notebooks/Example4_Polymer_Ensembles_Represented_by_Experimental_Molecular_Mass_Distributions_Colab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qJN4M4IQfWlV"
   },
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/olsenlabmit/Polymer-Ensemble-Similarity/blob/main/notebooks/Example4_Polymer_Ensembles_Represented_by_Experimental_Molecular_Mass_Distributions_Colab.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "zMN73XuAfrN0"
   },
   "outputs": [],
   "source": [
    "# @title Copyright & License (click to expand)\n",
    "\n",
    "# MIT License\n",
    "\n",
    "# Copyright (c) 2023 olsenlabmit\n",
    "\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
    "# of this software and associated documentation files (the \"Software\"), to deal\n",
    "# in the Software without restriction, including without limitation the rights\n",
    "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
    "# copies of the Software, and to permit persons to whom the Software is\n",
    "# furnished to do so, subject to the following conditions:\n",
    "\n",
    "# The above copyright notice and this permission notice shall be included in all\n",
    "# copies or substantial portions of the Software.\n",
    "\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
    "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
    "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
    "# SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_iZyqlBRb7ce"
   },
   "source": [
    "# Download Necessary Files\n",
    "\n",
    "Downloading local python functions, tables, and dataset\n",
    "from Github Repo [Polymer-Ensemble-Similarity](https://github.com/olsenlabmit/Polymer-Ensemble-Similarity.git)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_o8vi_-QrKij"
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/olsenlabmit/Polymer-Ensemble-Similarity.git\n",
    "!mv Polymer-Ensemble-Similarity/dataset .\n",
    "!mv Polymer-Ensemble-Similarity/tables .\n",
    "!mv Polymer-Ensemble-Similarity/utils ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mSdNpNOZcEdk"
   },
   "source": [
    "# Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "veQ7XTe5KHgx",
    "outputId": "dee994ce-1852-41ae-e96f-804aff587e3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Processing triggers for man-db (2.10.2-1) ...\n",
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      "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DGL backend not selected or invalid.  Assuming PyTorch for now.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable.  Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n",
      "Import Required Libraries Done!\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import shutil\n",
    "import sys\n",
    "import os.path\n",
    "import json\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "if not shutil.which(\"rdkit\"):\n",
    "    !pip install -q rdkit\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "\n",
    "if not shutil.which(\"grakel\"):\n",
    "    !pip install grakel\n",
    "\n",
    "if not shutil.which(\"dgl\"):\n",
    "    !pip install dgl\n",
    "\n",
    "if not shutil.which(\"umap\"):\n",
    "    !pip install umap-learn\n",
    "\n",
    "if not shutil.which(\"pyomo\"):\n",
    "    !pip install -q pyomo\n",
    "    assert shutil.which(\"pyomo\")\n",
    "\n",
    "from pyomo.environ import *\n",
    "\n",
    "if not (shutil.which(\"cbc\") or os.path.isfile(\"cbc\")):\n",
    "    if \"google.colab\" in sys.modules:\n",
    "        !apt-get install -y -qq coinor-cbc\n",
    "    else:\n",
    "        try:\n",
    "            !conda install -c conda-forge coincbc\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "assert shutil.which(\"cbc\") or os.path.isfile(\"cbc\")\n",
    "\n",
    "from utils.load_networkx import networkx_feat\n",
    "from utils import macro_unsupervised as unsup\n",
    "from utils import plot\n",
    "\n",
    "print(\"Import Required Libraries Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YteAN12TdcD2"
   },
   "source": [
    "# Load Polymer Ensembles Represented by Experimental Molecular Mass Distributions in Example4\n",
    "\n",
    "\n",
    "\n",
    "The raw data of these polystyrene molecular mass distributions (MMDs) about retention time ($t$) vs signal is provided by Prof. Brett P. Fors (Department of Chemistry and Chemical Biology, Cornell University) from their publication, [Kottish et al. “Shaping” the Future of Molecular Weight Distributions in Anionic Polymerization, ACS Macro Lett. 2016, 5, 7, 796-800](https://pubs.acs.org/doi/full/10.1021/acsmacrolett.6b00392)\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?export=view&id=1kXCZ7NjJZ3Q_rgUScDgkdTQtm_emoCMI\"  width=\"50%\">\n",
    "\n",
    "Six polystyrene MMDs from the work of [Kottish et al. ACS Macro Lett. 2016, 5, 7, 796-800](https://pubs.acs.org/doi/full/10.1021/acsmacrolett.6b00392) are generated by varying initiator (sec-butyllithium) addition rates (constant (C), linearly ramped (L), and exponentially ramped (E)) and addition time (40 min and 60 min). C-40, for example, refers to a constant rate of initiator addition with an addition time of 40 minutes.\n",
    "\n",
    "The parameters of these MMDs from  [Kottish et al. ACS Macro Lett. 2016, 5, 7, 796-800](https://pubs.acs.org/doi/full/10.1021/acsmacrolett.6b00392)\n",
    "are shown in the following tables.\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?export=view&id=1DO4uDVihgHgJWjHfQHvJvkUbDEOEqJCN\"  width=\"50%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hzjj-4AEssfC"
   },
   "source": [
    "\n",
    "Since Example 4 directly loads the raw data of polymer MMDs, the preprocessing and data loading of Example 4 is different from [Example1](https://github.com/olsenlabmit/Polymer-Ensemble-Similarity/blob/main/notebook/Example1_Two_Component_Polymer_Ensemble_Colab.ipynb), [Example2](https://github.com/olsenlabmit/Polymer-Ensemble-Similarity/blob/main/notebook/Example2_First_order_Markov_Copolymer_Ensemble_onehot_Colab.ipynb) and [Example3](https://github.com/olsenlabmit/Polymer-Ensemble-Similarity/blob/main/notebook/Example3_Nonlinear_Star_Polymer_Ensemble_Colab.ipynb).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yyegJQVSV7kI"
   },
   "source": [
    "## Loading the Raw MMDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "uwJvO6PjYKeK"
   },
   "outputs": [],
   "source": [
    "data_constant_rate_addition = pd.read_excel(\n",
    "    \"./dataset/Example4/ConstantRateAdditions.xlsx\"\n",
    ")\n",
    "data_linearramp_addition = pd.read_excel(\"./dataset/Example4/Linearrampadditions.xlsx\")\n",
    "data_exponentialramp_addition = pd.read_excel(\n",
    "    \"./dataset/Example4/ExpRampAdditions.xlsx\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CgQfhekGWDV0"
   },
   "source": [
    "## Load the MMD Ensembles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CqRg1bO8RSIJ"
   },
   "source": [
    "For these polystyrene MMDs, the relationship between retention time ($t$ (min)) and molecular mass ($M$ (g/mol)) is as follows:\n",
    "\n",
    " $\\lg (M) = -0.47*t + 10.92$.\n",
    "\n",
    "This relationship equation is provided by Prof. Brett P. Fors and Jenny Hu.\n",
    "\n",
    "Note: Different GPCs might have different relationship between retention time ($t$ (min)) and molecular mass ($M$ (g/mol)). If users want to use their own dataset, users might need to update the relationship equation based on the setting of their GPCs.\n",
    "\n",
    "Take C-40 ensemble for example, MMD_x_C_40 is the $M_i$, which is imported to the function **Similarity_Score_EMD_Calculation** as **ensemble** and MMD_y_C_40 is the mole fraction of $M_i$, which is imported to the **Similarity_Score_EMD_Calculation** as **weight**.\n",
    "The range (e.g. [1927:3478]) is determined by 2% of the maximum signal by referring to [Walsh et al. General route to design polymer molecular weight distributions through flow chemistry, Nat Commun 11, 3094 (2020)](https://doi.org/10.1038/s41467-020-16874-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "3SvkCVi05kzR"
   },
   "outputs": [],
   "source": [
    "MMD_x_C_40 = np.array(\n",
    "    pow(10, (-0.47) * data_constant_rate_addition[\"Chromatogram RI (159)\"] + 10.92)\n",
    ")[1927:3478]\n",
    "MMD_y_C_40_signal = np.array(data_constant_rate_addition[\"40 min\"])[1927:3478]\n",
    "MMD_y_C_40 = MMD_y_C_40_signal / MMD_x_C_40\n",
    "\n",
    "MMD_x_C_60 = np.array(\n",
    "    pow(10, (-0.47) * data_constant_rate_addition[\"Chromatogram RI (159)\"] + 10.92)\n",
    ")[1800:3660]\n",
    "MMD_y_C_60_signal = np.array(data_constant_rate_addition[\"60 min\"])[1800:3660]\n",
    "MMD_y_C_60 = MMD_y_C_60_signal / MMD_x_C_60\n",
    "\n",
    "MMD_x_L_40 = np.array(\n",
    "    pow(10, (-0.47) * data_linearramp_addition[\"Chromatogram RI (163)\"] + 10.92)\n",
    ")[1768:3451]\n",
    "MMD_y_L_40_signal = np.array(data_linearramp_addition[\"40 min\"])[1768:3451]\n",
    "MMD_y_L_40 = MMD_y_L_40_signal / MMD_x_L_40\n",
    "\n",
    "MMD_x_L_60 = np.array(\n",
    "    pow(10, (-0.47) * data_linearramp_addition[\"Chromatogram RI (163)\"] + 10.92)\n",
    ")[1597:3540]\n",
    "MMD_y_L_60_signal = np.array(data_linearramp_addition[\"60 min\"])[1597:3540]\n",
    "MMD_y_L_60 = MMD_y_L_60_signal / MMD_x_L_60\n",
    "\n",
    "MMD_x_E_40 = np.array(\n",
    "    pow(10, (-0.47) * data_exponentialramp_addition[\"Chromatogram RI (161)\"] + 10.92)\n",
    ")[1785:3269]\n",
    "MMD_y_E_40_signal = np.array(data_exponentialramp_addition[\"40 min\"])[1785:3269]\n",
    "MMD_y_E_40 = MMD_y_E_40_signal / MMD_x_E_40\n",
    "\n",
    "MMD_x_E_60 = np.array(\n",
    "    pow(10, (-0.47) * data_exponentialramp_addition[\"Chromatogram RI (161)\"] + 10.92)\n",
    ")[1647:3357]\n",
    "MMD_y_E_60_signal = np.array(data_exponentialramp_addition[\"60 min\"])[1647:3357]\n",
    "MMD_y_E_60 = MMD_y_E_60_signal / MMD_x_E_60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xQ5GAUEIVpEU"
   },
   "source": [
    "# Visualize MMD Ensembles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "id": "kLB6A6TcF_Qu",
    "outputId": "92d1a711-30ac-46a4-c06a-e37e80cf4c0f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RnDq+qJqvpHGLiIggOzu7ooe7Wq3Gy8uL4uLqu7mU8/SAYd2hVRSk58Jfu6FDLNx3FXRqCXuOwoe/QIg/TLoc4pqBVgOFxVBUcorYiFP4+8C+49Aycj+XeCyFym812YUeZOujiI/IQmUuqjyh9rEE9sylNS/Qvw94RkHun2A6l8Wj8oCQy8G/O5SmQ1m6pQ6NxhcCB0HEjWAssmzJ8ogE346WIIipDIwFoPa2vP5F1pJLCCGEEBenH374genTp9tk6AJkZWWRlZXFzp07+eCDD2jfvj1vvfUW48ePt53IASaTiXvvvbcikFFXK1asYOLEieTn51eMlZc62L59O4sWLWLlypW0bdu2hllEuSYbzMjPz+fff/+1GU9OTiY5ObniuKSkpM5zTps2jW7duvHmm2+ydetWdDodLVu2ZObMmcycOdNuYVBxTso79se7/wEhoyuP+x+GjGVw4lkoOlCnqY+WKb/vvbyy6XFrEY+8C1l58OKLL1YUmoyPHICfVwi6ksoCmwX6TI5n7KBds4H2X+BcPZRyMc88Q8qcOUyZMoX333/f7i2fAxHAuCpjocCAblej1lYGHkyGUs7uX2V3jrwz+9BlncAvrFXFWFzvWyqCGQDtg7rg76GsBbIry34HFZPZyOaMvxgbXbkVKj6gI/7aQAoN+XbvcZVLLrmE1NRUjh07hkajYdKkSeTl5fHjjz8CMHHiRG677TZ++OEHSkpKGDZsGIMHD6awsJDs7Gy6deuGr68v69ev58CBAwwcOJChQ4eSkpLCn79+Sbj3afoMuppmbUZz6sQBshLeoE1EGgE+JlLzA0nPLCTK/yxar1Dy87IIVu0lNKD6LSyDu8FD19b+dXVpbX881L+MUP/TYP0SptoDLQAU7rAdM5dB9i+WD2s5a+CkVVUXbQh4xVgyosxlljGNPwSPgvDrLdkjuv1QfBgwQ9BQy7m69C0WQgghhGjE5syZw3PPPVdxPGbMGK6++mo6d+5McHAw2dnZJCUlsWLFCn7//XcOHTrE008/7bJgxvz589m6dSuRkZGKrN+a7Nq1i1tuuYXi4mL8/f2ZOXMmI0eOpLi4mK+//pqPPvqIQ4cOMX78eLZv3y7PnnWgMrtq07pwWEZGBpGRkYqx9PT0plMA1GyCfzS245pAGJwFqhpiZvoTsLUjmO0HnUrNamZndcNE5bvN/wk8SntPS+vRe16H975Oxce/WcX5vcm/s/TfGYp5LomfwNU9n7KdPzWVzecyd8r13LCBoMGDAdi8eTNFRUXs3LmTP//8k40bN2I0GiuyA+YDXavcu/D2j8lqVtmW9kzCShLXzK32y4/tdRMdRk5VjG3+dCK67BMA3Nb6XtoGdqo4d0p3jE+PzK92Pg+1J493no2XpnITzJozP7Elw96mGaXBgwfTsmVLMjMz6dixIzqdjuXLl5OdnU2/fv1YuHAhX375JYcPH+bKK6/k7rvvprCwkOLiYpo3b47JZOLQoUN4e3tXdP3JzMwEUHbmMeRZMgk8oyuzCEyllrGsFZD1C5SeBd/OluyE7FWW7RoAmiBLwKC2zAdhn087iLgFPCPPZYSowDsOfDuBT3tQ2y9AK4QQQghber2e48eP07p1a7y9bbvriYaxePFi/vOf/wAQGRnJt99+y/Dhw6u9PiEhgWnTppGRkcHu3budfv3k5GQ6d+5MYWEhn376KXfeacnXrm2bybBhw1i/fj1arZZ//vmHQYMGKc6//vrrzJhheYZ5/vnnmT17dr3XVt+fyab+HNpkMzNEI5L3j/3xXptrDmQAeLeC3tvg2AzIse06crLMVxHIUGMmzkNXcbzoSeDQVdBrS8U7zt1jxnAicwKbjn5dcd2mo18zosNkAn2s/rF+9ZXiWBMQQED//hXH5b9kRo8eXdFVozz+p9frOb5wIRnTpwOQ7x+uCGQAnD1QcyeVMwm/0GbQZDy8K7Mvontcw6F18/BQe9LaX1krZGfWlhrnKzOVcliXSNfAyq47Q1qOYlv2BoxGI/fccw/XXnstv/zyC61atWLKlCmoVCpMJhP+/v428y1cuJDs7GwiIyNRq9X06dNHcT44OJjg4GDAsg1EUVPGVEY4GyxbJXT9wTsejj0JZz+0ZBJ4RFjGipPAkION/E22Y8a8Gr/+hpaWDc2susCaTKAvBd/a/n+h9juXuWGq5cIGVHwYTr1k/5xKawlo+HUF/54Qchn495JMDiGEEEI0GikpKUyZYqmF5+fnx99//11rTcOuXbvy22+/sXTpUpes4eGHH6agoIDJkyczbNiwOt2zdetW1q9fD8Ddd99tE8gAmD59OosXL+bAgQPMmzePp59+Wjrv1UKCGcJ5SZNtx/z7WAod1oV/N+hepa7E2UVw6F4AjpUpH7BjtEV4qaweBgu2WTJDgoZDaQp4t6Vv+J1sOqq87LNNj/HQyM/QnKudYSop4fTrryuuCbnsMtS1/NJQncsm8PHxoeNDD5H39tuUJidzIlb5oO/nreF4wj98seQzSktLGTlyJB9++CHFxcVcd9117N69m3bt2lEc1Imv/kypuK95p7Hoj/9ClDkKjbrKP1GVmTe+nMu+A3spLi7mnnvuYdmyZezYsYPrr7+ecePGcfr0aQqOFfPtY5VbW3wNARzddQLvSA+aNbNksNQ1xc7T09O2xkxZNmACj3Awmy3bhUpOQ2kaFO0H/XFLVk7WL1CWVv3kZRmWj4bk2+XcdqZzPzOe0RA40BIoKT1rGfNpDxE3g0coFB8DzJYxlQry/8WQs4lTaUbeWhbAruOBXDM8mNYBuyjITWXbQVi+AVKzoW00xEWBrhjSciCnALw9ISQA0nI13HHXPbSMicSnZAsD2qbToXUwfhH9oMWDlroYWSst20kMOZZ1ekZZ1lS469z3MsNSV0Pjbz/44yyzAYoSLR8Z38LxWZY1hIyxBGLUPpbvjV8XCB0HXs63/RVCCCGEqI+3336boiJLvbIXX3yxzs0Z1Go1EydOdPr1v//+e37++WfCwsJ4/fXXKSgoqNN9y5cvr/jcuuNe1TVOmjSJmTNnkpuby7p167jsssucXvOFTIIZwjlms2WriLUOThSdbH4PNJsI+8ZxLPe04lRrD9suJhXyzm2lKD5C8+zVtNC054yxshpwSu4B9p5eQ684y4N87t9/U3r2rGKKmHNZFnWl9vamyw8/sG/sWE7G9lac6xEfSFCgPw8//HDF2IIFCyo+v/rqqwE4m6Xn67UplG/48vAOYNF3myj9PYGt3+ypuD6udwxjrriUMVdcWjFWtQguWDpxmKJNBEb6k59e+b1K+vM4lz02tF5fG2Ap1pr7l6W4ZPAIOPZfS9tclQb8e1u2htSx9onLebe2bJUw5INPG0tNCNRQchI8W1h+jgL6WjqN5P5leTAPHmHZSmEqA90ey0O6b8fqi2ZGP4IWaAPMr9KV2Ww28+2333J8z2KiWqYRGF7EkSNHOJJim3WRmg1gZN68eYpxlUrF1KmdePXVMLy0XtDsdstHdYxFlv8OKjXoT0PBFihNtQQ+AvpYtuoUbIGMHyD3D0unFW0IBAywfE8c+e9UmgppS+yfCxoBsTMg5FJQy7sGQgghhGhYZrOZzz77DLBkZdx7773n9fXz8vJ49NFHAXjttdcICwurczBjw4YNgGXd1pnOVVXdLrNx40YJZtRCghnCOal2ghaRt1vS1J2h9qa0y0qSjw4DKqsEt6kpmFGFSgU3B5xiYV47SsyV9TwSk+bSK7Ij+MST++efinsC+vUjyE7KV20C+/Wj01df8fa3yi0Q7fTJQO3ZKc3DvOnTPpjtSbkVY3/tzqTZllOK69oOalWn9ag1arpd0YGNn1UWmdy36iCXPjoEtVplCUAV7rJkUgQNApUXnFloeRD2jAK1L5SegezV1WdOmA1QsLVO66kXtbdle4NHmKV+RlmmZStKh4/P1dT4BTQB0OwOS9ePuvBuCVGTrF7HwxLocJBKpeKWW27hlltuqRjLzc3lxx9/5LPPPmP9+vUVXWuqYzabeeedd/jzzz954okn6NevH+3atUOrrebXsqZKmy7vWMuHNd92lu+N2WwJ9Ki9Lf8YzGbLdrC0z6HokOXrV3lZatUUHbJkNNVX3l+WD5WXpRCpNgDU/pbPI2+GsGtki4oQQgghXGb//v0V9diGDh163gtkPvXUU5w9e5ahQ4dWm11RnQMHLG8qtW3btvq/9UCRaVJ+j6ieBDOEcw4/bDvW+hWXTH02LwmjuTKQYTKZKUrRQTUdJqxFafWM90vhh8KWFWOHCvMp2tIB386fkmMVzAi5tPrCQbUp6TOUvN92Ksb83p9N2a398AgJqeauSmP6RiqCGTsP5dI7OV/xDzR+UEub+2wYC6H4CD2uaKUIZuSnF3Lyp8dp3c0MmT9YtoW4i3criJ5q6cJhKoGgIZbWo2Yj+LS2bKMAS8Ck5Izl4bj8oTjsSrctuzbBwcFMnjyZyZMnU1JSwqFDh/j000+ZP38+paXVFyzdt29fReEoLy8vRo4cyeOPP86YMWOqvadWKhVofJTHwcMtH/YYckGXCLoES8ZKzu/nuqDUgbkE9FZ7ujK+Br9uEHHTuTocXaTAqBBCiIuSyWQiKyvL3cs4r8LCwlCrXf+Gxp49lRnLNWU3NISNGzfy4Ycf4uHhwcKFCyu2ndeFXq+vCMLExMTUeG1ISAh+fn7odDpOn3bj3+tNhAQzhHPKW0JW5dncdswBZ3KTFMdZp/X0nm7i/qth3qPeeGn1tc7RwUOZ+lVi1rBa15yrd95B4S7ltcERb8D6hZaH68hbIerOypNmAxRst2QueLeyeZ29R5WtT32KcwlJ3MLp11+nzSu1B3cGdA7BQ6OizGjZa2IwmskJDyIiLdsyX6AXzTs1s70x43vI/tXSjcK7DRx5GEpTaQY0i55BWkplvYukv4/ROvTnWtfiFM8oCLvKsvXDWGgJUkQ/atnmUbDFsu2h+b2gDap9LpXWklXRBHl5eVW0eX7ooYeYO3cu27ZtIygoiLCwMFavXo1eb/vzW1JSwurVq1m9ejUPPvggzz77LM2bu+bfU420wRB0ieWjXNFhy89W3npLgAOVJWNGtx+MdWj1q9tn+Sin8rLU34i40RKU8ghz9VchhBBCNDpZWVk23SIudA3VDaNqUOh8fk9LS0u57777MJvNPP7443Tp0qVe91fdimKv4L618mBGYWHdMtIvZhLMEK7nov3zp7MTFMdpJyztUP/3M4y5YzE33HCLZTtE2hdw/L925wjSlNHZM5fE0uCKse0lYfQ7mQqmyqwPlRYCOwMmHeT8Zvk4NdeS2l+aZnmdikmHQ+jlEDQMggZDwS727NwNtK+4JO70LlSYOfvhh8Q98zQajxJL0cmjT1g6WjS/1/JOdf4G8O+LX8RN9GoXxNaDuRVzZEeEVgQz4joXod7VDworsy1q06nbHkUw49D+zoy9zkXBDG2w5d18tTfE/hdazrQ84GpD7W8tCL/K8nERio+PZ9Ei5Xasffv2MWHCBBITE6u9b+HChSxcuJBhw4Zx++23c/vtt+Pn59fQy63k287yEfOoctxUYqmjkfKuMlhRG3MJZP9i+QBLNxuftpYirSGjIPw6y8+TEEIIIYQdVYMC5/NvoldffZXExERatWrFc889V+/7q76B5elZe5aql5cXAMXFxfV+rYuNBDOE40wltmORt7pk6lJDMfvPrFOMpR2v/Acd3/Zc0UavaGj5lCUb5PAD51pfYskOKNwNJae52i+Fw6WBlGF5yDah4ruA1vShMo3evz1orJ+jipOwK+/vymKj5+xP+VpxHJe8GwBDVhZpzwbT4mqD4jwnnlUeJ93JEK/xbGVmxVBOeBBGtQqNyUyr5j/XK5AB0KHrfv5afXnFcVZGJJlpEYQ3q6WDiNrXkp1iLrO8m97iAct42hfgEQItnwavlpYAjyYAtOfayqrD67W+i1m3bt3YsWMHCxYs4LfffmPfvn2ctSpGW+6ff/7hn3/+Yfbs2bz88stMmjQJjUZj99rzQu1lKa7a/B4oSQbdATAVWjJxStMh9eO6FRst72aTv9lSe8ezOcRMhxb3V241EkIIIYQ4p2qNDJ1O5/A8Op2O48ePV3u+a9euFZ8nJSXxyrks6/feew9fX9/qbquWt3flQ0ZNW4/LlZRYnrF8fHxquVJIMEM47uyHtmPx82zHHJCcs58Sg/KX1PFdltR2jUZDjx49lDdETbLszy/LUG5NKNhOcOIEOhflsaeksnZFeqgf6b0DidxpmTO4l+NrzdaHcqZIuf8tNmVvxedHFxgI7gW+duo1VjUoaiPqPUZMWB5UTRoNeaFBhGbm0rrd0ZpvtiMqOoWAwDwK8iu3dCTt70x4s7/PpfxfaulWYsiC5g9A3HOWIpvaQEuqirXwa5THXtH1XpOo5O3tzeOPP87jjz8OWFInly5dyowZM+xuQTl79iz/+c9/eO655xg5ciTXX389V155ZY1FpBqcV4zlo6qYxyDnT8j6yfLzVd66tzalZ+HYE3DqFQi7GjybWTKAAgdZMqDs/UwKIYQQ4qIRFla5RTUtLc3hebZt28bIkSOrPW8+12LQbDZz//33U1JSwnXXXceVVzpWu61qEKYuW0fKAzV12ZJysZO/DoXjjjxqO+YR6pKp0/KVD+8ZJ4vJSrFEKdeuXWu/6I7GBzRWNRYC+kK/A/TZeQt7TiojsEdujnJJMGN/djfFsY9KR2TmsYpjkx6SXoOe71bfARQg2CuXbmF72JNV2eI1KyKE6KIzRDa3/659TdRekbTr78nOPyrHDiffzeAuU8G/r/1uGGrX/PcT9RcWFsYjjzzCuHHjeOaZZ1ixYoXddx2Sk5NZsmQJS5YsoUOHDsyfP59LL73UzoxuotJA6GWWj3Jl2ZC9CjK+g+zfLFtOqmPIhrRPlWNesRD3LDS7S9rACiGEaFLCwsJIT0939zLOq6pBB1eq+mbmzp07a7jSNbZs2cLff1uysS+55BK+/vprm2syMiozno8fP15xTdeuXSsyPLy9vQkLCyMrK4vk5OQaXzMnJ6fi77/Y2FreCRUSzBAOMlfTdlLlmvT3LJ3yH3rGacs71Z6engwcOLB+k6k9aNvneyKyriej8GTFcF5bX4pDPfDNLyOoaw331yIpt5PiuFPYfkJ7G8mtsiskfx/k7IDQWrqBDm7+jyKYkRcaREvtUdRqc+VFft3P1RYwW97NDr3SUlgzexX494D4dyq2frTXH2fnHysqbj21N4dSvwl4essDYWMVHx/PV199RUlJCatWrWLhwoX8/vvvdq9NSkpizJgxTJ8+nVdeeaVO+zDdwiMUmk20fJhKQH8cio9asjayVlpavNak5DQcug9OvWrZihI2Drziao4OCiGEEI2AWq1ukGKYF6MuXboQHh5OZmYm69evJz8/n8DAwHrPM2LEiIrsi5qUb/cAePLJJ2u9vnxrMMDzzz+v2K7SuXNn1q9fz5EjRzAYDNVm1h48eLDi806dOtm9RlRyfc8ccXEwZNsZdN2DRbYuRXGcl27ZX9a9e3eHHtjUKjWPXmobTc3sGUDAgEvQXGaA4WbLxzATDC2FLj9A2/kQdQ/4dQXv1pZ2k4GDLHUjgoYBcDSvnWLO9iEH6TIbvKKU34+zP6ssQQhNAKAGTZDlneaoe6DFg9BiCn26xCvuKfX2JKB3f4idCX32WtbXdw/0/hd6b4WBp6H9QmjzCvTdDR0/s9S1UGlApaF1/1jU2sp/5sYyIye2SZunpsDLy4vrrruONWvW8Ouvv9KtW7dqr33zzTcZNGgQH330ERs3bmzcBaPUXuDbEcLGQ+wT0HMd9NoCYdfUfq/+mKVjz7+tYWMI7BkNp9+E4mO13yuEEEKIJk2lUlW0s9fpdDYF1huzIUOGAJZ179hRfR288kwQgMGDBzf4upo6CWYIx5TZCWZE3Oyy6XOsghm5aZbIaNUIZ315aLzpHnOZYix5dBgBAwYoM0pUKksqe/h1EP0wdPgI+u6DAceg717otQlavwQ9/8bc7whHdL0Vc7bt8xjaK83EPvOeYjxzo5qSNkdgSD4MzoZLMqDjYsv87RZAu/cIavk0nnplYaDclqMswQr/6h9mq+Pt70Vsd2VrzyObT1ZztWisxo4dy549e0hISGD+/Pn07NnT5pqdO3dy3333MWTIEKKjo1m4cGGd3nVoFAIHQNfl0O+AJbOo+f0QeTv49aj+HmMe5K611NnYGg+JN4MuofrrhRBCCNHkTZs2raII53PPPafIZKiJyWTiyy+/rNdrlWdw1PRRtZDonXfeWTE+e/ZsxVzXXnttxeeLFy+udo2ff/45AMHBwTXW9RAWEswQjjHk2I61ed0lU5vNZpvMjNw0ywP+FVdc4dTc7ZspI5y5HfzYOsBelkndZJXFkFuk3LIR36oVAM0mTkRdteKx0cipuXMtn2uD7O79P5OYRlBOvmLsaL7R4fUBxA+KU84nwYwmSaVS0aVLFx5++GF27NjBvHnzqs1SysnJ4aGHHuKuu+6irKzsPK/UCb4dIWYqtP8AOn0BfXZB9z8gYEDt92Z8B9u7wZ5LIXUxGPIafr1CCCGEOK+io6OZP38+YMlyGD58uCKbwZ7ExETGjh3L66+75lnFEf3792fo0KEAfPzxx2zevNnmmjfffJMDBywd4aZOnYqHh2wLr40EM4RjrIMZns3tF5R0QFFprk0nk9y0UtRqNVdffbVTc7fTdkajVwYH9mr2kJp3uJo7anY0RblOXy8NLcIs7Ze0QUE0u/12xfkz77/PmQ8+qHa+1KQMgrKVD2G7j+RhNDn+Dntbq2BG5okccs/kV3O1aArUajWPPvooW7ZsoX379tVe9/nnn3P11VcrilM1KSoVhIyGXpuh268QMcFSH6YmuX9C0n9gUzPYf9O5gqNNJENFCCGEELWaPHkyL774IgDp6emMGDGCyy+/nAULFrBu3Tp27drFn3/+ycKFC7nyyivp3r17tfXHzqd58+bh4+ODwWDgsssuY+7cuWzZsoV169Zx//33M2PGDADat2/P9OnT3bzapkEKgArHnHhBeVxa/24b1ckqVNZ0MBrMFGSV0qF9R0WfZkeU7Uyk86IU9k1Rdj1JOLOWqKB21dxVvSNnlMGMNi38UKsra2XEPvUUqZ99hrlKT+kjU6cSesUVeMcpgwwAqYcyCM5WBhp0eiMHThTQtU39CxwBNO8UiU+QN8V5le0+j209Re9rnah6KhqFXr16sXfvXr755ht+/vln9uzZw5EjRxTXrF69mtjYWK699lruvfdeRo0aZb8bUGOmUkHoWMuH2QwlyVC4HbJWQca3YLQTnDOXQOb3lo+A/pbWw6HjpGioEEIIcQF49tln6dKlC9OnT+fEiROsWbOGNWvWVHt9ly5deO21187jCm316tWLb775hokTJ5Kfn8+sWbNsrmnfvj0rV65UtHMV1ZPMDOGYIqu96TXtba+nIxlbFce5aSWYTdT4DnRd5W/aRMxf2bT4R7m15ODZ9Q7NZ52ZEd/CT3HsEx9Pu/ffV4yZS0tJO7cfzlraoQw8S8vwLShSjG9OdHwrjFqjpnW/GMXYqV1nHJ5PNC5eXl5MmjSJ77//nsOHD7N8+XJ8fHwU15SUlPDNN99w6aWXMnLkSJuAR5OiUlmywMKvs9SbGXgS4maDNrj6ewq2QsKVsLMPnHzJUltDsjWEEEKIJu36668nKSmJL7/8kokTJ9KhQwdCQkLQarWEhobSu3dvHnroIdauXcu+ffu47LLLap+0gV111VXs3buXadOm0b59e3x9fQkODqZv37783//9H7t27aJt27buXmaTIcEMUX+mEjAWKsc8m7ls+iPpymDGiT0FgGuCGbnr1gEQvU4ZHEjO2c+JzN31nu+IVTCjbbSfzTXN77mHyFtvVYylffmlTXFGfUEJOSmWd5hDMnMV5zbvdzyYAdCyV7Ti+OTOlGquFE3dNddcw59//lltG7i///6b7t2789VXX53nlTUQbTC0eh4GpkCnbyDsalBVs8e0cBeceNZSW2NbRzg1F8rs1P8RQgghRJPg6enJbbfdxpIlSzh48CDZ2dmUlZWRlZXFjh07eP/99xk5cmSDZaW2atWqoujnp59+Wqd74uLieOutt0hKSkKn05GTk8O2bduYMWNGRXFTUTcSzBD1pz9uO9bhU5dNn1GgnP/UfkvgpEOHDk7NW5qRQcG2bQCEHNDhUWBQnP9p99x6dX/QFRs4m6VXjMXbCWYAtJgyRXFcnJREvlXhn7TDlXUNQjOVD1in04s5na7M1qiPuN7KYEb26VwKMnXVXC2aukGDBrFt2zauu+46NBqNzfni4mJuu+02nnnmGYqKHP+5alQ0vhB5M3T9CQalWjoEecdXf33xITg+y9Lm9ewnkqkhhBBCCNHESDBD1F+RVbFMj0jwam7/2noqKdNRoM9UjGWnWNqyOpuZUbB9e8UDi8Zgpu3PymyHs3mHOJ5Zfd9na0et6mVo1CriouxHUwMHDcK7dWvF2LEnn8RsMlUcpx6q/Lr983V4G5TBFmeyM6LaR+Dlp+x8cWqXZGdcyOLi4vjhhx84e/Ys7777Li1btrS55uWXX6ZDhw58++23blhhA/IIhRYPQv+DlkCrTw31cIx5cOhu2Hc5FB89b0sUQgghhBDOkWCGqD+91X77mh4U6imnyLaWQ06aJZjhbGaGbu9exXHX9DjC/ZVFODcfrftDnfUWk7goXzy19v9JqVQqWjz8sGIsf9MmUj/+uOI47XBlMEMFtPE0Ka7fe9TxDiRqjZrYHsqAk2w1uThERETwyCOPsH//fu69916b88nJydxyyy1Mnz4do9G5NsCNjkoLUXdCvwPQ5WdoMQW8qum6lPM7bOsEByZCxvdgLD6/axVCCCGEEPUiwQxRf8VWmRk+ritSk1uUqjguyC7DWGYmNjaWyMhIp+YuTkpSHAd07kr/1tcrxval/M7e5Lq1brLOzLAu/mktesoUvNu0UYydeOEFzOcyMNIPKzNSOrVQFnHcfyIfkxMtWq3rZkgR0IuLv78///vf/3jzzTdRq21/9b/11ltceeWV5Obmnv/FNTSVBsKvgnbvwYCTllav4TfaXmcug/QvIfEm2NwMDt4JhXvO/3qFEEIIIUStJJgh6s86FdulwQxli9eCTEtL08cee8zpwj1Fh5VBGJ8OHejd8ko8NMp2rz/ufIkSQ+11BKw7mdgr/lmV2suLdgsXKsZKU1LI+vVXzGYz6UezFOf6dA1XHBcWGzmR6rq6GamHMtAXljg8n2h6VCoVjz/+ONu3b2fUqFE251evXs3AgQM5dOiQG1Z3nqhUEDgQunwHXVeCV4z964wFkPY57OgDx2ZKpoYQQgghRCMjwQxRf/oTymOfGors1VNusTIzI/9cMGP06NFOz11s9YDm064d/t6hXNn9CeV1ZfkcPPtPjXOZzWZSMpUPN62b1159OPSyywjo318xdmrOHHJS8ijRlSrG23dvRlSol2Js3zHHt5pEd4lCU2UbjNlk5vSeszXcIS5UvXr14o8//mDRokV4eCg7fyQlJdGjRw/uvvtutm/f7qYVnidh46BvAsQ8DirPai4ywulXYUdPyK3594IQQgghhDh/JJgh6q9MuR0CT9cU/wTbbSb5mWUAxMRU8+5pHRny8ylLT1eM+bSz1Pro3/p6Wof3VpyrbatJVn4p+lJlTYvocJ9qrlZqft99iuOCbds48NZixZh3gBeBkf50bR2oGE847ngww8NbS4suyha6UgT04qVSqbj77rtZu3atTRtXvV7PJ598Qr9+/bj33nsvnI4n9miDIP5NGHAM2s6H4FFYqtZYKT4Ee4bD/psgf6vteSGEEEIIcV5JMEPUj9kABquuGh7h9q91QI7VNpP8zFI8PT0JDQ11at5iqy0mqNX4nKtfoVKp6N3ySsXpA2f/Jr84g+qcyVS2ZPXUqgkPqu6dXaXI227Ds7kyAHT0+z8Vx83ah6NSqejWxjaYUZ/2sdast5qclGDGRW/IkCFs376dHj162D2/aNEirrrqqgs7oAHgFQ3RD0OPP2FgMrR8BlQettdlfg+7BsDuYZD1q7R0FUIIIYRwEwlmiPop2Gk75sJghk1mRkYZUVFRTtfLsA5meLdsidqrcgtHl+hRaFTaimOT2cjnmx+j1GB/n7x1MKNFuDdqdd3WqPHxof2iRYqxApUyWNOsreV7ap2ZkZlXypks5WvXR5xVEdCUfakYSg3VXC0uFi1btmTjxo088MADNttOANauXcu1116LXu/4z16T4tUCWs+BPrsgYID9a/LWQ8I42DtGioQKIYQQQriBBDNE/RyfZTumDXPJ1EaTgfxi5VaQ/MxSmjd3fhtLkXW9jPbtFce+nkF0ajFcMZack8j6w1/Ync+6XkZ0uLfd66oTNm4cze64o+JY56P8GiPbWYIZsZE+hAYoHy73HMmr12tVFduzBVXjQoZSI2f2pzk8n7hw+Pn5sXDhQk6ePMmcOXPw9/dXnP/999+57rrrLp6ABoBfF+i1EeLfAU2g/Wty/7QUCT06HYyF53V5QgghhBAXMwlmiPrJ/dN2TG0nFdsBBfoMzCjrULgqmGGdmVFeL6Oqcd2m4ecVohjbc/pXu/Ol2MnMqK9WL70EajUmlYYiL2XNgvLMDJVKRff4IMW53U4EM3wCvSsCJeVO7pYWraJS8+bNeeaZZ/j7778JDg5WnFu9ejUjR47kyJEj7lmcO6g0EDMVBp6C1v8HntF2LjJC8luwrTNkfCdbT4QQQgghzgMJZohGw3qLSaneSHGB0TXBDDudTKyF+kVzY5/ZirH0guOk5x+zudZ2m0ndin9W5d2yJWFXXonOqxlmlUZxLrJtZbZLz7bKYMaeI3lO1c1o2VP5MHZqp9TNELZ69+7N77//TlCQ8udvy5YtdO7cmWnTppGbm+uexbmDNghazrAUCu3wif2WriWnIfFm2D0U8jae/zUKIYQQQlxEJJgh6kdtlYHg39NlU1sHMwrOdTJpc65Qp6PMBgO6ffsUY74dO9q9tkPUEAK8lZkL/xz+XDmfnbas9d1mUq7FQw9RaLXFxJdCPDSVGSo9rIIZ2QVlnE63X8ujLqyLgJ7afQaTSd5JFrb69u3LmjVrCAxUbrEoKyvjnXfeoWvXrvz9999uWp2bqD0hajL0OwRxL4DKy/aa/I2wewgk3mLb/UkIIYQQQriEBDNE/VjvG2/9qsumzi227WQCMH78eKfmLTp4EFOx8uE/oE8fu9eqVWq6x1yuGNt+4idOZ++vOM4pKHO4Lau1kDFjMMQr1+Kbd4KU996rMre3TaeUPUcd32piXQRUX1BC+hF54BL29e/fn3/++YfWrVvbnEtJSWHs2LFs2LDBDStzM40PtHoO+u6D4NH2r8n4FrZ3g+zfzu/ahBBCCCEuAhLMEPVj1CmPtUH2r3OATSeTzDLi4uLo1KmTU/PqEhMVx16xsXiEV9+BZVCbm9BY1QH548AHFZ9bbzHx0Krq3JbVmkqtRt13lGLMtySdsx9+WLGVRKVS2WRnOFMENLCZPyHRyqDUSdlqImrQo0cPdu3axZNPPomnp/JnXa/Xc9VVV7HPKvvpouHbDrr/Dp2/B2/bgA+lqbBvLBx+BIyOZ1QJIYQQQgglCWaIujObwVSkHFP7uWx622BGKb169XJ6Xv0xZc0L604m1sID4hjT+UHFWFLqBk5m7QYg2WqLSYuwurdltScno0Rx7FuSgf7oUXLXrasY62FVBHTfsXzn6mZYt2hNSK3mSiEsgoKCeO211zh48CBjxoxRnMvNzeXyyy+/uAqDVqVSQcQN0O8gtH3XfoenM/NhZx/IWScFQoUQQgghXECCGaLuTMWA1R/hGtcFM3KKrLaZZJTSqlUrp+ctPnpUcewTH1/rPZfET8DfK1Qx9tXWWejLClxS/LOcyWQm+1SuYsy3xNKe9tgTT2A2GgHoHq/MpMguKLPpqFIf0V2jFMdnEqU9q6ib1q1bs2rVKq6++mrF+NmzZxkxYgSbNm1y08oaAbUnRD8C/RIg9Arb80UHYO8oS4HQ3H/O//qEEEIIIS4gEswQdWedlQGg8XXZ9Pa2mbgimGGdmeFdh4Kinlofhre/SzGWW3SWjUe+5oyLin8C5KcWYCg1KsZ8SzIAKNy1izMLFgCW7I/QQOXWl33H8h1+3eguzRTHmcezKdGVOjyfuLhotVq+/vprhgwZohhPSUlh2LBhvP/++25aWSPhGQVdV0Lb922LJoOlQOie4bD/BtCfPP/rE0IIIYS4AEgwQ9Sddb0McNk2E31ZASWGQsVYfmYDZWbUsTvKoLYTiAnpohj799h3tttMnAhmZJ3MURxrzaV4VPk+HH/mGcoyM1GpVHRvY73VxPG6Gc3aR6DWVv7zN5vh7MF0h+cTFx8fHx9WrFhB7969FeNGo5EpU6bw5ptvumlljYRKBdEPQZ9d4N/b/jWZP8COnpC54rwuTQghhBDiQiDBDFF3aUtsx1yUmWGdlWE2mSnIshQAdYaptJSS06cVY9512GYCoFV7cG2vWYqxvOIMkjOUQR1HO5kAZFoFM8LigqlafcOYn8+pVy0dY7q1UW41cSYzw8NLS7O2yiKostVE1FdwcDBr1qxh+PDhNueeeOIJXnvtNTesqpHx7Qi9tliyNLxibc8bcmH/1XBqrtTSEEIIIYSoBwlmiLo7+5HtmErjkqmtgxmFuQaMBrPTwYyS06fBpGyj6m2nxWR1YkI60yqssgipoSyAklJlsU9ntplYZ2ZEdmlJ5K23KsZS3nuPoqQkm2BGanYJGbnK4qH10aJzpPJ19kswQ9RfWFgYf/zxB7NmzbI599RTT/Hqq65r39xkqT0sWRr9j0Db9+wXCD0+C5L+AybH/00LIYQQF5vS0lK++uorJk2aRMeOHQkLC8PDw4Pw8HD69OnDgw8+yB9//IHJ6nnAUWVlZXz66aeMHz+eli1b4uXlRXh4ON26deOee+7hu+++q3WOkydPMn36dDp27Iifnx+hoaH069eP119/naIiO9v6RbUkmCHqzl7NDBfJ1ilbgxZklhIQEEBwcLBT8+pPnFAca4KC8AgJqdccl7StDC6U6CMU5zw0KsKDvRxeX5ZV8c+wuGBazZmDSqutGDOXlnLovvtoGeGNv48yeLT/uOPZGS26WBUBlWCGcJBWq+Xll1/mjTfesDk3c+ZMXnjhBae671ww1J4QPQX6H4aIW2zPp30Ke0ZDyVnbc0IIIYRQ+OGHH+jQoQO33XYbS5YsISkpiezsbAwGA1lZWezcuZMPPviAMWPG0KlTJ1auXOnU6+3du5fevXszefJkVq1axenTpyktLSUrK4uEhAQ+/vhj7r333hrnWLFiBd27d+ett94iKSmJoqIicnJy2L59OzNmzKBXr14Xb3c4B0gwQ9Sd9buJ0dNcNnVm4SnFcU5aCXFxcahUjrc8BdtghrcDNTi6tBhBuL8lQ8Q6mNE83BuNE21ZrTMzwuNC8ImPJ3rqVMV43j//kP7pYrq0stpq4kQww7oIaPbpXIrzHe+QIsT06dN56623bMZnz57N1KlTXfauSJPnEQKdvoI2rwNWvz/yN8K2jpDyHpgNblmeEEII0djNmTOHG264gRPn/tYfM2YM7733Hn/++Sc7duzg999/Z/78+Vx++eWo1WoOHTrE008/7fDr7d27l5EjR5KQkIC3tzdTpkzh559/ZseOHWzevJnPP/+c2267jcDAwGrn2LVrF7fccgv5+fn4+/vz8ssvs2nTJv7888+KIMihQ4cYP348BQUFDq/1YqKt/RIhzinLVB6HjXfZ1Nm6ZMVxztlSp7eYgJ1ghgNzatQe3HnJO8z7YwJ6q2BGizDHt5gYSg3knlEGI8LiLFkjrV54gcxlyxTrP/bf/9L5i638e6Dy+oTjjv+ii4gPQ+upUXRTOZOYRvxA57/v4uI1bdo01Go1jz32mGL8vffe48yZMyxZsgQfH8frzFwwVCqIfQJ82sKB25WZb8Z8OPIopH4C7RdBQB/3rVMIIYRoZBYvXsxzzz0HQGRkJN9++63d+l2XXnopDz/8MAkJCUybNo2MjAyHXk+v13PTTTeRnZ1Ny5Yt+eOPP2jXrp3imoEDB3LHHXdQWlp9d8CpU6dSXFyMVqtlzZo1DBo0qOLcqFGjaNeuHTNmzODQoUO8+eabzJ4926H1XkwkM0PUjdkMBmUWAR529n07qECvDJQUZDZQMMPB7igRAa24ud8cSoqVwYwA/8Jq7qhdTnIeZpMy9T60ZTAAGj8/2i1cqDhnyMqixf5/FGPHz+ooLHbs3Vuth4Zm7ZVfjxQBFa4wdepUFixYYJNZtWzZMi699FJ0OjudkS5W4ddCr432i4MW7oadA+DYLKmlIYQQQmBpAz9lyhQA/Pz8+Pvvv+0GMqrq2rUrv/32G0888YRDr/nGG29w6NAh1Go13377rU0goypPT0+741u3bmX9+vUA3H333YpARrnp06fTqVMnAObNm0dZWZlD672YSDBD1I1JB1iliGuC7F7qCF2pMlBSlG+gdT0KdVan5ORJxbGjwQyAzi1GUFairDNxKv8ndCU51dxRs6yTuYpj/3BfvP0r62+Ejh1L6BVXKK7xWPgKHtrKB0SzGQ6cdDw7w3qriQQzhKs8+OCDLF26FA8PD8X4pk2buP322zEajdXceRHy7wm9d0CzSXZOGuH0XNhzKZRlne+VCSGEEI3K22+/XVEk88UXX6Rjx451uk+tVjNx4sR6v57RaOSDDz4ALJkeAwYMqPccAMuXL6/4fPLkydWucdIky98Cubm5rFu3zqHXuphIMEPUjcFObQZt9XvC6ktXkqs4Lso30MqJwEM5V2VmAGhUWkqtghkm9TF+TXjXofls27LaFiaNfeopxbHh6GFaeyoLsSaecDyY0aKzMpghHU2EK02YMIFff/2VoCBl4POnn37iySefdNOqGinPCOj4GfT4G/y62p7P3wC7BkGxFAUTQghxcTKbzXz22WeAJSujtmKbrrBp0yZSUiyNCq6++uqKcb1ez9GjR0lJSanTGzQbNmwALOvu06f67aNVs0w2btzo6LIvGhLMEHVjtPPArAlwydSlhmLKjMrCk8UuyMwwlZZSkqLskuLlRDAjT2egtEzZTcTLO53tJ5ZzOG1LveezLv4Z1tI2mBE0bBgB/fsrr9v4s+I48YQzHU2UwYy8swUUZktLKOE6o0ePZsOGDYRYdRF6++23ef/99920qkYseJglSyNuNqisyloVH4adAyFvg1uWJoQQQrjT/v37ycy0bE0fOnQoAQGueRapyZYtlX/jd+vWjcOHD3PDDTcQGBhI27ZtiYmJISwsjEmTJnH06NFq5zlwwFL0rm3btmi11ZetrJppUn6PqJ4UABV1Y52ZofKytBl0AXvbNIryjU5nZpQkJ4NV9wRnMjPOZikDLiqVAU8vy9q/2/E8j136Lb6edd96k3Wq9swMlUpFy6efZv8111SMxZzaDb0q2zoeOFWA0WhGo6l/V5WI1qF4eGsp01fW3TibmEa7Ic5v8RGiXNeuXfnhhx+47LLLFPs/H330UVq3bs24cePcuLpGSO0JrZ6HsKsg4WoorRKUNWTBnlEQ/za0eMhSSFQIIUTjZTZdfNsEPcJA5fr3zPfs2VPxeU3ZDa6UmJhY8fnBgwcZN26cTe2vvLw8lixZwvLly/nhhx+49NJLFef1en1FECYmJqbG1wsJCcHPzw+dTsfp06dd9FVcuCSYIerGOjND67pIqK40V/lSBjMqkwehoaFOzWu9xUQTGIg2ONjh+dKylcEMT68cVCpLAc/84nRW7HmdW/q9VOf57LVltSfsqqsIv+EGMpctAyD6bILifHGJieOpOtpG+9f5tcupNWqad4rk1K4zFWMp+yWYIVxvxIgRLFq0iDvvvLNizGQycfPNN7Nu3Tr69evnxtU1UgG9ofe/kHClpRhoOXMZHJkCBf9C+w9B7XhXJSGEEA2sLAs2R7p7FefXoHTL9kkXy8qqDApFRp6f72l2dnbF51OnTkWv1zNt2jQeeughWrZsSXJyMh988AFvvPEGBQUF3HTTTezZs4eWLVtW3Fe1zaq/f+1/r5cHMwoLHW80cLGQbSaibgy5ymONK+tlKB/qiwsMREVF2XRCqC979TKcmTM1R9lNIDhA2UVk16mVpOQerNNcJUWlFGYqt3OUdzKxplKp6PTVV4Rfdx0A/kU5hOQqW9m6sm6GFAEVDWXSpEkVrdTK6XQ6hg8fzvfff++mVTVyXtHQ4x8ItZO9krYEdg+DkhTbc0IIIcQFpmpQwM/P77y8ZtUsDL1ez4svvshbb71F27Zt8fT0pE2bNrz22mu8/PLLgKVw59y5cxVz6PWVb4hW1+2kKi8vS0OA4uJiV3wJFzQJZoi6KdylPNbazyJwaOoSZepdUb6BZs2aVXN13bmy+CdAqlVmRteWHfD2UGaobDryVZ3myk2xrXMRElP9FhW1hwdt589HdW6PXcwZZXaGM8GM6C7KoqYSzBANafbs2dx2222KseLiYm666SZefPFFzGZzNXdexLQB0PUniLFTNLVgG+zsB/n1r9sjhBBCNCVVa2Q40+Zdp9ORkJBQ7UdV3t6V2Y/h4eE8ZVWcv9yTTz5JVJTlb+pvv/1W8fdM1TlKS0trXV9JieUNVB8fn7p/URcpCWaIuinarzwOdKwtkT0F+kzFcWF2WcUvA2eUuDiYkZatzMyIjQxmaDtli6cdJ39mYx0CGjkpeYrjgAg/PLxq3vXl1aIFETfdBEDMmX2Kc/tdWAS0IENHfpqktYmGoVKp+OSTTxg9erTNueeff56nn37aDatqAlRaiH8NuvwIGqsU1dKzsHs4pH7mnrUJIYQQ50FYWFjF52lpjr/5tm3bNrp161btR1VVAygjRoyoNrNCq9VW/G2TnZ3NsWPH7M5Rl60j5YGaumxJudhJzQxRN9aFi3zr1tO5LvKLMxTHupwyYhogM8MrLs6p+ayDGVEhXvRvfQNrD3yE0Vy55WTFntcI9YumU/Nh1c6Vk6wMZoRE161waPS0aaR/9ZVNZkZqdglZ+aWEBda/KGtobDBe/p6UFFZGis8kphHYTH6Biobh5eXFqlWrePjhh1m0aJHi3Ny5c+nWrRu33nqrm1bXyIVfC702Q8I1oK/8QwlzKSTdZamtEf+6bScUIYQQ7uERZqkhcTHxCKv9Ggf06NGj4vOdO3c2yGtYi42Ntft5bddmZGQQHx8PWDIzwsLCyMrKIjk5ubrbAcjJyakIZtT2ekIyM0RdlSmzJ/AId9nUNpkZuQbatGnj9Lyu3GZiNptJs6qZ0SzUmwDvMHrFjbe5ft3Bj2uczzozIzi6bjVIAvv1o9lddxGRdRyvEmVkd9/RvGruqplarbKpm5EiW01EA/P09OTDDz/knXfeQa1W/q/ovvvuq7G92UXPryv03gbBl9qeS3kH9o2Dsmzbc0IIIc4/ldpSDPNi+miATiYAXbp0ITzc8gyyfv168vMdy0weMWIEZrO52g/r1yxnNBprnLfqeev2q507dwbgyJEjGAzKuntVHTxYWX+vU6dOtX8xFzkJZoi60Z9UHmtdF8ywzswozClj+PDhTs1pKiujJEVZFM/bicyMnIIySg3KNq9RoZbiPFf3eIoOUUMU505l7+VE5u5q58s9o/zlW9fMDIB2CxYQ0L0bsSl7FePb/j1WzR21sykCuj/V4bmEqCuVSsXUqVNZsGCBYrywsJDbb79d0cZVWPEIhe6/QvRjtudyfoed/UG33/acEEII0USpVKqKrmg6nc4mu7MhDBtWmWlddeuIPVXfiImOjlacGzLE8qyg0+nYsWNHtXP8/fffFZ8PHjy4Xmu9GEkwQ9TOpAeTVZEdT+drWpTL1p1RHBdkltHKyfoWpSkpYFIGH5zJzEjPVWZlaDUqQgMsWzo8tT5MGvQWgT7KFlHLdr5IUan9bAlHt5kAaHx86PDJJ8Sl7FaM79ydgqkORYXsibbT0UQKMYrz5f7772fy5MmKsX///Zc5c+a4aUVNhEoLbd+GDp+AymqLmf4o7BoImSvcszYhhBCiAUybNg1fX18AnnvuOUUmQ01MJhNffvllvV+vdevW9OrVC4B169aRl2f/b/uCggL++OMPAOLj42nevLni/LXXXlvx+eLFi6td4+effw5AcHAwI0eOrPd6LzYSzBC1y/zRdsyrue2YA4ymMgpLlNtM8jNLK1LIHKU/qcwkUfv5oQ0NdXi+dKstJuFBnqjVlW1eNWoPhrZVFgPNKDjOt9ueswkKmM1mm20m9QlmAAT07s3AS3soxjL9mrH7hdfqNU856yKgRbl6m+wRIRrSu+++S9u2bRVjL7/8MuvWrXPTipqQqMnQ4y/bILOxEPZfA8lvu2VZQgghhKtFR0czf/58oLK9e9VsBnsSExMZO3Ysr7/+ukOv+d///rfi9R577DG71zz++OMV214eeOABm/P9+/dn6NChAHz88cds3rzZ5po333yTAwcOADB16lQ8PDwcWu/FRIIZonbW9TIAtI4HBqrKK87AjPJhX2P0t9lnVl/WwQzvuDhUKlU1V9fOOjMjMsTL5pp+ra/D30v5fTmY+g+bjn6tGNNlF1GmV+6Vq6kta3UGPzcV77IixdiWH/5Gl5hY77mCWwTiG+ytGDuzX+pmiPPH39+fpUuXKv7tm0wmJkyYIPUz6iJoEPTeDgH9rE6Y4ejjcOpVtyxLCCGEcLXJkyfz4osvApCens6IESO4/PLLWbBgAevWrWPXrl38+eefLFy4kCuvvJLu3bvz+++/O/x6N998M+PGjQPg008/ZezYsfz000/s2rWLn3/+mfHjx1dseenVqxdTpkyxO8+8efPw8fHBYDBw2WWXMXfuXLZs2cK6deu4//77mTFjBgDt27dn+vTpDq/3YiLlzkXt7FXFd1Fhn/xi5QNzqd5IsH+E0/OW2AlmOMM6MyMy2DaY4e3hz+TB8/ng7/9QZtRXjK/Zv4AuLUYS7Gt51zQnRZnxoNGqCYjwq/eaPLy96NIqgB0plcWGkqO6cPy//6Xrzz/Xay6VylIE9Mimyu9bSmIaXS5rX+91CeGofv36MWfOHGbOnFkxlp6ezujRo9mwYQMxMTFuXF0T4BUNPf6GQ/dB+hfKc8dngqkUWj3nnrUJIYQQLvTss8/SpUsXpk+fzokTJ1izZg1r1qyp9vouXbrw2muOZTADfPPNN9xwww2sWbOG3377jd9++83mmn79+vHzzz/j7e1tZwZLoOObb75h4sSJ5OfnM2vWLJtr2rdvz8qVKxXtXEX1JDND1M5gtTcsdJzLprbuZKLLMRAR4XwwQ3/8uOLY2basdcnMAIgO6cQdg95SjJUYCvls02OUGCxZFLnWnUxaBKLWOPZPsVt3ZXGh5BZdyVqxgtxa0u3sadFFmaJ+RjqaCDeYMWMGV1xxhWLs5MmTXHHFFeTm5rpnUU2Jxgc6fg6tX7E9d/J5OP4cSD0cIYQQF4Drr7+epKQkvvzySyZOnEiHDh0ICQlBq9USGhpK7969eeihh1i7di379u3jsssuc/i1/P39Wb16NV999RVjx44lKioKDw8PIiIiGDNmDJ988gmbN28mKqrmuoJXXXUVe/fuZdq0abRv3x5fX1+Cg4Pp27cv//d//8euXbtstt2K6klmhqiddTBDW/8tEdUpLFG2D9TllREZGVnN1XVXbJWW7uNkq9e6ZGaUa99sEL1bXsnOU79UjJ3NS2LN/gVc1eMJspOt27I6/v3s0koZtU2NbEep1ptD991Hr40b8ahH7RF7RUBNJrOiNogQDU2tVrN06VJGjhzJ7t27K8YTEhK45ppr+O2336p9x0Oco1JBy5mg9oWjjynPnZoD5jJLsMOJrXdCCCFEY+Dp6cltt93Gbbfd1uCvpVKpmDBhAhMmTHBqnri4ON566y3eeuut2i8WNZLMDFE7o1UhSE2gy6a2DWYYnC7+CaC3ap3kHR/v1HwZucouIdVlZpS7sseThPsrs0H+PfY9BfpMO21ZHf9+dogNQF3lX7FZreVsVEeKDx1iZ//+lKbWvcWqdXvWksJSsk/nOrw2IRwVHBzMmjVr6NChg2L8n3/+4Y477qi1z7s4J2YqtH3fdvz0q3DsScnQEEIIIUSTJsEMUTtjgfLYlcEMfZbiuCjP+W0mJr2ekpQUxZiPE8GMkjIjuYVlirGaMjMAfD0DufOSeWiq1BsxmEp454+byTiVobi2vp1MqvLx0hDfQllvI7l5V8Cy1Sbp7rvrPFdgM3+b2h1SBFS4S0REBKtXr7ZJ1/z+++954okn3LSqJij6IWj/IWCVhZH8piVrw2yyd5cQQgghRKMnwQxRO4N1MMPfZVMXluQojotckJlRfPy4zTuO3k5sM7HOygCICPas9b6IgDh6x12lGNOV5HD6mLKehzPBDIDOccrgUnKLbhWfZ69aRd6GDXWeyzo7I2V/3TM7hHC1Vq1asXr1agIDlT/j77zzDitXrnTTqpqg5vdCh0+wCWikvAuJN4NR55ZlCSGEEEI4Q4IZonbWmRla11XXtbfNxNnMDOstJh4REWidqAhsXS/D30eDr3fdys2M7Hg3WnWVwIdJhTlXud8/2IltJgCdrepmpER3UzS7PfHcc5jrmE5uHcyQIqDC3Xr06MGPP/5o02v97rvvJjPTTttoYV/UXZbCoNb/289cBruHgP6UO1YlhBBCCOEwCWaI2hkLlccuzMzQWQUzivLKnA5mWG8x8WrZ0qn5svKVmRnhQTVvMakq1C+aa3pVtpkkzw+VWfnPLiQm2Jnl2RQBLfIOJDs4tuI4d906Us/1vq5NdBdlMOPsgXRMRklDF+41atQo5s+frxhLS0vjP//5DyaT/HzWWbOJ0GkpoFGOF+6Gnf0gb6M7ViWEEEII4RAJZoja2dTMcGFmht46mOH8NpPSs2cVx14tWjg1X2aedTCj9i0mVfVrdS0Pj/wCAFWuMhDk5eeBT2DdgyP2RIZ4ERaoXNOZrsMUx4enTCF/27Za52pulZlRpjeQcTy7mquFOH/uvfdexo8frxhbsWIFL7/8sptW1ERF3gLdfgVtsHK8LB32jIbs39yyLCGEEEKI+pJghqhdUaLy2EWZGQZjKXqDMuvDFQVAS8+cURx7Oh3MUG4zqW8wAyA2tAt94q62CWYYg/MxmZ3rzKBSqejaWhlgyhp/l+LYXFpK4k03YcizarNrxT/Ul6DmyrmkCKhoDFQqFYsWLbIJdj7//PP88ssv1dwl7AodA73+BR9ltxjMJZBwDWT/7p51CSGEEELUQ5MPZnz33XeMGDGCkJAQ/Pz86NGjB6+99hplZWW132xFp9Mxd+5c+vbtS2BgIB4eHkRFRXHllVfy888/N8Dqm4CyHNsxF2Vm6Eps5y7R4Xwwwyozw9lgRpZVZkaYA8EMOFc/IzdEMVbin86Gw186vLZy3eOVRUQPG4KJefxx5WudPMnRadNqnct6q4nUzRCNRVRUFF9//TXqKv2IzWYzEydO5MiRI25cWRPk2x56b4GQscpxcwkk3gCFe9yzLiGEEEKIOmrSwYzHHnuMm2++mY0bN9K/f3/Gjh3LqVOneOqppxg1ahTFxcV1nisrK4v+/fsza9YskpKSGDRoENdffz3R0dGsXLmSa665hqlTpzbgV9NI5ayxHXNRZoZ18U+T0Uy71p1sCv3VV4lVZoZX8+ZOzWe9zcR6S0ddhfu3pLV2qGLMHFLI74kLScs/6vD6ALq1URYRTcspwfepFwgaqny91MWLyfjhhxrnsu1oIsEM0XiMHj2a1157TTGWl5fHTTfdVK/f+QLLVpNuKyDyduW4sQD2jYeSZLcsSwghhBCiLppsMGP58uXMmzcPf39//v33X3777TeWLVvG4cOH6datGxs2bODZZ5+t83wvvvgiiYmJ9OnTh5MnT/Lbb7/xzTffsGPHDlauXIlWq+Xdd99ly5YtDfhVNUKFu23HPKNcM7V18c98A71793F6XpdnZjhRANSaMUcZqDEH6TCYSli0/gGyCh1/cIhr5kugr7LDSsKpIjouWYLGqpNL0n/+Y2lfWw3rYEbaoQwMZc5thRHClR5//HEmTJigGNu9ezePPvqom1bUhKm00PEziLhFOV6aYgloGPLdsy4hhBBCiFo02WDGK6+8AsB///tfevfuXTEeHh7OggULAJg/fz55tdQIKLd27VoAnnrqKUJDQxXnxo0bx8iRIwHYvHmz02tvUtR2Hty9ol0ytU0wI89Ax44dnZrTbDRSmpqqGPN0IjPDaDKTXeBcAdCq8lKtiqkG6QAo0Gfy9daZGE0Gh+ZVq1U22Rl7j+bhHRdH23ffVYwb8/I4OHEiZqP9AIV1MMNQaiTjaJZD6xKiIahUKj766CM6deqkGF+0aJHUz3CESmMJaAQpM7nQ7YXEm8BU/22bQgghhBANrUkGM1JSUth2rjPDbbfdZnN+yJAhxMbGUlJSwqpVq+o0p7e3d52uc7bTRpNj1CmPI22/346yrplRlG+gpZNtVEvT08GqVaMz3UxyCkqtp3M4mGE0mCjIUH4/zYGVx6dzEvhx10uYzWaH5reum7H3mOUd1WZ33kmk1b+T/E2bSHnvPbvz+AR6ExobrBhLSUi1e60Q7uLv78+yZcvw8/NTjN93331kZ0sHnnpTe0GXH8GnvXI8Zw0k3gymEvv3CSGEEEK4SZMMZuzatQuA0NBQWrdubfeavn37Kq6tzRVXXAHA//3f/9n8Ibxq1SrWrVtHVFQUV199taPLrpfMzEwyMjJc8uEUk9UedE2g/escYBPMaIC2rKjVeDhRUNS6+KdWoyLIz7GaHgUZhZhNykBFSAtlQdDtJ37iiy1PYDAqX7curDMzzmTqycwrQaVS0e6DD/COj1ecPz5rFsXVFE1sIUVARRPQqVMn3rXKPDp79uzFWd/IFTzCLG1bPax+Z2Yth/3Xg0nvlmUJIYQQQtjTJIMZx8/t96/pXfzY2FjFtbV56qmnuPzyy9mxYwdxcXGMHTuWCRMm0LdvX8aPH8+AAQP466+/CAoKqn0yF+jcuTORkZEu+XCKqUh5rPZxbr4qCq2DGQUGwsLCnJrTpi1rVBQqjcbh+ayLf4YGeqJWqxyaK99qi4mHt5abhz2NCuV8+8+s5Yedc+o9f5sWfvh5K7/WvUct2RnagAA6fPyx4pypuJgDt95KqZ2Al21Hk/R6r0eI82Hy5MkVwehyX3zxBd9++62bVtTE+bSBritA7ascz14FCVeDscj+fUIIIYQQ51mTDGYUFFgeCq3Ti6vy97d03MjPr1vxMj8/P1asWMETTzyBTqdTFAANCwvj0ksvJTraNbUimhTrP1w1vvavc0BeofIhujjfBcEM6+KfjaSTCdjWywhsFkCr8F6M7Hi3zbU7T/3Ciczd9Zpfo1bRpbUyOyPheOXPf/Dw4bR4+GHF+YLt29nRsydFhw8rxm2KgB7JpKzEsXoeQjSk8voZwcHBivF77rmHw1Y/16KOAgdYMjSsO1fl/G4pCioBDSGEEEI0Ak0ymNEQzp49y+DBg3nvvfd46aWXOHbsGIWFhWzdupU+ffrwwgsvMGTIkIpAykXDJjPDdcGMgmLldp7iQqPTwQybtqwu72TiumBGUJSly8iYzg9xfe9nUauUWRUr972FyWxVsKMW3ayCGfuPK4N5bV59FW+rrVmlZ86QeOONmPSVKeTNO0aiqpIwYjKYSDvk5JYlIRpIdHS0zXaTgoICadfqjOBh0G2N7dbCvL8g8UYw1X8rnBBCCCGEKzXJYEbAuVaTOp2u2msKCwsBCAysW42HO++8k23btjFnzhxmzZpF69at8fPzo1+/fvzyyy9069aNPXv28MYbbzj/BTQlDZiZoStVbjMxlajx8XFuG0vJ6dOKY2fbstpkZjRAMEOlUtG/9fWM7/644vzp7H18uvERCvV1L2bY1apuxvHUIgqLKzMqNP7+dF62DK3Vu9i6vXtJOdcFCMDLz5Pw1squPin7pW6GaLwmTpzIpEmTFGN79uzhsccec8+CLgRBg6DHn6BV1vYh+1c4eAeYpWWzEEIIIdynSQYzWrVqBcBpqwfXqsrPlV9bk5SUFH7//XcAbr31VpvzHh4e3HjjjQD88ccf9VytYxITE0lPT3fJh1MaMDNDb7CqIaGqfttQXVkHM7yc7I6Slaes4B/uzDaTNPvBjHID2txEmF+sYuxQ2iYWb5yCsY6tEdvH+uOhrUypMJth/wlldkZAr1703rHDpiDoqZdfxpCbW3FsvdXkjAQzRCOmUqlYsGABnTt3Vox/+OGHfP31125a1QUgoC/0WGcb0Mj4Fg4/aPklI4QQQgjhBk0ymNGrVy8AsrKyqi3wuX37dgB69+5d63ynTp2q+Ly6TI7ywp/nq+VfeHg4ERERLvlwSgNlZpjMJgxmZfq3j4fzxVVLqvy3BPB2MpiR6cptJmetamZYBTO0ag+u6TUTldU/y5TcA2w88lWdXsNTq6ZjS+W8Ccds68b4tGlDpy+/VIwZsrM5MHEiZqPl3VbpaCKaGj8/P7777jt8fZW/px544AHF73lRT/49LDU01FYB57MfwbGn3LMmIYQQQlz0mmQwIyYmhn79+gGwdOlSm/MbNmzg9OnTeHl5MW7cuFrnq1rY899//7V7zZYtWwCqbQV7wSq1bnXqmmCGvqwAVMp39Py8gp2a02w2o3dxZob1NpPwIC+H58q3yswItgpmALRvNog7L3kHHw9lUO3XffPYfuKnOr2OdRHQ/Sfs13kJHDCA8OuuU4xlr1zJ4Ycfxmw2E90lSnEu43g2JUWyT140bp07d+aDDz5QjOXl5XHnnXdiMtWvBo2oInAAdP0JVFYB3eTX4ezH9u8RQgghhGhATTKYATBr1iwAXn31VXbu3FkxnpWVxUMPPQTAlClTFK1Uf/zxRzp27Mjo0aMVc7Vs2bIiODJ16lROnDihOP/FF1/wzTffAHDbbbe5/Gtp1Mqstqm4KDNDV5JrMxbkF+7UnIacHExWdVS8YmOrubp2xSVGivTKPeGO1swoLS6jKFevGLPOzCjXsflQ7hj0pmLMjIllO14gKXVjra9lXQT04KkCSg32H+Li334brVXR1bP/+x8nX3iBqPYRqDVVtqyYzKQelBatovG74447uP322xVjf/31Fx999JGbVnSBCBkNnb8BrNpdH34Q8mr/3SSEEEI466+//kKlUqFSqZg9e7bL5//1118r5q/PayQkJHD//fcTHx+Pj48PERERDB06lA8++ACDQToCNpQmG8y49tprefTRRyksLGTgwIFcccUV3HjjjbRt25Z9+/YxePBg5syZo7gnLy+PpKQkjh49ajPfJ598Qnh4OAcOHKBTp06MHDmSm266ia5du3LHHXdgNpuZOHGizR/IF7RiO1t4tM51GylXZFX8s1RvJCwk0qk5retloFLh5UQ73UyrehngeGtW66wMsK2ZUVWbiL4ManOLYsyMmaX//peMgpM1vlbnVgGKTiRlBjOHThfavdY7Lo6uy5ej8lR+XSdfeIHcVT8TGa8MMKXsl2CGaBrmz59PS6vMrFmzZpGZmemmFV0gwq+FDouUY+YySLgKdIluWZIQQgjhCjqdjgcffLDe93300Uf06dOHDz/8kGPHjqHX68nMzGTDhg08+OCDDB48WP7+aCBNNpgBMG/ePL755hsGDRrEpk2bWLVqFTExMbz66qusXbu2Xp0xunbtSkJCAk899RTt27dn27ZtLF++nPT0dC6//HK++eYblixZgqrqU+KFTm8nmBHQxyVTF1h16CjKMzjfljUlRXHs2awZak/Ha1xYt2X189bg46Wp5uqaWdfL8AnyxtPHo8Z7ruo5g+Ht71KMlRgKeXPNtfyV9Em19/n7aGkdpcygsVc3o1zQkCGW+hlq5a+Do489RvN2yqJ/UjdDNBXBwcF88ony30l2dnZFVp9wQtRdEDNdOWbIgX2Xg776wtxCCCFEY/bss89y8uRJIiPr/gbrqlWreOCBBygtLaVZs2a8++67/Pvvv/z6669cf/31AGzdupXrrrsOo1G6gLlakw5mANx88838/fff5OXlUVRUxL59+3jqqafwtPMQe9ddd2E2m222kZRr1qwZr776Knv27KGwsJCysjLS09NZvXo1N998cwN/JY2QMU957NMWVK75kSnUK6OTulyD0/VISlNTFceubsvqVPHPWjqZ2KNWqRnb9VE6txhpc251wntsO/5jtfda183YW0MwAyDixhtp++67irGS06fhnx8UY2f2K7/HQjRmo0eP5pZblBlOixYtYtu2bW5a0QWkzasQalWTqiQZ9o2FsvNTKFsIIYRwlR07dvDuu+/i5eXFyy+/XKd7ysrKeOSRRzCZTAQGBrJx40YeeeQR+vfvz9ixY1m2bFlF+YMNGzawZMmShvwSLkpNPpghGpDBKpihsd/pxRH5NsGMMpuWivVlE8yIiqrmyrqxDmaEOVH80zozoy7BDLC0m7yxz2xCfG0DM7/sfZO8IvuZEj3ilZ1h9h/Px2isuYVii4ceInikMnCi2bFGcZx1KpfifGXtDyEaszfeeAM/v8ouHGazmYcffliKgTpLpYXO30LAAOV4UaJly4l1JywhhBCikTIajdx7770YjUZmzZpF27Zt63Tfjz/+yLFjxwCYOXMm8fHxNte8/vrrhISEVHwuXEuCGaJ6Bqt3810YzMjKV3ZJ0eUaiHIy+FDm4mBGlpszM8r5egZyz9D/ERfWQzFeYtCxbOeLlBhsHxq6tVH+tyoqMXLkjP26GeVUKhUdPvsMj2aVLVn99WdRmZRFi85KEVDRhMTExPDcc88pxrZt28bHH0sHDqdp/KDbSvDtqBzP3wRJd4G55gCqEEII0Ri8/fbb7Nq1i/bt2/PUU3VvOb58+fKKz++66y671/j6+lZk+CcmJnLo0CFnliqsSDBDVM9oFczQui6YkV2gDDzo8gw0q/IQ7YiGzsxwKphhlZlRXSeT6oT5x/DgiE/pE3eNYvxQ2iZeXXUFCSl/KsZDAz2JjVDWjNl7tOatJgDesbGKgqBqsxF//RnFNSn7pW6GaFoee+wxOnZUPnD/97//JcWqzo5wgEcYdPsNPK2KLWd8B6fqlqYrhBBCuMuJEyd4/vnnAVi4cCFeXnXPxN6wYQMAHTp0qPFN2eHDh1d8vnGjdP9yJQlmiOoZrTpwaOr3AF6TwuIsxbGpRIu3t7dTc7o+mKHsZuJUMCNV+b0Mrmcwo9wV3R7F28NfMVZcls/Sf58iOWe/Yrx7vFXdjKNW24aqEThwIG3fe6/yuEhZ0O+MBDNEE+Pp6cl7VX6mwVIMdNKkSbLdxBW8W0L330Cj3N7GiWchs/raPkIIIYS7PfjggxQVFXH77bczatSoOt9XWFjI6XOdFK3fMLFW9fyBAwccW6iwS+vuBYhGzHqbiQszM4qt5vZU+1VzZd25Ophh3c3E0basZrPZpjVrfTMzyvl7hXJ972f56t//YqYyhdtkNvLVvzN5aOTn+HkFA5atJiu3VAYeEo7nYzSZ0ahr78jT/N57yd+0ibTPPiOg6BQwuOJcyl55N1s0PZdeeikTJkzg66+/rhhbu3Ytr7/+er1SSkU1/LpA529g3zigSoDowB3QazP4d3Pb0oQQwp3MJhNlWVm1X3gB8QgLQ6Vu/O+ZL126lNWrVxMcHMxbb71Vr3uTk5MrPo+Jianx2tjY2IrPywMgwjUkmCGq14CZGaUmnSIvyMsFc1sHMzycCGaYTGay88sUY2EOZmYU5eop0yvrTtSnZoa17jGXEegdwS9731RkY2TpTrPwrzu585J3iQiIo7tVEdDCYiPHz+poG+1vPaUNlUpF2/feI2/DBgJTkhXn8tKLKMzS4R/mfABKiPNpwYIFbNiwQfEHyDPPPMOoUaPo16+fG1d2gQi9HOLfgKOPV46ZdLD/aui9DTzC3bc2IYRwk7KsLDbXo9XnhWBQejqeERHuXkaNsrOzmTZtGgBz586tVztWgIKCyuckf/+a/7auWoi8sLDmGnaifhp/yEy4TwMFM0xmIyaVsiOGv3eIU3MadTqMBcr1OpOZkVtYhtGkLF4X7mA3E+usDJVaRUBE7QGFmrQK78WDIxbTPKiDYjyz8BSL1j+AriSHiGAvmocpt+7UpW5GOW1AAN3XrCGibThqkzJLJXH+UscXL4SbhISE8MUXX6BSVWYnGQwGbr31VsUfJcIJ0Y9Bs7uUY/oTsP8msPo9IoQQQrjLE088QXp6OgMGDOC+++6r9/16feWzjKdnzW94Vq3DUVxcXO/XEtWTYIaoXgN1M9GXFYLVTodA3zCn5ixNs63j4Ewww7r4p1oNwf4eDs1lXfwzIMIPjdb5f3oatQe39p+Lr6cyAyOvOJUvtjxBUWke3a26muw9Vre6GeV82rSh11/rCDQoO5gkLV5ByZkz1dwlROM1fPhwZs2apRg7evQoU6ZMcdOKLjAqFbT/AAIHKcfz/oLDD0qHEyGEEA0iNzeXhIQEux/WHUT++usvFi9ejEaj4YMPPkDtwJaYqrX+SktrDtaXlFTW4fPx8anhSlFfEswQ1bPOzNC6JjOjuNQ2OyAkwLn0O+stJmpfXzS1pHzVxLr4Z1iAZ51qTdhj05a1meu260QGtmbKqKU2GRrHM3fy9u83Et1M+b3edzQfk6l+DxMeYWHEDeuiGMtVRbBn1Cj0J044tG4h3On5559n4MCBirHPP/+cH374wU0rusCovaDLD7YdTlI/gdOvumdNQgghLmjLly+nW7dudj8uu+yyiutKSkq4//77AXj00Ufp2bOnQ68XEFD593xtW0d0Ol3F57VtSRH1IzUzRPUaaJtJcZnyAdtYZiIs1LXBDM+oKEUqeX3ZFP90qi2r8usNau66YAZAqF8LJg9+j3f/vJXCksoCUwX6TPYVvQQ8UTGWX2Tg6Bkd7WLq94s0/qpB7NmyunIe31iKE5PYOXAg3deswb97d6e/DiHOFw8PD5YuXUrPnj3Jz6/89zl9+nTGjRvndGclAXhGQdefYPdQMFVJqT0+C7zbQOQt7lubEEKcRx5hYQxKT6/9wguIR5hzGdcN6YcffuDQoUN4eHjQuXNnRWHwcomJiRWfJyQkVFwzYMAAWrduDUB0dGXAvmotLnuqFv2sWgxUOE+CGaJ6hlzlsXXbPQcVlyqDJHqdkYhw54oEub4tqwuDGWnKaG2gCzMzKub0ieCOQW+weOMjlm0855jUJ/D1zaGoqLImyY5DufUOZkR3aaY4LvUIokQbCGlp7L/2Wvrs2YM2wPVflxANpXXr1rz//vvccccdFWMnTpzg3XffZcaMGW5c2QUkoA90Wgr7r4cq3Zc4eCd4tYSgQdXeKoQQFwqVWt3oi2FeCO666y7uuuuuWq8r3/JRVlbGvffeW+v1y5YtY9myZQAsXry4IpgREBBAbGwsp0+f5uDBgzXOUfV8p06dan1NUXeyzUTYZzaDIUc55hHqkqmLSpV1G/SFRsKcjOC6vC2rVTAjPNCx4p/Q8JkZ5eLCejJtzDLiwnoqxv0C9imOdx7KrffcoS1D8PJTBnTyfS2RZf3x4xydOrXecwrhbrfffjtDhw5VjL300kukX2TvoDWo8Gsh/k3lmLnE0uGk+LhbliSEEEK4wpAhQwBISkoi1epZpKq///674vPBgwc3+LouJhLMEPaZdGBWtiZF61zHkXLW20z0OiPh4c617Ctr4MyMcCcyM/KtMjNcWTPDWpBPJJMHv0u4f1zFWECwMlqccCyfkjJjveZVq1U076TcClTgU5kml7p4MWlffOHAioVwH5VKZdNXvqCggOeee85NK7pART8GLR5SjpVlwv5rwaizd4cQQgjRIO666y7MZnONH+vWrau4/vnnn68Yt878uPbaays+//TTT+2+XlFREd9++y0AnTt3pn379q7+ki5qEswQ9pVl2465KDNDp89VHOsLDY0vM8NFNTOMBhP56VbBjAbKzCjn7RHApEFv4aW19LQOCDoMmCrOlxnNJJ6ofxtK660mBYGtFMcH77yTzOXL6z2vEO7Ut29fxVYTgI8++oiEhAQ3regCpFJB23kQeoVyXLcXkiZLhxMhhBBN0nXXXUebNm0AmDt3LkePHrW55sknnyQnJ6fic+FaEswQ9llvMUHtstas2fnKFG69zkhkpOsLgDrDupuJo5kZhZk6zFbdQxoyM6NcZGAb7hu+iJah3dFqi/H1O604//2m1ZQYiuo1Z4vOymCGLqwDiq/MZOLA7bdTsGuXg6sWwj1eeeUVRas0k8nE9OnTMctDtuuotNDpa/C12iuc8R2cmuueNQkhhBBO8PDw4L333kOtVpOfn8/gwYOZP38+W7du5bfffuPGG29kwYIFgGVLivWbJ8J5EswQ9llnZmiDQeWaH5fcAutghsnlmRkeTgQz9KVGCouV2zAcDWZY18vQemnwDTk//aWjgzvy0MjPmDjwzXPZGZUST5Twv7/vtqlfUpMWVpkZ+mIzwfdNU4yZiopIuOoqSs6ccXzhQpxnMTExNkU/16xZw08//eSmFV2gtIHQ5SfL/0+qOvEMZK+2e4sQQgjRmI0bN44PPvgAT09P0tLSeOSRRxgwYABjx46tKBzav39/fvzxRzQajZtXe+GRYIawz2j1kKt1TScTgPwiZaBEZfR06h+32Wx2aWaGdb0MgLBAB4MZduplONMy1hFdo0dxSec4xZiuII7krKN8uvFRm+4y1QmJDsInSNmy0uPGB4iaPFkxVpqSQsLVV1OWlYUQTcWTTz5JixYtFGOPPPIIBQX135IlauDbDjp9hfLPDzMcvAtKpfCqEEKIpufee+9lx44d3HvvvbRp0wZvb2/CwsIYMmQICxcuZOPGjU7XBxT2STBD2Ge0+gPeRVtMAIpKldkKnhpfp+Yz5ORgLlMWK3UmmGHdycTXS4Ovt2NdjPNSld/HwCj3tC+dNOJ2VKrKlHmz2YPCwtacyt7LO3/czKHUTbXOoVKpbLaanElMo90HHxA0bJhivHDHDnb06kVRLa2qhGgs/Pz8eO211xRjycnJPPPMM25a0QUsdCy0ttpaUpYm9TOEEELUasSIERUFOWfPnt1oXqNr1658+OGHHD16lOLiYjIzM1m/fj0PPPAAWq1jzxGidhLMEPYZrIIZWtcFM0oMymwFHw/n5rbOygDwdKIGh6uKfwLkpym/j+ejXoY9/j4etI9RvnZBXjsA8opTWbzxEfYl/17rPPaCGWpPT7osW4Z3fLziXMnp0+weMQL9yZNOrl6I8+O2225jzJgxirH33nuPbdu2uWlFF7DYJyH0SuVY9io487571iOEEEKIJkeCGcI+ozJ7Ao3rHsINKAtP+nsHOzWfdTBDGxqK2svL4flcVfwTsOlkEtjM3+G5nNUjXrlVqPBcMAPAjIlvtj1DUurGGuew7mhyZn86ZrMZj/Bwuv3yCx7NlOfL0tI4MHEiZoPBydUL0fBUKhULFy7E27tyO5XZbOb+++/HaKxfO2NRC5UKOnwMHsrfGRx9Agr3uWdNQgghhGhSJJgh7MtaqTxWebhkWrPZjFmjDBb4eznX8tX1nUyUmRlOBTPSGk8wo3tbZQZMUWFrTMbK/64GUymLN05hwbo7OZVt/2HCpghoYQnZp3IB8O3YkT67dhHQv7/imvwNGzj16qsu+AqEaHjx8fE8//zzirFdu3bx8ccfu2lFFzDPSOj4mXLMXAIHbwNjsXvWJIQQQogmQ4IZwr78DcrjLNdU9S81FKHSKPdEB/g4VxCnoYMZYUGOZ3lYbzMJdNM2E4CurQNRV/kXbzJriPGbZHPdqey9fPj3PXYDGoGR/viHKWucnElMq/jcq3lzuq9Zg3fr1oprTsyeTfZvvzn5FQhxfkyfPp0uXbooxmbNmlXRJ164UOjlEK3sioQuAY48IvUzhBBCCFEjCWaIuvFp75JpCktsHwaC/SKcmrP07FnFsbPBDOsCoOEOdjIxlhkpzNQpxgIj3ZeZ4eetpX2M8vV9GUebiH421xpMpXy55Ulyi5TfW3tFQFOqBDMAtEFBdPzyS6jaocZoZN/48SS/+66TX4UQDc/Dw4N58+YpxrKysnjuuefctKILXJu54NdDOZb6MZz9wD3rEUIIIUSTIMEMUTfN73HJNLoSZVvWshITIcFOBjNcnZmR75ptJoVZRTZvLLozMwNs62bsSMrnzkveYXDb2/HSKgMdecVpvP37zWw59h0ms6li3HqryZn9ymAGQNCgQcRZP/gZjRydOpXUTz917osQ4jwYPXo0N9xwg2JswYIF7Nsn9RxcTu1ladeq9lOOH3kUcte7Z01CCCGEaPQkmCHs84xWHvt1d8m0hVbBjKI8A6EhjadmhslkJttF3Uyst5hoPTX4BntXc/X50a9jiOL4SIoOXZGWq3o8wTNX/kHr8N6K8yWGQpbveoVPNz5CqcGyhz3aKjPj7IF0TEYT1uJmzSL8uutsxg8/+CAFu3Y5+6UI0eDeeOMNRTFQk8nEo48+ilm2P7ieXyfouFg5ZjZA4o1QkuyeNQkhhBCiUZNghrDPpFceq13zEG4dzNDllREcHOzUnGUuDGbk6cowGJUPKo5mZtgr/qlSqRxemyt0aR2Ar5dGMbbtoGXrj4fGi9sHvE6YX6zNfYfSNvHen7dxJH2rTWZGaXEZmSdstw+ptFo6f/+9TYaGSa8n8YYbKMvOtrlHiMakVatWPPXUU4qxv/76i5UrV1Zzh3BKxE0QO1M5VpYO+6+3/X+SEEIIIS56EswQ9jVUMEOvfIAtzjcQEhJSzdV1Y52Z4eFEMMO6XoZaBSH+jgUz8qyDGZHu3WICoNWo6d0+WDFWHswA8PcO5YERi+kec5nNvRmFJ1i0/n7+PPUegVHKLSln9qfaXA+gUqtp9cILtJgyRTGuP36cPaNGUZpmu0VFiMZkxowZtGzZUjE2c+ZMadXaUFrPgdArlGMF2+Dww+5ZjxBCCCEaLQlmCPsaKJiRp8tUHBcVGJ3KzDCVlVGWqZzTmcyMrAJlMCPY3wONxrFsivx0604m7iv+WVX/Tsrg0Y5DuRirZKMEeIdx24D/4+4hC/H1DLK+nc3HvkHVIlcxlpKYXuNrxr/5JoGDBinGdHv2sO+KKzAUFFRzlxDu5+vry0svvaQYS0hI4KuvvnLTii5wKg10/BK845XjqZ/A2UXuWZMQQgghGiUJZghbZgNg9a5jAwUz9IXOZWaUpds+RDsTzMjOL1MchzrYyQTsbzNpDPp1DFYcFxYbSUoutLmuXbOBPDB8MbEhXW3OZQXtVRyfSaw5w0Lt6Unn777Ds0UL5Wvv2kXijTdiKi2t5k4h3O+2226jW7duirFnn30WvV62PjQIjxDo+pNtQdDDU6Bgh3vWJIQQQohGR4IZwpa9vckuCmbkF2UpjvWFRgIDAx2ez3qLCRoNHmFhDs+XbZWZ4Vwwwzozw/3bTADCg7xoFeWrGNtx0LbmBUBkYGseHPkp1/aciUalrRg3tVD+d0xNSsdYVnPavVd0ND3Xr8e7dWvFeM6aNRy65x7MkrYvGimNRsMrr7yiGDtx4gRvv/22m1Z0EfDrAh0+UY6ZSywFQQ357lmTEEIIIRoVbe2X1M5gMLBy5UrWr1/PsWPHKCgoqHU/sUql4s8//3TFywtXa8BgRnZeGnhUHvt6BqHRaKq/oRY2nUyaNUOldjxGZ93JJDTAo5ora5efbl0zo3FkZgD06RDMidSiiuPth3K54/KWdq9VqzQMjL+ZAO8Ivvx3BiazAXNzZTDDUGLkTFIqsV2j7c5RzqdNG7qvWcOuSy6hLCOjYjxtyRLKsrLo9NVXaJ0IbgnRUMaPH8/QoUNZv76yVejLL7/MpEmTiI6u+edeOCjyZsjfDCnvVI7pT8CxJ6H9/9y1KiGEEEI0Ek4HMzZs2MAdd9zBqVOnKsZqalunUqkwm81u7+ogatCAwQxdSR6aKvGB2BZtnJrPlW1ZwXabSZiDmRkmo4mCDJ1irLFsMwHo0z6YZX+fqTg+eKqAwmID/j7V/0roEj2SWwfM5Zutz2DwKcEUmo86uzLw8O3yt7kl+j/EhHSu8bV92ral6y+/sGfkSExFlQGV7FWr2Dt6NN1++w2PUOfa9QrhaiqVinfeeYe+fftW/D9Op9Mxc+ZMPv/8czev7gLW5jVLAdD8jZVjZz+E8Bsg1LZQsRBCCCEuHk4FMw4ePMjYsWMpLi7GbDbj6elJu3btCA0NRe3Eu+PCzewGM3xcMrVZXaI4bh5hPxugrlwfzHDNNhNddhEmg0kx1li2mQB0jw/EQ6uizGB5KDOZYNfhPIZ2r3mLTrfoS4m+rDP/HvuOzTHHoEowI+dgCfPX3k73mMu4oc/zeGl9q50nsH9/On/7Lfuvuw5zWWUAqWD7dvaOHk3PjRvR+FZ/vxDu0Lt3b+6++24WLaosRLlkyRIeeughBg4c6MaVXcDUHtBxCWzvBqYqAeJDd0PfBNDaFikWQgghxMXBqWDGK6+8QlFRERqNhhdeeIFHH30Uf//G8+6zcJBNMEMFKse3W1Sl9lBuPwr0de4deJcHM1xUMyMvVbnFRK1V4xfaeB7OvTw0dGsTxM5DuRVj25Nyag1mAIT6teCKblPRjl7Dhr37K8bVKREA7E1eQ3r+cSZd8jahftWn34eNH0/3P/4g8YYbFB1pCnfv5siUKXT45JNq7xXCXV5++WW+++478vLyKsaeeOIJ1q9fLxmHDcWnNcS/AYcfrBwrSYajj0OHj923LiGEEEK4lVPpE2vXrkWlUjF16lRmzZolgYwLhbFIeaz2Bhf8kV5m1KO2iokE+Uc4Nacrgxlms5ksF9XMsGnLGuGPWt24HnT6dghWHP+bmFPjFjFrXQf0VByrcgJAZ9mOlJp/mHl/TGDjkaWYzCY7d1sEDxtGz40b8YqJUYynLl7MWQlmiEYoMjKS559/XjG2ceNGVq9e7aYVXSSa3w/BlyrHUj+BzBXuWY8QQggh3M6pYEbmuXdTr7vuOpcsRjQSRqtK8RrXFGQsLrWtQB8SEOnUnK4MZhQWGyu2XZRzNDOjsbZlrWpAJ2VL3Kz8Uo6m6Kq52lZk23A8fZTBHnVyZXCqxFDIij2v89mmqZSUVT+vb/v2dP/jDzRWwdDD99/PqVdfxWww1HlNQpwPDz/8MK2tuvI8/fTTmEzVB+6Ek1Qq6LAINFbb9ZImQ8kZ+/cIIYQQ4oLmVDAjIsLy4OLj45p6CqKRyFmrPC5Lc8m0xWUFNmMRIc2dmrPMhcEM6y0mAKEBjgYzrNuyNr5gRmykDy3ClYVdtxyw36LVHo1WTXRX5ffbP72tzXVJqRtYtP4BcnTVP3D4duhA+48+UoyZDQaOz5zJntGjMRTY/uwI4S6enp7Mnj1bMbZr1y6WLVvmngVdLLzjIP4t5ZghCw7eCTVkgAkhhBDiwuRUMGPIkCEAJCQkuGQxopHI/EF57N/bJdPqSnIVx3qdkZAQJ2tmVGnvCeAR6Ximh3XxzwAfLZ4ejv0Tsc3MaDzFP8upVCoGdlZmZ2zZn12vOWK6K4NRkXn96N3ySpvrTuck8H+rx/PhP/eSnn/c7lyREyYQ8/jjNuN5//zDobvvrtcWGCEa2u23306nTp0UY8899xwGySRqWFF3Q7hVNmjuH5D8pnvWI4QQQgi3cSqY8fjjj6PRaJg3b578AXch0SofcCk66JJps/OVGR76QgPBwcEOz2cqLcVYpQgfgEeE4zU4bDuZOF70ND/dKpgR2fgyMwAGdFIGk5JOF9p8H2oS20MZzEg9kMkNPWdz95CF+HmF2Fx/LGM7H/w9mROZu+3O1+b112k5a5ZNjZaM774jZd68Oq9LiIam0WiYM2eOYuzgwYN88cUXblrRRUKlgvYfgadVceHjs6Bgu3vWJIQQ4rz566+/UKlUqFQqmyxJR+l0Ot5//31Gjx5NdHQ0Xl5eNGvWjN69e/PII4+wZs2aWudISEjg/vvvJz4+Hh8fHyIiIhg6dCgffPCBPCc3IKeCGf369eOdd95hz549XH/99RU1NEQTZ7IqANpqjv3r6ik7L11xrC80OhXMKMvKshnzCA93eL7s/DLFsaP1MqBpbDMB6NYmEF8vjWJsaz22msR0UwYzyvQG0g5n0q7ZQO4f9jGBPraZMkWleSxafz+7T/1qUxxUpVbT+uWX6blhA1qrn41jTz5Jxg9WWUNCuNH1119P797KzLXZs2dTUlJSzR3CJTzCoNMXQJWgp9kABybaFrAWQggharBu3Tq6dOnClClTWLt2LWfOnKG0tJT09HR27drF/PnzmTFjRo1zfPTRR/Tp04cPP/yQY8eOodfryczMZMOGDTz44IMMHjxYnpMbiFOtWV988UUA+vfvzy+//EJcXBxjxoyhY8eO+PrW3obyueeec+blRUMxWhVr9Glt/7p6yi20CmbojAQFBTk8X5nVFhNUKjxCHd+2kuWitqwmk5n8dOX3sDFuMwHw0Krp0yGY9XsrA0NbDmQzdkCzOt3vF+JDWMtgsk7lVoyd3nuWFp2bERnYmkdHfcWqfW+z89QvivsMplK+3jaLPw58wGVdHqZb9BhFW8ugSy6h4xdfkHBl5ZYVs8FA4o030nbePKIfecTBr1gI11GpVLz88stcccUVFWMnT55k0aJFPPzww25c2UUgeAS0nAWnXq4cK06C4zOhrWRxCSGEqN0ff/zBVVddhV6vJzg4mAceeIARI0YQGRlJUVERBw4c4JdffiEtrfr6gatWreKBBx7AZDLRrFkznn76aQYMGEB2djYfffQRP/zwA1u3buW6667jr7/+QqPRVDuXqD+nghmzZ8+ueABRqVQUFxezYsUKVqyoW6s0CWY0UiarYIbazyXT5umUmRTGEhVqtePJQWVWEU5tSAgqreM/0jbbTBxsy1qUW4yxzKgYa6yZGQCDuoQqghk7knIpNZjw1Nbtv01M9+bKYMaeswyY0BMAf+9Qbu43h6t6zODrrTNJStuouDez8BRL/32Kzi1Wc3PfF/H2qPw+hY0fT8tnnuHUSy9V3mA2c+TRRzEWFdHyqafq/8UK4WKXX345Q4YMYcOGDRVjL730EpMnT65TUF84Ie55yFkDBdsqx1LehbCrIOTS6u8TQghx0cvIyGDChAno9Xp69uzJ6tWradZM+Wbe4MGDueeeeygttb8Fu6ysjEceeQSTyURgYCAbN24kPj6+4vzYsWN5+OGHWbBgARs2bGDJkiXcddddDfllXXSc2mYCYDabKz6sj2v7EI2USa881rjmD3KdXrl9wVzmeE0KsM3McKZeBtirmeGaTiYqtQr/MNcEhBpCv44hihIV+lITe47kVX+DFeu6Gcl7bbuW+HgGMOmSd+jf6nq7cySeWcfCvyZzKnufYrzV7Nk0v/dem+uP//e/pH72WZ3XKERDKc/OqCo1NZX58+e7aUUXEbUHdFwCamVXJpImgyHXLUsSQgjRNMycOZOsrCx8fX1Zvny5TSCjKk9P+88EP/74I8eOHauYr2ogo9zrr79OSEhIxefCtZwKZphMJqc+RCNlHcyw/kPRQUWl+cppzV5OzWedmeF8MMOqZobDbVmVxT8Dwv3Q1DHLwR2C/T3o1FK5DebfxLrXzYjt3kJxnJOST2GWzuY6jVrLdb2f4ZZ+L9Es0PaXfVr+ERasm8T3O2ZTXGoJCKk0Gtr973+0Orelraqku+8mbcmSOq9TiIYybNgwLr/8csXY//3f/5GXV/egoHCQbwdo85pyrCQZjjzqnvUIIYRo9HJycli6dCkAEydOJC4uzqF5li9fXvF5dRkXvr6+3HzzzQAkJiZy6NAhh15L2Nd4n7CEe5jNDRbMKDEoMxa0ODevTWaGE8U/AbKtamaEOZyZYdXJJKpx1suoaoBVi9Z/D2TXOXsqsm0Ynr7KLJvTe87avValUtGr5Xgeu/Q7bhvwGn6ewTbXbD/xEwv/upNs3ZmKe+KefZb4t95SXmg0cnDSJE69+qpkegm3e6nqdiggOztb3oE5X1o8DMGjlWNpSyBjmXvWI4QQolH75ZdfKC4uBuDqq6+uGC8qKuLIkSOkpqbW6W/L8i2mHTp0ICoqqtrrhg8fXvH5xo0bq71O1J8EM4SS2c6eMBcFM0qtuqR4OlmLwzozw9OJzIySMiM6vbLOhaOtWW06mTTStqxVDeysLJyaml3CybTiOt2r1qiJ7qr8BX56r/1gRjmVSkX3mDE8NPJzwvxibc6nFxznrTXX8fnmxzmZtQeAmGnTiHnySZtrj8+cyYlnnpGAhnCrvn37cv31ym1Ub7zxBkeOHHHTii4iKjV0WAwaq4LSh+6H0lT3rEkIIUSjtWXLlorPu3XrxrZt27jssssICAigXbt2NG/enGbNmjFlypRqi38WFhZy+vRpADp27Fjj61U9f+DAARd8BaKcUwVAxQXIOisDXBbMMKn0VRvp4eMZ6NR81pkZWhe2ZQUnamakW2VmNIFgRuvmvkQEe5KRWxnM2pKYTauoutVLie3enONbT1ccJ9cSzCgX5h/Lo5d+zeajX7P24MeUGioDXgZTKYln1nHgzF9c0vZWxnV7jDavvgpGI8lWWRqnXnkFlZcXraSosHCjOXPm8NNPP2E0WgKjJSUlTJ06lV9++UXRrUc0AO9YaDcfDt5ROWbIgqR7oevPIN9/IYSbmMwmikpz3b2M88rXMxi1qvG+Z56YmFjx+bp167jnnnswGAyKazIyMnj//fdZtmwZq1evpkePHorzycnJFZ/HxMTU+HqxsZVv3JUHQIRruCyYkZ2dzeLFi/njjz9ISEggOzsbgNDQULp27cqll17K5MmTCXWidaY4DxowmGHWlCmCGb6ejrdlBShNV7Z6dSYzI8uq+Ke3pxpfL8daJ9lsM2nEnUzKqVQqBnYOZcWmyncxt+zPZsKomn85l4vtriwCeiYxDWOZEY1H7d9DL60vIzr8hx4xY/l006Ok5R9VnDdjZuORpWQWnOTWAa8S/+abeEZHc2z6dMV1J59/noB+/Qir0iZTiPOpc+fOPProo7z99tsVY6tWrWLFihWKNFbRQCJvh8zlkFlle0n2L5D6CTS/223LEkJc3IpKc3npl9G1X3gBeebKP/H3arzPfOXPqQAPPPAAKpWKl156iUmTJtGsWTOOHDnC66+/zqeffkpqairXXnste/bsITCw8o3YgoLKTGx//5r/1vfzq8xGLywsrOFKUV8uCZn973//Iy4ujhkzZrBmzRpSUlIoLi6muLiYlJQU1qxZw4wZM4iLi+PDDz90xUuKhtJAwQyT2YTKQ7mNIyQg0qk5S1NSFMeeLVpUc2XtrDuZhAR4OvxOalPMzADbrSYHThaQV2ibsWJPjFUwo0xvIPVQRjVX2xfi14IHhi+mZ+w4u+eT0jbyf7+OZ+3BRUQ8ch/tFy2yebf14O23U7hnT71eVwhXmj17ts2+2RkzZti84yMagEoF7RaCh1VF+qOPQfFxtyxJCCFE46PTVRaq1+v1fPzxxzz99NPExsbi6elJ586dWbx4Mffddx8AJ06cYOHChYo59PrKZ6bqup2U8/KqbHpQXqtDuIbTwYxXX32Vhx56CJ1Oh9lsJjAwkBEjRjBhwgQmTJjAiBEjCAwMxGw2o9PpePDBB3nttddqn1i4RwMFM/RlhTZZvp079LB/cR2YzWZKrIIZXtHRDs9n05Y1wLF6GWazmQLrYEYTyMwA6Nk2CG/Pyl8JJjNsS6pbVxPfYB/C4pRFRGurm2GPj2cAE/q/zH+v+JUxnR9Eq1Z2vCkuy2fN/vf5v1/Hc2iIhtZvKAssGnJy2DlgAKdff11qaAi3CAwM5I033lCMJSUlsUQ675wfnhHQ/iPlmLEQku4Es9H+PUIIIZq83NxcEhIS7H5YdxDx9q58tunevTt33HGH9XQAvPLKKxWBiG+++abaOUpL7dQcrKKkpKTicx8fn7p9QaJOnApmJCQk8Oyzz2I2m2nevDlffPEF6enprF27lqVLl7J06VLWrl1LRkYGX375JS1atMBsNvPMM8+wf/9+V30NwpVsghkaUDm/Gyk7z7YIW8d23Ryez5iXh6nIqqCoE8GMrAKrtqwO1svQF5RQWqycK6CJZGZ4eqjp1S5YMVa/Fq3K7Izkajqa1EWwbxSjO93HvcP+h4+HbW2V4rJ8fk2Yx79D8wi9UVl00VxSwrEZMzg2Y4bDry+EM2699VZ69+6tGJs9e3atf+wIFwm/CqKstpXkrYfkt+1fL4QQoslbvnw53bp1s/tx2WWXKa4NCKjsNGh9rqqwsDD69u0LwJ49exT/H686R21bR6pmgtS2JUXUj1NPqfPnz8doNBIREcHmzZtp2bKl/RfRarn11lsZMmQI/fr1IyMjg/nz59uk64hGwGSV+mT1zrijUtJOKI6NZSaio+z/vNSFdVYGgJcT20xy8l3TltU6KwMgIKLp/NIa2DmEzfsr9xFuO5iDwWhCq6k97hnTozm7V1QWVHIkM8NaXFgPpoz6glX73mH/mbU253ecWsHRu6JoFzaAsP/9q6jJkvzGG3hFRxPz2GNOr0OI+lCr1bz88stcUaV+y6lTp/jiiy/4z3/+48aVXUTi34bcP0F/onLs+NMQOhb8urptWUKIi4+vZzDPXPmnu5dxXvl6Brt7CTWKjY2t6GhStThnddcCmEwmsrOzK7aSRld5E7VqMVB7qhb9rO31RP04FcxYu3YtKpWKmTNnVhvIqCo2NpannnqK6dOn8+efF9c/6iaj1KrOgYdrivekZSqDD3qdSVFEp75KzpxRHGvDwlB7OR54yS6w2mbiok4mviE+eHg1naZB/Tspt4ro9EYSjufTs21wrfdaZ2bknsmnIFNHQLhzLXjD/GO5Y9CbnMlN4o8D/yPxzDrl6+hT2TYGWvUaQ9vpa/HQVaaSH502Dc/mzYm85Ran1iBEfV1++eUMHjxY0U/+1Vdf5c4770Sjcay4sKgHbQB0+Az2jADObTkzl1q6nfT6F9SO/Y4XQoj6UqvUjboY5oXirrvu4q677qrTtV26dOG7774DqOhAVp2q57Xayr/pAwICiI2N5fTp0xw8eLDGOaqe79SpU53WKOrGqW0mKefeHb/kkkvqfM/gwYMBOGP1MCoaiVKr/y6ejmc7VJVltc3EoMepVoXWxT+dqZcBkGXVmtXRmhlNtfhnufAgL9rFKIMPW+q41SQyPgwvP+UDQl1btNZFi+AOTBr0FhMHvmlTSwPgRHgG2xYPIXVAEKYqz4oHJ00i4/vvXbYOIepCpVLx9NNPK8YOHz5c8ceTOA+Ch0GMsusRhbvh5ItuWY4QQojGYdiwYRWfHzt2rMZrjx61dNnz9va26co5ZMgQwFIbKzXVdkt9ub///rvi8/JnYeEaTgUzyt9dqk+V9vLollrdeHsPX9QaKJhRUJytODaWOvfOpE1bVqvuAfVlUwDU0cwM67asTSyYAbZdTf5NzK7mSiW1Rk10V2UXgdN7XB+07Bo9igdHLKZlaHebc3nqPHY92Zq/FnQmeXgIZsBcWkriTTdx2qoooxANbezYsTa1M1555RVMJpObVnQRaj0HfLsox07NhbzN7lmPEEIItxs2bBgREREArFixotrsjOPHj7N7927AEoSwfn699tprKz7/9NNP7c5RVFTEt99+C1hauLdv3965xQsFpyIK5VtL6rNlpPzaumxLEW5QahVV9Gpu/7p60ulzlQMG57ZelGVmKo49zv1CcoTRaCZP55oCoDaZGU2kk0lVAzort5okZ+hJzqhbG6mY7srglyvqZtgTHdKJB0d8yqRB7+DrGWRzviTMk32PxJHwYCxGD0sG0LEnn+TE7NnS5UScNyqVilmzZinG9u3bx8qVK920oouQ2hs6LgFV1Ww7Exy6G0xSkFUIIS5GGo2GJ554AoCTJ08yZ84cm2sMBgMPPfRQxRsQDzzwgM011113HW3atAFg7ty5FVkcVT355JPk5ORUfC5cy6lgxpgxYzCbzbzxxhvs27ev1usTEhJ4/fXXUalUNVaOFW5kyFMea0PsX1dPxWX5imOVybn9ymUZytoezgQzcgpLsX6+dXibiVVmRlPpZFJVu2h/m6+/rtkZ1nUzziSmYShrmHaI/8/efUdHUX0BHP/ObnonjZoQQu8diVSRjgrSxIJgQQGRItJsqEgVFBBBUVT4KUWaoPRegvTeQwkkQBrpPVt+f0SSzO4GsiXZlPc5J+cwb97OvsCS7Ny9715JkqhXqQOjn11tMEsDIPxZL459WYOUCtmvtztffMGtCRNEQEMoMi+++KLe/tjp06eL12BRcm0KVT+Xj6VegTDRJl4QBKGsGj16dE725BdffMHLL7/M9u3bOX36NGvXrqV9+/Zs374dgJ49e9KvXz+9a9ja2vLdd9+hUChITEykTZs2LFq0iOPHj7Njxw769+/P4sWLgewtKfm1gBVMZ1YwY+zYsdjb25OcnEzbtm2ZO3cuDx8+1Jv38OFD5s6dS7t27UhKSsLe3p6xosNA8aROkh8rXQ3PM1KmOkV2bIN5PZb1MjO8vU2+1kOdLSZKhYS7s2nBDN1uJu6+lvn7K0oKhaRXCLSgdTOq6AQzVBlqIq9F5zPbMjycKvBuh2UMaPGl4a0nNZ05uKgeR2bWJPwZT+7On0fI8OFojdgeJwimUigUTJkyRTZ27Ngx9u7V784jFCL/ieDSXD525ytIDbHOegRBEASrcnBw4J9//qF58+zfDatXr6ZHjx40b96cgQMH8u+/2dsRe/bsyerVq/Ot9dezZ09++OEH7OzsiIyM5P333+epp56ie/furF+/HoBWrVqxceNGUQC8EJgVzKhatSo//vgjkN1fd9KkSZQvX56aNWvSpk0b2rZtS82aNSlfvjyTJk0iISEBSZL48ccfxTaT4qqwghnaVNmxrcLJrOtZMpgRq1P8s5yrLQqFacVJE6Pkf38lMTMDIKi+vG7GhVuJpKQ9+ebfyd0B7wB5IKSwtprkpVTY0Lzq84x8ZjmvPDUbO6V+sCyhpjMX3vPn6PRa3F73K+d79CArrmBBGkEwx8svv0y1atVkY9OnT7fSasooyQZq/YjsbY82A0JGoJeaJwiCIJQJFStW5OjRo/zwww906NABHx8fbG1tqVChAi+88AIbNmxgy5YtuLo+/n5o2LBhnDp1imHDhhEYGIiDgwNeXl60bduWJUuWEBwcjLcZ9ypC/szuGfn666/j5eXFu+++y/3799Fqtdy8eTOnMmzeVNpKlSqxdOlSevbsae7TCoVFJd8Ogo3p7VPz0kjpsmNHG/OCJHrbTMz4ARFnobasWRkqUuPl32dJrJkB0LSmB7Y2Elmq7P+/ao2WE1fj6Nj0ydt5qjSqSExobpAg7PwDWr/StNDWqqtRla74ugbyv38/4GFKmN75hBpOBM+uRa1Vp0lv14Zm+w+a9foRhCexsbFh0qRJsv22+/bt499//yUoKMiKKytjXJtD5dFwb37uWPweiPoDyr9mtWUJgiAIBdexY0eLbtW0sbHh3Xff5d133zXrOg0aNGDp0qUWWpVQUBZpKdKrVy9CQ0NZs2YNw4YNIygoiFq1alGrVi2CgoIYNmwYa9as4fbt2yKQUdwVUmaGVinPfnA0ULTRGJYsAKq7zcTUehm6W0ygZHYzAXC0V9Kkhvzf6OB5/S1khujWzQg/V/iZGboquNfg/Wf/oH2tITjY6v8bZJaz5eJIf3a+o2XXez1Qp6QYuIogWM6QIUOoWFH+f8NQwTGhkAV8CfZV5GM3x0FWwX6+CYIgCIJQfJidmZFzIRsbBgwYwIABAyx1ScEaCimYobCVF4F0cTS9sKgmKwt1grxQqSW3mXhZqJOJnZMt9i7mFTq1prYNvThxNT7n+OjlWFLSVTg7PP7Hhl9jeUeT+AeJJEUn4+pTtIEdB1tXejYcS7f673EtIph/zs8jNiVcNielsgMHB6q4t6ALA99fj7urZbr3CIIuBwcHPvzwQ8aPH58ztm3bNg4fPpzTp14oAjauUON7uNQ7dywrBm5NhNrLrLcuQRAEQRCMZpHMDKEU0d1mYqFgho29Rnbs5uRl8rV0szLA3GCGPDOjnKtpAQjdzAw3X5d8iwWVBG0beWGjzF1/lkpL8IUndzXxCfTE3ln+d1gUdTPyo1TYUq9SR0Z1+p3aFQzfNN6sncbX//Rk27+zSc/Sz7ARBEt499138fX1lY1NmTJFdDYpat4vgPeL8rGIXyD+gHXWIwiCIAiCSUQwQ8ilycouiJaXBWpmZKkzUNrJb+o9XEzfFqJbLwNJwtbL9OBIrF7NDNO2mSTotGUtqfUyHnFzsqVlHXkGzZ7TT+5MolAqqNyggmwszApbTXQ52bkz9OmFvNl2MRUdqumdV9nBgfurmft3L0Iij1phhUJp5+zszCeffCIbO3z4MNu2bbPSisqwGgtBqfMzOmQ4aDIMzxcEQRAEodgRwQwhl+4WE7BIZkZaZqLemKd7eZOvlxUVJTu29fJCMqPVkW7NDFO3mehmZhT1torC0KmpPOPlbEi8XiaLIX6N5ds1ws7dt+i6TCVJErXKBzG653q6u7+CMk2jNydZm8iywyPZdmEBKk2WgasIguneeecdqlatKhv7+OOP0Wj0X4tCIbKvAgE6HWVSr0LYHOusRxAEQRAEoxUomKFUKlEqldjY2BgcN+VL91pCMaDWDzpYIpjxMCFCb8zXs5KBmQWj18lEJ23bGFqtlrgk+Q2rqd1MdNuyltTin3m1ru+Jo33ujwmNFvaf1d/mo0u3bsb9y1FkpT+5tWtRkSSJjp0nMLLON/ifyAS1bpq/lgPXf2PJviHciDqGWlN81i6UbPb29nzxxReysbNnz7J27VorragMq/weuLaQj92ZDqnXrbMeQRAEQRCMUqBghlarzfnKb9yUL6GYURnKzDD/hvxeRKjsOCNFTbWAQJOvl6mbmWFGJ5PEFBUqnRtZTxNrZiRGyTtiuJW3TL0Ra3KwU9KmgXwLT0G2mvg1roikyN1apM5SE37B+ltNdFVu8SzvTA7muX8q4nVOP5h3L/4KPx8azlf/PMvms6KehmAZr732GvXq1ZONff7556jV6nweIRQKSQk1lyJ7K6TNyN5uIt6jCIIgCEKxV6D0iKlTpxo1XpTWrl3L999/z7lz58jMzKRGjRq8+uqrjBs3Dltb02ofbNq0iWXLlnH8+HFiY2Px8PCgRo0adO/enc8++8zC30ExotfJxAUk83cixehkZmSkanB2djb5errbTOzMyMzQrZcBUM7E1qyJkaUvMwOgUzMfdp/KDWBcD0smPDqNKj6O+T7GwcWeinV8uH85998q9GQ41Vr6FepaTWHj4cHTyzbj+vxznDl9hmuvVURrK3/dp2UlcuTmaq5H/svgoHmUd6tupdUKpYFSqeSrr76ib9++OWNXr15l5cqVDB482IorK4Ncm0KVsRD+Te5Y/L7sgqAV37LasgRBEARBeLISHcwYO3YsCxYswMbGhk6dOuHi4sLevXuZNGkSf//9Nzt37sTRMf8bLl2ZmZm89tprrF27FkdHR4KCgihfvjwRERFcunSJhQsXlrFghmUyC+IS5cGHrHTzrqe3zcSMzAzdtqzuzjbY2hgfwNGoNSTH6GZmlI5gRrOaHni42BKfnPt3tf9sDK91eXxgomrzKvJgxqnwx8y2LoWtLQ3WrUf9zDN4TbnE2bFVSanioDcvJvkO3+8dTPcGo2lZ7UVslfZWWK1QGvTp04dmzZpx+vTpnLEvvviCQYMGmRyIF0wU8AVEr4WMsNyxm2PBoxM46hcLFgRBEASheCixBUD/+usvFixYgIuLC8eOHWPHjh2sX7+ekJAQGjZsyOHDh/n000+NuuawYcNYu3Ytffr04e7du+zZs4eVK1eyd+9eHjx4wD///FNI300xoVszw0LBjMTUh7JjbZbpxToBMiMjZcdmBTOSLNOWNflhKhqd7SqupSQzQ6mUaNdIvtVk35noJ24VC2heRXYcfuEBqsziW3tC6eJCgy1bKG9TmTYfXqPuL+G43UrVm5epTmPzudl8vf15jt1ah0YrtgYIxpMkiS+//FI2dvPmTVasWGGlFZVhSheouVg+pk6Ga0NB/P8WBEEQhGLLrGDGwYMHOXjwIGlpaQV+THp6es7jzDFjxgwAJk+eTLNmzXLGvb29Wbw4+03JokWLSEhIKND19uzZw4oVK2jQoAF//vkn3t7yLg4KhYLWrVubteZiT7dmhoWCGclpcbJjSW1awOAR3ZoZduVN74yi28nE1OKfup1MFDYKnD2dTF5XcdOpqTxgdDcyjVsP9G/086rarDJSno68qgw19y5F5v+AYsDO15eGO3fiUr0WAVtjaDPxOm3HXcXtpv73mpgezcYz0/l+72DuPDxnhdUKJV3Pnj156qmnZGPTpk0jM/PJHYMEC/N6Diq8KR9LOAjh31pnPYIgCIIgPJFZwYyOHTvSqVMnbt++XeDH3Lt3L+dxprp37x4nTpwA4JVXXtE737ZtW/z8/MjIyGDr1q0FuuZ3330HZG9dKbMpvhr5NglLFP8E/dasSvTT942RpZuZYUYwQ7fNqJebifUy9NqyOqPIUwCzpKsX4IqPhzzQs+8JhUAd3RzwrSkPCoaeLL5bTR5xrFaNZseP4/1fPQPXsHRafxJCld0PDc6/F3+FJfuHsvzIGMLjLhXlUoUSTpIkpk2bJhu7c+cOv//+u5VWVMZV/xYcAuRjtz+G5AtWWY4gCIIgCI9n9jYTU7uSmNPN5MyZMwB4enpSrZrh/awtWrSQzX0ctVrNnj17AGjfvj0RERHMnz+fESNGMHbsWJYvX05yctF2MYiJiSE6OtoiXwWm1vkeLRTMSNepxWGnML34J1g2M0M3mGF6JxP5311pKf75iEIh8YxOdsb+szFGbzUJPRmWz8zixcbNjXrr1uH/8ccAKLO0NPwhjFafheB1wUDXH+DKg4Ms2vsa//t3PEnpT25fKwgAnTt3pl27drKxb7/9VnT8sgYbN6i9AsgTiNZmwrXXQVt8t8gJgiAIQllVoAKglqTRaIDsau6mepQJ4u/vn+8cPz8/2dzHuXXrVk6w4ujRo4wcOVIveDFhwgRWr15tVkaJMXTb9pmjwG+K1YWTmaHSpsleaPY2pm9fUaekoEmRr9PWrG4m8gKgpm4zKa2dTPJ6pqk3f+67l3McGZfB5dAk6ldzy/cx1Vr6cWzV2ZzjsHPZdTNs7Ir8R4/RJEmi2ldfYevry80xYwDwupyC1xc3iWnowvWx9Uhw198OcOn+Xm7HnObFph/RsEqXol62UMJIksTkyZM5dOhQztjFixfZtWsXXbt2teLKyiiPdlDlQwj/Oncs+SzcW5Td9UQQBEEQhGKjyAuA3rlzBwB3d3eTr5GUlH3j+Lj2ni4u2TeTiYmJ+c555OHD3PTxt956i+bNm3PixAmSkpI4e/YsPXv2JDo6mt69exMSEmLyuos9vcwM8zIoHtEoMmTHzvam/9vrZmWAZTMzvEwNZuhmZpS3TL2R4qR6JWf8fOXdgfaeeXzmT9XmVWR1M7LSVcW+boauKqNHU23mTNmY94VkWr9znBbnfHG01X89p2bG88exifxxdCL346+JT9mFx+revTt16tSRjc2bN89KqxGoNg2c6svHQj+FjHuG5wuCIAiCYBVGBTPu3r0r+3rkwYMHeud0v0JCQti+fTtTpkxBkiTq16//mGcqWnlvNCpXrsyOHTto0aIFLi4uNG7cmM2bN9OgQQOSk5OZNWuWFVdayPQyMywTzJBs5Om5Lo7lTL6Wbr0Myd4epavpgQPdbiaeJtbMSIqS/92Vlk4meUmSxDNN5TUwDp57iFqd/426k7sD5WvKt6fcPlEytprk5T95MrV+/hkpTz0dhRp8pu3kxQ0V6V7/fZztPPQed+HeLhbuGcS8nX04eH0Fak2W3hxBUCgUfPDBB7KxnTt3cvHiRSutqIxT2EOtH+Vj6mS4Oc466xEEQRAEwSCjghnVqlWTfUF2IKBr165653S/6tSpQ69evTh9+jQAr7/+usmLdv3v5jVFZ7tBXo+2ibi55Z8Cr3s9gKFDh2Jvby87r1QqeffddwHYvXu30estMXQzMxSWuSFX2mtkx+7O3vnMfDJD9TIkybRCm2kZatIy5GsztTWr7jYT9/KlL5gB6NXNiE/O4syN+Mc+JqClbt2M4l8E1JCKb71Fw23bUDjKs1Pilq+m/LQ9jGn7Ow0rdzb42Jjku2y98C0/HnibuJT7RbFcoYR57bXX8NFpM/3tt6KThtW4t9HvbhK9FmK3W2c9giAIgiDoMSqYodVqZV/5jT/uy97engkTJvDmm28+5pkeLyAgAICwsPw/4X107tHcJ13v0Q1xYGCgwTmPxh88eGDESk13+fJloqKiLPJVYCqdbg0WyMxQa1TY6jQv8XStYPL1dDMz7Myol6HblhVM22ai1Wr1u5mUwswMgCo+jtSsIn9d7Dvz+GKX1Vr4yY7Dzt1HlVkyi+mVe/ZZGmzejMJB/qKOWrmSK83b0SO1B4NazsDR1nAQ9W7seebvfomToZtQa0rm34FQOBwdHRk5cqRs7Pfff+f+fRH8sprA2WDjKR8LeQ/UBW9HLwiCIAhC4TGqCt+vv/4qO37jjTdyWstVrlw538dJkoSDgwMVK1akadOmOfUsTNW0aVMgu9bF7du3DXY0OXnyJADNmjV74vVcXFyoXbs2V69eJSbG8I3Zo3Fz115Q3t7eep/SFbrEo/JjCxQATc1MRNJpUepTrpLJ19PNzLBkW1ZHewWO9sYXpk1PyiArXX5jWhoLgD7yTFMfQsJzs6IOX3jImH7VsbM1HBut2rwykgSP4p+qDDXhFyL0Op2UFOU6d6b+X39x8YUX0GbmvobSb9/mQufO1FiwgA/f2cThkN85E7aF+NQI2eMzVMmsO/U5u6/8QIdaQ2lZrS82ijLaDlqQGTlyJLNmzSIjI7vOUGZmJvPmzRP1M6zF1hsCv4brb+WOpd+CsK8h4DPrrUsQBEEQBMDIYMaQIUNkx2+88QYAffr0sWj3jSepUqUKLVu25MSJE6xcuZKP/2uf+Mjhw4cJCwvD3t6enj17FuiaAwYMYNq0aezevZtx4/T3xe7atQuAVq1amf8NFFdatfxY4Wh4nhEiou/qjVXyrWry9SyZmVFYbVmh9GZmAHRs4s1P/4TmBCdS09UcuxJHu0ZeBuc7ujlQobYvD67mBqJCT4aX2GAGgGe3btTfsIHLAwagScvzKa1Wy43Ro6lpa0u34aPoWv89bkYfZ+Pp6TxMkWeSxadGsOnsLI7cWM1zjT+kdoU2RfxdCMWNr68vb7zxBj/88EPO2A8//MCUKVPw9jZ9e55ghgpDIeIXSAzOHQubDRXfBPuS+zNMEARBEEoDs7qZ7Nu3j7179xrMjChsH330EQCzZs3KqcMB2dkaj1J1R40aJeuasnHjRurUqcOzzz6rd73Ro0dTrlw5tm7dyo8/ygt/rV69mj/++CNnXqmkVYEmVT7m2tzsy965f0N2nJmmxt8vwOTrZUbLu2cUj7as8mCGs6cTNramtx4u7nw87GkYKN9GsefU47cz6dbNuH285BUB1eXVqxfNz57FrW1bvXMhI0cSvX49kiRRw/cp3n92Jc38nzN4nejkUH4NHsVvwWOITrpT2MsWirmJEyfKWpenpqayYMECK66ojJMUUHMxsrdLmlS4NdlqSxIEQSjL9u/fjyRJRn2NHTvWomtITU0lMDAw5/oFKWvw6HFz5syhZcuWeHp64uzsTJ06dRg/fnxOx0/BOGYFMzp06ECHDh1wdDT/E3xj9enTh9GjR5OcnEzr1q3p0aMH/fv3p0aNGly4cIE2bdowbdo02WMSEhK4du0aN2/e1Luet7c3a9aswcHBgeHDh9OgQQMGDBhAs2bNePnll9FqtXz66acFzvQocXQ7mQDYmr/N5WG8vMZIRooWOzvTggYAWbrbTCxYM8NibVlLcVbGI882k782jl6OIy5JvwbJI9Va6tfNyEwr+Z09nGrVosmBA1T94gv5Ca2WK6+8QvSGDQA42LowsOU0Xg+aj1+5BgavdTXiIN/u6s8/5+aSnqWf7SOUDdWqVePVV1+VjX333XckJCRYaUUCLo2g4jvysag/9LdmCoIgCGXCZ599xu3bt416zI0bN2jSpAmTJk3i5MmTxMXFkZqayrVr1/jmm29o1KgR//zzTyGtuPQyK5ihVqs5ePAgBw8eLNAbrfj4+Jz5eQuImmrBggWsWbOGoKAgjhw5wtatW6lSpQqzZs1i7969RgdZunTpwrlz5xgyZAjx8fFs2rSJu3fv0rNnT3bs2MGXX35p9pqLLU26/pjSyezLxifLa5CoM03rPPKIbjDDnG0mujffprZl1e1k4uprmZa2xVmHxt7Y56mRodZoH1sINKB5FRQ2eearNNw5VTK7muiSFAoCPvuMqp/J99BrMzO53K8fd+fMyfl5V69SB97r9D/eaf8zVcrpt6fWaFUcvvEH83a+yJm7Wyzyc1IoeR61MH8kISGB77//3oorEgj4EpTu8rEbY0CrMTxfEARBKHQjRozgwoULT/yaPNly2XRnzpxh/vz5ODg4yDpiPk5SUhK9evUiJCQEgGHDhrFnzx6OHDnC9OnTcXFxITExkZdeeomzZ89abK1lgVE1M3T99ddfDBgwAC8vrwKlxtjZ2dG3b1/i4uLYtGkTzz1nOO3aGAMHDmTgwIEFmjt06FCGDh362Dm1atXit99+M3tdJY6hYIZkrz9mpKTUOMgTE9FmmfWS099mYkaR1IeJ8swA09uyyj9Fdy9fsB9sJZmzow1tG3qx53Tuv8eO45G82K6iwVa59s52VGlYgbtncjsz3Dx6l5pti36LWmGp+vnnZEZG8kBnm9rtSZNIPn2a2j//jPK/AsKBPs0Z+cwKTt/5m+0XF5KcESt7TFJ6DGtOfMKx2+t5ofEkKnnULrLvQ7C+OnXq0K9fP9atW5cz9u233zJmzBicnUt/sLRYsvOBgKlw84PcsaTjELEMKg6z3roEQRDKMF9fXxo0MJzxWhjUajXDhg1DrVYzdepUli1bRlJS0hMf9/XXX3P9+nUA5syZw4QJE3LOBQUF0bFjRzp06EBqaipjx45l//79hfUtlDpmZWZs3LgRyC6e6eT05E/xnZyceOmll9Bqtaxfv96cpxYszVAwQ+GgP2aklIx42bGkMX2LiVajIUun24xZNTMstM0kSXebSRkIZgB0bSn/u7/1IJWb9wxsV/pP9dbywq83j5auvYGSJFHz+++pOEz/xiZ6zRpOt25N2o3cGjIKSUGLgN582O0vOtQaitJAR5PQmDMs3DOIxfte59+bf5KpEi0hy4pHdaEeiYmJ4aeffrLSagQAKr0HjrXkY7cmQaYRLdAFQRCEEmvBggWcOnWK2rVrM2nSpAI9Jisri4ULFwJQt25dxo8frzfn6aef5q23sjtnHThwgBMnTlhu0aWcWcGMEydOIEkSnTp1KvBjHs09elTsNS1WNBk6AxJI5mVRAKRlJsqOlZie7aGKiwO1vONKcexm4la+9NfMAGhSwx3fcvJ/zx0n839TH/iUv+w46sZDkqJLV20ISamk5o8/Um32bL1zqZcucbZ9e1L/SzF8xMHWlR4NxzCuyzpqV9AvJgpwN/YCm87OZN7Ovly+v78wli4UM02bNtWr0TRv3jwyM/OvTSMUMoUd1FgkH1PFwa0JhucLgiAIpcadO3f47L8txT/88EOBawDu27cvpxzDkCFDUCgM337n3T3wKGFAeDKzghlhYdkdCYzpZvKo2uujxwrFhG5mhsIBDGwXMFamTmFRO4XpKdKZOm1ZAWxNbFeYpdKQmKqSjVmqZkZZCWYoFBJdWsi3+ew9FU2WyvAe8soNKmDvIv/Bf+uYfuvekk6SJPwnTqTB33+jdJfvsc988ICzbdqQeOyY3uO8XfwZ+vRCXg/6lnJOlQxeOyEtghX/jmPV8SmkZT45rVEo2XTbjoeHh+d01hKsxLML+L4sH4tcAXH7rLMeQRAEoUiMHDmSlJQUBg8eTMeOHQv8uMOHD+f8uUOHDvnOa9GiRc5Oh+Dg4HznCXLmf/QORhWpezRXpVI9YaZQpHQzMxTm18sAUGnTyXv76mBj+o1+5gN5ZxQbT08U9qatMy5Jv5OGKdtMMtOySEuU/92VhW4mj3Rt4csfu3ILeSamqjh6OY52jbz05iptFFRr4cfV/bndhG4evUvj5+oVyVqLmtdzz9H85Eku9e1LyoULOeNZ0dGce+YZ6q5ciXefPrLHSJJEvUodqVm+NQeuL+ffG6tJyYzXu/a5sO3ciDrG09VfJqj6QJzs3PXmCCXf008/Tbt27Th06FDO2OzZsx/7yY5QBKp/Aw+3gjpP4fOQEdDinMV+dwqCUHpoNFrS4svWNlFHD0cUCvM/FC0uVq9ezdatWylXrhzz5s0z6rGXL1/O+XOdOnXynWdjY0ONGjU4f/48V65cMXmtZY1ZwQwfHx/Cw8O5evUqLVq0KNBjrl69CmS3QhWKEUOZGRagluQ3+o72biZfSzeYYVexosnX0m3LaquUcHUy/r+D7hYTANcyFMyo5O1Iw0A3LtzK3U6080SkwWAGQGBrf1kw49axu2i1WoNFQ0sDxxo1aHLwIOc6dyb51KmccU1aGpf69qXWTz9R8b89knnZKh3oXPddnqn9JiGRRzkY8j9uRcv3T6ZkxLHr8mIOXPuVltX60q7mq3g4mf5/QiiepkyZIgtmXLt2jb/++ou+fftacVVlnF0FqDYDbryXO5Z2DcLmQtWP83+cIAhlUlp8GnM6/fjkiaXIxL3v4uxpflfEJ4mKiuLixYtPnFe7dm1sbU3LwI6Li2Ps2LEAzJo1Cx8jmw+Eh2d/6Ofs7IyHh8dj5/r5+XH+/Hmio6PJyMjA3sQPbcsSsz7aadmyJVqtlhUrVhT4Mb/99huSJNGsWTNznlqwNK1OZoYFOpkAYCPPgHB1LGfypTLu35cdmxPM0G3LWs7NzqQbat0tJg6u9tg7mV7ktCTSLQR6/GqcXj2SR6oHyYuAJkWnEH3zYaGtrTiw8fCg8b59lOvWTX5Cq+X6229zf8mSfB+rVNhSp2I7hrX7kYEtpuFgq19cNlOdRvCNP/h6+wvsvPQ9WWrd+jdCSda9e3caN24sG5s1a5Zo22ttld4F15bysbtfQdpNw/MFQRAEi1uyZAkNGzZ84te9e/dMfo4JEyYQGRlJUFAQwwwUeX+SR91OXFye/GFn3o5lycmlq65cYTErmNG/f38A9uzZU6CUm3nz5rF3714guwOKUIwUUmaG0k5eP8HN2fAn9gWhm5lhb8HMDE9XU+tllM3in3m1b+SFg13ujxKNBvacijY418vfA/eK8hvym0dLX90MXTaurjT4+28qvP223rmQkSMJnz//sY+XJIlmVZ9jbOc/qVOhncE5aq2KvVd/ZsHulzgXth2N1nDtEqFkkSSJyZMny8ZOnDiR87tUsBJJCTV/QPY2SpMOISNBBJoEQRBKhYMHD/LLL79gY2PDDz/8YNIHn+np2fdYBSkYmjcTIy2tbG1NMpVZwYyXXnqJxo0bo9VqmThxIv379+fw4cOyehgqlYpDhw7Rr18/Jk6ciCRJNGjQgNdee83sxQsWpBfMsExmho3OZTxcTd9elBkRITs2JzMjNlGeMWJyJxO9YEbZaMual5ODDe0ayf9dtx2PRKPRf0MvSZKBFq2lP5gBoLC1pdbSpfjrtNwEuDluHDcnTkT7hFpCHk4VGNpmIaOfXU1jv+5IBn6ExyTfYdXxKSzcPYjL9/eLT/BLgf79+xMYGCgb+/rrr620GiGHazOo/L58LG4nRP9pnfUIgiCUMVOnTkWr1T7x61EDCoCLFy/m+5WSktu4ICMjg3feeQetVsuYMWNo1KiRSWt0cMj+gLgg3cgyMnKzax0dHU16vrLGrJoZkiSxceNG2rRpw4MHD9i4cSMbN27E1tYWT09PAGJjY8nKyr5x1Gq1VKpUiU2bNpXaPfIlll4BUPMzM7JUmdg5ym+2vD1MD0BkRcnbftqWL2/ytWJ1tpl4mlD8Ewx0MilD9TLy6tbSl1152rKGRaVx4locT9X11Jsb+JQ/pzfm7m+8cyocVZYaG1tlkazVmiRJotr06SgcHAj9r73XI+Fff03ikSPUXbkSB3//fK6QrZJHbV5uNZNu9Udx6Pr/OHZ7PRqtPBASkRjCin/HEejTgr5NP8HbtWo+VxOKOxsbGyZMmMCIESNyxnbs2MH58+dNfnMlWEjAlxC9FjLzbIO8OQ48u4ONKMwrCEJ2McyJe9+19jKKlKNH8b0Rb9iwYb7n9u3bl9OpZPr06Vy7dg0/Pz+++OILk5/P1TX7g86CbBvJG0wpyLYUwQLdTAICAjhz5gzDhw9n06ZNaLVaMjMzidD5FF2SJPr27cvixYvx9fXN52qC1ei0ULVEZkZMXITemHc5M4IZ0fKtC3ZGFuDJS7emg8ltWaPENhOARtXdCKjgRGhEas7Yuv33DQczWvnJjjPTsgg/94CAFlUKfZ3FRdVPP0Wyt+f2pEmy8cTgYE41aULd1avx7Nr1idfxdK5M76aTaRXYlw2nphEWp18E61b0SebvfomOtd8gqPpAnO1Nr1sjWM+QIUP47LPPiM7zc3Du3LlG1awSCoGNG9RYAJfzbJ3NfAChn0KNhdZblyAIxYZCIRVJMUzBsmbPng1A586d+fvvvw3OeRR8SElJYfXq1QD4+vrSqVOnnDlVqlTh2LFjpKSkEB8f/9gioGFhYUB2kw1R/LNgLNKa1dfXlw0bNnD9+nW2bNnCmTNniImJAbK7ljRr1oxevXpRs2ZNSzydUBgif5UfJxwyPM8IUbH6xXYqeJt+w6obzLA1I5ihWzPDlLasILaZPCJJEv06VGLemhs5Y2dvJHA9LJlafvIAj7OnExXr+PLgam4mx82jd8pUMAPAf+JEbDw8uDFqFNqs3G1Pqrg4Lj7/PA3+/rtAAQ2Aiu61GPHMci7f38+uy0uITLwhO6/SZLD7yg/subKUOhXb8nT1l6nh+5TIkCtBHB0dGTVqFFOnTs0ZW7VqFdOnT8fPz+8xjxQKnXc/8OwBsdtyx+59D+VfB9eCdXoTBEEQikZBt98+2hby66+/8uuvvz52bkxMDC+//DIAHTp0kAUz6tWrx/r164Hsrp6tW7c2eA2VSsXNm9lFpOvWrVugNQpm1szQVatWLcaNG8eKFSvYunUrW7duZcWKFYwdO1YEMoq75LPyY6X5GQa6mRlqlYZy7qbVzNBqtWT9FyB7xJxghl7NDAttM3Evo5kZAJ2a+egVUt0c/MDg3MDW8m0Ut46VjboZuiq98w5NDh/GoVo12bg2M5NLvXsTZ0SRR4WkoEHlTozpvJpBLWcYbNOqRcOVBwdZdngEvx/9kKT0GANXEoqrkSNHyvbQqlQqFixYYMUVCQBIEtRYpLM9UwPXh4NWbbVlCYIgCNbXtm3bnD8fOHAg33knT57MyfRo06ZNoa+rtLBoMEMoRWz0twcYKzYhUnacmapFoTDtJaeKj9crjmhqMEOt0RKXrNvNxPhghipTRUqcvNKwaxmtmQFgZ6Ogd1v5DfT+szGkpOkXtayuE8y4dymStMR0vXllgVurVjQ/cwbvvn1l45r0dC4+/zxxu3cbdT2FpKSJfw/GdVlHmxqvIGE4++LS/b18u6s/Z+5uEUVCSwhvb2/efPNN2djSpUuJj4+3zoKEXI6B4P+pfCz5FET8ZpXlCIIgCOYpSGHRqlWz65FVrVo1Z2z//v2y63Ts2BF39+waSsuXL8/3Pddvv/2W8+cXX3yxUL6n0kgEM4RsTvXkx1WnGp5nhPhk+ae+qgzTU9p1t5iA6cGMxJQsNDpdK02pmaG7xQTKbgHQR7q3Ko9SkfvvnJGlYc9p/X87/6aVsbHPLfip1Wi5+e+dIlljcWTj7k69P//E95VXZOOa1FTOd+vGrSlT0BSgCnZe9jZOPN94AmM6r6FRla7YKvWL+qZmJrDmxCcs2T+Ec2E7UKmNew6h6H3wwQeyoHBSUhI//PCDFVck5PD7EJx0UoNvfwyqJMPzBUEQhFLPzs6O0aNHA3DlyhXmzp2rN+fff/9l2bJlQPY2lZYtWxbpGksyi9TMyCs0NJSYmBjS0tKe+Glf+/btLf30gqk0Or2MbU1vofpITFwE5Kk1qM0yvVuFbjBD4eyM0sSWRbFJ8i0mkgTlXIzPzEiIkL9BtXexw9HN/C4wJZmnmx1B9T05fOFhztiWo5E8/3QFWX0GW3sbqrXwIyQ4NGfs+qHbNOhWuyiXW6xISiV1li9Hm5VF9Nq1uSc0GsJmzSJ+zx7qrl6No06Lziep4F6TV56aTYYqldN3/mH35SWkZMbL5tyNvcDd45NxtvOgdfWBBAW+hIuD+dlZguUFBgbSr18/1uZ5jcyfP58xY8aINm7WprCD6gvgQp5aN1mRcHcmBM6w3roEQRBKqaioKC5e1C9+rsvR0ZHq1asXwYoMmzBhAmvWrOH69etMnDiRGzduMGjQIBwdHdm3bx8zZsxApVLh6OjI/PnzrbbOksgiwYxr164xY8YMNm/eTGJiYoEeI0kSKpV++rlgJepU+bHS/KrLUbH3yNs4wc7G2eRrFWYnE3dnW5RK47NGEh7IX+vuFcpm8U9dPVuXlwUzbt1P4XpYMrX95X8/NdtVkwUzQoJD0ag1KJRlN2FMsrGhzh9/gEJB9Jo1snNJJ05wqlkzGmzejIcJgWB7GyeCqg+kYZXObD47m/PhO/XmpGTGs+fKUg6H/EG3+qNoXX0ACqn0t8wtaSZNmiQLZkRGRrJ8+XKGDx9uxVUJAHh2Ac9eELsldyz8G6j0DjgEWG1ZgiAIpdGSJUtYsmTJE+c1btyYs2fPFv6C8uHq6sqWLVvo2bMnISEhLF26lKVLl8rmuLm58ccff9CkSRPrLLKEMvuu4a+//qJZs2b8/vvvJCQkFGh/0aMvoRjR6AQzFOYHM1LTE2THjram3+wXZicTU4t/xutkZrhXcDN5TaVJs1oelC8nbye15Wik3rxa7eRFL1Pj0rh3SX9eWaOwtaXuH39QbcYMJFv59id1QgIXuncndqd+IKKgXOw9eeWp2Qxu/Q2eTpUNzslQpbD53Gy+3zuYKw8OotFqDM4TrKN58+Z06dJFNjZnzhzxAUFxUX0uSHk+K9JmwK1J+c8XBEEQSr0aNWpw5swZZs+eTYsWLfDw8MDJyYnatWszbtw4zp8/z3PPPWftZZY4ZgUzwsLCeO2110hLS6NSpUrMnz8/J8okSRJ79uxh7dq1TJo0iUqVKgHZFV13797NXiOq9AuFTKstlMyMdJW8poSjnek3+5kWDGboZmZ4mVAvAyDhgU4wo6LIzABQKiS6P1VeNrbnVDQJyfLtPeUqu+MTKN/KcP3grUJfX0kgKZX4T5lCs+PHcaxVS3ZOk5bGxeee48HPP5v1HPUrP8MH3TYyqOUMAn0Mt4+8F3+F5UfGMH9Xf06E/oVGdGYoNiZPniw7vn37tixbQ7AipzpQaaR8LPpPSAi2znoEQRBKkY4dOxr14blWqy20rIzQ0FC0Wi2hoaEFmu/s7MzEiRM5ceIEcXFxpKSkcPXqVb755pucYqKCccwKZixcuJDU1FRcXV05duwYo0ePJigoKOf8M888Q79+/Zg5cyYhISEMGjSI4OBgli1bRocOHcxevGAhWhWgc5OiMH/vtRqdTh+Opu/Bt2Rmhl5bVhM6mYB+zQyxzSRX91a+skKgmSoN//wboTdPNzvj+qHbhb62ksSlSROanTyJV+/esnFtVhbXhw3j+ogRRhcGzctGYUsT/x680/4nxnZeSxO/HgbnRSXdZv2pL/h+72DC4y6Z/HyC5TzzzDO0atVKNjZr1iyR9VhcVJ0KNuXkYzfGiFatgiAIgmBBZgUzdu/ejSRJjBw5MifzIj+Ojo78/vvvNG3alNWrV7N+/XpznlqwJG2G/pjCXn/MSAo7+Zs2V8dy+cx8MosGM5Iss81EN5jhIYIZObzd7enYVF5EdnPwAzJV8u0KtdrJi1lGXIs22CWmLLNxdaXe2rV6nU4AHvzwA+eefZas2Fizn6eCew0GtZrBsPY/4eMSYHDOvfgrfL93MJvOzCIhLcrs5xRMJ0mSXnbG+fPn2b59u5VWJMjYeup3BUs+BQ/My6gSBEEQBCGXWcGMRyk1Tz/9dM5Y3o4Fuvt3FQoFo0ePRqvV8ssvv5jz1IIlaQwEMyTzgxlKe/knhC7FJjNDJ5jhavw2E61Wq18AVGwzkenXXh7gjE3KYp9Om1a/xhVxcJG/1q4fFtkZuhS2ttRZsYIq48frnUs8fJiz7duTce+eRZ6ruk8Lxnb5k/7Nv8DXVb9zihYt/95aw8yt3Vh6cBgX7+0R20+spHfv3tSuLe8ANGvWLCutRtBTaaSBVq1TICvG8HxBEARBEIxiVjAjJSUFAD8/v5wxJ6fcWgsJCQl6j6lfvz4A586dM+epBUsyFMwwMzNDo9Fg5yTvEOLm5GXy9SzazUSnNaspmRlpCelkpcuDdaIAqFzNKi40ri7/O1l34L4sDV5pq6T60/I9giFiq4lBklJJ9blzqfPHHyh0WnCmXrrEmTZtSL1+3SLPpVTY0iLgBcZ1Wccbbb4zGNQAuBV9kt+PfsjCPS9zO+a0RZ5bKDiFQsGkSfLCkgcPHuT0afFvUSwobKHGIvmYKg5uf2Sd9QiCIAhCKWNWMMPd3R2A9PT0nDEvr9wb1ps3b+o95lGAIyZGfDJRbBRCMCM1NRVHF3lLRw8X0wMQmZHyLhe2vr4mXUer1epnZpgQzNDdYiIpJFx9TG89W1r16yDvlhEakcrJa/GyMd26GbeO3UWVKboy5Kf8K6/QJDgYu8ryv9uMO3c426YNcbt3W+y5JEmidoW2jO68mu4N3sdW6WBwXkRCCD8eeIs1Jz4hMS3a4ByhcLz66qtUrFhRNrZgwQIrrUbQU64T+AyUjz34GRKPW2c9giAIglCKmBXMeJTeeutWbgcCV1fXnGqsOw20D9y1axcAHh4e5jy1YEmFUDMjITEeBxcb2Vg5VxMDEGo1mRHy4pF2T6jRkp/UdDUZWfK6DV6mBDN0Opm4+jijtFXmM7vseqpuOfx85FkE6w7clx3XbBNAnt1pZKZlEXrSMlsmSivXpk1pGhys1+kkKyaG8126cOODD8wqDKrLRmFLx9pvMq7LOloE9MHB1sXgvDN3tzBn+/NsPP0V0UmhFnt+IX92dna89957srFVq1YREaFfcFewkurzQJE32K2FG++JYqCCIAiCYCazghmPOpccPXpUNv7cc8+h1Wr5+uuv2bdvX874n3/+yYIFC5AkiTZt2pjz1IIl6WVmKECyMTi1oOIS9D+d9faoYNK1MqOiQCMPQNibGMx4mKh/g+dpQmvW+Aidehmi+KdBCoVE/47yf6vT1+O5eT8l59jZ04nKDeSvjeuHRIvWJ3GoWpUmhw/j2kK/req9b7/lbJs2ZISHW/Q5PZ0r07/5VD7ptYeXWk6nvFt1vTkqTQbHbq/nm519+fXwKC7fPyBqahSyd955BweH3KyZrKwslixZYsUVCTL2VaDqZ/KxpJPwYJl11iMIgiAIpYRZwYyePXui1WrZsGEDanXum9UJEybg5OREcnIynTt3xsfHB1dXV15++WXS09NRKBRMmDDB7MULFqIbzLBAJ5OHifqfCrq7eBuY+WSZuoUNlUqTC4DqdjJxdlBib0JGhW5mhntFUS8jP52b++LhIg8YrT8g/zetqbPVJOTwbdFisgDsfHxotHcv5bp31zuXdPIkZzt0IP3OHYs/r43Sjqb+PRn97GqebzwBexv9TA0tWq5FBrPi37F8u7M/N6NOWHwdQjYfHx9ee+012diSJUtkW0AFK6syFpzqyMdCP4asOKssRxAEQRBKA7OCGR07dmTq1Km88cYb3Mtzw+nv78/atWtxd3dHq9Xy8OFDUlJS0Gq12Nvb89NPP9G6dWuzFy9YiEbnDa8FghkJyfKaKKpMDXY2jvnMfryM+/JtCfYVKyIpTHvpxiaaX/wT9GtmiMyM/NnZKnihjTzzYu/pGKLicoNounUzYsMSeHhHvMkvCBtXVxpu2UKN775Dspf/302/dYuzHTqQdqtwMl2UChva1HiFD7v9RcuAF1Hmk9EVnRzKT4fe4c8TnxKbct/gHME8Y8aMkR1HR0ezcuVKK61G0KOwgxrfyceyYuDOVMPzBUEQBEF4IrOCGZIkMXXqVKZNm4a/v7/sXI8ePQgJCWHJkiWMGjWK4cOHM2/ePG7cuMHQoUPNeVrB0tTJ8mOl+TfmCSnyYEZmqunXytQJZphaLwP0t5mY0pYVRDDDWC88XRE7m9wfN2qNlnV5sjMq1vHVK6B6XXQ1KTBJoaDyqFE0O3YMxxo1ZOcy7tzhXIcOpFy5UmjP7+rgRb/mnzGpxxY61n4DB1vD/x9O3/2HuTt6s/bkVKKTLJ8xUpY1aNCAzp07y8bmz58vMpyKk3KdwbuvfOzeYki+YJ31CIIgCEIJZ1Yw40m8vLx49913WbhwIYsXL2bcuHFU1qnALxQDap0Wukrzt0wkp8s/VVcZqDFaULqZGeYEM+KSzO9kApDwQF4zw6OiCGY8jruLLd2fkheA3XYsksSU7EwZSZKo2VaenXF1v343JOHxXBo3psnhwzg3aCAbzwgP50zr1kStXl2oz+/m6Ev3BqP5qOcO+jWfSkX3WnpzNFoVp+5s5ttd/dhw+isS06IKdU1lybhx42THFy5ckNWtEoqB6vNAkbcrkBpujAYRdBIEQRAEo5kVzHjzzTd58803Wbt2raXWI1iDSn5jjo35wYyUjHjZsSbT9E4fupkZphb/BMtsM1FlqUmOSZGNuVcQNTOeZEDHyuTdHZSeqWHzkdzaKnU6Bsrm3z19jySdv2fhyezKl6fR3r04N24sG1cnJnLl5Ze5PGgQqoSEfB5toTXYONIyoA/vP7uS3k2mGKypodGqOX57PV/v6M32iwtJy0wycCXBGN27d6eWToeb+fPnW2cxgmEOAeA3ST6WsB+ixfsoQRAEQTCWWcGM5cuXs3z5ctzcxI1ciabWCWZYIDMjMeWhfEBl2nYOgMzISNmxbfnyJl9Ld5uJl6vxwYykqGS9D9HcRWbGE1XwdOCZJvLCrZsOPyAjK7t4cGDrqtg75/57aLVwZe+NIl1jaWHn40PjvXtxbdlS71z0mjWcbd9eL+OpMCgkJUHVBzK+6wba1HgVW6WD3pwsdTr7r/3KzK3dWHfqC+7HXyv0dZVWCoVCr3bGP//8w7Vr4u+0WPGbCPbyrbncGg9qEbwVBEEQBGOYFczw+a+jRHkzbi6FYqAQMjNiE+UBCGcHD5OvlRUlT0O3M+P1ptvNxJS2rLqdTOycbHFwNb9oalkw4Bn5NrP45Cx2n8xu42trb0Ot9vLsjMu7Q4psbaWNracnjffvx/fVV/XOpZw/z5mgoEKto5GXm6MPzzf+kEndt9Ch1lCDQY1MdRonQ/9i4Z5B/Br8PnceniuStZU2r7/+Oh4eHjnHWq2W2bNnW29Bgj6lE1T/Rj6WEQ53Z1lnPYIgCIJQQpkVzKhXrx4Adwqh9Z9QhPQyM9zNvmS6Sn7D7+bkZfK19IIZvr75zHyyhwnm18xIiJD/fblXcEWSJJPXVJZUr+RMs1oesrF1B+6h0WSnutTvUlN2LvRkOCmxZlSPLeOUTk7U/f136q5ahU25crJzGXfvcrZNGxIOHy6y9bg4eNKj4RgmdNtEq2r9UEiGt59dizjMkv1DWXPiE5LSHxqcIxjm4uLCiBEjZGP/+9//xO/p4sa7L3h0ko+FfQ1phdN5SBAEQRBKI7OCGa+99hparZbly5dbaj2CNRRCZoZKmyY7NieYkakTzLA1MZiRlqEmJV0tG/N2Nz6YEa+TmeFeUWyzMsbAjvLsjPDodP69FAtAjacDsHPMzZbRarRc2ScKgZrLd9Agmp08iWNNebBIFRfHuU6duPfdd0Xa9cLN0Ze+zT5hXJd1NKrSNd+gxpm7W5i3sw/BN1ai1mQZnCPoGzt2LI6Oua2wVSoVX3/9tRVXJOiRJKixEMjz2tdmwM0PrLYkQRAEQShpzApmvPHGGzz77LNs2rSJzz//XLSAK6kKoWYGNvIbDy/3CiZdRp2SgiZV/sm8qdtMdOtlAHiZkpnxQD8zQyi4ZrXcqV5J3ob1z/330Gq12DrYULNtgOzcVVE3wyIcAwNpEhyM61NPyca1WVncGD2aK4MGoU5OzufRhcPHNYBXnprN5B5b6VxvOM52Hnpz0rOS+fvc13y7qz/7ri4jPvVBka6xJPL19WXYsGGysZ9//pmIiIh8HiFYhXN9qPy+fOzhJojdYZ31CIIgCEIJY1Yw49ChQ3z44Yc0bNiQadOmUa9ePT7//HM2bNjAvn37OHjw4GO/hGLCwpkZaWlp2OpsiffxNK0DiW5WBpiemaEbzHCyV+LkYGP0dRIi5JkZHiIzwyiSJDGgo/z1cDk0ieNXs9v51u1UQ3bu1vEw0pPN6O0r5HhUGNTzuef0zkX/+SdngoJIDSn6OiVujr50rvsuk3pspVfDDwx2P4lJvsuOS4uYta0nvxx+j3vxV4t8nSXJhx9+iK1tbpZTRkYG3377rRVXJBgUMBVs5YWRuTEaNPrBd0EQBEEQ5Iy/k8ujY8eOsloB169fZ9q0aQV6rCRJqFQqc55esJT43fJjpXmZBlFRUTi4yNPGK/r45zP78XTrZUj29ihdTVufbr0MU7IyQD+YITqZGK9DE29+2XaXqLjcIMWPm0NpXsuDmu2qobRVov6vy4k6S03I4VAadq9treWWKkonJxps3MjtTz4hTKcwZMrFi5xu2ZI6K1bg/cILRb42OxtH2tUaTBP/Hmw5/y1nw7YanHc98gghkf/SrOoLdKv/Hm6OPgbnlWV+fn68/vrrLFu2LGds8eLFTJ48mXI69VMEK7LxgGqz4PpbuWNp1yF8PvhPtNaqBEEQBKFEMCszA7IrpZv6JRRTklkxLmJjY3FwkV/D26OiSdfSzcyw8/U1udimXjDDhHoZWq1Wr5uJ2GZiPBulgqHd5QGusKg0/vk3EgcXewJb+cnOXdkjuppYkmRjQ+CsWTT4+2+U7vKCv+qEBC717s2tSZPQZFmnToWrgzeDWk1neIdfqOxR1+AcLVpO3dnE3B292XNlKelZSQbnlWWTJk1Cocj9NZ+cnMx3331nxRUJBlUYCq46bZTvfAkZ96yyHEEQhOJs//79SJJk1NfYsWMt8ty7d+9m6NCh1KhRA2dnZ9zd3alVqxb9+/dnyZIlJD9hu25qaipz5syhZcuWeHp64uzsTJ06dRg/frwo1G0is+5a9+3bZ6l1CNaiMZC+r3DUHzNCdOwDlDbygIOTvWkdUrIi5S1eTd1iAhCTKP9eTSn+mZaYQWaa/AZPBDNM82wzHzYeuk9IeErO2MrdYfR4ypc6nWoQEhyaMx4SHEpWhgpbe/MCbYKc13PP0fzkSS717UvKhQuyc2Fz5pBw5Aj1Vq/GvnLlfK5QuAK8m/Jep98JjTnD2btbOX9vJ+lZ8jcKmeo0dl1ewsHrK2gdOIB2NQfj4uBplfUWNzVr1mTgwIGsXr06Z2zBggV88MEHuLjob+URrERSQI3v4cxTwH8f9GhS4NYEqLvSqksTBEEQIC4ujjfeeINNmzbpnUtMTCQkJIT169cTFBREkyZNDF7jxo0b9OzZkxCd7bzXrl3j2rVr/Pzzz/zxxx88Z2ArsJA/s+4MOnToYKl1CNaiStAf8zDv3zUmXr9An5OdaXUlDGVmmCrGAttMdIt/ShK4+YqbAlMoFBLDX6jG+MUXc8bikrLYfiyKZzsG8s9X8CiBKzM1i1vH7lK7faCVVlt6OdaoQdN//+X6sGFErVolO5d4+DCnmjal3tq1eFjp571CUhDo05xAn+Y832QCh0P+YN+1X8hUyQsDZ6hSOHD9N47cXM1Tgf3pUGsIrg7eVllzcfLRRx/JghmxsbH8+OOPjB8/3oqrEvS4tYQKb0HEz7ljUaug4rtm/04WBEEorUaMGMHIkSOfOM/b2/T3AwkJCXTp0oVTp04B8OKLL9K/f3+qV6+OUqkkLCyMAwcOsH79+nyvkZSURK9evXICGcOGDWPQoEE4Ojqyb98+Zs6cSWJiIi+99BLBwcH5BkQEfQUKZjRr1gxJkli3bh3VqlXLGb979y4AlStXRqk03FpPKObUKfpjZtbMiE2IhDyJGBoNBgv6FYRuzQyzMjN0ghmmZGbo1stw9XFBaSte+6ZqVN2dZrU8OH09Pmdszb5werZujl+TStw9cz9n/MreGyKYUUiUzs7U+eMP3Nq04ea4cWjzbC/Jio7mfOfO1PjuOyoNH27FVYKt0oFn6rxFi4De7Lj0PadCN6FFvmUxS53O4ZDfOXpzLa2q9aVDrSG4O5nWAak0aNiwIS+88AKbN2/OGfvuu+8YM2YMNjYi06lYqTYDYtaBKj537MYoaHYaFLb5PkwQBKGs8vX1pUGDBoX6HO+//z6nTp3C3t6eP//8kxd0aoq1aNGCF198kW+//Ra1Wm3wGl9//TXXr18HYM6cOUyYMCHnXFBQEB07dqRDhw6kpqYyduxY9u/fX2jfT2lToJoZZ8+e5ezZs6SlpcnGAwICCAwM5Nq1a4WyOKEIaFL1x8zcZpKQEiN/igzJ5DoXlgxm6NfMsDf6GnrFP8UWE7O92rmK7Dg6PpPdp6L1uppc3XsDVZbhXxKC+SRJovJ779EkOBj7qlVl57QqFSEjRnBn+vRiUe/I1cGb/s2n8v6zq2hYuTOSgV9lKk0GR26uYvb251h1fAp3Yy8YuFLZMGnSJNnxnTt3+Pvvv620GiFfdj4QMF0+lnIR7i+2znoEQRDKuMOHD/O///0PgK+++kovkJGXJEkGPyTIyspi4cKFANStW9dgZuTTTz/NW29lF4I+cOAAJ06csMTyy4QCBTMe3YhqNBq9c8Xhja1gBrVOMEPhkL1/1wxJqbGyY63K9E+U9LaZlDftE1aNRqvXmtWkzAydbSaik4n5GlV3p2GgfBvS6j3h1OpYXTaWlpjBjTx1NITC4dayJc1Pn8azZ0+9c6GffMLtyZOLzc/9Sh61ebX114zvuoHmVXujkPSzpDRaFefCtrN43+ss3jeEy/cPFJv1F5WgoCBatGghG1uwYIGVViM8VqV3waWJfCz0M8iMNDhdEARBKDyLFi0CwN3dnVGjRpl0jX379pGQkL2tf8iQIbLC3HkNHTo0588bN2406bnKogLdtbr/V+0+LCysUBcjWIFuZobCyexLpmTI63AoNMZnQDyim5lhas2MhJQsVGr5DYwltpmIzAzLeEUnO+P+w3SOhafj36SSbPz8litFuawyy9bTkwabN+P/0Ud658LmzOH622+jfkLF7qLk7VqVAS0+Z3zXv2gV0BdlPh2Z7saeZ8W/Y1m091VuRZ8q4lVajyRJjBkzRjZ24MABzp07Z6UVCfmSlFBjkXxMnQi39f8vCoIgCIUnMzMzp+Bnly5dcHBwAECtVhMWFkZoaCjp6elPvM7hw4dz/vy4epMtWrTAySn7Piw4ONicpZcpBQpmNGzYEMhOr7l69arefiBTtxAIxYBGvnXI3C0mAOkqefaCjeRg8rV0MzNM3Waiu8VEIYGnqymZGTrBjIqmFTYV5JrX8qC2n7yuym/b71KrW23Z2LWDt0hPMtCBR7A4Samk2vTp1DDQyjPil184Ua8eMQaqeluTl0sV+jb/lAndN9M6cCC2SsM/e+7FX2Hpwbf54+gEYlPKRvvLAQMGUF4ns+1R2qtQzLi3gfKD5WMRv0LSGeusRxAEoQw6d+5cTrCiYcOGJCYmMnbsWLy9vfH396datWq4u7vTpUuXx9a4uHz5cs6f69Spk+88GxsbatTI3mJ95Yr48K6gClT96+233+bQoUMcPXqU+vXry85ptVqTCq9IkoRKpTL6cYKF6QYzlOYHM7K08mwPexMLimo1GrKio2VjpgYzYnS2mJRztUOpND4IJzIzCockSbzVqyoTf7iUMxafnMVle0+UNgrUquwtbqoMNVf23KBpn/r5XUqwsMqjRqF0deXam29mV/P9T0ZYGJf69MHrhReo/csv2Hp5WXGVch5OFenTdApd64/kxO2NHLm5hoS0CL15F+7t5tL9/TSo3Img6i8R4NW01Abn7e3tGTFiBJ9//nnO2B9//MHs2bPNqvIuFJJqsyFmI6gfZUBp4eYYaHwgu42WIAglhkajJTG1bN3zuDnZoFAU/s+qqKgoLl68+MR5tWvXxtbWuG3veYMQGo2GFi1a6LVVzczMZPfu3ezZs4eZM2fq1agCCA8PB8DZ2RkPD4/HPqefnx/nz58nOjqajIwM7O1Nz24vKwoUzBg8eDAXLlzIt0prWdt/XKqoLZ+ZoZbkKVeOtqZlL6hiY2U3T2D6NhNLdDJRZ6lJipan1otghuU0relB63rlOHo5Lmds87FoercN5N7+Gzlj57ddEcGMIlZhyBBs3N258vLLaHRSKh9u3syZNm1otH07DgEB1llgPpzs3OlQeyhta77GubDt7Lu6jOjkUNkcjVbF+fCdnA/fiZezHy0CevNU4ACT20kXZ++++y7Tp08n679uNRkZGSxdupSPDGwnEqzMviL4fyTfXpJwCGLWg09/661LEASjJaaqGDD1uLWXUaTWftEKD5fC78K0ZMkSlixZ8sR5t2/fJsDI9yixsbk1AGfPnk16ejrdu3fnyy+/pFGjRiQmJrJ+/XomT55MQkICkydPpk6dOvTu3Vt2naSk7A9CXVye3NnR2dk558/JyckimFEABe7LNmfOHEaPHs2+ffu4d+8eGRkZfPHFF0iSxPDhw/E1o8uEYEWFsM0E2ywg9weYi4OnSZfR3WICYOvjY9K1YuLlWxNMCWYkRiWjG7cT20wsa9hzARy/GpcTw8pUabhVpSL25AYzbh8PIzEyGbfyprX7FUzj3acPzc+eJWTkSOL37pWdS7t2jTNBQTTctg2XYtgbXamwoVnV52js153jt9ez6/ISUjMT9OY9TAljx6VFHAr5H8/WfZfWgf1RlqKWmBUqVGDQoEE5ldkBFi9ezIQJE4z+xEooAlXGwYOlkB6aO3ZrAng9l12sWxAEQSg0KSkpOX9OT0+nS5cu/PPPPyiV2cXGfXx8GD58OA0aNKBDhw5oNBqmTJnCCy+8IMvyfLRVxc7uyfceeYMXul1EBcOMajJfpUoVBg/O3cf5xRdfAPDee+9Rr149y65MKBoancI1FniDpLRTkzeY4e5sWgpzZqS8eruNhweKAvwgMEQ3M8PLAsU/7RxtcXQTEVNL8i/vxHNBFdgcnLsl4NSDDJr7umMflX3zqdXChe1XaTOkRX6XEQqJU+3aNNq9m6iVK7n5wQeyAr2ZERGcbd+eumvW4NWjhxVXmT+lwoag6i/R2K8He68s5djt9WSp9Yt3pWYm8Pe5Ofx7czUda79FU/8epSaoMXr0aFkw4969e2zcuJGBAwdacVWCQQoHCPwaLg/IHUsPhfBvwX+K1ZYlCIJQXEydOlW2fbIgHrctpVq1ajnZEY8Kfj4ye/bsnEBGXm3btqVv376sW7eOK1eucOHCBRo1apRz/tF1MjMz9R6rKyMj98NXR0cLfMBcBpjVg9Pf3x9/f/8CRZqEYsrCmRmpqanY6TRE8SlX2aRr6XYysTWxLSvoBzN83I0PQujVy6joWmr311vT4K7+ODnIf1lENKlB3qSY81uvFu2ihBySJFH+1VdpdvIkTjo1lNRJSVzs2ZPbH3+MthjXRHKyc+O5xh8ypecOejYch49LgMF5Mcl3WXdqKl/veIEjN1YbDHyUNC1atODpp5+WjYk2rcWYdz9wbycfuzMdMh5YZz2CIAglXMOGDfP9OnHiRM48V9fcreQ+Pj40bdo032t269Yt5895r5H3OskF6AKXNxukINtSBCMzM3SFhoZaaBmC1aRckh+bGcyIiIjA0VX+sqro42/StSzVlhXgYaIFMjN0O5mIehmFwsPFlpefrcKyLXdyxu6plbh6eeD5MB6AiGvRRN2IwbeGKFxoLQ5+fjQ9fJiLvXuTcPCg7NzdGTOIP3iQuitX4uDnZ6UVPpmTnRvta71Ou5qDCYu9wKGQ37lwb5fevPjUCDafm83eqz/RtuZrtA4cgINtyX2TMXr0aI4cOZJzfOTIEU6ePEmLFiLbqdiRJKg+H063gEchXU0KhH4MtX+x5soEQSggNycb1n7RytrLKFJuTmbdYhYLfnnev1SpUqXAc6N1mhdUqVKFY8eOkZKSQnx8/GOLgIaFhQHZwRNRL6NgSv4rTTBP5G/y4zTzPvGOjIzE3kn+qbqXewWTrmWptqwAMQnm18xIiJC3nBXBjMLzYruKbA5+QHR8bhAqrE5VPI4koPivcMn5rVfpPLqttZYokL31q9GOHVwdOpToNWtk5xIPH+ZUkybU/u03vJ9/3korLBhJkvD3asSrXnMIjTnDP+fnER53SW9eckYs2y8uZP+1X3i6+iDa1HgFZ/tyVlixefr27UvlypW5dy+3Le2CBQtk20+EYsS1GVR4AyLyBC8ifoNK74Frc6stSxCEglEopCIphikUTEEbV+Tt4GmoAUZeec/b2Mhvr+vVq8f69esBuHr1Kq1btzZ4DZVKxc2bNwGoW7dugdYomLnNRCiFUs0LZsTERaLQaXnqaGfaTb+lMjPSM9Ukp8l/CJkUzNDNzBDFPwuNva2SN3tWlY2lONgTWTm3AOyFbdfQaEQnJWtTODhQd9Uqqn/7LZLOL3BVbCyXXniBmx9+iOa/DhrFXYB3U9575n+80eY7qno1MTgnPSuZvVd/Zta2nvx1ZibRSXcMziuubG1tGTlypGxs9erV3L1710orEp6o2nRQ5s0G0sKNMehVpRYEQRAsomrVqvj7Z2eXh4aGPjYI8igIAVC5snx7fdu2uR+8HThwIN9rnDx5MmebSZs2bUxac1kkghmCRcUm6HcgMTUd21KZGbr1MgC8LVEzQ2RmFKpOTX2oWcVZNhZWrTIqm+zMn/gHiYSdu2+NpQk6JEmiytixNAkOxqFaNb3z4fPmcbZ9e9LvlIybfkmSqF2hLcM7/MI77X+mpm+QwXlZ6nSO3vqTeTv78FvwGG5FnywxrcrfeecdWXExlUrFt99+a8UVCY9lVwH8P5aPJQZD9FrrrEcQBKEM6NevHwCJiYns2bMn33kbNmzI+XPe4AVAx44dcXd3B2D58uX5vk/47bffcv784osvmrrkMkcEMwQ5v0lmPTwuUT+YYW/jbGDmk2XpdDMxPZgh32Li7KDE0V6/GvHjaLVa4h+IbSZFSaGQGP6C/MZYZWdLeEClnOPzW64U9bKEx3Br1Yrmp0/j3b+/3rmko0c51bQpMX//bYWVmUaSJAJ9mvNWu8WM6vQ79St1ynfu1YiDLD04jB8PvMWNqGNFuErTeHt789Zbb8nGfvrpJ2JjY620IuGJqowFB51g4a0JoBbt+wRBEArD2LFjc7qRfPDBByQmJurN+f3339m/fz8AvXr1ktXPgOyWrKNHjwbgypUrzJ07V+8a//77L8uWLQOgQ4cOtGzZ0pLfRqkmghllnaSz3cLbvEhgYqr8jbA6S0KpMK00S8Z9+afudhVMq72hm5lhyhaT9KQMMlPlafLuFUUwo7A1qu5O24ZesrEHfuVJc8zOrLm0KwRV1uP3MQpFy8bDg3p//knNxYuRdDpdqeLiSty2k0eqlKvP4KB5jOuyjmb+z6GQDAdEQx+e4edDw1l2aCT3468V8SqNM378eFmbuZSUFL7//nsrrkh4rEetWvPKuAvh31hnPYIgCFYWFRXFxYsXn/iVdxuIMfz9/fnyyy8BuHDhAq1ateLXX3/l1KlT7Nu3j/fff5+hQ4cC4Obmlm+G44QJE6hVqxYAEydO5N1332Xfvn0cPXqUmTNn0rVrV1QqFY6OjsyfP9+ktZZVogBoWafVuRFUOBieV0DJqXHyCJnKuAyIR7QaDZk6wQz7yqa1eNUNZnhZYIuJJIGbb8ntZlCSvP1cVY5ejkWlzk7L0yoU3KnhR50LN0hLSOf6gVvU61zTyqsU8pIkiUojRuDaujWXBwwgXedNRPi8eSSdPEn99eux9fLK5yrFU3m36gxsOY2u9d/j35trOHZ7PelZSXrzQqL+JWTPvzTx68mzdYfh4xpQ9It9goCAAF566SVWrlyZM7Zw4ULGjx+Pk5PTYx4pWI13X3DvAAl59l3fnZldINS+Uv6PEwRBKIWWLFnCkiVLnjivcePGnD171qTnmDBhArGxscyePZtr167x5ptv6s3x9fXlr7/+omZNw+9HXV1d2bJlCz179iQkJISlS5eydOlS2Rw3Nzf++OMPmjRpYtI6yyqRmVGWadWAbjDDvDZAKRkJsmNJY3wWBEBWTAxanU9uTQ5mxMuDGT4mdTKR36y4eDtjYydigUWhsrcjfdpWlI3F+nqS4JGdGXNi7TlrLEsoANemTWl++jQ+AwbonUs4cIAzQUGkhoRYYWXm83CqQI+GY/io5w5eaDwJDyfDmWNnw7Yyb+eLLNn/BsdvbyAjK8XgPGuZOHGi7DgmJoZff/3VSqsRnkiSoPq3QJ5C25oUuD3FaksSBEEo7WbOnElwcDCDBw8mICAAe3t73N3dadmyJdOmTeP69esEBRmur/VIjRo1OHPmDLNnz6ZFixZ4eHjg5ORE7dq1GTduHOfPn+e5554rou+o9BDBjLJMo18YU2/biZGuXLsgO1ZiWnBENysDhcL0bSaJFmjLqtvJRNTLKFKvdvHT61keWtMfLXDrWBgxd+KsszDhiWzc3Ki7Zo3BbSdpISGcad2a+MdU9y7u7GwcebrGID7stpkXm36Cm4OPwXl3Hp5lw+lpfLWlM3+dmUliWrTBeUWtcePGdO/eXTY2d+5cVCqVlVYkPJFrU6ggr3dC5ApIOGyd9QiCIBShjh07otVqjfoyNSsjr6CgIFasWMHt27dJT08nPj6e48eP88knn+QU+HwSZ2dnJk6cyIkTJ4iLiyMlJYWrV6/yzTffULVq1SdfQNAjghllmTZDf8zMzIzUTHlhHFuFaanKGffuyY7typfXa/tYULqZGd4epmwzEcU/rcnF0YbXu/nLxlLcnHnglx3gOrn2vDWWJRTQo20nTY8cwa6SPBVeFRvL+c6dCZs7F20JvoG2UdjyVGA/Puy2iW71R2FvY3gb2qMOKF/veIEdFxfp/cy0Bt3sjNDQUNauFV0yirVqX4FS5/dQyAjQlKxaNIIgCIJgDosHMzQaDTExMdy9exe1WhTmK9Y0lg1mqNVqXMrZysZcHT1NupZuMMPULSZgmQKgepkZFd1MXo9gml5B5fEv7ygbu1O9CinOjpzdfIms9JJ7I1xWuDZvTrNjx3DR2Q+qVam4NWECp5o2Je4xrc9KAjsbR56p8xYTu2+mfa0hONt5GJyXpU5n37VlzNranb/PzSU+9UHRLjSPjh076lVOnz17dolpM1sm2ZWHgC/lYykX4d58qyxHEARBEKzBIsEMtVrNsmXLaNeuHU5OTpQvX57AwECuXZNXcv/nn3+YOHEi06dPt8TTCuay8DaT6OhoXMrJsyeqVa5r0rUydTMzTAxmqNVa4pJ0ghlu5tfMEJkZRc9GqWBMv+qyMa1SQUj96qQkZXJxR/HuHCFks69ShSaHDuH1/PN651IuXuR8585cHTIEVXx80S/Ogpzty9Gz4Vim9NrJa63nUbdiB4MdUDLVaQTf+IM5259nzYlPeJBwvcjXKkkSkybJ23KfO3eOnTt3FvlaBCNUHgUuTeRjoZ9D+l1rrEYQBEEQipzZwYyoqCjatWvHO++8Q3BwMJmZmTn7k3QFBAQwd+5cPvvsM4vsXRLMZOFtJnFxcbh4yjMzynv65zP78TIjImTHdhUr5jPz8WKTMtHovBRN22YighnFQaPq7vRtL38tpLo6cd+/AifEVpMSQ+niQv2NG/H/+GOD5yNXrOBMUBDpd+4U8cosz0ZhS4PKnRjy9Hwmdv+HlgEvIhn41avRqjlzdwsLdr/EL4ff42b0ySLNjOjTp49eFfY5c+YU2fMLJpBsoOYS5MVAU+HGGKstSRAEQRCKklnBDLVazfPPP8/Ro0eRJImBAweyaNGifOc3aNCAp556CoCNGzea89SCJRjMzLDVHyugpKQknD3kjy/nbFrRzsyoKNmxXfnyJl0nWmeLia1Swt3ZuNobapWGxKhk2Zh7RRHMsJa3egZQraK8Fkt4QCVuX3/I/SuRVlqVYCxJqaTaV1/R+MABnBs10jufevUqZ4KCSD5XerrVeDhVoF/zzxjXZR2Nq3QzGNQAuB55hJ8ODuOHA29yLSK4SIIaSqWSCRMmyMb27t3LyZMnC/25BTO4tYaK78jHHv4FMX9bZTmCIAiCUJTMCmYsX76cEydOYGtry5YtW1i9ejUjR4587GNeeOEFtFothw+LqttWp1szQ7IFyfSXRGJiIo6u8kCBs305k66VFSm/KbX19TXpOg8T5N+jl7sdkiTlM9uwpOhktDrpHe4VRM0Ma7GzVfDhSzXJ+8+osVFyp4YfJ/4U2RkljUf79jQ/fZqaP/yA0k3+/yrzwQPOtmtHzKZNVlpd4fB1q8bLT81iQvfNPF19ELZKB4Pz7jw8y6/Bo1i091UuhO9CrSncujCDBw+mgk7XqNmzZxfqcwoWUG0m2Op00bnxPqiLVxtgQRAEQbA0s4IZq1atQpIk3n33Xbp161agxzRt2hRAr56GYAVZOq0BteZVQU9MTMDBWb4n3MmuYK2KdFksM0O3k4m78VtM4u/rdGhxsMHJw/DNh1A0avm50K2lPMAVXdGbg0fukZ5kYPuUUKxJSiWV3n2XFufP41S/vuycOimJS336cHXo0BJfR0OXp3NlXmgyick9ttGl3oh8i4Xei7/CH8cmMntbT3ZdXkJiWpTBeeZycHBg7NixsrH169cTEhJSKM8nWIhtOQicKx/LuAN3RH0yQRAEoXQzK5hx/nz2p6AvvPBCgR/j+98n7A8fPjTnqQVLiFhm0cvFJeu/wTY1mKGbmWFqMOOhBTqZxIUlyI7LVXY3OrtDsLw3e1bFyU7+I+xqdX+ObrpkpRUJ5nKoWpUmhw7h3r693rnI5cs52bAhsaWwKKWzvQfP1n2HyT230afJFDycDNcISkyPZs+Vpcze1ot1pz4nOinU4msZPnw4bnkyZLRaLd98843Fn0ewsPKDwb2DfCx8HqTdsM56BEEQBKEImBXMiP/vUzIvL68CP+ZRu1alUr+qu1DEov+06OUSUmL0xhxNCGaoU1NRJ8trVJi6zSRGZ5uJKcGMh2HxsmNPPw+T1iJYVjlXO97oWVU2luloz+8HI0RLyRLMtlw5Gu3Ygc9LL+mdywgP50K3boS8/z6ajNKXgWOrdKB19YFM6LaJ/s0/x8vZz+A8tVbFydBNfLOzH3+e/IyHyeEWW4O7uzvDhw+XjS1fvlx8AFHcSRLUXJxdFPQRbSbc/MB6axIEQRCEQmZWMMPT0xOAsLCwAj/mUbqqj4/PE2YWzNq1a+nYsSPlypXD2dmZxo0bM2fOHLKyzNsyAbB161YkSUKSJDp37myB1ZZuyelxsmN1poSNwviCollR+hkelioAakowI1Y3mOHvYdJaBMt7oU1FavnItw7dcXXnn7/Fp5ElmcLBgbqrVlH7l1/06mgA3F+0iLNt25J286YVVlf4lApbWgT05oOuGxjUagb+ng0NztOi4fSdv5m380U2np5OVOItizz/6NGjsbHJvSlOS0tj6dKlFrm2UIic60FlnU4mD/+Gh9ussx5BEARBKGRmBTPq/7e3+cSJEwV+zJo1a5AkiZYtW5rz1ACMHTuWgQMHEhwcTKtWrejevTt3795l0qRJdOrUibS0NJOvHRcXx7Bhw0r3dgJnw2+QTZWaES871mYZ1zXkkUydLSaSnZ3BG5qC0N9mYnzNjLjweNmxp59pW2cEy1MoJD5+uz5KjUY2/vOBCJJSC7dYolC4JEmiwhtv0OLCBTwMBJOTTp7kVJMmRKxYUWozcZQKG5r49WDkMysY/exqWgX0xUahH5DVaFUcu72Ob3b1Y9HeVwm+sYqMLNOLP1auXJmBAwfKxhYtWkRmpoEOWELxUvVTsNXJZLw51nD3MkEQBEEo4cwKZvTp0wetVsuiRYuIi4t74vx169bx99/Z7cL69etnzlPz119/sWDBAlxcXDh27Bg7duzIKVTWsGFDDh8+zKeffmry9d9//30iIyP10m1LFVudbIdK75l1ubQseaFM1Ka1eTVU/NOUoJJWqyXGzMwMrVZL7F15zQyxzaR4qeTtSE9/eZAqFQUL11y30ooES3Lw96fRzp3U+P57FA7ywrvq5GSuDRnClZdfJqsAv4NKskoetenb/FMm9dhKx9pvYqd0NDgvPO4yf5+bw+ztvdh3dZnJQY1x48bJju/fv8+6detMupZQhGzcIXCWfCztOtz7zjrrEYQyorQG1YWSp6y9Fs0KZgwbNgx/f38SExPp2rUrly9fNjgvKiqKjz/+mFdeeQVJkmjQoIHepz7GmjFjBgCTJ0+mWbNmOePe3t4sXrwYyP4kKSEhweDjH2fjxo388ccffPDBB7Rq1cqsdRZrmlT5sXMjsy6nG8ywwfCb7SexVFvWhBQVmSr5J/Y+HsZlZqTGp5OeLN+bL4IZxc9bbzSkXJz8//r+i3GcCYm3zoIEi5IkicojR9L02DEca9XSOx+9Zg2nGjcm/uBBK6yuaLk6eNG9wftM7PEP7WoOxkZh+GdaamYCOy4tyglqpGclG5yXnxYtWtCmTRvZ2DfffFPm3iSVSOWHgKtO9uudLyAzwjrrEYRS7FENQJVKZIMKxcOj12JZqU9pVjDD3t6eTZs24ebmxqlTp2jYsCH16tXLOf/aa69Rq1YtKlWqxKxZs1CpVHh6erJ+/Xqztm/cu3cvZ2vLK6+8one+bdu2+Pn5kZGRwdatW426dkxMDMOHD6d27dp8+eWXJq/RXDExMURHR1vkK1+6wQylk1lrztTI3yzbKVxMu46FOplExqbLjpUKCS834zIzdLeYKG0UuFdwNWk9QuFx9nDk+Uo2KLPkbyYWrr9Jlk5ASyi5XBo1ovmpU1R48029cxlhYZzr2JE7M2ag1ZT+f3MXe096NfqAid03077m67g6eBuclxPU2NaLPVd+IjWz4AF+3eyMU6dOsX//fnOWLRQFSQE1dDIx1Elw+yPrrEcQSjEbGxvs7e1N+vBUEApDQkIC9vb2stpXpZlZwQyAxo0bc+LECYKCgtBqtVy9ejXn3Llz57hx4wYajQatVkurVq04duwYNWrUMOs5z5w5A2QXIK1WrZrBOS1atJDNLagRI0YQExPDsmXLcNBJaS5K9erVw9fX1yJf+VLrBDMU5gUzVMiDB462ptW5SA8NlR3bVTTcpvBJIuPkGRU+HnYolcYF0eLC5b+c3Cu6oVCa/d9GKATPvNSIqjflXR3Co9NZsbPgBYqF4k/p4kLtZcuot3YtNuXKyU9qtYR+/DGXXnwR1X/dtko7N0dfejYax+Qe23izzffUq9jR4Ly0rER2XV7MzK3d2XBqGhEJIU+8dp8+fQgMDJSNzZkzxxLLFgqb21NQfqh8LOI3SDptjdUIQqklSRIeHh4kJSUVaMu9IBSmuLg4kpKS8PDwKN11H/OwSMimRo0aBAcHc/jwYTZv3szJkyeJiopCrVbj5eVF06ZNeeGFF+jSpYslno7bt28D4O/vn+8cPz8/2dyCWL16NevWrWPMmDF66bWlklpnL7UZmRkqlYp0VSKQm7Xg5lTwlr15pYXI32QbSisviMhYeTCjfDnji3/G6gQzylURxT+Lq8oNytPEDSITk0lxy80KWrM3nGY13Wla08N6ixMszqd/f9xat+bq668Tv2+f7NzDzZs53bIl9TZswKWhZQsdF1dKhQ21KjxNrQpPcz/+GnuuLOXS/b1687LU6RwP3cDx0A1U825GUPWXqF/pGZQGOk8plUrGjx/Pe+/l1lPavn0758+fp1Ej87YlCkUgcCbErAP1o6xJbXar1sb7slu5CoJgEeXKlSMzM5OIiAgSExNxcXHBwcEBhUJRZm4oBevQarVoNBrS09NJTk4mNTWVcuXKUU73w55SzKL5J23btqVt27aWvKRBSUlJADg7O+c7x8Ul+2YmMTEx3zl5RURE8N5771G9evWcehylnu42EzMyM65evYqtTomM6lXrmHSttOvywo1ONWuadJ2IOHmmSHlP4zNt4u6JYEZJIUkSrQY04sa3R7jQoh5aRXYGjVYLc1aFsPTDprg6lY2Uu7LCvkoVGu3eTdjs2dz+5BPIs70k7cYNzrRuTbUZM6g0ciQKW9MKEpdElTxqMzho3mODGgC3Y05zO+Y0rg7eBFV/iTbVX8beVv57dejQoUydOpWYmJicsblz57JixYpC/R4EC7CrAH5TIPTj3LGEA/DwL/B+0WrLEoTSRpIkKlSogKOjI4mJicTExKApA9sdheJDoVDg5OREpUqVcHcvW/cq4p39f9555x3i4uJYv349Tk7mbbcoMXS3mZiRmXHv3j0cXeUvJ1/PykZfR5OeTuaDB7IxRxODGVFx5mdm6G4zKVe5bP2AKGka9qiD9zeH8L8Zzp2auZlbMQmZfL/xFpNfNS3LRyi+JIUC/ylTcG3ZMrurSZ6bbk1qKjfHjuXB0qVUnz8fTwtlB5YUeYMaB68v53z4LjRa/SJ1Sekx7Lz0PcEhf9Cx9pu0CuyHvU327wMnJydGjRrF559/njN/1apVTJ8+PScDUijGqoyDBz9Cxt3csZsTwLMn5FM8VhAE07i7u+Pu7o5Go0GlUomAhlAkFAoFNjY2KBRlcxt8iQxmuLpmb2VIScm/5VxycnZapZvbk+s2LF++nL///psRI0bQsWNHi6zRXJcvX8bb23BBN4vQqkErv9k3JzMjIiICRxd51VwnO+Nv/HWLf0L2p68mrckC20zi78kzezxFZkaxZudoS+Pn65K26izxnu4keOX+e+05HU2bhp60a1SI/68EqynXuTPNTp3icv/+JP1XIPqR1MuXudC1K76vvUbNRYuwKWOfWlTyqM2gVjPo2XAsR2+t4/jtDSRnPNSbl5IZz5YL37Dn6k88Va0fT9d4GXdHX9577z1mz55NWloakL2tcP78+cybN6+ovxXBWEpHCJwNV17OHUu/CfcWgd94661LEEoxhUKBnZ1xBecFQTBNiQxmBAQEABAWln9hv0fnHs19nI0bNwJw4sQJvWBGRER2K7NTp07lnFu9ejUVKlQwbtFG8vb2xsfHp/CeQJOmP2ZGZkZERAQOfvKXk5O9h9HXyYyQt46T7O1RmnDjodVq9TIzKhi5zUSVpSYhMkk2JraZFH+tX23K8TXnqHHlFmefaojaNvd1OX/tTRpUc6Ocq3iTURo5+PvT5NAhbo4bx/0lS/TOR/3+OwmHDlFvzRrcnnrKCiu0LjdHX7rWH0mnusO4dG8PR26u4c7Ds3rz0rOSOHD9Nw6F/E4Tv+60rzWEt956i0WLFuXMWbp0KZ988kmZ2pdbYvm8BPcWQuK/uWN3voTyr4NdIb7PEARBEIRCVqB8FKVSafEvc9rFNG3aFICHDx/mW+Dz5MmTADRr1qzA1z158iQHDhyQfV27dg2A+Pj4nLH09PQnXKkE0N1iAqDIvwbJk0Q/jMDWXv5yMikzQ2eLiV2FCiYVT0pKU5GaoZaN+RqZmZHwIAmtRisb86hkWocWoeh4VvGgftda2GdkEXj9juxcYqqKb/68gVarzefRQkmnsLen5uLFNAkOxqV5c73zGXfucLZ9ex4sW2aF1RUPNgpbGvt1Z0THX3n/2VXUrdjB4DyNVsXpu/8wf/cA6r6Qhl/d3MK6ycnJ/PDDD0W1ZMEckgTV58vH1Ilwe7JVliMIgiAIllKgYIZWqy2UL1NVqVKFli1bArBy5Uq984cPHyYsLAx7e3t69uz5xOv99ddf+a7x119/BeDZZ5/NGStItkexp1v8E8zKzEhOj9UbMymYoZOZYXJbVp0tJgopuzWrMeLC42XHDq72OLpZr12vUHBth2a3ZvaOeIhnlPy1efRyHGv23rPGsoQi5P700zQ7doxaP/2kl92lzczk+ttvc/nll8mK1f/ZVZZU9qjDkKfnM7LjcupW7ICE4eDx3cRTvDKtBq9+VYMaLd1Agvnz5+dsPRGKObdW4PuqfCziF0g4bJ31CIIgCIIFFCg9YurUqYW9DqN99NFHvPjii8yaNYsePXrkZGA8fPiQkSNHAjBq1ChZRdeNGzcyZcoUKleuzJ49e6yy7mLDYGaGo/5YAaVmJuKRd0ALDrau+czOn14wo3x5k9ajWy/D28MeG6VxhXFiw+TFPz39PExai1D0KtbxpUabAG4Eh1L9aiiJHq6o7HK7WSzbegffcvZ0aiZSrEszSamk4ttvU65LF64OHkzCoUOy89GrV5Nw4AC1fv4ZrwIEvkszf69GDHl6PtFJoRwO+Z1Td/5BpcnQm1eljgtV6rjw8F46Fw/E8vOKRbz/7gQrrFgwWuBseLgpT6tWIGQENDsNBlrzCoIgCEJxV2KDGX369GH06NEsXLiQ1q1b8+yzz+Ls7MyePXuIj4+nTZs2TJs2TfaYhIQErl27Vjq2iZhLrVM8VeEAkulVcNNV8toSktoehQnXy4qKkh2bHsyQ/xtX8DS++GdsWLzs2NNP1MsoSdq90ZIbwaHYZqmoeekWV5rUyk63/s+8NTeoXtmZquXLSPeiMsyhalUa793LrcmTCdcpWpn54AEXe/Wi4rBhBM6bh42r8UHY0sTHNYAXm31Cl/oj+ffmGv69uYbUzAS9eV6VHejwSiXCNX/wy6FQngrsT52K7VAqSmQprrLBvjIETIOb43LHUi7CvfngJwJSgiAIQslTonu4LFiwgDVr1hAUFMSRI0fYunUrVapUYdasWezduxdHR9MzDUo93W0mZnQyAcjSyoMjSkzbjpEZHS07tvX1Nek6uttMypczfj2xOttMRGZGyVK1eWX8GmVvUyoXm0DVG/KCwZkqDTN/v06mSrROKwskGxuqz51LvT//xMZA0coHP/3EqUaNSAgOtsLqih8Xe0+61BvBpB5bea7RBNwdDRe9lhQS16OO8L+jH/DNzr6cuvM3ao1++1ehmKg8ClyayMdCP4f0O4ZmC4IgCEKxVqKDGQADBw7kwIEDJCQkkJqayoULF5g0aZLBlkhDhw5Fq9USGhpa4Os/eszu3bstuOpiQHebiRn1MgDUkjwTwlYyrZioXmaGqcGMOAtkZtyNlx2LYEbJIkkSbd9smXNc6W4E5cPlr6+b91P4dat4E1+W+AwYQIuLFynXvbveufTQUM526MCdr75Cq1YbeHTZY2/jRNuarzCx+2YGtphGebca+c59mBLG2pOf8e2u/py9uw2NVvwdFjuSDdT8AfLWRtGkwo3RVluSIAiCIJjK4sGMyMhI9uzZw9q1a1m7di179uwhMjLS0k8jmMvCmRlaG3kmhIPStFRt3WCGrYntaXVrZpQ3spOJRqMl7p5uzQyxzaSkqdU+EJ9ATyD7rXtAyB3cVZmyOesO3Of09fiiX5xgNfaVKtFw61Zq/vgjCmedwKtaTeinn3Lu2WfJCA+3zgKLIaXClmZVn2Ns5z+pr3iDsztjSE8xHKyISb7D6hMfMX/XAI7e/JP0rCSD8wQrcXsKKr4rH3u4GWI2WWc9giAIgmAiiwQztFotP/74Iw0bNqRSpUp07dqVQYMGMWjQILp27UqlSpVo2LAhS5cuFS0RiwvdmhlmZmZItlmyY0db0278M3WDGSZkZmi1WgM1M4zbZpIUnYxKp7WryMwoeRQKibZv5GZnKDVaAk5ew0bnJ9+cVSEkpmQhlB2SJFHpnXdocf487u3a6Z1POHCAk40aEbFihfi9lYckSbzaexR3D7ny/bCLbFl0lwc3DBSUBqKSbvPX2ZlM39KVtSencufhWfF3WVxUmwG2Or9fb4zWf28gCIIgCMWY2cGMuLg42rdvz8iRI7l8+XK+LU4vX77MiBEjaN++PfHx8RZYumCW+0vkx5lRhucVgFarRWkvrzvg4qC/J/1J1CkpqHTaJNpVMLxP+3ESU1SkZ8rXU97IbSYPQ+Nkx7YONrh4m7Z1RrCuht1r414hN1PIOSWNJmnyrJuHiZl8/ttVMrJEWnxZ4xgYSON9+wj48ktQyH8lquLiuDZkCBd69CDjnmjn+4hCoWDSpEmoMrVc3B/LisnXWTv9Jm42fgbnZ6nTOXVnM0v2v8G3u/pz5MZqMlWipatV2ZaD6t/IxzLuwp0vrbMeQRAEQTCBWcEMrVZL7969CQ4ORqvV4unpyYgRI/jtt9/Yvn0727dv57fffmPkyJF4eXmh1Wo5cuQIvXv3ttT6BVMlHZUfZ943+VJ3797F0U3+UvJyr2j0ddIN1DJxqFrV6OvoZmUoFODjblwwI/q2kItr5wAAxnRJREFUPKjiXc0TKU8nDKHkUNoqeXpIc9mY7eFr1K8gz9a5cCuRmb9fR60RnxyXNZJSSdVPP6XJwYPY+/vrnY/bsYOTDRsSvW6dFVZXPL3yyiv45/m7unUmiQOL03k96FsqutfK93FRSbfYfG42s7b1YNflJaRkxOU7Vyhkvq+ARyf5WPg3kHzBOusRBEEQBCOZFcxYuXIlhw8fzk47ffVVbt26xffff8/rr79O165d6dq1K6+//jqLFi3i1q1bDB48GK1Wy+HDh1m1apWlvgfByo4fP45HeXnB1cDK9Y2+Tvrt27JjW19flLr72Qvg/kN5MMPXwx6l0rhARPQteTDDp5qn0esQio/mfRvi6pP7WpKAmtdu4+lmK5sXfDGW7zfeKuLVCcWFe5s2ND97Fp+XXtI7p4qL4/KAAVwdOhRVYqIVVle82NraMnHiRNnYzp27SL3vwvvPruKNNouoX6kTCslwq9bUzAT2XFnKrG092XRmFjFJohBvkZMkqLkYpDy/v7UqCBkBWtHlSRAEQSj+zA5mAHTo0IH//e9/uLrmX/TRxcWF5cuX06FDB7RaLb///rs5Ty0UI3fDQnH1lN8UerkYTjd+HN1ghkO1aiatRzeYUcnL+LasMQYyM4SSy9behvZvPyUbizoZxsjWnjg5KGXjfx+JYOcJ07ddCSWbbbly1Fu9mvp//YVdpUp65yOXL+dU48YkHD5shdUVL2+++Sa+OnWNZs6ciUJSULtCGwYHzWNKz210bzAaL+f8t6D8e2sNc3f24ccDb3Hqzt9iC0pRcqoNfpPkY4nBEPGbVZYjCIIgCMYwK5hx+vRpJEli1KhRBX7M+++/D8CZM2fMeWrB0iq8afJDo+PuISnkmQ/ujuWNvk6ahYIZD2LkwYyK3sYHM6JvP5QdP+qIIZRczV6sL6udAXDtfyf48o062NrIX78L19/k9gNRCK8s8+7dmxYXLuAzcKDeufTQUM62b0/IyJFk6dT5KUscHR354IMPZGMbNmzg8uXLOceuDt50rP0GH3bbxLB2S6lToX2+17sdc5q1Jz9jxtZubL0wn7gU07c/CkbwnwIOgfKxWxMgK8Y66xEEQRCEAjIrmBH735u4akbcdD6aG1uG3wAWC/Y6+8K9XzT5UvHJ+m94HO2Mb81aXDIz0hLTSY6RV+cX20xKPhs7G9q/3Uo2du9iBHZ3Y/jwpZqy8YwsDV8uv0pKuqoolygUM7aentRdvZo6//sfSjc3+UmtlvtLlnC8Vi0e/PQTWk3ZTMsfMWIE7u7y7lWzZ8/WmydJEtV9WzK0zQLGdl5Ls6rP57sFJT0riYPXlzNn+/P8fvRDbsecFl1QCpPSEWp+Lx9TxcKtiYbnC4IgCEIxYVYw49EbmPv3C/7pyYMHDwBw031jKBQtTYb8WDKuQGZeSWk6BdzUNigkpeHJj5GhUwDU0cRgRoROMKOil6NRj9etl6GwUYi2rKVE09719f4t9ywKpmNjL55/Wt45Jzw6nWnLr6FSl82bVCGbJEmUf+217Bau7fWzClQPH3L9nXc4ExRE6tWrVlihdbm5ueVkXD7yxx9/EGqgoPMjFdxrMLDFl0zs/jftag7Gyc7D4DwtGi7e28OPB95i4Z5B/HtzDWmZSRZcvZDDszv46GQhRfwK8Qetsx5BEARBKACzghkNGjQA4Ndffy3wYx7NffRYwUq0OsEMhfFbMR5JzZC3uVRqTbtWRni47NhQV4EnyczSEJOYKRurZOQ2E91OJp5+HihtjQ/OCMWP0lZJp5FBsrHoW7Gc33qV4b2rUcvPRXbu1PV4Fq6/JT4VFnCoWpXGe/cSOGcOCicnvfNJx49zqnlzHvz8c5l7vYwZMwanPH8narWar7/++omP83CqQK9GH/BRzx288tQcapV/Ot+5DxKus+nsLKZv6cKaE5+IbI3CUP1bUOp80BQyHDSZhucLgiAIgpWZFczo378/Wq2WjRs38vnnnz/xjcW0adNYv349kiQxYMAAc55aMJduZobC9MyM9Cz5J2W2CuMyIQDUaWlkxci3q9j7GV9E9EFsOrovw4qexn1vusU/Rb2M0qV+t9pUqOUjG9u35F8UGg2fvl4bDxd5MdttxyJZs/deUS5RKKYkpRK/CRNoeeUK3v366Z3XpKZyfdgwrrz0Eqr4+KJfoJV4e3vzzjvvyMaWLVtGREREgR5vo7SjUZUuvNn2e8Z3/Yug6i9hZ6MfMAJQaTI4c3cLPx54i/m7B3IydDMqtbjZtgj7SlBtunws9QqEzbXOegRBEAThCcwKZgwbNozatWuj1WqZNm0ajRs35ptvviE4OJiQkBBu3LhBcHAw33zzDY0bN+bzzz8HoE6dOgwbNswS6xdMZcFgRqZWXl/Cwcb4ehm6WRlgYjBDZ4uJh4stTg6G92XnR7RlLd0UColnR8k/AY6/n8iJP89TwdOBL9+si52N/Efjsq13OHBWFMMTsjn4+1N/3Toa7tyJY61aeuej167lZOPGxB86ZIXVWcf48eOxtc0NBGZkZPDtt98afR0f16r0bjKZj3pup1ej8Xg6Vc53bmTiDdadmsrs7b3Yf+0XUjNFy1yzVRoBri3kY3enQdpN66xHEARBEB7DrGCGra0t27Zto1q1ami1Wi5dusSECRNo3749derUoXbt2rRv354JEyZw8eJFtFotgYGBbNu2DRsb424wBQvSqgCdOgBmBDM0kjyA4GTvns/M/GWEhcmOle7u2Dym1W9+7ut2MjGpLatOJxMRzCh1ararhn8TedvNAz8dIy0xnbpVXZn8ai0keYMTZq+6zsVb4mZJyOXZpQvNz56l0ogReucy7t7lXPv2XB8+nKy4OAOPLl2qVKnCkCFDZGOLFy8mzsTv3cHWlXY1X+PD7pt4s833NKj8bL4FQ5PSY9h+8Ttmbe3O5rNziE0RmVQmk5RQ8wdkbw816RAyCr20R0EQBEGwMrOCGQABAQGcP3+e8ePH4+7ujlarNfjl7u7Ohx9+yNmzZ/E3oRaCYEG6WRlgcgHQtLQ0lPbyNzgujuWMvo5evYwqVUxaj25mhrGdTDLTsoi/L79h9RbbTEodSZLoMradbCwtIZ1Dy44D0K6RF8OeC5Cdz1Jp+WTZZW7eFy1bhVxKR0dqLl5M/Y0bsSmn/7PvwY8/cqJOHaJWrSr1NR4mTpyIQpH7tiI5OZlFixaZdU2FpKRWhad5rfVcPuq5gx4NxuLtYvg9RKY6jSM3V/H19hf44+hErkccQaNVm/X8ZZJrc6g8Sj4Wtx2i11pnPYIgCIKQD7ODGQDOzs58/fXXREREEBwczI8//sjMmTOZOXMmP/74I8HBwURERDBnzhxcXFyefEGhcGVF64+ZmJkRGxuLo4u8OKaHs08+s/Onm5lhyhYT0A9mGJuZ8fBOnN6HT94BIphRGvk3qUS9zvKWrEdXniXuXnZB2/4dKvFckLzDSUq6milLL3E/Jq3I1imUDN59+mR3POnQQe9cVlQUV155hQvdupF265YVVlc0atasycCB8o4Y8+fPJzk52SLXd3HwpEPtIXzQdSNDnl5AoE8Lg/O0aLhwbxe/BL/HzK3d2Xr+WyISQiyyhjIjYBrYybPXuDkWVAkGpwuCIAiCNVgkmPGInZ0dQUFBDBs2jEmTJjFp0iSGDRtGUFAQdnZ2lnwqwRxRq/XHzAhmOLjIU389XM0PZjiYGMy4r5uZYWwnE516GR4V3bBztM1ntlDSdR7dFmWe+hjqLDW7FhwGsrM3Rr0YSOt68k/b45KymPTjJR4miqKDgpx9lSo03rOHwLlzDXY8idu1i5MNGxK+cCFaTels+TtlyhTZcWxsLEuXLrXocygkBXUrtued9j8xqtPvNK7SLd924EnpMRwMWcH83QNZsHsQx29vIEudbnCukIeNG9RYIB/LfAC3P7HOegRBEATBAIsGM4QS4u5M/TETgxkPHz7E1Ut+s+/iYIFtJiYEMzQaLRGx5mVm6LZlFVtMSjcvfw9aDGgkG7u08zq3jt0FQKmU+OT12jQMlLcrjIjNYOovV0jPFCnsgpykVOI3fjwtL1/G6/nn9c5rUlO5OWYMZzt0IDWk9GULNGrUiOd1vu958+aRkWFge6MFVClXn5efmsWEbptpW+PVfLugADxIuMaG09OYubUHOy4uIiEtqlDWVGp49wPPnvKx+99D0knrrEcQBEEQdIhgRlmkNlDE0MSaGWHhd/CqJH+sj2uA0dexRM2MmIRMslTyPSLG1szQK/4pghmlXsd3W+PoLn+dbJ29D1VWdqDC3lbJtDfrUr2Ss2zOtbBk5qwKQaMp3XUQBNM4VK1K/U2bqL9hA3aV9TtyJB4+zKlGjQibNw+tunQFxT766CPZ8f3791mxYkWhPmc550o81/hDpvTYzvONJ1DZo26+c1Mz49l3bRmzt/Vi9fGPCI05U+rrmZhEkqDGIpC1W9fC9Xf/KyQuCIIgCNZV4JYiBw8etPiTt2/f3uLXFApA6aYf0MinSvyThEXeQBkgj4n5ulYz+jqZ9+TV5w29+X+SBzpZGQ52Csq5GrdFRLRlLXucPBx59v02/PPVnpyx6FuxHFt5hjZDsvfkOzvaMPOdeoxeeJ6I2NxPmA+df8iyrXf0ioUKAmRvVfJ+8UU8nn2W21OmcH/xYtl5TXo6tz78kOh166j9yy84183/Brwkad26Nc888wz79u3LGZs9ezZvvPFGoXcyc7RzpU2NV2hT4xUiE29y5u4WTt/dQqKBLAyNVsXZsG2cDduGr2s1Wga8SLOqz+Fsb3x2YanlWA2qfga382wfSj4N9xZDldHWW5cgCIIgAJK2gB9HKBQKJN1eheY8sSShUonIPkB0dDS+vr6ysaioKHx8jK89USCX+kHMBvlYB9M+lfpw6jvYNDqRc6zVSMzqf8qo14omPZ1Djo6ysRaXLuFcr55Ra9l+PJJ5a27kHAdUcOKnCU0L/Hi1SsP01t+hVuXuZX/r14H4NzU+sCKULBq1hqWvreLBldwbHjsnW97/ayhuvrlFi+9EpjJm4XlS0uWfpI/sU40X2+kUyxMEHXH79nH97bdJN1AEVLK3J+Dzz/H78EOkUtC6fNeuXXTt2lU2tnLlSl5++eUiX4tGqyYk8ijBN1ZyPfLIY+cqFbY0rNyF1oEDqOrV2KLve0osTSacagqpl3PHlK7Q8grYi9+PgiAIJVmR34damNHbTPJrvWrKl2AlCp2tF76mv7lMTNXpjKKyNfrNX8b9+3pj9iZkZkSY2ckkLjxeFsgA8A70MnodQsmjUCroNaWTbCwzNYud3x6SjVUt78RnQ+qgVMhf44v/us3y7XfFzzXhsco98wwtzp+n8ujR2Sn8eWgzMrg9ZQpngoJIuXjRSiu0nM6dO9OihbzbyIwZM9BYofCpQlJSu0Ib3mz7PR90Wc9T1fpjqzT8+0GtyeJs2FZ+OPAGC3a/xNFba8nIKuPtmBV2UPMH+Zg6CW6Msc56BEEQBOE/Rn/84+joSO/evenSpYusn7xQgmiz5MeONQ3PK4Dk9DjybsRQah3znZufDJ0tJgpnZ5RubvnMzp9uW9YKnsbVAdHdYuLs6YSTu3EBEaHk8mtUkaZ96nPmr0s5Yxe2XaVFv4YEtMit4dKslgdj+1dn3p83ZI//fVcYcUmZvN+vul6wQxAeUTo7U2PBAnwGDODam2+SplMENOnkSU41a0bVTz/Fb/JkFLYls5uSJEl89NFH9O3bN2fs4sWLbNmyRa9AaFHydQvkxWYf063BKE7c/osToRuISb5rcG5EYgh/nZnB1gvzaVi5Cy0CXiDAq2nZzNbwaAcV3oSIX3LHYtbDwy3g1ct66xIEQRDKtAIHM1xdXUlKSiItLY01a9Zw4MABXnnlFQYPHkyjRo2efAGh+NDotJSUTG+bm66S196wV7gafY1MneKfDn5+Jr1ZzFvLAKCCp3ltWUW9jLKny+i2XNlzg/Sk3NfSlll7Gb7qVZS2ua0fuz9VntikTH7dJr8J2nI0EoAx/auXzRseocDc27al+dmzhH72GeHffAN5snq0WVmEfvYZMRs2UOuXX3BtWvDtcsVJ7969qVu3LleuXMkZmzFjBs8995zV/3842bnTofYQ2td6nVsxpzhxewMX7u1GrcnSm5upSuXUnU2curMJL2c/mlV9nuZVn8fDqYIVVm5FgXMgZhOo8hTKvjEKPJ4BZf5dZARBEAShsBQ4tSIyMpJVq1bRs2dPlEolDx484JtvvqFp06Y0adKEb775hgcPHhTmWgVL0eoEMxSmBzMyNEmyYyc7D+OvoRPMMKX4J+gXAK3kbWxbVnknE9GWtexx9nSi03tPy8aibjzk+JpzenNf6ezH6H6B6CZhbDkayS9b74gtJ8ITKZ2cqD53Lk2PHMGpTh2988lnz3K6RQuuvf22we14xZ1CoWDKlCmysaNHj3LgwAErrUifJElU92nBoFYzmNJzOz0ajMHTOf9uWg9Twth1eTGzt/Xkt+DRXI84gkZb9FtnrMLWC6rPlY+lh8KdaVZZjiAIgiAUOJjh4ODASy+9xD///MO9e/f49ttvadq0KVqtlvPnzzNhwgT8/f3p3r07K1euJC0trTDXLZhD95MnybQ05qysLDQKeTaEm5PxNSYs0ZY1LUNNXJL8+6poZGZGzG2RmSFAi/6NqFBLXvRo3w//khSjv2/++acr8snrdbBVyiMaq/fe45dtooaGUDBurVvT/MwZ/CZPBt3tmxoNEcuWcbxmTe7MmIEmSz9zoDgbNGgQAQEBsrEZM2ZYZzFP4GLvSYfaQ/mw2ybeaLOIuhU7IOXzNkmLlqsRh/gl+D2+2dmX4BsrSc9KLuIVW0H5IeDeQT4WPhdSSn6dF0EQBKHkManohY+PD2PGjOHkyZNcunSJSZMmUaVKFdRqNTt37mTw4MGUL1+eoUOHsmfPnidfUChaupkZJm4zWbVqFU7u8p1K5VzKG30dSwQzInSyMgDKG1EzQ6PREnM7TjbmIzIzyiSljYJeU56RjWUkZ7Jr/iGD89s18mLKa7X1MjRW7wln6d+hIqAhFIjCwYHAmTNpduwYzg0a6J3XpKYS+vHHJa5AqK2tLRMnTpSN7dq1ixMnTuTzCOtTSApqV2jDkKfnM6XnNro3GI2PS0C+82OS7/D3ua+ZsbUbf52ZSVSifreaUkOSoOYS+YcgWhVcHwFlJUNFEARBKDbMruBZt25dZs6cyZ07d9i7dy9Dhw7FxcWF5ORkVqxYQdeuXfHz8+Pjjz+2xHoFS0g+LT82cZvJqVOncHKTBzN8PY0PROi2KTSlk4lu8U9PV1sc7JT5zNaXGJlEZpr8E0+faqKTSVnl37QyjZ+rKxs7988V7py5Z3B+u0ZejBtQQ2983YH7LN50WwQ0hAJzbdGCZidPEvDllyic9OsQJJ86xanmzbk7cybaEtLe/I033qB8eXmge+bMmVZajXHcHH3pWPsNPui6gZEdl9OqWj/sbVwMzs1UpXL01p98s6sfPx98l7Nh28hUlcIsVee64CcPUJF4GCJ+tc56BEEQhDLLou1IOnbsyC+//EJkZCQrV66kR48eKJXKnG0pQjGg1YJaJxVWtyBoAbm6uuKoE8zwdDOuIJomM5OUy5dlY0716hm9Fr3in0a2ZdUt/mnvbIerr7PR6xBKjy5j22HvIg/0bZ25T6997yPdnyrP+IE1dDtu8tehB3y34RYajQhoCAWjsLen6qef0urGDSq8/bbe1hNtZia3P/qIM08/TUqe4prFlYODA+PHj5eNbdy4kUuXLuXziOJHkiT8vRrRt9knfNRrBy82/ZjybtXznX8j+jirj3/EV/88y5oTn3D1wSGDxUVLLP+PwSFQPnZrImRGG54vCIIgCIWgUHqrSpKEQqFAkiSrVywXdGRF6Y9pM/THCiAxMVEvM8PZvpxR10i9ehVtpjyY4tK4sdFruRcj//SropnBDO9qnuK1W8a5ejvzzPAg2VjE9WhOrNUvBvpI96fKM2FQTb0tJ38fiWDenzdQq0VAQyg4+4oVqf3TTzT991+DBUKTTpzgVNOm3J0zp9jX0hg+fDgeHh6ysU8//dQ6izGTvY0TTwX2Z2zntQxr/xP1K3XKt7ZGpjqNM3e38NuR0Uzf0oUNp7/izsOzJT9bS+kINb+Xj6li4dYE66xHEARBKJMsGsw4cOAAb7/9NhUqVODll19m27ZtZGVlUbFiRUaPHm3JpxJMpU7SH3ML0h8rgITkOByc5Vs53By8jbpG8tmzsmOHgABsdN7wFkRoRKrsuIqPo1GPj9HpZCKKfwoArQY1wbeGfLvR7gWHiQmNzecR0KWFL5NeqaVXx3HniSim/36NzHwyOwQhP26tWmUXCJ04UT9LIyOD25MmcbJ+fWL++qvY3iS7uroyZswY2djGjRs5fvy4lVZkvkedUAYHzWNSj3/oWPtNnB/T0Ss1M4Hjt9ezZP8bfL/vNc7e3VayszU8u4PPQPlY5HKI32+V5QiCIAhlj9nBjCtXrvDRRx8REBBAp06d+PXXX0lISMDR0ZFXXnmFHTt2EBYWxqxZsyyxXsFcultMAFyfMulSaVlxemNuDj4GZuYv/eZN2bGTgcJ3T6LVarn9QB7MCKxoXM/7yBs6wQxR/FPgv2KgkzvJxrLSVaybsg1VZv71Cjo18+Hj12qj1EnROHT+IVN/uUJ6prpQ1iuUXgoHBwJnz6bJ4cM41qqldz4tJIRLL77IuY4dSTp50gorfLJx48bh5SUPDk6ePLnYBmCM4eFUke4N3mdyz+281PIravoG5ZutARAed5nVJz5i9rZe7Lu6jOT0/AOkxVr1+aB0k49dHw4a0zI+BUEQBMEYJgUzoqKiWLBgAS1atKBBgwbMnj2bu3fvIkkSnTp1Yvny5URGRvK///2PLl26oND9iFKwHrVOe0mlO3qb/AsoXZMgO5Y0NtjbGldnIvPBA9mxKZ1MHiZmkpQqv7GsVrHg69BotESFxMjGKtQ2LigjlF4BLarQvF9D2diDK1HsWXTksY9r39ibz9+og62N/P/XyWvxTFl6meS0klG8UShe3IOCaH72LFXGjzf4szvh4EFOt2zJlcGDyYyMtMIK8+fu7s6UKVNkY/v27WP37t1WWpHl2Srtaerfi7faLeajXjt4vvEE/D0b5js/MT2aHZcWMXNrN1Yem8SNqONoSlJXEPuKUE2n1W7aNQidap31CIIgCGVKgaMM6enprF69ml69elGlShU++OADTp8+jVarpV69ejkBjV27djF48GCcnUXxxGJJL5hh+r+TSpJnQ9hojb9WZkSE7NiugnEFRAHCouT1MhzsFJQvV/C2rHHhCXqdTMrXNG67jFC6df+wg162zpEVp7h17O5jH9e6niczhtXD0V7+o/bi7UQ++P4CMQni00vBeEpHR6rPnUuT4GBcW7UyOCfq9985Ub8+kStXFqvMh5EjR1JFJ2g9ZcqUYrVGS3F18KZNjVcY+cwKJnb/h271R+HhVNHgXLVWxfnwnfx86F3m7ejDvqvLSEwzUOOqOKo0HFxbyMfC5kDCYeusRxAEQSgzChzM8PX15dVXX2X79u2oVCrKly/PuHHjOH36NBcuXGDChAlUqlSpMNcqWIJGHoBAadx2DNmlFPIggr1uqmkBZEbJ36zZ6bTvK4jwaPk6qvg4otCtwPgYkdfl1dedyjni4i2CcUIuO0db+s/sidJWXiPmr6k7SU96fECiSQ0PZr/bAFdHebHc2w9S+eD7i0TGpufzSEF4PPegIJr++y91V67EvmpVvfOqhw+5+uqrXOrTh4z7962wQn2Ojo5MnSr/1P7UqVOsX7/eSisqGp7OlXmmzltM6LaZV1t/TYBX03znPkwJY8elRcza1pPfgsdw6d6+4l1bQ1JCrWUg2eYZ1MLVIYa3tgqCIAiChdg8eUq25ORkJEnCwcGBF154ga5du6JUKjl//jznz5836clff/11kx4nmEE3M0NhejAD20wgNwPC0cbd6EtkRcsDCba+vkZf4160/GbQ2OKfETrBjAq1vEUnE0FPhdo+dBnblu1fH8gZS4hIYtuc/bw4rdtjH1u3qivz3mvA5KWXiE3MvSl58DCd8Ysv8vWIBkZ34BEEAEmhwPfll/F+8UXCFy7k7vTpqBMTZXMebt5M/P79VJ06lcrvv4/C1jafqxWNoUOH8vXXX3P9+vWcsU8++YQ+ffpgY1PgtyUlklJhQ8PKnWlYuTPhcZcIvrGKC+G7UBloka7RqrkacZCrEQdxsfeiedXnaRHQBx9X/cCV1bk0goBpcHty7lj6Lbj5IdT6wXrrEgRBEEo1SVvA3M5HrVYt9sSShEol9owDREdH46tzEx8VFYWPTyHUbXjwE1x/J/fYtTU0+9foy2g0GnqOrEqTLrnbMQJdOvBOt/lGXeewqyvq5NxPbpocOoR727ZGXePjny9z/EpuMdJXu1RhaPeCv9lbNXYzV/fnFiINeq0Z3T/sYNQahLJBo9GyYvh6bh8Pk40PmN2TBt1qP/HxDx6mM2XpJe7FyANwnq62THqlFs1qeVhyuUIZlBkVxY1Ro4heu9bgeae6damxcCHlOncu4pXJrV27loED5Z0wfv75Z9566y0rrch6UjMTOHN3C8durScq6dYT5wd4N6VlQB8aVu6CnY1xwftCpVXD2faQqFNPqMFW8OphnTUJgiAIj1Wk96GFwKjKnFqt1qJfghWodbeZmLad4o8//sDJXf7pnpujcXUm1KmpskAGmJaZER6lv83EGIYyMwTBEIVCos/nXbF3tpONb562m7h7Cfk8KldFLwe+HdWQgAryjKjYpCwmL73Ez/+EolKXoOJ/QrFj5+tLvT//pN66dQZ/nqZeucL5Ll241K8f6WFhBq5QNPr160fz5s1lY59//jnp6WVv25WTnTttarzCuC7rGNHxV1oE9MZOmf/vsdCYM6w9OZXpW7qw4fRXhMVeLB7vqSQl1FkBCp33FdffgqyHhh8jCIIgCGYocD7nvn37CnMdQlHR3b9qYs2MvXv34tlYXmSznKtxxTt1i39C9htxY2RkqXmgU3PAz7fgwYz0pAzi78tTsivUNj6gIpQdHpXc6DXlGTZ8siNnLCM5k3VTtvHmsgF6dTV0lXO14+sRDZj0w0Vu5WkprNXCmn33uHo3iS/frIuTQ+lOtxcKl0+/fnh06MDN8eOJXLFC73zMhg3E7dxJwPTpVH7vPSTl41+3lqZQKJg5cyZdu3bNGQsPD2fx4sV88MEHRbqW4kKSJKp6NaGqVxOebzSB8+E7ORG6kbuxFwzOz1ClcPz2eo7fXk8Ft5q0DuxP06rPYW9jxvZRczlWh+rzIGR47ljmAwh5D+qttt66BEEQhFKpwNtMhMJTpOk9NydA+Nzc4/KDsz9JMdKgV/sT0PembGxEx9+o6tW4wNd4uHUrF3v1yjlWurvTJi7OqO1MN8KTGfHtOdnY5hmtcbQv2BvzO6fD+eXN3HRspY2Cj/4dhc0TbkgFYf3H2zi/5apsrP3brXh2VJsCPT4xNYuZv1/n5LV4vXN1q7oyY1g9XBxFQEMwX8KRI9x4/32ST582eN61ZUtqLFqEWz6dUQqLVqvl/+zdd3xT1fsH8E/2aNqm6d570LLKliUCIkMQRARc8HUrKioOXPxEcS8QUXFvURRQAdmyNxQoFEpL995tmjT7/v5IB7dJ2rR0JO3zfr36Su6559x72ty2uU/Oec6ECRNYH5Z4enoiIyMDbm5tTyjdUxXXXMGJrE1Iyt4Mla6qxboivgyDQ2/GdZFz4e0a1iX9s8AwQPJUoHIbu7zPb4DP7dbbEEII6Ra9apoJ6QGMzYbC89qetBMAtEwla5sxAYHyPm06hur8eda2S0JCm/OyZBWzp834KUR2BzIAoOhyGWvbK0JBgQxil2kvjIcimP37c+Dr48g8Yd/QfTepAG/cH4+HZoSBz2Nf9xezlVjy6XmUVNLSreTauY8ciUHHjyPmiy8g8LKcRqc8cQJJw4fj0oIFXbrqCYfDwVtvvcUqKy8vx/vvv2+jRe/k6xaJm/svwQvTduDOEe8h1ncUODbevmkNtTh8ZR0+2DELXx14GOfzd8NgtEwu2qk4HCD2a4DvwS5PXwToSq23IYQQQtqBghm9jaGKvc2Xt+swdQb21Aw+JODzhDZqW6e+cIG17ZKQ0OZ+ZBexgxmhvm0bXluU2jxfhnNEIUn3E8tEuO2tqeDym/6MMgzw54v/QlVZ10LLJlwuB7ddH4iVj/eDXMbOQZNRoMKjH51BUlpVR3ab9FIcHg/+DzyAoamp8Lv/fqt1in/4AcdjYpD9xhsw1tl3DV+r4cOHY+bMmayylStXoqKiokvO70z4XAH6BU7E/0Z/guenbMGN8Y/AQxpgs356yTH8dPQZvLl1EjaefgPZ5We6LreGKACI/pRdpi8D0h/rmvMTQgjpFSiY0dt0UDDDwGEHEQScticSVTULZkjbEczIah7M8GtbMCP/Ajtvh18sBTOI/QL7+llMK1GWqvDX8h1tummIDXbF+4/2hacbOyBYrTJg6doLWL833zES/BGnJ1AoEPvllxiwbx8ksZYr8JhUKmS9/DJO9OmD0vXru+S6W7FiBWtUnlKpxEcffdTp53VmcqkfJvR5EM9O/gf3jv4UffyvBwfWRzaqddU4lvkHPtv7P7y3fQZ2pnyGMmV253fSey7gdSu7rPR3oPSPzj83IYSQXoGCGb2NUcXe5snadRgTlz38XcRzbVN7xmSC+uJFVll7RmZcnUARACL87Q9maGq1KElnZ1gP6tu2JKaEjLxnMCJHhLDKUvdm4PhvZ220sC7UV4oPF/VFkLeYVW5igC/+ycJbP1+GRme85v4SAgDysWMx5OxZRLz3HnhW8lNos7ORcvvtSJ46FZqsrE7tS0JCAubNm8cq+/jjj2l0hh24HC5ifK/DgpEr8ezkzbg+ZiGkQrnN+hWqPOy++AXe3zETn++7F+fydsBo0ndO5zgc8+gMvoJdnvaoeZQGIYQQco0omNHbmJote8cVW6/XCg6f/eZHKmxb7g1NVhZManYgoq0jM1R1BoucAhEB9o8QyT9fBMbU9Kkjj8+Ff7xvm/pACJfLwawVk+HiwV5FZ/sH+5GXXNimYwV4SfDJ4gG4LkFhse+/pDIsXp2M/LKuGf5Pej6uSITgZ57BsMuXzVNPrOQsqty2DScSEpD30UdgjJ0XTHvllVdYozNqamqwcuXKTjtfT6RwCcCUfovxwtR/cdvg5QjzTGyxflZZEn459jze2XYzdl/8ErWaTggeCX2B6E/YZfpSIO3xjj8XIYSQXoeCGb1NBwQzjEYjeGL2m1qZ2MNGbeuaTzHhKxQQ+rVtVERms1EZPC4HQd72L8uae5Z9o+nfxwcCEa0eQdrO1csFs16/iVVm1Bux7ul/oCyttdHKOhcJH68ujMOCySEW95YZBSo8+uFZ7DtLn2qSjiP09UXsl19i0KlTcB871mK/Sa3GlaefRtKIEag927YRR/bq06cP5s6dyypbtWoVKisrbbQgtgh4YgwJm4GHx32D5yZvxo3xj8JLFmqzfk1dCXamfIq3/p2MdcdfRHrJcZgYU8d1yHse4HkLu6x0HVC6oePOQQghpFeiYEZv0wHBjNraWkhc2Tf9btK25ZpQp6SwtqXx8W1eySSziD1lJsRXAgHf/ks67xw7mBHU379N5yfkatGjwzHynsGsMmWpCr8t2QyDztCmY3G5HNx1YzBev7cPXMTs1XXUWiNW/JCK1RuuQGfowBsO0uu5JiZiwN69iP/9dwj9Lf8eKk+exKnBg5G+eDF0JSUdfn4andHxFC6BmNDnASyZtBGLbvgJIyPnwUVk/cMHo0mPM7n/4qsDD+H9bTOw++KXqFYXX3snOBwg+jPL1U3SHgH05dbbEEIIIXagYEZvY2q21GM7ghlHjhyBxI0dzJDL2hjMaJ4vIz6+zf3IKGCPzAhvQ/JPhmEskn8GUzCDXKOJT4xGxPBgVlnuuUJsffu/diVSHB6vwCdPDkCor+WIo78PFWHJmmSUVdPyraTjcDgceM+Zg6EXL8L/4YctKxiNyP/4YxyLiEDmyy/DUFXVYeeOj4+3GJ2xcuVKlJbScp7XisPhIFiRgBkDn8cLU7dj/rC3EOo5wGb9CnU+dqZ8irf/nYpvDz6G5PxdMFxLbg2RPxD1MbtMXwKkL27/MQkhhPR6FMzobTpgZMbJkychbTYyQ+HatikiFiMz+vRpcz8yC9kjM8L97c+XUVVQA3UV+2cRSMk/yTXi8bmY8840yAPYCRVPbTiPY+vOtOuYQd4SrF48ADcOsQwYXsqpxaKVZ5GSrWzXsQmxhe/ujpjPPsOA/fshjYuz2G9SqZDzxhs4FhGBnHfegVGlsnKUtrM2OuPVV1/tkGMTMz5XgAHBk/HIuO/w+PhfMDh0Bvhc60urMzAhtfgQfj76LN7eOhlbk1eiVJnVvhP73Al4TmeXlfwMlG1q3/EIIYT0ehTM6G06IJiRlZUJd2/2G5+25MxgGAbq1FRWWVuDGQzDILPZsqzhAfaPzMg/zx6VIZWLLW5ACWkPqVyC+R/NgEDMDvhte28fMo7ntOuYEhEPz86LxpLboyBsNpWqokaPZ9Yk45/DhTCZaPlW0rHkY8Zg8JkzCF22DByBwGK/obISmUuX4nhMDMo2bbrm88XHx+Ouu+5ila1duxYpzQLgpGMEevTBnCHL8cLUbbi5/zPwdYuyWbdWW4H9l7/HBztmYe2++3A6ezP0Ro3N+hY4HCD6c8sl4S8/RKubEEIIaRcKZvQ2HRDMqKgthMiFPY/f1y3S7vb60lIYa2pYZZKYmDb1oaRSC7WGnYQ03K8NK5lcYM8DDkjwa3PODkJs8Yv1xszlk1hljInBhpe2tTkhaAMOh4PJw32xenF/+Huyf2/1RgYf/5mBZz47j+xmQT5CrhVXJELY8uXmVU/+9z+Aa/nWQVdQgAuzZuHCbbdBW9i2VXyae+ONNyCRNE2tMhqNeOaZZ67pmKRlLiIPjI6+E09O/B2LbvgRw8JnQ8S3/T81s+w0fj/5Ct7YciM2nn4DeZUX7JtKJwoAIlexy/QlwOVHgHZMxSOEENK7UTCjN2EYYMB/QP+dQN9/gPj1gDiszYdR6Zol7GJ4cJfYv6RpXVoaa5sjFEIcEtKmPlzOYw9plkl48JZbHyZrTfN8GYEJtCQr6Vh9b4rFuIdGsMqUpSr88MgGqKvb8GlmMxEBLvjkyf4YFG25HHJyRg0e/vAMftyeAyON0iAdTBwWhthvvsHQCxfgPWeO1Tplf/6Jk/HxKPjii3Yv5RocHGwRvPj333+xffv2dh2P2M+cW6Mvbh30Ml6athNzhixHmJftJV41+locy/wDn+y5C6t2z8XBtF+g0la1fBLfuy2nm5T9AZT+du3fACGEkF6Fghm9CYcDyMcAHhMBz5sB79sAvuUNUWuyi9hTRERwa9OohrorV1jbkogIcHg8G7Wtu5DJHtkRE+xqdx9MRhMKU9iZ+ClfBukM1z80AlEj2UsilqSX46dHN0Cr0rX7uG5SAd58IAGzrw+w2GcwMvhhRy5e+jIF1bXXkLCPEBukcXGI//13DDp1CvKJEy32G6qqkPbQQzg1aBAqdu5s1zmee+45+DdbUWXJkiUwGNq2MhBpPyFfgsGhM/Dw9d/g6Rs3YGz0PXARym3WL6pOw+Zz7+HNrZPw67GlyCg9aX20BocDxHwB8BXs8rRFgPbaRvUQQgjpXSiYQdrk8uXLcPVkj4CQChQ2alunycxkbYsjItrejzz2UP2+Ya52ty3NrICujn2TRyMzSGfgcjmYtWIyFMFyVnn+hWKsf24LjNewtCqPx8HDM8Lx5gPx8FOILPafulyFRz46g0s5lByUdA7XQYPQf8cOxP30E/ienhb7VefOIXnSJJybMgWqCxfadGyZTIY33niDVXbhwgV89dVX19Rn0j4+buGY2v8pvDBtB+4Y/i6ifa4DB9Y/QDCa9Dibtx1f7H8AH+2cjUPpv6BO1+zvkNDPvFzr1QwVwKW7AKZ9I3oIIYT0PhTMIG1y9OhRuCrYSeA8XNo2qkGTlcXaFoeHt6k9wzDIapYXIDLQ/nwZOWcKWNvu/q6QedrfnpC2kCmkWLB2Ntx8ZazytENZ2Pb+vms+/tA4D3z5bCLmTQiySGVQWqXDU58k4/ttOdDp2x84IcQWDocD3zvvxNCLF+HTLHFng8pt23BywACkPf449BUVdh97wYIFSExkT3FYtmwZqqurr6nPpP34XAH6B92I+8Z8iucmb8bE+IfhIbUcIdagRJmJf86aR2v8cepV5FZcFdTyuR3wZi/Fi6o9QNbyTuo9IYSQnoaCGaRNdDodZM2CGUG+trOfW6NpNs1EHBbWpvYVSj2UavZQ47Ysy5qTlM/aDhkY2KbzE9JW8gA3LFg7G1IPCav8+LozOPpr0jUfXyzk4b6pofjgkX5QuLF/Pw1GBj/tzMWD7yfh9OWqaz4XIdYIvb3R58cf0X/nTrj062dZwWhEwSef4HhMDAo++wyMHdNFuFwuPvjgA1ZZaWkp3nzzzY7qNrkGHi4BmNjnITw7+R/cP+ZzDAiebHOJV71Rg5NZf2HNf3dh5a7bsTf1W1SpC4HoNYCw2f/gnNeB8n+74DsghBDi7CiYQdqkpKQELnL2kpNuYm+72+vLylBz9CirrK3BjMxCdvJPsZALXw/LYfa25CSxR2aEJNr+VImQjuIVpsD8D6eDJ2Dnh9n23j5cPpBpo1Xb9I1ww2dPDUT/SMtlhvPLNHh+7QW888tli2AgIR3FY+JEDE5KQsxXX0HoZzlqz1BejrRHH8WpwYNRtXdvq8e74YYbcMstt7DKVq5ciczMjvmdIdeOy+Eiymc45g97Cy9O24Gb+z8Db1mYzfpF1WnYdv5jvP3vVKw9/ByOuT0ONdPsf/iluwBN+5ayJoQQ0ntQMIO0SWVlpUXODJnY/pwZpevXg9E35avg8PlwHz26TX1oPsUkzE8KLte+5J/VxUpUFbCTh4Ym0sgM0jVCEgOtLtm6/vktyEsustGqbRRuQrz7UF+r004AYNepUjzwXhKOX6zskPMR0hyHx4P/ffdhWFoaQl55BVyx5RLgqnPncPaGG5B8881QJrU8Oum9994Dn98URNfpdHj++ec7vN/k2kmF7hgdfSeenrQBD4z5Av0CbwSXw7dZP7PsNDZeWoc3KhLwQ004LuncYGJgzp+RMgcwtT9RMiGEkJ6PghmkTapqyiH3YQczPF2C7W6vOn+ete01a5bVT+9aklVoGcywV/NRGWKZCN6RlonrCOks/afGWSzZqlPr8dOiDSi8VGKjVdvweBzcNzUUnz41EH1CLZPjltfo8NJXKXjnl8uoohVPSCfhyWQIf+01DL140eZSrhVbtuD0oEG4MGcO1JcvW60THR2Nxx57jFW2fv16HDp0qMP7TDoGh8NBpM9Q3DniXSydshWTEhZBLrX9v97ImJCic8d3NRF4pzIeO1V+qKpKAjKesdmGEEIIoWAGaZP88svg8q4aBcEAvm6RdrdvnvxT2rdvm/vQfCWTtuTLyDjGHrYakhhg96gOQjrKuIdHoN+UOFZZXY0W393/B7JP53XYeSIDXLDysX548rZIuIgtlz/edaoU9797GntOl1pfQpGQDiAOC0P8779jwH//waV/f6t1yv74Ayf79kXG0qUw1tZa7F+2bBkUCvYowKeeegomEyW2dXRuEm+Mj7sfz03ejAfGfIGhYbMgFthegazaJMTuOj+8UxmP71I248LF12E00dQ4QgghliiYQexWXV2NK3nskRVcowuEfImNFpYsVjIJDW1THyqVOmQ2G5kREyyzUZuNYRhkHGUHM8KHhbTp/IR0BA6Hg5nLb0TkdezrX1OrxQ+PbLAIul0LLpeDadf54ctnEzE4Rm6xv1plwFs/X8ayby6irFrbYeclpDn5uHEYfOoUoj/9FAIfH4v9jF6P3HfewfG4OJSsW8cKsHl4eOD//u//WPVPnDiBX3/9tdP7TToGl8NDpM9QzB68DC9P24W7R3yIfoETbSYNZcDBJb07fkzZgLe3TsK28x+jVJndxb0mhBDiyCiYQey2bt06uHmx33SI4G53e4ZhoMlmvxFpa/LPM+nsJfkkIi7iQuwLZlTmVaOqkJ0vI3IEBTNI9+AL+Zj34XSEDQ5ilRu0Rvyy+C9kney4ERoA4C0X4a0H422O0jiaUon73k3C1qNFNEqDdBoOn4+ARx7B8CtXEP7mm+DL5RZ1dPn5uDh/Ps7ecANrauIjjzyCmJgYVt2lS5dCrVY3PwRxcHyeEAmBN+DOEe/h5Zt3YVbiywiU97FZX6mtxN7Ub/HBjpn4bO//cDxzAzR6ZRf2mBBCiCOiYAax27Zt2+Adwk7kJuLI7W5vKC+HSdVsJZI2BjMuZLLfvPSLcAefZ99l3PzTbheFFD5RlC+DdB+hRIC71sxC7LgIVrleY8DPj29C1qmODWhwOOZRGl8/Pwhj+lte+2qNER+tv4Iln57H+cwaK0cgpGPwZDKEvPAChmdmIujZZ8HhWyaJrN63DycHDkT6k0/CUFUFgUCA999/n1UnLy8PH374YVd1m3QCscAVwyNm4/EJv+Dx8b9gePhtEFnLXlwvu/wMNpx+HSs2T8Qvx57HpcIDMJoo9w8hhPRGFMwgdvP19UX/CewboJYSejXXfIoJeDyIAtu2ksilHHYwIyHM9rzb5poHMyKGBYPDoXwZpHsJxHzMfX86+t7E/sRZV6fHT4s2duiUkwaebkIsWxCHZQti4eEqsNifnFGDpz5JxtNrkpGSTZ9+ks7Dl8sR+e67GJKcDI8bb7SsYDQif9UqHIuKQs4772DKuHGYMGECq8rbb7+NwsLCLuox6UyBHn0wa9BLeHHqdsxWmBDMV9msazDpcC5vB747/ATe2joZ/5x9H/lVl2hkGSGE9CIUzCB2YxjLRGsJUYPtbl935QprWxQUZPXTOFt0ehOuFLDf2MSF2BfMMJkYZBzPZZVF0BQT4iB4fC5uXTEZfcZHscr1GgN+fmIT0g5ldcp5x/T3wlfPJeLGId5W9ydn1ODJ1efw2V8ZqNMaO6UPhACANC4O/bZvR8KGDRBZyaVkKC9H5tKlOB4ZiTcGDMDVYwRVKhVefvnlruss6XQikReGDl+PRZ5FeFJ+CaPFJXDh2B59UautwKH0n7F693ys3HU79qV+j5q6jlkdihBCiOOiYAaxm1JTYVEW4Ztod/vKHTtY221N/pleoILB2PSJC4cDxNqZL6MotQR11RpWWcRwCmYQx8ET8HDbO1PRZwI7oGHQGvHrk3/j0t4rNlpeGzepAM/Nj8Eb98fDx0NksZ9hgA37C/HQB2eQlFbVKX0gBDBPg/KaNQtDU1IQumwZOCLL61FfWgrNhx9io0SCWQAasr98++23OHPmTFd2l3Q2aQwQ8wX8+BrcLCvAC4oU3O2aiQQpBzyO7Q9CimvS8e/5lXhr6xR8c3ARzuZug96osVmfEEKI86JgBrFbnaHaoszPPcpKTeuq9u1jbbuPHdum819qNtw91FcKF7F9IzsyjrFHZSiC5ZD7u7Xp/IR0Nr6AhzlvT0XfybGscqPeiN+e2YwLOy932rmH9fHAt88PwiO3hMPL3XJ1gcJyDZ77/AJe+/4S8svqOq0fhPCkUoQtX46hKSnwnDnTah1JXR0WA/gGwDCYE0w//vjjtFRrT+MzD/BdCADgcxgkiKpxtzQJL/YdjVsGvoBgRT+bTRmYcLn4MH49/gLe2HIjNpxegezyszQNhRBCehAKZhC7XUo/x9rmmkTgcS3n21tjVKmgaTbNxMvGm1RbLjYLZti7iglgJV8GTTEhDoon4GH2G5Mx4GZ2Zn+TwYQ/lm7Fua2XOu3cQgEXt44NwPcvDsb900Ih5Fv+izhwrhz3vnMa769LQ2E5fdpJOo8kIgJ9N27EoBMnoJg2zWqdUADvAngbQM7Bg/juu++6sIekS0SvBiTRrCKXwpW4zsMdi274AUsmbcQNcfe3mMNLo6/F8cw/8dnehfhgxyz8d+lrVNM0FEIIcXpOH8xYv349xo0bBw8PD7i4uGDAgAF49913ode3LbN1UlIS3nrrLUyYMAG+vr4QCATw8PDAmDFjsGbNmjYfr6cpKSkB+DpWmQAudrevS0tjF3A4kPaxvQybNc2Tf9qbL0NdrUF2s1UhImmKCXFgXB4XM5dPwqBZfVnlJiODDS/9i6S/LnTq+YV8LuaOD8LaZwaiX4TlCCaTCdh+ogQL3z6FD35LQ0WNzspRCOkYrkOGoN/mzUg8dgwekydbrTMCwLcA0h59FIUXOvf3g3Qxngzosw7gNPvw5NLdgLYA3q5huClhEZ6bvAUPjP0Sg0NvgZAvtXm4stpsbL/wCd6maSiEEOL0OIwTj7d78sknsWrVKvD5fIwfPx4ymQx79uxBVVUVRo8ejR07dkAikbR6HIPBAIHA/E9SJpNh6NCh8PX1RV5eHo4cOQKj0Yhhw4Zh+/btkMvlHf59lJaWwsfHh1VWUlICb2/rSfm6w+rVq/H9ttcw8d6gxjI3ThhevHWjXe1L1q3DxfnzG7fFYWEYnplp9/mravWY83/HWWWfLxmIyIDWAyqnNp7H38t3Nm7zBDw8u/tBSNzELbQipPuZTAy2vrUHJ9afs9h3/YPDMe7h68Dldu6KPCYTg81HivDdvzlQ1hms1pGKeJg3IQgzR/tDIuJZrUNIR6k+dAhXnnwSypMnre7X8/mIeuklBC1ZAr6r/SteEQeXtxK48hS7zP16YMAuoFkODZ2hDufzd+N09j+4UnoCDFp+qysWyDAgaDIGh05HsKIfrXRGCOk1nOE+tCVOOzJj06ZNWLVqFWQyGY4dO4bt27fjzz//RFpaGvr164eDBw/ilVdesft4gwcPxu+//46ysjLs2bMHv/76Kw4cOICkpCT4+/vj+PHjePrppzvxO3Js58+fR/gA9ptCAWx/8tGc+hJ7aLwkNtZGTeuaj8oQC7kI87Xv/Dmn81nbMWPDKZBBnAKXy8G0F8djxJ2WiXb3fXEMvz+7GXqt9QBDR/Zhxih/fP/iYNw+LhAigeW/DbXWiG+2ZmPBW6ew8UABdAbKW0A6j/uoUUg8dgyx334LoZ/l1AKBwYDs5ctxPCoK+Z9+ClMvH1nZYwQuBjxnsMuq9wHZr1tUFfIlGBR6M+4fuxbPT9mCSQmL4CWzPSJTo6/Fscw/8OneBXhv23RsO78aRdVpNusTQghxDE4bzHjzzTcBAEuXLsWgQYMay728vPDpp58CAD755BNUV1smrWyOz+fj5MmTmDNnDkTNsqf369cP7777LgBg3bp1vXa6iUwmQ+Rgd1aZRNqGYEZqKmtbGhfXpvM3z5cREywDj2ffJyc5ZwpY2+FDg9t0bkK6E4fDweRnrsfo/w2x2Hdxdzp+enQjNEptp/fDVcrHA9PD8ONLg3Hb9QFWgxqVSj0+3ZSJe98+jd2nSmAyOe3AP+LgOFwu/BYuxNDLl+Hx2GOw9hugLylB+qJFOJmQgNINGyjxo7PjcIDYbwFRs6BE9utA+WabzeRSf4yPux9LJm3Cw9d/i6FhsyDi2x7VWaHOx97Ub7By1+34aOdt2H3xS5TX5tqsTwghpPs4ZTAjPz8fJ06cAADccccdFvtHjx6N4OBgaLVabN269ZrPl5ho/lS0rq4OZWVl13w8e5SVlaG0tLRDvjpCda3l9x0bPMhKTetqk5JY29I2jsxIzqhhbdubL0NZpkJFbhWrLGRgQJvOTUh343A4mPjEaEx5bhy4zYJ4Wafy8O1966Esre2Svni4CvHQjHD88OJg3DTUx2qd4kot3v4lDYtWnsWp1Kou6Rfpnfiurui/ejUyXnsNWwEYrdSpS0tDyuzZOHv99TanphAnIVDU58+4eloJA1y8A1C1nCuFw+EgzGsgZg9ehpem7cTcoSsQ6T2sxTbFNVewM+VTvLd9Bj7ZcxcOpP1EiUMJIcSB2LeupYNJqr8xVigUCA8Pt1pnyJAhyM3NRVJSEuZflauhPdLqk1cKhUIoFIprOpa94uPjO+xYHfFpVJW2AF7NygaGTLGrrbawEHXNRma4Dh1q97nzSutwPpMdzLCWlNCazOPsT1NEMiF8opp/J4Q4Pg6HgxF3JMInyhPrnv4H2tqmpJtFl0vx1YLfcOfqmfCJ9OyS/ijchHhmXjRuGe2Pb//NwYlLlRZ10vNVWPrFBfSPdMMto/wxsq8CfJ5TxtCJg3vgxRcx4u+/sf7kSTwAYKSVOtUHDuD00KHwnjsXYa+/Dml0tJVaxOG5XweEvwVkPNtUZlQC52cAg44Bgtb/xwv5EiSGTENiyDRUqgpwOmczTmdvRrnK9giMvMoLyKu8gK3nPkSY1yAMDJ6MvoET4SKSd8A3RQghpD2c8l1lZn3iyJAQ2/Mfg4ODWXXbi2GYxmkmN998s8U0lN4iI8/yEw9v1zC72iqPHWNt89zcIEu0zAFgy5HzFbg6HiOT8DAwyt12g6tc3JPO2g4bHASeleUmCXEWEcNC8L+v5kDmyZ7mVVVQgy/u/AVn/u7alRyig2R484F4fPRYP5tBxnNXavD6D6m4+41T2HKkCAYj5dQgHYvH42Ht2rXI5nLxIoDFAFJs1C397Tec6NMHlx9+GNqCAhu1iEMLWgL43Mku02QAF24DTG1bXcnDJQAT+jyIZ276C4+N/xljou+Gu8TXZn0GDDLLTmFj0ht4Y8uN+PbgYzidvRlavao93wkhhJBr4JR3dUqlOX+Ci4vtOY8ymQwAUFNTY7OOPZYvX44jR45AJpPh7bffvqZjOSudTgeOgJ0rxI1j/9KmqhT2W0rZoEHg8Oxf8eBCFvs1vH6AF8TC1ttrlFpc3p/BKoseHWb3eQlxVP5xPrjv+7lQBMtZ5XqNARuX7cDGV7ZDV9e1+X36hrvhg0f74rV7+yDE1/oqUmXVOqz84wrufzcJe5NKKacG6VCDBg3CE088AQA4C+BRAK8CMFjLyG40onDtWhyPjETG889DX1HRhT0l14zDAWK/AlybTROp3gekPwa0Y0Qqh8NBkEc8pvV/Gs9P2YqHrv8aIyLmwEUot9nGxBiQWnwIv598Ba9vnoCfjj6D5PxdtNQrIYR0EacMZnSVH374Aa+99hq4XC6++eYbRPfSIamHDh2Ci5w9I8lVYv9UjayXXmJtu/Tp06bzX8ph5wJICLdvisnF3ekw6JpmUPP4XCTcGNOmcxPiqBRBctz3/VwExFt+gnjmnxR8edevqMxvPQFyR+JwOLguQYEvliTiqTmR8HQTWq2XX6bBGz9dxqKVZ3H8YiUlZiQd5rXXXkNQUNMS4nsBLORw4P/qq+C5W47oM2k0yH33XRyLiEDOW2/BqFZ3XWfJteGKgYRNgDCQXV74JVDwybUdmsNFuNcgzEx8ES9O24F7R63BoNDpEPFlNtsYTFqcz9+Nn48+ixWbJ+K3Ey8jteggjKbemTieEEK6glPmzHCtXzdepbI9pK+21nwD7OZm341vc+vXr8e9994LAPjyyy8xZ86cdh2nvVJSUuDl5Ri5Hfbu3QupO/tS8fcKs6ut8vRpizJpG/KBlFVrUV7DHjIaF2L7zcTVzv17kbUdNToMUrn1T4wJcUYyhRT3fns7tr+/DyfWn2PtK7lSji/u+hXzPpyO0MRAG0foHDweB1NH+GHiEB8cOFuGvw8VIaXZikSAOafGS1+lIMhbjJuv88ONQ33gJhV0aV9Jz+Lq6oqPP/4Yt956a2NZXkkJPioqwscZGch9913kr1oFk4b9ybmxuhqZL76Igk8/Rdgbb8D3rrvA4dLnPQ5P5A/0/Qs4MwYw1TWVpz8JSGIBxaRrPgWPK0CM30jE+I2EPlGL1KJDOJu7DRcL98Ngsr6SlNagQlLOFiTlbIGLUI6+gRMxIPgmhHkNApdD1xUhhHQUDuOEH4n9888/mDFjBjw9PW2uLnLrrbdi48aNeOaZZ/Dee++16fgbNmzA3LlzYTQasXbtWjzwwAMd0W2bSktL4ePDXhWgpKQE3taGxnYDDoeDO1dEISiuKYgwsc/DmBj/UKtti77/HqkLF7LKhmdmQhwWZte5DyaXY/l3lxq3pWIeNr4+HFxuy8uyqirr8N6EtWCuGsY+552p6HtT21ZRIcRZnN+eir9f2wWtih384/G5mPL8OAy5rT84HPuWM+4Mpy9X4Zut2UjNtb3qiljIxdQRvpgzLhBe7r0zPxG5dgzD4JZbbsE///zTWMbhcHD48GGMGDEC2vx8ZL/+Ogq/+gowWlv/BJAlJiLigw/gccMNXdVtci1K1wMpt7PLeO7mhKDSzvm/r9WrkFK4D2dzt+Fy8RGYGEOrbdwkPhgQdBMGhkxFgHtst/5NJoQQwPHvQ1vjlOHhhqVSy8vLbSb4PFm//NqgQfYvHwoAmzZtwrx582A0GvHZZ591eiDD0ZlM5kR93iHsEQ3erqF2tddYeX3sDWQAQEqzfBmxwbJWAxkAkLrvCiuQIRDzETM2wu7zEuJs+t4Ui4d+vRPeEewVl4wGEza/sQd/Ld/Z5Xk0rjYoRo7Vi/tj2YI4BPtYHyGl0ZmwYX8h7n7jFD5an478sjqr9QhpCYfDwerVqyGVNiXJZRgGDz30EPR6PUSBgYj5/HMMvXgR3vPmWT1GbVISzo0fj/MzZqD23DmrdYgD8Z4DhP4fu8xYDZy/GdCXd8opRQIXJIZMxcJRH+OlaTtx66BXEOE9FBzYfo9SU1eCA2k/YvXu+fho52zsufQVKlT5ndI/QgjpDZwymBEUFISh9Ut7/vLLLxb7Dx48iNzcXIhEIkydOtXu4/7zzz+4/fbbYTAY8Nlnn+Ghh1ofedDT1dTUwNVLAJGUnXAzRNHfrvaaDHYCzoDHHrP73HVaI7YdY6/nHhvialfb5quYRI0Kg1BCw9dJz+YZIsf9P8xD9Kgwi31Jmy7gs9t/QnZS971x5nA4GNPfE18+k4glc6NsBjUMRgZbjxbjf2+fxstfpeBYSgWMlCyUtEFoaCiWL1/OKjt37hw+/vjjxm1pdDTif/0Vg06dgnzCBKvHKf/nH5waMAAXbr0VtWfOdGaXybUKXWYOalytLh24MAuwMR2ko7iI5BgWfiseHPsFXpi6DTf3fxbBin4ttilRZmLHhTV4d9vN+GTPXdiX+j0FNgghpI2cMpgBAC+++CIA4O2338bpq/IylJeX49FHHwUAPPbYY3C/KuHXxo0bERcXhwlW3rRs3boVt912GwwGAz7//HMKZNSrqKhAcB92jgqGAeRSf7va1zUbmSGJsH90xMnUSijr2MM2bxjYeh4RdVUdrhzJYZX1GR9l93kJcWZimQh3fHwLRv9viMW+itwqfHvv79j2/j4YdK0Pie4sPB4Hk4f54uvnEvH+o30xbqAXrKUnYBjg2MVKvPz1RSx86xQ2HShAndb6tABCmlu8eDH69WPfUC5btgwZzYLsroMGof/Onei7eTOkNhJUl23ciFOJiTh/yy1QnjrVaX0m14DDBWK/A2TNRuRWHwAuP9iuFU7aw03ig9HRd2DRDT/g2Zv+wU0Jj8HPreUE8nmVF/Dv+ZV4d9vNWL37DuxL/Y4CG4QQYgenzJnRYPHixfj4448hEAgwYcIEuLi4YPfu3aiqqsKoUaOwc+dOSCRNn/x99913+N///ofQ0FBkZWU1lpeUlCAkJARarRZBQUFWgx0N3n///Q5PzOnIc5U2b96MbUVLIfNgj2p4e3ZSq20ZhsH+ZncoCRs3wmvmTLvOvWZjBjYdLGzcHhjljvce6dtqu6O/JuHfd/Y2bvMEPDy7+0FI3MR2nZeQnuLCjsvY9H87rE4v8Yv1xm1vT4V3uMJKy65XWK7B+r352Ha8GHqD7X9LrhI+pl3ni+kj/eHjQXk1SMuOHDmCUaNGsVbMmTBhAnbu3Gk1XwFjMKDwq6+QtWwZ9KWlNo+ruPlmhC5bBrf6UaLEgWgLgNPDAF2zYEDYCiD0JettukBRdTrO5P6LM7n/okpd2HoDAEEe8egXOAn9gm6EwiWgk3tICOmNHPk+1B5OHcwAgN9//x1r1qzBmTNnoNfrERkZibvuugtPPfUUhEL2soC2ghlZWVkIDw+363yZmZkIa0POB3s48kXE4XDw/B8DLcrtCWaUbdyIC1dllAeAwWfOQDZggF3nfvSjM0jLa1qx5n9TQnDHxOBW2315zzrknWt6o9D3phjMeWeaXeckpKcpy6rApmU7kHvO8s2zQMzHxMWjMWzuQLty0XSF8hod/tyXjy1Hi6HW2B6FweUCI/ooMHGIN4bHKyDkO+1AQ9LJHn30UXz22WessrVr1+LBBx+02cZQU4O8lSuRv3IlDJWVNut5zZ6NiPfeg8TO9xCki9SeAZJGA6Zmq971WQf4zO2WLjUwMSbklJ9FUs5WJOfvhFpn3xLawR590S/oRvQNnEiBDUJIh3Hk+1B7OH0woydw1ItIpVJBJpNZBDP6Bk7AXSPeb7V96r33oujbbxu3OUIhRlVUgOfi0mpbncGEW148CoOx6fJ8/5G+GBDl3kIroKqgBh9N/ZpVdufqmYgZQ280Se9lMppw5Ock7PnkEAw6ywBB+NBgTH9lIjxD5F3fORvqtEb8l1SKfw4XIT3f9jLcACCT8DBxsA9uHRsAf08agUXYampq0LdvX+Tm5jaWicViHD9+3GIaSnOGmhrkr16NvA8/hKGiwmodjkiEoKefRsgLL4Dval9eJ9IFyv4BLtwC4Kq3uRwRMOA/wP26buvW1YwmPa6UnkRy3k5cKPgPal2VXe0C5X0QH3AD+gaOh49rBK2KQghpN0e9D7UXBTMcgKNeRPn5+QgKCsLTP/eHQNT0qecj475FqOfAVtufHjYMyhMnGrc9brwR/XfssOvcp1KrsPSLC43bHA6wccVwuIj5LbbbveYw9n95rHFb4i7Gs7seBE/Aa6EVIb1DcVoZ/li6FSVXLLP780U8jHtoBEbePdihfl8YhkFyRg3W783H0RTbn5ADAJcDjOrnidvGBSI+lG4qSZNt27ZhypQprLLY2FicOHECrnYEIAxKJQrWrEHu++/DUG59dQyhnx/C3ngDfvfcAw6/5f9VpIvkrQSuPMUuE3gBAw922pKt7WU0GZBRehLJ+btwPn+33YENL1koEgJuQN/ACQj0iAeXQ6PUCCH2c9T7UHtRMMMBOOpFdPHiRSQO6Ycnf2B/cvXc5M1QuAS22JZhGBxyd4dRqWws67dtGxQ33WTXuVf9cQWbjxQ1bseFyLB6ccvTU4x6I96/8QuoqzSNZYNm9cUt/3ejXeckpDfQawzYueoAjv16xup+32gvzFg2EUH97Evy25VyitXYdLAQO0+WQKMztVg33F+KGwf74IZBXvByp9waxJwUfM2aNayyO+64Az/99JPdn2wba2tR8NlnyHn7bZsjNSRRUQhdtgw+d9wBDs9xAoO9EsMA6YuAAvY0I4iCgYGHAHHrU1e7Q2NgI28nzhfssTuw4SbxQULADegXeCPCvAaCy6HrjxDSMke9D7UXBTMcgKNeRMeOHcNdj03C7KXsFUhen3kUAl7LNwd1GRk4HhnJKhuenQ1xSEir5zWaGMx/7QQqlU1JC++bFop544NabJdxLAffP/Qnq2zRH3fDJ6pjE7YS0hNcOZqNv17dieoipcU+DgcYNm8gJjw2CiIXoZXW3UtVZ8CBc+XYeaoE567UtFiXywESo+W4cYg3xvT3glBAn1r2VlqtFqNGjcKpZquRtGcFM31lJbKXL0fBmjVgDNZXBpLExpqDGnPnUlCjOzEGIPlmoHI7u1waBwzYDwgd+w17w1SUc3k7kFKw1+7AhqvYC/0CJ6J/0CSEeA6gERuEEKsc9T7UXhTMcACOehFt3LgRO0tehpsX+2bGnuSfBV98gbSr3hxyXVwwWqm069Ovc1eqseTT86yyb5cOQpC3xEYLs81v7sGJ3882bgfE++KhX+5o9XyE9FZalQ571hzGsV+TrK5a6OYrw80vjkfs9ZGWOx1EUYUGmw4W4t+jxVC3smyruwsfU0b4Yfp1frQSSi+VmZmJxMREVFc3JV0UiUQ4ceJEq/kzrFFfuoQrTz+Nin//tVlH2qcPQl99Fd633QaOtTWISeczKIFzEwDlCXa5bDAwYA/Ad+uefrWR0WRAVlkSLhTswYWC/1BdV2xXOzexN/oGTkT/oBsR4tmfRmwQQho56n2ovSiY4QAc9SKSy+V4+Gt24kwOOHhr9ulW21665x4U//hj47bn9Ono+/ffdp33gfeSkFWkbtwO9ZXgq+cGtdAC0GsNWHXzN1CWNiUKnPD4KIy9b5hd5ySkN8tLLsLfr+9E8eUyq/sTbozGlOfGwdVb1sU9s5+qzoCtR4ux6VAhSiq1LdblcIDBMXJMHuaL6/rSSii9zcaNG3Frs5W2EhIScOLECdZy7m1RsX07sl55hZUnqjmXvn0R+uqr8Jo1i4Ia3UFfDpwZA6gvssvlNwD9tgJc50oezDAM8ipTcKFgN87n70FZbbZd7WQiBfr4X4+EgBsQ6TOs1ZG2hJCezVHvQ+1FwQwH4KgXkbVlWf3dY7F44rpW2+5rNgIjas0aBD76aKvtalR6zPm/4zBddVXOvj4AD89oeTWSbR/sw5Ef2UGWxzYugHe4otVzEkLMOWcO/3Qaez8/AoOVEQ5imQg3Pjkag27t5zDLuFpjMjE4l1GNnSdLceBcGeq0LefWcJXwMWaAJ8YP8ka/cDeH/t5Ix3nyySexatUqVtmiRYvwySeftPuYDMOgYssWZC1bhtok2yMYXfr3R9jy5fC85RZahaKrafOApFGANodd7nkLkPAHwHHOxK0Mw6BEmYHz+buRnLcLRTVpdrUT8iSI8RuJhIAbEOs3BlKhc4xQIYR0HEe9D7UXBTMcgCNeRAzDgC/g4tnfBrLKHxn3HUI9W07EWfjVV7j8wAOsssFJSZANHGi9wVX2JpXijZ8us8pam2KiUWrx3sS1rBuwwARfPPgzTTEhpK3Kc6qw+Y1dyDiWa3V/SGIAbnp6rEMmCG1OozPi8PkKbDtejKS06lbre7sLMS7RC+MTvREZ6EI3mj2YVqvFiBEjcObMGVb533//jenTp1/TsRmGQfnffyPr//4PqrNnbdaTDRyIkFdegdfMmTRSoyupL5tHaOhL2OW+dwOx3wE9ILdESU0mkvN34lzeThTXpNvVhsvhIdxrMOIDxiEhYBzkUsf/G08IuXaOeB/aFhTMcACOeBFVVVUhOMIbi77syyp/adpOuIpbTqh52M8P+uKmeZxcqRSjq6vtWqruw9/T8e+xprb9I93wwaMtz2NO3paKP5Zubdzm8bl48Jc74BfjHL+EhDgahmFwdvNFbP9gH2t1oKtFjQzFuIdGIHhAQBf3rn2yi9T461Ahdp8uhVrTcm4NAAj2kWD8IG/ckOiFQK/2TT0gju3SpUsYNGgQ6urqGss8PT1x7tw5BARc+3XNmEwo27gR2a++CtX58zbrufTti5CXXzbn1KBEoV1DmQScHQcYmyUQDngUiPrEPBethyipycC5/J1Izttld2ADAALkcfWBjRvg5xZNwV1CeihHvA9tCwpmOABHvIi+/PJLfLfjFYyZx47Mr5h5DHxey6sbHHR1hbG2llV2vR2XmUZnxPQXjrLKHpwehjnjWl4G9s+X/sW5LZcat6NHheGuNbNaPR8hpGWqCjW2fbCP9fvVXOy4CExecj0UwfKu69g10OiMOJhcjm3HinG2lZVQGsSFyDBpqA8mDPKGVOycw9CJdV9++SUefPBBVtnEiROxfft2cDtotARjMqH0jz+QvXw51CkpNutJ4+IQ8tJL8Jk3z67gP7lGVQeA5EmAqVnANvh5IPytHhXQaFBWm4OUgr1IKdyL7LIzYGDfLYCHNADxATcgIWAcQj0Hgsel65OQnsIR70PbgoIZDsARL6JHHnkE+eJ/ET/ag1Xe2komhupqHJLLWWWx334Lv4ULWz3n99ty8NNO9tD2NU8OQEyw7aSDpRnl+Oz2n2A0NM2Ln/LcOIy4I7HV8xFC7JN+OBub39iFynzrN/88AQ/X3T0IY+4dCrHMeZLJFVdo8F9SGXafLmUlHbZFLORi/CBvTBzsjfhQN/B4Pe9mp7dhGAa33XYbNmzYwCp/55138Nxzz3XsuYxGlP7+O7KWL0ddaqrNepKoKIS++qo5qEEjNTpX+b/AhVsARs8uD1sBhL7UPX3qIrWaClws2o+Ugr1IKz4Kg6nlxMkNpEI54vzHIMH/BkT7joCQTyPXCHFmjngf2hYUzHAAjngRzZ49G5Fzr4B71Zt1IU+C12YebrFd2V9/4cLMmayyMRoNuKLWb3DuWnESxVetQuCnEOG7FwaD10JCvnVL/sHF3U3DJjlcDhb/8z94BLq3ej5CiP10dXqcWH8Oh747CVWF9Rt/F4UU4x4agUGzEsAXOtcnd5mFKuxJKsN/p0tZf4dscZPyMayPB0YkKDAkVg4XGrHhtCoqKjBgwADk5eU1lvH5fBw+fBhDhw7t8PMxRiNK169H9ooVUF+4YLOeNCEB4a+/Ds+ZM2mIf2cq/QNImQugWbLgyFVA0BPd0qWupjPU4XLxEaQU/IdLRQeg1rWeYwgA+FwRon1HICHgBsT5j4FMREnXCXE2jngf2hYUzHAAjngRDR42EDc+z37zdOfwd9Ev6EabbRiTCfubfYrEl8sxqrKy1fOVVGpx54qTrLI3H4jH0DgPGy2AyvxqrJr+LZirlj4Zec9g3PT02FbPRwhpH12dHqf+TMaBb07YDGq4+cgw+t6hGDSrLwQi57rJZxgGKVlK7Ekqxf6z5aiq1bfaRsDjYFi8ByYO9sGwPh601KsT2rdvH2644QZc/ZYoMjISSUlJcHV17ZRzNubUeP31FhOFug4divA334THxImd0g8CoOh7IHWhZXnM14D/vV3ene5kNBmQVX4GKQX/4ULBf6hSF9rVjgMuQr0GIMH/BsQHjIOnLLiTe0oI6QiOeB/aFhTMcACOdhExDIMBYwMx9UlfVnlrK5koT53C6SFDWGXyCRMwYNeuVs+572wZVvzQNOxWJuHhz9eGt7hM4s5VB3Dw26YAiMhFiKe33+9Uw9wJcVYapRZ7vziKY7+egclgfflTmZcUoxYMxZDb+kEoEXRxD6+d0cjg5OVKbD1SjKMpFawlo21xlfIxqq8Co/p5YlC0HEIBBTacxSuvvIIVK1awyu6++2788MMPnXpehmFQ/s8/yHn9dShPnrRZTz5+PEJffRXuo0fTSI3OkP8JkP54s0Iu0OdXwOf2bulSd2MYBoXVl5FSsBcXCv5DYbXt6VHN+bpFoo//WMT5jUWIZz9wOTRlihBH5Gj3oW1FwQwH4IgX0aG0dfjn3DusstdnHoGAJ7bZpuj775HaLDdG5KpVCHqi9WGar31/CQfOlTduD4mV460HE2zWr6vR4OMZ37JWWhg+fyCmPn9Dq+cihHScsqwK7Fh5AKl7M2zWcfGQYMSdgzD09v6QuNn+G+LISqu02Ha8GAfOlSOzsPX8GoA5x8bQOA+M6qvA8HgFZBLnGqXS2xgMBowdOxZHjhxhlf/www+4++67O/38DMOgfPNmZL30ElTJyTbryRITEbh4MXzmzbNrCidpg5y3gMwX2WUcPpCwCfCc1i1dciQVqgJcLNyLCwV7kVV2Giam9ZWhAHOejVi/UYjzG4MY35GQCDtntBMhpO0c8T60LSiY4QAc8SJa+ic7gaaIL8PyWw602Cb9iSeQv3o1q2xMXR244pZvXi5mK/HEx+dYZQsnh+DOG20PUdz18UEc+OYEq2zRH3fDJ6rlZWMJIZ0j62Qe9q49iswTuTbriFyEGDy7H0bcmQh3X+d9M1tcocHRlEocTanA2fRq6I2t/xvlcTkYEOWO0f0UuC5BAS93ugl1RFlZWRgwYABqapqS3YrFYvz3338YMWJEl/SBMZnMiUKXLUNdWprNegIfHwQ88ggCHn4YQj+/Lulbr5DxIpD7FruMIwIS/gA8b+6ePjkgta4alwoP4ELBf7hcfBh6o/VlvJvjcngI80pEnN8Y9PEfC2/XsM7tKCGkRY54H9oWFMxwAI52EZUqs/HBjpmsspGR8zBj4PMtttvXbNhr4OLFiFq5stXzvfPLZew6Vdq4LRXz8O3zg6Bws74ErFatw6pp30BVWddY1md8FOZ9OL3VcxFCOlf26Tzs++IYrhzNsVmHy+ei3+RYXHfXIPjH+dis5wxq6wzYf7YMu06VIjnDvqVeAfNyr6P6eWJUXwWCfaSd2EPSVuvWrcP8+fNZZV5eXjh69CgiIyO7rB8mvR7F33+P7OXLob0qOWlzHKEQ/vfdh5CXXoIosOWlzIkdGMY83aRgDbucIzBPOfGe3T39cmB6owZpxceQUrgXFwv3QaVtPVdaA0+XYMT5j0Uf/zEI8xoEPtf5piQS4swc7T60rSiY4QAc7SI6nrkBG06/ziobF/s/TO5re7pI7ZkzOJXIHs0RtXo1Ah97rMVzVdfqccfrJ6G7as79fVNDMW9CkM02m17dgaRN7Azwi/68Bz6Rni2eixDSdXLPFWLfF8eQdjCzxXrBA/wxbO5AxN8YDb7AuedUF1docDC5HIfOV+BCZo1dOTYAIMRXglF9zYGNmGAZ5UNwAA8//DDWrl3LKouOjsbhw4fh5dW1IwBNGg0KvvgC+atWQZNhezoXRyRCwMMPI2TpUhqpca0YE5B6L1D8fbMdXCD6EyDgkW7pljMwMUbklCfjUtF+XCw8gOKa9NYb1RMLZIj1HYX4gBsQ6zcKYoGsE3tKCAEc7z60rSiY4QAc7SI6kPYTtpz7gFU2Y8DzGBk1z2abfXw+YGTPnRyenQ1xSEiL51r/Xz6+2JzVuM3ncfDLK0Pg4Wp9VEZVYQ0+mvI1qyx8aDAWfnlbi+chhHSPwkslOPDNCaTsSmOtPNSczFOKQbf2w9A5/eHm4/xvYCuVOhxNqcTh8+U4dbkKeoN9/2q93IUY2VeBUX090T/SDXweJRDtDnq9HtOnT8f27dtZ5SNHjsSuXbsgkUi6vE+M0YjyzZuRv2oVqv77z2Y9rkSCgEWLEPzccxA6yZtRh8QYgdT7rAQ0AAQ+CUS+D1BSy1ZVqAqQWnQAFwv3I6P0JAwmnV3teBw+IryHIj5gHOL9r4e71Lf1RoSQNnO0+9C2omCGA3C0i+j7w0/iYuE+VtlTN/4BXzfrw2t1JSU44mv5T+Z6Oy6tp9cks4Zmj0/0wgt3xdqsf+j7k9jxETt3xyO/3QW/WOf4hSOkt6rIrcLhH08j6a/zMGhtJ43jCXgYcHMfjLxnMLzDFV3Yw86j1hhwIrUKh5PLcfRiJdQa+5LmiYVcxAbL0CfMDX3DXZEQ5kZJRLuQUqnEmDFjcLbZsqlz5szBunXrwOV2X6Cp9tw55H/8MYp/+gmMVmu1Dlcqhd///oegJ5+EJCqqi3vYQzAmIG0RUPi55T7P6UCfXwCe8wdfu4rOUIf0kmO4VHgAF4v2Q6kps7utr1sUYn1HIsZvJMI8E8HnWf/QixDSNo52H9pWFMxwAI50EZkYE17cMNii/O3ZSTbbVO3fj7PXX88qC3zySUR99FGL50rPr8UjH7LfJL52bx9cl2D9Bkar0uGDSV9Cq2qK6g+cHo9Zr9/U4nkIIY5DVaHGifXncPKPc1CWqmzW43CAmLERSLwlAdFjwp1+CkoDvcGEs1eqcSi5AocvlKOiRm93Wy4X6B/hjjH9PTGyLyUR7Qr5+fkYMWIE8prlrHjmmWfw3nvvdVOvmmjz8pDz5pso/OorMHob1xKHA88ZMxD09NNwHzOGpjG1FcMAWcuAnBWW+2QDgb7/ACLbU2OJdSbGhMKqVFws2o9LhfuRV5lid1sBT4xI72GI8b0OMX4j4SVreRQwIcQ2R7oPbQ8KZjgAR7qIKlQFeHcbe/kxT5dgPDv5b5ttkqdPR8Xmzayy4VlZEIeG2mxjMjGY9cox1ieUHA6w4fXhNj953PjKdpz5h/3PbsEXsxExjP6JEeJsjHojLv13Bcd/O4usU7aTGwKAxF2MvjfFYuD0Pgjs69djbsZMJgaXcmtxKLkch5LLkV9m32oADWKCZUiMcsegGDniw1whFvaMgI+jSU5OxujRo1krnADAJ598gkWLFnVTr9g0WVnIXrECRd99ZzHl82qywYMR9PTT8J4zB1wBJVpsk6IfgMv3A0yzoJEoGOi/A5DGdU+/eoiauhKkFO5DSsFeXCk9AaPJ/kCvp0swYupHbUR4D4GIT0mVCbGXI92HtgcFMxyAI11EqUWH8O0hdtLOMdF3Y1r/p63W1+Tm4lizvBiSqCgMa2E5OQBIzVHisVXs5VgjAlywdslAq/Urcquwavq3rDLvCAUe/eMecLk948aGkN6qOK0Mx9adwdl/UmDQtTwFwytcgcG39sPA6X0glXd93oLOwjAMcorrcPC8ObCRlmd71Io1fB4HscEy9I90R/9INySEuUEiouBGR9m1axemTJkCg8HQWMblcrFx40bMmDGjG3vGVnflCrJfew3FP//cYlBDFBSEwCeegP8DD4Avl3ddB51d1X7gwizAUMEu53sC/bYAbsO7p189jEZfi8vFR5BS8B8uFR2ERq+0uy2Pw0e492DE+ZlXSPGUBXdiTwlxfo50H9oeFMxwAI50EX20c45F5uklkzbaXAc8/5NPkP7446wye6aY/LYnD19tyWaVvXRXDMYlWv+e93x6GPu+ONa4LRDz8cCP8+Eb3bVZ5Qkhnae2XIWjv5zBid/PQqO0ngegAU/AQ58JURh4cx9EDA8Br4dMQ2lQqdThUk4tUrKVuJBZg0s5SruTiALm4EafUFcMjHLHwCh3xIW6QsinZKLX4vvvv8fChQtZZRKJBPv27cPQoUO7p1M2aHJykP/JJyj84gsYq6tt1uPJZPC77z4ELl4MSXh4F/bQianTgPPTgLpmH9pwpUDCBkBBU187ktFkQF7leaQWHcbl4sPIr0wBA/v/Fnq7hqOP/1jE+1+PEM/+4FLSVkJYHOk+tD0omOEAHOkiemPLjRYJmVrKl5Hx/PPIffddVlnczz/D9447bLbRG0yY+vwRVlmglxjfvWCZqwMASjPK8fm8n1mf2A6e3Q8zXplo8xyEEOelVelwfnsqzm6+iOzT+a3Wl3pI0PemWPSfGoegfj1nGsrV6rRGnEytwqHkchxJqbA7iWgDkYCLhDBXJMbIkRjtjqhAGXg0qq3Nli9fjldffZVV5uPjg6NHjyLcAYMBBqUSRd9+i/yVK6HJbGGZZC4XXjNnInDxYsqrYQ99GZA8DVAeZ5dz+EDcD4DP/O7pVy+g0lYireQoLhcdxuXiI6jVltvd1kXkgTg/c2Aj2ncEhPyeM7qPkPZypPvQ9qBghgNwlIuoSl2Et/+dwipLDJmGuUOtJL2qt8/KG57RKhV4UtvzFXeeLMG7v7I/0XhsVgRuGe1vUZdhGHz/4J/IPJHLKl/45W0IH0pDBwnp6SryqnBuyyWc3ZyCilzbnzA3UAS7o9+UOPSdFAOfqJ45cktnMCElqwZJadU4fbkKl/NqYTK17RiuEj4GRLkjMdod/SPdEewjoeCGHRiGwb333ovvvvuOVR4bG4vDhw9DoXDMFXgYoxFlmzYh78MPUXP4cIt1Xfr3R+ATT8DnjjvA64YlaJ2GsRa4cBtQud1yX+QqIOiJru9TL2NiTCisvoy04sNILTqM7PKzMDGG1hsC4HEFCPNMRLTvCET7jIC/PBZcDo1eI72Po9yHthcFMxyAo1xEOy58ij2XvmSVPTD2S0R6D7Fav2rfPpwdN45VFrRkCSLff7/F8yz++BxSstnzH39dNsRqZv5L/13Br0+xk4/2mxyL2W9NoU+OCOlFGIZB1sk8nNpwHhd3p7WaWwMw59WJnxiN+InR8I326rF/M9QaAy5kKXHuSjXOXanBpVxlm4MbYiEXkQEuiAqUISJA2vicx+uZP7NrodPpMG3aNOzatYtVPmbMGOzYsQNisbibemafmqNHkffhhyj980+0dKHwFQoEPPwwAh57DCJ/yw8bCACTDkhdCJT8arkv+Dkg/C2AbpC7jEZfi/SSY7hYeACpRQdQq61ovVE9F6EcUfWBjWifEXCX+nZiTwlxHI5yH9peFMxwAI5yES39M9Gi7LnJm6FwCbRa39qojD6//gqfefNsniMlqwaLVyezyu6eFIx7brJckUSvNWDNrd+jMr8pg7y7nyse27gAQgllYSekt1JX1eHc1ktI/vcS8pKL7GrjHaFAvylx6D81Dh6B7p3cw+6l1hhwPlOJM+lVOJNejfR8Fdrzn14q5mFgpDsGRrujT6grIgJcKO9GverqaowZMwbJyez/Z5MnT8bGjRsdPqABAHWZmcj/+GMUffUVjLW1NutxhEL43nUXgpYsgUt8fBf20EkwJuDKk0D+ast93vOAuO8ALi2j3NVMjAn5lSm4WLgPKQX7UFTTcmL65nxcIxpHbUR4D6EpKaTHcpT70PaiYIYDcJSLyFow441ZJ8DjWi6Vqs3Lw9Fgy2keI8vLIWhhmO2LX17AiUtVjdsyCQ+/vDLUatb9LW//h+PrzrDKZr85Bf2n0vJnhBCz8pwqnNt6EclbL6E8p8quNiGJAeh7UyyirguFIkTeY0dsNKhR63E2vQZJaVU4k1aN3NK6dh1HwOMgMtAFcSGuiAuRITbEFYFe4h7/87MlNzcXI0aMQEFBAav8xhtvxF9//QWJk0zRMFRXo+i775C/ejU0V660WFcxdSqCnnkG8nHjeu3rbhXDADlvAlkvW+5zvx5I2AgIPLq+X6RRhSofKQX7cLFwHzLLTsHE2J93iMfhI9RroHnUhu8IBMj70JQU0mM4yn1oe1EwwwE4wkXEMAxe2DDIotxW8s+U229H6fr1rLLwt95CyNKlNs+xbk8evm62gomtURnZp/Pwzb3s44cMDMC9395Ob6AIIRYYhkH++WIk/3sJKbvSUFNi+5Pmq8kD3BA5IgSx10cg8rpQ8IWWwdueprRKa863kVaFc+nVKK3WtftYrhI+4kJl6BPiirhQV8SFuMJV2vN/hg3Onj2LMWPGQKlkT52cMGEC/v77b0hbyB/laBiTCRX//ov8jz9G5Y4dLdaVxsfD77774Hv33RA6yRveLlH4DXD5QQDNbpRFoUCfXwD3kd3SLcKm0dfiSukJpBcfQ1rJEZTV5rSpvVQoR5TPsMbghlxK07CI83KE+9BrQcEMB+AIF1F2+Rl8tvd/rLJbBi7FdZFzLerWHD+OpOGWa6mPNZlsBho0OiOmv3CUVSbgcfDLsqGQyyynjHz3wB8WST8XrJ2NiOGWgQ9CCLmaycQgP7kQF3alIWVXGqoLla03AiCSCRE3LhLxE6IRPjwYIqmwk3vqGCqVOqTlqZCWX4vMAhXS81XIL9O0+3h+ChGig2SIqh/FERMsg0zScwMcx44dw6RJk1BTU8MqHzduHDZv3gwXF5du6ln7qS5eRP5HH6Hohx/AaG0vk8wRCOB5yy3wv+8+eNx4Izg8WvYSFduAlDnmBKEsPCDs/4CQFwFaHtShVKgKkF5yFGnFR5BWcgwavX3/Mxp4y8LMU1J8r0OE12CIBM73O096L0e4D70WFMxwAI5wEf2bvAr7Ln/HKnvo+q8R7mU5WiNj6VLkvvMOqyxw8WJErVxp8/jJGdV4es15VtkNiV548a5Yi7qX92fg5yf+YpWFDQ7Cwq9uo1EZhJA2YRgGuWcKcHbrJVzYnoq6Gts3ZlfjCXgIGxyI6NHhiB4TDs9eMB3lamXVWpxJr8aZtGqkZCuRW9K+qSkNgr0liAmRIS5YhpgQV0T4SyEW9pwbuhMnTmDSpEmoqqpilY8dOxZbtmyBTCbrno5dI11xMfLXrEHBmjUwVLScTFEUHAzfhQvhf//9EIf08g8elEnA+amAzkpOH/frgbgfATGtyOaITIwR+ZUXcbn4CNJKjiCnPNnuFVIAgMvhI9RzAGL9RiLGdxT83WN61f8O4nwc4T70WlAwwwE4wkX0+4lXcDpnM6ts6ZR/IZf6WdRNnj4dFZvZdSPeew/Bzzxj8/h3rTiJ4kr2TYS1FUxqy1X45NYfUFfd9Kkgh8vBCwcf7TWfkhJCOodBb0T6oSyk7ErDlaPZqC1T291WEeyO6NHhiB0bgdAhQeALes6NuD1q6wy4nFuLizlKpObU4lKOEpVKfbuPx+UAgV4ShPpJEeQtRqCXBCG+EkQGukDkpD/b06dPY+LEiaisrGSVX3fdddi6dSvkcnn3dKwDGFUqFH3/PfI/+gh16ektV+bx4DVrFgKfeALuo0f33hs5TS5w6U6g+oDlPr4HEPMV4H1r1/eLtIlWr8KV0pNIKzmC9OJjKK3NalN7V7EXYnxHIsZ3JKJ9R0Aq7NkJqInzcYT70GtBwQwH4AgX0TcHF+FyMXvteWv5MhiTCfutDCMdcv48XBISrB47NUeJx1adY5VNGOyNpXfEWNTdtfoQDnx9nFU28p7BuOnpsa1+D4QQYi+GYVCSXo70I9lI3XcFOafz7V7xQ+QiROR1IYgYEYrIESFQBMk7ta+OiGEYlFbpcDFHiYvZSlzMUiItrxZ647W9peBxOYgIkCIuxBWxwTKE+kkR7COBi9g5pqmcOXMGEydORHl5Oat8wIAB2LFjh8X/emfDmEyo2rcPRV9/jbI//4RJ0/J0JFliIgIXL4bP3LngOsEKLx2OMQDZbwLZywFYWQbX/0Eg8iOA5zy5VXq7SlUB0kqOIr3kGNJLjkGtq7a7LQdcBHrEIcJrCCK8hyDMKxFigXOO2iI9hyPch14LCmY4AEe4iLae+wiZ5UnIrTAvMzdnyGsYHDrdol7ZX3/hwsyZrDKeTIbRStvzCz/4LQ3bjpewypbeEY0Jg9nfc3lOFdbM/gFGfVPiLEWwHI+uvxsCJ3kjSwhxTjUltUjZnYZLe64gOykfJoOVGw8bPILcET0qDNGjwxE2JKjXLh2tN5iQXaxGep4Kl/NqkZZXiyv5qmsOcACAp5sQIb4SBPtIEOwjRYiPBOH+Uni4Ot6IvXPnzmHChAkoKytjlcfExGDXrl0ItrISmDMyVFWh+JdfUPT116g9fbrFugJvb/jdey98FyyAS58+XdRDB1J9CLh4B6C1kmhS2gfo8ysgG9D1/SLXxDwl5RLSS47icvER5JSfhbENU1LMwY0+iPAeggivwRTcIN3CEe5DrwUFMxyAo11EGr0SXA7fYk1tbX4+jgYFWdSP/f57+N1zj9VjlVVr8b+3T0Oja7oxEAu52LRiBHi8pqGnujo93rjuE4v2j/5xN3yjvNr7rRBCSJtparXIOJaDtINZSDuYCWWpyu62fBEPIQMDET4sGBHDQhAQ7wMur/cu4aczmJBZqEJqTi1Sc2uRmqNETkmd3aNgWuPhKkC4nxShflKE+jY9dveKKikpKZg4cSIKCwtZ5SEhIdi1axeio6O7qWedo/bMGRR++SWKvv8eJlXLvy+uQ4fC95574DN/PgSenl3UQwdgqAIuPwSU/m65jyMCIt4FAh8Heuu0nB5Aa1Ajo/QkUosO4XLRIVSo89vUnsvhIVBeH9zwHowwz0RKJko6naPdh7YVBTMcgDNcRMa6OhwLCYG+2SdNADAkJcXqJy0GowlTnjvCKuNygB9fGgIfD3aujJ+f2ITL+zNZZXE3RGL+RzM6oPeEENI+DMOgKLUUlw9k4vL+DOQlW0no1wKxTISwIUGIHBmKqJGhvXJKSnManRFZRWpkFKiQX6pBflkdckvrkNuBQQ6FmwBhfi4I95cizE+KcD8pQnylkIi6Lh9HRkYGJk6ciMxM9v82Hx8fbNq0Cdddd12X9aWrGKqrUfTNN8hfvRqaZt93cxyBAIpp0+C3YAEUU6eCK3S8UTYdjmGAom+B9McBk5WcPYppQOy3gNBx3v+R9mEYBuW1OUgtPozLxYeRUXoSemPbVonicvgIVvRFlM9wRPkMQ7CiH/jc3jnyj3QeZ7gPbQkFMxyAM1xElbt349zEiRbl4vBwDM/IsNrmm63Z+HV3Hqts8jAfLJnL/kQq6a8L2PR/7DXt3XxleOT3uyF174VzbAkhDktZWov0Q9m4ciwHGcdyoKqwP4koAHiGyBE5MgxRI0MRmhgIsauo9Ua9hFpjwOW8WlzKNicYzSpSo7BcA1MHvUvhcAAfuQh+ChH8FGL4e4oR4itBmJ8UAZ4S1mjBjpKfn49JkyYhJSWFVS4QCPDJJ5/gwQcf7PBzOgLGaET5li3IX7UKVXv2tFqf7+kJn3nz4LtgAVyHDOn5SUPVqcDF+UCtZW4yCP2A2B8AxY1d3y/SafRGLXIqkpFRegIZpSeRU5EMo6ltSZSFPAnCvBIR7jUI4V6DEOSRAD6vFwQBSadyhvvQllAwwwE4w0VUsHYt0h5+2KJ8rMlk9U0HwzCY9Mxhi/LPlwxEZEDTkLmClGKsveMXi3p3rZmF6FFh19ZpQgjpRCYTg5K0MqQdykTawSzkni2AqQ35ITgcwDvCE0H9/RHU3x9hgwKh6GVLwLZGZzChoEyDnGI1ckvqzI+ldcgproNWb39ek9YIeBwE+5hXVwmr/wrxlcLHQwQh/9qmCZWVlWHy5Mk4deqUxb4HHngAq1evhkjUc4NaqvPnUfTddyj+6Sfoi4tbrS/t0wd+998P37vvhtCB3gd1OJMWyHwRyPvQ+v6gZ4Hw1wFuz702ejO9UYOc8mRcKT2BjLKTyC1PblO+DQDgc4UIUiQg3NMc3Ajx7E85N0ibOcN9aEsomOEAnOEi2mflzXXQkiWIfP99q/XfW5eGHSfYST8lIi7+emME6436z49vwuUD7KGoiTMTMPPVSR3Qa0II6Tp1NRpkncxDxvFcZB7PQWlGRZuP4e7nivBhwYgcHoKwocFw86E3ptYYTQwKyzW4UqBCTpEaWcVq5BSbp6oYO2ooRz2FmwC+HmL4eojMX/UjO0J9pfCWC+0KPtXU1GD27NnYtWuXxb4RI0bgzz//REBAQIf229EwBgMqdu5E8Q8/oHzTplZXQuEIBPCaORM+d98NxU039dxpKBXbgEsLAH2J5T5pH/MSru4ju75fpEvpDHX1IzdOIqP0JHIr2h7c4ICLAHkswrwSzSM4PAdBJlZ0Uo9JT+EM96EtoWCGA3D0i6jm+HEkDR9uUT7oxAm4DhliUa5UG3DrK8csyr96LhGhvk3Lj6Xuz8AvT/xlUW/J9gfg5ktv4Akhzq2mpBaZx3OQfjgbV45kQ1VZ1+ZjyAPcEDwgAMED/BEyMAA+UV7gXeNIgZ7MYDQhv1SDrGI1sgrVyCxSIatQjYJyTYfl47iaVMxDqK8UQd4S+CtE8PMUw18hhp+nCApXIbjcpkCHwWDAiy++iPfee8/iOH5+fvjzzz8xcmTvuGk1VFejdP16FP/wA6oPHGi1Pt/TEz5z58Jn/ny4jRwJDreH/Q7oioFLC4HKbVZ2cgD/B4DwNwABJUTvLXSGOmSWncaVkuNIKzmGwurUdh3HSxZaPzUlEeFeg+EhDaDRf4TF0e9DW0PBDAfgyBdR9cGDODNmjNV919u4dNbtycPXW7JZZYNi5HjnoYTGbVWFGiunfQNdHXu+4J0f34KYsRHX2GtCCHEsJhODotQSpB/ORvrhLOSdK2ItQ20voVSAoH5+9QGOAAT184PEjXILtUajMyK3pA6F5RoUVWhQVKFFbkkdsovVqFS2bd66vQR8DvwU4vr8HE15Os6e2IulTz2E2ppydn2BAKtXr8aDDz7Yq2426q5cQfFPP6H4hx+gsZGD62qi4GB4z5sHn3nzIEtM7Dk/K8YE5K8CMp4HGCvXJN8DCFsBBDwEcLoukS1xDCptJa6UnkRm2SlklSWhqDoNDNp+C+cm8UG4ZyLCvAYh3CsRPm6R4HJ6WHCQtIkj34fag4IZDsCRL6IzN9yA6r17LcojP/oIQU8+aVG+N6kUb/x02aL8w0V90S/CHQBg0Bux5tYfUJFbxaoTOy4Cd6y8pSO6TQghDk2vNaDoUglyzxUh71wBspPyUVvWtmSiQH3ejUhPBA8IQMgAfwQPCKC8G21Uo9Ijq0iN7GK1+bHI/FitatsQ77YyapWorcxDXXUBNNWFUFfnQ12Rg5lTRuPTj9+DRCJp/SA9CMMwqN6/H4VffYWyP/5odRoKAEhiYuAzfz585s2DNC6uC3rZBWrPAqn3AbWWOVYAAC4DgKjVgNz6B02kd6jTKZFdfgZZ5UnILEtCXuWFNicUBQCJwA2hngMRXj81JdAjnlZM6WUc+T7UHhTMcACOfBFZy5UBANeVlFgk5qrTGjHjxaMWdZ++PQpThvsCALRqHd4cucbqMZ/Z+QBcvWl6CSGk92EYBqUZFcg4loOM4znIPpkPTa22XceSekgQPMAfgQl+COjjA/8+PpB5urTekLCoNAaUVGpRVKFFcaUWxRUaFFdqUVShQX6pBmpt20fW2Isx1CEi0B0RQe7w8xDBSy6Ct1wIb3cRvOUiyCS8Hh2w0ldWonTdOhT/9BNqDlsmE7dGNnAgvObMgdctt0AaH+/cPx/GAOStArKWWV/CFQB87gAi3gVEgV3bN+KQ9EYNcivOI7MsCVllSciuOAudoe0BcgFPjGBFX4R7DUKYZyJCPPtDxJe23pA4LUe+D7UHBTMcgKNeRCaNBgesfDI0RqMB10rm9X1ny7DiB/acvqFxHnjzgfjG7Z0fH8TBb06w6ohlIjz6591w93XtoJ4TQohzMxlNKM2oQM7ZAuSeKUDu2UKL0Wxt4ertAv8+PvCP80FAvC+CBwTAxaN3ffLfkRiGQWmVrnFEh3n6ihZF5RoUVWqgN3TuWyuxkAtvuQiBXmIEekvMj14SBHlL4OXOztXh7OoyMlDy888oWbcO6mZL3NoijoyE1y23wPOWW+A+ciQ4fH4n97KTaHKBjGeB0t+s7+e6AKHLgKAnAW4PTZBK2sVoMqCw+jKyypKQWXYaWeVJUGkr23wcLoeHAHlcY0LRMK+BcBF5dEKPSXdx1PtQe1EwwwE46kVkrK3FIS8vMNqmTwdd+vfHkLNnLeqW1+gwb/kJi/KX74nF9QPMCasu7knHuqf/sahz+7vTkDAppgN7TgghPU9tuQq5ZwuRe9Yc3ChIKYZB1/7RAd4RCoQODkJoYiAC+/rBI8i9R90EdxeTiUGFUoeici0KKzQoKteYHyu0KCzXoLxG1ynJSBsIeBx4y0XwqV99xcdDBJ/6bR8P8wgPkcD5ci4wDANVcjJKfv0VpevWQZOVZVc7vqcnPG++GV633AKPm24CT+qEnzJX7QXSHwdU563vl0QBER8AntPNc88IaYZhGJQqs+qnpZxGVlkSKtUF7TqWj2t4Y86NMM9EeLj07JWYejpHvQ+1FwUzHIAjX0S60lKciI2FobIS4HCQ8Oef8Jo1y6Lel/9k4fe9+RblO94fCQ6HA12dHm9c94nF/sjrQnHPZ7d2St8JIaQnM+gMKLxYgpyGAMeZAtSWt31YcQOhVADfaC/4xXjDN9Yb/rE+8I3xgkDkpJ9qOyidwYSSSm1jMtLCcg0Ky7W4kleJ/DINuLzO/4RdLhM0Tlvxqn/0dBfCWy6El7sIXu5CiIWOG/BgGAbKY8dQsm4dSn/7DbqiIrvacV1c4DltGrznzIFiyhTwXJxo+hVjAAo+M089MVRZryOfCER+CMj6dWnXiHOqVhcjs/x0/eiNJBTXpLfrOHKpH8I8ByHMcwACPeLh5x4NAc9yBDdxTI58H2oPCmY4AEe/iHRFRSjfsgUu/fvDbehQi/3HL1bipa8sh34+Oy8ak4aav6+PZ3yL8pwq1n6ZlxTP7nqoU/pMCCG9DcMwqCqoMU9LSS5C0aUSFKWWWqwa1RZcPhc+kZ4IiPdFQLwP/ON8KcDRicorKnDX/xbh+NkMuChCIJEHQizzgcjVBxJXHwhdFAC65pN3D1cB/D3NK7AE1D/6e4rhpxBBLhOAz3OMFRAYoxFV+/ahbMMGlP/1F7R5eXa140qlUEydCu/bboPntGngyZwkZ5euFMh8ESj6GrC6mgUX8J4DhCwFZAO7uHPEmam0VeakomVJyCxPQn7lRZiYtidC5nJ48HGLQKA8HkEefRDoEY8A91jwuyBQS9rO0e9DW0PBDAfgzBdRSaUWd644aXXf5rdHQCTg4d/39uLoz0kW+x/ftABeYYrO7iIhhPRaJqMJ5TlVKLxYgsJLJSi4UIz8C0XQa9q/UgeXx4F3hCd8Y8yjOBpGcsgUTjh83wEZjUb83//9H9544w2LfRwuH17+4Vj4wGKMnTgDpTUm5JfWIa9Ug/yyOmh0pi7rp5uUDw9XATxchfBwFcBHLoKvQgRfD3Hj9BaJqGtHdzAMg9qkJJT/9RfK/voLKivTYq3hiERQTJoEz5kz4XnzzRA2e0/mkGpOmKeeKI/ZrqOYAgS/QCufkHbRGeqQU5GMrLIkZJWdRnbFOeiNra8yZA2Pw4e/PBbBHn0RrDB/ecpCaFlYB+DM96EABTMcgrNeRAajCVOeO2J13x/Lh0Em4uLjW75DVUGNxf5pL4zHsLkDOruLhBBCmjHojSi8WILs0/nIPp2HvOQiqCvrrvm4Mi8pfKO94RPpCe8IBXyivOAT6QmRC30a1x7btm3D/fffj/x8yymcAODv74/33nsPd9xxBzgcDhiGQYVSj+IKDUoqzSuwlFRpUVKpRUmVDqWVWijrOne52ebcXfjmPB3101g83YSNj17u5iktnbkyiyYrC2V//43yTZtQtW8fYLIv2CNLTIR84kR4TJwI99GjHTfPBmMCSn4FMp4HdNavEwCA2yjzSA3FNMqpQdrNaNKjoCoVmWWnkVl2GtnlZ6DWVbf7eGKBK4I9EhCs6Iug+iCHq9izA3tM7OGs96ENKJjhAJz1Itp6tAgfrb9iUb5oZjhmjgnA4R9PY/sH+yz2Sz0keGbng+DxKRpLCCHdjWEYKEtVKL5ciqLLpSi6XIai1FKUZ1V0SKJKeYAbfKPNgQ3faC94R3jCM9QDAjFNVWlNVVUVli5diq+++gpGo/VkrzfffDM+//xzBAa2vkSnWmNASZWuPsChRVm1FuXVOpRW6VBWrUVZtQ4qTectOWuNWMhtDGx4y4XwdBfB290c7PCWm/N3uLsIrjk5ra6kBGUbN6L0jz9Q9d9/gI2fZ3McoRDuo0Y1BjdcBw8Gh+dg+USMKiD3PSDvA8BYa7ueSz8geCngczvAod8/cm1MjAmlysz6hKJnkF91EWXKLDBWpz/Zx03ig0B5HALqvwLlcXCX+Dn3UssOzlnvQxtQMMMBOOtF9Oxn53EmnR2RlYp4+OvNEQCAtXf8goKUYtb+6FFhuPOTmfRHiRBCHJxWpTNPTUkpRkFKCQovFqM8u7JDAhwcDuDu7wavcAW8wjzgHa4wPw9XwMVDQv8jmklLS8OyZcuwbt06q/vd3NzwwQcf4L777rvmn12d1oji+gSlrK/6ZKWdveysNQIepz5Bqag+8NEU/Gh49HAVgmdnwENfVoayTZtQun49qvbsAWOwf8QKXy6H/IYbGoMbkuhox7le9ZVAwRogfxWgL7NdTxwBBD8L+C0EuOIu6x7p+bR6FQqqU5FfeQn5VSnIr0xB6TUGOKRCOSvAESCPg6csmKaodBBnvQ9tQMEMB+CsF9HU5w9bvKn58tlEhPlJoVFq8daYTy3avHz0cfo0jhBCnJRWpTOP3kg1fxVfLkXJlfJrysHRnMRNBK8wRWOgwytcAe9wBeSB7r1+RF9SUhKWLl2KHTt2WN0/YcIEfP7554iKiuqU85tMDKpVelTV6lGpbHjUoaxah+KGqS2VWlTVtj/pbHtxOYCbiwCuUj7cXQRwd+HDVcqHh2vTKi3e7kJ4yUVwd+E3BiD0lZWo2LIFZZs2oWLbNphUqjadVxQcDI+JE83BjQkTIPT17Yxvr22MaqDwKyDvfUCba7ue0A8IfAoIeBjgu3Vd/0ivotErkVd5EbkVycitvIDcimQoNS0E2+wg5EsR4B7bNILDIw4+ruHgcQUd1Ovew1nvQxtQMMMBOONFpKozYObL7KRT/SPd8MGj5uXALu/PwM9P/MXa/9iGe+AdQXPhCCGkJ2lIMtoQ2CjNKEfJlQrzKA5Tx73F4PG5UITI64McCniHezQGPXpbXo6NGzfikUceQXFxscU+oVCIJUuW4Kmnnuq29xF1WiNKqrQorjBPZ6mo0aG8xhz0KK82P++OgEcDAZ9Tn7dDBE83ATzdhVC4CqFw4UKclw5e0hFg33bojuwHx87pKA1c+vVrHLUhHzu2e1dJMemBkl+A3LcB9SXb9fhyIGAREPg4IHSAYAzp0RiGQU1dCXIqk5FXcQE5FcnIr0yBznhtuZv4XCH83KMQIO8Df/cY+LlHwc8tGhKhawf1vGdyxvvQq1EwwwE440VkMJqQmlOL3JI6pGQrcTFbiTVPDYCw/lOzkivlSPrrArJP5aHwUgmCBwbg3q9v7+ZeE0II6Sp6rQFlmRUouVKO4rQylKSXo+RKGaoLlR1+LjcfGbzCPeAZqoBHkDsUQe6QB7jBI9AdYldRh5/PEVRUVODpp5/G999/b3W/SCTCnXfeicWLF6N///5d3LvW6Q0mVNToUFqtQ1mV1vxYra3P32EuK6/RoQPjYW0m4HHgzjdAVlcFSWkOJEXZcFFXQFZbDldVGWSqMshU5ZCpKsFlLIMeHD4fbtddB/mECXAfPRquw4aB79oNN1aMCSj/C8h5C1CesF2PIwC8bgX87wfcrwfoU27SRUyMEWXKbORXXUJB1aXGR43+2v9fyKV+8HOLhp97NPzdzY9eshAaxVHPGe9Dr0bBDAfg7BdRa7QqHVQVaiiC5d3dFUIIId1MV6dHeXYlyjIrUJpZgbKsSpRlVaA8qxIGXccnn5S4i+ER6AZ5gDs8gtzhEWgOcngEusM9wA18gYMlc2yjf//9Fw899BByc21PJxg3bhwWL16M6dOng+doyStbYDQyqKw1Bzcak5RW6VBan6y0tMqcwFRv7Oa3sowJLuqq+sDG1V9lkKkqmrbVFZDHx8J95Ei41X+Jw8O7LucGwwBVe4Cct4GqXS3X5XsAntMBr5mAxySA59IlXSSkAcMwqFQXIL/qIgqqUlFQeRH5VZdQqy2/5mPzOHx4u4bDzz0Kvm6R8HOPhq9bFDyk/o6TA6eLOPt9KAUzHICzX0SEEELItTIZTaguUjYFORoCHZkVUHXA0rHWcDiAm68rPALdoQiRwzNEDs9QDyhC5FAEyZ0mx5NSqcTy5cuxatUqGFpIZhkaGopFixbhvvvug0Kh6MIedp6GPB7lNTrUqAyoUelRfdVjhdIc9Cir1qGim0d6AADfoIVEUwOxRgmxRgkpo4W7XAJvPw/4RQbCPyEC3l4yKNyEULgJG0e8driaE+bpJ2UbgdaSM3LFgGIq4HsPoJgCcHvXtC7iWGrqSlmjNwqqLqFSXdAhxxbxXeDrFgE/t2j4ukc2PspEPePvpTXOfh9KwQwH4OwXESGEENKZ1FV1KMuqvCrIUYGyzEpU5ld3aF6O5lwUUrj7uUIe4Ap3Pze4+7nC3d/VXObvBqmDrbySkpKCd955B+vWrYNOp7NZTyKRYMGCBXj++ecRFhbWdR3sZkYjYw5u1E9jacjlUV7TlMujO5anbYmrmAtPuRhymcD85SqA3EUAd5kA7i4CyGX8+kcBZBJ+25ewVV00Jwot+QUwaVqvz/cEfOabAxuuQ8wRQUK6mVpXbR69UXUJBVWpKKpJQ2lNJoxMxySnlgrl8JKFmL9cQ+ufh8JTFgwRX9oh5+guzn4fSsEMB+DsFxEhhBDSHQw6AypyqhoDHRU5VajMr0ZlfjWUJbUdsoxsS/gintUgh/m5G9x8ZRCIun50R1FRET7//HN89tlnKCkpsVmPz+dj9uzZuOeeezBp0iTw+c4xEqWz6fQmVCp1KFfqUVmjQ4VSh0qlHhVKHSpq9KioMY/yqFDqYezuoR5X4XIBN6k5sOHuwr8q4HHVo6xhtRfzF49XH4zQVwLF3wPFPwG1p+w7oTTOHNTwuQsQB3feN0ZIOxhMepQps1FUnYbC6ssoqklHUXUaqussEydfCzexN7xcQ+EtC4WXLBTermHwcg2FhzQAPK7j/0119vtQCmY4AGe/iAghhBBHY9AZUFWoRFV9cKMyvwaVedWoKjA/r6u241PoDiDzlMLdvz7g4ecKN18Z3HxkcPM1b8u8XDptyVmtVovffvsNq1evxsmTJ1us6+vri/nz5+POO+/E4MGDHWrEiaNqmOJSodQ3jvIwBzoaAh+6xn1avam7u2uVq4TfGOCQy8xL27qJ6uDKXIKb8STcDCfgKqiCm7AarsIauAlqIORZWYlGGg94TADkEwD5OIDv3uXfCyH2UOtqUFJzBUXVaSiquYKimjQUVad3SLLRq/E4fChkwfCShcDHNbw+4BEGb9cwuIjkHXqua+Hs96EUzHAAzn4REUIIIc5Go9Q2juKoyKlCeU4VynMqUZFTBWWpqsv6weFyIPOUws23WaDDVwZXHxlkXi6QKaQQyYTtDjAwDINjx45h9erV+P3331vMqwEAERERmDt3LubOnYv+/ftTYOMaMQwDtcaIylo9lGoDauvMOT0qc4tQlpaN8rwSlJWrUWUUoFbqiVqZJwx8x12FR8xTQy6qglxYBXdRZf2jeVsuqoS7sBruHoFw9R4IV5/r4OJzHbh8SXd3mxCbGIZBjaYUxdXp5hEcNekork5HcU0GDCZth59PKpTD29U8isNbFgaFSyDkUn/IpX5wESnA5XRSrhwrnP0+lIIZDsDZLyJCCCGkJ9GqdagqqEF1oRLVRQ2PSlQV1qC6SAllSS1MXbyCBl/Ig4tCCheFBC4KF7h6m79kXi5w85ZB5iWFzNMFLp7SFqe2FBQU4P3338fnn3+OurrWE6vGxcVh3rx5mD9/PmJiYjryWyLNGJRKKI8fR/Whwyg6dhqFF9JRbRKhVuYJlcQDaqkHaqUeUEvlUEs8oJbIoZbKoRM69px9DkyQCbVwlQAyFylcZW5wlZpHgTR+Sa56LhXATcqHTMrvvASohNjBxBhRUZuHstqc+q/sxsdqdTGY1pLntgOfK4S7xLc+uGEOcMilfpBL/OAh9Ye71A8CXscFO539PpSCGQ7A2S8iQgghpDcxGkyoLVOhuj64UVXYFPCoLlKiulAJTW3Hf5pnL5FMCJlCChdPKWSeUrgoXCCrf94Q8NBztdi6ezN++vUnHDt2zK7jDh48GOPHj8fw4cMxbNgwBAUF0aiNTsSYTFBfuoSaw4fNX8ePoy41FUyzkTUGnhCq+sCGWuJuDnJI5FBLmwIeqsYyOTRit276jtpOLOSyAhyuUn7jo6uUD5mEDxcJHy5iHmSS+m0xDy4SCoSQzqU3alBem1cf4MhGqbLpUa2r6tRzy0SKpkCHxM8i8OEi9LD7b7Oz34dSMMMBOPtFRAghhBA2jVJbH9ioQVV9kKOmWIma4tr6LyUMuu5fNUMoFUDoKoBSV43c0hwUVxVBZVBCZVCiVl/b9NyghN7EXiElICAAEyZMwE033YRJkybR+5YuYNJqoU5NhSo5mfWlzc21+xhGLg91YneLIIdaIked2A06T39oFX7QyBRQC6RQMUKoDc4XtBLyuXCR8CAT8+Ei4cFFzIdMwmsxACK7qlws5FKwjrSLSlt1VYAjC6XKbJQqs1Bem9NhK6y0RMAT1wc5/JqN7jAHPdylvuBzBQCc/z6UghkOwNkvIkIIIYS0DcMwUFfWoaak1vx1daCjpOm5rs5KssVuojNqUWtQQm2oRZ1RjTpjHTRGNeoMamhMdfAN8kG/QX0xfPRQDB4xCDKFDBI3MQRix8/o7+wMVVWoTU6G6tw5qM6eRe25c1AlJ8OkVnfI8U0cHuqkbjCGx8IYFgtdYAS0vsHQePhBJVVAJXBBjRaoqtWhSqmGUm2C3sTrkHN3Fy4X9QEQcxDEpT4oImM92g6KSMU88Nq6VC7p0YwmAyrVBShVZqFUmYUyZTZKa7NRrS5CVV0xTF0Q6AAADjiQiT3hIfUHV+uOR6Z8wtrvTPehFMxwABTMIIQQQog1ujo9VBXqxq/acvOXslSF2jIVlKW15uflahj13T/SwxounwOJuxhSdwkkbmKI3USQuInZz93FkLiKIHYTQ+ImgsRdDLGbGHyBc98QdyfGaERdRgbUFy5Adf48VBcuQH3+PNSpqWD0HR8kE/j4QBIVBUlkJESRUeBFRsPo5w29rAx1mlQoK1NRW1sDpc4NNTo3KPWuqNW7Qqlzg1Jv3q7RuUFtkHV437qLVMQzBz4k/MaRIawASMOokaue01SZ3snEGKHUlKNKXYgqdVH9VyGq6pq26/Q1HX5edbUBq+87zypzpvtQCmY4AApmEEIIIeRaMAwDjVKL2nI1VOVq1JarUFvR8PzqQIgKqnK1Q0xxsYdAzG8MdojdRJC41j+61wdDXBsCI02BEHF9OQVCrDPp9ahLS2MHOS5cgDotDTB2znXBlUgg9PWFwFsOoQcgcKuFUFoEobwWIm9A6A3zoxwwcnjmIEd9oKNG524OduhcUaN3awx+1OjcoDK4QWWUQ6V3Ra1OAp2xZ73mAh4HYhEPEhEPEiEXEhEPYiEP0voysYgLibBhf/12/XPz/qZ2DWUCPoemzzgpjb4W1XXFqFIXovLqoEddIarUhaipK4WJadvvMAUzyDWjYAYhhBBCugrDMNDW6uqDHarG0R7NAx4NZXpN1wx97mgCMR8imRBimQgimQhimdD86CpqKncRQigVQCAWQCDmQyAWQCjhQ9Swz0UIkVQAXi8IjDTm4zh/HuoLF1CXloa6jAxoMjJgqKzskj5weIDQCxB5AUIFIPAwPwrrHwUNzz0AnpXVXnVGAVQGF6j0ruYABycCtQiHCiFQMYGoNfqi1ugBlV4KlcaI2jojVBrzcrmqOiPUWucI8l0LLhdNARArgRJzGbc+OGIOgkjFDfu4lm1FXAj5lF/EERhNBig1ZRajOpoCH4XQGthLj1Mwo5utX78ea9aswdmzZ6HT6RAVFYU777wTTz31FAQCQZuPd+rUKbz99tvYv38/qqur4e/vj5tvvhmvvPKKRcCho1AwgxBCCCGOiGEY6NT6+hEf9aM9KuugqdGgrkaLumoNVFVqFOeVoKK4EpoaLbhGHsTW7jSdmEDMh1AqhMjF/CWQmAMfAhEffDEfAhEfAjEf/PpHoUQAgUQAkVQAobQ+YCIRQFT/3BwkEYIv4jnFTaC+shKazExoMjLMAY70dNRduYK6K1egzckBuuF2gidpCnYI5IDADRC4A3y3+uduls+5DbcGHBEgjQYkcYA0FpDGAeJwGLkeUJvcUat3hUrLharOaA50aOofr3peqzEHQMzBkIZ6BphMXf6j6HZcDhoDH5KG0SENo0WEXIiFPIjqH8VCLsQC8yiShm2RgF2voZ1ISIGSjqbRK1mjOrLz0jB/3MusOs50H+rUwYwnn3wSq1atAp/Px/jx4yGTybBnzx5UVVVh9OjR2LFjByQS+/+Z/vHHH5g/fz4MBgOGDh2K8PBwnDx5EhkZGfD19cXBgwcRFRXV4d8HBTMIIYQQ0lMUFhZi+7bt2PXvbqScuYiywnIIOSKIeRJIeFKIeVJIeBLzI18KccPz+v1Cnqi7v4Uuw+GgMQDCFzYFQ/giPitQ0lSHZ1HHPKLEHFzhW9nmC3ngCbjg8s2PPAEPvA68QTRptdBkZqIuPb3p68oVc8AjM7PTpq60B096VZDDHeC72giCuAMCuQgChQd47gpwBApA4AkI/c1fIn9AGNC0LfQBODzzdC+dyXYApP656qrnDaNCGoIjOkMvjIa0gMMBRIKrAiFCXv02F0KBOdgh4Dc9Fwo45kc+F4L6MpHAHBgRCcyBE5GQC7GAC1H9sRr2iwU88Hi9K3Di7PehThvM2LRpE2bNmgWZTIZ9+/Zh0KBBAICysjKMHz8eycnJWLJkCd5//327jldQUIDo6Gio1WqsXbsWDz74IADAaDRi4cKF+OmnnzB06FAcO3asw6ODzn4REUIIIYTYotfrkZWVhbS0NKSlpSE9Pb3xeVZWFkzNPsrmcnj1gY+m4EdDIKQp+FG/Xb9PzDcHQwRcYTd9l86Hy+eCL+CxAyb1ARS+kMcKpvAETYEQLr8pINLw2DxQ0lCfw2FgqqoAo6yGqboSpupKGKsqYKqqgLGiFMaSIugLC2AoKgDHoAOHMYDLGMFhTODAQW5RuPUBDhnAFdZ/ia56LgR48hDww28Dz90dfLkc/Kseea6u4Mlk4Lm4gCeTgeviAm4Lo8d1elPj1Jc6rQl1OiM0WiPqtEbU6Uz1jw1l5v11WiM0OvO2Wlu/r7GcgiNtweWiMUAi4HOues6FkM+xfM7jQChoqi/gNQVXmravqnNVfWFDm6ueCwVcCHjm7a4IrDj7fajTBjOGDRuGEydOYMWKFXjppZdY+w4ePIgxY8ZAJBKhuLgY7u7urR7vueeew3vvvYeJEydi586drH21tbUICgpCdXU1tm3bhptuuqlDvxdnv4gIIYQQQtqjtrYWx48fx6FDh3D48GEcP34cFRUV7T4erz4QYg6AiOGr8MMPX/8ITa0WGqUWmlodtPXPtbU6aFU66DV66LUG6OsM0Gv00NXpoVPpumPmBLkKBwy4HAYcmMBhjOCYDOAY9eCYzAEPc9CjIfhhBJcxgGMyNm5zGGN9W5O5DCaAYerL6x8ZprEOYAKXMQGMqbGsIajSULexDmt/8+dM4/Gs7zeBI+CDLxGD7yIBTyIGXywCVywCXyICTyIGTyIGVyIBVyIBTyIBRywGr36bK5GAKxbbtY8rkQBCEXQcPjS6qwIfVwVBGoIkDQGQxmDIVcETjc4ItaYhiGJuS78fna95YIXP44LP5YDH44Bf/8Xj1pc3Pq8v53Hq63Ityhqf87hQ11TgodsGss7rTPehTrnwd35+Pk6cOAEAuOOOOyz2jx49GsHBwcjNzcXWrVsxf/78Vo+5ceNGm8eTyWSYMWMGfvzxR2zYsKHDgxmEEEIIIb2RTCbD+PHjMX78+MYypVKJvLw85ObmNn5dvZ2TkwOVSmX1eEbGCJWhFipDLQDAPUyGhEkxbe6XycRAX6c3BzzUuvrAhxZalfm5XmOAXmuAQWuAXlP/qDVAX6eHQWuArs4AXZ0OOrW+/kvX+Eg3gfZhwIGR4QDgAuDXD4fo7l51Ik39V32u1REX34JUV9oxx+ZwWMEOnkSChL//hsuAPu06HMMw0OpNrGBI4/NmZVqdeXSIRmeERl//qDVBW/9c27Cv/lFvpF+QBiYT6n8unTe6RqfumuS+ncUpgxlJSUkAAIVCgfDwcKt1hgwZgtzcXCQlJbUazFAqlUhPT29sZ+t4P/74Y+O5O1tZWVmHHctZImuEEEIIIa6urujTpw/69LF+o8UwDCoqKpCVlYXs7GxkZ2cjNzcXZWVlKCsrQ2lpaeNzLy+vdvWBy+U0JvvsSAzDQK8xQKfWQavSQ6/RWwREDBoD9Dqj9fKrAyi6+sf68sa6Gr052OKkq9CQBh14A8swMKnVMKnVHXI4DodTn8OCBw/XDjlkI6ORgVbfFNzQ6k2NI0caAiAanQlanRF6AwOdwWT+0jc9NpbXl2l15uCJVl8fPNGbyyg/ifNzymBGZmYmACAkJMRmneDgYFbdlmRlZTU+t3XMthyvI8THx3fYsZx0JhEhhBBCiAUOhwNPT094enpi8ODBLdY1GBzrhp7D4UAoEUAoEUDm2bnnYhgGBp0RRr0RRr0JJoP50ag3wmgwPxqsBU2sBE/M9c1tTPVtm47TrLzhPFfVa2rTVE5aZp6q0nm4YnGnHr+9eDwOpDw+pF3QPZOJYQU7NDpzAtaGYIi+/rm+PmDS8Ny83fS8eX1dG+roDQyMJrpXay+nDGYolUoAgIuLi806MpkMAFBTU2P38Vo6ZluO11YdOQqDdD/KgdKz0OvZ89Br2rPQ69mzdPTryec75VvdDsHhcMyrnYi672dg6/X08vIyBzgMTcEVk94Eo8HIKm94bn5sCMo0D6ZYeawPsDAmBiaDCSYTA5PR3NZkNJnLjUz9cxNMBgYmkwkmIwPGaH40mUxg6us01W22bWxod1Xd+nNeXbc9Ah99GBKoYaqrg6muDsa6Opg0msZt01Xbxqu2GZ3OruPz2rDi49V60t9cLrdphEl3MposAyY6vQl6I1M/0sRcbjCZYDQyMNR/GU0Nz+vL67cb65gYGI0mi/rG+jYGE4OyYi32N+tPWVmZ07yevfcvPCGEEEIIIaTLcTic+hVPeIDE9soePUVDMKUpOFIfODE1C5RcFXjxCvUw/3zaiDEam4IeGk1ToKNZMITv4dEJ3ylpDx6XA143BVUuXgTWLO3y03YYpwxmuLqaJ2fZSv4EmLNjA4Cbm5vdx2s4prXVT9pyPEIIIYQQQggBzCMAuFwe0AVxGw6PZ14GtoUR7IT0FE4ZzAgLCwMA5Obm2qzTsK+hbktCQ0Mbn+fk5KBfv37XdLyOcPDgQcTEtD37NiGEEEIIIYQQ0tM5ZTAjMTERAFBeXo7MzEyrK5qcPHkSADBo0KBWj+fm5oaoqCikp6fj5MmTVoMZbTleR1AoFE4zV4kQQgghhBBCCOlKTrlac1BQEIYOHQoA+OWXXyz2Hzx4ELm5uRCJRJg6dapdx5w1a5bN49XW1uKff/4BANx6663t7TYhhBBCCCGEEEI6gFMGMwDgxRdfBAC8/fbbOH36dGN5eXk5Hn30UQDAY489xsp/sXHjRsTFxWHChAkWx3vyySchlUqxa9cufPnll43lRqMRjz76KKqqqjB06FBMmjSps74lQgghhBBCCCGE2MFpgxkzZ87EE088gdraWowYMQJTpkzBbbfdhqioKCQnJ2PUqFF4/fXXWW2qq6uRmpqKK1euWBwvICAA3333HXg8Hh588EGMGDEC8+bNQ0xMDH788Uf4+vril19+AYfD6apvkRBCCCGEEEIIIVY4bTADAFatWoXffvsN1113HQ4fPoytW7ciKCgIb7/9Nvbs2QNJG9dPnjNnDo4dO4Zbb70VGRkZ2LhxI4xGIxYtWoSzZ88iKiqqk74TQgghhBBCCCGE2MspE4Be7fbbb8ftt99uV92FCxdi4cKFLdYZPHgw/vzzzw7oGSGEEEIIIYQQQjqDU4/MIIQQQgghhBBCSO9DwQxCCCGEEEIIIYQ4FQpmEEIIIYQQQgghxKlQMIMQQgghhBBCCCFOhYIZhBBCCCGEEEIIcSoUzCCEEEIIIYQQQohToWAGIYQQQgghhBBCnAoFMwghhBBCCCGEEOJU+N3dAQKYTCaLsoqKCpSWlnZDb8i1Kisrs6uMOAd6PXseek17Fno9exZ6PXsWej17HnpNe5aKigqLMmv3po6KwzAM092d6O0OHTqE0aNHd3c3CCGEEEIIIYT0YgcPHsSoUaO6uxt2oWkmhBBCCCGEEEIIcSoUzHAACoWiu7tACCGEEEIIIaSXc6Z7UwpmEEIIIYQQQgghxKlQzgwHYDAYkJaWxipTKBTgcinWRAghhBBCCCGk45lMJoskoNHR0eDznWOdEApmEEIIIYQQQgghxKnQR/+EEEIIIYQQQghxKhTMIIQQQgghhBBCiFOhYAYhhBBCCCGEEEKcCgUzCCGEEEIIIYQQ4lQomEEIIYQQQgghhBCnQsEMQgghhBBCCCGEOBUKZhBCCCGEEEIIIcSpUDCDEEIIIYQQQgghToWCGYQQQgghhBBCCHEqFMwghBBCCCGEEEKIU6FgBiGEEEIIIYQQQpwKBTMIIYQQQgghhBDiVCiYQQghhBBCCCGEEKdCwQxCCCGEEEIIIYQ4FQpmEEIIIYQQQgghxKlQMIMQQgghhBBCCCFOhYIZhBBCCCGEEEIIcSoUzHAA69evx7hx4+Dh4QEXFxcMGDAA7777LvR6fXd3zamlpqZi9erVWLhwIfr16wc+nw8Oh4MVK1a02nbXrl2YOnUqvLy8IJFIEBcXh5deegm1tbUttktPT8fChQsRFBQEkUiEoKAgLFy4EBkZGS22UyqVePHFFxEbGwuJRAIvLy9MmzYNe/bsabGdyWTC2rVrMXz4cLi6usLV1RXDhw/HF198AYZhWv0+nYVer8fu3bvx7LPPYujQoZDL5RAIBPDz88OMGTOwZcuWFtvT6+mYfv75Z9xzzz0YMGAAfHx8IBAI4O7ujmHDhuGtt95q8fWh19TxPffcc+BwOK3+3aXX0jEtXLiw8fWz9aXRaKy2PXXqFObMmQNfX1+IxWKEh4fj8ccfR0lJSYvnLC4uxmOPPYbw8HCIRCL4+vpizpw5OH36dIvtdDod3nnnHQwYMAAuLi7w8PDAuHHj8Mcff7T6ffa292A6nQ4ff/wxRo8eDYVCAbFYjKCgIEyZMgW//fab1Tb0O+p4srKyWv39bPjav3+/RXt6TR1TTk4OHnvsscafU8PfzwULFuDs2bM22/Xq15Mh3Wrx4sUMAIbP5zOTJk1ibr31VkYulzMAmNGjRzNqtbq7u+i0Gn62zb9ef/31Ftt9+OGHDACGw+EwY8eOZebMmcP4+fkxAJjY2FimtLTUaruDBw8yUqmUAcAkJCQwc+fOZRISEhgAjIuLC3PkyBGr7YqLi5mYmBgGAOPv78/MmTOHGTt2LMPhcBgOh8N8/PHHVtsZDAbm1ltvZQAwUqmUmT59OjN9+nRGIpEwAJg5c+YwRqOxbT80B7Vz587G18/Pz4+ZNm0ac/vttzN9+/ZtLH/wwQcZk8lk0ZZeT8c1atQohsPhMPHx8cxNN93EzJ8/nxk/fnzj9xwVFcXk5+dbtKPX1PEdOnSI4XK5DIfDafHvLr2WjmvBggUMAGbUqFHMggULrH7pdDqLduvXr2f4fD4DgBk6dChz++23MxEREQwAxtfXl0lLS7N6vtTUVMbHx4cBwERERDC33347M3To0Mb3SBs2bLDaTqVSMSNHjmQAMHK5nLn11luZSZMmNfZhyZIlNr/H3vYeLDc3l4mPj2cAMF5eXszNN9/MzJ07lxk5ciQjlUqZ2bNnW7Sh31HHVFpaavP3csGCBY2/O66urkxtbS2rLb2mjuno0aOMq6srA4AJDAxkZsyYwcyaNYsJDw9v/Dv1+++/W7Tr7a8nBTO60caNGxkAjEwmY06dOtVYXlpayvTr16/Vf8KkZV9++SXzzDPPMD///DNz8eJF5u677241mHH69GmGw+EwPB6P2bp1a2O5SqViJkyYwACw+s9epVIxAQEBDADmhRdeYO174YUXGABMcHCw1TdGt9xyCwOAmTBhAqNSqRrLt2zZwvB4PIbL5TJnz561aPfRRx81/sHLyMhoLM/IyGjsy+rVq1v+ITmJ3bt3M7Nnz2b2799vsW/dunUMj8djADDff/89ax+9no7t6NGjTHl5uUV5WVkZM3r0aAYAM2/ePNY+ek0dn0qlYqKjo5nAwEBm5syZNv/u0mvp2BqCGd9++63dbfLz8xvfHK9du7ax3GAwMHfddVdjgKN54NlkMjGJiYkMAObuu+9mDAZD4761a9c2vlcqLCy0OGdDQKJfv36sN+0nT55kZDIZA4D5559/LNr1tvdgarWaiYuLYwAwr776qkUgSqVSMUlJSawy+h11XlOmTGEAMA888ACrnF5Tx9W/f//GD+eu/v00Go3Myy+/3Biwraura9xHrycFM7pVQ9R0xYoVFvsOHDjAAGBEIhFTVVXVDb3reRremLUUzJgzZw4DgLn//vst9mVlZTFcLpcBwFy8eJG1b82aNQwAJiYmxiKqaDQaGyOYn3/+OWvfhQsXGAAMj8djsrKyLM553333Wb2hMxqNjVHXn376yaLdjz/+yABgAgICelTU2paGn9OECRNY5fR6Oq/9+/czABiFQsEqp9fU8T3xxBMMAGbLli0t/t2l19KxtSeY8eyzzzIAmIkTJ1rsUyqVjLu7OwOA2bZtG2vfli1bGt+oK5VKi7YNb8qXLl3KKq+oqGCEQiEDgDl48KBFu9dff50BwIwYMcJiX297D/bKK6803ijZi35HnVNeXl7ja3P06FHWPnpNHVNZWRkDmEcal5SUWOw3GAyNIxhOnz7dWE6vJwUzuk1eXl7jRXt1dOpqwcHBDADml19+6eLe9UytBTO0Wm3jJ0p79uyxWmfMmDEMAObNN99klU+cOJEBwCxbtsxqu4Y3EZMmTWKVr1ixggHAjB071mq73bt3M4B5uNfVUdqDBw82vtG6OkLbQK1WN77BO3z4sNVj9ySffPJJ4x/lBvR6OrfDhw8zgHkIYwN6TR3ff//9x3A4HOaee+5hGMb23116LR1fe4IZUVFRDADmm2++sbq/YYRk8xvq+++/nwHQeN009/XXX1v8jWcYhvnpp58YAExISIjVdleuXGl8r3X1lLXe9h5Mp9MxXl5eDACb03yao99R59Xwc0xISGCV02vquJRKpd3BjJycHIZh6PVsQAlAu0lSUhIAQKFQIDw83GqdIUOGsOqSznX58mWo1WoATT/75my9Jg3bndVOpVIhLS3Nol1CQgLEYrFFO4lEgoSEBKvn7Ikafjb+/v6NZfR6Oi+lUolXX30VADBjxozGcnpNHVttbS3uvfde+Pr6YuXKlS3WpdfSefz3339YsmQJHnzwQbzwwgvYuHEjtFqtRT2lUon09HQAnffapKWlQaVS2d0uIiICCoUCAHDmzBmLdr3lPdjp06dRVlaGgIAAREVFITk5GcuXL8dDDz2EpUuXYsuWLTCZTKw29DvqvL777jsAwH333ccqp9fUcclkMowZMwYA8PLLL7MSEJtMJrz66quoq6vDlClTEBwcDIBezwYUzOgmmZmZAICQkBCbdRou1oa6pHM1/JzlcjlcXV2t1rH2miiVSpSXlwOw/Xo2tCstLWW9EWvtOnBzc4Obm5vFOen6aVJUVNT4j3v27NmN5fR6Oo8dO3Zg4cKFuOeee3DTTTchMDAQO3bswOTJk/HOO+801qPX1LE988wzyMzMxGeffQYPD48W69Jr6Tx++OEH/H97dx4V1Xn+Afw77IKKjIIoCkhYomJcKoIiCY1LkBAXcMEFY2O2amuMMXGJpz9zNEcxjTbVJCqJJsWt1BI9aiGoQSMucYmaIi0BUQOpIjuIbAPP7w97bxmHTSXCwPdzDueM933f+75zHwbvPPe9712/fj2ioqKwdu1ahIaGok+fPoiPj9erd/36dfV1Y7G5/zg1doyVdiKi109TYtOrVy+DPttbTH/44QcA947F0qVLMXDgQKxcuRJbt25FZGQkQkJCMHToUPz0009qG35GjdPx48eRnp4OCwsLRERE6JUxpq1bVFQU3NzcsHXrVvTp0wcTJ05EWFgY3N3dsW7dOkRERGDPnj1qfcbzHiYzWkhJSQkAwMbGpt46HTt2BAAUFxc/ljG1dw8bE6VdQ22VdvW1fdg+2/vvj06nw6xZs1BUVIQBAwbgtddeU8sYT+ORkpKCL7/8EtHR0UhISEBJSQlmzJiBL774Ara2tmo9xrT1SkhIwJYtWxAeHo6JEyc2Wp+xbP0GDhyIjz76CMnJySguLkZ2djYSEhIwYsQI3Lx5E+PHj8exY8fU+g8Sm/uPU2PHmDF9NMoXl4sXLyIyMhLz5s1DamoqioqKcPjwYXh6euLixYt4/vnn1SvC/Iwap23btgG4N6uxW7duemWMaevm5eWF06dPY+zYsfj555+xf/9+xMbG4tq1a3B3d0dgYKCaKAAYTwWTGURktF5//XUcPXoUXbt2xd69e2FhYdHSQ6KHsHDhQogIKisrkZ6ejg8//BBxcXHo168fvv3225YeHjWiqKgIc+fOhb29PTZu3NjSw6Fm8uabb2LBggXo378/OnXqBAcHB4wZMwZJSUmYMGECqqqqsHDhwpYeJjWBiAAAqqqqMH36dGzatAmenp7o3LkzRo8ejcOHD8PKygrJycl6V37JuBQXF2Pv3r0AgJdeeqmFR0MP6uTJkxgwYACSk5Oxa9cu3Lp1C/n5+Thw4ACqqqowd+5cg1uHiMmMFqNMB6o9ded+d+7cAQC9LBz9ch42JrWndtXXVmlXX9uH7bM9//688cYb+Pzzz2FnZ6deWaqN8TQ+5ubmeOKJJ7Bo0SLExcWhoKAAs2bNQllZGQDGtLVauHAhsrKysGnTJoMrgfVhLI2XRqPBe++9BwC4fPkyMjMzATxYbO4/To0dY8b00dSOTe0ZjApnZ2c8//zzAIAjR47oteFn1Hjs2bMHd+/eRa9evfDcc88ZlDOmrVdhYSEmTZqEnJwcxMbGYvr06ejevTvs7OwQEhKC+Ph4WFtbY9u2bUhMTATAeCqYzGghrq6uAKCeBNRFKVPq0i9LOc6FhYV6U7BqqysmnTp1UhcYq32/aV3tunXrpjfFStlPfe2Ki4vV6Va1+2ysXX1jbSveeust/PnPf0aXLl2QkJCAwYMHG9RhPI2br68v+vXrh8zMTJw/fx4AY9paffXVVzAzM8Mnn3yCwMBAvR9lXYXPP/8cgYGBCA8PB8BYGru+ffuqr7OysgAALi4u6rbGYnP/cWrsGCvtNBqNXj9NiY0yvrpi2l7Owdzc3Op8XVedmzdvAuBn1Bgpt5jMmTMHJiaGX/EY09br0KFDyMnJgZubG3x9fQ3Ka29XEo6M5z1MZrQQ5ctXXl5evQudKCfwQ4YMeWzjas+8vLxgbW0N4H/H/n71xUT59y/VzsbGRm/mgdLuypUrKC8vN2hXVlaGK1eu1NmnsXvnnXewfv162NraIiEhod4VlRlP46f8B3r79m0AjGlrptPpcPz4cYOf7OxsAPcWhzx+/DjOnDkDgLE0dsoaDMD/rrh17twZ7u7uAH652Hh4eOjdy91Yu4yMDOTn5wOAXtK7vZ2DDRkyBBqNBgCQm5tbZx1lu3J8+Rk1LikpKfjuu++g0Wjwm9/8ps46jGnrpXzJb2hWgrKGmPI3jfH8rwd6kCs1Kx8fHwEgq1evNig7ceKE+jzewsLCFhhd2/Piiy8KAFm1alW9daZMmSIA5OWXXzYou379upiamgoA+de//qVX9vHHHwsA8fT0lOrqar2y6upq8fT0FACyefNmvbLk5GQBIKampnLjxg2DPufOnSsAJDw83GCfjo6OAkB27Nhh0C46OloASM+ePQ3GY8yWLFkiAMTW1lbOnj3baH3G03jl5OSIpaWlAJAffvhB3c6YGpeG/u4ylsZr/fr1AkA6d+4slZWV6va3335bAMjo0aMN2pSUlEiXLl0EgMTHx+uVHTp0SABIly5d5M6dOwZtR40aJQBk6dKletvz8/PFwsJCAEhSUpJBu1WrVgkA8fPzMyhrb+dgAQEBAkDWrVtnUFZZWSlubm4CQNasWaNu52fUeCxatEgAyLPPPttgPca0dfryyy8FgHTo0KHOvzmVlZXi7OwsACQyMlLdzniKMJnRgr766isBIB07dpQLFy6o23Nzc2XAgAECQN56660WHGHb0pRkxoULF0Sj0YipqanExcWp20tLS9WTqbCwMIN2paWl0rNnTwEgy5cv1ytbvny5AJBevXrJ3bt3DdpOmDBBPfmrXf6Pf/xDTE1NxcTERC5fvmzQbsOGDQJAnJycJCMjQ92ekZEhTk5OAkA2btzY8EExIu+++656stuURIYI49maXblyRXbs2CFlZWUGZampqRIYGFjnlxDG1Lg09HeXsWy9Ll68KPv375eqqiq97dXV1fLZZ5+JlZWVAJAVK1bolf/8889ibW0tAGTr1q3qdp1OJxEREQJAfHx8pKamRq9dTU2NDB48WADI7NmzRafTqWVbtmxRz5Vu3rxpMNY33nhDAMhTTz0lubm56vYLFy5Ix44dBYAcOHDAoF17Owc7cuSIABA7Ozs5ffq0ur2qqkp+//vfCwDp1KmT3Lp1Sy3jZ9Q4VFZWioODgwCQnTt3NliXMW2dbt++LTY2NgJApkyZIiUlJWpZRUWFzJ8/XwCIubm5XL16VS1jPJnMaHELFixQfzmDgoIkLCxMvXLh7+9f5y8RNc2FCxfE19dX/enWrZv64ay9/T//+Y9eO+WKk0ajkcDAQJk6dar06NFDAIiXl5fk5OTU2V9SUpJ6Euft7S3h4eHi7e0tAMTGxkbv5KG27Oxs8fDwEADSo0cPmTp1qgQGBopGoxEA8tFHH9XZTqfTyaRJkwSAWFtby/jx42X8+PHqGCZPntwmstUiIvv37xcAAkCGDh0qL774Yp0/dZ14Mp6tU2JionosR44cKeHh4RIaGipDhw4VExMTASB9+/atM/PPmBqPxpLIjGXrpHzRt7Ozk1GjRsmMGTMkODhYvTIIQKZPn26Q7BARiYmJUa8G+vr6yrRp09Sr/t27d5e0tLQ6+/z3v/8t9vb2AkDc3Nxk2rRpMmzYMAEgZmZmEhsbW2e70tJSGT58uDresLAwCQoKEnNzcwEgixYtqvd9trdzMGWmipmZmYwYMUJCQ0PF1dVVvSJ88OBBgzb8jLZ+sbGx6sWeui4Q3I8xbZ2io6PFzMxMAIi9vb0EBwfLhAkT1C/6JiYm8umnnxq0a+/xZDKjFfjrX/8qTz/9tHTu3Fk6dOgg3t7esnbtWqmoqGjpoRk15ctSYz/Xrl0zaHv48GEJCgoSrVYrlpaW4uHhIcuWLZPi4uIG+0xLS5PZs2dLz549xdzcXHr27CmzZ8+W9PT0BtsVFRXJ0qVLxcPDQywtLUWr1UpQUJAcOXKkwXbV1dWyefNmGTp0qNjY2IiNjY34+PjI5s2bDa58GbPt27c3KZYuLi51tmc8W5/bt2/L+++/L0FBQeLq6io2NjZiYWEhjo6OMmbMGPn000+lvLy83vaMqXFoyow4xrL1ycjIkIULF8rIkSPFyclJrKysxNLSUpydnWXy5Mly6NChBtufP39eQkNDxd7eXiwsLMTFxUXmz5+vd9W/Ljdv3pT58+eLi4uLWFhYiL29vYSGhurNnKhLRUWFrFmzRry9vaVDhw5ia2srTz/9tMTExDT6XtvbOdjXX38t48aNE61WK+bm5tK7d2+ZM2eOwTT02vgZbd1CQkIEgMybN6/JbRjT1unSpUsyZ84ccXNzE0tLS/Xv58yZM+W7776rt117jqdG5L8PnyYiIiIiIiIiMgJ8mgkRERERERERGRUmM4iIiIiIiIjIqDCZQURERERERERGhckMIiIiIiIiIjIqTGYQERERERERkVFhMoOIiIiIiIiIjAqTGURERERERERkVJjMICIiIiIiIiKjwmQGERERNbu8vDxotVpoNBqcO3eupYdjVAIDA6HRaBAYGGhQdu7cOWg0Gmi1WuTn5z/+wREREbUSTGYQERG1AceOHYNGo1F/OnXqhLt37zbarqysDLa2tnptjx079sjj+cMf/oCCggIEBwfDx8fnkffXVJ6entBoNNiwYcNj6/Nx8vHxwXPPPYeCggKsXLmypYdDRETUYpjMICIiaoPu3LmDffv2NVpv//79KC4ubta+b9y4gaioKAD3khqPS2pqKtLS0gAAL7zwwmPr93FTjumWLVuQmZnZwqMhIiJqGUxmEBERtTFWVlYAgOjo6EbrKnWUNs0hMjISVVVV8Pf3h6+vb7PttzEHDhwAADz55JNwd3d/bP0+biNGjICfnx8qKyvxwQcftPRwiIiIWgSTGURERG3M+PHjAQCHDx/GrVu36q13+/ZtJCQkAAAmTJjQLH0XFhbiL3/5CwBg1qxZzbLPplKSGW15VoZixowZAIAvvvii2WfWEBERGQMmM4iIiNqYsWPHwtHREdXV1di9e3e99Xbv3g2dTgdHR0eMGTOmWfres2cPSktLYW5ujilTpjTLPpuioKAAJ0+eBACEhIQ8tn5byrRp02BqaoqSkhL87W9/a+nhEBERPXZMZhAREbUxpqammD59OoCGbzVRZlDMmDEDpqamzdJ3TEwMgHtP5OjatWuj9ZOSkhAWFgZHR0dYWVnBzc0Nr7/+OtLT09X91Pdkj9ri4uJQXV0NrVYLf39/vTJXV1doNBrMmTMHAPD9999j5syZ6N27Nzp06AB3d3csWrQIubm5eu1OnTqFKVOmwNnZGVZWVnjiiSewZMkSlJSUNPq+cnJysGLFCgwePBhdunSBlZUVXF1dERERgaSkpEbbN8bBwQEBAQEA0GDCioiIqK1iMoOIiKgNioiIAABcvHgRV65cMShPSUnB999/r1f3UVVUVODUqVMAAD8/v0brR0ZG4umnn0ZsbCyys7NRUVGBa9euYcuWLRgyZIh6C0xTKLeYjBs3rsHETHR0NIYPH45du3YhKysL5eXluHr1KjZs2AB/f3/1tpw//vGPGDlyJPbu3YvMzExUVFQgIyMD69atQ2BgIO7cuVNvHwkJCXB3d8f777+PS5cuoaioCBUVFbhx4wZ27NiBgIAA/O53v0NNTU2T319dlGN84sQJlJaWPtK+iIiIjA2TGURERG3Q4MGD0b9/fwB1z85Qtnl7e2PQoEHN0ue5c+dQUVEBAI0+jjUmJgZLly6FiECr1SIyMhKnTp3CqVOnEBkZCTMzM4SHh+PmzZuN9qvT6RAfHw+g4fUyLl++jJdffhnu7u7Ytm0bzp07h2+++UZd2+PHH3/E4sWLERsbi7fffhu+vr7YuXMnzp8/j/j4eAQHBwO4N7Nj9erVdfZx6dIlvPDCCyguLoa5uTnefPNNJCYm4uzZs9iyZQv69OkDAPj444+xbNmyRt9bQ4YNGwYAqKysxOnTpx9pX0REREZHiIiIyOglJiYKAAEg27dvFxGRyMhIASC9e/eWmpoatW5NTY307t1bAMi6detERGT79u1q+8TExIcag9IfAMnMzKy3Xnl5uXTv3l0ASLdu3SQtLc2gTmpqqmi1WnV/zzzzTL37++abbwSAmJmZSWFhoUG5i4uLup8RI0ZIaWmpQZ3JkycLADE1NRWtVithYWGi0+n06uh0OvHz8xMA0rVrV6mqqjLYj4+Pj7qfr7/+2qA8Pz9f+vXrJwDExMREkpOTDeo888wzjb5nEZEbN26o72vt2rUN1iUiImprODODiIiojZo5cyZMTEyQmZmJY8eOqduPHTuGzMxMmJiYqE/FaA5ZWVnqawcHh3rr7du3D9nZ2QCAlStX1vkYVU9PT/zf//1fk/o9ePAgACAgIAC2trb11tNoNPjss89gbW1tUDZv3jwAQHV1NcrLy7F161aD21VMTU3x6quvAgDy8vKQkpKiV3727FmcO3cOAPDKK69g7NixBv3Y2dlh69atAICamhp88sknTXqPdal9jDMyMh56P0RERMaIyQwiIqI2ysnJCb/+9a8B6N9qorx+9tln4eTk1Gz95eTkAACsra1hYWFRb70jR44AAExMTDBz5sx6682aNQsajabRfpv6SNannnoKffv2rbNs4MCB6usxY8ZAq9U2Wu/+BILyvgBg7ty59Y7D399fHUftNg/KysoKHTp0AIAGH8FLRETUFjGZQURE1IbNnj0bAPD3v/8dZWVlKCsrw969e/XKmkt+fj6Ae7MPGpKcnAwAcHNzQ5cuXeqtp9Vq4ebm1uC+UlNTkZaWBqDxZIanp2e9ZbXH0dR69z/VRHlfFhYWja5D4uvrCwBIS0tDZWVlg3UbohxrLgBKRETtDZMZREREbVhoaCisra1RXFyM/fv3Y9++fSgpKYGNjQ1CQ0ObtS8rKysAQFlZWYP1CgoKAAD29vaN7rOxOsqsDC8vrzpvV6mtrttLFCYmJg9cr7q6Wq9MSeZotVqYmZk1OBZHR0cAgIiox+NhKMfa3Nz8ofdBRERkjJjMICIiasM6duyISZMmAbh3e4lyi8mkSZNgY2PTrH0piYfCwkKISLPuuz7KehmNzcp4nJpya0xzqKmpQVFREQA0OMOFiIioLWIyg4iIqI1TbidJSEjA4cOH9bY1JyWZUftLdl2UWyOUNTYa0lCdgoICnDx5EkDrSGYo62zk5eVBp9M1WFdZ40Kj0TR6W059ioqKUFNTAwBwdnZ+qH0QEREZKyYziIiI2rhRo0ahR48e0Ol00Ol06NmzJ0aNGtXs/QwYMEB9/eOPP9Zbr3///gDuLaDZ0C0W+fn5DT6lIy4uDjqdDlqtFv7+/g8x4ubl7e0NAKisrMSlS5carHv27FkAgIeHR4OLpTak9jFWjikREVF7wWQGERFRG2dqaoqIiAhYWlrC0tISERERems/NJeAgAD1tfKI0rooiZSamhrs2rWr3no7duxo8HYVZb2McePGGTxGtSWMHj1afb1t27Z6650+fVp9rGvtNg+q9jFWFhQlIiJqL5jMICIiagciIyNRXl6O8vJyrF279hfpo3fv3nBxcQHwv5kHdZk0aRIcHBwAACtXrsTVq1cN6qSlpeG9996rdx86nQ7x8fEAgJCQkEcZdrMZNmwYhg4dCgCIiorC0aNHDeoUFRXhtddeA3BvMdHf/va3D92fcoydnZ3h5eX10PshIiIyRkxmEBERUbOZMGECACAxMbHeWRVWVlb405/+BADIzc2Fr68vPvjgA5w5cwZnzpzBunXr4Ofnh5qaGnh4eAAwXFQzKSkJhYWFMDMzQ1BQ0C/3hh5QVFQULCwsoNPpEBwcjMWLF+P48eM4f/48oqKiMGTIEPzzn/8EACxevFi9NeVBiQgSExMBQF3glYiIqD1hMoOIiIiazSuvvAIAyMzMxIkTJ+qtN336dKxevRoajQZ5eXl45513MHz4cAwfPhxLlixBeXk5du7cqT7CVHnsq0K5xSQgIKBVPclj0KBBOHDgADp37ozKykp8+OGHCAwMhI+PD1599VV1DZD58+djzZo1D93Pt99+i6ysLADArFmzmmXsRERExoTJDCIiImo23t7eGD58OAA0uB4GALz77rs4fvw4Jk6cCAcHB1haWsLFxQUvvfQSzp8/j+DgYBQXFwMAbG1t9doqyYzW8BST+40dOxbp6elYvnw5Bg0ahM6dO8PS0hLOzs6YOXMmTpw4gU2bNj3SuiXKsfXx8VFvbSEiImpPNPK4HgRPRERE7UJMTAymTZsGOzs7/PTTT+jYseND7aeqqgq2trYoKyvDihUrsGrVKgBAamoqnnzySQD3nuih3IrSXpSUlMDZ2RmFhYXYvXs3wsPDW3pIREREjx1nZhAREVGzmjJlCn71q1+hoKAAmzZteuj97Nu3D2VlZQAAPz8/dfvBgwcBAF5eXu0ukQEAmzZtQmFhIfr164epU6e29HCIiIhaBGdmEBERUbM7evQoRo8eDXt7e1y7dg02NjYGddLT0+Hu7l5n++vXryMgIABZWVno3r07srKyYGZmBuDezI+UlBQMHjxYXXC0vSgtLYWrqytyc3Nx6NAhBAcHt/SQiIiIWgSTGURERPSL2LhxI/Ly8jB16lT069fPoNzMzAzBwcEICQlB//79YWNjg9u3byMxMRGbN29GYWEhACA6OpqLXP5XSkoKYmJioNVqsWDBgpYeDhERUYthMoOIiIhaxP2PW72fiYkJVq9ejWXLlj2mEREREZGxMGvpARAREVH7dODAAcTFxeHUqVPIzs5GXl4eLC0t4eTkhMDAQMyfPx/e3t4tPUwiIiJqhTgzg4iIiIiIiIiMCp9mQkRERERERERGhckMIiIiIiIiIjIqTGYQERERERERkVFhMoOIiIiIiIiIjAqTGURERERERERkVJjMICIiIiIiIiKjwmQGERERERERERkVJjOIiIiIiIiIyKgwmUFERERERERERuX/AUCv7gegYqfUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "xmajorLocator = MultipleLocator(10000)\n",
    "\n",
    "ymajorLocator = MultipleLocator(0.2)\n",
    "\n",
    "plt.plot(MMD_x_C_40, MMD_y_C_40 / max(MMD_y_C_40), c=\"k\", label=\"C-40\", lw=3)\n",
    "plt.plot(MMD_x_C_60, MMD_y_C_60 / max(MMD_y_C_60), c=\"#FFC000\", label=\"C-60\", lw=3)\n",
    "\n",
    "\n",
    "plt.plot(MMD_x_L_40, MMD_y_L_40 / max(MMD_y_L_40), c=\"#C00000\", label=\"I-40\", lw=3)\n",
    "plt.plot(MMD_x_L_60, MMD_y_L_60 / max(MMD_y_L_60), c=\"#77AC30\", label=\"I-60\", lw=3)\n",
    "\n",
    "\n",
    "plt.plot(MMD_x_E_40, MMD_y_E_40 / max(MMD_y_E_40), c=\"#7E2F8E\", label=\"E-40\", lw=3)\n",
    "plt.plot(MMD_x_E_60, MMD_y_E_60 / max(MMD_y_E_60), c=\"#4472C4\", label=\"E-60\", lw=3)\n",
    "\n",
    "for side in ax.spines.keys():\n",
    "    ax.spines[side].set_linewidth(3)\n",
    "ax.xaxis.set_major_locator(xmajorLocator)\n",
    "ax.yaxis.set_major_locator(ymajorLocator)\n",
    "\n",
    "ax.tick_params(\n",
    "    which=\"both\",\n",
    "    direction=\"in\",\n",
    "    colors=\"black\",\n",
    "    bottom=True,\n",
    "    top=False,\n",
    "    left=True,\n",
    "    right=False,\n",
    "    pad=15,\n",
    ")\n",
    "ax.tick_params(which=\"major\", direction=\"in\", length=15, labelsize=16, width=3)\n",
    "\n",
    "\n",
    "plt.xlim(0, 80000)\n",
    "plt.legend(fontsize=20)\n",
    "plt.xlabel(\"M (g/mol)\", size=20)\n",
    "plt.ylabel(\"Mole fraction\", size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e0uCMi7Udzb9"
   },
   "source": [
    "# Polymer Ensembles Similarity Function via Earth Mover's Distance\n",
    "\n",
    "Use the earth mover's distance (EMD) to compute pairwise similarity scores between polymer ensembles.\n",
    "\n",
    "\n",
    "$ EMD (P,Q)= \\min_{F}\\sum_{i=1}^{m}\\sum_{j=1}^{n}f_{i,j}d_{i,j}$\n",
    "\n",
    "$ {\\rm subject\\ to\\ } f_{i,j} \\geq 0, {\\rm\\ for\\ any\\ } \\ 1 \\leq i \\leq m, 1 \\leq j \\leq n $\n",
    "\n",
    "$     \\sum_{j=1}^{n}f_{i,j} =w_{pi}, {\\rm for\\ any} \\ 1 \\leq i \\leq m $\n",
    "\n",
    "$     \\sum_{i=1}^{m}f_{i,j} =w_{qj}, {\\rm for\\ any} \\ 1 \\leq j \\leq n $\n",
    "\n",
    "$     \\sum_{i=1}^{m}\\sum_{j=1}^{n}f_{i,j} = \\sum_{i}^{m} w_{pi} = \\sum_{j}^{n} w_{qj} = 1 $\n",
    "\n",
    "\n",
    "One polymer ensemble is defined as $P = \\{(p_1,w_{p_1}) ,(p_2,w_{p_2}),...,(p_i,w_{p_i}),...,(p_m,w_{p_m})\\}$ has $m$ types of polymer chains, where $p_i$ represents a type of polymer chain and  $w_{p_i}>0$ is its corresponding weight, which can be the mole fraction of this polymer chain in the polymer ensemble. Similarly, the second ensemble $Q = {(q_1,w_{q_1}),(q_2,w_{q_2}),...,(q_j,w_{q_j}),...,(q_n,w_{q_n})}$ has $n$ types of polymer chains. The sums of the weights for $P$ and $Q$ are both normalized and equal to one.\n",
    "\n",
    "The EMD linear optimization code  refers to [Transportation Networks, ND Pyomo Notebook](https://jckantor.github.io/ND-Pyomo-Cookbook/notebooks/03.01-Transportation-Networks.html)\n",
    "\n",
    "Once $EMD (P,Q)$ is calculated, the similarity score between $P$ and $Q$ is\n",
    "\n",
    "$S(P,Q) = 1- EMD (P,Q)$\n",
    "\n",
    "\n",
    "The distance ($d_{i,j}$) between one polymer molecule $p_i$ and another polymer molecule $q_j$ is calculated through graph edit distance. This part of the code builds on [GLAMOUR](https://github.com/learningmatter-mit/GLAMOUR) and networkx function [graph_edit_distance](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.similarity.graph_edit_distance.html).\n",
    "\n",
    "$d(g_1, g_2) = 1- \\exp(-\\frac{\\alpha \\cdot GED(g_1, g_2)}{(N_1 + N_2)/2})$\n",
    "\n",
    "where $g_i$ is the graph representation of one polymer molecule, $N_i$ is the number of nodes in graph $g_i$, $\\alpha$ is a tunable parameter with the default value being 1. [Reference1](https://arxiv.org/abs/1808.05689), [Reference2](https://doi.org/10.1021/acs.macromol.3c00761)\n",
    "\n",
    "\n",
    "Since calculating the GED is computationally expensive, an analytic approach along with the knowledge that all these MMDs are about linear polystyrenes is used here.\n",
    "To evaluate $d_{i,j}$, a polymer graph representation is needed for every single value of measured molecular mass ($M_i$) in the MMDs. Since one of the end groups (*H) is implicit, the number of nodes can be computed as\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?export=view&id=1cWPWri7vla-yUi6z2H6ZXb3TQmsiJ7Hy\"  width=\"50%\">\n",
    "\n",
    "$N_i=dp_i+1$\n",
    "\n",
    "where $dp_i$ is the degree of polymerization computed from $M_i$. The value of 1 corresponds to the other end group, sec-butyl group. Since the polystyrene is linear, adding one repeat unit node means also adding one edge in the graph representation. GED between $M_i$ and $M_j$ can then be computed as two times the difference of the degree of polymerization. For nonlinear polymer, this equation may be also a suitable approximation approach if only the MMDs are known.\n",
    "\n",
    "$GED(M_i,M_j)=2\\cdot |dp_i-dp_j |$\n",
    "\n",
    "Then,\n",
    "\n",
    "$d_{i,j} (M_i,M_j )=1-\\exp(⁡{-\\frac{2 \\cdot |dp_i-dp_j |}{((dp_i+1)+(dp_j+1))/2}})$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VVUHsRD6IjrF"
   },
   "source": [
    "**Notes**\n",
    "\n",
    "Since Example 4 directly loads the raw data of polymer molecular mass distributions, the preprocessing and data loading of Example 4 is different from [Example 1](https://github.com/olsenlabmit/Polymer-Ensemble-Similarity/blob/main/notebook/Example1_Two_Component_Polymer_Ensemble_Colab.ipynb), [Example 2](https://github.com/olsenlabmit/Polymer-Ensemble-Similarity/blob/main/notebook/Example2_First_order_Markov_Copolymer_Ensemble_onehot_Colab.ipynb) and [Example 3](https://github.com/olsenlabmit/Polymer-Ensemble-Similarity/blob/main/notebook/Example3_Nonlinear_Star_Polymer_Ensemble_Colab.ipynb).\n",
    "\n",
    "In Example 4, after the data loading, $w_i$, $w_j$ and graph representations are all stored in the **.npy** format, while in Example 1, Example 2 and Example 3, $w_i$, $w_j$ and graph representations are all stored in ***.json** format.\n",
    "\n",
    "Therefore, the data loading in the function **Similarity_Score_EMD_Calculation** is differnt from Example 1, Example 2 and Example 3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eC6ocqMUwYP3"
   },
   "outputs": [],
   "source": [
    "## The Approximation of $d_{i,j}$\n",
    "\n",
    "\n",
    "def distribution_approximate_distance(x1, x2, alpha=1):\n",
    "    end_group_mass = 57.070425287999996  # mass of sec-butyl end group\n",
    "    repeat_unit_mass = 104.062600256  # mass of styrene repeat unit\n",
    "\n",
    "    dp1 = (x1 - end_group_mass) / repeat_unit_mass\n",
    "    dp2 = (x2 - end_group_mass) / repeat_unit_mass\n",
    "\n",
    "    gap_dp = abs(dp1 - dp2)\n",
    "\n",
    "    GED = gap_dp * 2\n",
    "\n",
    "    NGED = GED / ((dp1 + 1 + dp2 + 1) / 2)\n",
    "\n",
    "    distance_score = 1 - np.exp(-alpha * NGED)\n",
    "\n",
    "    return distance_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VkvicUCoMTFZ"
   },
   "outputs": [],
   "source": [
    "def Similarity_Score_EMD_Calculation(\n",
    "    ensembleP=None,\n",
    "    ensembleP_weight=None,\n",
    "    ensembleQ=None,\n",
    "    ensembleQ_weight=None,\n",
    "    alpha=1,\n",
    "    print_details=False,\n",
    "):\n",
    "    \"\"\"\n",
    "    Pairwise Similarity calculation between two polymer ensembles using EMD\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ensembleP: array-like of shape (m graphs), Ensemble P\n",
    "    ensembleP_weight: array-like of shape (m weights), Ensemble P weight\n",
    "    ensembleQ: array-like of shape (n graphs), Ensemble Q\n",
    "    ensembleQ_weight: array-like of shape (n graphs), Ensemble Q weight\n",
    "    alpha: float, a tuning parameter for d_{i,j} calculation, default is 1\n",
    "    print_details: bool, print the details of EMD calculation, default is False\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float, pairwise similarity score between two polymer ensembles, range from 0 to 1.\n",
    "    0 means two polymer ensembles are totally different.\n",
    "    1 means two polymer ensembles are identical.\n",
    "    The larger the similarity score, the more similar between two polymer ensembles.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # Data loading w_i, w_j, d_ij\n",
    "\n",
    "    ## load the weight w_i of Ensemble P\n",
    "    Demand = {}\n",
    "\n",
    "    ensembleP_number = len(ensembleP_weight)\n",
    "\n",
    "    ensembleP_weight_sum = sum(ensembleP_weight)\n",
    "\n",
    "    for i in range(0, ensembleP_number):\n",
    "        Demand[\"P\" + str(i + 1)] = ensembleP_weight[i] / ensembleP_weight_sum\n",
    "\n",
    "    ## load the weight w_j of Ensemble Q\n",
    "    Supply = {}\n",
    "\n",
    "    ensembleQ_number = len(ensembleQ_weight)\n",
    "\n",
    "    ensembleQ_weight_sum = sum(ensembleQ_weight)\n",
    "\n",
    "    for j in range(0, ensembleQ_number):\n",
    "        Supply[\"Q\" + str(j + 1)] = ensembleQ_weight[j] / ensembleQ_weight_sum\n",
    "\n",
    "    ## load the distance matrix D = [d_ij]\n",
    "    D = {}\n",
    "\n",
    "    for i in range(0, ensembleP_number):\n",
    "        for j in range(0, ensembleQ_number):\n",
    "            D[(\"P\" + str(i + 1), \"Q\" + str(j + 1))] = distribution_approximate_distance(\n",
    "                ensembleP[i], ensembleQ[j], alpha\n",
    "            )\n",
    "\n",
    "    # The following are optimization steps\n",
    "    ## Step 0: Create an instance of the model\n",
    "    model = ConcreteModel()\n",
    "    model.dual = Suffix(direction=Suffix.IMPORT)\n",
    "\n",
    "    ## Step 1: Define index sets\n",
    "    CUS = list(Demand.keys())\n",
    "    SRC = list(Supply.keys())\n",
    "\n",
    "    ## Step 2: Define the decision\n",
    "    model.x = Var(CUS, SRC, domain=NonNegativeReals)\n",
    "\n",
    "    ## Step 3: Define the objective\n",
    "    model.Cost = Objective(\n",
    "        expr=sum([D[c, s] * model.x[c, s] for c in CUS for s in SRC]), sense=minimize\n",
    "    )\n",
    "\n",
    "    ## Step 4: Add constraints\n",
    "    model.src = ConstraintList()\n",
    "    for s in SRC:\n",
    "        model.src.add(sum([model.x[c, s] for c in CUS]) <= Supply[s])\n",
    "\n",
    "    model.dmd = ConstraintList()\n",
    "    for c in CUS:\n",
    "        model.dmd.add(sum([model.x[c, s] for s in SRC]) == Demand[c])\n",
    "\n",
    "    ## Step 5: Call the cbc solver to solve this EMD problem\n",
    "    results = SolverFactory(\"cbc\").solve(model)\n",
    "\n",
    "    ## Step 6: Return EMD result\n",
    "    if \"ok\" == str(results.Solver.status):\n",
    "        if print_details == True:\n",
    "            for s in SRC:\n",
    "                for c in CUS:\n",
    "                    if model.x[c, s]() > 0:\n",
    "                        print(\n",
    "                            \"Ship from \",\n",
    "                            s,\n",
    "                            \" to \",\n",
    "                            c,\n",
    "                            \":\",\n",
    "                            model.x[c, s](),\n",
    "                            \"*\",\n",
    "                            D[c, s],\n",
    "                        )\n",
    "        return 1 - model.Cost()\n",
    "\n",
    "    else:\n",
    "        print(\"No Valid Solution Found\")\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lpFPLOC7d3DI"
   },
   "source": [
    "# Calculation of $S({\\rm C-40 , C-60})$\n",
    "\n",
    "This step will take about 30 min."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mYANYopl2mv8"
   },
   "outputs": [],
   "source": [
    "Similarity_Score = Similarity_Score_EMD_Calculation(\n",
    "    ensembleP=MMD_x_C_40,\n",
    "    ensembleP_weight=MMD_y_C_40,\n",
    "    ensembleQ=MMD_x_C_60,\n",
    "    ensembleQ_weight=MMD_y_C_60,\n",
    "    alpha=1,\n",
    "    print_details=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_Z2phPpt1hr5",
    "outputId": "1c8089d8-bef8-4816-f15e-1caaf10af667"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similarity Score between C-40 and C-60 = 0.85\n"
     ]
    }
   ],
   "source": [
    "print(\"Similarity Score between C-40 and C-60 = %.2f\" % Similarity_Score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N2MQ6BPDROGR"
   },
   "source": [
    "# Calculation of All the Pairwise S\n",
    "\n",
    "This step will take a long time. If users do not have the time to run the code to calculate all the pairwise S but still want to see the results, users can directly skip this following step and can load the results that are stored. If the users want to run the code to calculate all the pairwise S, uncomment the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dYATWiF1QShA"
   },
   "outputs": [],
   "source": [
    "# Users can uncomment the following to calculate all the pairwise\n",
    "# similarity scores between polymer ensembles.\n",
    "# This step will take a long time.\n",
    "\n",
    "\n",
    "# Example4_Ensembles = [MMD_x_C_40,\n",
    "#                      MMD_x_C_60,\n",
    "#                      MMD_x_L_40,\n",
    "#                      MMD_x_L_60,\n",
    "#                      MMD_x_E_40,\n",
    "#                      MMD_x_E_60]\n",
    "\n",
    "# Example4_Ensembles_weights = [MMD_y_C_40,\n",
    "#                              MMD_y_C_60,\n",
    "#                              MMD_y_L_40,\n",
    "#                              MMD_y_L_60,\n",
    "#                              MMD_y_E_40,\n",
    "#                              MMD_y_E_60]\n",
    "\n",
    "# Example4_Ensembles_names = [\"C-40\",\n",
    "#                           \"C-60\",\n",
    "#                           \"L-40\",\n",
    "#                           \"L-60\",\n",
    "#                           \"E-40\",\n",
    "#                           \"E-60\"]\n",
    "\n",
    "# S_Matrix = np.ones([6,6])\n",
    "\n",
    "# for i in range(0,5):\n",
    "\n",
    "#    for j in range(i+1,6):\n",
    "#\n",
    "#        S_ij = Similarity_Score_EMD_Calculation(\n",
    "#                        ensembleP=Example4_Ensembles[i],\n",
    "#                        ensembleP_weight=Example4_Ensembles_weights[i],\n",
    "#                        ensembleQ=Example4_Ensembles[j],\n",
    "#                        ensembleQ_weight=Example4_Ensembles_weights[j],\n",
    "#                        alpha=1,\n",
    "#                        print_details=False,\n",
    "#                      )\n",
    "#\n",
    "#        print(f\"S({Example4_Ensembles_names[i]},{Example4_Ensembles_names[j]})=\",\n",
    "#              S_ij)\n",
    "#        print('\\n')\n",
    "#\n",
    "#        S_Matrix[i,j] = S_ij\n",
    "\n",
    "#        S_Matrix[j,i] = S_ij"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sExxrLwhT8HD"
   },
   "source": [
    "## Load the  Stored Results of Similarity Matrix\n",
    "\n",
    "Similarity Matrix have been calculated and the results were stored.\n",
    "\n",
    "Users can directly load the Similarity Matrix if users want to see the results but do not have time to run the previous step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EKuV2K4fRmZB"
   },
   "outputs": [],
   "source": [
    "S_Matrix = np.load(\"./dataset/Example4/Example4_S_matrix.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JNGc-SNbU6VF"
   },
   "source": [
    "# Visualize Similarity Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 599
    },
    "id": "jzQl65HsTGFR",
    "outputId": "58d530dd-3bc8-493a-b124-dcc07bfb1303"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x660 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6.6), sharey=True)\n",
    "\n",
    "x_labels = [\"C-40\", \"C-60\", \"L-40\", \"L-60\", \"E-40\", \"E-60\"]\n",
    "y_labels = [\"C-40\", \"C-60\", \"L-40\", \"L-60\", \"E-40\", \"E-60\"]\n",
    "\n",
    "sns.heatmap(\n",
    "    S_Matrix,\n",
    "    linewidth=0.3,\n",
    "    annot=True,\n",
    "    fmt=\".2f\",\n",
    "    cmap=\"Blues\",\n",
    "    cbar_kws={\"label\": \"Similarity Score\"},\n",
    ")\n",
    "ax.set_title(r\"Pairwise Similarity Score for Polymer MMDs\", fontsize=16)\n",
    "ax.set_xticklabels(x_labels, rotation=0, fontsize=15, horizontalalignment=\"center\")\n",
    "ax.set_yticklabels(y_labels, rotation=360, fontsize=15, horizontalalignment=\"right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "01OQo_UsVFYg"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}