Parkinson's_Disease_Physionet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Final - Parkinson's Disease - Physionet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DhilipSanjay/Human-Biomechanic-Analysis/blob/main/Parkinson's_Disease_Physionet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18N8mGt7O8VE"
      },
      "source": [
        "# Human Biomechanic Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqbL6IWgaQ5p"
      },
      "source": [
        "## Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9hvphHtdOeQ7"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import cohen_kappa_score\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from google.colab import drive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRyUIR3JMzpm"
      },
      "source": [
        "## Mount Drive & Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_SSCedlPkk9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7e1eadf-97d4-4309-c1a8-8fc09b2d3a4e"
      },
      "source": [
        "drive.mount('/content/drive')\n",
        "dataset_dir = \"/content/drive/MyDrive/College Related/Project Work 1 - Sem 7/Dataset/\"\n",
        "dataset_zip = dataset_dir + \"gait-in-parkinsons-disease-1.0.0.zip\"\n",
        "\n",
        "!unzip -q \"$dataset_zip\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFz5KNkbn1aG"
      },
      "source": [
        "## Convert to CSV format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwzpX772xlpe"
      },
      "source": [
        "!rm -r CSV\n",
        "!rm -r Final"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juvXBryVulPi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68144649-5b50-4632-e595-cc91a0718264"
      },
      "source": [
        "!mv gait-in-parkinsons-disease-1.0.0 Physionet\n",
        "len(os.listdir('Physionet'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mv: cannot stat 'gait-in-parkinsons-disease-1.0.0': No such file or directory\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "313"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N67tA4Ingi0Z"
      },
      "source": [
        "features = ['Time', 'L1' , 'L2', 'L3', 'L4', 'L5', 'L6', 'L7', 'L8', \n",
        "            'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', \n",
        "            'Total_Force_Left', 'Total_Force_Right']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAYQK53fsUqi"
      },
      "source": [
        "!mkdir CSV\n",
        "\n",
        "# reading given csv file \n",
        "# and creating dataframe\n",
        "for name in os.listdir('Physionet'):\n",
        "  if 'Co' in name or 'Pt' in name:\n",
        "    # print(name)\n",
        "    df = pd.read_csv('Physionet/' + name, header = None, sep='\\t')\n",
        "      \n",
        "    # adding column headings\n",
        "    df.columns = features\n",
        "      \n",
        "    # store dataframe into csv file\n",
        "    name = 'CSV/' + name.split('.')[0]+'.csv'\n",
        "    # print(name)\n",
        "    df.to_csv(name, index = None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6y6TUUmLUVoY"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "YP5igmemhGzc",
        "outputId": "9d666eb4-9775-46aa-bb53-a3664d38ad97"
      },
      "source": [
        "pd_subject = 'GaPt03_01'\n",
        "control_subject = 'GaCo01_01'\n",
        "\n",
        "pd_df = pd.read_csv('CSV/' + pd_subject + '.csv').head(1001)\n",
        "control_df = pd.read_csv('CSV/' + control_subject + '.csv').head(1001)\n",
        "print(pd_df.shape)\n",
        "print(control_df.shape)\n",
        "\n",
        "nrows = 1\n",
        "ncols = 2\n",
        "\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(ncols * 5, nrows * 5)\n",
        "\n",
        "for i, fname in enumerate(['Total_Force_Left', 'Total_Force_Right']):\n",
        "  plt.subplot(nrows, ncols, i+1)\n",
        "  plt.tight_layout()\n",
        "  plt.title(\"Time vs \" + fname)\n",
        "  plt.plot(control_df.Time, control_df[fname])\n",
        "  plt.plot(pd_df.Time, pd_df[fname])\n",
        "  plt.xlabel('Time (in seconds)')\n",
        "\n",
        "plt.legend([\"Control\", \"PD\",]) \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1001, 19)\n",
            "(1001, 19)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZebQKk0nCiV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "d0c82485-77d7-4472-a549-1cfdbdcad856"
      },
      "source": [
        "nrows = 2\n",
        "ncols = 4\n",
        "subjects = ['GaCo03_01', 'GaPt06_01', 'GaPt04_01', 'GaPt03_01']\n",
        "category = ['Healthy Control', 'PD Severity 2', 'PD Severity 2.5', 'PD Severity 3']\n",
        "colors = ['blue', '#FFD600', 'orange', 'red']\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(ncols * 4, nrows * 4)\n",
        "\n",
        "i = 0\n",
        "for sub in subjects:\n",
        "  df = pd.read_csv('CSV/' + sub + '.csv').head(1001)\n",
        "  i += 1\n",
        "  for j, fname in enumerate(['Total_Force_Left', 'Total_Force_Right']):\n",
        "    plt.subplot(nrows, ncols, i + (j*4))\n",
        "    plt.tight_layout()\n",
        "    plt.plot(df.Time, df[fname], color=colors[i-1])\n",
        "    plt.xlabel('Time (in seconds)')\n",
        "    plt.ylabel(fname)\n",
        "    plt.title(category[i-1])\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e90hkv2NiAgYGur0XkUhkTqiNgSwEDeCO7Ce0yNsCOKvJcw3w6hbr+Tjw8YC7FomkwxiojEHvom7vSSQS6cTEBCxYEM8FIrMU6sBp1mrTGHOtMaZif70JrfsGbbX5eWPMlDHmfvRq1VPsz2ZjzH3GmGng8/a5kchBxa23wsKF8JnPdHtPIpFI4cw8Cr9ZBHs+3O09iUQiBxr3fhS+tBj23NX5uZFIpHvs3q0n/y99KTz0ULf3JrKf0O0MnL8Evm1ve7faFJGLROQWEbll+/btBexuJLIvQ0OwaRN897vFbudHP9LlF79Y7HYikUgbzAyMXKVXsItk/Ie63P2RYrcTiUQOPh7+mi43x/FlPiIix4rI7YmfYRH5OxFZISLXicg9drncPl9E5P02iuIOETmt269hXjAxAY88svd9W7fCySfrVdW54IEHdPmZz8Dhh8/NNiP7PV0TcETkzUAFCOYniJ0aIt3giitg82Z45zuL3c4vfqHLrU37lEQiBWEqsO0SGP5qt/dk/2DHe2DL82Dka8VuZ/Jme6Na7HYikcjBx7i9LrrnF93dj0gujDG/McacYow5BfgdYBz4GvAm4HpjzCbgevs7wLOBTfbnIuBDc7/X85CNG+HII2Fmpn7fpZfCnXfC+95X3HZrNfjYx2B0FLZsKW47kXlL0Rk4TRGRVwDPAc6y9eHQvqVmqlabkfzccAMcdxysWtXtPZl/3HmnLlesKHY7TsB5+OH2z4tEgjJxI+x8DyCwpNbtvek+07/V5e7/giV/Utx2xm/U5dTdKqJJV76uI5HIgUZlAobv1tvbf6JZOD2D3d2niA9nAfcaYx4UkecBz7D3XwH8AHgjGj3xKTvnuklElonIWtsRL9KMbdtgxw69vWePnuTfcgtcdpneVy4Xt+3nPx++8Q24/37YsGHvx6anoa+vuG1H5gVz7sARkfOANwDnG2PGEw9dBVwgIv0iciSqEv8U7c6wSUSOFJE+NOj4qrne7wOZW26BM8+El7yk23syP7n3Xl3ec0+x23nwQV3u3KnifCSyD7Wx8KU94z+xNwzUJsOuez4z+TMwgT+I1SFd58wWdeCUlgAzUN0TYN27YfJO//VE5j+j98EPngMzw93ek0g3GLoLTBXWP1eXow90e48iflwAfM7eXp0QZR4DVtvbqeIoYhRFguTr37MH3vEOOOOM+n3XXltMKfXUlIo3AO9+N1x8sd6+/HJdxhycYhkdVddV0bkYnhTdRrxZq80PAIuB62zd5ocBjDG/BL4I/Ar4DvBqY0zVBh6/BrgG+DXwRfvcSCA+/Wld/vrX3d2P+cp99+nynnuKE1ZmZjTHbOVKqFb1diSyF5UdGnq769LA603Yd6fvDrvumYdg7LtQGw273iKpWANobXjv/40vpgb3rIMH/wCmbJPFJS+y2wrwgb9nLdx/Moxe57+uyPzm9kvgkW/Blngt7KBk9+263PhCXY4HHMe2/QjGH+n8vEgQ7IXt84EvNT5m3TaZFIYYRZFgV6IHz/vfD//6r/XfL7wQHnusfgU3JOedt+99554LRx2lt++/P/w223HXXXv/Lw50Lr9cc4f+7u+6vSdtKboL1YXGmLXGmF5jzAZjzOXGmKONMYe52k1jzN8knv8uY8xRxphjjTHfTtx/tTHmGPvYu4rc54ORxx/X5Z4AF3kPNqantaRp5UoVzYu6YOHeoxNO0OW2bcVsJzKPGf6sXX457HqrQ/XbTlgIxb3HwkPPgnuPC7veXf8FvxYYvTrsekFFm/Khens6oO2u8jiYcZi4oR5gvOApuqwGEHDMlC7Hvt3+eZEDn5kRXdamu7sf85X7Pgl75rGbbWQzlPrhkDP19/FAddnVafjuH8D3nhlmfZE0PBv4uTHGniXyuIisBbBLd7bYLqbCn29+U0WO8fHOz50vbN5cv/2BD9Rvf/vbcMklevsnPyEoxsAPfrDvNt/7XnWFwNwKOMbASSfBMw+iz7QTbnbu7O5+dKDbXagi+wFOuBkZ0Y5KkfQ8+KC6bn7/9/X3xrD6ULjyrKc9TZcHu7M10oQp2w62vDTsemt7oP8EoAemApsfjS3JqmyFmYDRZrveq8vhwC3bjNHypkF7MuPycEJQSbz+Xf8JpUXQf7z+7ivg1CYS24lXxw96pmyuw1i04mfmse/BTf8Lrj6523uSn4mtMLAOFq4HJJwDZ9gK/MO/CbO+SBoupF4+BRox8XJ7++XAlYn7X2a7UZ0BDAXNv3nuc+F1r4PBAyhL6ec/hyVL4GUvq9/3utepQ+aEE2DpUrjxxrDbvO22+u1Xv7p++4QTYP166O2dWwHHlWv94iAJO59InCs99th+nVcRBZyUjIwcuGUrSedN7HCUDVc+VbSA4xw4p5yiy6IdON/4Rn2bAJOT8JGPHFwuynnHjD0Jnw5s6a0OQfkQ6NsUVsCpNuRvjAZ0hpSW6bISWOms7QYzAQueDLIwrIBT3VG/bcag7xgoL7fb9bRHJsWh0W9pVlLk4GVWwHmgq7sRjJHNMD1HFuItX6/frky0ft7+zPhWWLgOSr0wsCacA2dP4vuhWmBe2q/eC3e+o7j1zxNEZBB4FpBsEfke4Fkicg9wtv0d4GrgPmAz8FHg4mA78ld/tffvn/98sFV3lYce0rbdyW5T//t/67JUgtNOg9tvD7tNd3X2hht0ed11ekJeKmlo8saNcyvgODcQHBwizm8bzum+973u7EcKooCTkmc9SwPIk53kDhTGx2HRIr29Y0f750b2xgleT32qLosScJx4eMwxuizSgXPHHXD++fCUp9Tve+c74VWv0lKxIjLbDipmHtTynt8sgYmfh1tv1SpulS1h36TaHigthd71MHolmEBlFxU7aVj3KZC+wG4Wu+7Jn4VbJ9QFoZ7V0HcsTAUMDqtau27JOqj6jqkLUb4OHCfurXqb5g0Nf8VvfY1M3AT3nx72/xEpjlkB58Hu7kcIJnfANzbBrXOQVzAzDJs/XP99R+Dyibli4hF14AAsPCycA2cqcQI5WtAk0xi4/Q1w51tVuDuIMcaMGWNWGmOGEvftNMacZYzZZIw52xizy95vjDGvtlEUJxljbgmyE7ffXg/XFdHlhRcGWXXX2b1bW/OuWKHhk8bAscfWH9+wAR4N3MTLXVFfbi/enH02POc59cePPHJuBZxXvKJ++6ab5m673eKb39TlBz+oy+H9N+g/CjgpqFTg5pv1thPjvv51+OEPu7dPIZmaqnepiwJONpxgc+qpe/8eGud82bRJl0UKOD+zc96HHqofD8mLDD8PqDkclOz8D13WRmBnwEivymO6NNN7uzl8qQ5BeRmIVXnHvh9mvU5U6D0Seo8OJ+DUxlUMkQGobg/bOcuFLZcWw4ITAzuS7Id8wZN02XcMlJfY7XqeRDgHzpILoLwCJv7Hb32N7P4ITN4Kj72683Mj3adqcyoOBAfODlvCcP8VxW9rfCvUZuBUW6I5UkCAadEYo46bAXvSt3BDOAfOdMKiO1rQ/2Ym4bTaHdj9EMnO8uVa6nPttfA/gb9XGrntNjj99PqJ6WWXwetfX9xVxaEhLaECdcA0sm6dnig/GFAIdwLOsmXNHz/sMNgSMHS8HR//+N6/H+gZG9u3w1veorfPP79+335KFHBSkBQ7r78exsbgBS+AZzyja7sUlMnJuRVwXvMaeM97Oj9vPvDII+pKWbQIDj20uBK03bu1tHhwUL8viyyhuuOO+m1XInbfffX8tJDfVQclk/bC18JnwshXE226PTA1qGyDPhsGHLI7Um1IXSGH/h/9fTpQvsG0vXraexj0HxtuvU68WnCaLisBP5Q1G/5aWgx9J+j/OUSLb6g7cPps7k3f0VqmBf4lT07c61kL/SfCnv+GiZv91rnX+u1VyKl5HOx6sFCraOto0Il7rdLd/fFlh5009i4pfluT9nO0/DQtPxqb424wIZjaDtUJWGS/0EM6cKatU1DKsPOnYdbZyETC8bDnLph4HL79O3DPfxezvUh7Dj9cw3af9Sxtsf3KV8KaNcVs60Mfgltv1ZKm88+Hv/97uPTS8GVMjqEhzblpxStfqcsvBszacyJJq+2uWDF3eR7u9f35n2v51oEu4LgJD4DrwLYfuxqigJOCpKviV7/aW2Tej/ONUpN04BQtNlYq6ky75JIDoxTn0Udh7Vq9/YQn7Fs+GYpdu+qOykMOKfZ9uvZadY2CipfGaEc9l/MTc5I8qTwKS14Cg8/S3x86y3+dtSGgUhctZgKdkJuauj/Ky9QR0rMRdl0W5sM7+TMor9Z19h2j2T0mwGTSdc3qP0mX0wFt9rMOnEUqhEC4zlzVnVoydci/wMpLYPELQUoq4vgKOLPC0yJY/GK9/cAZMHqN33odrmStuiOs4ykSnqrNbVl8jAo5E3M8oD/2Pdge8Eq9y76ZGa4LCEUxYQWcgXWw8HAYfaDY7RWB2+fBw3W5cANURmA6wORsehcsegIsOgqGAncsnN1G0oHzc9h6pS5vfV0x24tk44gjNPx1ooB8qKttV8l3v1tzYRxFnZQOD9cdOM3YtAlWr4a77w63zT17oKcHFi5s/vjy5fq/nZoKt81OPO95KijNpYDzy8ANM5oxNbX3uawrL7juOujvh8WLowNnvuMEnGOP1QntXXfVHzsQJrOTk/rZXLSoeLHxscfqtw8EMfeRR9RFCRowfPvtxQhTu3fXBZxDDy3OgWOMHuMvfKH+fv/9On5NTKhztbd37tybByzVHVBeBSv/EShre2ffDkyuxMZ1LQrlwKmNAEYdOFKCla+Hmfth6JP+667u0SwZEc2TYQZmHvBfb80OLIvOBUowETCnIingLHACzl2tn5+F6k4or9T/yaHvhtICu63B+nbzUhtVIUjKsORP6/cPf8lvvY6Zh9XdAzB1R/vnRrrLrIBztC7H5/gk5ntnwXVPC7e+mcSJxFDAiVQznPtjYDUsOmJ+lqCNWwvt4BG6XLBal1MBJipTu6BvhYo4RWXguPd7ybGw/QbY+i393VSgVi1mm5H0FGnVbjXhKsqRMjqqk/h2HHZY2Ingnj1aPuXyhBo53AqvyW5VReBa34Lm8CxZMneTti98AU48sZ5HUwTDw7Bgwd7lIBfbXO+j7XfjqlXRgTPfcRlVv/d76kS4/vr6Y8lOPfOVqSkVG4t2dsDek/+i8mIc3/3u3o64InjkkboD59RTdXwrYpvj4/XujO59GhuDiy6qZ9aEYNcuPR6OOUYFo4cfht/YypZNm1SsKvp9O6Ax0yqK9KwC6YHD7FWkyVv91lu1DoveowCpl7T44roflW099vKLVWQYuy7AuoehZK9u9dl07qkAZVTOgdOzDnrWqLgQimQGTs9GFXJCCziNlBYFcOCM6npABaInGhh8dpiQ5+qwvpfOUTZd8KAb8cN1B1pobbdFu1aSJMWiaqAryDND9fKp4YJDtHfeBP0roXeZOlhChkAP/QqGC7LwOkbuhRtepLedA6fPjjlTO/3XP7MHepdqedbw3ZoXFBrnFHrC/9Jjd+tVKkybKkwGDpSNZOeII3T5wANh11ttIs65yViyzXcopqa0a43r8NKKtWv3vjLty5497cu2XMeUpMBSBP/HlszffruWbc2VA8cYLY0DuCVMznZT3MT+n/5Jl65dOtRFskMOiQLOfGfbNujrgzPP1In0t75VF+jmu4BjjDpw+vvnRmycKwHn9tu1JPeoo4rbBuhYu9Ke/5x5pi6LyPeZnISBAb3tBJwLL4SPfhT+LmDzDfeerFunFxYefrjuDn3iE7W0eb4f812lYk+Sy7ZGbcB+GU97fhkbKyyUl2oZTjXAyTjUxRDXGUlKsPAZMH6Dv9WsNqT7C3UBZyZAuVMtsc89hwXOA0qUIoloDk4wAWeXBgw3UhrUtuI+JAUcR/+xWl7m+z66Y23B6YDAdEGlE5EwOAfOwHpdJoNni2Z34qrxRKATgJlhWH4KlBfAUIG2+8qYthDf8Cf62V+wFqa2hXF91KrwrRPg+mf4r6sVD30ZvmFPXJc8Efrs2NsfUMCpjEPPIKx6mgZl7yiga41z4Bx+ASzepBdCTv+A3hcqyyeSn8MO0+XDAS+cQD3c97LL6vc9/elht5FkxH7Xd3LgLFlSf24InParlGcAACAASURBVAOnFe6KcRETqLe8Rce266+HT3xCw16fZJsqFCngPP/58Ad/oOciN95YF1f+9V/1SnURJNvs7txZF20+9rG6+2nVqlhCNd8ZGdHP8FmJqIoX2YsY830y69qiL1hwYAk4yTG+qDGnVtOxxTljnvhEXX7sY3UXzt/8jQb0+zI5qe8RaAnVjh31jn6/+pW+jx/6kP/758bNdes0F2nLFi2j6unR7+XVq8NebDjocAG7zmlRXqE/vgJONRGu27MqnIDT6MABWPh0zTypeB4I1YQDp7xKBYYQ7g0n4JSXakByIQ4c+6EfOAPGf1wXurzWPVwXtJJIoBKqRgGnZz2Y8boolXvd9hjpPQwWPDlcl7JIMTgBZ6EVcKbmUMAZS1zlDDXZnhmCvuWw9MRiuxKN3q+OkjVn6+8DazUjLETpkQtDnnhUhaLQVMbghhfWf39OQmR1As50gO+M6gSUB2ClnRwNBwqmT+IEnL6V8NzfwotGYdXv6X2humlF8uMEj1HP76xGXCvWlSvV3fOVr+iJ6aWX6v179rT801y4/e8k4CxaFPa1Dg21F3AWLVJHwR0FlCq/y3ZFPduOccmW6UUKOFdeCT/6kbajd84iJxz9+78Xs81ke3AX+gnwkpfUby9dGtuIz3dcGeTGjfX7XmxzIIvsBjQXTFo3dTdKqB4t0O2aFBmatb1+8EF/h+fEhArGSYfln/yJLj/8YZiehv/+b/iv//IXkZMCzurVKh5t367i/5496sa5+GL4l3/x244T1dauVSfsvfeqGLVxo4bQr1kTBRwvag2OFoDeTf4ttJPdkcori3PgAPQeocuK58my624FesWj9yiYCdB6NrnPvRtUwAkVTGVGQfpBevX3wbOBSph24klBK0noEipHj82+8Bbi7IlzaSkMnK4ZOAdCQv2BiiuhWrAGdUwFGivSkCw5Cta6ekjLdlacDrtuLe7Yc/vrSs8GbKedyQBfiFOJKy8jAUPXHdt+VL/9u5/a+7E+6/oL4cCpjkPPACw+SrN1tv3Af52NzAxpyVSPFdHL/TBoXR9jUcDpOu6KZmjnxK9teeThh+uPO9kuqmTLuWo6lVAtXhzegdOuhAp0cvHZz4bbZisuuaR+ey7EjJtv1qveUA+p/sAHitlWb+++933/+/WJFmjZQxFh3IGIAk4KRkfrn+HbblOnw8kn6zg13x04LsjcOXDmQsDZtEmFh6ICoO++G665RjOwoHmW2hFH1LPW8uK+n5Lj+1e+ok7AL31p7ywc30D1iYn6uOKcPgCvfW19u7B3GWce3PG8Zo3+/4aHNZDdlQyuWaMun5kCStsPCpJCi6PvSH+XSDJct7yyXqrly6wDJyngWCXbe5+H9l5v31HaicqX2hhQBlmgJVRmHGqBcj6qI3u/d7OdqAKUUdUa1u0oDRYj4JStgFP1/BKbdTwt0zDq2jBUA13ZGLkyXJeviOIcOD2D0LcszMQ9LeMPwYJD7e2ADpzepbDid/T26L3w+A/VHROCyW2w9eq6e2ihFQsWWAFnPICVOJlDNFJAtsUuexXrhUNw5Ev3fqxvmZbGBhFwJqC8UNe36vcKKqEahp7Fe4e89i7T4zmWUHWfvj51xoQWcNyJfPIEGIoTcNI6cBYv1hP0SoAOmtC5dTnU/wfNcoHy0mwym+zAVZQDJ3mcbNmiV6vXrKmX4u3a5T+xacQYfW0XXVS/7z/+A57xjL2fNzBQdznsh0QBJwUjI/VJ+imn1AXC1au7K+B84hNwxRV+63ACjnPgTExozk9RbNmipTmtOil9//v+bsRf/EKXl1+u3/HtxnWfMdftZ6NA/2d/ptv82tfq993v2ZAh6cA56aT6/Y3lWb4dooaH1WmzcCGstw77XbvgD/9Qb69Zo2PfflwWun+TDMF19KzxDx3ex4ET6A2adbMkLL099ovVx4FTm9JA56TjpPdI7XDlixmD0kLr6rH7GqqMyjQIIb0bgZL/+k3V7nczB04AAceM7isO9dgJaMXzS2zWgWMFHICpO/3W6dj6Ihjy/JKL7I1z4JQHYNHR9dyYyjg8+IVigmcdYw9pqVPvkjCTbWN0Qt+7RAUcgG9s0iyZzf/tv36AO94GP/xjuPs/NG/FOW+ckDMR4HUky9hGCwgBf/x6WHZSPew5iZS0BC2EE6syrscVwKFnqpgWuoyqMg49DS2WRfT9GA880YvkY3AwvIDjSqQay4uKduCkKaGCcK83jYDjTvxDZl64UqW3vAVe9Sr4+tf3ftwJOKEdjskr+d/5ji7dtt/6Vl3eHrg0dvt2zb05/nj4wQ/gH/6heZjoggXRgTPfSTpwkqxb172WyuPj8Jd/Ca94hd96nLi4YIEKONB8cn5rIGdyUsBp3M7Pfw7PfGa9PC0vSaF+/Xod1ycn69tLvo7feJxbOAHHOUYdL3iBLj/ykfp9vuNsYwaOY+1aOOccvf20p/m7msbG9FgXqQs4UN/GGnvuOt+dZ10jGYLr6Fmrk3efnJPkensOg8ojYAJMxJo5cMorQPr8RKdmpWTl5dpS3Xe/a+PaMhvCCziNThYpaX6Pr+OkmbDnKC0qJgOnbAd8X7EvmZO08GlAD4xd3/ZPUmGmrcjX4QQ6kg3nwCkPwNLjdZIN8MVB+MkF9S5FIdh1G3xWYNsN+vv4Fi1BGlgPEwEsuJVRwKgDZ9mJez/2aIBOeVAXuEZ+q9soWev9wFoNTt4ZoJNb0oFTRPvtoV/Wc2ma0bfS34FjjB5bTlw5/AJdfv/ZfuttxLl8Ghk8shjxK5KdRYuKEXAGB9Xdk2TFCt1eUQ6cNCVUEKaMqlrV7XYScJYv12XI3B/nrrnkEs2AeN7z9n586VLdv9BX+G+8UZcunBng9NN1+frX6/LOQBeEHG4yuGaNlkxcemnztu2uhGo/LQmPAk4KXAZOI8ce6ycA+JCcqPscW40OHNjXGXPNNfp5+vCH828HNLdl61YVcA45ZN/t3Go7KX/fMwPz4Yd1rFm8WMX5++/XXKpDD4VnP3tvMeXXHl1Hm5VQgQp7q1bp94nrHOVbOpoUcEBbpLsQ4899TrNrzj1X/6dTHt1ZR0frgtSpp8Lv/Z7+z049Ve974hPVgdbpokSkBc0m6mXnhPDIUjB2UiYD0H8cUAuUyzKkpUjSV79PBMqH+jk3avYDUU5cEXaii7fbZFwdOJBwCwVS2puVOfWs83cOuf9HqxKqIrpQuWBq3wDmWZfWEt1G3zFhyp6SZYGRcFScgLNAg4wnHoXt/1N/fCJgON1t/6DLu/5VS5omHlHxZuH6vVuK52U20HapCitPv7IuVIRyY4wkTvIWb6rfLvXA2vNg2w/9t+E6gS17Uj3QOBSVCS0DG2xTM94fQMCp2RMP58AZWAtLjtXXsydQpz6wItHAvvcPHhZGFIz4U4QDZ+fOunCRRERP9LtZQpV8vg9uotBJwHGlTSFLmh54AH73d9V+3wy3T6HLqFx51I036iTnq1/VMgDQydXixeEt/zvtWOdaCLdiYEAn2NPTYbcfiCjgpKCVA+e443TCvGsOmzg4kgKOj/DbGGIM+35WXNj5zzwvNG3friVLrRw4TsCpVv1KOx9+WLcBcMwx2qXpq1/V37/zHc10cfiEULcS6EXqAeonn6xZWaEFnLPOgqfaDtQrVqh47RwzPt29nAMHoFSCH/4QvvnNujh93HGaAeUycSIZaeXAAU9Hy4QVWsS2cyaMgGPGm0+ge4+Eqbvzr7dZOLLr7OSd95IUcNYA5eIcOKDBvZO3NX9+6vXa46LcrMTBllD5KPXN9tsJc775QLU9KjyJPenqPx6mPZTx2fU2yYuK+FNLlFANrNfyPVdutPRELUkKhv3iKPVpUK+pwMA6/Qkx2Xb72mM/NxvOh3NvhmNeo1kyvldOJ3eo+LH22dC/Cp78ob0fX/lk3c6055Xw6d2a67J4U3gHjvs/u/DlZvQt39sFlIeKvTJfTogrZ/1Qw4x/+iq/dTdup5kDp/8QFaFCZR9F8lOEgLNtm+ZWNKMIASdtiLF7PETArxNHOgk4RYgp999fL0ebq22CdrNZuVKDqScm6iUMjpUr64JLKNz6Vqxo/zwnzu2nnaiigJOCZAZOkmOO0eXmApoGdCI5SfcRkJIhxq0EHNd1yNc558rNnICzY8feQs2PbKOE6Wm/0rTHH6+78U49te64eYq9MPfJT9afu9vjnMV9PzWWUAGccYYuTztNxXKfz3+1qqHBSQGnGevW6dKnu1fSgQPqVi3Nk1FCRD4uIttE5K7EfStE5DoRuccul9v7RUTeLyKbReQOETkt8Tcvt8+/R0ReHnQna6NAL5T66/f1hHDgTOqEHBJlQ03Su7NSS6w3yYKTYNrDflhLuDYcYv8nvi3VkyVUUlaHjG/HrNl1NxFCeo/U9vA+wlM7saI0CNS0vCwPZkb/dh8BR6C0vJ5hk5fqnr0zkvqP1zDqmmf4X7uyskh+KokSKtdK/P5PwZpzYMPztFSoFiiQ0+Wf7LlD3TcAC9fZEqpHoeYZwjmdcOAkWXS0ijvJ7k55GHtAl5teBX+6vd5yO7kd8M/zmd6lIsqiI3WbpqYdu0IEC7t9ayvgrPAXcGbDsRPiysBqOPLlsOuWcMeUa1XeSP8qFSNnAk8wI9kpQsB5/PG5FXDSllA5F0eIK/ndEnBGR1XAOe64udum45FH6pOXZqxYUZyA08mB4x4PmTUUkHkyNesu7Rw4oE6IL395bvcp6cDxObbTOHCcWOSbr5IUcDZuVGHCrfPGG7WcyYksPq9pZKTuMHSlPwCXXabL667T74GBAb8SUpdtNdDkXOKf/klzsd7yFv/ue0mRrR3uNfs4ssbGmgtS84RPAuc13Pcm4HpjzCbgevs7wLOBTfbnIuBDoIIP8DbgqcBTgLc50ScItREotwqT9SyhKtkDsTSoZSwT/9P+b1Ktt4WA03OYOi/yihazJVSJExXn4Nj+z/nW6UiWUIEKWsEcOE1KqHoP1+WMR8lG1ZVQtWgjDvlzcNx7JE0+2OVl/gLOPt3EnoiW8HmWTTRzq0X8qSZKqJY9qX7/+ufC4EadBIcoozIGJm2Z5fiWehnN4BEqHJlq/fG8uMl6b8OEx5U6+XZ0mrQWXddxqpH+Vbr0FVqmd0P/ChVwatOaq3PlEfCj5/utF+qlagPrWz+nKAcOwJLj9DU5McyXapMQY6i/F5P752TroKIbAs7QUNhMmNFR62jucOK9yh53ISb5WQWcUM6QrVt1vD7qqM7bLMKBk8y/aWTlyvBlLmkFHPfehhaQAhEFnA5UKipyNCuDdA4cqIsDc0XSgeNzbCXFgSVLtNxn+3b467+GD3xAH3Miy8OecyBXrnToofUW3r/9LZx3nmatAPzpn+rSZ4wYHq6/X6ecUr//jDPqotvhh2uYvc94n8wPamThQs3FWrfO34GTDJpuR4gw/FZi5XzAGPMjoHGkfx7g2thcATw/cf+njHITsExE1gLnAtcZY3YZY3YD17GvKJSf2ihIYxbJSqDHs4SqQWjpPxmmf5t/fQ4zCaUmB16vvZo7k1PVbVZCtcSGXpY8FcSkAwcCCzjNHDhH6NInB6ejA4f8OTizTpYm/9fSMv8SqkYHzsCTdTn6bc/1xhKqQkh2oRo8vH7/mmfCwo16eyyAe68yoiVTTkzZfgMgOqFfagOHH73GbxuuhKpRwFlq2+zu+YXf+p3AtODQ5o/PCjiek7fpPdoK2+X3XGstvNtvqAsjeRm6U/OBFh3R+jl9K2Bmj58jalYYbBBXltgT5ZFAVvV2Dhzwfy8i/oQWcIzpXEIFYV047mS4WbhtkpACTtoMnNBiipsINcsYKmqbjh076u6BZhRRQrVrl7a773S12gk8UcCZn7QKqnW4PJXf/GZug6qTbhgfcTLpwBFRweHmm+FjH4PXvlbLp9y2fEXQZBfAJzxBb192mYYkgwYln3223vYZI0ZG6gLO4KAK9yMj+vpcC+7nPjecgNPX1/55oQScZk6fJO4Y9clSm+cOnGasNsY4ZeQxwJ0BrAeSs/ot9r5W9++DiFwkIreIyC3b04asNXPgSAl6VgcooUocIH1Hw/T9OoHyoVUJVY8TcHJO9GpNHCfSoyKObwlVowOn5zANMQ4xQNfG9nWy9J+gSx/HSacQY7ftXOse33s9ScqBSqjKCQGn7ygN5p7x7AgTS6iKoTqhE/pSWb8UT/xn2Phi7UjlymxC5NM4V0pSwBk8Qt0Tq56quTg//SuoeqTuzzpwGpxrg0do9sr2G/OvG2DKOXBaTBxDiQaVkXor9GRQMsC4p/g8er8GGJfbXAHqsxM3n/KjZHezJM695Ou2crTMwIkCzn5DaAFn927NEWgl4LgrwvfeG26baa9mLl2qobtz6cBxE5wDRcBp54QpKgNn5crO4lwUcOY3ncogzz4b/v3f9RgMVSZXqehx9cQnNn98elpFlhPtRaxQDhzQcfCHiaYKN9xQd/uMjvrNgfbs0XFucFBLqEol+Na39LHXvx6uvdbfGWjM3gIOqOPHvX+XXgrvfKdub/lyvwwcF0zezIGTZK4dOD4CTmMGzoGEMcYAwWRWY8xHjDGnG2NOP6TdFYQkzRw4oEHG3iVUiQOk72ig4u88aVVCteAUQGDipnzrdRk4jaG9fcfAzAPaPjovtSYlVGbKv122qVlHUsPkobxMs2SmPa4wt+u45I6XvAKOc+40FXCW1duA56U2tLeTCjQHZ/zHfgJiDDEuhmqDq+7kt8Pvf15vu4m8bygv1AWcFb+jy6FfqkgEKiacZDtT7bo1/zaSXaiSiMDy02DIs4xv4nHoGWxesgP1TBxf0WBmFHrs5/ycG9WhdPwb9XfffJ3Jba0FKEe/DfOc9rhKV7VCcWOHKLftUAJOqy5UC+x38FTgjjWR7IQWcB63x04rAefYY3X52wCuY0daAadU0ol+iE5JaQWccln3LZSY4iYo7TpuuSvIk57ZdklmZnTbzsXUjBUrdPLo09mmESfgdCIKOPObNEHkbuy4P1DzgPvshcu7764fs2Nj9ZbXZ5+tndfchD5EiLETIZyQ7bo43Xijfl7XrFFxxCfIeGhIxyURLdXaZC80nXGGimDLl/uLvJOT+j9rNQ5t3AhvfrO+3iJLqJLMlYDjhBcfAWdysrPTZ57xuC2Nwi5d37GtwGGJ522w97W6PwzNHDigOTghS6h6bbimj6gArUuoysugdyNM5+xE1aw9OUDfJqAG0x7ujcYSqh570uct4NgrzI0CDuj/YuTr+RVuM9l63bMOnJzhVu0ycErLoBqgC1XSgQOw/DUwcy9se4PHemMGTiGYirbAbsasgON5TEBdDFhzTv2+JyQy4Te+UJcjHpOu6SFA6uJHkiXH6Lp9rjpN74K+Nif65X7tHuXtwBmFXvsa+lfCH98JT3y9/r7zp37rntrWugTMEeJ9r7QooeoZVFfOnDlwooDTdUILOJ0ySwYHNbcgZNhsljyBxYvDvN60Ao57TigBx03s2l29LULAcZPXTg4cY8LmG6UVcEJMqgokCjgdSBNE7kSPUAJOUkR+yOZi/tVfwfHHq/Pmxz/W+/7jP7RLkM/nKVlCBVrGBPDHfwyHHQbf+57+7kQqn+N4zx4VTRwf/KAu3/zm+n0uiDfvuOQEt3ZCsmO+lFC5sOROAs7AgIpjPu/R1FTn1zPPuApws4aXA1cm7n+Z7UZ1BjBkS62uAc4RkeU2vPgce18YamPFOXCSJVQuo6biqT21cuCAii15y51qw/u6Ntw6wS+/p7GEqmy/qKueV1FcKZI0mTwsuUAFovHrc67bfsib/a9dmZmvgNOuhCq38GRUjCs1CDiLztXlrvflWy/EEqqiMFUtV2xGeUAnxxOPNH88C86B078KnvZ5WHsurD+//vhs2Y7Hl+PMMPQu1jLURhYfA5Uxvzyf6d31/WxF/6owJVSNIlT/Sm2N7Rv+O/l4CgHHOnCmAjhwGkuoRNSFE9KB0ywDp7xQxaKJQNuJ5GdwUEWBWqCW7u7EfkmTkH+Hr6W+kSwCTn9/GGFjaEhPwDud7IN/d5Qk7TqyONwkMaSA4wS3dg4c524PKc6lFXBKJT2Wo4AzP3HvWztBYI0t8Q3hoIO925LfbS9wX321Lt/xDl1+4Qtw5pn6Off5PDWWUP31X8N73gNvfasG/t5+u97vApt9jmPnwHGcdZaO8c95Tv0+N3blHZfSjPOO5cv9BBxXQtXb2/55c+XACTHWTE11dhTtr4jI54AbgWNFZIuIvBJ4D/AsEbkHONv+DnA1cB+wGfgocDGAMWYX8A7gZ/bn7fa+MDSW9zh61kB1m06u8tDolCnbLz1f10mrDByA3qPy55zUhvYtn4KEgJNTGDJmXwdO2Z4cVDxPAIzLkmny/i2/WJfjOTM3zCRQbj6xdh2eqjlV7VkBp8l+l5cBlfpry4oZA6p7d6Fy2+q3HY5mcjrLaiNAaW9hMuJPOweOCCw7ScudfJkVcFbA4S+GP/yOOlYcvfakatrjKvLM0L4Bxo5Dfl+XOzy68c3sgb5l7Z/jK+CYmgpNPU1OMgc3wphHd7vajL4P/R0EHJchVPE4UWnWRtyx4NB6Ry8fajN6/DYTcERgwVqYDNBBbZ4gIstE5MsicreI/FpEfldEVojIdSJyj10ut88VEXm/iGwWkTtE5LTCdsxNmHws+0ncCXS7E/sVK7on4CxYUJ9M+dA4SWpHEQ6chS1KRUHLtnp7wwo4abpBHWrHrm0Bxg/Hrl3pBBzQY8CntW+BRAGnA2kcOC73KVSns2S3p7vvVnHUHT8uM8YJKr7Cb6MDp7cX3vhGDTNOZvCEcuA0jk3NBF+fccm5GNPkuDgHTt6Lz07s6JSDtWSJ/p+nc8Z6pBVwQI/TvO+RMfNbwDHGXGiMWWuM6TXGbDDGXG6M2WmMOcsYs8kYc7YTY2z3qVcbY44yxpxkjLklsZ6PG2OOtj+fCLuTrQScDUANKjmvfNcaHDilxVqeVPEtG2oj4PSshuqufDkn1Sa5KQDlFeqYyR1kPANU596BU16i4tPU7fnW3e7/7P5PtZyDomkTYuycM3nLqFwAcqMDB2CNbWM4dm2+dddGtHyq0wAbyUatAlJu/fjC9WHcEtP28+bcHY2UenUi7iMatBNwBm1HrQkPZ2MaB07vYi2ByovrMtXb5CRz8HAY9xBwnPso2W2sGS7g2CdQulWIMYRz4LQTiQAG1oZxj80f/hP4jjHmOOBJwK+BNwHXG2M2Adfb3wGeDWyyPxcBHypsr9yEKdTEN421/kBx4KQVcJYsCS/gdMpPWLCg7tYJgXPVzKWAY0x6Bw7oMXcwOnBE5OMisk1E7krcl1kdFpGX2+ffIyIvb7atokiTgdPTo+9xqLFj61Y4+mj9LG3dWu9w9dSn1p9ztI248BV+2+W4uI5NUBdzfI7j8fF0worPuJTGCehYvFjzcvKOR2nLjdxFg7zfZVkFnLyluJWKHmfzVcCZF9TGmzsKZltRP5BvvY0CgIi6cLxzX1pk4IB1+Zh8wkirEiqwXaNyln7VmrhkZgUcTwdOs3Un6X1C/tBoMwmlFoNW2VPAaZeBU7aT07ydqNzfNWbgACyw9biVnCGstZFYPlUEptK6hArUrRFEwNmjrpJWbh9Q8cW3hKoxwDi57lKf32uZ3t3ZgVNeUBcW8uDEn2Y5PgutAyfvVaZR65BcfFT75znRxet1tCihAivgBJiAVdqIRAAD62Di4HDgiMhS4OnA5QDGmGljzB7gecAV9mlXAM+3t58HfMpevLoJWObyAYPjhJZQAk4aB043BZz57sCZmNCJbKdyAt+Sj0acA6ddCVVoAWd0VMOTV7S4sNCIz1XxginagfNJ4LyG+zKpwyKyAngb8FTgKcDbnOgzF6Rx4IAeC6EcOFu2aIjw6tUavu7Ci1/84vpz3P74fp4mJ9UZ19PkHOsP/7B+27mM5qI8x6e0M4vY4fsdMz2d7vW475y5eE0+JVRpQ5kjHpiJ5gJAnw3Sms4ZpNVMAAgl4LR04Ngv1jwun1YlVGADnXNeNW/mkiktVNHM14HTroQK/IKoaxOt/8/Sa/fft4SqjQMnbycqJyo1E+NKC9RRldtVNjovBRwR+XsR+aWI3CUinxORBSJypIjcbC9QfUFE07tFpN/+vtk+fkThO9guAwd0sj21U506PlRG6mVSrehd4ingDEFPi3FERFtYT3o6cHo7nG6WB4oTcAY36uNTOceuUft9Mnhk++fNOnA8TibbllCt1nBh45mJMtvpqlVXsAB5RPOHI4HtwCdE5DYR+ZiIDAKrbZ4fwGOAa920HkheYdhi79sLEblIRG4RkVu2582FcCfXoSa+aa6kd9uBM58FnPHx9uVTjqIEnHZuGCfuhBJw3DGSRcAJXUI1NgaPPurdWatQAccY8yOgUdbIqg6fC1xnjNlljNkNXMe+olBhpMnAgbBjx9atsH69CjiPPaYCTqkEL7RNG44/vv5c33Fjaqq1MHDUUfDpT+v+hGhRnUXA8elCBekdOJD/s5n29cylgOMjFkcBp2BMVdtZNyvB6dkISH4HTjMBoOdQ/xKqdhk4szk7Ob5Ya2PNHSFgA51zCiGtRJbyqnAOnGbvH9j9fjzfJKWdUAYaZOzrwGnVRhz8S6iaOXAAetbBTF4BZ2TedaASkfXA3wKnG2NOBMrABcC/Ae8zxhwN7AZeaf/klcBue//77POKpVaBUpsSqoHVgAnQGnsOBJzpodYOHPAr3alVNZumt0OgXnmg7gzJw4w9AWkm4Cw7WZe7b8u3bteRqVMb8WACjkCpyQnEgtX6/ed7TLUr0wI9FmaG/TqPzR96gNOADxljTgXGqF8QB7RUHMj0zzDGfMQYc7ox5vRDXIBsVkKXUA0P69XJcptxa/nycFfRjelOCdXwcLoATwjvwEkzaQot4OzYodttt+2enrAt09NO6h1FlFBdc43m0PHUZAAAIABJREFUlNx5p9dqupGBk1UdTqUaQyDluAH3vnUq/QnlwKnVVDDZsEHDkZ0D5wlP0PuuuQauvLL+/BAOnHYT9r/4Cz3OQgg4k5Ppw9V9S6jmwoGTtYQqCjgHObNtqJt8WZX67WTXw4HTKAD4OnCMSefAybON2kTrkqGeNfmFkFYiS3llAAdOmzbioPvNTD4xpF2pGqhAkteBY8Y0D6lpQLJ1F+R24LTJwAEbzp3XTTVvS6h6gAER6QEWAo8CzwS+bB9vvHDlLmh9GThLpODQnzQlVOBfRjXTpLNSI71L1EWTexttMnDAT8CZdXt0OPkL5cBpJna57Jq8ZUHTu7U7U7nDiYobe3xLqMoDzTOrFh6my/GcJabJbUDzNuKgx4Kp1t+7A5stwBZjzM329y+jgs7jrjTKLt0Vlq3AYYm/32DvC0/oEqqRkXRX0cfGtDzGl+lpzRSY6xKqNK/T4RuumaSbDpx25VOOxYvDHUtpJ/WOIkqoHrEXtdat81pNV0OM86jDHdbnrxw3MDKix2yzEqMkoRw4O3bo+LN+Paxdq2LOT38KT7INPc45p55/A2G6UKWZsM+lA8ena1NWsQMOrBKqKODsx8y2im7xRVk+NJ8Y4oSWRkGkx7eEqgLUOmTgAJUcDpzGtudJetbotqs5FPFWDpyeVcWGGINf56/aZPtuSz4Ootp4a7fTbIhx3gwcO/lu5cApLc/v7pmHJVTGmK3ApcBDqHAzBNwK7DFmNu07eRFq9gKVfXwISJmumHcnOwg4zq3hm1lSGWneWSmJdwnVcHuHzMCa/CHGFetcmysBp5nY1W8PhemcY1eaEGZQR1ap19+B09NiDHOB0j4dtdw2oPV23LHg09lsnmCMeQx4WERsixHOAn4FXAW4rNCXA+6S71XAy2ze6BnAUOJieliKyMDp5ExxOQ8hJmJpszMcoUqo0gaFQr3UKkQr8fHxdA6cgYHwDpw0YcK+rXyTuJDQtO9tEQLOo4+qqJBGvGpDNwScrOrw3KnGTUjrogvlwNli8x43bFDXza5d8NBDcOqpzZ9fZAlVklhCtS9zXUJVtMPRCflRwCmIThkq5ZyTXWMHgGYOnNqoigN5cH/XsoRqJSAFOHBsrmKeMqpWQcPllcWHGJfsB72WY0DpVEJVPiRfqRpoCVXLY861KM8rsjgHTgsXRN5jGualA8fm8z0PzadYBwwSoOQ7qLu4Vm0fLDywRpcTnqdZqUqoPEKMq1NQm2rvwOlfCdM5T8xmBZwOJ4A9BQo4vUu1Y1je0qPKaOf3wFFa4CngjLcubVroBJwH868fEiVUbRw44CcKzi9eC3xGRO4ATgHeDbwHeJaI3AOcbX8HuBq4D9gMfBS4uLC9KqIL1f4u4IQQNsbG0gs4vhOLJBMTB58DJ4uAEzoDZ3hYX1PJT4LphoCTVR2+BjhHRJbbk6Nz7H1zwuhoOkdbKAfOVnvOtH69ZtA4WhmKii6hSm6nVPI7jtOWUC1ZotvJU8Z8sJdQ+XyPRAdOwcw6OFp1G1qezwlh7BveLMQY8rtwTAcBR8oqjGTN2TFVYKZNloydQOYJMm71Py4HcOB0FOCcgJPjg24m2pdQ+eQZmbHm+TegAcmlRflLqKp79PholnsB9pjelW8wn4cZOOiE6X5jzHZjzAzwVeBpaKafU02SF6FmL1DZx5cC+xyoQd3FpkMb8cHD1aEz/Fu/7RTtwHGlV+0EnJ5FUJuGWo6yiiwOnNq0CmN5mHElVE2OdSmpCJVbwBlvLXY04t1Na6L1tvpX6vs0/Jv864f2na6g7sDxKcubRxhjbrfjwsnGmOcbY3YbY3YaY84yxmwyxpxtjNlln2uMMa82xhxljDnJGHNLYTsWOsTYTXjbEVLAcS6NtGJKiBIqY9KXMkF938YDlAumdeCEFnB2766/b+3oZgmVy8AJmauVRahrQ9FtxD8H3AgcKyJbROSVZFSH7eDzDuBn9uftbkCaC9I6cJYv189v3pbUDifgbNiwt4CzZk3z5/sKv2kdOCJ+TjJj0pccLV6sz8/TDvtA7EI1MdG6U1gjPo6sKOAUTK1Dhkp5OdRy5qcASMMb1+NKnDwFnLbZLDmcIe2ygCAh4ORw4LQSncor1QliPFL/O5VQORdKNccHvV1YNNTL63LlArUJjAYto8pbQlUbap1/A9qFioqKSJnXPf9KqNDSqTNEZKHNsnFlDd8H/sw+p/HClbug9WfA92xpeXF0KqEq9Wrb6RHPyXaWEOM8L9kJP+1CjJ2rpZLjxGXWGZNCwIH8TseKCzFu8b/qW5m/C1V1vHXHpkbKA/lfA6j400pYEYElx8Ho5vzrd9uA1q9p1oFzcAg4+y3z3YGTVcAJ4cCZnNRxMO023fPyTJQayeLA8Z3kJkmb+eOu6ocgTwlVtRqmRC65DwEEnBRTwvwYYy5s8dBZTZ5rgFe3WM/HgY8H3LXUjIykL6ECLXla3zRiOR2PPqrfdatX7z1enXZa8+f7Cr9pHTjgF8adpTwnKayk/Yw55rqEKo1QtHChupd8HDhptgN+x0MUcAqmk4Mjb16IsR+uRgGnaAcOWGEko54+mwXUyolkMziqOcJHW/4vVgFG/789OeuOzTgg+67bUfJx4Ey1Xi9Az2qgpu9lT4eOMo3U2jhwwAYke3ShKreZQJfsSXV1dzY3jalYV9L8EnCMMTeLyJeBn6MhUrcBHwG+BXxeRN5p77vc/snlwKdFZDPasfOC4neyQwkVwOAT/Mtd0rYRd6GznYSSRmYdOG0mdk7AmRlNlwWTJLUDx46PlYnsrwHal1CBX2vsynh7h1KScoASqnZi0YLVAUqoOjhw+g66Eqr9k3JZT8JDZuB0muiHdqRAejdMCAeOExbSbtM9L9TrTROoOzDQHQFn8eJwGTh5SqigHogbgixZR20oVMA5EMhSQgX+As7OnbBsmY5/ixbB3/6trnvDhubPDxFinPaY9HHguH3MKuCsXZttO25sSVPa5HuRYGoqXcc/Ef9g5rTvUXTg7Md0LKFapqJJrUNHokZmRYvehvV5Cji1Fs6eJKVF2bNlOjlwSouBsmceUMMA4DpmVR7LL+DUxvW9a9UkyEvAmd53n5P0WTvm9D35BJxym8lrablfF6q2DpyEgNN7WOvn7bNe+0Uz/0qoMMa8DXhbw933AU9p8txJ4IVzsV+z1CqdBZyBNbDnDr9tVCfTlVCBTrizih/TKUuoIKcDJ0MJFeR3r8yMquupVaeo/pUwck++dVfHYSDlSZRvCVU7Bw7AgkNh50/zrx/q7dpbZuAcXCVU+zUhy17SOHDcSXKoLBrI5sCp1bRzVRqrfDOcEJN2m6EFnLQhxqEEHNeqPa2A080SKvd3gZojMTqaXqhrQ1e7UM0H0h5fbmzxLfncuXPvUO7//E/4l39p/XzfEOMsDpwQHY6K7g7lxI40jVh7evwuEqQtoQI/ATmrgDMzo98lWYkCTsF0akOdnOxmWq/Nd9hHtPAtoXLr7W39nNLi+mQ7LZ2ELBEtvfEScBoO4l7bjnfG4wpwbbz1ewdQtl8UtRyTBzPT/v/cYxX8XGVl4wU6cIZad6CC/Mf0rIAzvxw484JOJVQAC9Zo++08JXtQLwtK48CBfI4J9zftBBy3fS8Bp4OI6ESLSs6JTWUUym0+n/2r8pdQZc3AyfsaZrfVQcCZylkG6pht7d4qA8ceCwdBF6r9nsHBMOICpHPghBRwsjpw3Emzz7azikYhBZy0JVQLF4Z7T8fHdaKS5ir44GCYUjHQ9fT1QW+b860kITr4NLJ7d71sx4Mo4HQgbQZOqM9So4DTiblqIw5z16Lap7RpcjKdkJzcVtFdqMCv+15WAQfqJWtZiAJOwXTMULGT3aw5OM6BQ8MXUmkZ0OPRStw5e9o4Q0qLsnddMh1KqKAefpsV02Kfe4/Q5cwD2dc5u+7x1u+d26YsyJeBw0z7/3PZftnn+Z/Uxtrvd9mj3XxtT+sOVJD/mHbHVBRwwlNLIeAMrFGhZypn3OBMh1wXh8+EO0sJVR4Bp5rRgZPXvdKpfKxvGczkDRnPkIFT6quL9rm2NdF+W/2HarnctEdGyWwXqlb5afZ4iw6c7hOqtfb0tP6kFXBCbDNPiLHvtrOKRiEzcLrhwHFXtNM4JAYHdbt5rkw3knZS7yhCwNm1K114cweigNOBtDks3RJwfLOzsogD3SihysrERLYyRd/MmDSlWuA37uURcPK8pijgFMxsBk6bLlQQzoEjYrsvFenAWZTDgdPBiQQqWOQKdLYHcWNXpPJqFYymc5YiQGcHDmgZVe4Sqjb/57L9UsjTSctMtt/vnkOhsi1fkGx1T0EOHHtS2s45FMmHqbbvQgXqwAGYzNEJDjrnujic+FLJ48BJUULlOjvN5DiZcN2hihZwKh1Kj3qXajlaNcdVmUqGbKFSb75uXY40JVQAkxlD75NUxnVslxZTl1JZ96ES6Gp9JD+hBJy0obMhXDCOg82Bk7b71cCAPjdEzr6b4KUVcCCMeDQ6mi1/xjcstRlpu291IAo4bTAmnXMPwgo4WZxVToDI+3maawdO0d2hsogd4J8Zk/Z/5+OUyiPg5NlWFHAKppMDJ7eA0yIDB7SMyreEqtHZk8SVUGUZgDpl4IANdPZx4DQcxCKw4HSYuCn7OmfXXaSAM0P7//MCPW7yCDhpOlxRyZeDUxvq4MCxJ0q1jBOq2Q5oGeyUkXSYlBk44CHguFKXDifKPiVUzrVTWBeqlA6cnhAOnDbjik9npWqGEirxFHA6lWuFEHA6iUSg71cUcLpPX19YAaeTwFBECVWWDBzwe73dCjGuVtXhlMaBs3BhvaWwL1kEHPdaQ7iNxsa668BxLZazduhpQhRw2jA2pnkiaRwxoT5Lo6PpSgIdbszK+3maKwfO/lpC5SPgZMnA8em+Fx04BwidMnBcGGzWiXQ7p4xPeUxaBw7V+oQ7DZ26UIEtofJw4DTL51/4NJi8NWeJEzbEuMPJVXlpMSHGYDt+5XXgtBlAZgOeM06salO67rZdqDwFnHbCUyQfaTNwACZyCjizWSUdPi9OwMlTQlUZ1sl8qZ1zzW6/kuPErDqmZUXt1g8BSqhSOHAgu4BTm9Gf1CVUPcU6cPrtifR0zjwft41OrycKOPsHoRw4acUUZ4efS9HIEbKEaq4dOG5iktaBk/wbH/I4cEIION0uoXKOixhiXCw77fdMGkdMqM9SWiebw9e5l9WB45MXA/tfCZXPRYKsJVRz4cDx+R6JAk7BzDpwWryZ3g6cJgdjz1wIOGQro0qVgbMivwNH+punmC96DlCBoU9kXy/YttYd1OHSknwCUacQY8gn4Bij+93RgQNUswo4dkLZrguVe49NxhOvWZEvCjjBqVU6l1DNOnAez7eNtA6cWYdMjhPz6aH2+TdQn+xXc5yYVcbSlR95l1B1CP/tyyngzObFpBVwelXcy4Mx1knUbkz3ENMcnf5XoO9ZNQo4XSe0gNNpYiTinymR3GZ/v7YDTkM3SqjKZZ2A+IoaeQScEGVb3RRwullClbU8rw1RwGnDLjt/mCsHTq2WPgzc4Sv8Zg3i9XGRwNx1oUrLfCmhSusqig6c/ZjauE5IW9XvuyyRzM6Tdg6cQ7M7KxzthCGHC5nNIuDUUpRQlZerQJC1Y4mZar2/C58GvUfD+I+zrdPRqRQJ8pVQmRpQLcaBM/sepnHgZBT6nIDTzoEjJXUtRQfO/oOpdi6h6lmsE+WJHF3PoC7IdBIPfASWmaH2+TfJ7ecRDSqj2QScvB2cOrlK8gY9z5aAzUEJVW1Gx7F277ev0OX+tqMDZ1F04OwPhM7ASTPp9u3qktxmnivpcxli7LbrW87ktpu2hArm3oETMu+n2yVUUcCZG5wDJ42AE0KYdJ+JLOKgr/CbRfAYGNCSsmo1+3ayiAOlkv4P5qqEKs/4Z4z+L+ZC/MriKooCzn6MmWifoSK9NhQ4oAOn9widaFfy2NazOHAyfFhdmHMnBw4me0tu58BpxYKTYOqubOucXfdU+3UDlJfk2OcU/2ewAk5GV1IaIaRs281XM7otqs6B02ESXRr0yMCJAk5w0pRQidjuRzm7+aQtoZoVP/IIOMMpBBx7/OQRDVI7cOyYUMs5eat2cJXkLaFy/9MsDpy8Ak6n7lDJx7wEnJQOnCjgdJ+5duBAOAFnfDzbRCxE/k5WBw5oK+wZj7JHyPb/PVAcOFkEnIEBnZSGEnDyvM8tiAJOG7KUUJVK+hn2Oa7zCHM+Dpx3v1vFmCwiBMxNQO7gYL7/5eRkNgEi73eME33SllDNdYixj4DT22EeGcnJ7g90FgBKObJf2oUN9x2jy5nN2daZXG9bAacgB45rP51VsKi1ceAA9J+onahqOSYRZqoYBw4tuog1ksuBk0II6Vmly8wOHJvV1M6BA/kEnJoTnmKIcXBqKUKMwToZcp60pi2hkpJOyPOUvMwMQ2+Hk38Ru/6cJVTlFCfZJU8BpzLRXmTJXUKVUkRzlHrztxGf3Vabz6tv2LP7206CVDkKOPsF3XLghNrmXDtwsubugE5AfB047spy2jbiyb/xwQkiacSUbpZQifjlhzQSHThzQ5YSKtD3w+f48hFw8ogDb36zLtM6Vnw+u1lKqCD/uDRXJVRZBan5EmLc3yI+JBKApS+DZa9q/5zyMm3NnIW2DpzDdDnzcLZ1QsouVHkycOzB2dYVkjMPiA4OnP4TgRpM/zbjelExpKMAZzNwMnXlatNFLIlz4GQpK0vjwJE+zbHJmpWUxYGTNQMnllAVR5o24qACzkxeASdlCRWowJArZDhli+y866+M1duQt6Nkx91qXgfORHvhY9aBk1EY7ooDJ0UJVd5SM0ifgRMFnO7TDQdOyAycPA4c3xKqLLk7MPcOnJAlVFmyd7pZQgV+HXwaCSjgpLgMc/CSxYED+n74HF95nFUhsrPSONigPkbl+exmFTzyCjhZcmnc/vg4cLK4l+ZDG/FYPlUg667o/Jxck902Tpnejbr0EXCCl1ClyNYp20E3lwOnzUHce7guZx6GBU/Ktm4zVb/a3orSUqBixZ6UyngaoQzs/6SmJVpO4OpELaUQkifsupZSwJE8JVTOpRUFnOCkKaECFXDyhsFmcX+UF+Z0yKSYzHutfwz6D0mx/qJLqHJ26poVVVJ+hnwycGbFojavQ0o6fhaegRMFnP2CgzEDx7eEKmtZTbccOCGElPFxFaB6UnwXhXA4gV5Yy1pCBcUIOLGEqlh27dL3LW2ZjK+AM5clVLXERdy0x3IIB05agSBvNs1cO3CylFDNhQPHtwtVFHC6TK7JbhtBpLRM11l5KPu+FBVibGaAUnsXgBMo8uQBtdvfHutIquQQtNKEGJftZCtLGVWa/zPUS5UyrTulgFM+JHvYtXPglNt0oQLPEqo4IAWnlkHAye3AGVdHR6cW3JB/wp1mMg8eDpyUIcZO1K3mnEh1Kgsq9aoIU8lo4XcdpdK8B+55vg6cNJlH3iVUnRw4OQW7SFi6lYETYptZJ/mhQoyzujL6+rqTgRPCgZPl9YYScKamNDckq3jiO7lPEkuo5oadO9OXT0F3BJy8wm+y1Cut298nAyeLWw72fwdOVkfRwICOG5WMXTqNmdsSqrSCVKQgcgW+tnHKiGgZVeEOnCwTvQ4iC0AppwOnU9Bwz2qgDDNbsq03zbpBS6ggo8iSMsS4ZE86shwfacOAy3kcOLbUr9TBwpk7xLgnndAQyYZJ0UYctHwobwZOdTx96U45bwlVism8W3+RIcalHnWX5HHg1CoqmnR6HaX+7CVatRwCTu4MnBQhxqClYj7iSmW8s0iU538VCU+IDkmQzYETqoRqeBiWLEn//FAhxlmFhd7e7nShCuXAmWsBxx1LWR04IZxOjfsQBZxi2bkzffkU5A/edeQpoco7biSPxSOOyLatPOJrVnGqry/fZzWrAyfvdrKWUOX931Uq6paaywycSBcpLQrrwAEto5p5IMfOFFhC1THvJWcGTicHjpRVxKlkbI9sqkAlXYgxaA5O6nWnDDEWJ+Bk+JJJW0JVWpzDJTOi+9RJDMibgdMu5DqSH1OFUpoMnEGPEOOU4gfkd0ykLaHyycDpSXmiX+rPJ+DMOlc6vI5yjvU7B05aEVR6ii2hco/7ZOCkEe3y/K8i4QnpwCmX03XXCFVCNTKSTcCZzw6cLFfXQzpwJibmXsDJEpzcuP0QxzJEB85csWvX/u/AyVsy4z7zb387nHlmur/x+exOTNjOpCkdHvPFgZOlhAqyf7dkDX+OAs48J49boZPQ0nc0TG/OFqwL6ZwhMgCUMpZQpXHgLFDRIbOAk6bV9/Icrb5d8HIRDpyUIcauBX0eB05HAWcgmzAE2smrlOLLKk9ZYJpytUg+TC19iLFPF6q0Dpw8JVTGpOtIBH5dqNKKUHldH2nCf936s4oSToxJ03EMrAMno0XYkbqEKqcbanY7KY6rUr+KlLVq/u1E/Onv16uPWW3njThnSpruGqFKqLI6cLqVgRPCgdOtLlTj4+k76IQWcLL+n0O5ySBm4MwVWR04AwPzp4TKCTjr1qX/G5/PrhOX03Y4yvN5MUb/JquAk1cocn+fhrzlZ1HAOcjwCjFu5cA5WgWLzOVIKcJ1RaxrKGMGTicBBzS0N/M+pxGH8nT6sh+oTiHGsxk4GQSitA4cV0KV5fhIW0IlC+vBwVnWnUZkyVtCFQWcgqiR6tTPR8Cppih1cZQHoJrxi7E2DZjOzhXIWX5UVcEk9WvI6fpI7VxZkP01zDpwspRQ1bJ1uXOkLaH6f+y9fZBsZ33f+fn1zNx7dSWhK8G1LOulhAHbhV8hd72kcKUcZG8C9lokwV4nXpsQapWNieOskwomtbVOtpItXMmGwLpCIpt1hOMytrFTsF7KFRZDZb1lcAmwFRvWhVYBIyHQFZKuXu/cmeln/zjn6emrO9Pn9/s9L+ec7vOtmpqZnu7znD59+pnzfPv7kpKBM99rvjpJosRQ6Ql5kGvRbbXapCpw5vN+FDjW4GTIm4GjIVPiJ9Y5yAzL6xrH7dNCNSlwxgWrAieVpPMQc14FTtxPS+ZJqgJHS7aCT4ETj4E1xPjSJbs4oZaFykvgTC1UI0VUK3hqqI8rFTzx0ub73uds+xL2gK1u1tVM4Fyis3EJGqLFHGKsUeCcsStw1FYkh4UKpQJHogLH8CmBSYFjJXCe09mcXATOc1MDVSmEeZPZ0oXta5oFsyec16xeMf7D0hIG0NR8z43PIS7+tQ1OqQocTa6LWYETM3AMChzw2ai0leVbJ+2vRURs4drpaL2bbQ6BIyKfF5H/JCJ/ICL3trfdICIfFpHPtd+vb28XEXmXiNwvIveJyCuL7lzO3BLtoiiHhSqqNCwETg6CwVpdDvUVOPE1rU3gzGbNc+3TQpUzA0cky2JrInCOQQjw+OM2BU4qSefJNkpV4GhspRGpGTiW5+U5llZVzPJ9vWSRlgDzKnAeb9ev13Vcs0TE/fGoVteZwBGR/0FE/lhE/khEfkVETonIi0XkE+0Fza+KNBIIETnZ/n5/+/fbq+3o7Gpgfqj40CDsATvHEy2RwLl0v21fNGoWaPNTrBk4mu1eY7f1hEsKm9N1fgWOZtswnBBjLfEkp4E9m4VCa3OaXU1TrW6YZCcLVTlYCBzwVYl3VWMvY+uUncDRKlfAp46ZKxV3KWPAobVLk+tiVhHFeUWbgZNA4Ghr42cn/MTKnpLAiQqczQky/vMhhO8IIZxrf/9p4CMhhJcBH2l/B3gt8LL26y7g3UX3KheB88QT+gvgHATOk+3/bguBE/MhUheAnhrxHBk42jrvuGDMQWZYMnAgTw6N10KVW4GjtQR2YCJwjsEzzzStQdp5A9KDqlMycLwhxhYCJ97XM1/UUOBY1Srg/x9jJYu85NeXvtR811rd4hzseY3WlcARkZuBvwOcCyF8C7AF/DDws8A7QggvBR4H3tw+5M3A4+3t72jvVweupqGOUODtr22+7xtbhsKeTn5fykLlUm7sKuq4zxw2KFm2C7owYChTI76wUBVS4IBNhROeazOQOiCec3oicIogBCCguvTbaQkcT5X4waWy5Ic2OwZ86ph4/y3lc/AqcPaVSiKPSik4FTieJqpotesKfZ6d8Net77Vz6k7HwnqDFDjH4E7gnvbne4DXL93+3tDg48AZEbmp2F7kJHCuv14/Zi4C59qOdsWjxq4dYpxLgaNdnInkUcKALQMH8hA4XgtV7hDjDPYpmAicY+EhYXNYqHZ2fKoYb4hxLQLHes56jmWKAqc0geNVSlkJnK02m3JS4FyBbeAqEdkGTgMPA68B3t/+/fkXOvEC6P3AHSIZ6HINXDknHYTI7DpA7KQFBgInZA4xhgQCp0sl02bgmGxqkQjp2vbJ5j4eBU6XrWxhofJk4HTsd9y2JQcnXNTZnLz151MLVQG057xGgbPVvm6eHJz5JQP54VDgaNubIM1CVVyBow3/TWmhMmTggE+Bs/d0o/SZdZHQjtdiMUarwDmhtFBthgInAP9BRD4pIne1t90YQog1i18Gbmx/vhn44tJjH2xvuwwicpeI3Csi954/b/zQZxm5CJzHH7cpcMa6yO9LgWMlUnJValufb98KnJwhxhOBUxaRwKmpwPFmWIFd2RHf85YMnKErcGoSONYMoXg/67F76KHm+03Kz0lEGhWOh8CxBkCPBSGEh4B/DvwpDXFzAfgk8EQIC8/I8sXM4kKn/fsF4Io0rGwXOpdtNC52rYTIqqDhWZPNYm50aq1ZXZhdCweZa8TBGeisIIe2zgAHRpJMaaGC9lgXqBH3tFCps3uiAsdSUa5U4HgJnEmBkx8xoFZD4EQFjpfA6VrMR3jIidIWqgMjgTM74czAUT4PVxBzzQycpxv1TdfnHCkZOFYL1WYocL4rhPBKGnvUW0Tkzy3/MYSF5E6NEMLdIYRzIYRzZ88byG14AAAgAElEQVSe9e9ZTgLHosDJReBYF/kp5FEIvgVgLgVO6U/Xj8LTT9tIspwETp8KHM/rfAwmAucYXGj/V1hzrGor6KKizfp+qm2hqpGB47FQebPHrGN5j92XvtQEaVuIle3tyUK1jDbE707gxcDXAVcDfzF1u9kudJbhWuwqLEku21DPChxP/bSGHJqdab5bcnC0bU7QEDglasRlqyGQXBaqLgVOu4C0KnBUGTjtxZKVeNKQZRNssBA42ykEzq6BwGkVOBZFXGkL1SLE2GChcrVQadubEmrEa2Tg7D99SPitQooCRx1i3J53G6DAaT+cIoTwCPDvge8EvhKtUe33R9q7PwTcuvTwW9rbyqAvC9XBQfPlhSeMNI7tfa6XLjXtV31l4PShwHn6absCJ3VcLzmXW4GToUIcJgLnWPShwPG+rjs7dsVFvL8mt2p5HBiuAieqkDwZONaxrPOAt33vS1+yVb2D73yA9SVwgO8B/nMI4XwIYQ/4TeDVNB7w+A5YvphZXOi0f78O+GqVPfUsdtGQFtc7gnstYcNDycDZ7z4WWw4CZ25U4JSoEQf7MQl7wKwhf1Zu19FwNTe0UIFR8aRUaU0wIlZEGwgcTwaORYGzyCwxXFiUbqHyKHDmjn+66vDfBAvVTGuhav8VpihwOsfIYKGaWqgAEJGrReTa+DPwXwF/BHwQeGN7tzcCH2h//iDwY20b1auAC0tWq/zIQeDs7jYX9mfO1BvTu8hPIXC8Y9bOwIE8gb4HB82n4H0ocE6csCkXco0dUdtCJSJXhHgedds6waPASSV/va/rzo6dVIn7uNVxXf/8ccCvwClNtj7Vujgs2WPe+d4aOO09dl/9KrzoRbbHeC1UFy/ayK8SKDTX/CnwKhE53WbZ3AF8Bvgo8Ib2Ps+/0IkXQG8AfqeVIpdHSQWOmcDRKnA8LVRKC5WVwNHk9kQFjkWRpA0xBti6rowCBxyqJOVrWFKB4wkxRknyJWLjrm1cChxHC5XJQtWeQ3NDDo4lA2frZENmxOeugVmBs+0L/9USUZ6mrkUGjlGBY2mii9h/xkDgJLZQdWXgjKyFKmEOuhH4XRH5Q+D3gf8zhPDbwNuB7xWRz9F8ePX29v4fAh4A7gd+HvjxDLt/PHJUTj/R/o/WKnC8oaDL6IPA8TTYQL4MHKs9IpcSpg8Cx2qfgsPjnGMZ0EMGzvcecdtrUwYeeq2vJ8TYm3MS4cmwgnEQOFbbn2eOqEngWFWWXgWO573utVB5z7/MyD7XhBA+QRNG/CngP9HMe3cDbwV+SkTup8m4eU/7kPcAL2xv/ykOazjLo5RaYXYG5o4MHHUL1TP6BZIlxJg928JIs88LBY5FJaO0IoE9AwdljTg0ShmLSkb9GkYFjrGFyqLAMZOSVRQ42eebQcNE4CSEGB8YQowjgWNZcC8ycJQWKuv2FwocrYpox1m/rbSCjSIDR3HxkNRCdaEhuroUReNT4LjmoBDCAyGEb2+/vjmE8E/b278aQrgjhPCyEML3hBAea28PIYS3hBBeEkL41hDCvZmfx+XwXvQu4/H2msWqwElpopoUOKuRw8rkCRPOlW/kIXBykJHL+5BpobVyZheRv0XD0r5ERO5b+tO1wP/jHXSp1vflIYTnROTXaGp9X0dT6/s+EfnXNHW+72ap1ldEYv3vf+MdX4N4nliVZfGxHiVDTQXOvL2OsxA48b61MnD295v9nClpxjgnWAgcLyn1zDPNe1p7/LzjWPPF4lhWBc583ozVF4FTaq6JCCH8DPAzz7v5ARrP+PPvexH4wdQxXXBVLivChreuh4sFW6gIba205gRSqiuWj8WW4gIuHDT70XksUhQ4WgtVgRpxsAc7a4OoFwoci4VKm4FT6JxOQOn5ZrgwWKgWSgbHYsiSgTNzjBPVOlua86/dj/klQHlBZ22hkh2nckUZYlwjAyelRnzvabhK0bSQFGL8ZHeFeBwDBq/AWfs5yBswuYxI4FgycFLHjGqYTVHgPPccfO3X2sbsS4HzrOH65ChYc3cilsnI1JyJjAqcrpn9PuC/ppHgvXXp9qciq5s49lUissfltb5/rf37PcA/oiFw7mx/huaT9J8TESlpa7C2DC3f13tuP/ss3HCD/XEpChwtOQKHgcm1CBxojqWWDPMocLzEilWJN3QFjvd/VkaUnGvGA9diV9m85Akx1rZQQWOjmilOIJMCBwOBo1SyuEKMDQu6LSuBo6wRhzbE2HCRWFKBoyXiFqqyAsohPzZzvrEocBZKBscFjctCZTivIzmhyXfxtBIdOCxUXgXObAdmXRlVjuyYsN8Gn3c0Qy3GyNBC1TlGYohxV/4NpJ23dbHec1AOBU60UPWRgWP9FP7kycPsDe+YfShwPDXifbRBnTx5SOiljJuiwNndtS0wj0JFAuddIYQ/IyLfEEL4QpYRaZLTRSTW+j4H/AcMtb4iEmt9H13erojcBdwFcNtttyXtYzw/LQROqspq6BaqlLF2d+1NdeAjcCzvTy+BYwnGTxnHS+BYFTjeDwAyoshcMzqUspvMzjRBw5aFsSXEGNocHMUnOZYMHNAfCy2Bs9UuAkwKHAPJMrvWR+CoVEknDhU72m2rjrUnA2ef7ksI/KqysgTOZs43FgLHW8c8P2jUcJYacTAqcCy2Q4ciw6PAcREfzyptYCfs2w/7evUNpBM42haqmEekOQeXsaclcBKyfOpiveegHLYTqwInVwbO6dO2T7ehPwXOfN4ssqyLuQirzD+HAmdsFqocZGRERQJnT0TuBm4WkXc9/48hhL/jGfR5tb5PAL9OplpfmmwLzp07l6TO6UuBM2QLlXes2A5lea96juXTTzcfNlnGSSFwrA1lUEeB47FQeUK7M6PIXDM6yFWAODJwuhQ47QXQwQXYVqZihz2dZSjab7QhpGEPKKHcUFoG5ATIaaMCJwaCalQyp5rXJATdp9+mEOMTtsYva4ixNl8nzIG5boG4UPcUCF/2YzPnm2CwUHnrmCMhqVavxAwcB4FjUuAY/vmaFThOC9XBc7YmLe2cAs0x0ubfQHqNuFaBE8fQHtuIvQvdAcaQ1qZVF+s9B+VU4NS0UHk/SU+tEQfbghMuX8CkEDjWDJxUK5PHQpVL+XPzzd33O2psSLerhWAPhF2Brtn9+2lSzP8CjUImFxa1vgAiclmtb6vCOarW98Fatb6XLjX/Jy3viVTLZx8tVFaS2TOWNfAXfMfyqaea+UB7fQN+AufCBb2q0ztOCL4MHI+F6tFWy3b2rO1xGVFqrhkXRBpywapWmHWcJJflvhgInJniH2wkebTWHq2yx6rcsKhktq4zWsoMjS7Lx0OTEaNVDkE5BY65har9B6I6HlstqTUoBc6GzjcGBY7M2nBe4wWNOQB4OaNGiWAgcBbbL6jASbFQdVWIw+WqEu37Yl5JgROCg8DZ9RE4V31d9/3icx6+Ame956CcGTi1Q4y9OSlesipesFvrrZeJBW+FrMdC9YQ1T/F58Fqo+mqhiq9LqgJnd7eZL2uEGIcQHgXeJyKfDSH8YZYRGyxqfWksVHcA93JY6/s+jq71/T0q1frGrCILGTBZqI6GRx3oIe8vXLArSFIUODcpMvsiPM8nvtctcyv4FDiPPNJ8f+ELbY/LhYJzzfgQW5200BAirtwX7eLfQ+D0aKGCliSz2IXiBKEhLNoLqbALaAicS8CsITo6t+0gcFS2r/gaKre9WBxpA1KN9eeFQ4w3dr6xWKjA2X7UnkOWBiewEQdzA6HqIYisChyvhergWb0CB9psIa0Fdl9/X/CHGB9cbM4rC4FzcMn+9lZn4MTzadgEztrPQbkUOFddpQ+OzWWh8izEtrYOF1ZWeAmcVGJhPm/ILqs9IlcGzqZZqDJnVWj1F8+JyEdE5I8AROTbROR/9A46hlrf3V27mi3lNfbkxESkWKhqKHA856yHDPvTP4Vbb9XfH/zz3xNPlFfgeN/rngyc++5ryMpv+Abb4wog61wzSlgXuxq7ycJCZQmBK0ngaBQ4kQjRWrMMNqfZKf12rdu2Hg+TXWjHSOAoybK4mrISONpP+MVwTi/sWVVqxDdrvrESOK72IyeBYyEOLBYqz4LerMDxtlAZLFRgI4msGTjitB7tt+9rDYHjsbNF7F3QtVB5n0d/WM85KEcGzr332kiNXC1UnoXY9nY6gbNteL9CntwOsBM4fbVQ5SCOUlqoUi1UPRE4Pw+8jTZkIIRwH03ttxshhJ8JIXxTCOFbQgg/GkLYDSE8EEL4zhDCS0MIPxhCcwUcQrjY/v7S9u8PpIytwaVLfgLHc46ltAANXYHzUGuEK52B88gjcOON+vtDPQuViN3alELgWJ/PJz8J3/iNtlyfQsg+14wO5qpogwLHFNyrDTG2Ejja9iIHEQKGnBrLRG35xL8QoQUFLVRi3La1othC4Bhex3Rs2HwTM3C0zUQn6hE4JnJiT9+w5LHUzI0KHLeF6lmlhcqhIrJm4HjDf/fbT9O1IcZgJ3DmB21Q8lqFGEes5xyUw0L1H/+j7fF9Wqi2tuyfnEbEx3kVOF5iwdN+dfJkvhDjmgROzJ/p00LVE4FzOoTw+8+7bTSzowcpBI7nNU55XYceYvzLv9w87tWv1j/GM/fv7tptoB5idW+vmQesZIe18S+GP9eyUHmyvQpg4+aaKyBXOew9XQqcaKEyKHC0NhaL4iTMAWWOg1nZYyFwThoVOHGC0Nic4n4bQp21ZIWcsCkVzNvWHuuSFqqqBM5mzTfReW6yUFkX25HAMahXwGih2tPbgzyhttYcH7eFSqvAicfIQuAY8nIuG8OqwGkXY9uKRaCXwIljaEKMx6fAWc85KNei96/8Ff1948VyXwRObQtVqgLHG1Cag8ARsS3YTp5MU8Ds7javT4qFKlWB4zneK6AlcB4VkZcAAUBE3gA8nGUPBgoPgZOiGPQQoRFDDzG+/374ru+yKVY8x3J3V2+VjfAQ2LGxyfJ8oDmfLON4g+k9CpynnoJrr7U9phA2bq65AnLSpg7RKFpmrfTc0mBUIgPHVJntJHC0OTXa1ixoCQvtJ/7G+mVtKxfYFTgWe5Zl2xZLGdhUZXUVONnnGxE5IyLvF5H/V0Q+KyJ/VkRuEJEPi8jn2u/Xt/cVEXmXiNwvIveJyCvTn9IqGFqowGehWuTHGMgPsBM46vPaociY7zaEhproai1U1mhGSwsVGIOe93W5WosxUgkcY4ixBXtPNt81FqrxKXDW85pHJG2xH0Lz9ZKX6B8TF8YpLUleAieHhWoMCpxcFipr48yJE80n01F9YIUndycitwKnRojxEt5Ck1HzTSLyEPCfgR/JsgcDhYcMGJMCp6aF6tFH4RWvsD3GG/o7ZALHqsCJKhqrLXZ7+1C9o8WACJyNm2uugJwwtAGhyzlZBAIXJHBUF+UOAsdEhKC3UM2f1G0XUKuG4rYhf6gz+CxUM+3FgmXbjgycg0d097W0iaWjxHzzTuC3QwhvEJETwGngHwIfCSG8XUR+mibH763Aa4GXtV//JfDu9nsZeEKMB2mh2tfbg7wKHK2CCJZsWge23Jn9Z2HLYqGyHiPDe8hbI+4icIwXyZHA2VZcpIxPgbO+1zwpi/2nn24IHEszSZ8EToqFqm8FTh8hxlYlzPJz9TRueXJ3nj/2GDNw2mya7wHOAt8UQvgu4C9l2YOBonYGToqyaugWquees9uAhkzgPPVU890z/1jGiSSblcDxWKiefnoYBM4mzjVXQE46gmo7JitPlXOJEOP4vPq2UM2sKidDIGhRC5U1xNjQ5uRR4KgtVIbQaMs5kojc842IXAf8OdoChhDCpRDCE8CdwD3t3e4BXt/+fCfw3tDg48AZETH0GxoxyBYqb76L9ryOC3qjAsdSc+1Vrxw8B9uFFDhzA8kF/haqPQeBY7XlmRQ4W4CMRoGz1tc8KYv9J9vX3JJVEBcafRE4tUOMUxU4fYUYe8KEU8kqT+5OrrEjesrAASCE8EwIoV2+8lNZ9mCg6CsDZwwWKis58Nxz/mwaawZODQIn2ms92TQeBY6VZPNYqDzHriQ2aa65AmZy4dIhabByu1cPwEIVF+cFLFTmEGNrBo6VwBlRiDG0512pFirDOW2x2WVCxvnmxcB54BdF5NMi8gsicjVwYwgh2iK+DMS4/ZuBLy49/sH2tssgIneJyL0icu/58+f9excqWKhqtVCpz2uHpcaqwHEHAGsVOI4MHGsLVQ0LlbeFar99a2oIHPCHSveItbzmSVnsR6m7RYFz8mRjy0klcGq3UHlDjFOJhc98pvleO8TYEyaci8BJsVCNUYFzDAzmtfGhrwycdbRQXbxoJzu8GTjW1yyFwPGQUkNV4Ozt2f93VMRazzVXwBImC819VYSIsZ5cXfddisA5od8uOCxUxgwcCxECtv22WKgwZG2UCjE2W6gMREDdDJyjkDLfbAOvBN4dQngF8AyNXWqBEEKgzbvQIoRwdwjhXAjh3NmzZxN2r4ICZxEAPJAQY4+lxrJ97xhgz8CxKFfmlTNwSrZQWRQ40LweI1HgHIP1uOZJWexHBY6FwBFpFhvWDIGI/f1mf8dioUolFn7iJ5rvloXgiRONhcNLVoHPQpVaS5/DQpVKXEVS0rMPRyCFwDGmtY0L667AiRaqGiHGNSxUcT6xqki22lxSy3OKqiArgVNTgbNmBM5azzVXwGKhCgfAgVKBc42RwCnRQmUhcGbAtiPEWNtCZawRN1uoCilwgIXaqBMGAseSgWO1UJnIod4JnJT55kHgwRDCJ9rf309D6HwlWqPa7zEQ6CHg1qXH39LeVgZWC1WKAqdkiLFFXeJRx1izbFw5PvPWqmUgcEwNdMbn4M7AaS9eS4UxwxKBo/R5z3ZslrnhYT2ueVIUOB4CB3yLlIiUhVgOC5X1Yj+VWPjar22+f8M32MdMycFJsVB5x02xUOVS4Dz8cLPovvHG7vsqsHJ2F5GnOHoiEcC4JB8Xdnft88aYMnBqKXD29pqxvASO9lh6Cez4GI8CxxNyXUOBY7VQzefNl3WcnNjkueYKmOwmBkLEYqEKB8A8v1LGao8xHQtDM5IlkwWMFqqW2VUrfKwKHPSkj1mBU9JCpd12+RDjUvNNCOHLIvJFEfnGEMKfAHcAn2m/3gi8vf3+gfYhHwT+toi8jya8+MKS1So/zBk4jnrsGiHGpRU44cCnXrHatAC2FJ8EeVuoTBk4ThVRnOdMBE7BFipoXvOBW6g24ponJQPHW82bQhql1AFvbTUX0iFgaleC5oJ9e9v+uFRi4Zu/Gb7+6+0ZONAcY68V6Jln4NZbu+933LgepFiocilwHnqoIW8yLbZWbiWEoKK7ReT6EMLjWfZoINgUBU5pAicqGUsrcLxkB/gJnKEqcKwWKq/9Nic2ea65Aha1gpnA0VY5x+0qWEoR/T4vFvHaxZ0nO0VZI24icEoqcBQ18Ittx4WihQwp0XBlONbgzMApNyEVnm9+AvjltoHqAeBNNGrnXxORNwNfAH6ove+HgNcB9wPPtvctCGMGjlQkcEzqEgOBE4kJswLH8I/XZdOKxIeGwGmfq8VCZc3AEcdxAjhon4fm9XaHGLfRMJoWKjisdR8wNuKaJ4VMiYsHK0lg/aR0GSkETlx8HBzYFyJeCXwqsfDYY3DzFZFrq5FqZYL0FioPhmCh+tKX4Ou+Lm0bS8j1mftHaGTCa4NNycApbaHyEjjLc6EG3sptqEfgDFWBMwQCx4C1m2uugCkvxEC0zK6BPaU7Iy62cytlLISTZbuALcS4VYSoPy2z1IhHAkdLslwCUU6Qywoc1baNIcZzZXaA2UI1LALHAPN8E0L4A+DcEX+644j7Bpoa4TrwKHCsC+E4dw1FgbPYfkECx/McFgoczdztbKHaNlxQiviUKwcXGxJKM496SaL9JxuFj1ZRNAIFjgHjveZJycBJ+fQ3dZHvWYjFT1o9BM7+vt8+AD7C6tIl+PSn4Vu/1fa4HGTG2FqoclmoHnoIbr89bRtLSMnAWcZ6BG4twUPgxNc4RYFjnaviuEO1UKUSOFolyRgInKFm4HgbDHvC2s01V8BkNzEQLbOrUdeIL7ar9AnKCR3pZK2IdtnJNKqh9s1rsmdpFw8OlYzZQqWcsCzbxqL8MtjVoN3vg9aa17VtI8lXFus131gJHM9CeKHA0c4dUb1irBFXn3uRNBiYhWpBdBkIHNNzMIYYQ0vYOQicmfJiyBuUvPek3j4Vxxm4AseA8c5BKWSKd1HUp4UKfDk4fShw3ve+5vsnP+kbMzUDxxtinJqB06eFKrMCJxeBsx6BW0vw1CrPZs0i2HN+Pfww3HCDXREDvtrooVuo4n6tE4Ez1BaqlPygHrB2c80VkBPA3uFiaxVMocCWDBzrIqygAsesRtKQWZHAUdqowp7BLmRVyThCjC1WpxIZOJ4WKtBtv4ca8RVYs/nGaKHyZOBEe4xWgSNiH8eS7+JS4BjzYzwWqoPCFqq50UIVx/EqcLTbBzu5YiVwPMTjY5+Cr3zU9pg6GO8clJKB4108WD8pXUZKloX1U+dlpBI4HmXIl77UfP/H/9g3pvcY7+8350QfFqqTJ/2LREhT4Fy8CI8+aresrUAuAmft4FHggF8xeN99diVbRCQgtM2yMFmolrGOCpw1tlCtP0yLXaOFSp2BU8pCVTLE2EJmxWOszcGxWKiMTVEuBU4hdU+pFipLtbpVpTVBD5eFyqnA0bZQgT1rxxNiXDIDx5PjY6lb9zZpWUioOE5JAsdbt773lFGB46gR/+w/g9//m7bHDAAisiUinxaR32p/f7GIfEJE7heRX22zuBCRk+3v97d/v734zo3VQtWHAieFWPA836efbhaAf/kv2x6Xq867toXqySfhWmWGVu6xAb74xeb7bbf5t/E8TBaqY+AlcLxzx5/8Cbz85fbHweF72DJv1CJwvGRHTQvV9rbt2A29hcqrwJksVAOBqZbbaKGaP6Njes0WqlIKHAupYCFwSlqoPCoZ7T+bgYQYW1uoYmi16ngPSoGzXvONq4XKmoFjVOAsxjESOOpspwotVAtiwtJCZVDguJq0HAocV2i1Q4HjsVBpA4zjONbz9mBXrziti6456CeBzy79/rPAO0IILwUeB97c3v5m4PH29ne09yuLVAvVyZP2RUpfIcZ9Wqg8z/fJJ5uqZWvzVV9ZNKnjnj8PL3qR77GzWfP6pihw+iRwROS7RORN7c9nReTFS3++Ipxv7EghcKyKwRCa99L119vHg8P3vWXRPp/blR1xLGuNONiPZU0LlTUz5uLF5jh4yK8hZ+AMRYGzaXPNFbAQACYFztXAfv7txvsVIXB20CtZHASOturbYqFi6/L96dz2pTIKnDCnqYK3EDjaf15GAmfACpyNmm+CtYXKk4FjDDEGu9LHpMBxqldKhxjH46QKMfY8B6eFypOBsw4Wqvmu7rUoAO8cJCK3AN8H/EL7uwCvAd7f3uUe4PXtz3e2v9P+/Y72/uWQaqHyhoKmWqg8IcZW28AyUkOMPc/3wgW47jr741IzcLxtUKkEzqOP+gmcOH6KAudP/qT5bq1PXwHVf3ER+RngrcDb2pt2gH8X/x5CeCzbHg0Eu7v1CJxLl5r3vCccG3z2vIMDX96OlcDxEitWMttLdsTHWAkcq6II7MSKN2g6jqO11KUcu9zYxLnmCpRS4MSmI41taDAtVIYFRXELlVaBE2vVLTYnawaO4phY25xM4dmOFioYXAbO5s03lSxUsq0fI45jUpc4asRLKnA8FipLiLGrptyY4wN+C5U2xNhtoaoQYmwhojIicQ76l8A/4DDc6oXAEyEsnvyDQAzduBn4YrvNfeBCe//n789dInKviNx7/vx513NaILVG3EPgWCX1y4iLN88FvvVT52X0ocC5cKFR4HjHTFXgWFVOOUKMvRYqSCdwPv5x+JqvgRe/uPu+Smj/w/4l4AeAZwBCCF8CEo7EsBFC8zpZLTLQvMbW929KODb4CJxaChwvOTCbNV9DtFB5Aq7jODUIHKulzmvVKoSNmmuOhBgWAy7SQvEPcO6xUFmUPdqLFQuBE5+XYtszh4XKogiRE0biqUQGjpXAqRFibCD5tMckDZs137gsVA4Cx6K+AUcGjoVQnQFStka8tIXKrcAxXjyk1Ihr4LVQ7VdS4PRjoXLNQSLy/cAjIQRjjdBqhBDuDiGcCyGcO3v2bNrGUjNwvEoYr9Ul7qvn0/uxZeA8+aRPgZOagdOXhcq7cFseP4XAeeIJuOkmn3LiGGi3dCmEEGjT0EXESTWMA3GR7XkPWxfp4FeURdRU4Gxt+bJ2PHOTRRmTMo71Ndvfr0MUpShwQH8+eMcphI2aa46Eh8BRBWEampesFqrZUBQ4OzpPt1hbqAwZOOBQ4JSwUBUkcBbbNuYCmarmq2TgbNZ846kRDwd6OSc0TUlWAmd2wl4jrlXggF2RYW1w8hATphBjr4rIY6EqqFyJhJLZQvUU7BgzcMzPozcCxzsHvRr4ARH5PPA+GuvUO4EzIosX/hbgofbnh4Bb2zG2geuAr+Z4AsciNQPHq8DxqGAgjcBJsVB5FTjWi/1leEN9U4mUCxea71b1zzoQOB7CbAW0S/hfE5F/QzMx/HfA/wX8fNY9GRBS38PW91IfCpyDA9+CfXu7Ue9Y7TmlCY+UcawWqoODOkRRKoFjJb8GQuBs1FxzJBYLbg2BExfEmRU4VguVWiljbaEyEjgWyxcUysABMIYvmzKBsBE4JUOMzRaqAk1ladiw+cZRIw5G8uOSjVyJ45SyUIFdkVHTQqXJXfHk+NSyUM0vwpZyoS1ify0Odptzan0zcFxzUAjhbSGEW0IItwM/DPxOCOFHgI8Cb2jv9kbgA+3PH2x/p/3777TEUTn0lYHTB4HTh4VKxJ/5c/Gi7/imEimxvvymm+qO2zeBc+ECnDnjf/wRUM3uIYR/LiLfCzwJfCPwPwShKswAACAASURBVIUQPpx1TwaEVAJnDAocr4VqWSWoITFS8lUsx7KmhSpFgVPTQjVGBc6mzTVHw7JILxTca26hUn7iWTrEWL1dowKHS/pjAe3xGKEChwPd4nXRQmXI14FCNjs/Nm6+8ViowKZ48VioSoYYL7Y/ZgvVgFuorNkx1td6v71A3jJ8wjnbgf2n9PeH3hQ4BeagtwLvE5F/AnwaeE97+3uAXxKR+4HHaEifskjNwPFaqFIInNg4ZEUfLVTgb91KaeoBPzH3yCPN96/5mrrjphI4KeHY0Chwvu3b/I8/AqrZvU1E/7/jpCIiV4nI7SGEz2fdm4Egnh+ec9tD/qY018UxoZ6FKj7eQuCUVsbUbKHa368TlpyqwLFm4AyBwCk114jIGZqmhm+hkSr/DeBPgF8Fbgc+D/xQCOHxtpnhncDrgGeBvx5C+FTK+LadtQTVGhbSnhwSNYGzzSIXRbXdUgocreXLmoFjaIoCfQZOCDQBycYQY4s6y5JjBO1z7fpkzpqBY6gRrxtivFHXNnYLladdyUiugEOBY7U4bdszcGaVWqhUIcaz5qtUTlCEN8TYSuCYiK7nmu/bBrWAS4HjOG8zIMccFEL4GPCx9ucHgO884j4XgR/MsMt6xAycEHTW5mU8+yy88IqM5W6kEDje9po4LvhbqDzByeAnFryERqoS5plnmm1Yj/OkwLkC2iX8r3OovQU4aG9bS8TXqEZQLYzPQhUfrx1n+XHWsdbNQlUzA2ekFqpSc807gd8OIXwT8O3AZ4GfBj4SQngZ8JH2d4DXAi9rv+4C3p1hfD1MGTiGLJKiFqptW2ZPkRBjh4VKnYFj2DYY7EgelQxKJYuVwLGQLCUtVFVrxDfq2sZcIx6VHyb1imMhbA4xdlioLKqPGhaqg5hfpp2zto2vgyPEuHSNODgsVO0crW26gua8tWbghAO75SwP1ncOOnGiIW88pEZKC1WKAsdL4KRaqLwtIt7WLe9zTQ0xfvZZn7JqzATOfO6vbV8BLYGzHcLhFWP7cxWDeh+onYHThwIn1UJlVcZMFqopA0eJ7HONiFwH/DlaKXEI4VII4QngTuCe9m73AK9vf74TeG9o8HEab7rRsJuyw5bFgEEJ4QoxtihlNAocay5LKQLHYCcDTFXfoCdwrGqTogSOJWejdAuVAFUmpI26tjmsEVd+Gu5SllRQ4MwNtsPF9mtYqIw5PnHfNDCrlByEROka8TiGiehq52iLAsf6eoPPcpYH6zsHpdhexkrg1LZQWT+BjkipWo6P98BL4GxtNV8pBI73tYU0AufixYbI9C7yj4GWwDkvIj8QfxGRO4FHs+7JgJBC4HgsVJF48ZKDfVmoNJgsVP5xvMSKlWQbGIFTYq55MXAe+EUR+bSI/ELb9HBjCOHh9j5fBm5sf74Z+OLS4x9sb7sMInKXiNwrIveeP38+cReXN2xR4MQXObeFykosaC1U+8CWfgFZmsAppsBRZuAsnpuWCLHkI8VPri02ONC/jjgIHKUqSU7YJfc+bNS1zWH7QGELlVU9VToDx6r68LZQBcPibW4kcMRqA6uUgTOvZKGykEQeC5VHsZQH6zsHpSz2JwJHB68Cx0topGbReAmcOLZn3IOD5qsvBU7cZ69N7hhoZ/f/HvhlEfm59vcHgR/NuicDQkoGzvZ2Q7ZZkEI+wGShgjSlTy0LVe0MnJESOCXmmm3glcBPhBA+ISLv5NAuBUAIIYiIqZEhhHA3cDfAuXPn8rU5mFqoLBYqi+rEuEBHK1k3tjkVCzE2KFms247bLxY0jG7bMWNDtBcNBruM2UJlsWcZ84bSsFHXNocKnJItVB4FzommKloLq02rVoixtSUK9Oe6meSq0EI1P2juX8NCZQ5Kdlio+lHgrO8clGK38ZIpqQSOd5FvvfBeRiqB41XgpBA4tRU40Lw2HgInJRcl4sSJZt89iPucMv4R6JytRGQL+FshhFeJyDUAIYSnUwcecqho7RaqPgic+TxNgTM0ZUwtogjSLFQh6I/9vL3Wtr5OYw0xLjXX0FwQPRhC+ET7+/tpCJyviMhNIYSHW4tUG4/PQ8CtS4+/pb2tEjwWqtwKHOsCXbm4sDQuWbYLRgLHmFkRLqG2fUG7H5lDqBfbRXmsrRYqyyK0pIXKaFdzouB8M1yYQ4yd1iCPhUr9Xpw3XyYi2JofY83AcbRQLchbA5mm3X6YA8ERYmzMClpUoZe0ULUKHG1VOThDjB2EVyLWfg5KWex7SQ1PjkVEXwoc76ICfIvOEJpjNCYLFTQWpJg5YkEOAuXEiaZJyoOo6siswOn8zxFCOAC+q/356YyTy2BDRVNea8/cEe8/BgWO10JVWhkzFgsVlCdWxqrAKTXXhBC+DHxRRL6xvekO4DPAB4E3tre9EfhA+/MHgR+TBq8CLixZrcrDE2KsIVrE0LxktshYLFSWN6mVwDFk60AZcggcIcZWJUuJDJy4D5bzTjlpmPbbSJY5UfDaZrhIqRHXonQGTiQxTAoc44K+RgZOPE5qO6mBXFmoe4z/1MVqb3KqY2qMYVbg1LdQrf0clGK3SSFwNs1CZX2+KSoFkbRK7RQC55prDlt/LMhF4KRaqGorcFp8WkQ+SJOMvqC/Qgi/6Rl0KVT0r7fbuQRcar2f393e7R6aWry3shQqCnxcRM7ET84943chRW3lycDpS4GzjhaqobdQQTOWZq7eNAKnRda5Zgk/QSNTPgE8ALyJhsD+NRF5M/AF4Ifa+36IRu13P43i702JY9vgycAxKXAKWKi0n3CXVOCwb9h2PNk1x/gAmBfKwDFkGC3frwSBY7ZQWbKM4rYVE7r1HElDqflmoLC2UDnaleZ7NrUEtPOH8p+9NfwX7MSEV4FjtZqVCmKO+1HaQuUhV6yNYDUVOP1YqNZ3DkqxUHlJDc8iLCKFwEm1UNVU4KRairxZNNAQONdf73vsROBcBu0Zcwr4KvCapdsC4J1glkNFvx34JPCT2ENFLyNwROQuGoUOt912m3PX0jNwxkDgpIYYD00ZM4YWKm+4sNdCNVICJ/dc02wghD8Azh3xpzuOuG8A3pIyXhJK1YjPPBYq7UmhJVosJAvtfQ9ay0THGyHsw0z5yY4I6n22BjoD6kwgy+sH7X5vF1LgGFuoTK+jJSDZqHZKQ5H5ZrBwK3CMxMTOC2z7JVv6Mazhv2C3BpkVOI6soGC07FhIiUiGmS1U1pwdT8V3BZJoXDXi6zsHee020eKzKQqcg4O6LVQpi9z4uL4UOE8ZstIiNpnACSHk/gR60KGitS1U8b3nff9OFqr6FirvuREfr0Fq0PQYCZwCc834YAkxxkC0WEKMwz4ws2VlqAkLa4hx+7guIsJqz1Ln9hgr1RfbLpAlE/djCC1UpsWhRd1TT4GzcfPNUDNwLAoca/hv3L61wcmymPdk4FitZhZbkNUCuzxGcQWOs4XKosAx18YHx5yWB2s9B3ktVPGidFMInNQMHOuYKRYqaBZAXjLjmWf8ddpXXQWPPWZ/XN8ETl8ZOAAicouI/HsReaT9+g0RuSVh3KNCRV9JGyrajtlbqOimhBhPFqoGVgI7JcQYbMdu0wicAnPNCGG1UG3rrCyWIFmMF7PqDByHhQpQ57JYG65MBI5VcZK5Bn6x7RO6/S7eQmVRKGzpt2099xKwcfNNqGShMmfgGBQTCwuVhWAp3UI1A6SshcqiwFnkBBnfR9Yacbc6ZmA2rQWxWf8iaK3nIK8CJy5mvATOwUFDylmRw0I1hhDjHBaqPhQ4XutWDgInJfenkAJHa874RZpgz69rv/6P9jYXhh4qmnKs+8jA8RC/k4Xq8nGsxy41A0c7TopKaowEDpnnmlHCaqFS22+2aOw3WgWOMWyY0P0puvVTTkvrEiNU4FgtVHE/SoYYl7BQLcgeZQZOhRDjFhs231QKMbYqqCwtUR4LlTUTxUrgQBuUbCGJNlSBY64qj01XhgtyswIn+tV7sVCt7xzUF4EDPiJldzddgeNR/9QmcHJYqFIycFJqxD0kSi4Cx6vs6pnAORtC+MUQwn779W+Bs4ljx1DR+4DvAP4X4O3A94rI54DvaX+HJlT0AZpQ0Z8Hfjxx7JVItVB5CBwRH6ESxwQ7CVHTQuUli6wEjlexUrOFarJQrUSJuWZcMDUkWbNIThZU4KBYYFgXd0Y1klndoznGBTNwLDXwEaUJnBJtYiLA1uAsVGzafOO2UBW0BoEvA8caAFxSgQN21Yc1NNejwDGTUNaK7woWKg8ZJdtAgLk2GNtJeOXB+s5B3mDfHASOZ7Hdp4XKewE+JgXOfN7YicaowEmx5vUcYvxVEflvgV9pf/+rNKFbbgw5VDTVQuWpEfeSr+AjfmtaqGpYO8dgobK+TrVtbgMhcLLPNaODOcTYYiU4ZQgxdtTosg+s+CcV9rHZhaxkllE1VEyBo1zMDUmBY86psdozCtns0rBZ8403xNhkR/FaqLQZOF4FTgUCZzAZOE5FifU5HHhCjK1EVwxktrSCLYdKKx4XnIRXHqzvHNQngbO3Z88c6ctClbJQ2tqyLzpTFTheJcxzbZ5VCoHTlwLHs7iP6DMDB/gbNBW7X6ZpfnoDtat1K6IPC1UKgeNV4NSyUKXMS1YVSY0Q41QLlYVY8bxGI1fgbNRccyRMuS8OBY42xNiaJwPdF+bmnJp4X431xrHPxTJwdtArWcBMaqkJnG272qKE8gtQK3DMKq0kbNZ8syBJCluoXCHGRnuQNWS4ZAsVOFQ+VgLHoMBJsVCFfX1+yLyChSo4CBxr+La3tSsP1ncO8pIafSpwvIt8r4UqhPRPumsSZOAnUp59tvmeYqHqMwNnTAocEXlVCOHjIYQvAD+QdeQBow8L1VgUOB4LVQ1lYO0a8clClRebOtccjbiQ1izSrYTISV0GjlnNolVvWBf+1mBdK4GjOcYOBQ6FFTjqUGejaggo00KFfpEe9kEMjTMObOx8Y1VmiJPAMWfgVLBQabcfQqNUMpMfVgXOvlFFtAPzZ/XbBh+BA/oPBrwWKlPFd1SNKYL6Iy5T4GiGcJCCidiIOWiyUHUj5dPn+LiaBBn4rUypBI6XOEq1jMEgLVRdH8P8q/iDiPxe1pEHjPhae+cOT434WBQ41rFqteONwUI1ETgrsZFzzZEQQU0AmIN7lQ1GHjJksT+rtutU4JSwUKkVOI4MHHONeIkMnEvOvKECZBm0r40yxLi8Amcz5xurVSQuaK3tSoO0UFnVGJ4MHONxssxXpqYuJyFhVVx5W6hKB0qbFTi9WKjWfw7qg8CJj6lN4HjVRqktNinVx5MCR4/t7UO1VB/jH4GuJfwy5Z3XvDVg7O4257WX4PC8l7zvI/ApcGo2HNWwUNVquwL/c/Icuw0icDZyrjkWsqNb0JgtVNrFvyNgE3QWKpNdyKgKKUJmOTNwtHkvgH2/NTlGTgVOqQwcbbBzHQJnM+cbqzLDY6HyZOCYLFROAsecH1OYwPG0UJUmJExNdCSEGFdoBAO7AqeuhWr956BNVOB4yZSaIcbx+HoXZ94MnEjgXH21b9y+M3DAd14VysDpevVmInI9DdETf15MOiGEx7LuzUCwu+t/nXd2GpJuPtcTQH0ocOZz3zzlsVDVIJZTW6jm8+Z106h0h67AGWmN+EbONcdCttHbZKxKmRKBwMoL5mC0DVhVISWORVELlUeBo60oNypwLGSZVfkF7SJsMBk4mznf1LJQeVqowoHun7BHgeMJAHbViBc8Tqaq9UQLlVWBMzNWfJduBFs8DytpV5XAWf85KJXAqXGhvYyxWqg2KQNnPrcvjnJl4EBzrK3b6amF6jrgkxxOKp9a+lsAvj7r3gwEOVR0+/v6bfSRgVPTQpVCLFvGmc3Sn5PmdUjNwLGEGG+QAmcj55rjob1Yt2bKaG1DQ7NQaY+FZSI1hhib27PmbZbGiknJk4HDltJSZlTgWPOGzCTLoBQ4mznfmC1UxiyRMG8X3A4FDrSP7XgveAJ6TQHACRYqaz22lfiwPofSFqp5uzCZGeYZM9GVYKHSBlf3UyO+/nOQl0xJsfiMjcBJtVBZMyCWxxwbgRNfm91d2zZSW7cg7bza3W1ep8yLrJVnTAjhds1GROSbQwh/nGWPBoAUBc5yg532XEmtEfdm4KybhSplHMs2UluoalmoxlQjvqlzzbFQqyyMNeLa3BcUC6nLtqtd/FstXyUtVDtKK5LXQgXNfq96nGPhINuogqi9CpwiZFncvtIWaFX3GLGx8421bceaJeJRx8CS5eWAztfelYFTQ4FjVJbM92D7Gv39xRLE7CQkxEjYeV8LC4FDBwl+FKwKnIViqd5F0EbMQWOyUB0cNOoOL6mRSlb1ocDxjpkaYuy1EsWF+aVLPgInh4XKUyWeQiqsgEOvcCR+KdN2BoGUJjlPfta6t1DVslClKH2g/HOaMnCyYK3mmmOhzlApmIFjWkRbMnAKWLPifYYUYhz3aeW249+NzVxqJcsYW6iq1oh3Yb3mG+vC3qzIiAt64yedFvLQZaEyqD689iNXiLHxOWgVJd5WpXh/6+ttIT5qWKi8NeJ1LVRaHDkHicgpEfl9EflDEfljEfnH7e0vFpFPiMj9IvKrIs0/ARE52f5+f/v324vvuTcXpg8CJ9VW1JcCp68WKo8CJ2bBXOVsmVxW4FgwhAyczPk3kI/AMfT7DR+7u3ksVFqkEjjRNlTTQmUhBzzjxLEsKpKU+Q/Kk0XWcSw5SinjjIzAWau55nhoFwOODBythaqEAse6OFdvN1DuWDgzcEB3PMChwFFm4FiJoeV9WrltKxHXbn98BM56zTdWpYHZQuVU4MT90SgmPDXiFntTUgaOkcAxhxhbFThW4sNI2MUgZlPFt9FClZKBY23tqmuh0uK4g7sLvCaE8O3AdwB/UUReBfws8I4QwkuBx4E3t/d/M/B4e/s72vuVRZ8KHE8dsHdMSCdwaoYYpz5Xb4hxLgLHOvbubnOcvItRSGs3G7gCJ2TaziCQy0KlRSqBI9K8960hxjUUON5x4li1LVQapFqopgycJKzVXHMsRPlpq1mBo7VQOQmcTouMd7tdJ/O8/W7N7SnZQoXiWMeFg5XUKqjAKaF2AgYWYqyFa74RkS0R+bSI/Fb7+zA+FV+QE8pLv14sVB3wVGTXCDE2t1Dt256D9n8C1Asxnu851FYVWqjGUSOuxZFzUGjwdPvrTvsVgNcA729vvwd4ffvzne3vtH+/Q8TCvDngrdbuU4FT65PgiLFaqFIInFQLlUeBk0qgpGbgDJjAWSvkCjHWIpXAiePWUOB4CJwUBU4NAqdWMHPtDJw1JXA2A7JDsepsFWlhtVBtLT0u53a1ShbPgsWqwLGGGIMq1BkwH5MiLVTKfY73cYUYaxboxqr5YeIngc8u/T6MT8Uj8aZdt7ktVN4Q45IKnMIhxh4FjslCZVHgGLOOlscAWwaOdR6IZFpQcqNVasSdx6tntETxHwCPAB8G/j/giRAWT/xB4Ob255uBLwK0f78AvPCIbd4lIveKyL3nz59P28E+FDhepUQOIgU2p4XKk4Hz3HPNdy+Bk6LAyUXgeDJw9vb8x3oFchE4DipuuEitEQc7gZP62lptkLXIgVQCp1bWTtxGF2JF/BhCjNeUwFmrueZYmPJCrLYhZQZOCaWMWTGkvRD3KFksQdHGbaszgQakwLHmDZlrxLXntEPdUw7m+UZEbgG+D/iF9ndhKJ+Kz/dti2GR5v7WUFtzC5XDQmW1HxEgzDvvmqbAKVwjblWUWDNdPIorN1mnvLgLc7+Fymo5G2YGzrFzUAjhIITwHcAtwHcC35Q6WAjh7hDCuRDCubNnz6ZtrA9VSh/NV+DP++mzhWpsCpw+CZwUC1UhAmflqycir1z19xDCp9rvr8q5U30jh4XK8hqntlCB/T1c00LlJXCGaKGKHxqlqJdKEziecURsNvbc2NS55nho5fhWJYTFQmX5J6u9KLcuIA3E0PJ+qLetPMbmbWtDjL014koFzszgNa/SQqUlnsoqcArPN/8S+AfAte3vL0T5qbiIxE/FH33e/t4F3AVw2223OXapRTjwLeoHZaFyhhhDs39bHRd3KS1U+8/q7x/27SRU6UwXq+IqOAic5TE052I40Fv+Itw14vU+xco5B4UQnhCRjwJ/FjgjItvtfHML8FB7t4eAW4EHRWSbpsb8qwlPoRspjVBQ5xPMiFRb0WzWfPWh/KkdYnzyZLO4sy5SLl5sFhqpFhfr801pJnr+2B4CJ4dK4wh0nTH/64q/Ra/l2uDixcNspb/wF3zb6CMDJ45rVeBMFqrDcUD3nObtB3gp4cJDzMAZgPpmo+aaTqgzcArViJutWfEE6iIsFDXBl6GkhcqgCLFuW53d41TgqC1UpVqojMovQEU8LcKoi1uoisw3IvL9wCMhhE+KyHd7tnHkDoVwN3A3wLlz5/w5YMFIoEJr3TEs6ONjLCjdQnXZ9rsInAT1irmFyjinWJu0zC1UkXguqMAxBwwnhBiba8SrKnCS5iAROQvsteTNVcD30lgwPwq8AXgf8EbgA+1DPtj+/nvt338nBK2PzQlPw8ry/cekwIlje8cdU4jxshLGEkgc25i8nxZ7X9shWKhSF/lHYOUWQwh/PvuIA8ZXvnL4c20LVWrDmFWBk6ruqEXgDM1ClYPAKU2sxH0rTRTlxKbNNd3QqhWsliRDjXiJFqpSChwcF+Bqa4gnH0FroXJk4Mj24T6thJUIUeYYLe7jWaR3bTs+r7IETsH55tXAD4jI64BTwAuAdzKUT8WtFiqwKT/cGTgGC9VBm70wM1ykWRb0KRaq0i1UhCavZdaxb+4WqqhUMrzeZruc0aa1pjXiGeagm4B7RGSLJg7j10IIvyUinwHeJyL/BPg08J72/u8BfklE7gceA344cfxuiPgIhj4UOKlKmPjYTQkxhoYY8RA4XnhJlL5DjPuwUC1DRL4FeDnNRQkAIYT3Zt+jHvHkk4c/e8+xPkOMrS1UQ1fgWC1UKW1XcRtdGAOBY/2fOQQCZxmbMNd0QpvPUqo622yRUVogrAocdY14QQvVYtsW4klrodpvtmv6RKqUAmcGzArm1GxDuNi9XaihwFkg53wTQngb8LZ2u98N/P0Qwo+IyK8zhE/Fw56N+ACfhcq7qFcpcFoCp8sKddT2Nc8jJcTY1EJlJXCWj1EXgZMYYlwyA8ds01r/GnHPHBRCuA94xRG3P0CTh/P82y8CP5i8s1Z4MlrGaKGKj/U+11QLVQj664gcIcZgz6J57rk8BE4fCpyUDJyeLFQAiMjPAN9NM8F8CHgt8LvAWi2qHn/88OdrrvFtoy8CxzpHWt7rzx8H6ipwNPs6BgtVrQwcGC+BsylzTScs5IK5aahPC5VVAaDNMhirhcphRbJkybhUMiUIPnQKBVcmkB8V55u3MoRPxQ927bXPMy3py5L9qGAGzkKBY3gelgV9rRrx+b7RBrZEfHSRV7UsVCkZOGpyxRFibA5jdiqWMmDtr3lqkxpjtVDlWMBot7G/f5jZ40EkQ6wETi4FTp8WqhEqcN4AfDvw6RDCm0TkRuDfZd+bnhEbziCdwBl6Bk4qgTNXlDnE+6UQOKCblyxz13HjDE2BM5/73/NjJXDYkLmmE7IN82e672cmWkorcDRWp4IKHDPJopk04308ChxNC5WDCCmhwAH0pJYxewlojp+C0IKaCpxi800I4WPAx9qfh/Gp+HzXplwBW321lziwKnBk2xZsW0uBoyUMwJ4jZanGrhVinNJCZVLgGC+4PDk70FcL1Xpf83hCdlNyYbw2m7FaqJY/GdZuIzWTxavASSVwvCqY3d30nJLUDJzTp9PGPwLaWfG5EMIc2BeRFwCP0Pi21wrLr8vVV/u2Ec8vK4GTSs7VUuBY81VSCJz4OA1ZVKuFagwhxnGskRI4GzHXdMOwkDZdpBfOwFlFiIQ5EHzEUKkWKvViaGa3OV22Xyu2bSYrLOeGdXGlrVZ3KnC0rVyFM3CWsFnzzfySXYHjqa82nxvGDBwPCQVKgsirwDEQXWHefHkVOJ3b974OBpIo7ot1jqlhoTKTRL1aqNZ7DhpLBk5fFqocIcZgI8lSFSHLGTgW9KnA8TZfRYytRnwJ94rIGeDngU8CT9N4ttcKy6RLqgLH+l4amwKnVgaOdqwxWKhqtkNZCL2BETgbMdd0Qp2B47FQhe6LYrMyRBOA61gYabNkSlqo3JXZ6EKMXYvdUgSO0p5lrq+P2+7KSKquwNms+eZg156BY7FQLTJwrMoPg4Vq7ngOJgWOV71isFB5wp4tChyvJahmBk6NFir169GfhYp1n4NqW6i8C+2xW6isuR05CByrAidVCZNij0slcFJrxGu3UEWEEH68/fFfi8hvAy9oA7TWCjkUOFMGzuUYE4EzNAvVJipwNmWu6YQp58RooYqPW3mx6lTgrLpgXizOPMqeQgoc9rsnQ3P1OUsEhIJ4KqXA4QCT7cuybY/1S7PtyiHGGzffzC+VtVB5M3BMFirnc9Buf2GnKZiB4yGJLOSKtwrdrI5xhmKbxvAQOI4xlvetItZ+DqqtShlbC1WOEGOwjZuqGvBm4Fy6lEakpLy2OVQS4LdQFVDgqJahIvKR+HMI4fMhhPuWb1sX5FTgrHsGzjoROLUsVLVDjMdUIx5Raq4RkS0R+bSI/Fb7+4tF5BMicr+I/KpIExYiIifb3+9v/3576tg+GBbS1hpx6LZRmbN1FBaqhUrGUSOutd647FldHs19xyezlv32qHvmzSS+cttO60GfFiqcCg4nNuXaZoG5I8TY1ELlzcAxWKhcth2LAiclA6egAketRsRPSFjGgEQFjpZc8YQYW4ko53mbAWs/B00WqtXoQ4GTy0K1iQTOgCxUK5ehInJKRG4AXiQi14vIDe3X7cDN2femZ0wKHN04UCfEeF0tVFMGzpWoMNf8JPDZpd9/FnhHCOGlwOPApsre4AAAIABJREFUm9vb3ww83t7+jvZ+9SHbygtca424hmjBsUBXWKg8ChxTODL5VUOLv3sXQooQY2/lcmcAs0OBo1F+hYCvPUth/apkodq0a5sFDhwZOKYWqlQLlZKc8GaiDKWFylO3biGh3C1URuVKUgaOxUJlDTE2vN7L96toodqYOWgsBE4OC5WnMj1niLFlzD5CjC9dSreogY+cS11kp9aI92Ch+pvA3wW+DvjU0u1PAj+XfW96Rg4Fjuc17kuB4yEhaoYYD9FCFfdlslBlR7G5RkRuAb4P+KfAT4mIAK8B/lp7l3uAfwS8G7iz/Rng/cDPiYiE0CV3yAzZQV0jbgp8NRAiuWuzPRfJxS1UcdsrbADhwJ+BU6JGfPk1XPVYz0JXpfyK7L3HQqXMwCkfYrxR1zYLeGufrS1U7swoxT/7cJBgDSpM4FiIj+X90sD0HLyvw1BbqEpn+TiPVxo2Yw4aW434pMDphjfEeFLgZMXKZxRCeCfwThH5iRDC/5Z99IFhkxQ48/n6WahSAn/jNrowZeCUQeG55l8C/wC4tv39hcATISxWqg9y+InXzcAX233aF5EL7f0fff5GReQu4C6A2267LfMuKxbSYQ7MB6I60WzXo8DRhCMv/d30aXAhMuuybZdU4HTtt1OBo6o+p0xAciUFzqZd2yzgJSPVCpzEDBwtOVGylSjFQmVVfFiIKG0wOtQLMfYSgvGxqjEOHOeTo+kKqlqoNmYOSlGlbJKFqmYLVWqI8ZSBY0OfGTjAvxGRvyMi72+//rZI+hXW0HIpcmbg9KHAqWGhEmm+hkjg1Awx9sy18ThoX6f5fHMInCVknWtE5PuBR0IIn8y4jwCEEO4OIZwLIZw7e/Zs3o2r5PgJtiGNosW03XgCraoR9yhwBNgagIXKmcFQLAOnY9sh0JB7BSxUrsYv0Kl7nOSQH0WubQaL+b5PvWJV4JjHUBK1kKbIMDU4lTxOHguVUYEjM/tFntV6lJKBUyPEuGSodD6s9xy0SRaqTQkxnjJw6o9/BLRL638F/Jn2e/z53RnGH1QuRV8KnBzk3NZWnRBj61g5CBxN3k6qWiVuowspCpw4liUDJ2WckRI4ueeaVwM/ICKfB95HY516J3BGZHG1dgvwUPvzQ8CtAO3frwO+mjC+D7LNItD1OAxJdaIiQ7zBmhrlRkEyi4IWKo8CR6VKipOm9Y2tqK/32NWgWYQVCaNOQqlrm2HCpV7ZsSkyoLyFaogKnEi6a9y2rhBj43PwvIeqWqi05IqXiMZhoerlQmi95yBrvgMcXvx6FilepUTfCpwxWqg2icBJycDpw0IlItutzeC/CCF8+9KffkdE/jBl4CHmUiy/308amxEj+mqhms1sY6YQOLNZnRBjS95OCglRy0IF9YgVi2p1CAROqbkmhPA24G3tGN8N/P0Qwo+IyK8Db6Ahdd4IfKB9yAfb33+v/fvvVM+/AXQWqviGz0xahDkQbNvVEEOLvxXIZXGRCqXsZOhDjF1hwBpyqGAQa9IifRgZOCWvbQYNz8J+ZrBQJWfgaP4Je56Dp8HJG5SsIBw8VjOL9cjTErc8Ro0QY4uFSv1ZcwsRWyZRDxaqjZmDvKRG6ieyUwbO6jH7yMDZ28tD4FjJuQ21UP1++/1ARF4SbxSRr6e7AqMLMZciUgHqXAog5lJcBhG5S0TuFZF7z58/b96h5dfl9Gnzw4F+M3DWVYGjGSvl+XgsVEMncEZYI15yrjkKb6Uhju+nmUve097+HuCF7e0/Bfx0gbG7IQYlhKlGXLNQ8qhZFBaqFAVO73ayUjXiDnJose1VdrX4t5IWKusFiYUcKu4gqD3fDANzx8LeYg1yZ+AYasQ978daNeKgJKEcJKhFueKxykFLfGwZyJWBWqjAl0lU10K1GXOQ11bkvSgVScvdSbVQWdVGfbRQTRaqemPnGv8IdG0xLon/PvBREXmg/f124E3eQZdzKdpPxbMghHA3cDfAuXPnzJ+aRwLnt34LbrrJtw9WldV83pAPE4Fz9DhQj8CpYaGqpYwZoYWqyFyzjBDCx4CPtT8/AHznEfe5CPxgjvGSoFpIe5QQBqVM9hpxpwLHYqHyqJE6L/ITLFS9hRg7F6HF1E4oX8dqBE7x+WaQCPuHeTNaiKFGPLW+upiFqqICZ74HWx0S7qQWqoIKHGiVK9qLh0sVLFQH9nMWbNa/fixUmzEH1SZwvGPmslBdvGh7TK4Q45oWqk0MMfZaqEJIVzwdg65ndFZEfqr9+d9weOV9ALwC+Khz3JhL8TrgFPAClnIpWpXNLVyZS/FgyVyKqO76vu/zb8N6fuVQ7cFE4EwKnH7GyYhSc81IYVlIOxQ4mrpvExkyA2YFiKF2P9TNSAOzUKlqxE8Zt60h4QoqcNyvoyWMujiBs5nzjWdhP7NYUVIzcJQEi3VBPzQFTnAQOCabmUc1yOE+Da2FyqXAUahYl8eA2gTOZsxB29v2hX7qgrsPK5N33FwhxtYWqlPG645l9KXA8ZIofSpwcii7jkHXMnQLuIamfnebhjGW9udrVzxuJUIIbwsh3BJCuB34YZqciR+hmbDe0N7tqFwKKJhLkcOm1ieBo82lgXQCp0YGTi0Cp3YGTg1r0wgJnCJzzWgR80JWTXOuxVIhCxXQvIQKC5VHudGnhYrSFiqHFQk6FkDtRCXW7IhCx3px/7nunDbbyszYzPnGlYHjaaEqbaFyEkTa7YNdRbQIAFYSLMv7pYFJgXPgz3OZWRRXCRk4apLIEWIMRuKxneO9F5E+bMYcNBYFTl8tVH2FGOewUFkycObzZh9TCBxrlS801xsHB/kW9578HehFgfNwCOF/zj7q8Xgr8D4R+SfAp7k8l+KX2lyKx2hIn+zISeBoX+Mcqj1ozmurAiclXHgdFTi1WqhqKXCefbb8OBlRe64ZNi5TcBw3KSWQFiqrU2aixVMjHrdbwkKlJlkOjNsFdYhxSAkxLpCBo1I7OUmWy/b7mMfWs1Bt5nzjyUaxWKi86iyTeuUAZsaWiRohxqUVOKbtJ1iotIRdCO1r4bRQmdQxjgsuU3tagmLJj82Ygzx5NGO2UI2FwEklqsC26IzHN4XAEbEf41SFU0RsRfO+vgUWWV3PqDgdPaRcij4VOLVrxOfzyUIVsekWqpTXKCOqfvQ1fCxd5B63mE0JMdZYqLLXfTtDjC3V1tmrz8FcqX7Zfmi27VXgKI61x0JVIjwbuEw5dNzxrJ+Bs1nwBgCXzhKx1IjPHf8Ya9WIg9Li5LFQGZQrnrDqxTgKEnd5P2bGBVm1EGODhSrlePmxGXOQV4EzNiKlr3H7aM7xKGGi3SqFwAH7Mc5lc4Fmod7HeXUMupZtd2QfccDIQeBYA8GnDJzV48B6WagsH0bM53VqxFPUWBmxUXNNJ1RKGY8SorCFamUz0v7h/SxQ2XoSWl1U6h7rGzGGOhdU4KzatnsRuoM+aNi5wF2pHKpG4GzmfOOxUImlhWrPZ0VZZNQoFTJmEqqiAkdloUoIMdYqcFIsVCqiy/Eclu9fmsCx1ohXrBBvsRlzkLeZKVWB04fVJYXASV0o1bRQRSWM5RivA4GTknFUQIGz8owJITyWfcQBIweBY1V4TQTO8YiP0+TtpJAQtRU4lgycGkqfFDVWLmzaXNOJxSJr1SI9QYFTykJVQoFTzEKlaM4C56K3jTIokYGjCkhOsVApyTLzflvIw7IEzsbONx4LlSkTZd++oIfDBXope5BFgTPP0ELVBY9i0BTEnGAJ0lqPPCQU+CxUHnLFFMZcX4GTOgeJyK0i8lER+YyI/LGI/GR7+w0i8mER+Vz7/fr2dhGRd4nI/SJyn4i8Msfz6MRYMnAiGZHyaWZfzzVuR4scrUjW57ouBI6VGMxl4ToC/X/uPiDkIHCgPwJnk0OMU0iI2hk42vd/qoVKO5+nnAsTSkFzkVtIdZLUFqUhhgpWWw/FQrXYvmK/S1g1kmwgWgVOAetXvRDjzURpC5V3IWyxULlaqCrWiJe2UNVoobI8h+Ihxs5zymyh6j8I0Ih94O+FEF4OvAp4i4i8HPhp4CMhhJcBH2l/B3gt8LL26y7g3VX2ciwETrQVpVwM90ngWEmNicCxI8VCVVuBs2nI0TQG/RA4nhBj7zy1biHGFgVi3JcaCpzUXB+LhWoicAYGzcW6p0a8pIVKOixUKQqcEvXTRS1UKMNf545t952B41y4qcjDahaqzUMMnfVYqNQLYYclEGzkh8tCVSEDx0ISecgP63NwhxgrCTu3AsdI4HhUYzAGC1USQggPhxA+1f78FPBZ4GbgTuCe9m73AK9vf74TeG9o8HHgjIjcVHxHU8gUL7wL7dqkBqRlJcQxoW4GDtiP8ToQOGOyUG0achI4WpXFZKFaPQ5sbojxROBsMEyL3cyqk1IWqpQMnKItVF0TjHcxpCGe5riqvoHVx9q5CLW0UBUJX47156P7NHz4CPHYeixU88PHrxzDu9i21Ig7CByLeqVGBs6iqtyRgVPqGEVoLVTeDJw436ktVAkKnAFbqHJCRG4HXgF8ArgxhPBw+6cvAze2P98MfHHpYQ+2t5WF13bSh4UqdSHmXeCn2ragbgZOHHcTCZzJQjVMpBKhER4FTuq4E4GTpijSjhMJnBrtUKkEjnaemQicIaKQUqZXC5W3haqwhaqYAkez33PM/4ZVaoUBK3A0BM50aZIfC9KgoHrFnYFjWNR7GoNqKHBcFioP+a5V4CRYqIoqcMRGrngbotbfQgWAiFwD/Abwd0MITy7/LYQQgGDc3l0icq+I3Hv+/Pn0HRyThaoPBc5kobKPayFR+lbgTBaqOkglQiMsyrJc6ioPgZNCrKwTgWMJnq7ZQpUazDxl4IwYqqYhh22otIVqVcPQ4m8ewkJpoTJtW7vgcipwZIeVIdRAQ1hY3+QFM3DQHOvEEOOV5OFE4BSDl5g1qVeci22RljxUZuCYyZUZIHr1CvgJHEsLlclCVUmBM9MoB/Fn4MTHWNQxNSxUI1TgiMgODXnzyyGE32xv/kq0RrXfH2lvfwi4denht7S3XYYQwt0hhHMhhHNnz55N38mdHZ9qoY867z4UOKnKAU8LVR/PNRI4tYmjeO71lYEzWajqoA8FTqqiI8IaYpwS+jubDS/EOJWE0BIrtVqoQvuZyaTA2VBomoY8ga8WBY45SLbrwj9aOBwEjkqBs207kbWfmIcDfP8qS1mo4oJ61Rvcq8BRfGpdxUI1XZpkh1cBZwmd9WbgQJu9UpCc0LZppVqoNGO4QoyNKiKr0moxTmEFDuhJIqhnoRpZBo6ICPAe4LMhhH+x9KcPAm9sf34j8IGl23+sbaN6FXBhyWpVDh4CJ0eN+KZZqKwtVJOFqvzYUNRCNa4ZqzByKXAsr3FqKG5EzRDjdVPgQD0FjnacVALHQhRPBM4QYciqyV0j7lbgdFmoopLboTgp0RSltVDhXDCqQ4wHlIFTsoVKRTxNCpxiiORI6Xpsz4Iemv0qFWIMrSLDosBxvi9NGTgW8j1WrVewUO1dVIyRQuAYLVReBY6JJBqdherVwI8C/0lE/qC97R8Cbwd+TUTeDHwB+KH2bx8CXgfcDzwLvKnKXnpVC5OFSj8mTBk4x6HvDJyCFqqJwFlC6vsowloVDVMGznHjxG10IQeBU6tG/KLm2iiDAmcicEYMFdHiCDE2EUPWfw9brF78xzey8WRT13E7FEPxsSu37WmKQrnfhSxUQ87AmSxU/cBDGoCxXSlByaC2UDnH0GaihIOGvLH+UzRVlTvm2Ggz05JQXiJNq1RKUeCog5LnQCivwPHm7PSIEMLvcvw/0zuOuH8A3lJ0p46CN/g11ULlWWj3pcDZhAyc+HqkEjhWQnAoCpzJQlUWY7dQWQgcGD6BYwkXnhQ4V44zWahGjMXFpCIDZygWqs4FWFTgWE+27e4FhSdoWB06euCz9Kg+AS7UQoVTRTCUFirzOTKhEzUsVCltPsUtVMpMlBSFDxhVPo7XorQCR0sS1bBQxf1wE3bra6EaDbyV3mO1UIVgz7OoSeDM581XjudqIcnWQYGTkoEztVCVRZ8hxqnjWkiVVHLAkrdT00KVcgxrZuBMFqoJ3dAsduNJkju4N8FCpVHglMjAYe63hnSqZA5wKXAobKFaqaKKE7Snbvlgye52FBJDjDsJHJkmpBLwLoYtFqqUDJziFiqLAsdJEIGNmPDMWcVDjI0KnJIhxm41KPpjBaMNMR4Fdnaa/yeWT5hTVSle0iiHKiVuS4vUhac1xDgXoTGWEOO+LVSTAqcOxqzAsQQLp5IDlrydWgROSigzrKcC56BrHbY01rReGhhUzS8OtYJq8e9U4NC1AHMqcFQtVKWsSNCoWQZUI26xULlJrRWTbnEL1XRZUgTBSRpYrUHuDBythcq52K6lwNGSRGAns7W2oCHXiFvGSCFwauTsTOhGXDhbFr59ZeDkIDXitrSobaHKRWhYSbLUxVNE3wTOVCM+TIw5xLi2AmdoBM6YMnBqETigH2sicAYG1WLXEQpssVC5FDirFv7OEGNVM5KjKUrdQuVQ9wDqGnFvC5UmxNhMPGkIvtIWqumypAiqtFClZuAo1Sve92NpixboQ4xl25GzozxGSRYq5XGa7zbft07ax7BaqEq2jkGa9W/CakTFhZXUqF0jnstCBf0QONpFWdy32kqYeP03ZgJnslANFzlDjIecgTMROFeilgJHa9WqTeDkIC4nZITabgI2RUthC1WRfJOeLVQUrhF3E0+KGnHvMVm17VQFTpdyaKoQL4N5wvsalMRBooWqNMFSMj/GZDVLIaFKK3CUxykSODMHgWO1UHlbqNQWqonAKYZIFFgUOH1k4PRloUpZJHnG7MtCtakKnMlCVQep76MITwtVDgVOCHrLTMqY60jgrGMGDuif06TAGRo0i3RHBs4QLFSu0N4CChyLhcq7oOvLQuVV4KgUWpOFapRYLIadFio1MeG8UC6tLimdH2Oymh1UaNIqbKE6SCBw1DatRAuVVoEzWajKYSwWqpwKnJrPddY25mmfb9y3vgic1MVG3wTOgGrEpyulJYxdgbO8Pc2Y6xZiPBYLlfb5pI4DurlmslANEKYMHMtJEu/bg4WqtAKnBFkB+GvElRYqrwJH1UJl3W/Neecl+CYLVa+o1UJVIwOnZCtRlQwcrwKncI4P6NUx8zaUdMvRKqO1y6UocCwZON7XY0I3xmKhypGBExuWYmCvBjkWnpbn25eFatMVOJOFqixyhRj30UJlrdyG8YQYa8iiWhaq1NfLatWaMnA2FGq1AthCjIVuQsT7qWfHdt0ZOFoLVaEMHLetp8f9TlXgdGbgOPI7tLbAES+kRORWEfmoiHxGRP5YRH6yvf0GEfmwiHyu/X59e7uIyLtE5H4RuU9EXlls55KIWXQL7lQFTpdCJsyBMFAFjsFC5Q5iVipw5qktVIYMnJIKHC/pGB+jtVClnLcTVmOTLFQeAifHwlNrIYD+LFSbnoEzKXDKos8Q41wKnBoETi0LlZWUqpmB4329hmihmgicIUKzaEogRDQKHKvCQrZWb9erwOnbQuWtEZeOT/y9hJalhaqUhcpbHdy57dErcPaBvxdCeDnwKuAtIvJy4KeBj4QQXgZ8pP0d4LXAy9qvu4B3F9szbyCsWYHjVX4oasSDV1lGeftRJDKitWgVvBk4pZu0LGMcpIQYa6vKK1movKquCd3wEDg5LFRWq0sOC9XJ9r1gVeDUJDX6tlDleK5WMjA+LhVTC9VwMeYa8SESODGTZwwWqnXLwJksVCOHKqg2gRDRZOC4FDir3qzOGvFiIcazdl8KZuCoCC0rATdrH6NQ4Jj3W3HeeQkcrYVqxCHGIYSHQwifan9+CvgscDNwJ3BPe7d7gNe3P98JvDc0+DhwRkRuKrNzTjuKlcBxhxgrLFRJBI6SmPCqV7ZOtY9XEDjBmblSuklrMUbpEGOtGirVQqW8EJ9CjMuhr2amTbNQWVuochA4FiJlUzNwJgtVHeRU4PQRYry8vVWoSeBAeo5LDRXJVCPuG2dCIahyTpyhwJ1hw/uH97NA20LlsQyVaHMCVORQSgZOieMBimNS0EKFV4GjOadHr8BZQERuB14BfAK4MYTwcPunLwM3tj/fDHxx6WEPtrc9f1t3ici9InLv+fPnfTvktaOYsl1SiAONhSrFUlNLgXNROYaHlNgum+MDh0qlrkaMGiHG7g8T4mNCQ8h1YbJQlYPXQpWy4PVYXXJaqHYVJG5ELgJHe3ynGnE/JgvVcLEpIcY5MnAsYcljIXDWSYEzWajGDkWYrCcDBxTKEG9bVIeFKjgVOMUsVCjJoZQa8VVKFqcCR7Vtb424xua0jz3AGJ2qbPwWKgBE5BrgN4C/G0J4cvlvIYTAoRxNhRDC3SGEcyGEc2fPnvXtVA0LVVL2isJC5X0OUN5+NNtqHqdR4LgtVBVqxBfv0w7iY77bjOGxzFktVF4FDigJL6ciakI3+rJQ9dFC1VcGzokT+jGnGvF6Yy+PPxE4ZTGFGOugVeCkvl+tVdgpx3DdCJzJQjVyqD71TshQURFDnu1qLFSezJewtF9HwRl+qyKdvNtWKpK8xJOmhcqsHCoZNKzc9sgvS0Rkh4a8+eUQwm+2N38lWqPa74+0tz8E3Lr08Fva2/KjioUqkTgomYFTw360daqsAkfd3pSowIHu13t+CWaOBiqwW6i8GTigH2dS4JRBXxYqT93zWDNwTp7Uj5krA8eqRslF4FjHnSxUm4E+QozXOQMnlwKnBglRKwNHO85kocqHQTfDHLvTcbLvIQOnWNhwynZZvW2vAqfTQhVJpxI14gUtVIuFbomGqzn2HCPltkeegSMiArwH+GwI4V8s/emDwBvbn98IfGDp9h9r55xXAReWrFZ5MQYLVekMnOL2o5O6EGNv2HMNBY5WuXKw67NPxTFUiq5UCxV6pc+I2+8GDa8CJ2XBe/JkY2PqsgEuo68WqhzWjxMn9LatvmvE+8rAyWWv8RI4kwKnLPoIMe4zAyeFhKhJ4KxbBs583v1/ZbJQZcU+Q22GORYGBY5rkd5Htk7K/tJxIe7NqVFmyZSoEU+yUCnzddwtVKuOdcCvGoI1b6F6NfCjwGtE5A/ar9cBbwe+V0Q+B3xP+zvAh4AHgPuBnwd+vNieee0otRQ4qhrxg8P7mrd/olGNdCFJgXNSH2Jcshq7igJn19dAFcfQWpsgzUKlPW8nC1UZ9FEjftVVzXdLFs2YLVSRsNJg02rE43mXSlgBnD4Nzz1ne0xBC1UvM5aI3Aq8lybILwB3hxDeKSI3AL8K3A58HvihEMLj7ada7wReBzwL/PXY9JATm6LASSVCt7bqZOCINGPVUODUtFBB9wcMuQKgJwsVtJ9oP9z+/JSILDfDfHd7t3uAjwFvZakZBvi4iJwRkZuKfTJ+FGIWSYkMnE6rk7cdaZtD684RSNlfKKTc6NpnrxUJfYhxSQuVeRGnOO+SjrVi2yMmcEIIv8vxJ/gdR9w/AG8pulOLwSpk4CQpcDQ14gkZOFunYF9x8Z1EfhS2UKmJjwwZOF1EUYoCZ7JQbQ76sFBFAufiRTh1SveYnC1U1hDjHBYq7ZjrUCPel4XqBS9ojvPu7qFdrgtraKEa5CfiOUOMa7dQxcfXCjGuoVYBvd1xbARO11ipJNtkoToag2uGOXZHFWoFb6ZM1+I/JRy5RGaPytZTKsQ4JXNjm94tVEVaqJwki7aFasQWqkGjloXKq2QobaHaugoOChM4WgWO17JjUeB4LFqgt1DVUOCkhBibLVQTgVMEfdiKImljUUuM3UI1lhDjvixUORQ4113XfH/qKf1j1s1CFUJ4OCpoQghPAcufiN/T3u0e4PXtz4tPxEMIHwfOxEDAnEgNwo3wWKimDJyjYQn9HUMGjpZYqW2hynHeDx2DbIY5Fu0bulRWzcqL2kLhyO79VTQjpQQNq8isEiRLwRYqL/Gktjl5MnC0qrINmIz6gDdPxKTAScgSsViohkrgqDNwnERXDQVOjRDjGjXi1vN2slCVQSRTLiqUaRGpapiowLEQODksVN4Q4z4sVH1l4IxdgQPw5JOr77eMdSNwljGkT8RzKXAsIdm5zul1JXB2dupZqIaU61OzhWo+X38FzmCbYY6DzICZjmgxKxa6FgFOZUhXo5N7fwsqcDpJpwQLlTqnpoCFym390nxq7czAUZOSvV+WrCfcFqp2ka7KdilsoZo7VURgI3C86hV1C5WT6JpVaNISpfXooEILVQ4FzmSh6henTzffn31W/5hcCpyapBFsXgaOJdcoVwZOXGBrFmxQhsC5cEH/mJzjPw+9XikN7RPxPkOM+8jAGXqIMdRT4AzNQjW1UOXDoJthVkF2KGN1OgFhxQVG8WydAi1UKbaeVWRFUm1xS7Icm1he0EKVqsDpIvhc+9ySkpOFqh94A4C32k+zi+fHKEKGkzJwrmq2P++4eKkSYpxQI14jTBq6lT5h71DlYh7DGGLsUuBE4lGxmJ5PCpxi8KhhcmXgjMVClUpqWCxUY8/AueGG5vvjj+vuv7/fLHBy2AwmBU6DIX4iPuYQ4zhvaBUXKWPWCjGGugoczWuWmlk0VAvVOhM4DLkZZiVKZcp0XTynZNUcHE9YLG53EjidwboekuWETo3krhGHY0OSS7ZQuTNw2n8kK0mtBJWMylY2EThF4F0Mz7abxfDBM4oxCtuPFiSUY4ztdlE37/hUvoaFyksYzLpUfRzOK8UtVCkETnucOus4EwicrVaF0UWohTkQ/MerR4jI/y4ij4jIHy3ddoOIfFhEPtd+v769XUTkXSJyv4jcJyKvrLKTVgVOCM3iIYVgsCpwQug3xHisCpz5XLcYhHwZOF/zNc33Rx5Zfb+IHK9rRMzA2WQCZ6ifiOdU4MQ5qAu5QowtxG8qsVIzxNhCho0hAyeeX0OyUK07gRNC+N0QgoQQvi2E8B0mTAE/AAAgAElEQVTt14dCCF8NIdwRQnhZCOF7QgiPtfcPIYS3hBBeEkL41hDCvb3seKfKIkWBoyEtcmfVJGTrQBkLlZyAsOrCJ7FGHFYc6wG2UEnr4V95TFJIFs05PRE4RTBPUK9sXw37igVYafVKagYOdCuJkp7DqW6CCPwWKo0CJ+UYwWGzVBcRNd9bmvON2LkGCN2WthQLVSRwuixt3nDvYeDfAn/xebf1WgZzBaxqmBwLXu+YfShw+rJQ5cjAAb2VKZeF6syZ5rvWxpSTwLn22ua7hcBZQwvVID8Rz6nAgTpqmAgL8btuFqoc84I1A8f7ek0WqglqdLYYOTNlZGe1hQrnybcIqT1u24UtVK4F0cnVZEVSjXgX8TTEFiolgeOdMDqJp8lCVQwhZTF8GvZLK3AU+TFJiox2UddFGqQSOMVrxAsGPcOSckXxWnhtR9vXNN/3n+4eA9IUOF2vRwpJ1DNCCP8ReOx5N/daBnMFTp5s/l/UJHCsCpxci+y+asT7aKGyyPwhn4UqKrq051NOAseb5wRFFDi9zFghhN/l+Kv4O464fwDeUnSnyBtiDM1501UVP0YFztBCjHMo82pn4HQdv8lCNaFTreDOwNlhJTHktbF02m8Khhi7M3A68oCSasS7FEmpLVR9KXC8IcZMFqo+kaI02L4aDjouWkNoXr+SCpx5BgVOSQJHQ0JBYo14YQWOmvhIUOAsCJwOUrCKAieBJBomrGUwl7kZROQuGoUOt912W/reiDSKGO2iN14c12yhypULI2IP961tocqZgQP9ETja8ykngXP11baxId8i/whMV0pLyGmhAhshkEuBs24EjkWBMyYCZ7JQTeiEdH3a6s2q6SItnDXRdChw3IRTSQtVlwInkWSB4wmt1IrylSRcQQVOEsnSVX8+ETjFkGShUihwUrNXtk41pMDifXHUGAMncLYtTVcDV+BoLFTeDJwaCpxZfB5dr/faETgL9FEGcySuusqmmIB+FDiptiJoFmNDJnByW6isBE7qYsNK4OSoh3/+2M8o1KgROQmk52G6UlpCbgtVjVDciKj0qZWBM6QQ4xwETq0MHCuBU3qcONZE4AwQGrtJc0fjhjUhxgUVOK5wZChnoVIocFJCjItYqJTKIeuxnhXOwJksVP0hxUKlycCpkb0ydAJHq8Dx1lbPtptWpVXhv4tj5L3A01qoMhA4ewUJnBhavcYWqmPQaxnMkTh92q7AqZmBkzOnxGJngjzKgT4sVJZPiSFfBk58bftQ4Jw40bxWVgVOAfsUTATOAiE0X7UVOLnscTUVODs7uvDxsSlwaqiKtOdGKlk9WajWAcoWKnNWjSLE2EUqFMrA6TPEOLlGHI5XyqQocDrsJuEAEMe5EQmcDoWWe8KYLFS9IcVCpcnASSGIoLFQQcd5nUJCDUmB47RQbV/dvEdWHaMUmxnYLFReAmdHqcBJIVfURFSCMm2Y6LUM5khYFDh9EDi5bEXQXHzXrhE/ebIZs6vVDfI91xe9qPn+5S/r7t9nBk4OZRU01z2nT9sUOBOBUx65zi3whRiPKQMnvhe02TRjUOBMFirfOBMKQjrsJsFpdeoKMXZvt1AGjrZG3KWS6bBQJdWId+x3koVKs9+ei4ZtQDpIrZQMnC4LyETgFMM8Rc2gycCpQBwMvYVqdqpVyHR9uuUMMd5uW1D2njr+PsmvQ1RCKZQrXtvRdpslMaQQ4xFaqETkV4DfA75RRB4UkTfTcxnMkbBk4ORQiGyihQp0C8BcCpwXv7j5/oUv6O7fl4Uqt4Xp6qttCpyCFqrxzViFkDMoet0VOHGsvb3VIc1jU+CsI4EzKXBGDNlhtd3Ea3UqZaEqnIGzspHLqxrShhh73vAlLVSnIKy6OHUuQkW6VUmpFqpOdc9E4BTBYjHsOL4qBU4mC1Wnssw5htZCNU9U4EBDGmyfPv5+XgXOTkvg7D8FHJNPkvw6aMN/K2Tg1KwRH6GFKoTwV4/5U29lMEfi9Ol+WqjGYKHKQeAsLwC7mnNykVU33NB8f/xx3f3nKcrdJfRpoQKbHRAmBU4N5FTgWOwr8ZxOPa/7IHC6xsqlwKlB4GxtNfvbpUBMJZHj+7j0c5osVOuALgtVqealBDIEOJ5oiW+uIVmoatSIF7JQde6386Khy56VQrJ0Ek+TAqcYorLEM9mvkwKndAYO6EgDD2Gw84Lm+yoFzoIYHrKFKj6PJ1ffr4YCZ41DjAeD2hk4s1mzUNEqcNbBQgW6IONcZNX11zffn3hCd/8Q8llcdnb6qRGHRoEzWaiGhT4VODnO6T4sVDUIHE0jX65xoDsHJ5VwmyxUE9Toqlz2ZpF01Yh7W6g6M3ASQ4w7j4Vj8p4pQ4xL1IinWKhmXRaqhEWoyp7lnDBmV0FYceE1ZeCUgzc4F5QtVBVCjFPsLrVaqEBHGrgycGpYqGJ2jKKFylsjvnNd8/1Sx+Iv5fWWGcxOKBRXE4FTHJ4WqtRFt2fMPixUOTJaLARO3LfUhed17XvYosDJVaVtIQT7VuBMLVTlkavOG+w14jnGjASO5v2b2ny1bKFahVoWqhzkmyVcuAZRVNNClXNenZARXVXRKW1RK7N1UluoOkKMzcG6HUTIYtslQowL1ognKXA6lCypCpxiGThXwXzVRfVkoSqGuZM0gNZCVUmB07eFKosCR0EaeAiDyyxUxyCZSNtuHltSgbN1snk99joInBTbHzTnlFaBM0IL1WhgWfTGT4bjQsOLU6f6UeBYLVR7e+kEjuUT/EgYpX5qu70N116rV+DkslBB/wTOpMAZFnLVecP6K3BqW6i6iKL42qW8R7XWplwETulg5q1WKT8pcMaMQiHGdIQYu20sUYFzzD6797eLCCHdQnWsdzJHjXjmUGdQ2JxSFTglzg9gdnq1AmeyUJWD17YDbfvRPhysCj+vEJ6bMkatFiooZ6HSKHBSW6hASXwkEDjQ2KguXegYY99v+wNbe9qkwCkHD4HTleXShT5UP2C3UOUgcKwWqlyExpkzegVOTiLDGoqdm8DRnlcwETg1UEKBo22hyqnAsRA43nGHZqGKc2/KcbQ8p5Tno32dcuT6nDypm88nAmegmJ0sGDYclvJdng+vNatLgXOAKze/c7vgtlAtcnuOszklLIZm7WLuOMIiqYXqFLB3fNtNSQVOUgZOhwJnslCVQ6qFClbn4IwlxLh0CxUoSKIcIcbHbTsDgTM7WbaFCnS5SqljnDgDex0k0cJCtTY14sODhUzJqcAZuoUqhH4sVLkIjeuv1xM4OYiqCAuJkpvAsZzL0BA4k4WqLHIqcCwBsrkUOFFxUbuFquQ40MzBXc8ph4UqBpt3KS5TXy9tu2EOAufUqYnAGTXkZHfgaxGiJbWF6jgFjnPREomQlQv/BAUOrCAsUhQ47aJ3ftwnwIkhxrB6v0tl4LiVVHRn4HAwWahKIcVCtah9XqFmqBJinGB30Wai1FLguCxUbfjvKuVKDgJn6xTMV70OoXktUhQ421fr1DEp1qad67pzdiYLVXn0YaG66qrhhxjHxUttC1UuQuGaa/R2otwEjvZ8yklYgU39A83xnhQ4ZRHP+xznVx8ZOCINCbtuFioNiZ5D/aglVlIVOHGcLmIlEjgpY508qfv/NRE4A4V0XUR7c180VqcSGThOVYi0b5pO641n2x2f+i8WQ47JZdYueufH/bNPqBGfdRA4KQqcTptTwQycSYFTDimL4a2WjFyVgzNPeK/w/7P33nGWVGX+//vpHKYnD8NEZoBhyFmSiKwgGcwIrphFfwYMi1kXxfBlV3dXXV1XVhFEBRNJyaIISM4ZZmAYJqee0D09PZ3O74/n1NzqOzf1rVP33p553q9Xv+p2VXWdc6vufbrOpz7PcyitiHFikai1hmrgJCliXAEBpyQhLcENcynpTUEcOMUEnISfW6M4kWuh2DSvkLk5DiHglOqUiPaLnuQmYSQ1cCLhqNIpVNUSUkK1O5pSqKyIcfqE+h5BdWrgQOlxYzTNQlVKHbKQDpxi38ukgttIhCKwFKqdGmkpkspS5s1tScWGkzh78glDZT513paKVMyNVI7oVCTNKUqtSiLguDQcOJGole+cJBmEjoHBItMUJ0mhcoVuvEzASY3BrRmRZKREDpzBQg6chKkoaU8jDukLOCOZhaocMa2uXkWcigg4hWYDi2aySejAKWVq+kQOnPGlz3RlDpz0aGvTm9pSBijVKGIciQFtbcnahJGlUIVy/lSrBk61BJyRFjEO1S6M3IHT25sZ+AXG7pQ8IQWcUgWOqN1Qn61KCTiVTqGqNQEnqSsGKpNCZQLOKKfoTEMDlFdTplj6jStTDCk2jXiZT50lxRSquijNKc8/5G1Tl5dznoukUCWqgZOmA2cMuO4COyQpYlzKLFRWiyIVhnozIslIKcWBEwkHdQmLGJdUA6dcJ1GJAk6576EUB45z5YvZAE3j0hdw6oo4cCIBp9xpxKH0FKrUHThWxDh1ImGklIFvyBSqUp0SURpQe3uyNmFkKVShau+MZBrikClF1RRwRksNHBNw0iekgFNqmkzU7mgVcCrhwCkljXVHTqGqRA0cm0a8RqkrUgOn3JvbYilJ5dY42VYQuIADp6w0p3p05qwUBv6RyJLPFZLk5n5bClURB065s1BBgcFuQgfOUAEBJ0mak7SixZfzFNC2FKr0GNxavoBTiRo4pRQxTlpwtqEGHDiRcFtuG43joH9TgeOHcOA0F07fDeHAKXWGqKQCTqkOHBNw0qPUJ6RQXQdOCAGnmilUla6BM1ocOKFTqAYGSndZmYCTPtG1SBozIHOtSolVo1HAqXQK1eBg4e/KaHLgVLKIcak1cEJ+Bo2AFKuBQ5kOnG1FgfMdO2kR4wI1cMquj1Gsdkq5DpxidWoS3NxLs/apWA2cRClU+US4Ms8H6DkpJOCUm2IHxWfmshSq9BjsTZBCVcIU3BUpYhwghaoSs1AVrF0WOZXKjIWN4ypTxLjgdYgEnISzUBVydIGKK4lSqMbCUF+RukoB3otRmJE4cLr9/54xY5K1WY4Dp1opVJWsgRNSUBiJE6ZaNXD6+sI7cGBk9ZVMwEmXNBw4lR48NzWV7riA0eHAKeVcRgJOku9opQSchgYVZYp9NkIIiqWkUA0N6fkLIVwagSklhaosYSGlQfQ2USGFtB5pKaGIcYIUqmIOnLKcQ6JiSL4aOElSqEoqkJySA6fcFDsoIR0uQX0dozBDCRw4I0mhSurAGc1FjKPp1gs6lRK6iBpLTKEqNw0MfApVgfMUXaO6BAOTSqRQNfhZu/oL1PQa8jey5YqbRnFGIuBE01KPH5+szZE4cEKmUJU6EINwBZtHkkK1eXOY9wmjw4GzeXNyMTDOSAUcc+CkT0gBp9QpqaN2R5sDp9I1cKDwuYxSqCpVXDjJ+xEp7f9KCBdpKSlUIZ1nRmCkBRiMiQjZlHlzW1fEvVH29OTR0+cCBYGTOHDSTKFKw4ETHT+NFKptwlM+cSjJNOJjVIQrVIw6SQ0cMAdONRhMUAMnEiYKFZ1NLK5EAk5K04hDcQEnaXpTgx8oFBIMktbxaRwLA6UcP8ENUWNHEdHD31iU+3kC/UwVK2Kc2IHjZ+0aKJByNhgJOHYTlBojGfRu8Clv48Ylb7PUQXZPjw4gQgzGSrW+A2z0QmxSsWokKVQ9PWGcRqDH6e3NDPYKUa0aOCEFq6htMAdOLZGGA8dSqJK1AyNz4IyGFCooTViJtjcneChUigMnVLqxkQJFp4out4hxERdE4uLIeb6siRw4rSWkJdRYEWPw6UhpzEJVzIGTsIgxFCi+vDVzrUdK0c+eCTipkSSFqr6EFKqkdVGkTv+2UA2cSNyJ+jNSigo4SV1EDb62SwmOj7IFnDElCkRJBJwiLp9t1yHBwKS+Xc/FUL4HFCR34EQCTikOnHq7CUqNkThwNmyAsWOT3dCD3miPdJAfYjaPkYgLodxG0fndXMTRFu0TUsCByg92W1v1yX0pA960HDilOoC6uvTznAJ2p+TZUVKoSvk8R4JH2ilU0fYk768UN1MlU6hCFHCvlAPHBJxRjhSpp1DuzW3DLrrc+mye45Y5QBcpkvaVxBWSkgOnFCcLJHAOtRdIz0qSQlVshquERYwBhvI8tXZ9yQWcgu4vuy1JhYHNGYfISCklhSoSXpKkotQ1F06hGtwCSPluibQFHPDulUKOj4TulYYOGChUZDzAe2gan76AU2q6WaJrEaVQFbgeQ+bASZ2RCjhJBQ3Qm/qtWzMFJQvR0xPOpRG1W4orJXIbJX2/HV6o7CogVEaEfK8jua4hhZRSZ4MZHNTBVkgHzkjcZM6F+zznwO6UPCHEhohSp4qGsAJOc3NpAk70uWst8yFWqemWm/z/zCTiYylupkqmUIVw45XisAyVQlVqO1bEuAbZVlMm30UsU8Bp3E2X3dfl3p7IYVGg8HK5zh4ooYhxP+XVqUk5haqgAycaaCWogZNPHEriwGmcocv+Jbm3u62xGcdGyLYC2oVq99htSSoMdKt7oxy2OXAK3KgPBhBw6osUbo/SwMp9Ul5sFqoQ4kdDqelH5RaUHqPXMt/ANJQDZ7A3v5gWRMDxMazgZypB2h/otYDSrofVwEmPkaZQhRjwjuRJend3dVwpoQScpiZ9vxsLiK4RmzZlBJ+kjLQ4dWgBp9i17ezUZUgBZSQOnN5eHWSZgJMukZNtwoTkx6qrq84MQM3NpcWMpAJOqWJvtD1JrKhUCtWYMfr3q1cX3i9E+mgpKVTmwDGKCjhlpzo1QesxMLAqz3H7yr+ZTc2BU6CIsev3xy4joBWb6jtpClWhGjiRIFVOv6WIAyfJuW7eX5c9d+XenqYDxw1QtvBkFGagq3wHTl29OhQKplAlFCagBAdOwgF9RRw4Y0tz4JQbYxvHqHsv3/sIMS12kx9w5HPhbBM9Agg4hRw4g1uSXW9z4NQG1XLgQGkDsTVrYMqU5G3G262kgANaM2hTgc95RGcnTJqUvD2ovoBTbICzcqUup00L0y5UR6DLgwk4nmjgvssuYY5XavplSAFn/PjM56UQUb/KFSLa21XsKCb2RgJOEgdO1MfuAo7hEDMANjbCQQfBAw8U3i+EA2fcOFiVZ+wcEcqB09VV2MlpRYxrmGJuhST1AcacCX0v5HZZJHFY1HcUSL1JUpelgAMnemJfV44Q0qIpQwMrcm93KTpwIvdMXRmBq5QUqrIdOLOhYTr0Lchz7L4Enw9/IzOY5x/V0OaMqGaEww0lS6ECX9uliFsCkjtwChUxTjqgLzaNeAhnSWNHkRo4AVKoIL+rJBJEGpLcEPkCsvmmK9/WRplPASGWlldIwOktv94RxIoYlzILld0EpcZIXAuhBZxSBmIrV8LUqcnbhEzx5cj9UYj169VNGELYGDu2uIDT26vnI4RTAUYm4HR1hUtlKjXNJRJwdt01TLtQPYEuBybgeJYv10FsqPNc6gx2PT3lO2GymTgx4yQq1iaU366IxqhiYlEUS5LEpunTdbl8ef59ovc8cWL57QCccALceSesW5d/nxD1vw47DB57rPA+IQScY47Rc3P//fn3iT6jJuDUIPX+n+xQvi91AgGn9Shdbn1++21JUqjqJ8Fgni9QknoGhWrguCROFoGm+dCX4zwA21KoyhaeCghakTAnZQQUaQGkgLiX4BoCNMyCrc+EP3a9f/KX7zNiAk46bPXnuznBU+ZiswYNBnDg1BeZvnqgJ9mAPnLg5Es/imrLNCSwDRd14CQUuiIRLl8dnMFIXEnwPYoEnHwOnD7/P6kxwQ3zNgdOoc/UloQCTgkOnG3Xw26CUmOkroVKp1AtWQIzZyZvE2DePF0uyPMAJM7y5SocJZ0ZBUoTcJYt02UoR0qpAk5vrwpaoUSyUq9tmgJOqWIkhBPMsjABx7NoEcydG+Z7BKULOOvXh7u2U6ao+FCs3aQpVKDfh4cfLrxPV5d+v5MU/Z01S//+6afz7xMJ3UnP44knqlvlqafy79PVlTx9dLfdNM4WSkELcY2OP16Xhc5dCJeUkRLRYHcg32B3SybNaqQ07aXLvhdzHDfBAL1uIgysyb1tqAvqyvyg1bUWSCXbktmnHJr2zi1kgfYZMoV9R0rDLjCYJy8ziQNHRIWffMWXBzdk3C7l0HEm9D6YO80ukQNnsi5zOZ6c03NSjqBlFGaLP9+tCW5kizlwQkwtXWyK7K1rkolQ9a2Ay7gusun3oki5tYIg/Ro4xVwlkaOlPoGAsy2FKs9TukjAaUpw07WtBk4BB85AQsdVKdO6967R91FnqZupUc0UqmKi0caN+rPbbsnbhEx6UinpEIsXh2t37NjiaRGLF+syVJulXtelS8O2W6qAEz35DyngRC6iUmb8itwAJuCAiJwiIi+IyEIR+WLIY7/0Euy+e7jjtbSUHqtCXdtDD1UBopi7o6tLhaok4sDcufDgg7B2bf59QtTKam1VJ8mtt+bfJ1T9ov320+UzeR48Q5jrtb8vMfHjH+ffJxLSk5y/adNU/Lr77vz7hKhTtCOSZqwpmYaomOwrubcProKGMp9oNEyD+imw8bLtn0a73vIFnJYD1LkxlOPp8OCazAB+pEihFKoEDhyA5n1g4FXoW7T9tsH1KiiUPf3yrnoutuSwwUXnqBwBB1Tg6/l77m1DG6AuwU1w62t1ueHnw9dH9YbKrX1R16KOp+4/b7/NbQHcTufAqUis2fCELsfOL/8Yxaavjhwh9UlSd4q4V3pXJhOhmr1Nd2sekXmbAyeBgFMshSoSwcp1ljT7AeKWPHnY0fFDOHDypVD1rtIp3xsT3Dhsm4UqRQeO1Ol5KHQ9eldCS8ABXo1TlXubxkb9KVQLAXQAs2lTGAGn1Km1Q4saUcpBsfcK8Oqr4dotJS0irfdarCBq6HZLdcEsWaLnJeQAJ3raXcqMX0t8iYJQ7q4sRo2AIyL1wI+BU4F9gXNFZN8kx1y/Hn70I/j611X0OOCAAB31HHAA/OEPevx89PZqrJpc5pgmmyOP1GWhlBlQUXDChGRuowsv1OV1WRPZOAcf/jD8+tfhip2fcgo8/njGDRfR2ws/+QksXKiiaNI0oOnT9bv+y1/m3u6cfmaS/m85+WRd/vu/ZwowZ7Npk16fJOlaDQ1w7rnwq1/BpZfmbwfMgRMnjVhTFg27aCpL70Pbbxvqhf7lWqukHERg7Dug91F49cRMrRfXD/0vQ+Pc8o7bfiLQD2v+dfh6NwADy8oXnKQ1v9uk398clHvs9pN0+dI8GMhSpAfXZVLZymHcubpcdu72Qln/EhW0ynWztByo12/p24eLW0NbYXAtNCQoVNj6GhWu1nwFlr8vsz46P+UKcQDNB0Hvw9D/6vD1g95KmeR8jzIqFmu6vTjZkUDAaZoIfQXqOmxeou6TxgT/TBrHFp6+ekvCwfaYPXXZlSe9IUo1iwSMcigmQm3136HmMr9Dxd5D33pNB0riXGmKUqjyDAg3vQAd88qbQS+ivkgRY+egb11GsCqXxnEZx1AukoqCo4iq3ttMmlT4iS/oDalzYQScyAlTqCYCqIgClRdwhobCCjiTJxc/v4sX673frFlh2ozOcSntAsyeHbbdYtd2yZJw7zUiGtSWUjB6yRIdiIVKHcsiQXJLxTkCWOicexlARK4G3gQ8W+4B16yBT34y8/tRRyXsYYz//E8VcD75SbjyytxpRFGdk1Cfr2nTYM4c+Oxnte18vPhi8iLkr3897LEHfOUr8ItfZNYPDmoh4J/9TMWQww9P1g6o4PHlL2uNmnhcf+yxjDsyxDkU0ff04IP6Wcie1aqrS6/ZjBnJ2qmvh298Ay66SOPZnDnb77NokcaJcmdLjbj0Urj6avjIR+CKK7bfft99ukwpvoxWgseasmk9CjZdlREpIoZ6gH5ofV35x576X9B9I/T8FRZMh6Z5mh7jtkLLwWX21zs3Ov/Lu078B9ht1XSktmPLO27jbupYeeVotnvuMLBC22kpM9i0vgamfBPWfA0WTNEZuiK2PgvNCZT9xtl6DbfcDy/toc6niC33Qttx5R972uXw8l7Q9UdYcCs0H6jrh7bodYyuRTnUtcMez8LLB8LGK6D3MU0ji4SiJALOpM9C1+/g1VOGizVRQeaGdJ5W1SjpxJoFP4VFsScRmxfpQLg+ybSGk2DZn+G2PJ+rtffCxMOS/dNqnQmv/h5uPTq3OLB1TTIBp8PXp3jwI7lTsXp9umN7goFGQ4fWVbntGLbFv2Ft+CdR5Qo4rdPUVfLsJbD4qu23dy+EtlnJrkPLruqwefqb8NJl22/f8ARMO6X840PGIfTk1+DFHE863ZCex6TumPY5sPQ6uO2F3NvXPwYz35ysjdFD9e5tJk+Ga6+FZws0FQ2KQgg40axSn/40fOtb+feLZrAJJS5EKTY/+AFcc03+/QYH9f2GEnCmTNH38toC//dfflkHiqGKXkbug0su0YFuPpYt03gUyokSXdvPfx6+9738+z31FBxb5j1nPurr9RpfeincdlvhfV96SQeMSaZILsBoEnBmAPEpU5YCR8Z3EJHzgfMBZpfwZdxjDxVxli2D//gPOOmkcJ2dNUtTcS68MDPTTzZtbXDmmSpMhOKii9T9UoiDD4bTT0/WjojGxJ//fPttu+yi57a9HT7+8WTtgPb3Qx+CV14Zvv61r4W//EXP3xvfmLwdUPfN5z6X+5q1tanY8o53JG/ns5/V2JLP8bjffnD00cnbaWmBW26Bf/u33LNRnXACHHJIuNnXdhCKxhoYebwpi4mfgKGNQNbFq2uD5nOgPUHwkCbY/TlY8f5YUdk2GPOmjCtlpNS1wpyHYc1XyRQA9sfteDu0n1becTveBD13qBCUTdMeMPYcqE/w5H/yV9Vd0peVP9l6OIw/v/zjAsy6EZZ/EFzWE7n2E2HCBeUft2ESzFsFKz6k7qaIujZofie0JwyKjbvB7k/Byo9n6kkfqT4AACAASURBVA/VtUHjWcnEoZYjYPxHoX/h8PV1bdD0Fmg7Jvff7ZikE2vqGofPQjRuP9jl9cl6Oue8wk6GXU+EPT6UrI3d3wubnss4ArOZdgrMfFP5x2+frX3c/Eru7WPmwNR/SiYazDhDxax872HM7rDrG8uvHyMC+3wB1tyVe/v4A2HGmeUdO6K+Gfb7Cqy5J/f2ycckv9at02Hue2BLgRkqpp+mP0nY6+Pw8i/yb59yLMx5d7I2Rg/Bx1El87GPFRY0QG+yzzgjU8AxCXPmwHnnwYo8M0zG93vDG8LVSamvV9GoUPHJiNNPh1NPDdPuW94Cjz6a39YPWr8hSgEIQVMTfOYzhQuHghZ2PvvscMLR9OnwgQ9k3FP5OPJI+OAHw7QZ51Of0if9xTjggHDXNwfi8lXjrzFE5O3AKc65D/nfzwOOdM59Itf+hx9+uHu4WJVdwzBSRUQecc4F8GFVjpHGGrB4YxjVxmKNYRiVYDTGGrBxlGGMRvLFm1FTAwdYBsQTZWb6dYZhGCGxWGMYRiWwWGMYRqWweGMYOwijScB5CJgnInNFpAk4B7ihyn0yDGPHw2KNYRiVwGKNYRiVwuKNYewgjJoaOM65ARH5BHArUA9c5pwrMOGzYRjGyLFYYxhGJbBYYxhGpbB4Yxg7DqNGwAFwzt0E3FTtfhiGsWNjscYwjEpgscYwjEph8cYwdgxGUwqVYRiGYRiGYRiGYRjGTokJOIZhGIZhGIZhGIZhGDWOCTiGYRiGYRiGYRiGYRg1jgk4hmEYhmEYhmEYhmEYNY4456rdh1QQkTXA4hJ3nwysTbE7IbA+hsH6mJyR9G8359yUNDtTC4wg3tT6tQXrYyisj2EotY8Wa4azI13bamJ9DMOO1EeLNduzI13falLrfaz1/sGO18ec8WaHFXBGgog87Jw7vNr9KIT1MQzWx+TUev9qmdFw7qyPYbA+hmE09LEWGQ3nzfoYButjGEZDH2uV0XDurI/JqfX+wc7TR0uhMgzDMAzDMAzDMAzDqHFMwDEMwzAMwzAMwzAMw6hxTMBRLq12B0rA+hgG62Nyar1/tcxoOHfWxzBYH8MwGvpYi4yG82Z9DIP1MQyjoY+1ymg4d9bH5NR6/2An6aPVwDEMwzAMwzAMwzAMw6hxzIFjGIZhGIZhGIZhGIZR45iAYxiGYRiGYRiGYRiGUePs1AKOiJwiIi+IyEIR+WK1+5ONiMwSkb+JyLMi8oyIfKrafcqHiNSLyGMi8udq9yUXIjJeRP4gIs+LyHMicnS1+5SNiHzGX+enReQqEWmpgT5dJiKrReTp2LqJInK7iCzwywnV7ONowGJNOCzWJKcWYw1YvAmFxZsw1HqsAYs3CfpksSYAFmvCUevxxmJN2X1KJdbstAKOiNQDPwZOBfYFzhWRfavbq+0YAP7FObcvcBTw8RrsY8SngOeq3YkC/AC4xTm3N3AQNdZXEZkBXAAc7pzbH6gHzqlurwC4HDgla90XgTucc/OAO/zvRh4s1gTHYk0CajjWgMWbxFi8CUqtxxqweFMul2OxJhEWa4JT6/HGYk15XE4KsWanFXCAI4CFzrmXnXN9wNXAm6rcp2E451Y45x71r7vQL8uM6vZqe0RkJnA68LNq9yUXIjIOOA74OYBzrs85t6G6vcpJA9AqIg1AG7C8yv3BOXcX0Jm1+k3AFf71FcCbK9qp0YfFmkBYrAlGzcUasHgTCIs3Aaj1WAMWb5JgsSYIFmsCUevxxmJN+aQVa3ZmAWcGsCT2+1Jq8EsdISJzgEOAB6rbk5x8H/g8MFTtjuRhLrAG+IW3J/5MRNqr3ak4zrllwPeAV4EVwEbn3G3V7VVepjrnVvjXK4Gp1ezMKMBiTTgs1iRklMUasHgzUizehKHWYw1YvAmNxZqRYbEmHLUebyzWhCVxrNmZBZxRg4iMAf4IfNo5t6na/YkjImcAq51zj1S7LwVoAA4FfuKcOwTYTI1ZY33+45vQIDkdaBeRd1e3V8VxzjnAVbsfRhgs1iTGYk2KWLzZsajVeDNKYg1YvEkNizU7FrUaa2DUxBuLNSlRbqzZmQWcZcCs2O8z/bqaQkQa0aDza+fcNdXuTw5eC5wlIq+g9sk3iMivqtul7VgKLHXORar7H9BAVEucCCxyzq1xzvUD1wDHVLlP+VglItMA/HJ1lftT61isCYPFmjCMplgDFm9GisWb5IyGWAMWb0JjsWZkWKwJw2iINxZrwpI41uzMAs5DwDwRmSsiTWihoxuq3KdhiIig+YbPOef+s9r9yYVz7kvOuZnOuTnoOfyrc66mFE/n3EpgiYjM96tOAJ6tYpdy8SpwlIi0+et+AjVWICzGDcB7/ev3AtdXsS+jAYs1AbBYE4zRFGvA4s1IsXiTkNEQa8DiTQpYrBkZFmsCMBrijcWa4CSONQ1BuzOKcM4NiMgngFvRStWXOeeeqXK3snktcB7wlIg87td92Tl3UxX7NFr5JPBr/0/mZeD9Ve7PMJxzD4jIH4BH0ar5jwGXVrdXICJXAccDk0VkKXARcAnwOxH5ILAYOLt6Pax9LNbsdFisKROLN8mxeLPTYfGmDCzWJMdizU6HxZoySCvWiKZeGYZhGIZhGIZhGIZhGLXKzpxCZRiGYRiGYRiGYRiGMSowAccwDMMwDMMwDMMwDKPGMQHHMAzDMAzDMAzDMAyjxjEBxzAMwzAMwzAMwzAMo8YxAccwDMMwDMMwDMMwDKPGMQFnJ0JEJonI4/5npYgs86+7ReR/Umrz0yLyHv/6YhE5MY120kJEvi4iFxbYfoaIXFzJPhlGrWOxZuRYrDGMkWOxZuRYrDGM8rB4M3Is3qSDTSO+kyIiXwe6nXPfS7GNBuBR4FDn3EBa7aRJsfMkIoK+x9c653oq2TfDGA1YrCkNizWGkQyLNaVhscYwkmPxpjQs3qSDOXAMROR4Efmzf/11EblCRO4WkcUi8lYR+XcReUpEbhGRRr/fYSLydxF5RERuFZFpOQ79BuDRKOiIyOUi8nb/+hUR+YaIPOqPvXeOfu0nIg96dftJEZnn1787tv6nIlLv15/ij/eEiNzh100Ukev8398vIgfG3udlInKniLwsIhfE2v2KiLwoIvcA82PrLxCRZ/2xrgZwqoDeCZyR9DoYxo6OxRqLNYZRCSzWWKwxjEph8cbiTaUxAcfIxR5o0DgL+BXwN+fcAcAW4HQffP4beLtz7jDgMuDbOY7zWuCRAu2sdc4dCvwEyGWv+yjwA+fcwcDhwFIR2Qd4J6rUHgwMAv8sIlOA/wPe5pw7CHiHP8Y3gMeccwcCXwZ+GTv+3sDJwBHARSLSKCKHAecABwOnAa+J7f9F4BB/rI/G1j8MvK7A+zQMIzcWayzWGEYlsFhjscYwKoXFG4s3qdJQ7Q4YNcnNzrl+EXkKqAdu8eufAuagaur+wO0igt9nRY7jTAOeK9DONX75CPDWHNvvA74iIjOBa5xzC0TkBOAw4CHfdiuwGjgKuMs5twjAOdfpj3Es8Da/7q+i+atj/bYbnXNbga0ishqYigaQayMbn4jcEOvPk8CvReQ64LrY+tXA9ALv0zCM3FissVhjGJXAYo3FGsOoFBZvLN6kigk4Ri62AjjnhkSk32UKJQ2hnxkBnnHOHV3kOFuAlmLtoOrvdp9F59xvROQB4HTgJhH5iG/7Cufcl+L7isiZRfpSqP28fcjidOA44Ew0IB7gbY0t6Hs1DGNkWKzJjcUawwiLxZrcWKwxjPBYvMmNxZtAWAqVUQ4vAFNE5GgAb5nbL8d+zwF7ltuIiOwOvOyc+yFwPXAgcAfwdhHZxe8zUUR2A+4HjhORudF6f5i7gX/2645H7YabCjR7F/BmEWkVkQ40yCAidcAs59zfgC8A44Ax/m/2Ap4u930ahpEXizUWawyjElissVhjGJXC4o3Fm0SYA8cYMc65PtEiWj8UkXHo5+j7wDNZu94MXJmgqbOB80SkH1gJfMc51ykiXwVu88GgH/i4c+5+ETkfuMavXw28Efg6cJmIPAn0AO8t8t4eFZHfAk/4YzzkN9UDv/LvV4AfOuc2+G3/BHxpu4MZhpEIizUWawyjElissVhjGJXC4o3Fm6TYNOJGqojItcDnnXMLqt2XNBCRqcBvnHMnVLsvhrEzY7HGMIxKYLHGMIxKYfHGyIUJOEaqiMh8YKpz7q5q9yUNROQ1QL9z7vFq98UwdmYs1hiGUQks1hiGUSks3hi5MAHHMAzDMAzDMAzDMAyjxrEixoZhGIZhGIZhGIZhGDWOCTiGYRiGYRiGYRiGYRg1jgk4hmEYhmEYhmEYhmEYNY4JOIZhGIZhGIZhGIZhGDWOCTiGYRiGYRiGYRiGYRg1jgk4hmEYhmEYhmEYhmEYNY4JOIZhGIZhGIZhGIZhGDWOCTiGYRiGYRiGYRiGYRg1jgk4hmEYhmEYhmEYhmEYNY4JOIZhGIZhGIZhGIZhGDWOCThGYkTEicieBba/IiInVrJP1UBEjheRpdXuh2EYlUNEXiciL1S7H4Zh7BhYTDEMo5pYDKp9TMDZCcgloIjI+0TknhTaulxEvhX6uLHjjxWR74vIqyLSLSIv+d8nBzh2QSHKMIzy8XFoi//ervKxYozfdqeI9IpIl4hsEpFHROSLItJc4HgzReSPIrJWRDaKyNMi8r6KvSGPc+5u59z8WL/KFqxFZC8RuV5E1ohIp4jcKiLzi/+lYex8WEwpzkhjij+Hff6cRj/15bRtGDs6FoOKIyKTReQfIrJORDaIyH0i8tpwvd05MQHHGDWISBNwB7AfcAowFjgaWAccUYH2G9JuwzB2cM50zo0BDgUOB74a2/YJ51wHMA34F+Ac4CYRkTzHuhJYAuwGTALOA1al1fFcpBATxgM3APOBqcCDwPWB2zCMHQmLKYUpJ6b8u3NuTOxnMHCfDGNHwmJQYbqBDwBTgAnAvwF/sjFVMkzAMQAQkele9V0jIotE5ILYtiO8YrpBRFaIyI+8mJJ9jPOBfwY+79XoP8U2HywiT3pF+bci0uL/5mkROTN2jEavPB+So5vvAWYDb3HOPeucG3LOrXbOfdM5d5P/+3286r1BRJ4RkbNix75cRH4sIjd6RfwBEdnDb7vL7/aE7/s7xadEicgXRGQl8AsRaRZ1/Cz3P98vpKYbhrE9zrllwM3A/jm2bXbO3QmchQq0p+c5zGuAy/3+A865x5xzN0cbReQoEbnXx4InROR4v/6dIvJw/EAi8hkRucG/bhaR74m6/FaJyP+KSKvflismbEudFJEr0Rj1Jx9HPu/jzSez2ntSRN6S470/6Jz7uXOu0znXD/wXMF9EJhU/q4ax82IxxWKKYVQTi0F5Y1Cvc+4F59wQIMAgKuRMLHZOjfyYgGMgInXAn4AngBnACcCnReRkv8sg8BlgMhp4TgA+ln0c59ylwK/JPL05M7b5bNQ1Mxc4EHifX/9L4N2x/U4DVjjnHsvR1ROBW5xz3XneR6N/H7cBuwCfBH4tw+3C5wDfQIPHQuDbvu/H+e0H+b7/1v++KxpkdgPOB74CHAUcDByEOn/iarthGEUQkVnodz3X9xwA59yrwMPA6/Lscj/wYxE5R0RmZx1/BnAj8C30+3sh8EcRmYLGiPkiMi/2J+8CfuNfXwLshX7H90Rj4r/G9s2OCfE+nwe8in8i55z7d+AKYjFORA7yx7wx33uPcRyw0jm3roR9DWOnxWJK0JjyMdF0q0dE5G0lHNMwdnosBhWOQSLyJNCLOgJ/5pxbnW9fozgm4Ow8XOcV2w0isgH4n9i21wBTnHMXO+f6nHMvA/+Hih045x5xzt3v1eBXgJ8Crx9h+z90zi13znWigeZgv/5XwGkiMtb/fh5qIczFJGBFgTaOAsYAl/j38Vfgz8C5sX2u9U+kBlCx6eAcx4kzBFzknNvqnNuCOowu9s6fNagYdF6RYxiGoVzn4889wN+B7xTZfzn5n9K8A7gb+BqwSEQeF5HX+G3vBm5yzt3knXq3ozdNpznnetAUgnMB/A3P3sANIiLozctn/BPrLt/Hc2LtZseEYtwA7BW7sToP+K1zrq/QH4nITODHwGdLaMMwdlYspoSNKT8E5qEPwb4GXC5Wr8IwCmExqIQY5Jw7EC198S70XBkJMAFn5+HNzrnx0Q/DHTS7AdOzBJ4vo/nSURG8P4vIShHZhH7xR1o0eGXsdQ8qtOCcWw78A3ibiIwHTkWFlVysQ/NI8zEdWOJtehGLUVW4YD8KsMY515vVxuKs408vcgzDMJQoDu3mnPtYCTcKM4DOXBucc+udc190zu2HxqrH0RspQWPaO7Ji2rFk4sdvyAi77wKu8zdAU4A24JHY393i10dkx4SC+H1/C7zbux3PJb9IDYB/onYb8D/OuatKbcswdkIspgSMKc65R51z6/wDu5vQ+7G3lto3w9gJsRhUQgyK/s7Hny96145RJibgGKAFsxbFBR7nXIdz7jS//SfA88A859xYVNzJV4DLldF+ZMV7B3Cf0zzSXPwFOFlE2vNsXw7M8sEkYjaQ73ilkP1+lqNBNH785QmObxhGDrwd+TD0aVRBnHNrge+hYupENKZdmRXT2p1zl/g/uR2YIiIHozcekc14LbAF2C/2d+OcFijc1lyx7uRYdwXq3jsB6HHO3Zfvj0VkAjrQusE59+0ibRmGUSIWU8qKKY7893uGYYyAnTUG5aAR2H0E+xtZmIBjgM5K0OULWLWKSL2I7B+z7XUAm4BuEdkb+P8KHGsVI/9SXodWb/8UWhMnH1F19j+KyN4iUicik0TkyyJyGvAA6qr5vGgx5OOBM4GrS+xHKX2/CviqiEwRnbr8X9E0MMMwAiAibSLyetQO/CBwU579/s3HqQYR6UDj0kKntR1+BZwpIif7eNYiWpRvJoDTYp6/B76L3hjd7tcPoemj/yUiu/h2ZkimHlgpbBdH/I3NEPAfFHhK5VNJbwX+4Zz74gjaNAwjDxZTSo8pIvJ2ERnj769OQh+u3TCCvhqGkcVOHoOOEpFjRaTJjzG/gLqLHhhB+0YWJuAYOJ0i8gy0HswiVK39GTDO73IhasfrQoPAb3McJuLnwL7epnddie1vAf6IFji+psB+W9FCxs+jgWkTGggnAw/43Msz0TSstWidn/c4554vpR/A14ErfN/PzrPPt9Cc0yeBp4BH/TrDMJLxIxHpQm8Uvo/GhFOyUiLjtAHXAhuAl1Fn3FkAzrklwJtQt+AaVPj9HMP/5/0GjSe/d1oTK+ILaIHz+33K6F/QKXhL5f+hIu8GEbkwtv6XwAEUFnzfgtYke7/obA/Rz+wCf2MYRm4sphSJKSLyzyLyTGz/T6Gu5Q3oQPDDTmfPMQxj5FgMgma09tY6NLacBpzuS2gYZSLOlZPxYhhhEZF/BfZyzr276M6GYRijDBF5D3C+c+7YavfFMIzRj8UUwzCqicWg6mEOHKPqiMhE4IPApdXui2EYRmhEpA0tHG8xzjCMxFhMMQyjmlgMqi4m4BhVRUQ+jNoAb3bO3VXt/hiGYYTE55mvQS3Uvymyu2EYRkEsphiGUU0sBlUfS6EyDMMwDMMwDMMwDMOoccyBYxiGYRiGYRiGYRiGUeM0VLsDaTF58mQ3Z86canfDMHZqHnnkkbXOuSnV7kfaWLwxjOpiscYwjEpgscYwjEqRL97ssALOnDlzePjhh6vdDcPYqRGRxdXuQyWweGMY1cVijWEYlcBijWEYlSJfvLEUKsMwDMMwDMMwDMMwjBrHBBzDMAzDMAzDMAzDMIwaxwQcwzAMwzAMwzAMwzCMGscEHMMwDMMwDMMwDMMwjBrHBBzDMAzDMAzDMAzDMIwaxwQcwzAMwzCMEhGRy0RktYg8HVs3UURuF5EFfjnBrxcR+aGILBSRJ0Xk0NjfvNfvv0BE3luN92IYhmEYxujCBBzDMAzDMIzSuRw4JWvdF4E7nHPzgDv87wCnAvP8z/nAT0AFH+Ai4EjgCOCiSPQxDMMwDMPIhwk4KbJgAdx6a7V7kQ6LF8Oll8LQULV7YhjGiOlbABuuqHYvDGNU4py7C+jMWv0mIPpSXQG8Obb+l065HxgvItOAk4HbnXOdzrn1wO1sLwoZRu3gHCz4KfRvqnZPDMMwtmfxYvj976vdi4pgAk6KHH00nHIKbNlS7Z6E53vfg498BK66qto9MQxjxLw0H1a8D/oWhT2uG4SuP4MLrOwOrIVVn4OBlWGPaxjhmOqcW+FfrwSm+tczgCWx/Zb6dfnWb4eInC8iD4vIw2vWrAnba8MolRW3wUMfhcc+X+2eGIZRyzz2GFx9deXbPeUUOPts6OqqfNsVxgSclHAO1q3T188+W92+pMH69bpcuLC6/TAMY4Q4Bzh9PbCi4K4jput6WHomrPtO4OP+ATq/B53fD3tcw0gB5+JfsiDHu9Q5d7hz7vApU6aEOqxhjIyNz+hy7b3V7YdhGLXN+94H554Lv/lN5dp0Dp5/Xl//4heVazebvj647TZYtSrVZkzASYkNGzKvX3ihev1Ii5X+Qfjatem31dcHzzyTfjuGsVPgejOvB1eHPfagdwd0/SnscYe6ddn7WNjjGkY4VvnUKPwy+nItA2bF9pvp1+Vbb1SaroWw8flq96L2WfegLvvWp9vOqjthqD/dNgzDSI8FC3T53e9Wrs1HH828/tvfKtduNt/5Dpx8sgpYKWICTkosX555vSOKD8v8bWYlBJwf/Qj23x9uuSX9tgxjh2coVr9gIPATggE/ZnWB80YHvZ1xIIXx7cA66PwBDG0Of2xje4a6oXuHDOY3ANFMUu8Fro+tf4+fjeooYKNPtboVOElEJvjixSf5dUYlGdwKf5oHN+4DA6M0371vPdx0IDz59XTbWfeALnuWwuZX02lj9T1wxz/B1U3pHN8wjPQZP16XTSl/j2+9Fa68Ul0FP/5xZv3tt3u3eRW46SZdbt2aajOpCjh5ptr8rog876fTvFZExse2fclPtfmCiJwcW3+KX7dQRL6Y3U4tEhdwHngg8zpKqwK47z545ztTv8apEKVQVULAedA/9LnjjvTbMowdnqFYbnBoAScShwbXFd5vpETH619SeL9y2PC/sOrTsPHK8MdOm7XfDu92SpuVF8CSU6H3qWr3pGxE5CrgPmC+iCwVkQ8ClwBvFJEFwIn+d4CbgJeBhcD/AR8DcM51At8EHvI/F/t1RiXZEPscrhqlNxmdj+n7ePobMNiXThtbVsHmV2D6Gfp7V0r588uuL76PYYyEu++Gz3wGBger3ZOdgwULYIVPz3/wwczr0Nxyi9a8ec97YNq0TNrUu94FmzfD+een024xojSuunQ9Mmk7cC5n+1kVbgf2d84dCLwIfAlARPYFzgH283/zPyJSLyL1wI/RqTj3Bc71+9Y00ed1//0hqjl4550weTL84Af6++mnw+9+p7WeRhtRYeZK1FOM0tF2xFQ0w6g4gzEHzmBoAWejP+7asE8/Bv24dmgTDG4Md1zQvgL0PhH2uABDW2Hp26Dn7vDHdkOw5quw9Kzwx06T3of9chT+4/M45851zk1zzjU652Y6537unFvnnDvBOTfPOXdiJMb42ac+7pzbwzl3gHPu4dhxLnPO7el/qpi0vxOz5p7M600vVq8fSYiLUF0L0mkjSp+a9VZd9qTgwHEOllyT+X20Xg+jdK67DkTgkUfSa+PMM+H734cXd8LPU0+POgWuvbZybUaD2s99TpeXXppOO3/5S+71b3+7Lq+owkyrf/5zpoDy00+n6gJKVcDJNdWmc+4259yA//V+NO8bdKrNq51zW51zi9CnVUf4n4XOuZedc33A1X7fmiZy4BxwQEbkuO02XX7uc1rXJXKxpBm30qLXl9F46aX0XWqd/hP03HPptmMYNcfQZugP/PTCxVKFBgLXwIkcOK4vU7cmBIOxfyP9gQcOUSHnvhRqYHT9AbqugdUpGEfjM3KFPNdp47xDoM8UeaMGWP8YtM2E+jZNDRqNrL0v87onBZciwGY/Y+GM06FxLKx7KHwbG56E7pdhT//kfOMOOAOIMZzP+xnNDj88vTYG/JAzXs/iAx+ozixJlebmm9Up8K1vVa7Na6+F+nr48pdhwgRYHfg+M2JwEDo6VACMeOQReMtb4PWvV1dOpbnwwszrDRtUQEuJatfA+QBws3+9Q021uXw5jBsHhx6qbpxVq+Af/9Bt/f3wv/+b2Xe0CThDQypATZoE3d3pp1HFZ7xan3LtvEpxzz16Hg2jIK+eDC/trtNzh2Io+ofSGD6FKu6OGQwYgwc7od7PyhxcwPFCSPxcbPglLHkzDPXm/ptS2fqkLodS+CceF3C2pvxkse8leE70JwluCPpf8cfcCZ+GGrXH5sXQPgfaZ6Unfqx9EJanWPep8xGY6AfAaYlQW1aCNEDzZBizu6ZThWbJtYDA3p/V37tfCt8GwNAgbLQnglXBOXj11czrBTHH2N0pOFUBxozRZSTgLFum6TYpF5nNya9+BUtSijO5iNwoixen/7T9739XMeXqq+GMM7QOzsSJ6Q3c1qzRgWhfnwpGu+2mg26AE07Qz1l3BR9uvfhiJlXk05/WZWd6WdFVE3BE5CvAAPDrUMespak2ly+H6dNh773196efhnvv1Ws6fTp86lOZfUO6+v72N3jzmzX9Ly0i981uu+kyba2sszNzHh9K4aFPnAUL4GMf03iQFn/9K7zudfBf/5VeG8YOwpZ/6KxRA8uL71sqkZjQNDeFFKp4elZAZXewE1oO0dcDKQk48XOx4r3QfT1sTlgTo98PpiLRIiTx/qYhhnTfDN2+GN+SUzPrk9wEDnWB80Xf4n1e9i5Y9dnyj2sY5bL5FRVw2lIUcO46C+48NR0Rp28jdC+EGWcAkt576F0JLVNB6qBtdjrtLL0GdnkdjJ0PTRPTq7Pzwg/gxn218LNRWa67TgcPf/rT8GKhAMcdF769ZcsyUu0GBgAAIABJREFU0zn//e+6fCkmDPanNNuZczoDS/wJ/SOPwHnnwQUXpNMmqGgRPVWP1/xZt06fgqfJ17+eeX2JLwE3eXJ602m/8op+lhoa1GX1yiuZbQcfrMsnUkiNz0e8UOvrXqfLHU3AEZH3AWcA/+zctrvBHWqqzUjA2XVX/f2mm/TzdcIJWl8p4o1vzMzoFIJLLoHrr8+4fXKxZk0yV1ck4MyercvIHffssyq+/vCH5R87m6EhFW9PPlmPff/94Y6di3/5F/jJT7SoeTbOwc9+lnl4UC5L/ZgubTHKGOXE3Sz9i8Id1/kvf+PuKRQx3ggN3iAZWsBp3htohP7F4Y4LMQGnU9N7XOyGri/hU9oB/2Uf2hDehRNPfwudjjSwGpacBktOh6Vvh77YU9Ik1zUqoF03Ro/pnNYJ2nQVdJqibVSYoQF1rKQp4PSugV4fZ1f9Nfzx1/t6E5OOhNZd03XgtPob2vbZ6lwK+US/v1tr+ez6Rv19zB7pOXAe+xddbngKBmz2wYry8su6/NnP4Etf0tfXXafLY48N3140C8rhh+uMMnEHEIQdgMW54gr45CfhqKMy637yE12mVfjUORU0pkzRQrrR4Oykk3SZdrrHnXfq8je/yTx1P+AAeCqlCQuWLoVZs3Jvi5w4jz+eTtu5iIoo33uvOo9gxxJwROQU4PPAWc65+B3tDcA5ItIsInOBecCD6OwM80Rkrog0oYWOb6h0v0fK+vV6/fbZR917//mfuv6AA1TEAWhu1s/Y8uXh0mmiWa5eKvB/b5dd4Pjjy28jKmCcLeC87326jLuLktLVpTFp1izYd9/hM3qlwUo/lsslgN1zD3z4w/D+9ydrI/rfEaXlGkZOBmI34lsD1meJhITGuSq4JE0TijO4SYUhgIFAAo5zKjrVdUDDrmHr9rg+Lzr5UmwDq2HLw5ntWxMKOP3LQdr19ZYHkx0rm8iBUz85vICzJRZou/6oy2bvgEriJoocWi2H6lTz3TfBun/LbB/cQXJkjdFB56Oanjpmjgo4W1bAUMAn8j1L4fmYMNn5aLhjbzumH5RNPMyLUClN771lGbT4mhJj94b+TSrihGKhryvQ4lNlO1IScIaybryszk5liQYpN9yQeVJ66qlw1lmZGUtC8vTT+vT3rW/VJ9eLFw8XcBYHfiAUET2hHRjQKa2HhjKFhBcvhkdTiAXx1KzvfU+f5oMKZU1N6c6YEwll48cPT02bMUMdQWnMALZmjYpVuZg2Ta97WvV3snFOC7VecAEcffToF3DyTLX5I6ADuF1EHheR/wVwzj0D/A54FrgF+LhzbtAXPP4EcCvwHPA7v29N09MD7e368+5367q2NhU9Tj5ZnTK33KKf7b6+cHVkIlEgn4ATpQMmcX9kO3CiFKq4qyfUg5morbY2OPLIjICeBuvXZwTqBTkmcojO2aKEZogoRbKrq/B+xk5OfDC75b78+42UqIhx01zfTsAcyKGN0LSHP26gf5xuK+BAWqF+nLpZnIOe+4a7ZcphWzqZ7/PAKui506/bN7kDZ2gjtHlbeF9g+/LgBqBBxZC+F1WMWvvt4QWfy2Vr1hOzWTfC9Mv1dRI3WOTAmfAJrWm07juw9qJYuykUkjaMfKzzlt5dT1TxAwdbAqWrbnoRbtgdnv1/+vsux+k05aGn+V7/BLTOgJYpMP6AjKATEudUTOnwcXKsf7oeUsCJXEoz36zLMXvq8UMKapBxWR30bWhohyf/NezxK4SIzPdjqOhnk4h8WkQmisjtIrLALyf4/UVEfigiC0XkSRE5tCod/9vftl/X1ARz5+rNdegb/Gee0WOfeab+/ve/D7fyJ7XU52NjzEF90knqBOnshG9+U9el8TQ6PlXvz38Of/wj7LmnFvQ95BC4L+B9ZDbROX3yyeHrJ0/WaxoJd6Ho6dGffAJOXZ2KSSkKKMPYtEkH2NHAeNIkXa5cmf9vEpL2LFS5ptrc0zk3yzl3sP/5aGz/b/upNuc7526Orb/JObeX3/btUP3bskU/c5GjJCQ9PSo6gNakAfjoR1UQFIEvfEFdMDN8tkEoF180fXm+4xVy5pRKdL5mzBgucMbjVTwVMURbLS3qgFy3LpOCFJonnlCRfPbs3HWJnvdji61bk7UTCTchroWxA7NtIC6ZmZJCsM2BM0eXodKo3JAO0BtmaorMQKCg5nwQqGuFuvEqXPTcCYuPgeebYCCBABV3I4G6WtZ8GZrmQdvr1YGTqObLJp/6VaepX4NdsOkaTRtKylAP1LVB03x14Gy+Q6cVf/Xk5MfufxXqp8Duz8GuP4H2U6HRFz1LksIWCTgNu8L4D8GWe/X36LOYxkxgRno4B5srWJAzNF0LdfapttlewCHc+1n114z4sPv7YcPT+nr138McP2LT8zBuH309Zg/oWw8DgW5qnYPBXnX1DGyGsb6dJv90uS/g4Kh7EbTsokIUqFjkBsO7lrp9Cs+ko2CvC2DFLenV2kkR59wL0TgKOAzoAa4Fvgjc4ZybB9zhfwc4Fc1smAecD/yk8r1GhYzPfCbz+89/rss5c7R4Z+iimi++CPPna0pPU5MKOo89Bm97m26P0l5Cs3GjiiYRv/+9Lj/8YWhtzf2UOCmRgHP00br8y1904CQC+++f7jTqCxbA2LEwc+bw9XP9vVXoaYSjz0mhercTJ1ZOwImEmqhuyvTpKuKkmMJV7VmoqsqSJfo5/8hHwh87LuCcfLK6O/7jP7bfL/qshxIlIgdiPrEzXsOq3BSeyBUzZoyKq6tX67plyzIFz++5Bz7+8UzqWLlEbbW06PcB0quHFdVTO+EEfU9xQQoyAk7SeBAJOIsWpVtsuhQiUTHt2kJGGUQOnOb9UhBw6qDRPykIJeAMdQNOXTINs8PNFjXkByPSCg1TVBiKF8Bd/9/lHztyI0VpXz3+CVXjburKGdowvDDziI7dr+6h+olaF6j/FXhxLCx7G7zQUn6ftx1/i56Tpr303K/4kK7vfViFoiQMdkL9JBWfJvggUTcWqPPOn3KP689lXQd0nJVZP9vXBgldk8lIl+U3wvWz4dl/r3ZPymPt/TD5SP18t3sBJ1QdnHX+Cfsux8NhP4DX+VTErQEHqP3d0PlQDmElUCrinafBb1thkU91Gb+/Lpv90+WQAs6au2HXkzK/R7VwQtcNitKyOvaA2f6GdX2KqSWV4QTgJefcYuBNwBV+/RWAf4TMm4BfOuV+YLyIVHae5S1b9KZ+8mR1aqxYodN5Axx2mC5vvTVce9EsV3vuqYVu995bXShLl2bay+UICsHGjToVcUeH/n7JJVqfZupU7U8aAs7zz6uI8oc/ZNa9/vW63G03HTwlfQKdj/vug732Gj6lN2gdEUieupBNKQLOhAmVm7o4W8AR0WudYvs7tYAzY4aKg1deCd/+drgUPeeGCzigTq5cRC6rENe4tzcze1K+lKy4gFOuEBIXVXbZRY/zyiv6vs84Q2PWHXfA//yPFgVOEi/ibUXf02yB/he/gBtvLL+NiEjAOeYYXWbP9BeJ2729mX6VQyTgDA3ld+H8939XZobDn/5Ul7mKNhtVJnLgNO07fMropDjv3NhW9yWU0OLzM+s6VBwKJeC4mIDTfIiKN13XA3W+GG6Cp0rxGbkA+nw9hAkfh/oJfp8yBYttBXs7VAjZ9Kvh213Cwmdui7qSmvfV3weWq3MIoPvPyY49tF6FpziRiDOUQByKn5OW10DH2TD1B+rAkeYw6V9G5Vjji8U9/oXkn+dK45zO3tQxX39vCy3gPAjTT4cT/waNHTDpNbp+c8CUjZW36zISO5oDO2NW+FmznvyaLsftp8tIKNoaqJ3eNdC7GibGsnraZkDLruHr4HS/DHWN0DpTZ7sC2BS4hljlOQe4yr+e6pyLnvisBHxRIWYA8Q/3Ur9uGCJyvog8LCIPrwnthokGOhMnakHQaLAL8NrXqjMlZJ2W9es1rWV3/4Bmv/306TJoUc13vlNfhypCGicScOIC0RveoMt589IRcJYt04Kh06fD176mTqfIoTB5si7TcKQsW6b1J6In+HGidkOnUEVuhQkT8u9TTQcOqJi2qcwHgCWwUws47e1w2WX6+qtfhV/+Msxx43VbijF2rC5DXOO4Y6QUAWdFmQ/1o7Sm1lb9rK5cqXXCQGPya14zXBB4PoErPt7WHj79+rnndKarr/l7ig98QIWjbMfMSFm+XD8T8/wYKC5wPfWUunIiN2CSWmtdXZnC6Q/mqWt6wQVw9dXJPxcPPaQzGWazfn2m8D/sGAWVReQyEVktIk/H1o04H1xE3uv3XyAi763GewF0EI3oAH1wDQwE+gc41KOFdRumqSgSqjZLVJNeWqFxVngHTl0btB6hrzffrAJU62uT9T8ScOonqxjU628e6ydpuhaU7zjZ5jYZCxM/nVk/xU+vmfT8DHkHTlts5o65T4C0QG/CKe4GN3rHTRZ1HeU7kiAm4IxVQWjmb2HiBfq6fqIJOKOJoUF49pLM72vurV5fymHTC+pUiUSDxg5oHBdGwOnfBBufg0lHZNY1tKvwEarI8NAg3P1Wfb3ribps8oOZEALOQI5Z85p8TGxoVxEklFD0jK+MEAlEES1TYGvggV/XSzrrWF29vo+22ZqGNkrxk7ucBfw+e5uf5XdEOcDOuUudc4c75w6fUsjdUA6RgJNr0F1Xp+tDFoeMbqDHjdNlNLU06GDlCP/9TKMgZSTgHHpoZuAQDWL2208HY1FR0lBs3qypEQAXX6wpEJEjJs2iutHsUyedtP22jg5obAxX6DUiSl9ob8+/jwk4OzbHH6+z2QFcc02YY0bFfCst4ETH2HXX/N+VuOOjXMEj7oqZNk2FoMcfh/p6FbUbG4cL2klqhMXbmjpVU86++12d6epb3xqevvpMwtLWq1ero2iqf16xapWe04svhhP9/dHpp+syyTTsXV36uZs6NbdzKC6mRDMuloNz+v/pk58cnoZ53XUa197ylsy60OJ4lbgcOCVr3YjywUVkInARcCRwBHBRJPpUnMFOdYG0HQc42BroyVTkwJE6TRPqC/SEMy60NM7WIsZDAWoxxIWhlsMz6yd8TFOdkgghUQpVXbumPEUFehumQX1CASfuNhlzGuzj9GeM/4huvq38fkPGgSNNMPdJ2HOx/l4/OVmaE6ibqq5j+/X1AR042dSNyVwPo/b5x9m6HLcf1LfAq9uNH2ubjf6GYeJhmXVts8JMw73uYcDp1N5x2maFq7HT5Z/gt82EhlZ9HTKFKkoBG7OnLo+LPfER0bZCCTgv/ECX4/Yfvr5pYngBp/tlrRUUMXb+aHfgnAo86pyLHjmuilKj/DKaTWAZEJ9zeaZfVzniDpxcdHSEHfBmD/IPjTm8dt89kxqRxuxXkYAjorNPfehDcN55mX4MDiZ7up2L7u6MgJNNmgLOvffqtTvwwO23iagLpxoCTqVTqBobh3+2TcBJnw9+UGuB3HlnmILGIxFwmpu1rlYIATgSZHbfXd9HrveycKGKv1C++JvLgbN4sYorLS3buxGTFOGOCzig7p748aKC7pC8Nk5Xl37f4gLOaafBRRepuHPhhXDsscP7VW47kybBccdp4WTwtQJ9Cl+8AHR2GtdIiM+OeP75mVqsv/tdZv055+h7qpRInSbOubuA7Hcy0nzwk4HbnXOdzrn1wO1sLwpVhsH1UDchU+C15+4wMzQMbVaRBaBxT+hbAP1LdBajJAxLdfI34r0BClBuE4ZaoWESiO/7hI9618b68s9L5MCRNj/bFVC/CzTtmXHghEihitN8gLpkkqR+gXfg+HPRckCmplH9eJ39KtGxu1VQySaxA2cT0KDpUtlIaxjBz0ifDc/AEv/E66R7Ycqx4Yvzps093u4fpdGAiiEhHDgb/CxuEw8Zvr59djgHTpRadGxMOAuZ2hQd47hr4F0OZr5p+PaQ4krjWBVV2qYPX988KWydnWg2rWECzt7qwElretP0OZdM+hTADUDkHH4vcH1s/Xu8+/goYGMs1aoyRDea+dJeOjrCumGiQU4kahyS9X2M+hFawBka0oF75Pw56CD4v//LzFC01166DF1UePPm/IJG2gLOkUfqU/xcVEvAmThRBZxKfLdXrtQBcbwGkAk4leHUU/W7fm8AF/BIBBwId40jASeKEdkOm/5+rd0ViaTlCjjZDpzeXnX2RKmOV16p7/0d79Dfk8SLuFgEKuCAig5nnz28LlbSuLRpk16L8eNVVFu5Ep71JTHOPhu++MVMP8oV+pzT/08dHfpeFi3SVNiPflRj+tDQ8NTYJGlhUfrypEmaSvXUU9r+zTdrfZ01azRtsJIuwyow0nzwkvLEK8JgpwoUjbtBwyxYe7EWwE3KUE9m4N+8j9Z9WTgbXjku+XFBhZZWr3QuPhZe2ldFonKJC0MAe29WJ0v9OF+nZaB858a2Prf5Qrr1MMcXMk7swImlUMXZ5nxKmAMfOXCyqRsXyIGTS8AZm6xA8lCXikDZhQ5Br68zAaem6dsIV9XDTV6gnf8ZHXxPPgY2PgX9KaQipEFc4GiIDQDaZgVKofLfv6ZJw9e3zQpXAyfqZ1vMVBEyharfx6/GHKmUEE5c6XpJ25r3/22/LbQDp68T+jfCmN0z68bOh4Fu2FJZLSMEItIOvBGI5w9cArxRRBYAJ/rfAW4CXgYWAv8HfKyCXVVKceCkIeBEg/xJk+BjH4PvfEd/jxw4oV0a3d16Mx8JONnsvrv+DwxdByeeQpVNVHA1tN1+cFBraBx2WP59Jk8OP7tYqQLO4GA6KXLZRAJOnLFjk9f2KIAJOJ6DDtJliELZIxVwQrkGo2NEM1tlH3PZMh3A7+trXpb7mY5P7R19Xp98MiPgTJmi362rr9bfk8y0lO3AOeccTSN9z3uGuyEhzOxQHX5sMW2a1qdZv17TSH/7W41/UT/KdeBs3qzXoKMjM9Pfyy/DpZfq8kc/Gh7Tk8Sd6Lx/4xu6fPJJFfA2bFABbPLkjONvB0mhKkg5+eCFSLXYH6izpH6CDvhnevt617Ww6MjCf1eMKIUKdCrniN4HYEOCQmAulkLVMDmzvu85WHJ6+VNnxx042dT5AUu5tVPiKVTt/wT7DECTv7mvT8mBAzpteZLpuMELcTnOSf348vu87dhpOXC6cp8P0OtrDpzaZvHVw4sVH/JdXU4+StevT2/K1KBE/XzDHcPXt83SYrqDCWdq6e/S6cnrsp5Ij99fxZ2NzyY7PmiqlzRood+IxrEg9WFSqIoJOE0Twzh9Hv6ELiccsv22SCQK9QQ9ckZ1zMusixxYXaMvjco5t9k5N8k5tzG2bp1z7gTn3Dzn3InOuU6/3jnnPu6c28M5d4Bz7uGKd7hQDRwIL+BEzpr4LDI//jF86UvD14d24ESD9nwCTkuLzgoV2oHT3V15B87q1Vr3IXIO5GLy5PCDjGigXSyFCirzhDqfgNPdHW6GpCxMwPHMnKmzzCWpORIRfa5ac9xb52Ls2DAxKxJWpvmJAbOFvygdJ2kKVSRetLZm2tq8OVOfK6KuTkWskALO3LmaBvbhD6srBvQ72tAQRsCJahJNn56py3VErA5hUgdOdJ07OjKFjKN2QGv7fOpTmd+T1DiLzvshh6hQ89RT+gPDU1UrmSZaBUaaD15ynniqxf4AhjozMwG1Hgqz/WwGvQ8mK/YaFTEGFSv2fBWm/0Z/X/FecGVWtB7KcspMvliXEz6pbpNya/hkO3DiROdnsMwPcDyFKpvIOZO0Bk59jgFQ/RQYTGgpzuvAGa9FiMs+bh/Qn9+Bk6gGTh5hCHwamwk4Nc3qv2uh3zOehzcvywgUkQtktLgYNr+iyzFzh6/fNpV4wjo4/ZtyCx9TT9Dl2geSHR+0j63ThotEIurCCeFaiQSchjyCa3OgGjibXtDPz9R/2n5b00QY6oOBALWxXr4cHv0M1LfC1OMz66NZyDYFHkwb29PZqZ/RfMJGaAEnGhTkc/xUS8ABFXCS1EjIRSEHTkeHpjiFFjOW+lgZOQdykZYDp65O0yXykWbaWDa5BJzo+ocuVu0xAcdTX6+uthcCiPDRtNnNOdL8cxHKgROJHVENl2wBJyomvPfeuiw3TsZFlQMOyKyfP3/7fdvbkwk42SlUcebOVZfP7beHSQPatEmvBaiAE7HPPpnXkZAUQsCJ2rjEG1xPyaq0IhLGgTNunF7zp59WFw4Mv26trclq+tQ4I80HvxU4SUQm+OLFJ/l1lSeqgRPRfjzM9N3vvqn847otUNeS+b1xFow7F8b5AnsDZdY1dLF0JIDJX4X5XVqrBqCvTHXcFXDgbBNwyvzyD2X1OY40eMdJ0hSqXMWAJ6uAk+TJcjQL1XbHHpfMgbNtOvg0HDg96nbKRV17pm2jNulepLM2jZ0/vF5Jyy66jGYTqnU2L1ZnY1vWoCPUVOIDXTqrVTYde0LDGFgfoDbY1rXQnOPBQfNk3ZaUgU3eRdSQe3uIIsbOwZZlMOddudMqtxVlTtjO1k64//3qvBrcMvzaNHu3aH8KhWyN4axfr6JJXZ6hZ1oCzqRJubenlUJVioAzfnzY9BrnCtfAEUnHbr/M3y8WE3A6O8M6UaL3mituREQOnLSeUPf2qmD2+terEymXAwdSq4NjAk6M/ffP1DxJQp+vBVpIGIzT0hJmAF2qgDN7tn7mkhYxbmnJpE1BxtkTJ6mAk+3Ayead79TUyxAukiiFCmCGr3qy667DxftISCr3esUFnMbG4dv++MdMWytX6rkL4cBpb1fB5qmn4LHH9PrH/680N2tcHe1TiYvIVcB9wHwRWSoiH2SE+eDebvxN4CH/c3FkQa44Q11QnzUIaD9ZB/+b/lD+cV2fzlyUzVgv4JSb2pPtwBFRESAqwtxfZn7qUGwWqmzqoxSqMr/8UQpVrmNDsnoygwVSqBqmaNHkJLMuuTwCTt147XPZhZ0LCTjegVP2sTfnF3DqJ5R/HY3KsHkRtM/dfv02IaHAzXRSnIPn/kNFpKRsfgVaZ+hU2HFCCTj5HDhSp6lCnQEEnL71mZo3cUIJOPneQ0TTRHXGDCa4ee3rVIdN6/Tc25snZfZLQtfCzOs9Pjx8W32Lpp31pzPIMmKsX58/fQrSqYFTV5c/HSIaYFfDgTN+fNh2t2zRGJnPgQN67kPXZIkEoXwiGehA0bmwQkohsSoiOhcpOWD45S+1H3fdpTWPTMCpHlOnhimUXY6AszVhyjVkhJVIwMn+zLz6qn7H2tuTxcneXh30R8JnJEQcc8z2+6Yt4MTbSTK19+Cg9jP6vu3iHyjGnSqQPIUq+ruoPtJVfu6ApiZdt3AhLF+u1zDp/7K4gHPggerWvOWW7a9TdG5DfAariXPuXOfcNOdco3NupnPu5+XkgzvnLnPO7el/fpG/xVTfjA7wJeuDX9cMHW+BngQzvrj+3AJO0xxd9pU5QMqX6lTXpqJTf5lpCfHaOtmEcOBIW/6nOPXjyp/RaahLz4XkeIJd75XvgQS24qE8KVT144HBjCNqxMctIODUdwCufOHJbc6drgZ+RrGA9S6MsAz0QO+q7dOOwBeOOxnqSrzpKcSyG+E3omJNnJ6l8NiFcPdbk7exeTG077b9+lafE96bcErL/q78qUcTD1UnyFDCp9F5BZwpsDVAukLfxtwuoogWL9olEYu2LNdldN6z2TarVkLXwFpfmP6sl+CInw7fJqJClQk46dPZmT+dCfSmt7dXZ1wJQU+P3ljn/f9erzf91RBwJk3SqW6TDJDilFLUN41ZkUqpRRM96Q85E1VPT3EBJ9qeZHBYiOXLh/8eDb4jIkeACTjpM2mSxpfsabBHSiTglJpC1dwcZvBczIHz0kuZuitJatNs2TJc0H7kEbjuutwidwgBp65ue7dKNm1tyb6jkUAbfd+i2j7vfOfw/ZIWMc4WpE46SZcXXKDLCRMybSdxScHwGRQjIWrTJpgzZ/h+0ed0tAs4Oxb+BibXdMtNe2uKTNmFe/uAHF+ohtmAQP8r5R23UDpSlDJU1nEL1cDxA5ihBDVwcvU3IknNl6FN+Qv21vvBT7nnxLkCDhx/01h27Z4iDhwofyaqgg6ciUB/MleSkR5R3ZhcDhzQGZdC1ER54Ye6fPri4es3+PzfEO6S3pW5RYN6HwsGEt7wF3KvTDgUBnugK2HNlb5OrUOTTUsoAadz+1m04kSpR70J2uqJBJy0HTgvqNg1Zvfcg3kTcCpDMQdO9AQ1lAsnEnAKscsumTSgUJQi4Bx7rN50h0j7gO2nTP//2Xv3INmu8srz92VV3aq690pXDy6SkJAlEIbmMTx8A0TgIWyEPYAZxExgTPslQN2aabvxg+gw4HA0Mz09E7jDHZgOh/HIyG7h8Riw2g4YD8GYBmO3u0FtCTBvjBASkpDQ1X1X1a1HZu75Y5+dlbceefb37bPPOflYERVZlXXO3ueVO89eZ6317YUcVZFiiKMcBM7qavm5Df+viiTbiS/uCO3fmSMS+s9EIM0InCFcfrknb1LJ2KYsVOfPe7IjjI/Dk3/n4G//dlt9sbxsn7Cvr1+oiHne8+Dmm/detooMnKWl0TZH8PtjVcXA9vdF+P746Z/25bbf+tbd/YTtsmBnPlLI7glVDYdRhQJHxB+/pw1Vzbxqx31rKik1Qwb0i5OxJ4FTpIVvftvWttsC2YPA6SzC/NWwZc2qGbHNKQSOO+8VQ7LH15UcAhbSqlDtRyhAUTbbeGPfP7e7hHhAqNJlDjLeAvr7kGWp1bNKMnDAnoMzKgMnVU01Q14EVcrBEZPtSsJziwlGvwu9ze33H/2Efz389N3raLF5em/1SmfO26p6iWHa+2XgwLbyJyXw2bkSC9WJC6uFWbD++LbKZi8sFOPMVsKE8HwJgVOVAufct0dfNzMCpx6cPFluoYJ6CZwXvAD+/u+r6S8ghsAJT1JDtkUqYoiUI0eqV4MMTzT2Qy4CJ1aBk4vAOXnywirgP1LTAAAgAElEQVQ3ocRzQOqEsQQzAmcIVV1jWgKnSgXO8rJXqywuXkjgPPaYv4bC9bW0lGYDKrM0BaRam3aSRfshVYEznE0Dvs9XvWo3cZQaYryXJezSS/dWGKVW8BrO+BpW3ezMKpopcFoIV5yMnRYq8AocgHXjTcd+GTjgq1KZw4Y3gIW9iZZUBc5+GTUiadkpwUK1H1JCe0eqTRItVKNygTrFxMpaiSoQODJCgWNVJY0izDozAqfVCKWp9yINwE+2t0574iUF5x/1Y0hvzWfuBJz4rxduRwr2I3DAq3BSCZxRCpz54vpPqazUW/PZMftZqFzP72MKNk9sK2D2QhX7sf6Yf126cu//B4VRqgJn9TtefbMf5i/ytrcZ8uLUqdEWqvC/qoJ2Ywicpz3Nkyip1othnDnj7Vmj+g6ZMVXZt8KEr24LVUyYcI5qUG0gcB57zIc3nzjhJ/47w7lnBE59CJ+n1LHDQuBUFWIciIGd9pvf/m3/+qIX+dcU1U8gimJQhYUqpq9UBU4Y0y4aYfmGdLWKpkJZajbS8Pi2tOSraR065APThzEjcFqIAYGzlwLnB73yZPOrxrb3UeAALDwtQYGz6VU8e2H+SdC1KnBGEE6wnZ1iQU4LlVvfm4CDdAtVsJXtmYFTPPVrpQJnVAZOovVrhrwoI3DCZDtFkQF+sn5xUfox2HNcH05/xf9+PtHu0FuH/sa2gmQn5parsVDtl4FTBfERypwvX737fyFQOtVG1V31FbP2Q9iPXsKx2jpTKD/3ucmbW/KE2kbixG/z5Lblay/MFDj14OzZ0aqUEAL72GPV9BdD4Fx5pZ+0VUlsnD3ryZJRpEbVAbtholVmocqRgVN2jHOE+cZk4Bw44Im0HATON78J3/qWL/F72WV7P4WfETj1oSr7ZZMhxnsROCdPwnvf64nCl7xku88UAkejwKnCQlWGVAVOWLdsHOp0POGRqsCJIXBSlVk7CerPfc6Tk/M7MlVnFqoWItiR9iJERGDhaugaJfhlBE73e9sWLlW7G/sTLUlls7t7BwEP2r7UnoFTaqFKUOC4jf0Jrc7FwHyarQxKFDgZM3AspJbrFcdkPwVOMfi6PDc7MySijMCZK67FFPXK1lnorsCR5/q/g21r7SFPFBx8qrfTJFU+CvsxgsBJ2YfehlfHlClwehUQOKFq1jACUZGaFdRd284E2gtVEFFbZ0cHJYMnBjcTnqo659VICyOIg4WLfdn0GfKh1/M3tKNusqu22sSQC6GUeJXZMDHkQiBaqrKLaSxUVRYKiFHC5CBwYjJwRNInofvhr4tCIrfcsv8yMwKnPlSVN9SkAidcL4cPb48Lv/ZrPtT9T/5kW+G1vGzvc2eI8ShUocCJIXBSFTjhWMTsVwr5FQiZmH1KzUbaOa5efPHexNFMgdNCjLJQAcxfBVvf2/t/I9t1wD5VqADmiwT0nuGG2W3urRiCwjLUtZEhrsueocuDthMVOKMsVHOhbLZBXu3W9z8eIgWpZXxK7kYpcEIGTqKFat8qVNjO4yDkep+bvXAe+nkC/6YKT9wND3yo2hv1zVO+3PJ+ypJA4HQTvohPFbbQpxaVps5+w79+/zP+9cqb/Ov64/Y+grVoPwXOfKKFKlhx9iMmKiE+is/2XmRayK1JCRfu97xKab4OAmdEqXLwQcopCpzemiePD5QQODMFTl7sLMG6F5rIwAmKoCorUcX0e+CA/6lKgRMTYnzkiLeKVVlWO4bAmZ/3x6NqAqesX8hH4PzlX/rr9WkjrJkzAqc+VGWXsxA4W1vpFsxhsuOii7Y/o3fc4V9f9rLtZcdFgRNroUpV4MSWK4c0sqhJBc5+mClwWoj+CAsVwPxTvFJGjSKfYj8FTqjqZCFEyhQ4YFOcuBIFTichA8et7U2CDNoOihPDDU9/jzLww5g/areVjarMVVkVqj0Gj5QqVKG6VKkCZ0bgJOObvw3/5R/D6S9X1+bmKa9a2bckb3EtWtR7AaHS1aXP92G/Zwqb6Hf+yL9eXVRLSLEHBQJnlAInxULVDQROxgycQDbs1cdAgZNwjAKBNUqBM6jYlZnASVXgDI7VjMBpFOEGfdQNfRMETlDg1E3gQHqp2WHEKHBCgPSpCnLEAmL3teoKWE0TOI88AjfeuNvSMIxwXGYETn5UrcApK30dECbQm5ujlyvDXhaq8ADu1a++8L4rNcRYo8DZ2vI/1r5iSZX1dTsJplHGpBA4WgVOCoGzsjKajA+YKXBaiFEVnWCbwNE+YXehPPl+REuRY2GxJJUqcDBahsosVCkKnBKSZZD5YrjJchv7Hw/Ip8DpLPl+UzJwZNmrLXa1naLAKW6iZKbAyY7rfta/VlFOOmDzzP6qFahGgbP6oH89eK23UZ35iu/3+5/yyp+BuiTBYrEVCJwRVrBUGxjsr1TqLPqQ5mwETsjASThGIddmpAKnJgLnwKVpwdUxBM78RX4/+j17PzOMRowCZ3nZ2wSaUOBUbaGKITVSS80OI0aBk4PAiSVSqszfcS6+39Sn+/vhiSfg6IgqfeAnVyIzAqcOVKnAmZvzPzEIE+hUBcTm5rbqJxA4IQvsJ37iwmXrUuCEMcx6TDVVqMLy1n4gPlw4tZ8YdVaqtW6mwBljjAoxBk/guPN6m4wLLPF+CpyESkCjFDihbLZFceK6QFkGzhkvk1e3XRKQHGxEpsyXMgLnsoTqWUGBs89NYuewjXQCv95e9ikoFD9ztuMRCJz9QqM7MwKnMoRslNQclGGMKo0NPnAW0siP1Qdh6QofanvkOXD2m3Dma/5/z/uX26RISn5MjAInKZi3RIEjAnOHKiJw9jgf88te5ZNioQrbNj/iBkI6hVopcT/mSwiczpLPFbJis/iOHEUUhf91K7SWzHAhYhQ4VWeWzBQ4FyIQOHVXg4JqS5hvbflMpSYVOMePb2c27YdQXn1G4ORHlQqcWPsUbE+gUxUQW1vbqp9LL/WBtffe6//+R/9od58pGTixBE6qukNThQrs564uC9XGhj9HO6vN7YUqq1CNwkyB00IEAqezXwbOU/yr1kY1UODsQ+B0UixUmRQ4bqtcgQNGy9CIPCAYUpxYCJwRGTihbXOFq5Ans1959UN2IsSNInDEHuzsSjJwZhaq6rBcjA/H/0t1bW6d219VAtWEGG+e2rYAHXm2DwP+/qf931f+OCwU1+VWwqQnqDmyZeCMIFcGfVRA4MwtQ2efcfzApbCV8JQ9WMhGWajA70cS2RWhwJk74K8DK7oj1EoB4X8zG1U+xChwID07YBhtzsCBC8NKU7G66ieeo6wfTVuoqiJwAiET028OAmdryyu2yggcSA9oHYEZgTOEqiqOaQmcqhQ43e62He/KK+H734e/+zt/z/3Sl164bEqIsaaMeNg3qz0slixKJcE0BE7K98vGho78milwphSlFqqr/Ku2ElWphSoQOBYLVaYMnJgqVJBg+8pF4GzsT8CFtq0EzqgMHPBkSN/4RTZKgQP20uoDBc5+g9ICXt0zI3CSsfQkX4p79TvVtVmmwAmloFMqRA1P6A9e41+/9Bv+9fD122WtU5QSWzEZOAk33GUZOOBJopzWo84S9BJIjxgLFVRDRJUROJ1EAic2A2d42RmqR4wCB+oncKp6cq/tF6olF1ZWym/4m7ZQVWVTi1EbBeQgcE4UmVwzAqc9EKnGLre5GWfFCahKAdHtbpOvV17p//7sZ+Haa3ePmUGBYylSobFQVaHAibU1heWt/Qy3Mwop3y+x+xP6SQm3nilwxhilFqon+9eushpLsFDtq8AJliFL5ssIBU5K2eyyKlSdFNtXZgJnpALnYq9ksQzCozJwwJMkuQicOaMCpzQDRzzxNFPgVIPDT9suN10FyhQ4nQosVFtntifTh35g+/0jz/bkURUBwJunPUkzt59acHE7RN6CsgwcKJQrGYmPZNVKpAKns5hmb4oicBbT9iXKQnXR9vbMkAd1K3C2tvwkqKy/HE8wYwmcKsmq1dXy0MsmCZwqLVRNEzihzP2MwGkXqjjXGxs6BU4gXaxBvwHDCpyrigf0f/M38IM/uHvZpSU/b7AoYzQhxnVZqKroZ7idsr7qUOCkqIo0GV9VWfhmqBCDKlT7XCwDokU7WJVYqGSuCMA1DIL9EQocEXvYcFkVqqTcnowETj/CQkV/m4xRtV2mwDm0XfVJ3XZuBc6IG1s5uL1vM6Th4DXVEjjdc9sWpr0wX4GFanhCf9EN8CMfh1f+DbzmK/69uWVA0hQ4m6f3V99AQRikkBIRCpwqMnBGKnASCZxYBU5KP70Nf5xrU+CMUkQVN0opdrAZRqNuBU7or4xI6XT8hK0pAie1ek1AjALnoou8zaTqDJy6LVTh3M4InHwQkT8QkcdF5CtD710mIp8UkW8Vr5cW74uI/DsRuU9EviQiLxpa55Zi+W+JyC05t7kqBU7TBM6VV/rXjY29CZywfdqxw7n6FTgaC1WKAufAgf0rpA7jwIH6FDhg62tjwyt3NFWoZhaqFqHMQhVsKNpJ+kCBM4q0OGRTQYxS4EBCsG4ZgVNMxrQZOM7lI3BcH5+vMyrEOJQot+TJlBA4klGB07kYehkycGCmwKkSF93gq1CtPFBNe9EZOBVZqACe8mp48n+7/cUsncK2k2ihGlVNqwpVCWzbvfZCbutR50AaCRUUOKNCjAf9GCegA6JrxDWV2gfEEThV5DfNMBqxhErdBA5UP8luSoFTRmiIeBVOVQqcbtdPIjVVqCyK453QZOAcPDgjcIz498Crdrz3TuBTzrlnAJ8q/gZ4NfCM4uc24P3gCR/g3cBLgBcD7w6kTw4cOjS+BM7W1m4FDowmcLR9bm76z1+dCpxYW1NKP9psGitpvrERT+CkkFIaheFMgdNClFmorFankIEzypIk1vyUiIpOJmvWFiOrUIVjoVac9ACXicAJRFlMiXKLmiXGQmX8IislcIzZPWUWKiiye8abwBGRXxWRr4rIV0TkT0RkSUSuF5G7iwdUHxbxF52ILBZ/31f8/7rKNuSq4tbr+N+mt+X6njQZWYWqIgVOWVWi+cOJCpxToxU4c4tpJFT3nCc+OiPKkNZC4FSgwCmzUM0l2JtCVtAoUhCKfdmyT/y2zvj96Ix6CFAB+dgAROQSEblLRL4hIl8XkZdaHpLXAo2FqgpViobASanqshO9nr+ZbqOFCnzVraoCmzVKmIsv9p/hKqpuaS1Ua2vVEEcB00DgOOf+Btip1boZuLP4/U7g9UPvf9B5fA64RESuAv474JPOuZPOuVPAJ9lNClWGKtRWTSpwQlvDBM5znrN/n9pxUpMVA9vHwWoDqlOBoyFwchNSoR+w9RXGyJjxLVwLMwVOi1BWhUoOAPN6oqWsChXYJ//9ssyXwzZiqMxCFRQdajKrpKQ6DJW2Vn4BlxFwUOQCYVSznAcW9j8uqRYqKbNQWTJwggKnzEI1vgSOiFwN/BJwzDn3XGAOeBPwm8B7nXM3AKeAW4tVbgVOFe+/t1iuGlz8TG+JPPuN9LYGZaUzlhHv9wqSqIzASSQ/NiMUOP2NBMLgbDkpUQWBM4roSiVwBuc7o4UqVoEzV9xE9o03yFGVrsZWgfM+4BPOuWcBzwe+jvIheW1oq4UqbFNVk+xwI103gRNjoQK/XVXtq0YJE6p9VWGj0hI4vV51VjXYJnAuv7x82XElcPbBFc65UDrlMeCK4vergYeGlnu4eG+/93dBRG4TkXtE5J7jx4+bNm5SLFTD1/WP/mh1fYbrsI4qVGGdmL6qqEJVB4HTRgWOSLXfIzNUgDILFdgIkRgLlRy0Tf5L7UgpFqpRJIsxDyjqWMwBC/qcmqjzl6LAWdtffQMJKiryKXCiSK2JsFDNA8siMg8cBB4FXgHcVfx/54Or8EDrLuAmkRgjbwQ6C3DoOjh3X3pbQfEyarLdWfCfF+skeNBHDQTOgREi7hBubCYMzuXfh26ZAmcxrQpVdIhxAoETzneMAgfslrCts3BgRAUqGEsCR0SOAC8H7gBwzm06506jf0heD+oOMW5KgaPptwkFTpWEglaBA9VUotJm4EC1NqonnvCEVMxEf8IInAGccw6oTNfknLvdOXfMOXfs6NGjpjbGXYEzP/RA9nOfg298w2d0VdVnuA7rUJFoK0MNr2PpS5NN03YFjobAgWq/v2aoAP0NQBhtHTpkt1DlUOCUVl06DM5ioeoy8jjIAWDBrsAZReCAV0E55YdjoKCKIXCMCpz98m/AXoXK9XzbI6tQFSHGWoWC2wDmfY7JfhhzBY5z7hHgt4Dv4ombM8C9wGnnXLdYbPgh1OABVfH/M8Cux3rmh1MX3QArFRA4W5F2l7lluw0lJqsEKsioiQgxhjTCoExVkttClVqFqo4Q44ECp2TSOSBwrP2cKbflBfVYSvn4+nE9cBz4QxH5goh8QEQOoX9IfgGqeBC+J6ZFgaPpNyVQcydiq5ZUua+aiUY471Xsr1aBM7xOFTh+PM4+BX6/qyxRP4QmCJzvB9a3eA11cB8Bnjq03DXFe/u9nwVV5B21IQMH4CUvgWc+c+9lrSHGgTw9UvJAI6AuAifVQqVRxkyaAgf8NZh6/c1QIdyGz08Z9TDeYpMZEDijlDJG9UYuBU6ZhcradiyBI0u+opSqbYWFyqRmOT9agdM55JdxfV27g0pRJQocnOHaKyH4YOwVOEXexM34ydVTgENUYPk2P5y66Blw7lvp/v9urN1lya5iiCVwUsp8OxeXgQN2kqgbqcDpGXMZehteHZQ7xLiz4H9GIUmBo8jAgQQCJ8ZCFUi7Cm0W+TEPvAh4v3PuhcAq23YpwPaQvIoH4Xtibc3f/O71RHkY06bAqcras7YWZ1doisCxTjpH9RtzjHMQOCdOwGWXxS07YQqcjwGhktQtwEeH3v/5ImjrRuBMwSL/f8CPi8ilxc3RjxfvZcE4hxjvVODk6LOtBE4VIcZ1EThtVODMCJyWwY0oyR2QRFqUKHBMVahKJuhiUAwBpVWowHgsIsgs8ASOVoFTVgYe0hQ4/QgFDuitX+EYlhI46LN73MZoRRJMQojxK4HvOOeOO+e2gD8DXoa3K4SLePgh1OABVfH/I8CJyrbm8A1+Arv6YFo7AwVOiVpibrkCAicm1NZKrqx6ldmoDJygxkhR4MRk4LiejTDYLGIlR9mCqggxLrNPpfYTY8sLfUAagVNmoQofTTdWN0EPAw875+4u/r4LT+hoH5LXg/Pn4wiGSSFwYve12/UlY1MRe3ybVBtBtQRObObP8DpV4Ny5bUtYGcaVwBGRPwE+CzxTRB4WkVuB9wA/JiLfwt/svKdY/OPA/cB9wO8DvwDgnDsJ/G/A3xU//6p4LwvGPQNnoeSByc4+ZwocDw2Bc+DAdjUuLdbW4gmcKhQ4MZZYmBE47UNJ7gsYS0VHWKisJaijFDiWdkuqUA3azmWhWjZYqGIycIwByVCuwAmVnrTHe0DgjLgxkoPb26BBjAJnzC1UeOvUjSJysMiyuQn4GvBXwBuKZXY+uAoPtN4AfLp4al4NLrrBv37rd9PaGYTaltwwzy3bbSjRmSgJ+S5bRfWVrBaqCAVOIEcsNqq1h/3r8p5RkB7JIcZr5fap0I/5XESSguF8mPs5E2HLK74P+93Ry7UIzrnHgIdEJOjsw1ijfUheD2ItPlUTOG0mNVKfPAc41wyB06QCZ2EhbsKbQ4GzsgIXlXxPBWQkcCL1GjY45/7xPv+6aY9lHfCL+7TzB8AfVLhp+6IqAieWDIBt1Uw38btDo8CxlhHXEjgpVagsBE5dChzwx05D1IHucz9T4EwxynJfwE+ye8qH9dEWKuUg6Pp40qksA2c9zhJ1QdsRZNZYWqiKmy0tOQQRCpxADlkJnFEKnOXtbdBgCixUzrm7ReQu4PNAF/gCcDvw/wIfEpF/Xbx3R7HKHcAfich9+Iqdb6p0g0Ip8ZSy2zCUiRJB4Gg/K7v6iCldbfyi3wwEzogQ4wFhYFX5RGTgpITmrn/fvy5duf8ydSlwUrJ2YhU4cxUocMoycKTjf6zB1c3hbcAfi8gB/APwt+AfzH+keGD+IPDGYtmPA6/BPyRfK5atD6ur9Qb7Nq3A0RI4sZVh9kJ4ojwtBM7aWvzkJheBE/t0fHnZn99+v9w+qERWAmcccfCg/xz3ejA3Z2tjki1UoQKcVoFj+czWHWIc+3kcHnO1BM65c/H9pChwNGXEYUbgtA6xuS9bSmtEVOlsS7ZOBBkyXC1qLnIAAeIsVIaJv4bAsahNoISwWAAkQYEzimQxKnBcBIETiCO1PSvCFhgUOM6Nzn9qMZxz7wbevePt+4EX77HsOvCT2TamMwdHXwZP3F2+7ChEK3CWEhQ4sZWPqiBwIjJwsipwEgicbgSZllyFajVegZNCrMh8hK0ygcBxLi4DB/xDgvGyUOGc+yJwbI9/qR6S14LYSfc0hRhXpcDRlAfOQeA0YaGK6RMufOJeFbQEDviJXOw2R6LRKlRtRBhfUq7vtoQYx/RptVDF2v/GpQpVHdk0/b4fd2IVOCnj3cxCNe7oFiWsR8BSaSimClUoQa1xckQpTgKBo1XKRKiRTCqZnFWoiuVHTU5EbPk64EmOqAwcJamlUeDksFB1DgI9Bla/GdJx9IfhzJfT1AWBwCkjV1IycDSVj6zqmM1T/nVUBk6KAqe34YmG0hDjBAInRg2VWoWquwZzEZPtlDyirTM+m6aMqE0hcLorgCvPwAFvoxo/Bc74oM0WqqYUOCkWhWE0ReBoynlXTeDEPp0Ok+KmCJxUe8gIzAicHQifuRQbVRMEjnNeNZTbQnX2rF9XazfKTeDMz/v7AOsxtFiotPsUrikNqQK2fVpZ8Qqy2H2aETgtQ6yFKotS5hC+ypDiAlcpcAwETpkCx0KExAQ6h7bV5FD4MMXk67QxA2eUAieEvOYicBj3HJx24chz/MT0XEI5cU0GjpXACcqdUpJoMUH1kVmBoym3Dja1UjeC6EqtQtVTZOBYz8XmGViIIVYCgWM5H5GVzWBG4OTG2prOQpUaBba2Fp+T0rQCJ5XU0BI4wdKTiiYzcDT2AqhugtPr+XMcO5Grcr93YEbg7EAVgdVNEDi93oVt5epzdTX+ugVPrHQ6+Qkc2A4XtqAOAifYmuoicA4fjnchzAictqGXufLSKAuVIT9lQIaMUuAYSQW2Io6FRckSW4XKEmJcDMgxxJMlMyS2ClUWAseY3eM240KMYUbgVIkjz/avZ75mb2NA4JSRK0vQy5yBU4WFKpcCpxtbCr0CBc4ooqtzoKhy1dO3D4UCpwYLVSyxAjZyJRA4ZRk4oZ8ZgZMPGgUOpN+QxhJG0I4MnBRoCRyoZn9XV/0kI2by1FQGTtUETiAH6ggzLcGMwNmBcVXghADk3BYqzZgYYFVEWgicOhQ4VtXjueLhXOznvgoCJxYzAqdliFKdHPKqBqdJP48gLcRgvxlYqCIUOC6ThSqXAsdEDhXnJIdyCLwCJybEWKvQCgSONBhiDGMdZNw6XPws/5pC4PTWPOkgJbeMSQqcQEyUfOHPLdotVFuFB3wUcVCFAqc0mLe4zi3Haqv4jM6N+vwX47A100WjwHF9G1G0FanAkRQCpzjfMRYqmR+7DJyxgpbASZ3waiYrQYFTRQHAcSFwqlAcBSIl5klx0xaq1CpBAdon8TMFTn0I10QKgaMNt22CwLFaqNpM4Cws1KvA0fZV5+d+ZSV+fIMZgdM6RFmohkKBo9uNIC0s6o3Q7qjMlyQLVU6bU0zbWrIi3CyUnUOjhapfYqEanMMMGTjWEOPYMuJgC3aeYW/MH4JD18OZr9rb6K6W26fAZ7ukZODMLZWTREm5K+f8+nOjiOYEBU6sZSclA2frjLdodUZkpA1KoVsrREUSOHMJ/cQSOFUocGYWquahsVBBvQROmGRUpQ6JVaWMO4GjIVKaCjGuWoGjncjNFDj1IVWB41wzCpywbu4qVBYCx2ptaqsCJ9VCVYfyb6bAGXfEVF6yEC0aC5VBgTMq88VC4DhH3LGw2JwijgUY1T1BgVMSRG0inly5Aifk1Gi3u78CzJcoqWYKnLHDkWfDyXvsT5hjCZy5ZbuFKtq2s+gn2s6Q4dBdiVDH1JiBYyJwTo/O8IFtBY61ElV3JT7EGDITOCl9KAgcmRE4WdFmBU5Kydf9+o1RpUwCgRN7jCclA2emwGkvUjNwej1/j9SUAkebgaO9pjSf14BJVeBYQ4zrII5nBM6FEJFfFZGvishXRORPRGRJRK4XkbtF5D4R+bCIn7WKyGLx933F/6+rfYNdF4ioQgU6m0yM6sTUbq4Q4zBJy2GhykjgRFuoLMTTJtAvCTE2kiz9FX+eRt18DsghA4FTVjJ4sN0JIXQz7MaVr4SVb8Pqg7b1u6tx5MrcUpoCR6X6sCgyzpWTK3UocFIInJjw37kE0gOge66c6II0cmXzTHx1KEizUMUqfWYETh44pydwUie8mslK1baiGCIFxp/A0WTRNJWB07SFqqrreQ/MCJwdSFXghHOkIXA6nbQKSlCvhUpjzYF6M3Asn5Fez//kJnA0YyzYCZzHHoO//uv4/Ql9TSqBIyJXA78EHHPOPRfPjLwJ+E3gvc65G4BTwK3FKrcCp4r331ssVzMiQ4xBqWjZxCssRk3QExQ4I8tmW9oNJEsMgbOheyofXUY8IcS41EK1ZCBCiuVlxM1pJ0GBM8o+BUNlxLXHJEaBs7S97AzV4ZLn+dfVB2zrqxQ4xiwJjQIHbAqZGGIiRYHTjc3ASahCpVHgWPaht+mJjJwEjnM+8FkTYmzJp9FaqFR5cjNEY3PTVz2q00K1shIfOFmlAufsWbg44nqD5sqID6+TAosSZtwVOCHMVKvAmVmo8iM1AyecIw2BA/4aSyEIrSHGbc7AWVmJt5KC3UIVti03gRO+G2IJnLk5G7H3pS/51xe9KH6dSSZwCswDyyIyDxwEHgVeAXBiOsoAACAASURBVNxV/P9O4PXF7zcXf1P8/yaR2HpeFSE2xBj0FqrS0N6EDJyR4cgWUkERBgzK0ucaBc6m0rKh2G6thSqoakYpcFgAxK7AGYkFoGNoOyYDp/j/jMCpFoeu869WAqe3Fk/ggJE4UCpwTAoZjQLHMJmrRYFzenQVLUhTxgQSaj5igmLtp7vqx9PcGTibhQKn7JyHfmYKnDzQlJuuksCJnWRXSWqcOQNHIq5rqL6MeMzT7qYInE7HT1BT9zWouZrOwNFWoZopcPIj1UIVxpxY1UhA6gTamoFTRxUq67498QRcfrn/3Mf2Y/mM1EXgaBU4YDt2p07515//+bz9jAucc48AvwV8F0/cnAHuBU47N3jk9jBwdfH71cBDxbrdYvnLd7YrIreJyD0ics/x48cr3uiYEGMj0VKqODFUMBpUoRoVYmyw9QyyZCIqRYGSHFIQOKAkhyJDjMUQYjxQ4IyyUInNnhVD4Ij48+y0T91jCJziWFtKq8+wPw4+FRBYecC2vkaBAzbyQ1O6GtqpwNmKJD9SCZxoBY4lNyYyxyelH421KaUK1fqjsHh0dODzcD8zAicPmiBwzp2LJ3CqVuBoCZxxVuBoJoRWm8Qw1tc9iTNuGTgzBU5+pFqotGRAQOoEui4LlSUDJ4XAOXo0fnnr2KA9Z9bxoG4C59JL8/YzLhCRS/GqmuuBpwCHgFeltuucu905d8w5d+yo5kKNQkxwr8VCFaHAEUMFoygFzjwwpyMVookQw8Q/tgpVx5D5EhtibClR7mIUOKHtHAoc/HnWqmSiCJyZAicL5g7Awath9Tu29aMJnOKzYiEmoktXp1Q+qiEDZ/5wRCWtOa/40B4n14f1x2DpySXtJxyjWBsY1EPgpJBRaw/DwWvilu3MyohnQxOltTUWqipJDY2Fqqp91cj7m8rAgWoIHA0ZCNsKgFkGzuQjVYGjzW0JqIrA0YYYWyxUGgIi9GUlcJ70pPjl67JQpRI4mmvDcuxCP7My4gO8EviOc+64c24L+DPgZcAlhaUK4BrgkeL3R4CnAhT/PwKcqHWLNSHGqsDXGAtVyKrRKHDCl2NZ21rFidZCpSFwIjNwAqmgUoUoQoytFqpRChxz2yvb19XItg9kUuDMCJxsOHR9WgZOVIhxgrKkdz6yjxSC5Uy5vakz7wmYXAqfgJAXpMHaI36di54xermUKlS1KHCU2TRgU8esPVyozyIws1Dlg2bSXZViQWOhCjfl40rgjEMGDlRD4AQyMLZfkWonONpywjMFTn2Yn/fn2nptj4sCJ+SraD9LW1v6fB/rZ/b4cZ0Cpy4LlZXA0WbghL60/ViCtCecwPkucKOIHCyybG4Cvgb8FfCGYplbgI8Wv3+s+Jvi/592zlp/14iYDJwBgaMMMS61UIUqVBoZYqay2bEKHIuFKjoDJ5AKisElNsRYEkKMyxQ4prZXIxU4SguV6+GDuWNDjGcWqspx6Afqs1BZwnm7WgWO4YZ48xQsXhbRx5JdgRNDSoCNwDn9Zf965Lklbadk4BTfJxoFjpYoCtk0mipUFnWMRoEzs1DlQ90Wqm7X33DHEjjhyX0VpMa5c/p8lCoInE4n7gn+uFuowrWk6bdqAufAgfgJVtMKHBG5Pua9ScHBg5NvoQKbYqXX8+SPBtZ9O3kSLou41woYBwtV7Bg73JeVwMndTw7kGGucc3fjw4g/D3wZP+7dDrwDeLuI3IfPuLmjWOUO4PLi/bcD70zp3wZFFSp1GfEywmLBL2NR4ESVzdZYkWKrUBkqI+UkcGIVOBYLVbC2lSlwLNWz+mujq1sFaC1Ug4ykSLVTTQqcqbq3Wb4SNh63VYiKDjFOyDDqKTNwtARLb90TJgcivMVzi/YMnBjlCngCp6u80TxdVCgIVcX2Q0pOUGyOD9iJIm15b9CTK9012DypsFA1Q+BMxRhUt4VKGzSbmp0xDA2BU2UVquXl0dVFA6bNQgV+YlyVhUpzfqEVCpz/sMd7d+3x3kTg4MHxU+BoQ4ytfdZJ4GgUkJBuoYq1NqUQOLFjbIBlvNvc9OdIc57aQuCQaaxxzr3bOfcs59xznXM/55zbcM7d75x7sXPuBufcTzrnZ43OufXi7xuK/9+f2r9+gyNCjMVideqVq2TA26hUGThBcVJ15ovSQqXOwJEI1VDxBWwKMS5re7kof66YVA8UOCU3w5YKV26tvF3QW6gGdrVIC1V9IcbTc2+zeLQgMZSTlH7Pr1eHAmcuQp5qDRk+/z3/unRl+bKdRZsCJ7Y0NhgVOF/ySqoy5UoVVahUGTjKY6UKMS7Gfi25svawf9UQOM1k4Ez+GFS3AkebU1IVgbO15be77gpFYXIRg6oInG7Xb/c4EDhVK3A0E9OMCpyRd8Ui8izgOcAREfkfh/51MaBMeRkfpChwms7A0RI42muqLgInVIrTfkbbrsCpIz9oc9NWxr5JAmdax5r9EVNGvONJHI2Fih6lpAJ4G5UqW0eT+ZKzCpXmpiTCTgY2VYjrAp1ytnY4u6dMURMQnYFjCTGOVeBoLVSxaqcQdJ1XgTOV481iESi3fhwOK75YA+GjqkLVwhDjc/f514tuKF/WrMA5C4efFreshcBZ+TZc9IPly41LFaoYC5WIHxfqIHBqVOBM1RhUN4FzrriG6yZwQr+xBM78vJflV6XAiUFVhILFytREBg40S+BkVOCUTfefCbwWuAT474fePwf808q3piVYXq7fQjU/X2+IMegVK85Bv18PgbO+7vuqQ4ETSLc6MnDqIPZSCBzndAqhCjGVY82+iAkxBj3REqvAkYO6DBxV5kvGKlRaC1UZoQBDChzNjUeEBQ52kEORN2DRVaiWlflIfX/8coQYx1rsoCCHsluopm+8CQTOxhNw+Lr49brF+BJD4MwbCRzX9yqfnCHG577lX8sCgMGuwNk6B/ORCpx5S4jxQ3BVSf4NDFXSamsVqrOAxNm0Qj/aPk7e619jCC9oIgNnesagabFQaQkc8PtbJ4FjDUDdCQuRMgkWKiuBU7cCxzn3UeCjIvJS59xnK++9pWgqAyfl+rIqcDTkQL/vX+sgcCyfUevYcLYoiBAbHD8OBI6GyAv9gL+OtOtWgWkda/ZFTIgx+Mm2JgMnuwKnzEKVuQqV2kIVQ+BYFTgxZIWBHNIocPrH49uNtWaBoYx4pMUOCgInr4VqKsebAYGjuCZgm8CJyqcJZcSV5y8QGVEKHKNt59x9fh+iLFQGwgCKEGNFBs6WgmDtbcL5x+KqKqWEGG+t+PF2LuIm1koUbZ7xx6ms3PqgHwO5svJtbxs8eHXc8i/9YPz2VICpGoPabqEK5Mc0EDihKlMTRMokhBifOzc2CpyA+0Tk14Hrhtdxzr218i1qAaqwUI1DiLH2M9wr7sHbSuBYx6TTp/3rJZfE9wP6fdrashErdSlwwLaNFWOqxpr9Eang6BxWqiwUGTi5FDi9U4p2w8U/bgqcSALORA4pyohrjkcg7KJDjJWKJIi89pbqLCM+PeNNyBsJJZxjobFQWRU4ITNnLuIJR8eowFl9AA5fHycx7SwMqf8i4VxRRlyRgbOuINPWHwNcHCGRlIGzoiChEixUMfk3AZZ8mvXHYenJij6UN7bVwTwGicgDeMVOD+g6546JyGXAh4v2HgDe6Jw7VVTgfB/wGmANeLNz7vOV7sl+0Kg1qrRQaaxMBw6kEzjhaXDdBM7a2nhUg5qUDJwnK8aVEEhatwJnCB8F/hPwH9l+lDaxOHgQHn/ctq42EDdgHEKMLSQR2D6zVnLXcgz/83/2r0ci7yc6Hf/T1gDoFAVOC4KMp2qs2RfRCg6tUqZhBY4sg/tefLOx1htTGfHcGTgxx9lQ4aofa6FSZuCE0OooBc6irUpZ1DVdi4UqYHrGm0DAdDWfa3QWKmsGTlDTqEKMlTcVGye2VUhlkHnoKwmc/oZXiUSTHwd1x2mQGxNRRSulClV3Je5cX9BPDQSOVoFz/ntxaqvmkToG/ahz7omhv98JfMo59x4ReWfx9zuAVwPPKH5eAry/eM2P1VV/gx4zeWhCgQNpT+4DmlLgaANDqyRStGqUs8oHCDvRdAaOVoED1ZzjPRA7FT/onHtH5b23FONcRjxnBs6kKnAee8z3E6vACX21lcDpdvUkW4sInKkaa/aHwkKlDTGOzcDpK1jsoMCpvGx2ZIix1UIVpcAxkCyxFjhThas1YD4yMFpzPDQEjraMuEKBI4t1VqGanvEm5I2oCZziuojKpwkWKiWBEyxXHYVtR0tObJ6KCzAG6MzrFTiD8N9YBc6SzmpWR7gw+OtDk01j6WfrbFyAcYAln2b1O3D163TrNIOqx6CbgR8pfr8T+AyewLkZ+KBzzgGfE5FLROQq59yjFfa9N1ZX45Ua8/NeJTctBE5VZIpGFWJ92j0MbfZE6LcJ5U/VGTia8wvV7PceiDV8/oWIvKby3luKlBDjmYVq737anIFz5gy8+MW68N42EziWfoLlqgUEzlSNNfsiVsEhSruJ0yhwNBYqRdlsUxWqDBYqtBYqZeZLLgtV/3wkyWJU4OS0UOW4ptMwPeONVYETJuedCLXaQIGjzcAplo+yUBXbobVQbZ6KU6+A/+yqCZxiQhOrwNHagrrFpHQhYlLaKcY1Syhvd6UGAudMPNEV+tH04VyhuDqq265mkDIGOeAvReReEbmteO+KIVLmMeCK4vergYeG1n24eO8CiMhtInKPiNxz/LgyL2s/rK3F38yLpCsWLETKoUPjq8BZWalfgXOmUATWTeCcO+cn6ZpJzjQqcETkHH6AEODXRWQD2Cr+ds45xZnb1favAv+kaP/LwFuAq4APAZcD9wI/55zbFJFF4IPADwEngJ9yzj1g7bsM46jACevmvKbHgcCxHMMzZ+DpT9etUyeBo1UcWvuB5gicnGPNWCI6xHgRulpiIUMVKk3YcI4qVMFOpCEsYhU4FptTtAXO0vb5uJLjnSUlWaa0UKm2OVKhNWg7exnx6Rtv5pYA2SYCYhFIgI7is2K2UEXcOM0lKHBiCZyOoWz1oHpT5KWjtQVpFDjScgJn80xcNbBBP8pj1Vv3Y04smdYAKhqDftg594iIPBn4pIh8Y/ifzjknIk6zXc6524HbAY4dO6Zad19oLT6Li2kT/aDA0fRZhQLHokoZVwtV2NfY7Imq+j13Tnd8oToCp9v1Ko2WKHDKqlBlGf1E5Grgl4BnO+fOi8hHgDfhw7Xe65z7kIj8HnAr3qd5K3DKOXeDiLwJ+E3gp3JsG4wngROuDU2/bbZQhf3RhPEGRZG2FPaZM7oxaLgvDdqswGmawMk11owvFAoOrQJHIm0KFotMmcKio1TgxBJDzAMdpWVoE4gZYCwKnJwWqvPl+TdQKGm24snAQQZOhjLiRCq0oK4qVNM33oh4FY5WgeMUBI6IrQT3wEKVKcS4t+nDmBcifdIyrxynMChwlKqSgQInxkJVfN61KiLwVaiWnxK3bJ0ZOCq1kpJMawBVjEHOuUeK18dF5M+BFwPfD9YoEbkKCF7oR4DhEmbXFO/lh8ZCBemkxsqKXqVRBYFz8qQfAzV5DE0QOFVUoWrKQnX2rJ5AqcpCZbHmQbMZOCLyoj3ePgM86JzlG2LQ97KIbAEHgUeBVwA/Xfz/TuB/wRM4Nxe/A9wF/I6ISOHlrBwHD/qqbFoiAPw5mp+vZ6I+DAuBU7eFSnM8Q8nyjqKqYyB7tKWwrQROXdamuogiaN5ClWmsGT9EKziWlHkh/bhSqaYJupS3rVbgxFahElvbKgVOhhBjMagJ+rEKnOKGrr8GcxE3WWoLVaYMnM6SMtfJjqkbb+YPGyxUCgIHbCW4BwqcGAInqEsUfWwVpSYPRE6sLFWotBk4ZgVOxORBOv7HqsCJsWlB8XkW/fnWVLqCvGqlhmEdg0TkENBxzp0rfv9x4F8BHwNuAd5TvH60WOVjwD8XkQ/hw4vP1JJ/AzoLFVRjodJO8qsicC65RHcDnrqvzjWrwNGGGFehwNGe24WFaggUK4HThAJnCL8LvAhvdQJ4HvAV4IiI/DPn3F9qOi0kf78FfBc4D/wl3jJ1emjAGvZnDrybzrmuiJzB26yeIAOWl/0keGtLX455fV2vvoF0Aidcm1rFima8CgSONSC314tf10IWDee4xBI4/b4fh9pK4EyLAmcIlY41YwnnyKrAiVZBaC0yMZavZaAXT564yBBj0Ack5ywj7mLPn6XtSAXOgMBZjSNw1CHGuTJwFqGf5at9L0zXeGNR4ISJc8xnBbzFSWtvGmTgRNw8iRQkkaKPEMQcW12pjgwcbTDvQFWSqf1BP6uK4yR6ws71vcVuTjHh1O5LsPDNK1QfzcE6Bl0B/LmvDs488H875z4hIn8HfEREbgUeBN5YLP9xvMvhPnwZ8bfk2Jk9sbpar61oZUU/yT540BMwKbA8DV5chFOn7H2ur/t7Ri2RUkWI8UUX6Z+yN0XgrFTwUMiScQTplsB9EHvkvwe80Dn3Q865HwJeANwP/Bjwb7SdisileFXN9cBTgEPAq7Tt7NFuJeFbQelnIWM3NmwETqrCy2Ld0n6Gw/ZZA3I1129Q4GgzfbT9rKz4sU875lrGP2t1qCkjcCoda8YTOfNCYgmcQmERLXLsRmbraKtFxVqoyKfAsQQNR1uojCHGMQqcoKSJzTIKZcGjFDjjnYEzhOkab+YP6TNwApHRifzy6izqFRmaEOPQh8pCFdqP+NxAkYGjvCGzZOC4bvwY213xn5+YSl2h/dwZOOAJnJ7GChbINAW5MnfARuDEXk/NwjQGOefud849v/h5jnPufy/eP+Gcu8k59wzn3CudcyeL951z7hedc093zj3POXdPDfvmoQ3ZrUKBYyFwUhU4WqUR+MlEyr7WWfFlGGfP6rNomszAqcJCdeKEf73sMt16qed4H8QSOD/onPtq+MM59zXgWc65+439vhL4jnPuuHNuC/gz4GXAJSKDO7xhf+bAu1n8/wg+zPgCOOdud84dc84dO3rUnj6fSuAsGb4zqrJQWTJjYpFioQJb3o7FQqXpJ5xjjT0X2q3A6ffHmsCpeqwZP8RWdAKD6iQ2xDhMEiK/9KIVJ8qw4dgqVKC3k7nNbQXMSIQ8iZwhxsoy4loFTlS7xTmJySHRWqgGRFzrqlBN13gzd9BnwWhgsVCpM3CK5WPJCa3KZzChjyRw6qpCBfH99Nb99sf60LW5MeDJJC2BM6dVQxXjUazKBwoFjuZmVUkINovJH4O0ypQqFDgWC9V5Ze7VTqyteQuHBqnqjGkjcKwZOFVMboJC6/LLdetlUuDE6gG+KiLvx1eIAh8g/LWiOpTlqHwXuFFEDuItVDcB9wB/Bbyh6Gend/MW4LPF/z+dK/8G0giccbNQ1RViDPn7sihwwjZpMnPC8m0lcHo9HfEV+oFWEDhVjzVjCINaITpgSqHAAd92tNUpknCCeNIptgoV6BU40WXExWBVi1UkGSxU/fMw9+SItpXkUJjsxZBacgBvhYskBGNDrqGWEOMhTNd4M7ekJ1fqsFD1DQqcnBN6MShwNBk1cGGp7xhyrL8ZT6KF9rUKnP6G/6xqFTiqc2EgcDoL2wRZVB+KUOzmMfljUBMEzqWRFecCqlDgnD+vfxqcuq9WAqeKilsWAidViWK1UFUxuQkKHC2Bc+DA9nmqELHTvDfjfZO/UvzcX7y3BfyotlPn3N34MOLP432fHXzZuncAbxeR+/AZN3cUq9wBXF68/3bgndo+NQifPwsZa7VQVaHAmZ/XTdy1fVpsTaEfyN9XsCcF8icGk0rgjLEC581UONaMJVSkRRhsIk9ctAJHSywoFTixFV4GpEJMvo4lIDnyg6+1DJFTgRNbhUpZonzwtF5jK4sdMBQhxvVaqN7MNI03JnKlhhDjQCpFEzhKlY9WgdOZ16tXts564iMmJB70FZz6m9vrxMCkIirsdTkJHIsCR6smGi8FzpuZ5DGo39fbXsbZQmVR4KSSVdBMFSqLlck53SRtJ9pA4GgtVE0qcJxz54F/W/zshCkZyDn3buDdO96+H18Gb+ey68BPWvqxIHz+6szASb2+LMqf+Xnd58hSGQrqs1AFAkdjdZwRONv9QPMETo6xZuygsg2FKkbrXspeCkWIMSgm/7FVl5QKHJQKHE3p39gMHPCElmbiG20py5mBoyxR7hRKiwsIvohJ0kCBE0vE1UPgTN14YynxrSkjbu1joJjIZaGqQYHTPacrWy1DCpwYaAkciwJnUKpcQ+Ao1VAhA0cTYqzdl74y86hBTPwYZAmaXFyE06fT+rRWobKUHw44fx6uvrp8uWE0pcBJIRR6PfiHf4DXvla33vBEQztJAT+5O39eTxxVVUb85EnflvbaypSBM/JuSkQ+4px7o4h8GdhlWXLO/TeVb1ELMI4ZOKdP6xWDc3O6a7pOAidFgTMjcPTV05omcKZ1rNkbytwXUEzStQqc2C+dyNDejjUDJ4ZUWFYqcGIzcLApcHJWoVIROBoFTidOQWC5PiBegaPJMjJgasebOhQ4dVmosipwLGXEz+pLY4OCwNlSKnAsBI5FHVODAkebgdNtf4jx1IxBZ874Vw2Bk0owaEOTYXvit76uV9EENKHAsRI4KTf73/62JzNe/nLdesMVbSwTZWsVqCoVOJdfrif4GlLg/HLxqqTZxhvjmIFz8qSewNEqcCyqGLBVoRoHBY7W0jhT4IzEVI41e0ITYqxWcORS4MS2G4I7Yy80BZnVWYKu4qmdVoGjzsCJCQM2KnByWKhiM4HAQA4pM3DYAtePt6PokW28EZFLgA8Az8VPzN4KfBP4MHAd8ADwRufcKfE1gN+HL++7BrzZOff5qrdpgM7StholFn3FZxD0Jb6hhhBjgwLHYj+aNxA4sWNhHRk4XaOFSlWFykoSWRQ47SVwmJZ7nrNFdpFGNVEFgaOd5A9bL1IInHHJwEk5vr/xG/71hS/UrZc60WgLgaPFwkL9Vaicc48Wrw8O/wAPAT9c+da0BE2UEU+1Bp46pbfltVmBYwkxnlQFTr+vJ9rGjcCZ1rFmbyhDjCGfAid6khSpOAn5KrGTI20VqhxlxKGGEGNN2xtD2UcxbSsUOJrjoWlblYETVGXVP7EKyDzevA/4hHPuWcDzga/jc/s+5Zx7BvAptnP8Xg08o/i5DXh/Yt+jYVHHDKx1kV8qVguVzENH0YcqONeQgaO1UPXXdYSBWoFjsFBpc3ysBI7qXNRRRrz9BM7U3PNYFDgpioXNTX8jaynnHda3whJiHMgUa12eQOBoMn9SCZx/+Af/Oi4ETlUWqhMn9BNtqK6M+Q6MnIqLyMUi8i4R+R0R+XHxeBs+q+aNlW9NS9BUiDHYr+s6FDjjEmI8SQSOpTT6OBI40zrW7AlNiLG2qpO6ClXVCpxQkltJ4ERn4GSoQgWFAidHiLEAC4rj7HzbqpwaTQaOVoET27YhmDtjJapc442IHAFeTlGAwTm36Zw7DdwM3Fksdifw+uL3m4EPOo/PAZeIyFXW/kthIVdClaRY2ficklwBTyqpyA9tiLEyE8WiwOltxCuIQJ+B06sxA0ddRlxD4CgDqyGhjHh7M3Cm5p6nbguVRZEC1dwEWy1UKf3WrcDZ2ICvfx1+9Vf1VqKqCBxLeHJVZcStCpwMBE7Z3dQfAafw5bv/CfDrgACvd859sfKtaQlSQozX123WvkA+bG3Z1p9UBU6bLVR1W5tir4t+fyzLiE/lWLMnTCHGVStwwkREEWIctb1hmdiqWYoqVGoFjjYDJ0OIMfhJnzpLRhs0HAGtpUzTtrYKFeQOMs413lwPHAf+UESeD9yLt0pcEZ64A48BVxS/X41/4h7wcPHeo0PvISK34RU6XHvttfats2bgxF4XYLRQrftti8XcImwp7JI9ZSaKSYGzAQuKSWoInY8lJlxkufEAUxCzQR3TObC9XgzC/qrIKC1JpAzFbgbTcc8TLFRaAsdqOWmKwOn1PClisVCB319teCXUT+CcPu3XffrT9eumHuNwLTVpoTp2TL/e/HyWyVXZHebTnHPPAxCRD+BvKq4tqkJNLJqyUIGdpLMocObm2l+FapIUON3u9jZq+oHJV+AwpWPN3jAQOJVn4GgtVLGEhdJCNahCFbHNWcuIGxQ4sQSOhhwylfpugYVKk4HTUQZz25BrvJkHXgS8zTl3t4i8j227FADOOSciKr28c+524HaAY8eOGbX2FAocbQaOkjgwqXw2ts97jj5MGTjKL8P+po6EqqMKlXoflFlEUJArCjJt0IdiX9QWqvN+/2Mtec1gOu55ggKnrgwci6UI0m+Cg2XDqsDZ2NATE+D3V0T35D+ljLiVIIN0m1pKBk7TFqoMk6uyqfigR+dcD3h44gaXPdAkgWM5x+vrfuzQXldaW6A1xDjFQpVbgROWtRArbQ0XHlMCZyrHmj2hCjE2WKhUKoiqy4hbLFTzcVJdrYUqdwZOjF0IdORQWC5GOaTO16lDgaNRlWX9+Ocabx4u2rq7+PsuPKHz/WCNKl4fL/7/CPDUofWvKd7Lg7klT6b1FU9uXNcrUqL7MFioLAocTR/9TUDi96Oz4EO0XT++D62FSpuBU4eFamBv0hI42nOBoaKWUoGjIQSbwXTc83zve/77++jR+HXG0UIVCJwUBY4FoeKWxs6UUoUqTIwtBE6TGTi9nj1nCPz5XV8fKwvV80Wk0CwhwHLxt+AfJCmNaOOBhQU/AR6XDJxTp/xrXQqcukgIbV8zAqfefirGVI41e8JiodKEGKsycCoOMdZaqFRKliVfYtu58psZ18eTWQoCp38ublmIDzEObWsVOK2xUGkzcFpjocoy3jjnHhORh0Tkmc65bwI3AV8rfm4B3lO8frRY5WPAPxeRDwEvAc4MWa2qR5iY9zegEznRqM1CpczA0fThejoSakA094iuhNbfUBIflipUWtJD+RTSoo4xEzhKksh148Z2diNHHwAAIABJREFU0F9PzWA67nkefBCe8hSdPSiFwFkpcpy0BMNwjoUFgdhIUeBYsLpqC2zu9WyThRQFTpMZOKFfi00NvPoGbAROExYq51zUmRWRS51zp6rZpHZgacmTbVpYM3CqIHAsCpxASsZ8J1otVBbVXF0hxhaiCGYETtWY5rFmNwKrmslCFaXACRO2WKVMJDGEQYETnSWzBPTx21wy4RwQIbEZOMoy4tGWMgoCR0GyQNyEumOwUKlDjJUWKlUVqnwPozOPN28D/lhEDuADSd+CVzt/RERuBR5kO6T04/gS4vfhy4i/RdmXDp0hAodIAkebvWJVfuS0UGkIVdgme/rd+H3PrcCp6zyAwUKlUccYFDiDvKCt7d9Hob/e6gBjmKJ7ngcfhB/4Ad06w5WZtEG5TVmoAoFjqUIF9RM44Pd1nAiclAyc0G8qgZNShcpyPY+AUnewLz6FlwlPDJaXx0eBc/Kkf7WEGIMnS2I+w5MYYpyyTzMCpxFM3FizCxoFjrYKVS6iJVqBoy0jvkW8Fam4Ye+vw1zZREdBhICOZAG9hSpaTaDZ7nCsW5CBMyAlW6PAiYV6vCkCSPdKOrxpj2Ud8Iu2TTMgKEQ05Ic6A8dAHPQtFqpMoeKgt3rChGTgFJ/n3BYq6ejyaQbHajOOwBkPBU4sxvue54EH4KUv1a0zXJlJO+Fu2kI1Lgoc8CSZVnEQjq+WqBruN0WBs7Cgn2RbJoc7ESbaVgUO+MmZ1u4xAspp676ojlJqCSwKnH7fX5fjYqEavqZiUGcGTl0WqpkCx95PQ5i4sWY3MlahilbgaJUyyjLi0coepYUK4siswcRGk1OjucHKHGIcZaEKJcoVbWezUCkycDqtInAma7wJhK8myFhL4IhR+aGyUCkVOH2lAsdE4CgVONoqVHVk4PQ3ANGRXdqS7v0N3X6EPkBxrM5PEoEzvmNQrwcPPWRT4IDNRmW1UFWlwNESOKmkRiqBY+kP0hQ4KSHGlqDnKiY4KRaqTBOsqgichGSgdsKiwAkEat0WquPH/av2ugqT/FjCo84MnH7fzz80arMZgWPvJyw/BgTOxI01u6DKC7FYWZokWsLESJOBEzl51KiRBiqnnCHGmgwcpYUqS/WsnCHGrcvAicVkjTda205YVpWBM7+dVxKL3npe246LJK4H7QcLleaLV0lMWBQ4MeqTQfuGMuK9IsdHc+NlCZTWEjjDFqoYTJYCZ3zHoO99z9+QX3edbr0mCIbUSXbYVq1iKLXf1dV67WJNZ+Bo82+q6BfSM3Cg8iDjqgiciYNFgRMInLoVOPff7yfg11yjW0+rwKnbQqXtR0tIhX6G141Fmwmcfl9/7ESyVbprBUTkEhG5S0S+ISJfF5GXishlIvJJEflW8XppsayIyL8TkftE5EsiUq98WRVirMyqiVXgqC1UymwdbRWqGKiyUxSKEFASISiVQwp1j0aBA35S3AYLlSYDR33tzRANbXAu6KtQDT7jmgoJygn33GJBEkVWiVLvw1AGTlT7fd+HJcRYQ+CoiDRjGXENkQY2C5W2j2ELVQwmi8AZX3z3u/712mt161VB4NSdgRPWW1B8RiG9tHbdCpwmq1CdPWtT4FRpobJm4EBrFTjjK/HbBxYFTiB86lbgPPGEJwW144ZVgVNXGXEt2VG3Aqff3z4msX1p+7GMs5Z+YGwIHOtY8z7gE865ZwHPB74OvBP4lHPuGXif+TuLZV8NPKP4uQ14f9IWq6EhFxSKlsET8YgPsLXcd2y70YST0ooEceWnB/sV2/a8bkKqDjGOJXAUZcQB0JJDmcuIRylwlCRfXkzWvY2WmACDAseg8rFYqMJ6MVArcJTXoCn8V1uFaqueMuIWe1NuBY7WQqU9Vu3G+I5Bjz/uX6+4QrdeFQSO1srUFIFThQKnCQLHkoFThQKnSQvVwYNpE/ymFDgi8sMi8pbi96Micv3Qv3eF8407LAoca4YVpBGEa2s2MnTSFDh1EzjQzmyacSdwqh5rROQI8HLgDgDn3KZz7jRwM3BnsdidwOuL328GPug8PgdcIiJX2fbGAA25oJrsaibRWqIlkrAQBeEESiWLYps1KqfBcoqBRRtinKMKFeiIp7Zk4KivvTRM1b2NRYFjCTFW96EkDuaU2V+acQT0BLYp/NdShUqphLJk4Gj2AYoS3z3ox4YpphA4Gb43WoCUMUhE5kTkCyLyF8Xf14vI3YWC+MNFNTxEZLH4+77i/9dl2p1thHyHo0d166VUZlpZ8RPtOiYqw5gWAifFapKqNmqawLHYpyC9RP0+iLrCReTdwDuAdxVvLQD/V/i/c+5kpVvVAlgUOCkETsr1tbZmI0O1ChxriHFdKhItITW8bG5ipd/3AogZgTMamcaa64HjwB8WNzofEJFDwBXOuUeLZR4DwmOiq4GHhtZ/uHhv57beJiL3iMg9x8ONSiXQWKjCMjEKnIw2lujMF227iipUaMgsJYHDvFIRkinEWF09S0E8mQgcZQZOyyxUU3dvY1HHWMpXa/voKyfcakWGNQMnlsAxlt8GxT4oSpqDMcTYYG8K+xGtJNrQZfnA0DWlOFZjQuBUMAb9Ml5RHPCbwHudczcAp4Bbi/dvBU4V77+3WC4vnnjCvz7pSbr1UhUiTdh7miRwrKXLLcd3c9NPAq2TDGguAydFAXPihM0+Ndx/Qxaq/wF4HbAK4Jz7HmCgwcYHdStwmiBwrAqcunJc6rBQ1bVP4RhrK8jVSeDMz1eu8LMgx1gzjy/D+X7n3AuLtt85vEBRzlcVFuicu905d8w5d+yo9gnTyIbHUYGTq4y4IsRY89RcExQd2tYQCqoQ44wKHOYVSogaMnA01149FqrpurcZTISVFqrcBA59JcFisVBlVOCE7chZRtwpK2lZMnB6RgUOKMiVGixU2syjZmEeg0TkGuAngA8UfwvwCuCuYpGdyuKgOL4LuKlYPh+OH/dZNFrbSaqFapwInFRVyvq6vfKVVYFjUd9A8xk4KQTKyZPpCpyGLFSbwxOc4un1RGOmwNmNSbZQ5d6ntlu1wB8/jXopE3KMNQ8DDzvn7i7+vgtP6Hw/WKOK18KwzSPAU4fWv6Z4rx44i1qhYgVOrjLig6+cHBk4GtJJG2I8D2zFVdZxDj8h1eTraBRJEJ9Vo2w7msAJRJyifD2gyl+KUZWlY7rubbQWRtCrGbTEBBQEi+JLWGvTcl3o1KHAUWbUQNxxcs5GQmmrUFlDjMFbo6L6SFD5TKaFKmUM+m3g14CQxng5cNq5waA/rB4eKIuL/58pls+HEyf06htIU0xYJ0Sp5ELY1joVOP2+X09LkKX0ubmpr7RVRb/g7XHacOoq+oU0C1XDCpyPiMj/ic+C+KfAfwR+v9ItaRmWl/UKnJDtNC4ETpszcMYhxBgmi8CZm2uFAqfyscY59xjwkIg8s3jrJuBrwMeAW4r3bgE+Wvz+MeDni2pUNwJnhqxWNUCTF9IBOtUrcCwWqqjtFVSqEE2WjIrMUoYYDwiTmNRyLTm0gMrmNFgnR9ux7Xbw51HxRJw54soT1xpiPF33NnVaqFSVrowhw9ETeqsCJ/YLPnOIcai2pbKZGRU45hLfORU4BgvV+ChwTGOQiLwWeNw5d2+VG1OpNTw1oNNyQzpuCpyUfkMeTZ0Kp83N5hQ45883Q85BNQROxROsqBHOOfdbIvJjwFngmcC/dM59stItaRkWF8dHgbO6qq/SB/Vl4Ij4dbTZNJMUYjwuBE7TCpyMY83bgD8uAv3uB96CJ7A/IiK3Ag8CbyyW/TjwGuA+YK1Ytj5YKiRVnYFjCjHOYUcyKHA0FipL22X7qSaHNISWxUKVQYEDRXaPogqV5vqAWgicqbu3sahj6rBQub5NgZPLfqSt1pU7xDh8FlQqogV/XDXH1hpiDIoS38ZKV5An/L5hJIxBLwNeJyKvAZaAi/HVNi8RkflCZTOsHg7K4odFZB44ApzYY3tuB24HOHbsmMpWvgsbGza1RmpVlyYm+U0QOEFloCVUUi1UVgVOSr+9nj9GdVcJAq+APHkSLr3Utn6mEOOoEa5IRP9PYVARkWURuc4590ClW9MiLC7qr7FxtVDlzsABfb5KSojxtBM4/b6e/IJ2EDi5xhrn3BeBY3v8a1eFh0LO/Isp/aVBG7C7kEGBUyxTdRlx0KtCVEoWIts2Ejh0gbKbJY0FDr/d0cdZWUY8l4UqbIMqA0dJ4NRQhWrq7m06BnKsjjLiWgWOukqUUoGjLSNuCTEekEQa8t2wD/2teFKmtwELyowJSwbOwhFbH9E2rfFR4FjHIOfcuyiCj0XkR4B/4Zz7GRH5U+ANwIfYrSy+Bfhs8f9PF/c6+WBVa6QqcLRly2E8M3ACgVO3AqcJC1VTE+ywXq9ns29V0f8+iJ3m/SkXasd7xXsTixQCp27yN9VClTsDJ/SlDRduM4GjJb+s/Whtbs5hqnYV+mqBhWrqxppdsChwKs/AEWBOMdHTTMA0pILFQqUJMW5B27EKKtheLkcVKk2IMRQEjiIDR1PxC+qyUE3XeGOqEKVU4JgycPrE346SX4GjzcAxhRhLUSkq4kZTqxgEo9oqIQNHQ+CoVT5KC9UYKXCofgx6B/B2EbkPn3FzR/H+HcDlxftvZ0chhyywBt6mKBamUYEzDRaqJgmclNLpkC3EOPqOyrntR23Ouc3CijCxOHDAnzPnIm37TI8Cx6ru0BIrbbZQ1UXgWG1u42qhYgrHml1QhRijULRoFDjoJv8qBY4yA6cT+dTDZKHKYOvRPi1XKXByWqg2MypwNFW5aiVwpmu8MZErWguVQlky6KOnswepKzjlVvgYFDjg9yNmH/rK8Qr0KiIwkis1VqGaQAsVFYxBzrnPAJ8pfr8fePEey6wDP5m0pVpsbNjKPjeZgWOtBjUtGTgpFqqmCJxUAsVqVQtoWIFzXEReF/4QkZuBJyrdkpZhcdGTN5rz3dT1df58PX1aM3BCX7ktVGG7JpHAyd1PWKcFBM7UjTW7oZXLxxIiCgXOoF1NVk3VhFNoty1VqGLb1j4tN2Tg5KpCFdsuZMzACdtQSxWq6RpvTJN6YxUqTR9OW0Zc2Ye2rLQ1A8cSzqvKwLHsgyZALyGfpg4CZwItVEzyGGSd7DdRhWp+3t8Ea6vXBIyTAqepKlQi/hinEDjafYXmFThNhhgD/zM+APR3ir8fBn4upWMRuQT4APBcwAFvBb4JfBi4DngAeKNz7pSICD6c6zX4UNE3O+c+n9J/GYYJytjPo/WzBPbryzk/6daOGVCvAkdbotqS4yJis2pBe4kVK8k2xmXEKx9rxg5qC06kgsMZFDiqMuIawiKnhSpjFapsFqpenNxTXYVqHlxkGr8pAyfSQqXJwNHmL6VhusYbkzqmjhDjHjoLlXI/1FWulAoci4UK4itFpWTgqMg6Q4hxWL4XORaYSKKJtlBN7hjUVAaOhcAB/yRcW70mIEze6gi6DGgixDjFQhX6tvTbBguVZXIPzYUYi8gc8M+cczeKyGEA59xKBX2/D/iEc+4NhVzwIPDrwKecc+8RkXfiPZrvAF4NPKP4eQnw/uI1G8L1ubERr8azErDD62jPbxjf5g3fVdYy4m0NMbb2A3qyaFIVOE1m4GQca8YMhoDdHAocrYWq8u0FnQJHM2GxVqGK2W6LBQ78+SnZnlwWKtcH+vksVJoMHHX+kg1TOd7UWYVKVcJaqcCxWKg6iptvrXrFklED8QqcoATSqEosZF1dGTi5LVRjosCZ+DEoNQNHe0Pa7/uJvsVCBekEzsJCfOZGwNycn4CMS4ix9ZwGLCyMXwbOuFqonHM94IeL31eqGFxE5AjwcopwLefcpnPuNHAzcGex2J3A64vfbwY+6Dw+hy+Td1XqdoyC5frudv1nUfv5hXQCJ0WBMykhxpZ+ej1/vixjblg/BlaiTUuyjbOFKsdYM5ZQl7iOtCRpFTgqC5W2THSOKlQKm5NJJZOpbY1ySB1iHKvO0hJD6CxUmgwcUIYv2zCV400dIcbqgGFH9jLifW2IsdaipbWnFojNwNFmdoW2h9eNQa+tIcZKC9WYKHAmfgyqm8AJE+0UBU6KhcoyEQM7qTFuVaigGQInNQNnzEOMvyAiH8Mno6+GN51zf2bs93rgOPCHIvJ84F7gl4ErnHOPFss8BoRacFcDDw2t/3Dx3qNkwrACJxaBwLFgnBQ4dWXgtDVrB2YhxhlR9VgzfhjccGuIlkwKnFxlxFXtKtQmg3Vi2iVPZSRLiHF024Yy4qrcnpxVqDSDkoY8TMJ0jTcmW03uMuJFJePcBEvODJzBZ15509I5EFmFymKhMipw6ggx1lxPMOkWqskdg6wZONYJ79qaf23KQlU3gdNEiHGqhWocFTipFqpMCpzYEW4JOAG8Yug9B1gHmHngRcDbnHN3i8j72FHSzjnnRMRpGhWR24DbAK699lrjpnmE61OrwLEQKbA94a6TwLGSA20mViaVwGkjyZYJVY81Y4hwEiJPYs4MnGgVRMYy4mqbk4awyFEZyWjPigntHRB1FecNqVVDFNdd7IRKk4GDjuRLw3SNN2prkDMEABvsTaAjPywWp5wZOFYFjtZCZcnAiSWhoLA35SZwlIqu4T6i8oL6/mcMLFQFJncMSs3A0U6IAoHTpIXKglQFjjUDx0oajZsCpy0WqiYUOM65t1Taq1fQPOycu7v4+y48gfN9EbnKOfdoYZF6vPj/I8BTh9a/pnhv53beDtwOcOzYMRX5sxPh+tQocHo9O4EjYruuw/IpBE4bM3DqtFC1mcCpM8R4bs5eQbEqZBhrxg8hEDjW15crA0eVn+LIUkbcVIVKEQraCguVRoGzhf7aiBk8isFdO4l2a5ELKzJwQts1VKGauvFGm08zICZyWqjCtWdQ4KhCjA0KnDoIHE3ouqrUusaaiSd6XD9/FSptoPRwHzEWKotaqUFM9BhktVBZFSLjqsCxBvuuFoIt7f6GrI8mLFRNhBi3xULVRBlxEblGRP5cRB4vfv6DiFxj7dQ59xjwkIg8s3jrJuBrwMeAW4r3bgE+Wvz+MeDnxeNG4MyQ1SoLrBYqK4EDNgKnCgVOIGbKkKLu0AbkWi1Uln7aTOBMWxnxqsea8YRS/p0rAydagaO1ZinKiGutWYN1ypDTQmVV90QqcFQ2p8hzqM5HAn01sfZZqKZuvFFnx2xduJ6mDzVJZMjA0ZATFgWO2kKVSYGTUoUquhS6tZJWHQTOHCBxfVgCnxvERI9BVgInKGhWV0cvtxPTSuAcPlxfnzMLVf3974PYb8w/xJMoTyl+/p/ivRS8DV8670vAC4D/A3gP8GMi8i3glcXfAB8H7gfuA34f+IXEvkthDTFuisCxjBuBIGlrGfE2K3DCMYglv6zEiojvqw4CpyVlxHOMNeMFtX+/4QwcS2hv9MRfEWKcMwNnQJooCK0s6h4lgRN9rA0EjrbMvCqAtbYMnOkab6SDnwgrPn+QOcTYoMCxZNRY2o8moQz7AIYQY0uOT+Q+hDLgOS1UzgFOf5xCPxq10pgocJjUMajb9TfIlsl+IGCmhcBZXNSpBQLC8bFYxqwEzsxCpUfDIcZHnXPDA8q/F5FfSenYOfdF4Nge/7ppj2Ud8Isp/WlRd4gx+HPchAJHSw60PcRYSxRZlT7QvvLe41xGvEDlY83YQV2xJzYDJ7CNsRe8ksBREUMaC1VsxaUaqlDlJIeiCAuDAidq0mNQEGgzklT5Opq2kzB9402sbQdsChxrBk70uIRB5aPN8WmZAqevHGND2xBPhFoVOHMWe5ORwInqY+wInMkcgwI5YJnsz815tYOWwLFaigKaInCWlmzVr1ZW/FNeizIkRYHTBIETjs84WqgaVuCcEJGfFZG54udn8aFbE4umFDja66tOAqfODJxZiPGFfbWtn4yYurFmN7QWqti8EK2Fag6dhUpDhmSwUIX9yhFirCr1bbVnxT5dzmChsihwopVf6ENk66tCNX3jTSxpAEYLlbb6UV0ZOC0sIx5dhcpgC2qlAic8BbQQOAsTaaFiUseg1EnvoUOeoNBgGhU4hw/HZ+INoykLVWoGjoWsalqBkynEOJbAeSvwRnxp70eBNwCTG7yFTYGTEmIM9YcYa21AdZcRnxE4Hpp9mgAL1dSNNbvglGoFYoMwc4UYG0J7s1ShEmBOJ7PPqcCJJso0IcZae13ssTbaWKJJltZWoZq+8UYWFMqShOpH0cqPhAycXCHGk5SBE30eiklVzhDjVAXOZFqoJnMMCpNea27IoUMzC1UZVlftFbcOHBi/KlSLi7YJqIifFFkJnEA4Wa/lTCHGI0c4EbnROfc559yDwOsq7bnlqLuMONSfgdPmEtUzAufCvtpm1aoa0zzW7IJ2ki7zQzaEUTCEGGvyU6rO1gFsaqRcqiHi2lZbqBTVs9BkAqHIMTLYWFRh1JYMknxVqKZ6vOkolFN1WKis5CEoyAmlAqxtVagsqhKtAicsN6ecnA36qYHAmSAFzsSPQeNI4CwtjZeFKoXAWVjQK2F6PXuu0XC/FrLq/HmbfWq431QCx0pcNWSh+t3wi4h8ttKeWw5LGfFxrUI1I3D0/dStwKnjHDVsoZrasWY3MpEW2pvn6HYtapaYJ6mhPHmOPCCrzSkDOaQqI67IBBpsQwYSDtDZnJQKiPwWqukdbywWKksZ8TqqUGkUOOOcgaPNGQttD69b2kc418qbSRGFFSyFwFmYtAycyR6DUi1Uhw/PFDhlWFlJU+BoCZxUIgPSFDgpBI52cjiMcG6s+50pxLjsG3PYWGekUccTszLiu5ESYlxXeW9tEHRdBE7qeaorxLhBAmdqx5pdUOW+QL5KQ5kUOLGWLzURAtlsX1qbk6ptbQZOjuORuwqVNpg7O4EzveNNbOUjSCsjXksVqkwZTIFMyq3Aia5CZbFQaRU4CcRHZ3E7Q2cU6rBQpexHvZjsMWgcFTjLy34CYbkRbjIDxwILgZNKZEBzBE6TCpxUC9c+KBvhOiJyKZ7oCb8PBh3n3MlKt6ZFsIYYp1ShaioDZ5IUOFoSYhwUOHVZqFII6gowtWPNbhjk/ppJusrqtBbRroGwiLF8mWw9sYoTq82pBW2r1Vk9X753ZNBhDVWoRHEjn78K1fSONx0N8WaxUHX8j7YKlUaBI6IkEJUKsNB+NPmROcTYZKEKRJoy70hzrgPmalLg5Ap8bgaTPQZVQeA8+qhunbU1P0Gxqn4CQbC+rle2bG2lWbfqthVZCJww4bQSVdZ+oXkCZ37eNvkd7r/mMuJHgHvZHlQ+P/Q/Bzyt0q1pEcYlxLhuC5X1+rWU964jiNfazySGGDeswJnasWYXtCHG2SoNZczAidpew5P52MpI2qBhjYUqd4ix+tqA0jLeFgWOxubkWhdiPL3jTe4qVKBT+VgUOOAn6Bp1iaV9tQJHeYOU1UKlUPZBWnZMLRaqiSsjPtljUKqFyjLRX1vzJIqlKhNsEwTnz9sInBQFjiUDZ3MTjhyx9Wk5vikTzoBxtFCllk4P/depwHHOXRfTiIg8xzn31Uq2qCWYhRjvRiqBo1XgWK1aWguVtZ+wfmw/w+tp+2pbP1VjmseaXVBXGsqpwMmUJ5MtWFezzZ34Gz2VSkZrd1BaqLTXRmh75HrWKlSxg60lAydfiPFUjzcqC5VxMqwhiSyfc1CSRMoQ7UH7GoWPYjwJiA0xTqlCFb0PxgwcqI/AyRX43AAmfgxKVeBYQnYDgWPFMIGjRWqIsUWBk0IsWKpQhQnCOBI4KQqc1Mpbqf3vgwQ90AX4o4raaQ2mKcRYk4FTJ4FThwJnHCxUmn2agDLiZZi4sWY3LFWoMihw1CHGVVehCgOTUrkRS7KorUjo1D3RFipN2KihClVM21ntaqH/9lShUmDyxhsVuWJU4MQSE5CgwNHsR1c/odcqcMy2oEy5Lpoxy9pHgNYKVoeFqv0KnFiM5xgUCJwUBY52QpRK4KRUC2oiAyeFwLEQZGESV3dWCDRvoapCgVNziHEsjHq19iJ8DuskcCznN4XAsWTgWD+3dWbgTFoVqjpDjBvMwInFxI01u2CyyeQIG9ZaqDSZL5p2cylwLEqWjCHGUZW5tNdGuKEs226LhUpjc9JOcLOHGMfCNN6IyJyIfEFE/qL4+3oRuVtE7hORD4vIgeL9xeLv+4r/X1fdpu+3cYpja7VQWRQ4FvtRToJFnbFj+NKNDjFOqEJVRwaOWoFjJIliLFTjE2Iciz3HIBFZEpH/KiJ/LyJfFZH/tXi/HWNNmDylKHDqJnAs4acBTViotrbSFDiWMuIwvgqcFAtVSun00H9LFTiuonZaAxH99T1uIcZtt1DNFDjb67StnwYxcWPNbmjzGjJVGtJaqFTZOrFlxDXthrZzhQET3zYozmHGMuKxxJPF2iDzQH/oPJW136oqVLGwjje/DHx96O/fBN7rnLsBOAXcWrx/K3CqeP+9xXJ5kbuMeFg+tg9T1hXxIcMDhY9BgaMqI264OYpW4FjCpOcvXLe0jzHIwNEoIFtuoVJgvzFoA3iFc+75wAuAV4nIjbRlrElV4FgmRKurzRE4KSqNYKFyyq+bVAtVEwqcpkKMUzJoqlDgZAgxrorAmUhox48qLFRWBU5KBk6shWocCJy6FDjhOGhLsOcOMU6tFDYGBM7kI3eIceVhwwYFjsrypVXgZMiSiVayYFfgRFkdchFPxjLiF6xb0r76eLeCwFFDRK4BfgL4QPG3AK8A7ioWuRN4ffH7zcXfFP+/qVg+HzT2JrMCRxEwnDsA2GrbqUOBE2092txePrptjTWTMcjAmVoL1Z5wHivFnwvFj6MtY00VGThNKXCasFA5p58ANkXgNKXAsV5LKf1Ca0OMqyJwDHRa+6E936lVqCznt04LVUoGTp3WppkCZ6ItVBM51lwANbmQSYETrezREhaRk3NTNoYmt0fbLrrjnKOMuLkKVQYFjtpWNpYKHMvFP2vAAAAgAElEQVR489vAr7Ed4nQ5cNq5wQ49DFxd/H418BBA8f8zxfIXQERuE5F7ROSe48ePGzZpCNrsGNCrGTRVxKyTem0AsHoflCSUlZTAbZch3w91KnDqIHA6OcmuiVPg7LvThVXzi8DjwCeBb9OWsaYJC1WqSqNJCxXoc3BmIcb5+4XWhhiPPAsi8qJR/3fOfb54vbHKjWoLxkmBM7NQ1dvPJIYYN2mhmvax5kJ0QTQ3PdqsmqrDhjNn66gUOLETR6OSJUoVkjHE2FyFqqxtS2C0xlZmycDJF2Kca7wRkdcCjzvn7hWRH7Fv4a7tuR24HeDYsWNpNlJRWIOsFioVSRSIWm0Vqsj9sOTHgD5jx0RKhM/+FiM/e5YwabUCJzUDJyLDI9VCpcoLajeBU8UY5JzrAS8QkUuAPweelbpdlY01VYQYa4mUFBIFtte1EjgpFirwx+zw4fj1xtFC1RSBc/CgrboYtDbEuGyE+7cj/hekehOLugkciwKn7gyclBBjDTlgJYu0JERKPxDfVyrRVleIcb/vlZyZRfx7YarHmgtgDTEuO3GWDJzoYF0UhMU80Iu40IylraOULBabGnkUSZoy4tYqVLEhxtrA6LBNZchmCzQj13jzMuB1IvIaYAm4GHgfcImIzBdPvq8BHimWfwR4KvCwiMwDR4ATxr7jUIuFSlOFqiYFTu4MHLMCB3+c50aQ9iYFTvDI15SBs3W2fLk6LFTjo8CpbAxyzp0Wkb8CXkpbxppADtSZgdPtphE4qRk446TAsVShqkqB0+/rnp47l07gHD4MDz1kW7elIcYjz4Jz7kcr7W3MYCFwUojJuqtQTWIGziQqcOqyUIVrKNUKaMG0jzUXwBlCjAFPeIxaz6KUibjwLGXEB9sz4kKzlrbOocAJ+5YjxFirwDERTyVfZGp7HcSre7Bd0xktVLnGG+fcu4B3ARQKnH/hnPsZEflT4A3Ah4BbgI8Wq3ys+Puzxf8/7Zw2yVKJusqIaxU4Wkf/JGTgyBCBMwqWDBwR5T4kKFfmagoxnqAMnNQxSESOAlsFebMM/Bg+mPivaMNYE8gB681kmOhrJh6pT9GbzMABHYHjXP1VqKoKMQbfdywhs7np9zeVwFlZKV9uv/5bGGIcfaWLyHOBZ+OfKgHgnPtgpVvTMliqUI1TiLGljHjbCRxLBo7lc1m3haouBU5oo24CZxg5xhoRmQPuAR5xzr1WRK7H3+BcDtwL/JxzblNEFoEPAj+Efzr1U865B1L61iNBHTLqxtikwMkQYjw88R+5jjEDx0XcBKlzhjpAR/fEP1cZ8RxVqNTkHjriifZWoarp3uYdwIdE5F8DXwDuKN6/A/gjEbkPOAm8qeJ+d0NTIcqqwNH0YZ3UR5fgTsnAqVGBMwoDK5tFRaStONbWEOPJslANwzgGXQXcWdzbdICPOOf+QkS+RhvGmjDptcq5w6Rmayte/VAVgaMlNoKixErgDFuoYhFIJmufTZURHyarYgmZYH1qisDZ2NBZ2/ZChhDjqLMgIu8GfgQ/wHwceDXwt/iJzsRiFmJ8IVJCjGdlxC9cT9tXLDFfFYHTFDKONaGs78XF36HU5odE5PfwJTbfz1CpTRF5U7HcTyX2rYMlxBjwZMuoGx1LBo7ihlmtwIkM1tUqcPqr5ctplSyh7RyWMk0ZcXV2T2TbFgWOKhdIOcGtqQpVznsb59xngM8Uv98PvHiPZdaBn0ztS4U6yohbFDjaDJlOJMlnzsCpQYETFDVl5Ee/sE5qJ8OyoCChUjJwFttThWp8LFSAfQxyzn0JeOEe77djrElVLYwTgRMmbtb9tShwwjaOmwLHsq9VEDgHD/oqZRZUpcBpqArVG4CbgMecc28Bno/3T040Jj3EWMQTMhoLVUoGTreI6YhB28uIT2KI8bCFqkFUPta0vqzvLmQKqlVP0mNDPI1Vl7IE68ZO/LWWHvDHI+bDEfqP/HrVlBHPVYXKosBRV+ZSbnc9Cpzpu7dRERPGybAlAyeXhcqs8FGoV1IVOKUWxy0jsaIIA0/NwOnHKB9nFqo9MJljUJUETiyqmISBncCp00JVBYETlEOxqKKMuGVfgzIphcBZXLRlG0E1GTgZQoxjvzHPO+f6QFdELsaXrHtqpVvSQjSRgVNniDF4AqcuC1VoIwZtV+CI+J+2lfcOxzclmLnhUuI5xprKy/oC1Zb2HYY55ySWENEQC71y1lWrwIkO7TUE60aXPi/LC9oD0QHJxfmL5f20ZcSzkHuGHBJtsHOLqlANYfrubeqwUGkUOCarJAoLlXFCX4sCJ9JC1dvU5d8Mt69V4LTdQlX2fTRmChwmdQxKJXAsapimFTgpdiZtv6mqH0veT5UWKs2+VqHAOXDAk0aW2KcxV+DcU5Sp+318XsTn8UFYEw0LgZMSgJ4SYmztV5MZk0LgaMmBtitwQl+TFGLcBgsVFY81w2V9K9q+AZxztzvnjjnnjh09erTChrWT3bBspAJHY6ECtst574dMChxnUeDE5vb0UT/xVwUkZ1Ky5K5CldNCleU8JmP67m0sVai0FipTBo5BgRPzmeknZOy0hcCxKnBibbDD22AlcHo1WKgg4ntj7BQ4kzkGjaMCxxpiXBWZoiE1UhU4FrXROFuoFhc9eWN5Qr2xMb4hxs65Xyh+/T0R+QRwceG/nGgcODA+Fqq6SIhUBU63G6dEa7sCB+qtDlV3iHFTyDDWtL+s7y7kUuBoJ+mRYcPqMuJhIhK7vdoqVJG2Cu2EMVYVorYLCTCX10IVa68zEU+xx7s9VagCpvLexpKBU0cVKktGjcpCZVDgxH7xZq9CZbVQ1ZSBE1uFykqmwYV5QaO2ccwInIkdg8aZwNEqcMLydSpwqrBQafusOsQ4FlUpcMBW7r0KBU6GEOOoO1gR+VT43Tn3gHPuS8PvTSoWFupV72lzYkKfnU6aMqauDBzIr/bRKnBSVUWTpMAZJtmaQtVjjXPuXc65a5xz1+GrLnzaOfczbJfahL1LbUJdZX13wvXyqCzMCpyqs3WUCpwsE/+MChy1zYn47J7cVagsCpzonCRt+HJ/SIWVB1N5b6MicDY80WkKAM6swNFaqLQhyXWUEVeFGBsVONHn2hj2DPVZqCCC7BovC9XEjkHTROBUZaHS7GsTBM64K3C0/QZUkYGTwUI18koXkSXgIPAkEbkUCKb+i9nOjJhYaI/31lY1+VmaalapfdadgZPbQjWpCpxJt1A1MNa0p6zvLmTKOdFO0qMn6FoFjtbWo1HgxNoe+jZLRXRAspbAiZ3wKtuODUi2TKxilVTOoT7eF9j3jF86o5qf5nsbDTHROw9zy5mrHxkn9eoQY4MCJ3Yf+pktVP2EDBxNYLXM6c81+G1zXU+4jiLiqrBQlRFFY6LAmfgxaBwJnKZCjGcKnNGoWoGjRVUKnJotVP8T8CvAU/C+zICzwO+kdi4ic8A9wCPOudeKyP/f3ruHSVaW596/p6p7uqdnBmYGhgGBkUEQNx5BPGcbA6h4hMtooomGGPPxZcdE/dStqN++tjGaaK5sjbqNW41GPGdvQ9QkeCCAMX4JKCCCiAgiyCgnYRiYU08f3u+Pd63umpmqWu9xrVVdz++6+qruqtXrXbWq6q1677qf+9kKfAEbGnoV8ApjzD4RmcK21Hs8tpzhN40xt8aOX0UTGTjgJ8rEzld1ZeD4CDjG2J+2Z+D4dPAqy89CPhv53KdRFXDIPNcA7W3re9BB5CyTEY8noet+PUOMfUOXvRf+mRw4zgHJ83gHJLt2/PJ+bviWq+XI7ol094Q4D6rJPt+0llL4MKZ6LigFnNAxXFgq0/L8gOwqToS2Ea/FgePYhSq0hMrLgeOZr9VLrzumO+Sb6iQOnCq30sg4cFb2HDROAk4TYkqqMX3PL4yfAydVBk6dJVTGmPcbY7YCbzTGbO35eawxJsUE81rghp6/3wO8zxhzArAdeFVx/auA7cX17yu2y47P+TbGzznTj5ASlljRqI0CTqxbxZj83a7A79zFdCiru4SqCQGnhrlmdPAtwfFytOQIqfX9htszl8VLaHEVWQKcHT5dqHI4cMwi1smSw51VimUZulDlztcJYKznm1IQMw4TfYyA4xyeu2/5f3xwFScWAx04dbYRzxVi7OvACRkD3MWVOkqoRsSBs+LnoLq7UBkTX5LQ7dpFjjpw+tN0G/Hp6fhxfR/bhQX7EyvgrFoV3sZ8AK6f1j4iIq8RkS8WP38kEve1mIgcAzwP+JvibwFOB75YbHIBcE7x+9nF3xS3n1FsnxWfEOMUzrLyte8r4MSWUPm6SEKoS8DxdZHUWUIVkx9UpwOn4Tbiyeea0SO0i5FDt6gc3ZFCHTiVYkhmB07QN/IubwieeS/lviuPO2Bhki3HyGPfOY87nvGbb1xFA4D5tjtwfNqIh+T4uD7/KkqHBo7hkYGT3YETIDyX1CLgOJZQLY6GgNPDypyD6nbglAuZmAVROa7vt5hNCjihY4a4jZoqoSqPMSaHpjzHvg6c2A5jJVNTYe6fIbi+4/w1tnzpr3t+/3Dk2H8FvImlT+kcBtxfdIUB2MZyHejRwO0Axe07iu2z4uPASSFM9pZQuVJ3CVVsiLEKOHnHKd/DQgOgofE24jnmmtHCu4uRjxMiR0htixw4zl2oMpdQeS8eHL4pX7o9w3ODkIWV7+MYkK+TvxPV+M03rmU7EO7A8WkjHurA8c3A8Q0x9ungFCp+OGfgzNWTgRNaduQs4EQGJXuNkT47KxMrcw6qW8BJsQgr/9+31KWJlt7jFGIcK1aFjtu7fWyI8fR0cgGnKsS4bLP7BGPMY3tuulREvh86qIg8H7jbGHOViDwjdD999nsecB7Ali1bovfn04UqxdwR4sBJ4Riso4TKx91RZyelURBw6goxbrKEKtdcM3IYg7cDx6fUKcd+gzNwqj4kldq+z5PZI8TYu4TKdd+5SqjKhUmGLlQxDpzKx7F9JVRjPd+Uj5uLONHmDBxxDBkOLanxysCZg07AB3znNuIVrbMH4dpqvTyGuhw4IUKRcxeq4n7kN+lHseLnoFEVcCYn/W3o41JC1ZQDJ4ULJjTEOKWAs3evW/acI1WfYL9TXC6IyMPKK0XkeJaTCUN4GvBCEbkVG1p8OvB+YL3I0jvIMcDPi99/DhxbjD0BHIoNM94PY8xHjTGnGWNO27RpU8ThWUbFgRObgePTRnyllVDFuIrqdOCMQQlVrrlmxCg7lmfIOfF24Di6IHy7vHhn6/g6cFyewAv+39D6ZOD4hhg7dbjKWYoU0oXKtzyrVSVU4zvf+JRQLeyBiZoycHyrRpxLqALLdnzLj0IzasBRlAgpoarZgbPgWt6UuQtV+wOMYaXPQU0JODELIgjrFtSEgBMraoxSiHGsWBU6bqqxy/EXF5MusKpmuVImeiNwmYjcUvx9HPDK0EGNMW8B3gJQOHDeaIz5bRH5P8CLsaLOucCXi3/5SvH3fxS3X2qMMQfuNzVNCTh1Z+D4iBCxAo7LWDFlQCEOnBhXUdtKqEa4C1WWuWb0KNVUD4U+lwPHOQDX80O5szAU4MBxFlkW/fYL2BIqxwycnA6cIHHPJSAZvN1O+x3XIAIWbflLqMZ3vvEtoZo8JGwM105XpXjRDcjAyRli7FN+FOpe8elCFfQ4TMC8TwZO4AK07DxVmU9TLJ66AWGkPl2oRiP/ZmXPQXWHGDdZQhUr4IxKCVXTGTgpSqiadOCU+4sVGQuqHoVNIvL64vePsPzpbgE4BbgsyVEs82bgCyLyTuB7wMeL6z8OfFpEbgbuA16aeNy++Ag45XajFmKsGTjtd+DUHWLckIBT91zTUgpdOls3oBCHRWphwTeXxbMLFcYKEkP/L6SEyiMDxzfE2CcDJ8SB49zqO8fzrhSHfB9HspVQMc7zja8DJzQDB4ruTBXP11AHjkza/VeJRDFtxJ27UIV2iXItPQrMwBGPHJ8kGTgVi7KF4vaQcjOfLlSjIeCs7DlonEqoYsWUTsced50hxqOYgRPz2IaGGKcWcPbuhbVr4/ZVUHU2usBaDv46eAJYl+IAjDHfBL5Z/H4L8MQ+2+wFXpJiPB98ulClcO+FlFCNSgZOnW3Ee/fhMlbbBZy624g3VEKVfa4ZCaIWu005cBYAcRdanEtvQh042AWPDHvDDegaIxNg9lZvl6uNeCmkeQk45RuSaxlcjuylkMcxuwNnfOcb19wViOtCVY5RJQoEd6HqmZ+GiT+huSteOT65S6giMnCcS9lqyMBZcuCECDgrroRqZc9BoyrgNFFCBf5tpptw/TTVRnxuzp6fmOyYUAdOqhKqNWvs5YMPwuGHx+2roOpRuMMY844kI40gpQPHxQXcVIhxCgfOSszAGecQY5Gwea5hB85YzzXLBAg4XlkkGbpQeWe+5HSF9AoWQxYJZsFvv4B7QLJnCDW4lX7V0YUqy+NYtoMPeU5nc+CM73zTcX3cgMW9geUuvaVBFQLQ4j5AwrpEQXWL7eA24jWWUDl1oQoUiGp14FQsjupw4IxOCdXKnoNGWcCpu4QK/AWc2HKmpkqoSreRrwMntuyoaQfOhg32cvt22Lo1bl8FVZ+o2h3jnpnJSSveuCxom8zAiXle152Bow6csHHqEqTKfTTAWM81ywQsdnM5cLyydXKICuW5yCQqhIgsTiVUnkIZ4FdClbELVcjzI0cJlfQKAFkY3/nGp4QqVDhY6nTlEjIcUR7kMkYZrOsrGsjEcolWFaElVM5dqEJDjD2DmEMzcJwdOHvtHBPVhWrFOHBW9hw0ygJO3SVU5f+GBArXKeCkKKECK4b4CjgpQoTLffmQSsDZuNFebt8et58eqj5RnZFspBHEZ/5osgvVKJRQ+ThjYkKM2+rAiXmcul37GdLFKZXCJdWQgDPWc80SQYvdTBk4Xov/XI4hwvbtJA6FdKFymZxDxaGqfdfQhSpHRlKIq8xZiAtmfOcbnxKqUDfDkkjk8o1NaHmQY5ndYlH26Fu24yV0RZZQuYQY+4Y8l/t3FUHr6EK1MBvmvgH3EqrRceCs7DkolYBTd4hxE23Ewd+Bk0rA8Vlwxny73ouvgFOWUMUQ6sApH5NYAeeQIoR+x464/fQw9BOVMea+ZCONIHULOKNQQtX2EOOV6MDxuU8pHDhNZOCM+1yzTMYSqlAHTnI3S0YHjpc45Kt0OjpwgsQhhzbiQV2ougf87yAWsTlGPl8K5yyhyuvAGev5xkeYcAkhHjaGU5vvQAdOKTZUOnBKAcezFMxVIIIicyviPFW2394X5o7xyvGJyMDpemTghOTfwIoLMY6dg0TkWBG5TER+KCLXi8hri+s3isjFInJTcbmhuF5E5AMicrOIXCsip6a4H30xRrtQ+TI5Wa8bJtSB0+nEZdFAmAMntoQqtI14uX2qDJzdu+P200Pgd/XjgY9A2ZQDJzbE2KeEqi53R10ZOKUzOvQ+dTru4lddpU0jXEKlAFlLqIK7UGVy4FS2tg5w4Dh3L8pZQhUg4Li0KA/J8hABuuldVOAeNBzjKnNpCa/44SWuBLoyvNwrgQ4c5xKqwNbVPmVgwaVmHft6dnHghJ4j505aNWXgBDtwVlwJVSzzwBuMMScDTwZeLSInA+cDlxhjTgQuKf4GeA5wYvFzHvDhfEdWzNsxi17fBXdKB45m4AweM/b8wniWUJUCzq5dcfvpQQWcITRVQlVnBk4bu1CVokhuB06sG7Duc6cCzhiwtNj1+IYjW9lQJgdO1u5FPrksISVULpPzQkCHK5fA1IDzAY6lX74dygh4HNvjwBlrloQJFzEyIJAbPMu0Qh04ruVHhQPHVzjwzQoK7uDkILLEtCmvEjyWxqghA2chMBQbVmIJVRTGmDuMMVcXvz8I3AAcDZwNXFBsdgFwTvH72cCnjOVyYL2IHJXl4FJkwjQl4ExPw549fv+TKgOnzhKq0C5UseVTMJ4lVDMz9lIdOPXg8/wuRZ5RLKFqa4hx7gycOgWcukqbUpRqNdRGXAGgDMzM4VYwYZ2AkgtDrqUJZbBuaBeqYYS4ZBy70phFgkSWSrEi4HwATqVfQcHLGUuo8mfgjC91llDldOC45uwsRAo4Tt265sJLg1xcMjEil1cr9MwOnLpKqMbDgbOEiBwHnAJcAWw2xtxR3HQnsLn4/Wjg9p5/21Zcd+C+zhORK0XkynvuuSfsgFIIGhMTdhFQt4AzM+Mv4IxyBo7vmE05cGJLqEIEK0hfQqUOnHoIceDEPMdCQ4xjBNE2ZuCsVAeOllCNObsugdkfV2yUMfDVFDknrriKIb5uFu/uRZlalHu7ZFxEFggPSM5xPnB090Q4cFw7XKkDpx24OlfMov2JKaFyefxCxQnXEqeFWXs8vm3KvUqoAjNqwObHDBvDmDiRy/U1FOUi8hBwcpdQjYkDp0RE1gJ/D7zOGPNA723GGMPyt0JOGGM+aow5zRhz2qZNm8IOqlwkxy72fRb6Kb5FB1i9erQEnJjOMp1OcyVUPuOmKKESsftoqo345KQ9577PrSGogDOEUQkxrquNeF0lVCvVgaMlVGPOz54F286p2Cgk8NVVtDD4dS71EENyhS5DnnMR6pJxKqFaDHDJeJRQhQhPTjlGvhNHeRw5SqhcO1wp3ri6Y0Jay/uOAfEOnCqBYmFvmGjgXKK1AJgwEQoKB86QxczS4xBaQjXn2Aq97Rk4jiVUIxJinAIRmcSKN581xlxYXH1XWRpVXN5dXP9z4Niefz+muC49sfksJT4L/VQOnBABZ98+O25MuG+IgNPtxo05PQ179/qPGUsTJVTluL4OnFQlVGDPt6+ANAQVcIYwKm3EYx04bRNwRsWBMzFRrwOnrhIqFXAysfopsHj/8G2yBr56CjjOIcaZulCFOE68Wltn6kIVFAjs4u4JcWeB23GHuIYEp/DlkG5izmV2ijeu+TRlaVLIYtgrA2dfXAZO5f0IzF1x3v++/bcPGWfYa6gcP2er9XKbYBdRsbjJmYGz5IjSEGOwXaWAjwM3GGPe23PTV4Bzi9/PBb7cc/3vFN2ongzs6Cm1SksT7aabFHDm5uJLfEJCjFOUi/lksoxyiHHIuJDO2VWO7yOYVaACzhCa6kLl48CJfT35lFDFuEhCnDGj4MBxfazqcuDEiGxNthEfC6Ye1VNOMoiMJVQYv/3mysBx3m9MFyqX4OUQkaXJNuIh5wM3ccgElFAt7TtHFyotocpGx1VALd+IM2fgLOyB7mr/MVzDmBdmw0QD1/0vCTiBC4yqnJryNZBT5IL2Z+CILDuKho4xNg6cpwGvAE4XkWuKn+cC7waeKSI3AWcWfwNcBNwC3Ax8DPjDbEfWpIATK6SMioCTIo/G97425cBJkYEDYSVUqZ5X4O94qmAsZrlQQhw4Mc/tJkKMfUuo2p6B45sXEzpO+X8ryYGjJVSZEZc8gogSKpdSFi+7bSYxxLd7kVeHK9fW1qFtsx0zcLzFkFxOFhxLv0JKqHATnqJKqFRNTo5zCVWEA8cnA2d+D0yuyzdG9hKqCIGlHGfYY7EQ4fDxcULFlB6V/7fgUEK1amPYGFCcKwcHTqhINEIYY77NYEvtGX22N8Crsx5UySg7cEJDjFOE7DYh4IyCA6fJEqoUi/uSxAKOOnCG4BNa3WQJVV1dqOpshQ1hY4UIRTH3qY4MnLpCjLWEKjMuAkDONuImsITKSVjweXIXT9As4bc+YlaIyOLahSpDG/EQJ8vSvjM5cJzLswjrJqYOnPR4l1AFOrNcxgBY2A0TM/5jrJgSqopW30sOnMAMHHBrJb44F+7AWXLHVCzKYhw44NYWfXwcOO1llAWc1avtmD4fhFOU+IRm4MTgK1Y16cBJIeCEOHBS5TlBWAnXEFTAGYKPAyfFfBXqwNEMnGXqduC4PlYp3Et1hRhrCVUmnBw4AW3EvUqocmTg+IYYd+z2WRwnuVqfU5yPxR4hJfG+XduIZ2l/HurAyVRCpW3E8+HcharschJTQuViGw0toXIUWNpeQlXVRjxFBo6zAyemI4aDuLIwC53ADJylMVzaiCcod1DCGXUBB/ycEqlKqHy+vU/hhgkpoRrVNuLluOrAGQ/qFnCacuD4tBFvexeqtrYRrzM/SEuo2kquEqoOVphJnIHjVUKVoawnxoHj4jgJEnBgWUgZRGCL8hwlZeW+XcShYKdFjhIqdeBkw1VcWep+lLmEKlTAcc3yCQ3ObUsJVXlbUBcqn3bukeG/Tu6YvZEOHMcSKnXgNMtKEHB8hI1RzsAZpxKq0AycTieu21eJCjj14RNinMJl1USIsU8GToog3rocOHWEGNfVhUrbiK8QZJJqB0fuTkOZulBlcYXEdKHKEGLs1T3L90XYdAlVyGMI+UqoNAMnG67lTSkycJxKqAJbS/uUUGXdf+4uVBEOn/J/qrJpIF74cHbg1FBCNQZdqFpNyjbioyDgNFVCVXfez6iXUIU6cFKIVqBtxOtkHEKM6wrirduBs5K6UNXdRlxLqDLh4ioIXqS7LKSN37cIubpQlft27ULlJbS4hhgHiCzOrpDADlcsFDlFQ/YL5AlfjulC5ZrrpA6cVuDsLInJwPEp3Ql97rmWUAU6cFxLqGK6RIFDF6oEpWxODpy5/AJOkgwcB+FRHTjNkqrspKkQYxgNB07s+W3KgePrhElZQhWSgZNKwNE24vUxKiVUKy0DJ+Zc1llCVZcDp64QY3XgZGbJAj/sBR7gVgDHUhbPDBznEqpMJUNBC//cGTjgVjIUIrJU7NuECFrg5JIxERk4OUqolp57oyXgiMixInKZiPxQRK4XkdcW128UkYtF5KbickNxvYjIB0TkZhG5VkROzX6Qzl2oamojbkICxT3GCM3Ace5yFdtGvEL4WBLScj8OkdkxnVVuXajqcOCogNMsWkLlj7YRH0zKEOMQB06K+wxaQlUnPl2oUgo4dbcRryMDx8dFEtMdqm4HTp3lZy5jpXiMVKyLG3YAACAASURBVMDJhJOrIGMJlfHMwHHN1gkqGfJx4OQooQoQnZxLqALydZwEi4CSMnAMzw7tQuVRCued69QZxRDjeeANxpiTgScDrxaRk4HzgUuMMScClxR/AzwHOLH4OQ/4cPYjrLOEykWAMyGvRZYFk9xtxHOXUFWFGJuAebCkri5UAN2qblqmcODEhBg7ZuBoCVWzpBJwfBa8TQs4dZdQpXCG+JZQpczAWVhwX2ykbCMekoGjJVSjR0gJVayY0unUG0RelwhRZkDV5cBxOYcxWTvl/9XRharuEioVcHLhU0LlGZjmVJLkmYEDbu2tc5VQRXUvcinrCQleJtP5cCn9CnVnObYRb1UXqmLflaVf7cIYc4cx5uri9weBG4CjgbOBC4rNLgDOKX4/G/iUsVwOrBeRo7IepIjb4xZTQuXj/AgJQd9vDJfg3IgSqsrnd+4SqggBx7ll/IIdJ6c7prxNS6hWPikFnKYcOD6lRSlKfFatsvfB9Rv1JkKMU3ahAvfHNlUJVWgbcS2hGj1CQoxj56uJCXdRwJh6BZwYEQLcS45ihBWfHKGYrB3wK6Gqy4ETI+CUx7fSMnBaU9aQ04HjtNj1LaEC55DaLCVf5RM+wIHTZAlVSEmIS/eskK5cUDzvXM5Hm0qowM3d015E5DjgFOAKYLMx5o7ipjuBzcXvRwO39/zbtuK6A/d1nohcKSJX3nPPPfEH15lwd+BkL6EKdH+5BvTGllBVlmhlDjGOcuC4CjjFoiZaXBkm4BRjaIjxyidlCdWoOHBiBQafkg9IW0I1NHvvgDFTlVCBm5hiTPMhxlpCNXrUnYFTjukTjAuj0UYc3MWpUQoxrjMDJ7cDR8SvK9kI0Y6yBicBp3gjzZFz4htiDB5ZNbm6ZuEZvOyQJVPuO8TJAo6t4ANLqIYed0x5nYMDJ6SMJVcXKnBsf95ORGQt8PfA64wxD/TeZowxLL3Q3TDGfNQYc5ox5rRNmzYlOMAK1wfEtRH3DjHOWEK1OBvYwcm3hCqzA6cTIXK5nCPIK64spBhD24iPBE2UUJULtVEVcMpxfQSr2PM7M2MfK9eyj5QlVOAm4JTPJS2hOggVcIYQIuDEPs4TE+6vpRRB7z4L9roEnBgHzkruQpXbgQN+rqJRoTVlDS4hxjlbReOZgQMe3aIyLM6DynocHDih59g51DlGHHIpocqUgZPLgRPznB7BNuIiMokVbz5rjLmwuPqucg4pLu8urv85cGzPvx9TXJeX7tTyon0QKcJznTJwcpdQzYW5Y5xekyQooarKjkngwHFxKUGkA2eqQsApFqZRGTgaYjwSjHIJVSku+Dg1UjhE1q2zlw8+6LZ9KgcOuItVTThwyschVQmVrwNHS6hGk7rbiJdj1u3AqaMMCFaeA6euLlR1lVCBnyg1ijRa1pC1hMol5yQgA8e5hCqkZChjmVMWIcS1hCpQ0ALHfKQc4l7GNuI5nUMtQ0QE+DhwgzHmvT03fQU4t/j9XODLPdf/TlG2+WRgR8+clI/O1PKifRBLJVShwh71lFDlykTpON6HFCVULm3EcwppdThwkpVpOTzeWkLVLKkdOC4lPqkEnFKI8Vnop3DgrF1rL3fudNs+hbDgK+A04cApH4eV5MBxLVmroBEBpzWZFBXU3YUKwhw4dWbgqANnmbrEr7pKqMqxVpoDp6T5sgYPASfIwZEhAydbCZVDvknIwm5pkZOp0xc4lgwFuGSA4ec6YwZOaIhx1hKqkczAeRrwCuB0Ebmm+Hku8G7gmSJyE3Bm8TfARcAtwM3Ax4A/rOUoXRw4MSVUnS4gyy6egWMEPjdgeW6o7HwU6cDJ2a2rHGfYGIs1ZOCkKG+q6kKlJVTjQ8oMHHBbhKX6Fr1pAadOB87MjL3ctct9zLodOOWCOIWAs26dPb8+i5zUGTiQrIyqqVmuzKS4WkTWAVeJyMXA72IzKd4tIudjMynezP6ZFE/CZlI8KfdBNpGB4xNinErA8cnAiS3PqcuBU8djVlcXqrodOCtRwBlW1mCMuaOWsoasbcRdS6haFGKcI2jYqYQqVAjJWELl00bceyHn4mQJdeBMwmLFh7+YLlQj5sAxxnybwS+yM/psb4BXZz2ofvg4cEIXw1XhvNAj4IQ896TakbG0/xj3iqMIFeJUKsdpuo14KnfMsOdUbFZQ+b9OJVSJFlxKGCkdOGBdOFMVz81S0PDN+TuQUAEnVmAoBRwfMaUUYOocc5QdOIcfbkX97dvt7y6kLqECe7+nI0pJCxpx4LQmk6ICEXdHTBMhxqOWgeMqeNTdhaqOEqq6HDh1uaRGidaUNbgIOLkzcHIEyQaXDDlk64TkvYBjCVVgTk2V0ynEkeRy3DmfGyFt1SFvFyonV5kSRHd6OZNkEDFtxKFamIAecSLwTavKkbGUTxPw7bhrCVWoILw0Tg0ZOFX3Yb5oJdyNWBDmvB/7jVH1/mm0hKppUpUz9Qo4LmOmWGiHCDgp2lz7ttZO4QzxFXCaLKFKkYFTija//KX7/6QuoYJkOTiNz3KRmRTZa8UnJ90zcMouPjH4lFDVnYEzCl2oOh37OPgIRbHCist5SdGFSh04wZRlDdeJyDXFdW/FljH8bxF5FXAb8BvFbRcBz8WWNewGXpnkKLzKe3xLnTJl4Dgv0H3fXB0cOEFCiENOzZIQEtrqO2d2z7DHMKKEysmB07IuVE77VoLouJRQRbQRB89OV6EiUVXuSoSLyLmTVoSLCPK2EXe9D/PFAm5ijf8YJbUIOFWCXUTZn5KO1A4cl4V+kwJOihIqXwEnhZjim7uTqpyodA7t3l29beoSKnC/v5CnhMqnw9kQGp3lDsykkB7rmzHGiIhXJoWInIdt+8uWLVuSHKOrgBO7cC5pcwlVihBjF3EgxoFTjlNXBg7YsarmlphzpyVUcbSnrCFjG3HnLlSjFGIcUtZTbO/iwMmWgRMihngITyEuqlwZOLkdOCNWQjUy5M7AAUcHTqz4UeXISODAcS2hinERmcWiG1effdRRQjUyAo6jYKcOnGbJUUJVRSoBxyf8tKQJASfF/V1TvN7rDE4Gv45bKUuofO8vpC2h2rDBXt5/f5LdNdaFKkerTf9Q0WomJ91DjFMIOCElVDHPLdcSKmPsT9szcELGqSObJoUDp44Q45XYRrw15C6hyhVinMspk2W/AnQr9h1YtuHchSoixNhJePJ9gWduI+66wA0ScNSBkwWXDJzYxbBTBk5s+VGFIyPKgdOxP5WhuQnKwGCwEFVLCVWNAk5oVtDSGC4duzQDp1FShxjXKeB0u/an7jbiTQg4TTlwDjnEXj7wwPDtIG0JlW/JGKQtodq40V7ed1+S3TXVhaodmRQO+JRQpXiMQ7pQ1eG4KLue1VlCNSoOHFdnzKg4cFZaBk5raEWIcYYORqFhwy5OlmBXiItIFlpC5VJSkaOEKqabk8NCOmjR4xiQDIGZQx4fohV3nBw4dWbgZHLgLEY4cKAoNasScCJdRFVlTikEnKrX6EIp4OTMwIk8T05jjFcJlYh8QkTuFpEf9FzXfDffUXbggBVjmnLguOajpMzAqduBUwo4O3ZUb5uyhCrEgZOyhGolCDiMSqtN7HOmzhKquh043e6yu2YYsXkx4N9GPEbwqKsLVe9+hjEqDpyVWELVGnIH7LoIC94dGhyFlhzhyEHdnHBwbtTQRjw4IDlH96xJYKFikm+jA8dBZFDCcAkxjl0MO2XgJMiPGbqgj3URuXTrShBiDIPvRy1dqMoOUZFdqFzElZhlR/l4D5rLYhxXo8kngbMOuO58bDffE4FLir9h/26+52G7+eZhnAQcY9IIOL4tpkfZgVMKKS4ZOCkdOOW4Pg6clCVUiQWcRma59mRSVNNEBk7dIcZQLTDEljXBynPglOfddazYsGTNwBlxXLoYZe00FFpClaODUcZclkrRKbILVY5OUT5txEOdQ8wzOGw6QixzzsDxPe4pWNzuf0xKNXWVUGXvQlWViVKML4Ef/p2cSgnaiEO1Aydk/6WAs+DQehvihI/SDWVM/y8KUmXggJ2D+z2mS4LdeJRQGWO+VTSC6eVs4BnF7xcA3wTeTE83X+ByEVkvIkdlqWZIJeCUros6Q4zLcV0FnHIBUHcJVQphoQwT9hFwUnWhEnEL802ZgRNaQpWg5TewnIEz4g6ckcFHwElVQlV3Bg5UL9pjg4XL/83dRhzcXUx1l1DVkbWTIgNHS6hykbGEKleIsawCU/VBJlMGTlBpFtXBusFOFpcMnEBHQVZ3Vrnoqfp2PIdYBuHHPQUmTbtN5QDqKqFyKjeMGcMxEyVUhOpOVwtdwY6+8t8q3heiulBJIdZVLJRiS80AuqXbJ8P9KOm4jjE2Dpx++Hbz3Q8ROU9ErhSRK++5556wI0idgeMipjQl4JQLxFEMMe50rCul7hIqESuK1N2FqukSqtWr7eOsAk49+GTgNFVCFSuqgLuA0/Y24iHj1BViHOvAcRW/Yh4jdeBkxCcDx7fUyTnEOGDx7yLgBLlZHBw4WXJZQheN5eM37HyECnAODpxgd5aDgBPU8QvHDlehxz0NRkuosuDTRjy4hGoifwaOVIUYJ8jAcS41a2EJFUB3tcN9SCDg5L4f0ONWGjTG2JVQDaVw23h1803SDCa1gNNmB04qAcfHbQTp7u/atfWXUIEVM3wcOClKqKan7QKpqRIqETjsMBVw6qLuLlQhIcapSqiGkUrAaVMb8dj7VFc2jbYRXyHk7ELlFGK8iL8DZ6p6EW0WCHPgZHD2gIPoFHiOy3yIYecjtoRqqBgS6hwqjzuDA8fZSQVB51sFnDz4ZLtEtRF3KZMkXPzoVuWuRC7ofUqoggWWwp4/P2AxEy3gTNfjwCnnx4HiSmSpGVSLRNpGHCK7+SZBBRx/ROy4PgJOikWnj4CTUszwFXBSOHBE/BxHkPZ5BTYHRwWcemhziHGKDBzfEip14CzjG2LcdqEItI14VpwcOOWXZZlKqLydPS6L6JASqikHASfQFdKZHl56E1xCVSy0Fod9m50xXye4PKt04Ax7HGPcTjlLqFTAyYJLiHEtbcRTlFDlduC4Cl2hb/Cr7eUgkSU2n8bFgbOUFRSZgQPVDpyoEOMaXD6jT/PdfJvIwJmba0bASSkwTE/Xm4ED7XfgpCyhAnt/fTNwUt1nsA6ce+9NsisVcCpoIgOnCQdO1aK9zhDjFA6cOrpQ1eWMqTvEWDNwMuESYjyqGTjeosJUhRBCsbgLceBUfWseIShAhQMnMl8nSwlVbgeOY5vlIJFPBZwsdKes8GaGWG+jM3AqxBWIX3B3KoSoaPGjDgdOhYBThwPHzNv9e3cp7KEOcaV0Kw16zMeshEpEPg/8B3CSiGwTkVfRhm6+KRYnMF4ZOGDvr08b8RT3t4kMHGimhArs/fUVcFrqwBmPWS4C1xKqVCJd3SHGvhk4dQTkjpoDp2qssuNlzP0BLaEafXwycHJ1ocqwiA4RWpwW56G5LBXZKUuL1gDRCSqOO3TfDm3El0qoAkQ4cDjuELFsGiiEgIHPWW0j3jrKcpeFWZhY3X+b6BKqKZh7YPg20e6ViudIbLZLZ9pRaIbg70OzCziODpyY8imoScBxdCuNSQmVMeZlA25qtpuvllCFMTVVfwbOmjXw4IPV2y0uVrcr9qGJEirwL6HasyddFyqwDpzLL0+yK3XgVFB3G3ENMR69LlRVY9U1TjmWllC1lJwZOE4hxpkycIJCjDMJQ0v7dihzCnayZCihcmkjXp6PkDI4qHbghIpl4CZqaReq9tAt80qGPG6xi+HudH73SlWJUwoHTu4SqokWOHAW5whutb40TtmyfMD5SingzA/oXqMlVO2gKQEnlUujqRIqXwEnxaJzzRq3blApMjt6mZkZjRKqPXuW262nYP162L49ya5UwKmgbgHHp4Qqxeup/N8qcaCJDJxQN29dDhxXd2fsuXPNKSq30RKqluLshIA8IcaZSqiCQoyngYWekqN+BJb1dKq6FwUuuKSDPc8ZQoydM3ACQ53B4ZyEnGsPUStIMKt6jihB9DpwBhFbQtX16eAUMcZQEaqGLlSlMB76gaVbLA6yhRivrnYR1eLAiRTrACaKczVQ7BqvEqrW0kQGzjg5cIyx46YYc2bGTdBILeCMSgnVnj32WFNx6KG2TM51oT8EFXAq8OlCleJ5HeLAiRm3nB+rnkspMnBcxYEy8DdGwKkjA6d01VWVrKZ4L/MRpbSEqqVIpxBEcoXgmgoBICDE2KkT0CLZypFCvtWucm6EllCBw/mIzMCpykcKPR+Q14EzbIEY00YcNAcnB0sOHJf8mBjnR+YW3FVj1NKFaiH8+KGeDJxB4tDSGHWWUGU8V7GOKyUNKcoDYHmBMk4ZOC4CTnlsKYSFmRk3B06qx7SkqRDj6Wm3ccEKZakFnEMOsZcPVJQXO6ACTgWuXahSZuDUGWJczjmuLpK6RIgYoch1Doy9T+VrukrASeFechVWYrpd+YyjBCKrYXHIm8fSYtdXaHFZ7EZk4JRBTn0JEBacWnKHButWiSwRmRVSlYkR+i1zWUI1ZIIMcTrBsgNn6EI09FyXz7sMoqTLc0QJo5wvhjpwCmEidMHdqSEAuKqEqnw9Ze1CFRDi3ksdGTiVDpz5eNGjDRk45RhjkoHTWvbts4unmFBswKu19koQcFy7UJUCRIpslqZKqFavdhs3tQNn9Wr3oOi5ObuoUgFnNGmihKrODJzyNVF1H1OVUNVRBuQq7Ma6iup24NRx7nyef0oAshrMsCdnaBvxwlq+OOQN0QRm4GAYGq4bGmIM+YJ1c7T6hmpxKHsJVaCgBc1n4GR5jihBuGTgmMhFvZcDJ3cJVeYcnxhXiXMGToRLqcqBM2ohxgMzcCJdY0oaZmfTOSZcv5FN2e657Rk45QInlQPHpaSoKQdODgHH1YGT8jyXrFljL33KuAagAk4FrgLO3Fya129bS6jqzMCJFdJ9BZzcJVSpHDgu3a6M0RKqVtOZzuRWKN5gKsWh0A5Gwz7MhGTgODpwgkSFKpElsMwJig5XOfJeHEKMg0uocnahcsjAiW5/rgJOclwzcGIWwq7uFbtx+BiL+wY7BJdKanI6cAKdcSUurhKJqCnvrnZoIz5iAo5m4LSbffuWQyJjcXXgpB6zzW3Ey21SOHBmZuziwjVHo+4MnDLrJ9bNVTI97e7AySHglPtyFZGGoAJOBa4Czr59aV6/dYcYuzpwUmTguAo4seeybQJOCgeOi7CSYhzX57sSSFUJVbAA4ODACRJwMmXViIMDIMaB49QVKWN5lrfQ0rU/lcJTpgyc0JbtnYwlVEvPEe1ElRzXLlSxDpzFivLLFPkuMEQ0iHXgOJaBxThwOpP2/g8VcCKDf+crvu2txYETWc4G1SHGi1pC1QpSO3BcxJS9e5sRcEbdgVM6QqrKmcqFQRMhxqncNz7jQh4Bp+xo5VI+VoEKOBX4OHBSPMfqduD4ZuDUIeDEnsu2CTh1nbsUAo5rCa4SSKeihCr0G2lXB04OocXkCjEOFRUytRGHfO4eEejMgBn2pp6xC1Vw3pBDiLGWULWPJQfOMOfUQtxCeEkkqijdA+hEZODA4PuxmCIDZ2+FCDUfL350Vw/vQhUl4KyBhV3D70OKNuJVAk5sW3rwcOBoCVWjpHTDuIoas7PNCDg7d9rLtWvjx3W9r6kdOFAtKKQcE+xCbd++6m+myzylVPgIOOU5UQfOaOL6Ok5VQlU6cIZmhhakKEn0LaGKdXe4zE2x59I1ZLwuASfV41R17lIJOK7uQiWAXPks2TJwyhfisEkwJgOnqvQmgwMnpmzDuQQuRAyZqXBQBYalupbBRWXgaBeqkaLrEmIc6cBZCkrOJKZCtZOodODEdKGq6u63OBdfsjOszCmFgGMWK4S0+XjXSingLFS4oWJdXeCQgaMOnEZpIgNndjaduOBatgXw4IP2ct26+HGbysCB6kyWHAJO734HkcodUTI9bRdLLm6C8jyX5ygF6sCpj1KsqxJUUpZQwbJgMoxRCzGemfEreQylbgdO1VjluY35cmDdumWhfxApBBzXElwlkMoQ41AHTvGmMHTf8yx1O3LF1WER6uzJ4cCRKStWmEGTaEQoqExXdBGLyNfprB7uwAkWtCrOdUxbdecwashTvqcE4VxCFVP3W84dDo61mC5UMFiISuHAGbZ/SJMfk1PA6RYlEsPKqFI4cKocVylafEuncEVpF6pWk9I14SqmpHTglK21Xb5JT+nAcbXAlx/S6yyhSjlm736qFlCp23j7OGByZuCogJOfdeusUFL1mkolErqKArB8TDGCaDnHutw/iO8O5ZKVlULAmZtzy4wRCc/GcnXgpCiRXbu2HgFHHTiZ6WRqIy7Fm8JQB07Ah/QlYWjYfnOFGMcKFoO+CY4QLDqHwOKQ9o/lmBLwQbLSgROagVPlkolwDblk4JgFIGCi1Tbi+XASJhKVUFWVaUGCDJxBAk6sA8dBhErRgntiiIATu/8JRwFnFDJwwObgDDtXoCVUTbN3bzqnhmsGTkoBx3XhB8sOnDpLqFK2EXd1hKQcs3fcqsXu7t1pHTBNCziu99sBFXAqKF1xVS3bU5VQrV9vL3fsqN42xXPr0EPdxivnlJixXF83KQQcl3Fiuw6WweguDsBy+1BUwFkhyOo8+SwuDpwQAWepNGvIh3+zz1+wmDjKXs79bMhGoW2zq0pvIso2uofCwpDJcknACXgz6MxUu3uCRJaKxzC2KxdUBHPvCzsf6sDJRx1txF1KqExgyWhJlUgUKxp0HYSuFOJH7hIqGC7g1NGFaklMixRXhp4rLaFqBbt2LTs7YnERNYxJL+DAsjgzjJ077eIvVYixTxeqJkqo6nbgpBZwXL95Bw0xHnVcX8epSqhKAef++6u33bvXCggx88bGjfbyvvuqx4I48dVVeKxLwJmdjXtdirgJHikcOOvWVT8HUziypqbcy0OVADrTDq2+CShJyuXAWTN8v2YRmPdfoK86AWQN7L16yEYLgWVOVfk6ESVUVQ4cykVKwARWFWJsZgOdPd3C3TNIAS4FnBBxqHiDHHZOFkOPW7tQZcMlxDi6hMpFJEpUQpXLgdNxKCFNFWI80FUyu3wuQ1jq3NQCB45049sBd1cPycDREqpWsHNnvQJOanHB9Zt7sB/MU+TfQDMOHN8SqtQZOFULtZRioM+4vdtoiPFo4irgpCqh2rTJXv7859Xb7tljX0sx74elYFQl4JRzSozA7Sr01iXgpHB5ugQm11VCVc6vMY/Rhg32sur5oATS3QTzd/W4Hg6gDHr0zapZclkMe5JElFANdG8ElgxJB6ZOgn03D94mpLsV9JT1VGW+hAg4h1qxYlBtfIwDp6qEyswuO1586awd/NyIWUR3NwIdmL9n8DahwpOLOKSE4ZRPExls23Vx4CQqoRrkkIl14EwWZRFzQ+bV3CHGC7PLc1oISw6cIXNLXV2oYkUiqBC71IHTClIuul0ycMpFRaoxfR04KcqnwH54n5+vDkFtwoGTuoTKNQsmVwmViwMmh4BTnj914OTHx4GTwkH3+MfbEphvfat62xTZThMTtoyqDgfOMcfYy58Nq5ogXsA55BB7+bWvDd8uhYCzYUP1uStLqGIFHFcHToyA85CH2EsXAVEJYOqx1oGz78b+t5funI7nC7t7uF1M7/3e4G3MHOAr4BQvpsUBlsAYwaJ7BCz8csgGoZkvVc6NiMyX7qH2/weKIZElVEMdOHvDhBCwYsi+Hw+4McKBI13oHgYLGQScbvFtxrB9K2G4ZuBElVC5jBFZQuXswAkUiCaKD4Bzw3KvMrcRX9y7LFQF7duhhGphj83hiaE8B8PEtBTCysTM4PuibcTbQeoSqqoMnKYFnFQOnHJBUiVYpRRTygVTlduoqRKq1A6c8rGq+kYc8gg4InZ/KuDkx/V1vGtXGpHwkEOsiPOud8Fttw3fNlVO2MaN9Qg4J5xgL3/4w+HbxZayvvCF9vJDHxq+XelgiuGII+CeivVF+d4TI0rNzMBddw3fJkUJ1aMeZS/f+97wfShDmD7FXu69pv/tZZaIr9NCOjDzq7D7sv63mwXA+H/LOnG0vZy7dcB+iyddyAJ94giY++ngFr2h2Q9VGThRnaJKQWvAh51YB87c7YNvN7PLC1ZfVj0cdl8KC30m+lgXxMQRsHD34NtjHDiyari7RwmjjgycrmP5EUQ4cCpEovI+hNqUJ4vX+/yQD4DZHTiRAo5LBs7CHnsMMYhYEWeYmJZCwFl9NOwZ8A3TfLEom0jkiFDCSC3gVOUUNCngPPhgOgdOKRTUmUdT2u63b3cbM5UDpwxgrcoLSe3A8SmPyyHglMfgIiBVoAJOBeVjfemlg7dZWLCL51RzxxvfaC8/+cnh26Vy7h1xBNw+ZN0Aadwdxx5rRYzXvnZ4d77YuX96Gp7xDLj22uHzYAoBbMsW65Ya5lhJUUJVOiq/9KXB26R4jE46CY46Cn7wg/B9KEOYeoRdzA5yypg9wGTYgmbm16zQsu/WPvsNzGbpTMHUY2DXJf1vjxEsVv8KLNxrRZy++961nMHjw1IGzqCgy2JSCNl3twgNm79jwL5jHDirYXEHzG0bsO9AIQRg/e/byz1X9Nlv8UGiE/hm0j0CHvyHIWVloRk4Asd8BTacF3ZcymBkApCKcN7IDBwXB05ZXhUqHlRl+SzOxZWBlQLOMAdO7hDjURFwAFZthNl7+98WW5JXsvZ42HlL//lmXxEwrwJOs6QUcI46yn7Arlo0QLoxS1eKSzeZlA4c1zKFlA6c1avt4qTqW/zUJVRHHmkvq76ZziXguIhzuQScQw91E5AqUAGngtI1ckWfz70lpRMq1dzxkpfYOevf/m34nPXAA8vzTAxPfjL867/CX/7l4G1KsTDmPna78IIX2BLPl7988HYp3Eyve529/Iu/GLzN3r3xofW/+ZtWOHnDGpxwGgAAF+9JREFUGwZvUworMQLOa15jL//Lfxn8nEiRgQPwu78L11wD//IvcftR+iCTMPVo2PHp/g/k4h7/8qmSmV+zl/1cOKECDsDqJ8K+H/Vkx/TuN8KBs+ph9vKBL/a/fXFnmKiw6kR7uffawfuFsH2vfpq9zCFoHfIye7l7QP3sYkQGztpn2ct+As5C8UGmE/ghdOox9nKgKBkhPK19tg28VtIiYt0rlQ6cmBBjhwycUrQIFSiqsnwWZpezWUKYdC2hythGPFrAcQgxnk8k4ExvhtkBbrzYx6Jk7fH2nNzV531ubocte+toCVVjzM/bby1TLYhOOMEusu4Y8KUJpBdwHvpQu2CpKheAtA6cLVvsZdU36nv32gVFJ8ESXsS6cFwdOKnEjE2b7Nh33jl8u9QlVKXj6N4BQnMvu3fbnJEUAbe9HHLI+Ak4InKWiNwoIjeLyPl1jFkuur/9bbj88v7bpBZwAJ77XLjkEvjKVwZvk0rAeelL7eV//a+wbcCXv6VYGTveF75g5+PPfQ5uvbX/Nrt3x5/L5z3PlqK94x3L7bUPJIVw/qIXwTnnwN/93eBzV4pfMWOdcIIVi+68Ez796f7bpHI4nnuuPf8veUmSoPSRJOtcM/OfYeEuuPPVB4siZs9yRylfph5pc0N2/lOfGyMEnDXPtE6Zu/744NvKTk8hpT2rn2TzU+77q/4hyTECjkzBnv+v/+1LAk7AC3LyITD1OHjwwgECXDFRSsAENvN0e0z3/vkAsWxPXIjx5Fb45TsP3ndZDtYJnNwPf5u93PX1/rcvPhju7hkDmvhcA9hg3D2/GHz7/O5l90YIVS2+e28LFQ+WRKIBb1RzO2Byfdi+oceBM+Tb2oW94aWNJd2ZwSHD87vs7aG4hBincuBMHwF7BnyjPrcDVkU8FiVHPdte3vfdg2+bvRemNsaPsYLJPt+kFlNOLL6Quemm6jFTCSlr1sCjHz140ddLSgeOqyslRQBqLxs31l9CNTEBhx8+/L4ak96Bs3mz3d9PflK9berzXLJxI9w9pOzckZERcESkC3wIeA5wMvAyETm5jrGPLmIg3va2/rlDpZCWau4AeN/77PP7vPMGOy527EjnwPnnf7a/H3ssvPOdB5cePfCAFXtjX0eTk/D1r1txe+tWeNWrrKjzp38Kb36zDfxN4cCZmICXvWz59x/3ye9MJYC96U328swz4eY+TXVK8Sv2+fH+99vLc8+F889fDkcuKeff9ZGfkU46yQpS999vHT/jRva55oj3QPdIuP/DtvSkl/lf2EyREERg/e9ZceHBA0ScUlgIWUiv+3V7uf2vYefF+982X7wJlYGzPnTWwEM+ZfNTfvmn+99m5grnRsDxSgemT4Mdn4K5PkFiMSIL2HO89zsw26fOcL74NmniSP/9ShdmnmH3u/MfD7594R6YCDjPJet/D1g4WGgpQ4K7G8L2O7EJJo6BPf/e//b5O8POxxjQ5Oca1m6F+68bfPvcjmUBI4SyjGVYfkysA6cUBPYNyFGYuz9ONCjvwzAHTgphYtV6e54We/LAtl8Lu26zjpbpwPcE6AkxHpC5sLDXuogmE3yAHebA2Xd/nJhWsu5hsPoo2HHDwbftuhXWPDR+jBVKLfNN+Y1lqgVRWQbR78N1SWrRCOzC6DvfGdwR6h//0drrt29fznOJ5YjidV7lStm9O52QAm7dWHbssAuzFK6fks2bh9/XnTvt+U+xUCvpdKwo2G9ReCC5BJyTT4brrx9eYuPAKPXaeyJwszHmFgAR+QJwNuDgcYvj6qutGHDppcvCLNjH9X/+z+XnwebN6cZctw7e+lbrIDn0UCsCvPzlduFe8otfwK/8Sprxnvtc+B//w5YC/bf/Zn+6PS7UhQX7Go9pWV5y/PHwh38IH/wgfOIT9qekLHmq6qLnwutfb4Wpyy6zokR5f4yxos6+ffDYx8aP85Sn2LKjT37Szgu9501keZ6NFek3b7bn6vd+D97zHvvT7S7fn9NOs9ttTPAF1Fln2ef3BRdYx4/I8jh/9mdW2EkpireMvHONTMIJt8KN09bVMvWI5dtmfwDTTwrf92FvgnvfA9teAMf3CAyzxaGHCC3ShaP+Bu74fbj9WbD1muUwyl3fsJehC/S1z4WJLfaY1z6vJ2Om+FZmInBSPerjcMsj4OatcNx392/DO3uDbQce+q15Waq27YVw7AFC2ex19j6Etv09+u/gxhnYdg5svW55wjX7bO5ON0IIOexNcN+H4L73wuSW5et3XQoITEV8jl93Nmz/EOy8CCZ7FlDG2GDmtc8J3/fKprHPNWw8DbZ9Ce6/vv/ts/fC+og3yKlirtnxw8Fj7CqyHkLdH5OHAgIP/rj/GPd9D9YeF7ZvKMTgI+EHfwJbXmzHOpDZe+OFiVXFvHfvFcv7+mrPuY8RcDpdmNkC170dNv8arDps/9v3FguoqYgxStZshVs/C3deasWcXu7/Phz2xPgxADacAj+9ALa+wj4+JTt/AkeemWaMlUn6+WbPHrjlluW/byiEtSMTifZbtthvfq+4wooq/SjdOSkFnCc9Cf7X/4KvfhWOO27/2773PXjFK+C3fssKG5sivljpZdUq+wH+Rz+yC/xB3Hxz2gXnkUfC978/fMyf/CTd/Sw56ij46U8Hj/uLwiGaetwTT4Srrhp+f8GKSzkEnC1brAh3//3LJV0BjJKAczTQWxi4DYhY6bhzxBFw8cXwt38L3/iGdT7cdJMVZ5/Y83507LFpxz3/fCtkfPWr9rn2mc/Yn162bk033utfbwWhP/5j+NVfPTiz5ZRT0o31/vfb+fF974NTT7XuvE9/Gp75THuujz8+fgwRW4b2kY/sX940O7uc95NqXvj4x22J07e/vf/173qXFZGnptK8t7zylfCEJ1jn0pln2vs4P2/FnH//d/s+FzEfLNHtwo03wsc+tiymLS7Cn/+5Ddl+6lOtcLVCyT/XdKZsiO+eb8Mtj9r/tvX/V/h+uxttydOuiw/eLyx3lfJl/atg7/dh+wfhp4/b/7bOuuXcmRCOeDf84rfgtqcffFto/snUSbDhtbD9/XDraQffPt3nOud9nwyr/hPsu6H/OZ751fB9d1bDuhdZF9VPH33w7asfH75vWQUb/gB++faDj3vqkXFlToe8zAo4tz+v/+1Tfe6LAg1+rmHmGJi9By7q8xwumY54g+yusov4H3/Q/gxiYk14hox0rLhx04ftTz+OOSds3yXH/Rb86L1w0ZDncIzAAtZRAnDxgG/kNpwat/9HvA6ufj38y5C5aSbBB9gtvw7XvxMuPaP/7Rse1/96X074A/jFRXBpH7FmXcR70con/Xxz/fX2Q+mBpFqcTEzAwx9uP4x+7GODtxNJ54QBeFqRd/f85w/e5nOfs5dHB36u6seWLfD5z9ufYZx9droxTzoJLrxwuQ3tIFJ/6H/oQ+2Cr2rclOcX4JGPhC9+sXpcgMclmrN6OaqY7++4I2rBJibSwlMXIvJi4CxjzO8Xf78CeJIx5o96tjkPOA9gy5Ytj7+tqg93BMZYMacsn9qwwS6oc3HhhTZzqbc8ptuFZz0rbelWG/jud61YNJFRXrz6avulwemnp3GsDOK226zQd9xx/d/jUnHttVZw2bLFCmO5uO46++XA6afDYYdVby8iVxljIlbL9eMy1xTXx803C/cXYbi9drOuFWC6EXatxT22TMYcUGPXOQTWPCvcRmcWYOdXD+7utOpEmI58k9t1ycEtrjtrYM1ZdpEWgjGw62vLmTe9TJ8SF4678IAVyehjFZx+Aqw6LnzfZh/s/NrBbdBltXWyxATLLu61jyEHtG6feixMPTx8vwC7v92/O5esso9jbE5IBTrXeDL3ANz5L8tt5A8+Oth8elymyPZr4cEbh2+z9njYGCFMbv++deD0pQNHng6rIr7VWJyDX3x1SOBzB448I66MamEf3PH1/Vuuz95rS8umDoejzooPSr7jYltS1o/ujM2WSdEl6s5LYF+fcgzpWndMTFleL3ddBrO/7DPGM5fDpzMxinMNZFpH3Xef/aa0l/Xrl79hTMGNN9oPucN4yEOWRZdUfPObcM89/W+7/Xb7rf2qVfDsZ6crafrxj60bpoqnPjWdsLFjh+1aUlX6cOqp8LCHpRkT7Ln95jeHbzMzY0sCuhGfew7kgQfs/R0UkNrLYx5jBa6UbNtm85XOPNMp82LQfDNKAs5TgLcbY55d/P0WAGPMn/fb/rTTTjNXXnlljUeoKMqBjOIHHd+5BnS+UZSm0blGUZQ6GMW5BnQdpSijyKD5ZmRCjIHvAieKyFYRWQW8FBjSo0lRFCUInWsURakDnWsURakLnW8UZYUwMhk4xph5Efkj4OtAF/iEMaYigUhRFMUPnWsURakDnWsURakLnW8UZeUwMgIOgDHmIuCipo9DUZSVjc41iqLUgc41iqLUhc43irIyGKUSKkVRFEVRFEVRFEVRlLFEBRxFURRFURRFURRFUZSWowKOoiiKoiiKoiiKoihKy1EBR1EURVEURVEURVEUpeWogKMoiqIoiqIoiqIoitJyxBjT9DFkQUTuAW5z3Pxw4JcZDycFeoxp0GOMx+f4HmqM2ZTzYNqAx3zT9scW9BhToceYBtdj1Llmf1bSY9skeoxpWEnHqHPNwaykx7dJ2n6MbT8+WHnH2He+WbECjg8icqUx5rSmj2MYeoxp0GOMp+3H12ZG4dzpMaZBjzENo3CMbWQUzpseYxr0GNMwCsfYVkbh3OkxxtP244PxOUYtoVIURVEURVEURVEURWk5KuAoiqIoiqIoiqIoiqK0HBVwLB9t+gAc0GNMgx5jPG0/vjYzCudOjzENeoxpGIVjbCOjcN70GNOgx5iGUTjGtjIK506PMZ62Hx+MyTFqBo6iKIqiKIqiKIqiKErLUQeOoiiKoiiKoiiKoihKy1EBR1EURVEURVEURVEUpeWMtYAjImeJyI0icrOInN/08RyIiBwrIpeJyA9F5HoReW3TxzQIEemKyPdE5J+aPpZ+iMh6EfmiiPxIRG4Qkac0fUwHIiL/T/E4/0BEPi8i0y04pk+IyN0i8oOe6zaKyMUiclNxuaHJYxwFdK5Jh8418bRxrgGdb1Kh800a2j7XgM43Ecekc00CdK5JR9vnG51rgo8py1wztgKOiHSBDwHPAU4GXiYiJzd7VAcxD7zBGHMy8GTg1S08xpLXAjc0fRBDeD/wNWPMI4DH0rJjFZGjgdcApxljHgV0gZc2e1QAfBI464DrzgcuMcacCFxS/K0MQOea5OhcE0GL5xrQ+SYanW+S0va5BnS+CeWT6FwThc41yWn7fKNzTRifJMNcM7YCDvBE4GZjzC3GmH3AF4CzGz6m/TDG3GGMubr4/UHsi+XoZo/qYETkGOB5wN80fSz9EJFDgacDHwcwxuwzxtzf7FH1ZQJYLSITwAzwi4aPB2PMt4D7Drj6bOCC4vcLgHNqPajRQ+eaROhck4zWzTWg800idL5JQNvnGtD5Jgada5Kgc00i2j7f6FwTTq65ZpwFnKOB23v+3kYLX9QlInIccApwRbNH0pe/At4ELDZ9IAPYCtwD/G1hT/wbEVnT9EH1Yoz5OfCXwM+AO4AdxphvNHtUA9lsjLmj+P1OYHOTBzMC6FyTDp1rIhmxuQZ0vvFF55s0tH2uAZ1vUqNzjR8616Sj7fONzjVpiZ5rxlnAGRlEZC3w98DrjDEPNH08vYjI84G7jTFXNX0sQ5gATgU+bIw5BdhFy6yxRf3j2dhJ8iHAGhF5ebNHVY0xxgCm6eNQ0qBzTTQ612RE55uVRVvnmxGZa0Dnm2zoXLOyaOtcAyMz3+hck4nQuWacBZyfA8f2/H1McV2rEJFJ7KTzWWPMhU0fTx+eBrxQRG7F2idPF5HPNHtIB7EN2GaMKVX3L2InojZxJvBTY8w9xpg54ELgqQ0f0yDuEpGjAIrLuxs+nrajc00adK5JwyjNNaDzjS8638QzCnMN6HyTGp1r/NC5Jg2jMN/oXJOW6LlmnAWc7wInishWEVmFDTr6SsPHtB8iIth6wxuMMe9t+nj6YYx5izHmGGPMcdhzeKkxplWKpzHmTuB2ETmpuOoM4IcNHlI/fgY8WURmisf9DFoWENbDV4Bzi9/PBb7c4LGMAjrXJEDnmmSM0lwDOt/4ovNNJKMw14DONxnQucYPnWsSMArzjc41yYmeayaSHs4IYYyZF5E/Ar6OTar+hDHm+oYP60CeBrwCuE5Erimue6sx5qIGj2lU+WPgs8WbzC3AKxs+nv0wxlwhIl8Ersam5n8P+GizRwUi8nngGcDhIrIN+O/Au4H/LSKvAm4DfqO5I2w/OteMHTrXBKLzTTw634wdOt8EoHNNPDrXjB061wSQa64RW3qlKIqiKIqiKIqiKIqitJVxLqFSFEVRFEVRFEVRFEUZCVTAURRFURRFURRFURRFaTkq4CiKoiiKoiiKoiiKorQcFXAURVEURVEURVEURVFajgo4iqIoiqIoiqIoiqIoLUcFnDFCRA4TkWuKnztF5OfF7ztF5K8zjfk6Efmd4vd3iMiZOcbJhYi8XUTeOOT254vIO+o8JkVpOzrX+KNzjaL4o3ONPzrXKEoYOt/4o/NNHrSN+JgiIm8Hdhpj/jLjGBPA1cCpxpj5XOPkpOo8iYhg7+PTjDG76zw2RRkFdK5xQ+caRYlD5xo3dK5RlHh0vnFD55s8qANHQUSeISL/VPz+dhG5QET+TURuE5EXichfiMh1IvI1EZkstnu8iPyriFwlIl8XkaP67Pp04Opy0hGRT4rIi4vfbxWRPxGRq4t9P6LPcT1SRL5TqNvXisiJxfUv77n+IyLSLa4/q9jf90XkkuK6jSLypeL/LxeRx/Tcz0+IyDdF5BYReU3PuG8TkR+LyLeBk3quf42I/LDY1xcAjFVAvwk8P/ZxUJSVjs41OtcoSh3oXKNzjaLUhc43Ot/UjQo4Sj8ehp00Xgh8BrjMGPNoYA/wvGLy+SDwYmPM44FPAO/qs5+nAVcNGeeXxphTgQ8D/ex1fwC83xjzOOA0YJuI/CfgN7FK7eOABeC3RWQT8DHg140xjwVeUuzjT4DvGWMeA7wV+FTP/h8BPBt4IvDfRWRSRB4PvBR4HPBc4Ak9258PnFLs6w96rr8S+M9D7qeiKP3RuUbnGkWpA51rdK5RlLrQ+Ubnm6xMNH0ASiv5qjFmTkSuA7rA14rrrwOOw6qpjwIuFhGKbe7os5+jgBuGjHNhcXkV8KI+t/8H8DYROQa40Bhzk4icATwe+G4x9mrgbuDJwLeMMT8FMMbcV+zjV4BfL667VGz96iHFbf9sjJkFZkXkbmAzdgL5h9LGJyJf6Tmea4HPisiXgC/1XH838JAh91NRlP7oXKNzjaLUgc41OtcoSl3ofKPzTVZUwFH6MQtgjFkUkTmzHJS0iH3OCHC9MeYpFfvZA0xXjYNVfw96LhpjPiciVwDPAy4Skf+7GPsCY8xbercVkRdUHMuw8QcewwE8D3g68ALshPjowtY4jb2viqL4oXNNf3SuUZS06FzTH51rFCU9Ot/0R+ebRGgJlRLCjcAmEXkKQGGZe2Sf7W4ATggdRESOB24xxnwA+DLwGOAS4MUickSxzUYReShwOfB0EdlaXl/s5t+A3y6uewbWbvjAkGG/BZwjIqtFZB12kkFEOsCxxpjLgDcDhwJri/95OPCD0PupKMpAdK7RuUZR6kDnGp1rFKUudL7R+SYKdeAo3hhj9okN0fqAiByKfR79FXD9AZt+Ffh0xFC/AbxCROaAO4E/M8bcJyL/L/CNYjKYA15tjLlcRM4DLiyuvxt4JvB24BMici2wGzi34r5dLSJ/B3y/2Md3i5u6wGeK+yvAB4wx9xe3/RrwloN2pihKFDrX6FyjKHWgc43ONYpSFzrf6HwTi7YRV7IiIv8AvMkYc1PTx5IDEdkMfM4Yc0bTx6Io44zONYqi1IHONYqi1IXON0o/VMBRsiIiJwGbjTHfavpYciAiTwDmjDHXNH0sijLO6FyjKEod6FyjKEpd6Hyj9EMFHEVRFEVRFEVRFEVRlJajIcaKoiiKoiiKoiiKoigtRwUcRVEURVEURVEURVGUlqMCjqIoiqIoiqIoiqIoSstRAUdRFEVRFEVRFEVRFKXlqICjKIqiKIqiKIqiKIrScv5/CDfzneRYwDYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SyZATrWqzqpp"
      },
      "source": [
        "## Preprocess the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a0S_ysgTX1_l"
      },
      "source": [
        "### Demographics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwSvyujwzxS5",
        "outputId": "1ed9d542-a726-4922-9a57-c8ae5e8634d8"
      },
      "source": [
        "demographics = pd.read_csv('Physionet/demographics.txt', delim_whitespace=True)\n",
        "sub_id = demographics.ID.to_list()\n",
        "sub_names = []\n",
        "for name in os.listdir('CSV'):\n",
        "  sub_name = name.split('_')[0]\n",
        "  sub_names.append(sub_name)\n",
        "\n",
        "print(\"Subjects Count (in demographics) = \", len(sub_id))\n",
        "print(\"Files count = \", len(sub_names))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Subjects Count (in demographics) =  166\n",
            "Files count =  306\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubE0i3V0UgQ2"
      },
      "source": [
        "### Preprocessing HoehnYahr Feature\n",
        "- Filling the missing data\n",
        "- Changing the classes \n",
        "\n",
        "| HY Score | Class |\n",
        "|----------|-------|\n",
        "| 0.0 | 0 |\n",
        "| 2.5 | 1 |\n",
        "| 2.0 | 2 |\n",
        "| 3.0 | 3 |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbRiGxHRQ_bd",
        "outputId": "0ee67b3b-de08-4bf4-921b-dd24eeb0f613"
      },
      "source": [
        "demographics.HoehnYahr\n",
        "demographics['HoehnYahr'] = demographics['HoehnYahr'].fillna(0)\n",
        "demographics.HoehnYahr.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    73\n",
              "2.0    55\n",
              "2.5    28\n",
              "3.0    10\n",
              "Name: HoehnYahr, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBi4fs8fsZiZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6524738-1b6c-483c-93e9-11fae0847cc4"
      },
      "source": [
        "# Remove the demographics of missing file\n",
        "demographics = demographics[demographics.ID != 'Juc010']\n",
        "print(\"Subject Count from different Study\")\n",
        "print(demographics.Study.value_counts())\n",
        "\n",
        "print(\"\\nSubject Count - Study & Gender\")\n",
        "print(pd.crosstab(demographics.Study, demographics.Gender))\n",
        "\n",
        "print(\"\\nSubject Count - Study & Groups\")\n",
        "print(pd.crosstab(demographics.Study, demographics.Group))\n",
        "\n",
        "print(\"\\nSubject Count - Groups & Gender\")\n",
        "print(pd.crosstab(demographics.Group, demographics.Gender))\n",
        "\n",
        "print(\"\\nSubject Count - Study + Groups & Gender\")\n",
        "print(pd.crosstab([demographics.Study, demographics.Group], demographics.Gender))\n",
        "\n",
        "print(\"\\nSubject Count - Study Group & HoehnYahr Severity\")\n",
        "print(pd.crosstab(demographics.Study, demographics.HoehnYahr))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Subject Count from different Study\n",
            "Si    64\n",
            "Ju    54\n",
            "Ga    47\n",
            "Name: Study, dtype: int64\n",
            "\n",
            "Subject Count - Study & Gender\n",
            "Gender   1   2\n",
            "Study         \n",
            "Ga      30  17\n",
            "Ju      28  26\n",
            "Si      40  24\n",
            "\n",
            "Subject Count - Study & Groups\n",
            "Group   1   2\n",
            "Study        \n",
            "Ga     29  18\n",
            "Ju     29  25\n",
            "Si     35  29\n",
            "\n",
            "Subject Count - Groups & Gender\n",
            "Gender   1   2\n",
            "Group         \n",
            "1       58  35\n",
            "2       40  32\n",
            "\n",
            "Subject Count - Study + Groups & Gender\n",
            "Gender        1   2\n",
            "Study Group        \n",
            "Ga    1      20   9\n",
            "      2      10   8\n",
            "Ju    1      16  13\n",
            "      2      12  13\n",
            "Si    1      22  13\n",
            "      2      18  11\n",
            "\n",
            "Subject Count - Study Group & HoehnYahr Severity\n",
            "HoehnYahr  0.0  2.0  2.5  3.0\n",
            "Study                        \n",
            "Ga          18   15    8    6\n",
            "Ju          25   12   13    4\n",
            "Si          29   28    7    0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxMtwLPxUaAH",
        "outputId": "b6294f14-e76f-498c-84c2-3424b6408575"
      },
      "source": [
        "demographics['HoehnYahr'].replace({2.5: 1}, inplace=True)\n",
        "demographics['HoehnYahr'] = demographics['HoehnYahr'].astype(int)\n",
        "demographics.HoehnYahr.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    72\n",
              "2    55\n",
              "1    28\n",
              "3    10\n",
              "Name: HoehnYahr, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "seMDNvgjVkif"
      },
      "source": [
        "## Segmentation of the Data\n",
        "- Each segment will be of the shape 100 x 19"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHlBADgVIHzZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e89b5cc2-61cf-4a31-9965-66e70273f166"
      },
      "source": [
        "!mkdir Final\n",
        "!mkdir Final/train\n",
        "!mkdir Final/test\n",
        "\n",
        "count = len(os.listdir('CSV'))\n",
        "train_count = int(70/100*count)\n",
        "test_count = count - train_count\n",
        "\n",
        "print(\"Training Count Subjects = \", train_count)\n",
        "print(\"Test Count Subjects = \", test_count)\n",
        "\n",
        "category = 'train'\n",
        "counter = 1\n",
        "\n",
        "ypath = 'Final/y_' + category + '.txt'\n",
        "yfile = open(ypath, \"a\")\n",
        "hypath = 'Final/hyscore_' + category + '.txt'\n",
        "hyfile = open(hypath, \"a\")\n",
        "\n",
        "for name in os.listdir('CSV'):\n",
        "  if counter == train_count + 1:\n",
        "    yfile.flush()\n",
        "    yfile.close()\n",
        "    hyfile.flush()\n",
        "    hyfile.close()\n",
        "    \n",
        "    category = 'test'    \n",
        "    ypath = 'Final/y_' + category + '.txt'\n",
        "    yfile = open(ypath, \"a\")\n",
        "    hypath = 'Final/hyscore_' + category + '.txt'\n",
        "    hyfile = open(hypath, \"a\")\n",
        "  print(counter, name, category)\n",
        "  \n",
        "  sub_name = name.split('_')[0]\n",
        "  sub_class = demographics[demographics['ID'] == sub_name]['Group'].to_string(index=False).strip()\n",
        "  hy_class = demographics[demographics['ID'] == sub_name]['HoehnYahr'].to_string(index=False).strip()\n",
        "  sub_data = pd.read_csv('CSV/' + name)\n",
        "  features = sub_data.columns.to_list()\n",
        "\n",
        "  full_size = 100\n",
        "  overlap = 0.5\n",
        "  overlap_size = int(full_size * overlap / 2)\n",
        "  entry_size = full_size - overlap_size\n",
        "  \n",
        "  for i in range(0, sub_data.shape[0], entry_size):\n",
        "    if sub_data.shape[0] >= i + entry_size + overlap_size:\n",
        "      yfile.write(sub_class + \"\\n\")\n",
        "      hyfile.write(hy_class + \"\\n\")\n",
        "      \n",
        "      for fname in features:\n",
        "        path_name = 'Final/' + category + '/'\n",
        "        file_name = fname + '_' + category + '.txt'\n",
        "        with open(path_name + file_name, 'a') as feat_file:\n",
        "          arr = sub_data.iloc[i:i + entry_size + overlap_size, ][fname].to_numpy()\n",
        "          # print(i, len(arr), end = ' | ')\n",
        "          feat_file.write(' '.join(map(str, arr)) + \"\\n\")\n",
        "\n",
        "  counter = counter + 1\n",
        "yfile.flush()\n",
        "yfile.close()\n",
        "hyfile.flush()\n",
        "yfile.close()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Count Subjects =  214\n",
            "Test Count Subjects =  92\n",
            "1 GaCo14_01.csv train\n",
            "2 GaCo06_01.csv train\n",
            "3 JuPt03_01.csv train\n",
            "4 JuCo23_01.csv train\n",
            "5 JuPt23_02.csv train\n",
            "6 GaPt05_01.csv train\n",
            "7 JuPt20_04.csv train\n",
            "8 SiCo20_01.csv train\n",
            "9 JuPt11_07.csv train\n",
            "10 JuPt10_06.csv train\n",
            "11 JuPt20_05.csv train\n",
            "12 GaPt25_10.csv train\n",
            "13 JuPt25_01.csv train\n",
            "14 GaPt31_02.csv train\n",
            "15 JuCo03_01.csv train\n",
            "16 JuCo21_01.csv train\n",
            "17 SiPt12_01.csv train\n",
            "18 SiCo06_01.csv train\n",
            "19 JuPt28_04.csv train\n",
            "20 JuPt03_03.csv train\n",
            "21 SiCo03_01.csv train\n",
            "22 SiPt39_01.csv train\n",
            "23 SiCo01_01.csv train\n",
            "24 JuPt03_05.csv train\n",
            "25 JuCo24_01.csv train\n",
            "26 SiCo09_01.csv train\n",
            "27 GaPt06_01.csv train\n",
            "28 GaCo05_01.csv train\n",
            "29 JuPt20_01.csv train\n",
            "30 GaPt23_10.csv train\n",
            "31 SiPt27_01.csv train\n",
            "32 JuPt28_01.csv train\n",
            "33 JuCo14_01.csv train\n",
            "34 GaCo12_01.csv train\n",
            "35 JuPt01_04.csv train\n",
            "36 JuPt21_07.csv train\n",
            "37 GaPt13_02.csv train\n",
            "38 GaPt29_02.csv train\n",
            "39 GaCo07_02.csv train\n",
            "40 GaCo17_01.csv train\n",
            "41 GaPt27_02.csv train\n",
            "42 JuPt15_02.csv train\n",
            "43 GaPt17_10.csv train\n",
            "44 JuCo18_01.csv train\n",
            "45 JuPt14_01.csv train\n",
            "46 JuPt01_01.csv train\n",
            "47 SiPt02_01.csv train\n",
            "48 GaPt28_10.csv train\n",
            "49 GaPt14_02.csv train\n",
            "50 GaPt23_02.csv train\n",
            "51 JuPt22_01.csv train\n",
            "52 SiPt17_01.csv train\n",
            "53 GaCo01_01.csv train\n",
            "54 JuPt15_06.csv train\n",
            "55 GaCo15_01.csv train\n",
            "56 GaCo15_10.csv train\n",
            "57 JuPt03_06.csv train\n",
            "58 GaPt24_01.csv train\n",
            "59 GaPt29_10.csv train\n",
            "60 GaCo07_01.csv train\n",
            "61 JuPt02_01.csv train\n",
            "62 JuPt01_03.csv train\n",
            "63 JuPt20_07.csv train\n",
            "64 SiCo28_01.csv train\n",
            "65 GaPt22_01.csv train\n",
            "66 GaCo02_02.csv train\n",
            "67 JuPt21_06.csv train\n",
            "68 JuPt15_05.csv train\n",
            "69 JuPt06_05.csv train\n",
            "70 SiCo13_01.csv train\n",
            "71 JuPt06_03.csv train\n",
            "72 GaCo10_02.csv train\n",
            "73 JuPt09_02.csv train\n",
            "74 SiPt19_01.csv train\n",
            "75 GaCo15_02.csv train\n",
            "76 JuPt20_02.csv train\n",
            "77 JuCo12_01.csv train\n",
            "78 SiPt33_01.csv train\n",
            "79 GaCo16_10.csv train\n",
            "80 GaPt22_10.csv train\n",
            "81 GaPt21_10.csv train\n",
            "82 GaPt03_01.csv train\n",
            "83 GaPt18_10.csv train\n",
            "84 SiCo10_01.csv train\n",
            "85 GaCo09_02.csv train\n",
            "86 JuPt18_01.csv train\n",
            "87 GaPt14_01.csv train\n",
            "88 JuPt10_05.csv train\n",
            "89 GaCo14_10.csv train\n",
            "90 GaCo03_02.csv train\n",
            "91 SiPt23_01.csv train\n",
            "92 JuPt10_02.csv train\n",
            "93 JuPt11_04.csv train\n",
            "94 SiPt13_01.csv train\n",
            "95 JuPt03_02.csv train\n",
            "96 JuPt03_07.csv train\n",
            "97 GaCo13_01.csv train\n",
            "98 GaPt12_02.csv train\n",
            "99 JuCo05_01.csv train\n",
            "100 JuPt10_01.csv train\n",
            "101 GaPt26_01.csv train\n",
            "102 GaPt19_02.csv train\n",
            "103 JuPt01_06.csv train\n",
            "104 JuPt29_02.csv train\n",
            "105 GaPt17_02.csv train\n",
            "106 JuPt28_02.csv train\n",
            "107 JuPt26_01.csv train\n",
            "108 SiCo23_01.csv train\n",
            "109 GaPt07_02.csv train\n",
            "110 SiCo27_01.csv train\n",
            "111 GaPt09_02.csv train\n",
            "112 GaCo03_01.csv train\n",
            "113 SiPt28_01.csv train\n",
            "114 JuPt29_07.csv train\n",
            "115 GaPt09_01.csv train\n",
            "116 GaPt15_01.csv train\n",
            "117 JuPt08_01.csv train\n",
            "118 GaPt31_01.csv train\n",
            "119 GaPt33_10.csv train\n",
            "120 JuPt29_01.csv train\n",
            "121 JuCo20_01.csv train\n",
            "122 SiCo04_01.csv train\n",
            "123 JuPt21_05.csv train\n",
            "124 SiPt36_01.csv train\n",
            "125 JuPt29_03.csv train\n",
            "126 GaPt28_02.csv train\n",
            "127 JuPt21_04.csv train\n",
            "128 GaPt28_01.csv train\n",
            "129 GaPt26_10.csv train\n",
            "130 JuCo25_01.csv train\n",
            "131 JuPt21_01.csv train\n",
            "132 GaCo17_02.csv train\n",
            "133 JuPt01_02.csv train\n",
            "134 SiCo08_01.csv train\n",
            "135 SiPt16_01.csv train\n",
            "136 GaPt24_10.csv train\n",
            "137 GaPt12_01.csv train\n",
            "138 SiPt25_01.csv train\n",
            "139 JuPt11_06.csv train\n",
            "140 SiCo21_01.csv train\n",
            "141 SiPt09_01.csv train\n",
            "142 GaCo10_01.csv train\n",
            "143 JuCo15_01.csv train\n",
            "144 JuPt29_04.csv train\n",
            "145 GaCo04_01.csv train\n",
            "146 JuCo04_01.csv train\n",
            "147 JuPt29_05.csv train\n",
            "148 GaCo02_01.csv train\n",
            "149 JuPt21_03.csv train\n",
            "150 SiCo17_01.csv train\n",
            "151 SiCo11_01.csv train\n",
            "152 JuPt03_04.csv train\n",
            "153 SiCo05_01.csv train\n",
            "154 JuPt12_01.csv train\n",
            "155 JuPt29_06.csv train\n",
            "156 JuPt28_05.csv train\n",
            "157 JuPt26_03.csv train\n",
            "158 GaPt24_02.csv train\n",
            "159 JuPt20_03.csv train\n",
            "160 GaCo14_02.csv train\n",
            "161 GaCo13_10.csv train\n",
            "162 SiCo07_01.csv train\n",
            "163 JuPt23_04.csv train\n",
            "164 GaPt32_01.csv train\n",
            "165 SiCo12_01.csv train\n",
            "166 GaPt17_01.csv train\n",
            "167 JuPt26_04.csv train\n",
            "168 JuPt15_03.csv train\n",
            "169 SiPt30_01.csv train\n",
            "170 SiPt08_01.csv train\n",
            "171 JuPt21_02.csv train\n",
            "172 GaCo16_01.csv train\n",
            "173 JuPt23_07.csv train\n",
            "174 JuPt10_03.csv train\n",
            "175 GaPt30_10.csv train\n",
            "176 GaPt16_01.csv train\n",
            "177 JuPt28_07.csv train\n",
            "178 SiCo29_01.csv train\n",
            "179 GaPt26_02.csv train\n",
            "180 GaPt27_01.csv train\n",
            "181 GaPt16_10.csv train\n",
            "182 JuCo13_01.csv train\n",
            "183 JuPt09_03.csv train\n",
            "184 SiCo18_01.csv train\n",
            "185 GaPt04_01.csv train\n",
            "186 GaPt13_01.csv train\n",
            "187 GaCo08_01.csv train\n",
            "188 JuCo06_01.csv train\n",
            "189 GaPt19_10.csv train\n",
            "190 JuPt26_06.csv train\n",
            "191 JuPt11_03.csv train\n",
            "192 JuPt24_02.csv train\n",
            "193 GaCo09_01.csv train\n",
            "194 JuCo02_01.csv train\n",
            "195 SiCo24_01.csv train\n",
            "196 JuCo07_01.csv train\n",
            "197 JuPt09_05.csv train\n",
            "198 GaCo06_02.csv train\n",
            "199 GaPt25_01.csv train\n",
            "200 JuPt06_01.csv train\n",
            "201 GaCo08_02.csv train\n",
            "202 GaPt20_10.csv train\n",
            "203 JuPt16_01.csv train\n",
            "204 JuPt28_03.csv train\n",
            "205 JuCo08_01.csv train\n",
            "206 SiCo14_01.csv train\n",
            "207 JuPt11_05.csv train\n",
            "208 GaCo04_02.csv train\n",
            "209 JuPt04_01.csv train\n",
            "210 GaPt18_01.csv train\n",
            "211 JuCo01_01.csv train\n",
            "212 GaCo05_02.csv train\n",
            "213 JuPt06_06.csv train\n",
            "214 GaPt21_01.csv train\n",
            "215 JuPt15_04.csv test\n",
            "216 JuPt11_02.csv test\n",
            "217 SiPt24_01.csv test\n",
            "218 GaPt16_02.csv test\n",
            "219 GaPt14_10.csv test\n",
            "220 GaCo22_01.csv test\n",
            "221 JuCo09_01.csv test\n",
            "222 GaCo11_01.csv test\n",
            "223 JuPt28_06.csv test\n",
            "224 SiCo26_01.csv test\n",
            "225 GaPt27_10.csv test\n",
            "226 SiPt21_01.csv test\n",
            "227 SiPt07_01.csv test\n",
            "228 JuPt10_07.csv test\n",
            "229 GaPt18_02.csv test\n",
            "230 SiPt14_01.csv test\n",
            "231 JuPt15_01.csv test\n",
            "232 GaPt30_01.csv test\n",
            "233 GaPt15_10.csv test\n",
            "234 SiPt22_01.csv test\n",
            "235 GaPt23_01.csv test\n",
            "236 GaPt13_10.csv test\n",
            "237 SiPt20_01.csv test\n",
            "238 GaPt30_02.csv test\n",
            "239 GaPt32_02.csv test\n",
            "240 SiCo30_01.csv test\n",
            "241 JuPt06_02.csv test\n",
            "242 SiPt34_01.csv test\n",
            "243 JuPt19_01.csv test\n",
            "244 SiPt04_01.csv test\n",
            "245 JuPt26_05.csv test\n",
            "246 SiPt37_01.csv test\n",
            "247 JuPt23_03.csv test\n",
            "248 SiPt05_01.csv test\n",
            "249 GaCo17_10.csv test\n",
            "250 GaPt32_10.csv test\n",
            "251 GaPt29_01.csv test\n",
            "252 GaPt20_01.csv test\n",
            "253 JuCo11_01.csv test\n",
            "254 SiPt18_01.csv test\n",
            "255 SiPt38_01.csv test\n",
            "256 GaPt21_02.csv test\n",
            "257 GaPt08_02.csv test\n",
            "258 JuCo16_01.csv test\n",
            "259 JuPt11_01.csv test\n",
            "260 JuCo19_01.csv test\n",
            "261 JuPt24_01.csv test\n",
            "262 JuPt23_05.csv test\n",
            "263 GaPt20_02.csv test\n",
            "264 SiPt15_01.csv test\n",
            "265 SiPt29_01.csv test\n",
            "266 SiPt32_01.csv test\n",
            "267 JuPt09_01.csv test\n",
            "268 GaCo16_02.csv test\n",
            "269 JuPt23_01.csv test\n",
            "270 SiCo22_01.csv test\n",
            "271 GaPt33_02.csv test\n",
            "272 SiCo15_01.csv test\n",
            "273 JuPt15_07.csv test\n",
            "274 GaPt33_01.csv test\n",
            "275 GaPt25_02.csv test\n",
            "276 JuCo17_01.csv test\n",
            "277 JuPt07_01.csv test\n",
            "278 JuPt17_01.csv test\n",
            "279 JuPt05_01.csv test\n",
            "280 SiPt35_01.csv test\n",
            "281 GaPt07_01.csv test\n",
            "282 GaPt22_02.csv test\n",
            "283 SiPt40_01.csv test\n",
            "284 SiCo19_01.csv test\n",
            "285 JuCo22_01.csv test\n",
            "286 JuPt06_04.csv test\n",
            "287 JuCo26_01.csv test\n",
            "288 JuPt20_06.csv test\n",
            "289 GaPt15_02.csv test\n",
            "290 JuPt01_05.csv test\n",
            "291 JuPt27_01.csv test\n",
            "292 JuPt23_06.csv test\n",
            "293 GaPt19_01.csv test\n",
            "294 SiCo16_01.csv test\n",
            "295 JuPt13_01.csv test\n",
            "296 GaCo22_10.csv test\n",
            "297 JuPt09_04.csv test\n",
            "298 SiPt31_01.csv test\n",
            "299 JuPt06_07.csv test\n",
            "300 GaPt31_10.csv test\n",
            "301 SiCo25_01.csv test\n",
            "302 SiPt10_01.csv test\n",
            "303 GaPt08_01.csv test\n",
            "304 GaCo13_02.csv test\n",
            "305 JuPt26_07.csv test\n",
            "306 JuPt10_04.csv test\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2N6yW62kgnO"
      },
      "source": [
        "## Load Dataset - Training and Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unHHUiZ2ZMiS"
      },
      "source": [
        "def load_file(filepath):\n",
        "\tdataframe = pd.read_csv(filepath, header=None, delim_whitespace=True)\n",
        "\treturn dataframe.values\n",
        "\n",
        "# load a list of files into a 3D array of [samples, timesteps, features]\n",
        "def load_category(filenames, prefix=''):\n",
        "\tloaded = list()\n",
        "\tfor name in filenames:\n",
        "\t\tdata = load_file(prefix + name)\n",
        "\t\tloaded.append(data)\n",
        "\t# stack group so that features are the 3rd dimension\n",
        "\tloaded = np.dstack(loaded)\n",
        "\treturn loaded\n",
        "\n",
        "\n",
        "# load a dataset category, such as train or test\n",
        "def load_dataset_category(category, prefix=''):\n",
        "\t# load all 19 files as a single array\n",
        "\tfilenames = []\n",
        "\tfor fname in features:\n",
        "\t\tfilenames.append(category + '/' + fname + '_' + category + '.txt')\n",
        "  \n",
        "\t# load input data\n",
        "\tX = load_category(filenames, prefix)\n",
        "\t# load class output\n",
        "\ty = load_file(prefix + 'y_'+ category +'.txt')\n",
        "\thy = load_file(prefix + 'hyscore_'+ category +'.txt')\n",
        "\treturn X, y, hy\n",
        "\n",
        "# load the dataset, returns train and test X and y elements\n",
        "def load_dataset(prefix=''):\n",
        "\t# load all train\n",
        "\ttrainX, trainy, trainHY = load_dataset_category('train', prefix)\n",
        "\tprint(trainX.shape, trainy.shape, trainHY.shape)\n",
        "\t# load all test\n",
        "\ttestX, testy, testHY = load_dataset_category('test', prefix)\n",
        "\tprint(testX.shape, testy.shape, testHY.shape)\n",
        " \n",
        "\t# zero-offset class values\n",
        "\ttrainy = trainy - 1\n",
        "\ttesty = testy - 1\n",
        "\t# one hot encode y\n",
        "\ttrainy = tf.keras.utils.to_categorical(trainy)\n",
        "\ttrainHY = tf.keras.utils.to_categorical(trainHY)\n",
        "\ttesty = tf.keras.utils.to_categorical(testy)\n",
        "\ttestHY = tf.keras.utils.to_categorical(testHY)  \n",
        " \n",
        "\t# print(trainX.shape, trainy.shape, trainHY.shape, testX.shape, testy.shape, testHY.shape)\n",
        "\treturn trainX, trainy, trainHY, testX, testy, testHY"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oALGmMo3ZOEd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f149d62-8175-46e5-b5f3-34cf0b6528a1"
      },
      "source": [
        "trainX, trainy, trainHY, testX, testy, testHY = load_dataset('Final/')\n",
        "print(\"Training Data = \", trainX.shape)\n",
        "print(\"Training Class = \", trainy.shape)\n",
        "print(\"Training HoehnYahr Class = \", trainHY.shape)\n",
        "print(\"Test Data = \", testX.shape)\n",
        "print(\"Test Class = \", testy.shape)\n",
        "print(\"Test HoehnYahr Class = \", testHY.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(30729, 100, 19) (30729, 1) (30729, 1)\n",
            "(13297, 100, 19) (13297, 1) (13297, 1)\n",
            "Training Data =  (30729, 100, 19)\n",
            "Training Class =  (30729, 2)\n",
            "Training HoehnYahr Class =  (30729, 4)\n",
            "Test Data =  (13297, 100, 19)\n",
            "Test Class =  (13297, 2)\n",
            "Test HoehnYahr Class =  (13297, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZvsAonDaCOg"
      },
      "source": [
        "## Detection of PD\n",
        "- PD - 0\n",
        "- Control - 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hVQqUU8cYesQ"
      },
      "source": [
        "### PD Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "af9c6_crX2bD",
        "outputId": "8ba0ebf3-3153-4450-e139-3a2b1f511dd7"
      },
      "source": [
        "verbose, epochs, batch_size = 1, 500, 32\n",
        "n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n",
        "\n",
        "DESIRED_ACCURACY = 0.97\n",
        "\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if logs.get('accuracy') > DESIRED_ACCURACY:\n",
        "      print(\"Reached \" + str(DESIRED_ACCURACY*100)  + \"% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "callbacks = myCallback()\n",
        "\n",
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))\n",
        "model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
        "model.add(tf.keras.layers.Dropout(0.5))\n",
        "model.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(100, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(n_outputs, activation='softmax'))\n",
        "\n",
        "model.compile(loss = 'binary_crossentropy', \n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate=0.001), \n",
        "              metrics =[tf.keras.metrics.TruePositives(name='tp'),\n",
        "                        tf.keras.metrics.FalsePositives(name='fp'),\n",
        "                        tf.keras.metrics.TrueNegatives(name='tn'),\n",
        "                        tf.keras.metrics.FalseNegatives(name='fn'),\n",
        "                        'accuracy',\n",
        "                        tf.keras.metrics.Precision(name='precision'),\n",
        "                        tf.keras.metrics.Recall(name='recall'),\n",
        "                        tf.keras.metrics.AUC(name='auc')])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_22 (Conv1D)          (None, 98, 64)            3712      \n",
            "                                                                 \n",
            " conv1d_23 (Conv1D)          (None, 96, 64)            12352     \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 96, 64)            0         \n",
            "                                                                 \n",
            " max_pooling1d_15 (MaxPoolin  (None, 48, 64)           0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " flatten_11 (Flatten)        (None, 3072)              0         \n",
            "                                                                 \n",
            " dense_22 (Dense)            (None, 100)               307300    \n",
            "                                                                 \n",
            " dense_23 (Dense)            (None, 2)                 202       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 323,566\n",
            "Trainable params: 323,566\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "olpaPBQBT_vB"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhCx2LelPoKF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ab3fae5-da05-4bd6-d0bd-33963bf3c16c"
      },
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  PD_history = model.fit(trainX,\n",
        "                        trainy,\n",
        "                        validation_data=(testX, testy),\n",
        "                        epochs = epochs,\n",
        "                        batch_size = batch_size,\n",
        "                        verbose = verbose,\n",
        "                        callbacks = [callbacks])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "961/961 [==============================] - 18s 16ms/step - loss: 1.4790 - tp: 23979.0000 - fp: 6750.0000 - tn: 23979.0000 - fn: 6750.0000 - accuracy: 0.7803 - precision: 0.7803 - recall: 0.7803 - auc: 0.8458 - val_loss: 0.5998 - val_tp: 9969.0000 - val_fp: 3328.0000 - val_tn: 9969.0000 - val_fn: 3328.0000 - val_accuracy: 0.7497 - val_precision: 0.7497 - val_recall: 0.7497 - val_auc: 0.7883\n",
            "Epoch 2/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.3438 - tp: 26513.0000 - fp: 4216.0000 - tn: 26513.0000 - fn: 4216.0000 - accuracy: 0.8628 - precision: 0.8628 - recall: 0.8628 - auc: 0.9279 - val_loss: 0.6580 - val_tp: 9628.0000 - val_fp: 3669.0000 - val_tn: 9628.0000 - val_fn: 3669.0000 - val_accuracy: 0.7241 - val_precision: 0.7241 - val_recall: 0.7241 - val_auc: 0.7573\n",
            "Epoch 3/500\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.2646 - tp: 27596.0000 - fp: 3133.0000 - tn: 27596.0000 - fn: 3133.0000 - accuracy: 0.8980 - precision: 0.8980 - recall: 0.8980 - auc: 0.9554 - val_loss: 0.6157 - val_tp: 10459.0000 - val_fp: 2838.0000 - val_tn: 10459.0000 - val_fn: 2838.0000 - val_accuracy: 0.7866 - val_precision: 0.7866 - val_recall: 0.7866 - val_auc: 0.8223\n",
            "Epoch 4/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.2184 - tp: 28139.0000 - fp: 2590.0000 - tn: 28139.0000 - fn: 2590.0000 - accuracy: 0.9157 - precision: 0.9157 - recall: 0.9157 - auc: 0.9675 - val_loss: 0.5539 - val_tp: 10657.0000 - val_fp: 2640.0000 - val_tn: 10657.0000 - val_fn: 2640.0000 - val_accuracy: 0.8015 - val_precision: 0.8015 - val_recall: 0.8015 - val_auc: 0.8468\n",
            "Epoch 5/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1903 - tp: 28447.0000 - fp: 2282.0000 - tn: 28447.0000 - fn: 2282.0000 - accuracy: 0.9257 - precision: 0.9257 - recall: 0.9257 - auc: 0.9741 - val_loss: 0.6177 - val_tp: 10685.0000 - val_fp: 2612.0000 - val_tn: 10685.0000 - val_fn: 2612.0000 - val_accuracy: 0.8036 - val_precision: 0.8036 - val_recall: 0.8036 - val_auc: 0.8405\n",
            "Epoch 6/500\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.1649 - tp: 28778.0000 - fp: 1951.0000 - tn: 28778.0000 - fn: 1951.0000 - accuracy: 0.9365 - precision: 0.9365 - recall: 0.9365 - auc: 0.9798 - val_loss: 0.6436 - val_tp: 10803.0000 - val_fp: 2494.0000 - val_tn: 10803.0000 - val_fn: 2494.0000 - val_accuracy: 0.8124 - val_precision: 0.8124 - val_recall: 0.8124 - val_auc: 0.8578\n",
            "Epoch 7/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1469 - tp: 28933.0000 - fp: 1796.0000 - tn: 28933.0000 - fn: 1796.0000 - accuracy: 0.9416 - precision: 0.9416 - recall: 0.9416 - auc: 0.9828 - val_loss: 0.6650 - val_tp: 10546.0000 - val_fp: 2751.0000 - val_tn: 10546.0000 - val_fn: 2751.0000 - val_accuracy: 0.7931 - val_precision: 0.7931 - val_recall: 0.7931 - val_auc: 0.8382\n",
            "Epoch 8/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1315 - tp: 29201.0000 - fp: 1528.0000 - tn: 29201.0000 - fn: 1528.0000 - accuracy: 0.9503 - precision: 0.9503 - recall: 0.9503 - auc: 0.9851 - val_loss: 0.7520 - val_tp: 10681.0000 - val_fp: 2616.0000 - val_tn: 10681.0000 - val_fn: 2616.0000 - val_accuracy: 0.8033 - val_precision: 0.8033 - val_recall: 0.8033 - val_auc: 0.8403\n",
            "Epoch 9/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1228 - tp: 29274.0000 - fp: 1455.0000 - tn: 29274.0000 - fn: 1455.0000 - accuracy: 0.9527 - precision: 0.9527 - recall: 0.9527 - auc: 0.9867 - val_loss: 0.7556 - val_tp: 10460.0000 - val_fp: 2837.0000 - val_tn: 10460.0000 - val_fn: 2837.0000 - val_accuracy: 0.7866 - val_precision: 0.7866 - val_recall: 0.7866 - val_auc: 0.8324\n",
            "Epoch 10/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1131 - tp: 29382.0000 - fp: 1347.0000 - tn: 29382.0000 - fn: 1347.0000 - accuracy: 0.9562 - precision: 0.9562 - recall: 0.9562 - auc: 0.9882 - val_loss: 0.7093 - val_tp: 10284.0000 - val_fp: 3013.0000 - val_tn: 10284.0000 - val_fn: 3013.0000 - val_accuracy: 0.7734 - val_precision: 0.7734 - val_recall: 0.7734 - val_auc: 0.8191\n",
            "Epoch 11/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1017 - tp: 29560.0000 - fp: 1169.0000 - tn: 29560.0000 - fn: 1169.0000 - accuracy: 0.9620 - precision: 0.9620 - recall: 0.9620 - auc: 0.9892 - val_loss: 0.7871 - val_tp: 10377.0000 - val_fp: 2920.0000 - val_tn: 10377.0000 - val_fn: 2920.0000 - val_accuracy: 0.7804 - val_precision: 0.7804 - val_recall: 0.7804 - val_auc: 0.8187\n",
            "Epoch 12/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.0927 - tp: 29640.0000 - fp: 1089.0000 - tn: 29640.0000 - fn: 1089.0000 - accuracy: 0.9646 - precision: 0.9646 - recall: 0.9646 - auc: 0.9906 - val_loss: 0.8872 - val_tp: 10298.0000 - val_fp: 2999.0000 - val_tn: 10298.0000 - val_fn: 2999.0000 - val_accuracy: 0.7745 - val_precision: 0.7745 - val_recall: 0.7745 - val_auc: 0.8111\n",
            "Epoch 13/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1032 - tp: 29543.0000 - fp: 1186.0000 - tn: 29543.0000 - fn: 1186.0000 - accuracy: 0.9614 - precision: 0.9614 - recall: 0.9614 - auc: 0.9896 - val_loss: 0.5802 - val_tp: 10868.0000 - val_fp: 2429.0000 - val_tn: 10868.0000 - val_fn: 2429.0000 - val_accuracy: 0.8173 - val_precision: 0.8173 - val_recall: 0.8173 - val_auc: 0.8645\n",
            "Epoch 14/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.0845 - tp: 29740.0000 - fp: 989.0000 - tn: 29740.0000 - fn: 989.0000 - accuracy: 0.9678 - precision: 0.9678 - recall: 0.9678 - auc: 0.9915 - val_loss: 0.6593 - val_tp: 10413.0000 - val_fp: 2884.0000 - val_tn: 10413.0000 - val_fn: 2884.0000 - val_accuracy: 0.7831 - val_precision: 0.7831 - val_recall: 0.7831 - val_auc: 0.8313\n",
            "Epoch 15/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.0806 - tp: 29793.0000 - fp: 936.0000 - tn: 29793.0000 - fn: 936.0000 - accuracy: 0.9695 - precision: 0.9695 - recall: 0.9695 - auc: 0.9923 - val_loss: 0.8946 - val_tp: 10712.0000 - val_fp: 2585.0000 - val_tn: 10712.0000 - val_fn: 2585.0000 - val_accuracy: 0.8056 - val_precision: 0.8056 - val_recall: 0.8056 - val_auc: 0.8417\n",
            "Epoch 16/500\n",
            "958/961 [============================>.] - ETA: 0s - loss: 0.0782 - tp: 29744.0000 - fp: 912.0000 - tn: 29744.0000 - fn: 912.0000 - accuracy: 0.9703 - precision: 0.9703 - recall: 0.9703 - auc: 0.9928Reached 97.0% accuracy so cancelling training!\n",
            "961/961 [==============================] - 16s 16ms/step - loss: 0.0781 - tp: 29816.0000 - fp: 913.0000 - tn: 29816.0000 - fn: 913.0000 - accuracy: 0.9703 - precision: 0.9703 - recall: 0.9703 - auc: 0.9929 - val_loss: 0.6612 - val_tp: 11001.0000 - val_fp: 2296.0000 - val_tn: 11001.0000 - val_fn: 2296.0000 - val_accuracy: 0.8273 - val_precision: 0.8273 - val_recall: 0.8273 - val_auc: 0.8711\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4oLUyK4HlaGp"
      },
      "source": [
        "### Save the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jROkett2lbpP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e8e8927-c34a-4875-ccba-bdc0395bf78b"
      },
      "source": [
        "model.save(\"/content/drive/MyDrive/College Related/Project Work 1 - Sem 7/Saved Models/PD_Model.h5\")\n",
        "print(\"PD_Model saved Successfully!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PD_Model saved Successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9w7ukjF4guhK"
      },
      "source": [
        "model = tf.keras.models.load_model(\"/content/drive/MyDrive/College Related/Project Work 1 - Sem 7/Saved Models/PD_Model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7mSdIh3YuDy"
      },
      "source": [
        "### Plot the graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzFgQlFWkDZL"
      },
      "source": [
        "# Plot the accuracy and loss functions\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.title('Training and Validation ' + string)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlmFpZh2Pmdk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "0cc07bfd-f790-4f6d-e5a0-327a85d1ea30"
      },
      "source": [
        "plot_graphs(PD_history, \"accuracy\")\n",
        "plot_graphs(PD_history, \"loss\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooFss27BGlkz"
      },
      "source": [
        "### Evaluation of Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_5ws8MSGlKt"
      },
      "source": [
        "def model_evaluate(model, target, testX, testy):\n",
        "  y_pred = model.predict(testX)\n",
        "  y_pred = np.argmax(y_pred, axis=1)\n",
        "  y_test = np.argmax(testy, axis=1)\n",
        "\n",
        "  # Confusion matrix\n",
        "  print('Confusion Matrix')\n",
        "\n",
        "  cm = tf.math.confusion_matrix(labels = y_test, predictions = y_pred)\n",
        "  # Calculate accuracy\n",
        "  cm_np = cm.numpy()\n",
        "  conf_acc = accuracy_score(y_test, y_pred) * 100\n",
        "  print(\"Accuracy for Test Data = \", conf_acc)\n",
        "\n",
        "  plt.figure(figsize = (12,8))\n",
        "  seaborn.heatmap(cm, annot=True, fmt='d')\n",
        "  plt.title(\"Confusion Matrix for Test Data\")\n",
        "  plt.xlabel('Predicted')\n",
        "  plt.ylabel('Expected')\n",
        "  plt.show()\n",
        "\n",
        "  # Classification Report\n",
        "  print('\\nClassification Report')\n",
        "  print(classification_report(y_test, y_pred))\n",
        "\n",
        "  # Cohen Kappa Score\n",
        "  print('\\nCohen Kappa Score')\n",
        "  print(cohen_kappa_score(y_test, y_pred))\n",
        "\n",
        "  # Precision vs Recall Curve\n",
        "  print('\\nPrecision vs Recall Curve')\n",
        "  fig, c_ax = plt.subplots(1,1, figsize = (12, 8))\n",
        "\n",
        "  y_test = tf.keras.utils.to_categorical(y_test)\n",
        "  y_pred = tf.keras.utils.to_categorical(y_pred)\n",
        "  n_classes = 2\n",
        "\n",
        "  precision = dict()\n",
        "  recall = dict()\n",
        "\n",
        "  for i in range(n_classes):\n",
        "    precision[i], recall[i], _ = precision_recall_curve(y_test[:, i],\n",
        "                                                          y_pred[:, i])\n",
        "    plt.plot(recall[i], precision[i], lw=2, label='class {}'.format(i))\n",
        "      \n",
        "  plt.xlabel(\"recall\")\n",
        "  plt.ylabel(\"precision\")\n",
        "  plt.legend(loc=\"best\")\n",
        "  plt.title(\"Precision vs. Recall curve\")\n",
        "  plt.show()\n",
        "\n",
        "  # ROC Curve\n",
        "  print('\\nROC Curve')\n",
        "  average = 'macro'\n",
        "  fig, c_ax = plt.subplots(1,1, figsize = (12, 8))\n",
        "  for (idx, c_label) in enumerate(target):\n",
        "      fpr, tpr, thresholds = roc_curve(y_test[:,idx].astype(int), y_pred[:,idx])\n",
        "      c_ax.plot(fpr, tpr, label = '%s (AUC:%0.2f)'  % (c_label, auc(fpr, tpr)))\n",
        "  c_ax.plot(fpr, fpr, 'b-', label = 'Random Guessing')\n",
        "  print('ROC AUC score:', roc_auc_score(y_test, y_pred, average=average))\n",
        "\n",
        "  c_ax.legend()\n",
        "  c_ax.set_xlabel('False Positive Rate')\n",
        "  c_ax.set_ylabel('True Positive Rate')\n",
        "  plt.title('ROC Curve')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpmalQLcHF01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "beda891f-cec0-4781-b237-aca37153b08c"
      },
      "source": [
        "model_evaluate(model, [0,1], testX, testy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            "Accuracy for Test Data =  94.12649469805218\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.97      0.96     10435\n",
            "           1       0.88      0.85      0.86      2862\n",
            "\n",
            "    accuracy                           0.94     13297\n",
            "   macro avg       0.92      0.91      0.91     13297\n",
            "weighted avg       0.94      0.94      0.94     13297\n",
            "\n",
            "\n",
            "Cohen Kappa Score\n",
            "0.8238795014003799\n",
            "\n",
            "Precision vs Recall Curve\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "ROC Curve\n",
            "ROC AUC score: 0.906664647578752\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dz-ceaiwaIA0"
      },
      "source": [
        "## Classification based on HoehnYahr Score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wA1E1EnZN_G"
      },
      "source": [
        "### HY Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQftQNotaOPU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd7e98dc-4d4e-414e-92ab-3033caeb0463"
      },
      "source": [
        "verbose, epochs, batch_size = 1, 500, 32\n",
        "n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainHY.shape[1]\n",
        "\n",
        "callbacks = myCallback()\n",
        "HYmodel = tf.keras.models.Sequential()\n",
        "HYmodel.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))\n",
        "HYmodel.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "HYmodel.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))\n",
        "HYmodel.add(tf.keras.layers.MaxPooling1D(pool_size=2))\n",
        "HYmodel.add(tf.keras.layers.Dropout(0.5))\n",
        "HYmodel.add(tf.keras.layers.Flatten())\n",
        "HYmodel.add(tf.keras.layers.Dense(100, activation='relu'))\n",
        "HYmodel.add(tf.keras.layers.Dense(n_outputs, activation='softmax'))\n",
        "\n",
        "HYmodel.compile(loss = 'categorical_crossentropy', \n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001), \n",
        "              metrics =[tf.keras.metrics.TruePositives(name='tp'),\n",
        "                        tf.keras.metrics.FalsePositives(name='fp'),\n",
        "                        tf.keras.metrics.TrueNegatives(name='tn'),\n",
        "                        tf.keras.metrics.FalseNegatives(name='fn'),\n",
        "                        'accuracy',\n",
        "                        tf.keras.metrics.Precision(name='precision'),\n",
        "                        tf.keras.metrics.Recall(name='recall'),\n",
        "                        tf.keras.metrics.AUC(name='auc')])\n",
        "HYmodel.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_18 (Conv1D)          (None, 98, 64)            3712      \n",
            "                                                                 \n",
            " max_pooling1d_10 (MaxPoolin  (None, 49, 64)           0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " conv1d_19 (Conv1D)          (None, 47, 64)            12352     \n",
            "                                                                 \n",
            " max_pooling1d_11 (MaxPoolin  (None, 23, 64)           0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 23, 64)            0         \n",
            "                                                                 \n",
            " flatten_9 (Flatten)         (None, 1472)              0         \n",
            "                                                                 \n",
            " dense_18 (Dense)            (None, 100)               147300    \n",
            "                                                                 \n",
            " dense_19 (Dense)            (None, 4)                 404       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 163,768\n",
            "Trainable params: 163,768\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TDIqhYBafd0"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYnKJq0RcReA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d14513e-eb50-43eb-c402-c2311e1335d2"
      },
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  HY_history = HYmodel.fit(trainX,\n",
        "                          trainHY,\n",
        "                          validation_data=(testX, testHY),\n",
        "                          epochs = epochs, \n",
        "                          batch_size = batch_size, \n",
        "                          verbose = verbose,\n",
        "                          callbacks = [callbacks])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "961/961 [==============================] - 18s 16ms/step - loss: 15.2232 - tp: 8588.0000 - fp: 18713.0000 - tn: 73474.0000 - fn: 22141.0000 - accuracy: 0.3126 - precision: 0.3146 - recall: 0.2795 - auc: 0.5614 - val_loss: 1.3759 - val_tp: 13.0000 - val_fp: 42.0000 - val_tn: 39849.0000 - val_fn: 13284.0000 - val_accuracy: 0.2290 - val_precision: 0.2364 - val_recall: 9.7766e-04 - val_auc: 0.5306\n",
            "Epoch 2/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 1.4522 - tp: 1798.0000 - fp: 3295.0000 - tn: 88892.0000 - fn: 28931.0000 - accuracy: 0.3304 - precision: 0.3530 - recall: 0.0585 - auc: 0.6024 - val_loss: 1.3398 - val_tp: 6.0000 - val_fp: 1.0000 - val_tn: 39890.0000 - val_fn: 13291.0000 - val_accuracy: 0.2166 - val_precision: 0.8571 - val_recall: 4.5123e-04 - val_auc: 0.5909\n",
            "Epoch 3/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 1.3195 - tp: 755.0000 - fp: 1066.0000 - tn: 91121.0000 - fn: 29974.0000 - accuracy: 0.3420 - precision: 0.4146 - recall: 0.0246 - auc: 0.6444 - val_loss: 1.2613 - val_tp: 11.0000 - val_fp: 4.0000 - val_tn: 39887.0000 - val_fn: 13286.0000 - val_accuracy: 0.2181 - val_precision: 0.7333 - val_recall: 8.2725e-04 - val_auc: 0.6551\n",
            "Epoch 4/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 1.2736 - tp: 1066.0000 - fp: 899.0000 - tn: 91288.0000 - fn: 29663.0000 - accuracy: 0.3673 - precision: 0.5425 - recall: 0.0347 - auc: 0.6701 - val_loss: 1.2352 - val_tp: 946.0000 - val_fp: 388.0000 - val_tn: 39503.0000 - val_fn: 12351.0000 - val_accuracy: 0.2510 - val_precision: 0.7091 - val_recall: 0.0711 - val_auc: 0.6829\n",
            "Epoch 5/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 1.2182 - tp: 3887.0000 - fp: 2465.0000 - tn: 89722.0000 - fn: 26842.0000 - accuracy: 0.4171 - precision: 0.6119 - recall: 0.1265 - auc: 0.7069 - val_loss: 1.1911 - val_tp: 2038.0000 - val_fp: 1690.0000 - val_tn: 38201.0000 - val_fn: 11259.0000 - val_accuracy: 0.3217 - val_precision: 0.5467 - val_recall: 0.1533 - val_auc: 0.7339\n",
            "Epoch 6/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 1.1477 - tp: 5670.0000 - fp: 2911.0000 - tn: 89276.0000 - fn: 25059.0000 - accuracy: 0.4733 - precision: 0.6608 - recall: 0.1845 - auc: 0.7477 - val_loss: 1.1022 - val_tp: 2122.0000 - val_fp: 1541.0000 - val_tn: 38350.0000 - val_fn: 11175.0000 - val_accuracy: 0.4364 - val_precision: 0.5793 - val_recall: 0.1596 - val_auc: 0.7687\n",
            "Epoch 7/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 1.0699 - tp: 7395.0000 - fp: 3666.0000 - tn: 88521.0000 - fn: 23334.0000 - accuracy: 0.5151 - precision: 0.6686 - recall: 0.2407 - auc: 0.7880 - val_loss: 1.0969 - val_tp: 2849.0000 - val_fp: 2281.0000 - val_tn: 37610.0000 - val_fn: 10448.0000 - val_accuracy: 0.4848 - val_precision: 0.5554 - val_recall: 0.2143 - val_auc: 0.7834\n",
            "Epoch 8/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.9850 - tp: 11458.0000 - fp: 5250.0000 - tn: 86937.0000 - fn: 19271.0000 - accuracy: 0.5645 - precision: 0.6858 - recall: 0.3729 - auc: 0.8257 - val_loss: 1.0647 - val_tp: 3897.0000 - val_fp: 2672.0000 - val_tn: 37219.0000 - val_fn: 9400.0000 - val_accuracy: 0.5534 - val_precision: 0.5932 - val_recall: 0.2931 - val_auc: 0.8070\n",
            "Epoch 9/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.8942 - tp: 14245.0000 - fp: 5513.0000 - tn: 86674.0000 - fn: 16484.0000 - accuracy: 0.6183 - precision: 0.7210 - recall: 0.4636 - auc: 0.8599 - val_loss: 1.0638 - val_tp: 4535.0000 - val_fp: 2735.0000 - val_tn: 37156.0000 - val_fn: 8762.0000 - val_accuracy: 0.5729 - val_precision: 0.6238 - val_recall: 0.3411 - val_auc: 0.8130\n",
            "Epoch 10/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.8170 - tp: 16518.0000 - fp: 5692.0000 - tn: 86495.0000 - fn: 14211.0000 - accuracy: 0.6580 - precision: 0.7437 - recall: 0.5375 - auc: 0.8848 - val_loss: 1.0983 - val_tp: 5299.0000 - val_fp: 3106.0000 - val_tn: 36785.0000 - val_fn: 7998.0000 - val_accuracy: 0.5774 - val_precision: 0.6305 - val_recall: 0.3985 - val_auc: 0.8128\n",
            "Epoch 11/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.7566 - tp: 18075.0000 - fp: 5750.0000 - tn: 86437.0000 - fn: 12654.0000 - accuracy: 0.6843 - precision: 0.7587 - recall: 0.5882 - auc: 0.9013 - val_loss: 1.0619 - val_tp: 5665.0000 - val_fp: 2952.0000 - val_tn: 36939.0000 - val_fn: 7632.0000 - val_accuracy: 0.6051 - val_precision: 0.6574 - val_recall: 0.4260 - val_auc: 0.8254\n",
            "Epoch 12/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.6923 - tp: 19460.0000 - fp: 5456.0000 - tn: 86731.0000 - fn: 11269.0000 - accuracy: 0.7142 - precision: 0.7810 - recall: 0.6333 - auc: 0.9180 - val_loss: 1.0770 - val_tp: 6544.0000 - val_fp: 3407.0000 - val_tn: 36484.0000 - val_fn: 6753.0000 - val_accuracy: 0.6074 - val_precision: 0.6576 - val_recall: 0.4921 - val_auc: 0.8299\n",
            "Epoch 13/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.6220 - tp: 20934.0000 - fp: 5195.0000 - tn: 86992.0000 - fn: 9795.0000 - accuracy: 0.7468 - precision: 0.8012 - recall: 0.6812 - auc: 0.9339 - val_loss: 1.0803 - val_tp: 7254.0000 - val_fp: 3880.0000 - val_tn: 36011.0000 - val_fn: 6043.0000 - val_accuracy: 0.6156 - val_precision: 0.6515 - val_recall: 0.5455 - val_auc: 0.8382\n",
            "Epoch 14/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.5658 - tp: 22091.0000 - fp: 4960.0000 - tn: 87227.0000 - fn: 8638.0000 - accuracy: 0.7741 - precision: 0.8166 - recall: 0.7189 - auc: 0.9452 - val_loss: 1.0214 - val_tp: 7806.0000 - val_fp: 3697.0000 - val_tn: 36194.0000 - val_fn: 5491.0000 - val_accuracy: 0.6423 - val_precision: 0.6786 - val_recall: 0.5870 - val_auc: 0.8532\n",
            "Epoch 15/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.5151 - tp: 23163.0000 - fp: 4644.0000 - tn: 87543.0000 - fn: 7566.0000 - accuracy: 0.7970 - precision: 0.8330 - recall: 0.7538 - auc: 0.9544 - val_loss: 1.0724 - val_tp: 7335.0000 - val_fp: 3700.0000 - val_tn: 36191.0000 - val_fn: 5962.0000 - val_accuracy: 0.6235 - val_precision: 0.6647 - val_recall: 0.5516 - val_auc: 0.8484\n",
            "Epoch 16/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.4730 - tp: 23816.0000 - fp: 4345.0000 - tn: 87842.0000 - fn: 6913.0000 - accuracy: 0.8113 - precision: 0.8457 - recall: 0.7750 - auc: 0.9613 - val_loss: 1.1111 - val_tp: 7703.0000 - val_fp: 3728.0000 - val_tn: 36163.0000 - val_fn: 5594.0000 - val_accuracy: 0.6387 - val_precision: 0.6739 - val_recall: 0.5793 - val_auc: 0.8530\n",
            "Epoch 17/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.4391 - tp: 24530.0000 - fp: 4014.0000 - tn: 88173.0000 - fn: 6199.0000 - accuracy: 0.8298 - precision: 0.8594 - recall: 0.7983 - auc: 0.9668 - val_loss: 1.0951 - val_tp: 7967.0000 - val_fp: 3933.0000 - val_tn: 35958.0000 - val_fn: 5330.0000 - val_accuracy: 0.6449 - val_precision: 0.6695 - val_recall: 0.5992 - val_auc: 0.8577\n",
            "Epoch 18/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.4075 - tp: 25098.0000 - fp: 3733.0000 - tn: 88454.0000 - fn: 5631.0000 - accuracy: 0.8453 - precision: 0.8705 - recall: 0.8168 - auc: 0.9710 - val_loss: 1.1641 - val_tp: 7864.0000 - val_fp: 3891.0000 - val_tn: 36000.0000 - val_fn: 5433.0000 - val_accuracy: 0.6417 - val_precision: 0.6690 - val_recall: 0.5914 - val_auc: 0.8500\n",
            "Epoch 19/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.3802 - tp: 25535.0000 - fp: 3517.0000 - tn: 88670.0000 - fn: 5194.0000 - accuracy: 0.8566 - precision: 0.8789 - recall: 0.8310 - auc: 0.9746 - val_loss: 1.0115 - val_tp: 8313.0000 - val_fp: 3654.0000 - val_tn: 36237.0000 - val_fn: 4984.0000 - val_accuracy: 0.6705 - val_precision: 0.6947 - val_recall: 0.6252 - val_auc: 0.8737\n",
            "Epoch 20/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.3530 - tp: 25963.0000 - fp: 3309.0000 - tn: 88878.0000 - fn: 4766.0000 - accuracy: 0.8672 - precision: 0.8870 - recall: 0.8449 - auc: 0.9778 - val_loss: 1.1314 - val_tp: 8039.0000 - val_fp: 3871.0000 - val_tn: 36020.0000 - val_fn: 5258.0000 - val_accuracy: 0.6498 - val_precision: 0.6750 - val_recall: 0.6046 - val_auc: 0.8604\n",
            "Epoch 21/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.3321 - tp: 26338.0000 - fp: 3133.0000 - tn: 89054.0000 - fn: 4391.0000 - accuracy: 0.8747 - precision: 0.8937 - recall: 0.8571 - auc: 0.9801 - val_loss: 1.1627 - val_tp: 8360.0000 - val_fp: 3936.0000 - val_tn: 35955.0000 - val_fn: 4937.0000 - val_accuracy: 0.6604 - val_precision: 0.6799 - val_recall: 0.6287 - val_auc: 0.8662\n",
            "Epoch 22/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.3086 - tp: 26695.0000 - fp: 2856.0000 - tn: 89331.0000 - fn: 4034.0000 - accuracy: 0.8863 - precision: 0.9034 - recall: 0.8687 - auc: 0.9828 - val_loss: 1.1278 - val_tp: 8226.0000 - val_fp: 4061.0000 - val_tn: 35830.0000 - val_fn: 5071.0000 - val_accuracy: 0.6518 - val_precision: 0.6695 - val_recall: 0.6186 - val_auc: 0.8701\n",
            "Epoch 23/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.2890 - tp: 26998.0000 - fp: 2688.0000 - tn: 89499.0000 - fn: 3731.0000 - accuracy: 0.8948 - precision: 0.9095 - recall: 0.8786 - auc: 0.9847 - val_loss: 1.1249 - val_tp: 8591.0000 - val_fp: 3898.0000 - val_tn: 35993.0000 - val_fn: 4706.0000 - val_accuracy: 0.6711 - val_precision: 0.6879 - val_recall: 0.6461 - val_auc: 0.8744\n",
            "Epoch 24/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.2784 - tp: 27140.0000 - fp: 2601.0000 - tn: 89586.0000 - fn: 3589.0000 - accuracy: 0.8982 - precision: 0.9125 - recall: 0.8832 - auc: 0.9856 - val_loss: 1.2451 - val_tp: 8407.0000 - val_fp: 4067.0000 - val_tn: 35824.0000 - val_fn: 4890.0000 - val_accuracy: 0.6565 - val_precision: 0.6740 - val_recall: 0.6322 - val_auc: 0.8679\n",
            "Epoch 25/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.2564 - tp: 27515.0000 - fp: 2380.0000 - tn: 89807.0000 - fn: 3214.0000 - accuracy: 0.9082 - precision: 0.9204 - recall: 0.8954 - auc: 0.9877 - val_loss: 1.2370 - val_tp: 8285.0000 - val_fp: 4157.0000 - val_tn: 35734.0000 - val_fn: 5012.0000 - val_accuracy: 0.6481 - val_precision: 0.6659 - val_recall: 0.6231 - val_auc: 0.8673\n",
            "Epoch 26/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.2439 - tp: 27721.0000 - fp: 2206.0000 - tn: 89981.0000 - fn: 3008.0000 - accuracy: 0.9139 - precision: 0.9263 - recall: 0.9021 - auc: 0.9887 - val_loss: 1.2643 - val_tp: 8341.0000 - val_fp: 4012.0000 - val_tn: 35879.0000 - val_fn: 4956.0000 - val_accuracy: 0.6538 - val_precision: 0.6752 - val_recall: 0.6273 - val_auc: 0.8666\n",
            "Epoch 27/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.2348 - tp: 27840.0000 - fp: 2137.0000 - tn: 90050.0000 - fn: 2889.0000 - accuracy: 0.9178 - precision: 0.9287 - recall: 0.9060 - auc: 0.9892 - val_loss: 1.3414 - val_tp: 8663.0000 - val_fp: 4121.0000 - val_tn: 35770.0000 - val_fn: 4634.0000 - val_accuracy: 0.6664 - val_precision: 0.6776 - val_recall: 0.6515 - val_auc: 0.8681\n",
            "Epoch 28/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.2217 - tp: 28013.0000 - fp: 2066.0000 - tn: 90121.0000 - fn: 2716.0000 - accuracy: 0.9213 - precision: 0.9313 - recall: 0.9116 - auc: 0.9903 - val_loss: 1.2871 - val_tp: 8536.0000 - val_fp: 4072.0000 - val_tn: 35819.0000 - val_fn: 4761.0000 - val_accuracy: 0.6627 - val_precision: 0.6770 - val_recall: 0.6419 - val_auc: 0.8677\n",
            "Epoch 29/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.2100 - tp: 28135.0000 - fp: 1977.0000 - tn: 90210.0000 - fn: 2594.0000 - accuracy: 0.9252 - precision: 0.9343 - recall: 0.9156 - auc: 0.9914 - val_loss: 1.2765 - val_tp: 8603.0000 - val_fp: 3911.0000 - val_tn: 35980.0000 - val_fn: 4694.0000 - val_accuracy: 0.6685 - val_precision: 0.6875 - val_recall: 0.6470 - val_auc: 0.8735\n",
            "Epoch 30/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.2019 - tp: 28268.0000 - fp: 1918.0000 - tn: 90269.0000 - fn: 2461.0000 - accuracy: 0.9279 - precision: 0.9365 - recall: 0.9199 - auc: 0.9920 - val_loss: 1.4462 - val_tp: 8415.0000 - val_fp: 4168.0000 - val_tn: 35723.0000 - val_fn: 4882.0000 - val_accuracy: 0.6516 - val_precision: 0.6688 - val_recall: 0.6328 - val_auc: 0.8568\n",
            "Epoch 31/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1921 - tp: 28360.0000 - fp: 1817.0000 - tn: 90370.0000 - fn: 2369.0000 - accuracy: 0.9306 - precision: 0.9398 - recall: 0.9229 - auc: 0.9927 - val_loss: 1.2449 - val_tp: 8752.0000 - val_fp: 3851.0000 - val_tn: 36040.0000 - val_fn: 4545.0000 - val_accuracy: 0.6767 - val_precision: 0.6944 - val_recall: 0.6582 - val_auc: 0.8804\n",
            "Epoch 32/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1805 - tp: 28513.0000 - fp: 1750.0000 - tn: 90437.0000 - fn: 2216.0000 - accuracy: 0.9341 - precision: 0.9422 - recall: 0.9279 - auc: 0.9934 - val_loss: 1.3719 - val_tp: 8576.0000 - val_fp: 4123.0000 - val_tn: 35768.0000 - val_fn: 4721.0000 - val_accuracy: 0.6598 - val_precision: 0.6753 - val_recall: 0.6450 - val_auc: 0.8677\n",
            "Epoch 33/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1770 - tp: 28582.0000 - fp: 1670.0000 - tn: 90517.0000 - fn: 2147.0000 - accuracy: 0.9369 - precision: 0.9448 - recall: 0.9301 - auc: 0.9936 - val_loss: 1.3755 - val_tp: 8658.0000 - val_fp: 4036.0000 - val_tn: 35855.0000 - val_fn: 4639.0000 - val_accuracy: 0.6671 - val_precision: 0.6821 - val_recall: 0.6511 - val_auc: 0.8727\n",
            "Epoch 34/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.1727 - tp: 28614.0000 - fp: 1646.0000 - tn: 90541.0000 - fn: 2115.0000 - accuracy: 0.9380 - precision: 0.9456 - recall: 0.9312 - auc: 0.9939 - val_loss: 1.6189 - val_tp: 8549.0000 - val_fp: 4216.0000 - val_tn: 35675.0000 - val_fn: 4748.0000 - val_accuracy: 0.6548 - val_precision: 0.6697 - val_recall: 0.6429 - val_auc: 0.8595\n",
            "Epoch 35/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1597 - tp: 28825.0000 - fp: 1505.0000 - tn: 90682.0000 - fn: 1904.0000 - accuracy: 0.9443 - precision: 0.9504 - recall: 0.9380 - auc: 0.9947 - val_loss: 1.4473 - val_tp: 8637.0000 - val_fp: 4126.0000 - val_tn: 35765.0000 - val_fn: 4660.0000 - val_accuracy: 0.6621 - val_precision: 0.6767 - val_recall: 0.6495 - val_auc: 0.8657\n",
            "Epoch 36/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1605 - tp: 28827.0000 - fp: 1522.0000 - tn: 90665.0000 - fn: 1902.0000 - accuracy: 0.9437 - precision: 0.9499 - recall: 0.9381 - auc: 0.9946 - val_loss: 1.5358 - val_tp: 8840.0000 - val_fp: 4037.0000 - val_tn: 35854.0000 - val_fn: 4457.0000 - val_accuracy: 0.6735 - val_precision: 0.6865 - val_recall: 0.6648 - val_auc: 0.8689\n",
            "Epoch 37/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1500 - tp: 28890.0000 - fp: 1457.0000 - tn: 90730.0000 - fn: 1839.0000 - accuracy: 0.9460 - precision: 0.9520 - recall: 0.9402 - auc: 0.9953 - val_loss: 1.7368 - val_tp: 8392.0000 - val_fp: 4391.0000 - val_tn: 35500.0000 - val_fn: 4905.0000 - val_accuracy: 0.6438 - val_precision: 0.6565 - val_recall: 0.6311 - val_auc: 0.8496\n",
            "Epoch 38/500\n",
            "961/961 [==============================] - 14s 14ms/step - loss: 0.1409 - tp: 29030.0000 - fp: 1367.0000 - tn: 90820.0000 - fn: 1699.0000 - accuracy: 0.9495 - precision: 0.9550 - recall: 0.9447 - auc: 0.9958 - val_loss: 1.5232 - val_tp: 8609.0000 - val_fp: 4151.0000 - val_tn: 35740.0000 - val_fn: 4688.0000 - val_accuracy: 0.6611 - val_precision: 0.6747 - val_recall: 0.6474 - val_auc: 0.8640\n",
            "Epoch 39/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1389 - tp: 29069.0000 - fp: 1329.0000 - tn: 90858.0000 - fn: 1660.0000 - accuracy: 0.9510 - precision: 0.9563 - recall: 0.9460 - auc: 0.9959 - val_loss: 1.5262 - val_tp: 8818.0000 - val_fp: 4017.0000 - val_tn: 35874.0000 - val_fn: 4479.0000 - val_accuracy: 0.6750 - val_precision: 0.6870 - val_recall: 0.6632 - val_auc: 0.8671\n",
            "Epoch 40/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1376 - tp: 29073.0000 - fp: 1325.0000 - tn: 90862.0000 - fn: 1656.0000 - accuracy: 0.9511 - precision: 0.9564 - recall: 0.9461 - auc: 0.9958 - val_loss: 1.6632 - val_tp: 8608.0000 - val_fp: 4244.0000 - val_tn: 35647.0000 - val_fn: 4689.0000 - val_accuracy: 0.6589 - val_precision: 0.6698 - val_recall: 0.6474 - val_auc: 0.8587\n",
            "Epoch 41/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1282 - tp: 29188.0000 - fp: 1245.0000 - tn: 90942.0000 - fn: 1541.0000 - accuracy: 0.9543 - precision: 0.9591 - recall: 0.9499 - auc: 0.9964 - val_loss: 1.6558 - val_tp: 8814.0000 - val_fp: 4107.0000 - val_tn: 35784.0000 - val_fn: 4483.0000 - val_accuracy: 0.6732 - val_precision: 0.6821 - val_recall: 0.6629 - val_auc: 0.8646\n",
            "Epoch 42/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1271 - tp: 29187.0000 - fp: 1258.0000 - tn: 90929.0000 - fn: 1542.0000 - accuracy: 0.9540 - precision: 0.9587 - recall: 0.9498 - auc: 0.9963 - val_loss: 1.5606 - val_tp: 8405.0000 - val_fp: 4291.0000 - val_tn: 35600.0000 - val_fn: 4892.0000 - val_accuracy: 0.6464 - val_precision: 0.6620 - val_recall: 0.6321 - val_auc: 0.8594\n",
            "Epoch 43/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1228 - tp: 29263.0000 - fp: 1163.0000 - tn: 91024.0000 - fn: 1466.0000 - accuracy: 0.9563 - precision: 0.9618 - recall: 0.9523 - auc: 0.9964 - val_loss: 1.7409 - val_tp: 8696.0000 - val_fp: 4221.0000 - val_tn: 35670.0000 - val_fn: 4601.0000 - val_accuracy: 0.6643 - val_precision: 0.6732 - val_recall: 0.6540 - val_auc: 0.8599\n",
            "Epoch 44/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1223 - tp: 29215.0000 - fp: 1231.0000 - tn: 90956.0000 - fn: 1514.0000 - accuracy: 0.9543 - precision: 0.9596 - recall: 0.9507 - auc: 0.9967 - val_loss: 1.7157 - val_tp: 8576.0000 - val_fp: 4286.0000 - val_tn: 35605.0000 - val_fn: 4721.0000 - val_accuracy: 0.6551 - val_precision: 0.6668 - val_recall: 0.6450 - val_auc: 0.8600\n",
            "Epoch 45/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1152 - tp: 29339.0000 - fp: 1155.0000 - tn: 91032.0000 - fn: 1390.0000 - accuracy: 0.9580 - precision: 0.9621 - recall: 0.9548 - auc: 0.9970 - val_loss: 1.6345 - val_tp: 8636.0000 - val_fp: 4241.0000 - val_tn: 35650.0000 - val_fn: 4661.0000 - val_accuracy: 0.6589 - val_precision: 0.6707 - val_recall: 0.6495 - val_auc: 0.8607\n",
            "Epoch 46/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1198 - tp: 29296.0000 - fp: 1181.0000 - tn: 91006.0000 - fn: 1433.0000 - accuracy: 0.9568 - precision: 0.9612 - recall: 0.9534 - auc: 0.9967 - val_loss: 1.7485 - val_tp: 8672.0000 - val_fp: 4249.0000 - val_tn: 35642.0000 - val_fn: 4625.0000 - val_accuracy: 0.6611 - val_precision: 0.6712 - val_recall: 0.6522 - val_auc: 0.8590\n",
            "Epoch 47/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1116 - tp: 29368.0000 - fp: 1114.0000 - tn: 91073.0000 - fn: 1361.0000 - accuracy: 0.9591 - precision: 0.9635 - recall: 0.9557 - auc: 0.9972 - val_loss: 1.8192 - val_tp: 8582.0000 - val_fp: 4242.0000 - val_tn: 35649.0000 - val_fn: 4715.0000 - val_accuracy: 0.6561 - val_precision: 0.6692 - val_recall: 0.6454 - val_auc: 0.8540\n",
            "Epoch 48/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1029 - tp: 29511.0000 - fp: 1021.0000 - tn: 91166.0000 - fn: 1218.0000 - accuracy: 0.9634 - precision: 0.9666 - recall: 0.9604 - auc: 0.9975 - val_loss: 1.7501 - val_tp: 8437.0000 - val_fp: 4471.0000 - val_tn: 35420.0000 - val_fn: 4860.0000 - val_accuracy: 0.6436 - val_precision: 0.6536 - val_recall: 0.6345 - val_auc: 0.8518\n",
            "Epoch 49/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1052 - tp: 29468.0000 - fp: 1051.0000 - tn: 91136.0000 - fn: 1261.0000 - accuracy: 0.9617 - precision: 0.9656 - recall: 0.9590 - auc: 0.9974 - val_loss: 1.9607 - val_tp: 8583.0000 - val_fp: 4400.0000 - val_tn: 35491.0000 - val_fn: 4714.0000 - val_accuracy: 0.6532 - val_precision: 0.6611 - val_recall: 0.6455 - val_auc: 0.8497\n",
            "Epoch 50/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.1056 - tp: 29461.0000 - fp: 1066.0000 - tn: 91121.0000 - fn: 1268.0000 - accuracy: 0.9616 - precision: 0.9651 - recall: 0.9587 - auc: 0.9972 - val_loss: 1.6254 - val_tp: 8622.0000 - val_fp: 4198.0000 - val_tn: 35693.0000 - val_fn: 4675.0000 - val_accuracy: 0.6601 - val_precision: 0.6725 - val_recall: 0.6484 - val_auc: 0.8647\n",
            "Epoch 51/500\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.0977 - tp: 29542.0000 - fp: 980.0000 - tn: 91207.0000 - fn: 1187.0000 - accuracy: 0.9645 - precision: 0.9679 - recall: 0.9614 - auc: 0.9977 - val_loss: 1.7303 - val_tp: 8843.0000 - val_fp: 4094.0000 - val_tn: 35797.0000 - val_fn: 4454.0000 - val_accuracy: 0.6744 - val_precision: 0.6835 - val_recall: 0.6650 - val_auc: 0.8610\n",
            "Epoch 52/500\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.1009 - tp: 29518.0000 - fp: 999.0000 - tn: 91188.0000 - fn: 1211.0000 - accuracy: 0.9637 - precision: 0.9673 - recall: 0.9606 - auc: 0.9976 - val_loss: 1.9345 - val_tp: 8680.0000 - val_fp: 4250.0000 - val_tn: 35641.0000 - val_fn: 4617.0000 - val_accuracy: 0.6621 - val_precision: 0.6713 - val_recall: 0.6528 - val_auc: 0.8509\n",
            "Epoch 53/500\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.0946 - tp: 29608.0000 - fp: 941.0000 - tn: 91246.0000 - fn: 1121.0000 - accuracy: 0.9661 - precision: 0.9692 - recall: 0.9635 - auc: 0.9978 - val_loss: 1.7868 - val_tp: 8802.0000 - val_fp: 4121.0000 - val_tn: 35770.0000 - val_fn: 4495.0000 - val_accuracy: 0.6705 - val_precision: 0.6811 - val_recall: 0.6620 - val_auc: 0.8561\n",
            "Epoch 54/500\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.0932 - tp: 29584.0000 - fp: 970.0000 - tn: 91217.0000 - fn: 1145.0000 - accuracy: 0.9652 - precision: 0.9683 - recall: 0.9627 - auc: 0.9981 - val_loss: 1.9667 - val_tp: 8639.0000 - val_fp: 4361.0000 - val_tn: 35530.0000 - val_fn: 4658.0000 - val_accuracy: 0.6562 - val_precision: 0.6645 - val_recall: 0.6497 - val_auc: 0.8491\n",
            "Epoch 55/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.0915 - tp: 29595.0000 - fp: 943.0000 - tn: 91244.0000 - fn: 1134.0000 - accuracy: 0.9662 - precision: 0.9691 - recall: 0.9631 - auc: 0.9980 - val_loss: 1.9408 - val_tp: 8716.0000 - val_fp: 4307.0000 - val_tn: 35584.0000 - val_fn: 4581.0000 - val_accuracy: 0.6614 - val_precision: 0.6693 - val_recall: 0.6555 - val_auc: 0.8546\n",
            "Epoch 56/500\n",
            "961/961 [==============================] - 14s 15ms/step - loss: 0.0890 - tp: 29657.0000 - fp: 901.0000 - tn: 91286.0000 - fn: 1072.0000 - accuracy: 0.9669 - precision: 0.9705 - recall: 0.9651 - auc: 0.9980 - val_loss: 1.9072 - val_tp: 8740.0000 - val_fp: 4226.0000 - val_tn: 35665.0000 - val_fn: 4557.0000 - val_accuracy: 0.6647 - val_precision: 0.6741 - val_recall: 0.6573 - val_auc: 0.8583\n",
            "Epoch 57/500\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.0877 - tp: 29646.0000 - fp: 918.0000 - tn: 91269.0000 - fn: 1083.0000 - accuracy: 0.9674 - precision: 0.9700 - recall: 0.9648 - auc: 0.9983 - val_loss: 1.8006 - val_tp: 8851.0000 - val_fp: 4122.0000 - val_tn: 35769.0000 - val_fn: 4446.0000 - val_accuracy: 0.6727 - val_precision: 0.6823 - val_recall: 0.6656 - val_auc: 0.8626\n",
            "Epoch 58/500\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.0841 - tp: 29719.0000 - fp: 856.0000 - tn: 91331.0000 - fn: 1010.0000 - accuracy: 0.9694 - precision: 0.9720 - recall: 0.9671 - auc: 0.9983 - val_loss: 2.1587 - val_tp: 8501.0000 - val_fp: 4447.0000 - val_tn: 35444.0000 - val_fn: 4796.0000 - val_accuracy: 0.6470 - val_precision: 0.6565 - val_recall: 0.6393 - val_auc: 0.8446\n",
            "Epoch 59/500\n",
            "960/961 [============================>.] - ETA: 0s - loss: 0.0801 - tp: 29741.0000 - fp: 844.0000 - tn: 91316.0000 - fn: 979.0000 - accuracy: 0.9702 - precision: 0.9724 - recall: 0.9681 - auc: 0.9983Reached 97.0% accuracy so cancelling training!\n",
            "961/961 [==============================] - 15s 15ms/step - loss: 0.0801 - tp: 29750.0000 - fp: 844.0000 - tn: 91343.0000 - fn: 979.0000 - accuracy: 0.9702 - precision: 0.9724 - recall: 0.9681 - auc: 0.9983 - val_loss: 1.8445 - val_tp: 8488.0000 - val_fp: 4401.0000 - val_tn: 35490.0000 - val_fn: 4809.0000 - val_accuracy: 0.6473 - val_precision: 0.6585 - val_recall: 0.6383 - val_auc: 0.8508\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtGcduByl12g"
      },
      "source": [
        "### Save the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eio5pdSXl3B5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc2e6073-d143-4115-87bb-2455efd7ffaa"
      },
      "source": [
        "HYmodel.save(\"/content/drive/MyDrive/College Related/Project Work 1 - Sem 7/Saved Models/HY_Model.h5\")\n",
        "print(\"HY_Model saved Successfully!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HY_Model saved Successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDQCaT3mfjhg"
      },
      "source": [
        "HYmodel = tf.keras.models.load_model(\"/content/drive/MyDrive/College Related/Project Work 1 - Sem 7/Saved Models/HY_Model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12DhrcDGaibe"
      },
      "source": [
        "### Plot the graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xF7XET8Ga3d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "34c3b734-2612-4bd0-91fd-ae6ee96540c3"
      },
      "source": [
        "plot_graphs(HY_history, \"accuracy\")\n",
        "plot_graphs(HY_history, \"loss\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zz82g8keHPt2"
      },
      "source": [
        "### Evaluation of Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHndGRA2HLa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7c9855cb-faa8-42cd-cd6a-5d9345814ff5"
      },
      "source": [
        "model_evaluate(HYmodel, [0, 1, 2, 3], testX, testHY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix\n",
            "Accuracy for Test Data =  89.67436263818907\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.90      0.86      2862\n",
            "           1       0.88      0.86      0.87      2708\n",
            "           2       0.93      0.91      0.92      6720\n",
            "           3       0.90      0.92      0.91      1007\n",
            "\n",
            "    accuracy                           0.90     13297\n",
            "   macro avg       0.89      0.90      0.89     13297\n",
            "weighted avg       0.90      0.90      0.90     13297\n",
            "\n",
            "\n",
            "Cohen Kappa Score\n",
            "0.8423538312049089\n",
            "\n",
            "Precision vs Recall Curve\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "ROC Curve\n",
            "ROC AUC score: 0.9286533824686953\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}