{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "expmkveO04pw" }, "source": [ "## Parameter Estimation with Adam in PyBOP\n", "\n", "In this notebook, we demonstrate an example of parameter estimation for a single-particle model using the Adam optimiser [1]. The ADAM optimiser is an algorithm for gradient-based optimisation, combining the advantages of the Adaptive Gradient Algorithm (AdaGrad) and Root Mean Square Propagation (RMSProp).\n", "\n", "[[1]: Adam: A Method for Stochastic Optimization](https://arxiv.org/abs/1412.6980)\n", "\n", "### Setting up the Environment\n", "\n", "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pip in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.3.2)\n", "Requirement already satisfied: ipywidgets in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (8.1.1)\n", "Requirement already satisfied: comm>=0.1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (0.2.0)\n", "Requirement already satisfied: ipython>=6.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (8.18.1)\n", "Requirement already satisfied: traitlets>=4.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (5.14.0)\n", "Requirement already satisfied: widgetsnbextension~=4.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (4.0.9)\n", "Requirement already satisfied: jupyterlab-widgets~=3.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (3.0.9)\n", "Requirement already satisfied: decorator in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", "Requirement already satisfied: jedi>=0.16 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", "Requirement already satisfied: matplotlib-inline in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.41)\n", "Requirement already satisfied: pygments>=2.4.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", "Requirement already satisfied: stack-data in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", "Requirement already satisfied: pexpect>4.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", "Requirement already satisfied: ptyprocess>=0.5 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", "Requirement already satisfied: wcwidth in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.12)\n", "Requirement already satisfied: executing>=1.2.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", "Requirement already satisfied: asttokens>=2.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", "Requirement already satisfied: pure-eval in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", "Requirement already satisfied: six>=1.12.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install --upgrade pip ipywidgets\n", "%pip install pybop -q" ] }, { "cell_type": "markdown", "metadata": { "id": "jAvD5fk104p0" }, "source": [ "### Importing Libraries\n", "\n", "With the environment set up, we can now import PyBOP alongside other libraries we will need:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "SQdt4brD04p1" }, "outputs": [], "source": [ "import pybop\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "id": "5XU-dMtU04p2" }, "source": [ "### Generate Synthetic Data\n", "\n", "To demonstrate parameter estimation, we first need some data. We will generate synthetic data using the PyBOP forward model, which requires defining a parameter set and the model itself.\n", "\n", "#### Defining Parameters and Model\n", "\n", "We start by creating an example parameter set and then instantiate the single-particle model (SPM):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulating Forward Model\n", "\n", "We can then simulate the model using the `predict` method, with a default constant current to generate voltage data." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "sBasxv8U04p3" }, "outputs": [], "source": [ "t_eval = np.arange(0, 900, 2)\n", "values = model.predict(t_eval=t_eval)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding Noise to Voltage Data\n", "\n", "To make the parameter estimation more realistic, we add Gaussian noise to the data." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "sigma = 0.001\n", "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(0, sigma, len(t_eval))" ] }, { "cell_type": "markdown", "metadata": { "id": "X8-tubYY04p_" }, "source": [ "## Identify the Parameters" ] }, { "cell_type": "markdown", "metadata": { "id": "PQqhvSZN04p_" }, "source": [ "We will now set up the parameter estimation process by defining the datasets for optimisation and selecting the model parameters we wish to estimate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating Optimisation Dataset\n", "\n", "The dataset for optimisation is composed of time, current, and the noisy voltage data:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "zuvGHWID04p_" }, "outputs": [], "source": [ "dataset = pybop.Dataset(\n", " {\n", " \"Time [s]\": t_eval,\n", " \"Current function [A]\": values[\"Current [A]\"].data,\n", " \"Voltage [V]\": corrupt_values,\n", " }\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "ffS3CF_704qA" }, "source": [ "### Defining Parameters to Estimate\n", "\n", "We select the parameters for estimation and set up their prior distributions and bounds:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "WPCybXIJ04qA" }, "outputs": [], "source": [ "parameters = [\n", " pybop.Parameter(\n", " \"Negative electrode active material volume fraction\",\n", " prior=pybop.Gaussian(0.6, 0.02),\n", " bounds=[0.5, 0.8],\n", " ),\n", " pybop.Parameter(\n", " \"Positive electrode active material volume fraction\",\n", " prior=pybop.Gaussian(0.48, 0.02),\n", " bounds=[0.4, 0.7],\n", " ),\n", "]" ] }, { "cell_type": "markdown", "metadata": { "id": "n4OHa-aF04qA" }, "source": [ "### Setting up the Optimisation Problem\n", "\n", "With the datasets and parameters defined, we can set up the optimisation problem, its cost function, and the optimiser." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "etMzRtx404qA" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NOTE: Boundaries ignored by Adam\n" ] } ], "source": [ "problem = pybop.FittingProblem(model, parameters, dataset)\n", "cost = pybop.SumSquaredError(problem)\n", "optim = pybop.Optimisation(cost, optimiser=pybop.Adam)\n", "optim.set_max_iterations(150)" ] }, { "cell_type": "markdown", "metadata": { "id": "caprp-bV04qB" }, "source": [ "### Running the Optimisation\n", "\n", "We proceed to run the Adam optimisation algorithm to estimate the parameters:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "-9OVt0EQ04qB" }, "outputs": [], "source": [ "x, final_cost = optim.run()" ] }, { "cell_type": "markdown", "metadata": { "id": "-4pZsDmS04qC" }, "source": [ "### Viewing the Estimated Parameters\n", "\n", "After the optimisation, we can examine the estimated parameter values:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Hgz8SV4i04qC", "outputId": "e1e42ae7-5075-4c47-dd68-1b22ecc170f6" }, "outputs": [ { "data": { "text/plain": [ "array([0.77574635, 0.66084088])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x # This will output the estimated parameters" ] }, { "cell_type": "markdown", "metadata": { "id": "KxKURtH704qC" }, "source": [ "## Plotting and Visualisation\n", "\n", "PyBOP provides various plotting utilities to visualise the results of the optimisation." ] }, { "cell_type": "markdown", "metadata": { "id": "-cWCOiqR04qC" }, "source": [ "### Comparing System Response\n", "\n", "We can quickly plot the system's response using the estimated parameters compared to the target:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", 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V7yTfi/NwB8YevPXE7OoJXfbB23be/+Rr4wYY7/W5Os2Y1z+WF16bIlerq2319bajn5wg73/6TanXB2sGW7btFH0Qqz5oVJ+toa8vHqyu5dVPQUGhjH5qgnHorJnbdhYvWy1nXHqXkdcrb4p3Mf0+Lav04aptWjY2tqLo22q+m/KcTxboM0Z6nzvS+LkWPnqFT7jkiY6djmFxeeZdqaMPkPVe47xd3Yo0+MpRxuG8gcoTM7G669FXZMpn3xurhC45p48P3c2jx8jnX/8o3lU6yBOH/+GieRCAAAQgEDYCyJOwoaVgCEAAAhCwM4HcvHw57eI7ZMXqDcaWnH49OhvbeJauWCvTZ803VqV0PrqVvPLULb5meuWJN30dtfJBXxerD2/Vt5vowzz1mRf6eXzMuzLh/c+NW3pO7XmsrN2wWS47t5/6faUyb9sJpTzRE/3kpEQldLoat+2sWL1e3vjwC+NWmC/UFcRaCuiJdvczbzCu0tVXLPfu3kFy1CqVX39fInN/+kOuvuQMuUrJDO/qBt2uAb2OU4eOJsms2b/4rvM1I0903qfGvS96S45+9BkdHdXtMPoKZV3+r38sKSFWvGKnbq3qMmRwb1XHPCV/PjK2UY0ecYlx2K9+QiVPdFn6jJdWzeqrWG0xRIV+9O1J+hYl/Qy5/mH5acE/hnA7uVt72bh5m8xUK5S8VxEHKk/MxGrj5u1y4lk3GPXQVw03b5wp3/2wwNiapbcRff3h/xnXaSNP7PxXibpDAAIQgEA0CSBPokmfd0MAAhCAgKUJ6Ct0nxn/obw5+QvfBFhXWMuR6y4dKBeffYrvcFX9c6880bfqFD9oVU+mn3vgeuNQWe+jJ7Gjn3xdvvz+J0PE6OeD8aMNQaOvKtYrOt58/k7j5/rmH30D0KSXRpW4Bte72qD4db06vfcsjk8mPCiNGxS98+CVJzqvPhvE+26dRq+WeOCWy+Skrkf76rlOiYK7HnvF2FJT/NHl3nX9hca5IvrRqyNGP/W6L4ku69j2R4i+PtisPNGZ9TXR+npkvVKj+KOl09nqWt0hg3obW4i03Hp8zDslDmfVgurWq88zbgHyPsGyK35VsS7TK0L06pYHbr3cEEreR7O68NoHjS1M3kcLtk3qoGB9rsx3U56ValUqGddJd+l/lXHF9ME3+AQTK72F6Orb/884mNj76LNOXnj4BuPsFf0c7t363BndH7w3QpUIAN9AAAIQgAAEXE4AeeLyDkDzIQABCEDAHAF9a4leHZJRJ923euTgnMW37VykJvlaAFRLq+TbwlHam/RkXJ8zUksd1KqlSyQffYbIBrU6YpNatVC9WprUrF7FuAmotEeLJF1PvTKlbq0axtaYgx+9SmSxui63ilrpUK92jaCaouXIilXr1UqXXMWmmrF6oix+ejVQXFysNMqsY6zwCeVTfKXGq0/dKus3bVWMYnxXQB/8Lr1daemKNbIna6/RV7QsCcXjT6x0HbXIaZhR27iumQcCEIAABCAAgeAJIE+CZ0gJEIAABCAAAYPA4W7bAZE9CZR3rbA9W0WtIQABCEAAAhDwlwDyxF9ipIcABCAAAQiUQQB54ryugTxxXkxpEQQgAAEIQCAQAsiTQKiRBwIQgAAEIFAKAX3by5ff/2wcJKvPmuCxPwG9fegtdeaNPotGHxjLAwEIQAACEICAOwkgT9wZd1oNAQhAAAIQgAAEIAABCEAAAhCAgEkCyBOToEgGAQhAAAIQgAAEIAABCEAAAhCAgDsJIE/cGXdaDQEIQAACEIAABCAAAQhAAAIQiCCBQvWu0m/1i2AlAn6Vn/LE3o0NmBIZIQABCEAAAhCAAAQgAAEIQAACEHAtAT/liWs50XAIQAACEIAABCAAAQhAAAIQgAAEXEoAeeLSwNNsCEAAAhCAAAQgAAEIQAACEIAABMwRQJ6Y40QqCEAAAhCAAAQgAAEIQAACEIAABFxKAHni0sDTbAhAAAIQgAAEIAABCEAAAhCAAATMEUCemONEKghAAAIQgAAEIAABCEAAAhCAAARcSgB54tLA02wIQAACEIAABCAAAQhAAAIQgAAEzBFAnpjjRCoIQAACEIAABCAAAQhAAAIQgAAEXEoAeeLSwNNsCEAAAhCAAAQgAAEIQAACEIAABMwRQJ6Y40QqCEDAbwKFKofH71xkgAAEIAABCEAAAhCAAAQgYDUCyBOrRYT6QAACEIAABCAAAQhAAAIQgAAEIGApAsgTS4WDykAAAhCAAAQgAAEIQAACEIAABCBgNQLIE6tFhPpAAAIQgAAEIAABCEAAAhCAAAQgYCkCyBNLhYPKQAACEIAABCAAAQhAAAIQgAAEIGA1AsgTq0WE+kAAAhCAAAQgAAEIQAACEIAABCBgKQLIE0uFg8pAAAIQgAAEIAABCEAAAhCAAAQgYDUCyBOrRYT6QAACEIAABCAAAQhAAAIQgAAEIGApAsgTS4WDykAAAhCAAAQgAAEIQAACEIAABCBgNQLIE6tFhPpAAAIQgAAEAiVQqDJ6As1MPghAAAIQgAAEIACBsgggT+gbEIAABCAAAQhAAAIQgAAEIAABCEDgMASQJ3QPCEAAAhCAAAQgAAEIQAACEIAABCCAPKEPQAACEIAABCAAAQhAAAIQgAAEIACBwAiw8iQwbuSCAAQgAAEIQAACEIAABCAAAQhAwCUEkCcuCTTNhAAEIAABCEAAAhCAAAQgAAEIQCAwAsiTwLiRCwIQgAAEIAABCEAAAhAIOwGuEQs7Yl4AAQiYIoA8MYWJRBCAAAQgAAEIQAACEIAABCAAAQi4lYCr5cnaLdlujTvthgAEIAABCEAAAhCAAAQgAAEIQOAgAnWqJZfKBHlCV4EABCAAAQhAAAIQgAAEIAABCEAAAooA8qSUbsDKE8YGBCAAAQhAAAIQgAAEIAABCEAAAl4CyBPkCaMBAhCAAAQgAAEIQAACEIAABCAAgcMQQJ64WJ7UrposHo/Iuq3ZUqgPLOeBAATKJFAxOU5i1IDZmZULJQhAoBwCerx41HjZxXihr0CgXAKparzofyFjvJSLigQQEMZL2Z2grIk93SZwAgfvSEGeIE+QJ4GPJ3K6iADyxEXBpqlBE0CeBI2QAlxEgMmgi4JNU4MmwHhBngTdifwoAHliApZbzjxh5YmJzkASCOwngDyhK0DAPAHkiXlWpIQAk0H6AATME2C8IE/M95bgUyJPTDBEnpiARBIIuIwA8sRlAae5QRFAngSFj8wuI8Bk0GUBp7lBEWC8IE+C6kB+ZkaemACGPDEBiSQQcBkB5InLAk5zgyKAPAkKH5ldRoDJoMsCTnODIsB4QZ4E1YH8zIw8MQEMeWICEkkg4DICyBOXBZzmBkUAeRIUPjK7jACTQZcFnOYGRYDxgjwJqgP5mRl5YgIY8sQEJJJAwGUEkCcuCzjNDYoA8iQofGR2GQEmgy4LOM0NigDjBXkSVAfyMzPyxAQw5IkJSCSBgMsIIE9cFnCaGxQB5ElQ+MjsMgJMBl0WcJobFAHGC/IkqA7kZ2bkiQlgyBMTkEgCAZcRQJ64LOA0NygCyJOg8JHZZQSYDLos4DQ3KAKMF+RJUB3Iz8zIExPAkCcmIJEEAi4jgDxxWcBpblAEkCdB4SOzywgwGXRZwGluUAQYL8iToDqQn5mRJyaAIU9MQCIJBFxGAHnisoDT3KAIIE+CwkdmlxFgMuiygNPcoAgwXuwpT3bs3CPHDri6zMpfc+kZMvyi04LqG2YzX3bzY3Lj0EFyRPOG5WZBnpSLSAR5YgISSSDgMgLIE5cFnOYGRQB5EhQ+MruMAJNBlwWc5gZFgPESfnlSsHGd5C36zXhRfIeu4klJDSpmOnNBQaH8u3yNr5wzLr1LtDDp0fUY42c1qlWWKpWDf4+ZirbufrGMefhGOaFL23KTI0/KRYQ8MYGIJBBwHQHkietCToODIIA8CQIeWV1HgMmg60JOg4MgwHgJrzzJX75Edo+6Vgqzdhsv8lSoKBVHPSexDZoGEbVDs7bpcancf8tlclrv44xf6tUgv/y+RHJycqVqWqqxMmRg3+ON391y/4vSqH4d2bp9l8z+caFcPPgU6d29o9z+8Evq+9+NNE0a1JUWTTLloduvML7//oeFMvrJCbJxy3bpckxrueL8/tK+bXO569FXZMpn30vFlGRJVR+DB5woQy84tcy2IU9MhJ2VJyYgkQQCLiOAPHFZwGluUASQJ0HhI7PLCDAZdFnAaW5QBBgvZeOrUy05KLY6866RF0v+in9LlBPfvquk3PJI0GUXL+BgeTJmwkfSqlkDSa+eJtO/+kFee/cz+W7Ks1KtSiUZNHSULFq83JAfx7Rppj6ay8RJM+S3P/+V6y8/yxAnj77wtiQmxMvbY+6WpSvWyoAhd8hl5/aVXt07yJTp38snM+fIj9NflD9VOWdfOVquvPBUOap1E6lXJ10aZdZGngQTXeRJMPTICwFnEkCeODOutCo8BJAn4eFKqc4kwGTQmXGlVeEhwHgJrzzZPrhrqS9Ie392SAN6sDzJzy+QBYuWysK/lsr6jVvljQ9mysRn7zBkiZYnrZrVl9EjLjHqkJW9Tzr0uVLuuO58OX9gT+NnDz37pvzx93+GPHnwmTdk2qz5xtYc/eTm5snFNzxi/K5tq8bCtp2QhpJtOyHGSXEQcAQB5IkjwkgjIkQAeRIh0LzGEQSYDDoijDQiQgQYL+GVJ7tHXeM778T7pnCvPNm5O0vOGTZaVq/bJB2OaiH1ateQD6Z+K689fZt0bNfCkCf688jh5xhVWrZynZx60e0y+ZX7pXnjjEPkyRUjnpAffl0kdWtVLwHrpisHS8/j2yNPQj1WWXkSaqKUBwH7E0Ce2D+GtCByBJAnkWPNm+xPgMmg/WNICyJHgPESXnmizzzZpQSKZO0pelGFFEkd9XxYzzz5eMYcuePh8TL74+d8h8bq1SFlyRN9+OxRPS+TR+64Uvr26HSIPNHnmujDad8de0+psHTZzz90vZx4bLtyOy5nnpSLiJUnJhCRBAKuI4A8cV3IaXAQBJAnQcAjq+sIMBl0XchpcBAEGC/hlSe69MI9uyT3f98bL4rv0C0kt+0cXOvi23a+mv2LXHvXs4bsqKNWi0z69Bt57tXJZcoTXdawW59UW3yWGdcb71IrV8a/PU1aqgNj9dacOf/7Q4aOfELuvP5CGXRqd9mmDpr96PPvpWvHI41zVQZcfKd0PrqVWslytuhVL/pclbIe5ImJwcrKExOQSAIBlxFAnrgs4DQ3KALIk6DwkdllBJgMuizgNDcoAoyX8MuToAJkMnNxeaJXklx03UPy6x9LjNxHtmwkvysxMuH/bjO28ehtO52Obikjhp3tK11v8bn9ofHqcNg16sDXOuoq5AJJTkqUV566xUgz4f3P5alx74s+S0U/lSulyMRn7pAmDeuKXukySt3Eo2/2Oee0k+TuGy9CnpiMW6nJkCfB0CMvBJxJAHnizLjSqvAQQJ6EhyulOpMAk0FnxpVWhYcA48UZ8qS0VmzYtE1iY2OketXK5XaevPx8iYuNNdJp+XLWFfcYt/Hccd0FvryFhYWyYfM24xaeKpVTS5SppcrmrTuM2308Hg/ypFzih0mAPAmGHnkh4EwCyBNnxpVWhYcA8iQ8XCnVmQSYDDozrrQqPAQYL86VJ/70mCdefE8+nTlXMuvWlJVrNhjbb6a/+ajUTq/qTzHlpmXbTrmIOPPEBCKSQMB1BJAnrgs5DQ6CAPIkCHhkdR0BJoORCXmMek3RAn4eOxNgvCBPNAG9bee7+Qtlx67dSphUkxOPayeVU1NC3rWRJyaQsvLEBCSSQMBlBJAnLgs4zQ2KAPIkKHxkdhkBJoMuCzjNDYoA4wV5ElQH8jMz8sQEMDfIk7hZk6XCovlSmLVL9h7RRXL7lX1QjglkJIGA4wkgTxwfYhoYQgLIkxDCpCjHE2Ay6PgQ08AQEmC8IE9C2J3KLQp5Ui4i59PnHGUAACAASURBVG/b0eIk4YOxJUjk9rtQcvsjUEx0D5K4lADyxKWBp9kBEUCeBISNTC4lwGTQpYGn2QERYLwgTwLqOAFmQp6YAOf0lSdJDw6T7VtWy4JaVeWP9Crym/p4cvYiSXjofRN0SAIBdxJAnrgz7rQ6MALIk8C4kcudBJgMujPutDowAowX5ElgPSewXMgTE9ycLk+S7zxfup7WQRalp/loTHt7phx1x9sm6JAEAu4kgDxxZ9xpdWAEkCeBcSOXOwkwGXRn3Gl1YAQYL8iTwHpOYLmQJya4OV2eJEwaI8NTNsgHrRr6aDy0ZLcM6XmNCTokgYA7CSBP3Bl3Wh0YAeRJYNzI5U4CTAbdGXdaHRgBxgvyJLCeE1gu5IkJbk6XJ6IOiR338ztyX0aCj8Z5yQ3k8ZrdTdAhCQTcSQB54s640+rACCBPAuNGLncSYDLozrjT6sAIMF6QJwcT2LBpm8z96Q85o0+3cjvV5OnfSdeObSS9+oEdGIfLhDwpF6nzD4zVCL7OXi0XbPjSR6NdQnWZWqe/CTokgYA7CSBP3Bl3Wh0YAeRJYNzI5U4CTAbdGXdaHRgBxou95UmHPldKVvY++XbyM1K9amWjMfr7zv2HS35+gfz+1WsSE+Pxq3N8O2+BXHX70/LnNxPKzde6+8Xy4qM3S7dOR5abVidAnpjA5PiVJ4rBhrwsOXr1gQNi4yVGljfgth0T3YMkLiWAPHFp4Gl2QASQJwFhI5NLCTAZdGngaXZABBgvzpAn5w88We647gKjMRPe+1weH/uu8TXyJKBhEd1MbpAnmnDrVW/L9vwcH+zv654hjeKLDCAPBCBQkgDyhB4BAfMEkCfmWZESAkwG6QMQME+A8eKfPBm97ifzcEOY8t7a7UstTa88Ob5zW/niu59k9sfPS0pykhw74Gq1EqSNfPbVDz558ve/K+WWB8bJ0uVrpGpaqtx05WDftpzCwkJ57tXJ8sYHM41VK3oFy+atO3wrT1au2WDkXbR4uTTKrCMXDeolA/seb9SHlSchDLK3KLfIkwu3fCFf7VrjIziuRnfpn9IgDEQpEgL2J4A8sX8MaUHkCCBPIseaN9mfAJNB+8eQFkSOAOPFP3ni+Xls5IJT7E2FxwwvU56MGnGJPPnie4YMaZhRWx594W0ZrX527V3PGvJkX06uHH/GtdK4QV25+uLT5bv5C+TtKbPkzefvlHZHNJX3P/1GRj85Qc47o4eccmIn+ejz2aLPMtHbdnJz8+SEM6+XI1s0kmEXDZAl/60x0k574xFpkFELeRKO3uAWefJE1i/y9MaFPoTXp7WVW9LahQMpZULA9gSQJ7YPIQ2IIAHkSQRh8yrbE2AyaPsQ0oAIEmC82F+ejB5xqRIkOXLf0xOlSuWKcuUFp0p6jSpyzR3PGPJk1uyf5YZ7npfP3npMMuumGw3udvq1xkGvD99xhQy4+E6pWb2KjH9ihPG74meefD33V6OcFx+9SVIrVjB+P2L0GDmz/wky/KLTkCfhGKtukSczClfIpSu+9iHslZwhr9XsEQ6klAkB2xNAntg+hDQgggSQJxGEzatsT4DJoO1DSAMiSIDx4gx50qt7ezluwDVSUFAgcz95QWb/73efPJn4wQx55uUP5deZ432N1UJkx6498sZzd0i7XlfIdZcOlEvO6XOIPJnwvjo/Zcy7PuniLeDkbu3l5mGDkSfhGKtukSfrk/bIMX9P8iHMKEyQ+Q3PCwdSyoSA7QkgT2wfQhoQQQLIkwjC5lW2J8Bk0PYhpAERJMB48U+eWPHME73ypG+PTsa5J1qe9O7eUbwrRvTKk+lfzZdb1Zkl3015VqpVqWQ0uM/5t0jLpg3kqVFXydlXjpYjWjSUu28suuyk+MqT6bN+kNseGic/zxgv8XGxh8DizJMwDFa3yJPKa/+U1H0/SqHnwHVQy9bXlMTORRaPBwIQOEAAeUJvgIB5AsgT86xICQEmg/QBCJgnwHjxT56YJxuZlPrAWK88Kf7G4vJk6/adctKgG2VAr+NkxLCz5fsfFioh8pI8fvdwQ7qMnfixcUPPU6Oulto1q8mDz7wh839eZJx5snX7LumuzjzpdUIHGXXzxcYrvpu/UHJyc+X0U7qy8iQcYXaLPKn03Ejp2Lmu/FUjzYdx+qTvpe3IV8KBlTIhYGsCyBNbh4/KR5gA8iTCwHmdrQkwGbR1+Kh8hAkwXuwvT+4bean0OalTiYYUlycxMR7j5p1bHxwn+fkFRrrBA06Ue28aYny9ftNWY/WJvmFHP80bZ8g/S1f5btuZ99OfcvN9Y2THzj3G72NjY+T+Wy6T03ofZ8iTlx4fIcd1OMJUzz3YC9SpllxqPo+6AqjQVIkOTOQWeZL6yHC5tE26fNCqoS+KT8z8Uc4dOsaBUaVJEAiOAPIkOH7kdhcB5Im74k1rgyPAZDA4fuR2FwHGi73liT+9VYuTVWs3Snr1NKmgrjQu/mhVsWrtJuOa4grJiaUWu23HLsnJyTPye4rttPCnDsgTE7TcIk/S3n1KHtu7QkZ3P3DDzpAlG+ShnreaoEQSCLiLAPLEXfGmtcERQJ4Ex4/c7iLAZNBd8aa1wRFgvLhHngTXU0KTG3ligqNb5EmtxDyZMu4uOat7Cx+VYzyp8kn9M01QIgkE3EUAeeKueNPa4AggT4LjR253EWAy6K5409rgCDBekCfB9SD/ciNPTPByizypXTVZ1uXtkboLJ/qoJHvi5N/6F5igRBIIuIsA8sRd8aa1wRFAngTHj9zuIsBk0F3xprXBEWC8IE+C60H+5UaemODlJnmit3+l/fqK7CjI8ZGZXXegNIwvuhaKBwIQKCKAPKEnQMA8AeSJeVakhACTQfoABMwTYLwgT8z3luBTukqeFBQUytoNm6Vmjaql3vNcFk63yZOui6bInOz1Phzj00+UvhXqB9/bKAECDiKAPHFQMGlK2AkgT8KOmBc4iACTQQcFk6aEnQDjBXkS9k5W7AW2lyfLV62XARffYdz7/MCtl5XJ7uDrjW4cOkguP6+fkf7jGXPkjofHH5L3p89fkuSkBHGbPBm65FsZv3ORj8eNaUfJCPXBAwEIHCCAPKE3QMA8AeSJeVakhACTQfoABMwTYLwgT8z3luBT2lqe6Lua+190m2zdvkvO6NOtTHmSlb1XOve/ypAlw4ecLtO+nCd3PvKyTJ34sDTMrC0ffT5b7nn8Vfnw5ftKEG3SoK5xjZHb5MnTKxfKTZvm+FicUiFTXkk/KfjeRgkQcBAB5ImDgklTwk4AeRJ2xLzAQQSYDDoomDQl7AQYL8iTsHeyYi+wrTzJzcuXc4aNljq1qsvOXXsko056mfJk+qwfZOT9Y+WXmeMlMSHeaP6xA66WCwb2lKsuPt2QJ6Ofel1+Vb8v7XGbPJm5bpX0XjvVh6J+frzMbXx+JPsl74KA5QkgTywfIipoIQLIEwsFg6pYngCTQcuHiApaiADjBXkSye5oW3ky4r6xsnjZavngpVFyxcgnDitPXn57mrz67nSZ+8kLPrbnDL9P9MoSvdVHyxO9EqVrxyMlMTFejmt/hAzsd4LvXBS3yZNV8+ZI/fgFUqhPj93//PenRxL6DYlk3+RdELA0AeSJpcND5SxGAHlisYBQHUsTYDJo6fBQOYsRYLwgTyLZJW0pT8a98akhQ6a/+ahUq1JJhlz/8GHlyRMvvifTZ82XryY97WN7yY2PSMWUCvLcA9fJTwv+kcnTv5Mqaamyau1GmfX9L9K7e0d5atRVRnqzkCIZuHC+a/eoa6RDl7ryd/U032s+f3OG9H76o3C+lrIhYAsCOXkFkhAXY4u6UkkIQAACEIAABEJHgH8HCB1LSoKAHQmY9QKeQvVYpYGd+g2Xumq7TrPGGUaVvp7zq6RUSJI+J3aSkVedc0g1y1t5cnCGCe9/Lo+PeVd++/IVY/WJ21aebL3rKhnaPFU+bNXAh+bJGT/KOVeOsUoXoB4QiDoBVp5EPQRUwEYEWHlio2BR1agT4L+kRz0EVMBGBBgvNgqWA6tap1pyqa2ylDwZ/9ZU2aYOifU+H82YLZVTU2Rg3+PlivP7H9IA75kn+kyThP1nnmgBM2RQb+PMk4OfGd/8T24a9YL877NxUiE50XXyZPOLT8pzWf/IfSe086G5ZMkGeaDnrQ7s8jQJAoERQJ4Exo1c7iSAPHFn3Gl1YASYDAbGjVzuJMB4cWfcrdJqW8iTg2EdvG3nu/kLZNSTE2TcYzdL04b1ZE/WXunYd5gMu2iA+jjtkNt2xk78WI5o3kiOadPUuLlnqDpDJS4uTj6Z8KDxKretPFm3eqN8O+15Obd9bR/qDlJJPmow0Cr9lHpAIOoEkCdRDwEVsBEB5ImNgkVVo06AyWDUQ0AFbESA8WKjYDmwqo6QJ1PVVcS3PjBO3h17jxzZspERpqlfqJ89OM4XsusuO1OuvPBU4/u7Hn1Fpnz2ve93ekuQFi/6GmNXypOt2bI2L0var3rfxyTZEyf/1r/AgV2eJkEgMALIk8C4kcudBJAn7ow7rQ6MAJPBwLiRy50EGC/ujLtVWm1LeWIWXn5+gXEgbJ2a1Xzbd7x5s7L3yfpNW6VSxQpSvWrlEkW6buWJkif6hJtWK9+WHQU5PhZz656pbuFJNYubdBBwNAHkiaPDS+NCTAB5EmKgFOdoAkwGHR1eGhdiAoyXEAOlOL8IOFqe+EWiWGK3ypPB6z+XOXvX+0i8kn6SnFIhM1CM5IOAowggTxwVThoTZgLIkzADpnhHEWAy6Khw0pgwE2C8hBkwxR+WAPKkFDxulSf3bv1BXt75l4/IiLSj5Eb1wQMBCIggT+gFEDBPAHlinhUpIcBkkD4AAfMEGC/mWZEy9ASQJ8gTWbd/2867u5bIzVvm+Ij0VatOxqvVJzwQgADyhD4AAX8IIE/8oUVatxNgMuj2HkD7/SHAePGHFmlDTQB5gjzxyZOF+zZLn3VTfUQaqvNOZqtzT3ggAAHkCX0AAv4QQJ74Q4u0bifAZNDtPYD2+0OA8eIPLdKGmgDyBHnikycaRd3lE0oQWZR5nlSOSQh1v6M8CNiOANt2bBcyKhxFAsiTKMLn1bYjwGTQdiGjwlEkwHiJInxeLcgT5EkJeXLi0rdlceyBG3feTOsqJ6Y1YahAwPUEkCeu7wIA8IMA8sQPWCR1PQEmg67vAgDwgwDjxQ9YJA05AeQJ8sQnT2J/myO3LZ8uE45q6qNyzbLtcvtJN4S841EgBOxGAHlit4hR32gSQJ5Ekz7vthsBJoN2ixj1jSYBxks06fNu5AnyxCdPkp6+WSbH7pArBnT1UTlm7Wb55KgrRSqkMlog4GoCyBNXh5/G+0kAeeInMJK7mgCTQVeHn8b7SYDx4icwkoeUAPIEeVJCnmxas1haXHPgkFhPYaH8m36GJKWkhbTjURgE7EYAeWK3iFHfaBJAnkSTPu+2GwEmg3aLGPWNJgHGSzTp827kCfLEJ0/i5s2QhIlPSIfL+8u/1Sr7yLxVs6d0T67LaIGAqwkgT1wdfhrvJwHkiZ/ASO5qAm6fDBaq6Htc3QNovD8E3D5e/GFF2tATQJ4gT0ocGBs/daLckvdPiXNPrq3cRm6rcnToex8lQsBGBJAnNgoWVY06AeRJ1ENABWxEgMmgjYJFVaNOgPES9RC4ugLIE+RJCXmicXy0e5lcvfk7H5n2ienyce2+rh4oNB4CyBP6AATME0CemGdFSggwGaQPQMA8AcaLeVakDD0B5Any5BB5sik/W45a9V4JMn+m9JC0Ghmh74GUCAGbEECe2CRQVNMSBJAnlggDlbAJASaDNgkU1bQEAcaLJcLg2kogT5Anh8gTffbJcQn/qHNPKvnojJ+zRPqe/6BrBwoNhwDyhD4AAfMEkCfmWZESAkwG6QMQME+A8WKeFSlDTwB5gjw5RJ7oK4vvqRcnL3Ro6aNz6j8rZdwxQ6Wweq3Q90JKhIANCCBPbBAkqmgZAsgTy4SCitiAAJNBGwSJKlqGAOPFMqFwZUWQJ8iTQ+RJ4th75KctS+SUC3r76CTl5skfdc6SZK4sduUfChotgjyhF0DAPAHkiXlWpIQAk0H6AATME2C8mGdFytATQJ4gTw6RJzGLf5PEp0dKi6sHysaKyT5CL6efJH0qZIa+F1IiBGxAAHligyBRRcsQQJ5YJhRUxAYEmAzaIEhU0TIEGC+WCYUrK4I8QZ4cIk80ktjf5sidW+bLK42r+QidmdJYnq3RzZUDhUZDAHlCH4CAeQLIE/OsSAkBJoP0AQiYJ8B4Mc+KlKEngDxBnpQqTzSWOXvXyeD1M3yEKubkyt/SXjzN2oW+J1IiBCxOAHli8QBRPUsRQJ5YKhxUxuIEmAxaPEBUz1IEGC+WCofrKoM8QZ6UKU88sz6UVnU2y/bkRB+lSR/Pk2OvH+e6gUKDIYA8oQ9AwDwB5Il5VqSEAJNB+gAEzBNgvJhnRcrQE0CeIE/KlCf61p0bGyXLxLZNfJSG/LZEHmk1SAqaHRX63kiJELAwAeSJhYND1SxHAHliuZBQIQsTYDJo4eBQNcsRYLxYLiSuqhDyBHlSpjxJmDRGvlk+XwYNOslHKS17n/yRea54Uiq5aqDQWAggT+gDEDBPAHlinhUpIcBkkD4AAfMEGC/mWZEy9ASQJ8iTsrftbF4v8Q8Pk4ZD+8nuxHgfqQ9qnSJdkmqFvjdSIgQsTAB5YuHgUDXLEUCeWC4kVMjCBJgMWjg4VM1yBBgvlguJqyqEPEGelClPNBqPEijXL58qk6rH+UhdVqml3Fe1k6sGCo2FAPKEPgAB8wSQJ+ZZkRICTAbpAxAwT4DxYp4VKUNPAHmCPDmsPNF4Ps9aKZdt/MpHqobEy28Nzg99b6RECFiYAPLEwsGhapYjgDyxXEiokIUJMBm0cHComuUIMF4sFxJXVQh5gjwpV57k7NkhLdd9KHvjYny0PludIm26DnLVYKGx7iaAPHF3/Gm9fwSQJ/7xIrW7CTAZdHf8ab1/BBgv/vEidWgJIE+QJ+XKk7hZk+XK3N/loxb1fbRumP+njBxwj0iF1ND2SEqDgEUJIE8sGhiqZUkCyBNLhoVKWZQAk0GLBoZqWZIA48WSYXFNpZAnyJNy5Ym+svij2O1y2YBuPlr1duyW/yV35cpi1/ypoKHIE/oABMwTQJ6YZ0VKCDAZpA9AwDwBxot5VqQMPQHkCfKkXHmiV57kf/SS1L9hkOTGxvqIfVn1ZGlZqV7oeyUlQsCCBJAnFgwKVbIsAeSJZUNDxSxIgMmgBYNClSxLgPFi2dC4omLIE+RJufJEsnZJ0tMj5YKO9eSzpgdkyc1pR8lN6oMHAm4ggDxxQ5RpY6gIIE9CRZJy3ECAyaAbokwbQ0WA8RIqkpQTCAHkCfKkfHmyn9GHf34m16Vs8BFrGVdJvqw3MJB+Rx4I2I4A8sR2IaPCUSSAPIkifF5tOwJMBm0XMiocRQKMlyjC59WCPEGemJYnOwtypOXKt0sQ+2tDTanUqQ9DCQKOJ4A8cXyIaWAICSBPQgiTohxPgMmg40NMA0NIgPESQpgU5TcB5AnyxLQ8iZs3Q87P+lm+bFzHR+32H/6Wa069k1t3/B56ZLAbAeSJ3SJGfaNJAHkSTfq8224EmAzaLWLUN5oEGC/RpM+7kSfIE9PyJOH1x+Xj7f/I5ad19VGrsSdbfonvLDHN2jGaIOBoAsgTR4eXxoWYAPIkxEApztEEmAw6Orw0LsQEGC8hBkpxfhFAniBPTMuT+KkTRT57U1pcPVC2VkjykXthb205vUVvvzoeiSFgNwLIE7tFjPpGkwDyJJr0ebfdCDAZtFvEqG80CTBeokmfdyNPkCem5Ym+dSf5rgvlns7N5PmOrXzk2q/fKtMKW0teFwQKf1KcSwB54tzY0rLQE0CehJ4pJTqXAJNB58aWloWeAOMl9Ewp0TwB5AnyxLw80ayUQFn94TPSqduBK4v1j38b95Gk3/yiFFavZb73kRICNiKAPLFRsKhq1AkgT6IeAipgIwJMBm0ULKoadQKMl6iHwNUVQJ4gT/yTJ4qX3r4zsNI6mZ1Z00fvjL9WyJjGp0lBs6NcPaBovHMJIE+cG1taFnoCyJPQM6VE5xJgMujc2NKy0BNgvISeKSWaJ4A8QZ74LU/iZk2WLxZMlQsGHl+C3tcxR0uzzDbmex8pIWAjAsgTGwWLqkadAPIk6iGgAjYiwGTQRsGiqlEnwHiJeghcXQHkCfLEb3linH3y4DDpdlp7+TO9io9g3wqZMj79JFcPKBrvXALIE+fGlpaFngDyJPRMKdG5BJgMOje2tCz0BBgvoWdKieYJIE+QJ/7LE81MCZRvf5ws5zUoLEFwZp0B0jqhqvkeSEoI2IQA8sQmgaKaliCAPLFEGKiETQgwGbRJoKimJQgwXiwRBtdWAnmCPAlMnuzn1m/tVPktZ7OPYs/kejKh5smuHVA03LkEkCfOjS0tCz0B5EnomVKicwkwGXRubGlZ6AkwXkLPlBLNE0CeIE+Ckidzl3wvg+KXlqDI6hPzA5CU9iGAPLFPrKhp9AkgT6IfA2pgHwJMBu0TK2oafQKMl+jHwM01QJ4gTwKWJ57N6yX57gul73kny7yMAzfvnJhQU96s08fN44q2O5AA8sSBQaVJYSOAPAkbWgp2IAEmgw4MKk0KGwHGS9jQUrAJAsgT5EnA8kRfWRw/7Q35uXY1OfmiU0qQnFq7n7RLrGGiC5IEAvYggDyxR5yopTUIIE+sEQdqYQ8CTAbtESdqaQ0CjBdrxMGttUCeIE+Clica4eCzTpQvGtfx0eyWVEferdXLreOKdjuQAPLEgUGlSWEjgDwJG1oKdiABJoMODCpNChsBxkvY0FKwCQLIE+RJwPJEb9tJemiYeLL3yB810qTbpf1K0GT1iYkRSBLbEECe2CZUVNQCBJAnFggCVbANASaDtgkVFbUAAcaLBYLg4iogT5AnAcsTjS5m8W+SoLbuxPyzQC4c1EOmNq7tI9pJKsnkBgNdPLxoupMIIE+cFE3aEm4CyJNwE6Z8JxFgMuikaNKWcBNgvISbMOUfjgDyBHkSlDzR+IyDYx8aLv+kxErny/qXIPrC6hg5vetFjEII2J4A8sT2IaQBESSAPIkgbF5lewJMBm0fQhoQQQKMlwjC5lWHEECeIE+ClidxsyZLwgdjFclCGdr/OJnUuqGPas3d2TKn8kmSnNGc4QcBWxNAntg6fFQ+wgSQJxEGzutsTYDJoK3DR+UjTIDxEmHgvK4EAeQJ8iRoeeK9dUcKC2VlWqq0HXZaCaqXbBF54JiLGXoQsDUB5Imtw0flI0wAeRJh4LzO1gSYDNo6fFQ+wgQYLxEGzuuQJ+X1gbVbsstL4ojf166aLB6PBC1PfAfHZu1We3g88sSxR8iD3dqWYDQuvbv0r9DAEdxohDsJIE/cGXdaHRgB5Elg3MjlTgJMBt0Zd1odGAHGS2DcyBUaAqw8KYUj8sT/zmUcHDvpRYlZ/a/K7JFjhp4qy6pU8hWU6omTGXVPk/pxqf4XTg4IWIAA8sQCQaAKtiGAPLFNqKioBQgwGbRAEKiCbQgwXmwTKkdWFHmCPAl65cnBCPUZKPOW/yCn92hZ4lfN4ivLZ3VOlSQlUnggYDcCyBO7RYz6RpMA8iSa9Hm33QgwGbRbxKhvNAkwXqJJn3cjT5AnIZcnMav+lSR1+879auvOU2oLT/FnYEojea7G8Yw8CNiOAPLEdiGjwlEkgDyJInxebTsCTAZtFzIqHEUCjJcowufVgjxBnoRcnngPkC1QbAeefZJ826B2CcqPxDSWCzO7MfwgYCsCyBNbhYvKRpkA8iTKAeD1tiLAZNBW4aKyUSbAeIlyAFz+euQJ8iTk8kSff5L09EiD7I6EOOl6WX9ZXSnFRzouv0A+X7BDWg680eXDj+bbiQDyxE7Roq7RJoA8iXYEeL+dCDAZtFO0qGu0CTBeoh0Bd78feYI8Cbk80UgTx94jsQvnqq888mf1ynLSkD6SExfro117V5Z8taehVOrcx90jkNbbhgDyxDahoqIWIIA8sUAQqIJtCDAZtE2oqKgFCDBeLBAEF1cBeYI8CYs80VgTJj4pcfM+FykslEmtGsjQAV1L0D52j8h7rYZIjL4vmQcCFieAPLF4gKiepQggTywVDipjcQJMBi0eIKpnKQKMF0uFw3WVQZ4gT8ImT7wHxyp7oih7ZOTJ7eXlY5qXIH7D3ioyssVprht4NNh+BJAn9osZNY4eAeRJ9NjzZvsRYDJov5hR4+gRYLxEjz1vFg6MLa0TrN2S7Yq+UbtqsuhFH+u2ZuvFIWF5El5/XOLmzzT8Sb561ykX9JKf6tY48C714ndij5DjMzuE5f0UCoFQEUCehIok5biBAPLEDVGmjaEiwGQwVCQpxw0EGC9uiLJ128jKk1JigzwJbYeNmzdDPFs2SMzyv2XL8t+l6yV9ZVNKsu8llfJFfmh4nlSKSQjtiykNAiEkgDwJIUyKcjwB5InjQ0wDQ0iAyWAIYVKU4wkwXhwfYks3EHmCPAnrypPieL238PyvTjU55fxeUhAT4/t18/hKMrlWP0mLTbT0gKFy7iWAPHFv7Gm5/wSQJ/4zI4d7CTAZdG/sabn/BBgv/jMjR+gIIE+QJxGTJ5K1S5IfHCaerRvl5aObycieJbfqNMvxyLjaJ0uz1Lqh6+GUBIEQEUCehAgkxbiCAPLEFWGmkSEiwGQwRCApxhUEGC+uCLNlG4k8QZ5ETp4o1p7N6yVh0hiJXTBXRvY5Vl5u26hEBCrn5MprNU+WTpUbWnbQUDF3EkCeuDPutDowAsiTwLiRy50EmAy6M+60OjACjJfAuJErNASQJ8iTiMoTL+4ktQJF38ZzfZ9OMrFt00Oi8H/Vu8mgio1D08spBQIhIIA8CQFEinANAeSJa0JNQ0NAgMlgCCBShGsIMF5cE2pLNtT1oO5C9wAAIABJREFU8uTux16VydO/kzkfPy9plSsaQeLA2PD2Vd8VxvqKH3Xdz7VKoLzZpskhL72u0pFya9VjwlsZSoeASQLIE5OgSAYBRQB5QjeAgHkCTAbNsyIlBBgv9IFoEnC1PJk4aYY8+sI7Bn/kSeS6offgWH0/cqG6vtij/u/5ji3l3u5HSYHnwCGyukZ9KmTKmBonSIInNnIV5E0QKIUA8oRuAQHzBJAn5lmREgJMBukDEDBPgPFinhUpQ0/AtfLku/kL5Jo7n5G7b7xIRj0xAXmiFoFE8km+83zj4FgR/WJlUJRImdm4rlx8ejfJjo8rUZV2CdXlzZo9uYknkgHiXYcQQJ7QKSBgngDyxDwrUkKAySB9AALmCTBezLMiZegJuFKeLPlvtZx5+T3y5L1XSd1a1WXQ0FHIkwjLE711J+GDsRKzeKEUpqaJZ9d2o3cvql5JzhzcQ9anVijR2+vvLZBJK2Ilo9VxUpBx6Baf0A8NSoRASQLIE3oEBMwTQJ6YZ0VKCDAZpA9AwDwBxot5VqQMPQHXyZMt23ZK3wtulUvO7iPDLhogixYvL1ee1ExLCj15C5QYG6tWfKgnPz/C5sTb9qLXS/6fv0jW/dcXfaOqsrFikpx91onyW62qJShV3Jcr987/S647/UaJbXDoIbMWQEoVHEggN69Q4uM8EmP0V48U6LN6eCAAgcMSYLzQQSBgngDjxTyrSKfMzStQ/w5Qckt5pOvA+0oSUMclFk0Z+Ncxuka4Cag+tmH73hJvcZ08+WDqt3LvE69J/5O7iEf9r9WWrTtl7k9/SM/j28uQwb2l3RFNDzkwNsahfzNrVknWG2Zkw7ZsY/NMxJ9iL0288XTxZO02DpDVz964WLmyfxf5pHn9Q6o1cMUWeaHjUJEKqRGvMi90JwGP6pcVkuIMgbI7O8+dEGg1BPwgoMeL/nO+h/HiBzWSupVAihov+l/IGC/W6wGFzNAtF5SKxnjxqH8fy7Vc3aiQwwiov8sFBSXb5Dp58teSFfLpzLk+Chs2b5PPv/5RBp/aXQYPOFFaNq3PbTtR6Pexv82RhNcfFc/e7P1vV2ZF/fPA8W3lyWOPPKRGdXfvlRHVO8hZtY6WGEMB8UAgvATYthNevpTuLAJs23FWPGlNeAmwDSG8fCndWQQYL86Kp91a4zp5cnCAzGzbsVtQzda3dlW18kR5h3Vb1cqTqCw9KVlTz+b1kvjawxKzbFHRWjxdOfX5w1YN5PIBXUttVjNJlttiG0rvjI5mm006CAREAHkSEDYyuZQA8sSlgafZARFgMhgQNjK5lADjxaWBt0izkScmzjyxSKxCXg2ryRNvA+OnjJf4me/v/7ZoBcof6VXk3DNPkNWVK5bKoeOmnXJPZi9pV7NFyDlRIAQ0AeQJ/QAC5gkgT8yzIiUEmAzSByBgngDjxTwrUoaegOvlSWlI127xbh0JPXArlWhVeaIZJd+kzkDJ3lOEa/+ymO3JifJw1zby0jHNy8TYN6GW3J7eRRrFVbYSauriAALIEwcEkSZEjADyJGKoeZEDCDAZdEAQaULECDBeIoaaF5VCAHlSChTkSfTHSty8GZIw8QlfRQrjE0XUyb2evVnG6pP71VkoH7SqLwWeQ0/zjVMrVc5NbSw3V+0gNWKceVNS9CPkvhogT9wXc1ocOAHkSeDsyOk+AkwG3RdzWhw4AcZL4OzIGTwB5AnyxDJnnhwcipjFv0ns4oXGj/M69zI+Jz88XETfyqMEyV810uTh446UT1tkljoSkgoKZURcPeldpyMrUYL/W+H6EpAnru8CAPCDAPLED1gkdT0BJoOu7wIA8IMA48UPWCQNOQHkCfLEsvKktN5eYXjPoh/vP1C2UFmUn+vUkHu7HyVzM2qWOUC6r9woPf9dLf1ia0iNQTdwzXHI/5Q4v0DkifNjTAtDRwB5EjqWlOR8AkwGnR9jWhg6AoyX0LGkJP8JIE+QJ7aSJwmvPy5x82cWEyj6y6Krimc2qiOjlURZVKPKYUfC0Ru2S5/MTtK/xhGSGZfq/6ghhysJIE9cGXYaHSAB5EmA4MjmSgJMBl0ZdhodIAHGS4DgyBYSAsgT5Imt5Ilk7ZKkcaMkZv92noJGrcSzbrl4stThsvpqY7US5eMW9eX+bm1ladVK5Q6SNglV5JSUhtK/QgNpHF+UPmbVvxI/7Q1jy1B+szaSc9ZwKaxeq9yySOBsAsgTZ8eX1oWWAPIktDwpzdkEmAw6O760LrQEGC+h5Ulp/hFAniBP7CVPvPFSEsV4KqSKZ/N6SXr8OvHs3GachWIsRFHbeqY3rScvdGgpczPL3s5TPPytlDw5RarLwNdfkdar1vp+VVitpmTfMZatPv79bXFcauSJ40JKg8JIAHkSRrgU7TgCTAYdF1IaFEYCjJcwwqXocgkgT5An9pQnB8Ut6cFhErN66f5rjYu28XglyqaKyTJNiZSPmmfKtw1qlzsodILj1BkpbTZslb7qnJSU3DxpNeBayT/qOFN5vYn0CpYCJV604OGxPwHkif1jSAsiRwB5EjnWvMn+BJgM2j+GtCByBBgvkWPNmw4lgDxBnjhCnsTNmiwJH6jVIfoxDpP12pOS329PSlArUurKJ80yZUaTen79TWgcV1GaqW0+rROrS+v4qtJid540nTxBYhfMlYKMxpLb90JDsGhpkqi2Fnm2bDDKzztpoOQMUrcE8diaAPLE1uGj8hEmgDyJMHBeZ2sCTAZtHT4qH2ECjJcIA+d1JQggT5AnjpAnOozxUydK3LwZRRFNSBLP+pUHorv/dh7vLT16e8+exDj5vHFd+VitSPlSHTabHR/n95+HJLUq5ciN26TF5h3SetMOadLrYjlmwrOStu7Ath9dqD43Ja/HwEPKj/1tju8A3NyTzpCCZkf5XQcyRIYA8iQynHmLMwggT5wRR1oRGQJMBiPDmbc4gwDjxRlxtGsrkCfIE8fIk+KhNM5Befpm8Wzd6PtxztnXSNxPX0vM0j9LnI2iD5rdFxsjs5RA0Vt79FkpexLigxrTNXdnyxFKqrTUUkV9Pn1DliQqYVNYtZZoSaJXqJRYLbP/bbm9zpbY//42vsvr0sv44LEGAeSJNeJALexBAHlijzhRS2sQYDJojThQC3sQYLzYI05OrSXyBHniSHlihFUdKhunttSIuoknv+2xxo05vquOix0s672lx3vlsc76afN6MjujliysWUX+qp4mO9R2n1A8FdRKlfrbd0tmag3JXLxY6m/ZKpnbsyRzxy71kSVV9u4r8ZrcfhdKbv+LQvFqygiSAPIkSIBkdxUB5Imrwk1jgyTAZDBIgGR3FQHGi6vCbbnGIk+QJ86VJ6XENmbxb2pFysii32iBov5foTofxaMPSfEKFePH+twU/TMjkaxJTZG/0yvL7+lVZJGSKf9UqywLa1UN+YCuuC9XMncqubJjj9TfsVvqZ+VK7TOvkTpxlSVDnblSOSYIiaNkUszqZVJQrxGH2AYQOeRJANDI4loCyBPXhp6GB0CAyWAA0MjiWgKMF9eG3hINR54gT1wlT3S49Tkj8dPeUAe6rlcioYnk128msTs2i2fHFsmv1UAKq1QXT36ecYZKsf096uv9MkVLFfXkq0//Vq0kf6ZXlT+rV5ZF6WmyqEaarKqUooTM/ht/QjzMKxeIZMYmS2ZSmmRsVatVsvZK3bqtpF61BlI3NlVSY4q2G+lDa402qlU3BdVrSkF6PUn4+NWi2iRXVAfYDlNbgnqHuHbOLg554uz40rrQEkCehJYnpTmbAJNBZ8eX1oWWAOMltDwpzT8CyBPkievkiakholZpJKvrjz1b9Y05+0WIdzVKiSUq6teJySJ7s4pWqqgnSx08q1eo/KWEyp/q8z/VK8nyyhVllfoI91PZEyf1C+IkY9kyydy6Q61eyVIfuyRj5x5poLYLpeTk+aqQff8bxlamokrvYjVKOcFBnoS791K+kwggT5wUTdoSbgJMBsNNmPKdRIDx4qRo2q8tyBPkCfKkrHGrhELCR6+oG3xmqtNbc/enUtt84pNk3xV3KWmSZBwA68neLQmvPSqedf8Vbf/Rj0+07P9afSqIiZF1FZNlZeUUWZFWUX1WH2qVyso09b36em3FCpKnDq4N51NVrVTJ3JklGUqk1K9ZXzKq1pMGH78vmZu3SOY+j8SdfnmpK1L0difdVp9sCWclLVo28sSigaFaliSAPLFkWKiURQkwGbRoYKiWJQkwXiwZFtdUCnmCPEGelDPc9c09CZPGqPNClqptPo2NA1wLMpockktfkxw/aaySKXtKypODUxYXK/p3HiVMCgskP8Yja1MrFAkVJVNWKMmyUksW4/sU9bsUI004n2rZ+yQzsaI0SKmmzlhJkYxtu6T+9I8lc9MWddDtLkmskCb57btL7olnlCtStHCJWzBPRMmlvM491VaiZRL3w0y1lWi3OsD3OMnpd4GtVrsgT8LZ8yjbaQSQJ06LKO0JJwEmg+GkS9lOI8B4cVpE7dUe5AnyBHkSwjGrRYtn63rjXBF9ZkrsQiUPfE+JE2mN41QKmrWVvPYnSsI7zxRLtv+wWu/2oP2yJU85ljVapBirVVJlpZIpK9SqFWMFi/pYl5okBVrEhPGpsWevZOiDbNXnzCZtpYG6NajeL79I/R/mScaubIk9foAhl/S5MonjRpXR9qIf56krm3MGXRXG2oa2aORJaHlSmrMJIE+cHV9aF1oCTAZDy5PSnE2A8eLs+Fq9dcgT5AnyJIyj1BAoSxYYb8jpcaYkzPpQYhYvPEQeGKs05n1h/Dxm9b/GrTi+o1WUPDFuBDLcy4EbgIp2CB24JShX/W7N/i1B+tDa5eprfc6K3hKkV65sqBgJuZItDSRBMjdsloyNm40bg/QVzPV37JR6artQYp468bbYkzW2qM12eJAndogSdbQKAeSJVSJBPexAgMmgHaJEHa1CgPFilUi4sx7IE+QJ8sRiY7/Eqo39VyUXxsdLYd3GUpiULDEbVhUdUKtWt3i2bVK137+ixStZvOeu6HalpIrsUYfBqidHnbmyWgsVJVb0mSvGtqD9567o7zemqDNcwnRLkKF5VP3S9YoVLVTUAbb11bkrDdQWoHqFSVInrZ7UTm8qcRJrsWgcqA7yxLKhoWIWJIA8sWBQqJJlCTAZtGxoqJgFCTBeLBgUF1UJeYI8QZ5YcMDrlSjxsyaLZ+8edV5Ir9KvFfbeCLRlQ9HKFN9VyqpB+jrifhdJXo+BxhYafV5LfrM26uyWF42VLSVuEDLMRtGKln+rVTZWqRiH2u5ftbLK+DpFNqWoW4XC/NTamyv1k6tIRp5HMnJipHZaXclIy5Cjf5grVXbvkcJqtRSLXmGuRenFI0+igp2X2pQA8sSmgaPaUSHAZDAq2HmpTQkwXmwaOIdUG3mCPEGe2HkwK4Git/vEbFgpMWv+E4mLMw61zel3YemHse6/QSh2zmfiKdi/hcZ7gO3Bn31c1MoW9c/exHh1zkoFtRVIn7uiVq4YtwQVfa1XsGxWK1fC+TTdskM6r94knZJqSvtWJ0rD1WtVWxuVenhvOOqBPAkHVcp0KgHkiVMjS7vCQYDJYDioUqZTCTBenBpZe7QLeYI8QZ7YY6yGtJYxq/6VxBfvVYfbbpTCtGoi6gYcT84+9Y4DW4CM1Sj7n8LkFMm55DZJHHO38RN1CkvJlS7qu71xsYZMWaEPszWkStGBtsY2ISVXtlYIrVxJ350tXbRMSakp7TqdJW0SVDvC+CBPwgiXoh1HAHniuJDSoDASYDIYRrgU7TgCjBfHhdRWDUKeIE+QJ7YasuGprN4mlPjiKN81y3nqLBKJT5CYrRukoKraKqNuxtHXMyffdLovjW+rkK5SghIjOXsPHHLrsy7qi/0OJis+bv+WoKLzVoyDbJVYWbX//JVtyYlBNS5VYqRjUjVjZUqHpNpyVFItdXRtrLoi+V/f6hR9G1LswrnG9p/8tsf69T7kiV+4SOxyAsgTl3cAmu8XASaDfuEiscsJMF5c3gGi3HzkCfIEeRLlQWil1xcXDaXVK27eDEmY+ETRr/QBtQmJknvWcMlrdYwkTHtT4ubPLPp5jdpSUDNDYtavEs8WdXWz7znocNtiNwjtTkqQFVqm7Bcry6qkGqtWFtSsImvV50CeDuu2SOeVG+TY1RulY068VF253FdMQUZj2XvD46VvbyrlZciTQCJAHrcSQJ64NfK0OxACTAYDoUYetxJgvLg18tZoN/IEeYI8scZYtE0t9OoNLUkK1aG0eV16HlY+xKlDbxM+GHuIO/HKF+NzbJxauaJWnegbhIzbhXz3Mu+XNCLr1TXL8zPSZX7dGvKj+vg9vYrkxcb4xUzf9tNsy07j3BQtU/Tn2l3PkNz+F5kqB3liChOJIGAQQJ7QESBgngCTQfOsSAkBxgt9IJoEkCfIE+RJNEegC96tBYqWLR51WG1e2+OKvs7e42t5zkUjjENukx4avl+WFB1QKzEHzlwxpIr3DBb19V51MO5PdasrkVJdflAy5X91qksg235qZ+dIl6qNpH2FOmrLT21pEV+l+EXPJaKDPHFBZ6WJISOAPAkZSgpyAQEmgy4IMk0MGQHGS8hQUlAABJAnyBPkSQADhyxBENh/Q5Ane7dxDXNh9VpGYXrLUNxXU9Q5K+slv2lb42fx094wPhuH2haoQ2p3bt0vWNRNQR698uTANqB/aqTJj0qiaJkyv14NWVq1kt+VTM3Nl46SIh3TW6lzU9KlU2JR3fSDPPEbJxlcTAB54uLg03S/CTAZ9BsZGVxMgPHi4uBboOnIk1KCsHZLtgVCE/4q1K6abPzH/HVbs4t2S/BAwIoElGyRCqnqRqBd6lyVNyRm9VJDrhSmVpb4Ka+I7MsST7GzU7wrVLYlJyiZUiRS9AqVX2pVk73q0Fp/niR1CO0JybWlV0pDObNaQ6kWmyQ7s3L9KYK0EHAlAeSJK8NOowMkwGQwQHCuyqb+o5H6dxIeEcYLvSCaBJAnyBPkSTRHIO8OmkDc9Dcl/ssP1Fag3cZClBK3ABlSZf/qFPXlL7WryQ/1a8v8WmqVilqhsr5iBb/e33lvjPRMzpT+1VpKvYo1Rd9SFK9Wy3iy9kh+szamz0/x66UkhoANCSBPbBg0qhw1AkwGo4aeF9uQAOPFhkFzUJWRJ8gT5ImDBrSbm+JZtVRiFy+Q+O8/FVE3/Hjy8gwc+prl3BMHSuwCfUVxTUkcN6roYFq17Erf7OPd6qO3+/ypDqLNL37WymGANt+eJX3/+k/6L1kt7dZvUY6m0HhXzqCr3BwG2g4BgwDyhI4AAfMEmAyaZ0VKCDBe6APRJIA8QZ4gT6I5Anl3eAioLT56e09h1Vq+M1X0i/S5KiUOpjUOoT2wMiVbbeuZnVFD3jqyiXzcItN03WruzpZT/l0jAxavks63vF4iX+xvc8SzdYMUNG0jBRlNTJdJQgjYmQDyxM7Ro+6RJsBkMNLEeZ+dCTBe7Bw9+9cdeYI8QZ7YfxzTAj8IJN95vpIZG33XIhdKgXjiEqQwP1fdtONRKkUdTLv/zp1NKcnyabMMmda0rszOrCk5sbHlvqmiOnT25JQ60ktt7+n78hipvGyxL0/OWcMlr8fAcssgAQTsTgB5YvcIUv9IEmAyGEnavMvuBBgvdo+gveuPPEGeIE/sPYapvZ8E9OqTxBfvLRIo6slv00X2Db9PHT47XuJnvn9AnnivR97/eU9CrMxsXE+mNqknXzSuI7sSE8p9c3xBgRy7cqP0W7JKBvyzUmruypLc4/uLJyHJuJ45r0uvcssgAQTsSAB5YseoUedoEWAyGC3yvNeOBBgvdoyac+qMPEGeIE+cM55piR8E9GGvB2/r0VtsEj58UTyb1/tWpugDZw+cO1u0zSc3JlZm168pU5vWk8+UTFmXau7g2bYbtkjfJWsMmdJ643ZDnuRcNLJErbXc0eez6C0++W2P9aNFJIWAdQggT6wTC2pifQJMBq0fI2poHQKMF+vEwo01QZ4gT5Anbhz5tLlsAvuvRI6dN9NIk9++u+R1OFGSd24SWb5YCn6dJx51IK3vUStTfq1TXaY1rivT1Bafv2ukmaKbuWO3EimrpWfHwdIhs53EqwNvE957QWKW/uHLr+XJvmGjTZVHIghYiQDyxErRoC5WJ8Bk0OoRon5WIsB4sVI03FcX5AnyBHnivnFPiwMgoCeDMeqA2Z1ZuRI3a7LEzZ+prijeJQV1GkrsHz8cuMGnUgX5RB02O61JhvxQr7oUeGLKfVsVdU5Kr39WSL9/V8vJy9ZJcm7RTUH62XflKMk/6rhyyyABBKxEAHlipWhQF6sTYDJo9QhRPysRYLxYKRruqwvyBHmCPHHfuKfFARAoLk8Ozm5s93n/BfFsU6tTit3esy05UR04W7S15+sGtWVfXPkHzuqyey1dI6eqm3tOV+ekJJ58juT2vyiAGpMFAtEjgDyJHnvebD8CTAbtFzNqHD0CjJfosefNIsgT5AnyhL8EEDBB4HDyxJs9YdIYiftqSgmBUlg1XQorVpactcvkq4a15HN16OwnzTNlR1L5B87qck/LqSgDMzrLycn1TNSSJBCwBgHkiTXiQC3sQYDJoD3iRC2tQYDxYo04uLUWyBPkCfLEraOfdvtFwIw88RaoD5wtrJAiMVs2GAe/6kevTvGo72OX/i6xv86WuRk1ZLra2jNFbfFZWyml3LrUyNong/5cJoOXrJfWGW0lr1MPKWjWttx8JIBANAggT6JBnXfalQCTQbtGjnpHgwDjJRrUeaeXAPIEeYI84e8BBEwQ8EeeHK44fZtO0kPDfWek6LSLqlc2Dpud3jRDfqtVtdzatNy0Xc798z85R4mUKs3bS96JZ/gkTbmZSQCBCBBAnkQAMq9wDAEmg44JJQ2JAAHGSwQg84oyCSBPkCfIE/5AQMAEgVDJE/2quHkzJP6d58STu6/Ym9WFyOqfFd37yIc5q+Xd1g1kSbXK5das+/J1ct4fy6TnaSMlIaNZuelJAIFIEECeRIIy73AKASaDTokk7YgEAcZLJCjzjrIIIE+QJ8gT/j5AwASBUMoT7+uSnrpZYpYs9L09v00XyevSWxLHjTJWpvyvXg15p3UjmdKyvmwv54yUinkFcqonTc5MbS6d01tLvBI0cfO+MMrOPekMbuwxEWOShI4A8iR0LCnJ+QSYDDo/xrQwdAQYL6FjSUn+E0CeIE+QJ/6PG3K4kEA45InGqFeh6LNQCqvVNMRJiW09OoG6HjknJlZmNKkj7xzRSL5sXEdyYw5//XG9PXvl7AVL1IqU/6TRtl1GtHxXHqvrlWNWLzN+VlCvkUiFVBdGkyaHmwDyJNyEKd9JBJgMOimatCXcBBgv4SZM+YcjgDxBniBP+BsBARMEwiVPSnt1wuuPS9z8mepXah+PfvSnxGQRJU22efLl/VYN5F0lUsycj3LM2s3G+Shn5KRISsdTJOGDF8WzY6u2MsY/+slve6zsu2gEIsVEPyCJOQLIE3OcSAUBTYDJIP0AAuYJMF7MsyJl6AkgT5AnyJPQjytKdCCBSMoTjc+3IiW5ouT1GOgjGrP4N4n76Rv18bUsTY6VN45sLO+3bijrUisclnpCfr70WrpGzvljufq8WuIL9ouZ/bnyOveSnCEjHRg5mhQNAsiTaFDnnXYlwGTQrpGj3tEgwHiJBnXe6SWAPEGeIE/4ewABEwQiLU/Kq5Jve49KWKC29nyfWVNt62konzbPlKz4uMNmr5q1Vwb+vVLOVdt6jl632Zc2a2zRGSk8EAiWAPIkWILkdxMBJoNuijZtDZYA4yVYguQPhgDyBHmCPAlmBJHXNQSsJk80+OQ7zxfP1o0lrj3W4uTj5hnGtp7ZmelKrBz+fJSmW3bIOX8ul3OXrJPK97/rmnjS0PASQJ6Ely+lO4sAk0FnxZPWhJcA4yW8fCn98ASQJ8gT5Al/JSBggoAV5YlefZL44r0+gVKozkTxqFt6vM/6lCR5p21TeadV/XKvPfaog1W6bs+Vs1ftlH41jpSEzr1NUCEJBEongDyhZ0DAPAEmg+ZZkRICjBf6QDQJIE+QJ8iTaI5A3m0bAlaUJ154WqIUqNt64hbMlYSJTxxgqledFBYYK1N+rVNdXXvcUD5U1x5vrZB0WO7JuXnSb0+cnNmgmxy/S+XPaGqbOFFRaxBAnlgjDtTCHgSYDNojTtTSGgQYL9aIg1trgTxBniBP3Dr6abdfBKwsT4o3xLN5vXFTT6E6aDbhg7HFfrV/RUpcgnwycpR8mLRbPslaWS6DWruz5PLflsoVvy+XCq07S85Zw7iVp1xqJECe0AcgYJ4Ak0HzrEgJAcYLfSCaBJAnyBPkSTRHIO+2DQG7yJPiQH1nohT7YX6bLrJv+H3GT7KmjJVPNv0hb6mDZn9RK1MO96Tuy5ELFy6VoVtipMawh20TNyoaHQLIk+hw5632JMBk0J5xo9bRIcB4iQ533lpEAHmCPEGe8NcAAiYI2FGe6GuNE18cJZ7sPUYLC5NTZN+NT0hBRhPj+6Snb5aYxQv1b2RZlUryttrW896RjWR1pZQyicSobUA9PFXl4lod5ISkuuIpllJvHxL9LvUe7ztMoCWJAwkgTxwYVJoUNgJMBsOGloIdSIDx4sCg2qhJyBPkCfLERgOWqkaPgB3liaalt/HEqrNQ9JPXpWeJLTc+eeI9ZFaZkEJ1TsrsjHR1W09D+aRZpuxOjC8TeiNPklyaV00G1W4vVae9I3FfTfGlze13oeT2vyh6AePNUSWAPIkqfl5uMwJMBm0WMKobVQKMl6jid/3LkSfIE+SJ6/8MAMAMAbvKk8O1Ta9MSXp65IEkWqJ49FoSfT6KR3apa49fat9CXujQQrYlJ5ZZVEqOuqXnz/9k+E//SJOtO33p9t4xlhUoZjqXA9MgTxwYVJoUNgJMBsOGloIdSIDx4sDhPWupAAAgAElEQVSg2qhJyBPkCfLERgOWqkaPgBPliaapBUrcvC/Ek7VL8o4+XuK//EBiVi8tEiix8VKYkip7PPnydpNaMr5dc/m3WqXDBqHbyvUy9OfF0mfJKsk/c7jk9RhYdnr1zpjVy9jmE71uHbY3I0/ChpaCHUiAyaADg0qTwkaA8RI2tBRsggDyBHmCPDExUEgCAafKk9Iiq4WKJ2uPFNRrLIXVa0ncrMn7b+4plK8b1pGX2jWVmU3qSoG+CrmMp97OPXLZgn/lwi0iqW26SV7nkluG4ubNkIRJL6ozUnYbJeS3PVb2DRtNR3MIAeSJQwJJMyJCgMlgRDDzEocQYLw4JJA2bQbyBHmCPLHp4KXakSXgJnlyMNmE1x83rj8W77YetShlVVqKkijN5M22TWR7UkKZwUjMy5eBf6+QYYtWSevE6uLJzZOCqtUl7vcfRfZmlciXc1Y5K1UiG3LeFgQB5EkQ8MjqOgJMBl0XchocBAHGSxDwyBo0AeQJ8gR5EvQwogA3EHCzPPGtPFHypFAdieLRR6IY9+wUyj51Lsr76pael45uJn+kVzlsV2i/drMM/eUfOX3RCon3HlJbLEfxa5Td0Kec3EbkiZOjS9tCTYDJYKiJUp6TCTBenBxd67cNeYI8QZ5Yf5xSQwsQcLM8EXU2iT5Y1ncWipInejtPYVKKxKxRZ5bsf+Zl1FASpblMbZohebFlb+mpsSdbLlZbei7/ZbGk79nry88NPRbo6CGqAvIkRCApxhUEmAy6Isw0MkQEGC8hAkkxARFAniBPkCcBDR0yuY2Aq+XJ/mDrc0o8WzYY55MUZDQxfqrPR9Fnl2ixUtC4lXg2rZdNeVny8jHNZILa0rMpJbnMrhKXXyD91cGyQ5VE6bx5j2Q/8EaJq5Td1sec1F7kiZOiSVvCTYDJYLgJU76TCDBenBRN+7XFL3myT11HuXT5GtOtrFurhlSulGI6vVUSrt2SbZWqhLUetasmG7eSrtuabRxlwAMBCJRNAHlirnd4Nq+X+GlvSOx/iyRv4xr5qFV9dTZKc/mpbvXDFnDElp1y+YpdckbjEySubddS0+qyjbNX1JPXuZex+oXHmgSQJ9aMC7WyJgEmg9aMC7WyJgHGizXj4pZa+SVP/lqyQs664l7TbO4beamc2e940+mtkhB5YpVIUA8IWIcA8iSAWKjtPvEz3pX4me/L7+lpMqZ9C5nSsoHsi4sts7C0vTlyXlwtGdLgJKkXV7EonSon7puPJeHT1w/kS64oe2983LcCJoDakSWMBJAnYYRL0Y4jwGTQcSGlQWEkwHgJI1yKLpeA3/LkplEvyGdvPVZuwTeNGiPHdTgCeVIuqeglYOVJ9NjzZvsRQJ4EHjO9YsSzdb2IEh67Fs2TN7f+Lq+2ri+rD7MyMUYthzs5ubZcmlNder30f6LLMJbKFXsKmrVRAuXJwCtGzrARQJ6EDS0FO5AAk8H/Z+884KuqsjX+3ZpKAiEQIIWQRq8iCsiIBRRpgnVUsI1I0ffGNs6zoqPOKCpjoalYwI5KEURQsVIUpEoNoSSBhBASID255e1zQiIlyb03ueWUbys/jNln77X+66wk+8vea2swqHTJZwSYLz5Dy4HdIOAz8eSBp2ZiQF+KJ27EIGBdKJ4EDD0nViEBiideDJrYSeI8lIGV9mN4J289fk5o+PhNcsFJ3LUxHTf/kYHwiqpaQ5whYSh7eZEXDeNQ3iJA8cRbJDmOHghwMaiHKNNHbxFgvniLJMdpDAGPxBNpguMnitE88tRW6sbMqIJneGxHBUGiiSTgZwIUT3wDPPjZidhXkoeZ/TrjE3Hlcam4+ri+Fibqbt34xz5M/H0PUoSgIl1tXDVivNy9poCtb6zkqJ4SoHjiKTH21zMBLgb1HH367ikB5ounxNjfmwQ8Fk+G3fwPmEwm/PXqSzHi8gF+LQh7oqgExwpPIjoqEhHhoS45VIoftHOPFqJtTEtYGjhjf/ZAFE9comUHEtAdAYonvgm5dBzH+v7LMO3ehCKrBR/0SMJbvdOQERXR4IQXZ+fjbwdO4qo16yAd8XHEJ6NiwlQWkfVNmDweleKJx8j4gI4JcDGo4+DTdY8JMF88RsYHvEjAY/Fk0x/p+HjRKiz//lfYxTWTF/bpgutHDcalA/vA0sBvDJtic3FJGYaP+yfyC07UDnPF4H6Y9vhEIeQY6xz65TmfYu5HX9V+7p/33IRx1w6VP168YjUe+feb5zy34es3EBJsBcWTpkSLz5KANglQPPFtXKUdKNJ1x07xDwxGrOrQBm/06YhvktrBeVatk9MtiRe7Ie/ctBfjt+5FRGofVEx62reGcnS3CFA8cQsTO5GATICLQb4IJOA+AeaL+6zY0/sEPBZPakyoqrLhh7Wb8dnSH/HLb9tkEeOqyy7E9SMvQZ/uqV61VNpx8upbn+Pma4agfWwMvvlpA6SaKm9Me1AuSnt2W/HDb5AK1j73f3dh+OUX4pPF3+O5V9/H5289jU4pCVj09S94Ytrb8sent5TEWFGP0EDxxKvR42AkoA0CFE98G0dpB0rQ3GdgPLD7jImyIsMwW4goH/RIxgkhbtfXgmx2XLPzAO688n50srTwrbEc3SUBiicuEbEDCdQS4GKQLwMJuE+A+eI+K/b0PoFGiyenmyLtDPlq1a94be7nKDhehDVLZvj0OE/NlckL3piKLmmJ51B5+Jk5WL9lF1YtmF77uUFX3yt2yFyCe+8YK4snT738HjatPHf3ifQAd554/0XjiCSgdgIUT/wTwZD7r4ahrATSBhScdrlOuTh6+eE1YzE3PhQ7TLYGjTk/pwB3/XEAV+/KgqW0WO7rSO2JivEP8liPf8IIiid+As1pNEGAi0FNhJFO+IkA88VPoDlNnQSaLJ5INUi+/GYNPl3yPQ5mH0F4WAi+/2w6QkOCvY58f2aOfBRn1eqNGHbJBXj8vupCgWe3J198B6vFbphvP3259lM3Tf4X4tq2wgviqI8knjz6n7dwUb/uCAqyYKC4FWjs8Itr66JQPPF66DggCaieAMUT/4TQvHYFrPNeFJMJ9cQp1JNTAopUHLbi1ocQPP0hrDMUYW6vVHzRJbFBo1oJYf9uUVz27o275Vt6pLoo5Y/M9o8jOp+F4onOXwC67xEBLgY9wsXOOifAfNH5CxBg9xslnpSWVWDlj+tlwWTLjgz5yM6Qv/SVd3b069VJPvriiybVW3lp9qfYvueAXGvllafvgVUUGDy7/bppJ+6473nZpov790TGwcP44ItvMWTQebJ4smHLbnzx1U9o0bwZsg7n4bufN0KqofLy1MnyUGeLJ9GRQb5wJ+BjWs3V9WIqbY6A20IDSECpBOwOJ0xGg/xHatLHbL4l4MjLgf2n5TCENYOhQxoMUa1hbN1WnrT0pr/UTp4THiIXl323VwoKQusX7CPLK0VdlD3iuuM9SHzm3dqx7Ds2our91+E4sBemvoNgueWe2s/51kPtj8580X6M6aH3CDBfvMfS2yPV/Azg7XE5XuMJMF8az45Pek4g/0TFGQ95LJ48+8p8fCJEE6lYbJ/uabhh9CWySBFUh4jhuXnuPVF4oggXj/1fPDzlr7h57JA6H1r1y0a89eEynCwuRZfU9lj23TpMHD9KPrZzdnv3068xbebH2PztXHn3ydniSY3I4J516unVUohC0nLwmHgpuBxUT9xoaWAIhASZYBTCcEl5w0dGAmOdfmY1zXgchs1r/nRYfPGqFF+3F3ZKEAVm07CxXXS9MCwOB64+7sSUvcfQc9PvwPGCM/o6xc4U+xNv6AemDz2V8kX6RUop88WHlDm0VgiEinwRCcN80UpA6YdPCTBffIqXg59F4OxNBh6LJ1I9Eano6qgrBqJli4avkvQl/QGjpmDMsEF4aNKNLqdZvf4PTHjoRcx/7dE6i9mu+GG9KDA7A+uXzxHHjYJY88QlUXYgAf0R4LEdZcTcmLUXQbOfhKEgr9qgYHFtfXlpbY2Uba1bYMb5nbCoU3tUNHBFff+sPEzesAvD07NhEFcd17Ty+6YBIeHiiE+KMhxWqRU8tqPSwNHsgBDgMYSAYOekKiXAfFFp4DRitkfiibTbpLi0DJHNwly6X1JaLu/iqOtYjcuHz+qw7vcd2Lx9L0YLwSZKCDYfL16FF2Z8hNnPP4BBF3THT+u2YOpL72LOCw8gtUOc/PTh3Hy0atkcu/Zm4r4nX0e0+O+PZz0hf27WvMXo1jEJ5/VIlQvcSsKK2WzGknefrX72WJmnJqqyf9uoEOkXHcgpKMNpawdV+kKjScDXBCie+JqwB+OXFsnXGkvNEZcMy6qFMK/6AoaqSvmqY0NVFY5HRmJxUmvM7dMJ21pF1jt4++PFsohy87YMhFVWAUFCjKkQ3wOEgCIVmLX3GuiBYexaQ4DiCd8FEnCfABeD7rNiTxIITL5Ie/W5T59vH+CReCLdciPt0Fj+wQsu2UlXBUvXCF8z/M/z6S4fqqfD+s27MOEfL6FS+sH2VDv9CM7Sb9dC2hEjiSPdOyfJPS6//n7k5BXU1mN55uG/IeTUNZePPT8XC5f/XDtWbJtoWXjpkFB9pp7iSWMjxedIQLsEKJ6oM7bGPZux8bNp8m6Ur1Ni4TBU13o6u0VUVGLclgxM/n0X2p0Qt/1IPyeZzKi64kZUjbxNnc4H0GqKJwGEz6lVRyAwi0HVYaLBJCATYL7wRQgkAY/Fk3sffQVfvP2MS5ul22wG9+/lFfFEmswptkZIN/tINUzixa05Fou5QRvyC05AKmwb365VnQVspc/lHi1ARHgooqPO/K0kxROX4WUHEtAdAYon6gx5kBDVTVvXyr8wOtAiHDPP64gPeqagtJ7vISZREHjEnizcs34n+h7Ol53mLT2ex57iiefM+IR+CXAxqN/Y03PPCTBfPGfGJ7xHwGPx5Nq7nnR79qcfusNr4onbk3qhI8UTL0DkECSgMQIUT9QZ0FrxRDZf2nJrwElR4Pzd3qmYIwrMHo6o/xiqJJ5MESLKSCGm2Mb/A84WreCMioEzuo06YfjRaoonfoTNqVRPgItB1YeQDviRAPPFj7A51TkEPBJPbHY7CgqL3MYYKX4o9ectPG4b5qIjxRNvkeQ4JKAdAhRP1BlL0+bVCJoz9U/jxS5GR6t2qPjfF1DVsjVW/vIuZpoLsalty3odjDtZgrt/341bN+9FM3F8tPLaSbBddu7Nbeok5BurKZ74hitH1SYBLga1GVd65RsCzBffcOWo7hHwSDxxb0j196J4ov4Y0gMS8DYBiifeJuq/8SxL51UXlC0rgSOtByrGPVS7e0T6nGXZfKyLayXXRfkqNa7euihSQdlbRGHZiRt2I7bfVYAlCM7QcNguHAKENvOfQyqYieKJCoJEExVDgItBxYSChqiAAPNFBUHSsIkUT+oILsUTDb/xdI0EGkmA4kkjwSn8MamgbPD0h2qtzIoMw4x+XfB+tw4oEcd76mpGpwPD9h7C5PW7MEBceSzXRPm7uOKYAkotLoonCn/xaZ6iCHAxqKhw0BiFE2C+KDxAGjeP4gnFE15VrPEkp3veIUDxxDsclTiKee0KWBbMknemOKNao2LiUyj7dQU+KNqNOaLAbHYDdVF65RYIEWUHRjbvDMe4B6rdE1cpW5e9D2NW9XXKlSNuETteeinRdZ/ZRPHEZ2g5sAYJcDGowaDSJZ8RYL74DC0HdoMAxROKJxRP3EgUdiEBiic6eAeE6FGze6SmVordaMCSjvF4vW9nbGwXXS+ENuImuAl78zCuMgrRv/0IlJw8o2/5fdN0JaBQPNFBvtBFrxHgYtBrKDmQDggwX3QQZAW7SPGE4gnFEwUnKE1TDgGKJ8qJhb8sCX52IozZ1btHIArN/hbfGq+fJ+qipMVDElXqaiFVNty4fT/u+W0nkk4rsG7v0R8Vk572l+kBn4fiScBDQANURICLQRUFi6YGnADzJeAh0LUBTRJPMg4extJv1iLj4CG8+q//wZcr1yD3aAHuunmEqqGy5omqw0fjScAnBCie+ASr4gc1f/cFjAW5cFhDYflxkXy0JysyFLOEiDK/RwqKg+qui2IQYsuQfYcxecMuXHxAPC8K1Zbf95Li/fWWgRRPvEWS4+iBABeDeogyffQWAeaLt0hynMYQaLR4sm3nPtwofovWJS0Rew8cwqaVb2Ljtj0Yd+9zWL98NkJDghtjjyKeoXiiiDDQCBJQFAGKJ4oKR0CMOb02ChwOFAUHYX7PFMw+L00IKuH12tQ1rxATgjpgdI9RsBiMAbHd35NSPPE3cc6nZgJcDKo5erTd3wSYL/4mzvlOJ9Bo8eT/nnsTzcJDMfnW0bjkuvtk8cThcKL7pbfj87eeRqeUBNWSpnii2tDRcBLwGQGKJz5Dq7qBpRt6HHHJMK/9BtYFM2E3GbFUHOWZ0bcT1se2qtefVuU23JlXhTs3ZyDKBlRdOgb2XgNV5787BlM8cYcS+5BANQEuBvkmkID7BJgv7rNiT+8TaLR4ct2EqRg1dABGDhlQK55UVlah99C7sPyDF5AQ29r71vppRIonfgLNaUhARQQonqgoWH401bRtHYJmPn5qRic2tYnGqxd0xpdpCfXWRQmy2XHdDlEXRVx1nHjNfZoUUCie+PEl5FSqJ8DFoOpDSAf8SID54kfYnOocAo0WT/775mdYsnI1XnxiMu584AWsWzoTT7zwNn5Yuxlrv5wJYz3F9NQQA4onaogSbSQB/xKgeOJf3mqazfreNJjXraw12RkShszJj+KNTZ+JuijJKAqy1uvO4Lwi/K37WFwSEqsml13aSvHEJSJ2IIFaAlwM8mUgAfcJMF/cZ8We3ifQaPFE2mVy5wPT5DonNc0kti7P+s/9GHh+N+9b6scRKZ74ETanIgGVEKB4opJABchMqR6KdL2xIz4FtguHwhndBqGThqDEasY8URdlTp+OONi8/rooqZZmuMvUHtcdrULYwXS5MK2tZ3/VXm9M8SRALyKnVSUBLgZVGTYaHSACzJcAgee0MoFGiyc1/HbtzUT6vmxERoShZ9cURDYLUz1aiieqDyEdIAGvE6B44nWkmh9QqodiXrUQTukfUSh2WWocZpzfGb/G1V8XJaq0HLdvTseEjXvQuqQcleMfhK3/FapjRfFEdSGjwQEkwMVgAOFzatURYL6oLmSaMrjR4onNbofd7jgHhtlkgrQDRc2N4omao0fbScA3BCie+Iar1ke1LJ0H08afYMw5CIjri2EANrVtidf7dsaSjgmw1fP90iq+x47ZeRD37slDh/tnqw4TxRPVhYwGB5AAF4MBhM+pVUeA+aK6kGnK4EaLJ/dPnYkVP/xWJwxpF8qU267GzWOHqBIWxRNVho1Gk4BPCVA88SlezQ9uyM+V66I4Q8JhWfouDOVlONwsBLP7dMK8Xik4EVx/XZSBBWWYvP0whpaYgJYxsKf2UHyRWYonmn+l6aAXCXAx6EWYHErzBJgvmg+xoh1stHjy2PNzkV9wHJNuvbrWwedeeR+tW7XAhX0647lXP8D81x5Fn+6pigZQl3EUT1QXMhpMAj4nQPHE54h1M4FUC0VuYiOKtBOlzGzG/J7JmH1eR+xv0axeDkmFJzFxw27csjUD1i4XoPK6SXJtFSU2iidKjAptUioBLgaVGhnapUQCzBclRkU/NjVaPLn69sdw3cjBYnfJ5bW0vvt5Ix6fNhdrlsyQb+Dp3TUV99wxRnU0KZ6oLmQ0mAR8ToDiic8R62aCmlootQ7Lx3kMspayLDUeM8/vhLXxrevlEVleiVtFXZRJv+9G1JBbYBt6PVBaBITWL7z4Gy7FE38T53xqJsDFoJqjR9v9TYD54m/inO90Ao0WT26/7z+w2Rxid8kjteO98f6XmDVvCTatfBMTH34JCbFt8Mj/3Kw64hRPVBcyGkwCPidA8cTniPUzgRA6rMvmw/zDYsBxqnaYJKCI5oyIEiJIOLahBK/164RFHdvXWxfFLOqOjdqdiXs27ELvnGNwiiM9FXdPlW/8CXSjeBLoCHB+NRHgYlBN0aKtgSbAfAl0BPQ9f6PFk9Xr/8CEh15EcmIsBl3QHYdy8rFq9UZcPqgvXp46GYOuvhcTbhmJcdcOVR1hiieqCxkNJgGfE6B44nPEupvAmLUXQdMflK8lloWTkDBU3PcirPNehDE7Qy4wm9ssVD7O816vVBxvoC5Kv+yjuGf9LlxVUIGKf70fcJYUTwIeAhqgIgJcDKooWDQ14ASYLwEPga4NaLR4IlH7bdMusdNkEfYdzEHXjokY8pe+uPKSCxAifsCTrjCOb9caYaHBqgNM8UR1IaPBJOBzAhRPfI5YnxOIXSjmLWvkQrL2tB7y0Rvre9Pk4rLVikr1jpQyqxkfdE/GrL4dsa9FRL2sEk4U487wjrihw2A0M1qq+4k5jNn74IhL8tvRHoon+nyd6XXjCHAx2DhufEqfBJgv+oy7UrxukniiFCe8bQfFE28T5XgkoH4CFE/UH0PVeCDEjuDpD1XvPpH0k+AQVN7xiNBRHLB+8F+saBWGGf06Y3VC/XVRwh0G/DUoARMzjiHpozm1rleOfxC2/lf4HAXFE58j5gQaIsDFoIaCSVd8ToD54nPEnKABAo0WT0rLyuUbdX7fuhtVVbYzplj87nOq3HFS4wTFE+YMCZDA2QQonvCd8DcB457NMJSW1O5IkecXwopl+UewfLsAW2NaYMb5nbGwc3tUGY11mmdyOHFVehamiCM9Fxw6Kvcp+9d8n9/SQ/HE328L51MzAS4G1Rw92u5vAswXfxPnfKcTaLR4Mm3Wx/hs6Y/o1qkDDmblilt3hmDFj+uRl1+Irz94AVbrqe3CKuRN8USFQaPJJOBjAhRPfAyYw3tEQKqXYlk6D4bj+ThqsGFOt3i8mxKDwgbqovQWtckkEWV0ViHMxScBcVSoQuxEsfca6NHc7nSmeOIOJfYhgWoCXAzyTSAB9wkwX9xnxZ7eJ9Bo8eTGSU9j2KUXoE2rKLz14TIseGMqDufmY8iND2L14tfRPDLc+9b6aUSKJ34CzWlIQEUEKJ6oKFg6M9WQn4uQ5yahvLIUH/ZIwUxRYDajZf11UdqdLMGEjem4XVx3HFFR6ZOdKBRPdPYS0t0mEeBisEn4+LDOCDBfdBZwhbnbaPHkmr89gbFX/QXn9UjDXyf/C+uXz4ZDXJvYe+hd4vriR9Gne6rCXHXfHIon7rNiTxLQCwGKJ3qJtPr8lHagWMTVx5Bqyxqq/1qZEosZfTvh5/Zt6nUotKoKN23dh0kHT6LdyAkw7d4C056tcETHoOqqcU062kPxRH3vES0OHAEuBgPHnjOrjwDzRX0x05LFjRZP7n3sVYQEWfH8Y3ej31UThViSBpPJiF9+2yaElDkI4rEdxb8nbaNCYBA/aOcUlNVc6KB4m2kgCQSKAMWTQJHnvK4IWBfMhHnVwlM384gv6lI79deO6Ei8dkFnfNE5EZUmU51DGcSNPldkHMJkcaRnUOaR6j7iSE/ZI7MaLaBQPHEVNX6eBP4kwMUg3wYScJ8A88V9VuzpfQKNFk8yDhzCscIi9OvdCRu27BbFY9+Xrbvn9jG49KI+3rfUjyNy54kfYXMqElAJAYonKgmUDs00bV6NoDlTqz2XrjaWtp4YT6knp/5fXngI3uidhnd6p6IgNLheSt2PFMgiyjU7DsDYvT8qbn2wUdcbUzzR4YtIlxtNgIvBRqPjgzokwHzRYdAV5HKjxZON2/agWXgoUjvE1bpTVl6Jpd+uwZhhg2Cu5zdcCvK9XlMonqghSrSRBPxLgOKJf3lzNs8I1B7dkfSTqNaouO0f8jEcqbCso3WcfDuP1MrFDtGPu3XATHFLT3rLyHonaV1chrs27cFteVUInfRvjwUUiieexY+99U2Ai0F9x5/ee0aA+eIZL/b2LoFGiyf3T52JjsnxuHvcyFqLnOI3Xt0uuR1L3n0WyYmx3rXUj6NRPPEjbE5FAiohQPFEJYHSuZlS8Vhn9Ll1TqzvTYN53cpqOuJ7tcNixXcJ0fJVxz8mtq2XWnCVDdcfyMeEjsPRIaF7bT9JlHGKoz11zSV1onii8xeR7ntEgItBj3Cxs84JMF90/gIE2H2PxZPSsnIUl5TjiWlzZYHk1uuulF1wOB3YsfsApFoovyx+DS0imwXYtcZPT/Gk8ez4JAlolQDFE61GVj9+SYKHMTsDjrhk2emg1/4JQ9EJ7BZ1UV7t1xmfi7ooFeb666IMNrTA3cY4DH3jvzAcq66NYu85QL7uGKFnfs+neKKf94qeNp0AF4NNZ8gR9EOA+aKfWCvRU4/Fk5fnfIq5H31Vpy9SwdjrRgzG4/eNV6KvbttE8cRtVOxIArohQPFEN6HWlaPB0x+AUdywI7X8ECveEDf0vC1qoxwLCaqXQ6ejxzH59924fvs+BNkcsF06BjYhokhFZh3xKfJzFE909RrR2SYS4GKwiQD5uK4IMF90FW7FOeuxeHLiZAkKTxRh5ruLENeuFUYNHVjrVGybaFgsZsU56alBFE88Jcb+JKB9AhRPtB9jvXpo/u4LmLeulnekOFrHwvnZDHzSNRkzhZAi7Uqpr0WXluOOjem4a1sGosXPBlJzpPZE+cQnEd6yhbjNzYCi0iq9YqXfJOA2AS4G3UbFjiQA5gtfgkAS8Fg8CaSx/pqb4om/SHMeElAPAYon6okVLW0aAaluSsi/JwGlxfiuQ1u8Lo70/NBAXZQgmx3X7DyAKeKWni5iV4qjfUeYnDZxXbIBld36o2qEunejNo0mnyYB1wS4GHTNiD1IoIYA84XvQiAJeCSe5B4twH/f/MylvU/efxtCgq0u+ym1A8UTpUaGdpFA4AhQPAkce87sfwJSfZSg2U8CBUdgEP9IO1BeE8VlP+tSf10Uycq/HMyVRZQh+w7DIF2bLJpt0HDAIo4BlRXD3mMA7L3+3LHqf884I8f+fOwAACAASURBVAkojwAXg8qLCS1SLgHmi3JjowfLPBJPDufm4+np77nk8vLUKQgNCXbZT6kdKJ4oNTK0iwQCR4DiSeDYc+bAEJAElODnxA4USQQRu0iklh8ahLdETRSpLsrRsPq/zycXnMTkDbtx01Yxht1xhgOV106C7bKxgXGKs5KAAglwMajAoNAkxRJgvig2NLowzCPxRBdEhJMUT/QSafpJAu4ToHjiPiv21A6B0ElDqp05TUCRPqw0GvFptw6YIeqi7GrVvF6HW5RV4NYte3G3KDDbpqi0up+4Jrn83ufgSOulHVD0hASaQICLwSbA46O6I8B80V3IFeVwk8STP3bvx1sfLMO+zBxENgvDxf17iquLr1B90ViKJ4p6R2kMCSiCAMUTRYSBRviZgGXpPFiWzZfUE/nfmh0ojqQuMOQchKGsBN8ntsEMcaRnlaiP4jy1Q+VsMy0OB67eeRD3iCM9PXKPVesxbRJg6zuYNVH8HFNOpzwCXAwqLya0SLkEmC/KjY0eLGu0eLJhy27c+r//RkyrFriwTxdkHT6KLTv24vxenTD3pX+omh3FE1WHj8aTgE8IUDzxCVYOqgICps2rYczOkC21d+wFR2wHILQZpMKypvQtcIoriq3zXkBGiFnURemET7olocJsqtez/ll54kjPLlyVng2j2NFiF9ccV0x8SgUkaCIJ+IYAF4O+4cpRtUmA+aLNuKrFq0aLJ3f/4yXY7PYzhJLft+7B+P95Dt98/CLaiWuL1doonqg1crSbBHxHgOKJ79hyZPUTsC6YCfOqhbIjBSGiLkqfVLk2ytGwkHqdSywsxt0bd2Hc1gwY/v2pLMiwkYAeCXAxqMeo0+fGEmC+NJYcn/MGgUaLJ2PvfBxDLu6LSeNH19pRKs42nz/sbnw483H07JLsDfsCMgbFk4Bg56QkoGgCFE8UHR4aF2gCpUWwiuM9prUrZUuMkVGoOHoYC7omYmbfjtghdqnW1yIqKnHLgULc2ecGtGmVGGhPOD8J+J0AF4N+R84JVUyA+aLi4GnA9EaLJ8/8dz6+WrUOrzx9L3p1S0VefiHeeP9LLFz+M9YtncnbdlTwcrSNCpGPr+cUlMm1ANlIgATqJ0DxhG8HCbhPIKz4KPCf++A8dkR+6IfUBMzs1xnfxrasty6KyeHEiEPHcXdZM/Rp3ZlXGruPmz1VToCLQZUHkOb7lQDzxa+4OdlZBBotnpSUluPmKc8gfX927ZAmkxH/eeRuXHXZBaoGzZ0nqg4fjScBnxCgeOITrBxUowSkfEFpMcq2/C57aE/rIR/Lyd79I2bn/o6P2kWg3CL61NP6Hs7HRCGiDL1sAkyoviaZjQS0SoCLQa1Gln75gkCg80Wune4LxzimKgh4LJ7MfHcRundOwsDzu8NoNGDbzn3ybTvNI8LlozrNI8NV4XhDRlI8UX0I6QAJeJ0AxROvI+WAGiYg5YtBbG0sKq2q08uKt57A29YivNEnDXnh9ddFibUZcEfrvrg5PA3NjBbUFK91xKfIhWbZSEALBAK9GNQCQ/qgHwLMF/3EWomeeiye3D91Jlb88Js4lhOE0VdchGtHXIxOKQlK9K3RNlE8aTQ6PkgCmiVA8USzoaVjPiDgSjwx7tmM4OkPoUoILJ+Luigz+nbGHzH110UJcxrw18zjmPL1D0g8XixbbOs/FJXjH/KB9RySBPxLgItB//LmbOomwHxRd/zUbr3H4onk8JGjhVj09S9Y8OX3yMkrQNvWUbhu5CUYM2wQWkc3VzsTUDxRfQjpAAl4nQDFE68j5YAaJuBKPJFcl3aRWJa8A2POQUDsg/4psY1cXHZlcmy9dVGMTgeG7T2Eyet3YYC48rj8kVmQdqGwkYCaCXAxqObo0XZ/E2C++Js45zudQKPEk9MHkGqefL7sJ7lQbHFJmXyk553p/0RIsFW1pCmeqDZ0NJwEfEaA4onP0HJgDRJwRzypcduydB4s4qYeWUERbV+LCLETpRM+6p6EsgbqovQ8UoAJhliMbNsH4XOegkEqThsSjorxD7LYrAbfKS27xMWglqNL37xNgPnibaIczxMCTRZPpMnsdgd+/nUrHn9hLgqOF2HNkhmIjAjzxA5F9aV4oqhw0BgSUAQBiieKCAONUAkBT8ST012yLHwTlpWfCh3FiePiePA7vVLxxnlpyA0PrdfzNsVlmLBxN27flI7m5ZVyv7J/zYczuo1KaNFMvRPgYlDvbwD994QA88UTWuzrbQJNEk+27MjAZ0t/xNJv16KyskqufXLDqEtwzfCLId28o9ZG8UStkaPdJOA7AhRPfMeWI2uPQGPFE4mE9b1pMK9bIf5L3GcgRJQqkwkLO7eXd6NsbRNVL6yQKhtu3L4f9/y2E/EDrkbViPHaA0uPNEmAi0FNhpVO+YgA88VHYDmsWwQ8Fk8yDx2RBRPpmI60yyQ6KhLXjRgsBJO/oG1MS7cmVXoniidKjxDtIwH/E6B44n/mnFG9BJoinkheG/JzEfTOv2HM2C4++PNSyNUJreXisitS2sFhqPuXNAYhuFxWWIUJsQMxMKZrNcTSIhiz94ljPWGskaLe10qzlnMxqNnQ0jEfEGC++AAqh3SbgMfiybCb/4FcUTB2xOX9cf3IwXKNE601iidaiyj9IYGmE6B40nSGHEE/BJoqntSQkm7lMf/8FUzb1sFQUQan2QqDrRIHIsMwo18XfNi9A0otlnrBdhE/r0xKz8f167fBWl4m95OuOK6Y+JR+gkFPFU+Ai0HFh4gGKogA80VBwdChKR6LJ3/s3i8fzzGLbbRabRRPtBpZ+kUCjSdA8aTx7Pik/gh4Szw5g5zYPWIoLUHwcxPlv6VTPSeDrKIuSgrmnNcROc3qr4vSqqQcd25Ox12/70ZUWQUqr50E22Vj9RcYeqxIAlwMKjIsNEqhBJgvCg2MTszyWDzRAxeKJ3qIMn0kAc8IUDzxjBd765uAT8STU0il3SjWD1+D8UhmLWSbEFIWdU7EjPM7Y3MDdVGCbHZct30fpuzKQacjhXBEx8B24VDYLqWQou83NrDeczEYWP6cXV0EmC/qipfWrKV4UkdEKZ5o7TWnPyTQdAIUT5rOkCPoh4AvxZMaiua1K2BZMAuGshI4g4LFsZ5yucDs2oQYubjs8tTYeuuiSGMMPpCDKet34fJ9h1Fx91Reb6yf11NxnnIxqLiQ0CAFE2C+KDg4OjCN4gnFE+QUiHPkTh287XSRBJpAgOJJE+DxUd0R8Id4UgNV2oniSOsFy9J5sCybL/63+IYm/j3QohlmCRHlg+5JKLHWXxcl7dhxTMwqwejRDyPYYNZdrOhw4AlwMRj4GNAC9RBgvqgnVlq0lOIJxROKJ1rMbPrkdQIUT7yOlANqmIA/xZPTMRqz9sK04XsYjx0Rt+tkwHAkW66L8l6vZMzp0wmHIuqvi9LCacK4sBTcedyC1rAIQaanhiNE15REgItBJUWDtiidAPNF6RHStn0UTyieUDzRdo7TOy8RoHjiJZAcRhcEAiWenA1XuvLYtGUNnBHNYX73P1jcsT1eP78jNrWNrjcOVrsdY3YexD2ZRUi+/mEYCnJFXwPFFF28uYFxkovBwHDnrOokwHxRZ9y0YjXFE4onFE+0ks30w6cEKJ74FC8H1xgBpYgnp2MNnv4AjHu2ynVRfo1rLYrLdsKytLgG66JcdOgoJq/bgSszDsEZl4SKCVPhjG6jsWjRnUAT4GIw0BHg/GoiwHxRU7S0ZyvFE4onFE+0l9f0yAcEKJ74ACqH1CwBJYonEFcdWxfMhknUSHEGhcKYcwBZkWGiuGxHURclBcVB9ddFSSo8iYkbduOvzmgYJzx1RtykmivyzhQhriC0mWZjSsd8R4CLQd+x5cjaI8B80V5M1eQRxROKJxRP1JSxtDVgBCieBAw9J1YhAUWKJ2dxDH52olwXRWpFoqDsvJ7JmC0KzGZHhNVLPLK8Eje17o07I7ugbYUDwf99CMas6jEQEo7yiU/KxWvZSMATAlwMekKLffVOgPmi9zcgsP5TPKF4QvEksDnI2VVCgOKJSgJFMxVBQA3iiVRc1jrvRSF+7BUbRwzycR67yYgv0+LFkZ7O2NCu/rooZrHTZMRxJ/5nydfonXPsT+ZCQCl9eaEiYkAj1EOAi0H1xIqWBp4A8yXwMdCzBRRPKJ5QPNHzVwD67jYBiiduo2JHEoAaxJOaMJnWr4L1o1dgKCuVBRRZSBFtfbuWeL1fZyxLTYDdWP3/6mr9so9iyoZdGLE7E0ZxR3LF5Gdg734h3wIScJsAF4Nuo2JHEgDzhS9BIAlQPKF4QvEkkBnIuVVDgOKJakJFQxVAQE3iiYRLvpUnfQucYueI9dMZMBQePUXRKddFmXVeJ7zfIxlF4trj+lrCiWK5Lsot6bmw3vh32HsNrO0q7W6xLJsvbu45ImqjJKPy2omsj6KA91QpJnAxqJRI0A41EGC+qCFK2rWR4gnFE4on2s1veuZFAhRPvAiTQ2megNrEk9MDYtq8GkFzptb+L6fYTWIQ/5RYTKIuSqqoi9IRmZHh9cYwvKIK47ZmYEJpGGKG3yX3C3luElBWXPuMIz4Z5Y/M1vx7QAfdI8DFoHuc2IsEJALMF74HgSRA8YTiCcWTQGYg51YNAYonqgkVDVUAATWLJxI+6QYd89pvZJKGkwUw7dggjvRIH0BcbQwsTUsQt/R0wm9xreqlbXI4MexQASYfqkD/H78/p1/5fdNYXFYB76oSTOBiUAlRoA1qIcB8UUuktGmnZsQTm92O3LwCOMQPK7FtomESRd9ctbz84zCKc8zRUZFndD18rMzVo5r4fNuoEPlod05BmXzMm40ESKB+AhRP+HaQgPsE1C6enOGpuOI4eLp0q86fhWVr6qJsatsSr4nislKRWVsDP3f0zsnHlPW7MFrURTGLn1OkZut/JZxRreAMDYft0rHuw2VPzRHgYlBzIaVDPiTAfPEhXA7tkoAmxJM3P1iK/775Wa2zoSFBmP38AzivR1qdAA5k5eJvD7yAHCG2SK1DQlu8/fLDaB3dXP6Y4onL94YdSEB3BCie6C7kdLgJBDQlnpziYNrwPazvvwxDufiFg/jFg3SUR27itw+HoltgdvckzOuVjJMN1EVpd7IEEzam4/YtexFRXlFL2JHaE+X3v9gE4nxUzQS4GFRz9Gi7vwkwX/xNnPOdTkAT4smHC79DyxbNcFG/HqiqsuGO+5+Hze7AknefrTPa9z72Kg7n5uPVZ/4HQVYLbpz4FJITYzHnhQconjA/SIAE6iRA8YQvBgm4T0CL4onkvVRY1rxuJVAlhA9pB0lQMGwXDhW7R8JgXTALFRu/x3xRWHZOn0440KL+uiih4meVm7btwyRxS09SYZEMlsd43H+/tNaTi0GtRZT++JIA88WXdDm2KwKaEE/OdvL2+/4jH99575X/O8f/46Ia/sDR9+DFJyZh2KUXyJ//cuUa/PO5N/DH9++InbgG7jxx9dbw8ySgQwIUT3QYdLrcaAJaFU9cAZGKzVo/fg1OUSdlWWocZp7fCeviWtf7mEHsWrki4xAmiyM9F/QbC9tlPL7jirEWP8/FoBajSp98RYD54iuyHNcdApoSTz744hssX/Ub9mUexqz/3I+eXZLPYXCiqAQDRk7By1On4IrB58uf37IjAzdN/he+/+y/8tEdHttx59VhHxLQFwGKJ/qKN71tGgG9iicStaBZT8C0de0pgE5sahuNGUJEWSyKzDZUF6X7sSJMiOyKUcmDYS4rka8ylgrXSlcbS//Npl0CXAxqN7b0zPsEmC/eZ8oR3SegKfHkyRffwfrNu3Cs8CReeGwiLu7fs04S102YigNZObj3jrGwWMzyzhNJQKlPPGkRbnWfqIp6hgSZZGvLKuwqspqmkoB/CdjFb4bNorC0yVRd4cBmZ3Vl/0aAs6mRgJ7zxfb1Atjef/3PsMkV2Q3If3YmZvy6AHOTo1HUQF2UrvkncOumPbhr457aMcxjb4N57O1qfBVosxsEzOL7i9T4/cUNWH7uYhM72U3iZwA2LxNowo9Scr6If222JgziZXc4nHYJFBZXnuGcpsSTGs+en/ERPl68CptWvllnJE+Igm0vzv4Em/5IR3hYiFwnZdfezHqP7QRbXd/co8ZXpkWzIHkxWFD0Z9E6NfpBm0nApwTkdY8BIVaTfKyvtMLm0+k4OAlogYCUL1LelOkxX0qKYJx2P5CVURtK541TxO6RMBjefgFl4pc274visrPEVcf7Rb22+lqrknLc+Mc+3LE5HYnHi+GY8jTQ5yItvB704SwCus4Xhb8NkvZpkO8pZ/MqgSboUXK+iKiUVfLnMa/GhIPVSaC80qF98WTh8p/x2PNzseW7uTCbqndXNNRG3fYopBt6PhZbbaXGYzuuiPHzJKA/Ajy2o7+Y0+PGE9DzsZ0aaua1K2A4dgT2ngPgiE+BZek8WJbNr4Uq3djzVUo8ZvbtiDUJMQ3CHph5BONLgnHFlffCYtDmL3Qa/7ap/0keQ1B/DOmB/wgwX/zHmjOdS0ATO0+efWU+Lht0Hnp0TkZefiEmPPQigoODam/beeWtz7Hih9/w1fvPywSkuifSb5Dtdjs+Ejf1zHh3kVxctm/PjhRPmCUkQAJ1EqB4wheDBNwnQPHkXFaSmGKdd9p1xOJX2tVXHgNbY1ri1X6d8HmXDg1Cjqiowg07M/G3DTuRWmWAo0MX2NN6iht/hrAuivuvp+J6cjGouJDQIAUTYL4oODg6ME0T4snEh1/Cz79uqw1XQmxrzPz3feiQ0Fb+f//33JtY9t1abP3ubfnjb37agL8/UX0eOap5M7k+Sv++XWuf584THbz5dJEEPCRA8cRDYOyuawIUT+oOv/W9adVXHZ9qtkHDYcxMh/FgdX2TzMhwvNS/Cz7pmoQKc8M7Zy/IPorbtqTj6l2ZCLaEoOyRWXBGt9H1e6dW57kYVGvkaHcgCDBfAkGdc9YQ0IR4IjlTUVmFw7n5aBYeiuioyAYjXGWzI+twHlq3bC7XPDm7UTxhgviSQHXpQDa1EaB4oraI0d5AEqB4Uj99Y9Ze+TiPs2WMfJwHpUUIeXYiDAV5wKnissWhQfikT1e8lxqDbTFRDYYyoqIS123fj1utCUgeMSmQYefcjSTAxWAjwfExXRJgvugy7IpxWjPiiTeJUjzxJk2ORQLaIEDxRBtxpBf+IUDxxDPOhvxcWBfMhDE7Q76auGrEeDhDwhHy+DhsahOF93qm4vPO7VEcZGlw4N6WSNwc0RmjwpMRZjirrxBpjNn75OcdcUk85uNZiHzam4tBn+Ll4BojwHzRWEBV5g7FkzoCRvFEZW8xzSUBPxCgeOIHyJxCMwQonngnlJKgYl61UOxIAcqsZllAeadnCja2i25wgjBRVHasLQK37DqC3s4QONqnwfrJ6/KOF6lJu14q7p5avfOFLeAEuBgMeAhogIoIMF9UFCwNmkrxhOIJcgrKqncKs5EACdRLgOIJXw4ScJ8AxRP3WbnqadyzGabNa2BK3wrpyA+CQrCrRRjm9uiABaLA7Ilga4NDdM0rFLVR9uL67QcgHfGpVk+csHfqg4q/v+Bqen7eDwS4GPQDZE6hGQLMF82EUpWOUDyheELxRJWpS6P9TYDiib+Jcz41E6B44vvoGbf9CsMbT2BRx0S82ysZ6+JaNzhpcJUNV+8+iFs378WFh/Llvo7Uniif+OQ5R3gkwUaqzsXjPb6PozQDF4P+4cxZtEGA+aKNOKrVC4onFE8onqg1e2m3XwlQPPErbk6mcgIUT/wTwJD7r4ahtFjoHAbsa9EMc3ul4pPuSTgWEtSgAWnHjuO2zRm46Y8MhKf1RcWkp8XultWwfLNArruCyvLq50XdFUlccaT18o9DOp2Fi0ElBl4q7c9t2UqMDPNFiVHRj00UTyieUDzRT77T0yYQoHjSBHh8VHcEKJ74J+TmtStgnffiGZNVGY1Y2jEO7/VIwU/t28AphJX6WpC4fXDEnkzcuj0Tg/ZlV9/2c3Z/IaCUvizqrrD5jAAXgz5Dy4E1SID5osGgqsgliicUTyieqChhaWrgCFA8CRx7zqw+AhRP/Bcz6YhN0IevwHAkW/ye3CkO25z6jbnQQbKah+HtXmn4sFsS8sJDGjQqqfAkxm/JwC1bM9CyrKL22mTpt++Vt9wH20XD/eeUzmbiYlBnAae7TSLAfGkSPj7cRAIUTyieUDxpYhLxcX0QoHiijzjTS+8QoHjiHY5ujyKuIbYumw/zmhVAeWn1Y6cqwTutQXDYq7A8RexGEULKqsTWcIjbeOprFocDV+7Jxm1b9+KSA7kwnBqn8tpJsF02Vn5MOuIjHe+x9xzAG3vcDlL9HbkY9AJEDqEbAswX3YRakY5SPKF4QvFEkalJo5RGgOKJ0iJCe5RMgOJJ4KIjCRvScR5naDPYLh0Dh7iW2LxlDZzi+I09rQeO7t2ID9K/xQepbXA4IqxBQ+NPFGO82IkyXtzW07q4DJVj7oBl1SIYThbWPifNUXnd5MA5rIGZuRjUQBDpgt8IMF/8hpoT1UGA4gnFE4on/NJAAm4QoHjiBiR2IYFTBCieKPtVCH52InBoH75Naov3eqZgZVIsbKb6d6OYHE4METVRbhXHeobsOwzpY7nJNVKAijsfhb3vJcp2WsHWcTGo4ODQNMURYL4oLiS6MojiCcUTiie6Snk621gCFE8aS47P6ZEAxRNlRz14+gMw7tl6SgABjoYFY54QUeb364aDwaYGjW9bVIpbtmXg1k3piBW7UWpa1fBxqBoxXtmOK9Q6LgYVGhiapUgCzBdFhkU3RlE8oXhC8UQ36U5Hm0KA4klT6PFZvRGgeKLsiEtFZoOnP3SGkVJNkypR0+Tnshx8eHwbvi47hCpj/Tf1GJ0ODBY1UW4TR3qGpWfDLHajlD8yS66BUlMTxdmyDWz9hyobhgKs42JQAUGgCaohwHxRTag0aSjFE4onFE80mdp0ytsEKJ54myjH0zIBiifKj64xay/M61YCpSVy4Vd7r4FnGF3gqMAXX7+KeW2CkBEV0aBDrUrKcNMf+3Bz7AAk7dgG06afa/tLY1dMfEr5QAJoofoWgw5Bq/5jXgFEyal1QEB9+aKDoOjIRYonFE8onugo4elq4wlQPGk8Oz6pPwIUT7QRc2kHSdCcqVid0Brv9UjBko4JqDDXf6xHuplnUOYRuTbKyN2ZkG7ukVrF3VPPEWe0Qcg7XnAx6B2OHEUfBJgv+oizUr2keELxhOKJUrOTdimKAMUTRYWDxiicAMUThQfIA/MsS+fBIq5Bltrx6Jb4YPRovN/KjF3OkgZHiSotx43b9+POjelI6HQh7Mnd5OuUDcfzAXHzT+V1E8WRnis8sES7XbkY1G5s6Zn3CTBfvM+UI7pPgOIJxROKJ+7nC3vqmADFEx0Hn657TIDiicfIlP9AaREgrj+uab9X5OHj9Z9gYUwIyizmBu2/MKt6N8qY3QcRZKvejSK18vumwZHWS/m++9hCLgZ9DJjDa4oA80VT4VSdMxRPKJ5QPFFd2tLgQBCgeBII6pxTrQQonqg1cp7ZbV67ApWfvIIFndvj3Z6p2NomqsEBIssrcb20G2VzOjrmn4Dt0jFiB8pkzybVYG8uBjUYVLrkMwLMF5+h5cBuEKB4QvGE4okbicIuJEDxhO8ACbhPgOKJ+6xU3VPsRpFu7ZGKz0ptc9uWcm2Uz7okojjI0qBrfQ7n49ZjDlxtaoPgFufeyiPdCCQd75Fu79F642JQ6xGmf94kwHzxJk2O5SkBiicUTyieeJo17K9LAhRPdBl2Ot1IAhRPGglOjY8JAcW89huYN/0I2OyA2YySZpFYYj+Cd3ol4/d2rRr0KryiCtfsPIjxR8rRtdeVMG1fD/MfvwJl1TVVHO07ofxvjwDRbdVIxy2buRh0CxM7kYBMgPnCFyGQBCieUDyheBLIDOTcqiFA8UQ1oaKhCiBA8UQBQQiwCdb3pslXIe+OjsDbvdLwadcOOB5sbdCqHrkFuHXrXly/4wDCKypP9TXIf8tXHo9/8Iy6KwF20WvTczHoNZQcSAcEmC86CLKCXaR4QvGE4omCE5SmKYcAxRPlxIKWKJ8AxRPlx8gfFko1UQzHjsB4JBuoLMeCDtGYF1aOtfGtG5w+SOxgGSWuOr7pj/0YfCCntm99NVKkIz7mdd/AUFoM24VDVXctMheD/ngbOYdWCDBftBJJdfpB8YTiCcUTdeYurfYzAYonfgbO6VRNgOKJqsPnM+MN+bkIeXwc9jUPx9u90/Bx9yQcCwlqcL6Y4jJcK3ai3LBjP7oGRcPe/UK5v7QTRaqHYtq8GkFzpp4xRtXwcagaMd5nfnh7YC4GvU2U42mZAPNFy9FVvm8UTyieUDxRfp7SQgUQoHiigCDQBNUQoHiimlD53VDrgpkwr1oo5nWiymjEsrR4+aaen9rHwGmoPqJTX+t09Lgsoty4bR/alJSJm3omwbzxZxgztp/5iCg0W/qyNIc6GheD6ogTrVQGAeaLMuKgVysonlA8oXii1+yn3x4RoHjiES521jkBiic6fwFcuC8dszHt2QoUn4Tlx8Vy74ORYXi3Vwo+7paE3PBQlwD7Z+Xh5j/2YfSeLISLK5DPbqWzvnE5hlI6cDGolEjQDjUQYL6oIUratZHiCcUTiifazW965kUCFE+8CJNDaZ4AxRPNh9hrDv65E0UM6XTCGRSMb+6YjM9jm2FpUQZKG96MIttx9a6D4mjPQQxPz5I/dka1hqEgr3q85q3E8Z4k2PtcDFv/oV6z25sDcTHoTZocS+sEmC9aj7Cy/aN4QvGE4omyc5TWKYQAxROFBIJmqIIAxRNVhEkxRkqFZY1iJ4qzZYxc8NUZ3Ua2rQJ2fP/qRHzSNRGrEtvCZjI2aHOLsgpcnZGDG7fsQb9sSTwR3aWjQEJEgfjL0aEzGSSABQAAIABJREFUKq++E460XorxXTKEi0FFhYPGKJwA80XhAdK4eRRPKJ5QPNF4ktM97xCgeOIdjhxFHwQonugjzv7wMnj6A7KwUhhixYLOiUJI6YCN7aJdTp1YWIzrdlbXR0kqLKoWUU41pRWU5WLQZTjZgQRqCTBf+DIEkgDFE4onFE8CmYGcWzUEKJ6oJlQ0VAEEKJ4oIAhaMaG0CMHiNh1JQJF2kDjatUdW2TF82DUZC7olYn/zZi497Z2Tjxu2S0d79qOl2J0ijSMXlA11/azLwb3QgYtBL0DkELohwHzRTagV6SjFE4onFE8UmZo0SmkEKJ4oLSK0R8kEKJ4oOTrqtE265tgZGiYLHsasvTCv+Bjm33/E+thofNIlEQs7tUdBaHCDzpntDlxyMAc3/LEfVx0phmHiM/J1x4FuXAwGOgKcX00EmC9qipb2bKV4QvGE4on28poe+YAAxRMfQOWQmiVA8USzoVWUY9KtPZbvvpBtsp8swCpzsXysZ3lKHCrMpgZtDauswsi92bi2NASDEAnEJgesoCwXg4p6rWiMwgkwXxQeII2bR/GE4gnFE40nOd3zDgGKJ97hyFH0QYDiiT7irCQvpZ0pwc9NhKGsBCVWMxaJnSifXH4xVlvK4ZCqxTbQYorLcM2OA7hRHOvp0kYUlR0+Hqb0LTBvXgNbrwGiiO0Qnx7x4WJQSW8SbVE6AeaL0iOkbfsonlA8oXii7Rynd14iQPHESyA5jC4IUDzRRZiV56Soj2LesgYoLYG95wD51p6j9jJ8XrIXXxxci+3NrC5t7nT0OG7YKYQUUWi2jRBVpOaIT0b5I7NdPtvYDlwMNpYcn9MjAeaLHqOuHJ8pnlA8oXiinHykJQomQPFEwcGhaYojQPFEcSHRvUFSnZTMOf/EB9064DNRI+VwhKif0kAziKKyA7KO4gaxG2WMEFMstz8Ge6+BPuHIxaBPsHJQjRJgvmg0sCpxi+IJxROKJypJVpoZWAIUTwLLn7OriwDFE3XFSy/WSgKKdd6LMBzah58TWuPTLh2wpGM8ioIa3pESZLPjin2HcEN2ES6JSILzqlu8eoyHi0G9vIH00xsEmC/eoMgxGkuA4gnFE4onjc0ePqcrAhRPdBVuOttEAhRPmgiQj/uUgHXBTJhXiauKnRCFZQ1Ynhov39jzXXIsqozGBuduIa46vnpPFq7LLcf5zeLgaJPQ5JooXAz6NNwcXGMEmC8aC6jK3KF4QvGE4onKkpbmBoYAxZPAcOes6iRA8USdcdOT1VKBWUNBLhASDuO2dbD89CVOVBTjcyGiSELK+thWLnG0P16E68Wxnht3ZiExMg6Vo26FI62X/Jw0vnndSjhbtoGtZ/8Gd6pwMegSNTuQQC0B5gtfhkASoHhC8YTiSSAzkHOrhgDFE9WEioYqgADFEwUEgSZ4TCD42YkwZmfIzx2MCMUn3ZPF0Z5EZERFuByrd04+bth+ECOvmILo7IPy8aCa5mwZg7JHZtUroHAx6BIvO5AAxRO+A4ogQPGE4gnFE0WkIo1QOgGKJ0qPEO1TEgGKJ0qKBm3xhID5uy/EdcfFYsdIjHy0R6qTsrFdK3zSNRFfdG6P/NDgBocz2x0YfPCIEFL2YYQ43hMs6qVIzdbrIjhjO8AZGn7OMR+KJ55EiH31ToD5ovc3ILD+UzyheELxJLA5yNlVQoDiiUoCRTMVQYDiiSLCQCOaSkBcfWxdNh+m7euB/BzYnHb8kNgWn3RLwlcpcSizmBucIayyCiOFgHLDH/vxl8wjMIobfOQWHIry+18SVyCnyB9yMdjUQPF5PRFgvugp2srzleIJxROKJ8rLS1qkQAIUTxQYFJqkWAIUTxQbGhrWBAKmX5bB+tUHMBTmocRqwZI0qdBsB/ycGAOHoeFCszHFZbhmxwGxI2U/euQVimK1Tjg6dELVJWMQ0ncADOERKCqtaoJ1fJQE9EGA4ok+4qxULymeUDyheKLU7KRdiiJA8URR4aAxCidA8UThAaJ5TSYgFYQ17vodFnHMJ78oDwukQrNdO2BbTJTLsTvmn8CN4ljPjWJHSpuiUiAoBIZr7kBZ8xixKyVMFJ3t6XIMdiABvRKgeKLXyCvDb4onFE8onigjF2mFwglQPFF4gGieoghQPFFUOGiMjwmEPHqzuLknT9x87ER6y+b4qFsHfCbElOyIsAZnNojdJwOy8uTdKGN2H0R4hU3ekSLVRbH3HQzboBG1R3t87AKHJwHVEKB4oppQadJQiicUTyieaDK16ZS3CVA88TZRjqdlAhRPtBxd+nY2AamorHS7jvS30yCuKRb/OA0GrG4fg087tceSjgk4EWxtEFyQKCw7NOOQLKQMzTgMi10UmhVjORI7w/aXkbD1H0rwJEACggDFE74GgSRA8YTiCcWTQGYg51YNAYonqgkVDVUAAYonCggCTQgIAek4j6EgFygvRfCsJ2ttWNgpQd6N8lVqvEu7Issrcc3OA7h++wFckJ1X3V+IMfaeA1Ax/sF6rzx2OTA7kIAGCFA80UAQVewCxROKJxRPVJzANN1/BCie+I81Z1I/AYon6o8hPWg6gZD7rxbXHpecMVBBYgoWd4jGJ+0i8GtcK5eTJBWexNidmfKtPT2OFMCR2lPc1PMiJJHG8v1CGI7lClFlIHemuCTJDlohQPFEK5FUpx8UTyieUDxRZ+7Saj8ToHjiZ+CcTtUEKJ6oOnw03ksEzGtXyMd5apozJAzlj8yGM7qNLH4c+e4DfGbLFUd74kStlEiXs3Y4XoRRuzIxKr8CfbbvPKO/dKyncvxDMO7ZDOvS92HMzoA9rQcqr50kz8dGAlohQPFEK5FUpx8UTyieUDxRZ+7Saj8ToHjiZ+CcTtUEKJ6oOnw03osEpDoopi1r4AwJF7tDhpxz5EYSUcI2fIuNjhP4KMyOhdFWHA0LdmlBwolijNydhatFodm+h/LlYz3OVm1hOHkcqCj7U7BpGYOyR2bxqI9LouygFgIUT9QSKW3aSfGE4gnFE23mNr3yMgGKJ14GyuE0TYDiiabDS+e8TOCMxWBpEX4+8CsW5m3CorgoVJhNLmdrd7IEo4WQMnJPJvpnHz2nf8XdU2HvNdDlOOxAAmogQPFEDVHSro0UTyieUDzRbn7TMy8SoHjiRZgcSvMEKJ5oPsR00IsE6lsMlucfwncZP2Np8X58k9AKZRazy1ljisvk+iijd2fK1yAbxdXHFE9cYmMHFRGgeKKiYGnQVIonFE8onmgwsemS9wlQPPE+U46oXQIUT7QbW3rmfQKuFoPB0x9Axf4dWJnUDgvF1cffJLdDqRtCSquScgxPz8bwlp1xwXkjEbLsA3GdcgYc8cmoHH4Lj/J4P5Qc0Q8EXOWLH0zgFDomQPGE4gnFEx1/AaDr7hOgeOI+K/YkAYonfAdIwH0CrhaD1vemwbxuZfWATlHSxGzEyuQ4LOzTFSvbNkOJ1eJysqjScly1N1vUSMnExQdyYWrRuroWimjWz2bDmH8EztAwVA0fJ8SVFJfjsQMJBIqAq3wJlF2cVx8EKJ5QPKF4oo9cp5dNJEDxpIkA+biuCFA80VW46WwTCbhaDEpFZYOfm3jGtcfSLTq2y8bClr4JP/44D4tjI/F1cqxbQkrz8koMEztSRhXYcMmBIwjKzTwlzAhlJigE5fc8J65E7t5Er/g4CfiGgKt88c2sHJUEqglQPKF4QvGEXw1IwA0CFE/cgMQuJHCKAMUTvgok4D4BtxaDopCsZdVCeVDpCmJHWq/aCSxL58GybL68I+XbDrFY1CleCClxKA5yvSOlWUUlrtx7SK6Rcvm+QwiyCwFFtJqrj933gj1JwD8E3MoX/5jCWXRIgOIJxROKJzpMfLrsOQGKJ54z4xP6JUDxRL+xp+eeE2jqYvCcnSmiSGyl2YzvEttiUecELE+JRVGQ1aVhYZVVGJpxWD7aM2TfYZj++ncholzh8jl2IAF/EmhqvvjTVs6lPQIUTyieUDzRXl7TIx8QoHjiA6gcUrMEKJ5oNrR0zAcEvLEYNGbthbQDxVCQB0dcMmCxwPzTUjgNQJXJjO87tMHCjvFYnhqPk24IKSFVNlyeX45hPYZjSGg8Qg3VN/1IQo1UG8WYnQGEhLM+ig/eBw7ZMAFv5AsZk0BjCVA8oXhC8aSx2cPndEWA4omuwk1nm0iA4kkTAfJxXRHw1WJQElSMezbD8u1nMBw/BmfzlqgqK8EPsVFY3CkBy4SQciLY9Y6UILsDl5YaMfqPvbhq3e9oVlElVBShyojmSO2J8vEPwHRov/yxXaqVEtpMV/Gjs/4l4Kt88a8XnE2tBCieUDyheKLW7KXdfiVA8cSvuDmZyglQPFF5AGm+Xwn4czEoCSrWN54Wt+vkwGY04AfpaE/HBHyVGofCkCCXflvtdgwWt/VINVJG7MlGREUFYBS7Uuw28awQVKo1FdguHcvrkF3SZIfGEPBnvjTGPj6jbQIUTyieUDzRdo7TOy8RoHjiJZAcRhcEKJ7oIsx00ksE/L0YrOv2HklI+X7IUCwxHsdXKXEoCA126Z3F4cCgg7miRkqWEFIy0aKs8rRnnLD3HoSqwaPFLUGl3JHikiY7uEvA3/nirl3spw8CFE8onlA80Ueu08smEqB40kSAfFxXBCie6CrcdLaJBAKxGDRtXg3rvGm11x9LVx/bew5AyOPjYBe7R36Sd6TEY1laAo65sSPF5HDioszqHSmjxJ+WpWJHirwL5dRWFFEfpfy+aayR0sR3hY8DgcgXcieBGgIUTyieUDzh1wMScIMAxRM3ILELCZwiQPGErwIJuE8gkItB6RiPIz6l1tigWU/AtHWt+FhcWSz+tZuM+CU+BgulGilp8ch3Y0eK0elA/6yjspAi/WldXFY9vsWKypukG3yGug+HPUngLAKBzBcGgwQonlA8oXjCrwMk4AYBiiduQGIXEqB4wneABDwmoKjFYGkRLKsWwrTrdxiO5sJwsqDWH7sRWC2ElEVdkvBlcjvkh7k+2mMQ1yb3O5SP0eJYz5idB9FGCClVw8ehasR4jznxARKQCCgqXxgS3RGgeELxhOKJ7tKeDjeGAMWTxlDjM3olwJ0neo08/W4MASUvBoOfnVh9LfFprXL8g3CIW3vW//oFFoujPUtEwdmjYSEuXZeElD45x0SNlExcMe7fiDWHuXyGHUjgbAJKzhdGS/sEKJ5QPKF4ov08p4deIEDxxAsQOYRuCFA80U2o6agXCCh6MSh2ogTPmSquPN4KZ0iYfItOza4RqW6Kee0KOMTVxL8M7I8vm5vwVdFeHDHY3aLS09oCw8M6YIT4097M643dgsZO3HnCdyCgBCieUDyheBLQFOTkaiFA8UQtkaKdSiBA8UQJUaANaiGgaPHEQ4iiTAo2ZG3E0sO/Y2mkAbnB4hpjN1pXSyRGhCdhpBBSOpgjINViMRTkwRGbJIraFsOybD5MW9bIRW2lYz+n12lxY3h20RABLeWLhsKiG1conlA8oXiim3Sno00hQPGkKfT4rN4IUDzRW8Tpb1MIaHUxKAspJw7gq02L8GVLK3KahbqFqdOxkxi984BcbLZzXiFgMonitdJop5q4uaf0mXmA2PHCpj8CWs0X/UVSnR6rSjw5UVSCY4UnER0ViYhw974AZx7KQ1zbVjCK++vdbYePnaoK7u4DKu3XNioEBoElp6DsjO9JKnWHZpOATwlQPPEpXg6uMQIUTzQWULrjUwJ6WAwa0rdi26LpWJTYWtRIaY9DEe79HJ8iCSm7D4qCs1nonisVr5V+nnfCNvBKVF5zNwUUn76ZyhxcD/miTPK0SiKgCvGkuKQMw8f9E/kFJ2qjdsXgfpj2+EQhRovS33W0197+Au9//g0cDgdsdgeGX3Yhnnn4Trnn4hWr8ci/3zznqQ1fv4GQYCsonjA5SIAEziZA8YTvBAm4T4Diifus2JME9LAYPOMKZCGAbGjXUlx/3F4uNpsd4V7h2A6FRRglbu0ZvTsLvUXhWUd8Msofmc0XSGcE9JAvOgupqtxVhXgi7Th59a3PcfM1Q9A+Ngbf/LQBDzw1E29MexADz+92DvCN2/Zg3L3PYea/78PF/Xti195MXPO3J/D29IdxQe/OWPT1L3hi2tv4/K2nz3g2JTFW7MQwUDxR1StMY0nAPwQonviHM2fRBgGKJ9qII73wDwE9LAaDpz8gF52Vdo3Iu0dO/SX9vbFdtBBSqm/tyYwMdwt6/IlijBLHekaEJaHH8In1PyMK3hqz9wGi2C3rpLiFVvGd9JAvig+Cjg1UhXhydnx2ph/EtXc9iQVvTEWXtMRzwvfDms2Y8sh/sfidZ5HSIVb+fO+hd+GhSTfipjGXyeLJUy+/h00rz919IvXlzhMdZwRdJ4F6CFA84atBAu4ToHjiPiv2JAE9LAatC2bCvGphdbBr6pdIZ8el/z7t701to7AoLQGLOyXgYHP3apq0K6vCVa27YURpEC56/02YMvcKsSQc9qTOMO3bAYhrlaUmFZutmPgUXziVE9BDvqg8RJo2X1Xiyf7MHMz96CusWr0Rwy65AI/fN77O4FRUVmHMHY8hO+coJtw8EieLS7Hih99kMaW5ULQl8eTR/7yFi/p1R1CQBQP7dsPY4RfDYhYFqSieaPqFp3Mk0FgCFE8aS47P6ZEAxRM9Rp0+N5aALhaDp115LOsnYidI1di7YF6/Cob8XPlmHWkXilPUKDSII/dOUQz29x5d8WWYDV+mxmK/u0LKyRLcsH0/rtl5EF2OFoo9LmfWPKy8dhJsl41tbKj4nAII6CJfFMCZJtRNQFXiyaY/0vHS7E+xfc8BXNinC155+h5YrZY6PZs262MsEbVNgsTnc/IKMPaqv+DJB26FWVTs3rBlN7746ie0EF+Isw7n4bufN0KqofLy1Ml1iicRoXXPofaXSvrhVmrFZTa1u0L7ScBnBGp2FlvM4kcw8duxyiqHz+biwCSgFQLMF61Ekn74g4BVfH+Rdl/o4fuLc+emaqQJKTCEnbmzxHk0B4ZWbetEvqX8GL7YtAyLHEexr0WEW2FJLCzGlRnZGLb3EAZkHYHZ4YSh90AY7/onnB/NgOPnr2GIbgPDRVfCOOa2Oses+RnArQnZyS8E9JQvfgHKSRokcLK06ozPq0o8qbG88EQRLh77v3h4yl9x89gh5zgs7TK5f+pMrF78urzTpKZGyr13jMVdN484p/+7n36NaTM/xuZv58q7T84+tlMjMmjt3aoRhc5+KbTmJ/0hgaYQqNlRHGQxyb+/Kq+yN2U4PksCuiAg5YvUKpgvuog3nWwaAeaL+/ycu7dg69tPYXFaPBaJgrMZUe4JKZHllbh83yEMO27DFeu3ILy84oxJDX97WBZRzm41PwO4byF7+poA88XXhDn+6QTO3mSgSvFEcmjAqCkYM2yQXMfk7Pbki+9g1S8b8fOi12o/dcPdTyE8LARzX/7HOf1X/LBeiC0zsH75HISGBLHmCXOGBEjgHAI8tsOXggTcJ8BjO+6zYk8S4DEEz94BuX7Kd1/Iu3W2t2ouRBRRI0UUm01vGenWQGZxC+eA7DyxIyUbI3dlIraoVL65p3L8Q+I4UTicYjcKm3IJMF+UGxs9WKYK8WTd7zuweftejL5iIKLEVr2PF6/CCzM+wuznH8CgC7rjp3VbMPWldzHnhQeQ2iEOny75Xi4I+9KTk8VxnPOxT9RKGXXrI/Kuk7/fdS1mzVuMbh2TcF6PVBQcL8KEh16E2WzGkneflWPOgrF6ePXpIwl4RoDiiWe82FvfBCie6Dv+9N4zAlwMesZL6m1M34LgOaL4a/HJ6oKzoh2MDMOa+Bj8ktAay1LjcSLY6tbAHfNPYHh6ljjicwjnH8qHIzYJlTdMgSO1h1vPs5N/CTBf/Mubs51JQBXiyfrNuzDhHy+hUhSCrWkTx4+CdAxHaku/XYuHn5mDj2c9ge6dk+AQZxr/8/qHWLziF5SJbXlS3ZPhl/XHo/97CywWMx57fi4WLv+5dqzYNtGy8NIhofqcJcUTpgkJkMDZBCie8J0gAfcJUDxxnxV7kgAXg418B0QR2qC3noVp5+/nDFDVtS9+bW7B8uQ4rIgyIyPI6NYkrUrKMDTjMK4Su1IGV4UA9z4PiOK1bMohwHxRTiz0aIkqxBMpME5x6PBY4Un55pz4tq1kEcSdlnnoCOLatoZRVO8+vZWWVSD3aAEiwkMRHXXmNj+KJ+6QZR8S0BcBiif6ije9bRoBiidN48en9UWAi8EmxFsIKCHPTqy+ree0Vvav+dXHb8TnQx8YKx/pWZ7STvyJw2+x0XAYXIspQTY7Buccx5D2F2BoXF+0NAY3wVA+6i0CzBdvkeQ4jSGgGvGkMc419hmKJ40lx+dIQLsEKJ5oN7b0zPsEKJ54nylH1C4BLgabFltj1l5Yls6DaetaOOJE7ZLrJsKR1kse1LhnM4KnP1Q9gVT9VZR+LwwNwtdpCVie1A6rEmNQUs/NnadbJf0KtpczFFc4IjHEEYEu4r8dcUl17kqR7EFZSfVxIjGnMyqGdVSaFuIznma+eBEmh/KYAMWTOpBRPPH4PeIDJKB5AhRPNB9iOuhFAhRPvAiTQ2meABeDvg1xyP1XwyCJGae1ymsnCeEjDIb3X8ZP7WPwVWocvk6OxeGIMLeMiT8hrkE+kIfLe4/ChfF9YD61kyVIlBCQRBycfsexKEIrCTq2/le4NTY7NUyA+cI3JJAEKJ5QPEFOQVm1GM9GAiRQLwGKJ3w5SMB9AhRP3GfFniTAxaBv3wHz2hWwLJhVK6A40nqg/O6p8q4RaWeKac9WGHIzYczJxB/2E1guhJTlKbHYEhPllmHN7E5cUgxcebQcw5cuRUSFdA3ymeUCIASU0mfmyXNKNwUZyorhbNlGCCpD3ZqDnf4kwHzh2xBIAhRPKJ5QPAlkBnJu1RCgeKKaUNFQBRCgeKKAINAE1RDgYtD3oTLk58IkbuhxtIypPdJT16ySmBI0/yVI/Y+EBYsdKfH4StRJ+VnsTqkwm1waahKXVlyYfVQuODs8PRPtj9fseHGi/L4XYV0wG8bsjNpxJPFEuiKZzX0CzBf3WbGn9wlQPKF4QvHE+3nFETVIgOKJBoNKl3xGgOKJz9ByYA0SqHMxKG1c4K7ggERbEk6Cpz8AFBwR+0eqA1FmseC7xLZiV0osVorjPfmh7hWPTT12AsPSs4WYIq5BPnwUxjpiWv7ILDjiUwLiqxonpXiixqhpx2aKJxRPKJ5oJ5/piQ8JUDzxIVwOrTkCFE80F1I65EMCXAz6EG5jhxa39ARPf1DsEtlXXWhWKv4qRBRJ+3CK/17frpUspEjHe/a0bO7WLC3FTZ9DMw5hmNiVcvm+HIRUVsnPVQ25HlXXTHBrDHYCmC98CwJJgOIJxROKJ4HMQM6tGgIUT1QTKhqqAAIUTxQQBJqgGgJcDCozVPIRntlTT9VKqd4y4kjsIq5FzoXhZGGtqJIVEYovO7aXhZR1sa1gM7l3DfKgg0cwTIgpw9Oz0DKpp9BnjDBtWQNHak9UjrilweNFyiTmH6uYL/7hzFnqJkDxhOIJxRN+dSABNwhQPHEDEruQwCkCFE/4KpCA+wS4GHSflb97Skd4zOtWytPaew6Qj9eYNq9G0JyptVcfw/Dn1Tong6xYkdJOrpPyXUo8isyuhRRp7J65x4SQchhXCiGl55ECwBKC8rseg6P7Bf52WfHzMV8UHyJNG0jxhOIJxRNNpzid8xYBiifeIslx9ECA4okeokwfvUWAi0FvkfTfOJKAYvn+i+oJTx6HUdzWc3Y7+dJn2DjnQSxPiMbXYldKZmS4Wwa2O1mCK8WOFKno7IA23eEcx4Kyp4Njvrj1GrGTjwhQPKF4QvHER8nFYbVFgOKJtuJJb3xLgOKJb/lydG0R4GJQ5fEU9VFCnp0ojvPk1TpSee0k2C4bK9/aY1k2X9ROScfOECO+bhshbvBJwKaWYXLtFFctTNRFufTISQzblYmhxyrQrMdFYjfKhbouMMt8cfXW8PO+JEDxhOIJxRNfZhjH1gwBiieaCSUd8QMBiid+gMwpNEOAi0ENhFIIKGZRr8Rw7Ejt8Z6GvCqeOxUrKnOFkBKHH9q3QbnF7BKC0elAv0P58u090hGfFASj6tKxsIk/tU3YYV32PsxrV8IZGgbbhUNRNWK8y7HV1IH5oqZoac9WiicUTyieaC+v6ZEPCFA88QFUDqlZAhRPNBtaOuYDAlwM+gCqCoY0f/sZrJ/PQbnZhO8T24iCs3FYIa5BzgsPccv65IIiXLk3C0MSL8R5/cbAKK5Vti6YCfOqhWc8X7MLxq1BVdCJ+aKCIGnYRIonFE8onmg4wema9whQPPEeS46kfQIUT7QfY3roPQJcDHqPpdpGOlvscLSKxSZzuVxw9mtxFfKOVi3ccqlFeSWGnLDjqnUbxDXIhxFWaTtV0FbcEBQtdrY8MgsIbebWWErvxHxReoS0bR/FE4onFE+0neP0zksEKJ54CSSH0QUBiie6CDOd9BIBLga9BFKlwxiz9oqaKBlwxCXLtUwsC9+EZeWnsjeHxC6UpR0T5GuQ1yTEoMro+vYeq92OAVl5uEoc7xkh/rQtKoWzZQzsnfvCtHODPK6950BUDr9FlYIK80WlL7pGzKZ4QvGE4olGkplu+JYAxRPf8uXo2iJA8URb8aQ3viXAxaBv+aptdOOezQiefuqGHad0DbJoorZskdWKb5PbyrtSvk2KxfFgq1uudcsrFMd7ssXtPYfQO+dY7TO2S8eg8rrJbo0BUUvFmL0PjrSe7vX3YS/miw/h+nRohxjdtfjnUxO8MDjFE4onFE+8kEgcQvsEKJ5oP8b00HsEKJ54jyVH0j4BLga1H2NPPQwWt/dIu1FqmjMkDFVX3QJjXjaM+3fBeXgf1sa2FgVnY8Xxnjjsb+7ekZyY4jIhpBwSBWezMTi3CPa/qedBAAAgAElEQVRpn9dpmiTgGI/lwZ7aQ76SubaOSkg4yic+KUSUXp665LX+zBevoeRAjSBA8YTiCcWTRiQOH9EfAYon+os5Pa6HgHS75qlfhtbHiOIJ3x4ScJ8AF4Pus9JNT+nWnAWzqwUUcWtO5fBxZwgWtbVSpK/F4mvy7ugIfJ0cL3alxGJDbEs4DK5/wx9is+OS0BgMbZaCy8I6oPX3y2D5bgEMx8XuFHnHi/gjj3/WlcpCQCl9Zl7AjvwwX3STBYp0lOIJxROKJ4pMTRqlNAIUT5QWEdqjZAIUT5QcHdqmNAJcDCotIuqwx7x2BUziSmJTZjpQUVZrdGFIkCyiLBc7Ur5PbItSt65BdqKPONIj1UmRdqV0Onq8WpU5Jc6cTaT8vmkB233CfFHH+6lVKymeUDyheKLV7KZfXiVA8cSrODmYxglQPNF4gOmeVwlwMehVnLocLHTSkHP8diR2hE0Uo/2xfYxcJ2WFEFRymoW6xSexsBhXChFlmBBTBmTnwew4c7uhdHuPVNw2EI35EgjqnLOGAMUTiicUT/j1gATcIEDxxA1I7EICpwhQPOGrQALuE+Bi0H1W7Fk3gbOvPJZ6Vdw9FfLulK1rax/aHBMl70iRbu/ZJv7bnRYprkG+fP9hsSvlEIZkHEJop76w9xoIw7EjsKf18PsOFOaLO1FjH18RoHhC8YTiia+yi+NqigDFE02Fk874mADFEx8D5vCaIsDFoKbCGTBnJAHFuGerXCPF1mMgbJeNxf+3dyfwVVSHHsf/NwtZCMhOWJVFVBRBbRFEK7KKolZbLbWKu0C1PEHQuoIbFaVatbIUd8qrioKtCwKyVCxI9SFgtYoIoiSEfU0Cyc3NOzMkYTGQCbmTO3fmNy2fNuTcM+d8zxzC/Jk5J7Q5x+zcc7tCWzfua1dSshQutNc02WCeQnmnXXP7qZSPzDbIBYmJFbbdegLlrKzN6rdyrS76+gc1M9sgV2rXngrPUHEB5kvFRpRwT4DwhPCE8MS9+UXNPhIgPPHRYNIV1wUIT1wn5gQ+EuBm0EeD6dGuWLvnlB4pE0crlL/bfFm6+nfIXhdlbqumZveepppttkHemp7qqCfW2ij9zO49vU/qqZ98+ZUSN5jdgL5epuL6jVScUVfFdRuo4JdDVNwg01F9TgoxX5woUcYtAcITwhPCE7dmF/X6SoDwxFfDSWdcFiA8cRmY6n0lwM2gr4bT851JMOugpEwyAYp57cbeVeeQ3XSKTu2qhdffojm7v9MH+T9oZdgKWio+6uXtUc8163X2DxvU/bsNarmj5HOV2J3HelIm8ZsVKk7PMNskdyh3Rx/mS8VjQQn3BAhPCE8IT9ybX9TsIwHCEx8NJl1xXYDwxHViTuAjAW4GfTSYcdKVxGX/sgMU+yhdCzY1TeFu59vbIiu9VllPvi/apTmL/qb3tU1LmjVSUcIhWxcfps/ttmxXn2/Xm19ZOv1M8wpRj8vKSlprsSSsW63itJoq7HGpfT67Ta+Mk+ynYkyz6jdWvlmY9sC2WL/PfImTi8ynzSQ8ITwhPPHp5KZb0RUgPImuJ7X5W4DwxN/jS++iK8DNYHQ9qc2ZgBVWJM+fLuXlqqjjWSrsP/CwH7TCjhom2NieWkOzzGs9M49vZr/mszvFrKHi4KgVjqh7rtRLddV3TY4azzLnLT1KnkxJu/dq8zqRKXTAEe7SRwXXjDzo95gvDsAp4poA4QnhCeGJa9OLiv0kQHjip9GkL24LEJ64LUz9fhLgZtBPo+nfvhy0o09CogrNIysftWxk1klpoffN7j3ratd01PmQeVWo44Zt6mPWSumzxjyVYhagDZn6VBw5+POmXOSEjtoz7I+EJ45kKVQdAoQnhCeEJ9Ux0zhH3AsQnsT9ENKBahQgPKlGbE4V9wKEJ3E/hIHpgLUmSWhrjrQnT6kTRu3rd8m6Kd/Wy9C/WmRq/rGNNc88nbLT4VMp1lop55tXe3qY9VJ6r16v2nv27qu3ZC2WsHndp+DyIWXGzJfAXG6e7CjhCeEJ4YknpyaN8poA4YnXRoT2eFmA8MTLo0PbvCbAzaDXRoT2OBGo8fLjSvp4dlmAEmnX0axfcpmKOnVTZPN6rfjLCL1vQpTZ5qmUrxsc46RKJZqtkDtnbVLfb7PV+9t1am928zk0RGG+OKKkkEsChCeEJ4QnLk0uqvWXAOGJv8aT3rgrQHjiri+1+0uAm0F/jWeQemPt3GPt2mMt7hpp0bas62UL0pY8lZKVkaaZ7VrY66UsNE+m7E0yr+k4OJrtzDMhSpb6rs7SuWs3KPmci5QycKgdqOzKK3RQA0UQiK4A4QnhCeFJdOcUtflUgPDEpwNLt1wRIDxxhZVKfSpAeOLTgQ1wtxJWLlPqk6ULvZrtfKz/mk16QuY/e0xw8s9jMzXLPJEy24QpWbXTHUmlhIvUbeNOnd+so/ou+lgtP/m3+VxI4a5mUdkLr9q/K0/eLiWYQOfAMMfRCSiEgAMBwhPCE8ITBxOFIggQnnANIOBcgPDEuRUlESA84Rrwo0Dqk7crYeWKkq4VqzjFhCTJNRTavcP+vcI+Vyi0c7u+XrfchChNNcs8kfJpZj3HWyG33bLT3ga5z5psnWU9lWI95VLypItVv/U0zN5BowlR/HhxxbBPhCeEJ4QnMZyAnDp+BAhP4mesaGnsBQhPYj8GtCB+BAhP4mesaGnlBJLmTleieQrFegqksMel9tMh1qKzxQ0yy61oR6RAC/J+0LxV/9Tc1LC2ma2RnRwZewvVfe16e60U61fD3Hz7Y5EWbbTn7olOqqAMAo4ECE8ITwhPHE0VCgVdgPAk6FcA/a+MAOFJZbQoG3QBwpOgXwH0vzyBiHnXZ/m8V/TBxi80xzyZ8kWjOo6grK2QO1hbIa/ONr+y1H7wEwo1aGJ/NmnxLCW/O8VepyVyfEezi89gRcwTKgnrVsta8JYDgYoECE8ITwhPKpolfB8BI0B4wmWAgHMBwhPnVpREgPCEawCBwwukD+ltf3N9RrreN+ukzGrTVB+aNVPyk5McsdUvKFL3tCbqtTdN5z87TnX2FOz/XFpNKT9339dpGfvClOZt9v2e+Z71xIwVuOxbFDfTXl+FI9gChCeEJ4Qnwf4zgN47FCA8cQhFMQRKwsYQuyFwLSDgSIDwxBEThQIqUGPaeCXNm3HQeiZ7E0Na2NJadLa5Zpsw5ftjMhzpWFsh/yR7s717j7WLzykbzVbIBx5JyVJ4/y4+VpCSsO7bshK8BuSI2deFCE8ITwhPfD3F6Vy0BAhPoiVJPUEQ4MmTIIwyfYyWAOFJtCSpx5cCZvecGtMmKunj2XaAEmrTXqFTOyv3jF52sJE8f7pWKl9zmtXVrBYNtaRRbYUTExxRNN2Zq96r19uv93Rfk6P0cHj/5w5YfPbAygoGjjBPoPR1VD+F/CdAeEJ4Qnjiv3lNj1wQIDxxAZUqfStAeOLboaVjLggQnriASpW+FahovuydPFoL8tdpllknZa75tTk91ZGFtRVy13UbzYKzWeq3ap2O3WZe3THbKx96FF54tQr7D3RUJ4X8J0B4QnhCeOK/eU2PXBAgPHEBlSp9K0B44tuhpWMuCFR0M+jCKakSgbgVqGi+JC77l1ImjTZPqZjddhJCWtqknlknxbzeY4KUFWYrZKdHm63WVsjWorPZ6rY2R8mRiB2mRNqcooJf3coWyE4hfVaO8ITwhPDEZ5Oa7rgjQHjijiu1+lOA8MSf40qv3BGo6GbQnbNSKwLxKeBkvlgBSvKM55Swcd1Ba6VsqplmFp1tasKUZlpgFp3NrWHWOHFw1Cwo1LkmQLGeSum7KkuNc/coctxJKrj0erNLTycHNVDELwKEJ4QnhCd+mc30w1UBwhNXeancZwKEJz4bULrjqoCTm0FXG0DlCMSRQGXmS8LKZUqe9ZoSVy7fvxCseSLFeoKkMCFBHx3b2H69Z7YJU9bUreVY4ZSNZitkE6RYT6Z0uOYBqcXxjj9LwfgWIDwhPCE8ie85TOurSYDwpJqgOY0vBAhPfDGMdKKaBCpzM1hNTeI0CHhW4Kjmi1l0NmnxHIXydyvSsKmSP3hj/y46JWHKqnq17RDF2gp5cYtGdrji5KhrtkI+L1TbLDybo97fZKluQVgF/a/iiRQneHFYhvCE8ITwJA4nLk2ufgHCk+o354zxK0B4Er9jR8urX+Cobgarv5mcEQFPCERlvhwQpiT8sEqJKxbv75vZZSc3JVnzjrNe77GeSmkq63UfJ0dCcURnZG8xr/dk69yuV6j98V1//DHr3MsXqzg9Q0XHd5DSnT/x4qQNlHFXgPCE8ITwxN05Ru0+ESA88clA0o1qESA8qRZmTuITgajcDPrEgm4gUJFAtOdLaHOO0u67+uDThsxTJyYIsY7ikFl0tv2JmpNpnkwx2yEvM4vOWr/n5MjcnafeJkjpVbOlzup5vTLWfafUJ0dK5gkYu+76jbXntnEqbpApqx32dszmCHfpY/8eh/cECE8ITwhPvDcvaZEHBQhPPDgoNMmzAoQnnh0aGuZBgWjfDHqwizQJgagJuDFfkhbPUvK0Cea1nlwVp9VUwcCRCm3ZYL/aE2neRuGuve0nRKynVHa9/oRmp0c0q1VTLWiVqV0pNRz1rYbZrafr+m3q+9V35hWfbLU1u/nYAUqj5tLePIV2bLO+Kqtr77V3SqnpCuXtNk+onEqY4kjZ/UKEJ4QnhCfuzzPO4AMBwhMfDCJdqDYBwpNqo+ZEPhBw42bQByx0AYFyBVydL+aVmopeoyl7UsW83lOYmGivj1K66Oyq+rUdj1qr7bvUx+zc02f1ep1tdvKxwpV9aYoVoJhfCYkl/998mZahvQNHqKhTN8f1U9AdAcITwhPCE3fmFrX6TIDwxGcDSndcFSA8cZWXyn0m4OrNoM+s6A4CXpgvSXOnq8YbE/Y9KGK/wWP+j/nv2roZmtm2ub0V8iITqhSYcMXJkV5YqJ+t3VC2FXKTXfkl9R7waROg5D0xw0l1lHFRgPCE8ITwxMUJRtX+ESA88c9Y0hP3BQhP3DfmDP4R8MLNoH806YnfBbwyX+yFZpcvUih3p0JbN9qv1STNKwk3TJCSl5yk+a2a2IvOzjG/cjLSHQ9N+03WVsjZ5qmUbHVet0mJ1vorps5w5x4qGPA7JZhXimReMYo0b13hkzKOT0pBRwKEJ4QnhCeOpgqFgi5AeBL0K4D+V0aA8KQyWpQNuoBXbgaDPg70Pz4EvDxfkt95RcnvTtkHab1+U7KwbLFJPj5vXN/euWdW62Za2rSeItaitA6OOnsK1MOEKNbrPb1WZ6l+QopkAhv7MGuiFFw0UOEev3BQE0WiIUB4QnhCeBKNmUQdvhcgPPH9ENPBKAoQnkQRk6p8L+Dlm0Hf49PBuBPw+nxJXPYv+4kUayedcMduSnljvBJWrjjIeUtaiv00ivV6z/zjmmhHqrNFZ62tkE9bv9V+vae3CVI65ViLzJrDvNKzZ/AoRdp1irvxjLcGE54QnhCexNuspb0xESA8iQk7J41TAcKTOB04mh0TAa/fDMYEhZMicBiBeJwvqQ/dpITs7/b3qGytFPMqTkJIS5o31KyOJ2v2yW30dbFZ78Th0Wh3vr1zT1/zik+PDeb1oftfsF/pibRoa9dgBTnWjkFFHc8q+z2HVVPsMAKEJ4QnhCf88YCAAwHCEwdIFEGgRIDwhEsBAecC8Xgz6Lx3lEQgugLxOF+s9VFSJo6y10axjki9xkrYatYtOeDYM+xx+8mR7F0bNXfdJ/ogtF0fpRRqj70gbcVHstmtp8u6jfZTKX3W7VCb5NpKWPt12QfDXfvYWzBzVE2A8ITwhPCkanOITwdEgPAkIANNN6MiQHgSFUYqCYhAPN4MBmRo6KYHBeJ2vphtkK2nQOzwxIQkpQvOWl8f7smQPcVhLVrypuZu/q/mtGykrNrOF509dvtus+istRVyts75Pkcp4Yj23D2BJ1CqeE0TnhCeEJ5UcRLx8WAIEJ4EY5zpZXQECE+i40gtwRCI25vBYAwPvfSYQBDnS8qE+5W4YrG+aFRHs82Cs7NaN9WnzRqqyLzy4+RIKwybAGWDeqc1V/cO/dTyk4+UtGyRIpnNVWzWS4m0bm8HOBwVCxCeEJ4QnlQ8TyiBgAhPuAgQcC5AeOLcipIIBPFmkFFH4GgFgjhfSsOTA822pyRrzk9P0+y6yZprtkTeZhahdXqcuGn7vqdS1mTrTLMVclKk2DyR0sY8mTLRaRWBLUd4QnhCeBLY6U/HKyNAeFIZLcoGXYDwJOhXAP2vjEAQbwYr40NZBA4UCOJ8SVi5TKlPHrxeSaTdqYo0b6OkeTMUMUBLmjewn0qZ3baZvmxY1/FFU3uv2Qp5zXo7TOmVm6SGa76xdwoqvPBqhbv2dVxPUAoSnhCeEJ4EZbbTzyoJEJ5UiY8PB0yA8CRgA053qyQQxJvBKoHx4UALBHW+WDvnJM+frtDmHBWZNVMKLh9sr6FSFqoUl2zhY97kWV8rXe+bbZBnme2QPzw2U/nJSY6umZCpo1POFvVdvS9MOanfzUrI3a2kf723b70W84pPYY9LVdh/oKP6/FiI8ITwhPDEjzObPkVdgPAk6qRU6GMBwhMfDy5di7pAUG8Gow5JhYEQYL4cPMxWqFJj2nh7J59Im/YKrVuj0N6S7Y5NnrI3OUELWzY2QUpzzTZhyvfHZDi+Thrm7tsK2Vp0tocJVGoVFNqfLRg4IrBPpRCeEJ4Qnjj+I4SCQRYgPAny6NP3ygoQnlRWjPJBFuBmMMijT98rK8B8ObKY9WRK8rtTzFbIOYrUz1Tiso8UysuTStaW/aphbbPgrHm9x/z6t1l0NpyY4GgIkooiOjNrk3kqJUu9N+zWcZcOtXcNCtpBeEJ4QngStFlPf49KgPDkqNj4UEAFCE8COvB0+6gEuBk8KjY+FFAB5kvlBt5aLyXl+TEK7dx28AfNKzpbel6iD9cvt3fv+cC85rOlEovOttixW70yWui8pmeoW2qmUkPOXg2qXOu9V5rwhPCE8MR785IWeVCA8MSDg0KTPCtAeOLZoaFhHhTgZtCDg0KTPCvAfDm6oUl+5xX7iZTSI9ylj71uStq9VyuUn2svOru0aX379R5rrZTPG9dzfKJnvt6lX7Y6JxBPohCeEJ4Qnjj+o4GCQRYgPAny6NP3ygoQnlRWjPJBFuBmMMijT98rK8B8qazY/vIJP6yyF361dumJtGhrfyNp8SwlT5tgByjWUZyYpFBRWBsyUsuClAXHZSovOfmwJ/7m6TfUIH+v9g4araJO3Y6+gXHwScITwhPCkziYqDQx9gKEJ7EfA1oQPwKEJ/EzVrQ09gLcDMZ+DGhB/AgwX1wYq7xddqhSXC/TLDybo5SJo0vClGIVN2qhvHMv0sdrFmtOnUR7rZQ1dWuVNaLDhq368KWZ9tfW9sl7hv3RhQZ6p0rCE8ITwhPvzEda4mEBwhMPDw5N85wA4YnnhoQGeViAm0EPDw5N85wA86V6hsRaK8UKU4obZNontJ5aqfHKODtkWVWvttm5p5neb9tUXdZt0t0LV9hlitNqKv+Jt6qngTE6C+EJ4QnhSYwmH6eNLwHCk/gaL1obWwHCk9j6c/b4EuBmML7Gi9bGVoD5Elv/9CG9Sxpg9kEu3cKn5HfsdVSuGRnbBrp8dsITwhPCE5cnGdX7Q4DwxB/j6I1eWMuyOdsa0BvtrXwrCE8qb8YngivAzWBwx56eV16A+VJ5s2h+osa08UqaN8M8ZlISnpRsgWy/smPWPFH6/ld6onler9Tlm/AkXFSknI1bFYkUq1lmAyU62LN6d26+9hYUqn7d2geNR/aWfK+Mj6vtaFIvTSFzwa/fmr/v+udAAIHDChCecHEg4FyA8MS5FSUR4GaQawAB5wLMF+dWbpW0du5JXL7IBCU1Fe7QVZETOpUtQOvWOb1Sry/Ck8lT39GfJr9RZppu9qieOPZ2nXFqu3Kds3I26/YHxuvLld/Z32/epKHG3HWTOp28b9VhwhOvXJ60AwHvCBCeeGcsaIn3BQhPvD9GtNA7AtwMemcsaIn3BZgv3h8jP7fQF+HJ/86Ya54eqaWzO5+qwsKwrh8+VuGiiP7x0iPljt0Nwx/Tth279OqE+5VgnlAZeu/T2rBpm9587kHCEz9f7fQNgSoIEJ5UAY+PBk6A8CRwQ06HqyDAzWAV8Pho4ASYL4Ebck912BfhyaGi1w171H595+Wn7ioXu99v7tCxzRvbT6dYxyvTZumZF6brk5mTCE88dXnSGAS8I0B44p2xoCXeFyA88f4Y0ULvCHAz6J2xoCXeF2C+eH+M/NxCX4UnU6fP0cx5/9bq77M14dHh6ti+Tblj9/bsRfr9mL/Y37/ysl4a8/RfddOV/XXdgH6EJ36+2ukbAlUQIDypAh4fDZwA4UnghpwOV0GAm8Eq4PHRwAkwXwI35J7qsK/Ck1HjXtQny77Slm079di9g3Vu147lYn/3Q46uvOUhtW7ZVCv++61ZXDZRU/98j9q3O67c8KRmapKnBi1ajTmmZrJd1Y7cwmhVST0I+E7AWks50aysXCM5wV5geW+BtVMKBwIIHEmA+cL1gYBzgRTz88Xa8ZOfL87NqqtkUXHE7I1Wsp1IdZ00AOepyj4VzJcAXCAe6mLunvBBrfFVeFLas7HP/k2v/n2ePps9uVz6XlcMV4+zT9fdQ6/Sjp25Gnrf0/rsP99oqSmfZIKUQxeMrZ3uz/AkI21feLI7n/DEQ3OUpnhMwLwBaIcmNZKsvz6FtDdc5LEW0hwEvCdgzRfrbrCA+eK9waFFnhNgvnhuSMoaZLIThfy9s3xM8KsSRzFfYjJkgT3pzrwAhCczZi7UvWOf1/K5z9thyIHHzt156tr/t3r07pt1UZ+z7G8t/Xylrv7dGL02aZROOaEVu+0EdnrQcQQOL8BrO1wdCDgX4LUd51aURIDXELgGEHAuwHxxbkXJ6Av44smTR56aop7nnKFTT2qjjZu36eaR45SamlK2285Tz72pWQv+rff+OtYWPPPCIfb2xJPHjVRGeqpGjXtJ//x4mT6c8XS5T55En90bNTapl2b/i/r6rfkqrsrzc97oDq1AwFUBwhNXeancZwKEJz4bULrjqgA3g67yUrnPBJgvPhvQOOuOL8KTwXf+UQuXfF5G37JZI43/wzC1atnE/r27xkzWu3MXa8XcF+yvrVd0Hh//qpZ/aa13kqAT2rTUHb8doJ92OtH+/qGv7cTZmDpuLuGJYyoKIiDCEy4CBJwLEJ44t6IkAtwMcg0g4FyA+eLcipLRF/BFeGKx7C0oVHbOZtXKSFeDesc4krJe4SksDKt+3doHlSc8ccRHIQQCJUB4EqjhprNVFCA8qSIgHw+UADeDgRpuOltFAeZLFQH5eJUEfBOeVEnhkA8TnkRTk7oQ8IcA4Yk/xpFeVI8A4Un1OHMWfwhwM+iPcaQX1SPAfKkeZ85SvgDhSTkuhCdMFwQQOFSA8IRrAgHnAoQnzq0oiQA3g1wDCDgXYL44t6Jk9AUITwhPWDA2+vOKGn0oQHjiw0GlS64JEJ64RkvFPhTgZtCHg0qXXBNgvrhGS8UOBAhPCE8ITxxMFIogQHjCNYCAcwHCE+dWlESAm0GuAQScCzBfnFtRMvoChCeEJ4Qn0Z9X1OhDAcITHw4qXXJNgPDENVoq9qEAN4M+HFS65JoA88U1Wip2IEB4QnhCeOJgolAEAcITrgEEnAsQnji3oiQC3AxyDSDgXID54tyKktEXIDwhPCE8if68okYfChCe+HBQ6ZJrAoQnrtFSsQ8FuBn04aDSJdcEmC+u0VKxAwHCE8ITwhMHE4UiCBCecA0g4FyA8MS5FSUR4GaQawAB5wLMF+dWlIy+AOEJ4QnhSfTnFTX6UIDwxIeDSpdcEyA8cY2Win0owM2gDweVLrkmwHxxjZaKHQgQnhCeEJ44mCgUQYDwhGsAAecChCfOrSiJADeDXAMIOBdgvji3omT0BQhPCE8IT6I/r6jRhwKEJz4cVLrkmgDhiWu0VOxDAW4GfTiodMk1AeaLa7RU7ECA8ITwhPDEwUShCAKEJ1wDCDgXIDxxbkVJBLgZ5BpAwLkA88W5FSWjL0B4QnhCeBL9eUWNPhQgPPHhoNIl1wQIT1yjpWIfCnAz6MNBpUuuCTBfXKOlYgcChCcBDk8cXB8UQQABBBBAAAEEEEAAAQQQQCDwAoQnhCeBnwQAIIAAAggggAACCCCAAAIIIHAkAcITwhNmCAIIIIAAAggggAACCCCAAAIIHEGA8ITwhAmCAAIIIIAAAggggAACCCCAAAKEJ1wDCCCAAAIIIIAAAggggAACCCCAQHQFQsXmiG6V1IYAAggggAACCCCAAAIIIIAAAgj4R4DwxD9jSU8QQAABBBBAAAEEEEAAAQQQQMAFAcITF1C9VuWOnbkqDIfVoN4xXmsa7UGgWgUikWJt2rJdyclJqlenVrnnPtJ8sT6fvWGzGjesp+SkxGptOydDwIsCzBcvjgptiqVA6c+JWjXTdUztmgc1hfkSy5Hh3F4TKAwXaePmbWqW2YC/j3ltcGjPYQUIT3x8cezOzdeNIx7X5/9dbffS+sPplWfuVqa58eNAIGgC8z5aqttG/VlFRRG7661aNtEDI67TGae2s7+uaL7MnLdEdz4yqezzw26+XDdeeWHQGOlvAAWsa3/EgxM07v4h6tfjTOZLAK8BulyxgBWMWPNk0af/sQuf3qGdppi/c/HzpWI7SgRLIFxUpJFmrixYvNzueFpqDd124y91xcXnMV+CdSnEZW8JT+Jy2Jw1etzE1zTt7QWa8fxDqpmepgFDHrBvGMf/YZizCiiFgI8E5i/6TNk5W3RBzzOVl79Xw0c9q4hZ8mnaX0bbvTzSfMnL36Mu/X9rhyVDrvm53v1gse559Dm988of7DnFgYBfBX2dQPEAAA0KSURBVKzw/Te3PmyHhgeGJ8wXv444/ToaAetpk16/Gq6EUMj+GXH+eZ21c1eumjSuz8+XowHlM74WePHVmXpy8jTNff0JNaxfR69Mm2X/HWzRP55VRs00/j7m69GP/84RnsT/GB62Bz0uH2b/K+HIIQPsMm+++6Huf/wF/Wf+iwqZH/AcCARZYOr0DzTm6b9q+dznlZSYqCPNl5nz/q2RD03Q0tmTlVIj2WY76+JbdNVlvfXba38eZEb67mOB9Ru36uJr7tLwQVfoD89M1dh7BpU9ecJ88fHA07VKC7wzZ7H9ZOJbLz6s41s1/9HnmS+VJuUDPhZ45Kkp+sfsRVrw5lP2Uyer1mTpkuvu0btTHtVxLTL5+5iPx94PXSM88cMoHqYPp/a8XqNvv1aXXfAzu8TSz7/R1b97RB/9/RnVPab89R58zEHXEDhI4KYR4/Tt2izNm/ak/ftHmi9W8PjCq+/Z/ypSegwY8qDaHtdMD995A7II+E7Aetrq4mvu1rldO+m+YQPt+XFgeMJ88d2Q06EqCNz58CTNnL9EZ3fuYN8IWmvMDb3xF+pyent+vlTBlY/6U8CaI5cPGq3aGem69fpLNeO9hfYT8pPHjWC++HPIfdUrwhNfDef+zlg7UJ9y3nV6/L4h9msK1vHfb9bqlzeN0sypj6lls0Y+7TndQqBigf+dMVfWv3w8/dBQ9TzndFU0X15/e77em/txWdBineG6YY+ax0vT9czDQys+ISUQiCMB6xUE6/q2jhef/L0SEkIHhSfMlzgaTJpaLQJX/vYhE8Zn6/oBF6ipeVXnrfc/0sdLv9SMF6wnUZod8e9j/HypliHiJB4SsML5G29/3H6FOitnk/2/Y+66SZf07cbfxzw0TjSlfAHCEx9fGda/DFoLYl7a7xy7lzx54uPBpmuOBeZ8+Kluu//Puu2mX+qm3/Qv+9yR5gtPnjjmpaAPBLJzNqv3gBE676zTlJGRZvfobfOI9SkntNKvLjnPfpqR+eKDgaYLUROwwpMWTRtp7L2D7DqtNYJ+0m+QBl11kQYPvJj5EjVpKvKDwH2PvaDP/vON/vHSGDsseeaF6Zo89R29NmmU/XOGny9+GOXq74O1HURCNZyW8KQakGN1Cusd2wt6dtGIwb+ym/DGO//UqHEvsuZJrAaE88ZcoHTdnztv+bUGXt73oPYcab6UrnnymVnzpEbJmidnXjhE15g6WPMk5sNKA6IsYO08Nf6ltw6q9WWzoF+XM9rriou6q2/3zvY76Yf7+cJ8ifKAUJ3nBe4d+7z9dO+bzz1YFp6c1udG3fDrC/U/5vUd5ovnh5AGVqNAryuGm50OTygLG62nHTv0uK7sH7WYL9U4GJyq0gKEJ5Umi58PPD7hVTswecs8NpqenqoBg9ltJ35Gj5ZGW2Dq9DlmgdipGjLwEvXv3bWseuvddGt19yPNl9y8Pep8wWD7XxAHm8+z2060R4f6vC5w6JonzBevjxjtq04B6xWdG4Y/pj89eKvO7dJRU96coycmva6pz96rTie35edLdQ4G5/K8wND7ntbCJZ/rjckPqLXZsdB6zc0KIEsXjOXni+eHMNANJDzx8fDv3J1n/zD/cuV3di+bNKqnKc/cU7Z1no+7TtcQ+JGA9aqO9crOoccd5ikU6wmSiuZL6W4KpZ8fesMvNOjqi5BGIBACh4YnzJdADDudrITAnya/Yb96UHqU/myxvma+VAKSor4X2Lx1hx544mX9c/Eyu6/WP2JZr1H/+uc97a+ZL76/BOK6g4QncT18zhq/dfsuFRQWKrNhPWcfoBQCARY40nyx3mP/IXujvSBg6es7Aaai6wiI+cJFgMB+AWvhy5xNW9WiSUMlJyf9iIb5wtWCwH6BwnCRNpj50tzMl/IO5gtXixcFCE+8OCq0CQEEEEAAAQQQQAABBBBAAAEEPCNAeOKZoaAhCCCAAAIIIIAAAggggAACCCDgRQHCEy+OCm1CAAEEEEAAAQQQQAABBBBAAAHPCBCeeGYoaAgCCCCAAAIIIIAAAggggAACCHhRgPDEi6NCmxBAAAEEEEAAAQQQQAABBBBAwDMChCeeGQoaggACCCCAAAIIIIAAAggggAACXhQgPPHiqNAmBBBAAAEEEEAAAQQQQAABBBDwjADhiWeGgoYggAACCCCAAAIIIIAAAggggIAXBQhPvDgqtAkBBBBAAAEEEEAAAQQQQAABBDwjQHjimaGgIQgggAACCCCAAAIIIIAAAggg4EUBwhMvjgptQgABBBBAAAEEEEAAAQQQQAABzwgQnnhmKGgIAggggAACCByNQG7eHqWkJCspMfFoPu74M9+uzdbSz1fa5RvWq6PuZ3U67GcLw0V66/2FZd+/sGdXpaelOD4XBRFAAAEEyhMoNr8ZggaBmAgQnsSEnZMigAACCCCAgBOB+x57QdPf+/CwRT+ZOVE/7TdYY+8ZpP69uzqp8qjLvPT6+3r2xRnqdc5P1Kplpm6+6qLD1pW/p0APPvGyCsNhzZy3RHNeHaemmQ2O+tx8EAEEEEAAAQRiK0B4Elt/zo4AAggggAACRxDYtGW7tu3YbZf421tz9d7cjzXlmXvsrxNCIbU+tqmWfbFKrVs2UZ1jMly1tMKTt2cv0pvPPej4PHn5e024M4jwxLEYBRFAAAEEEPCmAOGJN8eFViGAAAIIIIDAIQKTprytv745Wwvfeuag7/T7zR0ac9dNOu2U43XHQxOVaF7fWbn6B3216nsd36q5+d6N+uOk1/XJsq90Uttjde9tV6vDSa3tOr7P2qA7Hp6kL1d+ZwKYphp4eR9ddsHPyrUvLzyxwpF7xz6v+Ys+U1FRkV3HY/cNVrvWze06CE+4jBFAAAEEEPCHAOGJP8aRXiCAAAIIIOB7gcOFJyd3v1YTx96uc87soMtvHq2vv/1et1x7qQlOmumuP0zW7tx8Xd6/u71GyYSX/67atWpq8rgRKiwM69xf/I86nNhagwderG/WZOmBP76kd6c8quNaZP7Is7zwZNzE1zR1+gea8OgwJScl6Z0PFus8c56fdelIeOL7K5IOIoAAAggESYDwJEijTV8RQAABBBCIYwGn4Unn007UyCED7J6OHveS/vvNWr02aZT9tRVuWOuofDZ7sv20yK13P2WCl+GqlZFuf3/EA+P1i/7nasjASxyFJw89+Ypd53PjRuqUE1spZF4lOvDgyZM4vuBoOgIIIIAAAgcIEJ5wOSCAAAIIIIBAXAgcTXjy2LN/0/+tWFkWnpQGJl8seEnWkySPj39VLZs1Oqj/1oKwtw++wlF48n3WRt16z1P69rss87pQgi7u0013/e43qpmean+e8CQuLi0aiQACCCCAQIUChCcVElEAAQQQQAABBLwgcDThiRWOfLr863LDk/fmLtHvx0zS/82abF65qXib4yMtGGuFKAuXLNdYE9YMH3SFrr3ifMITL1w0tAEBBBBAAIEoCRCeRAmSahBAAAEEEEDAXYFohydbt+9Sd7PmSZ9zf6rRt19rN/7Dj1eooLBQPz//7B91przwZPxLb6njyW115uknadfuPPUZMEJ33PJre40V6+DJE3evCWpHAAEEEECgugQIT6pLmvMggAACCCCAQJUEjhSe/OXxEer201PsBWOtIGPE4F/Z5zr0yZMFi5Zp6H1Pa8XcF+zvL/70C93+4Hjt2Jlrf229evPQHTfokr7dHIUnT5hdfJ7/23tlnz3rJyfrz2NuU5LZ8YfwpErDzYcRQAABBBDwlADhiaeGg8YggAACCCCAQCwEtu3YpYKCsBo1qPOjRV9L23O413YKw0XauHmbGjesWxaalH6GJ09iMZqcEwEEEEAAgegLEJ5E35QaEUAAAQQQQMCHAlZ48tRzb+r0U47X8a2b6/e3XnnYXubl79Hv7nnavAIU1tLPV2rOq+PUNLOBD1XoEgIIIIAAAsEQIDwJxjjTSwQQQAABBBCookBWzmZ9vep7u5Y6x2To9A7tDltjuKhIHy5eXvb9bp07KKVGchVbwMcRQAABBBBAIFYChCexkue8CCCAAAIIIIAAAggggAACCCAQFwKEJ3ExTDQSAQQQQAABBBBAAAEEEEAAAQRiJUB4Eit5zosAAggggAACCCCAAAIIIIAAAnEhQHgSF8NEIxFAAAEEEEAAAQQQQAABBBBAIFYChCexkue8CCCAAAIIIIAAAggggAACCCAQFwKEJ3ExTDQSAQQQQAABBBBAAAEEEEAAAQRiJUB4Eit5zosAAggggAACCCCAAAIIIIAAAnEhQHgSF8NEIxFAAAEEEEAAAQQQQAABBBBAIFYChCexkue8CCCAAAIIIIAAAggggAACCCAQFwKEJ3ExTDQSAQQQQAABBBBAAAEEEEAAAQRiJUB4Eit5zosAAggggAACCCCAAAIIIIAAAnEhQHgSF8NEIxFAAAEEEEAAAQQQQAABBBBAIFYC/w9YuQ87yL9nhQAAAABJRU5ErkJggg==", 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