diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..1be2b17 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,9 @@ +examples/input_data/*.nc filter=lfs diff=lfs merge=lfs -text +tests/fixture/temperature_simh.zarr filter=lfs diff=lfs merge=lfs -text +tests/fixture/temperature_simp.zarr filter=lfs diff=lfs merge=lfs -text +tests/fixture/precipitation_obsh.zarr filter=lfs diff=lfs merge=lfs -text +tests/fixture/precipitation_obsp.zarr filter=lfs diff=lfs merge=lfs -text +tests/fixture/precipitation_simh.zarr filter=lfs diff=lfs merge=lfs -text +tests/fixture/precipitation_simp.zarr filter=lfs diff=lfs merge=lfs -text +tests/fixture/temperature_obsh.zarr filter=lfs diff=lfs merge=lfs -text +tests/fixture/temperature_obsp.zarr filter=lfs diff=lfs merge=lfs -text diff --git a/.github/codecov.yml b/.github/codecov.yml index 7b54980..84b310b 100644 --- a/.github/codecov.yml +++ b/.github/codecov.yml @@ -18,6 +18,6 @@ coverage: comment: layout: "reach, diff, flags, files" behavior: default - require_changes: true # if false: post the comment even if coverage dont change + require_changes: true # if false: post the comment even if coverage don't change require_base: no # [yes :: must have a base report to post] require_head: yes # [yes :: must have a head report to post] diff --git a/.github/workflows/_build.yml b/.github/workflows/_build.yaml similarity index 100% rename from .github/workflows/_build.yml rename to .github/workflows/_build.yaml diff --git a/.github/workflows/_build_doc.yml b/.github/workflows/_build_doc.yaml similarity index 89% rename from .github/workflows/_build_doc.yml rename to .github/workflows/_build_doc.yaml index ed76948..e1b428f 100644 --- a/.github/workflows/_build_doc.yml +++ b/.github/workflows/_build_doc.yaml @@ -32,7 +32,7 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install -r docs/requirements.txt + python -m pip install -r doc/requirements.txt - name: Build the documentation - run: cd docs && make html + run: cd doc && make html diff --git a/.github/workflows/_codecov.yml b/.github/workflows/_codecov.yaml similarity index 100% rename from .github/workflows/_codecov.yml rename to .github/workflows/_codecov.yaml diff --git a/.github/workflows/_codeql.yml b/.github/workflows/_codeql.yaml similarity index 100% rename from .github/workflows/_codeql.yml rename to .github/workflows/_codeql.yaml diff --git a/.github/workflows/_pre_commit.yml b/.github/workflows/_pre_commit.yaml similarity index 100% rename from .github/workflows/_pre_commit.yml rename to .github/workflows/_pre_commit.yaml diff --git a/.github/workflows/_pypi_publish.yml b/.github/workflows/_pypi_publish.yaml similarity index 100% rename from .github/workflows/_pypi_publish.yml rename to .github/workflows/_pypi_publish.yaml diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yaml similarity index 96% rename from .github/workflows/_test.yml rename to .github/workflows/_test.yaml index 83933e3..e59900b 100644 --- a/.github/workflows/_test.yml +++ b/.github/workflows/_test.yaml @@ -38,4 +38,4 @@ jobs: run: python -m pip install ".[dev]" - name: Run unit tests - run: pytest tests + run: pytest -vv tests diff --git a/.github/workflows/cicd.yml b/.github/workflows/cicd.yml index 1d03456..30922bf 100644 --- a/.github/workflows/cicd.yml +++ b/.github/workflows/cicd.yml @@ -11,6 +11,10 @@ on: push: branches: - "**" + schedule: + - cron: "20 4 * * 0" + release: + types: [created] concurrency: group: CICD-${{ github.ref }} @@ -20,24 +24,24 @@ jobs: ## Checks the code logic, style and more ## Pre-Commit: - uses: ./.github/workflows/_pre_commit.yml + uses: ./.github/workflows/_pre_commit.yaml ## Discover vulnerabilities ## CodeQL: - uses: ./.github/workflows/_codeql.yml + uses: ./.github/workflows/_codeql.yaml ## Builds the package on multiple OS for multiple ## Python versions ## Build: needs: [Pre-Commit] - uses: ./.github/workflows/_build.yml + uses: ./.github/workflows/_build.yaml strategy: fail-fast: false matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] with: os: ${{ matrix.os }} python-version: ${{ matrix.python-version }} @@ -46,7 +50,7 @@ jobs: ## Build-Doc: needs: [Pre-Commit] - uses: ./.github/workflows/_build_doc.yml + uses: ./.github/workflows/_build_doc.yaml with: os: ubuntu-latest python-version: "3.11" @@ -55,34 +59,57 @@ jobs: ## Test: needs: [Build] - uses: ./.github/workflows/_test.yml + uses: ./.github/workflows/_test.yaml strategy: matrix: os: [ubuntu-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] with: os: ${{ matrix.os }} python-version: ${{ matrix.python-version }} + ## Generates and uploads the coverage statistics to codecov + ## + CodeCov: + if: | + success() && + github.actor == 'btschwertfeger' && + github.event_name == 'push' + needs: [Test] + uses: ./.github/workflows/_codecov.yaml + with: + os: ubuntu-latest + python-version: "3.11" + secrets: inherit + ## Uploads the package to test.pypi.org on master if triggered by ## a regular commit/push. ## UploadTestPyPI: - if: success() && github.ref == 'refs/heads/master' + if: | + success() && + github.actor == 'btschwertfeger' && + github.ref == 'refs/heads/master' && + github.event_name == 'push' needs: [Test] name: Upload current development version to Test PyPI - uses: ./.github/workflows/_pypi_publish.yml + uses: ./.github/workflows/_pypi_publish.yaml with: REPOSITORY_URL: https://test.pypi.org/legacy/ secrets: API_TOKEN: ${{ secrets.TEST_PYPI_API_TOKEN }} - ## Generates and uploads the coverage statistics to codecov + ## Upload the python-kraken-sdk to Production PyPI ## - CodeCov: - needs: [Test] - uses: ./.github/workflows/_codecov.yml + UploadPyPI: + if: | + success() && + github.actor == 'btschwertfeger' && + github.event_name == 'release' + needs: [UploadTestPyPI] + name: Upload the current release to PyPI + uses: ./.github/workflows/_pypi_publish.yaml with: - os: ubuntu-latest - python-version: "3.11" - secrets: inherit + REPOSITORY_URL: https://upload.pypi.org/legacy/ + secrets: + API_TOKEN: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yaml similarity index 100% rename from .github/workflows/codeql.yml rename to .github/workflows/codeql.yaml diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml deleted file mode 100644 index 218d212..0000000 --- a/.github/workflows/release.yml +++ /dev/null @@ -1,79 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (C) 2023 Benjamin Thomas Schwertfeger -# GitHub: https://github.com/btschwertfeger -# -# Workflow that gets triggered, when a release was created in the -# GitHub UI. This enables the upload of the python-cmethods package -# for the latest tag to PyPI. -# - -name: PyPI Production Release - -on: - release: - types: [created] - -jobs: - ## Run pre-commit - just to make sure that the code is still - ## in the proper format - ## - Pre-Commit: - uses: ./.github/workflows/_pre_commit.yml - - ## Build the package - for all Python versions - ## - Build: - uses: ./.github/workflows/_build.yml - needs: [Pre-Commit] - strategy: - fail-fast: false - matrix: - os: [macos-latest, ubuntu-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] - with: - os: ${{ matrix.os }} - python-version: ${{ matrix.python-version }} - - ## Build the documentation - ## - Build-Doc: - needs: [Pre-Commit] - uses: ./.github/workflows/_build_doc.yml - with: - os: ubuntu-latest - python-version: "3.11" - - ## Run the unit tests for Python 3.8 until 3.11 - ## - Test: - needs: [Build] - uses: ./.github/workflows/_test.yml - strategy: - matrix: - os: [ubuntu-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] - with: - os: ${{ matrix.os }} - python-version: ${{ matrix.python-version }} - - ## Upload the python-cmethods to Test PyPI - ## - UploadTestPyPI: - needs: [Test] - name: Upload the current release to Test PyPI - uses: ./.github/workflows/_pypi_publish.yml - with: - REPOSITORY_URL: https://test.pypi.org/legacy/ - secrets: - API_TOKEN: ${{ secrets.TEST_PYPI_API_TOKEN }} - - ## Upload the python-cmethods to PyPI - ## - UploadPyPI: - needs: [Test] - name: Upload the current release to PyPI - uses: ./.github/workflows/_pypi_publish.yml - with: - REPOSITORY_URL: https://upload.pypi.org/legacy/ - secrets: - API_TOKEN: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.gitignore b/.gitignore index 543184d..624b176 100644 --- a/.gitignore +++ b/.gitignore @@ -4,7 +4,6 @@ __pycache__/ *$py.class # C extensions *.so -*.zip # Distribution / packaging .Python @@ -28,12 +27,6 @@ share/python-wheels/ MANIFEST _version.py -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - # Installer logs pip-log.txt pip-delete-this-directory.txt @@ -49,59 +42,18 @@ nosetests.xml coverage.xml *.cover *.py,cover -.hypothesis/ .pytest_cache/ # Translations *.mo *.pot -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - # Sphinx documentation -docs/_build/ - -# PyBuilder -target/ +doc/_build/ # Jupyter Notebook .ipynb_checkpoints -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - # Environments .env .venv @@ -110,13 +62,7 @@ venv/ ENV/ env.bak/ venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject +.vscode/ # mkdocs documentation /site @@ -125,7 +71,6 @@ venv.bak/ .mypy_cache/ .dmypy.json dmypy.json -del*.py # Pyre type checker .pyre/ @@ -135,7 +80,11 @@ del*.py *.csv *.log *.zip -.vscode/ +*.nc +!examples/input_data/*.nc +!tests/fixture/ +dev +del*.py *.egg-info/ conda.stuff/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 48cfa0d..a3c1ce5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,35 @@ repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.1.13 + hooks: + - id: ruff + args: + - --preview + - --fix + - --exit-non-zero-on-fix + - repo: https://github.com/pycqa/flake8 + rev: 7.0.0 + hooks: + - id: flake8 + args: ["--select=E9,F63,F7,F82", "--show-source", "--statistics"] + - repo: https://github.com/pycqa/pylint + rev: v3.0.3 + hooks: + - id: pylint + name: pylint + types: [python] + exclude: ^examples/|^tests/|^setup.py$ + args: ["--rcfile=pyproject.toml"] + # - repo: https://github.com/pre-commit/mirrors-mypy # FIXME + # rev: v1.7.1 + # hooks: + # - id: mypy + # name: mypy + # args: + # - --config-file=pyproject.toml + # - cmethods - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: # all available hooks can be found here: https://github.com/pre-commit/pre-commit-hooks/blob/main/.pre-commit-hooks.yaml - id: check-yaml @@ -19,6 +48,7 @@ repos: - id: mixed-line-ending - id: name-tests-test args: ["--pytest-test-first"] + exclude: tests/helper.py - id: end-of-file-fixer - id: pretty-format-json - id: detect-private-key @@ -31,29 +61,21 @@ repos: - id: rst-directive-colons - id: text-unicode-replacement-char - repo: https://github.com/psf/black - rev: 23.1.0 + rev: 23.12.1 hooks: - id: black + # - repo: https://github.com/adamchainz/blacken-docs + # rev: 1.16.0 + # hooks: + # - id: blacken-docs + # additional_dependencies: [black==23.12.0] - repo: https://github.com/PyCQA/isort - rev: 5.12.0 + rev: 5.13.2 hooks: - id: isort args: ["--profile=black"] # solves conflicts between black and isort - - repo: https://github.com/pycqa/flake8 - rev: 6.0.0 - hooks: - - id: flake8 - args: ["--select=E9,F63,F7,F82", "--show-source", "--statistics"] - - repo: https://github.com/pycqa/pylint - rev: v2.17.0 - hooks: - - id: pylint - name: pylint - types: [python] - exclude: ^examples/|^tests/|^setup.py$ - args: ["--rcfile=pyproject.toml"] - repo: https://github.com/pre-commit/mirrors-prettier - rev: v3.0.1 + rev: v3.1.0 hooks: - id: prettier -exclude: (\.ipynb$) +exclude: '\.nc$|^tests/fixture/|\.ipynb$' diff --git a/.pylintrc b/.pylintrc deleted file mode 100644 index 30be9ad..0000000 --- a/.pylintrc +++ /dev/null @@ -1,631 +0,0 @@ -[MAIN] - -# Analyse import fallback blocks. This can be used to support both Python 2 and -# 3 compatible code, which means that the block might have code that exists -# only in one or another interpreter, leading to false positives when analysed. -analyse-fallback-blocks=no - -# Load and enable all available extensions. Use --list-extensions to see a list -# all available extensions. -#enable-all-extensions= - -# In error mode, messages with a category besides ERROR or FATAL are -# suppressed, and no reports are done by default. Error mode is compatible with -# disabling specific errors. -#errors-only= - -# Always return a 0 (non-error) status code, even if lint errors are found. -# This is primarily useful in continuous integration scripts. -#exit-zero= - -# A comma-separated list of package or module names from where C extensions may -# be loaded. Extensions are loading into the active Python interpreter and may -# run arbitrary code. -extension-pkg-allow-list= - -# A comma-separated list of package or module names from where C extensions may -# be loaded. Extensions are loading into the active Python interpreter and may -# run arbitrary code. (This is an alternative name to extension-pkg-allow-list -# for backward compatibility.) -extension-pkg-whitelist= - -# Return non-zero exit code if any of these messages/categories are detected, -# even if score is above --fail-under value. Syntax same as enable. Messages -# specified are enabled, while categories only check already-enabled messages. -fail-on= - -# Specify a score threshold under which the program will exit with error. -fail-under=10 - -# Interpret the stdin as a python script, whose filename needs to be passed as -# the module_or_package argument. -#from-stdin= - -# Files or directories to be skipped. They should be base names, not paths. -ignore=CVS - -# Add files or directories matching the regular expressions patterns to the -# ignore-list. The regex matches against paths and can be in Posix or Windows -# format. Because '\' represents the directory delimiter on Windows systems, it -# can't be used as an escape character. -ignore-paths= - -# Files or directories matching the regular expression patterns are skipped. -# The regex matches against base names, not paths. The default value ignores -# Emacs file locks -ignore-patterns=^\.# - -# List of module names for which member attributes should not be checked -# (useful for modules/projects where namespaces are manipulated during runtime -# and thus existing member attributes cannot be deduced by static analysis). It -# supports qualified module names, as well as Unix pattern matching. -ignored-modules= - -# Python code to execute, usually for sys.path manipulation such as -# pygtk.require(). -#init-hook= - -# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the -# number of processors available to use, and will cap the count on Windows to -# avoid hangs. -jobs=1 - -# Control the amount of potential inferred values when inferring a single -# object. This can help the performance when dealing with large functions or -# complex, nested conditions. -limit-inference-results=100 - -# List of plugins (as comma separated values of python module names) to load, -# usually to register additional checkers. -load-plugins= - -# Pickle collected data for later comparisons. -persistent=yes - -# Minimum Python version to use for version dependent checks. Will default to -# the version used to run pylint. -py-version=3.10 - -# Discover python modules and packages in the file system subtree. -recursive=no - -# When enabled, pylint would attempt to guess common misconfiguration and emit -# user-friendly hints instead of false-positive error messages. -suggestion-mode=yes - -# Allow loading of arbitrary C extensions. Extensions are imported into the -# active Python interpreter and may run arbitrary code. -unsafe-load-any-extension=no - -# In verbose mode, extra non-checker-related info will be displayed. -#verbose= - - -[BASIC] - -# Naming style matching correct argument names. -argument-naming-style=snake_case - -# Regular expression matching correct argument names. Overrides argument- -# naming-style. If left empty, argument names will be checked with the set -# naming style. -#argument-rgx= - -# Naming style matching correct attribute names. -attr-naming-style=snake_case - -# Regular expression matching correct attribute names. Overrides attr-naming- -# style. If left empty, attribute names will be checked with the set naming -# style. -#attr-rgx= - -# Bad variable names which should always be refused, separated by a comma. -bad-names=foo, - bar, - baz, - toto, - tutu, - tata - -# Bad variable names regexes, separated by a comma. If names match any regex, -# they will always be refused -bad-names-rgxs= - -# Naming style matching correct class attribute names. -class-attribute-naming-style=any - -# Regular expression matching correct class attribute names. Overrides class- -# attribute-naming-style. If left empty, class attribute names will be checked -# with the set naming style. -#class-attribute-rgx= - -# Naming style matching correct class constant names. -class-const-naming-style=UPPER_CASE - -# Regular expression matching correct class constant names. Overrides class- -# const-naming-style. If left empty, class constant names will be checked with -# the set naming style. -#class-const-rgx= - -# Naming style matching correct class names. -class-naming-style=PascalCase - -# Regular expression matching correct class names. Overrides class-naming- -# style. If left empty, class names will be checked with the set naming style. -#class-rgx= - -# Naming style matching correct constant names. -const-naming-style=UPPER_CASE - -# Regular expression matching correct constant names. Overrides const-naming- -# style. If left empty, constant names will be checked with the set naming -# style. -#const-rgx= - -# Minimum line length for functions/classes that require docstrings, shorter -# ones are exempt. -docstring-min-length=-1 - -# Naming style matching correct function names. -function-naming-style=snake_case - -# Regular expression matching correct function names. Overrides function- -# naming-style. If left empty, function names will be checked with the set -# naming style. -#function-rgx= - -# Good variable names which should always be accepted, separated by a comma. -good-names=i, - x, - j, - k, - ex, - Run, - _, - X, - QDM1, - LS_simh,LS_simp,VS_1_simh,VS_2_simp,VS_1_simp, - CMethods - -# Good variable names regexes, separated by a comma. If names match any regex, -# they will always be accepted -good-names-rgxs= - -# Include a hint for the correct naming format with invalid-name. -include-naming-hint=no - -# Naming style matching correct inline iteration names. -inlinevar-naming-style=any - -# Regular expression matching correct inline iteration names. Overrides -# inlinevar-naming-style. If left empty, inline iteration names will be checked -# with the set naming style. -#inlinevar-rgx= - -# Naming style matching correct method names. -method-naming-style=snake_case - -# Regular expression matching correct method names. Overrides method-naming- -# style. If left empty, method names will be checked with the set naming style. -#method-rgx= - -# Naming style matching correct module names. -module-naming-style=snake_case - -# Regular expression matching correct module names. Overrides module-naming- -# style. If left empty, module names will be checked with the set naming style. -#module-rgx= - -# Colon-delimited sets of names that determine each other's naming style when -# the name regexes allow several styles. -name-group= - -# Regular expression which should only match function or class names that do -# not require a docstring. -no-docstring-rgx=^_ - -# List of decorators that produce properties, such as abc.abstractproperty. Add -# to this list to register other decorators that produce valid properties. -# These decorators are taken in consideration only for invalid-name. -property-classes=abc.abstractproperty - -# Regular expression matching correct type variable names. If left empty, type -# variable names will be checked with the set naming style. -#typevar-rgx= - -# Naming style matching correct variable names. -variable-naming-style=snake_case - -# Regular expression matching correct variable names. Overrides variable- -# naming-style. If left empty, variable names will be checked with the set -# naming style. -#variable-rgx= - - -[CLASSES] - -# Warn about protected attribute access inside special methods -check-protected-access-in-special-methods=no - -# List of method names used to declare (i.e. assign) instance attributes. -defining-attr-methods=__init__, - __new__, - setUp, - __post_init__ - -# List of member names, which should be excluded from the protected access -# warning. -exclude-protected=_asdict, - _fields, - _replace, - _source, - _make - -# List of valid names for the first argument in a class method. -valid-classmethod-first-arg=cls - -# List of valid names for the first argument in a metaclass class method. -valid-metaclass-classmethod-first-arg=cls - - -[DESIGN] - -# List of regular expressions of class ancestor names to ignore when counting -# public methods (see R0903) -exclude-too-few-public-methods= - -# List of qualified class names to ignore when counting class parents (see -# R0901) -ignored-parents= - -# Maximum number of arguments for function / method. -max-args=10 - -# Maximum number of attributes for a class (see R0902). -max-attributes=20 - -# Maximum number of boolean expressions in an if statement (see R0916). -max-bool-expr=5 - -# Maximum number of branch for function / method body. -max-branches=15 - -# Maximum number of locals for function / method body. -max-locals=25 - -# Maximum number of parents for a class (see R0901). -max-parents=7 - -# Maximum number of public methods for a class (see R0904). -max-public-methods=20 - -# Maximum number of return / yield for function / method body. -max-returns=6 - -# Maximum number of statements in function / method body. -max-statements=50 - -# Minimum number of public methods for a class (see R0903). -min-public-methods=2 - - -[EXCEPTIONS] - -# Exceptions that will emit a warning when caught. -overgeneral-exceptions=builtin.BaseException, - builtin.Exception - - -[FORMAT] - -# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. -expected-line-ending-format= - -# Regexp for a line that is allowed to be longer than the limit. -ignore-long-lines=^\s*(# )??$ - -# Number of spaces of indent required inside a hanging or continued line. -indent-after-paren=4 - -# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 -# tab). -indent-string=' ' - -# Maximum number of characters on a single line. -max-line-length=100 - -# Maximum number of lines in a module. -max-module-lines=10000 - -# Allow the body of a class to be on the same line as the declaration if body -# contains single statement. -single-line-class-stmt=no - -# Allow the body of an if to be on the same line as the test if there is no -# else. -single-line-if-stmt=no - - -[IMPORTS] - -# List of modules that can be imported at any level, not just the top level -# one. -allow-any-import-level= - -# Allow wildcard imports from modules that define __all__. -allow-wildcard-with-all=no - -# Deprecated modules which should not be used, separated by a comma. -deprecated-modules= - -# Output a graph (.gv or any supported image format) of external dependencies -# to the given file (report RP0402 must not be disabled). -ext-import-graph= - -# Output a graph (.gv or any supported image format) of all (i.e. internal and -# external) dependencies to the given file (report RP0402 must not be -# disabled). -import-graph= - -# Output a graph (.gv or any supported image format) of internal dependencies -# to the given file (report RP0402 must not be disabled). -int-import-graph= - -# Force import order to recognize a module as part of the standard -# compatibility libraries. -known-standard-library= - -# Force import order to recognize a module as part of a third party library. -known-third-party=enchant - -# Couples of modules and preferred modules, separated by a comma. -preferred-modules= - - -[LOGGING] - -# The type of string formatting that logging methods do. `old` means using % -# formatting, `new` is for `{}` formatting. -logging-format-style=old - -# Logging modules to check that the string format arguments are in logging -# function parameter format. -logging-modules=logging - - -[MESSAGES CONTROL] - -# Only show warnings with the listed confidence levels. Leave empty to show -# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE, -# UNDEFINED. -confidence=HIGH, - CONTROL_FLOW, - INFERENCE, - INFERENCE_FAILURE, - UNDEFINED - -# Disable the message, report, category or checker with the given id(s). You -# can either give multiple identifiers separated by comma (,) or put this -# option multiple times (only on the command line, not in the configuration -# file where it should appear only once). You can also use "--disable=all" to -# disable everything first and then re-enable specific checks. For example, if -# you want to run only the similarities checker, you can use "--disable=all -# --enable=similarities". If you want to run only the classes checker, but have -# no Warning level messages displayed, use "--disable=all --enable=classes -# --disable=W". -disable=raw-checker-failed, - bad-inline-option, - locally-disabled, - file-ignored, - suppressed-message, - useless-suppression, - deprecated-pragma, - use-symbolic-message-instead, - anomalous-backslash-in-string, - multiple-statements, - line-too-long, - import-error, - consider-using-with, - c-extension-no-member, - too-many-return-statements - - -# Enable the message, report, category or checker with the given id(s). You can -# either give multiple identifier separated by comma (,) or put this option -# multiple time (only on the command line, not in the configuration file where -# it should appear only once). See also the "--disable" option for examples. -enable= - - -[METHOD_ARGS] - -# List of qualified names (i.e., library.method) which require a timeout -# parameter e.g. 'requests.api.get,requests.api.post' -# timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request - - -[MISCELLANEOUS] - -# List of note tags to take in consideration, separated by a comma. -notes=FIXME, - XXX, - TODO - -# Regular expression of note tags to take in consideration. -notes-rgx= - - -[REFACTORING] - -# Maximum number of nested blocks for function / method body -max-nested-blocks=5 - -# Complete name of functions that never returns. When checking for -# inconsistent-return-statements if a never returning function is called then -# it will be considered as an explicit return statement and no message will be -# printed. -never-returning-functions=sys.exit,argparse.parse_error - - -[REPORTS] - -# Python expression which should return a score less than or equal to 10. You -# have access to the variables 'fatal', 'error', 'warning', 'refactor', -# 'convention', and 'info' which contain the number of messages in each -# category, as well as 'statement' which is the total number of statements -# analyzed. This score is used by the global evaluation report (RP0004). -evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)) - -# Template used to display messages. This is a python new-style format string -# used to format the message information. See doc for all details. -msg-template= - -# Set the output format. Available formats are text, parseable, colorized, json -# and msvs (visual studio). You can also give a reporter class, e.g. -# mypackage.mymodule.MyReporterClass. -#output-format= - -# Tells whether to display a full report or only the messages. -reports=no - -# Activate the evaluation score. -score=yes - - -[SIMILARITIES] - -# Comments are removed from the similarity computation -ignore-comments=yes - -# Docstrings are removed from the similarity computation -ignore-docstrings=yes - -# Imports are removed from the similarity computation -ignore-imports=yes - -# Signatures are removed from the similarity computation -ignore-signatures=yes - -# Minimum lines number of a similarity. -min-similarity-lines=4 - - -[SPELLING] - -# Limits count of emitted suggestions for spelling mistakes. -max-spelling-suggestions=4 - -# Spelling dictionary name. Available dictionaries: none. To make it work, -# install the 'python-enchant' package. -spelling-dict= - -# List of comma separated words that should be considered directives if they -# appear at the beginning of a comment and should not be checked. -spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy: - -# List of comma separated words that should not be checked. -spelling-ignore-words= - -# A path to a file that contains the private dictionary; one word per line. -spelling-private-dict-file= - -# Tells whether to store unknown words to the private dictionary (see the -# --spelling-private-dict-file option) instead of raising a message. -spelling-store-unknown-words=no - - -[STRING] - -# This flag controls whether inconsistent-quotes generates a warning when the -# character used as a quote delimiter is used inconsistently within a module. -check-quote-consistency=no - -# This flag controls whether the implicit-str-concat should generate a warning -# on implicit string concatenation in sequences defined over several lines. -check-str-concat-over-line-jumps=no - - -[TYPECHECK] - -# List of decorators that produce context managers, such as -# contextlib.contextmanager. Add to this list to register other decorators that -# produce valid context managers. -contextmanager-decorators=contextlib.contextmanager - -# List of members which are set dynamically and missed by pylint inference -# system, and so shouldn't trigger E1101 when accessed. Python regular -# expressions are accepted. -generated-members= - -# Tells whether to warn about missing members when the owner of the attribute -# is inferred to be None. -ignore-none=yes - -# This flag controls whether pylint should warn about no-member and similar -# checks whenever an opaque object is returned when inferring. The inference -# can return multiple potential results while evaluating a Python object, but -# some branches might not be evaluated, which results in partial inference. In -# that case, it might be useful to still emit no-member and other checks for -# the rest of the inferred objects. -ignore-on-opaque-inference=yes - -# List of symbolic message names to ignore for Mixin members. -ignored-checks-for-mixins=no-member, - not-async-context-manager, - not-context-manager, - attribute-defined-outside-init - -# List of class names for which member attributes should not be checked (useful -# for classes with dynamically set attributes). This supports the use of -# qualified names. -ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace - -# Show a hint with possible names when a member name was not found. The aspect -# of finding the hint is based on edit distance. -missing-member-hint=yes - -# The minimum edit distance a name should have in order to be considered a -# similar match for a missing member name. -missing-member-hint-distance=1 - -# The total number of similar names that should be taken in consideration when -# showing a hint for a missing member. -missing-member-max-choices=1 - -# Regex pattern to define which classes are considered mixins. -mixin-class-rgx=.*[Mm]ixin - -# List of decorators that change the signature of a decorated function. -signature-mutators= - - -[VARIABLES] - -# List of additional names supposed to be defined in builtins. Remember that -# you should avoid defining new builtins when possible. -additional-builtins= - -# Tells whether unused global variables should be treated as a violation. -allow-global-unused-variables=yes - -# List of names allowed to shadow builtins -allowed-redefined-builtins= - -# List of strings which can identify a callback function by name. A callback -# name must start or end with one of those strings. -callbacks=cb_, - _cb - -# A regular expression matching the name of dummy variables (i.e. expected to -# not be used). -dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ - -# Argument names that match this expression will be ignored. -ignored-argument-names=_.*|^ignored_|^unused_ - -# Tells whether we should check for unused import in __init__ files. -init-import=no - -# List of qualified module names which can have objects that can redefine -# builtins. -redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io diff --git a/.readthedocs.yaml b/.readthedocs.yaml index fa78b4e..9e653bd 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -13,9 +13,9 @@ build: # Build documentation in the docs/ directory with Sphinx sphinx: - configuration: docs/conf.py + configuration: doc/conf.py # Optionally declare the Python requirements required to build your docs python: install: - - requirements: docs/requirements.txt + - requirements: doc/requirements.txt diff --git a/CHANGELOG.md b/CHANGELOG.md index fe66399..c5f0a2d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,14 +1,23 @@ # Changelog -## [Unreleased](https://github.com/btschwertfeger/python-cmethods/tree/HEAD) +## [v1.0.3](https://github.com/btschwertfeger/python-cmethods/tree/v1.0.3) (2023-08-09) -[Full Changelog](https://github.com/btschwertfeger/python-cmethods/compare/v1.0.2...HEAD) +[Full Changelog](https://github.com/btschwertfeger/python-cmethods/compare/v1.0.2...v1.0.3) + +**Implemented enhancements:** + +- Validate types during runtime [\#42](https://github.com/btschwertfeger/python-cmethods/issues/42) **Fixed bugs:** - The tool fails when input time series include np.nan for distribution-based methods [\#41](https://github.com/btschwertfeger/python-cmethods/issues/41) - Fix error when time series includes nan values [\#40](https://github.com/btschwertfeger/python-cmethods/pull/40) ([btschwertfeger](https://github.com/btschwertfeger)) +**Merged pull requests:** + +- Merge `.pylintrc` and `.coveragerc` into `pyproject.toml` [\#44](https://github.com/btschwertfeger/python-cmethods/pull/44) ([btschwertfeger](https://github.com/btschwertfeger)) +- Add type checking for parameters of bias correction techniques [\#43](https://github.com/btschwertfeger/python-cmethods/pull/43) ([btschwertfeger](https://github.com/btschwertfeger)) + ## [v1.0.2](https://github.com/btschwertfeger/python-cmethods/tree/v1.0.2) (2023-06-18) [Full Changelog](https://github.com/btschwertfeger/python-cmethods/compare/v1.0.1...v1.0.2) diff --git a/MANIFEST.in b/MANIFEST.in index 64ad321..e9f05fc 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1 +1,12 @@ +# -*- coding: utf-8 -*- +# Copyright (C) 2023 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + include README.md LICENSE + +graft cmethods + +prune tests +prune doc +prune examples diff --git a/Makefile b/Makefile index e453570..dbdc017 100644 --- a/Makefile +++ b/Makefile @@ -2,51 +2,83 @@ # -*- coding: utf-8 -*- # Copyright (C) 2023 Benjamin Thomas Schwertfeger # GitHub: https://github.com/btschwertfeger +# VENV := venv -GLOBAL_PYTHON := $(shell which python3) PYTHON := $(VENV)/bin/python3 +TESTS := tests +PYTEST_OPTS := -vv -.PHONY := build dev install test tests doc doctest pre-commit changelog clean +.PHONY: help +help: + @grep "^##" Makefile | sed -e "s/##//" -## Builds the python-kraken-sdk +## build Builds python-cmethods ## +.PHONY: build build: $(PYTHON) -m pip wheel -w dist --no-deps . -## Installs the package in edit mode +## dev Installs the package in edit mode ## +.PHONY: dev dev: + @git lfs install $(PYTHON) -m pip install -e ".[dev]" -## Install the package +## install Install the package ## +.PHONY: install install: $(PYTHON) -m pip install . -## Run the unit tests +## test Run the unit tests ## +.PHONY: test test: - $(PYTHON) -m pytest tests/ + $(PYTHON) -m pytest $(PYTEST_OPTS) $(TESTS) +.PHONY: tests tests: test -## Build the documentation +## wip Run tests marked as wip ## +.PHONY: wip +wip: + $(PYTHON) -m pytest $(PYTEST_OPTS) -m "wip" $(TESTS) + +## doc Build the documentation +## +.PHONY: doc doc: - cd docs && make html + cd doc && make html -## Run the documentation tests +## doctest Run the documentation tests ## +.PHONY: doctest doctest: - cd docs && make doctest + cd doc && make doctest -## Pre-Commit +## pre-commit Pre-Commit +## +.PHONY: pre-commit pre-commit: @pre-commit run -a -## Create the changelog +## ruff Run ruff without fix +.PHONY: ruff +ruff: + ruff check --preview . + +## ruff-fix Run ruff with fix +## +.PHONY: ruff-fix +ruff-fix: + ruff check --fix --preview . + +## changelog Create the changelog ## +.PHONY: changelog changelog: docker run -it --rm \ -v "$(PWD)":/usr/local/src/your-app/ \ @@ -57,10 +89,11 @@ changelog: --breaking-labels Breaking \ --enhancement-labels Feature -## Clean the workspace +## clean Clean the workspace ## +.PHONY: clean clean: - rm -rf .pytest_cache \ + rm -rf .pytest_cache .cache \ build/ dist/ python_cmethods.egg-info \ docs/_build \ examples/.ipynb_checkpoints .ipynb_checkpoints \ diff --git a/README.md b/README.md index dc60265..26c6c8b 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@
[![GitHub](https://badgen.net/badge/icon/github?icon=github&label)](https://github.com/btschwertfeger/Bias-Adjustment-Python) -[![Generic badge](https://img.shields.io/badge/python-3.8_|_3.9_|_3.10_|_3.11-blue.svg)](https://shields.io/) +[![Generic badge](https://img.shields.io/badge/python-3.8_|_3.9_|_3.10_|_3.11|_3.12-blue.svg)](https://shields.io/) [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-orange.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Downloads](https://pepy.tech/badge/python-cmethods)](https://pepy.tech/project/python-cmethods) @@ -18,11 +18,17 @@
-This Python module serves as a collection of different scale- and distribution-based bias correction techniques for climatic research +This Python module serves as a collection of different scale- and +distribution-based bias correction techniques for climatic research The documentation is available at: [https://python-cmethods.readthedocs.io/en/stable/](https://python-cmethods.readthedocs.io/en/stable/) -> ⚠️ For the application of bias corrections on _lage data sets_ it is recommended to use the command-line tool [BiasAdjustCXX](https://github.com/btschwertfeger/BiasAdjustCXX) since bias corrections are complex statistical transformation which are very slow in Python compared to the C++ implementation. +Please cite this project as described in +https://zenodo.org/doi/10.5281/zenodo.7652755. + +> ⚠️ For the application of bias corrections on _large data sets_ it is +> recommended to also try the command-line tool +> [BiasAdjustCXX](https://github.com/btschwertfeger/BiasAdjustCXX). --- @@ -33,7 +39,8 @@ The documentation is available at: [https://python-cmethods.readthedocs.io/en/st 3. [ Installation ](#installation) 4. [ Usage and Examples ](#examples) 5. [ Notes ](#notes) -6. [ References ](#references) +6. [ Contribution ](#contribution) +7. [ References ](#references) --- @@ -41,56 +48,74 @@ The documentation is available at: [https://python-cmethods.readthedocs.io/en/st ## 1. About -These programs and data structures are developed with the aim of reducing discrepancies between modeled and observed climate data. Historical data is utilized to calibrate variables from current and future time series to achieve distributional properties that closely resemble the possible actual values. +These programs and data structures are developed with the aim of reducing +discrepancies between modeled and observed climate data. Historical data is +utilized to calibrate variables from current and future time series to achieve +distributional properties that closely resemble the possible actual values.
Schematic representation of a bias adjustment procedure
Figure 1: Schematic representation of a bias adjustment procedure
-For instance, modeled data typically indicate values that are colder than the actual values. To address this issue, an adjustment procedure is employed. The figure below illustrates the observed, modeled, and adjusted values, revealing that the delta adjusted time series ($T^{*DM}_{sim,p}$) are significantly more similar to the observed data ($T{obs,p}$) than the raw modeled data ($T_{sim,p}$). +For instance, modeled data typically indicate values that are colder than the +actual values. To address this issue, an adjustment procedure is employed. The +figure below illustrates the observed, modeled, and adjusted values, revealing +that the delta adjusted time series ($T^{*DM}_{sim,p}$) are significantly more +similar to the observed data ($T{obs,p}$) than the raw modeled data +($T_{sim,p}$).
Temperature per day of year in modeled, observed and bias-adjusted climate data
Figure 2: Temperature per day of year in observed, modeled, and bias-adjusted climate data
---- - -## 2. Available methods +## 2. Available Methods -All methods except the `adjust_3d` function requires that the input data sets only contain one dimension. +python-cmethods provides the following bias correction techniques: -| Function name | Description | -| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| `linear_scaling` | Linear Scaling (additive and multiplicative) | -| `variance_scaling` | Variance Scaling (additive) | -| `delta_method` | Delta (Change) Method (additive and multiplicative) | -| `quantile_mapping` | Quantile Mapping (additive and multiplicative) and Detrended Quantile Mapping (additive and multiplicative; to use DQM, set parameter `detrended` to `True`) | -| `quantile_delta_mapping` | Quantile Delta Mapping (additive and multiplicative) | -| `adjust_3d` | requires a method name and the respective parameters to adjust all time series of a 3-dimensional data set | +- Linear Scaling +- Variance Scaling +- Delta Method +- Quantile Mapping +- Detrended Quantile Mapping +- Quantile Delta Mapping -Except for the variance scaling, all methods can be applied on stochastic and non-stochastic -climate variables. Variance scaling can only be applied on non-stochastic climate variables. +Please refer to the official documentation for more information about these +methods as well as sample scripts: +https://python-cmethods.readthedocs.io/en/stable/ -- Non-stochastic climate variables are those that can be predicted with relative certainty based - on factors such as location, elevation, and season. Examples of non-stochastic climate variables - include air temperature, air pressure, and solar radiation. +- Except for the variance scaling, all methods can be applied on stochastic and + non-stochastic climate variables. Variance scaling can only be applied on + non-stochastic climate variables. -- Stochastic climate variables, on the other hand, are those that exhibit a high degree of - variability and unpredictability, making them difficult to forecast accurately. - Precipitation is an example of a stochastic climate variable because it can vary greatly in timing, - intensity, and location due to complex atmospheric and meteorological processes. + - Non-stochastic climate variables are those that can be predicted with relative + certainty based on factors such as location, elevation, and season. Examples + of non-stochastic climate variables include air temperature, air pressure, and + solar radiation. ---- + - Stochastic climate variables, on the other hand, are those that exhibit a high + degree of variability and unpredictability, making them difficult to forecast + accurately. Precipitation is an example of a stochastic climate variable + because it can vary greatly in timing, intensity, and location due to complex + atmospheric and meteorological processes. + +- Except for the detrended quantile mapping (DQM) technique, all methods can be + applied to 1- and 3-dimensional data sets. The implementation of DQM to + 3-dimensional data is still in progress. + +- Except for DQM, all methods can be applied using `cmethods.adjust`. Chunked + data for computing e.g. in a dask cluster is possible as well. + +- For any questions -- please open an issue at https://github.com/btschwertfeger/python-cmethods/issues @@ -108,36 +133,47 @@ python3 -m pip install python-cmethods ```python import xarray as xr -from cmethods import CMethods as cm - -obsh = xr.open_dataset('input_data/observations.nc') -simh = xr.open_dataset('input_data/control.nc') -simp = xr.open_dataset('input_data/scenario.nc') - -ls_result = cm.linear_scaling( - obs = obsh['tas'][:,0,0], - simh = simh['tas'][:,0,0], - simp = simp['tas'][:,0,0], - kind = '+' +from cmethods import adjust + +obsh = xr.open_dataset("input_data/observations.nc") +simh = xr.open_dataset("input_data/control.nc") +simp = xr.open_dataset("input_data/scenario.nc") + +# adjust only one grid cell +ls_result = adjust( + method="linear_scaling", + obs=obsh["tas"][:, 0, 0], + simh=simh["tas"][:, 0, 0], + simp=simp["tas"][:, 0, 0], + kind="+", + group="time.month", ) -qdm_result = cm.adjust_3d( # 3d = 2 spatial and 1 time dimension - method = 'quantile_delta_mapping', - obs = obsh['tas'], - simh = simh['tas'], - simp = simp['tas'], - n_quaniles = 1000, - kind = '+' +# adjust all grid cells +qdm_result = adjust( + method="quantile_delta_mapping", + obs=obsh["tas"], + simh=simh["tas"], + simp=simp["tas"], + n_quantiles=1000, + kind="+", ) + # to calculate the relative rather than the absolute change, # '*' can be used instead of '+' (this is preferred when adjusting # stochastic variables like precipitation) ``` +It is also possible to adjust chunked data sets. Feel free to have a look into +`tests/test_zarr_dask_compatibility.py` to get a starting point. + Notes: -- When using the `adjust_3d` method you have to specify the method by name. -- For the multiplicative techniques a maximum scaling factor of 10 is defined. This can be changed by the attribute `max_scaling_factor`. +- For the multiplicative techniques a maximum scaling factor of 10 is defined. + This can be changed by passing the optional parameter `max_scaling_factor`. +- Except for detrended quantile mapping, all implemented techniques can be + applied to single and multdimensional data sets by executing the + `cmethods.adjust` function. ## Examples (see repository on [GitHub](https://github.com/btschwertfeger/python-cmethods)) @@ -161,6 +197,7 @@ Help: --scen input_data/scenario.nc \ --kind "+" \ --variable "tas" \ + --quantiles 10 \ --method quantile_mapping ``` @@ -180,7 +217,8 @@ Help: Notes: - Data sets must have the same spatial resolutions. -- This script is far away from perfect - so please look at it, as a starting point. (: +- This script is far away from perfect - so please see it, as a starting point. + (: --- @@ -188,18 +226,46 @@ Notes: ## 5. Notes -- Computation in Python takes some time, so this is only for demonstration. When adjusting large datasets, its best to use the command-line tool [BiasAdjustCXX](https://github.com/btschwertfeger/BiasAdjustCXX). -- Formulas and references can be found in the implementations of the corresponding functions, on the bottom of the README.md and in the [documentation](https://python-kraken-sdk.readthedocs.io/en/stable/). +- Computation in Python takes some time, so this is only for demonstration. When + adjusting large datasets, you should either use chunked data using for example + a dask cluster or to apply the command-line tool + [BiasAdjustCXX](https://github.com/btschwertfeger/BiasAdjustCXX). +- Formulas and references can be found in the implementations of the + corresponding functions, on the bottom of the README.md and in the + [documentation](https://python-kraken-sdk.readthedocs.io/en/stable/). + +### Space for improvements + +- Since the scaling methods implemented so far scale by default over the mean + values of the respective months, unrealistic long-term mean values may occur + at the month transitions. This can be prevented either by selecting + `group='time.dayofyear'`. Alternatively, it is possible not to scale using + long-term mean values, but using a 31-day interval, which takes the 31 + surrounding values over all years as the basis for calculating the mean + values. This is not yet implemented, because even the computation for this + takes so much time, that it is not worth implementing it in python - but this + is available in + [BiasAdjustCXX](https://github.com/btschwertfeger/BiasAdjustCXX). -### Space for improvements: +--- -- Since the scaling methods implemented so far scale by default over the mean values of the respective months, unrealistic long-term mean values may occur at the month transitions. This can be prevented either by selecting `group='time.dayofyear'`. Alternatively, it is possible not to scale using long-term mean values, but using a 31-day interval, which takes the 31 surrounding values over all years as the basis for calculating the mean values. This is not yet implemented, because even the computation for this takes so much time, that it is not worth implementing it in python - but this is available in [BiasAdjustCXX](https://github.com/btschwertfeger/BiasAdjustCXX). + ---- +## 6. 🆕 Contributions + +… are welcome but: + +- First check if there is an existing issue or PR that addresses your + problem/solution. If not - create one first - before creating a PR. +- Typo fixes, project configuration, CI, documentation or style/formatting PRs will be + rejected. Please create an issue for that. +- PRs must provide a reasonable, easy to understand and maintain solution for an + existing problem. You may want to propose a solution when creating the issue + to discuss the approach before creating a PR. -## 6. References +## 7. References - Schwertfeger, Benjamin Thomas and Lohmann, Gerrit and Lipskoch, Henrik (2023) _"Introduction of the BiasAdjustCXX command-line tool for the application of fast and efficient bias corrections in climatic research"_, SoftwareX, Volume 22, 101379, ISSN 2352-7110, (https://doi.org/10.1016/j.softx.2023.101379) - Schwertfeger, Benjamin Thomas (2022) _"The influence of bias corrections on variability, distribution, and correlation of temperatures in comparison to observed and modeled climate data in Europe"_ (https://epic.awi.de/id/eprint/56689/) @@ -207,5 +273,10 @@ Notes: - Delta Method based on: Beyer, R. and Krapp, M. and Manica, A.: _"An empirical evaluation of bias correction methods for palaeoclimate simulations"_ (https://doi.org/10.5194/cp-16-1493-2020) - Quantile and Detrended Quantile Mapping based on: Alex J. Cannon and Stephen R. Sobie and Trevor Q. Murdock _"Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?"_ (https://doi.org/10.1175/JCLI-D-14-00754.1) - Quantile Delta Mapping based on: Tong, Y., Gao, X., Han, Z. et al. _"Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods"_. Clim Dyn 57, 1425–1443 (2021). (https://doi.org/10.1007/s00382-020-05447-4) +- I'd like to express my gratitude to @riley-brady for initiating and + contributing to the discussion on + https://github.com/btschwertfeger/python-cmethods/issues/47. I appreciate all + the valuable suggestions provided throughout the implementation of the + subsequent changes. --- diff --git a/cmethods/__init__.py b/cmethods/__init__.py index 778f528..44f6f1f 100644 --- a/cmethods/__init__.py +++ b/cmethods/__init__.py @@ -5,7 +5,10 @@ # r""" - Module to apply different bias correction techniques to time-series climate data + Module providing the a method named "adjust" to apply different bias + correction techniques to time-series climate data. + + Some variables used in this package: T = Temperatures ($T$) X = Some climate variable ($X$) @@ -21,14 +24,7 @@ _{m} = long-term monthly interval """ -from __future__ import annotations - -import multiprocessing -from typing import Any, Callable, List, Optional, Union - -import numpy as np -import xarray as xr -from tqdm import tqdm +from cmethods.core import adjust __author__ = "Benjamin Thomas Schwertfeger" __copyright__ = __author__ @@ -36,1391 +32,4 @@ __link__ = "https://github.com/btschwertfeger" __github__ = "https://github.com/btschwertfeger/python-cmethods" - -class UnknownMethodError(Exception): - """ - Exception raised for errors if unknown method called in CMethods class. - """ - - def __init__(self: UnknownMethodError, method: str, available_methods: list): - super().__init__( - f'Unknown method "{method}"! Available methods: {available_methods}' - ) - - -class CMethods: - """ - The CMethods class serves a collection of bias correction procedures to adjust - time-series of climate data. - - The following bias correction techniques are available: - Scaling-based techniques: - * Linear Scaling :func:`cmethods.CMethods.linear_scaling` - * Variance Scaling :func:`cmethods.CMethods.variance_scaling` - * Delta (change) Method :func:`cmethods.CMethods.delta_method` - - Distribution-based techniques: - * Quantile Mapping :func:`cmethods.CMethods.quantile_mapping` - * Detrended Quantile Mapping :func:`cmethods.CMethods.detrended_quantile_mapping` - * Quantile Delta Mapping :func:`cmethods.CMethods.quantile_delta_mapping` - - Except for the Variance Scaling all methods can be applied on both, stochastic and non-stochastic - variables. The Variance Scaling can only be applied on stochastic climate variables. - - - Non-stochastic climate variables are those that can be predicted with relative certainty based - on factors such as location, elevation, and season. Examples of non-stochastic climate variables - include air temperature, air pressure, and solar radiation. - - - Stochastic climate variables, on the other hand, are those that exhibit a high degree of - variability and unpredictability, making them difficult to forecast accurately. - Precipitation is an example of a stochastic climate variable because it can vary greatly in timing, - intensity, and location due to complex atmospheric and meteorological processes. - """ - - SCALING_METHODS: List[str] = ["linear_scaling", "variance_scaling", "delta_method"] - DISTRIBUTION_METHODS: List[str] = [ - "quantile_mapping", - "detrended_quantile_mapping", - "quantile_delta_mapping", - ] - - CUSTOM_METHODS: List[str] = SCALING_METHODS + DISTRIBUTION_METHODS - METHODS: List[str] = CUSTOM_METHODS - - ADDITIVE: List[str] = ["+", "add"] - MULTIPLICATIVE: List[str] = ["*", "mult"] - - MAX_SCALING_FACTOR: Union[int, float] = 10 - - @classmethod - def get_available_methods(cls: CMethods) -> List[str]: - """ - Function to return the available adjustment methods of the CMethods class. - - :return: List of available bias correction methods - :rtype: List[str] - - .. code-block:: python - :linenos: - :caption: Example: Get available methods - - >>> from cmethods import CMethods as cm - - >>> cm.get_available_methods() - [ - "linear_scaling", "variance_scaling", "delta_method", - "quantile_mapping", "quantile_delta_mapping" - ] - """ - return cls.METHODS - - @classmethod - def get_function(cls: CMethods, method: str) -> Callable: - """ - Returns the bias correction function corresponding to the ``method`` name. - - :param method: The method name to get the function for - :type method: str - :raises UnknownMethodError: If the function is not implemented - :return: The function of the corresponding method - :rtype: function - """ - if method == "linear_scaling": - return cls.linear_scaling - if method == "variance_scaling": - return cls.variance_scaling - if method == "delta_method": - return cls.delta_method - if method == "quantile_mapping": - return cls.quantile_mapping - if method == "detrended_quantile_mapping": - return cls.detrended_quantile_mapping - if method == "empirical_quantile_mapping": - return cls.empirical_quantile_mapping - if method == "quantile_delta_mapping": - return cls.quantile_delta_mapping - raise UnknownMethodError(method, cls.METHODS) - - @classmethod - def adjust_3d( - cls: CMethods, - method: str, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - n_quantiles: int = 100, - kind: str = "+", - group: Optional[str] = None, - n_jobs: int = 1, - **kwargs: Any, - ) -> xr.core.dataarray.DataArray: - """ - Function to apply a bias correction method on 3-dimensional climate data. - - *It is very important to pass ``group="time.month`` for scaling-based - techniques if the correction should be performed as described in the - referenced articles!* It is wanted to be the default. - - :param method: The bias correction method to use - :type method: str - :param obs: The reference data set of the control period - (in most cases the observational data) - :type obs: xr.core.dataarray.DataArray - :param simh: The modeled data of the control period - :type simh: xr.core.dataarray.DataArray - :param simp: The modeled data of the scenario period (this is the data set - on which the bias correction takes action) - :type simp: xr.core.dataarray.DataArray - :param n_quantiles: Number of quantiles to respect. Only applies to - distribution-based bias correction techniques, defaults to ``100`` - :type n_quantiles: int, optional - :param kind: The kind of adjustment - additive or multiplicative, defaults to ``"+"`` - :type kind: str, optional - :param group: The grouping base, Only applies to scaling-based techniques, defaults to ``None`` - :type group: str, optional - :param n_jobs: Number of parallels jobs to run the correction, defaults to ``1`` - :type n_jobs: int, optional - :raises UnknownMethodError: If the correction method is not implemented - :return: The bias-corrected time series - :rtype: xr.core.dataarray.DataArray - - .. code-block:: python - :linenos: - :caption: Example application - 3-dimensional bias correction - - >>> import xarray as xr - >>> from cmethods import CMethods as cm - - >>> # Note: The data sets must contain the dimensions "time", "lat", and "lon" - >>> # for the respective variable. - >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") - >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") - >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") - >>> variable = "tas" # temperatures - - ''' - In the following the Quantile Delta Mapping techniques is applied on - 3-dimensional time-series data sets containing the variable "tas". The - parameter "kind" is not specified, since the additive ("+") kind is the default. - When adjusting ratio-based variables like precipitation, the multiplicative - variant ("*") should be used. In addition "n_jobs" is set to 4 which means that - four processes are used. This can improve the overall execution time. - - After the execution, "qdm_adjusted" contains the fully bias-corrected - data - with the same shape, resolution, dimensions, coordinates and - attributes. - ''' - >>> qdm_adjusted = cm.adjust_3d( - ... method = "quantile_delta_mapping", - ... obs = obs[variable], - ... simh = simh[variable], - ... simp = simp[variable], - ... n_quantiles = 250, - ... n_jobs = 4 - ... ) - - ''' - The next example shows how to apply the Linear Scaling bias correction - technique based on long-term monthly means. - ''' - >>> ls_adjusted = cm.adjust_3d( - ... method="linear_scaling", - ... obs=obs[variable], - ... simh=simh[variable], - ... simp=simp[variable], - ... group="time.month", - ... kind="+" - ... ) - """ - - if not isinstance(obs, xr.core.dataarray.DataArray): - raise TypeError("'obs' must be type xarray.core.dataarray.DataArray") - if not isinstance(simh, xr.core.dataarray.DataArray): - raise TypeError("'simh' must be type xarray.core.dataarray.DataArray") - if not isinstance(simp, xr.core.dataarray.DataArray): - raise TypeError("'simp' must be type xarray.core.dataarray.DataArray") - if not isinstance(n_quantiles, int): - raise TypeError("'n_quantiles' must be type int") - if not isinstance(n_jobs, int): - raise TypeError("'n_jobs' must be type int") - - obs = obs.transpose("lat", "lon", "time") - simh = simh.transpose("lat", "lon", "time") - simp = simp.transpose("lat", "lon", "time").load() - - result = simp.copy(deep=True) - len_lat, len_lon = len(simp.lat), len(simp.lon) - - if method in cls.CUSTOM_METHODS: - if n_jobs == 1: - func = cls.get_function(method) - for lat in tqdm(range(len_lat)): - for lon in range(len_lon): - result[lat, lon] = func( - obs=obs[lat, lon], - simh=simh[lat, lon], - simp=simp[lat, lon], - group=group, - kind=kind, - n_quantiles=n_quantiles, - **kwargs, - ) - else: - try: - pool = multiprocessing.Pool(processes=n_jobs) - # with multiprocessing.Pool(processes=n_jobs) as pool: - # context manager is not used because than the coverage does not work. - # this may change when upgrading to only support Python 3.11+ - params: List[dict] = [ - { - "method": method, - "obs": obs[lat], - "simh": simh[lat], - "simp": simp[lat], - "group": group, - "kind": kind, - "n_quantiles": n_quantiles, - "kwargs": kwargs, - } - for lat in range(len_lat) - ] - for lat, corrected in enumerate(pool.map(cls.pool_adjust, params)): - result[lat] = corrected - finally: - pool.close() - pool.join() - - return result.transpose("time", "lat", "lon") - raise UnknownMethodError(method, cls.METHODS) - - @classmethod - def pool_adjust(cls: CMethods, params: dict) -> np.ndarray: - """ - Adjustment along the longitudes for one specific latitude - used by :func:`cmethods.CMethods.adjust_3d` - as callback function for :class:`multiprocessing.Pool`. - - **Not intended to be executed somewhere else.** - - :params params: The method specific parameters - :type params: dict - :raises UnknownMethodError: If the specified method is not implemented - :return: The bias-corrected time series as 2-dimensional (longitudes x time) - numpy array - :rtype: np.ndarray - """ - kwargs = params.get("kwargs", {}) - - result = params["simp"].copy(deep=True).load() - len_lon = len(params["obs"].lon) - - func_adjustment = None - if params["method"] in cls.CUSTOM_METHODS: - func_adjustment = cls.get_function(params["method"]) - kwargs["n_quantiles"] = params.get("n_quantiles", 100) - kwargs["kind"] = params.get("kind", "+") - for lon in range(len_lon): - result[lon] = func_adjustment( - obs=params["obs"][lon], - simh=params["simh"][lon], - simp=params["simp"][lon], - group=params.get("group", None), - **kwargs, - ) - return np.array(result) - - raise UnknownMethodError(params["method"], cls.METHODS) - - @classmethod - def grouped_correction( - cls: CMethods, - method: str, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - group: str, - kind: str = "+", - **kwargs: Any, - ) -> xr.core.dataarray.DataArray: - """Method to adjust 1 dimensional climate data while respecting adjustment groups. - - ----- P A R A M E T E R S ----- - method (str): adjustment method name - obs (xarray.core.dataarray.DataArray): observed / reference Data - simh (xarray.core.dataarray.DataArray): simulated historical Data - simp (xarray.core.dataarray.DataArray): future simulated Data - method (str): Scaling method (e.g.: 'linear_scaling') - group (str): [optional] Group / Period (either: 'time.month', 'time.year' or 'time.dayofyear') - - ----- R E T U R N ----- - - xarray.core.dataarray.DataArray: Adjusted data - """ - if not isinstance(simp, xr.core.dataarray.DataArray): - raise TypeError("'simp' must be type xarray.core.dataarray.DataArray") - - func_adjustment = cls.get_function(method) - result = simp.copy(deep=True).load() - groups = simh.groupby(group).groups - - for month in groups.keys(): - m_obs, m_simh, m_simp = [], [], [] - - for i in groups[month]: - m_obs.append(obs[i]) - m_simh.append(simh[i]) - m_simp.append(simp[i]) - - computed_result = func_adjustment( - obs=m_obs, simh=m_simh, simp=m_simp, kind=kind, group=None, **kwargs - ) - for i, index in enumerate(groups[month]): - result[index] = computed_result[i] - - return np.array(result) - - # ? -----========= L I N E A R - S C A L I N G =========------ - @classmethod - def linear_scaling( - cls: CMethods, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - group: str = "time.month", - kind: str = "+", - **kwargs: Any, - ) -> np.ndarray: - r""" - The Linear Scaling bias correction technique can be applied on stochastic and - non-stochastic climate variables to minimize deviations in the mean values - between predicted and observed time-series of past and future time periods. - - Since the multiplicative scaling can result in very high scaling factors, - a maximum scaling factor of 10 is set. This can be changed by passing - another value to the optional `max_scaling_factor` argument. - - This method must be applied on a 1-dimensional data set i.e., there is only one - time-series passed for each of ``obs``, ``simh``, and ``simp``. - - The Linear Scaling bias correction technique implemented here is based on the - method described in the equations of Teutschbein, Claudia and Seibert, Jan (2012) - *"Bias correction of regional climate model simulations for hydrological climate-change - impact studies: Review and evaluation of different methods"* - (https://doi.org/10.1016/j.jhydrol.2012.05.052). In the following the equations - for both additive and multiplicative Linear Scaling are shown: - - **Additive**: - - In Linear Scaling, the long-term monthly mean (:math:`\mu_m`) of the modeled data :math:`X_{sim,h}` is subtracted - from the long-term monthly mean of the reference data :math:`X_{obs,h}` at time step :math:`i`. - This difference in month-dependent long-term mean is than added to the value of time step :math:`i`, - in the time-series that is to be adjusted (:math:`X_{sim,p}`). - - .. math:: - - X^{*LS}_{sim,p}(i) = X_{sim,p}(i) + \mu_{m}(X_{obs,h}(i)) - \mu_{m}(X_{sim,h}(i)) - - - **Multiplicative**: - - The multiplicative Linear Scaling differs from the additive variant in such way, that the changes are not computed - in absolute but in relative values. - - .. math:: - - X^{*LS}_{sim,h}(i) = X_{sim,h}(i) \cdot \left[\frac{\mu_{m}(X_{obs,h}(i))}{\mu_{m}(X_{sim,h}(i))}\right] - - - :param obs: The reference data set of the control period - (in most cases the observational data) - :type obs: xr.core.dataarray.DataArray - :param simh: The modeled data of the control period - :type simh: xr.core.dataarray.DataArray - :param simp: The modeled data of the scenario period (this is the data set - on which the bias correction takes action) - :type simp: xr.core.dataarray.DataArray - :param group: The grouping defines the basis of the mean, defaults to ``time.month`` - :type group: str | None - :param kind: The kind of the correction, additive for non-stochastic and multiplicative - for stochastic climate variables, defaults to ``+`` - :type kind: str, optional - :raises NotImplementedError: If the kind is not in (``+``, ``*``, ``add``, ``mult``) - :return: The bias-corrected time series - :rtype: np.ndarray - - .. code-block:: python - :linenos: - :caption: Example: Linear Scaling - - >>> import xarray as xr - >>> from cmethods import CMethods as cm - - >>> # Note: The data sets must contain the dimension "time" - >>> # for the respective variable. - >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") - >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") - >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") - >>> variable = "tas" # temperatures - - >>> ls_adjusted = cm.linear_scaling( - ... obs=obs[variable], - ... simh=simh[variable], - ... simp=simp[variable], - ... kind="+" - ... ) - """ - cls.check_types(obs=obs, simh=simh, simp=simp) - - if group is not None: - return cls.grouped_correction( - method="linear_scaling", - obs=obs, - simh=simh, - simp=simp, - group=group, - kind=kind, - **kwargs, - ) - - if kind in cls.ADDITIVE: - return np.array(simp) + (np.nanmean(obs) - np.nanmean(simh)) # Eq. 1 - if kind in cls.MULTIPLICATIVE: - adj_scaling_factor = cls.get_adjusted_scaling_factor( - cls.ensure_devidable(np.nanmean(obs), np.nanmean(simh)), - kwargs.get("max_scaling_factor", cls.MAX_SCALING_FACTOR), - ) - return np.array(simp) * adj_scaling_factor # Eq. 2 - raise NotImplementedError( - f"{kind} not available for linear_scaling. Use '+' or '*' instead." - ) - - # ? -----========= V A R I A N C E - S C A L I N G =========------ - @classmethod - def variance_scaling( - cls: CMethods, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - group: Optional[str] = "time.month", - kind: str = "+", - **kwargs: Any, - ) -> np.ndarray: - r""" - The Variance Scaling bias correction technique can be applied only on non-stochastic - climate variables to minimize deviations in the mean and variance - between predicted and observed time-series of past and future time periods. - - This method must be applied on a 1-dimensional data set i.e., there is only one - time-series passed for each of ``obs``, ``simh``, and ``simp``. - - The Variance Scaling bias correction technique implemented here is based on the - method described in the equations of Teutschbein, Claudia and Seibert, Jan (2012) - *"Bias correction of regional climate model simulations for hydrological climate-change - impact studies: Review and evaluation of different methods"* - (https://doi.org/10.1016/j.jhydrol.2012.05.052). In the following the equations - of the Variance Scaling approach are shown: - - **(1)** First, the modeled data of the control and scenario period must be bias-corrected using - the additive linear scaling technique. This adjusts the deviation in the mean. - - .. math:: - - X^{*LS}_{sim,h}(i) = X_{sim,h}(i) + \mu_{m}(X_{obs,h}(i)) - \mu_{m}(X_{sim,h}(i)) - - X^{*LS}_{sim,p}(i) = X_{sim,p}(i) + \mu_{m}(X_{obs,h}(i)) - \mu_{m}(X_{sim,h}(i)) - - - **(2)** In the second step, the time-series are shifted to a zero mean. This enables the adjustment - of the standard deviation in the following step. - - .. math:: - - X^{VS(1)}_{sim,h}(i) = X^{*LS}_{sim,h}(i) - \mu_{m}(X^{*LS}_{sim,h}(i)) - - X^{VS(1)}_{sim,p}(i) = X^{*LS}_{sim,p}(i) - \mu_{m}(X^{*LS}_{sim,p}(i)) - - - **(3)** Now the standard deviation (so variance too) can be scaled. - - .. math:: - - X^{VS(2)}_{sim,p}(i) = X^{VS(1)}_{sim,p}(i) \cdot \left[\frac{\sigma_{m}(X_{obs,h}(i))}{\sigma_{m}(X^{VS(1)}_{sim,h}(i))}\right] - - - **(4)** Finally the previously removed mean is shifted back - - .. math:: - - X^{*VS}_{sim,p}(i) = X^{VS(2)}_{sim,p}(i) + \mu_{m}(X^{*LS}_{sim,p}(i)) - - - :param obs: The reference data set of the control period - (in most cases the observational data) - :type obs: xr.core.dataarray.DataArray - :param simh: The modeled data of the control period - :type simh: xr.core.dataarray.DataArray - :param simp: The modeled data of the scenario period (this is the data set - on which the bias correction takes action) - :type simp: xr.core.dataarray.DataArray - :param group: The grouping defines the basis of the mean, defaults to ``time.month`` - :type group: str | None - :param kind: The kind of the correction, additive for non-stochastic climate variables - no other kind is available so far, defaults to ``+`` - :type kind: str, optional - :raises NotImplementedError: If the kind is not in (``+``, ``add``) - :return: The bias-corrected time series - :rtype: np.ndarray - - .. code-block:: python - :linenos: - :caption: Example: Variance Scaling - - >>> import xarray as xr - >>> from cmethods import CMethods as cm - - >>> # Note: The data sets must contain the dimension "time" - >>> # for the respective variable. - >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") - >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") - >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") - >>> variable = "tas" # temperatures - - >>> vs_adjusted = cm.variance_scaling( - ... obs=obs[variable], - ... simh=simh[variable], - ... simp=simp[variable], - ... ) - """ - cls.check_types(obs=obs, simh=simh, simp=simp) - - if group is not None: - return cls.grouped_correction( - method="variance_scaling", - obs=obs, - simh=simh, - simp=simp, - group=group, - kind=kind, - **kwargs, - ) - - if kind in cls.ADDITIVE: - LS_simh = cls.linear_scaling(obs, simh, simh, group=None) # Eq. 1 - LS_simp = cls.linear_scaling(obs, simh, simp, group=None) # Eq. 2 - - VS_1_simh = LS_simh - np.nanmean(LS_simh) # Eq. 3 - VS_1_simp = LS_simp - np.nanmean(LS_simp) # Eq. 4 - - adj_scaling_factor = cls.get_adjusted_scaling_factor( - cls.ensure_devidable(np.std(np.array(obs)), np.std(VS_1_simh)), - kwargs.get("max_scaling_factor", cls.MAX_SCALING_FACTOR), - ) - - VS_2_simp = VS_1_simp * adj_scaling_factor # Eq. 5 - return VS_2_simp + np.nanmean(LS_simp) # Eq. 6 - - raise NotImplementedError( - f"{kind} not available for variance_scaling. Use '+' instead." - ) - - # ? -----========= D E L T A - M E T H O D =========------ - @classmethod - def delta_method( - cls: CMethods, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - group: Optional[str] = "time.month", - kind: str = "+", - **kwargs: Any, - ) -> np.ndarray: - r""" - The Delta Method bias correction technique can be applied on stochastic and - non-stochastic climate variables to minimize deviations in the mean values - between predicted and observed time-series of past and future time periods. - - Since the multiplicative scaling can result in very high scaling factors, - a maximum scaling factor of 10 is set. This can be changed by passing - another value to the optional `max_scaling_factor` argument. - - This method must be applied on a 1-dimensional data set i.e., there is only one - time-series passed for each of ``obs``, ``simh``, and ``simp``. - - The Delta Method bias correction technique implemented here is based on the - method described in the equations of Beyer, R. and Krapp, M. and Manica, A. (2020) - *"An empirical evaluation of bias correction methods for paleoclimate simulations"* - (https://doi.org/10.5194/cp-16-1493-2020). In the following the equations - for both additive and multiplicative Delta Method are shown: - - **Additive**: - - The Delta Method looks like the Linear Scaling method but the important difference is, that the Delta method - uses the change between the modeled data instead of the difference between the modeled and reference data of the control - period. This means that the long-term monthly mean (:math:`\mu_m`) of the modeled data of the control period :math:`T_{sim,h}` - is subtracted from the long-term monthly mean of the modeled data from the scenario period :math:`T_{sim,p}` at time step :math:`i`. - This change in month-dependent long-term mean is than added to the long-term monthly mean for time step :math:`i`, - in the time-series that represents the reference data of the control period (:math:`T_{obs,h}`). - - .. math:: - - X^{*DM}_{sim,p}(i) = X_{obs,h}(i) + \mu_{m}(X_{sim,p}(i)) - \mu_{m}(X_{sim,h}(i)) - - - **Multiplicative**: - - The multiplicative variant behaves like the additive, but with the difference that the change is computed using the relative change - instead of the absolute change. - - .. math:: - - X^{*DM}_{sim,p}(i) = X_{obs,h}(i) \cdot \left[\frac{ \mu_{m}(X_{sim,p}(i)) }{ \mu_{m}(X_{sim,h}(i))}\right] - - - :param obs: The reference data set of the control period - (in most cases the observational data) - :type obs: xr.core.dataarray.DataArray - :param simh: The modeled data of the control period - :type simh: xr.core.dataarray.DataArray - :param simp: The modeled data of the scenario period (this is the data set - on which the bias correction takes action) - :type simp: xr.core.dataarray.DataArray - :param group: The grouping defines the basis of the mean, defaults to ``time.month`` - :type group: str | None - :param kind: The kind of the correction, additive for non-stochastic and multiplicative - for stochastic climate variables, defaults to ``+`` - :type kind: str, optional - :raises NotImplementedError: If the kind is not in (``+``, ``*``, ``add``, ``mult``) - :return: The bias-corrected time series - :rtype: np.ndarray - - .. code-block:: python - :linenos: - :caption: Example: Delta Method - - >>> import xarray as xr - >>> from cmethods import CMethods as cm - - >>> # Note: The data sets must contain the dimension "time" - >>> # for the respective variable. - >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") - >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") - >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") - >>> variable = "tas" # temperatures - - >>> dm_adjusted = cm.delta_method( - ... obs=obs[variable], - ... simh=simh[variable], - ... simp=simp[variable], - ... ) - """ - cls.check_types(obs=obs, simh=simh, simp=simp) - - if group is not None: - return cls.grouped_correction( - method="delta_method", - obs=obs, - simh=simh, - simp=simp, - group=group, - kind=kind, - **kwargs, - ) - - if kind in cls.ADDITIVE: - return np.array(obs) + (np.nanmean(simp) - np.nanmean(simh)) # Eq. 1 - if kind in cls.MULTIPLICATIVE: - adj_scaling_factor = cls.get_adjusted_scaling_factor( - cls.ensure_devidable(np.nanmean(simp), np.nanmean(simh)), - kwargs.get("max_scaling_factor", cls.MAX_SCALING_FACTOR), - ) - return np.array(obs) * adj_scaling_factor # Eq. 2 - raise NotImplementedError( - f"{kind} not available for delta_method. Use '+' or '*' instead." - ) - - # ? -----========= Q U A N T I L E - M A P P I N G =========------ - @classmethod - def quantile_mapping( - cls: CMethods, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - n_quantiles: int, - kind: str = "+", - **kwargs: Any, - ) -> np.ndarray: - r""" - The Quantile Mapping bias correction technique can be used to minimize distributional - biases between modeled and observed time-series climate data. Its interval-independent - behavior ensures that the whole time series is taken into account to redistribute - its values, based on the distributions of the modeled and observed/reference data of the - control period. - - This method must be applied on a 1-dimensional data set i.e., there is only one - time-series passed for each of ``obs``, ``simh``, and ``simp``. - - The Quantile Mapping technique implemented here is based on the equations of - Alex J. Cannon and Stephen R. Sobie and Trevor Q. Murdock (2015) *"Bias Correction of GCM - Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles - and Extremes?"* (https://doi.org/10.1175/JCLI-D-14-00754.1). - - The regular Quantile Mapping is bounded to the value range of the modeled data - of the control period. To avoid this, the Detrended Quantile Mapping can be used. - - In the following the equations of Alex J. Cannon (2015) are shown and explained: - - **Additive**: - - .. math:: - - X^{*QM}_{sim,p}(i) = F^{-1}_{obs,h} \left\{F_{sim,h}\left[X_{sim,p}(i)\right]\right\} - - - The additive quantile mapping procedure consists of inserting the value to be - adjusted (:math:`X_{sim,p}(i)`) into the cumulative distribution function - of the modeled data of the control period (:math:`F_{sim,h}`). This determines, - in which quantile the value to be adjusted can be found in the modeled data of the control period - The following images show this by using :math:`T` for temperatures. - - .. figure:: ../_static/images/qm-cdf-plot-1.png - :width: 600 - :align: center - :alt: Determination of the quantile value - - Fig 1: Inserting :math:`X_{sim,p}(i)` into :math:`F_{sim,h}` to determine the quantile value - - This value, which of course lies between 0 and 1, is subsequently inserted - into the inverse cumulative distribution function of the reference data of the control period to - determine the bias-corrected value at time step :math:`i`. - - .. figure:: ../_static/images/qm-cdf-plot-2.png - :width: 600 - :align: center - :alt: Determination of the QM bias-corrected value - - Fig 1: Inserting the quantile value into :math:`F^{-1}_{obs,h}` to determine the bias-corrected value for time step :math:`i` - - **Multiplicative**: - - .. math:: - - X^{*QM}_{sim,p}(i) = F^{-1}_{obs,h}\Biggl\{F_{sim,h}\left[\frac{\mu{X_{sim,h}} \cdot \mu{X_{sim,p}(i)}}{\mu{X_{sim,p}(i)}}\right]\Biggr\}\frac{\mu{X_{sim,p}(i)}}{\mu{X_{sim,h}}} - - - :param obs: The reference data set of the control period - (in most cases the observational data) - :type obs: xr.core.dataarray.DataArray - :param simh: The modeled data of the control period - :type simh: xr.core.dataarray.DataArray - :param simp: The modeled data of the scenario period (this is the data set - on which the bias correction takes action) - :type simp: xr.core.dataarray.DataArray - :param n_quantiles: Number of quantiles to respect/use - :type n_quantiles: int - :param kind: The kind of the correction, additive for non-stochastic and multiplicative - for stochastic climate variables, defaults to ``+`` - :type kind: str, optional - :param val_min: Lower boundary for interpolation (only if ``kind="*"``, default: ``0.0``) - :type val_min: float, optional - :param val_max: Upper boundary for interpolation (only if ``kind="*"``, default: ``None``) - :type val_max: float, optional - :raises NotImplementedError: If the kind is not in (``+``, ``*``, ``add``, ``mult``) - :return: The bias-corrected time series - :rtype: np.ndarray - - .. code-block:: python - :linenos: - :caption: Example: Quantile Mapping - - >>> import xarray as xr - >>> from cmethods import CMethods as cm - - >>> # Note: The data sets must contain the dimension "time" - >>> # for the respective variable. - >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") - >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") - >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") - >>> variable = "tas" # temperatures - - >>> qm_adjusted = cm.quantile_mapping( - ... obs=obs[variable], - ... simh=simh[variable], - ... simp=simp[variable], - ... n_quantiles=250 - ... ) - """ - cls.check_types(obs=obs, simh=simh, simp=simp) - if not isinstance(n_quantiles, int): - raise TypeError("'n_quantiles' must be type int") - - obs, simh, simp = np.array(obs), np.array(simh), np.array(simp) - - global_max = max(np.nanmax(obs), np.nanmax(simh)) - global_min = min(np.nanmin(obs), np.nanmin(simh)) - - if cls.nan_or_equal(value1=global_max, value2=global_min): - return simp - - wide = abs(global_max - global_min) / n_quantiles - xbins = np.arange(global_min, global_max + wide, wide) - - cdf_obs = cls.get_cdf(obs, xbins) - cdf_simh = cls.get_cdf(simh, xbins) - - if kind in cls.ADDITIVE: - epsilon = np.interp(simp, xbins, cdf_simh) # Eq. 1 - return cls.get_inverse_of_cdf(cdf_obs, epsilon, xbins) # Eq. 1 - - if kind in cls.MULTIPLICATIVE: - epsilon = np.interp( # Eq. 2 - simp, - xbins, - cdf_simh, - left=kwargs.get("val_min", 0.0), - right=kwargs.get("val_max", None), - ) - return cls.get_inverse_of_cdf(cdf_obs, epsilon, xbins) # Eq. 2 - - raise NotImplementedError( - f"{kind} for quantile_mapping is not available. Use '+' or '*' instead." - ) - - @classmethod - def detrended_quantile_mapping( - cls: CMethods, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - n_quantiles: int, - kind: str = "+", - **kwargs: Any, - ) -> np.ndarray: - r""" - The Detrended Quantile Mapping bias correction technique can be used to minimize distributional - biases between modeled and observed time-series climate data like the regular Quantile Mapping. - Detrending means, that the values of :math:`X_{sim,p}` are shifted to the value range of - :math:`X_{sim,h}` before the regular Quantile Mapping is applied. - After the Quantile Mapping was applied, the mean is shifted back. Since it does not make sens - to take the whole mean to rescale the data, the month-dependent long-term mean is used. - - This method must be applied on a 1-dimensional data set i.e., there is only one - time-series passed for each of ``obs``, ``simh``, and ``simp``. Also this method requires - that the time series are groupable by ``time.month``. - - The Detrended Quantile Mapping technique implemented here is based on the equations of - Alex J. Cannon and Stephen R. Sobie and Trevor Q. Murdock (2015) *"Bias Correction of GCM - Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles - and Extremes?"* (https://doi.org/10.1175/JCLI-D-14-00754.1). - - In the following the equations of Alex J. Cannon (2015) are shown (without detrending; see QM - for explanations): - - **Additive**: - - .. math:: - - X^{*QM}_{sim,p}(i) = F^{-1}_{obs,h} \left\{F_{sim,h}\left[X_{sim,p}(i)\right]\right\} - - - **Multiplicative**: - - .. math:: - - X^{*QM}_{sim,p}(i) = F^{-1}_{obs,h}\Biggl\{F_{sim,h}\left[\frac{\mu{X_{sim,h}} \cdot \mu{X_{sim,p}(i)}}{\mu{X_{sim,p}(i)}}\right]\Biggr\}\frac{\mu{X_{sim,p}(i)}}{\mu{X_{sim,h}}} - - - :param obs: The reference data set of the control period - (in most cases the observational data) - :type obs: xr.core.dataarray.DataArray - :param simh: The modeled data of the control period - :type simh: xr.core.dataarray.DataArray - :param simp: The modeled data of the scenario period (this is the data set - on which the bias correction takes action) - :type simp: xr.core.dataarray.DataArray - :param n_quantiles: Number of quantiles to respect/use - :type n_quantiles: int - :param kind: The kind of the correction, additive for non-stochastic and multiplicative - for stochastic climate variables, defaults to ``+`` - :type kind: str, optional - :param val_min: Lower boundary for interpolation (only if ``kind="*"``, default: ``0.0``) - :type val_min: float, optional - :param val_max: Upper boundary for interpolation (only if ``kind="*"``, default: ``None``) - :type val_max: float, optional - :raises NotImplementedError: If the kind is not in (``+``, ``*``, ``add``, ``mult``) - :return: The bias-corrected time series - :rtype: np.ndarray - - .. code-block:: python - :linenos: - :caption: Example: Quantile Mapping - - >>> import xarray as xr - >>> from cmethods import CMethods as cm - - >>> # Note: The data sets must contain the dimension "time" - >>> # for the respective variable. - >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") - >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") - >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") - >>> variable = "tas" # temperatures - - >>> qm_adjusted = cm.detrended_quantile_mapping( - ... obs=obs[variable], - ... simh=simh[variable], - ... simp=simp[variable], - ... n_quantiles=250 - ... ) - """ - if kind not in cls.MULTIPLICATIVE and kind not in cls.ADDITIVE: - raise NotImplementedError( - f"{kind} for detrended_quantile_mapping is not available. Use '+' or '*' instead." - ) - - cls.check_types(obs=obs, simh=simh, simp=simp) - if not isinstance(n_quantiles, int): - raise TypeError("'n_quantiles' must be type int") - if not isinstance(simp, xr.core.dataarray.DataArray): - raise TypeError("'simp' must be type xarray.core.dataarray.DataArray") - - obs, simh = np.array(obs), np.array(simh) - - global_max = max(np.nanmax(obs), np.nanmax(simh)) - global_min = min(np.nanmin(obs), np.nanmin(simh)) - - if cls.nan_or_equal(value1=global_max, value2=global_min): - return np.array(simp.values) - - wide = abs(global_max - global_min) / n_quantiles - xbins = np.arange(global_min, global_max + wide, wide) - - cdf_obs = cls.get_cdf(obs, xbins) - cdf_simh = cls.get_cdf(simh, xbins) - - # detrended => shift mean of $X_{sim,p}$ to range of $X_{sim,h}$ to adjust extremes - res = np.zeros(len(simp.values)) - for indices in simp.groupby("time.month").groups.values(): - # detrended by long-term month - m_simh, m_simp = [], [] - for index in indices: - m_simh.append(simh[index]) - m_simp.append(simp[index]) - - m_simh = np.array(m_simh) - m_simp = np.array(m_simp) - m_simh_mean = np.nanmean(m_simh) - m_simp_mean = np.nanmean(m_simp) - - if kind in cls.ADDITIVE: - epsilon = np.interp( - m_simp - m_simp_mean + m_simh_mean, xbins, cdf_simh - ) # Eq. 1 - X = ( - cls.get_inverse_of_cdf(cdf_obs, epsilon, xbins) - + m_simp_mean - - m_simh_mean - ) # Eq. 1 - - else: # kind in cls.MULTIPLICATIVE: - epsilon = np.interp( # Eq. 2 - cls.ensure_devidable((m_simh_mean * m_simp), m_simp_mean), - xbins, - cdf_simh, - left=kwargs.get("val_min", 0.0), - right=kwargs.get("val_max", None), - ) - X = np.interp(epsilon, cdf_obs, xbins) * cls.ensure_devidable( - m_simp_mean, m_simh_mean - ) # Eq. 2 - for i, index in enumerate(indices): - res[index] = X[i] - return res - - # ? -----========= E M P I R I C A L - Q U A N T I L E - M A P P I N G =========------ - @classmethod - def empirical_quantile_mapping( - cls: CMethods, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - n_quantiles: int = 100, - extrapolate: Optional[str] = None, - **kwargs: Any, - ) -> xr.core.dataarray.DataArray: - """ - Method to adjust 1-dimensional climate data by empirical quantile mapping - - :param obs: The reference data set of the control period - (in most cases the observational data) - :type obs: xr.core.dataarray.DataArray - :param simh: The modeled data of the control period - :type simh: xr.core.dataarray.DataArray - :param simp: The modeled data of the scenario period (this is the data set - on which the bias correction takes action) - :type simp: xr.core.dataarray.DataArray - :param n_quantiles: Number of quantiles to respect/use, defaults to ``100`` - :type n_quantiles: int, optional - :type kind: str, optional - :param extrapolate: Bounded range or extrapolate, defaults to ``None`` - :type extrapolate: str | None - :return: The bias-corrected time series - :rtype: xr.core.dataarray.DataArray - :raises NotImplementedError: This method is not implemented - """ - raise NotImplementedError( - "Not implemented; please have a look at: https://svn.oss.deltares.nl/repos/openearthtools/trunk/python/applications/hydrotools/hydrotools/statistics/bias_correction.py " - ) - - # ? -----========= Q U A N T I L E - D E L T A - M A P P I N G =========------ - @classmethod - def quantile_delta_mapping( - cls: CMethods, - obs: xr.core.dataarray.DataArray, - simh: xr.core.dataarray.DataArray, - simp: xr.core.dataarray.DataArray, - n_quantiles: int, - kind: str = "+", - **kwargs: Any, - ) -> np.ndarray: - r""" - The Quantile Delta Mapping bias correction technique can be used to minimize distributional - biases between modeled and observed time-series climate data. Its interval-independent - behavior ensures that the whole time series is taken into account to redistribute - its values, based on the distributions of the modeled and observed/reference data of the - control period. In contrast to the regular Quantile Mapping (:func:`cmethods.CMethods.quantile_mapping`) - the Quantile Delta Mapping also takes the change between the modeled data of the control and scenario - period into account. - - This method must be applied on a 1-dimensional data set i.e., there is only one - time-series passed for each of ``obs``, ``simh``, and ``simp``. - - The Quantile Delta Mapping technique implemented here is based on the equations of - Tong, Y., Gao, X., Han, Z. et al. (2021) *"Bias correction of temperature and precipitation - over China for RCM simulations using the QM and QDM methods"*. Clim Dyn 57, 1425-1443 - (https://doi.org/10.1007/s00382-020-05447-4). In the following the additive and multiplicative - variant are shown. - - **Additive**: - - **(1.1)** In the first step the quantile value of the time step :math:`i` to adjust is stored in - :math:`\varepsilon(i)`. - - .. math:: - - \varepsilon(i) = F_{sim,p}\left[X_{sim,p}(i)\right], \hspace{1em} \varepsilon(i)\in\{0,1\} - - - **(1.2)** The bias corrected value at time step :math:`i` is now determined by inserting the - quantile value into the inverse cummulative distribution function of the reference data of the control - period. This results in a bias corrected value for time step :math:`i` but still without taking the - change in modeled data into account. - - .. math:: - - X^{QDM(1)}_{sim,p}(i) = F^{-1}_{obs,h}\left[\varepsilon(i)\right] - - - **(1.3)** The :math:`\Delta(i)` represents the absolute change in quantiles between the modeled value - in the control and scenario period. - - .. math:: - - \Delta(i) & = F^{-1}_{sim,p}\left[\varepsilon(i)\right] - F^{-1}_{sim,h}\left[\varepsilon(i)\right] \\[1pt] - & = X_{sim,p}(i) - F^{-1}_{sim,h}\left\{F^{}_{sim,p}\left[X_{sim,p}(i)\right]\right\} - - - **(1.4)** Finally the previously calculated change can be added to the bias-corrected value. - - .. math:: - - X^{*QDM}_{sim,p}(i) = X^{QDM(1)}_{sim,p}(i) + \Delta(i) - - - **Multiplicative**: - - The first two steps of the multiplicative Quantile Delta Mapping bias correction technique are the - same as for the additive variant. - - **(2.3)** The :math:`\Delta(i)` in the multiplicative Quantile Delta Mapping is calulated like the - additive variant, but using the relative than the absolute change. - - .. math:: - - \Delta(i) & = \frac{ F^{-1}_{sim,p}\left[\varepsilon(i)\right] }{ F^{-1}_{sim,h}\left[\varepsilon(i)\right] } \\[1pt] - & = \frac{ X_{sim,p}(i) }{ F^{-1}_{sim,h}\left\{F_{sim,p}\left[X_{sim,p}(i)\right]\right\} } - - - **(2.4)** The relative change between the modeled data of the control and scenario period is than - multiplicated with the bias-corrected value (see **1.2**). - - .. math:: - - X^{*QDM}_{sim,p}(i) = X^{QDM(1)}_{sim,p}(i) \cdot \Delta(i) - - - :param obs: The reference data set of the control period - (in most cases the observational data) - :type obs: xr.core.dataarray.DataArray - :param simh: The modeled data of the control period - :type simh: xr.core.dataarray.DataArray - :param simp: The modeled data of the scenario period (this is the data set - on which the bias correction takes action) - :type simp: xr.core.dataarray.DataArray - :param n_quantiles: Number of quantiles to respect/use - :type n_quantiles: int - :param kind: The kind of the correction, additive for non-stochastic and multiplicative - for non-stochastic climate variables, defaults to ``+`` - :type kind: str, optional - :raises NotImplementedError: If the kind is not in (``+``, ``*``, ``add``, ``mult``) - :return: The bias-corrected time series - :rtype: np.ndarray - - .. code-block:: python - :linenos: - :caption: Example: Quantile Delta Mapping - - >>> import xarray as xr - >>> from cmethods import CMethods as cm - - >>> # Note: The data sets must contain the dimension "time" - >>> # for the respective variable. - >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") - >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") - >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") - >>> variable = "tas" # temperatures - - >>> qdm_adjusted = cm.quantile_delta_mapping( - ... obs=obs[variable], - ... simh=simh[variable], - ... simp=simp[variable], - ... n_quantiles=250 - ... ) - """ - cls.check_types(obs=obs, simh=simh, simp=simp) - - if not isinstance(n_quantiles, int): - raise TypeError("'n_quantiles' must be type int") - - if kind in cls.ADDITIVE: - obs, simh, simp = ( - np.array(obs), - np.array(simh), - np.array(simp), - ) # to achieve higher accuracy - global_max = kwargs.get("global_max", max(np.nanmax(obs), np.nanmax(simh))) - global_min = kwargs.get("global_min", min(np.nanmin(obs), np.nanmin(simh))) - - if cls.nan_or_equal(value1=global_max, value2=global_min): - return simp - - wide = abs(global_max - global_min) / n_quantiles - xbins = np.arange(global_min, global_max + wide, wide) - - cdf_obs = cls.get_cdf(obs, xbins) - cdf_simh = cls.get_cdf(simh, xbins) - cdf_simp = cls.get_cdf(simp, xbins) - - # calculate exact cdf values of $F_{sim,p}[T_{sim,p}(t)]$ - epsilon = np.interp(simp, xbins, cdf_simp) # Eq. 1.1 - QDM1 = cls.get_inverse_of_cdf(cdf_obs, epsilon, xbins) # Eq. 1.2 - delta = simp - cls.get_inverse_of_cdf(cdf_simh, epsilon, xbins) # Eq. 1.3 - return QDM1 + delta # Eq. 1.4 - - if kind in cls.MULTIPLICATIVE: - obs, simh, simp = np.array(obs), np.array(simh), np.array(simp) - global_max = kwargs.get("global_max", max(np.nanmax(obs), np.nanmax(simh))) - global_min = kwargs.get("global_min", 0.0) - if cls.nan_or_equal(value1=global_max, value2=global_min): - return simp - - wide = global_max / n_quantiles - xbins = np.arange(global_min, global_max + wide, wide) - - cdf_obs = cls.get_cdf(obs, xbins) - cdf_simh = cls.get_cdf(simh, xbins) - cdf_simp = cls.get_cdf(simp, xbins) - - epsilon = np.interp(simp, xbins, cdf_simp) # Eq. 1.1 - QDM1 = cls.get_inverse_of_cdf(cdf_obs, epsilon, xbins) # Eq. 1.2 - - delta = cls.ensure_devidable( # Eq. 2.3 - simp, cls.get_inverse_of_cdf(cdf_simh, epsilon, xbins) - ) - return QDM1 * delta # Eq. 2.4 - raise NotImplementedError( - f"{kind} not available for quantile_delta_mapping. Use '+' or '*' instead." - ) - - @classmethod - def check_types( - cls: CMethods, - obs: Union[list, np.ndarray, np.generic, xr.core.dataarray.DataArray], - simh: Union[list, np.ndarray, np.generic, xr.core.dataarray.DataArray], - simp: Union[list, np.ndarray, np.generic, xr.core.dataarray.DataArray], - ) -> None: - """ - Checks if the parameters are in the correct type. **only used internally** - """ - phrase: str = "must be type list, np.ndarray or xarray.core.dataarray.DataArray" - valid_types: tuple = (list, np.ndarray, np.generic, xr.core.dataarray.DataArray) - - if not isinstance(obs, valid_types): - raise TypeError(f"'obs' {phrase}") - if not isinstance(simh, valid_types): - raise TypeError(f"'simh' {phrase}") - if not isinstance(simp, valid_types): - raise TypeError(f"'simp' {phrase}") - - @staticmethod - def nan_or_equal(value1: float, value2: float) -> bool: - """ - Returns True if the values are equal or at least one is NaN - - :param value1: First value to check - :type value1: float - :param value2: Second value to check - :type value2: float - :return: If any value is NaN or values are equal - :rtype: bool - """ - return np.isnan(value1) or np.isnan(value2) or value1 == value2 - - @classmethod - def ensure_devidable( - cls: CMethods, - numerator: Union[float, np.ndarray], - denominator: Union[float, np.ndarray], - ) -> np.ndarray: - """ - Ensures that the arrays can be devided. The numerator will be multiplied by - the maximum scaling factor of the CMethods class if division by zero. - - :param numerator: Numerator to use - :type numerator: np.ndarray - :param denominator: Denominator that can be zero - :type denominator: np.ndarray - :return: Zero-ensured devision - :rtype: np.ndarray | float - """ - with np.errstate(divide="ignore", invalid="ignore"): - result = numerator / denominator - - if isinstance(numerator, np.ndarray): - mask_inf = np.isinf(result) - result[mask_inf] = numerator[mask_inf] * cls.MAX_SCALING_FACTOR - - mask_nan = np.isnan(result) - result[mask_nan] = 0 - elif np.isinf(result): - result = numerator * cls.MAX_SCALING_FACTOR - elif np.isnan(result): - result = 0.0 - - return result - - @staticmethod - def get_pdf( - x: Union[list, np.ndarray], xbins: Union[list, np.ndarray] - ) -> np.ndarray: - r""" - Compuites and returns the the probability density function :math:`P(x)` - of ``x`` based on ``xbins``. - - :param x: The vector to get :math:`P(x)` from - :type x: Union[list, np.ndarray] - :param xbins: The boundaries/bins of :math:`P(x)` - :type xbins: Union[list, np.ndarray] - :return: The probability densitiy function of ``x`` - :rtype: np.ndarray - - .. code-block:: python - :linenos: - :caption: Compute the probability density function :math:`P(x)` - - >>> from cmethods import CMethods as cm - - >>> x = [1, 2, 3, 4, 5, 5, 5, 6, 7, 8, 9, 10] - >>> xbins = [0, 3, 6, 10] - >>> print(cm.get_pdf(x=x, xbins=xbins)) - [2, 5, 5] - """ - pdf, _ = np.histogram(x, xbins) - return pdf - - @staticmethod - def get_cdf( - x: Union[list, np.ndarray], xbins: Union[list, np.ndarray] - ) -> np.ndarray: - r""" - Computes and returns returns the cumulative distribution function :math:`F(x)` - of ``x`` based on ``xbins``. - - :param x: Vector to get :math:`F(x)` from - :type x: Union[list, np.ndarray] - :param xbins: The boundaries/bins of :math:`F(x)` - :type xbins: Union[list, np.ndarray] - :return: The cumulative distribution function of ``x`` - :rtype: np.ndarray - - - .. code-block:: python - :linenos: - :caption: Compute the cmmulative distribution function :math:`F(x)` - - >>> from cmethods import CMethods as cm - - >>> x = [1, 2, 3, 4, 5, 5, 5, 6, 7, 8, 9, 10] - >>> xbins = [0, 3, 6, 10] - >>> print(cm.get_cdf(x=x, xbins=xbins)) - [0, 2, 7, 12] - """ - pdf, _ = np.histogram(x, xbins) - return np.insert(np.cumsum(pdf), 0, 0.0) - - @staticmethod - def get_inverse_of_cdf( - base_cdf: Union[list, np.ndarray], - insert_cdf: Union[list, np.ndarray], - xbins: Union[list, np.ndarray], - ) -> np.ndarray: - r""" - Returns the inverse cumulative distribution function as: - :math:`F^{-1}_{x}\left[y\right]` where :math:`x` represents ``base_cdf`` and - ``insert_cdf`` is represented by :math:`y`. - - :param base_cdf: The basis - :type base_cdf: Union[list, np.ndarray] - :param insert_cdf: The CDF that gets inserted - :type insert_cdf: Union[list, np.ndarray] - :param xbins: Probability boundaries - :type xbins: Union[list, np.ndarray] - :return: The inverse CDF - :rtype: np.ndarray - """ - return np.interp(insert_cdf, base_cdf, xbins) - - @staticmethod - def get_adjusted_scaling_factor( - factor: Union[int, float], max_scaling_factor: Union[int, float] - ) -> float: - r""" - Returns: - - :math:`x` if :math:`-|y| \le x \le |y|`, - - :math:`|y|` if :math:`x > |y|`, or - - :math:`-|y|` if :math:`x < -|y|` - - where: - - :math:`x` is ``factor`` - - :math:`y` is ``max_scaling_factor`` - - :param factor: The value to check for - :type factor: Union[int, float] - :param max_scaling_factor: The maximum/minimum allowed value - :type max_scaling_factor: Union[int, float] - :return: The correct value - :rtype: float - """ - if factor > 0 and factor > abs(max_scaling_factor): - return abs(max_scaling_factor) - if factor < 0 and factor < -abs(max_scaling_factor): - return -abs(max_scaling_factor) - return factor +__all__ = ["adjust"] diff --git a/cmethods/core.py b/cmethods/core.py new file mode 100644 index 0000000..1860729 --- /dev/null +++ b/cmethods/core.py @@ -0,0 +1,154 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2024 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +""" +Module providing the main function that is used to apply the implemented bias +correction techniques. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Callable, Dict, Optional + +import xarray as xr + +from cmethods.distribution import quantile_delta_mapping as __quantile_delta_mapping +from cmethods.distribution import quantile_mapping as __quantile_mapping +from cmethods.scaling import delta_method as __delta_method +from cmethods.scaling import linear_scaling as __linear_scaling +from cmethods.scaling import variance_scaling as __variance_scaling +from cmethods.static import SCALING_METHODS +from cmethods.utils import UnknownMethodError, check_xr_types + +if TYPE_CHECKING: + from cmethods.types import XRData + +__METHODS_FUNC__: Dict[str, Callable] = { + "linear_scaling": __linear_scaling, + "variance_scaling": __variance_scaling, + "delta_method": __delta_method, + "quantile_mapping": __quantile_mapping, + "quantile_delta_mapping": __quantile_delta_mapping, +} + + +def apply_ufunc( + method: str, + obs: XRData, + simh: XRData, + simp: XRData, + **kwargs: dict, +) -> XRData: + """ + Internal function used to apply the bias correction technique to the + passed input data. + """ + check_xr_types(obs=obs, simh=simh, simp=simp) + if method not in __METHODS_FUNC__: + raise UnknownMethodError(method, __METHODS_FUNC__.keys()) + + result: XRData = xr.apply_ufunc( + __METHODS_FUNC__[method], + obs, + simh, + # Need to spoof a fake time axis since 'time' coord on full dataset is different + # than 'time' coord on training dataset. + simp.rename({"time": "t2"}), + dask="parallelized", + vectorize=True, + # This will vectorize over the time dimension, so will submit each grid cell + # independently + input_core_dims=[["time"], ["time"], ["t2"]], + # Need to denote that the final output dataset will be labeled with the + # spoofed time coordinate + output_core_dims=[["t2"]], + kwargs=dict(kwargs), + ) + + # Rename to proper coordinate name. + result = result.rename({"t2": "time"}) + + # ufunc will put the core dimension to the end (time), so want to preserve original + # order where time is commonly first. + return result.transpose(*obs.dims) + + +def adjust( + method: str, + obs: XRData, + simh: XRData, + simp: XRData, + **kwargs, +) -> XRData: + """ + Function to apply a bias correction technique on single and multidimensional + data sets. For more information please refer to the method specific + requirements and execution examples. + + See https://python-cmethods.readthedocs.io/en/latest/src/methods.html + + :param method: Technique to apply + :type method: str + :param obs: The reference/observational data set + :type obs: XRData + :param simh: The modeled data of the control period + :type simh: XRData + :param simp: The modeled data of the period to adjust + :type simp: XRData + :param kwargs: Any other method-specific parameter (like + ``n_quantiles`` and ``kind``) + :type kwargs: dict + :return: The bias corrected/adjusted data set + :rtype: XRData + """ + kwargs["adjust_called"] = True + check_xr_types(obs=obs, simh=simh, simp=simp) + + if method == "detrended_quantile_mapping": # noqa: PLR2004 + raise ValueError( + "This function is not available for detrended quantile mapping." + " Please use cmethods.CMethods.detrended_quantile_mapping", + ) + + # No grouped correction | distribution-based technique + # NOTE: This is disabled since using groups like "time.month" will lead + # to unrealistic monthly transitions. If such behavior is wanted, + # mock this function or apply ``CMethods.__apply_ufunc` directly + # on your data sets. + if kwargs.get("group", None) is None: + return apply_ufunc(method, obs, simh, simp, **kwargs).to_dataset() + + if method not in SCALING_METHODS: + raise ValueError( + "Can't use group for distribution based methods.", # except for DQM + ) + + # Grouped correction | scaling-based technique + group: str = kwargs["group"] + del kwargs["group"] + + result: Optional[XRData] = None + for (_, obs_gds), (_, simh_gds), (_, simp_gds) in zip( + obs.groupby(group), + simh.groupby(group), + simp.groupby(group), + ): + monthly_result = apply_ufunc( + method, + obs_gds, + simh_gds, + simp_gds, + **kwargs, + ) + + result = ( + monthly_result if result is None else xr.merge([result, monthly_result]) + ) + + return result + + +__all__ = ["adjust"] diff --git a/cmethods/distribution.py b/cmethods/distribution.py new file mode 100644 index 0000000..f9aa941 --- /dev/null +++ b/cmethods/distribution.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2024 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +""" +Module providing functions for distribution-based bias adjustments. Functions are not +intended to used directly - but as part of the adjustment procedure triggered by +:func:``cmethods.adjust``. +""" + +from typing import Any, Final + +import numpy as np +import xarray as xr + +from cmethods.static import ADDITIVE, MAX_SCALING_FACTOR, MULTIPLICATIVE +from cmethods.types import NPData +from cmethods.utils import ( + check_adjust_called, + check_np_types, + ensure_dividable, + get_cdf, + get_inverse_of_cdf, + nan_or_equal, +) + + +# ? -----========= Q U A N T I L E - M A P P I N G =========------ +def quantile_mapping( + obs: NPData, + simh: NPData, + simp: NPData, + n_quantiles: int, + kind: str = "+", + **kwargs: Any, +) -> np.ndarray: + r""" + **Do not call this function directly, please use :func:`cmethods.CMethods.adjust`** + See https://python-cmethods.readthedocs.io/en/latest/src/methods.html#quantile-mapping + """ + check_adjust_called( + function_name="quantile_mapping", + adjust_called=kwargs.get("adjust_called", None), + ) + check_np_types(obs=obs, simh=simh, simp=simp) + + if not isinstance(n_quantiles, int): + raise TypeError("'n_quantiles' must be type int") + + obs, simh, simp = np.array(obs), np.array(simh), np.array(simp) + + global_max = max(np.nanmax(obs), np.nanmax(simh)) + global_min = min(np.nanmin(obs), np.nanmin(simh)) + + if nan_or_equal(value1=global_max, value2=global_min): + return simp + + wide = abs(global_max - global_min) / n_quantiles + xbins = np.arange(global_min, global_max + wide, wide) + + cdf_obs = get_cdf(obs, xbins) + cdf_simh = get_cdf(simh, xbins) + + if kind in ADDITIVE: + epsilon = np.interp(simp, xbins, cdf_simh) # Eq. 1 + return get_inverse_of_cdf(cdf_obs, epsilon, xbins) # Eq. 1 + + if kind in MULTIPLICATIVE: + epsilon = np.interp( # Eq. 2 + simp, + xbins, + cdf_simh, + left=kwargs.get("val_min", 0.0), + right=kwargs.get("val_max", None), + ) + return get_inverse_of_cdf(cdf_obs, epsilon, xbins) # Eq. 2 + + raise NotImplementedError( + f"{kind=} for quantile_mapping is not available. Use '+' or '*' instead.", + ) + + +# ? -----========= D E T R E N D E D - Q U A N T I L E - M A P P I N G =========------ + + +def detrended_quantile_mapping( + obs: xr.core.dataarray.DataArray, + simh: xr.core.dataarray.DataArray, + simp: xr.core.dataarray.DataArray, + n_quantiles: int, + kind: str = "+", + **kwargs: Any, +) -> NPData: + r""" + See https://python-cmethods.readthedocs.io/en/latest/src/methods.html#detrended_quantile_mapping + + This function can only be applied to 1-dimensional data. + """ + + # TODO: this function should also benefit from ufunc -- but how? # pylint: disable=fixme + + if kind not in MULTIPLICATIVE and kind not in ADDITIVE: + raise NotImplementedError( + f"{kind=} for detrended_quantile_mapping is not available. Use '+' or '*' instead.", + ) + + if not isinstance(n_quantiles, int): + raise TypeError("'n_quantiles' must be type int") + if not isinstance(simp, xr.core.dataarray.DataArray): + raise TypeError("'simp' must be type xarray.core.dataarray.DataArray") + + obs, simh = np.array(obs), np.array(simh) + + global_max = max(np.nanmax(obs), np.nanmax(simh)) + global_min = min(np.nanmin(obs), np.nanmin(simh)) + + if nan_or_equal(value1=global_max, value2=global_min): + return np.array(simp.values) + + wide = abs(global_max - global_min) / n_quantiles + xbins = np.arange(global_min, global_max + wide, wide) + + cdf_obs = get_cdf(obs, xbins) + cdf_simh = get_cdf(simh, xbins) + + # detrended => shift mean of $X_{sim,p}$ to range of $X_{sim,h}$ to adjust extremes + res = np.zeros(len(simp.values)) + max_scaling_factor: Final[float] = kwargs.get( + "max_scaling_factor", + MAX_SCALING_FACTOR, + ) + for indices in simp.groupby("time.month").groups.values(): + # detrended by long-term month + m_simh, m_simp = [], [] + for index in indices: + m_simh.append(simh[index]) + m_simp.append(simp[index]) + + m_simh = np.array(m_simh) + m_simp = np.array(m_simp) + m_simh_mean = np.nanmean(m_simh) + m_simp_mean = np.nanmean(m_simp) + + if kind in ADDITIVE: + epsilon = np.interp( + m_simp - m_simp_mean + m_simh_mean, + xbins, + cdf_simh, + ) # Eq. 1 + X = ( + get_inverse_of_cdf(cdf_obs, epsilon, xbins) + m_simp_mean - m_simh_mean + ) # Eq. 1 + + else: # kind in cls.MULTIPLICATIVE: + epsilon = np.interp( # Eq. 2 + ensure_dividable( + (m_simh_mean * m_simp), + m_simp_mean, + max_scaling_factor=max_scaling_factor, + ), + xbins, + cdf_simh, + left=kwargs.get("val_min", 0.0), + right=kwargs.get("val_max", None), + ) + X = np.interp(epsilon, cdf_obs, xbins) * ensure_dividable( + m_simp_mean, + m_simh_mean, + max_scaling_factor=max_scaling_factor, + ) # Eq. 2 + for i, index in enumerate(indices): + res[index] = X[i] + return res + + +# ? -----========= E M P I R I C A L - Q U A N T I L E - M A P P I N G =========------ +# def __empirical_quantile_mapping( +# self: CMethods, +# obs: NPData, +# simh: NPData, +# simp: NPData, +# n_quantiles: int = 100, +# extrapolate: Optional[str] = None, +# **kwargs: Any, +# ) -> NPData: +# """ +# Method to adjust 1-dimensional climate data by empirical quantile mapping +# """ +# raise NotImplementedError( +# "Not implemented; please have a look at: +# https://svn.oss.deltares.nl/repos/openearthtools/trunk/python/applications/hydrotools/hydrotools/statistics/bias_correction.py " +# ) + +# ? -----========= Q U A N T I L E - D E L T A - M A P P I N G =========------ + + +def quantile_delta_mapping( + obs: NPData, + simh: NPData, + simp: NPData, + n_quantiles: int, + kind: str = "+", + **kwargs: Any, +) -> NPData: + r""" + **Do not call this function directly, please use :func:`cmethods.CMethods.adjust`** + + See https://python-cmethods.readthedocs.io/en/latest/src/methods.html#quantile-delta-mapping + """ + check_adjust_called( + function_name="quantile_delta_mapping", + adjust_called=kwargs.get("adjust_called", None), + ) + check_np_types(obs=obs, simh=simh, simp=simp) + + if not isinstance(n_quantiles, int): + raise TypeError("'n_quantiles' must be type int") + + if kind in ADDITIVE: + obs, simh, simp = ( + np.array(obs), + np.array(simh), + np.array(simp), + ) # to achieve higher accuracy + global_max = kwargs.get("global_max", max(np.nanmax(obs), np.nanmax(simh))) + global_min = kwargs.get("global_min", min(np.nanmin(obs), np.nanmin(simh))) + + if nan_or_equal(value1=global_max, value2=global_min): + return simp + + wide = abs(global_max - global_min) / n_quantiles + xbins = np.arange(global_min, global_max + wide, wide) + + cdf_obs = get_cdf(obs, xbins) + cdf_simh = get_cdf(simh, xbins) + cdf_simp = get_cdf(simp, xbins) + + # calculate exact CDF values of $F_{sim,p}[T_{sim,p}(t)]$ + epsilon = np.interp(simp, xbins, cdf_simp) # Eq. 1.1 + QDM1 = get_inverse_of_cdf(cdf_obs, epsilon, xbins) # Eq. 1.2 + delta = simp - get_inverse_of_cdf(cdf_simh, epsilon, xbins) # Eq. 1.3 + return QDM1 + delta # Eq. 1.4 + + if kind in MULTIPLICATIVE: + obs, simh, simp = np.array(obs), np.array(simh), np.array(simp) + global_max = kwargs.get("global_max", max(np.nanmax(obs), np.nanmax(simh))) + global_min = kwargs.get("global_min", 0.0) + if nan_or_equal(value1=global_max, value2=global_min): + return simp + + wide = global_max / n_quantiles + xbins = np.arange(global_min, global_max + wide, wide) + + cdf_obs = get_cdf(obs, xbins) + cdf_simh = get_cdf(simh, xbins) + cdf_simp = get_cdf(simp, xbins) + + epsilon = np.interp(simp, xbins, cdf_simp) # Eq. 1.1 + QDM1 = get_inverse_of_cdf(cdf_obs, epsilon, xbins) # Eq. 1.2 + + delta = ensure_dividable( # Eq. 2.3 + simp, + get_inverse_of_cdf(cdf_simh, epsilon, xbins), + max_scaling_factor=kwargs.get( + "max_scaling_scaling", + MAX_SCALING_FACTOR, + ), + ) + return QDM1 * delta # Eq. 2.4 + raise NotImplementedError( + f"{kind=} not available for quantile_delta_mapping. Use '+' or '*' instead.", + ) + + +__all__ = ["detrended_quantile_mapping"] diff --git a/cmethods/scaling.py b/cmethods/scaling.py new file mode 100644 index 0000000..2c46d97 --- /dev/null +++ b/cmethods/scaling.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2024 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +""" +Module providing functions for scaling-based bias adjustments. Functions are not +intended to used directly - but as part of the adjustment procedure triggered by +:func:``cmethods.adjust``. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Final + +import numpy as np + +from cmethods.static import ADDITIVE, MAX_SCALING_FACTOR, MULTIPLICATIVE +from cmethods.utils import ( + check_adjust_called, + check_np_types, + ensure_dividable, + get_adjusted_scaling_factor, +) + +if TYPE_CHECKING: + from cmethods.types import NPData + + +# ? -----========= L I N E A R - S C A L I N G =========------ +def linear_scaling( + obs: NPData, + simh: NPData, + simp: NPData, + kind: str = "+", + **kwargs: Any, +) -> NPData: + r""" + **Do not call this function directly, please use :func:`cmethods.CMethods.adjust`** + + See https://python-cmethods.readthedocs.io/en/latest/src/methods.html#linear-scaling + """ + check_adjust_called( + function_name="linear_scaling", + adjust_called=kwargs.get("adjust_called", None), + ) + check_np_types(obs=obs, simh=simh, simp=simp) + + if kind in ADDITIVE: + return np.array(simp) + (np.nanmean(obs) - np.nanmean(simh)) # Eq. 1 + if kind in MULTIPLICATIVE: + max_scaling_factor: Final[float] = kwargs.get( + "max_scaling_factor", + MAX_SCALING_FACTOR, + ) + adj_scaling_factor: Final[float] = get_adjusted_scaling_factor( + ensure_dividable( + np.nanmean(obs), + np.nanmean(simh), + max_scaling_factor, + ), + max_scaling_factor, + ) + return np.array(simp) * adj_scaling_factor # Eq. 2 + raise NotImplementedError( + f"{kind=} not available for linear_scaling. Use '+' or '*' instead.", + ) + + +# ? -----========= V A R I A N C E - S C A L I N G =========------ + + +def variance_scaling( + obs: NPData, + simh: NPData, + simp: NPData, + kind: str = "+", + **kwargs: Any, +) -> NPData: + r""" + **Do not call this function directly, please use :func:`cmethods.CMethods.adjust`** + + See https://python-cmethods.readthedocs.io/en/latest/src/methods.html#variance-scaling + """ + check_adjust_called( + function_name="variance_scaling", + adjust_called=kwargs.get("adjust_called", None), + ) + check_np_types(obs=obs, simh=simp, simp=simp) + + if kind in ADDITIVE: + LS_simh = linear_scaling(obs, simh, simh, kind="+", **kwargs) # Eq. 1 + LS_simp = linear_scaling(obs, simh, simp, kind="+", **kwargs) # Eq. 2 + + VS_1_simh = LS_simh - np.nanmean(LS_simh) # Eq. 3 + VS_1_simp = LS_simp - np.nanmean(LS_simp) # Eq. 4 + max_scaling_factor: Final[float] = kwargs.get( + "max_scaling_factor", + MAX_SCALING_FACTOR, + ) + adj_scaling_factor: Final[float] = get_adjusted_scaling_factor( + ensure_dividable( + np.std(np.array(obs)), + np.std(VS_1_simh), + max_scaling_factor, + ), + max_scaling_factor, + ) + + VS_2_simp = VS_1_simp * adj_scaling_factor # Eq. 5 + return VS_2_simp + np.nanmean(LS_simp) # Eq. 6 + + raise NotImplementedError( + f"{kind=} not available for variance_scaling. Use '+' instead.", + ) + + +# ? -----========= D E L T A - M E T H O D =========------ +def delta_method( + obs: NPData, + simh: NPData, + simp: NPData, + kind: str = "+", + **kwargs: Any, +) -> NPData: + r""" + **Do not call this function directly, please use :func:`cmethods.CMethods.adjust`** + See https://python-cmethods.readthedocs.io/en/latest/src/methods.html#delta-method + """ + check_adjust_called( + function_name="delta_method", + adjust_called=kwargs.get("adjust_called", None), + ) + check_np_types(obs=obs, simh=simh, simp=simp) + + if kind in ADDITIVE: + return np.array(obs) + (np.nanmean(simp) - np.nanmean(simh)) # Eq. 1 + if kind in MULTIPLICATIVE: + max_scaling_factor: Final[float] = kwargs.get( + "max_scaling_factor", + MAX_SCALING_FACTOR, + ) + adj_scaling_factor = get_adjusted_scaling_factor( + ensure_dividable( + np.nanmean(simp), + np.nanmean(simh), + max_scaling_factor, + ), + max_scaling_factor, + ) + return np.array(obs) * adj_scaling_factor # Eq. 2 + raise NotImplementedError( + f"{kind=} not available for delta_method. Use '+' or '*' instead.", + ) diff --git a/cmethods/static.py b/cmethods/static.py new file mode 100644 index 0000000..697666c --- /dev/null +++ b/cmethods/static.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2023 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +"""Module providing static information for the python-cmethods package""" + +from __future__ import annotations + +from typing import List + +SCALING_METHODS: List[str] = [ + "linear_scaling", + "variance_scaling", + "delta_method", +] +DISTRIBUTION_METHODS: List[str] = [ + "quantile_mapping", + "detrended_quantile_mapping", + "quantile_delta_mapping", +] + +CUSTOM_METHODS: List[str] = SCALING_METHODS + DISTRIBUTION_METHODS +METHODS: List[str] = CUSTOM_METHODS + +ADDITIVE: List[str] = ["+", "add"] +MULTIPLICATIVE: List[str] = ["*", "mult"] +MAX_SCALING_FACTOR: int = 10 + +__all__ = [ + "ADDITIVE", + "CUSTOM_METHODS", + "DISTRIBUTION_METHODS", + "MAX_SCALING_FACTOR", + "METHODS", + "MULTIPLICATIVE", + "SCALING_METHODS", +] diff --git a/cmethods/types.py b/cmethods/types.py new file mode 100644 index 0000000..925bf1d --- /dev/null +++ b/cmethods/types.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2023 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +"""Module providing custom types""" + +from __future__ import annotations + +from typing import TypeVar + +from numpy import generic, ndarray +from xarray.core.dataarray import DataArray, Dataset + +XRData_t = (Dataset, DataArray) +NPData_t = (list, ndarray, generic) +XRData = TypeVar("XRData", Dataset, DataArray) +NPData = TypeVar("NPData", list, ndarray, generic) diff --git a/cmethods/utils.py b/cmethods/utils.py new file mode 100644 index 0000000..e9a5f25 --- /dev/null +++ b/cmethods/utils.py @@ -0,0 +1,257 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2023 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +"""Module providing utility functions""" + +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING, Optional, Union + +import numpy as np + +from cmethods.types import NPData_t, XRData_t + +if TYPE_CHECKING: + from cmethods.types import NPData, XRData + + +class UnknownMethodError(Exception): + """ + Exception raised for errors if unknown method called in CMethods class. + """ + + def __init__(self: UnknownMethodError, method: str, available_methods: list): + super().__init__( + f'Unknown method "{method}"! Available methods: {available_methods}', + ) + + +def check_adjust_called( + function_name: str, + adjust_called: Optional[bool] = None, # noqa: FBT001 +) -> None: + """ + Displays a user warning in case a correction function was not called via + `CMethods.adjust`. + + :param adjust_called: If the function was called via adjust + :type adjust_called: Optional[bool] + :param function_name: The function that was called + :type function_name: str + """ + if not adjust_called: + warnings.warn( + message=f"Do not call {function_name} directly, use `CMethods.adjust` instead!", + category=UserWarning, + stacklevel=1, + ) + + +def check_xr_types(obs: XRData, simh: XRData, simp: XRData) -> None: + """ + Checks if the parameters are in the correct type. **only used internally** + """ + phrase: str = ( + "must be type xarray.core.dataarray.Dataset or xarray.core.dataarray.DataArray" + ) + + if not isinstance(obs, XRData_t): + raise TypeError(f"'obs' {phrase}") + if not isinstance(simh, XRData_t): + raise TypeError(f"'simh' {phrase}") + if not isinstance(simp, XRData_t): + raise TypeError(f"'simp' {phrase}") + + +def check_np_types( + obs: NPData, + simh: NPData, + simp: NPData, +) -> None: + """ + Checks if the parameters are in the correct type. **only used internally** + """ + phrase: str = "must be type list, np.ndarray or np.generic" + + if not isinstance(obs, NPData_t): + raise TypeError(f"'obs' {phrase}") + if not isinstance(simh, NPData_t): + raise TypeError(f"'simh' {phrase}") + if not isinstance(simp, NPData_t): + raise TypeError(f"'simp' {phrase}") + + +def nan_or_equal(value1: float, value2: float) -> bool: + """ + Returns True if the values are equal or at least one is NaN + + :param value1: First value to check + :type value1: float + :param value2: Second value to check + :type value2: float + :return: If any value is NaN or values are equal + :rtype: bool + """ + return np.isnan(value1) or np.isnan(value2) or value1 == value2 + + +def ensure_dividable( + numerator: Union[float, np.ndarray], + denominator: Union[float, np.ndarray], + max_scaling_factor: float, +) -> np.ndarray: + """ + Ensures that the arrays can be divided. The numerator will be multiplied by + the maximum scaling factor of the CMethods class if division by zero. + + :param numerator: Numerator to use + :type numerator: np.ndarray + :param denominator: Denominator that can be zero + :type denominator: np.ndarray + :return: Zero-ensured devision + :rtype: np.ndarray | float + """ + with np.errstate(divide="ignore", invalid="ignore"): + result = numerator / denominator + + if isinstance(numerator, np.ndarray): + mask_inf = np.isinf(result) + result[mask_inf] = numerator[mask_inf] * max_scaling_factor # type: ignore[index] + + mask_nan = np.isnan(result) + result[mask_nan] = 0 # type: ignore[index] + elif np.isinf(result): + result = numerator * max_scaling_factor + elif np.isnan(result): + result = 0.0 + + return result + + +def get_pdf( + x: Union[list, np.ndarray], + xbins: Union[list, np.ndarray], +) -> np.ndarray: + r""" + Compuites and returns the the probability density function :math:`P(x)` + of ``x`` based on ``xbins``. + + :param x: The vector to get :math:`P(x)` from + :type x: list | np.ndarray + :param xbins: The boundaries/bins of :math:`P(x)` + :type xbins: list | np.ndarray + :return: The probability densitiy function of ``x`` + :rtype: np.ndarray + + .. code-block:: python + :linenos: + :caption: Compute the probability density function :math:`P(x)` + + >>> from cmethods import CMethods as cm + + >>> x = [1, 2, 3, 4, 5, 5, 5, 6, 7, 8, 9, 10] + >>> xbins = [0, 3, 6, 10] + >>> print(cm.get_pdf(x=x, xbins=xbins)) + [2, 5, 5] + """ + pdf, _ = np.histogram(x, xbins) + return pdf + + +def get_cdf( + x: Union[list, np.ndarray], + xbins: Union[list, np.ndarray], +) -> np.ndarray: + r""" + Computes and returns returns the cumulative distribution function :math:`F(x)` + of ``x`` based on ``xbins``. + + :param x: Vector to get :math:`F(x)` from + :type x: list | np.ndarray + :param xbins: The boundaries/bins of :math:`F(x)` + :type xbins: list | np.ndarray + :return: The cumulative distribution function of ``x`` + :rtype: np.ndarray + + + .. code-block:: python + :linenos: + :caption: Compute the cmmulative distribution function :math:`F(x)` + + >>> from cmethods import CMethods as cm + + >>> x = [1, 2, 3, 4, 5, 5, 5, 6, 7, 8, 9, 10] + >>> xbins = [0, 3, 6, 10] + >>> print(cm.get_cdf(x=x, xbins=xbins)) + [0, 2, 7, 12] + """ + pdf, _ = np.histogram(x, xbins) + return np.insert(np.cumsum(pdf), 0, 0.0) + + +def get_inverse_of_cdf( + base_cdf: Union[list, np.ndarray], + insert_cdf: Union[list, np.ndarray], + xbins: Union[list, np.ndarray], +) -> np.ndarray: + r""" + Returns the inverse cumulative distribution function as: + :math:`F^{-1}_{x}\left[y\right]` where :math:`x` represents ``base_cdf`` and + ``insert_cdf`` is represented by :math:`y`. + + :param base_cdf: The basis + :type base_cdf: list | np.ndarray + :param insert_cdf: The CDF that gets inserted + :type insert_cdf: list | np.ndarray + :param xbins: Probability boundaries + :type xbins: list | np.ndarray + :return: The inverse CDF + :rtype: np.ndarray + """ + return np.interp(insert_cdf, base_cdf, xbins) + + +def get_adjusted_scaling_factor( + factor: float, + max_scaling_factor: float, +) -> float: + r""" + Returns: + - :math:`x` if :math:`-|y| \le x \le |y|`, + - :math:`|y|` if :math:`x > |y|`, or + - :math:`-|y|` if :math:`x < -|y|` + + where: + - :math:`x` is ``factor`` + - :math:`y` is ``max_scaling_factor`` + + :param factor: The value to check for + :type factor: int | float + :param max_scaling_factor: The maximum/minimum allowed value + :type max_scaling_factor: int | float + :return: The correct value + :rtype: float + """ + if factor > 0 and factor > abs(max_scaling_factor): + return abs(max_scaling_factor) + if factor < 0 and factor < -abs(max_scaling_factor): + return -abs(max_scaling_factor) + return factor + + +__all__ = [ + "UnknownMethodError", + "check_adjust_called", + "check_np_types", + "check_xr_types", + "ensure_dividable", + "get_adjusted_scaling_factor", + "get_cdf", + "get_inverse_of_cdf", + "get_pdf", + "nan_or_equal", +] diff --git a/docs/Makefile b/doc/Makefile similarity index 100% rename from docs/Makefile rename to doc/Makefile diff --git a/docs/_static/images/biasCdiagram.png b/doc/_static/images/biasCdiagram.png similarity index 100% rename from docs/_static/images/biasCdiagram.png rename to doc/_static/images/biasCdiagram.png diff --git a/docs/_static/images/dm-doy-plot.png b/doc/_static/images/dm-doy-plot.png similarity index 100% rename from docs/_static/images/dm-doy-plot.png rename to doc/_static/images/dm-doy-plot.png diff --git a/docs/_static/images/qm-cdf-plot-1.png b/doc/_static/images/qm-cdf-plot-1.png similarity index 100% rename from docs/_static/images/qm-cdf-plot-1.png rename to doc/_static/images/qm-cdf-plot-1.png diff --git a/docs/_static/images/qm-cdf-plot-2.png b/doc/_static/images/qm-cdf-plot-2.png similarity index 100% rename from docs/_static/images/qm-cdf-plot-2.png rename to doc/_static/images/qm-cdf-plot-2.png diff --git a/docs/conf.py b/doc/conf.py similarity index 99% rename from docs/conf.py rename to doc/conf.py index 36c02db..8aa9e13 100644 --- a/docs/conf.py +++ b/doc/conf.py @@ -3,12 +3,12 @@ # # For the full list of built-in configuration values, see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html +# pylint: disable=invalid-name # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information """This module is the configuration for the Sphinx documentation building process""" -# pylint: disable=invalid-name import os import sys @@ -17,7 +17,6 @@ copyright = "2023, Benjamin Thomas Schwertfeger" # pylint: disable=redefined-builtin author = "Benjamin Thomas Schwertfeger" - # to import the package sys.path.insert(0, os.path.abspath("..")) @@ -43,7 +42,6 @@ templates_path = ["_templates"] exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "links.rst"] - # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "sphinx_rtd_theme" diff --git a/docs/index.rst b/doc/index.rst similarity index 96% rename from docs/index.rst rename to doc/index.rst index f0ec1d3..56a3687 100644 --- a/docs/index.rst +++ b/doc/index.rst @@ -13,5 +13,6 @@ Welcome to python-cmethods's documentation! src/introduction.rst src/getting_started.rst src/cmethods.rst + src/methods.rst src/issues.rst src/license.rst diff --git a/docs/links.rst b/doc/links.rst similarity index 98% rename from docs/links.rst rename to doc/links.rst index ad0a6b9..41e9fab 100644 --- a/docs/links.rst +++ b/doc/links.rst @@ -21,7 +21,7 @@ .. |License badge| image:: https://img.shields.io/badge/License-GPLv3-orange.svg :target: https://www.gnu.org/licenses/gpl-3.0 -.. |PyVersions badge| image:: https://img.shields.io/badge/python-3.8_|_3.9_|_3.10_|_3.11-blue.svg +.. |PyVersions badge| image:: https://img.shields.io/badge/python-3.8_|_3.9_|_3.10_|_3.11|_3.12-blue.svg :target: https://github.com/btschwertfeger/python-cmethods .. |Downloads badge| image:: https://static.pepy.tech/personalized-badge/python-cmethods?period=total&units=abbreviation&left_color=grey&right_color=orange&left_text=downloads diff --git a/docs/make.bat b/doc/make.bat similarity index 100% rename from docs/make.bat rename to doc/make.bat diff --git a/docs/requirements.txt b/doc/requirements.txt similarity index 100% rename from docs/requirements.txt rename to doc/requirements.txt diff --git a/doc/src/cmethods.rst b/doc/src/cmethods.rst new file mode 100644 index 0000000..233c8cf --- /dev/null +++ b/doc/src/cmethods.rst @@ -0,0 +1,18 @@ + +Classes and Functions +===================== + +In past versions of the python-cmethods package (v1.x) there was a "CMethods" +class that implemented the bias correction methods. This class was removed in +version v2.0.0. Since then, the ``cmethods.adjust`` function is used to apply +the implemented techniques except for detrended quantile mapping. + +.. autofunction:: cmethods.adjust +.. automethod:: cmethods.distribution.detrended_quantile_mapping + +Some additional methods +----------------------- + +.. automethod:: cmethods.utils.get_pdf +.. automethod:: cmethods.utils.get_cdf +.. automethod:: cmethods.utils.get_inverse_of_cdf diff --git a/doc/src/getting_started.rst b/doc/src/getting_started.rst new file mode 100644 index 0000000..b5dc5f7 --- /dev/null +++ b/doc/src/getting_started.rst @@ -0,0 +1,58 @@ +Getting Started +=============== + +Installation +------------ + +The `python-cmethods`_ module can be installed using the package manager pip: + +.. code-block:: bash + + python3 -m pip install python-cmethods + + +Usage and Examples +------------------ + +The `python-cmethods`_ module can be imported and applied as showing in the following examples. +For more detailed description of the methods, please have a look at the +method specific documentation. + +.. code-block:: python + :linenos: + :caption: Apply the Linear Scaling bias correction technique on 1-dimensional data + + import xarray as xr + from cmethods import adjust + + obsh = xr.open_dataset("input_data/observations.nc") + simh = xr.open_dataset("input_data/control.nc") + simp = xr.open_dataset("input_data/scenario.nc") + + ls_result = adjust( + mathod="linear_scaling", + obs=obsh["tas"][:, 0, 0], + simh=simh["tas"][:, 0, 0], + simp=simp["tas"][:, 0, 0], + kind="+", + ) + +.. code-block:: python + :linenos: + :caption: Apply the Quantile Delta Mapping bias correction technique on 3-dimensional data + + import xarray as xr + from cmethods import adjust + + obsh = xr.open_dataset("input_data/observations.nc") + simh = xr.open_dataset("input_data/control.nc") + simp = xr.open_dataset("input_data/scenario.nc") + + qdm_result = adjust( + method="quantile_delta_mapping", + obs=obsh["tas"], + simh=simh["tas"], + simp=simp["tas"], + n_quaniles=1000, + kind="+", + ) diff --git a/docs/src/introduction.rst b/doc/src/introduction.rst similarity index 55% rename from docs/src/introduction.rst rename to doc/src/introduction.rst index faa7bd5..dabc0e2 100644 --- a/docs/src/introduction.rst +++ b/doc/src/introduction.rst @@ -10,11 +10,11 @@ Introduction About ----- -This Python module and the provided data structures are designed -to help minimize discrepancies between modeled and observed climate data of different -time periods. Data from past periods are used to adjust variables -from current and future time series so that their distributional -properties approximate possible actual values. +This Python module and the provided data structures are designed to help +minimize discrepancies between modeled and observed climate data of different +time periods. Data from past periods are used to adjust variables from current +and future time series so that their distributional properties approximate +possible actual values. .. figure:: ../_static/images/biasCdiagram.png :width: 600 @@ -24,12 +24,12 @@ properties approximate possible actual values. Fig 1: Schematic representation of a bias adjustment procedure -In this way, for example, modeled data, which on average represent values -that are too cold, can be bias-corrected by applying an adjustment procedure. -The following figure shows the observed, the modeled, and the bias-corrected values. +In this way, for example, modeled data, which on average represent values that +are too cold, can be bias-corrected by applying an adjustment procedure. The +following figure shows the observed, the modeled, and the bias-corrected values. It is directly visible that the delta adjusted time series -(:math:`T^{*DM}_{sim,p}`) are much more similar to the observed data (:math:`T_{obs,p}`) -than the raw modeled data (:math:`T_{sim,p}`). +(:math:`T^{*DM}_{sim,p}`) are much more similar to the observed data +(:math:`T_{obs,p}`) than the raw modeled data (:math:`T_{sim,p}`). .. figure:: ../_static/images/dm-doy-plot.png :width: 600 @@ -42,35 +42,41 @@ than the raw modeled data (:math:`T_{sim,p}`). Available Methods ----------------- -The following bias correction techniques are available: - Scaling-based techniques: - * Linear Scaling :func:`cmethods.CMethods.linear_scaling` - * Variance Scaling :func:`cmethods.CMethods.variance_scaling` - * Delta (change) Method :func:`cmethods.CMethods.delta_method` - - Distribution-based techniques: - * Quantile Mapping :func:`cmethods.CMethods.quantile_mapping` - * Detrended Quantile Mapping :func:`cmethods.CMethods.detrended_quantile_mapping` - * Quantile Delta Mapping :func:`cmethods.CMethods.quantile_delta_mapping` - -All of these methods are intended to be applied on 1-dimensional time-series climate data. -This module also provides the function :func:`cmethods.CMethods.adjust_3d` that enables -the application of the desired bias correction method on 3-dimensional data sets. - -Except for the variance scaling, all methods can be applied on stochastic and non-stochastic -climate variables. Variance scaling can only be applied on non-stochastic climate variables. - -- Non-stochastic climate variables are those that can be predicted with relative certainty based - on factors such as location, elevation, and season. Examples of non-stochastic climate variables - include air temperature, air pressure, and solar radiation. - -- Stochastic climate variables, on the other hand, are those that exhibit a high degree of - variability and unpredictability, making them difficult to forecast accurately. - Precipitation is an example of a stochastic climate variable because it can vary greatly in timing, - intensity, and location due to complex atmospheric and meteorological processes. - -Examples can be found in the `python-cmethods`_ repository and of course -within this documentation. +python-cmethods provides the following bias correction techniques: + +- Linear Scaling +- Variance Scaling +- Delta Method +- Quantile Mapping +- Detrended Quantile Mapping +- Quantile Delta Mapping + +Please refer to the official documentation for more information about these +methods as well as sample scripts: +https://python-cmethods.readthedocs.io/en/stable/ + +- Except for the variance scaling, all methods can be applied on stochastic and + non-stochastic climate variables. Variance scaling can only be applied on + non-stochastic climate variables. + + - Non-stochastic climate variables are those that can be predicted with relative + certainty based on factors such as location, elevation, and season. Examples + of non-stochastic climate variables include air temperature, air pressure, and + solar radiation. + + - Stochastic climate variables, on the other hand, are those that exhibit a high + degree of variability and unpredictability, making them difficult to forecast + accurately. Precipitation is an example of a stochastic climate variable + because it can vary greatly in timing, intensity, and location due to complex + atmospheric and meteorological processes. + +- Except for the detrended quantile mapping (DQM) technique, all methods can be + applied to single and multidimensional data sets. The implementation of DQM to + 3-dimensional data is still in progress. + +- For any questions -- please open an issue at + https://github.com/btschwertfeger/python-cmethods/issues. Examples can be found + in the `python-cmethods`_ repository and of course within this documentation. References ---------- diff --git a/doc/src/issues.rst b/doc/src/issues.rst new file mode 100644 index 0000000..3a37226 --- /dev/null +++ b/doc/src/issues.rst @@ -0,0 +1,19 @@ +Known Issues +============ + +- Since the scaling methods implemented so far scale by default over the mean + values of the respective months, unrealistic long-term mean values may occur + at the month transitions. This can be prevented either by selecting + ``group='time.dayofyear'``. Alternatively, it is possible not to scale using + long-term mean values, but using a 31-day interval, which takes the 31 + surrounding values over all years as the basis for calculating the mean + values. This is not yet implemented in this module, but is available in the + command-line tool `BiasAdjustCXX`_. +- Using this module or especially Python to apply bias correction techniques on + large data sets can be a very time-consuming task. So this module is more + about showing how to apply different methods on climate data and maybe even + to bias-correct small data sets. When it comes to large ensembles it is + preferred to use the way more efficient tool `BiasAdjustCXX`_. A speed + comparison between `python-cmethods`_, `BiasAdjustCXX`_, and `xclim`_ was + made this `tool comparison`_. Since the development of python-cmethods is + continuing, speed improvements have been done since the last bench. diff --git a/docs/src/license.rst b/doc/src/license.rst similarity index 100% rename from docs/src/license.rst rename to doc/src/license.rst diff --git a/doc/src/methods.rst b/doc/src/methods.rst new file mode 100644 index 0000000..a3f1e33 --- /dev/null +++ b/doc/src/methods.rst @@ -0,0 +1,436 @@ + +Bias Correction Methods +======================= + +Linear Scaling +-------------- + +The Linear Scaling bias correction technique can be applied on stochastic and +non-stochastic climate variables to minimize deviations in the mean values +between predicted and observed time-series of past and future time periods. + +Since the multiplicative scaling can result in very high scaling factors, a +maximum scaling factor of 10 is set. This can be changed by adjusting +:attr:`CMethods.MAX_SCALING_FACTOR`. + +This method must be applied on a 1-dimensional data set i.e., there is only one +time-series passed for each of ``obs``, ``simh``, and ``simp``. + +The Linear Scaling bias correction technique implemented here is based on the +method described in the equations of Teutschbein, Claudia and Seibert, Jan (2012) +*"Bias correction of regional climate model simulations for hydrological climate-change +impact studies: Review and evaluation of different methods"* +(https://doi.org/10.1016/j.jhydrol.2012.05.052). In the following the equations +for both additive and multiplicative Linear Scaling are shown: + +**Additive**: + + In Linear Scaling, the long-term monthly mean (:math:`\mu_m`) of the modeled data :math:`X_{sim,h}` is subtracted + from the long-term monthly mean of the reference data :math:`X_{obs,h}` at time step :math:`i`. + This difference in month-dependent long-term mean is than added to the value of time step :math:`i`, + in the time-series that is to be adjusted (:math:`X_{sim,p}`). + + .. math:: + + X^{*LS}_{sim,p}(i) = X_{sim,p}(i) + \mu_{m}(X_{obs,h}(i)) - \mu_{m}(X_{sim,h}(i)) + +**Multiplicative**: + + The multiplicative Linear Scaling differs from the additive variant in such way, that the changes are not computed + in absolute but in relative values. + + .. math:: + + X^{*LS}_{sim,h}(i) = X_{sim,h}(i) \cdot \left[\frac{\mu_{m}(X_{obs,h}(i))}{\mu_{m}(X_{sim,h}(i))}\right] + + +.. code-block:: python + :linenos: + :caption: Example: Linear Scaling + + >>> import xarray as xr + >>> from cmethods import adjust + + >>> # Note: The data sets must contain the dimension "time" + >>> # for the respective variable. + >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") + >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") + >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") + >>> variable = "tas" # temperatures + >>> result = adjust( + ... method="linear_scaling", + ... obs=obs[variable], + ... simh=simh[variable], + ... simp=simp[variable], + ... kind="+", + ... group="time.month" # this is important! + ... ) + + +Variance Scaling +---------------- + +The Variance Scaling bias correction technique can be applied only on non-stochastic +climate variables to minimize deviations in the mean and variance +between predicted and observed time-series of past and future time periods. + +This method must be applied on a 1-dimensional data set i.e., there is only one +time-series passed for each of ``obs``, ``simh``, and ``simp``. + +The Variance Scaling bias correction technique implemented here is based on the +method described in the equations of Teutschbein, Claudia and Seibert, Jan (2012) +*"Bias correction of regional climate model simulations for hydrological climate-change +impact studies: Review and evaluation of different methods"* +(https://doi.org/10.1016/j.jhydrol.2012.05.052). In the following the equations +of the Variance Scaling approach are shown: + +**(1)** First, the modeled data of the control and scenario period must be bias-corrected using +the additive linear scaling technique. This adjusts the deviation in the mean. + +.. math:: + + X^{*LS}_{sim,h}(i) = X_{sim,h}(i) + \mu_{m}(X_{obs,h}(i)) - \mu_{m}(X_{sim,h}(i)) + + X^{*LS}_{sim,p}(i) = X_{sim,p}(i) + \mu_{m}(X_{obs,h}(i)) - \mu_{m}(X_{sim,h}(i)) + +**(2)** In the second step, the time-series are shifted to a zero mean. This enables the adjustment +of the standard deviation in the following step. + +.. math:: + + X^{VS(1)}_{sim,h}(i) = X^{*LS}_{sim,h}(i) - \mu_{m}(X^{*LS}_{sim,h}(i)) + + X^{VS(1)}_{sim,p}(i) = X^{*LS}_{sim,p}(i) - \mu_{m}(X^{*LS}_{sim,p}(i)) + +**(3)** Now the standard deviation (so variance too) can be scaled. + +.. math:: + + X^{VS(2)}_{sim,p}(i) = X^{VS(1)}_{sim,p}(i) \cdot \left[\frac{\sigma_{m}(X_{obs,h}(i))}{\sigma_{m}(X^{VS(1)}_{sim,h}(i))}\right] + +**(4)** Finally the previously removed mean is shifted back + +.. math:: + + X^{*VS}_{sim,p}(i) = X^{VS(2)}_{sim,p}(i) + \mu_{m}(X^{*LS}_{sim,p}(i)) + +.. code-block:: python + :linenos: + :caption: Example: Variance Scaling + + >>> import xarray as xr + >>> from cmethods import adjust + + >>> # Note: The data sets must contain the dimension "time" + >>> # for the respective variable. + >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") + >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") + >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") + >>> variable = "tas" # temperatures + >>> result = adjust( + ... method="variance_scaling", + ... obs=obs[variable], + ... simh=simh[variable], + ... simp=simp[variable], + ... kind="+", + ... group="time.month" # this is important! + ... ) + + +Delta Method +------------ + +The Delta Method bias correction technique can be applied on stochastic and +non-stochastic climate variables to minimize deviations in the mean values +between predicted and observed time-series of past and future time periods. + +Since the multiplicative scaling can result in very high scaling factors, +a maximum scaling factor of 10 is set. This can be changed by adjusting +:attr:`CMethods.MAX_SCALING_FACTOR`. + +This method must be applied on a 1-dimensional data set i.e., there is only one +time-series passed for each of ``obs``, ``simh``, and ``simp``. + +The Delta Method bias correction technique implemented here is based on the +method described in the equations of Beyer, R. and Krapp, M. and Manica, A. (2020) +*"An empirical evaluation of bias correction methods for paleoclimate simulations"* +(https://doi.org/10.5194/cp-16-1493-2020). In the following the equations +for both additive and multiplicative Delta Method are shown: + +**Additive**: + + The Delta Method looks like the Linear Scaling method but the important difference is, that the Delta method + uses the change between the modeled data instead of the difference between the modeled and reference data of the control + period. This means that the long-term monthly mean (:math:`\mu_m`) of the modeled data of the control period :math:`T_{sim,h}` + is subtracted from the long-term monthly mean of the modeled data from the scenario period :math:`T_{sim,p}` at time step :math:`i`. + This change in month-dependent long-term mean is than added to the long-term monthly mean for time step :math:`i`, + in the time-series that represents the reference data of the control period (:math:`T_{obs,h}`). + + .. math:: + + X^{*DM}_{sim,p}(i) = X_{obs,h}(i) + \mu_{m}(X_{sim,p}(i)) - \mu_{m}(X_{sim,h}(i)) + +**Multiplicative**: + + The multiplicative variant behaves like the additive, but with the difference that the change is computed using the relative change + instead of the absolute change. + + .. math:: + + X^{*DM}_{sim,p}(i) = X_{obs,h}(i) \cdot \left[\frac{ \mu_{m}(X_{sim,p}(i)) }{ \mu_{m}(X_{sim,h}(i))}\right] + +.. code-block:: python + :linenos: + :caption: Example: Delta Method + + >>> import xarray as xr + >>> from cmethods import adjust + + >>> # Note: The data sets must contain the dimension "time" + >>> # for the respective variable. + >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") + >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") + >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") + >>> variable = "tas" # temperatures + >>> result = adjust( + ... method="delta_method", + ... obs=obs[variable], + ... simh=simh[variable], + ... simp=simp[variable], + ... kind="+", + ... group="time.month" # this is important! + ... ) + + +Quantile Mapping +---------------- +The Quantile Mapping bias correction technique can be used to minimize distributional +biases between modeled and observed time-series climate data. Its interval-independent +behavior ensures that the whole time series is taken into account to redistribute +its values, based on the distributions of the modeled and observed/reference data of the +control period. + +This method must be applied on a 1-dimensional data set i.e., there is only one +time-series passed for each of ``obs``, ``simh``, and ``simp``. + +The Quantile Mapping technique implemented here is based on the equations of +Alex J. Cannon and Stephen R. Sobie and Trevor Q. Murdock (2015) *"Bias Correction of GCM +Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles +and Extremes?"* (https://doi.org/10.1175/JCLI-D-14-00754.1). + +The regular Quantile Mapping is bounded to the value range of the modeled data +of the control period. To avoid this, the Detrended Quantile Mapping can be used. + +In the following the equations of Alex J. Cannon (2015) are shown and explained: + +**Additive**: + + .. math:: + + X^{*QM}_{sim,p}(i) = F^{-1}_{obs,h} \left\{F_{sim,h}\left[X_{sim,p}(i)\right]\right\} + + + The additive quantile mapping procedure consists of inserting the value to be + adjusted (:math:`X_{sim,p}(i)`) into the cumulative distribution function + of the modeled data of the control period (:math:`F_{sim,h}`). This determines, + in which quantile the value to be adjusted can be found in the modeled data of the control period + The following images show this by using :math:`T` for temperatures. + + .. figure:: ../_static/images/qm-cdf-plot-1.png + :width: 600 + :align: center + :alt: Determination of the quantile value + + Fig 1: Inserting :math:`X_{sim,p}(i)` into :math:`F_{sim,h}` to determine the quantile value + + This value, which of course lies between 0 and 1, is subsequently inserted + into the inverse cumulative distribution function of the reference data of the control period to + determine the bias-corrected value at time step :math:`i`. + + .. figure:: ../_static/images/qm-cdf-plot-2.png + :width: 600 + :align: center + :alt: Determination of the QM bias-corrected value + + Fig 1: Inserting the quantile value into :math:`F^{-1}_{obs,h}` to determine the bias-corrected value for time step :math:`i` + +**Multiplicative**: + + .. math:: + + X^{*QM}_{sim,p}(i) = F^{-1}_{obs,h}\Biggl\{F_{sim,h}\left[\frac{\mu{X_{sim,h}} \cdot \mu{X_{sim,p}(i)}}{\mu{X_{sim,p}(i)}}\right]\Biggr\}\frac{\mu{X_{sim,p}(i)}}{\mu{X_{sim,h}}} + +.. code-block:: python + :linenos: + :caption: Example: Quantile Mapping + + >>> import xarray as xr + >>> from cmethods import adjust + + >>> # Note: The data sets must contain the dimension "time" + >>> # for the respective variable. + >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") + >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") + >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") + >>> variable = "tas" # temperatures + >>> qm_adjusted = adjust( + ... method="quantile_mapping", + ... obs=obs[variable], + ... simh=simh[variable], + ... simp=simp[variable], + ... n_quantiles=250, + ... kind="+", + ... ) + + +Detrended Quantile Mapping +-------------------------- + +The Detrended Quantile Mapping bias correction technique can be used to minimize distributional +biases between modeled and observed time-series climate data like the regular Quantile Mapping. +Detrending means, that the values of :math:`X_{sim,p}` are shifted to the value range of +:math:`X_{sim,h}` before the regular Quantile Mapping is applied. +After the Quantile Mapping was applied, the mean is shifted back. Since it does not make sens +to take the whole mean to rescale the data, the month-dependent long-term mean is used. + +This method must be applied on a 1-dimensional data set i.e., there is only one +time-series passed for each of ``obs``, ``simh``, and ``simp``. Also this method requires +that the time series are groupable by ``time.month``. + +The Detrended Quantile Mapping technique implemented here is based on the equations of +Alex J. Cannon and Stephen R. Sobie and Trevor Q. Murdock (2015) *"Bias Correction of GCM +Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles +and Extremes?"* (https://doi.org/10.1175/JCLI-D-14-00754.1). + +In the following the equations of Alex J. Cannon (2015) are shown (without detrending; see QM +for explanations): + +**Additive**: + + .. math:: + + X^{*QM}_{sim,p}(i) = F^{-1}_{obs,h} \left\{F_{sim,h}\left[X_{sim,p}(i)\right]\right\} + + +**Multiplicative**: + + .. math:: + + X^{*QM}_{sim,p}(i) = F^{-1}_{obs,h}\Biggl\{F_{sim,h}\left[\frac{\mu{X_{sim,h}} \cdot \mu{X_{sim,p}(i)}}{\mu{X_{sim,p}(i)}}\right]\Biggr\}\frac{\mu{X_{sim,p}(i)}}{\mu{X_{sim,h}}} + + +.. code-block:: python + :linenos: + :caption: Example: Quantile Mapping + + >>> import xarray as xr + >>> from cmethods.distribution import detrended_quantile_mapping + + >>> # Note: The data sets must contain the dimension "time" + >>> # for the respective variable. + >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") + >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") + >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") + >>> variable = "tas" # temperatures + >>> qm_adjusted = detrended_quantile_mapping( + ... obs=obs[variable], + ... simh=simh[variable], + ... simp=simp[variable], + ... n_quantiles=250 + ... kind="+" + ... ) + + +Quantile Delta Mapping +----------------------- + +The Quantile Delta Mapping bias correction technique can be used to minimize distributional +biases between modeled and observed time-series climate data. Its interval-independent +behavior ensures that the whole time series is taken into account to redistribute +its values, based on the distributions of the modeled and observed/reference data of the +control period. In contrast to the regular Quantile Mapping (:func:`cmethods.CMethods.quantile_mapping`) +the Quantile Delta Mapping also takes the change between the modeled data of the control and scenario +period into account. + +This method must be applied on a 1-dimensional data set i.e., there is only one +time-series passed for each of ``obs``, ``simh``, and ``simp``. + +The Quantile Delta Mapping technique implemented here is based on the equations of +Tong, Y., Gao, X., Han, Z. et al. (2021) *"Bias correction of temperature and precipitation +over China for RCM simulations using the QM and QDM methods"*. Clim Dyn 57, 1425-1443 +(https://doi.org/10.1007/s00382-020-05447-4). In the following the additive and multiplicative +variant are shown. + +**Additive**: + + **(1.1)** In the first step the quantile value of the time step :math:`i` to adjust is stored in + :math:`\varepsilon(i)`. + + .. math:: + + \varepsilon(i) = F_{sim,p}\left[X_{sim,p}(i)\right], \hspace{1em} \varepsilon(i)\in\{0,1\} + + **(1.2)** The bias corrected value at time step :math:`i` is now determined by inserting the + quantile value into the inverse cummulative distribution function of the reference data of the control + period. This results in a bias corrected value for time step :math:`i` but still without taking the + change in modeled data into account. + + .. math:: + + X^{QDM(1)}_{sim,p}(i) = F^{-1}_{obs,h}\left[\varepsilon(i)\right] + + **(1.3)** The :math:`\Delta(i)` represents the absolute change in quantiles between the modeled value + in the control and scenario period. + + .. math:: + + \Delta(i) & = F^{-1}_{sim,p}\left[\varepsilon(i)\right] - F^{-1}_{sim,h}\left[\varepsilon(i)\right] \\[1pt] + & = X_{sim,p}(i) - F^{-1}_{sim,h}\left\{F^{}_{sim,p}\left[X_{sim,p}(i)\right]\right\} + + **(1.4)** Finally the previously calculated change can be added to the bias-corrected value. + + .. math:: + + X^{*QDM}_{sim,p}(i) = X^{QDM(1)}_{sim,p}(i) + \Delta(i) + +**Multiplicative**: + + The first two steps of the multiplicative Quantile Delta Mapping bias correction technique are the + same as for the additive variant. + + **(2.3)** The :math:`\Delta(i)` in the multiplicative Quantile Delta Mapping is calulated like the + additive variant, but using the relative than the absolute change. + + .. math:: + + \Delta(i) & = \frac{ F^{-1}_{sim,p}\left[\varepsilon(i)\right] }{ F^{-1}_{sim,h}\left[\varepsilon(i)\right] } \\[1pt] + & = \frac{ X_{sim,p}(i) }{ F^{-1}_{sim,h}\left\{F_{sim,p}\left[X_{sim,p}(i)\right]\right\} } + + **(2.4)** The relative change between the modeled data of the control and scenario period is than + multiplicated with the bias-corrected value (see **1.2**). + + .. math:: + + X^{*QDM}_{sim,p}(i) = X^{QDM(1)}_{sim,p}(i) \cdot \Delta(i) + +.. code-block:: python + :linenos: + :caption: Example: Quantile Delta Mapping + + >>> import xarray as xr + >>> from cmethods import adjust + + >>> # Note: The data sets must contain the dimension "time" + >>> # for the respective variable. + >>> obsh = xr.open_dataset("path/to/reference_data-control_period.nc") + >>> simh = xr.open_dataset("path/to/modeled_data-control_period.nc") + >>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc") + >>> variable = "tas" # temperatures + >>> qdm_adjusted = adjust( + ... method="quantile_delta_mapping", + ... obs=obs[variable], + ... simh=simh[variable], + ... simp=simp[variable], + ... n_quantiles=250, + ... kind="+" + ... ) diff --git a/docs/src/cmethods.rst b/docs/src/cmethods.rst deleted file mode 100644 index 0ff5d88..0000000 --- a/docs/src/cmethods.rst +++ /dev/null @@ -1,18 +0,0 @@ - -Classes and Functions -===================== - -.. currentmodule:: cmethods - -.. autoclass:: CMethods - :members: linear_scaling, variance_scaling, delta_method, quantile_mapping, detrended_quantile_mapping, quantile_delta_mapping, adjust_3d - - -Some helpful additional methods -------------------------------- - -.. automethod:: CMethods.get_pdf -.. automethod:: CMethods.get_cdf -.. automethod:: CMethods.get_inverse_of_cdf -.. automethod:: CMethods.get_adjusted_scaling_factor -.. automethod:: CMethods.get_available_methods diff --git a/docs/src/getting_started.rst b/docs/src/getting_started.rst deleted file mode 100644 index ffd4c4e..0000000 --- a/docs/src/getting_started.rst +++ /dev/null @@ -1,57 +0,0 @@ -Getting Started -=============== - -Installation ------------- - -The `python-cmethods`_ module can be installed using the package manager pip: - -.. code-block:: bash - - python3 -m pip install python-cmethods - - -Usage and Examples ------------------- - -The `python-cmethods`_ module can be imported and applied as showing in the following examples. -For more detailed description of the methods, please have a look at the -method specific documentation. - -.. code-block:: python - :linenos: - :caption: Apply the Linear Scaling bias correction technique on 1-dimensional data - - import xarray as xr - from cmethods import CMethods as cm - - obsh = xr.open_dataset('input_data/observations.nc') - simh = xr.open_dataset('input_data/control.nc') - simp = xr.open_dataset('input_data/scenario.nc') - - ls_result = cm.linear_scaling( - obs = obsh['tas'][:,0,0], - simh = simh['tas'][:,0,0], - simp = simp['tas'][:,0,0], - kind = '+' - ) - -.. code-block:: python - :linenos: - :caption: Apply the Quantile Delta Mapping bias correction technique on 1-dimensional data - - import xarray as xr - from cmethods import CMethods as cm - - obsh = xr.open_dataset('input_data/observations.nc') - simh = xr.open_dataset('input_data/control.nc') - simp = xr.open_dataset('input_data/scenario.nc') - - qdm_result = cm.adjust_3d( # 3d = 2 spatial and 1 time dimension - method = 'quantile_delta_mapping', - obs = obsh['tas'], - simh = simh['tas'], - simp = simp['tas'], - n_quaniles = 1000, - kind = '+' - ) diff --git a/docs/src/issues.rst b/docs/src/issues.rst deleted file mode 100644 index 17854ee..0000000 --- a/docs/src/issues.rst +++ /dev/null @@ -1,19 +0,0 @@ -Known Issues -============ - -- Since the scaling methods implemented so far scale by default over the mean - values of the respective months, unrealistic long-term mean values may occur - at the month transitions. This can be prevented either by selecting - ``group='time.dayofyear'```. Alternatively, it is possible not to scale - using long-term mean values, but using a 31-day interval, which takes - the 31 surrounding values over all years as the basis for calculating - the mean values. This is not yet implemented in this module, but is - available in the command-line tool `BiasAdjustCXX`_. -- Python can be very slow when applying mathematical calculations on large - data sets or when iterating over high resolution data. Using this module - or especially Python to apply bias correction techniques on large data sets - can be a very time-consuming task. So this module is more about showing - how to apply different methods on climate data and maybe even to bias-correct - small data sets. When it comes to large ensenblse it is preferred to use - the way more efficient tool `BiasAdjustCXX`_. A speed comparison between - `python-cmethods`_, `BiasAdjustCXX`_, and `xclim`_ was made this `tool comparison`_. diff --git a/examples/biasadjust.py b/examples/biasadjust.py index 3e84a1b..e11f544 100755 --- a/examples/biasadjust.py +++ b/examples/biasadjust.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (C) 2023 Benjamin Thomas Schwertfeger -# Github: https://github.com/btschwertfeger +# GitHub: https://github.com/btschwertfeger # # Note: This is just an example on how to use the python-cmethods module. # This is in no way an optimal solution and exists only for demonstration @@ -12,7 +12,8 @@ import click from xarray import open_dataset -from cmethods import CMethods as cm +from cmethods import adjust +from cmethods.static import DISTRIBUTION_METHODS, METHODS, SCALING_METHODS def save_to_netcdf(ds, **kwargs) -> None: @@ -23,7 +24,7 @@ def save_to_netcdf(ds, **kwargs) -> None: :type ds: xarray.core.dataarray.Dataset """ ds.to_netcdf( - f"{kwargs['method']}_result_var-{kwargs['variable']}{kwargs['descr1']}_kind-{kwargs['kind']}_group-{kwargs['group']}_{kwargs['start_date']}_{kwargs['end_date']}.nc" + f"{kwargs['method']}_result_var-{kwargs['variable']}{kwargs['descr1']}_kind-{kwargs['kind']}_group-{kwargs['group']}_{kwargs['start_date']}_{kwargs['end_date']}.nc", ) @@ -50,10 +51,18 @@ def save_to_netcdf(ds, **kwargs) -> None: help="The modeled data set to adjust (scenario period)", ) @click.option( - "-m", "--method", required=True, type=str, help="The bias correction method" + "-m", + "--method", + required=True, + type=str, + help="The bias correction method", ) @click.option( - "-v", "--variable", required=True, type=str, help="The variable to adjust" + "-v", + "--variable", + required=True, + type=str, + help="The variable to adjust", ) @click.option( "-k", @@ -87,9 +96,9 @@ def main(**kwargs) -> None: """ The Main program that uses the passed arguments to perform the bias correction procedure. """ - if kwargs["method"] not in cm.get_available_methods(): + if kwargs["method"] not in METHODS: raise ValueError( - f"Unknown method {kwargs['method']}. Available methods: {cm.get_available_methods()}" + f"Unknown method {kwargs['method']}. Available methods: {METHODS}", ) ds_obs = open_dataset(kwargs["ref"])[kwargs["variable"]] @@ -102,7 +111,7 @@ def main(**kwargs) -> None: end_date: str = ds_simp["time"][-1].dt.strftime("%Y%m%d").values.ravel()[0] descr1 = "" - if kwargs["method"] in cm.DISTRIBUTION_METHODS: + if kwargs["method"] in DISTRIBUTION_METHODS: descr1 = f"_quantiles-{kwargs['quantiles']}" kwargs["group"] = None else: @@ -110,37 +119,22 @@ def main(**kwargs) -> None: kwargs.update({"start_date": start_date, "end_date": end_date, "descr1": descr1}) + xkwargs = { + "obs": ds_obs, + "simh": ds_simh, + "simp": ds_simp, + "method": kwargs["method"], + "kind": kwargs["kind"], + } + + if kwargs["method"] in SCALING_METHODS: + xkwargs["group"] = kwargs["group"] + if kwargs["method"] in DISTRIBUTION_METHODS: + xkwargs["n_quantiles"] = kwargs["quantiles"] + print("**Starting correction**") - n_dims = len(ds_obs.dims) - if 1 == n_dims: - result = ds_simp.copy(deep=True) - result.values = cm.get_function(kwargs["m"])( - obs=ds_obs, - simh=ds_simh, - simp=ds_simp, - kind=kwargs["kind"], - n_quantiles=kwargs["quantiles"], - group=kwargs["group"], - **kwargs, - ) - result = result.to_dataset(name=kwargs["variable"]) - - elif 3 == n_dims: - result = cm.adjust_3d( - method=kwargs["method"], - obs=ds_obs, - simh=ds_simh, - simp=ds_simp, - n_quantiles=kwargs["quantiles"], - kind=kwargs["kind"], - group=kwargs["group"], - n_jobs=kwargs["processes"], - ) - else: - raise ValueError( - "Cannot correct this data! Idk how to handle more than 3 dimensions!" - ) + result = adjust(**xkwargs) print("**Computation done - saving the result now**") diff --git a/examples/examples.ipynb b/examples/examples.ipynb index 9903eb0..34dd71a 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -120,7 +120,7 @@ "outputs": [ { "data": { - "image/png": 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xceJEjXXh6emJ6OhoyGQyXLp0CXl5edi8eTNGjRqFXr161fu3aNMQ+9OBAwcwePBgVFZWQiaTISYmBn5+frW24+PHj2PUqFHYv38/rKx0dzBy6NAhDBkyROMJYGdnZ7Rs2RL29vZITk5GZmYmjhw5goEDB2LOnDlGLhVpKisr8dBDD2HLli0anzdt2hQhISHIz88Xt00A2Lp1K7p164Zdu3ZJfqL47NmzmDBhgnjsjIqKQlBQEO7evYtz586Jy27fvn3o378/9u3bJ56bavrpp5/w2GOPaXzm5+eHpk2bwsnJCSUlJbh9+zZu3rwpPkWur7VWcXExxowZgx07dmh8HhUVhcDAQMjlcnE7BoCTJ0+Kv79169Ya0zg7O2PMmDH48ccfAaiOVVLX28qVK8XXw4YNg7e3t9Z0S5YswTPPPKOxz3l7eyMqKgqOjo5IS0sTz3X5+fl45JFHcPv27VrnXXUKhQITJ07Exo0bNT5v1qwZgoODkZ+fLx4f5syZAzs7O0m/SarevXsjKysLV69exbVr1wCojiOdOnXSml7bNYcp16Muy5cvF7c9R0dHxMXFwdnZGdeuXcOtW7cAqPansWPH4tChQ2jXrh3Gjh2LzZs3A1CtpxYtWsDa2hrnzp3D3bt3Aaha0g0ZMgTnz5+Hs7OzpLJ899134rZlY2ODuLg4eHh44NatW+IyFAQBS5YsQU5ODtasWaP3eNQQy++TTz7Biy++CACwtrZGXFwcvLy8kJ2dXavVT0FBAW7cuCG+t7e3R7NmzcSWFDk5Obh06RIqKysBAOfPn0eXLl1w/PhxtGjRota8O3XqBAcHB6Snp+PcuXMAAAcHB/Tu3VtrWZs1aybpN5maIAh4+OGH8fvvvwMAXFxcEBMTAycnJ6SmptZqGWOO9fbee+/hrbfe0vgsJCREbPVSXFyMmzdv4vbt2+L3+o6xM2bMEI+JgKp1jNRzhyFXrlzRaB3i7e2NiIgIuLm5QS6XIzU1VWy5plQq8d133yE1NRVbt26V1FoNULX4HDRoEE6dOgXgn3qFTCbD+fPnxf24uLgYo0ePxsmTJxEXF6czv5KSEgwdOhSHDx/W+Dw2NhZ+fn7Izs7GhQsXUFhYiHHjxmH79u1GLZO6UCgUGDlyJA4cOABAVeeLiYmBUqnEpUuXxN+Yk5ODMWPG4Ndff8WoUaMk5W1svex+vl5NSkrCAw88IJ4vqoWFhSE4OBg2NjbIyspCcnKyWM78/Hwxnanq4uaqk5izHmVIq1atMHjwYOTl5Wm01NV1/6BVq1ZaP3/ttdfw/vvva3wWEBCAiIgI2NraIiUlRTymZGRkYOTIkVi2bBmmTZsmqZznzp3DxIkTUVJSAisrK8TGxsLHxwfZ2dka92kWLlyIgIAAzJkzBx999BFefvllAKrzVlxcHFxdXXHt2jXxnkNFRQUmT56MqKgotG3bVlJZTHltXs3cy+/o0aOYPn26eO6vvk4tLCzU2urelPXDZs2aYfDgwSgrK9NoWdyrVy84OjrWSi/12sgcjLneAky/3vbs2YMxY8aIdUdAdfyqbo1fVlaGrKwsXL9+Xbx+kNp7DpFFa8woKhFRfVhai8zPPvtMTGNrayt88MEHQmFhYa10SqVSSExMFObPny+0atVK5zzr2hJj69atGr8rODhY+P333wWFQiGmKSsrE7788kuNpzk9PDyEW7duSSqPq6urAEBo1aqVsGfPHkGpVIrpFAqFcP369Tr9lsOHDwu2trZiWmdnZ+G9997T2vLq9u3bwtdffy3ExcUJmzZt0lnmxx57TNi6datQUlJS63uFQiFs375daN26tThPFxcXIT09XWcZG6JFZvWTzLGxsbWeCi8uLhYef/xxjTKsWrVKaNWqlQConnJOTk7WmCYlJUXo2rWrmN7GxqbWOlKXmJio8XShp6ensGLFCqGyslJMI5fLhdWrV2s8dW1rayucOnVKZ77Hjh0TrKysNJ423LRpk8a2WVpaKnz88cfiE7HqTxnqe7L31q1bGi2QQ0JChJ9//lmoqKjQSJeXlye88sorGk9pv/766zrzlfokc2FhoUbrLS8vL2Hx4sW1nmAvKSkRPvzwQ43l+/DDD+vMNyUlRaP1mbu7u7Bs2TKNdVFZWSmsWLFC8PDwqLXMTNki0xz7U80n36vX4eOPP17ryeTbt28LDzzwQK1tX5eCggKNlp329vbCokWLNMquUCiEP/74Q0ynvuxM2SLzlVde0Sh39+7dhTNnzmikyc7OFp588kmNdF26dNH5hH7NY1F12Xv06CEkJSVppL19+7Ywbdo0jfSjR4/Wmq9cLtdYDp06dRKOHz+uNW1+fr6wdu1a4YEHHtDbInPixIliftbW1sLLL79ca7tQKBTCpk2bNJ5Ubt68udZWILt27dL4LYZa2gmCIKSlpWkcf3SdN7Zu3apxfOjRo4ewf/9+jfOcIAjClStXNFrWyGQy4a+//tI5/48++kijzJ07dxYSExM10mRlZQmzZs0SAAgODg4a+359W2RWq0+LbVOvR0HQfMLd2dlZcHBwEOzt7YWPPvpIKC0t1Uj722+/Cc7OzmL6oUOHCv/73//Ea52a55PKyspa14rvvPOOzt+nXhYXFxfBwcFBACDMnj1byM7O1kh7+vTpWq2fPvvss0Zdfo6OjoKNjY1gZWUlvPbaa0JeXp5G2vT0dI1lmpSUJAQGBgpvvPGGcOzYMa3HmpKSEuH777/XOL+2b99e7++sa8v2hmqRWX0N6+3tLSxfvrzWdULNayhTr7esrCyNa4Bhw4bpPIZlZWUJK1asEHr27Km3dUbNllKmujYVBEF46aWXhN69ewvffvutkJqaqjXNtWvXhNmzZ2uU4fPPP9ebr3rdoHr7atOmjbB3716NdFVVVcIXX3whWFtbi+kHDBigN+85c+ZolGXYsGHCtWvXNNLcuHFDPIbXbNFijhaZ1fPw9PQUVq5cqbG/VVZWCj/88IPg5uamsZ2ao152L16vSj025OTk1Op9YMaMGVpbSBcXFwu//vqrMGTIEGHu3Lla86trXdxcdRJz1qOMoa03FamWLFmiMe2DDz4onD59ula6kydPatRdHRwchLNnz2rNs+b1cHXddNasWbXqEufOndPovcjHx0fYsWOHYGVlpbWeIAiCsGnTJo17Jv369dP5+8x1bd6Qy6/6eDJq1Kha58Pi4mLh9u3bGp9Z0v2WhmqRaez1ljnWW9u2bcV0UVFRwq5du2rVVQRBdSzfvHmzMGnSJGHevHmSlgmRJWMgk4juWZYWyFTvVqNmVyW6VFVV6fyuLpUnuVyu0Y2Kv7+/3gu/HTt2aNwYGDt2rKTyAKpuTQsKCiSVS8pvkcvlQmRkpJjO09NTSEhIMJi3UqnUeYOvqKhIUvmKi4uFDh06SFp/DRHIBCDExMQId+/e1Zm+R48eYtrqmwyTJ0/WegErCKogifoNkgULFujMW/0C2tnZWTh58qTOtImJiWKFB4DQsWNHnWnj4+M1bpzo63pt06ZNtbqE0lcBHzJkiJiuVatWQm5urs60gqDqKq06va2trZCWlqY1ndSbJ0888YSYLjg42OB2sW3bNo2bEboCRePGjRPT2NvbC4cPH9aZ59GjR8Ub71JuDBnLHPuTti68PvjgA53pKyoqhJiYGEk3E15++WWNfKu7j9Pm6tWrtbriNlUg8+LFixrruk+fPnq73XvzzTc1ylHdzWhNNY9FAISePXvqzbtmoHT79u210hw4cEDjRkPNyrkuus5na9as0djXDHULmJqaKvj5+YnTfPjhh7XSKJVKjXPdq6++arB81QEvQHWTS/3marWioiKNm37Tpk3Te55WKpXCww8/LKZv2bKl1nTZ2dkaN8E6d+5cK0inbu7cubXWbWMHMs2xHgVB88YQoAoIb926VWe+S5cu1UhvZ2cn+Pr6Cjdv3tQ5zaRJk8T0kZGROtPVLAugvxuu4uJijW7wnZ2ddXZX3FDLz5htpby8XOt+oM3ly5c1riF27NihM62lBzKr11XNm8ramGO9/fTTT+L3ERERtYIbuug7FpkzkCn13C8IgvD++++LZQgNDZVczwEgxMfH653Xu+++q3GcSElJ0ZouKSlJ45w7atQojUCPOoVCIYwZM6bWPmSOQCaguvl94sQJnekPHDig8UDnxIkTdaata73sXrxelXpsmDp1qkaeUrt917Xd1TWQaa46iTnrUcaoayAzJSVFY73rC9oKguqav0+fPmL6YcOGaU2n7XpYX8Dm4sWLgo2NjcZ1hKFrD/V1pO/4Y65rc0Fo2OU3Y8YMnfcUarKk+y0NFcg05rhgjvV269Ytje3x8uXLUn6q3nMy0b2CgUwiumcZ24e+ob/6BjKbN28uptm2bVu9f19dKk/r1q3T+E1r1641OM0zzzwjpre2ttZ5M7BmhVnKuJrG/Bb1scuklt2UduzYIc47OjpaZ7qGCmTu27dPb/rVq1drpPfw8DB4A+PRRx8V0w8cOFBrmhMnTmjkqy+oVO3jjz/WmObgwYO10hw/flwjzZIlSwzmqx4g0LfvnT59WuMGgJTWWYIgCP369ROne/PNN7WmkbLv3759W2NMFV1jttZU3fIKgDB16tRa32dkZGhUtP/zn/8YzPP111/XWGamDGQaQ+r+VPNmSPfu3Q3mrX4zwdHRUWulrLy8XPDy8hLTTZgwwWC+33//vUZZTBXIfOqppzTKq+vmRzWFQqHxlG3z5s213kyoeSyytbU1WJEtLS0VQkJCxGlGjBhRK83PP/8sft+5c2fjfqwW6r9F135Wk/r5ICQkRGua1157TUwTGhpq8IZLy5YtxfRz5szRmubTTz8V0zRt2lTSOG8FBQVi6xIAwq5du2ql+fDDDzXOs+fOndObZ3l5ucaDPcZcBxhS10CmudZjzRtDM2fO1JtneXm5xsMzAISVK1fqnebgwYMa6XW1AqhZlqioKIOBvjNnzmjc5P/444+1pmuo5TdkyBBJedeF+j73xBNP6Ex3LwQy//e//0nK3xzrbeHChUadm6QwZyDTGAqFQggODhbLoa+uoF43sLKyEi5duqQ374KCAo2bwatXr9aaTv2c6+rqKmRlZenN986dO4K7u7vG8jNXIFNfXbPaiy++qHFe1zVedl3qZffq9aqUY8P169c1Hs598sknJf02fepSFzdXncSc9Shj1TWQ+dxzz4nT9OzZU9I0169fF7ctmUwmXL16tVaamtfDzZs3Nzje6MCBAzWmmTVrlt70ZWVlGi2OdR1/zHVtLggNt/x8fHyMeoDFGOa+39KQgUyp11vmWG+HDx8W8/Tz85OUJ9H9QvcgHkREZBT1fvtPnz7dKGXYtGmT+Do0NBTjxo0zOM0LL7wgjmGjUCjwxx9/GJymTZs26Ny5c90LqsXq1avF13FxcRg/frxJ8zeka9eu4uvLly+joKCgQeevLjo62uC4MV26dNF4P3HiRLi5uUme5sKFC1rTqI/f5uTkhCeffNJQcfH444/DxcVFfK++HWrL19XVFTNmzDCY77PPPmswDQCsWLFCfD18+HBER0dLmm769Oni6507d0qaRps1a9aI4wi1a9cO/fv3N3r+u3btqvX9H3/8IY57IZPJJI0D+PTTT8Pa2lrS/M2prvvTU089ZTCN+nhrZWVluH79eq00e/fuFccrA4BnnnnGYL5Tp06Fh4eHpHIaQ31/GDNmDMLCwvSmt7Ky0hj/6MqVKxpj+ugyePBgNG/eXG8aR0dHPProo+L7bdu2aYx9Vp1Gfd7q44saKzExURw7x9bWFs8995yk6caPHw8HBwcAqjF4r1y5UiuN+pgxqamp2LNnj878Tp48qbEM1fc9derHkmeffRb29vYGy+rm5obRo0eL77UdS9SPf3379kXLli315mlvb4/HH3/c4LwbijnXY02Gfre9vb3G2FRubm6YOHGi3mk6duyocVzUdf7TVhZbW1u9aVq3bq1xTNqwYUOtNJa0/OpD/bh+/Phxs83H3KytrTWOg7qYa72pH2PVx36ujxUrVkBQPaQOQRBMNj6msaysrDTqB1K3k/79+2sdd1Wdm5ubxr6v67yofrydMGECfH199ebbpEkTo8byritra2tJ1zjPPPOMWC+Ty+UmrZfdz9era9euFfclW1tbvPnmmybL2xjmqpOYsx7VEJRKpcY45S+99JKk6SIiItCzZ08AgCAIWre/mmbNmmVwbNCa9ejZs2frTe/g4KBx/JF6HWGqa/OGXH6TJ0/WqNebkiXdb6kvKddb5lpv6tcR2dnZSE9Pl5Qv0f3AdCM/ExE1Ml0Dzevz119/mWz+8fHxOHPmDABgwYIFaNKkCaZNmybpRqipHDlyRHz9wAMPiBVhfcLDw9GmTRvxZs2RI0cMVkANBdmMVVVVhUOHDonvpQRgjSEIAg4fPoxjx47h0qVLyM/PR3Fxsc4BzwVBQEZGBtzd3U1aDqnUL/J18ff3N3qagIAA8fXdu3e1plHfhnr37g1XV1eD+To7O2PAgAH47bffauVR7dixY+LrPn36iDf59OnUqROaNGmCnJwcven27dsnvh44cKDBfKu1adNGfH3y5EkIgiBpnzHH/DMyMpCRkYHAwEDxM/VlFhcXh+DgYIN5BgQEoF27dkhISJBcDmOZc3/q3r27wTQ1l0N+fn6tNOrLzt3dHd26dTOYr729PQYMGID169cbTCvVzZs3cfv2bfH9iBEjJE334IMParw/cuQI4uLi9E4zdOhQSXk/8MADmD9/PgDVsffUqVPo0aOH+H3Hjh0hk8kgCALu3r2LkSNH4quvvpJ8M06d+r7Rrl07eHt7S5rO3t4e0dHR4nkpISGh1o2gFi1aoHPnzuK6XrlyJfr166c1P/WbCLGxsejYsWOtNPn5+UhKShLf13VfrrnvVVZWajzcZMx6+s9//iO5DOZkzvWozs7ODh06dDCYr/r5r0OHDgaDjfb29vDy8sKdO3cA6D7/1WTMuqoOpJ86dQpyuVyjTA21/IC6X5+Vl5dj586dOH36NK5du4bCwkKUlZVBEAQxjfrDIWlpaXWajyWIjY2VtA7Mtd7i4+PF1xcvXsSUKVPw4YcfIiQkROIvaDypqanYvXs3zp49i6ysLBQVFaGyslIjjfpxVOp2IuXcD2ie/7Wd+1NTU5GZmSm+N2YfXrJkiaS0ddW+fXuDQVUACAsLQ8uWLXHu3DkAquuZxx57TO80Uvf7+/l6Vf239ejRQ6PO05DMVScxZz2qISQlJYnnXplMJjmIDqiWTfU5NiEhwWDQ0dh6tNRrDyn16JpMdW3ekMuvrtcR99r9lvqSspzMtd5iYmLg7OyMkpISCIKA4cOH47vvvtO4viC6XzGQSUT3je3btxs9TV2CFro899xzWLlyJSorK1FRUYHZs2fjpZdewuDBg9GrVy906dIF7dq1M1trqaqqKqSkpIjv1StEhrRu3Vq84XL16lWD6SMjI40tnl5paWkoLi4W32u7yVwXgiBg+fLlWLBgAVJTU42aVtsNkoZSM0ipjZOTU72mqfmkZzX19W/sNlQdyNS2Dam3SGjVqpXkfFu1aqW3pZUgCOLNHgBYunQpfv/9d0l5l5WVia8rKytRWFhYp8rU2bNnxdebN28WH2gw1p07dzRuDNVnmZkjkNkQ+5OU7djZ2VnjvbZtWX3ZtWzZUvKxvlWrViYNZNbcF6TuUx4eHggNDRWXs5TjstRtpHp5VAcorly5onGzJDg4GJMmTcLPP/8MQNX6IiYmBm3btsXAgQPRrVs3dO3aFX5+fgbnpb5v3Lx5E0OGDJFUxur01aoDUDVNmzZNvLm3YcMGLF68uNaxsaqqCr/88ovGNNokJSVp3Gx59tlnJZ+v1Z+ErlnW1NRUsQUMIH09tWjRAra2tpDL5ZLSm5O512M1b29vg60oAM1zmZRjRs1pdJ3/1Nna2hpsIVZNfZ2Wl5fj1q1bGtdJDbX83N3d4eXlJTlvACgpKcG7776LJUuWoLCwUPJ0jXmNVF9Sr2HNtd66d++Obt264fDhwwBUreTWrVuHLl26oF+/fuIx1hw9BNTVuXPn8MILL2Dnzp0awW1DTHnuBzTP/4bO/YD0460x11h1Zex1XPW1rZSW2HXZpu+369WLFy+Kr01VjzSWOesk5qpHNRT1bc/GxgZjxoyRPK36NbCh8yBgfJ24LtceUq4jANNdmzfk8jP2Ps+9er+lPqReb5lrvdnb2+O5557DwoULAah6kOjUqROaN2+OwYMHo3v37ujatStCQ0Mlz4/oXsFAJhGRicTFxWHNmjWYNm2aGJQrLCzEr7/+il9//RWAqlukAQMGYNq0aXjwwQdNGkiteSHo4+MjeVr1tFKeMDTUhamx1J/yByDpiWVDlEolZs6cqdESxxjqN54bmp2dXYNMo436+jflNqT+mdRWDVLSFhQUiN1ZAarWMHVVUFBQp0Bmbm6u+PrixYsaN1OMnb86cy2zumio/akuLdi13VS1lGVXc18wdp+qviEg5bgstewODg5wdnYWz1Pa8v7mm2+Qm5ur0WuBejeLgOqmy9ixY/Hoo4/qbH2hvm9kZWXVuRcEXV1PTZw4Ec8//zwqKytRXFyMjRs3YsqUKRpptm3bJt4AsLKyqvW9trICde9uWt9+DEhfTzY2NnB3d7eIlhTmXo/VGurcJyUQ4+HhITmQXXOd1lznDbX8jL02y8nJwYABA+oUzKjZCu9eInU5mXO9rV+/HiNGjMDJkycBqM6xhw8fFoObVlZWaN++PcaPH49HHnnE6AC1KW3ZsgVjxoyp03VxY577AenHW3NcN9VnHuppTVkvu5+vV9XrkqaoR9aFOesklrCM60N925PL5WY7DwLGXxeY6zoCMN21eUMuP2OuJe7l+y31UZdjrqnX24IFC5Ceno4ff/xR/OzKlSu4cuUKvvzySwBA06ZNMXr0aDz66KOSH84jsnQcI5OIyIRGjx6NK1eu4OWXX9Z4UrVaYWEhNm7ciFGjRqF9+/aSx1eQouaFoDEX5eo3EMrLyw2mt7Iy7emj5jyldJdjyJdffqlxUR0VFYX3338f+/fvR2pqKoqLi6FQKDTGFCLN7aiu25BcLq815pP6Tc+65qtNfcbvq0lX1zeGmKoMNedvrmVWF/fa/mQpy64hj8t1zVvbTQRXV1ds374dGzduxIABA7Q+qX7+/HksWLAAzZo1w4IFC7TuP+baN6p5eXlh+PDh4nttN1LUP+vfvz+CgoK05tUQ+zHQ+PtyXZh7PVqi+qynmvtUQy0/Y6/NHnvsMY0gZp8+ffDtt98iISEBWVlZKC0thVKpFI/pltCqxxSkLidzrreAgAAcO3YMy5YtQ5cuXWo92KhUKpGQkIBXXnkF4eHh+Oabb0xSFmOlp6djwoQJ4jbt5OSEJ554Aps2bcLFixeRn5+PiooKjXO/rjGIG0Jdj7cNcaw15Tm6psbepi3hmkv9OskU9ci6MGedxBKWcX38G68jANPt9w25/Iy5lrjX6oem0tjHXED1wOOKFSuwe/dujBw5Uuu+fu3aNXz00UeIjY3F008/fc8GjonUsUUmEZGJBQQE4MMPP8SHH36ICxcu4ODBgzhw4AB2796NjIwMMV1iYiJ69+6NkydPmqTbh5pdUBUVFUmeVr07scboysrT01PjfX0HflcqlWJXGwAwfPhwbNiwQW9lwpjldT/z8PAQWwDVdRtydXWt1ZLFzc1NfFq6rvlqU3N73bhxI0aPHi05f1NQX2affPIJnn/+eZPkq/60pymXmbHuxf3JUpadtuOyi4uL0WWRclyu6+/U1wp59OjRGD16NIqKinDw4EEcOnQI+/btw9GjR8VWBxUVFXj77bdRWlqKDz74QGN69XI/+OCDkrtYM8a0adOwceNGAKpucNXH7srPz8fmzZvFtPpusNdcxnl5ebXOTXVR86ntxtwe66oh1qOlqc96qrlPWeLyO3funNgdPAAsXLgQr776qt5pGvu4rk3Nh6ZMydzrzdraGjNnzsTMmTORm5uLAwcO4NChQ9i7d684Rh6gWu5PPvkkBEHAk08+adIyGPLpp5+KN2Ld3d1x+PBhxMbG6p2mMbcTbcdbKefchjjWmuMcbaz7+XrV09MT2dnZAOpfj6wrc9ZJzFWPaijqy8bNza3R1lFDM9V+b4nL716sH2rTUNcR5lpvffv2Rd++fVFWVoYjR47g0KFD2L9/Pw4ePCg+4KFUKrF48WLk5ORg7dq1Ji8DUUNii0wiIjOKjY3F7Nmz8dNPPyEtLQ2HDx/GAw88IH6fk5OD9957zyTzcnZ21hi74dq1a5KnVU/bGN3x1BzL4vLly/XK79SpU2JlFgC++OILg09E3rp1q17zvF+or39TbkPq4+nduHFDcr7Xr1/X+72zs7PGTaqsrCzJeZuK+vZryvmba5kZ617cnyxl2dXcF6TuU0qlUqPcUo7LUn9nWlqaxriLUsa6dHV1xdChQ/Hee+/hwIEDyM7Oxueff64R6Pvkk09qjY1jrn1D3bBhw9CkSRMAquW2evVq8bt169aJTx+7urrqvaFY8zxkqvLWXL5S11NOTo7F3PBpiPVoaQoLC2t1e69LzeNGzf3VEpef+rjy4eHhmDdvnsFpzH1cVz+vSB0bVkq3m3XVkOvN29sbo0aNwqJFi3DixAmkp6djwYIFGi3LXn31VcnjspmK+nby3HPPGQxiAo17/q/r8dbU535t6notIuUcLdX9fL0aEBAgvq5vPbKuzFknsYRlXB/q215hYaHGmKD3M1Ndm1vi8rPE+qElX0eYe705OjqiX79+ePPNN/H3338jJycHy5cv1xj+Y926dWIX9kT3KgYyiYgaiEwmQ9euXfHHH3+gV69e4ue6+spX77JCajccHTp0EF9LvUipqqrC8ePHxfcdO3aUNJ0peXp6Ijo6Wny/d+/eeuV38+ZN8XWTJk0QERFhcJqDBw/Wa573i7psQzXTatuG1PM9duyYpDwLCgok3Yzo1q2b+PrIkSOS8jYlc81ffZmdPHlSY9wdXaq7ozOle3F/Ul92165dqzX+oS5St02pWrVqBVtbW/G91H3q7NmzGt0RSTkuSy17zXTqy0oqT09PPPvss1i3bp34WVVVFXbt2qWRTn3fSExMNEsF3tbWFpMmTRLf//TTT+Jr9e6uxo4dq/GwT02tW7eGs7Oz+N5U+7Kvr6/GTYS6ridTqcu1RUOsR0tUl3UVGhpaayxcS1x+6sf1jh07ShqzXepxvS7bGKDZqktqEDkpKUly/sZqzPUWEBCAt956SxznClBdE5nruKCL+nbSqVMng+mLi4vrNOaqqbRu3VrjnNvYx1t1J06ckNStY1VVlcbYinU5R+tyP1+vdu3aVXy9b98+k3RhWd/zpbmWsanrUcaq2a2mlGWjvn4A4OjRoyYtk6Uy1bW5JS4/c9YP67KNAZrXEXfv3pU0nTmvIxpzvTk7O2PGjBnYsWOHxnmxruN0ElkKBjKJiBqYlZUVRo0aJb7PzMzUmk79hqrUmye9e/cWX+/cuVNn3uq2bNmicZNfPY+GNGTIEPH1+vXrxa6P6kLqE3jqfvjhhzrP736ivv4vXryIkydPGpzmzJkzGjeutG1D6p+dPXtW0viwa9eulXQzZOjQoeLrTZs2Sb4Bairq8z948KDJbhqoL7O8vDzs2LHD4DQ7d+40ecuRe3F/Un9YRKlUSupG58qVKyYPAjs4OKBz587i+1WrVkmqVP/444/iazs7O3Tp0sXgNOvWrZPUPZJ6i8WQkBBJNx50GTBggEb3VzXPOQMGDBArzxUVFVi1alWd56WPepexSUlJOH36NK5fv45Dhw6Jn0+bNk1vHra2thgwYID4funSpSYrn/q+vH79ekn7lPp6MqW6XFs01Hq0NL/88ovBNJWVlVi/fr34Xtv5zxKXn7HH9dzcXI2uaPWpyzYGAGFhYeLrs2fPGkxfXl6Obdu2Sc7fWJaw3saMGaPxXsp1vSkZu5389NNPtcapbEgODg4aAVcp+zAA/Pzzz+YqkigzM1PSg5pbt27VaCFkynrZ/Xy9ql6PTE1NxdatW+udZ12OZeaqk5izHmUs9eUCSFs2gYGBaNOmjfjelNdYlsxU1+aWuPzMWT+syzYGaF5HlJWV4erVqwanqR6ewhwsYb3FxMQgJiZGfN/Q1xFEpsZAJhGRiRjz5GdxcbH42svLS2sa9S5ykpOTJeX7yCOPiE+wyeVy/Oc//9GbvqKiQmNMpLCwMAwaNEjSvEztmWeeEcdVLCsrwxNPPFHnp2mrx0cDVN3zXbx4UW/6ZcuWabRK/TebMGGCxtOML730kt71IAgCXnzxRfG9i4sLJk+eXCvd+PHjNSolhrbN4uJivPvuu5LK/Mgjj4j7UVFREZ5++mlJ05nKgw8+iBYtWgBQBc0ee+yxOlXuaurXr59Ghez111/Xe0OiqqoKr7/+er3nW9O9uD9FRUWhR48e4vv//ve/BscJMrRN1tVjjz0mvk5KSsKKFSv0pk9OTsaSJUvE9+PHj5c0RmZKSgq++eYbvWmOHDmiEYx45JFHaqUx5rhbUVGhsa3XPJ/5+PhoBBnfeOONWt3PmkKHDh3QsmVL8f3KlSs1WmOGhYVJuhn88ssvi68PHz5scHlKNXPmTPF1RkYGvvjiC73pT506ZbYxbNSvLa5duyZpfTfUerQ0P//8s8HWZZ9++qnGTSFt+5QlLj/14/qRI0cM3ux+/vnnJd9IVN/G7ty5I3lMKPWW5xkZGQZbbnz00Ue4c+eOpLzrwlzrra71BUB3nWHGjBmQyWTiX0pKSn2KKFLfTvbv3683bVZWFt566y2TzLc+1I+3J06cwIYNG/Sm//333xusq73XXntNb1BDLpfjjTfeEN9HRkaib9++Jpv//Xy9Onz4cDRt2lR8/8wzz9R7PLq61sXNUScxZz3KWOrLBZC+bNSvsdasWWOSYLOlM9W1OWB5y8+c9UMPDw+NrtWlbmMxMTEava8YupY+cOAAtmzZIinvujLHejP2HpmUe49E9woGMomITKR169ZYsWKFRneA2ly/fh2LFy8W3+uqoKp3KbJ27VqkpaUZLEN4eLjGTZeVK1fi3Xff1XqxU1JSggkTJmhcdL755ptiMLGhRUZG4qmnnhLfb9iwAdOmTdM7RphSqcT69etrtRrs1KmTRmXvqaee0nkDbu3atXjyySfrWfr7h4uLi0Zgcu/evXj88ce13uioqqrCU089pdGd5Ny5czVaaFVzc3PDM888I77/888/8corr2jtZquwsBCjR4+WtM0DqrHvFi5cKL5fs2YNJk2ahPz8fIPTnjhxAtOmTavX0/hWVlb49NNPxa75Dhw4gCFDhiAjI8PgtBcuXMCcOXOwaNEirfmqP2iQmJiI6dOni2P+qauoqMCsWbNM3qIQuHf3J/WbgRkZGRg9erTWm1pKpRKvvvqq5NZGxpowYYJG19lPPfWUxrhj6lJSUjBs2DBxHdvb22tsA4a8+OKLOivkFy9exJgxY8TzgZeXl9Z19cEHH2D27NmSulp66623NMZs03Y+mz9/vjiGZXZ2Nnr37i2pa6WsrCx88MEHePjhhw2mBYCpU6eKr3/55ReNLmanTZsmqevM7t27Y+LEieL7OXPm4IMPPjAY5JHL5fjjjz/Qt29fja62qvXr10+ju7lXX31V5xPgV65cwciRIyV1QVgX6tcWeXl5WL58uaTpGmo9WhKFQoGRI0fqfKJ//fr1GseZnj176gyYW9ry69evn/g6PT1d43eoq6qqwksvvaSxPxlSs3vPTz75RNJ0nTt31uiG+ZlnntH5AMqyZcuwYMECyWWqK3Ost7lz5+Lll182OHZdVVWVRrDCwcGhVjd15qa+nXz99dc6rzFSU1MxcODAevWmYiqTJ09GZGSk+H7WrFkarfPVHTlyRKPeZG7Hjh3DY489prXVanl5OaZOnapx7n3ttdcknbukup+vV62trfHhhx+K72/cuIE+ffoY3M+OHz+u0apeXV3q4uaqk5izHmWswMBAjbH/PvvsM0mtDidNmoTu3bsDUF17jxs3DsuXLzcYlCktLcXq1atN2s1yQzLFtTlgecvPnPVDa2trtG3bVny/ePFilJeXG5zOxsZGo+ezRYsW4fz581rTnjhxAmPHjjVJN9T6mGO9rV69GhMnTpTUffXixYs1joOmfDiGqDHYNHYBiIjuF+fOncPMmTPx9NNPY+DAgejcuTNiYmLEp54yMjKwf/9+/PTTT+JTUXZ2djpvUk+ePBkffPABBEHA7du30bRpU7Rv3x4+Pj4a4wbUvPn+2WefYd++feIFy1tvvYU///wTM2bMQIsWLSCXy3Hq1Cl89913Gk9sjxo1SucTgA3lo48+wokTJ8SbRKtWrcL27dvx8MMPo0ePHvD19YVcLsetW7dw7Ngx/Pbbb8jMzMSmTZs0LvIcHBzw9NNPixXavXv3onXr1njyySfRtm1byGQyXL16FWvXrhWDcE888YTJWt/c61577TVs27ZNXA/ff/89Dh48iEcffRStWrWCTCbD+fPnsXTpUpw7d06cLj4+Xu/T+PPnz8fvv/8uBs8XLVqE3bt3Y9asWYiOjoZcLseJEyfw7bffIi0tDT4+PmjTpg127txpsMyPP/44Tp8+jW+//RaA6sbBn3/+iQkTJqBXr14ICgqCnZ0dCgoKkJqaitOnT+Pvv/8Wgw7qN+vqYujQoVi4cKG4P+/evRuRkZF46KGH0K9fP4SGhsLJyQmFhYVIT09HYmIidu/ejUuXLonLRpvZs2dj3bp12L17NwBVC6GEhATMnj0brVu3hkwmw9mzZ/H999/j0qVLcHBwwNChQ7Fp06Z6/R519+r+NHjwYMyYMUNsAbl79260bNkSjz/+OOLj42Fvb49Lly5h+fLlOHHiBGQyGSZMmIA1a9aYtBz29vZYtWoVunfvjoqKCpSXl2PYsGF46KGH8NBDDyE4OBj5+fnYs2cPli5dqvHU7AcffIDY2FhJ85k0aRJ++eUXDB8+HGPHjsXo0aMRHByMvLw8/P3331i2bJnGTYAvv/wSvr6+tfIpLy/H999/j++//x4xMTEYMGAA2rVrh8DAQLi4uKC4uBgXLlzAmjVrNJ6snjhxIpo3b14rv+DgYKxfvx6DBw9GRUUFUlJS0LVrV/Tr1w/Dhg1DTEwM3N3dUVJSgjt37iApKQmHDh3C4cOHoVQqJXerN2XKFLz22mtQKpW1uqsz1K2suh9++AFXrlzBqVOnoFAoMG/ePCxevBgTJkxAp06d4OPjA0EQkJ+fL3ZHvGPHDjFIru3GhEwmw9KlS9GxY0eUlpZCLpdjzJgxGDVqFMaOHYuQkBDcvXsXu3btwtKlS1FWVoaePXvixo0bJr8R2aJFC3Ts2FG8ifzII4/g/fffR1RUFOzs7MR0zz77rMZxsaHWo6UIDg5GREQEDhw4gDZt2uDRRx9Fv3794OnpiVu3buHXX3/F77//LqZ3cXHR22WYpS2/Hj16oFOnTuI+/MEHH+DYsWOYPn06IiMjUVZWhjNnzmD58uXieUrqcd3FxQUjR44UgwPvvPMOli1bhtjYWDg6OorpJk6cqPHggJWVFf7zn/+IN+sTExPRpk0bPPvss2jTpg0EQcCVK1ewZs0a7N+/HzY2NpgyZYpZu3w1x3orKCjAjz/+iI8++ggdO3ZE79690bZtW/j5+cHJyQn5+fk4e/YsfvrpJ42HDefOnavRY0ZDmDt3LlasWAGFQoGSkhL07NkTjz76KAYOHAgvLy9kZ2dj165dWLFiBUpLSxESEoJWrVo1akshR0dHfP/99xg0aBAUCgUKCwvRu3dvPPzwwxg+fDj8/PyQnZ2NLVu24KeffoJCocDkyZPN3r3sqFGjsH37dixfvhxHjx7FY489hlatWkEQBJw9exbffvutRqujQYMGmaVedj9frz700EN44YUXxIcnEhMTERMTg3HjxmHgwIEICQmBtbU1srKycOrUKWzZsgXnzp3Dc889h7Fjx9bKr651cXPVScxZjzLWlClT8NFHHwEAVqxYga1bt6JVq1ZwcXER0/Tr1w/PPvus+N7KygobNmxA586dcfPmTZSWlmLWrFlYtGgRxo4di/bt28Pb2xtyuRx3797FxYsXcfz4cezatUvjobl7iamuzQHLW37mrh9OmTJFvBexY8cOBAQEoG3btnBzcxMfxoiLi8N7772nMd3LL7+MtWvXisf/Ll264Omnn0afPn3g5OSEtLQ0bNmyRUyjXlc0B3Ost6qqKqxduxZr165FWFgYhgwZgvbt2yMkJARubm4oKyvDlStXsGnTJo39v1u3bhrDaBDdkwQionvU/PnzBQDiX12oTz9//nyd6aZPny6mmz59usG8pPw5ODgIGzZs0Fu+d955x2A+2ty6dUto2bKl5LI89NBDQnl5ud6yhIWFiemXL1+uN219pi0uLhYefPBBo5blpk2bauVTVlYmdOvWTdL0Q4YMESoqKjQ+27Nnj9by3bhxQyPdjRs3jFoWuvTu3VvStqhOSnnV7dmzR/I+k5+fL/Tq1UvyOujevbuQl5dnsAxpaWlC06ZNDebn7Ows/P3335L2vWpKpVJ47733BCsrK6P3R13bpTHzFwRBWLZsmeDg4GD0/PWt84KCAqFTp04G87CxsRFWrlypcWzs3bu3wTJLYa79yZhtUp3Ubb+yslLy8WThwoXC8uXLxfdhYWHGLSQD9uzZI7i7u0sqi0wmEz744AO9+dU8Fl29elUYNmyYpPwXLVqkM9+a51Ypf/379xcKCwv1lvf48eNCcHCw0Xkbsw0PHDiw1vTdunWTPH21oqIiYcyYMUaXFdB/Tti1a5fg5ORkMI/IyEghLS2tXuddfU6fPi14e3vrLYOu+Zl6PdZlnzP2uCwI0q5DapYlLS1NiIyMNPjbnJ2dhX379kkqhyUsv2rJycmCj4+PpOPR22+/bdTx+tatW0J4eLjefLWd9xQKhaRjto2NjbB06VLJv78u24w6U6439bJI/Xv44YeFyspKneUbMmSImNbPz08oKysz+jfq8vnnn0sqo4+Pj5CQkCB5Wdfl+GbMely1apVgbW1tsNwdOnQQioqKND6Tck0tRc1r+5UrVwo2NjYGy9S1a1eD59T6nh/upetVY/ffBQsWCDKZTPJveu6553TmVde6uDnqJIJg3nqUMQoLC4V27drpLYOu+WVmZgo9e/Y0ernoWtZ1qZub69rDXNfm6ixh+VUzV/1QEARBLpcLgwcP1punruPGokWLJJVp1qxZwvXr1yX9/vrWEU253tTLIvWvTZs2QkZGhtHlJrI07FqWiMhEvv76awwbNgyurq560zk4OGDSpElISkrCQw89pDftm2++iQMHDmDWrFlo2bIl3NzcNJ4A1SU4OBgnTpzAe++9Bx8fH53pmjdvjtWrV2P9+vWwt7c3mG9DcHZ2xu+//44NGzagffv2etP6+vpizpw56Ny5c63vHBwcsGvXLjz77LM6f1tAQAAWLVqErVu3arREIcDd3R27du3C4sWLERoaqjNdcHAwvvzyS+zduxeenp4G8w0KCsLJkyfxxBNP6FwvPXv2xPHjx41+YlAmk+H1119HUlISHn74YY0xMrTx9PTE2LFjsWHDBq3jetbFzJkzcfnyZTz99NNau9hV5+LiggceeAA//vijxvgZNbm5uWH//v14/fXXNZ50Vte2bVvs3btXo3tNU7pX9ydbW1ts2rQJn376Kby9vbWmadasGTZt2mRUF6510adPH5w/fx6zZs3SGPdFnUwmQ9++fXHkyBG88sorRuVvbW2NzZs3491339U5/kl0dDR27NiBl156SWc+48aNw3PPPSeOo6VPdHQ0vvnmG+zYscPguS8+Ph4XL17EBx98oPeYAqi6huratSs++OADo1rJaOsi0JjWmNVcXFywfv16bNu2DX369DHY5Xp4eDiefPJJHDx4EOHh4TrT9evXD6dPn8bAgQO1dhdoZ2eHqVOn4uTJkwgKCjK63FK1bdsW58+fx4IFC9CzZ0/4+PhI3mcbYj1aiurz1dSpU7UuH5lMhkGDBiExMRG9evWSlKclLb9mzZohISEBw4YN05mmVatW2LJli85WWLoEBwfjzJkz+Pjjj9G/f38EBAToPO6ps7Kywvr16/H6669rtN5U1759e+zfv79BexEx5Xp7/PHH8dhjj+k9VlRr37491q1bh1WrVml016tOoVBojPH4yiuvSFrWUj377LNYv369zvLa2dlh3LhxOHv2rEV1/fjwww/jyJEjiI+P1/q9i4sL5s6di0OHDum8tjK1qVOnYs+ePRpdJtYs05tvvok9e/YYPKfW1/16vQqoeiNKSEjA8OHDYWOjuxM6JycnjBkzBlOmTNGZpq51cXPVScxZjzKGq6srjhw5gm+//RZDhw5FcHCwzmN2TX5+fti7dy/WrFmDjh07Guw+OTo6Gi+++CISExNNUPKGZaprc3WWtPzMWT+0sbHB1q1b8fPPP2P06NGIiIiAs7OzpO62X3rpJaxevVpjHE91wcHBWLp0KX744QeTdt+tjynXW79+/TBv3jy0adPG4PEoNDQU77//Po4dO1ZrfFuie5FMEMzcITQR0b+MQqHAhQsXcOXKFaSlpaG4uBjW1tbw9PQUu3RrqAozoOqL/8SJEzh//jzu3LkDGxsb+Pr6Ij4+XmPcNkuVnp6Ow4cPIzMzEwUFBXB0dERQUBBatmyJuLg4SRefeXl52Lt3L27cuAG5XA5/f380a9YM3bp1k1QZJeDMmTNITExEdnY2AMDHxwdt27bVeTNGioKCAuzatQs3b96EQqFAYGAgOnfujKZNm5qkzJWVlTh27BiuXr2KnJwcyOVyuLi4ICgoCNHR0YiJiTHr+lcoFDh16hQuXLiA3NxclJWVwdnZGf7+/oiOjkbLli113pjUpaysDLt27cL169dRVlaGwMBAtGvXDnFxcWb6FbXdq/uTXC7H3r17ceXKFRQVFcHf3x+xsbHo1KlTg5elrKwM+/fvx40bN5CXlwcXFxcEBgaiV69eOruUMkZlZaW4jvLy8uDj44P27dsbfDikptzcXJw9exbXr19Hbm4uKisr4ezsLG532rqSlSo5ORkJCQnIyckRj+3e3t5o3rw5WrVqZfabuMYoKCjAoUOHkJaWhtzcXMhkMri7uyM8PByxsbEICwszOs+bN29i//79yMjIgKOjI4KDg9GnTx+dN7os1b20Hg1ZsWIFZs6cCQAICwvT6H6/+riXlpaG8vJyBAQEoHfv3gaDWoZYyvJLSUnB/v37cfv2bdjY2IhduEnt2tociouLsWfPHly/fl1c5u3bt2/Q850uplpvt2/fRlJSElJSUnD37l1UVVXBxcUFoaGhaN++vaRjS0JCghis8/X1xY0bNwwGTepCoVDg6NGjSExMRH5+Pjw9PREUFITevXvDw8PD5PMzpYsXL+Lo0aPIysqCm5sbQkND0bdvX43x3cyhT58+2LdvHwBVt6Bvv/22+F1SUhISExPFc0DTpk3Rr18/ycEgU7pfr1cBoKioCAcOHEBqairy8vJgY2MDHx8fREdHo3379g32IK856iTmrkc1lDt37uDQoUO4ffs27t69CxsbG3h4eCAyMhJxcXEa43FaupSUFERERIjvb9y4IT4EYqpr85osZflZYv2wqqoKR44cQVJSEvLz8+Hj44PmzZujZ8+ejV5nNdV6KyoqQmJiIq5fv447d+6grKwMTk5O8PPzQ5s2bSTfLyO6VzCQSURERERERP9q+gKZRJbq448/FlvyLFq0SHKrHjI/fYFMIrr/6AtkEhFR/VnuY/NERERERERERKRVdaDMx8cHTz75ZCOXhoiIiIjIPBjIJCIiIiIiIiK6hyiVShw8eBAA8OKLL5q9q1QiIiIiosaie+RrIiIiIiIiIiKyOFZWVsjLy2vsYhARERERmR1bZBIRERERERERERERERGRxWEgk4iIiIiIiIiIiIiIiIgsjkwQBKGxC0FEREREREREREREREREpI4tMomIiIiIiIiIiIiIiIjI4jCQSUREREREREREREREREQWh4FMIiIiIiIiIiIiIiIiIrI4DGQSERERERERERERERERkcVhIJOIiIiIiIiIiIiIiIiILA4DmURERERERERERERERERkcRjIJCIiIiIiIiIiIiIiIiKLY9PYBaB7X3Z2Nnbs2AEACA8Ph6OjYyOXiIiIiIiIiIiIiIiIiBpCWVkZUlJSAACDBg2Cr6+vyfJmIJPqbceOHZg6dWpjF4OIiIiIiIiIiIiIiIga0U8//YQpU6aYLD92LUtEREREREREREREREREFoctMqnewsPDxdcrVqxAXFxc4xWGiFBVVYX8/HwAgIeHB2xseKgnamzcL4ksC/dJIsvCfZLI8nC/JLIs3CeJLAv3ydouXrwo9typHjMyBS5dqjf1MTGjo6PRoUOHRiwNEcnlcuTm5gIAvL29YWtr28glIiLul0SWhfskkWXhPklkebhfElkW7pNEloX7pH7qMSNTYNeyRERERERERERERERERGRxGMgkIiIiIiIiIiIiIiIiIovDQCYRERERERERERERERERWRwGMomIiIiIiIiIiIiIiIjI4jCQSUREREREREREREREREQWh4FMIiIiIiIiIiIiIiIiIrI4DGQSERERERERERERERERkcVhIJOIiIiIiIiIiIiIiIiILA4DmURERERERERERERERERkcRjIJCIiIiIiIiIiIiIiIiKLw0AmEREREREREREREREREVkcm8YuABERERERERERERERkSWrrKxEUVERCgsLUVlZCaVS2dhFokYiCAKqqqoAAHl5eZDJZI1corqzsrKCnZ0d3Nzc4OrqCjs7u8YuUi0MZBIREREREREREREREemQl5eHrKysxi4GWRArq/ujw1OlUony8nKUl5cjOzsbfn5+8PLyauxiaWAgk4iIiIiIiIiIiIiISAsGMUmb+yWQWVP1tm5JwUwGMomIiIiIiIiIiIiIiGqorKzUCGI6ODjA09MTLi4usLa2vqe7FKW6UyqVUCgUAABra+t7NqgpCAIUCgWKi4tx9+5dlJeXA1AFM11cXCymm9l7c+kSERERERERERERERGZUVFRkfjawcEBYWFh8PDwgI2NDYOYdM+TyWSwsbGBh4cHwsLC4ODgIH6nvu03NgYyiYiIiIiIiIiIiIiIaigsLBRfe3p63rMt74gMsbKygqenp/hefdtvbNzriIiIiIiIiIiIiIiIaqisrBRfu7i4NGJJiMxPfRtX3/YbGwOZRERERERERERERERENSiVSvG1tbV1I5aEyPzUt3H1bb+xMZBJRERERERERERERESkB8fEpPudpW7jDGQSERERERERERERERERkcVhIJOIiIiIiIiIiIiIiIiILA4DmURERERERERERERERERkcRjIJCIiIiIiIiIiIiIiIiKLw0AmEREREREREREREREREVkcBjKJiIiIiIiIiIiIiIiIyOIwkElEREREREREREREREQWa8OGDZDJZCb5O3v2bGP/HDICA5lERERERERERERERERksY4fP26SfBwdHdGyZUuT5EUNw6axC0BERERERERERERERESky9ixY9G1a1et31VWVmLChAkAAGdnZ6xatUpnPq6urrC2tjZLGck8GMgkIiIiIiIiIiIiIiIiixUfH4/4+Hit3506dUp83aZNG4waNaqBSkUNgV3LEhERERERERERERER0T3p9OnT4ut27do1YknIHBjIJCIiIiIiIiIiIiIionuSeiCzbdu2jVcQMgt2LUtERERERERkQGphKjYmb0RpVSk6B3RG98DucLBxEL8XBAGX8i5hf9p+FFYWoplHM/QI6gEfJ59GLDURERER0f0vMTFRfM0WmfcfBjKJiIiIiIjoviYIAsoV5SiqLEJxZTEKKwtRpayCv7M//J39YWOlu2qcWpiKb89+iy3Xt0AhKAAAv1z6BY42jugV3Au9gnsh+W4ydt7cibTitFrTx3jFoEdQD3QJ6ILmns3h4eBhrp8JuVKOU1mnkF2aja6BXdHEsYnZ5kVEREREZAkEQcCZM2cAADY2NoiLi2vkEpGpMZBJRERERERE94WCigLsvLkTyfnJyC7Nxp3SO6r/ZXcgV8q1TmMjs0GgSyBCXEPg5+wHV1tXuNi5wNXOFRdyL2gEMNWVVZXhr5S/8FfKX3rLdDHvIi7mXcT3Sd8DALwdvNHMoxmaeTZDJ/9O6BHUA3bWdhrTnMs5h18u/YKTWSdRqaiEjZWN+Ods44xIj0i08GyB5l7NEe4WjqScJOxK3YX9aftRVFkEAHC0ccT7Pd9H/9D+dVmURERERGSkBZvP40JGYWMXo9HEBrph/oiWDT7f5ORkFBcXq8oQGwt7e/sGLwOZFwOZREREREREdM8SBAEJWQnYkLwBf6f8jUplpVHTVwlVSC1KRWpRqplKqCm3PBe5mbk4lnkMqy+uhqudKwaFDcLQiKHIK8/D6ourcebOGb15nMs9Z3A+ZVVleGHvC3i106uYGD3RVMUnIiIiIh0uZBTi2I28xi7Gvw7Hx7z/MZBJRERERERE9wxBEJBWlIbzuedxLucc9tza02BBSHMoqizChuQN2JC8weR5KwUl/nvsv7hdchvPtX8OVjIrk8+DiIiIiKgxcXzM+x8DmURERERERGRxqpRVuFV0C6mFqbhZeBOpRalIKUzBpbxLKKgoaPDy+Dv747FWj6FbYDfsT9uPv2/+jVPZp6AUlACAIJcgDAgdgAFhAxDlGYXjt4/jQPoBHEg/gMySzAYvr7pl55YhsyQTE6MnooljE/g4+sDBxqFRy0REREREZArqLTLNFcjctWsXBgwYgOnTp2PFihVmmYex9uzZg379+mHq1KlYuXJlYxfHrBjIJCIiIiIiIotQIi/BofRD2HNrD/an7UdhZd3GGLKxskGAcwB8nXzh6+gLHycfeDp4aox/CQDpxem4VXQLt4puIa0oDXfL76JYXowKRYWYV5BLEGbFzcKoZqPEsSwnx0zG5JjJyCnLQWphKlztXNHMoxlkMpk4Xd/Qvugb2heCIOBm4U0k5yfjav5VXMu/hmv513A1/6rB39HUvSna+raFXClHlbIKcqUcd0rv4MrdKyitKtVIK4MMbX3boldwL2xM3ohbRbc0vt96Yyu23tgqvne1c0Un/06YGTcTbXzaGL+QiYiIiEhDbKBbYxehUTXW768OZMpkMrN1LXv8+HEAQJcuXcySf12cOnUKANCxY8dGLon5MZBJREREREREjaZCUYGdN3fiz+t/4tjtY5Ar5XXOK8YrBmOixmBY5DAxWFkXcoUcRfIiKAUlvB28NQKU6po4NkETxyZ685LJZAh3D0e4ezgGhg0UP79dfBvbUrZh241tuJR36Z/0kKF3cG9MjpmMLgFdtM5bKSiRXpyOK3evIK0oDZ4OnugW2E0sy+hmozFn1xy9Y2kWVRZhV+ou7ErdhW6B3fBEmyfQzpddcRERERHV1fwRLRu7CP86GRkZyM7OBgCEh4fD3d3dLPOZNWsWRo8ejZCQELPkXxfVgcwOHTo0cknMj4FMIiIiIiIianDX869jffJ6/HHtj3p1FRvuFo7OAZ3xUNRDiPWONUnZbK1t4WXtZZK8dAlwCcCsuFmYFTcL1/KvYe+tvRAgYFDYIIS6heqd1kpmhRDXEIS4ar+R4u3ojR8G/4BX9r+CfWn7DJblcMZhHM44jI5+HRHrHYsmjk3g7egND3sP3C6+jWsF18SWpOWKcgwJH4JX4l+Bk61TXX46EREREZFJNNT4mH5+fvDz8zNb/nVx+vRpWFtbm60VqiVhIJOIiIiIiIjMRhAEpBenqwJh/x8QS76bjIt5F43Kx9HGESGuIQh3C0fLJi0R5x2HGO+YerW8tBRNPZqiqUdTk+bpZOuEz/p+hi9Of4E1l9agrKrM4DQJWQlIyEowmG5D8gZcyL2Ar/p/BV8nX1MUl4iIiIjIaPUZHzM5ORmLFi3Crl27kJ6eDltbW/j7+6Nz586YO3eu2GVrWloaQkJC0LlzZxw9elScPi0tDZGRkejUqRN27tyJ//3vf1i7di3S09MRGRmJ+fPnY/z48QCAzZs349NPP8WpU6fg6OiI0aNHY9GiRXB2dq7T7y4tLcXly5cRExMDKysrLFy4ED///DOuX78OPz8/TJ8+HW+++Sasra3rlL+lYSCTiIiIiIiITK5CUYGt17di5YWVksaDVCeDDG182qBvaF+0atIKoa6h8HXy1dnFK2lnY2WDFzq8gNmtZiO1KBU5ZTm4U3oH2WXZ2HlzJ67cvVLnvC/mXcSkLZPwdf+vEe0VbcJSExERERFJU9dA5q5duzBixAiUlZWhY8eO6NixI0pKSnD16lWsXr0ao0ePFgOZ1a0+a7Z8PHPmDADA29sbHTp0QGVlJbp06QJPT08kJCRg8uTJiIyMxM8//4zFixejT58+6NevH/766y8sWbIEdnZ2+Oyzz+r0uxMTE6FUKhEcHIyOHTsiLy8PPXr0QEhICP7++28sWLAALi4ueOmll+qUv6VhIJOIiIiIiIhMQhAE5JbnYv2V9fjl0i/IK8+TPK2VzArdArthYNhA9AruZXDsSZLOxc6lVre7j7d+HHtv7cU3Z74xunVstezSbEzbNg3v93gfbXzbiJ/bW9vfFy1liYiIiMiyqQcypXaxKggCHnnkEdjY2ODYsWPo1KlTrTxDQ/8Z6sFQIHPbtm2YN28e3n33XdjYqEJuU6dOxapVqzBx4kTY2tri/PnzaNpU1QPLsWPH0KVLF/z66691DmRWj4/5119/4cUXX8TChQthZ2cHAPjtt98wevRorF+/noFMIiIiIiIi+vdKLUzFz5d+xqmsUyioKECRvAgl8hIoBaVR+fg7++OhZg9hdNRo+Dv7m6m0VJOVzAr9Qvuhb0hfHEg/gF8u/YKr+VeRW5YLuVKukdbGygbhbuHwcfTBkdtHNL4rqyrD3L1zNT6TQYZ4/3jM6zQPUZ5R5v4pRERERPQvVFhYiBs3bgAAfHx8EBQUJGm6a9eu4ebNmxg0aFCtICZQu2WnoUDmgw8+iPfff1/ju2HDhmHVqlXIyMhAQkKCGMQEgM6dO8PLywuZmZmSyqtNdQB31KhR+OijjzS+Gzx4MAAgOzu7zvlbGgYyiYiIiIiISLKkO0lYfn45dt7cCQGC0dO72rqKY0L2C+2H7oHdYW11f4zdci+SyWToFdwLvYJ7AVA9oV4kL0JOWQ4KKgrgbueOELcQ2FrZAgB+v/o73j7yNqqUVTrzFCDgeOZxTN4yGW90eQMjm41skN9CRERERP8eiYmJEARVfcSYbmXd3d0hk8mwZ88efPnll5g5cyZcXFz0zsfKygqtW7fW+Pzs2bMAgDfffLPWNIWFhQCAsWPHIjZWs2cUpVKJ4uJieHt7Sy5zTdUtMhcsWFDru6ysLABAYGBgnfO3NAxkEhERERERkU7lVeW4mHcRZ++cxZ5be3Ay66RR01vLrDEobBBGNhuJ5p7N0cSxCce6tGAymQxudm5ws3PT+v3IZiMR6BKIuXvmorCyUG9e5YpyvHHoDZzKPoVXO70KBxsHcxSZiIiIiP6F6jo+po+PDxYuXIg33ngDzz77LF555RX069cPDz30ECZNmgQnJycxbVFREa5fv47mzZtr/Tw4OBjt27evNQ/11po1Xb16FZWVlbUCnFJVVlbi/PnziIiIqBVcBf4JsMbFxdUpf0vEQCYRERERERFpKKwsxE8XfsKBtAO4nHcZVYLu1ne6ONs6Y2zUWEyOmYxAl3v8aWClArjwG5CwHKgsBiJ6A7EjgcB2QHVQtigLuLQZuLIDKM8HfKKBqEFAZG/A/v4aLzLePx6rh63GnN1zcLPwpsH0G5M34lzOOcyMm4mW3i0R5hYGK5lVA5SUiIiIiO5XdRkfs9q8efMwYcIErFu3Dtu2bcP27duxdetWzJ8/HwcOHEBERAQAVUBSEASt3coKgqA1iKletvj4+FrfVXdVa0zwVV1SUhLkcrnWvNXnXdf8LREDmURERERERCTanbob7x19D3fK7khK7+fkh3j/eLjYusDVzhUudi4IcA5Az6CecLHT3UVTg1AqgZI7QHEmUJYPVBQC5QVAeSGgqATcgwHPCMArAnD0/CcoKU6vAM5vAvZ9CORc/ufzjNPAoc8A91AgaiCQfRFIPQKod7V76xhw6kfAyhYI66YKaPrFqQKc7iGAlYFAniCoyl5VAVjbqvKxtgFsnVTvtaXPvghc3goUZQLRDwBN+9ZxwRkW7h6OX0f8in1p+1BY8U/LzPKqcvxw7gfkledppL9y9wpePfAqAFX3wrHesYj3j8fIZiPhbVf3brWIiIiI6N9pxYoVWLFiRZ2nj4iIwH/+8x/85z//wc2bNzFlyhQcPHgQixcvxqJFiwAYHh9TW7BQqVTi7Nmz8PLyQlhYWK3v6xtorJ5eV/CWgUwiIiIiIiK6L+WV5+H9Y+9je8p2SembeTTDrLhZGBIxRBw/sVEJApByEDi7Fsi+oArmFWcBesZy1GDvDrgFAg7ugIMbYO8GZCZpBjBrKkgFEn7Qn69SDtzYp/qrZusM+EYDEb2AVuMAv5b/fFdRBCT+DBz/Dsi9Wjs/mRXg1RTwj1NN5x0FpCcAF/8E7t74J92J74GeLwL93qwdoDURRxtHDAkfUuvzIRFD8Mr+V3R2Q1wkL8KxzGM4lnkMi88sRo/AHhjkNwgdm3Q0SzmJiIiIiPQJCwvD9OnTcfDgQSgUCvFzXYFMXZ8DwOXLl1FaWoquXbtqnZe+aaWoHh9TXyDT2toarVq1qlP+loiBTCIiIiIion8hpaBEWlEaLuVdwsW8i1h/ZT3yK/L1TuNq64oOfh0wIXoCugd2t4yxLouygMTVwOmfgLzrdc+nogC4U2C6cukjLwHST6r+Dn4K+MYCcWOAkhzg9Cqgskj3tIISyE1W/Z3fpH8+Bz4GCjOAEV8ANnam/Q16+Dr5Yumgpfjy9JdYdm6Z3rRKQYn96fuxP30/fBx8MD12Oia3nGwZwXEiIiIiuq/89ddfUCgUGDRoEGxs/gmPZWRk4PPPPwegOa6loRaZ2oKJhlpEJiYmwt7evtYYmW+//TYWLFiA6dOn621pWh3I1JZ/Xl4eUlNTERsbC0dHR5153GsYyCQiIiIiIvoXOZF5Aj+c+wGJ2YkokZfoTWtvbY/hkcPRxqcN2vi0Qbh7eOOObahUqoKVGadU3bumnwLSTgCCwvC0puLso+ryVRcbR8AzHLhzUXqe2ReA3RfqXTStzvyiap06fqWqpWkDsbGywfMdnke8fzwWJy7GuZxzENS73tXiTvkdfHTqI2y8thHzOs1D10DtT7ETEREREdXF2rVrsXz5cnh5eaFjx45o0qQJsrKycODAAVRWVmLevHno06cPAKCqqgrnz5+Hn58f/P39xTyqP/f19UVQUFCteVQHMrWNn5mZmYnMzEx06NBBI5AKqLqkBQBbW90P9CkUCpw9e7ZWmWrO+37qVhZgIJOIiIiIiOhfIbs0Gx8lfIRtN7ZJSt/etz0WdFuAcPdw8xZMG0EAcq6oWizmXgPyrv3//+tAZXHDlwcAWjwA9H4FCGijCqJe+F31d/cGYOcCNB8MxDyoGjPTzlnVUvTqTiB5B3B9L1Ce3zjlBoDre4Dlw4A2EwAXf8DVD3ANUHVRa2isznrqEdQDPYJ6oFReiot5F3Eu5xzO55zH4duHUVChvQXs9YLrmP33bAwIHYDH2zwOZxtnCBCgFJSws7aDv7N/4wbUiYiIiOieNGnSJDg4OODw4cM4efIkCgoK4OXlhSFDhmDOnDkYOHCgmPbSpUsoLy9H7969NfKo/rxXr15a56EvmKivW9nq76ZNm6az/JcuXUJZWZnBede121pLJRMEQf8jkUQGnDx5Eh07qsYyOXr0KDp37tzIJSL6d5PL5cjNzQUAeHt7632Kh4gaBvdLIsvyb9snKxWV+Pniz1hyZglKq0oNpne0ccTzHZ7HhBYTGjZYpFQAt44Bl7YAl7fWr5tYvzhVd61uAaqAnas/4NTkn7EvHdxV6fJvAnk3VMHIvBtAaR5QUQiUF6j+C0ogOB7o9iwQ2Lb2fARBVW6Zlf6AoCAARbeB7IvAnUuqFpjX9gCF6bqnsbYHWo8DQjoDCrlqrE9Fpap1ZdY5IPMcUJrzT3rPcCB6ONB8CHD4C1UA1RDXQKD7s0CHGYBtw3Y9VaGowN83/8b6K+t1jqWpS3PP5vig5wdo5tnMTKUjon/buZLI0nGfbDwXL/7Ty0ZMTEwjloQsiVKpFMfStLa2hlU9Hw5UKBTw8vJCp06d8Pfff5uiiHVW121ePU6UkJCADh06mKxMbJFJRERERER0n8grz8P6K+txKe8SskqykFmSiZzyHCgFpcFpA50D0SWwCx5v/TgCXQIboLT/L+sCcHIFcG49UJpb93zs3YDW44F2U7UHHbVx8gIC69HtkkwGWEuoVstkgFug6q9Zf9VnSiWQegRI+hW48BtQdlf1uWsAEP8I0GEm4NxEd56CABRnq4KwTk0A76aq+QBAaFfgz7mqcUP1KcoAts8DDnyiCmh2nKVqTaqNUgkU3ALkZUCTKMDK2vDv1qO62+LhkcNxOecyliYuxV/pfxnsfhYArty9gslbJ+Pd7u9icPjgepWDiIiIiKixJSQkoLCwEO+8805jF8UiMZBJRERERER0H0jMTsQzu59BfkW+pPQxXjEY0XQEor2i0dyzOdzt3c1bQHWVJcD531QBzLTjdcxEBvi0UAUiI/uounW1czJdGc3NygoI7676G/qhajkISlUQ0lpCKwuZ7P+7iPWr/Z21DfDgl4B7MLD3fcN5lWQDO94A9n+kWqYuvoCzryrQW5CmakV65wpQPaaqXytg/I+q4KkJRLpH4sW4FzE8ZDi+Sf4G53LPGZymrKoML+17CUl3kjC3w1zYWPH2BhERERHdmzp37gx2nqobr/SJiIiIiIjucbtTd+OV/a+gQlFhMK27vTuebfcsxkSNgXU9W9VJUpKrGqOxulvVO5dV3cYKCul5uAaqgmZekUCT5qrgZUAbwN7FfOVuSDZ2QHgP0+YpkwF95gF+LVUtLjOTAKVc/zTl+arufQ3JSgKW9gcm/gyEdTNJcQGghXsLrBi0AttTt+PTk58it9xwC90fL/yIC3kX8Fqn19jVLBERERHRfYiBTCIiIiIionvY2ktrsfD4QoPdx8ogw5jmY/Bcu+fg4eBh/oLduQwc+Ro4swaQEGDV4BkBRD8AtBgKBLa/t1paWpqYEao/pVLVfW1xpmqczYt/AKdXGw5u6lJ2F1g5EnjwK6DNBJMV10pmhZHNRmJQ+CCczDqJ3LJcyGQyyCBDqbwUXyV+VavV8YnMExj9x2hEukdicPhgDAobxKAmEREREdF9goFMIiIiIiKie1BmSSZWXViFHy/8WOs7e2t7tPZpDT8nP/g7+8PfyR9dA7si1C3U9AWRlwPlBUBFIVBeCBTdBk79CCTvMC4f91Cg3RQg9kHAJ/qf8R7JNKysAGdv1Z9fS9VYnT1fAg5+qhpLU1FpfJ6KSmDTbCD7PBDc6Z/P7ZxVLTVt7OtcXEcbR/QIqt1KtWdwTzy/93lcyL1Q67vrBdex5MwSLDmzBLHesZjXaR7a+dZjDFQiIiIiImp0DGQSERERERHdAyoVlTieeRyHMw7jcPphXCu4pjWdp70nvuz/Jdr4tDFfYRRVwIXfgKOLgfRTAOo4nouVDdBiGNBhOhDZTxVso4bjEQIM/wTo+SJwehWQfQEouQMUZ6v+KgoAe3fVuJm+0YCLP3DkK0BeqpnPoc9r5+3gDgz7GGg9zqRFDnQJxMqhK7Hw2EJsTN6oM92F3AuYtm0axjYfi7nt5zbsGLBERERERGQyDGQSERERERFZKKWgxOns0/jz+p/YkbIDhZWFetOHuIZgyYAlCHMLM0+BKkuAUz8BR78G8lONn949RBUU84lW/UUNAlz9TF9OMo57ENDnP7U/V1QBVtaarWOjHwB+mahqeatPeQGw8VEg9TAw+H3A1sFkxbW3tseCbgvQzrcdvjz1JbLLsnWmXX9lPXan7sbc9nPh5+yHgooC5Ffko7iyGBHuEegX2g9WMgbQiYiIiIgsFQOZREREREREFuZO6R38cukXbLm+BRklGZKmifOOw1f9v4K3o7fpCiIvA26fAdJOqP6u7wPK843LwyMU6PIU0GYS4OhhurI1ArlCCUEA7Gz+JYEvay23DALbAo/uAn6eAGQlGc4jYRmQfhIY9yPgFWHS4o1qNgojIkfgVPYp7EjZgb9v/o3c8txa6fLK8/DW4be05tE3pC8+7PUhHGxMF2glIiIiIiLTYSCTiIiIiIjIQmQUZ2DZuWXYlLwJlUppYxbaWtlidLPReLHji3CydTJRQRKBXQuAG/sBZVXd8giOB7rOAaKHaw+I3UOyC8vxzb7rWJdwC6WVVYgP98KwVgEYEucPPzcHlMsVOJicg23nMrH7UhYKyuRo7ueKvtG+6NvCF+1DPWBjfR8FP92DgFnbgd+fAi78bjj97TPAt72BNhOAwHZAYHugSZSqtWc9WVtZI94/HvH+8ZjXaR6OZR7DRwkfIflusqTp99zagyd2PoEv+30JVzvXepeHiIiIiIhM696uTd5jCgoK8Ndff2HPnj04deoUrl69isLCQri4uCA0NBTdu3fHzJkzER8fLznP7du3Y/ny5Th69CiysrLg5uaGqKgojB07FrNnz4azs7MZfxEREREREZnCrcJb+C7pO/x57U9UCYYDhxHuEege2B3dAruhg18H0wUw5eXAvv8Bh74ABIWECWSqMS6b9gXs3QAHN9V/92DA00zd20pQpVAip7gSWYXlyCwsR3ZRBQrL5Cgsl6OwrApF5XLIFUoEuDsizNsJYd5OCPVyRoC7A5zsrCH7/65UswvLsWTfNfx8LBUVVUox/2M38nDsRh7m/3EeLQPdkJJTgpJKzeV1KbMIlzKLsGTvNbg52KBzpDei/V3R3E/1F9HEWW/LzjtFFTideheVCiVsrKxgay2DjbUVXOyt0czHFe5OtrXS77qYhZ0Xs5BVWIFBsX54ok9T2JorgGrvAoxfCeTfAsru/vN5VTmw4w3g1jHN9BUFwPHv/nlv5wKE9wDaTQUi+5ukSNZW1ugW2A1rh6/FqgursDhxMcoV5QanO5l1EjO3z8Q3A79BE8cmJikLERERERGZhkwQBKGxC/Fv8OGHH+Ktt95CRUWFwbRTpkzBt99+Cycn3TcjKioqMGPGDKxZs0ZnmqZNm2Ljxo1o3bp1ncos1cmTJ9GxY0cAwNGjR9G5c2ezzo+I9JPL5cjNVXWp5e3tDVtbWwNTEJG5cb8ksiyWtk/+lfIXXj/4OioU+usKTd2bYnjT4RgaMRRBLkGmL0jqUeD3OUCuhJZs1nZAm4lA12cAn+amL0sdZBeWY/2pNPx2Oh1Xs4uhrGNN19pKBlcHG7g62CC7sEIjgGlKNlYydAz3xMi2QRga5w8PJzsAQOKtfCw/dANbzt5GlZ4fEeThiGh/V0T6OCPxVj4Sbt5Fzdp9r+Y+WPxwe7jYN/AzzAo5sPNt4MhXkpILLv4oaT4apTHj4Bne2mT7ZHpxOhYeW4j9afslpQ92CcZ3A79DiFuISeZPdC+ztHMl0b8d98nGc/HiRfF1TExMI5aELIlSqYRCoXqQ0draGlZW90/vK3Xd5tXjRAkJCejQoYPJysQWmQ3kypUrYhAzMjISAwYMQNu2bdGkSRPcvXsXu3btwoYNG6BQKLBq1SpkZ2dj27ZtOneA6dOnY+3atQBUJ6/Zs2ejVatWyMnJwapVq3D8+HFcu3YNQ4YMwbFjxxASwooYEREREZElEQQBy84tw2enPtOZxs3OTTUOYNMRaOHZQmwpWG+KKiDnCpCZBGSeVXX9mXIQgJ7on707ENwRCOsGtJsCuPqbpiz1IFcocSD5Dn45fgu7L2VDUdfopRqFUkB+qRz5pXITlFC3KqWAo9fzcPR6Ht76/Rx6N/dBTnElEm/lS5o+Pb8M6fll2HVJd5r9V+5g4ndHsGxGPHxdG3AMSGtbYPB/gdAuwG9Pq1pj6iErzoTLqSVwPvUNhNhRwOD3VK166ynIJQhf9fsKGSUZSL6bDGdbZ3jYe8DD3gNLk5bi50s/a6RPK07DlG1T8FHvjxDvL72nJCIiIiIiMh8GMhuITCbDAw88gJdffhm9e/eu9f3s2bNx4MABDBs2DMXFxdixYwd+/PFHzJw5s1ba33//XQxihoaG4sCBAwgNDRW/f/rpp/Hoo49i+fLluH37Nl544QX8+uuv5vtxRERERERkFLlSjv8e/S82JG/Q+r23gzdmtJyBcS3GwdnWhMNFVFUCRxcDhz4HyvIMp/eMAHq+AIR0BryjgEZ80rhcrsDN3FKcSy/A2bR8nE0vwIWMQrO1mNTG08kWHcK8cPhaDkortXe92ybEA9F+rjh0LQdpd8sk5StXCNh5MduURRWdSy/EQ4sP48dZndDUx8Us89ApZgQQ0AY48rUqUJ59UW+XxTIIkF3YBFzZDvR8Eej2DGBbvwCsTCZDkEtQrVbM8zrNg5eDF75K1Gw1mleeh0d3PIpn2z2LWXGzTPfwABERERER1Qm7lm0gd+/ehaenp8F0X331FZ555hkAQK9evbBv375aadq1a4fExEQAwJYtWzBs2LBaacrKyhAdHY3U1FQAQFJSEuLi4urxC3Rj17JEloXdjRBZHu6XRJalsffJosoivLTvJRzOOFzrOy8HLzzR5gmMbjYaDjYmbkF3bTew9RVpXcfKrIAuTwF9XwfsTDT+phFyiiuw+1I2ztzKR0puCVJySpFRUFar61Rj2VlbwdXBBm6OqnWefrcMlQrDgVBPJ1s81isS07qGw8XeBuVyBfZduYNtSbdx7U4J3Bxt0D/aD0Pi/BHo4QhA1eL22p0S7L2cjYNXc3A5swi3CwyP11hf1lYyrS1T3R1t0b2ZN3xdHeDjag8/Nwd0jvBCiFcDrt/KUlUr4IxTwOVtwI3a9V0NHmFA9+cAe1dAEABBCdjYAWE9AFc/kxRp3eV1eO/oexC0tEbuE9IH/+3xX7jZuZlkXkT3ksY+VxKRJu6TjYddy5I27Fq2NnYtex+QEsQEgHHjxomBzKSkpFrfJycni0HMqKgorUFMAHB0dMRjjz2GN998EwCwbt06swUyiYiIiIjIsOLKYvx86WesvLASBVq62mzq3hRfD/jadONfKqqA4kygIE3VIu7iH9Km84kBRn4NBJuu4inF9TvF+PtCFv6+kIWTqbXHe5TK1cEGw1sHItrfFX5uDvBzUwXtvJzt4GBrrZFWoRSQWViOmzklSM0rRV5pJYrKq1BULkdReRWUAtAuxAPj40M0xpl0sLXG4Jb+GNxSd/e6MpkMzXxd0MzXBY/2jAQAFJbLkZxVjCtZRdh/5Q52XcpGpY4Wpc39XDCzewQ6hHlCrlCiSiFArlAiq7ACF28X4lJmIS7eLkJ6fhncHW3Rt4UPBrX0R6/mPvhu/3V8sUszYF1QJsfWpMwaZQSGtw7EM/2aobmfq1HLuU7snIDQzqq/Lk8CudeAUz9COL0astKc2unzbwJbXtCSjwvw4BdA3Jh6F2l8i/HwsPfAawdfqzVO7d5bezH6t9EIcw+DrZUtbK1s4WjjiG6B3TCq2Si21iQiIiIiagAMZFoYV9d/Ko9lZbW7Ifrrr7/E14MHD9ab15AhQ8RA5vbt2/HOO++YqJRERERERCRVUWURVl9cjZ8u/ITCykKtaToHdMYnfT6pf8uvm0eAQ58Bt8+qgpiClG5XZYB3M8C/FRDZB2gzEbCxr185JCqpqMLmMxn45XgqzqTpH0fRkE7hXpjYKQRD4wLgaGdteAKoWi8GeTgiyMMR3eo1d2ncHGzRIcwTHcI8MalTKArL5fjrXCb+OJOBQ1dVgbx+0X6Y2T0c3Zp66wyUPdA6QHwtVyhha635BPgLA5sjwN0Bb/x2Tu+4oYIAbD6Tgc1nMjCslT+e7tsMMf5usLLSnG9FlQI3ckqQnFWMskoF+kT7mGbMTe+mwMB3UNXjFZQeWQaX45/CulxCl8eVxcD6WUD6KWDAAsC6frc2BoUPQphbGF7Y+wJSi1I1vssuy0Z2mWa3v9tTtuPK3St4Jf4VBjOJiIiIiMyMgUwLc+7cOfF1WFiY3u8NNc1t27YtrK2toVAocOHCBQiCUKdK1smTJ/V+r97UuKqqCnK53Oh5EJHpyOVyVFVVia+JqPFxvySyLA25T+5L24cFxxYgvyJfZ5qRkSPxWvxrsJXZ1qs8VqdWwGr7K5BJCF4KkEHZfjqE1hMh+MQAds7qXwJmXC4FZXJczirC5rOZ2HzmNkp0jDWpj7WVDFE+zogLckdckBu6RXohokn1b1BCLm+4cTPrw9EaGNXGH6Pa+KOsUgGFIIgtP6u3USnkytrLcGy7AHg72eC5tWdQJmF5bE3KxNakTNhYyeDtbAdvFzt4ONkis6AcN/PKNAKizvbW+Gx8a/Rp7iO5jHrLL1ihqPlDKA4bAN8LS2FzagVkesbSFB35Csr0U1CMXgq4+NarDJGukfhp8E9YcGwBdt/abTD9qour4OPgg6kxU+s1XyJLxetXIsvCfbLxqI/Mp1TeG9eYZH6CIIjbhiAI99W2ob7NG3O8Mab+YiwGMi3Md999J75+4IEHan1/5coV8XV4eLjevGxsbBAUFITU1FSUlJQgPT0dwcHBRpepul9jKQoKCsT+2omocVRVVaGoqEh8b2PDQz1RY+N+SWRZGmKfVAgK/Hj1R/xy/Redaeys7DCj2QyMDR+LwnztLTUlEQS4nPgMLqe+kZS80q8tCnu8hSqflqoPisoBmGfsRkEQcOJWEQ5eL8D13DKk5JUjp0R6ZdjGSoZwLweEeNgjxMMBIZ72CPN0QHMfJzjYqrdCLEdurvnHn2wIFcWmy6t1EyusfDgG6xKzcSW7DLklcuSUyFGuoztbAKhSCsgqqkBWUYXONCUVCjy+6jTm9g7B+Lb1CyAC6vukDEK7F+DQdBRcD/8PdmmHIdMydqU6q9TDEJb2RXHHZ1AeMQCCg7RhXXSZFzMPUU5R+P7K91AaeCjg09OfwlHhiL4Bfes1TyJLxOtXIsvCfbLxVFVVwcrKClZWVuKYiESCIGhsD/dbLx1KpRJKpdKoWE9+fr7ZysMjngU5fPgwli9fDgBwcHDA888/XyuN+sbQpEkTg3l6e3sjNTVVnLYugUwiIiIiIpIuvzIfC88sxOm801q/t7eyx/CQ4RgfMR5e9l71m5lCDvd9b8LxyibDSZ18UNzpeZS1GA3IrAymr4/KKiV2XM7DL6ezcS2n9pAZ+rjaW6NbhDt6RXqgS5gbnO2ldRNL2oV4OODFPqHie0EQUFqpxJ6rd7HieCbSCnQHLPVRCsAne28h9W455vYOgY2V6W7eVHk1x93hyyArvwur8rsAZIDMCjJ5Cdz2vQW7O0ka6a1LsuC+7w24HXgblYFdUN50CMojB0Gwdzd63jKZDGPDx6KtV1tsTduK9NJ0yJVyyJVy5JTnIKdCcyzPRUmL4GXvhTZeberzk4mIiIiISAcGMi1EZmYmxo8fLzZBfvfdd7UGHYuL/3k818HB8Jgkjo6O4mv1p3aMkZCQoPf7ixcvYupUVXc67u7u8Pb2rtN8iMg01Jv8e3l5wdbWthFLQ0QA90siS2OufVKulONQxiF8kPABskqzan3vYO2AcVHjMC1mGrwd63nNXJYPWcp+WJ38AVY3D9X6WnAPhbLlaMA1EIJbIAS3IMA3Fk5WNnCq35w1FFdU4cLtQhSUVqGoQo6i8ipkF1Vg0+kM3CmulJyPrbUMA2N8MbZDELpEeNUa85FMb3qgLx7uHoXNZzOxeN91pOSW1imf9WfuIK2wCq2D/wkaOtnZoF8LH8QEuErKQ/c+qWU/aboNyr/mwSpxVa2vZMoq2KcdhH3aQbgdeR/KXvOgjH8MsDL+1oe3tzc6R3TW+KygogAz/56JlMKUf8ouyPF24ttYNnAZmnk0M3o+RJaK169EloX7ZOPJy/tn/G5raz5gRyrq3a9aW1vfdy0yq1shGxPr8fDwMFt5GMi0ACUlJRg5ciTS09MBqLqUffHFFxu5VP8wNBanOhsbG55IiSxAdRcjtra23CeJLAT3SyLLYqp9UhAEnM89j83XNmN7ynbkledpTdfcszk+7fMpQt1CtX4vSdZ54MIfwLVdQPpJQFe3lwFtIZu8DtaufnWflwG38krxw8EbWHviFsrkde9iK6KJMyZ1CsFD7YPRxMXehCUkKWxtgfGdwjCmYygOXc3Blawi3CmuQE5RJXKKK3C3tBLujrZo7ueK5n4u8HV1wNubz+NmjaDn0Rt3cfTGXY3PPtt1FTO6heO1YTGwszEcmJa8T9raAqO+BkI6AVtfAhTaA+ayyhJY73wT1ud+BUZ8BgRJr9fq0sS2Cb4Z+A2mbJ2CnLJ/WmYWy4sxZ88cfD/4e0S6R9Z7PkSWgtevRJaF+2TjUA9QWVnxYbvGtGHDBowdO9YkeZ05cwatW7eu8/RKpVLcNmQy2X21bahv88Yca8zZ5TUDmY2svLwcDz74II4fPw4A6N69O9auXaszgu/i4qIxrSFlZf904+TqKu1pWCIiIiIiMmx/2n58evJTXM2/qjfdiMgReLPrm3C0cdSbTquCdODceuDsOiDrnOH0zQYC41YA9i4Gk9bFufQCfLf/OrYk3YZCqX/swpqa+jgjOsANUb4uiPJVBcaa+brcd08v34usrWTo1dwHvZr7GEzbJsQDj/+UgBMpdw2mXXE4Badv5ePrye0Q7GnKtsAAOkwHAtsBe/8HXP1bZ0ATmWeB7/ur0rsFAaV5QFkeUF4INIkCejwPOEnv4jnIJQhLBizB9G3TUVr1T0A3uywbM7bNwLcDv0WMd0x9fx0RERER1VAdQ6kvR0dHtGzZ0iR5UcNgILMRVVZW4qGHHsLu3bsBAJ06dcLWrVvh7Oyscxr15rk5OTk601VTH4zVnE17iYiIiIj+LW4V3cKHxz/E3rS9etPZWtliXqd5GNd8nHHBOkEAru4CDn8B3NgPQGLAsN0UYPhngLVpntCvqFIgKa0Ap1PzcfrWXZxOzcftAsMPU6qzkgFD4wLwaM8ItAv1NEm5qHF5Odth1aOdMW9DEjadTjeY/sytfDzwxUF8OqEN+kWbuJVwQGtg0s9AeQFweTtwfpOqxXKtoKYAnFxRe/or24DL24CpmwCPEMmzjfaKxqd9P8XTO59GlVAlfn634i4e+esRfD3ga7TzbVe330REREREWo0dOxZdu3bV+l1lZSUmTJgAAHB2dsaqVbWHIajm6urKboLvMQxkNhK5XI5x48Zh27ZtAIB27dph+/btcHNz0ztd8+bNsWfPHgBASkqK3rRVVVVid7XOzs4ICgqqf8GJiIiIiP6lyqvKsezcMvyQ9AMqlfrHf2zv2x6vxL+Clk2MeNJXqQQu/Qkc+Bi4nShxIhkQ2BboNBtoMwkwQetGQRDwy/Fb+GjHZeSVSB/nUl0TF3s82CYQM7uHI8TLxC3xqNHZ21jjk/FtEBvghu8PXEd+2T/jdimVAqpqtNYtKJNj1ooEtA52R6sgd7QJ9kDrEHdE+brC2soELXId3IE2E1R/JbnA328BWsbQ1Co3GfhhkCqY6RsteZbdArvh/Z7vY96BeVAI/3SvXCQvwuN/P47P+n6GboHdjP0lRERERKRDfHw84uPjtX536tQp8XWbNm0watSoBioVNQQGMhtBVVUVJk2ahD/++AMA0KpVK/z999/w9DT8hHJcXJz4+uTJk5gxY4bOtImJiVAoVBWq2NhYdtlERERERFRHF3Iv4JX9r+Bm4U2daUJcQzAicgSGRw5HiJv01l0AgIubgd3vAXcuGU7r4g806w807QdE9gWcvY2blx43c0swb0MSjlzPNZwYgI+rPWZ2D0fPZj5wdbD5/z9bSWMi0r1NJpPhsV6ReKyX5piQlVVKLNx6ESsOp9Sa5mxaAc6mFWD1sVQAqoD32A7BGNs+ACbrDNnZWzWGZttJwJ/PAzlXDE9TlAEsHwJM/hUI0X5zTJshEUNgZ22Hl/a9BLnyn2BuWVUZntr5FNr5tkO3wG7oFtgNMd4xsJJxvyAiIiIyh9OnT4uv27Vjzxj3GwYyG5hCocCUKVOwYcMGAKoA486dO+HtLe3mw+DBg8XXf/31l96027dvF18PGTKkDqUlIiIiIvp3EwQBv1z6BR8lfKQRqFDX3rc95naYi7Y+bY1/eFCpBP5+Ezjylf509m5A7Eig9QQgrDtgZdqAiEIpYPmhG/hox2WUy5UG00f6OOPxXpEY1S4I9jbslon+YWdjhbcfbIn4cC/8Z8NZFFdU6UybU1yBb/Zdwzf7rqFTqCtGtfLBKA9P2Jqid+TwHsATB4FDXwDHvwVK7qg+t3VWjYlZcEszfdldYOWDwPifgKgBkmfTL7Qfvu7/NZ7b8xzKqsrEzxWCAglZCUjISsAXp7+Ah70HRkeNxuxWs+FiZ54xbImIiIj+rdQDmW3btm28gpBZMJDZgJRKJWbNmoW1a9cCAFq0aIFdu3bB19dXch5RUVFo164dTp8+jeTkZGzbtg1Dhw6tla68vBzff/+9+H78+PH1/wFERERERP8ihZWFePvw2/j75t9av2/i2AQvdnwRD0Q8ULfeTypLgU2zVa0xdQntBnSeDTQfCtg6GD8PHa7dKcaJG3m4cLsQFzIKcfF2IUoqFTrTuzrYoG2IB9qHeqJzhBe6RHrDyhRdgtJ964HWAYgJcMVTq0/hUmaRwfTHU4twPLUI3x3NxPwRLdE3Wno9WScbe6D3y0Cvl4CSHMDBTfUZAJxZC/z+FKBUC7TKS4GfxwMDFwBd50juqrlrYFd8N/A7PLXzKRTJtf/W/Ip8LD+3HH9c/QMvdHwBwyOHs4UmERERkYkkJiaKr9ki8/7DQGYDEQQBjz/+OFauXAkAaNasGXbv3g1/f3+j85o/f77Yx/OTTz6J/fv3IzQ0VPxeqVTi6aefRmqqqruesWPHanRJS0RERERE+l3Ku4S5e+YivTi91nfWMmtMjpmMp9o8VfeWVcXZwC8TgfST2r9vNgDo+SIQZtox9m7lleK9LRfw1/ksSemHtPTHC4Oao5mPCwOXZLRIHxf8MacHtp27jaPX83A2LR+XM4tqjaGpLiW3FDNXnEC/aF+8NTwW4U2c618QmQxw8dH8rM0EwNETWDcNUGtJCUEB7HgDSD0KjFqsGn9Tgra+bbFsyDLM2TUHWaW696/c8ly8fvB1rLu8Dq91fg2x3rF1+UVERERE9P8EQcCZM2cAADY2NoyF3IcYyGwgr7/+OpYuXQoAsLW1xXPPPYfjx48bnG7QoEFwcnLS+GzkyJGYMGEC1q5di5s3b6J9+/Z4/PHH0apVK+Tm5mLlypVi3gEBAfjkk09M/4OIiIiIiO5T1wuu45G/HkFhZWGt7/yc/PBhrw/R3q993WeQfhL4dQaQn1r7u6AOwAMfA4GmfYq4XK7Ad/uv4+s9V1FRZbjr2CYudnhnZByGtQowaTno38fOxgoj2wZhZNsgAKpt8eLtQuy6mI21Cbdwp6hC63S7L2XjYHIORrQJhIu9NQQASkGAnbU1+sf4onuzJvUvXPNBwLTfgZ/HAeUFmt9d+hP47gIwfiXg30pSdtFe0dg8ejN2pOzA4YzDOHr7KPLK87SmPXPnDCb+OREz42ZiTrs5sLUyRX+6RERE1Ki2zQMykxq7FI3HvxUw9H8NPtvk5GQUFxcDUA3lZ29v3+BlIPNiILOBHD58WHwtl8vxzDPPSJruxo0bCA8Pr/X5jz/+CJlMhjVr1iA3NxcLFy6slaZp06bYuHEjQkJC6lxuIiIiIqJ/kzuld/Dk309qDWL2Cu6F97q/B08Hz7plnn4K2PcBcGW79u9jRwKjvwVsHeuW//9TKAVkF5XjdkE5bueXIyO/DKuO3cTN3FJJ0z/ULghvDo+Fp7NdvcpBpI2DrTXahXqiXagnnhsQhV0Xs7H6aAoOXs1FzXaalQolNpxKq5XHskM38EiPCMwbGg1b63p2zxraGZi5HVgzCbibovld3nXgmx6AjQNgbQdY26rG2GzWDxj0HmDvWis7RxtHjGw2EiObjYRSUOJy3mXsuLkDP134CRUKzaCtAAHLzi1DQlYCPuz1IYJcgur3W4iIiKhxZSYBNw82din+dTg+5v2Pgcx7lL29PX755RdMnz4dy5Ytw9GjR5GdnQ1XV1dERUVh3LhxmD17NpydTdANDxERERHRv0CJvARP73oaGSUZGp/byGzwXPvnMK3ltLqNaZd+Ctj7PpC8Q3ea7s8B/d8GrOoelBEEAcsOpWDJ3mvIKdbeyq0mGysZovxcERvghthAN/Ro1gQt/GsHZ4jMwdbaCkPi/NG/hTeOXrqFj/feQmJ6saRpfzh4A0npBfh6cnv4uNbzqXu/WGD2PuC3J4HLW2t/X1Wu+gMA5AInVwB5N4CH1wM2ugP+VjIrxHjHIMY7BmOixmDRiUXYfWt3rXRn75zFuM3j8E63dzAgbED9fgsRERHRvwzHx7z/MZDZQPbu3WuWfIcMGYIhQ4aYJW8iIiIion8LuVKOF/a+gIt5FzU+t5ZZ47O+n6F3SG/jM1UqgL3/A/YvAmq1Nft/MmtVV7IdZxqfvxq5QonXNibh15O1W69p08TFHvOGRmNEmwDY21jXa95EphDl44QlY5vjSIYcH/6VjMzCcoPTHL+Rh+FfHsDihzugQ1gdW0pXc/QAJv4MHPoc2PWOaqxMfW7sA/6Yo2pFLTM8fmywazA+7/c5DqcfxvvH30dKYYrG90WVRXh+7/OY0GICXo5/GfbW7BKNiIiISAr1FpnmCmTu2rULAwYMwPTp07FixQqzzIN0YyCTiIiIiIj+1aqUVXj78Ns4nHG41ndvdnmzbkHMklxgwyPA9T260/hEA8MWARG9jM9ffVYVVXhq9Snsu3LHYFprKxmmdQ3D8wObw82BY/KRZZHJZBjROgCD4wLx7b5r+DPpNnKKKiCTyWAlA+QKAcUVVRrTZBVWYOJ3RzCmfTCGtQpA16bede9uViYDeswFgjsC62cBxVn6059dC7gFAgPeljyLbkHd8OuIX/FRwkdYe3ltre/XXl6LxOxELOq9CBHuEcaVn4iIiBqXxHG171uN9PurA5kymcxsXcseP34cANClSxez5E/6MZBJRERERET/SoIg4FDGIXx2+jNcK7hW6/sn2jyBMc3HGJ9xWgKwbjpQqKN1pG8s0PsVIGZkvbqSBYDsonLMWnEC59Jrj+mpzs7GCr2imuClwS0Q7e9Wr3kSmZuzvQ1eGNQCLwxqofF5uVyBBZvP45fjtzQ+lysErDlxC2tO3IKnky0Gt/THmA7BiA/3qlsBwnsAcxKAS1uAvGuAohKoqgTuXKr9cMLBTwG3IKDTY5Kzd7BxwBtd3kDngM6Yf2g+iuRFGt9fvnsZE/6cgNc7v46RzUbW7TcQERFRwxv6v8Yuwb9ORkYGsrOzAQDh4eFwd3c3y3xmzZqF0aNHIyQkxCz5k34MZBIRERER0b/O9aLr+PbytziVe0rr96ObjcZTbZ4yLtOcZCBxNXD4K0Apr/29dxTQ/00gekS9ApjlcgVOpORh/5U72HzmttYuOHtGNcHULmEIcHdEgIcDvJzsYGVluPtLIkvmYGuN9x9qjbYhHnjz9/OorFLWSnO3VC4GNR9oHYD5w2Ph6+ZQh5m5AW0naX6mqALWTAaS/9L8fOvLgGsAEDPcqFkMDBuIWO9YvLLvFZzNOavxXVlVGd449AaO3T6GN7q8ASdbJ+N/AxEREdF9rqHGx/Tz84Ofn5/Z8if96vf4LxERERER0T1ErpDj45Mf48nDT+oMYnYP6o43u74JmYRx71CSCxz7DviuL/BVR1XrLG1BzNYTgMf3AbF1a4VZUCrHL8dTMW3ZcbRZsANTfziO7w/c0BrEHNshGMtmxGNQS3+0CnZHExd7BjHpvjIhPhTrn+iKIA9Hvem2nL2N/h/vw09Hb0Kp1DFOrTGsbYBxy4HA9jW+EID1M1UtOI0U5BKEFUNXYGac9nFyN1/fjPF/jselvEt1KDARERHR/a0+42MmJydj9uzZaNq0KRwcHODq6oqoqChMmTIFCQkJYrq0tDTIZLJa3cqmpaXBzs4OXbt2RVFREV5//XU0a9YMjo6OaNmyJdatWyem3bx5M/r16wcPDw8EBATgqaeeQklJSR1/NXD37l2xK93k5GTMmDEDISEhsLOzQ1BQEF555RXI5VrqpfcotsgkIiIiIqJ/hcySTLy07yWcuXNGZ5qRTUfitc6vwdbKwPiRlaXAwU+AQ18Aigrd6axsVV1MdXxENf6eESqqFNhz6Q5+O52O3ZeyUamo3fqspmf7NcPzA5tLC8IS3cNaB3tg29ye+OnITfx59jYu3tbevXJRRRXe/O0cfjmWCj83e9wtlSO/tBJF5VVo6uOCtx9sidhAI7pbtnMGJq8DfhgI3L3xz+eKSmDtVGD0N0Dr8Ub9FlsrW7zQ4QV08u+E1w++jrzyPI3vbxbexOQtk/FSx5cwKXoS928iIiKi/1fXQOauXbswYsQIlJWVoWPHjujYsSNKSkpw9epVrF69GqNHj0bHjh0B/NPqs+b4m2fOqOqV3t7e6NChAyorK9GlSxd4enoiISEBkydPRmRkJH7++WcsXrwYffr0Qb9+/fDXX39hyZIlsLOzw2effVav3y0IAjp27AgbGxv06dMH+fn52LdvHxYtWoSKigp8/vnndcrf0jCQSURERERE973jt4/j5f0v1woQVOvg1wEvd3wZLZu01J+RIACX/gS2vwYUpOpP6xYMjP8RCO5oVFnzSyux/FAKfjySgvxSaU/RWlvJ8O7IOEzuHGrUvIjuZW4Otni6bzM83bcZbuSUYGvSbfyRmIHLWUW10l64XYgLtzU/yy3Jw/hvj+C7aR3QrWkT6TN28QGmbFAFM0tz//lcUAAbZwOVxUDHWUb/nh5BPfDriF/x6oFXcTzzuMZ3cqUc7x9/H8czj2NBtwVwtzfP+E9ERERE/8fefYdHVaxxHP/upockQEIVQu+99650FFB6x05VEUQRFRugqEhTeu9NsVAUBKSX0Kt0EAiQUNOT3b1/5IKcnE1IqAF+n+fxufi+Z+bMeFnYnPfMzOPk9kJmwkJjYhwOB6+88gqurq5s2bKFChUqmPrMkeO/n6vuVMhctmwZ77//Pp9//jmurvElt44dOzJz5kzatGmDm5sb+/fvJ2/evABs2bKFSpUqsWDBgrsuZO7YEb+70J49e2jTpg0TJ04kTZo0APz555/Uq1ePsWPHMmDAgCdiS1xtLSsiIiIiIk8sm93GpL2TeO3P15wWMXP45uD72t8zpf6UOxcxQ4/BzJdgXoeki5g+maFKb3hzXYqKmBdvRDFk6UGqDv2LEauOJKuImSdDGjpXzsmyt6qriClPtdwZ0tCjdj6WvVWdwc2L4+eZvPe2w6Lj6DJ5G8v3nb/zxbcLyAsdfwbvhAVQB/z2Dqz7DuJiUtYnkMk7E+PrjqdHqR5YLeZHNqtOr6Llry3Zc2mPk9YiIiIiT4/r169z4kT8DhkZM2YkW7ZsyWp37NgxTp06ReXKlU1FTIhf2RkQEHDr3+9UyHzhhRcYMmTIrSImQKNGjQA4d+4cixYtulXEBKhYsSL+/v4EBwcna7zO3CzgVq5cmZkzZ94qYgLUrVuXGjVqEBMTw/79++/6HqmJVmSKiIiIiMgTx+FwsP7ser7f8T3/XPnH6TUvBL7AgCoDSOOZxmn+ts5g50xY2g/iIp1f4+YNhZpAydaQu1b8WXrJFBNn59s/DzN1w0mi45LePtbdxUrNghmpVTAjNfJnJNDfO9n3EXkaWK0W2lXMQd0imfni9wMs2XXujm1ibHa6z9rBl82L07ZCCl4IyFoCui6D6U3hRoL7rPoU1n0LuapD3jrx/2TIl6xuXawuvFnyTcpnKc97f7/HxYiLhvz58PN0Wd6FARUH0KJAi+SPV0REROQJsmvXLhyO+HPQU7KtbNq0abFYLKxevZpRo0bRtWtXfHx8kryP1WqlRIkShviePfEvln300UemNtevxx970KJFC4oUKWLI2e12wsLCDMXSlLq5InPw4MG4uLiY8vny5ePvv//m4sWLptzjSIVMERERERF5ouwP2c93Qd+Ztma8ydPFk7eLvM2zzzyLu4t70p1Fh8Wvrto733ne6gqVukONfuCZgnP2/u9aRCxvzgxi0/HQJK+rmNuf5qWz0bBYVtJ63+H8ThEho68HI9qUpkXZ7EzfdIrDwTfw8XAlfRo30nm7s/bwJcKi425db3fAB4v3EhoWTY/a+ZJ/DmXGAvDycpj+Alw5aczFhME/y+L/AchZFRoMjS+AJkPZzGVZ9PwiBm4YyNp/1xpysfZYPt30KXtD9jKg4gA8XDySN14RERGRJ8Tdno+ZMWNGBg8ezMCBA+nduzfvvfcederU4cUXX6Rt27Z4e//3suiNGzc4fvw4BQoUcBrPnj07ZcqUMd3j9tWaCR09epSYmBhTgTO5wsPD+eeff8iSJQu1atVyek1ERATAPRVLUxMVMkVERERE5IkQERvB4C2DWXJsSaLX5PDNwbDqw/C3+d+5w+B9sKAzhB51ns9dAxp9AxkL3tV4z1yOoMuUrRy7FO40n8bdhQ6Vc9Kpci6ypfO6q3uIPO2q589I9fwZTfF9Z6/RZcpWQsKM279+88c/HDh/na9eKoGvZzJfGkifE7ouhxnN4NKhxK87tQHG14SyXaHOQPC+859D6TzTMarOKGYenMl3Qd8RZ48z5BcfWczhy4cZXms4WX2yJm+8IiIiIk+Auzkf86b333+f1q1bM3/+fJYtW8by5ctZunQpn3zyCevWrSN37txAfEHS4XA43VbW4XA4LWLePrby5cubcje3qk1J8TXhve12O2XLlk30mu3bt2OxWEyrSB9XOiNTREREREQee2fDztJpWacki5gNczdkbpO55E+X/84d7l0IE591XsRMkwlaTIFOv9x1EXPn6Ss0G7PBaREzrZcbbz+Xnw3v1+GDhoVVxBR5AIplS8vCN6sQ6G/+fC3dG0zT0Rs4FHw9+R36ZYUuSyF/vaSvc9hh+yQYVQa2TQK77Y5dWywWOhbpyLQG08jkncmU3x+6n1a/tWLTuU3JH6+IiIjIY27q1Kk4HA4cDgdt2rRJcfvcuXPTv39/1qxZw/Hjx6lWrRpnz57lhx9+uHXNnc7HdFaMtNvt7NmzB39/f3LmzGnK3yxy3m0h82Z7Pz/nOwJt27aNo0ePUrFiRTJnznxX90htVMgUEREREZHH2pbzW2jzWxsOXznsNF82c1lmNprJ1zW+xtfd984d/rMCFr8OcVHmXO6a8OZ6KPYiJHfrydtEx9mYtvEkbcZvJjQ8xpRvWyGQDe/X4e3nCpDO+w7b3orIPcmVIQ2L3qxCoSzmPxeOh4TTbMwG5mw9zYFz1zl6MYzToRFcuhF96ywmkzQB0H4B9NwODb+GAg3ALZEzeCOvwO994s/XvH4+WeMtkbEE85vMp3wW85v9V6Ov8ubKN5m4d2Li4xMRERERp3LmzEnnzp0BsNn+e9EssUJmYnGAw4cPExERkWihMqm2yXHzfMwTJ06Ycg6Hgw8++ACIX3X6pFAhU0REREREHksOh4OZB2byxp9vcDX6qimfL10+RtcZzZT6UyiZsWTyOj0bBAu6gCPBKimLFWp/CB1/At+Uv9UaFRtfwKz59Ro++WU/0XF20zUDGhVicPPi+HjoBBCRhyWTnyfz3qhMnULmlY5RsXY+WLyXRiPX8dx3a6kxbDXlv1xJwxHrOBnifEtoADLkh4pvQLt50P8ktJkD/nmdX3tyHYytBkdWJmu8AV4BjK87ns5FOptydoedETtG0GdNH8JiwpLVn4iIiMjTZMWKFSxdupS4OON2/efOnWPEiBGA8VzLO63IdFaMvNOKy127duHh4WE6I3PQoEFYLBa6dOmS5Bxu9r9lyxZWrFhxKx4bG0vv3r1ZtWoVL774Ik2bNk2yn8eJfkIWEREREZHHTqw9li83f8miI4uc5l8v8TrdS3bHxeqS/E5Dj8GsVhAbYYz7ZIaXJkHu6ikep83uYPaWU4xefZQL16OdXuPhamV461I0Kq7z7UQehbRebkzsVI4f1x7j2z8OY7/DgsZDwTfoNHkri7pVIaOvR9IXu7pDoUaQ71nY/AOsHQaxCYqgESEw6yWo0hue/Rhckj6b09XqSt/yfSmWsRgfb/iYyLhIQ37l6ZUcu3aML6p+QYmMT8a5SCIiIiL3w7x585gyZQr+/v6UK1eODBkycOHCBdatW0dMTAzvv/8+tWrVAiAuLo79+/eTOXNmsmTJcquPm/FMmTKRLVs20z1uFhqdnZ8ZHBxMcHAwZcuWxdXVWJ6z2+NfdnVzS/y7YExMDPv37yd79uxUrlyZxo0bU7NmTfz9/dm4cSPnzp2jcuXKTJkyJcX/bVIzFTJFREREROSxEh4bzrtr3mXDuQ2mnJerF19W+5K6OeumsNMQmNUivqBwO8900PnXuzoL83J4DG/P28Xf/1xK9JqANO5M6FyOMjnSp7h/Ebl/rFYLPWrno3RgOnrP3UlImHnr59udvhzBK9O2Mee1SqRJzipqVw+o9g6UaA0rPoT9i83XbBwJpzZCi0mQPtcdu2yQqwH50ubjnTXvcPL6SUPuxLUTtF/anoa5GvJW2bfI5mN+yCYiIiLytGnbti2enp5s3LiRoKAgrl27hr+/Pw0aNKBnz57Urfvfz5GHDh0iKiqKmjVrGvq4Ga9Ro4bTeyS1IjOpbWVv5jp16pTo+Pfv309MTAylS5dm2rRpZM6cmYULF3LlyhXy5cvH22+/Te/evfHwuMPLdo8ZFTJFREREROSxcTHiIj1W9eDQ5UOmXHaf7IysM5L86fOnrNPr52BeB7h83Bh38YjfGvIuipi7z1yl+6wdnL0a6TTvYrXwYuls9KlXgKxpvVLcv4g8GFXyZeD33tXpM38XG46GJnntnn+v0XP2DiZ0KoerSzJP7vF7BlpOgfz14Pd3zaszz26HsTXghZFQtNkdu8uXPh+zG8/mw/UfsvrMalN+2cllrDq9ig5FOvBq8VeTd06wiIiIyBOqbt26hmJlUooVK+b07PFixYphs9kMZ2nebuXKxI8MaNCggdM+bTYba9eu5bnnnqN69cR3Arp5PmaZMmXw8vJi1KhRjBo16k5TeeypkCkiIiIiIo+Fo1eO0m1VN4LDg025Slkr8U3Nb0jrkTbZ/Vliw7GunQBbfjBvJ4sFXpoIOSqlaIwOh4PZW0/z6S8HiLGZz8F0tVp4qUx2etTOR44A7xT1LSIPR2Y/T2a9Wonjl8I4GRpOTJyDGJudw8HXGbP6mOHa1YcvMfDnfQx5sTgWiyX5NynVFrKXgwVd4cJeYy76GizoDCdehvqDwS3plx183X35vvb3TNo7iVE7R+HA+HAsxh7D5H2TWXJ0CQMrDeS5nM8lf5wiIiIi8sBt376d69ev89lnnyV53Z3O33xSqZApIiIiIiKp3oHQA7y64lVuxN4w5Zrla8bHlT/GzZr0uXK32G14HVyAz7YRuEQksu1rw6+gyAvJHl9YdBzL9wWzMOgMm49fdnpN4+JZeb9hIQL9VcAUeRzkyehDnow+/wVKPoPDAT+sMRYz5247Q2Y/T96pWyBlN8iQH15dCX8MhG0TzPntk+HMVmgx+Y4rw60WK6+VeI1SmUoxZOsQjlw5YromNCqUd9a8Q/1c9RlQcQD+nv4pG6+IiIiIPBAVK1Z0ulIzodtXZD5NVMgUEREREZFU7WzYWXqs6uG0iNm9ZHfeLPlm8ldCXT+Hy/wupP13S+LXVOkNFd+4Y1cOh4ONx0JZsP0MK/ZfIDLW+dZCbi4WPmpShI6VcqZsxZaIpDr96hck+FoUi3eeNcRHrDqCw+HgnboFUvY5d/OExt9A7hrwS0+IumbMX9gH42tBo2FQqj3coe/yWcqzoMkCfj76M6N3jSYkMsR0zYqTK9h6fisDKg6gfq76+nNJRERE5DFgt9vZs2cPAQEBBAYGPurhPFQqZIqIiIiISKp1Lfoa3Vd2Nz2Md7W48kmVT2iWr1nyOzuxDhZ2xRqeyCpML3+oMxDKvXzHro5eDGPQL/tZf9RcJLhd1rSejGlfhjI50id/nCKSalksFoa+VIKLN6JNn/+Rfx3lRnQcHzUugtWawuJgkRcga0lY9Ar8u82Yi42AJT3g+Fpo8h14JH3OpYvVhZcKvETD3A2ZvG8yU/dPJdoWbbjmSvQV+v3dj63BW/mo0kcqZoqIiIikclarlbCwsEc9jEcimafRi4iIiIiIPFwxthjeXv02x68dN8Tdre6MfnZ08ouYDgdsGAHTm4KzIqaLe/wqzN47ofwrSa54Co+OY8iygzQc8fcdi5jV8mXgt17VVMQUecK4u1r5sUMZimT1M+WmbDjJ+4v3YLPfeWswk/Q5oesyqPq28/ze+TCuJpzfnazuvN286Vm6J4tfWEzZzGWdXrPgnwWM2jkq5WMVEREREXlIVMgUEREREZFUx+6wM3DDQLZf2G7KfVn9S6pmq5q8jsIuwvyO8OfH4DBv/Wov3BR6boN6n4NXuiS7Wrb3PM9+u5Zxa48Ta3NepHC1WniucCbGdijD9JcrEODjkbxxishjxdfTjZmvVqR4trSm3Pzt/9Jj1g72n7uGPaUFTRc3qPspdFgE3hnM+cvHYOJz8ednJuMcJYAcfjmYXH8yAyoOwMvVy5SfsHcC8w7NS9k4RUREREQeEm0tKyIiIiIiqUqsLZZh24ex7MQyU65P2T40yNXgzp1E34CNo2HjKIgNN6Udrp5cq/EZaSq/jNXNLcmuomJtDPplP3O3nUn0mhLZ0/JSmew0KZFVxUuRp4R/Gndmv1aRV6ZuZ+vJy4bc8v3BLN8fTAYfd6rly0D1/BmpVzQzvp5J/3lzS77noNsGWPw6nFhrzNli4Ld34GwQNPo2/pzNO7BarLQt1JYa2WswYN0AdlzcYcgP3jqYjN4ZqZOjTvLGJyIiIiLykGhFpoiIiIiIpBpHrxyl/dL2zDk0x5RrXbA1XYp2SbqDuBjYOgFGloa1Q50XMdPnJrT5PKIKNL3zeC6G0XT0hkSLmNnSeTG2QxmW9KhK5yq5VMQUecr4erox7eUK1CiQ0Wk+JCyGn3ed490Fu6k5bA1ztp5O/razvlmg40/xZ/danDy+2TkTJteHq6eTPd5sPtn48bkfKRpQ1BC3O+y89/d77Lq4K9l9iYiIiIg8DCpkioiIiIjII2d32Jm2fxqtf2vNwcsHTfla2WvxfoX3sSRxfiWhx2B8LVja1/lZmAAFGxP38kriAgrdcUyLgv7l+VHrOXzhhinn7mKlR+28/NmnBg2KZU16XCLyRPNyd2FCp7I0LJYlyesuh8fwweK9NBuzgR2nrySvc6sL1OgHXZaCXzZz/vyu+HMzj/2V7PF6u3kz5tkxZPfJbohH26Lp9VcvDl8+nOy+REREREQeNBUyRURERETkkToQeoBXVrzCN9u/IcYeY8qXyFCCr2p8has1iZMxjq6ECbXh4n7nec900OAraD0TPM1n2t3O4XDwxW8HeHfBbiJjzedqlsuZnhXv1KBf/UJ4u+u0DhEBD1cXRrUtTZ+6BUjnnfT2sXvPXuPFHzby7vzdXA43/5nnVM7K8PpayFXdnIu8DDNfgnXfJfvczACvAMbWHUt6j/SG+NXoq3Rc1pFVp1clb1wiIiIiIg+YCpkiIiIiIvJIHL58mLf+eovWv7Vm+4XtTq9pW6gtE+tPxNvN23knDgdsGAGzWkLUNXPexQOqvgVv7YJKb4L1zj8C/bj2GBPXn3Ca614rL3Nfr0TuDGnu2I+IPF1cXaz0fjY/QQPrsqRHVfrWK0CF3P64Wp2v2F60418ajVjHluOhybuBT0bo+DNU6W3OOeyw6lOY1wGirieru5x+ORn97Gg8XYxnbEbGRfL26reZsGcCjmQWRkVEREREHhS9PiwiIiIiIg/VmetnGLFzBCtOrkj0mkzemfi86udUeaZK4h3FRMAvvWDfQuf5Uu2h9gBIm9153onFO/7l6+XmbRX907gzvHUpaiZyDp6IyE0uVgslA9NRMjAdPevk59zVSAYvPchve86brg2+HkXbCZt569kC9KyTD5dEip7/de4K9T6HbGXg5x7mc4AP/QYTDsevPs905y20S2QswbCaw3hn9TvEOeIMuZE7R3Lk6hE+q/IZnq6eifQgIiIiIvJgaUWmiIiIiIg8NFvOb6Hlby2TLGI2zN2QxS8sTrqIGXYRpjZ2XsR094W2c6HZDykqYq4/EsJ7C/eY4qVzpGNp7+oqYorIXXkmnRej25VhzmuVKJjZ15S3O2D4yn9oP3EzF65HJa/Tos3htb8gIJ85F3oEJj4Lm8eCLfaOXdUKrMX4euNJ62HednvZiWW8vOJlQiJDkjcuEREREZH7TIVMERERERF5KFacXEG3ld0IT7iC6P/yps3L97W/5+saXzt9oH5LyBGY+Byc22HOBeSLf7hfsGGKxrb/3DXenBlEnN24jWKBzD5M7VqBLGm1GklE7k3lvAH83rsaHzUpgrur+XHM5uOXaThiHasPX0xeh5kKwWuroVATcy4mDJb3hzEV4eBvdzw7s3yW8sxpPIe8afOacntD9tL+9/YcvXI0eeMSEREREbmPVMgUEREREZEHbu6hufRb249Yu3l1UE6/nAytPpRFLyzi2RzPJt3RqU0wqS5cPWXO5a8fX8TMWCBFY1tz+CJdp2wjLNq4rWIWP0+mdq1AWi+3FPUnIpIYVxcrr1TLzc/dq5Ino/ms3cvhMXSdso0vfz9ATJz9zh16+sVvI/vsJ2Bx8ojn8jGY1x6mNoFzu5LsKtA3kJmNZlIze01T7lz4OTot68Smc5vuPCYRERERkftIhUwREREREXlgHA4Ho3eO5sstX+LAuCLIx82Hz6p8xs9Nf6Zxnsa4WF2S7mz/TzC9KUReMeeqvg1t54BnEis5EzgaEknXaUF0mbKNizeiDTlfD1emvlyeZ9J5Jbs/EZHkKvKMH7/1qkaLss63v56w7gQtx23idGjEnTuzWKB6H+iwGLz8nV9zaj1MqA1/fASxkYl25ePuw4jaI+hatKspdyP2Bt1XdmfxkcV3HpOIiIiIyH2iQqaIiIiIiDwQDoeDIVuHMG7POFMug1cGpjaYSvP8zXG1ut65s70LYUFXsBkLjlis0GQ41P0U7lQI/b/QsGgGrzxFp1kHWH801JR3c7EwrmNZCmXxS1Z/IiJ3w9vdlW9almR465J4u5v//Np95iqNR67j9z3nk9dh3trQfTOU7ghYzHmHHTaOhLHV4fSWRLtxsbrQp1wfPqn8CS4W47jiHHF8svETPt/0OZFxiRdERURERETuFxUyRURERETkvnM4HHy55UvmHJpjyuXwzcH0htMp6F8weZ2dXA8/d4MEKzpx84a2c6Hcy8ke1+bjoTQZs4lf9oVgd3JknIvVwjctS1IlX4Zk9ykici+al87Ob72qUSSr+eWJG9Fx9Ji9gwE/7SUq1nbnznwzQ9PR8OY6yFPL+TWhR2ByfVj+AcQkvuKzRYEW/PDcD/i4+Zhy8/+ZT6tfW7E/dP+dxyQiIiIicg9UyBQRERERkfvqZhFz3uF5plyRgCJMbzidQN/A5HV26TDMbQe2GGM8TSbo8jsUqJ/sMY3/+xjtJ24hJCzG6TXlc6Xnp+5VaFoqW/LGJiJyn+TJ6MPi7lXoXDmn0/zsLadpOnoDRy7cSF6HWYpDx5+h3QLwz+vkAgds/gGmvwAx4Yl2U+WZKkxvOJ2sabKacievn6TD7x2YuHciNnsyiqwiIiIiIndBhUwREREREblv7A57okXM8lnKM7n+ZAK8ApLX2Y0LMLMFRF0zxn2ywCt/QLYyyesmKpbus3YweOkhbE6WYeYK8GZshzLMf6MyJbKnS97YRETuM083Fz5tWoyxHcri52necvvwhRs8P3o987edweFwsqQ8IYsFCtSDbhugSu/4rbgT+nfb/7ftjku0m/zp8zOr0SxKZChhysU54hixYwSv/fkaN2KSWWQVEREREUkBFTJFREREROS+sDvsfLnZeRGzQpYKjHl2DGnc0iSvs+gwmN0Srp02xt19oP188M+drG42Hg2h6ZgNLNsXbMp5uFoY0LAgf7xTkwbFsmKxODlTTkTkIWtQLAtL36pO2ZzpTbmoWDvvLdpDv4V7krfVLICbF9T7HF75EzIWMuePrIDf+0ASxdGM3hmZ2nAqr5d4HauTgui24G288ecbKmaKiIiIyH2nQqaIiIiIiNyzm0XM+f/MN+UqZqnI6GdH4+XqlbzOrp2F2a3g/G5j3OICLadB1pJ37OLAuet0mryVdhO3cPySedvE7Gk9mNi6EF2r5MTdVT8WiUjqkj29N3Nfr0T3Wnlx9o7FwqB/aTF2I2cuJ37GpbnTcvDG31D+NXNuxzRY+3WSzd2sbvQq3YupDaaSzce8BffekL0qZoqIiMgDs2jRIiwWy335Z8+ePY96OpIC+oldRERERETuid1h54vNXyRaxBz17KjkFTEdDtizAH6sDKc2mPNNvoP8zyXZxdmrkbwzbxeNR63j738uOb3m2UIZmdK2EPkzet95TCIij4ibi5X3GhRi+ssVyODjbsrvO3ud50evT/TPOqdcPaDRMCjR2pxbMxh2zLhjF6UzlWbh8wt5Ie8LppyKmSIiIvKgbN269b704+XlRdGiRe9LX/JwmA9dEBERERERSSa7w87nmz9n4T8LTbmKWSsyqk4yi5gRl+O3Ntz/k/N89b5QtkuSXaw6eIG35+7iRrTzs96sFni3XkFerZKDK1cu33lMIiKpQPX8GVn6VnXenruLjcdCDbmrEbF0nrKVvvUK0q1mXqzWZGyRbbHAC6Mh7AIcX2PM/fpWfLGzRKsku/Bx9+GLql+QyTsTE/dONORuFjPH1R2Hr7tvcqYoIiIickctWrSgcuXKTnMxMTG0bh3/olaaNGmYOXNmov34+vri4uLyQMYoD4YKmSIiIiIicleSKmJWylqJkXVG3rmIGRcDO6fHb2kYdsH5NWW7QJ2BiXbhcDgY/ddRvlv5T6JHvJXJkY4PGxembE5/YmNjkx6TiEgqk8nXk+kvV2DYH4cZt/a4IedwwLAVh9l95irftiqJr6fbnTt0dYdWM2BKI7iw97bObLD4Nbh4EOp8BNbEN/KyWCz0Lt0bwGkx882VbzKh7gS83bT6XURERO5d+fLlKV++vNPcjh07bv26ZMmSNGvW7CGNSh4GbS0rIiIiIiIpdubGGd766627L2LaYiFoGowqA7+/67yI6ZEWXpwATb7H6SFxQHh0HN1n7eDbP50XMfNkTMPYDmVZ1K0KZXP6J3N2IiKpj6uLlQ8aFubH9mVI425eRfDHgQs0Hb2BIxeSua2rpx+0XwBpA8259d/BvA4QnXRfN4uZrxR7xZTbc2kPvf/qTbQtOnnjEREREblLO3fuvPXr0qVLP8KRyIOgQqaIiIiIiCRbeGw43wd9T9Ofm7Lm3zWmfKWsle68neyhpTC6PPzaG66dcX5N7prQfWP89oaJFDFPh0bw0o8bWbYv2JTz83Tly+bF+OPtGjQolgVLIn2IiDxuGhbPypKeVcmTMY0pdzwknKZjNvD7nvPJ68wvK3RYBD6ZzbnDv8Ok+nDlVJJdWCwW3irzltNi5pbgLfRd25dYu1bCi4iIyINzeyGzVKlSj24g8kCokCkiIiIiIsny67FfafJTEybtm+T0ofTNIqanq2finWwcBXPbwpUTzvOuntDgK+j4M6TNnmg3u85cpfkPGzgUbF4tlD+TD7/0rEb7ijlxddGPPCLy5MmXyZclPapSv6i5ABkRY6PH7B30nrOTf69E3LmzjAXhtdWQtaQ5d3E/TKgDZ7Yl2cXNYmaXol1MuTVn1jBw/UBsdtudxyIiIiJyF3bt2nXr11qR+eTRT/UiIiIiInJHU/dNZcD6AYREhjjNV89WPekipt0OfwyM/8cZixVKtYceW6DSm0mey/bH/mDajN9EaHiMKVevSGZ+6lGVXBnMK5VERJ4kvp5ujO1Qlv4NCmF1suj8l93nqPPtWoYuO8T1qDusiEybDbouh6LNzbmIEJjWBPb/lGQXFouFPmX70KpAK1Nu6YmlfLHlCxyJHWQsIiIicpccDge7d+8GwNXVlWLFij3iEcn9pkKmiIiIiIgkKehCEMN3DHeay+SVicHVBjP62dGJFzFtsbCke/xqTBMLFG8JPbZBsx8gfa4kxzJt40nemBlEVKzdlHv7ufyM7VAWHw/XO8xIROTJYLFY6FYrL9Nfrkh6bzdTPibOzti1x6g9bA2ztpzCbk+ikOjuDS2mQO0Pzbm4KFjQBdYPx+mBxLeN58NKH9I4T2NTbuE/C3lr9Vtcj7menKmJiIiIJMuRI0cICwsDoEiRInh4eDziEcn9pp/wRUREREQkUZejLvPe2vewO4yFQ3erO52LdubV4q/i7eadeAcx4fEPv4/8Yc5lKAgtp0LmIncch93uYOjyQ4z/+7gp5+Fq5fvWpWhYPOsd+xEReRJVy5+B33pXp/usHew+c9WUDw2P4cOf9vHLrnN83aIEOQMSWbVusUDN9yBDAfjpTYiLNOZXDoLLx6Hxd+BiLpwCWC1WPq/6ORGxEaw+s9qQW31mNW1+a8PwWsMp6F/wLmYqIiKSen219SsOXT70qIfxyBTyL0T/Cv0f+n11PuaTT4VMERERERFxyu6wM2DdAC5GXjTEc/jmYFzdcWT3TfwMSwCiw2BWSzi90ZzLXh7azQdv/2SN5esVh50WMdN7uzGxc3nK5kyfrH5ERJ5U2dJ5sbhbFRZsP8M3f/xDSFi06ZotJy5T//u/6Ve/EF2q5MLF2Z60AEWbQdpAmNMawi8ZczumQ/A+eHECZMjntLmb1Y1hNYfRY1UPtpzfYsiduXGG9kvbM7DSQJrla3YXMxUREUmdDl0+xPYL2x/1MJ46Oh/zyaetZUVERERExKlJeyex4dwGQ8zd6s63tb69tyJm/nrQaUmyi5gzNp9i7NpjpniuAG8Wd6+qIqaIyP+5WC20qZCDNf1q0atOPjzdzI99omLtfP7bAVqN28Tp0IjEO8teFl5dFb96PqFzO2Bcddg+JdGtZj1cPBhZeyTP5XjOlIu2RfPRho8YvGWwacW/iIiISErcviLzQRcy161bh8VioX379g/0PsmxevVqLBYLnTp1etRDeeBUyBQREREREZPtwdsZvWu0Kd6/Qn8K+RdKunF0GMxq4byIWbIttJkN7olsa5jAqoMX+GTJPlO8dI50LOpWhdwZktePiMjTxMfDlXfrFWR131o0TmTb7aBTV3hp7EbOXE6imJk+J7zyB+SuYc7FRsBvb8PcdhAe4rS5t5s339X6jr7l+uJicTHl5xyaw7Btw3Akce6miIiISFJuFjItFssD31r25r3KlSv3QO+THDt27ABSx1geNG0tKyIiIiIiBn//+zcfrv/QtEqmYa6GtCzQMunG0Tf+vxJzkzlX8U1oMDT+DLZk2PPvVXrO3ok9wfPtkoHpmPVqRbzd9eOMiEhSsqb1Ykz7MjTZe56PluwjJCzGkL90I5ouU7ayqFsV0nm7O+/EKx20XwQrPoBtE835w0th7M74lfYZzas3LRYLnYt2pmhAUfr93Y+QSGPRc+bBmfh5+NGtZLe7naaIiEiqcMcXPp9wj2L+586d4+LF+KNQcuXKRdq0aR/o/W4WD8uUKfNA75McN8dStmzZRzySB08/+YuIiIiICAAxthiGBw1n5sGZplxOv5x8XPljLEkVIaNvwMwWcGazOVepO9QfnOwi5pnLEbw8dRuRsTZDPIe/N5M6l1MRU0QkBRoWz0qlPAF8+ut+ft51zpA7dimcV6dtZ+arFfF0M6+aBMDVHRp/C/nqwpIeEJFgBeaN8zC1CXT5HTIWcNpFuSzlmN9kPn3X9mXHxR2G3A+7fsDXzZcORTrc9RxFREQetf4V+j/qITx1Hvb5mDt37sRisaSKQubOnTtxcXF54KtQUwNtLSsiIiIiIhy/dpz2S9s7LWK6W935puY3+Lj7JN7BfSxirj8SQsuxm0wrh9J5uzGla3ky+Hgkqx8REflP+jTufN+mNN+1KmnKbT91hbfn7sKWcAl8QgUbQPdNkL++ORd+EaY2hkv/JNo8o3dGfnzuR0pnMj9o/GrbV/x05Kc7zkNERETkpns9H3Pbtm106NCBwMBAPDw8CAwMpHfv3ly5csV0bXR0NAcOHKBAgQJYrVYGDRpE4cKF8fLyIl++fAwfPtzpPY4cOcLrr79O3rx58fT0xNfXl/z589OhQwe2b9+e4jEDREREcPjwYQoVKoTVamXw4MEUK1YMb29vcufOzaBBg7DZbHfu6DGhQqaIiIiIyFPM4XCw+Mhi2vzWhkOXD5nyXq5efF3j66S3CUqyiNkj2UXMyBgbg37ZT4dJWwi+HmXIubtamdCpHHkzJlFMFRGRO3qxTHYGNi5sii/fH8xnv+6/83mVPpmg3bz4FZoJz71MRjHT282bMc+OobC/eQyDNg1i+cnlyZqHiIiIyL0UMocNG0blypWZN28eBQoU4IUXXsDV1ZVRo0ZRrVo1rl+/brh+7969xMXFERgYSPny5Zk0aRIlS5akYsWKHD9+nD59+vD5558b2qxatYqSJUsyYcIE/P39adq0KTVr1sTFxYVZs2Zx6tSpu5r3rl27sNvtZM+enXLlyjFq1CgKFy5MzZo1OXPmDJ9++mmihdXHkQqZIiIiIiJPqesx1+m7ti+fbPyEyLhIU76wf2HmN5nPszmfTbyTqOsw8yXnRczKPaH+l8kqYu4+c5XGo9YxdeNJp/nhrUpRPpf/HfsREZE7e6VabrpWzWWKT9t0ikG/7CfOZjc3up3FAuVfhZZT7qqY6evuy4/P/UguP+MY7A477//9PstPqJgpIiIid3Z7ITMlW6xOmDCB9957j6JFi3L48GFWrVrFggULOHToEI0bN+bAgQN8++23hjY3z6RcuXIlNWrU4NixY8ydO5c1a9Ywd+5cAIYOHcqNGzeA+JeGX3nlFVxdXdmyZQvbtm1j3rx5/Pbbbxw6dIgdO3ZQq1atu5r3zbGsWLGChg0bcurUKRYsWMCyZctYuHAhwK3/fRKokCkiIiIi8hTaeXEnLX9pyR+n/nCa71SkEzMbzSRX2lyJdxJ1HWa1gDNbzLnKPaHeF3csYp4KDafP/F00/2EDxy+Fm/Lurla+eqk4jUtkTbIfERFJPovFwkeNi9CoeBZTbtqmU3SZso1rEbF37qhI08SLmdOeh8snEm0a4BXAhHoTyJrG+Oe7zWGj/7r+/H7892TNRURERJ5O169f58SJ+O8aGTNmJFu2bMlqd/78ed555x3Sp0/P0qVLyZMnz62ch4cHH374IQBLly41tLtZNK1Tpw4//vgj7u7ut3KtWrWiXLlyREREEBQUBMCxY8c4deoUlStXpkKFCqZxlC5dmoCAgBTM2DyWZs2a8c033xjGUr9+/BEAFy9evKu+UyMVMkVEREREniIOh4MJeybQZXkXzoWfM+X9Pf358bkf6Ve+H+4u7k56ABwOOLAExla76yLmv1ci6L9wD3W+XcviHWdxdixbkax+/NarGq3L50ju9EREJJmsVgvftSpFBSer3dcfDaHZDxs4dinszh0VaQotJpuLmWHBML0pXDf/XXNTljRZmFBvAhm9MhridoedAesH8OuxX5M1FxEREXn67Nq169aW+CnZVnbixImEh4fz2muvOS1+3ixshoaGGuI3V0EOGTIEi5OfdQsXjt82/+aWtGnTpsVisbB69WpGjRpFWFgyvlcl082xfPrpp6bchQsXAHjmmWfu2/0eNRUyRURERESeEg6Hg+E7hjNy50jsDvO2gVWfqcqiFxZRLVu1xDsJ3hu/ymZ+J7jq5DyPKr2SLGLa7A6GrThE7W/WMG/7GWxOKphWC/SonZefe1SlQGbfZM9PRERSxtPNhQmdylEuZ3pT7kRIOM3GbODvfy7duaOizZwXM6+egunNIDwk0aY5/XIyuf5kMnllMsTtDjsfrv+Qn478lIyZiIiIyNPmbs/H/P33+F0funbt6jQfGRl/7Er69P99P7LZbOzdu5fcuXM7XV0JEBUVBcSvDr35v4MHD8Zut9O7d28yZsxI48aNmTRpEhEREckeb0IxMTHs37+f3LlzU6JECVN+z549ABQrVuyu75HaqJApIiIiIvKUGLt7LFP2TTHFXa2u9C3Xlx+e+4EMXhmcN46Lgd/7wrgacHKd82uq9IK6nydaxIyOs9F77k7GrD5GrM3JEkwgV4A3C96sTL/6hXB31Y8rIiIPWlpvN2a9VpGWZbObcjei4ug6dRuztjh5cSWhos3gxfFAgr8DQg7DjOYQdS3RprnS5mJyg8lk8jYWMx04+Hjjx4zYMQKb3ZaM2YiIiMjT4m7Px9y7dy/u7u4ULFjQaX7//v0AlCxZ8lbs4MGDREZGUqZMmUT7DQoKws3NjaJFi96Kvf/++xw5coShQ4dSsWJFli9fzquvvkqBAgVubYubUnv37iU2Npby5cs7zd/875KS4m5qpycDIiIiIiJPgcn7JvPD7h9M8Zx+OZnZaCadi3bGaknkxwOHA37pCdsmgJOVnFhcoNaAJIuYN6Ji6TplG7/vOe80nzWtJ182L8Yf79SkbE7zNociIvLgeLi68HWLEgxsXBhrgj/GbXYHH/60j8FLD2J3tg/47Yq3gOdHmOPBe2BWqySLmTn9cjK1/lSypDGf2zlx70R6rOrBtejE24uIiMjTZerUqTgcDhwOB23atElWG5vNRkREBG5ubk63hwVYtGgRAA0bNrwVu1kc9PV1vmPQli1bOH78ODVr1sTPz8+Qy507N/3792fNmjUcP36catWqcfbsWX74wfzzeXLcHEtixVsVMkVERERE5LEz++BshgcNN8VLZCjB3MZzKRpQ1Emr2+ycAXvmOc/lrQPdNkKt/okWMS/diKbthM1sPBZqymX09WDQ80VY3bcW7Svm1CpMEZFHxGKx8Gr1PEzqUh5fD1dTfvzfx+k+aweRMXdYGVm2M9T70hw/sxnG14LgfYk2DfQLZHL9yWRNk9WU23BuA21+a8Phy4fvNBURERERp1xcXAgICCA8PJyLFy+a8rt372bGjBkEBgbywgsv3IrfPJPy2LFjpjYOh4MBAwYA0L9//yTvnzNnTjp37gzEF1Xvxs2xJFXIdHFxoXjx4nfVf2qkpwQiIiIiIk+wn478xJCtQ0zxwv6F+bHuj/i4+yTdwYX9sLSfOe6fF9rOgw6LIVOhRJufDo2gxdiN7Dt73ZSrXzQzf/erTZequfF0c3HSWkREHrbaBTPxU48qBPp7mXLL9wfTZsJmLlyPSrqTKj2h5vvm+OXjMPE52D030aaBvoFMazCNQv7mv1v+DfuXjss6svr06jvOQ0RERMSZmystv/jiCxyO/3ab2LFjB02aNMHhcDB+/Hg8PDxu5W6ucly/fj1//vnnrXhMTAzdu3fnr7/+ol27djz33HMArFixgqVLlxIXF2e497lz5xgxIn73itsLpQCDBg3CYrHQpUuXJMd/s5DpbMXl5cuXOX36NAULFsTLy/xd7nGlQqaIiIiIyBMq6EIQn236zBTPly4f4+qOw8/dz0mr20TfgPmdIS7BA+vcNaH7ZijYINFVmACnQsNpPX4Tp0IjTLm2FXLwQ/uyeLmrgCkiktrky+TLT92rUiownSm3+8xV6n//N8v3Od8q/JZa70OlHuZ4XCT89Ab89g7ERTttmtUnK9MbTqdxnsamXGRcJH3X9mV78PbkTEVERETE4LPPPiMgIIBRo0ZRvHhxWrduTbVq1ShXrhwhISFMnjyZBg0a3Lre4XCwa9cusmfPTrt27WjYsCENGjSgffv25M+fn/Hjx1OzZk3Gjx9/q828efNo3LgxmTNnpn79+rRv357nnnuO3Llzs2/fPt5//31q1aplGJfdHn+Mi5ubW6Jjt9ls7Nmzh8yZM5Mli3k7/idxW1lQIVNERERE5IkUHB5MnzV9iHMY3wDN5ZeLCfUmkN4zfdIdOBzxD5lDjxjjPpnhpYng6p5k85Mh4bQZv5nz18yrdno/m5/BzYvhkvAgNhERSTUy+Hgw9/VKNCxmfkh2NSKWN2fu4L2FuwmLjnPSmvgXXep/CXU/A2dnMG+fDNNegOgwp829XL0YUm0I/cv3x8VifOklxh5D79W9OXrlaIrnJSIiIk+33Llzs2XLFtq0aUNwcDBLlizh7NmzvPzyywQFBdGpUyfD9cePH+fatWsUKVKEyZMn884777Bv3z5++eUXMmTIwPDhw/nzzz9JkybNrTZt27alW7duBAYGEhQUxPz589m7dy8NGjTgjz/+YMgQ865Ju3btAjDd/3aHDh0iMjLyjudjJpZ/XFkct6+dFbkLQUFBlCtXDoDNmzdTsWLFRzwikadbbGwsoaHxZ5AFBAQk+RaPiDwc+lzKwxZti6bLsi7sCzWeQ5Y1TfwKlyxpzA+lTYKmwq9vGWMWK3RaArlrJNn0ZhEzOMHWgxYLfPZCUTpWzpWMWTw4+kyKpC76TKZudruDr1YcYtza407zOfy9+b5NKcrkSOIFmRPrYOHLEG4+i4rcNaHdfHDzTLT5tuBtvLvmXa5EXzHEM3tnZmajmcn7e01SRJ9LkdRFn8lH5+DBg7d+Xbhw4Uc4EklN7Hb7rTMuXVxcsFrvbc2gzWbD39+fChUqGLaufRTu9vf87XWi7du3U7Zs2fs2Jq3IFBERERF5gjgcDj7f9LmpiOnp4snIOiPv/LDXFgerB8evxkyo1gd3XcS0WuC7ViUfeRFTRERSxmq18EHDwnzXqiQ+Hq6m/OnLEbQet4k/D1xIvJPc1eGNvyFHZXPuxNr4IqctkZWdQPks5Rlbdyzert6G+IWIC3Rf1Z0bMTeSPR8RERGR1Gb79u1cv36dzz4zHw0jKmSKiIiIiDxR5hyaw5JjS0zxT6t8SiH/Qkk3vnoapjaGtV+Bw27M5akF1d9NsvmJJIuYpWheOntypiAiIqnQi2Wys+yt6pTNaV55GWtz0H1WUNLFTL+s0PlXKP+qOXf4d1jSA+x2c+7/igQUYXit4bhajMXUI1eO8Nbqt4ixxSR7LiIiIiKpScWKFXE4HFSu7OSlL1EhU0RERETkSbHx3EaGbRtmincu0plGeRol3Xj/T/BjNTiz2ZzzyQwvTgCrizn3f/FFzE2JFjGblc6WrDmIiEjqFejvzbzXK9G3XgFcE5xznKxiposbNPoGynYx5/bMheX9489oTkSVbFUYVGWQKb4teBv9/+5PnD3xVZ0iIiIi8nhSIVNERERE5AmwPXg7b/31FnEO40Pcilkr8nbZtxNvGBMOv/SGBV0g+po5H5AfOv0CPpkS7eJmEfPC9WhD3GqB4a1VxBQReZK4uljpWSc/C7tVISCNuyGXrGKmxQKNv4NiL5lzW8fDyk+SLGY2zdeU3qV7m+IrT69k0MZB2BPuKCAiIiIijzUVMkVEREREHnO7L+2mx6oeRNmMqyGfSfMMw2oMw9VqPtMMgOC9ML4W7JjmPF+mE7yxFjIlviXt8UthSRYxm5ZSEVNE5ElUKjAds1+rhH8ixcyVSRUzrS7QfBzkr2/ObRgBfwxMspj5avFXaV2wtSm+5NgShm0bhiOJtiIiIiLyeFEhU0RERETkMXYg9ADd/uxGRFyEIe7r5svIOiNJ72k+ywyHAzaPhQl1IOQfc94jLbScCi+MAvc0id77yIUbtJ2wWUVMEZGnVMEsvsxJtJi5g/VHQhJv7OIGraZBzmrm3KbRsGJAosVMi8XCBxU+oH4ucyF05sGZjN09NkXzEBEREZHUS4VMEREREZHH1D9X/uH1P1/nRuwNQ9zb1Zsf6/5IQf+C5kYxETC3Xfw5ZLYYcz6wInRbD0WbJ3rf61GxDF56kEYj16mIKSLylCuYxZfZr1U0FTNjbHZem76doFOXE2/s5gVt50D28ubc5h9g+fuJFjNdrC4MqTaEatnMhdAfdv/A1H1TUzINEREREUmlVMgUEREREXkMnQ87zxt/vsG1BOdaerl68cNzP1AyY0lzo7homNcBDi815yxWqNkfuiyFdDmc3tNmdzB362nqfLOG8X8fJ9ZmfLhstcD3bUqriCki8pQplMXPaTEzMtZGlynb2H/OyRnMN3n6QYfF8S/SJLRlLPz+LtjizDnAzcWN72p9R5lMZUy5b4O+5autX2Gz21I0FxERERFJXVTIFBERERF5zETERtDrr16ERBq37HO3ujOyzkjKZi5rbmSLhQVd4dgqc84vG3T+DWoPABfn52levB7Fiz9u5P3FewkJM6/ktFpgRJvSvFDymbuak4iIPN4KZfFj+ssV8PU0/j1yIyqOTpO2cvRiWOKNPf2gwyLIUdmc2z4JZr0EEc5Xdnq5ejH62dEU9i9sys08OJO317xNRGyEk5YiIiIpozOY5UmXWn+Pq5ApIiIiIvIYsdlt9F/Xn8NXDhvirlZXhtceTqWslcyN7DZY/Boc/t2cK9QE3lwPuaomes+YODuvzwhi95mrTvNZ/DyZ2Lkcz6uIKSLyVCuWLS1Tu5bHy83FEA8Nj6HjpC0cDr6RSEvAwxfaL4ScTv4+Or4GxteE83ucNvV192Vs3bHkTpvblFtzZg1dlnfhYsTFFMxEREQkntX6XwnFZtMqf3my3f57/Pbf+49a6hmJiIiIiIjc0YgdI1hzZo0p/mXVL6mRvYa5gd0OS3rC/p/MudIdoNUM8PZP8p5fLz/ELidFTA9XK72fzc9ffWtSp1Dm5E1ARESeaGVz+jO+U1ncXYyPnM5fi6LZmA38vPNs4o09fKD9AshV3Zy7ehom1YM985029ff0Z1qDaZTOVNqUO3j5IO1+b8fxa8dTNBcRERF39/+2TQ8LS2J3AZEnwO2/x2//vf+oqZApIiIiIvKY+OnIT0zZP8UU71ayG43yNHLeaPn7sHu2OV68JTw/Eu7wluUf+4OZuP6EKd6kRFZWvVuTPnUL4O3ufDtaERF5OlXPn5FR7UrjYrUY4pGxNt6et4uPft5HdFwiq1rc08SvzCzZzpyLi4zfYWDNV06bpvdMz4R6E2iYq6EpdyHiAm/8+QbB4cEpno+IiDy9/Pz8bv36ypUr2O32RzgakQfHbrdz5cqVW/9+++/9R02FTBERERGRx8Cfp/7ks82fmeINcjWgW8luzhsFTYOt48zxws9Ds7FgdTHnbnPmcgR9F+w2xTtVzsnodmXInt47WWMXEZGnT/2iWfi2ZUkS1DIBmLH5FK3Gbebs1Ujnjd08odkP0OgbsDp5WWbNYNg2yWlTDxcPhtYYyuslXjflgsOD6bayG9eir6VkKiIi8hTz9fW99euoqChOnTrF1atXiYuLS7XnCYokl8PhIC4ujqtXr3Lq1CmioqJu5W7/vf+o6dVpEREREZFULCI2gmHbh7Hwn4WmXPEMxfm86udYLE6eEp/ZBkv7muP568FLk8El6R8FYuLs9Jyzk+tRcYZ4sWx+fNi4cIrmICIiT6dmpbORyc+D3nN2EhIWY8jtPnOV5mM2sPDNKuQIcPJijMUCFV6DzEVhficIv2TML+0LvlmgUGNTU6vFSq/SvQj0DWTQxkHYHP+t/jx69Si9/urF+Lrj8XT1vC/zFBGRJ5e7uzuZM2fmwoULQHwx8/z58494VPKoJSxiO/2Z/DGWOXNmbS0rIiIiIiJ3djD0IK1/a+20iJnZOzMjao9w/hD2RjDM6wA240NjclSOPxPTNekfSOx2B1/8foDdCc7F9PVwZUy7Mni4Jr2SU0RE5KYqeTPwW6/qlM2Z3pS7eCOa9pM2c+F6lJOW/5ezCrzxN2Qpbow77LDwZTizNdGmzfI149Mqn5riOy/upN/f/YizxzlpJSIiYuTv70/mzJkf9TAklbHb7U/kVsOZM2fG39//UQ/DQIVMEREREZFUaPbB2bRf2p6T10+acr7uvox+djQZvTOaG8bFxK9cCUtwBphfNmg1PX67viRcuhFNl6nbmL7plCn3VYsS5AxIk5JpiIiIkCWtJ3Nfr8TLVXObcmcuR9Jh4hauhMc4afl/fs9Ah8WQPpcxHhcFs1tDyJFEmzbN15R3yr5jiq85s4bPN3+ubQFFRCRZ/P39yZs3L5kyZcLT0xOrVaWVp92TUsi0Wq14enqSKVMm8ubNm+qKmKCtZUVEREREUp0ZB2bw9bavneZKZCzB0OpDCfQNdN542XtwZosx5uIBrWeAT6Yk77v2n0u8O3+Xafs/gM6Vc9KoeNZkjV9ERCQhNxcrHz9fhGLZ/Hh3wW5urx8euRhG5ylbmfVqRXw93Zx34JMpvpg5qS5EhP4Xj7wMM1+EV/6M32rWia5FuxISGcKMAzMM8cVHFlMmUxma5mt6r9MTEZGngLu7OwEBAQQEBDzqocgjFhsbS2ho/PeRgIAA3NwS+f4i94VeGxARERERSUV+O/6b0yKm1WLljRJvMK3BtMSLmBtHQdAUc7zJcMhWNtF7xsTZ+fL3A3SevNVpEbNkYDoG6FxMERG5D14sk53BzYub4nv+vcar07YTFWtz0ur/AvJCu/ng6mWMXz0NM5pDxGWnzSwWC33L9aVR7kam3FdbvyI4PNhJKxERERFJDVTIFBERERFJJdafXc9H6z8yxbOmycrk+pPpWbonrtZENlXZOgH+GGiOV3gdSrdP9J4nQsJ56ceNTFh3wmm+cfGsTH+5gs7FFBGR+6ZthRwMaFTIFN9y4jKvTb9DMTN7OWg5FSwJHmldPACzWkD0DafNrBYrX1T9gopZKxriN2JvMGjjIG0xKyIiIpJKqZApIiIiIpIK7Lm0hz5r+hDniDPEc/nlYm6TuZTNnPiKSnZMh6V9zfGcVaH+YKdNHA4HC7afofHIdew9e82U93Jz4auXijO6XWnSemmbHBERub9er5GXnrXzmeLrjoTwyrRtRMYkUcws2ACafG+Onw2Cue0gNsppMzcXNwZXG4yvu68hvuHcBhYdWZSS4YuIiIjIQ6JCpoiIiIjII3bs6jG6r+pOZFykIZ7JOxPj647H39M/8cZ75sMvvc3xDAWh1XRwMRchr0fF8tbcXfRbuIcIJw+KC2f149de1WhdPgcWiyXF8xEREUmOd+sVoHPlnKb4hqOhvDx1GxExcU5a/V/ZzvDcp+b4ib9h4ctgc942k3cmPqjwgSk+bNswzoadTfbYRUREROThUCFTREREROQR+vvfv+m4tCPXoo2rIv3c/Rj33Diy+mRNvPGBJfDTm0CC7fD880DnXyBNBlOTs1cjeWHUen7Zfc5pl12r5uKn7lXIl8knpVMRERFJEYvFwifPF6VNefPZz5uOh9J1yjbCo5MoZlZ7G6q9Y44f/h0WvwZx0U6bNcnThNqBtQ2xiLgIPtnwCXaHPSVTEBEREZEHTIVMEREREZFHwOFwMHHvRHqu6smNWON5Xp4unox5dgz50pu33Lvl1CZY9Co4EqyoTJcDOv8KvllMTS5cj6LdhM2cDI0w5fzTuDO5Szk+eb4onm46D1NERB4Oq9XC4ObFaVcxhym35cRlOk/eSmiY84IkAM9+AuVeNsf3L4YZzSHisillsVj4uPLHpPNIZ7xf8BaGbRtGVJzzrWlFRERE5OFTIVNERERE5CGLiI3g3bXvMmLHCBwJVlO6WFz4puY3lMpUKvEOrpyEee3BFmOM+2WLL2KmzW5qEhIWTfuJWzjlpIhZLV8Glr9VnTqFMt/FbERERO6N1Wrhi6bF6FDJXMzcfuoKz49az95/zec5A2CxQKNvoXhLc+7UBpj4HIQeM6UyeGXgw0ofmuIzD86k2ZJm/P3v3ymeh4iIiIjcfypkioiIiIg8RKGRoXRc1pE/T/1pyvm4+TCyzkhqBtZMvIOo6zC7DUSEGuNpMkGnXyB9LlOTqxExdJy0laMXw0y59xoUZPrLFcjk55nSqYiIiNw3VquFz5sWc3pm5rlrUbw0diMLg/5NrDE0+xEKNTHnLh+LL2ae2mRKNcjVgHo565niZ8PO0mNVD95Z/Q7B4cEpnouIiIiI3D8qZIqIiIiIPCQRsRH0WNWDf678Y8rlTpub2Y1nUyN7jcQ7sMXBwpfh0kFj3NUL2s+HDOataG9ExdJ58lYOnr9uyn3erBjda+XDarWkeC4iIiL3m8ViYdALRXmlWm5TLibOTt8Fu/lkyT5ibU7OsXRxg5bToPxr5lzkZZj+AvyzwpT6qNJH5EvnfCv3ladX8tIvL3Eg9ECK5yIiIiIi94cKmSIiIiIiD0GsPZY+a/uwP3S/KVcrsBazG80md1rzg1uDPwbCUfNKTl4cD8+UNt/TZuf16UHsdrId38DGhelYybzqRURE5FGyWCwMbFyYL5sXw83F/KLNtE2n6DZzB3FOi5mu0GgYNBgKJGhri4H5neH0FkM4nWc6ZjWaRZeiXXCxmM+Ivh5zne4ru3Pmxpl7mZaIiIiI3CUVMkVEREREHjCHw8GnGz9lw9kNptwbJd5gRO0R+Lj7JN5BXEx8EXPLj+ZcnY+gyAtOm335+0E2HQ81xfvWK8Cr1fMke/wiIiIPk8VioX3FnMx9vRIZfT1M+ZUHL/DRkn04HA5njaFSN2gzC9y8jbm4SJjdCi4eMoS93bx5t9y7zH9+PqUzmV8MCo0KpdvKblyOunxP8xIRERGRlFMhU0RERETkARu9azRLji0xxV8r/ho9S/fEaknia3nIUZhUFzaOMudKtIbq7zpttijoX6ZuPGmK96idl5518id36CIiIo9M2Zz+/N6rGmVzpjfl5mw9w6i/jibeuFBj6LoUvPyN8airMPNFuGY+b7NA+gJMbTCVz6p8hpvVzZA7df0UPVf1JCI24m6mIiIiIiJ3SYVMEREREZEHaP7h+YzfM94Ub5q3Kb1K90q8ocMBO2bAuOpwfpc5H1gRnh8Zv/IkgT3/XuWDn/aa4m3KB9K3XsGUDF9EROSRyuTnyZzXKvF8yWdMue/+/If525PY8vWZ0tB+gXll5vWzMONFiDCvsLRarDTP35zB1QdjSbA97d6QvfRd25dYe+xdzUVEREREUk6FTBERERGRB2Rb8DYGbxlsilfNVpVPqnyCxUkREgBbLCx+HX7pCc5WfmQuBq1ngZunKRUSFs0bM4KIiTOeHVYmRzo+bVo08XuKiIikUu6uVr5tWZKq+QJMuQ8W72X14YuJN85eDlrNAKurMR5yGGa3hrhop80a5GpA/wr9TfF1Z9fx6cZPsTucnNEpIiIiIvedCpkiIiIiIg/AhfAL9F3bF5vDZogXDSjKdzW/M21Zd4vdDkt6wt75zvMV3oBXV4JPRlMq1man+6wdnL8WZYhn9PXgxw5l8XB1uau5iIiIPGrurlbGdihL4ax+hrjN7qD7zB3sPH0l8cb5n4OmY8zxf7fC2q8Sbda+cHteLvayKb7k2BIGbxns/IxOEREREbmvVMgUEREREbnPYm2x9Fnbh8tRxi3rsvlkY8yzY/BOuMXd7f78CPbMNce9A6DtPGj0Nbh5mdI2u4N35+9m6wnjPd1cLIztUIbMfubVmyIiIo8TX083pnYtT7Z0xr8HI2NtdJmyjcPBNxJvXLIN1PvCHF8/HP4NSrTZ22Xe5vk8z5vi8w7PY9j2YSpmioiIiDxgKmSKiIiIiNxnX237ij2X9hhini6ejKg9ggAv87Z4t2wYAZtGm+N5akO3jVCwgdNmdruD9xft4Zfd50y5T18oRtmc/ikav4iISGqV2c+TaS+XJ62XcWeDa5GxdJi0hVOh4Yk3rtILyr9qjDns8PObEBvltInFYuHTqp9SLVs1U27GgRmM3DlSxUwRERGRB0iFTBERERGR+2jJ0SXMOzzPFP+kyicU9C+YeMOds+DPj83x/PWh/QLwzeK0mcPh4ONf9rEg6F9Trm2FHLSrmCPZYxcREXkc5Mvky6TO5fB0Mz7WunQjmg6TthB8zXlREoC6n4N/XmMs5B9Y7WS15v+5Wd0YXms4FbNUNOUm7p3IuD3jUjR+EREREUk+FTJFRERERO6TA6EH+Hzz56Z4u0LtaJKnSRINl8Avvczx7BWg5VRwcX6epsPh4IvfDzJz82lTrm6RzHzWtGhyhy4iIvJYKZfLn7EdyuLmYjHEz1yOpOOkLVwOj3He0N0bmo8FS4JHYhtHw+nNid7P09WTkXVGUiZTGVNuzK4xzD3kZFt4EREREblnKmSKiIiIiNwHlyIu0fuv3kTbog3x0plK07dcX+eN7HZYMxTmdwKHzZjLWAjazYt/4JqI7/78h0nrT5jiNQtkZHS70ri56Ou+iIg8uWoVzMT3rUtjNdYyOXIxjHYTNnP+WqTzhoEVoHLPBEEH/NwNYiISvZ+3mzdjnh1D8QzFTbmvtn7FtuBtKZyBiIiIiNyJnmyIiIiIiNyjqLgoev/VmwsRFwzxDF4Z+Lbmt7g5W1EZdR3mdYA1Q8w5v+zQYTF4J3625bxtpxn111FTvEreAMZ1LIuHq0uK5yEiIvK4aVwiK0NeNBcWDwXfoNmYDew7e815w9ofQoYEW75fPg6zW0GI+e/Xm3zcffjxuR8p5F/IEI9zxNF3bV/Oh51P8RxEREREJHEqZIqIiIiI3AO7w87ADQPZF7rPEHe1uvJNzW/I6J3R3CjkKEx8Dg7/bs55B0DHxZA2W6L33HQslA9/2meKl8+Vnomdy+HppiKmiIg8PVqXz8GHjQqb4heuR9Nq3CZWHbxgbuTmCc1/BEuCvzNProMfK8NfX0Ks8xWdaT3SMq7uOLL5GP+uvhx1mbdWv0VkXCIrQUVEREQkxVTIFBERERG5Bz/u/pEVJ1eY4h9X+piymcuaG5zbBRPrQMhhcy5TEXh1JWQsaM7938mQcLrNCiLO7jDES2RPy+Qu5fF2d03pFERERB57r9XIQ996BUzxiBgbr03fzrSNJ82NspWFau+Y47YY+Ptr+KEyHFvt9H7+nv6MqD0CL1cvQ/zg5YN8svETHA6H03YiIiIikjIqZIqIiIiI3KWlx5cydvdYU7xr0a40z9/c3ODKSZjVEqKcbHNX+AV45U/wz5Po/a5FxvLKtG1cjYg1xJ9J68nEzuXw9XSyha2IiMhTomed/HzXqiRuLsZDM+0O+OSX/YxZ7WTL2FrvQ7GXnHd45QTMaA57FzpNF/QvyGdVPzPFl51YxrT901I8fhERERExUyFTREREROQu7A/Zz0cbPjLFawfW5q0yb5kbhIfCjBch/GKChAXqfAStpoOHT6L3i7PZ6Tl7B8cuhRvi3u4uTOpSnky+nnczDRERkSfKi2WyM+OViqT1Mr/cM2zFYeZuPW0MurjBS5Pi/x72fcZJjw746U04vtbp/RrkasArxV4xxYfvGM7W81vvZgoiIiIichsVMkVEREREUigkMoS3Vr9FjD3GEC+YviBDqw/FxZrgvK2YCJjdCi4fM8Zd3KHtXKjRFyzG1SO3O38tko6TtrLuSIghbrHAiDalKZzV757mIyIi8iSplCeAxd2rkDPA25Qb8NNe/jyQ4MxMiwWKNIWeW6FyT/O5mfZYmNsegvc6vV+v0r2olq2asYnDznt/v8eliEv3NBcRERGRp50KmSIiIiIiKRBri+XdNe9yIcL4EDSDVwZGPzsab7cED01tcbDwZTi7PUFPFnhxAhRskOT9lu87T4Pv17HpeKgp936DQtQtkvlupiEiIvJEy5vRh5+6V6VQFl9D3O6AnrN3sO3kZXMjD1+o/yW8sRZ8Evz9GnMDZraAK6dMzVysLnxV4yty+uU0xEOjQun3dz/i7HH3PB8RERGRp5UKmSIiIiIiKTB061B2XNxhiLlZ3RheazhZ0mQxXuxwwLJ+8M8yc0cNv4KizRK9T0RMHB8s3subM3dwLTLWlG9ZNjuv10j8PE0REZGnnX8ad6a9XIHs6b0M8eg4O69M3cbh4BvOG2YpDu0XgruxCEpYMMx8CSLMRVA/dz++q/Udni7Grd6DLgQxeufoe5qHiIiIyNNMhUwRERERkWRa8M8C5v8z3xQfWGkgpTKVMjcImgrbJ5vjVd+Cim8kep/QsGhe/GEjcxKe4/V/L5XJzpfNi2NJYjtaERERgcx+nkx/uQL+adwN8etRcXSevJXQsGjnDbOWgDazwJrgrM3QIzCrBUSbi6AF0hdgYKWBpvikfZNYe8b5GZsiIiIikjQVMkVEREREkmHXxV0M3jLYFG9TsA0v5n/R3ODfIFj2njlevBU8OyjR+4RHx/Hy1G0ccrJKxMfDle9bl+LbViVxd9VXeRERkeTIk9GHKV3K4+1uPPsy+HoUHy3Zh8PhSKRhTWg+1hw/GwSzWkFMuCnVNF9Tp98LBqwfwNmws3c1fhEREZGnmZ5+iIiIiIjcwbmwc7y1+i3TGVdlM5flvQpOipVhl2B+R7DFGOO5qkPTMWB1/jU8Js7OmzOD2P3vNVOudI50LO1dnWals931PERERJ5WJQPTMbZDWVytxt0Mlu4N5rc95xNvWLwF1De/yMTpjTCnDcRGmlIfVPiAgukLGmLXY67zzup3iIqLuqvxi4iIiDytVMgUEREREUlCeGw4Pf/qyeUo43lYmb0z823Nb3FLuOWcLQ4WdoXrCVZd+GWDFlPA1bi13U12u4N3F+xm3ZEQU65brbwseKMyOQK872kuIiIiT7MaBTLy8fNFTPGPluzj4o0kCoyVe0C1d8zxE3/DvI4QZ9ye1tPVk29rfUsatzSG+MHLB/l006eJrwAVERERERMVMkVEREREEmGz2+j/d3+OXDliiHu4eDCizggCvALMjVYNgpPrjDEXd2g1A3wyOr2Pw+Hgs98O8Ovuc6Zc91p56d+gEK4u+uouIiJyrzpUzEnVfMa/v69GxDJgcRJbzAI8+wlU7GaOH/0TFnQFW6whnNMvJ59X/dx0+W/Hf2PGgRl3NXYRERGRp5GehoiIiIiIJOL7Hd+z9t+1pvgX1b6gaEBRc4N9i2HjKHO84deQvazTe9jsDoYuO8TUjSdNudblAulXv6C5kYiIiNwVq9XCVy+VwMfD1RBfefACP+1M4gxLiwUaDIFyL5tzh3+Hpf1M4bo56/JyMfP13wV9x5bzW1I8dhEREZGnkQqZIiIiIiJO/HTkJ6bun2qKdy/ZnQa5GpgbnA2Cn52s1CjdAcp2cXqPa5GxvDptG+P+Pm7K1S2SmS+bF8NisThpKSIiIncre3pvBjYubIp/8st+zl8zn3l5i8UCjb6FUu3NuaApsH2yKdy7dG+qPlPVELM5bPRd25ezYUkUTkVEREQEUCFTRERERMRk+cnlfLb5M1O8Ya6GvFnyTXODa2dhTjuIS3C+VtZS8Q88nRQjj168QbMxG1h9+JIpVyGXP6PaltZ2siIiIg9I6/KB1Cxg3PL9RlQcTUdv4Nfd5xLfZtZqhRdGQbEW5tzS9+DUJkPIxerCVzW+ItA30BC/Gn2Vt/56i2vR1+5pHiIiIiJPOj0ZERERERG5zayDs3hv7XvE2eMM8WIBxfis6mfmFZIx4TCnDYQFG+PeGaD1DHDzNN3jj/3BNBuzkRMh4aZc8WxpmdC5HJ5uLvc8FxEREXHOYrEw9KXi+Hoat5i9eCOaXnN20mnyVqd/TwNgdYHmYyGncaUl9liY3yn+BafbpPVIy4jaI/By9TLED185TKdlnTgXZj4jW0RERETiqZApIiIiIgI4HA6+D/qeoVuH4sC4CiOzd2ZG1hmJp2uCoqTdDotfh+A9xriLO7SZDelymO7zy+5zvDEziLDoOFPuhZLPMP+NyqT1crvn+YiIiEjSsqb14tMXnJx5Daw7EkL97/9mzOqjzldnurhBy2ngl90YD78I89pDrHGL2vzp8/NltS9N3Ry/dpz2S9tzMPTgXc9DRERE5EmmQqaIiIiIPPVi7bEM3DCQSfsmmXIBngH88NwPZPTOaG7412dw6DdzvOkYyFHRFN54NIR35+8i4fNQqwUGNCrEiDal8HLXSkwREZGH5cUy2fm4SRHcXc2PyGLi7AxbcZivlh923tgnI7SZBQlWWnJuJ/z6Ngn/wq+bsy7dSprP0w6JDKHL8i5sOLvhbqchIiIi8sRSIVNEREREnmp2h53+f/fnl2O/mHI5/XIys9FMCqQvYG64ZwGsH26O1+gHJVqZwgfPX+eNGUHE2owPNdN6uTG1awVer5HXvG2tiIiIPHAvV8vNH2/XoEYBJy8tAWPXHmPqhhPOGz9TKv7MzIT2zIXtk03hbiW78W7Zd03xiLgIeqzqwe/Hf0/J0EVERESeeCpkioiIiMhTbcq+Kfx56k9TvFhAMaY3nE523+zmRud2wS89zfEiTaHWAFP47NVIukzZyo0E28lm8vVgSY+qiT44FRERkYcjV4Y0TOtanjHtypDZz8OU//S3A/y+57zzxiVaQpVe5vjy9+HsDkPIYrHQpVgXvq7xNW5W41byNoeNgRsGsuOCsY2IiIjI00yFTBERERF5am0L3sbInSNN8WrZqjGp/iT8Pf3NjcIuwbwOEBdljGctBc3GgtX4FftqRAydJ2/lwvVoQ9zHw5WpXSuQK0Oae52GiIiI3AcWi4XGJbKysk9NSgamM+QcDnhn3i42HQt13vi5TyFPbWPMFgMLOkPkFdPlDXM3ZFzdcfi6+RricfY43lnzDufCzt3LVERERESeGCpkioiIiMhT6VLEJfqt7YfdYTfEawfWZmSdkXi7eZsb2WJhQRe4dsYYT5MR2swGd2ObmDg7r88I4ujFMEPczcXC+I5lKfKM3/2YioiIiNxHvp5uTO5cjtwJXjaKsdl5fcZ2DgVfNzeyusBLE8H3GWP86mn4ubvpvEyA8lnKM73hdDJ5ZTLEL0ddpvdfvYmIjbjnuYiIiIg87lTIfIhsNhv79u1j6tSp9OrVi8qVK+Pt7Y3FYsFisTBo0KAU97l8+XJat25Nzpw58fT0JFOmTFStWpXhw4cTHh5+/ychIiIi8gSIs8fR7+9+hEYZV1Xk9MvJ4GqDTVu93bLiQzi13hizukGrGZA2m+nywUsPsvXEZVP8m5YlqZIvw12PX0RERB6sAB8PpnWtQAYf4zazN6Li6DhpKydCnDxzSZMBWk4Fq6sxfngpbDTvAAGQL30+Rj07Ck8XT2OTK4cZuGGg6YUrERERkaeNCpkPUatWrShevDhdu3Zl9OjRbN68mcjIyLvqKzo6mrZt29KwYUPmz5/P6dOniY6O5tKlS2zcuJE+ffpQsmRJ9uzZc59nISIiIvL4G7lzJEEXggwxTxdPvqv1HT7uPs4b7ZgBW8eZ442+hpyVTeGfdv7L1I0nTfEPGxWmaSlz0VNERERSlxwB3kztWp407i6G+KUb0bSbsJkzl52smMxREep+Zo6v/BRObnB6nyIBRfi82uem+J+n/mTcHiffPURERESeIipkPkQ2m83w7/7+/uTPn/+u+urcuTNz584FICAggA8++IDZs2czcuRIKlSoAMCxY8do0KABZ86cSaorERERkafKmjNrmLJviik+sNJACqQv4LzRqY3w2zvmeNkuUO5lU/jAuet8sHivKd6hUg5erZ47hSMWERGRR6VYtrSM7VgWNxeLIX7+WhTtJm7m3FUnL6hX6g6FnzfGHLb/b09/1ul9GuRqwBsl3jDFf9j1A6tOr7rb4YuIiIg89lTIfIgqVKjA+++/z4IFCzh+/DihoaEMGDAgxf0sWbKEefPmAZAjRw527NjB4MGDadu2Lb169WLTpk107doVgPPnz9OnT5/7Og8RERGRx1VIZAgfb/jYFH8p/0s0zdfUeaPLJ2Bue7DHGuOBFaHhMNPl1yJieXNmEFGxxq3gyuRIx8dNimKxWExtREREJPWqnj8j37cujTXBX+FnLkfSfuIWLl6PMiYsFmg6BtIneHkp/CLM7wixCa7/v+6lulMnsI4pPnD9QM5c10vqIiIi8nRSIfMhGjBgAEOGDKFFixbkzn33b+Lffpbmjz/+SI4cOQx5q9XKmDFjbsUXLlzIvn377vp+IiIiIk8Ch8PBRxs+4kr0FUO8sH9hPqj4gfNGUddgThuITHDOpe8z8ediurobwna7g7fn7eR0gq3mMvi480P7sri76uu3iIjI46hxiax826okCd9HOhESTruJWwgNizYmPNNCq+ngajz7krNB8Pu74HCY7mG1WBlSfQj50xt37wqLDaPP2j5ExTkvgIqIiIg8yfQk5TFz5MgRdu3aBUD+/Plp1KiR0+u8vLx47bXXbv37/PnzH8bwRERERFKtuYfnsv7sekPMy9WLb2p+g4eLh7mBLQ4WvgyXDhnjbt7Qbi74ZjY1Gb7yH1YfvmSIuVgtjG5XhixpPU3Xi4iIyOOjeensDH2xuCl+9GIY3WbtINZm3I2BrCXghVHmjnbNhG0Tnd7D282bEbVH4Ovma4gfunyIoVuH3vXYRURERB5XKmQ+ZlasWHHr1/Xr10/y2gYNGtz69fLlyx/YmERERERSu+NXj/Pt9m9N8Q8qfEAOvxxOWgB/DISjK83x5uMga0lT+OedZxn111HzPRoWolKegBSPWURERFKf1uVz8FnToqb41hOX+fy3A+YGJVpBpR7m+PL34eQGp/cI9A3ky2pfmuKLjixiydElKR6ziIiIyOPM9VEPQFLm9i1iy5Ytm+S1pUqVwsXFBZvNxoEDB3A4HHd1JlNQUFCS+YMHD976dVxcHLGxsUlcLSIPWmxsLHFxcbd+LSKPnj6Xj1asLZb+f/cn2mbc8q129to0ztnY/P+Jw4511SBctvxo6stWayD2/A0hQZudp6/y3qI9pusbF8tCp4rZ9f97KqPPpEjqos+kPG7alstGRHQsQ5f/Y4hP33SKQpnT0LJsdmOD2h/hErwH68l1/8XscTjmdyKu3SLIbC6MVstajc6FOzPt4DRD/IvNX5A/bX7yp8tvanM/6XMpkrroMymSuugzaXbzv8eDoELmY+aff/77kpwrV64kr3V1dSVbtmycPn2a8PBwzp49S/bs2ZNs40y5cuWSfe21a9cIDQ1N8T1E5P6Ji4vjxo0bt/7d1VV/1Is8avpcPlrjD4/n0BXj9rD+Hv70yN+Dy5cTnH1piyHtmgF4HfnV1E9k/he4VrADJPiuc/56NG/MPURMnHE7uQIZvXi3RhbzPeSR02dSJHXRZ1IeR80K+XL4bAZ+2htiiH/y60Eyutso/oyPIW6p+TUZQlrgEnb2v1hECC7TG3O13mhislc23aNt9rbsCN7B3it7b8WibFG8vfptvq3wLRk9M97nWf1Hn0uR1EWfSZHURZ9Js6tXrz6wvrW17GPm9t8MGTJkuOP1AQH/bWP2IH8jiYiIiKQ2cfY4Rh8czYKTC0y5vsX6ktY9rSFmiQkj/bI3nRYxYzKX5lrNLyDB7hbhMTb6LjnKlQjjm4cB3q5880I+vN1d7sNMREREJDXqUyuQEs+kMcRibQ4++P04l8JiDHGHlz9XGozB4Wo8M9saE0b6pa/h+Y95y1gXqwsflvyQ9O7pDfHzked5d+u7XIy8eJ9mIiIiIpJ6qUz8mAkLC7v1a09PzySujOfl5XXr17e/IZAS27dvTzJ/8OBBOnbsCEDatGkNxVMRefhu387A398fNze3RzgaEQF9Lh+Fq9FXGbB+ANsubDPl2hZoS4OCDYzBsIu4/twVS7B5e1h7tnJYWs0mwNvfELfZHXwweyfHQqMMcQ9XK+M7lqVwdmOhVFIPfSZFUhd9JuVxNraDH83HbubC9f+2sA8Jj2Xg8tPMeqU8Hq63rSEIqIat6ThcfnoVi/2/3/cWeyzp/noPm/069ipvGV6cCiCAodWH0u2vbtgd/+3+cD7yPP2C+jH+2fE84/PMfZ+XPpciqYs+kyKpiz6TZunSpXtgfauQKXd0p7M4b+fq6qoPrUgqcHM7Azc3N30mRVIJfS4fnqNXjtLrr178G/avKZc/fX76lO+Dm+tt/x/EhMOclnBxv7mzAg2wtpiC1d3blBq+/BCrD4eY4t+2KknZ3HfeOUMeLX0mRVIXfSblcfWMvxsTOpWjxdhNhm3md/97jWF/HOHTpsWMDYo3gzTpYF5HiL5uSLms+QKXmGtQ7wtDvEr2KnxY8UM+3/y5IX4u/ByvrXqNSfUnEegbeB9nFU+fS5HURZ9JkdRFn0mjB7m9rraWfcz4+Px3xkJUVFQSV8aLjIy89WtfX98HMiYRERGR1GLr+a10WNbBaRGzdKbSTKg7Ac/bt3RzOOC3d5wXMUt3hNazwEkR87c95/hhzTFTvE/dAjQpcf9XRYiIiEjqVSJ7OoY0L26KT9t0il93nzM3yFMLui4DXyffGTaOgqBppnCrgq34uPLHpvj58PN0Xd6VM9fP3M3QRURERFI9FTIfM7cvzw0JMa8ASCg0NNRpWxEREZEnzdErR3lr9VuEx4abci/mf5FJ9SYR4JVgC/ygKbBnnrmzGu/BC6PAxfxG4YFz1+m3wLwF7Qsln6FXnXx3PX4RERF5fL1UNjtdquQyxd9ftIdjl8LMDbIUg1dXQqYi5tzv78KpTaZwywIt+azKZ1gwntl9IeICr//5OiGRd35OJCIiIvK4USHzMVOgQIFbvz558mSS18bFxXH27FkA0qRJQ7Zs2R7k0EREREQemZDIEHqs6kFYrPFBoYvFhfcrvM+gyoNwc0mw1cu5nbCsv7mz6n2hzoeG86luuhIew+szthMZazPEiz7jx1cvlcDipI2IiIg8HQY0KkzpHOkMsfAYG91n7iAyxmZukDZb/MrM7OWNcXsszOsAV0+bmjTP35zPq35uKmb+G/Yv3VZ2IyzGSdFURERE5DGmQuZjplix/85WCAoKSvLaXbt2YbPFf1EuUqSIHqyJiIjIEykqLoq3/nqLc+HGrdvSuKXhx+d+pH3h9ubvQZFXYH4nsMUY43lqQ+0BTu8TZ7PTY/YO/r0SaYgHpHFnfKdyeLm73PNcRERE5PHl7mpldLsypPM2vjx1+MINPvx5Lw6Hw9zIKx20mQ1+CV4+jwiBOe3iz/JOoGm+pgyuPhirxfhY79DlQ7y9+m1iEn6/EREREXmMqZD5mKlfv/6tX69YsSLJa5cvX37r1w0aNHhgYxIRERF5VOwOOwPWD2BPiHGrVxeLC9/V/I7Kz1R20sgOP71pXuXg+wy8NBGszguSXy49yMZjoYaYi9XCmPZlyJbO657mISIiIk+GbOm8GN66lCm+eMdZ5m5L5BxLn0zxxUzXBN8nLuyN/85it5uaNMnThA8rfmiKbwnewoD1A7A7zG1EREREHkcqZD5m8ufPT+nSpQE4cuQIy5Ytc3pdVFQUEyZMuPXvrVq1eijjExEREXmYRu0cxZ+n/jTFB1QcQJVsVZw32jQa/llujFldodU0SJPBaZPZW04zZcNJU/zjJkWolCfA3EBERESeWrULZnJ6bvYnS/az4/QV542eKQXNfjDHD/4C679z2qRVwVZ0L9ndFF9xcgVDtw51vgJURERE5DGjQuZj6JNPPrn1627dunH6tHE1gd1up0ePHrfiLVq0MGxJKyIiIvIkmHFgBhP3TjTFOxfpTKuCibzEdX4PrPrMHK/3BQRWcNpk47EQPl6yzxRvWTY7nSrnTNGYRURE5Onw9nMFqJLX+LJTjM1Ot5lBXLwe5bxRsRehxnvm+Oov4cTfTpu8WfJNWhUwf++Zc2gOMw7MSPG4RURERFIb10c9gKfJiRMnmDRpkiG2Z89/26D99ddfxMXFGfIvvfTSrRWYNzVt2pTWrVszb948Tp06RZkyZXjjjTcoXrw4oaGhTJ8+na1btwKQNWtWvvvO+Zt7IiIiIo+rhf8s5OttX5vidQLr8E7Zd5w3io2ERa+CPdYYL9IUKr7ptMmJkHC6zdxBnN24oqFMjnR83qyYziAXERERp1ysFka0Kc3zo9YTfFvh8sL1aLrN2sGc1yrh7upkfUGtD+DiATj0238xhx0WvgxvrAO/rIbLLRYLAyoO4HLUZVaeXmnIfbP9GwJ9A6mdo/Z9nZuIiIjIw6RC5kN06tQpvvzyy0Tz69atY926dYZYvnz5TIVMgGnTpmGxWJg7dy6hoaEMHjzYdE3evHlZvHgxgYGB9z54ERERkVTit+O/8dkm86rKIgFFGFJ9CC6JnHHJn59AyGFjLG0gvDAKnBQkr0XE8sq0bVyLNBY+s6XzYlzHcni6JXIfERERESCjrwdjO5al1bhNxMT9d2Zl0KkrDPp1P4ObFzc3slqh2Y8w4RCEHv0vHn4pvpjZ+VdwMT7Oc7G6MLTGUN788022X9h+K+7AQf91/ZnaYCpFAorc9/mJiIiIPAzaWvYx5eHhwZw5c1i2bBktW7YkMDAQDw8PMmTIQOXKlfnuu+/YvXs3JUqUeNRDFREREblvVp1axcD1A3FgXCGZyy8XPzz7A95u3s4bHlkJW8clCFqg+TjwTGu6PDrORs85Ozh+KdwQ93Z3YWLncmT09biXaYiIiMhTolRgOr5oZj7uZ/aW08zZetpJC8DTD1pNB1cvY/z0RvjLyRb5gIeLB9/X/p5cfrkM8ci4SHqt6kVwePDdDF9ERETkkdOKzIeoVq1a9/2g9QYNGtCgQYP72qeIiIhIarTx7Eb6/t0Xm8NmiGfzycbEehMJ8Apw3jA8BJZ0N8ervQO5qprCN6JieWNGEBuPhRriFguMbFOawln97noOIiIi8vRpVS6Q/WevMW3TKUP84yX78HZ3oWmpbOZGmYtCk+Hwc4Lt7zeMgMCKUKixqUlaj7SMeXYM7Za241r0tVvxi5EX6fVXL6Y1mJb4S18iIiIiqZRWZIqIiIhIqhccHkzfv/sSZzeeJ57JOxMT600kc5rMzhvGRcPP3SHsgjGetWT8GVQJXLweRetxm01FTIAPGhbiuSKJ3EdEREQkCQObFKFCbn9DLNbm4K25uxi39pjzF99LtYUync3xxa/DqU1O75PDLwcjao/A1Wpcu3Do8iE+WPfBfX/BXkRERORBUyFTRERERFI1u8POwA0DuRFzwxD39/RnYr2JZPfN7rxh2EWY9jwcWWGMu3rBixPB1d0QPn4pjBd/3MiB89dNXbUql53Xque5p3mIiIjI08vNxcoP7cuQNa2nKTdk2SEG/bIfm91JkbHh15AlwbFBMWEw8yU4ud7pvcpmLstnVcxb0P515i+m7Z92V+MXEREReVRUyBQRERGRVG3OoTlsOb/FEPNx82F83fHkTpvbeaNzu2B8LTizxZyr9zlkLGAI7T5zlRZjN/HvlUjT5e0q5mDIiyWwWCx3OQMRERERyODjwdSuFcjk5KztaZtO0W1mEFGxxi30cfOEVtPAI8GZ3rHhMLMFHF/r9F7P532eN0q8YYp/v+N7gi4E3fUcRERERB42FTJFREREJNU6fvU4w4OGm+IfVvqQgv4FnTfatxgmN4DrZ825wi9A+VcNobNXI+kyZSuXw2NMl/epW4AvmxXDxaoipoiIiNy7gll8Wdy9Cvky+Zhyfxy4wGvTtxNnsxsT/nmg3VxwS2OMx0XC7FZwdJXTe/Uo1YM6gXUMMZvDRr+1/QiJDLmneYiIiIg8LCpkioiIiEiqFGuL5f117xNtizbE6+WsR+PcjZ032joBFnaNf7CXUMU3ocUUuG1lZUycnR6zdnAlItZwqdUCQ18sTu9n82slpoiIiNxX2dN7s+jNKqYzMwHWHQnhi98PmhvlrAIdfwJ3X2M8LgrmtIVjf5maWCwWPq/2Odl9jNvwX4q8RP+/+2Oz20xtRERERFIbFTJFREREJFUau2csBy8bH+Rl9MrIR5U+cl5cPLwMlvYzx61u8PxIaPgVuLgaUoOXHmTXmauGmIerlXEdy9GmQo57nYKIiIiIU2m93Zj+cgUal8hqyk3deJK5W0+bG+WoCJ1+Bg8/Y9wWDXM7wFnzlrF+7n58V+s73K3Gs8G3Bm9lzK4x9zIFERERkYdChUwRERERSXWCLgQxce9EU/zzqp+TzjOducH53bDwFcBhjHtngM6/QNnOpia/7j7H1I0nTfFvWpakbpHMdzdwERERkWTydHNhVJvSvFQmuyn30ZJ9bD1x2dwoeznotAQ8nZyZOaslhB4zNSkcUJgPK31oik/YO4GN5zbe9fhFREREHgYVMkVEREQkVTlz4wzvrH4Hu8N4PlTrgq2pmq2qucG1szC7dfwDvNv554HXV8dvxZbA0YthvL9ojynepUouni/5zD2NX0RERCS5rFYLg18sRtmc6Q3xWJuDbjOD+PdKhLlRtjLQ6RdzMTMiFGY0hxvBpibN8zWnad6mpvhnmz4j0tmW/CIiIiKphAqZIiIiIpJqXI+5Ts9VPbkSfcUQz+WXiz5l+5gbRIfBnNZw47wx7pUe2i+EdObtYSNi4ug+K4jwGOO5UKUC0zGgUeF7noOIiIhISni4ujC2Q1mypvU0xEPDY3htehARMXHmRs+UgrZzwdXYhqunYGYLiLpmCFssFj6s9CEF0hcwxM+GneXH3T/ej2mIiIiIPBAqZIqIiIhIqhBrj6Xvmr4cv3bcEPdy9eKrGl/h7eZtbGCLg0WvQPBeY9zqBq1nQUBep/cZ9Mt+/rkQZoil93ZjTPsyuLvq67GIiIg8fBl9PZjQqRyebsbvIgfPX+eDxXtxOBzmRjmrQIvJYEnw/eXCXpjbHuKiDWEvVy+GVB+Cq8V4Zvj0/dM5dPnQfZmHiIiIyP2mJzUiIiIi8sg5HA6GbhnKpvObTLkh1YZQJKCIMWi3w5Lu8M9yc2dNx0AuJ1vQAsv3nWf+9n8NMYsFvm9TmmzpvO56/CIiIiL3qli2tHzbspQpvmTXOWZtOe28UaHG0GS4OX5yHfz6FiQogBZIX4AuxboYYjaHjUEbB2GzG3erEBEREUkNVMgUERERkUdu9qHZzP9nvin+dpm3eTbns8agwwG/94E988wd1ewPJVs7vceF61G8v3ivKd6rTn5qFsh4V+MWERERuZ8al8hKrzr5TPHPfj3Ann+vOm9UtgvU/tAc3z0H1n1jCr9R4g0CfQMNsf2h+5lzaM5djFhERETkwVIhU0REREQeqf0h+xm2bZgp3ixfM14u9rIx6HDAig8haIq5o+ItodYHTu9htzvou2A3VyNiDfFyOdPz1rP573rsIiIiIvfb288VoHr+DIZYjM1Ot5k7uBoR47xRjX5Q7hVz/K8vYN8iQ8jT1ZOPK39sunTkzpGcDztviouIiIg8SipkioiIiMgjExUXxQfrP8DmMG5lVi5zOT6u9DEWi8XYYM0Q2DzG3FGBBtD0h/h9Yp2Ytukk646EGGI+Hq4Mb10KF6vzNiIiIiKPgovVwvetS5E1rachfvZqJO/O343d7uS8TIsFGn4N+Z4z537qBme2GkKVslbihbwvGGKRcZF8uulTbTErIiIiqYoKmSIiIiLyyIzcOZIT104YYtl9sjO81nDcXNyMF28aA2u/MneSuya0nAau7k7vcTj4BkOWHTLFB71QlEB/77seu4iIiMiDEuDjweh2ZXBN8MLVqkMX+XHtMeeNXFyhxRTIlOBscVs0zGkLV04awn3L9SW9R3pDbMO5DXy2+TMcDifFUhEREZFHQIVMEREREXkktgVvY+aBmYaY1WJlSPUhpPNMZ7z43yD44yNzJ4GVoO0ccPM054Dz1yLpOXsHMXF2Q7xR8Sy8VCbbvQxfRERE5IEqmzM9AxoVNsWHrTjMjM2nnDfy9IN28yBNJmM8IgTmtIOYiFuh9J7p6Ve+n6mLxUcW8/2u71XMFBERkVRBhUwREREReejCY8P5aMNHODA+IOtStAulMpUyXhwTDotfgwTbz5K1FLSfD+5pnN4j6NQVnh+1gSMXwwzxzH4efNmsuHnbWhEREZFUpmvVXDQslsUU/+jnfUzdcMJJCyBdDmg7F1wTvOh1cT/8/m78meP/1yRPE+rnqm/qYsbBGcw5Meeexi4iIiJyP6iQKSIiIiIP3bBtwzgbdtYQy5cuHz1K9TBf/OfHcDnBFmrpc0PHn8AzrdP+Fwb9S9vxmwkJizblvm1ZivRpnG9DKyIiIpKaWCwWvm5RgjwZzC9uDfr1ABPXHXfeMHtZaD7OHN89G3bOMPQ/uNpgqjxTxXTplCNT+PX0r3c9dhEREZH7QYVMEREREXmoVp1axaIjiwwxV4srg6sNxt0lQYHxyErYNtEYs1jhxQng7W/q22Z38MVvB+i7YDcxNrsp369+Qarlz3DPcxARERF5WHw93Zj5akVyBZjP9v7i94OMS+zMzKLNoPq75vjvfeH8nlv/6u7izvBawymZsaTp0lEHR7Hy9Mq7HbqIiIjIPVMhU0REREQemrVn1tLvb/NZTG+WfJPCAQnOgIq4DEucrNCs/i4EljeFHQ4HHyzew8T15m3WPFytjGhTih6189312EVEREQelWfSeTH39cpOV2YOWXYoANx/TQABAABJREFU8ZWZtQZArurGmC0a5neCyKu3Qt5u3ox5dgwF0hcwXOrAwSebP+HolaP3OgURERGRu6JCpoiIiIg8FGvOrOHtNW8Ta481xItnKM4rxV8xXuxwwG9vQ1iwMZ61FNTs77T/H9YcY/72f03xzH4ezH+jMk1LZbv7wYuIiIg8YlnSejL39Urky+Rjyn3x+0F+2mn+HoSLK7SYDD4Jztm8ciL+hbHbzstM65GWcXXHkcM3h+HSyLhI3lnzDjdibtyXeYiIiIikhAqZIiIiIvLArT69mnfWvEOcPc4QT+eRjsHVBuNqdTU2CJoKB5YYY66e8OJ4cHEz9f/r7nMMW3HYFC8ZmI5fe1ajZGC6e5yBiIiIyKOXyc+TOa9VomBmX1Ou34I9rP3nkrmRTyZoOQUsLsb4od9g+yRDKINXBsbVHUc6j3SG+MnrJxm4fiB2h3nrfhEREZEHSYVMEREREXmg/jr9F33W9nFaxJxYbyK50uYyNji+Fpb2NXf03KeQsaApHHTqCu8u2G2KV8+fgXmvVyKTn+e9DF9EREQkVcno68Hs1yqaVmbG2R10mxnE7jNXzY1yVoHnBpnjKz+F6+cMoey+2RlSdQjWBI8N/zrzF5P3Tb7H0YuIiIikjAqZIiIiIvLA7Lm0h75r+5qKmOk90jOx3kQK+icoTIYcgfkdIcH15KkFFV439X86NILXp28nJs64OqBAZh/GtC+Dp5uLqY2IiIjI4y7Ax4PpL1cgS4IXtiJibHSduo3jl8LMjar0goKNjbHo67DsPdOlFbNUpGv+rqb4qJ2j2Hhu4z2NXURERCQlVMgUERERkQfiStQV3l37rulMzPQe6ZlY30kRM+IyzG4FUdeM8bSB0Hw8WI1fXa+Ex/DytG2EhscY4hl8PJjcpTx+nuYtaEVERESeFM+k82L6KxXw8zRu0X85PIZOk7dy8UaUsYHFAs9/D57pjPGDv8LB30z9t87dmqqZqhpidoedfmv7cfiyeUt/ERERkQdBhUwRERERue/sDjsfrPuA4PBgQ/xmEbNA+gLGBnExMK8jXD5ujLv7QNu54JvZEL4eFUunyVs5etG42sDTzcrEzuXInt77vs1FREREJLUqkNmXyV3K4+FqfMT375VIXpseRFSszdjAJxPU+8Lc0dJ+EHXdELJYLPQr3o9cfrkM8esx13n9z9c5dvXY/ZiCiIiISJJUyBQRERGR+278nvFsOLfBEHO1ujLm2THmIibA733g1HpjzGKFFpMhSzFDOCImjpenbGPv2QQrN4HhrUpRKjDdvQ5fRERE5LFRLpc/o9uVwWoxxnefucq783djtzuMidIdIFd1Y+zGOfjrc1PfaVzT8E31b/By9TLEL0dd5pUVr3D82nFTGxEREZH7SYVMEREREbmvNp3bxA+7fjDF3yv/HsUzFjc32LcYds4wx+sPhgL1DaGoWBuvTw9i+6krpssHNCpEw+JZ73rcIiIiIo+rukUy82Vz8/es3/ee59s/E2wDa7FAk+/BxcMY3zoBzmwz9ZEnbR6+rfktrlbjFrahUaG8uuJVTl0/da/DFxEREUmUCpkiIiIict8EhwfT/+/+ODC++d8wV0PaFGxjbnAjOH41ZkLlXoGKbxpCsTY7PWfvYP3RENPlverk4/Uaee9p7CIiIiKPs7YVcvBGjTym+JjVx1iw/YwxmCEf1OiX4EoHLOkOkeYXxqpnr853Nb/D1WIsZl6KvMQrK17hzI0zpjYiIiIi94MKmSIiIiJyX9w8F/NKtPHhV560eRhUZRAWS4L9zhwO+KW3+WFZzmrQ8Kv41QK3eX/RXlYevGi67yvVctOnrpPtakVERESeMv0bFKJekcym+ICf9rLpWKgxWPUtyFjIGAv5B2a1gphwUx+1c9Tm65pf42JxMcQvRFygz5o+xNpj73n8IiIiIgmpkCkiIiIi98X8w/PZfmG7Iebl6sV3tb7D283b3GDnDDiywhhz94XmP4KLmyG8ZNdZFu3419RFu4o5GNi4sLlIKiIiIvIUslotfN+mFMWzpTXEY20Oeszewflrkf8FXd3h+RHmTv7disvCzmCLMaXq5qzL0OpDsVqMjxQPXT7E1H1T78cURERERAxUyBQRERGRe3Yu7BzDg4ab4h9X/pi86Zxs+XrlFCz/wBxvMATS5TCEzl+L5KOf95kubV46G180LaYipoiIiMhtvN1dmdi5HFnTehril8Nj6DFrB7E2+3/BHJXg2Y9NfVhPrCHdqr5gjzPlGuRuwJfVvjTFf9z9I8evHr/3CYiIiIjcRoVMEREREbknDoeDzzZ9RkRchCHeIFcDmuRpYm5gt8PP3SEmzBgv0ABKd0hwqYP3Fu7hepTxIVqlPP4Ma1ECq1VFTBEREZGEMvt5MrFzObzcjNvA7jh9laHLDhkvrtYHKvc09eF5fAV+f38cfxxAAk3yNKFlgZaGWKw9lo83fozNbrv3CYiIiIj8nwqZIiIiInJPlhxbwoZzGwyxdB7p+KCikxWXAGu/glPrjTGv9PD8SNO5mNM3nWTdkRBDzNfDlW9blcLVRV9lRURERBJT9Jm0DH2puCk+af0Jlu09/1/AYoF6X0DpjqZrvQ8twvr3V07771O2D5m9jedx7r60m7mH597bwEVERERuo6c/IiIiInLXLkVc4uttX5vi71d4H39Pf3ODtV/D2qHmeOPvwNf4IOzoxRsMSbhiAPisWVGypfO66zGLiIiIPC2alspGh0o5TPF+C/dwIiT8v4DFEn9eZpGmpmtd1n8Dh5eb4j7uPnxc2bwt7YgdIzgbdvbeBi4iIiLyfypkioiIiMhdcTgcfLnlS27E3DDEa2avSaPcjcwN1gyF1ebzlCj2EhR70RCKtdl5Z95uouPshnjj4llpVirbPY9dRERE5GnxUZMilMie1hALi46j28wgImNu2wbW6gIvToC8z5o7Wfw6hB4zhWtkr0HjPI0Nsci4SD7d+CkOJ1vSioiIiKSUCpkiIiIicldmH5rNqtOrDDEfNx8+qvQRlgRbxLJ6CKwZYu4kc7H41ZgJfPn7QfaevWaIZfL14Itmxcx9i4iIiEiiPFxdGNOuDGm93AzxQ8E3+PaPw8aLXT2g9QwcGQsb49HXYF5HiDGeiQ7Qv3x/0nukN8Q2nd/ElP1T7sv4RURE5OmmQqaIiIiIpNhPR35i6FbzFrF9y/UlcxrjFrGsHuJ8O9ksxaHzr+CVzhCevukkUzeeNF3+dYsSpE/jfg+jFhEREXk6Bfp7M7x1SVN88oYT7Evw8hjuaYhrMRW7u48xfnE//PoWJFhpmd4zvdOz0b8P+p5Vp1aZ4iIiIiIpoUKmiIiIiKTI8hPLGbRpkCleMWtFXsxv3CKW3fMSKWKWgE6/gLfxHM01hy8y6Jf9pss7VspJrYKZ7mXYIiIiIk+1OoUy82bNvIaY3QHvL95DnM24nT/+eblWZ5i5k73zYet4U7hBrgbUCaxjiDlw8P6699kfav5uJyIiIpJcKmSKiIiISLKtObOGD9Z9gN1hfNiVyy8XQ6sPNW77euEA/Pa2uZMsJaDTElMR83DwDXrO3ok9wXFK5XOlZ2CTBNubiYiIiEiKvf1cfnJnSGOI7Tt73eluGNG56hBWppu5k+UfwP6fDSGLxcLn1T4nT9o8hniULYpeq3oRHB58r0MXERGRp5QKmSIiIiKSLJvPb+bdNe8S54gzxJ9J8wwT6k0gg1eG/4LRN2B+J4hNcI5SIkXMkLBoXpm2jbBoY985/L0Z17EcHq4u93UuIiIiIk8jTzcXBjcvbop/+8c/nLlsPv8yrFwv7HmMKy1x2GDRK3Dod0PYz92P0c+ONp2XeSnyEr3+6kVEwu+FIiIiIsmgQqaIiIiI3NGui7vo/VdvYuwxhnhGr4xMrDeRLGmy/Bd0OOCXXhB6xNiJdwZoN89UxIyz2XlzRhD/Xok0xH09XZncpTz+OhdTRERE5L6pnDeAVuWyG2KRsTYG/rwPR4LzL7G6YGs2DtLlNMbtcTC/MxxebggH+gYyos4I3Kxuhvihy4cYsH6AuX8RERGRO1AhU0RERESSdCD0AN1XdicyzlhoTOeRjgn1JhDoF2hssHUC7P8pQS8WaDEJ/J4x9f/jmmNsP3XFEHOxWvixfVnyZfK5H1MQERERkdsMaFSYgAQvi6395xK/7jlvvtgrPXRYDD6ZjXF7LMzvCEdWGsKlM5Xms6qfmbpZdXoVcw/Pveexi4iIyNNFhUwRERERSdTRK0d54883uBF743/s3XVwVNf7BvDn7m5ciUAghCDBIWhwp8XdCpQWKkCLFivu7lLaUqEClAItDi0Ud09wCBoc4kJ05f7+yO+b5OxJNZuQhOcz05nm2feefbczmSZ57z1HyB2tHPHVm1+hlGsp8YLH54G9E+SFmk4ESjaR4qtPYrD8wG0pn9GxIhqU9pByIiIiIso6V3trTGlfQcqnbL+K2y/i5As8/IC+OwEHTzE3pgAbegP3jwpxu5LtMNB/oLTMonOLcDtK/tmPiIiI6M9wkElEREREmXoU+wgD9g1AdHK0kNvp7PDlG1+igrvZH79S4lPPSzLpxdzvTaDhKGn9ZIMRozZdgsEkbjHWM8AHb9f2leqJiIiIyHI6VCmCxmXEwWR0gh59Vp/Bo6hMzrP0LAu8uwOwdxdzYzKw+UMgUdxhY3DVwWji00TIUkwp+PTop0gyJFniIxAREdFrgINMIiIiIpKEJoTiwz8+RFhimJBba6yxotkKVC1YVb7o4CwgKkTMXHyALl8DGvnHziX7biHY7I5/Hzc7TGonPx1ARERERJalKApmdaoEe2utkL+ITUa/Hy4gPF4vX1SoAvDu9tTtZjN6+QLYO0laf0a9GShoV1DI70TfwaLziyzyGYiIiCj/4yCTiIiIiCSzTs/C0/inQqZTdFjcZDHqFK4jX/DoLHD6SzHT6IDuPwL2blL5+ZBIfH30npApCrCoWxU42uiy3D8RERER/T0fN3us7F0NOo0i5A8jEzF8623EJBnki7wqA+9sA3R2Yn5xHXDngBAVsC2AOQ3nQIG4/sbgjTj08JAlPgIRERHlcxxkEhEREZEgKDQIhx6Jf1hSoGBuw7nS9mAAAEMysH0IAHGLWNT/BChaQyqPTzZg1C+XoJqVf1C/BGqXdJfqiYiIiCj7NCtXCIt7VIEizhpxNzwRo7bdQXxyJsPMIlWBZhPlfOcnQPJLIapduDber/S+VDrl5BS8iH/x3xsnIiKi1wIHmURERESURlVVLLuwTMrH1x6PViVaZX7RkQVAeLCYeZQFGn8qlaYYTBix8SIeRIjnLvkVdMTolmX/a9tERERElAUdq3pjZsdKUn71eTzGbrkK1fwONACo/TFQpLqYxTwEDsyQSgdXG4xK7uL60cnRmHh8IowmY5Z6JyIiovyNg0wiIiIiSnP08VEEhgYKWXm38nir7FuZX/DsEnB8qVmoAB0/B3Q2QppiMGHw+kD8cV28816rUbCkRxXYWonnMxERERFRzulTxxdjMrmxbO/1UHxldiQAAECrS/2ZT2Ml5me/Bh6eFiIrjRUWNFoAe529kJ95fgY/XPshq60TERFRPsZBJhEREREBAIwmI5YFLpPy4dWHQ6Nk8mOjUQ9sHwyoZnfR1xkE+AQI0f+GmPuuy9uHDW7qB/+irlnonIiIiIgsYVCTUhjYqKSUL9hzEyfuhMsXFKoANBxlFqqpxw7ok4TUx9kHk+pMkpZYGbQSV8KuZKVtIiIiysc4yCQiIiIiAMDu+7txJ/qOkNXyqoV6RerJxSYTsGMY8Nzsj04FigPNxD9QJRuMGPTThUyHmO38C2NYM7+stk5EREREFqAoCsa2Kofm5TyF3KQCQ38OwtPoRPmihqMAz/JiFnEb2DEE5oeity/VHm1LthUyg2rA2GNjEa+Pt8hnICIiovyFg0wiIiIiQooxBZ8HfS7ln1T/BIqiiKGqAr+PAS6tlxfq8Blgnb5lmMFowuCfgrD/RqhcWqUIlr1VFTotfyQlIiIiyi00GgULu1aCj6t4TEBkfAo+/ikQyQaz3Th01qlbzJrv4HHll9Sz1M1Mqj0JRR2LCtmjuEeYc2aORfonIiKi/IV/NSIiIiIibAzeiKfxT4XsTd83UdmzslioqsD+qcC5b+VFan4AlGgkREv23cL+G/KTmB2rFsGSHlU4xCQiIiLKhZxsrTCvXSnY6sSf1S49isa0Hdegmj1piaI1gHrD5IUOzwGu/CpEjtaOmN9oPrSKeD76jrs7sPvebov0T0RERPkH/3JERERE9Jq7HXUbX13+Ssi0ihZDqw2Vi48uAk4sl/PSLYBW84TowI0X+OLwXam0U9UiWNydQ0wiIiKi3KyUhx0mvukr5T+ffYSFe4PlYWbzKUDplvJC2wYBj84Kkb+nPwZXHSyVzjg1A7ejbmepbyIiIspf+NcjIiIiotfYiScn8M7v7yAmOUbIO/l1QgmXEmLxqS+AQ7PkRYo3BHqsSd1W7P89ikzAiI0XpdL2VYpgcQ9uJ0tERESUF7xZ1g3v1ZOHmV8cvoul+80Gjhot0G01UKiSmBuTgZ97AVEhQvx+pfcR4BUgZAmGBAw5MAThieGWaJ+IiIjyAf4FiYiIiOg1tSl4EwYfGIx4fbyQ22pt8XGVj8XiCz8Ae8fLi3jXBHr9DFjZpUVJeiM+/ukCYpMMQmk5Lycs7OYPrUYxX4WIiIiIcqkxLUqjbkl3KV9x4DY+O2A2zLRxAnptABwKinlCOPBzb0CfmBZpNVrMbTAXLjYuQunT+KcYfnA4kgxJFvsMRERElHdxkElERET0mjGajFh0bhFmnp4Jo2oUXtMpOsysPxOFHAqlh5d/AXZ+Ii9UqDLQ59fUP1hlMHPXdVx9EitkTjY6fNmnBmytxLOQiIiIiCh3s9Jq8PW7NVDVx1V6bfG+W/jS/CgBV5/UYabOVsxDrwH7pghRIYdCWNR4EXSKTsgvh1/G5BOTYVJNlvgIRERElIdxkElERET0mpl3dh5+vP6jlDtZOeHLN79EqxKt0sObu4GtAwGYnYHkUQZ4ZytgV0CItwU9wU9nHkprL+zujxIeDpZon4iIiIhymJOtFX58vxb8i7pIr83fcxNrTz8Qw6I1gM5fSbU4+zUQ/LsQ1SlcBxPrTJRK94TswRcXv8hS30RERJT3cZBJRERE9BrZE7IHG4I3SLm3ozfWtlmLOoXrpId3DwK/9APMntqEqy/w7nbA0VOIn8UkYvK2q9LaHzYogVaVCluifSIiIiJ6RVzsrLDm/VqoUNhZem3Wruu4F/ZSDCt2AuoPlxfaNgiIfSZE3cp0Q98KfaXSry5/hV33dmWlbSIiIsrjOMgkIiIiek08inuE6SenS7m/pz9+avMTSrmWSg+fBKaeY2RMEYudCgN9dwDORYRYVVVM2noVccniuZg1fQtgbOtyFvsMRERERPTquNpbY92HtVHOSzxaINlgwrgtV2Ayme3i0XQSUKSamCVGAts+AkzitrEjaoxAE58m0ntOOzkN1yKuWaJ9IiIiyoM4yCQiIiJ6DeiNeow5MgYv9eKd8lU8q2B1i9Vwt3NPD00mYOdwwJAoLmLvnvokZoHi0vo7Lj3FgZuhQuZiZ4WVvavDSssfOYmIiIjyCzeH1GFmQScbIT97PxI/nTU7YkBnDXRdDViZHTFw7zBwcoUQaTVazG84H+XcxJvgko3J+OTQJ4hIjLDURyAiIqI8hH9VIiIiInoNLAtcJt3J7mTthAWNFsBWZysWX9sCPL8sZrYuwDvbAM+y0trhL5MxbYd8l/zkdhXg5WIr5URERESUt3k42mBWp0pSPu+3G3gSbXYznHspoO0ieZGDM1N3AcnA3soenzX7DB52HkL+PP45Rh8ZDb1Jn+XeiYiIKG/hIJOIiIgonzv6+CjWXF8j5TPrz0QRR3GLWBhSgIOz5EV6rAUK+2e6/tQd1xCVIP5RqXEZT3St7v2feyYiIiKi3K1FRS+08xfPQY9PMWLi1itQVbMtZqv0Aip1EzOTAdj8AZAcJ8ReDl5Y0mQJdBqdkJ9/cR6Lzy+2WP9ERESUN3CQSURERJSPvYh/gYnHJ0p5r3K90LxYc/mCwB+BqPtiVqY1ULJxpuvvvfYcuy8/EzIHay3mdKkMRVH+c99ERERElPtN61ARrvZWQnY4OAxbg56IhYoCtFsCuBYT88h7wG+fSutWK1gN42uNl/KfbvyEbXe2ZbVtIiIiykM4yCQiIiLKp1RVxdRTUxGdHC3k5d3KY1TNUfIFyS+BIwvMQgVoPiXT9aPiUzBp21UpH9emPLxd7f5j10RERESUV3g42mBq+wpSPmPXdbyITRJDWxeg63eAohXzS+uBK79Ka/Qo2wPdynST8pmnZuJm5M0s9U1ERER5BweZRERERPnU5tubceLJCSGz19ljYeOFsNHayBec/hKIDxWzKr2AQvIfp+KS9Oj3/VmExSULea0Sbni7VjGpnoiIiIjyp05VvdG0rKeQRSfo0X/NeSSmGMVinwCgqfykJXaNAKJCpHh8rfGo4llFyFJMKRh/bDySDElSPREREeU/HGQSERER5UNPXj7BwnMLpXx87fHwdfaVL4iPAE4sFzOtdaZ/aEpMMeKDH8/j0uMYIbfRaTC/qz80Gm4pS0RERPS6UBQFsztXhqONeKbl5ccxGLHxIkwms/MyG4wEfBuIWXIssLk/YDQIsbXWGkubLIWnnTgovRN9B8sDzX52JSIionyJg0wiIiKifMakmjD5xGQkGBKEvIlPE3Qs1THzi44tBlLixCygv3SOUbLBiIHrLuDs/UhpiQltyqOEh0OWeiciIiKivKeIqx1mdKwo5XuuPcfCP4LFUKMFunwF2LqK+eOzwJF50hqe9p6Y32g+FIg3y627sQ4nn5zMautERESUy3GQSURERJTP/HzzZ5x7fk7IXG1cMbXuVChKJk9LXt0MnP1azGycgYbiOZoGownDfg7C0Vth0hKDmpRC33rFs9o6EREREeVRXaoXxcdNSkn5l4fvYtP5R2LoUhTouFJe5Ogi4MYuKQ7wCsB7ld6T8kknJiE6Kfq/tkxERER5AAeZRERERPlISEwIll1YJuWT6kyCh52HGKoqcHQh8Ov7gEkvvlZvGODgnvalyaRizK+XsffaC2ntfvWKY0zLspZon4iIiIjysDEtyqJ1JS8pn7j1Ck7fixDD8u2BGubDSRXY0h94elFaY0jVISjnVk7IwhLDMP3UdKiqKtUTERFR/sBBJhEREVE+kWRIwvhj45FkTBLy1sVbo2XxlmKxIQXYNgg4OEteyKkIUOfjtC9VVcWk7VexNeiJVNq9RlFMaVch8yc9iYiIiOi1otEoWNKjKvyLugi53qhi0E+BCI0Vf05FyzmAZ3kx0ycAP/cEYp8KsZXWCvMazoON1kbI9z/cj613tlrsMxAREVHuwkEmERERUT5gUk2YdGISrkZcFXIPOw9MqD1BLE6MBtZ1AS6tlxdyKAj03gDYOAJIHWLO3n0D6888lErb+RfGvK7+0Gg4xCQiIiKiVHbWWnz7bk0UdrEV8sj4FIzcdAkmU4anJ63tgV7rATs3cZG4Z8D6t4Dkl0JcyrUURtYYKb3njFMz8EfIHxb7DERERJR7cJBJRERElA98fvFz7A3ZK+VT606Fq61remAyAhv7ACHH5EUKVgD6HwQKV0mLlu2/jW+P35dKm5criKVvVYWWQ0wiIiIiMlPQ2Rar+wbA3lor5MfvhOPb4/fEYreSQM/1gNZazJ9fBrYMSP35NYNe5Xqhvnd9ITOqRnx69NNMfx4mIiKivI2DTCIiIqI8bufdnfj68tdS3rdCXzTxaSKGRxdmPsQs1Rx4fy/g6pMWfX30LpYfuC2V1ivljs/frg4rLX+UJCIiIqLMVSjijGkdKkr5wr3BuPI4Rgx96wLtV8iLBO8GDkwXIkVRMLPeTHjaeQq5UTVi7NGx2HN/T5Z7JyIiotyDf30iIiIiysMCXwRi6smpUt7EpwlG1BghhiHHgSPz5UVqvg/03gTYOqdFuy8/w5zfbkqlNXwL4Jt3a8LWSiu9RkRERESUUfcaRdHOv7CQ6Y0qhm0IQnyyQSyu2gtoOFpe5MRy4OpmIfK098S3Lb+Fh52HkBtVI8YdG8dhJhERUT7CQSYRERFRHvXk5RMMPzQcepNeyMu5lcP8hvOh1WQYNsaHA5s/BFSTuEiVXkDbJYBWlxYl6Y2Yueu69H4Vizjju34BcLDRSa8REREREZlTFAWzO1eGt6udkN8Pj8e0HdfkC5pOBCp0kvNtg4HnV4SopEtJfNfyu8yfzDw2Fueen8tq+0RERJQLcJBJRERElEctOrcI0cnRQlbQriA+a/YZ7K3s00OTCdj2MRD3TFzAvTTQZhGgiOdc/nAyBM9jk4TMr6Aj1rxfCy52Vpb8CERERESUz7nYWWFFL/ls9V8uPMbOS0/FYo0G6LxKOLMdAGBIBDb0BhIihbiESwmsbrlaGmaaVBPmnJkDg8nsqU8iIiLKczjIJCIiIsqDgiODsf/hfiGz09lhRfMV8HLwEotPfw7c/kPMtDZA9x8AG0chjknQ44tDd4RMp1Hw7bs14e5oY6n2iYiIiOg1UsPXDcObl5byCVuu4FFkghha2QFv/QTYi9vGIvoh8Ot7gFEcTpZwKYHvWn6HgnYFhfxO9B1su7PNEu0TERHRK8RBJhEREVEetOrSKikbX2s8KrpXFMMngcD+afICreYAXpWk+MsjdxGbJP5xqHftYiju4ZCVdomIiIjoNTe4qR9qFXcTsrhkAz7ZeBEGo9nxB64+QI8fAcXsXPZ7h4H98vnwxV2KY1nTZVK+MmglEvQJUk5ERER5BweZRERERHlMZk9jFncujg6lOoiF+iRg60eA+ZZa5TsANT+Q1n0ek4TvT9wXMntrLYY2k++eJyIiIiL6N7QaBUt7VoWzrXje+oUHUVhx8I58QfEGQKu5cn5qJXBjlxRX9qyMNiXaCFlEUgS+v/Z9lvomIiKiV4uDTCIiIqI85qvLX0nZAP8B0GrM7lg/NAsIDxYz12JAh8+kczEBYPmBW0g2iHfDf9igBDyduKUsEREREWWdt6sd5nf1l/KVB2/j7P1I+YJaA4Cqb8v5jqFA3HMpHlZ9GKw11kL2w9Uf8CL+xX/umYiIiF4tDjKJiIiI8pDgyGDse7BPyIo7F0frEq3FwoengZMrza5WgM5fA3au0rp3w15i0/nHQubmYI3+jUpaoGsiIiIiolStKxdGr1o+QmZSgU82BCEmQS8WKwrQdglQpLqYJ0YC2wcDqirE3o7eeLuCOPhMMiZh5UXzn4uJiIgor+Agk4iIiCgP+bOnMXWaDFt0pcSnbikL8Q87qDcE8K2b6bqL9gbDaBLrBzf1g5OtVVZbJiIiIiISTG5XAaU8xTPYn8YkYdyWy1DNhpOwsgW6rQasHcX8zn7g7DfS2h9W/hCuNq5Ctv3OdgRHBku1RERElPtxkElERESUR9yKuiU9jenr7Cs/jblvKhAlnnUJj7JA00nSmkaTipm7ruP3q+LWXN6uduhTp5hF+iYiIiIiysjeWocVvarBWiv+afL3q8+xMrPzMt1KAq3ny/m+yUDoTSFytnbGx1U+FjIVKhaeWwijyZjl3omIiChncZBJRERElAeYVBM+D/pcygf6DxSfxrx3GDhndme6ogU6r0q9mz2DhBQDBq69gNXHzYaeAEa+WQY2Oq2UExERERFZQsUiLhjbupyUL953CzsvPZUvqPo2UL69mBmSgC0fAoYUIe5etjt8nX2F7MzzM5hzZo78xCcRERHlahxkEhEREeVyz14+w4A/BuDgo4NCXsypmPg0ZuzT/99S1kyj0YC3eK7Qi9gk9PjqFPbfeCGVV/VxRadq3hbpnYiIiIjoz7xfvzjeKF9Qykf9cgmBD6PEUFGA9isARy8xf34l9cnMDANKK40VRtQYIa276dYmLA9cbpHeiYiIKGdwkElERESUS6mqip13d6LLji448/yM9PrAKhmexkx+Cax/C4h7JhZ5VQYajhaiO6Fx6PT5CVx9EiutWb2YK1b3rQmtRrHY5yAiIiIiyoyiKFjWsxrKeTkJeYrBhAFrzuNRZIJ4gb0b0OkLeaEzq4AD04VhZjOfZnjT902pdPXV1Vh9ZbVF+iciIqLsx0EmERERUS70MuUlRh0ZhQnHJ+Cl/qX0elXPqmhTok3qFyYjsGUA8PyyWKS1Bjp/Beis06LYJD3e/+E8nsUkSWu28y+M9f3rwN3RxqKfhYiIiIjozzja6LC6XwA8zH4GDX+Zgg9/PI+4JL14gV9zoLZ4BiYA4PhSYP+0tGGmoiiY23AuannVkkqXBS7DpuBNlvoIRERElI04yCQiIiLKZZIMSRh8YDD2PdiX6esti7fEyuYr05/G3D8VCN4tF3ZYCRSqmPalqqqYsOUKHprf2Q5gSFM/rOhZDbZWPBeTiIiIiHKWt6sdvu1bEzY68U+VwS/iMHzDRZhMZudavjEVKBogL3RiGbBvStow00ZrgxXNVqCyR2WpdNbpWdj/YL+lPgIRERFlEw4yiYiIiHIRo8mI8cfGIzA0UHrNydoJ8xvOx6LGi+Bi45IaXvgROPmZvFCjMUCVt4Row7lH2HVZ3HpWq1GwsJs/RrcsCw23kyUiIiKiV6SqjysW96gi5QdvhmLp/ltiaGUH9NkMeNeUFzq5Qjgz08HKAV++8SX8XP2EMhUqJp2YhAexDyz2GYiIiMjyOMgkIiIiyiVUVcW8s/Ow/6F8Z3i9IvWwtcNWtCnZJj18eAbYPVJeqGJnoMkEIQp+HodpO65JpaNblEX3mj5Z7p2IiIiIKKva+RfBqDfLSPlnB+9gz1Wzs+BtXYB3tmT+ZObJz4CgtWlfuti44Os3v0ZRx6JCWbw+HqOPjEayMdki/RMREZHlcZBJRERElEusvroaG4I3SHnvcr2x6o1VKORQKD1UVWDveMBkEIu9awKdvgQ06T/mJaQYMHh9IJINJqG0URlPDGxU0qKfgYiIiIgoK4Y080M7/8JSPnLTJdx6ESeGti5Any1AUfkcTByYASTFpn3pae+Jr1t8DWdrZ6HsZuRNLDi7wCK9ExERkeVxkElERESUC2y/sx3LA5dLeQvfFhhbaywUxWzb1zv7gScXxMzFB+j1c+pWWxlM23ENd0JfCpmnkw2W9KjC7WSJiIiIKFdRFAULuvmjnJeTkCekGDFgzXnEJOjFC2ydU7eZ9akt5vFhwAnx52sfJx/MaTBHes9Ntzbht3u/WaR/IiIisiwOMomIiIhesesR1zHt5DQpr1moJuY0nAONYvYjm6oCh+fKC3X6AnAsKES7Lz/DpvOPhUxRgGVvVYWHo01WWyciIiIisjh7ax2+ebcmXO2thDwkIgHDNgTBaFLFC2ydga6rAZ2tmJ9aCcQ8EaLGPo3xXsX3pPecfmo6QmJCLNE+ERERWRAHmURERESvkEk1YfaZ2TCo4haxpQuUxvJmy2GjzWTYeOeA/DSmbwOgRCMhCo1LwqRtV6TLhzT1Q30/jyz3TkRERESUXXzc7LGyV3WYbyBy5FYYvj56T77A1QeoM0jMDEnAwVlS6dDqQ1HVs6qQJRgSMOrIKJ6XSURElMtwkElERET0Cu28uxOXwy4LmZeDF75s/qV0fg+AP38as8k4szIVE7ZcRZTZ1lsBxQtgePPSWe6biIiIiCi7NSjtgQltykv5kn3BuPokJpMLRgD2ZjfsXfoZeHZJiKw0VljYeCFcbVyF/FbULay9vjarbRMREZEFcZBJRERE9IrEpcRhyYUlUj6j3gwUciiU+UV3DgBPzouZbwOgREMh2hL4BPtvvBAyB2stlvSoCp2WPwISERERUd7wQYMS6FCliJDpjSqGbwhCYopRLLZ1lm7wA1Tgj0mpNwRm4OXglel5mV9f/hphCWGWaJ2IiIgsgH/FIiIiInpFvrz0JSKTIoXsjWJvoG6RuplfoKrAkXly3mSs8OXT6ERM23lNKpvUrgJ83Oz/c79ERERERDlNURTM6lwJ3q52Qn43LB7zfr8hX1CjH+BRRszuHwVu/yGVNizaEL3K9RKyREMilgcuz2rbREREZCEcZBIRERG9Anej7+LnGz8LmY3WBqMDRv/FRQeAx+fEzLc+UDz9aUxVVTF282XEJYlnbjYu44meAT5Z7puIiIiIKKc521ph6VtVpfMyfzz1AIeCQ8VQawW8OUNeZO8EIEnejnZw1cFwsXERsu13t+Nq+NWstk1EREQWwEEmERERUQ5TVRVzz86FQRWHjR9U/gDejt6ZX5QQCRzI5A8yTcYBSvpfdH468xDHbocLJc62Oszv6g9FUcyvJiIiIiLKE2qVcMPHTUpJ+ae/XkbEy2QxLNNKuNkPABBxB1j/FpCSIMQuNi4YXHWwtO68s/Ogmm1HS0RERDmPg0wiIiKiHLb/4X6ceXZGyLwdvfFexfcyv+DOfuDLesCzS2JerJ7wB5oLD6IwY9d16fIZHSvBy8U2y30TEREREb1Kw5uXQWVv8enJsLhkDN9wEUn6DOdlKgrQYqa8wMNTwKZ3AUOKEHcv0x1+rn5CdinsEn67/5vFeiciIqL/hoNMIiIiohwUlRSFeWfkcy7HBIyBrc5s2JgSD+weBazrCsQ9kxfL8DTmo8gEDFhzHikGk1DSqqIXOlYtYrH+iYiIiIheFWudBkvfqgpbK/FPmsfvhGPA2gviMLNINaDpRHmRO/uArQMAU3qtTqPDpwGfSqVLLyxFgj5ByomIiCjncJBJRERElENMqgmTT01GaKJ4jk+9IvXQzKeZWBx+G1jVEDj3beaLVXsHKNEIABCbpMcHP55DRLx4Z7mHow1mda7ELWWJiIiIKN/wK+iIiW0rSPnRW2Hov+a8OMxsNAao/bG8yLWtwK5PgAxbx9YtUhdNfJoIZS8SXmDV5VUW6pyIiIj+Cw4yiYiIiHLIpvubcPLZSSGz1lhjXK1x4rAx5gmwphMQeVdeRGsDtJwLtF8BKAoMRhOGrA/CrRcvhTIbnQbf9q0JD0ebbPgkRERERESvTp/axfBWTR8pP3Y7XBxmKgrQcg5Q9W15kcA1wJEFQjS65mjoNDoh+/7q91hzbY3FeiciIqJ/h4NMIiIiohxwNeoqvr/zvZSPrTUWJVxKpAeJ0cBP3YDYx/IihasAA48CdQcBmtQf42bsuo6jt8Kk0iU9qqKqj6uFuiciIiIiyj0URcHcLpX/dJj54Y/nkWz4/2GmRpN6E2C5dvJCh+cC9w6nfenr7It3yr8jlS08vxDrb6y3VPtERET0L3CQSURERJTNopOjMefyHJhU8fzKlsVbonuZ7umBIRnY8DYQel1cQNEAjT4FPtgPFCyXFm8LeoI1px5I7ze6RRm09S9s0c9ARERERJSbaDSpw8yeAfIw8/idcKw4cDs90OqAbt8BJZuaVarA5v5A3Iu05KMqH6F0gdLSmnPPzsWm4E2Wap+IiIj+IQ4yiYiIiLKRqqqYemoqwpLEpyZ9nHwwre609C1lTSZg60DgwXF5kQ4rgWYTAZ11WpRiMGHh3mCptEt1bwxu6mfRz0BERERElBtpNArmdK6MXrXkYea3x+7jSXRieqCzAd5aB3iUFQvjQ4HNHwCm1Cc47a3s8fWbX4u7pvy/madnYsvtLRb9DERERPTXOMgkIiIiykZrrq/BsafHhMxKY4VFjRfB0doxPdw7Abi2VV6g2WSgmnymz+bAx+IfZgAEFC+AuV0qi+dtEhERERHlYxqNgtmdKqN7jaJCnmwwYZH5jX82jkD3HwCdnZiHHAMOz0v70sPOA6tbrIavs6/0ftNOTsORR0cs1T4RERH9DQ4yiYiIiLLJ5bDLWHZhmZSPrjkaFdwrpAc3dgJnvpQXCPgQaDhKilMMJqw8eEfINAowr6s/bHTarLZNRERERJSnaDQKpnaoCA9HayHfGvQEVx7HiMWFKgBtF8uLHF0I3D2Y9qWnvSe+bfEtijqKA1IVKsYfH4/HcZmcaU9EREQWx0EmERERUTaISY7BmCNjYFANQt7Mpxl6leuVHqQkAHsmyAuUbw+0XgBk8nTllkyexuxQpQhKeTpKtURERERErwNHGx1GvFlGymf/dh2qqophtbeBqua7nvz/eZnx4WmJl4MXvmv5HbwdvYXKuJQ4jDoyCinGFEu1T0RERH+Cg0wiIiIiC1NVFVNOTMHT+KdC7mXnham1p4pbv55YDsQ8FBcoWgvo8g2gkZ+uTDGYsPKQ/DTmkGalLdY/EREREVFe9FZNH5QuKN7cd/peJPbfCJWL2ywCPMuLWUI4cHCmEBV2LIxVb6yCg5WDkF+PuI4F5xZYpG8iIiL6cxxkEhEREVnY+pvrcfDRQSHTKTpM9J8IJ2un9DAqBDi+VLxYowM6fg5YmZ3b8/+2BD7G4yj5aUy/gnwak4iIiIhebzqtBhPalJfyub/fgN5oEkNre6DHj4DZgBIXfgSeXRai4i7FMbO+OOAEgI3BG7Hr3q4s901ERER/joNMIiIiIgu6Fn4Ni84vkvIPy3yIcq7lxHDvRMCYLGa1PwI85S2xAEBvlJ/GVPg0JhERERFRmiZlPVHfz13I7oXFY8PZh3KxZ1mg2USzUAX2jAfMtqN90/dNvFPhHWmJGadm4G703ay2TURERH+Cg0wiIiIiCwlPDMeoI6NgMInnYjb2bowuvl3E4jsHgJtmd287FgIaj/3T9fk0JhERERHRX1MUBRPalJeOml+87xbuhr2ULwjoD7j7idmD48CNHVLpiBojUNWzqpAlGhIx4vAIJOgTstg5ERERZYaDTCIiIiILeJnyEoP2D8KTl0+E3MvBC9PqTBPPxTSkAL9nMrB8cwZg65zp+lcex2DJvltCpijAUD6NSUREREQkqFjEBV2qFRWy6AQ93vn2DJ5EizcGQmcNtJwjL/LHJECfJERWGissbLwQBWwKCPn9mPuYdnIaVLOnOImIiCjrOMgkIiIiyiK9UY9PDn+CG5E3hFyraLGw0UK42LiIF5xZBUTcFjOf2oD/W9LaBqMJy/ffRucvTuBFrLgNbXt/Po1JRERERJSZ0S3LwN5aK2RPY5LQ59szCIszO96hdAvA7w0xi34InFoprevl4IX5jeZDgfjI5+8hv2Nj8EaL9E5ERETpOMgkIiIiygKTasLEExNx5tkZ6bWJdSaiasGqYhh6HTg026xSAVovgPn+V3fDXqLrqlNYuv8WDCbx7m6NAgxrbrYFFhERERERAQAKu9hh2VtVodWIP2PfD4/Hu9+dRUyiPj1UlNSnMhVx8IljS4DYZ9LadYvUxaCqg6R8/rn5uBJ2xSL9ExERUSoOMomIiIiyYPH5xfj9/u9SPqjKIHQv013IFH08dFs+AAziFlWo0Q8oUlWIDt0MRdsVx3DpUbS0tqIA0ztWgl9Bpyx2T0RERESUf7Wo6IUFXf2l/MazWLz/wzkkpGQ4296zLFCrv1iojwd2jwSMBpgb4D8A9YvUFzKDyYBRR0YhOinaEu0TEREROMgkIiIi+s82BW/CmutrpLxr6a74qMpHUu58bAYU8y1lHQsBzacI0YUHUfj4pwtI0pukNYq52WPTwLp4p45v1ponIiIiInoNdK1RFNPaV5DyCw+iMObXy+K5lo3HAnbi+ZcI/g3Y9hFgMgqxRtFgbsO58HLwEvJn8c8w4fgEmFT5Z3kiIiL69zjIJCIiIvoPHsQ+wMJzC6W8iU8TTKozCYrZNrF2N7fA7tY2sVjRAF2/Bezd0qI7oS/xwY/nMh1i9qrlg9+GN0RAcTfpNSIiIiIiyly/+iUw6s0yUr778jOsO/0gPbB3A5pOlBe48guwfQhgEn9GL2BbAIsaL4JOoxPyY0+O4cdrP1qkdyIiotcdB5lERERE/5LRZMTE4xORZBS3iK3qWRULGi2Q/pCBsGA4HZ8hL9R4LFCiUdqXobFJ6PvdWUQn6IUyV3srfNevJuZ28Yejjc58FSIiIiIi+htDmvnhwwYlpHzmrhu48jgmPaj5AVC+g7zApfXAzmHSMLOKZxWMrjlaKl8ZtBL3ou9luW8iIqLXHQeZRERERP/SmutrcCnskpC52bphebPlsNPZicUpCdBt/QAaQ6KYl2gENBqT9mVckh59vz+HJ9Fina2VBt/3C0CzcoUs+hmIiIiIiF4niqJgQpvyaFLWU8hTjCYMXh+I2KT/v5lQowG6rgbKtpEXCVoL/DYKyLgdLYDe5XqjZfGW4rqmFEw6MQkGk3y+JhEREf1zHGQSERER/Qt3ou7gs6DPpHxa3Wlws81ky9ffP4USdlPMHDyBLt8AGi0AQG804aN1F3DjWaxQptUo+Lx3dVQrZnZODxERERER/WsajYIlPaqisIutkD+MTMDYjOdl6qyB7j8ApVvIi5z/DjixXIgURcGUulNQ0L6gkF8Jv4I119dY8iMQERG9djjIzONUVcXGjRvRrl07FC1aFDY2NihcuDCaN2+Ob7/9FgYD7/oiIiKyFL1Jj4knJkJvErd+7VCqA5oWaypfcHlT6l3bGahQgC5fA05eadkPJ0Jw4k6EdPnsTpXQvDyfxCQiIiIishQ3B2t81qsatBrxTPvfrz7HjydD0gOdDdBjLVCqmbzIgRlAyAkhcrZ2xrS606TSz4M+5xazREREWcBBZh4WFRWFN954Az179sTu3bvx5MkTpKSk4Pnz5zh48CD69++P2rVr4+HDh6+6VSIionxh9ZXVuB5xXcgK2hfE2Fpj5eLw28DOT6TYVH+E8MeQ0NgkLD9wW6ob8UYZ9KxVLMs9ExERERGRqGZxN3zasqyUz/7tBm6/iEsPrGyBnuuFc+0BAKoR+PU9IO6FEDcs2hCd/DoJGbeYJSIiyppsGWRqtdoc+Uen02HHjh3Z8RFyvZSUFHTs2BEHDx4EAPj4+GDmzJn4+eefsXDhQpQvXx4AEBgYiNatWyM2NvavliMiIqK/cSPiBr669JWUz6g3A87WzmKoTwR+6Qfo44U4pXBNmBp9KmTz9tzEy2Txjxqdq3ljWHM/i/RNRERERESy/g1Lonk5cStYvVHFjF3X07eYBQAru9QnMwuUEBd4+QLY/AFgFH+WHxMwhlvMEhERWVC2DDJVVc2xf15XX375JY4dOwYAqF69Oi5duoRJkyahZ8+eGD16NAIDA9GyZeoh49evX8fMmTNfZbtERER5WooxBRNPTIRBFf9I0b1Md9T3ri9fsHcC8OKqEJlsCyC6+RJAo0vLLjyIxJbAJ0Kdi50VJrerAEURt7oiIiIiIiLL0WgULO5RBUXMzss8djscB26EisV2rkCPNYDWRsxDjgGH5wjRn20xuzJoJc49P2eBzomIiF4vur8v+W8URUGFChXg4eGRLesfOXIkW9bNCwwGA2bPng0g9b/zmjVrUKBAAaHG1tYWa9asQcmSJREfH4/PPvsM48aNg7u7+6tomYiIKE9bdWkVbkeJ2796O3pjVM1RcvHVLcD576Q4utkCmBzTz7s0mlRM2X5NqhvVogzcHKyz3jQREREREf0lV3trTGlfER+tuyDks3+7gUZlPGGty/AMSGF/oO0iYMdQcZFjiwGf2kCZlmnR/7aY3XZnW1qmN+kx/OBw/ND6B5QpUCY7Pg4REVG+lG2DTACYPXs2OnTokC1razSv7/GeBw8eRFhYGACgefPmqFixYqZ1BQsWRM+ePbF69WokJydj+/bteP/993OyVSIiojzvcthlrL66Wspn1p8JBysHMXwSCGwfItUa6w5DSjHxXJ0N5x7i2lNx6/dyXk7ozXMxiYiIiIhyTMuKhVC3pDtO3YtIy+6Hx+PHkyHo36ikWFztHeDhaeDiT2K+ZQAw8ChQwDctGhMwBqeensKLhPRzNOP0cfh438dY12YdCjsWzpbPQ0RElN+8vtPAPOyPP/5I+/dWrVr9ZW3G1/fs2ZNtPREREeVHSYYkTDw+ESbVJOR9yvdBgFeAWBxxF/ipu3QuJnxqw9R4vBBFJ6Rg0d5g6f1mdKwEnZY/nhERERER5RRFUTClfQVozE52WHHgNsJfJpsXA20WAQXNHipIigZ+6QsY0uudrZ2xotkK2OvshdLQxFAM3D8Q0UnRlvsQRERE+Rj/UpYHXb2afuZWjRo1/rK2Zs2amV5HREREf29F0AqExIYIma+zL4ZVHyYWxr0A1nUBEsLF3K4A0HU1oLUS4oV7gxGVoBeyjlWLoFYJN0u1TkRERERE/1D5ws7oZbYzSlyyAYv/kG8+hLV96nmZ1k5i/jQI2DtBiCq4V8DSpkuh04ib4t2PuY8hB4cg0ZBokf6JiIjys2zZWvbQoUMAgEqVKmXH8jn2HrnVrVu30v69ePHif1lbtGhRaLVaGI1G3L59G6qqQlGUv7zG3IULF/7y9Rs3bqT9u8FggF6v/4tqIspuer0eBoMh7d+J6L8JDA3EuuvrhEyjaDCt9jToVF3691dyHHTrukKJChFqVZ0tjN3XQnXwEr4v9159hp/OPBRq7a21GP2mH79niXII/19JlLvwe5Io93kdvy+HNi2JHZeeIi7JkJZtOPcIb9XwRsUizmKxiy+U9iug2/yemJ/7FoYiNaFW6pYWBXgGYHqd6Zh4cqJQeinsEmaenInpdadb/LNQ/vM6fk8S5Wb8npT9779HdsiWQWbjxo2zY9kcf4/cKjo6Ou3fPTw8/rJWp9PB2dkZUVFRMBgMiI+Ph6Oj4796v4xPdf6dmJgYRERE/H0hEWUbg8GAuLi4tK91umw9DpkoXwpNDMWnZz6FClXIuxfvjqKaoun/rzOmoMBvA6G8uCLUqYoG0c0XI9m+NBARkfZ9+eKlHuN3PZXer1+AF6z08YiIiJdeIyLL4/8riXIXfk8S5T6v6/fl+7W8sPzo47SvVRUYtekivuhWFi52Zv8NPOvByb8fHC7/IMSa3SMRYeMDo5tfWlbLqRYGlh2Ir4K/Emp33t+Jxh6NUcWtisU/C+Uvr+v3JFFuxe9JWca5laVxa9k86OXLl2n/bmtr+7f1dnZ2af+e8ZuLiIiIZPGGeEwKnITI5EghL+5YHO/6vStkTqcWwObJSWmN2AZTkFziDSEzmFTMOxqKuGSjkJctaI9e1QtaqHsiIiIiIvqvulXxhG8BGyG7G5GEoVtuITZJftIkrvZopBSqJmQaQwIK7BsGRS/epNiteDd0K94N5lbeWAmDKfueYiEiIsrrOCamv3X+/Pm/fP3GjRt45513AAAuLi5wd3fPibaI6E9k3M7Azc0NVlZWf1FNRBkZTAZMOTIF91/eF3IrjRVmN5iNwm6F0zIl5Ch0V9dKaxgbjIJ9oyGwz5Dp9Xp8cfIpboYlC7WONjp8/nZ1eLnZg4hyDv9fSZS78HuSKPd5nb8vp7SviA/WBArZrbBEjN55Hz/0qwEnW7P/Fj1+hLq6KZSE9B3KdFF34Xl+MYztlgulY+uMxbXYa7gRmX5MU8jLEOwL34c+5ftY/sNQvvE6f08S5Ub8npS5urpm29rZOsg0Go04ceIEAMDKygp169b9V9efPn0aKSkpAIAGDRpAo+EDpADg6OiIqKgoAEBSUtLfbhWbmJh+cLiTk9NfVGauRo0a/7hWp9Pxm5YoF/jfdgZWVlb8niT6h1RVxdzTc3Hq2SnptRn1Z8C/kH96kBQL7PpEXqTaO9A2nwyt2XnUh2+FYX1gmFQ+v6s//Aq5ZLV1IvoP+P9KotyF35NEuc/r+n3ZvEJhjGlZFgv3Bgv55Sex+GBtENa8X0scZrr7Al2/BdZ2ATIcTaG59BM0lbsAfuk7tVjBCpPrTMbbv70tHGOx6soqtPVri4L23KmF/tzr+j1JlFvxe1KUndvrZutk8IsvvkDTpk3RtGlTnD179l9ff+bMGTRp0gRNmzbFN998kw0d5k0ZJ9vh4eF/WWswGBAbGwsg9RvKwcEhO1sjIiLKs3689iN+ufWLlA+uOhjtSrYTwz8mAjEPxaxQZaDtEsBsiPksJhGfbr4qrdunTjG09S8s5URERERE9GoNbuqHT94oLeVBD6Px3vfnkJgiHheBUs2AJuPkhXYMA5JihKiyZ2V0Kd1FyBIMCVh0flGW+yYiIsqPsm2QqdfrMWvWLADAG2+8geHDh//rNYYPH4433ngDqqpixowZMJlMlm4zTypTpkzav4eEhPxl7ePHj2E0pv5w5efnB8Xsj6tEREQE7HuwD4svLJbyDqU6YKD/QDG8vQ8IXCNmGiug85eAzlqIDUYThv0chKgEvZCXL+yMSW0rWKR3IiIiIiKyvOHNS2Nw01JSfv5BFCZuuwJVVcUXGo4GilQXs9gnwN6J8trVh8PFRtyZ5ff7v+PMszNZ7puIiCi/ybZB5u7duxEWlrqF2uzZs//zOv+79vnz5/jtt98s0lteV6lSpbR/v3Dhwl/WZjzfMuN1RERElOpy2GWMPzZeymt51cK0utPEm4ASo4AdQ+VFmowFvCpL8dL9t3AuJErIHKy1+Lx3NdhaabPcOxERERERZQ9FUTC6RVkMbFRSem1L4BNsPPdIDLU6oNOXgFa8uRFBa4E7+4WogG0BDK8uP/Qx58wc6I16KSciInqdZdsg8/fffweQOjyrWbPmf14nICAAlSun/mFw9+7dFuktr2vZsmXav+/du/cva/fs2ZP2761atcq2noiIiPKix3GPMfTgUCQbk4W8hEsJLGmyBFZaszMOfh8LxD0TsyLVgfojpLWP3grDF4fvSvmMDhVQ0vOvz7cmIiIiIqJXT1EUjGtdDv3qFZdem7LjGq4+EbeNRcFyQBP5JsnMtpjt4tcFldzFhw7uxdzD/HPzs9o2ERFRvpJtg8xz585BURSLDM9atWoFVVVx7tw5C3SW9zVt2hSenp4AgP379+PatWuZ1oWGhmLDhg0AAFtbW3Ts2DHHeiQiIsrtYpJjMOjAIEQmRQq5m60bPm/+ubTVE27sBC5vFDOtDdB5Verd1xmExiZhxMaLMN9tqkMlD3SownMxiYiIiIjyCkVRMLldBdQr5S7kKQYTBv0UiJhEsyco6w37R1vMajVaTKwzEQrEY6A2Bm/EhpsbLNY/ERFRXpdtg8zHjx8DAEqVkveS/7f+t8bDhw+zvFZ+oNPpMHFi6g8/qqri3XffRVSUuG1dUlIS+vbti/j4eADAkCFD4O7uLq1FRET0OtIb9Rh5eCTux9wXchutDVY0WwEfJx/xgvhwYOcn8kLNpwCeZYXIaFIxfMNFRMSnCHkpd1uMbGy2LhERERER5XpajYLlPauhoJONkD+MTMDoXy6J52X+1Raz574VokoeldCrXC/p/eadnYfTz05brH8iIqK8LNsGmTExqdslWGJ45ubmJqxJwMcff4yGDRsCAAIDA1GlShXMnj0bGzduxOLFi1G9evW0bWUrVKiASZMmvcp2iYiIcpXZZ2bj7POzUj6nwRxU8awihqoK7BoBJISLebG6QJ2PpTVWHLiNU/cihMzOSoNZbUvC1irbfvQiIiIiIqJs5Olkg5W9q0OrEZ+g3Hf9Bb45dk8s/rMtZnePBq7vEKLRNUejRqEaQmZUjRh1eBQexD6wSO9ERER5Wbb9Nc3BwQGAZYaPsbGxAAB7e/ssr5VfWFtbY/v27WjWrBkA4NGjR5g0aRJ69uyJ0aNH48aNGwCA6tWr4/fff4eLi8tfLUdERPTaOPDgADbf3izlI2uMRIviLeQLrm4Gboh/bICVPdDpC0CjFeKtQY+x/MBtaYlp7cujhJtdlvomIiIiIqJXq1YJN4xtVVbK5+8Jxtn74pEVqDcMKBpgVqkCmz8EQk6kJVZaKyxtshTejt5CZWxKLIYcGILYlFhLtU9ERJQnZdsg08PDAwAQEhKS5bX+t8b/1qRUBQoUwP79+7Fhwwa0bdsWRYoUgbW1NQoVKoRmzZrh66+/xpkzZ1CsWLFX3SoREVGuEJkUiRmnZ0h59zLd0a9iP/mCuOfA7lFy/uYMwK2kEB29FYYxv1yWSrtWL4ou1bylnIiIiIiI8p7+DUuiRYVCQmY0qRiyPhBhccnpoVYH9FwPFCghLmBMBn7uBby4lhYVsC2Az5p9Bnud+BBHSGwIxh4dC5NqsvjnICIiyiuybZBZoUIFqKqKffv2ZXmtffv2QVEUVKhQwQKd5S+KouCtt97Crl278OTJEyQnJ+P58+c4cOAA+vfvD51O96pbJCIiyjVmn56NyCTxTunqBatjQu0JUBRxiyioKrBzOJAULeYlGgM1PxCiy4+j8dG6CzCYVCEvU8gRMztVtFT7RERERET0iimKgoXdq6CYmzh0DI1LxrCfg2DM+DuBY0HgnS2Ag6e4SHIMsK4bEP0oLSpdoDQWNFoABeLvJcefHMeaa2ss/jmIiIjyimwbZDZv3hwAcPbsWZw9K59B9U+dOXMGZ86cEdYkIiIi+rf2hOzBHw/+EDI7nR1m1Z8FnSaTG3+C1gK39oiZtRPQ8XNAk/4jVEh4PN77/hwSUoxCqZezLX54rxbsrXlTERERERFRfuJiZ4Uv3q4Oa534p9VT9yKwdN8tsditJPD2L4CVg5jHPU3dZtaU/rRlY5/GGFFjhPR+ywOX40rYFYv1T0RElJdk2yCza9eusLGxAQB89NFHePny5b9e4+XLlxg4cCCA1DMhu3XrZtEeiYiI6PUQnhiO2adnS/mIGiPg4+wjX/DwNLB7tJy3mgu4pteHv0xG3+/PIiI+RShzstXhx/droYgrz8UkIiIiIsqPKnm7YGZHefeVlYfu4ODNF2JYpBrw1lrA/AbKR6eByxuEqF/FfmhZvKWQGVQDxhwdg7iUOIv0TkRElJdk2yCzSJEi+PDDD6GqKi5duoTWrVvj8ePH//j6R48eoVWrVrh8+TIURcEHH3yAIkWKZFe7RERElE+pqooZp2YgOjlayGt71cZbZd+SL4i4m3pmjTFZzEu3AKr1EdYduekSHkQkCGXWOg2+fbcmyno5WeojEBERERFRLtSjpg+61Sgq5SM2XsLjKPH3BPg1Bzp+IS/yx2QgMSrtS0VRMLXuVHg7egtlT14+wYxTM6CqqvkKRERE+Vq2DTIBYPbs2ShbtiwA4OTJk6hUqRJGjhyJwMBAmEzyIdUmkwmBgYEYMWIEKleujFOnTgEAypQpgzlz5mRnq0RERJRP7bq3C4ceHRIye509ptefDo1i9qNQQiTwU3cgUTxHEw4FgfYrgAznaP589hGO3goTyhQFWNGzKmqXdLfoZyAiIiIiotxHURTM7FgJ5cxuYoxJ1GPUpkswmcyGjlXeAsp3ELOEcOCguHuMk7UTFjRaAJ0iPsG5J2QPtt7ZarH+iYiI8oJsHWQ6Oztj586dKFq0KFRVRVxcHJYvX46AgAA4OzujYsWKqF+/PurXr4+KFSvC2dkZAQEBWLFiBWJjY6GqKooWLYqdO3fC2dk5O1slIiKifOhx3GPMPiNvKTsmYIx0hzMMycCGt4HIu2KuswN6bwCcC6dFjyITMHv3dWndae0rolWlwlJORERERET5k521Fl+8XR2ONuLQ8cz9SKw9/UC+oOUcwMpezM6vBp5eFCJ/T38Mqz5Munzumbm4G31XyomIiPKrbB1kAoCfnx+CgoLQpk0bqKqa9k9CQgJu3ryJ06dP4/Tp07h58yYSEhLSXgeANm3aICgoCH5+ftndJhEREeUzBpMBE45PQLw+XsjrF6mPrqW7isWqCmwfDDw8abaKAnRbDXjXSEtMJhWjf7mE+BSjUPlmhUJ4t66vJT8CERERERHlASU9HTGva2Upn/f7TTw0O4oCrj5Ao9FippqA30YDZjvY9a3YF/WL1BeyJGMSpp+aDpMq73ZHRESUH2X7IBMA3NzcsGvXLpw4cQI9evSAu3vqdmsZB5v/G166ubmhR48eOHHiBHbt2pVWS0RERPRvrL6yGkGhQULmbO2M6fWmQ8mwRSwA4PBc4Mov8iIt5wDl2grRj6dCcOa+uPVsAXsrzOlcWV6XiIiIiIheC+38i6Cdv7g7S6LeiDG/ZrLFbN0hgLvZgxuPzwEXfxIijaLBrAaz4G4r/n00KDQI2+9st1jvREREuZnu70ssp27duqhbty4A4ObNm3jy5AkiIiIAAO7u7ihSpAjKly+fky0RERFRPnQl7Aq+vPSllE+rNw2FHAqJ4cX1wJH58iIB/YE6HwvRvbCXmL/nplQ6q1NleDrZZKlnIiIiIiLK26Z3qIhTdyMQEZ+Slv1vi9m+9YqnF+psgNYLgHVdxAX2TwXKtgYcPNIiDzsPzKg/A4MPDBZKF19YjCY+TVDAtkB2fBQiIqJcI0eeyMxMuXLl0Lx5c/To0QM9evRA8+bNOcQkIiKiLEvQJ2DcsXEwquLWr538OuFN3zfF4vtHgR3yuTMo3RJoNQ/I8ISl8f+3lE3Si1s4ta9SBG39eS4mEREREdHrzt3RBrM6VZLyTLeY9WsOVOgoZgkRwKa+gCFFiBsVbST9LhOTHIOlF5ZapG8iIqLc7JUNMomIiIiyw/xz8/Ew7qGQ+Tj5YFytcWJhWDCwsQ9g0ou5V2Wg23eAVty44ptj9xD4MFrIPJ1sMKNDRUu1TkREREREeVzryoX/dItZg9HsXMuWcwArezF7cBz4fQygitvRfhrwKex1Yu3WO1sR+CLQYr0TERHlRhxkEhERUb6x4eYGbLm9Rci0ihbzGs6Dg5VDevgyDPipO5AUIy7gVATovQmwcRTi4OdxWPLHLen95naujAIO1hbrn4iIiIiI8r7pHSrC3ez3hDP3IzFp21WoGQeULkWBFjPlBS78AJz9Woi8HLwwuOpgqXTm6ZnQm9+cSURElI/kyCDTaDTixYsX0Ov5P1UiIiLKHkcfH8Xcs3OlfGCVgfD39E8PTEZg0ztA9AOx0NoReHsT4FxEiPVGE0b9chEpZndPd6tRFG9UMDtvk4iIiIiIXnt/tsXshnOPsHT/bTGs+QFQ4z15kT3jgDsHhKh3+d4oW6CskN2JvoN119dluWciIqLcKlsHmTExMejfvz9cXV1RpEgRuLi4oG/fvoiMjMzOtyUiIqLXzM3ImxhzZAxMqjhsrF6wOvpX7i8Wn/wMeHhKzBQt0P2H1G1lzXx+6A6uPokVssIutpjSvoIlWiciIiIionyodeXC6FajqJSvOHAb605nuKlSUYA2C4HiDcVC1QT88h4Qnj741Gl0mFRnkrTmFxe/wPWI6xbrnYiIKDfJtkFmXFwcGjVqhO+++w4GgwFVqlQBAKxbtw4NGzZEXFxcdr01ERERvUZexL/A4AODkWBIEPKijkWxtOlS6DQZzroMvQEcmi0v0mYhUPpNKb76JAYrD96R8vld/eFsa5Xl3omIiIiIKP+a07kyGvh5SPmU7Vex5+rz9EBrBfRYAxQoLhYmxwAbegMp8WlR1YJV0a1MN6EsyZiEoQeG4kX8C0u2T0RElCtk2yBz9uzZuHLlCtzc3BAUFITAwEBcvnwZHh4euHnzJmbNmpVdb01ERESviQR9AoYcHILQhFAhd7J2wudvfA43W7f00KgHtn4EGFPERaq+DQR8IK2dbDBi1KZLMJhUIX+7djE0KuNpsc9ARERERET5k7VOg1Xv1EAlb2chN6nAsA1BOB+SYdc6ezeg10bA2klcJPwW8PunQvRJ9U/E33UAhCaGYujBoUjQizd4EhER5XXZNsjctGkTFEXBkCFDUK5cOQCAn58fhgwZAlVVsWnTpux6ayIiInpNzDo9CzcjbwqZTqPD8qbLUdKlpFh8fBnw7KKYOXsDreRzNQFg0d5gBL8Qd5DwcbPDhDbls9g1ERERERG9LhxtdPi+Xy0Uc7MX8hSDCZ9svIgkvTE9LFgO6PYdoJj9yTZoHXDl17QvXWxcsKTJEnH3GQA3Im9gwvEJ0pEbREREeVm2DTKfPHkCAChbVjyAunTp0gCAZ8+eZddbExER0Wsg8EUgdt7bKeXT6k5DgFeAGD6/AhyZLy/S4TPA1kWKN5x9iG+O3RcyRQEWdasCBxudVE9ERERERPRnPJ1ssOb9WnB3sBbyx1GJ+OLwXbG4TAugkfgEJgBg5ydA5L20L2sUqoHp9aZLZQceHsDywOWWaJuIiChXyLZBZuHChQEAt27dEvLbt1MPqC5YsGB2vTURERHlcybVhHln50n5AP8B6OjXUQwNKcDWjwGTXsxr9AP8mktrHLsdhonbrkr5e/VKoHZJ96y0TUREREREr6niHg74rl8AdBpFyFcduYuQ8HixuNEYwLe+mKXEAb++n/r7zf/rUKoD+lfuL73Xd1e/w467OyzWOxER0auUbYPMbt26QVVVrFy5Enfu3AEA3Lt3DytXroSiKOjYsePfrEBERESUue13tuNG5A0hK1ugLAZVGSQXH5kPvLgiZi7FgBbyed3Bz+MwaF0gjGbnYlb2dsGnrcpK9URERERERP9UFR9XvN+ghJClGEyYtvMaVDXD7yBaHdDlG8CugLjA0yDggPgU5pBqQ/Cm75vSe806PQv3Yu5JORERUV6TbYPMyZMno2zZsggPD0eVKlVQs2ZNVK5cGWFhYfD19cW0adOy662JiIgoH3uZ8hLLApdJ+dhaY6HVaMXwwSng+BJ5kU6fAzZOQhQal4T3fziHuGSDkHu72mF1v5qwtTJbm4iIiIiI6F8a1rw0CjnbCNnh4DD8cf2FWOjiDXT6Ul7g1Ergzv60LzWKBrMbzEZF94pCWaIhEZ8e+RTJxmSL9U5ERPQqZNsg08XFBcePH0efPn2gqioCAwNhNBrRrVs3nDhxAu7u3JqNiIiI/r2vL3+NyKRIIWvh20I+FzMpBtgyAFBNYl5rAFCikRAlpBjw4Y/n8SQ6UcidbHT4rl8ACjrZWqx/IiIiIiJ6fTna6DCpbQUpn7HzOhJTjGJYtjVQ+2N5kV0jgZSEtC/tdHZY0WwF3GzdhLLgqGAsOZ/JjZ1ERER5SLYNMgHA3d0da9asQUxMDJ48eYKYmBhs2rQp7fxMIiIion/jQewDrL2xVshstDYYWXOkXLx7NBDzUMw8ygBviFsxGU0qhm+4iMuPY4Rcq1HwRZ/qKOslPrlJRERERESUFe38C6NeKfEhjyfRifj80B25+M3pgJe/mEU/AI4tFqKC9gUxu8Fs6fL1N9fj0MNDWe6ZiIjoVcnWQeb/WFlZoXDhwrCxsfn7YiIiIqI/sejcIhhM4tav/Sr2g7ejt1h45VfgyiYx01ilnjNjbS/Ec367gX3m2zgBmN2pEhqW9rRI30RERERERP+jKApmdKwInUYR8q+P3sPN57Fisc4G6PI1oNGJ+YnlQFiwEDXwboC+FfpK7zf55GQ8j39ukd6JiIhyWo4MMomIiIiyate9XTj8+LCQFbIvhPcrvS8WRj9M3WrJXLNJQJGqQrTmVAhWH78vlX7cpBR61iqWxY6JiIiIiIgy51fQCR80LCFkKUYTPvzxPCJemp1rWbA8UHewmJn0wO5RgKoK8fDqw6XzMmOSYzD+2HjpplAiIqK8gINMIiIiyvWCQoMw5cQUKR9ZYyTsrTI8YWnUA1s/ApLFbWJRvCFQb6gQHbz5AtN2XJPWbOtfGGNalLVI30RERERERH9mWLPS8HK2FbLHUYn4eF0gUgwmsbjxWMDFR8xCjgGXxZ1orLRWWNBoARysHIT8/IvzWHphqcV6JyIiyikcZBIREVGu9ijuEYYfHA69SS/k1QtWR+sSrdMDVQV2jwQenBAXsHUBOq8CNNq06OqTGAxZHwSTePMyqhdzxeLuVaAx2+KJiIiIiIjI0hxsdFjSowq0Zr9/nA2JxORtV6FmfNrS2gFoPV9eZO8EIDFKiIo5F8PkOpOl0jXX12DbnW2WaJ2IiCjHZMsgMzY2FrGxsTAajdmxfI69BxEREb1asSmxGHJgCKKSxV/MPe08Mb/RfChKhl/4TywHAtfIi7RbBrgUTV8zSY+Bay8gIUX8GaKYmz2+ebcmbK20ICIiIiIiygn1/DwwrX0FKd94/hG+PxEihuXaAmXbiFlCOLB/unR925Jt0dmvs5TPODUDF0MvZqFjIiKinJUtg0xXV1e4ublh9+7d2bF8jr0HERERvTp6kx6jD4/GvZh7Qm6rtcVnzT+Dl4NXenhtG7B/qrxI9XeBSl2EaPauG3gSnShkLnZW+P69ALg72liqfSIiIiIion/knbrF8U4dXymftfs6DgeHimHr+UDG4zUA4ML3wNXN0vUT60yEv6e/kOlNenxy6BM8j3+e5b6JiIhyQrZtLauaHTSdV9+DiIiIXo0FZxfg1LNTUj634VxUdK+YHjw6B2wdKC9QohHQZrEQHboZio3nHwmZlVbBV+/UQClPR4v0TURERERE9G9NaV8B9Uq5C5lJBYZvuIinGW/EdC0GNP5UXmDbIODxeSGy0dpgWZNlKGhfUMgjkiIw7OAwJBrEGzyJiIhyI56RSURERLnOoYeHsCF4g5SPqDECb/i+kR5EhQA/9wQMSWKhR1mgx1pAZ50WxSToMW7LZXnNN8ugTkl3KSciIiIiIsopVloNvni7Ooq7i09bxiTqMXxDEAxGU3pYdwjgVVlcwJCU+rtR9EMh9rT3xIqmK2CjFXefuRF5A9NPTeeDIkRElOvpsnPxFStWYNu2bdn5FkRERJTPhCeGY9qpaVLe2a8z3qv4XnqgqsCWAalnwmRk7wG8vQmwcxXi6Tuv4UVsspBV8XHFgIYlLdQ5ERERERHRf+dqb41v+wag8+cnEJdsSMvPhURhxYHbGNmibGqgtQJ6rge+aQ7EZ9h6Nj4MWN8T+GAvYOOUFlf0qIgZ9WZg7LGxwvvtvrcbdQvXRUe/jtn6uYiIiLIiWweZhw4dyra1FUXJtrWJiIjo1VBVFVNPTkVkUqSQ+3v4Y3KdyeL//6/8Cjw6Iy6gswV6bQAKFBfiP649x5agJ0JmrdNgcfcq0Gm5QQUREREREeUOfgUdMadLZQz9OUjIPzt0B3VKuaNeKY/UwLUY0Otn4Ps2gDHDDZuh14BfP0h9TaNNi9uUbIPb0bfx7ZVvhXVnn5mNKp5VUNyleHZ9JCIioizJ1jMyc+IfIiIiyj9+ufULjj4+KmR2OjvMbTgXVlqr9DAlHtg3RV6g0xeAT4AQRcWnYMLWq1LpmBZl4VeQ52ISEREREVHu0r5KEfQM8BEyVQVGbLyIiJcZhpZFawKdv5QXuL0XODBDiodWG4o6hesIWaIhEWOPjYXeqLdI70RERJaWLU9k3r9/PzuWzVTBggX/voiIiIhyvZCYECw6v0jKxwaMRTHnYmJ4fBkQ91TMyrYBKnUVIlVVMWHrFYS/FLeUrelbAO83KGGJtomIiIiIiCxuavuKOP8gCndCX6ZlL2KTMfqXS/iuX0D6bjWVugIRd4FDs8UFTiwDSjQC/JqnRRpFgzkN5qDrjq6ISo5Ky69HXMeKoBUYVXNUdn4kIiKi/yRbBpm+vr7ZsSwRERHlU3qTHuOPjUeiIVHIm/g0QZfSXcTiqAfAyRViprECWsyS1v3lwmP8fvW5kNlaabCwexVoNdymnoiIiIiIcic7ay1W9q6GjitPINlgSssPBYdh7ekHeLdu8fTiRmOA8NvAlU3iIls/Aj4+ATimPwjiae+JmfVnYsjBIULpD9d+QN3CdVHPu152fBwiIqL/jIdCERER0Sv3WeBnuBohbv/qZuuGaXWnyedi75sCGJLErO4gwL2UED2IiMf0Hdek9xrXqhxKeDhYpG8iIiIiIqLsUs7LGVPaV5DyhXuCERqb4XciRQE6fAYUqiQWxocCWwcCJpMQN/ZpjLfLvy2tO+H4BIQmhFqkdyIiIkvhIJOIiIheqX0P9uH7a99L+cz6M+Fu5y6GIceB69vEzKEg0HC0EOmNJgzfcBHxKUYhb1TGE33rFbdA10RERERERNmvd61iaF3JS8jikg2YufuGWGhlC3T7DrCyF/O7B4FTn0nrjqgxAmULlBWyiKQI9P+jPyISIyzSOxERkSVwkElERESvzL2Ye5h8YrKU9yjTA42KNhJDkxH4fZy8yBtTAVtnIfrs4B1cfBQtZG4O1ljUzV9+wpOIiIiIiCiXUhQFMztVgoudlZDvvPQUx26HicWeZYHW8+VFDswAHl8QIhutDRY0WgBbra2Q34u5h/77+iM6KdoS7RMREWUZB5lERET0SiToEzDi0AjE6+OFvLxbeYwJGCNfcGA68OKKmBWuClTpLUQXHkRi5cHb0uXzulRGQWdbKSciIiIiIsrNPBxt8GmrslI+edtVJOnFXWhQ7R2gYhcxMxmAze8DiVFCXNK1JCbWmSitezvqNgbsG4CY5Jgs905ERJRVHGQSERFRjlNVFVNOTsG9mHtC7mLjgqVNl8JWZzZwDFwDnFguL9R6AaBJ/3EmMj4Fn2y8CJMqlvWqVQwtKnqBiIiIiIgoL+oVUAxVfVyFLCQiAauO3BULFQVovwxw9RXzqBDg1w8Ao0GIO/l1wpia8o2kNyJv4OP9H+NlysusN09ERJQFHGQSERFRjlt7fS32huwVMgUK5jWcB29Hb7H43hFg1wh5kapvA8Vqp32ZpDdiwJrzeBSZKJSV9HDA5HblLdY7ERERERFRTtNoFMzuXAkas5Myvjh8F/fDxV1uYOuSel6mRifmdw8A+6dKa79b8V0Mrz5cyq+EX8HwQ8NhNBml14iIiHIKB5lERESUo65HXMfSC0ulfFDVQWjg3UAMw28Dm95J3QopoyLVgTaL0r5UVRVjN1/G+QfiVkk6jYJlPavC3trsF3giIiIiIqI8pmIRF/SrV0LIUgwmTN52FSbzbWmK1gTemC4vcmolEPSTFH9Y+UMMqjJIys8+P4vNtzdnqW8iIqKs4CCTiIiIckyKMQWTTkyCQRUHk42KNsIA/wFicUIk8FN3IMnsXBYXH6DXBsDaPi1auv82tl98Kr3fpLbl4V/U1VLtExERERERvVIjW5RBIWcbITt+JxyL9wXLxXUHA1V6yfmuT4BHZ6X4oyof4cPKH0r5ssBlCE8M/68tExERZQkHmURERJRjVl1ahdtRt4XM29EbcxrMgUbJ8GOJyQT80heIui8uYO2YOsR0KpQWbb7wGCsOiGsCQL96xdGvfgkpJyIiIiIiyqscbXSY0q6ilH9+6C42X3gshooCtFsGeNcUc2MKsOFtIOaJWbmCYdWGoVXxVkIelxKHxecXW6J9IiKif42DTCIiIsoRV8OvYvXV1VI+q/4suNi4iOGplcD9o2KmaIBu3wNeldKiM/ciMG7LZWnN5uUKYnK7Chbpm4iIiIiIKDdpU9kLbSp7Sfm4LZdx9n6kGFrZAj1/ApwKi3l8aOoxHka9ECuKgrG1xsLRylHId93bhTPPzlikfyIion+Dg0wiIiLKdsnGZEw8PhEm1STkfcr3QU0vs7uDn18BDsyQF2k1DyjTIu3LJL0Ro365BL1RPAumYhFnrOhVDVqNYrH+iYiIiIiIcgtFUbCoexVU8nYWcr1RxcC15/EgIl68wMkrdZipsxXzJxeAQ7Ol9T3sPDCs+jApn3V6FlKMKVnun4iI6N/IlYPMqKgo/PHHH/jtt9/w+PHjv7+AiIiIcrXPgz7HvZh7Qubr7Cv/cqxPAjb3B0ziXcGo3B2oPVCIVh+/j8dRiULm5WyL1X0D4GCjs1jvREREREREuY29tQ7fvhsgnZcZlaDH+z+cQ0yi2e9U3jWADivlhY4vA+4dluIeZXqgoru4hW1IbAi+v/p9FjsnIiL6d3J0kBkZGYkVK1ZgxYoVuHXrVqY18+bNg7e3N1q3bo327dujePHi6NevH5KTk3OyVSIiIrKQoNAg/HDtByHTKBrMqj8Ldjo7sfjAdCDshpg5FwXaLBKi5zFJ+PzQHSHTahR827cmvFzM7jImIiIiIiLKh7xcUm/ktLPSCvndsHiM2nQRqiruXgP/7kDAh2arqMCWgUB8uJBqNVpMrjsZGkX88/E3V77Bo9hHlvoIREREfytHB5kbN27EJ598gk8//RTu7u7S6+vXr8eECROQnJwMVVWhqipMJhPWrl2L999/PydbJSIiIgsITwzH6COjoUL8Bbpvhb6oWrCqWHz3EHD6C7MVFKDzKsDOVUgX7LmJhBSjkL1TxxeVvM3O2iQiIiIiIsrHKnm7YFnPqlDMTtbYfyMUP54MkS9oMQsoWEHMXj4Htg8BzAafFd0romfZnkKWbEzG1FNTpWNDiIiIskuODjIPHToEAGjUqJE0yFRVFZMmTUr7ulu3bhg9ejR8fX2hqio2bNiA48eP52S7RERElAUpxhSMODQCoQmhQl7SpSQGVxssFidEAts+lhepNxQo0VCIAh9GYUvQEyErYG+FEW+UsUjfREREREREeUnLil4Y26qclM/57SauPY0RQys7oOtq+bzMW78DZ7+R1hhSbQg87TyF7Nzzc1hzbU2W+yYiIvoncnSQeevWLSiKgrp160qvnThxAiEhIVAUBfPnz8emTZuwYMECnDt3Dm5ubgCAH3/8MSfbJSIiov9IVVXMOTMHF8MuCrmVxgpzGsyBjTbDOS4mE7D1IyDumbhIocpAs0lCZDKpmL7zuvR+I1uUhYu9laXaJyIiIiIiylMGNiqJFhUKCVmK0YShPwchIcUgFheqALScLS/yxyTg0TkhcrJ2wrha46TSFUErEBwZnOW+iYiI/k6ODjLDw1P3Wvfz85Ne279/PwDAzs4OgwYNSss9PDzQu3dvqKqK06dP50yjRERElCWbgjdh8+3NUj65zmRU9KgohscWAbf3ipnWBuj6DaCzEeKtQU9w6VG0kJXzckKvAB9LtE1ERERERJQnKYqCBd38UdhFfNLyXlg8pm6/Jl9Q8wOgXDsxMyYD67oCT4OEuEXxFmhbsq2Q6U16jDs2DsnGZIv0T0RE9GdydJAZEREBAHB0dJRe+9+2sY0bN4a9vb3wWuXKlQEADx8+zOYOiYiIKKvOPz+PeWfnSXnvcr3RuXRnMby9Hzg0R17kzRlAwfJCFJekx/w9N6XSKe0qQKfN0R9piIiIiIiIch1Xe2ss71kNGrPzMn+58BjbL4rHc0BRgA6fAU5FxDw5BljTCXh+RYgn1J4ALwcvIbsTfQcrAldYqHsiIqLM5ehf/TSa1Ld7+fKlkBsMBpw5cwaKoqBBgwbSdf/bWjYhISH7myQiIqL/7EX8C4w6MgoGVdy6qJZXLYwOGC0WR4UAmz8AoIp5xc5A7YFCZPj/LZFC48S7fVtV9EI9Pw8LdU9ERERERJS31SrhhmHNS0v5xK1XcSdU/Jss7N2AHj8CVuJDJUiKBtZ0BEJvpEXO1s6Y02AOFIhT0jXX1+DMszOWap+IiEiSo4NML6/Uu3auXRO3Mzh27Bji4+MBAPXq1ZOui4uLAwDpSU0iIiLKPYwmI8YfH4/IpEgh93b0xqLGi2ClyXCGpT4R2PRu6i/IGXmUBTqsTL07OINZu2/gcHCYkFnrNJjQRnxqk4iIiIiI6HU3tFlp1CrhJmQvkw0YsOY8YhL0YrFPLaDXz4BO3JIWCRHAjx2A8NtpUYBXAPpW7Cu934TjE/A8/rnF+iciIsooRweZNWvWhKqqWLduXdo2swDw2WefAUg9H7Nu3brSdbdu3QIAFC1aNGcaJSIion/t2yvf4tzzc0Jmp7PD8qbLUcC2gFj822jg2SUxs3YC3loH2Ihb0K85FYIfToZI7zfijTIo5s6bnIiIiIiIiDLSahQs71kVrvZWQn4vPB5DNwTBYDSJF5RsArz1E6C1FvP40NQzM5PTn+QcWm0oyhQoI5SFJoTig70fIDQh1JIfg4iICEAODzJ79+4NAHj27BkCAgIwYsQItGzZEtu2bYOiKOjevTusra2l606ePAlFUVCxYsWcbJeIiIj+oaDQIHx56Uspn1xnMsq6lRXD6zuAoHXyIp2+ADzFX4gPB4di2o5rUmmXat74qHHJLPVMRERERESUXxV2scOyt6pK52UevRWGeb/flC8o/QbQYw2g0Yl59APgyPy0L6211pjbcK644w6Ah3EP8eEfHyI8MdxSH4GIiAhADg8yO3bsiDZt2kBVVTx48AArVqzA/v37AQDOzs6YNm2adE1oaChOnDgBAJk+rUlERESvVkxyDMYeHQujahTyDqU6oH2p9mJxfDiwa4S8SP3hQIUOQhT8PA5D1gfBZHaEZkDxApjbtTIUs+1niYiIiIiIKF2TsgUxvrV8HMe3x+/jl/OP5AvKtga6fQcoWjE/9TnwIv0G0zIFymByncnS5fdj7qP/H/0RlRSV5d6JiIj+J0cHmQDw66+/Yvjw4XB2doaqqlBVFbVq1cL+/fvh6+sr1X/99dcwGlP/MNqyZcucbpeIiIj+gqqqmHZyGp7FPxNyX2dfTKw90bwY2D0SSDC7Q7dYPaDZFCF6mWzAgLXn8TLZIJa62eOrd2rCRmf2izURERERERFJPmxYAl2qe0v5xK1XceFBJgPHCh2BBmY3n6pGYNdIwJS+JW3n0p3l3/kA3Im+gwH7BiAmOSbLvRMREQGvYJBpa2uLpUuXIiIiAs+ePUN0dDROnz6NGjVqZFrfrl07HDp0CIcOHUKFChVyuFsiIiL6K7/c+gX7H+4XMp1GhwWNFsDeyuz8ymtbgOvbxczKIXVLWa24fdHMndfxICJByJxsdfiuXwDcHORt6ImIiIiIiEimKArmdK6Mqj6uQp5iNGHwT4GIik+RL2o0GihQXMwenQYuikeE9CzXE58GfCpdfjPyJsYfGw9VVaXXiIiI/q0cH2SmvbFGg0KFCsHZ2fkv66pWrYrGjRujUaNGOdQZERER/RO3o25jwbkFUj6yxkhUcDe7+SjuBbB7lLzIm9MBtxJCtPfac2w02+ZIq1Gwqk8N+BV0zHLfRERERERErxNbKy2+fqcGCjnbCPnz2CR8uvmyPHC0sgPaLJYX2jcFiI8QoncqvIMRNeTjQ449OYbDjw5nsXMiIqJXOMgkIiKivCvRkIgxR8Yg2Zgs5I2KNkKf8n3EYlVNPRcz0WzbohKNgJofCFFoXBLGb7kivd/w5qVR38/DIr0TERERERG9bgo62+Kbd2vCWif+OXjf9RdYd+ahfEHpN1K3mc0oMSp1mGnm/UrvY3DVwVK+4NwC6XdGIiKif4uDTCIiIvrXFpxbgLsxd4XM084TM+vPhKIoYvHF9UDwbjGzdgQ6fg5o0n8UUVUVn/56GZFmWxtVL+aKQU1KWbR/IiIiIiKi141/UVdMbFNeymftuo7g53HyBa3mpf7ultHFdUDICal0oP9A1PaqLWSPXz7G2utrs9QzERGR7u9Lso/RaMTly5fx+PFjxMbGwmg0/u017777bg50RkRERH9mb8he/HrrVyFToGBuw7lws3UTi58GAbtHyou0nA24FhOidWce4nBwmJA5WGux9K2q0Gl57xUREREREVFWvVvXF8duh2H/jdC0LNlgwtCfA7FjSAPYWmnTi52LAM0mAXvGiYtsHwx8dBywSR9yKoqCsbXGovvO7jCq6X/j/fry12hfsj0KORTKts9ERET52ysZZD58+BDTp0/Hxo0bkZiY+I+vUxSFg0wiIqJX6OnLp5h+crqUf1j5Q9QuLN59i5dhwIY+gCFJzEs1B6r3FaI7oS8xe/d1ad0p7SvA190hy30TERERERFR6t9XF3SrglbLjiI0Ln3b11svXmL27huY2amSeEFAf+DiT8DzDEeARN0H9o4HOnwmlJYuUBpvlX0L62+uT8sSDYlYGrgU8xrOy5bPQ0RE+V+OP95w4sQJVK1aFT/88AMSEhKgquq/+oeIiIheDb1Jj7FHxyJOL245VMWzCj6u+rFYbEgBNr0LxD4Wc4eCQMeVQIbtZ+OTDfho3QUk6U1CaYsKhdCjpo9FPwMREREREdHrzs3BGkvfqgrzU0HWnn6ATeceiaFWB7RfAWjMnocJXAPc/E1ae1DVQXC1cRWy3fd2Iyg0yAKdExHR6yhHB5mxsbHo0qULoqOj056uXLVqFYDUu4GGDh2KlStXYsyYMfD390/L+/Tpg++//x7fffddTrZLRERE/y/FmIJRh0fhYthFIXeycsL8RvNhpbESL9gzFnh4Usw0VsBb61K3J/p/qqri082XcSf0pVDq4WiDuV0qy+dtEhERERERUZbV9/PAR41LSfnYLZexNcjshlTv6kDjsfIiO4YCL0OFyMXGBUOrDZVK556ZC6Pp748VIyIiMpejg8xVq1YhLCwMiqJg3bp1+OGHHzBgwIC015s3b45BgwZh/vz5uHjxIrZu3YoCBQpgw4YNAIC+ffv+2dJERESUTRINiRh2cBgOPTokvTat3jR4O3qL4fnvgfOZ3HzUdhFQTNx+dvXx+9h9+ZmQKQqwuEcVuDvaZLl3IiIiIiIiytzIN8ugio+rkKkqMGrTJey6/FQsbjASKBogZgnhwPYhqRdl0LV0V5RzKydkNyJvYGPwRku1TkREr5EcHWT+/vvvAIAaNWqgZ8+ef1vfsWNH7N69G6qqYtCgQbh582Z2t0hEREQZxOvjMWj/IJx4ekJ6rXuZ7mhRvIUYPrkA/DZGXijgQ6BGPyE6cy8Cc3+X/98+4o0yaFzGMyttExERERER0d+w0mrw5dvVUbSAnZCbVGD4hovYe+15eqjVAV2+BqwcxEVu7wUufC9EWo0W42qNk95vWeAyPIp7JOVERER/JUcHmdevX4eiKOjUqVOmrxuN8vYCtWvXRo8ePZCYmJi2DS0RERFlv9iUWAzYNwDnX5yXXmtdvDXG1x4vhkmxwK/vAya9mPvWB1rNE6IXsUkYvD4IRpN4526zcgUxpKmfRfonIiIiIiKiv1bE1Q4/96+Dwi62Qm40qRiyPhCHbmbYOtatJNBqrrzIngnAA/FokRqFaqBNiTZClmhIxNSTU2FSTRbrn4iI8r8cHWRGR0cDAHx8fITcyir1XK34+PhMr2vevDkA4I8//si+5oiIiCiN0WTEkANDcDnssvRaZ7/OmNtwrngupqoCu0cCUSFisXNRoPuPgDa91mA0Ycj6QIS/TBZKi7nZY2mPqtBoeC4mERERERFRTvFxs8f6/nVQ0Ek83kNvTB1mPo1OTA+rvwuUFQeUMCQCP3UHHp0V4rG1xsLN1k3Izj0/h03BmyzaPxER5W85Osi0trYGANjainf4ODk5AQCePHmS6XV2dnZ/+ToRERFZ1sbgjQgKDZLyXuV6YVq9adBqtOILF9cDV34RM40O6PEj4ChuE/vNsfs4FxIlZDY6DVb1qQEXeysQERERERFRzirh4YD1/WvDw9FayONTjJix83p6oChA+xWAg9lxICkvgXVdgSeBaZGbrRsm1p4ovdeSC0vwOO6xRfsnIqL8K0cHmd7e3gCAiIgIIS9ZsiQA4Ny5c5leFxwcDAAwGAzZ2B0REREBQHhiOFYGrZTy9yq9h/G1xkOjmP34EH4b+G20vFDTiUDRmkIU/DwOS/fdkkrndK6MCkWcs9Q3ERERERER/Xd+BZ2w7sPacLETbzDdc+05Dt58kR44egI918vnZSbHAms7A8/Sd/ZpUbwFWhZvKZQlGhIx5eQUbjFLRET/SI4OMv39/QEAN27cEPI6depAVVX89ttvePDggfBadHQ0Vq1aBUVRUKJEiRzrlYiI6HW15PwSxOnjhKxtybYYUX0EFMVs21dDMvDre4A+QcxLNgHqfyJEeqMJo3+5hBSj+Mtq9xpF0bVGUQt1T0RERERERP9VOS9nTGxbXsqnbL+GxBRjeuBTC3j7F0BnJxYmRQNrOwGhN9OiCbUnoIBNAaHs3PNz2Bi80YKdExFRfpWjg8zGjRtDVVUcPnxYyPv06QMASE5ORqNGjfDll1/ijz/+wJdffokaNWogNDT1UOlOnTrlZLtERESvnfPPz2PnvZ1C5mTlhNE1R8tDTADYNwV4fkXM7D2Azl8BGvHHjC8P38WVJzFCVsTFFpPbV7BI70RERERERJR13aoXRUBxcfD4OCoRKw/dFguL1wd6bwB04jFiSIgAfukLGPUA/n+L2TryFrMLzi7Avgf7LNo7ERHlPzk6yOzQoQMA4OrVq7h27VpaXqtWLfTp0weqquLx48cYMmQIWrdujSFDhiAkJAQA4OPjg1GjRuVku0RERK8VvUmP2WdmS/nQ6kPhYechXxD8O3BmlZx3/gpw8hKia09jsOLAbal0fjd/ONvyXEwiIiIiIqLcQqNRMKtTZeg04s2sXx+9h9svxN17ULIJ0PMnQCuerYmwm8C5b9O+bFm8JVr4thBKDKoBY46MwZ6QPZZsn4iI8pkcHWT6+Pjg0KFD+O233+DsLJ6DtXr1anzwwQcAAFVVhX9q1KiBAwcOoECBApktS0RERBaw/sZ63Im+I2Tl3cqjR5kecnHsU2DbIDmvOwQo/YYQpRhMGLXpEgwmVch71y6GhqU9s9w3ERERERERWVZZLyd80FA85ktvVDFp21Woqvi7HfzeAHqsBWC2i8+huUB8eNqXE2pPgKed+DugUTVi3NFx+O3eb5Zsn4iI8pEcHWQCqdvLtmzZEj4+PkJuZWWFb775Bvfu3cPq1asxZ84cLFu2DCdOnMC5c+dQqlSpnG6ViIjotfE8/jm+uPiFkClQMKnOJGg1WrHYZAQ29wcSI8W8cFWg+VRp7cX7gnHzuXjXbtECdpjQRj53hYiIiIiIiHKH4c1Lw9tVPAPzzP1I/HrhsVxcthVQrY+YJccAB2akfelu545vWnwDd1t3ocyoGjH++HjsvCsec0JERAS8gkHm3/H19cV7772HcePGYdiwYahbt+6rbomIiChfU1UVM07NQIIhQci7lO4Cf09/+YJjS4AHx8XM2hHo9h2gE7cT2nnpKb46ck9aYkE3fzja6LLcOxEREREREWUPe2sdpneoKOUzdl7H46gE+YLmUwEbcRc+BK4BngalfVnKtRS+a/Wd9GSmSTVh4vGJOPLoiEV6JyKi/CPXDTKJiIgoZ/104ycce3JMyFxtXPFJ9U/k4oengcNz5bztEsBd3D3h6pMYjPn1klTar15x1CuVyZmbRERERERElKu8UaEQ3qxQSMjikg0YtekSjGbHh8DRE2gyzmwFFfh9LJBhO9qSLiXxXcvvUNCuoFmliqknpyI6KdqCn4CIiPK6HB1kajQa6HQ67Nix419dt3fvXmi1Wuh0fHKDiIjIkm5E3MCSC0ukfGSNkXC1dRXDl2HA5g8B1SjmVXoBVd4SooiXyRi49gKS9CYhr+TtjLGtylmidSIiIiIiIsoBMztWgoudlZCduR+Jb4/Ju++g1gDAo4yYPToDXPlFiIq7FMf3rb5HIXtxSBqRFIF55+ZZpG8iIsofcvyJTOkw6H9x3X+9loiIiGQJ+gR8evRT6E16IX/T90108uskFsc+A35oA8Q8EnO3UkCbRUKkN5ow6KdAPIlOFHJ3B2t89U5N2FmbnblJREREREREuZaXiy1md64k5Yv+CMb1p7FiqLUCWmUyiNw3BUh+KUTFnIvhmxbfwFZrK+S77+3GwYcHs9w3ERHlD9xaloiI6DU1/9x8hMSGCJmXgxem1p0KRVHSw+iHwPetgfBb4gIaq9RzMW0chXjWrus4cz9SyHQaBV/2qQFvVztLfgQiIiIiIiLKAe38i6BzNW8h0xtVfLIxCEl6s117/JoDZduIWdwz4OBMad0SLiUwvPpwKZ9xagZikmOy3DcREeV9eWKQGRcXBwCws+MfP4mIiCxhT8gebLm9Rcg0igbzG86Hi41LehhxF/iuNRB1X16k1VygSFUh2nHpKX489UAqndahImqVcLNE60RERERERPQKTO9YUbo59daLl1iwJ1gubjkb0FqL2ZlVQMgJqbR3+d6oXrC6kEUkRWDu2blZ7pmIiPK+PDHIPHDgAADAy8vrFXdCRESU992LuYcZJ2dI+UD/gaheKMMvj6E3U5/EjH0sL9JyDlCrvxA9jEjAhC1XpNJetYqhTx3fLPdNREREREREr46zrRUW96iCjBv4AMB3J+5j7WmzG1rdSgL1P5EX2T4YSEkQIo2iwYz6M7jFLBERZUqXXQsfOXIER44cyfS1DRs24OLFi395vaqqiI+PR2BgIA4dOgRFUVC3bt1s6JSIiOj1EZUUhcH7ByNOHyfk1QtWxwD/AelBUiywrivw8oW8SNslQMAHQqQ3mjB0QxBeJhuEvIZvAUzvUNFi/RMREREREdGrU6ekOwY0LImvjt4T8snbrsJaq+CtgGLpYaPRwM1dQOj19CzqPnBgBtBaPEfT19kXw6oPw4JzC4R85umZqFGohrhzEBERvVaybZB5+PBhzJghP+2hqio2btz4r9ZSVRU6nQ7Dhg2zVHtERESvnRRjCj459AkevxSfsHSydsK8hvOg02T4seDwPPlJTEUDdPwCqNpLWnvRH8G49ChayFzsrPBZr2qw1uWJDSCIiIiIiIjoHxjZogyO3Q7H9WexQj5uyxVYaTXoUr1oaqCzATp9AXzTHFAznKN5ZhVQoQPgW0+4vne53tj3YB+CQoPSsvDEcMw7Ow9zG3KbWSKi11W2/mVRVVXhnz/L/+6fatWqYceOHQgICMjOdomIiPItVVUx9eRUBIYGCvn/zsUs7Fg4PXxxLfUXS6FQB3T7LtMh5tFbYfjqyD0pX9jNH0Vceb41ERERERFRfmKj0+K7fgHwdbcXclUFRv9yCTsvPU0Pi1QDGowwW0HNdItZrUaLmfVnwkZrI+S77u3CoYeHLPkRiIgoD8m2JzL79euHJk2apH2tqiqaNWsGRVEwc+ZM1K9f/y+v12g0cHR0RIkSJeDq6ppdbRIREb0Wvr78NXbd2yXlYwPGomHRhumBqgK7R4l3ywJAw9FAxc7S9WFxyRi56ZKUv1vXFy0q8mxrIiIiIiKi/MjLxRbr+9fBW1+dwuOoxLTcpAKfbLwIe2stmpcvlBo2/hQI/k3cYjbyHrBvMtBmETIeuunr7Ith1YZh4fmFwvvNOD0D1QtV5xazRESvoWwbZPr6+sLX1zfT1ypVqoTGjRtn11sTERFRBntD9mLlxZVS3rtcb/Qu31sML/0MPDwlZgWKAw0+ka43mVSM3HQR4S+ThbyclxMmtCmfxa6JiIiIiIgoN/N2tcPP/z/MfBqTlJYbTSrG/HoZh0Y3gYudVeoWsx0/B759Q7xp9ty3gI0T0HyqMMx8u/zb2P9wv7TF7Pyz8zGn4Zwc+WxERJR75OihVYcOHcLBgwf/9mlMIiIisowX8S8w/eR0KW/g3QBjAsaIYWIU8MdkeZE2iwAreYvYb47dw7Hb4UJmZ6XFyt7VYGulzVLfRERERERElPv5uNljff86KOQsbgcbGZ+CZftvpQfe1TO9QRbHlwL7pqTuDvT/tBotZtSbIW0xu/PeThx+dNhyzRMRUZ6Qo4PMxo0bo3HjxnB3d8/JtyUiInotqaqK6aemI04fJ+SlC5TGwkYLodOYbcxwcBaQIA4mUa4dUPpNae2Lj6KxcG+wlE/rUAF+BZ2y3DsRERERERHlDcU9HPDTh3VgZ3ZD65pTD3D7RYbfRxuPBbxrygucXAH8MUkYZhZ3KY6h1YZKpTNOzUBMcozFeiciotwvRweZRERElHN23N2BY0+OCZmTlRNWNlsJR2tHsfhpEHButZhZ2QOt5knrxiXpMeznIBhMqpC39S+MHjV9LNI7ERERERER5R1+BR0xqEkpITOaVEzfeR3q/waUOhugz+bMh5mnVgJ7xgvDzD7l+6CqZ1WhLCwxDBOPT4TBZLD0RyAiolyKg0wiIqJ86EX8C8w/O1/Kx9YaiyKORcQwJQHY+hEAcTCJRmMAV3EwqaoqJm27ioeRCUJetIAd5napDCXDuSZERERERET0+ujfqCR83MRjSY7fCccf11+kB3auwDtbgKIB8gJnvkwdaP4/rUaLmfVnSlvMHnl8BFNPToVJNVmyfSIiyqU4yCQiIspnVFXFjNMzpC1lGxVthA6lOsgX7J0AhN0UM48yQN0hUumvFx5j+8WnQqbVKFjRqxqcba2y3DsRERERERHlTbZWWkxsU0HKZ+2+jiS9MUOhC9BnC+BTW17kwEwgLP1szT/bYnbH3R1YeG5h+tOeRESUb3GQSURElM/svLcTRx8fFTInKydMqTNFfmLy+g7gwvdmKyhAu2WAzlpIrzyOwdQd16T3G9WiDKoXK2CBzomIiIiIiCgva1mxEOr7uQvZo8hEfHvsnlho65y6zWyxumJuTAa2DwJM6YPPdyq8gzYl2kjvte7GOnxz5RuL9U5ERLkTB5lERET5yIv4F5h3Vj7X8tNan6KQQyExjHkM7JDvbEWj0UDx+kJ0PiQSvb85jYQUo5A38PPAR43Ec1CIiIiIiIjo9aQoCqa2rwitRryJ9vNDd/E0OlEstnECem0AnAqL+eNzwKnP077UKBrMajALDb0bSu/3WdBn2Hhzo8X6JyKi3IeDTCIionxCb9JjzNExiEsRt5Rt6N0QHUt1FItNRmBzfyApWsyL1gIajxOik3fC8c7qs4hLNgi5u4M1lvSoAo2G52ISERERERFRqjKFnPBOHV8hS9QbMWHrFXkrWDtXoP1yeZGDs4QtZq00VljcZDGqFawmlc4+Mxtnnp2xROtERJQLcZBJRESUT6wIXIGg0CAhc7JywtS6U+UtZY8uAh6eFDMbZ6Drt4BWlxYduhmKfj+cQ6JefBLTWqfBil7VUNDZ1qKfgYiIiIiIiPK+EW+UgZuDeFzJ4eAw/HrhsVxcpiVQpbeYGZOB7YOFLWbtdHZY2XwlyhQoI5SqUDHx+ETEJMdYrH8iIso9OMgkIiLKBw4+PIgfrv0g5RPrTJS3lL31B3BE3n4W7ZcBBdLvmv3j2nMMWHseKQaTUGZvrcUP/QJQ38/DAp0TERERERFRfuNib4WJbcpL+Yxd1/E8Jkm+oNWcTLaYPQuc/kKInK2d8dWbX8HHyUfIXyS8wOzTs7PcNxER5T4cZBIREeVxj+IeYdLxSVLevUx3tC3ZVgyfXwV+fQ9QxeEkqvYBKnVN+/JxVAKGb7gIvVHc9sfJRoe1H9RCPQ4xiYiIiIiI6C90qe6NZuUKCllckgHjt1zOZIvZAn++xWz4bSHysPPAsqbLYKWxEvLfQ37H7nu7LdI7ERHlHhxkEhER5WHJxmSMPjIacXrxXMzybuUxttZYsTjuBbD+LSDlpZi7+wGt5wvR9J3Xpe1kXe2tsL5/HdTwdbNY/0RERERERJQ/KYqCOZ0rw8lWJ+SHgsOwOfCJfEFmW8wakoBtg4QtZgGgTIEyGF59uLTE7NOz8ezlsyz3TkREuQcHmURERHnYonOLcD3iupA5WTlhcZPFsNHapIcpCcCGXkCs2Xkkti5Arw2AjWNadPDmC+y7/kIoK2BvhQ0D6qByUReLfwYiIiIiIiLKn7xcbDG1fUUpn77z2p9vMevoJWaPzwKnv5RK36nwDmp71RayOH0cJhyfAKPZ4JOIiPIuDjKJiIjyqMthl7EheIOUz2wwUzwvxGQCtn0EPLkgFmp0wFvrAI/SaVGS3oipO65Ja05qWwHlvJwt1jsRERERERG9Hrr+yRazIzddRJLZTkB/vsXsTGmLWY2iwawGs+Bk7STk51+cx5rrayzSOxERvXocZBIREeVBJtWEuWfmSnnfCn3RvFhzMTy6ALi+XV6k3VKgRCMh+uLwXTyKTBSyWsXd0KW6d5Z7JiIiIiIiotfPn20xe/JuBAauvSAPM8u2Aqr0EjNDErB9sLTFrJeDF6bUmSK954qgFQiODLZI/0RE9GpxkElERJQH7bi7A1cjrgqZn6sfhtcwOyPkSSBwZIG8QP3hQPV3hSgkPB6rjtwVMq1GwcxOlaAoikX6JiIiIiIiotePl4stprSrIOVHboWh/5rzSEwxG2a2mitvMfvoDHBmlbRGqxKt0K5kOyEzmAwYd2wckgyZbF9LRER5CgeZREREeczLlJdYdmGZlI+vNR5WGqv0wJAMbBsEqGa/EJZrBzSfJkSqqmLqjmtIMZiE/P36xVHWS9ymh4iIiIiIiOjf6lajKHrULCrlx26H44Mfz4nDTLsCQPtl8iIHZgDhd6R4Qu0JKOxQWMjuRN/B8sBMtqklIqI8hYNMIiKiPOary18hIilCyN70fRO1CtcSC48sAMJuiJlbSaDL14BG/BHg96vPceRWmJAVcrbB8DfKWKxvIiIiIiIien0pioK5XfzRvYY8zDx5NwLv/XBWHGaWbQ349xQLDUnAht5AvPg7sZO1E2Y3mA0F4m5C626sw8mnJy32GYiIKOdxkElERJSH3I+5j3U31gmZjdYGo2uOFgufBALHl5pdrQAdPwesHYT0eUwSJmy9Ir3X5HYV4Gijk3IiIiIiIiKi/0KrUTC/qz96BvhIr52+F4kZu66JYet58haz4cHAT92A5DghDvAKQL9K/aR1Jx+fjJjkmKy2TkRErwgHmURERHnIgnMLYDAZhOz9Su+jiGOR9MCQDGwfLG8pW/sjwLeeEBlNKkZsvIjoBL2QNyztgbaVxW15iIiIiIiIiLJKo1Ewp3NlvF27mPTaz2cf4WjG3YLsCgDtM9ke9mkgsOHt1N9/MxhSdQjKuZUTstDEUEw/NR2qqlqkfyIiylkcZBIREeUBqqpi7fW1OP7kuJB7OXjhvUrvicVHFwKh18WsQAmg+RRp3VVH7uLUPXFLHkcbHWZ3qgxFUaR6IiIiIiIioqzSaBTM6lQJferIw8xxmy8jLinDzbZlWwEtZsmL3D8CbP4QMKXfxGuttcbcBnNhrbEWSvc92IfNtzdbrH8iIso5HGQSERHlcsnGZEw5OQULzi2QXhtVcxTsdHbpweMLwLElZlUK0OkLwNpeSAMfRmHJvlvSmrM6VUIxd3spJyIiIiIiIrIURVEwrX1FVPZ2EfKnMUmY89tNsbjeUKDBSHmRGzuAXZ8AGZ629Cvgh5E15do5Z+bgcthlS7ROREQ5iINMIiKiXOx5/HP0+70ftt3ZJr1Ws1BNtPRtmR4kRAK/9P1HW8rGJukx7OcgGE3i1jpdqnujUzVvS7VPRERERERE9Kd0Wg0Wda8Ca634Z+qfzz7EsdthYnHzKUCNfvIigWuA898JUa9yvVCviPh7sN6kx4hDIxCeGG6J1omIKIdwkElERJRLBYUG4a1db+FqxFXpNW9Hb8xpMCd9+1eTCdj6ERDzSCwsUAJoPlmIVFXFxK1X8TgqUciLu9tjRsdKFv0MRERERERERH+lrJcThr9RWsrHbb4ibjGrKEDbJUCFTvIif0wCwu+kfalRNJjdYDYK2hcUykITQzHq8CjojXrzFYiIKJfiIJOIiCgXuhl5EwP3DURkUqT0Wu3CtbGh7QYUdiycHp5YBtzeKxZqrICu3wLWDkL89dF72HnpqZDpNApW9KoGRxudpT4CERERERER0T8ysFFJaYvZJ9GJmPu72RazGi3Q5WugZBMx1ycAW/oDGQaUHnYeWNZkGaw0VkJpYGgg5p+bb8n2iYgoG3GQSURElMuEJ4Zj6MGhSDQkSq/1q9gPq95YBVdb1/Qw5DhwcKa8UMvZQNGaQrTn6nPM23NTKh3Tsiz8i7pKOREREREREVF202k1WNjdH1ZaRcjXn3mI47fNtoLV2QBdvgUcPP+PvfuOjqrq1zj+nUnvECCA9N57VRABUVAEFJGO0gRFwIKIKIgFQQEpIkovAlKk2EF6770jvfcSQnpm5v6R96Ine0INEOD5rOW6ybPP3mfv964hyfzm7G3NT26Cpf0sUbEMxehZ0bpLEcC0vdOYvW92isxdRETuLhUyRUREUpE4RxzvLH6H05GnLbmvhy9fP/k1Xcp2wdP+n6cmI87AjNbgcloHKvwilG9nibYfD+edaZtxWY/FpEr+DLz+ZO4UXIWIiIiIiIjIrSmYKZi3nza3mO02cxtXYxOsYWAGqDfMHGT5ADi2zhK9lO8lGhVoZFzae01vdl/YfUdzFhGRu0+FTBERkVTC5XLx+erP2XpuqyX3tHnyfY3veT7380k7wOz2cPWMNQ/NA3WHJp4f8j+nwqNpM2E9MfHWgmfesECGNimF3W791KuIiIiIiIjIvdb+qTwUzRJsyU5cjqbPX24KjvlrQtnW1szlTNxiNjbCEncr143SYaUtWZwzjveXvs/VuKspMncREbk7VMgUERFJJX7c9SO/HvjVyD+u+DHlMpUzO+ycDQcXWzNPX2j4I/j++4dfZGwCrcdv4GxErOXS0ABvxr5WjhA/63khIiIiIiIiIveDl4edAa+UuLktZgGe7Q3p8lqzS4dhbvck43rxTdVvCPMLs+RHI47y6epPcSXdukhERFINFTJFRERSgZUnVjJw40Ajb1qwKQ3yNzA7xEXBPPOcD54fAJmKWqJuM7ex+9QVS+btYWdkizJkT+d/R/MWERERERERSUkFMwXTufpNbjHrHQD1R8J/j2AB2DwRdv9hidL7paffU/3wsHlY8r8P/820vdNSZO4iIpLyVMgUERG5zyLiIvhk5Sc4k5xzWTFzRbqW6+q+08rBcOW4Ncv3LJRuYYn+3HaKP7adMrr3a1CcsjlD72TaIiIiIiIiInfFG1XdbzHb190Ws1nKwFMfmvlvnSDitCUqk7EMHUt1NC7tt74fuy7suqM5i4jI3aFCpoiIyH02ZNMQzkaftWQ5gnMw4KkBeCb9VCnApSOwcog1s3tBzb6W6MLVWHr+usPo3vnpfLxYKssdz1tERERERETkbvDysNO/gbnF7OS1R1m+75zZofK7kLW8NYu+CL92hCTbxrYu2ppKWSpZsnhnPF2WdCEiznq2poiI3H8qZIqIiNxHm89uNraw8bZ7M6TaEEJ8Qtx3mtcDEmKsWcU3Ib31XJBPftvJxcg4S/Z47nS887S5RY+IiIiIiIhIalIoczCd3Gwx+9bkTcbxKXh4Qv0R4B1ozffPh/WjLZHdZqdv5b6E+VvPyzx+9TidFnUiKj4qReYvIiIpQ4VMERGR+yTeEc9nqz4z8vYl2pMnTR73nQ4tg92/WbOAMKhi3YL2r+2n+DPJlrL+3h70a1Acu936iVYRERERERGR1OjNqnko8ph1i9krMQm8OnYdRy5EWi8OzQ21vjIHmdcDzv1jidL6pqV/lf7GeZkbz2zkrYVvqZgpIpKKqJApIiJyn4zZMYYD4QcsWd40eWlVpJX7Do4EmNPNzGt8Cr7//mF34WosPX8xt5Tt/lxBsoX638mURURERERERO4ZLw873zQsgZ+XteB4LiKWFmPWcfZKkt2KSjWHgi9Ys4QYmNUWEqw7FpXOWJrOpTsb99xwZoOKmSIiqYgKmfeIy+Xin3/+4aeffqJLly5UrVqV4OBgbDYbNpuNli1b3ta4q1evpnXr1uTJkwd/f39CQ0MpU6YMvXv35vz58ym7CBERSTGHwg8xcttIS2bDxqdPfIqXh5fZIT4afnkTzu6y5lnKQIkmluiT33ZyIcmWshVzh9KsQo4UmbuIiIiIiIjIvVIwUzAjWpQxzss8ejGKFmPWER4V/29os0GdbyEwo3WQU1th/ifG2K2KtKJJwSZGrmKmiEjqoULmPfL+++9ToEABmjVrxsCBA1m6dCkREbd/eLTL5eK9996jUqVKjBs3joMHDxIdHc2lS5fYtGkTPXv2pGjRoixatCgFVyEiIinB6XLy2erPiHfGW/LGBRtTIkMJs8PlozDmWdg+3Wx7rh/Y//1x/svmE263lO3foIS2lBUREREREZEHUpX8GRjUqCS2JH/W7j0TQavx64iJd/wbBqSDet+bg6z9AbZOtUQ2m43u5bsnW8zsuKgj8Y54o01ERO4dFTLvEYfDYfk+KCiIwoUL3/Z43bt3Z9CgQbhcLgICAujcuTOTJk1i+PDhPPPMMwCcOXOGevXqsWXLljuZuoiIpLCJuyay8cxGSxbmH0bnUuaWNhxaBiOrwultZlvJ5pC17LVv95+N4KPZ243LPtSWsiIiIiIiIvKAe6H4Y3z5YjEj33T0Mv3/3msN89WA8u3MQX5/G05usUTXK2auP72eoVuG3sm0RUTkDqmQeY8ULlyYd999l8mTJ7Nnzx7Cw8MZNmzYbY21efNm+vXrB0BISAirVq1iyJAhNGvWjPbt2zNv3jx69eoFwNWrV2nXrh0ulyvF1iIiIrdv89nNDNo4yMh7VOhBoHegNVw/Gn58EaIumAPleRqe73ft26i4BN6ctImoOOsHZyrmDqW5tpQVERERERGRh0DTCtnpWrOAkY9ZcYgV+5Ics/Vsb8hazpolxMC05hBpvfZ6xczxO8az9tTaO567iIjcHhUy75F27doxcOBAmjZtSoECBbAl3QfhFnz++efXCpN9+vShePHixjW9evWifPnyAKxfv56//vrrtu8nIiIp42LMRd5f+j4Ol7XY+GyOZ6mWvZr14p2z4c8ukORaACq/B81+Bu8AIHG78Y9n72Df2auWy0IDvBnUqKS2lBUREREREZGHRoeqeWj5RE4jf//nrVyOivs38PSBhhPN8zLDj8HPLcGRYIn/v5j5cr6XLbkLFx8t/4jLMZdTZgEiInJLVMh8wERERDBnzhwAgoODadmypdvrbDYbnTp1uvb9tGnT7sX0REQkGU6Xk4+Wf8TZqLOWPGtgVno90ct68amtMPtNcxCvAHhlAtToBXaPa/GUdceYvfmE5VKbDYY0LknmEL8UW4OIiIiIiIjI/Waz2ej+fEEKZQ625KevxPDxLzusO9MFZ4aGP4LdyzrI4eUwv6f7sSt0J2+avJb8bPRZeq3qpV3vRETuAxUyHzBLly4lNjYWgCpVquDvn/yZZzVr1rz29dy5c+/63EREJHmjt49m5cmVlszL7sU3Vb8h2Ps/f3xFnIEpTSAh2jpA2pzQdgEUedES7zgRzqe/7zTu17l6Pp7MlyGFZi8iIiIiIiKSevh4ejC4UUm8Pa1vb/+57RS/bLF+0JfsFeG5r81B1nwPu341x/bwoV+VfnjbvS35omOLmLFvxh3PXUREbo0KmQ+YHTt2XPu6TJky1702Q4YM5MiReC7auXPnOHv27HWvFxGRu2PtqbUM22Kei/xh+Q8pnK7wv0FCbOJZHVeS/NHlnw5e/Q0yFrbEsQkOOk/ZTFyC05JXzpuezk/nS7H5i4iIiIiIiKQ2BTIF8WGtgkb+yS87OX4pyhqWbQ2lWpiD/NoRLh404nxp89GlbBcj77euHwfDzetFROTu8bzfE5Bb888//1z7OmfOnDe8PkeOHBw5cuRa37CwsFu+58aNG6/bvnv37mtfJyQkEB8ff8v3EJGUEx8fT0JCwrWv5f5aeXIlXZd3xemyFhufy/EcL+Z68d//H7lcePzeCfvxdZbrXHYvHC+PxxX4GCT5/+eoZYc4eD7SkmUM8mHAy0VwOhJwujleU+4PvS5FUhe9JkVSF70mRVIfvS7lQdGsXBYW7D7NqgMXr2URsQm8O20LE1uVxcNu+/fiZ/vicXoH9lOb/81ir+Ca/hoJr81JPFPzPxrkacDy48tZcXLFtSzGEUO3pd2YUHMCXkm3q72L9JoUSV30mjT9//8ed4MKmQ+Yy5cvX/s6ffr0N7w+Xbp0bvveirJly970teHh4Vy4cOG27iMiKSMhIYGIiIhr33t66p/6+2XhyYX039Efh8taUcwWkI03877JxYv//qHlv/1Hgreb5xlfebIX0QH5Icm/reeuxvH9kgOWzG6Dz2rlgNirXIi9moIrkTul16VI6qLXpEjqotekSOqj16U8SD6sloXmx8O5Evvv397rD1/i23m7eLVcJsu1HtUGkG7GS9jjrlzLbKe3EffHB0Q8+Ykxduf8ndl5fieX4i5dy/Zc2sOw9cNokdfNE553iV6TIqmLXpOm260/3QxtLfuAuXr13zemfX19b3i9n5/fta//+8ISEZG7a+bhmXy1/SujiOnr4UvPEj3x8/z332ePK8cIWvuNMUZksVeJLvSK2/G/X3mCqHjrU54vFctAySxBKTB7ERERERERkQdDWKA33Z7OYeQjV59k71nrFrOO4KyEV+trXBuwczI+B+YYeVqftHQt2tXIJx+czD/h/xi5iIikPJWJ/2f06NEcP348Rcb69NNPU2Sc1GLDhg3Xbd+9ezctWiR+AikkJMTyFKiI3Hv/3c4gNDQUL697t9WJgMvlYujWoYzfO95oC/YOZvBTgymZoeR/O+Ax7w1sCTGWa525quL9Qj/S2c0f1ZuPXWbO7ouWLI2fF91qFyGtv3cKrEJSml6XIqmLXpMiqYtekyKpj16X8qBp+Hg6NpyMZvaWU9eyBKeLz+cf5Zc3K+Lr5fHvxeka4bi0HY91wy1jpFnag4Q8FSFdXkteK10tNkds5ud9P1/LHC4H3+z+hsm1JuPjYd2S9m7Qa1IkddFr0pQmTZq7NrYKmf8zevRo1q5dmyJj3c1CZmBg4LWvY2JirnNloujo6GtfBwXd3lM6ZcqUuelrPT099aIVSQX+fzsDLy8vvSbvsVHbRjF+13gjD/MPY0SNEeRNa/2DiG3T4eBiaxaQAfsr47D7+JGU0+mi9197jbzLs/kJCwm4k6nLXabXpUjqotekSOqi16RI6qPXpTxoPnuxGOsOX+bE5X/fDz1wLpJvFhzg07pFrBc/+wWc2JD43//Y4q7iNeUVeO13CM1lufz9cu+z5vQajkUcu5YdDD/IyB0jea/se3dnQUnoNSmSuug1aXU3t9fV1rIPmP9Wtc+fP3/D6/97XuXdrIiLiAgsO76MoZuHGnmukFxMem6SWcSMvABzPzQHeu5r8A91e48ZG4+z7Xi4JSuYKYgm5bPf9rxFREREREREHnTBvl4MalQSm82aj191mKX/nLOGnt7wyjjwDbHm4cdgQh24eMgS+3v507tSb2xYBx+/czybzmxKqSWIiIgbKmT+z5o1a3C5XCny392UP3/+a18fPnz4htcfOXLEbV8REUlZR68c5cNlH+LC+nOgePriTKg1gcyBmc1O83pA1AVrlu9ZKFLf7T2uxMTT7+89Rt6rThE8PfQjXURERERERB5t5XOF8sZTeYy8689buRgZZw3TZIeXRkCS4uS1Yualw5a4dMbSvFbkNUvmwsXHKz4mKt56FqeIiKQcvev5gClatOi1rzdu3Hjda8+dO3etkJkhQwbCwsLu6txERB5VUfFRvL34bSLiIyx5rpBcjHhmBGl905qdDi6BrT9ZM68AqP0NxsdHgWMXo2g4fDXnr1r/8KpdLDOP59HZxCIiIiIiIiIA79bIT5HHgi3Z2YhY3p66GYczyUMoBZ6DF3/AbTFz/AtGMbNjqY7kCbEWSo9fPU735d1xupwptAIREfkvFTIfMFWrVsXHJ/EA6WXLllnOwEzq77//vvZ1rVq17vrcREQeRS6Xix4re7D/8n5LHugVyJBqQwj0DjQ7xUXB7++YefUeiZ8ITWL5vnPU+W4Fe05bC6W+Xna6P1/wTqYvIiIiIiIi8lDx9rQzpHFJfDytb30v33fe7S5HlGxynWJmncRjYf7Hx8OHL5/8Eg+bh+XSRccWMXjT4BRagYiI/JcKmQ+YwMBAnn/+eQCuXLnC+PHj3V7ncrn47rvvrn3fqFGjezE9EZFHzpgdY5h/ZL6R932yL7lCcrnvNKcrXLKet8FjpaBCe0vkcrkYsfQAr41dx+WoeGOYzk/nI2ta/9ueu4iIiIiIiMjDKG9YED1qFzLyEUsP8se2k2aHZIuZR+G3jvCf48SKpCvCmyXeNIYYt2Mcs/fNvtOpi4hIEipkPoB69uyJ7X/bDnbv3p1t27YZ13z++eesXbsWgHLlylG7du17OkcRkUfB9nPbGbp5qJF3KNmBqtmquu+0eTJsnmTNbB5QdyjY//1Ep9PpouuMbfSds4ekO98AdK6elzfdnPshIiIiIiIiItC8Yg7ql85i5F1/3sbuU1fMDskVM/f+BRvGWqLXi79OzZw1jSE+X/0560+vv5Npi4hIEp73ewKPisuXLzNgwABL9v/nVwJs3ryZHj16WNqrV69O9erVjbFKlSrFBx98wNdff014eDhPPPEEbdu2pXz58ly9epWZM2cyb948IPEJzpEjR96FFYmIPNpiHbH0WNnDOAOjWrZqtC/e3n2nM7vgzy5m/mQXyFTMEg1euI8ZG48blwZ4e/BNw5LUKprptucuIiIiIiIi8rCz2Wz0eakY+85cZfuJ8Gt5dLyD9hM38lvHSqTx97Z2KtkEYq/AnA+s+d8fQ45KEJZ4vIvdZqd3pd6cvHqS7ee3X7sswZXAu0veZfLzk8kRnOOurU1E5FGiQuY9cvnyZb788stk27dt22Y8Wenp6em2kAnQt29fYmNjGTJkCJGRkQwZMsS4JiwsjClTplCyZMk7mruIiJi+3/I9B8MPWrIcwTnoU7kPdpubDQ9iI2D6q5CQ5GzjnE9C1Q8t0R/bTvLtwn3GELnTBzDy1TLkDQu64/mLiIiIiIiIPOx8vTwY3qIMdYau4GJk3LX86MUoOk/dwriW5fCwJ3kCs3w7OLAY/pnzb5YQDTPbwusLwdMncWxPX76t/i1N/mzC6cjT1y4Njw2n06JOTK09FX8vHQcjInKntLXsA8pmszFo0CBWrlxJy5YtyZ07N76+vqRJk4bSpUvz+eefs3PnzmQLoSIicvu2ndvG+J3jLZkNG70r9SbQO9Ds4HLB72/DhSTFyYAweHmMZUvZHSfCef/nrcYQT+ZLzy8dK6mIKSIiIiIiInILsqTxY1jT0kbBctk/5/h+8X6zg80G9b6DwIzW/Mx2WPCZJUrvl57vqn+Hn6efJT8Ufoh+6/ulyPxFRB51eiLzHsmZMycul5tDzu7Q448/zuOPP57i44qIiHvJbSn7auFXKRlW0n2ntcNhx0xrZrNDgzEQ9O8fRmevxPD6jxuIibeOXTBTEMOblyHARz+2RURERERERG7V43nS8fHzhfj8j12WfNCCfyiTMy1P5Elv7RCQHl4aDhNfsuZrhkHe6pC3xrWoQGgB+lXpR+dFnXHx7/u/M/fNpFKWSjyT45kUX4+IyKNET2SKiIjcgmFbhnEo/JAlyxmck46lOpoXu1ywbADM/dBsq/YR5Kpy7duYeAftJm7kVHiM5bJ0Ad6Mfq2sipgiIiIiIiIid6BVpZzULfGYJXO64O2pWzgXEWt2yFMdHnfzt/4vb0HURUtUNVtV2hVvZ1z66apPLdvOiojIrVMhU0RE5CZtPbeVCTsnWDK7zc4Xlb7A19PXerHTCXO6waIvzIHyPA2Vu1ii3n/uYsuxy5bMy8PGiBZlyJpWZ2qIiIiIiIiI3AmbzUaf+sXInT7Akp+LiOWdaZtxON3spvf0J5CpmDW7ehr+6mpc+kaJNyiRoYQluxJ3hY9WfITD6bjj+YuIPKpUyBQREbkJsY5Yeq7seXNbyibEway2sG6EOVDanFB/FNj//RG8/vBFJq05alz65YvFKJszNAVmLyIiIiIiIiKBPp4Ma1YaH0/r2+Ir91/gu0Vuzsv09IGXx0DSDy/vmAE7Z1svtXvy1ZNfEeBlLZSuP72ecTvHpcj8RUQeRSpkioiI3IRhm91vKftWybesF8ZFwU8NzTMxATIWhVZzISDdtSg2wcGHM7cZl7aulIuG5bKlyNxFREREREREJFGhzMF8VreIkQ9e+A9L9p41O2QoAE/3MvM/3oOr1uuzBmWlZ8WexqXfbf6O9afX3/acRUQeZSpkioiI3MDWc1uZsMvcUrZ35d7WLWVdLvitIxxcbA6SoxK0/BOCM1viH5Yc4MC5SEtWMFMQ3Z8vmGLzFxEREREREZF/NSqXjZdKZbFkLhe0n7iRFfvOmx0qvAE5Kluz6Ivw+9uJHf+jdu7a1Mldx5I5XA7eXPAmy48vT5H5i4g8SlTIFBERuY6YhBh6rOhhbCn7WuHXjLMvWP2d+ycxC74AzWeCXxpLvP9sBN8vPmDJbDboW78YXh76ES0iIiIiIiJyN9hsNnq/WJQ8GazbwMYmOGkzYb1ZzLTb4cVh4B1ozff+BVunGON/VOEjsgZmtY7tiKXzos7MPTw3RdYgIvKo0LukIiIi1zFsyzAOXzlsyXKF5OKtUkm2lD2wGOZ/Yg5QqgW8MgG8/Cyx0+mi+6ztxDmSFEgfz0mp7GlTYuoiIiIiIiIikowAH09+aF6GIF9PS/7/xcyV+5MUM9PmhJp9zIHmdINLRyxRoHcgA54agL+nvyVPcCXQbVk3Zu2blRJLEBF5JKiQKSIikowtZ7cwYaebLWUr9cbHw+ff8NJhmNEKkjy1Sb5noc4Q8LD+UQQwdf0x1h++ZMkeC/Hl/ZoFUmr6IiIiIiIiInId+TMGMbltBbfFzNbj3RQzS78KeZ+xZrFXYEpjiLliiYukL8LoZ0cT4hNiyZ0uJ71W9WLirokptg4RkYeZCpkiIiJuRCdE03NlT1xYz7p4rchrFM9Q/N8gLgqmNYdoa1GS0NxQfxTYPYyxT4VH03fObiP/vF5RAn3MoqeIiIiIiIiI3B3Fs6ZJtpjZZsJ6/jkT8W9os0HdoeCbxjrI2V0wozU4EixxsQzFGFdzHOn90hv37be+H2tOrUmpZYiIPLRUyBQREXFjwPoBxpayuUNy81bJ/2wp63LB72/D6e3Wzt6B0Pgn40xMgHiHk04/bSYixvrHTe1imalROGMKzV5EREREREREblZyxcyYeCedp2wmJt7xbxicObGYmdT++TDvYyPOlzYfP9b6kSyBWYy2T1Z+wtW4q3c8fxGRh5kKmSIiIkksPLqQ6f9Mt2Rut5TdOQu2T8fw4g8QVsjt2AP+3suGI9anN4N8PelVt/Adz1tEREREREREbk9yxcw9pyP4eu4e68WF60L1HuYga4fDulFGnC04GxNqTSBncE5LfiryFAM2DLjTqYuIPNRUyBQREfmP05Gn6bWql5G3KdqGYhmK/RtcPQt/vm8O8GSXxD9o3Ji/6wwjlh008t4vFiUsyPe25ywiIiIiIiIid6541jT80KyMkY9beZjFe89awyffh+KNzEHmdIP9C404Y0BGvqn6DZ52a6F05r6ZrDyx8o7mLSLyMFMhU0RE5H8cTgcfr/iY8NhwS148fXHeLPnmv4HLBX++B9EXrQPkqgLVzG1kAI5djKLL9C1G3qxCduqVNLeXEREREREREZF7r3K+9LSrktvIu/68lXMRsf8G/39eZraK1gtdDpjRCsKPG2PkT5ufDiU6GPknqz7hStyVO567iMjDSIVMERGR/xm3cxzrTq+zZAFeAXxV5Su87F7/hjtmwu7frZ29A6HeMLB7GOPGJjjo+NMmriQ5F7PIY8H0fEFbyoqIiIiIiIikJu8/W4CiWYIt2fmrcXwwYysul+vf0NMHGk+GNDmsA8SEw6z24HSQVKuirSiSroglOxt1lv7r+6fY/EVEHiYqZIqIiABbz23lu83fGXmPij3IFpTt3+DqWfirqznAs19AmuxGnOBw8tGsHWw9bn3KM8jHk++blcbXyyx8ioiIiIiIiMj94+1pZ0jjUvgl+Zt98d5zTFh12HpxQHpoOh28g6z5kRWwYpAxtqfdky8rf2n9wDTwy/5fWHR0UUpMX0TkoaJCpoiIPPI2nN7Am/PfxOGyflKyTu46vJD7hX8Dlwv+eNfcUjZ3VSjTyhg3Jt5Bh8mbmLnJ3E6m/yvFyZEuICWmLyIiIiIiIiIpLE+GQD6pY+6i1HfOHg6cu2oNwwpCncHmIIv7wPEN5thp8vBWybeMvNuybqw9tfZ2pywi8lBSIVNERB5p84/Mp/389kTER1jyrIFZ+ajCR9aLd8yEPX9YM++gxDMxbDZLHB4dz6tj1zFv1xnjnm0q56JW0cwpMn8RERERERERuTsal8tGzSIZLVlsgpP3pm8lweG0XlysAZRoYs1cDpjZBmLM8y9fK/IaxdMXt2Qxjhg6LuzI+tPrU2T+IiIPAxUyRUTkkTV1z1S6LOlCnDPOknvZvfi6ytcEegf+G0acgb/eNwdxs6XsmSsxNBqxmnWHLhqXV8mfgW61CqbI/EVERERERETk7rHZbHxVvzhhQT6WfOuxy4xYdtDs8Hx/SJvTml06DHM+MC71tHvSu3JvAr0CLXmMI4a3Fr6lYqaIyP+okCkiIo8cl8vF0M1D+XLtl7hwWdr8Pf357unvKJ6h+H87wJ/vQfQl60C5q0GZlpbo5OVoXv5hFXtOW5/wBKhT4jFGv1oWb0/9+BURERERERF5EKQN8Obrl4sb+eAF/7DrZJInLX2C4OUxYLOercnWKbDmB2OMXCG5GPHMCKOYGZ0QrWKmiMj/6J1UERF55EzdO5WR20YaeahvKGNrjeWJx56wNmyfcVNbysbEO2g/cSPHL0UbY7d8IidDGpVUEVNERERERETkAVOtYBiNy2WzZPEOF+9N30JsgsN6cdayUC3JUTUAcz+EZf0TPyz9H8UzFGf4M8MJ8Aqw5P9fzNx/aX+KrEFE5EGld1NFROSRsvPCTvqv72/k2YKyMem5SRRJV8TaEHHa/ZayNb+ENP/+EeNyufho9na2nwg3Lu1aswC96hTGbrcZbSIiIiIiIiKS+n1cuxBZ0vhZsj2nI/h24T7z4srvQo7KZr6oN8z/xChmlshQghHPjHBbzPxoxUfEO+PveP4iIg8qFTJFROSREREXwftL3jf+ACicrjATn5tItmDrpytxueCPdyHmsjXP8zSUftUS/bj6CLM2nTDu2eelYrxVLS82m4qYIiIiIiIiIg+qIF8vBrxSwsh/WHKANQcvWEO7BzQYA6F5zIFWfQu/vw1O65OcJTKUYHgN88nM3Rd3M2bHmDuev4jIg0qFTBEReSS4XC56rerF8avHLXmmgEyMqDGCdH7pzE7bpsPev6yZTzDU/daypezagxf44o9dRvf2VXLTtEL2FJm/iIiIiIiIiNxfj+dJR6tKOS2Z0wVvTNrI4fOR1ouDMkHruZCxqDnQpgkw+w3jycySYSUZ+NRA4/IxO8ewN3zvnU5fROSBpEKmiIg8Eqbtncb8I/MtmYfNg/5V+pPGN43Z4cpJmPOBmdf8EkKyXvv25OVoOkzeRILT+sdH5bzp6VqzQEpMXURERERERERSiQ9qFiR3eutTk5ej4mk9YT3hUUm2gA0Mg5Z/QNby5kDbp8PGcUb8RJYnaFSgkSVzuBz0296POEfcHc9fRORBo0KmiIg89HZd2EW/9f2M/O3Sb1MyrKTZwemAWe3MLWXz1oBSLa59G+9w8ubkTVyItP4hkTWtH0OblMLTQz9mRURERERERB4mft4efNe0NP7eHpb84LlIOvy0kXiHM0mHtNBiNuSuag72dw+4dNiI3yvzHtmCrMffHI08yrj9ZuFTRORhp3dYRUTkoXYx5iLvLXnPOBfzySxP8lqR19x3WjkEDi+3Zj7BUMe6pezwJQfYeuyy5TJfLzsjW5QlbYB3SkxfRERERERERFKZwo8FM7hRyf++RQDAyv0X+OTXnbiSbBmLTyA0nZ74Aen/io+EXzuC01r89Pfy58vKX2LDeoOZh2ey8ezGlFqGiMgDQYVMERF5aMU54nh38bucuHrCkmf0z8iXlb/EbnPzY/D4Rlj8pZk/PwBCslz7dvepK3y7aJ9xWb8GJSj8WPAdz11EREREREREUq9ni2Si+3MFjXzKuqOMXXnY7ODpAy8OB/901vzwctgwxri8VFgpWhZtaclcuOixqgdnIs/cwcxFRB4sKmSKiMhDyeVy8dnqz9h0dpMl97B50P+p/qT1TWt2io2AmW3AmWDNizeCEv+eTxHvcPL+z1uJd1g/Ydm0QnbqlngsxdYgIiIiIiIiIqnX60/mplHZbEbe56/dbDp6yewQmAFqf2Pm8z+BCweMuGPJjuRNk9eSnYk6w1sL3yIyPvK25y0i8iBRIVNERB5KY3eM5bcDvxl5t/LdKBVWyn2nv7rCpUPWLG3OxKcx/+P7xQfYefKKJcuSxo+Pni90J1MWERERERERkQeIzWbjixeLUjF3qCV3OF28M3ULETHxZqciLyX+91/xUfDrW8YWs94e3vSp3AdPu6cl33tpL12WdDGO0REReRipkCkiIg+dhUcWMmTTECNvUrAJTQo2cd9p8yTYOsWa2Tzg5THg++9WsbtOXmGomy1l+zcoTqCPp5GLiIiIiIiIyMPL29PO8OZlyB7qb8mPXoyi16873Xd6/hsIyGDNjq6GFebTmoXSFaJn+Z5GvvLkSnqv6W2exyki8pBRIVNERB4qO8/vpPuK7riw/iJf6bFKfFDuA/ed1gyHXzuaebWPIGvZa9/+/5ayCU7r2M0rZueJvOnveO4iIiIiIiIi8uBJ4+/Nt01K4Wm3WfJZm0/wy+YTZoeAdPDCIDNf1BvW/GDEdXLX4dU8rxr5rH2zGLlt5G3PW0TkQaBCpoiIPDQOXD7AGwveIDoh2pLnDslN/6f6G1ux4HLB/F4wtxskKXyS80mo/K4lGrZ4P7tOWbeUzZrWj+7PaUtZERERERERkUdZyWxpePeZ/Ebe45cdHL0QZXYoVAeKvWLmcz9M/MB1Es3zNKdmlppG/t2W7/j78N+3NWcRkQeBCpkiIvJQOB5xnHbz2nE59rIlT+OThu+qf0eQd5C1gyMefnkTVg42BwvMBC+NALvHtWjnyXC+W7TfuLRfg+IEaEtZERERERERkUfeG0/lMc7LvBqbwNvTNpPgcJodnu8P6c3iJ3O7wdoRlshms/FO4XeomKmicflnqz7j5NWTdzR3EZHUSoVMERF54J2NOsvr817nbPRZS+5t92ZwtcFkC85m7ZAQC1OamGdiAoTmgTZ/Q0iWa1FcgpP3f95mbCnbomIOnsijLWVFREREREREBDzsNgY1KkmIn5cl33z0Ml1nbDOLmX5p4bXfIV0+c7A5H8Ba67axnnZP+j3Zj/xprcXPiPgIui/vjsPpSJF1iIikJipkiojIA+1SzCXazWvH8avHLbmnzZOBVQdSJmMZs9Pc7rB/vplnKQNt5kHanJZ42OL97E6ypWy2UD8+fK7gnU5fRERERERERB4imUP8+PrlYkY+e/MJOkzeREx8kmJjUCZo+Ucyxcyu8M88SxToFeh256lNZzcxevvoO56/iEhqo0KmiIg8sKIToumwoAMHwg9Ychs2+jzZh6eyPWV22jELNowx87zPJH4KMsD6hOXOk+EMW+xmS9mXS2hLWREREREREREx1CqamSblsxv5vF1naDNhPZGxCdaGa8XMvOZgv3WC6EuWKHNgZj55/BPj0h+2/sDWc1vvaO4iIqmNCpkiIvJAcrlcfLLyE3Zc2GG09Xy8J8/les7sdOEA/NbZzIs3hiZTwDvAEsclOOkyfauxpeyrj+fg8Tzp7mj+IiIiIiIiIvLw6lWnMNULhhn5yv0XaD5mLeFR8daGoEzwmpti5tXTePzdzRinVs5a1MtTz5I5XA66LevG1birdzx/EZHUQoVMERF5II3ZMYa5h+ca+Xtl3uOV/K+YHeJj4OeWEBdhzbOWh3rfgYeX0eW7xfvZc9p6fbZQP7rV0payIiIiIiIiIpI8Xy8PRrQoQ50Sjxltm49eptmYNeY2s8GZoclU8PS1xPads/A5MMcYp3uF7mQPsj75eeLqCXqv7Y3L5TKuFxF5EKmQKSIiD5wlx5bw7aZvjbxVkVa0KtrKfad5PeD0NmvmmwYajHVbxNx67DLfa0tZEREREREREblNXh52BjcqSdMK5jazO05cYcDfe81O6fNBjU+NOGT5p9ijzlmyAK8Avq7yNZ426/sUfx78k++2fHdHcxcRSS1UyBQRkQfK/kv76basGy6snyyskrUKb5d+232nnbNh/Sgzf2k4pMlmxFdjE+g8dbOxpexr2lJWRERERERERG6Bh93Gly8Wpf1TuY22MSsPsf7wRbNT+faQ80lLZI+5TPDSnpDkScui6YvyVqm3jCFGbhvJxF0T72zyIiKpgAqZIiLywAiPDafz4s5EJURZ8twhufn6ya/xsHuYnU7vgF87mvnjHaGAm3M0gV6/7uTIBes9sof60+05bSkrIiIiIiIiIrfGZrPxYa2CtKmcy5K7XND1561ExSVYO9jtUG8YeAdZYt8ji7Ftm2KM36pIK5547Akj77e+H78f+P3OFyAich+pkCkiIg8Eh9NB16VdORZxzJIHewcztPpQAr0DzU5Xz8KUxpD0kPus5dxu0wLw65YTzNx03JJ52G0MblwSf29tKSsiIiIiIiIit85ms/FBrQLkz2h9/+LwhSj6zXWzxWzaHFCrjxF7zP0Ajq23ZnYPBlYdSJF0RYzre67syZJjS+5k6iIi95UKmSIi8kD4bst3rD612pLZbXb6P9Wf7MHmWRPEx8DUphBuLXxe71zMYxej6DF7h5G/WyMfpbOnvZPpi4iIiIiIiMgjzsfTgwGvlMDDbrPk41cdZs3BC2aHUi0gX01LZEuIgSmN4OJBSx7gFcD3Nb4nZ3BOS+5wOXh/6fusObUmRdYgInKvqZApIiKp3oIjCxi9fbSRv1/2fbdbp+Bywa9vwXHrJxSxe0KjiZDGLHwmOJy8PXUzEbHW7Vwq5Arlzap572j+IiIiIiIiIiIAxbOmoUPVPEbedcZWIpO8J4HNBnW/xeWf3ppHXYBJDSDSWvwM9Q1l5DMjyeif0ZLHOmJ5a8FbLDy6MEXWICJyL6mQKSIiqdrB8IN8vOJjI6+Tuw7NCzV332lpP9gxw8xrD4RcVYzY6XTxxR+72HT0siUP8fNiUKOSxiclRURERERERERuV6fq+SiYyXr+5bGL0XwwcxsJDqf14qBMOBpOxuXpa80vHoCpTSA+2hJnDszMyGdHksYnjSWPc8bx3pL3+GX/Lym0ChGRe0OFTBERSbWuxl3lncXvEJUQZckLhhak5+M9sdncFBi3ToMl5hkSPN4RyrxmxHEJTt6dvoUJq48YbV+/XIzH0vjd9vxFRERERERERJLy9rQz4JUSeCb54PSf207x/s9bcThdltyVpQyXn/4GF0neBzm2Fma1A6e1+Jk7JDfDawwn0Mt6HqfT5aTnyp78uPPHlFuMiMhdpkKmiIikSi6Xix4re3Ao/JAlD/EJYVDVQfh5uikw7vkTfnnTzPPXgmc+N+KrsQm0Hr+eX7ecNNqalM9OraKZb3v+IiIiIiIiIiLJKZolhI7VzaNsftly0m0xMzZXDSIqmTtWsfs3WNbfiIukL8KYmmMI9Q012vpv6M/QzUNxuVxGm4hIaqNCpoiIpEojto0wzm6wYaPfk/3IGpTV7HBwCfzcElwOax5WBF4eDXYPS3wuIpbGI1ezYv95Y6iyOdLyyQuF73AFIiIiIiIiIiLJ61gtLzUKZTTy2ZtP0NVNMTOqWAscFdx8gHvpV3B0rREXTleYCbUmkCkgk9E2cttIxuwYc/uTFxG5R1TIFBGRVGfhkYUM2zLMyDuX7swTWZ4wOxxbB1OagiPOmgdngabTwMd67sS5iFgaDF/FjhNXjKFqFApjYpsK+Hl7GG0iIiIiIiIiIinF08PO981KU6NQmNE2a/MJPpixDWeSYqbz6c+gUF3rxS4nzGoLMeHGODlDcjLxuYnkCslltA3ZNITZ+2bf2SJERO4yFTJFRCRV2XtxL91XdDfyp7M/TZuibcwOp7fD5AYQH2nN/dNDi18gTTZL7HC6eHvqZo5csJ67CdC4XDaGNy+jIqaIiIiIiIiI3BPennaGNSvN0wXNYubMTccZv+qwNbTZ4cUfIDSPNb98FP583+09MgVkYnyt8RROZ+4+9enqT1l8dPHtTl9E5K5TIVNERFKNSzGXeHvx20QnRFvyvGny0qdyH2y2JIfahx+HifXNTxz6BEOLWZAhv3GPoYv2serABSPv/HQ++tYvhqeHfjSKiIiIiIiIyL3j4+nB981LU91NMfPruXs4cC7Jh7d9Av93jI6nNd8+HbZOc3uPUN9QRj4zkrxprOdyOl1Oui7rysYzG+9oDSIid4verRURkVQh3hnPe0ve48TVE5Y8jU8ahlYfir+Xv7VDXBRMbQqRZ625px80nQ6ZSxj3WLX/PEMW7jPyT14ozHvP5DcLpSIiIiIiIiIi94CPpwc/NC/NU/kzWPLYBCcfzNpOQpItZslSGqr3NAf6swtcPOT2HiE+IQyvMZzMAZmt93DE0mlhJ/Ze3HtHaxARuRtUyBQRkfvuStwVPlj6ARvObLDknjZPBlYdSNagrNYOLhf81hFObbXmdi9oPAlyPG7c42xEDJ2nbsGV5Pf++qWz0LqyeU6EiIiIiIiIiMi95OPpwZDGJQkL8rHk245fYeKG02aHJzpDrirWLC4CZraB+GjzeiBjQEaGPzOcND5pLHlEfATt5rfjn0v/3MkSRERSnAqZIiJyX205u4VXfnuFBUcXGG3dynejXKZyZqcVA2HHTDN/8XvIW8OIHU4X707bwvmrsZY8b1ggvV8settzFxERERERERFJSWn8vfn65eJGPmbNKf45G2UN7XZ4aQT4pbXmJzbCz63AEe/2HrlDcvP909/j5+lnyS/GXKTt3231ZKaIpCoqZIqIyH3hcDoYvnU4Lee25GTkSaP9lfyv0KhAI7Pj3jmw8Aszr/QOFG/o9l5DF+1j5X7ruZi+XnaGNS2Nv7en2z4iIiIiIiIiIvdDtYJhNCmfzZIlOF18Nu8wsQlO68XBj0HdoeYg/8yBXzuC02m2AcUyFGNw1cF4Jjln81LsJdrMa8Oei3vuaA0iIilFhUwREbnnouKjaDe/HcO2DMPhchjtVbNWpXv57uaZlWf3wMzXgST7w+arCU9/4vZec3ecYvAC81zMz+sWpUCmoNtdgoiIiIiIiIjIXfNx7cJkTWt9YvLA+WgGL9xvXlyoDjze0cy3TYV5H2Ocs/M/T2R5gm+e+sYoZobHhtPm7zbsurDrtucvIpJSVMgUEZF7rt/6fqw7vc7IPe2edCnThSHVh+Dl4WVtjAmHqU0Tz3r4r/T54eVRYPcwxttxIpx3p2018vqlsvBK2axGLiIiIiIiIiKSGgT6eDLglRIk/Yz36BWHmbHxuNnhmS+guJudrdZ8D8sHJHuf6tmrM6jqILzs1vdhrsRdoe28thwKP3Q70xcRSTEqZIqIyD2168IuZu2bZeQ5gnMw6flJtCzaErstyY8npxNmvwEXD1hz3xBoMjXx/yZx5koMbSasJzre+sRnvrBAvnixqPm0p4iIiIiIiIhIKlIxdzpaV8pl5B/O3MaKfeetod0O9YYl7lqV1KLesHpYsvepmq0qg6sNNoqZEXER9FjZA4fT3E1LROReUSFTRETuGZfLxdfrvsaVZGvY2rlrM/2F6RRJV8R9x+XfwN6/rJnNDg3GQbo8xuXRcQ7aTtjAmSuxljw0wJsxr5UjwEfnYoqIiIiIiIhI6te1ZgEKJTkaJ8Hp4o1JG9l96or1Yg8veGU8ZH/cHOjvjxILmslsM1slaxW+rf4t3nZvS77t3DYm7Z50J0sQEbkjKmSKiMg98/eRv9l0dpMlyxWSiy8qfYG/l7/7Tvvmw+IvzfzpXpD3aSN2Ol28N30L20+EW3JvDzsjWpQhe7pk7iMiIiIiIiIiksr4enkwqkUpMgZZn5a8GptAq3HrORUebe3g7Z+4e1XGYuZgy/rDX+8n7nzlRuUslen/VH8jH7p5KEeuHLntNYiI3AkVMkVE5J6ITohm4IaBRv5BuQ+MrUuuuXgQZraBJE9wUrgeVHrbuNzlcvHlX7uZs+O00da3fjHK5Qy9namLiIiIiIiIiNw3GYN9GVgvHwHe1rfzT1+JodW49UTExFs7+KWB5jMhfQFzsPWjYdbr4Ig320g8M7NunrqWLNYRS69VvXC63BdARUTuJhUyRUTknhi/czynIk9ZsiezPEnlLJXdd4iLhGktIMb6ZCUZCiae+eDmjMshC/cxZoV5CP2bVfPwcpmstz13EREREREREZH7KU96P76ukwcvD+v7IXtOR/DR7B24km4ZG5QRWs2Bx0qZg+2YAVObQkKs2Ubih87T+6W3ZBvPbGTa3ml3tAYRkduhQqaIiNx1pyNPM3b7WEvmafOka7mu7ju4XPDLm3BmhzX3CYZGk8AnyOgyZsUhBi/YZ+Q1i2Sk67NuPoEoIiIiIiIiIvIAKZstmD4vFjHy37eeZNamE2aHgHTw2u+Q80mzbd88mP0GOB1GU4hPCD0q9jDyQRsHcTzi+G3NXUTkdqmQKSIid93AjQOJccRYsqaFmpIrJJf7Dsv6w65fzfylEZA+nxFPX3+ML/7YZeRlc6RlUKOS2O3m05siIiIiIiIiIg+aF0s+xrs18hv5J7/u4MiFSLODTxA0mwEFapttO2fBnG6JHyhP4unsT/NczucsWXRCNJ+u+hSHm+KniMjdokKmiIjcVT/t/ok5h+ZYslDfUNqXaO++w+7fYfGXZl7lAyj4vBH/ue0UH87aZuRFHgtmbKty+Ht73ta8RURERERERERSo07V81IpbzpLFhnn4O2pW4h3uDnH0ssXGv4IxRubbetHJX6g3I0PK3xIWp+0lmzt6bUM2zLstucuInKrVMgUEZG7ZuGRhXy17isj71iqI8HewWaHMzthlpsCZ4HaULW7EW88cpF3p23BmeSDg7kzBDChdXmCfb1ud+oiIiIiIiIiIqmS3W5jYMOSpPW3vu+x5dhlhrg5dgcAD0+oNwwKvmC2Lf4SNow14lDfUD6q8JGRj9o+ioVHF97W3EVEbpUKmSIicldsObuFbsu74cJaZSyZoST189Y3O0RegCmNIT7JNihhhaH+CLBbf2SdvBxN+4mbiEvyScMsafyY3LYC6QN9UmQdIiIiIiIiIiKpTcZgX75+ubiRD1uyn7UHL7jv5OEJL4+BHJXMtj+7wM5fjLhmzprUzm1uS/vxio85GH7wVqctInLLVMgUEZEUdzj8MJ0WdSLWEWvJswdlZ0j1IXjYPawdTm+Hcc/B5aPW3C8tNP4p8TyH/4iJd9B+4kbOX7WOnz7Qh0ltK5A5xC/F1iIiIiIiIiIikho9WyQTzSpkt2QuF7w9dQsnL0e77+Tlm/heS8ai1tzlhBmtjWKmzWaj1+O9KJC2gCWPjI/kncXvcDXu6p0uQ0TkulTIFBGRFHU++jxvLHiDy7GXLXmobyg/1PiBUN/Qf0OXC9YMh1HV4fxe60A2j8TzG0JzWWKXy8UHM7ax/US4JffxtDPmtbLkSh+QkssREREREREREUm1etQuTN6wQEt2+koMLcas5WJknPtOfmmg+UxIk8OauxyJxcwdM62Xe/oxqNog45igQ+GH6LGyB06Xm3M5RURSiAqZIiKSYq7GXaXDgg6cuHrCkvt6+PJd9e/IHvyfTwlePQc/NYS53cDh5hfr576GXFWMePjSg/y29aSR92tQnBLZ0tzpEkREREREREREHhh+3h5827gU3h7Wt/oPnIuk1bh1XI1NcN8xKBO0mA0BYdbc5YCZbWH7DEucLSgbX1f5Ghs2S77w6EIGbxyMy2U9WkhEJKWokCkiIiki1hHL24vfZvfF3ZbcbrMz4KkBFMtQ7N/w0hEYUQX2zXMzkg2q94BybY2WxXvO0u/vPUb+xlN5qFcyy50uQURERERERETkgVP4sWD61i9m5FuPh9N+4gZiExzuO6bLA6/97qaY6YRZr8PWaZa4cpbKdCrVyRhm3M5xDN6kYqaI3B0qZIqIyB1zOB18uOxD1p1eZ7R9XOFjnsr21L+BywW/dYII86lKgrNAyz+gSlewWT/hd+h8JJ2nbibp78TVC4bRtab1nAYRERERERERkUfJy2Wy0qN2ISNfuf8C70zdgsOZTJExrCC0/BMCM1lzlxNmt4cDiyxxm2JtqJ6tujHM2B1j+XbztypmikiKUyFTRETuiMvl4os1X7Dg6AKjrUOJDjQs0NAa7v0LDi01BypUB95YATkrG02RsQm8MXEjETHW7VDyZAhgcOOSeNhtRh8RERERERERkUdJ2ydz07FaXiOfs+M070zbQlxCMmdZZsifWMwMypykwQW/doKYK9cSu81Onyf7UDx9cWOY0dtHM3TzUBUzRSRFqZApIiJ3ZOjmoczcN9PIGxVoxBsl3rCGCbHw90fmIDX7QMOJ4B9qNLlcLrrN3MbeMxGWPMjXk9GvlSPY1+uO5i8iIiIiIiIi8rDo8mx+mlbIbuS/bz1Ju4kbiI5LZpvZ9HkTi5nBSY7uuXIcFvSyRAFeAQx/ZrjbYuao7aNUzBSRFKVCpoiI3LaJuyYyavsoI6+Zsybdy3fHlmR7WNZ8D5cOW7NcT0HFDsZWsv9vzIpD/LHtlJEPaVySXOkDbnfqIiIiIiIiIiIPHZvNxhf1ilK7eNKnK2HJ3nO0GLOW8Oh4953T5YFmM8DD25pvGAuHllmiIO8ghj8znGLpzbM5R20fRZ+1fXC6knkCVETkFqiQKSIit+WPg3/Qb30/I6+YuSJ9KvfBw+5hbYg4A8sGWDObHWp9lWwRc9WB8/Sds8fI36mRj+oFM9723EVEREREREREHlYedhuDGpbk2cLmeycbjlyi8cg1nI2Icd85Y2F46gMz/60TxEVaov8vZhZNV9S4fOreqXy4/EPiHckUTUVEbpIKmSIicsuWH19OzxU9jbxIuiIMrjYY76Sf3ANY+DnEXbVmZdsk/oLsxonL0XT6abNxGP3TBcPoXD3fbc9dRERERERERORh5+1p5/tmpWlQJqvRtvvUFRoOX518MbPSO5ApyZOWlw7Dwi+MS4O9gxnx7Ai3xcw5h+bQaXEnouKjbmMFIiKJVMgUEZFbsuXsFt5b8h4JrgRLnjM4J9/X+J4ALzfbvZ7YCFsmWTPfNFDNzXmZQFRcAq9P2MCFyDjrPdL5M7BRSex2909wioiIiIiIiIhIIk8PO/1eLk6byrmMtsMXomg5dj1XYtw8MenhBfW+B7unNV87HI6uMS4P9g5m1LOjKJuxrNG28sRK2s1vR3hs+G2vQ0QebSpkiojITdt/aT9vLXyLGIf1E3th/mGMeGYEob6hZidHPMzpZubVPgJ/83qXy0XXn7ex69QVS+7n5cGIFmUJ8fO6ozWIiIiIiIiIiDwq7HYbPWoX4v1n8xttu05d4fUJG4iJd5gdMxeHyu8lCV3wSweIuWJcHugdyPBnhlMtWzWjbeu5rby75F0cTjf3ERG5ARUyRUTkppy6eor2C9pzJc76y2qwdzAjaozgscDHzE4uF/zxLhxfb80zFISyrd3e57tF+/lz+ykj7/9KcQpkCrrt+YuIiIiIiIiIPIpsNhsdq+fj0zrm8T5rD13knalbjKN9AKjyPmQoZM0uHoBf3gSn07jcx8OHgVUHUi9PPaNt/en1jNo+6rbXICKPLhUyRUTkhi7FXKLd/HacjTpryX09fBn29DDyps3rvuPSfrB5opnX6pu4TUkSc3ec5pv5/xh5p+p5eaG4m0KpiIiIiIiIiIjclJaVcvFOjXxGPnfnaXr8sgOXK0kx09MH6g0DW5Iywp4/YMU3bu/haffki0pf0LJIS6Pth60/sOnMptudvog8olTIFBGR64qKj6LDgg4cvnLYknvaPBlYdSAlw0q677hpIizpY+almkOe6ka86+QV3pu+xcifKZyRd2uY25+IiIiIiIiIiMitefvpfLSomMPIp6w7Sr+/95rFzKxloHoPc6BFX8K+BW7vYbPZ6FK2C3Xz1LXkTpeTbsu76bxMEbklKmSKiEiy4h3xvLP4HXZc2GG0fV7pc57M+qT7jvsWwO9vm3nualB7kCVyuVzM2HicBsNXERVnPSshf8ZABjUqid1uu+01iIiIiIiIiIhIIpvNxqd1i/B8sUxG2w9LDrgvZlZ+DwrVSXK1C2a2gYuHkr3XxxU+JkewtWh6OvI0vVb1Mu8hIpIMFTJFRMQtp8vJRys+YvWp1UbbB+U+oE6epL/A/s/JzTD9VXAlOcA9UzFo+CN4el+LrsTE03nqFt7/eatRxEzj78XoV8sR6ON5x2sREREREREREZFEHnYbgxqV5Ik86Yy2H5Yc4Ks5e6yFRpsNXvwB0ifZMSvmMkxrDnGRbu/j7+VPvyr98LRb39tZeHQh0/dOv9NliMgjQoVMERExuFwuvlr3FXMPzzXa2hZrS4vCLdx3vHAAJjWA+CS/wIZkg6Y/g2/wtWjT0Us8P2Q5v289aQzjYbfxfbPSZE/nf0frEBERERERERERk4+nByNalKF41hCjbcSyg/T+c7e1mOkTBI1/Au8g68VndsC0FhAf4/Y+hdMV5r0y7xl5v/X92HB6wx2tQUQeDSpkioiIYcS2EUzZM8XI6+erT+dSnd13unIKJr4IUeetuW8INJ8JwZmvResPX6TxiDUcvxRtDBMa4M2Y18ryRJ70d7IEERERERERERG5jiBfLya2qUCJbGmMtjErDvH5H7usxcz0+aD+CHOgAwthatNki5nNCzWnStYqlizOGUeHhR1Yc2rNnSxBRB4BKmSKiIjF9L3TGbZlmJFXz1adnhV7YrO5Oa8y+hJMqg+Xj1pzDx9oPAUyFLgWxTucdJu5jTiH0ximct70zH37SaoWCLvjdYiIiIiIiIiIyPWF+HkxsU15SmVPY7SNW3mYSWuOWMOCtaFKV3OgAwthWjO3xUybzcYXlb4gg18GSx6dEE3HhR1ZeWLlnSxBRB5yKmSKiMg184/Mp/ea3kZeNmNZ+j1lnmkAQFwU/NQYzu6y5jY7NBgLOStZ4h9XH+HgOevWs552Gx8+V5AfW5cnLNj3jtchIiIiIiIiIiI3J9jXix9bl6dMjrRGW985ezh2McoaVv0ISrk5dmj/gsQzM90UM0N9QxlYdSD+ntZjhGIdsXRa1Imlx5be0RpE5OGlQqaIiACw7tQ6ui3rhguXJS+QtgDfVv8WHw8fs5MjHn5uCcfcbANSZwgUesESXbgay+AF/1gymw0mta3AG0/lwW5387SniIiIiIiIiIjcVUG+XkxoXZ7yOUMteVScg+6ztlu3mLXboc63UKq5OdD++YnFzLgoo6lkWElGPDOCQK9ASx7vjOedJe+w8MjCFFmLiDxcVMgUERF2X9hN58WdiXfGW/KsgVkZ/sxwgpIe5A7gdMKvHWHf32bb072g9KtGPHD+P0TEJFiyRmWzUTF3ujuav4iIiIiIiIiI3JlAH09+aF6adAHelnzF/vNMW3/MerHdDnWGQslkipmT6kP0ZaOpZFhJRj87mmDvYEue4Ezg/aXvs+joojtdhog8ZFTIFBF5xB29cpQ3FrxBZLx1u9dQ31BGPjOS9H7pzU4uF8zvCdummm0V34LK7xrxrpNXmLLOeoZmoI8nXZ4tYFwrIiIiIiIiIiL3XrpAHz6rV8TIv/xzN6fCo62h3Q51h0LJZuZAR1fD+NoQccZoKpK+CGNqjiGtj3Ur2wRXAl2WdmHJsSV3sAIRediokCki8gg7F3WOdvPbcTHmoiUP8ApgeI3hZAvO5r7jyiGw+jszL94Inu2duF/sf7hcLj7/YydO6661dH46LxmC3GxZKyIiIiIiIiIi90XtYpmpWSSjJYuITeCjpFvMwvWLmWd2wNiacPGQ0VQwtCBjao4h1Ne6lW2CM4H3lrzHsuPL7ngdIvJwUCFTROQRFREXwZsL3uTE1ROW3MvuxbfVvqVQukLuO26aCAt6mXm+Z6HesMRfYJP4e+dp1hy0FktzpQ+g5RO5bnv+IiIiIiIiIiKS8mw2G1/UK0qIn5clX7z3HLM3nzA72D2g7ndQsYPZdulQYjHz7G6jKV/afIytOdYoZsY743l38busPLHyjtYhIg8HFTJFRB5BMQkxdFrUib2X9lpyu81Ovyr9KJ+5vPuOe/6C3zubebYK8MoE8PAymi5GxvHFH+Yvqz1qF8LbUz+GRERERERERERSm7BgXz55obCR9/ptJ4fPR5od7Hao2Qeq9zDbrp6BKY0h5orRlCdNHkY/O9rYZjbOGUfnRZ1Zd2rdba9BRB4OegdZROQRczryNK/NfY2NZzYabT0q9qBGjhruOx5eCTNagctpzTMUgiZTwdvf6BIT76DthPWcuGw9Q+HJfOmpXjDsttcgIiIiIiIiIiJ3V/3SWahWIIMli4hJ4I1JG4mKSzA72GxQpSvUHghYjx3i0mH4411IujUtiU9mjnp2FCE+IZY8zhlH58Wd2XVh1x2uREQeZCpkiog8Qrac3ULjPxq7/QWwY8mOvJL/FfcdT2+HKU0gIcaah2SDFrPAP9To4nC6eHvqZjYdvWzJPew2PnmhMLYk52iKiIiIiIiIiEjqYbPZ6FO/GEG+npZ8z+kIus10c17m/yvXBhqMBZuHNd8xA7ZMdtulQGgBRj0zimDvYEseGR/Jmwve5HD44dtdhog84FTIFBF5RMzeN5vWf7fmQswFo61JwSa0K97OfceLh2DSyxAbbs3900GL2RD8mNHF5XLx+e87+XvnGaOtR+1C5MsYdFtrEBERERERERGReydziB8DG5Y08t+3nmTMikPJdyxa3/02s391hXP/uO1SKF0hRj47kgCvAEt+MeYi7ee352zU2VuZuog8JFTIFBF5yLlcLgZtHMQnqz4h3hlvtLcq2ooPy3/o/gnJq2dh4kuJZxn8l3cgNJsB6fO5veeo5QeZsPqIkbetnItWlXLd1jpEREREREREROTee6ZwRjpXz2vkfefsYdWB88l3rPQO5K5qzeKjYEZriI9x14Mi6YowtPpQvO3elvxk5Enaz29PeNIP2ovIQ0+FTBGRh9z0vdMZu2Oskft4+ND3yb68V+Y97DY3Pw5iwmFSfbiU5NN1di9oNAmylHZ7v1+3nKDPX3uMvHbxzHz0fKHbWoOIiIiIiIiIiNw/79TIb5yX6XC66PTTZo5finLfyW6Hl0aAf3prfmY7zP8k2XuVy1SOfk/1M96v2n95P28tfIvI+MjbWoOIPJhUyBQReYhtP7+dr9Z/ZeRhfmGMrzWeF3K/4L5jfAxMbZZ4NqaFDeqPhDzV3HZbsvcsXaZvNfLyuUL55pUS2O06F1NERERERERE5EFjt9sY3KgUOdL5W/ILkXE0G72WU+HR7jsGZUosZia1bgSsG5Xs/Z7O/jS9Hu9l5FvPbaXjwo5EJyRzPxF56KiQKSLykLocd5kPVnxAgjPBkhdNV5SpL0ylaPqi7js6HTCrLRxebrbVHpB4xoEbm45e4s1Jm0hwWg96zxcWyKgWZfH18nDbT0REREREREREUr8Qfy9GtCiDX5L3eI5ciKLxyDXJFzPz1YDHO5r5X+/D6mHJ3q9+vvq8XfptI99wZgOdF3UmJsH99rQi8nBRIVNE5CHkcDnos7UPZ6KsZ1tmCsjE9zW+J4N/BvcdXS748z3Y/bvZ9tSHUK6t2277zkTQevx6ouMdljxjsA/jW5cnxN/rttYhIiIiIiIiIiKpR8FMwfRrUNzIj1yIosnINZwOT6a4+HQveKyUmf/9ESwfmOz92hRtQ8siLY18zak1vLPkHeIccTc7dRF5QKmQKSLyEBq/bzybL262ZF52LwY+NZC0vmmT77j4S9g43szLtYWqH7rtcuJyNK+OXcflqHhLHuLnxcQ2FciSxu9Wpy8iIiIiIiIiIqlUnRKP0ftFc6evwxeiaDxytftipqc3NP4JQvOYbQs/gyVfu72XzWbjvTLv0aRgE6Nt5YmVdFnShXhHvJueIvKwUCFTROQhs+jYIqYemmrkH5b/kGIZiiXfce0IWNbfzAu/CM/1A5t5vuXFyDhajFnLqSS/oPp62Rnbsiz5Mwbd6vRFRERERERERCSVa14xx3WLmccuRpmdgh+DVn9B+gJm25I+sKh34m5hSdhsNrqX784r+V8xux1fwscrP8bpct7WOkQk9VMhU0TkIbLu1Do+WvmRkdfNU9ftL3vXbJ8Bc7qZea6noP5IsJvnW0bGJtBq3DoOnou05J52Gz80L0OZHKG3PH8REREREREREXkwNK+Ygy+SKWY2GL6Kf85EmJ2CMkHLPyGsiNm2rD8s+crtvWw2Gz0q9uDFvC8abXMOzWHwxsG3OHsReVCokCki8pDYem4rHRd1JM5pPRsgf9r89KjYA5ubJyoB2L8QZr8BJPnEW+aS0HgyePoYXeISnLwxaSNbj4cbbf1fKU61AmG3uQoREREREREREXlQtKiYgy/qmUXJM1dieWX4ajYdvWR2CswAr/0OmdzsHLb0K1jaz+297DY7nz7+KS/kfsFoG7dzHJN3T77l+YtI6qdCpojIQ2Dvxb28ueBNohOiLXkanzQMrjoYP89kzqk8vhGmtQBnkrMEQvNAsxngY24N63S6eG/6FpbvO2+09ahdiJdKZb3tdYiIiIiIiIiIyIOlxeM53T6ZGR4dT7NRa1n6zzmzU0C6xGLmY6XMtsVfwrIBbu/lYffgi0pfUDVbVaPt63Vfs+DIgludvoikcipkiog84A6FH6Ld/HZExFm36/D39Oe7at+RLTib+47n98HkBhBv3RqWwEzQYnbip+OScLlcfPb7Tv7Ydspo61A1D22fzH3b6xARERERERERkQdTi4o5GNK4JJ52645g0fEO2k5Yz/xdZ8xOfmkT34PKXMJsW/QFrBjs9l6edk/6VelH8fTFLbkLFx8u/5DNZzff7jJEJBVSIVNE5AG26sQq2vzdhosxFy25j92HL0t/SeHQwu47XjoME1+CaGs/fEOgxSxIm8Ntt6GL9jNh9REjb1Q2G11rujmoXUREREREREREHgn1SmZh1Gtl8fWylh3iHS7enbaFIxcizU5+aaHFL+63mV3QC9aOcHsvP08/hj49lOxB2S15rCOWtxa8xZJjS25vESKS6qiQKSLyALoad5VPV31K+wXtORdt3Z7Dy+7FZ6U+o2hac0sPAA4th5HVIPyYNff0hSbTIKObw9aBSWuOMHD+P0b+bOGMfPlS0eTP4BQRERERERERkUdCtQJhTG5bgWBfT0t+NTaBzlM2E5fgNDv5h8Krv0FGN+9lzfkAtv3s9l6hvqEMrzGcUN9QSx4RH0GnRZ34ZsM3xCc9TklEHjgqZIqIPGBWn1xN/d/qM3PfTKPNw+bBV5W+okz6Mu47rx8NE180n8S0ecAr4yHH4267/bntFD1/3WHkFXKF8m2TUnh66MeJiIiIiIiIiIhAmRyhTH/jcUL8vCz51uPhfDNvr/tO/qHw6q8Q5mZ3sV/egH3uz77MFpyNYU8Pw8/Tz2gbv3M8Lee25NRV84gkEXlw6J1nEZEHhMvl4ttN39JufjtORZq/gHnZvehTuQ/VslUzOyfEwe/vwJ9dwJlgttf9Fgo85/a+K/ad551pm3G5rHmhzMH/2y7E4zZWIyIiIiIiIiIiD6uCmYLp36C4kY9YdpCl/5xz0wMISJ/4ZGa6vNbcmQDTW8CxdW67FU1flMFVBxPgFWC0bTu3jQa/N2DtqbW3vAYRSR1UyBQReQA4XU76ruvLqO2j3LYXTleYaS9M4/ncz5uNCXHw0yuwcZzZ5uUPDX+EUs3djrvt+GXaT9xAvMNaxcwe6s+E1uUI9vVy209ERERERERERB5tzxbJxKuP5zDyLtO3cDYixn2nwAzQfBYEZbbm8VEw+RU4u9tttyeyPMH0F6ZTKLSQ0XYl7gqdF3Vm78VkngYVkVRNhUwRkVTO4XTw+erPmbJnitHmafekc6nOTH5+MvnS5nM/wN8fwcElZh6SHdrMg8L13HY7eO4qLcetJzLOYcnTB/owsU15woJ8b3UpIiIiIiIiIiLyCPno+UIUzBRkyc5fjaPL9K04nS73ndLmSCxm+qax5jGXYeJLcPGg227Zg7Mz8fmJNCrQyGiLSoii46KOnItK5mlQEUm1VMgUEUnFEpwJfLTiI7fnYRZIW4BpL0zj9eKv42n3dNMbbNunw3o3T3HmqAztFkOmYm77nbkSQ4sx67gYGWfJg3w8mdC6HDnSmVt1iIiIiIiIiIiI/JevlwffNS2FX5KjiZbvO0/XGdtwJFfMzFgYmk6HpGdfRpyCCXXh0hG33Xw8fOhRsQf9n+pvbDV7OvI0nRd1Jjoh+rbXIyL3ngqZIiKpVLwjnq5Lu/LXob+MtjIZyzDhuQnkT5s/2f6eF/bg8VcXs6FEE2gxO/HcATfCo+J5dcw6Tly2/lLn7Wln1GtlKfJYyK0tREREREREREREHll5w4L4tG5hI5+56TjvTttCgsPpvmP2CtBoIiT9AH/4MZhQB8KPJ3vPWjlr8W21b/G0WfvuuLCDj1d8jNOVzD1FJNVRIVNEJBWKSYjh7cVvs+DoAqPticee4IcaP7g9wPz/2WKvkObvztiSfsIsa3mo8y14ervtFx3noM2E9ew9E2HJ7TYY2qQUFXOnu/XFiIiIiIiIiIjII61h2WzULfGYkf+29SSdp24mPrliZr5n4KURYEtSyrh8JLGYeeVUsvcsn7k8PR/vaeTzj8znu83f3dL8ReT+USFTRCSViYqPouPCjiw/sdxoq5qtKkOrD8Uv6bYa/+VyErL4QzyvJNliIyADNJyQbBEzweGk40+b2HDkktHWt34xahbJdEvrEBERERERERERAbDZbPRrUJxqBTIYbX9tP82bkzYRm+Bw37lYA6g3DLBZ84sH4ce6cPVssvetn68+rYq0MvJR20cxcdfEW1mCiNwnKmSKiKQiEXERvLHgDdaeXmu01cxZk4FVB+Lt4b4Q+f/sywfge3ihNbR5QINxEGx+8g3A5XLx4aztLNxj/uLXtWYBGpXLfvOLEBERERERERERScLXy4PhLcrwTOGMRtuC3Wd4Y+LG5IuZJZtCnSFmfv4fmNwAYiPMtv95p8w7VM9W3cj7re/H6O2jb3r+InJ/qJApIpJKhMeG8/q819l8drPRVjdPXb5+8mu87F7XH2TNcDyW9zPzGr0g15PJdvtq7h5mbDTPFWhdKRcdqua54dxFRERERERERERuxMfTg++blaZ2scxG2+K953hr8ibiEpLZZrbMa/D8ADM/tRV+bgmOeLfd7DY7fZ/sS6HQQkbbkE1DGLp5KC6X61aWISL3kAqZIiKpwP5L+2kxpwU7L+w02hrmb8gXlb7Aw+5x/UE2TYS53cy8UB14onOy3UYtO8iIpQeN/MWSj9GjdiFsNpubXiIiIiIiIiIiIrfOy8POkMYlebGkuXPYgt1neeunTcmfmVn+daj1lZnvXwC/vwPJFCT9vfwZ9vQwcoXkMtpGbhvJgA0DVMwUSaVUyBQRuc9+3f8rTf5swqHwQ0Zbi8It6FGxB/akB5ontWMW/G4WK11hRaHe95BMMXLmxuN8+dduI38qfwb6NSiB3a4ipoiIiIiIiIiIpCxPDzvfNCzJy6WzGm3zd52h00+bky9mVnwTqnxg5lsmwRI3Rc7/yeCfgXE1x5E/bX6j7cddP9J3XV8VM0VSIRUyRUTuk+iEaD5Z+Qk9VvYgxhFjtLcr3o6uZbve+InIf/6GWa+Dy/rLXUKaXCQ0/Rl8g912W7TnDB/M3GbkJbOl4YfmpfH21I8IERERERERERG5OzzsNvo1KM5LpbIYbXN3nuadqVtISK6YWe0jKNnMzJd+BRsnJHvPdH7pGFtzLEXSFTHapuyZwoSdyfcVkftD71KLiNwHpyNP0+yvZszeP9tos2Hj3TLv0qlUp+sXMR3xsLQfTG0GzgRrU2AWLr4wDgIyuO26ZO9ZOkzehMNp/ZRZ3rBAxrUsh7+3560vSkRERERERERE5BZ42G0MeKUEdUuY28z+uf0UH87a7v4pSZsN6gyBPNXNtt/fhoVfgCPBbANCfEIY9ewoSoWVMtoGbhzIwqMLb3kdInL3qJApInKPxTpi6bSoE/su7TPaQn1DGfnsSFoXbX39Qc7shNFPw+IvwWk9yNwVmJGLdcbjDDQPTXe5XIxadpDW49cTE2/9RFvmEF9+bF2etAHet74oERERERERERGR2+BhtzGwYQlqFzffy5qx8Th95+xJpqMXNPwRMhVP0uCC5QPgx3pw5ZTbrkHeQQyvMZyyGcsm6emi+/Lu7Lqw63aWIiJ3gQqZ90h0dDS///477777LpUrVyYsLAxvb2+Cg4MpVKgQrVq1YuHCW/+kx+rVq2ndujV58uTB39+f0NBQypQpQ+/evTl//vxdWImI3KmBGway56L5C1jZjGWZUWcGFTNXTL6zIwGWDYART8GprWa7XygJTWfiCMluNMXEO+gyfStf/rWbJA9iksbfix9bl+exNH63uhwREREREREREZE74ulhZ3CjkjxXNJPRNnLZQUYsPeC+o08QNPsZ3LwXxpEVMLwyHFjktqu/lz+Dqw0mZ3BOSx6dEE2nhZ04E3nmVpchIneBCpn3wOTJkwkLC6Nu3boMHjyYlStXcu7cOeLj44mIiGDPnj2MHz+eGjVq8Nxzz3Hu3LkbjulyuXjvvfeoVKkS48aN4+DBg0RHR3Pp0iU2bdpEz549KVq0KIsWuf9HWkTuj0VHF/HTnp+M/PVirzPq2VFk8He/FSwAcVHwU0NY9IXxFCaQ+Avbq79AhoJG05krMTQauYZZm08YbUE+noxtWY58GYNuZSkiIiIiIiIiIiIpxsvDzrdNSlG1gPn+WN85e5i+4Zj7jkGZ4LVfIayw2RZ1HibWhzXD3XYN8Qlh2NPDSOOTxpKfjT5Lp0WdiIqPutVliEgKUyHzHjh06BBXr14FIHPmzLz66qsMHTqUadOmMXbsWFq1aoWvry8Ac+fOpUaNGkRFXf8fyO7duzNo0CBcLhcBAQF07tyZSZMmMXz4cJ555hkAzpw5Q7169diyZctdXZ+I3JzTkaf5ZNUnRv5a4dfoXLoznvbrnEsZGwGTG8CBZJ7cLtsaOqyCzCWMpvNXY3n5h1VsPXbZaMuVPoDZb1WidPa0N7sMERERERERERGRu8LLw873zUpTOnsao+3DmduYt/O0+46huaHtQijVwk2jC+Z2g12/ue2aPTg7g6sNNt6b231xN03/bMqBy8k8DSoi94QKmfdIpUqV+P333zl27BgTJkygY8eONGzYkFatWjF27Fg2btxI5syJe4Bv27aNr7/+OtmxNm/eTL9+/QAICQlh1apVDBkyhGbNmtG+fXvmzZtHr169ALh69Srt2rVzfyCyiNwzCc4Eui3rRnhsuCUvkq4Ib5d++/qdoy/Bjy/CkZVmW0g2aPELvDAocSuNpPd1OOk8ZTPHL0UbbU/mS88vHSqRNyzwFlYiIiIiIiIiIiJy9/h7/2/3sCTvWTld0HHKZpb9k8yOht7+UO87eHE4ePmb7bPawYlNbruWyViGz574zMgPhB+gyZ9N+OPgH7e8DhFJGSpk3gNvvfUWK1as4IUXXsDDw8PtNYULF2bkyJHXvh8/fnyy433++efXCpN9+vShePGkhxlDr169KF++PADr16/nr7/+uoMViMidGrltJJvOWn9RCvAKoH+V/nh5eCXfMfI8TKgDJzaYbcUawpurIE+1ZLsPXniAVQcuGHnbyrkY17IcIf7XubeIiIiIiIiIiMh9kMbfmx/blCdLGj9LHpfg5PUfN7DqwPnkO5dsAq8vhtA81jwhGqY0gXDz6CWAunnq8nqx1408OiGa7su78/nqz4l1xN7yWkTkzqiQeQ+kTXtzWzY+99xzBAQEAHD06FGuXLliXBMREcGcOXMACA4OpmXLlm7HstlsdOrU6dr306ZNu8VZi0hK+fvw34zYNsLIez3ei2zB2ZLvGHEaxteG09vNtvLt4aUR4BucbPel+y8zYvkh8751CtPjhcJ4euhHgIiIiIiIiIiIpE6ZQ/yY2KY8oQHeljw2wUmb8RtYd+hi8p3DCkLzmeCfzppfPQ1TGkHsVbfdOpXqRIeSHbBhM9p+/udnXp3zKmejzt7yWkTk9uld7FTEw8MDf/9/H3mPjja3gly6dCmxsYmf+qhSpYrl+qRq1qx57eu5c+em4ExF5GYtO76MD5d/iNPltOT189XnuVzPJd/x8jEY9xyc22O2VXoHnvsa7Mn/E370UgyfzzOLmA3KZKXlEzlvcvYiIiIiIiIiIiL3T+4MgfzYujzBvtbzK6PjHbQat46NRy4l3zk0FzT+CTyshVBOb4eZbcHpMLrYbDbeLPEmw58ZTlof8wGlXRd20eyvZvxz6Z/bWo+I3DoVMlORs2fPcu5c4v7e/v7+ZMiQwbhmx44d174uU6bMdcfLkCEDOXLkAODcuXOcPatPiojcS+tPr+e9Je+R4Eyw5LlDctOtXLfkO148BOOeh4sHzbaqH0GNT8Fmfirs/0XFJdD9jwNExlmLp4UzB9P7xaLYrtNXREREREREREQkNSmaJYSJbSoQ5GMtZkbGOWg5dh3bj4cn3zl7Rag3zMz/mQOz24MjwWwDnnjsCX6u8zOlwkoZbacjT/PqnFdZeWLlLa1DRG6P540vkXvlv2dk1qpVC7ubp63++effT3rkzJnzhmPmyJGDI0eOXOsbFhZ2y/PauHHjddt379597euEhATi4+Nv+R4iD5sd53fQcVFHY9/89L7pGVhlIF54uX+tnN+H5+SXsF09bTQ5nv4UZ8WOkOD+FyyABIeTLj9v48CFGEse7OvJ0MbF8cBJfLwzmd4icrfEx8eT8L/Xrn5Oitx/ek2KpC56TYqkPnpdiqQuek1C4UwBjH61NK0nbCQy7t8nKSNiE2g5bh3T25Une2gyuxcWegl75b14rBhgzbf/jDMuGsdLI82nNoFQ71CGVx/Od1u/Y+LuiZa2yPhI3lr4Ft3KdqNBvgZ3vD55sOg1aUq4znvWd0qFzFTi4MGD9O3bF0h8fP3DDz90e93ly5evfZ0+ffobjpsu3b97gP+3760oW7bsTV8bHh7OhQsXbus+Ig+LnZd20nNTT6ISoix5kFcQfUr3ISAuwO3rxPPCHtL+3gpbjLm//5XKnxCVrwlc5/XldLnoPe8wC/aY/T95NgcBrmguXDC3rBaRuy8hIYGIiIhr33t66lcwkftJr0mR1EWvSZHUR69LkdRFr8lEOQJgQN08vPvLfmIS/v2g/oXIOFqOW8+oRgVJ45fM/zZF2hJyejd++/+0xPa9fxD3UxMuP/stePq67fpq9lcJ8whj8M7BOFz/FlEdLgd91vfhzOUzNM7d+M4XKA8MvSZNt1t/uhnaWjYViIyM5KWXXiIqKrHo0aFDB8qVK+f22qtX/z2E2NfX/T+s/+Xn53ft6/++sEQk5V2IvUC/7f14Z907RCRYX2/+Hv70LdOXXEG53Pb13fcHob80wSNJEdOFjfCqXxJVtNl17+1yufhm8TH+2m0WMVuVz0Tl3GlubTEiIiIiIiIiIiKpTKmsQQyolwdvD+vRSccux/L+r/uJSW4nMpuN8GpfEZOzutHke3Qpaee0xxYf5aZjolpZatG3TF8CPAOMtrH7xrL+3PpbW4iI3DSVif9n9OjRHD9+PEXG+vTTT2/6WofDQdOmTdm2bRsApUuXZsCAATfodW9t2LDhuu27d++mRYsWAISEhFieAhV5FMQ74vlp70+M2jHKeAoTwNfDl2+rfUvpsNJm54QY7PN74LFpvNHksnngqPc9/kVeJpmNMa4ZMG8fM7edM/JahcPoVrsYHnadiylyP/13m5HQ0FC8vLzu42xERK9JkdRFr0mR1EevS5HURa9Jq5rp0vGNlz+dp23F5fo333E6ki8XnWBo4xLJvxfWeDLOX9/AvvtXS+xzYg0Z5nfA0eTnZJ/MrJGuBrkz5qbzks6cjDx5LXfh4usdXzO51mQeC3zsjtcnqZ9ek6Y0adLctbFVyPyf0aNHs3bt2hQZ62YLmU6nk5YtW/Lbb78BUKBAAebMmXPdJy0DAwOvfR0TE5Psdf8vOvrfbSSDgoJual5JlSlT5qav9fT01ItWHimbzmyi16peHL5y2G27p92TQdUGUSFLBbPxwgH4uSWc3ma22b2wvTIOz0J1rnt/l8vFd4v2M2L5IaOtUq4QvnmlOL4+5h7/InLv/f82I15eXvpZKZIK6DUpkrroNSmS+uh1KZK66DVpVadkVs5HxvPZ77ss+fzdZ/nir718Xq+o+2Kmlxe8Mg5+7Qhbf7I02Y+uxj6nC7w0AmzuC6EF0hdgcu3JtP27LQfCD1zLw+PC+WDlB/z43I/4ePjc+QIl1dNr0upubq+rrWXvE5fLRfv27Zk0aRIAefLkYeHChYSFhV2333+r2ufPn7/hff57Dt/drIiLPGoi4yPps7YPLee2TLaImS9tPsbVHEflLJXNxiOrYWRV90VMv1Bo9jPcoIh5OjyG1uPX8838f4y2MlmD+LJ2brw99c+8iIiIiIiIiIg8fFpVykW7KrmNfPLao7Qct47zV2Pdd7R7QL1hULa12bZtGizrf937pvdLz6Bqgwjwsm4zu+vCLr5a99VNz19Ebo7e4f6fNWvW4HK5UuS/m9GxY0dGjx4NQI4cOVi0aBFZsmS5Yb/8+fNf+/rw4cM3vP7IkSNu+4rI7Vt5YiUv/foSU/ZMwYX5mg/2DuajCh8x/YXplAwraQ4QcQamt4DYK2ZbtgrwxnLIUy3Z+7tcLmZuPM6zg5ayeK+5nWyJrCH0q5sHXxUxRURERERERETkIfZhrYK8UDyzkS/fd57nhyxnzcELbnoBdjvUHghl25hti7+EHTOve99cIbn4otIXRj7jnxn8sv+Xm5m6iNwkvct9H7zzzjt8//33AGTNmpVFixaRPXv2m+pbtGjRa19v3LjxuteeO3fuWiEzQ4YMN3zaU0Suz+VyMXDDQN5Y8AanIk8Z7TZsNMzfkD9e+oMmBZvgaXfzOL3TCbPbQ6RZgOSJTtDyTwjJmuwczkXE8vqPG+jy81auxCQY7QUzBTHm1dIEeHvc0tpEREREREREREQeNHa7jW8alqBCrlCj7WxELE1HreG7RftwOt08gGSzwXP9IO8zZtvsN+HY+uve+5kcz/Ba4deMvPea3uw4v+Om1yAi16dC5j3WtWtXhgwZAkDmzJlZtGgRuXObj78np2rVqvj4JO6xvWzZMssZmEn9/fff176uVavWbc5YRP7fhJ0TGLdznNu2vGnyMvn5yfR8vCdpfdMmP8iqIXBwsTXz9IPGU+DZ3uCR/H7qxy5GUf+HlSzYfdZte7UCGfjp9YqE+GlPdhEREREREREReTT4eHowrlU56pV8zGhzumDAvH/o8vNW97spenhCg7EQVtiaO2JhahM4v++69367zNuUDittyWIdsXRY0IFD4YdueS0iYlIh8x7q0aMHAwYMACBjxowsWrSIfPny3dIYgYGBPP/88wBcuXKF8ePHu73O5XLx3XffXfu+UaNGtzdpEQFgwZEFDNw40Mg97Z50KNmB6S9Mp1iGYtcf5Nh6WNTbzJ/vDwWfv27Xg+eu0nDEao5dND+8EOTrSf8GxRnbshyhAd7Xn4OIiIiIiIiIiMhDxt/bk8GNStK3fjG83Ry3NHvzCQYvSKYo6RsMTadBQJIdDSPPwciqsGNWsvf1snsx4KkBpPdLb8kvxV7ijflvcDbK/QMJInLzVMi8R3r37s2XX34JJG7zunDhQgoWLHhbY/Xs2RObzQZA9+7d2bZtm3HN559/ztq1awEoV64ctWvXvs2Zi8j2c9vpvry7cR5modBCTH9hOm+WeBOv6zxJCUD0ZZjZGpxJtoMt+jKUan7drntPR9BwxBpOhccYbVULZGDeu1V4pWy2a/8uiIiIiIiIiIiIPGpsNhtNymfnlw6VyJ0+wGgfsnAfv2454b5zmuzQZAp4+lrzuKswoxX81RUSYt12zeCfgUFVB+Hj4WPJT0ae5I0Fb3Al7sptrUdEErk5wE1S2siRI+nZs+e17zt27Mi+ffvYt+/6j6VXrlyZ9OnTG3mpUqX44IMP+PrrrwkPD+eJJ56gbdu2lC9fnqtXrzJz5kzmzZsHJD7BOXLkyJRdkMgj5OTVk3Ra1IkYh7WImCskF6OeHUWIT8iNB3G54I934PJRa542J7wwKHE//mTsOBFOizFruRQVb8ltNuj1QmFeeyKnCpgiIiIiIiIiIiL/U/ixYH7rVJm3Jm9i6T/nLG1df95G1rR+lMlhnqlJ1rLw0nD4uRUkeaCBdSPh+AZ4ZTykzWF0LRlWkv5V+vPOkndwupzX8n2X9tFpYSf6PNmHzAGZsdv0bJnIrVIh8x5YtWqV5ftevXrdVL/FixdTtWpVt219+/YlNjaWIUOGEBkZee3czf8KCwtjypQplCxZ8lanLCJARFwEby18iwsxFyx5qG8ow54ednNFTICFn8PO2dbM/r/9932TH2PT0Uu8NnYdETHWpzjtNvimYQleKpX15u4vIiIiIiIiIiLyCAn08WRYs9I0+GEVe05HXMvjHE7a/biRX96qRLZQf7NjkZfAKwBmt4PoS9a2k5sSt5pt/BPkeNzoWi17NXo93oteq6zv/286u4laM2vh6+FLjuAc5ArJxdPZn6Zmzpp6QEHkJqj8/4Cy2WwMGjSIlStX0rJlS3Lnzo2vry9p0qShdOnSfP755+zcuZPq1avf76mKPJAi4yPpsKAD+y/vt+Tedm+GVBtCtqBsNzfQ8m9ghXm2JjU+hSxlku225uAFWoxeaxQxPe02vmtaWkVMERERERERERGR6wj08WRsy3JkCLJu+XohMo7W49cTnmQHtGvyPwvtl0OWsmZb9EX4sS5sn+G2a/189elcqrPbthhHDHsv7WXu4bl0XdaV8TvH38pyRB5ZKmTeA+PHj8flct3yf8k9jflfjz/+OOPGjePAgQNER0dz6dIlNm7cSM+ePd1uSysiNxYVH0WHBR3Ycm6L0fZl5S8pGVby5gZaOzLxacyk8j0LFd9KttvSf87Rctw6IuMcltzb086IFmV4vljmm7u/iIiIiIiIiIjII+yxNH6MfrUsvl7WUsi+s1dpMmoNF666P/eSNNmg1Ryo2MFsc8TBzDawrH/ikVJJtC3WlqYFm95wbt9u/pa9F/fe1DpEHmUqZIqI/EdUfBRvLXyLTWc3GW2dS3WmVq5aNzfQlp9gTlczz1ImcUtZu/t/fufvOsPrEzYQE++05L5edsa+Vo6nC2W8ufuLiIiIiIiIiIgIJbKlYWDDkka+69QVGo9cw9krMe47enpDrb7wygTwcrMN7aLe8GtHSIizxDabjW7lu/FK/leuO68EZwLdV3QnzhF33etEHnUqZIqI/E90QjSdF3Vmw5kNRturhV+lbbG2Nx7EEQ+rh8Gvbp64DCsCzWaAT5Dbrr9uOcGbkzYS57AWMQN9PPmxdQUq59NT1iIiIiIiIiIiIrfq+WKZ+aBWASPfd/YqDUes5sTl6OQ7F3kRWv4JgW4eMNgyCWa3N57MtNvsfPL4Jyx6ZRHDawynW7luNCrQiPR+1vf39l3ax/dbvr+dJYk8MlTIFBEBTkeepsOCDqw9vdZoa16oOe+Xff/Gh28fXALDn4S/PwKXtRhJaB549RfwDzW6xSU4+ez3nbw9dQsJTusvPcG+nkxqW4Hyucx+IiIiIiIiIiIicnPefCoP79bIb+SHL0TRcPhqjl6ISr5zltLQdiGEFTbbds6CNe6LkRn8M1ApSyWaF25Oj4o96Feln3HNuJ3j2HJ2y80uQ+SRo0KmiDzSXC4Xvx34jfq/1nf7JGbjAo35oNwH1y9iXjoC05rDj/Xg3G6zPSQbvPorBIYZTccuRvHKiNWMW3nYaAsN8GZKu4qUzJbmFlYkIiIiIiIiIiIiSdlsNt6ukY+Pni9otJ24HE3jkauT32YWEs/NbP035Kluts3rCUdW3XAO5TKVo3mh5pbM6XLy8YqPiYq/TiFV5BGmQqaIPLLOR5/n7cVv8/GKj4mIjzDaG+ZvyEcVPkq+iOl0wKrvYFgF2P27+2uCMicWMdNkM5rm7zpD7W+Xs/XYZaMtLMiHae0qUuSxkFtZkoiIiIiIiIiIiFxHuyp5+KJeESM/GR7D6z9uIDrOkXxn32BoOh3y17LmLgf83Aoiztzw/m+XfptcIbks2dGIo3Rd1pVVJ1cR74y/qXWIPCpUyBSRR9Kio4t46deXWHxssdv2V/K/wscVP06+iHl2N4x5BuZ9DAnJ7KFfqjm0Xw7p8hhNU9cd5fUfN3AlJsFoK541hJlvPkG+jO7P0hQREREREREREZHb1+LxnPRrUJykb/1tPR5Ol5+34Exy/JOFhxe8NBzS5rTmV0/DjFbgMN/v+y9fT1/6VO6Dh83Dki87voz289vz1LSn6L68O2tOrbmFFYk8vFTIFJFHSqwjlj5r+/D24re5HHvZaA/yDqLvk33pWbEndpubfyIT4mDJ14lnYZ7Y6P4mWcpC20VQbxgEZjCad5wI55Nfd7rt2vKJnPz8xuNkC/W/lWWJiIiIiIiIiIjILWhYNht9Xypm5H9tP8038/dev7NfWmg4ETx9rfmRlbDwsxveu2j6orQt1tZtW0RcBH8c/IPX571O37V9cbmuU1QVeQSokCkij4yD4Qdp9mczpuyZ4ra9UpZKzK47mxdyv+D+ScyEWJjcAJb0AXdbPARkgBd/gDbzIWsZt/eIjE2g85TNxDmcljzIx5Pvm5Xm07pF8PH0cNtXREREREREREREUk7j8tlpXyW3kQ9bfIAZG49fv3Pm4lD7GzNf9S1snXrDe7cv3p5i6c1C6n/9tOcnvl7/tYqZ8kjzvN8TEBG522ISYpi1bxaDNw0m2s02sP6e/nQt15WX872c/FayLhf82hEOLXXfXqIp1PwS/EOvO5dPft3JwfORlixziC9TXq9IzvQBN7UeERERERERERERSRndahXk0PlI5u2ynm/ZfdY2An08qFU0c/KdSzWHY+tg0wRr/utb4J8O8j2TbFcvDy9GPDOCybsnM+/IPPZd2uf2usm7J2O32elatmvy712KPMT0RKaIPLROR55myKYhPDPjGfqu6+u2iFk0XVFm1JlBg/wNrv+LwJKvYPt0Mw/JBs1nwks/3LCIOXvzcWZusn6Sy26DIY1LqYgpIiIiIiIiIiJyH9jtNgY3LknRLMGWPN7h4o1Jm+j9xy7ik+yuZvFcP8hcwpo5E2D6q3B8w3XvHeQdxBsl3mBW3Vn8+dKfdCnThbxp8hrXTdw1kUEbB+nJTHkkqZApIg+dM5Fn+GDZB9SaWYvR20e7PQsToFWRVvz43I9kC852/QG3ToOlX5l58cbQYTXkrXHDOR06H0mP2TuM/O2n81M+1/ULoCIiIiIiIiIiInL3+Ht7Mua1cmQK9jXaRq84RKMRqzl52XxIAgAvX2j8EwRnsebxUTD5FTj3z03NIXtwdloWbcnk5ydTJqN5bNW4neMYsmmIipnyyFEhU0QeKqeunqLpX02Zc2gODpfD7TWhvqEMrzGc98q+h5eH1/UHPLwSfuto5vlqwovfg0/QDecUE++g05RNRMZZ51MhVygdq5ufsBIREREREREREZF7K2OwL2NaliXEz3y/cNPRy9T+djlL/znnvnNI1sRd23zTWPPoizDxJdg+A05tg7ioG87D38ufYU8Po1RYKaNtzI4xvLP4Ha7EXbmZJYk8FFTIFJGHxuWYy7Rf0J6zUWfdtnvaPKmduzYz6sygUpZKNx7w/D6Y1gwccdY8YzFoMAbsHjccIi7ByRuTNrLjhPWXi7T+Xl3DVFQAAH1TSURBVAxpXAoPu/a1FxERERERERERSQ2KPBb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"text/plain": [ "
" ] @@ -160,7 +160,7 @@ "metadata": {}, "outputs": [], "source": [ - "from cmethods import CMethods as cm" + "from cmethods import adjust" ] }, { @@ -174,23 +174,15 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 23.67it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# to adjust a 3d dataset\n", - "qdm_result = cm.adjust_3d(\n", + "qdm_result = adjust(\n", " method = \"quantile_delta_mapping\",\n", " obs = obsh[\"tas\"],\n", " simh = simh[\"tas\"],\n", " simp = simp[\"tas\"],\n", - " n_quaniles = 1000,\n", + " n_quantiles = 1000,\n", " kind = \"+\", # to calculate the relative rather than the absolute change, \"*\" can be used instead of \"+\" (this is prefered when adjusting precipitation)\n", ")" ] @@ -566,80 +558,50 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.DataArray 'tas' (time: 10950, lat: 4, lon: 2)>\n",
-       "array([[[-23.10136938, -22.10136938],\n",
-       "        [-24.44140058, -23.44140058],\n",
-       "        [-26.01662437, -25.01662437],\n",
-       "        [-26.28269982, -25.28269982]],\n",
-       "\n",
-       "       [[-23.41589465, -22.41589465],\n",
-       "        [-24.56309161, -23.56309161],\n",
-       "        [-24.04938832, -23.04938832],\n",
-       "        [-26.01993313, -25.01993313]],\n",
-       "\n",
-       "       [[-23.18047936, -22.18047936],\n",
-       "        [-24.61759495, -23.61759495],\n",
-       "        [-24.38520952, -23.38520952],\n",
-       "        [-25.59637237, -24.59637237]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[-25.56795121, -24.56795121],\n",
-       "        [-26.43865472, -25.43865472],\n",
-       "        [-28.31595975, -27.31595975],\n",
-       "        [-29.04694175, -28.04694175]],\n",
-       "\n",
-       "       [[-26.37948592, -25.37948592],\n",
-       "        [-27.46839831, -26.46839831],\n",
-       "        [-28.41172919, -27.41172919],\n",
-       "        [-29.84433331, -28.84433331]],\n",
-       "\n",
-       "       [[-25.65145194, -24.65145194],\n",
-       "        [-28.06217378, -27.06217378],\n",
-       "        [-28.43720573, -27.43720573],\n",
-       "        [-29.98579602, -28.98579602]]])\n",
+       "
<xarray.Dataset>\n",
+       "Dimensions:  (lat: 4, lon: 2, time: 10950)\n",
        "Coordinates:\n",
-       "  * time     (time) object 2001-01-01 00:00:00 ... 2030-12-31 00:00:00\n",
        "  * lat      (lat) int64 23 24 25 26\n",
        "  * lon      (lon) int64 0 1\n",
-       "Attributes:\n",
-       "    units:    °C
    • " ], "text/plain": [ - "\n", - "array([[[-23.10136938, -22.10136938],\n", - " [-24.44140058, -23.44140058],\n", - " [-26.01662437, -25.01662437],\n", - " [-26.28269982, -25.28269982]],\n", - "\n", - " [[-23.41589465, -22.41589465],\n", - " [-24.56309161, -23.56309161],\n", - " [-24.04938832, -23.04938832],\n", - " [-26.01993313, -25.01993313]],\n", - "\n", - " [[-23.18047936, -22.18047936],\n", - " [-24.61759495, -23.61759495],\n", - " [-24.38520952, -23.38520952],\n", - " [-25.59637237, -24.59637237]],\n", - "\n", - " ...,\n", - "\n", - " [[-25.56795121, -24.56795121],\n", - " [-26.43865472, -25.43865472],\n", - " [-28.31595975, -27.31595975],\n", - " [-29.04694175, -28.04694175]],\n", - "\n", - " [[-26.37948592, -25.37948592],\n", - " [-27.46839831, -26.46839831],\n", - " [-28.41172919, -27.41172919],\n", - " [-29.84433331, -28.84433331]],\n", - "\n", - " [[-25.65145194, -24.65145194],\n", - " [-28.06217378, -27.06217378],\n", - " [-28.43720573, -27.43720573],\n", - " [-29.98579602, -28.98579602]]])\n", + "\n", + "Dimensions: (lat: 4, lon: 2, time: 10950)\n", "Coordinates:\n", - " * time (time) object 2001-01-01 00:00:00 ... 2030-12-31 00:00:00\n", " * lat (lat) int64 23 24 25 26\n", " * lon (lon) int64 0 1\n", - "Attributes:\n", - " units: °C" + " * time (time) object 2001-01-01 00:00:00 ... 2030-12-31 00:00:00\n", + "Data variables:\n", + " tas (time, lat, lon) float64 -23.09 -22.09 -24.46 ... -29.84 -28.84" ] }, "execution_count": 9, @@ -714,7 +646,7 @@ "outputs": [ { "data": { - "image/png": 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EkZGR8PT0RFpaGhITE3H79m2MGDEC69evN3udKZVKzJkzBz/88IPO9JCQELRq1QoANPtLzet7770Xf/zxB4YMGWL2fIx55ZVX8O677+pMCwwMRFhYGGQyGZKTkzUtxDMyMjBu3DisWrUKM2bMMFhmWVkZRo4ciWPHjulM79ixI/z9/ZGdnY0rV66guLgYkydPxo4dO6yyLLXxvFV/mZmZOq2ha1pweXh4QCwWIysrC9euXdMcgw4fPoxevXrh3LlzmmtKQ2zx+GUN8fHxGDZsmOacCVQf19q1awcPDw9UVVUhNzcXiYmJmvNO7evsNm3aYPjw4aioqMChQ4c00wcMGKB3m+zSpYveunz55ZeYP3++zjr29vZGREQEHB0dkZaWprkeLiwsxJw5c3D79u061+bamnvbHDhwILKyspCYmIgbN24AqN5uevbsqTe9vt8lpaWleOCBB7Br1y6d6REREQgKCoJCodBsfwBw9uxZ9O3bF3v37jW4rmtbvXo15s6dC6D6uiAyMhLOzs64ceMGUlNTAVS3FJ80aRKOHj2Kbt26YdKkSfjzzz8BVH9P7dq1g0QiwaVLlzTHsPT0dIwYMQKXL1+Gs7OzWXX55ptvNNdYUqkUkZGRmvNyzToUBAFffvklcnNzsW7dOojFhjvJa4r19+GHH+KFF14A8M+51svLC9nZ2YiPj9dJW1RUhKSkJM17e3t7tGnTRtO7Tc11nlwuBwBcvnwZvXv3xqlTp9CuXbs68+7ZsyccHByQnp6OS5cuAQAcHBwwcOBAvXU1dD3Y2ARBwLRp07BlyxYAgIuLCzp06AAnJyfcunWrTovKxvje3n77bbzxxhs601q2bImQkBA4ODigtLQUKSkpuH37tuZzUy1iiYjuSM0WQiUiIrIiW2uRuWLFCk0amUwmvP/++0JxcXGddGq1WoiNjRUWL14sdO7c2eA86/uU9bZt23SWKzg4WNiyZYugUqk0aSoqKoRPP/1UcHJy0qTz8PAQUlNTzaqPq6urAEDo3LmzsH//fkGtVmvSqVQq4ebNm/ValmPHjgkymUyT1tnZWXj77beFnJycOmlv374tfP7550JkZKSwefNmg3WeO3eusG3bNqGsrKzO5yqVStixY4fQpUsXzTxdXFyMtnporBaZn376qdCtWzdhxYoVQkJCgt40t2/fFhYtWqTTAu4///mP0XK1n36uaaEbGhpaZ5tQq9XC+vXrdbaJiIgIne+2tuXLl+usi169egmxsbE6abKysoRHH31UACA4ODgILi4u9dqujdHeP319fTXzWrFihVBRUaFJp1KphM2bNwuBgYGa9BKJRDh79qzBsrXXX812HxISIvz++++CUqnUSav9vSkUCqFfv36avI6OjsLSpUuF/Px8nTxyuVz47rvvBDc3N03aAQMG6Hw32oqKioQWLVpo0trb2wvLli3T2b5VKpXwxx9/aNLVrJOauhtibqucyspKoUePHjrf/dixY4Vz587pTbtt2zZhypQpwgMPPKC3PHNb4tRWXFys0xrGy8tL+OKLL+q0uCwrKxM++OADnafap02bZrDc5ORkne3U3d1dWLVqlSCXyzVp5HK5sGbNGk0rP+113FgtMr29vQWRSCS8/PLLQkFBgU7aI0eOCP7+/pq0nTp1En7++WfNsb12/dVqtfDVV18JEolEk+fRRx81Wp8HH3ywznde+1ifkpIiPPDAAzrpJk2aZLTc+++/Xyf9rFmzhNu3b+ukuXjxotCnT58669rUMXjhwoU6aWfPnq33+Lpv3z6hQ4cOOseRjIwMvWVacg748ssv66yz8+fP10l39uxZzfLVHL8uXrxosNxnnnlGp9xRo0YJN27cqFPPmnVbe51Zq0Umz1v19+effwoRERHC0qVLhYsXL+o95ufn5wsffPCB4OzsrJn/hAkTjJbbmMev5m6Rqd0C2d/fX9i0aZPeVm5VVVXCnj17hHnz5gnTp0/XW1ZDruW2bdum02KyX79+wqFDh+psd9evX9c5volEImHnzp0Gy7WVbbMhPQw89NBDOtdXCxYsqHNNXXMtpt1KvW3btgZ7S9BuUefs7Cw4ODgI9vb2wvLly4Xy8nKdtL///rvO/jJy5EjhvffeE4Dq30ObN2/W2dfkcnmd35NvvfWWweXTrouLi4vg4OAgABDmzZsnZGdn66Q9f/58nZ5rVqxY0azrz9HRUZBKpYJYLBZeeeWVOtek6enpOus0Li5OCAoKEl577TXh5MmTeve3srIyYeXKlTq9AHXv3t3ocprbSrK2pmqRWXO97+3tLaxevVqoqqrSSVv7fGft7y0rK0vnmnXUqFEGWwtnZWUJa9asEfr37292jw5ERHcSBjKJiOiuYGuBTO1uTF955RWz5l87EKKtPoFMhUKh091kQECA0Zszu3bt0rmRbeyGc+0up7p06SIUFRWZVS9zlkWhUAjh4eGadJ6ensKZM2dMlq1Wqw3+eDe3W8PS0lKdwIyx76+xApnm1lUQBOGXX37RualTO6ihrXY3TqGhoUa7L/3xxx910h86dEhvuuzsbJ2bx7169apzQ0nb888/r1OuNW+6ae+fNTcLtbsUqy0+Pl6nO8TevXsbTFt7/bVo0UJIS0szWaeaG2dAdfeOprbl8+fP66zPDRs26E23YMECnfrUdBupT2JiYp3upa0RyHz99dd1ynzjjTeMLlsNQ9t4fQOZTzzxhCZfcHCwyX1x+/btOl2injp1Sm+6yZMna9LY29sLx44dM1jmiRMnNDcy63vj1xB9XY1/9dVXBtPv2bNHJ62dnZ3g6OioN8BcY9GiRTrHEn0PfAiCIOzcuVOn7KlTpxoMFqnVamHGjBk66Q3dXNu6datOuvnz5xusa3l5uU6wz9Qx+Pjx4zrBhm+++cZg2YIgCIWFhTrBzKeeekpvOnPPAcnJyTrbxquvvmp0/lVVVcKgQYN0blzqExcXp7Mdjx8/3uCDDyqVqk5gGbBeIJPnrforKyszGnDVdvjwYc21mkgkEq5du2YwbWMev5ozkKlQKAR7e3tNml27dplVrqHr7Ppey5WUlOgEfmfMmGH0Wl6tVgvTpk3TpO/UqZPedLa0bdY3kLlu3TpNPplMZrKby1u3buk8gPPBBx/oTacdiKrZB7Zt22aw3G+//bbOudDPz09ISUkxmOfhhx/WpA8PDzeYrnZdAAgLFiwwmL60tFRnqAxnZ2chNzdXb9qmWn+WbCuVlZU6D0EYc+3aNZ2H8ozto7YeyKz5ruLi4kyW3xjfm/Y5LSwsrE4g1RBjxyIiojsVA5lERHRXsHScG1N/DQ1ktm3bVpNm+/btDV6++gQyN2zYoLNM69evN5ln/vz5mvQSicTgD/3agUxzxtW0ZFl++OEHi+tuTbt27dLMu3379gbTNVYg01J9+/bV1GHdunUG09W+IWzq5p9KpdJp8ffOO+/oTffBBx/obDeXLl0yWm5lZaVOoNqaN91q33ww5+bGp59+qpPHUKvM2uvP2LquUVlZqXOTwtyxsd566y1Nnv79++stV3t8xAcffNBkmStXrtSpf0MDmcXFxTpjDRoKtliiPoHM27dv69zUNjRObm01LVkA6G2pk5GRodNybOHChSbLfPXVV3XWcWMFMgcPHmwyT+3x2oy1LBEEQUhNTdVJf/ToUb3ptMeF9ff3N/kQS0lJiU6rgyFDhpgsNyQkRKcFtT5Xr16tMyaroWPwuHHjjH7X+hw6dEiTx8nJSW+vCuaeA5577jmj+7M+N2/e1CyfSCQSEhMT66R56qmnNOW6uroKWVlZRsvMyckR3N3ddepsrUCmpXjeqr+pU6dq6vDee+/pTdPYx6/mDGRmZGTolGvqWGFKfa/lPvroI02e1q1bC5WVlSbzFBUV6Zw39+7dWyeNLW2b9Q1kRkVFafK9/vrrZuXRvvZv2bKl3jS1A3GzZ882WmZlZaWmRV3N3w8//GA0z5EjR3TSG+qZpXZdIiIiTAb6Lly4oPPwyf/+9z+96Zpq/Y0YMcKssuvjlVde0czniSeeMJjuTghkGjrO1tYY39vSpUs1n5tzrU9EdDcz3CE7ERER1Zv22DraY+I1pc2bN2tet2rVCpMnTzaZ57///S9EIhGA6vF5/vjjD5N5unbtil69etW/onr89NNPmteRkZGYMmWKVcs3pU+fPprX165dQ1FRUZPO31La9a09Zp0hERERGDp0qNE0YrEY/fv317zXHvNVm/YYTvfeey86depktFx7e3s8/vjjZtWzoebPn28yzaOPPgoXFxfN+02bNpnM4+PjgwceeMBkuu3bt2vGH/Tz8zM61p22mTNnal4fP34c5eXlOp8fOHBAM74OYN5yTp8+HR4eHmbN3xxbt27VGetyyZIlVivbEuvWrdOMe9mtWzfcd999ZuXTXsd79+6t8/kff/wBpVIJABCJRGaNL/r0009DIpGYNf+GmDdvnsk0vXv3tihPcHCwznisV65cqZOmvLxcZ9ynuXPnws3NzWi5Li4uOvv7vn37NOMR1ygrK8POnTt1ynVwcDBabvv27U0ewwBoxv+r8eKLL5rMAwD9+/dHWFgYgOrlPn78uFn5alOr1Trjcpo7/7CwMM3xVxAEvduo9rH3wQcfhJ+fn9EyfXx8mmXcZ3143qo/c9adLR+/Gqr2+JXNdZ29Zs0azetnn30W9vb2JvO4ublhwoQJmvd79uypk+ZO3jaB6jHpa8all8lkeO6558zKN2XKFM1xPzU1VWesZENMLbe9vT2ioqI0793c3PDQQw8ZzRMdHa2zH+g7Fxqqi0wmM5qmS5cuOmNA6rvetKX11xD1OcbbIolEgscee8xkusb63rSPdxcvXtQZi5eI6N9G2twVICIiagzDhw+3OI/2TdSGiomJwYULFwBU39z38fHBjBkzzLrJYS3aN11Hjx6tCVAaExoaiq5du2p+iB0/ftzkza8BAwY0qJ61KZVKHD16VPPenACsJQRBwLFjx3Dy5EnEx8ejsLAQpaWlUKvVBtNnZGTA3d3dqvUwV05ODnbv3o0LFy4gIyMDxcXFmqBNjcTERM3rtLQ0s8q95557zEqnHdjQDlrVkMvlOjcRR44caVa5o0ePxsKFC81KW19+fn7o0aOHyXROTk4YNGgQ/vrrLwDAyZMnTebp06cPpFLTl9IHDx7UvB40aJBZeYDqhw88PDxQWFgIpVKJ2NhY9O3bV/O5dh3d3d11PjPE3t4eQ4YMwcaNG82qgynayxYWFobo6GirlNuQepgT2KrRtWtXzeuMjAxkZGQgKChIM017HUdGRursC4YEBgaiW7duOHPmjNn1qA/tG4SGBAQEaF6HhYXB39/fZJ7AwEDNMaSgoKDO56dPn9YERwBgzJgx5lQX48aNw+LFiwFUB/ZOnjyp812dPXtW5+acJceR7du3G01z+PBhzfHdz88PXbp0MatsoHobSUpKAgCcOXMGw4YNMztvjbi4OM26FIlEZgfaa+a/f/9+zfy1g9G3bt1CZmam5r0l6+zLL780uw71wfNW/RUXF2P37t2IjY1FSkoKSkpKUFlZCUEQNGnS09M1rw2tO1s+fjWUh4cH2rRpo9mGpk6dipUrV2LIkCFNVofCwkLExcVp3tf33FN7Xdvytmku7XNyt27d4O3tbVY+e3t7tG/fXvMb5MyZM2jbtq3B9HZ2dmZd42mfC3v06GEy2Ghvbw8vLy/k5OQA0H8u1MeS76rmuH7u3DkoFAqdOjXV+gPq/xuusrISe/bswfnz53Hjxg0UFxejoqJC5zil/bCducd4W9SxY0ezvoPG+t5iYmI0r69evYpHHnkEH3zwAVq2bGnmEhAR3T0YyCQiorvSjh07LM5jTqDPXM899xx++OEHyOVyVFVVYd68eXjxxRcxfPhwDBgwAL1790a3bt0a7cl3pVKJ5ORkzXvtmyamdOnSRfNjSvtGoyHh4eGWVs+otLQ0lJaWat5bKzgiCAJWr16NJUuW4NatWxbl1XcjtLGlpKRgwYIF2Lx5s07gwBRz66p9Y8cYZ2dnzevarQKB6pvp2jeoO3fubFa57dq1g0wmg0KhMCt9fURGRpqdtnPnzppApjlPsZu73V+8eFHz+tSpUxgxYoTZdaqsrNS8rrmhVkO7jp06dTL7+NW5c2erBTKvXr2qed1cQUxAdx3/+eefmodILJWTk6MTyNRex+Zu1zVpGzsQYM7+6+TkZFH62nn07e/a5wSRSGR2ULBjx46QSqWaY1liYqLOjX/tdS0SiUy2QKphzveivX1UVlZatA9qBypq74Pm0p6/VCo1qyV3De31bewYAJi/jVqyLVuK5636y8vLwyuvvIIffvhB59hviqF1Z8vHL2t46aWXNIH95ORkDB06FC1btsSIESPQv39/9O7dGxEREY02/7i4OJ0H4J599lmzr+m1A9G192tb3DYtpX3MS0lJseiYm5KSonlt6pjr7e1t1sNhjXEurE0mk6Fdu3Zmla39nVZWViI1NVXnmrKp1p+7uzu8vLzMLhuo7j3h//7v//Dll1/W6VnBmOb4HWUt9bnet+b3ds8996Bv3744duwYgOpeSDZs2IDevXtj8ODB6Nu3L/r06WPVHleIiGwVA5lERESNIDIyEuvWrcOMGTM0Qbni4mL8+uuv+PXXXwFUd280ZMgQzJgxA2PHjrVqILX2D0ZfX1+z82qnNecpZFPdClpK+wleACa7yjOHWq3G7Nmzdbr3s0TtliSN7fTp0xg2bFi9fvibW9f6tA7WftK6Ru1txNwnkKVSKdzd3ZGbm2txPcxlbl1qp7Xmdp+Xl6d5nZycrPOAgSVqd2+sXcf6LmdDae+r1thP60t7HV+9elUnwGoJW1zHhtjZ2TVqesD0/u7i4mKy+9caMpkM7u7umu+q9j5Wu1xzj0/mrGvt7aO4uLjevS/Ut4tx7fkrFAqrzb++x97G2j553qq/mzdv4t5777X4ISvA8Lqz5eOXNcydOxdJSUl47733NN9xamoqVq5ciZUrVwIAgoKCMHbsWMyZM8fqD9to79eA/i5izWGt/boprqnMpb1usrKyGu2YW5/zmrXOhbV5eHiYHciu/Z3W/s6bav1Z+vstNzcXQ4YMqdfDYnK53OI8tqI+1/vW/t42btyIMWPG4OzZswCqf9ceO3ZME9wUi8Xo3r07pkyZgjlz5lgcoCYiulNwjEwiIqJGMmHCBFy/fh0LFizQaeVTo7i4GL/99hvGjx+P7t27mz0Gizlq39iy5Ie79o1Cc1oFiMXWvZyoPU9zb5Qb8+mnn+oEMSMiIvDuu+/i0KFDuHXrFkpLS6FSqSAIguavuZSVlWHixImam8EymQyPPPII1q1bh7i4OOTn52u6mav5q+mysTnUvjlR322tMdS3LubcVDd3uy8rKzO7DsbU7vpYe7031zrX3letsZ/W1928jm2N9r5h6Q1hY+eWxlzXjbV9mKsptk/A/PXWGNsnz1v1p1arMWXKFE0QUyQSYdy4cVizZg1iY2ORm5ur6bKx5m/16tUmy/03HL+WLl2KM2fOYNq0aTrjXNfIyMjAV199hZiYGEyZMsWqrcJsbb8GbOe7a+5jbnNoyPdU+5qzqdafpb/f5s6dqxPEHDRoEL7++mucOXMGWVlZKC8vh1qt1hynarrPvdM19/U+UN3t98mTJ7Fq1Sr07t27zsPParUaZ86cwUsvvYTQ0FB89dVXVqkLEZGtYYtMIiKiRhQYGIgPPvgAH3zwAa5cuYIjR47g8OHD2LdvHzIyMjTpYmNjMXDgQJw9exatWrVq8Hxrdy9TUlJidl7troKao5saT09Pnff1bQVTQ61WY+nSpZr3999/PzZt2mT0poMl68vaVq9erRlLRiaTYffu3Rg4cKDRPM1Z39pPKtd3W2sM9a2LNcdD1d6Hnn32WXz88cdWKVd7vTfXOtfeVxu6nzaEh4eHphXKhx9+iP/85z9WKdcW1rGt0d6eLT3uGDu3NOa61p5Xly5d6t31cH1pz9/Nzc1q+4q+Y6++YE5tjbF98rxVf9u2bdO0sgGAtWvXYurUqUbzmFPfO/n4pT1erindu3fH2rVroVAocPr0aRw9ehSHDx/GgQMHdJb7119/RVpaGg4fPmyVYR1qH8Py8/PrXL/Why1tm/WlvW7Gjh2LLVu2NF9lmkhDvqfa15y2uP4uXbqE33//XfN+6dKlWLRokdE8zXmMN8SSY4ulGvt7k0gkmD17NmbPno28vDwcPnwYR48exYEDB3D27FnNQ7glJSV48sknIQgCnnzySavWgYioubFFJhERURPp2LEj5s2bhx9//BFpaWk4duwYRo8erfk8NzcXb7/9tlXm5ezsrDO+y40bN8zOq522ObqLrD1+zbVr1xpU3rlz55Cdna15/8knn5h8cjo1NbVB82wI7fFdH374YZM3g4Hmra+/v7/O+6SkJLPy5ebmNvpNDnPrAlR37Vej9jI1hPb2nJWVZbVytetY3+VsqMDAQM3rhu6nDXE3r2Nbo31OkMvlmuCVKdnZ2TpjH9c+t2iva7lcrvOgjzHmrOvG2j7MpT3/4uJiVFRUWKXc+h57G2P75Hmr/rTX3YABA0wGMQHz1p2tHL9kMpnmtbnjN5rTvbu++fTt2xcLFizAH3/8gdzcXGzatAkdOnTQpDl+/DjWr19vcdn61L5WtdaxxZa2zfpq7mNucyguLq4zNIYhtfex2udDW1x/2sep0NBQvPzyyybzNPYxXvu3XGMeW8zVlN+bt7c3xo8fj2XLluH06dNIT0/HkiVLdHpHWbRokVnjuxIR3UkYyCQiImoGIpEIffr0wR9//IEBAwZophsaT0O7Wxtzuz3t0aOH5nXNGBqmKJVKnDp1SvPe2mMKmcPT0xPt27fXvD9w4ECDyktJSdG89vHxQVhYmMk8R44cadA8G0K7vj179jSZXhAEs7/fxuDn54fg4GDN+5MnT5qVz9x0DREfH292CwXt+mjvOw3Vt29fzevjx49brVztOt64caPOeF2GWHO99+nTR/P6zJkzOoGq+qrPsa4p1vHZs2ehVCpN5qnp3utuVXvfMPfYUztd7XNL7XKteRzR3j6ysrIsCupYg/Z+AgAnTpywSrldunTRCRI157GX5636s3TdAeZdo9jK8Uu7haE5gZ6qqiokJCQ0eL52dnaYOHEi9u/frzMmob7r7NpdR5pz7unSpQucnZ0176117rGlbRNo+Dk5NjbWag9v2Lr6fFetWrWCr6+vzue2uP60j1PR0dF1ujbVx9zfUvXZxgDLjy0AEBcXZ3b5lmrO7y0wMBBvvPEGPv30U820oqKiJvmtRUTUlBjIJCIiakZisRjjx4/XvM/MzNSbTvtmibk/jLRbROzZs8dg2dq2bt2qExAxp1VFYxgxYoTm9caNGzXdRtaHuU/pavvuu+/qPb+GsrS+O3bsQHp6eiPVxjza28nGjRvNWoaffvqpMasEoHpdbty40WS6ixcv4tKlS5r31tzuR44cqXl969Yt7N692yrlaj8AoVarzWplcv36davepNbeTysqKvD99983uMz6HOu01/GRI0es1jpUezvIz8/Hrl27TObZs2ePzbSgaAzh4eE6N9l//PFHs/Jpbxv+/v5o166dzuetW7fWKfeXX34xWaZSqcSGDRtMpouJidEJZHz77bfmVNlqgoKC0LVrV6vP38HBQSfwZc46A4Cff/7ZKvPXxvNW/Vm67q5cuWJW0MxWjl8hISGa1xcvXjSZ/q+//qozTmRD+Pv745577tG813ctrH3eAcw798hkMgwZMkTz3prHFVvZNoH6nZOHDBmieciiqqoKa9eubZS62RpzjsFyuVznulTf9aYtrj9Lj1N5eXk6XdEaU59tDLD82FJZWYnt27ebXb6lbOF7e+CBB3Tem/Pbn4joTsJAJhERUSOw5IlS7VZMXl5eetNod+Fo7pPqc+bM0TzlqlAosHDhQqPpq6qqdMY7CQkJwbBhw8yal7XNnz9fM4ZRRUUFnnjiCYvWqbagoCDN69zcXFy9etVo+lWrVum0Sm1q2vU9dOiQ0bTl5eVWGw+wIWbPnq15nZGRgU8++cRo+nPnzlmtezdT3nrrLZSVlRlNo71vuLq6YsqUKVabf1RUlM7Nzvnz51tljLyIiAj069dP8/6dd94x2frU1DHAUl27dsWgQYM0719//XXcunWrQWXW51g3duxYTWBMrVZj7ty59XqAobbBgwfr3Ch79dVXjbZqUiqVePXVVxs8X1s3d+5czeu//voL+/fvN5r+0KFDOjc0586dq7c1x6xZszSvN23ahNOnTxst9/PPPzdre5NKpTrHyRUrVuD8+fMm81nTggULNK/XrVuHbdu2WaVc7WPv6dOnsWnTJqPpt2zZ0igtIXneqj9L1p1arcbTTz9tVrm2cvzSbn29ceNGqNVqg2krKyuxePFik2Vaej1o6jrbw8NDp0tGc8892vv1sWPH8NVXX1lUL0NsZdsEdM/JN27cMGvd+/r6YubMmZr3r732WoOvDe4EP//8s8kxmD/66COd4NKcOXPqpLHF9ad9nDp+/LjJFt7/+c9/zA5Kam9jOTk5Zl8jax9bMjIyTLYAXb58OXJycswquz4a63ur7z0FwPB9BSKiOxUDmURERI2gS5cuWLNmjckAys2bN/HFF19o3t97771602l3EbZ+/XqzxiULDQ3V+UH1ww8/4P/+7//0/iAqKyvDgw8+qBPke/311zXBxKYWHh6Op556SvN+06ZNmDFjhtHxf9RqNTZu3IizZ8/qTO/Zs6fO075PPfWUwR/X69evx5NPPtnA2jfM4MGDNa83btyIv/76S2+6vLw83H///c06NmGNwYMH63SptGjRIvz22296016/fh3jxo0zejPTmlJSUjBp0iS9245KpcILL7ygM/bPc889BxcXF6vWYdmyZZqbpNeuXcPAgQMRHx9vMl9KSgpeffVVvPDCC3o/f+211zSvMzIyMGHCBL03gNRqNRYtWmT20/GWeO+99zRPoBcUFGDgwIEmg0RXr17F6tWr9X6mfazbtWuXWd2AicVifPTRR5rg2OHDhzFixAizxlm8cuUKnnnmGSxbtkxvudoPd8TGxmLmzJmoqqqqk7aqqgqPPvroXd2tbI2nnnpKpyu8yZMn1znu1jh//jwmTZqkee/t7Y358+frTfvMM8/Aw8MDQPU2O2HCBIP7yZYtW3SCCKY8++yziIiIAFAdSBs6dCi2bt1qMl9hYSG+/PLLBj/U8/DDD2tahanVakyePBmrV682eYOyvLwcP/30k8HurqdOnYrw8HDN+0cffRRHjx7Vm/b48eM61wTWxPNWw+pR49SpU/jyyy/1pisvL8cjjzxidnf7tnL80t7/r1+/jnfeeUdvutLSUkyZMgWXL182WeahQ4cwcuRI7Nq1CyqVymjaP//8U2ed6bvOlkgkiIqK0rz/4osvUFlZabIe99xzDx566CHN+2eeeQbvv/++ySCPQqHAH3/8gXvvvVeny84atrJtArrn5Pz8fIPn7toWL14MHx8fANVjJA8cONCsbrWzsrLw/vvvY9q0afWrcDNSqVQYN24cEhMT9X6+ceNGneu2/v37G+wBxNbWn/ZxKj09XWc5tCmVSrz44otm99YA1O0m/cMPPzQrX69evXR6cpg/f77BB/pWrVqFJUuWmF2n+mqM7+3555/HggULTI5frFQqdR5YdHBwqNO1PRHRnU7a3BUgIiK6G126dAmzZ8/G008/jaFDh6JXr17o0KGD5snIjIwMHDp0CD/++KPm6Uk7Ozudm07apk6divfffx+CIOD27dto3bo1unfvDl9fX52xRWoHKlasWIGDBw9qfvy88cYb+OuvvzBr1iy0a9cOCoUC586dwzfffIPk5GRNvvHjx+t9SrgpLV++HKdPn9b8AFy7di127NiBadOmoV+/fvDz84NCoUBqaipOnjyJ33//HZmZmdi8ebPOjRcHBwc8/fTT+OCDDwBUj7nZpUsXPPnkk4iKioJIJEJiYiLWr1+PvXv3AgCeeOIJqz1Zb6l58+bh/fffR2lpKdRqNcaNG4fp06djzJgx8Pf3R0FBAQ4fPoxVq1YhLy8Pbm5uGD16tNndCjYGkUiEb7/9FtHR0SgvL4dCocADDzyA8ePHY9KkSWjZsiUKCgqwd+9efPvtt6ioqED//v2RlJRkVlC+voYMGYLY2Fjs2LEDnTp1wuOPP47o6GjIZDLEx8fju+++w7lz5zTpIyMjDd6caYioqCh89913mD59OtRqNS5cuIBOnTrh/vvvx7Bhw9C6dWu4urqipKQEmZmZuHDhAg4dOqSpm6Hgw/DhwzFr1iysWbMGALBv3z7NcsbExMDe3h7x8fFYvXo1Tp8+DZFIhAcffBDr1q2z2rL16tULy5cvx3PPPQcASE5ORnR0NMaOHYtRo0YhNDQU9vb2yM3NxYULF7Bz506cPHkS48aN02l1UuOBBx7A888/j8rKSpSXlyMqKgpRUVEICgrSebDim2++gZ+fn+b9yJEjsXTpUs0xdN++fQgPD8fEiRMxePBgtGrVCk5OTiguLkZ6ejpiY2Oxb98+TaDMUCugefPmYcOGDdi3bx+A6hYXZ86cwbx589ClSxeIRCJcvHgRK1euRHx8PBwcHDBy5Ehs3rzZOivYBvn4+OC7777DuHHjIAgC8vLy0Lt3b0yfPh2jRo2Cn58fcnJysGPHDnz//fea1rEikQgrV67U+d60+fv7Y8WKFZqWmenp6ejWrRvmzJmDIUOGwMvLC2lpadi0aZPmpv7UqVPN6irV1dUVW7ZswT333IOCggJNQC0mJgbjxo1Dly5d4OnpicrKSuTl5eHy5cs4ceIEDhw4AIVCodOyrT7EYjE2bdqEXr16ISUlBeXl5Xj00UexbNkyTJo0Cd27d4e3tzcUCgUKCgpw9epVnDp1Cnv37kV5ebnBch0dHbFy5UoMGzYMKpUKxcXFGDhwIKZNm4b7778f/v7+yM7OxtatW/Hjjz9CpVKZvc4swfNW/U2ePBmvvPIKUlNTAVQ/KLBr1y5MmTIFwcHBKCkpwalTp7Bq1SqkpqZCJpNhxowZZnWBbwvHr/vuuw8xMTGaFtZvvPEGTp06halTpyI4OBjFxcU4ceIEvv32W2RmZqJz586ws7Mz+HAEUN1CaceOHdixYwf8/f0xYsQIREdHIywsDO7u7lAoFLh58ya2bt2KLVu2aIJ8rVu3xiOPPKK3zEceeURzvblr1y4EBgYiKioKbm5umodkIiMj8fbbb+vk++6773D9+nWcO3cOKpUKL7/8Mr744gs8+OCD6NmzJ3x9fSEIAgoLCzXdu+/atUvz0JG+hxlsZdsEgHbt2iE6OloT5J4zZw7effddREREwM7OTpPu2Wef1Ql2BQcHY+PGjRg+fDiqqqqQnJyMPn36YPDgwRg1ahQ6dOgAd3d3lJWVIScnB3FxcTh69CiOHTsGtVrdbENb1FdwcDDCwsJw+PBhdO3aFY899hgGDx4MT09PpKam4tdff8WWLVs06V1cXIx2R2xr669fv37o2bOnpsea999/HydPnsTMmTMRHh6OiooKXLhwAatXr9ZcV5n7W8rFxQXjxo3TdLn71ltvYdWqVejYsSMcHR016R566CGdBwfEYjEWLlyoeTgqNjYWXbt2xbPPPouuXbtCEARcv34d69atw6FDhyCVSvHII480apevjfG9FRUV4fvvv8fy5csRHR2NgQMHIioqCv7+/nByckJhYSEuXryIH3/8UeeB5Oeff15nHFEioruCQEREdBdYvHixAEDzVx/a+RcvXmww3cyZMzXpZs6cabIsc/4cHByETZs2Ga3fW2+9ZbIcfVJTU4VOnTqZXZeJEycKlZWVRusSEhKiSb969WqjaRuSt7S0VBg7dqxF63Lz5s11yqmoqBD69u1rVv4RI0YIVVVVOtP279+vt35JSUk66ZKSkixaF4Zs2rRJkEqlJuvq7OwsbNu2TWf7HzhwoMFyBw4caNY2rs3csgVBEPbu3Ss4OTmZrHd4eLiQlpbWoO3IkNr75+7duwVnZ2eTdYqIiBAyMjKMll2f9adt69atgoeHh8XHB0PHGUEQBLlcbvY+snTpUmH16tWa9yEhIQbLteR7FwRB+PbbbwWZTGb2Mo0bN85gWatXrza5/Rva11atWiU4ODhYvI6NfZ9FRUVCz549TZYhlUqFH374weJ1Z479+/frzMsc9amHJdv4unXrBDs7O7PWr0wmE3766Sez6vDuu++aVeaoUaOExMREi47BCQkJFp0PTe0rlp4DMjMzhf79+1s8f1Pf+dq1awWJRGKyjB49egglJSU60wyd3yzF81b9HT9+3Kw6yGQy4dtvvzX7OC4IjXf8MudauMaVK1cEX19fk3UICwsTkpKSTH7ntY+H5vy1atVKuHr1qsE6KhQKYfjw4UbLMLQ+SkpKhAceeKBe+7WxY4YtbJuCIAjnz58XvL29jdbB0PxOnTolBAcHW7xeDK1rS7b9GpZsqzXMWZe165KWliaEh4ebdQw8ePCgWfWwhfVXIyEhwaz9WCQSCW+++aZF1y2pqalCaGio0XL1HQtUKpVZ18BSqdSiY2d9thlt1vzetOti7t+0adMEuVxucb2JiGwdu5YlIiJqBJ9//jlGjRoFV1dXo+kcHBzw8MMPIy4uDhMnTjSa9vXXX8fhw4fx6KOPolOnTnBzc9NpjWlIcHAwTp8+jbffflunO8Da2rZti59++gkbN26Evb29yXKbgrOzM7Zs2YJNmzahe/fuRtP6+fnhmWeeQa9evep85uDggL179+LZZ581uGyBgYFYtmwZtm3bpvOUeXOYOHEi9uzZg8jISL2fSyQSDBs2DOfOncPIkSObuHaGDR48GOfPn8fQoUP1joFnZ2eH6dOn4+zZs2jRokWT1GnIkCE4deoU+vfvr/dze3t7PPXUUzhz5ozOOD2NYdSoUUhISMCiRYsMtkrTrtfgwYPx+eefG+1mSyaTYfPmzfjoo4/g7e2tN02bNm2wefNmgy2+rWHOnDm4cuUKpk2bpjPWWG12dnYYPnw4nnnmGYNpZs2ahbNnz+Lpp59Gt27d4OHhAanUvI5kZs+ejWvXruHpp5+Gu7u70bQuLi4YPXo0vv/+e6PdlLq5ueHQoUN49dVXDXY7HBUVhQMHDmD69Olm1fNu8OCDD+LixYuYOHGiTrdw2qRSKcaNG4cLFy5g6tSpZpX78ssvY8eOHZpxT2vz9vbG22+/jT///NPi7s/btGmDc+fO4auvvkL79u2NphWJRIiKisIbb7yBPXv2WDQfQ/z9/XHgwAGsW7cO0dHReo+T2tq3b48XXngBsbGxRtNNmzYNx48fR0xMjN7PXVxc8Pzzz+Po0aNW7zq7Bs9b9de7d2+cOHFCpzvR2vr06YMjR45Y3FtGUxy/TG3HHTp0wNGjRzF06FC9n9vb22P27Nk4f/48QkNDTc6vc+fOeOutt9C7d2+Dx54avr6+eOmllxAXF2d0n5dKpdi2bRt+/vlnTJgwAWFhYXB2dja5bED1/rVx40Zs374dgwYNMnlcCg0NxZNPPokjR44YXV5b2DaB6u3j8uXLWLJkCfr37w9fX1+zr5NjYmJw9epVvP/++2jVqpXRtFKpFH369MH7779v9VbjTaFFixY4e/Yspk+frnf9iEQiDBs2DLGxsRgwYIBZZdrS+mvTpg3OnDmDUaNGGUzTuXNnbN261ayxbrUFBwfjwoUL+N///of77rsPgYGBRq8la4jFYmzcuBGvvvqqTutNbd27d8ehQ4eatKcha35vjz/+OObOnWvWsbF79+7YsGED1q5da/LYSER0JxIJgoUjpRMREZHZVCoVrly5guvXryMtLQ2lpaWQSCTw9PTUdNfUWDcV9VGr1Th9+jQuX76MnJwcSKVS+Pn5ISYmxuRNXVuQnp6OY8eOITMzE0VFRXB0dESLFi3QqVMnREZGmnXDKT8/HwcOHEBSUhIUCgUCAgLQpk0b9O3b16zAcFMSBAHnzp3DmTNnkJeXB1dXVwQGBqJfv34ICAho7uoZlZKSgkOHDiEjIwOOjo4IDg7GoEGDNN0rN5ZZs2bh+++/B1DdJWtNt6sAcOPGDZw8eRIZGRmQSCQICQnBkCFDmqXrJUEQEBcXh4sXLyI3NxelpaVwdnaGr68v2rVrh8jISIM3ZQxRKBQ4cOAArl+/jpKSEgQEBKBjx47o2bNnIy2FfpWVlThy5AiSkpKQm5sLsVgMLy8vtG3bFtHR0Tpj1jYmlUqFc+fO4cqVK8jLy0NFRQWcnZ0REBCA9u3bo1OnThbf6KmoqMDevXtx8+ZNVFRUICgoCN26dTMYvPm3KC4uxsGDB5GamorCwkK4u7ujZcuWGDhwoMmAsjFnz57F+fPnkZOTAy8vL4SFhWHQoEFWe9gkNTUVJ06cQHZ2NgoLC2Fvbw9PT0+0adMGnTt3bvTjVU5ODo4ePYrbt2+joKAAUqkUHh4eCA8PR2RkZL2O81evXsWJEyeQlZUFNzc3tGrVCvfee2+T7Xc8bzXM1atXcezYMWRnZ8PR0RGBgYHo2bMnwsLCGly2NY9f06ZN09x0f+KJJwyO7VlbUlISDh06hMzMTNjb22u2T09PT4vrAEDTpWViYiKys7NRXl4OBwcH+Pj4IDIyElFRUWY/CGMtRUVFOHr0KNLS0pCXlweRSAR3d3eEhoaiY8eO9eqq2ha2TWtISEjAmTNnkJubq7mO9/b2Rtu2bdG5c2eTD4DakjVr1mi6yA8JCdEZoqPmt0ZaWhoqKysRGBiIgQMHmgxqmWIr6y85ORmHDh3C7du3IZVKNV0xd+zYsUnmr09paSn279+PmzdvatZ59+7dbeL6zFrf2+3btxEXF4fk5GQUFBRAqVTCxcUFrVq1Qvfu3RvcDT4Rka1jIJOIiIiI7hrGAplERETUcCNHjsSOHTsAAAsXLsR7773XzDUialrGAplERERkfbbV7ICIiIiIiIiIiGySIAi4ePGi5v2d0KMHEREREd3ZGMgkIiIiIiIiIiKjBEHAhx9+iIyMDADV4/4NHDiwmWtFRERERHe7ph0sgIiIiIiIiIiI7hiLFy/GwYMHkZCQoAliAsCoUaOsMn4nEREREZExDGQSEREREREREZFeFy5cwMGDB3WmRURE4KuvvmqmGhERERHRvwkDmUREREREREREZJBIJIKbmxvat2+PsWPH4plnnoGbm1tzV4uIiIiI/gVEgiAIzV0JIiIiIiIiIiIiIiIiIiJt4uauABERERERERERERERERFRbQxkEhEREREREREREREREZHNYSCTiIiIiIiIiIiIiIiIiGwOA5lEREREREREREREREREZHMYyCQiIiIiIiIiIiIiIiIim8NAJhERERERERERERERERHZHAYyiYiIiIiIiIiIiIiIiMjmSJu7AnTny87Oxq5duwAAoaGhcHR0bOYaERERERERERERERERUVOoqKhAcnIyAGDYsGHw8/OzWtkMZFKD7dq1C9OnT2/uahAREREREREREREREVEz+vHHH/HII49YrTx2LUtERERERERERERERERENoctMqnBQkNDNa/XrFmDyMjI5qsMEUGpVKKwsBAA4OHhAamUh3qi5sb9ksi2cJ8ksi3cJ4lsD/dLItvCfZLItnCfrOvq1auanju1Y0bWwLVLDaY9Jmb79u3Ro0ePZqwNESkUCuTl5QEAvL29IZPJmrlGRMT9ksi2cJ8ksi3cJ4lsD/dLItvCfZLItnCfNE47ZmQN7FqWiIiIiIiIiIiIiIiIiGwOA5lEREREREREREREREREZHMYyCQiIiIiIiIiIiIiIiIim8NAJhERERERERERERERERHZHAYyiYiIiIiIiIiIiIiIiMjmMJBJRERERERERERERERERDaHgUwiIiIiIiIiIiIiIiIisjkMZBIRERERERERERERERGRzWEgk4iIiIiIiIiIiIiIiIhsDgOZRERERERERERERERERGRzGMgkIiIiIiIiIiIiIiIiIpsjbe4KEBERERERERERERER2SK5XI6SkhIUFxdDLpdDrVY3d5WomQmCAKVSCQDIz8+HSCRq5hrVn1gshp2dHdzc3ODq6go7O7vmrlIdDGQSERERERERERERERHVkp+fj6ysrOauBtkgsfju6PBUrVajsrISlZWVyM7Ohr+/P7y8vJq7WjoYyCQiIiIiIiIiIiIiItLCICYZc7cEMmur2eZtKZjJQCYREREREREREREREdHf5HK5ThDTwcEBnp6ecHFxgUQiuaO7EqWGU6vVUKlUAACJRHLHBjUFQYBKpUJpaSkKCgpQWVkJoDqY6eLiYjPdzN6Za5eIiIiIiIiIiIiIiKgRlJSUaF47ODggJCQEHh4ekEqlDGLSXUMkEkEqlcLDwwMhISFwcHDQfKa9DzQ3BjKJiIiIiIiIiIiIiIj+VlxcrHnt6el5x7a4IzKXWCyGp6en5r32PtDcuPcRERERERERERERERH9TS6Xa167uLg0Y02Imo72tq69DzQ3BjKJiIiIiIiIiIiIiIj+plarNa8lEkkz1oSo6Whv69r7QHNjIJOIiIiIiIiIiIiIiEgPjolJ/xa2uq0zkElERERERERERERERERENoeBTCIiIiIiIiIiIiIiIiKyOQxkEhEREREREREREREREZHNYSCTiIiIiIiIiIiIiIiIiGwOA5lEREREREREREREREREZHMYyCQiIiIiIiIiIiIiIiIim8NAJhEREREREREREREREdmcTZs2QSQSWeXv4sWLzb04VA8MZBIREREREREREREREZHNOXXqlFXKcXR0RKdOnaxSFjUtaXNXgIiIiIiIiIiIiIiIiKi2SZMmoU+fPno/k8vlePDBBwEAzs7OWLt2rcFyXF1dIZFIGqWO1LgYyCQiIiIiIiIiIiIiIiKbExMTg5iYGL2fnTt3TvO6a9euGD9+fBPVipoSu5YlIiIiIiIiIiIiIiKiO8r58+c1r7t169aMNaHGxEAmERERERERERERERER3VG0A5lRUVHNVxFqVOxaloiIiIiIiMgEeUoKCjdugrq8HE69e8GlXz+IHR110yQno/TwEaiKi2DfJgLOfXpD4ubWTDUmIiIiIrq7xcbGal6zRebdi4FMIiIiIiIiuusJCgVUpaVQFxVBVVICQS6H1D8AsgB/iKSGfxrLU1KQ+9XXKPrjD0ClAgAU/PQTRA4OcOnfHy6DBkKelISSffshv3lTN7NYDMcuXeDcrx+cYmJg36Y1JF5eEIlEjbaM5efOQ5mdBee+fSH19m6U+RARERERNTdBEHDhwgUAgFQqRWRkZDPXiBoLA5lERERERER0V1Dm56Nk505UJSRCmZcHZW4ulLk5UOXmQV1Wpj+TVApZQABkwcGQ+ftB7OYOiZsbJO5uqLwarxPA1CZUVqJk926U7N5tuEJqNSpiY1Gh9aS42N0d9mFhsAsPh1NMDFzvG6zTalMQBFRevIjCjZtQfvYshKoqQCKBSCwGxGKIHR1h1zoc9hERcGjbFrJWrVB56TJK9+9D6eEjUJeUAABEjo4I+uB9uA0dWq91SURERERkyxISElBaWgoA6NixI+zt7Zu5RtRYGMgkIiIiIiKiO5agVKL0yBEUbfoNJQcOAAqFZQUolVCkpUGRltYo9atNXVSkCW4W/fYbbstkcO7bB27DR0BdWorCjRtRdf260TIqL182OR+hogLpzz4H5csL4TVzprWqT0RERES1LPnzMq5kFDd3NZpNxyA3LB7Tqcnny/Ex/z0YyCQiIiIiIqI7hrqqClXXrqHy8mVUXLqEskOHoczJae5q1Z9CgbKDh1B28JD1yxYEZL37HuRp6fB/eSFEEon150FERET0L3cloxgnk/Kbuxr/Ohwf89+DgUwiIiIiIiKyOcq8PFRevgx5aioUqWmQp6VCcSsVVTdvAkplk9dHGhAAn8fnwbn/AJQdPoSS3btRdvKUpttZWXAwXAbfC9fBg2EXHo7y06dRduQoyo4ehTI7u8nrq63gxx+hSEuD+8QJkHp7Q+rtDYm3DyQuzs1aLyIiIiKi+tJukdlYgcy9e/diyJAhmDlzJtasWdMo87DU/v37MXjwYEyfPh0//PBDc1enSTCQSURERERERDZBnpKCkj17UbJ3LyrOnwcEoV7liOzsIAsMhMTXB1If3+rAnZcnJK5uELu5QuLmDohFUGRkQJGWrulaVllQAHVREdTl5ZqyZC1awPuxOXB/4AGI7ewAAHYPPwzPhx+GsqAAilu3IHZ1hV1YGEQikSaf++jRcB89GoIgQJ6cDPmNG6i6mQT5zZuounkTlVeumAzI2rVuDcfIThBUakCtgqBSQ5mXi6rrCVAX1+q+TCKBU48ecBnQH4W/boQ8JUXn49L9+1G6f7/ONIeOHeE151G4jRjB1ppEREREdEepCWSKRKJG61r21KlTAIDevXs3Svn1ce7cOQBAdHR0M9ek6TCQSURERERERM1GmZODwt9/R/Eff6IqIaFBZTlG94DHhIlwGzEcYuf6tzYUlEqoSkoAlQoSb2+dAKU2qacnpJ6eRssSiUSwDwuDfVgYXLWmq4qKULJ3H4p37kDZseOasT1FDg5wGzkSHpMnwbFbN73zFgQByuxsVF2/DnlqKqReXnDu0wcSd3cAgPsDDyDtmfmoOHvWaN0qr1xBxgsvIvezz+H9+Dy4338/RFLDtwlUhYVQV1RAGhBgcJ0QERER/dt0DHJr7io0q+ZY/oyMDGT/3etJaGgo3P++Dra2Rx99FBMmTEDLli0bpfz6qAlk9ujRo5lr0nQYyCQiIiIiIqImJahUKDtyBIUbN6Jk/4F6dxUrsreHfft2cO7dB+7jx8E+LMwq9RNJpSYDlA0lcXeHx8QJ8Jg4AaqiIpQdPw6o1XDu3x8SV1ejeUUiEWT+/pD5++v9XOrpiVarvsPtRa+geNs2k3WRJyXh9suLkPvJp7Bv1w4SL09IvbwhcXeD4nYmqhITUXXjBlS5uQAAx+7d0eJ/yyELDLR8wYmIiIjuMovHdGruKvzrNNX4mP7+/vA3cM3dXM6fPw+JRNJorVBtEQOZRERERERE1GhUhYUoO3ES8qSbkCcnoyo5GfKk5LpdoxojlULWIgh2LYJhFxoCh06d4NCpE+xbt4ZIJmu8yjcRibs73EaMsGqZYnt7BC1fBvsO7ZG/eg1U+fkm8ygyMqDIyDCZruLcOSRNnoKWn38Gx65drVFdIiIiIiKzNWR8zISEBCxbtgx79+5Feno6ZDIZAgIC0KtXLzz//POaLlvT0tLQsmVL9OrVCydOnNDkr5keExODHTt2YPny5diwYQPS09MRHh6OxYsXY8qUKQCAP//8Ex999BHOnTsHR0dHTJgwAcuWLYNzPXuPKS8vx7Vr19ChQweIxWIsXboUP//8M27evAl/f3/MnDkTr7/+OiR32bARDGQSERERERGR1VUlJCD/x7Uo2rIFQlWVRXnFbm5wGTQQroPvg2PnyOquTO+yH+NNQSQWw2fuXHjPmgVlbi6UeflQ5eVCmZuHkr17UbpvX73LVuXmImX6DAQuXQr3+0dbsdZERERERMbVN5C5d+9ejBkzBhUVFYiOjkZ0dDTKysqQmJiIn376CRMmTNAEMmtafdZu+Vgz3cfHB7169YJCoUDv3r3h6emJM2fOYOrUqQgPD8fPP/+ML774AoMGDcLgwYOxc+dOfPnll7Czs8OKFSvqtdyxsbFQq9UIDg5GdHQ08vPz0a9fP7Rs2RK7d+/GkiVL4OLighdffLFe5dsqBjKJiIiIiIio3tTl5VAVF0NVXAx1SQmUWVko3LgJZceOWVSO2MUFbqNHw234MDjFxNwVLS1thUgmgywwUKcrWI8HJqIyPh65X36Fkl27AEGwuFxBLkfGiy+i8uoVOEZFVQebxWJInJ2r3/M7JCIiIqJGoB3INLeLVUEQMGfOHEilUpw8eRI9e/asU2arVq00700FMrdv344FCxbg7bffhp2dHQBg+vTpWLt2LR566CHIZDJcvnwZrVu3BgCcPHkSvXv3xq+//lrvQGbN+Jg7d+7ECy+8gKVLl2rm/fvvv2PChAnYuHEjA5lERERERET076aWy1H811YUrF2LyitXGlSWY48e8Jg0CW4jhkPs6GilGpI5HNq3R/DHK1CVmIiC9RtQlZgAVV4+lAX5UOUXACoVIJPBPjQU9hFtIPX1Q8Evv0CQy3XKyf9uVZ2yJR4eCHhzsdW7zCUiIiKif7fi4mIkJSUBAHx9fdGiRQuz8t24cQMpKSkYNmxYnSAmULdlp6lA5pgxY/DOO+/odOM6atQorF27FhkZGThz5owmiAkAvXr1gpeXFzIzM82qrz41Adzx48dj+fLlOp8NHz4cAJCdnV3v8m0VA5lERERERERkFmVBAQrXr0f+Tz9BlZNrcX6JpyfsQkOr/8LD4Dp4MOy1ftxT87Bv0wYBr76iM01Qq6EuLYXYyQki6T+3DtxGjUTq089AlWv8+1cVFiL9+f+g7OGT8H/5ZYjt7Rul7kRERET07xIbGwvh795ELOlW1t3dHSKRCPv378enn36K2bNnw8XFxeh8xGIxunTpUmc6ALz22mt18hQXFwMAJk2ahI4dO+p8plarUVpaCm9vb7PrXFtNi8wlS5bU+SwrKwsAEBQUVO/ybRUDmURERERERKSXMi8PFRcuouLCheq/2FgIlZUWlSELDobX9EfgNmYMpF5ejVTTplFQWYBKZSUCnAMgEon0pilXlKNcWQ5Pe09IxHfuuJ4isRgSN7c60x27dkXYhvVIfeppVMXHmyyn8Jd1qDgfixYffQj7sLDGqCoRERER/YvUd3xMX19fLF26FK+99hqeffZZvPTSSxg8eDAmTpyIhx9+GE5OTpq0JSUluHnzJtq2bat3enBwsN55X7hwAQAwduzYOp8lJiZCLpfXCXCaSy6X4/LlywgLC6sTXAWAixcvAgAiIyPrVb4tYyCTiIiIiIiIdJQdP47s5f9D5eXL9S7DqWdPeM2YDpd7760eO/EOdi3/Gr6++DX2pOyBAAGtXFvhvpD7MKTVEET6RCKnPAf7Uvdh7629OJN5BipBBQ97D/QN6ot+Lfrhnhb3wMvhzg7iapMFBSH0p7W4/frrKN623WT6qvh4JD0wCW7DhsGhcyQcIyNh3749W2kSERERkcXqMz5mjZdffhkPPvggNmzYgO3bt2PHjh3Ytm0bFi9ejMOHDyPs7wfvLly4AEEQ6pRfM71Hjx5G6xYTE1Pns5qWnJYEX7XFxcVBoVDoLVt73vUt35YxkElEREREREQAqrsDzfpgGYp++828DGIxXIcPg3Ov3pC4u0Hs6gaJmytkgYGQ+vo2bmUNUKgUyCjLQHZ5NnLKc5BTkYOc8hwUy4tRqihFibwEpfJSKNQKBDgHoKVrSwS7BqOla0v4OfnBReYCVztXOMuccS3/Gr66+BUOpB7QmcetkltYfWk1Vl9aDQ97DxRWFdapR2FVIbYlbcO2pG0QQYQIzwi08WiD1h6t0dq9NcI9whHiFgKxSFwnryAIuJx3GWezzkKukkMqlmr+nGXOmvyO0uoxRdWCGnG5cTiQegAH0w4iuzwbg1sOxoKYBXC1c7X+SgYgdnZGiw8/hN/Cl6EqLATUKggqNYTKCmQv/x8q/r5Ro1mm8nIU/f47in7/vXqCVArHqK5wHzcOTkOGNEodiYiIiOjus2bNGqxZs6be+cPCwrBw4UIsXLgQKSkpeOSRR3DkyBF88cUXWLZsGQDT42N27969TrlqtRoXL16El5cXQkJC6nze0EBjTX5DwVsGMomIiIiIiOiuJQgCSrZvR+Y7S6HKyzOZXuzkBI/Jk+A5fQbsgls0QQ2NEwQB57LPYXPCZuxK2YUKZYVZ+a4VXGvwvPUFMWsTIOB6wXVcL7iuM93fyR8jQkdgZNhIdPTuCLlajp3JO/Hz1Z9xOc94a1gRRAhxC0GIWwgu511GboXumJWbEzfjUt4lfHHfFwhwDrB4ucwl8/eDzN9PZ1rIjz8g5+OPkfftd4YzKpWoOHMWFWfOQrR0KWT9+8Nu5EgIgwc3Wl2JiIiIiLSFhIRg5syZOHLkCFQqlWa6qUCmvmDitWvXUF5ejj59+uidl7G85qgZH9NYIFMikaBz5871Kt+WMZBJRERERET0L6PIykZFbCyqrsWjMv4aquLjocjIMJpHFhwMx65d4RTdA2733w+Ja+O09DOXSq1CcnEyDqQewO+JvyO5OLlZ61MfWeVZ+P7K9/j+yvdo6doSpfJSFFQVmJVXgIDk4mSjy51QkIBpW6fh8yGfo71XeyvV2jSRTAa/F1+EU0wMMmpabBohVFRCvms35Lt2Q9GhAwJeexVOBrrrIiIiIiKy1M6dO6FSqTBs2DBIpf+ExTIyMvDxxx8D0B3X0lQgU1+rR1MtImNjY2Fvb19njMw333wTS5YswcyZM422NK0JZOorPz8/H7du3ULHjh3h6OhosIw7FQOZRERERERE/xLK3Fxk/+/D6u49BcFkeodOneD9xONw6tYNUh+fxq+gHoIgoKCqALeKbyGlOAXx+fG4kncFV/Ovmt3y0lpC3ULRK7AXjqQfQXppep3PHaWO6NeiH9p4tMGpzFOIzY6FSlDpKamu1JJUa1cXAJBdkY2Z22di+cDl6B/cv1HmYYjLwIEI+2MLCn76GeWnT6PyyhUIlZVG81RdvYqUaY/AbfRo+L34AmSBgU1UWyIiIiK6W61fvx6rV6+Gl5cXoqOj4ePjg6ysLBw+fBhyuRwvv/wyBg0aBABQKpW4fPky/P39ERDwT88mNdP9/PzQokULnRacwD+BTH3dzmZmZiIzMxM9evTQCaQC1V3SAoBMJjNYf5VKhYsXL9apU+15343dygIMZBIREREREd31BIUCBb/8gpxPPoW6tNRkepGDA3yffRZeM6ZDJG3an41VqiqcvH0Sh9IO4WLORaSWpKJUYbrOxkjFUnjae8LFzgWuMtfqcSNFQHpJOtJL06FQK4zmb+PRBvO6zMOwkGGQiCUQBAHXCq5hd8puJBUlwc3ODQODB6JPUB84SB0AAE/hKZTIS3Dy9kmcuH0CNwpv4EbhDbNbXNaXn6MfsiuydaaVK8vx9N6nEegcCB9HH/g4+sDXyRcxATG4r9V9kIob7zuW+fnB7z/PAwAEpRJVN2+i8tJllB48iJJ9+wCF/nVfvHUrSvbtg8eE8RA7u1RPFIkgdnWB631DYB8e1mh1JiIiIqK7y8MPPwwHBwccO3YMZ8+eRVFREby8vDBixAg888wzGDp0qCZtfHw8KisrMXDgQJ0yDE2vYSyYaKxb2ZrPZsyYYbD+8fHxqKiowIABA4zOu77d1to6kSCY8RgukRFnz55FdHQ0AODEiRPo1atXM9eI6N9NoVAg7++xrby9vY0+zUNETYP7JZFtudv3SUGlgjI3D8rsbChzsqHIzEThL7+gKiHRrPzOffsiYMmbsGvZspFr+o9ieTH2puzFgdQDOH77eINaWrb1bIuxrceijUcb+Dr5ws/RD+727hCJRHrTq9Qq5FTkILUkFYVVhSiVl6JUUYpSeSnUUKOzT2f0a9EPYpG43nXSll+ZjxuFN3As4xi2J23X27JTIpJgcKvBeLj9w+ji2wUqtQoKtQIKtQK5FbnV423mX0dCYQJSS1Lhae+Jfi364d5W96KdZztsTdqKN46+YTJACwDBLsGY03kOxrYeCzuJnVWW0VzKggIU/7UVBZs2QR4fb35GkQg+Tz4Bn6efhkgiabwKEv2L3e3nSqI7DffJpnf16lXN6w4dOjRjTcgWqdVqTYtMiUQCsbh+vxVUKhW8vLzQs2dP7N6925pVrLf6bvvacaIzZ86ghxWHimCLTCIiIiIioruAIAjI//575H33HVQ5uWbnkwYEwL5dWzi0aw/nPr3h1Lu3waCfNQmCgDNZZ/Bbwm/YnbIbVaqqepflKnPFqPBRmBAxAR29OlpUf4lYggDnAAQ41+2iqTF4OXjBK8ALMQExeLbbs7iUewnbkrbhUNohqAU1RoaNxJR2U3TroxWr83H0qR7vsrXhedwffj/8nfzx/P7nUSwvNlqftNI0LDm+BF9d+AozOs5AB+8O8Hb0hreDN9zs3JBdno0bRTeQVJSEG4U3UKGswMiwkRgQrP9pcEtIPT3hNf0RuD70IDL37UPFp59BlWhGwF0QkPvFlyg/fx4tli1rtm6PiYiIiIga4syZMyguLsZbb73V3FWxaQxkEhERERER3eEEuRy3X38DRVu2mJVe7OoK3/nz4Tbmfkg9PRu5dtWqVFVILkpGUlESrhdcx87knbhVcsvicsQiMcLdw9HRuyM6endEJ+9O6ODdAfYS+0aodeMSiUTo7NsZnX07Y2HPhVYtOyYgBj+O+hFP73kaaaVpJtNnlWdh2ZllOtPEIjHUgrpO2r9u/oXZnWbjue7PQSK2TotIWdeukH79FWSHDiH/k0+hKjDdBW/58RNImjARLT76EE5/P/1NRERERHSn6NWrF9hpqmkMZBIREREREd3BVCUlSH/uOZQdO25WevcHJsLvv/+F1Nu7kWsGpJWk4Zf4X3Aw7SBSS1L1BsWMaefZDpE+kWjl1gqtXFuhpWtLtHRtCSeZUyPV+O4S7h6O38f/jl3JuxCfH4/cilzkVuQipyIHyUXJEGD8pomx72v15dVIKk7Ce/3fg7PM2Sr1FUkkcJ80CZ6jRyP3q69RvH07VHl5wN83dwS1Gvi7C68aypwcpMycBddhQ+E2fDhc+veH2Nk69SEiIiIioubHQCYREREREdEdSpGZidR5j6Pq+nXjCUUiOPXqBb//PA/Hrl0btU6CIOBs1lmsvboW+1P3WxS8lIll6BnYE4OCB2Fg8EAEugQ2Yk3/Hewl9hjTegzGtB6jM/1G4Q18G/cttiVtszjAXONA6gHM2D4Dnw7+FEEuQVaobTWJmxv8X1oA/5cW6ExXl5cjc8lbdVseq1Qo2b4DJdt3QGRvD+f+/eA+bhxchwxpkm6SiYiIiIio8TCQSUREREREdAdR5uej/MQJlB0/jpI9e/V2wencrx88pkyGzM8PUj8/SH18ILKzs2o9FGoF9qbsxbnscyiqKkKZogwl8hLkVuRa3GVsB68OmBgxEaPCR8HNzs2q9ST9Wnu0xrv938WTXZ/Eqkur8MeNP6BQK4zm0dfV7PWC65j4x0R4O/zTwtdJ5oSBwQMxO3K21VprAoDYyQmB770Lp5hoZP7f2xCq6o6rKlRVoXTPXpTu2Qun6GgELH4D9hERVqsDERERERE1LQYyiYiIiIiIbJw8ORlFf21FyZ49qIqPN5rWY/IkBLzxBkQyWaPUpURego3XN2Lt1bXILs+udzl+jn64t9W9mBgxER29O1qxhmSJVm6t8GbfN/FSzEu4VXILeRV5yKvMQ15FHgqrCuFh74HWHq3R2qM1PO09sfjYYuxI3qFTRpmiDGWKMp1p8fnx2JG8A8sHLkd7r/ZWq69IJILHpElwiIxE2nPPQZFiOGhefuYMbk6YCK+ZM+D71FOaLmcFQYCgUEBs5eA+ERERERFZHwOZRERERERENkiRlY3i7dtQ/NdWVF66ZFYe3+efg/fjj1u9O81SeSku5V3CwdSD2Jy4uU7QymS9HH0xMmwk2ni0QbhHOMLcw9jy0sY4yZzMCjh+MOADhHuE44vYL0ymTSlOwbSt0/BSzEuY0m6KVbdLh/btEb55Mwo3b0bJjp0oP3sWUOvpIlepRP53q1C0+XdI3NygKi6GqqQEUCgga9ECAW8tgcs991itXkREREREZF0MZBIREREREdmQqsRE5K1ciaK/tgIqlXmZZDIEvfM23MeOtUodVGoV9tzagyPpRxCXE4ebRTchQLC4nI7eHTG943QMDxkOmaRxWog2mazLwNnvAXkZEDYAaDsccPT453O1Crh1AkjYBVQWAr4dgIihgFc4cBeN0ygSifBk1ycR5h6G1468hipV3e5dtcnVcrx98m2czjqNxzo/htYerSETW2dbEDs5wWvaNHhNmwZlbi5K9uxF8bZtKD91qk5aVX4+VPn5OtMU6elIffyJ6n1n3Dir1ImIiIiIiKyLgUwiIiIiIiIbUBEXh7xvvkHJ7j3mZRCL4RAZCefeveEx6QHYtWpllXpcL7iON4+9ibjcOLPSiyBCkEsQXO1c4SxzhovMBQHOARgZNhLd/bpbvXVovVSVVgcXK4uBquLq/yo54N4C8AzTDUjWlhELHFoGxP/1z7TYtYBYCoQNrA5WZl8F4rcC5bm6eXcsBDxDgTZDq4Offh0BrzBALLH+MgLVy3VzP1B8G2g7rDqI2khGhI5AhEcE1l9bj6KqIs10hVqBfbf2QSXoBuF3Ju/EzuSdsBPboZ1XO3T07oiYgBjc2/JeiNDwbUTq4wPPhx6E50MPomT/fmS9/Q4U6emmMyqVyFj4MpS5ufB69FHb2F6JiIiIiEiDgUwiIiIiIqJmpCwowO1XX0Ppvn0m00q8vOA6bChc+vWDU8+ekLhZr3vWKlUVvrn4DVbFrYJSUJpM7yh1xLjW4zC943S0crNOELXBCpKBuI1ATjxQkgmU3K7+Ly81ns/Bozrg6BYE2LsBDm7V/zPjgISd+vOolcCNvdV/pup0emX1HwBI7AGfCMC3XXVws8NYwMlLq1w1kLgbOP0tkHIMUFYBEhkglgESKWDnUp3XvxPg1wnwbg2knwWubQeSDgFqRXU5u14F7l8BdJ9ucrXVV2uP1nil1yt1pp/NOouXDr2kdwxVuVqOuNw4xOXGYf219fCw98Do0NG41+dehLiEWKVervfeC+c+fZD3zTfIW/ktBIXCZJ7sZcuhzM6B38KXIBKLrVIPIiIiIiJqOAYyiYiIiIiImknVzSSkPvkEFCm3DKYROzvDdehQuN1/P5x794JIar2fceWKciQUJuBa/jX8eOVHJBcnm8zj7+SPKe2mYErbKfBw8LBaXepNUQFc/RM4/2N1IK8+KguB27HVf41NVQVkXar+u7QJ2PoC0HowEPkAUJZbHfAsSNbNo9YKxJXnAYUp1V3YGqNWAn88U5323lebtHvbHv49sHHMRrxy5BUcST9iNG1hVSF+uvYTfrr2Ezp6dMSjnR/FsLBhDW4ZKXZwgO+zz8JtzBgUbvgVVQkJEDs7Q+LmBom7G4r+2gplZqZOnvzvv4cyJweB77wNsaNjg+ZPRERERETWwUAmERERERFRMyg7fhxpzz0PdXGx3s9lLVvC+7HH4D5uLMQODlabb2JBIr6/8j3OZ5/HreJbJse+7OLTBV18u6Crb1d09u2MIOeg5u1+s6IAuH0ByDhf3e3rjf2AVtemNkEs0w0+GqNWVgclTQUm6+vQMqDwFjD2U0Bq3zjz0MPTwROf3/c5frj8A76J+wYl8hKTea4UXsGLh19ETEIMFsYsRDuvdg2uh31YGPwXvlS3ftOm4dbcuZAn3tCZXrxtG6oSE9FixQrYh4c1eP5ERERERNQwDGQSERERERE1sYING5D51v8ByrpduNpHRMB73jy4jRxh1daXxfJifBn7JX6J/6XO+IX6tHBpgTd6v4G+LfparQ4WKcmsDlbm3/znLy+xOijXHHzaAQMWAK3vBRJ2V7cCvbEXUFZWfx7QBegwpvrPqzWQeqI6XeJeIPty89S5xsX1QFEaEDUNcPEHXP0BlwDAxbdRZysWiTErchamdpiKhIIEXM67jCt5V3Al7wri8+MNBtFPZ57GlL+m4IGIBzC381w4yZwgCALUUMNObAcXO5cG100WGIjQtWuR+tTTqDh3TuezquvXkTxpEgLf/j+4jRrV4HkREREREVH9MZBJRERERETURKoSEpDzxRco2b6j7odiMfwXvgTP6dOtOkafSq3C5sTN+OTcJyioKjCZXiwS45EOj+DpqKfhJHOyWj1MEoTqMSmv7wCubasOYtaXoyfg3QZwDQBcA6uDd84+WuNfuld3tVqQDBQk/f0/pbrb1qoSoLIYqCoGRGIgKAro+2z1WJY130vUw9V/VaVAUSrg4F49vqa2sAHVf8P+r7rL2Oyr1WN35sQD2fFA2unqbmaNCekHBEdXt9pUKwGVHCjJqu6WtjBFN63UEWhzHxAxFDizqrrVqraUo9V/2nzbVy9blynV43A2EjuJHTr5dEInn06aaZllmdicsBmbEzfjdtntOnnUghq/Xv8Vv17/tc5n3f26451+7yDYNbhB9ZJ4eKDVqu+Q/t8X6oxRqy4vr55+5CgcO0dCJJNV/zk6wjEqCjI/vwbNm4iIiIiIzMNAJhERERERUSOrjI9H7pdfoWTnTr2fi52d0eLD/8Fl4MAGzaeoqghbErcgPj8eWeVZ1X9lWahUVZrMKxPLEO0fjee6P6cTcGp0eTeqx7eM21gdFKwvkRhoMxToPh2IGA5I7UznCY42/JlaDQgq4wE+exfAr4Pp+Tj7AGH9q/9qVBYB8duqx8m8ub86UAkAMiegy4NAz7mAv5HvoaqkOjianwQ4ewMh9wCyv8d1jJwEbHwUSNC/vWnkxANbngIOvAf0ew6IegSQGejGWBCA0mxAUQZ4hjV4zM0A5wA8GfUk5nWZh8Oph7E6bjXO5Z0znRHAuexzmPLXFLzX/z0MCB7QoHqIHRwQ/MnHyPnsM+R99XWdz4t++w1Fv/2mm8fJCS0+XgGX/v3rpCciIiIiIutiIJOIiIiIiKiRKHNykPl/b6Nkl+HxD2VBQQj+8ks4tGvboHldzr2Mp/c+jbzKPLPSt3BpgaEhQ9HWsy3ae7VHqHsoZOLGa5WnQ1EBXPkDOPcDkHKk/uU4egKBUUD4QKDLQ4BboNWqWN360notY+twcP+nZWdZHnBjHyCogbbDqpfLFHtXoGXP6r86n7kAD/0MbH8JOPOd6bKKbgFbX6gOaPq2B5x9ARe/6noUpQG516uDnpV/j0XaogcwaTXgGWLZMushEUtwT9A9aGfXDqdyT+GbhG9wq8R098El8hI8vfdpzO08F09HPQ2JWFLvOoikUvg9/zycundHxksLoSosNJpeXV6OtGefQ8jqVXCMiqr3fImIiIiIyDQGMomIiIiIiBpB2YmTSH/xRahycw2mcYyKQvBnn0Lq49OgeR1IPYCXDr2ECmWFybSOUkc81vkxzOw0E/YS+wbN1yxludVBuuyr1QGx3OvV412q644PapDUEfAKB7zCqruMDepW3eWrR0iDWwbaBGdvoMtk65YpkQKj/wcEdgGOfFTdfa4pZTnVf6aknwVWDgYe+glo1bvBVQUAkUiEXr69MDRiKH698Su+uvAVShWlJvOtjFuJizkXsbDnQrTxaANRA7YHlwEDELb5N6T/57+oiI01mlaoqEDq408g5OefYN+6db3nSURERERExjGQSUREREREZEWCWo28b75BziefVndPqoesVSv4PP443MeNhUjasJ9lP1/9Ge+ffh9qQf+8tI0MG4n/9vgvApwDGjRPs2TGASe+AuI2VI/taAlnX6DtcKDtSKBF9+pxLu+GgGVTE4mAHrOA7jMBeWl117AlmUBpJhC/Fbj0GwChfmWX5wLfjwHGfFLdqtRKZBIZZnaaiXGtx+FoxlHkV+ZDBBFEIhEqlBX4Lu67OgHOk5knMfGPiQh1C8XQkKEYGjIU7b3a1yuoKQsMRMiPPyDn889RuOFXqPLzDaZVFRXh1mNzEfrzT5AFWrE1MBERERERaTCQSUREREREZCWK7GzcfvU1lB0+rPdzu9BQ+Dz5BNxGj25QAFMQBCQVJ+GXq79g3bV1dT6XiWVo79Ue/k7+8Hf2h7+TP/oG9UU7r3b1nqdB5flARQFQVQxUFgOlWdVdxibrXwcGuQQAXR8EOowFgrr/3bUrWYVIVN0Vrb0r4P1368HIB4BBi6pba15YVz0eqKVUcuD3J4DbsdXdzdaQOVV392vvWu8qezh4YHT46DrTh4YMxX8P/BfXC67X+Sy5OBkr41ZiZdxKtPFog5diXkKfoD4Wz1skk8Hv+efh+8wzUJeWQlAoICgUqLpxA2nzn4VQ+c+Ys8rbt3Fr7lyErl0LiYeHxfMiIiIiIiLjGMgkIiIiIiJqAFVpGUr27Ebxn3+h7Phxva0wRfb28F+0CB6TJ0Ekqd9YfqXyUhxJP4JjGcdw/PZxZJZl6k3nYe+BTwZ/gm5+3eo1H7PIy4ELPwMnv67uKra+RJLqlpfdZwBthlZ3h0pNxycCGP8FMPAl4NyP1d3/luUAZdlAaQ6gKANkzoBvu+o/14DqVra1uzA++VXdsqWOwIh3q1uEWrE1bYhbCNaOWou3T7yNP278YTBdYmEi5u2eh5GhI/FizIvwc/KzeF4iqVQnOCkLCkKLFR8h7elnANU/gV954g3cevxxtFq5EhI3N4vnQ0REREREhvFXIhERERERUT3IU1KQ89nnKNm1C0JVlcF0dqGhaPHxCji0s7w1ZJWqCofTDmNb0jYcTD0Iudp4F62tXFvhiyFfIMQtxOJ5maU0Gzi1Ejj9LVBhuMtNg9xbAj5t/w6MtQcihgFu7JKz2XmGAve9Xne6sgqQ2OkGIjuOA36ZCpRkGC9TWQH89Xx1y9z7VwAO1gvwOUod8fY9byMmIAafnvsU2RXZBtNuT96OQ+mH8HiXx+Hr5IuiqiIUVRWhRF6CMPcwTGgzATKJzOx5uw4ahMB33sbtlxfpTK+8cBEpM2ai1bcrGzzmLRERERH9Y9OmTZg0aZJVyrpw4QK6dOlilbKo6TCQSUREREREZAF1eTlyv/4G+atWQVAojKZ1GzUKAW+9BYmLs0XzSCxIxI9Xf8Su5F11xgM0JMo3Cp8M/gSeDp4WzcsgQQCKUoG0M0D62er/GecsH+/SqzXQ+0mgyxTAwd06dWsm+WVyVChUCHJ3MDj+YlmVEmVVSni72EMivsPH9ZTa150W1A2Yuw9Y9zCQcd50GZc2VaebvAYI7Gq1qolEIoxvMx5jwsfgQs4F7E7Zjd0pu5FVnlUnbZmiDB+e/VBvOVtvbsUngz+Bu73526bH+PFQ5eUhe9lynelV8fFImfYIWq1eBVlQkGULRERERER6nTp1yirlODo6olOnTlYpi5oWA5lNqKioCDt37sT+/ftx7tw5JCYmori4GC4uLmjVqhXuuecezJ49GzExMWaXuWPHDqxevRonTpxAVlYW3NzcEBERgUmTJmHevHlwdrbshgkREREREeknCAJKduxA1vsfQJmpv1vXGmJXV/i98F94PPigwYCXPrHZsfju0nc4kHrA7DxOUidMajsJ87vNh4PUwex8Rl3bAex+A8i9Vv8ywgYAvZ+ubnV5h493eTmjCJ/tS8SOy5kQBCDE2wkjOgVgeGQAooI9kFVSiV2Xs7DzciZOJuVDpRbg6STDgLa+GNjWFwPa+sLHRU9Q8E7lFgjM2gZsfaG6i2FT8m8C3w6p7j44KAoIjKr+72J5d6+1ScQSdPfvju7+3bEgZgFis2PxvzP/w8Xci2blP5d9DrN2zMKXQ75EgHOA2fP1evRRqAoKkPftdzrT5SkpSJ72CFp99x3sw8MsWhYiIiIiqmvSpEno00f/uOdyuRwPPvggAMDZ2Rlr1641WI6rqysk9Rzmg5qXSBAEobkr8W/wwQcf4I033kCVkS6najzyyCP4+uuv4eTkZDBNVVUVZs2ahXXr1hlM07p1a/z222+N3lT67NmziI6OBgCcOHECvXr1atT5EZFxCoUCeXl5AABvb2/IZOZ3lUVEjYP7JZFtqc8+qcjKxu1Fi1B27JjhRFIpXPr1g9v998N18L0QG7mer+105ml8EfsFzmSdMZlWLBIj0icSfQL7oE9QH3Tx7QKZ2ErHldJsYPtC4PJv5qUXy4DOk4CwgdVdh9q7AvZugHsw4Nw83WvKlWqkFZQjq7gK2SWVyCquRHZxFYoqFCipVKKkqvq/QiUgyN0BLb2cEOLthFZeTvB3c4CbgwyuDlK4OkhxOaMYn+5LwJ6rhrsu9XCSobDceMtcAGgf4Iq2/q5o6++CNn6uiPB3QZi3M8R6Wm0KgoCLaUU4mZQHuVINqUQMqVgEmUQMZ3spIvxc0NbfFY521Tdi1GoBsWmF2H0lC/uuZiOzuBLDOvrj9TEd4ebQyOecsjygsvCf98oqYPfrQOIe03l92wPdHoGi4yTkVVavB2ucJ9WCGpsSNmHF2RUolheblSfAOQBfDfkKrT1amz0fQRCQ++WXyP3k0zqfSdzd4TpqJJz79oVz796QuLqaXS6RLeD1K5Ft4T7Z9K5evap53aFDh2asCRlz7tw59OjRAwDQt29fHD16tEnmq1arofp7zHSJRALxHf7gprb6bvvacaIzZ85ovhdrYIvMJnL9+nVNEDM8PBxDhgxBVFQUfHx8UFBQgL1792LTpk1QqVRYu3YtsrOzsX37doM7wMyZM7F+/XoA1SevefPmoXPnzsjNzcXatWtx6tQp3LhxAyNGjMDJkyfRsmXLJltWIiIiIqK7Senhw8hY+DJU+frHhJT6+8N73ly4jRoFqadl3boKgoBv477FJ+c/MZpOBBGiA6IxKmwUhrQaAg8HD4vmY0ZFgNifgJ2v6galDLF3B6JnA70eB9yavwtNQRBwOrkA60+nYvul2yiXq8zKd/W2eUEuY8wJYgJAfGYJ4jNLdKYFuDng/i6BGNM1CF2C3VGlVGPrxdv4/ngyLqYVGS1PJAJCvZ0R5uOMi2lFyC3VfWj217NpuJhWhNWzYxDk4WjZQlnC2bv6T9vUX4FjHwN7/w8QjHwXOfHArtcg3bME7qH3oaLDZMBrTIOrJBaJMbntZAxuORgfnv0Qf938C2pBbTRPZlkmZmyfgc/u+wzd/LqZNR+RSATfp56CxNUNWe+8o/OZqqgIhb+sQ+Ev6wCJBI5dusBj0iS4jx8HEVsCEBEREVnF+fP/DHXQrZt513B052Egs4mIRCKMHj0aCxYswMCBA+t8Pm/ePBw+fBijRo1CaWkpdu3ahe+//x6zZ8+uk3bLli2aIGarVq1w+PBhtGrVSvP5008/jcceewyrV6/G7du38d///he//vpr4y0cEREREdFdSFAokPPJJ8hb+a3+BDIZvGfNgs8Tj0NcjyEd5Co5lhxfgj9u/GEwTUvXlpjSdgpGhI2wqNtLkwpSgNSTQGYckHUJyLwElBludQgA8GkLtIgGQvoAnSZUt75sRiq1gBs5pdhzNQu/nklDUm5Zs9anPjKLK/HtkSR8eyQJrbycUFqlRH6ZeWOQCgKQlFtmdLmvZZVg/OdHsWpWDCJbNOH4pGIx0O8/QKs+wMZHgeJ0o8lFagUcb+6A480dEE53Aka+D4T1b3A1vB298U6/d/Byz5dxs+gmXGWucLd3h5u9G364/ANWnFuhk75YXozHdj6GhT0XYnLbyWZ3C+01/RGIXV1w+9XXAJWewK1KhYrz51Fx/jzyf1qLgEWL4GTBkDJEREREpJ92IDMqKqr5KkKNil3LNpGCggJ4mvF09meffYb58+cDAAYMGICDBw/WSdOtWzfExsYCALZu3YpRo0bVSVNRUYH27dvj1q1bAIC4uDhERkY2YAkMY9eyRLaF3Y0Q2R7ul0S2xZx9UnH7NtL/819U/H3dXZvzgP4IeOUV2IWG1qsOBZUFeH7/8ziXfU7v5+082+Gxzo9haMhQSMRWbL1VlF499uWljealb9kbGLAACO4BOFrW2tRaBEFAXpkcKXlluJlThiu3ixGXVoTLGcWoUJjX8tJawn2d0a+ND/Zfy0ZqfkWdz53sJLi3nR8i/F1w/EYezqYUQKlu/p/cTnYSfD61O+5t3/AxKS1WUQic+wFIOQqknzMdMK/RYSww7P8Az9BGq9qWxC1YfGwxVHpajY4MG4nFfRbDWWb+Qwole/Yg/b8vQJCbDka7jhgB/wUvQtaihUV1JmoqvH4lsi3cJ5seu5a9M/Tr10/Tnay1uzM1hl3L1sWuZe8C5gQxAWDy5MmaQGZcXFydzxMSEjRBzIiICL1BTABwdHTE3Llz8frrrwMANmzY0GiBTCIiIiKiu4kiKxsp0x6BIiOjzmciJycEvPE63MeNM7u1Vm2Xci9hwcEFSCtNq/NZa/fWeCH6BfRr0a/e5eulqASOfwYc/h+gKDed3s4VGLoE6DG7unVdE6pUqHDsRi72Xs3GhbRCpOSWo6RK2aAy7SRieDrL4KoZ/1IGEYDUgnKk5VdArjLe7Wg7f1c8M7gNRnUOhEQsgiAIuHq7BDsu3caNnDK4OUpxX3t/9IvwgYOsOvD8/BCgpFKBo4l5OHYjF9cyS5CYXYo8M1tc1leAmwMyiyt1ppXLVXj0+9MIcneEr6s9fF3t4e9mjz7hPhjeyR9SSSN+x44ewD3PVv8JAlByG8iIBa7vAC5tAuSl+vNd/QO4vhPoPBmwcwYgAIIakNgDbYcB4YMaXLVxbcbBy8ELLxx8ARVK3cD09qTtuJp3FcsHLkc7r3Zmlec6ZAjCt21FwS+/oOzYcVRp3QSqrWTHDpTu3w/f556D16yZEN1FN7+IiIiImoIgCLhw4QIAQCqVMv5xF2Mg08a4uv7TPVNFRd0nfHfu3Kl5PXz4cKNljRgxQhPI3LFjB9566y0r1ZKIiIiI6O6kKi5G6ty5eoOY9u3aocVHH8I+PLxeZcdmx+Lri1/jSPoRvZ/f0+IeLB+wHC52LvUqX0Mpr+7Kszij+q8oFTj3PVCQbF7+dqOB0cubdOzLonIFdl7OxO6rWTiSkNuglpbtA1wxqUcwIvxd4e9mD39XB3g4yQwGhlVqAVnFlbiVX46CMjlKqpQoqVSipFIBtQB0a+WBgRG+EIv/yS8SidAxyA0dg9yM1sXVQYYRkQEYEflPt8B5pVVIyC7F4YQc/HnhNm7l1w0sS8QiDOvojxl9QtGtlQdUagFKlQCFWo3s4irEZxYjPrMEV28X41Z+Obyc7TCorR+GdvRHh0BX/HEhAwt+vagToBUEIL2wAumF//zOXHviFlp5OeHJQa0xsXsL2EsbeexGkah6u3ILAtqPAoYvBS7/BvWZNRBnnK2bXlUFxK6tO/3E50CvJ4GhbwFSuwZVqX9wf3w37Ds8v/95ZFfothZNLk7GpD8nQSqWwk5sB5lEBkepI/oG9cWC6AV691W74GD4L1gAAFDm5aHs2HGU7NqFkt2766QVqqqQ/cEHKDt2DEHvvQupj0+DloWIiIiayfaXq4ds+LcK6AyMfK/JZ5uQkIDS0uqH4jp27Ah7e/smrwM1DQYybcylS5c0r0NCQox+bqppblRUFCQSCVQqFa5cuQJBEKz7VDcRERER0V1EXVmJ1KeeQtX163U+83j4IfgvXAixg4PF5Z7NOosvY7/EycyTBtNMbT8VC2IWQCpuwE80tRo4+hFw7DOgIt/8fJ5h1TcfAjpXt3ILjqkOODUyQRBw4mY+1p++he2XMlGlNN4q0hhXBynGRQXhwehWiGzhZtHvHolYhCAPRwR5ONZ7/pbwdrGHt4s9eod748Vh7XAxrQh/XMjAvvhsqAUB93cJxLReIQbr4+NibzKAOi6qBQLcHDDvx7MoqlAYTXsrvxyLfovDx3sS8Fj/MHQKcoevqx18XOzh5iBDVkklErJKkZhdioTsUlTIlRgbFYTB7f3rvQ407F2A7jOg6vwwCi7vhuvRpbDLMfMm4MkvgfSzwOQ1gHvDumft7NsZG8ZswCtHXsGxjGN1PleqlVCqlYASKKoqwm8Jv+FW8S18NfQr2EsM3zCTenvDfcz9cB9zP8rPnkXW0ndReflynXRlR47g5rjxCHrvPbj079egZSEiIqJmkBkHpOh/WJEaD8fH/PdgINPGfPPNN5rXo0ePrvP5da2bKqEmxuORSqVo0aIFbt26hbKyMqSnpyM4ONjiOp09q+fJWC3afSYrlUooFMZ/KBNR41IoFFAqlZrXRNT8uF8S2RZ9+6SgVCLzhRdRcabuta/vG2/AffIkqACoLNiHFSoFPon9BD9d+8lgGrFIjAU9FuDBtg9CUAlQqOp5jFBWQvLH0xBf3WJWcsHJG6qBr0DoNBGwd9X9UNmwblwNqVSokJRbjhs5pbieVYptl7KQoqc1oiliEdDa1xmRLdwRGeSGyCA3dAp0hf3fXboqG6n+jaVjgDM6BkTg5eEROtMber7o3tIN6+f2xNwfzyG1oG5vP7VlFlfi7a26XaGKRNUtOWv7PTYDc+4JwYJhbSERNzzorVAoUOHTBRVjf4bv7X2wO/gOROaMpZl2CsLXA6Aa/w2EsAENqoOb1A2fDPwEqy6vwldxX0EtGA+sn8k6g4UHF+K9e94zaxxbWZcuaPHzTyjZ8gfyPv4Yqr/HOauhystD6ty58JgxA97PPQuRXcNamhI1FK9fiWwL98mmJ2hdBKnVxq8LRBDwb24+JECAYGIdNYbagUxT35M1CYKg2UYEQWjSeTc27W3fkuNNY/4OYyDThhw7dgyrV68GADg4OOA///lPnTSFhYWa1z5mdDvj7e2NW7duafLWJ5BZM0CrOYqKijQDTxNR81AqlSgpKdG8l0p5qCdqbtwviWxL7X1SIhKh/KOPIN+3r05ah7mPQTn4XouvcbMrsvH2hbdxtcjwGHkhziF4usPT6ObdrUHX0KKKfHjufBrizHMm0woiCco7TUVpzHwI9u5AqRwobbzr9/SiKvwam40jSUVIL6yCnpiYURE+jugQ4IyWHvYI9rBHSw8HBLvbw0GmPZ6gCqXFhTAw0uK/mocYWDutPfYlFCAhpwK5ZQrklSmQW6bArYJKk9+HviBmje+OpuDa7UIsGREGZ7uGdUmrs08GDYHswX5wjl0Jhxs7IK7IAyACRGJALYe41hivovJcSH6ZhKrQIagMH46qkEEQGtA984TACQi3D8fSi0uRX2W8ZfPe1L146+hbmN9hvvmtgAf0h0u3bij/33IoDhys83HhDz+g5MRxOL/+OiQtW9ZnEYisgtevRLaF+2TTUyqVEIvFEIvFUKmMD3sgEf7lgUxBMLmOGoN2ILNLly6NUod9+/ZhxIgRmD59Or777jvN9NrLfLf1hKlWq6FWqy36naodu7I2HvFsRGZmJqZMmaKJ3P/f//2f3qBjTZ/PQHWw0xRHx3+6I9I+2REREREREaA8exbl33wD1Y2bdT6zf2AiHKZOtbjMUzmn8F7ceyhR6L/+bu3aGtPCp+Ee/3sgFon1pjGXpCgFntvmQVqUbDSdWuoEect7UBrzHJReEUbTNpQgCIhNL8W689k4dKPQouClTCJCj2BX9At3R78wDwS4sVVaQ9lLxRjZwRsjO+hOT8qrwPenM7HrWj7UlkaY/3bkZhHmbbiG5WNbI9DNemMSCXYuKO35H5T2rPVwr0oO12PvwvnyzzqTRYIaDkm74JC0C4LEDlXB96Ci7XhUhQ+vVzfJXb26YnW/1TiafRQZ5RlQqpVQqBW4VXYLp3NP66T9M/VPeNt7Y1rraWaXL3Z1gfPixZDHbEP5p58BlZW6i3k9AcVz58HpuedgN2L4XXdjjIiI6G4j+HfG3dMez3KCf+dmmW9sbCyA6iBi165dG2Uep09XX/v16tWrUcon8zCQaQPKysowbtw4pKenA6juUvaFF15o5lr948yZM0Y/v3r1KqZPnw4AcHd3h7e3d1NUi4gM0G7y7+XlBZlM1oy1ISKA+yWRrVEoFFClpKDq669Rceq03jQuI0bA/403IBKbF2hUqVU4nXUafyX9hW3J2/SmaevRFk91fQr9g/o3LDBRlAZR0gGIb+6H6MZeiOR12yKqI0ZA3X0WBLcgwDUIcHCHRCSCe/3nWodcqcbuq9k4m1KAwgoFSquUKKlUIqdEbnGXsZ2CXDG5ewuM6RIIN0ceI5uCtzcQ3TYYKfnlWHk4Cb+dz4BCZTyiKRahTtDzRm4FHll7Fd4u/wSdneykGNzOF4/1C4Wrg+nbDhadJ8d/AmWbAZBs+y9EirrbmUglh0PKfjik7Ie6ZW+oRiwD/DroKcg4b3jjIf+HdKap1CosOroIe1L36Exfk7gGwV7BmNhmomUzmTED8n79kfnSS5Bfu6b7WWUlyt9/H+KLF+H7+muQuLrqL4OokfD6lci2cJ9sevn5//TMIJGY6H1i5HuNXBvbJgLQsP45LJeRkYHs7OqhCEJDQ+Hl5dUo85kzZw4mTpyIli1b6mwH2t2vSiSSu+7Bs5rWyJbEejw8PBqtPgxkNrPKykqMHTsWp06dAgDcc889WL9+vcEN38XFRSevKRUV/4yF4lrPHz49evQwO61UKuWJlMgG1HQxIpPJuE8S2Qjul0S2QZDLkfvppyj//gfAwDgmzn37oMUH70Nsxhh11/KvYcuNLdietB25FbkG0z3U7iEsiFkAO0k9WxhmXQHifgWu/gnkJRhP2+tJiIe/A7EZ4/bVR1GFAr+cuoU1R5ORWWz6N4khAW4OGNrRHw/GtERkC2uGWMkSbfzd8f6kKCweG4nk3HLkllZp/grKFfBwlKGtvyva+LnA28UOCzfF4c8LGTpllMlVKMvXHYczPrME2y5l4rOp3c36fi06T3Z7GGgRBayfbnR/EKeegPjbQUCfp4CBLwP2f/+eVqsAeSlg72ZRi00ZZHhv4Ht4cs+TOJ2p+xDEO6fegVqkxsPtHza7PACQtWuLsA3rkf2//6Hghx/rfF66fTuq4uLQYvkyOEZFWVQ2UUPx+pXItnCfbFra9+fFZj7cSE3n4sWLmtfdunVrtO8oMDAQgYGBdaar1WrNNiISie6qbUR727fkWNOYXV4zkNmM5HI5Jk6ciH1/j8XTs2dPbNu2Dc7OzgbzaEe1c3MN3yipod2HcWNGxImIiIiIbJ0iPR1p//0vKi9cNJjGbewYBC5ebDKImVmWiQ/PfIjtyduNpnOSOmFJ3yUYETbC8goX3gLiNlb/ZV82I4MIGPEu0PtJy+dlQnGlAhdSC7EvPhsbTqeiTG7Z+DN+rvYY2zUIbQOqA2KtfV3gzpaXNsXJToqOQW4m033yUBQi/Fzw4e7rJtMm55Vj4pfH8ProDnikd4h1n1T36wA8cRi48Atw+Xcg+Qgg6NkuBRVw7FMg9hfAwQ0ozwcqiwAIgEsAMOZjoJ35+6e9xB4f3/sxZu+YjWsF/7SiFCBg6cmlKKoqwuNdHrdoWcX29gh45RU49+2L24tegaqgQOdzRVoakqc9At9nn4X3Y3MgMtUqhIiIiOgupz0+Zrdu3SzKm5CQgGXLlmHv3r1IT0+HTCZDQEAAevXqheeffx7R0dEAgLS0NLRs2RK9evXCiRMnNPlrpsfExGDHjh1Yvnw5NmzYgPT0dISHh2Px4sWYMmUKAODPP//ERx99hHPnzsHR0RETJkzAsmXLjMaAjCkoKICXlxe6du2KX3/9Fe+88w727t2LrKws+Pr6Ytq0aXjnnXfuuocdGMhsJgqFApMnT8b27dU3Prp164YdO3bAzc34D8e2bdti//79AIDk5GSjaZVKpaa7WmdnZ7Ro0aLhFSciIiIiugOV7N+PjJcXQV1UpPdzx27d4P/yQjiaGFulSlWF7y9/j2/jvkWFssJo2jYebfDhoA8R5h5mWWUzYoEjHwFXtgDmjjApcwImrgQ63G/ZvAxQqQVsv3QbB67lIDa1EDdySiHUYxzFzi3cMadfGEZ1DoSd9O55SvnfTCQS4dn7ItDa1wUv/BqLSoXxEaHkSjVe33IZx2/m4alBbdDW39V624LMEYh+tPqvLA+4trU68J90sG7a8tzqP22lmcC6h4H7PwJ6zDJ7tq52rvhyyJeYvn060kvTdT77PPZzFFUVYUHMAovHwHUdNAgOW35HxsKFKD9+QvdDlQo5H32EsuPHEfT++5D5+1lUNhEREdHdpL6BzL1792LMmDGoqKhAdHQ0oqOjUVZWhsTERPz000+YMGGCJpBZMwZnVK1eMWqm+/j4oFevXlAoFOjduzc8PT1x5swZTJ06FeHh4fj555/xxRdfYNCgQRg8eDB27tyJL7/8EnZ2dlixYkWDllsQBERHR0MqlWLQoEH4f/buMjqq62vA+DMzcSUhISFKcHd3l0Jxd3cv1tIWCkWK9I+7F6dIS/Hi7hrcJSQhIcR1Zt4PKbwMd2YSKG2R/VuLVbr3Peee6SokufuefV68eMHBgweZPHkyiYmJTJ8+/Z3m/1BJIfM/kJKSQqtWrfj9998BKFCgAHv27MHFxSXNsfnz53/1+7Nnz9KxY0eT1164cAGtNvWN1Lx5835yfZqFEEIIIYRIiy4hgWczZvJ8yRKjeQtvbzyGDsWxVs00v18+9PgQ40+OVxQu3pQ3Y17qZ6tPkxxNsLGwSd9C9frUHWVHfoY7+9I3RmMFfqUhaxUo3BocPdM3Lg3Xg6MYvvEyFx+9SNf1KhX4uNjiaG2Jg40FTjYWeGWw5ctCXhT3d5GfQz5RdQtmJqeHA6tOPuRFXNKreGKKjt1XQ9C+cZjm9svBbL8cjJWFmjyejhTwcaZMVjeq583Eeylr2meEou1Tf93cDduHwIsHaY/T62DrAIgOgUrD0t1q1t3OncW1FtN9d3ceRj80yK28tpKIxAi65u9KtgzZ3urPgGWmTPgtXkz44sU8mz4DUlIM8nEnTnCvQQO8Jk/CoUKFdM8rhBBCCPEpeb2Q+Wah0RS9Xk+XLl2wsLDg5MmTlCxZUjGnn5/fq39Pq5C5Y8cOhg4dyo8//ojVXx192rVrx8qVK2nZsiWWlpYEBgaSLVs2AE6ePEnp0qXZsGHDOxcyz507B6S21m3ZsiWLFi16tbtzz5491KxZk3nz5vHNN9/g4eHxTvf4EEkh81+m1Wpp27YtGzduBFILjH/++We6D02tVavWq9/v2rXL7LU7d+589fvatd+hlZUQQgghhBAfKX1KCpFbtvBs1mxSgoONXmPdqBHeI7/B+rVz6I1JSElgypkprLuxzuQ13g7e1M1al7pZ65LVOevbLTY6GDZ1g3uH0r7W0QvyNYTs1cCvLFjZvd29zEhI1jJr323mHbxDii7t7Ze2lhqaFfehc7kAsri9W2sk8XHL4eHI6Pr5FPFT957Tf815o2eoJqXouPg4kouPI1l54iEZ7CxpWCgzNbM7kDWj7ftZWM6aEHASDv8MR6eBNinNIRwYn7pD84spkM7zZb0dvFleZzm9/uzF9efXDXLb7m5j291tuNu6Uzpzacp4laGqX1XsLdP+s6JSq3Hr1g37UqV48tUQkh89MshrX7zgUfceuPXpg1vvXqg+oTOZhBBCCCHSEhUVxb179wBwd3dPdyfKO3fu8ODBA2rWrKkoYoJyZ2dahcwvv/yScePGoXmt7f8XX3zBypUrCQoK4syZM6+KmAClSpXC1dWVYBM/n6bHywJumTJlWLlypcG9a9SoQcWKFTl06BCBgYFSyBTvRqfT0blzZ9atS30AkitXLvbu3UumTOlvCZMjRw6KFCnC+fPnuXXrFjt27KBOnTqK6xISEli4cOGrf3/Zk1kIIYQQQohPmV6vJ3rPHp5Nm07S3btGr1HZ22M3dAhWlSujtrY2O9/dF3cZcmgItyJuGc27WLswoOgAGmZviCadxQ8DwZdhdUuIemz6GpsMqcXLAs1Si5fvsWgRm5jC9eBorj6NYumRe9wNi01zTGZnG9qW9qdNKT8y2Jk/S1R8nkoGuLJ9QAUGr7/AgRvPzF77Ii6ZZccfsuw4FMhsT49K2albyPvv7+S1tIWqI6Fgczi3HEKupp6RaesKti5weX3qObSvO7MkdWdm4/lg7Ziu27jZurG41mL67e3HudBzivyz+GdsvbuVrXe34mLtQt8ifWmSo0m6/r6wLViQgM2bCP5hDFFbtxom9XrCZs0i/uJFvCb9hEU6OjwJIYQQQnwKLly4gP6vcy/epq2ss7MzKpWK/fv3M3PmTDp16oSDmZdaL1y4gFqtpmDBgoo4wLfffqsYExUVBUDTpk3JmzevQU6n0xETE5PuTW3GvNyROX78eIMi5kvZs2fn0KFDhIaGvvM9PkRSyPyX6PV6evTowYoVK4DU/6H27duHp+fbt38aNWoUDRs2BKBXr14cOnTIYMuzTqejT58+PHyY+kNZ06ZNDVrSCiGEEEII8SlKefaMJ0OGEnfypMlrrPPkwWPKZKLtze+K0uv1bLq1iYmnJpKgVe4q06g0tMrdil6Fe+FkZf6ce5Nu7IBfu0CyieKhWy4oPwjyNwGL91cwvBkSzYJDdzlz/zkPnselefZlEb8MFPVzobBvBgr7ZsDHxVbaxYo0udpbsaRDCRYfuces/beJjE9Oc8zlp7H0XXuRX04+YtSX+cjr9Y5/tl7nlgNq/qiMl+wGK5tCyGXD+I1tML8SNF8OngXSdQsnKyfm1ZjHkINDOPTY9M7qiMQIxp4Yy/ob6xlecjglPEukObfGwQHvyZNwKF+O4B/GoIuLM8jHHj7MvSZN8Jk+HdsC6VuvEEIIIcTH7F3Px3R3d2f8+PF8++239O/fn2HDhlG1alUaN25Mq1atsLP7/2430dHR3L17l5w5cxqN+/j4GL33xYsXAahfv74id/v2bZKSkhQFzvSKjY3l5s2beHp6UrlyZaPXxP31veLfKZZ+iKSQ+S8ZOXIkixYtAsDS0pIBAwZw6tSpNMfVrFnT4A8KQIMGDWjRogXr1q3jwYMHFC1alB49elCgQAHCw8NZsWLFq7kzZ87Mzz///P4/kBBCCCGEEB+QuPPnedJ/ACnPTOz+srDAtU0b3AcPQqtWQ3i4ybn0ej0/nviR9TfXG80XdCvI6LKjyeGS490Wq9fDiTmwayRgpIroXQzKD4ZcX7zX3ZeRccn878+b/HLigeL8QmN8XW0Z36gAFXK4v7c1iM+LWq2iW8WstC/rz83gGC49ecGVJ5FcfhLJ1aAoTP1vePLec+rNPEzLkn70qZIdeysNOn3qn01LCzVONpZ/f3GOntBpG6xrq2zr/PwOLKwGdX6CYh3TdW6mrYUt06pMY+6Fuay5voaY5BiT196IuEHnXZ2p6V+T4SWHk8ku7S5Nzg0aYFOgII/79yPp9h2DXErQUx60boPHyJFkaNFcXjQQQgghxCftXc7HfGnEiBG0aNGC9evXs2PHDnbu3Mn27dsZNWoUhw8fJiAgAEgtSOr1esX8L+PFihUzu7YSJZQvrL3cyfk2xdc3763T6UzeG+DMmTOoVCrFLtKPnRQy/yXHjh179fvk5GT69euXrnH37t0jS5Ysivjy5ctRqVSsXbuW8PBwxo8fr7gmW7ZsbNq0CV9f33detxBCCCGEEB8yvV7Pi7VrCR4/AZKN7/hyqlsX9wH9sfqri4nWxHUvzTg/w2QRs0v+LvQp0gdL9TsWUh6fhYM/wS0j592rLaDe/6BIu3QVTtJLq9Oz7vQjpuy+wfPYtM8KVKugS/kABtXIiZ2V/Mgo/j5rCw0FfJwp4OP8Khb0Ip4NZx6z/swjnryIV4zR6WH1yYesPvlQkSuRxYWpzQrjl/FvnhFr4wxtfoXNPSFwk2FOmwh/DITbf4JXEdBYpf6ysgP/cpAxm2I6S7Ul/Yv2p3fh3lwJu8Lxp8c5EXSCS88ukaJPUVy/+8FuTgWfYkzZMVTxq5Lmcq2zBhCwbh1Pvx9F1LZtBjl9cjLBo0cTf/48nqNHobZ9T+eNCiGEEEJ8YJYtW8ayZcveeXxAQADDhw9n+PDhPHjwgLZt23LkyBHmzJnD5MmTgbTPxyxatKhiXp1Ox6VLl3B1dcXf31+Rf1nkfNdC5svxTk7Gu5acPn2a27dvU7p06U/qfEyQQuZHy9ramjVr1tChQweWLFnCiRMnCA0NxdHRkRw5ctCsWTO6d++OfRots4QQQgghhPhY6RISCP5hDJGbNxvN25cvT6bBg7B5i9Y9q66tYtHlRYp4RpuMjK8wnrJeZd9tsQ+OwaHJcGef8bxNBmixEgIqvNv8f4mMS2bD2UcEBkXxNDKe4MgEnkYmkJiiS3OslYWaUgGuDK2Vi4I+Gf7WOoRIi1cGWwZUz0Hfqtk5eD2Y+Qduc/JhVLrGnr4fQb2Zh/lfi8JUy/M3H9JYWEOTxeCRD/aPA/0bf1au/5H663UaK2i8MPXsWmNTqi0onKkwhTMVplehXoTEhvC/c/9j291timtfJL6g//7+tMzVkq+Kf4WNhY3Z5art7fGaMhnbIkUI+eknxQsckb/9RsK1a/jMmI6VkZeihRBCCCHE//P396dDhw4cOXIErVb7Kp5WIdPYTtAbN24QFxdHmTJljN7L3Nj0eHk+5r179xQ5vV7P119/DaTuOv3USCHzX3LgwIF/ZN7atWtTu3btf2RuIYQQQgghPlQpz57xqE9fEi5dUuRUVlZ4jhpFhiaN32rOnfd38tOpnxTx4h7FmVxpMm62bm+/0IgH8Hs/uHfQ9DWu2aDNBqM7vN7GxUcv6LL8NGExae+6hNTWsV/kz0yezE7k9XIiwM0eS837a2UrRHpo1Coq5HAjjwscux/FrCNB3AuPS3NcVEIKXZafoXflbAyukROLv/P/rloNFYeAXxn4tTPEBJu/XpsEm7qBnSsEVExzeg97DyZWmEjLXC2ZeGoigeGBimvW3ljLmZAzTK44mewu2c3Op1KpcG3bBpt8eXkycBApISEG+cSbN7nXtBnuAwfi0rwZKqv3d8auEEIIIcTHaNeuXWi1WmrWrImFxf+XxYKCgpg+fTpgeK5lWoVMY7sq09pxeeHCBaytrRVnZI4ePZoffviBDh06mN1p+nL+kydPsmvXLmrVqgWkdgAdPHgwe/fupXHjxjRo0MDkHB8rKWQKIYQQQgghPioJN27yqFdPUoKeKnIWXpnxmTET2/z53mrOk09P8s3hb9C/cWZlvoz5mF1tNnaW79DC8sYO2NwDEiJNXxNQEZotTy2I/A27A4Ppv/Y8Cclp77y0tdTQt2p2upQPwMZS87fuK8T7olKpKBfgTO3CWVhz5gnT/7xFdKKyHeub5hy4w/mHLxhVPy+5PBz/3vmQWcpBzyOpRcq7+81fq02CNa1Tz9nMXChd0xfOVJjVdVez+dZmJp2eRFyKYcH29ovbtNzWkqHFh9I8V9pnXdoVKULA5k08+eor4o6fMMjpYmII+fFHni9fjvvAATjVqYPqPZ65K4QQQgjxMVm3bh1Lly7F1dWV4sWL4+bmRkhICIcPHyYpKYkRI0ZQuXJlAFJSUggMDMTDwwNPT89Xc7yMZ8qUCW9vb4MdnPD/hUZjbWeDg4MJDg6mWLFiBoVUSG1JC2Bpafr4kqSkJAIDA/Hx8aFMmTLUrVuXSpUq4erqyrFjxwgKCqJMmTIsXbr0nf77fOikkCmEEEIIIYT4aMQcPMiTQYPRxSl3bNmXLYPX1KlYuLike75kbTIbbm9gxvkZJOsMWzT6O/m/WxFTmwz7xsLR6aavyZQvdQdY3oapu8H+hmVH7/HDH1fR69O+tn4hL77+IjeZneX8PPFhsrJQ07VCVpoW8+HgzWeExyShUoFapSI2KYW5++8oCpzH74ZTe9phsrrZU6eAJ3XyZyafl9O7FTUd3KHtJjgxG04vhghl665XkqJhZVPoshtcA9I1vVqlpknOJpTwLMHwQ8O5En7FIJ+oTeTHkz9yLOgYP5T9gQw2GczOZ+Hqit+iRTybOZPwefMV+eRHjwj6agjhixfjMWwY9qVLp2udQgghhBCfklatWmFjY8OxY8c4e/YskZGRuLq6Urt2bfr27UuNGjVeXXv9+nUSEhKoVKmSwRym4i+Z25Fprq3sy1z79u1Nrj8wMJCkpCSKFCnC8uXL8fDw4NdffyUiIoLs2bMzcOBA+vfvj7W1tbn/DB8tlV6fnh93hTDt7NmzFC9eHIATJ05QqlSp/3hFQnzekpOTCQ8PByBjxoxm3+YRQvw75M+lEO/H85WrCBk/HnTKXYeunTuT6avBqDRp7zBMTk4mLCyMwyGHWXZnGY9iHimucbN145c6v+Dj6PN2iwy/A7/1gYfHjeczF4ZKwyBnnb9VwNTr9dx5FsPyYw/45cQDRd5KoyavlxOZnW3wdLYhs7MNFXK4kyez0zvfU4h/ytt8nbwfFkvPlWe5Hhxtds6cHg58Xy8f5XO8Q0vo1+n1qS8naBPh2Q34pREkvnGWp2tW6Lw7tQj6FpK1ycy6MIulV5YqdoMDZLLLxMQKEynhWSJd80Xv30/Q8BHookyfNercqBEeI4ajcXZ+q7WKz498/yrEh0X+TP77rl279ur3efLk+Q9XIj5EOp3u1Y5MjUaD+h1/ttNqtbi6ulKyZEn27Nlj8rrFixfTtWtXRo0axejRo9/pXun1rv/vv14nOnPmDMWKFXtva5IdmUIIIYQQQogPXvjSZYT+pDy/EgsLMo8eRYamTdM9V2B4IONOjuN65HWjeXtLe+ZWn5v+ImZsOARugssb4NFJ49dYO8GX0yBfY3jH1pdRCckcuPGMwzefceR2GE8jE4xe52JnyaIOxSnm//fa1QrxIcriZs/m3uX47rcr/Hr2scnrbobE0HbxSeoVzMx39fLi4WTzbjdUqcDCKvWXT3FouRpWNk5tLfvS87upsXZbwD5juqe21FgyqNggyniV4ZvD3/As/plBPjQulC67utC1QFd6Fe6Fpdr8Q2vHKlXIuvV3ns2YQeTmLUZf+ojcvJmYI4fx/O47nGrWTPdahRBCCCHE+3fmzBmioqIYM2aM2evSOn/zUyeFTCGEEEIIIcQHLXLbNqNFTLWTEz4zpqe7VaJer2fVtVVMPTOVFL3xs/ccLB2YUXUGuV1zpz1hyFXYPw5u7gSdmbP8PAtC8+Wpu7beUkKylgM3QvntQhB7r4eSlGL+DMwsGe1Y1qkkWdzs3/peQnwsbK00TG5akFIBrkzdfZPgKONFfYA/Lj3lwI1n9K2aHU8nGyLikoiISyYmIYVsmexpVswXK4u3eIM+oAI0XggbOsLruyiDL8HS2tBuMzi/3U7u0plL82v9X/n+6PccfHzQIKdHz8LLCzkZfJKfKvyU5gsWlh4eeI0bR8aOHQn93zRi9u1TXKN9FsaT/gOIqlkTz1HfY5Ex/cVXIYQQQgjx/pQqVYr0NE09d+4cYPz8zc+BFDKFEEIIIYQQH6zYEyd5OuJrRdzSzw/fefOwzpq+c+likmL4/tj37HlgvF2PWqWmQbYG9C7cG097T/OTxUfA/glwehHoteavLdYJak8Ey7fbDXYjOJolR+6x/fJTxXmAphT3d2FB++K42lu91b2E+BipVCqaFfelSVEfzj2MYPvlYHZceWp0p3JMYgoTdxjfgb3l/BMWtS+Bs91btOjL1xBiJ8P2IYbxsJuwpHbqzky37OmfD3C1cWVm1Zmsub6GqWemkqRLMshfenaJZlub8V3p7/gi6xdpzmedIwe+c2YTd/YsIRMmknDliuKa6N27SbhxnSyrVmHh9jdb8AohhBBCiH+ETqfj0qVLZMyYEV9f3/96Of+Jdz+URQghhBBCCCH+QQk3bvK4b1/0yckGcausWcmydk26i5g3nt+g5baWJouYFbwrsOHLDYwpN8Z8EVOnhTNLYUZRODXffBHT3j1119aX096qiHn2QQRdl5+m1rRDrDvzKF1FTAdrC7pVCGBl11JSxBSfHbVaRfEsrnz/ZV6ODq/Khp5lKOybId3jT9+PoNn8YzyNjH+7G5fsBpW/UcYjH8GSWhB04e3mI7U42zpPa1bXXU1WZ+UO7pjkGIYfHs63R74lLjkuXXPaFStGlnVryTRsGCob5d9FyQ8e8qh7D7QxMW+9XiGEEEII8c9Tq9XExMQQFhb2Xy/lPyM7MoUQQgghhBAfnOSnT3nUvTu6Nx6uW7i747dwARauaZ//GJUUxbIry1hxdQWJ2kRF3sPGg9FlR1Pet3zaCwq/Axu7QtA509dY2EDuulCgOWSvBpr07/A6cTecn/fc5NS952leq1ZBId8MVMjhToUcbhT2zYClRt5RFUKtVlEiiyubepVl7elH/LTzOpHxyWmOuxkSQ+M5x1jRuSQ5PBzTf8PKw8HaEXa9sWs8LgyW1oEcNSBrZchaBVzT9+IFQC7XXKytt5ZJpyfx681fFfnf7vzGlbAr/FzlZ6MFzzepNBoydu6EY7WqPP3ue+JOnTLIJ1y9yuN+/fCdPx+1lbwMIYQQQgghPixSyBRCCCGEEEJ8UGKOHuXpt9+REhJiEFfb2+O7YD6W3t5mx8enxLPm+hoWX15MVFKU0WtKuZdiWP5hBHimo7hwcR1sGwxJJnYsuWSBisMgb/3UosZb0Ov1zN5/mym7b5q9TqWCUgGuNCjsTe18nrjIzkshTFKrVbQu5UfNfB5M2H6dzecfo0vj6KGnkQk0nXecxR2KUzxL2i9KvFKmN9hmgN/6Gu7STo6Dq7+l/gLI4A/FOkDp3mBpm+a0tha2jCozinJe5Rh1bJTi77I7kXdo9UcrxpQbQ60stdK1VCt/f/yWLeX50qWETp5ikIs7foKg4cPxnjoVlVpejBBCCCGEEB8OKWQKIYQQQgghPgi62FhCpkzhxZq1yqSlJT6zZmKTJ4/ZObbe2cq0s9MIjQ81mler1PQp2Id6HvVQq9J4WJ8YA9uHwsXVxvOWdlBxCJTu89ZnYAIkpmgZsfEym88/MXmNf0Y72pTy48tCXmR2Trv4IYT4f24O1kxtXohR9fNyOzQGJxsLMthZ4WxryaLD9/hpp+G5mZHxybRedJLv6+WlTSk/VCpV+m5UuDXYOMOGTmBk9zcALx7A3jFwdhnU/BHy1E99QyEN1f2rk98tPyMOj+BsyFmDXFxKHEMODuFC6AUGFx+MpTrtXeAqtZqMXbqgi40jbM4cg1z0jp2EuGbE49uR6f/sQgghhBBC/MOkkCmEEEIIIYT4z8WdPk3QNyNJfvTIaN5r/Djsy5QxOT5Rm8iEkxPYeGujyWvcbN2YVHEShTMWJjw83PyCQq7C+nYQftt4Pn9TqDEGnM3vDjUlPCaRHr+c5cyDCKP5vJmd6F0lG3XyZ0ajloKCEH+Hk40lRf1cDGK9KmfD3dGa4RsvoX1tu2ZSio5vt1zh5L3nTGhcAAfrdD42yV0X2v4Ka9tCYqTp6148hPXtIUsFqD0RPPOnObWnvSeLay5m/qX5zLs4Dz2G20tXXlvJlbArTKk0BQ97j3Qt161fX1LCwnixfr1BPGLVKvQpKXh+OxKVZfrbYwshhBBCCPFPkX4hQgghhBBCiP9M/KVLPOrZiwft2hstYqosLfEcPRrnL780OUdwbDCddnYyWcS0tbCle8Hu/N7wd0p4lkh7UWG3YFld40VMW1dotQ6aLn7nIuaFRy9oNOeY0SJmjkwOLOtUgm39y1OvoJcUMYX4BzUt5sOiDsWxtdQoclsvBvHlzCNcDTLentqogIrQ/1zqSw7Zqqaem2vK/cMwvwL8+QNo0z7HU6PW0Ltwb+ZUn4OztbMif+HZBZr/0ZyTT0+ma6kqlQrPUd/jWKO6Ivdi3Toedu5CSoTxFy2EEEIIIYT4N8mOTCGEEEIIIcS/Lu7cOcLmzCX2yBGT19jkzUvmiROwyZnT5DWng08z5OAQnic8V+Qs1BY0z9mcbgW74Wbrlr6FRT2FXxpDvHI+/MtDk4Xg5JW+ud5w9sFzZuy9zcGbz4zmK+dyZ2arIjjayC4oIf4tVXJlYk330vT45QwhUYZtYe+FxfLFjMNYalRYatRYatTYWWmokMON7+rlNf5n1d4Nyg1I/ZWcAI9OwrXfU1vK6lIMr9Xr4MjPcO8gNFkErlnTXG957/Ksr7eerw58xZXwKwa55wnP6b6nO/2K9KNz/s5pts9WaTR4TZnCoy5diTtzxiAXd/o095s2w2fOHGxymf47WAghhBBCiH+a7MgUQgghhBBC/Gt0cXE8HjiIB63bmC5iWljg1q8vWdatNVvEXHN9Dd12dzNaxCzuUZytDbfydamv01/ETIiEVU0h8qFhXKWGyt9Ah9/fqYh58m44rRacoMnc4yaLmB3LZmFR++JSxBTiP1DYNwPb+1egYk53o/lkrZ64JC2R8ck8jUxg/ZnHdF1+hoRkrfmJLW0gayWoOxV6HYNs1Yxf9+QszKsIl9Ybz7/By8GL5XWW0yJXC0VOp9cx/dx0+u/rT1h8WJpzqa2t8Zk7B7sSyt3qyU+ecL9VK6L//DNd6xJCCCGEEOKfIIVMIYQQQgghxL8iJTycBx06Er1zp8lrrHPlIsu6tbj36WPyfDatTsvEUxMZf3I8Wr2ykNA+b3sW1lyIj6NP+heXnABrWkOI4Q4nVGposRIqDwe1sv2kOUkpOsb+cZUWC05w/K7xMzk1ahVjG+RjdP18WGjkxzMh/isZHaxZ1rEEQ2vlIj0dnU/ee86gdRcMztc0yz0XtN2Y2pra2M7LpGjY1A029YDE6DSns9JY8W3pbxlffjw2GmUL24OPD1J3U13mXZxHXHKc2bk0jo74LV5EhhbKwqg+Lo7HffsRNncuen06P6sQQgghhBDvkfykLIQQQgghhPjHJT14wP1WrUm4fNlo3jpnTryn/Y+AzZuwzZfP5DxxyXEM3D+QVddWKXI2Ght+qvATQ0sMxUL9FqdoJMenFhAeGNkh+uV0yF03/XP95cmLeFosOM7iI/dMXpMjkwO/dC5JuzJZ3np+IcT7p1ar6FMlO2u6lSaTo3Wa1++4Eszo3wPTX+BTqSBX7dTdmcW7GL/m0lqYVyF1l2Y6fJntS1bXXU0WpyyKXFxKHLMvzObLzV+y6dYmtDrTO0hVVlZk/mE0nqO+B43ypY1n02cQ9NVX6OLj07UuIYQQQggh3hc5I1MIIYQQQgjxj4q/eJFHPXuhjYhQ5Kzz5sG9d28cqlZFpTb/nmVoXCh99/bl2vNripyPgw/Tqkwjl2uu9C9Mp0V1cTUcnAjRQcp81W+haPv0z/eX/TdCGbTuAi/iko3m83k50a9qdmrm9USdnq1fQoh/VamsGdk3pDJ7rgZzPyyOZK2OpBQdd57FsP+GYXvoX048IJOjNf2q5Uj/DSxtod7PkK0q/N4X4t/4uzHiHiyumfp3UNkBkMbfjTlccrCm7hq+P/Y9ex7sUeRD40MZdWwUq66t4oeyP5DfLb/JuVxatcIqICtPBgxAGxlpkIvavoOk+w/wmT0Ly8yZ0/95hRBCCCGE+BukkCmEEEIIIYT4x8QcOsTj/gPQJyQochm7dcN90MA0C5gAD6Me0nlXZ0LiQhS5IpmKML3KdFxsXNK3KL0eq4eHcDwxGYvnN41fU6IbVBiSvvkArU7PsTthbDr3hM3nnxi9Jm9mJ4bUykmVXJlQqaSAKcSHzMHagkZFDNtTa3V6+q05x/bLwQbxqXtu4uZoTauSfm93kzz1wKsIbO4B9w8b5nQp8OdouLMfGs0HJ/OFQwcrB6ZWmsraG2uZeX4m0UnK9rQ3I27SZnsb2uVpR58ifbC1sDU6l33pUmTZsJ5HvXuTdPuOQS7h6lXuNWuO95Qp2Jcu9VYfVwghhBBCiHchrWWFEEIIIYQQ/4jovXt51KevsoipUuHx/Xdk+mpwuoqY4fHh9Pyzp9EiZp2AOiysuTD9Rcz4CDS/dsB1ezcsTRUx8zaEOj+ltoFMQ2BQJGP/uErpCXtpt/iUySJmhzL+bO5Tlqq5PaSIKcRHSqNW8XPzwpTO6qrIfbP5MiuO33/7SZ29of1vUPU7UBk5h/feQZhXDm6YPlv4JZVKRavcrdjReAft87bHUq08Z1in17H86nIa/9aYk09PmpzLys+PLGvX4lCliiKnDQvjYceOhEz8CV1iYprrEkIIIYQQ4u+QQqYQQgghhBDivYvatZvHAwZCsmF7VZW1NT4zZ+DaunW65olLjqPP3j48in6kyHUv2J2JFSZirUn7LDsAgs7D/Iqob243nldbQtl+0HghqI0UFF6f6kU8fVafo+6MIyw+co9n0cYf5ttbaZjZqgg/NMiPtYX5OYUQHz4bSw0L2hcnT2Yng7heD9//FsiMvbfSf2bmS2oNVBwCnXdBBn9lPi4c1rSA7cMgWbm7/U3O1s4MLTGU3xv+Tp2AOkaveRzzmK67uzL93HST69U4OOAzexYZu3c3mn++bBn3mzYl4fr1NNckhBBCCCHEu5JCphBCCCGEEOK9itq+nSeDB0NKikFc4+yM39KlOFavnq55knXJfHXwKwLDAw3iKlSMKTuGfkX6oVal40cavR5OL0o9c+7FQ+PX5G8CfU9DzR/BwsrkVAnJWmbuvUXVqQfYdump2dvm9nRka7/yfFnIK+01CiE+Gk42lizvVAJfV2Vr1p/33GTMH1fR6d6ymAngWwJ6Hob8TY3nT82HRdXg2Y10Tefj6MOkipNYUWcFWZyyGL1m0eVFLL6y2OQcKrWaTIMH4TVlCipr5Usjibduc69Zc8IXL3n7Aq4QQgghhBDpIIVMIYQQQgghxHsT+fvvPBkyFLRag7jG1RW/FcuxK1okXfPo9XrGHh/LkSdHFLmvS31NoxyN0reg2HDY1A22fQXaJEVa51cGuu6DpkvANcDsVHuvhVDjfweZuucmCck6k9cV8HZm1Jd52dKnHFndHdK3TiHERyWTkw1rupUmwM1ekVt69D6D11/gZkj02xf3bJyhySJoOBcslXMTcgXmV4Lzq9I9ZZFMRfi1/q90K9ANC5WFIj/93HQ239psdg7nenXJsn4d1jlzKpPJyYROnkzIhAlSzBRCCCGEEO+d8jtYIYQQQgghhHgHLzZt5unIkak7IF+jcXPDf9lSrLNnT9c8ydpkfj77M5tvKx+sdy3QlVa5W6U9SWI0HJ8Dx2ZCUrQirVdbEF16KLaVB6O2Mr0DE1J3YY794yqrTprYzQn4utrSsLA3DQp7kz2TFC+F+Bz4uNixvkcZOiw5xdWnUQa5LReC2HIhiEyO1pTP7ka57G7UzOeBo43y3EoFlQoKtwafkrCxCzy9YJhPiYffesOjk1BnEljapDmltcaa/kX7UytLLb49+i3Xnxu2g/3h+A+42rhSybeSyTlscuUiy68beDZ9Os+XLFX8XR+x4hf0ycl4fvddus4/FkIIIYQQIj3kO0shhBBCCCHE3xaxfj1Pv/lG8WDbwt0d/xXL013EvBlxk9bbW7Py2kpFrn62+vQv0t/8BCmJcGIeTC8MB8YbL2I6evG8/i/EFeyYWjAwt56QaBrMOmqyiOlqb8XExgU4MKQKX9XMJUVMIT4z7o7WrO1RmpJZXI3mQ6MT2XT+CV9tuEjFSftZeeIB2vS2nXXLDl32pJ7da8y55bCkJkTcT/d6c7nmYnGtxeRwyWEQ1+q1DDk4hAuhF8yOV1tZ4TF0KH7Ll2HhlVmRf7FmLU+//x79G7vyhRBCCCGEeFdSyBRCCCGEEEL8Lc9Xryb4+1GKuIWnJ/6/rMA6a9Y050jRpbDo8iJa/NFCsVMIoJxXOUaXHY3KXOHx2Q2YVwF2Doe4MOPXZKtKSpd9JHsWNbsevV7PmlMPqT/rCDdClMVQjVpFp3JZ2D+kMi1L+qFRmy+ICiE+XU42lizvXJKquTOZvS4iLplvt1yh7ozDnLgbnr7JLaxSz+5tuwnsjcz/9CLMrwg3d6V/vVZOzKs+j8z2hoXIBG0Cffb2ITAs0MTI/2dfsiRZf/sN22LFFLnIXzcS9PXX6N84J1kIIYQQ4l1s3LgRlUr1Xn5dunTpv/444h1IIVMIIYQQQgjxzp6vWEHImLGKuIVXZvx/WYFVlixpznE6+DTtd7Rn+rnppOiUD77zZ8zP1MpTsVSbacl49XdYWBXCbhjPWztBjTHQ5lewdzO7Hr1ez8gtV/h602WjZ2EW9s3AjgEVGPVlPpxt09EmUgjxybO10jC/XTH6VsmOo7X5U3yuB0fTcsEJ+qw6R3BkQvpukL0a9DwMfmWVuYRIWN0c9o4FXfp2Qmayy8S8GvPIYJ3BIB6VFEWHnR3YemdrmnNoHB3xW7gAu9KlFbmo37cSNGy47MwUQgghxN926tSp9zKPra0t+fLley9ziX+XnJEphBBCCCGEeGva6GjCZs/h+bJlipyljw/+y5dh6e1tdo7TwaeZe3Eup4NPm7ymZa6WDCo2CDtLO+MX6LSwbywc+Z/xvMYaSnWH8oPB7q/Wj1plcfJ1k3fdYLWRVrIqFfSqlI1BNXJiqZF3QoUQhiw1aobUysXA6jm4+DiSI7fCOHo7jHMPI0gx0k522+WnHL0TxqQmBamZzzPtGzh6QoffYe8Pqef/vunwFHh8GpouSfOFDYCszlmZXW02XXd3JT4l/lU8UZvIN0e+4WbETQYWHYhGrTE5h9rODt95c3nctx+xR44Y5KK2b0dlZUXm8ePkzEwhhBBCvLOmTZtSpkwZo7mkpCRatGgBgL29PStXKo8oecnR0RGNxvT3NeLDJYVMIYQQQgghRLrpk5OJWLeesNmz0UZEKPKW/n74L1+Opafph/KB4YFMPTPVbAEzs31mxpQbQ+nMyp0+r8Q9h41d4M4+I0kVFG0HlUaAs/mC6uuWHb3HnAN3FHE3B2umtShM+RxpFweEEJ83C42aYv4uFPN3YUD1HARHJvDTzutsPv9Ece2LuGS6/3KWdqX9GVk3DzaWaTxc01imtpr1LQVbekNilGH+3sHUFtvNl4NvyTTXWtC9IFMrTWXA/gEk65INcssCl3Ez4iaTKk7C2drZ5BxqGxt8Zs/iyYCBxBw4YJCL3LIFlY01nqNGmW8NLoQQQghhQokSJShRooTR3Llz5179vlChQjRs2PBfWpX4N8krcUIIIYQQQoh0iTl4kLtf1ifkxx+NFjGtAgLwX/GL2SLmjns7aLe9ndkiZuMcjdlUf5P5Imb4HVhUzXgR09YF2m6E+jPfqoi59WIQP/xxVREvFeDKjgEVpIgphHgnns42/K9FYTb2KktBH+MFwV9OPKDBrKPcCFaeyWtUni+h+wHIZKQ9WnQQLK0Du7+D+BdpTlXBpwJLai0ho01GRe5Y0DHabG/D/cj7ZudQW1vjM2M6DtWrKXIv1q4jdOJE9HrlrlQhhBBCiL/j/Pnzr35fpEiR/3Al4p8khUwhhBBCCCFEmiLWredRj54k3b9vNG+dIwf+K5Zj6ZHJ5BzLA5cz7NAwxa6flwq4FWBBjQX8UPYHHKwcTC/mwbHUIubzu8qcZ4HUh/vZlQ/TzTl6O4zB6y/w5nP2Qj7OLOlYAndH67eaTwgh3lTM34UtvcsxsXEB7K2UOy9vhERTf9YRfjnxIH1Fv4zZoOufUKiVMqdLgWMzYEZhOD4HUhLNTlU4U2HW1ltLvozKwuiDqAe02d6GU0/Nn0+lsrLC++efsa9YQZF7vnwFz/43TYqZQgghhHivXi9kFi5c+L9biPhHSSFTCCGEEEIIYVb8pUsE//ij0ZzKxga33r3Jsm4tFu7uRq/R6XVMOj2JKWemGM3nz5ifOdXmsOqLVZTxMn72ySuXNsCKBhCv3BFKwZbQeTe4ZDE/x2uSUnQsO3qP7ivOkKw1fMAe4GbPko4lsLeWEzmEEO+HWq2iZUk/tvWvQCEjuzMTU3R8t+UKPX45S0RsUtoTWtlBw7lQbxporJT5+AjY9TXMKgGBm1G8rfEaT3tPltVexpdZv1TkopKi6LGnBxtvbjS7HLWVFT4zZmBXWrmjPnzBAoKGD0cXG5vmxxJCCCGESI8LFy68+r3syPx0SSFTCCGEEEIIYVJKRASPBw6E5Dd2UapUODduTLZdO3Hv3w+1nZ3R8UnaJIYdGsYvV39R5Nxs3ZhdbTar666mgk8F8+en6fVwcBJs6graNx/uq6DWBGg0L/Whfjro9Xr23oygzsyjjN56ldgkrUHe3dGaFZ1LktFBdmIKId6/LG72bOhZlp6VsmHsr77dV0OoM/0wx++Epz2ZSgXFO0HnXZDBz/g1Lx7Aho6wpiVEKs/qfMnGwoZx5ccxpPgQVBguLEWfwujjo5lyegpandbEDKlnZvrOmY1tsWKKXNTvW7nXvAWJt2+n/bmEEEIIIczQ6/VcvHgRAAsLC/Lnz/8fr0j8U6SQKYQQQgghhDBKr9MRNGIEKUFPDeIWmTMTsGkjXuPHYenhYXJ8fEo8fff2Zdf9XYpcgHMAK79YSUWfiuYLmC8d/An2j1PGLe2g1Roo0xuj1QAjLj2OpMva64zcfpeHz+MVeUdrC5Z3Komva/qKokII8S6sLNSMqJOblV1KkclI++rgqARaLzrB5F3XSdbq0p7Quyj0Og6VRqT+3WjMzZ0wuxScWQI643OqVCo65OvAzKozsbNQzrP86nI67+rM4+jHJpeitrPDd/48bAoWVOSS7tzhXrPmRP72W9qfSQghhBDChFu3bhETEwNA3rx5sbaWl1A/VdIjSQghhBBCCGFU+MJFxB48ZBi0tMRn2v+wyZPH7Ni45Dj67evHqWDlmWpFMhVhZtWZOFsr2yoadXY5HJigjDt4Quu14JW+FkJ6vZ6lR+8zfvs1UnTG2ys6WluwsENx8no5pW9tQgjxN5XL7saOARUY9usl9l4PNcjp9TB7/x2O3QlnRssiab9gYe0AVb5O3aF5YAKcWwH6NwqWSdHwxyC4vBEazjbZjruSbyVW1FlBv339eBpr+ELLudBzNPm9CSNKjqBh9oZGX0jRODjgt3ABTwYNIvbYccPPFR9P0PARxF+8hMe3I1Gp5T17IYQQH6+fTv3E9efX/+tl/Gdyu+ZmeMnh//p95XzMz4cUMoUQQgghhBAKsSdO8mz6dEXcY9gwbAsVMj82OZbef/bmXOg5Ra6aXzUmVpiIjYVN+hZyY2fqA/c3ZcoHbdaDs0+6polOSGbYr5fYcSXYaF6tgubFfRlYPSeezulcmxBCvCcZHaxZ1KE4K44/YNz2aySlGBYfzz98wRfTD/Njo/w0KOyd9oSOnvDldCjVC3aOgLv7ldc8OAKLa0GX3eDib3SaXK65WF13NQP2DeBS2CWDXFxKHN8f+559j/Yxqswo3GzdFOM1zs74LlxI2Nx5hM2erTijM2L1alCpUouZ6dxVL4QQQnxorj+/zpmQM//1Mj47cj7m50NeeRNCCCGEEEIYiNq5i8f9+yvaDjrWro1L2zZmx0YnRdNjTw+jRcwmOZowtdLU9BcxH59JPdNN/8ZZbJ4FoPOOdBcxrz2Nov6soyaLmFVzZ2LHgIpMbFJQiphCiP+MSqWiQ9ks/NanHNkzOSjy0YkpDFh7ga/WXyQ2MSV9k2bKDe02Q8O5YJNBmY8JhpWNITbM5BRutm4srrWY+tnqG80feHSAZlubcSviltG8SqPBvW8f/BYvQuPqqshHrFpF6JQp6PXGd8oLIYQQQhjz+o7Mf7qQefjwYVQqFa1bt/5H75Me+/fvR6VS0b59+/96Kf8aKWQKIYQQQgghANDGxBL09Tc8GTgQXVSUQc4qSxYy/zjW7I6ZyMRIeuzpwcVnFxW5lrla8n2Z79GoNelbTPgdWN0cUt44wzKDH7TZCDZpt6WNjE9m0s7rNJx9lHthsYq8p6MVKzoVY0nHEuTydEzfuoQQ4h+WJ7MTW/uWp1VJP6P5jece03D2UW6HxqRvQpUKCreGvqchb0NlPvx26t+3iabns7GwYVz5cUytNJUM1hkU+bD4MLrs6sKN5zdMzmFftiwBmzdjW6yYIvd88RLCZs9Jz6cRQgghhAD+v5CpUqn+8dayL+9VvHjxf/Q+6XHuXOpLwx/CWv4t0lpWCCGEEEIIQdz58wQNG07yo0eKnMrGBu/p09E4KHcIvfQi4QXd93Tn2vNrilzbPG0ZVmJY+tsGBm6BbYMhLtwwbusCbTeBo4fZ4fFJWpYfv8/cA3eIjE82ek25AGe+r5WFrN4Z07cmIYT4F9laaZjQuACVcroxfONlxd9lt0JjaDDrCJOaFqJuwczpm9QhEzRfDhdWw5ZehrknZ2F9e2i9DjSWJqeomaUmRTIVYdSxURx+ctggF5EYQZfdXVhQYwF5M+Y1Ot7SIxN+ixbyqEdP4k4ZnqEcNmsWahtrMnbtmr7PI4QQQnwgcrvm/q+X8J/6Lz5/UFAQoaGpZ4tnyZIFZ+e0X3T9O14WD4sZeSHr3/YhreXfIoVMIYQQQgghPmP6lJTUs8vmzQOtVpFXOzjgNWUyNrlympzjecJzuu3uxs2Im4pcp3ydGFRsUPqKmHHPYfsQuLJRmbOwhdbrwS2H2Sk2nXvMTzuvExKVaDSvVsHg6jlonNcRtZzHJoT4wNXOn5mCPhkYuO4Cp+49N8jFJmnps/ocZx8E8PUXubHUpLPpVuHWkBAFO4cbxu/shd/6QMN5oDY9l7udO7OrzebXW78y/uR4UnT/3+Y2MjGSrru7sqDGAvK75Tc6Xm1ri8+cOTzq2pX41862AgidMhWVpSWuHTqk77MIIYQQH4DhJYenfZF4r/7t8zHPnz+PSqWiaNGi//i90rMWjUbzj+9C/ZBIa1khhBBCCCE+U0kPH/KgTVvCZs82WsS0LVaMgC1bcKxc2eQcL9sJGitidivQLf1FzBs7YE5p40VMlRqaLgHfkiaHJyRrGbHxEoPXXzRZxHR3tGZ1t9L0qBggRUwhxEfDK4Mta7qVZkC1HBj7q2vJ0Xu0XHCC4MiE9E9auieUH6SMX1oHv3aCJGU77tepVCqa5WzGtMrTsFQb7uCMToqm2+5uRtuMv6RxsMd3wXxs8ip3boZMmEjYwoXp+xxCCCGE+Cz93fMxT58+Tdu2bfH19cXa2hpfX1/69+9PRESE4trExESuXr1Kzpw5UavVfP/99+TMmRMHBwfy5MnDtGnTjN7j1q1bdO/enWzZsmFjY4OjoyM5cuSgbdu2nDlz5q3XDBAXF8eNGzfInTs3arWa8ePHkz9/fuzs7AgICGD06NFojfxs/7GTQqYQQgghhBCfGb1ez4tNm7nXsBHxF408aLawwH3gQPxXLMfKx9vkPCGxIXTe1ZnbL24rcr0L9aZfkX5pFzHvH4GldWFNS4gJUeatHKDxQsj9hckpgl7E02L+cdaeVrbFBbC11NCvanb2flWJ0lmllawQ4uOjUasYVCMnSzuWIIOdsvXr2QcR1Jt5mGO3w9I/abVRULitMn51CyyuBREP0pyikm8lpleZjpXayiAekxxDzz09CQwPNDlW4+SE7+JFWOfIrsg9m/ozz+bImZlCCCGEMO7vFDInT55MmTJlWLduHTlz5qR+/fpYWFgwc+ZMypcvT1RUlMH1ly9fJiUlBV9fX4oVK8aCBQsoXLgwJUuW5O7du3z11VeMHTvWYMzevXspVKgQCxcuxNXVlQYNGlCpUiU0Gg2rVq3iwYO0v88y5sKFC+h0Onx8fChevDgzZ84kT548VKpUiUePHvHDDz/wv//9753m/pBJIVMIIYQQQojPiC4xkaCvhvD0m2/QxcUp8lb+/mRZsxq3nj1QaTRG50jUJrLo8iIa/NaAe5H3FPl+RfrRq3Av80XM+0dgWT1YVhceHDF+jX956HUUCjQ1Oc3xO+F8OfMIFx9HKnKWGhUdy2bh0LAqfFUzF042ps99E0KIj0HlXJn4o195Cvkoz4EKi0mi7eKTzN5/G51On/ZkKhV8OR1y1FLmQi7Dgspw77Ay94YKPhWYWXUm1hprg3hMcgw99vQwumP/JQsXF/yWLMEqa1ZFLmzGTEKnTUOvT8dnEUIIIcRn5fVC5tu0WF24cCHDhg0jX7583Lhxg71797JhwwauX79O3bp1uXr1KlOnTjUY8/JMyj///JNKlSrx8OFD1q9fz759+1i5ciUAEydOJDo6Gkh9cbhLly5YWFhw8uRJTp8+zbp16/jjjz+4fv06586do7KZrkfmvFzLrl27qFOnDg8ePGDDhg3s2LGDX3/9FeDVPz8lUsgUQgghhBDiM6FPSuLJgIFEbd9uNJ+hWTMCNm3EtkAB4+P1enbe30mDLQ2Yfm46scnK1oODiw2me8HupheRGAPr2qYWMO+beEBuYQu1f4IOW8Eli8mplh+7T9vFJwmPTVLkSmd1Zd9XlRldPx/ujtZGRgshxMfJx8WO9T3L0K60vyKn08PkXTfotuIMz6KNt9k2oLGAZssgl5Fd7/HPYUUDOJV2m9ey3mWZXW02tha2BvHIxEi67e7G3ci7JsdauLvjv2I51jmUZyCHz5tP6MSf0H+CLdKEEEII8W6ioqK4dy/1hVp3d3e8vU13EXrd06dPGTRoEC4uLmzfvp2sr71IZW1tzciRIwHY/sbPyy+LplWrVmXevHlYWf1/J4pmzZpRvHhx4uLiOHv2LAB37tzhwYMHlClThpIllcejFClShIwZ361T0Mu1NGzYkClTphispVat1JfTQkND32nuD5kUMoUQQgghhPgM6JOTefLVEGIOHFDkNBky4DNrJpnHjkFtb290/POE53Te1ZmhB4fyJOaJ0WuGlRhGp/ydTC8iJjS1gHltq+lr/MpAzyOp57epjf+4otXpGf17IKN+D0RrZNdR1/IBrOxSCl9XO9P3EUKIj5i1hYaxDfMzrUVhbC2Vu+f3Xg+l8uT9zNh7i7ikFPOTWdlBi1VQcZgyp9fC9iGwbQhozc9TKnMpZlSdoWgz+zzhOd12deNRlPH23wAWbm74rViOdZ48itzz5ct51KMnKUbOrBJCCCHE5+fChQuvOja8TVvZRYsWERsbS7du3YwWP18WNsPDww3iL3dBTpgwwWjXody5cwO8aknr7OyMSqVi//79zJw5k5iYmHSvMS0v1/LDDz8ociEhqUe1eHl5vbf7fSikkCmEEEIIIcQnTq/VEjR8BNF79ihy9uXKEfD7bzhWr25yfLIumUH7B3Em5IzRvKOlIz+W+5F2eduZXkT4HVhcA55eMJ73LgZtfoVOO8BNeV7aS7GJKfT45QzLjt1X5GwtNcxoVYRv6+XFQiM/6gghPn0Ni3izpU85sropX0KJTdLy856bVJ58gLWnHpKi1ZmeSK2GqiOh+QqwNPJCy+mFsLoZJCjbeL+udObS/K/K/7BQWxjEQ+ND6bq7K3de3DE51sLFBf+lS7Ax0hUg9sgR7jdpSvzlK2bvL4QQQohP37uej7lt2zYAOnUy/vJtfHw8AC4uLq9iWq2Wy5cvExAQYHR3JUBCQgKQujv05T/Hjx+PTqejf//+uLu7U7duXRYvXkyckeNd0ispKYnAwEACAgIoWLCgIn/p0iUA8ufP/873+FDJT/dCCCGEEEJ8wvQ6HU+/GWm0naxT/S/xXTAfy0yZzM4x49wMzoWeU8Q1Kg2tcrdiW+NtNMjewPQEj8+mFjEj7itzXkWh9Qbouhdy1Eg9s82EkKgEms8/zp/XlK1y/Fzt2NS7LPULfXpvnwohhDm5PB35rW85vijgaTQfGp3IiE2XqTfzCBcevTA/Wd4G0GU3ZPBT5u7sg0U14LnybOTXVfSpyJSKU9CoDHeKBsUG0WpbK7beMb0rX5MhA35LFmNr5KFkclAQD1q3JmLDBvOfQQghhBCftHc9H/Py5ctYWVmRK1cuo/nAwEAAChUq9Cp27do14uPjKVq0qMl5z507h6WlJfny5XsVGzFiBLdu3WLixImUKlWKnTt30rVrV3LmzPmqLe7bunz5MsnJyZQoUcJo/uV/l7cp7n4spJAphBBCCCHEJ0qv1xM8diyRv/2myDnWro3X+PGoNMqWhK/b+2AvywKXKeIVfSqyqcEmvin1DS42LsqBL93aA8vrQVy4MlfhK+i2D3LWNFvABLgfFkvD2UcJDIpS5EpkcWFLn3Lkyexkdg4hhPhUOdpYMrt1UcY2yIeTjYXRa64HR9N4zlF+/OOq+Xaznvmh2wHwK6vMhd2ARdXg9l6z66nmX43x5cejwvDv9viUeL458g2jj40mISXB6FiNoyN+ixfhVK+eIqdPTib4u+8J+WnSq5ZyQgghhPi8LFu2DL1ej16vp2XLlukao9VqiYuLw9LS0mh7WICNGzcCUKdOnVexl8VBR0dHo2NOnTrF3bt3qVSpEk5Ohj+PBgQEMHz4cA4cOMDdu3cpX748T548Yc6cOela85tersVU8VYKmUIIIYQQQoiPTsTq1bxYs1YRd6heDe/Jk1BZGH/Y/dKjqEd8e/RbRbxZzmbMrjabrM5ZzS/g+nZY0wqS32ifo1JD3alQ7fs0C5gAz6ITab/kFE8jlQ+9GxT2YmXXUrjaWxkZKYQQnw+VSkW7Mlk4NKwK3SoEYGWkxbZOD4uO3KP2tMMcux1mejL7jNB+CxRqrczFhcPKxqnnZiaZbo/2RdYvGFNujKKYCbDx1kbabm/Lw6iHRseq7ezwmjwJj2+/BUtLRf750qWEL1pkev1CCCGEEK/RaDRkzJiR2NhYQkOVHX4uXrzIL7/8gq+vL/Xr138Vf3km5Z07yvb4er2e7777DoDhw4ebvb+/vz8dOnQAUouq7+LlWswVMjUaDQWMtOn/2EkhUwghhBBCiE9Q7IkThIyfoIg7VKqE988/ozLyYPh1CSkJDD44mJjkGIN4Htc8DC9p/oc0AK7+BuvbgS7ZMG5hA81/gRJd056D1DMxuyw/zcPnyofl/avlYFqLwlhbmN9VKoQQn5MMdlaMrJuXvV9VMtlu++HzOFovOsmknddN72y0sIaGc6DaKOP50wthfgV4bPz8ZICG2Rsyr8Y8XG1cFbkbETdou72tyXMzVSoVrm3b4L9iORYeHor8s6k/82LzFpP3FkIIIYR43cudlj/++KPB9z/nzp2jXr166PV6FixYgLW19avcy12OR44cYc+ePa/iSUlJ9O3bl/3799OqVSuqV68OwK5du9i+fTspKYbdL4KCgpg+fTqAQaEUYPTo0ahUKjp27Gh2/S8LmcZ2XD5//pyHDx+SK1cubG1tzc7zMZJCphBCCCGEEJ+YpIcPeTJgILzxpqddiRJ4z5iO2sr87sVkXTJjjo/h+vPrBnFHK0d+rvwz1hprEyP/cmUjbOgEujdaF9pkgPa/Qx5lu0Cj69Dq6LP6HJceRxrE1SqY3LQgg2vkNNkWSAghPne+rnbMaFWEX3uWIau7vdFr5hy4w5wDxguJQOqu+QqDU19AsTDyUCz8NiyuCQd+AhMF0bJeZVlfbz1FMynPlopIjKDb7m4md2YC2BUpQsCmjdga2X3w9NtviTl40PT6hRBCCCH+MmbMGDJmzMjMmTMpUKAALVq0oHz58hQvXpywsDCWLFlC7dq1X12v1+u5cOECPj4+tG7dmjp16lCrVi1atmxJ1qxZWbhwIRUrVmT+/Pmvxqxbt466devi4eFBrVq1aNOmDdWrVycgIIArV64wYsQIKleubLAunU4HgKWZl421Wi2XLl3Cw8MDT0/lueifcltZkEKmEEIIIYQQnxRtTCyP+/RBG2lY/LP08UktYlqbL0I+iHpAu+3t2Hp3qyI3rtw4fBx9zC/g4jrY2BX0b7TLscsIHbeBX6l0fQ69Xs83my5z4MYzRe6HBvlpVtw3XfMIIcTnrngWV7b3r0DfKtmxUCtf/pi86wbrTz8yP0ne+tD9AGQupMzptXBgPGwbDH89iHuTh70Hi2stpnP+zorcs/hndN3dlaCYIJO3t8iYEd/587DOkcMwodXyeOAg4i9cML9+IYQQQnz2AgICOHnyJC1btiQ4OJjffvuNJ0+e0LlzZ86ePUv79u0Nrr979y6RkZHkzZuXJUuWMGjQIC5fvsyWLVtwd3dn6tSp7NixA3v7/39hrFWrVvTq1QtfX1/Onj3L+vXruXz5MrVr12b37t1MmKDsmnThr+9j3rz/665fv058fHya52Oayn/sVHo5HV38TWfPnqV48eIAnDhxglKl0vdwSgjxz0hOTiY8PByAjBkzmn2bRwjx75A/l+LfotfpeNy3HzH79hnE1XZ2+K9dg03OnKbH6vVsurWJn07/RHxKvCLfOX9nBhUbZH4B51fBb32AN37EsM8EHX6HTHnS9TmSUnRM2HGNpUfvK3J9qmRjaK3c6ZrHFPkzKcSHRf5M/nuuBkUxZMNFrj6NMohr1Crmty1G9bzKFq4GUpLg0CQ4PBX0RoqWRdtDvemgNv3e/J4Hexh2cBgpesNd+36OfiyrvQx3O3eTY5ODg7nfqjUpT58art/ZGf9VK7HOnt38+kW6yZ9LIT4s8mfy33ft2rVXv8+TJ30/x4jPh06ne3XWpUajQW3mex9ztFotrq6ulCxZ0qB17X/pXf/ff71OdObMGYoVK/be1iQ7MoUQQgghhPgE6PV6gseMURQxAbwmTzJbxHyR8IJBBwYx+vhoo0XMUplL0a9IP/MLOLvMeBHTwTN1J2Y6i5jXnkbRcPZRo0XMJkV9GFIzV7rmEUIIoZTXy4k13UuT29PRIK7V6emz+hxnHzw3P4GFFVT9FjrvBtesyvy5FfB7X9Bplbm/1PCvwYQKE1CrDB9JPYx+SLfd3XieYHoNlp6e+C1aiMbZ2XD9kZE87NyFpEdp7CwVQgghhPiAnDlzhqioKMaMGfNfL+WDJoVMIYQQQgghPnJ6vZ6QH8fxYu06Rc594AAcq1UzOfZ40HGa/N6EvQ/3Gs23zNWSWVVnYaG2ML2AUwth6wAURUwnb+i0HdxNF1FfStHqmL3/NvVnHVHsFAKomNOdiU0KyJmYQgjxNznbWrK8c0m8MxieeZmYoqPzsjNcfPQi7Ul8S0CPwxBQUZm7sAq29AJtijL3l9oBtfmh7A+K+J3IO3TZ1YWw+DCTY62zZcNn3lxUNjYG8ZTQUB526kxySEja6xdCCCGE+ACUKlUKvV5PmTJl/uulfNCkkCmEEEIIIcRHTK/XEzJhAhGrVilyjnVqk7FHD6PjkrRJTDk9he57uhMaH6rIu9q4MrvabEaWHomNhY2RGf5yYi5sH6KMO/um7sTMmC3Nz3D8TjhN5h5j8q4bJGuVJ18U8nFmbpuiWGrkxxchhHgfPJxsWNGlJC52hq0JI+OTaTb/OJvPP057EmsHaLUOslZR5i6tgy09ze7MbJi9ISNLjVTEb7+4TaednQiJNV2QtCtSBO9p/wMLw5dskh8/5mGnzqT81X5RCCGEEEJ8/ORJgBBCCCGEEB8pvV5P6OQpRKz4RZGzK10arwkTjO5gvPPiDq23tWb51eVG5y3vXZ6N9TdS0cfITpuXEqJg10jYOUKZy+CfuhPTNcDs+k/cDaflguO0WniCi48jjV7ToYw/a7qXxt7azI5QIYQQby2buwNLOpbA1lJjEE9K0TFo3UXGbbtKitbIOZivs7KDVmshew1l7vIG+L0/6EzP0TJ3S74q9pUifj/qPp12deJpzFMjo1I5Vq6M18SJ8MbXuaS7d3nYtRvaKOXufiGEEEII8fGRQqYQQgghhBAfqWfTpvN8yRJF3K5ECXznzkFto9xJeTbkLK22teJGxA1Fzkptxdclv2ZOtTm42boZv6k2Bc4sgRlF4PgsZd41a2oRM4OfyXVfevyClguO03LBCU7cNX4WmncGW1Z3LcUPDfJjZyVFTCGE+CcU8XNhbtuiWBnZ8b7w8D06LTvNi7gk85NY2kDLVZCztjJ3YSXsGAp65W77lzrm78igYoMU8UfRj+i4syOPok2fe+lcry6eY5QtahOvXfurzayy44AQQgghhPi4SCFTCCGEEEKIj1DE2rWEz5+viNsWK4bvvLmobW0VuQuhF+j1Zy/iU+IVuZwuOVlXbx2t87Q2fQ7l3QMwvwL8MQjijJxfljEHdNwOzj4m1/3bhSc0mXvMZAEToGUJX3YOrEDZ7CaKqUIIId6byrkysaZ7KdwcrBW5w7fCaDj7KHeexZifxMIamv8Cub5Q5k4vgt3fmi1mds7fmeElhiviQbFBdNjRgQuhF0yOdWnWDI+vld0BEgIDud+8OfGBgebXLoQQQgghPmhSyBRCCCGEEOIjE3P4CMFjf1TEbQsXxnf+fNT29orcpWeX6PlnT6NFzHZ527G67mqyu2Q3fdPzq2BFQwi9ajyfKW/qmZhOmY2m9Xo9Cw/dZcDaC0bPwQQo7JuBVV1LMbFJQRxtLI1eI4QQ4v0r5u/K1n7lKOTjrMjdD4+j0eyjHL1t5AWW11lYQbNlkL26Mnd8Fuwfb3Z427xt+a70d4r4s/hndNrViTXX16A3UQx17dABt/79FPGUkBAetGlL1K7d5tcuhBBCCCE+WFLIFEIIIYQQ4iOScPMmTwYNAq3WIG5ToAC+ixaicVAWMQPDAum5pyexybEGcTsLO+ZWn8uwEsOw1ih34rwSdCF1FyZGHiBb2EDFodD1T3D0MDpcp9Mz9o9rjNt+zWi+kG8GlnUqwebeZSknuzCFEOI/kdnZlnU9ytC4qLciF5WQQoclp1hz6qH5SSysocVKyFJBmTs0Cbb0gaQ4k8Ob52rOmLJjUGHYGSBFl8L4k+P55sg3Rl/IAXDr1Qu33r0UcX1CAk8GDCBs3jyThVAhhBBCCPHhkkKmEEIIIYQQH4mUsDAe9+yFLsawxZ+ltze+8+aicXBQjLkWfo1ue7oRnRxtELe1sGVO9TmU9y5v/qbxEbC+PWgTlbmCLaDfWaj6LVgpC6gACcla+q09z5Kj9xS5TI7WLO1Ygi29y1I5VybTLW2FEEL8K2wsNUxtVohv6+ZB/cZfySk6PV9vusyPf1xFqzNTELS0hVZrwbeUMndhJSyqBmG3TA5vlKMREytMxEptpcj9cfcP2mxvY/TcTJVKhXv//mSeMAEslbv6n02bTuhPk6SYKYQQQgjxkZFCphBCCCGEEB8BXXw8j3r3ITkoyCCudnTEd/48LDJmVIwJiQ2h997eRCcZFjFtNDbMrjabYh7FzN9Ur4ctveHFA8O4Y2boug8aLzB7HmZ8kpauy8+w7dJTRS6buz2bepelSm4pYAohxIdEpVLRtUJWFnUojr2VRpFfdOQerRac4NFz0zsrsXaANhvAq4gyF3oVFlSGy7+aHP5F1i9YUWcFme2V7cpvRdyiy64uhMSGGB2boVFD/JctRePiosg9X7aM8PkLTK9bCCGEEEJ8cKSQKYQQQgghxAcu4cYN7jdvQcKlS4YJCwt8pk/DOrvybMuElAQG7B9AWLzhmWbWGmtmVptJCc8Sad/46HS4sd0wptKknoHmY74IGpuYQqdlpzhi5Ey14v4ubOxVFh8Xu7TXIIQQ4j9RNbcHG3uXxTuDrSJ36v5zak87xLrTD03vcLRxhrabjLeZTYqBjV1g59epL80Ykc8tH+vqraOsV1lF7mnsU3r+2ZPIxEijY+2KFSPLhvVY51B+fXw2bRoRa9YYX7MQQgghhPjgSCFTCCGEEEKID5RepyN82TLuN21G4i1lGz7P77/DvqzyAa9er2f08dEEhgcaxC3VlkyvMp3SmUunffP7R2DvGGW8xhjwMz8+JjGFjktPceLuc0WuVj4PVnYtRQY7ZctAIYQQH5bcnk5s6VOOIn4ZFLnYJC3DN16m24ozPIs20n4cwM4V2m1JPUvZmBNzYNc3JouZLjYuzKk2h+4Fuytyt1/cpv++/iSkJBgda+Xjg/+aNdgWLarIBY8ZS+Qf24yvSQghhBBCfFCkkCmEEEIIIcQHKDkklEdduxI68Sf0ycmKvGuXzrg0b2507LLAZWy7q3xAO6bcGMp5l0v75te2wto2oNcaxnPXgzJ9zA6NSkim3eKTnL4foci1KunLnDbFsLFUtioUQgjxYXJ3tGZNt9I0LWa8lfif10KpM/0w14OjjE+gsUg9S7nNRrB1VeZPzIF9P5q8v0atoV+Rfowpq3y55lzoOYYeGkqKLsX4WAcHfOfNxTp3bsOEXk/QiBHEHDxo8r5CCCGEEOLDIIVMIYQQQgghPjCJ9+5xr2kTYo8dVyZVKjJ2706mr74yOvbw48P87+z/FPFO+TpRL2s98zdOioXf+8O6tpDwwjDnEgAN54CZ8ywj45Jpt+gk5x++UOTal/FnXMMCaNRyHqYQQnxsbCw1TGlWiLltiuJiZ6nIh8Uk0mrBCa4GmShmAuSoDj0Pg4+R1uaHp8DhqWbX0ChHIwYWHaiIH3h0gLEnxppscatxcsJv4QIs/fwMEykpPB4wkMQ7d8zeVwghhBBC/LekkCmEEEIIIcQHJDkoiIedu6B9pjxb0sLTE79ly8g0eBAqtfJb+dsRtxl+aDh6DB/mlvcuz4CiA8zf+Mk5mF8Rzi1X5ixsoPmK1PPOTIiITaL1ohNcfKw8r6xzuQB+qJ8PtRQxhRDio1anQGZ2DapIlVzuilxEXDKtF53gyhPj51YC4OwD7X8DvzLK3N4xcGKu2ft3zt+ZtnnaKuKbbm3if2f/Z7KYaeHujt+SxVhkymQQ1yckEDRsuNHOB0IIIYQQ4sMghUwhhBBCCCE+ECnh4Tzs3IWUp08VOacv6pD1ty3YlyppdOzdF3fpsrsL0cnRBvEsTln4qeJPaNQm2rnqtHD4Z1hcA8JvK/NWDtB0KWQuaHLd4TGJtFp4gkAjO3F6VMzKd/XyoDKzk1MIIcTHI5OjDUs6lmB8owJYagz/bn8Rl0zrhSe4+OiF6Qms7KH1evAqosztHAGnF5kcqlKpGFpiKHUC6ihySwOXMu/SPNO39fHBb/EiNM6GL+UkBAYSNn+B6fUKIYQQQoj/lBQyhRBCCCGE+ABoo6N52K0bSffvGybUajKPG4fX1KmKh68v3Yu8R5fdXXie8Nwg7mjpyIyqM3CycjJ+0xePYHl92PsDGDtfzKck9DwCub8wue7Q6ARaLjjB9eBoRa5vleyMqJNbiphCCPGJUalUtC7lx7y2xbDSGD5aikpIoe2ik5x7qDwr+RUbJ2i7CTLlU+a2fQXH55gcqlapGVduHGUyK3d1zrkwh+WBRjoL/MU6Rw68pkxWxMPmziX+8hXT6xVCCCGEEP8ZKWQKIYQQQgjxH9PFx/OoZy8Sr15T5DKPG0eGJo1NFgMfRD2gy64uhMUbtqK1UFkwqdIkApwDjN/0ykaYWw4eHFHmVGqo/DV02gGuJsYDTyPjabngBLdCYxS5QdVzMqRWLiliCiHEJ6xaHg/mty+GlYXh46XoxBQ6LD7FZSPtxl+xc4X2WyBjDmVu19dwRHne80uWGkumVZlGIfdCityUM1NYd32dybEOFSqQoWULw6BWS9Dw4egSEkyvVwghhBBC/CekkCmEEEIIIcR/KDkklIedOhN/9qwi5/HN12Ro1NDk2EdRj+i8qzPP4p8ZxDUqDVMqTaG8d3nlIG0y/NYHfu0MiUYeMGfwh047ofII0FgYvW9CspY5B25T4+dD3H0Wq8gPrZWLAdWNPJgWQgjxyamSKxOL2hfH2kgxs92Sk1x7qmw7/opDptQzM12MvDTz52g4MBFMnHtpZ2nHnOpzyOOaR5H78eSPbL612eRtPYYOxdLPzyCWdPcuz/5nungqhBBCCCH+G1LIFEIIIYQQ4j8Sd/o095o0If7CBUXOrU8fXNu3Nzk2LD6Mrru7EhoXahDXqDRMqjiJav7VlIO0KbCxC5xfaXzSQq1SW8n6lTKa1uv1bL0YRLWpB5m08wYxicp2tN98kZs+VbKbXLcQQohPT8Wc7iztWAJbS8PzmF/EJdN20UluG9m5/4qzN3Tabnxn5oEJ8Oco0OmMDnWycmJ+jflkc86myH1/7HtmX5iN3kghVG1vj9fECaA2fCz2fPkKonbtNr1WIYQQQnxSli1bRuXKldN1rVarZd26dTRo0ICsWbPi6OiIh4cHVatWZcGCBSQnJ5scO2TIEFQqlcEvGxsbvLy8qFmzJj///DMREabb8r8+vlo1Iz/r/yUhIQF/f/9X106cODFdn+1DJ4VMIYQQQggh/mV6vZ7ny5fzoGMntGFhirxLu3a49e1jcnx8Sjz99vYjKDbIIK5WqZlYYSI1s9RUDtKmwObucPU3Zc7aGZoshkbzUs8tMyI8JpGWC07Qb815nryIN3rN9/Xy0r2i8mGyEEKIT1/Z7G4s6lBc0WY2PDaJNotO8CBcuYP/FSev1GJmprzK3NHpsLYVxL8wOtTFxoWFNRfi5+inyM27OI9hh4aRkKJsGWtXtCgZu3RRxJ8MGMDjfv1IfvLE9HqFEEII8dEyV3A0lQ8MDKRUqVK0bNmS3bt34+3tTYMGDShSpAgnTpygR48eVKlShRcvXhid88JfLy/XrFmTDh060KFDBxo0aEBAQACHDh3iq6++IleuXOzebfyFqguvvfx89epVk2ufPn06Dx8+fPXvhQsXNvtZPxZSyBRCCCGEEOJfpEtKImjIUEImTAStVpF3ad8Oj69HmDxbUqfXMfLISK6EXzGIq1VqxpcfT+2A2kYGaWFLr9RzMd/kXw56HYUCTU2uOVmro+fKs5y899xo3snGgqnNCtG5vOnzNIUQQnz6ymV3Y367YlhqDL+GhUQl0nrhSW6FRJse7JAJOvwBngWUuZs7YUFlCL5sdKi7nTuLai4is31mRW7n/Z103tVZcZY0gFu/vljnyqWIR+/5kzt16/Fszhx0iYmm1yyEEEKIj8qlS5fIkSMHv/2mfMH34cOH1K9fnxEjRhjEz58/T4UKFTh79iwDBgzg4cOHHD58mJUrV7Jz505u3LhB4cKFOXr0KJ07dzZ634sXLwIwZ84cli1bxrJly1i3bh1Hjx7l6dOn9OnTh2fPntGwYUOuXLlicnyJEiUIDg42unszPDycCRMmkC1bNhwcHAApZAohhBBCCCHekl6rJWjYcKK2bVPkVDY2eE36Cc9vvkGlNv1t+rRz09jzYI8i/l3p76ibta5ygE4Hv/eDy+uVubwNof3vkMHX7Lon77rB6fvKH5Q0ahUdy2bh4NAqNCnmY3YOIYQQn4cquTIxs1VRNGrDYuaTF/HUn3WUTecemx5snxE6bAWvospcxD1YVAMurjM6NLNDZlbUWUFu19yK3OWwy7Ta1oq7kXcN4morK7wm/YTa3l4xRp+QQNiMmdxr0oTkkBDTaxZCCCHERyNPnjz079+fTp060aRJE4KCgtBqtUybNo38+fNjb29P//79X10fGhpK7dq1iYiIYNq0aUybNg13d3eDOX19fdm4cSMWFhZs3ryZc+fOGeSfPHlCWFgYDg4OZM2aVbEmFxcXZs2aRbNmzYiPj2f06NFGx3t7e1O1alXA+K7MsWPHEhkZSe/evYmJicHDwwNPT893/U/1QZFCphBCCCGEEP8CvV5PyLjxRO/cqchZ+vqSZe0anOvXNzvHrzd/ZemVpYp4p/ydaJrTyI5KvR62D4ELq5S53PWgySLQWJi9584rwSw4dFcRr5Y7E7sGVmR0/Xy42FuZnUMIIcTnpXZ+T/7XojBvNheIT9YyeP1FRmy8REKysisBALYu0OH31Jdt3pQSn9omfd+PRod62nuyvPZyKvtWVuSCY4Ppvrs7T2OeGsRtcuXCf/VqbE3sWEi6fYdHXbuhjYw0vl4hhBBCfDQsLS0ZPHgwt2/fxtvbm9GjR3PkyBHWrVvH7t27WbNmDf7+/q+u79+/P6GhoTRu3JgBAwaYnDdr1qyviozb3nhx+WVb2AIFCpjsvATQr18/AHbu3GnQ3vbl+EKFClGgQGrnisDAQIOxd+7cYe7cuZQpU4YcOXK8uv5TIYVMIYQQQggh/gXh8+cTsXq1Im5fsQIBv27AJrdyB8lLer2e3+/8zrgT4xS5Gv41GFh0oPGBpxfBmcXKeM460HQpaCzNrvlBeCxDN1xUxNuU8mNxxxJkz+RgdrwQQojPV/1CXkxqUlBRzARYe/oRjeYc416YiXMzrR2h2TKoOQ5UGmX+0GQ4PtvoUDtLO6ZVnkanfJ0UuZC4ELrv6c7zBMNW6Ta5cuK/ehWZJ0xAkzGjYlzirVs86t0HXYLyrE0hhBBCfHxUKhXq1zohvfnvANeuXWP9+vVYWVkxffr0NOfMmzf1rO/bt28bxF8vRJrzskgZGxtLcHCwYnzhwoVfXfPmjsyvv/6apKQkJk+e/KoN7afSVhakkCmEEEIIIcQ/LmLDBp5NU/7g41ijBr5z56JxdjY5NjIxkqGHhjLyyEhS9CkGuQJuBRhXfhxqlZFv6+8dgh3DlfEcNaH5crAwv4syIVlLz5XniE58457eznxXL6/ZsUIIIQRAs+K+/NK5FBmN7Ny/9jSKJnOPmT43U6WCsn1Td2fauyvzu76BS0bapgMatYbBxQczpuwYNG8UQu9H3afXn72ISYoxvJ1aTYZGDcm2Yzsubdsq5ow/e5Yng79Cn5KiyAkhhBDi45CSksLMmTPJnj07Dx8+ZPTo0ZQvX55mzZpRvXp12rVrx+PHqW3w58+fj16vp2nTpvj4pH2UiqVl6ovC8fHxBvGXhciCBQuaHW9l9f/fLyW+dkb364XQPHnyYGlpaVDIPHnyJBs2bKBRo0aUK1fOoPD5qZBCphBCCCGEEP+g6L17CR41WhG3K14crymTUWmM7DT5y8mnJ2nyexN23d+lyGW2z8yMqjOwtbBVDoy4D+s7gP6Ntn3+5aH5L2BhbXbNSSk6vt50mWtPowziTjYWzGlTFBtL02sWQgghXlc+hxvbB1SgZICrIvc8Nok2i05y39TOTIAs5aHHIchcWJnb0gtu7zU5tFGORowtN1YRvxp+lQH7B5CoTVTkNE5OeH47EvfBgxW5mH37eDp6NHq93vR6hRBCCPHBCgwMZMqUKSxZsoRNmzbh5eWFRqNh0KBBXLlyhRcvXvDzzz8DsGtX6s/h9dM4AualkL/O1HZ1Nfye5+UOybQKmS9evHj1ezc3N8X4QoUKYWlpSa5cuQxayw4dOhQLCwsmTpwIIIVMIYQQQgghRPolXLvGk6+GgE5nELfOlQufObNRWxsvKOr1eqafm07X3V0JiQtR5F2sXZhdbTZutm7KwYkxsKY1xBu2zSODHzRfAZY2Ztd851kMTeYeY/P5J4rcz80L4+tqZ3a8EEII8SYPJxtWdy1Fr8rZFLnQ6ETaLDrJ44g40xM4eUHbTZAxh2FclwLr2sGTsyaHfpntS0aUHKGInwo+xbCDw9DpdUZGQcZuXXFp304Rj/x1I89+/lmKmUIIIcS/ZOPGjahUqvf26/bt2zRo0EBxHz8/P7Zu3cpPP/1EYmIi169fB6BIkSLpWufZs6nfj7xs/woQExPDnTt3UKlUaRYy79y5A0DGjBnJkCGDwXg7O7tXZ18WLFiQoKAgIiMj+e233zh8+DDdu3cnZ86cxMTEcPfuXWxtbcmZM2e61v0xkEKmEEIIIYQQ/4CUiAge9+2H/o3ztCy9vfFdsACNk5PRcXq9np/P/syiy4uM5ktnLs2GLzeQwyWHMqnTweYeEBpoGLe0h1ZrwV557tfr911z6iH1Zhzh8pNIRb5X5WxUz+thcrwQQghhjoVGzfDauZnXtigWasODM5+8iKfNopOERJk5g9I+I7TbBI6ZDePJsbCqGTy7aXJomzxt6FmopyK+79E+Vl5daXSMSqXCY8QInOrWVeTCFy4ibOYs02sVQgghxHtz6tSp9zKPra0t+fLle9UC1hRLS0tCQ0Nf/bu7u5EW92+4c+fOq12SNWrUeBW/dOkSOp2OLFmy4OjoaHaOo0ePAlCtWjXF+AIFCrw6w/NlofTSpUsMHz4cR0dHRo0aBaTu3tTr9eTPnx+Nme5PHxuL/3oBQgghhBBCfGr0KSk8GTyY5CeGuxo1Li74LlqIpUcmk2MXX1nMssBliril2pKBRQfSNm9b42di6vWwczhc/0OZazwfPPKZvOfz2CRGbLzE7qvK3Z8A5bO78VWNT+dtTiGEEP+d2vkzM62lnv5rzqN7bVPjg/A42iw6ybrupcnoYKIFega/1J2ZS2tDwmsv3cSFw4r60GkHuAYYHdq7UG9eJLxg7Y21BvHp56ZT1qss2V2yK8ao1Gq8JoxH++IFsX89XHwpbM4cUKtx79snfR9cCCGEEO+kadOmlClTxmguKSmJFi1aAGBvb8/KlcZfUAJwdHRUFPc6duxIx44dFddav9Y9KTk5Oc01zpqV+oJThQoVDHZCvn6+pTl6vZ6lS5cC0Lx5c8X419vEvtzZOXToUG7cuMHYsWPJlCn1GcPLNrSfUltZkEKmEEIIIYQQ713olKnEHT9hGLSwwGfGdKwDjD9gBVh/Yz3Tz01XxHO45GBihYnkdDFRTNTrYefXcGqBMlf5a8jzpcl7Hr71jK/WXyQ0WnlOGED7Mv5880UeLDTSzEUIIcT7Ua+gFwnJOoZsuGgQvx0aQ6uFJ1jZtRSZHE20QvfIm9pl4JdGkPLaDs7op7CiQWox09lbMUylUvF1qa8JTwhnz4M9r+JJuiS+OfINq75YhaVGuUNDZWWFz4zpPOjUmYRLlwxyYbNmodKocevV6y0+vRBCCCHeRokSJShRooTR3Llz5179vlChQjRs2PC93NPNzQ1nZ2ciIyO5evXqq0KhMZcvX2bu3LkAjB8/3iCX3vMxFyxYwM2bN8mZMyeNGjVSjH+9EPpyR+bJkyfx8vJi8Gvnen+K52OCtJYVQgghhBDivYrc+gfPly1TxD1GjMDOxA9fADvv7eTHEz8q4oXcC7GyzkrzRczd38LJucpcni+h4jCjwxJTtPz4x1XaLT5ltIjp5mDFko7FGdMgPzaWn05LGiGEEB+GpsV8GNswvyJ+MySGlvNPEBxpps2sf1lougRUb3x9evEgtZgZ88zoMLVKzagyo8hkZ/gw8trza8y/NN/k7dT29vgtWohNPmV3g2fTZxA2z/RYIYQQQvxzzp8//+r36T3LMj3UajW1a9cGeLVT0phnz57Rpk0bEhMTGThwIOXLlzfIp2dH5p49exg8eDAajYbFixe/aiH7+vjXC5O+vr5kzZqVjBkzMm7cOOzs7N7qfh8jKWQKIYQQQgjxnsQHBvL0228VcefGjXFp09rkuH0P9/H1ka/RozeI53DJwexqs7GztDM+UK+HP0fBcSPndPmXh0bzQa38lv9WSDQNZh1l0ZF7RqetksudHQMqUjW3nIkphBDin9OutD8jv8ijiN8Ni6X5/OM8jogzPTh33dSvcxiet0n4LfilIcQ9NzrM2dqZseXGKuKLLi/i0rNLRkak0jg54bd4EdZ5let9Nm0az1evNr1WIYQQQvwjXi9kvu9diCNHjsTa2poVK1Ywffp09HrDn9f37dtHxYoVCQwMpFGjRkyaNMkgr9PpuHLlCmB8R2ZwcDBDhw6lbt26JCYmMnfuXINC6MvxarX61S7Ml+7cuUNYWJhBW1ytVsuVK1dQqVRSyBRCCCGEEEIoJYeE8LhXb/SJhrsbbQoWxHPU96hUKsUYnV7H3ItzGbh/ICm6FIOcj4MP86vPx9na2fRN94+Do8pWtPiVhdbrwMpekTp5N5wGs49yPThakbO2UDOmQT6WdCyBu6OJ88mEEEKI96hbxax8XSe3Iv7weRwt5p/gQXis6cEFm8GXRr4OhlxJPTMz8okyB5T1KkvLXC0NYlq9lpFHRhKfEm/ydpoMGfBfsgTrPMpiZsi48cSeOGFklBBCCCH+KS93IML73ZEJqS1cf/nlF2xsbBg4cCDZsmWjRYsW1K9fn5w5c1K7dm3u37/P119/zfr167G0NGxRf+PGDeLiUl/KGjt27KvzOBs2bEjhwoXx8vJiypQpZM2alR07dtCtWzej47Nly4aDg0Oa67158ybx8fHpvv5jIoVMIYQQQggh/iZdbCyPevUiJTTUIK5xc8NnxnTU1sqiYFRSFP339WfOhTmKnZjutu4sqLkAdzt30zc9sxQOTVbGfUtBm/VgrfzB5cz953Radpq4JK0il9vTka39ytO+TBajRVchhBDin9KjUjZGfZlXEX/yIp5m845z9oHx3ZUAFOsAtScq48GXYWFVeHJOmQMGFRuEv5O/Qex+1H167unJ7YjbJm+nyZABvyWLsc6VyzCh1fJkwECSHj0yvVYhhBBCvDd6vf7VGZIWFhbkz69sWf93NWvWjEuXLtGrVy8sLCz4/fff2bp1K3fu3CFXrlxcunSJH3/8EQsLC8XYl2sDWLFiBcuXL2ft2rWcPHkStVpNr1692LJlC1evXqVGjRomx6d3d+XL6z+18zFBCplCCCGEEEL8LXqtlidDh5F49ZphwtISn+nTsPT0VIy5FXGL1ttac/DxQUXOycqJeTXm4evoa/qmt/fCtq+Uce/i0OZXsHZUpM49jKDjUuNFzK7lA/itbzlyeijHCSGEEP+GTuUCGNdI+QAyNDqRFvNPsPzYfUVLt1dK94Kq3ynjMcGw9AsI3KJI2VnaMa78ONQqw0dj50LP0WxrM6aemUpssvHdoBYuLvgtXYKlt7dBXBsZyePefdDGmNlFKoQQQoj34tatW8TExACQN29erI28QPw+5MiRgzlz5rza8Xjv3j2cnJy4desWISEhJse1bNkSvV5v8CshIYGnT59y7tw5Zs+eTYMGDQzOxDQ2fsOGDela59te/zGRQqYQQgghhBB/Q+jkKcTs26eIe/04FrtixRTx86HnabO9DQ+iHihy2TNkZ9UXq8jpktP0DUOuwoaOoH+jIOlVBNptAhsnxZCLj17QYfEpYhIN29faW2lY3rkk39bLi7WFxvQ9hRBCiH9Bm1L+TG5akDcbA6To9Iz6PZBB6y4Ql5RifHDFIVD5G2U8JR42dEjtYvBGIbSQeyG65O+iHKJPYVngMupvrs/eB3uN3s7C1RWfOXNQ2RmeY5146xZBw4ej1+lMf1AhhBBC/G3/5PmY5mTJkoUZM2ag0+no1KkTkZGR/9q9P1fK/a5CCCGEEEKIdIlYu5bny5Yp4m69e+HcoIEifiviFn329jF6/latLLUYU3YMdpZ2itwrMaGwugUkRhnGM/hB6/VgozxP88qTSNotPkn0G0VMW0sNSzuVpGSAq+n7CSGEEP+yZsV9sbbUMGTDRZJSDIuBWy4Ece1pNAvbF8cvo5Gvl5WHg0sW+L0vaJMMc/t+hIQoqDnWINyrcC+CYoPYdnebYrrQ+FAGHhjI92W+p1nOZoq8Ta6ceP00kSf9+hvEY/bu5dnMmWQaMCB9H1oIIcRHLXj8eBKvXf+vl/Gfsc6TG89vjLxM9A/7J8/HTEu7du2IiooiNDSUS5cuUalSpX/1/p8bKWQKIYQQQgjxDmKPHyd47I+KuNMXX+DWr58iHhQTRM89PYlOijaIq1VqBhcbTPu87c2fTZkUB2taQuRDw7i1E7TeAA6ZFEOeRsbTadlpohIMi5g2lmqWdCwhRUwhhBAfpPqFvAjIaE/PlWd58sLw5Z8bIdG0WniCX3uVIbOzrXJwoRbg4g9r20BcmGHu2Aywc4Xyg16FLNWWTKwwkdpZajPx1ESexDxRTPnjiR/JYJ2BGv7K86ucatQgsV9fwmbOMoiHz52Hbb58OFav/hafXAghxMco8dp14k6f/q+X8dl5fUfmv13IBOjZsycAGo10N/qnSWtZIYQQQggh3lLSw4c8HjgItIbtXW0LFybzhPGKgmREQgQ99vQgND7U8HoLW+bXmE+HfB3MFzGfXoKFVeDJWcO4SgPNl0Om3IohCclauq84y7PoRIO4tYWaxR1KUCZbxnR8UiGEEOK/UcDHmT/6ladiTndF7smLeDosOUVkXLLxwX6lods+cM+jzP05Gs4uU4Qr+1ZmS4Mt9CzUEyu1lUFOp9cx/NBwTj09ZfR2br164VirliIeNHwEiXfvGV+jEEIIIf6Wl4VMlUr1r7aWFf8+KWQKIYQQQgjxFrQxMTzq1RvdG+dgWHp74zN7Fmpra4N4XHIcffb24X7UfYO4hcqC/1X+H6UzlzZ9M50Ojs6AhVXhmZFWRXWnQraqirBer2f4xktcfmK4RiuNmgXti1Muu5v5DymEEEJ8AFzsrVjasQT9q+VQ5G6GxNBl+WkSkrVGRpK6K7PLbvApqcz9MQiu/qYI21jY0KdwHzY32Iy3g7dBLlmXTP/9/bkWfk0xTqVW4zVhPNa5DV8s0sXG8rh/P3SxsWY+pRBCCCHeVlBQEKGhqS8KZ8mSBWdn5TEr4tMhrWWFEEIIIYRIJ71WS9BXQ0i6c8cgrrazw2fuHCwyGu5y1Oq0DDk4hMthlxVzjS0/lnLe5UzfLPIxbO4J9w8bz5ftB8U7GU3NP3SX3y4EKeKTmhakkpGdLUIIIcSHSqNWMbhGTnxdbBn66yWD3JkHEfRdfZ55bYtioTHyrr6NE7ReB8vqQujV/4/rdbCxa2p79mxVFMP8nPxYUGMB7Xa043nC81fx2ORYev7Zk1/q/IKfk5/BGLWdHT4zZ3CvaTODl52Sbt8haOS3eP/vZ/PdF4QQQny0rPMoO+R8Tv6Lz/9vnY+5d+9eqlevTocOHVi2bNk/dh9hnhQyhRBCCCGESKdn06YRc/CgYVClwmvKZGxy5lRcP+P8DA4/URYihxYfSr2s9czc6AYs/UJ5tlfqDaHCYKjyrdGh+6+H8tNO5e7NHpWy0rCIt5ERQgghxIevWXFfwmKSFF/j/rwWwjebL/NTk4LGC4V2rtB2EyypBS8e/H9cmwTr2kL3g+CWXTHMz8mPedXn0WlXJ2KT/39H5fOE53Tb3Y2ltZfi5eBlMMbK1xfvyZN41KMn6PWv4tE7d/K8YEEydjb+ApIQQoiPm+c33/zXS/js/FvnY546ldpWvnRpM52UxD9OWssKIYQQQgiRDpG//074wkWKuPuAAThWVbZ33Xl/J0uuLFHEO+fvTPt87U3f6MVD+KWR8SKmsy90/AOqfQ9q5bfyN4Kj6b/m/OvPTgGoksudYbU+77eEhRBCfPx6VspK53IBivj6M4+ZsOM6+je/AL7klBnabQb7TIbxpBjY0hO0KUaH5cmYh5lVZ2KptjSIB8UG0XlXZ57GPFWMcahYEbe+fRTx0KlTiT1p/IxNIYQQQrydf6uQ2blzZ65du0a7du3+sXuItEkhUwghhBBCiDTEnT3L05HKHZBOdeuSsUd3RfxmxE2+P/q9Iv5FwBcMLDrQ9I1insGKhhD1RJkr0Bx6HoEs5Y0OvR0aTZtFJ4hONHwYm9XdnumtiqBRSzs7IYQQHzeVSsW3dfNQv5CXIrfg0F2m771lenDGbNBuE1i/cYbW49NwdJrJYSU8SzCp4iTUKsNHaE9intBldxeCY4MVY9x69cKhcmXDoFbLk4EDSXps5Gu8EEIIId7K64XMwoUL/2P38fDwIHfu3Njb2/9j9xBpk0KmEEIIIYQQZiQ9fMjjvv3QJycbxG3y5SPzuB8VbewiEyMZsG8A8SnxBvE8rnn4oewPps/HSoiElY3gueH5m6g00Gg+NFkIthmMDr3zLIZWC08SFpNkEHe0sWBR++I42VgaHSeEEEJ8bNRqFVOaFaJCDjdFbtqft1hw6I6RUX/xLACN5yvjBybA04smh1X3r86YsmNQYfg1/FH0I7ru7kpoXKhBXKVW4zXpJyz9DM/R1EZE8LhvX3RxcabXKIQQQgizoqKiuHfvHgDu7u54e7/dESq3bt2ie/fuZMuWDRsbGxwdHcmRIwdt27blzJkzr657/PgxKpVK0Vb28ePHaDQaypUrR3R0NN9++y3Zs2fH1taWfPnysX79+lfXbt26lapVq5IhQwYyZ85M7969iY2N5V1FRESgUqkoXLgwt27domPHjvj6+mJlZYW3tzfDhg0j+Y1nF58CKWQKIYQQQghhgjYqikc9e6GNiDCIW7i74zN7FmobG8PrdVqGHRrG45jHBnEXaxemV5mOjYXh9a8kx8PqlhB8WZlrNA8KtTS5xnthsbRacIJn0YkGcUuNilmti5LV3cHMJxRCCCE+PlYWaua1LUZRvwyK3Pjt1/nl+H3Tg3PVgSJvtIfTpcDmnpCcYHJYg+wNGF12tCL+IOoBXXZ1ISzesCW8xskJn5kzUdnaGsQTr18n6JuRptvgCiGEEMKsCxcuvPo6+rZtZffu3UuhQoVYuHAhrq6uNGjQgEqVKqHRaFi1ahUPHvz/edoXLlwAlDs+X8bd3NwoVaoUq1evpnjx4uTPn5+rV6/SunVrzpw5w+DBg2nWrBlWVlZUrVqVqKgo5s6dy8iRI9/5s7/ciarX6ylevDhbt26lZMmSVKhQgZCQECZPnsyQIUPeef4PlRQyhRBCCCGEMEKfnMzjAQNIunvXIK6ytcVn7lwsPT0VY34++zPHgo4ZxDQqDVMrTyWzQ2bjN4oOTm0n+/CYMldnEhRsbnKND8JTi5ihbxQxLdQqZrYqSqWc7ibHCiGEEB8ze2sLlnYqST4vJ0Xuu98CWX/6kenBtcZDBsPdkoRehf3jzN6zcY7GfF9G2Tr+ftR9BuwfQKLW8OuxTa6ceE0Yr7g+eudOwucvMHsvIYQQQhj3rudj6vV6unTpgoWFBSdPnuT06dOsW7eOP/74g+vXr3Pu3Dkqv9YaPq1C5o4dO2jUqBE3b95k7dq1nD59mrZt26LVamnZsiU7duwgMDCQnTt3smnTJvbt2wfAhg0b3ulzA5w7dw6AS5cu8cUXX/Dw4UM2btzI3r172bFjBwDz5s0jJCTkne/xIZJCphBCCCGEEG/Q6/UEjxlL3PEThgmVCq9JP2GbP59izPob61lxdYUiPqT4EEp4ljB+o4cnYH5FeHRCmav8DZTqYXKNodEJtF54kuAow90jGrWKGa2KUDu/stAqhBBCfEqcbS35pUspcmRSdh8YtvESP+++gU5nZOejjRM0nAtvtIrl2Ey4sdPsPZvlbMa3pZTnZl96dokxx8codlo61a5Nxl49Fdc/mz6d6H37zd5LCCGEEErvej7mnTt3ePDgAWXKlKFkLLFOEgABAABJREFUyZKKfJEiRciYMeOrf0+rkPnll18ybtw4LCwsXuW++OILAIKCgti4cSPZsmV7lStVqhSurq4EByvP106vl5+9TJkyrFy50uDszho1alCxYkWSkpIIDAx853t8iKSQKYQQQgghxGv0yck8/e47Xhh5SzLTkK9wqlFDET/y5AjjTyp3XNTPVp82edoYuYkeTi2EZXUhxsibkqV6QaVhJtcYn6Sl2/IzPHlheA6nWgXTWhTmiwImdn8KIYQQnxhXeytWdS1Flox2ityMfbfpt/Y8Ccla5cAs5aFMnzeCeljTAjZ2hainJu/ZIncLRpQcoYj/fud3lgcuV8Td+/XDoWrVN26lJ2jIEGJPGHmZSQghhBAmLVu2DL1ej16vp2VL08ewvMnZ2RmVSsX+/fuZOXMmMTExZq+/cOECarWaggULKuIA336rfLEpKioKgKZNm5I3b16DnE6nIyYmxqBY+rZe7sgcP348Go1Gkc+ePTsAoaGhitzHTAqZQgghhBBC/EUbE8uj3n2I/HWjIufctAmunTsr4jcjbjLk4BC0esOHpAXdC/Jd6e9Qqd7Y7ZGSBL/1ge1DUs/kelPJHqkt794c9xedTs+gdRe4+DjSIK5Wwf9aFObLQl5pfEohhBDi05LJyYZV3UrjncFWkdt26SktFpwgNMrI+ZdVvwP3PMr45Q0wqwQcnw3aZKP3bJOnDe3ytlPEfz77M4ceHzKIqdRqvCb9hFX2bAZxXVwcD7t248WmzWY+nRBCCCHeB3d3d8aPH49Op6N///64u7tTt25dFi9eTFxcnMG10dHR3L17lxw5cmBnZ6eI+/j4GG1re/HiRQDq16+vyN2+fZukpCRFgTO9YmNjuXnzJp6engYtcF/38nP8nWLph0gKmUIIIYQQQgDJoaE8aN+O2MOHFTm7UqXI/P33iqLks7hn9Nnbh9jkWIO4t4M3M6rMwMbCxnAinQ5+6w0XVikXYGGT2ubui0mgNv1t+k+7rrMzUNmKZlyjAjQo7G3mEwohhBCfLu8Mtvzaqwx5MyvPzLz46AUNZh/ldugbOy8sbaDxfLC0V4whKRp2fQOLqkPMM6P3HFxsMOW8yhnE9OgZfmg4d18YnrGtcXDAd/Zs1E5vrC8lhafffEPo9OmKtrRCCCGEeL9GjBjBrVu3mDhxIqVKlWLnzp107dqVnDlzcu/evVfXXbx4Eb1er2gr+zJerFgxo/O/bP1aooTyeJmXOznf5lzPN++t0+lM3hvgzJkzqFQqxS7Sj50UMoUQQgghxGcv8e5dHrRsReLVa4qcfdmy+MyehcrKyiAenxJPv339CI41LCo6Wjoyp9ocMtq+8QakXg+7R6bu8niTsx903gWFW5td55pTD5l/8K4i3qNSVlqV9DM7VgghhPjUZXa2ZUPPMtTI66HIPY1MoN3ik4q27GQuBJ13gJeJh4pPL8CqJpAQpUhZqC2YVGkSWZyyGMRjkmPot68fUUmGY6z8/fGZNRO1vbJwGj53HkFDh6FLSjL7GYUQQgjx9wQEBDB8+HAOHDjA3bt3KV++PE+ePGHOnDmvrknrfMyiRYsq5tXpdFy6dAlXV1f8/f0V+ZdFznctZL4c7/TmS1F/OX36NLdv36ZUqVJ4eCi/F/qYSSFTCCGEEEJ81lLCwnjYqTPJQUGKnHPDhvjOn4fGwcEgrtfr+f7o9wSGBxrELVQW/FzlZ7JmyKq80dFpcGKOMp61MnQ/AF6FTa5Rr9ez8exjvttyRZGrk9+T4bVymxwrhBBCfE7srS2Y37YYPSopvxY/jUyg3aKThMckGiYyF4Kue6He/8Amg3LSpxdhXRtISVSknKycmFF1Bo6Wjgbxh9EPGXl4JDq9znB9JUviv3oVFp6eirmi/viDx337ok8x0npeCCGEEO+dv78/HTp0AECr/f/jYtIqZL4ZB7hx4wZxcXEmC5XmxqbHy/MxX985+pJer+frr78GUnedfmqkkCmEEEIIIT5b+uRkngwaTEpIiCLn1rsXmSeMR2VpqcgturyInfd3KuLfl/me0plLK290fiX8OVoZz1kH2mwEe9PnV7yIS6LvmvN8teEiKTrDlnOFfDPwc/PCqNXGz9MUQgghPkdqtYqv6+RhUpOCaN74Gnk3LJYOS08RnfDG2ZdqDRTvDP3OQRHl2ZfcOwSbuoFOq0gFOAcwudJk1CrDx2wHHh9g8eXFiuttcuUiy7p1WOdVns8Ze+gwIZMmpeNTCiGEECK9du3axfbt20l542WhoKAgpk+fDhiea5lWIdNYsTKtHZcXLlzA2tpacUbm6NGjUalUdOzY0exneDn/yZMn2bVr16t4cnIy/fv3Z+/evTRu3JgGDRqYnedjJIVMIYQQQgjx2QqdMpW406cNgxoNnmPH4N6/v+JMTIB9D/cx4/wMRbxT/k40ytFIeZMbO+D3/sq4b2lougQ0FibXd/R2GLWnHWbbpaeKnHcGWxa2L4atlcbkeCGEEOJz1ryEL1OaKc+IuvIkim4rzpCQrCxKYp8RGsyCSsOVuau/wbavUtvFv6GcdzkGFR2kiM+6MIvjQccVcUuPTGT55RccKldW5CJW/ELE+vXGP5QQQggh3tq6deuoW7cuHh4e1KpVizZt2lC9enUCAgK4cuUKI0aMoPJfX5NTUlIIDAzEw8MDz9c6KLyMZ8qUCW9vb8U9XhYajbWdDQ4OJjg4mPz582NhYfgMQKdL7d5gaeQl6peSkpIIDAzEx8eHpk2bUrduXapVq0azZs3IkiULs2bNokyZMixduvSt/9t8DKSQKYQQQgghPkuR27bxfPlyRdxz9ChcmjUzOuZmxE2+Pvy1Il7JpxIDigxQDgi+Ar92Bv0bD0rd80DrtWBlZ/Q+er2eSTuv02bRSYKjEhR5V3srlnQsQSZHG6PjhRBCCJGqUREfRn2ZVxE/cfc5/dacR6dTFiUBqPx16g7NN51dCvvHGR3SIV8HavjXMIjp9DqGHxquOFMbQG1vj8/sWTh9+aUiFzxmLLEnTxlfmxBCCCHeSqtWrejVqxe+vr6cPXuW9evXc/nyZWrXrs3u3buZMGHCq2uvX79OQkKCYjfmy7ipHZfmdmSaayv7Mte+fXuT6w8MDCQpKYkiRYqwfPlyevXqxdWrV9m6dSsuLi5MmjSJ/fv3mzw/82Nn+vVvIYQQQgghPlEJN27y9NvvFPEMzZqZLGJGJETQf19/4lLiDOLZnLMxscJENOo3dkbGPYe1rSHZ8HqcfaHdJrB1MXofvV7PhB3XWXDortF8+exuTGlWCE9nKWIKIYQQ6dGpXAARccnM2HvLIL7naghLjt6jawUjZ1urVPDFFIgLT92J+bpDk0FjBZWGvTFExZiyY7gVcYv7UfdfxSMSIxh8YDDLai/DSmNlOEajIfOPY0l+9Ij4vx5kApCSwpP+/cmyYT1Wfn7v8rGFEEII8ZcaNWpQo0aNtC8E8ufPj95I94XX4y93Ub7uzz//NDln7dq1jc6p1Wo5ePAg1atXp0KFCibHvzwfs2jRotja2jJz5kxmzpyZ5mf5VMiOTCGEEEII8VnRRkXxuF8/9PHxBnGbggXx+O5bo2OStckMPjCYJzFPDOJOVk7MrDoTBysHwwE6LWzsAi8eGMZtXaHtJnDyMrm+uQfvGC1iWlmo+a5eXlZ0LilFTCGEEOItDaqeg/Zl/BXxSbtucDMk2vggtQYaL4SAisrc/nFwaIoi7GDlwLQq07C1sDWIXw67zIRTExTXA6itrfGZNRMLr8wGcW1kJI969UYbbWJ9QgghhPionTlzhqioKMaMGWP2urTO3/zUSSFTCCGEEEJ8NvR6PU+//Y7khw8N4hpXV3ymT0NtZWV0zOjjozkTcsZwjErD1MpT8XX6P/buOsqKY90C+D42bowr7u7uEiRBkgBBgwUN7u7u7hIkeEgIIcEdgrs74+569P3BuzPU1AADQQbYv7XuWnN2V1V3vXfPTae/01U+8okOTwAeHREzhQpouQFwyf/S6/v13DPM3HdPygu62+LP3lXQpWouKJXyvp1ERET0agqFAuMbF0HDou5CrtUbMWDbVWj18psVAAC1OdBqM+CZwYPDI5OAU/OkOI9DHkysLD+Q3Hl/J7bd3ZbxaZyd4bN0KRRW4rLz2kePEDhsOEwZvPlBREREn7YKFSrAZDKhUqVKr2z34huZXyIWMomIiIjoixG9cyfiDhwQQ6USXnPnQuPhkWGfVTdW4c9Hf0r50HJDUdGjotzh5m/A6QVyXn8KkOvlS8XsuRaI0X/clPLS2R2wq1dlFHT/PPe6ICIi+lCUSgWmf1cc7nbiyga3AmOx+MiDl/QCYG77fEUF92LysUPjgTPy0m4NcjVAu0LtpHz6+em4EHwhw9NYFCwIr5kzni9r+4L4I0cQvmzZy6+PiIiIPltGoxHXr1+Hk5MTfHwy+CH1F4CFTCIiIiL6IqQ8foyQqfKSbq6DBsK6YoUM++x7sg+LrsgPJ38o8ANaF2wtdwi+AezuLeclWgMVerz02g7cCsbA7VeRfsuMgu62WNexPKzMuLU9ERHRu2BvpcGsFsWlfMmxR7jiG/XyjlaOwI9/Am5F5WMHRgOX1kvxwLIDUc69nJDpTXoMPDYQ/nH+GZ7Gtm5duAwYIOXhixYj7ujRl18fERERfZaUSiXi4+MRHh7+sS/lo2Ehk4iIiIg+e0atFgGDBkv7YlpXqwbHTp0y7HM19CpGnRol5VW8qmB4+eFQpHtbAnHBwOZWgC5RzD1KAt/Mk96uAACj0YT5h+6j28ZL0BnEKmZ2Ryts6Fwe9laa10+QiIiIMq1aPhdpv0yD0YRB268hSWt4eUcrR+DH3YBrYfnY3kHAszNCpFFqMKfGHHjZeAl5dEo0+h7ti8T09wz/z6nrT7Bt0EDKA4cMRcqTJy+/PiIiIqLPEAuZRERERPTZC5szFyl37giZytkZntOmQqGUb4nvRt5Fv6P9oDVqhTxftnyYXX021Mp0b0hqE4DNPwCx6d6usHIGftgEaCylc8Qk6dB1w0XMPyQvZedqa45NXSrANd3Sd0RERPRuDG9YELmcrYXscXgCOqw9j/shcS/vaO38/M1Ml4JibtQB29oBUc+EOJtFNiyotQCWavFe4EHUA4w4OQJ6o146hUKhgOeUyTDPl088RXw8/Hv3gSE+IRMzJCIiIvo8sJBJRERERJ+1+JMnEbleXu7Nc9pUqJ2dpfxM4Bl03NcRkcmRQu5k4YQltZfAxsxG7GA0AL91BYKuirlSDbT4BXCQ97C4GxyLpotP4fDdUOmYg5UGG7tUQHYnq9fOjYiIiN6OlZkac1qWgDLdggnnn0ai0YKTmLL3NuJT5CIjAMDG5Xkx0z7dP+MTI4AtrYEUsRBawLEApladKg1zxO8I+h7J+M1MpbU1vBcvgtJO3CNb++gRAocOhUmne/0kiYiIiD4DLGQSERER0Wcr4cwZBAweIuWOHTrAplo1Kf/j4R/4+dDPSNCJbzpYqCywuM5ieNh4yCc5OBa4t1fOGy8AcsnnuPg0Et8tPYOnEfJDy/xuNvi9VxUUcLd9xayIiIjoXSidPRt61cwr5XqjCatOPkGdOcew72ZQxp1t3YBWmwFNuh8ehd4CdnUHjEYhrpujLnqV6CUNczLgJDru64jQRPnHTWY5csBr1kxpefr4I0cQMHAQTFqt1IeIiIjoc8NCJhERERF9dkwmEyLW/QLfn7rCGBMjHDMvXAgugwZK7ZddW4Yxp8dAbxLfvlAr1ZhRfQaKOheVT3RhDfDvYjmvNggo1U6K7wXHofMvF5CYwf5bXxf3wO+9qkjL3BEREdH7069uPnxbyivDYyGxKeix6TJ+Pfcsw+PwKA58u1zO7+0Fjk6R4u4luqNejnpSfifyDtrsbYP7UfelYzY1asClX18pjzt4EP79+sPIYiYRERF95ljIJCIiIqLPijE5GYFDhyF0xgzpbQiFpSW8Zs+B0sxMyGdemImlV5dKY9lqbLGy3krUzl5bPtGjo8Df8tueKPIdUGu0FPtHJeLHtecQmywWSpUKYFSjQljcuhSszdVSPyIiInp/NCol5v1QEqt/LAsfR3lPawAY/cdN/HPjJW9mFm4K1Bwh5ydnA3f2CJFSocT0atPxTe5vpOYhiSH48Z8fcS7onHTMqVs32DZsIOXxR4/Cv3dvGFNSMr42IiIios8AC5lERERE9NnQhYTgWZu2iN2zRzqmtLKC9/x5MM+dS8h3PdiFTXc2Se3drd2xoeEGlHMvJ58o4hGwoyNgSvdmpXc5oNlSQCneZkcmaPHj2vMIiRUfNFpqVNjYpQK6Vs8NRbpl44iIiOjDqVvYDQcH1EC/Ovlgphb/OW4yAf22XsWZR+EZd64+9HlBM73fewLhD4XITGWGqVWnokeJHlLzBF0C+h7pi3uR94RcoVTCa+ZM2DaQi5kJJ07Cv2cvGJOSXjNDIiIiok8TC5lERERE9FkwxMTAt0sXJN++LR3T5MiOnNu2wqZGDSG/FX4LU87KS7/lz5YfmxpuQt5s8r5ZSI4FtrQGkqPF3CEH0GoLoBHf5khI0aPTLxfwOEzcd1OtVGB5+zKoktc5cxMkIiKi98pCo8KAevlxoH91ZHcU977UGozotuESbgbEyB2VSqDZMsC9mJhr44Dt7QGteA+gUCjwc8mfMbnKZKgV4moMifpE9DnSB+FJYtFUodHAa/Ys2H0jv82ZcOYM/Pv2456ZRERE9FliIfMDMhgMuHnzJn755Rf06dMHlSpVgpWVFRQKBRQKBcaPH//GY+7btw8//PADcuTIAQsLC7i6uqJKlSqYN28eEhISXj8AERER0WfAqNXC/+fe0D58JB2zrlYNubZvh3m+fEIelRyFAccGQGsUH/qVci2F9Q3Ww83aLYMTGYDffgLCxTclYGYDtNkG2LgIscFows+bL+OaX7Q01OwWJVAjv4uUExER0ceV09kaG7uUh7ONuZDHp+jRYe15PAnP4HmLmTXQciNgYS/mobeBPf2ev9aZTtO8TbG83nLYaGyEPCghCAOODkCKQVzJQaFWw3PGdNg3ayaNlXDyJAKGDYPJIO/DTURE/40pg/8NJ/ocZdX/rrOQ+QG1bNkSxYoVQ6dOnbB48WKcPXsWSW+59EdKSgpat26Nhg0bYvv27fD19UVKSgrCwsJw5swZDBw4ECVKlMD169ff8SyIiIiIshaT0YjAYcOQePGidMypa1f4LF8Glb34UNFgNGD4yeEIShD3u3K1dMXcmnNhYyY+UEx1eCLwYH+6UAF8vxpwLSQ1n7X/Ho7dC5PyMd8URrNSXq+eGBEREX00OZys8UuncrBJt391RIIW7Vafg19kotzJMRfw3Wo5v7EDOL8qw/NU8KiABbUWSG9mXg27iglnJkgPFBUqFTymToFDi+bSWHH/7EPwhIlZ9iEkEdGnRPnCdiEG/kiEvhAv/nddqcw65cOscyVfgPT/g+fo6Ih86d4MyKwOHTpg69atAAAnJyeMGDECmzdvxsKFC1G+fHkAwKNHj9CgQQP4+fn9twsnIiIiysJCZ81G3D/7pNxl0EC4DhoIhUolHVtydQnOBJ4RMrVSjTk158DZ8iVLvV7fDpyeL+d1xgAFGkrxX9cDsfy4/IZojxp50KVqLiknIiKirKWolz1W/VgWZirx8VlAdBJarzqLgOgMfpye/yugxjA53z8C8D2X4XnKe5THiAojpHzP4z1Yc3ONlCuUSrhPmAD7b7+VjkVv346wuXNfMiMiIsosMzOz1L/j4+M/4pUQfTgv/nf9xe/Ax8ZC5gdUvnx5DB8+HDt27MDjx48RERGBkSNHvvE4u3fvxrZt2wAA2bNnx+XLlzF16lS0bt0affr0wb///otOnToBAIKCgjBw4MB3Og8iIiKirCJywwZErlsn5dnatIHTTz9l2Oe433GsuiG/FTG03FCUdC2Z8Yl8zwK7f5bzot8DVeV7rTtBsRiyQ14Zo1lJTwxrUCDjcxAREVGWUymPExa2LgmlQsz9o5LQeuVZBMVkUMysMQzIU0fMjPrn+2XGBmZ4npYFWqJdoXZSvuDyAhz1PSrlCqUSHpMmwrZePelYxKrVCF+V8RugRESUOXZ2dql/R0VFwWg0fsSrIXr/jEYjoqKiUj+/+B342FjI/IBGjhyJadOmoXnz5siV6+1/hf/iXprLli1D9uzZheNKpRJLlixJzXfu3ImbN2++9fmIiIiIsqLo3/9AyLTpUm5Ttw7cRo2EQqGQjoUlhmHM6TFS/k3ub9CqQKuMTxT5GNjaBjCIe2nCoyTQZDGQ7jzRiVp023gRSTpxNY5iXvaY/n3xDK+LiIiIsq4GRT0ws3mJ9P/Ih29kIlqvPIvgmGTxgFL1fNl5e/F5DeJDgK1tAV3G2wwNKjsIVbyqSPnIUyPhG+sr5Qq1Gp5zZsOqUkXpWNicuYj+449XzouIiF7O1tY29e/k5GQ8e/YM0dHR0Ov1XMKbPhsmkwl6vR7R0dF49uwZkpPT7mle/A58bOrXN6Gs5MGDB7h69SoAIF++fGjUqFGG7SwtLdG1a1eMGfP8Qd327dtRtGjRD3WZRERERO9VzF97ETRqFJDuXyAtS5aE1+zZGS4nazQZMerUKESlRAl5/mz5MbbS2IwLjElRwK8tgcQIMbdxB1ptBsyshNhgNKHPlivwixQfUDpZm2F5+zKw0MjXRURERFlf8zLeMBiNGPbbDSF/GpGINqvOYmv3inC1tUg7YOUItFwPrG0AGFLS8sDLwJ99ge9WSj+GUivVmFV9Ftr/3R6PYtKWp4/XxaP/sf74tdGvsFRbCn2UZmbwWbwYzzp3RvI1cTWIoNFjoHF3h3VFudBJRESvZmZmBjc3N4SEhAB4XswMCgr6yFdFWYW0h/Vn9oNlNzc3Li1Lb2///v2pf9evX/+VbRs0aJD697598r5RRERERJ+i2H37EThsGJBuaR+zHDngvWwplBYWGfbbeHsj/g36V8gs1ZaYW3Ou9FAQAKDXAtvaAxEPxFxjBbTZCth7SV2m/X0HJx+EC5lKqcDiNqXh5ZDBOYiIiOiT8UO57Jj6bTEpfxyegG4bLiE53WoM8CoNNFkkD3RjO3BmYYbnsDWzxaLai2CrEd+CeBD1ABP/nZjhW0BKa2tkX7EC5vnyiQf0evj36Yvk+/dfPTEiIsqQo6Mj3NzcPvZlUBZlNBo/yyWH3dzc4Ojo+LEvQ8BC5ifmxSViy5Qp88q2JUuWhOr/30a4ffs2X3knIiKiT17c4cMIGDwYMIgPCtUeHvBZswbqbNky7Hc74jbmX54v5aMqjEIOuxxyB5MJ+GsA8PRkugMK4LtVgGcpqcvGs8+w+tQTKR/9dSFUyuP00jkRERHRp6NNheyY1Exe8eqqXzRG7rohP3sp8QNQua880MFxwP0DGZ7Dx84H06pNk/K/Hv+Frfe2ZthH5eAAn1UroU73wN0YFwe/7j2gCwl9yYyIiOhVHB0dkSdPHri6usLCwgJKJUsq9NznUshUKpWwsLCAq6sr8uTJk+WKmACXlv3k3H/hV3Q5c+Z8ZVu1Wg0vLy/4+voiISEBAQEB8Pb2fuNzXrp06ZXH79y5k/q3Xq+HTqd743MQ0buj0+mg1+tT/yaij4/fy3cj4dQpBPXrD/z//y3/R+XqCq81q6Fwc83w/75J+iQMPT4UeqPY76vsX6Fh9oZyH4MWqr0DoLyxTRrLUGc8jHnrA+n6nHgQjvF/3pLaf1vSA23LefH/71kMv5NEWQu/k/SpaVXGEzqdHhP33hXyXVcCkM/VGj9VzSl2qDEKqpDbUD469EJogmlnJxhaboIpR1XpHJXdK+OnIj9h9a3VQj7zwkzks8uHEi4l5AtzcoLHkiXw79ABpoSE1FgfFAS/Ht3h9csvUFpZyf0ywO8lUdbC7+THpVAoYGdnBzs7u499KZRF6HQ6REZGAnhe7NZoNB/5it6dt/3fGH26ZzXvEguZn5jo6OjUv52dnV/b3snJCb6+vql936aQWbZs2Uy3jYmJQURExOsbEtF7o9frERcXl/pZreb/1BN9bPxe/neG4GDEDR4iFTEVjo6wnj0LsVZWQAb3IEaTEbNvzsazuGdC7mrhip55e6b+i0fqeClxcDjQF5qAM9JYiYV/QGzeH6TzPAxPQp/td2Ewim9gFPOwRr+q7tI56OPjd5Ioa+F3kj5FjfJZ40FpV2y5LL7pOHP/fbhZGFE5l72QK6pPg1PEI6ij01ZvUGjjodrcAjE1pyA5f1PpHM29muNK8BVcikj7gbneqMfA4wMxs9xM5LTJKV+YsxOsJ4xH/LDhwgoWKXfuwm/AQFhPngRFJt4m4veSKGvhd5Ioa+F3UvZi7epd43vQn5j4+PjUvy1esv/Tiywt0/ZievGLRURERPSpMBkMSJw6TXizAAAUDg6wnTsHquzZM+yXoEvAuCvjcDDwoJArocSI4iNgo7ER8/gQOP7ZFuYZFDFTvKsitsoYQKEQ8vAEHQbtfoBErbicjKedGWY2zgMLNW+3iYiIPlc/V/VGxRzi2zkmAGP+eYwnkUlibm6HqAZLYTQT975UGHVwODIU1hcXP1/a/gUqhQoji4+Em4W4XGyUNgqDzw/Gw9iHGV6XpmxZWA0eJOW6M2eQvGlTZqdHRERElCWwTEyvdfHixVcev3PnDtq3bw8AsLe3h5MT94Ai+phefP3/c1vagOhTxe/lfxO5ejX0168LmdLWFl6rV8O8QP4M+zyNfYqB/w7E09in0rGfiv6EGnlriGHYXah3t4IiLlBqb8xTF8rvVsPJTCx8JmkNGLHjAkLixGVXbC3UWNOhLPK6iu0p6+B3kihr4XeSPmVL2tqjxcpzeByemJolaI0Y9tcT/N6jIuwsX/jvs5MTjC3WQ7G9PRQ68QdathcXwVobDkOjOYDKLK0LnDCnxhx0OtgJOmPadyVGF4OhF4diSa0lKOos79mJdu0QERODqOUrhDh53S/IVq4crKvKy9m+iN9LoqyF30mirIXfSZmDg8N7G5uFzE+MjU3aA7Hk5OTXtk9KSvsFoK2t7StavlyZMmUy3VatVvNLS5QF/G85A41Gw+8kURbB7+XbSbpxA5FLlkq5x6SJsClaJMM+x/2OY/jJ4YjXxUvHyrqVRc9SPaFWvnAbHBcMbG4OxAfLg5X+Ecqv50GpEm+bTSYTRu64gRsBsUKuViqwol0ZFPLKlonZ0cfE7yRR1sLvJH2qnDQarO5QDk2XnEZcctoS+L6RSRj2+y2sbF8WSuULKzrkqwN0/gfY/AMQFySMpby+BcqUWOCHjYBSlZqXcC+BmdVnYsjxIdCb0s4Rp4tDz6M9saTOEpRxk5/duPXrB/2zZ4j7Z19aaDIhZPgI5PptJ8xes/0Qv5dEWQu/k0RZC7+Tove5vC7XuvrEvFjVDg8Pf237F/erfJ8VcSIiIqJ3zZiQgMAM9sW0b9YMdg0aZNjnt/u/oc+RPhkWMevnrI8ldZaIRUyDDtjRMeMiZq3RQOOFgEq+GV967BH+uh4k5VO/LYbKeV+/jzkRERF9PnK72GBJm9JQiivQ49CdUCw7/kju4FEC+Okw4JbBm5T39gKHxklx3Rx1Mb/WfGiU4oPSBF0Ceh7qiQvBF6Q+CoUCnpMnwyxPHiE3xsTAv29fGDPxA3kiIiKij42FzE9M/vxpy6c9ffr0lW31ej0CAgIAANbW1vDy8nqfl0ZERET0ToVMnwHts2dCpvHxgdvoURm2P+F/AhPPToQJ4v5SCijQv3R/zKo+C1YaK7HTwbGA779iplQDzZYBNYZIe2ICwIFbwZi1/56U96iRBy3L+WRiZkRERPS5qZ7fBSMaFpLyOQfu4dSDDH6Ibu8FdPoHyFNHPnZmEXB5oxTX8KmBxXUWw0JlIeRJ+iT0OdIHtyJuSX2U1tbwXrQQSmtrIU+5fQfBEybClG5fTiIiIqKshoXMT0zRomm/1rt06dIr2169ehUGgwEAULhwYSgyeBBHRERElBXF/LUX0Tt2iKFKBc+ZM6CykfeevBVxC4OPD4bRZBRyWzNbLK27FF2KdZHvhW7sBM7Ky9ai8QKgZJsMr+tucCwGbLsq5XULuWJo/QKvnBMRERF93n6qlguNirkLmdEE9N16BYHRSXIHCzugzTagyLfysb8GAE9PS3Flz8pYWncprNTij7MSdAnoebAnnsQ8kfqY584Nj2lTpTzm998Rs3v3a2ZFRERE9HGxkPmJqV+/furf+/fvf2XbffvS9kBo8JLl14iIiIiymsQLFxA0YoSUO/fsCatSpaQ8MD4QvQ/3RpJefEDoZeOFLV9vQVWvqvJJQu8Af/aR8zKdgFLtMryuyAQtum64iAStQcjzu9lg3g8lxf2viIiI6IujUCgws3kJ5HYR336MTNCi56+XkaI3yJ1UmucrQXil2+PSqAO2tQMi5cJkOfdyWFFvBaw14nmiUqLQ/WB3BCfIS+bbffUVnLr+JOUh06ZDn4mti4iIiIg+FhYyPzH58uVDqf9/gPfgwQP8888/GbZLTk7GqlWrUj+3bNnyg1wfERER0X+R8vgJ/Hr3gUmnE3LLkiXh3KO71D5WG4teh3ohPEl8AGdnZodldZchh10O+STJMcDWtoAuUcy9ygANZ2R4XVq9ET03XYJfpFgsdbDSYNWPZWFrocmwHxEREX1ZbMzVWNGuDKzMVEJ+zS8a4/+8lfFSrhpLoNVmwC7dlkBJkcCWVs/vXdIp6VoSi2ovgpnSTMiDEoLQ/WB3RCdHS31c+vWDVcWKQmaMiUHwlCmZmxwRERHRR8BC5ido3Li0Td979uwJX19f4bjRaMTPP/+cmjdv3lxYkpaIiIgoK9JHRMCvWzcYY8SHdSoXZ3jNnQOFWi3kKYYUDDg6AI9iHgm5RqnBwtoLkcs+l3wSkwnY0x+IFPvAyglouQFQm2fQxYThu67j3JNI8bqUCixtUxo5nKylPkRERPTlyudmixnfF5fyLef9sOnsswx6ALB1B1pvAdLv5x12F/i9J2A0Sl3KuZfDrBqzoFSIj/cexzxGr8O9kJjuR1sKtRqe06dJ+2XG/bMPcUeOZGJmRERERB+e+vVN6F158uQJ1qxZI2TXr19P/fvIkSPQ6/XC8e+//z71Dcz/adq0KX744Qds27YNz549Q+nSpdG9e3cUK1YMERER2LBhA86fPw8A8PDwwNy5c9/TjIiIiIjeDWNSEvx69oLO31/IFVZW8Fm+HBpPTyHXGXQYdGwQzgefl8aaWnUqyriVkXIAwNXNwK1dYqZQAs3XAfbeGXZZfOQhdl0OkPJxjQujcl7nV8yKiIiIvlSNS3jiim801p4Wl4adsOc28rjaoHKeDO4hPEoA3618vqTsi+7tBU7PB6oNlLrUzl4b4yuNx9gzY4X8RvgNDDsxDPNrzYdKmfZ2qMbdHa5DBiN4/AShffD4CbAqVw4qW9s3mygRERHRe8ZC5gf07NkzTHnFch0nT57EyZMnhSxv3rxSIRMA1q9fD4VCga1btyIiIgJTp8qbtufJkwe7du2Cj4/Pf794IiIiovfEZDIhcOgwJL/wAy8AgFIJr7lzYFmkiBDrjDoMOTEEx/2PS2P1L90fDXK9ZG/wiEfA30PkvPYYIHeNDLv8eS0Qcw7el/L2FXOgfcUMlq0lIiIi+n8jGhXE7aAYnH2ctqqD3mjCz79exp+9q8LH0UruVKgxUGcscHiimB+ZBHiVBnLXlLp8m+9bxGpjMfvibCE/5n8MMy7MwIjyI6BQpO3l7dCyJWL/2ovEixfTris0FKGz58Bjwvi3misRERHR+8KlZT9R5ubm2LJlC/755x+0aNECPj4+MDc3h7OzMypVqoS5c+fi2rVrKF5cXsqEiIiIKCuJ3rYNcQcPSrn7mNGwrVlTyPRGPUaeHInDvoel9i3zt0Tnop0zPoleC/zWBdAliHnuWkCV/hl2ufQsEoN3XJPymgVcMK5xYeGBIBEREVF6GpUSS9uWgXc2SyGPStThp/UXEZ+iz7hj1YFAwW/EzGQEdnYGYvwz7NKhSIcM74O23N2CTXc2CZlCqYT7pIlQmIn7a0Zv24aE8/JqF0REREQfEwuZH1DNmjVhMpne6D8dO3Z85ZgNGjTA9u3b4evri+TkZISFheHMmTMYMGAArK25XxMRERFlbdqnTxEyY6aUO3bpjGytWwuZwWjA2NNjse/pPql949yNMariqJcXF49NBQKviJmVE/DtckAp3xLfCoxB1w2XoNWL+1EVdLfFotaloFbxNpqIiIhez9HaDKt+LAsrM5WQ3wuJw8BtV6E3yHtfQqEAmi0DnPKKeWIEsP1HQJ+S4bn6le6HhrkaSvmsC7Nw+Jn4IzDzXLng3Lu31DZoxEjoQkJfMysiIiKiD4dPYIiIiIjoozDp9QgYNgympCQht6lZE66DBkntZ1yYgT2P90h5g5wNMLHKRCgVL7m1fXICODVfzpsuBWzdpfjE/TC0XP4vIhO0Qu5ia441HcvB1kLz8kkRERERpVPIww5zW5aQ8gO3Q9B1w0vezLSwA37YBGjS/Ug94NLzpfJNJqmLUqHE5CqTUdq1tJCbYMKwk8NwPUxcxt+pU0eYFyokZLqAAPh26gR9REQmZ0dERET0frGQSUREREQfRfjKlUi+Jj5QUzk5wWPKZCjSvSV5+NlhbLm7RRqjbva6mFptKtTKl2z97nsW2NEJQLqHfeW6AgXkvTR3XPRD518uIEFrEHILjRJrOpSFl4Ol1IeIiIjodRoU9UD/uvmk/Oi95z+gCo5Jlju5FgKaLpLzy+uBvwcDRvltTjOVGRbUWoCcdjmFPMWQgv5H+yM8KTw1U2g08Jg8CVCJb4tqHz9GYLfuMMbGZm5yRERERO8RC5lERERE9MEl3biB8CVLpdxj0iSonZyELDQxFOP+HSe1reldEzOrz4RG+ZI3JC+uA375BkgMF3OXQsBXk4TIZDJhwaEHGLLzOvRGseipUSmwsFUpFPd2eP3EiIiIiF6ib+18aFRMXg3idlAsvl16GneCMigcFv0eqNhLzi+sBv7sAxgN0iEHCwcsrbsUjhaOQh6WFIbBxwdDZ9SlZpZFisB93FhpDO39+4gfMhSm+PhMzIyIiIjo/WEhk4iIiIg+KGNSEgKHDgMM4oM3hxYtYFu7ltjWZMToU6MRkxIj5MVdimN2zdnQqDIoYuq1wJ7+wF/9gRce1AEAVOZA8zWARnyzcuJftzHv0H1pKFsLNTZ0roCvisgPHYmIiIjehFKpwLwfSuLbUl7SsaCYZLRY/i9OPwyXO9abCOSsJudXNwG7ugIGnXTIx9YHi2ovgrnKXMgvhVzC/EvzhSxby5ZwGzFcGsNw7x7ihg+HMTHx1RMjIiIieo9YyCQiIiKiD8ak0yFw+AhonzwRco2PD9yGD5Pab7q9Cf8G/Stk1hprTK82XXowBwBICAfWNwYurZOPqS2A71cDbkWEeNsFX6w7/VRq7mlvgd96VkalPE7SMSIiIqK3Ya5WYW7LEuhbR15mNj5Fj24bLspvZqo0QOutGRczb/4G7OgI6FOkQ8VdimN0xdFSvuH2Bux7sk/IHDt0gMuAAVJbw81bCBk5CqYM9uQkIiIi+hBYyCQiIiKiD8Kk1SJg4CDE7d8vHlAq4TljBpTW1kJ8L/Ie5l+eL40zovwI+Nj6yCdIiQc2fQ/4nZWP2XkDnfcDhZsI8XX/aIzZfUtqXtDdFrt6VUF+N9vXzouIiIjoTSgUCgyslx+zW5SAWqkQjiVoDfhp/UWExaUrTJrbAG13AHnrygPe/Qv4rUuGy8w2y9sMLfK3kPKxZ8biYdRDIXPu3g1OPXtIbRMOH0bEylWZmBkRERHRu8dCJhERERG9dyatFv4DByLu4EHpmFPXrrAqXUrIUgwpGH5yuLCHEwB8leMrNMkjFiMBAAb98wd4QVflYzmqAN2OAZ4lhTgyQYuemy5DqzcKedkc2bCjRyW421tkZmpEREREb6V5GW9s6FwetuZqIQ+ITkKPTZeQrEtXmNRYAq02AwW+lge7s+f5svoZvDk5vPxwFHUqKmRJ+iQMODYA8VpxD0yXvn3h2KmTNEbY/PmIP3kqcxMjIiIieodYyCQiIiKi98qo1cK//wDEHzosHbOtXx8ufXoLmclkwvgz4/EwWnxLwNXKFWMrjYVCIb65AJMJ+GcocF9cIg0AUL4b8ONuwMZFiA1GE/ptvYKA6CQhd7Mzx7J2ZWBrkcHem0RERETvWOW8zljxYxnpzcxLz6IwctcNeUlXtTnQcj1Q5Dt5sMsbgMMTpNhMZYa5Necim3k2IX8a+xRzLs0RMoVCAdchg2Fdu7Y4iMmEgMGDofXzy/zkiIiIiN4BFjKJiIiI6L0x6fUI6Ncf8UeOSMfsGjWE15zZUKjFtxCWXluKvx7/JWQKKDCt6jTYm9vLJzmzELi4Rs6rDQIazXq+r1Q68w7ex8kH4UKmUSmwtG1puNhmsPcmERER0XtSOY8zJjQtIuW7rgRg6bFHcgeV5vm+34WbycdOzQNOL5RiDxsPzKg+A0qF+Chw5/2duBB8QcgUSiXcpkyGMnt2ITfGxMC/dx8YExNfPykiIiKid4SFTCIiIiJ6b8IWLED80aNSbvf11/CcOVMqYu5+uBvLry2X2ncs2hHlPcrLJ7j5G3BwrJwXawnUHpPhNR2+E4LFRx9K+ZhvCqNMDseXzISIiIjo/WlbIQc6Vckp5bP238OBW8FyB6UK+G4lkLuWfOzgGODKJimu5FkJPUv0lPLxZ8YjWZ8sDm9jA5tJkwArKyFPuXcPQaPHwGQUl+YnIiIiel9YyCQiIiKi9yLu0CFErFot5XaNG8NzxnSpiHk+6DzG/zteal/Nqxr6luorn+DREeB3+WEcclYDmi4G0i9BCyA0NhlDdl6X8m9LeaF9xRwvnwwRERHRezaqUSHULOAi5YO2X8OT8AS5g9oc+GET4FVGPvZnH+D+ASnuUqwLCjoWFDLfOF8su7ZMaqvKkR3WI0dIeezffyN4wkQWM4mIiOiDYCGTiIiIiN457dOnCBwuP/iy+/preE6fJhUxH0c/Rv+j/aE36oW8oGNBzK4xG2ql2B5PTgJbWgOGFDF3LgD8sPH5g710jEYTBu24hsgErXgOd1tM/baYvPcmERER0QekVimxsHUp5HO1EfK4FD16brqERK1e7mRuA7Td+fwe6EUmI7CjIxB0TYg1Sg0mVJ4AlUIl5OtvrcftiNvS8GZVqyJbt25SHr1tG4InTpT38CQiIiJ6x1jIJCIiIqJ3ypiUBP++/WCMjxdyi8KF4TF1ChQq8cFZvDYePx/+GXG6OCF3tXLF4tqLYaURlzSD71lg8w9AuiXQYOMGtNsJWGbL8LrWnHoi7YtpZabCsnZlYGmmyrAPERER0YdkZ6HB6g5lYWch/ojrbnAcRuy6kXHh0MoRaP87YO8j5rqE5/dMMf5CXNipMH4s8qOQGUwGjD8zXvpRGQA49uoJm5o1pTx6K4uZRERE9P6xkElERERE74zJZELw+PFIuX9fyJV2dvBauABKc/lNyRkXZsA/XnzAZqW2wtI6S+Fm7SY29r8IbGr+/MHciyzsgbY7AIfsGV7XzYAYzNx/V8onNi2KXM7WmZgZERER0YeRw8ka81uVlPLdVwOx4d9nGXey9wLa7Xp+T/SiuCDg15ZAcowQ9yrRC9ltxfumO5F3sP7WemlohUoFr/nzYF25knQsestWhEyaxGImERERvTcsZBIRERHROxO9bTtidv8p5Z4zZ8DM21vKDz87jD8e/iFkKoUKc2rOQQHHdEukBV4FNn4HaMU3N2FmC7T7HfAokeE1JWr16LvlCnQG8QFb4xKe+L6012vnRERERPSh1S7ohr6180r5pL9u49KzyIw7ueQHWm0GlBoxD70FbO8AGHSpkYXaAuMrj5eGWHRlEY76HpVypYUFvJcsgVWlitKxqM1bELZw4asnRERERPSWWMgkIiIiondC++wZQqZPl3Knnj1gm8FyZOFJ4Zjw7wQp/7nkz6jqVVUMEyOf74mZIr5NAI010O43wLtMhtdkNJow5o9beBwuvsHp5WCJyc2Kcl9MIiIiyrL61c2P6vldhExvNKH7xku4FxyXcaecVYFmS+X88VHg78FCVM69HL7P972QGUwGDDo+COeCz0lDKC0t4bN0KawqVJCORaxYiaTr118zIyIiIqI3x0ImEREREf1nJqMRgaNGwZQs7ltpXbkSXHr3ltubTBh7eiyiUqKEvKRLSXQu2jl9Y2BPPyAuUMzVlkDb7UB2+WEaAMQm69B1w0X8dllctlapABa0Kgl7S02G/YiIiIiyApVSgQU/lISXg6WQh8dr0Wrlv7gZEJNxx+Itgdqj5fzSL8DlDUI0sOxAeFh7CJnOqMOA4wNwM+qmNITS0hI+y5bCqnx58YDRiKBRo2DUal87LyIiIqI3wUImEREREf1nUb9uRtLFS0KmcnaG5+zZUKhUUvsd93fgZMBJIbNSW2FqtalQKdO1v/orcCfdcrUqM6D1ludvHWTgYWg8mi05jcN3Q6VjfWrnQ9mcjpmYFREREdHHlc3aDMvalYaZWnyEF5WoQ5tVZ3HFNyrjjtUGA6XayfnewUDgldSPdmZ2WF5vORwtxHujZEMyRl0ehXsx96QhlFZW8Fm+DGa5cgl5yoOHiFi+PJMzIyIiIsocFjKJiIiI6D/R+voidO5cKfcYPw5qR7lg+DTmKWZfnC3lw8sPh4+tjxhGPAL+GSaftN5EIE+tDK/n0O0QNFtyGo/DEqRjFXM7ok8G+00RERERZVXFvR2wsFVJqJXikvixyXq0W30O5x5HyJ0UCuCb+UCOdD/6MqQA2358vmz//8ttnxsr6q2ArZmt0DRRn4gRl0bgccxjaXillRU8pkx+fp4XhK9cheQ7d95sgkRERESvwEImEREREb01k9GIoFGjYUpKEnK7r7+Gbd26Unu/WD90PdgVSXqxfS2fWmiWt5nY2KADdnUDtPFinqc2UL57htez5bwvum68iPgUvXTsm+IeWNuxHNQq3gITERHRp6VBUQ+saF8GZunuYxK0BnRYdx5nHoXLnVQaoMU6wFZcOhYxvsBvPwFGQ2pU0LEgltVdBku1uIxtnC4OA44PQEz6fcoBWJUujWzt0731qdc/325Ap3uzCRIRERG9BJ/iEBEREdFbi9qyBYkXLgiZyskJbqNHSW2fxDxBx30dEZwQLOSOFo4YV2kcFOl+0Y8Ts4CAi2Jm6Qg0WwYo5dvYw3dCMOr3GzCZxFyhAIY1KIhFrUvBykyd+ckRERERZSF1CrlhTceysNCI90HJOiO6rr+I6/7RcicbV6DFekCZ7h7o0WHg2HQhKuFSAkvqLIG5ylzI/eL9MPzkcBheKHz+j2v//tB4ewtZyu07iFizNvMTIyIiInoFFjKJiIiI6K2kPHqE0DnykrLu48ZCnS2bkD2KfoRO+zohNEncs1IBBSZVmQQnSydxkKennhcy02u6GLB1l+Ib/jHovfkKjOmKmHYWaqzrWA49a+aRC6VEREREn5hq+VywvlN5WJuJe4onaA3ouO4CHoXFy52yVwDqT5PzEzOBh4eFqJx7OcytORdKhfjI8FTAKSy5ukQaQmllBY9JE6U8fMkSJN++nYkZEREREb0aC5lERERE9Ma0z57Bt2MnmBIThdyuUUPYffWVkN2LvIfO+zsjIlncv0mpUGJK1Smo7l1dHDz8AbC1LWAyinnpDkDBr6Vr8Y9KROf1F5CkE98S8M5mid29q6JmAdc3nB0RERFR1lUhtxM2/lQBNubiW5aRCVq0X30OgdFJcqfyXYFiLeV8Tz8gRSx+Vveujv4l+0tNV91YhYPPDkq5daVKcGgpjm3S6eDXoyd0wcFSeyIiIqI3wUImEREREb0RXUAAnnXqBH1YmJCrHB3hNnq0kPnF+eGnAz8hMjlSbKtQYXq16Wicp7E4eEI48GsLIDlazB3zAA3kNwliknTotO4CwuJShNzBSoP1ncsjl7P1m02OiIiI6BNQOns2rO5QFmZq8dFeYEwy2q85h8gErdhBoQAazwdci4h5jB9wZJI0ftuCbVHHo46Ujzo1Cg+iHki565DBULuLq2boQ0Ph170HDPEZvCVKRERElEksZBIRERFRpulCQvCsYyfoA4PEAyoVPKdNhdrRMTVK1idj4LGBiE6JFpqqFWrMqjELDXM1TDd4MrC1DRD1RMw11kCLdYCZWJRM1hnQY+MlPAgVH46ZqZRY2b4s8rjYvNUciYiIiD4FFXM7YUmb0lApxeXzH4UloNO680jU6sUOZtZA8zWAUiPm51YAfueFSKFQoH+R/shjm0fIk/RJ6HukL0ITxe0CVLa28Jo1E9CIY6fcu4eAAQNh0qe7FiIiIqJMYiGTiIiIiDJFHx4O346doPPzEw8olfCaNRM2NWqkRiaTCZPPTsbdyLtCU41Sg7k156JejnriGEYj8EdPwO+cmCuUQPO1gEcJIdbqjej162X8+1hcrhYA5rQsgfK5HKWciIiI6HNTr7AbZnxfXMqv+ceg75YrMKTfQNy1EFBtULrWJuDPPoBeXOHCQmWB8aXGw8HcQcj94/3RZX8XqZhpVa4cPKdMlq4l4eRJBE+aDJPJJB0jIiIieh0WMomIiIjotfRRUfDt3AXaJ0+kYx5TpsCuUSMh2/lgJ3Y/2i21nVF9BmplryWf4Ohk4NYuOW8wAyjQQIh0BiN6b76MI3dDpebDGxZE4xKer5kNERER0eejeRlvjP66kJQfuhOKiXtuyQXEagMBl4JiFnYXODlXGsPd0h3Tq0yHUiE+Qnwa+zTDYqZ9kyZw7tNbGid62zZErl2XyRkRERERpWEhk4iIiIheyRAbC78uPyHl/n3pmPv48XD4tpmQ3Qi7gWnn5P0suxTtIr+JCQAPDwEn58h5hZ5AhW5CpDcY0X/bVRy4HSI1b1cxO7pXz/3qyRARERF9hn6qlhs9auSR8vX/PsOaU+l+iKY2B5osAiAuSYuTc4DQO9IY5d3LY1SFUVL+smKmc69esG/WTGofOmcOEi9efO1ciIiIiF7EQiYRERERvZQhPgF+Xbsh+fZt6ZjbyBHI1uoHIYtMjsTA4wOhM+qEvIJHBfQuJf86H4mRwB8/y3mBr4H6U8RrMZowZOd17L0eJDX/rrQXJjYpCoVCIR0jIiIi+hIMrV8ATTJYmWLK33ew72a6+yef8kCF7mJm1AG/9wBSxP3HAaBlgZYYWWGklP+vmBmWGJaaKRQKeEycAKsKFdKNb0TAkKEwREdnek5ERERELGQSERERUYaMSUnw79EDSdeuScdcBg6E448/CpnJZMLIUyMRnBAs5G5WbphZfSbUSjXSdQD+GgDEi+3hVgz4fhWgVAnxhD238PuVAOlaGpfwxKzmJaBUsohJREREXy6lUoFZLYqjfE5xr3CTCei39Sou+0aJHWqPAeyzi1nQVWBrG0CfLI3fumDrlxYzBxwbAIPRkJopzMzgvXABzHLkENrqg4IQOHo098skIiKiTGMhk4iIiIgkxpQU+P/cO8Plv5x79YJzt65S/vvD33E64LSQqZVqzK05F44WjlJ7XN8O3P5DzNQWwPerATNrId53Mwgb/n0mDdGgiDvmtiwBFYuYRERERDBXq7CifRnkdhbvpVL0RnTfeAmhsS8UKM1tgMbz5EGeHIfqt86AQScdelkx81rYNWy6s0nIVPb28Jo3FwqNRsjjDx1G1ObNbzArIiIi+pKxkElEREREkrB585Fw5oyUO/3UBc595CVigxOCMevCLCkfXm44irsUl08Q7Qf8PVjO604AXAuKY8ckY/iuG3LTQq5Y2LoUNCre0hIRERH9TzZrM/zSqTycrM2EPCwuBb03X4HOYEwL89YFqvSXxlA+PAD7I0OAF96y/J+XFTMXXVmEJzHifpwWhQvDdcgQqW3ojJlIvns3kzMiIiKiLxmf+hARERGRQPvsGSI3bZLybO3bw2XQIGkfSpPJhPFnxiNeJ+6nVNOnJloWaCmfwGgE/ugJpMSKee6aQPlu6ZqaMHjHNUQnim8ElMrugMVtSsNMzdtZIiIiovSyO1lhdYeyME93r3T+aSSm/5OugFh3PFC2szSG5aN/YHd8NGAySsdaF2yNJnmaCFmKIQVjT48VlpgFgGzt28GmVi0hM2m1CBgwEMbExDeYFREREX2J+OSHiIiIiASh8+YDer2Q2Tf/Hm4jR0hFTOD/l5QNFJeUtTOzw9iKY+X2JhNwdDLw9KSYWzgAzZYBSvH2dO3pJzj1MFzIbMzVWPBDKVhoxD00iYiIiChNqezZMLlZUSlfc+oJ9lwLTAsUCqDRHKB4K6mt1b1dUB6bluH4Q8sNhYuli5BdDbuKX+/8KmQKhQIeU6dA7eYm5NonTxA8eUpmp0NERERfKBYyiYiIiChV0rVriNu3T8jU7u5wHz06wyLmS5eULT8cLlbigy2YTMDhCcDJOfKJv5kL2HkK0e3AWMzcd09qOr5JEWR3ssrEbIiIiIi+bC3K+qBNhexSPuy367gfEpcWKJVA0yVAocZSW9WZecDt3VJub26PsZXGSvmiK4vwLFbc21ydLRs8Z82UfrQWs2sXYvb8ldnpEBER0ReIhUwiIiIiAvB8idiQmXJR0qVvXygtLDJs/7IlZb/J/U36xsD+UcCpefKJi7UAin4vRMk6A/pvuwKtQVzK7OviHvi+tFcmZ0RERERE4xoXRglveyFL1BrQY+MlxCW/sHy/Sg18v+b5vpnp/d4TCLktxRnd9yUbkjH29FgY0y1Ja12+PJx79pTGCB4/Hlpf3zeYEREREX1JWMgkIiIiIgBA/JEjSLp0ScjMCxSAfdMmGbZfem2ptKSsrZmtvKSs0Qj8PQQ4u0QexKMk8LX4hqbJZPr/twTEAqmHvQWmNiuW4ZuhRERERJQxc7UKS9uVgaO1mZA/Dk/A1L/T7ZepNgdaboTJrZiY6xKArW2ApChp/OHlh8PJwknILodexoLLC6S2zj17wKpsWSEzJiQgYOAgmLTaN5gVERERfSlYyCQiIiIimPR6hM6Wl3x1HTwYCpW8F+Wq66uw/NpyKR9RfoS4pKzJBOwdCFxYJZ/Uuxzw427AQnxDYN7B+9h9NVDIFApgTssSsLfSZHJGRERERPQ/Xg6WWNS6FJTpfg+25bwvzj6OEEMzK+ibr4fRwkHMo54AO7sARoMQv2yJ2bU312LH/R1CplCr4Tl7FlT24v1f8s2bz/dpJyIiIkqHhUwiIiIiQvTOndA+eSJk1pUrwbpqFant+lvrsfDKQimv6Z3BkrL/LgYurZNPmL0S0G4XYOkgxDsu+mHhkYdS827Vc6NyHufXT4SIiIiIMlQlrzMG1M0v5cN/u45knVichEN2RNedD5Mi3aPDR4eBI5OkMWpnry3fBwKYcnYKTgWcEjKNuzs8pk2V2kauW4f4EycyMRMiIiL6krCQSURERPSFS3nwAGELF4mhQvH8bcx0y7huubsFsy/OlsYo5FgIU6pNEds/OQkcHCefMGc1oO1OwMJOiM88DMeIXTek5rUKuGDIVwUyPyEiIiIiylCPmnlQ2EO8B3sakYj5hx5IbbXelRBXcag8yKl5wGn5R21jK41FUaeiQmYwGTD4+GDci7wn5La1ayNbu3bSGIFDh3G/TCIiIhKwkElERET0BYs/cQJPW7WGITJSyO2bNIZF4cJC9tv93zD1nPzr+XzZ8mFlvZWwM3vhoVhsILCzE2BK9+v+XDWANtsBcxshfhgah+6bLkFvNAl5YQ87LG5TGmoVb1uJiIiI/iuNSomZzYtDlW6N2VUnH+NmQIzUPrF4RxiLNpcHOjhGKmZaqi2xqM4ieNl4CXmCLgE/H/4ZIQkhQu46ZDDMCxUSMkN0NHy7doU+3b0pERERfbn4RIiIiIjoC2QymRC5fj38evSEMSFBOKYwM4NLv35CtufRHkz4d4I0Ti77XFhVbxUcXtxDSa8FtncAEsLExtlyAS03AGZWQhyXrEPnXy4iLlkv5O52FljbsRyszdVvPkEiIiIiylBRL3v8VC2XkBmMJgz77Tr0BqPYWKGAodFcwL24PFAGxUxnS2csqbMEthpbIQ9JDEHfo32hNWhTM6W5ObzmzIHCSrw31D3zhX/PXjAmJb3F7IiIiOhzw0ImERER0RfGpNMheOw4hEybDhjTPaxSqeAxeRI0np6p0b6n+zD69GiYIL4tmd02O1Z/tRpOlk7iGAdGAf7nxUxtCfywSdoTEwAm7rkN38hEIbM2U2Ftx3Jwt7d44/kRERER0asNqJsfOZ3EAuKtwFisPPlYbqyxer63uUtB+VgGxcw8Dnkwr9Y8qJXij9FuR9zGvEvzhMw8dy54zpgOpNvOIOnaNQQMHgKTId3qHkRERPTFYSGTiIiI6AtiMpkQNHo0onfskI4p7ezgs3IF7Js0Sc0O+x7G8BPDYTSJBU8vGy+sqb8Grlau4iDXdwDnV8onbrwAcC8qxftuBmHHJX8hUykVWNK2NAp72kntiYiIiOi/s9CoMO07+S3LOQfu48CtYLmDjQvQ4a+XFzMvrBaiCh4VML7SeKnppjubcMzvmJDZ1asHt5Ejpbbxhw8jZMpUmEwm6RgRERF9OVjIJCIiIvqCRO/ciZjdf0q5WY4cyLl1K2yqVEnNTvifwODjg2FIt8+lm5UbVn+1Gu7W7ukG9wP+GiCftFxXoMQPUhwal4wRu25I+cB6+VGzgKuUExEREdG7UymPE1qXzy5kBqMJvTdfwamHEXKHVxUz940Ewu4LUdO8TdG+cHup6ejToxGcIBZLHdu3g2OnTlLbqM2bEbnul9dPhoiIiD5bLGQSERERfSGS791DyOQpUm5VoQJybtsK89xpeyVdDb2KAUcHQG8U9610tnTGmvpr4G3rLQ5iMgF7+gHaODH3LgfUnyqd02QyYdjO64hK1Al52RzZ0KNGnjecGRERERG9jRGNCsLLwVLItAYjem6+gqsBcXKHlxUzDSnA7p8Bo/gDuP6l+6OwU2Ehi0mJwbATw6T7TNchg2HbsIF0ytDZsxF/8uQbzIqIiIg+JyxkEhEREX0BDPEJCOjXH6aUFCG3rlwZ2VevgsrBIa2t0YAJ/06A1qgV2jpaOGL1V6uRwy6HfIIrG4FHh8XMwgFosR5Qm0nNfz3ni6P3wsRrMVNhbsuSUCkVUnsiIiIievfsLDRY37k8nKzF+7VknREDdz/EnZAEudP/ipm2HmLufx44u0yIzFRmmFV9Fqw11kJ+OfQyVlxfIWQKpRKe06fDqmxZcVyjEQEDByHl8ZM3mxwRERF9FljIJCIiIvrMmUwmBI8bB+3Tp0KudnGB56yZUGg0Qv7noz/xMPqhkNmb22NlvZXI45DB25Ix/sD+UXLeaBZg7yXFj8LiMWXvHSkf17gIsjtZvX5CRERERPTO5HW1wcYuFWBnoRbyRK0R/X5/gAeh8XInG5fne6Cnd2QSEC7eR2a3y46xFcdKTVdcW4HzQeeFTGluDu/Fi2CWQ/zhnDEuDv69esEQG5vJWREREdHngoVMIiIios9c9PYdiN27VwyVSnjOmQ21k5MQJ+mTsPjqYmmMhbUWooBjAXlwkwn4sy+Qku6hUoFGQLEWUvOn4Qlov/ocknTismP1CruhRVlvqT0RERERvX+FPe3wS+fysDZTCXlssgE9fr2CmHTbAQAA8tcHSrQWM31yhkvMNsrdCN/m/VbITDBhxMkRiEyOFHKVgwO8ly2F0sZGyLVPnyJg0GCYDOLYRERE9HljIZOIiIjoM5Z87z5Cpsj7Yrr07QPr8uWl/Nc7vyI0MVTIGuRsgNJupTM+wZVNGS8p+808QCEuEfswNB4/rPwXgTHJQu5sY4Zp3xWDQsElZYmIiIg+ltLZs2F1h3IwV4uPC30jk9B36xUYjCa5U4NpgI27mPmdBc6vlJoOLz8cuexzCVloUihGnxoNo8ko5Oa5c8NrzmzpfjLh5EmEzp37BrMiIiKiTx0LmURERESfKaNWi8AhQ2DSintdWlepAqdu3aT2UclRWHNjjZCplWr0LdU34xNEPgH2j5TzhjMBW/GB1v2QOLRaeRYhseIenQoFMLN5cTjbmGdiRkRERET0PlXK44Tl7ctIe5Yfvx+GOQfuyR0sswGN58v5oQlAqLiVgJXGCrOqz4KZUtyP82TASWy8vVEawqZGDbgOHiTlkWvWIvbAgddPhoiIiD4LLGQSERERfabCFixAyv37QqZ2dYXnzBlQKOXbwJXXVyJeJ+6B9EOBH+Bj5yMPHu0HrG+S8ZKyxVsK0e3AWLRaeRbh8WIRU6kA5rQogdoF3d5gVkRERET0PtUq4Iph9fNL+dJjj/D3jSC5Q4GGQDHx/g/6JODXlkBciNjUsQCGlR8mDTH/8nzcDL8p5Y6dO8OuSWMpDxozFrrg4NfMhIiIiD4HLGQSERERfYYSzp9H5Np1Uu45c4a0LyYA+MX6Yeu9rUJmo7FBt+Lym5uIDQLWNwZifMU8gyVlfSMS0Xb1WUQmiG+FqpQKzG9VCt+V5r6YRERERFlNx0rZUb+go5QP3nEN94Lj5A4NZwDWrmIW4wtsaQVoE4W4Rf4WqJejnpDpjXoMOT4E8VrxR3UKhQIeEyfColgxITfGxCBw2HDul0lERPQFYCGTiIiI6DNjiItD4PDhgEncx8ixY0dYV6yYYZ+FVxZCb9QLWZdiXeBoke4BVnwosKEJEPUk3QgKoMkiYUnZZJ0BPTZdQlSiTmipViqwuHUpNCnh+WYTIyIiIqIPQqFQYESdHMjvYinkiVoDum64iOB0e57DyhH4bgWgUIl54GVgV1fAmFZwVCgUGFdpHDytxXtB/3h/TPh3Akzp7mGVFhbwmjcXSmtr8VrOnUPEmrVvOUMiIiL6VLCQSURERPSZCZk8BfpAcdkv83x54TKgv9TWYDRg5oWZ2Pd0n5C7WrmibaG2YuOECGBDUyBcXK4WwPMiZuEmqR9NJhNG/X4Tt4PEpWfNVEosa1cGDYt5vNmkiIiIiOiDstAoMf2bPMhmpRFy38hEtF51FiGx6YqZeWoD38yVB7r7F3BgtBDZm9tjRvUZUKUrfO57ug8rr6+UhjDz9ob7+PFSHrZwIZJu3MjchIiIiOiTxEImERER0Wckdt9+xOzeLYYaDTxnzYLS3FyI47Xx6Hu0Lzbe3iiN07tkb1iqX/gFflIUsLEZEHpbPunXc4HS7YVo83lf/HbZX2q6oFVJ1CvMPTGJiIiIPgWe9uZY8ENxqJQKIX8SnoBWKzMoZpbpCFQdIA90dilwTixQlnQtiT6l+khNF19djL2P90q5feNv5P0y9XoEDB4MQ3xCpuZDREREnx4WMomIiIg+EykPHiBo9Ggpd+3XFxYFCwpZQHwA2v/THif8T0jtizoVRZM8aW9XIjkW2PQ9EHxdPmn9aUC5LkJ0xTcK4/+8JTXtWTMP38QkIiIi+sRUyu2EiU2LSPlLi5m1xwJFvpMH2j8C8L8oRJ2KdkJVr6pS0zGnx+Bi8EUpdx87FhpvcY913TNfBI0aBZNeL7UnIiKiTx8LmURERESfAX1YGPy694AxPl7ILcuWgWOnTkJ2O+I22uxtg4fRD6VxirsUx6I6i6BS/v8yXynxwK8tgIBL8knrjgcq9RKiiPgU9Pr1MnQGcW+jKnmdMKhe/jefGBERERF9dG0r5Mh8MVOpBJotA3wqiI2NemBnJyApOq2pQolZ1WchX7Z8QlOdUYd+R/vhSYy4L7vKxgZes2cBKnFJ2rj9+xEwcBBMWu3bTZCIiIiyLBYyiYiIiD5xxqQk+PX6GbrAQCFX2tjAc/p0KF540BOZHIk+R/ogMjlSGqdRrkZYW38tnC2dnwfaRGBLK8DvrHzSmiOlZcNMJhMGbr+GoBjxV/ke9hZY2KoU1CreehIRERF9qn6slPOlxcwemy7BYHzhh2waC6DVFsAhh9g42hfY0w8wpbW1MbPB0jpL4WLpIjSN1cai16Fe0n2rZcmScOn9s3QdcQcOwL9ffxhZzCQiIvqs8GkSERER0SfMZDAgcOhQJN+4IR5Qq+G1YD7MXlh6y2gyYuTJkQhNDJXG6V2yN6ZXmw5z1f/vo6lPAba1BZ6elE9adSBQY6gUb7/oh+P3w4TMTKXEsnZl4GRjLrUnIiIiok/Ly4qZV3yjsfaU+PYkrJ2A5usApVrMb/8BXPpFiNyt3bGkzhJxj3YA/vH+GHB0AHQGnZA7desGm7p1pOuIP3oU/r1+hjEpKdNzIiIioqyNhUwiIiKiT1jo7DmIO3hIyt3HjYVNlSpCtubGGpwOPC1kGqUGs2vMRvcS3aFQKNIOHJ4IPDoin7Diz0CdscCLbQEERidh8l93pOZjGxdGSR+HzE+IiIiIiLK0lxUzZx+4h0dh4jYH8C4D1BknD7JvOBByW4gKORXC7BqzoVSIjysvh17GlHNTYHrhLU6FSgXvefNg26CBNHTCqVPw69ETxuRk6RgRERF9eljIJCIiIvpExezdi8h166TcqWtXZGvRQsguBl/E4quLpbbDyw9H/Zz1xTDyCXBuhXzCcj8B9adIRUyTyYThu24gLkUv5PUKu6FtheyZnA0RERERfSp+rJQTzct4C1mK3oihO6+LS8wCQKXeQN56YqZPfr5fpjZRiKt7V8eI8iOk8/324DdsvbdVyBQaDbxmz4Jd48ZS+8Rz5xC2aNEbzIiIiIiyKhYyiYiIiD5BJq0WYXPnSbltwwZwGdBfyCKSIjD0xFAYTUYhb5izIVrkFwueAIAjkwCjuHwXSrYDGs6SipjA8yVlT6RbUtbeUoMp3xYV3/IkIiIios/GmK8Lw9VW3D7g0rMorDudbolZpRJotgywcRfzsLvAvmHSuK0KtkKrAq2kfMb5GTgXdE7IFGo1PKdPg33z76X2kRs2Quvrm8nZEBERUVbFQiYRERHRJyj69z+gCwgQMssSJeA5bRoUyrRbPIPRgBEnRyAsSSw05rDLgbGVxsqFxoBLwM3fxMzWE2g06/lDqHRetqTshCZF4Gpr8YazIiIiIqJPhb2VBlO/LSbls/bfw5PwBDG0cQG+Wwkg3b3n5Q3AjZ3SGEPLD0V59/JCZjAZMOj4IPjF+Qm5QqWCx8SJcEi3Igl0OoTOmp3p+RAREVHWxEImERER0SfGqNUifPlyMVQq4TFtGpQWYvFww+0N+DfoXyEzV5ljTo05sDGzEccwmYCDGexhVGskYGYlxS9bUrZuITc0LemZ+QkRERER0SepbmE3fFfKS8ieLzF7Dcb0S8zmrgFUHywPsqc/EPlYiP63j7uXjTh2TEoM+h7pi0SduCStQqmE24jhULu5CXncwYNIOH/+zSZFREREWQoLmURERESfmOgdO6APChIy+8aNYZ47l5Ddi7yHRVfkvYGGlx+OAo4F5IEfHASenhQzl0JAyTYZXse8Qw8yXFJ2KpeUJSIiIvpijG1cGC7plpi98DQKM/ffkxvXGA5kryRm2jhgZ2dArxXibBbZsLD2QliqLYX8YfRDzLgwQxpaaWUF14EDpDx0+gyYjEYpJyIiok8DC5lEREREnxBjcjIiVqwUQ5UKzr16CpHWoMWIUyOgS7fXZaNcjfB9PnkPIRgNwMGxcl5vAqBUSfGSow+x8PADKR/fpDBc7bikLBEREdGXwsHKDFOaFZXy5ccfYfO5dHtUqtTA96sBCwcxD7wCHJ4gjZE/W35MqzpNync92IUDTw9IuV3jxrAoKl5L8u3biPlj9+snQkRERFkSC5lEREREn5Do7duhDw0VMvtmTWGWI4eQLb66GA+ixEKju7U7RlccnfHbklc3A2Hp9rrMWQ3I95XUdPXJx5iVwS/s6xZyQ7OSXlJORERERJ+3r4q449tS8n3gmN03ceyeeO8Ke2+g2TJ5kH8XA/f3S3GdHHXQvXh3KR//73gEJwQL2f+WmE0vbN48GBMSpJyIiIiyPhYyiYiIiD4RxqQkhK9cJYZqNZx7im9jXgq5hF9u/iL1n1xlMmzNbOWBIx8DRybLeb0JQLqi54Z/n2Ly3jtS02Je9pjTsgSXlCUiIiL6Qk39thhKeNsLmcFoQu/NV3A7MFZsXLARUKGHPMjvPaT9MgGgR4keKOVaSsjitHEYfnI4DEaDkFuVKQPbBg2ETB8Whog1a95gNkRERJRVsJBJRERE9ImI2rIVhvBwIXP47juYeXunfo7XxmPUqVEwwSS0a1+4PSp4VJAHfXoaWFUHiBd/zY6i3wNeZYRo63lfjN19SxqioLstNnQuD3tLzRvOiIiIiIg+F5ZmKqzuUA7e2cQ9LeNT9Oj8ywUExySLHepNBNyLi1lSJPBrCyAxUojVSjWmVZsGG42NkF8KuYQ1N+UCpevgQVBoxHvT8FWrEX/y1BvOioiIiD42FjKJiIiIsjiTyYSYPXsQvmSJeECjgXOP7kK7aeenISA+QGiWxz4P+pXuJw98ZROwoenzB0YvUmqA2mOE6LdL/hjx+w1piLyuNtj0UwVkszZ7s0kRERER0WfHxdYc6zqWg62FWsiDY5Px04YLSNG/8Pak2hxovg7QWIuDRDwEtrYF9ClC7GXjhbGV5D3dl15dimth14TMzNsbjh07iA11Ovj36YPEixfffGJERET00bCQSURERJSF6cPC4N+7DwKHDJX29cnWojk0np6pn9feXIs/H/0ptFErnv963VxlnhYaDcCB0cDunwGjLt0ZFUCjWYBjrtRkz7VADNl5DSbxJU/kdLLC5p8qwNnGHEREREREAJDPzRYr2pWBRiVuOXAzIBbT/7krNnbOC3y3EkC67Ql8zzy/V013A9owV0M0ydNEyAwmA/oe6SvtD+/UvTvUbm5CZkpOhl/3Hki6If9Aj4iIiLImFjKJiIiIsqiYvXvx+JvGiD98WDqmsLSEU7duqZ/3Pd2H+ZfnS+16luyJQk6FxPCfYcCZRfIJNVbAD5uAsp3Sxr0ZjP7brsKYrojp42iJzV0rwtXO4o3mRERERESfv8p5nTHtu+JSvu70Uxy8HSKGhb4B6k+VB7mxAzg6RYpHVhgJH1sfIYtMjkSX/V1wL/JeaqaysYHPiuVQ2tkJbY0JCfD7qSuS799/gxkRERHRx8JCJhEREVEWFLZ4CQIHDYYhJkY6prSxgfeC+dC4uwMAroZexaiTo6R2FdwroHPRzmJ4YydwYZV8QjsvoPO+5w+S/t+RuyHos+UyDOmqmJ72Ftj8U0V4OlimH4WIiIiICADQvIw3ulTNJeVDdl5DYHSSGFbsCZTvJrXFiVnA1c1CZK2xxoxqM6BWisvXRqVEofP+zrgVkbanu0XBgsi+aiWUVlZCW0NMDHw7d4H22bM3nBURERF9aCxkEhEREWUxUVu3IXzx4gyPWVetitx7/oRN9eoAAN9YX/Q50gdao1Zol8c+D+bWmis+4Al/AOzJYK9Mz9JA1yOAR4nU6NKzSPTYdBk6g1jEdLU1x69dK8LH0Sr9KEREREREgqENCqCYl72QRSfq0H/rVegNxrRQoQAaTAfyN5AH2dMP8DsvRMVcimFW9VlQK8RiZqw2Fl33d8X1sOupmWWJEvBetgwKc3E7BEN4OPx794ExKV1RlYiIiLIUFjKJiIiIspC4I0cQPHGilCutreE+aSJ8Vq2ExsMDABCTEoNeh3shOiVaaOtk4YQldZfAzuyFZbR0ScCOjoA2XhzYpwLQ6W/A1j01MhhNGLHrBrR6o9DU2cYMm7tWQC5n6/80RyIiIiL6MpirVVjUuhRszMWC4/mnkVh45KHYWKkCvl8DuKdbktagBba2BWIChLhujrqYV2seNEqNkMfp4tDtYDc8jEob37pCeXgvXABoxLYpDx4geNLkt5wdERERfQgsZBIRERFlEYlXriBg4CDAKBYQzQsXQu49fyJbixZQKBSp+bxL8/AsVlwOy0JlgcV1FsPLxksc/J+hQMhNMbN0BJqvAzTiErG/XfLH/RCx4OlgpcGmnyogr6vtW86OiIiIiL5EOZ2tMeXbolK+6MgDnHkULobmNkCb7YCtp5gnhAJb2zz/cd4LavrUxMLaC2GmNBOb6xIw6ewkmExpq4vY1KgBr1kzpeuI2bUL0b/tesNZERER0YfCQiYRERFRFpDy+An8e/aCKTlZyDXe3si+YgU0nuLDHL9YP/zx8A8hU0CB6dWno6hzugdF17YBlzfIJ/1uJWAvFjyTtAbMOXhParr6x7Io6G4n5UREREREr9O0pBdalvUWMpMJ6L/1KiLiU8TGdh5Aq18BtYWYB10Fdvd+3vEFVb2qYkndJbBQie0vh17GId9D4tANGsCpR3fp+oInTkTyPfkemIiIiD4+FjKJiIiIPjJDdDT8unaFITpayFXZsiH76lVQu7hIfVZcXwGDySBkvUv1Rp3sdcSG4Q+Av/rLJ606EMhXT4rXnn6CkFjxYdI3xT1QNqdjpuZCRERERJSR8U2KIK+rjZCFxqVg0I5rMBrF4iS8SgNNMtgz/uZO4PR8Ka7oUREzq8tvW869OBdag7iXvEvv3rAqX17ITCkpCOjXH4b4dNswEBER0UfHQiYRERHRRxY6dx50AeKePwoLC/gsXwaznDml9s9in2HP4z1C5mrlig5FOogNDTpgVzdAlyjmOaoAtUZJ40bEp2DZsUdCplEpMKR+gcxPhoiIiIgoA1ZmaixuUwpmavFx5LF7YVh7+oncoXgLoEp/OT80AXh8TIprZa+FKp5VhMw/3h+/3vlVyBRqNTxnz4LK2VnItU+fImjMGGE5WiIiIvr4WMgkIiIi+oiSrl9H9I4dYqhUwmveXFiWKJFhn+XXlsNoEvfR7FasG8xV5mLDE7OBwMtiZuUMfL8GUKmlcRcdeYj4FL2QtauYAzmcrDM3GSIiIiKiVyjoboex3xSW8hn77uKaX7Tcoc5YIF/9dKEJ+L0HkBAhNR9cdjBUCpWQrby+EhFJYluNqyu8Zs8GlOKj0bh/9iFu375MzYWIiIg+DBYyiYiIiD4Sk8GA4PETpH1+XPr3h22tWhn2eRz9GHsf7xUyD2sPfJvvW7Gh/yXgxCx5gGbLnu87lM6ziAT8eu6ZkNmaq9Gndr5MzISIiIiIKHPaVsiOhkXdhUxnMKHPliuITdaJjZUq4PtVgHN+MY8LAv7sI91H582WF83zNxeyeF08ll5dKl2HdcUKcOnbR8pDZsyEMTFRyomIiOjjYCGTiIiI6COJ2rYNybdvC5lZ3jxw6tTxpX2WXVsGE8QHNt2Kd4OZyiwt0CYCv3cD0u2hiTKdgPxfZTjuzP33oDOI4/aomQeO1mYZticiIiIiehsKhQLTvy8OLwdLIfeNTMTIXTfkpV0t7IEW64H0q4/c2wtcWieN36tkL9hqbIVs54OdeBD1QGrr1K0brCtXEjJ9cDDCV6x8gxkRERHR+8RCJhEREdFHoI+IQNj8BVLuPmYsFBpNhn3uR93H/qf7hczLxgtN8zYVGx4cC0Q8FLNsuYCvJktjGowmTNhzC3uvB4nXYWeBzlVyZWImRERERERvxt5Sg0VtSkGlVAj5X9eDsPjIQ7mDW2Gg/hQ53zcSCLsnRI4WjuhWvJuQGU1GzLgwAwaj+EM/hVIJtzFjgHT335Fr10L79GnmJ0RERETvDQuZRERERB9B6KzZMMbGCpld48awrlA+w/YGowGLLi+S3sbsXrw7NMoXHrw8PAxcWCV2ViiB71YC5jZCHJesQ5f1F7Du9FPpfAO/yg9LM5WUExERERG9C6WzZ8PgrwpI+ZyD9/HHlQC5Q7mfgPwNxEyfBOzsAuhThLhNoTbwsfURsnNB5zDx7ERpr3nzXLng1LGDkJl0OgRPm/YGsyEiIqL3hYVMIiIiog8s8eJFxPzxh5ApbWzgNnRIhu2fxjxFh30dcMz/mJBnt82OxnkapwVRT4Hfu8sDVB0I+IgFUr/IRHy/7AyO3QuTmpfwccD3pb0zMxUiIiIiorfWvXpu1CrgIuVDd17HuccRYqhQAE2XADZuYh5yA/hnqLBfppnKDAPLDJTG3fVgF2ZemCktX+vcowfUrq5ClnD8BOKOHn3DGREREdG7xkImERER0QdkTExE0NhxUu7Sty/ULuJDHKPJiF/v/IoWe1rgWtg1qU+PEj2gVqqff0iKAn5tCSSkK0y6FwdqDBOimwExaLbkNO6HxEtjls7ugDUdykrLfBERERERvWtKpQILW5dCQXdxT0utwYhuGy/hUVi6+1VrZ6DZMnmgS78AewcBxrS3Letkr4N6OepJTX+98ysWXVkkXoe1NVyHDZXahkydBmNKipQTERHRh8NCJhEREdEHYjKZEDpxErSPHwu5eaFCyNamtZCFJ4Wj64GumH5+OpINydJYZd3KolGuRs8/6LXAtvZAuLg/ENQWwHerALVZahQRn4Kf1l9ERIJWGrNZSU9s7loRzjbmbzlDIiIiIqI3Y2uhwdqO5eBmJ96DxiTp0GndBUTEpysk5q0DVOotD3RxDfBXv9RipkKhwPRq01HZs7LUdNWNVVh1XdyOwa5RI1iVKydkOj8/hC9f/hazIiIioneFhUwiIiKiD0S7Zw/i9+4VQ4UC7mPHQKFWp0ax2lh0O9gN54PPZzhO0zxNsaj2IqiUqudLaO3pBzw9KTf8djngWjD1o9FowoDt1xAcKxdGB3+VH/N+KAkLDffFJCIiIqIPy9PBEms6lINVuj3afSMT0X3jJWj14r6WqDMWyFFVHujyBmB3L8BoAPB8idn5teajtGtpqenCKwux8/7O1M8KhQJuo0cDKvEaIpYtR+Svv77lzIiIiOi/YiGTiIiI6APQ37+PxEWLpdz5559hVapU6udkfTL6HO6DB1EPpLZOFk5YVHsRJledDBszm+fhiVnAtc3yCetOAIp8K0TLjj/Cifvi0rNmKiWWti2N3rXzQaHgcrJERERE9HEU9bLH4jalkH6Hg4vPojB2901xX0u1OdB2O5CrujzQtS3Arm6pxUxLtSWW1FmCok5FpaZTz03FrfBbqZ8tCuRHtjZtpHYhkyYjavv2t5sYERER/ScsZBIRERG9Z4bYWCSMnwDodEJuXbkynHv2SP2sN+ox5MQQXA69LI3RMGdD/NH0D9T0qZkW3vsHODpFPmGZTkCVfkJ09nEE5hy4JzUd27gwGhXzeLMJERERERG9B7ULumFCkyJSvvWCHzb8+0wMzayBNtuBPLXlgW7uBE4vSP1oY2aD5fWWI3+2/EIznVGHgccGIiYlJjVz6dcXZrlzS0MGjxuP6F2/v+GMiIiI6L9iIZOIiIjoPTKZTAgdMxbGwEAhV7u5wXP2LCj+f+kqk8mEif9OxDG/Y9IYvUv2xswaM+Fg4ZAWGvTAgdHyCfPWBRrNBl54uzIsLgV9t1yB0SQ2bVzCE20rZH/LmRERERERvXvtK+XEj5VySPnEv27jzMNwMdRYAq22APnqywOdnAPEhaR+tDe3x4p6K+Bq5So0C0wIxKhTo2A0PV++VmVjg+zr1kGTI919ssmEoFGjELPnr7ebGBEREb0VFjKJiIiI3qPIX9Yj4cgRMVSp4DV3DtSOjqnRgssL8PtD+RfebQu1Rbfi3eSBb+wAIh6KmVtRoPk6QJW236bBaMKAbVcRGpciNM3tbI1p3xXjcrJERERElOWM+aYwKuV2EjKD0YRemy/DNyJRbKyxAH7YBBRoJObaeODIJCFytnTGnBpzoFaohfy4/3Gsvbk2bUg3V+T45RdovL3FMU0mBA4bhvhTp99uYkRERPTGWMgkIiIiek8SL19B6Jw5Uu46cCCsypRJ/Xzc7zjW3FwjtWuYqyGGlhsqFxsNOuD4DPmE364ALOyEaNXJxziV7pfr5mollrQtDRtz8QEOEREREVFWoPn/fdx9HC2FPDpRh582XEBCil7soDYDmi4BXlzBBACubAKCrgtRSdeSGFR2kHTORVcW4XzQ+bRr8PBA9l9+gdoz3TYMRiMCBw2C1j/gjedFREREb46FTCIiIqL3QB8VhYCBAwG9+JDFulYtOHbulPo5WZ+MaeenSf0re1bGlCpToFRkcLt2bQsQ9UTMCjcD3IsK0Z2gWMw9cF/qPr5JERTysJNyIiIiIqKsIpu1GVb/WA7WZiohvx8Sjwl7bskdrByBmsPThSZg/0jAJO6x0LZQW9TLUU/IjCYjhpwYgsjkyNTMzNsLOX75BWo3N6GtISYGAf36wZgirnpCRERE7x4LmURERETvmMloROCQodAHBwu50sMDrpMmCW9Yrrm5BgHx4q+5CzsVxrya86BRaeTB9Vrg+Kx0oUJ6aJOiN2DAtqvQGoxC3rSkJ1qV83nzSRERERERfWAF3G0x74eSUr79oj/2Xg+SO5T7CXDKK2ZPTwL3/hYihUKBiZUnIqddTiGPTI7EwssLhcwse3ZkX7sGSisrIU++dQshkydnei5ERET0dljIJCIiInrHwpcvR8KpU2Ko0cB6/Dio7NPehPSN9cXaG2uFZmqFGlOqTIGVRnxQkurqJiDGV8yKfg+4FhKiuQfu425wnJB52ltgUrOi3BeTiIiIiD4ZXxVxR986+aR8xK7rCIhOEkOVBvgqg+LigdHPfxD4AhszG8ypOQcWKgsh3/VgF26Fi298mufJA4+pU6Vho3fsRNSOHZmcCREREb0NFjKJiIiI3qGEf/9F+KLFUm7V+2eoCxRI/WwymTDt/DRojeIDlXaF2yFvtrzpuz+nTwFOzBYzhRKoMUyIzj2OwMqTj6Xus1uWgJ1FBm95EhERERFlYX1r50XZHNmELDZZjwHbrsJgFJeNRf4GQK4aYhb5GLiwSho3f7b86F2qt5CZ8Pw+3WgSVzaxa1Afjp07S2OETJqMpBs332A2RERE9CZYyCQiIiJ6R1KePEHAwEHSHjw2DRvCrEkTITvqdxSnAsS3Nl0tXdGjRI+Xn+DyBiBWXIYWxVoCLvlTP8Yl6zBox7X0l4AuVXOhch7nzE+GiIiIiCiLUKuUmN+qJGwt1EJ+/kkklh17KDZWKID6U5//4O9FR6cBgVelsdsUbINc9rmE7FrYNex9vFdq6zpwAKzKlxcyk1YL/359oY+KyvyEiIiIKNNYyCQiIiJ6B3QhofDr8hMM6R5gmOXODdfx44TlXJP0SZhxfoY0xpByQ2Ctsc74BPcPAEeniJlCBdQYmvrRZDJh3J+34B8lLrGVz9UGQ+oXABERERHRp8o7mxWmfFtMyucdeoDLvumKiO5FgVLtxUwbB2z8Fgi9I8QalQbDy4v7zQPA3EtzkaBLEDKFWg2vuXOgdnUVcn1gEAIHD4HJYHiDGREREVFmsJBJRERE9B8ZYmPh17UrdIGBQq6wsIDX/HlQWon7Xa66vgqBCWLbCu4VUD9nfXnw5Fhgd29gcwsgKd0DmpKtAac8qR+XHX+EXZfFNzbVSgXm/VASFhrVW8yMiIiIiCjraFLCE83LeAuZwWhCtw0XcS/d/vCoPRqwsBezpEhgQ1Mg4pEQV/asjNo+tYUsPCkcK66tkK5B7ewMrwXzAY24ZUPC6dMIWyxvMUFERET/DQuZRERERP+BMTkZfr16IeX+ffGAUgmvuXNgkT+/EF8MuYg1N9cImVqpxsiKI4W3NgEAj48ByyoDVzbKJ1aZAdWHpH7ccy0QM/fdk5oNqJcfRb3spZyIiIiI6FM0vkkR5HASfygYHq9F61VncTswNi20cQVabgRU5uIA8SHA+iZAtK8QDy43GGZKMyHbeGcjnsQ8ka7BqlQpuA0fJuURy5Yj7sjRN5wRERERvQoLmURERERvyaTXI2DQYCRdvCQd85g4Aba1xV91R6ZEYuTpkTCajEL+Y+Efkds+tzjAhdXPfy0e4yefWGUOfLcSyJYTAHDxaSQG7bgmNauS1wndq+eWciIiIiKiT5WNuRoLWpWCRiX+CDAy4Xkx87p/dFqYuwbQcgOgFPfWRKz/82JmXHBq5GPrg45FOwrN9EY9Rp0ahXhtvHQd2dq0gX3TJlIeOGwYtM+evfG8iIiIKGMsZBIRERG9pbD58xF/+LCUu/TvD4fmzYXMYDJg2vVpCE8OF/IcdjnQvXh3cYAbO4G9gzM+qVcZoMcpoMi3AICn4QnouuEitHqxOJrP1QZL25aBWsXbPSIiIiL6vJT0ccCi1qWlYmZMkg5tV50T98ws0AD4fjWgSHdfHPUE2P4jYNClRl2KdoG7tbvQ7Eb4DfQ81FMqZioUCriPHw/zAuJe9Ma4OPj36QtjkrhvPREREb0dPtkiIiIiegsJ588jYs1aKc/Wvj2cuneT8g0PN+Bq5FUhM1eZY06NObDSvLA01sPDwO89AJjEAZQaoPYYoPMBwOX5crXRiVp0+uUCohJ1QlNnG3Os61QO9pbivj1ERERERJ+LBkXdsbxdGZil++FeXIoe7VefE/fMLPIt0HSJPIjfOeDguNSPVhorDC4r/6DwathV9DzUEwm6BCFXWlrCe9FCKG1thTzl/n2ETJv+FrMiIiKi9FjIJCIiInpDhvh4BI0YCZjEYqNdo0ZwGzFc2uvydOBpbH68WRpnVIVRKOD4wi+4/S8B29oDRrEwiWy5gG7HgOqDAVXaslgz9t3Fk3DxYYqFRok1HcrCO5u4bxARERER0eemTiE3rOpQFuZq8RFngtaA0X/cgOnF+/WSbYCv58iDnF0C3Poj9WP9nPXRqUgnqdnVsKvocbCHVMw0y54dnjNnSO2jt29H/MlTbzYhIiIikrCQSURERPSGQqZPhy4gQMjM8+WFx7SpUCjF26vghGCM+XeMNEbTPE3xbb5v04Kw+8CvzYF0D0Zg4w78+AfgXlSIn0UkYPtFfyFTKICFrUqhhI/DG8+JiIiIiOhTVCO/C9Z2LAcLjXgffuFpFP65GSw2LvcTULaLPMju3kD4w9SPA8oMQMciHaVm/ytmJuoShdy2Vi049ewhtQ8aPRqGmJjMT4aIiIgkLGQSERERvYG4I0cQs/M3MVSr4TljBpTm5kKsM+gw6PggRKdEC3m+bPkwquKotCAxEtj0HZAUKY5rbg+0+w3IllO6joWHH8JgFN8IHfxVAXxVxF1qS0RERET0OauS1xmLW5eW8mn/3EGyziCGDaYBnqXETBv3fL9M7fMCpUKhwMAyA9GhcAdpzKthVzHh3wni254AXH7+GRbFigmZPiQEIVOnvsWMiIiI6H9YyCQiIiLKJH1kJILGjJVyl94/w6JwYSmfd3keroddFzIrtRXm1JgDS7VlWnhoPBDjJ3ZWWwBttkpvYgLA47B4/H5FfBvT3c4CXarmyvxkiIiIiIg+I3ULu6FGfhch84tMwi9nnooN1eZAi/WAhYOYh94C9g5M3T5CoVBgUNlBGRYz/37yNzbfFbeOUKjV8Jw+DQozMyGP2f0n4g4deqs5EREREQuZRERERJliMpkQPG4cDBERQm5ZogScfvpJan/o2SFsvL1RysdUGINc9i8UHP0vApc3iI0UKqD5OiBH5QyvZeHhB0j3MiZ+rp0XFhpV5iZDRERERPQZGv11IaiU4n71i488RHh8itgwWw7gu1XyANe2AHf3pn78XzGzXaF2UtPZF2bjauhVITPPkwcu/ftLbYPGjYc+KirT8yAiIqI0LGQSERERZULM7t2IOyj+klphaQnPGdOhUKuF3C/WD2NOy/tiNvFpgvo56qcFRsPzX30jXVWyzligYKMMr+NhaBx2XwsUMi8HS7Qs6535yRARERERfYbyudmiTfnsQhafosfcg/flxvm/AqoNlvP9IwBdUupHhUKBwWUHo4pXFaGZ3qTHoGODEJ4ULuSOHX6EZZkyQmaIiEDwuPHScrRERET0eixkEhEREb2GLjAQIZOnSLnb0CEwy5lTyFIMKRh0fBDidfFCnt8uP7oX7C4OcGkdEHRNzFwKAZV+fum1zD/0AOmff/SunRfmar6NSUREREQ0oF5+2FqIPzTcet4Xd4Nj5ca1RgI5xAIlon2BM4uFSKVUYUa1GfCy8RLy0KRQDD4+GDqjLjVTqFTwnDYVCktLoW3cgQMIW7jwLWZERET0ZWMhk4iIiOgVTEYjAkeMhDFeLExaV60Kh1atxLYmE6adm4Y7kXeE3FZji9ElRsNM+cJ+OQnhwOGJ8gm/ng2oNBley93gWOy9ESRkPo6WaF6Gb2MSEREREQGAo7UZ+tbOJ2RGEzBxz20Y0u/PoFQBX895vrXDi07OAWLEPentze0xt+Zc8Z4ewKWQS1hwaYGQmWXPDtch8tueEcuWI3L9+jecERER0ZeNhUwiIiKiV4jauBGJ584JmdLeHh5TJkOhEPffWXl9JX578Js0xsRKE+Fh5SGGh8YByTFiVqwlkLNqhtdhMJowe/896W3MvrXzQaPiLR0RERER0f/8WDkHcjhZCdmZRxEYses6jOmLma6FgPLdxEyfBByQt4oo7FQYoyuOlvL1t9dj/9P9QpatVStYV68mtQ2ZNh0xu3dnciZERETEp15EREREL5Hy8CFC58yVcvexY6BxcxOy7fe2Y/HVxVLbTkU6oYZ3DTH0Ow9c2SRm5nbAV5MzvI7HYfFosfwMDt0JFfKcTlb4tpRXhn2IiIiIiL5U5moVRjQsJOXbL/pj4l+35b0qaw4HrJzE7NYu4MlJaYxv832L5vmbS/mY02PwOPpx6meFUgmvufNgUbSo1DZw5CjEHTuWuckQERF94VjIJCIiIsqASadD4LDhMGm1Qm7XqCHsv/5ayA48PYDJZ+UiZFm3suhTuo8Y6hKBPf3kE9YaCdiKxVGj0YS1p56g0cKTuOwbLXXpVzcf1Hwbk4iIiIhIUr+IG74u5iHlv5x5iln774mhpQNQZ5w8yD/DAINeikeUH4GiTmKBMkmfhH5H+yFem7YlhcrGGj4rV8AsVy5xAIMBAf36I/HylUzPh4iI6Ev1Xp58qVSqD/IftVqNP//8831MgYiIiL5wYYsWI/nWLSFTu7jAfexYITsbdBbDTw6HCeKvugtkK4AFtRdAoxT3u1TtGw6E3hZP5loEKNdVPH9cCtqsPouJf91Gss4oXV+FXI5oUoJvYxIRERERZUShUGDuDyVQLZ+zdGzpsUdYfOSBGJZqD3iWErPQW8CFVVJ/M5UZ5tacCwdzByF/GvsUY8+MFd74VDs6Ivua1VC7uwttTSkpCBwyBMakpDebGBER0RfmvRQyTSbTB/sPERER0bsW/dsuRKxcKeUeU6dA5eCQ+vlu5F30O9IPOqNOaOdt443l9ZbDzsxOyC3v/gbl9c3yCb+eA6jUqR9jknRov+Yczj6OzPD6mpfxxuoOZaFSKjI8TkREREREz5eYXdm+LMrncpSOzT5wH5vP+aYFSiXQcKY8yP5RwM3fpNjDxgMzqs+AAuI9+cFnB7H+1noh03h6Ivua1cK/SwCALiAA4StWZH5CREREXyD165u8HYVCgcKFC8PZWf7V07tw/Pjx9zIuERERfdniT51G0Dh5WSmH1q1gU61a6uckfRKGHB+CRH2i0M7Jwgkr662Es6V4D6SOuAe7UxPlE1YbBOSolPoxWWdA1/UXcTc4TmrqYmuO6d8VQ51CbtIxIiIiIiKSWZqpsLZjObRdfQ7X/KKFY+P/vIWiXnYo7u3wPPApD5RoDVzbktbIZAB++//VU4p+L/Sv7FkZfUr1wcIrC4V83uV5KOJcBOXcy6Vm5nnywGfFcjxt2w7Qpy1XG7FmLeybNIF57tz/ea5ERESfo/dWyASAKVOmoEmTJu9lbKWS+0ERERHRu5V85w4C+vYVHiwAgHnBgnAbMkTI5l2ah6exT4XMRmOD5fWWw8fORxw4JQ4OB/pCoU8W8xxVgZojUz/qDUb03nwF55/Kb2I2KeGJCU2KIJu12ZtPjIiIiIjoC2Zjrsb6TuXQauVZ4QeDWoMRPTddxt6+VeFg9f/32fUmAo+PA3GBaQO8opjZpVgXXA+/jmN+x1Izo8mI0adG47cmv8HGzCY1tyxRAk4dOyBi9Zq0AXQ6BE+chOzr1kKh4IorRERE6bEaSERERARAFxQEv+49YEwU37BUe3jAZ8UKKK2sUrMzgWew5e4WoZ1SocTC2gtR0LGgOLDJBNXfA6COeSrm1q5A8zWpS8qaTCaM/P0GDt0Jka5t8Ff5sbB1KRYxiYiIiIjekoOVGTZ2qQAPewshD4hOwsDt12A0/v8WVjauQIc9gI24p2VqMTPdMrNKhRJTqk5BdtvsQh6YEIjZF2dL1+HcsyfUHh5Clnj2LGL/2vuWMyMiIvq8sZBJREREXzxDbCz8unWDPjRUyJW2tsi+cgU0bq6pWUxKDMacHiON0aVoF2HpqFQXVkN5+w8xUyifFzFt0x6OzNx/D9sv+kvdO1XJiZ9r5X2zCRERERERkcTF1hyL25SGOt1e80fuhmLZ8UdpgXNeoOPelxczHx0VYjszO8ytORdqpbj43W8PfsMJ/xNCprS2hvuokUgvZMYMGOLk7SWIiIi+dO+lkHn06FEcOXIEVapUeR/Df7BzfApMJhO2bduGb775Bt7e3jA3N4eHhwfq1KmD1atXQ59uaTwiIiISmbRa+Pfpi5QHD8UDGg28Fy2Ceb58Qjz13FSEJooFz0KOhdCzRE958IDLwH75IQVqjQRyVU/9ePhOCJYdeyQ1a1rSE2O+LswlpoiIiIiI3pEyObJhZKNCUj7nwD2ceRieFjjnBTr+lXExc2dnINpPiAs4FkCvEr2kccefGY+YlBghs6lTBzY1agiZITwcYfMXvOFsiIiIPn/vpZBZo0YN1KhRA05OTu9j+A92jqwuKioKdevWRatWrbB3714EBARAq9UiODgYR44cQdeuXVGhQgX4+vp+7EslIiLKkkwmEwJHj0biuXPSMc+pU2BdsYKQ7Xu6D38/+VvIzJRmmFp1KjQqjThAUhSwowNg0AqxMXcdoOqg1M/JOgPG/XlLOn/1/C6Y1bwElEoWMYmIiIiI3qVOVXLi62Li8q5GE9B36xVExKekhc75Mi5mJkUC238E9ClC3KloJxRzLiZkYUlhmHpuqpApFAq4jRkNhbm5kEdt2YKE8+ffclZERESfJy4t+4nSarVo2rQpjhw5AgDw8fHBpEmTsGXLFsyaNQuFCj3/Zdnly5fRsGFDxMbGfszLJSIiypLCFixA7J97pNxlwADYN24sZKGJoZj07ySpbd/SfZE3W7qlX00m4I9eQLT4YyKDtTsMTZcCyrRbsKXHHsE/KkloV9zbHsvaloaZmrdqRERERETvmkKhwPTviyG3s7WQh8drMWv/PbGxcz7gxz8AjdgWgZeBf4YJkVqpxuSqk2GuEguUfz/5GweeHhAyM29vOPfsIY5pND5fLebJkzeeExER0eeKT8c+UcuWLcPJkycBAKVLl8a1a9cwevRotGrVCoMHD8bly5dRv359AMDt27cxaZL84JWIiOhLFrV9OyKWr5Byh5Yt4dStq5CZTCaMPTMWsVrxh0Hl3MuhfeH28uBnFgL3xDc3TUo1ouvNB6zSVpN4Gp6A5cfFJWXVSgVmtygBa3Nxfx0iIiIiInp3bC00WNauDCw04uPRbRf9cN0/WmzsWghoulge5NI64MomIcptnxv9SveTmk46OwnPYp8JmWPnzjDLlUvIjDEx8OvRA/qoqMxPhoiI6DP2XguZBoMBJ06cwIkTJ/Dvv/++cf+zZ8+m9jcaje/hCj9Ner0eU6ZMAfD8F2QbNmxAtmzZhDYWFhbYsGEDrK2f/1ps0aJFiIiI+ODXSkRElBXFnziB4AkTpdy6RnW4jx0j7Um54/4OnA44LbbVWGNylclQKtLdTj37Fzg0QRo7ruIQ6NxLpX42mUwYv+cWtHrxHqdz1VzI72b7plMiIiIiIqI3VMDdFoO/KiBkJhMw/s9bMBpNYuOi3wGVesuD/DUQCLwqRG0LtUUZtzJCFp0Sje4HuyM0MTQ1U5qZwWvuHCisrIS2ume+8O/dB0atuE0FERHRl+i9FjKXLl2KWrVqoVatWjj/Fuu7nzt3DjVr1kStWrWwatWq93CFn6YjR44gLCwMAFCnTh0UKVIkw3aurq5o1aoVACAlJQW7d+/+YNdIRESUVemCghAwaDBgMAi5RZEi8J47Fwq1+Cbks9hnmH1xtjTO8PLD4WnjKYZRT5/vi2kSxzYW+AaJxToI2YHbITh2L0zI3O0s0LdOvjecERERERERva0OlXMir6uNkF32jcYfVwPkxnXHAzmqiJkhBdjeHkiMTI2UCiUmVZkES7Wl0DQgPgA9DvVATEpMamZRqBC85swWtp8AgKRLlxA0ejRMpnQFVSIioi/Meytk6nQ6TJ48GQBQt25d9OsnL6nwOv369UPdunVhMpkwceJEvpX5/w4cSFtTv0GDBq9s++Lxffv2vbdrIiIi+hSYjEYEjhgJY1yckGs8PeGzfBmU1uK+N3qjHiNPjUSSXtzDsrZPbTTN01QcPD4U2NAMiA8R82w5YfhmIfDCW55JWgMm7rktXd/obwrBhkvKEhERERF9MBqVEuMaF5byaf/cRVyyTgxVGqD5OsDGXcyjfYFdXYEXnl362PpgWtVp0gouD6IeoM+RPsK/Y9jWqgW34cOla4j9cw/Cly17i1kRERF9Pt5bIXPv3r2pbw3+bxnUt/G/vsHBwfj7779f0/rLcPPmzdS/y5Qp84qWQNmyZTPsR0RE9CWK2vQrEs+eFTKlrS18Vq2E2sVFar/u5jpcD7suZI4WjhhXeZy4/GxyDLDpOyDqiTiAyhxouQGwsBPiRUceICBaLI5WyeuEr4t5vMWsiIiIiIjov6iWzwVfFXYTsrC4FCw+8lBubOsGtFwPKNP9APHhIeD4DCGqk6MOxlQcIw1xJfQKhhwfAp0xrVDq+GN7ZGvbVmobvmgxEs6ceYPZEBERfV7e20/+//nnHwBA0aJFhWLamypXrhyKFSuGmzdvYu/evfjmm2/e1SV+su7fv5/6d86cOV/Z1tvbGyqVCgaDAQ8ePIDJZJL2/XqdS5cuvfL4nTt3Uv/W6/XQ6XSvaE1E75tOp4Ner0/9m4ie0z5+jNA5c6TcZdRIKLNnl74vdyPvYunVpVL7MeXHwFZlm9ZenwzV1tZQBt8Q2pmggKHJYpicCwvfy9MPQrH8+COhrUalwJhGBVPbENH7xX9WEmUt/E4SZT1f4vdyeIN8OHY/TNjDfu3pJ/iupAdyu4grt8CjDJR1J0F1YISYH58OvVtxmPJ9lRo1zdUUEYkRWHxtsdjU/zhmnZuFIWWHpGaOgwchxdcXiSdPpjU0mRAwZCh8du6A2tn5v0+UPklf4neSKCvjd1L2Pp9pvbdC5oULF6BQKF679GlmNGjQADdu3MCFCxfewZV9+qKjo1P/dn7NDYxarYadnR2ioqKg1+uRkJAAGxubV/ZJ700K0TExMYiIiHij8Yno3dLr9Yh7YdlMtZrLVBKZ9HrEDRkKU0qKkGtq1URKhQrQpvtnV1RKFAaeGwi9SbwJa+DVAEUti6b9s85ogMPBftA8Oy2dM7baWCS5VQciIlK/l5FJegz8KxDGdNvctCrlCgdlMiIikv/zXIno9fjPSqKshd9JoqznS/xeWgFoV8YNa88FpWY6gwkDtl3Bou/yw9pcJXbI9S3s856G5cO/hFj5Rw9ENN8Fg51PatbErQmCcgTht2e/CW233t+Kytkqo6BDwdTMbMRwJPv5wvj0WWpmiIiA/6BBsJk1CwpVuuugL8KX+J0kysr4nZS9WLd6197b0rL+/v4AgDx58vznsf43hq+v738e63MQHx+f+reFhcVr21tapm0sHpduTzAiIqIvQfLGjTC8sKIBACicnGA1YIC0UkGSPgljLo9BcFKwkLtbuqNHwR5CZnN+HiyeHJTOF1e2D5KKtBEyg9GEGSdCEZkoFkezZzNH5wpcUpaIiIiI6GP7saw73Gw1QnY7JBH9/3iAhBSD2FihQGyNSdA55hdipTYWDvt7A7qkF5oq0K1AN9TxqCO0NcGEBbcXwGBMG1thZQWbceMAc3Ohrf7yFST/+ut/mR4REdEn6b2ViWNiYgAATk5O/3ksR0dHYUz6sC5evPjK43fu3EH79u0BAPb29u/k/+dE9PZeXM7A0dERGo3mFa2JPn/J168japP8L/zukyfBOlcuIdMb9Zh0chLuxd4TcqVCiUlVJiG7a/bUTOF7Bqqrq6VxDWW6wOKrsbB4oUCq0+mw8mwQrgeLb1yaq5VY2qY0vN1t32puRPR2+M9KoqyF30mirOdL/l6OalQIfbddF7IbQQkYsvcpVrcvDVuLFx+nOgEtN8C0rh4UKWkvD2gi7sLlynwYG84WxplafSpa72uNxzGPU7OHcQ9xOPIwWhdo/cKwTrAYNRKhY8cJ/ZN/WQ+nqtVgWe7tt/GiT9OX/J0kyor4nZQ5ODi8t7HfWyHT2toaMTEx76T4GBsbCwCwsrL6z2N9DmxsbBAVFQUASE5Ofu1SsUlJab8As7V98welZcqUyXRbtVrNLy1RFvC/5Qw0Gg2/k/RFM8QnIGT4CMAg/nraoXUrONSqJWQmkwkzzs3AiYAT0jijKoxCRa+KaUFyLLCnN4B0a8QW+Q6qr2dDpRQXvTh+PwwbLoZK405qVhRFfRzfbFJE9E7wn5VEWQu/k0RZz5f6vWxSyge3guKx4sRjIb/sG42um67gl07lYGvxwv893AsB364Atoorsqgu/wJV4SZA3rS3MDUaDcZUHINO+zsJbZdeX4oGuRvA1co1NXNs0QLJFy8i9s89aQ2NRoQMH45cf/wOtSP/PeJL86V+J4myKn4nRe9zed33trTs//ZufPr06X8e639jvG4/yC/Fi5Xt8PDwV7bV6/WphWCNRgNra+tXticiIvqchEyaBJ2fn5CZ5cgBtyFDpLZrb67FtnvbpLxL0S5oWaClGO4fCUSnW/LepRDQbBmQrogZEJ2EwTtvSuO2KOONlmV9pJyIiIiIiD6u4Q0Lolv13FJ+6VkUOq67gEStuF0ECn4NVB0gD/RnHyApWojKupdFkzxNhCxBl4BZF2YJmUKhgPvYcTDLmVPI9aGhCJk8OdNzISIi+tS9t0Jm4cKFYTKZcPCgvG/Umzp48CAUCgUKFy78Dq7s05c/f9ra+68rFPv7+8Pw/2+h5M2bV9oHjIiI6HMVs+cvxOzeLYYqFTxnzoAy3SoPex/vxfzL86UxGuVqhL6l+4rh3b+BKxvFTKkBvlsBaMS9q7V6I3pvvozoJJ2QF3S3xcSmRd9oPkRERERE9GEoFAqMeEUxc8SuGzCZ0q3OUmsU4FFSzGIDgH0jpDEGlR0EOzM7Idv3dB/OBJ4RMpWNNbzmz4PCzEwc9u9/EH9CXkmGiIjoc/TeCpl16jxfNuH8+fM4f/78W49z7tw5nDt3ThjzS1e0aNqDz0uXLr2y7Yv7W77Yj4iI6HOm9fND8PjxUu7Spw8sS5QQsgvBFzD69GipbTn3cphUZRKUihdulxLCgT19pbaoORzwKCHF0/+5iyu+0UJmbabCkralYWmmytRciIiIiIjow3tVMXP31UCsP/NUDFUa4NvlgEosOuLaZuDuXiFytHBE/zL9pXGnnJ2CFEOKkFkULAjXYUOltsHjJ8CYmJipuRAREX3K3lsh8/vvv4e5uTkAoEePHoiPj3/jMeLj49G9e3cAgJmZGZo3b/5Or/FTVb9+/dS/9+/f/8q2+/btS/27QYMG7+2aiIiIsgqTTofAwUNgTEgQcqvy5eHU9Schexj1EP2O9IPeKC4NldchL+bXmg+zFx9CmEzAnn5AQph4Qu9yQJX+0nXsuxmEtaefSPnUZkWQx+XV+1sTEREREdHH979iZpequaRjk/fewaVnkWLoWgioLf9I8vm/R0QI0ff5vkdxl+JC5hvni7Gnx0pve2Zr3RqWpUoJmS4wEGGLl7zBbIiIiD5N762Q6enpiZ9++gkmkwnXrl1Dw4YN4e/vn+n+fn5+aNCgAa5fvw6FQoEuXbrA09PzfV3uJ6VWrVpwcXEBABw6dAi3bt3KsF1oaCi2bt0KALCwsEDTpk0/2DUSERF9LGFLlyLp2jUhU9rbw3PmDChUaW9BhiaGoufhnojTxQltXSxdsLTOUmmpJ1zbAtz9S8w0VsC3KwCVuKH50/AEDNlxXbq25iVc0KiY+1vMioiIiIiIPgaFQoFRjQqhRn4XIdcbTej162WExiWLHSr1BnwqiFlCGLB3wPMfR/4/pUKJMRXHiCvAAPj7yd9YcX2FeA1KJdwnjAfU4r93RK5fj+Q7d95uYvR/7N13dBXV2gbwZ05J771BqNJrKNJRREAEBCnSmyIgRXoNvXcQEBWlK01FRfCK9N5C7zWQQnqvp8z3R7xJdjbflSAJ7fmtxVqc57xnz+x7Vwxz3pm9iYjoJVFgjUwAmDlzJsqUKQMAOHbsGCpWrIjhw4cjKCgIZrNZqjebzQgKCsKwYcNQqVIlHD9+HEDWnpCzZs0qyFN9qeh0OkyYMAEAoKoqevTogbi4OKEmPT0dPXv2RMrfT6MMGjQIrq6uhX6uREREhSlx927ErPpKyr2nT4PeK6eBmGJIwWd7P8OjlEdCnY3OBivfWQlvO29xgPgHwC55OSe8OwNwLSlE6QYTBm4KQlKG+JRneU8bDGngl88ZERERERHR86bRKFj6UVX4OVsLeURiBgZ9fw4GU67vOTVa4IMvs256zO3qL8B+8fvNsi5l0btCb+l4K86vwB/3/hAyqzfegGvfvmKhyYTwwElQTab8T4qIiOglUaCNTAcHB/z222/w8/ODqqpISkrC0qVLUbNmTTg4OKBChQqoV68e6tWrhwoVKsDBwQE1a9bEsmXLkJiYCFVV4efnh99++w0ODg7/fMDXyIABA9CgQQMAQFBQEKpUqYKZM2diy5YtWLhwIapXr569rGz58uUxceJjlrUgIiJ6haScOoWw0WOEu5wBwKljRzi8+272a1VVMebQGFyPvS7UaRUtFjVehLIuZcWBzWZgx0AgU3xyE6XeAWr0ESJVVTH5lyu4Gp4o5A5WOsx4rwQsdAX6Ty8iIiIiIiogTjYWWNUtQPo3/al7sZj3h3htAdeSQNNp8iCH5gEnxRsvB1cbjIZ+DaXSCUcm4EKUuNKM24D+0BctKmTply8jbtP3+ZgJERHRy6XAv00rVaoUzp07h/feew+qqmb/SU1NxfXr13HixAmcOHEC169fR2pqavb7APDee+/h3LlzKFWqVEGf5kvHwsICv/zyC95++20AWUvxTpw4ER999BFGjhyJa38vK1G9enXs3r0bjo6Oz/N0iYiIClT6jZsI+WwQVINByC1KloTn2DFCtvXGVhwMOSiNMbnOZNTzrScPfvJL4P5hMbN2BlovBxRFiL/YdxtbzjyUhpj3YUX4OFo+4WyIiIiIiOhFVNHXETM+qCjl3xy+h98vhothjb5ZNz/mtXs0cGl79kutRot5DeehtHNpoSzTnIkh+4YgLDksO9NYWcF7ymRpyMglS5B+/bqUExERvQoK5bEAFxcX7Ny5E0ePHkXHjh2zlzjN3dj8b/PSxcUFHTt2xNGjR7Fz504uh/o/ODs746+//sLmzZvRsmVL+Pj4wMLCAp6ennj77bfx9ddf4+TJkyia504tIiKiV4khPBwP+/WDOUl8YlLr5oYiX62CxiZnSafgxGAsPLtQGmNAlQFoW7qtPHjkNeCvqXLechHgIC4/u+FEMBbtuSmVftqoBJqU9XjC2RARERER0YusY40i6FxL/q5t1PYLuB2Z65pEowE6rAW8q8qD/PwpcOuv7Je2elssf3s5XK3E70Fj02MxZN8QZJgycmrr1oVD61ZCnZqaioef9ochIuKp5kRERPQi0/1zybNTp04d1KlTBwBw/fp1hIaGIiYmBgDg6uoKHx8flCtXrjBP6aWnKAo6deqETp06Pe9TISIiKnSmhAQ8+OQTGPNcsGtsbFDkq1Ww8MvZk9JoNmLCkQlIM6YJtU2KNsGAKgPkwY2ZwE/9gFxfGgAAKrYHKrYTot8vhmPSL5elId4s4YJR75aBauaeNUREREREr4oprcvjalgCLoQkZGepmSb023AWv3xWD/ZW+qzQ0h7ouh1Y0xyIuZ0zgNkIbO0O9NwJ+AUAAHzsfLDs7WXo858+QuPyRtwNzDs1D4F1ArMzz7FjkXLoMEzx8dmZMSICDwcMQLENG6CxtS2YiRMRET0Hz22jprJly6JJkybo2LEjOnbsiCZNmrCJSURERPkSPnEiMm/fEUOdDr5fLIN1hQpCvPbKWmmPGRcrF0yqMwlKniViAQD7ZwCPLoqZvQ/QcoEQHbkVjc+3nMu7NSfKeTvg6x41oNNyX0wiIiIioleJpU6Lld0C4GyjF/K7USkYvf1i9spzAAA7d6D7z4C9uKILDKnAj30AQ86NlpXdK2NG/RnS8bbe3Io/7v2R/Vrn4gLfpUsBvXj8jKvXEDpyFFQTb6QkIqJXB79ZIyIiopdS8uEjSNrzl5T7zJoJu3riXpfXY69jxfkVUu2UOlPgYuUiD352LXB0qZx/sCJrf8y/XQ5NwKcbzsBgEruYRV1ssK5PTThY6fOOQERERERErwBfJ2t80bk6NHnuidx9+RG+OXxXDJ2KZjUzrZzEPO4+cFjc+qJ5seboXbG3dLwpx6cgODE4+7Vt7VrwnjZNqkvevx8Rc+fmZypEREQvNDYyiYiI6KWjZmYiYtYsKXcfPhyOrVsLWaYpE+MOj4PRbBTytqXa4q2ib8mD39oD7Bwu5zU/AUq+nf0yLdOEIT+cQ0qmeLezm50lNvStBQ97q3zMiIiIiIiIXjb1S7thxLtlpHzO7us4cTdGDD3KAV22AopWzI8sAaJvCdHgaoNR1b2qkKUYUjDy4Ehh2Vmnth/AdUB/6fhx6zcgbvOWfM2FiIjoRVUojUyTyYSIiAgYDIbCOBwRERG94mI3bETmvXtCZl29Olw/+ViqXRa0DLfjbwuZj60PRtccLQ8cdg7Y2hNQ8yzF5F0FaCre7Txn9zXcjU4RMntLHdb3qQV/V+5JQ0RERET0OhjQqCTeKecpZGYVGLH1ApLS83wXWrQ28OYAMTMbgN+HI/deFXqNHvMazoOjpaNQej32Ouafni9k7kOGwKFlS+m8ImbORPrVq08xIyIiohdLgTYyExIS8Mknn8DJyQk+Pj5wdHREz549ERsbW5CHJSIioleYITIS0SvyLBOrKPCaOEHa6/Jo6FGsu7pOLIWCGfVnwM7CThwjLhjY1BEwiM1JOBUFumwDLGyyo8O3orDueLBQplGAr3vUQHkfh6ebGBERERERvXQ0GgULO1ZBMVcbIQ+NT8PM36/JH2g8DnDwFbN7h4BL24XI284bM+vNlD6+5cYW7A3em/1aURR4z5oJ6+rVhTrVYEDIsGEwJSfnc0ZEREQvlgJrZCYlJaFhw4b47rvvYDQaUaVKFQDAxo0b0aBBAyQlJRXUoYmIiOgVFrVwEcypqULm1LEjrMqXF7KYtBhMODJB+nz38t1R06umGKbGApvaAymRYm7lBHT9EbDPucM6IdWAUdsuSuN+9lYp1Cnpmr/JEBERERHRS8/RWo+VXQOg14o3Vm4+/RD7r+e5xrC0A5rPkQf5z3ggLV6IGhVphF4VekmlM0/ORFJmznerGktL+C3/AjpP8clQQ/ADPJo0CWqupz2JiIheNgXWyJw5cyYuXboEFxcXnDt3DkFBQbh48SLc3Nxw/fp1zJgxo6AOTURERK+o1KBzSPjlFyHTODrC/fOhQmZWzZh4dCJi0sV9aco4l8HQ6mItjBnAlm5A9E0x11oCnX8A3N8Q4km/XsajxHQhq+DjgMFvl36KGRERERER0augvI8DPn/nDSkf8+NFxKdmimG5VkDpd8UsJRLYJ39fOqT6EFR2ryxkUWlRWBa0TMh0Li7wXbQQ0Ip7cCbu2o34LVvzMRMiIqIXS4E1Mrdu3QpFUTBo0CCULVsWAFCqVCkMGjQIqqpi61b+AiUiIqInp5pMiJgpL63kPmQwdM7OQvb9te9xJPSIkFlprTCv4TxYaC1yQrMZ+Lk/EHxUPmDbVYB/XSHaeTEMv5wPEzILnQZLOlWFha5Qth4nIiIiIqIX1KcNS6BKESchi0zKwORfr4iFigK0mAforMT89Grg3mEh0mv0mF1/Niw0FkK+5cYWXIi6IGQ2AQFwHzJEOq+IWbOQfv16/iZDRET0giiwb9xCQ0MBAGXKlBHy0qWznlYIDw8vqEMTERHRKyhq2RdIvyJ+AWBZpgycO3USsuux17Ho7CLp86NrjUYJpxJi+Ndk4MpP8sHenQFUbCdEEYnpmPDzZXncZmVQ2tP+CWdBRERERESvKp1Wg4UdqsAyz02Ov5wPw65Leb4LdSkONByVZwQV2NodiLkjpEUdiuLTKp/mqVQx7fg0GMwGIXf95GPY1q8v1mZmInTo5zBxqy8iInoJFVgj09vbGwBw86a4TNutW7cAAB4eHgV1aCIiInrFxO/YgZivvpJyr4kToOh02a8TMxMx6uAo6WL+naLvoH3p9uKHT30DHBOXYwIA1OoH1BkkRKqqYtT2i0hIE8d9s4QL+tQrns/ZEBERERHRq6qUhx1GNy8r5RN+voR70SliWHcI4JZnOdq0OOD7jkBqrBD3rtAbJR1LCtnNuJvYcHWDkCkaDXzmzYUuz3evmcHBCB0xAqrJlM8ZERERPV8F1shs3749VFXF8uXLcfv2bQDA3bt3sXz5ciiKgjZt2hTUoYmIiOgVknrmDMIDJ0m5Y5vWsKlZM/u1wWTAsP3DcD/xvlDnaeOJKXWnQFGUnPD678Du0fLByr4PNJ+TtdRTLptOPsChm1FCZm+pw4IOVaDRiLVERERERPR66123GGoXdxGyuFQDun97EhGJ6TmhzgJo/x2gtxUHiLkNbO0BmHJupNRr9ZhUR74u+vL8lwhJChEynYsLfBcuADTiV78phw4jct78p5wVERHR81FgjczAwECUKVMG0dHRqFKlCmrUqIFKlSohKioK/v7+mDJlSkEdmoiIiF4RmQ8eIGTQYMAgPglpVaECvCZPzn6tqiomHZuEU49OCXUaRYM5DebA0dIxJ4wLBn76FFDN4sH8agLtvgE0WiG+F52Cmb9fk85tcusK8HO2ecqZERERERHRq0qjUbCgQxXYWojXFiFxaej53SkkpOa6vvGqBLT/FkCeGyTvHwZ+Hw6oanZU3bM6Piz9oVCWbkrHjBMzYM5zfWNTsybch30unVvsunWI3779qeZFRET0PBRYI9PR0RFHjhxBt27doKoqgoKCYDKZ0L59exw9ehSurq4FdWgiIiJ6BZgSE/Gw/wCY4uOFXOfpCb+VK6GxyWkiLj+/HDvv7pTGGFxtMGp41cgJzGZgxwAgM8/eMC4lgM6bAQuxMWk0mTF863mkGcTll94t74kPq/s+3cSIiIiIiOiVV8TFBos6VUXeBVyuP0pC33WnkZaZ6xqjTAvg3RnyIEHrgePLhWhYwDC4WIlPex4NO4ovzn0hfdz144/h0LqVlIdPnYbU06effDJERETPUYE1MgHA1dUV69evR0JCAkJDQ5GQkICtW7dm759JRERE9Diq2YzQESORefeukCvW1vBbuQJ6z5z9Xn68+SO+vvi1NEaHNzqgb8W+YnhiJRB8VMysnYGu2wFbN2mMrw7dxbkH8ULmZmeB2e0qiUvVEhERERER5dGsghdmta0k5WeC4/DZ90EwmHI9RVnnM6B6T3mQPZOB4OPZLx0tHTGm5hipbPWl1fjx5o9CpigKvKdPh3WVKmKxwYCQwUOQ+fBh/iZERET0HBRoI/O/9Ho9vL29YWlpWRiHIyIiopdc7Lr1SDl8WAwVBb7z58G6QoXs6FjYMUw/MV36fAPfBhhfe7zYbIy8BuydJh/s/SWAa0kpvhyagMV7bkr57HaV4WrHf9MQEREREdE/+6hWUYxqVkbK912PFLewUBSg5UKgeCOxUDUB2/sAKTHZUYviLdDUv6k05vQT03Es7JiQaSwt4bf8C+jyPFhiio9HyKDBMKeng4iI6EVWKI1MIiIioieVfvUqIhctknKPkSNg/8472a9TDakIPBoIkyou+1rOpRwWNFoAnUaXExozgZ/6AaYMcdBKHYEKH0jHikrKwIBNZ2E0q0LesYYfmpb3zP+kiIiIiIjotTWwcUn0rV9cytcdv4/zD+NzAq0e6Lgua+uL3JLCgB39s7bKQNaTljPqzUAF1wpCmUk1YcSBEbgVd0vIde7uKLJyBRRrayHPuHEDEXPnPv3EiIiICgEbmURERPTCMKemInTESMBgEHL75s3h0qePkH1z6RtEpkYKmY+tD1a+sxI2enGvSxyaDzy6KGb2PsB786RzSM00ou+603gYmybkfs7WCHy/fD5nRERERERErztFUTDhvXJoW81XyFUVmPDzJRhzLzFr7Qx0WAtoLcRBbv0JHFuW/dJGb4PlTZbD21Z80jLZkIzP9n6GqNQoIbcqVw4+8+SmZfwPm5H4xx9PNzEiIqJCwEYmERERvTAiZs9B5r17Qqbz8Yb31CnCMrEPEh9g3ZV1Qp1eo8fyJsvhZp1nr8uQM8DhhfLB2izP+pIgF6PJjMHfn8PFkAQh1yjAwg5VYG+lf4pZERERERHR606jUTD3w8oo7WEn5FfCErHhRLBY7F0FaDZLHmTvNODByeyXbtZuWNFkBez04pjhKeEYeXAkjGajkDs0bQrXj/tKw4ZPDOR+mURE9MIqkEZmYmIiEhMTYTKZ/rn4BT4GERERFZ7EP/6D+G3bxFCjge+8edA6Ogrx3NNzYTCLT232KN8DpZ1Li5/PSAJ+/DhrX5ncan4MlGoiRKqqYvKvV7D3uviUJwBM/6Aiapdwzd+EiIiIiIiIcrHQaTDjg4pSvvDPm4hIzLNXZc2PgfIfiJlqArb3BlJjs6PSzqWxsPFC6BSdUBoUGYSV51dKx3IfOhTWVaoImTk5GaHDR0DNzMzfhIiIiApBgTQynZyc4OLigt9//70ghi+0YxAREVHhMISFIXzSJCl3698fNjVqCNmhkEM4FHJIyDxsPNCvcj954N9HAnHiE55wKQE0nSaVfnnwDjadfCDlAxuXRNfa/k8wCyIiIiIiov+tdglXfFjdT8iSM4yYvvOqWKgoQOtlgHMxMU8MBXaPEaK6PnUx8c2J0rFWX1qNY2HHxGH1evguWgiNg4OQp1+6hMjFS/I1FyIiosJQYEvLqqpaUEMX6jGIiIioYJnT0xEyZCjMiYlCbl2tGtwGDhCyTFMm5p6S93UZETBC3hfz4lbg4mYxU7RA268BC1sh/uV8KOb9cUMat01VH4x8t0w+ZkNERERERPS/jX+vLBytxW0rdl4Mx6Gb4r6WsHJ8/H6Zl7YCd/YJ0YdvfIh2pdsJmQoV4w6PQ3RatJDrfX3hPWO6dF6xa9YgfseOfM2FiIiooHGPTCIiInpuVFXFoylTkX75spBr7OzgM38+FJ24PNL6q+vxIEl8ajLAMwAtircQB469B+wcLh/wrXFAkZpCdPxODEZuuyCVvlnCBfPaV4ZGo0jvERERERERPS1XO0uMaV5Wyif9chnphjzbYvhUA96ZKg+yczhgSBOisbXGopRTKSGLTY/F2ENjYTKL4zq8+y6cu3aVhg0fNx4Jv/32hDMhIiIqeLp/Lnl6y5Ytww7exUNERET/j7iNm5DwmH8reE+fBgs/XyF7mPQQX1/8Wsg0igbjao2DouRqNpoMWftiZiaJgxZrANQXm5s3I5LQb8MZGEziKg9veNrhq+41YKnT5n9SRERERERE/+CjmkWw7exDnHsQn53dj0nFmB8vYkmnquI1Tu1PgcvbgdCzOVncPeDQfKBJzhYd1jprLGi0AJ1/74w0Y06T8+Sjk/jm0jfoX6W/cA4eo0chNSgIGdeu5YSqirAxYwFFA8f3Wz6z+RIRET2tAm1k7t+/v8DGFn6ZExER0Usn5dQpRMyZI+UuffrAoYX4hGVCRgIG/jVQuBgHgI5vdEQZlzxLvx6YDYSeETNrZ6DtV4AmpzEZkZiOXt+dQlK6USj1dLDEmt61pKWeiIiIiIiInhWNRsGMDyqi1RdHYM51X+Uv58NQws0OQ98pnatYC7RaCnzVCFBzPVl5dClQqQPgUS47KulUEuNrj0fg0UDheF9e+BIV3Sqivm/9nGEtLeH3xRcI7t4dxvDwnGKzGWGjR0PRaqRrMyIiosJWoHtkFsYfIiIievkYwsMR+vkwwCQub2Rbtw48hg8TskxTJj7f/znuJ94XcidLJwyqNkgc+MrPwOGF8gFbLwccc57wTM4wovea0whLSBePb6HFd71qwtfJOv+TIiIiIiIiyocKPo74tFFJKV/81038eiFMDL0qAXU+EzOzEfhtKGA2C3Gbkm3QqkQrsVQ1Y9TBUbgTf0fILfx84b9uLXReXnnGNiN05Cgk/vln/iZFRET0jBXIE5n37t0riGEfy8PDo9CORURERP+empmJkCFDYYqNFXK9nx98Fi4U9sVUVRWTj03GmQjxCUsFCqbUmQJHS8ecMPg48NOn8gFr9AHKvS+MOeSHc7ganiiU6TQKvuwWgAo+jnlHICIiIiIiKhAj3y2DG4+SsO96pJhvuwA/Z2tUL+qcEzYeC1zZASQ8yMkengSC1gE1emdHiqJg4psTcTnmMu4l5HxPm2xIxqC9g/B9y+/hbJUzrkXRovBftxbB3XvAGJnrPEwmhI0eA6ty5WBRpMgzmzMREVF+FEgj09/fvyCGJSIioldA9KpVSL90ScgUKyv4Lf8COmdnIV95YSV23t0pjTE8YDia+DfJNegtYHNnwJQhFrqXA96dKUQbTwRLXxIAwKx2ldDwDfd8zoaIiIiIiOjpaTUKlnWuhvZfHsP1R0nZeabRjH7rz+DngfVQxMUmK7SwBd5fBGxqLw6yZxLgXxdwz9l2w0ZvgyVvLUG337shyZAzbkhyCIYdGIZvmn4DvTZnOw0Lf38UXbcWD3r0hDEqKjtX09PxaMpUFFn9Dbf6IiKi56LAlpYlIiIiyivt0iVEf/W1lHvPnAGrsmWF7Nc7v2LVhVVSbacyndCzQs+cIDkS2PghkBYnFtp6AF02AxY22dG96BTM3HVNGnNok9LoWIN3GBMRERERUeGzs9Th21414WZnKeTRyZn4ZP0ZpBtybclRuilQoa04QEZiVnMzWbxhs4RjCSxovABaRSvkZyPOYtqJadK2XZbFi6PourXQurgIecrRo0jc+ftTzo6IiOjfYSOTiIiICoU5PR1hY8dJ+2I6d+kMx5YthexO/B1MOz5NGqOBbwOMrTU2507gzBTg+05AfLBYqLcBumwBnItlR0aTGcO3nke6Qdw/pnkFL3z+TumnnxgREREREdG/5OtkjW96BMBSJ35de/1REqbtvCoWN58DWOXZEiP+AfBDZ8CQJsR1fepiTK0x0vF23N6B9VfXS7lliRLwmjhByiNmz4YpPv7JJkNERPQMsZFJREREhSJq6TJk3rkjZHr/ovAYOVLIMk2ZGHNoDDLyLBNb1qUs5jeaD50m18r4u0YBYUHigRQN0H4N4FtdiL86dBfnHsQLmZudJWa2rcglkoiIiIiI6LmrVtQZCztWkfLvTz7ArxfCcgJ7L6D9d0CeJy0Regb4qR9gFm/e7Fy2MzqV6SSNu/jsYpyPPC/l9i1awLZhAyEzxcYicuHCJ58MERHRM8JGJhERERW41NOnEbt2rRhqNPCZPQcaGxshXhK0BDfibgiZm7Ublr+9HLZ625zwwQng/Cb5YO8tAMo0F6LLoQlYvOemVDqnXSW45lm+iYiIiIiI6Hl5v7IP+jcqKeXjfryIe9EpOUGpd4CWC+QBrv0K/DVZisfWGos3vd8UMpNqwuhDo5GQkSDkiqLAa9IkKFZWQh6/bTtSz5zJx2yIiIj+PTYyiYiIqECZU1IQNm48kGf/Fdc+vWFTvZqQHQs9hg1XN0hjzKw/E562nrkGNQG7Rkp1qDcUqNlXiNINJozYegFGs3j8TjWK4J3yniAiIiIiInqRjHj3DQT4OwtZSqYJn20KEvfLrNEHqDtYHuDYMiBIXDZWp9FhYeOF8LPzE/LwlHBMPjZZ2i/Tws8P7oMHSUOHT5oMc2ZmPmdERET09NjIJCIiogKjqirCp06FISREyC1Ll4LbYPGCOzY9FhOOynux9CjfA3V96orh2bXAo0ti5lUJaCLfebzgPzdwIyJJyHydrDHx/XJPPhEiIiIiIqJCotdq8EXnanCy0Qv51fBEzPg9z36Z70wDyrWWB9k1Cnh0WYgcLBywoNECcbsOAHsf7MXmG5ulIVx69IBl2bJClnn3LmJWrcrHbIiIiP4dNjKJiIiowMSsXo3EX38TQ50O3nPmQGOZs6SrqqqYdHQSotOihdKyLmUxtPpQ8fOpscC+6fLBWswHNOIeMQdvRmH1kXtCpijAwo5VYG8lfilARERERET0ovBxssaix+yXufHEA/xxOTwn0GiAtl8BvgFioTEd2NYLyEgW4gpuFTCs+jBp3Pmn5+N67HUhU/R6eE+bmnURlUv0qq+QGnQufxMiIiJ6SmxkEhERUYFI2rsXUYsWS7lb//6wrlBByDZe24iDIQeFzEprhbkN5sJCayEOsG8GkBYnZpU6Av51hCg6OQMjtl6Qjt+3XnG8WcI1HzMhIiIiIiIqfG+X9cSnDUtI+aRfriAx3ZATWNgAnTcDdl5iYcwt4PcR0jYf3ct3RyO/RkJmMBsw6uAoJGeKjU/rypXh3LWrOK7ZjLBRo2BKFmuJiIgKAhuZRERE9MylX7+O0FGjpQtm2wYN4Nb/UyE7/eg0Fp5ZKI0xquYolHDKc9EefhE4u0bMLOyAptOEyGxWMXLbBUQnZwh5OW8HjGpeJp+zISIiIiIiej5GNiuD6kWdhCwyKQPz/7ghFtp5AB+uBpQ8X/de3Ayc/16IFEXB9HrT4WHjIeT3E+9jwF8DkGJIEXKP4cNgUayYkBlCQxExfUa+50NERJRfbGQSERHRM2WMjsbDgQOhpqYKuUXJkvBdtBCKLmc/lvDkcIw8OBIm1STUNi7SGB3e6CAOrKrA7tGAahbzhqMAB28hWnPsPg7ciBIyK70GX3SuCkuduPwsERERERHRi0qv1WBRx6qw1Ilf4248GYygB3lWqineAGg0Vh5k10ggUlw21tnKGfMazoMmT+PzfNR5qZmpsbGBz/z5gE7cWzPhl1+Q8PvvTzErIiKiJ8dGJhERET0zamYmQgYPgTEsXMi1jo4o8uVKaO3ts7N0Yzo+P/A5YtNjhVo/Oz/MqDcDSp59WHB4IfDguJi5lgLeHChEV8ISMHe3eJEOAJNbVUApD3spJyIiIiIiepEVc7PFkCalhUxVgfE/XYLBlPdGz5FA8YZiZkh97H6ZAZ4BGFxtsHS8c5HnMPCvgUg15Nycal2pItyHDJFqH02ZCkNYWP4mRERElA8vZCMzLi4Of/75J3bt2oWQkJDnfTpERET0hCIXL0HauXNiqNPBd9kyWBQtmh2pqorpJ6bjasxVodRaZ40lby2Bo6WjOMal7cC+6fIBm88FdDl7aCakGTD4h3PIzHMx36KiFz6qWeTpJkVERERERPScfdKgBN7wtBOy64+S8O2Re2KhRgu0Ww3Yuot51DXg508Bs3it1LdiX/Qs31M6XlBkEAbuFZuZrn37wKZmTaHOnJSE0NGjYc7MfIpZERER/bNCbWTGxsZi2bJlWLZsGW7evPnYmjlz5sDX1xctWrRAq1atUKxYMfTq1QsZGRmPrSciIqIXQ/KhQ4hds0bKvQIDYVu7lpBtvrEZv975VaqdWncqyrjk2cMy+BiwY4B8wLLvA6XfyX6ZaTSj/4azuBsl7ufi7WiF2e0qyU94EhERERERvSQsdBrMaltJypf8dRMPY8VtPWDvCbT7BkCea6DrO6UbRBVFwYgaI9C9fHdp7LMRZzHswDCY/97eQ9Fq4TNvLjQODkJd2pmzCB36OVQ2M4mIqAAUaiNzy5Yt+PzzzzF69Gi4urpK73///fcYP348MjIyoKoqVFWF2WzGhg0b0KdPn8I8VSIiIsoHQ2QkwsaOk3LnLp3h3KmjkF2PvY55p+dJtb0q9EKL4i3EMPo2sLkLYMpzQexeDvhgZfZLVVUx5seLOH43RijTKMCSTlXhZGMBIiIiIiKil1mNYi7oXKuokKUbzJiw4zLMZlUsLvkW0Pgx+2UeWQRc2CxEiqJgVI1R6Faum1R+LOwYfrr1U/Zrvbc3vKdOkeqS9+9HyPDhUA2GJ58QERHREyjURub+/fsBAA0bNpQamaqqYuLEidmv27dvj5EjR8Lf3x+qqmLz5s04cuRIYZ4uERERPQHVbEbYmDEwxYp7XVqWKwePMWOELNOUiXGHx8FoNgp5be/aGFp9qDhwSjSwqT2QFifmdp5A122AVc7ys4v23MTP50KlcxvVrCxql5BvniIiIiIiInoZjW1eFm52lkJ26GYUFvx5Qy5uOBoo/4Gc/zoYeHBSiBRFweiao9G1XFepfNHZRYhOi85+7dCiBZw6dZLqkv/ai9DhI9jMJCKiZ6pQG5k3b96EoiioU6eO9N7Ro0dx//59KIqCuXPnYuvWrZg3bx5Onz4NFxcXAMC6desK83SJiIjoCcR8sxqpx08ImWJjA9+FC6GxFC+wl59fjtvxt4XMy9YL8xvOh06jywnNJmBrDyAuz34vehugyxbAKWe/y82nHuCLfeKYANC1dlH0b1TiKWdFRERERET04nG00WNSq/JSvvLAHWw+9UAMNRrggy8B76pibsrMWvkmXqxXFAVjao5BU/+mQp6UmSStquM1KRAOrVpJ55G0Zw9CR45iM5OIiJ6ZQm1kRkdn3blTqlQp6b2//voLAGBtbY2BAwdm525ubujSpQtUVcWJEyekzxEREdHzk3ruHKKWLZNyr4kTYVmiuJAFRQRh7eW1Uu2s+rPgbOUshocXAsFHxUzRAO2/A3yq5ZTdisKEHZelMd8u64GprStwX0wiIiIiInrltKrsjRYVvaR8wo7LOHwrSgwtbIDOPwD23mKeGg1s7goYM4RYURSMqzUO9np7Id99bzeOhOaslqdotfCZPQsO770nnUfSf/6DiNlz8jkrIiKixyvURmZMTNa+VXZ2dtJ7/102tlGjRrCxsRHeq1QpayPrBw8eSJ8jIiKi58MUH4+wESMBk0nIHd5/H45tPxCyFEMKxh8ZDxXivi3dy3dHTa+a4sAPTwEHHnPR23wuUCZnD82UDCNGbbsIU569YCr5OuKLztWg0xbqP3OIiIiIiIgKhaIoWNixCir7OQq5yaxi4MYg3HiUJH7AwQf46HtAZy3mjy4CfwZK47vbuOPzgM+lfMaJGUgzpuWch04Hn3lzYd+iuVQb9/33SLtw4cknRURE9P8o1G/4NJqswyUnJwu50WjEyZMnoSgK6tevL33uv0vLpqamFvxJEhER0T9SzWaEjR0HQ1iYkOuLFoXXlMnSk5ALzixAaLK4h2UJxxIYUm2IOHB6IvDjx4AqNkdRtStQu58Qrdh/G48S04XM18ka3/aqAVtLHYiIiIiIiF5VNhY6rO5ZA75OYnMyKcOI3mtOITLPtRJ8qwNtV8kDnfoKuLZTitu/0R5V3KsIWWhyKFZdEMdQdDr4zp8P+2bNpDEeTZsONc+Nr0RERPlVqI1ML6+sJQ+uXLki5IcPH0ZKSgoAoG7dutLnkpKy7iLK+6QmERERPR+xa9Yg+cABMdTr4btwAbR5Vl44+PAgtt/cLmQ6RYdZDWbBSmcljrF7NBAfLGbOxYEWc4XofnQKVh8W98+00GqwpndNeNjnGZOIiIiIiOgV5GFvhTW9a8I+z42cYQnp+Oz7IBhNZvEDFT4A6gySB/rlMyD+oRBpFA0m15kMnSKOve7KOtyIvSFkik4H3wXzYVm6tJCnX7mC+G3b8jcpIiKiPAq1kVmjRg2oqoqNGzdmLzMLAF988QWArP0x69SpI33u5s2bAAA/P7/COVEiIiL6f6UGBSFy0WIp9xw1EtZ/Lwf/X/cS7mHc4XFSbb8q/VDBtYIYXtoOXPhBzDQ64MNvAUtxf5ZpO68iM89F+ccNiuMNT7GOiIiIiIjoVfaGpz2+7BYAnUZcFef0/Tgs+euW/IEmkwHfADFLjwd+7AuYDEJc2rk0elXsJWQm1YQJRyYg05Qp5IpeD8/AidLhIhcvgTEu7onnQ0RElFehNjK7dOkCAAgPD0fNmjUxbNgwNGvWDDt27ICiKOjQoQMsLCykzx07dgyKoqBChQrSe0RERFR4jHFxCB02XNoX075pUzh37y5kCRkJGLJvCJIM4v4sFV0r4uNKH4sDxwUDO4fLB2w8DvATL7L3XY/AvuuRQublYIXP3iqVz9kQERERERG9/OqXdsOMDypK+YoDt3H4VpQY6iz+vlnUQcwfngT2z5TG+LTyp/CzEx8uuRF3A8uClkm1trVqwaFlSyEzJyQg6jE3whIRET2pQm1ktmnTBu+99x5UVUVwcDCWLVuGv/76CwDg4OCAKVOmSJ+JjIzE0aNHAeCxT2sSERFR4VDNZoSNHgNjRISQ64sUgfesmcK+mEazEaMPjcb9xPtCra3eFrMazIJeo88JDWnAlm5ARoJ4QP/6QP1hQpRhNGHab1elcxvfshz3xSQiIiIiotfWR7WKolONIkKmqsCwLefl/TJdigOt5UYkjiwGLv8oRFY6K0yuO1kqXXd1HY6FHZNyj9GjocmzPVj89u1Iu3TpCWdCREQkKtRGJgBs374dQ4cOhYODA1RVhaqqqFWrFv766y/4+/tL9V9//TVMfz/10ewxm0YTERFRwVONRoSPG4+Uw4eFXNHr4btkMbT24pKuC88slC5qFSiY13AeijsWzzWwCvz2OfDoonhAK0eg3VeARivEqw/fw/2YVCGrXdwFrSp7P93EiIiIiIiIXhFTWldAmTzbbUQnZ2Lo5vMwmVWxuEJbIKC3PMiPnwBXfhaiN73fRI/yPaTSiUcmIi5dXDZW7+kBt0F59uFUVTyaOg1qnpV9iIiInkShNzKtrKywePFixMTEIDw8HPHx8Thx4gQCAgIeW//+++9j//792L9/P8qXL1/IZ0tERERqZiZCR45Cwi+/SO95jBsL6zxLv/906ydsvLZRqh0eMBwN/RqK4amvgYub5YO2/gJwFJcvCo5JwfJ9t4VMo2RdrOd+GpSIiIiIiOh1ZG2hxfIu1WCtF28IPX43RrqWAgA0nw145Pm+VTUB2/sCV8Xrv6HVh6KMcxkhi0qLwuRjk6GqYpPUpXs3WJQqKWTply8j7ofHXPsRERH9g0JvZGYfWKOBp6cnHBwc/mdd1apV0ahRIzRs2PB/1hEREdGzZ87IQMjgIUj64w/pPYf3WsC5c2chuxx9GdNPTJdqW5dsjZ4Veorh/SPAH+Pkg9YfBpRvI0TxqZnovfY00gziHbzd3/RHOe///W8JIiIiIiKi10VpT3tMa1NBypfuvYn9NyLFUG8NfLQJsM+zwo1qArb3Aa7+mh1ZaC0wt+FcWGothdL9D/dj+63tQqbo9fCaGCidQ+TChch88CCfMyIiotfdc2tkEhER0YvNnJKCh5/2R/LBg9J7do0awXvWLOFJyMTMRIw8OBJGs1GorexeGZPqTBKfmkwIAbb2zLpAzq3k28Db4gVvhtGEfhvO4m5UipC72FpgeFPxjmAiIiIiIqLXXYcaRdCuuq+QmVVg8PfncDMiSSx2KQH0/A2w8xJzsxHY3hu4/nt2VNKpJEbWGCkdb+6puTgVfkrIbN+sDYeWLYVMTUtD+PgJUM3mp5gVERG9rtjIJCIiIolqNiNk8GCknjghvWffrBn8vlgGjZVVTr2qYsqxKQhNDhVqPW08sfStpeJduyYDsLUHkBotDuzkD3z4rbAvpqqqGLP9Ik7dixVKFQWY3a4SHG30/2KWREREREREr6bpbSqipLutkCVnGNF33WnEpmSKxW6l/25meoq52Qj8+DEQF5wddSrTCY38GgllGaYMfLb3M5x+dFrIPSeMh9bFRchSz5xB3MZNTzkrIiJ6Heme58FNJhMuXryIkJAQJCYmwvQEGz736CFvLE1ERETPVvzWbUg5dlzKHdu0gffMGVB04j8hNt/YjD3Be4RMp+iwqPEiuFm7iYMcmg+EnhUz3d9LGtmIF7mL99zEjvNh0nmMb1EOzSp4STkREREREREBtpY6fN2jBj5YcRRJ6Tmr5jyMTUP/DWex4eNasNTl2kvT/Y2sZuba94GUXEvQGlKBXSOBLlsBRYGiKJhWbxo+/PVDRKfl3JyabkrHZ3s/w4omK1DTqyYAQOfiAq/JkxE6dKhwbpGLFsGuYQNYFCtWIHMnIqJXy3NpZD548ABTp07Fli1bkJaW9sSfUxSFjUwiIqICZoyORuSiRVLu9FEneE2aBEUjLuhwNeYq5p+eL9V/HvA5KrtXFsOQs8ChBfJB2ywHvCoJ0fazIVi277ZU2v1Nf3zcoPgTzISIiIiIiOj1VdLdDiu7VkevNadhMqvZ+an7sZj482XMa19Z3ALEvczfzcz3gNSYnPzWn8CVn4GK7QAALlYuWPbWMvTb0w/JhuTssjRjGj7b+xlWNlmJGl41AAAOzd5FUsuWSPw9Z4laNT0dYeMnwH/DeijaXM1UIiKixyj0pWWPHj2KqlWrYu3atUhNTYWqqvn6Q0RERAUrYs5cmBMThcy+WTN4TZ4sNTGTM5Mx6uAoGMwGIW/o1xDdy3cXB85MBX7+VN4Xs0ZfoFJ7IboZkYQJP1+Szu2tMu6Y3Kq8eLFNREREREREj9WgtDumtCov5dvOhmD14XvyBzzKAi3myfkfY4G0+OyXldwrYVXTVbDVi8vXphnTMHDvQJyPPJ+deU6cAK2buFJPWlAQYteuy9dciIjo9VSojczExES0a9cO8fHx2U9Xrlq1CkDW05aDBw/G8uXLMWrUKFSuXDk779atG9asWYPvvvuuME+XiIjotZNy7BgSd+4UMo2dHTwnjJeah6qqYtrxaXiQ9EDIPW08MbPeTGiUPP/M2DsViLklZi4lgHenC1G6wYQhP5xDhtEs5OW9HbC8S3XotNzim4iIiIiI6El1r1MMPer4S/mcP67jbHCs/IGKHwIlm4hZckTWNV0uVdyr4KumXz22mTn60GikGbNW4tM5O8N76hTpMJELFiD+xx/zNxkiInrtFOo3gatWrUJUVBQURcHGjRuxdu1a9OvXL/v9Jk2aYODAgZg7dy7Onz+Pn3/+Gc7Ozti8eTMAoGfPnoV5ukRERK8Vc0YGHk2dJuXuw4dB7+Eh5T/e+hG77+8WMq2ixbyG8+Bk5SQW3z0AnFwlZooGaPsVYCFe9M774wauP0oSz8HeEt/1qglby+e6vTcREREREdFLadL75VG/lPhUpMmsYsgP5xGfmikWKwrQciGgsxbzM98BD04KURX3Klj1zirY6GyEPDwlHKsvrc5+bd+kCRxatxLHU1WET5iIuL+/+yUiInqcQm1k7t6d9WVnQEAAPvroo3+sb9OmDX7//XeoqoqBAwfi+vXrBX2KREREr62Yr75GZnCwkFlVrgznTp2k2huxNzDn1BwpH1RtEKp7VhfDtHhgx0D5gPWHAUVqCdGBG5H47qi8vNGijlXg5Wj1z5MgIiIiIiIiiU6rwYqu1VHURWw4hsanYfT2i/KWXi7FgcZj5IF2fg6YxK1FqnpUxaqmq6DTiDeerrm8Bg8Sc1bw8Ro/HnofH2nIR1OmInb9+vxNiIiIXhuF2si8evUqFEXBBx988Nj3TSaTlNWuXRsdO3ZEWlpa9jK0RERE9Gxl3L2HmG++EUONBt5Tp0DRaoU41ZCKUYdGIcOUIeR1feqiT8U+4hhmM/DrYCAxVMw9KwGNxgpRdHIGRm67KJ3bJw2Ko0Fp9/xNiIiIiIiIiASO1nos71INeq24bcifVyOw/niw/IE6gwCPCmIWeRU4slgqreZRDd3Ldxcyg9mAuafnZr/WOjmh6Lq1j21mRsyajZjVq6WciIioUBuZ8fHxAIAiRYoIuV6vBwCkpKQ89nNNmmStyf7nn38W3MkRERG9ptTMTISNGQPVIN5V69K9O6zKlZPqZ56ciXsJ4lOT7tbumFV/lrwv5sG5wLVfxUxrAbT7CtBZ5JyDqmLM9ouIThabo+W9HTCyWZmnmBURERERERHlVdnPCWOal5Xymb9fw+XQBDHU6oFWSwCIjU8cmAOEnJXG+LTyp/CwFrclORRyCAcfHsx+bVGkCPw3boC+aFHp85ELFnLPTCIikhRqI9PCIusLSysrcWk4e3t7AEBoaKj0GQCwtrb+n+8TERHR04tcvATply4Jmc7LC+5DBku1v9z+Bb/eERuTGkWDuQ3nwtXaVSy+8jNwUF5+Fm9NADzFu3q/PXIPe69HCpmVXoNlnavBUic+EUpERERERERPr2/94mhSVmw4ZprMGPzDOSSmize4okgtoEaelXdUE/BjXyAjSYht9bYYWXOkdLw5p+YIK/rofXzgv2EDLIoXl2ojZs5CZkhIPmdERESvskJtZPr6+gIAYmJihLxEiRIAgNOnTz/2czdu3AAAGI3GAjw7IiKi10/ywYOIXbNGyr0mT4LG1lbILkZdxMyTM6Xa/lX6o6ZXTTEMvwD8PEA+YNn3gbpDhOjYnWjM3i3vgx34fnmU8rB7glkQERERERHRk1IUBQs6VIG3o/iwyb3oFPT67hSSM/J8B9t0KuCcp+kYdw/YLW4XAgDNizVHDc8aQhaSHII1l8XrTr2nB/w3rIdl6dJCbk5NRfi48VDN5nzOioiIXlWF2sisXLkyAODatWtC/uabb0JVVezatQvBweJ67PHx8Vi1ahUURUHxx9ylQ0RERE/HEBGJsLHjpNy5WzfYv/WWkJ0MP4mP//wYacY0Ia/tVRv9KvUTB0iKAH7oAuSphWdFoO1XgCbnnx+h8WkY9P05mMyqUNq0vCe61JKXGiIiIiIiIqJ/z9nWAks/qgZNnlVjgx7Ey81MS3vgw9WAkme1nPMbs1biyUVRFIyvPR7aPLWrL61GaLK42p7OzQ1Fv/sWWicnIU89fRpxGzc+1byIiOjVU6iNzEaNGkFVVRw4cEDIu3XrBgDIyMhAw4YN8eWXX+LPP//El19+iYCAAERGZi0198EHHxTm6RIREb2yVJMJYaNHwxQXJ+SW5crBY5S4FNC+B/sw8K+BUhPTxcoFsxvMhlaT6wLVmAls6QYk5lkKyMYV+Oh7wDLnCct0gwn9N5xFbEqmUFrUxQYL2leBouS5oiYiIiIiIqJnplZxF4xsVkbKzwTHofeaU0jJ3cz0qwG8Jd8Ii9+GAgni9V9p59LoXLazkGWYMjD56GSYVfFJS527O7ymTJGGjVy4CBl37z35ZIiI6JVVqI3M1q1bAwAuX76MK1euZOe1atVCt27doKoqQkJCMGjQILRo0QKDBg3C/fv3AQBFihTBiBEjCvN0iYiIXlnRX32F1JMnhUyxsYHvooXQWFpmZzvv7sTwA8ORaRabjVZaKyxotADuNu7iwH9NBkJOiZlGD3TaCDj7Z0eqqmLCz5dxKTRBKLXWa/F1jwA42uj/xeyIiIiIiIjoSQxoVBL9GpaQ8tP349B77WmkZuZqZtYfDhStKxamJwA/9cu6qTWXgVUHwtXKVchOPjqJzdc3S8dyaN4MDi1bCpmakYGwcWOhcqsxIqLXXqE2MosUKYL9+/dj165dcHBwEN779ttv0bdvXwBZX27m/hMQEIC9e/fC2dm5ME+XiIjolZS0bx+il6+Qcu/Jk2CZaxn3n279hPGHx8OkmoQ6O70dvmr6lbwv5vXfgRMr5QO+vwjwFy92N5wIxo9BIVLpvPaVUdbLQcqJiIiIiIjo2VMUBeNalMXH9eUtvU7di8WnG87C/N+tQDRaoN1XgKWjWBh8FNjWU2hm2lvYY2xteQ/NxWcXIzgxWMq9AidC6+4mZOkXLiLmuzVSLRERvV4KtZEJZC0v26xZMxQpUkTI9Xo9vvnmG9y9exfffvstZs2ahSVLluDo0aM4ffo0SpYsWdinSkRE9MpJu3gRocNHAGZxOR/HNm3g2KZN9utbcbcw/fh0qBD3rnS2dMa3zb5Fdc/q4sDxD4AdA+QD1uoHVO8hRFfCEjB951Wp9NOGJdCqik8+Z0RERERERET/hqIomNCyHPrUk5uZh29FY/PphzmBU9Gsm1XzurEL2NZLaGY2L9YczYo1E8rSTemYeGQiTGbxhlmtkxO8p0+Xho364guk37iZvwkREdErpdAbmf/E398fvXv3xtixYzFkyBDUqVPneZ8SERHRKyHz4UM87D8Aanq6kFsUKwavSYHZr82qGTNOzIBRFZfw8bTxxNoWa1Hetbw4sMkAbO+TtaRQbt5VgXdnCFGG0YThWy7AYBIbpPVLuWHUY/ZmISIiIiIiooKnKAoC3y+HXnWLSe/N2X0NkUm5riMrtQcCesmD3Pgd2N476xrxbxNqT5CWmD0fdR7rr66XPm7fuDEcP2wnhgYDwsaOhZqZKdUTEdHr4YVrZBIREdGzZ4yLw8NP+sEUGyvkWicnFFn1JTS2ttnZL7d/QVBkkFDnZeuF9S3Wo4SjvHcK9k0HQk6LmaUD0GENoLMU4kV7buJGRJKQ+TpZY1nnatBp+c8SIiIiIiKi50VRFExuVR4tKnoJeWK6EdN3XhOLWy4CKnWQB7m+M+vJzL+bmc5WzphcZ7JU9sW5L3A77raUe44bB52Pt5BlXLuG6FWr8jcZIiJ6ZRTqN4YajQY6nQ6//vprvj73n//8B1qtFjqdroDOjIiI6NVlzshAyGeDkHn/vpArlpbw+3IlLIoVy87i0uOw8OxCaYzANwPhY/eYZV9v/gkcXSrnrZcBLmLT8/T9WHx96K54DgqwsGMVuNhaPPF8iIiIiIiIqGAoioKprSvA3kr8Hva3C2E4cCMyJ9BogQ9WARXby4Nc3wnsHp398q2ib6F1ydZCicFswIiDIxCdFi3kWjs7+MycKQ0Z/dXXSLt06SlmREREL7tCf/RBVdV/Lvp/Pve0nyUiInqdPZo6DWlB4hOWUBT4zJsHm2rVhHjR2UVIyBCXiG3q3xQN/RrKAyeEAj9/Kuc1+gIV2gpRcoYRw7eeR95f5X3rFcebJcRlhoiIiIiIiOj58XCwwpjmZaU88JfLSMvMtbelVge0/Qqo+KE8yJnvgIensl+OqTUGnjaeQsndhLvo/UdvRKRECLltnTpw7tpVHM9kQtjYcTBnZOR/QkRE9FLjGm5ERESvsKR9+5Dw009S7jFmNByavStkZx6dwY7bO4TMRmeD0TVHQ2IyAj9+DKSJS9XCsxLQbJZUPvP3a3gYmyZkpT3sMJL7YhIREREREb1wutQqiupFnYTsYWwaluy9KRZqdUDbr4EKefa2BIBdIwFzVuPTwcIB0+pNk0ruJ95Hrz96ISw5TMg9RgyH3r+okGXeuYOopcvyPxkiInqpvRSNzKSkrL20rK2tn/OZEBERvTyMcXEInyTvReLcvTtcevYUMoPJgBknZki1g6oNgpetl5Tj4BzgwTEx09sCHdYCeish3nstAj+ceiBkOo2CRR2rwkqvfbLJEBERERERUaHRaBTMblcZOo0i5KsP38PVsESxWKsD2n0NeFQQ8/ALwNm12S/r+tTFyBojpWOFJIeg1x+98DDxYc7xbWzgM3sOoBG/vo5dswapp08/3aSIiOil9FI0Mvfu3QsA8PJ6zBepRERE9FgR06fDFC3uN2JdrRo8x46BouS5GL28GncS7ghZWZey6Fy2szzwnf3AoQVy3moJ4FZKLI1KxudbzkulQ5qURiU/xyeaBxERERERERW+Ml726NewhJCZzCpGbLuADKNJLNbqgffmy4Psmw6k5qzk07NCT4ytNVYqC08JR68/eiEkKSQ7s6leDa59eouFqoqQQYORcetW/idEREQvJd0/lzydgwcP4uDBg499b/PmzTh//vz//LyqqkhJSUFQUBD2798PRVFQp06dAjhTIiKiV0/i7t1I3LVbyBQrK/jMngVFKz4FeTbiLFZdWCXWQkHgm4HQafL8UyEpAvjpEwB5Nrus1h2o3FGIElIN+GTdGSSlG4W8ip8jBjYumf9JERERERERUaEa0qQ0dl4Mx4PY1OzsWngi5v9xAxPfLy8WF6sHVOoAXNqWk6XFAXunAq2WZkddy3WFXqPH9BPThY9HpkVi7OGxWNd8HbSarOtWt8GDkXTgADJv59x4a0pIwIM+feH//SZYFCnyDGdLREQvogJrZB44cADTpsnrnquqii1btuRrLFVVodPpMGTIkGd1ekRERK8sY3Q0Hk2Vfwd7jBgBi2LFhCwuPQ6jD42GWTULeYc3OqCye2VxALMJ+OljICVKzN3LAS3miedgMmPQD0G4G50i5PaWOizqVBU67UuxKAQREREREdFrzUqvxZx2ldBl9UkhX33kHhq+4Y6Gb7iLH2g6HbixG8hMzsnOrgOq9wR8q2dHHct0hKXWEpOOTRKuRy9EXcC6q+vQp2IfAIDG0hI+c+ciuEtXqBkZ2XXGqCg86NUb/t9vgt7T8xnOmIiIXjQF+i2iqqrCn/8v/6c/1apVw6+//oqaNWsW5OkSERG99FRVRfjkKTDFxwu5Te3acO7aRaoNPBqIyNRIIfd38MfwGsPFgY2ZwI8fA/cOibneJmtfTAsbIZ656xoO3xKXtVUUYFnnaijpbpfveREREREREdHzUbeUGz5pUFzKR2y7gNiUTDF08AYajclTqQK7RgFm8QbaNqXaYEa9GdK4y88tx+2429mvrStUgN+ypYBOfCbHEBqKB336whgbm3cIIiJ6hRTYE5m9evVC48aNs1+rqoq3334biqJg+vTpqFev3v/8vEajgZ2dHYoXLw4nJ6eCOk0iIqJXSvzWbUj+e2/p/9LY2MB75kwoGvH+pQ1XN+BgiLgMvF6jx4JGC2Crt80JM1OBbT2BW3/KB3xvAeBRVoi2nH6ANUfvS6XjW5TDW2U98jchIiIiIiIieu5GNiuDo7djcDU8MTuLSsrA6O0X8U2PACiKklP85gDg3EYg+kZOFnoGOPsdUPNjYdxWJVvhZPhJ/HLnl+zMYDZg/JHx2NRyE/QaPQDArlEj+M6bi9ARI4FcD8xk3rmDhx9/Av+NG6CxEW+wJSKiV0OBNTL9/f3h7+//2PcqVqyIRo0aFdShiYiIXktpl68gYoZ8N6vH2DGw8PMVsivRV7A4aLFUO7LGSJR1ydWYTE8EfvgICD4qH7BKZ6Cq+JTn+YfxmLjjslTaPsAPHz/mDl4iIiIiIiJ68VnqtFjWuSpaLjuCDGPOk5V/XYvA96ceoGvtXN8Da/XAe/OA9W3EQf6cBJRsAriI14Zjao3BifATiEiNyM6uxV7D6ourMaDqgOzM4b33YEpJwaPAScLn069eRcTsOfCeLm+xQkREL79C3aBq//792Ldv3z8+jUlERET5Y4qPR+jQoVANBiG3bdAATh06CFl8ejxGHhwJo9ko5E2KNkHnsp1zgpQYYH3rxzcxy74PtFqatV7s3xLSDBj0fRAMJlUorV7UCTPbVhTv0CUiIiIiIqKXSikPe0x8v7yUT/vtKo7dFrcWQYnGQIW2YmZIAXYMlJaYtbewx7S6chPy64tf42rMVSFz7tABHmPyLl0LxG/bhsQ9e55sIkRE9FIp1EZmo0aN0KhRI7i6uhbmYYmIiF5pqtmMsDFjYQgNFXKdpyd85swWGogZpgwM3T8UIckhQq2PrQ+m1p2aU2syAJvaA2Hn5ANW6QJ0WAfoLHPOQVUxZvtFhMSlCaXejlZY1T0Aljrtv5wlERERERERPW/dahfFO+XELUMyjGb0WXcax+7kaWa2mA/YuInZg2PAyS+lcev61kWnMp2EzKgaMeHIBGSaxH04XXv3gusn4hK1APBoYiAMERFSTkREL7dCbWQSERHRsxfz9ddIPijudQmdDr5LFkOX6+Yhs2pG4JFABEUGCaVaRYu5DefC0dIxJzyxEggT6wAAtT4F2qwAtOLq9BtOBOOPK4/EU9AoWNm1OjzsrZ5uYkRERERERPRCURQFcz+sDHd7SyFPN5jRZ+1pHL8TkxPauQPvy1ua4K+pQNRNKR4eMBx+dn5Cdjv+NlacXyHVug8dCuuqVYXMlJCAsLFjoeZ54pOIiF5ubGQSERG9xFKOH0fUsi+k3HP0KNhUqyZky88tx+77u6XaUTVHoapH1Zwg/gFwYI58sIajgRZzAY34z4fLoQmYsfOaVD62RVlUK+r8ZBMhIiIiIiKil4KrnSVW96gBe0vxBtf/NjNP3s3VzCzfGqjUURzAlAHs6A+YxO1ObPQ2mFF/BhSI25KsvbIW5yPPC5mi08Fn/jxobG2FPPX4CcSuWfN0EyMiohcSG5lEREQvKVNyMsJGj5H3F2nRHM7duwvZjzd/xDeXvpHG6FquK7qW6yqGu8cAhlQxq94DeHuCsCcmACSlZ+2LmWkSz6FJWQ/0rV88nzMiIiIiIiKil0GVIk5Y37eW1MxMM5jQe+1pnA2OzQnfmwfYe4sDhJ4FDi+Uxg3wDED38uL1rFk1Y+LRiUgziluZWBQpAq9JgdIYkUuWIu3KlXzOiIiIXlRsZBIREb2koleshDEqSsgsiheH9/QZwr6Yx8OOY/qJ6dLnGxdpjFE1Ronh9d+BG7vEzMYNeGeq9HlVVTH+58u4HyM2Pb0drbCgQxXhHIiIiIiIiOjVUq2oM9b1rQW7PM3M1EwTBn9/DmmZpqzA2hloLa8khAOzgOMrpXhwtcEo7ijeGBucGIylQUulWofWreHQsqUYGgwIGzkK5tRUqZ6IiF4+bGQSERG9hDJu30bshg1Cplhawm/ZUmjtcpbWiUuPw9jDY2FSTUJtedfymNtgLrQaba5Bk4Fdo+WDNZsJ2LhI8ebTD/HbhTAh02oUfNG5GpxtLZ5iVkRERERERPQyqV7UGev61IKthVbIwxLSsWL/7ZygdNOslX7y+s844PAiIbLSWWFmvZnQKOJX15uubcKp8FNCpigKvKZMht7HR8gz791DxJy5TzEjIiJ60bCRSURE9JJRVRWPZs4EjOJ+Iq6ffALL0qWFuuknpiM2PVao87H1wYomK2CjtxEHPjgHSAwRs2INgMqdpHO4Fp6IKb/KS/WMePcN1CgmNz2JiIiIiIjo1RTgn9XM1GvFVXm+PnQX96NTcoJ3ZwKupeQB9k4F9s8GVDU7quReCX0r9pVKA48GIsWQImRae3v4zJ8HaMSvuuO3bkXSX389xYyIiOhFwkYmERHRSybpzz1IPX5CyPS+vnD9WLzI++P+H9gTvEfIrHXWWNFkBdys3cRBH12Wl/TR6IGWi6R9MVMyjBj0fRAyjOK+mA3fcEf/hiWfYkZERERERET0MqtRzAV96ovLwWaazJi282pOYOUA9NwJuL0hD3BwTlZDM1czc0CVAXjDWawNSwlD4NFAmMziqkM2AQFw6/+pNGz4xEAYIiKfYkZERPSiYCOTiIjoJWJOS0PEnDlS7jluLDRWVtmvo1KjMPPkTKluWMAwlHLOcwesIQ346RMgz/KzqD8McJcvMAN/uYw7UeIdsB72lljUsQo0Gu6LSURERERE9Doa/HZpeDpYCtm+65H462pETuDgDfT6HfCoIA9wZDFwclX2S71Wj1n1Z0GnEffg3BO8B1OPT4VZFW+udRs4ENZVqgiZKT4eYWPHQDWLtURE9PJgI5OIiOglEv311zCGhwuZbf36sGvSJPu1qqqYenwqEjIShLraXrXRqYy8TCz+GAdEXhUz5+JAg+FS6fazIfgpKFTINAqwrHM1uNlZSvVERERERET0erCz1GFCy/JSPnXnFaQbct04a+cB9NoJeFeRarFnMhB1M/tlGZcyGFhloFT28+2fMf/0fKi5nuBUdDr4LJgPja2tUJt6/ARi1657ihkREdGLgI1MIiKil0T6jZuI/fY7MdTr4Tl+PJRcy7/+cucXHAw5KJTZ6m0xrd40aJQ8v/ov/wScXSMfrNUSQG8tROcexCFwx2WpdGiTN/BmCdd8zYWIiIiIiIhePa0qe6N2cRchexibhq8P3RULbVyAHr8CvjXE3JQB7OgPmIzZUZ+KfdDUv6l0rI3XNuLLC18KmUWRIvCaFCjVRi5ejLRL8vUsERG9+NjIJCIiegkk/vkngjt3hpqZKeSuPXvAskTOPiShyaGYe2qu9PkxNcfAx85HDGPvAb8NlQ9WfxhQorEQHbsdja6rTyLNIC4/W7ekKwa9nWepWiIiIiIiInotKYqCaW0qQptn25EV+2/jYWyqWGztBHTdBth7i3noWeDokuyXWo0WcxrMQT2fetLxvrzwJdZdEZ+2dGjdGg4tW4qFBgNChgyBMSYmv1MiIqLnjI1MIiKiF5hqMiFy0WKEDhkKc6p40afz8IBr/wHZrzNMGRh+YDiSDclCXUO/hvig1AfiwMZMYHtvICNRzP1qAW9NEKI/rzxCr7WnkZopNjHd7Cyw5KOq0gUqERERERERvb7KeNmjZ51iQpZhNGP09oswm1Wx2MYFaP2FPMiBOcCjS9kvLbQWWPzWYlT3qC6VLjizAAcf5qxKpCgKvCZPgt5HvJnXGB6OkKFDpRuEiYjoxcZGJhER0QvKGBeHh/0+RczXX8tvajTwmjYVWrucvT/mnJqDqzHiXpcOFg6YUmeKsPQsAGDvVCDsnJhZOQLtvwW0+uxox7lQDNgUhEyjWSzVa7C8S3V42Fs93eSIiIiIiIjolfV509Jws7MUsuN3Y7DhRLBcXLopUL2HmJkNwM/9s27C/Zu1zhrLmyxHOZdy0hCBRwMRnRad/Vrr4ACfhQsAvV6oSztzFo9mz36KGRER0fPCRiYREdELyBQfj+Bu3ZFy9Kj0ntbREUW++Rr2jRtnZztu78D2m9ul2il1p8Ddxl0ML2wGji+XD9pmJeBUNPvlltMPMGzreZjy3DFrb6nDxr61uS8mERERERERPZaDlR5TWpeX8tm7r+FedIr8gXdnAo5FxSziMnBonhDZW9hjVdNVKOFYQsjjMuIw8chEmNWcm3BtqlWDV+BE6VDxP2xG3Nat+ZgNERE9T2xkEhERvWDUzEyEDP0cmXfuSO9Zli+HYj/+CLt6OXuDXI+9jhknZki1Pcv3RFP/pmJ47zDwyyD5oLU+Bcq9n/3yxqMkTPj5MtQ8q/642Frgh35vokYxl/xNioiIiIiIiF4r71f2QcvK4v6X6QYzRjzmhllYOQAfrJAHObwICDkrRC5WLlj61lJY66yF/GjYUWy6tknInDt2hFPnj6RhH02fgdSgc1JOREQvHjYyiYiIXiCqqiJ82jSknjwpvefYpjWKff89LPx8s7OEjAQM2z8MGaYMoTbAMwCfB3wuDhB1A9jSNWuJnty8KgPvTs9+aTarmLjjEox5Liy9Ha2w9dM6qOjr+HSTIyIiIiIiotfK9DYVpSVmgx7EY/Xhu3Jx8YZZN9nmppqAHf0BQ5oQF3MshrG1xkpDLD67GDdibwiZ17hxsK4RIBYaDAgZOgSGyMgnnwwRET0XbGQSERG9QGK/+w4J23+UcvfPh8J7zhxorHL2pFRVFZOOTkJIcohQ62bthvkN50On0eWEyZHApvZAeoI4sL030HkzoMu5sNx+NgSn78cJZT6OVtjWvw5Kedj9i9kRERERERHR68TF1gKz21WS8oV/3sTNiCT5A+9MAVxKiln0TWCfvApR21JtpVWIDGYDxhwag3RjenamWFjAb+lS6LzFp0NNUdEIGzESqtH45BMiIqJCx0YmERHRCyLpr78QuWChlDt17AjXTz+FoihCfuDhAex7uE/ItIoWCxotEPfFzEwFfvgIiH8gDmxhB3TZCjjmPOEZm5KJWbuvSecws20l+Dnb5H9SRERERERE9FprWt4TH1b3E7JMkxkDNwUhKklcXQgWNsAHXwJKnq+tj68Ago8JkaIomFxnMjxtPIX8TsIdLDizQMh0rq7wW/4FFEvx6dDU06cRtXTZU8yKiIgKCxuZREREL4D0q1cROmo08m5KaVu3DrwCJ0pNzHRjOuaeniuNMyxgGAI88yyZs/NzIFTcUwSKFuiwFvCuLMSzd11DfKq49GyLil54q6xHvuZDRERERERE9F+TWpWHt6OVkN2OTEanr4/jUUK6WFy0NlB3SJ4RVGDHACAjWUgdLR0xu8FsKBCvmbfc2IIDDw8ImXWFCvCaMkU6t5hvvkHSvv35mA0RERUmNjKJiIieM3NGBkJHjoKaJu75YVGiBHyXLIGi10ufWXN5DUKTQ4Wsllct9CjfQyy8sgO4uEU+aMsFQGlxCZ6Td2Ow7ay4TK2thRaTWpV/8skQERERERER5eForce89pWl/G5UCjp+dRwhcaniG2+NB9zLiVncfWDPJGmMml410adiHymfdHQSolKjhMyp7Qdw6tBeqg0bOxaZISFSTkREzx8bmURERM9Z9JdfIvPuXSHTOjmhyKovoXVwkOpDkkLw7eVvhUyn6DC+9njxyc3kKOD34fIB6w4BaogXeZlGMybuuCyVDn+3DLwdrfMxGyIiIiIiIiJZg9LumPBeOSl/EJuKTl+dQHBMSk6oswTafglodGLxmW+B239JY3xW9TNUcK0gZHEZcZh4dCLMqlnIPSdMgGU58TzMiYkIHTIU5ow8S90SEdFzx0YmERHRc5R+7RpiVotNSWg08F22FBZFiz72M/NPz0eGSby46lKuC0o6lcwJVDVrSdnUGPHD/vWAd6ZKY648cBu3IsUlesp7O6BnHf8nngsRERERERHR//JJwxKY/JhVf0Lj09Dxq+N4GJvryUyfakDDUfIgW3sBoUFCpNfqMbfhXFjrxBtxj4Udw8arG4VMY2UFv6VLoLGzE/L0q1cRNnIk1MzM/E2KiIgKFBuZREREz4lqNCJ8wkTAaBRylx49YFur1mM/cyT0CPY93CdkrlauGFBlgFh4cStwfaeY6W2BNisAjfjr/+TdGCzbe0vIFAWY1a4SdFr+U4GIiIiIiIiend71imNW20pQxG0tEZGYgaGbz8FkVnPCBiMA7ypiYWYSsLEdEHlNiP0d/DGu1jjpeEuCluB67HUhsyhaFN6zZ0m1SXv+QsjQz2FmM5OI6IXBbyeJiIiek9i1a5F+9aqQ6f384D5k8GPrM02ZmHNqjpSPqDECdha57iRNDAN2Peau1WYzAJfi4jmkZGLo5vPIfZ0IAF1rF0XVIk5PNA8iIiIiIiKi/OhSuyjmt68CTZ5mZtCDeHxzONfWK1o90PZrwEJ8ehJpccD6D4DYe0L8QakP0NS/qZAZzAaMOTQGacY0IXdo2hQuvXtL55a8fz9CBg/mMrNERC8INjKJiIieg4x79xD1xXIp954+DRobGymPT4/H4H2DEZwYLOTVParj/RLv5wSqCvw6GMhIEAco+TYQIF6gqaqKUdsu4FFiupCXcLPFuBbyviVEREREREREz0r7AD8s7FhFyhf9eRM3HiXlBB5lgc6bAZ2VWJj8CFjfOutm3r8pioLJdSbD08ZTKL2bcBfTjk+Dqop38XqMGA6H996TziHl4CGEDPwM5vR06T0iIipcbGQSEREVMtVsxqPASVDz3N3p2P5D2NapI9VfibmCTjs74VjYMSHXKBqMqz0OSu71eE6uAm7/JQ5g6Qi0Xo686/Z8e+Qe9l6PFDILnQZfdKkGW0vdU8yMiIiIiIiI6Mm1reaH9gF+QpZpMmP41vMwmMw5YfEGQMf1gCbPtWr8g6wnM9PisyNHS0fMbjAbCsRr4J13d2LdlXVCpuh08Jk3Fw6tWknnlnL0KEIGDoRqMDzV3IiI6NlgI5OIiKiQxaz+FqlnzgiZzt0dnqNHS7U/3/oZPXb1QFhKmPRe57KdUdalbE7w4ATw50T5gO/NAxx9hejCw3jM/eO6VDqxZTlU8HF8wpkQERERERER/TuTWpWHj6P4tOWVsESs2H9bLHyjGdDuayBPgxLRN4DfR2StUPS3ml418XGlj6VjLQ5ajCOhR4RM0engM2c2HD/4QKpPOXYcUcuW5Ws+RET0bLGRSUREVIhSTpxE1JIlUu41eRK0Dg7Zr1VVxdxTczHp2CRkmjOl+vdLvI8RASNygqQIYGtPwGwUC8u+D1TuJESxKZkY9EMQDCZxSZ1mFTzR/U3//E+KiIiIiIiI6Ck5WOkxr728xOzyfbdxKSTPtikVPwRaLZUHubwduLhFiD6r+hnq+dYTMrNqxuiDo3E/4b6QK1otvGfNhGP7D6WhY75ZjeQjR59sMkRE9MyxkUlERFRIDBERCB0xAjCbhdzhvRawf+cdIfvu8nfYeG2jNIZO0WF87fGYVX8W9Fp9VmgyAtv7ZO0PkpuTP9BGXFI23WBCv/Vn8DA2TSj1dbLGvA+riMvUEhERERERERWC+qXd0O3NokJmNKsYse08MowmsTigJ/D2Y1Yj+n0kEHsv+6VWo8W8hvPg7yDesJtkSMKQ/UOQlJkk5IpGA+9p0+DYtq00dNiYMTBGReVzVkRE9CywkUlERFQIVIMBoZ8PgykmRsgt/P3hNW2akO0N3oulQfIdpu7W7ljTfA06l+0sNhz3TgWCxaVxoLUEOm0ArJ2zI7NZxajtF3EmOE4o1WkUfNGlGhxt9E85OyIiIiIiIqJ/Z1yLcijqYiNkNyOSsXjPLbm4/gigeEMxy0wCfvoEMOXsaelg4YBlby+Dnd5OKL2XcA/jDo+DWRVvNFY0GnhNmQzLMmWE3BQTg7AxY6HmuTGZiIgKHhuZREREhSBywQKknTsnZIq1NXy/WAatXc4F1dWYqxh3ZBxUiMu+VnWviq2ttqKqR1Vx4Gu/Acces19Hy4WAt7g0z4I/b+C3C/JemxNblkP1os5STkRERERERFRYbC11WNChCvIuFPT1oTs4GxwrhhoN0PYr4eZdAEDIaeDgPCEq4VgCcxvOhZJnb82DIQfx7aVvpfPQWFrCd9FCKNbWQp5y7BhivpXriYioYLGRSUREVMASftuJ2HXrpdx72lRYvfFG9uvI1EgM3jsYaUZx2dcSjiWw8p2VcLN2EwdIiQZ+GSQfsHoPoHp3Idp86gFWHrgjlfauVwy96hXPx2yIiIiIiIiICkat4i7om+ca1awCI7ddRFpmniVmHXyA1svlQQ4vAIKPCVFDv4YYUn2IVLr8/HKcDD8p5ZYlS8Jr4gQpj1q6DGnnz//zRIiI6JlhI5OIiKgAxW3ejLAxY6TcqfNHcGzVKvt1mjENg/cNRmRapFhn6YTlTZbD3sJeHnzvVCA9Xsy8qwAt5gvR4VtRmLDjsvTxpuU9MbFl+SefDBEREREREVEBG9msDEq62wrZvegUzP3julxc7n0goJeYqWbgp35AWrwQ963YF82KNRMys2rG6EOjEZESIQ3t2K4dHN57TwyNRoSOHAVTcsqTToeIiP4lNjKJiIgKgKqqiFyyBI+mTAXy7KFhVakSPMeNE7JZJ2fhasxVIdNpdFjy1hIUsS8iHyDkLBC0Qcws7IGO6wG9VXYUk5yBYVvOw2QWl6qt7OeIpR9VhVaTZ80eIiIiIiIioufISq/Fwo7y9eraY/dx7E60/IFmswDX0mKW8BDYOQxQc66FFUXB1LpTUcyhmFAamx6L0YdGw2A2CLmiKPCaOgX6IuI1uSEkBBFzZud/YkRE9FTYyCQiInrGVIMB4ePGI2bVV9J7WldX+C1ZDI2FRXZ2LPQYdtzeIdVOqTMFAZ4B8gHMZmDXSCDPPppoPBZwLpZzHqqKwF8uIzo5UyjzdbLG6p41YGOhy8+0iIiIiIiIiApF1SJOGNCopJSP2nYRyRlGMbSwBdp/C2j0Yn7lJ+DCD0Jkq7fF4saLYa0T978MigzCsqBl0vG09vbwXbgA0InXzwnbf0TS3r35mBERET0tNjKJiIieIXNaGh72H4CEHTuk9/RFiqDYpo3Q+/pmZ6mGVEw7MU2q7VuxL9qUavP4g5zbAIQFiZl7WaD2p0L028Vw7Lr0SMis9Vqs6V0THvZWICIiIiIiInpRDWlSGmW9xG1WQuPTMGrbBWnVIXhXAZpMkgfZNQqIuSNEpZxLIfDNQKl07ZW1+P3u71JuXbky3D8bKOXhgZNgjH7ME6JERPRMsZFJRET0jKiqivAJE5By9Kj0nlWFCij2w/ewKFZMyL849wVCk0OFrJJbJQyuNvjxB0mNBf6aIuct5gHanLtPIxPTEfiYfTHHvVcWb3g+Zr9NIiIiIiIioheIhU6DRR2rQq8Vl5jdffkRxv10Eea8zcw6g4ASjcUsMxn46RPAJC4b26pkK3R4o4N0zPFHxmPn3Z1S7vrJJ7CuUkXITLGxCJ8YCFVVpXoiInp22MgkIiJ6RmJWrULirt1SbtuwAfzXr4POzU3IL0RdwKZrm4RMp+gwpe4UaDXaxx9k/ywgLVbMKrQFSjTKfqmqKsb+dAkJaeKFWr1SruhW2z8fMyIiIiIiIiJ6fsr7OODzd96Q8q1nQjDj92tiE1GjAT5YBVi7iMWhZ4EDc6QxxtQag3Iu5YTMrJox/vB4/HL7FyFXdDr4zJsLxcZGyJMPHED8tm35nBUREeUHG5mFRFVV3Lx5E99//z1GjBiBxo0bw8HBAYqiQFEU9OrV66nGPX78OPr06YOSJUvCxsYGLi4uCAgIwIwZMxDNpQ2IiApN4p49iFoq76fh2K4diqxYAY2trZBnmjIx+ehkqHn2ufy48sd4w1m+SAMA3NkPnPlWzPQ2wLszhWjb2RDsux4pZHaWOsxrXwUajXgnKxEREREREdGLrH+jkmhR0UvKvzt6D0v+uiWGDt5Am+XyIIcXAncPCpGl1hKL31oMd2t3IVehIvBoIH669ZOQW/j7w3PsGGnoiDlzkXHnjpQTEdGzwUZmIRk5ciTKlCmDrl27YtGiRTh48CCSkpKeejxVVTF8+HDUq1cPa9aswd27d5GWloa4uDgEBQUhMDAQFStWxL59+57hLIiI6HHSr19H2Gj5Ysbu7bfhPWM6FL1eem/1pdW4kyBe6JRwLIFPKn0iH0BVgaPLgI0fAqpZfK/hKMAxZ8/Nh7GpmPbbVWmISe+Xh6+T9RPOiIiIiIiIiOjFoNUoWPJRVTR6w116b+neW1h9+K4Ylm0JBPTOU6kC23oBcfeF1NfOF2uar4GHjUeeahWTj03G1htbhdypQwfYNW4s1qam4uGn/WGMicnHrIiI6EmxkVlITCaT8Nre3h7ly5d/6vHGjRuHxYsXQ1VV2NraYsiQIdi4cSNWrVqFpk2bAgAiIiLQpk0bnD9//t+cOhER/Q/GmBg8HDgQalqakFuWLg2fefOgaORftVdiruCbS98ImQIFU+tOhYXWQizOSMq62NoTCKji7xK4lATqfJb9Mt1gwsBNQUjOMAplb5f1QIcafvmfHBEREREREdELwFKnxapuAahVzEV6b8bv17D3WoQYNpsFuOVZ7SgtFtjcFchIFmJ/B3+sbbYW3rbe8tgnZuBY6LHs14qiwHvGdGhdxPMwhITg4cCBMOf5boCIiP49NjILSfny5TFs2DBs2rQJ169fR0JCAlasWPFUY507dw7z5s0DADg6OuLYsWNYunQpunbtik8//RR//vknJk+eDABITk5Gv379uOk0EVEBUE0mhA79HMawcCHXOjnB78uV0NrZSp9JyEjAiAMjYDSLzcau5bqiqkdVsTj6FvBNE+DqDvngehug7SpAZ5kdTf3tCi6FJghljtZ6zGlXCYrCJWWJiIiIiIjo5WVtocXqXjVQ0ddBem/U9ouITErPCSxsgA5rs66dc4u4DOwYAJjF1Y6KOBTBmuZr4GvnK+QqVIw/Mh7RaTlbeOnc3OAzdy6g1Qq16RcuImz0aKh5HmghIqJ/h43MQtKvXz8sWrQIXbp0QZkyZf7VF8rTpk3LbkzOmjULlStXlmomT56MWrVqAQBOnz6NXbt2PfXxiIjo8WK+/Q6pZ86IoU4H32VLYeEnPwFpVs0Yd3gcQpNDhdzH1geDqw0Wi2PvAd81B6JvyAd2Lg58/BdQpFZ2tPXMQ/xw6qFUOvfDSvBwsHrySRERERERERG9oBys9FjfpzZKedgJeWxKJkZuuwizOdfDHJ4Vsm4Azuvar8DhBVLsa+eLNc3WoIh9ESGPSY/BxCMTYc611Ytdg/rwCgyUxkja8xci58tjExHR02Mj8yWTlJSE3bt3AwAcHBzQq1evx9YpioLBg3O+FN+yZUthnB4R0Wsj/cYNRH3xhZR7TZwI21q1HvMJ4JuL3+Bw6GEh0yk6zG04Fza57xJNTwB++AhIjYbkjeZAvwNZF2R/uxyagMAdl6XSfg1LoHlFeWkcIiIiIiIiopeVi60FvulRAzYW4hORh25GYc2x+2Jx+TZAozHyIPtnAtd2SrG3nTeWv70cVlrxhuCjYUex/sp6IXP+qBNcP+4rjRG7di1iN216sskQEdE/YiPzJXPw4EFkZGQAABo2bAgbG5v/t7ZZs2bZf//jjz8K/NyIiF4XamYmwsaMBQwGIXf84AM4f9TpsZ85FnYMK87LS4qPrDlSXFLWZAS29QairuepVIC3JgAf/QBYO2WnCakGDNh0FhlGcVmcWsVdMLpZmfxMi4iIiIiIiOilUNzNFlNaVZDyubuv42pYohg2GguUfV8e5OdPs7Z0yaOEUwmMrTVWypcGLcXlaPEmYvfhw2HfvLlUGzF7DtLOn//fkyAioifCRuZL5vLlnF+WAQEB/7PW3d0d/v7+AICoqChERkYW6LkREb0uolauRMZ1sdGo8/GG54Txj60PTw7HmENjoELcr7h5seboUraLWPyf8cCdvfIg7b8DGo0GNDm/ulVVxajtF/AwNk0odbe3xPIu1aDT8tc8ERERERERvZo61PDDe5W8hCzTZMbQzeeQbsi1T6VGk7XErHs5cYDMZGBLdyAzRRq7Xel2aFasmZAZVSNGHRyF5Mzk7EzRaOAzZzasq1YVBzAaETpiJEyJeZqqRESUb7rnfQKUPzdv3sz+e7Fixf6x3t/fH8HBwdmf9fDwyPcxz549+z/fv3btWvbfjUYjDHmeUCKiwmUwGGA0GrP/Ts9W+sWLiPn6Gyn3mDYNZisrmPP8bx6WEoZB+wchPiNeyIs7FMfEmhOz/78CAM2Z76A99ZU0tqnxRJjLtJKeAP3rWiT+vBohZFqNgqUdK8PZSsv//18g/LkkerHwZ5LoxcKfSaIXD38u6WUxrVU5BAXH4VFiRnZ2KzIZ03+7gimtcjUuNVZAh/XQrXkXSlpcTh51DebfPoep1QpAUYSxx9cYj0tRlxCWEpadhSSHYPrx6ZhRd0ZOoVYLr6VL8LBLVxhDQ7NjQ2goQidMhNfCBVDyjJ1f/JkkerHwZ1KW+zvOZ42NzJdMfHx89t/d3Nz+sd7V1fWxn82PGjVqPHFtQkICYmJinuo4RPRsGI1GJCUlZb/W6fif+mdFTU9H4thxgFlcxtWyXVuklSqFtDz//budeBsTgiYgNiNWyK20VphQaQLSEtOQhqynKS1CjsP5z3HSMdPe+AAJZboBecZON5oxbedVqX5QfV+UsDfzv8UvGP5cEr1Y+DNJ9GLhzyTRi4c/l/QymfSuPz7bflNYA2nTqYeo6mWBBiWccqUOsHhrLpx3fQolV7Xm0lYkOVdCWvmO0thjKo7BsFPDYFZzvgfYdX8XqjtWRyOvRkKt9cQJSBo0GDDlPA2asmcPwteshWWb1v9qjvyZJHqx8GdS9rT9pyfBNedeMsnJOUsXWFlZ/Y/KLNbW1tl/z/2DRURE+Zf2zWqYHz4UMk2RIrDu10+qDYoJwohTI6QmJgAMrzAc/nb+2a+VjCQ47h8DRTUJdZleAUhoNF26KxQANp55hPDETCEL8LPHR9Xy/+Q9ERERERER0cuqup89etT0kvKZe4IRnSI+KZVZtBFSAgZItQ5Hp0MXdUXKyzuVR69SvaR86ZWliE6PFjJduXKP/X4gdflyGO/c+adpEBHR/4Nt4r+tXr0aISEhz2SsKVOmPJNxXhRnzpz5n+9fu3YN3bt3BwA4OjoKT4ESUeHLvZyBi4sL9Hr9czybV0fqqVOI+/FHMdRo4DN7Fqx8fYV49/3dmBw0GUazvKTC8GrD0b5ce3GYXbOgTRGXiFWd/KF0/h6uNvJ/Ux/GpWLDGbFep1EwvW0luLnZ5WdaVEj4c0n0YuHPJNGLhT+TRC8e/lzSy2ZMS2ecC0vFxdCcPSnj04yYvS8U3/WoDo0m1w3C706GOeYyNPcPZUeKKROue4fB2GcvYO0kjD3QZSAuJlzEmcic70iTjElYdnMZljdeLiwbq/b/FOGXLiH1yJGcAQwGpM+YiSKbf4DGxuap5sefSaIXC38mZU5OTgU2NhuZf1u9ejVOnjz5TMYqyEamnV3OF9Tp6en/WJ+Wlpb9d3t7+6c6ZkBAwBPX6nQ6/tASvQD+u5yBXq/nz+QzYEpORlTgJCl3/eQT2OdZfvs/9/+DCccmSLU6jQ4z683EeyXeE9+4fwQ4t07MtJZQumyB3lG+oxQAZv9xCxlGcXnbXnWLobyv8xPMhp4X/lwSvVj4M0n0YuHPJNGLhz+X9DLR64FlnavjvWWHkZqZs9rR0Tsx2HAqBB83KJG7Gmj/HfBVAyApPDtV4oOh39oF6LYdsHIUxp/ZYCY+/PVDJBtyVss7Hn4cP939CR+V/Uio9Z03F/fafABjVFR2Zrh3D48+GwS/L1dC+5Tf0fJnkujFwp9JUUEur8ulZV8yubva0dHR/3/h33LvkVaQHXEioldZxOzZMISFCZllmTJw+2ygkF2PvY6JRyZKn7fV22LVO6vkJqYhDfh1sHzAxmMAj3KPPZf9NyKx56r4NKa7vSWGvlP6CWZCRERERERE9Goq5maLKa0rSPm8P27gSliCGNq5Ax3WApo8X7yHnAI2tAXS4oXYx84H42qPk8ZeeGYh7iXcEzKdiwt85s+XtolJPXMGwT17wpjr+1oiIvpnbGT+7cSJE1BV9Zn8KUhvvPFG9t/v37//j/XBwcGP/SwRET2ZpP37kfDjT2Ko18Nn3lxoLCyyo5i0GAzZNwTpJvFpeXdrd6xrvg61vWvLgx+YDcTeFTOvSkDdIY89lwyjCVN/lffsGP9eWdhb8c4vIiIiIiIier11CPBDy0reQpZpMmPID+eQlutJTQBA0TeBptPkQULPAutbA6mxQtyqRCs09W8qZOmmdIw/PB4Gs7gXp+2bteE2ULz5GQAyrl5DcJeuMISG5mNWRESvNzYyXzIVK1bM/vvZs2f/Z21UVFR2I9Pd3R0eHh4Fem5ERK8aY1wcwifJS8q6DxoEqzJlsl8bTAYMPzAc4SnhQp2LlQvWt1iPMi5l8g4BhJ0Dji0XM0ULtF4OaOWmZGxKJj5edwb3Y1KFvGYxZ3xQ1VeqJyIiIiIiInrdKIqCWW0rwcfRSsjvRKVg3E8X5YdQ3hwI1BsqDxR+AVjXGkjJeXpSURQEvhkIN2s3ofRyzGXMOTlHGtvts4Fw7tZNGjozOBj3u3RFxu3b+ZwdEdHriY3Ml0zjxo1haWkJADh06JCwB2Ze//nPf7L/3rx58wI/NyKiV82jadNgihKX8bauUgWuffsI2ZxTcxAUGSRkOkWHRY0Xwc/eTx7YZAB+GQyoee4GrTsI8KkqlV8KSUCrL47g8C3xXDQKMLV1RSh5lqshIiIiIiIiel052uixqFPVvCu7Ysf5MHx9KM+qSIoCvDMVaDBCHijiUtaTmRlJ2ZGzlTOm1ZWf4tx6cyvWX10vDq3RwHPCeLh99plUb4yIQHCv3sI+mkRE9HhsZL5k7Ozs8N57WXusJSYmYu3atY+tU1UVy5fnPOnTqVOnwjg9IqJXRvyOHUja/YeQKVZW8Jk7B0quzau33tiKrTe3Sp8fV3scAjwDHj/4nslZF0S5uZQAGsv7bWw9/RAfrjqG0Hj5xpWedYuhvI/DE8yGiIiIiIiI6PXxZglXfNa4lJTP+eM6DtyIFENFAd4OBBqNlQeKuAzsGiVEDfwaoFMZ+bvWhWcW4q/gv/IMrcB98CB4jh8v1ZuioxEeOKnAtyojInrZsZH5EgoMDMx++mbcuHG4ePGiVDNt2jScPHkSAFCzZk20bNmyUM+RiOhllnHvHh5Nmy7lHiNHwqJYsezXN+NuYvap2VJdxzc6omOZjo8f/MrPwIkVct76C0Bvnf1SVVVM/e0KRv94EZlGs1Terpovxr9X7p8nQ0RERERERPQaGtb0DTR6w13IVBUY/MM53I1KFosVBXhrHPDWRHmgCz8AF7YI0ZiaY1DTq6Y4NlSMOzwOl6Ly3LgMwKVHd/jMmwtotUKefOAAEn76KR+zIiJ6/ej+uYSehfj4eCxYsEDI/rt/JQCcO3cOEyeKvyjffvttvP3229JY1apVw+jRozF37lwkJCSgbt26+Pjjj1GrVi0kJyfjxx9/xJ9//gkg6wnOr7/+ugBmRET0ajJnZiJ0+AioqeJelDZ13oRzl87Zrw0mAyYcmQCj2SjUBXgGYGytx9zFCQBRN4FfBsl5zU+AYvWF6MuDd7Dm6H2pVK9VMOn98uj2pj+XlCUiIiIiIiL6f2g1CpZ1roa2K47ibnRKdp6UbsTH689gx2f14GClFz/UaBRgygAOzRfz34cDfjUA15IAAL1Wj8WNF6Pbrm64n3g/uyzdlI5B+wbh+5bfw9fOVxjCsXVrmNPT8WjSZCGPmDUbtm++Cb2vWE9ERFnYyCwk8fHxmDlz5v/7/sWLF6UnK3U63WMbmQAwe/ZsZGRkYOnSpUhJScHSpUulGg8PD/zwww+oWrXqvzp3IqLXSeT8Bci4dk3ItC4u8JkzF4omZyGDVRdX4XrsdaHO08YTCxsthF6b50IIADKSga3dgcw8d336VAOaib8f/rj8CPP+uCEN4elgiZVdAxDg75zPWRERERERERG9fhyt9fi6Rw20XXEUSRk5NyLfjUrB55vPY3WPGtBo8twk3Hgc8OAEcP9wTpaZDGzvA/TdA+gsssa2dMTKd1ai265uiE2PzS6NTY/FoL1ZzUxrnbUwtFOHDkjeuw/JBw9mZ+aUFISNn4Cia74TvncgIqIs/C/jS0pRFCxevBhHjx5Fr169UKJECVhZWcHJyQnVq1fHtGnTcOXKlf+3EUpERLKkffsQt2GDlPvMmQ29p0f260tRl/DtpW+luhn1Z8DV2lUeWFWB34YCUWLjE9bOQMf1gM4yO7ocmoBhW85LQ9Twd8Zvg+uziUlERERERESUD6U87LCsczXkXdRo3/VIrDxwW/6ARgu0+xqwdhHz8PPA3qlCVMS+CJa9vQyWWkshvx1/G/NP53mqE1nf6XpNnwato6OQp548ibiNm554TkRErxM2MgtJsWLFoKpqvv5MmTLlH8etU6cO1qxZgzt37iAtLQ1xcXE4e/YsAgMD4ebmVvATIyJ6RRgePUL4uPFS7tKrF+waNsx+nW5Mx4SjE2BSTUJd57Kd8ab3m48f/NgXwOXteUIFaLcacCqanUQkps9EgzgAAQAASURBVKPvutNIM4hjl/aww3e9a8LD3ip/kyIiIiIiIiIivFXWA6OblZXyRXtu4ujtaPkDDj7AByvl/Phy4NYeIariXgUz68sr8W27uQ3/uf8fKdd7eMBr8iQpj1y4EBl37/2PWRARvZ7YyCQioteeITQUD/p+DFNCgpBbVagAj+HDhGzZuWW4lyBeWPg7+GNYgFgHIOtJzL3TgD2B8nuNxwKl38l+mZZpwsfrziAiMUMoc7G1wLc9a8r7dhARERERERHRE+vfqATer+wtZGYVGLr5HB4lpMsfKNMCqN1fzncMAFLE5mezYs3wSaVPpNKpx6YiNDlUyh3eew8O77UQMjUjAw8//RSGULmeiOh1xkYmERG91tKuXMG9jz5C5p07Qq6xsYHvooVQLCyys9OPTmPj1Y1inaLBjHozpH0vYDIAOwYChxfKBy3ZBGg4Wogm/XIZl0LFRqqFVoOvugegqKvNU8yMiIiIiIiIiP5LURTM+bAySrjbCnl0ciYG/xAEg8ksf6jpNMCrkpilRGVtH6OqQjyw6kBUda8qZEmGJIw+NBoGs0Ea2jMwEFp3cUU9w8OHCO7eA5khIU8+MSKiVxwbmURE9NpKPnwYwd17wBQlLyPjNXUKLPz9s1+nGFIQeDQQKsQLld4VeqOqR1XxwxnJwPedgAvfywd1KQG0+wbQ5PwKPnQzCtvOyhcps9tVQs1iLlJORERERERERPlnZ6nDqm4BsNZrhfz0/TjM++O6/AGdJdB+DaDPc4Px9Z3AeXFPS51Gh7kN58Lewl7IL0ZdxMrz8jK1Omdn+MyUl6Q1hIVlNTODg59wVkRErzY2MomI6LUUv307HvYfADU1VXrPY/RoOLZqJWQLziyQloMp5VQKA6sOFD+cFgesbQnc2Ssf1Kca0Oc/gK1rdpSSYcS4ny5JpQMbl8SHAX75mBERERERERER/ZM3PO0xu10lKf/m8D3sOPeYZV3dSgPvTpfz3WOBuPtC5GPng6l1p0ql3176FnuC90i5XcOG8Joq1xvDwxHcvQf3zCQiAhuZRET0Gkr84w+ETwwETCYhV/R6+CxcANc+vYX8cMhhbL+5Xch0ig6z6s+ChTZn6VmYjMC23kD4efmgpZoCPXcCdh5CvODPGwiNTxOyKkWcMOLdMvmfGBERERERERH9ow+q+aJr7aJSPnzrefx87jHLutboC5R6R8wyk4CfBwBm8buFpv5N0fGNjkKmQsXIgyPx862fpaGdO3WE98yZgKIIuTEyEsE9e8Dw6NETzoqI6NXERiYREb1WMu7eQ/j4CVKucXBAkW9Xw7FlSyFPyEjA5GOTpfr+VfqjnGs5MdwzCbi7Xz5o1W5A5x8ASzshPhsch7XH7guZXqtg3oeVodWIFzBERERERERE9OwEvl8elXwdhcysAsO3XsC2Mw/FYkUB2qwArJ3F/MEx4NgX0tijao5CKadSecY2Y9KxSfju8ndSvdOH7eAzZ7awDQ0AmKKiET5+PFTzY/bvJCJ6TbCRSURErw1zaipChw6BOc9ysjofbxT7fhNsa9WSPjPr5CxEpUUJWUXXiuhbqa9YeP574MQK+aANRgBtlgNavRBnGE0Y8+NFqOKWmxjYuBTKeIn7aRARERERERHRs2Wl12Jl1+rwdLAUclUFRv94EZtPPRA/YO8FtFoqD7RvBhB6VhxbZ4WFjRfCxcpFKl98djEWnVkENc8XAo5t2sBn3jxAK+7fmXLsOBI2b8nHzIiIXi1sZBIR0WtBVVU8mjoVGbduC7nW0RHFNmyAZalS0mf+vP8ndt3bJWSWWkvMbDATOo0uJww5A/z2uXzQql2BtwOl5WEAYMX+O7gdmSxkpT3sMPCtkk8+KSIiIiIiIiJ6akVcbLClXx14O1oJuaoCY3+6hI0ngsUPlG8DVOksZmYD8ENnIF5sfJZwLIH1LdbDx9ZHOu6aK2sw7cQ0uZn5fkt4T5f344xZvBimBw+knIjodcBGJhERvRbit21Dwi+/iqGiwGfBfOh9faX6qNQozDgxQ8qHVBuCEo4lcoLEcGBzV8CUIRb61gBaLnpsE/NSSAJW7hcbqooCzG1fGZY6rVRPRERERERERAWjmJsttvSrA18na+m9wF8u49BNcZUmtJgLOBYRs+QIYFNHID1BiP0d/LG+xXqUdJRvWt5+czt23N4h5Y5tP4B98+ZCpqanI2XWbKhG45NNiojoFcJGJhERvfLSrlxBxIyZUu42oD/sGjSQcpPZhHFHxiEuI07Ia3jWQLfy3XIVGoCt3YHkR+IA9t7AR5sAvXhHJwDEp2ai/8azMJrFuy571S2G6kWdpXoiIiIiIiIiKlhFXW2wud+bKOIiNjPVv/fMjErKdfOylSPQ/jtAKy5Ji6hrwNaeWd8V5OJp64l1Ldahsntl6bhzT89FaHKokCmKAq/Jk6B1dxNy0/XrSN+46SlmR0T0cmMjk4iIXmnGmBiEDhkKNTNTyG3qvAm3zz577Ge+u/wdToafFOt1Nphebzo0Sq5fnUcWAyGnxQ9rLYFOm7L2zsjDbFbx+ZbzCI1PE3JfJ2uMfLdMPmZFRERERERERM/Sf5eZ9Xe1EfLo5AwM33oe5tw3JBepBXywUh7k7n7g9+FZHdBcHC0d8U3Tb1DTq6aQpxhSEHg0EGbVLOQ6Z2f4zJRvyE5fvx7ply/nc2ZERC83NjKJiOiVZc7IQMhng2AIFe9u1Hl4wHfBAihaeRnXc5HnsOL8CikfV3sc/Oz9coKw88DBufJBWy0F/AIeez4r9t/GgRvikjQ6jYJlnavB1lL32M8QERERERERUeHwcbLGtz1rwEovfm1++FY0vjl8Vyyu1B54O1AeJGg9cHSpFNvobTC3wVw4WjoK+elHp7HpmvykpV3DhnDq1EkMzWY8GjUaxujoJ5sQEdErgI1MIiJ6JamqivDxE5B2/rz4hlYL38WLoHN1lT6TkJGAMYfGwKSahLxliZZoU7JNTmDMAH7uD5jz7E0R0Auo2vmx53P4VhQW/XVTyie2LIcAfy4pS0RERERERPQiKOVhjymtKkj5/P/cwIWH8WLYYARQrZtUi71TgftHpNjdxh0T35wo5UuDluJuwl0p9xw9CvqiRYXMGBKCh/0HwJyS8r8nQkT0imAjk4iIXknRK1Yi8fffpdwrcCJsAuQnJlVVxeRjkxGeEi7kRe2LIvDNQCiKkhPun5W190VuTv7Au/KyLwAQGp+GIT+cy7uyDFpV8UHPusWeaD5EREREREREVDg61SyClpW9hcxoVjH4h3NISs+1B6aiAO8vAYo3EgdQzcCPnwCpsdLYzYs1R4viLYQsw5SB8YfHw2AW99fU2NrCZ+4cQCN+jZ9++TJChg2DahDriYheRWxkEhHRKydh5++IXr5cyl169oDzRx899jPfXf4Oex/sFTKdRod5jebBVm+bEz44CRxblufTCtB2FWBpJ40bEpeKHt+eRFyqeHFRysMOc9pVEhukRERERERERPTcKYqC2e0qwc/ZWsgfxKYicEeePSq1eqDjesC1tJgnhQE7Bkr7ZQLAhNoT4G7tLmRXYq5gydklUPPU21SrBvcJ46UxUg4dRvikyVI9EdGrho1MIiJ6paSeOYPw8fI/8O0aN4bH6NFSblbNWHhmIZYELZHeGx4wHBVccy0nk5kC7OifdWdlbnU+A/zrSp+/EpaAdiuP4U6UuNyLjYUWq7pV576YRERERERERC8oBys9lnWuBq1GvAF5x/kw/HYhTCy2dgI6rAG0lmJ+czdw8itpbEdLR0ytO1XK119dj8VnF0vNSceOHWHVTV7CNuHnnxG1LO/N1kRErxY2MomI6JWRdvEiHn7aH2pmppBblikDnwULoGi1Qp5pysSYQ2Ow9spaaaxGfo3QrVyei4Q9k4HYPHtWuJUB3g6UPn/0djQ6fXUCkUkZ0ntzP6yMUh72TzYpIiIiIiIiInouqhd1xoh335DyiTsu41FCuhh6VQKaPWbLmT2BQPgFKW7g1wAd3ugg5WuurMH8M/OlZqZV3z6waNZMqo/5chUSfvnlH2ZCRPTyYiOTiIheCek3buDBJ/2kze617m4osupLaO1shTwhIwGf7vkUf9z/QxrL38Ef0+tNF5d9vbMfOP2NWKhos5aU1VsJ8Y5zoei15hSSM4zS2BNblkOrKj75nB0RERERERERPQ/9G5bEmyVchCwhzYBR2y/Iy7rW/Bgo+76YmTKBbb2BjCRp7JE1RqKyW2Up33B1A+acmiOMrygKbEaNhE09eUWo8KnTkHH3Xj5mRUT08mAjk4iIXnoZd+/iQe8+MCckCLnGxgZFVq6E3ttbyOPT49Hrj144E3FGGquye2Wsb7EezlbOOWF6AvDLIPnADUcCvtWF6PeL4fh8y3kYTOLFjF6rYFnnavi4QYl8zo6IiIiIiIiInheNRsGCDlVgn2d7mMO3orHhRLBYrChA6y8AB18xj70DbGwPpMUJsY3eBl81/QpV3KtIx/3++veYeXKm2MzU6eC1aBGsKlQQatXUVISOGAFznhWqiIheBWxkEhHRSy3zwQM86NUbpthYIVcsLeG36ktYV6ok5GbVjLGHx+J2/G1prLeKvIXV766Gi5V4pyX+GAckhoiZV2WgwUghuhKWgBHbzkvj2lvqsK53LbTmk5hERERERERELx0/ZxtMbVNBymftuobbkcliaOMCfLgaUPJ89f7wBLCmJZD0SIjtLOzwVdOvUN1DvFEaALbc2IL/Y++uo6O61jaAPzOTibsbCe7uLoW2UNxdgrt7kUJxd5fg7l6Ku7s7EYi7jn1/pJf2ZJ9J0n4tDfT5rXXXgvc97z77rHtD5p539t47nu2QxJSWlvBetgwqBwdJPOXJE4TOnvMnnoqI6MvARiYREX2xtJGReN+1G7ShoZK4Qq2G95IlsCpfXqhZfX81LgVfEuKtC7TG/JrzYWFiIU08PQrc3SKNqUyBpisBE9NPofD4FPTceAvJGr3kUjdbM+zsXQmV8zr/yacjIiIiIiIiouyiaSkv1CvqLokla/QYuvMuNDrpuwD4VgZqjhUHCX0ErP0OiHwtCVuprbC8znKUcSsjlMy9ORfvYqUrP9VurvCcMV24NmrTJsSdPp3FJyIi+jKwkUlERF8kfWoqAgcMhCYw3UpJlQpeC+bDulpVoebqh6tYenepEO9fsj9+rPAjVEqVNJEQARwaJN681o+AW+FPf03V6tF3820ERSdJLnOyMsXu3pVRyMM26w9GRERERERERNmOQqHA1KbF4GJjJonfD4zBxIOPxPMyqw8HKskcUxP9Dlj7PfDxgSRsqbbEstrLUM69nCSerEvGuMvjoNVrJXHrGjXg6OcnDP9hzFhoPnz4E09GRJS9sZFJRERfHIPBgI8/TULSrVvShEIBz5kzYVO7tlATkhCCUedHwQDp/7Gol6seehbvCYVCkf4mwJGhQIJ0tSdyVAAqD5CEJh16hOtvpVvbmigVWN6hDHI4Wv65hyMiIiIiIiKibMnRyhSzWhQX4luvvcfqC9JVllAogO+mALUniAMlhAIbGgExQZKwpdoSc2rMgZO5kyT+KPIRtr3eJgzjOmQwzIsWlcR0MTEIGjYc+pSULD4VEVH2xkYmERF9cSLX+yNm714h7j5hPOwa1BfiGr0GI8+PRGSytNmYyy4Xfqr0k9jEBIC7W4HH+6UxtSXQZDnwh5Wbm6++w5Zr74XySY2LoHwuRyFORERERERERF+uWgVc0bGirxCfdvQpjj1ItxJSoQCqDQMaLACQ7t1DUiRwsH/aF6n/wNHcEZOrTBbG3/x6M55GP5UOb2oKr3lzobSykg59+zaChg6DQStdxUlE9CViI5OIiL4ocWfPInT2bCHu0L49HNq2la1ZdHsRbofelsQsTCwwr8Y8WKplVkyGPQeODhfj304GnPJ8+uvTj7GYdOiRcFmHij5oX0H8PzVERERERERE9OUb36AwKuYWv7w8eMdd3HkfJRaU9QNa+gNKtTT+6jRwc61weXXv6miZv6UkpjfoMePBDCRppcfamPr4wH3yJGGM+FOn8GHceBj0eiFHRPQlYSOTiIi+GCkvXiB42HDh24pWlSvDbcxo2Zp9L/bB/5G/EB9fcTzyOuQVCzTJwG4/QJMojeeuBZTt9umvqVo9hu64B41OOpfyuRwxsWGRrD0QEREREREREX1xTE2UWNmhLPK4SFdCpmj16LHxJgIiE8WiIk2ARovF+C/jgYhXQnh42eHwsfGRxIISgzDn1hzhPE67+vXh0KGDMEbM/v0ImTFDPL+TiOgLwkYmERF9EXQxMQjo1x/6hARJ3DRnTnjNnweFiYlQc/XDVUy+Im7H0iJ/CzTM01D+Rr+MA0IeSmNWLkDTlYDy91+bi0+/wOMPsZLLPO3Msbx9aahV/PVKRERERERE9DWzs1RjfZfycLIylcTD41PRbcMNJKXqxKISbYBC6d5HaBKB/X0AvfR6S7UlplWbBqVC+o5h36t9WP9ovTC029gxsK0vHrcTtXETwpcvz+JTERFlP3zTSkRE2Z5Bp0PQiBHQvJeeRam0tYX38mVQ2dkJNa+jX2PomaHQGqTnQRRxKoLR5eVXb+LJIeDGajHedAVg4/bpr3cDorHsrPhtyTktS8DJ2iwLT0REREREREREXzofJ0us7lwWZibS1+zPQ+Ix+fBjsUChSDsv08pFGg+4BlxeJFxewqUEehTrIcTn35qPQ68OSYdWKuE5YzqsalQXrg9ftBjhq1ZzZSYRfZHYyCQiomwvbPFiJJy/IA2qVPBeMB9muXIJ10ckRaDvqb6I08RJ4u5W7lj8zWKYqWSajdHvgQP9xHiVQUDeOp/+mqzRYdjOu9DppR/+u1TOicp5nbP+UERERERERET0xSvt44D5rUsK8W3X3+PI/Q9igZUz0HChGD89Ffj4QAj3KtELZVzLCPEJlybgcvBlSUyhVsN7wQJYlBGvD5s3DyHTp/PMTCL64rCRSURE2VrsL78gYsVKIe46YjisKlcW4snaZAw8MxBB8UGSuJXaCktrL4WLpYtQg8g3wMYmQHKMNO5VFvhmvCQ0+8QzvAqTbm+by9kKo+oWzNoDEREREREREdFX5YdiHuhfK68QH733vvx5mQXrAyXbS2N6DbClFRD+UhJWK9WYW30uclrnlMS1Bi2GnBmCJxFPJHGlhQVyLF8Gs0KFhNtGbdyE4BEjYUhNzdqDERFlA2xkEhFRtpXy8iU+jB4jxG0bNoRj585CXKPTYOjZobgfdl8SVylUmFtjLvI75BdvEnQLWPstEJluq1gzW6DFWkCl/hS6+joC6y69kVymVKRtKWthqvoTT0ZEREREREREX5PBdfKhjK+DJBaXrMWAbXeg0cmsgqw7HbDLIY3FBQP+9YGw55KwraktppaeCmcz6U5QidpE9Pm1D4LjgyVxla0tfFavglk+sbkae+QIAnr3gS4+QcgREWVHbGQSEVG2pIuNRWC//tAnSr+5aFaoEDwmT4JCoZDEtXotRl0YhQtB6bagBTC2wlhU8aoi3uT5CcC/AZAQJuYaLgQccn76a3yKFsN33UP64yR61cgj/B8VIiIiIiIiIvpvMVEpsbBNSdiam0jidwOiMe/kc7HA3A5osgxQpHtFH/8xrZkZ+lQSdrVwxbQy02CttpbEI5IjMOL8CGj0Gul8nJ3hu3kzLEqXFm6dcPky3nfpAl1cnJAjIspu2MgkIqJsx6DVImjIUKS+eyeJq+zs4L14MZQWFpK43qDH+EvjcfLdSWGszoU7o1WBVuJNbvkD29oAmvRbvCiAujOBos0k0alHniAwKkkSK+hug8F18mX5uYiIiIiIiIjo6+XtYImZzYsL8eVnX+HoA5nzMnNVB5quEpuZCaHAhgZAyGPp5Ta5MK/6PKiVakn8fth9LLu7TBheZWcHn3VrYf3NN0Iu+eFDBPTpA31SkpAjIspO2MgkIqJsJ2TmLCRcuiQNKpXwmj8Ppt5ekrDBYMDkK5Nx+PVhYZxGeRphaNmh4g0e7AYODQIM6bZ2MTEHWm0AKvaWhM88C8W26+8lMbVKgbmtSsDMhFvKEhEREREREVGaesU80L6CjxDvv/U2dtx4LxYUbwk0XwMo0r1fSAgDNjQEoqU1Zd3KYkqVKcIwax+sxdUPV4W40twc3osWwr5lCyGXdPMWAgcPhkGjEXJERNkFG5lERJStRO3YiahNm4S467BhsKpcWRIzGAyYdWMW9rzYI1z/fc7vManyJCjTf6sx7BlwcKB4YwsHoNMBoHBjSTg6MRWjdt8XLh9UOx+KeNpl4YmIiIiIiIiI6L9kfIPCKOBmI4npDcCoPQ+w4twrsaBoc6DlekAp3ZYWieHAvt6AXicJ/5D7B7TM31ISM8CAMRfGICIpQhheYWIC98mT4dSjh5BLOHcewaPHwKCXOceTiCgbYCOTiIiyjYSr1/Dx55+FuF2TJnDs6ifEtz3dhs1PNgvxmt41Mb3adJik/z8AqQnAzk6AJt2B9va+QLeTgE9FYayJBx8hNC5FEivhbYfeNfJk4YmIiIiIiIiI6L/GXK3Csg6l4WxtKuRmHHuKaUefwGAwSBOFGwMt/cVm5rtLUF5fLowzstxI5LXPK4mFJ4Vj3KVx0KffgQqAQqGAy9AhcOjUUcjFHjmCkClTxDkREWUDbGQSEVG2kPr2LYIGDQK0WknconRpuE+eBIVCIYlfDrqMmTdmCuNU8qiEOTXnCOdFwGAADg8Fwp5K45ZOgN9RwFk86/Logw84cDdYEjMzUWJuq5IwUfFXKBERERERERHJy+NijV29K8PL3kLIrTr/GmP3PRAbh4UaAk1WCNcrz06DSYT0fYa5iTlmV58NM5WZJH4x6CI2PRZ3ugLSmpluo0fDrnFjIRe1dRvC5s5lM5OIsh2+hSUion+VwWBAzKFDeNOqNXQxMZKc2tMT3osXQWkq/Qbj65jXGH5uuPANw1KupbCg1gLhQzwA4PYG4P72dEEF0Gw1YOctXB4am4xx+x8K8ZF1CyKvq3XWHo6IiIiIiIiI/rNyOVthT5/KyO8mvkfYdj0Au24FikXFWwLFW0tCCl0q7E6NALTSHaPyOuTFqPKjhCHm35qP0+9Py85JoVTCY+oUWNeuLeQi1qxFyPTpbGYSUbbCRiYREf1rtJGRCBo0GMEjRkIfGyvJKSwt4b18GUycnCTxmJQYDDg1AHGaOEnc29obi2otgqXaUrzRh3vA0ZFivMZIIK/4wV2nN2DwjruITEiVxCvkcoRf5ZxZezgiIiIiIiIi+s9ztzPHzl6VUMrHXshNPvQYAZGJYlG9WYCt9EvX6sjnsLmxQLi0Rb4W+M73O0lMZ9Bh+LnhuPrhquycFCYm8Jo3F5YVKgi5qI2b8PGnSTwzk4iyDTYyiYjoXxF35gxeN2yEuF9+EZMKBbzmzIZ5gQKSsEavwbCzw/A+7r0kbq22xpLaS2Bvbi+OFR8G7OgA6KTfWkTumkAN8VuLALDi3CtcfhUhiVmZqjCnZQkolQrZGiIiIiIiIiIiOfaWptjSvQIq5ZZ+WTs+RYsRu+9Br0+3AtLCHmi6HID0HYTlvfVQvLsoiSkUCkysPBFe1l6SuEavwcDTA3E39K7snJRmZvBeuhQWJUoIuegdO/Dhx3Ew6HRZej4ion8SG5lERPTZxZ0+g8C+/aCLiBByShsbeM2fB5tvvhFyM6/PxLWP16TXK5SYVX0W8tjnEW+kSQK2twWipY1P2HgAzdYASpVQcvNtJOadfC7Ef2pUBDkcZVZ7EhERERERERFlwtLUBAvblIS9pVoSv/o6EusvvxULclUHKvWThBQwQLWvJxD1ThK3NbXFsjrL4GjuKIknaZPQ91RfPIt8JjsnlbUVcqxdC4uyZYRczL59CB4xks1MIvrXsZFJRESflT4hAR8nTQJkzluwqlwJuQ8egG3dukJuz/M92PFshxAfXnY4qnlXk7mRHtjXGwi8IY0rVECL9YC1i1ASnZiKQdvvQpfum5BNS3mhRRnxHE0iIiIiIiIioqxytTXHlCZFhfjM40/xMjROLPhmPOBaWBJSJIQCm5sDiZGSeG673FhRZwVs1DaSeFxqHHqe7InXMa9l56SytoLPqlWwrFRRyMUePYrw5Ssyeywion8UG5lERPRZha9cBW1IiCSmMDeH2/hxyLFmDdQeHkLN3dC7mHJtihBvnq85OhTqIH+j0z8Dj/eL8R9mAb6VhLDBYMCoPfcRFJ0kied0ssTPTYpCoeCWskRERERERET0/9OguCcalfCUxFK1egzdeQ8aXbpzKdXmQLNVMKhMpfGIF8C2Nmk7Uf1BIadCWFpnKSxMLCTxyORIdDnWBQ/DH8rOSWlpiRzLl8OqRnUhF75iBZIfP87i0xER/f3YyCQios8m5c0bRKxfL4kp1Grk3LEDju3bQ6EUfy2FJoZiyNkh0Oq1knhp19L4scKP8g3G25uAi/PEeKX+QLnusnNbce41TjySNljVKgWWtCsNazOTTJ6MiIiIiIiIiChrJjcuAlcbM0nsfmAMZhx7CkP6Hazci0HXcIk4SMA1YE93QC/d+rWUayksqLkAaqV0C9uolCh0PdEVl4Muy85JaW4O78WLYV27tjSh1SJ49BgYUlOz9nBERH8zNjKJiOizMBgMCJk2HdBoJHHHrl1hXiC/bE2qLhVDzgxBeFK4JO5m6Ya5NedCrVKLRS9/BQ4PFuMFGwDfThbCer0B044+wczjT4XcmHqFUNTLzvhDERERERERERH9SfaWppjVorgQX3vxDabLNDMNRZohttJocaCnh4GjI4Tjeyp7Vcas6rOgUqgk8SRtEvqd7oejr4/KzktpagqvWTOh9pYer5Py/DnCli/PyqMREf3t2MgkIqLPIv7MGSRcuCCJmbi7w7lXT9nrDQYDplydgvvh9yVxM5UZFtZaCGcLZ7Ho7UVgewcg3epNeJYCmq0ClNIP8ClaHQZuv4NV58VzImoXdIVflZyZPxgRERERERER0Z9Us4Ar2lXwEeKrzr/Gz4efCM3MxBJ+SCjeRRzo5lrg+iohXMe3DhbUWgAzlXTlp1avxagLo7DlyRbZeSmtrOAxbaoQj1i1GkkP5LemJSL6J7GRSURE/zh9cnLaasx03EaNhNLSUrZmy5Mt2PdynxCfWGkiijgXEQsCbgBbWwNa6fkQsMsBtN0BmFpJwjGJGnRaex2H738QhsrtbIXZLUvwXEwiIiIiIiIi+seMq18IJXPYC/F1l97gp4OPhGZmXKVR0BdqLA70yzjgwz0hXDNHTaz6dhVsTG2E3IzrM7Dj6Q7ZeVmVLw+Hjh2lQZ0OwWNGQ5+SYvyBiIj+AWxkEhHRPy5i3TpoAgMlMcsKFWBTt67s9RcCL2D2zdlCvGPhjmiYp6FY8OEesLk5kBovjZvbA+12AjZuknBkQiparryMa28ihaFK+dhjV+9KcLQyzfihiIiIiIiIiIj+HyxNTbCpW3mU8XUQchuuvMP4Aw+lzUyFErpGy4Cc1aQX61KB3V2BlHTvRQCUdisN/7r+cLVwFXJTr03FsTfHZOfmOmQw1L7SFaOpL18hfPHiLDwZEdHfh41MIiL6RyXeuIGIFSulQZUK7uN+lF3x+DLqJUacHwG9QS+JV3CvgKFlhoo3CH0CbGoKpMRI46Y2QMe9gFthSVivN2Dwjrt4HiJ+uP+usBu2dq8IJ2szIUdERERERERE9HezMVdjQ9fyKJdTbGZuvvoem6++kwZNzIBWGwFbL2k84iVwbKTsPfI75MfGHzbC19ZXEjfAgLEXxuJC4AWhRmlpCc9p04B0724i1q5D3OkzWXgyIqK/BxuZRET0j0l+9hwBffvBkJoqiTt26ACzfPmE66OSo9D/dH8kaBIkcR8bH8ytORcmShNpQUwQsLEJkBghjastgfa7AK8ywj2WnHmJ88/DhHinSr5Y3qEMLExVQo6IiIiIiIiI6J9ibWYCf7/yKJ/LUchNPfoEb8Kl70lg6Qg0XwMo0r3ev7sFuL9L9h5e1l7wr+uPHDY5JHGtQYuhZ4fiTugdocayTBk4du4sDRoMCB4xAimvXmX+YEREfwM2MomI6B+hCQ5GQM+e0MfFSeImrq5w7t9PvF6nwZCzQxAUHySJ26htsLj2YtiZ2UkLUhOA7W2B+I/SuMoMaLMV8K0k3OPii3DM//W5EB/+XX5MalQEKiXPxCQiIiIiIiKiz8/KzAT+fuVQMbe0mZms0WP47gfQ6qTnZcK3MlBjlDjQ4SFA5GvZezhbOGPVt6vgYuEivYcuGf1+7YenkU+FGpfBg2CWL68kpk9IQGDfftDFxAjXExH93djIJCKiv50uOhrve/SENiREEldaWsJ72TKobKSHzEclR2Hw2cG4FXJLElcpVJhTcw5y2+WW3kCvB/b1Fg+yV5qkba+Sp5Ywp48xyRi0/Q4M6T73NyrhiX618spuc0tERERERERE9LlYmppgWfsycE535M39oFisv/5BLKg+AvCtIo2lxgG7usielwkA3jbeWPntStia2kricZo4dP+lOx5HPJbElebm8F6yBEpb6fWp794haPgIGHS6rD0cEdFfxEYmERH9rfRJSQjo3Qep6bcYMTGB1+JFsChaRBK+FHQJzQ42w/nA88JYo8qPQmXPyuJNzk4HnhwU442XAQXqCmGNTo8B224jIkG6xW0eFytMb1aMTUwiIiIiIiIiyhYcrUwxu0VxIe5//QMefki3xaxSBTRbDVikO1/zwz1geztAkyx7j3wO+bCszjJYmFhI4jEpMeh+ojvuh92XxE19feE1dy6glLYTEi5cQNj8+Vl8MiKiv4aNTCIi+tsYNBoEDR6CpLt3hZzn9GmwrvL7twSTtcmYcX0Gev/aG+FJ4cL1rQu0RtuCbcWbPNgNnJ8lxqsNA0q0lp3X7BPPcONtlCRmoVZheYcysDIzka0hIiIiIiIiIvo31CroivYVfCQxnQGYdOINElO10ovtvIDGS8VB3pwDdncFdBrZe5RwKYEFtRbARCl9LxKniUOPX3oIu2ZZV6sK1+HDhXEi1qxF1I6dWXgqIqK/ho1MIiL6Wxh0OgSPGo34c+eEnOuoUbBr2PDT36OTo9HhaAdsebJFdqxvfb/FqPIy5zwE3gL29xXjBRsAtcbJjrXzRgBWnRfPhpjWrCjyu9nIVBARERERERER/bt+rF8IOZ0sJbGA6BRMPfoMhvTn5hSsD1QdKg7y7AhwoF/aET0yKntWxsJaC2GqNJXEE7WJ6PNrH1z9cFUSd/TrAttGDZHex4kTEbVtWxaeiojoz2Mjk4iI/t8MBgM+Tv4ZsUePCjlHPz84+XWRxH6++jOeRT0TrjVXmWNchXGYW2Mu1Eq1NBn3EdjRHtClSOPuxYCmK4XtTQDg0stwjN33QIi3Le+DpqW8M38wIiIiIiIiIqJ/gaWpCea3LgmVUnoczs5bQVhxTvzCNmpPAMr4ifH7O4BjI4D0zc/fVPeujiW1l8BcZS6JJ2mT0P9UfzwMf/gpplAo4DF5MsyLFhXG+ThpMiI3bc7CkxER/TlsZBIR0f9b2Lx5iN6xQ4jbNW4M1xHSbUeufbiGX979IlxbyLEQdjTYgdYFW4tnVmpTgB0dgbh0B9tbuQJttwNm1sJ4L0Li0HvzLWj10g/qxb3tMLFh4Sw+GRERERERERHRv6OUjwP61corxGcef4q9twOlQYUCqD8XKNpCHOjGGuDYSKMrMyt5VsLyOsthaSJdAZqiS8HYi2ORrP39rE2luTm8ly6B2lv8gnjI1KmIWO+f+YMREf0JbGQSEdH/S/iq1YhYvUaIW9epDY+pU6D4w0pJrV6LGddnCNe2L9QeW37Ygtz2ucUbGAzA0eFA4HVpXGUKtNkK2IkfnMPiUuDnfwNxydJzIzztzLGmU1mYq1VZfDoiIiIiIiIion/PgG/yonxOByE+cvd9nH8eJg0qVUDTFUD+uuJA11cBe7sD2lTZ+5R1L4tV362CjVp6DM+bmDdYeld6BqfazQ2+mzZC7SM9xxMAQmfORPjKVeL2t0REfxEbmURE9JdFbduGsHnzhLhV5UrwmjcPChPpgfE7nu3Ay+iXklhRp6IYWW4k1Kp0W8n+z811wO2NYrzBfCBHOSGcrNGhx8abCIxKksStzUywzq8cXG3NhRoiIiIiIiIiouxIrVJiWbuSyO0kfZ+h1RvQZ/MtPAyKkRao1EBLfyBnNXGwh3uArS2BlDjZe5VwKYGldZZCqZC2DTY82oC7oXel8/LwgO+mjTDNmVMYJ2z+fITOmAGDkRWgRER/BhuZRET0l8QcOoSPk38W4hYlSsB78WIoTaUHxUckRWDpnaXC9WMqjBE+IH/y7nLa1ifple8FlOoghFO1evTZfAt3A6IlcZVSgaXtS6Ogu63xByIiIiIiIiIiyobsLNSY3yQfXKylXwJPSNWhy/obCIxKlBaoLYC22wDfKuJgr88CGxoCCeGy9yrlWgqdi3SWxAwwYPyl8ZItZoHfV2aa5skjjBO5YSOChg2DPiUl8wckIsoAG5lERPSnxZ0+g+DRY4SD4s3y50eOVSuhtLISahbdWYQ4jfQbf03yNkFxl+LyN4kOAHZ2AvTS7WGRsxrw/VThcq1Oj0Hb7+DMszAh93PjoqiR3yWTpyIiIiIiIiIiyp7cbEyxoEk+2JhLd78Kj0/BgG13oNGlW/1oZgN02AMUbCAOFnwHWPc9EB8qe69+Jfsht530+J+3sW+x+M5i4VoTFxf4btwAs/z5hVzcseMI6N4DutjYTJ6OiMg4NjKJiOhPSbh6DUGDBwM6nSSu9vWBz9o1UNnZCTUPwx9i34t9kpi12hqDSg+Sv0lKPLCtLZCQrilplyNte5R029Dq9AYM33UPxx5+FIbqWT032lUQz2wgIiIiIiIiIvqS5HG2wPJ2JWGqkr7Wv/M+GvNPPhcL1BZAyw1A6U5iLuIlsKkZkBQtpMxUZphSZYqwg9amx5twJ/SOcL2JkxN8N26ARenSQi7xxg28a98empCQjB+OiMgINjKJiCjLku7dQ2DfvjCkSg+GN3F3h++6dTBxEVc9anQaTLs2DQZIV2/2KdEHzhbO4k30emBfLyDkgTRuYgG02QJYSWsMBgPG7X+A/XeDhaFalPHG6LoFs/h0RERERERERETZW4VcjpjTqoQQX37uFS6+kNkuVmUCNFwEVBsu5kIeAFtbA6mJQqqYSzH4FfGTxAww4MeLPyI2VVxhqbK3h8+6tbD5to6QS3nxEgF9+nCbWSL6S9jIJCKiLEl+/hzve/aCPlH64Vbl4ACfdWuh9vISavQGPX689CMehEubknns8qBtobbyNzr9M/D0sBhvvATwkH5QNxgM+PnwE2y7HiBc3qC4B2Y2Lw6lUpHJkxERERERERERfTkalfBE+3S7TxkMwJCddxEeL9MsVCiA2uOB76eJuYCraUf7aFOFVN+SfZHXPq/08rgAjDo/Cjq9TrheaW4OrwUL4NBOfOeT8vgJQmbMyOTJiIhEbGQSEVGmUt+/x/tu3aCPiZHEldbWyLFmNcxy5xZqDAYD5tycg2Nvjgm50RVGQ61UC3Hc2w5cnCfGq48EirUQwhuvvMO6S2+EeJ1CbpjfuiRUbGISERERERER0VdofIPCKOBmI4mFxaVg2M570OsN8kWV+gG1fhTjL08C+/uk7ZL1B6YqU0ypMgUqhUoSvxh0Ufa8TABQqFRwGz8eLkOGCLnobdsRe/RoBk9FRCRiI5OIiDKkCQnBe7+u0IVJtydRmJsjx4rlsChSRLbO/5E/Nj3eJMRb5W+Fih4VxYK3F4GDA8R44cZAzTFC+PzzMEw69EiIV8vnjCXtSkGt4q84IiIiIiIiIvo6matVWNyuFMzV0vcf556HYe1F8Uvfn1QfAVTsK8Yf7gYODxKamUWci2Bw6cHC5WsfrsXxt8dlb6FQKODcqyccu3YVch/GT0Dq27fG50dElA7f8hIRkVHaqCi879oNmqAgaUKthvfiRbAsW1a27tCrQ5h3S1xZWTNHTYypkK4pqdcB52cDGxoBunTbmHiUBJqsAJTSX1cvQ+PRb+ttpP+CYbmcDljVsSzM1dJvChIRERERERERfW3yu9lgQgPxC+Yzjj/FiUcf5YsUCuC7qUCJdmLu9kbgQL+0dzV/0LlIZ/yQ6wfh8gmXJuBZ5DOj83MdMhgWJUtKYvqEBAQOGcrzMokoy9jIJCIiWbr4eAR074HUV6+kCaUSXrNnwbpaNdm6i0EXMeHSBCFe0qUkZlWfBROlye/BqHeAf33g9BTAkO5sBWt3oO02wNRSEo5OTEX3DTcQl6yVxH2dLLGqY1lYmLKJSURERERERET/DW3L50D9Yh6SmE5vwICtd3DxRbh8kVIJNFoMFBCbk7i3FdjbA9BpPoUUCgV+qvwTCjkWklyapE3CwNMDEZkcKXsbhVoNr3lzobKzk8RTnvC8TCLKOjYyiYhIoE9ORmCfvkh+JG7d6jF5Emzr1pWtux1yG0PODIHWIG0y5rHLgyW1l8DCxOL34IPdwIqqwPsr4kAmFmlNTFtPSVij06PP5tt4G5EoiduYmWBt57JwsDLN4hMSEREREREREX35FAoFpjUrBm8HC0k8VadHz003cetdlHyhygRosR7IXVPMPdwD7OoCaH/fOcvCxAILay2Eo7mj5NLghGD0+KWH0Wam2tMTHjOmC/HobdsRsWZNhs9GRASwkUlEROkYNBoEDRmKxBs3hJzrqFGwb9FCtu5p5FP0P9UfybpkSdzN0g0rvl0BO7M/fPvu0T5gTzcgJVYcyCEn0OUI4FVaSE069AhXXkdIYkoFsLhdKeR1tRGuJyIiIiIiIiL62tlZqLGha3k4pfuCd2KqDn7rr+NxsMz7FwBQmwNtdwD5vhNzTw8DOzpImpke1h6YW2MuTBQmkkufRz1HtxPdEJEUkX4UAIBNrVpw7Caelxk6Zy7CV6zI5OmI6L+OjUwiIvrEoNcj+McfEX/mjJBz6tMbTn5dZOvexrxFr5O9EKeJk8RtTW2xos4KuFu5/x5MjgWOjpSfQMkOQO+LgHcZIbXt+ntsvvpeiI+rXxg1C7gafygiIiIiIiIioq9cHhdrbOxWHjbm0iZjbLIWndZdw8vQePlCtTnQejNQsIGYe3ECOP2zJFTWvSxGlR8lXPoy+iW6neiG8CT57WxdBw+GRalSQjxswUKELVkKg8Fg5MmI6L+OjUwiIgIAGAwGhEydhtiDh4ScQ/v2cBk4ULbuY8JH9DzZU9hCxMLEAsvrLEdeh7zSgnMzgYRQaczcHmi5AWiyFDATV1beeheJCQceCvG25XPAr0rODJ+LiIiIiIiIiOi/oIinHfz9ysFCrZLEw+NT0WrlFdwLiJYvNDEDWvoDRZqJuStLgADprl1tCrbBwFLie6JXMa/gd9wPoYmhQk6hVsN7yWKY5csn5MKXLEHYokVsZhKRLDYyiYgIABC+eAmitmwR4raNGsLtx7FQKBRCLjI5Ej1P9sSHhA+SuFqpxqJvFqG4S3FpQdgz4Fq6LUNMzIEep4EiTWTn9TEmGb0334ZGJ/0wWz6nIyY1Kio7LyIiIiIiIiKi/6Iyvo5Y1akMTFXSV/+RCalou/oqzj8Pky9UqYHma4DiraVxgx7Y3wfQJEnCPYr3wJAyQ4Rh3sa+hd9xPwTGBQo5Eycn+GzcALOCBYVcxPIVCJs3j81MIhKwkUlERIjcsAHhy5YJcetateA5dSoUSvHXRXxqPPr82gdvYt5I4kqFErOrz0ZFj4rSAoMBOD4a0Gul8SqDAac8svNK1ujQa/MthMWlSOIeduZY1qE0TE34a4yIiIiIiIiI6I+q5XPBoraloFJKv/ydmKpDV/8bOHA3SL5QqQIaLgKc80vjES+A01OEy7sW7YrhZYcL8fdx79HxWEc8jXwq5EwcHOCzfh3MChcSchGr1yB05iw2M4lIgm+AiYj+46L37kPI9BlC3LJcOXjNnweFWi3kkrXJ6H+6Px5HPBZykytPRm3f2uKNnh4BXp2Wxux8gKqDZedlMBgw4cBDYdsTMxMlVnUsC2drM6PPRERERERERET0X1a3qDtWdigDs3RfAtfqDRi0/S7WX3ojX6g2B5osBxTpWgdXlgLvrwmXdy7SGaPKiWdmhieFw++4H258vCHkTBwc4Lt+PcyLFRNykf7+CJk2nc1MIvqEjUwiov+wuF9/xYdx44S4eZEi8F6+DEpzcyGn0Wsw/Nxw3Aq5JeRGlhuJxnkbizfSJAEnxojx76cCagvZua279BY7b4rbkExvVgzFvO1ka4iIiIiIiIiIKE2dwm7Y0r0CbM1NhNykQ4+x6eo7+ULvskDl9GdgGtK2mE1NFC7vULgDxlUYBwWkK0DjNfHodbIXfnn7i1CjsrODz7q1sChRQshFbdqEj5Mnw6DXG384IvrPYCOTiOg/KuHKFQQNGQqk+1Bomjs3cqxeBZW1tVCjN+gx7uI4nAs8J+R6l+iNjoU7yt/s0iIg+r00lqsGUKih7OVnnoVi6hFxtWe3qrnQrLS3kSciIiIiIiIiIqI/KpvTEbt6V4abrbiz1YQDD41vM1tzDOCS7izLyFfA6Z9lL29dsDVm1ZgFtVK6s9f/vhB/6NUhoUZlY4Mca9fAonRpIRe9bTs+TpzIZiYRsZFJRPRflHD5MgL69YdBo5HETTw94LN2DUwcHYUag8GA6dem4+ibo0KuXcF26Fuir/zN3l0GLsyVxpQmQL1ZgEIhXP48JA4Dtt6BPt0OIpXzOGFMPfEweCIiIiIiIiIiMq6Auw329KmM3M5WkrjBAAzbeQ9nnoaKRWpzoMkyQKGSxq8uAx4flL1P3Zx1sbzOclip090HBky6MgnPIp8JNSpra/isXgXLsmWFXPSu3WkrM7nNLNF/GhuZRET/IQaDARFr1+F99x4wJEq3AlE5OcFn7VqoPTxka5fcXYLtz7YL8Ya5G2JU+VFQyDQlEfIY2NYG0KVI4+V7Aa5iUzIyIRXdNtxAfIpWEs/pZIll7UvDRMVfW0REREREREREf5a3gyW296oIXydLSVyrN6DPllu48TZSLPIqA1QZJMb39U575yOjgkcFrP9+PZzMnSTxFF0Khp8bjkSNuDWt0soKOVathGXFikIuevsOhM6Zw2Ym0X8Y3wgTEf1H6JOSEDx8BEJnzxa2k1VaW8NnzWqY5colW7vh0Qasur9KiNfMUROTqkyCMv0B8AAQHQBsbg4kx0jj1u5ATfEQ+FStHr033UJAZJIkbmNugrVdysHe0jSTJyQiIiIiIiIiImNcbcyxuVsFYZvZZI0eXf1v4FFwjFhUczTgXkwa0yQA29sCiTLNTwCFnAph0w+b4GLhIom/jX2Ln6/+LNuUVFpaIsfyZbCqXFnIRa5dh4iVKzN5OiL6WrGRSUT0H5AaGIi3bdsh9sgRIae0tESOlStgXqiQbO2+F/sw5+YcIV7evTzm1JgjnH0AIO2D7ObmQFywNK62AtpuA8ztJOGI+BT02nQT19N9+0+lVGBZ+9LI4yKe10lERERERERERH9ODkdLbOpWAfaW0vc5cclatF9zDQ+D0jUzTcyA1lsAi3THEEW9BXZ3BXTSXbU+3ccmB2ZWnyl8+f3w68PY/3K/bI3SwgLey5fJrswMW7AQkRs3ZvhsRPR1YiOTiOgrp/n4Ee/atkPK06dCztTXFzl37oBlmTKytSffncRPV34S4kWcimDRN4tgphIPikdqIrC1NRCe7twDpQnQehPgJT3A/eyzUNRdeAFnnoUJQ/3UsDCq5XMR4kRERERERERE9Nfkd7PB+i7lYGkqPf8yOlGDtquv4s77KGmBgy/Q0l88L/P1GeDXiUbvU869HPqU6CPEp12bhpdRL2VrlGZmyLF0CSxKlBByIdOmI3rPHqP3I6KvExuZRERfMUNqKgIHDoI2TGwSWtWojpy7dsIsb17Z2ivBVzDq/CjoDdJtaHPb5ZY9uB0AoE0FdnUGAq+LuSbLgby1P/01WaPDTwcfocv6GwiLSxEu71TJFx0r5cz4AYmIiIiIiIiI6E8r5eOAVR3LwlQlbRHEJWvRYc01XH+TbtvY3DWA76eKA11ZAtxcZ/Q+PYr1QAWPCpJYsi4Zw84NQ1xqnGzN/87MNCtYUMh9GDceMQcPGr0fEX192MgkIvqKhU2fgeT794W4U5/eyLF8OVS2trJ198LuYdCZQdDoNZK4p5UnVn67Eg7mDmKRXgfs7w28+EXMfTcFKN7q01+jElLRdNll+F9+K3v/+sU8MKFBYeMPRkRERERERERE/y9V8zljZccyMDWRtgkSUnXovO46Lr0MlxZU6A2UaCcOdHgIcGON7D1UShVmVJsBJ3MnSfx1zGv0+KUHopOj5evs7OCzdg1Mc+WSJgwGBI8eg5jD4vFJRPR1YiOTiOgrlXLkCGJ37xbiHtOmwXXQICiU8r8Cnkc9R99f+yJJmySJO5k7YfV3q+Fu5S4WGQzA0eHAQ5ntPSr1ByoPkITG7X+IJx9ihUvN1UpMaVIUS9qVgomKv6KIiIiIiIiIiP5JtQq6Yl3ncjBXS9/DJGl06Op/A/cCon8PKhRAg/mAp/TYIADAkWHAtZWy93C2cMaM6jOggEISfxTxCH4n/BCeFC5bZ+LkBJ/166D28pIm9HoEjxqF2OPHM30+Ivry8S0xEdFXSPv0KRIXLBTijp07wb5ZU6N1AXEB6HWyF2JTpU1GG7UNVn67Ej62PvKFpybLbyNSoh3w7c+S0OWX4Tjy4INwaRFPWxweUA0dKvpCoVAIeSIiIiIiIiIi+vtVzeeMDX7lYZXuzMwUrR5Dd95Fskb3e1BtDrTZAjjkFAc6NhK4slT2HhU9KqJvyb5C/GX0S/gd98PHhI+ydWp3d/j4r4eJe7ov1ut0CBo2HLG/yOwMRkRfFTYyiYi+MrrISMRPmAhopNvCWpYtC9fhw43WhSaGoscvPYRvwZmrzLG0zlIUcCwgX3hpIXBxnhgv2ABotBj4w8pPjU6Pnw49Ei71q5IT+/pWQV5X6wyejIiIiIiIiIiI/gkVcjthU/cKsDE3kcRfhSVg3snn0ottPYEuRwDH3OJAJ8amvSuS0at4L/Qo1kOIv419iy7HuyAgLkC2zjRHDvhu8IeJq6s0odMhaOgwxJ05Y/zBiOiLx0YmEdFXRBefgOABA2EIDZXETVxd4bVgPhRqtWxdTEoMep3shaD4IGmd0gQLai1AKddS8je85Q+cnCDGc1UHmq8FVNIPv5uvvsPzkHhJrEQOe4yvX1g4j4GIiIiIiIiIiD6f0j4OWN+lHJTpNspafeE1br2LlAbtvNOamU55xYFOTgBOTkw7iugPFAoFBpYeiEGlBwklQfFB8Dvuh/ex72XnZurrC58N/lC5OEsTWi2ChgxF4p07mT4fEX2Z+NaYiOgroU9KQmCfPki5f1+aUKvhvWghTJydZesSNYnoe6ovXka/lMQVUGB6temo4lVF/oYP9wKHBotxrzJAm61pW438QXh8ivgNPgCTGhWBMv0nZCIiIiIiIiIi+uzK5nREj+rSlZYGAzBi133pFrPA7ysznfOLA11aAOzvC+g0Qqp7se4YXX60EA9JDIHfCePNTLNcueDr7w+Vk5N0fsnJCOzdBymvX2f8cET0RWIjk4joK6BPTUXggIFIvHFDyLn/OBYWJUvK1qXqUjH4zGDcD7sv5MZXGo+6OevK3/DFr8DengCk36yDSyGg/W7AzEYomX38GeKStZJYq7LeKJnDXv4eRERERERERET02Q2pk184/ud1eALmnHgmXmzjntbMdCko5u5tBba3A1IThFT7Qu0xqfIkKCD9cntoYij8jvvhXew72bmZ5ckDX//1UNnbS+K6mBgEdO8BTUiobB0RfbnYyCQi+sIZNBoEDR2KhIsXhZy9nx/sW7eWrdPpdRh9YTSufLgi5AaXHoyW+VvK3/D9VWBHB0Cf7ht19r5Ax32ApaNQci8gGjtvSc85sDEzwYjvZT7kEhERERERERHRv8ZcrcKcliWELWbXXnqDG28jxQJrV6DL0bRdutJ78QuwoRGQKNY1y9cMU6tOhVIhbVOEJoWi6/GueBvzVnZ+ZvnyIceK5VCYS3cD0wQHI6BnT+ji4jJ8PiL6srCRSUT0BdMnJCBoxEjE/3pKyJk1bQqnIYOhUIjbthoMBky+Ohkn350Ucn5F/dCtWDf5G358AGxpBWiTpHFrN6DTfsDWQyhJ1eox4cDD9MciYPC3+eFiY2b02YiIiIiIiIiI6N9RMoc9etXII4kZDMDwXfcQkyRuFwsrJ6DzISBvHTEXdBPY3h7QaYVUwzwNMa3qNPlm5omuRldmWpQsCa/58wCVShJPefYMgX36QhsVlckTEtGXgo1MIqIvVPzFS3jdsBHijh8XcqY/1IPFgP6yTcxETSJGnB+BvS/2Crnm+ZpjSOkh8jeMeAVsagqkxEjj5vZpKzEdcwsler0Bw3fdw71AaU0+V2t0quRr/OGIiIiIiIiIiOhfNbhOPuR3k24x+y4iEQO33YFObxALTK2AttuB4m3E3PvLwNlpsvepn7s+pledLjQzw5LC0OOXHviY8FG2zqZWLXhMniTEE2/exJsmTZF486aRJyOiLwkbmUREXxhdTAyCx4xFQPfu0AQHC3nrevVgOWwYFErxn/iAuAB0PNYRJ96eEHLf+X6H8RXHyzY/ERMEbGwCJIRJ42qrtDMx3YoIJQaDAVOOPMHBe+Icf2pUBGoVfwUREREREREREWVXZiZpW8yq0u0xe+55GOb8InNeJgCo1ECT5UDlgWLuwjzgpbirGAD8kPsHzKg2Q2hmfkj4gJ4neyIyWWZLWwD2zZvDZZB4L21ICN516oywZctg0Onk50pEXwS+RSYi+oIkXL6MVw0aIGbfPtm8dZ3acJs6BYp022oAwJXgK2h7pC2eRz0XcpU9K2NGtRlQKcU6JESkrcSMeS+Nq0yBNluAHOVk57Ly/Gusu/RGiHeo6IMqeZ1la4iIiIiIiIiIKPso7m2P0XULCvHlZ1/hkMyX1wEASiXw3c9A6U7pEgZgb08g9oNsWb1c9TCz2kyhmfkm5g16n+yNuFT5sy+deveGQ/v2YkKvR/iixXjfrTu04eHycyWibI+NTCKiL0Ts8RN436s3dGEyH7zUajj36wfv+fOhUKuF9JYnW9D7196ISb8tLIAa3jUwv+Z8qFViHZJjgc3NgPB037JTKIHma4E8tWTnuudWIGYceyrE6xRyw08NxdWbRERERERERESUPXWvlgtNSnoK8RG77+FRsPiu6ZO6MwHXwtJYYjiwtwegl18lWTdXXfxU6Sch/iTyCQacHoBkbbKQUygUcBv3I9zGj5N9L5Z49SredekCXWys8bkSUbbFRiYR0Rcgeu8+BA0dCmjEw9TNixVDrj274TKgv+yHteNvjmPG9RnQG/RCrneJ3lj0zSJYqi3Fm2qSgG1tgQ93xVyjxUDhRrJzPfMsFKP23BfiZX0dsKRdKZhwS1kiIiIiIiIioi+GQqHAjObFUdTLVhJP1ujRc+MtRMSnyBeaWgIt/YH0753eXgDOzTJ6v6b5mmJkuZFC/FbILQw5OwSpulTZOTq2b4+cO3fANGdOIZ/68hUCBw2CQebdGhFlb3ybTESUzUVu3IQPY8cCemkjUmFuDteRI5Fz+zaY588vW/si+gUmXJ4gxC1NLLGg5gL0K9lP2K4DAKDTALv8gHcXxdz304BSHWTvd/1NJPpsvgVtugPf87laY03nsjBXy2xdS0RERERERERE2Zq5WoWVHcvCycpUEg+KTkL7NdeMNzNdCgD154rxczOBezuM3q9j4Y7oXaK3EL8YdBFDzw6VbWYCgHmhQsi1ZzfsGotfwE+8chUfJk2CwWCQqSSi7IqNTCKibMpgMCBs2TKETJsm5FTOzsi5YzucuvrJnocJAHGaOAw7PwxJ2iRJ3MfGB1vrb0Vt39ryN9brgQP9gefHxFz1EUClfrJlD4Ni0M3/BpI10oarh505NnQtD3tLU9k6IiIiIiIiIiLK/rzsLbCsfWmYKBWS+NOPcWi7+irC4ow0M0u2A0q0Sxc0APt6ATfXG71f3xJ90a5g+jrgXOA5DDs7DBqd/OpKpZUVPGfOhMvgwUIuZvceRKxeY/SeRJT9sJFJRJQNGQwGhM6eg/BFi4Wc2tMTObdshnmBAkbrdQYdpt+fjsD4QEncwcwBa75bgzz2eYzdGDg+Gri/XcyV6wHU+lG27HVYPDqvu464FK0kbmehxoau5eFpb2F0rkRERERERERE9GWokNsJExsVEeLPQ+LRdvVVhMaJZ1gCAOrPAZzTv8syAIcHA1eWypYoFAqMKj8KjfM0FnJnA89i2DnjzUwAcOrVE/YtWwjxsHnzEHtM5gv8RJQtsZFJRJTNGHQ6fJz4EyLXrRNypjlzwnfLZpj6+mY4xqaXm3Aj/IYkplKoMKfGHHhYexgvPDcTuL5SjBdrCdSbBSgUQio4Ogkd1lxDRIJ0Sw9LUxX8/cohv5tNhnMlIiIiIiIiIqIvR8eKvhjxvfgF+5eh8Wiz6ipCYmWamaZWQNttgK2XmDsxFjg/W/ZeSoUSkypPQoPcDYTcmYAzGH5uuNFmpkKhgPuECbCqXFnIBY8ajfhLl2TriCh7YSOTiCgbMWg0CB4xEtE7dwo5s0KF4LtlM9QeGTQiAZwKOIUtr7cI8SFlhqC8R3njhVdXAGeni/F83wNNlgNK8VdGRHwKOqy9huAY6QdUU5USqzuVRSkfhwznSkREREREREREX55+tfJidL2CQvx1WAJar7yCgMhEscgpD+B3DHDIKeZOTwFOTU7bLSwdlVKFKVWmoH7u+mJZwGmMvTgWeoNeyAGAQq2G18IFMMuXVxI3pKYisHcfxJ48Kf+ARJRtsJFJRJRN6JOTEdh/AGKPHhVyFqVKwXeDP0ycnDIc41LQJYy9NFaI18tZD50KdzJeeG87cHyUGPetArTaAKjUQio2WYPO66/jdViCJK5UAIvalkKVvM4ZzpWIiIiIiIiIiL5cvWvkwbj6hYT424hENFt+GY+CY8QiB9+0ZqZzfjF3YS5wapLRZubUKlPxQ64fhNzxt8cx+8ZsGGTqAEBlY4McK1ZA5Sx9V2XQaBA0eAhiDh408oRElB2wkUlElA1oPn7Ee7+uiD93TshZVa4Mn7VroLK1zXCMGx9vYNCZQdDopdtp5HfIj58q/wSFzLawAICnR4H9fcW4e/G0LT/U4vmWyRodum+4iYdBsUJuVosSqFvUPcO5EhERERERERHRl697tdyY0KCwEA+LS0HrlVdx+VW4WGTrCXQ5CrgVE3MX5wO/TjTezKw6FfVy1hNym59sxvpH643OU+3lhRwrVkCZ/v2aTofgkaMQtW2b0Voi+nexkUlE9C+LO3sWb5o0RdKdO0LOuk5teK9YDqWlZYZj3A29i36n+iFFlyKJ25vZY0HNBbBUG6l/cwHY1QUw6KRxp7xAh72AuZ1QotHp0XfLbVx/EynkxjcojBZlvDOcKxERERERERERfT26Vs2Fn5sURfrv0MenaNFl3Q0cuf9BLLJ2AbocArzKiLlLC4GTE2SbmSZKE0yrNg21ctQScvNvzcfBV8ZXV1oULQLfjRugcnQUch8nTUbE2nVGa4no38NGJhHRv8Sg0SBk1mwE9u4DXXS0kLdr3AjeCxZAaWqa4TiPwh+hz699kKRNksStTayx/JvlyGGbQ74w6DawrS2QrvkJW2+g4/60D5Tp6PUGDN91D6efhgq5gbXzoVvVXBnOlYiIiIiIiIiIvj4dK/picdtSMFVJWw6pOj36b7uNPbcCxSILB6DjPsCrrJi7vAj4ZZzRZuas6rNQ2rW0kJtwaQIuBF4wOk/zggXhu3kzTNzF3cRCZ89GzIEDRmuJ6N/BRiYR0b9AGx6Odx06InKd/De9HDp2hMf06VCYmBgdw2AwYN+Lfej+S3fEa+IlOUuVJWaUnYECDgXki4PvAJubA6lx0rilE9BpP2AvNj8NBgMmHnyEA3eDhVyXyjkxpE4+o3MlIiIiIiIiIqKvW4PinvD3KwdrM+n7LIMBGLPvAR4Hi0cUwdwO6LgX8C4n5q4sAU5Nlr2XuYk5Fn2zCHnt80riOoMOg88Mxs5nO42emWmWOxdybtkMtY+PkAseNx4JV68ZeUIi+jewkUlE9JkZUlMR0Lcfku7dE3JKS0t4zp4N9x/HQqE0/k90cHwwep3shQmXJwhNTHOVOaaWmYoCdkaamPd2AOvqAknptoY1tUnbTtZZviE595fn2HT1nRBvVsoLExoUNn4GJxERERERERER/SdUzuuM7T0rwtnaTBJP1erRf+ttxKdoxSJzu7R3Ut7lxdzFeWlbzcqwM7PD8jrL4W4lXV2Zqk/Fz1d/xrBzwxCbKtM8RdqZmb6bN8E0bx5pQqNB4IABSHn50vhDEtFnxUYmEdFnFjpvPpLv3xfiZoUKIeee3bBr2MBord6gx46nO9D0QFNc+XBFHENlhgU1FqCoQ1GxWKcFjo8F9vUEtMnSnIk50G474FlS9r6rz7/GkjPiB7g6hdwws0VxKJVsYhIREREREREREVDUyw57+1SGm620mfk6PAHj9j2QXylpbgt02APkqCDmTk4AbvnL3svdyh0r6qyAramtWPbuJFodaoX7YeJ7OABQu7rCZ80amLi5SeL6uDgE9OwFbViY/AMS0WfFRiYR0WcUd/o0Iv39hbhDu7bIuX0bzHIZP2MyVZeK4eeGY8q1KUjUJgp5G7UNFtVahPLuMt9eS4gANjcFri4Vc0o10HIDkLOq7H133HiPqUefCPFKuZ2wpF0pqFX8VUJERERERERERL/zcbLEojalkP677/vvBmPXTZnzMoG0Zmb73YCnePYlDg0GHu6RLctjnwcr6qyAi4WLkAuKD0LnY52x78U+2Vq1uztyrFwBpZWVJK4JDkZA7z7QxSfIz5WIPhu+fSYi+kw0QUEIHjNWiDu0bw/3CROgNDOTqUqTqEnEgNMDcPLdSdl8Te+a2N9kPyp7VZYpjgTWfQe8OS/mrN2ALoeBAnVlxz364APG7H0gxEt422F157IwV6uMzpmIiIiIiIiIiP67KuR2wpA6+YX4hIMP8TwkTr7ofyszXQqmSxiAvT2BF/Lvxoq5FMOuhrtQ1Uv8or7WoMVPV37CuYBz8rcsWBBeCxYAKul7ruRHj/C2ZUskP3smP1ci+izYyCQi+gwMGg2Chg6DPiZGEjcvXBiuo0ZmWBubGovev/bG5eDLQs7ezB4zq83Eom8WwdXSVSzW64A93YAImX39vcoCPc8CPhVl73vhRRgGbb8DfbrdPvK5WmO9X3nh4HYiIiIiIiIiIqI/6lsrL6rkdZLEkjV69N1yG7HJGvkiS0eg4z7A3kca12uBHR2AV2dky5wsnLC09lIMLzscJgrpeyu9QY8R50fgccRj2VrralXh/tNEIZ765g3etmyFqB075bfEJaJ/HBuZRESfQej8BUi6d08SU1pbw2vBfChNTY3WRSZHovuJ7rgTekfI1cxRE/sb78cPuX+AQiF/RqXy3DTg1WkxUaoD4HcUsPWUrbv1Lgo9N96CRif9gOZlb4FN3SrA0cr4nImIiIiIiIiIiABApVRgQetScLaW7kT2MjQeHdZcQ1RCqnyhrSfQ6UDabmJ/pE0GtrUBXp+VLVMqlOhcpDM21tsIDysPSS5Jm4T+p/rjY8JH2VqHli3h1KuXEDekpuLjxIkIHjYcuvh4+fkS0T+GjUwion9Y1M6diFy3Toh7TPkZpj4+MhVpPiZ8RJfjXfAkUjyfsnm+5lhQcwGcLJxkKtOYvT4B1eWFYqL2RKDREsBEfivbpx9j0dX/BpI0Oknc2doMW7pXgLududF7EhERERERERER/ZGLjRkWtimJ9N/Dvx8YgzarriI0Llm+0DE30HE/YG4vjWuTga1t5I9R+k0xl2JY+e1K2JraSuJhSWHoe6ov4lPlG5IugwfBZdBAQCm2TmKPHsXbFi2R+vat0fsS0d+PjUwion9Q9P79+DjxJyFu37YNbOvKn0sJAAGxAehyvAvexLwRcp0Kd8LEShOhUho/n1IV+RJ2Z0aLieJtgKpDIHxy/M37iER0XHsdMUnSrT1szU2wsWt55HS2kq0jIiIiIiIiIiIypkpeZ9nzMp+FxKHNyqv4EJMkX+hWGOi4FzCzk8a1ScCWVsCbC0bvmcsuFxbWWgi1Ui2Jv4h6geHnhkOjF7e2VSgUcO7TBz7r18PExUXIp759i7et2yDxxg2j9yWivxcbmURE/5DYo0fxYeyPQLr9880KFYLbaJkm429eRL1A5+OdERQfJOT6luyL4WWHG91KFgCQHAuHE/2g1CRK4+7FgYYLjDYxQ2KT0X7tVYTFpUji5mol1vuVQ2FPW9k6IiIiIiIiIiKizAz4Ji8GfJNXiL8OT0DLFVfwPiJRpgqAV5m0MzPN0r2b0iYBW1sB7y4bvWdZ97KYXGWyEL8UfAlDzwxFii5FpgqwqlAeufbvg1WVKkJOFxODd127IXr/fqP3JaK/DxuZRET/gLhTpxA0YiSg10viam9v5Fi+DEoz+W1dH4Y/hN8JP4QlhQm5keVGok+JPhk3MTVJUO3pApOYt9K4hSPQejOgtpAti05MRae11xEQKf32m1qlwMqOZVHG19H4PYmIiIiIiIiIiDKhUCgw7LsCGFm3gJALjEpCq5VX8DY8Qb7Yu0zaNrPpm5maxLRtZkMeGb1vg9wN0K9kPyF+NvAs+v3aD4npFwP8xsTJCTlWr4LL4MHiwgCNBh9Gj0HoggUwpHv/R0R/LzYyiYj+Rga9HtF79iBo8BBAJz1j0sTDAz7+/lC7u8vW3vh4A91/6Y6YlBhJXKlQYnLlyehYuGPGN9emAjs7Qfk23fkACiXQYi3g4CtblpCihZ//DTwLiZOWKYD5rUuiRn5xGw0iIiIiIiIiIqK/om/NvPipYWEh/jE2GW1WXcWbDJuZ+wBTG2k8JQbY3ByIfm/0nr2K90LjPI2F+LWP19DjZA/hfdz/KJRKOPfuBe+lS6CwEBcIRKxYiZApU43el4j+/9jIJCL6myQ9eIC3bdviw4/jYNBI99g3cXGBr/96mHp7CXV6gx5rHqxBj196IEEj/aBmojTBrOqz0DRf04xvrtMAu/2AF7+IudoTgDzfyJalaHXovfkW7ryPFnLTmhZDg+KeGd+XiIiIiIiIiIjoT+pSJRdmNS8uLHRMa2ZeweuwePlC77K/NTOtpfG4D8CmZkBipGyZQqHAxMoT8UOuH4Tc/bD76HqiK8KTwo3O1+abb5Bzy2aYuLkJuaitWxG1a5fRWiL6/2Ejk4jo/0kbEYHgcePwtlVrJN+7L+RVjo7w8V8PU19xRWREUgT6/NoHC28vhM4gXcFppjLDolqL8H3O7zOegF4H7OsFPD0spHSlOgFVBsuW6fQGDN5+FxdeiB/SRtcriLblfTK+LxERERERERER0V/UqlwOzG9VEsp0zcyQ2BS0WXUVr4w1M3OUA1pvApRqaTziRdqZmanyKzrVSjWmV5uOVvlbCbnnUc/R7UQ3oyszAcC8cGHk3LkDZoULCbmQyT8j6d49o7VE9NexkUlE9BcZ9HpE7diJV/V+QMzuPYDBIFyjsreHz/p1MMuTR8hd+3ANLQ61wOVg8UByK7UVltdZjmre1TKehCYJ2N8XeLhHSCXlbwJ9vTniHv4ADAYDxu59gGMPPwq53jXyoHcNcb5ERERERERERER/pyalvDC/tdjMDI1LQdtVV42vzMzzDdB0hRgPvAHs7AQkx8qWKRVKjKs4Dn5F/YTc65jXGHJ2CDQ6jUxlGrWbG3Ju3gyLsmUkcYNGg8CBg6ANN76qk4j+GjYyiYj+guTnz/GufQd8nDgR+lj5D0Y2332HXHv3wLyAeID54deH0eOXHrJbVuSwyYH1369HOfdyGU/i3RVgeRXg/nYhlZTnB8TUnJp2PmY6iala9N92BztuBgi5tuVzYJTMgetERERERERERET/hMYlvbCgTSnZZmbHtdcRHJ0kX1isBfD9NDH+8ldgZXUg+I5smUKhwNAyQzGo9CAhd+PjDUy8PBEGmQUL/6O0tIT3ggXCNrPakBAEDh4sHDlFRP8/bGR+JklJSTh06BCGDBmCqlWrwtXVFaamprC1tUWhQoXg5+eHU6dO/elxr1y5gq5duyJPnjywtLSEo6MjypQpgylTpiCc3/4g+tvpk5MROnce3jRrjqQ78h+GTPPmgc+6tfBetBBqT/GMyedRz/HT5Z9ggPiBqF7OetjZYCcKOYlbVHySEg8cHQmsrwdEvhLnmP8HxHwzC1CaCLn3EYlotuwyjtz/IOTqF/PAlCbFoJBZwUlERERERERERPRPaVTCEwvblIIqXTczKDoJHdZeQ0R8inxhpX5A5QFiPOoNsOZb4Moy2V3UAKB7se4YUmaIED/0+hCW31ue4XxNnJ3hvXgRFGrp9rZJN28hZOasDGuJ6M8R33LT327Lli3o3bs34uPFZfAajQZPnz7F06dP4e/vj7p162Ljxo1wcXHJcEyDwYBhw4ZhwYIFkm+HJCUlISoqCrdv38aSJUuwdetWfPPNN3/7MxH9F+kTE/G+azck3b0rm1daW8Nl4AA4tG0rfIj5n0RNIoadHYYUnfTDl5nKDGPKj0GzfM0ybiQG3AD2dAOi38nn89aBrulqIEb89+bii3D033Yb0Ynit8Kq5XPG/NYlhQ+LREREREREREREn0PDEp5QKICB2+5A/4fe4+uwBHRadx3belaErbnMO7c6k4GEcODeNmlcrwFOjAHenAOaLAcsHYVSvyJ+CI4Pxo5nOyTx5feWw9vGG43yNDI6X4vixeE+cQI+jBsviUdt3gzTXDnh2L595g9NRJniiszP4M2bN5+amB4eHujUqRMWL16MHTt2YN26dfDz84O5uTkA4Pjx46hTpw4SExMzHHPMmDGYP38+DAYDrKysMHDgQGzevBkrVqzAt99+CwAICQlB48aNcddI04WIss6g0yFoxEijTUzbH+oh99EjcOzUyWgT02Aw4OerP+Nt7FtJ3NnCGdvqb0Pz/M0zbmK++BXY0FC+ialQAlUGAW22ASZmQnr9pTfotO6abBOzdkFXrOxYBqYm/JVARERERERERET/ngbFPTGzeXEh/ig4Ft39byIpVScWKZVA42VA7QmAQiXmnx8HNjZO2+UsHYVCgdHlR6OaVzUhN/HyRBx7cyzD+dq3aAH7Nq2FeMiUqYg5ciTDWiLKGr61/kyqVKmCQ4cOISAgABs2bED//v3RqlUr+Pn5Yd26dbh16xY8PDwAAPfv38fMmTONjnXnzh3MmpW2PN3Ozg6XL1/GwoUL0b59e/Tq1Qu//PILJk6cCACIj49Hz549M9zTm4gyFzJzJuJltn9We3khx6qV8Jo3D2pX1wzH2P9yPw6/PiyJKRVKzKw2E/kc8mU8gccHgG1tAK3MmQCuRYDup4BvJwMmpkL64L1gTDr0WPJNtv8ZWDsfVncqC0tTLtAnIiIiIiIiIqJ/X8uyOTC+QWEhfv1tJPpsuYUUrZFmZrVhgN9RwNZbzH+8n7bLmV6sNVGaYHaN2SjoWFAS1+q1GHl+JCZfmYxkbbLR+bqPHQuLUqWkQYMBwaPHIP7CRaN1RJQ1bGR+Bv369cPFixfRoEEDqFQy3wgBULhwYaxaterT3/39/Y2ON3ny5E+NyWnTpqF4cfEbKhMnTkT58uUBADdu3MDRo0f/H09A9N8WuWkzojZuEuL2rVoh96GDsK5ePdMxXkS9wLRr4uHjvUv0RnmP8hkX39kC7OqSth3GHylNgBqjgZ5nAa/SsqVPP8Zh1O77QtzKVIWVHctg6Lf5oeR2skRERERERERElI10q5oLA2uLX/w/+ywMfTbflm9mAoBPRaD3BaBgAzH3/DhwfLTsmZlWaiss+WYJXC3FhQq7nu9C+6Pt8SbmjewtFaam8F68CGpfH2lCo0HgwIFGd3gjoqxhI/MzcHBwyNJ19erVg5WVFQDg/fv3iI2NFa6Ji4vDsWNpy9ltbW3RpUsX2bEUCgUGDPj9kOMdO3bIXkdEGYs7fQYh06cLcZt6deH+00QoLS0zHSM2NRbDzw1Hsk76za0KHhXQs1jPjIuvrQQO9AUMemnc3B7wOw7UGiO7ChMAYpO16Lv1LpI00g92OZ0ssb9fFXxfxD3TuRMREREREREREf0bhtTJhy6Vcwrx009D0W/LbaRq9WIRkHYWZuvNQNUhYu76KuDqctkyNys3LKu9DPZm9kLuedRztD7cGkdey28Xa+LsDJ+162Di4iKJG5KS8L5Xb4SvWIHYY8eQ/OQJ9AkJ8vMmIllsZGYjKpUKln9oiiQliVtInjt3DikpKQCA6tWrS65P7/vvv//05+PHj/+NMyX6b0i6fx9Bw4YBeumHIotSpeA5YwYUysz/CU3QJKDPr33wOua1JO5k7oQZ1WZApZRfpQ2DATg/Gzg2UsxZuaZtk5GjnNH76vQGTDz+BgFR0n9HnKxMsbVHReRzs8l07kRERERERERERP8WhUKBCQ0Ko0UZcavYX5+Eot/WDJqZCgVQeyJQrKWYOzEWeHJYjAMo4FgAOxvsRAmXEkIuSZuE0RdGY9HtRdCnX3QAwNTbCznWroHS1lYS18fEIGzBQgQNGYo3TZvhWZmyeN+1G7QREfJzJyIJNjKzkdDQUISFhQEALC0t4ZLu2xsA8PDhw09/LlOmTIbjubi4wNfXFwAQFhaG0NDQv3G2RF+3xDt38L5rNxjSfaFA7eMD72VLoTQzy3SMJG0S+p3qh/th0q1dFVBgRvUZcLZwli80GICTE4DTU8ScXQ6g63HArUiG915zNRhX3kpXdauUCixpVxqe9haZzp2IiIiIiIiIiOjfplQqMLN5cTQr5SXkTj4OQf/MmpmNlwI+ldMlDMCe7sCbC7JlHtYeWF93PfyK+snmVz9YjeHnhiNJKy5EMs+fHzlWrIDC3DzD50q4fBlBQ4fBoDcydyL6xOTfngD97o9nZNatWxdKmdVez58///TnnDlzZjqmr68v3r1796nW1VXc4zszt27dyjD/5MmTT3/WarXQaDQZXE2U/SXdvo3gPn1hSEyUxJV2dvBYugQGa+tM/3eeokvBkHNDcCtE/PnpW6IvyjiXkR9Dr4Py+Eio7mwQUgbHPNC23wvYegEZ3P/o/WCsv/5RiI/6Pj/K+tjyZ5ToX6DRaKDVaj/9mYj+XfyZJMpe+DNJlP3w55Ioe+HPJDCtSWFodXocvP9BEv/lcQiGbL+DeS2LQalUyFQqgeb+MNlQF4rIP+yYpk2CYUsL6JqvhyHvt7L3HFB8AEo5lcKEqxMQnRItyZ18dxJBcUGYX2M+XCykC5LUxYrCfd5cfBg4CPjtvzc5ideuIdzfH/YdO2b47JT98GdSpM3gf+v/X2xkZhOvX7/G9N/O4VMoFBg9erTsddHR0Z/+7OxsZDXXHzg5OcnW/hlly5bN8rUxMTGI4JJ4+oJp7txF/JgxQLL0PEuYmsLy58mIs7EBMvnfuEavweS7k3E17KqQa5WzFRq7NZb/OdFpYHdmNCxeiltbaJwKIqr+Wug15hne/+rbGIw4+EqIf1fAEQ3zW/Hnk+hfotVqERcX9+nvJib8CEb0b+LPJFH2wp9JouyHP5dE2Qt/JtOMqumB5JQU/PIsUhI/8vAjrE30GFzDGwqFXDMTUH2/Ak77WkOZHPUpptAmQ7WrI2K+mY3kvD/I1hUyL4RlFZdh4p2JeBH7QpJ7HPkYHY51wNTSU5HLJpe0sHBhWM+YgeTNm6F7/RqGWOnOaf8TPn8BUgsXhioLi5Yo++DPpOiv9p+yglvLZgMJCQlo2rQpEn9b/dW3b1+UKyd/9l18fPynP5tnsjwdACwsft9C8o8/WEQk0ty8ifjRo8Umprk5rGdMh7p48UzHuBd5D/2v9pdtYjbxaYLu+bvLfqBSJEXC4Xhv2SZmqlspRDbaCL1lxl9euBUQh1GHXkGjN0jieZ0tMLaOr9EPckRERERERERERNmdSqnAhO9z4rsCDkJux91QbLoZYrRWZ+eLqLrLoVdbSeIKvRZ2vw6FxZNdRmtdzF0wt9xcVHGtIuTCksPw4+0fEZUSJeTUZcvAZsF82B88ALuDB2CzbCnUVdKNodEgYdo0GLiqj8gotol/s2bNGgQGBv4tY/30009Zvlan06Fdu3a4fz/tDL3SpUtjzpw5f8s8/i43b97MMP/kyRN0/G35u52dnWQVKNGXwJCaiohlyxG/fj2Qbl96hYUFPJcthUUmK5MD4wOx4M4CnA44LZtvmqcpfiz/I5QK8fsjiveXodrXE4p4cTtYfc7qULTcCEdT6wzvf+tdFEYceoUUnbSJ6WCpxsqOZeDlaJlhPRH9s/64zYijoyPUavW/OBsi4s8kUfbCn0mi7Ic/l0TZC38mpRa2dcTAHfdx8kmoJL7sUhB83exlz9MEADjVgd5hHxTbW0OR9IeVmTDA7tw4WJsqoC/fy/h9XRdiyb0l8H/sL4mHJYdh1uNZWPbNMpgojbRcnJyAXLmgK14c75s1hy7097nrnr+AYtduOA3on/GDU7bBn0mRvb39PzY2G5m/WbNmDa5du/a3jJXVRqZer0eXLl1w8OBBAECBAgVw7NixDFdaWlv/3sxITr9qTEZS0u8HDtvY2GRpXumVKVMmy9eamJjwh5a+KEkPH+HDmNFIefFSyCmtrJBj9WpYli5lvF6bhFX3V2HDow3Q6OW/OdUgdwNMrDwRKqVKmtDrgAtzgbPTAYPMwd4F6kPZYh2U6oxXX98LiEaPTXeQmKqTxG3MVPDvUgZ53OwyrCeiz+N/24yo1Wr+riTKBvgzSZS98GeSKPvhzyVR9sKfyd+p1cDidqXRae11XH8r3WZ27P7HcLW1RK2CrvLFvhWALkeBTU2BdIsKVCd/hMrWHSjWwui9h5UbhjwOeTDpyiRo9b+fCXgz9CaW3l+K4eWGZzx3Z2d4Tp2KgB49JPGoNWtgW6smLEsZfw9J2Qt/JqX+ye11ubXsv8RgMKBXr17YvHkzACBPnjw4deoUXF2N/AP7mz92tcPDwzO9zx/Pw/snO+JEXxpDairCFi3C29at5ZuYNjbwWb8uwybm1Q9X0fxgc6x5sMZoE7Nl/pb4ucrPYhMzMRLY1AQ4M1W+iVm8DdBqA5BBE9NgMGDnjQB0WHMNcSnSw5QtTZVY2DQfCnvYGq0nIiIiIiIiIiL6EpmrVVjdqSzyu0l3MdPpDei1+RY2XX0Hg8EgX+xWGOh6DLD3EXP7+wDvrmR47yZ5m2BshbFCfMPjDTj25limc7euVhUO7dpKg3o93rXvgLcdOiBi7VqkvHplfP5E/zFsZP7m6tWrMBgMf8t/sqJ///5Ys2YNAMDX1xenT5+Gl5eRJe9/kD9//k9/fvv2babXv3v3TraW6L8s9f17vG3fAeHLlgM6nZA3zZULvhs3wMLImZgxKTEYf2k8evzSAwFxAbLXFHUqik31NmFCpQnilhI6LbCjI/DmvFioMgPqzwWargBUxr/JExiViE7rrmPknvtCE9NCrcS8xvlQ2N3KSDUREREREREREdGXzc5SjQ1dy8PTTroQIFWrx/j9D9F3y23EJBk5e9IxN9D1BOCc7p25LhXY3haIeJXhvVvka4Fm+ZoJ8YmXJ+J51PNM5+46fDhMfX2lQb0eSTdvIXT2HLyu3wBvW7RE8tOnmY5F9LVjI/NfMHjwYCxbtgwA4O3tjdOnT8PHR+bbHzKKFi366c+3bt3K8NqwsLBPjUwXF5dMV3sS/RfEHDqEN02bIfnBAzGpUMCxSxfk2rcX5oUKydZfDLqIRvsbYf/L/bJ5FwsXTK06FVvqb0FJ15Lykzg3A3h3UYw75QV6nALKdQcUCtlSvd6ATVff4fv553Hhhbgq28xEiZUdSqGkV8ZnahIREREREREREX3pPOwssKFredhZiAsCjj38iB8WXsDt91EylQBsPYEOewFrN2k8KQrY0gJIiJCvA6BQKDC2wlgUdSoqiSdpkzD4zGDEpMRkOG+lpSU8Z84AlMZbNMmPHuFd5y5IevQow7GIvnZsZH5mI0aMwMKFCwEAHh4eOH36NHLnzp3l+po1a8LMzAwAcP78eckZmOmdOHHi05/r1q37F2dM9HXQJyQgePQYBI8YCX1CgpBX+/jAd9NGuI0eBaWRc2pvfLyBgacHIjI5UsipFCr4FfXD4aaH0ShPIygVRv55fXUaOD9HjJdoC/Q8B7gXM/oMKVod+m65jfH7HyIhVVxJamtugtWdyqJSbiejYxAREREREREREX1N8rnZYGPX8nCzNRNyQdFJaLniCnbceC9fbJ8DaLcDUFtK45Gvge3tAI3x9+9mKjPMrzUfjuaOknhAXAD6nuqLRE1ihvO2KFkS7hMnQmEmzvt/9DExeN+1G5KfPMlwLKKvGRuZn9G4ceMwZ05aA8PNzQ2nT59Gvnz5/tQY1tbW+OGHHwAAsbGx8Pf3l73OYDBgyZIln/7eunXrvzZpoi+cJiQEYYuX4FXdeojZv1/2God2bZF7/z5Yli1rdJzX0a8x6Mwg2bMwCzsVxvYG2zG0zFBYpv/Q80dxH4E9PQCk24K6VIe0rWTNjK+iTEzVovuGmzj+6KNs/tvCbvh1aA1Uz+9i/P5ERERERERERERfoRI57HF0YDXUKiC+G9PpDRiz9wFOPQmRL/YsBbRYB6RfmBBwFVj7bYbbzLpbuWN29dlQKVSS+P2w+xhydgg0OiNb2/7GoXUr5LtwHp5z58C2QQMo7eyEa/QxMXjfxY/bzNJ/FhuZn8mUKVMwdepUAGnbvJ46dQoFCxb8S2ONHz8eit+2nRwzZgzu378vXDN58mRcu3YNAFCuXDnUr1//L86c6MuUcPUaAgcMxMtvaiN86VJow8KEa1R2dvBeugTuEyZAaWm8ARmeFI4+v/ZBXGqcJG6uMsfwssOx5YctKOiYyc+zXgfs6Q4kptsO1rUwUG92hqUxSRp0XHtdditZRytTLG5bCqs6loGrrfxKUiIiIiIiIiIioq+dk7UZ1nYuh3H1C0Gtkh7bpDcA/bfewcMgI1u+FqgH1J0hxj8+AFZWBx7sNnrf8h7lMbzscCF+OfgyxlwcA51e3Fntj1S2trCrXx9ec2Yj/6WL8FqwAApTU8k1uv81M589y3Asoq+Ryb89gf+CVatWYfz48Z/+3r9/f7x48QIvXrzIsK5q1apwdnYW4qVKlcLIkSMxc+ZMxMTEoHLlyujevTvKly+P+Ph47NmzB7/88guAtBWcq1at+nsfiCgbM+j1+DhxIqJ3Gf9wAQCW5crBc/YsqN3dM7wuUZOIfqf6ITghWFpvYgn/uv4o5CR/lqbg3Ezg7QVpTG0JtPQHTDNoosanoNPa63j8IVbIfVvYDTOaFYOTtfHtJ4iIiIiIiIiIiP4rlEoFulfLjXI5HdF3y20ERf++NWySRoeu/jewv18VeNpbiMUVegGRb4Bry6Xx1HhgTzfgzTmg7kzZd3kdCndAaFIo1j9cL4mfeHsCtqa2GF/x98VJGVGYmMC27vdQWlkisG8/GDS/r+jURUfjfafO8Jw7F9ZVq2Q6FtHXgo3Mz+Dy5cuSv0+cODFLdWfOnEHNmjVlc9OnT0dKSgoWLlyIhISET+du/pGrqyu2bduGkiVL/tkpE32RDAYDQqZOy7iJqVLBuV9fOPfqBYVKZfw6AFq9FiPPj8TjiMfSIRQqzK05N+tNzPu7gHOzxHj9eYBLAaNlH2KS0H7NNbwOE8/0bFMuB6Y2LQaVMvMPQERERERERERERP8lJXLYY2O38mi27DJikn5vBobGpaCr/w3s6l0JNuZqsfD7qYBKDVxeJOZubwSCbgNtt6edrZnOkNJDEJMSg70v9kriu57vwvG3x5HLNhdy2uVELrtcqO5dHfkd8hudv3W1avBeshiB/QdIm5kxMQjo0QPOffrAuV/fTN9vEn0NuLXsF0qhUGD+/Pm4dOkSunTpgty5c8Pc3Bz29vYoXbo0Jk+ejEePHuGbb775t6dK9NlErFiBqC1bZHMqOzs4duuKPMePwaVv5r/kdXodJlyagHOB54Tc+IrjUdWratYm9fgAsK8XhHMxS7YHSrY1WvY2PAEtll+RbWL2qJYL05uxiUlERERERERERGRMHhdrrOpYBqYqaRvk6cc49N1yGxqdXixSqoDvfgba7QQsHMV8yENg9TdA4E0hpVAoML7ieNTxqSPk4lLjcD/8Pg6+OoiFtxei5aGWOPnuZIbzt65RA16LFgLqdA1XgwHhy5YhoEcPaCMiMhyD6GvARuZn4O/vD4PB8Kf/Y2w15h9VqlQJ69evx6tXr5CUlISoqCjcunUL48ePl92WluhrFbV9B8IWit+UMitUCB4zpiPvubNwGzECpjnEb0ulp9PrMO7SOBx6fUjI9SjWA83zN8/apJ6fAHZ3Awzp9sF3KQj8YPxczGcf49By5RXJ1hf/M/Tb/Bj7Q6EsbUVBRERERERERET0X1YhtxNmtSguxC+8CEefzbeQrDFyfmX+74HeFwGfSmIuIRTwrw883CukTJQmmFF9Biq4V8hwXnqDHhMvTcSH+A8ZXmdTqxa8Fy+CwlLczjbh8hW8adoM8RcuZjgG0ZeOjUwi+uLFHj+Bj5MmCXHLcuWQc/s22DdpAqW5eZbG0ul1+PHSjzj8+rCQa5C7AQaUGpC1Sb06A+zoCOg10riNZ9r2E6ZWsmX3AqLRetUVhMWlCLkJDQpjYO18bGISERERERERERFlUZNSXhj6rbiN669PQuG3/gbiU7TyhXZeQOfDQNWhYk6bDOz2A87NBgzSndjMVGZY+M1ClHItleG84jRxGHdpHPQGmZWhf2BTsyZy7doJs3x5xWmEhiKgRw8EDhgITXBwhuMQfanYyCSiL5ZBp0Pkhg0IHjFC/MBQsCC8ly2F0swsy+Np9VqMvTgWR14fEXI1vGtgUuVJmTcRDQbg0T5gW1tAl64ZaeUKdD4EOOaSLb38KhztVl9FdKK0+alUALOaF0fXqvJ1REREREREREREZNyAb/KiRRlvIX7ldQQ6rLmG6MRU+UKVCVBnItB8LaCSec94Zgpw4kchbKW2wtrv12JZ7WUYVHoQGudpjBIuJWCqNJVcd/3jdWx6vCnT+ZvlyYOcO3bArnEj2XzcyZN4Vb8Bwleugj7VyLMQfaHYyCSiL1LKq1d4174DQqbPkBx4DQDqHDngs3oVVDY2WR4vJiUGI86NwNE3R4VcTe+amFdzHkxVpjKVf/DhPuDfANjVBdCm2xbWwhHodABwFr85pdcbsOzsS3Rcex0JqdLtLNQqBRa3LY1W5TLfEpeIiIiIiIiIiIhECoUC05sVQ5OSnkLubkA02qy6itC4ZOMDFGsBdDkCWLmIuatLgbvbhLBaqUY172roXqw7plSdgs0/bMaUqlOE6xbeXogXUS8yfQalpSU8ZsyA++RJUJiK7ykNSUkImz8fr+v9gIj1/tDFxmY6JtGXgI1MIvqiGDQahK9YiTdNmiLp7l0hr3J2hs/aNTBxkflQYcTZgLNoeqApfn3/q5CrmaMm5tacm3ETMz4UODgAWFkdeCezJ725HdBpP+BWWEiFxaWg8/rrmHX8GXT6dKtKTZRY1aks6hf3yPKzEBERERERERERkUitUmJeq5JoX8FHyD39GIe2q64iJt1OaRI5ygE9TgOuRcTc4cHAh3uZzqFernqol6ueJKbRazD6wmik6jJfSalQKODQqhVy7toFi1LyW9dqgoIQOnMmXtSshQ+TJiHl1atMxyXKztjIJKIvhi4+Hu/8/BC2YIGwChMAVC7O8FmzGqY+4ocROTEpMRh7YSwGnB6AsKQwIV8rRy3Mq5HBSky9Hri5HlhcFri9EYBBvMbUBuiwF/AoIaQuvwzHD4su4MKLcCFnbWaCjV3Lo1YB1yw9CxEREREREREREWVMqVRgSpOi6F0jj5B7FZaAXptvIkWrk6n8jb0P0O0E4F1eGtcmAzs6AImRmc7hxwo/wtVS+s7vedRzjL04FndD70Knz+D+vzEvkB++WzbDY9o0qBwdZa8xJCYiett2vK7fAKFz5sBgkHl3SfQFYCOTiL4Iurg4vO/WDUk3b8nm7Ro3Qu6DB2FesGCWxrsYdBFNDjTBodeHZPO1fWpjbo25UKvU8gOEvwA2NEj7tlVKjPw1eWoDPc8C3mWF1L47gWi/9hrC4lKEXE4nS+zoVREVcjtl6VmIiIiIiIiIiIgoaxQKBUbXK4iRdQsIuauvIzFmz4OMm35mNkCrjYC1mzQe/R7Y3RXIpBFpZ2aHqVWnCvETb0+g47GO+GbXN/jp8k+4FSL/HvTTcyiVsG/WFHmOHYVDu3aA0ni7J2LNWoQvX57heETZFRuZRJTt6WJj8b5rNyTfuy/kTNzdkWPlCnjOnAkTB4dMx0rVpWLm9Zno82sfhCeJKyHNVGYYUXaE8SamTgOcnwMsrwK8uyR/E6e8QLudQIc9smdiPvkQi9F7HkDu81DDEp44NKAqinjaZfosRERERERERERE9Nf0rZkXY+qJiyL23gnCgl8zObPS1gNouQFQmkjjr88Ap8VzMNOr6FERHQp1kM1FJkdiz4s96HK8C2bfmJ3pSkqVnR3cJ4xH7kMHYd+6NRTm5rLXhS9ajJjDRzKdG1F2w0YmEWVruuhovPfriuQHD4ScXbNmyH34EKxr1MjSWK+jX6PdkXbY/GSzbL6kS0nsbrgbnYp0gkqpEi/QJAObmwOnfwZ04kpKmNkB308D+lwB8n8PKBTCJQkpWvTbehspWr0kbq5WYkazYljUpiRszI2sAiUiIiIiIiIiIqK/Tc/qudGhonhM1cJTL7D7VmDGxb6VgO+ni/GL84C72zK996DSg5DfIX+G12x8vBEzb8zM0rawZnnywGPST8h39gxcR4yA2tNTuObDmDFIvH0707GIshM2Moko29JGReFd165IfvRIyDn16AGPqVOgsrbOdJwETQI2PNqA1odb41nUMyH/v1WY/nX9kdMup/wgej1woC/w5px8vnBjoP91oFI/wET+TE2DwYBx+x/idViCJO5iY4YD/aqiTXkfKGSan0RERERERERERPT3UygU+KlhEdQq4CLkRu+5j4P3gjMeoHwPoERbMX6gH/DsWIal5ibmWPPdGvgV8YOXtZfR67Y82ZLlZiYAqOzt4dStK3IdPACz/NJGqUGjQWC//kh9/z5LYxFlB2xkElG2lPz8Od62ao2Ux0+EnFPvXnAZOiTTpt+bmDeYfm06au+qjTk35yBZlyxcU8ixEHY23Gl8Feb/nJoEPNwjxm08gTZb0/bFt3HPcD67bgZi350gSUypABa1KYUC7jYZ1hIREREREREREdHfz0SlxJJ2pVHYw1YS1+oNGLjtDsbsfYBkjZFzLxUKoMF8wL2YNG7QAbu6AG+NHE31GwdzBwwtOxTHmh3DnkZ70K9kP+SyyyVct+XJFsy4PiPLzUwAUFlbI8eK5VC5OEviuqgoBPTqDW1UVJbHIvo3sZFJRNlO3KlTeNemLTQBAULOuW9fuAwalGET813sO/T+tTca7W+ErU+3IkGTIHtd58KdsfmHzchtlzvjCd1YC1xaIMaLtwb6XQMK1s+4HsDTj7EYf+ChEB9cJz8q5XHKtJ6IiIiIiIiIiIj+GVZmJljXpRzcbcXzJbddf48mSy/hVVi8fLHaAmi7HbD1lsa1ycC2NsCHe5neX6FQIL9DfvQu0Rvb629HGbcywjVbn27FtGvToNVrs/RMAKD29ESOZcuFczNT37zB2zZtkPLqVZbHIvq3sJFJRNmGwWBA+PLlCOzXH/rERCHv3L8/XAYOyLCJ+Tr6NToc7YBLQca/7eRk7oSVdVZieLnhMFXJbwP7yfMTwNHhYrxAfaDJcsDcVsylE5OkQb8t4rmYVfI6oV+tvJnWExERERERERER0T/L3c4c6/3KwcFSLeSefoxDw8UXceBukEwlADtvoOM+wDLdgoWUWGBTM+DqcuDFr0DUW0BvZHXnbyzVllhWe5lsM3P7s+3oebInwhLDsvpYsChWFF5zZqetHv0Dzbv3eNuqNeJOn8nyWET/BjYyiShb0CckIGjIUIQtXCQmVSq4/fgjXPr3y3CMD/Ef0PNkT0SnRMvmTZWmaJavGfY02oPKXpUzn1TgLWCXH2CQNiDhVQZovgbIaCva38SnaNFl/XW8kjkXc0HrUlApeSYmERERERERERFRdlDIwxZHBlZDGV8HIZeYqsOg7Xcx6/hT6PUyW7y65Afa7wZMrdMVhgPHRwNbmgMLSwDTPIGdnYHkGKPz+F8zs6xbWSF34+MNtDjUApeDL2f5uWzq1IHriBFCXJ+QgMB+/RC+YuWf2raW6HNiI5OI/nUpL1/iTctWiDt+XMgp7ezgs2Y1HDt2yHCMyORI9DzZEyGJIULO3codg0oPwsmWJzGp8iQ4WWRhK9fAW8CmpkD6bWntfYG2OwBTy0yHSErVoav/Ddx5Hy2JKxTAwtYl4WJjlvk8iIiIiIiIiIiI6LPxtLfA9p4V0btGHtn8srOv0HfLbSSlyqys9CoNtNkKZLQLnDYZeLwf2NMDyKB5aKm2xNLaS1HOvZyQi0yORO+TvbHkzhLoMlnh+T+Ofl3gOmoUoEzXFjIYELZgAYKGDoU+OTlLYxF9TmxkEtG/KubIEbxp1Rqpr18LOdO8eZBr105YVaqU4RjxqfHo82sfvI19K4mrFCpMrjwZx5odQ/di3eFo7pi1SQXeBDY1AVLSfSvKwgHosAewdsl0iGSNDj023sT1N5FCbvh3BVA5r7NMFREREREREREREf3b1ColRtcrCH+/cnC0EpuSxx99RKuVVxASK9P4y10DaLEOUGTSfnlxArjln+El/1uZ2TRvUyFngAEr769Eu6Pt8Cj8Ucb3Qto5nE5+XZBj1SoobcXjsuKOHce7zp2hDQ/PdCyiz4mNTCL67PQpKUh68BAfJ09G8LDhMMich2ldqxZybt8OUx+fDMeKT43HoDOD8DjisZD7ucrPaJqvKUyUJlmfXMCNtJWYKbHSuIl52repnPNlOkSqVo++W27j4kvxl36vGrnRt6b8t7mIiIiIiIiIiIgo+6hZwBVHBlZFUS+x8fcgKAaNl1zC04+xYmGhhmnvEr3KAiYWxm9wYiwQ8SrDOZibmGNylcmYWnUqLGTGehzxGG2PtMXUq1MRmyozl3Ssq1ZBrl07YZpXfEeZfO8+3rZug5SXLzMdh+hzYSOTiD6LpAcPETz2R7xu3ATPypTF25YtEbV1m3ihQgHngQPgvXQJVNbWYv4PzgeeR5MDTXD943UhN7r8aDTM0/DPTTLgupEmpgXQbgfgm/m5mpdfhaPRkos4/TRUyHWpnBOj6xaEQsFzMYmIiIiIiIiIiL4EHnYW2NmrEuoWcRdyH2OT4bf+BsLjU8TCAvWAHqeAscHA4IdAx31Anm+k12gSgb09AZ0203k0ytMI2+pvQx47sQFpgAHbn21Ho32NsO/FPmh0mgzHMvX1Rc7tO2Bdu7aQ0wQF4W3bdki4nPUzOIn+SWxkEtE/LnLTZrxt2RIxe/ci5dkzQCv/i1nl4IAca1bDpW9fKNLv1f4HMSkxGHthLPqd6id7Jmav4r3QvlD7rE9QpwUuzAP86wOpcdLc/5qYuWtmOMT7iET02nQT7VZfw9OPcUK+bfkcmNiwMJuYREREREREREREXxhLUxMsa19adqe1DzHJ6LflNrQ6vXyxUgnY50hrYjZdBVilO7Yq6CZwYW6W5pHHPg+21t8qu9UsAEQkR2DC5Qn4dve3WHZ3GcISw4yOpbK2gvfiRXDs1lXI6ePi8L5nL0Ru3ARDBud4En0ObGQS0T8q5sABhEydmul1FiVKINfePbCuUiXD684Hnkfj/Y1x6PUh2XzrAq3Rr2S/rE8w9Amw9lvg1CRAlyrNfWpi1jBanpSqw6zjT1Fn3jmceCQ2VQGgWSkvTG1SjE1MIiIiIiIiIiKiL5RSqcDIugUxp2UJqFXS93zX3kRixrGnmQ9i7QI0WizGz80EAm9laR6WaktMrjIZa79bi1x2uWSviUiOwPJ7y/Hdnu8w6vwofEz4KHudQqmE24gRcJ80CVCppEmtFiHTpiGwX39oo6KyNDeifwIbmUT0j4k7fQbBY3/M8BqlpSWcevSA76aNUHt4ZHjt4deHMeD0AEQkRwg5tVKNQaUHYWyFsVlrGOq0ad90WlkdCL4t5k0sgPY7M2xiXnoZjroLz2PZ2VdINfKNq/YVfDCrRXEolWxiEhERERERERERfelalPHG5MZFhfiai29w4G5Q5gMUqAeU7iyNGXTAuu+AjU2A66uBmMzHKe9RHnsa7sHAUgNhpjKTvUar1+Lom6Nofbg1AmIDjI7l0LoVcqxcCaWVlZCLP30ab5o0RcI18Xgvos/B5N+eABF9nRJv3EDQkCGATieJm3h4wOabb2BeuDDMixSGWe7cUJiaZjregZcHMP7SeBggbmVQ3KU4fq78M3Lb587a5KLeAXt7AAHX5PO2XkDztYBvJfnyhFRMPfoEu28FGr1FCW87TGhYBGV8HbI2JyIiIiIiIiIiIvoitC3vg3sB0dh+Q9ocHLXnPvK72aCQh23GA3w/DXhzHoh683tMrwVen0n7z9HhQK7qQIMFgJO4ne3/qFVq9CjeA/Vy1cO8W/Nw6v0p6A3igovI5Ej0PdUXm3/YDDszO9mxrKtWge+2rQjo3Rva4A+SnDYkBO+7dIFDhw6wb94MZgUKcPc5+my4IpOI/lYGgwEJV68hoE9fGFKkh1ybuLsj55bNcB8/DvbNm8G8YMEsNTH3PN8j28Q0V5ljZLmR2Fh3Y9abmA92AyuqGm9iluoI9L1itIl59lkovp1/zmgT083WDPNalcC+vlXYxCQiIiIiIiIiIvpK/dSoCEp4S5uCyRo9em26haiEVCNVvzGzBpqtAhQZtGjenAfW1QVCM9+y1tvGG/NqzsPRZkfhV8QPtqZiI/Vt7FsMOTsEGp3G6Djm+fMj1549sK5dW0waDIjatAlvmjTF6wYNEbZsGVLfvct0bkT/X2xkEtHfQhcfj6ht2/CmaTO879IF+vh4SV5lbw+ftWug9vT8U+PueLoDP135SWhiulm6YUfDHehYuCNUSpWR6j9IiQP29QH2dANSYsW8rRfQfg/QeAlgLv+tpCuvItBj402Ex4sfRFRKBXrXyIPTw2qiWWlvbiVLRERERERERET0FTNXq7C8Qxk4WUkXaryPTISf/w0kpGgzHiBHeeCHOYCRbWEBAAmhwIYGWWpmAoCXtReGlh2KX1v+ipHlRkIB6TvKGx9vpL1rNYi73v2PiYMDvJcshtv4cUYXoaS+eoXwRYvx6vu6CB41imdo0j+KjUwi+ssMej0Srl5D8LhxeFG9Bj5OmoyUp+IvVaWlJXKsXg2zPMa3QRDGNhiw+v5qTLk2Rch5WHlgfd31yG2XhVWYcR+BM9OBxWWAe1vlrynZIW0VZr46Rod5ERKHXptuQqOT2drW2w4H+1fB6HoFYWXGHbuJiIiIiIiIiIj+CzztLbC4XSmo0i1quBsQjd6bbyFFqzNS+Zty3YBhT4GmK4FCDQG1pXhNQthvzcwnWZ6XhYkFOhbuiGFlhwm5g68OYvWD1RnWKxQKOLZvj5w7d8A0d8bvYGMOHMTr+g0Qc/hIhg1Sor+KjUwi+tOSnzxByKzZeFnrG7zv0gUxu/fAkJgoe61CrYb3sqWwKCYegG10fG0yRl0YhUV3Fgk5L2svrK+7HjlscmQ8SOBNYHc3YH5R4NwMID5EvMbcDmi5AWiy1OgqTAAIjU1Gl/U3EJss/RaVhVqFcfULYW+fyijiabyeiIiIiIiIiIiIvk6V8zjjxx8KCfELL8IxdOc96PSZNPcsHYESbYDWm4GRr4FGSwBlusUSCWHAhoZ/qpkJAJ0Kd0LL/C2F+OI7i7HlyZZMG4/mBQsi1+5dcO7bBybu7kav00VGInj4cAT27gPNhw9GryP6K9jIJKIs08XHI6Bff7xp2gyR69ZBGyLTHPwDs3x54bN+HawqVszyPT4mfETn451x7M0xIZfDJgf86/rDy9rL+AAGA3BsFLCmNvBwN6A3sue7bxWgz2WgSJMM55OQokXXDTcQFJ0kiVuZqrCrdyV0r5YbJir+U0pERERERERERPRf1bVqLvSqIa5cPHL/A8YfeJj1lYpqC6B0R6Clv3wzc/0PwMtTWZ6XQqHAmApjUNmzspCbcX0GfrryE1J1GZ/nqbS0hMvAgch7+hR8N2+Cfds2UDk4yF4bf+4cXtVvgLDFS6CLT8jyPIkywrfvRJQl2rAwvOvYCfGnMv5FqTA1hW2jhvDdshm5Dh6EZdmyWb7HvbB7aHukLR5HPBZyue1yY/336+FuZfybPwCAkxOAayuM55UmwDfjgM6HADvvDIfS6vQYsO0OHgZJz9RUKRVY1qEMinpxFSYREREREREREREBo+sWROuy4i5yW6+9R4lJv6Dx0ksYuuMulp55iacfY2VG+INCDeWbmUmRwObmwOkpgD6TbWt/o1aqMafGHOS1zyvk9r7Yi64nuiIsMSzTcRRKJSzLloXHxInId/4c3MaOgcLCQrjOkJiI8KVL8eq77xC5eQsMqRk3Sokyw0YmEWUq9e1bvG3bDilPjG9dYFagAFxHjULec2fhNWsWLMuUgUKhMHr9HxkMBux8thN+x/0QnhQu5Kt7V8eWH7bAzcot44Euzgcui9vRAgDM7YHKA4ABt4HqIwClKsOhdHoDRuy+j9NPQ4Xc1CZFUSO/S8ZzISIiIiIiIiIiov8MhUKBqU2Lom4RcSFGbLIW9wKisfdOEGafeIYfFl7AsQeZbMFaqGHasVjpm5kwAOdnAxsbA3EfszQ3G1MbLK29VHanu3th99D6cGvcC7uXpbGAtOPEHDt1Qu5Dh2BVpYrsNbrISIRMmYJX9Rsg5sgRGPT6LI9P9EdsZBJRhpIePMDbtu2gCQwUcmpPTzj17IlcBw8g94H9cPLrAhMj2woYHV+bhHGXxuHnqz9DI7MNbNeiXbGo1iJYm1pnPNAtf+DXn8S4S0Gg4UJg6BPguymAg2+mc9LpDRi28y723QkScv1r5UWb8j6ZjkFERERERERERET/LSYqJRa2LYkqeZ0yvE5vAEbuuS8cZyUo1CDt7Ey1pZh7ewFYURV4eiRLc/O09sS2+ttQ3r28kAtLCkPX411x/M3xLI31P6beXsixZjU8Z86Ayt5e9hpNQACChw3H2xYtkXD58p8anwhgI5OIMhB35gzede4CXVSUkHPu3x95fj0J16FDYJ4//18a/33se3Q42gEHXx0UcqZKU0yvNh1DygyBKpPVk3h8ADg8RIx7lwd6nAbKdAFMZX7Zy9Dq9Bi68y723w0Wck1LeWHYd3/tWYmIiIiIiIiIiOjrZ2aiwsqOZVEuZ8YLPuKStRi+8x70+kzOzyxQD+h5FnApJOYSwoDt7YCtbYDo95nOzcHcASu+XYEOhToIuVR9KkacH4HV91dn/UxPpK1EtWvcGHlOHIdT925QmJrKXpf8+DHed+2G9127IenuXa7QpCxLvyaZiAgGvR7hS5chfOlSMalUwn3iRDi0bvWnx30X+w5PIp7gaeRTPI16ijshd5CoTRSuc7N0w/ya81HMpVjGA+p1aedh/voTYEj3i8+1MNBuB2BqleX5pTUx7+HgPbGJWbugK2Y0L5bl7XKJiIiIiIiIiIjov8nazARbe1TEpZfhePIhDq/D4vE6PAEPg2KQov39PeaV1xFYd+kNulfLnfGALgXSFmwcHQHc3Szmnx8DXp8FaowEKvUHTOSbiUDamZmjyo9CQceCmHxlMlL10jMsF91ZhMD4QIyrOA5qpTrLz6yys4Pr8OFwaN8eYUuWIGbffkCmWZlw+TISLl+Gyt4eFmXLwLJsWViVLw+zQoX47pVksZFJRBK6mBgEjRyJhHPnhZzCzAxe8+bCpnbtPzXmx4SPmHBpAq58uJLptRU9KmJm9ZlwNHfM+MLQp8DB/kDgDTFn7wt02AtYZjLGH0QnpmLUnvs48ShEyNUp5Iql7UvDzCSTlaFEREREREREREREANQqJWoWcEXNAq6fYgfvBWPgtjuS62Ydf4aq+ZxR0N024wFNLYEmS4GcVYDDQwFtum1ptUnAqUnA7Y1Ahd5AyXaAufExG+dtjNx2uTHg9ABEJEdIcntf7EVQfBDm1ZwHW9NM5pWO2sMDnlOnwqlLF4TOX4D406dlr9NFRyP+11OI//UUAMCidGl4zZkNtafnn7offf24tSwRfZL89CnetGgp28RU2trCZ/26P93EjEyORI9femSpidmjWA+sqLMi4yamTgOcnwOsrCbfxLRyBTruA2w9sjzHk49D8O3880aamG5Y1r4Mm5hERERERERERET0/9KohCcal5Q26lJ1egzefhcpWl3WBinZDuh1HvCtKp+PegMcHwX8X3v3HR91kf9x/LXpvRNaQkjoPXRpAgKCIAKigAVBRdG7s3v+1Ct656nnnadyZztFsSGCooAiSBEB6b2GToAAIY2E9GR3v78/VvYI2SSbZCGLvJ+Pxz74Zme+M7OBTybsZ2fmtTa2FZwZBytsqkO9Dnw+4nOahzUvV7bh9AZuXXAr29O2Ozeui/i2aEHs228RN/Mz/Dt3rrJ+4datHB03nsKdO2vUn/x6KZEpIhQlJXHy8Sc4evNYSk+cKFfu26IF8V/OIaBLl2q1m1eSx4PLHiT5XHKl9YJ9gnnzujd5uMvDlZ+HWZQDH98EP74AlpLy5UH1YeLXENnMqfFlF5Tw2Ozt3PfJZtJzi8uVD2lbn7fv6IKPl35UioiIiIiIiIiISO399ab2NAz1K/PcvtRcHp+9g83JWViqOjMToF5LmPwdjPkvBEQ5rlOSBxvfgze7/XI0l+N2GwU14pMbPqFXw17lyk7ln2Ly4sm8s+MdLFYnE60XCejalbjPZxLz1pv4NKv8fVtLRgbHJt7FuUWLatSX/Dppa1mRq5RhGBRs2kTm+9PJX726wnohw4fT8G8v4BEQUK32iy3FPLziYfZm7nVY3jioMa0jWtM+qj2jm48myr+CCfe8wmz47GY4ucVxeafbYOhLTm8nu2J/Gk99tdNhAhNgaLv6/Oc2JTFFRERERERERETEdUIDvHn11k7cMX1DmecX7jrNwl2niQryYXCb+tzcJYYe8ZW812kyQacJ0HIoLP8rbJ4BVJAE/fl18AmEa3/vsDjYJ5i3Br/Fi+tfZO7BuWXKLIaFt7e/zfpT63m538s0Cqr+1q8mk4ngQYMIGjiQop07yd+0iYLNmyncshVrXl6ZukZxMScfe5yS5GQiH3hA52aKEpkiVxvDaiVvxQoy33ufwh07Kq7o6Un9p35P+F13VXuyMFvNPLnySTallt/69ZEujzCu1bjq7a1ekAWfjobTDsYb0hhufANaXu9UU0WlFl5ZvI8Za5Idlvt6efD7oa24u088nh6aJEVERERERERERMS1+jSP4p4+8Xy45mi5soy8Er7YdIIvNp1gSt94/jCiTeXvz/qHw42vQ/cpsP4d2PUlmIvK1/vxbxAeDx1ucdiMt4c3z/V6joTQBF7f+jpmq7lM+da0rQz/ejg9G/bkhvgbGNRkEME+wdV63SYPD/wTE/FPTIT77sOwWDi3cCGn//gnjJKyO/ClT/s3xQcP0fCFv+IRGFitfuTXRYlMkauEUVpKzncLyZw+nZLDhyut69WoIY1e/juBPXtUq49iSzFLjy1l1r5Z7Ewvv5f51I5TmdJhSrXaJD8DPhkNZ3aVL+t8Jwx9udJDqy908EwuD83axr7UXIflXePC+ectHUmoF1S9MYqIiIiIiIiIiIhUw1PDWrHuSCZJp89VWGf6z0cxWw2eG9m26sUm9dvBqDdhyF9h68ewcTqcSylbZ95vIDQWmvR02ITJZOKudnfRtX5Xnlr1FMdzj5cptxgW1p5ay9pTa/nrur/SP6Y/93e8nzaRbZx6zeX68/Qk9Kab8GnShBO//R2WzMwy5ee+/57igwdo/O9/4xsfX6M+5MqnRKbIr5S1qIiiXbso2LKFgi1bKdy2rdwy/Yv5xMcTOeVeQkeOxOTj43Rfh84eYu7BuXx75FtyinMc1pnQagK/TfxttV4DuWdsKzHTHGxP2+8JuO5Pti0UqmqmqJRZG4/zryUHKDZby5VrFaaIiIiIiIiIiIhcTn7ensyc0pP/rjrMol2pHM8qcFjvo7XJAM4lM8F29Fbfx6DbvfDhMEjb878ySzF8cRtMWQYRCRU20S6qHXNGzuGlDS+x4PACh3VKraUsO76MFSdWcFe7u3iw04P4e/lXPT4H/BMTiZ8zmxMPPEjxwYNlyooPHiL5lltp+PeXCRkypEbty5VNiUyRXxlzZibpb75JztffYBQ7Pv/xYn4dOhB53xSCBw3C5Onp1D2p+aksOrqIhUcWsv/s/krr3hB/A8/0fKZ6W9Se2g5f3A7nTpYv6/80DHi6yiTm4fQ8PlmbzFdbUsgvcXwYdbtGIUyb0Jnm0VqFKSIiIiIiIiIiIpdPRKAPz9zQhqeHtWb/mVyW7jnDvO0nOZyeX6ZetZOZYNvF7vbZMH0Q5J353/MFmTBzHNy7xJb0rECgdyAv9n2RPo368NLGlypcwGIxLMzYPYNlx5bxXK/n6NnQ8WrPqng3bkzcrM85+cQT5K9cVabMmp/PyYcepmDiRCImTcInpnGN+pArkxKZIr8S1uJisj75hMx3/4s1P7/qG4DA3r2JvP8+Anr2dHoC3HB6A+/ueJctZ7ZgVHR49AWGxA3hxT4v4mHycKp9AHZ/bdvmwFxYvmzgH6G/40Opzzuakc9zC/aw6kB6pfXu6xfPk0Nb4evlXPJWRERERERERERExNVMJhOtG4TQukEI9/SN5+6PNrHxaFaZOh+tTcbyyzazXp5OvtcaFgu3fQEzhpd9rzXzoC3BOWEWRLeutInhCcMZEDuAFSdWsOjoItacWlPu/EyAE7knmLJkCqObj+bhzg9TL6Cec2O8gGdQELHvvEPGW2+T8fbbYJR9//nsp59y9tNPCejRg9AxYwi5fojOz7wKKJEpcoUzDIPcRYtIe/VflJ46VfUNJhPBQ4cSOWUK/u3bVauvz5M+5+8b/15lAtPD5MG1Mddya8tb6de4n/OfErJa4aeXYdU/HJcP/gv0fbTSJg6l5XLru+s4W1BaYZ16wb68Nq4T/VpUfzIVERERERERERERuVQCfb346O7u3D1jExsuSmZ+uv4YB87k8u/bOlM/xM+5Bht3gbHvw+yJcOH7ullHbMnMMf+FNjdW2kSAdwAjEkYwImEEOcU5LDu2jA93f1juDE2AeYfm8UPyD9zV9i7ubn83gd7VSzSaPDyo99Dv8O/YgZNP/R/WnPIrQQs2bqRg40ZSX3iB0BtvJPzOO/Br2bJa/ciVoxpLpETE3ZSeSePEAw9w8vEnKk1iekZEEDxkCNFP/x/NflhMzBuvVyuJaRgGb257k5c3vlxpErNhYEN+m/hbloxdwn+u+w/XxlzrfBKz6BzMmeg4ienhDSP/XWUS82R2IRM/2FhhEtPHy4MJ3WNZ/Eg/JTFFRERERERERETELQX4eDHj7u5ck1B+69cNR7MYPm01qw9WvhtdGW1GwvUvlH++JA9m3wErXrYtMnFCqG8oY1uOZe5Nc7mn/T14msrvdldoLuS/O//L8K+H83nS55RaKl50UpGg/v2Jn/sVvm3bVFjHKCgge84cjt40imN3TeLckiUY5vKrReXKphWZIlcgwzA4t/B7Ul94weEnUgA8goOJvO8+gocMxqdp0+qdT3kBi9XC3zb8ja8OfOWwPNg7mCFNhzA8fjjd6nfD06MG27Se2Quz74Ssw+XLAqJg/KcQ17vSJjLzipk4fQOnc4rKlTUK9ePOXnFM6N6EiECf6o9PRERERERERERE5DIK8PHiw8ndufejzaw7klmmLDO/hLs+3MjvBjbnkUEtnNtqttfvwOQBS/4IxkVJy5V/hzO74eb3wMe5FZR+Xn481vUxhjUdxnNrnyMpK6lcnayiLF7e+DKfJX3Gw10eZmjc0Gq9T+0TE0PTzz8n4+13yJ4zB0t2doV1z6/S9GrUkPAJtxF26y14hYc73Ze4L5NhGFUfcidSiS1bttCtWzcA1q9fT8+eNTvMV6pmGAalJ06Q9q/XyP3hB8eVPD0JnzCBqN/9ttY/qIstxTy96mmWHV9WrqxJcBMe7/o4fWP64uvpW/NOds6Bbx+B0oLyZfU7wG2fQ1iTSpvILSrl9vc3sOtk2aSup4eJv9/cgTGdGzu/b/yvQGlpKZmZtl9uIiMj8fb2ruMRiYjiUsS9KCZF3ItiUsT9KC5F3Iti8upWVGrhufl7mL35hMPy9o1DeHF0BzrFhjnX4OEV8OVkKMouX9YwEW6fDcENqjVGs9XMrH2zeHfHu5wrOVdhvfaR7Xm82+N0b9C9Wu0DGCUl5K5cSc68+eStXAlVrLw0+foSMmIEEXfegW+bNjVe6OOIYrK8C/NEmzdvpmvXri5rW4lMqTUlMi8dwzAo2LSJgk2bKNq5i8Jdu7BkZVVYP/DaftR/+ml8ExJq3XdqfipP/PQEOzN2litrE9GGdwa/Q6R/ZM0aLymAtL2wfSZs/tBxnTYjYfS74BtUaVO5RaXc/8mWcp9KAnh9fCfGdI6p2RivYJpIRdyP4lLEvSgmRdyLYlLE/SguRdyLYlIA5m5J4Y/zdlNYailXZjLBHT2b8PuhrQn1d+LfR9ZR+OIOSNtTviwkBu6YA/WdP5rsvHMl5/hg1wfMTJpJsaW4wno9G/Skb+O+dK3fldaRrfH2qN6/aXNmJjnz5nN21ixKU1KqrO8ZFoZvixb4tmiOb4sWBHTvjm/z5tXq80KKyfKUyBS3pkTmpZG/YSNpr75K0a5dVdb1CAqi/rPPEjpmtEs+WbIpdRNPrnySrKLySdOeDXvyxoA3CPKpPMFYzvH1sGk6nN4BmYfKb19wnskDBv0Zej8CHpWvolyxL41nv9nlcDvZ50a25e4+8dUb46+EJlIR96O4FHEvikkR96KYFHE/iksR96KYlPMOnsnlNzO3cjAtz2F5VJAPTw1tzajOjfD1quIIsOI8mP8b2Du/fJlPMIz7GJoPqtE4U/NTeWv7Wyw4vABrRe8D/8Lfy5/Eeonc1vo2BjYZWK1+DIuFvJWrOPvZZ+SvXVute0NGjqTBc3/GM6ia73OjmHTkUiYyr569FkWuEMUHD3Ji6gMcnzTJqSRmQK9rSFgwn7Cbx9Q6iWkYBh/v+Zj7ltznMIk5tOlQ3h70dvWTmGumwYdDYdeXkHGg4iRmQBRMnAd9H6s0iZldUMLjs7dz90ebHCYxHx7U4qpNYoqIiIiIiIiIiMivU4v6wcz/XR/Gd4t1WJ6RV8JTc3fS5+8/8trSA6SdK//eqZ1vENzyEfR7onxZSS7MvBXWvQU1WAvXILABL/R5ga9GfsW1MddWWrfQXMi60+t4eMXDvLLxFSzW8itOK2Ly9CT4uoE0+fADEr5fSPjtt+MREODUvee+/Zajo8dQuH270/1J3VAiU8QNmNPTyf5mHimPPMqRUaNte3xXwSM4mPp/+iNNPvgA70aNaj2GA2cP8MiKR3h186tYjPKTxcS2E3ml3yv4ePpUr+EtH8PSP1ddL6YHPLAaEvpXWu3HfWcY/Noqvt520mH5xGvieGxwi+qNUUREREREREREROQKEODjxSu3dGTWfdfQrF6gwzoZeSX8e/lBev/9Rx6etY2T2YWOG/P4ZXe8m94ED6+yZYYFfnjWltDMS6/RWFuEt+CtQW/x4dAP6RDVocr6nyV9xsMrHia/NL/affkmJNDgz3+i+aqV1H/2WXzi4qq8pzQlheQ77iTj3XcxLM4nUOXy0tayUmvaWrZmSpKTyf5mHnmrV1G8N6nK+p71ovDv2An/Dh3w79gB/8REpz9dUpmtZ7bywe4PWJWyymG5v5c/z/d6nuEJw6vf+J558NXdFa/ABPAJgh73w4BnwKvyJOk321J4Ys4OrA5+avl4evDokBY8cG0zPDxcd3DzlUhbG4i4H8WliHtRTIq4F8WkiPtRXIq4F8WkVKTEbOX91Uf4z48HKSqt+D3YiEAfvn6wN02jHCc+ATi8AubcBcXnypcFRsOYd2u81SzYdgNcfnw58w/NZ0vaFnJLcius2yK8BW9e9yaNgmq+gMewWinYuInCHTsoPniQ4oMHKTlyBKO01GF9vw4diLhrIsHXX4+Hr2+lbSsmy9MZmeLWlMh0nmG1kr9mDVmffkr+qtVO3ePXoQPRTz5JQI/uLjn/EmyTxuqTq5m+azrb0rZVWC82OJY3Br5By/CW1e/k8I8wcxxYL5oYwuKg9Y3QsCM06ABRLcGz6h/0X24+wVNzdzrcyaBzkzD+eUtHmkcHV3+cv0KaSEXcj+JSxL0oJkXci2JSxP0oLkXci2JSqnIiq4C/L9rHot2nHS4CAUiICuTr3/QmLKCSxSRpSbb3dHOOOy7vchd0nggx3aEW71VbDSsHzx5ky5ktzD88n72Ze8vVifSLZNp10+hUr1ON+7mYUVpK5kcfkT7t32A2O6zjGRpK6OhRhN16K77Nmzuso5gsT4lMcWtKZFbOkpdH0e49FO7YQc4331CSnOzUfd6xsUQ//hjBw4a5LIFptppZnLyYD3d/yMGzByut2z+mPy/1e4kQn5DqdWK1wsEl8NU9cPEWAGFN4J4lENKwWk3O2nicZ7/ZVS6J6eftwe+HtmZy76Z4XuWrMC+kiVTE/SguRdyLYlLEvSgmRdyP4lLEvSgmxVkpZwv4dP0xvth4gpzC8isPr0mI4JN7euLjVcmpgwVZ8O3DkPRtxXXCmkD7sdDhVqjfrlZjLrYU8+c1f+b7o9+XK/Py8OKZHs9wa8tbXfYeOUDhrl2cfOJJSo9XkLD9RUCPHoTfcQfBg67D5PW/rXcVk+VdykSmV9VVRKS68jdsJGf+fAp37KDkyBHnD0T29iagSxdCbriBsJvHYPKp5nmUDpRaStmTuYfNZzbz1YGvOJnn+GzJ8+JC4ri3/b2Maj4KD1M1jtHNS4cdn8OWjyDrSPnywGiYOK/aScxP1yXzp/l7yj3fMNSPT+/tSfPooGq1JyIiIiIiIiIiIvJrFBMewDM3tOHRQS2ZtfE4LyzcW+at6fVHsnjm6128emvHihODAREw7lPY+jEsehrMDs7XzD4OP79ue7QZCTf8s9rv+57n6+nL3/v9nfjQeN7a/laZMrPVzAvrX2B3xm7+cM0f8PWsfMtXZ/l36ED8119z5oUXyJk/v8J6BRs3UrBxI14NGhA+YQJht96CV2SkS8YgzlMiU8SFCrZtI33avylYv97pe7yiowkadB1B/foR0KMnnkGV7FPupNN5p5l3eB6bUzezM30nRZaiKu9pE9GGKR2mMKjJIDw9PJ3ryFwMh5bBztmw7/vy28ie5xsKE7+GyGZOvwbDMHhrxSFeXXKgXFnjMH9m3XcNTSJrf0aoiIiIiIiIiIiIyK+Jv48n9/SNx2oY/G1hUpmyuVtTiI8K4HfXtai4AZMJuk6GJr3gq3vhzK6K6yZ9C0dWwfV/hS6TarTlrMlk4oFOD9A0tCl//PmPFFuKy5R/c+gb9p/dz+sDXq/VuZkX8gwKpNErfyfkppGcnfk5eT/9ZNtt0AFzairpb7xBxltvETL8BoLHj4dGrhmHVE2JTBEXKNq7l/Rp/yZv5Uqn7wno1o3wO+8kePCgMsvSayO3JJfpu6bz2d7PKLGWOHVPzwY9uafDPfRq2Mu55fmGAck/w645sHc+FOVUXt/LH26fbTsP00mFJRaemruTb3ecKlcWG+HP51OuITZCSUwRERERERERERGRitzbN56jGfnM3FB2C9VXlxwgyNeLSb2bVv6ecL1WMGUZrH8btn0GWYcd1yvOgW8fgZ1fwshpEOX4bMmqDGs6jNigWB796VFS81PLlO3N3Mvo+aMZEjeEm5rdRPcG3au3o2AFgvr0IahPH0pTU8meO5fsr+ZiPn3aYV2jtJSc+QvImb8Az9at8R0zGuvNN4O2lr2kdEam1NrVfEZmwZYtZL4/3fZpDSd4hoYSNHgQEXfeiV+bNi4bh9lqZu6Buby9422yirKqrG/CxKAmg7in/T10qOd8gpH8TPhyEiSvdq5+oy4w/FWIcX4/7NM5hdz/yRZ2nSyfIG0aGcDn911DozB/p9u7GmmPdhH3o7gUcS+KSRH3opgUcT+KSxH3opiU2jBbrNzz8WZWHUgvVzauWwwvjG6Pr5cTO/QZBpzeDrvnwu6v4VwFR5h5+kCP+6HfE7ZtamsgqyiLp1Y+xYbUDRXWaRjYkJHNRjK2xViXrdIEMCwW8lau4uzMmeSvWVNlfY+gIIKHDSVs1Cj8u3bF5FH75OqV6FKekalEptTa1ZbItBYXk792LZnvT6dw69ZK6/q2aUNgjx74deiAf8cOeMfGuvRQYoDtadt5fu3zHM6p4NMwFwjzDeO6Jtcxqd0kEkITqtdR9gn4dAxkHqy8nk8wdLzVto1Ao8RqdbH1+FmmfrqF9NzicmUtooP49N6eNAj1q1abVyP9civifhSXIu5FMSniXhSTIu5HcSniXhSTUlu5RaXc8s469p/JLVfWuUkY797Zlfoh1Xjf1WqFbZ/Ckj/ZVmM64hsK/R6Dng+Ad/UXppitZv699d/M2DOj0noeJg8GxAzg9ja306NBD5e+/1585ChnZ80i5+uvsebnV1nfu1EjQkYMJ7BXL/w7d8bD/+pZkKNEpri1X2Mis/RMGkV79tgeSUmYU1OxZGdjzs7GKCio8n7fNm2o98jDBPXv7/LE5XnFlmLe2vYWH+35CAPHYRzmG0avRr3oVr8bXet3JT40vmbL7dOS4NObIbf8Vq92TXpDpwnQfiz4BlWrecMwmLnhOH/9di8llvL7kA9qHc0bExIJ9tMvac7QL7ci7kdxKeJeFJMi7kUxKeJ+FJci7kUxKa5wMruQCe+t40RWYbmy6GBf3p3YlS5NwqvXaG4qfP97SFpQcZ2QxjDwWeh0G3g4sfLzIj8k/8Dza58nrzSvyrrNQptxW+vbGNlsJAHerjuazJKXz7lvF5A1cyYlh6peUARg8vbGr1NHAnteQ8jwG/Bt1sxl43FHSmSKW7sSE5nmrCzOLfyevJ9XY83OwbBYMCwWMJsxZ5/Fkp5Ro3Z9EhKo9/BDBF9//SVdQr4nYw9/+PkPFa7C9PP04652d3FP+3sI9A6sXWfHN8Dn46Aou3xZ/fbQ4RZofwuExdao+cISC3+Yt4uvtzreiuCB/s34/dBWeHpcmoTwr5F+uRVxP4pLEfeimBRxL4pJEfejuBRxL4pJcZWs/BJ+9/lW1h7OLFfm4+XBq7d24qZONdimNelbW0Iz1/HZkgDUawODn4eWQ6Gai3/OFp1l4ZGFLDi8gKSspCrrB3sHM6r5KG5rfRtNQppUq6/KGIZBwYaNZH72Kfk/rrCtTHVS8JDBRE59AP/27Vw2HneiRKa4tSslkWktKSFvxU/kzJ9P3qpVYDa7rG3ftm2Iuu8+WwLTs/qfKqlKqbWUA2cPsCNtB9vTtrPk2BIshsVh3Zua3cRDnR+iQWCD2nec9B3MnQLm8p/SYehL0Ou3tWo+OSOfBz7bwr7U8lsa+Hh58I+xHRnduXGt+rga6ZdbEfejuBRxL4pJEfeimBRxP4pLEfeimBRXMlusvPT9Pj5cc9Rh+e+HtuI3A5pVf6fB4jxY/zasmQYllayejOsDA/8ATa6p0QrNA2cP8O3hb1lweAFZRVlV1u/buC+9GvaieXhzWoS1IMo/qta7KJaWlpK+bx8ly5djWbackkOHnL43sG9fwm+/Hc/wMEw+Pnj4+GDy9cUrMhKPwFouSqpDSmSKW3PnRKZhGBRu307O/PmcW7QYa04F+3XXUMA11xB53xQCe/d2+RayhmGw9tRaPt7zMdvStlFkKaq0fkJoAn/r8zc61OtQ+86tFljxIqz+V/kyDy8Y/Q50HFetJg3DIDmzgN0nc9h7+hx7T51jc3IW+SXlE7INQ/14586uJMaG1fAFXN30y62I+1FcirgXxaSIe1FMirgfxaWIe1FMyqUwd0sKz3yzixJz+VWFt3aN4cUxHfDxqsGug3npsOofsPlDsFaymMg3BGJ7QlxvW3IzpjtUY5fDUkspPxz7gVn7ZrEzfafT94X4hNCtfjceTHyQ1hGtnb6vTN8XxGRERATWw4fJmb+A3KVLKT3peOdBZ3iEhuLdsCHeDRrg07QpIcOG4tep0yU7vs6VlMgUt+ZOiUzDYqH09GlKjx+nYNs2chYsoPTY8Vq16RUdjW+LFnhGRuAVHo5nWBie4RH4d+6MX6uWLhp5WXsy9/D6ltfZcHpDlXVNmJjcbjK/7fxbfD19a995QRbMvRcO/1i+zDsAxn0KLQZXq8lT2YU89dVOfj5U9Za9fZtHMW1CIpFBLngtVyn9civifhSXIu5FMSniXhSTIu5HcSniXhSTcqnsOJHNlE82k55bXK6sd7NI3rmjK6EBNfz3lnkYfnwB9nzjXP2GneDWjyAiodpd7cnYw+f7PmfR0UWUWkudusfbw5vHuj7GnW3urHaisLKYLEk5ScGGDRRs3ED+2nWY09Or1fbFfNu2IeL22wkZMQIPf/9atXUpKZEpbs2ViUxzRgalp1PxbtQQr8jIKutbCwrI/XEFuUuWULx/PyWnTkGpcz+oAEz+/vgndsLk44PJ0wuTpycmb298EhLwa98O/3bt8KpXr8avpzoMw+BIzhH+u/O/LDq6yKl7mgQ34W99/0bn6M6uGcSpbTD7LshxkPz1j4A7voSYbtVqMjOvmFv/u44j6flV1n3ouuY8OrilzsOsJf1yK+J+FJci7kUxKeJeFJMi7kdxKeJeFJNyKZ3MLuSeGZvYf6b88V+Nw/yZNiGRbk0jatHBFlj6HCSvrrqufwSM/wya9qlRV5mFmcw9OJfZ+2eTVpDm1D39Gvfjb33/RoSf86/R2Zg0zGbOLVpMxn/fpeTQYafbd8QjJAS/tm0xioqwFhVhFBZi8vUl4JqehI8fj2+zZrVqv7aUyBS3VttEpmG1kr9mLVmffUr+ylX2573jmhDQuQv+nTvjmxBvSzb6+GDy9qb01ClyvvuO3GXLMQoKqjdgk4mAa3oSOmoUIUOG1Om+04XmQjalbmJ1ymp+PvkzKXkpVd7j7+VP+6j29G3clwmtJhDgHVD7gZzcAj+/YTuUGQc/Ehp0sE0g4U2r1WxuUSm3vb+e3SfPVVovxM+LNyYkcl3r+tVqXxzTL7ci7kdxKeJeFJMi7kUxKeJ+FJci7kUxKZfauaJSfjtzK6sPlt9Rz8MEjwxqye+ua17zBSiGAQeXwrLnIW1P5XU9vGHkNOh8R836Akqtpfx4/Ee+OvAVO9J3UGgurLR+lH8Uf+n9F/o17ufU6szqxqRhtZK7fDmZ7/6Xoj1VvP4aCujenbAJ4wkeMgQPH59L0kdllMgUt3ZxIjMxKgpLTg4mH19MPt62w2p/ObAWw8Cal4clLx9rXi7FBw5ydtYsSo46PljYlXwSEggdPZrQkTfi3bDhJe+vMvuy9jEzaSaLjy6u8uxLgC7RXRiRMIKO9TrSPKw5Xh5etR+EYcCRn+Dn1+HoyorrdZwAN74OPtVLmBaVWpj04UY2HHV84HKTiADaNgyhU2wYt3aLIUpbybqMfrkVcT+KSxH3opgUcS+KSRH3o7gUcS+KSbkcSi1W/jx/N7M2nnBY3qNpBK+N70RMeC0W1hgGpO6EY2vh2BrbnwWZjuv2fQyu+3O1zs10xGpYOZ1/mkNnD3Ew+yBf7PuCMwVnHNZtHtac8a3Gc2PCjQT5BFXYZk1j0jAMSpKTMZ9JwygtwSgtxSgpwZpfgDntDKWnTlN6+jQlycmUplS96MkRz4gIQm4cQeioUfi1bVvr8zUt2dnkr19PUdI+PAICCLymJ37t22Py9CxTT4lMcWsXJzJj5nzJuYUL63hUNp7h4YSM+CVo27er00NxzVYzPx7/kZlJM9mattWpexJCE3i0y6MMiB3gurFbLbaVlz+/Dqe3V1zPwwuG/R26T4Fq9m22WHngs60sSyo/ITw1rBV3XhNHiJ9+4bpU9MutiPtRXIq4F8WkiHtRTIq4H8WliHtRTMrlYhgGH/x8lFcW76PUUj515OlhonezSEZ2asTQdg0I9a/lv0XDsL1X/c1UKHWw82Kr4TDmXfALrV0/F8gpzuG5tc+x/PjyCusEeAUwstlIxrcaT4vwFuXKL3VMGoZB4ZYtnP38c84tWQpmc43a8WnejNAbR+IRHIQlOxtLdg6WnGyM4hLbe/4mbHkHkwceAQF4hATjGRyCZ2gIpalnyF+zxraC9KI0omdoKIF9ehPYtx+BvXvh3aCBEpni3i5OZDb+7DNyly67vIPw8CCgRw/8WrXCu0ksPk3i8GkSi3dMTLlPBlwux88dZ3fGbvZm7mVv1l6SMpPIK81z6t7ogGh+0+k3jGo+yjWrLwHMxbBzNqyZBpmHKq8b3hTG/BeaXFOtLopKLSzafZqP1x5j+4nscuUPX9ecx69vVa02pfr0y62I+1FcirgXxaSIe1FMirgfxaWIe1FMyuW2KyWHh7/YxtGM/Arr+Hh6MLB1PR66rgXtG9cy0Xh6B3w+AXJPlS+LaAYTZkJ0m9r1cQHDMPjywJf8Y9M/KLYUV1q3S3QXbmt9G4OaDMLb0xZ7lzMmzenpZH/1FfnrN2CUluLh54fJ3x8PXx8Ktm/HfOr0JevbWd5xTTgc24RhH0wHXJ/IdFGGROR/rCUlNb7Xv1tXQm8cSWnKCQq2bado1y6MStrz69iR0BtvJGT4DXhFRdW4X1falLqJaVunsSN9R7XuaxHegr6N+9KvcT8SoxPx9qjlD7/SQtvZl8fW2Zbpn9gIpRVPPADUa2Nbst/+ZvB0vv/9qbl8sek4X289SU5hqcM6d/WK47EhLavzCkRERERERERERETkMusQE8p3D/Xl+QV7+HKL4y1OSyxWfthzhmVJaUzpF8+jg1ri71PDRUUNO8F9P8IXt8GpbWXLsg7D+4Ng9FvQbkzN2r+IyWRiXKtxdI7uzPPrnmdn+s4K625N28rWtK1E+kUytuVYxrUcR4RPhEvG4QyvevWIevBBoh58sFyZYbGQt2oV2V/MJm/VqnIrJy+X0mPHyd1/4JK1r0SmuFxliUdHTN7ehNx4I+F33oF/u3bl2io6cBDruRzbftG/PDCZ8GvbFp8mTVw59FpJykxi2rZprDm5xul7moY0ZXyr8QyOG0yDwAauGUheOvz0MmyfCeaqz98EILYn9H0cWlzv9J7jJ7ML+XbHKeZtO8m+1NxK645KbMTzI+t2a18RERERERERERERcU6grxf/vLUT/VrW47n5uzlb4HgBi8Vq8N+VR1i8O5WXx3Sgd/MaLjgKaQiTv4d5D8LeeWXLSvPhy8m2MzWv+Q1ExNesj4u0CG/BZzd8xq6MXczeP5vFRxdTYnWc38gsyuS9ne/x4a4PGRg7kBsa3ED7sPYuGUdNmTw9CR44kOCBAylJOUnO11+Ts2BBjc/XdFfaWlZq7eKtZTuFhGDOzMIoKcEoKf7lzxKsxbYl2p5BQXgEBeERaPvTN74pHoGBdfkSauXg2YO8v+t9Fh1d5PQ9fRv35Y42d9C7UW88TLU7rNiutAjWvw2rX4OSyhOLdi2ut63AjOvtdDdrDmUwbflBNh7Ncqr+8A4NmDahM96eLnqdUiVtNyLifhSXIu5FMSniXhSTIu5HcSniXhSTUtcKSsws3XuGb3ecZuWBNIfnZ553a9cYfj+0FdEhfjXrzDBsx6Mt/wsYVsd1mvSGxNug7WjwC6lZPw6cLTrLvEPzmL1/NifzTlZZPyE4gVta3sKI5iOI8Lt8qzQrYxgGhdu2kTNvPucWLcKa+79cgUdAAJ5hYZj8/W1PWK1gGBhWK9b8fCznzkHp/xLW3o0aEdinD4F9+hDQswfmtDTyf/6ZvNU/U7BlS5m6e4qKuPVYMqAzMsUNXZzI7NmzZx2P6NIzDINNqZuYsWcGP5/8udK6JkzEhcTRNrItbSPb0j+mP01Dm7pyMLB7Lix7HnJOVF3f5AHtx0KfR6BBh2p19dGao/zlu71VrlD3MMF1retzR88mDGhVTysxLzP9civifhSXIu5FMSniXhSTIu5HcSniXhST4k5yCkpZvOc07/x0mOTMAod1/L09ua9fPPf3b0aQbw03Bj28Ar66BworWVDj5Q8dxkKPqdCwY836ccBitbDm1Bpm75/N6pTVGFT+hrinyZNejXoxImEE18VeR4B3gMvGUhtGaSmlZ87g4euLR2goHj4+ldc3DIziYiznzmHy8MAzMrLC9/at+fkUbN1KwYYN5G/YyJbNm7k1+SigRKa4oV9zItNsNXM05yip+amcLT5LVmEWWcVZbDi9gb2Zeyu9t0NUBx7o9ABd63cl0PsSrTg9dwrm/w4OL6+8XmC0bdVlXG9oOQzC46rVjWEYvLb0AP/58VCl9RqH+TO+eyzjusXSILSGn7iRWtMvtyLuR3Ep4l4UkyLuRTEp4n4UlyLuRTEp7qio1MK05Qd5b9URLFbHaabIQB8eHtSC23o0wcerBjv2ZR+H2RPh9Paq68ZeAz3vhzY3gafrYiQlN4U5B+bw9cGvySnOqbK+t4c3idGJ9GzQk54Ne9Iuqh3eHr/+mN24ahU9+/cHlMgUN3SlJjKthpXknGSyi7MxW82UWksxW81kFWWxN3Mve7P2ciDrAEUWJ8+Z/EV8aDyPdH6E65pcd+lWIhoG7JwNi56Cogp+ePqGQt9Hoe0oiEiAGo7FYjX447zdzNp43GF5sJ8Xw9s3ZFRiI3omROLpodWXdU2/3Iq4H8WliHtRTIq4F8WkiPtRXIq4F8WkuLM9p3J4eu4udp2sOMkXFxnAk9e34saODav/nnlpEaz6J2yZAQWZVdcPbgjd7oGukyEounp9VaLIXMSio4uYtW8WSVlJTt8X4BXA9U2v55729xAf6pqzPd3RhXkiJTLF7VxpiczknGS+PfItC48sdGqfa2fFBscypcMUbmp2E14eNVwuXxXDgMxDtm1k933nuI6HF3S7F/r/HwRG1qq7olILj36xncV7UsuVNY0M4Okb2jCgVT38vD1r1Y+4ln65FXE/iksR96KYFHEvikkR96O4FHEviklxd2aLlU/WHePfPx4ku6C0wnodY0J5+obW9G4WVf1OLKVwcCns+Bz2LwZrxf0A4OkD7cZAj/uhcdcaL/S5mGEYbDm9hc92f8aatDVOL4QyYWJo06FM6TCFVhGtXDIWd3IpE5mXKNsi4l5yinNYfHQxC44sYGf6Tpe23TGqI5PbT+a62Ovw9HBxQs8w4MQGOLoaUjbZHpXtCd5iKAx9CaKa17rrU9mFPDhzKztOZJcr69A4lBl3dycqyLfW/YiIiIiIiIiIiIjIlcvL04N7+sYztmsM7648zIc/H6XYbC1Xb2dKDre/v4FeCZEMaFWPHvERtG8cirenE9vOenpD6+G2R0EW7JwDG9+DrMOO61tKbLsa7pwNviFQrzVEt4bottC0LzToUKPXajKZ6FSvEzEdYyg0F7KzYCeLji9i/an1WAxLhfcZGCxOXszi5MV0ie5CqG8oXh5eeJm88Pb0pp5/PRoFNSImKIZGQY1oFNQIH8/Kz7S8WiiRKb9apZZSVp1cxbeHv2VlykrMVrNL2vUyeRHuF05idCJ3tLmDLtFdLs0WssfWwdI/Q8rGquv6hsLwf0DH8S75ZMnawxk89Pk2MvNLypX1aR7Jfyd2q/khzSIiIiIiIiIiIiLyqxPq783/DWvNXb3ieH3pAb7akoKj4zPXHclk3RHbKmN/b0+6xoVzV684rm/XwLmOAiLgmgdsqy2P/Agb3oODS4AKNiAtPmd7n/3C99rbjYEb3wD/sOq8xDL8vfwZHj+cUS1HkVGYwcoTK9mQuoGNpzeSWVTxNrhb07ZW2baPhw8DYgcwofUEutXvdumOsbsCKBMhVzzDMDiYfZBDZw9xMu+k/ZGUleTU4buOeJo8aR7WnLaRbWkb2ZbmYc2J9I8kwi+CEJ+QS/tDI20fLP8L7P/eufrNBsFN/4HQxrXu2jAM3l99hFcW73d4QPOIjg15bVwnfL20layIiIiIiIiIiIiIlNcw1J9/3NKJKf0SeGXRPpbvS6uwbmGphZ8PZfDzoQzu6RPPH0a0wdPDyfffPTyg+WDbI/MwbPoAtn0GzuQF9nwDKVtg7HRoUvvj8qL8oxjbcixjW47FMAyO5Bzh55M/83nS55zKP1Xt9kqsJSw5toQlx5aQEJrAuFbjSIxOpMhcRKG5kEJzIb6evnSq14lQ39Baj9+dKZEpbiMpM4kFhxeQmp9Kw6CGJNZLpFO9TtQPrO+w/pn8M3x35DsWHF7AkZwj1e4vwi+CEQkj6NWwF35efnh7eOPt4Y2Ppw+xwbH4efnV9iU5Ly8dDi+H/YsgaQEY5Zfdl+MfDoOfhy6TXLIKM+n0Of615ADLks44LJ/SN55nhldjEhERERERERERERGRq1bL+sF8MLk7G45k8vKifWx3cIzZhT5cc5TkzHymTUgk2K+aZ8FGNoNhL8HAZ23byW58H9KTKr8n5zjMuAEGPgN9HwcXHR1nMploFtaMZmHNuL3N7Sw8spAPdn1A8rnkGrV3JOcIf9/4d4dlvp6+3BB/AxNaTaBdVLtajNp9KZEpdarEYvtUwRf7vmBH+o4yZZ/yKQANAxuSEJaAv6c/fl5++Hr6kpqfyrrT67A6k/C7gK+nL9fFXsfIZiPp1agXXh51GAJnk2H7LNuS91PbqHDZ+3mB9SCmB8R0g9getgOKvf1rPYyNR7N456dDrNif7rDc39uTV27pyE2dGtW6LxERERERERERERG5uvRMiOSb3/Tmhz1n+GpLCpuSs8gpLHVY98d9adzyzjo+mNyNmPCA6nfmGwTd74Vu98CJDXBiI6Ql2ZKaafvAXFi2vmGBH/8G+xdD799B6xtt53G6iLeHN6Obj2ZkwkiWHlvKwqMLSStIw2K1YLaaMRtmCksLSS9Mx6gqR+BAsaWYeYfmMe/QPNpHtmdcq3EMbTqUAO8afO/clMkwjOp/Z0QusGXLFrp16wbA+vXr2ea3jc1nNttWN3r44O35vz8NwyCvNI/80nzySvM4ce4EZ4vPXvIxdm/QnZEJIxkSN4Qgn6BL3l+FDAOOroIN79pWXzrzg6lhJxj8F0gY4JKVlwBWq8GK/Wm889NhNh+r+PvfNDKAdyd2pXWDEJf0K5dHaWkpmZm2PdgjIyPx9nbdxCsiNaO4FHEvikkR96KYFHE/iksR96KYlF8bq9XgQFouG49m8dWWFHamlN8KNirIh/9O7EbXuHAXdmyB9e/AsufB6jiRSlAD6DoZuk6CEMeLey5FTJZaSknNT+Vk/kmOZB9h/uH57M3cW6O2/L38GRg7kJHNRnJNw2vw8vDCMAzOlZwjsyiTYnMxJpMJEyZMJhMeeBDkE0Sobyj+XrbFU8WWYranbWfdqXWsP72e/Vn78ffyp2v9rvRs2JNrGl5Ds7Bm9mP4LswTbd68ma5du9b6e3KeEplSaxcnMmcXzmbpsaV1Np5g72AaBzemUWAjOtTrwPD44TQKqsPVhEXnIHUnnNwK2z+vejn7eWFxMOjP0O5m217fLlBqsfLdzlO8+9MR9p/JrbTu4DbR/GtcIqH++sXoSqNfbkXcj+JSxL0oJkXci2JSxP0oLkXci2JSfs2KSi08PXcn87aXP0fS29PEcyPbcUfPJvaEmUuc3Apz74WsSo6sM3lC6xHQ4z5o2q/MIqPLFZO7M3Yze/9sFh1dRLGluEZthPmG4ePhQ1ZRFmbDXGV9X09fQn1CySnJqbLPKP8oejbsSc8GPQlIDWDYtcMA1ycytbWsuFxpRZ9kcFKYbxjZxdlO1w/2DmZY/DBGJIygRXgLQnzqePWgYdjOu9wxG05thcxDzt/r4QVNekG7MdB5Inj51Ho4JWYru05ms/5IFrM2HiflbGGl9eOjAnlwQDNu6RKDh87DFBEREREREREREZFLxM/bk9fHJ9KsXhD/WnqgTFmpxeCP83az40Q2L4xuj5+3a86wpHEXmLoKFj4JO79wXMewQNIC2yOqJXSfAp0mgF+oa8bghPZR7Wkf1Z4nuz3JzvSdlFpL8fPyI8ArAD8vP7ae2crs/bM5klNxQrY6uRawrcRMK0xzqm5GYQYLjyxk4ZGFFCZXnneoDSUyxeVKLdVPZHqYPLg25lpua3Ub1zS6htySXHam72RH+g52Z+wmuzibInMRRZYiisxFGBi0jWzLTc1uYmDsQPy8/C7BK6mBExtty9KPrXH+nsBoaDUMmg+xbR/rV/tEbMrZAr7aksL6I5lsO55Nsbnqs0Q7NA7lwQHNGNquAZ5KYIqIiIiIiIiIiIjIZWAymXhoUAvi6wXyxJwd5d7P/nJLCkmp53j3zq41OzfTEd9guPm/0PFW23azh5ZVXDfjACx6Cpb9xZbM7HI3eNRzzTicEOobSr+YfuWebx3Rmtta38bmM5v5Yt8X/Hj8R6dWXV5plMgUl0sISyC/NJ9Sayml1lJKLCX2a4Ag7yCCvIMI9A4kyCeIFuEtGNVsVJntX88HpqPgdEtp+2D5X2H/QufviekOPR+ANje5ZOUlwLmiUt5acYgZa5IpcSJ5CdCneSQP9m9On+aRrl2eLyIiIiIiIiIiIiLipBs7NqJJRAAPfLqFUzlFZcp2nzzHkNdWcUOHBtzSJYZrEiJds6Ng88G2R9YR2PwhbPsMCs86rluaD5s/wHvzB4Q3voaC9ndC+C1A3W33bDKZ6N6gO90bdCerKIvFRxez8MhCdmbsdFkfMUEx9GjYg6zCLDad2UR+ab7L2naGzsiUWrv4jMyePXvW8Yguo5TNsGYaJH0LOBFKviHQ6gboMRViXLdHtNliZdbG47y+7CBZ+SVV1jeZYGjbBjwwoBmJsWEuG4e4B52bIOJ+FJci7kUxKeJeFJMi7kdxKeJeFJNytcnMK+bhL7ax5lBmhXUah/kzpnNjxnePJTbCRas0AUoLYc83sPF929FxVTAC62FqPxY6jLNtWesmi4WSc5JZdHQRB7MP4u/lT6R/JJF+kUT4RRDgHQAGGBhYDSsWw0JuSS45xTm2R0kOAB2iOtCrUS9ig2Pt7ZZaS9mTsYf1p9ez4fQGtqdvx2w1U5hcyOHnDwOuPyNTiUyptasukVlaCEdW2hKYx9dWXrdRF2jaBxomQqPOEB4PHh4uHc7m5Cye/noXh9LyqqwbHuDN0HYNmNIvgebRQS4dh7gP/XIr4n4UlyLuRTEp4l4UkyLuR3Ep4l4Uk3I1Mlus/HPJfv67suLzHwE8TDCoTX0m925K72Yu3nXw5BbY9CHs/grMRVXXj2gG7cZAs4G2HRm9fF03FjdVUFrAtrRtfPXjV7xx1xuA6xOZ2lpWxJH8DDi1HU5vg9M74dxJ23P5Gbbl41Wp3wEGP2dbkn6JPoFRVGrhtaUHeH/1ESr6OEJ4gDfXtqxHj/gIejSNoHl0kLaPFRERERERERERERG35uXpwTM3tKFTTBj/99VOcosdn/1oNWDp3jMs3XuG5tFBTOoVx81dYgj0dUH6q3FX2+P6F2xbzm56H7KPV1w/6zCsftX28PKHJtdAQn9oOxoi4ms/HjcU4B1An8Z98Gvrxxu8cUn6UCJTrk5F5yBpge0A34IssJrBUmJ75GfCuZSatRveFK77E7S72eUrLy+040Q2T3y5o8JVmH7eHtzfL4H7+zcjyBU/sEVERERERERERERELrPhHRrSKyGSedtPMndrCrtPnquw7qG0PP40fw//WLyfW7rFcFevpsRHBdZ+EAER0Odh6PVbOPAD1g3/xePoT5XfYy6EIytsj+V/hfa3QL/HIbpN7cdzlVGGQ64eFjMc+Ql2fgFJ39l+kLhKdDvo/RC0HwtePq5r9xelFitJp8+x9dhZNh87y6LdqVis5Zdhmkxwc+cYnhzakoah/i4fh4iIiIiIiIiIiIjI5RQe6MPdfeK5u088+1LP8fXWk3y9NYWMvBKH9XOLzcxYk8yMNcn0b1mPfi2iaFk/mFYNgokO9q35roUentB6OJZmQ8g8vAX/g98SeGQhprNHK7/PsMKuObZH6xuhx33gHw4e3uDhZcspBNUHb72n74gSmfLrl7obdsyCXV9C3hnXth1/LfR+BJoPcvkWsoZhsPJAOtNXH2VTchbFZmul9VtEB/HqrZ3oFBvm0nGIiIiIiIiIiIiIiLiD1g1CeHZ4CE9e34rvd53m43XJbDueXWH9lQfSWXkg3f51iJ8XPRMieXRwC9o1Cq3xOCxhTcnr/hC+Q5/DO22XLUmZ9K3tmLrK7PvO9nAkqD6ExUFYE4hqYduSNrp1jcf4a6FEpvy6WK2Ql2rbpzplE+yYDWd21a7NwHq25d6B0RAYBQFRtj9je0L9tq4Z90V2peTw8qIk1h7OrLKuyQT3X5vAY4Nb4ufteUnGIyIiIiIiIiIiIiLiLny8PBjduTGjOzdmZ0o2H61N5rsdpymxVL4g6FyRmaV7z7Byfzr/d0Nr7unTtOYrNMH2Bn1MV9tj2N8h64htZ8ijq2yPwizn28o7Y3ukbLR9/dPLENcXut8DrUdekt0grwRKZIp7KciC3NMQ0hj8w6quX1oE+xfCnm8gLQmyT4Cl2Pn+PLyhYSfbkm0PL/D0AS9fqNcKGiZCo0TbWFy82tIRwzA4mJbHf348xLc7Tjl1T9PIAF69tRPdmkZc4tGJiIiIiIiIiIiIiLifjjFhvDYukWeHt+GLjcf5bP1xUs8VVXpPicXKC9/t5eeD6fzz1k5EBfnWfiAmE0Q2sz263wvmEttukT+/BmeTa9bmsZ9tj8BoaNQZSgt+eRTa8hkJ/aHr3RARX/vxuyklMqXuWa22A283TYcDi237RQPUaw2xPSCmh+3aJ8CWcPTyh5wU2PE57J4LRTnV77NxN+g0AdrdDIGRrn091VBYYmHdkQx+2p/Oiv1pnMiq+tzOAB9POsWEMaBVPSb2iiPAR2EsIiIiIiIiIiIiIle3qCBffnddC6b2b8aSPWeYtfE4246fJb/EUuE9K/anc8O01bwytgMDW0XXbnXmxbx8oOskSLwD9nwNq16FjP01ays/DQ7+UP7509thzb+hxRDoPgWaD7ad5fkrogyIuF7aPig8a/s0gKe3bYWjp7fta8OA4tz/PdKTYPMMyDpcvp30fbbH1k9cM67QWOg43pbAjGrhmjZraPfJHD5am8y3O05VefYlQPem4YxKbEznJmG0qh+Ml6fHZRiliIiIiIiIiIiIiMiVxdvTgxEdGzKiY0MMw+BkdiEHzuSyLzWXz9Yd41RO2dWa6bnF3PPRZlpEBzGxVxxjOjcm2M/bdQPy9IKO46D9Lbaj8M6dBqsZrKVgMUNpvm3xVvZx2yPjIBRkVKMDAw4usT1Cm9hyIB3HQ1Tz2o+9OBeOrYXUneAdCHG9oUFH8Lh8OQolMsX1Vr5i+3SBO/AJhnajoNNt0KT3ZQ2ui5ktVpbsPcOMNUfZlHzWqXtaRAfxf8NaM6iNiz8JIiIiIiIiIiIiIiLyK2cymYgJDyAmPIDrWtfn9h5NeHruLhbvSS1X92BaHn+ev4e/L9rHmM6NmdgrjtYNQlw3GA8P21F3DTtVXs9qgUPLbLtYHlwKGM73kXMcVv3D9mjc1ZY89QmA/HTIS7f9aS4CTLatcE0mMHmAbwj4h0NAhO3P3DO2sz5TNtqSrhfyD4f4ayG+PyQMgIiE6n0fqkmJTHE9S8nl79PkAU37QnQ7CGsC4XG2P6Na2laE1oGjGfnsTMlm98kcdp3MYc/Jc+QWm6u+EWgQ4sdjQ1owtkuMVl+KiIiIiIiIiIiIiLhAWIAP79zZhc83Huev3+51uGNiQYmFmRuOM3PDcXo0jeDOXnEMa9cAH6/L9F69hye0HGp7nE227WqZvNqWe/EO/OUYvgBI2QR5Zypu5+QW28PVCs/C3vm2B9h2w7S2cX0/v1AiU1yvNonM2J7QfqwtOE9shNM7bMurKxLVEhJvty2TDmlU835daO3hDP75w362Hc+u1n2tGwQzoFU0A1vVo0tcON5KYIqIiIiIiIiIiIiIuJTJZOKOnnF0bxrB03N3srWS9/I3JmexMTmLqCAfJnRvwh3XNCEq4DKm1sKbwpC/OC6zlELSt7DpAzj28+Ub08VyTsCp5EvWvBKZ4nrVTWR6+UPHW6H7fdCwY9my0kJI3W3L8JcW2JY8lxbYztpslAiNutiWPruB3Sdz+McP+1l1IN3pexKiApnYK45h7RvQMNT/Eo5ORERERERERERERETOa1k/mLkP9mZHSg6frjvGtztPUeJghSZARl4Jb644xDsrD3N9m2huahNKYuOgyzzii3h6Q/ubbY+0JNj6Cez6CvLT6nZcLqZEprje9X+D/AzbpwEsxbbEprnkfwlO32Dbfsu+wbZHRAJ4+zluy9sfYrtfvrHXwL7Uc7y14jDf7jjl9D0DW9Vjcp94+jWPwsPDPRKxIiIiIiIiIiIiIiJXE5PJRGJsGImxYfxxRBvmbD7BZxuOcSKr0GF9i9Vg0Z4zLNpzhhZR/kzoGceozjFEBdXNEXd20W1g2Msw5AU4+hPsmA37vrMtDDvPJwgCo8AnGDBsC8YMq+0MzOJzUJBVdofMoAbQbKDtHMym/Wznax75CY6uhGPrwOz4e+RqSmSK6zXoUNcjuOQMw2DdkUz+u/IIK6tYgWkyQXxUIB0ah9K+USiD29YnPirwMo1URERERERERERERESqEh7ow9T+zbivXwIrD6bz6bpjrNifhmE4rn8wo5AXFu7jpUX76ds8itGdG3F92wYE+tZh6s3TC5oPtj1KC+HsMduZmgFRtj8rYxi2xGdBli2xEdK47I6YoY1tO2X2fRTMxbbjAY+utCU3T2+8ZC9JiUyRSpgtVg6n53Mqp5CsvBIy84vJzC9h7aFMdp3MqfTeTrFhPDqoBd3jIwiqyx9cIiIiIiIiIiIiIiLiFA8PEwNbRTOwVTQnsgr4bMMxZm86QXZBqcP6FqvBygPprDyQjo/nLjo3CaNP8yh6N4ukU2wY3p4el/kV/MLbH6JbO1/fZAKfQNujKl6+EN/P9rjuj9B2Jbw3oMZDrbSrS9KqyBXAajU4nJ7H2YJSzBYrJRYrZotBVn4Ju07msOtkDkmnz1FcwZ7YFWlWL5DfD23N0Hb1MbnJ+Z0iIiIiIiIiIiIiIlI9sREBPHNDGx4b3JIFO07x8dpk9pw6V2H9EouVDUez2HA0i9eWQqCPJzd0aMgD/ZvRPLqOz9S8lHwv3WtTIlOuOkfS8/hm20m+3nqSk9mu28O5aWQAvxnQnJu7NMarrj5hISIiIiIiIiIiIiIiLuXn7cm4brHc2jWGjUcy+HD1IVYeyqaoioVQ+SUWvtqSwtytKQzv0JDfDmhO20Yhl2nUvw5KZMpV4Wx+Cd/tPMXX206y7Xi2S9vu3CSMqdcmMKRtAzw9tAJTREREREREREREROTXyGQy0aVJGHHD4ikosbA1zcJ3u1JZfTADi7WCwzSxHT+5cOdpFu48Tfem4YT6++DtacLTw4SPpwfRIX7EhPsTGxFATLg/MeH++Hp5XsZX5r6UyJRfrRKzlRX70/h6awo/7kuj1FLxD5Hq8PIwERnkQ9e4cO7uE0+3uHBtISsiIiIiIiIiIiIichUJ8PFkVKdobunWhIy8YpYnnWHt4UzWHs4kPbe4wvs2JZ+tsm0fTw+GtK3PHdc0oVdC5FWdg1AiU654hmGwLzWXA2dySTlbyImsAlLOFrL7VE6Fh+9WxdPDRMv6wXRoHEKHmDBa1Q8mKsiHyCBfQvy8ruofGiIiIiIiIiIiIiIi8j9RQb6M796E8d2bYBgGh9Pz+Gl/Oh+tTSblbPWPuCuxWFm46zQLd52meXQQd/RsQte4cApLLBSUWigqseDj5UHXuHDCAnwuwStyH0pkitvYfTKHuVtTOJVdSEx4AF2ahNM1LpwGoX4O66fmFPHNtpN8teUEh9Pzq91fVJAvoxMb0bdFFH7ennh7mvD29MDXy5O4yAD8vLVsW0REREREREREREREnGcymWgeHUzz6GAm9W7K/O2nePunQxypQR4D4FBaHn/5dq/DMh8vD0Z2bMRdveLoFBtWi1G7LyUypU4Vmy18v+s0n6w7Vu7syg84CkCjUD+a1w8mwNsTfx9P/Lw9OJldxM8H06lky2mHfL08GNquATd3aUzf5lF4eXq46JWIiIiIiIiIiIiIiIj8j7enB7d0jWFM58Ys2n2a+dtPceZcEWaLgdlqxWwxKCixkJZbVO18B9iO2Ju7NYW5W1PoGBPKnT3jGN6xIUG+v57036/nlYjb+Pfyg6w/kom3pwc+Xh74/PKnt6cJw4C8YjN5xWZyi8wcy8znbBXbv57KKeJUTlGtxnRNQgQ3d4nhhvYNCPbzrlVbIiIiIiIiIiIiIiIizvL0MHFjx0bc2LGRw/ISs5XTOYWknC3kUFoec7emsDMlp1p97EzJ4amUnfx5wW6ub9uAMV0a0++XBV1Wq0FOYSmZ+cUUlVoxmcDDZLL/GeznRZi/D37eHphMJopKLWw5dpbVBzNYcyiDvafPEeDjSc/4CHo3i6JviyhaRAddlmP4lMgUl9uXeo61hzPrrP8QPy9iIwKICfenU2wYN3VqREx4QJ2NR0REREREREREREREpCI+Xh7ERQYSFxlIn+ZRTOrdlB0nsvls/TEW7DhFsdnqdFtFpVYW7DjFgh2nCAvwxsfTg6z8EsxOLPn08fIgzN+bnMLScn3mFplZlpTGsqQ0AOoF+9KnWSS9m0cRnFe7xWiVUSJTXK7EXIP1zxcID/CucpXmhYL9vBjZqRGjExvTqkEwof5acSkiIiIiIiIiIiIiIleuTrFhdIoN448j2rLtxFlKLQb+vxzB5+/tyeZjWXy67hgH0/IqbCO7GrkWsK0MTcstdqpuem4x87afYt72UxSnHqpWP9WhRKa4XInF+U8GnOdhgkFt6nNXrzj6NIsit8jMthNn2Xo8mx0nsskuKKGw1EJRqZXCUguGAR0ah3BzlxiGtK2Pn7fnJXglIiIiIiIiIiIiIiIidSc0wJsBraLLPd+2UQgTr4ljw9EsPl1/jB92pzq16vJKo0SmuFzL6CAKis2UWqwUm62UWqyUWKyU/rJSM8jPiyBfL4J/+bNNwxDGdo2hcZi/vY3zgekoOEVERERERERERERERK52JpOJaxIiuSYhkqz8EhbuPMXX206y7Xi2y/poEhFAr4RIMvOLWX8ki7xis8vadoYSmeJyf7yxbV0PQURERERERERERERE5KoREejDxF5NmdirKUcz8lmw/RQHzuTi7+NJVJAvUUE+RAb5EOjjhQEYBhiGgdlqkFtk5mxBCTmFpWQXlACQGBtO3+ZRNIkMsPdRarGyMyWHNYcyWHMog63HbVveXkpKZIqIiIiIiIiIiIiIiIj8SsRHBfLI4BYub9fb04OuceF0jQvn4UEtKCgxsyn5LF8uKuStj13eHaBEpoiIiIiIiIiIiIiIiIhUU4CPF/1b1iMoN563LlEfHpeoXRERERERERERERERERGRGlMiU0RERERERERERERERETcjhKZIiIiIiIiIiIiIiIiIuJ2lMgUEREREREREREREREREbejRKaIiIiIiIiIiIiIiIiIuB0lMkVERERERERERERERETE7SiRKSIiIiIiIiIiIiIiIiJux6uuB3C12Lp1K+vXr2fz5s3s2rWL9PR0MjIyMJvNhIeH07ZtW4YMGcLkyZNp0KCB0+2uW7eO999/n5UrV3L69Gn8/PyIj49nzJgxPPDAA0RFRV3CVyUiIiIiIiIiIiIiIiJyaSiReZkMHz6cM2fOOCxLTU0lNTWVH3/8kRdffJF//etf3H///ZW2ZxgGTzzxBG+88QaGYdifLyws5OzZs2zdupU333yTzz//nOuuu86lr0VERERERERERERERETkUlMi8zKKiorimmuuoVOnTsTHxxMaGkpJSQmHDh1i3rx5bNu2jby8PKZOnYqXlxf33HNPhW0988wzvP766wAEBgZy77330qNHD/Ly8pg7dy5Lly7lzJkzjBo1itWrV5OYmHiZXqWIiIiIiIiIiIiIiIhI7SmReZksX76ctm3bYjKZHJb/+c9/5uWXX+bZZ58F4IknnuCOO+7A19e3XN1t27bxj3/8A4DQ0FBWrVpFx44d7eVTp07l+eef5y9/+Qt5eXncf//9bNiwocK+RURERERERERERERERNyNR10P4GrRrl27KhOJzzzzjD0hmZ2dzZo1axzW++tf/2rfTvall14qk8Q877nnnqNHjx4AbNq0ie+//742wxcRERERERERERERERG5rJTIdDNt27a1X6emppYrz83NZdGiRQCEhIQwefJkh+2YTCYeeugh+9ezZ8927UBFRERERERERERERERELiElMt3M4cOH7dcNGjQoV75y5UqKi4sBuPbaawkICKiwraFDh9qvFy9e7MJRioiIiIiIiIiIiIiIiFxaSmS6kXfffZdNmzYBUL9+ffr06VOuzu7du+3XXbt2rbS9evXqERcXB0B6ejppaWkuHK2IiIiIiIiIiIiIiIjIpeNV1wO4Gq1atYqsrCwAiouLSU5O5rvvvuPnn38GwN/fnxkzZuDr61vu3gMHDtivmzZtWmVfcXFxHDt2zH5vdHR0tce7ZcuWSsuTkpLs12azmdLS0mr3ISKuU1paitlstl+LSN1TXIq4F8WkiHtRTIq4H8WliHtRTIq4F8Vkeee/H5eCEpl14KmnnmLDhg3lnvf09GTw4MG8/PLLdO7c2eG92dnZ9uuoqKgq+4qMjHR4b3V069bN6bo5OTlkZmbWqB8RcQ2z2Uxubq79ay8v/agXqWuKSxH3opgUcS+KSRH3o7gUcS+KSRH3opgsr6b5J2doa1k3EhcXx/XXX0+TJk0qrJOXl2e/9vPzq7JNf39/+/WFgSUiIiIiIiIiIiIiIiLizpQm/sX06dNJSUlxSVvPP/98peXr16+3X+fn57N//36+/PJLpk2bxhNPPMHrr7/OvHnzqjwD83LZvHlzpeVJSUlMnDgRgNDQ0DKrQEXk8rtwO4OIiAi8vb3rcDQiAopLEXejmBRxL4pJEfejuBRxL4pJEfeimCwvLCzskrWtROYvpk+f7nC715qoKpF5ocDAQLp06UKXLl0YN24c/fv3JyUlhcGDB7Nnzx4aNWpUpn5QUJD9uqioqMr2CwsL7dfBwcFOj+tC1Umoenl5KWhF3MD57Qy8vb0VkyJuQnEp4l4UkyLuRTEp4n4UlyLuRTEp4l4Uk2Vdyu11tbWsG+ncuTNPPfUUYNtPeNq0aeXqXJjVzsjIqLLNC8+rvJQZcRERERERERERERERERFXUiLzF+vXr8cwDJc8amPYsGH2659++qlcecuWLe3XycnJVbZ37Ngxh/eKiIiIiIiIiIiIiIiIuDMlMt3Mhdu/Zmdnlytv3769/XrLli2VtpWenm5PZNarV4/o6GjXDFJERERERERERERERETkElMi080cOnTIfh0VFVWufMCAAfj6+gKwatWqMmdgXuyHH36wX1+40lNERERERERERERERETE3SmR6Wbee+89+3Xv3r3LlQcFBTF8+HAAzp07x0cffeSwHcMwePPNN+1fjx8/3rUDFREREREREREREREREbmElMi8DD766COWLFlS6fmZJSUlPPnkkyxYsAAAHx8fpkyZ4rDun/70J0wmEwDPPPMMO3fuLFfnr3/9Kxs2bACge/fujBgxorYvQ0REREREREREREREROSy8arrAVwNtm/fzt13301MTAxDhgyhY8eOREdH4+PjQ1ZWFjt37uSbb77h1KlT9nteffVVWrVq5bC9zp0789RTT/HKK6+Qk5ND7969mTJlCj169CAvL4+5c+eyZMkSwLaC88JVniIiIiIiIiIiIiIiIiJXAiUyL6OUlBRmzJhRaZ3o6GimTZvGhAkTKq338ssvU1xczLRp08jPz2fatGkO25o1axaJiYm1GbaIiIiIiIiIiIiIiIjIZadE5mXw4osvMmjQIH766Se2bt3KoUOHyMjIoLS0lKCgIBo2bEhiYiI33HADY8eOJTAwsMo2TSYTr7/+OuPGjeO9995j1apVnDp1Cj8/PxISEhg9ejQPPvggUVFRl+EVioiIiIiIiIiIiIiIiLiWEpmXQWBgICNHjmTkyJEub7tXr1706tXL5e2KiIiIiIiIiIiIiIiI1CWPuh6AiIiIiIiIiIiIiIiIiMjFlMgUEREREREREREREREREbejrWWl1goLC+3X+/btw8tL/6xE6pLZbCY7OxuAsLAwxaSIG1BcirgXxaSIe1FMirgfxaWIe1FMirgXxWR5SUlJ9usLc0auoO+u1FpycrL9evLkyXU2DhEREREREREREREREak7ycnJ9O3b12XtaWtZqbWjR4/W9RBERERERERERERERETkV0YrMqXWrrnmGvv19OnTSUxMrLvBiAhJSUlMnDgRgE8//ZQ2bdrU8YhERHEp4l4UkyLuRTEp4n4UlyLuRTEp4l4Uk+UVFhbad++8/vrrXdq2EplSaxEREfbrxMREunbtWoejEZELtWnTRjEp4mYUlyLuRTEp4l4UkyLuR3Ep4l4UkyLuRTH5P67cTvZC2lpWRERERERERERERERERNyOEpkiIiIiIiIiIiIiIiIi4naUyBQRERERERERERERERERt6NEpoiIiIiIiIiIiIiIiIi4HSUyRURERERERERERERERMTtKJEpIiIiIiIiIiIiIiIiIm5HiUwRERERERERERERERERcTtKZIqIiIiIiIiIiIiIiIiI2zEZhmHU9SBERERERERERERERERERC6kFZkiIiIiIiIiIiIiIiIi4naUyBQRERERERERERERERERt6NEpoiIiIiIiIiIiIiIiIi4HSUyRURERERERERERERERMTtKJEpIiIiIiIiIiIiIiIiIm5HiUwRERERERERERERERERcTtKZIqIiIiIiIiIiIiIiIiI21EiU0RERERERERERERERETcjhKZIiIiIiIiIiIiIiIiIuJ2lMgUEREREREREREREREREbejRKaIiIiIiIiIiIiIiIiIuB0lMqVGDMNg9uzZ3HjjjcTExODr60vDhg0ZNGgQ06dPx2w21/UQRa54AwYMwGQyOfVo2rSpU20uXryY8ePHExcXh5+fH9HR0fTp04fXX3+d/Pz8S/uCRNyYxWJh9+7dfPTRRzz00EP06tWLgIAAe4w9//zz1W7TlfG2bt067rnnHpo1a0ZAQAARERF07dqVv/3tb2RkZFR7bCJXAlfF5eTJk52eT00mk9PjU1zK1SYnJ4c5c+bw4IMP0rNnTyIjI/H29iY8PJxOnTrxm9/8hk2bNlWrTc2VIjXnqpjUPClSe4ZhsGbNGt544w3uuOMOunTpQmxsLP7+/gQEBBATE8MNN9zAm2++SXZ2ttPtap4UqTlXxqXmSjdgiFRTVlaWcd111xlAhY8uXboYx44dq+uhilzR+vfvX2mcXfiIi4urtK2ioiJjwoQJlbbRrFkzY8eOHZfnxYm4mZtvvrnS+HjuueecbsuV8Wa1Wo3HHnvMMJlMFbZVv359Y/ny5bV49SLuyVVxOWnSJKfnU2f+e6S4lKvRK6+8Yvj6+joVQ3feeaeRn59faXuaK0Vqx5UxqXlSpPYKCwudjqF69eoZ8+bNq7Q9zZMitefKuNRcWfe8EKmGkpISRo0axerVqwGIjY3l/vvvp3nz5qSkpPDhhx+SlJTE1q1bueGGG1i3bh0hISF1PGqRK98333xTaXlAQECl5ZMmTWL27NkAREZGcv/999OhQwcyMjL47LPP2LhxI4cPH2bYsGFs2LCB2NhYl41d5EpgsVjKfB0REUFkZCQHDx6sdluujLdnnnmG119/HYDAwEDuvfdeevToQV5eHnPnzmXp0qWcOXPGPjcnJiZWe7wi7sqVcXnef//7X6Kjo2s1LsWlXI0OHDhAcXExAAkJCQwePJjExESioqI4e/Ysy5cvZ+7cuVgsFj777DPS0tJYtGgRHh6ON4HSXClSO66OyfM0T4rUTuPGjenZsycdO3YkLi6O4OBgCgoK2LdvH19++SUHDx4kPT2dsWPHsmjRIoYMGeKwHc2TIq7jqrg8T3NlHanrTKpcWd544w37pwK6dOliZGVllSkvLCw0hg4daq/z5JNP1tFIRa58F67IrI158+bZ22nSpEm51dIWi8W4++677XVuueWWWvUnciV68cUXjaefftr48ssvjSNHjhiGYRgzZsywx4WzK79cGW9bt261fzovNDTU4adtn3vuOXtb3bt3N6xWq/MvWsTNuSouL/z07NGjR2s1JsWlXK2mTJlijBgxwvjpp58qrLNq1SojKCjI/u//ww8/dFhPc6VI7bkyJjVPitSexWIx9uzZU2kds9lsPPjgg/Z//61bt3ZYT/OkiGu4Mi41V9Y9JTLFaaWlpUa9evUMwDCZTMbu3bsd1jtz5owRGBhoAIavr6+RkZFxmUcq8uvgqkRmYmKivZ2FCxc6rFNQUGA0adLEXm/Xrl216lPk16AmCRNXxtvo0aPtdd566y2HdaxWq9GjRw97ve+++86pcYpcqeo6kam4lKvVxR9grch//vMf+7/9a6+91mEdzZUitefKmNQ8KXL5lJSUGJGRkfZ//4cPHy5XR/OkyOXlTFxqrqx7le8pIXKBH3/8kfT0dAAGDRpEu3btHNaLjo5mwoQJABQXFzN//vzLNkYRKevgwYNs374dgBYtWjB8+HCH9fz9/bnvvvvsX8+ZM+dyDE/kV8WV8Zabm8uiRYsACAkJYfLkyQ7bMplMPPTQQ/avz28/JCKup7iUq1l4eLhT9W699Vb79a5du8qVa64UcQ1XxaQrKSZFqubt7U2LFi3sX6emppYp1zwpcvlVFZeupLisOSUyxWlLliyxXw8bNqzSuheWL168+JKNSUQq98MPP9ivhw4dWmldxa1I7bgy3lauXGk/9+jaa6+t9BzcC/tS7IpcOopLkaoFBwfbrwsLC8uVa64UubyqiklXUkyKVM1qtZKcnGz/ukGDBmXKNU+KXH5VxaUrKS5rTolMcdru3bvt1127dq20brdu3RzeJyI1M2LECBo2bIiPjw+RkZEkJiby0EMP2T+pV5HqxG1iYiKenp4A7N27F8Mwaj1ukauJK+OtOm3Vq1ePuLg4ANLT00lLS6vWuEWuJvfddx9NmjTB19eXsLAw2rZty3333ceqVauqvFdxKVK1C+PkfAxUVK65UuTSqyomL6Z5UuTSMQyDP/7xj/bVXomJiSQkJJSpo3lS5PJyJi4vprmybnjV9QDkynHgwAH7ddOmTSutGxMTg6enJxaLhYMHD2IYBiaT6RKPUOTX6/vvv7dfZ2VlkZWVxY4dO3jzzTe5++67eeutt/D39y93X3Xi1svLi8aNG3P8+HHy8/M5efIkMTExLnsNIr92roy36rQFtjemjh07Zr83Ojq6eoMXuUosW7bMfl1SUkJOTg5JSUlMnz6dESNG8MknnxAREeHwXsWlSNXee+89+/WIESPKlWuuFLm8qorJi2meFHGNxYsXU1RUBEBBQQGHDh3i66+/ZseOHQBERkbywQcflLtP86TIpVPTuLyY5sq6oUSmOC07O9t+HRUVVWldLy8vQkJCOHv2LGazmfz8fIKCgi7xCEV+fSIjIxk6dChdu3alUaNGGIZBcnIy3333HWvXrgVgxowZHD9+nMWLF+PlVfbHenXi9nx/x48ft9+rRKaI81wZbzVpy9G9ImITHBzMkCFD6NGjB7GxsXh6epKSksKSJUvsxycsXLiQ/v37s2bNGkJCQsq1obgUqdzatWuZMWMGAH5+fjz22GPl6miuFLl8nInJ8zRPirjW5MmTOXPmTLnnfXx8uOmmm/jHP/5BfHx8uXLNkyKXTk3j8jzNlXVLiUxxWl5env3az8+vyvr+/v6cPXsWsB1kq0SmSPW8/PLLdOvWDW9v73JlzzzzDN988w133nknBQUFLF++nFdeeYU//OEPZerVJG7Py83NrcXoRa4+row3xa6I6zz00EO89dZbBAYGlit74oknWL16NbfccgtpaWns3r2bJ554gvfff79cXcWlSMVSU1MZN24cVqsVgBdeeMHhB+I0V4pcHs7GJGieFLmcWrduzeDBgytcVaV5UuTyqyouQXOlO9AZmSIibqpXr14Ok5jnjRkzpsyk+M9//tN+YLSIiIjYdO3a1eF/OM/r168fX3/9tf0YhBkzZnDy5MnLNTyRK15+fj6jRo2yx82IESN44okn6nhUIlev6sak5kkR10tNTcUwDKxWKzk5OaxZs4YHH3yQPXv28MADD9CzZ08OHz5c18MUuarUJi41V9Y9JTLFaReuqDy/n3RlCgsL7dfBwcGXZEwiV7vbb7+dVq1aAdgn4QspbkUuH1fGm2JX5PLq06cP119/PQAWi4UffvihXB3FpUh5RUVF3HTTTWzcuBGwxdLs2bPtb+JcTHOlyKVV3Zh0luZJkZoxmUyEhITQu3dv3n77bRYuXIinpyd79uxhyJAh5Ofnl6mveVLk0qtuXDpLc+WlpUSmOC0sLMx+nZGRUWlds9nMuXPnAPD29q70EwsiUjsDBgywX+/bt69MWXXiFiAzM9PhvSJSNVfGm2JX5PKrbD4FxaXIxUpKSrj55pv58ccfAejRowfff/99pf/301wpcunUJCarQ/OkSO0NHTqUyZMnA3D06FE++eSTMuWaJ0Uuv6risjo0V146SmSK01q2bGm/Tk5OrrRuSkoKFosFgObNm9f6038iUrHKDn6uTtyazWb7tgeBgYE0btzYZWMUuRq4Mt6q0xbAsWPHHN4rIs6rbD4FxaXIhUpLS7n11ltZtGgRAJ07d2bx4sWEhIRUep/mSpFLo6YxWR2aJ0VcY9iwYfbrn376qUyZ5kmRulFZXFaH5spLR4lMcVr79u3t11u2bKm07ubNmx3eJyKuV9mnc6oTt9u3b7d/AKFt27b6AIJINbky3qrTVnp6uv2X23r16lV6QL2IVKyqT7sqLkVszGYzt912GwsWLACgQ4cOLF26lPDw8Crv1Vwp4nq1icnq0Dwp4hoXbg95caJD86RI3agsLqtDc+Wlo0SmOG3o0KH2a0d7PF9o8eLF9usLP9EgIq63cuVK+/XFn85R3IpcPq6MtwEDBuDr6wvAqlWrypyLcLEL+1LsitRcZfMpKC5FwHbez5133sncuXMB2xuny5YtK/Pp88porhRxrdrGZHVonhRxjUOHDtmvo6KiypRpnhSpG5XFZXVorrx0lMgUpw0cOJB69eoBsGzZMvbs2eOwXlpaGl988QUAfn5+jBo16rKNUeRqM2vWLPue68HBwfTt27dMeYsWLejcuTMABw8etG81dLGioiLef/99+9fjxo27RCMW+fVyZbwFBQUxfPhwAM6dO8dHH33ksC3DMHjzzTftX48fP76mwxe5qq1Zs8b+H0UPD48ybyKdp7iUq53VauWee+5h9uzZALRq1Yrly5dX69PhmitFXMcVMekszZMirmG1Wvnggw/sX/fu3btMueZJkcuvqrh0lubKS8wQqYY33njDAAzA6NKli5GVlVWmvLCw0Bg2bJi9zpNPPllHIxW5sk2bNs1Yv359pXW++eYbIyAgwB5vzz//vMN68+bNs9eJi4szjh07VqbcYrEY99xzj73OLbfc4rLXIXIlmzFjhj0unnvuOafucWW8bd261TCZTAZghIaGGjt27ChX5/nnn7e31b1792q9PpErUXXj8uOPPzaWLFliWK3WCuusXr3aiI6Otrc7efLkCusqLuVqZbVajSlTptj/bTdv3tw4efJkjdrSXClSe66KSc2TIq7x+uuvG+vWrau0zrlz54zbb7/d/u8/IiLCyMjIKFdP86SIa7gqLjVXugeTYRiGq5Ki8utXUlLC4MGDWb16NQCxsbFMnTqV5s2bk5KSwgcffEBSUhJg29Jk7dq1hIaG1uWQRa5Io0ePZv78+bRq1YpBgwbRrl07IiMjMQyD5ORkvv32W9auXWuvP3DgQBYvXoyPj4/D9iZMmGD/pG5kZCRTp06lQ4cOZGZm8sknn7Bx40YAGjZsyIYNG4iNjb30L1LEjRw9erTMJ/AAdu7cybfffgtAv379uPbaa8uUjx071v5p2Qu5Mt6efvppXnnlFQACAwOZMmUKPXr0IC8vj7lz57JkyRLA9qm+1atXk5iYWLNvgIgbckVcPvroo0ybNo3Y2FiGDh1Khw4dqFevHp6enqSkpLBkyRKWLFnC+f8StWvXjp9//tnheSbnKS7lavTss8/y8ssvA+Dt7c1rr71GTExMlfddf/31BAQElHtec6VI7bgqJjVPirjG+fdwWrRowXXXXUf79u2JiorC09OT9PR0tm7dyjfffENWVhYAXl5ezJkzhzFjxjhsT/OkSO25Ki41V7qJusyiypUpKyvLuO666+yfCnD06NKlS7lPDImI80aNGlVpjJ1/mEwm4/777zfy8/Mrba+oqMiYMGFCpW01a9bM4aeARK4GK1ascCrmLnzMmDHDYVuujDer1Wo8+uij9k/rOXpER0cby5cvd/F3RKTuuSIuH3nkEafvHTNmjMNPxV9McSlXo/79+1c7HgHj6NGjDtvTXClSO66KSc2TIq7h7Hs4gJGQkGAsXbq00vY0T4rUnqviUnOle9CKTKkRwzCYM2cOn376Kdu2bSMjI4Pw8HDatWvHhAkTuPvuu/Hy8qrrYYpcsQ4fPsyKFStYv349O3bsIC0tjYyMDMxmM2FhYbRs2ZK+ffty9913Ozw8uiKLFy/mww8/ZP369aSlpREcHEyLFi249dZbuf/++wkMDLyEr0rEff30008MHDiwWvfMmDGDyZMnV1juynhbt24d7733HqtWreLUqVP4+fmRkJDA6NGjefDBB2t1GL2Iu3JFXJ48eZIVK1awbt06tm7dypkzZ8jIyKCoqIjQ0FDi4+Pp3bs3d911F126dKlWX4pLuZoMGDCAlStXVvu+o0eP0rRp0wrLNVeK1IyrYlLzpIhrnD17lqVLl7Jq1Sq2b9/OkSNHyMzMxGq1EhwcTGxsLJ07d+amm27ixhtvrHA3rYtpnhSpOVfFpeZK96BEpoiIiIiIiIiIiIiIiIi4HY+6HoCIiIiIiIiIiIiIiIiIyMWUyBQRERERERERERERERERt6NEpoiIiIiIiIiIiIiIiIi4HSUyRURERERERERERERERMTtKJEpIiIiIiIiIiIiIiIiIm5HiUwRERERERERERERERERcTtKZIqIiIiIiIiIiIiIiIiI21EiU0RERERERERERERERETcjhKZIiIiIiIiIiIiIiIiIuJ2lMgUEREREREREREREREREbejRKaIiIiIiIiIiIiIiIiIuB0lMkVERERERERERERERETE7SiRKSIiIiIiIiIiIiIiIiJuR4lMEREREREREREREREREXE7SmSKiIiIiIiIiIiIiIiIiNtRIlNERERERERERERERERE3I4SmSIiIiIiIiIiIiIiIiLidpTIFBEREREREZf66KOPMJlMmEwmPvroo7oezmV34sQJHnnkEdq2bUtQUJD9e5GYmFjXQxMREREREbmieNX1AERERERERER+Lfbt20fv3r05e/ZsXQ9FRERERETkiqdEpoiIiIiIiIiLPPXUU/Yk5o033sioUaOIiooCIDQ0tC6HJiIiIiIicsVRIlNERERERETEBUpLS1m6dCkAbdq0YcGCBZhMpjoelYiIiIiIyJVLZ2SKiIiIiIiIuEBGRgZFRUUAdOrUSUlMERERERGRWlIiU0RERERERMQFiouL7de+vr51OBIREREREZFfByUyRUREREREpFq+++47Ro4cSYMGDfDz86Np06bccccdrFu3zqn7CwsL+eabb/jtb39Lz549iYyMxNvbm9DQUNq1a8eDDz7Ijh07Krz/3XffxWQyYTKZeO2115zq86abbrLfk5SU5LBOVlYWL7zwAr169aJevXr4+PjQsGFDBg8ezH/+8x/7asuLTZ48GZPJRHx8vP25jz/+2N7fhf2ev77pppucGvdrr71mv+edd96psN66det48MEHadu2LWFhYfj5+dGkSRPGjx/PwoULq+zn3LlzzJw5k3vvvZfOnTsTFhaGt7c3ERERdOnShSeeeILDhw9X2U7Tpk0xmUw0bdoUgKKiIv7973/Tt29f6tevj4eHBwMGDHDqtYuIiIiIiJgMwzDqehAiIiIiIiLi/iwWC/feey8ff/yxw3IPDw9efvlloqOjufvuuwGYMWMGkydPLlMvPj6e5OTkKvt75plneOmll8o9n5ubS6NGjcjLy6Nt27bs2bOn0nZOnjxJXFwcFouFvn37snr16nJ15s+fz+TJk8nOzq6wnSZNmjBv3jw6d+5c5vnJkydX+D250NGjR5k0aRKrVq3C09OT48eP06hRo0rvadu2LUlJSQQEBHD69GlCQkLKlOfn53Pvvfcye/bsStsZMWIEs2bNIjg4uFxZSUkJISEhZVaUOuLl5cUbb7zBb3/72wrrNG3alGPHjhEXF8eKFSsYOXJkub+f/v3789NPP1Xal4iIiIiICIBXXQ9ARERERERErgwPP/ywPWHn4+PDpEmT6Nu3Lx4eHmzcuJEPPviA//u//2P06NGVtlNYWEhERARDhgyhc+fONG7cGG9vb06ePMnWrVuZM2cOpaWl9qToo48+Wub+4OBgbr/9dt577z327t3LmjVr6NOnT4X9zZgxA4vFAsB9991Xrvz7779n7Nix9jrXXnstt9xyC/Xr1+fYsWN8+umn7Nq1i+PHj9O/f382btxI69aty3xfRo8eTVpaGlOnTgVg4MCBPPzww2X6iY6O5oEHHmDVqlVYLBZmzJjBH/7whwrH/fPPP9tXj44fP75cErO4uJjBgwezfv16AJo1a8b48eNp06YN3t7eHDp0iE8++YQDBw6wcOFCRo8ezdKlS/HwKLs5k9Vqpbi4mEaNGjFkyBA6duxoXz154sQJ1q5dy4IFCzCbzfzud7+jUaNGjBkzpsJxnx/bzTffzJ49e+jbty9jx46lUaNGpKenc+bMmUrvFREREREROU8rMkVERERERKRKq1ev5tprrwUgLCyMZcuW0bVr1zJ19u/fz4ABA0hNTbU/52hF5uLFixk8eDBeXo4/W3vs2DGGDRvGvn37CA4O5uTJk+VWEm7dutXe/6RJk/joo48ctmUYBgkJCSQnJxMWFsapU6fw9/e3l587d44WLVqQlpYGwL/+9S8ef/zxMm2YzWZ+85vf8P777wPQrVs3Nm3aVK6v5ORk+/ayFY2ppKSExo0bk5GRQXx8PIcPH8ZkMjkc+6RJk/jkk08AWLt2Lb169SpT/thjj/HGG28A8Pvf/56XXnqp3Pe0tLSUKVOm2Nt55513eOCBB8rUsVgsLF26lKFDh1Y4lp07dzJ06FBSU1NJSEjg4MGD5RKi8L8Vmee99tprPPbYYw7bFBERERERqYrOyBQREREREZEq/etf/7Jfv/HGG+WSmACtWrVi+vTpVbY1bNiwCpOYAHFxcbz99tuAbRvZ+fPnl6vTpUsXunfvDsCXX37JuXPnHLa1bNky+za2d9xxR5kkJsBHH31kT2KOGzeuXBITbFuqvvPOO3Ts2BGAzZs3s2zZsipepWM+Pj72bXePHj3K8uXLHdbLycnhq6++AqB9+/blkpinT5+2f49uvvlm/vGPfzj8nnp7ezN9+nQSEhIAHJ4p6unpybBhwypMYgJ07NjRvs3vkSNHWLt2bVUvlTFjxiiJKSIiIiIitaJEpoiIiIiIiFSquLiYRYsWAVC/fn3uvPPOCuuOGDGCNm3a1LrP3r172683bNjgsM75lYUFBQXMnDnTYZ3zqygB7r///nLlX3/9tf36//7v/yocj6enJ7///e8d3lddU6dOtScNLxzfhWbOnElBQQHgeDvcOXPmUFJSAsCTTz5ZaX/e3t6MHz8egIMHDzp1PqkjzvydXOihhx6qUT8iIiIiIiLn6YxMERERERERqdSOHTvsSbP+/fvj6elZaf1BgwbZz3asSFpaGp988glLlixh7969nD171p64u1hKSorD5ydMmMDjjz9OTk4O77//Pg8++GCZ8vT0dPtqzh49ethXVJ5nGIZ9i9ioqCi6dOlS6Zivv/56+7UzibyKNGvWjEGDBrFs2TLmzZtHRkYGUVFRZeqcX9nq5+fHxIkTy7WxevVq+3VKSgrz5s2rtM+zZ8/ar5OSkmjatGm5OsnJyXz88cf89NNP7Nu3j+zsbIqKihy2V9HfyXmenp7lVpGKiIiIiIhUlxKZIiIiIiIiUqlTp07Zr5s3b15l/arqzJ49m6lTp5KTk+NU/xVtGxsQEMDEiRN588032bZtG1u3bi2TjPzkk0/sCVhHqxrPnTtnT562aNGiynFER0cTGhpKTk4Op0+fdmrsFXnggQdYtmwZJSUlfPLJJ2W2tN2yZQvbtm0DYOzYsYSHh5e7/8JVlePGjatW3xcmNc974403ePrppykuLnaqjYr+Ts6LjIzEz8+vWuMSERERERG5mBKZIiIiIiIiUqm8vDz7dUBAQJX1AwMDKyxbtWoVt99+O1arFbCddTl48GCaNWtGaGgovr6+9rpjxowBwGKxVNje1KlTefPNNwHbNq3vvPOOvez8qsagoCAmTJhQ7t7c3FynxnyhoKAgcnJyytxbE6NGjaJBgwakpqYyffr0MonMC7ebdZSABZxOAjtyPrl73syZM8ucZdmvXz/69+9P06ZNCQ4OxsfHB7Ctop06dSpQ+d8JUO4sUhERERERkZpQIlNEREREREQqFRQUZL+uaPvXC+Xn51dY9vzzz9uTmO+9916FibrK2rhQ+/bt6dOnD2vWrOHzzz/nX//6FwEBAaxevZp9+/YBcNttt5V5DecFBwdXu7/zSd0L760JLy8v7r33Xl588UWSkpL4+eef6du3L/n5+cyaNQuAli1b0r9/f4f3n389JpMJs9mMh4dHjcfypz/9yT6mBQsWcMMNNzist2fPnhr3ISIiIiIiUhM1/5+OiIiIiIiIXBUaNWpkvz506FCV9SuqU1JSYj/bsVu3bhUmMQGOHTvm9PgeeOABwLbd6Zw5c4D/rcYEuP/++x3eFxK2+cT8AAAICUlEQVQSYl9h6szrSk9Pt6+EvPB7UlP33XefPQF5frxz5syxb9ta2fencePGgO2cz5MnT9Z4DEeOHOHo0aMAjB49usIkJlTv70RERERERMQVlMgUERERERGRSnXq1Mm+vejKlSur3FZ0+fLlDp/PzMzEbDYD0KxZs0rb+OGHH5we3y233EJkZCRg25Y1OzubL7/8EoDExES6devm8D6TyUT37t0BW5Jy+/btlfazZMkS+3WPHj2cHl9F4uLiGDZsGABffvklOTk59m1lfXx8mDRpUoX3XrhS88JxVdeZM2fs1678OxEREREREXEFJTJFRERERESkUr6+vgwfPhywJb4+//zzCusuWrSIpKQkh2UXnq95+PDhCtvIzc3l9ddfd3p8fn5+9qTf2rVr+cMf/kBhYSFQ+apGgLFjx9qv//nPf1ZYz2Kx8Oqrrzq8rzbOryYtKCjg2WefZd26dYDtDM169epVeN+ECRPsyeVXXnnF6a1xL+bs38mJEyeYMWNGjfoQERERERGpKSUyRUREREREpEpPPPGE/fqRRx5xuHrx4MGD3HvvvRW2ERoaSosWLQDYvHkz33zzTbk6eXl53HrrrZw4caJa45s6dar9+u233wZsSbo77rij0vsmT55MdHQ0AJ9//jn//ve/y9WxWCz87ne/s7/m7t27M2jQoGqNryLDhw8nNja2zLih6gRsbGwsDz30EGD7vo8cOZLU1NQK61utVpYtW8bf/va3Ms+3adOGwMBAAObPn8/GjRvL3XvmzBlGjx5Nbm6ucy9KRERERETERZTIFBERERERkSr17duX3/zmNwCcPXuWa665hqlTp/Lpp58yc+ZMHnnkETp37szp06cZPXp0he2cT76BbUvYu+66i/fee49Zs2bxhz/8gVatWvHDDz9w1113VWt8LVu2ZODAgWWeGzduHKGhoZXeFxwczIwZM/D09ARsSdqBAwfy1ltvMWfOHF599VW6dOnCu+++a6//ySefVGtslfH09GTKlCllnktISGDw4MFV3vvyyy/bE6orVqwgISGBiRMn8vbbb/Pll18yc+ZM/vWvf3HXXXfRuHFjhgwZwk8//VSmDR8fH3sSuLS0lGuvvZbf/OY3zJgxg88++4zHH3+c1q1bs3Xr1mr/nYiIiIiIiNSWyTAMo64HISIiIiIiIu7PYrFw99138+mnnzos9/Dw4O9//zv16tXj7rvvBmDGjBlMnjzZXscwDCZOnMjMmTMr7GfUqFHMmjXLvu1p//79yyXgHJkzZw7jx4+3f71mzRp69+7txCuzrUacNGkSOTk5FdZp0qQJ33zzDV26dHFYnpycTHx8PACTJk3io48+cqrvU6dOERcXZz8/9MUXX+TZZ5916t6SkhKeeOIJ3nnnnSrPLgW46667+Pjjj8s8V1RUxPDhw1mxYkWF902dOpWnnnrKfo5mRa+vadOmHDt2jLi4OJKTk516DSIiIiIiIhXRikwRERERERFxiqenJ5988gnffvstI0aMoF69evj6+tKkSRNuu+02fv75Z37/+99X2obJZOKzzz7j888/Z+DAgYSFheHj40NMTAw33ngjs2fPZt68efj7+1d7fBeuYmzbtq3TSUywJU8PHz7MX//6V3r27ElkZCTe3t5ER0dz3XXXMW3aNPbt21dhErM2GjVqRJs2bQDw8vKyJ4Gd4ePjw3/+8x/27dvH008/Tc+ePalXrx5eXl4EBAQQHx/P8OHDeemll9i5c2e5JCbYzhhdsmQJb7/9Nr169SI4OBhfX1/i4uIYN24cP/zwA++++y4eHnoLQURERERELi+tyBQREREREZFfhenTp9vPlnz99dd59NFH63ZATjp48CAtW7YEbAnVefPm1e2ARERERERE3IQ+TikiIiIiIiK/CufPsfT397+iznM8P27Afl6liIiIiIiIKJEpIiIiIiIivwILFixgy5YtANxxxx1ERETU8Yicc+rUKd577z0AWrZsybBhw+p4RCIiIiIiIu7Dq64HICIiIiIiIlJdhYWFrFy5ErPZzI4dO/jnP/8J2M6MfOaZZ+p4dJVbuXIlBQUFHD9+nNdee428vDwA/vznP2Mymep4dCIiIiIiIu5DZ2SKiIiIiIjIFSc5OZn4+Phyz7/22ms89thjdTAi5zVt2pRjx46VeU5nY4qIiIiIiJSnFZkiIiIiIiJyRQsLC6NNmzY8+eST3HzzzXU9HKf5+fnRrFkzJk2axMMPP1zXwxEREREREXE7WpEpIiIiIiIiIiIiIiIiIm7Ho64HICIiIiIiIiIiIiIiIiJyMSUyRURERERERERERERERMTtKJEpIiIiIiIiIiIiIiIiIm5HiUwRERERERERERERERERcTtKZIqIiIiIiIiIiIiIiIiI21EiU0RERERERERERERERETcjhKZIiIiIiIiIiIiIiIiIuJ2lMgUEREREREREREREREREbejRKaIiIiIiIiIiIiIiIiIuB0lMkVERERERERERERERETE7SiRKSIiIiIiIiIiIiIiIiJuR4lMEREREREREREREREREXE7SmSKiIiIiIiIiIiIiIiIiNtRIlNERERERERERERERERE3I4SmSIiIiIiIiIiIiIiIiLidpTIFBERERERERERERERERG3o0SmiIiIiIiIiIiIiIiIiLgdJTJFRERERERERERERERExO0okSkiIiIiIiIiIiIiIiIibkeJTBERERERERERERERERFxO/8PmlqOCFaJRdkAAAAASUVORK5CYII=", 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npyeSk5ORkJCA+/fvY/Dgwdi4caPZ60ypVGLmzJlYt26dzvTQ0FCEhIQAgGZ/qXz/6KOP4s8//0T//v3Nno8xb775Jv7zn//oTAsMDER4eDhkMhnu3LmjaSGempqKESNGYPXq1Zg6darBMouLizFkyBCcOHFCZ3qrVq3g7++PjIwMXL16FQUFBRg3bhx27dpllWWpiuetmktLS9NpDV3ZgsvDwwNisRjp6em4ceOG5hh09OhRdO3aFRcuXNBcUxpii8cva7h+/ToGDhyoOWcCFce15s2bw8PDA+Xl5cjKykJCQoLmvFP1Ortp06YYNGgQSktLceTIEc303r17690m27Ztq7cuX3/9NebOnauzjr29vREZGQlHR0ckJydrrofz8vIwc+ZM3L9/v9q1ubaG3jb79OmD9PR0JCQk4NatWwAqtpsuXbroTa/vd0lRURHGjBmDPXv26EyPjIxEUFAQFAqFZvsDgPPnz6NHjx7Yv3+/wXVd1Zo1azBr1iwAFdcFUVFRcHZ2xq1bt5CUlASgoqX42LFjcfz4cXTo0AFjx47Ftm3bAFR8T82bN4dEIsHly5c1x7CUlBQMHjwYV65cgbOzs1l1+e677zTXWFKpFFFRUZrzcuU6FAQBX3/9NbKysrBhwwaIxYY7yauP9ffZZ5/htddeA/D3udbLywsZGRm4fv26Ttr8/HwkJiZq/re3t0fTpk01vdtUXufJ5XIAwJUrV9CtWzecOXMGzZs3rzbvLl26wMHBASkpKbh8+TIAwMHBAX369NFbV0PXg3VNEARMmjQJf/zxBwDAxcUFLVu2hJOTE+7du1etRWVdfG/vv/8+3n33XZ1pjRs3RmhoKBwcHFBUVIS7d+/i/v37ms9NtYglInogNVgIlYiIyIpsrUXm8uXLNWlkMpnw0UcfCQUFBdXSqdVqISYmRli0aJHQpk0bg/Os6VPWO3bs0Fmu4OBg4Y8//hBUKpUmTWlpqfDFF18ITk5OmnQeHh5CUlKSWfVxdXUVAAht2rQRDh48KKjVak06lUol3L59u0bLcuLECUEmk2nSOjs7C++//76QmZlZLe39+/eFr776SoiKihK2bt1qsM6zZs0SduzYIRQXF1f7XKVSCbt27RLatm2rmaeLi4vRVg911SLziy++EDp06CAsX75ciI+P15vm/v37wsKFC3VawL366qtGy9V++rmyhW5YWFi1bUKtVgsbN27U2SYiIyN1vtuqPvnkE5110bVrVyEmJkYnTXp6uvD0008LAAQHBwfBxcWlRtu1Mdr7p6+vr2Zey5cvF0pLSzXpVCqVsHXrViEwMFCTXiKRCOfPnzdYtvb6q9zuQ0NDhd9//11QKpU6abW/N4VCIfTs2VOT19HRUVi6dKmQk5Ojk0culwvff/+94Obmpknbu3dvne9GW35+vtCoUSNNWnt7e2HZsmU627dKpRL+/PNPTbrKdVJZd0PMbZVTVlYmdOrUSee7f+KJJ4QLFy7oTbtjxw5h/PjxwpgxY/SWZ25LnKoKCgp0WsN4eXkJK1asqNbisri4WPj44491nmqfNGmSwXLv3Lmjs526u7sLq1evFuRyuSaNXC4X1q5dq2nlp72O66pFpre3tyASiYQFCxYIubm5OmmPHTsm+Pv7a9K2bt1a+PnnnzXH9qr1V6vVwjfffCNIJBJNnqefftpofSZMmFDtO696rL97964wZswYnXRjx441Wu7jjz+uk3769OnC/fv3ddLExsYK3bt3r7auTR2D58+fr5N2xowZeo+vBw4cEFq2bKlzHElNTdVbpiXngK+//rraOrt48WK1dOfPn9csX+XxKzY21mC5c+bM0Sl36NChwq1bt6rVs3LdVl1n1mqRyfNWzW3btk2IjIwUli5dKsTGxuo95ufk5Agff/yx4OzsrJn/qFGjjJZbl8evhm6Rqd0C2d/fX9iyZYveVm7l5eXCvn37hNmzZwtTpkzRW1ZtruV27Nih02KyZ8+ewpEjR6ptdzdv3tQ5volEImH37t0Gy7WVbbM2PQw8+eSTOtdX8+bNq3ZNXXktpt1KvVmzZgZ7S9BuUefs7Cw4ODgI9vb2wieffCKUlJTopP3999919pchQ4YIH374oQBU/B7aunWrzr4ml8ur/Z5csmSJweXTrouLi4vg4OAgABBmz54tZGRk6KS9ePFitZ5rli9f3qDrz9HRUZBKpYJYLBbefPPNatekKSkpOus0Li5OCAoKEt5++23h9OnTeve34uJiYeXKlTq9AHXs2NHocprbSrKq+mqRWXm97+3tLaxZs0YoLy/XSVv1fGft7y09PV3nmnXo0KEGWwunp6cLa9euFXr16mV2jw5ERA8SBjKJiOihYGuBTO1uTN98802z5l81EKKtJoFMhUKh091kQECA0Zsze/bs0bmRbeyGc9Uup9q2bSvk5+ebVS9zlkWhUAgRERGadJ6ensK5c+dMlq1Wqw3+eDe3W8OioiKdwIyx76+uApnm1lUQBOGXX37RualTNaihrWo3TmFhYUa7L/3xxx910h85ckRvuoyMDJ2bx127dq12Q0nbK6+8olOuNW+6ae+flTcLtbsUq+r69es63SF269bNYNqq669Ro0ZCcnKyyTpV3jgDKrp3NLUtX7x4UWd9btq0SW+6efPm6dSnsttIfRISEqp1L22NQOY777yjU+a7775rdNkqGdrGaxrIfO655zT5goODTe6LO3fu1OkS9cyZM3rTjRs3TpPG3t5eOHHihMEyT506pbmRWdMbv4bo62r8m2++MZh+3759Omnt7OwER0dHvQHmSgsXLtQ5luh74EMQBGH37t06ZU+cONFgsEitVgtTp07VSW/o5tr27dt10s2dO9dgXUtKSnSCfaaOwSdPntQJNnz33XcGyxYEQcjLy9MJZr7wwgt605l7Drhz547OtvHWW28ZnX95ebnQt29fnRuX+sTFxelsxyNHjjT44INKpaoWWAasF8jkeavmiouLjQZctR09elRzrSYSiYQbN24YTFuXx6+GDGQqFArB3t5ek2bPnj1mlWvoOrum13KFhYU6gd+pU6cavZZXq9XCpEmTNOlbt26tN50tbZs1DWRu2LBBk08mk5ns5vLevXs6D+B8/PHHetNpB6Iq94EdO3YYLHfVqlXVzoV+fn7C3bt3DeZ56qmnNOkjIiIMpqtaFwDCvHnzDKYvKirSGSrD2dlZyMrK0pu2vtafJdtKWVmZzkMQxty4cUPnoTxj+6itBzIrv6u4uDiT5dfF96Z9TgsPD68WSDXE2LGIiOhBxUAmERE9FCwd58bUq7aBzGbNmmnS7Ny5s9bLV5NA5qZNm3SWaePGjSbzzJ07V5NeIpEY/KFfNZBpzrializLunXrLK67Ne3Zs0cz7xYtWhhMV1eBTEv16NFDU4cNGzYYTFf1hrCpm38qlUqnxd8HH3ygN93HH3+ss91cvnzZaLllZWU6gWpr3nSrevPBnJsbX3zxhU4eQ60yq64/Y+u6UllZmc5NCnPHxlqyZIkmT69evfSWqz0+4oQJE0yWuXLlSp361zaQWVBQoDPWoKFgiyVqEsi8f/++zk1tQ+PkVlXZkgWA3pY6qampOi3H5s+fb7LMt956S2cd11Ugs1+/fibzVB2vzVjLEkEQhKSkJJ30x48f15tOe1xYf39/kw+xFBYW6rQ66N+/v8lyQ0NDdVpQ63Pt2rVqY7IaOgaPGDHC6Hetz5EjRzR5nJyc9PaqYO454OWXXza6P+tz+/ZtzfKJRCIhISGhWpoXXnhBU66rq6uQnp5utMzMzEzB3d1dp87WCmRaiuetmps4caKmDh9++KHeNHV9/GrIQGZqaqpOuaaOFabU9Fru888/1+Rp0qSJUFZWZjJPfn6+znlz//791dLY0rZZ00Bm+/btNfneeecds/JoX/s3btxYb5qqgbgZM2YYLbOsrEzToq7ytW7dOqN5jh07ppPeUM8sVesSGRlpMtB36dIlnYdPPv30U73p6mv9DR482Kyya+LNN9/UzOe5554zmO5BCGQaOs5WVRff29KlSzWfm3OtT0T0MDPcITsRERHVmPbYOtpj4tWnrVu3at6HhIRg3LhxJvP861//gkgkAlAxPs+ff/5pMk+7du3QtWvXmldUj59++knzPioqCuPHj7dq+aZ0795d8/7GjRvIz8+v1/lbSru+VcesMyQyMhIDBgwwmkYsFqNXr16a/7XHfNWmPYbTo48+itatWxst197eHs8++6xZ9aytuXPnmkzz9NNPw8XFRfP/li1bTObx8fHBmDFjTKbbuXOnZvxBPz8/o2PdaZs2bZrm/cmTJ1FSUqLz+aFDhzTj6wDmLeeUKVPg4eFh1vzNsX37dp2xLhcvXmy1si2xYcMGzbiXHTp0wGOPPWZWPu11vH///mqf//nnn1AqlQAAkUhk1viiL774IiQSiVnzr43Zs2ebTNOtWzeL8gQHB+uMx3r16tVqaUpKSnTGfZo1axbc3NyMluvi4qKzvx84cEAzHnGl4uJi7N69W6dcBwcHo+W2aNHC5DEMgGb8v0qvv/66yTwA0KtXL4SHhwOoWO6TJ0+ala8qtVqtMy6nufMPDw/XHH8FQdC7jWofeydMmAA/Pz+jZfr4+DTIuM/68LxVc+asO1s+ftVW1fErG+o6e+3atZr3L730Euzt7U3mcXNzw6hRozT/79u3r1qaB3nbBCrGpK8cl14mk+Hll182K9/48eM1x/2kpCSdsZINMbXc9vb2aN++veZ/Nzc3PPnkk0bzREdH6+wH+s6Fhuoik8mMpmnbtq3OGJD6rjdtaf3VRk2O8bZIIpHgmWeeMZmurr437eNdbGyszli8RET/NNKGrgAREVFdGDRokMV5tG+i1lbnzp1x6dIlABU39318fDB16lSzbnJYi/ZN12HDhmkClMaEhYWhXbt2mh9iJ0+eNHnzq3fv3rWqZ1VKpRLHjx/X/G9OANYSgiDgxIkTOH36NK5fv468vDwUFRVBrVYbTJ+amgp3d3er1sNcmZmZ2Lt3Ly5duoTU1FQUFBRogjaVEhISNO+Tk5PNKveRRx4xK512YEM7aFVJLpfr3EQcMmSIWeUOGzYM8+fPNyttTfn5+aFTp04m0zk5OaFv377466+/AACnT582mad79+6QSk1fSh8+fFjzvm/fvmblASoePvDw8EBeXh6USiViYmLQo0cPzefadXR3d9f5zBB7e3v0798fmzdvNqsOpmgvW3h4OKKjo61Sbm3qYU5gq1K7du0071NTU5GamoqgoCDNNO11HBUVpbMvGBIYGIgOHTrg3LlzZtejJrRvEBoSEBCgeR8eHg5/f3+TeQIDAzXHkNzc3Gqfnz17VhMcAYDhw4ebU12MGDECixYtAlAR2Dt9+rTOd3X+/Hmdm3OWHEd27txpNM3Ro0c1x3c/Pz+0bdvWrLKBim0kMTERAHDu3DkMHDjQ7LyV4uLiNOtSJBKZHWivnP/Bgwc189cORt+7dw9paWma/y1ZZ19//bXZdagJnrdqrqCgAHv37kVMTAzu3r2LwsJClJWVQRAETZqUlBTNe0PrzpaPX7Xl4eGBpk2barahiRMnYuXKlejfv3+91SEvLw9xcXGa/2t67qm6rm152zSX9jm5Q4cO8Pb2Niufvb09WrRoofkNcu7cOTRr1sxgejs7O7Ou8bTPhZ06dTIZbLS3t4eXlxcyMzMB6D8X6mPJd1V5XL9w4QIUCoVOnepr/QE1/w1XVlaGffv24eLFi7h16xYKCgpQWlqqc5zSftjO3GO8LWrVqpVZ30FdfW+dO3fWvL927RomT56Mjz/+GI0bNzZzCYiIHh4MZBIR0UNp165dFucxJ9Bnrpdffhnr1q2DXC5HeXk5Zs+ejddffx2DBg1C79690a1bN3To0KHOnnxXKpW4c+eO5n/tmyamtG3bVvNjSvtGoyERERGWVs+o5ORkFBUVaf63VnBEEASsWbMGixcvxr179yzKq+9GaF27e/cu5s2bh61bt+oEDkwxt67aN3aMcXZ21ryv2ioQqLiZrn2Duk2bNmaV27x5c8hkMigUCrPS10RUVJTZadu0aaMJZJrzFLu5231sbKzm/ZkzZzB48GCz61RWVqZ5X3lDrZJ2HVu3bm328atNmzZWC2Reu3ZN876hgpiA7jretm2b5iESS2VmZuoEMrXXsbnbdWXaug4EmLP/Ojk5WZS+ah59+7v2OUEkEpkdFGzVqhWkUqnmWJaQkKBz4197XYtEIpMtkCqZ871obx9lZWUW7YPagYqq+6C5tOcvlUrNasldSXt9GzsGAOZvo5Zsy5bieavmsrOz8eabb2LdunU6x35TDK07Wz5+WcMbb7yhCezfuXMHAwYMQOPGjTF48GD06tUL3bp1Q2RkZJ3NPy4uTucBuJdeesnsa3rtQHTV/doWt01LaR/z7t69a9Ex9+7du5r3po653t7eZj0cVhfnwqpkMhmaN29uVtna32lZWRmSkpJ0rinra/25u7vDy8vL7LKBit4T/v3vf+Prr7+u1rOCMQ3xO8paanK9b83v7ZFHHkGPHj1w4sQJABW9kGzatAndunVDv3790KNHD3Tv3t2qPa4QEdkqBjKJiIjqQFRUFDZs2ICpU6dqgnIFBQX49ddf8euvvwKo6N6of//+mDp1Kp544gmrBlKr/mD09fU1O692WnOeQjbVraCltJ/gBWCyqzxzqNVqzJgxQ6d7P0tUbUlS186ePYuBAwfW6Ie/uXWtSetg7SetK1XdRsx9AlkqlcLd3R1ZWVkW18Nc5talalprbvfZ2dma93fu3NF5wMASVbs31q5jTZeztrT3VWvspzWlvY6vXbumE2C1hC2uY0Ps7OzqND1gen93cXEx2f1rJZlMBnd3d813VXUfq1quuccnc9a19vZRUFBQ494XatrFuPb8FQqF1eZf02NvXW2fPG/V3O3bt/Hoo49a/JAVYHjd2fLxyxpmzZqFxMREfPjhh5rvOCkpCStXrsTKlSsBAEFBQXjiiScwc+ZMqz9so71fA/q7iDWHtfbr+rimMpf2uklPT6+zY25NzmvWOhdW5eHhYXYgu+p3WvU7r6/1Z+nvt6ysLPTv379GD4vJ5XKL89iKmlzvW/t727x5M4YPH47z588DqPhde+LECU1wUywWo2PHjhg/fjxmzpxpcYCaiOhBwTEyiYiI6sioUaNw8+ZNzJs3T6eVT6WCggL89ttvGDlyJDp27Gj2GCzmqHpjy5If7to3Cs1pFSAWW/dyouo8zb1RbswXX3yhE8SMjIzEf/7zHxw5cgT37t1DUVERVCoVBEHQvBpKcXExRo8erbkZLJPJMHnyZGzYsAFxcXHIycnRdDNX+arssrEhVL05UdNtrS7UtC7m3FQ3d7svLi42uw7GVO36WHu9N9Q6195XrbGf1tTDvI5tjfa+YekNYWPnlrpc13W1fZirPrZPwPz1VhfbJ89bNadWqzF+/HhNEFMkEmHEiBFYu3YtYmJikJWVpemysfK1Zs0ak+X+E45fS5cuxblz5zBp0iSdca4rpaam4ptvvkHnzp0xfvx4q7YKs7X9GrCd766hj7kNoTbfU9Vrzvpaf5b+fps1a5ZOELNv37749ttvce7cOaSnp6OkpARqtVpznKrsPvdB19DX+0BFt9+nT5/G6tWr0a1bt2oPP6vVapw7dw5vvPEGwsLC8M0331ilLkREtoYtMomIiOpQYGAgPv74Y3z88ce4evUqjh07hqNHj+LAgQNITU3VpIuJiUGfPn1w/vx5hISE1Hq+VbuXKSwsNDuvdldBDdFNjaenp87/NW0FU0mtVmPp0qWa/x9//HFs2bLF6E0HS9aXta1Zs0YzloxMJsPevXvRp08fo3kasr5Vn1Su6bZWF2paF2uOh6q9D7300kv473//a5Vytdd7Q61z7X21tvtpbXh4eGhaoXz22Wd49dVXrVKuLaxjW6O9PVt63DF2bqnLda09r7Zt29a46+Ga0p6/m5ub1fYVfcdefcGcqupi++R5q+Z27NihaWUDAOvXr8fEiRON5jGnvg/y8Ut7vFxTOnbsiPXr10OhUODs2bM4fvw4jh49ikOHDuks96+//ork5GQcPXrUKsM6VD2G5eTkVLt+rQlb2jZrSnvdPPHEE/jjjz8arjL1pDbfU9VrTltcf5cvX8bvv/+u+X/p0qVYuHCh0TwNeYw3xJJji6Xq+nuTSCSYMWMGZsyYgezsbBw9ehTHjx/HoUOHcP78ec1DuIWFhXj++echCAKef/55q9aBiKihsUUmERFRPWnVqhVmz56NH3/8EcnJyThx4gSGDRum+TwrKwvvv/++Vebl7OysM77LrVu3zM6rnbYhuousOn7NjRs3alXehQsXkJGRofn///7v/0w+OZ2UlFSredaG9viuTz31lMmbwUDD1tff31/n/8TERLPyZWVl1flNDnPrAlR07Vep6jLVhvb2nJ6ebrVytetY0+WsrcDAQM372u6ntfEwr2Nbo31OkMvlmuCVKRkZGTpjH1c9t2iva7lcrvOgjzHmrOu62j7MpT3/goIClJaWWqXcmh5762L75Hmr5rTXXe/evU0GMQHz1p2tHL9kMpnmvbnjN5rTvbu++fTo0QPz5s3Dn3/+iaysLGzZsgUtW7bUpDl58iQ2btxocdn6VL1WtdaxxZa2zZpq6GNuQygoKKg2NIYhVfexqudDW1x/2sepsLAwLFiwwGSeuj7Ga/+Wq8tji7nq83vz9vbGyJEjsWzZMpw9exYpKSlYvHixTu8oCxcuNGt8VyKiBwkDmURERA1AJBKhe/fu+PPPP9G7d2/NdEPjaWh3a2Nut6edOnXSvK8cQ8MUpVKJM2fOaP639phC5vD09ESLFi00/x86dKhW5d29e1fz3sfHB+Hh4SbzHDt2rFbzrA3t+nbp0sVkekEQzP5+64Kfnx+Cg4M1/58+fdqsfOamq43r16+b3UJBuz7a+05t9ejRQ/P+5MmTVitXu463bt2qNl6XIdZc7927d9e8P3funE6gqqZqcqyrj3V8/vx5KJVKk3kqu/d6WFXdN8w99lRNV/XcUrVcax5HtLeP9PR0i4I61qC9nwDAqVOnrFJu27ZtdYJEDXns5Xmr5ixdd4B51yi2cvzSbmFoTqCnvLwc8fHxtZ6vnZ0dRo8ejYMHD+qMSajvOrtq15HmnHvatm0LZ2dnzf/WOvfY0rYJ1P6cHBMTY7WHN2xdTb6rkJAQ+Pr66nxui+tP+zgVHR1drWtTfcz9LVWTbQyw/NgCAHFxcWaXb6mG/N4CAwPx7rvv4osvvtBMy8/Pr5ffWkRE9YmBTCIiogYkFosxcuRIzf9paWl602nfLDH3h5F2i4h9+/YZLFvb9u3bdQIi5rSqqAuDBw/WvN+8ebOm28iaMPcpXW3ff/99jedXW5bWd9euXUhJSamj2phHezvZvHmzWcvw008/1WWVAFSsy82bN5tMFxsbi8uXL2v+t+Z2P2TIEM37e/fuYe/evVYpV/sBCLVabVYrk5s3b1r1JrX2flpaWooffvih1mXW5FinvY6PHTtmtdah2ttBTk4O9uzZYzLPvn37bKYFRV2IiIjQucn+448/mpVPe9vw9/dH8+bNdT5v0qSJTrm//PKLyTKVSiU2bdpkMl3nzp11AhmrVq0yp8pWExQUhHbt2ll9/g4ODjqBL3PWGQD8/PPPVpm/Np63as7SdXf16lWzgma2cvwKDQ3VvI+NjTWZ/q+//qo2TmRt+Pv745FHHtH8r+9aWPu8A5h37pHJZOjfv7/mf2seV2xl2wRqdk7u37+/5iGL8vJyrF+/vk7qZmvMOQbL5XKd61J915u2uP4sPU5lZ2frdEVrTE22McDyY0tZWRl27txpdvmWsoXvbcyYMTr/m/Pbn4joQcJAJhERUR2w5IlS7VZMXl5eetNod+Fo7pPqM2fO1DzlqlAoMH/+fKPpy8vLdcY7CQ0NxcCBA82al7XNnTtXM4ZRaWkpnnvuOYvWqbagoCDN+6ysLFy7ds1o+tWrV+u0Sq1v2vU9cuSI0bQlJSVWGw+wNmbMmKF5n5qaiv/7v/8zmv7ChQtW697NlCVLlqC4uNhoGu19w9XVFePHj7fa/Nu3b69zs3Pu3LlWGSMvMjISPXv21Pz/wQcfmGx9auoYYKl27dqhb9++mv/feecd3Lt3r1Zl1uRY98QTT2gCY2q1GrNmzarRAwxV9evXT+dG2VtvvWW0VZNSqcRbb71V6/naulmzZmne//XXXzh48KDR9EeOHNG5oTlr1iy9rTmmT5+ueb9lyxacPXvWaLlfffWVWdubVCrVOU4uX74cFy9eNJnPmubNm6d5v2HDBuzYscMq5Wofe8+ePYstW7YYTf/HH3/USUtInrdqzpJ1p1ar8eKLL5pVrq0cv7RbX2/evBlqtdpg2rKyMixatMhkmZZeD5q6zvbw8NDpktHcc4/2fn3ixAl88803FtXLEFvZNgHdc/KtW7fMWve+vr6YNm2a5v+333671tcGD4Kff/7Z5BjMn3/+uU5waebMmdXS2OL60z5OnTx50mQL71dffdXsoKT2NpaZmWn2NbL2sSU1NdVkC9BPPvkEmZmZZpVdE3X1vdX0ngJg+L4CEdGDioFMIiKiOtC2bVusXbvWZADl9u3bWLFiheb/Rx99VG867S7CNm7caNa4ZGFhYTo/qNatW4d///vfen8QFRcXY8KECTpBvnfeeUcTTKxvEREReOGFFzT/b9myBVOnTjU6/o9arcbmzZtx/vx5neldunTRedr3hRdeMPjjeuPGjXj++edrWfva6devn+b95s2b8ddff+lNl52djccff7xBxyas1K9fP50ulRYuXIjffvtNb9qbN29ixIgRRm9mWtPdu3cxduxYvduOSqXCa6+9pjP2z8svvwwXFxer1mHZsmWam6Q3btxAnz59cP36dZP57t69i7feeguvvfaa3s/ffvttzfvU1FSMGjVK7w0gtVqNhQsXmv10vCU+/PBDzRPoubm56NOnj8kg0bVr17BmzRq9n2kf6/bs2WNWN2BisRiff/65Jjh29OhRDB482KxxFq9evYo5c+Zg2bJlesvVfrgjJiYG06ZNQ3l5ebW05eXlePrppx/qbmUrvfDCCzpd4Y0bN67acbfSxYsXMXbsWM3/3t7emDt3rt60c+bMgYeHB4CKbXbUqFEG95M//vhDJ4hgyksvvYTIyEgAFYG0AQMGYPv27Sbz5eXl4euvv671Qz1PPfWUplWYWq3GuHHjsGbNGpM3KEtKSvDTTz8Z7O564sSJiIiI0Pz/9NNP4/jx43rTnjx5UueawJp43qpdPSqdOXMGX3/9td50JSUlmDx5stnd7dvK8Ut7/7958yY++OADvemKioowfvx4XLlyxWSZR44cwZAhQ7Bnzx6oVCqjabdt26azzvRdZ0skErRv317z/4oVK1BWVmayHo888giefPJJzf9z5szBRx99ZDLIo1Ao8Oeff+LRRx/V6bKzkq1sm4DuOTknJ8fgubuqRYsWwcfHB0DFGMl9+vQxq1vt9PR0fPTRR5g0aVLNKtyAVCoVRowYgYSEBL2fb968Wee6rVevXgZ7ALG19ad9nEpJSdFZDm1KpRKvv/662b01ANW7Sf/ss8/Myte1a1ednhzmzp1r8IG+1atXY/HixWbXqabq4nt75ZVXMG/ePJPjFyuVSp0HFh0cHKp1bU9E9KCTNnQFiIiIHkaXL1/GjBkz8OKLL2LAgAHo2rUrWrZsqXkyMjU1FUeOHMGPP/6oeXrSzs5O56aTtokTJ+Kjjz6CIAi4f/8+mjRpgo4dO8LX11dnbJGqgYrly5fj8OHDmh8/7777Lv766y9Mnz4dzZs3h0KhwIULF/Ddd9/hzp07mnwjR47U+5Rwffrkk09w9uxZzQ/A9evXY9euXZg0aRJ69uwJPz8/KBQKJCUl4fTp0/j999+RlpaGrVu36tx4cXBwwIsvvoiPP/4YQMWYm23btsXzzz+P9u3bQyQSISEhARs3bsT+/fsBAM8995zVnqy31OzZs/HRRx+hqKgIarUaI0aMwJQpUzB8+HD4+/sjNzcXR48exerVq5GdnQ03NzcMGzbM7G4F64JIJMKqVasQHR2NkpISKBQKjBkzBiNHjsTYsWPRuHFj5ObmYv/+/Vi1ahVKS0vRq1cvJCYmmhWUr6n+/fsjJiYGu3btQuvWrfHss88iOjoaMpkM169fx/fff48LFy5o0kdFRRm8OVMb7du3x/fff48pU6ZArVbj0qVLaN26NR5//HEMHDgQTZo0gaurKwoLC5GWloZLly7hyJEjmroZCj4MGjQI06dPx9q1awEABw4c0Cxn586dYW9vj+vXr2PNmjU4e/YsRCIRJkyYgA0bNlht2bp27YpPPvkEL7/8MgDgzp07iI6OxhNPPIGhQ4ciLCwM9vb2yMrKwqVLl7B7926cPn0aI0aM0Gl1UmnMmDF45ZVXUFZWhpKSErRv3x7t27dHUFCQzoMV3333Hfz8/DT/DxkyBEuXLtUcQw8cOICIiAiMHj0a/fr1Q0hICJycnFBQUICUlBTExMTgwIEDmkCZoVZAs2fPxqZNm3DgwAEAFS0uzp07h9mzZ6Nt27YQiUSIjY3FypUrcf36dTg4OGDIkCHYunWrdVawDfLx8cH333+PESNGQBAEZGdno1u3bpgyZQqGDh0KPz8/ZGZmYteuXfjhhx80rWNFIhFWrlyp871p8/f3x/LlyzUtM1NSUtChQwfMnDkT/fv3h5eXF5KTk7FlyxbNTf2JEyea1VWqq6sr/vjjDzzyyCPIzc3VBNQ6d+6MESNGoG3btvD09ERZWRmys7Nx5coVnDp1CocOHYJCodBp2VYTYrEYW7ZsQdeuXXH37l2UlJTg6aefxrJlyzB27Fh07NgR3t7eUCgUyM3NxbVr13DmzBns378fJSUlBst1dHTEypUrMXDgQKhUKhQUFKBPnz6YNGkSHn/8cfj7+yMjIwPbt2/Hjz/+CJVKZfY6swTPWzU3btw4vPnmm0hKSgJQ8aDAnj17MH78eAQHB6OwsBBnzpzB6tWrkZSUBJlMhqlTp5rVBb4tHL8ee+wxdO7cWdPC+t1338WZM2cwceJEBAcHo6CgAKdOncKqVauQlpaGNm3awM7OzuDDEUBFC6Vdu3Zh165d8Pf3x+DBgxEdHY3w8HC4u7tDoVDg9u3b2L59O/744w9NkK9JkyaYPHmy3jInT56sud7cs2cPAgMD0b59e7i5uWkekomKisL777+vk+/777/HzZs3ceHCBahUKixYsAArVqzAhAkT0KVLF/j6+kIQBOTl5Wm6d9+zZ4/moSN9DzPYyrYJAM2bN0d0dLQmyD1z5kz85z//QWRkJOzs7DTpXnrpJZ1gV3BwMDZv3oxBgwahvLwcd+7cQffu3dGvXz8MHToULVu2hLu7O4qLi5GZmYm4uDgcP34cJ06cgFqtbrChLWoqODgY4eHhOHr0KNq1a4dnnnkG/fr1g6enJ5KSkvDrr7/ijz/+0KR3cXEx2h2xra2/nj17okuXLpoeaz766COcPn0a06ZNQ0REBEpLS3Hp0iWsWbNGc11l7m8pFxcXjBgxQtPl7pIlS7B69Wq0atUKjo6OmnRPPvmkzoMDYrEY8+fP1zwcFRMTg3bt2uGll15Cu3btIAgCbt68iQ0bNuDIkSOQSqWYPHlynXb5WhffW35+Pn744Qd88skniI6ORp8+fdC+fXv4+/vDyckJeXl5iI2NxY8//qjzQPIrr7yiM44oEdFDQSAiInoILFq0SACgedWEdv5FixYZTDdt2jRNumnTppksy5yXg4ODsGXLFqP1W7Jkicly9ElKShJat25tdl1Gjx4tlJWVGa1LaGioJv2aNWuMpq1N3qKiIuGJJ56waF1u3bq1WjmlpaVCjx49zMo/ePBgoby8XGfawYMH9dYvMTFRJ11iYqJF68KQLVu2CFKp1GRdnZ2dhR07duhs/3369DFYbp8+fczaxrWZW7YgCML+/fsFJycnk/WOiIgQkpOTa7UdGVJ1/9y7d6/g7Oxssk6RkZFCamqq0bJrsv60bd++XfDw8LD4+GDoOCMIgiCXy83eR5YuXSqsWbNG839oaKjBci353gVBEFatWiXIZDKzl2nEiBEGy1qzZo3J7d/QvrZ69WrBwcHB4nVs7PvMz88XunTpYrIMqVQqrFu3zuJ1Z46DBw/qzMscNamHJdv4hg0bBDs7O7PWr0wmE3766Sez6vCf//zHrDKHDh0qJCQkWHQMjo+Pt+h8aGpfsfQckJaWJvTq1cvi+Zv6ztevXy9IJBKTZXTq1EkoLCzUmWbo/GYpnrdq7uTJk2bVQSaTCatWrTL7OC4IdXf8MudauNLVq1cFX19fk3UIDw8XEhMTTX7nVY+H5rxCQkKEa9euGayjQqEQBg0aZLQMQ+ujsLBQGDNmTI32a2PHDFvYNgVBEC5evCh4e3sbrYOh+Z05c0YIDg62eL0YWteWbPuVLNlWK5mzLqvWJTk5WYiIiDDrGHj48GGz6mEL669SfHy8WfuxSCQS3nvvPYuuW5KSkoSwsDCj5eo7FqhUKrOugaVSqUXHzppsM9qs+b1p18Xc16RJkwS5XG5xvYmIbB27liUiIqoDX331FYYOHQpXV1ej6RwcHPDUU08hLi4Oo0ePNpr2nXfewdGjR/H000+jdevWcHNz02mNaUhwcDDOnj2L999/X6c7wKqaNWuGn376CZs3b4a9vb3JcuuDs7Mz/vjjD2zZsgUdO3Y0mtbPzw9z5sxB165dq33m4OCA/fv346WXXjK4bIGBgVi2bBl27Nih85R5Qxg9ejT27duHqKgovZ9LJBIMHDgQFy5cwJAhQ+q5dob169cPFy9exIABA/SOgWdnZ4cpU6bg/PnzaNSoUb3UqX///jhz5gx69eql93N7e3u88MILOHfunM44PXVh6NChiI+Px8KFCw22StOuV79+/fDVV18Z7WZLJpNh69at+Pzzz+Ht7a03TdOmTbF161aDLb6tYebMmbh69SomTZqkM9ZYVXZ2dhg0aBDmzJljMM306dNx/vx5vPjii+jQoQM8PDwglZrXkcyMGTNw48YNvPjii3B3dzea1sXFBcOGDcMPP/xgtJtSNzc3HDlyBG+99ZbBbofbt2+PQ4cOYcqUKWbV82EwYcIExMbGYvTo0TrdwmmTSqUYMWIELl26hIkTJ5pV7oIFC7Br1y7NuKdVeXt74/3338e2bdss7v68adOmuHDhAr755hu0aNHCaFqRSIT27dvj3Xffxb59+yyajyH+/v44dOgQNmzYgOjoaL3HSW0tWrTAa6+9hpiYGKPpJk2ahJMnT6Jz5856P3dxccErr7yC48ePW73r7Eo8b9Vct27dcOrUKZ3uRKvq3r07jh07ZnFvGfVx/DK1Hbds2RLHjx/HgAED9H5ub2+PGTNm4OLFiwgLCzM5vzZt2mDJkiXo1q2bwWNPJV9fX7zxxhuIi4szus9LpVLs2LEDP//8M0aNGoXw8HA4OzubXDagYv/avHkzdu7cib59+5o8LoWFheH555/HsWPHjC6vLWybQMX2ceXKFSxevBi9evWCr6+v2dfJnTt3xrVr1/DRRx8hJCTEaFqpVIru3bvjo48+snqr8frQqFEjnD9/HlOmTNG7fkQiEQYOHIiYmBj07t3brDJtaf01bdoU586dw9ChQw2madOmDbZv327WWLfagoODcenSJXz66ad47LHHEBgYaPRaspJYLMbmzZvx1ltv6bTe1NaxY0ccOXKkXnsasub39uyzz2LWrFlmHRs7duyITZs2Yf369SaPjUREDyKRIFg4UjoRERGZTaVS4erVq7h58yaSk5NRVFQEiUQCT09PTXdNdXVTUR+1Wo2zZ8/iypUryMzMhFQqhZ+fHzp37mzypq4tSElJwYkTJ5CWlob8/Hw4OjqiUaNGaN26NaKiosy64ZSTk4NDhw4hMTERCoUCAQEBaNq0KXr06GFWYLg+CYKACxcu4Ny5c8jOzoarqysCAwPRs2dPBAQENHT1jLp79y6OHDmC1NRUODo6Ijg4GH379tV0r1xXpk+fjh9++AFARZesld2uAsCtW7dw+vRppKamQiKRIDQ0FP3792+QrpcEQUBcXBxiY2ORlZWFoqIiODs7w9fXF82bN0dUVJTBmzKGKBQKHDp0CDdv3kRhYSECAgLQqlUrdOnSpY6WQr+ysjIcO3YMiYmJyMrKglgshpeXF5o1a4bo6GidMWvrkkqlwoULF3D16lVkZ2ejtLQUzs7OCAgIQIsWLdC6dWuLb/SUlpZi//79uH37NkpLSxEUFIQOHToYDN78UxQUFODw4cNISkpCXl4e3N3d0bhxY/Tp08dkQNmY8+fP4+LFi8jMzISXlxfCw8PRt29fqz1skpSUhFOnTiEjIwN5eXmwt7eHp6cnmjZtijZt2tT58SozMxPHjx/H/fv3kZubC6lUCg8PD0RERCAqKqpGx/lr167h1KlTSE9Ph5ubG0JCQvDoo4/W237H81btXLt2DSdOnEBGRgYcHR0RGBiILl26IDw8vNZlW/P4NWnSJM1N9+eee87g2J5VJSYm4siRI0hLS4O9vb1m+/T09LS4DgA0XVomJCQgIyMDJSUlcHBwgI+PD6KiotC+fXuzH4Sxlvz8fBw/fhzJycnIzs6GSCSCu7s7wsLC0KpVqxp1VW0L26Y1xMfH49y5c8jKytJcx3t7e6NZs2Zo06aNyQdAbcnatWs1XeSHhobqDNFR+VsjOTkZZWVlCAwMRJ8+fUwGtUyxlfV3584dHDlyBPfv34dUKtV0xdyqVat6mb8+RUVFOHjwIG7fvq1Z5x07drSJ6zNrfW/3799HXFwc7ty5g9zcXCiVSri4uCAkJAQdO3asdTf4RES2joFMIiIiInpoGAtkEhERUe0NGTIEu3btAgDMnz8fH374YQPXiKh+GQtkEhERkfXZVrMDIiIiIiIiIiKySYIgIDY2VvP/g9CjBxERERE92BjIJCIiIiIiIiIiowRBwGeffYbU1FQAFeP+9enTp4FrRUREREQPu/odLICIiIiIiIiIiB4YixYtwuHDhxEfH68JYgLA0KFDrTJ+JxERERGRMQxkEhERERERERGRXpcuXcLhw4d1pkVGRuKbb75poBoRERER0T8JA5lERERERERERGSQSCSCm5sbWrRogSeeeAJz5syBm5tbQ1eLiIiIiP4BRIIgCA1dCSIiIiIiIiIiIiIiIiIibeKGrgARERERERERERERERERUVUMZBIRERERERERERERERGRzWEgk4iIiIiIiIiIiIiIiIhsDgOZRERERERERERERERERGRzGMgkIiIiIiIiIiIiIiIiIpvDQCYRERERERERERERERER2RwGMomIiIiIiIiIiIiIiIjI5kgbugL04MvIyMCePXsAAGFhYXB0dGzgGhEREREREREREREREVF9KC0txZ07dwAAAwcOhJ+fn9XKZiCTam3Pnj2YMmVKQ1eDiIiIiIiIiIiIiIiIGtCPP/6IyZMnW608di1LRERERERERERERERERDaHLTKp1sLCwjTv165di6ioqIarDBFBqVQiLy8PAODh4QGplId6oobG/ZLItnCfJLIt3CeJbA/3SyLbwn2SyLZwn6zu2rVrmp47tWNG1sC1S7WmPSZmixYt0KlTpwasDREpFApkZ2cDALy9vSGTyRq4RkTE/ZLItnCfJLIt3CeJbA/3SyLbwn2SyLZwnzROO2ZkDexaloiIiIiIiIiIiIiIiIhsDgOZRERERERERERERERERGRzGMgkIiIiIiIiIiIiIiIiIpvDQCYRERERERERERERERER2RwGMomIiIiIiIiIiIiIiIjI5jCQSUREREREREREREREREQ2h4FMIiIiIiIiIiIiIiIiIrI5DGQSERERERERERERERERkc1hIJOIiIiIiIiIiIiIiIiIbA4DmURERERERERERERERERkcxjIJCIiIiIiIiIiIiIiIiKbI23oChAREREREREREREREdkiuVyOwsJCFBQUQC6XQ61WN3SVqIEJggClUgkAyMnJgUgkauAa1ZxYLIadnR3c3Nzg6uoKOzu7hq5SNQxkEhERERERERERERERVZGTk4P09PSGrgbZILH44ejwVK1Wo6ysDGVlZcjIyIC/vz+8vLwaulo6GMgkIiIiIiIiIiIiIiLSwiAmGfOwBDKrqtzmbSmYyUAmERERERERERERERHR/8jlcp0gpoODAzw9PeHi4gKJRPJAdyVKtadWq6FSqQAAEonkgQ1qCoIAlUqFoqIi5ObmoqysDEBFMNPFxcVmupl9MNcuERERERERERERERFRHSgsLNS8d3BwQGhoKDw8PCCVShnEpIeGSCSCVCqFh4cHQkND4eDgoPlMex9oaAxkEhERERERERERERER/U9BQYHmvaen5wPb4o7IXGKxGJ6enpr/tfeBhsa9j4iIiIiIiIiIiIiI6H/kcrnmvYuLSwPWhKj+aG/r2vtAQ2Mgk4iIiIiIiIiIiIiI6H/UarXmvUQiacCaENUf7W1dex9oaAxkEhERERERERERERER6cExMemfwla3dQYyiYiIiIiIiIiIiIiIiMjmMJBJRERERERERERERERERDaHgUwiIiIiIiIiIiIiIiIisjkMZBIRERERERERERERERGRzWEgk4iIiIiIiIiIiIiIiIhsDgOZRERERERERERERERERGRzGMgkIiIiIiIiIiIiIiIim7NlyxaIRCKrvGJjYxt6cagGGMgkIiIiIiIiIiIiIiIim3PmzBmrlOPo6IjWrVtbpSyqX9KGrgARERERERERERERERFRVWPHjkX37t31fiaXyzFhwgQAgLOzM9avX2+wHFdXV0gkkjqpI9UtBjKJiIiIiIiIiIiIiIjI5nTu3BmdO3fW+9mFCxc079u1a4eRI0fWU62oPrFrWSIiIiIiIiIiIiIiInqgXLx4UfO+Q4cODVgTqksMZBIREREREREREREREdEDRTuQ2b59+4arCNUpdi1LREREREREZIL87l3kbd4CdUkJnLp1hUvPnhA7Omo+FwQBZVeuoujwIagLCmDXtClcevWCLCCgAWtNRERERPTwiomJ0bxni8yHFwOZRERERERE9FATlEqoCguhLizU/BUUCsgCAiALDtYJSFZVnpCArG+/Q8H27YBaDQDI/ekniBwd4dKrF1z69Eb5zXgU7t0LRWpqtfz2kZFw7t0Lzt26wT4yElJ/f4hEojpZTnVZGUpOn4YiPb0iiBoYWCfzISIiIiJqaIIg4NKlSwAAqVSKqKioBq4R1RUGMomIiIiIiOihoEhPR8HOnSiPj4cyIxPKzEwoMzKgys0FBMFgPomvD+wah0Dq7weJiyvErq6QuLqg7Np1FO7dqzevUFqKwj17ULhnj9E6lcfHozw+HjnfrwYAiJ2dYde0CeybNIVz1y5weewxSFxc/i5XEFBy+gxyf/kFJWfPQpDLIZJKAakUIqkUYicn2DdpAvvmzeHQojnswsJQGhuHwgP7UXz8BITSUgCAyM4OgUuXwv3xYTVZlURERERENi0+Ph5FRUUAgFatWsHe3r6Ba0R1hYFMIiIiIiIiemAJcjkKDx1C/pbfUHT0qKbVpCVUmVkozcyqg9pVpy4uRtmlWJRdikX+b79BZG8Plz594DZ0KFS5Ocj56SfIE24ZLUN++3ZFgNUIQS5H6uuvQ5GSAu/Zs+qsFSgRERHRP93ibVdwNbWgoavRYFoFuWHR8Nb1Pl+Oj/nPwUAmERERERERPTDUpaUou3YdZVeuoOxyHIqOHoMqJ6ehq6VLJDLaAlSbUF5uVsvOmsr8/HMokpMRsOjdipadRERERGRVV1MLcDrRxq5H/wE4PuY/B3/FEBERERERkc1R5uai7OpVKO7dg/xeEuT37kF+9w7kiXcAlare6yMLDob37Flw7vEIio4cRuHevSg5c1ZTF7vQULgOHAjXgQNg36QJSs6eRdHRYyg6egSKu/fqvb7a8n79FYr79+ExZjSkPj6Q+PhA6usHiYtzg9aLiIiIiKimtFtk1lUgc//+/ejfvz+mTZuGtWvX1sk8LHXw4EH069cPU6ZMwbp16xq6OvWCgUwiIiIiIiJqcIIgQJ6YiKKDB1F44CBKL16sUTexACB2coIsJARSX19I/Xwr/np6QuzqBrGrCySuboBIBEVKChTJSZAnJUORlARVbi5URUVQFxZCkMsBAPbNmsHr6Rlwf/xxTYtGr4kT4TVxIpS5uVDcuwexqxvswsN0um916dMHLn36AHgLitTUirEyE26h/NYtlN9KQNmVq4BSaXQ5HNq2hWO7doBKBUGphKBUQpmRgfIbN6DMzNRNLJPBuUsXuPTpg7xfN6E8PkHn4+Jjx1B87Jhu+a1awWvm03AbPBgiiaRG65qIiIiIqCFUBjJFIlGddS175swZAEC3bt3qpPyauHDhAgAgOjq6gWtSfxjIJCIiIiIiogajSE1F3m9bUbBtG+R379aqLKfu3eAxegxcB/SH2MGhVmWp5XJAqYTYyclgGqmnJ6SenibLkgUFQRYU9L/AZgVlbi4K9+xFwY4dKDlzRtMVrUgmg9vQofCcNBGObdsaLFOZk4Pymzchv3cPEk9POHfrBomrKwDAfdRIJL/0EkpOnjJar7KrV5H62uvI+uJLeD/7LNwfHwaRTGYwvaqoCEJpKSQ+Phxzk4iIiOh/WgW5NXQVGlRDLH9qaioyMjIAAGFhYXB3d6+T+Tz99NMYNWoUGjduXCfl10RlILNTp04NXJP6w0AmERERERER1StBLkfhwUPI27y5opWgmeNJViVydIRDq1Zw7tYN7qNGwi442Gp1FNvZAXZ2ViuvKqmnJzwnjIfnhPFQpGeg+OgRCGo1XB97DFJvb9P5vbwg7dYNznqeDpe4uiLk229x/91FyP/9d5Nlye/cwf2FC5H5f/8H+2aRkHr7QOrtBYm7OxRp6ZDfvoXyhFtQ/u9mkVOXLgha9jFk/v4WLzcRERHRw2bR8NYNXYV/nPoaH9Pf3x/+NnbNe/HiRUgkkjprhWqLGMgkIiIiIiKiOqMqKEDxqVOQ37qF8lu3UX77FuSJdyCUlppfiEQCWXAj2DUOgV1YGByiWsMxKgp24eEPRZeoMn8/eIwda9UyRXZ2CPzPUji0bIHsVd9X74pWD+X9+1Dev28yXcmZM7gzdhyCV3wFxzZtrFFdIiIiIiKz1WZ8zPj4eCxbtgz79+9HSkoKZDIZAgIC0LVrV7zyyiuaLluTk5PRuHFjdO3aFadO/d3TSeX0zp07Y9euXfjkk0+wadMmpKSkICIiAosWLcL48eMBANu2bcPnn3+OCxcuwNHREaNGjcKyZcvg7FyzsepLSkpw48YNtGzZEmKxGEuXLsXPP/+M27dvw9/fH9OmTcM777wDyUPwG0kbA5lERERERERkdeW3E5G7/kfkbf3dsqAlALG7O1x694ZL3z5wbNMGssBAo12ekn4ikQhe06bBc/JkqHJzoczKgjIzC8rMTBTu24eiAwdqXLYyMxN3J09B0H+Wwm3oUCvWmoiIiIjIuJoGMvfv34/hw4ejtLQU0dHRiI6ORnFxMRISEvDTTz9h1KhRmkBmZavPqi0fK6f7+Piga9euUCgU6NatGzw9PXHu3DlMnDgRERER+Pnnn7FixQr07dsX/fr1w+7du/H111/Dzs4Oy5cvr9Fyx8TEQK1WIzg4GNHR0cjJyUHPnj3RuHFj7N27F4sXL4aLiwtef/31GpVvqxjIJCIiIiIiohpTl5RAVVAAdVERVIWFUGZmIm/LFhQfPmJRORIPD7g9MRyu/fvDqWNHiKT8uWotIokEUh8fSH18gBYV0zxGj0LZ9evI+vZbFO7aXaPufYXycqT86zWUXr4Cx7ZtAFSMmyl2doZT5+haj1NKRERERKSPdiDT3C5WBUHAzJkzIZVKcfr0aXTp0qVamSEhIZr/TQUyd+7ciXnz5uH999+H3f+GpJgyZQrWr1+PJ598EjKZDFeuXEGTJk0AAKdPn0a3bt3w66+/1jiQWTk+5u7du/Haa69h6dKlmnn//vvvGDVqFDZv3sxAJhEREREREf2zqeVyFGz7Czk/rUf51Wu1Ksu5Rw94jBsLl8ceqxiXkuqNQ4sWCP78c5TPvY28jZtQnpAAZXY2VNnZUObkACoVIJPBPiwUdhFNIPX1Rd7GjRAUCp1yclavrla22NkZ/m+/DY9RI+tpaYiIiIjon6CgoACJiYkAAF9fXzRq1MisfLdu3cLdu3cxcODAakFMoHrLTlOBzOHDh+ODDz7Q6cZ16NChWL9+PVJTU3Hu3DlNEBMAunbtCi8vL6SlpZlVX30qA7gjR47EJ598ovPZoEGDAAAZ/xvX/mHCQCYRERERERGZRZmbi7yNG5Hz009QZWZZnF/i6Qm7JhGwj2gC+6ZN4dLvUdgFB9dBTckS9hER8F+4QGeaoFZDXVQEsZOTTutYt2FDkTxnLlTZ2UbLVBcX4/7ChSg5fRoB774DsZNTndSdiIiIiP5ZYmJiIPyvNxFLupV1d3eHSCTCwYMH8cUXX2DGjBlwcXExOh+xWIy2bdtWmw4Ab7/9drU8BQUFAICxY8eiVatWOp+p1WoUFRXB29vb7DpXVdkic/HixdU+S09PBwAEBQXVuHxbxUAmERERERER6aXIyEBZbCxKL8WiNC4OpTExEMrKLCpDGhQIr0mT4T7iiYquTR9gRfIiyNVyeDl4GUyjUClQpiqDi8wFIpGoHmtnXSKxGBI3t2rTnTp0QPivm5D0wosov37dZDn5v/+O0thYNFr+ORyaNauLqhIRERHRP0hNx8f09fXF0qVL8fbbb+Oll17CG2+8gX79+mH06NF46qmn4KT14F1hYSFu376NZs2a6Z0eHBysd96XLl0CADzxxBPVPktISIBcLq8W4DSXXC7HlStXEB4eXi24CgCxsbEAgKioqBqVb8sYyCQiIiIiIiIdRceOI+OTT8wKVBni2LEjvKZOhWv/xx748S5v59/GytiV2Jm4EypBhaYeTdE/tD/6h/RHM89myC3PxaGkQ9h/bz9OpZ6CXC2Hn5MfejbqiZ6NeqJbYDe42rk29GJYjSwoCGE/rcf9dxehYPt2k+nlt2/jzrjxcOn3KByjouDQOgoOrVtB4vrwrBMiIiIiqh81GR+z0oIFCzBhwgRs2rQJO3fuxK5du7Bjxw4sWrQIR48eRXh4OICKgKQgCNXKr5zeqVMno3Xr3Llztc8qW3JaEnzVFhcXB4VCobds7XnXtHxb9mD/miQiIiIiIiKrUebmIuPDD5H/x5/mZRCL4TpoIJy7doPY1QUSV1eIXVwgCwyELDCwbitrgEqtQmZpJjJLMpFRmoHMkkxklmaioLwARYoiFMmLUKgohEKtQIBTAIJdg9HYtTGCXYPh5+QHV5krXOxc4CBxQHxePL6L/Q577uyBAEEzj4S8BCTkJeCbS9/Az9EPWWVZUAtqnXpklGTgt/jf8Fv8b5CIJGjt3RpNPZsiwj0CTTyaoIl7EwQ4BxhstRmfG4/z6echV8khFUshFUshE8vgJHNChHsEwtzDIBPLAACCIOBm7k0cSDqAI0lHkFGSgUdDHsW/Ov0LTrK66dJV7OyMRp9+Ar/5b0BdUAAIAgRBgFAuR8ayZSg5c0YnvVBejsKdu1C4c5dmmkOrVnAfMxpOgwfXSR2JiIiI6OGzdu1arF27tsb5w8PDMX/+fMyfPx93797F5MmTcezYMaxYsQLLli0DYHp8zI4dO1YrV61WIzY2Fl5eXggNDa32eW0DjZX5DQVvGcgkIiIiIiKih5YgCCj4azvSly6FKjfXZHqxkxM8xo2F55SpsAtuVA81NE4QBFzNvorf4n/Dzjs7USgvNCtfLGINfiYVSaEUlCbLyCjNMJlGJagQmxWL2Czd+TV2bYzBYYMxJHwIIj0joVArsP/efvxy7RdcyLhgtEypWIoI9wiEuoXiStYVpBan6ny+8cZGxGbG4qvHvoKvk6/JOtaUzM8P8PPTmRayZjWyVnyNrBUrAEEwkBMou3oVZVevQvTxMsj69IbdsGEQHn20zupKRERERKQtNDQU06ZNw7Fjx6BSqTTTTQUy9QUTb9y4gZKSEnTv3l3vvIzlNUfl+JjGApkSiQRt2rSpUfm2jIFMIiIiIiKifxhFegZKY2JQfuM6yq5dR9n161Dev280j6xxYzi2bQun6E5wGzZM7/iJ9UkQBCQXJeNw0mH8lvAb4nPjrVq+OUHM2koqTMLKuJVYGbcSTT2aokBegIwS04FRAFCqlbiZexM3c28aTHMt5xom7ZiEFY+tQFPPptaqtkkiiQS+c+fAqXM0UubNgyozy2h6obwc8j17Id+zF4qoKAS89SacHsInyYmIiIioYezevRsqlQoDBw6EVGvYi9TUVPz3v/8FoDuupalApr5Wj6ZaRMbExMDe3r7aGJnvvfceFi9ejGnTphltaVoZyNRXfk5ODu7du4dWrVrB0dHRYBkPKgYyiYiIiIiI/iGUWVnI+Oxz5G/darSlXCWHNm3g8/xzcGzfHlIvr3qooX7FimIkFSbhTsEd3Mi5gStZV3Al+woK5AX1Wo9GLo3QOaAzjqccR2ZpZrXPZWIZugd1RxOPJjh7/yyuZF/R6ZLWmIS8BGtXFwBwv/g+pu6cis8f/RxdA7vWyTwMce7WDRF//om8DRtQcvYsSi9fqeiG1ojyy5dx96mJcBs+HH6v/QuygIB6qi0RERERPaw2btyINWvWwMvLC9HR0fDx8UF6ejqOHj0KuVyOBQsWoG/fvgAApVKJK1euwN/fHwFa16KV0/38/NCoUSOdFpzA34FMfd3OpqWlIS0tDZ06ddIJpAIVXdICgEwmM1h/lUqF2NjYanWqOu+HsVtZgIFMIiIiIiKih56gUCBn/U/I+uorqIuKTKYXOTnB75WX4TlpEkQSST3U8G8KtQIX0y/icPJhxGXF4V7BPWSXZdeqTBFEcLVzhaudK1xkLnCxc4EIIqQUpSCtOM1ksDHULRTPtHkGwyKGQSaWQS2oEZsZi3139yGxIBGudq7oG9wXPRv1hIudiyZfTlkOTqSewOn7p3Er7xZu5d1CibKkVstiioe9B/LK83SmFSoK8ezeZxHiFgIfRx/4OPrA19EXXQK6oFdwL4hF4jqrj9TTEz7PPw88/zwEQYAiKQllV66g6NAhFOzaDaG8XG++gm3bULhvHzxGjYTYyQmCIABqASI7O7j06QOnjg/nTRoiIiIisr6nnnoKDg4OOHHiBM6fP4/8/Hx4eXlh8ODBmDNnDgYMGKBJe/36dZSVlaFPnz46ZRiaXslYMNFYt7KVn02dOtVg/a9fv47S0lL07t3b6Lxr2m2trRMJghmP4RIZcf78eURHRwMATp06ha5d6/cpXyLSpVAokJ1dcbPP29vb6NM8RFQ/uF8S2ZaHfZ8UVCoos7OhTE+HMj0ditT7yN24EfJbt8zK79y7FwIXLYKsUf2NfVmiKMGhpEM4lHwIx1KOmT3GpT6hbqEYHjEcTT2bws/RD75OvvB29IZMrP97lqvkSC1KRXJRMnLLclGsKEaRogiF8kIIgoC2vm3Rt3FfSMW1fw5YEASkFachIS8BJ1JPYPed3XpbdgJAr0a98FSLp9DWty1UggpKtRIKtQKZJZmaLmXjc+ORXJgMTwdP9GzUE4+GPIo2Pm3we8LvWHJyCVSCSm/Z2iLcI/BMm2cwJHyIVZbREqqCAuT/9RdyN/0K+fXrFuX1njULvi+/BJGUz2cT1YWH/VxJ9KDhPln/rl27pnnfsmXLBqwJ2SK1Wq1pkSmRSCAW1+zBQJVKBS8vL3Tp0gV79+61ZhVrrKbbvnac6Ny5c+jUqZPV6sQrfiIiIiIiooeAIAjI/fFHZK9cBWWm/uCYPtLAQDg0bw77li3g3K07nLp0hkgkqsOaVhAEAZezLmNL/BbsTNxZq5aKjlJHDAwdiNGRo9HBr4NF9beT2CHMPQxh7mE1nr+5RCIRAl0CEegSiF7BvfB69Ou4kHEBuxJ34XDyYQiCgEHhg/Bk8ycR4hait4xGLo3Q3q+90fmMjhyNAKcA/Ovwv1CsKDaa9nb+bbx57E2siFmBGVEz0NKrJbwdveHt6A17iT3yyvJwO/82buXfwu282yhVlmJI+BCrdFMrcXOD18SJcB03Dmn79qP0yy+hMjPgnr1yJUpjY9Ho008g9fGpdV2IiIiIiOrbuXPnUFBQgCVLljR0VWwaA5lEREREREQPOEEux/13FyH/99/NSi92c4PvnDlwf2I4JB4edVq3Skq1EilFKbiddxvxefHYdWcX4nPja1RWsEswWnm3Qmuf1mjl3QptfdrCSeZk5RrXPYlYgs4BndE5oDPewTtWLbtHox5YN2QdXtj3AtJL0k2mTy5Kxr9P/VtnmoPEAWWqsmppt8RvwXPtnsPz7Z63Wre0sg7tIf3uW8gOHUbOF19AlZdnMk/J6dNIHDUajZZ/DicrPvFNRERERFQfunbtCnaaahoDmURERERERA8wVUEBkl96GSWnTplOLBLBY+xY+L76CqReXnVet/TidGy8sRGHkg/hTv4dKNQKi/KHuYWhtU9rhLqGorFbY4S4hiDULRTu9u51VOOHSzPPZvhz5J/YnrgdN3JuIKs0C5mlmcgqyUJqcarJ/PqCmJW+ufQNEvMT8e9H/g1HqaNV6iuSSOA+fhw8Hx+GrG++RcGuXVBlZQEiESAWAyoVBIXuNqTMzMTdqdPgNnQo3AYNhHPPnhA7OFilPkRERERE1PAYyCQiIiIiInpAyZNTkPTcs5AnmOiOUyKBc/fu8H35ZTi2iarzel3Ouowfr/6IPXf2QCkozc4nFonR0a8j+jbuiz7Bfeqlu9eHnZPMCeOajas2/Wr2VayKW4W9d2s+Fs/uO7uRXJiM/+v3f/Bz8qtNNXVI3N3hP/8N+M9/Q2e6uqwMaf/+N/K3/KabQaVCwbZtKNi2DSInJ7j06Q33ESPg0qdPvXSTTEREREREdYeBTCIiIiIiogeIMjMTxSdPovj4CRQeOgR1fn61NM6PPAKPCeMh8/eHNCAAUm9viKTW/fmnUqtwJPkILmZcRL48H0XyIhQpipBZmmlxl7Hh7uEYEzkGj0c8Dm9Hb6vWk/Rr5d0Kn/X9DAm5CVh1eRV2Ju6EWlBbXM6V7CsY9cco+Dv7a6Y5Sh3RN7gvJreabLXWmgAgdnBA0AcfwKlDB6Qt+TcEubxaGqGkBIU7d6Fw5y449+gB/3fehn14uNXqQERERERE9YuBTCIiIiIiIhsnv3MH+X9tR+HevSi/ccNoWo/x4xHw7jtWD1xWKlGUYGvCVqy/uh7JRck1Lsfd3h19g/tiTLMxaO/bni3nGkhTz6b4sNeHWNhlIRLzE5FTloPssmxkl2YjvzwfbvZuaOrRFBHuEfBx9MG7x9/FgaQDOmUUyAtQIC/QmRabGYsdiTvwSZ9P0MSjiVXr7DF2LBxatULyy69AkZRkMF3xiRNIfGIEvJ6ZCZ9nn9V0OSsIAqBQQGRnZ9V6ERERERGR9TGQSUREREREZIMUGRko3LkT+dv+Qtnly2bl8Xv9NXjNnGn1oGCZsgzXcq7hcNJh/Hrz12pBK1M87D0wKGyQJiAW4REBbwdvBi9tiLu9O9r7tTeZ7vNHP8cXF7/AqrhVJtMm5CXgyb+exFvd3sLIpiNrX0ktDq1aIeL3rcjbsgUFO3eh9OJFvekEhQLZX3+D/C2/QeLuDlV+PlR5eRDkcsiCgxG4ZDGce/Swat2IiIiIiMh6GMgkIiIiIiKyIWU3byJ71SoUbN8BqFRm5RHZ2SHow//AbehQq9RBLahxJPkIjqUcQ2xmLOJz4y0a67JSU4+mmNxyMoZFDIOD1MEqdWswWQnAxXWAvBgI7w007Q/YOf/9uSAAqReB+D1AaR7g16IijXtwg1W5LohFYrzc8WVEuEdg0YlFUKgVRtOXqcrwzvF3cDbtLGa2mYkwtzCIRWLr1MXZGV5Tp8Jr6lQo0tNRuHcfCrZv1xvUVGZkQJmRoTNNkZyMe7OfRdAH78N9xAir1ImIiIiIiKyLgUwiIiIiIiIbUHLxIrK/W4migwfNyyCRwLFdOzj36AGP0aMgCwqySj0S8xOx+ORinE8/b3YeH0cfuMhcKl52LghwDsCQsCHoHtTdNlpdqhRAWX7Fq7wAKCuomObeCPAMA2RGxnHMuAYc+QS48htQOYbk2VWA1KEiUNm0P5B5Hbj2F1Cgp6td35ZAZH8gvA/g1xJwawTU1TpRlAKJR4HC1DoPog5vMhyRnpH45fovyC//e5xWuUqOYynHIEDQSf/nrT/x560/4SxzRguvFmjt3RpdArrgkUaPWKU+Mn9/eE2eBM9JE1G4dy/Sl/4HyrQ00xmVSqTOXwBlVha8nn7aNrZXIiIiIiLSYCCTiIiIiIioASlzcpC6YAGKjxw1mVbi7Q3XgQPg0qsXnLp0gcTFxWr1UKgUWHNlDb699C3karnJ9DKxDI9HPI6praaiqWdTq9WjVgpSgStbgYyrQGHa/173gZJs4/lcAysCmq6BgIMbYO9W8TctDrj6J1AlKAcAUJYB1/+qeBmTea3ideKLiv/tXAHf5oBvi4qWnc2HVMyrkiAAiUcqgqV3jwNKOSCRAmIZIJEBdi4V+f1bV7y8mwIp54HrO4BbBwBlaUU5EntgxFdA23Hmrj2LtfBqgcU9FlebfjL1JBYcXYCcspxqnxUrinE+/TzOp5/Huqvr4Ovoi8fDH0df774Icqp9MF4kEsFt4EC4PPIIMlesQM4P6wCl6dbEGcs+gTIjE37z34BIbJ0Wo0REREREVHsMZBIRERERETWQ8oQEJD33PBTJelry/Y/Y2RmuAwbA7fHH4dytK0RS6/2MU6gUuJV/C9dzrmPd1XWIz403mcfT3hNjm43FxJYT4ePoY7W61JhSDtzcBVxcDyTs/bvVpCUK71e86oO8EEg5V/GKWV/RsjNyIBA1BijJAs6srGjhaUzWDeDan8bTqMqB354B8u4CvV6ru1agenQP6o7NwzdjwdEFOJN2xmjazNJMrLm6BmuwBu292mNm25noG9q31nUQOzvDf948uI8YgbyNm1AeHw+xqysk7u6QuLmhYNeuai02c374AcrMDAR+8AHEjkZa6RIRERERUb1hIJOIiIiIiKgBFB07jpRXXoG6qEjv57KQEHjPnAn3EU9A7GC98SXvFtzFj1d/xIWMC0jMSzQ59mUzz2Zo59sObX3boq1PW4S5W2+MwxqRF1e0lEyNqRiTMmFfRQDQpoigtxWnPsqyiqCkqcBkTR34d0Uwc9hnFS0664mvky++G/AdVsWtwurLq1GiLDGZJyYnBnMPzcUjjR7BG53fQIR7RK3r4dCsGQLeebvadK9pU3Fv1izIE27pTC/YsRPl8fFotHw57Js0qfX8iYiIiIiodhjIJCIiIiIiqme5v/yCtPc/AFSqap/Zt2gB71nPwG3QIKu2vixWFOO72O+w7uo6KNWmu9r0c/LDW13fQr+Qflarg0VKcoD7l4CcW0BOIpBzG8hOqHjVpNVlbXmGVbRsbNIPiN8LXNsGJB4GKtelT3Og5eNAy+EV7+8er0iXsK9iGRrShXVAXhLQYTLgGgC4+Ff8tXet09lKxBI82+5ZTGk1BddzruNK9hVcyb6Cq9lXkZifaDDf8ZTjGJM6Bk+2eBKz2s6Ck9QJAgQIggCpWAo7iV2t6yYLDETY+vVIenEOSs/rjgdbHp+AxLHjEPjeIriPGFHreRERERERUc0xkElERERERFRPym7cQNZXK1C4Z0/1DyUS+C9cCM9JEyGyYjeggiDgr9t/4fPznyOzNNOsPBOaT8ArHV+Bi531xuA0S1Y8cGNnxSvpVM0DlnYugGd4RbCu8uXkozv+JUQVLRVz7/z9KskGygqA8gJA9b9xQv1aA4+8BESNrRirEgCiZ1S8SnMr8jm4A15VWg9GDqh4AUB+MpBx/X/jZV6veH8/5u8gqCGNooHgzhXp1ApApagY9zP9ClCk2y0qxLKKMTebDQLOr60YJ1Tb7YMVL22B7YAeLwGtRwFiiRkrtmacZE7o6N8RHf07aqbdLbiL3+J/wx8JfyC7rPoYpkpBifXX1mP9tfXVPuse2B1LHlmCAOeAWtVL4uGBkO9XIeX111G0b7/OZ0JpKVLnL0DRkaNwiIqCSCaDSCqF2MkRTp06QdaoUa3mTURERERE5mEgk4iIiIiIqI6Vxl1G1jffoGj/fr2fi11c0Ojzz+HSq2et5lOsKMb229txLeca0orTkF6SjrTiNBTKC03mFYvEaO/bHq90egUd/DrUqh4WyUsCYn4C4n6taG1ZG+F9gI5TgRbDAJk5Yxz2MvyRoqwigGhvJJjr6FnxMsU9uOIV2f/vacXZFd3JXt4C3DkGTVe0EruK8TK7zAIadTJcZnE2kHGlorWqk3dFENPBreKzdk8Cm6YCtw8Zr9f9S8CWmcDBpUCvfwFtJxjvfrY0D1CUAK6BtR5zM9QtFK92ehVzOszBgTsHsDZuLS7nXTYr78n7JzF+23h82PtD9AjqUat6iB0cEPzf/yLrqxXI+vprQNDtErhg+3YUbN+uM01kb49Gn30K18ceq9W8iYiIiIjINAYyiYiIiIiI6ogiPR1pi95D0aFDBtPIGjVC42++hn1kZK3mdSPnBl7Y/wIySjLMSu/n5Id+jfuhuVdztPBqgSYeTeAoNSf4ZwVKOXBjB3DxRyBhP8weT7IqmTMQ2LYigNl+IuAZar06yqw3Lqlezt5/t+wsuA8k7K1ogdp8GODia17+8N4Vr6oc3IFJm4FtL1cEiU3JuQX88SJw4APAvxXg7FdRB0cvoCClohVp5s2/W4GG9ADGrALca98qUSaWoV/jfmjr2BZH049iVcIq3C++bzJfbnkuntv7HF5o/wJmt51dq3FbRRIJfF+aC8dOHZE67w2ocnKMphfKy5Hy6r8Qsvp7OEVH13i+RERERERkGgOZREREREREdaDo2HGkzpsHVW6uwTSOnToh+Iv/g9TLq1bzOp5yHP869C+UKEtMppWJZZjeejqeafMMnGROtZqvWUrzgMQj/wuG3QCybgBZCYCy1PwyxFLAI7Si+1bvpkBQeyCoQ8X7OuwStd64BVa0JLUmiQwY8RUQ0BY4/l+gMNV0nsJU89LdOwGs7Ac8+TMQbKTVqAVEIhF6B/TGkOZD8PPNn/H95e9RamIbESDgq5ivcCnzEhZ2WYgQt5Ba1cHlkUcQvnUrUl97DSXnzhmft1yOpBdeROj6H+HQrFmt5ktERERERIYxkElERERERGRFgkqFrK++QtbX31TrprKSXVgYvJ99Fu7DH4dIWrufZZtvbsb7p96HSlCZTPto40cxL3oeGrs1rtU8zZJ5Ezj9NRDzi2VBSwBw8AAiB1aM99ioI+Ae8vf4lGQ+kQjo9hzQZTZQmlMxvmZRGlCYDlzfDtzYbroMQ4rSgLVDgZErKrrCtRIHqQOebfcsxjQbgyPJR5BTVtE6UiwSo0RRgnVX11ULcB5LOYZhW4ehuWdzDAgdgAGhAxDhEaGveJNk/n4IWbsG2atWIXfDRijT0gymVRcUIGnWbIRt+AWywMAazY+IiIiIiIzjL0EiIiIiIiIrUaSmIvXNt1By6pTez+0jm8L7uefgNngwRJLatSRMKUrBL9d+wQ9Xf6j2mVQkRYRHBPyd/OHv7A9/J3/0COqBtr5tazVPvcqLgLI8oLwQKCuoCHBd+LGiq1RLOHoBbccDLYcDjbsxcGlNYjHg7FPxQlTFtA6TgLTLwNFPgStbUaPufZVlwOanK8ba1B7P084ZCH3EzHFK9fNx9MHoyNHVpg8JH4JXD72KxPzEap/dyL2BG7k38GXMl2jt3RpvdH4DHf07WjxvkVQKn+eeg/ezz0KQyyEoFBAUCpTHxyPpuechlPzd8lmZno57z8xC2E/rIfHwsHheRERERERkHH8ZEhERERER1YKqoACFe/Ygf9tfKDlzRm8rTJGTEwLefhvuI0dAJK7ZWH6lylKcTD2JE6kncOr+KdwtuKs3navMFcsfXY4ugV1qNB+zKMuBuF+B098AaXG1Kyvi0YpuVVsMA6T21qkfmScgChi3Bnj0TeD8WiDjGlCcARRlAsWZgKACJHYVXfj6NgdcAoBzqwFVuW45x/9bvWw7V2DoxxVjl1pRE48m+GXYL3jvxHvYdWeXwXRXsq9g2q5pGNl0JF7t9Cq8HCzvvlkkEkFkbw/YV2yX0i5dEPzf/yLp+ecBpVKTTn7rFpKefQ6NV34HiZub5QtFREREREQGMZBJRERERERUA+W3E5H15Rco3LcfglxuMJ19ZFM0Wr4c9k2aWDwPhVqBU6mnsCNxB/bf229yzMAg5yCs6L8CTTwsn5dZSnIqAllnvgOK0i3P7+RTERDzaQb4tazoOtYzzOrVJAv5RAKDPtCdplYD5QWAnYtu69io0cCGiRWBTmPkhcDvzwOJR4Fhn1S00rQSZ5kzPu79MaL9o/FVzFfILTc8Du3vCb/jwL0DeKH9C/B38kdeeR7yyvNQrChGuHs4hoYPhVRs/q0Rl149EfTB+0idv0BneumlS7g7dRpCVn4Hqa9vjZeNiIiIiHRt2bIFY8eOtUpZly5dQtu2ddBLDdUpBjKJiIiIiIgsoCoqRtbXK5Cz7kdAoTCa1n3ECAQsehdiJyeL5nG34C5+uvYTdiXuMhqk0dbauzW+fOxL+Dj6WDQvo4oygZRzQPK5ir/3Tls+3qV744oxGts9Cbj4Wa9uDaCgTAG5Ug0fF8MtR8uVKpQp1HBzkEIkEtVj7axMLAYcPapPb9wFmHUA+OUpIP2y6XIu/QyknAfGrQX8W1mteiKRCBNaTMDoZqNxNu0s9t3dh/339mvG1NRWIC/Ah2c+1FvOn7f+xPK+y+Fi52L2vN1HjIAyKwsZyz7RmV5+/TruTJqMkNXfwy442LIFIiIiIiK9zpw5Y5VyHB0d0bp1a6uURfWLgcx6lJ+fj927d+PgwYO4cOECEhISUFBQABcXF4SEhOCRRx7BjBkz0LlzZ7PL3LVrF9asWYNTp04hPT0dbm5uiIyMxNixYzF79mw4O1vvqVciIiIion8yQRBQ8OefSP/kE6gys4ymlbi7w++NeXAfPdqiYNa17Gv4/vL32Ht3L9SC2qw8dmI7jIochX91+hecZJYFTA26fQjY+27F2Ic11bgb0O15oMXjD/x4lwkZhfjyQAK2xd6HSi2gmb8LBrcOwKCoALQKdEN2sRz7rqZj95U0HE/Ihlylhr+bPfo080Xf5n54pKkP3B1lDb0Y1uMRAjy9G/hzzv/G1zQh6wawsh/QfAgQ1AEIag8EtgMc3GtdFZlYhh5BPdAjqAfe6voWTqedxmfnPsON3Btm5T99/zSe3v00VvRfYdFDAF5PPw1lTg5yvl+tM11x7x7uTpyExqtWwqFZM4uWhYiIiIiqGzt2LLp37673M7lcjgkTJgAAnJ2dsX79eoPluLq6QiKR1EkdqW6JBEHPAC5kdR9//DHeffddlJeXm0w7efJkfPvtt3Ay8tR2eXk5pk+fjg0bNhhM06RJE/z222913lT6/PnziI6OBgCcOnUKXbt2rdP5EZFxCoUC2dnZAABvb2/IZA/RTTOiBxT3SyLbUpN9UpGaitQ35qPk3DmDaUQyGVz69oHb48Ph0rcPxPbmj/cYmxmLFTErcDz1uFnpm3s2R4+gHugW1A0d/TrCQepg9ryMKskB9rwNxPxkXnqRGGg1AgjvAzi4AfZugL0r4B5c8WoAKrWA9IKy/73KkVFYhoyCcuSXKlBYpkBhmRKFZUrIVWoEeTigsZcTQr2cEeLlhAB3e7g6yODqIIWjTILraRUBzB2X7+sb+hQA4Odqj6yicqiN/LKWiEWIauSO5v4uiPRzRVN/F0T6uaCRh6PBQPf1tAKcScyBXKmGVCyCVCKGTCKCs70UTf1cEOHjAjtpxXirgiDg6v0C7L2ajgPXM5CWX4aBrf2xcEhLONvXcRC5IBUo1Wo1rCwD9i4C7hw1nTewHdBhChQtRyG7uGLMSWucJ5VqJTZc34AvY75EsaLYrDyNXBrh2wHfItQt1Oz5CIKArK+/Rtb/fVHtM7GLC1wf6wenbt3h3L0bZAEBZpdLZAt4/UpkW7hP1r9r165p3rds2bIBa0LGXLhwAZ06dQIA9OjRA8ePm/d7qrbUajVUKhUAQCKRQCwW18t860NNt33tONG5c+c034s1PNiPxT5Abt68qQliRkREoH///mjfvj18fHyQm5uL/fv3Y8uWLVCpVFi/fj0yMjKwc+dOgzvAtGnTsHHjRgAVJ6/Zs2ejTZs2yMrKwvr163HmzBncunULgwcPxunTp9G4ceN6W1YiIiIioodJwZ49uP/2O1AXFOj9XBoYCJ9nn4XbkMGQuFvWwkwQBKy7ug6fnvsUAow/Y9rWpy2GRgzFwNCB8HWy8hh8ggBc+Q3YOd/02IcAIHMGOk4Fuj1nE2NcCoKA2OR8bDqXhG2XUlFQpjQrX0yS4c8kYhFUxqKT/5NRaPphVZVawKWkPFxKytOZHuLlhOHtAjG8XRBaBLhBoVJj1+U0rDt5B2fvGO9SWCYRoYmvC8J9nBGbnI+UPN0uf9efuoeL9/Kwenpn+LtZKdCtj1tQxUvb1D+Awx8Dhz8CjG3X9y8B9y9BuudtuEcMQknL8YDXwFpXSSqWYnKryRgYNhDLzi7Drju7TOZJKUrBlB1TsKL/CkT5RJk1H5FIBN8XXoDEwwPp/34f2tFudVER8v/4E/l//AkAsIuIgMe4cfCcNBFiO7uaLRgRERER6bh48aLmfYcOHRqwJlSX2CKznsyaNQv379/HvHnz0KdPH71pjh49iqFDh6KoqAgAsHr1asyYMaNauj/++AMjR44EAISEhODo0aMICQnRfK5Wq/HMM89gzZo1ACqaXv/6669WXqK/sUUmkW3hU3pEtof7JZFtMXefVJeVIf3DD5G3YaPez0V2dvB+Zia8Z82C2NHR8nqoFVh6eik239xsMI2/kz/GNhuLYeHD0NjNig8nFqYBSWcqxjhMuwykxQH594zn8QgBGkUDoT2ANmMBR0/r1acGBEHAvZwS7LuWgV/PJeF6WmGD1qe2Iv1cUFCmQHqB6cCoJYLcHbBmRhc0D3C1arlmuX0Y2PIMUJxhdhZ1UEeIh3wMNDZ/yBVTskuzcSvvFlzsXOBh7wF3e3f8cOUHfH3p62ppHSQOeLvb2xjRdIRF88j/aztSFywAlMaD6HahofBbuACufftaVD5RfeP1K5Ft4T5Z/9gi88EwZ84cfPXVVwCAlStX4plnnqmX+bJFZnV12SKTgcx6kpubC09P0z/0v/zyS8ydOxcA0Lt3bxw+fLhamg4dOiAmJgYAsH37dgwdOrRamtLSUrRo0QL37lXcjIiLi0NUlHlPlVqKgUwi28KLWyLbw/2SyLaYs0+W305Eyssvozw+Xm8ZLo89Bv8F82FXw55P8svz8dqh13A67bTez8Pdw/F01NMYFj4MMokVjxlFGcC+xcClnwFzxuAM6gD0eh1o3AVw8bNePSxUWKbA3ewSJGYV4+r9AsQl5yMuJR/5pYp6rUewpyN6NPHG4ZuZegOOdhIxekX6INLfFSdvZyM2Oc9gl7T1ydVeim+mdMIjTc0fA9JqirOBc98Dd44B92OAsnzz8rWdAPR/r3prTyvadGMTPjj9gd7xaEc2HYk3u74JR6n5DykUHT2K5JdfgVBSYjKtc+9e8F+wEPYR4RbVmai+8PqVyLZwn6x/DGQ+GHr27KnpTtbawTNjGMisjl3LPgTMCWICwLhx4zSBzLi4uGqfx8fHa4KYkZGReoOYAODo6IhZs2bhnXfeAQBs2rSpzgKZREREREQPE3lSEu5OnQpVVla1z8QuLghcshhuBq7DzXEj5wZeP/w67hTcqfZZiGsIXu30KvqF9INYZMUfw0o5cObbiu4+y/V3katD5gT0exvo+hwgllivHmZQqNQ4m5iD/dczEJOUh7vZxcgqkteqTJEIcHesGPvS1b7ir0gEJOWU4n5+qdHxLQEg3McZLz7aFCPaB0EmEUOtFhCTnIddl9NwK6MIbo4yPNbSD32b+8FFa0zK7KJyHEvIwvGELNxML0JCRhGKys3r9ramvJztkFOsu74Ky5WYuvoMQr2d4OtiDz83B/i52qN7hDf6tfCDWKx/nE6rcPYG+rxR8RIEIDcRSL0I3NwDXP29YkxNfWI3Ate2Ae2eAuycAQgV+SV2QLNBQEi3WldtfPPx8Hb0xhuH34BcrbvOfk/4HZezLuPTPp8iwiPCrPJcevVCk107kbd5M0pOnETJpUuAQn+wvfjIUdw+eQq+LzwP79mzIZLU735GRERE9KATBAGXLl0CAEilUsY/HmIMZNoYV9e/u/spLS2t9vnu3bs17wcNGmS0rMGDB2sCmbt27cKSJUusVEsiIiIiooeTMisL92Y+ozeI6diuHYI+/QR2wcE1Kvtq9lV8F/sd9t/br/fzLgFd8Fnfz+Bub9k4m9WoVRUtLwtSgYIUID8ZOLcayNbfurSaJo8Bj39Wr2NfFpcrse9aOvZdy8ChGxkoNHOMS30ifJwxumMjNPN3hb+bA/zdHODjYgepRH9gWK5UIyWvFPdySpBbLEdhuRKFZQoUlimhFgR0aOyJAa38IdEK9onFInQM8UTHEOMPrHq72GNE+0YY0b4RgIqbLffzy3AzvRBH47PwV2yq3padIhHwaHM/TO0eig4hnlCpBShVashVamQUluP6/UJcTyvA9bRC3MsugZezHfo298WAVv5oF+yBzeeT8ebWOCi1IrQqtYDbmcW4nVmsmfb9sUQ09XPBC32b4Il2QQbXkdWIRIBXRMUragww5EMgbjOEc2shyrhcPb2ipKI1Z1XHPgMeeQXo9w4gqd1tjcdCHsPKgSvx6qFXkVOWo/NZQl4CRv05Ck5SJ8jEMsjEMjjKHNE9sDte7fQqnGRO1cqT+fnB94UXgBdegLq4GCXnz6Ng927kb/0dUFdp+alQIPO//4fiEycR9PFHkAUG1mpZiIiIqIHsXFAxVMM/VUCbiuu6ehYfH68Zpq9Vq1awt7ev9zpQ/WAg08Zcvvz3j7fQ0FCjn5tqmtu+fXtIJBKoVCpcvXoVgiBAJKrDJ22JiIiIiB5gqqJiJM1+Fop7VcaKFIngPWsWfOfOgagG3XjFZcbhm9hvcCT5iME0oyNH4+2ub9euG1lBAE59DRxfDhSlm5/PNaji5kNAFBDeBwjvXRFwqmOCIOBiUh42nU3CtkupKJaralyWk50Ew9oEYkLnxugU6mnR7x47qRjhPs4I93Gu8fzNJRKJEOThiCAPR/Rt7oe3hrbE2Ts52BabigPXMqAWgMfbBmJK91CEeuuvT7Cnk8kA6vjOjRHo4YAX1l9AoYkWoAkZRfjXpktYvi8es3pHICrIDT4u9vB1tYeDTIKcYjkSMooQn1GIhIwilMpVGN4uyDrd1Dp6Al1mQdl+GgrjtsP1xH8gy75hXt7jy4GU88DY1bXu9rijf0f8OvxXzD8yH+fSz+l8phbUKFIU/T2hFLhbcBf3Cu/hy35fGt1nxc7OcOndGy69e8NryhSkv/8BSs6dq5au5OxZ3B45CoHv/xtuAwbUalmIiIioAaTFAXePNXQt/nEuXryoed++ffuGqwjVOQYybcx3332neT9s2LBqn9+8eVPzPiwszGhZUqkUjRo1wr1791BcXIyUlBQE1+Dp8fPnzxv9XLvPZKVSCYWBrnOIqH4oFAoolUrNeyJqeNwviWyLvn1SUCiQOmcOyq5e1U0sEsH/44/hOngQlBUZzJ6PUq3EN3HfYPWV1QbTiCDCKx1eweQWkwE1oFDX8BihkkOy/VWI4zaalVxwcIe693yoW48FnLyqVLxuuj5VqtRIyi3Frcxi3Egvwo64NNzMKDKdUY8QL0dEBbkhqpEbooLc0C7YHU52FT9vlXVU/7rSsbEbOjZ2w6JhLXSm1/Z80S3MA7880xmz1l/E/XwD3bdquZdTgnd+120VaS8Vo1xZffzIDWeTMKdvBOY+2sQq3dIqlEqU+HdGyYhf4Ze8E7KjH0FUmmM6452jEL7pCdXo7yE0rl1Xs54yT6x4dAW+i/sO31/5HgKM9zd8IvUE3jr6Fv7d499mdQMtadIEgau/R9HuPcj+9FMo09J0Plfn5yNl7ksoHDcOPvNeh9jR/LE5ieoCr1+JbAv3yfonaA10rq7aq0IVIgj4JzcfEiBAMLGO6kLVQKap78maBEHQbCOCINTrvOua9rZvyfGmLn+HMZBpQ06cOIE1a9YAABwcHPDqq69WS5OXl6d57+Nj+glYb29v3PvfE+V5eXk1CmRWDtBqjvz8fM3A00TUMJRKJQoLCzX/S6U81BM1NO6XRLal6j4pUatR/NFHUJw6VS2t48svQd452uJr3OzybCy9tBSxubEG0wQ5BuGFli+gq29X5OSYEbQxQFReAI89cyFLqV7/qgSIUNpyPAq7vALB0QsoFYDSurt+zyiUY0tsJo7dzsPd3HKdrk7NEeppj5b+zmjsYY9gDwcEe9ijsYc93Bx0j6OlhfmoPjAH+ciAnye3wO7rOYjPLEV2iQLZxQpkFStwv8D0uKP6gpiVvjx0G1dTcvDuwHA4yGrXJa3OPhnyOGRPPgqXC9/A4dZOiEuzAYggiMQQCSqIVLr1FhWlQ/LjCJQ1HYqyiEEob9wLkDrUuC4TgiegiUMTfBT3EfLkeUbT7ry7E05wwnMtnjN/Bp2j4bL6e5T89/8g1xo6plLBr7+i6OwZOL/zDqRNmlhYeyLr4fUrkW3hPln/lEolxGIxxGIxVCrjPYdIhH94IFMQTK6juqAdyGzbtm2d1OHAgQMYPHgwpkyZgu+//3vIg6rL/LD1hKlWq6FWqy36Hawdu7I2HvFsRFpaGsaPH6+J3P/73//WG3Ss7PMZqAh2muKo9RSn9smOiIiIiOgfT62G4sABFK9eA3WV1lEA4DBlChxGjrS42JjsGCyNXYpcea7ez0OcQzAxYiL6BvSFRCyxuHxt4sIUeO6YDVlugtF0glgGeaPuKOzyCpS+rWs1T3NcSSvGxovp2B+fC5UFDydLREC7Ri7oGeGBnuHuCPGseUCKKjjKJBjZxrfa9BsZJfjh7H0cjM8z0fbQsAPxeUjNv4GPn2gCPxe72lVUi2DvjsLu81HYfb7uB8oyuB1bAqfrW3QmiwQVHOO3wTF+G9RSJ5SH9EZZ85EoD+lbo26So32isbrnahxNO4qUkhQoBSUUagWSipMQkxOjk3bL3S3wtvfGuPBxZpcvcnKC88IFkHWORvFnnwMlJTqfq+/cReFzz8Px+edgP2rUQ3djjIiI6GEj+LfBw9Mez3KCf5sGmW9MTAyAiiBiu3bt6mQeZ8+eBQB07dq1Tson8zCQaQOKi4sxYsQIpKSkAKjoUva1115r4Fr97ZyeMTy0Xbt2DVOmTAEAuLu7w9vbuz6qRUQGaDf59/LygqwGY3kRkXVxvySyLQqFAsqLF1G+ciXKbsbrTeM2ZjR8571udgBBLahxKfMS/kr8C3/c/gNqofqtjHC3cDzf9nn0a9zPrK4oDSrOhCjxMMSJhyCK3w1RafWAqTqiH9QdpgFugRBcgwBnX4jFErjXfK7VKFVqHLqZhXN3c5FfqkRRuRKFZUpkFpZb3GVsU19njOvUCCPaB8Hb2XoBMTKsh7c3erRsjPiMInx7JBF/xaVBZWGLWQC4nlGCyeuvIcDt76Czo50E/Zr7YkaPUDjamQ7WW3SeHPMtlDG9Idk9HyJl9S5zxcoSON7eBcfbu6AO7wPVoI8A76aWLRQAb3gjLCBMZ5pSrcS8o/NwOOWwzvTvbn6HYO9gPB7+uGUzmTABih6PIG3BfJTHxul+plCg9P++gOjSJfgvWQKJl5f+MojqCK9fiWwL98n6p91jikRi4npmyId1XBvbJgJQu8czLZeamoqMjAwAFUPwedXRtdLMmTMxevRoNG7cWGc70O5+VSKRPHQPnlW2RrYk1uPh4VFn9WEgs4GVlZXhiSeewJkzZwAAjzzyCDZu3Ghww3dxcdHJa0pp6d8dLLm6utaojp06dTI7rVQq5YmUyAZUdjEik8m4TxLZCO6XRLZBVVSE7LffQemuXQbTuPTrh6DFiyEyo8uu2/m38detv7D99nakFqcaTDcmcgwWdFkAh5p2eZl9C7i8Bbj2J5AWZzxtpxkQD/0EYknd/NwrLldi07kkrD6eiKScmnfo6ukkw4BW/pjQOQQdQzweuh//D4pWjTzx36c8sWSEAreyipBdJEdWUTmyCsuRW6KAh5MMzfxd0NTPBb6uDnhj8yXsvpKuU0ZBmRIFZbrB60vJ+fgrLg1fTeqIZv6mf4tadJ7sPB0I7gBsmgrk3jGYTJx4GOKVvYFHXgZ6vQbI/tdjkSAAKgUgtSxoLoMMy/ouw+w9sxGTGaPz2Xun3gNEwKjIUZaVGRGO8J9+QuZXXyH72+8q6qal5PARJI0dh6CPP4Jz9+4WlU1UW7x+JbIt3Cfrl/a1qVhcu270yfpiY/8ewqNDhw519h0FBgYiMDCw2nS1Wq3ZRkQi0UO1jWhv+5Yca+qyy2sGMhuQXC7H6NGjceDAAQBAly5dsGPHDjg7OxvMox3VzsrKMjkP7T6M6zIiTkRERERk68pu3EDKSy9Dfveu/gQSCTwnTIDf/DdMBjGzSrOw/Pxy/HHrD6PpHCQOeLvb2xjRdITlFS5MAy7/BsT9CqReMC9P/8UVQRsrBwVL5SrEpeTjwPUM/Hz6LgrKlBbl93K2w7A2gWgW4IqmvhVBMR8XOwYvbYi7kwwdQzxNpvt6Uid8uvcGvjp4y2Ta+IwiPPHlMSwZEYVxnYKt+30HtgOeOw5c/BG48juQZGCMWJUcOLIMuPgT4OgBlOQApbmAqhzwCAGG/xdo0s/s2TpKHfHlY19i2s5puJX/9zpQC2q8e+Jd5JfnY3rUdIsWRSSTwe+VV+DcrTtS58+HMl03UKzMzMS9p2fC+5mZ8H3pJYh485qIiIj+4bTHx+zQoYNFeePj47Fs2TLs378fKSkpkMlkCAgIQNeuXfHKK68gOjoaAJCcnIzGjRuja9euOHXq72vNyumdO3fGrl278Mknn2DTpk1ISUlBREQEFi1ahPHjxwMAtm3bhs8//xwXLlyAo6MjRo0ahWXLlhmNARmTm5sLLy8vtGvXDr/++is++OAD7N+/H+np6fD19cWkSZPwwQcfPHQPOzCQ2UAUCgXGjRuHnTt3AqjY2Xbt2gU3Nzej+Zo1a4aDBw8CAO7cuWM0rVKp1HRX6+zsjEaNGtW+4kRERERED6C837YibfFiCOXlej93eewx+P3rVdg3aWK0HIVKgZ+v/4yvL32NYkWx0bTh7uH4tM+niPSMtKyyGdeAY8srApiCyrw8Entg1NdA1BjL5mWAWi1g//UMHLqRgZikPFxPK6xRt6PN/F0ws2c4RrRvBAdZfXc4RXVBLBZh3qAWaOLrggVb4iA3MQhqmUKNNzbH4uStbLz4aBOE+7hAIrZSQNPeBej2fMWr4D5w/S8gbrP+oGZhasVLW9494KdxwIivgHZPmj1bd3t3fDPgG0zeMRnpJbpBx0/Pf4q88jy83PFliwO3zt26Ivz3rbj/9jso2r9f90NBQPbKVSg+dRqNPv0EdiEhFpVNRERE9DCpaSBz//79GD58OEpLSxEdHY3o6GgUFxcjISEBP/30E0aNGqUJZFaOwdm+fXudMiqn+/j4oGvXrlAoFOjWrRs8PT1x7tw5TJw4EREREfj555+xYsUK9O3bF/369cPu3bvx9ddfw87ODsuXL6/VcguCgOjoaEilUvTt2xd5eXk4fPgwli1bhvLycvz3v/+tUfm2ioHMBqBUKvH/7N11dFTXFsDh38zElRhxILi7e3FpcXd3pxQptLS4lOIWXEqBIi20uBQrLsHdQgghIcR95v2RhsdwZyaBQovsb62uR/e+59wzXQ/C3H3PPq1bt+a3334DoFChQuzZswcnp/Tffi1YsOCLX585c4ZOnToZvfb8+fOkpKQ++MifP7+87SyEEEIIIT45KZGRPJk6lYhfNhnMWxYujMfwr7DJwHEKfwX9xcQTE7kXec/kdTkcc/BFji9onbc1NuY2GV/sw1NwZAZc/yNj16s04FMSsn8GxdpBJt+M38uEWyHRjNp8kZP3nqV/8d8y21tib2WGnZU5DlZmeDpa8UURLyrmdJXvIR+pJsV9yONhz+q/7vM89v/ndiWmaDlwPeTVDqlsOfeILeceYWuhoYCXI4V8HCmX3YUqedzezoIcPKF0dyjVDa5ug50jIPJR+uO0ybClZ+oO6NfYzexh68Gy2svosacHj6L177P00lLCE8LpVqgbvvav9/vSzMkJn7lzeP7zzzyZPEXx8kX8xYvcbdQYz/HjcKhX77XmFkIIIYT4WLxcyHy10GiMTqeja9eumJmZceLECUqXLq2YM8tLL4ulV8jcsWMHw4YNY/z48VhYpB5Z0L59e9asWUOrVq0wNzfn8uXL5Pj7ZdkTJ05QtmxZNm7c+MaFzLNnUzv1BAQE0KpVK5YsWfJid+eePXuoVasWCxcuZNSoUbi7u7/RPd5HUsj8l6WkpNCuXTs2bUp9kJI/f3727t2b4UNTa9eu/eLXu3btMnntzpfO/alTp84brFYIIYQQQogPkzY+nvC1awld7I82IkJ5gVqNdbduePXtg4Wlpcm5ElMSmXl2JquvrDZ6TWbrzNT1q8vnOT4nj1Oe1yvexYTC1t5wc3f619q4Qv4GkLMGZKsIVo4Zv086EpO1LPrzNnP230p3lx2AhUZN42LedKvkR64MnIEoPj4FvByZ3LSwIn70VigDfz5PaLRyB3RMYgon7z3j5L1nLD1yFzd7S5oU9aJmDlt8Mpn+vZghKlXq75Ec1eDPKXB8fmqxMj17v4XoJ1BrAmTwjKMsDllYWWclvfb24tbzW3q5zTc3s/nmZnzsfCjrVZaynmWp7FMZazPrDHwEFU6tW2NdogRBQ78k4eZNvbw2NpZHQ4YSe+487sO+RGXxemd9CiGEEEJ8yCIjI7l79y4Abm5uGe5Eefv2be7fv0+tWrUURUxQ7uxMr5D5xRdfMGHCBDSa/3eeqVevHmvWrCEoKIjTp0+/KGIClClTBmdnZ4KDgzO0XkPSCrjlypVjzZo1eveuWbMmlStX5tChQ1y+fFkKmeLNaLVaunTpwvr16wHIkycP+/btI3PmzBmeI1euXBQrVoxz585x8+ZNduzYQd26dRXXxcfH4+/v/+Lf03oyCyGEEEII8THTJSXxfPMWQufNIzkkxOA1GhcXrEePxrxYUVTpFCweRD5g2KFhXAm7YjBvb2FP36J9aZmnJWbqN/h6FXINfmoBz42c2wlgYQf5voBCzcCvKmje3te4hOQUbj6J5srjSJYevsv1J1HpjnGxtaBNmSy0L5eVzPZWb20t4uNRIacrfwysyOD15zl6K8zktU+jElh0+C6LDkMJH3t6fZaTGvk9//lOXks7qDUOirSGM8vhyRWwcgBr59SzMi9vUe7YPD4foh6ntpq1yNi5Re627qyos4I+e/sQEBqgyAdGB/LLjV/45cYvuFq7Mqj4IL7I8QVqVfrFUqvcucm2cQMhU6cR/tNPinz46tXEX7yI98wfMffwyNB6hRBCCCE+dOfPn0f3d/uP12kr6+joiEql4sCBA8yZM4fOnTtjZ2dn8j5qtZrChQsr4gCjR49WjImMjASgWbNm5M+fXy+n1WqJjo7O8KY2Q9J2ZE6cOFGviJkmZ86cHDp0iBAj34U/VFLI/JfodDp69uzJqlWrgNT/Q+3fvx+PN/iy8e2339KoUSMAevfuzaFDh/S2PGu1Wvr27cuDBw+A1N80L7ekFUIIIYQQ4mOUeP8+gQMHkXDtmtFrbEqVIvOUyURkYMfVttvbGH98PLHJsYqcChXNcjejX7F+OFs5v9mCb++HDR0hIdJw3skvtdVl4ZZg8RotatNxNzSGJYfvcPpeOLefRpOcztmX+TwdKJ4lE8WyOFHUNxPZXW1Rv60zDsVHK7O9Fau6lGHhn7dZcPA20Qnp74o8ExhF99XnqJw7kG8+z0fOzG9hp697fqg3TRkv2xvWNIWnr/x5cXlL6jm1zVdC5rwZuoWjpSP+tfwZeGAgxx8bOJ/zb6FxoYw+Opr119czvPRwirgVSXdutZUVHt+MwbZiBR6P+pqU58/18nHnz3O3cRO8f5iObfnyGVqvEEIIIcSH7E3Px3Rzc2PixImMHj2aAQMG8NVXX1GtWjWaNGlC69atsbH5/3euqKgo7ty5Q+7cuQ3GfXx8DN77woULADRo0ECRu3XrFomJiYoCZ0bFxMRw48YNPDw8qFq1qsFrYmNTv7v+k2Lp+0gKmf+Sr7/+miVLlgBgbm7OwIEDOXnyZLrjatWqpfcbBaBhw4a0bNmS9evXc//+fYoXL07Pnj0pVKgQYWFhrFq16sXcnp6ezJgx4+1/ICGEEEIIId4j0X/+yaNhX6GNNFwUVFlb49KtK649e5Ks00GY8V1iOp2OqaemsubqGoP5fM75GFt+LPld3uwLKACnlsAfX4EuRZlzLwQVB0H+Rm9192V0QjJz9t9k2ZG7JKWYLl4CeDpaMa5hQWrk/3haEol/l0atou9nOelSwY/LQREEBEZw8VHqP7dCoo2OO3TjKbVnhtKhXFb6fpYTWwszdOjQ6sBMrcLKXPn2+Wtz9IHOO+DnNvDgL/3c02vg/xnUnwFFW2doOhtzG+ZVn8fss7NZf3098SnxRq+9GHqRdn+044vsXzC05FBcrNN/0GRfrRpWmzcROHgw8Rf0d36mhIfzoGs33Ab0x6Vnz3R3mgshhBBCfMje5HzMNCNGjKBly5Zs2LCBHTt2sHPnTv744w++/fZbDh8+jJ+fH5BakNTpdIr50+IlSpQwubZSpUopcmk7OV+n+PrqvbVardF7A5w+fRqVSqXYRfqhk0Lmv+TYsWMvfp2UlET//v0zNO7u3btky5ZNEV+5ciUqlYqff/6ZsLAwJk6cqLgmR44cbN68GV9f3zdetxBCCCGEEO8znVZL6MKFhM6ZCzoDxTkzM5xatMC1dy/M3NxSY0lJJudcGLDQaBGzXb52DC4xGAvNG55JF3wR/pwKV39T5lQaqDsFSnVLPefvLdFqdWw594jJO6/xNEp5ZqFiGSpoXzYrw2rnwd7K/K2tQ3y6rC00lMzmTMls/9+9fC80hvWnH7LxdKDBszRTtDqWH73H8qP3FLmKOV2Z2qwwXpnSP2/SJBtnaL8FNnWDa9v1c0mxsLUX3NoLXsXAzBI05mBuC1nKQibl92wLjQVflvqS/sX7cz7kPMcfH+evoL+4EnYFHco/n7bd2caxoGNMrDiR8t7p76Y09/Ii2+rVPJkylfC1a/WTOh1PZ80m9vx5vKdMQZMp0+v8lxBCCCGE+GCsWLGCFStWvPF4Pz8/hg8fzvDhw7l//z7t2rXjyJEjzJ8/n2nTUjt5pHc+ZvHixRXzarVaAgICcHZ2JmvWrIp8WpHzTQuZaeMdHBwM5k+dOsWtW7coW7bsR3U+Jkgh84NlaWnJunXr6NixI8uWLeP48eOEhIRgb29Prly5aN68OT169MDWNmPnegghhBBCCPGhSYmKImj4CKL37zeYd/jiC9wG9MfiNV7s23hjI/PPz1fEM1lmYnyF8VTxrfJmi310Bg5Nh+t/GM5bOkDz5ZCzxpvN/7fohGS2nHvElaAIgp7HExwRT1BEHFHx6bf1VKugZDZnhtfJQ4msb9guV4gMyuZqy/A6eRlSMzd7Lj1m8aFbnH9kfJfmy47cCqX+7MPMalWMyrnd/tlCzK2hxSr4c0rqSwavFhwv/ZL6z8vMrKHZMshbz+CUlhpLyniWoYxnGQYWH0hgVCAzzsxgz/09imvD4sPoubcnHfN3ZGDxgZhrTL88oLKwwGPMaKyLFePxmDHo4uL08jF/HuJuk6Z4z5qFdSE5YkYIIYQQwpSsWbPSsWNHjhw5QkrK/7vlpFfINLQT9Pr168TGxlKuXDmD9zI1NiPSzse8e/euIqfT6Rg5ciSQuuv0YyOFzH/JwYMH38m8derUoU6dOu9kbiGEEEIIId5XiYGPeNirJ4m3bityajs7vKZOxb7aZ681574H+xh/fLwiXsStCD9U+QF32zd4qzXiEWwfDDd3Gb8mU1ZosyHD5/EZczkogi4rTvEkMv1dlwAeDlbULuBOPk8H8ns5kNvd/u207BTiNZhr1NTMn5limdUcuPWceUeDePTceFvWNOGxSXRcfpJB1XPTv1rOf3Zuq1oDn41K3Wm5qTvEhpq+PjkOfukM7bdCVsMPql7mY+/DjKozOPn4JJNPTeZm+E3FNSuvrORk8EmmVp5KNsds6c7p+Hl9rPLmIXDAQBLv3NHLJQUFcb9NG5y7dcWlazc0dvKCsxBCCCE+bbt27SIlJYVatWphZvb/slhQUBCzZs0C9M+1TK+QaWhXZXo7Ls+fP4+lpaXijMyxY8fy3Xff0bFjR5M7TdPmP3HiBLt27aJ27dpAagfQIUOGsG/fPpo0aULDhg2NzvGhkkKmEEIIIYQQ4oMSFxDAw959SDFwzqVFzhz4zJmD5d9nm2TUmSdnGH5oOFqdVi+exykPC2oswN7C/vUXemsfbO4OscbP4yRLOWi5BmxdX3/+lxy8HkLftWeJSTRw5uYrLMzU9Kycnd5Vc2BjIV8JxftBpVJRLZcTX5TwY+Xxh8w7cJu4JNP/f9bp4Me9Nzj7IJyxDQrg5/oPC3Y5qkGvI7CpK9w/avra5HhY1xI67wT3jJ2XW9qzNBs+38CG6xv48cyPinM0rz67SovtLRhVZhQNczRElU6LacucOcm2YQPB34wh8o8dejldUhJhCxbyfMNG3Pr1JVOzZqjMpVW0EEIIIT5N69evZ/ny5Tg7O1OyZElcXV158uQJhw8fJjExkREjRlC1alUAkpOTuXz5Mu7u7nh4eLyYIy2eOXNmvL299XZwwv8LjYbazgYHBxMcHEyJEiX0CqmQ2pIWwNzE39USExO5fPkyPj4+lCtXjvr161OlShWcnZ05duwYQUFBlCtXjuXLl7/Rf5/3nXxrFUIIIYQQQnwwInftJuirr9AlKHcd2teujeeECa+1+yhZm8yvN35lxukZJKToz+lt5/1mRUxtChycDIemoWhTmcYlJ1T6Ego1B80/+1r204kHjPn1EilaI/d6Se0C7nxdLz9ZXGz+0T2FeFeszDX0q5aLFqV8OXjtKaExCahQoVZBTGIKSw7fIfaVgv2fN57y2fSD5PWwp14hT+oW9CCX+xu8fADg4AkdfoOjP8KpZRAVZPza+AhY0xS67jZ4ZqYhZmoz2uRrQ1nPsnx16Cuuh1/Xy8clxzHm6BiOPTrGmHJj0v3zR2Nni9cPP2BdrDhPpkyBZP020ilhYQR/9z3PVq0m81fDsP/s9XaqCyGEEEJ8DFq3bo2VlRXHjh3jzJkzRERE4OzsTJ06dejXrx81a9Z8ce21a9eIj4+nShX9Y0WMxdOY2pFpqq1sWq5Dhw5G13/58mUSExMpVqwYK1euxN3dnV9++YXw8HBy5szJoEGDGDBgAJaWlqb+M3ywVDqdLv1vu0KYcObMGUqWLAnA8ePHKVOmzH+8IiE+bUlJSYT9vUPFxcXF5Ns8Qoh/h/y+FOKf0+l0PFu2nJDp01O3Yb3CbdAgXHr2SHcHE6T+ngwNDeX40+Msv72cu5HKM0acrZxZVXcVWR2yvt5CIwJhax+4+6fhfOb8UPlLyN8otZ3lP/DwWSwrj91jyRHl+s3UKnK52+PpaIWHoxWeDlZUyu1GUd9M/+ieQrwLr/Nz8uaTKHqvPcutENPnaRbydmTM5/kp7fcPznvV6VJ3XqYkQkoSPL0Ga1tAUoz+da65ocsusHm9eyWkJDDzzEzWXF1jMO9t582UylMo4lYkQ/PFnjvHo8FDSA4ONnqNfZ06eIz+GjPXf7YLXHz85O+vQrxf5Pfkv+/q1asvfp0vX77/cCXifaTVal/syNRoNKjV6jeaJyUlBWdnZ0qXLs2ePcrz1NMsXbqUbt268e233zJ27Ng3uldGven/91+uE50+fZoSJUq8tTXJjkwhhBBCCCHEey/MfwlPZ8xQxFUWFnhNmYxD3boZnutG+A0mnppIQHiAwby1mTXzq8/PeBEzPgKu/AYXN8DdwxjchWluC/WnQ+FW8IZfcmMTkzl8M5TDN59y5GYo98JiDV7nYGXGovYlKZfD5Y3uI8T7LJe7Pb/2rcCIzRfZdsH4bsmLjyJosegvmhb3YVS9vLjYvcHb6SoVmFun/gNgWxFaroafWoD2pZ2PoTdgbTNotwmsnTI8vaXGkuGlh1POqxyjj4wmPCFcL/8o+hEdd3SkT9E+dC3YFU06Lz/YFCtG9m2/EbbYn2crV6JLTFRcE7VzJzF//YX7iBE4Nkq/fa0QQgghhHh3Tp8+TWRkJN9//73J69I7f/NjJ4VMIYQQQgghxHvt+datBouYGmdnfObNxSaDX+Z0Oh0bb2xk8snJJGmTDF5jbWbNzM9mUsC1QPoTht6CgxPh6nZIUba6fcEtH7RYBW65M7TOlyWlaDlyM5Rfzz9i95Unipaar/JxsmZF51LkzPyGbTWF+ADYWpoxu1VRyvg58+OeG4TFKAt2aTadDWTv1ScMqpELDwcrwmOTCI9NJDohmRxudjQq6oWZ5jVeLshZHRotSD3/9mWPzsDy+tB+M9h7GB5rRGWfymxqsIlRR0Zx/PFxvVyKLoU55+Zw/PFxJlaciIet6bk19vZkHjoEp9ateDprFhG/bVPsYtdGRPB45Egit2/Hc/w4zD09X2u9QgghhBDi7ShTpgwZaZp69uxZwPD5m58CKWQKIYQQQggh3lvRR47yePQYRdwie3Z8Fy3Ewjdj59LFJsUy7vg4tt/ZbvSaen716F+sPz72PqYni49MPf/y+AIwUhB9oUhrqP8DWGT83E6AO0+jWXHsHtsuBBEem8490m7l48iSjqVws/84z0UR4mUqlYp2ZbPSspQvJ+48Y8elx+y6HExotLKoGRGXxHfbrhicZ/PZQBa1L4G91Wu06CvcAqJDYPfX+vGQy7CsNrTfCs5+r/FpwM3GjUU1F7Hy8kpmn51Nsk7/rMtTwadotq0Z35f/nmpZqqU7n7mXF15TpuDcsSPBEycSd/qM4pqYo0e517oN2db9JMVMIYQQQoj3lFarJSAgABcXF3wz+P33Y/NmPY2EEEIIIYQQ4h2Lu3yZRwMGQLL+A33LXDnJ9tPaDBcx7zy/Q5vf2xgtYpb2KM3Pn//MlMpTTBcxtVo4vw7mloRjs00XMa0yQYO5qTu3XqOIeelRBH3XnqX6jD9Z9df9DBUxLc3UdCiXlXU9ykoRU3xyzDVqKuZyZULjQpwYVYM1XcuQ39Mhw+OP3Q6j5aLjhETGv96Ny/eDikOU8fB7sKwOPLn8evMBapWazgU7s7reanztlX++RSREMPDAQMYfH098csbWa5U/P1lXrcJj7LeobZV/FiUHB/Oge3dSnj9/7fUKIYQQQoh3T61WEx0dTWho6H+9lP+M7MgUQgghhBBCvHcSAwN52LMX2lj9cyDNPDzw9fdHkylTunPEJsWy6soqll1aRlxynCLvaunKN2W/oWrWqumfExd+H7b0hAd/Gb9GbQ65a0Oh5qn/m3auXgacfRDOj3tucPhmxr6c5vd0oFJuVyrldKNkNieszE2fnSfEp0CjVlExlyu/9avA6uP3+WH3DaITktMdd+VxJE0WHGNVl9Jkd7PL+A2rfwOWdrDvlTONooNhaW3IWw+yV039x8Erw9MWdC3Ixi82MvHERH67/Zsiv/76egKeBvBD1R8MFjxfpVKrcWrVCruqVQke+x3RBw/q5RNv3eZhn75kWbYUtZVVhtcphBBCCCHEv0EKmUIIIYQQQoj3StSBAzz+5htSXnnjVO3ggO/iRZh7mD4jLjElkY03NrI4YDHP4p8ZvKa4S3FGFh5JDs8c6RcxL2+F3wZAQoThvIMPVBoCBRqDjbPpuV6h0+lYcvguE3dcffUYO4ViWTLRsIgX9Qp5ktlBig1CGGOmUdO5gh/1CnkybvsVtgc8TndMYHgczRb+xbJOpSjqmyljN1KpoNJQsHaC7UOAl34TJ0ZBwPrUfwBc80CJjlCqO5hZpDu1rbktEypOoLxXecYdH0dMUoxe/uqzq7Tc3pKJFSdS1bdqhpZr7uGBz4L5PF+/geCxY/VycWfP8mjIUHxmz0JlJo+KhBBCCCHE+0P+diqEEEIIIYR4L6RERfFk4iQitmxR5FTm5vjOm4tV7twm59h1bxczTs8gKCbIYF6Fiu4Fu9PEqwkaVTq7GJPiYOdIOLPccF5jCRUGQsVBr30GJkBSipZvfr3EupMPjV7j5WhF69JZaFjUmywuNq99DyE+Ze4OVsxtU5zvGiRw40k09lZmONlakMnaHP/Dd5i596be9c9iEmm1+C/GNSxI85Kvcf5QyS5g5QibexpvOR16HXaNglNLoc4kyFUrtRCajvrZ61PYrTDDDw3nYuhFvVxUYhT99/enW6Fu9C3aFzN1+o94VCoVTq1aoo2OImT6D3q56P37Cf7uOzy+/z79FzyEEEIIIYT4l0ghUwghhBBCCPGfiz5ylMdjxpD82MDOKZUKr2lTsSlVyuj4pJQkpp6ays/XfzZ6jZOlE5MrTaZU5lKEhYWZXtDTG7ChAzy9ajif93OoPQGcspmex4iI2CR6rz3DsduG15Ezsx29quSgYVEvzDXqN7qHECKVi50l5ez0z48dVCM3me2tGL31ItqXNlLGJ2kZ9ksAJ+4+Y1zDglhbZLBtc8GmqcXMDR0hMdr4dc9uw08tIGcNqD0J3Ey/nAHga+/LyrormXNuDssvKV+sWHJxCQFPA5hSeQqu1q4ZWq5z164kP33Ks5Wr9OLPN/6CLjEJj++/Q20pZ+4KIYQQQoj/nnwjFkIIIYQQQvxn4s6f50HXbjzs1s1gEVNlaYnnhAk41KljdI7QuFC67e5mtIhpobagU4FO/NboN8p7l09/Uc/uwIp6houYlo7QfCW0WvvGRcwrQZE0nn/UYBHTz9WWhe1KsHtQZZqV8JEiphDvUJsyWVjQrgSWZsrfZ7+cCaThvCPcConK+IQ5a0D/M/DZ15C1ApjaIXlrLywoBwenQEr653iaq80ZUmIIMz+bib25vSJ/MvgkLbe15FzIuQwtVaVSkXn4cBzq11fkIn79lfvtO5D05EmG5hJCCCGEEOJdkh2ZQgghhBBCiH9dXEAAT+fMJebwYaPXWBcpguekSVhm9zN6zfmQ8ww5OISncU8VOY1KQ6OcjehVpBcetqbP1XwhOgRWN4EY5Xz4lIamS8Apa8bmesXFwAjm7L/J7iuGiwPlc7iwoG0JHG3M32h+IcTrq13Ag7XdytBrzRlCoxP1cjeeRFPrx0PYWJhhrlFhrlFjY6GhUi43RtbLi42FgUcq9h5Q5avUfxKi4f4xuPornP8JdFr9a7XJcHAi3DkITRZDpvTb2VbPUp3cn+dm8MHBXA+/rpcLiQuhy84uDC4xmPb526fbHlalVuM1aSIp4c+IOfaXXi4+IIC7zZrhM3s2NsWKpbsuIYQQQggh3hV5vVcIIYQQQgjxr9HGx/Poq6+416Kl0SKmytwct6FDyPrTWpNFzE03NtF5V2eDRcwibkXY0nALY8uPzXgRMyEK1jaD8LuvrggqDoHOf7xREfPcg3A6Lz/JF3OPGC1itirly8oupaWIKcR/oGQ2Z/4YUImy2Z0VOa0OohOSCY9NIiQqgXthsaw+fp+eq8+QmKw1MNtLLO0gdy1oOA96HoKsFQ1f9+AYLKwAV37N0Hp9HXxZU28NjXI2UuSSdclMOz2NoX8OJSIhIt25VBYWeM+eg22FCopcytNQHnToyPNNmzK0LiGEEEIIId4FKWQKIYQQQggh/hUpz5/zoEtXIn/bZvQaq0KFyLbpF1y7d0elMXw2nVan5cczPzL2r7Eka5UtGVvmacny2svxczReBFVIToT17eDxhVcSKmi2DGp8C5rXKzImp2iZuvMajecf48B1Azs8AZUKvq6Xj0lNCkkbWSH+Q5kdrFjbrSwDquUknY2MABy+GcqXGy+gffmATVM8CkGn7amtqR2zKPPxEann8v42ABJj053OysyKcRXG8V3577BQWyjye+7voe7muiy/tJyElASTc2nsbPFdtBDnzp0VOV1SEo+/Hs2TqdPQpaSkuy4hhBBCCCHeNvmmLIQQQgghhHjnkoKCuNe2HXFnzxrMW+bNi8/cOWTbsB6r3LmNzhOfHM+Xf37JskvLFDkLtQXjKoxjdNnRmL9O0TE5Abb2Tm3v+Kr606Fgk4zP9beQyHjaLjnB/IO3jV6TzcWGZZ1K0b1y9nRbQAoh3j2NWsWQWnlY1aU0LrbK4uCrfrsQxIQ/rqLTZbCYqVJBgUbQ5y8o2s7wNWdXwuKqEHwxQ1M2ydWE1fVW423nrchFJUYx48wMvtjyBdtub0P7amvbl5dmZob78K/wmjoFlYXysz9btozA/gPQxsRkaF1CCCGEEEK8LVLIFEIIIYQQQrxT8deuca9VaxJvK4t6lrlz4z17Fn6bN2Ffo4bJgl5YXBhdd3Vlz/09ipyHrQer6q4y2GrRKJ0W1eVNMLckXPpFma/8FZTqlvH5/nbsVij1Zh/mxN1nBvO5Mtsxq1VR9g6pwmd5Mr/2/EKId6tSLjf2D63K5CaF6FUlB10r+tG+bFYq5HRRXLv0yF0WH7rzejewtING81J3e1s6KPOh18G/GhxfCBkokuZ3yc/6z9dT1aeqwfzjmMeMOjKKNr+34fqz6wavSePYoAFZ167BzN1dkYvev597bduRFBSU7pqEEEIIIYR4WwycTC+EEEIIIYQQb0fMiZME9u2LNjpakXPu2IHMw4ejUqf/fuWj6Ed03dWVR9GPFLmCLgWZU30OrtauGV6XxaPj2B+fitnTy4YvKN4RPhuV4fm0Wh2n74ez+WwgG04/xFC3yVyZ7RhSMze1C3igVssOTCHeZ4425rQqrd8CNjlFS681Z9l7Vf+s20k7ruFqZ0nTEj6vd5OCTcG7JGzqBoEn9XMpibBzONzeD43mg63pP98cLR2ZVW0Wq6+sZv75+cQmK9vTXg67TKvtrehcsDM9i/TEUmNpcC7rQoXw+2UjD/v1I/5CgF4u4do17rZoifeMH7AtXfr1Pq8QQgghhBBvQHZkCiGEEEIIId6J6CNHedijh8EiZuavvsJ95MgMFTGfxz+n155eBouY1bNUZ1mdZRkvYsZHotnSHedtHTE3VsTMUx/qzyAjB+XdColi6s5rVJp6gBaL/uLnU4aLmK1K+bKtf0XqFvKUIqYQHygzjZq5bYpRMquTIjfslwusP/Xg9Sd1ygqdd6TuAFcZ+PPw5i5YUD61oJkOtUpNxwId+aPJH7TM0xKNSnnOcLIuGf+L/jT7rRlnnxhu9Q1g5uZG1pUrcahXV5FLCQ3lQcdOPJkyFW2C6fM3hRBCCCGE+KekkCmEEEIIIYR466L//JPAPn3QvfqQ29wcr+nTcenSOUPzxCfH029/P+5F3lPkOhXoxIyqM7A2s87YooIvweKqqK9sMZxXaaB0j9R2jxrTzWtCIuMZuuECNWYcYv7B2zx6HmfwOmtzDT80L8LkpoWxMlcWFYQQHxYrcw1LOpYkV2Y7vbhWB8M3XWTRn8bPxTVKYwbVvoaO28FBedYl0U9gdWPYPQaSE9OdzsXahdFlR7Ol4RZqZKlh8Jp7kffouLMjCy4sMDqP2soKrx9+wLVvX2VSp+PZ8uXca9aM+CtX0l2TEEIIIYQQb0oKmUIIIYQQQoi3KmrfPh72648uUf+Bu9rWliz+i3H8vH6G5knRpvDVoa+48PSCIje6zGiGlhyK2tAOJkPOrYUl1eGZkSJDnvrQ5y+oNw3MrYxOk5isZfGh21T74U82nQ00ecscbrb82q/C67ebFEK81zLZWLCqa2m8HJV/VkzacY3JO66hy8DZlgrZKkCvI5D3c8P5Y7NhWS0Iy1ix1M/Rjx8/+5EltZbgY2f4z6H55+ez8vJKo3OoVCrc+vfDa/p0VBYWinzCzVvcbdmKsCVL3uwzCyGEEEIIkQ4pZAohhBBCCCHemshduwkcOAiSkvTiagcHsqxYjm3ZshmaR6fTMenkJA48PKDIDSs5jJZ5W2ZsQXHP4de+8GsfSI5XpLVeJVLbOrb+CdzymJzq8M2n1Jl5iIl/XCM6Idnodbnd7RhRNy/b+lckt7t9xtYphPigeDpas65HWXydlTvCF/55m+GbArgfFvP6E9s4Q8s18PmPYGbgpYqgc7CoMlz8JcNTlvEsw+aGm+mYv6PBlz+mn57O9jvbTc7h+Hl9sm1Yj2WuXMpkUhIh03/gyaRJUswUQgghhBBvnel+SUIIIYQQQgiRQRG//07QV8MhJUUvrnF0JMvyZVjlz5+heZK1ycw5N4f119crch3yd6BDgQ7pT5IYCycXw5EfIf65Iq1TqYkuPRir6sNRW1ianCohOYUpO66z7Ohdo9e4O1jSsKg3jYp6k8/THlUGztcUQnzYsrrY8kuv8rRfeoIbT/TPAt5wOpANpwPxdbamYk5XKuR0pXped6wtMtBiWqWCkl0gSzn4pSuEvHKeb2I0bOoKD09CrfFgptwp+SprM2u+LPUldfzqMPrIaG5H6O/qHHNkDM6WzpT3Lm90Dqu8ecn2y0aezprNs+XL4ZWiZfiq1ZCcgvuY0fJnoBBCCCGEeGtkR6YQQgghhBDiH4v49VeChn2lLGI6O5Nl1coMFzHvRNyhw44OLLu0TJGrm60uQ0sONT1BShKcXg5zisPebw0XMW0zE/75CmKK9YB0WtPeDY2h6YJjRouYjtbmfNegAEeHV2NUvXzk93KQB/hCfELcHazY0LMcxbJkMph/+CyOdScf0u+nc1SedoCNpx+i1WZw12LmfNB9f+rZvYacXAQr6kHEowyvt6BrQVbUWYGfo59ePFmXzKCDg7gUesnkeLWlJe5fDSPLyhWYe3kp8uE//UTwd9+h02ozvCYhhBBCCCFMkUKmEEIIIYQQ4h95vmkTQSNGwisPrjWurmRdtRKrPKZbtgJodVpWXV5Fi20tuBh6UZEv7VGa8RXHmz4TM+w2LK4K2wdB1GPD12StSHLX/SR6l0l3TZvPBvL57MNcehSpyKlU0LZMFg58WZWO5bNhppGvVkJ8qjLZWLCmaxkq5XI1ed3TqASG/RJAo/lHOXM/PGOTm1ulnt3b+mewdlbmA0/BokpwW9mG2+h6rTKxqMYiMttk1ovHJcfRZ28frj+7nu4ctqVL4/frVqxLlFDknv+8nuBvv5ViphBCCCHeik2bNqFSqd7KPwEBAf/1xxFvQL5tCyGEEEIIId5Y+M/refz1aEWLQbPMmcm6ahWWOXOmO8eFpxfovLMz005PIyElQZHP65yXmZ/NxEJjon3ijV2w+DN4YmQ3kbkNfPY1dPgV7D1Mrken0/HdtssM2XCBmMQURb6AlwPb+lVkQuNCONum39JRCPHxs7U0Y0nHkvSonB0rc9OPWgICI2i64BiD15/naZTyzzyD8tSFXofBu6QyFxsGqxvDn9MUL5QY42nnycIaC7G30D/HNzwhnPY72rP73u5059DY25Nl8SJsSpVS5J5v/IXHo8dIMVMIIYQQ/9jJkyffyjzW1tYUKFDgrcwl/l1yRqYQQgghhBDitWljYwldtJiwRYsUOTNPT7KuWI5F1qwm57jw9AILLizg6KOjRq9pmqspw0oNw9bc1shCtHBoGhycBBho16g2h5KdofIwsPt795E2yeS6Zu27yfKj9wzmulTwY3jdPFiaZeCcOyHEJ8XSTMOoevkYWis3Z+6Hc/RWKEduhhLwKOLVdz0A2HLuEYdvPuWHFkWpktst/Rs4+kDnHbB7dGpbWT06ODAeAk9C40VgY2D35ityOeVibrW59NjTQ+8lkrjkOIb+OZQe4T3oW7SvyZ3waltbfBct5GGfvsQeP66Xi9i8GbWVJe5jxkjLbSGEEEK8sWbNmlGuXDmDucTERFq2bAmAra0ta9asMTqPvb09Go18j/sQSSFTCCGEEEIIkWE6rZbIbdsImfEjyU+eKPLm3t5kWbkCCx8fo3PcCL/BjDMzTBYw3azd+K78d1TyqWR8MfERsLkn3NhhOF+4JVQdCc5+hvMGrD1xn5l7byriTjbm/NCiCNXyumd4LiHEp8nSTEP5HK6Uz+HKsNrw8Fksk3Zc5Y+LwYprQ6MT6bjsJN0q+jGsTgZekjCzgHpTwbc0/NYfkmL18zd3w6Iq0GIleBdPd63F3YsztfJUhh4cSrIuWS+3OGAxN57dYFKlSdhZ2BmdQ21jg+/CBQT27UfMUf0/18N/WofK0orMXw2TYqYQQggh3kipUqUoZaADBMDZs2df/LpIkSI0atToX1qV+DdJa1khhBBCCCFEhsSeO8e9lq0IGj7CcBEzSxayrl5lsoi57/4+2vzexmQRs372+mxpuMV0ETP8HiypabiIaekArddDk8WvVcTcdTmYMVuVrWlLZHVix8DKUsQUQrwRX2cb5rctwbruZcnrYW/wmiVH7tJ0wTHuPI3O2KSFmkH3A+CaW5mLeADLasP+CZAQle5U1bJUY3GtxThZOilyBwMP0vaPtjyMemhyDrWVFT7z52FbsaIi92z5ckLnzEl3HUIIIYQQr+vcuXMvfl2sWLH/cCXiXZJCphBCCCGEECJdkTt2cL9de+IvXjSYt/DzI+vqVZh7eRmdY921dQw+ONjgOZgAeZzyMLfaXCZXmoyjpaPxxQSehiU1IPS6MueWD3ochDx1TH0chZN3n9F/3Tm0r7R/zO/pwIrOpfBwtHqt+YQQ4lXlcriwvX9Fvm9YAGtz5c7LS48i+XzOETacfojOUC/aV2XOC933Q4HGylxKIhyaCrOLwaklkGK6pXYpj1Ks+3wdeZzyKHJ3Iu7Q9ve2nH1y1sDI/1NbWuIzdw42ZcoocqHzFxC6aLHpzyOEEEII8ZpeLmQWLVr0v1uIeKekkCmEEEIIIYQwKf7GDYJGfQ0pKcqkuTnOXbqQbeMGzN0N71jU6XTMOjuLiScmojNwjmVup9zMrDqTDV9soIpvFdOLufIrrKgPMU+VuQKNodtecMmRkY8FQHKKlp9OPKDrylMkJmv1cr7O1qzoUgp7K/MMzyeEEKaYadR0KJeNbf0rkt/TQZGPTUzhq18C6L/uHBFxpouPAFjaQ7PlUGcKqA2cHhTzFH4fCvPLwXUjbbj/5m3nzaq6q6iVtZYiF54QTrfd3dh2e5vJOdRWVvjOn4e1gR0RT3/8keDvx6FNTDT9mYQQQgghMuj8+fMvfi07Mj9eUsgUQgghhBBCGJUSHcOjgYPQxcUpcvY1a5Bj+zbcvxqGxs7w+WlJKUmMPjqaJReXKHLOVs7MqDqDjV9spHrW6qhVJr6e6HRwdBZs6ADJ8cp89W9TH+ZbGj/HTX86HYfvPOfzeX8xastFouL1z4ZzsbVgVZcyZLaXnZhCiLcvZ2Y7tvQtT5cKhttfbw94TL1Zhzlz/1n6k6lUULYXdPoD7I3sig+7CetawYaOEB1idCobcxumV5nOgGIDFLkkbRKjjoxi9tnZaHVaA6NTqW1t8V28CKuCBRW58J9+4n7rNiQ+NN2qVgghhBAiPTqdjgsXLgBgZmZGQQN/9xAfBylkCiGEEEIIIQzS6XQ8HjOaxLt39eIaFxeyrFyJz5w5WGTNanR8QkoCgw4O4rfbvylyvva+rK67mppZa5ouYKY5MgP2fKOMm1lB85VQaUjqw/wMuPo4ij6/3GDYb7e5/TRGkbex0LC8cyn8XG0zNJ8QQrwJSzMN33yRn+WdSuFia6HIP3oeR4tFx5mz7yYpr/a9NiRLGeh7HCoOTv2z0ZArW2FuKTj/U+oLIgaoVCq6F+7Oj1V/xEqjnMf/oj899vQgOCbY6FI09vZkWeKPZR5lq9r4y5e526QpUXv3pv+ZhBBCCCGMuHnzJtHRqeeL58+fH0tLy/94ReJdMdB3RAghhBBCCCEgfPUaonbs1A+q1Xj/8AO2ZUqbHBufHM/AAwM5FnRMkSvoUpC51efiYu2SsYWc/wn2fa+M27hC65/Bt1SGptHpdKw98YDvtl0mKcXwA3wbCw2L2pegsE+mjK1NCCH+oc/yZmbHoEoM3XCBwzdD9XIpWh0/7LnBkVuhzGxVFE9Ha9OTWTlCjbFQsiscmAAXfoZXW3rHP4etveHiRmgwFxy9DU5VI2sNPO08GbBvACFx+rs4Tzw+QZNfmzCq7Cjq+9VHZeBFEk2mTGRZtpTAPn2J+3u3RBptVBSB/frj0r07bkMGGxwvhBBCfCimnJzCtWfX/utl/GfyOudleOnh//p95XzMT4cUMoUQQgghhBAKsefO8WTqVEXcbcAAbMuWMT02KZYB+wdwIviEIlfJuxLTq0zHxtwmYwu5tRd+66+Mu+aGthvBKVuGpolJSObrLRfZej7I6DWNinoxtFYefJ0zuDYhhHhLMttbsbJzaZYcucO0XdcVL1ucuPuMOjMPM6VpYeoU9Eh/wky+0HghlO0DO0fA/aPKa27vh6W1oOsucPQxOE0BlwL8VP8n+u/vz9VnV/VyUUlRjDw8kv0P9vNN2W/IZJVJMd7MxYWsq1cRMuNHnq1YociH+fsDSDFTCCHEB+3as2ucfnL6v17GJ0fOx/x0SGtZIYQQQgghhJ7oQ4cI7NsPkvXPjbSrUgWXHt1Njo1NiqXPvj4Gi5gNcjRgdrXZGS9iBp2D9R1Aq78O3PJBl10ZLmLeComi4byjRouY5XO4sL1/RWa2KiZFTCHEf0atVtGjcg429S5PNhfln0URcUn0WnOGr7dcJD4pJWOTehaGjtvh8x/B0kGZjwyENU0h1vhZnO627qyos4I62eoYzO+5v4em25pyJ+KOwbzKwgL3EcPxmTsHtb29Ih/m70/YwoUZ+zxCCCGEEH97eUfmuy5kHj58GJVKRZs2bd7pfTLiwIEDqFQqOnTo8F8v5V8jhUwhhBBCCCEEANr4eILHT+Bhj56kPNN/qG3u7Y3XlMmo1Ma/QkQnRtNrby/OPDmjyDXJ1YRxFcZhps5gU5hnd2Ftc0h65QxLB29otwlsnNOdIjohmVl7b/LFnKPcColW5N3szFnSvhhru5WhoLdjxtYlhBDvWGGfTGwfUImmxQ3vklx74gFN5h/jQVhsxiZUq6FkF+hzHHLXVeafXoN1rSHR+Hw25jZMrTyVSZUmYW+uLEaGxIbQZWcXboXfMjqHfY0a+G3ehFX+/MolzJpNmIEdm0IIIYQQxqQVMlUq1TtvLZt2r5IlS77T+2TE2bNngfdjLf8WaS0rhBBCCCGEIP76DYK+/JKEmzcVOZW5Od4zZ6LJlMno+MjESHrv6U1AaIAi1yJ3C74u+zVqVQbfo7y+A34bADFP9eNWjqlFTCPnuaVJSE5h7fEHzDtwi7CYRIPXlMniwNg62cjp6ybtDIUQ7x07SzN+aFGEyrld+XrLJaIT9HemX3kcSf05h5nRoig187tnbFJHb2i9Ds4sh+2D9XMPj8MvXaDlGtAYflSkUqn4PPvnlHQvyeijoznxWH/nfVh8GF13d2VxzcXkcc5jcA4LX1+yrFrFgy5diA/Q/3kRMnkKamsbnFq2yNjnEUIIId4TeZ3z/tdL+E/9F58/KCiIkJDUM7yzZcuGo+O7fTE1rXhYokSJd3qfjHif1vJvkUKmEEIIIYQQnzCdVkv46tWETP8BXVKSIq+yssJr8mSsCxU0OkdEQgQ99/TkcthlRa5N3jaMKD0iY8XC+AjYORLOr1XmNBbQah1kzmdyiu0BQUz64xqPnscZzKtU0L9qDloWckSjlgKmEOL91rCoN8V8nRjw8znOP3yul4uKT6b7qtP0rpqDoTVzY6bJwMsiKlXq7syEKNjzjX7uxg7YPhAazE29zggPWw8W11zMumvrmHZqGim6/7e5fRb/jG67u+Ffy9/oQ02NnS1ZFi/ifsdOJFy/rpcLHjsWlbk5mZo0Tv+zCCGEEO+J4aWH/9dL+OT82+djnjt3DpVKRfHixd/5vTKyFo1G8853ob5PpLWsEEIIIYQQn6ikkBAedu/Bk0mTDRYxrfLnx2/zJhzq1DY6R3h8ON12dzNYxOyQv0PGi5i398P88oaLmKigiT9kq2B0eGKylm9+vUS/n84ZLWK62Fqwqktp+lfLIUVMIcQHI4uLDRt7laNXlRwG8wsO3qb90pM8jUrI+KTlB0DZvsr4uTWwuQckGf5zNI1apaZtvrZMqzINM5X+O/LPE57TdVdXgz8X0mgyZSLLsqVYZM+un9DpeDxqFOHrN2T4owghhBDi0/NPz8c8deoU7dq1w9fXF0tLS3x9fRkwYADh4eGKaxMSErhy5Qq5c+dGrVbzzTffkDt3buzs7MiXLx8zZ840eI+bN2/So0cPcuTIgZWVFfb29uTKlYt27dpx+vTp114zQGxsLNevXydv3ryo1WomTpxIwYIFsbGxwc/Pj7Fjx5KSksGz1D8gUsgUQgghhBDiExS1bx93GzQk5uhRZVKlwqV7N7L9vA7LVx8yvyQ0LpSuu7ty7dk1Ra5rwa58WfLL9IuYD0/B6iawujFEBirzZtbQaAEUaGR0ipDIeNr4H2fVX/cN5i3N1PSsnJ19Q6tQKZeb6fUIIcR7yFyjZkTdvPh3KIm9lbK51l93wqg/+zCn7j0zMNoAlQpqjYdCzZW5ixtgWR2IeJTuNDWz1uSHqj8ozj+OTIykx+4eXH923chIMHNxIcvyZZj7KM8CDf72W56tWp3+5xBCCCHEJ+mfFDKnTZtGuXLlWL9+Pblz56ZBgwaYmZkxZ84cKlasSGRkpN71Fy9eJDk5GV9fX0qUKMHixYspWrQopUuX5s6dOwwdOpRx48bpjdm3bx9FihTB398fZ2dnGjZsSJUqVdBoNKxdu5b79w1/d03P+fPn0Wq1+Pj4ULJkSebMmUO+fPmoUqUKDx8+5LvvvuPHH398o7nfZ1LIFEIIIYQQ4hOiS04m+PvvCezbj5TnzxV5Mw8PsqxYQeahQ1FZWBicIykliTVX1tBwa0NuhivP1OxZuCcDiw80XcQMPA1rmsLSGnB7n+FrfMtA76NQtLXRac7cf8bnc45w+r7yzVmNWkXr0r4cHFaVkfXykcnG8OcRQogPRc387mzvX5F8ng6KXEhUAq0WH2fJ4TvodLr0J1OroeF8yFFNmXt8HhZXhQfH052mWpZqzKw6E3O1uV48MjGSHnt6cOf5HaNjzd3dybJiOWaenorck4kTCVuyJN37CyGEEOLT83Ih83VarPr7+/PVV19RoEABrl+/zr59+9i4cSPXrl2jfv36XLlyhR9++EFvTNqZlHv37qVKlSo8ePCADRs2sH//ftasWQPA5MmTiYqKAkCn09G1a1fMzMw4ceIEp06dYv369Wzfvp1r165x9uxZqlat+kafO20tu3btom7duty/f5+NGzeyY8cOfvnlF4AX//sxkUKmEEIIIYQQnwhdSgpBI0YS/tM6g3n7OnXI/utWbMuUNjxep+PAgwM0/q0xU05NITIxUnFN36J96Vesn/EiZmIsbOoGS6rDrb2Gr9FYQM3vofMOcDHcShFg3ckHtFp8nBAD7RRLZHViz+DKTGpSGE9Ha6NzCCHEhyariy1b+pSnRUnlTsYUrY7xv1+lz9qzhMckpj+ZmQW0WA05qitzMSGw4nM4szLdaar4VmF2tdlYqPVfGEk7M/NB5AOjYy18fMi6ejXmvr6KXMj0H3g6Z27GCrNCCCGE+CRERkZy9+5dANzc3PD29s7QuMePHzN48GCcnJz4448/yP5S9yFLS0u+/vprAP744w+9cWlF02rVqrFw4UIsXnrht3nz5pQsWZLY2FjOnDkDwO3bt7l//z7lypWjdGnld+tixYrh4uLyGp9YuZZGjRoxffp0vbXUrp16JExISMgbzf0+k0KmEEIIIYQQnwBdSgqPR31N5PbtipzaxgbPSZPw/nEGGkdHg+MjEiLovbc3Aw4M4H6k4TY4A4sPpFeRXsYXEfsMVjWEixuNX+NVHHoeggoDQa0xeIlWq2PSjquM3HyRpBTlw+0O5bKyrntZsrvZGb+PEEJ8wKzMNUxtVoQpTQthYaZ8tLPjUjCVpx1g4Z+3iU9K55wkSztouzH13MxXaZNg2wDY9TVoTc9T0bsis6rNUrSZfRr3lG67uxEUHWR0rIWPN1nXrMYiWzZFLnTePB4NGkxKdIzpzyGEEEKIT8L58+dfvOT0Om1llyxZQkxMDN27dzdY/EwrbIaFhenF03ZBTpo0yeALu3nz5gV40ZLW0dERlUrFgQMHmDNnDtHR0RleY3rS1vLdd98pck+ePAHAy8vrrd3vfSGFTCGEEEIIIT5yOq2Wx998Q8Svvypy1kWK4Ld1C5kaNzK6izJZm8yQg0M4GmTgPE3AxsyGb8p9Q7dC3YwvIvweLK0FgScN590LQcu10H0/ZM5ndJr4pBT6/nSWRX8qWxVamKmZ3rwI3zcsaPDBvhBCfGxalsrC5t7lyeJso8hFxSczecc1qv/wJ5vPBqLVmtjVqNZArXHQxB/MrJT5v+bCz20gIcrkeip6V2R6leloVPovojyOeUy33d2MvggDqW1ms65ehWWunMrPsmsX91q0IOH2bZP3F0IIIcTH703Px/z9998B6Ny5s8F8XFwcAE5OTi9iKSkpXLx4ET8/P4O7KwHi4+OB1N2haf87ceJEtFotAwYMwM3Njfr167N06VJiY2MzvN5XJSYmcvnyZfz8/ChcuLAiHxAQAEDBggXf+B7vK/l2L4QQQgghxEdMp9USPPY7IjZtVuTsqlcn6+pVWGTJYnKO+efnczJYWYBUoaJprqb83uR3mudubnyCxxdSi5hhyvM0XxQwex6CfJ+DiXM1n0Yl0HLxcXZcClbkvDNZs6lXeZqVULZaFEKIj1lBb0e29atIjXyZDeYfPY9jyIYLNJx3lMtBEaYnK9wCuuwEBwMt2m7shGV14PlDk1NUz1KdyZUmo1bpP3J6GPWQlttbsuveLqNjzdzcyLJqFZb5lC+0JN65w73mLYjcaXy8EEIIIT5+b3o+5sWLF7GwsCBPnjwG85cvXwagSJEiL2JXr14lLi6O4sWLG5337NmzmJubU6BAgRexESNGcPPmTSZPnkyZMmXYuXMn3bp1I3fu3C/a4r6uixcvkpSURKlSpQzm0/67vE5x90MhhUwhhBBCCCE+YiFTpvB8wwZF3K5KFbx/nIHKwsLAqP87FHgI/4v+ingZzzJs/GIjY8uPxdXa1fgEdw7C8noQ/USZK9fv/wVMtemvJg+fxdJ4/lEuPHyuyBXxzcTWvhUo5GO4La4QQnzsHG3MWdy+JKPr58PO0szgNRcfRdBg7lGm7rxmut2sVzHocRB8DDwke3IJ/KvBvSMm11PHrw7fl/9eEY9JiuHLP79k4omJJKYYPsPTzMmJrKtWYletmiKnjY3l0aBBhC5caPL+QgghhPh4rVixAp1Oh06no1WrVhkak5KSQmxsLObm5kY7EW3atAmAunXrvoilFQft7e0Njjl58iR37tyhSpUqODg46OX8/PwYPnw4Bw8e5M6dO1SsWJFHjx4xf/78DK35VWlrMVa8lUKmEEIIIYQQ4oMTvmEDz1auUsRtK1bEe/Ys1OkUMR9FP2Lk4ZGKeMMcDfGv6U8eZ8Nvsr5wcy+sbQGJr54JooLak6D2hHQLmADPYhLpuOwkgeFxilydAh783L0sbvaW6c4jhBAfM7VaRbdK2Tk4rCodymXFTK18SJei1TH/4G3qzTrMqXvPjE9mlxk6boOCTZW5mBBY8XnquZlJ8UanaJizIWPKjjGYW3dtHR12dOBR9CODeY29PT5z5+A2eLDBnxNPZ84ifN064+sXQgghhHiJRqPBxcWFmJgYQkJCFPkLFy6wevVqfH19adCgwYt42pmUtw20t9fpdIwZk/p3neHDh5u8f9asWenYsSOQWlR9E2lrMVXI1Gg0FCpU6I3mf59JIVMIIYQQQoiPUOyZMwSPG6+I25Yvh8/cOagtTRf+ElMSGXpwKJGJkXrx3E65+brs10bfYn3h+k74uTWkJOjHNRbQbBmU65OhzxGXmEK3lae4ExqjyPWsnJ35bYtjbaExMFIIIT5NrnaWfN+wILsHV6ZuQQ+D19wJjaH5wr+YufeG8YnMraHpUqiqfKEFdKnnZi6umto+3IgWeVowr/o8HCwcFLnLYZdp+3tb7kXcMzhWpVbj2rMHvv6L0WTKpMgHfz9O2swKIYQQIsPSdlqOHz8ene7/Z4efPXuWzz//HJ1Ox+LFi7F86bty2i7HI0eOsGfPnhfxxMRE+vXrx4EDB2jdujU1atQAYNeuXfzxxx8kJyfr3TsoKIhZs2YB6BVKAcaOHYtKpaJTp04m159WyDS04/LZs2c8ePCAPHnyYG1tbXKeD5EUMoUQQgghhPjIJAUFEThgICQl6cWtixXDZ9481FZWJscna5OZcGICl8Mu68VtzW2ZUXUG1mbpfDG6ug3Wt4NX2wZaOkC7zVCwSYY+R3KKlv7rznH2wXNFbnyjgoyslw+1gR1HQgghILubHQvaleCnbmXI4mxj8JqZe2+y5PAd45OoVFB1RGpBU2PgBZinV8G/OhyZCS89EHxZZZ/KbPxiI4VclbsDwuLD6La7m9GdmQB2FSrgt3kTlnnz6id0OoKGDSPm+Anj6xdCCCGE+Nv333+Pi4sLc+bMoVChQrRs2ZKKFStSsmRJQkNDWbZsGXXq1HlxvU6n4/z58/j4+NCmTRvq1q1L7dq1adWqFdmzZ8ff35/KlSuzaNGiF2PWr19P/fr1cXd3p3bt2rRt25YaNWrg5+fHpUuXGDFiBFWrVtVbl1arBcDc3Nzo2lNSUggICMDd3R0PD+WLah9zW1mQQqYQQgghhBAfFW1cHA/79SMlLEwvbubpic+c2ajTeTszMCqQzjs7s/nmZkVuXIVxZHXIanoBl7fAho6g1S+iYpUJOv4GfpUy8jHQ6XR889tl9l5Vnq055vP8tCubzjqEEEIAUD6nK7sGVaZH5ewYevdj/O9X2XrOeCERgELNoNtecMunzGmTYO+3sHOk0WKml50XK+uspF2+dorck9gndNvVjScxBs5S/pu5lxdZlvhjniWLXlyXlERg377EX7liev1CCCGE+OT5+flx4sQJWrVqRXBwML/++iuPHj2iS5cunDlzhg4dOuhdf+fOHSIiIsifPz/Lli1j8ODBXLx4ka1bt+Lm5sYPP/zAjh07sLW1fTGmdevW9O7dG19fX86cOcOGDRu4ePEiderUYffu3UyaNEmxrvPnzwMo7v+ya9euERcXl+75mMbyHzqVTmfkb5lCZNCZM2coWbIkAMePH6dMmTL/8YqE+LQlJSUR9vfDaxcXF5Nv8wgh/h3y+1L8W3Q6HUFDhxL5xw69uMrKiqxr12BdoIDJsdvvbGfCiQnEJCnbuLbP356vSn1legEBG2FLD9Bp9ePWzqlFTI+MndWRnKJl2u7rLPpTuUuoeyU/vq6fP0PzGCO/J4V4v8jvyX/PhYfPGfbLBW480T+72EytYmmnUlTJ7WZ6gqR4ODAejs0FDDxOKtUd6k41ef7x73d+Z9SRUWhf+Vnh5+jH8trLcbF2MTo28cED7rVpS0poqF5c4+pKtrVrsMgqL7m8LfL7Uoj3i/ye/PddvXr1xa/z5TPwIo/4pGm12hdnXWo0GtQm/u5jSkpKCs7OzpQuXVqvde1/6U3/v/9ynej06dOUKFHira1JdmQKIYQQQgjxEdDpdIRMm64oYgJ4TZxgsogZmRjJ8MPDGXVklMEiZvHMxRlcYrDpBZxfZ7iIaesGnX7PcBHzVkgUTRccM1jEbFDEi5F15SGCEEK8qSK+mVjfoxw5M9vpxZO1OnqvOcP5h89NT2BuBbXGQ6ft4JhFmT/lD78PAa1Wmftb/ez1GV9hPCr0t4fejbhLzz09iUiIMDrWIksWsixehPqlnQ8AKaGh3O/cmaSgINPrF0IIIYR4j5w+fZrIyEi+//77/3op7zUpZAohhBBCCPGB0+l0PJ3xI8+WLVPkXHr0wKFePaNjzzw5Q7PfmrHjrrIACtAkVxMW1FiAudrEW99nV8HW3soipp17ahHTPf0dlClaHUsO36He7CNcCFQ+xC6fw4VpzQvLmZhCCPEPOdlasKpLaTwc9M9Ljk1MocuKU1wJikx/kmwVofcRyFJOmTuzHLYNMFnM/CLHF4wuO1oRvx5+ne67uxMeH250rFX+/PjMm4vqld1IyUGPU4uZISHpr18IIYQQ4j1QpkwZdDod5coZ+DuVeEEKmUIIIYQQQnzAdDodT2fNIszfX5Gzq1oVt0EDDY5L0iYx++xsuuzqwuOYx4q8o6UjP1b9ke/Kf4eNuY3xBZxaCr/1R9Fi0N4LOv0BbnnS/Qxn7ofTctFfjP/9KonJygff+T0dWNi+BJZmmnTnEkIIkT6vTNas6loaByszvfizmESaLjjG7wHKnwsKVo7Q9hfIZuDs43Or4bd+JouZLfK0YFjJYYr41WdX6bKrC6FxoQZGpbItWxavacoWtkn3H/Cwa1eSw40XQoUQQgghxIdFCplCCCGEEEJ8wELnzSds4SJF3LpoUbymT0dl4KyO+5H36fBHB/wv+ivOKAMo41mGTV9sokbWGsZvnBgD+75PbSH4Kkdf6Pw7uOY0ufYz98PpsOwkTRcc4/R9ww+dW5f2ZUOvcjhYyTlAQgjxNuV2t2dZp1JYmun/nIhLSqHvT2eZvus6Wq2BczBfZmkHbTaAXxVl7vxa+ONL0Bmfo0OBDvQr2k8Rv/X8Fp13duZJzBOjYx3q1MFz/HhFPOHmLR52605KVJTptQshhBBCiA+CFDKFEEIIIYT4QIUuXETo3LmKuFWRwvgu8UdjZ6vIXXh6gVbbW3Ep7JIiZ6Y248uSX7K45mLcbd0N31SrhXNrYXZxOPyDMp8pS2o7WefsRtd99XHkiwLmoRtPDV7j7mDJ8s6lmNSkMHaWZgavEUII8c+UzObM3DbFMTPQtnvugVt0X3WayPgk05NY2ECb9ZCjujJ3eins+tpkMbNH4R70KdpHEb8XeY9OOzsRFG383MtMTRrj/s0YRTz+8mUedO5C8lPDP2OEEEIIIcSHQwqZQgghhBBCfICeb97C05kzFXGrggXJ4u+Pxs5OkbsUeolee3oRnRStyGV3zM66+uvoWKAjapWRrwn3j4F/Vfi1D0QHK/NOfqntZJ2yGl33zkuPaTjvqNECJkDjYt7sHlSFz/JkNnqNEEKIt6NmfnfWdCuDs62FIrfvWghN5h/jQVis6UnMraHVT5CzpjJ3fB7sV+6cTKNSqehdpDeDig9S5AKjA+mwowOXQy8bHe/cpg2Zh32piMdfusTdFi2Jv3bN9NqFEEIIIcR7TQqZQgghhBBCfGBijh/n8TffKOJW+fOTZekSNA4OityVsCv02NPDYBGzZZ6W/Pz5z+R1zmv8pgEbYEV9eHzBcN4lF3T+AzL5Gp1i5bF79F571uA5mJB6FuayTiX5sWVRHG2klawQQvxbymZ34bd+Fcjvqfz5cSskmobzjnDy7jPTk5hbQcvV4FdZmTs8HQ5NMzm8a6GuDC81XBF/EvuE9jvas+nGJqNjXbp2xbWPcldn8uPH3GvTlqj9+02vXQghhBBCvLekkCmEEEIIIcQHJOH2bQIHDITkZL24ZZ48ZFm2FI2jo2LMtWfX6L67O1GJ+ueFWWmsmP3ZbEaXHY21mbXxmwZfhN/6g4HzNNFYQIWB0H0/OHgZHK7T6Ziy8xrf/nbZYHfBfJ4OLGpfgt8HVKRaXiMtbYUQQrxTPk42/NK7HPULeypy4bFJtF1ynE1nAk1PYm4NrX8G37LK3P7xsH0wJMUbHd4ufzvGlFW2ik3SJjH2r7F8e+xbElISDI517d8Pl149FXFdbCyBffsRtnQZOhMtboUQQgghxPtJCplCCCGEEEJ8IJLDwnjYsxfayEi9uJmHB76LF6PJlEkx5mb4Tbrv7k5kov4YS40lc6vP5bMsn5m+aXwEbOgAyQYePOdvCH1PQs3vwUq5iwcgMVnL0A0XWHDwtiLnYmvBwnbF+b1/RWoX8EClUp7RJoQQ4t9jY2HG3NbFGFY7jyKXlKJj6MYLTN15Da3WREHQwhbabgSv4src6WWwtCY8u2N0eIs8LRhfYTxmauX5yJtvbqb9H+15HP1YkVOpVGQeNAjPCePB7JWxOh0h06bxdOYs4+sWQgghhBDvJSlkCiGEEEII8QHQxsUR2KcvSYH6u2HUNjb4LlqIubvyPMmnsU/ptbcXzxOe68Ut1BbMrjabMp5lTN9Up4Nf+ykfONtmhs47ocUqcPYzOjw+KYWeq0+z+dwjRS6biw2b+5SnTkFP1GopYAohxPtCpVLR97OcLGxXHCtz5WOj+Qdv037ZCYKexxmfxMoB2m0C90LKXHAALKoCV34zOrxhzoYsr72czNbKn21Xn12ly64uhMaFGhybqWlTsiw13KEgbNEiwpavML5uIYQQQgjx3pFCphBCCCGEEO+5+Os3uNeiJXEXXjmfUqPBe9ZMrPIod84kpiQy+OBgQmJD9OLmanNmfjaT8l7l07/x8QVw9ZUHzSo1NF8OWcuZXnNSCt1XnebA9aeKXBHfTGzqXZ6sLrbpr0EIIcR/ok5BTzb2LI+7g6Uid/RWGLVnHmLLuUDj7VptnKHDVsNtZhMiYUN72PMtBnuOA0UzF2X9F+sp5VFKkQuMDqT33t6KlulpbMuUJtuG9Vj4KV+2CZkyheebNhtesxBCCCGEeO9IIVMIIYQQQoj3lE6r5dmqVdxr3pyEmzcVeY/RX2NXqZJynE7HuOPjuPBUv/BppjZjRtUZVPJRjlF4cAL2KM8po/o3kK2iyaGxicl0WXGKwzeVu2Wq5c3Muu5lcLFTPhgXQgjxfink48ivfStS0FvZPjwqPpnB6y/Q96ezPItJNDyBrSt02g7l+xvOH50Je8caLWa6WruyuOZiOhXopMhde3aNgQcGGj0z0yJrVrL9vA6rQspdoY/HjCFq717DaxJCCCGEEO8VKWQKIYQQQgjxHkoKCeFhj548mTgJXaLyAbFz5844tW5tcOzaq2vZemurIv5N2W+o6ls1/Zvf3AM/twFtsn48d10oP9Dk0OiEZDotP8Wx22GKXJPi3ixuXwIbC+W5Z0IIId5PHo5WbOhZji+KeBnM/3ExmHqzDnMrxPDuSDTmUGs8tPoJrJTtXjk6Ew5NM3p/M7UZQ0sOZUxZ5cs1p4JPMfLwSFK0KYZv7eiI7+JFWOTIoZ/Qank0ZCgxx08Yva8QQgghhHg/SCFTCCGEEEKI90zivXvca9qMmCNHlEmVCpfu3cg87EuDY/8K+ovpp6cr4u3ytaNxrsamb5wUB38Mg7XNIPaV3ZSZskDjBaA2/hUiKj6JjstOcvLuM0WuVSlfpjcrgplGvoIIIcSHxsbCjNmtijKrVVEcrJQvowRHxtNq8XFuPDFSzATIWx96HgLPosrcgQlwbI7JNbTI04I+Rfoo4nvu72HSyUlGW9yaOTmRZekSzLw89eK6xEQC+/Yl8f59k/cVQgghhBD/LXmKIIQQQgghxHsk6ckTHnTpSvJT5dmSZu7uZFm+jMxDh6IyUFC8G3GXL//8khSd/s6Usp5lGVpyqOkbB1+ExVXh5GJlTmMBLVaBtZPR4RFxSbRbepIz98MVuXZlszCxcSHUapXpNQghhHhvqVQqGhb1ZtfgylTM6arIh0Yn0mrxca4+jjQ+iVO21Faz3iWVud2j4dQSk2voVaQXLXK3UMTXX1/PnHPGC6HmHh5kWboUjbOzXlwbE0PQiJHoUgzv6BRCCCGEEP89KWQKIYQQQgjxnkgOD+dB164kBQUpcva1a5P9163Yli1rcOz9yPt03dWVyET9B8i+9r5MrzIdM7WRdq5aLfw1D/yrwdNryry5DTTxB69iRtf9PDaRdktOcOHhc0WuU/lsjGtYUIqYQgjxkfB0tGZVl9KM/SI/mlf+bH8Wk0hr/+NcehRhfAJLe2j3C3goz67k96FwdrXRoSqVilFlRlEza01Fzv+iP0suGi+EWvr54eu/GLWtrV487tw5wpYtM75eIYQQQgjxn5JCphBCCCGEEO+BlOgYHvbsReKt2/oJtRqPcd/jPfNHNJkyGRz7MPIhXXZ14Wmc/i5OW3NbZn82G0dLA2eSAUQ+hjVNYNcoSFGew4ln0dQ2gAUaGV136kPrE1w08NC6eyU/vv0iPyqVFDGFEOJjolar6FTBj3ltimH2SjHzeWwSbfyPExD43PgE1k7Qfiu45lHmfusHp5YaHapRa5hUaRKlPEopcrPOzmLt1bXGb1ugAF5TJiviT2fPIf76dePrFUIIIYQQ/xkpZAohhBBCCPEf0yYkENivH/EBAYqc57jvcWre3GgxMDAqkC67uxASG6IX16g0TKk0hZxOOQ3f9Op2WFAe7hwwkFRBxcHQdQ+45jK67qdRCbQ20kawT9UcjKqXT4qYQgjxEatT0JMF7UpgrtH/sz4yPpn2S09yJchEm1lbV+jwKzj5KXO/D4G/5hsdaqmxZNZnsyjgUkCRm3xyMltubjE61r5GDRwbNdIPJiUR9NVwtIkGXuoRQgghhBD/KSlkCiGEEEII8R9KDgvjYY+exB4/rshlHjaMTE2bGh0bFB1E111dCY4J1ourVWomV5pMFd8qykEpybB9CKxvC3HPlHkHb+i4DWqMBTMLg/dNTNay9Mhdasz4k+tPohT5AdVzMax2HiliCiHEJ6BmfncWtS+BhUb/EVNEXBLtl57gpoGfEy84eELH38AxizK3ayQc+dHoUHsLexbWWEjOTMoXdr499i3b72w3Otb961GYeXrqxRKuXyd0zlzjaxVCCCGEEP8JKWQKIYQQQgjxH4kLCOBu02bEnjihyLn06IFL1y5Gxz6Lf0a33d0IitE/T1OtUjOx4kTq+NVRDtKmwJaecNpIy778jaD3UfCrZDCt0+nYfTmY2jMPMW77FSLikhTXDK2ZmyE1c0sRUwghPiHV8rrj37EkFmb6j5nCYhJpu+QE90JjjA/OlAU6/2F4Z+besXBgIuh0hodaZcK/lj/ZHLLpxXXoGHl4JP4B/ugMjNXY2+M1aaIiHrZ0KdGHDxtfqxBCCCE+KitWrKBq1aoZujYlJYX169fTsGFDsmfPjr29Pe7u7lSrVo3FixeTlKT8fpzmyy+/RKVS6f1jZWWFl5cXtWrVYsaMGYSHh2dofPXq1Y1eFx8fT9asWV9cO3mysqX+h0gKmUIIIYQQQvwHwjds4H7bdiQHBytymVq2xG3wIKNjE1ISGLh/IA+jHurFVagYV2Ec9bPXVw7SpsDWPnDpF2XOwg4aLYDmK1LPLTPgeWwiHZadpMfqM9w18kB6eJ289K9uvBWtEEKIj1eV3G74dyip2JkZEpVA2yUnCAyPNT44ky903gGuuZW5P6fAhg6QYHhnp6u1K/61/PGy9VLkZp+bzddHvibRwDnQtmXL4tShvX5Qq+Vh9x4EjRhJcliY8fUKIYQQ4oNlquBoLH/58mXKlClDq1at2L17N97e3jRs2JBixYpx/PhxevbsyWeffcbz588Nznn+/HkAatWqRceOHenYsSMNGzbEz8+PQ4cOMXToUPLkycPu3btNjge4cuWK0bXPmjWLBw8evPj3okWLmvysHwopZAohhBBCCPEv0iUl8XjMNwR/8y06A1+QMjVvjsc3Y4zuaNTqtIw5MobzT8/rxVWo+K78dzTI0cDAIC38NgACflbmvEtCr8NQtA0YuWdyipZea85w+GaowbythYaJjQvRu2oOg3khhBCfhiq53Zjbphhmav2fJ4+ex9F2yQmjL8IAqW1mO/0OmfMrc1d/A/9q8PS6waEeth4sqb2EzNaZFbltd7bRbXc3nsUr26lnHjIEi+zZFfGIrVu5Xbcez376CV1KivE1CyGEEOKDEhAQQK5cufj1118VuQcPHtCgQQNGjBihFz937hyVKlXizJkzDBw4kAcPHnD48GHWrFnDzp07uX79OkWLFuXo0aN06WK4q9KFCxcAmD9/PitWrGDFihWsX7+eo0eP8vjxY/r27cvTp09p1KgRly5dMjq+VKlSBAcHG9y9GRYWxqRJk8iRIwd2dnaAFDKFEEIIIYQQr0mn0/F4zDc837hRmTQ3x+O77/D4/jtUGo3ROeadn8eOezsU8RGlR9A4V2PlAK0Wfh8M59coc3nqpe6AcVY+xH3ZjD03OH5H+QBYrYLWpX05OOwz2pQxcL6ZEEKIT06tAh7MbFWUV2qZ3A+L5Ys5R9geEGR4IIBdZui4HTwKK3OhN1KLmZe3GBzqa+/LyroryeGofKnmXMg52vzehgeRD/TiaisrvKZMQWVlpRijjYzkyffjUrsnmGj1JoQQQogPR758+RgwYACdO3emadOmBAUFkZKSwsyZMylYsCC2trYMGDDgxfUhISHUqVOH8PBwZs6cycyZM3Fzc9Ob09fXl02bNmFmZsaWLVs4e/asXv7Ro0eEhoZiZ2dHdgMvUDk5OTF37lyaN29OXFwcY8eONTje29ubatWqAYZ3ZY4bN46IiAj69OlDdHQ07u7ueHh4vOl/qveKFDKFEEIIIYT4lzyd8SMRW7cq4mbu7mRbsxqnli1Mni35661fWRywWBFvl68dbfK1MTxo99dwZoUynqt2aitZMwuTa9539QnzD95WxCvkdOH3AZWY1KQwbvaWJucQQgjxafm8sBfTmhVRbPSPTkim30/n+PbXSyQkG9npaOsCnbZD7rrKXGI0bOwEh6YZHOpj78Pqequp4F1BkXsU/Yjuu7vzJOaJXty6UEGyrl6NZd68BueMO3+eh716oY010RpXCCGEEB8Ec3NzhgwZwq1bt/D29mbs2LEcOXKE9evXs3v3btatW0fWrFlfXD9gwABCQkJo0qQJAwcONDpv9uzZXxQZf//9d71cWlvYQoUKmfy+379/fwB27typ1942bXyRIkUoVKgQkNrq9mW3b99mwYIFlCtXjly5cr24/mMhhUwhhBBCCCH+Bc9WriTM318RtyldGr9Nv2CdzpeMnXd3MvavsYp4Vd+qfFnyS8ODzqyA4/OV8RzVocUqMDNdgHz4LJbB688r4s1L+LCmaxnyeTqYHC+EEOLT1bSEDxMaFTKYW/nXfZov/IuHz4wUB60codVPUG0MYOCB3/7xcGqJwaH2FvbMrTaXtvnaKnJBMUH02tuLiIQIvbh1oYL4/bIR91GjUP/diu1l8RcCCBwwEF2i8qxNIYQQQnx4VCoVarXa6L8DXL16lQ0bNmBhYcGsWbPSnTN//tT2+Ldu3dKLv1yINCWtSBkTE0NwcLBifNGiRV9c8+qOzJEjR5KYmMi0adNetKH9WNrKghQyhRBCCCGEeOcitv/Ok0mTFXHbypXIsnQJZq6uRsdGJ0Yz6vAohh0aRrI2WS+XzzkfUypNQaM20Ir2/l/wu4ECZ/aq0GotmCvb6L0sITmFPmvPEhmvf8+8HvaMa1TQ5JukQgghBECbMllY1qkkjtbmilxAYASN5x/jztNow4PVaqj8JbTbBNbOyvzvX8LlrQaHmqnNGFF6BF+X+Rq1Sv/R163nt+iztw+xSfpFVJWZGc4d2pNjxx84fPGFYs6YI0cIGjkKnVZreL1CCCGEeO8lJyczZ84ccubMyYMHDxg7diwVK1akefPm1KhRg/bt2xMYGAjAokWL0Ol0NGvWDB8fn3TnNjdP/ftOXFycXjytEFm4sIHW+S+xsPh/t6SEhATF+CJFipAvXz7Mzc31CpknTpxg48aNNG7cmAoVKugVPj8WUsgUQgghhBDiHYo+epSgkSMVcavChfGZOROVufLhbpqzT87SbFsztt3ZpshltsnMnGpzsDG3UQ58/hDWtwNtkn7ctyy0Wgfm1ibXnJyiZczWS1x8pL9jxd7SjAXtSmBlbvwMTyGEEOJl1fK68/uAihT1zaTIhUYn0HbJCeM7MwFyVoeef4L7q7s7dbC5O9w9ZHRoq7ytGFturCIeEBrAoAODSExR7rA0c3PDe9pUXLp3U+Qif/+dJxMnodPpjK9XCCGEEO+ty5cvM336dJYtW8bmzZvx8vJCo9EwePBgLl26xPPnz5kxYwYAu3btAqBBgwYZmvvJk9T29c7O+i9gpe2QTK+Q+fz58xe/dn3pZee08UWKFMHc3Jw8efLotZYdNmwYZmZmTJ6c+vK0FDKFEEIIIYQQGZZw5w6PBgyEJP2CooWfH76LFqK2MVCEBHQ6HQvOL6Dzrs48in6kyDtYODCv+jzcbd2VgxNj4OfWEBv6yiAfaLkGLAzfM82DsFhaLPqLDacDFblpzQvj52prcrwQQgjxKh8nGzb0LEeXCn6K3OOIeNosOU5wRLzxCTJlgfabwemV8SmJsK4NPL5gdGjjXI0ZUmKIIv7X478YdWQUWp3hHZZuQ4bg2LSJIh6+Zg2h8wy0bRdCCCHEO7Fp0yZUKtVb++fWrVs0bNhQcZ8sWbKwbds2pkyZQkJCAteuXQOgWLFiGVrnmTNngP+3iAWIjo7m9u3bqFSqdAuZt2/fBsDFxYVMmTLpjbexsXlx9mXhwoUJCgoiIiKCX3/9lcOHD9OjRw9y585NdHQ0d+7cwdramty5c2do3R8CKWQKIYQQQgjxDqRERRHYpy/amBi9uFnmzGRZ4o+Zk5PRsXPOzWH+hfkGH64Wz1ycjV9sJK9zXuVAnQ629oHgi/pxM2to/RPYuRm9p06nY9OZQOrNPszZB88V+W4V/ahT0NPoeCGEEMIUCzM133yRn1mtiqJ+pTv5w2dxtF1ynNDoBMODAewypxYzbTPrxxOjYE0zCLttdGjngp3pXLCzIr7r3i7WXVtncIxKpcLzu++wq15dkQudO5fQxcpzr4UQQgjx9p08efKtzGNtbU2BAgVetIA1xtzcnJCQkBf/7uZm/Ht0mtu3b7/YJVmzZs0X8YCAALRaLdmyZcPe3t7kHEePHgWg+kt/90gbX6hQoRdneKYVSgMCAhg+fDj29vZ8++23QOruTZ1OR8GCBdFoPp5OSmb/9QKEEEIIIYT42Oi0WoKGfUXivXt6cbW9Pb7+/ph7exsdu/LySvwvKh+OmqnM6FusL50LdDZ8JqZOB7tHw5WtylyjeeBZxOg9I2KT+HrrRbYHPDaYL+3nzPC6BgqnQgghxGtqWNQbrU7HkA0XeLlD6+2nMbRbcoKfe5Qlk42F4cHO2aHdL7C8fmoBM01MCKxqCJ13QCZfg0MHFx9MREIEm29u1ov/eOZHynmVI7tjdsUYlZkZ3j9M52G37sSePq2XezpjBiqNBpeuXTL2wYUQQgjxRpo1a0a5cuUM5hITE2nZsiUAtra2rFmzxug89vb2iuJep06d6NSpk+JaS0vLF79OeqXDkiFz584FoFKlSno7IV8+39IUnU7H8uXLAWjRooVi/MttYtN2dg4bNozr168zbtw4MmdOfdErrQ3tx9RWFqSQKYQQQgghxFsXOncu0QcP6gfVarxnzMAqj/H2LltubmH66emKeDaHbEypPIX8LvkND9TpYO+38NdcZa7SUCjY1Og9j98JY8j68wQZaenXqpQv33yRH3ONNHMRQgjxdjQu5kNcopZRW/Q7CFwLjqKN/wlWdy2Ni52l4cGeRVK7DKxpmtpaNk3EQ1jVILWYae+hGKZSqfim7Dc8i3vGwcCDL+IJKQl8ffhrVtdbjZla+ZhMbWWFz4L53O/YkYQrV/VyIdOmoTLT4NyxY8Y/vBBCCCFeS6lSpShVqpTB3NmzZ1/8ukiRIjRq1Oit3NPV1RVHR0ciIiK4cuXKi0KhIRcvXmTBggUATJw4US+X0fMxFy9ezI0bN8idOzeNGzdWjH+5EJq2I/PEiRN4eXkxZMj/W+h/jOdjgrSWFUIIIYQQ4q2K3LOH0PkLFPHMQwZjV6mi0XH77u9j7F9jFfECLgVYV3+d6SLm/nFwdJYyl7sufDba4LCkFC3Tdl2jtf9xg0XMTDbmLGxXgslNC2NjIe8/CiGEeLvalMnCmM+VP9uuPI6ktf9xQqJMnJnpVxma+IPqlcdaz+7AqkYQE2ZwmEat4fsK3+Nq7aoXvxR2iSUXlxi9ncbenixLl2Jp4KypJ5Mm82zNWuNrFUIIIcQ7c+7cuRe/zuhZlhmhVqupU6cOwIudkoY8ffqUtm3bkpCQwKBBg6hYUf87f0Z2ZO7Zs4chQ4ag0WhYunTpixayL49/uTDp6+tL9uzZcXFxYcKECdjY2LzW/T5EUsgUQgghhBDiLUm4eZPHw0co4g716uLctavRcYcDDzPs0DDFmZh+jn4sqLEAOws74zc9OAkO/6CM+5aBpv6gVv6V/25oDE0XHGPegdt6bf3SVMzpyq5BlalTULmjRQghhHhbulb048tayuLgjSfRtFp0nGAj3QIAKNAIGhjoRPD0KqxpDPERBoc5WTnxXfnvFPFFFxZxOeyy0duZOTmRZcVyLHPlVOSejB/P8y1bja9VCCGEEO/Ey4XMt70L8euvv8bS0pJVq1Yxa9YsdK98ed6/fz+VK1fm8uXLNG7cmKlTp+rltVotly5dAgzvyAwODmbYsGHUr1+fhIQEFixYoFcITRuvVqtf7MJMc/v2bUJDQ/Xa4qakpHDp0iVUKpUUMoUQQgghhBBKyaGhPOzVG21srF7cMk8ePMePR6VSKcbodDqWXFxCv/39SNLqn7vhaevJ4pqLcbJyMn7TP6fBn1OUcZ9S0PYXsLRXpM7cD6fBnCMEBCof8Fpo1Iyun49VXUrj7mBl/L5CCCHEW9KvWi6G1FQWM++ExtBi0V8EhscaGPW3Ym2hnrIlO48vpJ6ZGfXE4LDKPpVplruZXixZl8yow6OITzZePDVzdibL8uVYZFeepxn8zTfEnj1nYJQQQggh3pW0HYjwdndkQmoL19WrV2NlZcWgQYPIkSMHLVu2pEGDBuTOnZs6depw7949Ro4cyYYNGzA3N9cbf/36dWL/fj4wbty4F+dxNmrUiKJFi+Ll5cX06dPJnj07O3bsoHv37gbH58iRAzs7Ey83/+3GjRvExcVl+PoPiRQyhRBCCCGE+Ie08fE87NuXpEeP9OIaR0d85s1F/VKrlzQxSTEMOTiEWWdnKXZiOls5s7jmYjxsTeyIPLcGDoxXxr2KQ7tNYOWgSJ1/+JxOy04SlZCsyOXMbMeWvuXpVik7arWy6CqEEEK8KwOq52JE3byK+INnsTRf+BcXHj43Prh0d6j5vTIedA78q0HwRWUOGFZyGD52PnqxOxF36LevH/ci7hm9nZmrK1lWLMciWza9uC4picABA0h6/Nj4WoUQQgjx1uh0uhdnSJqZmVGwYMG3fo/mzZsTEBBA7969MTMz47fffmPbtm3cvn2bPHnyEBAQwPjx4zEzUx7HkrY2gFWrVrFy5Up+/vlnTpw4gVqtpnfv3mzdupUrV65Qs2ZNo+Mzursy7fqP7XxMkEKmEEIIIYQQ/4hOqyVoxEjiLwToJzQavH+cgYWPj2LM3Yi7tPm9DXsf7FXk7M3tWVhjIdkcsxm/6d1DsG2gMu5ZFNpvAStHRepiYATtl54wWMTsUC4r2/pVpICXcpwQQgjxb+hVJQfffqE8M/NxRDzNF/7FupMPFC3dXqgwEKooW7sTGQhLa8P1HYqUjbkNEypOQIX+yzsngk/Q5LcmzD47m7jkOIO3M8+cmSwrV2CWObNePCU0lId9+6KNMzxOCCGEEG/PzZs3iY6OBiB//vxYWlq+k/vkypWL+fPnv9jxePfuXRwcHLh58yZPnhju/gDQqlUrdDqd3j/x8fE8fvyYs2fPMm/ePBo2bKh3Jqah8Rs3bszQOl/3+g+JFDKFEEIIIYT4B57OnEXUzp2KuMeYMdiWL6+IX3x6kTa/t+FOxB1FLptDNlbXW00+l3wmbngD1rcD7SsFSfdCqUVM60yKIZeDImi39ARR8fpjrM01LOlQku8bFsTaQmP8nkIIIcS/oHMFPyY0Vu6mSEzRMnLzRYZvCiA+KcXw4KojoNKXynhSDKxrDUdn8+rB0MXdi9OpYCflEG0S/hf9abi1IYcCDxm8nbm7Oz7z5qJ65aFpwpWrBI0aZbzoKoQQQoi34l2ej2lKtmzZmD17Nlqtls6dOxMRYfhcbvH2KPe7CiGEEEIIITLk+abNhC1erIg7d+6MU6uWividiDv02deH6KRoRe4z38+YUHEC9hbKcy1fiAmFn5pD/CtflBy8oe1GsHFWDLkWHEm7JSeIiNM/g9PKXM3STiUpn8PV+P2EEEKIf1nbMlmxMtMwYnMASSn6xcANpwO5HBTJovYl8HF6pW27SgXVx4BTNtg+6JUXfnSwZ0zqz8/qY/SG9Svaj8CoQPbc36NYy+OYx/Td15dxFcbRKGcjRd66UCE8x48naNgwvXjUjp2E5sqFW58+r/HJhRBCfKiCJ04k4eq1/3oZ/xnLfHnxGDXqX7/vuzwfMz3t27cnMjKSkJAQAgICqFKlyr96/0+NFDKFEEIIIYR4A7GnT/P4228Vcbsa1cn85VBF/EnME3rt6cXzhOd6cRUq+hXrR7dC3VCrTDRMSYqHn9tA+D39uIUdtNkADp6KISGR8XRadorwWP0ipoWZmiUdSkkRUwghxHupaQkf/Nxs6bPmLMGR8Xq5y0GRtPY/zqbe5clsb6UcXLx9ajFzQ3uIC9fPHZ6e+tJPub4vQhYaC36o8gN77u9h6qmpPIlVtogbe2wsmSwzUdW3qiLn+MXnJNy8qXixKXT2HKzy58e+qnKMEEKIj0vC1WvEnjr1Xy/jk/Pyjsx/u5AJ0KtXLwA0Gulu9K5Ja1khhBBCCCFeU9KjRwT2HwDJ+q1arQoUwHvqVFSvfJGJSIig195ePI55rBe31Fgyr/o8ehTuYbqIGXIVltSAhyf04yo1NFsOHso2fPFJKfRcc0bxANhCo2Zx+xJUzCVFTCGEEO+v4lmc2D6gIuWyuyhyD5/F0XHZKSLjkwyMBPwqQbd94JJLmds1Cs6v0wupVCpqZavFb41+o0vBLpip9N/7T9Gl8OWfX3L2yVmDt3MbNBC7atUU8aCvhpP48KGRTyiEEEKIfyKtkKlSqf7V1rLi3yeFTCGEEEIIIV6DNiaGh336khKuv8vDzNMTnwXzUdvot7qLS46j//7+3Hp+Sy+uUWmYXmU6lXwqGb+ZTgcnFsHiqvDkojJfZwrkrmVgmI7RWy9x7sFzvbi5RsWCdsWpmiezyc8ohBBCvA9c7SxZ3bU0PatkV+SuPo6kx6rTxs/MdMkB3faAV3Fl7te+cO0PRdjG3IbBJQazqcEmPG31Ox0kpCTQb18/rj+7rhinUqvxmjoVy1w59eLayEgCBwxEGx+vGCOEEEKINxcUFERISAiQemalo6Pjf7wi8S5Ja1khhBBCCCEySKfVEjRyFAnX9R9iqqyt8Z0/D/PM+gVCrU7L8EPDORdyjld9W+5bgy3qXogKhq194PY+w/kyvaBMD4OpZUfv8cuZQEV8QuNCVM/nbvyeQgghxHvGTKNmZN18ZHG24estl/Ryx+88Y/D688xtUxyNWqUcbO0EbX+B5XUg9Mb/47oU2NgJ2m+GbBUVw7Jnys6imovouKMj4Qn/f3EpKimK3nt7s6ruKnzsffTGaOxs8Zkzh7vNmqON/v9Z2AlXrxI8bhxeEya82X8AIYQQ7z3LfHn/6yX8p/6Lz/9vnY+5b98+atSoQceOHVmxYsU7u48wTQqZQgghhBBCZFDo/AVE7d6tiHtNnoxVvnyK+Pzz8znw8IAiPrD4QBrnamz8RmG3YXk9iA42nC/XD2p+bzB1+OZTJvx+RRHvUsGPFiV9jd9TCCGEeI+1LZOV0KhEftx7Qy++41Iw3/x6ifGNCqJSGShm2rpA+y2wtDZEvvSST0oCrGsNPf8EZ+WOTz9HP+bXmE+XXV2IS457EX8a95Tuu7uzvM5yPGw99MZYZMuG1+RJBPbrrxeP2LQZ66JFcWre/A0+uRBCiPedx6hR//USPjn/1vmYJ0+eBKBs2bLv7B4ifdJaVgghhBBCiAyI3LWb0LlzFXHXPn1wqK1s77rv/j4WBSxSxNvla0fXgl2N3yjiEaxqZLiIae+Z+jC29gRQaxTpWyHR9PvpHFqdfrxSLldG1fu03xIWQgjx4RtQPSftymZRxNeeeMD03cqWry84+kCHrWDzynmbCZGp3Q+0htvTFnQtyMzPZmKm1t8HEBgdSLfd3QiJDVGMsa9RA5fu3RTxJ+PGE3fxkiIuhBBCiNf3bxUyu3TpwtWrV2nfvv07u4dInxQyhRBCCCGESEfcxYsEDR+uiNvXrIFrv76K+O3ntxl1RPlWbq2stRhWapjhHSMAMWGwujFEPFDm8jWA3scgRzWDQ++GxtDG/zgRcUl68awuNsxpXQwzjfzVXwghxIdNpVLxXYOC1C3oocjNO3CbeQduGRj1N9dc0G4TWNjpxx/8BX8pX1RKU96rPJMqTUKF/s/u+5H36bqrK09jnyrGuA0ciE2ZMnoxXWIigf37kxwaanyNQgghhMiQlwuZRYsWfWf3cXd3J2/evNja2r6ze4j0ydMMIYQQQgghTEh69IiHvfugi4/Xi1vmzo3X5Mmo1Pp/pY5MjGTggYHEJsfqxXM75WZchXGoVUb+Cp4QBWubQegrO0pUavhiNrRYBTbOBofeC42h9eLjhEQl6MXtLM1Y0qEkmWwsMvBJhRBCiPefRq3ix5ZFKZtd+TNx2q7rLD961/hgr2LQeKEyvn88PLlsdFidbHX4ptw3ivi9yHt03d2V0Dj94qTKzAzvGT9g5q5/LnVycDCB/QegTUw0vkYhhBBCmBQZGcndu6k/793c3PD29n6t8Tdv3qRHjx7kyJEDKysr7O3tyZUrF+3ateP06dMvrgsMDESlUinaygYGBqLRaKhQoQJRUVGMHj2anDlzYm1tTYECBdiwYcOLa7dt20a1atXIlCkTnp6e9OnTh5iYmDf+7OHh4ahUKooWLcrNmzfp1KkTvr6+WFhY4O3tzVdffUVSUlL6E31gpJAphBBCCCGEESlRUTzs1ZuUV3ZPaJyc8Jk/D/Urb2VqdVpGHh7J/cj7enEHCwdmfjYTG3MbwzdKioef20DQWWXui9lQoiMY2cX5ICyW1v7HCY7UL7Rq1CpmtSpKLnf7dD6lEEII8WGxMtfg36EkhX0cFbnvtl1h/SkDnQ3S5PsCirTWj6UkwuaekGy8wNgsdzNGlxmtiN+NuEu3Xd14Fv9ML27m4oL3zB/B3FwvHnfuHMHffYdO90ofeCGEEEJkyPnz51/8HH3dtrL79u2jSJEi+Pv74+zsTMOGDalSpQoajYa1a9dy//7/v8ufP38eUO74TIu7urpSpkwZfvrpJ0qWLEnBggW5cuUKbdq04fTp0wwZMoTmzZtjYWFBtWrViIyMZMGCBXz99ddv/NnTdqLqdDpKlizJtm3bKF26NJUqVeLJkydMmzaNL7/88o3nf19JIVMIIYQQQggDdElJPBo0mISbN/XiKgsLfObPw8LHRzFm9tnZHAo8pBdTq9RMqzINX3tfwzeKfpq6E/PuIWWu1gQobvwsjofPUouYjyP0i5hqFcxqVZTq+dyNjBRCCCE+bPZW5qzqUpq8HsoXdkZsvsiWc4HGB9eZDA6v/Bx/chH+nGzyni3ztmRk6ZGK+O2I2ww+MJikFP0dEDbFiuH5rXInZ8SmzYSvXmPyXkIIIYQw7E3Px9TpdHTt2hUzMzNOnDjBqVOnWL9+Pdu3b+fatWucPXuWqlWrvrg+vULmjh07aNy4MTdu3ODnn3/m1KlTtGvXjpSUFFq1asWOHTu4fPkyO3fuZPPmzezfvx+AjRs3vtHnBjh7NvXl54CAAOrVq8eDBw/YtGkT+/btY8eOHQAsXLiQJ0+evPE93kdSyBRCCCGEEOIVOp2O4PETiDl6VJHzmjwJGwNflrbc3MLSS0sV8UHFB1Heq7zhGwWegcVV4N5hZa7SUCjfz+gaw6ITaLvkBI+ex+nF1Sr4sWVRPi/sZXSsEEII8THIZGPB6q5lyO6q3yFBp4PB6y8wd/9NwzsfrTNBo3nK+JEf4dY+k/dsk68NX5X6ShE/G3KWCScmKO6XqVkznNorX0p6Mnky0Qb+niGEEEII0970fMzbt29z//59ypUrR+nSpRX5YsWK4eLi8uLf0ytkfvHFF0yYMAEzM7MXuXr16gEQFBTEpk2byJEjx4tcmTJlcHZ2Jjg4OMNrflXaZy9Xrhxr1qzRO7uzZs2aVK5cmcTERC5fNt4y/0MkhUwhhBBCCCFeoktOJvibb3m+fr0i5zZoIA5/fzF5bu3H/AABAABJREFU2fHHx/n+r+8V8brZ6tKpQCfDNzqzEpbXgchHylzJLlBtjNE1xiel0H3VaR480z+HU6WC6c2L0LDo650RIoQQQnyo3OwtWdu9DD5O1orc9N03GLLhAgnJKcqB2atCmV76MZ0W1jSBrX1TOyYY0T5/e4aWGKqIb7q5iZ+u/aSIuw//Cpty+udrodXyaPAQYl96GCuEEEKI9K1YsQKdTodOp6NVq1YZHufo6IhKpeLAgQPMmTOH6Ohok9efP38etVpN4cKFFXGA0aOVLecjIyMBaNasGfnz59fLabVaoqOj9YqlryttR+bEiRPRaDSKfM6cOQEICQl543u8j6SQKYQQQgghxN+0MTE87NuX5wZavTg2aoRLz56K+O3ntxlyYAjJumS9eAGXAowtPxbVq2dbpiTDtoGwbUDqmVyvKtEJ6k03eiamVqvjy40XOPvguV5cpYKpTQvTpLiy5a0QQgjxMfN0tOanbmXxcLBS5Lace0Qb/xOERicoB9YYC665lfHza2BuCTjpD1oDRVCgU8FOtMqjfHg69dRUjgUd04upzMzwnjEDc1/9NvPayEgedOpM5K7dxj+cEEIIId4KNzc3Jk6ciFarZcCAAbi5uVG/fn2WLl1KbKz+S8JRUVHcuXOHXLlyYWNjo4j7+PgYbGt74cIFABo0aKDI3bp1i8TEREWBM6NiYmK4ceMGHh4eei1wX5b2Of5JsfR9JIVMIYQQQgghgOTQUO537ETMn8qzKm1Kl8bz++8URcnQuFD67utLVFKUXtzD1oM51eZgY26jF0enSy1gnlmhXIDaHD6fCV/MArXyzco0M/bcYHvAY0X8uwYFaF7SyDmcQgghxEcui4sNG3uVI4+78szMM/fDaTTvKHdDY/QT5tbQeBGYKXdzEh8Bf3wJy2pDTJjBe35V+ivKeJTRi2l1Wr7880vuRdzTi5s5OeE7fx5qG/2/G+gSEng0aBBhy1cYboMrhBBCiLdmxIgR3Lx5k8mTJ1OmTBl27txJt27dyJ07N3fv3n1x3YULF9DpdIq2smnxEiVKGJw/rfVrqVKlFLm0nZyvc67nq/fWarVG7w1w+vRpVCqVYhfph04KmUIIIYQQ4pOXcPcu91q1Jv7SJUXOtnw5fObPQ2VhoT8mJYGBBwbyKFq/NaytuS3zqs/DzcZNeaO9Y+H8WmXc3gs674CSnU2uc+Pph8w9cEsR71LBjw7lspkcK4QQQnzsfJ1t+KV3OarmUf4MDgyPo92SEzyO0D9bGu/i0Pl3cC9oeNLAU/BTc0hQtp8zV5szvcp0fO31XySKSoyi//7+RCfqj7HMlQvvWbNQWb2yc1SnI2TKFJ6Mn4AuxfAOUCGEEEK8HX5+fgwfPpyDBw9y584dKlasyKNHj5g/f/6La9I7H7N48eKKebVaLQEBATg7O5M1a1ZFPq3I+aaFzLTxDg4OBvOnTp3i1q1blClTBnd39ze6x/tKCplCCCGEEOKTlvzsGQ+6dCUpMFCRc2zYAN+FC9HY2enFdTod3x37joCnAXpxjUrDD1V+ILeTgTZ1f82DozOV8awVoOef4Kt8Y/Nlv10IYtSWi4p4jXzufF0/n8mxQgghxKfC3sqcJR1K0ql8NkXu0fM4Oiw9SXjMK63dvUtAjz+hzhSwNPBw8NEZ2NABkpUt4TNZZWJOtTnYmtvqxe9F3mPM0TGKXZZ2lSqSddVKNAZavoWvXUvQ8BGyM1MIIYT4l2TNmpWOHTsCkPLSy0TpFTJfjQNcv36d2NhYo4VKU2MzIu18zJd3jqbR6XSMHDkSSN11+rGRQqYQQgghhPhk6ZKTeTRkKMmPla1aXXr1xHPyZMVOTIAVl1ew7c42RXxUmVFU8K6gvFHABtg1ShnPUR3abwW7zEbXGBmfxJD15xmw7hxJKfoPNgt4OTCrVVE0asPnaQohhBCfIjONmrENCjCuUUFe/RF5MySazitOEZOgf7Y1GjMo2wv6nYbCLZWT3t4HW3uDVqtI5ciUgymVpqBC/2Z7H+xl5eWViuutCxcm28/rsPDzU+Qit28ndO689D+kEEIIITJs165d/PHHHyQn6//8DwoKYtasWYD+uZbpFTINFSvT23F5/vx5LC0tFWdkjh07FpVKRadOnUx+hrT5T5w4wa5du17Ek5KSGDBgAPv27aNJkyY0bNjQ5DwfIilkCiGEEEKIT9bTWbOJPX5cP6hW4zF2LJkHDVKciQlwKPAQP575URFvl68dLfK0UN7k1t7UB5+v8i4BLVaBmbJQmubk3WfUnXmYzeceKXIeDlYs7VgKW0szo+OFEEKIT1n7slmZ3FR5RtT5h8/pteYMCckG2rjau0OTxVBxsDJ36RfYOSL1zOtXVPGtQv9i/RXxmWdncir4lCJu4etLtnU/YV1Sec5V6Lx5RGz/3cinEkIIIcTrWr9+PfXr18fd3Z3atWvTtm1batSogZ+fH5cuXWLEiBFUrVoVgOTkZC5fvoy7uzseHh4v5kiLZ86cGW9vb8U90gqNhtrOBgcHExwcTMGCBTEz0/8Or/37JSlzc3Oj609MTOTy5cv4+PjQrFkz6tevT/Xq1WnevDnZsmVj7ty5lCtXjuXLl7/2f5sPgRQyhRBCCCHEJylyzx7C/P0VcY9vxuDUysBODODO8zsMPzQcHfoPMCt4VWBoyaHKASHXYENH0L6y68MlF7TZCJZ2yjGktoX5cc8NWi7+i0fP4xR5R2tzlnYqiYejlYHRQgghhEjToqQvo+rlVcQP3wxlyPoLaLVG2rhW/xaKtVPGTy6CQ9MMDulaqCtVfavqxVJ0KQz7cxghsSGK6zWZMpFl6VLsqlZV5B6PGkXc37s+hBBCCPHPtG7dmt69e+Pr68uZM2fYsGEDFy9epE6dOuzevZtJkya9uPbatWvEx8crdmOmxY3tuDS1I9NUW9m0XIcOHYyu//LlyyQmJlKsWDFWrlxJ7969uXLlCtu2bcPJyYmpU6dy4MABo+dnfujk9W0hhBBCCPHJSbhzl8cjRirijo0bk6ml4SJmREIE/ff3JzopWi+ezSEbU6tMxUz9yl+t457Dz20gUf967L2g/RawVZ6NlWb67uvMO3DbYK60nzMzWhTBx8nG6HghhBBC/F+Pyjl4FpPEwj/1f7b+fvExpbI50amCssUrKhV8Pgtiw+H6K7sjD0wAjQVUHKQXVqvUTKg4gVbbW/Ew6uGLeFh8GF/++SVLay/FXK2/20JtaYn3D9O517YdCdeuvYjrEhN52K8/fhvWY+7l9WYfXAghhBAA1KxZk5o1a2bo2oIFCxo8r/rluNZAq/m9e/canbNOnToG50xJSeHPP/+kRo0aVKpUyej4tPMxixcvjrW1NXPmzGHOnDnpfpaPhezIFEIIIYQQnxRtTAyBA/qjjYnRi1vmy4fHt98YbCebpE1i6J9DeRD1QC9ub27P7GqzcbB45a1HrRY294BnrxQjrRyh3SbI5Gt0ff6H7hgsYpprVAyvk5d13ctKEVMIIYR4TcPr5KFVKeXP30k7rnErJNrACFLPzWy2FLIaOP9677dwTPkA0cHCgR+r/oiVRr9rwrmQc0w7ZXgnp9rWFt/589C4uurFU0JDedi7DynRMQbHCSGEEOLDdvr0aSIjI/n+++9NXpfe+ZsfOylkCiGEEEKIT0rw9+NIvKVfKFQ7OuIzZzZqK2WrVp1Ox4TjEzjx+IT+GJWaaVWm4edoYBfHwYlwc5d+TKWG5ivAPb/RtW049ZAJf1xVxHO42bKlTwV6V82BRq0stAohhBDCNJVKxYTGhaiR73/s3XWUFce6BfB9bNwYFwaH4O6E4AFCSLDgTgju7gR3dwshOAkJMSTBg7s7DCMw7nqs3x+8O0OdOiRAAgywf2u9tebsrq7uevd20re/7iofIc8wmjFk+0UYTfKXFQAAnT3QZgvgW0Letm8scGKZFH/g/gHGVxkv5VtubsEPt3+wfhh/fwQuXQKVra14frdu4fHYsVa/4iAiIqK3W6VKlaAoCqpUqfK37Z7+IvN9xEImEREREb03En77DQm7domhSoWA2bNgkzOn1X3WX1uPH+7IDx2HlBuCagFWvtC4/rP1tbPqTgTy137mue25+hgjd16W8pI5XbGr74coHuD6zH2JiIjon2nUKsxuURJezmKx8FJoApYdsj6lO4AnMyp0+AnwtvIy0t5RwKmVUtw4f2O0+kCern7KqSk4H3He6mHsS5WC//RpUp60Zw9iv/322edHRERE7yyz2YzLly/Dw8MDgYHPnt3pXcZCJhERERG9F/ShYQif+LWUe/bpA6ePPrK6z58P/8T8c/OlvFnBZuhQtIO8Q+QN4Kdecl68OVC1/zPP7eDNSPTfchFmi48tCng7YX2XinCy5dL2RERE/4UcjjaY1byklC/afwdXQhOevaOjJ9DxZ8CrsLxt93Dg4mYpHlFhBMp4i1PAGc1GDDo0CI+TH1s9jMsnn8Czb18pj5w9B6lnzz77/IiIiOidpFarkZycjOjo6Dd9Km8MC5lERERE9M5TjEY8Gj4c5qQkIXeoUAGevXpa3edq9FWMOjoKCsTqYiW/Shhbeay8lmZyJLC5JaC3WGfLpzjw2WLAytqbiqJg6cG76PrtGegtprQLcLPHd90qwt3R5jlHSURERM+jVmFvtKmYS8iMZgWDtl9EusH07B2dvIBOvwCeheRtvwwAgsVp6HUaHebVnAdfR18hj02PRf+D/ZFqSLV6GM/eveBUs6YYmkwIHTQIxqioZ58fERER0TuIhUwiIiIieudFr1yJtPPiNG5qFxf4z5oJlUYjtb8bdxf9DvRDuildyPO55sO8mvOgU+vEHQxpwJY2QHywmNvnAFpvAmwcpWMkpRvQc+M5zN57C5bLXnk62eC7bhXh52r//IMkIiKi5za2URHkcncQsruRyei6/gzuRyU/Yy8ATt5PipkeBcTcpAe2tQcSQoXY094TC2sthJ1GXIf7ZuxNjDs2DiazXDhVqdXwnzkDOovp40xR0QgbNBiKwfAcIyQiIiJ6N7CQSURERETvtNTzFxC9bLmU+02aBJ2fn5SfCT+Djrs7IjpNnLbF3c4dS+sshYuNi7iD2Qz82BMIs5juTaUBWnwD5MgjHeNuZDKaLD2GvdcipG3Odlp827Ui8nk5/fPgiIiI6KU42moxr2UpacKE4/diUH/BEczeexOpeqP1nZ19nxQznf3FPCXyyYtN+hQhLupRFJOrTZa62fdwHwYdGmT1y0yNqytyLloIla24nmfq2bOInDMHiuVbUERERETvKBYyiYiIiOidlX79OsIGDQJM4tcOrs2bwaVBfan97/d/R48/eiDJIE5Ba6O2wcJaC5HTOad8kAOTgOs/yXmjOUD+WlJ8ITgOTZcew72oFGlbPk9H7OxVFcX8Xf9+YERERPSvlc/jjh4f5Zdyg0nB0oP3UG/eEey/Ib90BABw8X8y64JW/NIS4ZeBn3rDcrqFBnkboHuJ7lI3B0MOotvebtILVABgV6QIfMePl/LYbzcgat48FjOJiIjovcBCJhERERG9kxJ++RVBbdvBGCE+gLTJnRu+o0cLmaIoWHtlLUYcHQGDWZyuTavSYnr16SjtXVo+yPnvgL/my3nVfkD5rlL8vynrkjLkLzzqFfXBrr7VUNDH+Z8HR0RERP+JwfUKoVEJeYYGAAiLT0O3b89i+9kQ6zsHlAU+Xyrn138CDs+S4r5l+qJmYE0pvxpzFe1/b4978fekbW7Nm8Htiy+kPGb1GkRMnQbFbJa2EREREb1LWMgkIiIioneKYjQiYsZMPBo2DEq6uMYltFr4z5kDtaO4ZuX8c/Ox4PwCqS9HnSOW1lmKj/N8LB8o6C/g14FyXvhToO4kKX6ckIaOa08hLlUslKpUwLD6H2Bl+3JwttNJ+xEREdGrY6NVY0nbMljeriz8XO2sthm18wr+uP6MLzNLtACqD5HzQ9OAW3uESK1SY06NOaifR54VIiw5DB1+74Cz4WelbT5jx8CuZEkpj9u4EeETJkAxyetsEhEREb0rWMgkIiIioneGKSEBwd27I3b9enmjVgv/qVNgX6K4EP9872d8c+0bqbmXvRfWN1iPqgFV5b7igoBtHQCzxZeV/mWAZqsBtXibHZ+qR6d1p/EoQSys2mrVWNe5AvrUKgC12mKRLiIiInotVCoVGpbww/4hNdCrZn7oNOK/k01mBX03n8fpB7HWO6g1FvigkZz/+BUQ+0CIbDW2mPXRLHQtLs/ckGRIQr8D/aQvM9W2tsi1ehXsSsnFzPgd3+PRyFFQjM9Yz5OIiIjoLcdCJhERERG9E8zp6Qjp0ROpJ05K2zSensj97Xq4fv65kN+MvYlJJ+SvJ/O75semTzahsHth+UAZScCWtkCaxcNMl5xAm62AjYMQp+lN6PbtWdyOSBbPSa3CsnZlUesD7+ccIREREb1KDjZajGhQGLsHfIQAN3thW4bRjG7fnsGNx4nyjmo10Gwl4F1UzNMTgO0dAEOa2FylxqBygzC+ynhoVBphW7IhGX3390VcepyQa1xdkWvtOjiULy8dPvGXX/Bo9GhOM0tERETvJBYyXyOTyYSrV69i/fr16NevH6pUqQIHBweoVCqoVCpMnDjxhfvcs2cPWrVqhdy5c8POzg7e3t6oVq0a5s+fj5SUlP9+EERERETZkGI249HwEUi7eFHaZleyJPJ+vwMO5coJeUJGAgYdHIQMU4aQl/QsiW8bfgs/JyvrZZnNwI89gchrYq5zANpuBZx9LZor6LflAs49FB9GAsCMZiVQp4jP8w2QiIiIXpsC3k7Y0K0i3B1thDwp3YiO604jJDZV3snWGWi1EbB1EfPwK8CvgwFFkXb5otAXWFJnCRy04ktQocmhGHRoEAwmcTp6jZMjAlevgmNVebaIxJ9/QcT0GVCsHIeIiP4d/rOV3hfZ9b/rLGS+Ri1btkSJEiXQpUsXLFmyBCdPnkRaWto/72hFRkYG2rRpg4YNG2L79u0IDg5GRkYGoqKicPz4cQwePBilSpXC5cuX/+NREBEREWU/kbNmI2nfPil3bd4Mub/bAJ2vRYFRMWPU0VEITQ4Vcg87D8yvNR+utq7WD3RoOnDzVzlvuhLwLSHF8/+8jT9vyGtqjWhQGF+UD/ybEREREdGblN/LCeu7VICjjfjFZFRSBtqtOYXHCVae53jkB5qukPNLm4Fz8jT2APBhwIeYX3O+9GXmuYhzmHxysvRAUW1vj5zLl8GpVi2pr7jvvkPMCivHJyKiF6Z+arkQE9cipvfE0/9dV6uzT/kw+5zJe8DyH3ju7u4oWLDgS/XVqVMnbN26FQDg4eGBUaNGYfPmzVi0aBEqVqwIALh37x4aNGiAkJCQf3fiRERERNlY7MZNVtfEdO/cGX5TpkBtayttW3l5JY6GHRUyjUqDOTXmwNvhGVO9Xt0JHJkl5zVHA0U/k+I9Vx9j8YG7Ut7tw7zoWSOf9WMQERFRtlEypxtWdigvrZkZHJuKNqtOItxi7WsAQOFGwIeD5Hz3CCD0nNXjVA2oiuEVhkv5j3d/xIbrG6RcbWuLnIsWwqlmTWlb1MJFiNuyxfqAiIjoudnYZH2Vn5yc/Dctid4dT/93/elr4E1jIfM1qlixIkaOHIkdO3bg/v37iImJwejRo1+4n127dmHbtm0AgFy5cuH8+fOYNm0a2rRpg379+uHEiRPo0qULAODx48cYPHjwfzoOIiIiouwi6cABREybJuXO9evDe/gwqFQqaduxsGNYfnG5lA8uNxjlfeV1pwAAIWeAn3rJedHPgY+GSfGdiCQM2X5JyhuV9MOYT4pYPS8iIiLKfj4s6In5rUrD8l/dQTGpaLv6JCITrRQza40F8n4kZiY9sK09kCTP1AAAbQq3QasPWkn53LNzcTT0qJSrdDoELJgP+/LlpG3hkyYj8fffnz0oIiL6Ry4uWVOFx8XFwcx1iOkdZzabEReXtSzO09fAm8ZC5ms0evRoTJ8+HS1atEDevHlfup+n19Jcvnw5cuXKJWxXq9VYunRpZv7999/j6tWrL308IiIiouwo9cwZhA0e8mTdyqfYly4N/5kzoLIyDUp0WjRG/zUaCsRp2hrkaYAORTtYP1DcQ2BrG8Bo8aDStwTQZDlgcZyENAO++u4cUvTibByFfZ0xu0VJqNUsYhIREb1NPi3pj2lN5Snk70enoM3qk4hMsrhH0GiB5usAlwAxT3r0pJhpFNfnBgCVSoURFUegkl8lIVegYOTRkQhNCpX2UdvZIXDZMtgWLixuUBSEjRiJlJMnn2+AREQkcXZ2zvw7PT0dDx8+RHx8PIxGY7ZdR5DoRSmKAqPRiPj4eDx8+BDp6Vn3NE9fA2+a9k2fAL2YO3fu4OLFiwCAggUL4pNPPrHazt7eHt27d8e4ceMAANu3b0fx4sVf12kSERERvVKpFy4gpEdPKOnig0Nd7lzIuXwZ1HZ20j6KomDcsXGITY8V8vyu+fF11a+tfyWZngBsbgmkRIm5oxfQegtg4yjEZrOCgVsv4EF0ipC7OeiwumN5ONjw9puIiOht1KZiLpjMCsb+JL4ofi8qBe1Wn8LWryrDw+mp6eydvICWG4BvGj75GvN/Qk8Dvw4CPl8Ky888dWod5taYi/a/t0dQYlBmnqhPxOBDg7Gh4QbYacV7HI2LC3KtXoWgdu1hCA7O2mAwILRff+TZvAm2L7msERHR+8zGxgY+Pj6IiHjyJX16ejoeP378hs+KsgvLYva7NuuSj48Pp5all7d3797Mv+vXr/+3bRs0aJD59549e17ZORERERG9TmlXriCk+1cwp6YKucbNDblWrYI2Rw6r+22+uRl/hf0lZPZae8yrNQ8OOgd5B5MB2N4JiLop5lo7oM1WwC1Q2mXW3ls4eEsseqpVwJI2ZRHobuUYRERE9NZoXzk3Jn1eTMrvRCaj58Zz0Bstph3MWR5oNE/u6OIm4KQ8zT0AuNq6YnHtxXDUiS9L3Yi9gamnplr9Ckjr5YVc69ZC6+Ul5OakJAT36AFjVJS0DxER/TN3d3f4+Pi86dOgbMpsNr+TUw77+PjA3d39TZ+GgIXMt8zTU8SWKyevg/C00qVLQ6PRAACuX7/OT96JiIjorZd+/TqCu30J81ML0AOA2sEBgSuWwyZ3bqv73Yq9hbln50r5iAojkM81n7yDogC/DwXuH5S3NV355MGkha2ng7Hi8D0pH9mwMD4s6PmMEREREdHbpGOVPBj/aVEpPxMUh3E/XZWfvZTtAFSyss72vjHA3f1Wj5HHNQ+mVpsq5T/d/Qk/3PnB6j42OXMicM1qqB3FAqjx0WOE9OwFc0qK1f2IiOjvubu7I3/+/PD29oadnR3UVpYwoffTu1LIVKvVsLOzg7e3N/Lnz5/tipgAp5Z969y+fTvz7zx58vxtW61Wi4CAAAQHByMlJQVhYWHImTPnCx/z3Llzf7v9xo0bmX8bjUYYDIYXPgYR/XcMBgOMRmPm30T05vG6/G9k3L6NsK7dYE5MFHKVvR38li+Dtlgxq///TTOmYfiR4TCYxW21A2ujcZ7G8j4mAzR7hkF9caPUl6nWOJgLNQIs9jl2L0aaag4APi3hi86VA/mfezbDa5Ioe+E1SW+bDpVyQm80Ysae20K+7WwICng7oHMVixerak+AJvI61A8OZ2WKGcr3XWBquRlKoLguJgB85P8ROhftjPXX1wv5tFPTUMClAIp5yF+GavLlg8/cOXjcpy9gylqrO/3aNYQMHgy/hQuh+v8X3v8Jr0ui7IXX5JulUqng4uICFxeXN30qlE0YDAbExj5Ztsbd3R06ne4Nn9F/52X/GfO/f0a9CixkvmXi4+Mz//b0/Oc3+z08PBD8/2skxMfHv1Qhs3x5+YuDZ0lISEBMTMwLH4OI/jtGoxFJSUmZv7Va/qOe6E3jdfnvmZOSkdS7D8wJCeIGGxs4TpuG1Ny5kWrlHkRRFMy/Nh/3E+4LuaetJ/oU6JP5Pzz+R2VIgdu+gdCFHJH6Si3cHImF2gEWx3kQk4a+227BaBa/wCjq44ChH/lJx6A3j9ckUfbCa5LeRk0KO+PuYy98f0mctnX67lvwtjWjUm7xYbeqxix4xHwBbWLWOpaq9ARoNjVFQq2ZSC/wiXSM1gGtcTH8Ii7GXszMDGYDBh4aiNkVZiPQUZ7mHoULw2HwIKTOniPEqYePIHTyZDj06/dc4+N1SZS98Jokyl54Tcqerl391/gd9Fsm+alp1Ozs7P6m5RP29vaZfz99YRERERG9TVIXzIc5IkIMdTo4TZ0CXZky1vcxpmLSxUnYHbZbyFVQYWTJkXCxER8wqlOj4L6rA2ytFDEz/CshsfpEQKUS8pgUAwbvuotkvUnIfZ1tMPuzArDT8XabiIjoXTWwRiDKBzoLmVkBxv5+H8Fx6UKu2LkhvsEymC3WvlSZ9HD7cxAcL6x+MrX9UzRqDUaXHA0vO3Hty5iMGAw5PQQPkh5YPS/bRo1g1769lGf8sBMZf/zx3OMjIiIiyg5YJqZ/dPbs2b/dfuPGDXTo0AEA4OrqCg8Pj9dxWkT0DE9//v+uTW1A9LbidfnvJP36Gwz7D4ihVgu/BfPh+NFHVvcJSQrB4JODcS9BXrOyS7EuqF2wthjG3IF2V1uoEoKl9ua8NaBuvh4etuKDynSDCT2+P4vHiXohd7TVYG2ncijkI7an7IPXJFH2wmuS3mbL27ui+cqTCI5Ny8ySMkwY+dsDfN+jMpztnnr05uEBc/NvoPq+E1TGNKEf51Nz4KCPhLnBLECdtY8HPDDnozno9mc3GM1ZU7bF6eMw/NxwLK21FEXci0jnpQwfhojYWCT//ruQp82dB48yZWH7QaG/HRevS6LshdckUfbCa1Lm5ub2yvpmIfMt4+TklPl3enr637R8Ii0t68bY2fnlHqaVK1fuudtqtVpetETZwP+mM9DpdLwmibIJXpcvRx8ahqipU6Xce9BAuNWpY3WfY2HHMOzIMCTp5dkoyniXQd+yfaFTP/WfQXIUsLkFkBgmd1aqDdSNF0GttRFiRVEwfuc1XAoVp7rVqFVY1q4ciuV0f47R0ZvEa5Ioe+E1SW8rL1cd1naqgKbLjiM5I6vQeD86FaN/uo7l7ctC9fSMDoXrA11+Aza3AlLEaWk1FzZAk5EAtFgPqLNmdSjrVxbTP5yOkUdHwqRkzQIRnxGPnvt7Ynm95SjlVUo6t4AZ0xH86BHSLl7MzJT0dIQPGYy8O3ZA8w9rvfG6JMpeeE0SZS+8JkWvcnpdznX1lnm6qh0dHf2P7Z9er/JVVsSJiIiI/muKyYRHI0fA/NTU+gDgUKkS3Lt0sbrPrru70Ht/b6tFzNqBtbG87nKxiGkyAt93sV7E/GgY0GQ5YFHEBIBVR+7jxwvyPpM+L4YahbyknIiIiN5dBX2csahNacsZ6LHnWjhWHbkv7xBQDvhyP+D5gbzt+i7g4BQpbpC3AebUmAOtWnxImGRIwlf7vsL5iPPSPmobGwQsXACNxcxZhofBeDRiJBSz+Z8HR0RERPSGsZD5lilUKGvqj6CgoL9tazQaERb25AGbo6MjAgICXuWpEREREf2nYlavQdrZc0KmdnWF/4zpUKnl29hjYccw4fgEmBX5oVyf0n0wv9Z8OFqsS4X9XwNBR8VMpQEaLwRqj5XWxASAgzcjMWPPTSnv9mFetKuU+zlGRkRERO+a2oV9MPRjuTA5c89NnLgXI++QIzfQbR+Q18o0+UfnApe2SXHd3HWxsNZC2KjFl6xSjanou78vbsXekvbR+fggYN48QKMR8uSDBxGzatU/jIqIiIjozWMh8y1TvHjxzL/PnTv3Ny2BixcvwmR6MuVI0aJFxalMiIiIiLKx1AsXELVkiZT7fT0ROj8/Kb8VewuDDw0WplsDAEedIxbVWoSepXpCrbK49b2+Czi+SD54o7lAuc5Wz+tuZBL6b7kARRHzGoW8MPoTeX0qIiIien/0qpEf9Yr6CJlZAfptOY/wBCvLA9m7Ae1+AAp/Km/7uS8QclqKP8r5EZbWXQp7rb2QJxmS0OOPHghJDJH2caxUEd6DB0t51MJFSDklH4OIiIgoO2Eh8y1Tv379zL/37t37t2337NmT+XeDBg1e2TkRERER/Zf0ISEI7dMXMBqF3LVJE7hYuacJTwlH7/29kWpMFXJfR19s/mQzauWqJR8k6hbwU285L9P+mUXM+FQ9vvz2LJIyxPPK5+WIRW3KQKPmS2NERETvM7VahbktSyGPh4OQRyfr0WfzeeiNVqZy1doAzVYDfqXF3KQHtrYF4oOlXSr7VcbyusulYmZMegy6/9EdUalR0j7uXbvA+eOPxVBR8HjcOJjT0p5rfERERERvAguZb5mCBQuiTJkyAIA7d+5g9+7dVtulp6dj9erVmb9btmz5Ws6PiIiI6N8wxccj5KseMMXGCrkuMBA+Y8dI7ZP1yeizvw8iUyOF3FnnjOV1liOfWz75IBlJwLb2gF5cexN+pYBP5lidTtZgMqPP5vMIihGLpc52WqzpWB6u9jppHyIiInr/uNjpsLx9OdjpxEdu5x7GYcpv163vZOMAtNkCOPmKeUoUsLn1k3sXC+V8ymFhrYXSmplhyWHo8WcPJGQkCLlKpYLftGmwySfeGxmCg63OgkFERESUXbCQ+RaaMGFC5t+9evVCcLD4dp7ZbEafPn0y8xYtWghT0hIRERFlR2a9HqH9+kP/4IGQq2xtETBvLjROTkJuMBkw+NBg3I67LeRatRYLai1AgRwFrB/otyFAtLgP7HMALb8DdPZSc0VRMPbHqzh2V1zfSq0ClrYti3xeTtI+RERE9P4q4ueC6c1KSPmGEw+x5bT8hSUAwMX/STFTayfmkdeAXX0gzWsPoIp/FcysPhMqiC9h3Ym7g777+yLdKE5nq3FyRMDcOdJ6mbHfrEfa1WvPMTIiIiKi10/7z03ov/LgwQOsXbtWyC5fvpz594EDB2C0mEKtefPmmV9g/s/nn3+OVq1aYdu2bXj48CHKli2LHj16oESJEoiJicGGDRtw+vSTNQ78/Pwwb968VzQiIiIiov+GoigIHzcOqWfOiBtUKvjPngX7EuLDQIPZgGFHhuHE4xNSX5OqTkJFv4rWD3RpG3B5m0WoApqvAXLktrrLyiP3se2svN7UmEZF8VEhr2eOiYiIiN5fTcvkxIXgeGw48VDIx++6igLeTqiQx13eKaAs0HQFsKOzmF/fBZxYAlTtJ+3ycZ6PMU4/DpNOTBLyi1EXMfqv0ZhTY46wTrhdkSLw6NoVMU/N4gWzGY/HjkXeHduh0nGWCSIiIspeWMh8jR4+fIipU6c+c/vRo0dx9OhRIStQoIBUyASAb7/9FiqVClu3bkVMTAymTZsmtcmfPz927tyJwMDAf3/yRERERK9Q9JKlSNj1s5R7DxsGF4v1nIxmI0YdHYX9wful9n1L90Xj/I2tHyT2wZOvMS3VGg0UqGt1l91XHmPG7ptS3rpCILpWy2P9OEREREQAxjYqihuPE3EmKC4zM5gU9PzuHH7u9yEC3OSZIFCsKRB1Gzhk8ZznjwmAfxkgz4fSLl8U+gIJGQlYeH6huMvDPzDv7DwMrTBUyD379EbSvn3QP8wqsmbcvImYdd/As8dXLzFSIiIioleHU8u+pWxtbbFlyxbs3r0bX3zxBQIDA2FrawtPT09UqVIF8+bNw6VLl1CyZMk3fapEREREfyvl+HFEL10q5W5tWsO9S2chM5lNGHdsHPYG7ZXaNyvYDF+VfMbDN5MR2Nkd0FusMZWnOlDdSnETwMWQeAzcdlHKPyzgiclNikNlZS1NIiIiov+x0aqxvH05+LuK08XGpOjR/duzSNUbre/40TCgUEMxU0xPvtRMfGR1l27Fu6FD0Q5S/u31b7Hl5hYhU9vZwW/KZKlt9NKlyLj/QMqJiIiI3iQWMl+jmjVrQlGUF/q/zp07/22fDRo0wPbt2xEcHIz09HRERUXh+PHjGDRoEBwdHV/PwIiIiIhekikhAY9GjZZyxxofwXfMGKFYaFbMmHhiIn69/6vUvmGehhhfefyzi4uHZwKhFtPW2ucAmq4E1Bqp+a3wJHz57VlkGM1CXsDbCUvblYVOw9toIiIi+meeTrZY1bE87HTivcP1x4kYuuMSTGZ57Uuo1U+mmM2RV8xTop4UM416aReVSoWh5YeiXu560rYZp2fgUMghIXOoUAFurVoJmaLX4/GoUTCni2trEhEREb1JfAJDRERERG9M+KTJMEZECJltwYIImDsPKq24CsKcs3Pw092fpD7q5qqLqdWnQmOlIAkAeHgcODpHzj9bDLgGSPHxe9FoseI4opMzhNzD0QbfdK4AV3uuHUVERETPr3iAK+Z8UUrKf78Sjp4bz1n/MtPeDWi1EdBaTD8bcgrYMxJQ5AKoWqXGtA+noaSXODuXWTFj+JHhuBZ9Tci9hw6B1sdHyNIuXULogAFQDIbnGxwRERHRK8ZCJhERERG9EQm//obE334TMpVOB/+5c6BxEmeWOBh8EN9d/07qo2bOmpj10Szo1M8oLoaee/LlgiJ+WYlynYEi8lqauy6GodO600hKFx8o2mjVWNWxPALdHf5xXERERESWPi3pj761Ckj5H9cj0HrVSUQmWfkK0rc40HihnJ9dC+wZZbWYaae1w+LaixHoHCjkacY09D/YHzFpMZmZxtkZvhMmSH2kHD6C8BEjoRhNzzEyIiIioleLhUwiIiIieu0Mjx8jfNIkKfcaPBh2hQoJWXRaNCYclx+yVfOvhrk150KneUYR88Im4JuGQLL4xSc8CwH1pwmRoihYduguBmy9CINJfCioVauwoFVplMud4zlGRkRERGTd4HqFUK+oj5RfDk1A06XHcSciSd6pVCugQnc5P7Uc+HUQYDZLm9zt3LGszjK42roKeWRqJIYfGQ6jOeuFLefatZCjfXupj5Q//kDqrJlQrPRPRERE9DqxkElEREREr5ViNuPR6NEwJyYKuUOlSnDv1FFsqygYe2ws4jLihLyYRzEsqLUANhob+QAmA7B7BLCrN2ASp4eFxgZovgawEb/4nL77JmbtuSV15WSrxTddKuCTEn4vMEIiIiIimVqtwuI2ZfBpSfm+Iiw+Dc2WH8fJ+zHyjvWnAbmqyPm5b/7/fkeemjaPax4sqrVImrXidPhpLL6wWMh8Ro+Ca5MmUh/6fX8gdcFCKFa+/CQiIiJ6XVjIJCIiIqLXKnrJUqSeOClkamdn+M+YDpVavD3dcnMLjoUdEzJ7rT1mfjQTdlo7ufPUWOC7psCpFfI2jQ3QZDngJ65R9f25UKw6cl9q7uNii+09qqB6Qa/nHBkRERHR37PTabCodRn0qplf2paUbkT3b8/KX2ZqbYC224FcVeUOL20Bdn755EUuC2V9ymJ0pdFSvu7qOvz58M/M3yq1Gn5TJsO5QQOprf7nnxG3wsp9FREREdFrwkImEREREb0WiqIgcuFCRC9bJm3zHT8eOj/x64S7cXcx9+xcqe2ICiOQ2yW3fAB9KrCpBRB0VN7m7Ad02Q2UaCHE1x4lYMyPV6TmhXycsLN3NRT1d/mHURERERG9GLVahRENCmN6sxLQqFXCtqQMI7p+ewaxKXpxJzsXoP33QL6acofXfgR+7GF1mtnmBZujaYGmUj722Fg8SHiQ+Vul1SJg1kw41aghtY1dthxJBw483+CIiIiI/mMsZBIRERHRK6coCqLmzUPMcvmNfpdPGsLl00ZCpjfpMeLoCOjN4kO8OrnqoFnBZvIBzCZgZ3cg7Jy8LWdF4KtDQM7yQpyQakDPjeeQYRQf+pUOdMOOnlUR4Gb/fIMjIiIiegltKubCus4V4GijEfKQ2DT0/O4c9Bb3KLBxBNpsAwp+LHd29Qdg93DAYhpYlUqF0ZVGo4h7ESFPMaRg0MFBSDWkZrW1sUHAwgVwqFxZ6v7RsOHIuC/PYEFERET0qrGQSURERESvlKIoiJw5CzGr10jbbIsUge/EiVCpVEL7SScm4XbcbaGtl70XJlYR22baOwa4+aucl+0EdP4VcPYVYrNZwcBtFxASmyYew9kWKzuUg6u9uJ4UERER0atQo5AXVnQoJ32ZeTooFmN+vCKvT6mzA1ptAoo0ljs7sxo4NF2K7bR2mF9rPlxtXYX8XsI9zDs3T8jUdnYIXLoENgUKCLk5JQWhffvBlJz8AqMjIiIi+vdYyCQiIiKiV0ZRFERMm47Y9eulbXbFiyP3N+ugcRGnb111eRV23dsltZ/y4RS42bnJBzmxDDi1XM6r9gc+WwRobaVNiw7cwcFbUUKmUauwtG1Z+LhYWXuTiIiI6BWpXtALExoXlfId50Kx5ugDeQetDdBiPfBBI3nb4ZnASXkGjACnAMyoPgMqiAXTbbe24XzEeSFTOzrCb9FCqJychFx//z4eDR8BxcoUtkRERESvCguZRERERPTKxG3chLjvvpNyu1IlkWvdWmjc3IT8t/u/YcnFJVL7DkU7oKp/VfkA138G9o6W82LNgLpfWz2nQ7cisXD/HSkf/UkRVMzrbn0gRERERK9Qxyp50KGyvAb4tN03cPBmpLyDRgu0WAfk/lDetmcEcHm7FH8Y8CF6lOoh5ROOT0CGKUPIdIGBcBw3DrCYCSP5wAFEL7PyAhkRERHRK8JCJhERERG9EqnnzyNi5kwpty9bFrnWrpW+xDwXcQ7jjo2T2lf1r4pB5QbJB3hw9Mm6mLCYci1XFaDJckAt3+pGJWVg6I5LlstH4dOSfuhaLc8/DYmIiIjolRnfuCg+LOApZIoCDNh6AcExqfIOOjugzRbAr5S87adewL0DUvxVia9QMEdBIQtKDMLKSyvl7itVhN2X3aQ8eskSxG7a9A+jISIiIvpvsJBJRERERP85Y1QUwgYMBIxGIXcoXx65Vq+CxmKqsqCEIAw4OAAGs0HIC7gVwJwac6BTW6xZ+fAEsLkVYEwXc48CQOvNTx7sWTCbFQzdcQnRyXohL+TjhJnNS1pfe5OIiIjoNdFp1FjatizyeToKeWK6ET03nkO6wSTvZOcCtPvhyT3Q08xGYHsnIOKaxTF0mFR1EtQq8ZHgN1e/wa3YW3L3bdvCsV49KY+YPAWxG1nMJCIiolePhUwiIiIi+k8pBgPCBg2GMUpcg1IXEICcSxZD7Sg+nEs1pKLfgX5IyEgQck97TyyrswzONs7iAULPApu+AAwpYu7gCbTbAThYnx52/fEgHL4tnpO9ToNl7crB0Vb7AiMkIiIiejVcHXRY06k8nC3uTa4/TsTYn65CsZxWAgCcvIAOPwLO/mKekQhsagkkPhbi4p7F0b5IeyEzKkZMOD4BRrP4EppKpYLPlMmwLSh+xQkAEVOmIPa7jS8wOiIiIqIXx0ImEREREf2nIufOQ+rZs0KmsrFBwKKF0pqYADD77GwEJQYJmb3WHkvqLIGfk5/Y+NEF4LtmgD5JzG2cgXbbAfd8Vs/p+qNEzNh9U8onNC6KAt5OVvYgIiIiejPyeTlhTkt5utjvz4Viy+kQ6zu55QLa/wDYilP3IzEU2PwFkCHeO/Up3QcBTgFCdi3mGjbdkL+yVDs4IHDNatjkltfwjJg6lcVMIiIieqVYyCQiIiKi/0zi7t2IXb9eyn0nTIB9sWJSfijkEL6//b2QqVVqzPpoFop5WLQPvwp81xSw+HITOscnD+4Cylk9pzS9CQO2XoDeZBbyhsV90apC4D+OiYiIiOh1q1/MF71q5pfyiT9fw6WQeOs7+RQFWn0HqC1mmgi/AnzfFTBlfW3poHPAhCoTpC4WnF+AI6FHpFzn44NcG759ZjEzbseOvx8QERER0UtiIZOIiIiI/hOGiAg8Hi8/EHNr2RJuzZtJeUxaDCYcl9v3LNUTNQNrimFa3JM1MdPixFxr/2Q62VyVrJ6T2azg61+u4U5kspD7udpherMSXBeTiIiIsq0h9QqhWgEPIdObzOi+4SzuWtzbZMpXE2i8SM7v7AP2jhKiKv5V0KRAEyEzmo0YfGgwzkaIs2sA/ytmboBNnjzStoip06APCvqb0RARERG9HBYyiYiIiOhfUxQFj8ePhzlJnLbMrkQJ+IwdY7X91ye+Rmx6rJCX9CyJ7iW6WzYGfh38ZGq0p2lsgTZbgDzVrJ5TcoYRvTedx9Yz4hRsKhUwv1VpuDnYPOfoiIiIiF4/rUaNRa3LwM/VTsgjkzLQetUJ3AxPtL5jmXZAjRFyfnoVcHGLEA0tPxTeDt5ClmHKwIDDA3A9/rrUhc7HG7m+/VYqZirp6Xg0ZiwUs1nah4iIiOjfYCGTiIiIiP61hJ92IeWwOA2Z2tkZORcugNpGLhj+ePdHHAw5KGT2WntMqz4NWsvp0C5vA67tFDO1Dmi9Cchfy+r5BEWnoNmyY9hzLVza1rtmflTO52FlLyIiIqLsxcPJFsvalYVOI84iEZ2sR+tVJ3ElNMH6jjVHASVby/mvA4HHlzN/utq6YkXdFXC1dRWapRnTMObcGNxNvCt18b9ipsbLU9zn3DnEbZTX2CQiIiL6N1jIJCIiIqJ/xRARgYhp06TcZ/Ro6Pz9pTwkMQQzT8+U8qHlhyK3i8W6S3FBwG9D5YPWnQAUrGf1fA7fjsJnS/7C7Qh5yrXyuXNgYN1C1gdCRERElA2VyZUD81qWhkYtFjPjUw1ou+Ykzj2Mk3dSqYDPFgOBlcXcmA5s7yBM118wR0GsrLcSTjonoWmyMRkjz45EUGKQ1L3Oxxt+EydKeeS8edA/fPjcYyMiIiL6JyxkEhEREdFLUxQF4eMnSFPKOtWoAdcmn0vtw5LD0P2P7kg1pgr5Rzk/wheFvhAbm4zAzh6AXuwbeWsAlftYPZ8dZ0PQ5ZvTSEw3StvqF/PB+q4VodPwFpiIiIjeLo1L+WNpW/nLzKR0IzqsPYUzQbHyTloboOW3gKM4dSzigp7cYz01DWwxj2JYWmcp7LX2QtMEQwIGHxmMJMv7MQDOderA5dNPhUxJT8djTjFLRERE/yE+xSEiIiKil5bw0y4kHz4sZGpnZ/hO+hoqlfigLSQxBJ33dEZYcpiQ57DNga+ryu3x13wg5KSY2bkBTZYDavk29vDtKIzceQVmRT7PIfUKYXm7cnCy1cobiYiIiN4CDYr7YlWH8rDRivdBqXoTun5zBtceWZlm1tkX+GI9oNKI+Z29wNE5QlTWpywW1loInVon5EGJQRh9dDTMilyc9BkzGhoPccr+1LNnEbd5i9SWiIiI6GWwkElEREREL0UfGvbsKWV9fITsQcIDdN7TGeEp8pqVE6tOhKe9uMYSgk8Ch6bLB/1sEeAaIMXXHiWg98ZzMFlUMZ1stVjTsTz61SkItcV0bERERERvm1qFvfFN5wqw14mFyaQMIzqtO4Og6BR5pzzVgI8ny/nBacB98YW0Kv5VMKfGHKhV4iPDQ6GHsOLSCqkLbY4c8J04Qcoj585Fxv37zzEiIiIior/HQiYRERERvTBDRASCu3R5rill78XfQ5c9XRCZFinkKqjwddWvUTtXbbHz2PvA1raAYhLz0u2BovJ0tY/i09B1/Rmk6MX2AW72+KlPVdQt6iPtQ0RERPS2qlbAExu6VYSDjVjMjE7OQPu1pxCRmC7vVLk3UKypRagAP/cD9GLxs3au2uhbqq/UxfJLy3Eg+ICUu9SrB5dPGoo9p6UhpEdPGGNinm9QRERERM/AQiYRERERvRBjVBSCO3WGISREyK1NKfso+RG67u2KmHTxIZZapcaUD6egWcFmYuepscCmlkCqxUOvHHmBhjOkc0lMN6Dr+jOISMwQcmc7Lb7pUgEFvJ1fYoRERERE2VuFPO5Pppm1WPs7NC4NHdeeRkKqQdxBpQI+WwJ4fiDm8Q+ffJlpoVORTqjhW0PKR/81GvcT5C8tfcaOhcbdXcgMISEI7d0H5nQrhVUiIiKi58RCJhERERE9N2NcHIK7doU+KEja5jfpa2FK2QxTBgYfGozY9FihnUalwfQPp+Oz/J9ZdJ4BbGsPxNwRc6090GIdYCsWJfVGM/psOo+b4eJXoTqNCis7lEMhHxYxiYiI6N31YUFPLGxdGpaz59+KSEKX9aeRZjFbBWydgBZrAbXFmuEnlwFh54RIpVJhSLEhyOuUV8hTDCnof6A/otOihVzr7g7/mTOkdczTLl3Co+EjoJjl9TWJiIiIngcLmURERET0XEwJCQju2g0Zd+5K23y//houDcUpxaafmo5rMdeETKvSYnaN2fgk3ydiB8r/T2328JhFzyqg+WogoKyQGkxm9N18HkfvRMPSrBYlUTW/p5QTERERvWsalvDD1KYlpPx8cDwGbbsorR8O3xJAtQFippiBXf0Ao16I7bX2mFhmIlxsXIT8YeJDdNvbTSpmOlWvDp+xY6RzSdq3D5Fz5r7AqIiIiIiysJBJRERERP/IlJyM4C+7I+PGDWmbz+jRyNGqpZDtvLMTP9z5QWo7rfo01MtdTz7AoRnA5W1y/vEUoEhjITKazBi49SL2XY+Qmg/9uBCalsn5D6MhIiIiene0qZgLwxt8IOV7roVj+u/yvRs+Gg54FBSzyGvAsYVSU38Hf0yrOg1qlfgI8X7CfavFTPe2beHepYvUT+y6dYjbsuU5RkNEREQkYiGTiIiIiP6WOSUFIV/1QPqVK9I272FD4d6xg5Bdi7mGqSenSm07Fe2EhnkbSjnuHwIOy+tfosKXQJU+QmQyKxi64xJ+u/JYat66QiD61Crw94MhIiIiegf1qpEfX36YV8rX/PUA3x4PEkOdHfDZYrmTI7OAqFtSXNW/KoZXGC7lzypmeg8bCuePP5bah0+bjjQr95NEREREf4eFTCIiIiJ6JnNaGkJ69Uba+fPSNs/+/eDRrZuQxafHY/DBwdCbxanJyvuUx8ByA+UDpMUBP/WW84IfAw1mAqqsRZ/MZgUjfriMny4+kpp/VsofU5uWgEqlkrYRERERvetUKhVGf1IEn5TwlbZ9/cs1/Gk5k0XuKk9eGnuaSQ/81AvQp0p9tCvSDsPKD5Nya8VMlVoN/1kzYVeqpNjYYEDY4CEwJSWBiIiI6HmxkElEREREVpkzMhDatx9ST5+Wtnn06AHPXr2ETFEUjD02Fo9SxEKjl70XZteYDa1aKx/k92FAYpiYeRcFWqwDNGL7Kb/dwPfnQqUuGhb3xbyWpaBRs4hJRERE7y+1WoV5LUujbC43ITcrQL8tF3A5NF7coc4EwCVAzMLOAdvaA8YMqf+OxTo+s5g55NAQmMymrHOxs0PgsmXQ+vsJbQ0hIQifMAGKolh2Q0RERGQVC5lEREREJFH0eoQNHISUY8ekbe6dO8Nr4ADp68ef7/2Mw6GHhUyr0mJuzbnwtPeUD3Lle+DKDjHT2ADN1wK2zkL8x/UIrDv2QOqibhFvLGxdBloNb2uJiIiI7HQarO5YHrk9HIQ8zWDCVxvOITr5qQKlnQvw6Xy5k3v7ofnpK8BslDY9q5h5PvI8ttwU18DUenggYO5cQKMR8sTfdyPhB3ktdSIiIiJr+MSHiIiIiCRRi5cg+eBBKc/Rtg28RwyXipgRKRGYeXqm1H5ohaEo411GPkBCGPDbYDmvMwHwKSpEkUnpGPHDZalpjUJeWNquLGy0vKUlIiIi+h8PJ1us71IRORx0Qh6emI5+my/AaDJnhYXqA5X7wJL61m9wPTgSUMzStmcVMxeeX4jgxGAhcyhTBl4DB0htw6dMRcadO887JCIiInqP8akPEREREQn0oWGIXb9eyl1bNIfP2LFSEVNRFEw6OQlJBnG9o49yfoS2hdvKBzCbn6y/lJ4g5nmqA5XF9TIVRcGwHZcRmyKuuVkqpytWdigHW634hj8RERERAXk9HbG6Y3nYWMxaceJ+DGbvuyU2/ngKUKaD1If9nV/gcmQiYGUa2I7FOqJRvkZClm5Kx7hj42C2KH56dOsGx2rVhExJT0fY4MEwp6c//6CIiIjovcRCJhEREREJohYthGIwCJlL48bw+/prqNTy7ePP937GkdAjQuasc8b4yuOloicA4PBM4IE4BS1sXYEmywGL/r89HoTDt6OEzMFGg4Wty8BOxyImERER0bOUz+OOSZ8Xk/KVh+9j95XHWYFaDTReCBRvLrV1uLEN6qOzrPY/ssJIeNh5CJm1KWZVajX8Z86AxlNcaiDjzl1ETJv+vMMhIiKi9xQLmURERESUKf36dST+/IuQabw84TdxAlQauXD4rCllh1ccDh9HHzFUFODAVODwDPnAjeYAboFCdDsiCdN235SaTmhcFHk8HZ9jNERERETvt9YVc6FV+UApH/b9ZdyNTM4K1Bqg6UqgUEOprebobODGr1LuZueGcVXGSfnC8wsRkhQiZFpPTwTMmglYvOQWv307Enfvft7hEBER0XuIhUwiIiIiAvBkGteI2bOl3KtvP6gd5cLhs6aUrR5QHZ/n/9yyMfDnROCIlTf6izUFSnwhRBlGE/pvuQC9UZyarH4xH7S08jCOiIiIiKz7+vNiKBHgKmTJGUb03HgOKRnGrFCjA75YD+StIXfyYw8g6pYU18lVBw3ziMXPNGMaJhyfIE0x61i1Kjy++krq4/G48dCHhj7/gIiIiOi9wkImEREREQEAUv46htQTJ4XMJl8+uDVvZrX9qsurrE4pO6HKBHFKWUUB9o4Bji2QO/EpAXy6QHg7X1EUjN55FTfDxQKpj4stZjQraX26WiIiIiKyyk6nwbJ2ZeHmoBPyu5HJmL77hthYZwe03gzFu6iY65OBrW3lNc4BjKo0Cu527kJ2JvwMll5cKrX16tcX9mXKCJk5ORlhg4dA0eul9kREREQsZBIRERERFJMJkXPmSLn30CFQabVSvv7qeiy5uETKh1UYJk4pqyjA7uHASflBFvzLAJ1+BuzdhHjxgbv44bz8Vv6cL0ohh6PNPw+GiIiIiASB7g5Y2LqM5cyu2HgyGKcfxIqhrROMLTbAbCt+xYmYu8DOrwCz+KVlDrscGFt5rHTMVZdX4cc7PwqZSqtFwNw5ULu4CHn65cuIXLjwxQZFRERE7wUWMomIiIgICb/8goxb4nRh9uXLwalWLant5hubMffcXCn/MOBDNCnQRAxPrQBOr5IPmLMC0HEX4CC+vf/ThTDM++O21PzLD/OiekGvfx4IEREREVlVo5AX+tUuKOUjf7iMdINJDHPkQXzdeVBUFo8Ob+8BDk2X+qiXu540xSwATDoxCScfizN+6Pz94T9tqtQ2du06JB89+hwjISIiovcJC5lERERE7zlDWBii5i+Qcp+hQ6VpXHfc3oHpp+WHV4VyFMKM6jPE9g9PAPvkt/ORqwrQfidgJ77lf+p+DIZ/f1lqXr2gJ0Y0LPx8gyEiIiKiZ+pbqwAK+zoL2f3oFCw+cEdqqw/8EMkVB8udHJkFnFwhxROrTkQR9yJCZlSMGHRwEO7G3RVy57p1kaNdO6mPR8NHQB8S8jxDISIiovcEC5lERERE77G0ixfxoGUrGCMihNy5fn3Yly4tZLvu7sLkE5OlPvK75sfqj1fD9enpx5LCgR2dALNRbJz7Q6Dd94CdOJ3Y/ahk9Nh4DnqTOFVZYV9nLGtXFjoNb1uJiIiI/i0brRozm5eE2mKK2ZWH7+P6o0SpfUrpL2Eu8rnc0Z4RUjHTQeeAJXWWwNfRV8iTDcnovb83otOihdx7+DDYFhELn6a4OIT06AlTgrwWJxEREb2f+ESIiIiI6D2V8OtveNixE0wxMeIGrRbegwYK0e4HuzH++HgoUIQ8t0turP54Ndztnpoi1mQAdnQBksXiKNxyAa2+A2ydhDglw4hu355FfKpByL2dbbGucwU42+leanxEREREJCsV6Iau1fIKmdGsYMQPl2G0eKkMKhVMny4CfIrLHVkpZno7eGNpnaVw1DkK+eOUx+h/oD8Mpqz7PbWtLQLmzoXKwUFoq79/H6H9B0DR619idERERPSuYSGTiIiI6D2jKAqiFi/Bo6FDrT4g8hk1EjZ58mT+3v9wP0YdHQWzIj7YCnAKwJqP18DLwWLtyj8mAMHHxUxrB7TaKK2JCQBTfruOB9EpQuZgo8G6zhXg72b/YoMjIiIion80+ONCyOUuFhCvhCVg3bEHcmMbR6D9D4CHvL6mtWJmoRyFMK/mPGhVWrH/6CtYdGGRkNnmywv/mTMAi+UMUk+dwuPxE6Ao4kt0RERE9P5hIZOIiIjoPRM5ew6ily6VcpWtLQLmz4P7U+sVHQ45jKFHhsKkmIS2vo6+WFt/rTR1GK7uBE7KfaPRPMCvlBT/eT0CW06L6yCpVcDiNmVQPMBVak9ERERE/56DjRbTm5WQ8ll7buHAzQh5B2dfoPOvzy5mnvtWiKr6V8W4KuOkpuuvrcfR0KNC5lKvHryHDZPaJvz0E6KXL/+HkRAREdG7joVMIiIiovdI4r59iF23Tso1np7I/d0GuDRsmJkdDzuOQYcGwWixzqWXvRfWfrwWAU4BFp0/An4ZKB+0XBegTDspjk7OwMidl6V8YN1CqFPE5/kGREREREQvpVoBT7Qsn1PIjGYFPTeex8n7sfIOf1fM3D0ciLknRM0KNkObwm2kpmP+GoPI1Eghc+/SGW6tW0ltoxctRsIvvz7HaIiIiOhdxUImERER0XtCHxyMx6PHSLlt4cLIu30b7EuWzMyuRF3BgIMDYDCL61a627ljTf01yOWSS+xEUYBfBgAZCWLuXxZoOFM6pqIoGPnDFUQni1Pblsnlht4187/gyIiIiIjoZYz5pCh8XeyETG80o8emC7jyKFne4VnFTGM6sKsPYBaXIhhSfggKuxcWsriMOIw6Ogomc9aMHyqVCr5jx8KxenXpkI/HjkXalSsvODIiIiJ6V7CQSURERPQeMGdkIGzgIJiTxQdSdsWLI8+mjdD5+2dmJrMJX5/4GummdKGtm60b1ny8Bvlc88kHuLQFuLNPzGxdgZYbAK2t1Hz72RD8eUOctsxep8H8lqWh1fAWlYiIiOh1cHXQ4duuFZHDQSfkqXoTBv10F7ciU+Wd/lfMdLKYQSP4BHB6lRDZamwx66NZsNeK656fDj+NNVfWCJlKq0XA/HmwLVRIyJWMDIT26QtDpPgVJxEREb0f+JSIiIiI6D0QOXMm0q9fFzK1iwsCFiyA2tFRyH978Btuxd0SMmcbZ6ystxIFc1iZSizxEbB7pJw3nAG4BUpxUHQKvv7lupSP+7Qo8ng6SjkRERERvTof+DpjQ9dKcLbVCnmy3oQBP97B/agUeSdnX+DT+XL+50Rpitm8rnkxtvJYqemyS8twLuKckGmcnBC4cgU0np5CboyMRGi/fjBnZDzfoIiIiOidwUImERER0Tsu8fffEbd5i5T7z5gOm5ziOpcZpgwsvrBYaruw1kIU9Sgqd64oT9bFtJxStmB9oJS8JlJIbCrarz2FVL1JyGsX9kabinLRk4iIiIhevRI5XfFNlwqw12mEPD7NiJ6bLiAx3SDvVLgRUOILMTOmAT/3k6aY/Sz/Z/gs/2dCZlbMGHFkBOLT44Vc5+eHnIsXQaUTvxJNv3QZ4ePHQ1GUFxscERERvdVYyCQiIiJ6h+kfPsTjseOk3L1LFzjXri3lm29sRnhKuJDVy10PFXwrWD/Apa3Anb1iZusKNF4AqFRCHBSdglYrTyA0Lk08F0cbzGheAiqL9kRERET0+pTP447VHcvDxmKa/wcxqRi09SLMZisFxIazAEcvMXt4DDizRmo6ptIY5HbJLWQRqREYd3ycVJx0KFMGvpMmSX0k7PoZseu+ec4RERER0buAhUwiIiKid5RiNOLR8BEwp4prG9mXLg3vwYOk9vHp8Vh9ebWQaVVa9C/T3/oB4h4Ce0bIeYPpgIu/EN2NTEarVSfwKCFdaj6zeUl4O9v9w2iIiIiI6FX7sKAnlrYrC7XF+2X7b0Ziwf478g4O7s+YYnYCEC22d9A5YE6NOdCpxS8tD4Ucwuabm6Uu3Jo2gXvnzlIeOWcOUk6e+qehEBER0TuChUwiIiKid1TMmjVIu3RJyDRubgiYP0+aqgsAVl9ZjSRDkpC1KNQCeVzzyJ0nPgI2fAakW04p+zFQuq0Q3Y5IQutVJxGRKK5ppFIBs5qXRL2iPs8/KCIiIiJ6peoV9cGQevK66Iv238Hea+HyDkUaA8Wbi5khFdjUAkiJFuLC7oUxtPxQqYu5Z+fieoy8hrr30CFw/PBDMVQUPBoxAsa4uH8eDBEREb31WMgkIiIiegelXb2GqCVLpdxv2lTo/PykPCw5DFtuiutoOmgd0LNUT7nzpAjg28ZAXJCY27oCjRcKU8qGxqWizaqTiE4Wi5hqFTD3i1JoWYHrYhIRERFlN90/zIO6hXJI+eBtF3E3MkneoeFswMFTzOKCgC2tAYO4rECbwm1QK7CWkBnMBgw/MhwphhQhV2m1CJg3FzZ58gi5MSIC4eMncL1MIiKi9wALmURERETvGHN6Oh6NGAEYjULu2qK51XUxAWDxhcUwmA1C1rV4V3jYe4gNU2KADZ8DMXflThrPF6aUTTeY0GvjecSk6IVmGrUKC1qXQbOyOV9gVERERET0uqhUKoyplxv5Pe2FPEVvwpffnkVkksVyAY4eQNMVgMriUWPoGWDnV4DZLPQ9udpk+Dr6Ck0fJj7E5JOTpeKkxsUFAQsXQGVjI+RJf/yB+B07XnKERERE9LZgIZOIiIjoHRM5bx709+4JmS5nTviMHCW1NStmzD83H7/d/03Ivey90KFoB7Fxaizw3edA1A35oJ8ukKYUm/jzNVwJE6ee1apVWNKmDD4rJa6hSURERETZi71Og1mN88PVXivkQTGpaLv6lFzMLFgPaDhL7ujGz0/WzHyKq60rZlafCY1KI+S/3f8N666uk7qw++ADeA8bJuUR06Yj4/795xwRERERvY1YyCQiIiJ6h6ScOIG4Dd+JoUoF/5kzoHFyFOJUQyoGHRxk9WFR79K94aBzyArSE4CNzYHwK/JBG8wEyncRoq2ng7H1TIjUdH6r0mhYQp7aloiIiIiynwBXW8xvWRJqlZjfjUy2Xsys2B2o0lfu6Pgi4MxaISrrUxa9S/eWmi44vwB7gvZIeY727eBY4yMhU9LTETZ0KMx6vdSeiIiI3g0sZBIRERG9IwyPHiFs+HAp9/iyGxzKlROy8JRwdNrTCQdCDkjti7gXQZMCTbKCjGRg0xfAo/PyQetNAiqL62heCU3A+J+vSU27V8+LxvwSk4iIiOitUr2AJyY0Liblzyxm1psMFGksd7R7BBAm3k92K94NVfyqSE3HHB2DC5EXhEylUsF/2jRoPMW1ODOu30DE9OlcL5OIiOgdxUImERER0TvAlJSEkB49YYqKFnLbwoXh2a+fkN2KvYU2v7XBzdibUj/FPIphaZ2l0Kr/fwoxfSqwpTUQcko+aK0xQLUBQhSXokfPjeegN5qFvFJed4xoUPglRkZEREREb1qnqnkw7tOiUv6/YmZUUkZWqFYDTVcBAeKLdDAbgO+7AumJmZFGrcGcmnOQ3zW/0FRv1qP/gf4ITgwWcq2HB/ynT5POI37LVkRMmQrFbJa2ERER0duNhUwiIiKit5xiMCBswEBk3Lkj5CobG/jPnAm1jU1mFp8ej74H+iI6LdqyG9TPUx/fNPgGXg5eTwJDOrC1LRB0VD5o9aFADfHrT0VRMGTHJYTFpwm5j4stlrQtC62Gt55EREREb6tuH+Z9ZjGz96ZzMJmf+iLSxgFosw1wDRQbxz0Afh0IPPX1pIuNC5bVXQYPOw+haXxGPHrv74349Hghd6peHe6dOkrnEbdpE8Infs1iJhER0TuGT5OIiIiI3mKKoiB80iSkHD8ubfObNg12HxTK/G1WzBhzbAzCU8Kltj1L9cSsj2bBXmv/JDDqge0dgfsH5YNW6QvUHivF358LxYGbkUKmVauwrF1ZeDnbvuDIiIiIiCi7eVYx80xQHNYfDxJDJy+gxTpApRHzqz8AFzYKkb+TP5bUWQI7jZ2QP0x8iMGHB8NgNgi515AhsLdYOgEA4rdvx+MxY6GYTM8/KCIiIsrWWMgkIiIieovFrFmD+B3fS7nXwAFw/bSRkH177VscCT0iZFq1FjOqz0Cf0n2gVj11a3hwKnBnr3zAil8BH08BVCohDk9Ix6Rfr0vNxzYqgnK53V9gRERERESUnT2rmDl7700ERaeIYWBFqy/A4fdhQNQtISruWRwzP5oJFcT7zDPhZzDz9EwhU9vYINeqlXCoUEHqOuHHH/FoxEgWM4mIiN4RLGQSERERvaWSjxxB1Nx5Uu7avBk8evQQsguRF7Dw/EKp7dDyQ9Eon1jwRNxD4OQy+YBlOwENZkpFTEVRMGrnZSSlG4W8dmFvdKqa5/kGQ0RERERvjW4f5kXTMgFClm4wY/j3l2F+eopZAKg2EMhXU8yMacCOzoBBXJKgdq7aGF5BXL4AALbd2obtt7YLmdrREYGrVsKxahWpfeKvvyL2m2+edzhERESUjbGQSURERPQWUkwmRMycJeUOVSrDb+JEqJ4qNsalx2Ho4aEwKeJb6fVy10Pbwm3lzg9MAUx6MSvZCvh0AaCWbx9/OB+Gg7eihMzFTovpzUoI50FERERE744JjYvC00lcPuB0UCw2nAgSG6rVQNNVgKOXmEdeB/aOlvptV6QdWhRqIeXTT03H2fCzYtf29si5fDkcP6outY9evgLGaHldeCIiInq7sJBJRERE9BZK/H039PfuCZlNgfzIuXAhVDpdZmZWzBj11yhEpoprVwY6B+Lrql/LhcZHF4Er4tvucPIBPp1vtYgZnpCOr3+5JuXjGxeDj4udlBMRERHRu8HNwQZTmxaX8pl7buFhjMUUs84+QLNVcidn1wHXfhIilUqF0RVHo6x3WSE3KkYMPjQYYclhQq62tUXOJUvgVKuWkJtTUhC1aPHzD4iIiIiyJRYyiYiIiN4yitGI6KVLpdx/5kxoXFyE7Lvr3+FY2DEh06l1mFNjDpxtnC06VoA/xssHrDUasHGUz0NRMPrHK1anlG1eNkBqT0RERETvlvrFfPFZKX8hSzOYrE8xm7828OEguZOf+z9Z2uApOo0O82rOg5+jn5DHZcSh/4H+SDOKU9KqbWzgN20q1Bb3wvHff4/0W7dfcFRERESUnbCQSURERPSWSfjlV+iDgoTMuV5d2BcrJmR34+5i0flF0v4jKoxAUY+icsf39gMPDouZZyGgdHur57H4wF0cuCl+6elsp8W0ppxSloiIiOh9MfGzYvB0shGyUw9iMe8PKwXEWmOAnBXFLCMB+KEbYDIIsYe9BxbVXgR7rb2Q3467jdlnZktda3PkgGfvXmJoNiNy5gwoiiK1JyIiorcDC5lEREREbxHFYED0smVS7tm3r/DbYDJg1F+joDeLa13Wz1MfLT9oKXdsNgF/TJDzul8DGq0Urzx8z+rDqfGfFoWvK6eUJSIiInpfuDvaYPLn8hSzSw7exfYzIWKo0QHN1wB2rmIeeubJOu0WCrsXxpRqcr7j9g7sf7hfPpe2baHLnUvIUo6fQPLhw1JbIiIiejuwkElERET0FknYtQuGEPGBkHODBrD74AMhW35pOW7G3hQybwdvjKs8zvrXkpe3ARFXxSxXVeCDhlLT9cceYPrum1Je6wMvtCiX8zlHQkRERETvioYl/NDYYopZABj94xX8dSdaDHPkBj6zsnblsQXAXbk4+XGej9G9RHcpn3BiAiJSIoRMZWMDn2HDpLaRM2dBMRiknIiIiLI/FjKJiIiI3hKKXo/oZcvFUKWCV98+QnQx8iLWXl0r7T+52mS42rpKOeIeAvsny/nHkwGLoufmU8GY+Mt1qWkRPxfMb1WaU8oSERERvadmNCuB4gHiGpVGs4JeG8/hVniS2Ljo50D5bnInO78C4oKkuFfpXijpVVLIEjISMOavMTArZiF3qlMHDhXF6Wv1Dx4gbtv25x8MERERZRssZBIRERG9JeJ37oTh0SMhc2nUCLYFCmT+TjWkYtTRUdIDnbaF26Kqf1W50+BTwOraQJLYL4p+DuQsL0TfnwvF6B+vSF0U9HbCxm4V4eZgI20jIiIioveDo60W6zpVgL/FMgNJGUZ0XX8GkYnp4g71pwE+FlPSpkYDm1oCaXFCrFPrMKP6DDjqHIX8VPgprL+2XshUKhW8RwyXXsiLWrgQ6bduvfjAiIiI6I1iIZOIiIjoLZBy/DiiFllMwaVWw7N378yfiqJgxukZCE0OFZrlccmDgeUGyp1e2gZ8++mTB0ZCv1qgjrhe5q6LYRj+/SWpi3yejtjUvRI8nGxfaDxERERE9O7xdrHDN10qwtlWXGM9LD4N3b87B73xqZftdHZAi3WAzkHsJPoWsK0DYBTXeg90DsSYSmOkYy4+vxjXoq8JmX2xYnBt0kTIzElJCO7aDRn377/4wIiIiOiNYSGTiIiIKBszp6Tg8ddfI7hrN5hiY4Vtro0bwzZf3szfG65vwI93fxTaaFQaTK8+HfZa+6c6NQP7JwE/fgWYxAdEAIAGMwCP/Jk/91x9jMHbL8GsiM1yuTtgU/dK8Ha2AxERERERAHzg64xl7ctCqxa/iLwUEo/Zey3WWff6AGhisXQCAAQdBX4ZACjiDWjj/I3RKF8jITMqRvQ70A/3E8QCpdfAgVA7il9wmmJiENylK/QWa84TERFR9sVCJhEREVE2lXrmDO43aYr4LVvljVotPHv3yvz558M/MffsXKlZj5I9UNzTYsqufWOAo3JbaO2BL9YDFbtnRvtvRKDflgswWVQxA9zssbl7Jfi52oOIiIiI6GnVC3phatPiUr766AMcvBkphsWaAHW/lju5tBk4MluKx1QagwCnACGLSotClz1dcCfuTmam8/FGwIL5gE4ntDVGRCC4cxcYHj9+/gERERHRG8NCJhEREVE2FLd9Ox527ASDtbfFdTr4T58Gm9y5AQCXoy5j5NGRUCAWG8v7lMeXJb8U9732I3Bymdynky/Q5XegWNPM6MjtKPTaeB4Gk9ivj4stNn1ZCTlzOFj2QkREREQEAGhVIRc6V80j5UN2XEKE5XqZ1QYA5brInRycClzeLkTONs6YUX0GtCpx+trY9Fh029sNt2Kz1sF0ql4dAfPmAhqN0NYQFobgLl1hjLZYYoGIiIiyHRYyiYiIiLKZxL37ED5hojSVFgDYFi2CvN9/D9fGjQEAoUmh6HegHzJMGUK7PC55sKDWAujUT72BHnMP2NVPPqBvSaD7ASCgbGZ0ITgO3Techd5kFpp6Otli05eVkcfT0bIXIiIiIiLByIaFUdTPRchiU/QYuPWiOOOHSgV8MgcoUFfuZFdfIOycEJX2Lo3pH02HRiUWKOMy4tB1b1dci8laM9OlXj34z5jx5BhP0QcFIXTgQChG40uOjoiIiF4HFjKJiIiIspGUU6fxaOhQuYip1cKzb1/k3bYNdh8UAgAk6hPRe39vxKaLa2e627ljWd1lcLV1zQoN6cCOToA+Sew3oDzQdQ/gmjU9l8msYNTOK8gwikXMHA46bPqyEgp4O/37gRIRERHRO89Op8GStmXgYCMWHE/cj8HSg3fFxhot0OIbwMdiSlpTBrC1HZAULsQN8jTA3BpzoVWLX2Ym6hPRfW933I/PWjPTtfGn8Js8STq/tLPnELVgwYsPjIiIiF4bFjKJiIiIson0GzcQ2qcPFINByHU5cyLPtq3w6tsHqqfW+Fl0fhEeJDwQ2tpqbLGo9iIEOgeKne8dBYRfETM7N+CLbwAb8evKHy+E4Wa4WPB0sdPiu26V8IGv88sNjoiIiIjeS/m8nDClibxe5oI/b+P0A/GFPNi5AG23P1n24GlJj58UMw3ilLR1ctfBgpoWs5AASDIkYcqpKVCeejnQrUUL+IwZI51HzJq1SDpw4AVHRURERK8LC5lERERE2YA+OBjB3b+COTlZyDWensi1bi3sixUT8rDkMPxw+wepn2kfTkMpr1JieOV74Ow6+aBNVwJuuYQo3WDCvH23pKarOpZH8QBXKSciIiIi+ifNyuZEs7IBQmZWgAFbLyAuRS82dg0AWm0ENDZiHnYW+GWANHNJjcAaWFx7MWw1tkJ+JvwMDoSIBUr3Du3h9sUX0vk9GjkKemtr0xMREdEbx0ImERER0RtmSkxE8JfdYYqOFnK1oyNyrVoJm1y5pH1WXV4FoyKu59O7dG98nOdjsWHMvScPfCxV7Q980ECK1x8PwqME8U33T0r4onI+j+ccDRERERGRbPLnxZHPYp31xwnpGPb9JeHLSQBAYAWg8UK5k8tbgRNLpLhaQDXMqD5DyuednQeDSZztxGfsGNgWLSJk5sREhA0YCHOGuO48ERERvXksZBIRERG9YVELFsIQHCxkKp0OOZcuhV3RolL7kMQQ7Lq7S8i87L3QpVgXsaHJCPzYA9CLX3kisBJQZ7zUb1yKXlqrSKtWYVj9wi8wGiIiIiIimaOtFovbloGNRnwc+eeNSKw/HiTvULotUKWvnP8xHnhwVIrr5KqDyn6VhSw4KRibb24WMrWtLXIuWAC1s7hkQvr164iYNv35BkNERESvDQuZRERERG9Q2rVriNu6VQxVKvjPng3HypWs7rPi8gqYFJOQfVniS9hp7cSGf80HQs+Imb070GIdoBHXEQKApQfvIild/MqzXaVcyGvx5jwRERER0cso5u+KMY2KSPn032/ialiCvEPdr4H8tcVMMT95WS9VXF9TpVJhWIVhUKvEx50rL61EXHqckNnkygX/6dOkw8Vv24bkI0eeczRERET0OrCQSURERPSGKGYzwidNAsxmIffq3w8uDepb3edBwgP8ev9XIfNx8EHzQs3Fho8uAIfl6bXw+VLANacUh8SmYsOJh0LmaKNBvzoFn2MkRERERETPp2OV3Pi4qI+Q6U1m9N18HskZ4kt10GifvITnnl/ME8OsrpdZKEchNCvYTMiSDElYdnGZdB7OdevCvUsXKY+YOg2KXi/lRERE9GawkElERET0hsT/8APSL10WMpu8eeHRrdsz91lxaQXMilj4/KrkV7DV2GYFhjRgZw/AbPEgqEwHoPAnVvuds+8W9Cax35418sPTydZqeyIiIiKil6FSqTCrRUn4u4qziQTFpGLMj1fk9TLtcwAtNwAaGzG/8TNw4Tup/z6l+8BRJ84osuP2DtyLvye19R48CPalSgmZ/uFDxG7Y8AIjIiIioleJhUwiIiKiN8AYF4eoufOk3HfcWKhsbKzsAdyLv4fdD3YLmb+jP5oWaCo23D8JiL4lZm65gQbymj8ms4Lpv9/ArouPhNzb2Rbdqud9jpEQEREREb0YNwcbLGpTBhq1Ssh3XXyE5YflgiN8iwP1Jsn57hFA9B0h8rT3RPcS3YXMpJgw68ws6YVAlU4H34kTALX4iDR62XIYIiJfYERERET0qrCQSURERPQGRC1YCFN8vJA5N2wAx6pVrbY3K2YsOr8ICsQ31HuU6gHd0+td3j8EnLScOksFNF0B2DoLaUqGET03nsPKI/el4w2uVwgONtrnHQ4RERER0Qspn8cdg+sVkvJZe27hl0uP5B0q9QQK1BMzQyrwQzfAKE4F275oewQ4BQjZ8UfHMfXkVOmLT7siReDWqqWQmVNTETl3zguMhoiIiF4VFjKJiIiIXrO0K1cQv327kKkcHOAzcqTV9iFJIei6tysOhBwQ8pxOOdE4f+OsID4E2PmV3EG1AUBusUD6KD4NLVacwB/XI6TmxQNc0KKcvI4mEREREdF/qWeN/Khe0FPKh+y4hLNBsWKoUgFNlgGOXmL++BKwd7SwXqatxhaDyg2S+t1+ezvmnp0rFTO9+veHxtVVyBJ//gWp58+/4IiIiIjov8ZCJhEREdFrZM7IwONx44UHLQDg1acPdD4+QqYoCrbf2o7mPzfHuYhzUl89S/WETv3/X2OmJwCbWwLJFoVJn+JArdFCdP1RIj5fegw3HidKfZbK6Yp1nSpAq+FtIhERERG9Whq1CkvalkUhHych1xvN6L7hLIKiU8QdnLyBzy1nHwFwZjWwZ5Rwj/1x7o9RO7C21PTb699i+aXlQqbNkQNeAwdIbcOnTIFiMr3AiIiIiOi/xidURERERK9R9IyZyLh5U8hsCuSHe8cOQhaXHocef/TA5JOTkWZMk/op410GjfI1evLDZAC2dwIir4uNNDZAs1WA1jar3xQ9vvz2DKKSMqQ+G5X0w7YeVeDtYveSoyMiIiIiejGu9jqs61wBXs62Qh6XakCX9WcQlyJOG4tCHwOVeskdnVoO/DYEMD9ZB1OlUmHmRzNRybeS1HT5peX45uo3QubWsiVsCxcWsozrNxC3bdtLjIqIiIj+KyxkEhEREb0mGfv+QOL330u577jxUOmy1rlM1iejxx89cOLxCav9fJL3EyypswRatfbJW+e/DgLuH5QbNlkO+BTL/Gk2Kxiy4xIeJaRLTQfUKYglbcrATqd5iZEREREREb28nDkcsLZTedhb3Is+iE5Bz43nYDCZxR3qTgQCK8sdnV0L/NIfMD/5itJOa4dFtRehlFcpqem8c/Pw450fM3+rNBr4jh0jtYuYNh2Jf/zx4oMiIiKi/wQLmURERESvgenBA6TOmyfl7t26wrFSxczfGaYMDDg4ADdib8ht7dwxv+Z8zPxoJlxsXJ6Ef80DLnwnH7D2WKBECyFadfQ+DtyMFDKdRoWFrUtjUL1CUKlULzEyIiIiIqJ/r2RONyxsXRqWt6SnHsTi61+uiaHODmj/PZC7mtzRhe+An3pnFjMddA5YVncZirgXkZpOOTkFN2Ky7rsdypeHy6efio2MRoQNGoykP/98qXERERHRv8NCJhEREdErZk5NRfLEr4F08UtI+3Ll4D1wYOZvk9mEUUdH4XT4aamPurnqYudnO1E3d92s8PY+YP8k+YBl2gPVhwrRmaBYzN57S2o6tlFRfF464MUGRERERET0CnxczBfjPy0q5RtPBmPjyYdiaOsMtNsB5P1I7ujyVuDE0syfLjYuWFlvJfK75hea6c16DD40GIn6rLXjvYcPg8bTU+zPaETowEFI2r//xQdFRERE/woLmURERESvkKIoiPx6EswPxQcvGnd3BMybmzmlrKIomHpqKv54KE9b1bNUT8yrOQ8e9h5ZodkE7JOnvkK+msCnC/D0q+wxyRnot/kCTGZFaNqohB86Vsn90mMjIiIiIvqvdamWF20r5ZLyiT9fw8n7MWJo4wi03Q7kry13dHgWkJw1G0kOuxxY9fEqeNl7Cc1Ck0Mx9q+xUJQn98o6b2/k/mYdNB4eQjsYjQgdMJDFTCIioteMhUwiIiKiVyh+2zYk//67GKpUCJgzGzofn8xo2aVl2HF7h7R/qw9aoXep3vK0r1d/AKJvi5lXEaDlBkCTtd6m2axg0PZLCE8UvwbN7eGA6c1LcDpZIiIiIsp2JjYuhop53YXMaFbQe9N5hMSmio119kDrLUDBj8VcnwQcnCpE3g7emF1jNjQqcS3OgyEHsf7a+szftgULIvf6b6BxF8/hf19mppyWZ1AhIiKiV4OFTCIiIqJXJO3qNURMnSblnn37wLFq1czfR0OPYsWlFVK7j3N/jFEVR8nFRpMRODRDPmCzlYCdqxCtO/YAR25HCZmNRo2lbcvCxU4HIiIiIqLsxkarxvJ2ZRHgZi/ksSl6dN9wFql6o7iDzg5osgKwFe+FcX4DECGur1nOpxwGlh0oHXPh+YU4G34287dtwYLI/e16uZhpMCBs4CAYHj9+4XERERHRi2Mhk4iIiOgVMCUmImzgQCgGg5DbV60Kz169Mn9nmDIw7ZRc7KzkVwnTq0+HRq2RtuHyNiD2npgVaQz4lRKiW+FJmLVHXhdzXOOiKB7gKuVERERERNmFh5MtVncsD3udeD98MzwJk3+9Ie/g6AHUGC5mihnYOxpQxCUWOhXrhNqB4nS0JsWEYUeGIS49LjOzLVgQuax8mWmKjUVo/wEwZ2S8xMiIiIjoRbCQSURERPQfUxQFj0aPhiE0VMhVnp7wnT4NKnXWLdi6q+sQmiy2K+JeBAtrLYSNxkbu3GQADs+U85qjhJ96oxkDt12E3mQW8kYl/dDeyppDRERERETZTVF/F8xrWUrKt5wOxp6r4fIOFb8C3POJ2f1DwO29QqRSqTD5w8kIdA4U8ui0aCy5sETI7AoVQq61a6CyF78OTb9yBeGTJmWurUlERESvBguZRERERP+x2G/WI/nP/WKoVsNpwnjhbe6QpBCsvbJWaKZRaTDlwylw1Dla7/ziZiD+oZgVawr4FBOiBX/exo3HiULm62KHaU24LiYRERERvT0alvBDn1r5pXzkzst4nJAmhloboN5kuZN9Y5+8EPgUFxsXzKs5D7YaWyHfcXsHbsSIX3zaFSkCvylyvwk/7ET8tm3PORIiIiJ6GSxkEhEREf2HUs+fR+TcuVJu36MHtCVKCNms07OQYRKno2pbpC0K5ShkvXOjHjgy2yJUSV9jng2KxYrDFlPPApjzRSm4OnBdTCIiIiJ6uwyqWwhlc7kJWXyqAYO3XYLJbPFFZOFGQJ7qYhZzBzgjvkAIAIXdC6NP6T5CpkDB9NPTpS8tXRs1gnuXLlIf4VOnIfX8hecfDBEREb0QFjKJiIiI/iP60FCEDhgAmExC7li7NmxbfiFkh0IO4VDoISHztPdE71K9n32AC98BCSFiVuILwOuDzJ/JGUYM3n4Jls9zOlfNgw8Lej7vUIiIiIiIsg2tRo2FrcvAyVYr5Cfux2DVkftiY5UKqD8NgMUsJAenAuFXpL7bF2mPPC55hOxC5AX8/uB3qa33kMFwqFxZDA0GhA0YAGNU1PMOh4iIiF4AC5lERERE/wFjTAyCu3WDKSpayHWBgfCePEmYzjXdmI4Zp2dIfQwtPxRONk7WD3DvwJOHL09TqYEaIzJ/KoqCr3++huDYVKFZfi9HjGhQ+AVHRERERESUfQS6O2BKk+JSPnffLVwKiRdDv5JAmXZilpEIbGgCRN0SYp1GhxEVR8DSvLPzkGoQ76tVWi0C5s2F1t9PyI1RUQgbMhSK0fjc4yEiIqLnw0ImERER0b9kSk5GSPevYHgYLOQqnQ4BC+ZD4+Ii5GuurEFYcpiQVfCtgE/yfiJ3npEM/DoY+K4pkBojbivZGvAskNXv0QfYcS5UaKJVqzC/VWnY22heYmRERERERNlHkzIBaFLaX8iMZgXdN5zF3cgksXHtcYCteB+O1Ghgw+dA7AMh/jDgQ9TMWVPIItMiseryKukctO7uyLl4MVS24tqaqadPI2rhwhcbEBEREf0jFjKJiIiI/gWzXo/Qvv2Qfv26uEGlgt+M6bAvVkyIz0eex5ora4RMq9JidMXRwlebAICgY8CKasBZeT0fqHVAjWGZP3dfeYxpu29IzfrVLoiSOd1eaExERERERNnVpCbFEehuL2SRSRloveokboU/Vcx09gW+WA9obMQOkh4DGz4DEsQXAIdVGAadWlxPfsP1DXiY+FA6B/tixeA7fpyUx6xeg6QDB15sQERERPS3WMgkIiIiekmKyYRHw4Yj9eRJaZvPmDFwbdRIyOIy4jDq2CiYFHENzfZF26NAjgJChnPrgfWNgLgg+cAaG6DpCsA9HwDgQnAcBm67CMViXcxKed3Rp1b+Fx0WEREREVG25WKnw4JWZaBViy8BRifr0XrVCVx7lJAVFqjzpJipspidJD74yZeZyZGZUS6XXOhUrJPQzGA2YMxfY6QpZgHArXlzuDZvJuWPRoyEPjhYyomIiOjlsJBJRERE9JKiFi1G0t69Uu7Zuzfc24tr8pgUE2ZcmYGotCghD3QORM9SPcUOrv0E/DIQgEVlEgD8SgFfHQZKtAAABMek4stvzyLDaBaa5fNyxKoO5aHV8HaPiIiIiN4t5XLnwMLWZaCxKGbGpRrQdvUpXA6NzwoLNwKarQJgMftJzF1geyfAZMiMupfoDm8Hb6HZpahL6PVnL6vFTN9x42BbpIiQmZOSENp/AMzp6S81NiIiIhLxyRYRERHRS0g9fx4xq1dLuVvrVvDs11fKN93bhPMx54XMRm2DeTXnwVHnmBXePwzs7A6piKnWAjVHAV/uB3yKAgAS0gzosv40YlL0QlMPRxus71wRrg7i1FhERERERO+KRiX9sKxdWeg0YoEyIc2AdqtPiWtmlmgBfLZI7iT4OLB/UuZPB50DhpYfKjU7H3neajFTbWeHnIsWQu3sLOQZN28ictaslxgVERERWWIhk4iIiOgFmZJT8GjESMAsfgXpXL8+fMeNk9a6PPn4JDbe2yj1M6rSKBR2L5wVPLoIbG0HmMTCJNxyPylg1hwJaLKKk7P23MS9qBShqa1WjTWdyiOXh8PLDY6IiIiI6C1Rv5gvVnYoBxuLWUiSMowY8+NVKE+vvVC2I9BgptzJ8UXAjV8zfzbI0wAdinaQmp2PPI/e+3tLxUybwED4z5whtY/bvAUpx4+/4IiIiIjIEguZRERERC8ocuZMGEJChMwmf374z5wBlUZcfyciJQJjjo+BYvGFZeN8jdG8YPOsIOYesKkFoE8S2sHRG+j4E+BfWohDYlOx7Yx4DioVsKBVaZTJleOlxkVERERE9LapXdgHqzuVh61WfMx56kEs9l6LEBtX7gmUFdfBBAD81AuIvQ8AUKlUGFZ+GNoXaS81OxdxDr3390aaMU3InWvXhkf37lL7R2PGwpSUJOVERET0/FjIJCIiInoBSYcOIX7HDjHUauE/cybUdnZCbDAbMOzIMMRlxAl5ftf8GFt5bNaXm2lxwMZmQIq4fiZsXYD23wPu+aTzWHzgDoxmsTg6sE4hNCzh93IDIyIiIiJ6S9Uo5IWFrctI+fTdN5BhNIlhw1mAb0kxy0gEtnUEDE8KlCqVCsMrDH9mMXPyicni154AvPr3g21Rcb1M4+PHiJguf61JREREz4+FTCIiIqLnZIyLw+Ox46Tcs3cv2BcvJuWLzy/GhcgLQmanscO8mvPgoHtq6tf9k4C4IHFnjS3QejPgV0rqNyg6BT+cDxMyHxdb9KghFzyJiIiIiN4H9Yv5oHpBTyF7GJOKDccfig11dkDLDYCdq5hHXAF+z1of83/FzHZF2knH+uX+L9h+a7uQqXQ6+E+fAZVOXKc+YedOJB08+BIjIiIiIoCFTCIiIqLnoigKwidMhCk6WsjtSpaE51dfSe0PBh/EN9e+kfKxFccin9tTBcew88BZi3YqNdB8DZC3utVzWXTgDkwWX2P2qVUAdjqN1fZERERERO86lUqFsY2KQi0uV49FB+4gJjlDDN3zAk1WyJ1c2Ajc2iP0OaLCCLQp3EZqOuPMDFyOuixkdh8Ugmf/flLbx+PHwxgXJ+VERET0z1jIJCIiInoOib/8gqR9+4RMZWf3ZF1MrVbIQ5NCMebYGKmPRjkb4ZO8n2QFZjPw2xDAYv1M1B4LFP3M6nnci0rGTxfErzH9XO3QqkLg8w+GiIiIiOgd9IGvM1pXzCVkSelGLPjzjty48CdAtYFyvmckYMwqfP6vmFnZr7LQzGg2YvChwYhJixFyj65dYV9KnFXFFBWNiMnydLRERET0z1jIJCIiIvoHhsePET55ipR7DxsK27x5hUxv0mPo4aFI0icJeQHnAuhduLfYwYUNwKPzYub5AVBFfov7fxb+eQcWH2Oib+0CsNXya0wiIiIiosH1CsHZVnzRcPPpYNyJSJIb1x4HBIoFSsQ9AE4sESKNWoNZH82Cn6O4Hn1EagSGHxkOo9mYmak0GvjNmA6VnZ3QNvH33YhevvwlRkRERPR+YyGTiIiI6G8oZjMejRoNc5L44MOxalXkaNtWbKsomHl6Jq7FXBNyJ50TxpUeBxuNTVaYEgP8OVE+4CezAa2NnAO4HZGEXy4/ErIAN3t8UY5fYxIRERERAYCnky361C4gZCazgkm/XofZ8o1AjRb4dN6TpR2edmQOkCDOgpLDLgfm1ZwHnVpcA/N0+GksvrBYyGzz5oX34MHSuUUvWoy4rVtfcERERETvNxYyiYiIiP5G3MaNSD15UsjULi7wmz4NKpW4AM/aq2ux/fZ2qY+JlSfC38FfDPd/DaRZrJNTvDmQr4bV8zCZFczeewuWs1H1r1MANlre0hERERER/U+XankQ6G4vZEfvRGPsrqvy9K4+xYAKX4qZIRX4c4LUb3HP4hhVaZSUr7u6Dn8+/FPIcrRvB8eqVaW24V9PQuKevc85EiIiIuJTLyIiIqJnyLh3D5Fz50m57/jx0Pn4CNkPt3/AwvMLpbYdinZA7cDaYhh6Fji/QcxsnICPp1o9j4cxKWi96gT+uB4h5LncHdCsbM7nGAkRERER0fvDVqvBqIZFpHzzqWBM/e2GXMysOQqwdxezKzuAh8elPloUbIEmBZpI+dhjY/Eg4UHmb5VajYCFC2BbuLDYUFHwaNgwpJw48dzjISIiep+xkElERERkhWIw4NHwEVAyMoTc5ZOGcP20kZDtD96PSScnSX2U9S6LQeUGiaEhDfhlAAArD09cxDV3zGYF350IQoMFR3EmyOLrTQD96xSETsPbOSIiIiIiSw2L+6J+MR8pX/PXA8z/844YOrgDdcbLnfw+HDCbhEilUmFMpTEo4i4WSlMMKRh0cBBSDamZmcbZGblWr4IuUFwKQjEYENqnL9KuiUtSEBERkYxPvoiIiIisiFq8BOkWDxa0Xl7wHS8+4DgTfgbDDw+HWTELeQG3AlhUe5G0ho5m32gg4qp4MK8iQKUeQhSTnIGO605j3K5rSDOID08AoHzuHGhS2l/KiYiIiIjoScFxYesyqJrfQ9q2aP8drDh8TwzLdgR8S4pZxBXgzFppfzutHebVnAcXGxchv5dwDxOOTxC++NR6eSHX2jXQeHoKbc2pqXg0ZCjMev0LjoyIiOj9wkImERERkYX4H3YiZtUqKfebNhUaN7fM37dib6H/gf7Qm8WHD/6O/lhRdwVcbV2F3O72T1Bf/E4+YKM5gCar4JmUbkDHdafx191oq+fXpLQ/1nauAC2/xiQiIiIieiY7nQarO5ZHudw5pG0zdt/E9jMhWYFaA3wyW+5k7yjg+i4pzumcEzOqz4AKKiHfE7QHm25sEjKbXLmQa/UqqJ2chFwfFISYNWteYERERETvHz79IiIiInpK8l/H8HjCBCl3a9MaTtWrZ/5ON6Zj2JFhSDYkC+3c7dyxst5K+DiK01hpY+/A5chE+YDVBgB5Pszq12DCVxvO4dqjRKmph6MNVrQvhwWty8DVXidtJyIiIiIikaOtFt90qYDiAS7StrG7ruJqWEJWkKsyUKKl2MhsBL7varWYWT1ndfQq3UvK556diwuRF4TMrkgR5Fy6FFCLj2NjVqyE/uHDFxgRERHR+4WFTCIiIqL/l37jBsL69weMRiG3LVIEPsOGCdnC8wvxIOGBkDnqHLGs7jLkcc0jdqxPhtsfA6A2pol5rqpA7aypak1mBQO3XsSJ+zHSuTUs7ot9gz5Cg+K+Lz4wIiIiIqL3mIudDhu6VkIhH4svIo1m9Np0Dgmphqzw48mAk8Xamn9TzOxRsgeqB1QXMqNixKijo4T1MgHAsVJF5GjXTsgUvR7hkyYL09ESERFRFhYyiYiIiAAYHj9GSI+eMKeKDxu0/n4IXLECageHzOzU41PYeGOj0E6tUmN+zfko5lFM7FhRoPl9CLRxFmvwOHgCLdYCGu3/N1Mw9qer2HMtXDq3AXUKYlm7svBwsv0XIyQiIiIien+5O9pgY7dK8HER76lDYtMwZMelrEKisy/Q6RfA0Vvs4BnFTLVKjenVpyOnU04hD0sOw9yzc6Xz8BrQH1ovLyFLOXYMSXv2vOTIiIiI3m0sZBIREdF7z5SYiJCvvoIxMlLI1c7OyLVyJXQ+WQ8xEvWJGHtsrNRHl2JdUMW/itz5uW+gvvaDRagCmq8BXPwzk/l/3MaW08HS7h2r5MbAugWhUqmkbURERERE9Py8XeywpG1ZaNTivfWfNyKw8sj9rMDrA6Dzr88uZj44IsSutq6YW3MutCqtkG+/vR3Hwo4JmcbJCT6jR0nnFjFtOkzJyVJORET0vmMh8y2nKAq2bduGTz/9FDlz5oStrS38/PxQp04drFmzBkaLqfGIiIhIpOj1CO3XHxl37oobdDrkXLwYtgULCvGMUzMQniJ+NVkoRyH0Lt1b7vzRRWD3CDmvORLIXyvz58FbkVh04K7UrFFJP0xoXIxFTCIiIiKi/0iFPO4Y2aCwlM/eewsnn17iweuDZ3+ZuaMLkBAmxEU9iuKrUl9J/Y4/Ph6J+kQhc27QAI7VqgmZMSoKUQsXveBoiIiI3n0sZL7F4uLiULduXbRu3Rq//fYbwsLCoNfrER4ejgMHDqB79+6oVKkSgoPlrzuIiIjoyQtBj8aOReqpU9I2/2nT4Fi5kpD98fAP/HL/FyHTqXWYXn06bDQ2Ygdp8cCOToBJL8TmvDWBj7LW20w3mDBh1zXp+NUKeGBey1LS2+JERERERPTvfFk9L+oXE9fBNJkV9NtyAbEpT92/exe2XsxMjQa2dwSMGWK/Jb5EUY+iQhaZGomZp2cKmUqlgu/4cVDZiP8bIm7TJqSeOfOSoyIiIno3sZD5ltLr9fj8889x4MABAEBgYCAmT56MLVu2YPbs2ShSpAgA4Pz582jYsCESExP/rjsiIqL3UtSChUj8+Rcp9xo0CK6NPxWy6LRoTDoxSWrbr0w/FMpRSAwVBdjVB4gLEmKTow9Mn68A1JrMbOXh+wiOFdflLObvgpUdysNWqwEREREREf23VCoVZn9RCnk8HIQ8KikDc/bdEht7FwY6/AjoxLYIOwvsHS1EOrUOU6tNhY1aLFD+fO9n7A/eL2Q2uXPDo4fFF5xmM0L79YeeHyUQERFlYiHzLbV8+XIcPXoUAFC2bFlcunQJY8eORevWrTF06FCcP38e9evXBwBcv34dkydPfpOnS0RElO3EbduOmJUrpdytVSt4fNVdyBRFwYTjExCfES/kZb3LomPRjnLnJ5cBN38V+1BpEF93HuDomZkFx6Ri2SFxSlmNWoW5LUvByVZcX4eIiIiIiP47LnY6LGtXDrZa8fHoltPBuBqWIDb2LQ58tlju5Mwa4NJWISqQowD6leknNZ10YhJCkkKEzKN7d9jkySNkpvh4hPTqDRM/SiAiIgLAQuZbyWg0YurUqQCevEG2YcMG5MiRQ2hjZ2eHDRs2wNHREQCwePFixMTESH0RERG9j5IPH0b4JPnrSqcaNeA7bqy0JuUPd37AkdAjQuagdcDUD6dCo7b4ajLkNPDHeKnvpEpDYPArL2Rf/3INGUazkHWumgeFfV1eZDhERERERPQSivq7YHA9cXYVRQEm/nwNiqKIjUu0ACr1lDv5ZSAQfkWIOhTtgDLeZYQsNj0WPf7ogei06MxMbWMD/7lzoLK3F9rq791D2MBBUIzGFx8UERHRO4aFzLfQgQMHEBUVBQCoU6cOihUrZrWdt7c3WrduDQDIyMjArl27Xts5EhERZVeGx48RNnQYYDIJuV2xYgiYNxcqrfglZEhSCGadmSX1M6LiCOR0zimG8cFP1soxiw8czAUbILVUVyH783oE9t+MFDJvZ1sMrFvwRYdEREREREQvqUu1vMjn6ShkZx/GYdfFR3LjepOBwMpiZkwDtrUH0uIyI41agynVpsBeKxYoQ5JC0OvPXkjSJ2Vm9sWKIWD2LMDiZcqU48cRPnWqXFAlIiJ6z7CQ+Rbat29f5t8NGjT427ZPb9+zZ88rOyciIqK3gWI249Ho0TAnJQm5LiAAgSuWQ+0oPsAwmU0Y89cYpBnThLxmzppoWqCp2HlKNPBdUyDpsZi75YKp8RLhwUS6wYSJv1yTzm9MoyJwttO9xMiIiIiIiOhl2GjVGN+4qJRP+/0GkjMsvojU2gBfrAccvcU8Lgj4sSdgzpptJZdLLkyuNhkqiAXKm7E30f9Af2SYMjIz57p14T1ksHQO8Vu2Im7jphceExER0buEhcy30NWrVzP/Lleu3N+2LV8+awq7p/cjIiJ6H8Vt2ozUEyeFTO3sjMBVK6H18pLar7+2HhciLwhZDtscmFB1gjj9bEYSsKkFECOudwmNDfDFt4C9mxAvO3gXoXFicbRyPnd8Vsr/xQdFRERERET/Ss0PvFG3iFicjEzKwNKDd+XGLn5PipkqiyUmbu8Bjs4Vovp56mNMpTFSF2cjzmL44eEwPjWTi3u3bnBt1kxqGzFzJtIuXnzusRAREb1rtP/chLKb27dvZ/6dx2JBcEs5c+aERqOByWTCnTt3oCiKtO7XPzl37tzfbr9x40bm30ajEQaD4YX6J6L/lsFggPH/19Hg9UiURX//ASLnzJFyr7FjoM6VS7pebsfdxpKLS6T2YyqOgavWNau9MQOabW2hfnRBamv8dBEU7xLCdXnibhSWHrontNOqVRj/SeHMNkT0avHflUTZC69JouznfbwuRzYohMO3o2AwZU3luubofTQr7Ys8HuLMLQioCHWdidD8OU6IlYNTYfIpCSV/7cysWf5miE6NxoorK4S2B0IOYO6ZuRhcNutLTM+xY5Dx8CHSn34WZzQidNBgBO7YDo2r638wUnobvY/XJFF2xmtS9iqfabGQ+RaKj4/P/NvT0/Nv22q1Wri4uCAuLg5GoxEpKSlwcnJ6oeM9/VXnP0lISEBMTMwL9U9E/y2j0Yikp6bN1Gr5j3oixWhC0ogRUDIyhFxXqyYyKlaE3uLfXfH6eAw+NVh4QxoA6vnXQymHUln/rjOb4PbnIOiCjkjHTKw2Bql+tYCYmMzrMj7NhEG/hsFkFte5aVXGG+7aDMTEZEj9ENF/j/+uJMpeeE0SZT/v43XpBKBtWR98eyY8MzOYFAzaehELmxWEo43FF5j5v4Drg+Owv7c7M1JBgfrHrxDT/AeYXHJm5s38muFxwmPsCt4ldLH55mZUdquMD1w/yMxsx4+DvmcvmB9nLVlhfPwYoSNGwHHKlBf+QIHeDe/jNUmUnfGalD1dt/qvcWrZt1BycnLm33Z2dv/Y3t4+a2HxJIs1wYiIiN4H6Zs3wXTzppCp3N3hMHCg9CAg3ZSO8efH41HqIyH3tvNGn8J9hMzpzCLY3d8rHS+5bC+klugoZCazgplHIxGTKhZHc7raomslvxceExERERER/bc6VfCFl5O4Zv3V8BQM/ukuUvUmsbFKhcSaU2HMkV+I1RnxcNvXHzBmPNVUhd6Fe6OWby2hrRlmLLy+ECYlq2+1qyscJ0wALB6KG44dR8b33/+b4REREb2VWCamf3T27Nm/3X7jxg106NABAODq6goPD4/XcVpE9AxPT2fg7u4OnU73N62J3n3p164jbsN3Uu47eTIc8+UTMpPZhOF/DceNhBtCroIKk6tNRi6fXFlZyCloLq6S+jWV6QTbBpNg+1SB1GAwYO2pcFx8LK6LaaNVY2m7Msjl5/JSYyOil8N/VxJlL7wmibKf9/W69AAwuqEJg3ZcEfJLj5Ix/LcgrOlQFo62WmEPpeVGKN/UhUqfkpnqoq/B6+IimOvPEPqZXmM6Wu9ujaDEoMzsTuIdHIw9iFaFWmU1rFYVtkOHIHrGTGH/tJWr4FG1KuxKlvy3Q6W3zPt6TRJlV7wmZW5ubq+sbxYy30JOTk6Ii4sDAKSnp//jVLFpaVkPTZ2dnV/4eOXKlXvutlqtlhctUTbwv+kMdDodr0l6r5lTUhA5ciRgMU+/W8uWcKtTW8gURcGc03NwMPSg1M/IiiNRNWfVrCAjGfilD6CYxYZFPoOm8Xxo1OK0U0fvRmP9mQip30mfFUOpXHwBiOhN4L8ribIXXpNE2c/7el02KRuIa4+TseavB0J+9mE8vtp4Ed90qSAWM/2KAk2WA9vFGVk0Z9dAU7QxkK9mZqbT6TCu8jh029dNaLv00lI0yNcAnvZZS0h5duqEjHPnkPTHn1kNjUZEDBuOvD/u5HqZ76H39Zokyq54TYpe5fS6nFr2LfR0ZTs6Ovpv2xqNRiQmJgJ4ckE5Ojr+bXsiIqJ3Sfi0adAHBQmZLjAQPiOGS203XN+AzTc3S3nnYp3RtkhbMdw3BogT+4VnIaDZKsCiiPk4IQ1DdlyBuCom0KxMAFpVCHzOkRARERER0eugUqkwplERdK2WV9p2OigWXb45gzTLaWaLfg5U7Sd3tqsvkJ4oRBX9KuLTfJ8KWbIhGbPPzJbOw2/KFOgCAoTc8OgRIqaLX3oSERG9y1jIfAsVKlQo8+8gi4ezlkJDQ2EyPbm5KlCgABcEJyKi90bi7t1I+GGnGKrV8J85A2qLF3v2BO3BnLNzpD4a5GmAQeUGieHtvcC59Rb9ap8UMXX2QmwwmdFv8wXEpRqEvKC3E6Y0Lc5/LxMRERERZUMqlQrjPi2CLtXySNtOB8VizI9XoCgWryrWHg/4lhCzhBBg72ipjyHlh8DZRpw17fcHv+Pk45NCpnF1RcD8eYDFlz4JP/2ElJOnnn9AREREbzEWMt9CxYsXz/z73Llzf9v26fUtn96PiIjoXaYPDcPj8ROk3LN3bziULStk5yLOYfRR+eFCOZ9ymPLhFKhVT90upcQ8eava0kfDAf8yUjx77y2cfRgnZA42GixvXxYONpzhn4iIiIgou1KpVBj/aVGrxcydF8Kw8VSwGGptgKYrAbXF9IIXvnvyMuRTPO09MaDMAKnfqSenIsOUIWT2JUvCe+BAqW34xIkwZ2RIORER0buGhcy3UP369TP/3rt379+0BPbs2ZP5d4MGDV7ZOREREWUXitGIR8OGwZyUJOT25crBs2cPIbufcB/9D/SHwSx+MZnXNS8W1loIW43tUx0rwG+DgJRI8YAB5YDqQ6Tz2HctHKuO3JfyyZ8VRQHvF1+zmoiIiIiIXq//FTM7V80jbZv0yzWcDxZfWoRPMaCW/JIkfu4HpMYKUYtCLVDcQ/zoICgxCJNOTJK+9nTv3Al2xYoJmT4oCDGrVj//YIiIiN5SLGS+hWrVqgUvLy8AwJ9//olr165ZbRcZGYmtW7cCAOzs7PD555+/tnMkIiJ6U6KXr0DahQtCpnZxQcDsWVA9tfB4dFo0ev/ZG4l6cc0aDzsPLK+7HK62rmLHl7cD13eJmdb+yVvXGvHryuCYVAzZcUk6t6YlPPFZKb+XGBUREREREb0JT6aZLYrqBT2F3GBS0HvjeUQnW3wVWbU/kLOCmCVHAL8NefJy5P/TqDUYV2WcOAMMgJ/v/Yy1V9eK56DRwPfrrwG12DZm1Spk3JdfniQiInqXsJD5FtJqtRgzZgwAQFEUdOzYEXFx4htg6enp6NSpE1JSUgAAffv2hYeHx2s/VyIiotcp6c8/Eb1smZT7TZoEnb9/5u9UQyr67O+DsOQwoZ291h5L6y5FgFOA2EFCKPD7MPmA9SYBngWFKN1gQp/N55GUbhTyD7wdMLBG4AuOiIiIiIiI3jSNWoWFrcsgwM1eyMMT09Fv8wUYTeanGmuBJiuevPT4tGs7gSOzhaioR1F0LNpROt7C8wuxL2ifkNkXLwb3Du2FTDEYED5horxeJxER0TuEhcy3VK9evVC9enUAwPnz51GqVClMnToV27Ztw9y5c1G2bNnMaWWLFi2KsWPHvsnTJSIieuXSLl1C2JChwlvOAOD2RQu4NMiall1RFIw8OhLXY64L7TQqDebUmINiHuKUTTCbgZ96AxkJYp6vFlDhSyFSFAWTfr2OK2FiW2c7LaY1ygdbLW+9iIiIiIjeRu6ONljWrixsNOI9/Yn7MZi975bY2LMAUHei3MnBqcCZNUI0oOwAVPOvJjUd89cYXI2+KmRe/ftD6yfO8JJ65gwSdv74/AMhIiJ6y/Bp2lvKxsYGu3btQu3atQEAISEhGDt2LFq3bo2hQ4fixo0bAICyZcti9+7dcHV1/bvuiIiI3mr64GCE9OoNJUOc1skmXz74jBolZN/f+R4HQw5KfYytPBYf5fxI7vz0KuDBYTGzcwU+XypN7bTs0P+xd9dRVlV9H8C/58Z0NzMM3d1dggiigAiIhIgg3Q1Dd3ejlCIlICqiIkjn0N01yXTXjfP+wfPMsO/mfWSQmIHvZy3X4n7v7+xz9vuueeDM75y972LTqUfSELNalYGfs7WUExERERFR7lHe3wWTWpaW8lWH7uGPK2FiWK3Hk4cfLf02DLiyM/OjTqPDnPpzUNi5sFCWZkpD/7/7Izw5PDPT2NvDZ5z8ssLj2bOR8eBB9iZDRESUS7CRmYu5urpi37592LJlCz766CP4+vrCysoK3t7eaNiwIVavXo1Tp04hX758b/pSiYiIXhljbCyCuveAKSZGyLWurvBfsRwaO7vMLCghCHMC51gOge5lu6NNsTby4JE3gX0T5LzZPMBZXH52y+lHmPPnTan06zoF0biU13POhoiIiIiIcrLPq/rjsyp5pXzYj5dwNzIpK9BogM82AD5lLSpVYGcP4O7fmYmjlSOWNloKNxs3oTIqNQoD/h6AdFPWA5uODRvCsfH7Qp05Ph6PevaE0WLrKSIiorcBG5m5nKIoaNeuHXbv3o2QkBCkp6cjPDwc+/fvR/fu3aHT6d70JRIREb0y5rQ0BPfpi4yHD4VcsbaG/4rlsMqfPzMzmU0Yc2wMUo2pQm0D/wboX7G/PLjJAPzUEzCmiXnpVkBZsen5x5VwBPx0WRqiWgE3jPywRDZnRUREREREOZWiKJjcsgzK+DkJeVK6Eb2+P4vkdGNWaOMMdNoJuBYUBzEbgC2dgJBzmVFex7xY9N4i6DV6ofR6zHXMDZwrZN5jxkBjby9khoePENy3H8wWq9QQERHldmxkEhERUa71ePoMpJ4/L4aKAt85s2FboYIQb7i2AecjxFo3GzdMrDkRiqLIgx+cAYRajO3gA3w0H3iq/sTdaAzYch5mcWtOFPd2xDedq0Cv5T+3iIiIiIjeJjZ6LVZ0rAwXO7HpeDsiCSN3XIKqPnVz4OAFfPET4OAtDmJIBnZ0AwxZD05W8KqAybUnS+fbcnML9j7Ym/lZ7+MD3zmzhfsSAEg9dw5howOgms3/YnZEREQ5C3+zRkRERLlS6sWLiNu2Tcq9R4+C0wcfCNnNmJtYen6pVDu+xni427rLg5/fCByZJ+ctlwJ2Wcs9XQ9LQPfvziDDKP6iIK+rLb7rVg3OFr/YICIiIiKit4O/mx0Wtqtg2UvE7kthWHvsgRi6FXzyZqa1s5jH3AOOLhCijwt9jM6lOkvnm3B8AoISgzI/OzZsCO/Ro6S6hD17ELlocbbmQkRElJOxkUlERES5jmo2I3zKVCl37fwF3DqLN/0ZpgwEHA2AwWwQ8haFW6BR/kby4Hf2A78OlPMqXYGijTM/phlM6LfpHJKeXjoKgLu9Fb7vVh3eTjbZmBEREREREeU2DYp7YfD7xaR8+p7rCHwQI4Y+ZYAOWwDF4texR+cD0XeFaFClQSjnUU7IkgxJGHZoGDJMGZmZW+fOcP3iC+n80atWIe6nXdmbDBERUQ7FRiYRERHlOnE7diDtyhUhsy5aFN4jRki1Sy8sxa3YW0LmY++DUdXkp5cRdgnY1hkwi81J+JQFPhAbp7P+uIG7kclC5mCtw4au1VDQQ9yvhoiIiIiI3k793iuChiW8hMxkVjFk2wXpoUfkrwVU7yVmpgzgt6HAU8vR6rV6zK4/G45WjkLptehrmH92vpB5jxoJh4YNpesKnzQJ6bdvv8CMiIiIcpYc2ciMjY3F3r17sWfPHgQHB7/pyyEiIqIcxBQfj8j5C6Tce+xYKDqdkJ0IPYF1V9ZJtVNrT5V+KYC4IOCHtkBGkpg7+wMdfgSsspqTx+5EYZ3FclEaBVj1RWWU8bNYLoqIiIiIiN5aGo2CBZ9VQD43OyEPiknF9D3X5QPeCwAc84jZvQPA1Z1C5Ofgh6m15VVofrj+Aw48OpD5WdFq4Td3DmxKlxbq1LQ0BA8eDHNKSjZnRERElLO81kZmTEwMFi9ejMWLF+PWrVvPrJk5cyb8/Pzw4Ycfonnz5ihQoAC6dOmC9PT013mpRERElENFLlkKU2yskDl+2BT21asJWUxaDMYcHSMd37FkR1TPU10MU+OeNDGTwsXc2hno+CPglPWLhvhUA4b9eFEat2f9wqhdxCN7kyEiIiIiolzP2U6P5R0rQacRN8zcdOoRDt2KFIutHYGmM+RB/ggA0hKEqGG+huhUspNUOvXUVCQ99QCmxs4OeVcsh9ZDvB/JuHMX4VOnZXM2REREOctrbWRu3boVgwYNwogRI+Du7i59v2nTJgQEBCA9PR2qqkJVVZjNZnz//ffo2rXr67xUIiIiyoHSbt5C7ObNQqbY2kpLyqqqignHJiAyVfylQRGXIhhUaZA4qDEd2NoJiLR4WlqjBz7fCHiVFOJJv1xFWHyakJXM4/TMvXGIiIiIiOjdUMbPGQMaFZXykdsvIT7FIIalPgEKNxKzpHDggNx0HFJ5CMq4lxGyiJQILL2wVMj0Xl7wmzMbUMRmavzOnYj/+efnnwgREVEO81obmQcOPFn2oF69elIjU1VVjB07NvNzmzZtMGzYMOTPnx+qqmLLli04evTo67xcIiIiykFUVcXjqVMBk0nIPXr2hD6PuDTT5hubcTD4oJBZa60xp94c2OhsskKzGfi5L/DgiHzCT1YABesJ0e+Xw7DzfIiQWWk1WNiuAqx0OXLFfiIiIiIiek16NyiMcnnFrSbCE9Iw6derYqGiAM3mAFprMT+9Gnh4XIj0Wj1m1J0BvUYv5Juub8KVqCtCZl+zJjx6W+zBCSBs0mSk37uXzdkQERHlDK/1N263bt2CoiioWbOm9N2xY8fw4MEDKIqCWbNmYdu2bZg9ezYCAwPh5uYGANiwYcPrvFwiIiLKQWLWrEFKYKCQ6fPlg1vXr4TsZsxNzDszTzp+eJXhKOJaRAz/ngJc/lE+WaMJQLm2QhSRkIaAny5LpcOaFENxH0cpJyIiIiKid4teq8G8tuWlhxx3ng/BH1cstrFwLwzUHSpmqhnY0hGIEZuOBZwLoHu57mIpVEw6MQlGs1HIPfr2hV3VqmJtSgpCBg2GOTX1BWZFRET0Zr3WRmZUVBQAoEiRItJ3+/btAwDY2tqiT58+mbmHhwc6dOgAVVVx8uTJ13OhRERElKMk/PEHIubKzUnv0aOgsbLK/JyUkYSRh0ciw5wh1DX0b4jPin8mHnxmLXB0vnyyKt2AOoOFSFVVjNxxCbEWS0JVK+CGbnUKZXM2RERERET0tirq7YjhHxSX8oCfLuNRdIoY1hkEuFv8njQ1BtjUDkiNE+JuZbqhoHNBIbsRcwM/XP9ByBStFr5z50Dr6irk6bduIXTUaKhmc7bmQ0RE9Ka91kZmdHQ0AMDBwUH67r/LxtavXx92dnbCd2XLlgUAPHr06BVfIREREeU0KefOI3TESCl3aNgQju+9l/nZYDZgyMEhuBt/V6jzsvPCpFqToDy9V8zNP4DfLJ5+BoBiTYEP5X1ltgQG4cBNcb9Neyst5n1WHlqNWEtERERERO+2rnUKomoBsZEYk5yBL9aeQkRiWlaoswbarAX04u9CEXUL+PFLwJT1IKWV1grja4yXzrXswjKEJoUKmd7bG76zZ0m1iX/+iaily15gRkRERG/Oa21kajRPTpeUlCTkRqMRp06dgqIoqFOnjnTcf5eWTUlJkb4jIiKit1fGw4cI7tMHaob4hqVVkcLwnTkj87Oqqph8YjJOhJ0Q6hQomFFnBlxsXLLCuEfAzu5Plm16mm+lJ79E0OqE+GF0MqbsviZd24TmpeHvZiflRERERET0btNqFMxtWx62eq2QP4xOwZdrA5GQ9tRKL3nKA59+A8DiAcl7B4E9wwFVzYyq+FRBqyKthLJUYyqmnpwK9ak6AHCoWxfuvXpK1xa1fDnid//2QvMiIiJ6E15rI9PHxwcAcPWquMH1kSNHkJycDACoVauWdFxiYiIASG9qEhER0dvLGBuLoB49YYqLE3Kthwf8V66C1skpM1t5cSV23dkljdG3Ql9Uy1MtKzCbgV19gPQEsdAlP9BhK2BlL8Qms4qh2y4iJcMk5O+X9ELbKnlfaF5ERERERPT2y+9uj3mflbdc7AXXwxLw9YYzSDM8dY9R8mPg/YnyIGfXAadWCtHQKkPhZuMmZEdCjmDZBflNS88BA+DwfiMpDwsIQOrFi889FyIiojfptTYyq1SpAlVVsXHjxsxlZgFgyZIlAJ7sj1mzZk3puFu3bgEA8ublLwyJiIjeBarJhJBBg5Hx8KGQKzY28F+xHFZ5/TKzXXd2YfnF5dIYrYq0Qo9yPcTw9CrgwRExs3EGOu0AHLykMVYdvoszD2OFzN3eCjM+LScuVUtERERERGShWdk8mNKyjJSfvh+D/pvPw2h6apWY2gOBip3kQf4cAzw6lfnR2doZw6oMk8pWXVqFn+/8LGSKRgO/WbNgXaKEkKsZGQjq1w+GsLBszoiIiOj1e62NzA4dOgAAwsLCULVqVQwePBhNmjTBrl27oCgK2rZtCysrK+m448ePQ1EUlC5d+nVeLhEREb0h0d98i5RTp8RQUeA3by5s/7N3NgCcCjuFSccnScfX9q2NcTXHic3GyFvAvonyyT5eAHgUleJroQlY8NctKZ/+aVl4Olo/91yIiIiIiOjd1alGfgxpXEzK/7r2GDN+v5EVKArw0QIgv8W2W6oJ2N4VSInJjD4u9DHe839PGnPiiYk4HXZayDT29vBfvgxaDw8hN0VGIbj/AGkbDyIiopzmtTYyW7ZsiWbNmkFVVTx8+BCLFy/Gvn37AABOTk6YOHGidExERASOHTsGAM98W5OIiIjeLqkXLyLyP6s1PM179Cg4NspaFinFkIKxx8bCqBqFuhJuJTCvwTzoNfqs0GQAfuoBGNPEQcu0fvKfheikdPT+4SwMJnGfmTaV86JJaZ8XmBUREREREb2r+jcsgi61Ckj52mP3cSk4LivQWQHtvgdcLWoTgoFdvTP3y1QUBTPrzkRJt5JCmdFsxKADg3Av7p6Q63194b90CRSLF0jSrlxBxLz5LzotIiKi1+K1NjIBYPv27Rg4cCCcnJygqipUVUW1atWwb98+5M+fX6pfvXo1TKYna8Y3adLkdV8uERERvUampGSEDBsOmMQ9KZ1btoBb585CtubKGoQnhwuZj70PljVaBnu9uNcljswHQs+LmYMP0GyudA2pGSZ023AGD6NThNzPxRbjm5fK5oyIiIiIiOhdpygKxn9cCi3K+wq5qgJjfroCk/mpByjt3IC26wGtxap1t/4ATizNKtPbYWmjpfC28xbKEg2J6LO/D6JSo4TctkIF5Jk2Tbq2mA0bkPj3gRebGBER0Wvw2huZNjY2WLBgAaKjoxEWFoa4uDicPHkSlStXfmb9xx9/jAMHDuDAgQMoVYq/PCQiInqbPZ46FYagICHT+/vDe9w4IQtKDML6K+uFTKfRYWnDpfCys9jrMuQccHi2fLKWS5/8kuApJrOKAVvO40JQnJBrFGBu2/JwstGDiIiIiIgouzQaBXPalkNhT/Ghy8sh8fjh1EOx2Lci8MFUeZB9E4GgwMyPXnZeWNZoGex0dkJZSFIIRhweAZPZ4gHR5h/D9YsvpGHDRo/mfplERJRjvfZGZuaJNRp4e3vDycnpf9ZVqFAB9evXR7169V7TlREREdGbkLBnD+J37RJDrRZ+c+dA6+AgxLMDZyPDLO7l8kXJL1Dcrbh4fHoSsONrwCwuP4vKXwFFGwuRqqqY/OtV/HXtsXRtE1uURs3C7tmaDxERERER0dOsdVpM+aSMlM/54yYiEiy2wajWAyjZXMzMRmD7V8J+mcXdimNeg3nQKlqhNDA8EKsurZLO5TV8GGwsXhYxxccjZNhwqEajVE9ERPSmvbFGJhEREdF/GUJCEDZhopR79usL2/LlhexoyFEcDDoo1tl6omf5nvLAv48EYu6KmWuBZz7d/M2Re9hw4qGU96xXCJ1rFvjfEyAiIiIiInoOtQp7oFVFPyFLTDdi6m/XxUJFAVosBVwstuKKDwL+DBCiOn51EFBdzABg5cWVOBV2Ssg0VlbwWzAfGnvxzdDUs2cRuXQpiIiIcho2MomIiOiNMqelIXjAQJgTE4XctkpluPfoIWQGkwGzTs+SxhhcebC8L+aVHcCFjWKmaIBWqwFr8Q3P3ZdCMX3PDWnc5uV9MbJpiWzMhoiIiIiI6H8LaFYSTjY6IfvlYiiO3hb3tYStC9B2HaCx2OLi4mbg3kEh+qz4Z2hRuIWQqVAx6sgoab9Mq/z54TNpknRd0atWI+GPP7M1FyIioldN988lr47JZMKlS5cQHByMhIQEmEymfzymc+fOr+HKiIiI6HVQVRXhEych7epVIdc4OsJv9mwoWnF5pO+vf48HCQ+ErKJXRXxc6GNx4NiHwK+D5BPWHwXkqy5Ep+/HYMjWi1JptYJumNu2HDQa5bnnQ0RERERE9E88Ha0xvGkJjNt1RcjH/3wFewbWhY3+qfsgv8rA+xOBvWPEQXYPBnqfAPQ2mdGY6mNwOeoy7sffz8yiUqMQcCQAKxuvhEbJeqfF+eOPkHLqJOJ+3J41pqoiZNgwQKuBU2NxKw4iIqI35Y00Mh89eoRJkyZh69atSE1Nfe7jFEVhI5OIiOgtErtpk7wvJoA8U6ZA7+srZCFJIVh1UdzjRaNoEFA9AIryVLPRZAR2dgfSE8RB89UC6g0TojsRiej+3RlkmMxCXsTLAd98UQXWOrGRSkRERERE9DJ0qJYP288G42JQXGZ2LyoZATsvY95n5cV7nBq9gSvbgdDzWVnMPeDIPKBhVoPTTm+HufXnosNvHZBuSs/MT4SdwJrLa9C9XHfhGrwDApB64QLSb9/JCo1GhAweAmXRQjg2avTS5ktERPSiXvvSsseOHUOFChWwfv16pKSkQFXVbP1HREREb4eUs2fxeMZMKXfr1hVOTZsIWUJGAvrs64MUY4qQty3WFiXcLJZ+PTwbCBL3gYGNM/DpakCT1ZiMSEzDl2sDEZ9qEEo9Ha2x/quqcLazWL6JiIiIiIjoJdFqFEz7pAwsF4DZeT4Eyw/eFUONFmi+6MlWGU87ugCIELfIKOZaDKOrjZbOt/TCUhwPOS4Oa2uLvEuWQOvpIRYbjQgeNBiJfx/I1pyIiIhehdfayExISMCnn36KuLi4zLcrV65cCeDJ25b9+/fH0qVLMXz4cJQrVy4z79SpE9atW4e1a9e+zsslIiKiV8TwOALBgwYBRqOQ29WsAa/Bg8VakwFDDgzBvfh7Qu5s7Yx+FfqJA1//FTg0Wz5hiyWAi3/mx+R0I7quD0RInLgyhJ2VFuu6VEVeV7vsT4qIiIiIiCgbyvg5o3vdQlI+58+b+O1SmBjmKQ/U6CNmZgOwexBgFleY+bTop2hWsJlYqpox7NAw6b7KqkAB5F+/HloPi2amwYCQgQORdOhQtuZERET0sr3WRubKlSsRGRkJRVGwceNGrF+/Hj169Mj8vlGjRujTpw9mzZqFCxcu4KeffoKrqyu2bNkCAPjyyy9f5+USERHRK6AajQgZNAimyCgh1/v6wm/+fCi6rJXvVVXFxBMTcSr8lOUwGF9jPFxsXLKCoEBgx9cALFZwqPQlUKqlMObALRdwJURcelarUbCsYyWU8XN+4bkRERERERFlx/AmxVG/mKeUD9l2QVh2FgDQYDTg7C9mj04A578XIkVRML7meBRwKiDkiYZE9NvfD3Fp4rjWhQsj//p10Lq5CblqMCBk8BAYQkOzMyUiIqKX6rU2Mn///XcAQOXKlfH555//Y33Lli3x22+/QVVV9OnTBzdu3PjHY4iIiChni1q9GqnnzwuZYm2NvEuXQOfqKuSrLq3CL3d/kcYYWGkgPijwQVYQfRfY3A4wpomFHsWApjOEaNPpR9h3/bE05rRPyuC94l7ZnA0REREREdGL02k1WNKhIop5Owh5utGMr787g9CnV5GxdgCazZUH2TsOiLotRPZ6eyx8byEc9OK4QYlBGHJoCAwmcYsN6yJFkG/9Omgt7snMKSkInzKVW34REdEb81obmdeuXYOiKPjkk0+e+b3JZJKy6tWr47PPPkNqamrmMrRERESUO6VeuYqo5SukPM/kSbApVUrIdt/bjWUXlkm1rYu2Rrcy3bKC5CjghzZASrRYaOcBdNgKWNlnRg+ikjF193VpzP4Ni+DzavmyORsiIiIiIqJ/z8lGjzVfVoW7vZWQRyamo8f3Z5BmeOp3psWbCivOAADS45/cEyWLq94UdimMOfXnQGOxt2ZgeCCmnZomNSdtihVDvnVroXFyEvKkAweQ+NdfLzg7IiKif+e1NjLj4uIAAP7+4hIIer0eAJCcnPzM4xo1agQA2Lt376u7OCIiInqlzGlpCB05UtoX06VtWzi3FG/E78Xfw6Tjk6QxavnWwpgaY6AoypPAkAps/hyIEfd5gc72SRPTLWu/GZNZxdAfLyLVID441biUN4Y0LvYvZkZERERERPTv+LvZYXXnKrDSib+uvRKSgOl7LB7GbDoLsBabjYh9AGxu/+Qe6Sl1/OpgRNUR0vl23N6BH67/IOU2JUrAe/RoKX88dRpMiYnPNxkiIqKX6LU2Mq2snjxVZGNjI+SOjo4AgJCQkGceZ2tr+z+/JyIiopwvcuEiZNy9K2R6f394jxopZBmmDIw6PAppJnGZ2KKuRTGv/jzoNfqscM9wIDjQ4kwK0GYNkLeKkK4+fA9nH8YKmbu9FWZ8WjarMUpERERERPSGVM7vijltykn5dyceYs/lsKzAKQ/Q+lvA4k1LBJ8GdvUGzGYh7lCiA9oWayuNO+/MPFyKvCTlzp+0hF2NGkJmjIhA5IKFzz8ZIiKil+S1NjL9/PwAANHR4tJvhQo9eVsiMNDyF5FP3Lx5EwBgtHiDg4iIiHKH5FOnEbNhgxgqCnxnzoDG3l6Il5xfgusx4hPHbjZuWN5oORysntrf5dEp4Pz38sk+nA2U+EiIroclYP5fN6XSGZ+WhYeDdfYmQ0RERERE9Iq0rOCH7nULSvnI7ZfwMPqp1eyKNXly72Pp6k/A35OFSFEUjK4+GtV9qgu5UTVixOERSMhIkOrzTJwAxUpc6jZ282akXriQvQkRERH9S6+1kVmu3JMniq5fF385WaNGDaiqij179uDhw4fCd3FxcVi5ciUURUHBgvJf4kRERJSzmZKSEDZ6NGCx/4p7t66wq1xZyE6EnsD6q+ulMabVmQYfe5+swGwCfh8un6xmP6B6DyFKN5oweOsFGEzi+dtUzosPSvuAiIiIiIgoJxnRtAQq5nMRssR0I/puOod041NbZVTrDtToKw9wdAFwXlw2Vq/RY16DefBz8BPykKQQTDw+Udov06pAAXj07iWOq6oIGz8BqsGQ7TkRERG9qNfayKxfvz5UVcXBgweFvFOnTgCA9PR01KtXDytWrMDevXuxYsUKVK5cGREREQCATz755HVeLhEREf1LqqoifPx4GEJDhdy6aFF4DBggZLFpsRhzdIw0RqeSnVDHr44YnvsOCLsoZl6lgfflfTXn772FG+HiXi5+LrYY37xUNmZCRERERET0eui1GixpXxHOtnohvxKSgBl7bojFH0wBiosr0gAAfhsKPL4mRM7WzphTbw50ik7I/3r4F3689aM0hHu3brAqUljI0m/dQvSatdmYDRER0b/zWhuZLVq0AABcuXIFV69ezcyrVauGTp06QVVVBAcHo1+/fvjwww/Rr18/PHjwAADg7++PoUOHvs7LJSIion8peuVKJOz5XQz1evjOngXNU8sUqaqKCccnIDI1Uigt5loMgyoPEo9PiQH2i0slAQCazQa04g350dtRWHX4nlQ6p205ONnopZyIiIiIiCgnyOtqh7lty0v5+uMPsPdqeFag0QKtvwF8K4qFxlTgxy5ARrIQl/Usi4GVBkrjzjo9CzdjxO04FCsr5JkkPywauXQpUi9fef7JEBER/QuvtZHp7++PAwcOYM+ePXBychK+W7NmDbp16wbgyS8zn/6vcuXK2L9/P1xdXV/n5RIREdG/kPDXX4hctFjKPfv2hU3JkkK26cYmHAg6IGTWWmvMqjsL1lqLPSwPTAdSY8SsTGuggPjWZnRSOgZvuyCd/6vaBVCrsMfzT4SIiIiIiOgNaFzKG93qyFttjf/5KhLTnlre1coeaL8FsPcSC6NuAntGSMd3Lt1ZWvUmw5yB4YeHI9kgNj7tKleGy2efiQMYjQgdNgzmZLGWiIjoVXitjUzgyfKyTZo0gb+/v5Dr9Xp88803uHfvHtasWYPp06dj4cKFOHbsGAIDA1G4cOH/Z0QiIiLKadJu3kToyFFS7tCgAdy7fy1kZ8LPYG7gXKl2aJWhKOJaRAzDLwNn1oiZ3g5oPEWIVFXF8O2XEJmYLuQlfBwxsmmJbMyEiIiIiIjozRnZtATK+7sIWXhCGubtvSUWOvo8eTMTiphf2Ahc3CJEGkWDaXWmwctWbHzej7+PPvv6IMWQIuReI4ZDnzevkGU8fIjHM2dmez5ERETZ9dobmf8kf/78+OqrrzBq1CgMGDAANWvWfNOXRERERNlgjI5GcO8+UFPEm1/rokXgO3cOFK02MwtPDsfQQ0NhVI1Cbb289fB58c/FgVX1ydPEqlnM6w0DnP2EaMPxB/j7RoR4ft2TfWZs9FoQERERERHlBlY6DRZ8Vh5WOvHXuBtOPMDFoDixuFADoL78BiZ2DwEixcanm40bZtabCY0ijnsu4hz67BebmVoHB/jOmQ1oxXupuB+3I+HPvdmeExERUXa81kamRqOBTqfDL7/8kq3j/vzzT2i1Wuh0un8uJiIiojdGzchA8ICBMISGCrnWxQV5ly+H1sEhM0s3pWPIwSGISROXifVz8MO02tOgKBZPEh9bCDw6LmZuhYCa/YToelgCpv9+Q7q2cR+XQlFvx+xPioiIiIiI6A0q5OmAfu+Jq9WoKjB652UYTRYPetYfCeQXl42FIfk/+2WKD5tW9amK3uV7S+c7+/gs+v3dD6nG1MzMrmJFePSWa8PGj4chPFzKiYiIXpbX/kamqqovfNyLHktERESvR8SChUg9e1YMdTr4LVoEq6eWlVdVFdNPTcflqMtCqY3WBgvfWwgXGxdxjCs7gX0T5RM2nQnosvbQTEwzoP/m88gwijfzH5TyRsfq+V5kSkRERERERG9cz/qFUNjTXsiuhSVg3bEHYqFGC7T+FrDzEPOIq8CuXoBZvFfqWa4nOpXsJJ0vMDwQ/ff3F5qZHr16wrZiRaHOHB+P0JGjoBoMlkMQERG9FDluaVkiIiLKnRIPHEDMunVS7jN2LOyrVxOyH2/9iJ23d0q1E2tNRAk3iz0sH50Cfuoln7B4M6BYk8yPBpMZvTeew52IJPH8TjaY1bqc/IYnERERERFRLmGt02J6q7JSPv+vWwiKEd+0hFMe4NNV8iDXfgYOzhAiRVEwouoIdCjRQSo/FX4KQw8Ohfk/23soOh1858yG5qmVdgAg5dQphAwZwmYmERG9ErmikZmYmAgAsLW1fcNXQkRERM9iCAtD2KjRUu7yeTu4ft5OyG7G3MTM0zOl2i9KfYGPCn0khtF3gS3tAVO6mHsUBz5ZkflRVVWM3nkZR+9ECWWKAsxvVx6u9lbZnBEREREREVHOUr2QO9pV8ReyVIMJ436+Iq9kV+R9oN5weZDDs4FLPwqRoigYVW0UPi/+uVR+JOQIdt3ZlfnZKm9e+EwYL9Ul/rWPzUwiInolckUjc//+/QAAHx+fN3wlREREZEk1GBAydBhM8fFCbl2qJLxHi83NDFMGAo4GwGAWb26r+lTFkMpDxIFTYoAf2gIp0WJu7wl0/BGwdcmMFu2/je1ng6VrG9q4GGoV9pByIiIiIiKi3Gh0sxJwt3hQ8+DNSMz/65Zc3CAAKPGxnP/cFwg+I0SKoiCgegDaFW8nlc87Mw/RqVn3Zc7Nm8P500+luifNzKFsZhIR0Uule1UDHzp0CIcOHXrmd1u2bMGFCxf+5/GqqiI5ORnnzp3DgQMHoCgKatas+QqulIiIiP6NyCVLkXrunJBp7OyQd/58aKythXzFxRW4FSveYHvbeWNu/bnQaZ76Z4nZBGzrDMTcFU+mswU6bAVc82dG284EYeG+29J1tavij77vFXnBWREREREREeU8LnZWGPdxKQzaekHIl/x9B/nc7ND26Tc2NRrg09XA2iZA+OWs3JQObG4PdP8bcMmq/28zMzIlEn8H/Z2ZJ2QkYM6ZOZhZN2tlnTyTJ0FNS0XCnt+F60j86y+EDB0Gv3lzoej1L2fSRET0TntljcyDBw9i8uTJUq6qKrZu3ZqtsVRVhU6nw4ABA17W5REREdFLkHTkKKJXr5ZynymTYVWggJBdiLiAtVfWSrXT6kyDm42bGB5bCDw4YlGpAG3WAH6Vs8ruRCFg52VYqlfME1NbleG+mERERERE9NZpWcEXey6HYe+1x0I+eudl+LnYolaRp1alsbIH2m8BvmkIJD1VnxwBbO0EdNsL6LIeQNUoGgRUD8Cp8FNINiRn5r/d+w0tCrdALd9aAP6zX+bs2QAgNzP37kX4tGnIM3HiS5oxERG9y17p0rKqqgr//X/5P/1XsWJF/PLLL6hateqrvFwiIiLKBsPjCISOHCnlLp99BuePxL0uUwwpCDgaALNqFvKOJTuiep7q4gAhZ4ED0+UTNp0BlMgaNyXDiGE/XoTRLO4FUyqPE5Z3rAS9NlesoE9ERERERJQtiqJgQbsKKO3rJORGs4qeG8/iTkSieIBzXuDzzYDORszDLgD7Jkrje9t7Y2ClgVI+9eRUpBnTsq7jP81Mxw+bSrVxW7Yi5fz5554TERHR/+eVvZHZpUsXNGjQIPOzqqpo2LAhFEXBlClTULt27f95vEajgYODAwoWLAgXF5dXdZlERET0AlSTCaHDh8MUEyPk1sWKwTtgtFQ//+x8BCUGCVkBpwLyzXF6ErDja8BsFPNynwM1egvR8gN3ERafJmS+zjZY91VVOFi/sn/iEBERERERvXH21jqs7VIVnyw7JtwXJaYZ0WVdIH7qUxuejk9t9ZG3MtByGbCjmzjQyeVAgbpAiWZC/Fmxz7D77m5cirqUmQUlBmHVpVXCfZyi08FvzhyEAEj8/Q9hjMdTpqLAj9ugaLX/fsJERPTOemW/5cufPz/y58//zO/KlCmD+vXrv6pTExER0SsWtWw5Uk6fFjLFzg5+CxdAYyM+5Xs05Ci23hSXldcqWkyvMx22Oltx4N9HAjH3xMy1ANBsjhA9jE7G6sNinV6rYE2XqvB2snjKmIiIiIiI6C3k7WSDtV2qou3KE0hKz3oYNDg2Ff02ncOm7jWg1Ty13UbZNk9WwDm5XBzo5z5AnqNP3tz8D61Gi/E1x6Pd7nYwqabMfP2V9fiw4Ico5losM1N0OvjNno37d+8h/datzDzt2jXE/fgjXD///CXOmoiI3jWvdc21AwcO4O+///7HtzGJiIgo50o+eRJRK1ZIeZ4J42FdqJCQPUx4iJGH5eVnu5frjrKeZcXw6k/AhY1ipmiBT78FbMQlk6bsvoYMk7hMbdc6BVEyj1hHRERERET0NiuZxwnLOlYSG5YATt2PwaL9t+UD3p8I5KkgZqmxwPZugElcGae4W3F0Lt1ZyIyqEWOPjoXBZBByRa+Hz7ix0ukiFyyEMTb2uedDRERk6bU2MuvXr4/69evD3d39dZ6WiIiIXhJjVBRChg8HVHFfSudWreDcsqWQJWYkov/f/ZGQkSDkJd1Koke5HuLAcUHAr/IeLGgwCvAX98g+cDMC+65HCJmXozX6NyyazdkQERERERHlfvWLeWJyy9JSvuTv2zh+J0oMddZA23WAlaOYB50EDs6Qxuhdvjf8HPyE7HrMdSw5v0SqtataFU7NmwuZKT4ekQsWPt9EiIiInuG1NjKJiIgo91JNJoSOGAFTpHgjbFW4sPTkrclswojDI3A//r6Q2+nsMLPuTOg1+qzQkAZs+wJIixdPmK8mUHeoEKUbTZj86zXp2gKaleS+mERERERE9M7qWD0/2lTOK2SqCgzcegGRielisVshoPlCeZAj84Cru4TIVmeL8TXGS6Xrrq7DybCTUu41bBg0dnZCFvfjj0i9fOW55kFERGSJjUwiIiL6R6rBgNARI5F8/ISQKzY28FswX7pRXXB2AY6GHBVroWBWvVko5PLU8rOqCvw2BAg9L57Q2hn4dDWg0Qrx2qMPcD8qWciqFnBFywq+LzgzIiIiIiKit8PklqVRxMtByCIT0zF46wWYzeKqOijbBqgkLhsLqMCObsC1X4S0ll8tdCrZSTrfmCNjEJcWJ2R6by949O1rMayK8KlToJrF7UGIiIieBxuZRERE9D+Z09IQ3K8/En77TfrOZ+wY2BQrJmQ/3/kZG65tkGoHVBqABv4NxDDwW+DCD/JJmy8EXPIJUVBMCpb8Le7xolGAiS1KQ1HE/WCIiIiIiIjeNXZWOizrUAk2evFXvkfvRGH5wTvyAU1nAZ4lxcxsBLZ/BVzfLcSDKg9CUVdxO4+I1AhMPDERqsXWI26dv4BV4cJClnbxEuK2bs3mjIiIiNjIJCIiov/BlJSEoO49kHTokPSdU/PmcG7dWsiuRl/FpBOTpNqPCn2EbmW6ieHDE8Afo+ST1uoPlPlUiOJTDPhqfSBSMkxC3qF6PpT2dX7O2RAREREREb3divs4YlILeb/M+X/dwuFbkWJoZQd8/gPg4C3mZiPw45fAjayHWa211phVdxasNFZC6f5H+7Hz9k4hU/R6+IwdI13D4zlzkREUlM0ZERHRu46NTCIiInomY2wsHnX5CimBgdJ3Do0aIc/UKcKbkIkZiRh2cBgMZoNQW9ajLCbVmiS+NZkQCmzr/OQG+WkF6wONJgpRhtGMXhvP4k5EkpC72OkxtHHxF5scERERERHRW+qzKv74xGL7DbMK9N10Trqvgnth4MtfAXsvMTcbgW1fAjf/yIyKuhbF0CpDpfPNPD0TZ8LPCJl9zZpw/LCpkKkpKQgbHcAlZomIKFvYyCQiIiKJajAgqEdPpF25In3n3LIF8i5aCI21dVa9qmLSiUkITgoWar1svbDwvYWw1mbVwmR4ckOcHGExcD6gzTpAqxPGHbXzEk7ci5auY0arsnC1t5JyIiIiIiKid5miKJjaqiwKedgLeWKaEd02BCI2OUM8wLP4f5qZnmJuNgDbuwJxjzKj9iXao65fXaEszZSGPvv74Ozjs0LuM3YstK6uQpZy5gxiv//+BWdGRETvIjYyiYiISBLz3fdIu3xZyl07dkSeGTOg6HRC/uOtH/Hngz+FTKtoMa/BPHjZWTzZe2Q+EHxazHQ2QLvvAXt3IV68/w52nguRrmNk0xL4sGyebMyIiIiIiIjo3eFgrcOqLyrD0Vq8d3sYnYJeG88iw2jxVqRXiSfNTDsPMTckA3uGA//ZB1NRFEyuPRluNm5CWaoxFb339ca5x+cyM527O3wmTZSuLWL+AqTfu/fikyMioncKG5lEREQkMISGInLpUil3790L3mPHQNGI/3y4EXMDs07PkuoHVBqACl4VxDDkHHB4tnzS5osAX7F21/kQLNh3SyptXy0fetUv9I/zICIiIiIiepcV9XbEkg4VoVHE/NT9GEz45QrU/zQnM3mVfNLMtBXfosStP4Drv2R+9LD1wOKGi2GnsxPK/tvMPB9xPjNz+uADOH38sVCnpqcjdNRoqEaLrUaIiIiegY1MIiIiEoRPnw41NVXInNu0htfAgeI+lwCSDckYfmg4Mszi0kR1/OqgS+ku4sCGVOCnXvK+mJW+BMp/LkR3IhIxaucl6drqFfPElJalpesgIiIiIiIiWYPiXhj3cSkp33w6CGuPPZAP8C4FfPiMh0/3jADS4jM/lvcsj1WNV0nNzBRjCnr91QuXIrPu53zGjoHOU1y2Nu3SJUStXp29yRAR0TuJjUwiIiLKlPj330jat1/ItK6u8B42TKpVVRVTTk7Bg4QHQu5l64VpdaZBo1j8M2P/FCDqppi5FgCaTBeidKMJAzZfQJpBXOqohI8jlnWoCJ2W/3whIiIiIiJ6Xl1qFUCH6vmkfMae6zj3KFY+oGxboNB7YpYUDuyfLEQVvCpgZeOVz2xmDj80HGnGNACA1sUFPlPEYwEgavESxHzH/TKJiOh/428CiYiICABgTklB+NSpUu41cgS0Li5SvuvOLvx27zch0ygazKo3S9ovBfcPAyeXWYygAK1WAdYOQjr3z5u4FpYgZB4O1lj3VVU42uifez5ERERERET0ZF/LSS1Ko1ZhdyE3mlUM2Hwe8akGywOAj+cDOhsxD1wDBAUKUUWviljx/grY6myFPDQ5FGuurMn87NigAZxbfypd2+Pp0xG9Zo2UExER/RcbmURERAQAiFq+HMbQMCGzq1oVzi1bSrV3Yu9g+qnpUt6nfB9U8akihmkJwK4+8glrDwTy1RCiI7cj8c2R+1LpvM/KI4+zrZQTERERERHRP9NrNVjesRL83cT7quDYVIzacUneL9OtEFBvuMUoKvDrQMAkNj4reVfCivdXQKfohHzt5bUISgzK/Ow9ejSs8ueXri1izlxErViR/UkREdE7gY1MIiIiQtqtW4hev0EM9Xr4TJwg7UeZYkjBsEPDkGZKE/IaeWrg67Jfi2OoKvDrACA+SMy9SgPvBQhRTHIGhm67KF1b19oFUb+Yp5QTERERERHR83Oxs8KS9pWg04j3eL9fCcfGU4/kA2oNADxLilnEVeDYIqm0sndldCzZUcgyzBmYHZi136bWwQH5NqyHVYEC0vGRixYjYuFCuaFKRETvPDYyiYiI3nHm9HSEjhgJGI1C7t61K6wLF5bqZ5yegbvxd8VaG3fMqDsDWo1WLD48F7j6k5hp9MCnqwCddWakqipGbL+EiMR0obSEjyNGNC3+ArMiIiIiIiIiSxX8XZ55jzVl9zVcCxW3+IDOCmi+UB7k4Awg5KwU9yrfC5624kOoB4MO4nDw4czPeh8f5P/+O1gVke81o1euQuwPm55rHkRE9O5gI5OIiOgdFzFrFtJv3BAyfd688OjVU6r99e6v2HVnl5ApUDCz3kx42HqIxdd+AQ7Ie27ivdGAT1khWn/8AfZdfyxk1joNlrSvCBu9RXOUiIiIiIiIXtjXdQrhveJiwzHDaEa/zeeQlC4+4Ip8NYDKX4mZ2Qjs6A6kJwmxg5UDhlQZIp1v5umZSDdlPbSq8/RE/u++g3XJklJtxJw5SL8nbzdCRETvLjYyiYiI3mEJf+5F7KbNUu4zcSI0tuLeKVeirmDKySlSbc/yPVEjj7jXJcIvAz/JjVAU+xCoPViITt6LxtTfrkulYz8qiaLejs8xCyIiIiIiInpeGo2CeZ9VgLeTtZDfi0zGV+tOI9mymdl4MuBaQMxi7gJ/jJLG/qjgR6jkVUnIghKD8N3V74RM5+aG/OvWwqas+JCrmp6OsNGjoZpM2ZsUERG9tdjIJCIiekdlBAcjbOxYKXfr1hUOdWoLWWB4ILr92Q2pxlQhr+JdBb3K9RIHSIoENrcHDCli7lUKaP0NoMn650dYfCr6bToHk1ncB6VRCS90qpH/BWZFRERERERE/8TN3gqLPq8Ii+0yEfggFl+tCxSbmTZOwKffAIrFajnnvweu/SxEiqIgoHoAtBa1qy+tRmhSqJBpXVzgv3oVdJ7i26GpFy8ieu3aF5sYERG9ddjIJCIiegepGRkIGTIU5sREIbctXx5egwYJ2eHgw+i9rzdSjGJj0tXaFbPqzRL3xTRmANu+AOKDxBPaugHtNwPWWW9YphlM6LXxHKKSMoTSvK62mNu2PBTF4o6aiIiIiIiIXpoahdwx+P1iUn76QQy6rg9ESsZTzUz/akD9kfIgvwwA4kOEqLhbcXxe4nMhSzOlYcLxCVBV8SFWnasrfKZMloaNWrwEaTdvZWM2RET0tmIjk4iI6B0UsXAR0i5dEjKNkxN8582DotdnZr/f/x0D/x4o7GcCANZaa8ypPwdedl7iwPsnAY9OiJlGB7T7XliKSFVVjP/5Ci4GxQmlNnoNVn1RGa72Vi88NyIiIiIiIno+/RoWQdfaBaX81P0YdFt/BqkZTy3xWnco4G+xrUha3JNtRYziA6p9KvSBm42bkJ0MO4ltN7dJ53Js0ADOrT8VMtVgQOjoUVANhuxNiIiI3jpsZBIREb1j4n/9FTHPWKYnz9QpsMrrl/l5151dGHl4JIyquD+Kvd4eK95fgep5qosD3PwDOLFUPmGzuUCBOkL0w6lH2HYmWCqd1bocSvs6Z2M2RERERERE9KIURcG4j0uiS60C0ncn7kWj9w9nYf7vViBaHfDpasDaSSx8cATY/hVgymo6Olk5YWRV+Q3OeWfnISghSMq9R42CLk8eIUu/dh1RK1dlf1JERPRWYSOTiIjoHZJ8+jTCAsZIuWuHDnD64IPMz3fj7mLSiUlQIS7742LtgjUfrEFVn6riAPHBwC6LvTIBoEo3oMpXQnQ9LAGTfr0qlX5dpyBaVvCTciIiIiIiInp1FEXBhOal8GXN/NJ3B29G4sezTzUeXfMDH82XB7mxW2pmfljwQzTO31goSzWmYuyxsTCZTUKudXSE77Sp0rBRK1ci9Yp8/0hERO8ONjKJiIjeEen37iO4/wBpaR7rkiXhNXJE5mdVVTHl5BQYzeKbmJ62nljfdD1Ke5QWBzYZgO3dgNRYMfcpBzSZLl6D0YTBWy/AYBIbpLUKu2PUhyVecGZERERERET0byiKgoktSuOLGnIzc/qeG4hKemq7kXJtgYqd5EGu/wps75rZzFQUBWNrjJWWmD0XcQ4br2+UDrevVQuuHTqIocmE0FEjYU5Pl+qJiOjdwEYmERHRO8AYE4Ognj1hjo8Xcp2XF/yXL4PG2joz+/nuzzj7+KxQ52XnhQ0fbkBhl8Ly4AemA0EnxczKEWi7HtDbCPHCfbdxIzxRyPI422BJ+4rQafnPEiIiIiIiojdFURRMblkaTUp7C3l8qgFTd18Tiz9eBJQW97UEAFz/BdjxdWYz083GDeNrjpfKFp9bjHtx96Tca9hQ6PPlE7KMO3cRtWRJNmdDRERvC/7GkIiI6C1nTktDcO8+MASJ+5AodnbwX7kC+qf2IYlLi8O8M/OkMcbXGA9/R3958Dv7gKPPWFao+ULAXWx6nn0Yg1WH7kql89qWh7uDtZQTERERERHR6/WkmVkGjtY6Id91IRRHbkdmBVod8Ok3QOlW8iDXdgF/jM782ChfIzQv1FwoyTBnYOihoYhOjRZyjZ0dfGdMBxRFyKPXrEXKufMvNikiIsrV2MgkIiJ6y4VPnITUixfFUKNB3gXzYVOqlBAvOLcAcelxQtYoXyPU968vD5wQBuzsKeeVuwBl2whRcroRQ7ZdhFlcURZf1S6AWkU8nnMmRERERERE9Kp5O9lgRNPiUj7mpytIzXhqb0utDvj0W6DUJ/Iggd8AwWcyP46sNhJetl5CyZ24O+j6Z1dEpkQKuV3lynD76itxPFVF6OhRMKekZHs+RESUu7GRSURE9BZL+OsvxO/aJeXeY8fAob7YnDz3+Bx23t4pZLY6W4yqNkoe2GwCdnYHUqLE3Ks00HSmVD7j9+t4GC3ecBb2tMfIptwXk4iIiIiIKKfpUD0/Kvi7CNmjmBQs+fu2WKjVAa2/BUq2kAf5beiTe0cAztbOmFR7klRyL/4euvzRBeHJ4ULuOXAArAqLq/wYHj5CxPwF2Z8MERHlamxkEhERvaWM0dEInzBRyt2++gpuHToImcFswJSTU6TavhX6wsfeRx780GzgwREx09v9Z19MWyE+cDMCG08+EjKtRsH8zyrARq99rrkQERERERHR66PVKJjxaVnoNOISr6sP38ON8ASLYj3QZi3gWVLMwy4A57/P/FjHrw4GVRoknetR4iN0+aMLghODMzONtTV8Z84AtOI9Y+zGjUg+fvyF5kRERLkTG5lERERvIVVVET5xEkwxMUJuW6UyvIYNlerXXl6LO3F3hKy4a3F0LNlRHvzeIeDQLDn/aD7gWUyI7kclY+BmeR+Tfu8VQXmLp3uJiIiIiIgo5yiZxwlf1y0kZEaziqHbLiLdaBKLtXqg2Rx5kH2TgJSs+9JuZbthWJVhUllIUgi6/NEFoUmhmZlt2bLw6NlDqg0eMBBp165lczZERJRbsZFJRET0FkrYvRuJf/0lZIqdHXynT4di8UTr+YjzWHFxhVgLBeNqjoNOoxMHTop4sqQsLDa7rNARqNBevIY0A77eEIiENKOQl/VzRr+GRbI/KSIiIiIiInqtBjYqCn83cdWdq6EJmL/3llxcsC5QprWYpcYAf08Voi9Lf4mA6gHS4Y9THmP0kdEwmbOapB69esG6pPimpzkpCY+6fY30u3ezORsiIsqN2MgkIiJ6yxgeP0b4lKlS7j1iOKzy5ROy+PR4jDg8AiZVfJq2dbHWKO9ZXhzAbAJ29gCSHou5R3HpyVuTWUX/TedxNzJZyO2ttFjQrjz0Wv4ThIiIiIiIKKeztdJiequyUr7q8D0cuxMlH9B4CqC3F7Mza4HQC0LUvkR7TKo1CQrEpWvPRZzDxusbMz8rVlbwnTkTip2dUGeKjcWjrt2QERySvQkREVGuw98iEhERvUVUVUXYmLEwJ4h7ltjXrg2Xdu2k2rHHxiI8OVzI8znmk5f6MRmAn3oC9w6Iuc72yb6YVuKN6ow913HoVqSQKQqwuH1FFPFyzP7EiIiIiIiI6I2oW9QTXWsXlPIh2y4gNjlDDJ39gPojLCpVYM9wwGwW0k+LforJtSdL4y4+txh347LetrQpXgz+y5ZC0euFOuPjx3jUtSsMERHZmxAREeUqbGQSERG9RWK/+w7JR48KmcbREXmmTYWiiE+6brqxCQeDDgqZXqPHnPpzYP/0E7SGNGDrF8DlH+UTNpsNeJcSoh/PBOHbo/el0hFNSqBRSe9szYeIiIiIiIjevBFNi6OEj/hQ6uOEdIzaeQmqarH1SI0+gHtRMQs+DZzbII37SZFP0LxQcyHLMGdgzNExMJqztimxr1kTfgsXABZbpRgePUJQt24wJSW9wKyIiCg3YCOTiIjoLZFy7hwez5kr5T5jx0Dv4yNkV6OvYt6ZeVLt0CpDUcr9qcZkeiLwQxvg1u/yCct+BlT8QoguBcdhzE9XpNJWFf3Qq36h55wJERERERER5SQ2ei0Wt68Ia5346+Q/rz7G1sAgsVhnBXw4Sx5k71gg9oEUj6w2El52XkJ2Nfoq1lxeI2SOjRrBd+aMJ8v9PCX99h2ET5bf7CQiorcDG5lERERvAWN0NEIGDQaMRiF3bPw+nFq0ELL49HgMPzQcBrNByN/zfw8dSnTIClJigO9aAg+OyCcs/hHQYolwA5mQZkC/TeeRYRKXC6rg74IZn5aV3gglIiIiIiKi3KOYtyMCmpWU8om/XsXJe9FiWKQRUFK8F0VGErCrr7TErLO1MybVmiSNu/LiStyIuSHWNm8OnwkTpNqEX35F/K+7n3MmRESUm7CRSURElMupJhNChg6D0WJfEH3evMgzVVxSNsOUgYEHBiIoUXxi1sfeB1NqT8mqNRmBH9oCIWflE5ZrB3y2AdDbZF2DqmLUjkt4FJMijutkg9VfVIaNXms5ChEREREREeUynWvmx3vFPYUszWDGV+sCccqymfnRPMDWTcweHgVOr5bGreNXB22KtREyo2rEmKNjkGES9+F0/bwdPPr3k8YInzQJGcHB2ZgNERHlBmxkEhER5XKRi5cg5eRJIVOsrOC3aCG0zs6ZmaqqGHdsHM4+FpuTWkWL2fVmw9k6qxanVgIhZ+STVekGfLIS0OqFeOOpR9hzOVzIdBoFyzpWgpeTDYiIiIiIiCj3UxQFc9qWh4eDlZCnGkz4an0gTt+PyQodvICPF8iD7JsARN2W4mFVhsHPwU/IbsXewsqLK6Vaj169YFetmpCZk5IQOnwEVIuVioiIKHdjI5OIiCgXSzxwANGrVkm597ixsC1dWsiWXViGPff3SLVDKg9BRa+KWUF8MHBgunyyOkOePFGrEf/5cDU0HlN2X5PKhzcpjsr5XZ9zJkRERERERJQbeDhYY3XnKnCw1gl5SoYJXdadRuCDp5qZpT8ByrQWBzCmAbt6A2aTENvr7TGl9hTpfGuurMGlyEtCpmi18J01E5qnHt4FgNTz5xG1Ur5HJiKi3IuNTCIiolzKFBeHsIAxUu7cqhVc2ohL8vx0+yesuiTfzLUr3g5flPpCDH8fCRiSxaxCJ+D9CcKemACQlG58si+mUdzjpEFxT3SvWygbsyEiIiIiIqLcolI+V2zoWhX2VuI2IikZJnRZexrnHsVmhc3mAg7e4gDBgcDR+dK4VX2qolPJTkJmVs0Yc3QM0oxpQq7Pkwd5Jsl7a0YtX46Uc+ezOSMiIsqp2MgkIiLKpSIWLYIpNlbIrIsXh8/4ccK+mKfCTmHyicnS8fXy1sOoaqOEWtzYA9zYLRbauQMfyE/FqqqKMT9dxv0osenp7WSN+Z9VgEajSMcQERERERHR26Fyfjd8162a1MxMzjCh/6bzSM34zxuXdm5A88XyAH9PBU7J+2UOqDQABZwKCNmDhAdYfF4ew6lpEzi3/lQMzWaEDh8OU2JituZDREQ5ExuZREREuVDq1auI27JVyBQ7O+RdvAgaW9vMLC4tDiMPj4RRFfcIKelWEnPqzYFO89RSQBnJwO8j5JN9MPXJjaeFbWeC8POFUCHTKMDizyvCzd5KqiciIiIiIqK3S+X8btjQtRrsLJqZIXGpWHHoblZQvOmTlX4s/T4cOCY2KG11tphaZyo0ivir643XNiIwPFAawicgAFb58wuZISQE4ZPlB3KJiCj3YSOTiIgol1HNZjyeMhVQVSH37NNbunmbfmo6otOihczH3gdLGy2Fnd5OHPjQLCA+SMzy1wHKt5eu4WZ4Iib8clXKhzQuhuqF3LMxGyIiIiIiIsrNqhRww/qvqkFnsSrPykN38TD6qRV8mk4H3J6xBclf44BDc4SovGd5dC3TVchUqBh3bBxSDClCrrG3h+/cOYBO3LMz4ddfEf/rry8wIyIiyknYyCQiIspl4n/5BakXLgiZVYECcOvcWcj+ePAHfn/wu5DZaG2wtOFSeNl5iYM+vgacWCZmGj3w8XxpX8yUDCP6bjqHNIO4L2bdoh7o06BI9idEREREREREuVq1gm7oWqegkGUYzZiy+1pWYOMMfLkbcH/GfeOBqcD+KcIDu73L90ZR16JCWUhSCMYfHw+T2STktmXLwnPAAGnY8ImTkBEc/AIzIiKinIKNTCIiolzElJiIiLnzpNx77FgoVlnLuUalRmHayWlS3aDKg1DcrbgYGtKAnd0Bs7j8LGoPADwtagGM//kq7kQkCZmnI/fFJCIiIiIiepcNaFQUXo7WQrbvegT+vvE4K3D2A7rsATxLyAMcmQucztoz00prhWm1p0GniG9a/vngT0w5OQWqxSpF7t26wq5aNSEzJycjdNhwqEaL+10iIso12MgkIiLKRaKWLoUpKkrIHBu/D4c6tTM/q6qKSScmIS49Tqir6lMV7UvIy8Ri7xjg8RUxc8kP1B0mle44G4ztZ8WnWRUFWNSuAjwtbliJiIiIiIjo3eFgrcOYj0pK+aRfryHN8NQblI7eQJffAO+y8iB/jQeibmd+LOleEj3L95TKdtzegXln5gnNTEWrhe/sWdA4Owu1qRcuIGrlqheYERER5QRsZBIREeUSKYGBiNn4g5Ap1tbwHjVKyH699ysOBh0UMjudHabUngKNYvFX/7WfgcBv5ZN9vACwEvfQvBAUh3E/X5FK+zcsilpFPJ57HkRERERERPR2alHeF9UKugnZw+gUfHvknlho7wF8+QvgW0nMjWnArt6AKesNyq/Lfo2G/g2lc224tgGrLokNSr2PD/JMnizVRi1fjpTAwGzOhoiIcgI2MomIiHI4VVURs/EHPPyqK2AS9wFx79kDej+/zM9hSWGYeWqmNMbwqsPh5+AnhrEPgJ/7yyesNQAo0kiITtyNRsdvTiIlQzx/jUJuGNhI3LOEiIiIiIiI3k2KomBSi9LQWmw7svTAHQTHpojFdm5Apx2Ag4+YBwcCxxdnftRpdJhTfw5q5KkhnW/ZhWXYeG2jkDk1+QDObVqLhWYzggcOgiE0NPuTIiKiN4qNTCIiohzMnJKC0BEj8XjqVMBiTw+9vz/cu3XL/JxhysCQg0OQaEgU6mr71UbrohY3cSYDsL0bkB4v5nmrAo3GC9H+64/x5brTSLZoYrrbW2HR5xWlG1QiIiIiIiJ6d5XM44QvauQXsjSDGaN2XIbZLO5rCTs3oMViSA5MBx5fzfxopbXCovcWobxneal0VuAsHAk+ImQ+o0fDKr94DaaYGAT16wdzamo2Z0RERG8SG5lEREQ5VMbDh3jweXsk/Pqr9J1iZQXf6dOgsc7al3J24GxciRaXfnW0csSkmpOgKBbNxv2TgZAzYmbjDLReA2j1mdHPF0LQ8/uzyDCahVJrnQZLOlSEt5PNC86OiIiIiIiI3laDGxeDh4OVkB29E4UfTj2Ui4s1ASp2EjOzAfipJ2DMyIzs9HZY/v5ylHArIQ0x9thYRKVGZX7W2NvDb+ECKDbiPWv6tesIGzNW2FuTiIhyNjYyiYiIciBDSAgedOyE9Fu3pO/0vr7Iv2kT7KpWzcx+vfsrtt7cKtVOqDkB3vbeYnjpR2GZnkwtlgKuWU+sbjsThEFbL8Bo8cSsg7UO33WthlqFuS8mERERERERyZxt9RjfvLSUT99zAw+ikuUDmswAnP3FLPwycGSuEDlZOWHl+ytRwKmAkMekxWD8sfFCg9KmZEn4Tp8mnSphzx5Ef/Pt80+GiIjeKDYyiYiIchhTUhKCevWGKSpK+s6+Th0U2LEdtmWybghvxtzE5BOTpdpOJTuhSYEmYvjwOPBzH/mkVb8GSrXI/Hj7cSLG/HQZlg+putrpsbl7DVQv5J69SREREREREdE7pXm5PGhWVtz/MtVgwvDtF2GyXGLWxglouVQe5PBcIOScELnbumNRw0Ww0YpvWx4JOYJNNzYJmVOzZnDv0UMaNnLBAiQePPj8kyEiojeGjUwiIqIcRDWZEDp0GNJv35a+8+jTB/6rVkLn6pqZJWQkYPDBwUgzpQm1Fb0qYkiVIeIAUbeBLR0AU4aY+5QFPsh6SlVVVYz56QoMJvHG0tvJGtt61kTZvM4vODsiIiIiIiJ6VyiKgikty0hLzAY+iMXao/flAwo1AKp2FzPVBPzUCzCI97yFnAthRLUR0hDzz8zHrVhxZSPPQQPh0KCBxbgqQocNR8bDZyx1S0REOQobmURERDlIxOw5SDp0SMp958yB54D+ULRaIZ9wbAKCEoOEzN3GHXPrz4Vek7XXJZKjgB/aAKmx4sAOPkD7LYA+60nW7WeDcfpBjFCWx9kG23vVQlFvxxecGREREREREb1r3B2sMa1VWSmfs/cmbj9OlA9oPAlwLShmUTeBA/ISsW2KtkGjfI2ELMOcgZGHRyLNmNX4VDQa+M6ZDatChYRac1ISggcOgjlNbJISEVHOwkYmERFRDhG7ZStiNmyQcs+BA+Dc/GMpPxR0CPse7RMyraLFnPpz4GXnlRUaUoHNnwOxD8QB9PZAh62Ac96sa0jOwPQ916VzTf2kDPzd7LI3ISIiIiIiInrnNSntg08r+glZhtGMvpvOISopXSy2sgdarQSgiPnxJcCjk0KkKAom1pwIL1svIb8TdwcLzi4QMq2jI/yXL4PGyUnI02/cQPiUKdmfFBERvTZsZBIREeUAySdPPfPmyal5c7j36iXl6aZ0zDw9U8oHVhqIqj5VxXD3YCA4UMwUDdB2HeBbQYhn/n4DsSkGIWtS2huNSno/30SIiIiIiIiILExoXho+TuKelrceJ6HdqhN4nGDxRmS+GkCtfhYjqE+WmM1IFlIXGxdMqzsNikXjc9ONTTgcfFjIrAoUgO9M+T46fsdOxO3Ykb0JERHRa8NGJhER0RtmSkpC6KhRgMkk5LYVKyLP1ClQFEU6Zt2VdQhOChayKt5V0KV0F7Hw2i/Axc3yST+cDRRrIkSBD2Kw9Yy4TK29lRYTW5R+/skQERERERERWXC202NWm3JSfjcyGe1WnUBoXKr4xXtjAY/iYhZ7H/hrgjRGjTw15HthAOOOjUNUapSQOTZ8D+7du0u14ZOnIO26vDoRERG9eWxkEhERvWGR8+fDGB4uZHo/P+RdugQaa2upPiQpBN9e/lbItIoWAdUDxKZnctSTtzEt1ewHVBNv3DKMZoz56bJUOrhxMeRxts3GbIiIiIiIiIhk9Yt5YtSHJaT8QXQKPlt1AkExKVmh3ubJErOKViwO/Aa4s18ao3/F/ijpVlLIYtJiMO7YOKiqKuSeAwfArlo1IVPT0xE8cBBMCQnZnBUREb1qbGQSERG9QSlnziB2k/jGpKLXI+/y5dC5uz/zmDmBc5BuEvcRaV+iPYq6Fs0KVBXYPQhIEZ8+Rb5aQOPJ0pirDt3FrcdJQlYyjxO61Crw3HMhIiIiIiIi+l961S+MsR+VlPLg2FS5melXCag7VB7kxy5A6AUh0mv1mFVvFmx14oO4R0OOYtONTUKm6HTwmzcXOk9PITc8eoSgXr1hShLvjYmI6M1iI5OIiOgNMaenI2zsOCn36NMbNsWLPfOYYyHHsP+R+PSpm40b+lToIxZe3g5c/1XM9HbAJ8sAjfhEa+CDGCzcf1vIFAWY3qoMdFr+U4GIiIiIiIhenq/rFsKUT8pIeVh8GgZvvQCT+ak3KOsNB3zKioXpCcDGT4HIm0Jc0LkgRlYdKY07/8x83Iq9JWQ6T0/4zZ8HaMX749Rz5xDU7WuYEhOzOSsiInpV+NtJIiKiNyRq2XJkPHggZNbFi8O9W7dn1htMBsw8PVPKh1QeAkcrx6wgIQzY84ynVj+YArgVEqLY5AwM2HxevFEE0L5aPlTM5/p8EyEiIiIiIiLKhi9q5Mfs1uXw9O4oAHDmYSzWHL2XFeisgFarAb29WJgSDXzXEoh9IMSfFv0U7+d7X8gyzBkYeXgk0oxpQm5XtSq8hgyRri314kU86toNpvj4bM+LiIhePjYyiYiI3oC069cRvWaNGGo0yDN1KhQrK6k+Pj0e/Q/0x4OEB0JewbMCmhdunhWoKvDrACDN4oar0HtAFbFBqqoqhm+/hLB48WaugLsdAprJS/0QERERERERvSyfVfXHnDblpXzu3lu4/fipNyK9SwHtNwNaa7EwMexJMzMhLDNSFAUTa02El52XUHon7g6mnJwi7Zfp1vUruHXpIl1D2uXLePRVV5ji4rI9LyIiernYyCQiInrNVKMRYWPGAiaTkLt9+SVsy8rL61yPvo52u9vhWMgxIdcoGgRUD4BGeeqv89Orgdt7xQGsnYCWS2H5qOu6Yw+w7/pjIbPSarC0QyU4WOteYGZEREREREREz69N5bz4tJKfkGUYzRj640UYTOassFB94LMNgCIuBYvYB8D3rYSHeZ2tnTGjzgwoEO+Bf7n7C76/9r2QKYoCr5Ej4N79a+na0q5dw8OuXWFOTn6xyRER0UvBRiYREdFrFrFgAdKuXRMyvb8/PAf0l2p/ufsLvvj9C4QkhUjffV78c5R0f+rNyUengD8D5BN+OAtwzitEl4PjMeP361JpQLMSKOPn/JwzISIiIiIiIvp3JjQvDR8nGyG7FByPFQfvioXFPwQ+XQ1YNCgReR34bZgQVctTDV3LdJXONe/sPBwPPS5kiqLAc8gQuPfuJdWnX7uO8GnTn38yRET00rGRSURE9Bol7N2LmDVrpTzPlMnQ2NpmflZVFXMD52LM0TFIN6VL9c0KNsOwKk/dqCVFAj9+CZiNYmHxZkD59kIUl5KBfpvPwWASl9T5oJQ3vqxVIPuTIiIiIiIiInpBzrZ6zGpTTsoX77+Nq6EW26aUbQN8vEAe5PI24NI2IepXsR9q5qkpZGbVjOGHhuNRwiMhVxQFXgMHwqNfP2no+J07Eb/7t+ecDRERvWxsZBIREb0m6ffvI2y0/MakS7t2sK9RQ8g2XN2ADdc2SLU6RYdR1UZhZt2Z0Gv1T0KTEdj+1ZP9QZ7mnA9ouUxYUjbdaEKP787iYXSKUOrnYovZbcpBsVh+loiIiIiIiOhVq1/MEx2q5xMyo1nF0G0XkW4Ut2VBla+A98bIg+we8mSp2f/QaXSYU38O8jmK4yZkJKD/3/2RlJEkDeHZry/ce/SQ8vAJE5Dx6JGUExHRq8dGJhER0WtgTklByICB0t4aNqVKwTtgtJAdeHQA88/Ol8bwsPXAmiZr0LFkR7HheGAq8OCIWKy1Btp9B9i5ZUaqqmLE9ks4/SBGLNUoWNy+AlzsrF5wdkRERERERET/TkCzksjraitkN8ITsWjfbbm47lCgQF0xy0gEdnR/8rDvfzhbO2Nxw8Ww19sLpffi72H00dEwq2ZY8hw4AHZVqgiZOTkZIUOHQc3IyOasiIjo32Ijk4iI6BVTVRVhEyci/bZ486VxcoLf4kXQWFtnZjdibmDkkZFQIS77Ws6zHLZ9vA2VvCuJg9/4DTj6jGV1ms0BfCsK0fy/buHnC6FS6egPS6ByfjcpJyIiIiIiInpdHKx1mNu2vJSvPHQX5x7FiqFGC7RaBdi4iHnwaeDwHCEq7FIYM+vOhGKxt+bBoINYe0Xe+kXRauE7dw60zs5Cnnb5MiIWLXru+RAR0cvBRiYREdErFvvdd0j45Vcp9509C1Z582Z+jkyJRP+/+yPVmCrUFXQuiBXvr4Cnnac4QEoM8HNf+YQVOgGVOgvRtsAgLPn7jlTauWZ+dKtTMBuzISIiIiIiIno1ahRyR9fa4j2qWQWGbbuI1AyLJWad/YAWi+VBDs8GHp0Uogb+DdC/Yn+pdMn5JQgMD5RyvY8P8kyfJuUxa9Yi6cjR55gJERG9LGxkEhERvSKqqiJy8RI8njFT+s6jT284NmiQ+TnNmIaBBwYiPDlcqHO2dsbShkvhZOUkn2D/JCDV4qlUn7LAR3OFfTGP3YlCwE+XpcMblfDC+I9LcV9MIiIiIiIiyjFGNC2OQp4WS8FGJWPOnzfl4lItpQd5oZqfLDGbFi/EX5f9Go3zNxYys2rG8EPDEZkSKQ3t2KgRXDt2lPLQ0aNhjI2VciIiejXYyCQiInoFVKMRYePGIWr5cuk7+1q14NFXfJNy5umZuBwlNht1Gh0WNFiAfE755BOEnAPObhAzKwfgs+8BfdaeIrHJGRi45QKMZnGp2tK+TljcviJ0Wv5TgIiIiIiIiHIOG70W89qWh8bimdu1x+7j5L1o+YCmMwH3ImIW/wj4bagQKYqCybUmI79TfiGPTovG8MPDYTQbYclrxHBYlyghZKaoKISPnwBVVaV6IiJ6+fjbSyIiopfMnJKC4L79EL99h/Sd3t8fvvPmQtFqM7MToSew47ZcO77GeFT1qfqME5iBPcMBi3000WAU4CYuwTPu5yuISkoXMl9nG6ztUhX21rrnnxQRERERERHRa1Ixnyt61S8s5cO3X0RSukXD0coeaP0toNGL+eUfgYtbhcjBygHzG8yHjdZGyM8+PovF5+VlajXW1vCbPw+KjVif+NdfiP/552zMiIiIXhQbmURERC+RKSEBD7/6CkmHDknfWZcqiQKbfoDO1TUzSzGkYNKJSVLtV6W/QquirZ59koubgJAzYuZRHKjeS4h2XwrF7kthQmaj12DtV1Xh7STehBERERERERHlJAPfL4oSPo5CFhSTipHbL8FkseoQfCsCDcfKg/w2FIi5L0TFXIthXM1xUum6K+vw+/3fpdy6UCF4jRgu5Y+nTEVGcMhzzISIiP4NNjKJiIheEtVkQsjQYUi7eEn6zr5WLeT/7nvoPD2FfOmFpQhJEm98SruXxoBKA559ktRY4K8Jct5sNqDNevo0IjEN43ZdkcpGNi2BEj7P2G+TiIiIiIiIKAex1mkx77Py0FmsMfvb5TCM+emyvLRrrQFAwXpilpEI7OwOmMS3OFsUboHWRVtL5xx9ZPQzm5mu7dvDvk4dITMnJyNs1CioJlM2ZkVERNnFRiYREdFLEjF/PpKPHJFypxbN4b9yBbQO9kJ+KfISfrj+g5DpFB0m1ZoEneb/Wfb1wAwgJUrMSn0CFGqQ+VFVVQTsvILYFINQVqOQG76sWeB5p0NERERERET0RpX2dcbARkWlfEtgEKb9dl1sZmo0QKtVgK2rWBwcCByaJY0xuvpolHQrKWQm1YRRR0bh17u/CrmiKMgzbRq0zs5CnnLmDGLWb8jmrIiIKDvYyHxNVFXFrVu3sGnTJgwdOhQNGjSAk5MTFEWBoijo0qXLC4174sQJdO3aFYULF4adnR3c3NxQuXJlTJ06FVFRUf88ABERvRTxv/yCmDVrpdytW1f4zpoFxcpKyA0mAyYcnwCzahbyrmW7orhb8Wef5P5hIPAbMdPbAR9MFaKd50Kw7/pjIbO30mJOm/LQWDzJSkRERERERJST9XmvCJqU9pbyb4/ex+L9d8TQyRdosUQe5Mhc4L744LG11hrzGsyDh62HkJtVM8YcHYNdd3YJud7bCz6TJkpDRy5ciNTL8opIRET0crCR+ZoMGzYMxYsXR8eOHTF//nwcOnQIiYmJLzyeqqoYMmQIateujXXr1uHevXtITU1FbGwszp07h3HjxqFMmTL4+++/X+IsiIjoWVIvX0bYWHl/DacWzeE1bBgURW4efnv5W9yJE2+4CjoXRM9yPeUTqCpwYjnw3SeAReMTdYcCLv6ZH0PiUjHx16vSEOM+LgV/N7vnmxARERERERFRDqHVKFjcviLqFvWQvluw7xbWHBX3wETJ5kClL8VMNQM/fgnEPRJif0d/rG2yFl62XmI5VIw/Nh47bu0QcqemTeHUorlYazAgqE9vGEJDszkzIiJ6HmxkviYmi7XSHR0dUapUqRceb/To0ViwYAFUVYW9vT0GDBiAjRs3YuXKlWjcuDEA4PHjx2jZsiUuXLjwby6diIj+B0NEBIL79oOakSHkNmXLIs/kyc9sYl6Pvo7Vl1cLmQIFk2tNhpVWfHMT6UnAjm7An6MB1WLfDbdCQK3+WaVGE/psPIvENHHvjwbFPdGuqj+IiIiIiIiIciNrnRarvqiMyvldpe+m7L6GAzcixLDpDMDdYknalGhgSwcgI1mICzoXxLqm6+BtJ771qULFpBOTcCL0hJD7jB0LXZ48QmaKjEJQr94wJSVlc2ZERPRP2Mh8TUqVKoXBgwfjhx9+wI0bNxAfH49ly5a90Fjnz5/H7NmzAQDOzs44fvw4Fi1ahI4dO6Jnz57Yu3cvJkyYAABISkpCjx495M2viYjoX1ONRoQMGAhjhHjDpPP0RN6lS6CxsZGOiU+Px+CDg2E0i83G9iXao4JXBbE4+i7w7fvAFfEJ0CcnsQU+WQHorDOjyb9ew8XgeKHM2VaPWa3LPbOhSkRERERERJRb2FnpsLZLVZTK4yR9N3z7RUQmpmcFVvZA23VPtmN5WvhlYFefJysfPSWfUz6sa7oOeezFBqUKFQFHAxCdGp2ZaZ2c4DdvLhS9XqhNv3ULIQMHQTUYXnCGRET0LGxkviY9evTA/Pnz0aFDBxQvXvxf/UJ58uTJmY3J6dOno1y5clLNhAkTUK1aNQBAYGAg9uzZ88LnIyKiZ4v+5hukWrz1ruj1yLtkMfTe8v4d/91nIyQpRMjz2OfBgEoDxOLYh8DapkDkdfnErgWAbnuBfDUyox1ng/HDqUdS6fRWZeHtJDdUiYiIiIiIiHIbZ1s9vutWDYU87YU8KikDw7dfFF/m8CkLfLJcHuTaLuDIPCn2d/TH+qbr4efgJ46dGoWxx8bC/NRWL3aVKiHPjBnSGMnHjiF86jS+VEJE9BKxkZnLJCYm4vfffwcAODk5oUuXLs+sUxQF/ftnLTe4devW13F5RETvjLTr1xG5TL4h8pk8GbYVKjzzmDWX1+BQ8CEh0yk6zKo3C/b6p27C0hKAzZ8DyRZL4wBA0Q+AHgeBPFkPsVwLTUDAT5el0m51CuKjcnmknIiIiIiIiCi38nCwxjedq8BWrxXygzcjsf74A7G4dCug3nB5kL+nAjd/l2JfB18sbbgU1lprIT8achTfX/teyJw//gieAy0eSgYQt3UrYtaue77JEBHRP2IjM5c5dOgQ0tOfLJNQr1492NnZ/b+1TZo0yfzzH3/88cqvjYjoXWHOyEDoiJGAUVwe1qVtW7i0+uSZx5wIPYGlF5ZK+eDKg1HRq+JTg5ue7IkZcc2iUgEajAbabwVss/YEiU81oPcPZ5FuNAvVVQu4YtSHJbI1LyIiIiIiIqLcoLCnA8Y3LyXlM36/gRvhCWLYIAAo3syiUgV2dH+ypYuFIq5FMKLqCClfeG4hrkZfFTL3Xr3g3KqVVBsxbx5Szpz554kQEdE/YiMzl7ly5UrmnytXrvw/az09PZE/f34AQGRkJCIinvFmDxERZVvUkqVIv31byPR588Jr5Mhn1ocnh2Pk4ZHCMjQA0Dh/Y3xR6guxeO9Y4PZeeZDW3wINRgGarL+6VVXFyO2X8DA6RSj1cLDGsg6VoNfyr3kiIiIiIiJ6O31e1R9NSovbumQYzRiw+TzSDKasUKMBWq0CPC0e9s1IBLZ1BjLEe2oAaFusLRrnbyxkRrMRIw6NQLIhOTNTFAV5Jk2EXfXq4gBmM0KGDYcxNvbFJkdERJl0b/oCKHtu3bqV+ecCBQr8Y33+/Pnx8OHDzGO9vLyyfc6zZ8/+z++vX8/av81oNMLADa2J3iiDwQDjf94U5M/jy5d64QKi16wRQ0WB1+TJMFtbwWzxf/Pw5HD0O9gPsenizUsBpwIYV21c5v+vAEBzbj20J+Xlak31A2Au0RKwGHv/jQj8cTVcyLQaBYvalYWrrZb//89B+HNJlLPwZ5IoZ+HPJFHOw59Lyi2mtCiJC0FxeJyQnpndepyEabuvYvzHJbMKtbZAm++gW/cBlLS4rPzxFZh3D4Wp+WJp7DFVx+By5GWEp2Tddz9KfISpJ6Zics3JWYWKAu958xDcqSMMDx5mxsbwcIQGjIHPooVQFOVfzZM/k0Q5C38mZUaLleteJjYyc5m4uLjMP3t4ePxjvbu7+zOPzY4qVao8d218fDyio6Nf6DxE9HIYjUYkJiZmftbp+D/1L4uamoqEUaMBs/hmpXWb1kgpVBApFv/7dz/xPgLOBiAqPUrIbbQ2GFN2DNIT0pGOJzdbViEn4fqH/EZnatEWiC/RGbAYO91oxuRfLZefBfrU9kVhR5X/W5zD8OeSKGfhzyRRzsKfSaKchz+XlJuMa5wP/XfchvpU9v2pIFTwsUbtgs5Ppc6wfm8WXH/vKRyvubQJiW6lkVqijTT2qDKjMCRwiLDC0u77u1HRqSLq+dQTam3GjIWhTx/hIeTkAwcQumYNbJ6x/Gx28GeSKGfhz6TsRftPz4NrzuUySUlJmX+2sbH5x3pbW9vMPz/9g0VERNmXuvobmENChEyTLx9sv/5aqr0QfQGDTg+SmpgAMLjUYBRwKJD5WclIgvPfI6GoJqEuw7si4utPBZ7x5ObGM+EITcgQsop+DuhQyVuqJSIiIiIiInpbVfF3QsfK8r3w1L0PEJ0svimVnr8Bkir2kmqdjkyGLuq6lJd2LY3OhTtL+cJrCxGdLj5ArCtaBLa9ekq1qctXwHjnzj/Og4iIno1t4v/49ttvERwc/FLGmjhx4ksZJ6c48w8bU1+/fh1ffPFkjzdnZ2fhLVAiev2eXs7Azc0Ner3+DV7N2yPl5EnE/vSTGGq18J05Aza+vkL858M/Mf7ceBjM8tISAysMRNtSbYVM8/tMaJPFJWJV53xQ2m+Cu72nNEZwbCq+O/NYvBSNgqmtysLDwzE706LXhD+XRDkLfyaJchb+TBLlPPy5pNxm9MeuuBCWiiuhCZlZbKoRsw6G4JtOlaDRPPWAcNNJMMdehebBkcxIMaXDff9gGLvuA2yefosT6OvaF5cSLuFcxLnMLNGQiCU3l2Bxg8XCsrFq9+4Iu3wZKQcPZQ1gMCBt2nT4b9kMjZ3dC82PP5NEOQt/JmUuLi6vbGw2Mv/j22+/xalTp17KWK+ykeng4JD557S0tH+sT01Nzfyzo+OL/XK7cuXKz12r0+n4Q0uUA/x3OQO9Xs+fyZfAlJiIiPETpNy9R3c4VqokZH89/AsBxwKgCovaADpFh8m1J6N54ebiIA+PA+fWiZnWCkqHLdC7iA3S/5r550WkG8Xlbb+sWQCl87o954zoTeDPJVHOwp9JopyFP5NEOQ9/Lik30euBRe0r4uPFR5FqyFrt6PDtaPwQGIKudQo+XQ20WQusrAskZT1UrMTeh35bR6Djj4CN01PVekyvOx2tf2mNZENyZn4s7Bh23d+Fz4p/JlyL34wZuN/yExgjIjIzw/37CO/XH3mXL4P2BX9Hy59JopyFP5OiV7m8LpeWzWWe7mpHRcnLFVp6eo+0V9kRJyJ6mz2eMRPGsDAhsy5ZEp69ewvZzZibGHN0jNTEtNPZYdn7y+QmpiEN+KW/fML6IwDv0s+8lkO3IvHnVfFtTA8HawxqXPQ5Z0NERERERET09ins6YAJzUtJ+czfb+B6WIIYOngBbdcBilbMg04CGz8F0uKF2M/BD6OqjZLGnntmLh7EPxAynasrfOfMkbaJSQkMxMPOX8L4HL/TJSKiLGxk/sfJkyehqupL+e9VKlasWOafHzx48I/1Dx8+fOaxRET0fBL/PoD4nTuFTNHr4TtzJhQrq8wsNi0WAw8MRKoxVaj1sPXA+qbrUcu3ljz4oVlAtMU+Gd5lgNqDnnkt6UYTJv1yVcpHf1gCTjZ88ouIiIiIiIjebe2q+qNpaR8hyzCZMWDzeaQ99aYmACB/LeD9ifIgwYHAdy2B1Fghblm4JRrlayRkqcZUBBwNgNFsFHL76tXgYfHwMwCkX7+OBx07IuMlbXFGRPQuYCMzlylTpkzmn8+ePfs/ayMjIzMbmZ6envDy8nql10ZE9LYxxsYibPx4KfcY0B82xbMeDjGYDRh6aChCkkKEOldrV3zX9DuUdC8pDx52ETi2SMwUDdBiCaCVm5JxKRno8d1Z3ItKFvLK+V3RqqJfNmZFRERERERE9HZSFAUzW5eFj5ONkN+OSELAzsvySyi1+gM1+8kDhZ4HNrQAUmKEscfXHA93G3eh9HLUZcw8PVMa26NfX7h27CgNbXj4CA/bd0DazVvZnB0R0buJjcxcpkGDBrC2tgYAHD58WNgD09Kff/6Z+eemTZu+8msjInqbqKqK8EmTYbJY8sW2QgW4d+0qZLNPz0ZgeKCQ6RQd5jWYB38nf3lwk+HJkrKqxdOgNfsBfpWk8ish8fh4yVEcuhUp5BoFmNyyNDQaRTqGiIiIiIiI6F3kYmeF+e3KW67sip3nQ7Dm6H0xVBTgg6nPXhkp/BLwXQsgPSkzcrNxw+Tak6XSrTe3YuP1jeLQGg28x46BRz+5UWqMjMSjLl1gePxY+o6IiERsZOYyDg4OaNasGQAgISEB69evf2adqqpYunRp5ud27dq9jssjInprxO/6GYl//CFkio0NfGfOgKLN2kNj+63t2HJzi3T8yGojUdWn6rMH3z/pyRuZT3MtCDQYLZVuPxuM1iuOIzhWfnClc80CKO3r/ByzISIiIiIiInp31Crsgd71C0v59D3XcdjiIWEoypMlZusNlwcKvwz8PkKI6uWth7bF2kqlcwLnYP+j/RZDK/Ds1xfe48dJe2aaYmMRNnbcK9+qjIgot2MjMxcaN24clP/8xTd69GhcunRJqpk8eTJOnToFAKhatSo++uij13qNRES5Wfq9+wifLD9h6TVsGKwKFMj8fDv2Nqafmi7VtSnWBu2K/z8PkFz7BTi+RM5bLAas7DI/qqqKqbuvYdiPF5FuNEvlLSv4IqDZM5asJSIiIiIiIiIMaVwMdYt6CJlZBfptOof7Ftu2QFGAhmOf+YAxLvwAXPpRiEZVG4XK3pWFTIWKUYdH4WrUVWkItw4d4DdvLqAXt5JJPnIEcVu3ZWNWRETvHt2bvoB3RVxcHObOnStk/92/EgDOnz+PsWPHCt83bNgQDRs2lMaqWLEiRowYgVmzZiE+Ph61atXC119/jWrVqiEpKQk7duzA3r17ATx5g3P16tWvYEZERG8nc3o6QoYMgWqxdLd9rZpw7dA+87PBbMCYo2NgMBuEukpelRBQLSDzgRNB1B1gVx85r9INKFhPiFYfvodvLZe8AaDTKBj7UUl8WavAs89BRERERERERNBpNVjavhJaLjuKB9EpmXlCmhHdvzuDn/rUgqON2FhEg1GAMR04Ol/Mdw8G8lYG3AoBAKy0Vlj03iJ02tMJDxIeZJalmdLQd39fbPpoE3wdfIUhnJo1gzktHWEBAUL+ePZs2NeqCat8+f79pImI3kJsZL4mcXFxmDZt2v/7/aVLl6Q3K3U63TMbmQAwY8YMpKenY9GiRUhOTsaiRYukGi8vL2zevBkVKlT4V9dORPQuiZg9B+k3bgiZ1s0NeWbOhKLJWshg9aXVuB5zXajzsvPCvAbzoNda3AgBQEYysO0LICNRzPOUB5qIb3XuvRqOmX+I1wAAno7WWN6xEqoWcMvmrIiIiIiIiIjePc52enz7ZRV8suw4ktKNmfmdiCQM3noBq7+oAo3G4iHhhmOBoFPAw2NZWUYisL0r0HUvoLN6Mra1M5Y3Wo6OezoiNj02szQ6LRr9/u6HTc02wUZnI15Pq0+QuH8/kvZnLUGrpqQgdHQA8n+3QdjKhoiInuDSsrmUoihYsGABjh07hi5duqBQoUKwsbGBi4sLKlWqhMmTJ+Pq1av/byOUiIhkCX/9hdgffpBy31kzoffyyvx8Neoqvrn0jVQ3tfZUeNh6SDlU9cnTmxHXxNzGBfjsO0CfdWNzNTQeA7dcgOUWGZXyueC3/nXYxCQiIiIiIiLKhiJejljYroLlFpXYdz0CKw7dlQ/QaIFPvwFsXcU89Dzwt7gNjb+TPxY3XAwrjZWQ3469jblnxNX5gCe/080zeRK0buK9ferZs4hZv/6550RE9C5hI/M1KVCgAFRVzdZ/EydO/Mdxa9asiXXr1uHu3btITU1FbGwszp49i3HjxsHD4xm/TCciomcyhIQgbMxYKXfr1hUOdetmfk4zpiHgaABMqkmoa1e8HWr61nz24CeXA5e2yvmn3wCuBTI/RiSk4esNZ5BqEMcu7GmPdV9Vg5eTDYiIiIiIiIgoe94v5Y1hHxSX8nl7b+L43Sj5AGc/oOUyOT++BLi9T4gqeFXAtDrySnxbb27FXw//knKduzt8Jk2U8siFi5B269b/PwkioncUG5lERPTOywgKwqOu3WBOSBBym3Ll4DVwoJAtPb8U9+LvCZm/oz+GVB4iD6yqwN/TgD8D5O/qjQCKfZD5Mc1gQvfvziAsPk0oc7XTY22XqnC2fcZytURERERERET0XPo0KIxmZX2EzKwCAzafx+OENPmAEh8B1XrI+a7eQHK0EDUt2BTdynSTSiccn4DQpFApd2rcGM4tWwqZajAgqGcvZDx69ByzISJ6d7CRSURE77TUy5fx4PP2yHj4UMg1Dg7wmzcXilXW8jBnH5/Fd9e+E+oUKJhWZxrs9HbiwCYD8Es/4PBs+aSF3gMajBKiCT9fxcXgeCHTaxWs7FQZ+d3tX2BmRERERERERPRfiqJgVutyKOQh3mNHJWWg/6bzMJrM8kGNpwDeZcUsOQLYPRCWe8L0rdgX5TzLCVliRiJGHB4Bg9kgDe09JgA6H7GxagwLw8MvOiPjwYPnnxgR0VuOjUwiInpnJR44gIedv4QpOlr6Ls/kSbDy98/8nGJIwdijY6FCvFHpUroLKnpVFA/OSAa2dADOb5RP6loAaL3myZ4b/3H0dhS2ngmSSqe3KovqhdyzNykiIiIiIiIieiZHGz2Wd6oEG734a/HTD2IwZ+9N+QC9DdBmLaCzFfPrvwIXt4ilGj1m15sNR72jkF+MvIgVF1ZIQ2udnOA7YzqgEa/F+PgxHn7RGen37mdjZkREby82MomI6J0Uu3Ubgvv2g5qaKn6hKPAOGA2nZs2EeN6ZeQhOChayIi5F0LdiX/H41Dhg/cfA7b3ySX3KAV33AvZZzcmUDCNG7bwklfasXwhtq/hLORERERERERG9uBI+Tpj2SVkpX3XoHn65KC8DC89iwAdT5HzPcCBWXN3Jz8EPE2pNkEq/vfwt9j3cJ+X2NWs+u5kZGYmHX3ZG+t27/zAbIqK3HxuZRET0zon/7TeET5gAmMVlYxQrK/gtXAi3zp2F/FjIMWy7tU3IdIoO0+pMg7XWOis0m4DtXYHQc/JJCzcEvtoDOHoL8by9txAcKzZTy+d1xvAPir/AzIiIiIiIiIjon7SunBftq+WT8sFbL+DnCyHyAVW/Bgo3ErOMxCf7ZZpNQtykQBO0KdZGyFSoGHpoKH66/ZM0tHPLlvCdNUtqZpoio/Cw85cwhD6juUpE9A5hI5OIiN4p6ffuIWzceCnXOjsj3/p1cGrygZDHp8dj/HG5vkf5HijlXkoM900A7u6XT1ruc6D9VsBaXF7m3KNYrD0mLhWj0yiY2bocdFr+FU1ERERERET0qkxoXgqlfZ2EzGRWMXjrBew8J67IBEUBWi4DbFzE/OEx4MQyaewRVUegiEsRITOrZow/Ph4brm6Q6p2bfwy/uXMArVbITdHRCB0dANX8jP07iYjeEfwtKRERvTPMqakIGTgQakqKkOv9/JB/82bYVaokHTPz9ExEpEQIWSn3Uvi67Ndi4cUtwPEl8klrDwRarQR0VkKcYTRj1I5LUMUtN9GnQWGUzCPeSBERERERERHRy2Wj12Jlp8rwdLQWcrMKDP3xIradCRIPcMoDNF8oD/T3FCBEXJnJVmeLefXnwdXaVSqfe2YuFp5dCNXiFwJOzZrBb948QKcT8pRTpxD3/cbnnxgR0VuGjUwiInonqKqK8EmTkX77jpBrXVyQ//vvYF2ooHTMvof7sPvebiGz0lhhep3p0Gv0WWHIWeCXAfJJy30OvD/pyZObFpYfvINbj5OErIiXA/o2LCLVEhEREREREdHL5+9mh609asDbSWxmqiowYvslbD79SDygdCug7GdiZsoANn8OxImNz0IuhbDhww3wsfeRzrvmyhpMPTlVbmY2bQLfaVOl+uhFi2C6d1/KiYjeBWxkEhHROyF+xw7E79olhooC3zmzoff1leqjUqMw5eQUKR9QaQAKuxTOChLDgS0dAVO6WOhbCWi+6JlNzCsh8Vh2QGyoKgowq3VZWOu0Uj0RERERERERvRqFPB2wtUdN+DrbSN8F/HQZR25HimGzOYCTn5glPQY2tQPSEoS4oHNBfP/h9yJ6pF0AAQAASURBVCjkXEgae9utbfjl7i9S7tyyJZw++kgMDQYkT5sGNSPj+SZFRPQWYSOTiIjeemk3biB8ivxEo3uvnnCoW1fKzaoZAUcCEJMWI+SVvCqhU8lOWYHJCGzrDCSGiQM4+ACf/wDo5Zug+BQDev9wFgaT+NTllzULoHJ+t2zMioiIiIiIiIhehgIe9tjasyb8XGyFXFWBIdsuIirpqYeXbV2ANmsBrbiFDCKuAj92efK7gqf42PtgQ9MNKOtRVjrvzNMzEZYUJuU+48dB5yO+yWm6exep69ZnZ1pERG8FNjKJiOitZoyNRfCAgVDTxTcm7apXh2e/fs88Zt2VdTgRdkLIbHW2mFp7KrSap96YPLYACDolHqy1AtptBJzktzzNZhWDt11AUEyqkPu52GJ4k+LZmBURERERERERvUz+bnbY2rMG/N3EZmZkYjqGbrsIs/mpB5Lz1QBaLpcHubsf2DPsSQf0KS42Lvj2g29R2buykCcZkjDu2DiYVbOQa52d4TtjujR8+pYtSD1zJpszIyLK3djIJCKit5Y5PR3BffvB8Ejc00Lr6QG/uXOgaOVlXC9EXMCS80ukfFS1UfB38s8Kwi4BB2fJJ/14IeBf9ZnXs/zgHfx9I0LIdBoFiz6vAHtr3T9PiIiIiIiIiIhembyudljzZVXY6MVfmx+6FYm1xyz2qCzXFnhvrDzI2XXAiaVSbKe3w+x6s+Fk5STkp8JPYfONzVK9fc2acPuysxiqKsJHjoIhNPT5JkRE9BZgI5OIiN5KqtmMsNEBSD13TvxCo4HfvHnQeXpKxyRkJGDk4ZEwqSYh/7Dgh2hVpFVWYEwHfuoFmA3iAJU6AxU7PvN6jtyOxLy/bkn56GYlUaUAl5QlIiIiIiIiygmKeTti/MelpXzWHzdwOTheDOsNAyo84/cAf40HHh6XYi87L4ytITc/F5xdgPvx96Xcc/BgWBUpLGSmiAg86t4Dpri4/z0RIqK3BBuZRET0VopcsgQJe/ZIuXdAAOyrVZNyVVUx8fhEhCaLTzXmdciL8TXGQ1GUrPDgzCd7XzzNJR/QRF72BQBC41IxcMsFy5Vl8FHZPOhau8BzzYeIiIiIiIiIXo/21fzxUdk8QmYwqei/+RyS0p/aA1NRnqzMVKCuOIBqBnZ8DaTESGN/WPBDNCnQRMjSTekYc3QMjGZxf02NjQ385swB9Hohz7h7F0F9+sKclpb9yRER5TJsZBIR0Vsn7qddiF6xUspdO38Bt07PfmNyw9UN+OvhX0Km0+gwt/5cOFg5ZIVBp4FjC+UBPlkBWDtKcUhcKjqvPY2Y5AwhL+Rpj1ltyokNUiIiIiIiIiJ64xRFwfRPy8LPRdwv80F0CsbvuiIW66yAdt8D7kXEPCEE+LmftF8mAIytPhYeth5CdjnqMhadWwTVot6mZEl4T5ksjZF67hxChw+HajJJ3xERvU3YyCQiordK0rFjCBs/Xsod3nsP3iNHSrlZNWPB2QWYd3ae9N2gSoNQ2uOp5WQyUp4sKauaxcIafYACdaTjr4cl4NPlx3AnIknI7ay0WNWpMhy4LyYRERERERFRjuRsq8fi9hWh1YgPIO88H4LfLoWJxbauQJt1gNZKzG/+BgR+K43tYuOCSbUmSfn6q+uf2cx0/Ogj2PbqJdUn/rUPj6dNk+qJiN4mbGQSEdFbIyUwEMF9+wEGce9K61Il4Td3DhStVsgzTBkYfWQ01l5ZK41V168uvij1hRjumwjE3BUz96JAI7lxevxOFD5beQKPE9Kl72Z8WhZFveW3N4mIiIiIiIgo56ic3xVDGheT8jG7LiMiwWJZ1zzlgA+myoP8GQCEXZLiennroXXR1lK+5soazD87X2pOWrf7DNZt2kj1sZs2I/aHTf8wEyKi3IuNTCIieiukXryIoJ69oFrsD6Hz8YH/ipXQ2NsLeUJGAnrv64099+V9NPM55sPUOlOhUZ76a/LeIeD0KrFQ0QKtVgF6camZXy6G4st1p5GYLu5tAQABzUqgZQW/bM6OiIiIiIiIiN6EXvULo1pBNyGLSzFg+PZL8puQ1XoAxZuJmSkD2N4VSBdXawKAEVVHoIx7GSlff3U9ZgfOFsZXFAW2fXrDoUkTqT5i1iykXb+ejVkREeUebGQSEVGul3b9Oh517wFzSoqQaxwd4b9yBfTeXkIenx6PLn90wenw09JYZdzL4LsPv4ObzVM3KWnxwM995RPXHQLkrSxEv18Ow8At52EwiTczeq2Che0qoEe9wtmcHRERERERERG9KVqNgnlty0vbwxy6FYmNpx6JxYoCtFwGOPqKefRt4Ie2QGqcENvp7bDqg1Uo51FOOu/G6xsx8/RMsZmp0cB7+jTYVasm1KoGA0KGDJV+L0JE9DZgI5OIiHK19Dt38KhrN5gTEoRcY2eHfN+shk2JEkJuVs0YdWQUbsfelsZqkLcB1jRZA3dbd/GLPwOA+CAx8ykL1BshRNdCEzBk20VYPpDpYK3Dui7V8ElFvolJRERERERElNv4u9lhYovSUj7tt2u4F2nxpqWdG9D6G0Cx+NX7o+PA+o+BxMdC7GTlhFWNV6GCZwVp/E03NuHHWz8KmWJlhbyLF0Hn4yPkGffvI3zatOefFBFRLsFGJhER5VqGx4/xqGs3mGJjhVyxsUHelStgW6GCdMyay2twNOSolH9W7DMseG8B7PR24hc3/wDObxQzjf7JkrI6q8woOikd3b87g1SDSSj1dLTG1p41UKeoR/YmR0REREREREQ5RutKfmhaWmwephnMGLz1Agwms1hcoA5Qf6Q8yOPLwNomQOwDIXawcsDKxitRyauSdMjcM3PxKEF881Pr4gK/ObMBjfjr/fgdOxH/22/PPykiolyAjUwiIsqVzKmpCO7TF8aICCFX9HrkXboU9hbLrADA6bDTWHphqZT3Kd8HY2uMhU4jLhODlBjg1wHyyd8LALyznsQ0mMzo88M5hMSlCmVu9lbY3qsmSvs6Z2NmRERERERERJTTKIqC6Z+WhYeDtZBfDI7HpF+vyvtl1hsBVO8tDxR7H1jTBHh8VYjt9fZY8f4KVPYWt7BJNaZi7ImxMJnFB6ftqlaFR295/PAJE5ERFCTlRES5FRuZRESU66hmM0JHByDtqviPfuh08Fu0CA51akvHRKZEYsThETCr4lOSTQo0Qa/yvaAoinyi34YCSeKSL8hbFag9UIgm/3oNp+7HiJeiUbC8YyXkd7d//okRERERERERUY7lZm+F2W3KSvnGk4+w5uh9MdRogKYzgPfGygMlhQMbmgMJYUJsp7fDvPrz4GbjJuRXoq9g071N0jAevXvBtorY+DQnJSFk8BDul0lEbw02MomIKNeJWrYciX/8IeV5pk6BY8P3pNxoNmL44eGITosW8gJOBTCp1qRnNzEvbAau7hQznS3wyUpAo82MNp16hO9PPpQOn9CiNGoUcpdyIiIiIiIiIsq9GpbwRsfq+aR82p7r+ONKuBgqClB/OPDRPAAWv3tIiQZ+6QdYvMnpbuuOSbUmSeNvvLcRN+NvisPrdPCbMwcaZ3ElqLQrVxDcrz/MGRnPPzEiohyKjUwiIspVEn7/HVHLlkm5e/fucPnkk2ces/j8Ypx9fFbIbLQ2mN9gPuz1z3hjMur2k7cxLTWeBHgUyfx4IzwBE3+5KpV1qJ4PX9TI/78nQkRERERERES50vjmpVCtoPjWpKoCg7aex8WgOPmAql8DbdYAGr2Y39kHnF0nlTfwb4DWRVsLmVk1Y+almUg1itva6PPkQZ6pU6Qxko8fR+jQoVCNxuebFBFRDsVGJhER5Rqpl68gdNRoKXdo1Aiegwc985hdd3Zh3RX5pmBsjbEo6lpUPsCQBmz/CjAki3nB+kDV7pkfM4xmDN12ERkmcanaagXcMLF5aRARERERERHR28lap8XqLyqjkIf4cHSawYxuG84gOPYZy7qWaQ00XyTnf44FYu5J8YiqI5DXIa+QBacEY965edJ+nE6NG8P9627SGIl/7UPY2HFQzWbpOyKi3IKNTCIiyhWMUVEI7tcPanq6kFuXKAG/2bOgaOS/0k6HncakE/JyLK2LtkbLIi2ffaK/xgPhl8XMzgNoterJ/hb/sfTAHVwNTRDKfJxssLxTJVjp+NcrERERERER0dvMxc4K676qCjd7KyGPSkrH1xvOIM1gkg+q0AEo8bGYGZKBn3oDZrHeTm+HGXVnQKOIv2PYeWcnNlzdIA3tOXQoXNq2kfL4XbvwePoMqflJRJRb8DetRESU46kZGQgeMBDGx4+FXOvuDv/ly6Cxl5eHvRd/D4MODoLRLC6hUtKtJEZVG/XsE934DTi9Ss5brQSc8mR+vBgUh2UH7khlc9uWh4eD9XPMiIiIiIiIiIhyu/zu9vimc2XpgeYb4YmYsvuafICiAB8vfPLA9NOCTgInlkrlFbwqoFsZ+U3LeWfn4bd7v1kMrcDn/9i77ygpqq3v49/unswE4pCGnHMGEQVEUECRoCTJOYtEMSEiQVAJkqMkQaIoKKiIgJIkgwhIzmFgmJy7+/mDe73WVE3QRxH091nrrqV71z516l3vOPPUrnPOqFEENm5kuv7usmXcHD9eKzNF5KGkRqaIiDzwbowbR9zBg4aYzdOTkGnT8MyTx3R9WHwY/bb0IyoxyhDP6ZeT6U9Ox8fDx3yTiCuwvq85/ugAKNbg13+NT3IyZPURnC7jl4wdaxbgsWLZU1aLiIiIiIiIyD9YlQJZmdSqgin+8d5LbDp23VzgnwOaTDHHt46Bm8dN4T4V+lAxR0VT/I2db7Dn+h5DzOZwkOfdd8lUp7bp+rtLlnJt+Cu4ExNTfRYRkQeRGpkiIvJAu7tyFeGfrDTFc701Er/KlUzxBGcCA7cO5Er0FUPcz8OPGU/OINgv2OImF2BJM4gPN8bzVIZ6Iw2h9786xZlb0YZYwWx+jGhUMiOPIyIiIiIiIiL/MM+Wz0PfukVM8eFrj3I5zOK8zFJNoEJbY8yZCB+3hDtnDWFPhyeTa08mf6b8hniyK5mXv3uZU2GnDHGblxchU6bgV7Wq6baRGzdyuU9fXDExGXwyEZG/nxqZIiLywIo9eJAbY8aY4llefJHML5jPfUhyJTF021AOhx42xO02O+/XeZ8SWUuYb3LtEMxvAHdOG+PegfDCQvD431kXe8/dYcHO8ynGhg9aVcDPyyPjDyYiIiIiIiIi/yiDGhSncv7MhlhUfDIDPzlEktNiS9eG70JgXmMs8iosegZuG4+zCfIOYnyV8WTzzmaIxyTF0GdLH65HG1d+2n19CZk9C98qVUy3jdm5k4udu5AcFpbxhxMR+RupkSkiIg+kpBs3uPLSQEhKMsT9qlYl56vmMy6TXcmM2DGCbVe2mXKvVn+Vx0MeN9/k9Dfw0TMQc8uce3YyZC30679GJyQzdM0R3MYdZelRuzBVCmTN0DOJiIiIiIiIyD+Tp8PO1DaVCPAxfuh88FI4k7/5xVzgmxmazQRbilf0UddhUWMINdYE+wYzrso4/D39DfHQuFCG7xhOsivZEHf4+5N/wXz869Uz3Tr+2DEutmtP8u3bGX9AEZG/iRqZIiLywHHFxnK5b1+cKf6g9sidm7xTp2Dz9DRe73bx1q63+Pri16ax2pdqT5uSbcw3ObgElreGJIvtVJ4eD+WMKz7HfXmCy2FxhljxnP4MblA8g08lIiIiIiIiIv9k+bL6MeH58qb4rO1n2fyTxXmZhetCs9nmZmb0zXsrM2+dNF4eUJj3H38fD7uxWXo49DAzD880DW/38SHkw6kEPd/ClEs8f55LPXrijIxM/8FERP5GamSKiMgDxe1yce2VEST8fMIQt3l7EzJtGh7ZjNuouN1uxu4Zy+dnPzeN1aRwE4ZVG2a+yU/r4PMB4HYa4w5vaLkIavY1hLf/EsryvZcMMQ+7jUmtKuLt4cj4w4mIiIiIiIjIP1rjcrl5sYbxPEu3G/p+fJBV+y+bCyq0hhbzzM3MmFuw+FkIN9ZUz1Wdd2q9Yxpm/rH57L2+1xS3eXiQe8wYsvXqZcolnDhx78zMuDhTTkTkQaFGpoiIPFBCP/yQqG++McVzj3kH37JlTPEP9n/Aql9WmeINCjRgdK3R2FP+HwK3T99rYqbkkxk6rocyzQ3hiNgkhq85Yrp8QL1ilM0blOaziIiIiIiIiMi/z8hnS1M8p3ELWJcbhq85ypztZ80F5V6A5xeALcXH0jGhsL4PuI1nbD5b+FmeL/a8IebGzavfv0pYvPnsS5vNRvCglwl+5RVTLu7AAa68/DLuFEf7iIg8KNTIFBGRB0bEho3cmT3HFM/WuxdBTZqY4p+c/ITFPy82xWuH1GbC4xNMW62QGAurOkJitDEelB+6fQ0FHjWNNWrDcW5GJhhi5UOC6PtEkQw8kYiIiIiIiIj82/h4OpjZrgrZMnmZcuM3nWT8phO43W5jomwLaPkRpHyXceF77D/ONo3zSvVXKBJkfDcRGhfKmzvfNI/9H9m6dCb7S+aPu2O27+Daq6/hdrksqkRE/l5qZIqIyAMh9sABrr/+uike0KABOV56yRTffW037/74rileI3cNJtWdhKfDeI4mbjd8MQRu/WyM+2aBLl9AjhKmsTb/dJ1PD101xLw87HzQsgKeDv0KFRERERERERFrRYP9WdW7Jnkz+5pyc7af4/X1P5kbjqWbQrNZpuvt343B484pQ8zXw5eJdSbi7fA2xHdc2cHSn5emOq/sffqQpWMHUzxy40ZuvDVKzUwReeDoLayIiPyt3E4nt+fO42KnzrgTEw0579KlyDPhXWx246+rCxEXGLJ9CM4UZ1xWzFGRD5/40PRHPACHlsKR5SmCNmgxHzLnN11+Kyqe1z79yRQf/nQJiuUMyNjDiYiIiIiIiMi/VpEc/qzpU5Oiwf6m3PK9l1hz4Iq5qHwrKNfKELI5EwnaOgycxvcmxbMUZ1jVYaYhJh2YxLbL2yznZLPZyDliBEFNm5py4atXc/31N3A7nRaVIiJ/DzUyRUTkb5N09SqXOnUmdNIkSE425Bw5spNv5kzsfn6GeERCBP239icqMcoQz+uflw/rfYifp/F6AK4fhS+GmuO1h0Kx+qawy+Vm8MojhMUY/w+E6gWz0qVWoQw+nYiIiIiIiIj82+UO8mV1r5pUzJfZlBu94Weu3I01FzV+DwLzGkKed07hv2+q6dJWJVpRP7/x3YbT7WTItiHsvb7Xck42u53cY8fgX6+eKRfx6adcG/4K7hTvaURE/i5qZIqIyN8i8ssvOdesObH795tyNm9v8s2YgWeuXIZ4kiuJIduHcDHyoiGeyTMT0+tNJ4tPFvONYm7DyvbgNJ5zSaHaUPdVy7nN3nGWH87cNsT8vBy837ICDrstA08nIiIiIiIiInJPlkxefNy9BjUKZTXEoxKSGbb6KC5Xii1mfTNbbjGb6fACbJd2GWI2m41Rj44iT6Y8hniiK5EBWwdwNPSo5ZxsHh7knTyJTI/WNOUiv/iCq4OHmHbOEhH5O6iRKSIi913k5q+4OngIrqgoU84zb14KLF6Eb/nyptzEHyeavia02+xMrD2RolmKmm+UFA8r2kK4sfGJfy54fgHYHaaSAxfv8sHXv5jio5qUIX82i9WeIiIiIiIiIiLpyOTtwYdtKxHk62mI7z53h8W7L5gLCteBR/oZQjbcOD7tCeGXDfEg7yBm1Z9FZu/Mhnhcchx9tvThl7vm9xwAdm9vQmbOJFPtx025qK+/5srAl9XMFJG/nRqZIiJyXzmjo7kxdoxlLqhpUwp9th7fihVNuXWn1/HJqU9M8cFVBlM7pLZ5MJcL1veBKz8a4zYHtPwI/INNJRGxSby04hDOFF9CNq2Yh5ZVQ1J/KBERERERERGRdOQM9GFMs7Km+LubTnLmVrS54MmRkKOkIWSLvgHLnofYMEO8cObCzG4wG39P43mckYmR9Py6JxciLljOye7jQ8j06fg/+aQpF/3dd4R++GE6TyUi8tdSI1NERO6r2zNn4Qw1bttqDwwk7+RJ5JnwLg5/f1PNkdAjjNljbn42L9qcjqU7Wt/ou7FwfJ053mgCFHjUFHa73YxYd5Sr4XGGeIFsfoxpVhabTVvKioiIiIiIiMj/T5MKeXi2fG5DLCHZxZBVh0l2uowXe/pAi3m47cZVnNw+BZ+8eG8nqt8ok60M05+cjo/DxxC/E3+HTps7cfzOccs52b28CJkymYCGDU25Ows/Iu7w4Yw9nIjIX0CNTBERuW8Szp0jbMkSQ8zm5UXBlZ8Q2KiRZc2t2FsM+m4QSa4kQ7xScCXefORN6wbjoWXw/fvm+CP9oHoPy/vM3XGOTT/dMMQ8HTamta1EgI+nZY2IiIiIiIiIyO/1TtOyBAd4G2JHrkQwYfNJ3O4U52XmLo+zicWqyEu7YV13cDkN4So5qzD5icl42D0M8bD4MLpu7srua7st52Tz9CTv++8R+OyzxoTLxbVXX8MVH29ZJyLyV1MjU0RE7gu3283NseMgOdkQz9a9O96FClnWJDoTGbRtEKFxoYZ4sF8wk+pOwtNh0WA88y1sGGiOl3gGnnrHFHa53Ly76STjN5005V5pWJLyIZlTfygRERERERERkd8pSyYvJjxf3hSf9/15Jmw+ZWpmusu2JPKRYeaBTmyATa9Aiusfy/sYEx6fgN1mfP0fmxxL32/7svn8Zst52Tw8yD3mHbxSvKdJPH+e0A+nZeTRRET+dGpkiojIfRG9dSsxO3caYh55cpOtR3fL691uN+P2juNo6FFD3MvuxdQnppLdN7u56MJO+KQduIzNUnJXhOfngd1hCCckO3l55WFmbz9rGqpeyWC6PWbdYBURERERERER+f94omQwbavnM8Vnbz/LuC9PmJqZsRW6EVPO4nidffNg33xT+KmCTzGp7iS87F6GeLIrmeE7hrPi5ArLedl9fMgzfhzYja2DsI8+IvbgofQeS0TkT6dGpoiI/OVc8fHcHP+uKZ7zlRHYfX0ta5afXM7a02tN8TdrvknZ7GXNBVf2w/JWkGw845LAEGj7CXhlMoQj4pLotPBHPj9yzTRUwWx+vPdCeZ2LKSIiIiIiIiJ/mTeeKU2FfJlN8Xnfn2f0xp+NzUybjahHX8VVqql5oK9ehxvHTOEn8z/JnAZzCPAMMMTd3Pt4fNWpVZbz8q1YkWxduxiDbjfXX30VV1ycZY2IyF9FjUwREfnL3Vm4kKQrVwwxv5qPEPBUA8vrf7j6AxP3TTTF25VqR7OizcwF14/CshaQGG2M+wRBu1UQmNsQvhuTSKvZu9lzLsw0VIV8mVnT51Gy+XubciIiIiIiIiIif5ZM3h4s7VadSvkzm3If7bzAqM+Pp2hm2nE+NwMK1DJe7EyA1V0gMcY0TtVcVfmo4UeWO1uN2TMm1W1msw8YgFeRIoZY4sWLhE6Zku5ziYj8mdTIFBGRv1TsgQPcmT3HGHQ4yPXaa5YrHs+Fn2PY9mG43C5DvFquagypOsR8g1snYWkziI8wxr38of06yFnGEHa53AxadZhTN6NMQ9UvFcyKHjXIriamiIiIiIiIiNwHgT6eLOlanSoFsphyi3df5OO9l4xBDx9ovQwC8hjjd07DpuGW9yiRtQRLGy0lf0B+Q9yNm1d/eJWdV3eaauze3tZbzC5eQuTXX2fgyURE/hxqZIqIyF8m4fRpLvfpizsx0RDP2r4d3sWKma4Pjw+n37f9iE4yrqzMF5CPSXUm4Wn3NBZEXrvXxIy9Y4x7+MKLqyCkqukeM7edYdupUFO8/SP5md2+Cn5eHhl7OBERERERERGRP0GAjyeLu1anWkFzM3PsFyc4fzvFSku/rPD8PLCleL1/aBkcW2N5j5CAEBY1XESIf4ghnuxKZtC2QRy+ddhU41u+PNm6dTPFr414lfhffkn7oURE/iRqZIqIyF8i6cYNLvXoiSsy0hD3yJGD7P37m693JjFo2yCuRBu3oPX39Gd6velk9slsLEiMhRVtIeq6Me7wgjYfQ8EU26wAu87cZtI35j+0B9UvzjtNy+Lh0K9FEREREREREbn//L09WNSlOtULZTXE45KcDF17jGSX21hQ8DGoPcw80IaXIey85T1y+OVg7lNzTdvMxiXH0ffbvpwKO2WqyT6gP94lSxpi7thYrvTrjzM8PN3nEhH5/9IbWxER+dM5IyK43KMHyTduGOI2Pz9CZs7AEWA8ZD48PpzB2waz/+Z+Q9xus/N+nfcpnLmw8QYuF6zvA9cPG+N2D2i5GIo+aZrTzch4XvrkECn/7m9SIQ8vPVnUcptbEREREREREZH7JZO3B7PaVSa7v5chfvRKJIt/vG4uqD0c8j9qjCVGwRrr8zLh3q5XcxrMIcDL+G4mKjGK7l9358SdE4a43cuLkOnTcAQFGeJJly9zdchQ3E5nBp9OROSPUSNTRET+VK6EBC7360fC6TPGhIcHIVOn4FuunCG869ouWnzegm1XtpnGGl5tOLXymldWsn0C/LzeHH9uOpRsbAonO10MWHGI29HGLW4L58jE+Bbl1MQUERERERERkQdCNn9vJjxf3hRfuPc6P99I0Zx0eNzbYjblLlbXDsEn7SA5wfIexbMUZ+aTM/H18DXEwxPC6fZ1N366/ZMh7hUSQt4pk8HhMMRjdu7k1qRJGXswEZE/SI1MERH507iTk7k6eAhx+w+YcrnHvIP/44//+u8JzgQm7ptIr296ERpnPrOyVfFWvFjyRfNNfloL2981xx8bBBXbWs7rva9O8eP5MEPMx9POrHZV8PfWmZgiIiIiIiIi8uB4slRO2lbPb4g53TDqq/PEJiYbLw4KgWYzzYOc+w7WdAVnsjkHVAyuyOS6k/GwG9+L/Hdl5qFbhwzxTDVrknO4eSvbsAULubtqVQaeSkTkj1EjU0RE/hRul4vrr79B9LffmnI5hgwmc7Nmv/57REIEHb7swNKfl1qOVT9/fUbUGGFeKXn1IKzvay4o8QzUG2k51qr9l5mz45wpPrZZOUrkCrCoEBERERERERH5e73xTCkKZPMzxC7dTWDcplO43SnOzSn5DNR62TzIyY3wef97R/RYqJW3FlPqTsHT7mmIxyTF0OubXvx4/UdDPEvHjgQ1fc40zo2Rb3F3pZqZIvLXUCNTRET+39xuNzfHv0vEZ5+Zclnatydb9+6G2Jg9YzgRdsJ0rbfDm9dqvMakupNMf0QTdfM/26LEG+M5y0GLuWA3/0rbdeY2r607Zoq3qZaP56uEZODJRERERERERETuv0zeHkxqVRF7im+8V+6/ylyLD7apPwoqdzLHj6yAza9Ayubnf9TJV4fp9abj7fA2xOOS4+j7bV+O3z7+a8xms5Hr7bfxKVvWNM6Nt97i7ooV6T6XiMjvpUamiIj8v92ePoO7S82rKwOfeYacrxpXVu67sY/NFzabri2RpQQrn11J25JtzSsxkxNhVUeIumaMZ8oBbVeAt79pvDO3oum97ADJLuMf6uXyBjHquTK/4+lERERERERERO6/KgWy0O+Joqb4+E0nWX/oqjFos8Gzk6FMC/NAP86FzSNSXZn5aN5HmVV/lunMzARnAq/98BoJzv+dtWn38SFk+jQ8Q8wfiN94ezRhyz7OwJOJiGScGpkiIvL/ErZkCbdnzDDF/evUIc+747H95iD4ZFcy438cb7q2bcm2LH9mOUUyF7G+yeZX4PIeY8zhBa0/hsz5TJffiU6gy6IfiYw3ngORO8iH+Z2q4uPpMNWIiIiIiIiIiDxoXnqyGFULZDbFh605wg+nbxuDdse9XauKPW0eaO9s+LTnvY/FLVTLVY25Debi72n8WPxcxDlmHDa+9/HMlYsCSxbjmc/8TubmmDGELV6c9kOJiPwOamSKiMgfFv7pem6OMzcm/apWJe/UKdg8jdvDrjy1ktN3TxtiZbKVYUT1EXg5vKxvsv8j2L/QHH9mEuSvYQrHJznpsWQ/l8PiDPFMXg4Wdq5GzkCfdJ5KREREREREROTB4OmwM7tdJQplNb7PSHK66b3sAMevRRgLHJ7QajEUeMw82LHVsKINJERb3qticEWmPzkdG8adshYfX8yR0CPGeeXJQ4GlS/AskN80zs3x73JryhTzWZ4iIn+AGpkiIvKHRG3ZwvU33jDFfUqXJmT2LOw+xj+ww+LDTF/wAbxW4zXstlR+HV3aA18OM8er9YDKHUzhxGQX/T4+yMFL4Ya4w25jRrvKlModmPoDiYiIiIiIiIg8gIJ8PZncrBg5Mhk/GI9OSKbzR/u4Gm78mBtP33tH8eSvaR7s7Lew5DmIuWN5ryo5q9CpjPGsTZfbxRs/vEF8crzxNrlyUWDJErwKFjSNc2f2HK6/+Sbu5GRTTkTk91AjU0REfreY3bu5OmgwOJ2GuFfhwuSbPw+Hv/nMyg8PfkhUYpQh1rRIU8rnKG99k4irsLIDuJKM8QK1oKF5FWiy08WglYf59uQtU27Uc2WoWyI4nacSEREREREREXkw5Qr0YlKzovh7exjioVEJDFh+kCRnivMvfQKhw6dQorF5sKsH4KOGEB1qea9+FftRMLCgIXYh8oLlB+qeOXOSf8livAoVMuUi1qzlyoCXcMXFmXIiIhmlRqaIiPwucUeOcLlff9xJxgajR57c5F8wH4+sWU01x28fZ93pdYaYv6c/L1d52fomiTH3tjqJSdGUDAyBlovvbZPyGy6Xm+FrjvLFseumobo9VogOjxRI/8FERERERERERB5gxXL4MevFing6jFu/HrwUzpQtv5gLPH2h1VKo1N6cu/0LLGsB8RGmlI+HD2MeG2PaQWvx8cUcvnXYfJvgYAosW4pP2bKmXPR333GpS1ec4eFpPpuISGrUyBQRkQyL/+UXLvfshTs21hB3ZMtG/gUL8Myd21ST5Exi3N5xuDGei9C7Qm+y+2Y338Tlgk97wY2jxriHD7RZBv45DGG3280bn/3EukNXTUO1qJSX1xuXyuDTiYiIiIiIiIg82B4pnJX3Xqhgis/cdpZdZ26bCxwe8Nx0eGyQOXfjKCxvA0nmFZMVclQwbTHrxs3rP7xu2nELwCNbNgosXkSmWrVMubjDh7nSf4C2mRWRP0SNTBERyZDEy5e53K07zgjjl3p2f3/yz5+Ht8UWIi63izd3vcnR28amZOGgwrxY6kXrG303Fk5sMMefmw55KpnCY784wfK9l0zxZ8rlZuIL5bHbbaaciIiIiIiIiMjDqlmlvLStnt8Qc7vh5ZWHuROdYC6w2aD+KGjwjjl3aRes7gzOJFOqX8V+FA4qbLw86hIjvh+B0+U0XW/PlIl8s2YS2KSJKRe7fz+h06en+VwiIlbUyBQRkXQl3brFpa7dSA41np1g8/Eh35zZ+JSyXvU4af8kvjj3hSn+SvVX8LR7mguOroLv3zfHaw+D8i1N4SW7LzD/h/Om+JMlg5ncuiIeDv2aExEREREREZF/npHPlqZ4Tn9D7FZUAkNXH8HlclsX1XoJ6r5qjv+yGdb3vbdL1m94O7wZ+9hY0xazO67ssDwvE8Dm5UWeCe+StUsXU+7OnLlE79yZxlOJiJjpDa+IiKTJGR7O5W7dSbp82Zjw8CDkw6n4ValiWbf4+GIW/7zYFH+h+As8mudRc8HFXfBZf3O81HNQ9zVT+PvToby94WdT/LGi2ZnRrjJeHvoVJyIiIiIiIiL/TL5eDqa1rYx3ivcf350KZeFO80ffv6rzClTvZY4fWwVfDDY1M8tmL8vAygNNl887No+vLnxleQub3U7OV4YT9MLzxoTbzbXhr5B061bq8xMRSUFveUVEJFWumBgu9epFwunTxoTNRt6JE/CvXduybsPZDby/37yysm5IXV6v8XqKmzjh+w9gcRNwptj+JFd5aD4b7MZfV2dDo+n78UGcKb4wrFogC3M7VsHH05GxBxQREREREREReUiVyBXAm8+WNsXHbzrJNz/ftC6y2aDhu1C+tTl34CP4fMC9dzW/0aVMFxoVamS6/M2db3Iq7FSq88v1+ut4FytmiDnv3OHasOG4neataUVErKiRKSIillyJiVwZMID4I0dNuVyjRhHYuLFl3c6rOxm5c6QpXiFHBSbWmYiH3eN/wfBL9xqY344GV4oD3/1zQttPwCuTIRwem0j3xfuJijdenz+rH3M7VsXPywMRERERERERkX+DdjXy06hsLkPM6XLTb/lBdp65bV1kt0PTGVDc3Jzk8DL4tBc4//fexWaz8fajb1Mya0nDpXHJcQz8biB34+9a38bXl7xTJmPz9TXEY/fu5fas2Rl4OhERNTJFRMSCOzmZa0OGErNrtymXY8hgsrRuZVl3+NZhBm0bRLLb2GQsHFSY6fWm4+vxmz9cf1oLsx6DixZnI3j4QpsVEJTXEE5yuuj78UHO344xxP29PZjfqSpZM3ll8AlFRERERERERB5+NpuNd1uUJ29mY7MwMdlFjyX7OXTJusmIwxNafgSFLHbbOrYa1nSB5MRfQ74evkx9YipZvLMYLr0afZWe3/RMtZnpXaQIuUaaP3i/PWMGERu/SOfpRETUyBQRkRTcbjfXR75F1DffmHLZenQne48elnWnwk7Rd0tf4pLjDPFgv2Bm159NZp/M/wseXw9rukJChHmgzAWg0wYIMZ+9+c7Gn9l19o4hZrfBtLaVKJ4zIN1nExERERERERH5pwny82Rx12qmD7xjE510/mgfJ65HWhd6+sKLq6BofXPuxOewqqOhmZnHPw8f1P0Ah814pM/JsJN0+7obYfFhlrfJ3LwZQc2aGYNuN9eGDyd8/fr0Hk9E/uXUyBQRkV+53W5uTZhIxLp1plzmli3JMXiwZd3FyIv0+qYXUUlRhniAVwCz688mt3/u/wXjI2HTcOsJVGgLvX+AfNVMqU9+vMSS3RdN8dcal+KJksFpPJWIiIiIiIiIyD9b0eAAlnStToC38cidiLgkOiz4kbOh0daFnr7QZjmUsDhC6JdN8N0YQ6harmq8Uv0V06Wn756m21fduB1nvZ1trjffwKtwYWPQ5eL6q68RvmZN6g8mIv96amSKiMiv7syZQ9iiRaZ4QKOG5Br1FjabzZS7EXODnl/35E68caWkr4cvM5+cSbEsxkPd2TERolMcOO8TBC98BM1ng0+g6R4HLt5l5GfHTfHWVfPR7bFC6T+YiIiIiIiIiMg/XNm8QSzsUg0fT+Nr/9vRCbScvZujV8KtCz28odUSKN3MnNs1DS7vM4TalmxLv4r9TJeeCT9D16+6EhobasrZM2Ui38wZeASn+Bjd7eb6G29y95NP0no0EfkXUyNTREQACFu+nNApU03xTI8/Tt4JE7A5HKbc3fi79PqmF9dirhniHnYPpjwxhYrBFY0Fob/AnlnGmMMbum+Fsi0s53UzMp4+yw6Q6HQZ4lULZOGdZmUtm6siIiIiIiIiIv9G1QpmZU6Hqng6jO9LwmISaTt3D9+fNjcZgXtnZj6/AMq1NMbdLvisLyTFG8K9K/RmYOWBpmHOR5yny1dduBp91ZTzKliQAkuX4JE7tyl3Y9TbhC1Zms7Tici/kRqZIiJCxIaN3HxnjCnuW7kyIVOnYPPyMuWiE6Pps6UP5yLOGeJ2m52JtSfyaJ5HjQVuN2x+BVzJxvhjL0P2opbzSkh20nvZAW5FJRjiuQJ9mNm+Ml4e+jUmIiIiIiIiIvJbdYrn4MM2lbCn+PY7JtFJ10X7+PzINetChwc8Nw2ypdhd6/Yv8N1Y0+Xdy3VncBXzMUQXIy/S4csOnAo7Zcp5FShAgaVL8Myb15S7OW4cdxYsTP3BRORfSW+ARUT+5aK++45rI0bcazT+hnfJkuSbPQu7n5+pJj45npe+e4njd8zbvY6qOYoGBRqYb3TqSzi71RgLyge1Xracl9vtZuT64xy6FG6Ie3nYmdOhCsEBPmk+l4iIiIiIiIjIv1WjcrmZ3b4K3ik+Ak9yuhn4ySEW77pgXejpC81mgS1F62DXNLj8o+nyLmW7MKzqMFM8NC6Uzps7s+/GPlPOKyTkXjMzf35T7tZ773F79pzUH0xE/nXUyBQR+ReL3bePqy8PAqfTEPcskJ/88+fhCDSfV5nkSmLY9mGWf4gOrTqU5sWam2+UFAebXzXHnxoDXuZGKcCiXRdYuf+yKT6+eTkq5Mts/UAiIiIiIiIiIgLAU2VysbRbDQJ8PAxxtxve+vw4y/ZctC7MVw0eHZAi6Ib1fe6940mhY5mOvFbjNWwYl4BGJ0XT+5vebLm4xVTjmScPBZYuwatQIVMudMoUQqdNx53io3sR+XdSI1NE5F8q7vhxLvfpizvBuG2rR86c5F+wEI/s2U01LreLkTtHsu3KNlOuZ/medCrTyfpmu6ZDeIo/jgvVhtJNLS/fduoW72z82RTvUqsgz1cJsb6HiIiIiIiIiIgYVC+UlVW9ahIc4G3KvfnZT2xIbZvZuq9B9hLG2J0zsNV8NBFA25JtmVh7Ih52Y9M00ZXI4G2D2XB2g6nGM2dOCixZjFfRIqbc7RkzCJ0yVc1MEVEjU0Tk3yj+55+53L0HruhoQ9yROTP5F8zHK8R8ToHb7ebdH99l47mNplybEm3oX7G/9c0u7YEd7xljNgc0mgg2m+ny0zejGLD8EK4Uf6fWLJyN1xqXSvvBRERERERERETEoFTuQNb2eZRC2TMZ4m43DFp5mG2nbpmLPH2st5jdPR1OmJuSAA0LNWTmkzPx8zDuvuXGzdu737Y8M9MjRw4KLFmCd4kSptydOXO4PW1aOk8nIv90amSKiPzLRGzYyIUX2+G8e9cQt/v5kW/eXLyLFrWsm3lkJitOrjDFnyn8DK/WeBWbRVOSWydgeStwGld9UqMXBJubkmExiXRbvJ+ohGRDvEA2P2a2q4ynQ7+2RERERERERER+r3xZ/VjZ8xHyZzU2GZNdbnovO8D+C2HmopAqUGugOf5p73vvfCzUzFOTjxp+RFafrIZ4gjOBoduHEpsUa6rxyJqV/Is+wru0+V3R7ZmzuLNoUeoPJiL/eHojLCLyL+FOTubmhIlcGzYMd3y8IWfz8iJk5kx8y5WzrF3681JmH5ltitcJqcM7td7BnvLrPICIK7DseYiPMMb9c0LdEabLE5Nd9F52gEthxj9oA3w8WNCpGlkyeaXzhCIiIiIiIiIikprgQB+Wdath2mY2PslFl0X7+PlapLmo7quQM8X7osRoWNEWYi2an0DpbKVZ1mgZOXxzGOIXIi8wZs8Yy+1iPbJkocBHH+Fj8W7q1rsTCF+7Np2nE5F/KjUyRUT+BZLv3uVSjx6EffSROenhQd7Jk8j0SA3L2vVn1jNx30RTvGrOqrxf53087Z7motgwWNoCIq8a455+0GYF+AQZwmExifRZdoAfzxv/AHbYbcx4sTJFg/3TfkAREREREREREUlX/mx+LO1WgyBf4/ucqPhk2s3fw09XU3yQ7uENbZaBbxZj/O55WNsNnMZdtf4rX2A+JtSeYPr4fcO5DXx29jPLGkdQEPkXLsCndGlT7vqbI4nc/FU6Tyci/0RqZIqI/MM5w8O52PZFYnfvMeUc2bJRYNFHBDz5pGXttxe/5a1db5nipbKWYlq9afh4+JiLkuLufZV3O8W5B3YPaLX03rYkv7Hjl1CenrKDb0+az2MY+WxpahfPYYqLiIiIiIiIiMgfUyJXAAs7V8PX02GI341N4sV5ezh8OdxYkKUgtFwENuP1nN0K345K9T7VclWjd4Xepvi4veM4G37WssYREEC++fPwKlzYmHC5uDpsGNE/7Ez1fiLyz6RGpojIP5jb6eTqkKEkXrhgyvmUK0ehtWvwq1rVsnbP9T0M2zEMl9tliBcKKsTsBrPx97JYJelMgtWd4bK5aUrTGVCs/q//Gp/kZPSGn+m48EdCoxJMl7d/JD8daxZI8/lEREREREREROT3q1IgC3M6VMHTYTPEI+OTaT9/L/tSnplZuC48NcY80K5psN9iB7D/6FmuJzVyGXcBi0uOY8i2IUQnRlvWeGTNSv6FC/DMk8eYSEriSv/+xOz9MdX7icg/jxqZIiL/YGHTpxOz0/ylWlDz5hRYthTPXLks646GHuWlrS+R5EoyxHNnys3cBnNNB7YD4HLB+j7wy2ZzrsFoqNDm13+9G5NIi5m7WLjzvOX9G5XNxVtNymCz2SzzIiIiIiIiIiLy/1O7eA7mdKiCl4exTRCdkEzHBT+y68xtY8EjfaB8G0w2vgz7F1rew2F38G7td03vks5GnKXH1z2ISIiwrPPMlYv8CxfgyJ7dEHfHx3O5d29iflQzU+TfQo1MEZF/qMQdO7g7f4EpnmPQIHKPG4vd29uiCk7fPU2fLX2IS44zxLP6ZGVug7nkymTR/HS7YdMwOLbanHukHzz6kiH0xmc/8fN18wHy3h523mlahpntKuPp0K8oEREREREREZG/Ur2SOZnfsSo+nsb3MHFJTros2sfRK+H/C9ps0GQK5KlkHmjjIPhxnuU9svtm593H38WG8YP1n+78RNevunIn7o5lnVfBguRfMB97YKAh7o6L43LvPsTu35/u84nIw09viUVE/oGcFy8SM/5dUzzwuSZk69kj1ZWOV6Ku0OubXkQmGpuMAZ4BzGkwh4JBBa1vuHUM7Jtvjpdvc2/bkd/cb9fZ23xx9Lrp0tK5A9k44DE61CyolZgiIiIiIiIiIvdJ7eI5WNSlOn5exjMwE5JdDFp5mPgk5/+Cnr7Q+mPIbHEc0JdDYc8sy3vUzFOTPhX6mOK/3P2Fzps7czPmpmWdT4kS5J83F7u/8Ygjd2wsl3r2IvbgwXSeTkQedmpkioj8w7iio4l+402IM66o9C5Vitxvv51qkzA0NpQeX/cgNC7UEPdx+DD9yemUzFrS+oa7psH375vjJRpD0+lg/9+vmmSni7c//9l0aedHC/Jpv0cpljMgnacTEREREREREZE/2yOFs7G0W3UCvD0M8bOhMUz+5hfjxUF5ofMXkKWQeaDNI+69K7LQu0JvupfrbopfiLxAp82duBJ1xbLOt0IF8s2biz1TJkPcHRvL5e49iDt8OPUHE5GHnhqZIiL/IK6EBK4PHoLr8mVD3BEURMi0D7H7+lrWRSRE0GtLL65EG/9g9LB5MKnuJCrnrGx9w4NL4Os3zPGCj8MLH4HD0xBetucip25GGWIVQoIY+WxpvD2MX/2JiIiIiIiIiMj9U6VAVhZ0roY9xTfw874/x8FLd43BzPnuNTOzFjEP9PUbsGXUvaOIfsNmszGw8kBeqvSSqeRq9FW6fNWFy5GXTTkAv0qVyDdvHnY/P0PcFRvL5V69STh7Nt3nE5GHkxqZIiL/EO7ERK4OfJm43buNCZuNPB98gFdIiGVdbFIs/b7tx+m7p41l2Bj3+DgeD3nc+obH18OGgeZ4nsrQdgV4+hjCd6ITmJTyCz5g1HNlsKf8C1lERERERERERO676oWy0uPxwoaYyw1DVx8xbjEL/1uZma2YeaAfJsNn/cGZbEr1KN+D4dWGm+I3Ym6k3cysXIl88+ZiS9HMdEZEcKl7D5Ju3Ejn6UTkYaRGpojIP4A7OZmrQ4cRvW2bKZfj5Zfxf6yWZV2iM5FB2wZxJPSIKffGI2/QqFAj6xue+RbWdge3yxjPXgLarwVv8xax7311ish44x+vL1QJoVL+LNb3EBERERERERGR+25Qg+IUyWHcxvVcaAwffH3KfHFgbui88d47oZQOL4OV7SAx1pTqULoDI2uOxIbx4/absTfp/FVnLkVespybX5Uq5J87B1uKXceSr1/nUvfuOMPD0344EXnoqJEpIvKQczudXBvxKlFff23KBT7fgmw9e1jWOV1OXv3+VXZd22XKDaw8kFYlWlnf8NJeWNkeXEnGeOb80HE9+GU1lRy9Es7K/cav6QK8PXilYSrnboqIiIiIiIiIyN/Cx9PB+y0rmLaYnf/DeQ5cDDMXBOSCLl/e26UrpV82w5KmEGuua1m8JWMfG2tqZt6KvUWXr7pwMfKi5fz8qlYlZOoUcBiPKUo8c5bLffvhio9P8/lE5OGiRqaIyEPMlZDA9dffIHLjRlPO66kG5Bg5EpvNvG2r2+3mnT3v8PVFc/Ozc5nOdCvbzfqGN36C5S0hKcWXdJmCocN6CMxjKklMdvHmZ8dTHovAwPrFyBHgneqziYiIiIiIiIjI36NS/iz0rG08/9LthsGrjhARl2QuyJQdOm2AIk+ac1d+vPdRvMU2s02KNGHsY2Ox24ytiluxt+i6uWuqKzP9a9cm99gxpnjcwYNcHfgyrpiYNJ5ORB4mamSKiDykYg8e4nyz5kSsX2/Kedatg9/wV7DZzf+Zj02K5ZUdr7D29FpTrkWxFgyuMtiy+cmds7C0OcRHGOM+QdDhU8hmPtzd5XIzbM0RjlwON8SLBvvT6dGCaT2eiIiIiIiIiIj8jV6uX4xiwf6G2MU7sbz8ySGcLre5wNsf2n4C5Vubcxd3wrbxlvdJtZkZd4seX/fgRoz12ZeZmzUjeOgQUzx6+3bOP/8C8SdOpPJkIvIwUSNTROQh44qN5eb48Vxs147E8+dN+UxPPEGmN97A5uEw5a5GX6Xjpo5surDJlGtQoAEjH7FewUnkNVjaDGJuGeOeftBuDeQqaznXcV+e4LPD10zxUU3K4OnQryARERERERERkQfVf7eYdaTYY/a7U6HW52UCeHhBs9lQs7859/0HcHarZdmzhZ9l3GPjTM3MazHX6PVNL+7G37Wsy9qtG1k7dTLFEy9c4ELrNoR9/DHulNuEichDRW+RRUQeInFHj3KuaTPCFi/BtFcrkOnxx8n1/nvYPDxMuT3X99BmYxtO3TX/oVkzd03effxdHHZz85PYsHsrMcNTbOVh94TWyyBfdcu5zt1xlvk/mButL9bIz2PFsqfyhCIiIiIiIiIi8qCokC8zw58uYYrP3HaWjUfNH68DYLfD02OhUvsUCTes6wlR1issnyn8DO8+/q6pmXku4hy9t/QmOjHaVGOz2Qh+ZTiBzzUx5dyJidx8ZwxXXxqIMzLSeq4i8sBTI1NE5CERvXMnFzt1JunyZXPSbidrly6EzJiOzcvLlF5xcgW9v+lNeEK4KVc7pDZTnpiCl8NcR0IULHseQk8a4zY7PD8filqcewCsO3iFcV+eNMXrlwpm9HNlLGtEREREREREROTB07N2YZ6rkMcUH7b6KD9fS6NB2Og9yFHKGIsJhbXdweW0LinUiFE1R5niP9/5mQFbBxCfHG/K2ex28owfT/aXBtxroqYQ9c03XOraDVdsbOpzFZEHlhqZIiIPgchvvuFK7z644+JMOa+iRSi4Yjk5XxmO3aKJufnCZsbtHYfTbf4DsWf5nkyrNw0/Tz/zTZPiYUVbuHbQnGsyFco0s5zrtlO3GL7mqCleOX9mprWtjIe2lBUREREREREReWjYbDYmPF+eMnkCDfG4JCc9l+4nLCbRutDLD1ouunc00W9d+B52vJfq/ZoXa87QqkNN8f039zNk+xASneb72RwOcvTtS4HFi/DImdOUj//pJ64OHYbbad1AFZEHl94mi4g84CI++4yrLw/CnZRkTDgcZOvTm0Lr1uFboYJl7ZnwM4zcOdIU9/XwZXLdyQyoNMC0XQcAzmRY0/XeH5YpPTUGKne0vN/+C2H0XnaA5BQHvhcN9mdh52r4ellsXSsiIiIiIiIiIg80Xy8HczpUIWsm40f0V+7G0W7+3tSbmcElofH75vi2d+Ho6lTv16lMJ3qW72mK77iygyHbh5DkTLKoAr9q1Si0/lP869Qx5aK3buXmhAmp3lNEHkxqZIqIPMDCli/n2isjIMXXYvbAQAosXUrwwIGWqzABopOiGbJjCHHJxlWc+QLy8XHjj6lfoL71TV0u+HwAnPrCnHt8CDw6wLLs52uRdFm0j/gklyGeK9CHJV2rk9nPep4iIiIiIiIiIvLgC8nix8x2lfGw2wzxE9cjeXHeHm5HJ1gXVmoHFdqmCLphXQ84sDjV+/Wv2J+2JVPWwbbL29JsZnpkyULI7Flk7drVlLu7ZClhS5amek8RefCokSki8oC6PWcuN0e/Y4o7smWjwJLF+FWulGqty+1i/NHxXI42nqeZxTsL85+aT7EsxawL3W746jU4stycq9oN6r1pWXb+dgwdF+4lKj7ZEA/y9WRx1+rkyeyb6lxFREREREREROTh8EjhbLzVpLQpfvJGFG3n7iE0KpVmZuP3IXvxFEE3bHgJ9syyLLHZbIyoPoLnijxnyn13+TuGbh9Kksu6mWmz2QgeOoTAxo1MuZvjxxO1dav1PEXkgaNGpojIA8btdnPrgw8InTzZlPPInZsCy5biU7JkmmMsPbuUH2//aIjZbXbeq/MeefzNh7P/asd7sNfij8eyL9z7g9NmM6WuR8TRfv5ebkcbtxDx83LwUZdqlMgVkOZcRURERERERETk4dGhZkGGNEjZlITTt6JpM3c3tyLjzUXe/tD2EwiweC+1eQR8/4Hlvew2O6MfHU3jQo1Nua2XtzJs+7DUm5l2O7nHj8e3cmVjwu3m6pChxO7bZ1knIg8WNTJFRB4gbpeLG6NHc2fefFPOq0ABCn68DO9ChdIcY+vlrSw7u8wUH1R5EDVy10i9cO9c+G6sOV7sKWg+G+zmXxlhMYm0n7+Xq+HG7Wu9HHbmdqhK5fxZ0pyriIiIiIiIiIg8fAY8WYzhDUuY4mdDY2g1ZzeXw2LNRdmKQNdNkLmAOfftaNg65t5uYSk47A7GPjaWRoXMqyu/vfQtr//wOi63y5QDsHt7EzJjOp4F8hvi7rg4LnXvQfT27ak8oYg8KNTIFBF5QLiTkrj2ygjCV3xiynkXL06Bj5fhmSeN1ZTArmu7eHXnq6Z4w4IN6VSmU+qFR1fBpmHmeP5HoeVicHiaUlHxSXRa+CNnQ2MMcbsNPmxbkceKZU9zriIiIiIiIiIi8vDqW7corzU27xp24U4sLWbt4udrkeaiLAWhyybIZnHs0Y73YOs7ls1MD7sH4x4bR6OC5mbmpvObeH//+7gt6uDemZn558zBkTmzIe5OSOByv/5EbtpkWSciDwY1MkVEHgDJd+9yuV8/IjdsMOV8KpSnwJLFeGRPuzG478Y+Bm4daNpOo2jmorz96NvYLLaFBeDUJvi0tzmeqxy8+Al4+ZlS8UlOui/ez7GrEabcu8+Xp2HZ3GnOVUREREREREREHn49axfhzWfNZ2aGRiXQes5udp+9Yy4KygtdvoTgMubc9x/At2+n3sx8fBwNCzY05Zb+vJRFxxelOk+vggUJmTkTu1+K91zJyVwdMpTwNWtSrRWRv5camSIif7PY/fs537wFMTu+N+X8atQg/4KFpi/GUjp86zD9v+1PvNN4BkFm78xMfWIqfp7mZiQA57+HVZ3A7TTGsxWF9p+CT5CpJMnpov/yg+w9H2bKvfFMKVpVzZfmXEVERERERERE5J+j22OFeKdpGVJ+Qx+VkEynhT+y6dh1c5F/MHTeCHkqmXM/TIYto1JtZo5/fDx189U15SYdmMSGs+ZFAv/lV7kS+Rd9hCMoxfsul4vrb7xJ2JKlqdaKyN9HjUwRkb+J2+nk9qxZXOzYieQbN0x5/yeeIN/cOTj8M6U5zvE7x+mzpQ+xycazB/w9/Jn5xEzyB+a3Lrx2CFa0BWeCMR6YFzp8Cv45TCUul5vha46y5cQtU+6lekXp/njhNOcqIiIiIiIiIiL/PB1qFmRa20p4OozdzESni77LD/LpoSvmIr+s0GE95K1izu2cAt+MTLWZObH2RCrmqGjKjdw5kh+u/pDqPH3Llyf/0iU4cph3Prs5bhwRX3yRaq2I/D3UyBQR+Rs4w8O51L07oVM/BJf5MPLAJk0I+XAqdm/vVMdwu92sP7OeHl/1IDop2pDzc/gxrso4SmY1n1MAwPUjsOx5SIwyxv2y3fsDMrO5+el2uxm14TifHrpqynWqWYBBDYqnOlcREREREREREflne7Z8HhZ1qY6/t4ch7nbDiLXHOHHd4sxM38z3PqjPW9Wc2/UhbB1jeS9fD1+mPzmdIkFFDPFkdzIvf/cyq06tSvXMTJ/ixSn48cd45s1ryl1/9TViDx60fkAR+VuokSkicp+5k5O5MuAlYnfvMeVsnp7kfPMN8kycgM3TM9Uxrkdfp8+WPry5802ikozNSB+HD2OqjKFU5lLWxUdXw4KnITbFGQVeAdB+LeSwbkhO/uYXluy+aIo3q5iHt5qUSf0MThERERERERER+VeoVTQ7n/R8hOz+XoZ4QrKLfssPEpOQbC7yCYIO6yCkmjn3/fuwa5rlvYK8g5jdYDY5/XIa7+VM4J097zB0+1AiEy2ap4BX/vwU+HgZXoUKGeLuxESu9O1H4oULqT+kiNxXamSKiNxnoTNmELtvnynuVaAABVd+QtZ27VJtCrrcLladWkWzz5qx89pO8xh2LybXmUy5LOXMxc5k+Op1WNcdkuOMOYc3tF1hfS4BMP/7c3y49YwpXr9UMO+1rIDdriamiIiIiIiIiIhA2bxBrO3zKMEBxp3GzoXG8Ob6n6xXSvoEQft1EFLdnPv6DTi4xPJeuTLlYnb92QR4BZjLLn5Nqw2tOBp61LLWM1cu8i+Yb9pm1hkezqVevUi+ezeVJxSR+0mNTBGR+yhm1y7uzJ5jigc2aULBtWvxKV061dpEZyJDtw/lnT3vmM7DBAjwDODDeh9SI1cNc3FsGCxrAbunm3N2T2i5CAo9bnnfVfsvM+aLE6Z4jUJZmf5iZTwd+lUiIiIiIiIiIiL/UyBbJj5sW4mU376vO3SV1QcszssE8Am8t1uY1Yf2GwbC8U8ty4pmKcrs+rPJ4ZvDlLsafZVOmzrx6WnrWs88ecg3azY2X19DPOniJa70648rPt56riJy3+jts4jIfZJ06xZXhw03HVIe1KIFeSZOwOGfKdXa2KRYBmwdwDcXv7HM1w2py6dNP6VW3loWxWGwoAGc327OZQqGThugZGPLcTf/dJ0Ra81frZXLG8T8TlXx8XSkOmcREREREREREfn3eqRwNgY+aT7CaORnP/HLzSiLCu41M9uthRwljXG3C9b2gNNbLMvK5yjP6iarqZXH/G4s2Z3MqN2j2HFlh2Wtb9ky5P3gA7Ab2yVxBw9yoe2L2mZW5G+mRqaIyH3gdjq5Nmw4zjvGcym9ihYh1xuvp3m+ZFRiFL239GbXtV2mXGbvzEx4fAIf1vuQnJlymotdTljbDe6Yt4UlbxXouQ0K1LS87w+nb/PSisO4Uuz2USRHJhZ3rU6AT+pneIqIiIiIiIiIiPSvV5RHi2QzxOKTXPT7+CBR8UnWRZmyQYdPIXN+Y9yVBCvbwzmLj/WBbL7ZmFl/JoOrDMbD5mEsdbsYtn0YJ8NOWtYG1HuCnK++aoonnDjB+RbPE7Hxi1SeUET+ampkiojcB7dnzSZ2715DzObjQ8jkydj9/FKtC4sPo9tX3Th065Ap999VmI0LN061EWrfPh7ObjUnKraHzl9CUF7LukOX7tJz6X4SnS5DPG9mX5Z1r0HWTF6WdSIiIiIiIiIiIv/lsNuY0qYi2f2N52WevhVNu/l7CY9NtC4MzAMd1t/bTey3kuNgeWs4b7260m6z06VsFxY3WkyuTLkMudjkWPpt6ceNmBuWtVk7tCdLxw6muCs2lmtDh3L9zTdxxcVZz1dE/jJqZIqI/MUivviC2zNmmOK5Ro7Eu1ixVOtuxtyk8+bOnAgzn0/ZolgLpjwxhey+2S0q7/E+9zWOXVPMiSdHQtPp4OljWXfqRhSdP9pHbKLTEM/u78Wy7jXIHeRrWSciIiIiIiIiIpJScIAPU9tUJOV3+EevRNBm7h5CoxKsC7MVubcy0yfIGE+Og49bwfnvU71n+RzlmdNgDoFegYb4rbhb9P+2PzFJMZZ1OV95hazdulrmwlev4UKbtiRdu5bqfUXkz6dGpojIXyjym2+4NvwV87mYTZuSuUXzVOsuR12m0+ZOnI84b8p1KN2BUTVH4bCnfj6lI+wMQd+9Yk6Ubw2PDcb0l+N/XLoTS4cFe4mIM27tEeDjweKu1SmUPfVzPEVERERERERERKzUKpqdly3Oyzx5I4rWc3dzPSKVlY65ykL7T8Hb2JC8tzKzFVz4IdV7Fg4qzJQnpuBhN24ze+ruKYZsH0KSy7y1rc3hIOewYYTMnoUjc2ZTPuHUKc63ak3c0aOp3ldE/lxqZIqI/EWitm3j6uAh4DSubPQqXJhcI99Mte7M3TN02tSJq9FXTbm+FfoyrOqwNM/UJD6SLF/1w54Ua4znKgfPTkm1iXkrMp72C/ZyK8VXcD6edj7qXI0yeYIs60RERERERERERNLz0pNF6fdEEVP8XGgMrebs5nJYrEUVEFLl3srMlM3MpFj4uCVc3J3qPavlqsboR0eb4juv7mTItiEkOK1XgwbUrUuh9Z/iW6WKKee8fZuLHToSuWlTqvcVkT+PGpkiIn+B6B92cvWlgZBk/LLLIziYfLNnYc9kvbLx+O3jdP6qM6FxoabcsKrD6FOxT9pNzKR4HOu64BFxwRj3zQKtPwYv6/M4w2MT6bDgRy6l+IPR02FjdvsqVC2YNfV7ioiIiIiIiIiIpMNmszHs6ZIMe7qEKXc5LI5Wc3Zz8Y71lq+EVIX268ArwBhPioUVreHmz6net0mRJvSt0NcU/+7yd/T7th+xKRcD/IdnrlwUWLyIbL16mXLuhASuDhrM7VmzcKfYiU1E/lxqZIqI/IncbjeRX37Jlf79cScaDyt3ZM9O/kWL8Mqf37J23419dPu6GxEJEYa4DRujao6iY5mOad88ORFWdcR+frsxbrPDCwshSwHLstjEZLos2sepm1HGMhtMalWRuiWCLetERERERERERER+r35PFGXks6VN8esR8bSZu4cLt1NpZuarBh0smpnxEbDseQi/nOo9e1fozXNFnjPF917fS89veprex/2XzcOD4EEvk+eD97F5eZnyoVM/5Na7E1K9r4j8/6mRKSLyJ4k/cYJLHTpydfAQ3PHxhpwjc2byL1yAd+FCpjqX28XCnxbS4+sepoPGPWweTKw9keeLP5/2zZ3JsLYrnP7KnHtyJBSpZ1mWkOyk19IDHLoUbsqNaVaWJhXypH1fERERERERERGR36nrY4UY36Kc6QSk/zYzz6fazKwO7deCZ4rdzqKuwbIWEBtmWWaz2Rj16CgaFWpkyh0JPUK3r7pxO+52qvMNeuYZCixZjCNbNlMubPFiIj77LNVaEfn/USNTROT/KfnOHa6/OZLzLZ4ndv9+U94eGEi+BfPxKW4+0PxO3B36bunL5AOTcbqNZ2l6O7yZWm8qDQs1THsCLid82gtObDCnKraHWi9bljldbgatPMz3p81/pA1vWIJ2NaxXcIqIiIiIiIiIiPx/ta2en0mtKmBP0cy8ERlPm7m7U29m5q8BrZeC3cMYv/0LLG8NialsFWv3ZPxj43mh+Aum3Km7p+j2lXmntN/yrViRQqtW4l2smCl3feRbxP+c+va2IvLHqZEpIvIHud1uwtes4WyjxoSvXg0W++HbM2Ui/7y5+JYpY8rtu7GPlhtasvPaTlPOz8OPWfVnUTukdtqTSIqDz/rBT2tMqbjiTXE2+gDTp23/mftr647x5bEbplyv2oXpU8d88LqIiIiIiIiIiMifqXmlECa1qmhqZt6MTKD1nN2pbzNb9EloNsscv/IjrOoICVHmHOCwOxj5yEi6lOliyp2LOMfgbYNJcialOl/PvHkpsGI5vhUqGOLuhASuDHiJ5Lt3U60VkT9GjUwRkT8g8eJFLnXuwvU33sQVGWl5TabHH6fQ2jWmP2wAvjj3Bd2/7k5oXKgpF+IfwkcNP6JarmppT+LSHpj9OBxZYUrFFW5IRN1xYHeYcrGJyQxYcYiV+83nBrSplo8RjUpis2h+ioiIiIiIiIiI/NmaVcrL5NbmZuatqATazd/L9Yg468LyreCpMeb4mW9gTm24dtiyzGazMajKIAZUGmDK/XjjR0btHoXbYsHCfzn8/cn74Yc4cmQ3xJOuXuXakKG4nc5UKkXkj1Aj8z6Ji4tjw4YNDBo0iMcee4zg4GC8vLwIDAykVKlSdOnShW+//fZ3j7t79266du1KkSJF8PPzI2vWrFSpUoUxY8Zw+3bqe3qLyB/jTk7m9rx5nHuuKbF791pe41WoEPnmzCb/vLl4FSxoyp++e5pRu0bhcrtMuacLPs2qJqsonc184PmvEmNg0whY2BDunDalXcUbEfHk++btNYDLYbG0mLmLjUevm3LPlMvN2Obl1MQUEREREREREZH7qmlF62bm1fA42s/fy53oBOvCRwdAzf7meNg5WNAA9sy23EXNZrPRs3xPBlYeaMp9fvZzZh+dneZ8PXMGEzJlCngY37/F7NpF6JQpadaKyO9jfsstf7qPP/6Y3r17Ex0dbcolJSVx8uRJTp48yaJFi2jYsCFLliwhR44caY7pdrsZMmQIU6ZMMXwdEhcXx927dzl48CDTp09n+fLl1KtX709/JpF/I1diIlf69CVmp3krWAC7vz/Z+/cj64svYvPysrwmNimWIduHEO+MN8S9Hd68Uv0VXij2QtqNxCv7YW03uHvBOl+0Ps7m8yHC/N+bnWdu02/5QcJjzdtjPF4sO5NaV8CR8q9FERERERERERGR+6BpxbzYbTYGfnII1296j2dDY+j80T6W96hBgI+nubDBOxATCkdXGuPORNj8CpzfDs1mgm8WU2m3st24Fn2N1b+sNsRnHp5JiH8ITYo0SXW+flWqkHPECG6OMa4KvTNvPl6Fi5C5ebN0n1lE0qcVmffB+fPnf21i5s6dm44dOzJt2jRWrlzJwoUL6dKlCz4+PgBs3ryZ+vXrExtrfSDxf7366qtMnjwZt9tNpkyZeOmll1i2bBmzZ8+mQYMGANy8eZOmTZty+PDhv/T5RP4N3G43119/I9UmZkCD+hT+4guyde6cahMTYOzesZyPOG+IZffNzvJnltOyeMu0m5hnvoXFTaybmDb7vS/Q2iwHD29TevGuC3RYsNeyiVmvZDBzOlTB28O8Da2IiIiIiIiIiMj90qRCHt5tUd4UP3Y1gm6L9xOfZLFtq91+77zMem/ce0eW0qkvYUlTSDB/+G+z2XitxmvUylvLlBu5aySbz29Oc75Z2r1IUNOmpvj1N94gaut3adaKSMaokXmf1KpViw0bNnD58mUWL15M//79adWqFV26dGHhwoUcOHCA3LlzA3D06FEmTJiQ6liHDh1i4sSJAAQFBbFr1y6mTp1Ku3bt6NWrF19//TVvvfUWANHR0fTs2TPNPb1FJH2hU6YSuWGDKe7IkZ28H04lZNo0PHMGpznG+jPr+fzs54aY3WZnwuMTKJ6leNoT+PlzWNEGkiw+cshRCrptuXcmgEUTc+PRa7z1+XHDl2z/NaBeUeZ3rIqflxboi4iIiIiIiIjI369VtXy88UwpU/zH82H0WXaAhGSrZqYDag+Dzl9CYF5z/voRWNsdXOZaD7sHH9T5gBJZShjiya5khu0Yxju73yHBab21rc1mI9fbo/AunWK+TidXBw0idt++1B9URDJEjcz7oF+/fvzwww88++yzOBzWK55Kly7N3Llzf/33RYsWpTre6NGjf21Mjhs3jvLlzV+ovPXWW1SvXh2Affv28eWXX/4/nkDk3+3uylXcmTPHFA9s3IgiX3xB4FNPpTvGmbtnGLtnrCneu0Jvqueunnbx4RWwutO97TB+y+4BdV6BXtshpIpl6akbUQxbfdQUz+TlYHb7ygx5qgR2bScrIiIiIiIiIiIPkO6PF+alekVN8e9OhdLv44MkJrusCwvUhN4/QInG5twvm2Dzq5ZlmTwzMf3J6QT7mRcqrPplFe2+aMeFiAuWtXYfH/LNmIHHfxYq/Zc7IYHLffoSf+KE9VxFJEPUyLwPsmQx771tpVGjRmTKlAmAS5cuERkZabomKiqKTZs2ARAYGEjnzp0tx7LZbAwYMODXf1+5cqXldSKStujt27kxerQp7l+nDnkmTsQRGJjuGFGJUQzdPtR0LmaNXDXoWa5n2sU/zoP1vcGd4o8zn6B7X5g98ZrlKkyAyPhk+q44TFyKLTcKZPPj0361aFg2t2WdiIiIiIiIiIjI321Qg+J0frSgKb7lxC36ptXM9Mt67/ilWi+bcz/OgT2zLMtyZcrFzCdnEuQdZMqdunuKVhtb8eU56wVDnrlzk3/BfByZMxviruhoLnXvQdiSpUR//wOJV67idqUybxGxpEbmA8ThcODn5/frv8fFxZmu2b59OwkJ95ax165d23B9Sk8//fSv/7x5c9p7eYuIWdyxY1wZNBicxkagT5ky5J30ATaP9LdjjU2Kpe+WvpyNOGuIZ/PJxru138VhT+Ncyu8/gC+HmuOZctxrYuavkWqpy+1m1ObzXAoz/nckayYvlvd4hOI5A9Kdu4iIiIiIiIiIyN/FZrMx8tnSPF85xJTbcuIm/Zan0cy02aD+KCj7gjm3+VU4+YVlWYmsJVj17CrK5zDvghiXHMcr37/Chwc/xJVy0QHgXbgw+ebNxZ7inb3zzh1ujhvH5R49OFu/PqcqV+Hq4ME4o6Ks5y4iBmpkPkBu3bpFaGgoAH5+fuTIkcN0zU8//fTrP1epYr2V5H/lyJGDAgUKABAaGsqtW7f+xNmK/LPFHTvGpa7dcMcaz6T0zJuXfLNnYf/P6uk0x0iOo//W/hwOPWyI27Dxbu13ye6b3brQ7YZv3oJvzStBCQyBLpshV9k07z1/z3V2XTCu6rbbYHrbSuTN7Jvu3EVERERERERERP5udruNiS+Up1nFPKbcNz/fZMCKgyQ502hmNpsJ+WumSLhhTTe48INlWR7/PCxquIguZbpY5ucdm8ew7cOISzYvRPItV46QGdOxeXqm+kzu+Hgiv9zEtWHDfz1CTkRSl/5yIrlvfntGZsOGDbHbzX3mX3755dd/LliwYLpjFihQgIsXL/5aGxxs3uM7PQcOHEgzf+I3e3wnJyeTlJT0u+8h8iCJP3aMa71640rxVZQ9MJDcM2fgzpw53f9/nuhM5OXtL7PvhvlA774V+lIlexXrMdwu7JuH4zi4yJzKWpjkF9dBUAikcf9Nx66xcO91U3z408WpViBIP6Mif4OkpCSSk5N//WcR+XvpZ1LkwaKfSZEHj34uRR4s+pmEd5uXwelyseHoDUP8q+M3efmTQ0x6oRx2u82i0g7PL8ZjcUNsYef+F06Ow73seZzPf4S7aAPLew6oMICK2Svy1p63CE8IN+S+vvg1V6OuMqnOJHL4GhckeVWtSs4J73Jj6DBIYxvZ6G3buLPiE4JaWqwalQeafibN/vv/Hn8FNTIfEOfOnWP8+PHAvSXzI0aMsLwuPDz813/Onj2V1Vy/kS1bNsva36Nq1aoZvjYiIoI7d+78ofuIPAiST5wgaugwiIkxJry88HtnNFFBQZDO/x9PciUx+vBo9oTuMeVaFmxJ05xNrX9OnEkEbXsV39MbzGNmK8HdZxbiSvZN8/57L0Yy9LMzpniD4lloWsJfP58if5Pk5GSifvNxhEcGtqYWkb+OfiZFHiz6mRR58OjnUuTBop/Je16pm4eEhAS+PnXXEP/i2A0CPFwMrB2CzWbVzATH07PJ9mkr7PHhv8ZsyfE4Vncg/Mn3SSjSyLKutE9pZtSYwVuH3uJMlPGd2/Gw47Tf1J5xVcZR0L+gsbByZTKNfpv4pctwnjuX6qKE0IkTSSheHEdI3rQfXh4o+pk0+6P9p4zQ1rIPgJiYGJo3b07sf7aw7Nu3L9WqVbO8Njo6+td/9vHxSXdsX9//bSEZpT23RdKU/PPPqTYx/ceNw7NChXTHOBJ2hAF7Blg2MZvmb0qP4j0s/6Cyxd8ly+a+lk3MxOAKhD23FJdf2h8vHLwSxfANZ0hyGbekKJLdl9caFEj1DzkREREREREREZEHnYfdxsinC9GgeBZT7pNDt1h24Gaqtc6gAtxtOBuXp/H8SpsrmcxbBuN7ck2qtcG+wUyqPolawbVMudD4UF478BrhieGmnNdjjxE4ZzaZN28icMVy/CdMwCPlcXHx8cSMHYs72Znq/UX+7dQm/o/58+dz5cqVP2WsUaNGZfhap9PJiy++yNGjRwGoXLky77///p8yjz/L/v3708yfOHGCDh06ABAUFGRYBSryMHA7nYQvWUL09BmQmGjI2Xx8yD3tQ/weeSTNMa5GX2XKoSl8e/lby3zzIs15vfrr2G3m70dsl3bjWN8LW9Q1U85V8HFsLywhq3dAmvc/eCmcoZ+fJSHZ2MTM7OvJnA5VCMnql0qliNwPv91mJGvWrHimcVaGiPz19DMp8mDRz6TIg0c/lyIPFv1MGn34YlYGfHKELSdDDfEZP1ylQHAWmlcyn6cJQLb6uDKvx7ayNba4/63qtLldBG17HX8vG65qPVO979TgqUw7PI3FJxYb4qHxoUz8eSIznpiBhz2VlktwMJQtS3L16lx+vgXOsP/d33niBPZPPyVr717pPLk8KPQzaZY5c+a/bGw1Mv9j/vz57N27908ZK6ONTJfLRefOnfn8888BKFGiBJs2bUpzpaW/v/+v/xwfH5/uPeLi/nfgcEBA2o2Q1FRJ+ZVIGjw8PPRDKw+VhLNnufbaa8QfOWrK2Xx8yDdrJplqpjwQ/H/ik+OZe3Qui48vJtGVaHnNM4Wf4a1H38JhdxgTLid8Pwm2jQO3xX75xRthb7kIu2faq6+PXgmn+5KDxCYav9wK8HawuEsViuYMSrNeRO6P/24z4unpqd+VIg8A/UyKPFj0Myny4NHPpciDRT+T/+PpCdPbVaH9/L3sv2jcZvbV9cfJEeTLEyWCrYsL1oDOX8LSZhBtXMHp+Po1HAE5oVzqZ1YOrT6UIlmKMHr3aJLd/zsTcN/Nfcw8NpMhVYekPffcucg9ZgxX+vYzxMNmzyawTm18y5dPs14eHPqZNPort9fV1rJ/E7fbTa9evVi2bBkARYoU4dtvvyU4OJX/wP7Hb7vat2/fTvc+vz0P76/siIs8bNzJydyeN4/zzVuk3sScPSvNJube63t5/vPnmXdsXqpNzBeKv8CYWmPMTcy4u7C0OXw3xrqJWa4VtF4KaTQx3W43q/dfpt38vUQlGA9T9vOyM6V5MUrnDky1XkRERERERERE5GHk4+lgfqeqFAv2N8SdLje9lh5g2Z6LuN1u6+KcpaHrZsic35xb3xcumY+M+q3mxZrzao1XTfFFxxex+fzmdOceUK8emVumaJY6nVx4sR2XunXn7ooVJN1MfZtckX8bNTL/Y8+ePbjd7j/lfxnRv39/5s+fD0CBAgXYunUrefOmf6Bv8eLFf/3nCxcupHv9xYsXLWtF/s2Srl7lYqfOhH4wCXeiuQHpERxM/vnzyJTKdrIRCRGM3DmS7l9351LUJctrSmcrzZJGS3ir5lvmLSWcybCyA5zfbi50eEHj96HFXHCk/iXP1fA4On20j2FrjhIVb2xi+nramdS0GGVyZUq1XkRERERERERE5GGW2c+LxV2rkzvIuBAgMdnFG+t/ot/yg0TEJVkXZy0MXTZDtmLGuDMBVrSFO2fTvHfL4i1pXrS5KT5y10hO3z2d7tyDXxmBZ758xmByMjE7d3Lj7dGcqVOXS127kfib9/si/1ZqZP4NXn75ZWbOnAlASEgIW7duJX9+i68/LJQtW/bXfz5w4ECa14aGhv7ayMyRI0e6qz1F/g0iv/ySc82aE5fKz09QixYU3rgBv6pVLfM7r+7kufXP8emZTy3z2X2z806td1jxzAoqBVeynsT2CXDhe3M8axHo/i1U7wE2m2Wpy+Vm2Z6LPDVpOzt+CTXlvTzszG5XiYp5/S2qRURERERERERE/jnyZPZlSdfqBPmaFwR8eewGz3z4PYcu3bWoBILyQod14J/TGI8Lg49fgJg71nWAzWbj9Udep0y2MsbS5DgGfjeQiISINOft8M9EngkTwJ56iyZm1y4utG9PwrlzaY4l8k+nRuZ9NmzYMKZOnQpA7ty52bp1K4ULF85wfd26dfH29gZgx44dhjMwU/rqq69+/eeGDRv+wRmL/DM4o2O49uprXB08BFdUlCnvkTMn+ebOIc+4sTgCrbdj3XdjHwO2DiAsPsyUc9gcdC7TmY3NN9KsaDPstlT+83r2O9jxnjlerhX02g65U98HPyHZSb/lB3lj/U/EpDgPEyDAx4N5HavyaJFsqY4hIiIiIiIiIiLyT1IsZwCLu1YnOMDblLtyN46Ws3ezat9l6+LM+aHtJ+DpZ4yHnYNPXoSk1N+/ezu8mVx3Mlm8sxjil6Mu0+/bfsQmxaY5b7/Klcj15hvYvLxSvcYZepuLnTqpmSn/ampk3kdvvPEG77//PgA5c+Zk69atFCtWLJ0qI39/fxo3bgxAZGQkixYtsrzO7XYzffr0X/+9devWf2zSIg+55Nu3CZ0xg3ONGhHxqfUqyqDmzSm8cQP+tWunOs658HMM/G4gSS7zdhSlspZi+TPLGVJ1CJk809jONeoGrOsBpNiCumL7e1vJegekWhqbmEz3xfvZ9NMNy3z9UsFsGVyHOsVzpH5/ERERERERERGRf6CK+TLz5cDHqVvC/G4s2eVmxLqjbD2ZyrmTeSvD8wuAFDukXd4DC55Kc5vZ3P65ea/Oe6ZFDUdCjzB422CSnKlsbfsfWdq2pej2beR+dzwBDepj8/ExXeMMvc3Fjp1IOJv2drci/1RqZN4nY8aMYezYscC9bV6//fZbSpYs+YfGevPNN7H9Z9vJV199laNHj5quGT16NHv37gWgWrVqPPPMM39w5iIPp7hjP3HtlVc480Q9bk+bTnKoeRtWe2AgeadMJs/4cTgCUm8i3o67Td9v+xKVaFzJ6ePwYUiVISx/Zjmls5VOe0IuJ6ztDjEp5pGjFDR+L9WtZAEi4pLouOBHvj9925TL4ufJ1DYVmdexKjkDzX/oiIiIiIiIiIiI/Btk9/dmYadqvNa4JB5247s2lxv6Lz/ET1dT2fK1ZGNo+K45fuMozKkDx9aket8auWswpMoQU3zntZ289sNrOF3mndV+yyNLFjI3a0bItGkU372LXKPfBofDcI3z9m0uduqsZqb8K3n83RP4N5g7dy5vvvnmr//ev39/Tp8+zenTaR/6+9hjj5E9e3ZTvFKlSgwfPpwJEyYQERHBo48+Svfu3alevTrR0dGsXbuWr7/+Gri3gnPu3Ll/7gOJPMDcbje3JkwkLJXVyv/lW7UKeSdOxDNPnjSvi02Kpd+3/bgafdVY7+HLooaL0m9g/tf2ieZzMT39oOUi8PKzLAG4E51Ax4U/cvxapClXv1RO3n2+HNn9zdtmiIiIiIiIiIiI/NvY7TZ61i5C9ULZ6LvsANci4n/NxSY66bZ4H+v71SJ3kK+5+JHecPc87J1tjCdGwdpu997tNXwXPM21HUp3IDQulEXHFxnimy9sJsg7iNdrvP7r4qQ05+/rS5ZWrXAEBnF1yBBw/q8J6rx9m4sdOpL3g/fJVLNmumOJ/FOokXkf7Nq1y/Dvb731VobqvvvuO+rWrWuZGz9+PAkJCUydOpWYmJhfz938reDgYFasWEHFihV/75RFHlqhkyan3cT08CB73z5k79ULW4ovm1JKdiUzfMdwfr7zsyHusDn4oM4HGW9iHlsD2yeY4898AMGpr8y+HhFH+/l7ORsaY8q1qZaPsc3L4bCn/weQiIiIiIiIiIjIv0nFfJlZ2r0GLWbuIiLuf9u73oxMoMtH+1jduyYBPp7mwqfHgcMTdk0z5w4sgqsH7p2pGRRiSNlsNgZXGUxEQgSfnjEeb7Xy1Eo2nd9EwaCCFAwsSKGgQtQJqUOxLKkfOxfY8Gmw2bg6eLCxmRkWxqWu3cjerx/Z+/RO9/2myD+BtpZ9SNlsNiZPnszOnTvp3LkzhQsXxsfHh8yZM1O5cmVGjx7N8ePHqVev3t89VZH75s6ChdyZN88yZw8IIGvnzhTZ9CU5+vZN95e80+XkrV1vsf3KdlPu9Ude5/GQxzM2qZ8/h3U9MZ2LWeFFqPhiqmUX78TQcvZuyyZm98cKMb6FmpgiIiIiIiIiIiKpKZLDnzkdquDpML5DO3kjiv7LD5HkdJmL7A54agy0XQm+Wcz5G8dgXj24csCUstlsjKw5knr5zO/kIxMjORp6lM/Pfs7Ug1NpuaElWy5uSXP+gU8/Rd5Jk8AjxXo0t5vb06dzqXt3km+bj6IS+adRI/M+WLRoEW63+3f/L7XVmL9Vs2ZNPvroI86ePUtcXBx3797lwIEDvPnmm5bb0or8U4WvXcet994zxb0KFiTXqLcotu07co54Ba98+dIdy+ly8ubON/n87OemXPdy3WlZvGXGJvXL17CmK7hT7IOfvQQ8836qZaduRNFy9m6u3I0z5QbVL87rz5TK0FYUIiIiIiIiIiIi/2aPFM7GxBfKm+Lbfwmlz7KDxCelcn5liYbQ+wfIV8Oci74JixrD8U9NKQ+7BxPrTKRGLou633C6nYzcOZIbMTfSvC7w6acImTIZm4+PKRe7ew/nmjcnatu2NMcQedipkSkiD72oLVu4/ptzaP/Lp3x5Cq1dQ5Y2bbBnypShsZwuJ2/sfIMN5zaYco0LNWZApQEZm9TZ72Ble3AlGeMBeeDFleBlPZ8jl8NpPXc3t6ISTLk3ny3NwPrF1MQUERERERERERHJoOaVQhhUv7gpvuXETbot3kdMQrJ1YVAIdP4Car1sziXHw+rOsOM9cBt3YvN2eDO13lQq5qiY5ryikqJ4/YfXcbktVob+RkD9+hRctRKvwoVNOWfoba707sOVAQNIunYtzXFEHlZqZIrIQ8vtchG2dBlXBw8Bl/EXvleRIuSbMzvDDUy418R8fefrbDy30ZSrHVKbd2q9g92Wzn823W44vh5WtAVnimZkpmDo9DlkLWRZuvvsHdrN30t4rLH5abPBhOfL0e0x6zoRERERERERERFJ3UtPFuX5yiGm+M4zd2i/YC8RKd7H/crhCQ3ehhbzweFtzm8dA1+/YQpn8szEwqcXMuPJGQysPJDnijxH+Rzl8bJ7Ga778caPLPt5Wbrz9ylenEKrVxH4XBPLfNQ3Wzj7zLPcmT8fd2JiuuOJPEzUyBSRh1LCufNcbN+Bm2PHmn45e+TJTf4F8/HIYrGPfSoiEiIYtmMYX5z7wpSrE1KHyXUn4+Xwsqj8jRvHYHETWN0JklNsC+ubFTp+BtnNh3i7XG5mbTtL+wV7iU7xBZiH3ca0tpVoXS1/hp9FRERERERERERE/sdms/Hu8+VoWjGPKXfo0r0d0kItdkj7VfmW0HkjZMphzu2eDkdWmsKeDk9qh9Sme7nujH1sLB83/ph3ar1jum7qwamcvns63WewZ8pEngkTyD3mHWze5qaqOy6OW+9/wNlnmxC2dBnO6Jh0xxR5GKiRKSIPFXdyMrfnzeN8s2bEHTxoyjuyZiX/ggV45sqV4TG3X95O88+a883Fb0y5uiF1mVR3UtpNzOhQ+PwlmP04XPjenPcOgg6fQs7SptTt6AQ6L9rHhM0ncbpSbEPhYWdex6o8W978B5aIiIiIiIiIiIhknKfDzqRWFXmxhnnBwMkbUbSdtyf1lZkA+apD928h2PyOjw0D4frRdOfQuHBjGhVsZIgluhJ59ftXSXSmv5LSZrOR+YUXKLRmNb5Vqlhek3TpEjfHjuVMnTrcGDeOxIsX0x1X5EGmRqaIPDSc0dFc6tKV0A8mWW6R4MiWjXzz5uJdKGNbsEYkRPD6D6/Tf2t/QuNCTfm6IXX5oO4HqTcx3W44sBimVYGDiwG3+Rovf2i/FvJUNKV2nb1No6nfs+MX870zeTlY3LU6T5QMztCziIiIiIiIiIiISNocdhtjm5WlVx3zeZNnbkXTa9l+EpPTOLMySwHo+hWEVDPGk+NgZTuIDUt3Dq8/8jrBfsZ3fqfunuK1H17j8K3D6Z6ZCeBdrBgFli0l9/jxOLJmtbzGFRPD3SVLOduwEbemTMHttnh3KfIQUCNTRB4KzshILnXrRuy+fZb5wGefpfDGDfiWKZOh8XZe3Unzz5rz+dnPLfP18tVLeyXm7TOw6FnY8BIkRFhfU6Qe9NwG+aqZUp8dvkq7+Xstt6zIn9WPlb1q8kjhbBl6FhEREREREREREckYm83GiIYlGfZ0CVNuz7kwRqw9mnbTzycQWi2FTCkWIIRfgrXdwOVM8/5B3kGMqTXGFP/qwld02NSBeqvq8fbutzl061C6z5G5eTOKbPqSzG3bgM1mfaHbzZ3Zc7gzd16a44k8qNTIFJEHnjM8nEtduxF/xLw9g0dwMCEzZ5L3/fcydCZmojORifsm0ntLb8tVmN4Ob4ZWHcqkupPwdHhaTCYJdrwPsx6Fiz9Y3yRrEWi7EtqvszwT8+SNSIavOYrV30PPls/Nxpceo2zeoHSfRURERERERERERH4/m81GvyeK8krDkqbcukNXmfptOmdWBuaGVovB7mGMn90K341N9/4189Skfan2lrk78XdY88saOm7qyAf7P0h3JaUjKIjcb71FofXrCXq+BTYv64UZoZMnE/nll+nOTeRBo0amiDzQku/e5WKXrsT/9JMpF9SsGYU3biCg3hMZGutc+Dle/OJFlv681DJfIUcFVjdZTacynXDYHRaTSYCPX4Ct74DT4vBv70B4aiz03QMlGlp+BRWTkEy/jw+SkGKLCm8PO+NblGNa20oE+lg0UEVERERERERERORP1btOYdpZnJk5Zctp1h64knZxgUfh6XHm+PcfwJFP0r33wMoDKZbFvAjitxYdX8TEfRMztC2sT4ni5Bk7lqLbt5Fj0CA8cuY0XXNtxKvEHkp7pafIg0aNTBF5YCXfvs2lTp1JOHHClMvWowe5x4/DERiY7jgxSTEsPr6Y1htbc+ruKVP+v6swFzdcTKGgVM7XdLlgfV84t806X+o56L8PHu0PHtZfPbndbt5c/xNnQ2MM8RwB3nzWvxZtq+fHltoWECIiIiIiIiIiIvKnstlsvP1cGeqWyGHKjVh3lM+PXEt7gOo9oXwbc3x9Xzi1Kc1SHw8f5j81n85lOpPXP2+q1y07sSzDzUwAjyxZyN6rJ4U3bsC7WFFDzp2YyJW+/Ui8fDlDY4k8CNTIFJEHUvyJE5xv1YqEX34x5bL37UOOwYPSbfpdiLjAuz++S/3V9Xl///vEO+NN15TIUoJVz65KfRXmf219B35aY44H5IbWH0PrpRCQK835rD5whXWHrhpidhtMbVORkrnSb8iKiIiIiIiIiIjIn8vDYWf6i5Upldv4fi7J6ealFYd47dNjxCelcu6lzQbPToZc5YxxtxNWd4YLO9O8d1afrAypOoRNLTaxpska+lbsS8HAgqbrfm8zE8AREEDIrNk4smc3xJ1373K5V2+c4eEZHkvk76RGpog8cCK/+poLL7Yj+dp1Uy77SwPI8dJLaTYxL0Veos+WPjRZ34SPT3xMdFK05XUdS3dk+TPLKZy5cNoT2r8QfphkjpdrBf32Qqln064HfrkZxcjPzNvjDnyyOI8WyW5RISIiIiIiIiIiIveDv7cHH3WuRq5AH1Nu+d5LNJuxk7Oh1u8Y8fKDNisgMMWqyuR4WNEGrh9N9/42m40SWUvQp0IfVj67ksrBlU3XLDuxjPE/jsfpSqWpajW1kLzkmzkDm4/xuRLPneNC6zYknD2b4bFE/i5qZIrIA8PtchE6bTpXBw7EHRdnyucYPJgcffumOca5iHO0+7IdP1z9IdVrsvlkY3b92QyrNgwvh/U2sL/65Wv4Yog5XqIxNJ8NPkFp1wOR8Un0/fgg8UnGczFrFc1G/3pFU6kSERERERERERGR+yVXkA8LO1cji5+nKXfyRhRNpv3AZ4evWlQCmfNBh0/BN6sxnhAJy1rAnllwZgvcvXjvCKs0+Hn6Mav+LKrkrGLKrTi5gh7f9CA0NjTDz+Vbvjx5Jky4t3r0NxIvXuRCq9ZEbf0uw2OJ/B3UyBSRB4IzOoarA1/m9owZ5qSHB7nefpvsPXukOcb16Ov0/Lon4QnhlnkvuxfNizZn7XNrqZW3VvqTunrg3hYQ7hR/XOSpDM8vgLS2ov2P6IRkOi/8kTO3jF9sZff3ZkrrSjjsOhNTRERERERERETkQVA6TyAbBjxG5fyZTbnYRCcDPznM+1+dwuWy2OI1Rwlovwa8/I3xmFDYPAKWPQ9Ty8O4PPfeOcZHpDoPP08/Zj4507KZue/GPl7Y8AK7r+3O8HMFPv0UwUPNizVcMTFc6deP27Nm/a5ta0XuJzUyReRvl3D6NBdatiTqm29MOUeWLBT4aCFZWrdKc4yw+DB6ftOTm7E3Tbmcfjl5qdJLfNPyG0bXGk0232zpT+rqAVjSHJJijPHMBeDFlfe2jEhHXKKTbov2cfBSuCFu+8+5mDkCvNOfh4iIiIiIiIiIiNw3IVn8WNmrJr3qWB9HNf27M/RfcZC4RIstXvNWgTYfQ1q7wCXHwfFPYV1PSKN5+N9mZtWcVU25sPgwen3TixmHZ2R4q9msXbsSPHw42FO0hdxuQqd+yNWXB+Gy2CVP5O+mRqaI/K0iNn7B+VatSTx/3pTzLlGCgqtX41etWppjxCTF0HdLXy5EXjDEHTYHox8dzebnN9OjfA+y+mS1HiClK/9pYiak+CrKJzO0Xwv+wekOEZ/kpOfS/ew9H2bKDX2qBLWK6lxMERERERERERGRB5Gnw86rjUrxUSpbzX557Aat5+7mVmS8ubhw3Xu7udnSab/8shkOLk7zEj9PP2bWn0mzos1MOTduZh+ZTbsv23H89vG078W9czizde1CvjlzsAcGmvJRX33FxY6dSA7N+La1IveDGpkict+54uOJO3aMG6NHc23oUMvzMAMa1Kfg8o/xCslrMcL/RCdGM3DrQI7fMf+yfqfWOzQv1hwPu0fGJ3dlPyxtZm5ievhAm+WQvVi6QyQmu+i//CDfn75tyvWqXZi+dYtkfD4iIiIiIiIiIiLyt3iiZDBfDnycsnnNjb+jVyJoOmMnp25EmQtLP3fvXWLeKvfeK6Zm82tw52yac/D18OWdWu8wptYYfBzmsY7fOU7bL9oyds9YohIt5pKC/+OPUWj1KryKmt9Rxh87xoXWbUg4fTrdcUTuFzUyReS+iD14iKvDhnP22Wc5VbkKF1q24u7yFeYL7XZyvPwyeadOxZ4pU5pj/nD1B5p/3py9N/aacq9Ue4UmRZr8vkle3gdLm987hPu3PHyg7SdQMP1zNXefvUPTGTvZcuKWKdf50YKMaFQSm03nYoqIiIiIiIiIiDwMcgf5sqpXTZ4uk9OUux4RT+ePfuR2dIK5sEQj6LEVXrsOLx+D9uug8BPGa5Ji4NNe4ExOdx5NizZlxTMrKBJkbkC6cfPJqU9o8mkT1p9ZT5IzKc2xvAoUoOAnK/Gv/6Qpl3TtGhfavkjMrl3pzknkflAjU0T+cncWLOTiiy8SuWEDiWfOgstleZ0jSxbyz59H9t69sKXcq/03IhIieP2H1+mzpQ83Ym6Y8j3L96R96fYZn6AzGX6YDIuesWhi+t47E7PIE9a1/3HpTiy9lx6g7bw9nLgeacq3qZaPkc+WVhNTRERERERERETkIePn5cGsdlXoXcfcRLweEc+A5YdIdlq/88Ruh8z5oeiT0GIe+KU4curKPvhhUobmUTRLUZY/s5zmRZtb5u/E3+HNnW/y1NqnmHV4FrfjzDvG/ZfDPxMhH35Ith7dTTlXdDSXevYibMkS3Gmc4ylyP6iRKSJ/qfA1a7j13nvpXudboQKFPl1HpkcfTfO67698T7PPmvH52c8t861LtKZ/xf4Zn+Ctk7DwKdgyCpwpvpz6bxOzcN1Uy+OTnLz31UnqT9rO5uPmpipA80p5Gdu8HHa7mpgiIiIiIiIiIiIPI7vdxohGJXnvhfJ4Oozv+Xafu8OEzSfTH8Q/Bzw3zRzf9i5cPZChefh5+jG61mjmPzWfgoEFLa+5HXebmUdm0mBNA0Z8P8JyMQiAzW4neMgQco1+GxwOYzI5mZvjxnOldx+Sw8IyNDeRv4IamSLyl4n8+muuj3wrzWtsfn5k69GdAkuX4JkrV5rXfnHuC/pv7W/5JZGn3ZOBlQfyWo3XMrbq0ZkM30+COY9b/5HwaxOzTqpD7Dpzm6en7GDGd2dJTOWLqxdr5Oe9F8rjUBNTRERERERERETkodeyaj7efq6sKT7v+/NsOHIt/QFKNobKHY0xtxMWPH3v2Ksf50HE1XSHqZG7BmufW8tLlV7C2+FteU2yK5kvzn1B642tuRx5OdWxsrRqRb65c7D7+5ty0du3c65pU6J/2JnunET+Ch5/9wRE5J8pZtcurg0ZatpG1jNvXgLqP4l3yVL4lCqJd+HC2Ly80h3v87Of8+bON3G5zQ3D8tnLM7rWaIpkNm/tYCn8EqztAZf3WOcD8sALC6FATevy2ETGfnGC1QeupHqL8iFBvNWkNFUKZM3YnEREREREREREROSh0LZ6Po5cDmflfmNzcPiaoxTL6U/JXIFpD/D0ODi/A+5e+F/MlQRnt97735dDoVAdeHYyZEv9naeXw4se5XvQqFAjJh2YxJaLW3Bj3go2LD6Mvt/2ZVnjZQR5B1mO5V+rFgWWf8yV3n1IumZsyDpDb3O5e3cyt2xJ5udb4FOhgo7QkvtGKzJF5E/ldruJ3rGDy/0H4E4yHirtmTcvBZZ/TM5XXyVz82b4lCyZoSbmutPreOOHN0xNTB+HD0OrDmVJoyUZb2L+tBZmPZZ6E7NSe+i7O9Um5rZTt6g/aXuqTczgAG/eb1mB9X1rqYkpIiIiIiIiIiLyD2Sz2Xi7aRkqhBibgnFJTnotPUB4bGLaA3gHQPM5YEujRXN+OyxsCKGn0p1PSEAIk+pO4ssWX9K5TGcCvAJM11yIvMDgbYNJciZZjHCPT/HiFFq3loAG9S3z4atXc6FNW842eIpbkyYT/8sv6c5N5P9LjUwR+VMk373LnYUfca5RYy737IU7NtaQd2TLRv4F8/HMmfN3jbvq1Cre2vWW6UuiYL9gVj67kk5lOuGwO1Kp/o2EKFjfF9Z0hYQIcz4wL7RbC01ngG9myyH2nLtDzyUHuB1t/kPEYbfRq3ZhvhtalxeqhOg8TBERERERERERkX8wH08Hs9pXIVsm40KNi3di6bJoH7GJyWkPkP8RaPwepLItLAAxt2DRs3ArA+dvcq+hOaTqELa8sIVhVYdhw/iO8scbP/L27rdxu82rNv/LkTkzeT/8kFyjRmHztp5b0pUr3Jk7l/PPNeXKwJdJDg3N0PxE/gg1MkXkD3MnJhK19TuuDh7Mmdp1uDVxIokXLpius/v7k3/+PLwKFsz42G4384/N550975hyuTPlZtHTiyicuXD6A0XdvHdY9rQqcPhj62sqtru3CrOY9ZdGAGduRdFzyX7LszDL5Q3is361eLVxKTJ5a8duERERERERERGRf4M8mX2Z9mIlUq5pOHQpnF5LD5CYbH6XaFCtOww5Cc1mQ8lnwdPPfE3MLVic8WYmgJ+nHx3LdGRwlcGm3GdnP2P+sflp1ttsNrK0aU2hNavxLl48zWujvvqKs888S/iaNWk2SEX+KL1xF5Hfxe12E7tvH5EbvyDyq69wRVisbvwNm7c3+WbPwqdUqQzfIz45npG7RrLp/CZTLq9/XhY8vYC8/nnTHuTKAdg7G45/em9/eSs+QdBkKpRpnuZQt6Li6bRwH5Hxxq+ofDztDGlQgi61CuLh0HchIiIiIiIiIiIi/zaPFsnO68+U5p2NPxvi35++zeBVh5naphKOtHZv88sKFdve+19SHBxbDRsHges37yJjQu81MztthOCSGZ5bpzKduBh1kTW/rDHEPzz0IZk8M9G2ZNs0z7r0LlaMgqtXEbZoMeHr1pJ08ZLlda7ISK6/8SYRGzaSe/TbeBUokOE5iqRHb95FJMOS797lUucuXOrYifBVq9JtYvqULk2BxYvwq1o1w/e4GXOTzps7WzYx8wXk46OnP0q7iel2w6YRML8eHFuVehMz/6PQe2e6TcyYhGS6LdrP1fA4QzyTl4M1vR+lR+3CamKKiIiIiIiIiIj8i3V7rBC9apt3j9t49DojP/sp4ysVPX2hckd44SOwp1iHFhMKixrD2a0ZnpfNZuO1Gq9RM3dNU278j+N5e/fbJDrTPs/T7u1N9l49KbJ5MwVXryZr5854pHJ8WOzevZxr8hw3J76HM513xyIZpbfvIpIhSdeucbFde2L37k3zOpufH5lbtqTg6tUUWrcW34oVM3yPo6FHaftFW47fOW7KFQ4qzEdPf0Ru/9xpD7LlLdg7K/W83QOeeAM6b4TM+dIcKtnp4qUVhzh21fhL12G3MaNdZcrmDUqlUkRERERERERERP5NRjQqSeuq5veNH++9RIW3v6bZjJ0MXnWYGd+d4eSNyLQHK/0ctFxkbmbG3oGlLWDrWHA5MzQvT7snH9T9gKKZi5pya0+vpetXXQmNTf+MS5vNhm+5suQc8QpFv9tKrtFvYw8IMF3nTkwkbOFCzjR4ijsLFuBKSMjQPEVSo0amiKQr4fRpLrR9kcRz51K9xrdyZXKNGkWxHTvI/c5ofMuVzfD4breb1b+spvPmzoTGmX9pPp73cZY1XkbOTNZf+vzqhymwc6p1zicIavaH/vuhzjCwO9IcyulyM3zNUb49ecuUG9OsLHVLBKc9FxEREREREREREfnXsNlsjG1elqfLmN9hRsYnc/hyOOsOXuW9r07xzIc/sPmn62kPWKqJdTMTN+yYCEuaQtSNDM0twCuAGU/OsNzp7kjoEdpsbMPR0KMZGgvAZreTpVUrCm/cSECD+pbXuCIjufXe+5xt2IjwT9fjdmas8SqSkhqZIpKm2IOHuNC+A8k3b5py3sWKkmPQIIps2ULB5R+TpU1rHP6Zftf4cclxvLHzDUbvHk2SxTawXcp2YVq9aQR4mb/uMTiw+N5qzJSyl4Bnp8DgE/D0WMhaKN05OV1uhq4+wrpDV025fk8UoW31/OmOISIiIiIiIiIiIv8uHg47U9tU4tEi2dK8zulyM2zNUdNxVialmkCrpeDpZ85d+B5mPwYnv8zQ3PL452H5M8uplquaKXcr7hZdNndh84XNGRrrvzxzBhMybRp5P5yKR44cltckX7/O9Vdf5XzzFkTv2JHxbXZF/kONTBFJVdSWLVzq2tXyLMzg4cMpvGED2Xv1xCskjTMr03Ap8hIdvuzA52c/N+W87F6Me2wcg6sMxpHO6kl+/hw2vmyOh1SDnt9B1S7glbEGq9PlZsiqw3xq0cRsWjEPQ58qkaFxRERERERERERE5N/Hx9PB3I5VqVogS5rXRcUnM3TVEVyudBp7JRtDj+8gR0lzLiYUPmkLK9pC+KV055bVJytzGsyhfan2plyiK5Fh24cx/9j8391sDHzqKYps3kT2/v2x+Vk0XYGEX37hcs9eXOrchdiDB7VCUzIs5ZpkERHcTieh06ZxZ/Ycc9LDgzxjxxDUtOnvHvdS5CV+DvuZU2GnOBl2koM3DxKbHGu6LtgvmMl1J1M+R/m0B3Q5Ye+ceysx3S5jLkcpeHFVhhuYcO9MzCGrj/DZ4WumXL2SwUx8oTw2my3D44mIiIiIiIiIiMi/j7+3Byt6PsIPZ25z4nok50JjOBcazU/XIklM/t97zN3n7rBw53m6P1447QGDS0KPrfDlMDj8sTl/6ks4tw3qDL93vJbDM9WhPO2evFL9FUpkLcE7u98h0ZVoyE89OJXLUZd545E38LSnPk5K9kyZyNG/H1natOb2zJncXbkKLJqVsXv3cvHFdtgDAvCtXAm/KlXxq1oV34oVsNm19k7M1MgUEYPku3e5Nmw4MT/8YMrZfH0JmToF/9q1f9eYN2Ju8Naut9h1bVe619bIVYMJtSeQzTft7RcIPQWf9YcrP5pzmfNDh0/BL2uG5xgRm8Qra4+y+bh5X/n6pYKZ0a4y3h7prAwVERERERERERERATwddp4oEcwTJYJ/jX1+5BovrThkuG7i5lM8XiwHJXKlc7SWVyZoNhMK1IIvhkByim1pk2Jhyyg4uARq9IYKbcEnMNXhmhVtRuGgwry09SXuxN8x5NadXse16Gt8UPcDAr1SH8OKR/bs5Bo5kiwdOhA6ZSpRX31leZ0rKoqY7TuI2b4DAJ/Spck7eRJeBQr8rvvJP5/a2yLyq7jjx7nwQkvLJqYjKIgCHy383U3MsPgwen7TM0NNzB7lejCnwZy0m5jOJPj+g3v7v1s1MTPlgA7rITB3hue45eebNJi8XU1MERERERERERER+cs8VyEPz1XIY4glOl0M/OQQCckZ3Gq1UjvotQMKPGadDzsHm4bDpNLw5XC4fSbVocrnKM/Hz3xMkaAiptye63totaEVh28dzti8UvAuVIiQqVMouPIT/KpWTff6+J9/5kKr1sTu2/eH7if/XGpkighxPx3nysuDuPBCS5Kums+G9C5ZkoJrVuNbseLvGjc6MZo+W/pwPuJ8mtcFeAYwrd40Xqr8UtrnYcZHwuLn4NvR4Ew05/1zQvt1kM38i9dKeGwig1cepvuS/dyKSjDl65fKycx2VdTEFBERERERERERkT/FO03LkivQxxA7eSOKwauOcOBiWPpnZgLkKA6dN0Kz2eCX3fqaxCj4cQ5Mr3JvpWYq517m9c/LksZLeCT3I6bc1eirdN7cmdlHZuN0/bEzLX0rVCD/0iWEzJqJd7GiaV7rjIjgYtduhK/79A/dS/6ZtLWsyL+U2+0mdu9e7sydR8yu1FdLBj77LLnfGY3d1/d3jZ/gTGDgdwP5+c7Plvm8/nkpkaUE5XKUo1nRZmT3TeUX7n/FhcOyFnD1gHW+fBtoOD7D28luO3WLYWuOEmrRwAR4qnROpr9YGS8Pfe8hIiIiIiIiIiIif44gP08+aFWBdvP3GuJfHL3OF0evk93fmwalg2lROYRqBdN412mzQcW2UKLhvYUf+z8CUmmC/jD53ta0tYdZpgO9AplZfyZj9oxh3el1hpzT7WTG4Rnsub6HcY+NI49/Hssx0mKz2Qh44gn869Qh/vjPxO7fT+yB/cTtP4AzPNx4cVIS1197jcTz58gxaJDOzRQ1MkX+bdwuF1FbtnBn3nzijx1L/UIPD3IOH06WDu2x2Wy/6x7JrmSGbR/GjzfMW78OrDyQViVa/b691WPDYGlzuH7YnAvIA02mQPGnMzRUQrKTCZtOsXCn9SpRbw87w54uQZdahXDYf99zi4iIiIiIiIiIiKSnVtHsdK1VyPId5e3oBFb8eJkVP16mx+OFeK1xqbTfz/pmgWcnQ9VusHcWHFsDyfHm67aOgSyFoNwLlsN42j0ZVXMUhYMKM+XAFJLdyYb8gZsHaLyuMY/kfoRGhRpRL389ArzSOdczBZvdjm+5sviWK0u2Lp3vvav+ZgvXRozAHWc88/POvPkknDlLnokTcAT8vvvIP4samSL/Eq7ERCI3bODO/AUknk97q1fPvHnJ8+54/KpV+133SHAm8M3Fb1hxcgVHQ4+a8j3L96R7ue6/a0xi7sCSpnDToulasd29VZg+QRka6sytKAasOMyJ65GW+SoFsvDeC+UpnMP/981RRERERERERERE5HcY3rAEu87e5uSNqFSvmff9eZJdbkY+Wzr9xSa5ykLTGVB/NBxcDPvmQ2SKY8TW94WgfJC/huUQNpuNTmU6UTVnVYbvGM6lqEuGvNPtZOe1ney8thOv3V7UyVeHnuV7UjJryQw9s+l+djuBTz+FV74QLvfpS/LNm4Z89HffceGFloRMn4Z3sWJ/6B7y8FMjU+QfypWQQPzRo8QeOEDsvv3EHTqEKzY2zRqvokXI1q07Qc80xublleF7nQ0/y9rTa/n87OdEJERYXtO6RGv6V+z/u56BqJuwtBncstie9rHB8OTIe1sopCM6IZkVey/xwTeniE9ymfJahSkiIiIiIiIiIiL3k4+ng4+712DujnN8+dN1LofFWV730c4LuN3wVpMMNDMBMmWDxwdDtW6wsKHx3aozAT5pC923QNbCqQ5RJnsZVjVZxbi94/j87OeW1yS6Evnm4jdsvbSVTmU60adCH3w8fCyvTY9P6dIUXLWKK/36Ef/TT8b7XLzI+dZtyDNuLIENG/6h8eXhpkamyD9M8p073J4xg/B1n+KOt9hCwIJvhQpk69UT/7p1M7zn+I2YG2w+v5kvzn/BybCTaV7bqGAjXqvx2u/bovb6EVjxIkReMefqvAJ1X023iXk2NJqluy+y5sAVohOSLa8pkyeQqW0qUTRYqzBFREREREREROT/2rvv8Kiq/I/jn5n03kNJQui9hN6lC4IIigIWBEVF/a2ubV1dd1e3qOvuWth17QpiBUUQUZCiFOm9IzWUUNJDejIz9/fHLLOETJJJMpBB3q/nuU9ucs4950zCNzfM955zgMsnKthPT49so6eua639Z3K1dO9Zzd+eoiNp+WXqzVybLKkayUzJvordbbOld4dI+an/+3pBhvTJeGnqEimw4j04g3yC9Hy/59W3YV+9sPGFCiewWA2rPtj9gZYdW6Znez+rHg16uDa+i/jUi1XiR7N06unfKXfx4jJlRkGBUh55VAV3bFHk5Dvlm5BQoz5wZSKRCfxC2IqKlPnhLGW8845s+flVXyAp6Jr+ir73XgV06+byDXDj6Y16a+db2nxms4yKNo++wNBGQ/V8v+dlNlVjU+Y986R5D0gWJ08hDXpGGvBkpZcfTc/Xcwv2aOWBtErr3du/iZ4Y3kp+3l6ujw0AAAAAAAAA3MhkMqlNg1C1aRCqqf2a6K6Zm7TxaGaZOjPXJstm2JeZ9fZy8b3W8EbSbZ9LM0aVfa8146D03lBp4qdSbOXLwo5sOlIDEwbqhxM/aNHRRVqbsrbc/pmSdDz3uKYumaobm9+oh7s8rOiAaNfGeAFzQIDiXn1Fme3bKfWVVyVb2dX1sj7+WFkff6yAbl0VNmaMQkeMYP/MqwCJTOAKZxiGzi38VqmvviLLqdNVX2A2K/S66xR17z3yb129tcs/2/+ZXtzwYpUJTLPJrGvir9EtLW9R/7j+rj8lZLNJK/8mrXzJefnQ56R+j1baxKHUXN3y1jplFZRWWCc62E+vjO+ka1rGuDYuAAAAAAAAALgMgvy8NWNKd6fJzFnrjunA2Vz9a2JnxYa6uIxrXFdp3LvS7EnShe/rZh62JzNveltqParSJgJ9AnV90+t1fdPrlVOco6XHluqD3R/oRO6JcnXnHZqnxcmLNbndZE1pN0VBPkGujfO/TCaTou65R/5t2yrlscdlzc4uV6dw8xYVbt6is399XqEjRyrijtsV0K5dtfrBlaMaU6QAeJrSM2d04t77dOo3v6k0iekVFaWQ4cNV73e/U7MlSxT38j+rlcQ0DEP/2f4fvbDhhUqTmPWD6uvBpAf1/bjv9e/B/9Y18de4nsQsOifNmeQ8iWn2kUZPrzKJmZJdqEnvb6wwienrbdaEbgla/Eh/kpgAAAAAAAAAPFKQn7dm3tVdPZuUX/p1/ZFMjfzXaq0+WPlqdGW0GS0N+3P5r5fkSp/fJq14qdzsx4qE+YXp5pY3a+4Nc3VX+7vkZSq/2l2hpVBv7XhLI78aqc/2f6ZSW8WTTioS1KePGn/5pfzbtq2wjlFcrJx585Q87mYl33qbcr79VkZp9fuCZ2NGJnAFMgxD5xYs0Jm/Pi9bbq7TOuaQEEXde69Chg2Vb+PG1duf8gJWm1XPb3heXxz4wml5iE+IhiYO1aimo9StXjd5mWuwTOvZvdLsO+xPAV0sMEqa8LGU2KfSJjLyijXp/Q06nVN+X9CGYf66vVeiJnZPUFSwX/XHBwAAAAAAAACXUaCvt2bc1V13z9yk9UfKzsxMzyvRnR9s1EODmuvXQ1vKy+zCe799HpJMZmnpHyTjoqTlihekMzulm96RfF2bQRngHaDHuj6mEY1H6Lm1z2lf5r5ydTKLMvXChhf08d6P9VCXhzQ8cXi13qf2jY9T4qefKP3Nt5Q9Z46sWVkV1i3ctk2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"text/plain": [ "
    " ] @@ -728,7 +660,7 @@ "simh[\"tas\"].groupby(\"time.dayofyear\").mean(...).plot(label=\"$T_{sim,h}$\")\n", "simp[\"tas\"].groupby(\"time.dayofyear\").mean(...).plot(label=\"$T_{sim,p}$\")\n", "obsh[\"tas\"].groupby(\"time.dayofyear\").mean(...).plot(label=\"$T_{obs,h}$\")\n", - "qdm_result.groupby(\"time.dayofyear\").mean(...).plot(label=\"$T^{*QDM}_{sim,p}$\")\n", + "qdm_result.groupby(\"time.dayofyear\").mean(...).tas.plot(label=\"$T^{*QDM}_{sim,p}$\")\n", "plt.title(\"Historical and predicted modeled and adjusted temperatures\")\n", "plt.xlim(0,365)\n", "plt.gca().grid(alpha=.3)\n", @@ -746,17 +678,9 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [01:04<00:00, 16.07s/it]\n" - ] - } - ], + "outputs": [], "source": [ - "ls_result = cm.adjust_3d(\n", + "ls_result = adjust(\n", " method=\"linear_scaling\",\n", " obs=obsh[\"tas\"],\n", " simh=simh[\"tas\"],\n", @@ -770,17 +694,9 @@ "cell_type": "code", "execution_count": 12, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [01:15<00:00, 18.78s/it]\n" - ] - } - ], + "outputs": [], "source": [ - "vs_result = cm.adjust_3d(\n", + "vs_result = adjust(\n", " method=\"variance_scaling\",\n", " obs=obsh[\"tas\"],\n", " simh=simh[\"tas\"],\n", @@ -794,17 +710,9 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [01:11<00:00, 17.90s/it]\n" - ] - } - ], + "outputs": [], "source": [ - "dm_result = cm.adjust_3d(\n", + "dm_result = adjust(\n", " method=\"delta_method\",\n", " obs=obsh[\"tas\"],\n", " simh=simh[\"tas\"],\n", @@ -821,7 +729,7 @@ "outputs": [ { "data": { - "image/png": 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RSNCzZ094enoiKysLaWlpuHHjBiZOnIgffvjB4nWmUqmwdOlSfPPNN3qfh4eHIywsDAC0+0vj69GjR+OXX37B2LFjLZ6PKc8//zz+/e9/630WGBiIyMhIyGQyZGRkaFuI5+TkYNq0aVi9ejUWLlxotMyqqipMmjQJR44c0fu8e/fu8Pf3R35+Pi5evIjy8nLMmTMHO3bssMmyNMXzVsvl5ubqtYZubMHl4eEBsViMvLw8XLp0SXsMio+Px6BBg3D69GntNaUx9nj8soWUlBSMHz9ee84EGo5rXbp0gYeHB+rq6lBYWIi0tDTteafpdXZ0dDQmTJiAmpoaHDx4UPv5iBEjDG6TvXv3NliXTz75BI8++qjeOvb29kZMTAwcHR2RlZWlvR4uLS3F0qVLcePGjWbX5ro6etscOXIk8vLykJaWhitXrgBo2G4GDhxoML2h3yWVlZWYNWsWdu3apfd5TEwMgoKCoFQqtdsfAJw6dQpDhw7Fnj17jK7rptasWYNly5YBaLgu6NmzJ5ydnXHlyhVkZmYCaGgpPnv2bBw+fBh9+/bF7Nmz8euvvwJo+J66dOkCiUSC8+fPa49h2dnZmDhxIi5cuABnZ2eL6vL5559rr7GkUil69uypPS83rkNBEPDJJ5+gsLAQGzZsgFhsvJO89lh/7733Hp566ikAf55rvby8kJ+fj5SUFL20ZWVlSE9P176Xy+WIjo7W9m7TeJ1XX18PALhw4QIGDx6MEydOoEuXLs3mPXDgQCgUCmRnZ+P8+fMAAIVCgZEjRxqsq7HrwbYmCALmz5+PrVu3AgBcXFzQrVs3ODk54fr1681aVLbF9/b666/j5Zdf1vssNDQU4eHhUCgUqKysxLVr13Djxg3tdHMtYomIbkodFkIlIiKyIXtrkfnBBx9o08hkMuGtt94SysvLm6XTaDRCYmKisGLFCqFXr15G59nSp6y3bdumt1whISHC1q1bBbVarU1TU1MjfPjhh4KTk5M2nYeHh5CZmWlRfVxdXQUAQq9evYR9+/YJGo1Gm06tVgtXr15t0bIcOXJEkMlk2rTOzs7C66+/LhQUFDRLe+PGDeGjjz4SevbsKWzZssVonZctWyZs27ZNqKqqajZdrVYLO3bsEHr37q2dp4uLi8lWD23VIvPDDz8U+vbtK3zwwQdCamqqwTQ3btwQli9frtcC7sknnzRZru7Tz40tdCMiIpptExqNRvjhhx/0tomYmBi977apd955R29dDBo0SEhMTNRLk5eXJ9x3330CAEGhUAguLi4t2q5N0d0/fX19tfP64IMPhJqaGm06tVotbNmyRQgMDNSml0gkwqlTp4yWrbv+Grf78PBw4eeffxZUKpVeWt3vTalUCsOGDdPmdXR0FFauXCkUFxfr5amvrxe++uorwc3NTZt2xIgRet+NrrKyMiE4OFibVi6XC6tWrdLbvtVqtfDLL79o0zWuk8a6G2Npq5za2lqhf//+et/9nXfeKZw+fdpg2m3btglz584VZs2aZbA8S1viNFVeXq7XGsbLy0v4+OOPm7W4rKqqEt5++229p9rnz59vtNyMjAy97dTd3V1YvXq1UF9fr01TX18vrF27VtvKT3cdt1WLTG9vb0EkEgnPPfecUFJSopf20KFDgr+/vzZtjx49hO+++057bG9af41GI3z66aeCRCLR5rnvvvtM1ueuu+5q9p03PdZfu3ZNmDVrll662bNnmyz3jjvu0Eu/ePFi4caNG3ppkpKShCFDhjRb1+aOwc8++6xe2iVLlhg8vu7du1fo1q2b3nEkJyfHYJnWnAM++eSTZuvszJkzzdKdOnVKu3yNx6+kpCSj5T7yyCN65U6ePFm4cuVKs3o2rtum68xWLTJ53mq5X3/9VYiJiRFWrlwpJCUlGTzmFxcXC2+//bbg7Oysnf+MGTNMltuWx6+ObpGp2wLZ399f2Lx5s8FWbnV1dcLu3buFBx54QFiwYIHBslpzLbdt2za9FpPDhg0TDh482Gy7u3z5st7xTSQSCTt37jRarr1sm63pYeDuu+/Wu756+umnm11TN16L6bZS79y5s9HeEnRb1Dk7OwsKhUKQy+XCO++8I1RXV+ul/fnnn/X2l0mTJglvvvmmADT8HtqyZYvevlZfX9/s9+Rrr71mdPl06+Li4iIoFAoBgPDAAw8I+fn5emnPnDnTrOeaDz74oEPXn6OjoyCVSgWxWCw8//zzza5Js7Oz9dbpuXPnhKCgIOHFF18Ujh8/bnB/q6qqEr744gu9XoD69etncjktbSXZVHu1yGy83vf29hbWrFkj1NXV6aVter6z9feWl5end806efJko62F8/LyhLVr1wrDhw+3uEcHIqKbCQOZRER0S7C3QKZuN6bPP/+8RfNvGgjR1ZJAplKp1OtuMiAgwOTNmV27dundyDZ1w7lpl1O9e/cWysrKLKqXJcuiVCqFqKgobTpPT08hISHBbNkajcboj3dLuzWsrKzUC8yY+v7aKpBpaV0FQRC+//57vZs6TYMaupp24xQREWGy+9J169bppT948KDBdPn5+Xo3jwcNGtTshpKuJ554Qq9cW950090/G28W6nYp1lRKSoped4iDBw82mrbp+gsODhaysrLM1qnxxhnQ0L2juW35zJkzeutz48aNBtM9/fTTevVp7DbSkLS0tGbdS9sikPnSSy/plfnyyy+bXLZGxrbxlgYyH3zwQW2+kJAQs/vi9u3b9bpEPXHihMF0c+bM0aaRy+XCkSNHjJZ57Ngx7Y3Mlt74NcZQV+Offvqp0fS7d+/WS+vg4CA4OjoaDDA3Wr58ud6xxNADH4IgCDt37tQre968eUaDRRqNRli4cKFeemM3137//Xe9dI8++qjRulZXV+sF+8wdg48ePaoXbPj888+Nli0IglBaWqoXzPz73/9uMJ2l54CMjAy9beOFF14wOf+6ujph1KhRejcuDTl37pzedjx9+nSjDz6o1epmgWXAdoFMnrdarqqqymTAVVd8fLz2Wk0kEgmXLl0ymrYtj18dGchUKpWCXC7Xptm1a5dF5Rq7zm7ptVxFRYVe4HfhwoUmr+U1Go0wf/58bfoePXoYTGdP22ZLA5kbNmzQ5pPJZGa7ubx+/breAzhvv/22wXS6gajGfWDbtm1Gy/3yyy+bnQv9/PyEa9euGc1zzz33aNNHRUUZTde0LgCEp59+2mj6yspKvaEynJ2dhcLCQoNp22v9WbOt1NbW6j0EYcqlS5f0HsoztY/aeyCz8bs6d+6c2fLb4nvTPadFRkY2C6QaY+pYRER0s2Igk4iIbgnWjnNj7q+1gczOnTtr02zfvr3Vy9eSQObGjRv1lumHH34wm+fRRx/VppdIJEZ/6DcNZFoyrqY1y/LNN99YXXdb2rVrl3beXbt2NZqurQKZ1ho6dKi2Dhs2bDCarukNYXM3/9RqtV6LvzfeeMNgurfffltvuzl//rzJcmtra/UC1ba86db05oMlNzc+/PBDvTzGWmU2XX+m1nWj2tpavZsUlo6N9dprr2nzDB8+3GC5uuMj3nXXXWbL/OKLL/Tq39pAZnl5ud5Yg8aCLdZoSSDzxo0beje1jY2T21RjSxYABlvq5OTk6LUce/bZZ82W+cILL+it47YKZI4ZM8ZsnqbjtZlqWSIIgpCZmamX/vDhwwbT6Y4L6+/vb/YhloqKCr1WB2PHjjVbbnh4uF4LakOSk5Objclq7Bg8bdo0k9+1IQcPHtTmcXJyMtirgqXngMcff9zk/mzI1atXtcsnEomEtLS0Zmn+/ve/a8t1dXUV8vLyTJZZUFAguLu769XZVoFMa/G81XLz5s3T1uHNN980mKatj18dGcjMycnRK9fcscKcll7Lvf/++9o8nTp1Empra83mKSsr0ztv7tmzp1kae9o2WxrIjI2N1eZ76aWXLMqje+0fGhpqME3TQNySJUtMlllbW6ttUdf4980335jMc+jQIb30xnpmaVqXmJgYs4G+s2fP6j188u677xpM117rb+LEiRaV3RLPP/+8dj4PPvig0XQ3QyDT2HG2qbb43lauXKmdbsm1PhHRrcx4h+xERETUYrpj6+iOideetmzZon0dFhaGOXPmmM3zj3/8AyKRCEDD+Dy//PKL2Tx9+vTBoEGDWl5RA7799lvt6549e2Lu3Lk2Ld+cIUOGaF9funQJZWVl7Tp/a+nWt+mYdcbExMRg3LhxJtOIxWIMHz5c+153zFddumM4jR49Gj169DBZrlwux9/+9jeL6tlajz76qNk09913H1xcXLTvN2/ebDaPj48PZs2aZTbd9u3bteMP+vn5mRzrTteiRYu0r48ePYrq6mq96fv379eOrwNYtpwLFiyAh4eHRfO3xO+//6431uWrr75qs7KtsWHDBu24l3379sXtt99uUT7ddbxnz55m03/55ReoVCoAgEgksmh80YcffhgSicSi+bfGAw88YDbN4MGDrcoTEhKiNx7rxYsXm6Wprq7WG/dp2bJlcHNzM1mui4uL3v6+d+9e7XjEjaqqqrBz5069chUKhclyu3btavYYBkA7/l+jf/7zn2bzAMDw4cMRGRkJoGG5jx49alG+pjQajd64nJbOPzIyUnv8FQTB4Daqe+y966674OfnZ7JMHx+fDhn32RCet1rOknVnz8ev1mo6fmVHXWevXbtW+/qxxx6DXC43m8fNzQ0zZszQvt+9e3ezNDfztgk0jEnfOC69TCbD448/blG+uXPnao/7mZmZemMlG2NuueVyOWJjY7Xv3dzccPfdd5vMM2DAAL39wNC50FhdZDKZyTS9e/fWGwPS0PWmPa2/1mjJMd4eSSQS3H///WbTtdX3pnu8S0pK0huLl4jor0ba0RUgIiJqCxMmTLA6j+5N1NaKi4vD2bNnATTc3Pfx8cHChQstuslhK7o3XadMmaINUJoSERGBPn36aH+IHT161OzNrxEjRrSqnk2pVCocPnxY+96SAKw1BEHAkSNHcPz4caSkpKC0tBSVlZXQaDRG0+fk5MDd3d2m9bBUQUEB/vjjD5w9exY5OTkoLy/XBm0apaWlaV9nZWVZVO5tt91mUTrdwIZu0KpRfX293k3ESZMmWVTulClT8Oyzz1qUtqX8/PzQv39/s+mcnJwwatQo/PbbbwCA48ePm80zZMgQSKXmL6UPHDigfT1q1CiL8gANDx94eHigtLQUKpUKiYmJGDp0qHa6bh3d3d31phkjl8sxduxYbNq0yaI6mKO7bJGRkRgwYIBNym1NPSwJbDXq06eP9nVOTg5ycnIQFBSk/Ux3Hffs2VNvXzAmMDAQffv2RUJCgsX1aAndG4TGBAQEaF9HRkbC39/fbJ7AwEDtMaSkpKTZ9JMnT2qDIwAwdepUS6qLadOmYcWKFQAaAnvHjx/X+65OnTqld3POmuPI9u3bTaaJj4/XHt/9/PzQu3dvi8oGGraR9PR0AEBCQgLGjx9vcd5G586d065LkUhkcaC9cf779u3Tzl83GH39+nXk5uZq31uzzj755BOL69ASPG+1XHl5Of744w8kJibi2rVrqKioQG1tLQRB0KbJzs7Wvja27uz5+NVaHh4eiI6O1m5D8+bNwxdffIGxY8e2Wx1KS0tx7tw57fuWnnuarmt73jYtpXtO7tu3L7y9vS3KJ5fL0bVrV+1vkISEBHTu3NloegcHB4uu8XTPhf379zcbbJTL5fDy8kJBQQEAw+dCQ6z5rhqP66dPn4ZSqdSrU3utP6Dlv+Fqa2uxe/dunDlzBleuXEF5eTlqamr0jlO6D9tZeoy3R927d7foO2ir7y0uLk77Ojk5Gffeey/efvtthIaGWrgERES3DgYyiYjolrRjxw6r81gS6LPU448/jm+++Qb19fWoq6vDAw88gH/+85+YMGECRowYgcGDB6Nv375t9uS7SqVCRkaG9r3uTRNzevfurf0xpXuj0ZioqChrq2dSVlYWKisrte9tFRwRBAFr1qzBq6++iuvXr1uV19CN0LZ27do1PP3009iyZYte4MAcS+uqe2PHFGdnZ+3rpq0CgYab6bo3qHv16mVRuV26dIFMJoNSqbQofUv07NnT4rS9evXSBjIteYrd0u0+KSlJ+/rEiROYOHGixXWqra3Vvm68odZIt449evSw+PjVq1cvmwUyk5OTta87KogJ6K/jX3/9VfsQibUKCgr0Apm669jS7boxbVsHAizZf52cnKxK3zSPof1d95wgEoksDgp2794dUqlUeyxLS0vTu/Gvu65FIpHZFkiNLPledLeP2tpaq/ZB3UBF033QUrrzl0qlFrXkbqS7vk0dAwDLt1FrtmVr8bzVckVFRXj++efxzTff6B37zTG27uz5+GULzzzzjDawn5GRgXHjxiE0NBQTJ07E8OHDMXjwYMTExLTZ/M+dO6f3ANxjjz1m8TW9biC66X5tj9umtXSPedeuXbPqmHvt2jXta3PHXG9vb4seDmuLc2FTMpkMXbp0sahs3e+0trYWmZmZeteU7bX+3N3d4eXlZXHZQEPvCf/617/wySefNOtZwZSO+B1lKy253rfl93bbbbdh6NChOHLkCICGXkg2btyIwYMHY8yYMRg6dCiGDBli0x5XiIjsFQOZREREbaBnz57YsGEDFi5cqA3KlZeX48cff8SPP/4IoKF7o7Fjx2LhwoW48847bRpIbfqD0dfX1+K8umkteQrZXLeC1tJ9gheA2a7yLKHRaLBkyRK97v2s0bQlSVs7efIkxo8f36If/pbWtSWtg3WftG7UdBux9AlkqVQKd3d3FBYWWl0PS1lal6ZpbbndFxUVaV9nZGToPWBgjabdG+vWsaXL2Vq6+6ot9tOW0l3HycnJegFWa9jjOjbGwcGhTdMD5vd3FxcXs92/NpLJZHB3d9d+V033sablWnp8smRd624f5eXlLe59oaVdjOvOX6lU2mz+LT32ttX2yfNWy129ehWjR4+2+iErwPi6s+fjly0sW7YM6enpePPNN7XfcWZmJr744gt88cUXAICgoCDceeedWLp0qc0fttHdrwHDXcRawlb7dXtcU1lKd93k5eW12TG3Jec1W50Lm/Lw8LA4kN30O236nbfX+rP291thYSHGjh3boofF6uvrrc5jL1pyvW/r723Tpk2YOnUqTp06BaDhd+2RI0e0wU2xWIx+/fph7ty5WLp0qdUBaiKimwXHyCQiImojM2bMwOXLl/H000/rtfJpVF5ejp9++gnTp09Hv379LB6DxRJNb2xZ88Nd90ahJa0CxGLbXk40naelN8pN+fDDD/WCmDExMfj3v/+NgwcP4vr166isrIRarYYgCNq/jlJVVYWZM2dqbwbLZDLce++92LBhA86dO4fi4mJtN3ONf41dNnaEpjcnWrqttYWW1sWSm+qWbvdVVVUW18GUpl0f6673jlrnuvuqLfbTlrqV17G90d03rL0hbOrc0pbruq22D0u1x/YJWL7e2mL75Hmr5TQaDebOnasNYopEIkybNg1r165FYmIiCgsLtV02Nv6tWbPGbLl/hePXypUrkZCQgPnz5+uNc90oJycHn376KeLi4jB37lybtgqzt/0asJ/vrqOPuR2hNd9T02vO9lp/1v5+W7ZsmV4Qc9SoUfjss8+QkJCAvLw8VFdXQ6PRaI9Tjd3n3uw6+nofaOj2+/jx41i9ejUGDx7c7OFnjUaDhIQEPPPMM4iIiMCnn35qk7oQEdkbtsgkIiJqQ4GBgXj77bfx9ttv4+LFizh06BDi4+Oxd+9e5OTkaNMlJiZi5MiROHXqFMLCwlo936bdy1RUVFicV7eroI7opsbT01PvfUtbwTTSaDRYuXKl9v0dd9yBzZs3m7zpYM36srU1a9Zox5KRyWT4448/MHLkSJN5OrK+TZ9Ubum21hZaWhdbjoequw899thj+M9//mOTcnXXe0etc919tbX7aWt4eHhoW6G89957ePLJJ21Srj2sY3ujuz1be9wxdW5py3WtO6/evXu3uOvhltKdv5ubm832FUPHXkPBnKbaYvvkeavltm3bpm1lAwDr16/HvHnzTOaxpL438/FLd7xcc/r164f169dDqVTi5MmTOHz4MOLj47F//3695f7xxx+RlZWF+Ph4mwzr0PQYVlxc3Oz6tSXsadtsKd11c+edd2Lr1q0dV5l20prvqek1pz2uv/Pnz+Pnn3/Wvl+5ciWWL19uMk9HHuONsebYYq22/t4kEgmWLFmCJUuWoKioCPHx8Th8+DD279+PU6dOaR/CraiowEMPPQRBEPDQQw/ZtA5ERB2NLTKJiIjaSffu3fHAAw9g3bp1yMrKwpEjRzBlyhTt9MLCQrz++us2mZezs7Pe+C5XrlyxOK9u2o7oLrLp+DWXLl1qVXmnT59Gfn6+9v1///tfs09OZ2ZmtmqeraE7vus999xj9mYw0LH19ff313ufnp5uUb7CwsI2v8lhaV2Ahq79GjVdptbQ3Z7z8vJsVq5uHVu6nK0VGBiofd3a/bQ1buV1bG90zwn19fXa4JU5+fn5emMfNz236K7r+vp6vQd9TLFkXbfV9mEp3fmXl5ejpqbGJuW29NjbFtsnz1stp7vuRowYYTaICVi27uzl+CWTybSvLR2/0ZLu3Q3NZ+jQoXj66afxyy+/oLCwEJs3b0a3bt20aY4ePYoffvjB6rINaXqtaqtjiz1tmy3V0cfcjlBeXt5saAxjmu5jTc+H9rj+dI9TEREReO6558zmaetjvO5vubY8tliqPb83b29vTJ8+HatWrcLJkyeRnZ2NV199Va93lOXLl1s0visR0c2EgUwiIqIOIBKJMGTIEPzyyy8YMWKE9nNj42nodmtjaben/fv3175uHEPDHJVKhRMnTmjf23pMIUt4enqia9eu2vf79+9vVXnXrl3Tvvbx8UFkZKTZPIcOHWrVPFtDt74DBw40m14QBIu/37bg5+eHkJAQ7fvjx49blM/SdK2RkpJicQsF3fro7jutNXToUO3ro0eP2qxc3TpeuXKl2XhdxthyvQ8ZMkT7OiEhQS9Q1VItOda1xzo+deoUVCqV2TyN3XvdqpruG5Yee5qma3puaVquLY8juttHXl6eVUEdW9DdTwDg2LFjNim3d+/eekGijjz28rzVctauO8CyaxR7OX7ptjC0JNBTV1eH1NTUVs/XwcEBM2fOxL59+/TGJDR0nd2060hLzj29e/eGs7Oz9r2tzj32tG0CrT8nJyYm2uzhDXvXku8qLCwMvr6+etPtcf3pHqcGDBjQrGtTQyz9LdWSbQyw/tgCAOfOnbO4fGt15PcWGBiIl19+GR9++KH2s7Kysnb5rUVE1J4YyCQiIupAYrEY06dP177Pzc01mE73ZomlP4x0W0Ts3r3baNm6fv/9d72AiCWtKtrCxIkTta83bdqk7TayJSx9SlfXV1991eL5tZa19d2xYweys7PbqDaW0d1ONm3aZNEyfPvtt21ZJQAN63LTpk1m0yUlJeH8+fPa97bc7idNmqR9ff36dfzxxx82KVf3AQiNRmNRK5PLly/b9Ca17n5aU1ODr7/+utVltuRYp7uODx06ZLPWobrbQXFxMXbt2mU2z+7du+2mBUVbiIqK0rvJvm7dOovy6W4b/v7+6NKli970Tp066ZX7/fffmy1TpVJh48aNZtPFxcXpBTK+/PJLS6psM0FBQejTp4/N569QKPQCX5asMwD47rvvbDJ/XTxvtZy16+7ixYsWBc3s5fgVHh6ufZ2UlGQ2/W+//dZsnMjW8Pf3x2233aZ9b+haWPe8A1h27pHJZBg7dqz2vS2PK/aybQItOyePHTtW+5BFXV0d1q9f3yZ1szeWHIPr6+v1rksNXW/a4/qz9jhVVFSk1xWtKS3ZxgDrjy21tbXYvn27xeVbyx6+t1mzZum9t+S3PxHRzYSBTCIiojZgzROluq2YvLy8DKbR7cLR0ifVly5dqn3KValU4tlnnzWZvq6uTm+8k/DwcIwfP96iednao48+qh3DqKamBg8++KBV61RXUFCQ9nVhYSGSk5NNpl+9erVeq9T2plvfgwcPmkxbXV1ts/EAW2PJkiXa1zk5Ofjvf/9rMv3p06dt1r2bOa+99hqqqqpMptHdN1xdXTF37lybzT82NlbvZuejjz5qkzHyYmJiMGzYMO37N954w2zrU3PHAGv16dMHo0aN0r5/6aWXcP369VaV2ZJj3Z133qkNjGk0GixbtqxFDzA0NWbMGL0bZS+88ILJVk0qlQovvPBCq+dr75YtW6Z9/dtvv2Hfvn0m0x88eFDvhuayZcsMtuZYvHix9vXmzZtx8uRJk+V+9NFHFm1vUqlU7zj5wQcf4MyZM2bz2dLTTz+tfb1hwwZs27bNJuXqHntPnjyJzZs3m0y/devWNmkJyfNWy1mz7jQaDR5++GGLyrWX45du6+tNmzZBo9EYTVtbW4sVK1aYLdPa60Fz19keHh56XTJaeu7R3a+PHDmCTz/91Kp6GWMv2yagf06+cuWKReve19cXixYt0r5/8cUXW31tcDP47rvvzI7B/P777+sFl5YuXdosjT2uP93j1NGjR8228H7yySctDkrqbmMFBQUWXyPrHltycnLMtgB95513UFBQYFHZLdFW31tL7ykAxu8rEBHdrBjIJCIiagO9e/fG2rVrzQZQrl69io8//lj7fvTo0QbT6XYR9sMPP1g0LllERITeD6pvvvkG//rXvwz+IKqqqsJdd92lF+R76aWXtMHE9hYVFYW///3v2vebN2/GwoULTY7/o9FosGnTJpw6dUrv84EDB+o97fv3v//d6I/rH374AQ899FAra986Y8aM0b7etGkTfvvtN4PpioqKcMcdd3To2ISNxowZo9el0vLly/HTTz8ZTHv58mVMmzbN5M1MW7p27Rpmz55tcNtRq9V46qmn9Mb+efzxx+Hi4mLTOqxatUp7k/TSpUsYOXIkUlJSzOa7du0aXnjhBTz11FMGp7/44ova1zk5OZgxY4bBG0AajQbLly+3+Ol4a7z55pvaJ9BLSkowcuRIs0Gi5ORkrFmzxuA03WPdrl27LOoGTCwW4/3339cGx+Lj4zFx4kSLxlm8ePEiHnnkEaxatcpguboPdyQmJmLRokWoq6trlraurg733XffLd2tbKO///3vel3hzZkzp9lxt9GZM2cwe/Zs7Xtvb288+uijBtM+8sgj8PDwANCwzc6YMcPofrJ161a9III5jz32GGJiYgA0BNLGjRuH33//3Wy+0tJSfPLJJ61+qOeee+7RtgrTaDSYM2cO1qxZY/YGZXV1Nb799luj3V3PmzcPUVFR2vf33XcfDh8+bDDt0aNH9a4JbInnrdbVo9GJEyfwySefGExXXV2Ne++91+Lu9u3l+KW7/1++fBlvvPGGwXSVlZWYO3cuLly4YLbMgwcPYtKkSdi1axfUarXJtL/++qveOjN0nS2RSBAbG6t9//HHH6O2ttZsPW677Tbcfffd2vePPPII3nrrLbNBHqVSiV9++QWjR4/W67Kzkb1sm4D+Obm4uNjoubupFStWwMfHB0DDGMkjR460qFvtvLw8vPXWW5g/f37LKtyB1Go1pk2bhrS0NIPTN23apHfdNnz4cKM9gNjb+tM9TmVnZ+sthy6VSoV//vOfFvfWADTvJv29996zKN+gQYP0enJ49NFHjT7Qt3r1arz66qsW16ml2uJ7e+KJJ/D000+bHb9YpVLpPbCoUCiadW1PRHSzk3Z0BYiIiG5F58+fx5IlS/Dwww9j3LhxGDRoELp166Z9MjInJwcHDx7EunXrtE9POjg46N100jVv3jy89dZbEAQBN27cQKdOndCvXz/4+vrqjS3SNFDxwQcf4MCBA9ofPy+//DJ+++03LF68GF26dIFSqcTp06fx+eefIyMjQ5tv+vTpBp8Sbk/vvPMOTp48qf0BuH79euzYsQPz58/HsGHD4OfnB6VSiczMTBw/fhw///wzcnNzsWXLFr0bLwqFAg8//DDefvttAA1jbvbu3RsPPfQQYmNjIRKJkJaWhh9++AF79uwBADz44IM2e7LeWg888ADeeustVFZWQqPRYNq0aViwYAGmTp0Kf39/lJSUID4+HqtXr0ZRURHc3NwwZcoUi7sVbAsikQhffvklBgwYgOrqaiiVSsyaNQvTp0/H7NmzERoaipKSEuzZswdffvklampqMHz4cKSnp1sUlG+psWPHIjExETt27ECPHj3wt7/9DQMGDIBMJkNKSgq++uornD59Wpu+Z8+eRm/OtEZsbCy++uorLFiwABqNBmfPnkWPHj1wxx13YPz48ejUqRNcXV1RUVGB3NxcnD17FgcPHtTWzVjwYcKECVi8eDHWrl0LANi7d692OePi4iCXy5GSkoI1a9bg5MmTEIlEuOuuu7BhwwabLdugQYPwzjvv4PHHHwcAZGRkYMCAAbjzzjsxefJkREREQC6Xo7CwEGfPnsXOnTtx/PhxTJs2Ta/VSaNZs2bhiSeeQG1tLaqrqxEbG4vY2FgEBQXpPVjx+eefw8/PT/t+0qRJWLlypfYYunfvXkRFRWHmzJkYM2YMwsLC4OTkhPLycmRnZyMxMRF79+7VBsqMtQJ64IEHsHHjRuzduxdAQ4uLhIQEPPDAA+jduzdEIhGSkpLwxRdfICUlBQqFApMmTcKWLVtss4LtkI+PD7766itMmzYNgiCgqKgIgwcPxoIFCzB58mT4+fmhoKAAO3bswNdff61tHSsSifDFF1/ofW+6/P398cEHH2hbZmZnZ6Nv375YunQpxo4dCy8vL2RlZWHz5s3am/rz5s2zqKtUV1dXbN26FbfddhtKSkq0AbW4uDhMmzYNvXv3hqenJ2pra1FUVIQLFy7g2LFj2L9/P5RKpV7LtpYQi8XYvHkzBg0ahGvXrqG6uhr33XcfVq1ahdmzZ6Nfv37w9vaGUqlESUkJkpOTceLECezZswfV1dVGy3V0dMQXX3yB8ePHQ61Wo7y8HCNHjsT8+fNxxx13wN/fH/n5+fj999+xbt06qNVqi9eZNXjeark5c+bg+eefR2ZmJoCGBwV27dqFuXPnIiQkBBUVFThx4gRWr16NzMxMyGQyLFy40KIu8O3h+HX77bcjLi5O28L65ZdfxokTJzBv3jyEhISgvLwcx44dw5dffonc3Fz06tULDg4ORh+OABpaKO3YsQM7duyAv78/Jk6ciAEDBiAyMhLu7u5QKpW4evUqfv/9d2zdulUb5OvUqRPuvfdeg2Xee++92uvNXbt2ITAwELGxsXBzc9M+JNOzZ0+8/vrrevm++uorXL58GadPn4ZarcZzzz2Hjz/+GHfddRcGDhwIX19fCIKA0tJSbffuu3bt0j50ZOhhBnvZNgGgS5cuGDBggDbIvXTpUvz73/9GTEwMHBwctOkee+wxvWBXSEgINm3ahAkTJqCurg4ZGRkYMmQIxowZg8mTJ6Nbt25wd3dHVVUVCgoKcO7cORw+fBhHjhyBRqPpsKEtWiokJASRkZGIj49Hnz59cP/992PMmDHw9PREZmYmfvzxR2zdulWb3sXFxWR3xPa2/oYNG4aBAwdqe6x56623cPz4cSxatAhRUVGoqanB2bNnsWbNGu11laW/pVxcXDBt2jRtl7uvvfYaVq9eje7du8PR0VGb7u6779Z7cEAsFuPZZ5/VPhyVmJiIPn364LHHHkOfPn0gCAIuX76MDRs24ODBg5BKpbj33nvbtMvXtvjeysrK8PXXX+Odd97BgAEDMHLkSMTGxsLf3x9OTk4oLS1FUlIS1q1bp/dA8hNPPKE3jigR0S1BICIiugWsWLFCAKD9awnd/CtWrDCabtGiRdp0ixYtMluWJX8KhULYvHmzyfq99tprZssxJDMzU+jRo4fFdZk5c6ZQW1trsi7h4eHa9GvWrDGZtjV5KysrhTvvvNOqdblly5Zm5dTU1AhDhw61KP/EiROFuro6vc/27dtnsH7p6el66dLT061aF8Zs3rxZkEqlZuvq7OwsbNu2TW/7HzlypNFyR44cadE2rsvSsgVBEPbs2SM4OTmZrXdUVJSQlZXVqu3ImKb75x9//CE4OzubrVNMTIyQk5NjsuyWrD9dv//+u+Dh4WH18cHYcUYQBKG+vt7ifWTlypXCmjVrtO/Dw8ONlmvN9y4IgvDll18KMpnM4mWaNm2a0bLWrFljdvs3tq+tXr1aUCgUVq9jU99nWVmZMHDgQLNlSKVS4ZtvvrF63Vli3759evOyREvqYc02vmHDBsHBwcGi9SuTyYRvv/3Wojr8+9//tqjMyZMnC2lpaVYdg1NTU606H5rbV6w9B+Tm5grDhw+3ev7mvvP169cLEonEbBn9+/cXKioq9D4zdn6zFs9bLXf06FGL6iCTyYQvv/zS4uO4ILTd8cuSa+FGFy9eFHx9fc3WITIyUkhPTzf7nTc9HlryFxYWJiQnJxuto1KpFCZMmGCyDGPro6KiQpg1a1aL9mtTxwx72DYFQRDOnDkjeHt7m6yDsfmdOHFCCAkJsXq9GFvX1mz7jazZVhtZsi6b1iUrK0uIioqy6Bh44MABi+phD+uvUWpqqkX7sUgkEl555RWrrlsyMzOFiIgIk+UaOhao1WqLroGlUqlVx86WbDO6bPm96dbF0r/58+cL9fX1VtebiMjesWtZIiKiNvDRRx9h8uTJcHV1NZlOoVDgnnvuwblz5zBz5kyTaV966SXEx8fjvvvuQ48ePeDm5qbXGtOYkJAQnDx5Eq+//rped4BNde7cGd9++y02bdoEuVxuttz24OzsjK1bt2Lz5s3o16+fybR+fn545JFHMGjQoGbTFAoF9uzZg8cee8zosgUGBmLVqlXYtm2b3lPmHWHmzJnYvXs3evbsaXC6RCLB+PHjcfr0aUyaNKmda2fcmDFjcObMGYwbN87gGHgODg5YsGABTp06heDg4Hap09ixY3HixAkMHz7c4HS5XI6///3vSEhI0Bunpy1MnjwZqampWL58udFWabr1GjNmDD766COT3WzJZDJs2bIF77//Pry9vQ2miY6OxpYtW4y2+LaFpUuX4uLFi5g/f77eWGNNOTg4YMKECXjkkUeMplm8eDFOnTqFhx9+GH379oWHhwekUss6klmyZAkuXbqEhx9+GO7u7ibTuri4YMqUKfj6669NdlPq5uaGgwcP4oUXXjDa7XBsbCz279+PBQsWWFTPW8Fdd92FpKQkzJw5U69bOF1SqRTTpk3D2bNnMW/ePIvKfe6557Bjxw7tuKdNeXt74/XXX8evv/5qdffn0dHROH36ND799FN07drVZFqRSITY2Fi8/PLL2L17t1XzMcbf3x/79+/Hhg0bMGDAAIPHSV1du3bFU089hcTERJPp5s+fj6NHjyIuLs7gdBcXFzzxxBM4fPiwzbvObsTzVssNHjwYx44d0+tOtKkhQ4bg0KFDVveW0R7HL3Pbcbdu3XD48GGMGzfO4HS5XI4lS5bgzJkziIiIMDu/Xr164bXXXsPgwYONHnsa+fr64plnnsG5c+dM7vNSqRTbtm3Dd999hxkzZiAyMhLOzs5mlw1o2L82bdqE7du3Y9SoUWaPSxEREXjooYdw6NAhk8trD9sm0LB9XLhwAa+++iqGDx8OX19fi6+T4+LikJycjLfeegthYWEm00qlUgwZMgRvvfWWzVuNt4fg4GCcOnUKCxYsMLh+RCIRxo8fj8TERIwYMcKiMu1p/UVHRyMhIQGTJ082mqZXr174/fffLRrrVldISAjOnj2Ld999F7fffjsCAwNNXks2EovF2LRpE1544QW91pu6+vXrh4MHD7ZrT0O2/N7+9re/YdmyZRYdG/v164eNGzdi/fr1Zo+NREQ3I5EgWDlSOhEREVlMrVbj4sWLuHz5MrKyslBZWQmJRAJPT09td01tdVPREI1Gg5MnT+LChQsoKCiAVCqFn58f4uLizN7UtQfZ2dk4cuQIcnNzUVZWBkdHRwQHB6NHjx7o2bOnRTeciouLsX//fqSnp0OpVCIgIADR0dEYOnSoRYHh9iQIAk6fPo2EhAQUFRXB1dUVgYGBGDZsGAICAjq6eiZdu3YNBw8eRE5ODhwdHRESEoJRo0Zpu1duK4sXL8bXX38NoKFL1sZuVwHgypUrOH78OHJyciCRSBAeHo6xY8d2SNdLgiDg3LlzSEpKQmFhISorK+Hs7AxfX1906dIFPXv2NHpTxhilUon9+/fj8uXLqKioQEBAALp3746BAwe20VIYVltbi0OHDiE9PR2FhYUQi8Xw8vJC586dMWDAAL0xa9uSWq3G6dOncfHiRRQVFaGmpgbOzs4ICAhA165d0aNHD6tv9NTU1GDPnj24evUqampqEBQUhL59+xoN3vxVlJeX48CBA8jMzERpaSnc3d0RGhqKkSNHmg0om3Lq1CmcOXMGBQUF8PLyQmRkJEaNGmWzh00yMzNx7Ngx5Ofno7S0FHK5HJ6enoiOjkavXr3a/HhVUFCAw4cP48aNGygpKYFUKoWHhweioqLQs2fPFh3nk5OTcezYMeTl5cHNzQ1hYWEYPXp0u+13PG+1TnJyMo4cOYL8/Hw4OjoiMDAQAwcORGRkZKvLtuXxa/78+dqb7g8++KDRsT2bSk9Px8GDB5Gbmwu5XK7dPj09Pa2uAwBtl5ZpaWnIz89HdXU1FAoFfHx80LNnT8TGxlr8IIytlJWV4fDhw8jKykJRURFEIhHc3d0RERGB7t27t6iranvYNm0hNTUVCQkJKCws1F7He3t7o3PnzujVq5fZB0Dtydq1a7Vd5IeHh+sN0dH4WyMrKwu1tbUIDAzEyJEjzQa1zLGX9ZeRkYGDBw/ixo0bkEql2q6Yu3fv3i7zN6SyshL79u3D1atXteu8X79+dnF9Zqvv7caNGzh37hwyMjJQUlIClUoFFxcXhIWFoV+/fq3uBp+IyN4xkElEREREtwxTgUwiIiJqvUmTJmHHjh0AgGeffRZvvvlmB9eIqH2ZCmQSERGR7dlXswMiIiIiIiIiIrJLgiAgKSlJ+/5m6NGDiIiIiG5uDGQSEREREREREZFJgiDgvffeQ05ODoCGcf9GjhzZwbUiIiIioltd+w4WQEREREREREREN40VK1bgwIEDSE1N1QYxAWDy5Mk2Gb+TiIiIiMgUBjKJiIiIiIiIiMigs2fP4sCBA3qfxcTE4NNPP+2gGhERERHRXwkDmUREREREREREZJRIJIKbmxu6du2KO++8E4888gjc3Nw6ulpERERE9BcgEgRB6OhKEBERERERERERERERERHpEnd0BYiIiIiIiIiIiIiIiIiImmIgk4iIiIiIiIiIiIiIiIjsDgOZRERERERERERERERERGR3GMgkIiIiIiIiIiIiIiIiIrvDQCYRERERERERERERERER2R0GMomIiIiIiIiIiIiIiIjI7jCQSURERERERERERERERER2R9rRFaCbX35+Pnbt2gUAiIiIgKOjYwfXiIiIiIiIiIiIiIiIiNpDTU0NMjIyAADjx4+Hn5+fzcpmIJNabdeuXViwYEFHV4OIiIiIiIiIiIiIiIg60Lp163DvvffarDx2LUtEREREREREREREREREdoctMqnVIiIitK/Xrl2Lnj17dlxliAgqlQqlpaUAAA8PD0ilPNQTdTTul0T2hfskkX3hPklkf7hfEtkX7pNE9oX7ZHPJycnanjt1Y0a2wLVLraY7JmbXrl3Rv3//DqwNESmVShQVFQEAvL29IZPJOrhGRMT9ksi+cJ8ksi/cJ4nsD/dLIvvCfZLIvnCfNE03ZmQL7FqWiIiIiIiIiIiIiIiIiOwOA5lEREREREREREREREREZHcYyCQiIiIiIiIiIiIiIiIiu8NAJhERERERERERERERERHZHQYyiYiIiIiIiIiIiIiIiMjuMJBJRERERERERERERERERHaHgUwiIiIiIiIiIiIiIiIisjsMZBIRERERERERERERERGR3WEgk4iIiIiIiIiIiIiIiIjsDgOZRERERERERERERERERGR3GMgkIiIiIiIiIiIiIiIiIrsj7egKEBERERERERERERER2aP6+npUVFSgvLwc9fX10Gg0HV0l6mCCIEClUgEAiouLIRKJOrhGLScWi+Hg4AA3Nze4urrCwcGho6vUDAOZRERERERERERERERETRQXFyMvL6+jq0F2SCy+NTo81Wg0qK2tRW1tLfLz8+Hv7w8vL6+OrpYeBjKJiIiIiIiIiIiIiIh0MIhJptwqgcymGrd5ewpmMpBJRERERERERERERET0/+rr6/WCmAqFAp6ennBxcYFEIrmpuxKl1tNoNFCr1QAAiURy0wY1BUGAWq1GZWUlSkpKUFtbC6AhmOni4mI33czenGuXiIiIiIiIiIiIiIioDVRUVGhfKxQKhIeHw8PDA1KplEFMumWIRCJIpVJ4eHggPDwcCoVCO013H+hoDGQSERERERERERERERH9v/Lycu1rT0/Pm7bFHZGlxGIxPD09te9194GOxr2PiIiIiIiIiIiIiIjo/9XX12tfu7i4dGBNiNqP7rauuw90NAYyiYiIiIiIiIiIiIiI/p9Go9G+lkgkHVgTovaju63r7gMdjYFMIiIiIiIiIiIiIiIiAzgmJv1V2Ou2zkAmEREREREREREREREREdkdBjKJiIiIiIiIiIiIiIiIyO4wkElEREREREREREREREREdoeBTCIiIiIiIiIiIiIiIiKyOwxkEhEREREREREREREREZHdYSCTiIiIiIiIiIiIiIiIiOwOA5lERERERERERERERERkdzZv3gyRSGSTv6SkpI5eHGoBBjKJiIiIiIiIiIiIiIjI7pw4ccIm5Tg6OqJHjx42KYval7SjK0BERERERERERERERETU1OzZszFkyBCD0+rr63HXXXcBAJydnbF+/Xqj5bi6ukIikbRJHaltMZBJREREREREREREREREdicuLg5xcXEGp50+fVr7uk+fPpg+fXo71YraE7uWJSIiIiIiIiIiIiIiopvKmTNntK/79u3bgTWhtsRAJhEREREREREREREREd1UdAOZsbGxHVcRalPsWpaIiIiIiIjIjCvbjiB75wWINCJI/eToef8kuAb6aKer1Wpc/H4XCk6mAxoRJC4SdJo+GCGDendgrYmIiIiIbl2JiYna12yReetiIJOIiIiIiIhuacq6OpRdy0FFdgGqc0tQW1QBdb0KHtFBCB7WG85eHkbzXt5yAPl7ryDUKRJRjl0bPqwCCt47g/O1OVA5q6CpUsNT6g0vuR88HXs1pBEA9U9FOPXd96gQyuAY5oHgYd0RGNcDEomkTZazKr8Y59duR11JDaLujEPIkD5tMh8iIiIioo4mCALOnj0LAJBKpejZs2cH14jaCgOZREREREREdEvIOX4eqT8egahWBKlIBrnYAXKJAnKxIxwkjpCJJHCHE9zh1JAhH8g/dArVqgpUq6tQr6mDGmpoRBoIEkCmlCLUOQoRzjHN5uUgUSDUOarhjbPh+khEUvg7hsAfIUAhgJ/LcGXTTlQqy1GjqYHIU4yeSyfALTxAm0etVuPMxz+h+lIpPGRekIjEENBwo0aAALWgQrW6CkqpEnIfZ3j3CEXeyTRIysXwUwQiWBIGAKj/KR+Hd6zFba8utt0KJiIiIiKyE6mpqaisrAQAdO/eHXK5vINrRG2FgUwiIiIiIiK6adVWVOLMf7YAeRoEOoaik7Qb4GJ5fpnYAe4O3nCHd/OJbXAvxEnqCiepa8ObeqD4o4tIqd0NwUcEZVU93OpdESAPAFwCTBcEABUAjgGR6NwsmOogUSC0NhL7n/wMw9+5v81agRIRERH91b366wVczCnv6Gp0mO5BblgxtUe7z5fjY/51MJBJREREREREN42q/GKkbT2Essv5ENeK4ePgh1BZhNFWkfZOKpYhyCkcqAYggk2Dp2KRGNHy7jj8xGoMfvNeODg72q5wIiIiIgIAXMwpx/H04o6uxl8Ox8f862Agk4iIiIiIiOxOcXIGMnYmoDq3HEKNBjKNAxylTnCXecJb7AlviWe7Bi9r1dUolOQjYGRX3Nh3EU5KF3jL/bTTa1RVKFYVwqWLF4JH9saVn45CKFTBS+YDB4mi/SpqQJRzV5xZ/iNUXgIcfV3hHuEP397RcAv179B6ERERERG1lG6LzLYKZO7Zswdjx47FokWLsHbt2jaZh7X27duHMWPGYMGCBfjmm286ujrtgoFMIiIiIiIisgtXfjuMnH3JcFK5wFcRAD+RPwB/wMqGhBpBg1p1Neo1tajX1EMtUkGQCoBUDIlcAomLDBCJUFdYBaFGgAMcoJA4QS6WQyaWQywSa8uqVVejWFaI3s9MRbSXGwAgYlIcAKAwMQ15J1Lh6OOGyKljESP78yd2/+5zAAAqpQpZu86g6Gw66otqIFXJ4CRxhpvMEyKRyORyVKkqUKlq6KZMhIa0MrEDXKUekIqb/5wvritAtbQSnoI3nKVuetOCnSKBWgCZADI1KDt4CVfrDqLaowYDnpsLB0XHBluJiIiIiKzRGMgUiURt1rXsiRMnAACDBw9uk/Jb4vTp0wCAAQMGdHBN2g8DmURERERERNRhsg4l4sqm4/AUecNL7o9IWRdA1rKySuoLUe1UhW7LxsMt3M98BgMEQYCqRomanGKo65QI7haCaLHhgKNPbDR8YqNNlieVSRExJQ4RU+L0Ps9LSEbaxmNwqneGjzxAG9RUC2rk12ZD0c0D3ZZOgFTW/Ge7oBGQe/Qico+koK6wChInGaLmDEXv7sMBAEUX0pHzZSI85T5G6yUSieCjCABqgcvPb0eZUyn6PzsHClfjA4yWXs1GTWEp/Pp35ZibRERERP+ve5Cb+US3sI5Y/pycHOTn5wMAIiIi4O7u3ibzue+++zBjxgyEhoa2Sfkt0RjI7N+/fwfXpP0wkElERERERETtqraiEqf/sxmiPCDIMQydFN1bVI5G0KBCWYoKoQz+o2PQ684Zra6bSCSCzMkBsuiAVpdliv+AbvAf0A0AkHUwERnbTgMC0Gn2EMTFjTJdR7EIgbf1QOBtPQxO9+4RCecXPHHh9e3wV4SYrYuHgw88VD64+spelCtLoRJU0IjVEElEgEoER5EjXKRucJa6QSQSIfHbH+E/pxtChvWxermJiIiIbjUrphq+JqO2017jY/r7+8Pf376GYzhz5gwkEkmbtUK1RwxkEhERERERUZspTs1E6g8HUFdUA4lKAkexE1xl7giTRlk1xmWNqgrV6irUCTUQnADPnsGImDoIYa5ObVf5dhIyIhYhI2JtWqbCxwN9Vs1BwvPfw08UCAex3GweN5kn3GSe+h86NE/n7xiM6p+zcf5yNnreN9lGNSYiIiIiskxrxsdMTU3FqlWrsGfPHmRnZ0MmkyEgIACDBg3CE088oe2yNSsrC6GhoRg0aBCOHTumzd/4eVxcHHbs2IF33nkHGzduRHZ2NqKiorBixQrMnTsXAPDrr7/i/fffx+nTp+Ho6IgZM2Zg1apVcHa24oeQjurqaly6dAndunWDWCzGypUr8d133+Hq1avw9/fHokWL8NJLL91yvacwkElEREREREQ2d+WXeOTuTkWQIgzB4nBAjoY/C6g0ShTU5ULtpoJv/0gED+8NhXfbdBd1K5PKpBi8agHqK2tQdCYNpVduoDq3FMqyWshrFPBVBLa4bCepKxxSlDj6yjoMeWWBDWtNRERERGRaSwOZe/bswdSpU1FTU4MBAwZgwIABqKqqQlpaGr799lvMmDFDG8hsbPXZtOVj4+c+Pj4YNGgQlEolBg8eDE9PTyQkJGDevHmIiorCd999h48//hijRo3CmDFjsHPnTnzyySdwcHDABx980KLlTkxMhEajQUhICAYMGIDi4mIMGzYMoaGh+OOPP/Dqq6/CxcUF//znP1tUvr1iIJOIiIiIiIharCq/GCVXslGTW4KawjLU5JZBXChGgCIE4U4xFpej0iiRX5cDWbgTui0ZhwhP1zas9V+Lg4sjAof3QuDwXnqfJ3+9C9VniuGvCG5RuVKxDCE14Yh//CvIfBuabgoQIHVyQPdFE+Hs7dHaqhMRERERNaMbyLS0i1VBELB06VJIpVIcP34cAwcObFZmWFiY9r25QOb27dvx9NNP4/XXX4eDQ8O18IIFC7B+/XrcfffdkMlkuHDhAjp16gQAOH78OAYPHowff/yxxYHMxvExd+7ciaeeegorV67Uzvvnn3/GjBkzsGnTJgYyiYiIiIiI6K+ttrQcp1ZthnOVC7zkfhCLxHCECI7wAOABOFpeVml9Eaqdq9H94YmICBjTRjUmQ7otGg8sAlJ/PIjiw9fgKHKCg1gOucQRMvH/ByYFAVWqclSqKqCCEoGKMEhEf3ZVJRKJEOnYGajUKbgSyP73EeQpbmD460vbeamIiIiI6FZWXl6O9PR0AICvry+Cgy17KO/KlSu4du0axo8f3yyICTRv2WkukDl16lS88cYbet24Tp48GevXr0dOTg4SEhK0QUwAGDRoELy8vJCbm2tRfQ1pDOBOnz4d77zzjt60CRMmAADy8/NbXL69YiCTiIiIiIiILFKcnIELX+yGnzgQ4dJoQGFdfpVGiUpVOWrU1VA7qOE/PAY975zeJnUly8XMGQHM0f+svrIG5Wk5cAn3Q6hO69izH2+FS7oj5BLT0WonqSsiVa6If+RLxL4wHa6BPm1RdSIiIiL6i0lMTIQgCACs61bW3d0dIpEI+/btw4cffoglS5bAxcXF5HzEYjF69+7d7HMAePHFF5vlKS8vBwDMnj0b3bt315um0WhQWVkJb29vi+vcVGOLzFdffbXZtLy8PABAUFBQi8u3VwxkEhERERERkUH5SWnI2HYCdbnVcBSc4CX3Q6RDF6vKqFZVokCTi6jZcQge0gMikaiNatv2qpRVqFfXw1PhaTSNUq1EnboOzjLnm3pZHVwc4RPbqdnnff4+DRk7T6J81w24yYyvh0aRLl2Q/vZ+KIb5oPOsUW1QUyIiIiL6K2np+Ji+vr5YuXIlXnzxRTz22GN45plnMGbMGMycORP33HMPnJyctGkrKipw9epVdO7c2eDnISEhBud99uxZAMCdd97ZbFpaWhrq6+ubBTgtVV9fjwsXLiAyMrJZcBUAkpKSAAA9e/ZsUfn2jIFMIiIiIiIi0nPqgx8hzhDgIw9AkCjU6paXAFBcV4Bqt2rELp+Gzu4393iX6WXp+PLcl9h2dRtUggqdPTtjbNhY3B5+O2I8YlBaV4r9mfux9/peHMk5gnpNPQKcA3Bb0G0YFjwMgwMHw8XB+NPeN5uICXEojc5B2n8PIMAxxGx6L7k/6o/V4vD+1VA5qOAS6YVOU4fCI/zWe1qciIiIiNpWS8bHbPTcc8/hrrvuwsaNG7F9+3bs2LED27Ztw4oVKxAfH4/IyEgADQFJQRCald/4ef/+/U3WLS4urtm0xpac1gRfdZ07dw5KpdJg2brzbmn59oyBTCIiIiIiIgLQ0HVs8sd7EeYUDZHCstaEuTVZUMrrIJJLIHGUQeaqgFeXEPQaN6NDWiSqNWoU1hSioKYA+dX5KKguQEFNAcrry1FZX4kKZQUq6yuh1CgR4ByAUNdQhLiEINQ1FH5OfnBxcIGrgyvkEjnSStLwedLn2JGxAwIE7Twul1zG5ZLL+Pjsx/Bz8kNRTRHUglqvHrlVudicuhmbUzdDKpKiu093xHjEIMo9Cp08OqGTRyf4O/kbXUdpJWk4lXcK9Zp6SMXShj+RFM4yZ3Ty6IRwt3BIxQ0/6QVBwOWSy9ifuR8Hsw4irzoPY8LG4Il+T8BJ5mSw/Nby6BSE/h/cjay9Z1CZUwQIIkAA1NV1QKoSHg76XWY5SBQId45peJMFlH6UgrS6/ahWVKPbfbcD7lYMrEpEREREf1lr167F2rVrW5w/MjISzz77LJ599llcu3YN9957Lw4dOoSPP/4Yq1atAmB+fMx+/fo1K1ej0SApKQleXl4IDw9vNr21gcbG/MaCtwxkEhERERER0S1LrVbjxOvfwrvC989gkwkqjRK5dVnwH98ZA6bc0w41NE0QBFwsvogtqVuwPX07yuvLLcp3tuCs0WkysQxKjdJsGfnV+WbTqAQVkgqSkFSQpPd5mGsYJkZOxKSISYj2jIZSo8S+6/vwfcr3SMhLMFmmTCzTBjTPF55HdmW23vTvU77H2YKz+Oj2j+Dj2DbjU4pEIoTe3vwmTn15NU6v2IwQeYTRvFKxDAGOoQCAii/TkFubBXGEI9z/Pg0ymaxN6ktEREREpCs8PByLFi3CoUOHoFb/+WCiuUCmoWDipUuXUF1djSFDhhicl6m8lmgcH9NUIFMikaBXr14tKt+eMZBJRERERET0F3PjxAVc23UGysJaOGjkcJW6IdQh0uQvxFp1NUrri1DvUIfOC0ZgcO8x7VdhAwRBQE5VDvZn7seW1C24VHLJpuVbEsRsresV1/F50uf4POlzRHtEo7y+3KLAKNBQv5TiFKQUpxhNc7HoIub/Ph8f3f4Roj2jbVVtsxzcnDD4/QU4s2oz3Avc4SCWm04vUSDMORooAC4t/w3iHgr0Xja1nWpLRERERLe6nTt3Qq1WY/z48ZBK//zRk5OTg//85z8A9Me1NBfINNTq0VyLyMTERMjl8mZjZL7yyit49dVXsWjRIpMtTRsDmYbKLy4uxvXr19G9e3c4Ot56PZ0wkElERERERPQXkXc6BWlrjiLUKRIhonCzY1+W1Rejyr0KIbf3QqfbhnVIV7EAUK2sRmZFJjLKM3Cp+BIuFF3AxaKLKK0rbdd6BDkHIS4gDodzDqOwprDZdJlYhsGBgxHtEY0TuSdwoeiCxWWnlabZsqpaOVU5WLh9IT4Y/QEGBg5sk3kY0/fpWcg7cQnp3x+Hi8gNbjIPiEUSk3l8FAEQ0gQcfexrhN7VDyG33XpPlBMRERFR+/rhhx+wZs0aeHl5YcCAAfDx8UFeXh7i4+NRX1+P5557DqNGjQIAqFQqXLhwAf7+/ggICNCW0fi5n58fgoOD9VpwAn8GMg11O5ubm4vc3Fz0799fL5AKNHRJC8BkryRqtRpJSUnN6tR03rdit7IAA5lERERERES3vNrySpx47XsEi8IR7my+ZZ5aUCFbdQ0DXp0LB9e2GWPRGJVGhTP5Z3Ag8wDOFZ7D9YrrBoOG1nKVucLFwaVhDEyZK0QiEbIrs5FXlac3/qUhoa6hWNZrGe7odAdkYhk0ggZJBUn449ofSC9Lh6uDK0aFjsLw4OFwcXDR5iuqKcKRnCM4fuM4rpRewZWyK6hR1bR6WUxxl7ujrK5M77MKZQUe+OMBhLuFw8fRBz6OPvB19MXAwIEYFjwMYpG4zerjP7AL/Ad2AQCoqupwfUcCChOvQVohgY88wOC8RSIRQp2iUL81D4c2nIBGrEHDVyRADTU8egei7/3T26zORERERHRrueeee6BQKHDkyBGcOnUKZWVl8PLywsSJE/HII49g3Lhx2rQpKSmora3FyJEj9cow9nkjU8FEU93KNk5buHCh0fqnpKSgpqYGI0aMMDnvlnZba+9EgiCY/sVGZMapU6cwYMAAAMCxY8cwaNCgDq4R0V+bUqlEUVERAMDb25tjDBHZAe6XRPblVt8nVXX1KEhKRdGFa6jMKkJ9aS081F7wkvtZlL+wNg/u40IQOWVwG9f0T9XKahzMOoh9mftwKPuQxWNcGhLuFo6pUVMR7RENXydf+Dn5wVvhDZnE8Pdcp65DTmUOsiqyUFpXikplJSrrK1GhrIAgCOjl0wtjwsZAKm79c8AaQYPcqlyklabhaM5R7MzYiYKaAoNphwUPwz1d70Ef3z5QaVRQaVRQapQorCnE5ZLLuFxyGaklqcisyISHwgPDgodhTOgY9PLphZ/Tfsa/jv0LakFtsGxdndw74f7e92NixESbLKM1ChOvIPXbQ/DQeMFV5mFxPo2gQUbNJQx+ayEcHM00KyaiFrnVz5VENxvuk+0vOTlZ+7pbt24dWBOyRxqNRtsiUyKRQCxu2YOBarUaXl5eGDhwIP744w9bVrHFWrrt68aJEhIS0L9/f5vViS0yiYiIiIiIbgEqlQpHl6+FnxAIZ6kbPOAKD7g2/Ooz8cuvTl2DcmUpalEDRYgzej0+HVJZ2/9UFAQBF4ouYHPqZmxP344qZVWLy3KUOmJc+DjMjJmJfn79rOoCVy6RI9I9EpHukS2ev6XEIjGCXIIQ5BKEESEj8M8B/8Tp/NPYlr4NBzMPQgMNJkZMxN1d70a4W7jBMkJcQxDrF2tyPrM6z0KgcyD+ceAfZtfrlbIrWB6/HB8nfozFPRaju3d3eCu84e3oDQeJA8rqynC17Cqull7VtiidFDHJJt3U+sR2gk9sJ9TU1CDxvZ/gXuQKNwdPs/nEIjGinLoh6bmfELqoH/z7dW11XYiIiIiI2ltCQgLKy8vx2muvdXRV7BoDmURERERERDe52tJynH5pMyKdu1iUXqmpxw1NJjrdNQTBAzq3y9iXao0a2ZXZuFp2FaklqdiRsQOXSy63qKxgl2B09+6OHt490N27O/r49oGTrH27wLUFiViCuIA4xAXEAUNsW/bQ4KH4euLXeHjPw8irzjObPrMiE/869i+9zxyljga7wt10eRMe6vMQHurzkE22HalUisilt0NZWYvrn+9DgBAKB4ncbL4Ax1CUfncFucdT0Oeh6a2uBxERERFRexo0aBDYaap5DGQSERERERHdxIrTMnH1w3iEWTD2JQDcqLmOsPn9MHTg7W1cMyC/Oh8/XPoBBzIPIL0sHfWaeqvyh7uFo4d3D4S7hSPUNRRhbmGIcIuAu9y9jWp8a+ni1QW/TP8Fv139DZeKL6GwphCFNYUoqCnAjaobZvObGs/zk7Of4GrZVbx+2+tQSG3TvavMRYG4N+5FWfJ1pH51CL4OgVBIHE3mcZa6QZGhxrHHvobIX4aeD0yCs6/5Vp1ERERERHRzYCCTiIiIiIjoJpW57xRKfrmGAMdQs2mL6/Ih6e6EuGXz27xeFwovYF3yOuxM3wmVoLI4n1gkRl+/vhgVMgojQ0e2S3evtzonmRPmdpnb7PMLRRfwZdKX2H19d4vL3pmxE9kV2fjPmP/Az8myMVgt4dO7E3z+06nh6XSdB9Qrsgpw6d0/EOgYppdeIpIgxCkKqADyVp1CQe0NiINk6PvP2ZBKeduDiIiIiOhmxit6IiIiIiKim0he4mWk/XQEQokafvIgeMl9m6UprM1FvYcSjgFu8OwSAt/YGIR4Otu0HmqNGvHZ8TidfxrldeWoVFaisr4SBTUFVncZG+EWgVkxs3BHpzvg4+hj03qSYT28e+D90e8jtSQVX577EjsydkAjaKwu53zRecz8ZSb8nfy1nzlKHTEqdBTmd5sPR6npFpWmiEQiQKfnWrcwP/R99y6cfPFbBAnhkIgkzfI4iOUIdooASoGzT22Cx+2h6HTnbS2uAxERERERdSwGMomIiIiIiOxc+o7jyNp5Dm6CBzzlvggXdQKMxCVzaq6h96vToPBwbZO6VCursfXKVqy7uA6ZFZktLsfNwQ2jQkdhVsws9PXr2y7jdFJzMZ4xeGvEW3h+0PNIL0tHUW0RimqKUFRbhLK6Mrg7uKOTRydEuUfB18kXLx5+Efsz9+uVUVZXhrK6Mr3Pzhacxe9Xf8e7I99FlEeUzeorlUox5M1FuLB6B2QXNXCSGt/O/R2DoT5Uj0N7V6Pv8zPg7Pdnl7P1VTVwcG55kJWIiIiIiNoHA5lERERERER26EZCMtJ+OAIXpSt8FYGIlHcxmyez/iri3p1v8+40a1W1SClOwcGsg9h4eWOzoJU5HnIPjA8fj2jPaES5RyHKPQo+jj4MXtoRd7k7Yv1izab7z+j/4D+n/4PV51ebTZtWmoa7f78bLwx6AdOip9mgln/qcd9ElF7JwYVPdsJV7W6wZTIASMRSRChikP32UdRp6uAgdoBM5ACp2AEVylLUhtaj/5NzbFo3IiIiIiKyHQYyiYiIiIiI7Mjlnw6gcN9VBDtGIFLSGWjee2YzGkGDGw5ZGPLmIpvUQSNoEJ8Vj/jseJwrPIfLxZetGuuyUSf3Tri3+72YEjWlVV2M2oWiK8Dpb4D6KiByBBB9O+Cg0yxWEIAbicDlXUBtKeDbFYgeC7gHd1SN24RYJMaT/Z9ElHsUXjn6ClQa09tFjaoGLx5+ESdyT2BZr2UIcwuDWCS2SV08OgXhtneWAACy9ifi2u9n4Kpyg5e8+XidTlJXOEG/9aa7gxdcclU48tJaDP3XYpvUiYiIiIiIbIuBTCIiIiIiIjtw7qvfUJVYjCCncIQ5R1uUp6y+GBVCGYImdMegifNtUo+Msgy8evRVJOQlWJzHW+ENVwdXOMuc4eLgggCnAEyKnIShQUPto9WlWgXUlQO1Zf//vxxQ1wPuIYBHGCAzEWTNTwHi3wHObwYax5A8+QUgdWwIZsaMa0iT8htQZqCrXb/uDQHNyJGAXzfALQhoq3WirAEyDgHlOW0eRJ0WPQ0xnjHYkLJBr4VuvaYeh7MPQ4Cgl/6XK7/glyu/wEXmgq5eXdHDuwcGBg7E0KChNqlPyKhYhIyKhUqlQsLr38Or0gdOUhez+SQiKULro3DwqS8x4t37bVIXIiIiIiKyHQYyiYiIiIiIOlB+UhqufnkEIU6R8HR2N5m2XlOHgrobEHlJ0Gn2EIT0GG6zeig1Sqw9vxafnv0U9Zp6s+mlYinuiLoDC7svRIxnjM3q0SoVucD5n4D8iw2vK3Mb/lcVAk0Ca3pcgwDPCMAtEJC7AQq3hv+554CLWw3nVdU0BC9TfjNdp/yLDX9H/tvwXu4G+HZp+IscCXSZBMh1WgoKApARD5z8Erh2BFDVAxIpIJYBEhng4AL4dgb8egD+PQDvaCD7FHBpG3BlL6CsbihHIgemfwz0mm3FCrROd+/ueO2215p9fiTnCJbHL0dxbXGzaZXKSiTkJSAhLwFfX/wavo6+mBo1FaO8RiHQKbDVdZJKpRj8ygKUXr2BC//ZgVBFlNkWoCKRCFGyLoh/7EsMWbUQUrlDq+tBRERERES2wUAmERERERFRB0ndchD18SUIcYo0mkalUSK/7gbEAVL0fGgyojxcjaa1llKtxNWyq0gpTsHXF79Gakmq2Tyeck/M7jwb93S9B75OhsclbFdqJXB5B3BmPZD6ByCorS+jIqfhrz3UlQNZJxv+zqwHpAqg8wSg56yGgOuJL4CCZNNlFCT/f4DVBHUdsHkpUHoNGPaPtmsFasDQoKHYNHUTnot/DidyT5hMW1BTgNUXVmM1VqOvV18s7b0UI8NHtroOHlGBuO0/S3Dx2z9QejwbTmJnqAUVVFBBAw18HPzhKHHWyxPp1AUn//k9ei6fDNcgO9i2iYiIiIiIgUwiIiIiIqKOcOq9jXC/4Q53By+D06tVlSgU56HXwxMRET7GZvPNLM/E1xe/xpn8M7hadtXsGIcxnjHo49sHvX16o7dvb0S6R9psjMMWqa9uaCl5IxHIOQOk7QaqCjquPq2lqm0ISpoLTLbUnteAkmvAlHcbWnS2E18nX3w+7nN8ce4LrDm/BtWqarN5zhSfwSP7H8Hw4OF4Ou5pRLobD/Bbqvv8cYCBXpczd59CxfYbcJXpt4IOdY5C+qqDcBzhi5gZI1o9fyIiIiIiah0GMomIiIiIiNpZ/LNfIQxRkEia/ySrVJahzKkUfZ+fic5uTjabZ7WyGp8nfY5vLn4DpUZpNr2fox+eH/w8bg+73WZ1sEpNCXDjLFB0BSi+ChSnA0VpQFHqn2NVtifPiIaWjZ1GN7T8TP61oQvYxkCwdwzQ7Q6g21TApwtw7XBDkDX1D6Akvf3rq+v01w3jd/a9F3AJAFz8AdcAQG5+DMnWkIgleLDPg1jYfSGSi5NxofACLhRdwMWii8gozzCaLz47HkdzjuKebvdgWa9lcJI5QRAEaAQNZGIZZDYIyIaO7Y9Cvyu4seYsPOX6rS+95H6oP1KLQ6dWY9jr97V6XkRERERE1HIMZBIREREREbWT5A27UXo4G5HOnQ1Oz1SnI+7f8yB1sN1PNUEQ8Hv673g/4X3k1+RblGdu57l4ov8TcHWwXTe2FilMAy5vBy5tB64fa1k3sQAgcwa8IhuCda4BDcE7Zx/98S9FooaWiiXpQElGw191EVBXAdSWA43BXr/uwNDHgF5zGsaqBIC4pQ1/NSUNAVaFO+DdSb8OnSc0/AFAaSaQn9zQJWzBpYbXN86aX76gfkBIXEOwVKNs6Ea3IhfIuwBUNfkuxTIgcjgQMwE4tbZ597RX9jb86ZXfFxj6KNB9OiCWWLZuW8BJ5oT+/v3R37+/9rNr5dewOXUztqZtNTiWpkpQYd3FdVh3cZ3e5yKIMDRoKF4Z+goCnANaVS+f3p3g+LQ7Lr29B36KIL1pDhIFIlQxOPToV1DL1RCJRYBEBImDBGG3xyJ0RN9WzZuIiIiIiCzDQCYREREREVEbO/f1dlQm5CHYKRKuzlHNpqs0ShR5FWHIcwtbNZ9qZTV+u/obUopTkFedh7yqPORW56KsrsxsXhFEiPWLxeP9HtcLOLW5siwg8TsgaWNDa8vWiBgO9FvY0CpS5mg+vbGeSwWhoctXjdp0q0VHTyDY0/x8PEIb/jqP//OzqsKG7mTP/9TQehNCw+cSB6DHDGDg34AQE99DZQGQf6EhkOrsA0SOaAioAkDsPcAPC4D0A6brlXMG2HQf4L0SGP7U/wdrTbR2rC0DlDUNLTpbOeZmuFs4/tH/H3i076PYk7EHa8+txYXSC2bzCRBwOOcw7vrtLrw5/E0MCRrSqno4B/mg57+m4tRLPyJU0XzfjNB96EAAUAfU/1qI44e/xaDlBvqsJSIiIiIim2Igk4iIiIiIqI1kHTmLzO/PINgpEp7OnQymqVFVQt1Pjv7z5rRqXpeKL+HhPQ8jrzrPovR+jn4YHTYaXby6oKtnV0R7RsNRakHwzxbUyoZWl6e/Aa7saXlXsTInIKA3EDUS6HNPQytMWxCJLAuEtoazz58tO8tzGrqgFTRA1ymAi5/5/C6+gMsoIGpU82kKd2D+JuC3J4DEb82XVZQG/PwQsPf1hhaoLn6Asy/g5AWUZQOFlxpaklbcaEgffhsw60vALch0uRaQiWW4PfR29HHsg4N5B/Fl6pfIrc41m6+4thh/++NveKTvI7i/1/2tGrfVwdUJQz5YhFNv/QjPIk84iOWm00sU8CvxR+KnvyD2wTtbPF8iIiIiIjKPgUwiIiIiIqI2cPLdH+B2wxWhRgKYAFBSVwi/eV3hH9etVfM6nH0YTx14ClXKKrNpZWIZFvdYjPt73Q8nme3G4DSqprRhLMmCFKDgckNQrDAVUFZbXoZIAniENXTf6h0NBMY2dIvqE9OmXaK2G7cgoP8i25YpdQCmfQQE9AIO/+fPIKQp5dkNf+ZcOwx8Phq453sguF/r6wpAJBJhZMBITO4yGd9e/hZfnfsKtepak3kECPjwzIdIzE/E8oHLEeoW2qo69H92DrIOJKJ0azo8HHxMppWJ5XC5osKlH/eiy5wxrZovEREREREZx0AmERERERGRDdXX1uLIs98gStEZYqnhIFu1qhLFDoXo9+osOLi1Lpi4+fJm/OvYv6C2YDzJ0aGj8fSAp1sd8LFIYSpw/NOGbmOtCVoCgNwdiBkHdJ7YECjzCDPd5SkZJhIBgx8CBj7QMP5nZR5QkQdU5gIpvwOXtrW87MpcYM0kYPonQM+ZNquyQqrAg30exKyYWTiYdRDFtcUQiUQQQYRqVTXWXVyHGlWNXp747HjEb4lHV6+uGBc+DmPDxyLKvXk3sZYIGRkLv76dcfqVzfCTBppsnamQOKPyWBky/U4jdKRtArpERERERKSPgUwiIiIiIiIbyYpPRM6P5xHtZLiFZZWqAqWOJej70kx0dm5d16U5lTn4PuV7rL2wttk0iUiCSPdI+Dv7I8ApAP5O/hgaPBR9fPu0ap4G1VcDtaVAXQVQW94Q4Dq9DkjdaV05jp4NYzR2mwqEDWHg0pbEkobuYl38GlpoAkDfe4Hcc0D8u8CFn6Edo9Maqlpg0xLgxlkgWGc8Twfnhu5nZYoWV9nXyRezOs9q9vmkiEl4cv+TyCjPaDYtpTgFKcUp+PDMh+jp3RPPDHwGff36Wj1vBzcnDH5vAVTVdajOKYKyogb1ldXIO5EKzzxPSMR/3kpxkbmjdOs1OPp4wKdHy4KnRERERERkHAOZRERERERErVCano3zX+6EQ4UD/B2DEeQU3iyNWlAhV5yN/v+aA6mj6fH3jKlR1eBYzjEcyTmCYzeOGQzkAICrzBXvj34fgwIHtWg+FlHVA+c3NbS4vHG2dWVFjQL6LQS6TGlV4ItaIKAXMGctMOoycPprIP8iUFkAVOUDVYWAoAbEsobufH07A66BQMJqQF2vX87hD5qXLXcDJr0NxN5j0ypHe0Zjwx0bsOLICuzMMB4sP190Hgu3L8SM6Bl4sv+T8FR4Wj0vqZMcbtF/jgMaOLwXTr35I3xL/PTG5PRw8EHWF6cgfkgCry7N938iIiIiImo5BjKJiIiIiIha4MrvR5C34zICHEMQIY4BjPQQW6Uqh3iAMwbdfa/V81BqlDh+4zi2Xd2GPdf3oFpluovWQOdAfHz7x4j2jLZ6XhapKQES1gDHP2toeWktJ2/Ap0tDUMy3G9BlIuAZYfNqkpV8OwMT3tD/TKMB6soABxf91rE9ZwEb5gFVBabLrCsHfn6wYXzUyasaWmnaiLPMGatGrMIA/wH4KPEjlNaVGk27JW0L9mbuxcOxD8PP0Q9l9WUorStFZX0lIt0jMSlyEqRiy2+N9H9uDo69uA4hqgi9z30UAcj6NAF191QicGCPFi4ZERERETW1efNmzJ492yZlnT17Fr1797ZJWdR+GMgkIiIiIiKyQvn1XCS++yvC5J0Q5tzJZNrcmix0fnI03CICrJpHZnkm1ievx46MHSiuLbYoT3fv7vjo9o/g4+hj1bxMqioEshKA7ISG/5nHrR/v0i0EGPQA0Oeehq5Nb2IVtUrUqzTwdjHeqrZepUGtSg1XuRQikagda2djYnFDd79NhQ4Elu0FvrsbyL9gvpzEb4HsUw0tP/0Md7ncEiKRCHd3vRuzYmbhRO4J/HHtD+y9vhcldSXN0pbVlWHl8ZUGy/nlyi/4YPQHcJZZHmgd/PoCHH3qa4TK9LuS9ZL7oXhDKmpLqxA5fqB1C0REREREBp04ccIm5Tg6OqJHDz5wdjNiILMdlZWVYefOndi3bx9Onz6NtLQ0lJeXw8XFBWFhYbjtttuwZMkSxMXFWVzmjh07sGbNGhw7dgx5eXlwc3NDTEwMZs+ejQceeADOzrZ76pWIiIiI6K9MpVLh+Kvr4F3tiyjHrqbTapTIFWdjwLv3QCq1/GdXSnEKVp9bjZ3XdkIjaCzKIxPLMCN6Bp4a8BScZEaahVrr6gHgj5eBG4ktLyN0EDD4IaDrVEByc//0TMuvxMf70vDL2RyoNAK6BrhiQo8ATOwZgK4BriipVmL3xTzsvJCL+LRC1Ks0CHRXYGRnX4zs7IvbYnzgpriFxvz0CAOW7gS2PgJc/Nl8+oIU4PPRQNfJQFBfIDAWCOwDKNxaXRWZRIbbgm/DbcG34cXBL+LEjRN479R7uFRyyaL8x24cw5IdS/Dx2I+teghg8DsLcezJbxCq0A9mujt4o3JnPpJL96Db3NutWhYiIiIiam727NkYMmSIwWn19fW46667AADOzs5Yv3690XJcXV0hkUjapI7UtkSCIAgdXYm/grfffhsvv/wy6urqzKa999578dlnn8HJyfhNiLq6OixevBgbNmwwmqZTp0746aef2ryp9KlTpzBgwAAAwLFjxzBoUBuOxUNEZimVShQVFQEAvL29IZPdQjfNiG5S3C+J7EtL9snM/aeR+1MyAp3CjKYRBAGFdblQuqvQfdl4uIRYHhRJKkjCJ2c/waHsQxal7+zZGUMCh2BI0BD08+8HR6mjxfMyqboY2PUSkGj8BoAekRjoNhWIHAko3AG5a8PYiO4hgEeobepkJbVGQH5FLfLK65BXXov88lrkV9ShrEaJiloVKmqVKK9VQanWIMjdEWHeTgjzckK4lxP83RVwVUjhppBBLhXjcl4lPtybit/P3YCxX87+bnIUVNRBY+KXtUQsQq9gd3Txd0WMvwui/VwQ4++KIHeF0Vabl3IrcCK9CHUqDWQSMaQSEWRiMZzlUsT4uyDSxxkyScM4jYIgIPlGBXYn52FPSj7yymoxrrs/npvUFc7yNg4il2U3dDncSFUH7F7R0KWsOUF9gb73Qtl1BoqqlABsc55UaVT4PuV7/O/M/8x2x9woxCUEn437DGFuxvfxZvNRqXDymW8R6hDVbFqNqgo3ajMBTxGi7xyCoDg+/U83F16/EtkX7pPtLzk5Wfu6Wzfb9SpBtnX69Gn0798fADB06FAcPny4Xear0WigVqsBABKJBGKx2EyOm0dLt33dOFFCQoL2e7GFm/ux2JvI5cuXtUHMqKgojB07FrGxsfDx8UFJSQn27NmDzZs3Q61WY/369cjPz8f27duN7gCLFi3CDz/8AKDh5PXAAw+gV69eKCwsxPr163HixAlcuXIFEydOxPHjxxEa2jE3EYiIiIiIbnZHXv8aPqV+RoOYtepqFIhyEX33bejbb4RVZQuCgHUX1+GdhHcgwPQzpr18emFy5GSMjxgPPycbd9EqCMCFLcD2Z8yPfQgAMmeg3wJg0IOAV6Rt69ICgiDgXHYZNiZk4tezN1BWo7Qo3xmUGp0mk4igVJt/7jev3PzDqmqNgMTMUiRm6s8v3NsJU3sHYWqfIHQJcIVSrcHOC7n45sg1nMgw3aWwg0SMTn4uiPJxxtmsUmSV1OhNX3fsGs5klmD1ojj4uSnM1rHF3IMb/nQt3AoceAs48DZgarvOOQPknIF054twj5qI6m6zAa/xra6SVCzFgu4LMD58PFYlrMLOjJ1m82RVZmHB9gX4+PaP0cPHsqCjVCrFkPcW4dgz3yBErL8fOEqdEeXSFVACyh/zcWp9Eipk5Rj80j1QuLe+JSoRERERAWfOnNG+7tu3bwfWhNoSW2S2k2XLluHGjRt4+umnMXLkSINp4uPjMXnyZFRWVgIAVq9ejSVLljRLt3XrVkyfPh0AEBYWhvj4eISF/XlTRaPR4P7778eaNWsANDS9/vHHH228RH9ii0wi+8Kn9IjsD/dLIvti6T5ZlV+Mk6/9iCjnrhCLmj9gqBE0yKnPQPfHJ1g9BiYAKDVKrDy+EpsubzKaxt/JH7M6z8KUyClWtRQzqyIPyDoB5J4H8s4DuUlA6XXTedxDgeD+QPhQoPdcw+MntiNBEJBVUoPdyXn44WQmUnIrOrQ+rdXZ3wXlNSrkltfatNxgD0esWRKHzv6uNi3XIlf3A5uXAVX5FmfRBPWHePLbQMgAm1WjsKYQV0qvwMXBBR5yD7g7uOPri1/j07OfNkvrKHXES4NfwtROU62ax4mXvkVgfajZcVELa3NRF6zCoKfvsap8ovbG61ci+8J9sv2xRebN4ZFHHsFHH30EAPjiiy9w//33t8t82SKzubZskclAZjspKSmBp6f5H/r/+9//8OijjwIARowYgQMHDjRL07dvXyQmJgIAfv/9d0yePLlZmpqaGnTt2hXXrzfcjDh37hx69uzZiiUwjoFMIvvCi1si+8P9ksi+WLJPpv0Wj/Ld2fBTBDebBjQEI9xvD0HkVMNjtZhTXl+Op/Y/hWM3jhmcHuEWgft63oc7ou6ATGLDY0ZlPrDnVSDxO8CSMTgDY4ER/wRCBgKu/rarh5Uq61S4VlSF9MIqXMwpx7nsMpzLLkNptWUtL20lxNMRQ6K8ceByAfIrmrfEdJCIMSzGBzH+Ljh2pQhJ2WVGu6RtT64KKT67tz+GRlve3bHNVBUCJ78Crh0Ccs4CdWWW5et9NzD2FcAtsM2qtvHSRrx+7HWDraFnxszEcwOfs6rb5lP/3gjfUj+IRebHXrpedQV+Ezoh+o7hVtWZqL3w+pXIvnCfbH8MZN4chg0bpu1O1tbBM1MYyGyOXcveAiwJYgLAnDlztIHMc+fONZuempqqDWLGxMQYDGICgKOjI5YtW4aXXnoJALBx48Y2C2QSEREREd1K0ncdh3JfqcEgplqjQr7iBgb8+x6zLa+MSS1JxVMHnkJ6WXqzaaGuoXiy/5MYEzoGErH5YIjF1Erg+GcN3X3WlZtPL3MCRr/Q0HWspH1/NirVGpzMKMbe5HwkZpYio6gahZXmu281x00hhatC1jAGpqMMIgBZJTXIKasxG2yM8HbC30dHY0bfYMgkYmg0As5klmLH+Ru4UlAFN4UUt3fzx6guvnBV/HljsaiyDvGphTicVojL+ZVIy6tAVb261ctiiqeTDCVNArwVtSosWH0CEd5O8HWVw89VAT9XOYZ08sboLn4Qi1u2LVvE2QcY9SyAZwGNBihJb+hSNnUXcHEroDLSAjVpA5D8KxA7D3Bw/v/AuwBIHICYCUBY6x+gndtlLrwUXnj24LOo19TrTfsp9SckFSTh3VHvIsq9+RiYhvRfPhdZ+5KQ/WsS3EQecJW5G00b5twJyoP12L/7Uwx9czEcFG3Y/S8RERHRLUgQBJw9exZAQ5f/jH/cuhjItDOurn9291NTU9Ns+s6df47tMWHCBJNlTZw4URvI3LFjB1577TUb1ZKIiIiI6NaUl5iCyu1Z8JQ3H4OyXFkKx9F+iLtjXovKTilOwedJn+OPa38YnB4XEIf3R70Pd7nx4IdFNOqGcS7Ls4HyHKAsC0hYDRRetix/pzHAHe8DnhGtq4cVqupU2J2chz3J+dh/KR/ltaoWlxXp44yZfYPROcAV/m4K+LvJ4eMih0xi+CnpOpUa2SU1uF5cjZLqelTWqlBeq0JFrQqCICA21APjuvtDqpNfLBahf7gn+oebfmDV20WO6X2DMb1vQ1BcEATklNXicl4F4i8X4rekHIMtOwFgdBdfLBwagX6hnlBpNFBpBNSrNMivqENKbjlSblTgUm4FrhVXwdPJAaO6+GFcd3/0DfXAj6cy8fyW81Br/ozQqjUCrhRU4UpBlfazLw+lI9rPBQ+P7oSpvYP0lrFNiMWAd6eGv16zgUlvAUk/Qji1FqL8C83TK6uAk180/zz+XWDYPxqC7a0MtI8NH4vPx3+Of+z/B4pr9cclTStNw8ytM+EkdYJMIoNULIWT1AlDg4bi8X6Pw0nm1Ky8kNG9ETK6NwCg5MJ1XN1yDOJCwFfRvGWpTOyAaEUPJD6zCYGzeyB0BMd1IiIiuiltfw7Ibd4o6S8joBcw6c12n21qaqp2mL7u3btDLpe3ex2ofTCQaWfOnz+vfR0eHm5yurmmubGxsZBIJFCr1bh48SIEQWjxU+NERERERLe68uu5yF6TCD/H5i0xb9RlotdLd0Dh5WZ1uRcKL+DTs59if9Z+o2lmRM/AS4Nfal03soIAHP8UOPQBUJlreT7XICCgJ+DfE4gaCUSOBNrhd4MgCDibVYYNJ67j17M5rWqp6CiTYErvQNwVF4oB4Z5W/e6RSyWI8nVBlK9Li+dvKZFIhGAPRwR7OGJ0Fz+8MKUbTmYU45ezOdibnA+NIGBqnyAsGByOCB9ng2WEejmZDaDeFReGQHdH/P3b06isMx0UTsuvxJM/nMX7f6TigRFR6BnsDh8XB/i4yKGQSVBaXY/U/Eqk5VciNa8SNUoVpvYOsk03tY6ewKAHoOq7GBVJv8P1yErIii0MuB96D8g6CcxeDbg0f/DAGv39++PHqT/imYPP4FTeKb1pakGNCmUFoNPINaM8AxnlGfjfmP+Z3Gc9e4Shf4+GsW2Tv9oF4UIt3Byaf3dBTuGo/uUGjhxMwtAXF7VqWYiIiKgD5J5r6Eaf2tWZM2e0r2NjYzuuItTmGMi0M59//rn29ZQpU5pNv3z5zx91ERERJsuSSqUIDg7G9evXUVVVhezsbISEhFhdp1OnTpmcrttnskqlglLZvuPUEJE+pVIJlUqlfU1EHY/7JZF9MbRP1pZX4uI7uxDiFNks/Q1ZJmL/NUcvvSVUGhU+O/cZvrrwldE0IojwWOxjWNhtIaABlJoWHiPU9ZBsewripO8tSi7I3aAZ8Sw0PWcDTt5NKt7y1pCmqNQaZJXW4Ep+FS7lVWLb+VxcyqtsUVmhno7oGeSGnsFu6Bnkhj4h7nCWN/y8VbVR/dtK/1A39A91w6t3dNX7vLXniyGRHthwfxzuX3caueXmu+a9XlyNF38+r/eZo0yMGmXzsVS/P5GJR0ZF4dHRnWzSLa1SpUJ1QByqp/8Iv8ztkB16G6KaYvMZM+IhfDoM6plfQQgd3Ko6eMo88cnoT8zus42O5BzBC4dewL+G/AtikfmWrNELR0NVr8L5N7fAq9YXcol+V7JOUleEVrjgwCOfofdzd8LFvwPGMyXSwetXIvvCfbL9CTpjD2g0pseWF0HAX7n5kAABgpl11BaaBjLNfU+2JAiCdhsRBKFd593WdLd9q37/tuHvMAYy7ciRI0ewZs0aAIBCocCTTz7ZLE1paan2tY+P+R823t7euH79ujZvSwKZjQO0WqKsrEw78DQRdQyVSoWKigrte6mUh3qijsb9ksi+NN0nNTV1uPaf/Yh06dwsbaYyHZFPjLf6Greorgj/PvtvnC05azRNoGMgHu72MAb5DkJxsQVBGyNEdeXw2PUYZNlHzaYVIEJNtzmoGPgkBEcvoAZATdtdv+dX1uOnpALEXy3D9ZJaKNVmBqNsIsxTjm7+zgj1kCPEQ44QdwXCPOVwU+gfR2sry1DbspjoLc1HBnx3bzfsulSMywU1KKpSoqhKicIqJXIr6s3mNxTEbPS//VeRnF2Cl8ZHQCFrXZe0evtk+FTIQsbA5dQnUFzZDnFNUUMLYZEY0KghahLsF1XmQbJuGmqjp6A2agLqQocB0paPN3l3yN3opOiEt5LeQpmyzGTa7Rnb4Qxn/K3L3ywuP/ThMajKyEfehovwbzIOr0gkQieX7rj2ziGoessQMrl1wVmi1uD1K5F94T7Z/lQqFcRiMcRiMdRq0z2HSIS/eCBTEMyuo7agG8js3bt3m9Rh7969mDhxIhYsWICvvvrzYbemy3yr9YSp0Wig0Wis+h2sG7uyNR7x7ERubi7mzp2rjdz/61//Mhh0bOzzGWgIdprj6Oiofa17siMiIiIi+qvTqNW49Mlv8K32MRjEzK7NQMSz46wuN7EoESuTVqKkvsTg9FDnUMyLmofRAaMhEUusLl+XuCIbntv+BllJqsl0gliK+uDBqBj4JFS+PVs1T0tczK3ChjP52JNaDLUVDyeLRUCfIBcMi/LA8Ch3hHm2PCBFDZwcJJjey7fZ5yn51fj6xA3sSyttcdl7UkuQU16Ht6d2gq+LQytqqU+Qu6Ni6HOoGPqc/gRVLdziX4XTpZ/0PhYJajim/gLH1F+gkTqhLnwkajtPR11Yy7pJjvOJw+phqxGfF4+c6hwoNUooBSWyqrKQWJyol3ZTxiZ4yb0wJ2KOxeU7R/jB6VlfZPz3DwSpQyER69+a8VEEoD65FhfO/4SuT02DRNK64wQRERG1LcG/F26d9njWE/x7dch8ExMTATQEEfv06dMm8zh58iQAYNCgQW1SPlmGgUw7UFVVhWnTpiE7OxtAQ5eyTz31VAfX6k8JCQkmpycnJ2PBggUAAHd3d3h7e5tMT0RtS7fJv5eXF2SyVoy1RUQ2wf2SyL4olUpc/W4P5JkidHHuAcibp8mtzULvlbMhlVn2k0kQBJwtPIvf0n/Dz1d+hkZofisjwi0CD/V+CGNCxrQugFlVCFHGAYiv7ocodQdENc0Dppqo0dD0XQS4BkFwCwSc/SAWS+De8rk2o9YI2H+pAAnXS1FWo0RlrQoVdSoUVNRZ3WVslI8z5g4IxvQ+gfB2MfCFkM3d5u2N27qFIjWvEp8eTMdv525AY12DWQBAcl417v02GQFufwadHR0kGNPFF4uHhMPRwfy2btV5cvbnUCWOgGTHsxCpm3eZK1ZVw/HKdjhe2Q5N5CioJ7wJeEdbtUwA4A1vRAbqdzWt0qjwz/h/4mD2Qb3PP7/0OUK8QnBH5B1WzcPntXuQue0kqg4WwU3moTfNQaJAF0kvZLy5C1FLh8CnRyerl4GoNXj9SmRfuE+2P90eU8w+VDTpzTaujX0TAWjvx65ycnKQn58PoGEIPi8vrzaZz9KlSzFz5kyEhobqbQe63a9KJJJbrkVmY2tka2I9Hh4ebVYfBjI7WG1tLe68806cOHECAHDbbbfhhx9+MLrhu7i46OU1p6amRvva1dW1RXXs37+/xWmlUilPpER2oLGLEZlMxn2SyE5wvySyD6XXc3H6rZ/QyaU7JM6Gf24X1uWh92vT4ODkaHC6rvSydPx65VdsS9+G7Mpso+lmxszE8oHLoWhpl5fFV4Fzm4HkX4DcJNNp+y2CeMq7EEva5lhTXa/CjwlZ+OpQOq4XV7e4HA8nGcZ288fdcaHoH+55y/34v1l0D/HEf+d54l/VSqQVVKKosg6FlfUorKxDSXU9PBwd0NnfBdF+LvBzVeDpTWex62KeXhllNSqU1egHrxMzy/DbuVx8NK8fYvzN/xa16jwZtwQI7gtsXAiUXjOaTJy+H+IvRgC3PQEM/wcg09mn1UrAyn1EBhneGfUOlu1ahrMF+t1Gv3LsFUAEzIiZYVWZUdOGonZEBZJe24ogeXiz6eHO0ShZdxlZYWcR9/hdVpVN1Fq8fiWyL9wn25futalY3Lpu9Mn2kpL+/E3Ut2/fNvuOAgMDERgY2OxzjUaj3UZEItEttY3obvvWHGvasstrBjI7UH19PWbOnIm9e/cCAAYOHIht27bB2dnZaB7dqHZhYaHZeej2YdyWEXEiIiIiInuXvGkPag4VoLOr8a6P8mqz0eXpsXBwczJZVmFNIf57+r/YkrbFZDqFRIEXB7+IadHTrK9wRR5wYQtw7kcg23QvKVpjX2kI2tg4KFirVON8dhn2puTj2+PXUVajNJ9Jh6eTDFN6B6KLvys6+TUExXxd5Axe2hF3Jxn6h3uaTffpvf2xatclfLL/itm0l/Mqcef/DuO1aT0wZ0CoLar5p6BY4KHDwOl1wMWfgczjhtOp64GDbwNn1gNOXkB1MVBTDKhqAc8I4I4PgE6jLZ6to9QRH93+ERZuX4irZVe1n2sEDV4+8jLK68uxqMciqxZF4emKge/fi8R3t8At1wUOEv0HHtxknnDOccW+xz7BkDcWQOHqYqQkIiIior8G3fEx+/bta1Xe1NRUrFq1Cnv27EF2djZkMhkCAgIwaNAgPPHEExgwYAAAICsrC6GhoRg0aBCOHTumzd/4eVxcHHbs2IF33nkHGzduRHZ2NqKiorBixQrMnTsXAPDrr7/i/fffx+nTp+Ho6IgZM2Zg1apVJmNAppSUlMDLywt9+vTBjz/+iDfeeAN79uxBXl4efH19MX/+fLzxxhu33MMODGR2EKVSiTlz5mD79u0AGna2HTt2wM3NzWS+zp07Y9++fQCAjIwMk2lVKpW2u1pnZ2cEBwe3vuJERERERDehAy98geD6ULgqDF8Tl9YXQd7fA/3n322yHKVGie+Sv8OnZz9FpdJ096kRbhF4d9S76OzZfPxNk/JTgMMfNAQwNSrL8kjkwPSPgV6zrZuXERqNgL0p+dh/OR+JmaVIuVEBVQv6He3s74L7bovE9L7BUMg4zt+tQCwW4dmJ/8fefcfXeLYBHP+dkb0TkUSWECP2CLGpPapU7b1VtdoapaWtDqOlRrWovWqWam1q7x2xVxASEQmy18k57x95qeM5JwmlNa7v5+Pzeq/rucfj04zzXM9938UJdLfn09WnyMjlENTUzCyG/hbGgfA4BrwRSICbHWr1MypgWzlA1fey/yREwbm1cOo3uHlYeW1iVPafR927Br+2hhbToGzeVzs6WTnxS4Nf6LyhM7dTjFenTjg6gfvp9xlYfuATF+rLDX6bO8cuEbU4DDer/EY5jUpLEdtSnBrxB27NClOoUZUn6lsIIYQQ4lXytIXMbdu20bx5c1JTUwkODiY4OJjk5GQuX77Mr7/+yttvv/2wkPngDM5y5coZ9fEgni9fPkJCQsjMzKRKlSq4uLhw9OhROnbsSKFChViyZAnTpk2jTp061K1bl82bNzN9+nQsLS2ZPHnyP7pvg8FAcHAwWq2WOnXqcP/+fXbt2sX48eNJT09nypQpT9X/i0oKmf8BnU5Hhw4d+PPPPwEoXbo0W7duxcUl97dfS5Uq9fDvx44do3v37mavDQ0NJSsrC4ASJUrI285CCCGEEOK1czf8JmET11HYrgQqjfL34RRdMmle6ZT+uAWqXIorB28dZMyhMVyNv5rjdYWcCtG8cHM6Fu+IrUXOKzuN3DwKeyfB+XV5u16lBu/g7NVk5TuDs1/ex8rBlTtJfLr6FIev3s394v9zd7DCwVqLg5UWB2sLvJysaV62ADWL5JPPIa+odyr6UMzTgUUHrnM/NeNhPEOnZ+fFOxgeq3uvPh7J6uOR2FtpKVHAkTLeTlQt7Ebtou7PZkKOBSCkH1Tum70F88bhysKlKXod/N4Xkm5DtQ/yvJrZ086TOY3m0HdLX6KSjceZfWo2d9Pu0qd0H3wcfJ7oNtwrFsGldADHRi7DS+WLWmW8TZmXrR+pW++x98A8aozq8UR9CyGEEEK8Kh4tZD5eaDTHYDDQq1cvtFothw4donLlyoo+/fz+/kyVWyFz48aNDB06lG+//RZLS0sAunTpwuLFi2nfvj0WFhacOXOGwoWzzzo/dOgQVapUYeXKlU9dyDx+/DiQvbVu+/btmT179sPVnVu3bqVhw4bMmDGDzz77DA8Pj6ca40Ukhcx/WVZWFp07d2bVqlVAdoHxr7/+yvOhqY0aNXr4982bN+d47aZNmx7+vXHjxk8xWyGEEEIIIV5OyXfucmjscgqofAi0L2nymlsZNyj1eXNsXHI+vy8zK5PJxyez8OxCs9e427jTJKAJbxZ6k+KuxZ+seJccB2v6w6Wcf78HwNYNgppDYH0oWBNsnPM+Ti4ydHpm7r7Cj9svk6HLeZUdgKVGTcvyBehdsxBF83AGonj1lPJ24rvWZRTxvZdi+Wj5CWKTMhS5pHQdh6/e5fDVu8zee5X8Dla0Kl+A+oXs8HG2+ueTUqmgRAsoXBd2joOD08GQlXu7rZ9DYjQ0/BbyeMaRv6M/C5sspN/WflyJN95qd/Wl1ay+tBofex+qFqhKFa8q1PKpladzcrWWWkK+78yFxdtRhSZjqzX++rLR2uOXWogdH0yn6hjZalYIIYQQr5eEhASuXs1+udTd3T3PO1FeuXKF69ev07BhQ0URE5QrO3MrZDZv3pzRo0ej0fy980zTpk1ZvHgxUVFRHD169GEREyAkJARXV1eio6PzNF9THhRwq1atyuLFi43GbtCgAbVq1WL37t2cOXNGCpni6ej1enr27Mny5csBKFasGNu2bSN//vy5tPxbkSJFKF++PCdOnODSpUts3LiRJk2aKK5LS0tj1qxZD///gz2ZhRBCCCGEeJVlJKey75uF5EvPR1GrUiav0ekzueMUi2+vumjtcy4qRCRE8MnuTzgTd8Zk3sHCgQHlB9C2WFss1E9xDsmdC7CkbfYWl+ZY2EHQm1C6DRSqA5pnd95Jhk7PpZhEzkYlMGfvVc5HJ+baxtXOko6V/ehazZ/8DrkXZcTrp0aRfGwYWJMPl4VyIDwux2tjEtOZsfsqM3ZDsK8D79YJpH7JAv98ElYO0Gg0lO0Ax+bB7bNg7Qg2rtlnZZ5erVyxefBnSLwFLX4Gy7ytpvaw82B+4/m8t+09TsWeUuRvJt1k5cWVrLy4Encbdz6q+BFvFnpTsdLSlGKd65JUJ5YLE7biYW28slOtUlPErhRhI36nQMey+FRRFpSFEEIIIV5FoaGhGP6//ceTbCvr5OSESqVix44dTJ06lR49emBvb/6FsNDQUNRqNWXKlFHEAUaOHKlok5CQAEDr1q0pUaKEUU6v15OUlJTnRW2mPFiROWbMGKMi5gOBgYHs3r2bmJiYpx7jRSSFzH+JwWCgX79+LFyY/RZ3YGAg27dvx9PT84n7+vLLL2nZsiUA/fv3Z/fu3UZLnvV6PQMGDCAiIgLI/qJ5dEtaIYQQQgghXkXhWw4St+4KRWxLgJmFXQkZ93Bo6oNvqRKmL3jEuvB1fHPgG1J0KYqcChWtirRiYIWBuFq7Pt2Er2yHFd0hPd503qUgVBsIZduDpd3TjWHCtdhkZu8N5+i1e1yOScr17Mving5U8HehvK8z5f2cKZTP/tmdcSheWfkdrVncO4RpOy4zY9cVkjNyXxV59EYivRedoHbRm3z+ZgkC8z+DlYaepaDZD8p4lf6wqBXEXjCOn1kNMeeg7QJwL5anIZytnZnVcBYf7viQQ7cOmb3uTuodRuwdwfLzyxlWeRhl3HMvPtr75KPCpPYc/2Ylbkn50D72wkQB24IkrrzOkUPnqPRh3s/5FEIIIYR4WT3t+Zju7u6MGTOGkSNHMnDgQD755BPq1q1Lq1at6NChA7a2f7/IlpiYSHh4OEWLFjUZ9/HxMTn2yZMnAXjrrbcUucuXL5ORkaEocOZVcnIyFy9exNPTkzp16pi8JiUl+7PrPymWvoikkPkvGTFiBLNnzwbAwsKCDz/8kMOHD+farmHDhkZfKAAtWrSgXbt2LF++nOvXr1OhQgX69etH6dKliYuLY+HChQ/79vLyYuLEic/+hoQQQgghhHiBHBz/Ky63nfGyNX1OpN6gJzrzJuW+aInKzpK4OPOrxAwGA+OPjmfR2UUm88VdizOq2ihKupnesjZPjsyBDUNNb3npUQpqfAwlWoLm2X1kS07X8dOOy8zZc5WMrNy3jvVwtOKbFqVoWPLJX74UAkCjVvFBvSL0qhnAmagEwm7Gc+rmfU5FxnPlTrLZdrsu3mHf5N10rVqQAW8Uxs5Ki8EAeoMBrUaFlVb59vkTc/KBnptgaQe4cdA4d+cczKwDb07KfpEgD+ws7JhWbxqTj09mxYUVpGelm702LDaMThs68VbhtxhUcRBuNjk/aFKpVFT8oi03Nh8laUs09hZORnkHC2dsIu3Y9tE06nzfB43ls1u1LYQQQgjxonma8zEfGD58OO3atWPFihVs3LiRTZs2sWHDBr788kv27NlDQEAAkF2QNBgMiv4fxCtWrJjj3CpVqqTIPVjJ+STF18fH1uv1ZscGOHr0KCqVSrGK9GUnhcx/yf79+x/+PTMzkw8++CBP7a5evUrBggUV8QULFqBSqVi2bBlxcXGMGTNGcU3hwoVZvXo1vr6+Tz1vIYQQQgghXmRZGZnsGjqLwlZBaLSmP97EpEfh2SKIynVqA9m/j+fkl7BfzBYxOwV1YlDFQVhqLJ9uwtGnYff3cPYPZU6lgcbjoHKf7HP+nhGDwcCa0EjGbTzP7QTzxZVHda7ixyeNi+NoLQUR8c/ZWmqpVNCVSgX/Xr18NTaZ5Udu8Nuxm8QmKf+71OkNzN13lbn7ripyNYvk4/vWZfBysvmHE3OFrmvgt15wYb1xLjMFfu8Hl/+CAuVBY5n9x9IO/KpkF0IfY6mx5JNKnzCw/EBC74RyIOoAB28d5FzcOQwoVz7/eeVP9kXuY0yNMVTzrpbrdH0bBZNc9j7nv9uEh5XxWVBatQXFrEtzYNACSgxsgGtR/yf6pxBCCCGEeFnMnz+f+fPnP3X7gIAAhg0bxrBhw7h+/TqdO3dm7969TJs2jfHjxwO5n49ZoUIFRb96vZ6wsDBcXV3x91f+LvagyPm0hcwH7R0dHU3mjxw5wuXLl6lSpcordT4mSCHzpWVlZcXSpUvp1q0bc+fO5eDBg8TExODg4ECRIkVo06YNffv2xc7u2W1BJYQQQgghxIvkbvhNzk7cTFH70ibzcekxONYqQIW3877d4m8Xf+Pn0J8VcWcrZ76p/g11fOs83WQjj8PuCcpiyQNWjtBmHgTWf7r+/y8pXceaE5GciUogOj6VW/Fp3IpPIz415+ItgFoFFf1d+KRxcaOCkxDPQ0A+O4Y3Kc7ghkXZcjqKmbuucDIqKU9t91yKpdmPe/mxfXlqFMn3zyZiYQPtFsHOcbB7PDxecDy1MvvPo7Q22V+vxZqY7NJaa00VrypU8aoCwI3EG0w8OpG/Iv5SXBuXFke/v/rRvWR3BpYfiEUuZ+DaeTpTYWI7jn2+jPyZBRRnbRa0L8bN6UeIqnCWUl1Mz08IIYQQQmTz9/enW7du7N27l6ysv3fLya2QaWol6IULF0hJSaFq1aomx8qpbV48OB/z6lXli34Gg4FPP/0UyF51+qqRQua/ZOfOnc+l38aNG9O4cePn0rcQQgghhBAvqmvbj3LvzysUtC+qyGUZsoizv0P5sa1RPcHKxu0R2/nm4DeKeBn3MvxQ+wc87Z5ii9WEKFj3MVzcZP4aZz/ouALyBz15/484G5VAz/lHiE5Iy9P1no7WNCzpQZCXIyW8HCnq4YCN5TPYslOIJ2ChUdOwhAcV8mvYcfk+P++LIvJ+7v8N303OoMvcQ3xcvyjvvxH4z85tVWug7ojslZar+0JKbM7X61JhZXfo+kd2m1z4Ovgy6Y1JHLx1kO8Of8fl+5cV18w/M5/D0Yf5vtb3+DvmvJpSpVIR/G0HLi3egepkGtYa4+NoXK08SA9LZdvAaVT65B0cfV6tN/KFEEIIIZ7U5s2bycrKomHDhmgf2cknKiqKKVOmAMbnWuZWyDS1qjK3FZehoaFYWVkpzsgcNWoUX331Fd26dctxpemD/g8dOsTmzZtp1KgRkL3j0KBBg9i2bRutWrWiRYsWZvt4WUkhUwghhBBCCPFSOTVvPdrTetytCyhyqbpkVJXtqNC2zRP1efz2cT7Z/Ql6g/HZkcVcijGj/gwcLB2efKJXtsOq3pBi/jxOfKtAu8Vg7/7k/T9i18U7vLf4GMkZJs7cfIylRk3fWoV4743C2FrKR0LxYlCpVNQt4kLzigHMP3CDaTuvkJqZ83/PBgNM3HqR4xH3GNW8JAXz/cMdiQLrwbt7YVUvuL4v52t1abCkLfTcnOeXEKp4VWFl85Usv7Ccyccmk5ZlXLA9G3eWNmvbMCJkBG8VfivXFzGKdH6De8GRRPxyEDer/EY5K40NxWxLc2PiQWItb1P18y5Y2v3DrXiFEEIIIV5Sy5cvZ968ebi6uhIcHEy+fPm4ffs2e/bsISMjg+HDh1OnTh0AdDodZ86cwcPDA0/Pv19mfRDPnz8/3t7eRis44e9Co6ltZ6Ojo4mOjqZixYpGhVTI3pIWwMLC/M4cGRkZnDlzBh8fH6pWrUqzZs2oXbs2rq6u7N+/n6ioKKpWrcq8efOe6t/nRSefWoUQQgghhBAvjQPfLMQjwQtLC2tFLi49Br93q+BSzNtES9N0eh1rL61lwtEJpGcZn9Pnbe/N9PrTn7yIqc+CXd/Dru9QbFP5gFsg1BwMpduC5p99LFt6OIKRa06TpTcz1iMalPBgZLMg/N3kCArxYrK20PBBvSK0q+zLjvMxxCZloFKBWqUiJV3HrD1XFQXOnRfuUOfCToK8HGlaypMmpT0JzP8ULx8AOHpB1z9h7yQ4OhcSo8xfmxYPi9+BXltMnplpilatpVNQJ6p4VWHo7qFcunfJKJ+qS2XkvpHsi9rH51U+z/X7j0txbxy+e4sTI1bipfFV5J0sXXHClbMj1pHulUnIsI55mqcQQgghxKukQ4cOWFtbs3//fo4dO0Z8fDyurq40btyY999/nwYNGjy89vz586SlpVG7dm2jPszFH8hpRWZO28o+yHXt2tXs/M+cOUNGRgbly5dnwYIFeHh48Ntvv3Hv3j0CAwP56KOPGDhwIFZWVjn9M7y0VAaDIfdPu0Lk4NixYwQHBwNw8OBBQkJC/uMZCfF6y8zMJC4ue+WHm5tbjm/zCCH+HfJ1KcQ/l5WVxd5PZhOgLYZapdz+NCojggpj26K1yv3rKzMzk9jYWA7HHmbelXlcib+iuMbFyoWFTRZS0Kngk000/ib8MQDCd5rOuwdBrSFQ8u3s7Sz/gRt3U1h44Bqz9ijPSNGqVQTmt8fTyRovJ2s8HW2oVTQf5f1c/tGYQjwPT/Jz8tLtRN5dfIwrd5Jz7LOMjxOfv1nin533qtdnr7zMysj+c+c8LGkPmY+Nna8Y9NwEtk82VnpWOhOPTmTJ+SUm89723nxX6zvKupfNU3+nflyL/U1bLNSWZq+5nnSRwF7V8Shf/InmKl4/8vurEC8W+Zr89507d+7h34OC/tkREOLVo9frH67I1Gg0qNXqXFqYlpWVhaurK5UrV2br1q1mr5szZw69e/fmyy+/ZNSoUU81Vl497X/7j9aJjh49SsWKFZ/ZnGRFphBCCCGEEOKFt3foHApbllDE9QY9t61uUXlcpzz3dfHeRcYeGcvJeydN5m20NkyrPy3vRcy0eDi3FsJWwNXdmFyFaWEHTcdD2Q7wlB9yUzOy2HPpDnsuxbL3cixXY00XchystfzSuSLVAvM91ThCvMiKeDjwx/s1GL4qjHVht8xeF3YznjYzDtCmog/DmxTHzf4p3k5Xq8HSFvj/GZT2+aHdQljSDvS6v6+LvQC/toHOv4FN3l8WsNJY8WnIp1QrUI3P933OvfR7RvnIpEi6bezGgHID6FmqJ5pcXn4oPbA5cSeucG3hYTysTK8Q9bcvyv1fr3Bx1T6qfdUdjUbOxRVCCCGE+K8cPXqUhIQEvv766xyvy+38zVedFDKFEEIIIYQQL7S9I+dSyEK5eihTn06SfxqVBrTPUz8Gg4FVl1Yx9tBYMvQZJq+x0dowuc5kSuUrlXuHcVdgxxg4vy571ZY57sWh7UJwL5aneT4qM0vP3sux/BkaxeYz0aTkcgamt7MN83tUoojHU26rKcRLwN5Ky9QO5Qkp5MakrRe5m2z66xlg5bGbbDl7m4/rF8HTyZr7KZncS8kkMS2Twu72tChXAK3mCV4uCKwPLabB732N45FHYf6b0Hk1OHg80f3U9q3Nb2/9xmd7P+PQrUNGuSxDFj+e+JGDtw4ypsYYPOxy7tutfGHcyhcmYv0R4rZew93aU3GNndaRAJ0jhz9ahG/H8vhUzduKTyGEEEII8WyFhISQl01Tjx8/Dpg+f/N1IIVMIYQQQgghxAvr6A/L8ckoiEqtMoon6xKxrO1C6eb189RPSmYK3x78lrXha81e0ySgCQPLD8THIZez7tITYfcEOPAz6DNzvrZMe3hzIlg+2ZmUV2OTmb/vKmvDbuVYpDEayseJ2d2Cye+gPD9UiFeNSqWiSxV/2lfy5WB4HBtORbPlTDRxJr5e4lMzGbX2rMl+Vp+4yYzOFXGwfoIt+sq2g+QY2DLSOH77NMxtBF3XgEvBvPcH5LfNz8wGM5l3eh4/nfgJnUFnlD8cfZh31r7D19W+pq5f3Vz782tWCb9mlTg7ezOGs2k4WSq3vfW1K0z8ymugM+BTs9wTzVcIIYQQQvw79Ho9YWFhuLm54eurPBP9dSCFTCGEEEIIIcQL6czCTbhEu6LVGBcYkjLjcetcHPfygXnqJzw+nME7B3P5/mWT+UqelRhccTAl85XMuSODIXv72K1fQFJ0ztdaO0ODr6FCV1Cpcr72Eacj45m+6wobT91Cn/uLuQBYatW0Dfbhs6ZB2FrKRzzxerHQqKlZxJ2aRdz5tmUp9l+JZcyG85y7lZCn9vsux9F+5kHm9aj0ZC8BVPsAUuJg7yTj+L2rMKcRdPkdPJTbYedErVLTq3QvKntWZtieYdxIvGGUj0+P58MdH9KuWDuGVhqKlSb37XJL9G6ELjWdY6NW4ok3GpXx9wgnS1fiVl/G1t0Z1+IFn2i+QgghhBDi+VOr1SQlJf3X0/hPyadcIYQQQgghxAvn6pbDaMOysNIar2RMy0rBrmmBPBUxUzJTWHR2EXNOzyFVl6rIu1m58UWVL3jD/w1UuRUb79+A3/vB9X3mr1FbQJEGUKYtFG0MFja5zvGBExH3mPTXJXZfvJOn64O8HKlZJB81AvNROcAVaws5504IjVpFzSLurH3fjYUHrjNx60WS0nW5tjsTlcA70/ezsGcIAfmeYPV0vS/B0h62f2McT4rOXplZrCkUqpP9x9Erz92Wdi/NyuYrGX1wtMlV5MsvLCfsThgT60zMfQU5oLWxIuS7ztzYeoy7G67jauVulHez8iB8+n6sPnPEzkO5clMIIYQQQoj/khQyhRBCCCGEEC+UQ+OW4HTHEXsLJ6O4Tp+JrpyWwHo5nwuSkZXByosrmRk2k7tpd01eU8GtAsNLDyewQGDuRcyzf8Kf70NavOm8ozfUHAQlW4HtkxUBDAYDc/ZeZfSGc+R2NEo5X2dalCtAs9Je5HeU7WOFMEerUdOzRgDNynjxzbqzrAu7lWubG3dTeWf6fuZ1r0RZX+e8DaRSQa0hYOMC6wcDj3wRpydA2LLsP5B9Vm6FblCpN2gtc+3azsKOMTXHULVAVb49+C0puhSj/Lm752i7ri3jao6jlk+tPE3Xt0FFvOqU5diIZXir/Y1ynja+hH27lorj2mJpl/eXMIQQQgghhHjepJAphBBCCCGEeCHcvxrJqYkbKWhbFJWFcXFRb9Bz3yuecp3ezrGPLde2MPHYRCKTIk3mVajoXao37xR4B40ql1WMmamweQQcnWM6r7GC6gOhxsdPfAYmQGaWni/+OMPSwxFmr/FysqZDZT9alCuAv9uTjyHE68zD0ZqfOlZg1FvpXLydiKO1Bc62FjjbWjJrdzhTtl0yuv5ucgbtZh7g25alaV0x95WOD1XqBTbOsLov6M2sAL1zHjZ/CkdmQ+OxULRRnrpuXrg55dzLMWzPME7FnjLKJWYkMmDbAPqU7sOAcgPQqHNfma210BLyfWcODl6Ej0VBo5yvXSEODltM9Sk90WhklbcQQgghhHgxSCFTCCGEEEII8Z87OnkFtjesCbArZjIfbR1J5Y87mm2fqc9kwpEJLDm/xOw1LlYujK05lsr5KxMXF5fzhGIvwYpuEHPGdL74m9DwW3ANyLkfM+JTMnlvyTH2XTY9j8LudrxbuzAtynljqVU/1RhCiGz57K3IZ298nuTHDYqS39GKz9ecNjqPNi1Tz5CVJzkUHsfXLUphY5nHgl6pd8DKCVZ0hcxk89fdvQJL2kJgg+yCZr4iuXbt6+jLgiYLmHp8KvPOzFPkZ52aRVhsGN/V/A43G7c8TTd4XAeOD11OAWs/o3gh++Ls/3AeFb5qjZ2bc576EkIIIYQQ4nmST8RCCCGEEEKI/8yp2es4NHARHrc8cbRwUeT1Bj2R6utU/sp8ETM2NZbem3ubLWJaqi3pVqIbf7b8k+re1XOf1N2rMK+p6SKmlRO0mQ/tf33qIub56ATenr7PZBGzoJstMzpXYOvHtWkT7CtFTCGeo04h/kzvXNHk19nKYzdp8fNeLsck5r3DIvXhg2NQ5zPwqwbqHN4dv7wVplWBXd+DPivXri3UFgwKHsTkOpOxt7BX5A/dOkTbdW0JjQnN01S1FloqjG1NTJpy9XqAfTHOf72RmwdO5qkvIYQQQgghnidZkSmEEEIIIYT4152avY60sHi8bP1wsXUyeU18xj0sq7kQ0rqz2X5CY0IZvHMwMakxipxapaZlYEv6l+2Pp51n3iaWdAcWt4JkZX/4VIJ35oCLvzKXB6cj4/lp+2U2nYk2ma9ayI3pnSvgbJv7+XlCiGejUUlPfu0dwruLjhGXnGGUu3g7iYaTdmNnqcVCq8ZCo8LWUkvNIvkY3qQ4tpYmHqk4ekGdYdl/0hPh+v7sc3ZPLgGD3vhavQ52jIbwndBqJjjlvp1tPf96BLoEMmjnIC7eu2iUi0mJocemHgwOHkynoE65nv+rtbGixMjGXBy9DVer/EY5DxsfEn67Tuipa5Tr2yLXeQkhhBBCCPG8yOu9QgghhBBCiH9NcnQcBwcuxOWyE162fiav0Rv03My4SpFR9SnSuqbZvlZfWk2PzT1MFjHLuJfh97d+56tqX+W9iJmeCL+2hrvhjyVUUGMQ9Nj4VEXM0Bv36Tn/CG9O3Wu2iNku2JcFPStLEVOI/0Clgq5s+LAmIQGuipzeAInpOu4mZ3A7IZ2rscksPHCdfouOkaHTm+jtEVYO2WdhtvwZ+u4CfzMrwq/vg+nVswueeeDv6M/ipot5q/BbipzOoOO7I98xdPdQ4tPjc+3LOp8zfgOqcjdd+X3U0cIFx0u27PpsZp7mJYQQQgghxPMghUwhhBBCCCHEv+JO2BUujduOj635LVkTM++TWlpHlYldsXS0NXmN3qBn8rHJfLn/S3R6nSLftmhb5jWaRyHnQnmfnC4DlneBW6GPJVTQeg7U/xI0FnnvD9Bl6Rm/+Twtf97H9vMmVngCKhWMaBrEuHdKyzayQvyHPByt+bV3CO+/EUguCxkB2HMplqG/nUT/6AGbOfEqA93XZ29N7eSrzKfdhxVdYO2HkJGSa3c2Whu+rf4tX1b9Eku18gWIzdc203R1UxacWUBGVoaJHv7mHFiA4l814VZahCJnqbEmIKsYOwZOJyMtLdd5CSGEEEII8azJJ2UhhBBCCCHEcxex9Sh35p8hn7Xp1ZGJmfe5ZXOTIt82oljnemb7SdOlMXTXUOacnqPIWaot+bra13xe9XMsNU+wslGXDn+8B+E7lLmm46HUO3nv6/9iEtLoNPsQP++4YvYafzdb5narRJ9ahXLdAlII8fxpNWqGNCrG/B6VcbXL/XvIH6FRjNlwLu8DqFRQ8m147yCUM7Nl9rH5MLMORJ/KQ3cqWhdtzcKmC/G291bkEzISmHB0Am+teYv14evRP7617SMsHW0JntSRW5obGAzGxVm1Sk0R21IcHbqMhAjTq8qFEEIIIYR4XuSMTCGEEEIIIcRzdW7RFrQn9ThauihyCRn3SHJKosKo1mitcy4cxKXGMXDHQMLuhClynnaeTKoziVL5SuV9YgY9qrO/w45v4f51Zb7WUKjcJ+/9/d/+K7EMXBpKbFK6yXxgfnvefyOQN8t4odXIu6VCvGhqF3Vnx+A6bDh9i2txyWTqDGRkZRF+J5n9V+KMrp299yr5Ha3oW6tw3gewss/ebrbwG7DuY0hPMM7HXoBZ9aDhN1C5L7ktES3pVpLlby7ns72fsfvmbkU+MimS4XuG8+u5X/my6pcUcy1msh+VSkWl0R05O3MT1pe1WKqtjPJ+doW5Omkvbm8H4lOrXN7vVwghhBBCiH9ACplCCCGEEEKI5+b4pFW43HLGQmujyEWkXqHydx1zLWACRCVF0XNzTyKTIhW5km4l+aneT+SzyZfneVlGHsL+4Hi0d8yseqrQFd4Ykef+9HoDxyLusfr4TZYfuYGp3SYD89szqEFRGpf0RK2WFZhCvMicbC3oUNn4HF9dlp53Fx/nr3O3jeJjNpwnn70VrSr4PNkgpVuDTzCs6g03jxjnstJh4ydwZTu0+Bnscv7+5mTlxNS6U1l4ZiHTT04nRafcnvZU7Cnar2tPz9I96Vemn9mV6yX6NubW7lPE/3Edewsno5yblQcpf0Zx4mQ45T9o9WT3K4QQQgghxFOQ13+FEEIIIYQQz8XR71fgdssVCxPnt13TXaLalO55KmLeT7tPv639TBYx6/nVY17jeXkvYqYnolnTF9e1XbE0V8Qs1hSaTcp1FRTA5Zgkxm8+T83vd9BmxgGWHjZdxGxfyZd1H9SgaWkvKWIK8ZLSatRM7VCeiv7K1eVDVp5kxZEbT96pS0HosRFqDgFMfG+4uAmmV4fwnbl2pVap6V6qO+tbraddsXZoVBrFNTqDjplhM2m9tjUnYk6Y7curVml8Pq7CnbRbipyt1gG3m27s/XAOKXHxuc5LCCGEEEKIf0IKmUIIIYQQQohn7sjYZbjHuaNRG28Ck2XQccPyKjUm9MxTP2m6ND7Y/gHXEq4pct1KdOOH2j9gY2K1p0m3z8DMOqjPrDadV6mhUm9oPQ80OW9eE5OYxpCVJ6k/cRc/77hC5P1Uk9dZW6iZ0KYs494pg7WFsqgghHi52FhqmNMtmCL57Y3iegN8siqMmbvNn4trlsYC6n0O3daCQwFlPikaFraErV9CVmau3eWzycfIKiNZ3WI1b/i+YfKaq/FX6baxGzNOzjDbj71PPkqPbcnN9KuKnFqlpqBNUa58u42zS7bmOichhBBCCCGelhQyhRBCCCGEEM/UoW+XkP++JxqVcTEwQ59OnOddqn7dNU/9ZOmzGLZ7GKF3QhW5ESEjGFJpCBp1HouDoUuyz5yLu2w6X6wp9D8AzX4AC2uz3WTo9MzaHU7dCbv47djNHIcs7G7HHwNq0LriE243KYR4oTnbWrKgZ2W8nJTfK8ZsOM93m85jMJhYmp2bgJrQfx8Uf9NE0gD7JsOchnA3PE/dFXIqxI91f2Rmg5l423ub6NHAz6E/s/DMQrN9aG0sqTKpK5EW18kyZCnyLlbu2IZq2D1oFjqdLk/zEkIIIYQQ4klIIVMIIYQQQgjxzBz6ajFeiQUUWxqmZ6WRUjyDCh+/k6d+DAYDYw+PZfuN7YrckOAhtC/ePm8TSr0Pf7wPa/qDTrlqUl+gAnTfAB2WQv7iOXa191IsjafsZvSGcySlm39gXyS/PcMaF2ftBzUo5umQt3kKIV4qBZxtWNqnCj4uyhXh03deYfiqU0TEKc+pzJWtK7RbDM0mgtbESxVRx2FGTThtZmW5CVULVGX1W6vpUqILapXyMdD4o+NZH74+xz5CvulMclA6iZn3FTmt2oJClsU5MGi+FDOFEEIIIcQzl/N+SUIIIYQQQgiRRwe+WIh3ur/iQXl6VirppQyU6tY0T/3o9Dp+OvETyy8sV+S6lOhCt5Ldcu8kMxUOz4Q9EyHtviJtUKlJqvQR1vWHo7a0yrGrdF0W32+6wJy9yu0VH/BwtOKtsgVoWd6bEl6OqPJwvqYQ4uVWMJ8dv71bjS5zDnEpJskot/zoDZYfvYGfqy3VA/NRIzAfdYvnx8YyD6vIVSqo1Av8q8FvvSDmjHE+Iwl+6wE3DkODr0Gb+1nDtha2fFLpExoXbMzn+z4nPN54VefIfSNxsXKhmnc1s32U6N6I5Jh7hI3+E1+bQop8gG0xDg1aSMjErmi18rhJCCGEEEI8G7IiUwghhBBCCPGPHRi5wGQRMy0rhYzyakp0a5Snfh6c2zbn9BxFrnHBxgwJHpJzB1mZcHQe/Fgetn5huohpl597b84juUK/7HMxc3AtNpnW0w+YLWI6WmsZ1bwE+4bVZUSzEpQs4CRFTCFeI55O1qzoV5Wyvs4m8xF3U1h6OIIBS45Ta/wOfjt2E70+j9vO5g+CPtugcl/T+UPTYX4ziI/M83zLuJdhfuP5FHQsaBTX6XV8tPMjTseezrG9XX4Xqk7pxh2PO6TqkhV5f9siHB60GF1aRp7nJIQQQgghRE6kkCmEEEIIIYT4R/Z/ugCfzABFETNVl4y+khVBHevn2ofeoGfR2UW0WduGsNgwRb6SZyVG1xhtclvEh+KuwMw3YN1HkHjL9DX+1dH12k6Gd5Vc5/T7iZs0+3EPpyLjFTmVCjqG+LFjSB26Vw9Aq5GPVkK8rlzsLFnSO4TqgW45XncnMZ0hK0/y9vT9nIi4l7fOLWyg6XhovxRsXJX5m4fhl1oQvjPv87V24ZcGv5DfJr9RPFWXynt/vceFuxdy7aP8x61w61Wcu+l3FDk/28Ic+WSpFDOFEEII8UysWrUKlUr1TP6EhSk/a4oXn3zaFkIIIYQQQjy1fcPn4WcopFiFmKJLgup2FG1bJ9c+wu6E0WNTD74/8j3pWemKfDGXYkx+YzKWmhy2T7y4BWa9AbdPmc5b2EKdz6Drn+DgmeN8DAYDX689y8fLT5KckaXIlyzgyNr3azDm7dK42ee8La0Q4vVgZ6VlbvdK9K4RgJU250ctJ2/c5+1p+xm0PJTYJOX3PJOKN4V+u8G7ojKXEguL3obd40Gvz1N3BewLML3BdBwsjM/xvZd+jy4bu7D1+tZc+3ANKkjBgVWJS7utyPnaFuLYsGVSzBRCCCHEP3b48OFn0o+NjQ0lS5Z8Jn2Jf5ccWiCEEEIIIYR4Yok3bhP2w3r8rYsocsm6RLR1nCn8pvmz1iC7gDn95HT2Ru41e02rIq0YGjwUe0t70xfo9bBnAuwYA5jYrlGthYo9oNZQcPD4f5vMHOf147bLzN1neivZntUDGNakGFbaPJxzJ4R4rVhpNYx8swRDGhXj+PV77Lkcy77LsZyKjMdg4tvT6hOR7L4Uy8S2ZalV1D33AZx9ocdG2DwCjswyzhn0sP1buHEEWv0CNi65dlfUpShT602l75a+ZOj/Ljim6lIZtHMQ/cr0471y7+W4Et4xwItCg2pyeeIu3K29jHLeNgEcGbaUSj90kjMzhRBCCPHUWrduTdWqVU3mMjIyaNeuHQB2dnYsXrzYbD8ODg5oNPI57mUkv0kKIYQQQggh8kyXlsGRb5aRL93dTBEzAcv6+QhoHGK2j4v3LjLp2KQcC5juNu6MqjaKWj61zE8mLR5+fxcubDCdL90W3vgMXAPM9/GYJYcimPTXRUXcxdaCCW3KUi/II899CSFeT9YWGqoF5qNaYD4AbtxNYcyGc2w8Ha24NjYpna5zD9OnZgBDGxXHMpfVnGitoNkE8A2BtQMhM8U4f2lz9lazbRdCgfK5zrWiR0W+r/09Q3YOQWfQGeV+CfuFC/cuMLbGWPMvkwAOfh4EDn2DS+O3k9+6gFHO16YQhwcvpvIPnaWYKYQQQoinUqlSJSpVqmQyd/z48Yd/L1u2LC1btvyXZiX+TbK1rBBCCCGEECJPwn7+gwufbcA3KwAbrfKhdlJmPDaNPHIsYm6L2EbH9R1zLGI2DWjK7y1+z7mIee86zGlouohp5QgdlsE7s56oiLn5TDQj1yi3pq3g58zGD2tJEVMI8VR8XW2Z3rkiS3qHUMzDweQ1s/ZcpdX0fYTfScpbp2XaQJ/t4KZ8oYT7ETCnUfZK9fTc+6vnV4+ZDWfibOWsyO28sZPOGzpzI/FGjn04eLtTbERDbqfeVOT8bApzeMhiDKaWpQohhBBC/AMnTpx4+Pfy5XN/iUu8nKSQKYQQQgghhMjVwc8X4xLhgpOlm8l8YuZ97Jv74Ncg2Gwfy84vY9DOQSbPwYT/b3NYdyrf1foOJysn85O5eQxm14M755U59+LQZwcUa5Lj/Tzu8NW7DFx6Av1jz9mDvByZ37Mynk7WT9SfEEI8rlpgPtYPrMGo5iWwtlA+jjkdmcCbU/ey8uiNvBX98gdB3x1Q8m1lLisddn0HP5aHI3MgS6e85hGVPCux7M1lFHUpqshdib9Cp/WdOBFzwkTLv9m5u1D0k/rEpEUpcn7Whdk/eEHO9yOEEEII8YQeLWSWK1fuv5uIeK6kkCmEEEIIIYTI0fkl2/BK90alUilyWQYd11Mv4dGvHD51yplsbzAYmHJ8CqMPjUZv0CvyRVyKMKnOJFY2X0kd3zo5T+bsnzC/KSTfUeZKtIDe2yBfYB7uKpsuS8/SwxH0WnCEdJ3x3HxcbFjQoxKO1hZ57k8IIXKi1ajpXj2AdR/UoLincnVmSkYWQ38LY+CyUBLScj7PFwArB2g9DxqPyz4T+HHJMbB+EEyvChc25diVt703i5osoqF/Q0XuXvo9em3uxdora3Psw8HbncCPaxObptxG19+yMAc/XEjKnfic70kIIYQQIo9CQ0Mf/l1WZL66pJAphBBCCCGEMOv+5ZtwLBWNiQfkkSnXyKptQ/UpPXEt6muyfaY+k5H7RjL71GxFzsXKhR9q/8BvzX+jvn991KocPp4YDLDvR1jRFXRpyny9L6DNArAyf46bcXcG9oTf582fD/Dp6lMkphmvVnKzs2RRrxDyO8pKTCHEsxeY34E1A6rTvVpBk/m1J6NoOmUPxyPu5d6ZSgVV+kP39eBQwPQ1sRdhaTtY2R2SYsx2ZWthy4TaE/ig/AeKXKY+k8/2fsbUE1NNvpTygKO/J34DqhCXfluR87EJIHzsDq5uPJTrbQkhhBBC5MRgMHDy5EkAtFotpUqV+o9nJJ4XKWQKIYQQQgghTNLpdFyeuht7C+NtXlN0SdzxvkPIj10o1LSK2fbpWel8vONj/rzypyLn6+DL4qaLaViwYc4FzAf2TYatnwOPbbeosYI286Hm4OyH+XlwPjqR9367yNA/r3DlTrIib2upYV6PSgTks8tTf0II8TSsLTSMeqskc7oF42pnqcjfvJdKmxkH+Gn7JbIe3/faFL8q8N4BqP5h9vdGU878Dj9VgtCl2S+ImKBSqehbpi8T60zEWqN8mWNm2Eze3fout5OVhcoHXIv4UqBHee5lxCpyzpZusCORw98uzf2ehBBCCCHMuHTpEklJ2eeBlyhRAisrM7//iJeeiX1HhBBCCCGEEAIOf7oIPxvjbVr1Bj1JvilU+OCdHNum6dL4aMdH7Ivap8iVdCvJz/V+xs3G9HmbCieXwV+jlHHbfNBhKfhWzlM3BoOBpYdvMGrtGTJ0plcT2Vpq+KVLRcr4OOdtbkII8Q/VC/Jg04c1GbTiJHsvGxf+svQGJmy5yN7LsUxqVw4vJ5ucO7NxhgZfQ6U+sP1bCFuO4gWQtPuw5l04tRLemgpO3ia7auDfgAJ2Bfhg+wfcSTXezvvArQO8/efbjAwZSdNCTU22dy9TGH2nTG4tOkU+a0+jnIXaigJJPuz/aD6VJ3RGq5XHU0IIIV5e3x3+jvN3z//X0/jPFHctzrDKw/71ceV8zNeH/KYohBBCCCGEUAib/ic+6kKK+PWMy9T8sFeObVN1qXyw/QMO3VJuHVjDuwY/1P4BWwvbvE3k8jb4Y4Aynq8odFwBrgF56iYlQ8eI30/z+4lIs9e0KFeAIQ2L4euax7kJIcQzkt/RmoU9KzNzTzgTNl9A99gKzIPhd2kyZQ/fv1OGhiU9zfTyCGdfaPULVH0PNg6HiP3Ka65sgzkNodcWs8XMkvlKsqTZEj7Y/oHiAW1iRiLD9gxjx40djKwyEicrJ0V7j4rFcSriw9HPl1PQpqgi72ddmMNDFlN1UjeT5zALIYQQL4Pzd89z9PbR/3oarx05H/P1IVvLCiGEEEIIIYwc/W4FtuFWii1fb6VEUHVclxzbpmSm8N5f75ksYjYv1Jwf6/6Y9yJmVGj2mZh64/MrcQ+CnpvzXMS8HJNIy5/3mS1iVi3kxtr3azClfXkpYgoh/jNqtYp3axdmVf9q+LspvxfdT8mk76JjjFxzirTMrLx16lU2++zMZhPB0kGZT7gJi9+BVPNncXraebKg8QIaFWxkMr/p2iZar23N1firJvPWjvbUmNKLSPvrpGcpzzj2sy7MgcHz83Q7QgghhBAPPLoi83kXMvfs2YNKpaJjx47PdZy82LFjByqViq5du/7XU/nXSCFTCCGEEEIIAUByzD0OfLgAz3teWGuMH6InZcYT0LcqWivlOW4Pr8lI4t2/3jX5NvLbgW/zTfVvsFBb5G0y967Br20gI8k47lAAOv8Gtq65dpGcruPHbZd466d9XLydpMi721swq0t5lvQJobSPciWREEL8F8r6OrN+YE1aVTC9SnLxwQjemb6fiLiUvHWoVkOlXjDgEBRtrMzfOQdL2kNmqtkubC1sGV9rPGNqjMHBQlkQjU6OpsemHly5f8VsHyEjO6Ou58jd9BhFzs8ykP1D5ufpdoQQQggh4O9Cpkqleu5byz4YKzg4+LmOkxfHjx8HXoy5/Ftka1khhBBCCCEE53/dhv54Mr42yu1ks/Q6dCXV5CulzD2QmJHIu3+9S9idMEWuTdE2jKwyUrHC06wLm2DtQEh+7GG3lRN0XgVOPjk2T9dlseRQBD9tv0xccobJayr7OfBV4wACfd1lO0MhxAvH3krLxLblqF3UnRG/nyYp3Xhl+pmoBN6cuodJ7cpRL8gjb506eUOHZXB0LqwfZJy7cRB+6wltF4HG9KMilUpF88LNqeRZiZF7R3Io2njlfVxaHD0392R2w9kUcSliso+AxiHcL+LDtakHyGdtPG8/bWH2D1tAte+65e1+hBBCiBdEcdfi//UU/lP/xf1HRUURE5P9ebFgwYI4OT3fF1MfFA8rVqz4XMfJixdpLv8WKWQKIYQQQgjxGtOlZXB45BK8NQXRWLgo8/pMom0jqdLT/Jay8enx9NvajzNxZxS5DsU78GnlT/NWLEyLh02fQehiZU5jCR2WgEeJHLtYH3aLMRvOEXnf9MoilQrer1OI9qWd0ailgCmEeLG1KOdNeV8XPlh2gpM37hvlEtJ09FpwlAFvFGZQg2J5+56mUmWvzkxPhL++NM5d2ADrPoK3pmZfZ4annSczG85kybklTDg6gSzD39vc3k27+7CYWcy1mMn2zoW98elTgajZobhauRvl/FSFOPDpQqqOfX22ShNCCPHyG1Z52H89hdfOv30+5okTJ1CpVFSoUOG5j5WXuWg0mue+CvVFIlvLCiGEEEII8ZqK2neKU8PX4KctjEalUeTvpseQWUVLlVHmi5j30u7Re0tvk0XMLiW65L2IeWUHTKtmuoiJClrNhII1zDbPzNLz5R+nGbDkuNkipqudJQt6VGZg3UApYgohXhp+brb89m5V+tU2vSr+5x1X6DLnELFJ6XnvtPqHUOU9ZfzEIvj93Ry3mQVQq9R0LtGZ8bXHo1UZvyN/P/0+PTf35GzcWbPt85UshEeXEsRnxClyvoYADgxfkLf7EEIIIcRr6Z+ej3nkyBE6d+6Mr68vVlZW+Pr6MnDgQO7dU54bnp6eztmzZylatChqtZovvviCokWLYm9vT1BQEJMnTzY5xqVLl+jbty+FCxfG2toaBwcHihQpQufOnTl6VHkcS16kpKRw4cIFihcvjlqtZsyYMZQqVQpbW1sCAgIYNWoUWVl5PEv9JSKFTCGEEEIIIV5DR75dRtqaaNytvRQ5vUHPtdSLFP2iAUVa1TLbR2xqLL229OL83fOKXM9SPRkaPDT3IubNo7D4HVjUEhJuKvNaG2g5DUq+bbaLmMQ0Os46yIID103mLbVq+tYqxLZBtalV1N3kNUII8SKz0Kj5tEkQv3SpiIO1cnOt/VfiaPbjHo5eu5u3DlUqaDgaSrVW5sKWwbymkBCVazcN/BvwQ50f0KqN55SQkUCfLX24cPeC2bYeFYrj9E4AiZnKB4a+FGL/0Pm5ji+EEEKI19M/KWSOHz+eqlWrsnz5cooWLcpbb72FVqtl6tSp1KhRg4SEBKPrT506hU6nw9fXl4oVKzJz5kzKlStH5cqVCQ8PZ/DgwXzzzTdGbbZt20bZsmWZNWsWrq6utGjRgtq1a6PRaPj111+5ft30Z9fchIaGotfr8fHxITg4mKlTpxIUFETt2rW5ceMGX331FZMmTXqqvl9kUsgUQgghhBDiNZKRmMKhDxfhleSNpcZakU/WJXDH6w41pvTC2sXRZB+Z+kx+PfcrLda04NK9S4p83zJ9+ajCRzkXMW8eg8WtYXY9uPyX6Wt8KsG7e6FcR7PdHLt+jzd/3MuRa8oH4WoVtK/ky84hdfisaRAudpbm5yOEEC+BRiU9WfdBDYK8lN+fbyek037mQebsvYrBYMi9M7UaWk6HQm8oc1HHYWYdiDikzD2mrl9dJteZjIXawiiekJFA3619CY8PN9vWp3pZbBp7kZh5X5Hz0xRm/+D5uY4vhBBCiNfPo4XMJ9liddasWXzyySeULFmSCxcusG3bNlauXMn58+dp1qwZZ8+e5YcffjBq8+BMyr/++ovatWsTERHBihUr2L59O4sXZ+8oNG7cOBITEwEwGAz06tULrVbLoUOHOHLkCMuXL2fdunWcP3+e48ePU6dOnae67wdz2bx5M02aNOH69eusXLmSjRs38ttvvwE8/N9XiRQyhRBCCCGEeE1kpKRxcuQavG0KmszfTL6KW48gKn5kYoUO2R/Idt7YSas/WjHu8DgSMhIU17xX7j0+KP+B+SJmZiqs6gOz68Llraav0VhC/a+g52bIF2j2fpYdjqD9zAPEJCq3U6zg58yWj2sz7p0yFHC2MduHEEK8bPzd7Pj9vWq0qeijyOn0Br5Zd5b3l5zgfkpG7p1pLaHdYihcT5lLug3zm8Hxhbl2U9u3NlPemIKl2viFkbtpd+mzuQ83Em6YbVuwQSXsmniRYGJlpp9FYfZ/PA+dTpf7vQghhBDitZCQkMDVq1cBcHd3x9vbO0/tbt26xccff4yLiwsbNmygUKG/t+23srJixIgRAGzYsMGo3YOiad26dZkxYwaWln//vtOmTRuCg4NJSUnh2LFjAFy5coXr169TtWpVKleurJhH+fLlcXNze4I7Vs6lZcuWTJgwwWgujRo1AiAmJuap+n6RSSFTCCGEEEKI14AuLYPQT3/Dy8ZXkcvISuOa6hKVJnciX0nTZ7DFp8fTf1t/Ptj+AdcSrpm85sMKH9K/bH/zk0i5CwtbwKkV5q8pUB767oIaH4FaeW4ngF5vYNzG8wxffYrMLOWqo85V/FjWtyqB+e3NjyOEEC8xawsN49uU5bt3SmOpVT7aWX/qFrW+38HM3VdIy8zlnCQre+i0EqoNVOb0mfDnB7B5BOhz7qemT02m1J2i2GY2JjWGXlt6cSvpltm2fvWDcWzlT3yGcmtcP6tAQgev4P6V3Le6FUIIIcSrLzQ09OHuE0+yrezs2bNJTk6mT58+JoufDwqbcXHGZ3g/WAU5duxYky/sFi9eHODhlrROTk6oVCp27NjB1KlTSUpKyvMcc/NgLl999ZUid/v2bQAKFCjwzMZ7UUghUwghhBBCiFecLi2Do58so4CNvyJ3J+0Whjp21BjbE43GdOFQp9cxaOcg9kXuM5m30drweZXP6V26t/lJ3LsOcxrCDTPbFHqUyl4V1Hs7eJQw201aZhbvLz3OjF1XFDlLrZrvW5fh25amH+wLIcSrpl0lP1b3r4avq3LleUKajjEbzlPvh12sORGJXp/DdrNqDTT8BlrNAq1y23EO/ATLO0N6zg/ianjXYELtCWhUxj9PbiXfoteWXkQkRJht61O9LK7tArmfEavIedr4cmvacS79tjvH8YUQQgjx6nva8zHXr18PQI8ePUzmU1NTAXBxcXkYy8rK4tSpUwQEBJhcXQmQlpYGZK8OffC/Y8aMQa/XM3DgQNzd3WnWrBlz5swhJSUlz/N9XEZGBmfOnCEgIIAyZcoo8mFhYQCUKlXqqcd4UcmneyGEEEIIIV5hOp2OI58sxcc2QJGLTLlG0KjGFG5WLcc+poVO43D0YUVchYpWRVqx/u31tC3W1nwHt8JgTgOIU56n+bCA2W8PBDXPPrPNjNik7PPfNpyKVuQKOFnz27tVaRusXHEqhBCvslLeTqx7vyb1g/KbzEfeT+Wj5aG8PW0fZ6OUW4IbKdMWemwERxNbtF3YAHMbQ/zNHLuo51ePsTXHolYZfz+/kXiDtuvasuXaFrNtvUJKkr9bae6l31HkHCycsDis4/CoJTnfgxBCCCFeaU97PuapU6ewtLSkWLFiJvNnzpwBoGzZsg9j586dIzU1lQoVKpjt9/jx41hYWFCyZMmHseHDh3Pp0iXGjRtHSEgImzZtonfv3hQtWvThtrhP6tSpU2RmZlKpUiWT+Qf/Lk9S3H1ZSCFTCCGEEEKIV9jhwYvwtVVuF3srJYLSX7bA2tkxx/a7b+5m1qlZiniIZwgrm6/kq2pf4W7rbr6D8F0wr2n2WWuPq/p+ngqYADfupvD2tH2E3rivyJX1cWLN+9Up4+OcYx9CCPGqcrK1YGaXYEY0DcLO0vTq+pM343nrp72M33w+5+1mvStAnx3gHazM3T4Fs+rCNdMr9B9oEtCEr6t9rYgnZyYzeNdgxh0eR2ZWpsm2+csG4v1uRaJSrityWrUFBdJ82T9oQY7jCyGEEOLVNX/+fAwGAwaDgfbt2+epTVZWFikpKVhYWJjcHhZg1apVADRp0uRh7EFx0MHBwWSbw4cPEx4eTu3atXF0NP5sHRAQwLBhw9i5cyfh4eHUqFGDyMhIpk2blqc5P+7BXMwVb6WQKYQQQgghhHjp7Bs+Hz+bQEU8OvUmJT5viq1bzkXMyKRIPt3zqSLeonALZjWcRTFX02+yPnT5L1jSFjISlbmGo6HR6FwLmAD3kjPoNu8wN+6mKnKNSnqwrG9V8juY2ApRCCFeI2q1ij61CrHrkzfoUsUfjVr5kE6nN/Dzjis0/XEPR64pz6N8yMEDuq+Dkq2UuaTbML8ZbBkJmWlmu2gR2ILPq3xuMvfruV/purErkUmRJvOuxfypML4t4enn0Rv0iryfZSEODJNiphBCCCHyRqPR4ObmRnJyMjExMYr8yZMnWbRoEb6+vrz11lsP4w/OpLxyRXm0icFg4PPPs3/XGTZsWI7j+/v7061bNyC7qPo0Hswlp0KmRqOhdOnST9X/i0wKmUIIIYQQQryCTk77Ax9DQUU8JjWKoE8bYufuomz0iIysDAbvHExChvE2hEVcijCiygizb7E+dGETLO0Auscecmss4Z05UO39vNwGaZlZ9FpwhPA7yYpc31qFmN6pIjZmVh8JIcTrKJ+9Fd+0LMWWj2vRuKSnyWvC7yTTZsYBpvxlYsvvByxsoPVcqD3cRNIA+6fCrDeytw83o22xtvxU9yccLZUvzpyOO03nDZ25nqBceQmgtbKk1qQ+3M4XTVqW8meAr6oQh7761fz8hRBCCCEe8WCl5bfffovB8PfZ4cePH+fNN9/EYDAwc+ZMrKysHuYerHLcu3cvW7dufRjPyMjg/fffZ8eOHXTo0IH69esDsHnzZjZs2IBOpzMaOyoqiilTpgAYFUoBRo0ahUqlonv37jnO/0Eh09SKy7t37xIREUGxYsWwsVGenf6yk0KmEEIIIYQQr5gbO45jd9Uajcq4wBeXdpsin9TFztMtx/Y6vY7Rh0ZzJu6MUdzOwo6JtSdio83lg9G5dbC8M2RlGMetHKHzKijdOk/3kaU38MHSExyPuK/IfdOyFJ81DUJtYsWREEIIKOxuz4wuFVnSOwQ/V1uT10z66yKz94Sb70Slgjc+zX4BRWOlzMeczd5qdt8UeOSB4KNq+9ZmZfOVlM6nXB0QmxpL7y29iUqKMjuFSkPboW3kxl0T52Z6pnhzYvJq8/MXQgghhPi/r7/+Gjc3N6ZOnUrp0qVp164dNWrUIDg4mNjYWObOnUvjxo0fXm8wGAgNDcXHx4eOHTvSpEkTGjVqRPv27SlUqBCzZs2iVq1a/PLLLw/bLF++nGbNmuHh4UGjRo3o1KkT9evXJyAggNOnTzN8+HDq1KljNC+9Pnv3CQsLC7Nzz8rKIiwsDA8PDzw9lS+qvcrbyoIUMoUQQgghhHilJEbeIX5tBNYa44fWybpE8ncsiYN3DudZAjcTb9JjUw9WX1I+GP662tcUdCqY8wTOrIGV3UD/2Nln1s7Q9Q8IqJX7TZD9ofHLP0+z9azybM2RzYLoUsU/T/0IIcTrrlpgPjZ/VIs+NQMw9e7Ht+vPseaE6S1eHyrdGnr/Be5Bypw+E7Z+AZs/M1vMLGBfgAWNF9ApqJMiF50cTa/NvYhJUW7z9kDB+pVw71CMhMx7RnGNSoNzlBPnFm8101IIIYQQIltAQACHDh2iffv2REdH88cffxAZGUnPnj05duwYXbt2Nbo+PDyc+Ph4SpQowdy5c/n44485deoUa9aswd3dnR9++IGNGzdiZ2f3sE2HDh3o378/vr6+HDt2jBUrVnDq1CkaN27Mli1bGDt2rGJeoaGhAIrxH3X+/HlSU1NzPR/TXP5lpzIYzPyWKUQeHTt2jODgYAAOHjxISEjIfzwjIV5vmZmZxMXFAeDm5pbj2zxCiH+HfF2Kf4suU8eJIcvxsvEzjuszSQpKp1SPJjm2X3tlLaMPjSY5U7mFX+egzgyrnPO5H5z6DVb3BcNjZ37YuGYXMb3K5O0+svRM2HKRGbuU55D0rhHAyDdL5Kkfc+RrUogXi3xN/ntO3rjPkJUnuRSTZBTXqlXM7V6JWkVzftmFzDTY/g0c+Bkw8Tipcl9o8n32Sk4z1oWvY8TeEYqzLws5FWJe43m4WruabRu+8SBZ2+5ho7U3iqdlpaCu40ShZlVynr/IM/m6FOLFIl+T/75z5849/HtQkIkXecRrTa/XPzzrUqPRoFY/3ZrBrKwsXF1dqVy5stHWtf+lp/1v/9E60dGjR6lYseIzm5OsyBRCCCGEEOIVoNPpODJ0saKICRBlfTPHImZCRgLDdg/js72fmSxiVshfgUEVB+U8gZPLYHUfZRHTNh90X5fnIublmCTemXHAZBGzedkCfNZUHiIIIcTTKuvrzIp+VSnsbmcU1+kNvLv4GCdv3M+5AwtraDQauq0FJ19l/vBMWD8I9Hpl7v/eLPQm31T/RhEPjw+n75a+xKfHm21bqEkVMkurycgyPn/ZWmNL5o673NhxPOf5CyGEEEK8QI4ePUpCQgJff/31fz2VF5oUMoUQQgghhHjJ6XQ6Dg9ZjK91YUXuWupFqn1tfouaY7eP0ebPNmy4usFk/u3At5lefzoWmhze+j6+CH5/Fx5bXYO9B3RfDx4lc70Hvd7A7D3hNPtxj8kH6VULuTGhTRk5E1MIIf4hFztLFvYKwdPR2iiekpFFj/lHOBuVkHsnATWh/z7wq6rMHZ0L6z7MsZj5VuG3+LzK54r4hXsX6LOlD/fT7pttW6JbI+57x5Ol1xnF7bQOJK2L5NbBM2ZaCiGEEEK8WEJCQjAYDFStauJ3KvGQFDKFEEIIIYR4iRkMBg4PWYyfiSLmrdQIqnzXxWS7TH0mU09MpefmnkQlRynyjpaOTKwzka+rf42tha2JHv7v6Fz4830UWww6eGUXMfMXz/Uejkfco93MA3y7/hzpOuWD7xJejvzStSJWWk2ufQkhhMidt7MNC3pWxtFaaxS/m5zBO9P3s+HUrdw7sXaCTr+Bfw1l7vhC+PODHIuZbYu1ZUjwEEX83N1z9Njcg9jUWLNtK3zUmlu2NxXb0zpYOBO34hJ3wpSr+oUQQgghxMtJCplCCCGEEEK8xPYPmm+yiBmbFk2Rj+qitbZU5CISIui2sRszw2YqHgIDhHiGsOqtVTTwb2B+4Ixk2PYNrPtYmXP0yS5i5iuS49yPR9yj29zDtJq2nyPX7pm8pn0lX1a8WxVHazkHSAghnqVing7M7lYJK63xo6HUzCze+/U4P2y5gF5v4hzMR1nZQ6cVEFBLmQtdDBuGgMF8H91KdmNAuQGK+OX7l+mxqQe3k2+bbVtlVBduqMMVcWdLN27NC+XupRs5z10IIYQQQrwUpJAphBBCCCHES2rfx/PwtwpUxGPTbuM3oArOAV6KXNidMNqta8ep2FOKnFatZXDFwcxsOBNPO0/Tg+r1ELoEplaEPROUeWc/6LEe3JTF1QfORyc8LGDuunjH5DX5HayY16MS494pg72V1uQ1Qggh/pnKAa5M7VAerYltu6duv0zfRUdJTMvMuRNLO+i4AgrXVeaOzoEtI3MsZvYr04/3yr6niF9LuEaPzT24lWR+dWj1sT24nnVJEXe1ys+NaYe4dUi2mRVCCCGEeNlJIVMIIYQQQoiX0P4RC0wWMePSb+PXvxKuRXwVudOxp+m3tR9JmUmKXIBTAEuaLqF7qe6oVWY+Jlw/ALPqwJr+kGjiwbJLQei+Ift/zdh0OpoWP+0zW8AEaFmuAFs+rsUbxfKbvUYIIcSz0bCkJ4t6heBqp1zB/9e5GN6etp+IuJScO7GwgfZLIbC+MnfgJ9gx2mxTlUpF/3L9+bDCh4rcjcQbdN3UlTNx5guS1cf35FqGspjpZuVBwsprnFvyV85zF0IIIYQQLzQpZAohhBBCCPGSOTF5NT46f0X8bnoMvn2CcS2mzJ2NO0vfrX1NFjHbFWvH8jeXE+QWZH7QU7/B/KZw66TpvFsR6LERnJUF1AcWHrhG/1+PmTwHEyDIy5E53YKZ3L48zrbKB+pCCCGej6qF3fhjQHWCvBwVucsxSbScto8j1+7m3ImFNbRbbHqb2d3jYbeJVfyP6F26N59U+kQRj06OpuuGrqy+tNps2xoTexKRdlkRt9M6Yh2q4sjYZTnPXQghhBBCvLCkkCmEEEIIIcRL5NLve3CMdECt0hjF72XE4tWjHK4lCiraXLh7gb5b+5KYkWgUt9ZYM+WNKYysMhIbrY35QaNPwx/vg4nzNNFYQrWB0Gc7OBYw2dxgMPD9pvN88ccZk7sLFvd0YEbniqz/oAb1gjzMz0MIIcRz4+tqy6r+VWlWWrkt+d3kDDrNOsTq4zdz7uTBykzfKsrc9m9g3SDITDPbvEuJLowMGamIZ+gz+HL/l4zaP4r0rHSTbStP6GKymGmhtsTzfgH2DpmHTqfLef5CCCGEEOKFI4VMIYQQQgghXhIxoZfI2huPlcbaKJ6iS8SlVSHcyyjPpbx07xJ9tvQhPj3eKG6lsWJqvanU9TNxptmj0hJgRVfQpSpzJVrAgEPQ8BuwVq7iAcjQ6Rm88iTTdl5R5NzsLJneqQIbBtakcSlP1CbOaBNCCPHvsbXU8lPH8gxtVEyRy8jSM2jFSSZsvoBeb/7MS6zsodMKKFBemTs6B+Y2hLtXzTZvV7wd31T/Bq1KeT7yqkur6Lqxq8lzM7VaLdUm9yCCK+gNWUY5lUpFQW0gh4YsMj9vIYQQQgjxQpJCphBCCCGEEC+B5Dv3iJp/EnsLJ6N4pj4DXVktPjXKKtrEpsby7l/vci/9nlHcUm3Jj2/8SBUvEytmHmUwwJ/vw93HipB2+bO3kW27EFwLmW2elpnFu4uPsfp4pCLn72bLqv7VaFLaSwqYQgjxAlGpVAx4I5AZnStgbaF8bPTTjst0nXuYW/EmXnB5wNoJOq8Gj1LK3K2T8EttOLfWbPOWgS2Z13ge+W2UZyWfjTtLz809iU2NNdm22rjuxHrGkZ6lXPnpbx3IviHzzM9bCCGEEEK8cKSQKYQQQgghxAvu4m+7CB+zk3zWxtuu6g16Yl3uUKJLQ0WbjKwMPtrxETEpMUZxC7UFk96YRDXvarkPfGgGnP3DOKZSQ+u54J9z+7TMLPosPMr28zGKXFkfJ1b1r0bBfHa5z0EIIcR/onEpL1b2q4aHo5Uit/dyLA0n7WbNiUgMpvYMB7B1ha5/gG+IMpceD8s7w1+jMLnnOFAufzmWN19OsEewIncz6Sbv/fUeSRnKc58BKnz8DtSwJjHzviLnrw3kwMiFpucshBBCCCFeOFLIFEIIIYQQ4gWl0+nY+8lcLA9n4WKVT5GP0F+h0qftFXGDwcC3B7/l5J2TRnGtSsvEOhOp5VMr98FvHIYtynPKqPs5BNTMsWlKho6e84+w55JytcwbxdxZ2rcK+eyVD8aFEEK8WEr7OPHHgBqULKDcPjwxTcdHy0MZsOQ495IzTHdglw+6r4eq75vO750E274yW8zMZ5OPWQ1n0b1kd0Xu3N1zfLjjQzKyTI9duEVN3HuU5G668oUa70x/Do9eanpOQgghhBDihSKFTCGEEEIIIV5Atw6dIXTwCgqqi6BVWyjyESmXqTG+p8m2S84v4ffLvyviX1T9gjq+dXIf/NJWWNoB9DrjeNHGUP2jHJsmp+voPu8I+6/EKXKtKngzq2swtpbKc8+EEEK8mDydrFn5blXeLONlMr/hVDRNf9zD5RjTqyPRWECj0dB+SfaWs4/bOwl2TzA7vlatZXDwYEaGKF+uORx9mOF7hpOlzzLREvKVKoRHl9LEZ9w1iqtVajwSPAmdovxZKYQQQgghXixSyBRCCCGEEOIFc2XdfhJXXsfTxtdkPiL1MsHfdTCZO3jrIOOPjFfEOwV14u0ib+c8cGYqbPgEfm0NKY+tpnT2g5bTQW3+I0RiWibd5h7m8NW7ily7YF8mtC6LViMfQYQQ4mVja6llaofyTG5XDgdr5csot+LTaD/zIBdvJ5rvpHgz6LcbvMopczu+hQM/5ziHdsXb0b9sf0V86/WtjDs8zuwWtx4ViuL4pg/JugSjuEalxSnSkasbDuY4rhBCCCGE+G/JUwQhhBBCCCFeIDf3niRzx11stQ6KXKoumWjXW1Sb0gNLG2tF/mr8VQbvHEyWwXhlSohXCEOCh+Q88O0zMKsuHP5FmdNYQpsF2eedmRGfmkmXOYc5ev2eItcpxI+xrUqjVqtynoMQQogXlkqlomV5bzZ/VIvqgW6KfGxSOh1mHuR8dIKJ1v/nUhC6rwPvisrc5s/gyJwc59C/bH/aFG2jiC+7sIyfQn8y2863bkXU1RxIy0oxiluoLUnYepOMlLQcxxVCCCGEEP8dKWQKIYQQQgjxgrhz8jLxq69hb6Hcei869QZOnQsT/Elbk20jEiLovbk3CRnGD5B9HXyZUGsCWrWZ7Vz1ejgwDWbWgZizyrzWBlrNAu8KZud9PyWDLnMOEXrjviLXvVpBvm1ZSoqYQgjxiijgbMOiniF82bwEmse+t8clZ9Bh5kHORMWb78DKATqvAo/Sytz6QXBisdmmKpWKESEjqO9XX5GbGTaTOafMF0KLtKpFalAWGVnGRUs3Kw+OfC7nZQohhBBCvKikkCmEEEIIIcQLIOFaNLfmn8TJ0njVo8FgIIIrlPuhLfnLFTHZ9kbCDXpu7klMaoxR3FZry49v/IiztbPpQROj4dd3YPOnkJWhzHuVzd4GsGRLs/O+m5xBx1mHCLupfGjdu0YAXzYvgUolRUwhhHiVqNUqelQP4KcO5dE+Vsy8l5JJx1mHOGXi58JDNi7Q5XfIV1SZ+2MAHJ1rtqlGrWFcrXEEewQrcpOPT2bJuSVm25bu2ZQY+2hF3EcbwPml283PVwghhBBC/GekkCmEEEIIIcR/LCU2niuTd+FqlV+Ru6EOp9q47mi1pldU3ky8Sc8tPbmdctsorlFp+K7WdwS6BJoe9Px6mFYVrph6cKuC6h9Br7/A3cRD5v+7k5hOx1kHOXtLuY1g/zqFGdEsSIqYQgjxCmtS2otpnSpgoTH+Xh+fmknnOYc4Z+Lnw0P27tD1T3AJUObWfQwHp5ttaqWx4se6P1LCrYQiN/bwWH6/9LvZtlW+7MKNlCtGMY1KQ9aRRNLu5lB8FUIIIYQQ/wkpZAohhBBCCPEfun3sAhe+3Yy7dQFF7nrmJaqN7W62bVRSFL239CY62Xh1iVqlZmzNsdTxraNslKWD9YNhWUdIvavMOxSAbn9Cg69Aa2ly3Aydnrl7r1J/4i7ORycq8gPrBvJJo2JSxBRCiNdAw5KezOhcEUuN8SOm+NRMOs8+xOUY5c+Jhxy9sn/mOPkpc5uGw97JZps6WDrwS/1fCHRWvrAz6sAo1oevN9u22IA3SNYZF1mdLF05/vVq83MVQgghhBD/CSlkCiGEEEII8R85M2cj8UvDcbf2UuSup16i+g89zba9m3aX3lt6E5kUaRRXoWJ0jdE0CWiibKTPgjXvwpHZpjst0QL674OAWibTBoOBrWdv02jybr5ed5b41EzFNYMaFGVQQyliCiHE66RekAczu1bEUmv8mCnu/9uPX4tNNt/Y2Q96rDe9MvOvL2HHWDAYTDe1dmZWw1n4O/obxfUGPcP3DGf2qdkYTLR1LeZPkleyIudrVZjjE34zP1chhBBCCPGvk0KmEEIIIYQQ/zKDwcCBTxdif9EGW629Ih+RcoWQH7qabZ+elc6H2z/kRuINo7gKFd9U/4Y3C72pbKTPyj537NRKZc7CDlpMgzYLwNZVmQfup2TQbd4R+iw8ylUzD6Q/aVyMgfVMn+MphBDi1VanWH5mdqmo2GY2JjGdTrMPcfNeivnGzn7QYwO4mfgZsmscrOwG6aZXduazycfshrMpYKfc2WDK8SmM3DeSDBPnQFcc3JaI1MtGMZVKRf5YDw5/uJjbRy+Yn68QQgghXgnz58+nTp06OV7z0UcfoVKpGDlyZJ77jYmJYdSoUVSuXBlnZ2esrKwoUKAAlStXZsiQIRw+fPgfzvz1IoVMIYQQQggh/kUZiSkc+mgxvoYANCqNIn8j5QrB37U3eyamwWDg832fE3onVJH7qtpXtAhsoWyk18PagXByqTLnHQzv7oHyncDMKkpdlp7+i4+z++Idk3k7Sw1j3i7Ne3XMnMcphBDitVCnWH5+6lgBjdr450nk/VQ6zT5k9kUYABwLZBcz8yvPveTsHzCrLtwxXVz0tPNkdsPZ5LdRnjX955U/6bOlD/fS7ilyZYY3Jz5Duc16ARt/Ulbc5MCwhWSkpJqfsxBCCCFeOpmZyp2FcsqfPHkSgHLlyuWp/82bNxMUFMRXX33FrVu3qFKlCq1atSIgIICwsDB++OEH/vjjj6ea++tKCplCCCGEEEL8S3SZOk6MXIWPTUFFLsug47rhEpUndcHSxtpsHz+H/szGqxsV8U8rf8rbRd5WNjAYYP0gOLFYmSvaBHpsBLfCOc570l8XORAep4irVNC+ki87htahY4iJ882EEEK8dhqV9GRyu3I8VsvkelwKzafuZX3YLfON7fNDt3XgWVqZi72YXcw8s8ZkU19HXxY0WUBhJ+XPtOMxx+mwvgMRCRFGcYcC7uhLaNDplQ80LdSW+KoCuDhyM3dCLyvyQgghhHj5hIWFUaRIEZOFxIiICN566y2GDx9uFH9QyCxbtmyu/Z8+fZqWLVuSlpbGwoULuXbtGps2bWLp0qXs27ePmJgYZsyYQaNGjZ7NDb0mpJAphBBCCCHEv+Tw0MV4myhiJusSuR+QSPXveqLRKFdpPvDnlT/5JewXRbxzUGc6BnU03WjzCDg2Txkv0hDaLgCtZY5z3n7+Nj/vuKKIVw90Y/0HNRn3ThnyO5gvvAohhHj9NC9bgO9bKx/2JaXrGLDkOF/+cZp0XZbpxnZu2cXMoibOes5Iyt5mdvcEk019HHxY1HQR1QtUV+QikyLpu7UvMSkxRvHSvd8koUgyd9NN7zrgbOnG7QWnSLwebXq+QgghhHhpBAUFMXDgQHr06ME777xDVFQUWVlZTJ48mVKlSmFnZ8fAgQMfXh8REcG9e/ews7OjcOGcXwAG+Oqrr0hLS+Prr7+mS5cuis/3jo6O9OvXj1q1aj3ze3uVSSFTCCGEEEKIf8G+IfPxs1Z+8IlJi8KxjT9l330rx/abr23my/1fKuJ1fOowJHiI6UbH5sPBn5XxwnWh7SLQWuU45o27KXy8/KQi3rqiD4t7hVCigGOO7YUQQry+Wlf0YfTbpUzmFhy4TpsZB7hx18y5mTbO0H4J1B0JmNj2fPs3cGSOyaYOlg78VO8nOhZXvuATmRRJv639iE+PN4qX6dOcol815KruAulZyq1kna3ycXnSbtLumz6nUwghhBAvBwsLCwYNGsTly5fx9vZm1KhR7N27l+XLl7NlyxaWLl2Kv7//w+tDQ0MBKF26NGp17uW0HTt2AFCjRo3nMv/XlRQyhRBCCCGEeM4Ofr4If62yiBmdeoMSXzbBK6Sk2bZJGUmM3DuSIbuGoNPrjHJBrkF8V+s7NGoTqzivH4D1JgqcAbWzHw5b5LyKMl2XxYAlx4lPNd5ur7inA9+0KIXKzHmaQgghxAOdQvyZ2z0YJxsLRS7sZjxvT9tP+J0k043Vaqg1FDqvAhsXZX79YLPbzGrVWj4N+ZTPQj5DrTJ+9HX5/mXe2/YeKZnGRVRrR3tqTuiNzTsFiEhW7kTgbu3FqS//RJeWYXq+QgghhHguVq1ahUqleiZ/wsLCAFCpVEaFycf//wNPsq0sQFpaGgB79uz5p7ctHiGFTCGEEEIIIZ6jY+NXUiDDVxGPS79N0UF1sXYxv6rxRMwJWq9tzR9XlOd35LfNz9S6U7G1sFU2vH8DVnSBx8/88g2BDsvAwibHOeuy9Hyx5gxhN41XrNhbaZneuSI2lua3vxVCCCEeVbe4B+sH1qCsr7MiF5uUTqfZh8yvzAQIrAf9doPH46s7DbC6D1zdbbZph+Id+LKqcjeDsDthfLzzYzKzlGdjFqhSimpTuxOecl6R87Lx48iwJeh0OkVOCCGEEM/H4cOHn0k/NjY2FCtWjKlTpxIYGEhERASjRo2iRo0atGnThvr169OlSxdu3rz5sM2DFZl5LWRWr569vf3QoUOpV68e06dP59y5c89k/q8z7X89ASGEEEIIIV5VF3/bhesdV9SPrZhMyLiLZ5cyOPp7mmxnMBj4JewXpp+cjt6gV+QdLB34qe5PeNh5KBtnpMCyjpD82Flfjj7QbjFYmih8PuLG3RQ+Wh7Ksev3FLnxrcsQkM8ux/ZCCCHE43xcbFnZryrjNp5n7r6rRrlb8Wl0mn2IFf2q4ulkZrcAZz/o8jvMaQj3HmmflQFLO0KP9eBl+gFjqyKtuJ9+n0nHJhnF90ft57O9n/Fdre8UqzYBqk3sweGPF+FnF2gU97UpzKGhi6g+qUce7lwIIYQQ/1Tr1q2pWrWqyVxGRgbt2rUDwM7OjsWLF5vtx8HBgfPnzzNhwgTmzp1LixYtmD9/PhqNho8//ph33nmHAQMGMHHiRCZOnAj8vSKzXLlyeZrrjBkzaN26NcePH2f79u1s374dAD8/P3r06MGgQYNwdJQjWp6UFDKFEEIIIYR4Du5ejCDrQAK2Fs5G8RRdIvZNvfGoUNRs259Df+aXsF9M5irkr8CYmmPwtvdWJg0G+GMARIcZx7U20P5XsM9vdkyDwcCa0Eg+X3OGpHTlSpNeNQJoUtrLbHshhBAiJ5ZaNV80L0FZXyc+Xh6K3vB3LuJuCp1mH2R5v6rkszdzfrN9fuiyOruY+ejLOhmJsLg19NwEbspt3AF6lurJ/fT7zDs9zyi+6domyucvT8cg5XmaWq2W4LHtCf1sFQVs/Y1y/laB7Bs8j+o/SDFTCCGEeN4qVapEpUqVTOaOHz/+8O9ly5alZcuWufZ3+fJlLCyU2977+fmxdu1aMjOzd2xITEwkPDwclUpF6dKl8zTXgIAAjh07xu7du1mzZg1btmzhzJkzRERE8NVXX7Fs2TKOHz+OrW3OLxgLY7K1rBBCCCGEEM+YLi2Dqz/vx+GxImZ6Vhr6YGv86gebbbvwzEKTRUytSsuHFT5kbqO55ouYWz+HM6uVuZY/Q4FyZseMT83kw2WhfLz8pMkiZuWCrgxvUtxseyGEECKvWpTzZkKbsjx+1PKVO8l0mXOY+yk5nEHpWij7zExLB+N4cgwsbJG9tboZH1f4mFZFWinik45N4mr8VRMtwNLehpKfNeFO2i1Fzt8ikP1D5pufqxBCCCGeuxMnTjz8e/ny5fPUxlQR01Q+LCwMg8FA4cKFsbe3f6J51apVi4kTJ3L69Glu3LjBsGHDALhw4QIzZ858or6EFDKFEEIIIYR45g4P/xUPG+Nio96g517+exRvX9dsuzWX1zD+6HhFvKBjQRY3XUzv0r3RqE2cT2kwwLavYP9UZa7GICj1jvm5Xr1L0yl7+PNklMl8u2Bf5veshIVGPjoIIYR4NlpV8OHblo+feQnnbiXQafYh7ibnUMz0Kpu9y4DG0jgefyO7mJl422QzlUrF51U+p45PHaN4WlYaI/aOQKc3fe6lnacb/u+GcC89VpHz0xZm/9D55ucqhBBCiOfq0UJmXrd/fVT37t3ZuXOnydyTno9pjo+PD+PGjaN27doAnD59+h/19zqSpxFCCCGEEEI8Q4e/WYKfdaAiHpF5meChbc222xaxjS/3f6mIl3ArwdJmSymZr6TphgYDbP8W9k5S5oo2hrqfm2yWmaVnwuYLtJ95gMj7qYq8s60FMzpX4LvWZbC1lBMphBBCPFudQvwZ2SxIET8TlUCHmQe5k5huvnGh2tBqJjx+tuXdK7CoJaTcNdlMq9bydfWvyWeTzyh+KvYUc07NMTuca3F/8ncpyf2MOEXOT1OY/Z/MNz9XIYQQQjw3D4qNkPcVmXn14HzMf1rIfMDbO/tlZzs7u2fS3+tECplCCCGEEEI8IxdX7CR/oqciHplyjSrfdzPbbm/kXobuGoreoDeKBzgFML3+dOwtc9jGZuc42DNBGfepDO/MBrXyV/6rscm0nr6fn3ZcNjqj7IHqgW5s+rAWjUvJmZhCCCGen941CzG4gfLM6Au3E2k38wDR8WnmG5d8G94ysRNBzFlY9DakxZts5mLtwlfVvlLEZ5ycwdm4s2aH86hQFLcOxYg3VcxUF+bAyIXm5yqEEEKIZ85gMDwsNmq1WkqVUu728E88yYpMg8HEB+tHJCcns2PHDgDq1av3j+f2upFCphBCCCGEEM/A7aPnMBxOQas2Pm/jXnosJYY2RKtVrmo0GAzMPT2XAdsGkKnPNMp52Xkxs8FMXK1dzQ+6ezzsGqeMewdnnyFm5aBIHY+4x1tT93LypvIBr4VGxYimQSzqGYKnk7X5cYUQQohn5P26gXxcX1nMDL+TTNtfDnDzXor5xuU7Q1MTL/PcCoWFLc1uM1vLpxbvFDHedl1n0PHZns9IzzK/EtSrUhCu7YoQn6Fc8Vkg04+w6X+an6sQQgghnqlLly6RlJQEQIkSJbCysnpmfWdlZT3cAjYvW9YOGzaMQYMGcf36dUXu3LlzNG/enFu3blGrVi2aN2/+zOb5upA9ooQQQgghhPiHkm/FcfvXs7ha5TeKp2WlYN+oAA6+Hso2mcl8vu9ztl7fqsi5Wrsys8FMPO2UqzsfOvFr9payjytQAbqsBmtHRerkjft0m3OYxHTlOWCB+e2Z0r4cJQs4mR9TCCGEeMZUKhUf1i+CpVbNd5vOG+Ui7qbQZsYBfulSkTI+zqY7qNwHMpLhr8e2Z486DrPqQsfl4KlcoTG00lAO3jpIZFLkw9iV+CsM2DaAz6t8jr+jv8nhvEJKkqXTk7AqHEfLv1820qg02IZbcWPHcXzfqJC3mxdCCCHEU/un52Pm5OLFi6SmZh/B0rVrV5PXGAwGhg8fTqNGjdi/fz/79u1j8uTJlCxZkmLFigFw9epVQkND0ev11KtXj99++w2VSvVM5/o6kEKmEEIIIYQQ/4AuLYMzYzdSwNb4gafekMV9j3iCGzdStLkaf5WPdnxEeHy4ImdvYc/0+tMp6FTQ/KBX98DaD5Vxr7L/L2Iqi5GnI+PpMueQySJmlyr+fNY0CBtLjfkxhRBCiOeof53CWGnVfL3OeHvXW/FptJ5+gK9blKR9ZT/TjWt8BJkpsOs743jCTZjTEFrPgWJNjFJ2FnaMrjGaHpt6YODv7eAO3TrE23+8TY9SPehdujc2WhvFcD7VS3MzK4vkP6Ow0/69+4G1xpb7a6/jXNQXB2/3J/sHEEII8dKKHjOG9HPnc7/wFWUVVBzPzz7718f9N87HBNi1a5fZ68aPHw/ArFmzWL9+PVu3buXq1ats3ryZzMxM8ufPT8uWLenSpQstWrSQIuZTkkKmEEIIIYQQ/8ChYYvxty2iiEcYrlBjSC9F/HTsafpu6UtiZqIi5+/oz+Q6kwl0CTQ/YOwlWN4ZHtuKFo/S0GUN2LgompyJiqfT7EMkpBkXMa0t1EztUIEGJZQrRoUQQoh/W88aAVhq1Yxcc9oonpGlZ/jqU5yIuM9XLUpibWHixZs6n4JeB3t+MI5nJsPSDtDwW6g6AB55gFjRoyLdS3Zn3pl5xk30mcwMm8m6K+sYUWUEtXxqKYbzqVWOU5eisLqQabStvLNlPs59t5kKE9ub3FZeCCHEqyf93HlSjhz5r6fx2nl0ReazLmS2b9+e9u3bm83r9XqysrIe/v+goCCCgoIYMmTIM52HyCZnZAohhBBCCPGU9n86H38bZRHzWsolanyvLGJejb9K/7/6myxi1vGtw9JmS3MuYibHwa9tIO2+cdzRGzqtBFvleZoXohPpPPsQ8anGhU8rrZq53SpJEVMIIcQLpXMVfya0KYuFRrliYfnRG7SesZ/I+6nKhioV1PsC3poK6scLiAbYMgJ2jFY0e7/8+zTwb2ByLlHJUQzYNoA1l9eYzJfu1ZQoq5uKeAFbfw4OXWCyjRBCCCGejQeFTJVK9cy3lhUvFilkCiGEEEII8RRCf/wdb31BRTwqJYLK33VUxG8n36bf1n7cT79vFFeh4v1y7zPljSk4WDoo2j2UmQbLOsK9q8ZxS/vs878cvRRNYhLS6Db3MPdSjIuYllo1s7sFUy0wn/nxhBBCiP9I64o+LOtbFQ9HK0XudGQCHWYeJCYxzXTjCl2zdyiwdlbmdo+HA9OMQpYaS36o/QMTak8gv21+ZRtg1P5R7Lyx02Su2jdduZZ6SREvaFWUI2OXmZ6jEEIIIf6RqKgoYmJiAChYsCBOTsrjVcSrQ/a4EEIIIYQQ4gld23IE+xu2aDTGW9vdTb9D0ND6WNpYG8Xj0+N59693uZV8yyhupbFiYp2JJresMxJzHlb3huhTxnGVGlrPA8/SiibpuizeXXyM6ATjB72WGjW/dKlIzSJydpcQQogXV0V/F9Z9UJMPlh7nYPhdo1zE3RS6zz3Csn5VcLS2UDYOqAl9tsOSthB32Ti3+dPsbdjLdXgYUqlUNCrYiJreNZkRNoNFZxahM/y9HXuWIYshu4Yws8FMKnhUUAxX5bvOnBj2G142vkZxt7tuXN1wiICmIU/xLyCEEOJlYRVU/L+ewn/qv7j/53k+pnjxSCFTCCGEEEKIJ5BwLZqkTZE4W7oZxVN0ibi+UwQHX+OtWtN0aQzcPpDL940fpGpUGsbXGp9zEdNggMOzYOvnoDOx8qTxd1C0oYlmBkb+fprjEfeN4hYaFdM6VeCNYqZXnAghhBAvEncHKxb3CuH7zReYuTvcKHf2VgJ9Fx5lfo/Kps/MdCsMvf+CRW9D1Anj3B8DwMYZijUxCtta2DKo4iBaFG7Bu3+9S3Ry9MNcelY6729/n/mN51PUpahRO621JUU+fIPInw7jZPn3Nu+WGmvu/3WL5PJx2HkZ/94ghBDi1eH52Wf/9RReO8/zfMxHbdu2jfr169OtWzfmz5//3MYROZOtZYUQQgghhMgjXVoGFyZtUxQxdfpMsspq8alhvDJSb9AzbPcwjsccV/T1ZdUvecPvDfODJUbDr61h41DTRczK/SCkr8mm8/ZdY+Ux5Zldo98uTX05E1MIIcRLRKtR81nTIL5tWUqROxh+l0ErQsnSG0w3tnGBTqvA7bHzrA1ZsLI7XNtnsllh58L80uAXnK2cjeKJGYm8u/VdIpMiFW2cC3lhXduVDH26cdzSjdPjNqLT6RRthBBCCPF0/q1C5uHDhwGoUqXKcxtD5E4KmUIIIYQQQuTRoeG/4mXjp4jftosiqItyZeT0k9PZfmO7Iv5hhQ95u8jb5geKuwK/1IbLf5nOV30fGo81mdp7KZbRG84p4j2qF6RtsK+JFkIIIcSLr3MVfz6qX0QR33AqmlF/nsFgMFPMtHODrmvA0cc4rkuDpe3h7lWTzQo5FWJavWnYaG2M4ndS79BnSx9uJ99WtCncoiYxDtGKuLeNP0dG/mp6fkIIIYR4Yo8WMsuVK/fcxunZsyfnzp2jS5cuz20MkTspZAohhBBCCJEHB0ctxt86UBG/lnaJkC87K+LbIrYx4+QMRbxzUGd6leplfqCEKFjYEpKUD0Kx94Quv0Oj0aBWbqN35U4SA5YcV6xMqRGYjxFNg8yPKYQQQrwEPqxXhE4hyheKFh28zg9bLppv6OST/fPT9rHtXdMTYE1/0GeZbFbavTST35iMVm18MtONxBv02tKLmJQYRZsqn3fmWqpyLt4Gf87M3mh+jkIIIYTIk4SEBK5ezX4Ryd3dHW9v7+c2loeHB8WLF8fOzu65jSFyJ4VMIYQQQgghcnF61jo8Uwoo4lEpEVQZpyxiht8P57M9ynNSGvg3YGiloahUKtMDpdzNPssrPkKZC2oO7x2AwnVNNr0am0yHmQeJT800ivu72fJTx/JoNfKrvxBCiJebSqXi6xalaFLKU5H7acdlpu28bKLV/7kXhU6/gaW9cTziABz4yWyzagWqMbbGWFQY/+y+nnCdXpt7EZsaq2hTeXRHbqcab/GuVmmwvKDi1oHT5ucohBBCiFyFhoY+3InhabaVvXTpEn379qVw4cJYW1vj4OBAkSJF6Ny5M0ePHn143c2bN1GpVIptZW/evIlGo6F69eokJiYycuRIAgMDsbGxoWTJkqxYseLhtWvXrqVu3bo4Ozvj5eXFe++9R3Jy8lPeOdy7dw+VSkW5cuW4dOkS3bt3x9fXF0tLS7y9vfnkk0/IzMzMvaOXjDzNEEIIIYQQIgcRW49idVGLVm1hFL+fEUvQ0PporS2N4okZiXy440NSdClG8SIuRfi2+reoVWZ+BU9PzD4T885547hKDc2nQNtFYOtqsun1uOwiZkyi8blc9lZaZnUNxtnW0mQ7IYQQ4mWjUauY1K4cVQopfyZ+v+kC8/eZ3ioWAO8K0HK6Mr79W7h91myzxgGN+bzq54r4tYRrJouZlvY2+HQrT1JmvFHcRmNH7IqLpNwxjgshhBAi7/7J+Zjbtm2jbNmyzJo1C1dXV1q0aEHt2rXRaDT8+uuvXL9+/eG1oaGhgHLr2gfxfPnyERISwpIlSwgODqZUqVKcPXuWjh07cvToUQYNGkSbNm2wtLSkbt26JCQkMH36dEaMGPFU9w1/37vBYCA4OJi1a9dSuXJlatasye3btxk/fjxDhgx56v5fVFLIFEIIIYQQwoy7l26QuPEmNhrjbWTSslJwbOKLg6+HUVxv0PPpnk+5lnDNKO5o6ciUN6Zga2FreqDMNFjWESKPKXPNf4SK3cHMKs4bd1PoMPMg0QlpRnGNWsWU9uUo6uGQ4z0KIYQQLxtrCw0zuwZTxsdJkRu19iwrjtww37jEW1CmvXEsKwN+7wu6DLPN2hRtw4gQ5YPH8Phw+mzpw720e0ZxjwrFSS+iR6c3XhXhYuXOqW//RKfTmZ+jEEIIIcx62vMxDQYDvXr1QqvVcujQIY4cOcLy5ctZt24d58+f5/jx49SpU+fh9bkVMjdu3Mjbb7/NxYsXWbZsGUeOHKFz585kZWXRvn17Nm7cyJkzZ9i0aROrV69m+/btAKxcufJpbhuA48ePAxAWFkbTpk2JiIhg1apVbNu2jY0bs7ewnzFjBrdvK8/yfplJIVMIIYQQQggTMpJSufrTXpwsjc/TytLrSAnMwK9BsKLN1BNT2XVzl1FMrVIzvtZ4fB18TQ+UHJu9EvPqbmWu4bdQoYvZOd68l0L7mQeJijcuYqpVMLldOeoFeZhpKYQQQrzcHK0tWNCjMsVMvLAzbHUYa05Emm/c5Dtw9DGORZ+CXd/lOGb74u0ZXnm4In75/mU+2vERmVnGRcuy777FTc01xfXeNgU5/OmiHMcSQgghhGnz58/HYDBgMBho37597g3+78qVK1y/fp2qVatSuXJlRb58+fK4uf39+T+3Qmbz5s0ZPXo0Wu3fZ2k3bdoUgKioKFatWkXhwoUf5kJCQnB1dSU6OjrPc37cgyJu1apVWbx4sdHZnQ0aNKBWrVpkZGRw5syZpx7jRSSFTCGEEEIIIR6j0+k48tkyPGx8FLlbNpGU6ddcEf/90u/MPjVbEf+wwodU865meqDIY/BLbbi2R5mrMQiqfWB2jnFJ6XSafYjI+6lGcbUKJrUrR/OyyjM9hRBCiFeJi50li3uHUCif8c4JBgN8tDyUn3dcfniGlhEbZ2j5szK+dyJc2Z7jmJ2COjE0eKgifjzmOKMPjVaMV2NsT66mXFRc76MuxPEJT78iQwghhBBPxsnJCZVKxY4dO5g6dSpJSUk5Xh8aGoparaZMmTKKOMDIkSMVbRISEgBo3bo1JUqUMMrp9XqSkpKMiqVP6sGKzDFjxqDRaBT5wMBAAGJiYp56jBeRFDKFEEIIIYR4REZSKocHLcLfNlCRu55+iSpfdVbED906xNcHvlbEGxdsTI+SPUwPdHwhzG0MCTeVuYo9oN4XZueYlplF30XHuB5nfA6nSgXjW5elRTlvs22FEEKIV4m7gxWLe4fg7WyjyI3ffIHBK06SrstSNixUB0LeNY4Z9LDobfhjQPaOCWZ0LdmVQRUHKeKrLq1iyfklinilb9txO9X4571apcb5tjNh0/80O44QQgghnh13d3fGjBmDXq9n4MCBuLu706xZM+bMmUNKivFn68TERMLDwylSpAi2traKuI+Pj8nzOU+ePAnAW2+9pchdvnyZjIwMRYEzr5KTk7l48SKenp5GW+A+6sF9/JNi6YtICplCCCGEEEL8X8L1aE5+9jt+JoqYkSnXCBnfVREPvx/Oxzs+RmcwPuuqhFsJvqr2FarHz7bM0sG6j+HPD7LP5Hpcha7Q7AezZ2Lq9QaG/hbGsevGZ3GpVPDdO2V4p6JyFakQQgjxKivgbMPSPlXwcLRS5FafiKTTrEPEJaUrG9b7EtyKKOMnFsPUCnBkNuhNFEGBHqV60K5YO0V8/JHxHIg6YBSzdrTHr1dlEjPvG8UtNdY4XXPg0Khfzd+cEEIIIZ6Z4cOHc+nSJcaNG0dISAibNm2id+/eFC1alKtXrz68ocnsiQABAABJREFU7uTJkxgMBsW2sg/iFStWNNn/g61fK1WqpMg9WMlpqgCaFydPnkSv15sdG+Do0aOoVCrFKtKXnRQyhRBCCCGEAG4dOcfVyXvxslWeZRmbFk3ZUW8ZnX0BEJcax3vb3iMxM9Eo7mnnyU91f8LWwtYojsEAaz+Eo3OVE1BbwJuToPmPoFZuEfPApL8usvZklCI+qnlJ2gabOYdTCCGEeMX5udny27vVKJLfXpE7ev0eLaft41pssnHC0hZa/QJa5WpO0uJh/eDs3RNS7pocc1jlYVT2ND5jK8uQxeBdg7mecN0o7l6mMOoQOzL0xgVVjUqLd5of+wbPQ6czfilKCCGEEM9eQEAAw4YNY+fOnYSHh1OjRg0iIyOZNm3aw2tyOx+zQoUKin71ej1hYWG4urri7++vyD8ocj5tIfNBe0dHR5P5I0eOcPnyZUJCQvDw8HiqMV5UUsgUQgghhBCvvSt/7CF+2RXcrJS/7MemReP3XgjWrk5G8fSsdAbuGEhkUqRR3M7Cjp/q/oS7rbtyoG1fQehiZdzBC3pshOCeZldiAvx27CZTt19WxHtWD6BbtYJm2wkhhBCvA19XW1a9V43aRZU/g2/cTaXT7ENEx6cZJ7wrQvf1kL+k6U5vHoZf20BGsiJlobbgh9o/4GNvvBtCYkYiH2z/gKQM47O3irWtS1y+OHT6TEVf/haBHB28hIzEFEVOCCGEEM+Hv78/3bp1AyAr6+9dGHIrZD4eB7hw4QIpKSlmC5U5tc2LB+djPrpy9AGDwcCnn34KZK86fdVIIVMIIYQQQrzWbh87j25PAvYWTopcVMp1in/RENeifkZxg8HAV/u/IuxOmFFco9IwofYEirkWUw50YBrsnaSM+1eHfrvBV7n1zKPWnozi09Vhinj9IA9GNAvKsa0QQgjxunC0tmBOt2C6m3jBJ/J+Kl3nHuJ+ymNbu/tUzP5Z3HgcWJlY5RB5FJZ3AZ1yS3hna2em1p2KnYWdUfxq/FW+2P8FBoPBKF7pk3YkFk0hVacsjPrYBBA2cg26NBNbzwshhBDiqW3evJkNGzYodj+IiopiypQpgPG5lrkVMk0VK3NbcRkaGoqVlZXijMxRo0ahUqno3r17jvfwoP9Dhw6xefPmh/HMzEwGDhzItm3baNWqFS1atMixn5eRFDKFEEIIIcRrKyMplciFodhplQ8tI9KuUOGH9oqVmAALzixgbfhaRfzTyp9Sw7uGcqCwFbD5U2W8cD3osgbs85udY2JaJoOWh/LB0hNkZhk/DC1ZwJEp7cuhUZtfxSmEEEK8brQaNaPeKsk3LUry+I/Ii7eT6D7vCMnpj23jqtFClf7w/hEo3VbZ6ZVt8Md7oNcrUoEugXxX8ztUGA+29fpWFp5dqLi+dO830TZwJT4jTpHztPHl8PAlud+kEEIIIfJs+fLlNGvWDA8PDxo1akSnTp2oX78+AQEBnD59muHDh1OnTh0AdDodZ86cwcPDA09Pz4d9PIjnz58fb29vxRgPCo2mtp2Njo4mOjqaUqVKKY6s0f//dwsLCwuz88/IyODMmTP4+PjQunVrmjVrRr169WjTpg0FCxbkp59+omrVqsybN++J/21eBlLIFEIIIYQQr63DI5aS38b4A4jBYCDCcIVqk7ujtdAq2uy+uZuJxyYq4p2DOtOueDvlIJf/gjX9lXHvitB2IWgtzc7vyLW7NJmyh9UnIhU5T0dr5nSrhJ2Vco5CCCGEgC5VCzKuVRlFPPTGfd5dfIx0XZaykYMnvDMLqn+kzJ1amf1i0mOrLAFq+9bmg/IfKOKTjk3iaPRRRTygUWW8+pTjdqryZ7yfdWEOfq4sgAohhBDi6XTo0IH+/fvj6+vLsWPHWLFiBadOnaJx48Zs2bKFsWPHPrz2/PnzpKWlKVZjPoibW3GZ04rMnLaVfZDr2rWr2fmfOXOGjIwMypcvz4IFC+jfvz9nz55l7dq1uLi48P3337Njxw6z52e+7OSphxBCCCGEeC0d+vpXCtoUUcRvEE6177qbbBMeH86w3cMwYPwAs3qB6gwOHqxsEHMeVnQD/WOrPtyKQMeVYGVvchyDwcCUbZf4cdsl9MpnpTjZWDCnezCeTtYm2wshhBAiW9tKvtxLyWDsxvNG8T2XYhm04iRT25dHbWpng/qjICUWTjx2tvWhGWCbD2oPVTTpVboXYXfC2Hlz58NYliGLIbuGsKL5CvLbGu/A4BpUENtRroR9+ScFbP2Ncl4ZvoRN/5My/d9CCCGEEP9MgwYNaNCgQZ6uLVWqlGJr+MfjehM7NPz1119m+2zcuLHJPrOysti1axf169enZs2aZts/OB+zQoUK2NjYMHXqVKZOnZrrvbwqZEWmEEIIIYR47VxatZv8SZ6K+M2Uq1Qe3dlkm/j0eAZuH0hSZpJRvKBjQb6v/T1a9WPvCKbeh2UdIcP4ehwKQJfVYOdmdn4/bLnI5L9MFzErF3Rl/cAalCyg3PJWCCGEEEr9ahemX+1Civj6sFssPHDNdCOVCt6cAsWaKnM7voV9UxRhtUrN6Jqj8XXwNYrHpcUxZNcQMvWZijbWLo4EfdKAu+kxRnGNSottuBURW5WrOYUQQgjxajh69CgJCQl8/fXXOV6X2/mbrzopZAohhBBCiNdKwtVbZO67j4XaeEvXe+mxlP60meK8CoBMfSZDdg3hesJ1o7iDhQM/1v0RR8vHtm/R62F1X7h7xThu7QSdV4Gzn9n5zd4Tzk87LiviWrWKTxoXY2nfKvi42OZyl0IIIYR41PDGxWkX7KuIj914nssxSSZakH1uZuu54FdNmdv6Bez/SRF2tHRkUp1JWGuMd004EXOCCUcmmBzGwccD15aFSdEZz8NaY0vCxpskXIs2c1dCCCGEeJmFhIRgMBioWrVqjtc9uiLzdSSFTCGEEEII8dowGAycn7wNR0sXo3h6Vir2jQpg56VcJWkwGBh9cDQHbx00iqtVasbXHk+AU4ByoJ1j4dJm45hKDW3mg0cJs/NbcfQG364/p4gXcrdjzYDqvFcnEI2p7e+EEEIIkSOVSsWYVqWpH2S8vWu6Ts/gFaHospRbxAFgYQMdloJHaWVuywg4OF0RLuZajC+qfqGILzm/hNWXVpscxqd2OdKDDOgeW7XpbOnGhUnb0Ol0JtsJIYQQ4tWm1+sJCwvDzc0NX1/lS1mvAylkCiGEEEKI18ahLxZRwMb4DCqDwcA993sENA4x2WbBmQWsurRKER9UcRDVvasrG5xbC7u/V8brj4LCdc3ObdPpaIavClPEy/g48ceA6pTylq1khRBCiH9Co1bxfeuyuDtYGcVP3oxn2s4rZloBNs7QdQ3kN/Ey0qbhcGimIty8cHPaFWuniH9z8BtOxJwwOUzpXk25ZRupiHvZ+HF4+GITLYQQQgjxqlOr/8fefUdXVaxtAH9OS+89JCGB0GuooYM0EaR3pIsFUBSkSVGKFAUpgoBwVVA6giAqRemClARCSKgppBJCCunJqd8ffCRMJlTpPL+17lrk2bNnn/Gay7n73fsdJbKzs5GSkvKsP8ozw0ImEREREb0SonedgHuBl5RfLbiCuuPlG40AsC9mHxYEL5DyruW6YmCVgfIJyReBX9+X82rdgUaj7vrZDlxKxqgNZ6Q9Mf1drbF6SH3YWmjuei4RERE9OCdrM3zZXX678pt9V3AuPuPuJ1q7AAN/A1wrycd2jQNCNkjx+HrjEeAaIGR6ox4fH/gY17KvlXiZhtMG4Gr+FSn3UZXF2cXb7/75iIiIiF5SLGQSERER0UtPm5WL7L0J0r6YyfmJCJzzVonnhKWEYeKRiTBBrC4GegRiasOpUCiKtXjNvgGs7wloi+2z5V4N6LQEKD4et94GXXYwAm+vPgVtsZZ2Xg6WWDssEE7WZtJ5RERE9OhaVnJH3/riftV6owmjN4cgX2e4+4k2rreKmS4V5GM7RwFxJ4XITGWGha8thLuVu5Cn5afhowMfIU+fV+JlGsztj2t5sUKmUChgG2+N+ENy9wYiIiKilxkLmURERET00gv6bDMczV2FrMCQB9cu5WFmaSGNj0iPwIf7P0S+IV/Iy9iXwdctvoZGWewNSV0esLEvcFO86QhLR6DPOsDMWrpGdoEew9eexle7L0lvYjpbm+Hnt+vD097ywRdJRERED2xKh8oo7WQlZBHJ2Ri6+hSibmTf5SwAtu7AoJ2AczkxN2iBjW8BGfFC7GLpgm9afgMLlfh940LaBUw9OhUGo1w4VVuYwX9EE2Rq04XcTGWBjB3RyE/LfIAVEhEREb0cWMgkIiIiopdayJLtKG3uL+U3bK7Dp0VtKT+VdAoDdw1ESp64/4SjuSO+bfUt7M2L7VVpNN5qJxt/SswVKqDHD4Cjn3SNyBvZ6PLtUewOT5KO2VqosWZofZR1tbn/4oiIiOiRWJur8XWvmlLDhGORqWi36Ajm7bmIPO1d3s609bhVzLT1FPOcZGBjP0CbK8RVnKtgZuOZ0jR7ru7BmINjSnwz06miL1DXEjpjgZDbmzkhbMYf0Ov0918kERER0UuAhUwiIiIiemmFrfoTNrFWUh6TcwUNPh8g5buid+G9v95Dli5LyM2UZvim5TfwsfWRL7J/JnB+u5y3nwf4t5TikLib6LL0KCKS5bc9yrpYY9vwRqjmZS8dIyIiosernp8T3m1WVsq1BiO+PRCJ1gsOYf/F6yWfbFcK6LMeUBfr7HDtLLBjBGAS2y20K9MOw6oPk6bZH7cfQ3cPlR6gAoAq/dogwSxWyj0svBEyfgv0WhYziYiI6OXHQiYRERERvXRMJhNOTP4ZdhFWsFCJ7VkztKmoMaGDNP6HsB8w/vB46Iw64ZhaocbsprMR4BYgX+j0z8A/C+S84QdAvbelOCI5G0N+PImsAvnGY5sq7tj+QWOUd7e9/wKJiIjosRjTpgLaV/co8VjCzTwMXR2ELUFxJZ/sVRvo/K2ch/8KHJ4nxR/W+hAtfFpIeVhqGPr/2R9RN6OkY02+GIqInPNS7mH+/8XMvALpGBEREdHLhIVMIiIiInqpaDNzcXL0WngZ/KBUqIRjeqMOCLCErbe7kC88vRALgxdKc1mprbC01VK87ve6fKGrR4HfP5bzSm8CbWZIcVJGPgb9cBLpuWKhVKEAxratgO/614GdhUY6j4iIiJ4cc7UK3/arjWVv1YanvbxvNgBM3HYOf5+/y5uZ1XsATcbI+YFZwKXdQqRUKDGv2Ty09W0rDU/ITkD/Xf0RfD1YOlZ/Rl8k5F6Vcg8Lb5z99Fdoc+TWtEREREQvCxYyiYiIiOilkRIaiYuf74KXhZ90TG/UIcEiDlUHikXJnZE78WPYj9J4V0tXrG63Go29GssXSo8BNg8AjMXerPQMALqtBJRiATUjV4dBP5xEwk3xRqO5WokfBtfDBy3LQ6kstkkXERERPRUKhQLtq3vi7zHN8X5zf6iL/Z1sMJowcv1pnLqaVvIELacCFdvL+a/vAmnRQmShtsC85vMwpNoQaXiWNgsf7PtAejPTytEOFce0QmJujHSOu4UXzk3egfybWdIxIiIiopcBC5lERERE9FLISUhB0upzcDJ3k4/pM5Hhn4XGMwYJ+aW0S5jxr/z2ZFn7sljbfi0qO1eWL1SQDWzoC+SmirmdN9BvE2BmLcR5WgPeXnMKl66LNxhVSgW+7Vcbr1WUPy8RERE9fdbmakx8oxJ2f9wUXg5ia/oCvRFDV5/ChWuZ8olK5a0HmVyLfW/Iz7j14JNOfJBJqVBiTJ0xmNpgKlTFukdk67Ixct9I3My/KeQOfqVQeUJbJOSIhVHgVjEz7PPf2WaWiIiIXkosZD5FBoMBYWFhWL16NT788EM0bNgQVlZWUCgUUCgUmDZt2kPPuXv3bvTu3Ru+vr6wsLCAm5sbGjdujIULFyInJ+fxL4KIiIjoOaTNycPlefvhYOYsHUvOT4R1t9Ko+V5nIc8oyMDHBz5GviFfyKu7VMdPb/yEUjal5AsZjcCv7wHJ4WKusQL6bgBsPYoNN2HUxjMIikmXpprTrTpaV3GXciIiInq2yrnZ4qe368PJ2kzIs/L1GPTDScSl5conmdsCfdYB5nZinnQO+OMTwGSSTulVsReWtloKK7WVkMdnx2P0wdHQGcR29LZebqg29U3El1DM9LD0xumJW6DXyftwExEREb3IWMh8inr16oXq1atjyJAhWLp0KY4fP468vEfbx6CgoAB9+/bFG2+8gc2bNyM2NhYFBQW4ceMGjh07hjFjxqBmzZoIDQ19zKsgIiIier6YTCacnbwNrhae0rGrOZdR6bM34N2oupAbTUZM+mcS4rPjhdzZwhmLXlsEe3P7ki92aC5w8Xc577oC8KwhxYv+voy/SthTa3y7iuhV1+ceqyIiIqJnyd/VBj8OrgcrM/GNyeSsArz1vxO4llHC/Rxnf6DLcjkPWQcEry7xOk28mmBBiwVQKsRbdEHXg/DFiS9gKlYAtXZzQo3pXRCXI7afBYBSlr4IHr9BOoeIiIjoRcZC5lNkMBiEn52cnFC+fPlHmmvQoEHYuHEjAMDZ2Rmffvop1q9fj2+++Qb169cHAERGRqJdu3aIi4v7bx+ciIiI6Dl2avw6eFqUlvKo3AtouGgwrJzspGMrQ1ficPxhIVMpVJjXfB7crO7S6jX8V+DQl3Le4lOgSmcp3h2WhG/2R0j50MZlMLy5/11WQ0RERM+Lmj4O+G5AHWhU4p6ZsWm56LfqBK5n5ssnVX4TaPyxnO8aDyQEl3idxl6NMb7eeCnfdmUbfj7/s5RbOdmh1qzuiM+V38z0MvfDyQnrSl4QERER0QuIhcynqH79+pg4cSK2bNmCqKgopKamYtKkSQ89z44dO7Bp0yYAQOnSpXH69GnMnj0bffv2xYcffoh///0XQ4bc2jT+2rVrGDNmzGNdBxEREdHzInjGJpRS+Up5bE4EGs0bDJVKJR07lnAMy0KWSfnoOqNRz6NeyReKDwJ+fV/OK3cCmsk3Hq9cz8Inm0OkvEN1T0zpUBkKhUI6RkRERM+fpuVdsbB3AIr/1R2dkoO+K48juaRiZsupQJlmYmbQApsGAFlypwYA6FepH3pV6CXl84Pm40j8ESm3sLNB9clvIilPfnjdS+mLU1PX331RRERERC8QFjKfokmTJmHOnDno0aMHypQp88jz3LmX5vLly1G6tPgGglKpxLfffluY//LLLwgLC3vk6xERERE9j0IX7IBrjoeUJ+XGoea0rlCbm0nHUvJS8Ok/n8IEseXa636vY2CVgSVfKD0G2NAH0Be7Uele/VZLWaX4lTojT4d3fw5GjlbsxlHJwxbzetaAUskiJhER0YvkzRqlMLtrdSmPSslB31XHcSOrQDygUgPdfwBsi+23nZkAbOoP6IuNB6BQKDAxcCICPQOF3AQTJh6ZiPiseOkca3cnlB3VFDfyE6VjnjofhMzb9gCrIyIiInq+sZD5grly5QpCQkIAAOXLl0f79u1LHGdpaYl33nmn8OfNmzc/jY9HRERE9FSc+2YnHK47SPtJpRUko/Q79WHt7CCdYzKZ8NnRz5CWnybk/vb+mNFoRslvSeZnAOt7Azk3xNzaFei7HjCzFmKj0YTRm0IQnZIj5PaWGqwcUBdWZuoHXyQRERE9N/rWL42ZXapJeeSNHPRbdRyp2cWKkzauQK+fAKVGzONPAr+PAUrYx1Kj1ODr5l/Dz85PyDO1mRhzcAzyiz9UBcDJ3welBtVEesEN6ZhjiiMurd1//8URERERPcdYyHzB7Nmzp/DPr7/++j3HtmvXrvDPu3fvfmKfiYiIiOhpOr/8T9gn2EpFzGzdTdi084Jb9XIlnrf+4nocSRBbs1moLLCgxQJYaazkEww6YMtg4MYFMVdbAH03Ag7yvpzz9l7C/ovJQqZUAEv71UJp5xKuQURERC+MAQ18Mb1TVSm/kpyN4WtPQ6s3igd86gFvLpQnClkLnFhR4jXsze3xTctvYK0RH5a6kHYBs07MgqmEAqh7rUqwfbM0MnXpQq5SqKE+a0D8wdD7rIyIiIjo+cVC5gvmzhaxderUuefYgICAwn2hzp8/X+KXXSIiIqIXyYVVe2B91QpKhbj3Za4+G9qqQNnXG5R43qW0S1gQtEDKJ9SfgLIOZeUTTCbgz3FAZAlvMXRdAXjXleLNp+Kw/GCkfI12ldC0vOtdVkREREQvkkGN/DD1zSpSfvJqGj7bESbfe6k9AAgsYZ/tPZNK/p4BoIx9GcxqPEvKt0dsx9YrW0s8x69VXSgCrZCnF7tCmKsskLMzDhkRcvtZIiIiohcBe1u9YC5fvlz4Zz8/v3uOVavV8PLyQmxsLHJycpCQkABvb++HvmZwcPA9j1+4UPSWgl6vh06ne+hrENHjo9PpoNfrC/9MRM8efy8fj4i1B2B1xQwqpVjEzNPnIKtsDmoM6FziP998fT7GHx4PrVEr5C19WqKTXyf5HKMeql1joQxZK81laDEFxgpvAsXOORaZikm/npPGd6jugSENffjf+3OGv5NEzxf+TtKLZmCgN7Q6Pb7cc1nIN56KQzlXKwxq6Cue0HIaVNfPQ3n1cFFmMsK0ZTAMvTfA5F1fukazUs0wqPIgrLmwRshnn5iNcnblUNVZfjO0XNfmOBP3C9TXNdAoi/YKt9bYIfbbEyg7uSXM7B+sQwR/L4meL/ydfPrufDDFaDTeYyS9ikwmU+G/IyaT6aX6d+TOf/cf5n9vbv9v1JPAQuYL5ubNm4V/dnFxue94Z2dnxMbGFp77KIXMunXlNw7uJiMjA6mpqQ99DSJ6fPR6PbKysgp/Vqv5P/VEzxp/L/+7rEvXYHZeAZVK/GeXb8jFdfcbKNetTYnfQUwmExadX4SojCghdzF3wchyI5GWJu6XqdDlwGHvx9DEHUZxuZW6I7Nif6DYdaJT8zBy0yXojeIbGJXdrTC2mYd0DXr2+DtJ9Hzh7yS9iLpWtkVkkit+OSvuTTl71yW4mhsR6Gsn5IoWX8F5Wy+oM2OLsvwMqNZ2wc2WX6HA/w3pGn29++Ls9bMISQspzHRGHT4++DHm15sPb2v5Hk/pt17D+QW/opypitCG39HcBRe/2IVSE5qXvC94Mfy9JHq+8Hfy6dPr9VAqlVAqlTAYDM/649BzxmQyCf9ePMjfrS8So9EIo9H4ULWeO2tXjxtby75gsrOzC/9sYWFx3/GWlpaFf77zLzsiIiKiF4XJZELO9giYq8TvPgWGPCQ6XUO5AW1KPC9Pn4eZZ2fiz/g/hVwBBSbWmAg7M/EGozL3Bpx2DIB5CUXMglKByGw6DSj2f05Sc3QYsyMC2Vrx/9i622owr1M5WGrEt0eJiIjo5fFxMx/U9bEVMqMJmPJnFGLT84XcZOGIm+2WwVhsX26FQQvHvz6G9ZlVt1rb30GlVGFSjUlwMRcfZE8tSMWYk2MQnRVd4ueqMqYrLueGSbm7hRdiFu974PURERG97H766Se0bt26xGN//fUXzMzM7nr8Trm5ufD19YWZmRm2b98uHEtOTsaMGTPQqFEjuLq6wsbGBr6+vmjUqBEmTJiAU6dOPY6lvNT46AbdV1BQ0D2PX7hwAQMGDAAA2Nvbw9nZ+Wl8LCK6iztf+XdycoJGo3mGn4aIAP5e/lch07bA09xHyHTGAiQ7X0fDT94q8Zy4rDh8cvwTRGRESMcGVxmMluVbimHqFah39IMiI1YabyzTHMruq+FsLt6oLNAZ8P7WIFzLFFvWWpur8P3AuqjoIY6n5wd/J4meL/ydpBfZsv526PHdCcSm5RVmWQUGTPwjGr+81wC2FnfcenN2hrH7aih+GQSFPk+Yx/bEfFjpUmB8fS6gLDrHGc6Y33w+hv09DHpjUcu2dG06xgePx7LXlqGSUyXpczWZ9zZOfLIG/rbifp6lTD5I2xWK8v1fu+e6+HtJ9Hzh7+TTd2dnHZWKD6i+THQ6XeHvkEKhgEKhEP47vn28evXqAICLFy9K/w7c2X5VpVLhm2++wbVr19C0aVN079698NiePXvQv39/pKWlwdvbG4GBgXB0dERcXByCg4MRFBQECwsLNGjQ4Eku+aHdfhv5YWo9Dg4OT+zzsJD5grGxsSn8c35+/j1G3pKXV/TF2Nb20W6m1alT54HHqtVq/kVK9By43WJEo9Hwd5LoOcHfy0dz9Y9TcNOXAop1abmqv4LXJg4v8Zxjiccw7tA4ZGozpWO13GrhwzofQqO847+D7BvA+h5AZoI8WY0+UHZaAqXaTIhNJhMmbAtHSFyGkKuUCix7qw6q+Tg92ALpmeHvJNHzhb+T9KJys9fg+0H10HXZMWQXFBUao1JyMWn7eSzvX1tsN1fpdWDIH8D63kCO2JZWdXo1VPk3gR4/AsqiJmp1POtgdpPZ+PTIpzCYirpA3Cy4iff2vYflbZajpmtNYS6NRoN6X/RF2Oc7UMrKrzBXKpQwPw9kXoiHc40y91wbfy+Jni/8nXy67vzfbqWSjS1fFqGhoejUqRMWL16Mzp07F/53q1QqERsbiw8++ADly5fH119/DR8fH9jb2yM5ORk3b96Ek1PR/883Go2F/46kpqZi/vz5UCgUmD9/fuGcYWFh6NatG5RKJX766Sf069dPKIhmZmZiw4YNqFy58nP179id/+4/zP/WPMmW18/PPx16IHdWtVNSUu47/s4exk+yIk5ERET0uGmzcpG7PwkqhfjkY1xOJJrNHVbiOb9F/obhfw8vsYjZ0qcllrVaJhYxDXrglyElFzGbjgW6rgCKFTEBYOXhKGw7I58zvVNVNK/gep+VERER0cukvLstFvcJKN6BHrvDk7DqSJR8glcdYNg+wKWifOz8duDALCl+o8wbmNd8HtRK8SZhli4L7+59F2eSz0jnWDnaoVS/AGTq0oXcXGWJxNUh0OcW3HdtREREL5PKlStj1KhRGDJkCLp3747ExEQYDAYsWrQI1apVg7W1NUaNGlU4vmrVqgCA8+fP33XOWbNmITMzE71790b9+vUL8+nTpyM/Px8zZszAgAEDpLc67ezs8N5776FZs2aPeZUvHxYyXzAVKlQo/PPVq1fvOVav1yMh4dYNNmtra3h5eT3Jj0ZERET0WIV8/ivsNA5ClqPLhE/vWlCZyU8FHks4hs+OfgajySgdG1FzBBa+thA2Zjbigf0zgKtHxEyhBN5cBLSaKu2JCQAHLiZj7u6LUj60cRn0b+B733URERHRy6dVZXeMbSsXJr/cfQnHo1LlExx9gbf3AH5N5WNH5gOhm6W4jW8bLH5tMcyU4kNWufpcjPx7JC6lXZLO8W5QHXlltNAbxVb4jmYuODN1631WRURE9HLRaDQYM2YMIiIi4OXlhWnTpuGff/7Bpk2bsHfvXmzYsAG+vkX/v/52IfPChQslzhcdHY0VK1bA3Nwcs2fPFo4dOHAAANCkSZMntJpXBwuZL5hq1aoV/jk4OPieY0NCQmAw3Go5UqVKFbGVCREREdFzLGzJ7yhlVlrIjCYjbtglw7tJTWn8pbRLGHNojNBuDQCs1FZY/NpiDA8YDqWi2Fff8zuAo4vli3f4Gqg7pMTPFZGchVEbzuCO7TAAAM0quGJSe3l/KiIiInp1DG/uj9aV3YXMYDThg/VncD2zhO2BLB2B/tuASm/Kx3Z8AMSdlOJm3s2wtNVSWKgshDxLl4X3/34fcZlx0jl1PuiBKL1c5PTU+ODs/O33XhQREdEztnXr1sK9LP/rf0JDQwHcap96ZzvX4j/fdr9C5ueffw6tVosPPvgAZcqILdtvbw145MiRkk6lh8BC5gvm9ddfL/zznj177jl29+7dhX9u167dE/tMRERERI/T1e3HYRNnLeVR2RfQ5HO5wJiUk4QR+0YgR5cj5B7WHljfYT1alm4pX+TGZWD7CDkP6A/UKbmImZGrw7A1Qci6Y/8rACjrYo0lfWtBreJXayIioleZUqnA171qwtfZSshTsgswct1p6Axy1wiozYBuKwHPYg9qGQqAjf2Am7HSKQ1LNcSKNitgqbYUr5OXgnf/ehc3cm9I5zSf9x6isuWOEjbXrZAZc/0BVkdERPRsnDwpP9jzKCwtLVGxYkUsWbIE5cqVQ2xsLKZNm4YmTZqgZ8+eaN26NQYMGID4+PjCc+5VyDxz5gw2bdoEJycnTJkyRTreuHFjAMC4cePQqlUrLF++/K4FUbo33m15wZQvXx61atUCAFy5cgW7du0qcVx+fj5WrVpV+HOvXr2eyucjIiIi+i9uBF+B4WimtP9TWsF1BIzrJI3P1mZj5L6RSM5NFnJbjS2Wt1oOfwd/+SIFWcCm/oA2W8w9awId5pfYTlZnMGLE+mBcTc0Vr2OhxqpBdWFvKbe6JSIiolePvaUGK/rXgYVGvOUWFJOOL36/y/5aZtZAnw2AjYeY59wA1ve59d2lmDrudbDotUXSd6b47Hi89/d7yCjIEHKVSoUa4zriRn6ikJurLHFp0b4HXB0REdHT16NHD/z6668l/mfTpk2F46ytre867tdff8XOnTtx8eJFzJ8/Hz/88AO2bduGUqVKQaVSYfTo0QgLC8PNmzexYMGCwjnvtUfm5MmTYTKZMGXKFDg4OEjHV6xYgdq1awMA9u/fjxEjRqBKlSrw9fXFtGnTkJmZ+Zj/Sb281PcfQs+bzz//HF26dAEADB8+HIcPH0bp0kWt14xGI0aOHInY2FtP7fXo0UNoSUtERET0PMpLSkfq+kuw0dgLeb4hF6hpCQe/UkKuM+jwyaFPcDn9spCrlWosfG0hyjmWK/lCf3wCpBRrr2bpCPT6GdBYSsNNJhOmbg/D0QhxfyulAljarzb8XW2kc4iIiOjVVdnTDnO6VcfoTWeFfM2/MajsaYc+9UvLJ9l7AX3XAz+2B/R3tKFNDgd2jAR6rpEetmpUqhHmNp2LcYfGwYSivvdX0q/gw/0fYmWblbBQF7WgdfD1gFl9B+hCtNDcsc+mp7kPwr/bharvvfEfV05ERPT41atXD/Xq1Svx2OnTpwv/XLNmzcK6yb1ERERAo5EfRi5dujR27twJnU5XmHl6esLR0RFxcXHIycmBtfWt7lH79u3D33//DX9/fwwfPrzE65QpUwbBwcE4fPgwtm/fjr179yI8PByxsbGYPn06Nm7ciNOnT8PKyqrE86kIC5lPUXR0NL7//nshu92TGbhVldfrxVZl3bt3L3wD87bOnTujd+/e2LRpE2JiYlC7dm289957qF69OlJTU/HTTz8Vvm7t6ekpPEFARERE9DzS5+tw5asDcDJzFXOjDtftrqHx4MFCrjPqMO7wOBxLPCbNNaPRDAR6BpZ8odDNQOimYqEC6PY/wNG3xFNWHo7CxlPyflOT2ldG8wquJZxBREREr7qutbxxOuYmfj4eI+RTd4TB380G9fyc5JO86gBdlgO/FGtzf34H8O+3QKMPpFNe93sdGQUZmHl8ppCfST6DSf9Mwvzm84V9wqv2ex37jy9HBSvxgXfNFSAvNROWznYPuVIiIqJn58yZM4V/Ll5HuZuSipj3Ol61alX8888/uHjxIurUqQOTyYRPP/0UADBz5kyYmZmVNE2hZs2aoVmzZgCA+Ph4LF26FF9++SUuXbqElStX4uOPP36gz/0qYyHzKYqJicGsWbPuevzIkSPSxq/lypUr8RdwzZo1UCgU2LhxI1JTUzF79mxpjL+/P7Zt2wYfH5///uGJiIiInhCTyYTQyb/CzcxTyiP059F6qnjTTm/UY9KRSdgXK7dBGxkwEh39O5Z8obRo4Pcxct7iU6B86xJP2R12DXN3y/tJ9a7rg7eblLnLioiIiIiAqW9WwYVrmQiKSS/MdAYThq8Nxo4PmsDLQe4EgWrdgJTLwME5Yv7XZ0CpAMCviXRKr4q9kFGQgW/OfCOeEvMXFgYvxCd1PxHywM/74srMv+BiUdTK1kptg3OzdqL+grcefqFERETPyJ2FzICAgIc+f/DgwRhc7MHp4m4XMi9cuIA6depg48aNCA4ORoMGDdCjR4+Hup63tzfmzp2L48eP49ChQwgLC3voz/wq4h6ZLyhzc3Ns2LABu3btQs+ePeHj4wNzc3O4uLigYcOGWLBgAc6ePYsaNWo8649KREREdE/n5u+Am8ZTyi9mheK1eWKLFoPRgM+OfobdV3dL47uW64r3arxX8kUMemDbO4C22B5Tfk2BZmNLPOVs3E18vCkEJpOYNy7njC+6VoOihL00iYiIiG4zUyuxvH8dlLK3EPKUbC3eWROEXK2+5BObjQcqFGvzajIAW4YAmddKPGVY9WHoX7m/lK8OX42NFzcKmbWzA4zllTCYxOt7anwQsenwfVZFRET0/AgJCSn884O+kfmw7twnU6fTYcqUKQCAL7/88pHn9PLyAoDCVrV0byxkPkUtWrSAyWR6qP/c72mAdu3aYfPmzYiNjUV+fj5u3LiBY8eOYfTo0fwlICIioude6tko2N6wlfLIrPNoMX8YVCpVYWY0GTH93+nYGbVTGt/Orx0+a/jZ3YuLh74E4k+JmYUD0PU7QKmShl++noVhPwUhX2cU8nJuNlj2Vh1oVPwaTURERPfnamuOlQPrwkIjfnc4fy0T47aEwmA0yScplUDXFYBjse4POcnAlkGAXiudolAoMK7eOLTxbSMdm3NyDg7FHRKy2iO7IypH3DNcoVBAfzITmdHXH3B1REREz47JZMLZs7f2o1ar1ahWrdp9zng0twuZFy5cwIoVKxAVFYVu3bqhYcOGd/1c95KTk4MDBw4AAFq1avV4P+xLindgiIiIiOiZMBlNSFgdAo1S3E8iKS8WNSd0hsZKfHvh66Cv8WvEr9I8rUq3wuyms6FW3mXXhJhjwJH5ct5pCWDvJcX/Rqai+/JjuJFVIOTO1mb4cXA92Fveez8NIiIiojtV87LHvB41pfyPc9fw/trgkt/MtHQAev8MqIu1n407Aez5FFLLCABKhRKzm8xGDVexO5fRZMS4w+MQnhou5LXGdsJNbaqQ2WjsEb/kBPKS0kFERPQ8u3LlCrKzswEAVapUgbm5+RO5zu1C5unTpzFz5kxoNBrMmTPnruMnTJiAMWPGICYmRjp24cIFdOzYEdeuXUOzZs3QseNdtsYhAffIJCIiIqJn4vTnm+FuXkrICgy5sG7iDofSHkJ+KO4Qfjr/kzRHc+/mmNdsHjTKuxQXE4KBLYMBk/hmJeoMBqp0kobvCEnAuC2h0BrE8WZqJVYOrAsfJ6v7rouIiIiouI41S+FiUia+PRAp5H+dv46+K4/jf4PqwdW22A1Yj+pAx0XAr8Va55/6H6AyA16fDRTrRmGhtsA3r32D/n/2R3x2fGGep8/DR/s/wqY3N8HZ0hkA4OBXChfcsmGX7gilouhdBzszR8QuPAb7d2tCU9I+nkREJDiy+TJS4rKf9cd4Zlx8bNC0V4Wnft3/uj/mg3J3d4ezszNiY2MBAB999BHKlSsHg8FQ4vhjx47h6NGjWLRoEapWrYqKFSsCAKKjoxESEgKj0YhWrVrhl19+4ZY1D4iFTCIiIiJ66uL3nYGL1h0o9p39qiECrXqOFLKUvBR8duwzaY5GpRrh6xZfQ6O6SxEzZD2w82PAIL5ZCZcKt2783cFkMmHFoSh8ufuiNI1aqcCi3gGo4+t433URERER3c0nbSriUlI2/r4gtm49G5+BrsuO4sfB9VDevVjL/Zp9brXHP/U/MT++DNDnA+2/vtWK9g7Ols5Y3no5+u/qj4yCjML8eu51jD88Ht+1+a6wk0XDif3x9wdLUclGfGPUwcwZ6StD4DiyNuD8HxdORPSSS4nLRuKVm8/6Y7xynsb+mLdVrVoVhw8fhoODAz77TL4/cadVq1bhjz/+wF9//YXo6Gjs2bMHOp0Obm5u6NKlCwYMGIDOnTuziPkQ2FqWiIiIiJ4qfU4BMv+Mg0oh7k0Zk30FLea+K2QmkwmfHf0MaflpQl7VuSoWvbYI5qoSWscYdMCuCcD24XIRU6kBuv8PMBP3Ep+762KJRUwbczV+GFwP7at7PsQKiYiIiGRKpQJL+9VChxry94r49Dx0W34MJ6JS5RNfnwP4NJDzoB+AHSMBo/xGiJ+9H7557Rupa8XJpJNYcmaJkL224D1cygyV5nA0c0Hat0HQ58l7chIRET1rd76R+aQLmYcOHYLJZEJ6ejqcnJzuObZy5coYO3Ys9uzZg8uXLyMrKwv5+fmIjY3F1q1b0aVLFxYxHxILmURERET01JgMJoR+th12GvHtxhxdBnx61ITKTLzZtvHSRhxJOCJklmpLzG06F5bF94wCgNw04OeuwIkV8jGVGdB1BeApvnGwNTge3x2Okoa72Zpj03sN0KyC6wOujoiIiOjeLDQqLOlTC+8395eOZeXrMWxNEK5czxIPqM2At7YApRvKE55dD2wddutBrmJqu9fGp4GfSvkPYT/g75i/C39WmWnQbOEwXM48J411MnPDhem/w1TCnpxERETP0u1CpkKheKKtZenZY2tZIiIiInoq9Fo9QiduhZuZuC+myWRCklUimrd4U8gjb0bi66CvpXkm1JsAP3s/+QLaXGBdTyAhSD5m4wH0Xgv41BPi8MQMTPpVvmlX3s0Gq4fWhxf3hSIiIqLHTKlUYOIblVDayQpTd4TBYCwqEmYV6PH2miBsH9kYTtZmRSdZ2AH9twIb+gLRh8QJw7cBCiXQbZXUZrZH+R4IvRGK7RHbhXzK0Snwd/BHGfsyAACNuTkazemP45PWo5xtVWGsm6YUQmZuQ63Puv/3xRMRvYRcfGye9Ud4pp7F+hMTE5GcnAwA8PPzg729/VP/DPT0sJBJRERERE+cXqvD2Ynb4F6siAkAUdkX0OSbYUKmNWgx4fAEFBRrDdvSpyW6le8mX8BoALa9U3IR07s+0PtnwNZDiDNydRi+9jQK9EYhD/BxwJoh9WFvdZe9N4mIiIgeg36BpVHKwQIj151GjraoPWxsWi7eXxuMtW8Hwkx9R2HSzBrotwnYPBC4slecLOwXwNIRaD8PuKNdnUKhwOTAybiUdgkX0i4U5jm6HIw+MBrrO6yHlcYKAGBhb4d6M/sgeOpmlLWtLEzvnOOCiA2HUa5vs8f4T4CI6OXQtFeFZ/0RXjlPc39MevbYWpaIiIiInii9VofQCSUXMRNyr6L6x+2gUhXtl2kymTDz+ExcSr8kjHW1dMW0RtNK3ktiz2Tg4u9yXnsgMPh3qYhpNJrw8aYziE3LFXIXG3N8N6AOi5hERET0VLSo6Ibl/etApRS/35yMTsOU7efklq4ay1tdJiqJnSwAAKdWAQfnSrGF2gILWiyAnZmdkEdmRGJh8EIhs3ZyQMCkzkjMjRFypUIJxel8pIZdffDFERERPSFPa3/Mffv2QaFQYPDgwU/sGnR/LGQSERER0ROj1+lxdsJWuJmXVMSMRtn3G8OpvK+Qrzq3Smp/BgBfNP4CjhaOUo7jy4ETy+W80YdAx28Atbl0aMn+CBy4dEPIVEoFvu1XC+52FvdeFBEREdFj1KyCKz57s4qUbw6Kx/f/RMsnqM2BnquBih3kY4fmAie+k2JvW2/MbToXCogF002XNuFM8hkhs/Nyh+MbfsjSpQu5ucoS134IgT5H7JhBRET0tD2tQubJkycBAA0aNHhi16D7YyGTiIiIiJ6Y4Ekb4W7uJeUJOdEoP6oFXKuUFfI/o/7EkjNLpPH9K/dHI69G8gUu7AR2fyrnVbsCrWcIrdVuO3T5Bhbtuyzln75RCYFlne+xGiIiIqInY2BDX/RvUFrKZ/15AQcuJcsnqDRAjx8A3ybysV3jgdDNUtzUuynerfGukJlgwufHPpfa+fu1DkSyUwp0Rq2QO5g5I2TqNvlNUSIioqfozkJmQEDAE7vO0KFDceHCBQwYMOCJXYPuj4VMIiIiInoizi39DV4qXymPz4lCxU9aw6mceLMu+HowphydIo1v6NkQY+qOkS8QfQTYOgxAsRtpPg2ALisApfxV90ZWAT7ZHILi997erOGJt5uUue+aiIiIiJ4EhUKBzztWReNy4kNVJhPw8cYQxBVrhw8A0FgAfTcAnjXlY9uHA5EHpPi9Gu+hnEM5IYvOiMbK0JXS2Ipvt8fl/HAp9zDzxukJm1jMJCKiZyIzMxPR0bc6Fri6usLLS354+nFxd3dHpUqVYG1t/cSuQffHQiYRERERPXbXjobBOsZSyhNyo1FlQjs4+ImtZq9mXMVHBz6CzqgT8nIO5fB1i6+hURbbszL2OLC+N6DPF3Pncrdu6Gnk9rAmkwnjfjmLlGzxzYLybjb4snuNkvfeJCIiInpKNCollvWrg7Iu4s3SjDwd3l8bjHydQT7Jwg54ayvg5C/mRj2weSBwXSxEalQazGg0A0qFeEvwh3M/4FKauD85AFSb0B0RWeel3F3pxWImERE9EyEhIYV//zxKW9krV67g3Xffhb+/PywsLGBra4vy5cujf//+CAoKKhwXHx8PhUIhtZWNj4+HSqVC48aNkZWVhSlTpqBcuXKwtLRE1apVsXlzUVeEnTt3omXLlnBwcICnpydGjBiBnJycR1w5kJ6eDoVCgYCAAFy5cgWDBw+Gj48PzMzM4OXlhfHjx0On091/ohcMC5lERERE9Fjl38xC6tYImKnEYuJNbQr8hzeBnbe7kOfqcvHh/g+RUZAh5C6WLljWahlszWzFC8QHAWt7ALpiX/6tXIC3tgBWTiV+rtXHruJgsX0xLTUqLO9fB9bm6odYIREREdGTYW+lwf8G1YVNse8m4YmZmLo9rOTCoY0rMHA7YFtsT/KCTGBdLyDzmhBXd62Otyq/JWR6kx7Tjk2DwSgWS1UqFWqMfxNJeXHSZd2VXgiesJHFTCIieqr+y/6Y+/btQ82aNbFq1So4OTmhc+fOaN68OVQqFdatW4eYmJjCsSEhIQDk1rW3cxcXFwQGBmL9+vWoW7cuqlWrhvPnz6Nfv34ICgrCmDFj0LNnT5iZmaFly5bIzMzE8uXLMXny5EdaN1C0dpPJhLp162Lnzp2oX78+mjZtiuvXr2PevHkYO3bsI8//vGIhk4iIiIgeq7PTtsPBTGyLpjUWAAEWcKlcVho/L2germZeFTJLtSWWtloKTxtPcXBiCPBzN0CbJeZmtsBbmwEneX4AuHAtE3P+vCjln3esgnJuNvddExEREdHTUtbVBvN7yu1itwTHY+MpuaAIAHAoDfTfCpjbiXlmPLC+F1CQLcQfBHwALxuxFV9YahjWXlgrTW1byg0e/WrgRn6idMxD6c1iJhERPVWPuj+myWTC22+/DbVajRMnTuDUqVPYtGkTfv/9d1y8eBGnT59GixYtCsffr5C5a9cudO3aFZcvX8bGjRtx6tQp9O/fHwaDAX369MGuXbsQHh6O3bt3Y9u2bdi/fz8AYMuWLY+ybADA6dOnAQChoaFo3749YmNjsXXrVuzbtw+7du0CAKxYsQLXr19/5Gs8j1jIJCIiIqLH5uS0dfCy8JPyBHUMqg18Q8oPxR3CL5d/ETIFFPiy6Zeo6lxVHJwUBvzcBSj25iY01rdu3HnVKfEz5WkNGLXhDLQGo5C/Uc0Dvev53HdNRERERE9bu2oeeL+5v5R/viMcZ+NulnySexWg10+AsliniaRQ4JehgEFfGFlprPB5w8+lKRadXoQj8Uek3DuwGlz7Vr1rMTNo0oZ7L4iIiOgxWb16NUwmE0wmE/r06fPA50VGRiImJgYNGzZE/fr1peO1atWCs3PRQ9n3K2R27NgRs2bNglpd9Pdu+/btAQCJiYnYunUr/P2L/i4PDAyEk5MTkpKSHvgzF3e7iNuwYUOsXbtW2LuzTZs2aNasGbRaLcLD5T2uX2QsZBIRERHRYxF/OBSuuR5SHp1zCU1nvy3lqXmp+OzYZ1I+vOZwvFb6NTHMuwls6APkpYu52vLWm5ilA0v8TCaTCdN3huNKsvgWgqe9BeZ0q859MYmIiOi5NbZtBTTyL9blwmDEuz8HIfJGdskn+b8GdPxGzq/sAfZMEqKGpRqis39nIdMb9Rh9cDSCrgehuKJi5jXpmLvRC1Hbj91nRURERM+Ovb09FAoFDhw4gCVLliA7+y5/l/6/kJAQKJVK1KhRQ8oBYMqUKdI5mZmZAIAePXqgSpUqwjGj0Yjs7GyhWPqwbr+ROXv2bKhUKul4uXLlAADJycmPfI3nEQuZRERERPSf6XV63Nh2CRqlmZDfyE9E7Wk9pPEmkwnT/52OtPw0Ia/uUh3v1HhHvsAfnwAZxVqpqcyBvhsAvyYlfqbsAj2Grz0ttWBTKICFvQPgYGVW4nlEREREzwO1Solv+taCp7247/j1zAL0/u44LiVllXxirbeA5hPk/OR3wNmNQjSu3ji4WbkJWYGhAB8f+hgXbl6QpvAOrAbXPlWQUqyYqVQokXf4BvS5BQ+wMiIioqfP1dUVs2fPhtFoxKhRo+Dq6ooOHTrg+++/R25urjA2KysLUVFRKF++PKysrKTc29u7xP05z549CwDo1KmTdCwiIgJarVYqcD6onJwcXL58GR4eHkIL3DvdXsd/KZY+j1jIJCIiIqL/LGjaBrhaiG9j5hty4dzRH9bODtL47RHbcSDugJBZqi0xp+kcqIu3QwvdDISJ7Weh1AB91t1666AEMak56LbsKHaHyy1bRrTwR4OyL9eXeiIiIno5udiYY9lbtaFRiV0kUrIL0GflvwhLyCj5xBafAjVKaLe382Mg6Vzhj/bm9ljeejnsze2FYbn6XEwKnoTIzEhpCu8G1eDSpwqydDeF3N7MCUGfb36gdRERET0LEydOxJUrVzB37lwEBgZi9+7dGDZsGCpUqIDo6OjCcWfPnoXJZJLayt7O69QpeWub261f69WrJx27/SZnSQXQB3H27FkYjca7XhsAgoKCoFAopLdIX3QsZBIRERHRfxJ/OBTuulJSfk2TgNKt6kp5XGYc5p6cK+Vj646Fr52vGKbH3Hobs7hWnwHl25T4eQ5dvoGOS/7B5etym5i6vo74uHWFu6yEiIiI6PlTq7QjFvQKgEopFjPTc3Xou+o4TsemyycpFECnJYBPAzHX5wGb+gvt+is4VsB3rb+DtcZaGJqtz8bE4ImIyYyRpvduUA03HeXreip9EL3z34dYHRER0dNVpkwZTJgwAQcPHkRUVBSaNGmChIQELFu2rHDM/fbHrF27tjSv0WhEaGgonJyc4OvrKx2/XeR81ELm7fPt7OxKPH7q1ClEREQgMDAQ7u7uj3SN5xULmURERET0yPR6PW5suyi1lE3IvYpGMwdJ4xOyE/DOX+8gVy+2bWnq1RQ9K/QUBxv0wLZ3gYJMMS/TDGj4QYmf55fgeAz58SQy8/XSsbZV3LF6aH1oVPwKTERERC+WjjVL4dt+taQ3M7Py9RjwvxM4dTVNPkltBvRcDViLrWORfhX49X3AaCyMqrpUxbJWy2ChEtvY3tTexOjDo5GlldvYNpwyEFdzLguZSqFC1v5r0OdpH2p9REREz4Kvry8GDbp178JgMBTm9ytkFs8B4NKlS8jNzb1rofJe5z6I2/tj3vnm6G0mkwmffvopgFtvnb5seBeHiIiIiB7ZrZaynkJWYMiDV+8a0sbzcZlxGLJ7CBKyE4TcwdwBMxrPgEIh3pjDPwuBuONiZuEAdFkBKOWvsYcu38CEraEwmuTPOaZNBazoXwc25mr5IBEREdELoF01T6wcUBdmavF7UI7WgKGrT+F8YqZ8kp3nrWKmQvxehsu7gSNfC1Ft99pY3HIxNEqNkF/NvIpJRybBaDKiuArvNkeOXryuk7krTn2+4cEXRkRE9ITt2bMHf/75J/R68aHnxMRELF68GIC4r+X9CpklFSvv98ZlSEgIzM3NpT0yp02bBoVCgcGDB99zDbfnP3HiBPbs2VOY63Q6jBo1Cvv27UO3bt3QuXPne87zImIhk4iIiIgeSeyeILhrS2gpq46Hd+OaQhadEY3BewbjWs41afy0RtPgYulSbPLjwME58kU7LgbsvaT4fGImRqwNhqFYFdPGXI1VA+tiVKvyUBZrx0ZERET0onmtkht+HFwPlhqxMJmVr8fAH04iJjVHPsmvMdBmhpwfmAVEHRKiRqUaYX7z+VAqxFuGB+MPYsXZFdIUbtXLIdUuVcpLoTSubD3yACsiIiJ68jZt2oQOHTrA3d0dr7/+Ot566y20bt0aZcqUQVhYGCZOnIgWLVoAuNV5Kjw8HO7u7vDw8Cic43bu5uYGLy/5vsTtQmNJbWeTkpKQlJSEatWqQa0WH7A2/n+HBI1GI513m1arRXh4OLy9vdGjRw906NABrVq1Qs+ePeHn54elS5eiYcOG+PHHHx/6n82LgIVMIiIiInpo8YdDkf9XstRSNjE3Bg1mDhSyyJuRGLpnKJJzk4VcAQWmNZyGVqVbiZOnRQEb+wEmg5gHvAVU7SJ9lmsZeRi6+hRytOL4UvYW2D6yEdpUebn2hiAiIqJXW+NyLlgztD6szMRiZkp2Afp/fwLJmfnySQ1HAlW6FAtNwM5RgFZs+d+ydEt8UFNu47/87HLsj90v5Y0+G4jYnEghUynVMP2bhetBlx5oTURERE9S3759MXz4cPj4+CA4OBibN2/GuXPn0K5dO+zduxdz5hQ9SH3x4kXk5+dLb2Pezu/2xuW93si8V1vZ28cGDhwoHbstPDwcWq0WtWrVwpo1azB8+HCcP38eO3fuhKOjI7766iscOHDgrvtnvujYW4uIiIiIHsq1E+eRvSMONhrxC3KBIQ+leohPF17Lvoahe4YiLV/ct0mpUGJGoxnoXK5Yy5PcNGBdLyC32JP9jn7AG19KnyUrX4chP55CUrEbdrYWaqweWh/l3GwffoFEREREz7n6ZZywckBdDFl9EjpDUUeKuLQ8DPzhJDa92xD2Vne82aFQAJ2XAskXgJQ7iovpV2+9mfn6LGH+QZUHIeRaCA5fPyzkk/6ZhPUd1qOsfVkh9x/aCJnrImGptinMrNS2SF53HjalnGFdqlj3DSIioqeoTZs2aNOmzQONrVatGkwmec+aO3OjUW63/vfff991znbt2pU4p8FgwKFDh9C6dWs0bdr0ruff3h+zdu3asLS0xJIlS7BkyZL7ruVlwTcyiYiIiOiBJZ++gpubI6UiptFkxHXLa/BuFlCYFRgKMPrgaKmIqVKoMLvJbLmIqS8ANvUHUq+IudoS6PEDYC4WJbV6I0asO42LSVlCrlEp8N2AOqjgziImERERvbyalHfB4j61UHyb8YtJWRi65hTydcW6W5jbAj2+B5TF3ms4vgxICBYihUKBsdXGooxNGSHP0eVg1P5RSMlLEXL3WhWQ7noTxmIdNRzNXXDhy7+gz9c+/AKJiIheckFBQcjMzMSMGSW0gL/D/fbffNmxkElEREREDyQtPBo31p6HrcZByE0mE2JwBY1mDBLyuSfnIjw1XMjUCjW+avYVOpTtIE5uMgG/fQjEHC12VQXQfRXgVUdIdQYjPtxwGkeupKC4L7vXQCN/PvVPREREL7/21T0xq0t1KQ+OScfoTSEwFts/HB7VgcYfiZnJCPw2CjDohNhSbYlptabBzkx8gC0mMwbD9gyTipn1J/RFjDFC+iwelt44NXHDQ6yKiIjo1RAYGAiTyYSGDRvec9ydb2S+iljIJCIiIqL7unklHgn/C4G9mZN0LNpwCU2/HCZkv175Fb9c/kUaO6vJLLT1aytf4NCXQOgmOW87E6jcUYj0BiM+3hSCPeHXpeGftKmAbrW977MaIiIiopdHv8DSGPd6RSnfFZaEObsuyCc0Gw84lxez62HA0UXS0FJWpTC70WwoFeItxMiMyBKLmU3nDUN0rrwvpo9FWfw7cc39F0NEREQCo9GI0NBQODs7w8fH51l/nGeChUwiIiIiuqfM6GuIXXYCjubyW45R2otoNv8dITufeh5fHP9CGjuwykC0L9tevkDUQeDgHDmv+zbQ8AMhMhhNGPdLKP4IvSYN713XBx+0LHfvxRARERG9hEa08MfbTcpI+aoj0fjp36tiqLEAOn0jT3LoK+DGZSluVKoRxtcbL+V3K2YGftUf8blXpfFeJj+E/7D7nusgIiIikVKpRHZ2NlJS5I5UrwoWMomIiIjorrISbiBq8T9wMneTjkXlX0KzBWIR82b+TYw5OAZao7gPUh33Ovi4zsfyBfJuAttHyHn5tsAbX+HOTZ+MRhMmbg3Fr2cSpOEda5bC7G7VoSi+SRQRERHRK0ChUGBy+8p4o5qHdGzab+H4+3yxTha+jW49NHYngxbYPhzQ5kpzvFX5LYytO1bKSypmmllYoOrEdkjJTxLGKhVKqM8bcDMi/iFWRkRERK86FjKJiIiIqEQ5yemImLcfLhbyDbHovEto/PUQITOZTJh6dCoSssVCo6ulK+Y3nw+NUiNf5M+xQGaxwqRbFaDHD4BKLcSz/ryALcHyja92VT2woFdNqJQsYhIREdGrS6lUYGHvANQq7SDkRhPw4YYzCI2/KZ7Qehpg5yVmCUHA5gGAvkCaf1DVQXctZn5y8BMYTcbCzLaUK9z7V0O2LkMYa622w+Ulh6DX6x9maURERPQKYyGTiIiIiCT56Rm4OGs3XC1KSceicy+j4deDoVKphPy3yN9wMP6gkKkVanzd4mu4WMptaXHuF+DcFjFTmQHd/weY2wrx3+ev4/t/oqUpWld2wzd9a0Gj4tdaIiIiIguNCv8bWBe+zlZCnqcz4N2fgpGSfUeB0sIOeHOhPEnE31BtfxcwysXGuxUzTyefxoaLG4TMs25l5JfTw2gyCHkpy9I4NXXdQ6yKiIiIXmW840NEREREkuBp2+Bu6S3lMblX0HDBIKjV4tuS13Ou48uTX0rjx9Ybi1puteQLZCYCf4yR81afAe5VhSg5Kx8TtoZKQ5tVcMW3b9WGmZpfaYmIiIhuc7Yxx4+D68HBSuyGkZSZjw/Xn4HeUPTmJCq8DjSQ2/wrL/0B+wOfAne8ZXnb3YqZi4IXITYzVsgChndBjDZCGutp8MHlXw496JKIiIjoFca7PkREREQkuLr3FLwt/KQ8NjcSgfP6S0VMk8mEGcdnIEuXJeRNvZqiX6V+8gWMxlv7YuaLrcbg1xRoMFKae9yWUKTmiHtu1vC2x8oBdWCuFt8KJSIiIiKgrKsNVg2sC7NiXSv+jUrFvL2XxMFtZwG1+ktzWF75DXZHpgEmk3RsUNVB6FC2g5DlG/Lx2bHPhBazANBwzkBczxO3B1ArNdAdu4mca6kPsSoiIiJ6FbGQSURERESC5N8vQaUQi5XxudGo+2UfqM3NpPE7o3bicPxhIbPV2OLzhp9DoShh38rDXwFRB8TM3A7oshxQil9Pf/o3Bocu3xAyKzMVFvepBQsNi5hEREREd1PPzwnTO1eV8u8ORWHXuWtFgVIJdPwGqNpNGmt1fhOUR74qcf6J9SbC2cJZyIKvB2PjxY1CprYwQ6l+NZCnzxFyezMnhM79/UGXQ0RERK8oFjKJiIiIqND51XtQyspXyPL02ajySVuYWVpI45NzkzH35FwpH19/PNyt3cXQZAIOzAYOzpEv3H4+4OAjRJevZ2H2nxekoZ+9WQVlXKwfYDVEREREr7Y+9XzQq668XcC4X0IRkZxdFChVQLeVQIU3pLGqI/OAi39IuYOFA6Y2mCrli04vQlxWnJB5BlbFTfcMmIq93eljWRYnpq990OUQERHRK4iFTCIiIiICcKuNa0FIhpQnq5Ng5+tR4vgZ/85AllZsKdvEqwk6+3cuPhjYNx04JO+jiSpdgBq9hKhAb8CoDWdQoBdbk71e1R2964kFTyIiIiIqmUKhwIzO1VDNy07Iswv0eH9tMHIK9EWhSgP0XA2UaS5PtO094MZlKW7l2wpv+InFzzx9Hj4/9rnUYrbeuN6IyZf3y3TNdsfVvacefFFERET0SmEhk4iIiIgAAKfn/wJXC08hy9Cmod6UPiWOX3VuFQ7FHxKyElvKmkzA3inAPwvlSdyrAx0XAXeMN5lMmPxrGC4miQVSN1tzzO1Wo+R2tURERERUIguNCsvfqgMHK42QRyRnY+6ui+JgjQXQZz1MblXEXJsFbOwr73EO4NPAT+Fk4SRkp5JOYVnIMmls3Rm9kJp/XcjMVBbI2BWL/IxsaTwRERERC5lEREREBG1uPiyuyftfZjvlwMzGUsrXhK/BkjNLpHxcvXHwsL7j7U2TCdg1Afh3qXxRzwBg0G+ApaMQL90fgV+C46XhX/eqCUdr+TMSERER0b35OFlhUe8AFH8e7OfjMTh1NU0MzW2g77EGRnN7MU+NALa9CxjFNy0dLRwxOXCydM3vQr/D9ojtQmZhbwP79r7QGvKF3NncDcGfbX6oNREREdGrgYVMIiIiIsLpWZthbyY+SZ+cn4g6E3tJYzdc3ID5QfOlvIlXE3Qp10UMT3wHnPxOvqBXXWDgDsBKvOaOkAR8/ZfctuztJmXQtLzr/RdCRERERCVqUdENH7YsL+UTtoYiX2cQQ8cyuNn6a5gUxW4dXt4NHJL3R2/r1xbt/NpJ+fRj03H82nEh82tbD8nWSdLY0hblEDSPxUwiIiISsZBJRERE9IqL3RMEZ62blKurWEGtVgvZL5d/wewTs6WxFRwrYG7TuWLb15h/gb3y0/nwaQAM+BWwdBDik9FpGLclVBretLwLJr5R6cEWQ0RERER39cFr5VDJw1bIom7kYOl+ee9KrU9TZNcfLU9y6MtbD6sVM73RdFR2qixkepMeYw6MQeTNSCFvMG0AYnOjhEyhUMAh2Z77ZRIREZGAhUwiIiKiV9i5ZTuh/zsdliprIY/PvYoa73YUst8if8OMf2dIc/jb+2NV21Wwv7P9WNZ1YMtgwKgXB/s2BvpvBSzshDjqRjbe/TkIWoPYqqyiuy2+fas2NCp+bSUiIiL6r8zUSnzZvQaUxVrMrjgUiQvXMqXxOQHvwFi5szzRrvFSMdNKY4WlrZbC3cpdyLN0WRjx9wik5KUIec1P30SGNlXILFRWyN1zDWkXrj74ooiIiOilxjtCRERERK+ok1PXwT7GDmYqCyE3mPRwbe0nZLujd2Pq0akwwSTkvna+WNV2FZws7mgRa9ABvwwBsou1DHMoDfReC5jbCHFOgR5vrwnCzVydkLvamuOHIfVgZ6F5tAUSERERkaSmjwOGNi4jZHqjCRO2hkJf7KEyKBQwvLkYcKsqT1RCMdPNyg3LWi+DtUZ8SC4xJxEf7f8IOkPR9z1rT2doGjhAbxS/A9ppHBH7XRDy0zIeYXVERET0smEhk4iIiOgVo8/T4sTon1FKVxrKYvseGU1GxBgi4N+paWG2L2YfJh6ZCKNJvLHlZeOF/7X9H1ytiu1d+fc0IOaomKktbhUxi+2JCQBf/HEe0Sk5QmapUeGHQfXg5WD58AskIiIionsa07YCfJzE71mh8Rn48ehVebCZDTBgG+BcTj5WQjGzgmMFLGi+ACqFSpw/JRRLziwRx/ZqgSSrBGlaFwt3nJv+B/Q6vXSMiIiIXi0sZBIRERG9YoI/3QQvcz8p1xkLEG8RjWbz3ynMDscfxtjDY2EwGYSxHtYe+P717+Fh7SFOEv4r8O9S+aIdFgCeNaX47/PXseFknJApFcCSvrVQ3dteGk9ERERE/52VmRpzutaQ8q/2XMSBi8nyCbYewOA/7l7MPP2TEDXyaoSpDaZKQ38M/xFH4o8IWYNpAxCtvSyN9bT0QdCE9fdZCREREb3sWMgkIiIieoWcnLYeXhZ+Up6ty0B2xXw0mj64MDuWeAyjD4yGvtg+l66Wrvi+7ffwsvESJ8lMBH77SL5oncFArbekOCW7ABO3hUr5R60qoHUVdyknIiIiosenSXkX9KzjLWQ6gwnvrw3Gieg0+YR7FTP/HA+kRgpR9wrd0adiH2no5H8mIzlXLJY2/GoQYnKuSGO9zcrg5FQWM4mIiF5lLGQSERERvSKid/4Lt1wPKU/JvwbrzqVQ/e03C7OwlDB8tP8jaI1aYayThRP+9/r/UNqutDiJyQTs/BgoKLaXUanawBtfSdc0mUyYuPUcUrLF+QN8HDDyNf+HWxgRERERPZIpHarAw07cL71Ab8S7a88g7Fq2fMLdipn6PGDHSMAobkUwtt5YVHSsKGTpBemYdGQSDMaijh9qtRp1ZvVCYm6MdEkPrRfOr9r1kCsjIiKilwULmURERESvgJzr6cg7cANqpUbIk/MTUXZMM/g0q12YGU1GTDs2DfmGfGGsvbk9VrVdhbL2ZeULnN0IXNkjZub2QK+fALW5NHxzUBz+vnBdyCw1KizsHQC1il9RiYiIiJ4GeysN1gytDwcr8TtirtaA0dsjcDk5Vz7pdjHTplgHjdh/gZMrhchcZY55zefBUi3ux3ki6QS+D/teyCzsbFBh9GtIzU8ScqVCCfPLaiQePveQqyMiIrq31atXo0WLFvccM3bsWCgUCuE/VlZW8PX1Rfv27bFw4UKkp6c/8BytWrW667j8/Hz4+voWjp07d+6jLOulw7tERERERC85k8mE8Nl/wM7MUcjz9Dlw6egPu9LiW5p/RP2BS+mXhMxWY4uVbVaigmMF+QKZ14DdE+T8jbmAg48Ux6TmYMbO81I+5c3KKONi/QArIiIiIqLHpaKHLX4eGghbc7WQZxUYMOrXK4i6kSOfZOsBvLlQzvdNB9KihKiMfRlMaTBFGvptyLc4ff20kDmU8YJTN39k68QuH+YqC9zcHo2cpBJa3hIRET0EnU73UMdDQkIAAG3btsWgQYMwaNAgdOrUCX5+fjhy5AjGjh2LihUrYu/evXed8/YcAHD+vHw/5LbFixcjNja28OeAgIB7ftZXBQuZRERERC+5k5+vRylLXyEzmUxIdUpF6VZ1hbzAUIBvznwjzbG45WJUca4iT24yATs/AvKLtZQt3xao2VcaHpeWi/7fn0CO1iDkLSu5oV/90tJ4IiIiInryqnvb44ch9WCpUQn5zTw9hq8/g8z8Em76VuoAVO8pZrpcYMeHUovZTv6d0LFsRyEzmowYf3g8MoptTeDTrDYKKhmhK7bFgZ2ZIy7O3Qu9Tty/nYiI6EGFhoaifPny2LFjh3QsNjYWnTp1wsSJE4X87NmzAIBly5Zh9erVWL16NTZu3IhDhw4hNjYWI0aMwI0bN9ClSxeEhYWVeN3bc9SrVw9JSUklvsGZmpqKOXPmwN/fHzY2NgBYyLyNhUwiIiKil1jU78fhnu8p5dF5l9Fg0ltSvv7CeiTliO282vi2QT2PeiVf4G4tZTsuBhQKIY5JzUHv7/5FXFqekDtZm2Fu9+pQFBtPRERERE9PPT8nrBpYF2bF2vxHpeRizKYQGI0m+aQ3vgKsXcUs5h8g6Htp6OQGk+FrJz5cdz33OqYenQqTSZy75judEKe+Ks3hbuGF4E83PNiCiIiIiqlcuTJGjRqFIUOGoHv37khMTITBYMCiRYtQrVo1WFtbY9SoUYXjExISkJKSAhsbG5QtK2+z4+joiCVLlqBnz57Iy8vDtGnTpDG35/Dy8kLLli0BlPxW5syZM5GRkYERI0YgOzsb7u7u8PDwkMa9iljIJCIiInpJabNykbvvmrwvZl4i6n/RRxqfUZCBVedWCZlaocaoWqOksQCAm7Elt5RtNwewKyVEkTey0eu7f5GYkS8Nn9utOtxsLe6zGiIiIiJ60pqUd8HSfrWgLPZ82d8XkrF43xX5BCsnoMMCOf/rcyBFHG+tsca8ZvOgKfbd9EDcAay/uF6aotnstxGRI9/o9VL74ezC7fddCxERUXEajQZjxoxBREQEvLy8MG3aNPzzzz/YtGkT9u7diw0bNsDXt+ihm9stYatXv/fD1x9++CEAYPfu3XdtTVuzZk1Ur14dABAeHi6MiYyMxPLly9GwYUOUL1++cDzdwkImERER0Usq+PMtcDB3EbI8fQ5cu5WHhZ2NNH5V6CpkabOErHuF7vCz95Mnz0wE1nQquaVsQD8hunI9C72/O47rmQVCrlAAX3avjrZV+YQhERER0fOibVUPjGldXsoX77uCveFJ8glVOgFVu4mZLgdY1xPISRHiys6V8UndT6Qpvg76GhdSL0h5o7kDEZ8bLeU2iVZIDY2SciIievls3boVCoXisfwnNDQUAKBQKKBUFpXHiv98251FyHu5XaDMyclBUpL4d+XtOQICAgrHFX8j89NPP4VWq8W8efMK29CyrWwRFjKJiIiIXkLnf9wDL42flKc6psKneW0pT8hOkJ6Et1Jb4f2a78uTZyffKmKmF7upVEJL2fj0XPRddRwp2WIRU6kA5veoid71uC8mERER0fPm3aZ+aFXeUcrHbD6LiORs+YT28wAr8QE6pEcDG/oCOnFbgX6V+qGFTwsh0xl1GHd4HHJ0OUJuZm2JCiOaIb3ghpCbqywR+0MQ98skInoFnDx58rHMY2lpiYoVK2LJkiUoV64cYmNjMW3aNDRp0gQ9e/ZE69atMWDAAMTHxxeec7sIWaNGjXvObWZmVvjnggLx/sedxdDKlStDo9EIhcwTJ05gy5Yt6Nq1Kxo3biwUPukW9bP+AERERET0eOUkpUEZpodSIz6zFpNzBY3nDi3xnCVnlkBnFNufDKk2BC6WxW5I5aQCP3UGUktoLdZxodBSNl9nwIh1p5GSrRWGqZQKLOhVE50DvB5iVURERET0tCgUCkxp64ur6fmITCkqRGYX6PHOT0HY/F5DuNqaF51g7QJ0XQGs7wWYjEV5/Eng1/eAHquB/3/TRaFQYGajmeixsweu514vHBqTGYMvjn+B2U1mC+37nCr5IanuJejOaqFRFt0odrXwRPBnGxE4p//j/wdARETPjR49eqBhw4YlHtNqtejduzcAwNraGmvXrr3rPLa2trh48SLmz5+PH374AZ07d8bq1auhUqkwevRodO/eHSNHjsSCBQuwYMGttum33468XyHz5s2bhX92cRHvo9yeo2bNmtBoNKhYsaLQWnbcuHFQq9WYO3cuALCQWQIWMomIiIheMmFz/4CXhZ+QZWrTUHXM69JYo8mIb05/gz+i/hByF0sXDKwyUByclw783BlIlvcqwpsLgWrdhWj6znCExoutZ9VKBZb0rYU3qns++IKIiIiI6Kmz1KjwVUd/DN14ERl5RW8+RqfkoO+q49jwTgOxmFm+DfDGV8CfY8WJzu8A9k0D2swojBwsHPBVs68wZM8QGO8ofP4e9TvKO5bH0Griw3dV3nodh86shL+yspB7GL0RufUI/Ls3/e8LJiKi51K9evVQr169Eo+dPn268M81a9ZEly5d7jtfREQENBqNlJcuXRo7d+4s3OMyOzsbkZGRUCgU9y1kRkZGAgCcnZ3h4OBQmN+ew8rKqnDvyxo1amD9+vXIyMjAwYMHceTIEYwYMQIVKlRAdnY2oqKiYGlpiQoVKtx3La8KtpYlIiIieomcWbBNKmIaTQYU+BvgUEZ8AzJXl4vRB0bj+7DvpXlGBoyElcaqKMjPAH7uBiSdky/abi5QV7zZtOlULDacjJOGLugdwCImERER0QvCy94cC3vVgFIh5hHJ2ei76jhuZInt81D/HaDhB/JERxcDQT8IUW332hhRc4Q0dGHwQuy+ulvKG88aLO2XqVKooD12E/kpNx9oPURE9HI5c+ZM4Z9r1ar1QOeUVMQs6XhoaCiMRiP8/Pxga2t7z3OOHj0KAGjVqpWQ356jevXqhXtw3t4nMzQ0FBMmTICtrS0+//xzALfe3jSZTKhWrRpUKtUDredVwEImERER0Usi7u/TsLsmf7mOzruMWiO7CVlSThIG7R6E/XH7pfGVnSqjS7kuRUFBNrCuJ5B4WhqL1tOBBsOF6Fx8BqbuCJeGvtO0DDrVLCXlRERERPT8alrOBZ+9WUXK71rMbDMTqPSmPNGf44EE8fvksOrD0NBTbhc4+chkhCSHCJna3Ay+A+ogR58p5LYaB5z74k/ul0lE9Aq6s5D5KK1YBw8ejIMHD5Z47M69Le/FZDLhxx9/BAD06tWrxDnu/Gy33+4cN24cLl26hPHjx8PNzQ1AURtatpUVsZBJRERE9BJIvxyPnF2JMFdZCHlqfhLqTesjZJfSLqHvH31xMe2iNE9V56r4ttW3UCv/fwcCbS6woQ8Qd0K+6GuTgSYfi58jR4v31wZDqzcKeWAZJ0xoV+nhF0ZEREREz9zgxmUwpUNlKS+xmKlUAt1WAV51xMFGHfDLUCC/qBCpUqowv8V8+Nv7C0O1Ri1G7R+FuEyxw4dnvaq46ZohtKMFAE8LH4SM2wy9VtzznYiIXm63C4XAg7+R+aAedH/MlStX4vLly6hQoQK6du1a4hx3FkNvv5F54sQJlCpVCmPGjCk8xv0xS8ZCJhEREdELLj89C3HLT8BGYy/kOqMWZg0cYeVkV5hlFGTgw/0fIiUvRZqnjW8b/NjuR7hauf7/BPnAxn7A1SPyRZt+AjQbJ0Qmkwljt5xFws08IXezNceSfrWgVvGrJxEREdGLaljTsnctZo5YFwyD0VQUmlkBfTcC9j7i4PRo4PfRgKlorJ2ZHb5t/S2cLZzFoQXpGLFvBG7m3xTywAn9EJ17SfocHhY+ODt+K/S5BdIxIiJ6+ZhMpsJCoVqtRrVq1R7r/A/yRubff/+NsWPHQqVS4fvvvy9sH1t8jjsLkz4+PihbtiycnZ0xa9YsWFlZSePv9xboq4Z3k4iIiIheYHqdHuem7YSTuZuQG01GJJjHoHKf1kI2+Z/JuJZzTZrn3RrvYn7z+bBUW/7/xFpg80Ag6oB80YYfAC2nAgpxs6StpxOw72KykKmVCix7qzbcbMU3RYmIiIjoxXO3Yuapq+lYc+yqGNq4Ad2/BxTF9vgK+wUIWSdEXjZeWNpqKSyKdRe5mnkVYw6Ngc4ovmlZd1ovJOXK+7G7W3ghdNJ26HNYzCQietlduXIF2dnZAIAqVarA3Nz8sc1tNBoRFhYGoOQ3MpOSkjBx4kR07twZBQUFWL58OZo0aVLiHEqlsvAtzNsiIyORkpKCwYMHF2YGgwFhYWFQKBQsZBbDQiYRERHRC+zUxPXwtPSR8qiCC2gyc6iQrQlfg0Pxh4RMrVRjTtM5+LDWh1Aq7vhqeGAWcGWPfMF67wBtv5CKmEkZ+Zi+U94Xc3KHyqjr5/QQKyIiIiKi59ndiplf7bmIqyk5Ylg6EGg5WZ7kz3HADfGtymou1TC32VwoIH7PPJV0Cl+d/ErIrJ0dUO6T15CYGyNN7WZRCmFTfuObmUREL7n/uj/mvVy6dAm5ubkAgJkzZ2Lw4MEYPHgwunTpgoCAAHh7e2PBggUoU6YM/vjjD7zzzjt3ncPf3x82Njb3vebly5eRl5f3wONfJepn/QGIiIiI6NEEz90MH00ZKY/OvoSmi8Uv0WeSz2Dx6cXS2LF1x+LNsm+KYXoMcHyZfMHaA4E3vpKKmCaTCZN+PYesfL2Qt6zkhsGN/B5sMURERET0whjWtCzCEjKwPSSxMMvXGTH+l1BsfLcBlMo7vi82Hg1EHwaiDhZlulxgyxDgnX2AxrIwblW6FcbVG4evTomFy42XNqKCUwX0rNCzMHPw9UTlCW0RPnc3vK3F78Qu5h4I/mwzAucPeDwLJiK6hwOrVyI5JupZf4xnxs23LF4b/O5Tv+7T2B8TAH766ScAgLm5ORwdHeHp6Yn3338frVq1wptvvgmNRnPPOR707crb47k/poyFTCIiIqIXkDY3D9Y3rIBi35cTc2NQb1YvqFRFLbzS89Mx9tBYGEwGYWwb3zboV6mfPPn+LwCDVsyq9wLeXAQo5YYeW08nYH+xlrK2FmrM7lodimJFTyIiIiJ6OXzesSr+iUhFSnbRm48nr6bhp3+vYnDjOwqLSiXQdSWwojGQc6MoTw4H9kwG3lwgzNu/cn9EZUThl8u/CPns47NRxq4M6nrULcxsvdxQdXJ7nJv1O0pb+wvjPVTeuH78AtwbyG+PEhE9TskxUYg/H/asP8Yr5843Mh93IbNPnz7o06fPXY8bjUYYDIa7Hn+QOf7r+FcJW8sSERERvYDOzNkKG429kKUVJKPcB01hYW9XmBlNRkz6ZxKSc8VCo7eNN6Y3mi4XGhNDgHObxczGHei4CFAW298Id28p+9mbVeBhz30xiYiIiF5WjtZmmNW1mpR/ufsSYlKLtZi1dQe6fidPEvQ9cH6HECkUCkyqPwm13WoLud6kx5iDY5CYnSjkth4uqDm9C2JzIoRcpVAjbuMZEBHRy+l2IVOhUPAtxpccC5lERERELxhtVi4c8hyFzGQyQRlgDacKvkK+9vxa/JPwj5BplBp83eJr2JrZihObTMBfn8kXbPEpYGYtxXdrKftaRVf0qOP9ECsiIiIiohfR61U90LFmKSHL0xkwYWsojEaTOLhcK6Dxx/IkOz68tbXBHTQqDRa0WABPa08hTy9Ix4f7P0SePk/IrZ0cUO7dpsjTZwu5h4UPLv287+EWRUREz73ExEQkJ996YNvPzw/29vb3OYNeZGwtS0RERPSCOT3nF3irxX2AEnKvosGggUIWkR5R4r6Y4+uNRxXnKvLEkfuA6ENi5lIBqFXy3kJL90eU2FJ2TrcabClLRERE9IqY3qkqjkWkIDWnaGuC41FpWPj3ZXzStqI4uOUUIOYoEH+qKCvIALa+DQzZBaiK9k1wtnTGNy2/wcBdA4XC5eX0y5h3ah4+ayg+gOdWvRzOm/ahHMTvudrTmdD30UOt4W1QInoy3HzLPuuP8Ew9i/U/yf0x6fnDv8GJiIiIXiD5N7PgpHUVvsUZTUbYNxKfhNcZdJj0zyRojeJel21926J3xd7yxEYD8Nfnct56OqCSvzKuPByJr/+6LOVT2VKWiIiI6JXiZG2GL7pUw/B1p4V8yf4I+DhZoVddn6JQpQG6fw+saHqrgHlb/CngwCyg9TRhjkpOlTCz8UyMPTRWyLdc3oLGXo3RqnQrIa8/pS8iZ/wNR3PXwszR3AVn521DnUm9/ttCiYju4rXB7z7rj/DKeZL7Y95p3759aN26NQYNGoTVq1c/sevQvbG1LBEREdEL5MyXv8JKbSNkCblXUblvayFbfnY5LqRdEDI3Kzd81vCzkt+WDN0MXA8Ts9KNgIpvSEPXHLuK2X9elPLXKrqiJ1vKEhEREb1y3qjuKbWYBYBJ287haESKGDr6Ap2XyJP8sxCIkNvAvu73OoZVHyblnx/7HNdzrguZlaMdspyzpLG2abbIT5dzIiJ6MT2tQubJkycBAA0aNHhi16D7YyGTiIiI6AWRn5oBF52bkBlNRjg3F/fFDEkOwfdh30vnz2w8E/bmJewbcTMW2DdDztvOBIoVPdefiMXnv4VLQyt72mFh7wC2lCUiIiJ6Rc3tVh1VS9kJmd5owvs/B+Py9WJFxCqdgbpvy5Nse1faLxMARgSMQA2XGkKWUZCByf9MhtFkFPLAT/sjMVecw0ptg5DZ2x98MURE9Fy7s5AZEBDwxK4zdOhQXLhwAQMGlLzlDj0dLGQSERERvSDOfLUdlmprIYvPjUaFHi0Kf87V5WLSP5OkGzp9K/VFo1KN5EljTwCrWgJZiWJepTPgXVeIfgmOx+Tt56QpyrvZYO3b9eFgZfZwCyIiIiKil4a1uRo/DK6HUsW2Gcgq0GPIj6eQnJUvnvD6LMCtqpjlpgDregJ5N4VYo9RgbrO5sNaI34VPJJ3A6vDVQqZSqWBdzwUGk0HIPRXeuLhGfuOTiIheLJmZmYiOjgYAuLq6wsvL64ldy93dHZUqVYK1tfX9B9MTw0ImERER0QvgzPxtcDeJX86NJgNcW/kX/mwymfDlqS8RlxUnjPOz88PoOqPlSUM3A2veBHJuiLlSDbQS98v87Wwixv9yFiaTOLSsizXWvRMIZxvzh18UEREREb1U3O0s8MOQerAxF/dYT7iZh3d+CoZWf8fDdhpLoOePgMZKnCTlErB5AKAX93r3sfXB5MDJ0jWXnF6C8BSxY0jVAe0QkxMhZCqlGubhQMSmw4+wMiIiel6EhITA9P83Jx6lreyVK1fw7rvvwt/fHxYWFrC1tUX58uXRv39/BAUFFY6Lj4+HQqGQ2srGx8dDpVKhcePGyMrKwpQpU1CuXDlYWlqiatWq2Lx5c+HYnTt3omXLlnBwcICnpydGjBiBnJycR1w5kJ6eDoVCgYCAAFy5cgWDBw+Gj48PzMzM4OXlhfHjx0On0z3y/M8rFjKJiIiInmOZUdcQ9PEGuKa4wkwpFgvjc6NRvkvTwp9/Pv8ztl3ZJoxRKVSY03QOLNWWRaHRCOybCWx7BzCIN4gAAO3mAs5FBdLdYUkYvSkExmJFTB8nS6x7JxButhYgIiIiIgKASh52WN6/NtRKccuBs3E3MX/vJXGwa0WgyzJ5kujDwO8fo/hTdB39O6JD2Q5Cpjfp8eH+DxGVESXk5QY0QJ5evFmsUZpBGaxF9O8nHm5RRET03Pgv+2Pu27cPNWvWxKpVq+Dk5ITOnTujefPmUKlUWLduHWJiilqTh4SEAJBb197OXVxcEBgYiPXr16Nu3bqoVq0azp8/j379+iEoKAhjxoxBz549YWZmhpYtWyIzMxPLly/H5MnyQzkP6vbaTSYT6tati507d6J+/fpo2rQprl+/jnnz5mHs2LGPPP/zioVMIiIioufUucW/IXl5KDwsvKVjeqMO7u0qFv68L2Yf5gfNl8a9V+M9VHOpJoZ7JwNH5LFQWwA9fgTqv1M074Xr+HDDaRiKVTFL2Vtg/bAG8LS3LD4LEREREb3impZ3xayu1aR85eEoHLiULIZVuwKtp8uThKwDDs+T4smBk+FlI3YquZF3A0N3D8WV9CuFWanAakjzSIPWWCCMNVOaw3DoJuL/PgMiInrxPOr+mCaTCW+//TbUajVOnDiBU6dOYdOmTfj9999x8eJFnD59Gi1atCgcf79C5q5du9C1a1dcvnwZGzduxKlTp9C/f38YDAb06dMHu3btQnh4OHbv3o1t27Zh//79AIAtW7Y8yrIBAKdPnwYAhIaGon379oiNjcXWrVuxb98+7Nq1CwCwYsUKXL9+/ZGv8TxiIZOIiIjoOXTy07VwvOYIC5WVdExn1CLBPAb+HW7teXnuxjlMPDIRJojFxjrudTCsxjDx5PBfgeMlPPVu4wEM+ROo1q0wOnz5BoavPQ2dQZzX3c4c699pAB8n+bMREREREQFA73qlMaihr5R/svksrmcW2y+z8UdAncHyJAdmAaHiDV9bM1vMbToXaoXYvjY1PxVv73kbl9KK3voMHNsPSXYJ0BvFLiQWKivk7U7C9eMXHm5RRET0zK1evRomkwkmkwl9+vR54PMiIyMRExODhg0bon79+tLxWrVqwdnZufDn+xUyO3bsiFmzZkGtLvr7qH379gCAxMREbN26Ff7+Rd2uAgMD4eTkhKSkpAf+zMXdLuI2bNgQa9euFfbubNOmDZo1awatVovw8PC7TfFCYiGTiIiI6DlzavoGlDLJN30AIDk/Afl1TGg8cwgAID4rHh/s/wD5BvFmkJ+dHxa/thgapaYoTI0EdnwoT+pRA3hnP+BVpzAKibuJd38OgtZgFIa62Jhh3bAG8HPhRvdEREREdG+ftq+MKp52QpaWo8XHG0PEjh8KBdD+a6Bca3mSHSOBhGAhCnALwJymc6BSqIQ8vSAdQ/cMRXhq0Q3cRlMGIc78KgwmvTDWUm2DG5svQZuR+4irIyKiF4m9vT0UCgUOHDiAJUuWIDs7+57jQ0JCoFQqUaNGDSkHgClTpkjnZGZmAgB69OiBKlWqCMeMRiOys7OFYunDuv1G5uzZs6FSqaTj5cqVAwAkJydLx15kLGQSERERPUfOLNgG99xSUq43anElLxxVprdH5d63bvBkabMwct9IpOWnCWMdzR2xrNUy2JvbF4W6fGDLYECbJU7sVRcYuhuwL2rPZTCa8Om2c8jXiUVMRysN1g1rgHJuNv9tkURERET0SrDQqLCkXy1YmYk3W/+NSsWyAxHiYJX61jYH7sVa0hoKgI1vAVniGyztyrTD/ObzoVaKb2ZmajPxzp53EHWzaM/MpjPfxlXFFRhN4vdbBzNnnPl86yOujoiIXiSurq6YPXs2jEYjRo0aBVdXV3To0AHff/89cnPFh1qysrIQFRWF8uXLw8rKSsq9vb1L3J/z7NmzAIBOnTpJxyIiIqDVaqUC54PKycnB5cuX4eHhIbTAvdPtdfyXYunziIVMIiIioufE+dV74JjkAKVC/Ip2U5uCrKoFeG3x+7CwL3qiffHpxYjKiBLGminN8E3Lb+Bj5yNOvmcSkBQqZhYOQM8fATPx7crtZxJw4VqmkNlZqPHz24Go6GH7aIsjIiIioleSv6sNZnaW98tc+PdlnIwWH8iDhR3Qb/OtbQ/ulHXtVjFTJ3Yhae3bGgtbLBS7kADI0mXhixNfwGQqeuuz+dx3EWWQW8l6Wfjh1MyND7kqIiJ6EU2cOBFXrlzB3LlzERgYiN27d2PYsGGoUKECoqOjC8edPXsWJpNJait7O69Tpw5Kcrv1a7169aRjt9/kLKkA+iDOnj0Lo9F412sDQFBQEBQKhfQW6YuOhUwiIiKi50Dk78dgFg6oi92EydFnwrK1O6oPai/kCdkJ2HpZfnp8dtPZCHALEMOwrUDQ9/JFu64AHEoLUb7OgK/3XpKGrhxYF9W87KWciIiIiOh+utfxRrfaXkJmNAEfbTyD9Bxx/0rYewG91wIqMzFPCAJ+Hw2YxP3bW/i0wDctv4GZUhx/KukU9sftF8fOfx9R2Relz+ea5Yao348/5KqIiOhFVKZMGUyYMAEHDx5EVFQUmjRpgoSEBCxbtqxwzP32x6xdu7Y0r9FoRGhoKJycnODrK28XdLvI+aiFzNvn29nZlXj81KlTiIiIQGBgINzd3R/pGs8rFjKJiIiInrG081ehO5AGC5WVkBcY8qGvoYJ/+0bSOStDV0JfbJ+fETVH4HW/18WBqZHAb6Pkizb6EKj4hhSvOXYViRnik+5vVPNAg7IvV1sSIiIiInq6ZnauhjLF9lm/lpGPcb+ECm9OAgB86gEdF8uTnF0P/PutFDfxaoK5zeZK+YKgBdAZdEJWZ2p3pORfEzK1UoP8/cnISUx5wNUQEdHLwNfXF4MGDQIAGAyGwvx+hcziOQBcunQJubm5dy1U3uvcB3F7f8w73xy9zWQy4dNPPwVw663Tlw0LmURERETP2MUVB2GjEd921Bt1uOmVgaoD20nj4zLjsCNih5C5WrpiSLUh4kCDHvj1PUBbbAN7n0Cg1efSvOk5WiwttleRWqnAuNcrPsRqiIiIiIhk1uZqLO1XC2Yq8Xbk3xeuY82xq/IJAf2ABiPl/K+pwNV/pLh16dZo4NlAyGKzYrH+4nrxc7g7wa5daeQbxP3Q7MwcEf7lbrmoSkREL7w9e/bgzz//hF4vPhCemJiIxYtvPThz576W9ytkllSsvN8blyEhITA3N5f2yJw2bRoUCgUGDx58zzXcnv/EiRPYs2dPYa7T6TBq1Cjs27cP3bp1Q+fOne85z4uIhUwiIiKiZyjsx13wsSwrZEaTEUnWiajzcY8Sz1kRugIGk0HIhlUfBgu1hTjw6EIg/pSYWToBPX4AVGILWwD49kAEsvLFL/X9AkujrKvNA66GiIiIiOjuqpayx+QOlaV89p8XEZaQIZ/QZgbg31LMTEZg27tAXroQKxQKjKs3Ttpv/ruz3yE9Xxxbtl0DpDjckIqWpSx9ETRn00OsiIiIXgSbNm1Chw4d4O7ujtdffx1vvfUWWrdujTJlyiAsLAwTJ05EixYtAAB6vR7h4eFwd3eHh0fRns23czc3N3h5eUnXuF1oLKntbFJSEpKSklCtWjWo1WrhmNFoBABoNPJ9mtu0Wi3Cw8Ph7e2NHj16oEOHDmjVqhV69uwJPz8/LF26FA0bNsSPP/740P9sXgQsZBIRERE9I/p8LYyhuVAoFEIep41Eg8/7l3jO1Yyr+D3qdyFzt3JH9wrdxYGJIcBBub0WOn8L2HtLcVxaLn76N0bIrM1UGNWq/P0XQkRERET0gAY29EWbKuLeXVqDER9uOIPsAvGhOqjUtx7Cc/IX88wEYOdH0n6ZFRwroFv5bkKWpcvCspBlKK7B5P6IzpP3hrdLs0XujRKKqkRE9MLq27cvhg8fDh8fHwQHB2Pz5s04d+4c2rVrh71792LOnDmFYy9evIj8/Hzpbczb+d3euLzXG5n3ait7+9jAgQPv+vnDw8Oh1WpRq1YtrFmzBsOHD8f58+exc+dOODo64quvvsKBAwfuun/mi059/yFERERE9CScmLYevubiTZlMXTrqfNb9LmcAy88uh9FkFLJ3a7wLc5V5UaDLu9VS1ljsRlCtAUCl9iXO+/XeS9AaxHnfb+4PFxvzEscTERERET0KhUKBeT1qoP3iI8Le7NEpOZjy6zks7B0gPuhn6Qj0WgOsagkYtEX5+R3AmZ+B2uKN35EBI7ErehdydDmF2ZbLW9CnUh/4O4jfvRvM7o/wKb/B1aJUYWattkPInF/RaMHgx7NgIiJ65tq0aYM2bdo80Nhq1aqV2Gb8zvz2W5R3+vvvv+86Z7t27Uqc02Aw4NChQ2jdujWaNm161/Nv749Zu3ZtWFpaYsmSJViyZMl91/Ky4BuZRERERM/AjbMR8DCUkvI8Ly0sHO1LOAOIvBmJXdG7hMzT2hNdy3UVB+6bAdy4KGYOvkC7OSjOaDRh7q6L2B6SKORutuZ4u2mZB1gJEREREdHDcbAyw+K+taBSip1JtockYsWhKPkEj+pA6+lyvmsCkHJFiFwsXfBO9XeEzGAy4KtTX0kPBJrZWMIi0BnGYts2eKl9EX8o5MEXRERE9AiCgoKQmZmJGTNm3HPc/fbffNmxkElERET0DET+cAzmKkshS8yNQZ0xJe+LaTQZseTMEpggPsH3Xo33oLlzv8uoQ8Dx4q2zFEDXFYC5rZDmFOjx3tpgrDgUKV1vdJsKsDJj8w4iIiIiejLq+TlhdGt5G4Mvd1/E76GJ8gmB7wPlWouZLhfY+jag1wpx/yr94WUj7l92LPEYZp+YLb0RU7lva8Tmit+HVUo1krZdeIjVEBERPbzAwECYTCY0bNjwnuPufCPzVcRCJhEREdFTFrbyD3hbim876oxalOpVvcTxcVlxGLpnKPbF7hNyLxsvdCrXqSjIiAe2vStP0HgU4NtIiBJv5qHHin/x1/nr0vBqXnboWUfeR5OIiIiI6HEa3qIcmpZ3kfIxm88i6GqaGCqVQJflgLWrmF87C+yZJOyXaa4yx+g6o6V5N13ahAXBC6RiZqX3X0OePlvIPCy9Ebr0t4dcERER0eNlNBoRGhoKZ2dn+Pj4POuP80ywkElERET0FOUkp0NxUS/lCYar8G5SU8hMJhM2X9qM7r91R/D1YOmc92u+D43y/9/GzM8E1vUCspPEQe7VgNcmC9H5xEx0/vYoLlzLlOas4W2PHwbVg1rFr4lERERE9GSplAos7Vcb5d1shFyrN+Kdn4JwNSVHPMHGDehcvPsIgFOrgN2fCsXMtr5t0dKnpTR0dfhqrDi7QshcqpZFkipBGquJVkCbk/cQKyIiInq8lEolsrOzkZKS8qw/yjPDO1RERERET9Glr/bA3sxJyDK0qaj7WW8hS89Px/t/v4+Zx2ciTy/fPAlwDcCbZd+89YNBB2wZBCSHi4NUZkDX7wC1edG8OVoMW3MKN7IKpDk71PDEpncbws3O4hFXR0RERET0cOwtNfhxSD242JgLeXquDkNWn0J6jtg2FhXa3mozW9yJ5cAfnwDGW/tgKhQKfNnsS9T3qC8NXXZ2GVaHrRaywOn9kZJ/TchsNQ44Pf2Xh18UERERPTYsZBIRERE9JRGLdsHL0k/KC3wNsLAvego9W5uN9/56D8cSj5U4zxtl3sDSVkuhVqpvPXX+xxggcr88sPMywKNa4Y9GowmfbDmLxIx8aeioVuWxpE8tWJqpHn5hRERERET/gbejFb4fVBcWGvFWZXRKDoavC4bOYBRPaD0d8AmUJwr6Htg5qrCYaaG2wJKWS1DTtaY09Ovgr7E9Ynvhz2YWFjBV0MBoEq/lrfRD0PSNj7YwIiIi+s9YyCQiIiJ6ChJ3BcHb6Cvl0bmXUPuj7oU/aw1afHzgY1xIuyCNdTR3xIIWC/BVs69gb25/K/xnIXD6J/mCr00BavQUolVHorD/YrKQaVQKLO4TgDFtKkCpVDzCyoiIiIiI/ruaPg74pk8tKIp9JT0elYYZO8+LocYC6L8VKC3uAw8AOPMzsGMEYDQAAKw0VljWehkqO1WWhs78dyYupl0s/LnWiK6IzY2QxrnnlsKpGRseflFERET0n7GQSURERPSEZcUlwzLMBPXt/Sz/X3J+Iup90afwZ4PRgIlHJuJE0glpjlalW+HXzr+ijW+bovDyXmDfdPmCAf2BZmOFKOhqGr7ac0kaOrl9ZXQO8HrIFRERERERPX5tq3pgaocqUv7z8RisOxEjhua2QP9fAL+m8kRnNwD/flv4o52ZHb5r8x387f2FYVqjFmMOjkGmtmjv+LL9A5GlSxfGKRQKeOR44eSM9Y+wKiIiIvovWMgkIiIieoL0ej0iFx+ErcZByPP0OXDrWQkWdrdayppMJsw+MRt/xfwlzfFejfewsMVCOFs6F4VGA7B3snzBMs2Bjotw56PsaTlafLD+DAxGkzC0fXUPDGrk96hLIyIiIiJ67IY09kPf+qWl/PMd4TgRlSqGZtZAv82Af0t5okNfAdlF3UgcLRyxsu1KuFq6CsPisuIw9Z+pMJlufVcuFVgNqsZ2yNZlCONuFzNPzFj3iCsjIiKiR8FCJhEREdETdGrKOnhaijdijCYjMjwz4d24aK+e5WeXY/PlzdL5vSr0wsiAkVAU77EVthVIuSxmrpWAXj8BqqI3P41GE0ZvCkFSprgvpq+zFeZ2ryHPS0RERET0DCkUCkzvVBX1/ZyEXG80Yfi604hLyxVPMLMC+mwAyrcVc20WcGCWELlZuWFe83lQKcR94ffH7cea8DWFP1fo1gLKRjbI0WcK45QKJTxzvHBm0bZHXB0RERE9LBYyiYiIiJ6Q86v3oJRJ3hfzasEV1P2kV+HP/yT8g+Vnl0vj2vi2waTASXKx0aAHDs6VL9htJWDpIEQ/HI3Gocs3hMxMpcS3/WrDzkJsdUtERERE9DwwUyuxrH9teDlYCnlajhbv/BSEPK1BPEFjAXRZAdzeR/620z8B18OFqI57HXxc+2PpmotOL0JQUlDhzxW6t4CigTVy9dnCOKVCBdsEa8QfOfvwCyMiIqKHxkImERER0ROQdikWqjCD9LR3Ul4cGszpX/hzgaEAs0/Mls4P9AjE3KZzoVKqpGM4txlIixSzyh0Bz5pCdPl6Von7Yk7tWAXVvOylnIiIiIjoeeFiY46VA+vAUiN+H76YlIWZf5yXT7B2BpqPFzOTEdgzCTCJWywMqjoILX3EdrQGkwHjDo/DzfybhVmFHi2ABpbIK1bMtFBZIW1bBPLTxPazRERE9PixkElERET0mOl1ekQvOwZrta2Q5+gz4dGvBswsLQqzH8N+RFxWnDCuklMlLHptEcxUZvLkBh1w6Es5b/Gp8KNWb8THG0Og1RuFvEMNT/QPlPccIiIiIiJ63lQtZY+ve9WU8vUnYrE7LEk+of67gFNZMYs6CFzZK0QKhQIzm8yEj62PkKfkpWDJmSVCVqFHC+hra6A36oTcydwNZ6Ztf+C1EBER0aNhIZOIiIjoMTv56Tq4W3oJmcFkQKpnBtxrVyzM4rPi8b9z/xPGqRQqfNH4C9iY2ZQ8ech6IP2qmFXtCrhXFaLF+y7j/DVxTx8POwvM7lKd+2ISERER0QujfXVPjHzNX8onbgtFUoa4DzzUZkCbmfIkeybfeiDwDnZmdljQYgHMVeZCvuXyFlxIvSBklfu1xjVz8eFDAPCxKot/p/z0gCshIiKiR8FCJhEREdFjdG7FTnir/KQ8Ov8Syr4ltq/68uSXKDAUCFnfSn1R0akiSqTXAofnFQsVQPOJQhIck4blB4u1ngUwr2cN2FtxX0wiIiIierF83LoCapV2ELKbuTqM2RwCo1FsG4tKHQC/pmKWegUI+kGat5JTJYwIGCFkJpgw9+RcmIq1o204cxBiciOkOTy13riw7u8HXwwRERE9FBYyiYiIiB6TuL+DYRlpBqVC/IqVkHsV5T55Q8gOxh3EwfiDQuZi6SLdSBGc+RnIKPYkePUegFulwh9zCvQYveksit/PGdzID03Luz7oUoiIiIiInhsalRKLe9eCjblayI9FpmLlkShxsEIBvD4LQLEuJPtnAUnnpLkHVB4APzs/ITudfBp/Rv8pja0zvTtS8sWWtmqlBorT+Ui7ePVBl0NEREQPgYVMIiIioscg+fQV5Oy6BguVlZBn6W7C752GUJsV7XeZr8/H3JNzpTnG1h0LWzNbKQcARB4ADswWM4USaD6h8EeTyYTpO8MRm5YrDCvrao0J7SqBiIiIiOhFVdrZCl90qSbl8/dcQmj8TTH0rAkEvCVmBRnAT12AG5eFWKPSYHy98dK8C4IWIFcnfq+2cLSDS49KyNPnCLmNxh7Ry/+FPl/7wOshIiKiB8NCJhEREdF/dDMqEclrw2CjsRdyvVEHfVUlnCr5Cvn3Yd8jITtByOq610X7Mu3lyQuygd/HAD93AXJTxGM1+gAu5Yvm/Scam4PihSEqpQILewXA0kz18AsjIiIiInqOdKnlhS4BpYRMbzRh2JogRCRni4NbTQXM7cQsNwX4qROQFi3ETb2borl3cyFLzkvGqnOrpM/g3aQ6skvnwmgyCLm7pTdOfLruIVdERERE98NCJhEREdF/kJuSgehv/oGDmYuQm0wmJJjFoPrQDkJ+JvkMVoWKN0RUChUmB06GQlGs/VXMMWBFYyDoe/nCSg3QfFzhj7vDrmHWnxekYR+2LIeaPg4PtygiIiIioufUjC7V4ONkKWTJWQXos/JfXErKKgptPYCeP9763nynrGu3ipkZ4oOF4+uNh6bY2DXhaxCbGSt9hlqjuiFGL+9JX9rMH8HztjzkioiIiOheWMgkIiIiekTavHyEzfwdrhae0rGrhsto/MUQIUsvSMfEoxNhKPb0dv/K/VHOsZw4QfAa4Mf2QPpV+cIqM6DLcsCpLADgTGw6PtoYAlOxfTEDyzhh5Gvl5POJiIiIiF5QdhYaLOpdC2ql+BBgSrYWfVb+i/DEjKKwXGug52pAUaw7yc3YW8XM7BuFUWm70hhUdZAwTGfUYfI/k6UWswDQ8MtBSMi9KmQKhQIOyQ6I/SvokdZGREREMhYyiYiIiB7RqU/Xo5RlaSmPzruEpvOHCZnBZMDcc3NxI++GkPvY+mB4wHBxgvM7gJ0fAShWmQQAjxrAu4eAGj0BAHFpuRi2JggFeqMwrKyrNb4bUAcaFb/uEREREdHLpY6vIxb1CYCqWDEzPVeHfqtOiHtmVn4T6PodgGLdT1IjgC2DAIO+MHqn+jtws3QThoXcCMGIfSOkYqZarUbFj1sjQ5sm5OYqC2T8GYec5PRHXh8REREV4Z0tIiIiokdwdtlv8DH3l/LY3Eg0/HqwlK+PXI/TqaeFzExphq+bfw1rjXVRGH0Y2DoMUhFTqQaaTwTe2Q+4VwEAZOTpMPjHk0jN0QpDna3NsHpwfThYmT3S2oiIiIiInndv1iiFb/vVhkYlFigz8nR4a9UJRCTf0Wa2Rk+g42J5kpijwP4ZhT9aaazwSd1PpGHB14NLLGY6lPWEprEDdMYCIXc0d0HorN8eYVVERERUHAuZRERERA8p82oSzCOVUCrEr1KJubGo+2UfqNVqIT9+7Th+jvxZmmdi4ERUdq5cFFw7C2zoBxjEwiQcSgPD/gZe+xRQFe3b89Xui4i8kSMMNVcrsWpQXZR2tnrE1RERERERvRjaVfPAiv51YFasC0lWgR5TtofBdOfeC3UGAe3mypMcXQxc/KPwxzfKvIH+lftLw+5WzKzQvTmuW12TxvtYlsXphVsfckVERERUHAuZRERERA8pfMFu2GjshSxTm47KE9rCzNJCyK/nXMfkY5NhKvaG5Ztl30SP8j2KgtRIYG13QJsljIO1KzBwB1CqlhDHpeVi06k46bMt7B2A2qUdH2FVREREREQvnlaV3bFqUF2Yq8XbnMej0rD3/HVxcIPhQG1xH0wAwK/DgbQoALf2uRxfbzzeqvyWNCz4ejBG7huJfH2+OO20Abiad0Uab51giZsR8Q+5IiIielpWr16NFi1aPNBYg8GATZs2oXPnzihbtixsbW3h7u6Oli1bYuXKldDpdHc9d+zYsVAoFMJ/LCwsUKpUKbRt2xYLFixAevrdW5LfeX6rVq3uOi4/Px++vr6FY+fOLeEBnhcQC5lERERED+HUlxvhYyW2lDWajDBWVcPWy1XIdUYdxh8ej/QC8ctoWfuymNpgKhSK/2+DlZcOrO0G5Ij7Z8LMFui/FXAqK32OpfsjoDeKxdHRrSugfXXPR1wZEREREdGLqXkFVyzuEyDlc/68AG2xveTxxle39p2/U0EGsHkgoMsDcKuYOaHehBKLmUHXgzDz+EzxbU8Adaf1QHqB+H3eWm2Li9/sf/gFERHRE3OvguPdjoeHhyMwMBB9+vTB3r174eXlhc6dO6NWrVo4fvw43nvvPbz22mu4efNmiXOGhIQAANq2bYtBgwZh0KBB6Ny5M8qUKYPDhw/jk08+QcWKFbF37957ng8A58+fv+tnX7x4MWJjYwt/DggIuOdaXxQsZBIRERE9oBtnI+Bww0HKYwsiUe3t9lK+5MwSnE4W98W0UFlgQYsFsNLc0fp13wwg/ap4ssoM6Lse8KwpzXs1JQe/nBaf7HazNcd7zeWCJxERERHRq+D1qh5oUs5FyK6m5uKnf6+KAzUWQK+fAHOxwwqSzgF/jiv88XYxs1+lftK1fov8DVsubxEyC0c7qOvZwWDSC7m3VRkEzd308AsiIqLHLjQ0FOXLl8eOHTukY7GxsejUqRMmTpwo5GfOnEHTpk0RHByMjz76CLGxsThy5AjWrl2L3bt349KlSwgICMDRo0cxdOjQEq979uxZAMCyZcuwevVqrF69Gps2bcLRo0dx7do1jBw5Ejdu3ECXLl0QFhZ21/Pr1auHpKSkEt/eTE1NxZw5c+Dv7w8bGxsALGQSERERvVL0ej2ivz8OS7W1kKcV3EDd6b2k8QdiD+DHsB+lfHL9yfB3uOONzoTTQFCxcQol0P17oEyzEj/LN/uvwFDsbcyRr5WDhUb1gKshIiIiInq5KBQKTHmzMpQKMV+87wrScortQe9UBui6Qp7kzM/Apd3CnBPrT0Sfin2koXNOzkHojVAhq9yvNWJ10dJY+xR7pJyLfPDFEBHRE1G5cmWMGjUKQ4YMQffu3ZGYmAiDwYBFixahWrVqsLa2xqhRowrHJycno127dkhPT8eiRYuwaNEiuLqK3bh8fHywdetWqNVq/Prrrzh9WnygPSEhASkpKbCxsUHZsvID6I6Ojli6dCl69uyJvLw8TJs2rcTzvby80LJlSwAlv5U5c+ZMZGRkYMSIEcjOzoa7uzs8PDwe9R/Vc4WFTCIiIqIHcGr6OpSy8hUyvVEHyyYusLC3EfL4rHhMPjpZmqODdwd0KNOhKDAagT8+AYrtn4nXJgNVOpX4OSJvZGP7mQQh87S3QO96Pg++GCIiIiKil1AlDzv0rldayLLy9Vj09+USBrcHGn8s57snAvqCwh9vFzMbeDYQhumNeow5OAapealCHjijL1ILxL05LdXWiPrfv9Drxbc1iYjo6dJoNBgzZgwiIiLg5eWFadOm4Z9//sGmTZuwd+9ebNiwAb6+Rfd+Ro0aheTkZHTr1g0fffTRXectW7ZsYZHxjz/+EI7dbgtbvXr1oi2GSvDhhx8CAHbv3i20t719fs2aNVG9enUAt1rd3ikyMhLLly9Hw4YNUb58+cLxLwsWMomIiIjuI+7gabjnl5LyBFMMyncX35rUGrQYe2gssrRZQu5v648RlUaIE5z5CUgUn9SDSwWg0SjczTf7rqDYy5h8G5OIiIiI6P+NaVMBNuZqIVt3IhZXrmfJg1tOBXzEAiXSo4F/lwqRSqnCl82+hIe1+GbL/7F339FVVGsUwPfcll5ISCEkIfROIPQOAoIIIr0IAioqiqj0Jh2kK6CIKEVEpChSBKX3kgRC7yRAEkIS0ntum/dHHjc5GRSCJLT9W8u1uPt+c2bO0zxy7zdzTkxGDEYdGgWjObdBqbO3gW1TdxjN4h5rXjalEDhy9WPMiIiInjRJkqBSqf7xNQBcvnwZGzZsgE6nw8KFCx86ZpUqVQAAN27cEPK8jch/c79JmZ6ejujoaMXxNWvWtNTkfyJz7Nix0Ov1mDt3rmUZ2hdlWVmAjUwiIiKif2XM0uPeH9ehU1sLeWxWFOpPe0vIZFnGnOA5uBgv3hlnr7XHxJoToVPrcsOMBGDPZOUJ288DNDplDuB6TCq2no0SspLONuhRh09jEhEREREBgJuDFT5uWU7ITGYZU/+8BHP+OwLVGuD1+TlbO+R1aB6QLK6C4mLtggXNF0Cr0gp5UHQQFp9eLGTluzRDpFm5xGwpq/I4NnpVwSZERERPjNFoxOLFi1GuXDmEh4dj8uTJaNKkCbp3747WrVujX79+iIyMBAB8//33kGUZ3bp1g7e390PH1mpz/n7IzMwU8vuNyBo1avzr8Tpd7ndB2dm5KwPkbYRWrlwZWq1WaGQGBgZi48aN6Ny5Mxo3biw0Pl8UbGQSERER/YsTX6yBu434NKbelAW3TuWhsRYbjisurMD6q+sVY0xqMAletvme6Nw7BcjMtzl7ta5AmeYPvA6zWcbcnVch5/vuZWirctBp+CsdEREREdF9Axv7wbuYjZAdvh6HL7ZcgJz/F2rPakDd98TMkAHsmaQYt7pbdYytP1aRr7iwAntv7xWyBtP7ITbrjqLWB2UQOHnNI86EiIiepIsXL2LevHlYsWIFNm3aBC8vL6jVanz++ee4cOECkpKSsGDBAgDAzp07AQBvvPHgrX/yi4nJWVbcxcVFyO8/IfmwRmZSUpLlz8WLF1cc7+/vD61Wi4oVKwpLy44cORIajQazZs0CADYyiYiIiF4m1zcdgrfkp8hjrO/Cp2WAkG26vglfh3ytqO1XpR9a+bQSw8hTwKmfxExnD7w6/YHXER6fgV4/nMCuS+JeO74utugS8PC7AomIiIiIXibWWjXGvlZZkf8SGI6ZOy4rm5ktxgI24hfPOL8RuH1cMUa38t3wZrk3Ffn4o+NxK/mW5bXGWge/jxojUR8n1EmShBKZ3ghZ8Nsjz4eI6GX2+++/Q5KkJ/bPjRs30KlTJ8V5fH19sW3bNsyePRvZ2dm4cuUKAKBWrVqPdJ2nTp0CkLtELACkpaUhNDQUkiQ9tJEZGhoKAHB1dYWzs7NwvK2trWXvyxo1aiAqKgrJycnYsmULDh8+jPfffx8VKlRAWloawsLCYGNjgwoVKjzSdT8P2MgkIiIieoCslDRkHr4HTb6lo+5mhqPupD5Ctjd8L6Ycn6IYI8A9AJ8HfC6Ghkxg21AA+b88GQM4ik9tyrKMNSduo93CQwi6maAYf2ir8tCq+escEREREVF+7at74tUqHor8h8M38fWe62Jo6wK0+kI5yF8jAbNJiCRJwvj641HZRWyUphvS8fmBz5FhyLBkLhV8ULxHBaQakoRalaSGS7QLLq74u2CTIiJ6CQUFBT2RcWxsbFC1alXLErD/RKvVIjY21vLazc3toWOHhoZanpJs06aNJT937hzMZjP8/Pzg4ODwr2McPXoUANCqVe7N8PePr169umUPz/uN0nPnzmH06NFwcHDApEk5qwicPXsWsiyjWrVqUKvVD73u54Xm4SUFV1T/A0mShE2bNj3yo71EREREj+rkF+vhZyPevZZpSoffO/Wh0eT+ChUcHYxRB0fBLJuF2nLO5bDolUXQqrUwmA2WXLV7PBBzQTyZW2Wg/odClJCux6frTuPwdfEO7vvqlCqGN2t6PfA9IiIiIqKXnSRJWNS7Ft5ZFYxjofHCewv3XoetTo0PmpfNDQP6AydXANHnc7Po8zlZvUHC8dYaayxosQA9/+yJFH2KJb+RdAOTjk3CnGZzIEkSAKBEg6rITEhF5r5E2GjsLbUalRbqixlIj02EnXuxJzhzIqIXS7du3dCwYcMHvqfX69GzZ08AgJ2dHdas+eelux0cHBS9qwEDBmDAgAGKWisrK8ufDQaD4v38vvnmGwBA06ZNhSch8+5v+W9kWcbKlSsBAD169FAcn3eZ2PtPdo4cORJXr17FtGnT4O7uDiB3GdoXaVlZoJAamYrlGYiIiIieI0fHrVI0MQEgyTUJ5f3LWV5fS7yGofuGQm/WC3Vedl5Y2nopnKychNz62haoT69WnvD1eYA6947A1CwD+i0PxMWoFGUtgE41vTC1UzVo+DQmEREREdE/staq8cPbdfD2iiCcui3uT//lX1dQzE6HHnV8cgKVGnhtLrCynTjI32MAew+givgghbeDN2Y1nYWP934MOc9qK3/f+hv+bv7oW6WvJSvTvgEuJe6G+kwWdGprS26vdcKZLzej8VcDn9CMiYhePHXr1kXdunUf+F5ISIjlz/7+/njzzTefyDmLFy8OJycnJCcn49KlS5ZG4YOcP38e3333HQBg5syZwnuPuj/msmXLcO3aNVSoUAGdO3dWHJ+3EXr/iczAwEB4eXlh2LBhlvdexP0xgUJqZAI5dz1VqVJF2JT0STp48GChjEtEREQvt5NzN8DH5AdIYh6REYaGs/pbXmcZszDy4EikGdKEOhdrF3zf5nt42InLWKkTbsDx0CTlCRt/Cvg1yR3XYML7q089sInpaqfDjM7V0K5aiYJPjIiIiIjoJWRnpcHKgXXR54cTuHBH/B17wuYLqFLCEdVK/v8GxFINgeo9gPMbcovMRuC3gUC3lYpmZlPvphhcczCWnFki5PNPzkfV4lVRyz13X7Uqb7XB6fhNcL2jg0rKvSHRW+eHsG3HUKZjoyc0YyKil8fp06ctf37UvSwfhUqlQrt27bB+/XqsXLkSLVq0eGDdvXv38NZbbyE7OxufffYZmjRpIrz/KE9k7t69G8OGDYNarcby5cstS8jmPT5vY9LHxwdlypRBcnIyZsyYAVtb2wKd73lUaI1MAJgxY0ahLfua918mERER0ZNw6ZfdcLnnApVKXGokWZ+AqsPaCtnCkIUISw4TMluNLZa0XgI/Jz9xYH0aiu0eCpUxU8x9GwGvTLS8NJllfLbuDI6HiUtfAUC7qp6Y3rkaittbKd4jIiIiIqJ/5mitxep36qPXsuO4FpN7I6LeaMZHv4Rg2ydN4GTz/xVSXp0G3DwIpMXkDvAvzcwPanyA8/fO4/Cdw5bMKBsx9vBYbHpjE2y1uV8w1xraBUc/XYFSNuUtmVrSIH5vBIyvGYUtLIiI6OHyNjKf9FOI48ePx+bNm7F69WoEBARg6NChlmXDAWDfvn34+OOPERoais6dO2POnDnC8WazGRcu5Gwt9KAnMqOjozF//nwsXLgQZrMZ3333ndAIvX+8SqWyPIV5X2hoqGI8k8mECxcuQJKkF66RyW4gEREREYCIAyHQnDZApxIbhRnGNDh19oOjn6clC7obhDWXxX0XJEj4uuXXqOpaVRxYlqH+awQ0ifl+ybQtDnRbDqg1/y+T8cWWC/j7YrTi2oa2Ko/v+gawiUlERERE9Jhc7HRY8259eDiKv1OHJ2RgxMazuVtlOXgCb28F7PItI3i/mXlpqxCrJBW+bPolvO29hfxO2h3MPzlfcR1Vh7ZFhlFc1cXd2gsnp617zJkREb287j+BCDzZJzKBnCVcf/75Z1hbW+Ozzz5D2bJl0bNnT7zxxhuoUKEC2rVrh1u3bmHs2LHYsGEDtFqtcPzVq1eRkZEBAJg2bZplP84333wTNWvWhJeXF+bNm4cyZcrgr7/+wqBBgx54fNmyZWFvb4+HuXbtGjIzMx+5/nnCRiYRERG99BKu3kby1tuw1TgIucGcDXNtK3g3zb2TLVWfivFHxyvGGFhtIBp6PWDz+VOroLrwW75QArr+CDh6WZKv9lzH2sBwxeH9GpTC563LC3f9ERERERFRwbk7WuObPgFQq8TfrXdfisGyQ3lWW3GvBPTf9s/NzJuHhNjJygnzW8yHRhKfqNxwbQOO3jkqZM5lSyLePk5xba7prkgKvfMYsyIiejnJsmzZQ1Kj0aBatWpP/Bzdu3fHuXPnMHjwYGg0GmzduhXbtm1DaGgoKlasiHPnzmH69OkPfKL+/rUBwOrVq/HTTz9h3bp1CAwMhEqlwuDBg7F582ZcunQJbdq0+cfjH/Xpyvv1L9r+mEAhNTL379+Pffv2oXHjxoUxfJGd43kgyzLWr1+PDh06wNvbG1ZWVihRogRatWqFH3/8EUaj8WlfIhER0TMtKzEFN5cch7NO3NfbLJuQWCIZlXq/IuSzgmYhOl18arJCsQr4uObHysGjzgB/jVbmLcYAZVtaXu6/GotFe68ryl6vXgKT36jKJiYRERER0RNS188FY9pVUuRzdl5FYN4tHv6tmblxIJAsNh2ruFbB+/7vK8adeGwiUvTi3px1J/RGTGakkNlo7HFp0a4CzoaI6OV1/fp1pKXlPOFepUoVWFkVzipW5cuXx5IlSyxPPN68eROOjo64fv06YmJi/vG4Xr16QZZl4Z+srCzcvXsXISEh+Pbbb9GpU6d/3Ebx/vEbN258pOssaP3zpFAWXm/evHlhDFvk53jWJSYmolu3bti3b5+QR0dHIzo6Gvv27cN3332HP/74A76+vk/pKomIiJ5dRqMRpyf9AR/bMor3orQRaPB5PyHbc3sPtoaKS0lpVVrMbDITOrVOHCArGdjYHzBlC7G5dAuomo3MLTOYMGnLRcX5G5dzxYKe/oq7xYmIiIiI6L95r2lpnLydgJ0Xc7+ANpllDPn1NHZ+1gwudv//3f5+M/OnjkB6bO4AGXE5v+sP2AFocj8HvFf9PRyIOIBL8ZcsWWxGLGYHzcaMJjMsmUajgXObUjAdyoZalfv1rLd1GZz5djNqfvzmE58zERWepG2h0EelP+3LeGp0XnZw7li2yM9bmPtj/hs/Pz8sWrQIAwYMwMCBA3H69GkUK1asyM7/MuIO0s8pvV6PTp064fDhnI3EfXx88P7776NcuXKIjIzEihUrcPnyZYSEhOC1117D8ePH4ejo+JSvmoiI6NlyfPgqlLatqMhvZ99A41kDhSwuMw5Tjk9R1A6pNQQVXfKNIcvA5o+AxFtCbLLzgLnTUqhUakv2/cEwhCdkCHVVvRzxfb86sNKoQURERERET5YkSZjb3R9Xoo/gdnzu7+L3UrMxf9dVzOhcPbfYvRLQ7w9geRvAkOf39shgYOc44PV5lkir0mJG4xno+WdP6M16S741dCta+bbCK765q72U7dAIR/asgJ91eUumklRwuGWLmzsCUbp9/Sc8ayIqLPqodOhvJj/ty3jpFOb+mA/Tr18/pKSkIDY2FufOneODd4WMe2Q+p7777jtLEzMgIABnz57FhAkT0KtXL4wYMQIhISFo27YtAODSpUuYNm3a07xcIiKiZ86RMStQ2kbZxIzMvIn6c8UnMWVZxuRjk5GUnSTkAe4B6F+lv3LwE0uAK3+KY0hqJLVeANjlLmEbHp+BJQduCHVqlYT5Pfxhb8X7zYiIiIiICoujtRZL3gqAlUb8enRtUDgu3MnXkPCsBryxWDlI8A/A2XVCVK5YOXxS6xNF6ZTjUxCZKi4nGzChK1INSUJmpbZB5t4YJFwLf/TJEBG9hPI+kVnUjUwA+PDDDzFx4kQ0bdq0yM/9sinURqbJZMKhQ4dw6NAhHD9+vMDHnzhxwnK82WwuhCt8PhmNRsyYkbMchSRJWL16teLRZWtra6xevRp2dnYAgMWLFyM+Pl4xFhER0csoeNY6+MrK5WRjM+8gYHo3xSbtm65vwsHIg0Jmq7HF9CbToVble2oyIgjYPVExdmr94TCUqCNkU/+8iGyj+DvOgEZ+qOTJVRSIiIiIiApbVS8nDGtTQchkGZi89SJkWRaLq3cD6n+oHGTbZ0D0eSHqV6UfarmLX6onZCXg/d3vIy4zzpLZFneCqZoGRrNBqHXUFsPtJSegTxVXbiEiolz3G5mSJBXp0rJU9Ar1Vv8lS5bgs88+AwAsWLAADRs2LNDxgYGB+PzzzyFJEpYsWYIPPvigEK7y+bNv3z7cu3cPANCqVStUrVr1gXXu7u7o1asXli9fjuzsbGzZsgXvvPNOUV4qERHRMyfiQAhc4l2hUosNyCR9HMp93gI6B1uxPjUCc4LnKMYZVXcUfBx8xDApHNjwNmA2CrG5fDtk+It/B++5FIM9l2OFzM3BCp+1Lg8iIiIiIioaAxuXxvrgCITF5e5vd/J2IraejUKnmiXF4jbTgKgzQMSJ3MyYCazvB7x/ALBxBgCoVWpMbzwd3bZ1Q6Yx01IakRqBwXsGY0XbFXDQOQAAqg18DYFT18Ar3ReSJFlq3axL4PSE31F7fm/FjZZE9GzRedk97Ut4qp7G/KOiohAbm/Odip+fH5ycnIr8GqjoFNrfggaDAdOnTwcAtG7dGp9++mmBx/j000+xfft27NmzB1OnTsWgQYOgUnE13F27dln+3K5du3+tbdeuHZYvXw4A+Pvvv9nIJCKil5oxW4/YP67Bw0b8QiLDmArX7uXh6Ocp5CazCROOTECGUbwTuoV3C3Qp30UcPD0O+LkzkHpXzJ19Yer4DZBhskRZBhOm/HlRcX0TXq8MB2vtY8yMiIiIiIgeh06jwsSOVTBgZbCQz9xxGa0re8Au75YPGh3QfRXwfTMgPc9NiYk3gT8+BHqtBf7/3aWvoy+mNZ6GkQdHQkbu051XEq5g6L6hWNpmKazUVgCA+hP74vCw5SitE58OLWnjh+Cxv6Dh3AdsZ0FEzwznjmWf9iW8dIpqf8y9e/eidevW6N+/P1atWlVo56F/V2hdwe3bt1ueGry/DOrjuH9sdHQ0duzY8USu7Xl34cIFy59r1679r7V16uQuYZf3OCIiopfRiQlrFE1MvTkbUkN7lGhYTVH/06WfEBIbImTFrIphUqNJwt3SyE4FfukGxIv7XUKtA7r/ZLkz+74l+28gIiFTyBqUccEb/l4FnxQREREREf0nLSq6o3VldyGLScnGN/tvKIsdSwDdVwJSvi0mrv0FHJkvRG392mJ8/fGKIU7GnMSog6NgzLOSS8M5/RGeHqqoLanyw7kl2wowGyKiF19R7Y8ZFBQEAGjQoEGhnYMertCeyPzrr78AANWqVROaaQVVt25dVK9eHRcuXMD27dvRoUOHJ3WJz61r165Z/uzn5/evtd7e3lCr1TCZTLh+/TpkWRa/eH0Ep06d+tf3L1++bPmz0WiEwWD4l2oiKmwGgwFGo9HyZyLKEbr5CLwlP0UeYxWFOm/0Vvy8XEu8hm9Of6OoH1dvHJw0Trn1xmyo1/eBKuq0otbYYSFk9+rCz+XxG/fw7QHxCwqNSsLE9pUsNURUuPh3JdGzhT+TRM+el/HnckzbCjh47R4MptynJ388HIYuNT3h55pv2cSS9aFqNQnqPROFWN43AyYPf8hlWlqyLmW74F7GPXx//nuhdl/EPswPno9hAcMsWfWJnXBt+t/wsPG2ZCpJBZswLaJPX4VrtTJPYqr0HHoZfyaftrz75JrN5qd4JfQgISG5N537+/sX2r+jAQMGoFOnTvDx8RHOIcuy5b8RWZZfqP9G8v63X5D/vynM77QKrZEZHBwMSZIeuvTpo2jXrh3Onz+P4ODghxe/BJKSkix/Ll68+L/WajQaODo6IjExEUajEenp6bC3ty/Q+QrSiE5OTkZ8fHyBxieiJ8toNCI1NdXymntpEAGG9CzojyXA3kq8yzoqIxw+o15R/N2VrE/G8MDhMJjFX9jaeLVBTduaufVmE5z3fA7trUOKc6Y0GoeMEq8A8fGWn8ukTBOGbb8Dk1kWanvWcoeLJhvx8dlPYLZE9DD8u5Lo2cKfSaJnz8v4c2kPoE+AB34KjrZkBpOMYevPYGHn8rDV5XsCs2wPOIUdg03Y35ZIggzVpkGI7/o7TI65zciuJboiOjkaW8K3CEOsvbIWDZwboKJTRUtm16UsUrZGw1FbzJLZaOwQteoMjCPtXop/F6T0Mv5MPm1GoxEqlQoqlQomk+nhB1CRyru0bPXq1Qvt31Hx4sUtPZi855BlWXhd0IfHnnVmsxlms7lAvZ68fasnrdCWlo2MjAQAlC3739eHvj9GeHj4fx7rRZCWlmb5s7W19UPrbWxsLH/O+xceERHRy+Lmoj1wydfEzDSlw7FzecUHwGxTNiaenog7GXeE3N3aHR9X+ljI7E8ugnXYTsX50gI+REYNcR8bk1nGnMOxiEsX71DzdrLCO/VLFHhORERERET0ZPWv6wk3e3HP+vN30zFsyw1k6PN9SS5JSGkxA8Zi4nefquwkOO8eChiz85RK+KjSR2jh2UKoNcOMhZcWwiTnju1c0RepZfQw5rup0t3GC7cW7PoPsyMiejGkpKTg5s2bAAA3NzeULFnyIUeIrl+/jsGDB6NSpUpwcHCAi4sLqlSpgv79+wurU0ZGRkKn06FJkybC8ZGRkbCyskKzZs2QmpqKiRMnonLlynB0dIS/vz82btxoqf3zzz/x6quvws3NDb6+vvjkk0+Qnp7+2HNPTEyETqdDnTp1cP36dbz77rsoU6YM7Ozs4OfnhzFjxryQT20X2q0bycnJAABXV9f/PJaLi4swJhWtkydP/uv7ly9fRr9+/QAATk5OT+TfORE9vrx/Wbm4uECr1f5LNdGL78rq3fCzLqfIE4slolaTVkJmMpsw6sgoXEq6JOQSJExrPA2+Hr65WWQQ1KeXKcY11XobVu2mwSrP3XgGgwHLA6Nx+q64L6ZOo8K3b9WCbwnHx5obET0e/l1J9GzhzyTRs+dl/bl0BTDuNRM+33heyM/cScOo7bfwY78A2FlphCPkHmsgr2wNSZ/7xbT23kW4nVkMc9svhXFmNZ+FXn/1wq2UW5bsesp17E/Yj54VeuaO+n4nHB+7CmVUlYTjfbVlEbM5CFXefe0/z5WeLy/rz+TTlJCQYPmzWq3+l0oqaufPn7csf1qzZs0C/fvZu3cvOnXqhMzMTNSpUwe1a9dGeno6QkND8euvv6Jz586oV6+e5TwPOsf9vHjx4mjcuDEMBgPq16+PYsWK4eTJk3j77bdRrlw5/Prrr/juu+/QvHlztGzZErt27cL3338PKysrfPXVV48193Pnzln+3LBhQ2g0GjRv3hzJyck4ePAgFixYAIPBgK+//vqxxr/v/tPIBen1ODs7/6dz/ptCa2Ta2dkhOTn5iTQfU1JSAAC2trb/eawXgb29PRITEwEAWVlZD10qNjMz90tTBweHAp+vdu3aj1yr0Wj4FynRM+D+E2ZarZY/k/RSS7kVDc0lE1Ra8ZfaiIwwNJwlPjEpyzLmB8/H/sj9inFG1xuNRt6NcoPsNGDrR4Ccbw+Eyh2h7vg11CrxfEduxGNVcIxi3ClvVIW/L28AInoa+Hcl0bOFP5NEz56X9efyzQAfXLibhuVHbgr5ydtJeP+XM1g5oK7YzCxRBXjzO2DD20K9+uQPUFfpCJRpbsm0Wi0mNJiA93a9J9R+e/ZbtCvTDsVtcreQajRrIE4N+xUlbf0smUpSweaGBqnXIuFStfQTmC09T17Wn8mnJe9SoSpVoS1sSY/h7Nmzlj8HBAQ88r8fWZYxaNAgaDQaBAYGWhqW950+fRq+vr6W8e43DWvVqiWc437+999/Y+TIkZg+fTp0Oh0AoF+/flizZg369OkDrVaLixcvWlYcDQwMRIMGDfDbb79h4cKFjzX3+0vqnjt3Dr169cKPP/4IO7ucfZx3796NV199Fd9//z3Gjx8PDw+PxzpH3v/2C/L/NYW55HWh/QTeXzf41q1b/3ms+2M8bD/Il0XeznZcXNy/1hqNRksjWKvVWv6jJiIiehlcWrATDlpnIUszJKPqsLaK2tWXVuOXy78o8v5V+uOtym+J4a4JQOItMSteAejyA5CviRmdnIXhv52DuCsm0KVWSfSq6/OIMyEiIiIioqIgSRImvF4ZAxv7Kd4LupmAgauCkZl/mdkqnYBGnygH2/IxkJUiRPVL1MfrZV4XsjRDGuYGzxUyjUaD8oObItWQJOS2GgdcX3bkkedDRPSiOX36tOXPNWvWfOTjQkNDcfv2bTRs2FDRxARyGpZ5n0C83zTMf477eceOHTFjxgyhgde+fXsAQFRUFH7//Xdh68X69evDxcUF0dHReFz3596wYUOsWbNG6Pe0adMGzZo1g16vx8WLFx/7HM+iQmtkVqlSBbIsY/fu3f95rN27d0OSJFSpUuUJXNnzr0KFCpY/P6xRHBkZadl0tly5ci/cprNERET/5MSkn+FtW0bIzLIZ2WVNcPTzFPKdt3Zi3sl5ijHa+rXFsDrDxPDaLuDUSjFTaYDO3wNaGyE2mMwYsjYECeni/gTl3e0xvXM1/r1MRERERPQMkiQJEztU+cdm5vg/cpc1tHhlIuBRXcySI4Cd4xRjjKgzAg5acdW0HTd34MTdE0LmUtkPxsoSTLJRyEva+CHkq98ffUJERC+QVatWQZZlyLKMXr16PfJxTk5OkCQJ+/fvx+LFi5GWlvav9WfOnIFKpUKNGjUUOQBMmDBBccz9h8q6deum6GeZzWakpaX9p635QkJCAAAzZ8584JK65crlbK0UGxv72Od4FhVaI7NVq5w9p4KCghAUFPTY4wQGBiIwMFAY82VXrVo1y5/zbj77IHn3t8x7HBER0Yvs1q5guGd4KvLw7FD4f9RJyEJiQjDusPLLhQD3AMxoMgMqKc+vS+nxwNYhyhM2GwWUDFDE83ZexcnbiUJmo1Xhu74BsNUV3pIbRERERET039xvZg5o5Kd4b9PpO1gTGC6GGh3QeSmgyrcM3+mfgWs7hai4TXEMDRiqGHfGiRnQm/RCVv29Dgg3hClqbSOtkB6bqMiJiOjB3NzcMHPmTJjNZgwdOhRubm54/fXXsXz5cmRkZAi1qampCAsLQ/ny5YUtD+/n3t7eqFWrluIc95e9feONNxTv3bhxA3q9/rEf2EtPT8e1a9fg6emJFi1aPLDm/jz+S7P0WVRojcyuXbvCysoKAPDhhx8+tLv9IGlpafjggw8AADqdDt26dXui1/i8ats2dzm8nTt3/ktlzjrN97Vr167QromIiOhZoU/LRNKOW9CprYU8Lisa9ab3FrKbyTfxyb5PoDeLXxaUdiqNRa8sgpXaKjeUZWD750Bavr0uvQKApvme2gSw+1IMvj+k/MJhWqeqKOde8D2riYiIiIioaEmShEkdq6B/w1KK96Zuu4iQ8HyNRM9qQMuxyoG2fgJkJAhR9wrdUdW1qpDdSrmFKcenKJ72rD/jLcRni59D7LVOODtzSwFmQ0REY8aMwfXr1zFr1izUr18ff//9N9577z1UqFABN2/m7o189uxZyLKsWFb2fl67du0Hjn9/6de6desq3rv/JOeDGqCP4uzZszCbzf94biDnwTZJkhRPkT7vCq2R6eXlhffeew+yLOPs2bN47bXXEBkZ+cjHR0REoF27djh37hwkScK7774LLy+vwrrc50rLli3h5uYGANizZ88/rnccGxuLdevWAQCsra3RqVOnB9YRERG9SIIm/Iri1uLTmHpTFlxeLw2dfe7Sr3GZcRi8ZzBS9OKeNa7Wrviu9XdwsnISBz6/EbiU74sCjQ3QZRmgFu+6jkjIwPANZxTX1rl6cXTyL1HwSRERERER0VMhSRImdqyKJuWKC7nBJOPjX0IQl5YtHtDoU6BkHTFLiwF2jMi5OfL/1Co1vmj4hbgCDICtoVux/MJyIdPZWEPjbw+zbBZybys/XNt48DFnRkT0cipdujRGjx6NAwcOICwsDE2aNMGdO3ewZMkSS83D9scMCFCuymU2m3Hu3Dm4uLigVCnlDTD3m5yP28i8f7yjo+MD3w8ODsaNGzdQv359eHh4PNY5nlWF1sgEgBkzZqBixYoAgGPHjqFatWoYNmwYQkJCYDabFfVmsxkhISH4/PPPUb16dRw/fhxAzp6QM2fOLMxLfa5oNBqMHz8eACDLMt5++20kJop3gGVlZaF///5IT08HAAwZMuSFe5yYiIgov6Bpv6CUVTlFHmsXDd82uV8mZBgyMGTvENxJuyPU2Whs8G3rb1HSvqQ4QHIksH2E8oRtpgLFywtRttGEj9eGICVL3MemgpsNPmvuU8AZERERERHR06ZWSVjUuxZKOtsI+d3kLHyy9jSMpjzfc6o1OUvMasQVYnDhd+DQXCGq6loVb1d5W3G+hSELsfv2brF2QDuEZ94QMpWkRtaxOBizxBVmiIjo0ZQqVQr9+/cHAJhMJkv+sEZm/hwArl69ioyMjH9sVP7bsY/i/v6YeZ8cvU+WZYwdm7MiwJgxYx5r/GdZoTYyHR0dsW3bNnh7e0OWZaSmpmLhwoWoW7cuHB0dUbVqVTRu3BiNGzdG1apV4ejoiLp162LRokVISUmBLMvw9vbGtm3b/rHL/LIaPHgwmjZtCiDnP2B/f3/MmDED69evx/z58xEQEGBZVrZKlSoP3HiWiIjoRXJ+6TZ4pHpBkiQhj8y4iQaT+1ley7KMsYfH4mK8uKKBSlJhXvN5iuWdYDYDmz8CspPFvEwLoO57QiTLMqZuu4RzkWKtg7UGM18vCytNof7qRUREREREhcTFToclbwVApxZ/pz8eFo95u66JxcXLA60nKwfZPwMIFp+2/DTgUzT2aqwoHXd4HC7GiZ9Zagx/HWkG8bOGi5U7gqf8+ugTISJ6Ce3cuRM7duyA0SjedB4VFYWFCxcCEPe1fFgj80HNyoc9cXnmzBlYWVkp9sicPHkyJEnCgAED/nUO98cPDAwUthw0GAwYOnQo9u7diy5duryQK3NqCvsE5cqVw+nTp/H2229jx44dljwjIwNXrlwRavOv/96+fXv89NNPfJLwAXQ6HbZs2YJu3bph3759iIiIeGCzMiAgAH/88QecnJweMAoREdGLIWzrMdiGWkOtFn+1STUkoeqItkL2+/XfsS9in2KMCQ0moJl3M+XgQcuAm/mWa7J2AjotAVTilxjfHQzFL4HhiiFmda4Kb+dC/7WLiIiIiIgKkb+PM6Z0qoqxm84L+dKDoajp44R21fJsI1HvA+Da30DYAXGQ7cMBWxegamcAgEalwdzmc9FvRz+EJodayrJMWfhk3ydY+/paeNrlbJ3hWMoTl11TYJ8ifs/nafJG6B+HUbZz0yc3WSKiF8j69euxcuVKuLi4oE6dOihevDhiYmJw+PBh6PV6jBkzBi1atAAAGI1GXLx4ER4eHvD0zN266H7u7u6OkiVLCk9wArmNxgctOxsdHY3o6GjUrl0bGo34/dD91Uu1Wq3iuPv0ej0uXrwIb29vNGzYEK+//jqaN28OFxcXHDt2DFFRUWjYsCFWrlz5WP/7POuK5LEAFxcX/Pnnnzh69Ch69OhhaUzKsiz8c7+2R48eOHr0KP788082Mf9FsWLFsGfPHqxbtw6vv/46vLy8oNPp4OHhgVdeeQXLli1DYGAgfH19n/alEhERFZrYU9dhPJQEK7W4dFOWKQO6lq5w8M3dFyAiNQJzgucoxhhUfRC6V+iuHPzeVWDPJGXefj7gJC4/uy4oHHP+vqoofbdJabxa5cXam4CIiIiI6GXVq64PetTxVuQjNp5D6L203EClAnqsBjyr56uUgd8HAaH7LYmDzgHftPoGLtYuQuW9zHsYum8o9KbcpWPrj+uDyIxbQp1WpYPxaCpiQ64/9ryIiF5kvXv3xuDBg+Hj44NTp05hw4YNOH/+PNq1a4ddu3bhyy+/tNReuXIFWVlZiqcx7+f/9MTlvz2R+W/Lyt5/7+23lUuN33fx4kXo9XrUqlULP/30EwYPHoxLly5h27ZtKFasGObMmYP9+/e/sCubFumjAQ0bNkTDhg0B5PxLv3PnDuLj4wEArq6u8PLyQuXKlYvykp57kiShZ8+e6Nmz59O+FCIioiKXdicO9365BCed+IHfaDYgo5weNTo0smQmswkTjkxApjFTqG3h0wKf1PpEObjJAPzxAWDMEvMqbwLVuwnRzovRGPeHeFc2ANT1K4Yxr1UCzCbFe0RERERE9PyRJAlTO1XDpbspuHAnxZKnZRvx4c+nsPnjxrCz+v9XrtZOQN9NwPJXgcQ8e5qZDcC6t4CB2wGvnC+8vR28sbDlQryz8x0YzAZL6eWEy5h3ch7G1R9nybx71kD25hhYqXP37LTTOCD65/Ow8y4OO/dihTR7IqLnU5s2bdCmTZtHqq1WrZpi9dD8+f2nKPPas2fPP47Zrl27B45pMplw8OBBtG7d2rKV4IPc3x8zICAANjY2WLx4MRYvXvzQubwontpGTZUqVUKrVq3Qo0cP9OjRA61atWITk4iIiArk8pxdiiamWTYj2jEKNT7oKOSrL61GSGyIkLlYu2Byw8mKfTUBAAe+BKJOi5m9J9DhKyBP/fHQeHzy62mY8/0+WtHDAT++XRdaNffFJCIiIiJ6kVhr1fjurdpwthWXAbwem4bRv58Tv6y2dwf6/QHY51ulxZAO/PYOYMi9cbKme01MbTxVcb5fr/yK3bd3W157N/ZHnEsczLJ4w6SLlRsuz/gLxix9/iGIiOgZdPLkSaSkpGDqVOX/9+f1sP03X3T8Zo2IiIieS+eXbEMJGx9Ffst0DQ0m9BWyqwlXsfi08k61iQ0mwtXmAcvYn14DHJ6vzDt9k7Ofzf9dvpuC91efhN4o3olX0tkGq9+tByfbf97fgIiIiIiInl8+Lrb4umdN5L8n8s9zd7Hi6C0xdCmd82Smlbi3JRLCgKNfC1GHMh3wdhXl8oITj05ERGqE5XX9sX1w2xyqqPO08UHQmLUFmQoRET0l9evXhyzLlpVM/0neJzJfRmxkEhER0XNHn5EJ9Q3lkhyh6ZfRbN4gsdakx7gj44TlmQDgjbJvoFWpVsrBb+wFtn2qzGsPBMrnLkOSZTBhyNoQpGYbhTJXOx3WvFcfHo7W+UcgIiIiIqIXSIuK7visVQVFPnPHZQTfShBDz2pA718BKd/XsYcXAPFiQ/KzgM9Qvbi4t2aaIQ0jD44U9stsOvdd3My4qji/r3VZHB//UwFnQ0REzyKz2Yxz587B1dUVPj7KG/pfBkXSyDSZTIiJiYHBYHh4MREREdFDhEzdCEeduO9LfHYMGs7qp6j99sy3uJZ4Tcg87Twxut5o5cB3zwEb3gbMYnMSntWBtjOEaPbfVxB6L13I7K00+Omdeihd3K4AsyEiIiIioufVJ6+UQ8uKbkJmMssYtuEM0vLd9Ai/xkD9D8XMlA3sGAHkWY5Wq9ZibvO5cNA5CKUX4y9iwakFQlZ/dl/cybituC4vgy+urdv3GDMiIqJniUqlQlpaGuLi4p72pTw1hdrITE5OxqBBg+Ds7AwvLy84OTmhf//+SEhIePjBRERERA+QcP4m3M1eQibLMkxlJOjsbIT8xN0TWHlhpWKMaY2nwVHnKIbJkcDaHoA+TcwdvYE+GwFdbnPy6I04rMy3XJRKApb2rY1qJfMtF0VERERERC8slUrC1z1rwdfFVsgjEjIxc8dl5QEtxwEOJcQsdB9w8Q8hKmlfEtMaT1Mc/svlX3Ag4oDltc7GGlVGtUF8doxQp1ZpYApOR2p4dIHmQ0RE9KwptEZmamoqmjVrhhUrVsBoNMLf3x8AsGbNGjRt2hSpqamFdWoiIiJ6gV3/8Qh0Kishi8gIRcAnXYUsISsB4w6PgwxxCdo+lfqgQYkG4qCZScCabkDqXTG3cgL6/gY45n7RkJxpwIiNZxXX9UHzsmhSvnjBJ0RERERERM81J1stlrwVAI1K3DBzbWA4Dl67JxZbOQDtvlQO8vdYICtFiFr5tkLfyn0VpdNOTEO6IXd1GAdvD7j1qIRMo3hTpoPWGZfn74EsK7flICIiel4UWiNzxowZOH/+PFxcXHD69GmEhITg3LlzKF68OK5cuYLp06cX1qmJiIjoBXXl5z0oaeMnZHpTFnx71BIyWZYx6egk3MsUvzQo51wOn9f+XBzUmA2s7wvcy3e3tEoL9FoDuFcW4ilbL+JucpaQVfJ0wGetyxd8QkRERERE9EKoVtIJQ1spPxOM/u0ckjPzbbdV5U2gbCsxS4sG9s9UHD+s9jBUc60mZLEZsVh8erGQeTWshpQSqTDLZjG3KYXAL9Y8+kSIiIieMYXWyNywYQMkScKQIUNQqVIlAEC5cuUwZMgQyLKMDRs2FNapiYiI6AVkNBihD0lR5BHGW/BuWlPI1l1dhwORB4TMSm2FOc3mwFpjnRvKMrBlCHDrsPKEb34HlG4mRH+dv4tNp+8ImU6twte9asJKoy7QfIiIiIiI6MUyuEVZ1PAWt5qITsnClG0XxUJJAtrPBdTiSjMI+h64fUyItGotZjadCa1KK+RrL6/FhbgLQlZ7eA/czrquuC5PfUlc//0Bn3mIiIieA4XWyLxzJ+dLvooVKwp5+fI5dybdvXtXcQwRERHRPwke8wtcrNyELFkfj7pf9BSya4nXMC94nuL4EXVGoHyxfHdI750KnH/AzVWtJgE1ugtRbGoWxv1xXlE6/NUKqOTpqMiJiIiIiOjlolWrML+7P3Qa8SvXTSF3sPNivr0qXcsCTYeLmWzOWS0m4aYQl3YqjUHVB4mlkDH1+FQYzUYhrz/jLcRmijdfalRaGI4lIjUy9jFmRURE9HQVWiOzRImcvaSuXbsm5Nev59wV5O7uXlinJiIiohdM4ORf4KMto8jT3NJh65LbREzTp2HUwVHQm/VCXUufluhZUWx44uQK4MgC5cnqvAM0EZeflWUZY34/j8QMcUmoen4ueK+p8rqIiIiIiOjlVN7DASNfrajIx206j/D4DDFs8hngWk7MMuKBtT2BzCQhfrf6u/Bz9BOyywmX8cvlX4RMZ28D9x6VkWlMF3JHbTFcnbsXxizxsxIREdGzrtAamd26dYMsy/jmm29w48YNAEBYWBi++eYbSJKETp06FdapiYiI6AVy7rut8Mz0UuSRGTdRf+xbltcGswHDDw5HaHKoUOdu444pjaZAkqTc8NpOYHu+u58BoEI74LW5OUs95bEuOAL7roh3L9vp1Jjfwx9qlVhLREREREQvt3ealEZdv2JCFp+uR78VgYhNzcoNNVZA1+WA1lYcIO4qsHEAYMq9kVKn1mFiw4mKc3175ltEpUUJmXdjfyS5J0OWZSH3tPFG0Ni1jzcpIiKip6TQGplffPEFKlasiLi4OPj7+6NOnTqoXr067t27h1KlSmHy5MmFdWoiIiJ6QYTtOAHbMCuoJY2QJ+njUOGj3P0rZVnGtOPTcCxK3E9GgoQvm36JYtZ5vkRIigB+fy9n2aa8vGoB3VYAavFct+PTMe3PS4prm9ixCnxcbBU5ERERERG93NQqCfO6+8NGqxby2/EZ6L8iGClZeVZ68aoJdFmmHCRsP/DXKCBPM7KuZ110LtdZKMs0ZmJG4AxF07LuqJ64lXVVMayvVVmc+GJNwSdFRET0lBRaI9PJyQlHjhxB3759IcsyQkJCYDKZ0K1bNxw9ehSurq6FdWoiIiJ6AcRdDEPmnhhYq8VmYaYxDXZtS8Klkp8lW3puKf648YdijI9rfox6JerlBmYzsOUjIDtFLHQuBfTZAOjshNhkljF8w1lk6E1C3rqyO3rU8Xm8iRERERER0QuvlKsd5nX3z7/YCy7fTcF7P51EliHPZ4zKHYHWk5WDnFwBBH4vRMPrDIeLtYuQHYo8hCVnlygObzinP+5k3FLkJfQlcW7Zn486FaKXXv4bBYheVM/qf+uF1sgEAFdXV6xevRrJycm4c+cOkpOTsWHDBsv+mUREREQPos/Kwq1lJ+CkEz+gG80GZFc0oXTb3ObklhtbsOSM8kN753Kd8X6N98UwaBlw85CYWTsBfX8H7JX7dy87FIaTtxOFzMVOhy+71BCXqiUiIiIiIsrn9RolMLVTNUUedDMBn/x6GkZTnlViGn8G1OyrHGTnOCAiyPLSycoJI+qMUJQtPbsUW25sETKNlQ6VR7RBQra4TYZa0sD2uhYR+0MKNiGil4hKlds6MZlM/1JJ9OLI+9963p+Bp61IrkSr1aJEiRKwsrIqitMRERHRc+7E2J/haaN84jHG/i6qDepgeR10NwiTj01W1DX2aowvGn4hNhvvXQP2TFKe7PUFQPHyivhSVAoW7FYuxTSzc3W4OfB3GiIiIiIierh+DUrh89YVFPnuSzH48q8ruYEkAR2+Ako1EQtlE7BxIJCRYIk6lOmAlj4tFWNOPj4ZQXeDhMzR1xPFOpVGhjFNyK3VtkjaehsZccmPMSuiF59Op7P8OS0t7V8qiV4cef9bz/sz8LQ9Oy1VIiIiIgDnV2xHKV05RR5uDEP9iW9ZXmcaMzHh6AQYZaNQV8mlEua3mA+tSpsbmgzAH+8Dxixx0KpdgOrdFOeKT8vGR7+cgsEkLqnRNcAb7ap5PsasiIiIiIjoZTW0VTn0b1hKka84ehPnIpNyA40O6PlzztYXeaVEAps/suyXKUkSZjWdhcoulYUyo9mIzw58hrCkMCH3aRGArIpGGM0GIS9mVRxnpim36CAiwNHR0fLnxMREmM3mf6kmev6ZzWYkJuauSpb3Z+BpYyOTiIiInhkpt6OhvmCEWtIIeXRmBOrNekvIlp9fjrvpd4XM084T37b6FnZaca9LHF4ARJ0WM3tP4PX5imvI1Jvw3uqTuBWfIeQlnW0w6Y0qBZwRERERERG97CRJwqSOVfGGv5eQyzIw/o8LMJnz3EBp6wJ0XwXkvTETAK79BRz/NrdMa4vFryyGu624RUaqPhUf7f0I8ZnxQl5jUEdEWUcqrs3XpiyCZvz6eBMjeoE5ODhY/pyVlYXbt28jKSkJRqPxmd1HkKigZFmG0WhEUlISbt++jays3AcA8v4MPG2ah5cQERERFY0L8/+Cr634NGaGMQ3lhjSDRpP7a0tEagRWXlgp1GlUGnzzyjeKD/KIOg0cmqM8Wadvcr4kyMNklvHputM4HZ4k5JIEzO1eA47W+b5MICIiIiIiegQqlYS53WvgYlQyQu+lW/Lzd5LxS+BtvN3QL7e4ZADw6nTg79HiIHsmAT71AZ+6AAAPOw8sabUEb//1NjKMuTdi3km7g5GHRuKHNj9ArVJb8kZT38bRoStQylbcWqN4UnFE7A+BT8uAJzdhouecTqeDh4cHYmJiAOQ0M+/evfuQo+hlkb+ZLWxt9ALw8PB48ZeWTUlJQUpKSqFuglsU5yAiIqKic3zyz4ompizLyCpjhHN5byGfEzwHerNeyPpV7oeKLhXFQbPTgN/eBczi8rOoPRAo30Zxrml/XsKuSzGKa5vcsSoalS1ewBkRERERERHlstKoMe3Naop87t9XEZuabxuM+h8AlTqImdkI/PaOsF9mRZeKmNd8HtSSWigNjg7G9+e+V5yr1qSuSMiOFTKd2hoJW29Cn5qhqCd6mbm4uMDDw+NpXwY9o8xm8wu55LCHhwdcXFweXliECqWR6ezsDBcXF2zfvr0whi+ycxAREVHRiDgQAvd05YeDCH0YanzUUciO3DmCAxEHhMzNxg0f+H+gHPiv0UBCqJgV88u5uzmfHw/fxKpjtxT5+83KoH8jv3+fABERERER0SNoVLY4OtcqKWSp2UbM2H5ZLJQkoNO3gLOvmCeHAzvHC1FT76YYV3+c4lxLzy5F4N1AIbN1dYJdqxLQm7OF3NXKHSe/WF/A2RC9+FxcXFC2bFm4u7vD2toaKhV366McL0ojU6VSwdraGu7u7ihbtuwz18QECnFp2aJYJ5prURMRET3/0u8lImHLTbhaiUvCJmTHos7UHkJmMBkwO2i2YozPa3+u3Bfzwu/AmTViJqmAzt8DVvZC/Oe5KMzYke+LAwAdapTAmHaVCjAbIiIiIiKifzeufWXsvRyDlKzclWO2nIlCjzo+aFwuz0owNs5At1XAiraA2ZCbn10L+PcCyjS3RD0q9sDZe2exNXSrJZMhY8zhMdjYcSOK2+SOW7ZDIxwLWg1ffWnhunytyyFwyi+oP+mtJzZXoheBTqeDq6srXF1dn/al0DPCYDAgPj5nL2JXV1dotdyKqDDx9gEiIiJ6aoxGI85N26poYhrM2XBs5wudg62Q/3z5Z9xKuSVktdxroUOZfEsuJd4Gtn2uPGHzMYBvAyEKupmAYevPKkrrlXbBvO7+UKlerH0OiIiIiIjo6XJzsMLIB9ww+cXmC8gy5NtGy7s20HqScpA/PwcM4nK04+uPR2knsTkZlxmHcYfHwSyLTw01mvo2wjPyrV4DoESGN4Km/vKIMyEiIip8hfZEJgAsWrQImzdvLsxTEBER0XMscNzPKJVvX0wAiLG5iwZtWwvZnbQ7+P6suMeLBAlj640VN1U3GYFNg4DsZHFQ34ZA0+FCdCM2DYNWn4TeJH6oL+tmh2X9asNaK+4zQ0RERERE9CT0qeeL305G4Gxk7ueWsLh0jPvjPOZ39xc/4zT4CDj/G3D3TG6WEAocng+8krvMrK3WFvOaz0Of7X2QbcpdOvb43eNYfn45BtUYJFxD9dGv4/b8I3DW5T6tqZJU8Ez3RtD0X1FvQu8nN2EiIqLHVKiNzP379xfa2MJf5kRERPTcOfPtZvhIpRV5eGYoGs0aIGQp+hR8vOdjZBgzhLx7he6o7FpZHODQHCBC3AcG1k5Alx8Ade6vPrGpWRiwMgjJmQahtLi9FVYNrAdnW13BJ0VERERERPQI1CoJMzpXxxvfHIE5z+5Zm0LuoKybPT5umeeGT5Ua6LgQ+KElkPfJyiNfAdW7AW4VLVGFYhUwtt5YTD4+WTjfN2e+QdXiVdHIq5ElcyjpBpsWxZFxKA22mtztN1SSCp6pXgia8SvqjWczk4iInq5CW1pWluUi+YeIiIieP5FHzsLuljVUkvjE472su6gzo6eQGUwGDNs/DKHJ4rJHTlZO+KTWJ+LAl7cBB+coT9hxEeDsY3mZnm3Eu6tOIjIxUyiz1amxckBd+LjY5h+BiIiIiIjoiapW0gmDmpZR5HN3XsWO83fF0KtmzpOZeZkNwLbPALO4wkyX8l3wWunXxFLZjBEHRiAsOUzIy77RFGhgg0xjmpCrJBU8U0og+Mt1BZoTERHRk1YoT2TevHmzMIZ9IHd394cXERER0TMjKzkN8b/fUOyLmW5MQckBtaCzt7FksixjyvEpCIwOzD8MvmjwBZytnXODiGDg9/cA5LvRKeBtoOqbwpifrT+D83fEpWfVKgnf9glAdW+nx50aERERERFRgYxsWxFXolNx8No9IR+24QxKOtvA38c5N2wxFri0BUiOyM3CjwFn1uR87vk/SZIwqeEkXIq/hNspty15qiEVn+z9BGtfXwsnq9zPPRW6tcBV8z5kBqXDRmNnyVWSGm6J7gjffRK+beo8uUkTEREVQKE0MkuVKlUYwxIREdEL4OSkDfCzLi9kRrMBxmpquNcU82XnlmFL6BbFGJ8GfIq2fm1zg/hQ4NeegDFLLCxeAWg3S4h+DYrA7ksxijGnv1kNLSvxBikiIiIiIio6GrUKi/vUQrfvjuFaTO5TkVkGM95bfRJbPm4ML+f/3+xpZQ+0n5fz2SevnRMA34ZA8dzPU3ZaOyxsuRB9d/RFmiF33PDUcAw7MAxL2yyFVqW15BV7vIIr5r3IPJUOG3VuM1Oj0iLmzxvwalkTGk2h7lJGRET0QIW2tCwRERFRfhdW/gVfK+XSSVG6cFQd0E7ItodtxzdnvlHUdi3fFe9Wezc3SI8DfukGZMSLhbbFgT7rAV3uh/Db8emYvv2SYswhLcuhdz3fAs6GiIiIiIjov3O01mJ5/7pwtdMJ+b3UbLz/80lkGUy5YcV2QOU3xAGyk3M+E6XHCXFZ57KY02wOVJL4FXBQdBBmBs5UbNtVqVcrmAK0yDaJW3B42JTEqRnrH3N2RERE/w0bmURERFQk0qPjIZ3XK/bFDM8IRaPpA4QsLDkMk49NVozRyKsRxjcYD0mScgJDJvBrLyBB3OcFGpucJqZLbtPUZJYxfMNZZOhNQmnryh4Y/mqFx54XERERERHRf+XjYotlb9eGTiN+XXvhTgpm7rgsFr82G7ByFLPEW8C6PoBBXKWmqXdTjKwzUnG+3679hrVX1irySr1bIcYqSpG7pLki4Vr4o02GiIjoCWIjk4iIiIrE6Zlb4KRzEbI0QzKqDROfxNSb9BhzaAyyTOIH8PLFymN+8/nC8kfYMRKIDM53Jgno+iPgLe7h8sPhMJy8nShkrnY6zOpaPbcxSkRERERE9JTULuWCud1qKPLVx29jx/m7uYGjV85nnnxPWiIiENg8GDCbhfitym+hW4VuinHnBc/D+XvnFXm9yX0Rk3lHyGzUdrj67f4CzIaIiOjJYCOTiIiICl3Iwt9RyqackMmyjKzSRjj6eQr54tOLcTlBvOPYxdoFS1otgb3OPjeMCAJO/6w82WtzgModhOjy3RQs2HVNUTqjc3UUt7cq4GyIiIiIiIgKR6eaJTGoaWlFPvq3c7gdn54bVGib89knv4ubgH3ThEiSJIyrPw71POsJuVE2YuShkUjVpwq5RqOBcxtfGM0GIfexKYPz328r4IyIiIj+GzYyiYiIqFAlhd2BTbiV4qnHiKxQ1BzyppAdjzqOVRdXKcaY3ng6PO3yNDzNppynMfNrOASo/74QZRtN+Hz9GehN4l3JXQO80a6a2EQlIiIiIiJ62ka1q4SaPs5ClpptxJC1p5FtzLNVRr1BQIOPlAMcWQCc/kWItCotFrRYgJL2JYX8TtodTD42WbFfZtkOjRBpuKkYWnXVjKzkVEVORERUWNjIJCIiokJjNBpx+avdcNA6CXmKPhE1v+giZIlZiRh/ZLxijLcqv4Wm3k3F8PTPwN0zYuZeFWg9RXH8gt3XcCVa/KDt5WSNSW9UefSJEBERERERFRGtWoVv+tSCo7VGyM/fScaXO66Ixa9OByq+rhxk+3AgVlzpxsnKCXOazYFGEsfddXsXNl7bqBiizsTuSNLHi2PoXHBq8m8FmA0REdF/w0YmERERFZrjI1fCx66skJllMyR/a9gWz21uyrKMSccm4V7mPaG2fLHy+Lz25+KgmYnA3qnKk7WfA6jFD+RHrsfh+4NhitJ53f3haK1V5ERERERERM8C72K2mNfdX5GvOnYLuy/F5AYqNdD1B6BETbHQmAlsHADo04W4hlsNDA0Yqhh3TvAcXEsUt+OwLuYIU3lJ8bSmj64MLi7/q0DzISIielxsZBIREVGhCJy+Bn66ioo80nATlfu/KmS/XvkV+yP2C5lOpcPsprNhpc63h+X+mUCGeFcwqnUF/JoIUXxaNoZtOKM4/4BGfmhUrvijT4SIiIiIiOgpeLWqJ95totwv84vNF5CalWf/Sp0d0Gc9YOcuFt67AuwYpTi+f9X+aFJS/PyUbcrGiIMjkG4QG5/+gzshIjNUyFSSCupLJqSGRxdwRkRERAXHRiYRERE9cZfX7oF7iqdiX8x7WXdRZ3ovITsVcwpzg+cqxhheZzjKFysvhtEXgOAfxUxrC7SZJkSyLGP07+cQm5ot5BU9HDDmtUoFnA0REREREdHTMbpdJfjn2y8zOiUL83eJT0/CwTPnyUyIn8FwZg1wdp0QqSQVZjSZATcbNyG/mXwTH+35CBmGDCGv8umrSDMkC5m91gkX5+0q8HyIiIgKio1MIiIieqJiTl2BdCobWpX4JGWqIQmlP24Ena21JYtOj8awA8NglI1CbTPvZuhdqbc4sCwDf40CZLOYNxsBOJUUotXHb2PP5Vghs9KosLhPLVhr1Y85MyIiIiIioqKl06jwVQ9/6DTi17g/Hb+FsxFJYnGZFkCzkcpB/hwG3BMbny7WLpjVdBakfI3PkNgQfLz3Y6GZ6Vy2JLLLm2HO91nM27Y0Aqf+UuA5ERERFQQbmURERPTEZCWn4e7P52GvdRRyvSkL1q3c4FzeO0+mx7ADw5CQlSDUlrQviRmNZyie5sTRhcDto2LmUgZoOESILt9NwYwdlxXXNqFDFVTwcHiMWRERERERET09ZdzsMaRlOSGTZWDspvMwmvLd6NliDFBKXDYWhvT/75cpPmlZr0Q9DK45WHG+kzEnMWTfEGQaMy2Z/4dvIDz7hqLWLdUdkYfPFmxCREREBfBMNjITExOxa9cu7NixA5GRkU/7coiIiOgRnZy4AcWtPYXMLJuR5JWC0u0bWDJZljEjcAbOx50Xaq3V1vi65ddwtnYWB774B7BnkvKE7WYBmtwnP1OzDBj662nojeKH+VereKBvfd/HmxQREREREdFT9kHzMijrZidkl+6mYOXRW2KhSg10/RGwdRXz2IvA5g8Bs/hZ6cMaH+Ktym8pzhccHYxP9n4iNDPrTe+DuCxxX0yd2hr3fr+KrOTUgk+KiIjoERRpIzMhIQGLFi3CokWLcO3atQfWzJo1CyVLlsRrr72Gjh07ws/PDwMGDEB2dvYD64mIiOjZEPTlr/CzKa/IIxGGgM+7CtnGaxux6fomRe2kRpNQySXfHpbhgcCmD5QnrNgeqNDW8tJgMuOjX0JwPTZNKPN0tMbsrjWUT3gSERERERE9J6w0aszsXF2RL9h9DZGJ4pOWcCwBdF6mHOTSFuDAl0IkSRJG1x2NPpX6KMoDowMx/MBwyLIMANDZ28CpnS/0ZvF7WjfrEjg/cSuyktMUYxAREf1XRdrIXL9+PT777DOMGjUKrq6uivfXrl2LcePGITs7G7IsQ5ZlmM1m/Pzzz3jnnXeK8lKJiIioACIPnUGxhGKKPCIjDI1mDxSyqwlXMStolqK2b+W+6FCmgxgmhAHregOmfDc0Fa8IvLnE8lKWZYzbdB6Hr8cJZZIELOjpj2J2ugLOiIiIiIiI6NlSv4wretbxEbJMgwlfbL5gaTZalG/94P0yD80Bzm0UIkmSMKbeGPSq2EtRfvjOYWy+sdnyunS7+oixuqOoK2Hji3MTt7CZSURET1yRNjL3798PAGjWrJmikSnLMiZMmGB53a1bN4wYMQKlSpWCLMtYt24djhw5UpSXS0RERI9An56JmN8vw0YtLnOUrI9HjQlvCJnBZMD4I+NhMBuEvK5nXQyrM0wcOCMB+KU7kBEv5nZuwFsbAZvcxunCvdex8ZRyOfrhbSqgUdnijzErIiIiIiKiZ8/Y9pXgmu9Gzf1X7+Gr3Q9Y/a7FOKBSB2W+5WMg8qQQSZKEcfXHoWfFnoryeSfnIT4z93NZw2n9EZERpqjzsvHFuS82Q5+WqXiPiIjocRVpI/PatWuQJAkNGzZUvHf06FHcunULkiRh9uzZ2LBhA+bMmYPg4GC4uLgAAH766aeivFwiIiJ6BMfHroaHjbeQGc0G2DR3g527+JTmd2e/w9XEq0LmbuuOuc3mQqvS5oZmE7CxPxB/QzyZxgbovR4oVsoSbTwZga/3XFdcV886Pvi4ZbnHnBUREREREdGzx9lWhy86VFHki/bdwMaTEWKoUgFdlgGe+ZakNWUDv/YGksT6+83MV3xeEfIUfQrmnZwnZLVn9sCdjNuK6/CyLYXT435jM5OIiJ6YIm1kxsXlLPdWrpzyS8U9e/YAAGxsbPDRRx9Z8uLFi6NPnz6QZRknTpwomgslIiKiRxI0fx1K21RU5DFWd1GmU2MhOxN7BssvLFfUzmgyA642+ZacP/o1cPNQvkoJ6Poj4F3bkhy7EYexm84rxmxWwQ3TO1fjvphERERERPTC6VTTC69W8VDkYzedx7Eb4nYb0NkBvdcB9vnq02OB9X0Bo7iNh0pSYVz9cbDTiivu/Bn2J45FHcsd1t4G/tPfRFRmuOI6Str64eS4dQWcFRER0YMVaSMzPj5nCQJ7e3vFe/eXjW3evDlsbW2F96pXz7lrKDxc+RcjERERPR1RgRfgGGUPlaQW8ruZEag3tY+QZRgyMP7IeJhls5C/VfktNCjRQBz4zilg/0zlCdvOBCrnLouUoTdixMazMJrFvWCqlHDEkrcCoFUX6a85RERERERERUKSJHzVsyaqejkKudEs44M1p3AjNlU8wMkb6PUroLEW87tngD2TFeN72Hng04BPFfn0E9ORZcyyvLZ2tEeNqZ1wN0P5na2PTVlc+OHPR54TERHRPynSb/hUqpzTpaWJmz4bjUYEBgZCkiQ0adJEcdz9pWUzMjIK/yKJiIjoofRZWYj85QzstU5Cnm5MQaURrRVPQi44tQDhqeKHWz9HP+WH4+w04Pf3ALNRzGv0BBoMFqLvDoQiKjlLyLycrLFyYF3YW2keY1ZERERERETPBzsrDVYMqIsSTmJzMjXLiAErg3EvVXzSEt61gU7fKgc6sQS4skMR96jQAzWK1xCyiNQILDu3TMisnexRfVon3M1ULlNrvpQNY5a+ALMiIiJSKtJGpqenJwDg4sWLQn748GGkp6cDABo1aqQ4LjU15y6i/E9qEhER0dNxfMxqeNmWEjKzbIKqrj0cfNyF/MidI1h/db2QqSU1ZjaZCRuNjTjwX6OBhDAxcy4FtJ8H5GmOhsdn4PtDYp1WLWH5gLrwcMx3lzEREREREdELyMPRGisGKG/kjEzMxJC1ITDlW70G1bsB9cUbRAEAWz4CkiOFSK1SY2LDiVDnW4Fn5YWVuJ54XcisnexRffLrSNEnCLmLlTsCJ/9SwFkRERGJirSRWadOHciyjDVr1liWmQWAxYsXA8jZH7Nhw4aK465duwYA8Pb2LpoLJSIion906uvfUNqqgiK/q76D8j2bC9ntlNsYfWi0onZQjUGo7lZdDC/+AZxZI2aSOmdfTGtxyaSpf16C3iguU/tOk9KoXEKsIyIiIiIiepFVLuGIb98KgFolrooTeDMBC/deVx7QZgpQwl/MMhOB394FTOLKOBVdKuLtqm8LmVE2YvyR8TCYDEJuXcwJWT75VtYB4GkqibgLoQWYERERkahIG5l9+uTsl3X37l3UrVsXn3/+Odq2bYvNmzdDkiR0794dOp1OcdyxY8cgSRKqVq1alJdLRERE+cSEXIF9pI1iX8yYrEjUmyHui5mqT8Un+z5Bij5FyCu7VMb7Nd4XB06KALYp92BBizGATz0hOnA1FnsuxwiZu4MVPnmlfAFnQ0RERERE9PxrXsENUzspvzddvO86jt2IE0ONFdBtJaBzEPOIE8CBLxVjfFjjQ5S0LylklxMuY/GZxYragM+7IirjtpBZqW1wfdmRR5wJERGRUpE2Mjt16oT27dtDlmXcvn0bixYtwp49ewAAjo6OmDx5suKY2NhYHD16FAAe+LQmERERFQ1jlh4RP4XATiM+9ZhuSEH5z1oK+2KazCaMOjQKN5NvCrW2GlvMajoLWpU2NzRkARv6AVnJ4gl9GwJNhwuR3mjG1G2XFNc2rn1l7otJREREREQvrbfql0K32uJqdrIMfLr+jHK/TNeyQMevlYMcng9c3CxEtlpbTGwwUVG66sIqBN4NVOReXarBYBb3xfS2KY1Lq3Y+0jyIiIjyK9JGJgD89ttv+PTTT+Ho6AhZliHLMurVq4c9e/agVKlSivply5bBZDIBANq2bVvUl0tEREQAslLSEDRyLTxtfITcJJsgB1jD0c9TyL869RWO3BHvupUgYXaz2SjjXCY3lGVg+zAg6rR4QisnoMsyQCU++bni6E2ExaULWV2/YuhU0+sxZ0ZERERERPRimNqpKsq52wvZvdRsDNtwBuYH7ZdZq1++EWTg93eBS1uFtFHJRnir8lv5KmWMOzIOSVlJQu7doibuGG8JmSRJMJ5NgzFLbHASERE9iiJvZFpbW+Orr75CfHw87t69i6SkJJw4cQK1a9d+YH2HDh2wf/9+7N+/H1WqVCniqyUiIqL0uCScmbAJvnZlFe9FSeGo9FYrIdtyYwt+uvSTonZowFC08GkhhsE/Amd+UZ6049eAs68QRSRkYHG+PV5UEjD5jarC06BEREREREQvI1udBt/2CYC1VvzK9/D1OHx38AH7VL42B3CrJGZmI/DbQODyn0L8ee3PUc65nJDFZsRi6ompkGWxSVrni25I0ScKmYuVO4Imry3gjIiIiJ5CI9NyYpUKHh4ecHR0/Ne6mjVronnz5mjWrFkRXRkRERHdlxIejUtTt8PbtrTivbuZ4ag7XdwX82L8RUw5PkVR+3qZ1/FutXfF8PZx4O8xypM2+gSo1kWIkjMNGLgqGOl6k5D3qe+Lql5OjzgbIiIiIiKiF1tFTwdMeUO5X+b8XVdx6No9MdTZAj1/AezcxdxsBDYOAK7ssERWaivMaTYHOpVOKN19ezf+uPGHkFkXc0JGiSzFNZQw++DWruCCTYiIiF56T62RSURERM+2uMthuD5/P0rY+irei82KQpXx7aHR5O5LmapPxYgDI2AwG4Ta6sWrY0qjKeJTkylRwIa3cz4g51W6OdBqshDpjWYMXnMKN2LThNzZVovhbSo+3uSIiIiIiIheUD3q+Ci23zDLwMdrQxSfq1C8HDDgT8DOTczNhpzPbNdy97YsX6w8htUZpjjfl4Ff4mT0SSGrM7IH7mTcFjKtSofEHTe5xCwRERUIG5lERESkoE/PxO2lgfCw8Va8F50ZiSqT28POvZglk2UZU45PQWRapFDrbuOOr1t+DSu1VW5oMgAb+gPpseLATr5At5WAOrc5Kssyxm46j2Oh8YrrmNm5OorZ6RQ5ERERERHRy0ySJMzoXB1litsJeWqWEe/+FIzE9HyNRLeKQP8/AdviYm42ABsHAknhlqhPpT5oUrKJUJZlysJHez/CqZhTQu7brzayTBniqaxL4MQXax5zZkRE9DLSPLyk8JhMJpw7dw6RkZFISUmByWR66DFvv/12EVwZERHRy+34xJ9R1qayIr+bGY5as7pBY2Ml5BuvbcTOWzuFTC2pMb/FfLjb5lum6PACIDJIzDTWQM+fATtXIV687wZ+DxGbowAwul0ltK9eogAzIiIiIiIiennYW2nwfb/a6LLkGFKzc1fCuR2fgcG/nMLqd+pDp8nzjIt7JaD/NuCnjkBGXG5uSAd2jAR6rwMkCZIkYVrjaei6tSsSshIsZZnGTAzeMxhLWy9FgEcAAKBE3co4sf0kvLP8hGvzlvwQuvUwyr7RtFDmTkREL5an0sgMDw/HlClTsH79emRmZj7ycZIksZFJRERUyCIOhcBb5afIo7JvI2BeL2i04q8PVxOuYnbQbEX90IChqOleUwzvhACH5ihP2nEh4CXWbj59Bwt2X1OU9q7ngw+bl3nYNIiIiIiIiF5q5T0csLhPLbyzKhhmOTc/EZaASVsvYGbn6uIWIB5VcpqZq9oDmYm5+bW/gctbgSqdAADFbYpj0SuL8P6u95FhzH3i0tLMbLMUtdxrAQAaTO6HoKG/wCvPliUalRaJ++5C3yYLOhvrwpk8ERG9MIp8admjR4+iZs2aWLVqFTIyMiDLcoH+ISIiosIVufE8rNQ2QhadFYm6899SNDHTDekYcXAE9GZxaaImJZtgQNUB4sCGTOCPD5X7Ygb0B/x7CdGN2DSM2XROcW3NKrhhaqdq4odtIiIiIiIieqAWFd3xRYcqivzXoAisPHpLeYBHFaCd8kZV7BgFZCVbXvq7+eP7Nt/DVmMrlGUYM/Dh7g9x7l7u57nS79Z/8BKz434u2GSIiOilVKSNzJSUFHTp0gVJSUmWpyuXLl0KIOdpy08++QTffPMNRo4ciRo1aljyvn37YuXKlVixYkVRXi4REdFL58TMX+BjJz7taDBno1T/upBUYvNQlmVMOzENt1JuCbm7jTtmNJkBlZTv14y904C4q2JWzA9oO1OIso0mfLruNLIMZiGv5OmAb/vUglbNLb6JiIiIiIge1YBGfuhT31eRz9xxGafDE5UH1OgBlGkhZmnRwN6pQlTTvSaWtlkKG414I2yGMQOjDo1CljELAODmXw6Jzsrz+Okq4NCoHwo2GSIieukU6TeBS5cuxb179yBJEtasWYNVq1bh/ffft7zfqlUrfPTRR5g9ezbOnDmDP/74A8WKFcO6desAAP379y/KyyUiInqppEbGwDHeQZHHaO7CrVY5Rb75xmZsD9suZCpJhdnNZsPF2kUsvnkYOPFtvhEk4M2lgJW9kM7fdQ0Xo1KErLi9DisG1IWDtfbRJ0RERERERESQJAlT3qiKRmVdhdxolvHJr6eRnGnIfwDw+gJAk2/Z1+DlQESwENVyr4XvWn+naGbeSbuDFRdyH0qpO74XojJvCzUqSYXSUkUcHL7sMWdGREQvgyJtZP71118AgNq1a6NXr14PqQY6deqE7du3Q5ZlfPTRR7hy5UphXyIREdFL6/ScrXDUFhOyxOx7qDOlt6L2RuINzAycqcg/8v8IdTzriGFWCrD5I+UJGw8FSjUUoiPX47DsUJiidF53f3g52yhyIiIiIiIiejitWoUlbwXAx0X8XBWZmImxm84pt/RyLQs0G5lvFBnY9ilgEhuftT1qY0mrJdBI4lYky88vR0RqhOV1uY+aIcOYJtRIkoSy2so4+DmbmURE9GBF2si8dOkSJEnCm2+++cD3TSaTIqtfvz569OiBzMxMyzK0RERE9GRdWb8XpazFpy7NshnWDVwU+2JmGDIw4uAIZJmyhLx+ifp4r/p74sDy/z/oJoeLuXsVoOV4IUpI12PYhjOKaxvY2A8tKroXbEJEREREREQkcLbVYXHvAGjybRuy43w0fgkMVx7QaCjgVlnMYi8CRxcqSut41sFbld8SMr1ZjznBcyyvXSr6wuY1D2QYUxXHl7WqjIOfLnvg98NERPRyK9JGZlJSEgDAx8dHyLXanGXi0tPTH3hcq1atAAC7du0qvIsjIiJ6SaXHJSH7RCLUKrFheSf7Fsr3bKGonxU0C6HJoULmau2KWU1nQa1Si8WH5gEXN4mZSgt0/h7QWFkiWZYx+vdziE3NFkoreTpgdLtKBZ8UERERERERKdT0ccaodhUV+dQ/L+HyXXGLD2h0QMevlYMc+BK4E6KIP/T/EMVtioulEQdwKPKQ5bVPqwDYv+mDNEO+cwEoa1MZR8eufKR5EBHRy6NIG5k6nQ4AYG0trq/u4JCzH9edO3ceeJyNjc2/vk9ERESPL2TKb3C18hCyDGMaqo14TVG7LXQb/rjxh5BJkDCr2SzFB1Zc3gbsn648YYsxQIkaQvTTsVvYfSlGyKw0KizqXQvW2nzNUSIiIiIiInps7zUpgxYV3YRMbzRjyNoQpGUbxWLfBkDtgWJmNgK/vwdki8vE2uvsMbzOcMX5ZgfNht6kt7z2alIdxXqWQaohSVHrAz/c2Hq4YBMiIqIXWpE2MkuWLAkAiI+PF/IyZcoAAIKDgxXHAMDVq1cBAEaj8YHvExER0eM5PvVnlLZT3o2b6p4OB29xOdeLcRcx7cQ0Re37Nd5HgxINxDD6ArDpA+UJK7wGNPlciALD4jF9+2VF6fjXK6OCh8MjzIKIiIiIiIgelUolYX53f3g4Wgl56L10vLMyGOn5m5ltpgLOpcQsIRTYOVYx9uulX0eAe4CQhaeG46eLPwmZR73KcHu7ClL0iUKuVVkhZW8U9JniViZERPTyKtJGZo0aOU9fXL4sflnZoEEDyLKMHTt24Pbt28J7SUlJWLp0KSRJQunSpYvsWomIiF50t/achFuqcu/JyKybqD2qm5AFRwfj3V3vItOYKeR1POpgsP9gcYC0e8CvvQFDviXj3SoDXX8A8iw/ezc5Ex+vDYHRLAulr1RyR78G+T4oExERERER0RPham+Fr3vWQr7tMhF0KwEDVwUjQ5+nmWntCHT9EZDyrZYTshq4tEWIJEnCuPrjoJLEr52XnVuGqLQoISvuXxZu/aog2yR+znS38cLx8T8/3sSIiOiFU6SNzObNm0OWZRw4cEDI+/btCwDIzs5Gs2bN8N1332HXrl347rvvULt2bcTGxgIA3nzzzaK8XCIiohdWVkoaErbdgLXaVsiT9PGoObGTkB2KPITBewYjPV9jsphVMeW+mEY9sKEfkBwuntDGBej9K2CV+4RllsGED9eEIC5NL5R6F7PB/O7+kKR8n6iJiIiIiIjoiWlY1hWft66gyINuJuCd/M1Mn3pA89HKQbYOBZLF7cAqulREr4q9hCzLlIVJxyZBlsWbWN1qlUOCc4Ji2FKasriyfm8BZkNERC+qIm1kvvHGGwCACxcu4OLFi5a8Xr166Nu3L2RZRmRkJIYMGYLXXnsNQ4YMwa1btwAAPj4+GD5cucY6ERERFVzghLVwtykpZEazAfavloS1i5Ml+/vm3/h036fINmULtVZqK8xtPhceduLemtg7BQg/LmYqDdBjNeCSu7KCLMuYtOUizkYkCaXWWhW+71cbxex0jz85IiIiIiIieiRDXimHdxorV8E7EZaAd1edRKbelBs2HQ745NtWJCsJ+OMDwGQQ4o9rfQwXaxdxzLsnsOHqBsW56o7vhbuZ4s2wGpUW2ccTkJWSpqgnIqKXS5E2Mn18fLB//37s2LEDjo6OwnvLly/Hu+++CyDny828/9SuXRt79+5FsWLFivJyiYiIXkhHJq5CaRvlvpj3bKPh166u5fWWG1sw6tAoGGVxfxRbjS2+a/0d6peoLw5w9W/g+DfKE7afC5RuKkRrg8Kx/mSEonR21xqo6uWkyImIiIiIiOjJkyQJX3SojAGN/BTvHQ+Lx+BfTsF8fysQtQbosgywEr/Xxa3DwG8DhWamo84Ro+qOUow5/9R8RKQoPwuWHdwUmSZxFSBXa08EffFrwSdFREQvlCJtZAI5y8u2bdsWPj4+Qq7VavHDDz8gLCwMy5cvx8yZM/H111/j6NGjCA4ORtmyZYv6UomIiF44Z5b8Ae8sH8V+JVFZ4agzqbfldVhSGCYfnwwZ4rI/TlZOWN52Oep61hVyJEcCmz9UnrDOOzn/5HH5bgqmbL2kKH23SWl0qllSkRMREREREVHhkSQJkzpWQf+GpRTvHbh6DxtP5Wk8FisFvD5fOcjlbcBv7wjNzPal26NNqTZCWaYxExOOToDJbBJyl0q+SPNQPn1ZyrocLqz6q4AzIiKiF0mRNzIfplSpUhg4cCDGjBmDoUOHomHDhk/7koiIiF4IoVsPwy7MBhqVuGxrqiEZVce8ZtmTUpZlTD0xFUaz+CSmm40bVrVdhWrFq4kDm4zAb+8CmYli7lkDaPulEGUbTfh8/RnoTWYhb1DGBWNfq/QfZkdERERERESPS5IkTH6jKvo1UDYzZ+64gri0PNuN1OgB1OqrHOTyVuD3dy3NTEmSMKHBBMUSsyGxIVhzeY3i8FojuuFO5i0hU0sayGczkR6XVOA5ERHRi+GZa2QSERHRkxdz+hqyDyTCRmMn5NmmTNi3LQE7z9wPlltDt+JUzCmhzt3WHT+99hPKFSunHHz/DCDihJjp7IHuqwCttRAv3HMdV6JThayEkzW+7RMAjZq/lhARERERET0tkiRhaqeqaFvVQ8iTMw2Y/me+VXU6LASqdlEOcmkL8Pt7OTe8AnCxdsHEBhMVZYtCFiEsKUyRV/6sNTKM4mfGYlZuODV1YwFnQ0REL4oi/cZQpVJBo9Fg69atBTpu586dUKvV0Gg0hXRlREREL6706HhE/XQGjjpxr2mT2YisSmb4vFrbkiVlJWH+SeUyQV80+AI+Dj6KHDf2AEcWKPOOCwFXcVn4U7cTsPRgqKJ0fnd/uNpbPeJsiIiIiIiIqLDkNDOrwd5K/B5285koHL5+LzdQa4AuPwBV3lQOcmkz8PcYy8tWpVqhQ5kOQonerMfwg8ORkJUg5I6lSyCzlAH5+dmUx5mlBftOmYiIXgxF/uiDLMsPL/qH4x73WCIiopfZuZnb4GZdQpHHOsWg6rvthOzrkK+RmC0uEfuKzyto4dNCOXDKXWDTB8o8oD9QvZsQpWcbMWzDWZjz/VU+sLEfGpUr/kjzICIiIiIiosLn4WiNUe0qKvIJmy8gy5Bnb0u1Buj6I1Clk3KQ4B+AyNyVfsbUGwN3G3eh5EbSDbzz9zu4l3FPyP0/6YTIrJtCppLU0F4zIzUy5jFmREREzzOu4UZERPQCOzFtDXxsyyryCHMY6o7vJWSnY0/j9+u/C5mNxgZj6o2BgtkEbBoEZMSJuXtV4LXZivIv/7qM2/EZQlbWzQ6j23FfTCIiIiIiomfNW/VLoaaPs5Ddjs/Aor3XxUK1Fui6HKj8hnKQHcNzPjsCcLJywpTGUxQlocmhGLhzIKLTo4W8+tgOSDMkC5mTzhVn5vxZ8MkQEdFz7bloZKam5qyLbmNj85SvhIiI6PkRc/oaXJOVTztGZIahwey3hcxgNmDq8amK2o9rfowS9sqnOXFwDnDrsJhpbf+/L6b49/X+q7FYcyJcyNQqCQt61IS1Vv1okyEiIiIiIqIio1ZJ+LJLdahVkpAvOxSGK9Ep+Yq1QLcVgFtlMY86DZz+2fKySckm+DTgU8W5bqfcxoC/ByAyNdKS2Xm4wFhJpVihr5RNOZxcsOExZ0VERM+j56KRuXfvXgCAp6fnU74SIiKi54PJZMLNlSdgo7ET8vjsWNT+sickSfwwuvLCStxIuiFkFYpVQJ/KfZSDhx0EDiqfusTrCwC3CkJ0My4dn/56WlE6pGU5+Oe7u5eIiIiIiIieHZVLOOK9pqWFzGiWMXzDWeiNZrFYrQXaz1UOsmcKkJG7D+Z71d/DiDojFGV30u5gwN8DEJUWZcmqDWqPyOz8S8yq4HzXCRdX//0YMyIioueR5uElj+fgwYM4ePDgA99bt24dzpw586/Hy7KM9PR0hISEYP/+/ZAkCQ0bNiyEKyUiInrxBE5ZA1/bMkJmNBvg8EoJ6Gythfx07GksObNEyCRI+KLBF9CqtOLAabE5S8oi32aXNd8CavYWopQsA977KRgpWUYhr17SCUNeKVfwSREREREREVGR+qxVBWw/dxeRiZmW7GJUCubvuoqx7fM9gVm6KVCtK3Ahz5YlmQnAvulAhwWWqH/V/tCpdZgZOFM4PCYjBmMPj8WKtiugVuWs3lNr4psIm34QjrpiljprtS3M51Jx/Y9DKN+52ROcLRERPYsKrZF54MABTJ2qXKJOlmWsX7++QGPJsgyNRoOhQ4c+qcsjIiJ6YUUdvwD3TE8g36qtUQhHo44DhCw5OxmjDo2CSTYJedcKXVHTvaY4gNkEbHofSIsR8+IVFXfemswyPll7GqH30oXcTqfGVz39oVU/F4tCEBERERERvdRsdGrM7Fwdb68IEvLvD4WhWQU3NC6XbzuTNtOAq38DhjyfBU+uAALeBrxqWqLelXrDSm2FyccmQ85zo2xIbAjWXF6D/lX7AwCsXZygqm0H01kj1Krcr7JtNQ5IPZKEWw4n4de6zpObMBERPXMK9VtEWZaFf/4pf9g/tWrVwtatW1G3bt3CvFwiIqLnntFoROSvZ2CtthXyuKxo1JsqLhMryzImHp2I6PRoIfdx8MHw2sPFgU0G4I8PgLD9Yq6xztkXUycuYfvljss4eO2ekEkSsKh3LZRzdyj4xIiIiIiIiOipaFbBDQMb+ynyYRvOIDFdL4ZOJYHmo/JVysCOkYBZXI62S/kumNpY+SDMopBFCE0Ktbyu9FYrJLjHwyyLxztonZG+IxJRJy4UaD5ERPR8KbQnMgcMGIAWLVpYXsuyjFdeeQWSJGHatGlo3Ljxvx6vUqlgb2+P0qVLw9nZubAuk4iI6IVyfNxPKG0r7lNpMOvh2qEMNNY6IV97ZS32RewTMo1Kg7nN58JeZ59ngCxg4wDg2l/KE742B/CoIkQbT0bgxyM3FaWj2lZCq8oeBZsQERERERERPXWj21XC8dB4XIlOtWQxKdkYs+kclvatDUmScosbfAScXgPEX8/NIoOAkJ+AOgOFcd8s9yaC7gZhW9g2S6Y36zH+yHisab8Gmv8/hVlrRDcET1sHzzQv4VxOOlfErb8KW/dicC5T8gnPmoiIngWF1sgsVaoUSpUq9cD3qlWrhubNmxfWqYmIiF5K55ZthS/KKPJo9R00bN1KyC7FX8L8k/MVtSPqjEBV16q5QXYq8Gtv4NZh5Qmr98hZHijvNUQmYfwfyrthO9cqiQ+bK6+NiIiIiIiInn3WWjUW9qqFjt8cgd6Y+2TkzosxWB8cgV71fHOLNTrgtdnAmi7iILsmAGVfAYqJ3xmPrjcagXcDEZsZa8kuxl/E8vPL8YH/B5as7he9EDhhDUoaxeNdrNxx8audaLz4nScwUyIietYU6QZV+/fvx759+x76NCYREREVTMzpK9BdVQl7hgBAbFYU6k4Tl5RNzk7GyIMjYTAbhLylT0v0qZSnNiMBWN3pwU3Miu2BNxbnrBf7fylZBgxZexp6k7jcT00fZ3zZpbp4hy4RERERERE9Vyp6OmB8+8qKfPK2iwgMixfDcq2Aym+ImT4N2PKxYolZJysnTGk8RTHu0nNLcSXhipDVn94XkbJyBaBSduVxdOKqR5sIERE9V4q0kdm8eXM0b94crq6uRXlaIiKiF5o+MwvhK0/BXusk5JnGdPgMqA2NJre5qTfp8dn+zxCeGi7Uetp5YlrjabnNRpMR+KU7cOeU8oTVewA9VgNaa0skyzLG/H4O4QkZ4riO1ljWrzaster/OEsiIiIiIiJ62t5uWAotK7oJWZbBjIGrghF0M0Esfn0+YOMiZrcOA0HLFOM2KdkE3Sp0EzKj2YjxR8ZDbxL34Www+21EZIcpxvDILIGbu4IKMBsiInoeFGkjk4iIiJ6846NXo4Str5CZZTP0lWS41SxnyWRZxqRjk3Ay5qRQq5bUmNNsDpys8jRCA5cCd8Q6AECdd4HO3wNqrRCvCQzHjvPRQqZRSfj2rQC4O1qDiIiIiIiInn+SJGFON38Ut9cJeYbehAErg8Rmpr070OEr5SB7JgNxNxTxiDojUNJe3OfyWuI1LD27VFFbe0YPJGTfEzJrtS0StodBn5756BMiIqJnHhuZREREz7ETM39BaZuKijxKuo2q770mZEvOLsGfYX8qaofVHoZa7rVyg+RIYP9M5cmaDMu5o1Yl/vpwMSoZ0/68pCgf2bYiapcq9ogzISIiIiIioueBm4MVlr1dB/ZW4tYm95uZwbfyNDOrvglU6yoOYMwENn8ImE1CbKe1w7TG0xTnW35hOc7dOydkOlsbFOvgB4NZfFrTw6Ykjo//ueCTIiKiZxYbmURERM+pexdC4ZLgApUk/nUenRmBOtPFfTE339j8wLtYe1bsiX5V+onhX6MBQ7qY1ewLtJ4k7IkJAGnZRnyy9jT0RnGPkxYV3TCoaZkCzoiIiIiIiIieBwG+xfDTO3VhpxO3EcnQmzBgRRBOhyfmhu3nAfYe4gCRwcCRBYpx63rWRd/KfYXMLJsx/sh4ZBmzhNynVQDu2cYoxvCzKo8zS7cUcEZERPSsYiOTiIjoOXXl+/2w1dgLWaohCRVHtBb2xQy6G4Qpx6Yojm/m3Qxj6o3J3RcTAK7sAK7ke2rT1hV4VXlXrCzLmPDHeYTFiU1PD0crLOhREyqVpDiGiIiIiIiIXgy1S7ngp3fqKZqZ6XoThqw9jSzD/5+4tHUBOi5SDrBvOhCo3C9zaMBQ+Dn6CdmtlFtYdFo5Rt1JvXE3M1zI1JIGumtAwo2Igk2IiIieSWxkEhERPYfO//QXStmWEzKT2QjrFsXh4ONuyZKykjDq0CgYZaNQW9mlMuY2mwuNKs9SQPp04K9RypO9Oj3ng2c+G09GYvOZKCFTScCiXrXgYqdT1BMREREREdGLpY6fC1a9Uw+2+ZqZd5IyseRAaG5QsV3OSj/5/TUSOLZYiGw0NpjeZLpi9aE1l9bgZPRJIZMkCRU/fQUZxlQhd9S54PLC3Y8xIyIietawkUlERPScMWbroQ9JgkoSPyhGmSJQumNDIZsZOBPxWfFC5mnniW9afQNbra048MHZQHK+O1ZLNQH8eyuu4VpMKiZuvaDIh7WpgPplXAswGyIiIiIiInqe1fVzwaqB9aDJtyrP0oOhCI/PyA3azQRcHrAFya4JwKG5QuTv5o+BVQcKmQwZE45OQIYhQ8gdy5SAvmLOqkF5lbIrj6NfrHyMGRER0bOEjUwiIqLnzPEpP8PDxlvI0o2pqDWuk5D9fetv/HXrLyGzVlvjm1e+gbutu5Aj5hJw/FsxU2mBDgsU+2Jm6I34+JcQZBnEfTGblCuOwS3Ep0SJiIiIiIjoxVevtAsGNvYTMr3RjKl/XswNrJ2A/n8CLmWVA+ybnvNPnmbkRzU/Qjln8TPmnbQ7mHhsIkxmk5BXG9QekYabimE9srxwc1dQwSdERETPDDYyiYiIniMJ12/DXe+pyNM9MmDt6mR5HZcZhxknZijqPqv9GSq6VBRDQxawaRBgFpefReOhgFu+WgCTtlzE9dg0IStub4WvetaEmvtiEhERERERvZSGtioPdwcrIdtzORb7rsTkBk4lgYE7ALdKygEOzQWCfrC81Kl1mNlkJjSSRijbeWsnpp2YpngCs/b0HkjIvidk1mpbJGwPhT498zFnRURETxsbmURERM+R84t2wk7jKGT3su6i1oiulteyLGPK8SlIyk4S6up61kXvSsplYrFrPBCTb5lY51JA0xGK0k0hkdh4KlLIJAlY2Ksm3PJ9YCUiIiIiIqKXh4O1FuPaV1bkU7ZdQpYhzxOUDp7AgO2AR3XlILu/AOKuW15Wdq2M9/3fV5T9fv13LDi1QGhm6mxt4NKxNAxmvVDrYeONY+N+fowZERHRs4CNTCIioufEycUb4WdTXsjMsgnF25eFlGf5121h23Ag4oBQZ6uxxdRGU6GS8v3Vf2kLEPyj8mQdvgJ04h6aZyOSMGGzcl/MT14pj8blihdoLkRERERERPTi6VTTC/X8XITsdnwGfjwcJhbaFQf6bwW8aom5MQvYPBjIs3TsoOqD8IrPK4pzrbq4CsvOLRMy71dqIc42VlFb2ro8Tn+zqYCzISKiZwEbmURERM84k8mEvcOXwCWiGNQqcUmdKEM4fF6tbXl9N+0uZgXOUowxsu5IeDuI+2oi8Raw5RPlCRsNBcq1EqLjofHo88MJZOjFfUjql3bBp63E5ioRERERERG9nCRJwpROVRXbjnyz/wYiEzPEYlsXoO8mwN5DzCODgWOLLC81Kg3mNp+LBiUaKM73zZlvsObSGiGrM6kX7mZGCJla0sD2lhXC9516jFkREdHTxEYmERHRMyw1KhaHhi5DRW116FTi0q2ZxnRUH9XB8lpv0mP4weFINSxQdi8AAQAASURBVKQKdY1LNkbX8l2FDCYD8Nu7QHaymJesA7SaKET7rsRgwMogpOdrYrrY6bCody3ui0lEREREREQWlUs4ol+DUkKWZTBjzO/nYTaL+1rC1gXouAgK+2cCMRctL3VqHRa2XAh/N39F6ezg2TgcedjyWpIkVPy0JTKM4mdjO40j4rbeQOrduMeYFRERPS1sZBIRET2jQncew5XZu1HeoZriPVmWkemrh51n7pI9c4Ln4HzceaHOQeeAKQ2nCEvPAgD2TgXunBQzKyeg2wpArbVEW89G4f3Vp5BtNIulGhW+6V0LHo7Wjzk7IiIiIiIielF93qYCXO10QnbkRhx+CbytLK7YDqjZV8xMeuCPDwFj7n6XtlpbLGm9BJVcKimGmHB0AuIycxuUjmVKwFRNA7Ms3pDrbu2FMzM2w2Qy5R+CiIieUWxkEhERPYPCD5xC1s5YlLDxVbynN2UhvUI2agx9w5JtC92G9VfXK2onNpwID7t8y/Sc2ygs02PRaTFQLPeu2Y0nI/DputMw5rtj1t5Kg9Xv1EMj7otJRERERERED+Bko8XEjlUU+cwdV3A7Pl15QLuZgGO+7VCizwGH5wmRo84RS1svhZ+jn5AnZCVg4tGJkOXcz6+VB7yKGOu7ilOVtq+IgyN+ePTJEBHRU8VGJhER0TMm6XY07v1xDU46V8V7yfpEOPQqjUrvtrFkVxOuYurxqYravpX7op1fOzG8fQzY8pHypHXfA6p0sry8HpOKcX+ch5xv1Z9itlqsHVQf9csor42IiIiIiIjovjf8vdC+uqeQZRpMGLHxLEz5l5i1dgI6faMc5NA8IOq0ELnauGLhKwthrRZXCDp85zDWXlkrZHUm90JUtvIp0LK6ijj+5c8FmA0RET0tbGQSERE9Q/RZWbgwdzs8bLwV78Xoo1B+Smu41S5nyVL0KRh2YBiyTFlCbS33WhhWZ5g4QNx1YF2fnCV68vKoDrw6w/JSlmWM33wBBpP4wdLdwQobPmiIGt7Ojzc5IiIiIiIiemlIkoRpnaoplpgNvpWIFUduKg8o2xKoO0jMZFPOErMG8TNvGacyGFVvlGKIBScX4FriNeEaAmZ0R0J2rFCnljQoHl8coduPFnBWRERU1NjIJCIieoYcGbECfvYVFHmMJgoB83tA52Ar5JOPTUZ4ariQuVq7Yl7zedCqcve6RHoc8Es3IDNRHNjeE+j9K6DNvZP1t1ORCLqZIJR5Olrjtw8bobyHw2POjIiIiIiIiF42rvZWmNmluiKfu+sqbsSmKg9oMwUoVlrM7l0B9s9QlHYr3w2tfFsJmd6sx+hDo5FlzG18amyt4PNBPWQY04RaG409kndHID1W/PxLRETPFjYyiYiInhEHx3yP8nbVFHm0PgIB03pAkiSxPuIgdt/eLWRqSY25zefC3dY9NzRkAr/2AhJviQNr7YA+6wFnH0uUmK7HzB2XFdcwo3M1+LraKnIiIiIiIiKif9O2qie61CopZHqjGR/9EoK4tGyxWGcHvPkdAPHzL44tBsIDhUiSJExuOBnuNu5CfiPpBr469ZWQFavkA01DR5jMRiF3ty6Jk1M3FnxSRERUZNjIJCIiegacWvQbfM3lFM3KuOwY1PyyuyLPNmVjVtAsxTifBnyKup51xfDPz4HIYDGTVEC3FYBXTSGe9dcVJGYYhKxtVQ+0quxRsAkRERERERER/d+kjlXh4WglZNdi0tBr2QnEpojLxqJUQ6DRkHwjyMDmDwF9upA6WztjRtMZkPI1PtdeWYtDkYeErEzXxoh3ilNcW1n7Kjg07oeCTYiIiIoMG5lERERPWdKtKFjf0kKrEvcNSTUkodzIltBYaRXHrLqwCpFpkUJWx6MOBlQdIBZe3gac/VV50tfmABXbCVHwrQSsPxkhZLY6NSZ1rProkyEiIiIiIiLKx8lWi9ldayjyG7Fp6PH9cUQlZYpvtJwAFK8oZglhwJ7JijEalGig/CwM4IujXyAuU2xcBozvjrvZ4YrakoZSuLh250PnQURERY+NTCIioqfs7Pw/4aRzEbJsUxacu5SBvZeroj4qLQo/nv9RyNSSGmPrjxWf3EyPA7Z9pjxhwyFAvUFCZDCZMf6P84rSYW0qwMvZ5tEnQ0RERERERPQALSq6Y3S7Sor8VnwGenx/HBEJGbmh1hrovBSQ1GJx0DIgdJ9ijE9qfYLKLpWFLCErAV8c/QKyLAu5/+Q3kawX98W0UtvAFJyGhDDxhmEiInr62MgkIiJ6ikKWbEJp2wpCJssyDNVUKNH4wU9Czg2eiyyTuPRO70q9UaFYnnFkOWdJ2Yx8y+b4NgTaTFWMufRAKK7FpAlZ5RKOGNDI79EnQ0RERERERPQvBrcoiwmvV1bkkYmZymZmyQCg6XDlIBv6A1FnhEir1mJ2s9mw0Yg34h65cwRrr6wVMp2THVy6lIXBrBdyFyt3XPt6H5JuRxdsUkREVKjYyCQiInpK0uOToLkuQ5XvDtMow21U6t/qgcccu3MMe8L3CJmLtQs+qvmRWHjhd+DyVjHT2gJvLgFU4vlO3krA13uvC5kkATM6V4NGzV8ViIiIiIiI6Ml5r2kZTOukvHH3bnIWPl9/BiZznicom40EPKuLhdkpwJouwL2rQlzaqTRG1x2tGHfByQW4lnhNyEo0qYbUEmmKWm/b0rg6fzefzCQieobw20kiIqKnJHjKerhYuQtZujEF/uM6PrDeYDLgy6AvFfmw2sPgoHPIDVLuAtsfcNfqq9MAlzJClJShx9BfT4sfFAH0quuLAN9ijzgTIiIiIiIiokfXr6Ef5nStgby7owDAyduJWH4kLDfQ6IDOywCtnViYEQ+sfhNIvC3EXcp3QWvf1kKmN+sx+tBoZBnFlY1qfN4JUcYH7Jdp64cbXx9AwrXbiveIiKjosZFJRET0FFz6ZSf8rMsrclNFNayLOyvy5OxkDN0/FLdSbgm5v5s/OpbN0/iUZWDbUCArSRygTAugzrtCJMsyRmw8h6hk8cOcn6stxj9gqR8iIiIiIiKiJ6VHXR/M7eavyOftuobrMam5gUcVoPdaQK0TC1OjgNVv5NzM+3+SJGFyo8lwtxVvGr6RdAPTTkxT7JcZMLMnYrLvKK7By7YUQr85gnsXwxTvERFR0WIjk4iIqIjp0zORFZwEjUor5FFZ4agyqJ2i/krCFfT8syeO3Dki5BIkjK8/Hiopz1/nQT8A13eJA1g5Am98g/y3uq46dgt7LscImU6twjd9AmBvpXmMmRERERERERE9um61vdEloKSQ6Y1mDN94FgaTOTcs0wLo/hOQb2sWJN4Cfu4MZKVYIicrJ3zZ5EtIED8Dbw3dijWX1wiZRqeB/+xuiMlWLiVbwtYXt5edQEpEjOI9IiIqOmxkEhERFbFjY3+Cu7WXkGWZMlBpWBtF7dbQrei7oy/upCnvEO1VqRcqu+Z5cjIiCNg5TnnCdrMAZx8hOh+ZjC93XFGUjm1fCdVKOj3iTIiIiIiIiIj+m0kdq8LT0VrIzkUm47sDoWJhpfZA5++BfA1K3LsM7BghRPVK1MM71d5RnGveyXk4FnVMyHKamd0RrVc2Mz1tfBAye/Mjz4WIiJ48NjKJiIiK0LFpq1HGRrlsa5pnBhx9c5e+kWUZ84LnYfyR8cg2ZSvqX/N7DSPrjMwzwD1gQ3/AbBALK7wG1OwjREkZegz5NQT6vHe3AmhTxQMDGvkVfFJEREREREREj8nJRovZ3Woo8kV7r+NiVLIY1ugOdFigHOTceuDcRiEaUmsIGpZoKGRm2YyRB0ciPEXcG1Oj06DmPzQzy9pVxuGJKx5xNkRE9KSxkUlERFREbvx5GG7J7uJSsACisyJRc3hnIVt9aTV+uvSTYgyNpMHouqMxu9lsaNX/X5rWZAR+G5izP0heTr7Am0uEJWWzjSa8//Mp3I7PEEpLOttgbrcakPItP0tERERERERU2JpXcEOf+r5CZjTLGL7hLLKNJrG4zjtAiwesRrR9WM5Ss/+nUWkwt/lc+DiIKxSl6FMwdN9QpOnThFyj1aD2vF6Izo4QckmS4JlRAjd3BRZ8YkRE9J+xkUlERFQEUu/EIm3PXdho7IQ8zZCMSmPaCtn+8P2Yf3K+YgxXa1f82PZH9K3SV2w47p8O3DosFqt1QM/VgK2LJZJlGaN+O4egmwliqUrCot414Wyre8zZEREREREREf0349pXhncxGyG7Ep2KhXuuK4ubjQD8mopZdgrw+6Ccm33/z8nKCYtfWQxbja1QGpocirFHxsIsiysVSSoJNSa/iWS9+LnZRmOP+O2hyEoVm59ERFT42MgkIiIqZCaTCae/3ILi1iWE3Gg2wKqlG+w9c5uNVxKuYPTh0ZAhC7U1itfAho4bUNujtjj4le3Aka+UJ20/F/CqJURf7b6GLWeiFKVjX6uE2qVcFDkRERERERFRUbG30mBed39FvvRgKELCE8VQpc7ZL9PaWcwjg4BDc4WorHNZzGo6C1K+vTUPRBzAygsrFefTOdnC8TVvGPNt3eJp44Pj49c88nyIiOjJYCOTiIiokB0a/QPK2FdS5AnO8Sjdob7l9b2Me/hk3yfINGYKdX6OfljSegncbd3FATISgC0fK09Ysy8Q0F+INpyMwKJ9NxSlbzcshXeblC7AbIiIiIiIiIgKR4MyrninsfgZ1SwDIzacRaY+3xKzTiWBNxYpBzk0Bwg/IUQtfVtiSK0hitJFpxchODpYkfu0qY14+zhFXtamEoLmr3uEmRAR0ZPCRiYREVEhMZlM2Dt0CcqqKyvei8q+jYBx3S2vs4xZ+HT/p4hOjxbqnKyc8G2rb+Fk5aQ8wd4pQGa+u1I9qwOvzxP2xTx2Iw7jNp1XHN6qkjsmdqjCfTGJiIiIiIjomTGqXUWUcRO3ZQmLS8fcnVeVxVU6AbX6iZlszlliNitZiAdVH4Q2pdoImVk2Y+TBkbiXcU8xdMAX3RGdFSlkKkkNhyg7xJ5X3ihMRESFg41MIiKiQqBPz8ThT39ARdvqUEniX7eJ+jjUnNZVyGYFzcL5OLHZqFFp8FWLr+Dr6Ks8wZ0Q4NRPYqazB3r8DGhz9xRJTNdj6LozMJrFpWqrejliUe9a0Kj5qwARERERERE9O6y1aszv7g9VvntuVxy9iRNh8coD2s0CXMqKWXI4sH24EEmShKmNpqKUYykhj8+Kx8hDI2E0GxX1lca0RboxRcgdtM64/v0hmEz5nhAlIqJCwW8viYiInrDUO7EIHv0rytlXVbyXbcqEe6/K0NnnNhuPRx3H79d/V9RObDARdT3rKk9gNgM7RgL59tFE89GAi7gEzxdbLiAuLVvIvJyssWJAXdhZaR59UkRERERERERFpJZvMXzYvKwiH/nbWaRliw1HWNkDXX8EVPk+457fCJxdL0T2OnssaLEA1mprIT8VcwqLTy9WnM/e0wWquvYwy2YhL2VfHscmrXr0CRER0WNjI5OIiOgJSrh+G1dm70Yp+/KK99KNKbDpUAIe9XL3y8wwZGDK8SmK2oFVB6Jz+c4PPsnZtfgfe/cdXVXxbwF835bc9F4gPaF3CL2joqDSpDepghRRepEm0kSKWBAV6YhgQ1CagID03ntoIQTS+83t7w98gcmcUPwhBtiftd5ayZ4zc868967cnO85M4g7JGa+JYAabwvRbydu4rcT8UKm16mxsEc1BLiLf7QRERERERERFSbvvlQcpQLdhCw2xYCRP56ANd+qQwiqArwwVh7k96FAyhUhKuFVAmNryscuPLUQG69slPLi7Rsg3n5dyosYg3B92+GHmAkREf0vWMgkIiJ6TEy5ubjw6Q4UcZaXgk0zJcO/dwUUbVBeyD8/9jnisuKErKxPWQyqMkj5JIZU4I8Jct50BqB1yPs1ITMX49ackg4b2aQUSgW6P8RsiIiIiIiIiP47jloNZrWrCG2+NWZ/PxmP9385Cbs9XzGz9rtAeD0xM2UCP/cBrOJbnC2KtUDr4uKWLwAw6q9RisXM6MkdkWoU99HUa1xwa81ZWIymR5gVERE9KhYyiYiIHpO9I5Yi2CVCyhNz4xE1uiG8SoUI+YnEE1hxdoWQaVVafFD7A2jzL4nz//6cBuQkiVmZFkBUo7xf7XY7xvx8Cqk5ZuGwmpHe6FYr/OEnRERERERERPQfKlvUA+++KK949P3BWEz5/axYzFSrgVZfAXpP8eAbB4CdM6QxRlUfhVLepYTMardi5F8jsS5mnZBrHbTwahEJi038O7uocxh2j1n8SHMiIqJHw0LmE2K323HhwgV89913GDp0KBo2bAh3d3eoVCqoVCp07979H427d+9e9OzZE1FRUXB2doa3tzeio6MxefJkJCUlPXgAIiJ6LHaPW4Qo59JSHm+MRfmpLeDk5ynkZqsZE/ZMkPbZ6Fm+J0p6l1Q+yZWdwMFvxEznDLw8RYh+PhKHLWdvC5mLgwYft6kIdb4nWYmIiIiIiIgKs/6NiuGVsgFSvmDXFXy69ZIYegQBzeW9LrHzY+DqLiHSa/WY3XA2fJ18hdxmt+H9Xe/j10u/Cnlww0pIcLolDR2qicKppRsecjZERPSoWMh8QoYNG4aSJUuic+fOmD17Nnbs2IHMzMx/PJ7dbseQIUNQp04dLFq0CJcvX4bBYEBqaiqOHDmCcePGoVy5cti2bdtjnAURESk5tWg9ihrl5WQTcuNQeUY7aJ0dpbYFJxfgUpr4B1eERwT6Vugrn8BuB/bOA5a2BPIVPlFvKOB5903PuDQDJq47LQ0x7vUyCPF2frgJERERERERERUSGrUKn3asjHrFfaW2OVsu4Ntd4h6YKNMcqPKmmNltwOo3gTRxr8sQtxAsfGUh/J38xcNhx7jd4/DzxZ+FvNrEjkjIvSlkOrUDNMctiN155BFnRkRED4OFzCfEarUKv7u5uaFMmTL/eLzRo0djzpw5sNvtcHFxwaBBg7B8+XLMnz8fjRs3BgDcvn0bLVq0wLFjx/6XSyciovu4ue8UtKds0KkdhDzDlIqSw1+CVicvEXs2+Sy+Pvm1kKmgwge1P4CDRhwHpmzgp17AptGAXfy3BN6RQO138n41Wqzov/wwMnPFvT8alvRD+2risrZERERERERETwtHrQZfdY1GdJiX1Pbhb2fw57kEMWwyHfApJmY5ycD3ne78nX2PCI8ILGqyCAHO4lufdtgxcc9E7L25Ny9TqVQIe6s6jFaDcKybzhMpP8cg7UrcP5gdERHdDwuZT0iZMmUwePBgrFixAufOnUN6ejq++OKLfzTW0aNHMWPGnXXdPTw8sGfPHsydOxedO3dG3759sXnzZkyYMAEAkJWVhT59+sibXxMR0f/MlGXA7e9Pw1XnIeRGqwHuLULhEiQ/LZpuTMfg7YNhsYnFxg6lOqCyf2Xx4OQYYMFLwKmf5JNrnYCWXwLau297Tlp3BsdvpAuHeTjp8FHrClCpuKQsERERERERPb2cHbRY2L0ayhRxl9qG/3gciZnGu4GDC9Bm0Z2/ne916ySwpv+dlY/uEeoeikVNFqGISxEht8OOMbvGINmQnJf5lI1AdqhB2irGRx+I83O2wZQtFjmJiOh/w0LmE9KnTx/Mnj0bnTp1QsmSJf+nG8qTJk3KK0xOnToVFSpUkI6ZMGECqlevDgA4ePAg1q9f/4/PR0REyvaP+w5++qJCZrPbkB1hRHCDStLx/7/PRlyW+IRmoEsg3q3yrnhw6jVgYRMg4Yx8Yq9woNdmILRmXvTT4RtYsf+6dOjUVuUR4K5/6DkRERERERERFVYeTjos7VUdkX4uQp6UZcLwH4+LL3MUqQC0nCcPcmYN8NcsKQ5xC8GiJosQ5Bokjm1IwtjdY4XCZYV3WuCW7oY0RpBzOPaPWiGtzkdERP8cC5lPmczMTGzYcGfzaHd3d3Tv3l3xOJVKhXfeubvc4KpVq57E5RERPTfOrPgDoQ6RUh6nvoYK/Zsp9ll4aiF23NghZBqVBh/V+wguunv+CMvNAFZ2ALLzLY0DAMVfBvpsv/MH2d/Oxmfg/TUnpUN71Y3AaxWKSDkRERERERHR08rX1RHfvFkVTjqNkG8/n4jFe66KB5d7A6g3TB5k22Tg/AYpDnINwucvfA5HjaOQ74rbhWVnlglZ9cmdccOYb39OABEuJbF7xMKHmwwRET0QC5lPmR07dsBovLNMQv369eHs7Fzgsa+88krezxs3bvzXr42I6HmRk5wO66EsaFTi/pdxhquoNe1NxT774vfhs6OfSfmQ6CGoElDlbmCz3tkTU3oTUwU0HA10XAU43d0TJN1gxtvLDyPXLC5pUy3cC6Oalnq0iRERERERERE9BaL8XDG+WRkpn7bhHM7dyhDDRu8DJZrmO9IO/PTWnS1d8inmVQwjqo2Q8k+OfILTyaeFrOqMTrhliJWODdcWx7Ev1jxwHkRE9GAsZD5lTp06lfdzdHT0fY/18/NDWFgYACAxMREJCQpv9hAR0SM7MulHeDmK+1/mWDJR8p1Gisffyr6FkTtHSvtnNA5rjK5luooHbx4LXNwsD9J6AdBwFKAW/+ke+eMJXEvOETJfV0d80akKdBr+M09ERERERETPpg7VQvBK2QAhM1lsGLTyKHLN9yztqlYDb3wN+OV72NeUCax+EzCJf1MDQNsSbdE4rLGQWWwWjNgxAtnm7LxMq9Oi3PjXkGIU77uqVRroL2uRdPryP5wdERH9P+2DD6HC5MKFC3k/h4eHP/D4sLAwXLt2La+vv7//I5/z8OHD920/e/Zs3s8WiwVms/mRz0FEj4/ZbIbFYsn7mR6v09/+jlB9MSnPDMpBRFiA9L/zW9m3MHD7QKTkpgh5mFsYxlUfl/d/KwBQH1kMzT55/w5rg9GwlWoB5Bt72/lEbDx9S8g0ahXmti8PLycN/+9fiPBzSVS48DNJVLjwM0lU+PBzSU+LD5uXxrHYNNzOMOZlF25nYcpvpzH+9dJ3D9Q4AW2WQrvoZahy0+7mt0/B9ttQWJt9Ko39frX3cTLxJG7l3P27+3rmdUzeOxmTak26O7S7MwK6VUDa8gtw0brn5a46D8R8tRsuHxWBVvu/3YbnZ5KocOFnUnbvPc7HjYXMp0xaWlrez76+vgUf+DcfHx/Fvo+iatWqD31seno6kpOT/9F5iOjxsFgsyMzMzPv9f/2yTHcZbqfC4YIaagfxTcfrOTGI7NhE+u/flcwrGHN4DJKMSUKu1+jxfvn3Ycwwwog7f2w5xO2D18aR8jmLN0N6qW5AvrGNFhsmrTstHd+/TlFEudn53+JChp9LosKFn0miwoWfSaLCh59LepqMaxyKd366CPs92bL9sagU6Ig6ER73pB5wbPQRvDb0FfqrT3yHTO+yMJRqI409qtwoDDk4RFhh6bcrv6Gye2XUD6x/98BAN2RGmuF0zQq16u7enUHO4dg/dhlKDW/+P82Rn0miwoWfSdk/rT89DK4595TJysrK+1mv1z/weCcnp7yf7/1gERHRo4tbsBfuDl5ClmlOQ9HeNaVjj6ccx+ADg6UiJgC8V+Y9RLhF5P2uMmXBY9tIqOxW4ThTQGWkN5gCqFTSGCsO30ZcuknIKge5olOVAOlYIiIiIiIiomdV1RB3dI6W/xaevPkqkrPFN6WMYQ2RVflt6Vj3vyZBm3RWyst6lcWbUW9K+SdnPkGyUXyAOPiNmrhulpeSDdcUw421ex84DyIiUsYy8d8WLFiAGzduPJaxJk6c+FjGKSwOHTp03/azZ8+ia9c7e7x5eHgIb4ES0ZN373IG3t7e0Ol0/+HVPDuOf74GYc7ikrI2uw2m4nYUKx0l5JuubcL4w+NhtslLS7xb6V20K9NOyNQbP4ImW1wi1u4RClXH7+Dj4ieNcSPVgCUH5SVlJ7cqD19ft0eaFz0Z/FwSFS78TBIVLvxMEhU+/FzS02b06144Fm/AqZsZeVmqwYKPtsfhmy5VoFbf84Bwkw9gSz0N9dW/8iKV1QifrYNh6bkF0N/7FicwwGsAjqcfx9HEo3lZpjkTn53/DJ82/BSqex4+9pjcEWfH/Ap/fVBeplXr4HRODYeXLHAL+WcPH/MzSVS48DMp8/T0/NfGZiHzbwsWLMD+/fsfy1j/ZiHT1dU17+fc3NwHHm8wGPJ+dnP7Zze3o6OjH/pYrVbLDy1RIfD/yxnodDp+Jh+DtJg4uN5whkonvhl5w3gZtd/uIWR/XPsDY3aPgV1Y1AbQqrSYVGcSmkU1Ewe/tgc4vFDMNA5QdfoeOs+iitczfdNxGC02IetWKxxlg70fYVb0pPFzSVS48DNJVLjwM0lU+PBzSU8TnQ6Y27EyXv90Fwzmu6sd7byYjBUH49CzbsS9RwNtFgLz6wFZdx8SVqVegW51Z6DzD4De/Z6jdZhWfxpar22NbHN2Xr47fjfWXFmDdiXvPqys0+kQ3C0a6Sti4KR1ycs9HLxx7dPdCBtQG97FQ/7RHPmZJCpc+JkU/ZvL63Jp2afMvVXtpCR5ucL87t0j7d+siBMRPcvOzt0CV527kKWZklFl/BtCdj7lPN7f9b5UxHTWOuOLl76Qi5jmXGDtO/IJG4wAAsoqXsuOC4nYdPq2kPm6OuK9xsUfcjZEREREREREz54oP1dMaFZGyqdvOIez8Rli6OoPtF0E3LOfJQAgdh+w/A0gN12Ig1yDMLLaSGnsmYdm4mr6VSHzr1wc2SEG2O3ivQE/fVHcmHcQ8QflJWyJiKhgLGT+bd++fbDb7Y/lf/5NJUqUyPv56tWrDzz+2rVrin2JiOjhHJz2PUKcI4XMardCE+0Kvffd5WZSc1Px7p/vwmAxCMf6OvlicZPFqF20tjz4jo+A5EtiFlAOqPOe4rWYLDZ8sPa0lI9uWgruej75RURERERERM+39tVC0KRsoJCZrDYMWnkUufe8qQkACKsNvDRRHuTGQWBpC8CQKsQti7XECyEvCJnBYsCYXWNgsVmEvNK7rXDdGCMN7e3oh9TvL+LKpgMPPykiouccC5lPmXLlyuX9fPjw4fsem5iYmFfI9PPzg7+//796bUREz5rEEzHwTPaQ8ljzFZTu/FLe72abGUN3DEVcVpxwnJejF5Y2WYrSPqXlweOPA7vniplKDTT/DNDIRcm0HBPeWnoIl5OyhTw6zAutKgdJxxMRERERERE9b1QqFaa3Lo9Ad72QX0zIwpifT8ovodR+B6g1UB7o5lFgSXMgJ0UYe0LtCfDWi9u6nEw6iekHpktj15jeGbGGy9LQ7jovmP5IwrlVWx9xdkREzycWMp8yDRs2hKOjIwBg586dwh6Y+W3atCnv5yZNmvzr10ZE9Ky5vGAPnLSuQpZiTED1SR2EbMaBGTh466CQaVVazGo4CyHuCntfWM13lpS153satNYAIKiKdPipuHS8/tku7LiQKORqFfBB87JQq1VSHyIiIiIiIqLnkaezA2a3rwhVvj+Vfz4ah293XRFDlQp4ebLyyki3TgBLmwPGrLzIW++NSbUnSYeuOr8Ky88uFzKt3gHVZnXGNeMl6XgXrRs0B824sfvkQ8+LiOh5xULmU8bV1RWvvvoqACAjIwOLFy9WPM5ut+Pzzz/P+719+/ZP4vKIiJ4Ze8YuQZBzuJBZbGa4NPCHg6tTXvbjhR/x/fnvpf4jq49EtcBqyoNv/eDOG5n38ooAGo6RDv3x8A20/nIPbqTKD668WSsc5YLkN0aJiIiIiIiInme1o3zRr0GUlE9dfxY78z0kDJXqzhKz9YfLA906CWwQ98ZsENIAbUu0lQ79+ODH2HZ9m5BptVrUmdMDsTZ5mVknrQviV52AxWKR2oiI6C4WMp9C48aNg+rvR4pGjx6NEydOSMdMmjQJ+/fvBwBUq1YNr7322hO9RiKip1nMr3+hiClYyuPsVxHVsl7e75dSL2Hq/qnScW1KtEH7kgU8QHJmLbDnMzlv/ing4Jz3q91ux+TfzmDYD8dhtNikw1tUKooxryosWUtEREREREREGNK4BOoV9xUymx0Y+N0RXM23bQtUKuCFsUDD0fJAx5YDJ38UolHVRyE6IFrI7LBj1F+jcDrptDRErRndccPhGmx28e/7Is6h2Dd2ySPMiojo+aP9ry/geZGWloaZM2cK2f/vXwkAR48exdixY4X2F154AS+8IG4gDQCVK1fGiBEj8NFHHyE9PR21a9dG7969Ub16dWRlZeGnn37C5s2bAdx5g/Prr7/+F2ZERPRsyklKR872RHg5+gl5Ym48akzvkve72WbGmF1jYLaZheOq+FfBmOpj8h44ESRdAtb0l/OqvYCI+kL09c7LWJB/yRsAWrUK779WGt1rhyufg4iIiIiIiIig1ajxeccqaPHFLlxNzsnLM3It6L30EH7pXxtuep3YqeEowGIEds0W83Xv3dkKxjsSAOCgccDcRnPRZX0XXM24mneYwWLAwG0DseLVFSjqWlQYouakLtg7eilC7BFCXtQWhisb9yOiSY3/ec5ERM8iFjKfkLS0NEyZMqXA9hMnTkhvVmq1WsVCJgBMmzYNRqMRc+fORXZ2NubOnSsd4+/vj5UrV6JSpUr/07UTET1Pjk76GWHOxYTMaDXAr01paPUOedk3J77B2ZSzwnH+zv6Y1XAWdJp8fwgBgCkbWN0VMGWKeZGKwCviW52bT9/C9I3npCH83Bwxr3MVVAv3fsRZERERERERET1/PJx1+ObNqmg1bw+yjHeXcL2UkIXBq47j667RUKvzPST8wlggdj9wbffdzJQJ/NgL6LkJ0N65N+Dh6IEvXvwCXdZ3QaoxNe/QJEMS3tn2Dla8ugJ6rV4YutqHnXB86I8IcArKyxzUjkjYeBVBDSrCwUk8noiIuLTsU0ulUmHOnDnYvXs3unfvjsjISOj1enh6eqJKlSqYNGkSTp8+XWAhlIiIZPunfCcVMQEgxTsFwXXL5/1+Ouk0vj4hv+0+uc5k+Dr5SjnsduC3wUDCGTHXewLtlgK6u3+onL6Zjne/Pwa7XTy0cqgnfn+nLouYRERERERERI+geIAbPmlfCfkXNdpy9jbm75T3roRaA7zxDeDkJeY3jwDbPhSiUPdQzH1hLhzUDkJ+IfUCZh4SV+cD7ry4EtylIozWXCH31xfF/veXP/ykiIieIyxkPiHh4eGw2+2P9D8TJ0584Li1atXCokWLEBMTA4PBgNTUVBw+fBjjxo2Dr6/CzXQiIlIUu/0IfNJ8pPy6IQbVRnfI+91oNWLMrjGw2q3Cce1LtketorWUB983DzixSs7f+AbwCs/7NSEjF72XHILBLI4d5eeCxT2qw9+dT2YSERERERERPaqXygRg2MslpXzmpvPYG5Msd/AIAlp8Ied7PgUubRGiyv6VMaWuvBLfqvOrsOXaFikPqFYaya6JUh6ijcS5VVvvMwsioucTC5lERPTcu/rHQWSsjYVe4yzkqcYkVJnQSsg+P/o5LqdfFrIQtxAMiR4iD2y3A9umAJvGyG31RwAlXs77NddsxVtLDyE+XXwq08tZh4Xdq8HDSWG5WiIiIiIiIiJ6KP0bRuHV8oFCZrMD76w8ioSMXLlDqdeA6n3kfE1/IFssfjaJaIJe5XpJh47fMx43s25KebXxHRFvuC5kWrUO9gM5uLxh30PMhojo+cFCJhERPddOL94I06YkeDiIS7aabSa4vBAAvbdHXnb49mEsOb1EOE4FFabUnQJnnVgEhdUMrB0I7JwhnzSyEdBwlBBN+PU0jt9IFzKdRoX5XaIR5uPyD2ZGRERERERERP9PpVLho9YVEOEr/o2dlGXEwJVHYbHa5E6NPwQCyotZ1m3gt3eRf0+YAZUHoIJfBSHLNGVi5M6RsNgsQq5SqRDVtw4M1mwhd9N5wrQlCTHrdoOIiO5gIZOIiJ5bB2esgvMZLZy1rlLbbcebiGxWO+/3HHMOxu4aCzvEP1S6l+2Oyv6Vxc6mbOD7TsBRhf0tvMKB1t/e2XPjb7suJmHVoVjp0CmtyqNGpLzcLRERERERERE9Oje9Dl92qQK9TrwtfuBKCj7efF7uoNMDbRYCWicxP7sOOP69eKhah4/qfQRXnXiP4VjiMcw7Nk8a2rtMODIDsqTcVecBy440XFzz10POiojo2cZCJhERPZd2j16EgOQA6NSOUts1yyXU/LCrkM06NAs3sm4IWTHPYhhQeYDY2ZAGLH4duLhZPmlgBaDnZsDlbnEyx2TBqJ9PSIf2bRCJdlVDHn5CRERERERERPRApQLdMaVleSn/asdlrD0uLwMLvxLAyx/K+frhQJq4PGywWzAm1p4oHbrg5AJsvSbvf1llWBvc0F6VchetO+y7MnHhpx0FzoOI6HnBQiYRET139k1chjB7MahVGiG32q244XgVdWb2EPLdcbux+sJqIdOqtJhSdwocNfcUQm1W4MeewM0j8kkjGwE91gNuAUI8a/MF3Eg1CFnFYA8Mf7nkP5gZERERERERET1I6+hgdKwuPzw8eNUx5WJmtd5A1ItiZsoEfukH2MQlaV8JfwWti7cWMjvsGLpjKNZcWiMNXXNyV8TprsGeb6laZ60bsDcHsdsOP9ykiIieUSxkEhHRcyXm178QkFNEyk3WXKQGpaLmB+KbmBmmDIzfM146vk/FPijjU0YMt0wAYuQnLFGhA9BpNeDoJsRHrqdi4e4rQqZVqzC9dQVoNfwnmoiIiIiIiOjfMqFZWZQt6i5kVpsd731/FL8cFVdkgkoFtPgC0HuK+bVdwL4vpLFHVh+JKI8ocWy7FeN2j8OS00uk42t82AU3HWMVipmuSPj1AixG08NPjIjoGcO7pERE9NzIjEuEYUeytJxsjiUTlmo6VBrUSuozff90JOQkCFkZnzLoXb63eODx74E9n8knrfMu0Go+oHUQYpPFhlE/nUC+v1HQr2EUShcR/5AiIiIiIiIiosdLr9Pgy87R8HMT7xHY7MCQ1cfxw6FYsYN7EaDZJ/JAWycBN48KkZPWCbMbzoaXo5d0+MxDMzH3yFypaFljUmfcdIqFzS6+4RngFIyD4797+IkRET1jWMgkIqLnxumPNsDTwUfIjNZc6F/xR4l2DaXjt17binWX1wmZg9oBU+tOhU6tuxvGHQbWDpJPWKE98NIHd57czGfe9ku4cDtLyKL8XDDwhWIPPyEiIiIiIiIi+sdCfZzxfZ+aCHAXi5l2OzDipxP4/oC4BybKtgLKtxMzqwn4rgOQLr7FGekZiSVNlyDQJVA674KTCzBl/xS5mDmxM+J01+Tr1EYh7vcDjzAzIqJnBwuZRET0XNgzejGCnSOkPMUnGaGNq0p5kiEJk/ZNkvJBVQYhyvOe5WEybwHfdwasRvHAolWAZnMVi5in4tLxxZ+XhEylAma0qQBHrUY6noiIiIiIiIj+HVF+rljVpxaKeuiF3G4HRv9yEn9dTBQ7vPox4B4kZlm3gBXtgNwMIY7wiMCypssQ6REpnXfV+VVYG7NWymt+2BXxBvFtUK1aB90pM4xpmY8wMyKiZwMLmURE9Mw7u/wPFLWGSvm13IuoNqqDlNvsNoz5awxSclOEvIp/FXQp3eVuYLUAq98EMuPFAVwDgA4rAJ2TNHZ6jhn9VhyG2So+ddmtVjiiw7wfYVZERERERERE9DiE+7pgVd9aCPIU/463/73MbFLWPQ8vO3kCbRYCGnELGSScBn7ofudewT0CXQKxuMlilPMpJ513+oHpiM8S7ymoVCpE9akFgzVbyL0d/XH1iz8feW5ERE87FjKJiOiZdvvIeaiOGqG9dylYAIm58ag2tZNin0WnFmFv/F4hc9I6YXKdydCo73ljcvccIHa/2FnjALRfAbgXlca12ewYsvoYYlMMQh7k6YRhr5R8hFkRERERERER0eMU4u2MVX1rIsRbLGYmZhoxdPVx2Gz3PJAcWhNoMU8eJGYrsH7YnQroPbz0XljwygJEB0QLeZY5C+N2j5P2xfQuG4HMQLGQCQARziVw4iv5LU4iomcZC5lERPTMyk5MRfzSE3DVeQi5wZKFwA5l4eCsl/ocSziGz45+JuWjqo9CiHvI3SD+BLD9I/mkr88BQqopXs+XO2Kw9VyCkGnVKsztUAmujtqHmBERERERERER/VuCvZzxbbdq0OvE2+Y7LiRi4e4r4sEV2gKN3pcHObwI2Pu5FLvoXDCj/gy4O7gL+f5b+7Hy3Erp+CpDW+NG7lUhU6s0cL7igNjtRx5uQkREzwAWMomI6JlkMZpw8sN18NUHCrnNbkNWiAFFapaV+mSYMjBy50hY7VYhbxrRFK2Ktbp3cOCXtwGbWRygclegchco2XUxCbM2n5fyUU1LoWo4l5QlIiIiIiIiKgxKBLhh/OvyPYOPNp7DyRvpYlh/OFBRYbWnP8YD1/ZIsb+zP8bWHCvlcw7PwZX0K1JefkRTZJnFc7rpPJH66xWknLt6/4kQET0jWMgkIqJn0t4RSxHsHCHlsbYYVH73DSm32+2YuGcibmbfFPJg12CMrzkeKpXqbrh9+p29L+7lGQo0maZ4LfHpBgz6/ihs4soyeLV8IHrVla+RiIiIiIiIiP47HauH4LXyRYTMbLXjnZVHkGW8Zw9MlQpoNhcIrycOYLcBP/UGclKksZtGNMUr4a8ImdFqxPu73ofFJu6v6VLUF7ZyOnnpWUd/XP5yD7KT0x59ckRETxkWMomI6Jmza8xCRDjJe05eN8Sgzsc9FfssOb0Ef1z7Q8i0ai1mNpgJVwfXu2HsAWD3J/IALeYBjm5SfDPNgDe/PYCUbJOQR/q54KPWFcQCKRERERERERH951QqFaa+UR5BnuJ+mVeTczB+zSnxYK0D0H4Z4FNMzDPigF8HSvtlAsDYGmPh6+QrZCeTTmLukbmw5zu+TI9XcFMbK40R6BSC4xN/gSk39xFmRkT09GEhk4iInimHZq1CiEV+y/G2IQ5Vp3WQcpvdhjmH52DW4VlS23tV3kNZ33uWkzHl3FlSNt+TkKjZH4jI9/QlgHO3MvDGvD24mJAl5E46DeZ3iYabXveQsyIiIiIiIiKiJ8nDSYdPO1aGRi0+gPzz0Tj8fiJePNjJC2izCNA4iPn534GDC6SxPfWe+KD2B1K++PRixWJm9MT2uG68LB0f6lIMe4cvhdVqldqIiJ4VLGQSEdEz4+gXv8D7tg80aq2Qp5tSUPy9BnBw1gu5yWrC6L9GY+GphdJY9YLqoWuZrmK4ZSKQEiNmPsWBF8dL/ffEJKHtl3txK0N+MnJ66/IoESC/vUlEREREREREhUd0mBeGNC4h5e+vOYmE/H/vF6kAvDxZHmTTGCD+hBTXD66P1sVbS/m3p77F7MOzpWJm6NAXEWe4Jh0f5VIau0fL9zWIiJ4VLGQSEdEz4eS3v8H9qgsc1I5CbrBmw6NlBNwjxL0tMkwZ6LelH9ZfWS+NFeoWisl1J0Otuuefycs7gANfiQeq1ECrrwCduNTMuuM30X3hQWQaxb0tAGDMq6XQolLQI86OiIiIiIiIiP4LbzeIQvUIbyFLyzFj+I8npGIjqvcBSr4qZlYT8GNPwCiu1gQAI6qNQDmfclK++PRizDg4Qxhfq9Ui6N16SMyNl44PQSTOrPxDyomIngUsZBIR0VPvzHd/QH9WC0eNWFA020ywVNAguH4FIU83pqP7xu44cOuANFY5n3JY2nQpvPX3/JGSmw78OkA+cd0hQHC0EG04GY9B3x+FySouP6vTqPBJ+0roUz/qEWdHRERERERERP8VjVqFWW0rwtVRXP1px4VELN9/XTxYpQJafAG4FRXz5IvAiraAIU2InXXO+Orlr1DBV7xvAQDLzy7H9APTxWKmsyMihjVAmilZOFandoD1QCYyb9x+9AkSERVyLGQSEdFT7cIvO6A9YoaT1kXILTYLsiIMKN21sZDb7DaM+msULqZelMZqGNwQ377yLXycfMSGTWOA9FgxCygPNBgpRGduZmDI6uPI/0Cmq6MWi7pXR8vKfBOTiIiIiIiI6GkT4u2MCc3KSPmU38/gcmK+Ny2dvYHW39xZxele1/cAi18HMsVio7uDO+Y3no+KfhWl8b879x1+uPCDkLkEeCOgSzkYrQYh93L0w/GPfnuEWRERPR1YyCQioqfWjT0nYd2VAWetuN+k1W5FWtE0lO/3utTn25PfYlfcLilvV6Id5jSaA2eds9hwfiNwdLmYqXVAq/mA1iEvSsk24a2lh2AwW4VD/dwcsapvTdQt7vuIsyMiIiIiIiKiwqJNdDBeKRsgZLlmGwavOgZzvlWZEF5XevgZAHD7JLDwFSD1qhC7Objhq8ZfoYp/FanLzEMzcT1DfPPTr0oxZBbNlo4NdymBPROWPNyEiIieEixkEhHRUykzPgmJq8/CTecp5Da7DUneiaj0Xiupz4H4A/j82OdS3r9if4ytORZatbhMDHJSgHWD5JM3Gg0E3t3Dwmy1of+Kw4hLE5+G9HZxwI9v10LZoh4PPzEiIiIiIiIiKnRUKhWmtioPX1dHIT9+Ix0frDst75dZfwRQ4215oNQrwLevALdPC7GLzgVfvvQlogPELWwMFgPG7h0Lq018cLrS4Fa4YbwiDR+QUwRXNslb6RARPa1YyCQioqeOxWLByanr4KcvIuR2ux23XeMRPbKt1CcxJxEjdo6AzS4+JflK+Ct4u+LbUKlU8ol+Hwpk5dtfIrgaUPtdIfrwtzPYdzlFyLRqFeZ1roIwH3HJWyIiIiIiIiJ6Ovm4OmJGm/JSvnzfdXy7K19RUa0GmkwHGr0vD5R1C1jSDMiIF2JnnTNmNZgFb723kJ9KPoWVV1ZKw1T5oA1STUlC5qhxQvqGa9wvk4ieGSxkEhHRU2f3sIUIdSkm5XGaa6g2roOUW2wWDN85HMm5yUIe7h6OD2p/oFzEPLYSOP2zmGmdgJbzAc3dNzdXHriOpXuvSd0nNC+LmpE+Uk5ERERERERET68XSgWgU41QKZ+y/iw2nrolhioV0GAE8NosAPnuPeQkA2sHAvne5PRx8sEHtT+Qxl8Wswzn088LmYO7MzybhsJsMwm5rz4QZ2f8gZyUjIefGBFRIcVCJhERPVX2TFyCCMeSUn4j9wpqTu2q2OfTo5/i8O3DQqbX6DG74Wy46BTemEy6dOdtzPwafwD43i2gnr+ViQm/npYO61QjFF1rhj1gJkRERERERET0NJrQrAyqR4hvTdrtwHurjuJ4bJrcoVpvoM23gFon5pe2AIcXS4c3DGmI1sVbC5nNbsNHJz+CwSJuaxPSOBqJrvLbl0Wdw3Bsws8wZRukNiKipwkLmURE9NQ4tXg9AnOCoFaJ/3wl5saj6rT2in3WXFqDRacWSfnYmmNR3Ku43MFiBH7sDpizxTyiPlDtrbxfzVYbhqw+BpNVXKq2erg3JjYr+3ATIiIiIiIiIqKnjqNWg6+7RiPSV3w4OtdsQ68lh3AjNUfuVK410GyunG96H0i5LMUjqo1AsGuwkMVmx2L2kdnSfpzVx3fCjVx5v8xQlyjsH7kCFqNJaiMielqwkElERE+F+MNnoTlpgYPaUcgzzWmIGFgXWidHqc+B+AP4YK+8HEvr4q3RolgL5RP9MR64dVLMnH2AVl/f2d/ib59tu4TTN8UlWgLd9ZjXpQoctPznlYiIiIiIiOhZ5unsgEU9qsHbxUHIk7KM6L3kEHLNVrlTpU5AydfEzJwNrOkP2MTjnXXOmFZvmvQw90+XfsLSM0uloat+3Anxhlgpj3AtiV3DFsJqVbgeIqKnAO+0EhFRoZebnoEbS4/ATecl5CZrLlxeLgLPYkWlPpfTL+O97e/BYrMIeWnv0hhVfZTyic79DuyfL+ct5wPuRfJ+PXEjDV/8eUk6bGbbivB1lQuqRERERERERPTsCfNxwTdvRksPNJ+7lYkPfzsjd1Cp7ryV6ewr5tf3Ans/lw6v5F8Jvcr1kvKZh2Zi/eX1QqbVaVFxaisk5N6Uji/mUhY73/saVpP5IWZFRFS4sJBJRESF3v73v0cRp1Ahs9ltyAo3IPSVqtLxKbkpGLBlADJNmUIe4ByAz1/8HHqtXj5J+o07T0DmV2sgUOLlvF9zzVYMWX0cVpu4jMubtcJQt7hv/t5ERERERERE9AyLDvPGrLYVpXzF/uvYcDJe7uDqBzT7RM63TQZun5bifhX7oZJfJSl/f/f72Be/T8gcXJxQZuwrSDYmSMcXdymH3UMWIzc9Q2ojIirMWMgkIqJCbceorxHlWlrKb6quo8KA5lJutBrx7rZ3cSPrhpA7a53xxYtfwN/ZXz5J6jVgWSsgN03Mi1YBXpwgRLM2n8elhCwhC/dxxqimpR5uQkRERERERET0TGlWsSj6NYyS8pE/nVDeL7N0M6BiRzGzmoAV7YDkGCHWaXSYU38OQlxChNxis+C9P9/D+ZTzQq739UTE4HpIN6VIp410LYWjY39GZpxc6CQiKqxYyCQiokLr6Fe/ItQWKeXxubGoMa2LlJttZgzbPgzHEo8JuVqlxswGM1HSu6R8kptHgQUvAUkXxNzRHWizENDe3eviwJUULNh1Jd/YwKx2FeHsoH34iRERERERERHRM2VI4xKoHOopZBm5FgxaeRRmq03u0GQ64B4kZhk3gMWvA0nidjYejh6YFj0NPo4+Qp5tzka/Lf1wK/uWkLuH+qNonyrIMKdKpw1xicKFGVuQeCpGaiMiKoxYyCQiokLpxu7jcLwA6NTinpPpphSUH/sqVCqVkFtsFozaOQrbb2yXxhpdfTTqBdeTT3LxD2DRa0C2wpOIr88BvCPyfs02WjDsh+OwiyvK4q36kYgO837oeRERERERERHRs0enUePTDpXhphcfdD5yPQ2fbLkgd3DyBFrOA1T5btFn3gQWvwYkin0CnAIwNXoqXHWuQp5oSMTwHcNhsVmE3KtUCIIH1USKwjKzAU4hiP/2KG4fPffwEyQi+o+wkElERIVO5s0E3Fp9Gu46sUBotObCu01x6H09hdxmt2HCngnYfG2zNFaX0l3QoVQH+SRHlgLftQfM2XLbK9OA8m2EaOr6s7ieIi4HUyLAFUMal3i4SRERERERERHRMy3E2xnT36gg5fO2x2DjKYX9MiMbAi3ny8XMrFt3ipkJYqEx0i0SM+vNhFYtFkuPJR7DvGPzpOHdw/xRamITJOTelNq8HQNwc8lxpMTEPnhiRET/IRYyiYioULGazDg6ZQ0CncS9H2x2G4wlrChSu4yQ2+12TNk3BWtj1kpjvR75OoZXGy6f5NTPwNp3ALtVzDWOQNvFQK3+QrzjQiJW7L8uZFq1CrPbVYKjVvPwkyMiIiIiIiKiZ9prFYqgY/VQIbPbgf4rjmD1IYWiYcX2wBvfyMXM7ARgyetAmtinemB1fFjnQ2mYBScXYH/8fil38HBBhRmtcdN4XWrz0xdFzNydyL4t76dJRFRYsJBJRESFyvZhXyPSrbSUJ+huosxbTaR81qFZWH1htZQ3DmuMD+t8CHX+PwSSLt4pYuan9wTeXAOUbSXE6TlmjPzxhHT4Oy8UR7kgj/vOhYiIiIiIiIieP+NfL4MSAeISsDY7MOLHE/hm52W5Q/k2QOtvAVW+h6WzE4E1/QC7uMfm65Gvo3Xx1kJmhx2j/xqNlFy5KKl10KLa7E6IM1+V2oo4h+LE5HXIzcx6uMkRET1hLGQSEVGhsXP8tyjuVFbKbxljEf2hvDzs9+e+x5IzS6S8fnB9fFTvI2mpFZhygNVvAqZ8X849QoFem4Gw2tJYE9edxq2MXCGrEOyB/o2iHmJGRERERERERPS8cXLQYF7nKvBxcZDapqw/i482noPdbhcbyr0BtF0E5L+XcfUvqA/Ml8YZWX0kojzEexOJhkSM2z1OHhuASqVC9ZldEGe8KrWFuETi0OhVsBhND54cEdETxkImEREVCkfm/4wgQzDU+Z4+TDEmotLk1lCpVEK+9+ZeTD8wXRqnRmANzG44GzqNTmyw24HfhwIJZ8TcyQvo8TvgV1Iaa+OpePxyNE7IHLRqzGpbEToN/wklIiIiIiIiImXF/N2w+u1aCPJ0ktq+3B6DsWtOyQXHMi2AFvJel+o/J0ObfF7InLROmNFgBhw1jkK+88ZOLD+7XPGaVCoVomd0xM1ceZnZcNcS2DNsCYuZRFTo8C4sERH9p8xGI7a+9wU8YlzhqHEW2rItmQgfWAtaF/FL+dX0qxi6Yyis+fa4rOhXEZ++8Kn0JR4AcHQZcPw7OX/jG8AzVIoTMnPx/i+npHzEKyVRPMDtIWZGRERERERERM+zKD9X/NivFor5u0ptK/Zfx09H4uROFdsD5dsKkcpqgse24YBVLDKW8CqB4VWHS0PMOjQL22O3K16TVqdFlWltcDtXPnekSynsHbYUptxchZ5ERP8NFjKJiOg/E7vjCA4PW4WS+gpw1IhPKJptJji/HAD3qCJCnm5Mxzvb3kGmKVPIg1yD8OkLn8JZJxZDAQC3TgLr5S/2qD8cKN5Yim02O4auPo7kbPEPhOrh3uhRJ+IhZ0dEREREREREz7siHk74oW8tVAzxlNo+WHsacWkGudOrHwPuQUKkSz4P14NzpUPblWyHl0JfEjKr3Yqh24fiQPwBxWvSOjmi7ITXkGS8JbVFuJTEweHfwZStcF1ERP8BFjKJiOg/sWfiEuSuS0Cwi3Jh0BBuRMjL0UJmtpkxbMcwXM24KuQuOhd89sJn8NZ7ywNlJwGrugCWfE8TRtQHGo5WPPf8nTH462KSkDk7aDCzbUVo1CrFPkRERERERERESrxcHPBd7xqoHiHet8g0WjD8h+Ow2fItMevkBbSUl5h1OfYtVNf3CJlKpcLE2hNR1KWokJtsJryz7R2cSDyheE16LzcUH9YIKcZEqS3MpTgOjVqF3Iysh5keEdG/ioVMIiJ64vZNWoZgQzictC5Sm9lmRHpwBsr0f1Vq+/jgx9gXv0/IVFBhRv0ZKO5VXD6RORf4vhOQelXMXQOB1t8Cao3U5fC1VMzafEHKJzYri1Afhbc9iYiIiIiIiIgewMVRi886VoaHk07I98QkY8neq3KHyIZAzQFCpIIdml/6AGmxQu7h6IEvX/oSno6eQp5jyUG/Lf1wIVW+zwEALkG+iBpWH8nG21JbqEsUjr3/M4uZRPSfYyGTiIieqLQr8fDO8IFaJf8TlGS8Dc/uJVB24GtS2y8Xf8HKcyulfGjVoagfXF8+kc0GrOkHxO4Xc5UGaLMQcPWXuqTnmDFo5VFY8z0J2aJSUbStGvyAmRERERERERERFSzAXY8PW5aT8ukbzuFSgkLB8MXxgF8pIVJl3QKWtwZyUoQ80jMS8xvPh6tO3I8zw5SBPpv74Gr6VcVrcgnyRfHRLyExV15mNtglAgfGyvdiiIieJBYyiYjoiTr1yXo4a92EzGKzIN7hBirOag2vMqFSnxOJJ/Dhvg+lvFWxVnizzJvKJ/pzCnD6Zzlv+hEQXkeK7XY7Rv18QtqbIszHGZNbloNKxSVliYiIiIiIiOh/07xiUbxeoYiQGS02DF19DBarTTxYpwfe+AZ2tfgWJ5LO31mByixuo1PWpyw+f/Fz6DV6IU/OTUa3jd1wOvm04jU5+3ui9LiXkZB7U2oLdyqOE9+se8jZERE9fixkEhHRE3Pxl78Qoo8UMqvdCnMNLapN6giVwv6TiTmJGPznYJhtZiGv7F8Z42qOUy4wHl0O/DVTzmv2B6q/pXhtX++8jA2nxKcPdRoVPutYGW56nWIfIiIiIiIiIqJH9WGLcvBzcxSy4zfS8dHGc7Db8+2XWaQCrM0+lQe5vhf4+S3AZhXi6IBozGk0B1q1VshTclPQc2NP7L25V/Ga9D4eKDepGW7n3hBytUoD9VkLspNSH3J2RESPFwuZRET0RFitVqRvj4VGJX6RjjNeRfHW9RT7mKwmDN4+GAmGBCH3d/bH7IazodMoFBhjtgHr3pXzkq8CL0+WYpvNjukbzmHahnNS28gmpVAh2LPgSRERERERERERPSIvFwfMaF1Byr/56wo+2nheKmbay7VFRs3h8kBn1wIbRwH5jq8bVBcf1ftI2tYnx5KD/lv7Y+OVjYrX5eDujFIjGiPbnCHkng6+OPLhTw8zNSKix46FTCIieiIOT1+FQKcQIcuxZKH8MHk/TODOUq9T90/F8cTjQu6gdsDcRnPh6+Qrd7q6G1jZCbBZxLxIRaD1AkCtEWKjxYr3Vh3D/B0x0lAvlPJHr7oRDzEzIiIiIiIiIqJH06iUPzpWD5Hy+TtiMHX9WamYmVOxF7LLK2yvc+Br4OACKX45/GXMbjgbDmoHIbfYLBixcwRWnlPe+9KlqC+MUTYpD9MXx4mv195vSkRE/woWMomI6F+XnZgK9xR3KU9xTYZbsJ9in5XnVuKni/LTfuNqjUM533JyhxuHge/aARZxj0u4BwEdVwEOLkKcbjCj28IDWHtcYf8HH2d83KYC98UkIiIiIiIion/N2NfKoGKwh5R/89cVTPrtjFjMVKmQWXs0bKVbyANteh+4dVKKXwx9EfMbz4erzlXI7bjz8Pjq86sVr6tC/2aIzb0sZGqVGppzdmTfSn6ImRERPT4sZBIR0b/u6NSf4aoTv5gn5saj6tgOisfviduDjw5+JOWdS3dGy2It5Q7xJ4DlrQBTlpjrPYBOqwH3IkKcmm1Cu/l7se9yijRUxRBP/NivNnxcHaU2IiIiIiIiIqLHxcVRi2W9a6BSiKfUtmj3VUxcezpfMVMNa/MvgLA64sFWI/BDD8CULY1TLbAaFjdZrLiy1eR9kwtcZrbC6ObIMqcLmYeDN45OXfPAeRERPU4sZBIR0b/q+LxfEaKLFDKb3QaXWr7QarXS8ZfTLmPYjmGw2cVlTKoFVsPQqkPlEySeB5a1BHLFL9dwcAW6/AwEim9v2mx2DF59DOdvZ0pDvVTaHyvfqgFfFjGJiIiIiIiI6Alw1+uwrFd1RId5SW1L9l7Div3XxVCrB9ovB9yKinnyRWDDCMVzlPQuiaVNlyLULVTI7bBj9K7R2B23W+rjEuAFcwlIS9yGORXH/snLH2JmRESPBwuZRET0r7mwahvcrjhDoxYLljdyL6NE+xek49Ny0zBw20BkmsUiY4hbCGY3mA2dWid2yLgJLG0J5ORb1kTrBHRaBQRXlc4xb/slbD+fKOVdaoZifpdoODvIxVUiIiIiIiIion+Lm16HJT2ro1q4XMyc8vtZXE3O96alszfQ+htAle/2/tHlwMkfFc8R4haCxU0WI8g1SMgtNgsGbx+MYwnHpD7l+76OWOMVIVOpVPDLCMT51dsePDEioseAhUwiIvpXxP55BDiYCweNXsgNliyU6tdIOt5sNWPw9sGIzYwVcledKz5/4XN46j3FDqYcYGVHIDPfHpcaB6DDCiC8rnSOPZeSMPuPC1I++KUS+LBFOWg1/GeRiIiIiIiIiJ48V0ctFveojuoR3kJuMFsx7MdTsNjENyMRXheoP1weaN17QMoVOQfg5+yHbxp/Iy0za7AY0H9rf5xPOS/1qTimOTJMqULmoHaEbX8WUs5cfeC8iIj+V7xjS0REj13KmSvIWBcLZ62bkJttJhhLWuFdKlzI03LTMGT7EBy6fUjI1So1ZjaYiUhPcWla2GzAmn5A/DExV2uBtkuAYi9K13Q7IxeDvj+K/N/7m1UsikEvFoNKpXqUKRIRERERERERPVYujlp82bkKfF0dhPz4jXQsORAvd6g/AgitLWamTOBH5f0yASDEPQRfNf4Kbg7iPZtMUyZ6b+6Ns8lnxWvy94LTS/4wWXOF3E3nhctf7YXJIOZERI8bC5lERPRYZccn4/rXh+DhID5BaLNbkeSTiHJvvS7ke27uQeu1rbH9xnZprBHVRqBOUB0px46PgDNr5Lz550CpV6XYYrXhnZVHkZRlEvJIPxdMe6M8i5hEREREREREVCj4uDrio9YVpHzh/nicuZWvOKnR3lliNv8qVjePAt93BixGxXOU8CqBeS/Ogz7fKlppxjT02twLp5JOCXnEqzWRFpAOm90m5IFOwTgw6ruHmxgR0T/EQiYRET02uelZODt9E3z1gVLbDe01VBvZIe93o9WIGQdnoO8ffZFgSJCOb1uiLTqV6iSf5NRPwI7pcl53MFCpo+J1fbzpPA5cSREyvU6NLztHw9WRe2ISERERERERUeHxYukAdKweKmRWOzBx0xUYTFbxYI9goMUX8iCX/wR+7AlYLYrnqORfCZ80+gRatXhf5P/fzDyacFTIqwxrg1jrZWmccKfi2D1q8YMnRUT0D7GQSUREj4Ul14Tj435BoFOI1HbVdBG1p3TL+z3dmI6u67ti2ZllimO9GPoiRtcYLb8pGXcEWNNf7lDyNeCF8YpjrT4Ui692yl+0p7Qsj5KBbgo9iIiIiIiIiIj+W2NfK40wH2chu55qxJQN52G359s3p/TrQJ335EHO/QasHXhnix4FdYLqYE7DOdCpdUKebc5G3z/64kD8ASGvMb0r4nKuSuOEIQq7Ry164JyIiP4JFjKJiOh/ZrFYcHDESgQ5h0tt1w2XUHPGm0I2ed9knE05Kx3rqHHE6OqjFb9EI/P238ui5Nt7IaA88MbXgFr+J23PpSSM+fmklHeoFoLW0cEPnhgRERERERER0X/AxVGL2e0qQZ3vGe9Vh27gm7/kB7bx0kSgyptyfnwlsHEUkL/4+beGIQ3x2QufwVHjKOQGiwH9t/bH6aTTeZlWq0W50a8i1ZgojROGYtg9gsVMInr8WMgkIqL/2f5hyxDiHCnlcTlXUf2jztBq7y5TcvDWQWy8ulE6toRXCXz/2vfoVLqT/CamxQSsfhPIvCnmLn5Ax5WAo6s03qWELLy9/DAsNvGLevkgD0xsXvYRZkdERERERERE9ORFh3lhQKNiUj51/Tn8eixODFUq4PVPgLJvyAMd+ArYOPq+b2Z++dKXcNI6CbnRasSYXWNgtN7da9OliA+8WkQi25whjROmLobdw1nMJKLHi4VMIiL6n+wetghhevlLdbwhFhU/bAWt3iEvs9gsmHZgmnRsx1IdsfK1lSjmJY8DANg4EojdJ2YaB6D9CsBTXso2OcuIHosPICNX3AeiiIceC7pVhV6neYiZERERERERERH9twa9WBxVwzylfNgPx7H7UpIYqjVAq6+A4i/LA+3/Evilz52HxRVUC6yGrxp/BRedi5BfTr+ML46Je3AGN6wEpyYByDKnS+OEaYph9zAWM4no8WEhk4iI/rG9o5cgTCsXHxNz41FmbFPoPcQ3JVefX42LqReFrKxPWYyqPgoOGgcoOrQIOLRQzl+bDYTWkOJcsxVvLT2E2BSDkLs4aLCwezUEuOsfMCsiIiIiIiIiosJBp1FjfufKiPAW72eYrXb0XXYYp2/mKyZqHYB2S4GwOvJgJ38AVnYAjFmK56rsXxlfvPgFVBBXylpyegmOJx4XstDGVeHSrCgyzWnSOGHaYtj73mJYzBapjYjoUbGQSURE/8j+D1Yg2BYh5SnGBES8Uwcufl5inpuCz499Lh0/usZoqFUF/HN0fR+wfricV3sLqNJVik0WGwasOIIj19OEXKNW4YvOVVC6iHvBEyIiIiIiIiIiKoQ8nHSY07I4/Fx0Qp5ltKD7ooOISxMf5obOCej4PRBaSx4sZiuwtDmQnax4ruiAaHQr203IbHYbxu4ai1xLrpCHNKwCt1ahyDClSuOE6KNwdNgqmDJyHmKGREQFYyGTiIge2ZGPf0CRnCBpL8t0UwoCu1eEZ1SQ1OfTI58i05QpZM2jmqOiX0Xlk6THAau6AjazmIfVAZrIy9NarDYMXnUMW88lSG0Tm5dFw5L+D5gVEREREREREVHhFOjugNkti8HVUSvkiZlGDFp5FBZrvv0v9e5A11+AEk3lweIOA4uaAFmJiucaUGkAwt3DhexqxlVpiVkACK5bEZ5tI5FuSpHaijiF4sz435EZK9+rISJ6WCxkEhHRIzk5/zd4J/pArRL3mcwyp8OjRRj8K8pLzZ5OPo2fL/4sZC46FwyOHqx8ElP2naVOsvN90XUPBtouATTiE4g2mx0jfjyB30/GS0P1qhuBrjXDHmJmRERERERERESFV3E/Z3zZqRJ0GvHB8sPXUvHJlotyB50T0H45UKmL3JZ0AVj+BpAr73Op1+oxue5kaQWtJaeX4FjCMen4orXKwbdLaSTn3pbafPWBuD5nL5KOxdx/ckREBWAhk4iIHtq5FVvhetkJWrVYSMyxZEHXyBvBDSpJfcxWM6bumwo77ELer2I/+Dr5yiex2YBf+gK3Toi5Vg90WA64+gmx3W7H2F9P4eejcdJQb1QOwvuvln64yRERERERERERFXI1I73xcRt5dasvtl/CnktJcgeNFmjxOVBX4WHyWyeA7zoAZoPUVNGvorTErB12vL/rfWnFLQAIqFICEYPrIt5wXWrzcPBG/NITXGaWiP4RFjKJiOihXF67B7pjVujUDkJutBpgraJD1Ou1pT42uw3j9ozDiSSxKBnpEYlOpTspn+jPKcDZdXLe/DOgaGUpnvL7WXy3X/6S/Gr5QMxoUwFqtUpqIyIiIiIiIiJ6WrWsHISO1UOFzG4H3lt1DMlZRrmDSgW8NBFo/KHcdn0P8EN3wGqWmgZUGoBIj0jx8MzrGPXXKFhtVul497BAlP+gGa5ly2+Hejn44uiEn+47LyIiJSxkEhHRA93YcQzWnRlw1DgJudlmRGZULkp3ekmx3+xDs/H75d+lfGT1kdDle6sTAHBiNfDXTDmvNwyo0E6Kl+69igW7rkj5C6X88Un7ytBq+M8cERERERERET17xr9eBiUCXIUsIdOI4T+egN1uV+5UZxDQcLScX9gIrOl/Z5WsezhqHDGl7hRpidmdN3Yq7pcJAHpPd9SY2QWXss9IbUGO4Tj6MYuZRPRoeIeXiIjuK/HoJWStvQEnrYuQW2xmJPklo9LbzRX7LTm9BEvOLJHyNiXaoHZR+e1NXNsD/DpQzks3Axq9L8V/XUzEB+vkL8V1ivlgXucqcNDynzgiIiIiIiIiejY5OWjwWccqcMx3/2PbuQR8q/DQd54GI4HqfeX85Grg9yFSMbOcbzm8W+Vd6fBvTn6DTVc3KZ5C6+iAhp/1xZXs81KbZ6In4nedKvj6iIjy4V1eIiIqUNrFm0hYdhquOg8ht9mtuOkSi2rD2yv2WxezDjMPyW9WNghugPdr5CtK2qzAX7OAJc0Aa77lTwIrAK2+AtTiP1cxiVkYsOIIrDbxCcPoMC9882ZV6HWah5whEREREREREdHTqWSgG8a9XkbKp204hz/O3FbupFIBTaYDFRTu6RxeBKx95869mnv0KNsDTSOaSoeP2z0O51PkYuX/qzSmJZKNt4RMp3ZA8s+XYMqS9+UkIlLCQiYRESnKvpWC2Hn74OHgLeR2ux3XVDGoPaGbYr/dcbsxfvd4Ka/oVxEfN/gYWrX2bph2/U4Bc+skwGYRO7gGAB2/BxzEN0HTckzoveQQMnLF40O8nfDNm1Xh7KAFEREREREREdHzoHONUDQtFyhkVpsdA747gj2XkpQ7qdVAiy+AEnJxEseWA7/0Bax377uoVCp8UPsDlPIuJRxqsBjw7p/vIjU3VfE0bkV8oa/jA6NVLFp6Ovjg2HguMUtED4eFTCIikpgycnBx+lZ4OfpJbZfN51Bvei/FfscSjmHw9sGw2MUiY6RHJD5/4XM4ae/ZY/PUz8CXdYFru+WBtE5Ah5WAR5AQm602DPjuCK4kZQu5q6MW33arBm8Xh4ecIRERERERERHR00+lUmH6GxUQ5Okk5CaLDb2XHsLR68pFRmh0QNtFQER9ue3kD8CPPQCLKS9y0jphbqO58HL0Eg6Ny4pDnz/6FFjMLNnmBcTprkt5UYcwHBz7/QNmR0TEQiYREeVjMZhwcvxa+OoDpbZLOWfQYHYfxX7nU86j/5b+MFjEp+z8nf0x/6X58NR73g3P/HrnC7ExXR7IMwzotg4IjpaaPvztDHZfShYytQr4rGNllAhwe/DkiIiIiIiIiIieMR7OOizpKT/gnWOyovuigzgbn6HcUecEdFoNFHtJbju7Flj9plDMLOpaFLMazoJGJW7pcy7lHHpt7oWU3BTF09T5sAcuZ52T8iKWIBwYvfwBsyOi5x0LmURElMdituDI6B8QoA+S2mKyzqLenN6K/a5lXEPfP/oi05wp5G4Obpj/0nwUcS1yN8zNANYPV76Aih2Bt3cBIdWkplUHr2Pp3mtSPubV0mhUyv8+syIiIiIiIiIierYV83fD0p7V4eYobrmTbjCj67cHEJOYpdxR5wR0+A4o+arcdmED8OcUIaoWWA0jq4+UDr2YehG9NvVCkkFezlaj0aDSyOZIzr0ltRW1h2Hf8KX3mRkRPe9YyCQiIgB39r48PHIliupDpbarWRdQa+ab0Gg0Utut7Fvos7kPknPFNyWdtE6Y9+I8FPcqLnbYOQPIyrfhvN4DaLMIaDUf0LtL5zhyPRXj1pyW8vZVQ9CrbsRDzI6IiIiIiIiI6NlWLsgDC3tUg14n3vZPyjKi7fy9OHEjTbmj1hFotxQo01Ju2/MpcOOQEHUs1REDKg2QDr2Udgk9N/VEYk6i1OYeEgCXlwKRbkqW2oI1Edg7ZAnsdnuBcyOi5xcLmUREBAA4MGI5ghzCpTw2+zKqTmsPB71eakvNTUXfP/riZvZNIdeqtfik0Seo5F9J7JB4Adj3pZhpHIHe24Bybyhe1+2MXLy97DBMVpuQVw3zwocty0GlUj1wbkREREREREREz4Nq4d74qmtV6DTi/ZKUbBM6fr0Pf12Ui4wA7uyZ2fpboHxbMbfbgDX9AHOuEL9d8W28W+VdaZgr6VfQY1MPxGXFSW3FXq8H5yZFkGaS39oMcYjE3sGLYbFYHjBDInresJBJREQ4MHYFgjThUh5vuI5yY16D3s1VassyZaHfln64nH5ZyNUqNWbUn4HaRWuLHex2YONIwJbvC2nd9wDfYorXZbRY8fbyw0jINAp5oLse87pUgYOW/4wREREREREREd2rQQk/fNqhMtT5nv3ONlnRc/FBrD1+U7mjRgs0/wzwybe6VtIFaYlZAOhdvjeGRA+R8msZ19B1fVecTzkvtUW8XAOerSKQapQLqqH6YjgwbDmLmUQk4B1gIqLn3KHJq1DUIi8nm5h7E1ED68KtiK/UlmvJxaA/B+F0srzc68RaE9E4rLF8ovPrgZhtYuYRAtR5T/G67HY7xq85jaPX04TcQavGV12j4e8mvyFKRERERERERERA0/JFML9LtPQQuNlqx7vfH8WSPVeVO+qcgJZfAqp8pYO9nwOxB6TDe5TrgWFVh0l5oiERPTb2wMFbB6W24HqV4Nu5NFIUi5lRODhsBYuZRJSHhUwioufY8Tlr4J8ZKOWpxkQEdq0A7+JhUpvZZsbwHcMVv4gOqzoMrYq3kk9kNgAbR8v5y5MBB2fFa1u85ypWHYqV8mmtyqNiiKdiHyIiIiIiIiIiuuPlsoFY1rM63PRaIbfbgQlrT2P5vmvKHUOqAbXfEbO8JWYN0uHdynbD6OryfZ9Mcybe/uNtbLm2RWorUrU0ivSoiBRjgnx6fSQODfsOFjOLmUTEQiYR0XPrzLeb4BnvAXW+J+wyzKnwaBaGItGlpT42uw3jd4/H9hvbpbY+FfqgW9luyifb8zmQlu/LcUR9oEwLxcO3n0/Ah7+dkfIedcLROjpY+RxERERERERERCSoEemD1X1rwc/NUWob9+sprCtomdmGYwDfkmKWfAnYNlnx8E6lO2FG/RnQqsWiqclmwpDtQ7AuZp3Ux69CFIL7VkVy7m2pLVgfgcPDv2cxk4hYyCQieh6d/XYznM5rocn35TLbkgltXQ+EvhAt9bHb7Zh+YDp+u/yb1NahZAcMrDRQ+WTX9wE7PxYzlQZoOgNQqaTDLyVk4p3vjsJmF/NakT4Y86pcXCUiIiIiIiIiooKVLuKOn/vVRriPuCqW3Q4MXnUM28/Lb0VCpy94idmzclESAJpGNMW8F+fBWZvvPLDjg70fKO6Z6V0qDCH9qiExN15qC9KH4fCIlbDb7VIbET0/WMgkInqO2O12HBy7Ei4XHKFTOwhtudYcmMoBJVo1UOw77/g8rDy3Uspfi3wNo2uMhkqhKImEs8B37QCrUcyr9wH85aJkarYJPRcfQqZRfNouzMcZ8zpXgU7Df7aIiIiIiIiIiB5ViLczVvethVBvschosdnx9vLDOHQ1Re4UHA3UeVfOf3n7zj0fBbWK1sLCJgvhrfcWcqPViOE7hyPHnCP18S4ZhrCBtZCYK78dGuQYjgPDl99nZkT0rOMdYSKi54QpPRuHhnyPIpZgaTlZk82ItOAMlO/+qmLfZWeWYf7x+VLeILgBPqzzoTQeACD9BrC8NZCbLuauAUDDUfL1WWx4e/lhXE8Rv9C66bX4tls1eLk4SH2IiIiIiIiIiOjh+LvrsbxXDWmZ2VyzDT0WH8SZmxlyp4ajgYByYmbKAlZ2BAypiucp61MWy5oug6+Tr5BfSb+CKfunKPbxLhaCsIG1cdsQJ7UFacNxYPSK+8yMiJ5lLGQSET0HEo9cwoUP/kARR3l/SbPNhNse8aj6blvFvmsurcGMgzOkvGpAVcxsMBM6tU7ulJMCLHsDyMj35VPnDHRYCTh5CnFKtgn9lh/G/ivi038atQpfdKqCYv6u958gERERERERERE9UKiPM5b1qg53vbjdUGauBZ0X7MOpuHwPpGsdgfbLAScvMU+9AvzYE7BZlc/jHoqP6n0kPfy+NmYt1lxao9jHu1gIIgbVQYLCm5lF7aE4NOH7+0+OiJ5JLGQSET3jko5dQtp3l+Dp4CO1ZZrTkBqejlrvd1Xsu/XaVkzYM0HKS3uXxmcvfAa9Vi93MhvuPJWXlG/fA7UWaLfszrIk99h5IRFNPtmJrefk/RjGvVYa9Uv43Wd2RERERERERET0KEoFumNRj+pw0mmEPDXHjE7f7MOx2DSxg3cE0GaRvF9mzDZgy8QCz1O9SHW8XeFtKZ+6fypi0mIU+3hHhSC4VzSSjbeltkBjEI5O/6nA8xHRs4mFTCKiZ5gp24CbS07ASesitd0yxMKtVSiq9H9Dse+++H0YvnM4bHabkEd4RGB+4/lwdVB4S9JqBn7oDsTuk9tafAEUfynv11yzFZPWncGbCw8gIdMoHd65Rii61Q6/7/yIiIiIiIiIiOjRRYd5YX7XaOg0KiHPyLWgy4L9OJh/z8yoRsDLk+WB9nwKHF5c4Hn6VOiDGoE1hMxgMWDYjmHIMmUp9vEvXww+rYshzZQktfmk+uL4zF8KPB8RPXtYyCQieoad+uBXeDvKbzTGZJ1F6fGvIrhuRcV+JxJPYNC2QTDbzEJexKUIvm78tbRhOwDAZgPW9AMubJTbGk8CKnbI+zU124Q35u3Bwt1XFM/fpGwgJjYvC5VKpdhORERERERERET/mwYl/DC/SzQcNGKZIMtowZvfHsCeS/kKiTX7AxU6QLLuXeDQIsVzaNQaTKs3TbqXdCntEvr80QfpxnTFfsF1K8GpcQAyzeI+nGqVGl6J3jg+a839J0dEzwwWMomInlFXvvgDQY7hUn4x9zTqzu0FFx9PxX6XUi+h/9b+MFgMQu6t98bXjb9GoEug3MluBzYMB07+ILfV7A/UHiREY389hTPx8gbyjlo1PmxRFl92qQKdhv9EERERERERERH9m14sHYAF3apCrxPvwxjMVvRYfBAnbqTdDVUqoNknQNHK8kC/vQcc+EbxHH7OfphWbxpUEB9YP5l0Ej039USyIVmxX1TT2rBXd0K2RbyHpFap4ZXgheOz1zxgdkT0LOBdYiKiZ9CtDUdQ1Bgs5VeyzqP+rLeg0WgUegE3Mm+g7x99pafh3HRu+KrxVwj3CFc+4Z9TgIML5LxCe+DlKXe+6P5tT0wSfj8RLx1auog7fnunLrrWCuebmERERERERERET0j9En5Y1L06nB3E+0VGiw2DVx1Drtl6N9Q5Ae1XAJ5h8kDrhwH75iueo3bR2uhXsZ+UX0i9gO4bu+N2trwnJgCUad8YuaWtyLFkCrlapYbXbU+c+OTXB8yOiJ52LGQSET1j0i/Gwfk0oFFrhTwxNx6V3m9ZYBEzyZCEPn/0QYIhQcj1Gj0+f/FzlPIupXzCPZ8DOz+W85Kv3tkXU333nxqL1YYP1p6RDu1WKwxrBtRG8QC3B8yOiIiIiIiIiIget1pRPljWqzrcHMX7STGJ2ZjzxwXxYI8goPvvgFeEPNDGkcCezxTP8XbFt9G7fG8pv5pxFd02dsONzBuK/Sr2ag5DKTMMFnFPTbVKA494D5z4fO19ZkZETzsWMomIniHZt1MQ981hOGvFgqDBkgXPV8PgVsRXsV+6MR19/uiD2MxYIdeqtJjdcDaqBFRRPuGRZcDm9+U8vB7QZhGg0Qnx8n3XcP62+ARdhWAPTGhWFo5a5QIrERERERERERH9+6LDvPFt92pQ51so65u/LuPIdXGvSniG3ClmekfJA20eC2yZeGcronuoVCq8W+VdDKo8SOoSlxWHHpt6IDYjVmoDgIq9WyCnlBkGS7aQa1QauF93xfkV2x44PyJ6OrGQSUT0jMhNTseFaVvh4+gv5Fa7FamB6Yh4uYZivxxzDgZsHYCLqReFXAUVptabinrB9ZRPeHoNsE7+4omilYGOKwGdXoiTs4yYnf8JPgAfNC8Ldf5vyERERERERERE9MRVj/DGW/UihcxmB4b9cFxcYha4+2amT3F5oF1zgF8HAlaL1PRWhbcwotoIKb+VfesBxczmyC5hRK41R8i1ah20xyyI3Xz4AbMjoqcRC5lERM8AU0YOTk/aAD99oNR2xXQB1Yd1UO5nNWHw9sE4nnhcahtbcyyaRjRVPmHMNuCn3oDdJua+JYHOPwGO8hKxMzefR0au+OW1TXQwKod6FTArIiIiIiIiIiJ60gY3LoEoPxchu5yYjVmbz8sHuxcBuv92555QfseWA6s6A6Ycqalrma4YX2s8VBAfbr+dcxvdN3XH9YzritdWqU8LZBXLhdFqEHJHjRNyNt1C0vGYB8yOiJ42LGQSET3lTDm5ODFuDQKcgqS2K9nnUPfjnor9rDYrRv81Gntu7pHa3q3yLtqVbKd8wtgDwPedAZtZzD1DgTfXAC4+UpcTN9Lw/UHxaTo3Ry1GNilg300iIiIiIiIiIvpP6HUazGxbUVpidsGuKzh8LUXu4BYI9FgPFFXYmujCRmBpCyBH7te2RFtMqTtFKmYm5CSgx6YeuJZxTfH6KvVtgZSAVFjy3Zty0bnj1pKTyIxNuP8EieipwkImEdFTLPt2Co6N/gmBTiFS29WsC6gytQO0Wq3UZrfb8eG+D7H52maprXvZ7uhVrpfyCW+dAla0Acz5nqRz8Qe6rgHci0pdTBYbxv96Ov+2CHj3peLwc3MscG5ERERERERERPTfqBzqhT71xf0v7XZgyOrjSDeY5Q4uvkC3dUDUi3LbjQPAqi6Ky8w2i2qGKXWnQK0SSxUJOQnoubFngW9mVhvWHjccrsKWb7UwTwcfxMzagbSLNx8wQyJ6WrCQSUT0lDrxxTrcmLEXRZ1Cpbbr2TEIG/IiHPR6qc1gMWDkXyPx08WfpLY3ir+BIdFDoFIp7FmZHAMsawXkpou53gPo+gvgI2/ubrPZMfzH4zgWmybkxfxd0a12+H3nR0RERERERERE/533XiqO4v6uQnYtOQfvfX8UVptd7uDoCnT8HqjQXm67thvYMV3xPAUWMw0JeGvzW7iVfUuxX93JPXHFLC9366sPRMJXx3Fm0aYCZkZETxMWMomInjIZV29h/3vL4R3rCRedu9Qel3MVIe82gM5ZLmLGZcXhzQ1vYsOVDVJb47DGGF9zvHIRM+MmsKwlkJ1vaQ6dM9D5RyCwnOK1Tl1/Fr8ek5+Am9isLHQa/hNERERERERERFRY/f8Ss5p8a8z+eT5Reb9MANA6AC3nA7UGym07ZwIxfyp2ez3ydUytO1UqZt7Mvom+f/RFam6qYr8Gs/vgcvZZKXfWusL1nB57hy+FxSy/CUpETw/eRSYieoqc/up33P78GIL0YYrt8YbrKDm6CXSuTlLb/vj96PBbB5xLOSe11SxSE9PrTYdGrZEHzUm58yZmWr6lPNQ6oP1yIKS64rV8vTMGC3ZdkfJONUJRt7ivYh8iIiIiIiIiIio8KoZ4YsQrJaV83vYY/HaigOVb1WrglSlA5S75GuzAz28BmcpvWL4W+Rqm15suFTMvp1/G21veRpYpS7Ff7ZndcS37onwZKjVCNBE4OfxnpJyS71ER0dOBhUwioqfEkY9/hOtlZzhpXaQ2m92KKznnUXZ8M7j4ekrtK8+tRN8/+iLNmCa11Quqh7mN5sJB4yCf1JgJLG8NJOYrfqrUQOsFQDGFfQ8A/HL0BqaulwumL5X2x6TmZRX7EBERERERERFR4dOnfiSaVywq5cN/OIEzNzMK7tj0Y8CvtJhlJwI/9QZsVuUuEU0xsdZEKT+TfAbvbHsHuZZcqU3r6IBqH3dCjPEMrHb57Us/fRHELzyBzGvKBVQiKtxYyCQiegocnLQSvkl+0Ki0UluKMQGpxTNR79PecPaRl5rdeHUjpu6fCqtd/oL4Vvm38NkLn8FZ5yyf1JwLrOwI3DwitzWbC5RtqXit288nYPgPJ6S8SqgnPutYBVouKUtERERERERE9NRQqVT4qHUFlC0q3ncymK3os+wQUrJNyh0dnIG2i+9sTXSvq38BOz8u8HytirfCsKrDpPzQ7UMYumMoTFb5fA56PRrM6YuU0FRkmtOkdg8Hb1ycswOmHEOB5yWiwol3k4mICrn9Y5ejSE6wtKyGxWZGjOEsio1rjIq9myn2vZR2CeN3j5dyJ60TZjecjUFVBikvJ2u1AD/2vPPFMr+XJwNV3lQ836GrKXh7+WFY8m34XszfFQu7V4OTg8K5iIiIiIiIiIioUHNy0OCrrtHwdhFX9LqRakDnBfsLLmb6lwJenSnn26cDJ34o8HzdynZDnwp9pHznjZ0YumMozFazYr/KA96Ab48yuJEtLyXrry+KI6N/hN1uV+hJRIUVC5lERIWU3W7H/hFLEWSR98M0WLKRFpmJBnP7wNlbfgsTALLMWRi6cygMFvFJs2DXYKx4dQUahzVWPrHNBqx9Bzj/u9xWbyhQ+x3FbmduZqDH4oPINduEPNBdj6U9q8PTWWHpWiIiIiIiIiIieioEezljXucq0KpVQn42PgOdvtmHpCyjcsfKnYGKHfOFf++XeXhJgecbWGkgOpbK3w/YHrv9vsVMv3JRqDqnEy7nnJXn4BSOfSMKPicRFT4sZBIRFUJ2ux37hy5HkDpCasu2ZMJcRYNKb7cosL/NbsO0E9MQmxUr5F6OXvj2lW9R3Kt4QScGNo0Bjn8nt1XtBbwwTrHblaRsvLlwPzJzxX0IPJx0WNKzOop6OhV4rURERERERERE9HSoGemDCc3KSPm5W5no+PU+JGYWUMx8dSbgWyJfaAfWDQL2fanYRaVSYVT1UWge1Vxq+zP2TwzfORxmm3IxU6vVovbH3XE9O0ZqC9FE4eDk75Wvk4gKHRYyiYgKGYvZggODlyPYIVxqyzSnQdPADWU6FfA25d+WxSzDgaQDQqZWqfFxg49R1FXenD3Pzo+B/QpfHsu1ufOFU6WSmuLTDeiyYD+SssQlRJwdNFjUoxpKBrrd91qJiIiIiIiIiOjp0bVWOIY2zl+UBC4mZKHD13uRkJErd3J0BTp+D7gp3JfaOAr4a5biudQqNSbVnoRXI16V2rZe34oRO0YUXMx0dEDFCS2RkHtTavPPCMCxub8o9iOiwoWFTCKiQsRiMOLI8FUI0odLbWmmJLi+XhTFmtW77xjbYrdhecxyKR9cZTBqFKlRcMf9XwN/TpHz4i8DreYDavmfjJRsE7os2I+4NHH5WgeNGl93rYoqoV73vVYiIiIiIiIiInr6vPNicYxoUlLKYxKz0f7rfYhNyZE7+UQBPTcAnvI2Stg6Cdg2+c5qYflo1BpMqTsFTSOaSm1brm/B2F1jYbPbpDYAcPH1RHCPaKSbUvKNqYXXTU8cmr6qgBkSUWHBQiYRUSFhysjB8dG/oKg+VGpLNt6GT8cSCGkUfd8x9tzcg9G7R0t5k/Am6Fa2W8EdT6wGNgyX89DaQNslgEYnNWXmmtFt4QHEJGYLuVoFfNqxEuoW973vtRIRERERERER0dOrf8NiGPNqKSm/kpSN1l/uwdn4DLmTVzjQYwPgo7Dt0c6PgW0fKhYztWotptadiqbhcjFz/ZX1mHVI+Y1OAPCvWAz6Rr4wWMV7WBqVFv6pgdg/UX4hgIgKDxYyiYgKgcQjl3B2wnoE6IOktgRDHILeqoIi1cred4yDtw7i3W3vSstpFPMshg9qfwCVwrKwAIDzG4Bf3pbzwPJAp+8BB2epKddsRe8lh3AyLl1qm966ApqUK3LfayUiIiIiIiIioqdfn/pRGPtaaSlPyDSi3fy92BuTLHfyCAJ6rAf8Fe51/TUL2PpBwcXMelPRJLyJ1Lb0zFIsPrW4wOuMalYH2ZEmmKzisrdqlRpFDaHYO3ppgX2J6L/FQiYR0X/sxGe/In3lZfg4Bkht8TnXETm4AXzLRN53jGMJxzBw60Dk5vsy5unoibmN5sJZJxcjAQBX/gJWdwPsVjH3KQZ0+QXQe0hdzFYbBn53BPuvpEhtY18rjXZVQ+57rURERERERERE9OzoXS8Sk1qURf5n6DONFnRbeAAbTsbLnVz9ge6/AUUry2275gBbJhZYzJxWbxoahjSU2mYdnoV1MesKvM5K/Zoju6QRuVZx2VuVSoUQewT2DF9cYF8i+u+wkElE9B+xGIzYN2QpvOO8odc4Se03si+jzPjX4Bl+/7cbTyefRr8t/ZBjEb+EuWpdMa/RPIS6y0vVAgBuHgVWdgSsRjF3DwK6/gK4+kldbDY7Rvx4AlvOJkhtg14oht717l9wJSIiIiIiIiKiZ8+btcLxaYfK0GnEaqbJakP/747gl6M35E7O3kDXNUCQwlZKuz8B/hhfYDFzRv0ZqORXSWobv3s8dsXtKvA6y/d+HZZoLXIsWVJbqCYK+yZwmVmiwoaFTCKi/0DKqSs4PeY3BDtEKLZfzbqASlNbw8XXs8Ax7HY71lxag7c2vYUss/jly1njjKnRU1HKW96nAAAQfxxY3howZYq5s8+dL5CecvHTbrdj4rrT+OVonNTWrVYYBjcuUeC1EhERERERERHRs61ZxaJY1L06XBw0Qm63A6N+Oqm8Z6aT550H6oOqym17PgW2TVY8l5PWCZ+/+DkiPcSH6i12C9778z38cOEH2BWKoABQqsOL0DTyQJZZ3jKpSG4wjs8r+K1OInryWMgkInrCTNkG3Pj2CHwc/aU2s82EGOs51JrbHXo31wLHiM+KR78t/TBu9zhkmsVipF6jx+ToySjtKe9PAAA48QPw7StATr49ChzcgC4/AX7KBck5f1zA0r3XpLxlpaKY0KxswXtwEhERERERERHRc6FucV+s6lsLvq4OQm602DDguyPINlrkTnoPoOvPQHA1ue2vmcCezxTP5eHoga8afwV/Z/Eem9FqxKS9kzB853Bk5n+I/29Rr9WGa/NgZJjErZM0Kg3cruhxee2e+8ySiJ4kFjKJiJ6ww2NXw1uhiJlmSkJOeTMafPwWNBqNQk/AZrdh9fnVaPlrS+y+uVtqd1A7YE6DOSjvVV7ubLUAm94Hfu4NWAxim8YR6LhSeV8CAAv+uoxPt12S8pdK++PjthWhVrOISUREREREREREQLkgD/zUrzb83RyF/HJiNsatOaX8pqTeA+hSQDFz81jgyFLFcwW6BOKrl76Cm4Ob1Lbp6ia0XdcWJxNPKvYNblAJrq8GScvMOmj0MO9IReIx+V4YET15LGQSET1BR2f+iBBHeR/Ja9kXETyoJsp2bVJgX5PVhOE7huPDfR9K+2ECgKvOFZ++8ClqBNaQO+ekACtaA3s/l9vUOqDtYiCinuJ5Vx+KxeTfz0p5jQhvfN6pCnQa/lNCRERERERERER3hfm4YG6Hysj/7PvPR+Pww2GF/TIBQO9+p5ip9KD9uneB078odivmVQzzX5oPXydfqS0uKw5vbngTay6tUewb+lJVGMvYYbaZhNxF545bS04g+3aKYj8ienJ495mI6AmJ33MK7rc9pPxa9iXU/KQbPMOLFNjXYDFg0LZB2Hxts2J7g+AGWNNiDeoE1ZEbc1KAb18GLm+X21z8gW7rgFKvKo678VQ8Rv10QsrLB3lgQbeq0OuU3xwlIiIiIiIiIqLnW60oH7z7oryF0fhfT+HibeUlX6F3Bzr/BPiWFHO7DfjpLeDiFsVuFfwq4MdmP6JOUfnemMVuwYQ9E7Dzxk7FvuV7vorb7rdgs9uE3MvRD9dm7MLlX7nMLNF/iYVMIqInwJRjQMIP5+Go0Qt5qjERFUa+WuBSsgCQacrE23+8rbiUrIejB6bVm4bPXvgMAS4BcmebFfipN5B8UW4Ligb6bAfCaimed9fFJAxaeQy2fKt9RPm5YEnP6nDT6wq8ZiIiIiIiIiIiooEvFEPtKB8hyzXb0H/FEWTmmpU7ufgAb64BPEPF3GYGVnUBLu9Q7Obj5IN5L83DkOgh0Kq0Yle7DcN3DMe5lHOKfWuO7YyrVvn+mbvOC+rdRhwYv0J5SVwi+texkElE9AQcen81fPLti2m2GeFQ2xNuwQoFyL+l5qai16ZeOJJwRGr7/7cwX498HSqV8h6V6h3TgZitckOlLkD39YBHkGK/o9dT0WfZIZis4pNoQZ5OWN67BrxdHBT7ERERERERERER/T+NWoVPOlSCr6t4L+liQha6LNiPtByTckf3okDXNXdWE7uXxQB81x64ovx2pVqlRo9yPbCk6RIEugQKbTmWHAzYMgC3sm8p9q0/szeu5JyXcq1ai6KmUBx67ztk30xSvl4i+tewkElE9C/bP345QhX2xYzTXkfJti8U2O929m1039gdZ1Pk/SlbFWuFuY3mKq79//8cL2+GZs8cueHF8UCLzwGdXm4DcP5WJrovOogck1XIfV0dsLx3DRTxcCrwnERERERERERERPfyd9Pjk/aVkf85/OM30tHh631IzDQqd/SJArr+AujzbdVkMQAr2gFX/irwnBX8KuCrxl/B3cFdyBMMCRi4dSCyzdmK/WrM6ILLOecU374s4hSK67P2Inar/MIBEf17WMgkIvoXHZz0HYoaQ6X8WvYl1J3as8B+sZmx6LaxGy6nX5baupTugom1J0KjLng5Wk3KJXj8OVJuqNAeqDsE0jfHv11PzkHXb/cj3SAu7eGm12JJz+qI8HUp8JxERERERERERERK6hb3xXsK+2Weu5WJ9l/vRXy6QbljYDmgyy+Ao1iQvPNmZjvg6q4CzxnpEYlPGn0CrVpcZvZ86nkM3TEUZpu8tK2DXo/6n76FOI9YGCxysdNN5wnTxiSc+mZ9gecloseLhUwion/J4WmrEZAdJC37mmZKQvnhTQvsF5MWg24buiEuK05q61exH0ZUGwG16j7/+c7NgNemAVCbc8Q8sDzw+icFFjETMnLR5dv9SMj3FJxep8bC7tVQtqiHYj8iIiIiIiIiIqIHGfRiMQxoFCXllxOz0e6rvYhNyVHoBSA4+s6bmfmLmeYcYEVb4NreAs9ZLbAaJtWeJOW743Zj6PahMFqV3watOaYz9K/54bZBvj/nqHGC2yUn7J+4osDzEtHjw0ImEdG/4Oisn+CX5i8VHLPM6XBuXATuoYGK/U4nn0b3jd2RaEiU2oZVHYb+lfoXuB8mAMCcC83PPaBNvyrmTl5A++WAg7Nit7QcE7p+ewDX831h1KpV+LJLNKqFexd8TiIiIiIiIiIiogdQqVQY/kopDH+lpNQWm2JAu6/24lqy8pKvCK4KdPkZcHATc3MOsLI9cPtMgedtFtUM/Sv2l/I/Y//EgK0DkJP/ZYC/hTSKRtnJr+NyzllpqVmNSoug3FDsGbIEFoulwHMT0f+OhUwiosfIYrbg4MSV8E7whlolLv2aY8mEpq47IpvWVOx76NYh9NrUC2nGNCFXQYWJtSaiW9luDzi5CVj9JtRXdoi5Sg20/hbwClfslmOyoMfigzh/O1PspgLmtK+ERiX9FfsRERERERERERE9qgGNimH862WkPD49Fx2+3oerSQUUM0OqAV0Vipm56cDy1kBabIHnfLvi22ge1VzK98fvR58/+iDDlKHYT+/mivqf9sF1hxhYbCapPdQhEgeH8s1Mon8TC5lERI/JueVbcHrEryiSGwyNSlx732DJhq2qI4q3qi/1s9ltWHhqId7a/Ja00bhWpcWM+jPQukTr+5/cagF+6glc3CS3vTAOKPaiYjejxYq+yw7j6PU0qW1yy3JoVrHo/c9LRERERERERET0iHrWjcC0N8pLOyD9fzHzSoHFzOpAl58AnYuYZ94Elr8B5KQodlOpVJhYeyKaRsjbPR1PPI6eG3si2ZBc4PXW+bAH0iKzkWPJki/JKRL7xi4tsC8R/W9YyCQi+h/dPnQO+wYtgctJB/g4ym8v5lpzYCxnR6kOcjExJTcF/bf2x5zDc2Cxi8tQOGocMfeFuWgS0eT+F2CzAr/0Bc6uk5sqdQHqDlbsZrXZMXjVMfx1MUlqG9GkJDrXCLv/eYmIiIiIiIiIiP6hjtVDMbtdRajzFTNvZeSiw9d7Cy5mhtYA2i8D1OKLBEi6AHzXHjApLxWrU+swre40tCnRRmo7n3oevTb1QroxvcDrrfR2czi84otUk3wvrYg5BGeXbC6wLxH9cyxkEhH9QxazBXtGL0b2qlgEO0cq7l1ptBqQXSwX5brLT3sdvHUQbda2we643VKbs9YZX770JeoHy29wCswG4NcBwKkfpSZD8eawNp0F6dE2AHa7HWN+Pon1J29JbX3rR6JfA3njdSIiIiIiIiIiosepVeVgzG5XSSpm3s4wov1XewteZrbYi0DLL+X8xgFg9ZuAMVNuA6BRazC+5nj0KNtDaotJj8GQ7UNgtpoLvN7wxtUQMqA6knLFe2oalRaak1YknYgpsC8R/TMsZBIR/QOX1+zG6eFrEGqPgoNGr3jMzZxrsNXRo2LfFlLb75d/R+/NvZFoSJTagl2DsajJIlQLrHb/i7i+D5hfDzi+UmoyRDZBeqNpgFojteWYLHhn5VGsOiTvG9C+aghGNS2lWJQlIiIiIiIiIiJ63FpWDsKc9nIxMyHTiM4L9iM+3aDcsUI74OXJcn7pD+Cr+sDNY4rdVCoVBkcPxjuV35HaDtw6gIl7J8Jutxd4vZ5RQfBtW0JaZtZZ64obC4/AlFPA9RLRP8JC5hNiMBiwbt06DB48GHXr1oW/vz8cHBzg7u6O0qVLo0ePHti6desjj7t371707NkTUVFRcHZ2hre3N6KjozF58mQkJcmvuBPR/yY3LRN7hyyBdq8ZPvoAxWMyTCmIc7+O6DkdUbyl/EblxdSLmLhnImx2m9T2SvgrWN1sNcr4yBue5zFlAxtGAQubAMkXpWZb8SZIf3GmvLwGgNiUHLwxbw9+OxEvtb1aPhBT3yjPIiYRERERERERET1RLSopFzPj0gzosmA/krOMyh1rvwPUGijnKZeBbxsD++YDCkVJlUqFPhX64N0q70pta2PWYv6J+fe93uA6FZEdngtrvq2ifPWBODJm9X37EtGjYSHzCVixYgX8/f3RvHlzfPLJJ9i9ezcSExNhNpuRmZmJc+fOYfHixXjppZfQtGlTJCbKb2jlZ7fbMWTIENSpUweLFi3C5cuXYTAYkJqaiiNHjmDcuHEoV64ctm3b9gRmSPR8yElMx/kPNiPEIRJqlfymo9lmxFXzBUSOa4QaYzpDo1F4G9Kcg6E7hiLXmivkjhpHjK81Hh/X/xhuDm4FX8SNQ8CXtYH9XwJQeDIs6kVY3/gW0Oikpt2XktDs8104d0teWqNecV/MaV8JmvzfFomIiIiIiIiIiJ6AFpWCMLdDZamYGZOYje6LDiIzt4AlXxt/CFRoL+dWE7BxJPB9J8CQqti1V7leaFuirZTPOzYP62LW3fd6Kw9shetWeSnZYH0k9o9dft++RPTwWMh8Aq5cuYKsrDuvmRcpUgRvvvkmPvvsM6xatQoLFy5Ejx49oNffWZpy48aNeOmll5CTo7wh8f8bPXo05syZA7vdDhcXFwwaNAjLly/H/Pnz0bhxYwDA7du30aJFCxw7duxfnR/R88CSa8KZyb/Dx9Ffsf1mzjXomgeg7qxe0Hu5FzjOlP1TcCX9ipD5Ovniu9e+Q9sSbe//NuSlrcCSZkDqVblNpb7zBFrHlYDWUWpesucqun67H2k58he+F0r5Y36XaDhq5cIrERERERERERHRk9KsYlFMf6OClJ+MS0evJYeQa7bKndTqO/tlvjD2zj2y/M6vB5a2AIxZUpNKpcKYGmNQJ6iO1DZ+z3hsvLrxvtdb+6MeuJ59ScqLmkNxaOqq+/YloofDQuYTUqdOHaxbtw6xsbFYsmQJBg4ciHbt2qFHjx5YuHAhDh8+jCJFigAATpw4gY8++qjAsY4ePYoZM2YAADw8PLBnzx7MnTsXnTt3Rt++fbF582ZMmDABAJCVlYU+ffrcd01vInqwg6NWItApRMqzLRm44XIV1T/tguC65e87xppLa7A2Zq2QqVVqfFTvI5TwKnH/CzizFljZATArPOTgVxroteXOngAKRczfTtzEhLWnYVP4z8A7LxTDgjerwsVRXoaWiIiIiIiIiIjoSWtXLQRjXyst5QeupKDf8sMwWpSKmRqg/nCg+3rAPUhujz8O/NQbsMl9tWotZtafKd2fs9gsGL5jOCbvmwyjVXlpW41Gg/KjX0ey8baQq1Qq+KUH4PinawqeKBE9FBYyn4ABAwZg165deP311xWXmgSAMmXK4Ouvv877ffHixQWON2nSpLzC5NSpU1GhgvyEyoQJE1C9enUAwMGDB7F+/fr/YQZEz7c9oxYjRB8p5dezL8GvTznUHNf1gWNcSr2EKfumSPnbFd5G9SLV79/52Ergh253lsO4l1oLNBgJ9N0BBEcrdj1/KxPDfzgh5S4OGszvUgVDXy4JNZeTJSIiIiIiIiKiQqR3vUgMeqGYlP95PhEDVhyByWJT7hhWC3h7F1DyVbntwgZg42jFbq4OrvjixS/g7ySvxrbq/Cp0Wd8F1zKuKfZ1K+oH7+YRyDSnCblGpYH7DTecXbJZ+VqJ6KGwkPkEeHl5PdRxTZs2hYuLCwDg+vXryMjIkI7JzMzEhg0bAADu7u7o3r274lgqlQrvvPNO3u+rVvE1dqJ/4tD0VQi2R0h5XM5VVJ3eAd4lwh44RqYpE8N2DJP2xawRWAN9KvS5f+cD3wBr3gbs+b6c6T3uPGHWaIziW5gAkJFrQf+Vx2DIt+RGmI8zfhlQB03KFXngtRMREREREREREf0XBjcuge61w6V8y9kE9L9fMdPZG+jwHVDnPbntwFfAvi8VuwW6BOKLl76Ah6OH1HYu5RzarWuHDVc2KPYNaRQNTR13GCzZQq5TO8DhFLB3+BIcmfEjrm84BEtWruIYRKSMhcxCRKPRwNnZOe93g8EgHbNjxw4YjXdeY69fv75wfH6vvPJK3s8bN95/LW8ikp1auAE+Kb5Q51tbPzn3NsqMeBkOLk4PHCPHnIP+W/ojJl3c+NtH74Pp9adDo77PvpR/zQLWD5NzF787RczQGgV2tdntmLjxCq6niP8d8XZxwHdv1USJALcHXjsREREREREREdF/RaVSYfzrZdC6SrDUtuXsbQz47j7FTJUKeGkiUK6N3LZxNHDud8VupbxLYfXrq1HBT14FMceSgxE7R+Czo5/Blv+lAwAl3miA3DJWmPK9zOCocUKIJhL+KQFQ7zAg/sMDOPreaqReuKF87UQkYCGzEElISEBiYiIAwNnZGX5+ftIxp06dyvs5Olp5Kcn/5+fnh7CwO2+LJSYmIiEh4TFeLdGz7fS3G+B8Vgud2kHIs8zp8O9cFm7BAQ8cw2AxYOC2gTiWeEzIVVBhev3p8HXyVe5otwN/TAC2TpLb3IOBHhuBwHL3PfeCffHYc1V8q1utAj7vWBlBng8uwBIREREREREREf3X1GoVZrSpgJaVikptf5y5jXdWHoHZep9iZst5QGitfA124MdewNVdit2KuhbF4iaL0aNsD8X2r098jRE7RyDXIr9ZWb7na0gpmgaLzVzgnFQqFfz0RXB13h5YzJYCjyOiO7T/9QXQXffukdmkSROo1XKd+cKFC3k/h4eHP3DMsLAwXLt2La+vv7+8xveDHD58+L7tZ8+ezfvZYrHAbC74P9JET4OzCzfBLUYPnUZcstVozQVqOMO3UrEH/v+5yWrCezvew8FbB6W2fhX6Ido3WnkMuw3qjSOgObJYbvKOhKXTz4BHMHCf8284eRML98dL+YhXSqBamAc/o0T/AbPZDIvFkvczEf23+JkkKlz4mSQqfPi5JCpc+JkEprcqC6vNhnUnbgn5ptO38d73RzG7TXmo1SqFnmqg9RJolzSBKuXy3dhigH15a1hbL4K9WGPFc75T8R1U8q2ECfsmIM2YJp736ibEZcZhdv3Z0ssKFQe2xMGpKxGcEwq1quDV2Pz0RXBw3ApU/bDTfedOhQ8/k7L//9/Hv4GFzELi8uXLmDZtGoA7T2SMGjVK8bi0tLS8n319C3ib6x4+Pj6KfR9F1apVH/rY9PR0JCcn/6PzEBUGsat2wTfOS3oT02IzI8E/CRH1Gz3w/8ctNgsmHZuEfYn7pLY24W3QMrCl8hhWMzy2j4bTxXVSk9mnJFJfWwibxQm4z/n3X8vAsF8vSXnjEl5oUdKVn0+i/4jFYkFmZmbe71otv4IR/Zf4mSQqXPiZJCp8+LkkKlz4mbxjZMOiMBqN2Hw+Vch/P3kLblob3q0fDJVKqZgJaF6ZD59f2kGdm5aXqSy50PzQFWkvzoQxqqlivzL6MviixheYcHQCLmWK99xOJZ9C5w2dMTV6KsJdw4W2yL4v4+KXG+Ca7gwPnTccNHrF8YvYQnBm9TYEvFjxAbOnwoSfSdk/rT89DC4tWwhkZ2ejVatWyMnJAQD0798f1apVUzw2Kysr72e9Xvk/fvdycrq7hOS9HywiksWu3AW/OG/FIuZNz5uI6NrogWOcSDmBd/a9g72Je6W25iHN0adEH8UvVKrcVHht7K9YxDT5V0RK82WwOd//4YUjNzIxYt0lmG12IY/ydcKYxmEFfpEjIiIiIiIiIiIq7LRqFca/EoHGJbyktu+PJmDF4dsF9rV6hCG1yZew6ZyFXGWzwHPLEDid+7HAvv5O/phdfTbq+NeR2hJzEzHm8BikmdKktuL9mqLIqAZwGFACydVzccU1BvE51/PNSQf1oWyYs+RlaonoDpaJ/7ZgwQLcuPF4NtedOHHiQx9rtVrRqVMnnDhxAgBQpUoVzJw587Fcx+Ny6NCh+7afPXsWXbt2BQB4eHgIb4ESPQ1M2Qac/HANimrDpCWdzTYTkvyTUO3d9vcdIy4rDnOPzsWW2C2K7S2jWmJs9bFQq+TnR1TX90Kzpi9UmTelNlt4PajaLIW3o9t9z3/kehqGrY2B0SIWMT2ddPiqazSCvZ0L6ElET8K9y4x4e3tDp9P9h1dDRPxMEhUu/EwSFT78XBIVLvxMij7t5I13vj+OLecShfzzXXEIC/BS3E8TAODTGDbPNVCtag+V4e5bnSq7DR7b34ergxq2am8VeN65/nPx2bHPsOTsEiFPzE3EjDMz8EWjL6BVK5dcAoKKAs2A24fPw/BjLJy0rnltXo5+uPbFn6g+o8uDpk6FBD+TMk9Pz39tbBYy/7ZgwQLs37//sYz1sIVMm82G7t27Y+3atQCAkiVLYsOGDfd909LV9e5/4HJzH/yUhsFgyPvZze3+hZCCREdHP/SxWq2WH1p6qlz8YSdMe1MR7BAhtZltRiQXSUW1wQUXMXMtufj6xNdYcnoJTDaT4jGvRb6GibUnQqPOtya+zQr8NRvYPhWwK2xKXqIp1G0XQ627/9vXJ26koffSI8gxWYXczVGDJT2iUSzA4779iejJ+P9lRnQ6Hf+tJCoE+JkkKlz4mSQqfPi5JCpc+Jm8S6cDPu8cjS4L9uPQNXGZ2dG/nIafuxMalvRX7hxeA+i+HljWEsgS3+DUbB4NjXsAUK51geceVn0YoryiMGnvJFjsd/cEPHj7IOadnIehVYfe99qDa5bDvi3HEZzlKuQhDpE4v+QPlOv96n37U+HBz6To31xel0vL/kfsdjv69u2L5cuXAwCioqKwdetW+PsX8B/Yv91b1U5KSnrgee7dD+/frIgTPW1MOQbsHboEjofs8HDwltutuUgJSkXVwW0LHONA/AG0Xtsa35z8psAiZpsSbTC5zmS5iGlIBZa1Av6crFzELN8OaL8MuE8R026344dDsei8YD8yjeJmys4OanzSqjjKFHEvsD8REREREREREdHTSK/TYEG3qijuLxYELTY7+i47jBX7r8Futyt3DigD9NwIeIbKbb/0A67vu++5WxVvhdE1Rkv54tOLsfHqxgdee82xnXE9R9xvU61Sw/2iC4699wP2jliCG9uPPXAcoucFC5l/27dvH+x2+2P5n4cxcOBALFiwAAAQFhaGbdu2ISgo6IH9SpQokffz1atXH3j8tWvXFPsSPc9itxzGufc3IEQXqbjUa4Y5FWkRmYh+V7mImW5Mx/jd49Frcy9cz7yueEwZnzJY2nQpJtSaIC8pYbUAq7oCV3bIHTUOwKszgTe+BjQFP8kTl2ZAt0UHMfzHE8jMFYuYTjo1ZrcojrKBLgX2JyIiIiIiIiIiepp5OjtgSc/qKOIhvghgtNjw/i+nMPC7o0g3mJU7e0cCPTYCPsXF3GoEVnYEkmPue+62JdqiVbFWUj5+93hcTL34wGsvM7gJMs1pQqZWqeGrD0SIOhLYmImTg3/G5V/3PHAsomcdC5n/gffeew/z5s0DAAQHB2Pbtm0IDVV4+kNBuXLl8n4+fPjwfY9NTEzMK2T6+fk98G1PoufBvgnLYdmcCm9HP6nNbrfjStZ5BPatiCr931DsvyduD1qsaYFfLv2i2O7r5IsP63yIla+tRGX/ysoXseMj4Opfcu4dBfTeClR/C1CpFLvabHYs33cNL8/egZ0XEqV2B60a8ztXRqUgV4XeREREREREREREz46ink5Y2rM6PJzkFwJ+PxmP1z79C0evpyr0BOARBHT9GXANEHNDCrCiLZCTUuB5VSoV3q/5Psr6lBW7Wgx478/3kG5Mv+91e0YUgamYDTalldr+5uXoB/uubFz8Yed9xyJ61rGQ+YQNHz4cc+fOBQAUKVIE27ZtQ2Rk5EP3b9iwIRwdHQEAO3fuFPbAzG/Tpk15Pzdp0uQfXjHRsyHj6i3sG7QUwcYw6NSOUnuWOR3x3jdQ7/Pe8C4RpjjGoVuHMHDbQCTnJkttGpUG3ct2x2+tfkPLYi0V3/QEAMT8Cez8WM7LtwP67gCKVChwDkaLFQNXHsHYNaeQnW8/TABw02vxzZtVUTvKp8AxiIiIiIj+j737jo+qSv84/pmW3hMCpBAIvYfeERFEQewFO/bys61trWvXVVeRXXXtoKKIiCKioFjpvYReElqAkEZ6mfr7Y5QlzCSZQIABvu99zWZyz7nnnsnyZLL3mfMcERERkVNJ68bhfHxjb+LDPe/5ZR2o4LJ3FvHl8t3eT45qBld+AZaQ6scLMtwrM201338PNAUybsg4ogOjqx3fVbKL//vl/yi3ldc67653XMBuVwZ2Zw2rRoFAUxDGpZVsneqlspvIaUKJzOPoiSee4F//+hcAjRs35tdff6V169Z1nFVdWFgYI0e6N/wtLi5m4sSJXvu5XC7efPPNg99fccUVRzZpkZPcviUbWPC3j9j/5iqSQlp47bOrbBvxt3Wm99+vqnGczMJM7vntHmxe/rBoH9Oez0d9zgM9HyDUUks515Js+PoW4LAS1GnXuEvJBobXeGq51c7NHy/nh7XZXtuHtY9nzt/O4Iw2nitNRURERERERERETmVpyVH8cO8gr/fG7E4Xj0xL59dN+72fnNgdLvkQOKxC2u7F8OHZtZaZbRrWlFfPeNVjUcOa3DXc//v92Bw1JykBBrxyIyFXN2OHcRtZ5duxOio9+gSagjEurWLLl7/XOpbIqUqJzOPk+eef54UXXgDcZV5/+eUX2rVrd0RjPfnkkxj+LDv56KOPkp6e7tHn2WefZcmSJQD06tWLUaNGHeHMRU5O697/gWX3fobt61xSAlsTbPYstVrlqGR30Hb6/+eGGldhAuRV5HHnL3dSYi2pdjzIFMQDPR7g81Gf0yG2Q+0Tcjpg2s1Qdlg52EbtYeSrNZaSBSiutHHdh0uZtzXPoy06xML4MWm8f11Pmhy2H4CIiIiIiIiIiMjpIi4skAlje/HYyHaYjdXvtTldcNfnq1i3p4aSr+1Gwjn/9DyenQ7vngFrv6rxun2a9uGBHg94HF+wdwGPzX8Mh9Ozstqh4ru2YuCLN9D339fR6IFuZLIZh9NerU+gKRjjMquSmXJaMp/oCZwO3nvvPZ588smD3991111s3bqVrVtr3/R34MCBxMXFeRzv1q0bDz/8MC+//DJFRUX079+fm2++md69e1NaWsq0adP46aefAPcKzvfee69hX5CIH7Pb7Cx/aDJJQc2JCq55hWNu5T5iz0ul37DhtY5Xbivnrl/uYk/pnmrHg83BTDxnYt0JzL/88YrnvpiWELhsIgSEeD0FIL+0iusnLGXdnmKPtmHt43np4i408lI2Q0RERERERERE5HRjNBq4dXBLereI5c5JK9hb9L8VjuVWBzd9vIzp/zeAppHBnif3vR0ObIcl71Q/bi2BaTe57+2d80+weJ57bYdrya3IZeL6idWOz94xm8jASB7v8/jBxUm1CW8Sx+B/3syiZz4hoTwZk+F/KZwgUzBVyypYuXsq3R+4rM6xRE4VSmQeBwsXLqz2/VNPPeXTeb/99htDhgzx2vbSSy9RVVXF+PHjKSsrO7jv5qHi4+OZPHkyaWlp9Z2yyEnJ5XKx7KHPSA6qed9Zp8vJbmsGvV66ioDg2lcwOpwO/j7376zPX1/tuMlg4rUzXvM9ibn2K/jjZc/jo16D+JpXZmcXVXL1B4vJyC3zaBvTK5kXLuqMyVj3H0AiIiIiIiIiIiKnk7TkKD69uQ8Xv72Qoor/lXfdX1zFDROWMfX2foQHWTxPHPEimCyw8D+ebSsmwp4V7j01I5OqNRkMBu7vcT+FVYVM3za9WtuUzVOYtX0WLSJb0DyiOS0iWzA4aTCto2vedq7fU9ex6JlP/0xmmg4eDzQF0ygniPn3fURvH+5vipwKVFr2JGUwGBg3bhwLFixg7NixpKamEhQURFRUFN27d+fZZ59l/fr1DB069ERPVeS4WfjgxzUmMasclews34r9jEAGjLvRpyTmPxb+g9+zfvdoe7zv4wxKGuTbpDbMgK9vxWNfzK5XQVrNe3Luyi/nsncXek1i3jSwBS9drCSmiIiIiIiIiIhITVo2CuPda3tgMVW/h7Ypu4S7Pl+FzeH0PMlogrOfhyunQHC0Z3v2Wnh/KGSt8GgyGAw81e8phiZ73pMvthazJncN32Z8yxsr3+Cy7y7jl52/1Dr/fk9dy77QLByu6qVpDQYDzYNas+6R6exbsr6Gs0VOHUpkHgcTJ07E5XLV+1HTasxD9evXjwkTJpCRkUFFRQUHDhxgxYoVPPnkk17L0oqcqhY+OpEUS0uP44XWPLa7ttDo7s4M+PeNpI7sW+dYDqeDJxc8yYyMGR5tN3e+mcva+Fi6YctP8NWNcNgfG8S1hVH/qvm0/SVc+s5CdhdUeLTdN6w1T4xq71MpChERERERERERkdNZ39RYXrm0i8fxP7bkcseklVTaati/su05cPt8SO7j2Va6HyaOhPXfeDSZjWZeOeMVejfpXeu8HC4HTy58kuyy7Nrn/49ryA7Lwu60ebTFBydSMnUXS178rNYxRE52SmSKyElvybOfkeRs4XE8t3Ifze7rx6CXbyKiWROfxnI4HTyx4Am+y/zOo21ki5Hc3e1u3yaV8RtMuQYO/yMjPAGumgIBoV5PS88q5PJ3F5FTUuXR9sSo9tw3rI2SmCIiIiIiIiIiIj66qFsSfxvWxuP4zxv3c9PHyyirsns/MTIJxn4PA+7zbLNXwtSxMPdVcFWvxBZoCmT8meNJa5RW67xKrCU8Mf8JnC4vK0MP0efJa7D2NFJsLfBoCzGHkVjcjKX3TGL3L56rREVOBUpkishJy1pRyfwHP6JJWQJGQ/VfZ4XWPJJu6uFzAhPcSczHFzzOzMyZHm2DEgfx3IDnPK7jweWC9dNh8pXgOCwZGRoP18+AGM+kK8DizHyuen8JheXVk58GA7x8SWduHlTz3p8iIiIiIiIiIiLi3T1nteKS7kkexxdsy+eaD5dQVO654hFw75c5/Bm4+AMwBXq2//o8/PSEx+GwgDA+GvERb531Fvd2v5fzW55Pl0ZdCDAGVOu3JHsJn22se0Vlm8uHkHxfX/ZU7PDanhCSguPHIhbc/xGVRSV1jidyMjGf6AmIiByJrdPmUj4vh+ZBnptil9qKiLoklUadPUvN1qSoqohnFj3DnJ1zPNoGJw1m3JBxBJgCvJx5iOy1MPtR2DHPsy04Bq77FuI85+t0unhvXiav/rgZh7P6J7jMRgNvjEnjvC4JPr8WERERERERERER+R+DwcA/L+mM3enk29V7q7Wt2lXImPcX88mNvWkU7iVZCdDlMohOgS+ugrLc6m2L3oQmXaDrFdUOW0wWBicNZnDS4IPHfsj8gb/P+3u1fm+seIO+TfvSOtrzvuGhwps1pvcb17D48U9JcCRjMlRP75iNFlICWrPj2bkUGHPpcPvZRLXUPUU5+WlFpoicVKylFcy//yMCljiIDWrs0V7hKMNyRjRJA7r6POYfu//gom8v8prEPCPpjLqTmKW5MOMeeGeQ9yRmYCRc+w007uDRlFdaxdiJy/jnrE0eScxAs5H3r+upJKaIiIiIiIiIiMhRspiMvH55Glf1aebRtnFfMVe+v7jmlZkAyb3h5l8g3vMeH9/dC/vS65zDyNSRnNv83GrHrE4rj857FKvDWuf5BoOBfi9eR1VPI/lVOV77hFkiaGZqyYF3N7Lw3olkfrugznFF/JkSmSJy0ijM2MOGJ2bSPKA1JqPngvIKexn2LkZanj/Ap/GKqop4fP7j3PXrXeRW5Hq0D0kawutDXq85ielywYqP4T89YOXHgMuzT0AYXDMNEtI8mhZm5DFy/DzmbvG8dmiAiY9v7M2Z7eJ9ei0iIiIiIiIiIiJSO5PRwAsXduK2Mzy3cNqWU8rtk1ZgtdeyZ2V0Ctz4IyT1qn7cXgFTroZyz30sD/d438eJD6l+z2/zgc08Pv9x1uSuqXPPTIA2l51Bx1cuYJcpk0pHudc+FmMAzYJbErDIyYJ7J2C317AXqIifUyJTRE4KBZt2svPNRcQFed/zck/5DqKubUX7a8/2abwFexZw0bcXMSNjhtf2M5PPrD2JmbcNJp4H390DVUXe+6SeCbf+Dsm9PJq+Xb2Hqz9YQk5JlUdbs5gQptzWj76psT69FhEREREREREREfGNwWDgkXPa8dCIth5tizLzeeTrdFwuLwsW/hIUAZd/CqGHLUAo3AXTbgKno9brRwZG8vyA5z2Oz94xm2t+uIazpp7FM4ueYVXOqlrHMVvM9H/heqKub82u8m21JkBTglux+KFPah1PxF8pkSkifi9vXSZZ7y4nNtCzlGy5vZS9EVn0+fe1NOraqs6xrA4rryx7hdt/vt3rKsxAUyAP9nyQcUPGYTFZPAdw2GDuv+C//WHnfO8XiWkJV05xl5P1sifmpuxiHv4qHW9/D53XpSkz7xlIp8TIOl+LiIiIiIiIiIiI1J/BYOD/zmzF389p59H29co9jP9la+0DRDSFyz+Gw6vGZfwKv71Q5/X7JfTjmvbXeG3Lq8jjqy1fcd2s63ht+Wu1J1WBuE6p9P/3DZR2sbKrPAO703t53OaBrVn81Kd1zk3E3yiRKSJ+LWfNNvZ+uIqYQM8Sq1nl24m7tRO9H7vSp7EyCzO56vur+HSD9zfsro26MnX0VK7veD0mo8mzg70KPrsUfn0OHJ4rKQmMgLNfgDsXQ9tzwGDw6FJWZef/PltJ1WElKgLNRl66uDP/ubIbEUFeEqgiIiIiIiIiIiLSoG4/I5WrveyZ+cbPW/l6ZVbtJ6f0hxEveh6f9xqs+aLOa9/b/V5aR3sugjjUxPUTeWXZK3UmMwE6XD2c/v8eS/CYJLZbt1BmL/bo07QiifT/eq9QJ+KvPDeZExHxE/uWbyTvs41ek5g7K7fR5/VrMZvr/jVWZivjqy1f8eaqN6l0VHq0B5oCubvb3VzT/hrvCUwApxOm3wmZv3tvb38+jHwVwr2XvgVwuVw8OX0dGbll1Y43Cg/k05t6065JRJ2vRURERERERERERBqGwWDgmfM7sqewgt83V6/e9vdp6VhMRkZ3Tah5gN63wp6VkH5Y4nL6nRAUCW3PrfHUIHMQH5z9ARPWTWDOzjnsKd3jtd+kjZMAeLjXwxi8LJw4XOPubWjcvQ0FW3ez978riAr43/ZVJqOZkMxAts9aQotz+9Q5log/0IpMEfFLGyb9SNHkDKIDG3m07arcRp9/1Z3E3FG0g38u/SfDpg7jX8v/5TWJ2Ta6LV+e92XNqzD/8utzsO4rz+PhTeGKz+CKT2tNYgJMXZHF16uq/0FiNMD4MWlKYoqIiIiIiIiIiJwAZpORN6/qTvum1e/P2Rwu7p68ise/WUulrYZ9Lw0GOG8cNOlc/bjLAVPHwo4FtV47JiiGB3o+wKyLZ/HV6K+4M+1Omkc09+g3aeMkn1dmHhy7dTIR5yZRYa++qCLIFELFnP3krcvweSyRE0mJTBHxO4uf+ZTgdDNhFs99IndVbaPfuLG1JjF3Fe/ijp/vYPT00Xy28TNKbaVe+13X4To+H/U5qVGptU9o+Ucw/3XP450vg/9bAu3Pq/18YMv+Ev7x7TqP4/ee1Yb+LePqPF9ERERERERERESOjbBAMx+N7UmTiCCPts+W7OLCtxaQkev9HiMBITBmMkQkVj9ur4TJY2Bfep3XNxgMtI1pyx1d72DKeVPoHt/do8+kjZN4aelLOJw1JFW9aDa8J1Vt7R77ZkYERJM7YQNbpv3h81giJ4oSmSLiN+xVVubd9wGJ5SlYjAEe7busGfR7fWytJRQyizK55odrmL9nfo19YoNieWfYOzzU6yECTJ7XqWbLT/D9A57H25wLF73rLhFRh+JKG3d+tpJKW/V9Mfu3jOWuoa3qPF9ERERERERERESOraaRwXw0thdRIRaPtk3ZJYz+z3y+Xe29/CtRyXDtNxAcU/14VTFMuhgW/xe2/QwHdrq3sKpFiCWE/w77Lz0a9/Bom7xpMrfMuYXc8lwvZ3rX6ZbzyA7d67GaM9wShXmJjaUvfO7zWCInghKZIuIXCjP3seKhKbQIauuRqHS6nOwmk/51JDH3le7jtjm3caDqgNf2AGMAF7W6iGnnT2NA4oC6J7VnhbsEhOuwPy4SusOlH0JtpWj/VFplZ+xHS9mWU/0TW3FhgbwxJg2Tse669iIiIiIiIiIiInLsdUiIYObdA+neLMqjrdzq4N4vVvOvHzfjdHop8dqoLVzzFQSEVT9elguzH4FJl8D4LvBigvueY2VRjfMIsYTw9llve01mLstexqXfXcqivYt8fl19n7qGnfZtHscDjIE0LU5i/t8+xF5l9Xk8keNJiUwROeE2f/kre99cSmJIc4+2KkclBQkF9Pvn9bWOUVBZwK1zbiW7LNujrXFIY+7pdg9zLpvDswOeJTY41ssIh9mzAj65CGzVa8gTlQJXTYGA0DqHqLA6uGniMlbuKqx23PDnvpjx4Z6lKkREREREREREROTESYoOYcpt/bjtDO/bUb352zbumrySCquXEq+JPWDMZ1BbFTh7Baz/Br6+FWrZ8/KvZGbPxj092goqC7htzm28tfotn0vN9n35OnbYtuI8bNGGwWCgeWAbVjz0JSVZ+30aS+R4UiJTRE6ohU9+QsByFxEBMR5tRdYCTMOiSLv3olrHKLOVcefPd7KjeEe14yaDiWf7P8vsS2ZzS5dbiAnyvIZXWX8mMasO+1RUUBRc/RWExdc5RKXNwa2fLmfJ9gKPtgfPbsuAVtoXU0RERERERERExB9ZTEYePbc9E8b2ItpLqdkf1mYz5r1F5BRXep6cOgQu+RAMdaRftsyGlR/X2iXEEsLbw97mwlYXerS5cPHOmne4+oerWZ+3vvZrAWazmYGv3UhOXA5Wh+e8E0NSyHxtHvuW1D2WyPGkRKaIHHdl2fms/fAHFt4zkWa2Fl73w8yuyCL57t40H9Gr1rFKraXc++u9rM/3fIN9bsBzXNT6IsxGs++Ty1oBn17omcQ0B8GYz6FRmzqHsNqd3PX5SuZtzfNou3VwKncOaen7fEREREREREREROSEOLNdPD/cO4hOiREebWuyirjgrQVszi7xPLHD+e57iQnd3fcVazL7McjPqHUOweZgnhvwHM8PeJ4gk+dY6/PXc+X3V/LC4hcosXqZy2F6PnQZDAmn2Oq5PVdsYGOKvtzBpi9+qXMckeNFiUwROS7WvDODRXd/zLr7vyF/3Fqit4bTLMQzoedyudhZtY20ly8hokXTWsecv2c+F824iCXZSzza/t7r74xuObp+k8xa/mcSs7j6cXMQXPkFNK97X81FGflc8NYCft6Y49F2fb8UHj23Xa37fIqIiIiIiIiIiIj/aBoZzJe39WNEx8YebfuKKhk7YSl5pVWeJ7Y9F279DR7bB/emwzXTIPXM6n1sZfDNbeCw1zmPC1pdwORRk0mN9Cx568LFF5u/YPQ3o5m+bTo2h63WsVJH9SXhrp5kV+z2aAuzRBCwwsmKf31Z55xEjgclMkXkmJv3wAfE7ogmOTSVqIA4jAaT135VjgpyG+1nwLgbMAfVXEe+qKqIx+c/zh0/3+F1T8xbu9zKNR2u8X2CTgfMfwMmjPSSxAx274nZ8kyvp/5lV345t3+6givfX8zGfcUe7WN6JfPU6I5KYoqIiIiIiIiIiJxkQgLM/PfqHtx+hufCjH1Fldz9+SrsDqeXMwGjEaJToNUwuPg9CDlsy6msZTD/dZ/m0Sq6FZNHTfZaahYgvzKfJxc8ydnTzua/q/9LXoVnxbi/RKUmkPbypeyq9FwRGmAKolFuPPMf/Ai7ve4kq8ixpESmiBxTCx79iBaWtnX2K6jKIXBUPN0fvKzWfvOy5nHhtxcyI2OG1/Yr2l7BXWl3+T7B3M3w4dnw81PgOOyTU38lMVOH1Hh6pc3Bqz9uYtjrfzB7vWdSFeCibom8cFFnjEYlMUVERERERERERE5GRqOBR85tx6uXdsFiqn6fb1FmPi/P3lT3IGHxcP5/PI///k/Ys8KneYRYQnhuwHN8cPYHNI9o7rVPXkUeb695m+FfDeeReY94XQwCYA4KoP8bY9lFBk5X9USs0WCiubk1qx74kv0rt/g0N5FjQYlMETlmFj87iWRn7ftB2pxWdlZso80/hpE0JK3Wvt9nfs9dv97l9ZNEFqOFe7vfy2N9HvNt1aPTAfPHwTuDYM9yz/aDScwzahxi4bY8Rrwxl7d+y8BawyeururTjFcv7YJJSUwREREREREREZGT3mU9k3nm/E4ex9+ft53v1uyte4B2I6H7ddWPuRzw4Qj49CJY+j4U7alzmD5N+zDt/Gnc3e1uAk2BXvvYnXa+z/yeK2Zewe5izzKyf+n/z7HkNsrB5vQskds0OJmizzNY/ppKzcqJoUSmiBwTK177kialCRgN1X/NlNtL2F2RyW5TJiWdKmn6aE8GjL+BoOjIWsebkTGDx+Y/5vHJIIAucV2YOnoqN3e+2eN6XhXugomj4OenPVdhAoQnwLXf1JjELCy38tDUNVz1wRJ25pd77dMlKZJpd/TjxYs6YzbpV62IiIiIiIiIiMip4sreyVzRM9nj+MNfpbM5u6TuAUa8CNHNqx9z2iDjV/jhQRjXAT4+H/I9y74eKsAUwK1dbuWbC75heMpwDHhfTFFQWcCdv9xJUVVRjWP1ePAyqroZKbN7bpsVYg4jPqcxC+7+iDUffIfD4ajzJYo0FPOJnoCInFrsdjsrX5tKXH4jzEZLtbZyewkh5yfSZvDIeo359daveXrh07hwVTseZArirm53cU37azAZve+76WHd1/DdfVDTm3a3a+DsFyA4ymvz75tzeHDqGvJKrV7b48MDeficdlzcLVGlZEVERERERERERE5BBoOBZy7oyKbsYtZk/e8+Y4XNwa2fLufb/xtAVEhAzQMEhsNF78KEc8HLwg0Atv8BH50DY2dCo9q37koOT+b1Ia+TVZLFlM1TmLZ1GiXW6gnVHcU7uP/3+3ln2DtYTBav47S7cih5nTLY8cESmgRXT9QaDUZSQlvDNtj00HcUOHKJG5BK+8vPqnVuIkdLy4REpEHsX7OFP+5/n40PzSDhQBIBxurlDKocFZgHRZI0uGu9xv1y85c8tfApjyRmfEg8U86bwvUdr/ctiVlVCtP/D766wXsSMyIRrp4GF7xVYxJzcWY+t36ywmsS02Q0cNvgVH57cAiX9khSElNEREREREREROQUFmQx8d9rehAbWj1huTO/nBsnLqPcaq99gGZ9YeSrUENZWADKcmDieZDjw/6bQFJ4Eg/0fICfL/2Zh3o+5LFCc2n2Up5Z9Awul6uGESCuc0vSXrucXa4MHC7vryEyIJYWwe0IXxnAors/Zt/S9T7NT+RIKJEpIkessqiYxS9OYsHdEyj/bDctA9oRHdjIo5/NacXaCVIvGODz2C6Xiw/WfsBzi5/zaGsa2pSJIyaSGpVa90Al++H3l+E/PWD1JO990q6GOxdB62E1DrMtp4RbP1nudS/MzomRfPt/A3h0ZHtCA7XQXURERERERERE5HSQEBXMf67qxuFrGlbuKuT2SSux2mtYbfmXXjfDA5vgwneg3XlgCfHsU5YDH/uezAQIsYRwXcfruL/H/R5t32Z8ywdrP6j1fLPZTP+Xx1LVzUWx7UCtfZNDUymbupt5j36I3V5H8lbkCOiOu4jUi8PhYPXb31C2+QBNghJJMqdAaC39XXaKm5XS9foLfL5Gpb2Sfyz8B7O2z/JoSwxL5MMRH5IYllj7IFkrYMk7sP4bd315b4IiYfR46HhRrUPllFRy/UfLKK6s/kYcZDHywPC23DCgufbBFBEREREREREROQ31bxnH46M68NzMDdWOz92Sy/1frmb8mG6YaqveFhIDaVe6H7YKWDsVZv4NnIfciyzLdSczr58J8e18ntv1Ha9nZ8lOvtryVbXj/171b0ItoVzZ7koMhprn1mbMUMrOPMCal2cQZ4wnxBzmtV+QKZQWrjasemAKMSNa0HJkf5/nKFIX3XkXEZ/lrs1gxd8m03hPY1LD2hFiDq+1f7H1AMWpZXS9y/ck5v6y/YydPdZrEjM5PJkJIybUnsR0uWDWI/DBUFj7Zc1JzGb94fYFdSYxy612bpq4nD2FFdWOhwaY+Or2/twyOFVJTBERERERERERkdPYTQNbcNtgz+pxM9P38Y9v19VayrUaSzB0vw4unQDGw9ahleXCxJGQ8avP8zIYDDzW5zH6Ne3n0fbS0pd4ZtEzWB2e22gdKrRxNP1fv57Ul4ZT1KqUrMrtVDkqvPZtGtwM4+8VzL3nffLWZ/o8T5HaaEWmiPhk928rKJyxk4SQlFr7OVx29lfuJbRLLB1uHF3rJ3oOl56bzn2/3UduRa5HW2pkKu8Nf4/GoY1rH+Tnp2DJf2tuN5rhjEdg0P1Qx96adoeTuz9fxdo91ffUNBkNvHV1dzolRtY+FxERERERERERETktPHJuOw6UW/lyeVa1458t2cV3a/aS2iiM1EahtGwUxlnt42nXJKLmwTqc705mfnVD9ZWZ5fnw6cUw+CEY8kid9zcBLEYLrw15jetmXce2wm3V2qZtnca2wm2MGzKORiGeW4Ydymwx0/HmcwGwV1Sx7JkpNHUmYTZaDrteAKkh7SicuIUNjl/o8feLCW0cW+c8RWqiZUQiUqctX/1O2cx9Xve//EuRtYAsthNxc1t6j7+ajjed43MS0+VyMXXLVMbOHus1iTkocRCTRk6qO4k5/w1YMN57W1Ak9LsL7loOZzxU55u8w+ni4Wnp/LIpx6Pt+Qs7MaRtfO1zERERERERERERkdOGwWDgxYs6M6Kj5z3M4ko7q3cX8vXKPbz642ZG/Xs+s9ftq33ADufDZRM9V2bigrmvwCcXQEm2T3MLDwjnrbPe8lrpbk3uGsbMHEN6brpPYwGYgwPp98/rMJ8XR07lXq99gkyhpAa0Y9crC5n/2EdYKyp9Hl/kUEpkikit1rwzA+OSKsIsnqsPy+wlZFm3Yx8cRMfXL6DvP68junVCvcavsFfwxIIneHbRs9i8lIG9odMN/GfofwgPqL2MLSs+dq/GPFxcGzhvHNy/EUa8ADEt6pyTw+nioalr+HrlHo+2/zuzJVf2blbnGCIiIiIiIiIiInJ6MZuMjB/Tjf4ta1+B6HC6eOirdPYWei/RelD70XD5J2AJ8WzbMQ/eGQibfvBpbglhCXw+6nN6Nenl0ZZTkcMNs29g9o7ZPo31l6TBXeg27nL2BWfVWG423BJFc2drNj32A0v/ORmHw1Gva4gokSkiNVry/CQiMkMJMnm+Ue62Z9LmlXPp+/p1NB/p+ebni13Fu7j2h2uZkTHDoy3AGMCLA1/k/h73Y6qrRMKGGTDzPs/jSb3g1t+h540QEOrTnBxOFw98uZqvV3kmMS9IS+DBs9v6NI6IiIiIiIiIiIicfoIsJt67ric9UqJr7VdSaeeBL9fgdNaxf2a7UXDLb9ConWdbWS58cSVMvhIKd9U5t5igGN4d/i7XtL/Go83qtPLQHw/xwdoPfN/TE/dK1F5PXUncHZ3ZXZWJ49BSuIdeO7ARCYVJrL5/Kqvfmq4VmuIz7ZEpIh6sFZUseWQSLYLbenzcwelysteyi37/vL7e4+4q3sWGgg1sLtjMpoJNrNy/knJ7uUe/+JB4xg0ZR5dGXWof0OmAJe+6V2K6nNXbGrWHq770OYEJ7j0xH5i6hm9Xe5ZDGNounlcu7VKvPT9FRERERERERETk9BMWaOaLW/syf1seG/cVk5lbRmZuKev2FmO1/+8+5qLMfD5asJ2bB6XWPmB8O7jlV/jhIVj9mWf75h8g83c442H39lomi2efP1mMFv7e+++0jWnLc4uew+q0Vmsfv3I8u0t280TfJ7AYax7ncJGpTek37nr2Ld7ArskraBrUDKPBcy1d4+BE2A27/zGX/KocKs2VRLWLp8ONIzGblbIST/pXISLV5KZnsPPDJe4k5mHsTht5Mbn0feTaeo2ZXZbNUwufYuHehXX27dOkDy8PfpnY4Do2gM7dDN/eBVlLPduimsG1X0NIjM9zLCq38fdp6cxe71lX/qx28bx9TXcCzXVvni0iIiIiIiIiIiJiMRk5s208Z7aNP3hsxpq93DN5VbV+r8zezKDWjWjbpI6ttQJC4cK3IWUAfP8A2A8r5Worh5+fhpWfQJ/boeuVEBRR43AXtrqQ1MhU7vn1HvIr86u1fb31a/aW7uW1Ia8REVDzGN407duBpn07kDl9AQW/7aJJcJLXfoGmYBJCUtzfZMK6B78hYkQyqef2rdf15NSn0rIictC6ibPI/3gjTYKTPdqsjkpKUsvo+cgV9RrzQOUBbp1zq09JzFs638K7w9+tPYnpsMG819z1370lMUMbwbXTIcL3vTp/3rCf4eP+8JrEHNZeSUwRERERERERERE5eud3TeD8rtXvW1odTu79YhVVdh/3jux2Ndw2F1IGem8vyIRZD8PrHeCHhyFvW41DdWnUhc9GfUbLyJYebYv3Leby7y5ndc5q3+Z1mNQLB9Bz/JUUphaTX5VTZ/+4oCY4filk9VvTj+h6cupSIlNEWDdhFkvu+ZSIjSGEWSI92ousBTAknM63j67XuKXWUu74+Q62F22vtV+4JZz/DP0P93S/p/b9MCuL4ePz4ZdnwWH1bA9rDNd8DbGeb7zeFJZbuX/Kam7+ZDk5JVUe7cPaN+btq3soiSkiIiIiIiIiIiIN4rkLOtEkIqjasU3ZJdz/5RpW7Cyoe89MgEZtYOxMuPAdCInz3sdaAkvfhTd7wM/PQA37XiaGJfLJyE/o29RzJeSe0j2MnT2Wd9a8g8PpY6L1MJ1uHUXHVy9gX+Qeim0Hau0bbA4lamcEC56YcETXklOTSsuKnKbsdjvpb07Hud1Gk+AkokLCvPbbW76T9n8fQXhio3qNX+Wo4t7f7mV9/nqv7YlhibSNbkvnRp25sNWFxAXX8Ib7l4pCmHQx7Fnhvb3LGDjnJZ/Lyf6+OYeHvkon10sCE+DsDo1586ruBJj1eQ8RERERERERERFpGJEhFl67vCtXf7Ck2vHv0/fxffo+4sICGd4hnou7J9GreS33Og0GSLsS2p7jXvixfAJQQxJ0/usQEAKDH/LaHBEQwdvD3ub5xc/z9davq7U5XA7eWv0Wi/ct5sWBL5IQ5nslvL+YzWZ6PToGu83O5k9/pmhDNoG2IGIC47AYA6v3NVpIsbdi7r3v0/+V6zEHBtT7enJqUSJT5DRjr7Sy/JUvCS4IIj6oMQR77+d0Oclybqf369fUe5Nlu9POQ388xNJsz9Kv93a/l8vbXl6/2urlBfDpRbBvtWdbeAKMfgPajPBpqCq7g5dnbeajBd5XiQaajTx4dltuHNgCk9Hg+xxFREREREREREREfDCgVRw3Dmjh9R5lXmkVk5fuZvLS3dwyqAWPjWyPwVDLfcrgaDhvHPS8CZb8F9Z+BfZKz36/Pg8xqdDpEq/DWIwWnu73NKmRqbyx4g3sLnu19hX7VzDy65H0bdqXc1ucy9BmQwkPqGNfz8OYLWY63njOwe/tFVZWvDyVJuUJmIzV70GnBrdj+UNf0O6+s4hKTazXdeTUokSmyGmi8kAxK16ZRlRVDEkBKRBUc99yewkVzW30v2tsva5R5aji550/M3nTZNbkrvFov7XLrdzc+eb6TbwsHz65APav9WxLu9q9CjPIsxyuN9tySrh78mo27iv22t4jJZpXLu1Cy0beV6eKiIiIiIiIiIiINISHz2nLwow8NmWX1Njn/XnbsTtd/OO8DrUnMwGadIIL3oJhz8LKj2HZB1C8p3qfb+6AiCRo1sfrEAaDges7Xk/Pxj15eO7D7CrZVa3d4XKwYO8CFuxdQMCiAM5IPoNbu9xKu5h2Pr3mw5mDA+jz9NVs+Gg2ARutBJlCqrUnhbRgz5vLyOm3lTaXDTmia8jJT4lMkVNUWe4Btk75ndKMAoKdIUQHxJFiagW1rMQvsRVSGHSAbo9cRFCU75+mySjMYNrWaczImEFRVZHXPle0vYK70u6q34so2Q+fXgg5GzzbBt4PZ/3DXUKhDqVVdiYv2cVrczZTaXN6tAeajTw0oi03DNAqTBERERERERERETn2giwmPru5D+/NzeSHdfvYXVDhtd+EBTtwueCp0T4kMwFCY2HQ/dDrJvjonOr3Vh1V8MWVcPMvENOixiE6xnXky9Ff8uKSF5mRMcNrH6vTypydc/h1169c3/F67uh6B0HmWlbP1KLDjeeQNXc1RdN3EhlQvZxuZEAs1mVVLN4wib5PXXNE48vJTYlMkVPM/uWbyPhkMQlBzYgzxhEXWMfek8CBqlwqY610e/BizEG+1RzPLstm9vbZfL/9ezYVbKq177nNz+WxPo/59kb7l31rYPJVUJzl2XbG32HIo3UmMTNyS/l00U6+WpFFaZXda58OTSP495VptIqvXxkEERERERERERERkaMRGxbIoyPb88i57diUXcKcDfuZvnoPmbll1fpNXLgDqEcyE9xV7K6aAu+fBWU5/zteng+fXQY3/QQhNe/BGWoJ5YWBL9A/oT8vLnmRYqv3KncOl4OP1n3Ezzt/5ql+T9G7aW/f5neYpMFpRKY0Ycvrv9M4uHop2QBjIEkVKSy4ZwKJ53Wi+dm9jugacnJSIlPkFFGWe4BVL31NgjmFZiEtfTonp3IvxhYBdPq/833eB3PpvqW8k/4Oy7OX46pp8+hDDGs2jBcGvoDRYPRpfADWfwPT7wRbuWfbmY/DGQ/Xevr2vDKenrGeP7bk1trvlkEteHBEWwLNJt/nJiIiIiIiIiIiItKADAYD7ZtG0L5pBDcNbMENE5exdHtBtT4TF+7A6XKXmTWbfLzXGtUMrvoCJowC+yErPvO3wgfDYMznEF97WdhRqaM4M/lMft39K7O2z2LhnoUe+2cC7CrZxU0/3cRFrS7inu73EBdc9wKbw4WnNKHrvy5l6SOTaGbxvMedEtIKfq1k1Q9fUh5cTsdbRhCV2rTe15GTixKZIic5u93Osmc/J7osluYBbers73K5yK7cTViPRnS/7op6XWvypsm8tOSlOhOYRoORwUmDuazNZQxKHOT7p4ScTvjjn/DHy97bhz0NA/9W6xDbckq57J2FHCi31dgnLiyQ1y/vyuA2jXybl4iIiIiIiIiIiMhxEBpoZsLYXl6TmZ8s2smW/SX8e0w34iN8LOOa2AMueR+mXAuH3tctyHAnMy9+F9qNqnWIEEsI56Wex3mp51FUVcScnXP4aN1H7C7Z7dH3m23fMHvHbK7veD1jO44l1BLq2zz/ZLaY6f/aWFa+OpWonCgCTJ6vs1FQU3DBgXc2sKHyR6L7JtH+qmH1uo6cPOqxREpE/E3W/DWsfmAqydYWhFkiauxX6ShnX8Vudjm2YR8SSK/xV9P+urN9vo7L5eKt1W/x4pIXa01iNgltwp1pd/LjJT/yn6H/YXDSYN+TmJXF8OW13pOYRguMHl9nEnNPYQXXfrikxiRmgNnIFT2TmX3fICUxRURERERERERExC+FBpqZeEMv+rTwLP26OLOAkf+ex7yttVejq6b9aBj+rOdxawl8cRX8/rJ7kYkPIgMjubTNpUw7fxo3dLoBk8Gz2l2FvYJ31rzDyK9HMnnTZGzOmhed1KT7Q5dhHh5NoTWvxj4WYwDNQloSnh7IqvumsPjpT6gsLq33tcS/aUWmyEnI4XCw5KlPaVyVSJPgJK99rM4q9tp30XRwa5qfdxatfCwd63Etp4MXlrzA1C1TvbaHW8IZljKMUamj6Nm4JybjEZRpzdkIU66B/G2ebSGxcMUkSOlf6xD5pVVc++ES9hVVerQlRAZxdd8UxvRKJjYssP7zExERERERERERETmOQgLMTLihFzdOXMbizOorM/NKrVz30VLuPrMV9w5rg8now2KS/neDwQhzngTXYUnL31+E7HS4+D0I8G0FZbA5mPt73M85zc/h6YVPs7Fgo0efgsoCXlzyIpM2TOKe7vdwdsrZvi98AZqd3ZOyzi1Y/c8ZNAlIJNAUXGPfRkEJUAk7n/2DHEc2ba4dQOPutZfNlZODweVy1b3JnUgtVqxYQc+ePQFYvHgxffr0OcEzOnXZ7XZ2/rCIvJ93khiS4rWP0+Ukq2I7rW4cQHxa66O6ntVh5ZF5jzBn5xyPtuTwZO7vcT+DkgYRaDqK5ODar2DG3d73w2zcCa6c7K7lXouSShtXvb+EtXuKqh03GQ28dHFnLu6W6Hvd+FOAzWYjPz8fgNjYWCwWywmekYgoLkX8i2JSxL8oJkX8j+JSxL8oJk9vlTYH//h2HV8uz/La3jkxkhcu6kSXpCjfBsz4FabeAJWFnm0J3eDKKRDeuF5ztDvtfL7xc95Nf5dia3GN/TrHdeZvPf5Grya96jU+QGVBEelv/4Ax10V8UCJGQ+33e+1OG/sqdxOW1ogO14/AZDqCBTg1UEx6OjRPtHz5cnr06NFgYyuRKUdNicxjx263s+H9WZRsySPQGURUQAxBppAa+++v2EPEGYm0vnjQUV97f9l+7v/jftJz0z3a2se05+1hbx/Rhs0AWMvdqzBXfwbLP/Tep/1ouPAdCAyrdaiSShu3frKCRZn5Hm3jrujKRd28r1g9lemNVMT/KC5F/ItiUsS/KCZF/I/iUsS/KCYFYNqKLJ6Yvo4Km8OjzWCAa/um8MDZbYkM9uHfR0EmfHE15GzwbItMhqu+hMYd6j3HoqoiPlz3IZ9t+Ayr01pjvz5N+zAocRA9GvegXUw7zMb6VRPMWbWVbZ/NJ9YVT2gtW679pdJRTomtiApnGY5AJ426N6fdlWfV65qHUkx6OpaJTJWWFfFTa97+FsNWOzGB8cQERtXa1+asItuyl16vXYX5CEvIHmpZ9jIe/ONBCioLPNr6NOnDG2e+QVhA7QlGD7uWwLL3YV865G/1LF/wF4MRhj4JA+4DY+2fqvltcw6Pf72WvV7KyT41usNpmcQUERERERERERGRU88lPZLokhTJnZ+tZGtO9X0gXS74ZNFOflibzcPntOXCtEQCzLXcW41JhZvmwPQ7YOOM6m1Fu+GjEXDZRGhVv2RfZGAk9/e4n6vaXcWbq95kRsYMXHiupVuybwlL9i0BIMQcQlp8Gle2u5IhyUN8uk58t9bEd2uNvdLK6nHfYNwP8UEJNfYPMoVUXyC0BpYs+pTWdw8hpnVyvV6jHH+nT61FkZPElq/+YPm9k4ndFUNMYHyd/XMr92E+N4Z+L1x31ElMl8vFJ+s/4ZafbvGaxByeMpy3h71d/yTmgvHw0dmwdirkba45iRkSB9d+A4PurzWJWVhu5YEv13DDhGVek5j3DG3FDQNa1G+OIiIiIiIiIiIiIn6sdeNwvr1rAJf39L6AI6+0ioe/Sqf/P39l3Jwt5BR73js9KDAMLvsYBj3g2VZVDJ9dBovedmdJ66lJaBOeH/g8X53/FYMSa68eWG4vZ+Hehdz96928vPRlHE7PFac1MQcF0PPRK+j+xhVY+xnZXZGBrZaVoIdKDGlOzjtrSH9nRt2d5YRSIlPED+xfsZlFT3zC0nsmEbQMmgTXvZLQ6qxilyuDzq9eTPKZ3Y96DlsObOG+3+7j1eWv4nB5vllc0/4aXh38KgGmgPoNvOJjmPOPuvsl9Ybb5kLqkFq7/bYph2Gvz2XaSu814a/p24y/DW9TvzmKiIiIiIiIiIiInARCAsy8cmlXPr+lDy0bhXrtk1daxfhftjLg5V+594tV7C2s8D6Y0Qhn/QPO/w8cXt7V5YAfH4XPL4fS3COaa5voNrw97G0+GvERnWI71dl/0sZJ3PPbPZTZyup9rdQLBtBv/Fiib2vPLuc2SmyFdZ4TZokkansk8//2EdaKWpK+ckJpj0w5atoj88jsmL2MvT+uI5woogPr3muywlFGobWAKlMlIcmRtL1iCKEJsUc9j1U5q/hw7Yf8kfWH1/YgUxBP93+aUamj6j/4hm9h6tiaV2ACBIRB71tgyGNgrj1JOn3VHu7/cjVOL7+1AkxG7hvemtsHt8RoNNR/rqcQ1WgX8T+KSxH/opgU8S+KSRH/o7gU8S+KSamJ1e7k/XmZ/PuXrVTZa74HGxsawNd39icl1nviE4CM3+DL69yrMQ8XGg8XvVPvUrOHcrlc/LzrZ77d9i0r96+kxFZSY9/W0a15c+ibJITVXC62LnabnXXvzKRsxwEsdguhpjDCLFGYDCav/QuqcqiIrKTrfecTFBVe69iKSU/aI1PkFGG32Vn7nxk4d1ppHJxIs8BWdZ5TUJWDNdFB2n0XN8j+l+B+05i3Zx4frv2QlTkra+yXHJ7MuCHjaBvTtv4XyfgNpt3smcSMagbtzoMmXaBJZ4hrU2cCE+CrFVk89NUar5UMujWL4tVLu9AqvvY3GBEREREREREREZFTRYDZyP+d2Yrzuybw0qyNzF6X7XURSH6ZlRsmLuObOwYQGVJD0q3lmXDTT/DZ5VC0q3pbWQ5Muhi6Xw/droWknmCo32ISg8HA8JThDE8ZjsPpYFvhNpbvX86MjBlsyN9Qre/WA1u56vurGD90PF0bda3Xdf5itphJu/vCases5RUse/ILks0tMB6W0IwJjIdK2PPCInLt2SSdn0bSmV2O6NrSsLQiU46aVmTWrjBzHztmLqZsZyHRxBJhifbpvFJbEQUhBfR+fAzmoHqWc62B3Wnnxx0/8uG6D9l6YGutfQcnDebFgS8SGRhZv4u4XLD1J5h6AxxeAiCqGdz4I0TU75M0XyzdxaPfrPVIYgZZjDw0oh1j+zfHdJqvwjyUPhEk4n8UlyL+RTEp4l8UkyL+R3Ep4l8Uk+Kr3QXlTFq8ky+W7aaowubR3jc1hk9u7EOAuZZdB8sL4Lt7YON3NfeJagadLoHOl0Hjjkc15ypHFU8ueJJZ22d5tJmNZh7t/SiXtbkMQz0Tp7VZ9+EPmDc4CbPUfu87rzIbks10uns05oD/LTJSTHrSikyRk4jD4WDVuGnYdlYQYYkiwhJNnCGOOEvd5WOdLgf5VTmUB5TS7bFLaBdbzySiFzanjQ35G1iWvYyvtnzFntI9tfZvFt6MmzrfxIWtLsRoqMc2uqW5sOZz956YBRme7aHxcO30eicxP128kyenr/M43jQyiE9v6kOr+LB6jSciIiIiIiIiIiJyKkqOCeHRke25b1gbPl+6i+e/31BtccjizAIe/Xot/7qsS82JwZAYuPxTWDEBZj8Gdi/7axbugvnj3I/258PIVyG8yRHNOdAUyMuDXqZFZAveXv12tTa7085zi59jXd46Hu/7OIGmwCO6xuE63TSSwow9bBr/C0khLWrsFxfUBHIh87GfKDDk0eamIcS0a9YgcxDfKZEp0oDS35uJfUMZTYISoJZy44cqt5eSZ83GGGumzVVDaNZ6yFHPI7ssm+nbprM8eznpeelUeHuzOUz7mPbc1PkmhjUbhsnovU64B3sVbPsZ0qfAph/A6fkpHwACI+HaryG2pc+vweVy8fbvGbz642aPtsSoYCbf0pdmsSE+jyciIiIiIiIiIiJyOggOMHHTwBY4nS5e+GFjtbZpK7NoERfCXUNb1zyAwQA9b4Rm/WHaTbDfc6HJQRtnQOYfcPZz0P26epecdV/OwB1d76BFRAueWPAEVY6qau3fbPuGzQc288aQN2ga1rTe43sT1TKRvv++juWvTMG1x0HjoMQaF/aEmMMJIZySCRlsq5xLRN9Eos5s3yDzkLopkSnSALZO/YPi+ftoHJwIQb6tosyt3EdFeDnd/34xbaIjGmQepdZSPlj7AZ9u+BSr0+rTOb2b9OamTjfRL6Gfb8vzXS7YMR/WfgkbvoXKotr7m4Phqinu/TB9VGlz8PBX6cxYs9ejLTkmmM9v7ktyjJKYIiIiIiIiIiIiIjW5eVALtueX8fmS6nte/uunLYQFmrm+f/Pa7wnHt4Obf4HFb8OqSd4r8QFUFbnL0a6dCqPH12tBy6HOaXEOSeFJ3Pfbfewv31+tbUP+Bi749gKGpwzngpYX0LNJz/pVFKxBz4evACDjh4XsmbWeJoGJhJjDvfY1GcwkBKfAGshZupCy8DLC7z0fSwNUVpSaaY9MOWqn8x6Z6z74gaq1xe4Epg+qHBXkVO0jtHMMXW4Z3WDzsDvtfL31a95a/RYFlQU+nXNWs7O4sdONdGlUjw2Lywvgy+tgxzzf+id0g5GvQZLv9bD3FVVw6ycrWLvHM0GaEhvC5Fv6khAV7PN4pyPVaBfxP4pLEf+imBTxL4pJEf+juBTxL4pJORo2h5MbJy5j3tY8j7Yreibz7IUdCTT7UKHP5YJ9q2HdNFj3NRTXsIWZKQB63wqDHnCXqT0C+RX5PDz3YZZmL62xT0JoAue1PI9LWl9CQlj9tjOrTUl2Hqv+PZ3QslAaByfV2d/qrCLXuo/QjrG0G3s2ZsvpuX7wWO6RqUSmHLXTLZFZlnuADR/8iGW/yV0juxaF1nyKHYUYIozE9WhBi3P7YA4MaND5rM5ZzdMLnyajqIZPwxwiMjCSoclDGdtxLKlRqfW7UOFumHQx5G2pvV9AmHuT5x5jISGtXpdYuesAt326gtySKo+2VvFhTLqpD00ig+o15ulIf9yK+B/FpYh/UUyK+BfFpIj/UVyK+BfFpByt4kobl/53IVv2l3q0dW8WxTvX9CA+oh73XZ1OWPUp/PSkezWmN0GRMPB+6HMbWOq/MMXutPPvlf9mwvoJtfYzGowMSRrCVe2voneT3r5VHfTR2o++o2hVDonBKViMde/PWWYrJs+RQ2iLKNpdM5Sg02ilphKZ4tdOxURm7qpt7JqzisrsMgIdAQQagwgwBRJgDMJirDsRWWjNo6JRFd0euhSz+dh8AqPKUcVbq9/i4/Uf43Q5vfaJCoyiX9N+9Gjcgx6Ne5AalXpky+1zNsKnF0OJZ6nXg5r1g65joNOlEBhWr+FdLhefL93FMzM2YHV4vpah7eJ5Y0waEUH6I80X+uNWxP8oLkX8i2JSxL8oJkX8j+JSxL8oJqUhZB0oZ8x7i8k6UOHRFh8eyDvX9qB7s+j6DVqSDT885N4nsyYRiXDm4+57x0YfVn4e5scdP/LUwqcos5XV2bdlZEuubHclo1uOJsTScFuT5azdxsYJvxJHYyIDfFtl6nDZOVCVR7mxjKbD2tNi5MmfN6mNEpni107GROaBrXvZPOEXAsotmA0WDBgwGIwYgABjEMHm0CMat9h2gMLgAno/cRXmoIZdeXmo9fnreXze4zWuwgw0BXJdh+u4qfNNhFqO7LUctGsJfH45VBZ6tsV3hC6XQadLIKrZEQ1fYXXw+PS1fL3SeymC285I5eER7TAZG+6TNKc6/XEr4n8UlyL+RTEp4l8UkyL+R3Ep4l8Uk9JQCsqs/N9nK1mUme/RFmA28tplXRnd9QjKtG78zp3QLNlXc5/4DjDsaWh9NtRz1eSBygPMzJzJjIwZbCrYVGf/cEs4F7S6gCvbXUmziCO7b+2NtbKSFa99hWm/iybBzeq1aCi7YjeR/RJoPWZIg83HnyiRKX7tZElkWssqWPfW9zj32IgPaorRUP9Pf9TkgDWPoqAD9Hz4UoKiIhps3L/YnDa2HNhCem46q3JW8dOOn3C4HF77jk4dzT3d76FJaO1lb32ycSZMuxnsnp/S4ewXoP9dRzX8jrwybp+0gk3ZJR5tAWYjL1/SmYu61V2HXKrTH7ci/kdxKeJfFJMi/kUxKeJ/FJci/kUxKQ3J5nDy4g8bmbBgh9f2h0a05c4hLetforWqFBa/DQvGg9WzhO1BKQNh6OOQ3OeIVmhuLtjMdxnf8V3mdxRUFtTZf2DiQPo17Uer6Fa0jmpNXHDcUZeftdlsrJv6M0WLd9HI3JjIgFifz82p3IuhmYWguHDMgRaMASZMIUE0SmtFaON6roj1I8cykXl67joqpw2Xy8WWz37lwPK9NLI0Id7UGOpfjrtGuZX7sDdx0u3BY1NCduGehUxcP5FVOauodFTW2jc1MpXnBjxHl0Zdjv7CTgf89gLMe82zzWiGC96GrlfUe9jteWWs21PEhn3FbNhbzPIdBZRZPROyTSKC+O813elW31IGIiIiIiIiIiIiIlIji8nIU6M70jEhkse+WYvVXn2rr1d/3Myu/HKev6gTFlM9tikLDIMzHoYeN8DcV2D5R+C0e/bbOR8mnAuBEe5kZkp/aD4Qknr5tFKzbUxb2sa05Z7u9/Djjh+ZvGkya/PW1th//p75zN8z/+D3EQER9GzckzvT7qRtTFvfX99hmg7tRtOh3YiMjGTrp3MoWZdHXEBjQs21L3SKD0qAHNyPgyo58Ns6sh0VVDjKqXRUYDPaiOubQqvLBzfovp8nIyUy5ZRir7SSvXQTBWt3Ura9gGhXLGGWSEKDmh/xmOX2UkpsRdhdVuw4cJlcGAKMxHZNpttVlzfc5A+xIX8D41aMY/G+xXX2NWDg+o7Xc1e3uwg01b3hcJ3KC2DaTZDxq2ebJQQu/wRaD6/XkHsLK/j7tHTmbc2rs++AVrH8e0w3YsMa4LWIiIiIiIiIiIiIiIdLeyTRKj6Mmz9eTl5pVbW2Kct3k1VYzttX9yAyuJ4rgMMawchXoc/t8OtzsP4b7/2qimHbHPcDoGkaXDYBYlJ9ukyAKYDRLUczuuVo1uauZfKmyczeMRub01brecXWYn7d/Svz9szjgZ4PcFW7q44qUWg2m+ly6/kAOBwOMr9bSPbCLVgqLcQGxNdrG7tAUzCBpkNWYq2CdUumUxFeQZf7RhMUHX7E8zyZqbSsHLWGLC2bl55B3rqdxKelEtOheZ39y/YfYN1/v8dQYCDcEkGIORxTPUrG2p1WCqpyceLChROXy4ULF1ZDFZa4IBIHtidhcBomU8OVoa1NZmEm76a/yw/bf/Cpf3J4Ms8PeJ7ujbs3zAT2roIp10HRLs+24Gi4+itI6lmvIfNLq7js3UVk5ta9GfP/ndmS+4e31X6YR0nlRkT8j+JSxL8oJkX8i2JSxP8oLkX8i2JSjqWsA+XcOHEZW/Z7loNNjArm31em0SMl5sgvsGcFzHkKdsyru29wDIz5zL1K8wjkVeQxbcs0vtz8JTkVOXWfAAxOGsxzA54jJsj31+hrTJYXFLPilanEWOOIDDiKnyFgdVRSZDuA0WDEZDBjMphwupwUOwtpcnY7Wow8sVv+aY9M8WtHm8i0V1pZ8+a3sNtB4+DEg8dLbIUU2wtxhDgJbx6LJSyEwIgQAsNDKNi4m/LNB2gU0BSLMaBe13O5XORW7qU0oJS21wymcdc29Tq/IVXYK1iWvezg8vbdJbvrPCfYHEzH2I4MTBzIle2uJMQScvQT2bMS5o9zb8qMl18JTTrD5Z9CTIt6DVtSaeOq95ewdk9Rrf3Cg8yMuzyNYR0a12t88U5/3Ir4H8WliH9RTIr4F8WkiP9RXIr4F8WkHGvFlTb+77OVXivqmYwG7hnamruGtjryBSguF2ydAz8/DTnra+9rtMD5/4a0q47sWoDNaeOXXb/w1ZavSM9Np8JeUWv/RsGNeKb/MwxMHOjT6sz6xqS1rIJlL31BeFkEMYHxvr+QesirzMbR2EXne0YTENqA++v5SIlM8WuHJzKjd1dSkVOIMdCCOTgAc1AA5tAgLOGBYHdRtr+AyvxSrCUVVGaXEksjIizHfi/EYmsBeY79ND2zHa0vHHzMr1ebTQWb+Hzj58zaPqvOvS8BusV3Y1SLUXRp1IXW0a0xGxugKrTLBZm/uxOY2/+ouV+XMXDeOAioX8K00uZg7ISlLM70vuFyckwwHZpG0DU5ist6JNMoXKVkG4r+uBXxP4pLEf+imBTxL4pJEf+juBTxL4pJOR5sDidPTl/HF8u8L7bp3TyG16/oSlL0USyscblg3xrYuRB2LnB/rfB+/5iB98PQJ8FYj306vXC6nOwt3UtGYQZbC7fyxaYv2F++32vfVlGtGNN2DOe1PI9QS80lYY80Ju1VVjZN+YXSrAJwOHE6XOBw4bI5cFY4MTnNBBoDCTWHE2aJrP+LBSod5eTZ9xM/qCUtLup/1PtrFmXuY9uUeVhzK3AZIapTE9qMORNzUPUFZkpkil87PJHJZ1tIDGl+Yif1p0pHOdmVewhuHUHX287HHFi/1ZsNye6089vu3/hs42es2L/Cp3NaRLbgvu73cWbymQ23oa/TAZtmuhOYe1fV3M9ohnP+Cb1u9mmT5UPZHU5un7SSnzd6viE8fE5bru6TUv/a6uIz/XEr4n8UlyL+RTEp4l8UkyL+R3Ep4l8Uk3K8uFwuPpy/nZdnb8Lm8EwdmYwG+reM5fyuCYzo1ISIoKP8t+hywcYZ8M3tYCv3bG87Ci76LwQdWVLPm6KqIp5a+BS/7Pqlxj4h5hBGtxzNmLZjaBXdyqP9WMektbKSleOmwT4HCcHNMNZjO71DFVkLKDYW4jKDq8oJDjBhwog7Oew65L+dOHEaHGAyYAgwQpWLMCKIDojzyE1UOSrIq9qPNdhKwhntaT6iD6tXr1YiU/zX4YlM4+fbaBrc7LjOwelykl+1n3JnGU6LE0t0IBEtGtH83H6ExEQc17n8ZXfxbtblr2ND/gY25G9gY/5GSmwlPp0bHxLPnV3v5IJWFzTM6ksAexWkT4EF4yF/W+19o5vDRe9Cs771ukSlzcHsddl8vGgHq3YVerTfM7QV95/dtl5jSv3pj1sR/6O4FPEvikkR/6KYFPE/iksR/6KYlOMtPauQeyavYke+l+TinwJMRoa2i+euoa3olHiUicZ9a+DzMVCy17MtthVc8RnEtzu6axzC5XIxdctUXln2ClWOqlr79mzckyvaXcFZyWdhMblj73jG5PYfl7D7+3TCjBEYMeJwOXDiwOlyEhUQQ6j5xOQ/DlViK2TxnhXcOOUZoOETmQ2UIRH5n7+y+UcirzKbEnMxBiuEGd3ZflMtibyCqv0UcoDUi3rRbfAZR3zdhrQsexnjV45nTe6aep3XKqoVgxIHMShpEGnxaViMR/nLz1bp3kh510L3Ev3dS8HquWFzNY3aw8D7oNMlYPL9+lv2l/DF0t18vSqLwnKb1z7X9Uvhb8NP3H6kIiIiIiIiIiIiIlK3LklRzLxnEE99u55pK7O89rE6nMxen82cjfu5eVAL7jurDcEBR7ZykKZd4ZZfYfIY2Le6elv+Nnh/KFz4NnS88MjGP4zBYODytpfTLb4bTy98mvS89Br7Lt+/nOX7lxMXHMclrS/h8raXE30ctsr7S4sRfWgxoo/XtsrCYlaO+4aAQguNg5IarqpjPYVbomgclHTMxlciUxqcwVC/RKbDZSe7cjdh3eJJG3tZtbay7Hy2TV9A5YFSHFU2HDYHTpsdcBHVtimdxl6EyXSEvxwb2Mb8jYxfNZ4Fexb4fE7ziOZc0fYKhqUMo0lok4aZSGku/P4SrP4c6tjE+KCk3jDofmg9wuea43sLK/huzV6mr97Lxn3FtfY9v2sCT4/ueMJ+kYqIiIiIiIiIiIiI78ICzbx2eVcGt4njqRnra1zA4nC6ePePTGavy+alizrTv1XckV0woincMAum3w4bvq3eZiuDqdfDztug353uioINoHV0ayaNnER6XjpTNk1h9o7Z2JzeX2deRR7vpr/Lh2s/5MzkMxnZZCQdozo2yDyOVFBUBP2fuR6AjV/+Qu78DOItCUe8v6a/UmlZOWqHl5aN2FxE2f4inDY7LrsTl82Jy+nC9WdNbVOACVOIhYDwIAKiwkg+syuhCUf4y80PbD2wlffXvs+s7bN8Pmdg4kCubn81/RP6Y6xn4rdGtkpY/DbMex2svpWwpdVwGPg3SOnv8z6YC7flMf6XrSzdUYAvvz3O7dSE8WO6EWBuoNcpdVK5ERH/o7gU8S+KSRH/opgU8T+KSxH/opiUE62sys7PG/czY/Ve5m7N9bp/5l8u65HEQyPaEh8RdGQXc7lgwRvwy7PgcnrvkzIAul4JHS6AoIYrrVpQWcD0bdP5cvOX7CndU2f/luEtuaTNJYxqNYqYoJgGm8fRsJZVsOqtb7DvrqJpUBIBpv/972BzWrE6q3A47e4DBjD8+R+z0YLFGIjpkP04y+zF5Ffl4op00ezsrhRl7qcwfS+hznBiAuOr9U3P3syoj28BtEem+KHDE5l9+nhf5nwqcblcLMtexoT1E5i/Z36d/ZtHNKd9bHs6xHRgSPIQmkc2b8jJwLpp8PPTULS77v4GI3S82F1Ctknnel3q44U7ePq79XUmMI0GGNquMVf3acaQto20EvM40x+3Iv5HcSniXxSTIv5FMSnifxSXIv5FMSn+pLDcyux12bzzR0aNe2gGW0zcMqgFt57RkrDAIywMmvEbfHUjVBTU3MccDJ0vhT631fted20cTgcL9i7gi01fMH/PfFzUfkPcZDDRP6E/o1JHcWbymYRYQhpsLkejZF8eWfPWEBQVTlyHFMKTGtfa3263U7o7l+JMdxI34Yw0zGbv//uV7S9gy2e/U7GrmHBDBLsK8jjvk9sAJTLFD53KiUy7086Ooh1kl2dzoPIABZUFFFQWsGTfEtbnr6/13M5xnbm96+10j+9OWEDYsZlg8V749i7I+KX2fqHx7lWXKf2hzTkQnVKvy7hcLsbN2cK/f91Wa7/EqGCu6JXM5T2TaRJ5hJ+4kaOmP25F/I/iUsS/KCZF/ItiUsT/KC5F/ItiUvxRpc3BGz9v5f15mTic3tNMsaEB3HNWa67s3ezIKvYV7oIp13rum+lNs/7Q+xZoPxpMDRcju0t2M3XzVL7e9jVFVUV19g8wBpAWn0afpn3o3aQ3neI6YTae+rs8/vr195x1yXlAwycyT/2fnkgNnC4nO4p2UFhViN1px+a0YXfaKagsYEP+BjYUbGBLwRYqHZX1Grd5RHPu7X4vZzU769itRHS5IH0KzHoYKmv45RkY6V512eECiEn1uXTs4RxOF09MX8fkpbu8tocHmRnZqSkXpCXQJzUWk1GrL0VEREREREREREROZUEWE4+c247zujTlka/TWben2KNPfpmVp2as56MF23loRFtGdW5av3vmUc3gxh9h7quwYgKU59fcd9dC9yM8AXreCD3GQlij+r+wwySHJ3N/z/u5M+1OZm2fxeebPmdTwaYa+1udVpZmL2Vp9lIAQi2hDE8Zzo2dbqRFZIujno+/ikxpcszG1opMOWon24rMncU7+S7jO2ZmzvSpzrWvksOTubnzzZzf8vxj9wkLlwvyt7nLyG6a6b2P0Qw9b4Iz/g6hsUd1uUqbg/u+WM3s9dkebc1jQ3jk3PYMaduIIIvJy9lyouhTeiL+R3Ep4l8UkyL+RTEp4n8UlyL+RTEp/s7ucPLxop38+5etFFXYauzXNSmSR85tT7+WR3Df2mGDrXNgzeeweTY4a74OAKYA6HgR9L4NErsf8UKfw7lcLpbvW86kdZNYmLPQ54VQBgyMaD6CmzvfTNuYtg0yF39yaJ5IKzJFjkBRVRGzt89mRuYM0nPTG3TsLnFdGNtpLEOTh2IyNnBCz+WC3Uthx1zIWg5Zy2r/1EnrETDiRYhrddSX3ldUwR2TVrJ6d6FHW6fECCbe0Ju4sMCjvo6IiIiIiIiIiIiInLzMJiM3DWzBpT2S+O/vGUxYsJ0qu9Oj35qsIq58fzH9W8YypG0jereIpWNCBBaTD2VnTRZoN9L9KC9wVyxc+h4UZHrv77C6+6RPgcAIiG8PjdpBfAdoPuCI99Q0GAykNUojuUsyFfYK1pSvYfau2SzeuxiHy1HjeS5czN4xm9k7ZtM9vjuRgZGYjWbMBjMWk4X4kHgSwhJIDEskMSyRhNAELA1YIvdkpkSmnLJsDhvz9szju4zv+CPrD2x1fULDRyaDieigaLrFd+Pq9lfTPb77sSkhu3MRzPkHZC2tu29gJIx8Bbpc0SCfLFmUkc9dn68kv8zq0TagVSzvXNOD8CD9EhURERERERERERERt8hgC4+c247r+qUwbs4Wpq3Mwtv2mQsz8lmY4V6wExJgokdKNNf1a87wDo19u1BIDPS9w73aMuMXWPIubJtTc/+qYti9xP34S8eL4bxxEBzl+ws8TLA5mFEtRnFhmwvJq8jjj91/sCR7CUv3LSW/suYFSStzVtY5doAxgCHJQxjTbgw9G/c8dtvYnQSUyJSTnsvlYmvhVrYd2Mbesr1klWSxt3QvGwo2+LT5rjcmg4mWUS3pENuBjrEdaRXVitjgWGKCYggPCMdoOIKNiX2Vswl+eQY2/+Bb/5Znwfn/gcjEo760y+Xig3nb+efsTV43aB7VpSmvX96VQLNKyYqIiIiIiIiIiIiIp4SoYF69rCs3D0rl5dmb+HVTTo19y60O5m3NY97WPG4c0ILHR7XHZPQxaWc0Quvh7kd+Biz7AFZNcicu67L+a3cVxEs+gGZHv11eXHAcl7S5hEvaXILL5SKjMIMFexfw2cbP2Fe2r97jWZ1Wftr5Ez/t/InUyFQub3s5afFpVNorqbBXUGmvJMAUQNdGXYkMjDzq+fszJTLFb2zM38iMjBlkl2WTEJZA10ZdSYtPIz4k3mv/nPIcZmbO5Ntt35JZVMPy8VrEBMUwKnUU/Zr2I8gchNloxmK0EGAKoFl4M4LMQUf7knxXmuv+5MjmWbBxBrg8l917CIqCYU+7Ny1ugE9jbMou5rWftjBnw36v7TcNbMFjI+vxJiIiIiIiIiIiIiIip622TcL5aGwvFmfm89KsTazxso3ZoT5asJ0d+WWMH5NW/4qAsS3hnJfgzMch/QtY+j7kbqr9nKJdMOFcOPNRGHg/NNDWcQaDgVbRrWgV3Yqr2l/FzIyZfLjuQ3YW7zyi8TKLMvnn0n96bQs0BXJui3MZ03YMHeM6Hs20/ZYSmXJCWR3uTxV8sekL1uSu8dqnaWhTUqNSCTGHEGQKItAcyL6yfSzauwinLwm/QwSaAhmaPJTRLUfTL6EfZuMJDIEDO2D1ZNj6E+xdBXhZY3+okDhI7g1JvdxfE3uAJfiop7FsRwH//T2jxk/FBFtM/POSzlyQdvQrPkVERERERERERETk9NI3NZbpd/bnx/XZfLUii6XbCyiutHvt++umHC797yI+HNuTpOiQ+l8sMAx63Qw9b4JdiyFrGeRshJwNkLsZ7BXV+7sc8OvzsHk29L8L2p3n3o+zgViMFi5qfRHntzyfOTvn8P3278kpz8HutONwOrC77FTYKsityMVVV47AiypHFdO3TWf6tul0iu3E5W0vZ0TzEYRYjuBn56cMLper/j8ZkUOsWLGCnj17ArB48WJWBa1i+f7l7tWNxgAspv99dblclNpKKbOVUWorZXfxbg5UHTjmc+zZuCejW45meMpwwgPCj/n1auRywfa57prdm3+gzuQlQJMuMPwZSD2zQVZeAjidLn7bnMN/f89g+c6af/4psSG8e20P2jWJaJDryvFhs9nIz3fXYI+NjcVi0X6mIiea4lLEvygmRfyLYlLE/yguRfyLYlJONU6ni837S1i6vYCvVmSxdo/nFnFxYQG8e21PeqREN+CFHbD4bfj5GXDavPcJa+KugtjjeohI8NrlWMSkzWFjX9k+9pTuIbMokxkZM9iQv+GIxgo2B3Nm8pmMbjmavk37YjaacblcFFuLKagsoNJeidFgxGAwYMCA0WAkzBJGZGDkwUqVVY4qVuesZtHeRSzet5jNBZsJtgTTo3EP+jbtS9+mfUmNTD24d+eheaLly5fTo0ePo/6Z/EWJTDlqhycyp1RMYc7OWjbWPcbCLeEkhieSEJpA50adObfFuSSGncDVhJXFkJ0Oe1bC6s8hd6Nv50WlwFn/cG86bGyYPTltDicz0/fyzu+ZbN5fUmvfs9rF8/oVaUQG6w+jk43+uBXxP4pLEf+imBTxL4pJEf+juBTxL4pJOZVV2hz8fVo6367e69FmMRl4anRHru7T7GDCrEHsWQnTboKCWrasM5ig3SjofQs0H1RtkdHxisl1eev4YtMXzN4xmypH1RGNER0YjcVkoaCyALvT+yrYQwWaAokMiKTIWlTnNRsFN6JP0z70adqH4Oxgzhl0DtDwiUyVlpUGZ6vpkww+igqMorCq0Of+4ZZwRrQYwagWo2gd3frEb2zrckHGr7DmC9i7EvK3+X6u0QzN+kHHC6HbtWAOPOrpWO1O1u4pZHFmAZOX7iLrQEWt/VvEhXLHkJZc2j0Jo/bDFBEREREREREREZFjJMhi4o0r0mjZKIzX52yp1mZzuHhi+jrW7C7kuQs7EWRpmD0sSewOt82F7x9076fpjcsBG2e4H3Ft3OVqu46BoOOXf+gU14nnBz7PQ70eIj03HZvTRpA5yL0NnzmIlftXMmXzFDKLak7I1rciZpWjipwK79vQHS63IpeZmTOZmTmTih215x2OhhKZ0uBsjvonMo0GI4OTBnNl2yvpm9CXEmsJ6bnprM5dzbq8dRRVFVFpr6TSUUmlvRIXLjrEduD8ludzZvKZB5c7n3C7l8HPT8PO+b6fE9oI2pwDrc+G1CEQdPRlXPcUVjB1+W4WZ+azalchVfa69xLtnBjJHUNaMqJjE0xKYIqIiIiIiIiIiIjIcWAwGLjnrNakNgrlgS/XeNzPnroii43ZxbxzTY8j2zfTm8BwuPhd6HIZLP4vbPu55r55W2DWw+6StF3HQPcbwRjXMPPwQWRgJIOSBnkcbxfTjivbXcny/cv5YtMX/LrrV+yuulddnmyUyJQGlxqVSpmtDJvThtVpxeawYXPaDiY4QwNCCbOEEWpxf20T04YLWl5AQtj/6k3/FZjegtMv5W6GX56FTTN9PyexJ/S5HTpcAOaABplGcaWNt3/L4KMF27H6kLwEGNAqljvOaMWAVrENuzxfRERERERERERERMRH53VJoFlMCLd/uoK9RZXV2tbtKWb463M5t3MTLu2eRN/U2IapKNhqmPtRkAnLP4JVk6CihlWMtjJY/iGW5R8SndiX8k7XQvQlwIkr92wwGOjVpBe9mvSioLKA2dtn833m96TnpTfYNRLDEundpDcFlQUsy15Gub28wcb2hfbIlKN2+B6Zffr0OcEzOo6yVsDC8bDxO3D5kDgMjHCvvuxzGyT1bLBp2B1OJi/bzRtztpBfZq2zv8EAIzo04fYhLUlLjmqweYh/0L4JIv5HcSniXxSTIv5FMSnifxSXIv5FMSmnm/zSKu6evIqFGfk19kmMCuaibolc0SuZ5JgGWqUJYKuA9d/A0vfdW8fVwRXaCEOnS6Dz5e6StX6yWGhH0Q5mbZ/F1sKtBJuDiQ2OJTYolpigGEIsIeACFy6cLicOl4MSawlFVUXuh7UIgM5xnemX0I/k8OSD49qcNtbnrWfRvkUs2beENblrsDvtVOyoIOPpDKDh98hUIlOO2mmXyLRVQOYfsPDfsHNB7X0TukHKAPfXpmkQkwpGY4NOZ8XOAh6ZtpatOaV19o0OsXB2hybcMjiVVvFhDToP8R/641bE/yguRfyLYlLEvygmRfyP4lLEvygm5XRkdzh59cfNvDu35v0fAYwGOKt9Y8b2b07/lg1cdXDPClj2Eaz7CuyVdfePaQkdL4KWZ0JSLzAHNtxc/FS5rZyVOSuZ9us03rjuDaDhE5kqLSviTVk+7FsFe1fDvjVQvAfK8twPW1nd5zfuBGc9Ba2HH7NPYFTaHIybs4X35mVS08cRokMsDGrdiN4tYujTIoaWjcIaZrm9iIiIiIiIiIiIiMgxYjYZeXRke7okRfHItHRKqrzv/eh0wZwN+5mzYT+t4sO4vl8KF3dPIjSwAdJfiT3cj7Ofc5ecXfY+FO6quX9BBsz7l/thDoZmfSH1DOhwIcS0OPr5+KEQSwgDEwcS3CGYN3jjmFxDiUw5PVWVwIYZsG0OlBeA0w4OKzhsUJ4PRbuPbNyoFBj6JHS6pMFXXh4qPauQB75cU+MqzCCLkVsGpXLbGS0Ja4hf2CIiIiIiIiIiIiIix9moLk3p1zKW6av2MG1lFuv3FtfYd1tOKU9+u55XZm/m0p5JXNevOS3iQo9+EiExMOAe6Pd/sOVHnEvexbj999rPsVdA5m/uxy/PQqdLYdD9EN/+6OdzmlGGQ04fTof7l8aaL2DjTPcvkoYS3wH63+NOYJoDGm7cP9kcTjbuK2bVrkKW7zzAD2v34XB6X4Z5cfdEHhrRlqaRwQ0+DxERERERERERERGR4ykmNIAbB7bgxoEt2JRdzNcr9/D1yizySq1e+5dU2ZmwYAcTFuzgjDaNGNQ6jjaNw2nbJJz48MAjLz9rNEG7kThaDic/YwXBW78jNPN7DAe2136eywlrv3Q/2p0HvW+B4GgwWsD05yOsMVh0T98bJTLl1Je9DtZMhrVfQWl2w47dYjD0vxdandXgJWRdLhdzt+bx/txMlu0ooMrurLV/q/gw/nVZV9KSoxp0HiIiIiIiIiIiIiIi/qBdkwgeGxnBg2e35Ye1+/h40Q5W7Sqssf8fW3L5Y0vuwe8jgsz0SY3lvmGt6ZgQecTzcEQ1p7TX3QSOeApLTjqkfwkbv4OSvbWfuGmm++FNWGN31ceoZhDX2l2SNr7dEc/xVKFEppxanE53srJwF2QtgzVTYP/aoxszJM693DssHkIbub8PjYPkPtC4Q8PM+zBrs4r45+yNLNiWX2dfgwFuHZTK34a3IchiOibzERERERERERERERHxFwFmIxd2S+TCbomkZxUyceEOZq7Zh9VR+4Kg4ko7czbs54/Nufz93HbcOKD5ka/QBPcN+qSe7se5L0N+Bmz/A7bPdT8qCnwfq3S/+5G11P397y9BykDodZN7JecxqAZ5MlAiU/xLeQGU7IOIRAiOqru/rRI2/wDrv4acjVC4GxxVvl/PaIamXd0b75osYAoAcyDEtYGEbpCQ5p5LA6+29MblcrE1p5T//LqN79bU8amNPzWPDeFfl3WlZ/OYYzw7ERERERERERERERH/0yUpitcvT+Oxke35YukuJi3eRXZxZa3nWB1Onpu5gflbc/nXZV2JDQs8+okYDBDXyv3odRPYre5qkfNfhwM7jmzMnfPdj9B4SOwOtnKwlru/mgPdVSN73gjRzY9+/n5KiUw58ZxO996Vyz6ALbPd9aIBGrWD5N6Q1Nv9PCDEXSPaHAxFWbDmc1g3DSqL6n/NxB7Q9UroeDGExjbs66mHCquDRZl5/L45l98257C7oO59O4MtJromRzKkbTzX9UshJEBhLCIiIiIiIiIiIiKnt7iwQO4a2prbzmjJT+v3M3npLlbtOkCZ1VHjOb9tzuWc8fN45ZIuDGnb6OhWZx7OHAA9roe0q92Lseb+C/I2H9lYZTnu/Mnh9q6CBf+G1sOh183Qaph7L89TiDIg0vByNkHFAffqRpPF/amAv1Y7ulxQVfK/R+5GWD4BCjI8x8nd5H6s/KRh5hWZDF2ugK5j3PWlT6B1e4qYuHAH363ZW+felwC9mkdzQVoi3ZpF0bZxOGaT8TjMUkRERERERERERETk5GIxGRnVpSmjujTF6XSxp7CCrTklbMouYdKinewtqr5aM7ekihsmLqNN4zCu7ZvChd0SCQ+yNNyETGbocjl0utS9FV7xPnDawWkDhx1sZe7FW4W73I+8rVCeV48LuGDrT+5HVDPoMsadC4lrdfRzryqBnQshOx0soZDSH5p0AePxy1EokSkN74+X3Z8u8AcB4dDxAvfqy2b9j2twHc7ucPLThv1MWLCdZTsO+HRO6/gw/n5OO85qH9+wnwQRERERERERERERETnFGY0GkmNCSI4JYWi7xlzVuxl/n5bOj+v3e/Tdsr+UJ79dzz9nbeKi7olc27c5bZuEN+Rk3FvdNe1aez+nA7b97K5iuXUO4PL9GoW7YO4r7kdiD3fyNCAEynKhNNf91V4JGNylcA0GMBghMAKCoyEkxv21ZD9k/u7er9Npr36N4Gh3SdsWZ0DqEIhJrd/PoZ6UyJSG57CegIsaoMUgiO/o/sRBdIr7a1wb94rQE2BHXhlrsgpZt6eItXuKWL+nmJIqe90nAk0igvjb8NZc0j1Jqy9FRERERERERERERBpAVEgA71zTg8+W7OK5mRu8VkwsszqYtNi912bv5jFc2y+FER2bEGA+TvfqjSZoM8L9OLDDXdVyxzx37sUS8r9H1jJ3ydma7FnhfjS0igOw4Vv3A9zVMJ3tG/46f1IiUxre0SQyk3pDp0vcwZm1FPalu5dX1yS2NaRd5V4mHZl45NdtQAsz8nj1x82s2lVYr/PaNQlnSNt4zmzbiO4p0ViUwBQRERERERERERERaVAGg4Fr+qbQq3kMj3ydXuu9/KU7Cli6o4C4sECu7J3M1X1SiA05jntQRjeH4c94b7NbYdNM98rNnQuO35wOV7Qb9u44ZsMrkSkNr76JTHMwdL4Uet/iuaTaVgHZ66CiAGzlYKt0f8UFTbtBYnf30mc/sG5PEa/8uJm5W3J9Pic1LpRr+6VwTqcmNI0MPoazExERERERERERERGRv7RtEs7Xd/Rn9e5CPl28k5np+7B6WaEJkFdaxX9+3cbbv2dwdvt4LugQSdeEsOM848OYA6DTxe7H/g2w6lNY+1XtqzRPQkpkSsM7+3koywOHDRxV7sSm3fq/BGdguLvecmC4+xGTCpYg72NZgiG51/Gb+xHYlF3MW79l8N2avT6fc2bbRowd0IJBreIwGv0jESsiIiIiIiIiIiIicjoxGAx0axZNt2bRPDGqA1OX72bSkp3sLqjw2t/hdDFr/X5mrd9P67hgxvRJ4YJuScSFnZgt7g5q3AHOeQmGP+fe2zL9C9g4E+yHvI6AMAiNg4BwwAUuF7ic7j0wq4qhvKB6hcywJu49MFueCc0HuffXzPwdtv8BOxdVH/sYUiJTGl6Tzid6Bsecy+ViUWY+7/6RyR91rMA0GKBFXCidEyPplBDJsA6NaREXepxmKiIiIiIiIiIiIiIidYkJDeC2M1py86BU5m7J5dPFO/ltcw4ul/f+W/MqeO77Tbw4azODWsdxYVoiZ3dsTEjACUy9mczQepj7YS2Hwp3u/TRDG0FASO3nulxgLXPvgQkQmVS9ImZkIiSkwcD7wF4Fu5e6k5qZv8O+pcfoBSmRKVIru8NJRm4Ze4sqKCi1kl9WRX6ZlYXb8lm7p6jWc7smR3HfWa3p1SKGsECFmoiIiIiIiIiIiIiIvzMZDZzZLp4z28Wzu6CcSUt2MmXZbgrLbV77O5wuft+cy++bcwkwGemeEsWAlnH0bxVLl6QoLCbjcX4FfwoIgfj2vvc3GCAwzP2oizkQWgxyP4Y+AR3+gPeGHPFUa73UMRlV5CTgdLrIyC3lQLkNu8OJ1eHE7nBRUGZl7Z4i1u4pYuO+YqpqqIldk5aNQnloRDtGdGyMwU/27xQRERERERERERERkfpJjgnh0XPb87dhbZixZi8fL9zB+r3FNfa3OpwszixgcWYBr82B0AAT53Zuyu1ntKRV/AneU/NY8iX5eYSUyJTTTmZuKd+s2sPXK/ewp7Dhajg3jw3hziGtuLh7IuYT9QkLERERERERERERERFpUEEWE5f3TOayHkkszczjo3nb+GNbIZV1LIQqszr4akUW01ZmMbJzU/5vSCs6JEQcp1mfGpTIlNPCgTIrM9P38vWqPazaVdigY3drFsVtg1MZ3qEJJqNWYIqIiIiIiIiIiIiInIoMBgPdm0WRck4Lyq0OVuY4mLk2m3lb83A4a9hME/f2k9+n7+P79H30ah5NZHAAFpMBk9FAgNlI44ggkqKDSY4OISk6mMToYALNpuP4yvyXEplyyrLanfy2OYevV2bx66YcbI6af4nUh9loICY0gJ7No7lhQAt6pkSrhKyIiIiIiIiIiIiIyGkkJMDEBV3jubRnM/JKq/hl434WZuSzMCOf3JKqGs9btuNAnWMHmIwM79CYq/s2o19q7Gmdg1AiU056LpeLTdklbNlfQtaBCnYXlJN1oIJ1e4tq3Hy3LiajgTaNw+mcGEHnpCjaNg4nLiyA2NBAIoLNp/UvDRERERERERERERER+Z+4sECu6NWMK3o1w+VykZFbyu+bc5m4cAdZB+q/xZ3V4eT7tfv4fu0+WsWHcXWfZvRIiabC6qDc5qDS6iDAbKRHSjRRIQHH4BX5DyUyxW+s21PEtJVZ7C2sICk6hO7NoumREk2TyCCv/bOLKvlm1R6+WrGbjNyyel8vLiyQC9MSGNg6jiCLCYvJgMVkJNBsIiU2hCCLlm2LiIiIiIiIiIiIiIjvDAYDreLDaRUfzvX9m/Pt6r28/fs2Mo8gjwGwLaeUZ77b4LUtwGxkdJcEruuXQtfkqKOYtf9SIlNOqCq7gx/W7uOTRTs99q78kO0AJEQG0apxOCEWE8EBJoIsRvYUVjJ/ay61lJz2KtBsZETHJlzcPZGBreIwm4wN9EpERERERERERERERET+x2IycmmPJC7qlsisdfv4dvVe9hdXYne4sDud2B0uyq0Ockoq653vAPcWe9NWZjFtZRZdkiK5pk8KI7s0JSzw1En/nTqvRPzGv3/ZyuLMfCwmIwFmIwF/frWYDLhcUFplp7TKTkmlnZ35ZRyoo/zr3qJK9hZVHtWc+qbGcHH3JM7t1ITwIMtRjSUiIiIiIiIiIiIiIuIrk9HAeV0SOK9Lgtd2q93JvqIKsg5UsC2nlGkrs0jPKqrXNdKzing4K51/zFjH2R2acFH3RAb9uaDL6XRRVGEjv6yKSpsTgwGMBsPBr+FBZqKCAwiyGDEYDFTaHKzYeYB5W/NYsC2PDfuKCQkw0adFDP1bxjGwdRyt48OOyzZ8SmRKg9uUXczCjPwTdv2IIDPJMSEkRQfTNTmK87smkBQdcsLmIyIiIiIiIiIiIiIiUpMAs5GU2FBSYkMZ0CqO6/s3Z83uQiYt3smMNXupsjt9HqvS5mTGmr3MWLOXqBALASYjBWVW7D4s+QwwG4kKtlBUYfO4ZkmlnZ835vDzxhwAGoUHMqBlLP1bxRFeenSL0WqjRKY0OKv9CNY/HyI6xFLnKs1DhQeZGd01gQvTEmnbJJzIYK24FBERERERERERERGRk1fX5Ci6JkfxxKgOrNp9AJvDRfCfW/AFW0ws31nAp4t2sjWntMYxCuuRawH3ytCckiqf+uaWVDF99V6mr95LVfa2el2nPpTIlAZndfj+yYC/GA1wVvvGXNcvhQEt4yiptLNq9wFW7jzA6qwiisqtVNgcVNqcVNgcuFzQOTGCi7snMbxDY4IspmPwSkRERERERERERERERE6cyBALQ9rGexzvkBDBtX1TWLK9gE8X7+THddk+rbo82SiRKQ2uTXwY5VV2bA4nVXYnNocTq8OJ7c+VmmFBZsICzYT/+bV90wgu6ZFEYlTwwTH+CkxvwSkiIiIiIiIiIiIiInK6MxgM9E2NpW9qLAVlVr5P38vXq/awaldhg12jWUwI/VJjyS+rYnFmAaVV9gYb2xdKZEqDe+K8Did6CiIiIiIiIiIiIiIiIqeNmNAAru3XnGv7NWd7XhkzVu9ly/4SggNMxIUFEhcWQGxYAKEBZlyAywUulwu700VJpZ0D5VaKKmwUllsBSEuOZmCrOJrFhhy8hs3hJD2riAXb8liwLY+Vu9wlb48lJTJFREREREREREREREREThEt4kK5d1jrBh/XYjLSIyWaHinR3HNWa8qtdpbtOMDUWRW89XGDXw5QIlNERERERERERERERERE6ikkwMwZbRoRVtKCt47RNYzHaFwRERERERERERERERERkSOmRKaIiIiIiIiIiIiIiIiI+B0lMkVERERERERERERERETE7yiRKSIiIiIiIiIiIiIiIiJ+R4lMEREREREREREREREREfE7SmSKiIiIiIiIiIiIiIiIiN9RIlNERERERERERERERERE/I75RE/gdLFy5UoWL17M8uXLWbt2Lbnkx24iAAAvVElEQVS5ueTl5WG324mOjqZDhw4MHz6csWPH0qRJE5/HXbRoEe+//z5//PEH+/btIygoiBYtWnDRRRdx++23ExcXdwxflYiIiIiIiIiIiIiIiMixoUTmcTJy5Ej279/vtS07O5vs7Gx+/fVXXnjhBV577TVuvfXWWsdzuVw88MADvPHGG7hcroPHKyoqOHDgACtXruTNN9/k888/Z+jQoQ36WkRERERERERERERERESONSUyj6O4uDj69u1L165dadGiBZGRkVitVrZt28b06dNZtWoVpaWl3HbbbZjNZm688cYax3r00UcZN24cAKGhodx000307t2b0tJSpk2bxpw5c9i/fz8XXHAB8+bNIy0t7Ti9ShEREREREREREREREZGjp0TmcfLLL7/QoUMHDAaD1/Z//OMfvPTSSzz22GMAPPDAA1x99dUEBgZ69F21ahWvvPIKAJGRkcydO5cuXbocbL/tttt4+umneeaZZygtLeXWW29lyZIlNV5bRERERERERERERERExN8YT/QEThcdO3asM5H46KOPHkxIFhYWsmDBAq/9nn322YPlZF988cVqScy/PPXUU/Tu3RuAZcuW8cMPPxzN9EVERERERERERERERESOKyUy/UyHDh0OPs/OzvZoLykpYdasWQBEREQwduxYr+MYDAbuvvvug99PmTKlYScqIiIiIiIiIiIiIiIicgwpkelnMjIyDj5v0qSJR/sff/xBVVUVAIMHDyYkJKTGsUaMGHHw+ezZsxtwliIiIiIiIiIiIiIiIiLHlhKZfuSdd95h2bJlADRu3JgBAwZ49Fm3bt3B5z169Kh1vEaNGpGSkgJAbm4uOTk5DThbERERERERERERERERkWPHfKIncDqaO3cuBQUFAFRVVbFjxw5mzpzJ/PnzAQgODmbChAkEBgZ6nLtly5aDz5s3b17ntVJSUti5c+fBc+Pj4+s93xUrVtTavnHjxoPP7XY7Nput3tcQkYZjs9mw2+0Hn4vIiae4FPEvikkR/6KYFPE/iksR/6KYFPEviklPf/08jgUlMk+Ahx9+mCVLlngcN5lMDBs2jJdeeolu3bp5PbewsPDg87i4uDqvFRsb6/Xc+ujZs6fPfYuKisjPzz+i64hIw7Db7ZSUlBz83mzWr3qRE01xKeJfFJMi/kUxKeJ/FJci/kUxKeJfFJOejjT/5AuVlvUjKSkpnH322TRr1qzGPqWlpQefBwUF1TlmcHDwweeHBpaIiIiIiIiIiIiIiIiIP1Oa+E8ffPABWVlZDTLW008/XWv74sWLDz4vKytj8+bNTJ06lfHjx/PAAw8wbtw4pk+fXucemMfL8uXLa23fuHEj1157LQCRkZHVVoGKyPF3aDmDmJgYLBbLCZyNiIDiUsTfKCZF/ItiUsT/KC5F/ItiUsS/KCY9RUVFHbOxlcj80wcffOC13OuRqCuReajQ0FC6d+9O9+7dufzyyznjjDPIyspi2LBhrF+/noSEhGr9w8LCDj6vrKysc/yKioqDz8PDw32e16Hqk1A1m80KWhE/8Fc5A4vFopgU8ROKSxH/opgU8S+KSRH/o7gU8S+KSRH/opis7liW11VpWT/SrVs3Hn74YcBdT3j8+PEefQ7Naufl5dU55qH7VR7LjLiIiIiIiIiIiIiIiIhIQ1Ii80+LFy/G5XI1yONonHPOOQef//777x7tbdq0Ofh8x44ddY63c+dOr+eKiIiIiIiIiIiIiIiI+DMlMv3MoeVfCwsLPdo7dep08PmKFStqHSs3N/dgIrNRo0bEx8c3zCRFREREREREREREREREjjElMv3Mtm3bDj6Pi4vzaB8yZAiBgYEAzJ07t9oemIf78ccfDz4/dKWniIiIiIiIiIiIiIiIiL9TItPPvPfeewef9+/f36M9LCyMkSNHAlBcXMzEiRO9juNyuXjzzTcPfn/FFVc07ERFREREREREREREREREjiElMo+DiRMn8tNPP9W6f6bVauXBBx9kxowZAAQEBHDzzTd77fvkk09iMBgAePTRR0lPT/fo8+yzz7JkyRIAevXqxahRo472ZYiIiIiIiIiIiIiIiIgcN+YTPYHTwerVq7nhhhtISkpi+PDhdOnShfj4eAICAigoKCA9PZ1vvvmGvXv3HjznX//6F23btvU6Xrdu3Xj44Yd5+eWXKSoqon///tx888307t2b0tJSpk2bxk8//QS4V3AeuspTRERERERERERERERE5GSgROZxlJWVxYQJE2rtEx8fz/jx4xkzZkyt/V566SWqqqoYP348ZWVljB8/3utYkydPJi0t7WimLSIiIiIiIiIiIiIiInLcKZF5HLzwwgucddZZ/P7776xcuZJt27aRl5eHzWYjLCyMpk2bkpaWxrnnnssll1xCaGhonWMaDAbGjRvH5ZdfznvvvcfcuXPZu3cvQUFBpKamcuGFF3LHHXcQFxd3HF6hiIiIiIiIiIiIiIiISMNSIvM4CA0NZfTo0YwePbrBx+7Xrx/9+vVr8HFFRERERERERERERERETiTjiZ6AiIiIiIiIiIiIiIiIiMjhlMgUEREREREREREREREREb+j0rJy1CoqKg4+37RpE2az/lmJnEh2u53CwkIAoqKiFJMifkBxKeJfFJMi/kUxKeJ/FJci/kUxKeJfFJOeNm7cePD5oTmjhqCfrhy1HTt2HHw+duzYEzYPEREREREREREREREROXF27NjBwIEDG2w8lZaVo7Z9+/YTPQURERERERERERERERE5xWhFphy1vn37Hnz+wQcfkJaWduImIyJs3LiRa6+9FoBPP/2U9u3bn+AZiYjiUsS/KCZF/ItiUsT/KC5F/ItiUsS/KCY9VVRUHKzeefbZZzfo2EpkylGLiYk5+DwtLY0ePXqcwNmIyKHat2+vmBTxM4pLEf+imBTxL4pJEf+juBTxL4pJEf+imPyfhiwneyiVlhURERERERERERERERERv6NEpoiIiIiIiIiIiIiIiIj4HSUyRURERERERERERERERMTvKJEpIiIiIiIiIiIiIiIiIn5HiUwRERERERERERERERER8TtKZIqIiIiIiIiIiIiIiIiI31EiU0RERERERERERERERET8jhKZIiIiIiIiIiIiIiIiIuJ3DC6Xy3WiJyEiIiIiIiIiIiIiIiIiciityBQRERERERERERERERERv6NEpoiIiIiIiIiIiIiIiIj4HSUyRURERERERERERERERMTvKJEpIiIiIiIiIiIiIiIiIn5HiUwRERERERERERERERER8TtKZIqIiIiIiIiIiIiIiIiI31EiU0RERERERERERERERET8jhKZIiIiIiIiIiIiIiIiIuJ3lMgUEREREREREREREREREb+jRKaIiIiIiIiIiIiIiIiI+B0lMkVERERERERERERERETE7yiRKUfE5XIxZcoUzjvvPJKSkggMDKRp06acddZZfPDBB9jt9hM9RZGT3pAhQzAYDD49mjdv7tOYs2fP5oorriAlJYWgoCDi4+MZMGAA48aNo6ys7Ni+IBE/5nA4WLduHRMnTuTuu++mX79+hISEHIyxp59+ut5jNmS8LVq0iBtvvJGWLVsSEhJCTEwMPXr04PnnnycvL6/ecxM5GTRUXI4dO9bn91ODweDz/BSXcropKiriyy+/5I477qBPnz7ExsZisViIjo6ma9eu3HnnnSxbtqxeY+q9UuTINVRM6n1S5Oi5XC4WLFjAG2+8wdVXX0337t1JTk4mODiYkJAQkpKSOPfcc3nzzTcpLCz0eVy9T4ocuYaMS71X+gGXSD0VFBS4hg4d6gJqfHTv3t21c+fOEz1VkZPaGWecUWucHfpISUmpdazKykrXmDFjah2jZcuWrjVr1hyfFyfiZy6++OJa4+Opp57yeayGjDen0+n629/+5jIYDDWO1bhxY9cvv/xyFK9exD81VFxef/31Pr+f+vJ/jxSXcjp6+eWXXYGBgT7F0DXXXOMqKyurdTy9V4ocnYaMSb1Pihy9iooKn2OoUaNGrunTp9c6nt4nRY5eQ8al3itPPDMi9WC1WrnggguYN28eAMnJydx66620atWKrKwsPvroIzZu3MjKlSs599xzWbRoERERESd41iInv2+++abW9pCQkFrbr7/+eqZMmQJAbGwst956K507dyYvL49JkyaxdOlSMjIyOOecc1iyZAnJyckNNneRk4HD4aj2fUxMDLGxsWzdurXeYzVkvD366KOMGzcOgNDQUG666SZ69+5NaWkp06ZNY86cOezfv//ge3NaWlq95yvirxoyLv/y7rvvEh8ff1TzUlzK6WjLli1UVVUBkJqayrBhw0hLSyMuLo4DBw7wyy+/MG3aNBwOB5MmTSInJ4dZs2ZhNHovAqX3SpGj09Ax+Re9T4ocncTERPr06UOXLl1ISUkhPDyc8vJyNm3axNSpU9m6dSu5ublccsklzJo1i+HDh3sdR++TIg2noeLyL3qvPEFOdCZVTi5vvPHGwU8FdO/e3VVQUFCtvaKiwjVixIiDfR588METNFORk9+hKzKPxvTp0w+O06xZM4/V0g6Hw3XDDTcc7HPppZce1fVETkYvvPCC65FHHnFNnTrVlZmZ6XK5XK4JEyYcjAtfV341ZLytXLny4KfzIiMjvX7a9qmnnjo4Vq9evVxOp9P3Fy3i5xoqLg/99Oz27duPak6KSzld3Xzzza5Ro0a5fv/99xr7zJ071xUWFnbw3/9HH33ktZ/eK0WOXkPGpN4nRY6ew+FwrV+/vtY+drvddccddxz899+uXTuv/fQ+KdIwGjIu9V554imRKT6z2WyuRo0auQCXwWBwrVu3zmu//fv3u0JDQ12AKzAw0JWXl3ecZypyamioRGZaWtrBcb7//nuvfcrLy13NmjU72G/t2rVHdU2RU8GRJEwaMt4uvPDCg33eeustr32cTqerd+/eB/vNnDnTp3mKnKxOdCJTcSmnq8M/wFqT//znPwf/7Q8ePNhrH71Xihy9hoxJvU+KHD9Wq9UVGxt78N9/RkaGRx+9T4ocX77Epd4r/7+9O4+u+c7/OP662cSShUjaxpKFUEtbYhuqQlEZWmJXiqgltFVU2+kyS890WtOFaqv0WCZEMWJaS6uIqCWEmlZpq0xRCWKJNQgRSe7vj5x8f7ly781NXHLV83GOcz6538/n833fe/uezyTv7/f7qXj2nykBFPPNN9/o9OnTkqTOnTurSZMmVvsFBQVp0KBBkqRr165p5cqVty1GAJYOHDig3bt3S5IiIiLUvXt3q/0qV66s0aNHGz8nJibejvCA3xVn5tulS5e0Zs0aSZKvr69iY2OtzmUymTR+/Hjj56LHDwFwPvISd7Pq1as71K9///5G+6effipxnLUScA5n5aQzkZNA6Tw9PRUREWH8fPLkSYvjrJPA7VdaXjoTeVl+FDLhsKSkJKMdHR1tt2/x42vXrr1lMQGwb926dUa7W7dudvuSt8DNcWa+bd682dj3qEOHDnb3wS1+LnIXuHXIS6B0Pj4+Rvvq1asljrNWArdXaTnpTOQkULqCggKlpaUZP997770Wx1kngduvtLx0JvKy/ChkwmE///yz0W7RooXdvi1btrQ6DkD59OjRQ/fdd5+8vLwUEBCgZs2aafz48caVeraUJW+bNWsmd3d3SdIvv/wis9l803EDdxNn5ltZ5goMDFRISIgk6fTp08rMzCxT3MDdZPTo0apbt64qVaokf39/NW7cWKNHj9aWLVtKHUteAqUrnidFOWDrOGslcOuVlpM3Yp0Ebh2z2aw///nPxt1ezZo1U3h4uEUf1kng9nIkL2/EWlkxPCo6ANw5fv31V6MdGhpqt2/t2rXl7u6u/Px8HThwQGazWSaT6RZHCPx+ff3110b73LlzOnfunPbs2aMZM2ZoxIgR+uSTT1S5cuUS48qStx4eHqpVq5aOHDmi7OxsZWRkqHbt2k57D8DvnTPzrSxzSYV/mEpPTzfGBgUFlS144C6RnJxstHNzc5WVlaV9+/Zp7ty56tGjhxISElSjRg2rY8lLoHSzZ8822j169ChxnLUSuL1Ky8kbsU4CzrF27Vrl5ORIkq5cuaKDBw/qiy++0J49eyRJAQEBmjdvXolxrJPArVPevLwRa2XFoJAJh124cMFo16xZ025fDw8P+fr66vz588rLy1N2draqVat2iyMEfn8CAgLUrVs3tWjRQsHBwTKbzUpLS9NXX32l1NRUSVJ8fLyOHDmitWvXysPD8n/Wy5K3Rec7cuSIMZZCJuA4Z+ZbeeayNhZAIR8fH3Xt2lWtW7dWnTp15O7urmPHjikpKcnYPmH16tWKiorStm3b5OvrW2IO8hKwLzU1VfHx8ZIkb29vTZo0qUQf1krg9nEkJ4uwTgLOFRsbq1OnTpV43cvLSz179tS7776rsLCwEsdZJ4Fbp7x5WYS1smJRyITDLl++bLS9vb1L7V+5cmWdP39eUuFGthQygbKZMmWKWrZsKU9PzxLHXn31VS1fvlxPPfWUrly5og0bNuidd97R66+/btGvPHlb5NKlSzcRPXD3cWa+kbuA84wfP16ffPKJqlatWuLY5MmTlZKSon79+ikzM1M///yzJk+erDlz5pToS14Ctp08eVIDBgxQQUGBJOnNN9+0ekEcayVweziakxLrJHA73X///erSpYvNu6pYJ4Hbr7S8lFgrXQF7ZAKAi2rbtq3VImaR3r17WyyK7733nrFhNAAAKNSiRQurv3AWeeSRR/TFF18Y2yDEx8crIyPjdoUH3PGys7PVq1cvI2969OihyZMnV3BUwN2rrDnJOgk438mTJ2U2m1VQUKCsrCxt27ZN48aN0969ezV27Fi1adNGhw4dqugwgbvKzeQla2XFo5AJhxW/o7LoedL2XL161Wj7+PjckpiAu93gwYPVsGFDSTIW4eLIW+D2cWa+kbvA7fXwww/rsccekyTl5+dr3bp1JfqQl0BJOTk56tmzp3bu3CmpMJeWLl1q/BHnRqyVwK1V1px0FOskUD4mk0m+vr5q166dZs6cqdWrV8vd3V179+5V165dlZ2dbdGfdRK49cqal45irby1KGTCYf7+/kb7zJkzdvvm5eXp4sWLkiRPT0+7VywAuDkdO3Y02vv377c4Vpa8laSzZ89aHQugdM7MN3IXuP3sracSeQncKDc3V3369NE333wjSWrdurW+/vpru7/7sVYCt055crIsWCeBm9etWzfFxsZKkg4fPqyEhASL46yTwO1XWl6WBWvlrUMhEw5r0KCB0U5LS7Pb99ixY8rPz5ck1a9f/6av/gNgm72Nn8uSt3l5ecZjD6pWrapatWo5LUbgbuDMfCvLXJKUnp5udSwAx9lbTyXyEiju+vXr6t+/v9asWSNJat68udauXStfX1+741grgVujvDlZFqyTgHNER0cb7U2bNlkcY50EKoa9vCwL1spbh0ImHNa0aVOj/f3339vt+91331kdB8D57F2dU5a83b17t3EBQuPGjbkAASgjZ+ZbWeY6ffq08X9uAwMD7W5QD8C20q52JS+BQnl5eXryySe1atUqSdIDDzyg9evXq3r16qWOZa0EnO9mcrIsWCcB5yj+eMgbCx2sk0DFsJeXZcFaeetQyITDunX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    " ] @@ -835,10 +743,10 @@ "simh[\"tas\"].groupby(\"time.dayofyear\").mean(...).plot(label=\"$T_{sim,h}$\")\n", "simp[\"tas\"].groupby(\"time.dayofyear\").mean(...).plot(label=\"$T_{sim,p}$\")\n", "obsh[\"tas\"].groupby(\"time.dayofyear\").mean(...).plot(label=\"$T_{obs,h}$\")\n", - "ls_result.groupby(\"time.dayofyear\").mean(...).plot(label=\"$T^{*LS}_{sim,p}$\")\n", - "vs_result.groupby(\"time.dayofyear\").mean(...).plot(label=\"$T^{*VS}_{sim,p}$\")\n", - "dm_result.groupby(\"time.dayofyear\").mean(...).plot(label=\"$T^{*DM}_{sim,p}$\")\n", - "qdm_result.groupby(\"time.dayofyear\").mean(...).plot(label=\"$T^{*QDM}_{sim,p}$\")\n", + "ls_result.groupby(\"time.dayofyear\").mean(...).tas.plot(label=\"$T^{*LS}_{sim,p}$\")\n", + "vs_result.groupby(\"time.dayofyear\").mean(...).tas.plot(label=\"$T^{*VS}_{sim,p}$\")\n", + "dm_result.groupby(\"time.dayofyear\").mean(...).tas.plot(label=\"$T^{*DM}_{sim,p}$\")\n", + "qdm_result.groupby(\"time.dayofyear\").mean(...).tas.plot(label=\"$T^{*QDM}_{sim,p}$\")\n", "plt.title(\"Historical and predicted modeled and adjusted temperatures\")\n", "plt.xlim(0,365)\n", "plt.gca().grid(alpha=.3)\n", @@ -860,7 +768,8 @@ "metadata": {}, "outputs": [], "source": [ - "ls_result = cm.linear_scaling(\n", + "ls_result = adjust(\n", + " method=\"linear_scaling\",\n", " obs=obsh[\"tas\"].sel(lat=23, lon=0, method=\"nearest\"),\n", " simh=simh[\"tas\"].sel(lat=23, lon=0, method=\"nearest\"),\n", " simp=simp[\"tas\"].sel(lat=23, lon=0, method=\"nearest\"),\n", @@ -1240,22 +1149,21 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.DataArray 'tas' (time: 10950)>\n",
-       "array([-23.09421931, -23.41702292, -23.17541147, ..., -25.57722903,\n",
-       "       -26.33119336, -25.63539539])\n",
+       "
    <xarray.Dataset>\n",
+       "Dimensions:  (time: 10950)\n",
        "Coordinates:\n",
        "  * time     (time) object 2001-01-01 00:00:00 ... 2030-12-31 00:00:00\n",
        "    lat      int64 23\n",
        "    lon      int64 0\n",
-       "Attributes:\n",
-       "    units:    °C
    • " ], "text/plain": [ - "\n", - "array([-23.09421931, -23.41702292, -23.17541147, ..., -25.57722903,\n", - " -26.33119336, -25.63539539])\n", + "\n", + "Dimensions: (time: 10950)\n", "Coordinates:\n", " * time (time) object 2001-01-01 00:00:00 ... 2030-12-31 00:00:00\n", " lat int64 23\n", " lon int64 0\n", - "Attributes:\n", - " units: °C" + "Data variables:\n", + " tas (time) float64 -23.09 -23.42 -23.18 -23.04 ... -25.58 -26.33 -25.64" ] }, "execution_count": 16, @@ -1304,9 +1211,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:python-cmethods]", + "display_name": "🐍 venv repositories/awi-workspace/python-cmethods/venv | Python 3.11.2", "language": "python", - "name": "conda-env-python-cmethods-py" + "name": "myvenv" }, "language_info": { "codemirror_mode": { @@ -1318,7 +1225,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.2" }, "vscode": { "interpreter": { diff --git a/pyproject.toml b/pyproject.toml index acca8b0..a2a365b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,11 +8,11 @@ dynamic = ["version"] authors = [ { name = "Benjamin Thomas Schwertfeger", email = "contact@b-schwertfeger.de" }, ] -description = "Collection of bias correction procedures for 1- and 3-dimensional climate data" +description = "Collection of bias correction procedures for single and multidimensional climate data" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.8" -dependencies = ["xarray>=2022.11.0", "netCDF4>=1.6.1", "numpy", "tqdm"] +dependencies = ["xarray>=2022.11.0", "netCDF4>=1.6.1", "numpy"] keywords = [ "climate-science", "bias", @@ -34,6 +34,7 @@ classifiers = [ "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", "Natural Language :: English", "Operating System :: MacOS", "Operating System :: Unix", @@ -48,7 +49,7 @@ include-package-data = false [tool.setuptools.packages.find] include = ["cmethods*"] -exclude = ["docs*", "tests*", "examples*", ".env"] +exclude = ["doc*", "tests*", "examples*", ".env"] [tool.setuptools_scm] write_to = "cmethods/_version.py" @@ -60,6 +61,7 @@ junit_family = "xunit2" testpaths = ["tests"] [tool.pytest.ini_options] +cache_dir = ".cache/pytest" markers = ["wip: Used to run a specific test by hand."] [tool.coverage.run] @@ -72,31 +74,249 @@ exclude_lines = ["coverage: disable", "if TYPE_CHECKING:"] skip_empty = true [project.optional-dependencies] +jupyter = ["venv-kernel"] dev = [ # testing "pytest", "pytest-cov", + "zarr", + "dask[distributed]", "scikit-learn", "scipy", # building "setuptools_scm", - # examples - "click", - # ducumentatoin + # documentation "sphinx", "sphinx-rtd-theme", + # linting + "pylint", + "flake8", + "ruff==0.1.13", + # typing + "mypy", ] -examples = ["matplotlib"] + +examples = ["click", "matplotlib"] + + +# ========= T Y P I N G ======================================================== +# +[tool.mypy] +python_version = "3.11" + +# junit_xml = "mypy.xml" +files = ["cmethods/**/*.py"] +exclude = ["tests/**/*.py"] + +cache_dir = ".cache/mypy" +sqlite_cache = true +cache_fine_grained = true + +# Disallow dynamic typing +disallow_any_unimported = false +disallow_any_expr = false +disallow_any_decorated = false +disallow_any_explicit = false +disallow_any_generics = false +disallow_subclassing_any = false + +# # Untyped definitions and calls +check_untyped_defs = true +disallow_untyped_calls = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +disallow_untyped_decorators = false + +# None and Optional handling +implicit_optional = true +strict_optional = false + +# Configuring warnings +warn_redundant_casts = true +warn_unused_ignores = true +warn_unused_configs = true +warn_no_return = true +warn_return_any = true +warn_unreachable = true + +# Suppressing errors +ignore_errors = false + +# Configuring error messages +show_error_context = true +show_column_numbers = true +hide_error_codes = false +pretty = true +color_output = true +show_absolute_path = true +ignore_missing_imports = true + +# Miscellaneous strictness flags +allow_untyped_globals = false +allow_redefinition = false +local_partial_types = false +# disable_error_code = xxx,xxx +implicit_reexport = true +strict_concatenate = false +strict_equality = true +strict = true + +# ========= L I N T I N G ====================================================== +# +[tool.ruff] +# https://beta.ruff.rs/docs/rules/ +# https://beta.ruff.rs/docs/settings/ +# src = ["cmethods"] + +select = [ + "A", # flake8-builtins + "AIR", # Airflow + "ASYNC", # flake8-async + "B", # flake8-bugbear + "BLE", # blind-except + "C4", # flake8-comprehensions + "COM", # flake8-commas + "E", # pycodestyle + "F", # pyflakes + "FA", # flake8-future-annotations + "FLY", # flynt + "G", # flake8-logging-format + "I", # isort + "ICN", # flake8-import-conventions + "INT", # flake8-gettext + "ISC", # flake8-implicit-string-concat + "LOG", # flake8-logging + "N", # PEP8 naming + "PERF", # Perflint # maybe + "PGH", # pygrep-hooks + "PIE", # flake8-pie + "PL", # PyLint + "PT", # flake8-pytest-style + "PYI", # flake8-pyi + "Q", # flake8-quotes + "RET", # flake8-return + "RSE", # flake8-raise + "RUF", # Ruff-specific rules + "S", # flake8-bandit + "SIM", # flake8-simplify + "SLF", # flake8-self + "SLOT", # flake8-slots + "T20", # flake8-print + "TCH", # flake8-type-checking + "TID", # flake8-tidy-imports + "ARG", # flake8-unused-arguments + "CPY", # flake8-copyright + "FBT", # boolean trap + "PTH", # flake8-use-pathlib + "FURB", # refurb + # "ERA", # eradicate # commented-out code + # "FIX", # flake8-fixme + # "TD", # flake8-todos + # "TRY", # tryceratops # specify exception messages in class; not important +] +fixable = [ + "I", + "C4", + "Q", + "PT", + "ICN", + "COM", + "RSE", + "PT", + "FA", + "FA100", + "PLR6201", +] + +ignore = [ + # "B019", # use of lru_cache or cache + # "PLR2004", # magic value in comparison + # "E203", # Whitespace before ':' # false positive on list slices + # "PLR6301", # Method `…` could be a function or static method # false positive for no-self-use +] + +respect-gitignore = true +exclude = [] + +line-length = 130 +cache-dir = ".cache/ruff" +task-tags = ["todo", "TODO"] + +[tool.ruff.per-file-ignores] +"doc/*.py" = [ + "CPY001", # Missing copyright notice at top of file + "PTH118", # `os.path.join()` should be replaced by `Path` with `/` operator, + "PTH123", # `open()` should be replaced by `Path.open()` + "PTH100", # `os.path.abspath()` should be replaced by `Path.resolve()` + "FURB101", # `open` and `read` should be replaced by `Path("links.rst").read_text(encoding="utf-8")` + "A001", # Variable `copyright` is shadowing a Python builtin +] + +"tests/*.py" = [ + "E501", # line to long + "F841", # unused variable + "N802", # PEP8 naming + "S101", # assert use + "S110", # try-except-pass without logging + "S311", # pseudo-random-generator + "SLF001", # private member access + "TID252", # ban relative imports + "PTH118", # `os.path.join()` should be replaced by `Path` with `/` operator, + "PTH120", # `os.path.dirname()` should be replaced by `Path.parent` +] +"examples/biasadjust.py" = [ + "T201", # `print` found +] + +[tool.ruff.flake8-copyright] +author = "Benjamin Thomas Schwertfeger" +notice-rgx = "(?i)Copyright \\(C\\) \\d{4}" +min-file-size = 1024 + +[tool.ruff.flake8-quotes] +docstring-quotes = "double" + +[tool.ruff.flake8-tidy-imports] +ban-relative-imports = "all" + +[tool.ruff.flake8-bandit] +check-typed-exception = true + +[tool.ruff.flake8-type-checking] +strict = true + +[tool.ruff.pep8-naming] +ignore-names = [ + "i", + "j", + "k", + "_", + "X", + "LS_simh", + "LS_simp", + "VS_1_simh", + "VS_1_simp", + "VS_2_simp", + "QDM1", +] + +[tool.ruff.pylint] +max-args = 8 +max-branches = 15 +max-locals = 25 +max-returns = 6 +max-statements = 50 +allow-magic-value-types = ["int"] # ------------------------------------------------------------------------------ # Pylint [tool.pylint.main] # Analyse import fallback blocks. This can be used to support both Python 2 and 3 # compatible code, which means that the block might have code that exists only in -# one or another interpreter, leading to false positives when analysed. +# one or another interpreter, leading to false positives when analyzed. # analyse-fallback-blocks = -# Clear in-memory caches upon conclusion of linting. Useful if running pylint in +# Clear in-memory caches upon conclusion of linting. Useful if running PyLint in # a server-like mode. # clear-cache-post-run = @@ -151,7 +371,7 @@ ignore-patterns = ["^\\.#"] # pygtk.require(). # init-hook = -# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the +# Use multiple processes to speed up PyLint. Specifying 0 will auto-detect the # number of processors available to use, and will cap the count on Windows to # avoid hangs. jobs = 1 @@ -169,7 +389,7 @@ limit-inference-results = 100 persistent = true # Minimum Python version to use for version dependent checks. Will default to the -# version used to run pylint. +# version used to run PyLint. py-version = "3.10" # Discover python modules and packages in the file system subtree. @@ -181,7 +401,7 @@ py-version = "3.10" # source root. # source-roots = -# When enabled, pylint would attempt to guess common misconfiguration and emit +# When enabled, PyLint would attempt to guess common misconfiguration and emit # user-friendly hints instead of false-positive error messages. suggestion-mode = true @@ -396,7 +616,7 @@ overgeneral-exceptions = ["builtin.BaseException", "builtin.Exception"] ignore-long-lines = "^\\s*(# )??$" # Number of spaces of indent required inside a hanging or continued line. -indent-after-paren = 4 +indent-after-parent = 4 # String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 # tab). diff --git a/setup.py b/setup.py index 9273ed3..5a37f97 100644 --- a/setup.py +++ b/setup.py @@ -2,8 +2,9 @@ # -*- coding: utf-8 -*- # Copyright (C) 2023 Benjamin Thomas Schwertfeger # GitHub: https://github.com/btschwertfeger +# +# This file is only used by sphinx for liking the package to the documentation. -import setuptools_scm # pylint: disable=unused-import from setuptools import setup setup() diff --git a/tests/README.rst b/tests/README.rst new file mode 100644 index 0000000..d88ded3 --- /dev/null +++ b/tests/README.rst @@ -0,0 +1,10 @@ +Unit tests for python-cmethods +############################## + +The input data sets are generated before the tests are executed. They are based +on simple equations to simulate temperatures and precipitation for different +locations. The bias correction methods are then applied to those to to then +validate if the methods improved the data. + +Additionally some input data is saved as .zarr, so the dask compatibility can be +tested as well. diff --git a/tests/__init__.py b/tests/__init__.py index 70e3b52..cde4d87 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -3,3 +3,5 @@ # Copyright (C) 2023 Benjamin Thomas Schwertfeger # GitHub: https://github.com/btschwertfeger # + +# This file is required for being able to import from ".". diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..f52130c --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2023 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +"""Module providing fixtures for testing.""" + +from __future__ import annotations + +import os +from functools import lru_cache +from typing import Any + +import pytest +import xarray as xr +from dask.distributed import LocalCluster + +from .helper import get_datasets + +FIXTURE_DIR: str = os.path.join(os.path.dirname(__file__), "fixture") + + +@pytest.fixture(scope="session") +def dask_cluster() -> Any: + # Create a Dask LocalCluster + cluster = LocalCluster() + + # Create a Dask Client connected to the LocalCluster + client = cluster.get_client() + + # Yield the client, making it available for the tests + yield client + + # After the tests are done, close the cluster + client.close() + + +@pytest.fixture() +def datasets() -> dict: + obsh_add, obsp_add, simh_add, simp_add = get_datasets(kind="+") + obsh_mult, obsp_mult, simh_mult, simp_mult = get_datasets(kind="*") + + return { + "+": { + "obsh": obsh_add["+"], + "obsp": obsp_add["+"], + "simh": simh_add["+"], + "simp": simp_add["+"], + }, + "*": { + "obsh": obsh_mult["*"], + "obsp": obsp_mult["*"], + "simh": simh_mult["*"], + "simp": simp_mult["*"], + }, + } + + +@lru_cache(maxsize=None) +@pytest.fixture() +def datasets_from_zarr() -> dict: + return { + "+": { + "obsh": xr.open_zarr( + os.path.join(FIXTURE_DIR, "temperature_obsh.zarr"), + ).chunk({"time": -1}), + "obsp": xr.open_zarr( + os.path.join(FIXTURE_DIR, "temperature_obsp.zarr"), + ).chunk({"time": -1}), + "simh": xr.open_zarr( + os.path.join(FIXTURE_DIR, "temperature_simh.zarr"), + ).chunk({"time": -1}), + "simp": xr.open_zarr( + os.path.join(FIXTURE_DIR, "temperature_simp.zarr"), + ).chunk({"time": -1}), + }, + "*": { + "obsh": xr.open_zarr( + os.path.join(FIXTURE_DIR, "precipitation_obsh.zarr"), + ).chunk({"time": -1}), + "obsp": xr.open_zarr( + os.path.join(FIXTURE_DIR, "precipitation_obsp.zarr"), + ).chunk({"time": -1}), + "simh": xr.open_zarr( + os.path.join(FIXTURE_DIR, "precipitation_simh.zarr"), + ).chunk({"time": -1}), + "simp": xr.open_zarr( + os.path.join(FIXTURE_DIR, "precipitation_simp.zarr"), + ).chunk({"time": -1}), + }, + } diff --git a/tests/fixture/precipitation_obsh.zarr/.zattrs b/tests/fixture/precipitation_obsh.zarr/.zattrs new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/tests/fixture/precipitation_obsh.zarr/.zattrs @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/tests/fixture/precipitation_obsh.zarr/.zgroup b/tests/fixture/precipitation_obsh.zarr/.zgroup new file mode 100644 index 0000000..3b7daf2 --- /dev/null +++ b/tests/fixture/precipitation_obsh.zarr/.zgroup @@ -0,0 +1,3 @@ +{ + "zarr_format": 2 +} \ No newline at end of file diff --git a/tests/fixture/precipitation_obsh.zarr/.zmetadata b/tests/fixture/precipitation_obsh.zarr/.zmetadata new file mode 100644 index 0000000..60ede38 --- /dev/null +++ b/tests/fixture/precipitation_obsh.zarr/.zmetadata @@ -0,0 +1,117 @@ +{ + "metadata": { + ".zattrs": {}, + ".zgroup": { + "zarr_format": 2 + }, + "lat/.zarray": { + "chunks": [ + 4 + ], + "compressor": { + "blocksize": 0, + "clevel": 5, + "cname": "lz4", + "id": "blosc", + "shuffle": 1 + }, + "dtype": " bool: + return np.sqrt(mean_squared_error(result, obsp)) < np.sqrt( + mean_squared_error(simp, obsp), + ) + + +def is_3d_rmse_better(result, obsp, simp) -> bool: + result_reshaped = result.stack(z=("lat", "lon")) + obsp_reshaped = obsp.stack(z=("lat", "lon")) + simp_reshaped = simp.stack(z=("lat", "lon")) + + # Compute RMSE + rmse_values_old = np.sqrt( + mean_squared_error(simp_reshaped, obsp_reshaped, multioutput="raw_values"), + ) + rmse_values_new = np.sqrt( + mean_squared_error(result_reshaped, obsp_reshaped, multioutput="raw_values"), + ) + # Convert the flattened array back to the original grid shape + rmse_values_old_ds = xr.DataArray( + rmse_values_old.reshape(obsp.lat.size, obsp.lon.size), + coords={"lat": obsp.lat, "lon": obsp.lon}, + dims=["lat", "lon"], + ) + rmse_values_new_ds = xr.DataArray( + rmse_values_new.reshape(obsp.lat.size, obsp.lon.size), + coords={"lat": obsp.lat, "lon": obsp.lon}, + dims=["lat", "lon"], + ) + return (rmse_values_new_ds < rmse_values_old_ds).all() + + +def get_datasets(kind: str) -> tuple[xr.Dataset, xr.Dataset, xr.Dataset, xr.Dataset]: + historical_time = xr.cftime_range( + "1971-01-01", + "2000-12-31", + freq="D", + calendar="noleap", + ) + future_time = xr.cftime_range( + "2001-01-01", + "2030-12-31", + freq="D", + calendar="noleap", + ) + latitudes = np.arange(23, 27, 1) + + def get_hist_temp_for_lat(lat: int) -> List[float]: + """Returns a fake interval time series by latitude value""" + return 273.15 - ( + lat * np.cos(2 * np.pi * historical_time.dayofyear / 365) + + 2 * np.random.random_sample((historical_time.size,)) + + 273.15 + + 0.1 * (historical_time - historical_time[0]).days / 365 + ) + + def get_fake_hist_precipitation_data() -> List[float]: + """Returns ratio based fake time series""" + pr = ( + np.cos(2 * np.pi * historical_time.dayofyear / 365) + * np.cos(2 * np.pi * historical_time.dayofyear / 365) + * np.random.random_sample((historical_time.size,)) + ) + + pr *= 0.0004 / pr.max() # scaling + years = 30 + days_without_rain_per_year = 239 + + c = days_without_rain_per_year * years # avoid rain every day + pr.ravel()[np.random.choice(pr.size, c, replace=False)] = 0 + return pr + + def get_dataset(data, time, kind: str) -> xr.Dataset: + """Returns a data set by data and time""" + return ( + xr.DataArray( + data, + dims=("lon", "lat", "time"), + coords={"time": time, "lat": latitudes, "lon": [0, 1, 3]}, + ) + .transpose("time", "lat", "lon") + .to_dataset(name=kind) + ) + + if kind == "+": # noqa: PLR2004 + some_data = [get_hist_temp_for_lat(val) for val in latitudes] + data = np.array( + [ + np.array(some_data), + np.array(some_data) + 0.5, + np.array(some_data) + 1, + ], + ) + obsh = get_dataset(data, historical_time, kind=kind) + obsp = get_dataset(data + 1, historical_time, kind=kind) + simh = get_dataset(data - 2, historical_time, kind=kind) + simp = get_dataset(data - 1, future_time, kind=kind) + + else: # precipitation + some_data = [get_fake_hist_precipitation_data() for _ in latitudes] + data = np.array( + [some_data, np.array(some_data) + np.random.rand(), np.array(some_data)], + ) + obsh = get_dataset(data, historical_time, kind=kind) + obsp = get_dataset(data * 1.02, historical_time, kind=kind) + simh = get_dataset(data * 0.98, historical_time, kind=kind) + simp = get_dataset(data * 0.09, future_time, kind=kind) + + return obsh, obsp, simh, simp diff --git a/tests/test_methods.py b/tests/test_methods.py index 159128a..f7eeee6 100644 --- a/tests/test_methods.py +++ b/tests/test_methods.py @@ -1,412 +1,209 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (C) 2023 Benjamin Thomas Schwertfeger -# GitGub: https://github.com/btschwertfeger +# GitHub: https://github.com/btschwertfeger # -"""Module to to test the bias adjustment methods""" +""" +Module implementing the unit tests for all implemented bias correction +techniques. +""" -import unittest -from typing import List, Tuple +from __future__ import annotations -import numpy as np import pytest -import xarray as xr -from sklearn.metrics import mean_squared_error -from cmethods import CMethods as cm -from cmethods import UnknownMethodError - - -class TestMethods(unittest.TestCase): - def setUp(self) -> None: - obsh_add, obsp_add, simh_add, simp_add = self.get_datasets(kind="+") - obsh_mult, obsp_mult, simh_mult, simp_mult = self.get_datasets(kind="*") - - self.data = { - "+": { - "obsh": obsh_add["+"], - "obsp": obsp_add["+"], - "simh": simh_add["+"], - "simp": simp_add["+"], - }, - "*": { - "obsh": obsh_mult["*"], - "obsp": obsp_mult["*"], - "simh": simh_mult["*"], - "simp": simp_mult["*"], - }, - } - - def get_datasets( - self, - kind: str, - ) -> Tuple[xr.Dataset, xr.Dataset, xr.Dataset, xr.Dataset]: - historical_time = xr.cftime_range( - "1971-01-01", "2000-12-31", freq="D", calendar="noleap" - ) - future_time = xr.cftime_range( - "2001-01-01", "2030-12-31", freq="D", calendar="noleap" - ) - latitudes = np.arange(23, 27, 1) - - def get_hist_temp_for_lat(lat: int) -> List[float]: - """Returns a fake interval time series by latitude value""" - return 273.15 - ( - lat * np.cos(2 * np.pi * historical_time.dayofyear / 365) - + 2 * np.random.random_sample((historical_time.size,)) - + 273.15 - + 0.1 * (historical_time - historical_time[0]).days / 365 - ) - - def get_fake_hist_precipitation_data() -> List[float]: - """Returns ratio based fake time series""" - pr = ( - np.cos(2 * np.pi * historical_time.dayofyear / 365) - * np.cos(2 * np.pi * historical_time.dayofyear / 365) - * np.random.random_sample((historical_time.size,)) - ) - - pr *= 0.0004 / pr.max() # scaling - years = 30 - days_without_rain_per_year = 239 - - c = days_without_rain_per_year * years # avoid rain every day - pr.ravel()[np.random.choice(pr.size, c, replace=False)] = 0 - return pr - - def get_dataset(data, time, kind: str) -> xr.Dataset: - """Returns a data set by data and time""" - return ( - xr.DataArray( - data, - dims=("lon", "lat", "time"), - coords={"time": time, "lat": latitudes, "lon": [0, 1, 3]}, - ) - .transpose("time", "lat", "lon") - .to_dataset(name=kind) - ) - - if kind == "+": - some_data = [get_hist_temp_for_lat(val) for val in latitudes] - data = np.array( - [ - np.array(some_data), - np.array(some_data) + 0.5, - np.array(some_data) + 1, - ] - ) - obsh = get_dataset(data, historical_time, kind=kind) - obsp = get_dataset(data + 1, historical_time, kind=kind) - simh = get_dataset(data - 2, historical_time, kind=kind) - simp = get_dataset(data - 1, future_time, kind=kind) - - else: # precipitation - some_data = [get_fake_hist_precipitation_data() for _ in latitudes] - data = np.array( - [some_data, np.array(some_data) + np.random.rand(), np.array(some_data)] - ) - obsh = get_dataset(data, historical_time, kind=kind) - obsp = get_dataset(data * 1.02, historical_time, kind=kind) - simh = get_dataset(data * 0.98, historical_time, kind=kind) - simp = get_dataset(data * 0.09, future_time, kind=kind) - - return obsh, obsp, simh, simp - - # -------------------------------------------------------------------------- - # 1-dimensional - - def test_linear_scaling(self) -> None: - """Tests the linear scaling method""" - - for kind in ("+", "*"): - ls_result = cm.linear_scaling( - obs=self.data[kind]["obsh"][:, 0, 0], - simh=self.data[kind]["simh"][:, 0, 0], - simp=self.data[kind]["simp"][:, 0, 0], - kind=kind, - ) - assert isinstance(ls_result, (np.ndarray, np.generic)) - assert mean_squared_error( - ls_result, self.data[kind]["obsp"][:, 0, 0], squared=False - ) < mean_squared_error( - self.data[kind]["simp"][:, 0, 0], - self.data[kind]["obsp"][:, 0, 0], - squared=False, - ) - - def test_variance_scaling(self) -> None: - """Tests the variance scaling method""" - kind = "+" - vs_result = cm.variance_scaling( - obs=self.data[kind]["obsh"][:, 0, 0], - simh=self.data[kind]["simh"][:, 0, 0], - simp=self.data[kind]["simp"][:, 0, 0], - kind="+", - ) - assert isinstance(vs_result, (np.ndarray, np.generic)) - assert mean_squared_error( - vs_result, self.data[kind]["obsp"][:, 0, 0], squared=False - ) < mean_squared_error( - self.data[kind]["simp"][:, 0, 0], - self.data[kind]["obsp"][:, 0, 0], - squared=False, - ) - - def test_delta_method(self) -> None: - """Tests the delta method""" - - for kind in ("+", "*"): - dm_result = cm.delta_method( - obs=self.data[kind]["obsh"][:, 0, 0], - simh=self.data[kind]["simh"][:, 0, 0], - simp=self.data[kind]["simp"][:, 0, 0], - kind=kind, - ) - assert isinstance(dm_result, (np.ndarray, np.generic)) - assert mean_squared_error( - dm_result, self.data[kind]["obsp"][:, 0, 0], squared=False - ) < mean_squared_error( - self.data[kind]["simp"][:, 0, 0], - self.data[kind]["obsp"][:, 0, 0], - squared=False, - ) - - def test_quantile_mapping(self) -> None: - """Tests the quantile mapping method""" - - for kind in ("+", "*"): - qm_result = cm.quantile_mapping( - obs=self.data[kind]["obsh"][:, 0, 0], - simh=self.data[kind]["simh"][:, 0, 0], - simp=self.data[kind]["simp"][:, 0, 0], - n_quantiles=100, - kind=kind, - ) - assert isinstance(qm_result, (np.ndarray, np.generic)) - assert mean_squared_error( - qm_result, self.data[kind]["obsp"][:, 0, 0], squared=False - ) < mean_squared_error( - self.data[kind]["simp"][:, 0, 0], - self.data[kind]["obsp"][:, 0, 0], - squared=False, - ) - - def test_detrended_quantile_mapping(self) -> None: - """Tests the detrendeed quantile mapping method""" - - for kind in ("+", "*"): - dqm_result = cm.detrended_quantile_mapping( - obs=self.data[kind]["obsh"][:, 0, 0], - simh=self.data[kind]["simh"][:, 0, 0], - simp=self.data[kind]["simp"][:, 0, 0], - n_quantiles=100, - kind=kind, - detrended=True, - ) - assert isinstance(dqm_result, (np.ndarray, np.generic)) - assert mean_squared_error( - dqm_result, self.data[kind]["obsp"][:, 0, 0], squared=False - ) < mean_squared_error( - self.data[kind]["simp"][:, 0, 0], - self.data[kind]["obsp"][:, 0, 0], - squared=False, - ) - - def test_quantile_delta_mapping(self) -> None: - """Tests the quantile delta mapping method""" - - for kind in ("+", "*"): - qdm_result = cm.quantile_delta_mapping( - obs=self.data[kind]["obsh"][:, 0, 0], - simh=self.data[kind]["simh"][:, 0, 0], - simp=self.data[kind]["simp"][:, 0, 0], - n_quantiles=100, - kind=kind, - ) - - assert isinstance(qdm_result, (np.ndarray, np.generic)) - if np.isnan(qdm_result).any(): - raise ValueError(qdm_result) - assert mean_squared_error( - qdm_result, self.data[kind]["obsp"][:, 0, 0], squared=False - ) < mean_squared_error( - self.data[kind]["simp"][:, 0, 0], - self.data[kind]["obsp"][:, 0, 0], - squared=False, - ) - - # -------------------------------------------------------------------------- - # 3-dimensional - - def test_3d_sclaing_methods(self) -> None: - """Tests the scaling based methods for 3-dimensional data sets""" - - for kind in ("+", "*"): - for method in cm.SCALING_METHODS: - if kind == "*" and method == "variance_scaling": - continue - - result = cm.adjust_3d( - method=method, - obs=self.data[kind]["obsh"], - simh=self.data[kind]["simh"], - simp=self.data[kind]["simp"], - kind=kind, - group="time.month", # default - ) - assert isinstance(result, xr.core.dataarray.DataArray) - for lat in range(len(self.data[kind]["obsh"].lat)): - for lon in range(len(self.data[kind]["obsh"].lon)): - assert mean_squared_error( - result[:, lat, lon], - self.data[kind]["obsp"][:, lat, lon], - squared=False, - ) < mean_squared_error( - self.data[kind]["simp"][:, lat, lon], - self.data[kind]["obsp"][:, lat, lon], - squared=False, - ) - - def test_3d_distribution_methods(self) -> None: - """Tests the distribution based methods for 3-dimensional data sets""" - - for kind in ("+", "*"): - for method in cm.DISTRIBUTION_METHODS: - result = cm.adjust_3d( - method=method, - obs=self.data[kind]["obsh"], - simh=self.data[kind]["simh"], - simp=self.data[kind]["simp"], - n_quantiles=25, - ) - assert isinstance(result, xr.core.dataarray.DataArray) - for lat in range(len(self.data[kind]["obsh"].lat)): - for lon in range(len(self.data[kind]["obsh"].lon)): - assert mean_squared_error( - result[:, lat, lon], - self.data[kind]["obsp"][:, lat, lon], - squared=False, - ) < mean_squared_error( - self.data[kind]["simp"][:, lat, lon], - self.data[kind]["obsp"][:, lat, lon], - squared=False, - ) - - # -------------------------------------------------------------------------- - # misc - def test_n_jobs(self) -> None: - kind = "+" - result = cm.adjust_3d( - method="quantile_mapping", - obs=self.data[kind]["obsh"], - simh=self.data[kind]["simh"], - simp=self.data[kind]["simp"], - n_quantiles=25, - n_jobs=2, - ) - assert isinstance(result, xr.core.dataarray.DataArray) - for lat in range(len(self.data[kind]["obsh"].lat)): - for lon in range(len(self.data[kind]["obsh"].lon)): - assert mean_squared_error( - result[:, lat, lon], - self.data[kind]["obsp"][:, lat, lon], - squared=False, - ) < mean_squared_error( - self.data[kind]["simp"][:, lat, lon], - self.data[kind]["obsp"][:, lat, lon], - squared=False, - ) - - # -------------------------------------------------------------------------- - # test failures - def test_unknown_method(self) -> None: - with pytest.raises(UnknownMethodError): - cm.get_function("LOCI_INTENSITY_SCALING") - - kind = "+" - with pytest.raises(UnknownMethodError): - cm.adjust_3d( - method="distribution_mapping", - obs=self.data[kind]["obsh"], - simh=self.data[kind]["simh"], - simp=self.data[kind]["simp"], - kind=kind, - ) - - with pytest.raises(UnknownMethodError): - cm.pool_adjust( - params=dict( - method="distribution_mapping", - obs=self.data[kind]["obsh"], - simh=self.data[kind]["simh"], - simp=self.data[kind]["simp"], - kind=kind, - ) - ) - - def test_not_implemented_methods(self) -> None: - kind = "+" - with pytest.raises(NotImplementedError): - cm.get_function(method="empirical_quantile_mapping")( - self.data[kind]["obsh"], - self.data[kind]["simh"], - self.data[kind]["simp"], - n_quantiles=10, - ) - - def test_invalid_adjustment_type(self) -> None: - kind = "+" - with pytest.raises(NotImplementedError): - cm.linear_scaling( - self.data[kind]["obsh"], - self.data[kind]["simh"], - self.data[kind]["simp"], - kind="/", - ) - - with pytest.raises(NotImplementedError): - cm.variance_scaling( - self.data[kind]["obsh"], - self.data[kind]["simh"], - self.data[kind]["simp"], - kind="*", - ) - - with pytest.raises(NotImplementedError): - cm.delta_method( - self.data[kind]["obsh"], - self.data[kind]["simh"], - self.data[kind]["simp"], - kind="/", - ) - - with pytest.raises(NotImplementedError): - cm.quantile_mapping( - self.data[kind]["obsh"], - self.data[kind]["simh"], - self.data[kind]["simp"], - kind="/", - n_quantiles=10, - ) - with pytest.raises(NotImplementedError): - cm.detrended_quantile_mapping( - self.data[kind]["obsh"], - self.data[kind]["simh"], - self.data[kind]["simp"], - kind="/", - n_quantiles=10, - ) - - with pytest.raises(NotImplementedError): - cm.quantile_delta_mapping( - self.data[kind]["obsh"], - self.data[kind]["simh"], - self.data[kind]["simp"], - kind="/", - n_quantiles=10, - ) - - -if __name__ == "__main__": - unittest.main() +from cmethods import adjust +from cmethods.distribution import detrended_quantile_mapping +from cmethods.types import NPData_t, XRData_t + +from .helper import is_1d_rmse_better, is_3d_rmse_better + +GROUP: str = "time.month" +N_QUANTILES: int = 100 + + +@pytest.mark.parametrize( + ("method", "kind"), + [ + ("linear_scaling", "+"), + ("variance_scaling", "+"), + ("delta_method", "+"), + ("linear_scaling", "*"), + ("delta_method", "*"), + ], +) +def test_1d_scaling( + datasets: dict, + method: str, + kind: str, +) -> None: + obsh: XRData_t = datasets[kind]["obsh"][:, 0, 0] + obsp: XRData_t = datasets[kind]["obsp"][:, 0, 0] + simh: XRData_t = datasets[kind]["simh"][:, 0, 0] + simp: XRData_t = datasets[kind]["simp"][:, 0, 0] + + # not group + result: XRData_t = adjust(method=method, obs=obsh, simh=simh, simp=simp, kind=kind) + assert isinstance(result, XRData_t) + assert is_1d_rmse_better(result=result[kind], obsp=obsp, simp=simp) + + # grouped + result = adjust( + method=method, + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + group=GROUP, + ) + assert isinstance(result, XRData_t) + assert is_1d_rmse_better(result=result[kind], obsp=obsp, simp=simp) + + +@pytest.mark.parametrize( + ("method", "kind"), + [ + ("linear_scaling", "+"), + ("variance_scaling", "+"), + ("delta_method", "+"), + ("linear_scaling", "*"), + ("delta_method", "*"), + ], +) +def test_3d_scaling( + datasets: dict, + method: str, + kind: str, +) -> None: + obsh: XRData_t = datasets[kind]["obsh"] + obsp: XRData_t = datasets[kind]["obsp"] + simh: XRData_t = datasets[kind]["simh"] + simp: XRData_t = datasets[kind]["simp"] + + # not grouped + result: XRData_t = adjust( + method=method, + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + ) + + assert isinstance(result, XRData_t) + assert is_3d_rmse_better(result=result[kind], obsp=obsp, simp=simp) + + # grouped + result: XRData_t = adjust( + method=method, + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + group=GROUP, + ) + + assert isinstance(result, XRData_t) + assert is_3d_rmse_better(result=result[kind], obsp=obsp, simp=simp) + + +@pytest.mark.parametrize( + ("method", "kind"), + [ + ("quantile_mapping", "+"), + ("quantile_delta_mapping", "+"), + ("quantile_mapping", "*"), + ("quantile_delta_mapping", "*"), + ], +) +def test_1d_distribution( + datasets: dict, + method: str, + kind: str, +) -> None: + obsh: XRData_t = datasets[kind]["obsh"][:, 0, 0] + obsp: XRData_t = datasets[kind]["obsp"][:, 0, 0] + simh: XRData_t = datasets[kind]["simh"][:, 0, 0] + simp: XRData_t = datasets[kind]["simp"][:, 0, 0] + + result: XRData_t = adjust( + method=method, + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + n_quantiles=N_QUANTILES, + ) + + assert isinstance(result, XRData_t) + assert is_1d_rmse_better(result=result[kind], obsp=obsp, simp=simp) + + +@pytest.mark.parametrize( + ("method", "kind"), + [ + ("quantile_mapping", "+"), + ("quantile_delta_mapping", "+"), + ("quantile_mapping", "*"), + ("quantile_delta_mapping", "*"), + ], +) +def test_3d_distribution( + datasets: dict, + method: str, + kind: str, +) -> None: + obsh: XRData_t = datasets[kind]["obsh"] + obsp: XRData_t = datasets[kind]["obsp"] + simh: XRData_t = datasets[kind]["simh"] + simp: XRData_t = datasets[kind]["simp"] + + result: XRData_t = adjust( + method=method, + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + n_quantiles=N_QUANTILES, + ) + + assert isinstance(result, XRData_t) + assert is_3d_rmse_better(result=result[kind], obsp=obsp, simp=simp) + + +def test_1d_detrended_quantile_mapping_add(datasets: dict) -> None: + kind: str = "+" + obsh: XRData_t = datasets[kind]["obsh"][:, 0, 0] + obsp: XRData_t = datasets[kind]["obsp"][:, 0, 0] + simh: XRData_t = datasets[kind]["simh"][:, 0, 0] + simp: XRData_t = datasets[kind]["simp"][:, 0, 0] + + # not group + result: XRData_t = detrended_quantile_mapping( + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + n_quantiles=N_QUANTILES, + ) + assert isinstance(result, NPData_t) + assert is_1d_rmse_better(result=result, obsp=obsp, simp=simp) + + +def test_1d_detrended_quantile_mapping_mult(datasets: dict) -> None: + kind: str = "*" + obsh: XRData_t = datasets[kind]["obsh"][:, 0, 0] + obsp: XRData_t = datasets[kind]["obsp"][:, 0, 0] + simh: XRData_t = datasets[kind]["simh"][:, 0, 0] + simp: XRData_t = datasets[kind]["simp"][:, 0, 0] + + # not group + result: XRData_t = detrended_quantile_mapping( + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + n_quantiles=N_QUANTILES, + ) + assert isinstance(result, NPData_t) + assert is_1d_rmse_better(result=result, obsp=obsp, simp=simp) diff --git a/tests/test_misc.py b/tests/test_misc.py new file mode 100644 index 0000000..12797f5 --- /dev/null +++ b/tests/test_misc.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2023 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +"""Module implementing even more tests""" + +from __future__ import annotations + +import logging +import re +from typing import Any + +import numpy as np +import pytest + +from cmethods import adjust +from cmethods.distribution import ( + detrended_quantile_mapping, + quantile_delta_mapping, + quantile_mapping, +) +from cmethods.scaling import delta_method, linear_scaling, variance_scaling + + +def test_not_implemented_errors( + datasets: dict, + caplog: Any, +) -> None: + caplog.set_level(logging.INFO) + + with pytest.raises( + NotImplementedError, + match=re.escape(r"kind='/' not available for linear_scaling."), + ), pytest.warns(UserWarning, match="Do not call linear_scaling"): + linear_scaling(obs=[], simh=[], simp=[], kind="/") + + with pytest.raises( + NotImplementedError, + match=re.escape(r"kind='/' not available for variance_scaling."), + ), pytest.warns(UserWarning, match="Do not call variance_scaling"): + variance_scaling(obs=[], simh=[], simp=[], kind="/") + + with pytest.raises( + NotImplementedError, + match=re.escape(r"kind='/' not available for delta_method. "), + ), pytest.warns(UserWarning, match="Do not call delta_method"): + delta_method(obs=[], simh=[], simp=[], kind="/") + + with pytest.raises( + NotImplementedError, + match=re.escape(r"kind='/' for quantile_mapping is not available."), + ), pytest.warns(UserWarning, match="Do not call quantile_mapping"): + quantile_mapping( + obs=np.array(datasets["+"]["obsh"][:, 0, 0]), + simh=np.array(datasets["+"]["simh"][:, 0, 0]), + simp=np.array(datasets["+"]["simp"][:, 0, 0]), + kind="/", + n_quantiles=100, + ) + with pytest.raises( + NotImplementedError, + match=re.escape(r"kind='/' for detrended_quantile_mapping is not available."), + ): + detrended_quantile_mapping( + obs=np.array(datasets["+"]["obsh"][:, 0, 0]), + simh=np.array(datasets["+"]["simh"][:, 0, 0]), + simp=np.array(datasets["+"]["simp"][:, 0, 0]), + kind="/", + n_quantiles=100, + ) + + with pytest.raises( + NotImplementedError, + match=re.escape(r"kind='/' not available for quantile_delta_mapping."), + ), pytest.warns(UserWarning, match="Do not call quantile_delta_mapping"): + quantile_delta_mapping( + obs=np.array(datasets["+"]["obsh"][:, 0, 0]), + simh=np.array(datasets["+"]["simh"][:, 0, 0]), + simp=np.array(datasets["+"]["simp"][:, 0, 0]), + kind="/", + n_quantiles=100, + ) + + +def test_adjust_failing_dqm(datasets: dict) -> None: + with pytest.raises( + ValueError, + match="This function is not available for detrended quantile mapping. " + "Please use cmethods.CMethods.detrended_quantile_mapping", + ): + adjust( + method="detrended_quantile_mapping", + obs=datasets["+"]["obsh"][:, 0, 0], + simh=datasets["+"]["simh"][:, 0, 0], + simp=datasets["+"]["simp"][:, 0, 0], + kind="/", + n_quantiles=100, + ) + + +def test_adjust_failing_no_group_for_distribution(datasets: dict) -> None: + with pytest.raises( + ValueError, + match="Can't use group for distribution based methods.", + ): + adjust( + method="quantile_mapping", + obs=datasets["+"]["obsh"][:, 0, 0], + simh=datasets["+"]["simh"][:, 0, 0], + simp=datasets["+"]["simp"][:, 0, 0], + kind="/", + n_quantiles=100, + group="time.month", + ) diff --git a/tests/test_utils.py b/tests/test_utils.py index 6bfc915..2b5cb8e 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -10,53 +10,72 @@ Data types are ignored for simplicity. """ +import re + import numpy as np import pytest import xarray as xr -from cmethods import CMethods as cm +from cmethods import adjust +from cmethods.distribution import ( + detrended_quantile_mapping, + quantile_delta_mapping, + quantile_mapping, +) +from cmethods.static import MAX_SCALING_FACTOR +from cmethods.utils import ( + check_np_types, + check_xr_types, + ensure_dividable, + get_adjusted_scaling_factor, + get_pdf, + nan_or_equal, +) + # -------------------------------------------------------------------------- # test for nan values - - +@pytest.mark.filterwarnings("ignore:Do not call quantile_mapping directly") def test_quantile_mapping_single_nan() -> None: obs, simh, simp = list(np.arange(10)), list(np.arange(10)), list(np.arange(10)) obs[0] = np.nan expected = np.array([0.0, 1.9, 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.0]) - res = cm.quantile_mapping(obs=obs, simh=simh, simp=simp, n_quantiles=5) + res = quantile_mapping(obs=obs, simh=simh, simp=simp, n_quantiles=5) assert np.allclose(res, expected) @pytest.mark.filterwarnings("ignore:All-NaN slice encountered") +@pytest.mark.filterwarnings("ignore:Do not call quantile_mapping directly") def test_quantile_mapping_all_nan() -> None: obs, simh, simp = ( list(np.full(10, np.nan)), list(np.arange(10)), list(np.arange(10)), ) - res = cm.quantile_mapping(obs=obs, simh=simh, simp=simp, n_quantiles=5) + res = quantile_mapping(obs=obs, simh=simh, simp=simp, n_quantiles=5) assert np.allclose(res, simp) +@pytest.mark.filterwarnings("ignore:Do not call quantile_delta_mapping directly") def test_quantile_delta_mapping_single_nan() -> None: obs, simh, simp = list(np.arange(10)), list(np.arange(10)), list(np.arange(10)) obs[0] = np.nan expected = np.array([0.0, 1.9, 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.0]) - res = cm.quantile_delta_mapping(obs=obs, simh=simh, simp=simp, n_quantiles=5) + res = quantile_delta_mapping(obs=obs, simh=simh, simp=simp, n_quantiles=5) assert np.allclose(res, expected) @pytest.mark.filterwarnings("ignore:All-NaN slice encountered") +@pytest.mark.filterwarnings("ignore:Do not call quantile_delta_mapping directly") def test_quantile_delta_mapping_all_nan() -> None: obs, simh, simp = ( list(np.full(10, np.nan)), list(np.arange(10)), list(np.arange(10)), ) - res = cm.quantile_delta_mapping(obs=obs, simh=simh, simp=simp, n_quantiles=5) + res = quantile_delta_mapping(obs=obs, simh=simh, simp=simp, n_quantiles=5) assert np.allclose(res, simp) @@ -64,37 +83,30 @@ def test_quantile_delta_mapping_all_nan() -> None: # test utils -def test_get_available_methods() -> None: - assert cm.get_available_methods() == [ - "linear_scaling", - "variance_scaling", - "delta_method", - "quantile_mapping", - "detrended_quantile_mapping", - "quantile_delta_mapping", - ] - - def test_nan_or_equal() -> None: - assert cm.nan_or_equal(0, 0) - assert cm.nan_or_equal(np.NaN, np.NaN) - assert not cm.nan_or_equal(0, 1) + assert nan_or_equal(0, 0) + assert nan_or_equal(np.NaN, np.NaN) + assert not nan_or_equal(0, 1) def test_get_pdf() -> None: - assert (cm.get_pdf(np.arange(10), [0, 5, 11]) == np.array((5, 5))).all() + assert (get_pdf(np.arange(10), [0, 5, 11]) == np.array((5, 5))).all() def test_get_adjusted_scaling_factor() -> None: - assert cm.get_adjusted_scaling_factor(10, 5) == 5 - assert cm.get_adjusted_scaling_factor(10, 11) == 10 - assert cm.get_adjusted_scaling_factor(-10, -11) == -10 - assert cm.get_adjusted_scaling_factor(-11, -10) == -10 + assert get_adjusted_scaling_factor(10, 5) == 5 + assert get_adjusted_scaling_factor(10, 11) == 10 + assert get_adjusted_scaling_factor(-10, -11) == -10 + assert get_adjusted_scaling_factor(-11, -10) == -10 def test_ensure_devidable() -> None: assert np.array_equal( - cm.ensure_devidable(np.array((1, 2, 3, 4, 5, 0)), np.array((0, 1, 0, 2, 3, 0))), + ensure_dividable( + np.array((1, 2, 3, 4, 5, 0)), + np.array((0, 1, 0, 2, 3, 0)), + MAX_SCALING_FACTOR, + ), np.array((10, 2, 30, 2, 5 / 3, 0)), ) @@ -106,13 +118,22 @@ def test_ensure_devidable() -> None: # valid values. -def test_type_check() -> None: +def test_np_type_check() -> None: """ Checks the correctness of the type checking function when the types are correct. No error should occur. """ - cm.check_types(obs=[], simh=[], simp=[]) + check_np_types(obs=[], simh=[], simp=[]) + + +def test_xr_type_check() -> None: + """ + Checks the correctness of the type checking function when the types are + correct. No error should occur. + """ + ds: xr.core.dataarray.Dataset = xr.core.dataarray.Dataset() + check_xr_types(obs=ds, simh=ds, simp=ds) def test_type_check_failing() -> None: @@ -121,112 +142,116 @@ def test_type_check_failing() -> None: have the correct type. """ - phrase: str = "must be type list, np.ndarray or xarray.core.dataarray.DataArray" + phrase: str = "must be type list, np.ndarray or np.generic" with pytest.raises(TypeError, match=f"'obs' {phrase}"): - cm.check_types(obs=1, simh=[], simp=[]) + check_np_types(obs=1, simh=[], simp=[]) with pytest.raises(TypeError, match=f"'simh' {phrase}"): - cm.check_types(obs=[], simh=1, simp=[]) + check_np_types(obs=[], simh=1, simp=[]) with pytest.raises(TypeError, match=f"'simp' {phrase}"): - cm.check_types(obs=[], simh=[], simp=1) + check_np_types(obs=[], simh=[], simp=1) +@pytest.mark.filterwarnings("ignore:Do not call quantile_mapping directly") def test_quantile_mapping_type_check_n_quantiles_failing() -> None: """n_quantiles must by type int""" with pytest.raises(TypeError, match="'n_quantiles' must be type int"): - cm.quantile_delta_mapping(obs=[], simh=[], simp=[], n_quantiles="100") + quantile_mapping(obs=[], simh=[], simp=[], n_quantiles="100") -def test_detrended_quantile_mapping_type_check_n_quantiles_failing() -> None: +def test_detrended_quantile_mapping_type_check_n_quantiles_failing( + datasets: dict, +) -> None: """n_quantiles must by type int""" - with pytest.raises(TypeError, match="'n_quantiles' must be type int"): - cm.detrended_quantile_mapping(obs=[], simh=[], simp=[], n_quantiles="100") + with pytest.raises(TypeError, match=re.escape("'n_quantiles' must be type int")): + detrended_quantile_mapping( # type: ignore[attr-defined] + obs=datasets["+"]["obsh"][:, 0, 0], + simh=datasets["+"]["simh"][:, 0, 0], + simp=datasets["+"]["simp"][:, 0, 0], + n_quantiles="100", + ) -def test_detrended_quantile_mapping_type_check_simp_failing() -> None: - """simp must be type xarray.core.dataarray.DataArray""" +def test_detrended_quantile_mapping_type_check_simp_failing(datasets: dict) -> None: + """n_quantiles must by type int""" with pytest.raises( - TypeError, match="'simp' must be type xarray.core.dataarray.DataArray" + TypeError, + match="'simp' must be type xarray.core.dataarray.DataArray", ): - cm.detrended_quantile_mapping(obs=[], simh=[], simp=[], n_quantiles=100) + detrended_quantile_mapping( # type: ignore[attr-defined] + obs=datasets["+"]["obsh"][:, 0, 0], + simh=datasets["+"]["simh"][:, 0, 0], + simp=[], + n_quantiles=100, + ) -def test_quantile_delta_mapping_type_check_n_quantiles_failing() -> None: +@pytest.mark.filterwarnings("ignore:Do not call quantile_delta_mapping directly") +def test_quantile_delta_mapping_type_check_n_quantiles() -> None: """n_quantiles must by type int""" with pytest.raises(TypeError, match="'n_quantiles' must be type int"): - cm.quantile_delta_mapping(obs=[], simh=[], simp=[], n_quantiles="100") + quantile_delta_mapping( # type: ignore[attr-defined] + obs=[], + simh=[], + simp=[], + n_quantiles="100", + ) -def test_grouped_correction_worng_type_failing() -> None: - """simp must be type xarray.core.dataarray.DataArray""" - with pytest.raises( - TypeError, match="'simp' must be type xarray.core.dataarray.DataArray" - ): - cm.linear_scaling(obs=[], simh=[], simp=[], n_quantiles=100, group="time.month") +@pytest.mark.filterwarnings("ignore:Do not call quantile_delta_mapping directly") +def test_quantile_delta_mapping_type_check_n_quantiles_failing() -> None: + """n_quantiles must by type int""" + with pytest.raises(TypeError, match="'n_quantiles' must be type int"): + quantile_delta_mapping( # type: ignore[attr-defined] + obs=[], + simh=[], + simp=[], + n_quantiles="100", + ) -@pytest.mark.wip() -def test_adjust_3d_type_checking_failing() -> None: +def test_adjust_type_checking_failing() -> None: """ Checks for all types that are expected to be passed to the adjust_3d method """ # Create the DataArray data: xr.core.dataarray.DataArray = xr.DataArray( - [10, 20, 30, 40, 50], dims=["time"] + [10, 20, 30, 40, 50], + dims=["time"], ) with pytest.raises( - TypeError, match="'obs' must be type xarray.core.dataarray.DataArray" + TypeError, + match="'obs' must be type xarray.core.dataarray.Dataset" + " or xarray.core.dataarray.DataArray", ): - cm.adjust_3d( + adjust( method="linear_scaling", obs=[], simh=data, simp=data, - n_quantiles=100, group="time.month", ) with pytest.raises( - TypeError, match="'simh' must be type xarray.core.dataarray.DataArray" + TypeError, + match="'simh' must be type xarray.core.dataarray.Dataset or xarray.core.dataarray.DataArray", ): - cm.adjust_3d( + adjust( method="linear_scaling", obs=data, simh=[], simp=data, - n_quantiles=100, group="time.month", ) with pytest.raises( - TypeError, match="'simp' must be type xarray.core.dataarray.DataArray" + TypeError, + match="'simp' must be type xarray.core.dataarray.Dataset or xarray.core.dataarray.DataArray", ): - cm.adjust_3d( + adjust( method="linear_scaling", obs=data, simh=data, simp=[], - n_quantiles=100, - group="time.month", - ) - - with pytest.raises(TypeError, match="'n_quantiles' must be type int"): - cm.adjust_3d( - method="linear_scaling", - obs=data, - simh=data, - simp=data, - n_quantiles="100", - group="time.month", - ) - - with pytest.raises(TypeError, match="'n_jobs' must be type int"): - cm.adjust_3d( - method="linear_scaling", - obs=data, - simh=data, - simp=data, - n_quantiles=100, group="time.month", - n_jobs="1", ) diff --git a/tests/test_zarr_dask_compatibility.py b/tests/test_zarr_dask_compatibility.py new file mode 100644 index 0000000..7e5fbf5 --- /dev/null +++ b/tests/test_zarr_dask_compatibility.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# Copyright (C) 2024 Benjamin Thomas Schwertfeger +# GitHub: https://github.com/btschwertfeger +# + +from typing import Any + +import pytest +import xarray as xr + +from cmethods import adjust +from cmethods.types import XRData_t + +from .helper import is_3d_rmse_better + +GROUP: str = "time.month" +N_QUANTILES: int = 100 + + +@pytest.mark.parametrize( + ("method", "kind"), + [ + ("linear_scaling", "+"), + ("variance_scaling", "+"), + ("delta_method", "+"), + ("linear_scaling", "*"), + ("delta_method", "*"), + ], +) +def test_3d_scaling_zarr( + datasets_from_zarr: xr.Dataset, + method: str, + kind: str, + dask_cluster: Any, # noqa: ARG001 +) -> None: + variable: str = "tas" if kind == "+" else "pr" # noqa: PLR2004 + obsh: xr.DataArray = datasets_from_zarr[kind]["obsh"][variable] + obsp: xr.DataArray = datasets_from_zarr[kind]["obsp"][variable] + simh: xr.DataArray = datasets_from_zarr[kind]["simh"][variable] + simp: xr.DataArray = datasets_from_zarr[kind]["simp"][variable] + + result: XRData_t = adjust( + method=method, + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + ) + assert isinstance(result, XRData_t) + assert is_3d_rmse_better(result=result[variable], obsp=obsp, simp=simp) + + # grouped + result = adjust( + method=method, + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + group=GROUP, + ) + assert isinstance(result, XRData_t) + assert is_3d_rmse_better(result=result[variable], obsp=obsp, simp=simp) + + +@pytest.mark.parametrize( + ("method", "kind"), + [ + ("quantile_mapping", "+"), + ("quantile_delta_mapping", "+"), + ("quantile_mapping", "*"), + ("quantile_delta_mapping", "*"), + ], +) +def test_3d_distribution_zarr( + datasets_from_zarr: dict, + method: str, + kind: str, + dask_cluster: Any, # noqa: ARG001 +) -> None: + variable: str = "tas" if kind == "+" else "pr" # noqa: PLR2004 + obsh: XRData_t = datasets_from_zarr[kind]["obsh"][variable] + obsp: XRData_t = datasets_from_zarr[kind]["obsp"][variable] + simh: XRData_t = datasets_from_zarr[kind]["simh"][variable] + simp: XRData_t = datasets_from_zarr[kind]["simp"][variable] + + result: XRData_t = adjust( + method=method, + obs=obsh, + simh=simh, + simp=simp, + kind=kind, + n_quantiles=N_QUANTILES, + ) + + assert isinstance(result, XRData_t) + assert is_3d_rmse_better(result=result[variable], obsp=obsp, simp=simp)