SoftComputingLab_(VI_SEM).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ramprasathmk/Soft-Computing-Lab-2024/blob/main/SoftComputingLab_(VI_SEM).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4TlICmLtIp9"
      },
      "source": [
        "## Install the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBSzWCTns0z-",
        "outputId": "3348bf1e-f831-444c-9cab-d72a065e43b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.4.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "%pip install numpy matplotlib scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMPNSzJDJbGo",
        "outputId": "543d63e2-9833-45af-d14c-470375340944"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting scikit-fuzzy\n",
            "  Downloading scikit-fuzzy-0.4.2.tar.gz (993 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m994.0/994.0 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-fuzzy) (1.25.2)\n",
            "Requirement already satisfied: scipy>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from scikit-fuzzy) (1.11.4)\n",
            "Requirement already satisfied: networkx>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from scikit-fuzzy) (3.3)\n",
            "Building wheels for collected packages: scikit-fuzzy\n",
            "  Building wheel for scikit-fuzzy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-fuzzy: filename=scikit_fuzzy-0.4.2-py3-none-any.whl size=894078 sha256=0f863a88582d984f3528a57d0741d160cef636c90f2b9b642195a536db6581a8\n",
            "  Stored in directory: /root/.cache/pip/wheels/4f/86/1b/dfd97134a2c8313e519bcebd95d3fedc7be7944db022094bc8\n",
            "Successfully built scikit-fuzzy\n",
            "Installing collected packages: scikit-fuzzy\n",
            "Successfully installed scikit-fuzzy-0.4.2\n"
          ]
        }
      ],
      "source": [
        "%pip install scikit-fuzzy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WrpcsvsBNxOn",
        "outputId": "e12082a2-2622-4953-973b-e6e88e3627d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting colorama\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Installing collected packages: colorama\n",
            "Successfully installed colorama-0.4.6\n"
          ]
        }
      ],
      "source": [
        "%pip install colorama"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o5OGqsIeMvw2"
      },
      "source": [
        "**GitHub:** [@ramprasathmk/Soft-Computing-Lab-2024](https://github.com/ramprasathmk/Soft-Computing-Lab-2024.git)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9ZwsSh_T72z"
      },
      "source": [
        "**REFERENCE LINKS:**\n",
        " - [scikit-fuzzy Examples](https://scikit-fuzzy.readthedocs.io/en/latest/auto_examples/index.html)\n",
        " - [fuzzy-k-means](https://www.xlstat.com/en/solutions/features/fuzzy-k-means-clustering)\n",
        " - [examples](https://scikit-fuzzy.readthedocs.io/en/latest/auto_examples/plot_cmeans.html)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YtjxAjRvJIVy"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6VHaxmrANFh1"
      },
      "source": [
        "## EX: 1.A Fuzzy Set Operations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "3KDP2u3-NKyN",
        "outputId": "fbd7f1c6-4fe5-455b-99af-e044927363f6"
      },
      "outputs": [
        {
          "data": {
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88IHWzs5Oa2trq+3UqZP27t27WS61980332jd3d21SqVSbzm2rJbfu3r1qrZjx45aOzs7rZmZmbZ69eraLVu2ZPlc/7m02rP3z7Jly7RarVYbGRmp/eijj7SlS5fWmpmZae3t7bUNGzbU/vXXXy97yYUQQrxjFFrtP8aFCSGEECJP/fHHH7Rr144DBw7olugqjPbt20fDhg3ZsGEDHTt2NHQ6QgghRIEg16QLIYQQb9nixYspVaqU3trbQgghhBAg16QLIYQQb83atWs5e/YsW7duZc6cOTmaZVwIIYQQ7wYp0oUQQoi3pFu3blhZWfHxxx8zYMAAQ6cjhBBCiHxIrkkXQgghhBBCCCHyCbkmXQghhBBCCCGEyCekSBdCCCGEEEIIIfKJd+6adI1Gw927d7G2tpYJe4QQQgghhBBC5DmtVsujR48oWrQoSuWr+8rfuSL97t27eHh4GDoNIYQQQgghhBDvmFu3blGsWLFXtnnninRra2vg6YtjY2Nj4GxeTa1Ws2vXLpo1a4axsbGh0xFCFDJyjhFC5DU5zwgh8lpBOc8kJibi4eGhq0df5Z0r0p8NcbexsSkQRbqFhQU2Njb5+g0nhCiY5BwjhMhrcp4RQuS1gnaeyc4l1zJxnBBCCCGEEEIIkU9IkS6EEEIIIYQQQuQTUqQLIYQQQgghhBD5xDt3TboQQgghhBCiYNJqtajVatLT0w2disgn1Go1xsbGJCcnG/yadFNTU1Qq1RsfR4p0IYQQQgghRL6XmprK9evXefz4saFTEfmMi4sLERERhk4DhUKBl5fXG09QLkW6EEIIIYQQIl/TaDSEhYVhZGREyZIlMTU1zdYs2UK8LRqNhqioKCIiIqhYseIb9ahLkS6EEEIIIYTI11JSUtBoNJQsWRIrKytDpyNEltzc3EhMTCQ1NRULC4vXPo5MHCeEEEIIIYQoEJRKKV9E/pVb7095lwshhBBCCCGEEPmEFOlCCCGEEEIIIUQ+IdekCyGEEEIIIQq9lJspqOPUL91u7GiMWXGzt5iREFmTnnQhhBBCCCFEoZZyM4Vj3sc4WeXkS2/HvI+RcjMl1x+7d+/eKBSKTLf8sGRYVhYvXkzFihWxsrLCzs6OSpUqMXXq1Gzvf/36dRQKBaGhoXmXZCEnPelCCCGEEEKIQk0dp0aTonllG02KBnWcOk9601u0aMGyZcv0Yk5OTrn+OG9q6dKlfPbZZ8ydO5f69euTmprK2bNnOX/+vKFTe6dIkZ7PHL/whOvRaQBkpKcTetGKJ2aJqIye/lN5uppQzd/ckCkKIQowOccIIYQQb5+pqSmurq6Z4r179yY+Pp5NmzbpYp999hmhoaHs27eP69evU7JkyUz71a9fn3379tGgQQP279+fafu1a9f4+uuviY2NZcuWLbq4Wq3G3d2dqVOn8vHHH2fa788//6Rz58562/z9/TO1W7JkCTNnzuTatWt4enoyZMgQBgwYAKDLt1KlSnq5/lNGRgaffPIJe/bsITo6muLFizNgwACGDh2aqe27xuBF+oIFC5g+fTrR0dFUrFiRefPmUb169SzbqtVqpk6dyooVK7hz5w7e3t5MmzaNFi1avOWs88bxC0/ouGwfCiP9X/nW7j2i+1ubruTXPg3kS7QQIsfkHCOEEEIULB4eHkRFRenuR0dH06RJE+rVqwfAxo0bSUtL020fOHAgFy5cwMXFhb59+1KvXj2ioqJwc3MDYMuWLSQnJ9OlS5csH8/V1ZX9+/dz48YNSpQokWWbX375hXHjxjF//nwqVarE6dOn6devH5aWlvTq1Ytjx45RvXp1/vrrL/z9/TExMcnyOBqNhmLFirFhwwYcHBw4dOgQn3zyCW5ubnTu3Pm1Xq/CwqDXpK9bt46goCDGjx/PqVOnqFixIs2bNyc2NjbL9l999RWLFi1i3rx5hIWF0b9/fz744ANOnz79ljPPG9ej0zJ9ef4nhZFG1wsmhBA5IecYIYQQwjC2bNmClZWV7tapU6ds7adSqXB1dcXV1RU7Ozv69+9PrVq1mDBhAgD29va67WvWrGHPnj38+eefmJubU7t2bby9vVm1apXueMuWLaNTp05YWVll+Xjjx4/Hzs4OT09PvL296d27N+vXr0ej0ei1mTlzJu3bt6dkyZK0b9+ezz//nEWLFgHPh/E7ODjg6uqKvb19lo9lbGzMxIkTqVq1KiVLlqR79+706dOH9evXZ+u1KcwM2pM+a9Ys+vXrR58+fQD44Ycf2Lp1K0uXLmXUqFGZ2q9atYoxY8bQqlUrAD799FP++usvZs6cyc8///xWczekczFxWJ9PNnQaQogC5lyMnDeEEEIIQ2jYsCELFy7U3be0tMzxMT766CMePXrE7t27USr1+1q3b9/OqFGj2Lx5M2XLltXF+/bty48//siIESOIiYlh+/bt7Nmz56WP4ebmxuHDhzl//jwHDhzg0KFD9OrViyVLlrBjxw6ePHnC1atX+fjjj+nXr59uv/T0dGxtbXP8nBYsWMDSpUu5efMmT548IS0tjYCAgBwfp7AxWJGelpbGyZMnGT16tC6mVCpp0qQJhw8fznKf1NRUzMz0J3IwNzfn77//funjpKamkpqaqrufmJgIPB06r1a/fAkGQ8hIT89Wu5VnL7LybB4nI4R4Z2Wkp+e786MQomB6di6Rc4p4UwX9PWRpaYmXl1emuFKpRKvV6sWyeq6TJk1i586dHDt2DGtra71tYWFhdO3alW+//ZZmzZrpbevZsyejRo3i8OHDHDp0iJIlS1K3bt1/zbdcuXKUK1eOAQMG0L9/f+rWrcv+/fvx8/MDns4AX6NGDb19VCrVvx73RWvXrmXYsGHMnDmTWrVqYW1tzfTp0zl69GiOjpMfZVVr5uQ9bLAiPS4ujoyMDFxcXPTiLi4uXLx4Mct9mjdvzqxZs6hXrx6lS5cmODiYjRs3kpGR8dLHmTp1KhMnTswU37VrFxYWFm/2JHJZ6MWsh538k6ORAmvzVw9ZFUKIf3r0RElcuvZf24WGhmKe8vgtZCSEeFfs3r3b0CmIAs7Y2DhT3VAYODk5ZZo5PTQ0FGNjY9393377ja+//prt27dTunRpvbZxcXG0adOGDh068Pnnn2c6voODA+3atWPZsmUcPnxYN4I5J54V5klJSbi4uFC0aFEiIyPp3r17lu2fXYP+qhoNICQkhNq1a+smnAO4evVqjvPLj0JCQjIV5cnJ2R/RaPCJ43Jizpw59OvXDx8fHxQKBaVLl6ZPnz4sXbr0pfuMHj2aoKAg3f3ExEQ8PDxo1qwZNjY2byPtbHtilqg3gdPLDKtbgw6N8lfuQoj877c9iYzKxjnmqpk1Q+rWwMna9C1kJYQozNRqNbt376Zp06Z6RYcQOZWcnPxG64obOxqjNFO+chk2pZkSY8e3+z5t1KgR06dPZ+XKldSqVYuff/6Z8+fP62ZGP3/+PD179mTkyJH4+/sTHR0NPC2E7e3t6dChAxYWFkyYMEG3DZ4W/896tvv27ct7771HRkYGvXr1emU+n376KUWLFqVRo0YUK1aMqKgoJk2ahJOTE7Vq1QJg4sSJDBkyBFtbW1q0aEFqaionTpzg4cOHBAUF4ezsjLm5OTt27KBYsWKYmZllORS+TJkyrFy5kp07d1KyZElWrVrF8ePHs5zNvqAJDAzM1CH8bER3dhisSHd0dESlUhETE6MXj4mJyXJ5Anj6Ztu0aRMpKSncv3+fokWLMmrUKEqVKvXSxzE1NcXUNPMXTWNj43z3YfFsCaTstMtvuQsh8r/snmOOR92jyey/6V+/NP3qlsLcJGfD14QQ4p/y4/cuUbC86fvHrLgZ1S9VRx338iHHxo7GebJG+qs0b96csWPHMmLECFJSUvjoo4/o2bMn586dA+DEiRMkJyczadIkJk2apNvv2bJmBw4cAMg0E/uzpdEAmjRpgpubG/7+/hQtWvSV+TRp0oSlS5eycOFC7t+/j6OjI7Vq1SI4OBgHBwfgadFvYWHB9OnTGT58OJaWlpQvX57PPvsMACMjI+bOncvXX3/NuHHjqFu3bpZLsP33v//l9OnTdOnSBYVCQbdu3RgwYADbt29/nZcyX8nqnJeT97BC+8+LIN6iGjVqUL16debNmwc8nYa/ePHiDBo0KMuJ4/5JrVbj6+tL586dmTJlSrYeMzExEVtbWxISEvJdT/qG4ASG73759fXPTG9ah06Ncz4xgxDi3Zbdc4yHjRW3Ep8Od3e1MWNYc2/aV3JHqVTkdYpCiEJGrVazbds2WrVqJUW6eCPJycmEh4fj6+ub7y5Zze8eP36Mu7s7y5Yto3379oZOp1B71fs0J3WoQZdgCwoKYvHixaxYsYLw8HA+/fRTkpKSdNdK9OzZU29iuaNHj7Jx40YiIyM5ePAgLVq0QKPRMGLECEM9hVzl6WqCNv3V/yTadCWerlmvNSiEEK+S3XPMzPerMbdbJdztzIlOTGHYhjO0mf83h67GvaVMhRBCCPGmNBoNsbGxfPPNN9jZ2fH+++8bOiWRTQa9Jr1Lly7cu3ePcePGER0dTUBAADt27NBNCnHz5k295QVSUlL46quviIyMxMrKilatWrFq1Srs7OwM9AxyVzV/c37t00C3RnFGejqhoaEEBATohql6uppQzd/cgFkKIQqqnJ1jLGjm58LyQ9dZsCeCC3cT+XDxUZr4OjO6lS+lnbI30aUQQgghDOPmzZuULFmSYsWKsXz5coyyedmbMDyDDnc3hPw83P2fZIiYECIvZfccc/9xKrP/usLqYzfJ0GgxUiroXqM4Q5uUxd5SRvYIIV5OvsuI3CLD3UVBUCiGuwshhMj/HKxM+aZdOXZ+VpfGPs6ka7SsOHyD+tP3smj/VVLUr15iRQghhBBCZJ8U6UIIIbLFy9man3pXY3XfGvi52fAoJZ2p2y/SZNZ+Np+5yzs2MEsIIYQQIk9IkS6EECJHans5snlwHaZ3rICLjSm3Hz5h8JrTtF94iJM3Hho6PSGEEEKIAk2KdCGEEDmmUiroVNWDvcMa8HmTsliYqDh9M54OCw8x8JdT3LyfbOgUhRBCCCEKJCnShRBCvDYLEyOGNinDvmEN6FLVA4UCtp6Losms/UzeGkbCE7WhUxRCCCGEKFCkSBdCCPHGnG3MmNaxAtuG1KVuGUfSMjQsPniNBtP3sjzkGuoMjaFTFEIIIYQoEGSxPCGEELnG182GlR9VZ9/le0zZGs6V2MdM2BzGysM3GNXSh6Z+LigUCkOnKYQQ4h108ybExb18u6MjFC/+9vJ51+3bt4+GDRvy8OFD7OzsDJ1OviI96UIIIXKVQqGgobcz24fWZfIH5XC0MiEyLolPVp2k649HOHc7wdApCiGEeMfcvAne3lClystv3t5P2+W23r17o1AoMt0iIiJy/8FyweLFi6lYsSJWVlbY2dlRqVIlpk6dmu39r1+/jkKhIDQ0NO+SLOSkSBdCCJEnjFRKutcowd5hDRjQoDSmRkqOXntAm/l/E7QulLvxTwydohBCiHdEXBykpLy6TUrKq3va30SLFi2IiorSu5UsWTJvHuwNLF26lM8++4whQ4YQGhpKSEgII0aM4PHjx4ZO7Z0iRboQQog8ZW1mzIgWPuwZ1oB2AUUB2Hj6Dg1n7GPGzks8Tk03cIZCCCFE3jI1NcXV1VXvplKp6N27N+3atdNr+9lnn9GgQQPgea/0P2/Ptjdo0CDL7devX+ejjz7ivffe0zu2Wq3G2dmZn376Kcs8//zzTzp37szHH3+Ml5cX/v7+dOvWjcmTJ+u1W7JkCb6+vpiZmeHj48P333+v2/bsx4dKlSrp5foyISEhVKhQATMzM2rWrMn58+f/5dUs/KRIF0II8Va425kzu2sl/hgYSHVPe1LTNczfG0GD6ftYffQm6TK5nBBCCKHHw8NDr/f99OnTODg4UK9ePQA2btyot719+/Z4e3vj4uJC37592bFjB1FRUbrjbdmyheTkZLp06ZLl47m6unLkyBFu3Ljx0px++eUXxo0bx+TJkwkPD2fKlCmMHTuWFStWAHDs2DEA/vrrL6Kioti4ceMrn+Pw4cOZOXMmx48fx8nJiTZt2qBWv9urw0iRLoQQ4q2q6GHHuv/W5If/VMHTwYK4x6l8+fs5Ws09yL5LsYZOTwghhMh1W7ZswcrKSnfr1KlTtvZTqVS6nnc7Ozv69+9PrVq1mDBhAgD29va67WvWrGHPnj38+eefmJubU7t2bby9vVm1apXueMuWLaNTp05YWVll+Xjjx4/Hzs4OT09PvL296d27N+vXr0ej0ei1mTlzJu3bt6dkyZK0b9+ezz//nEWLFgHg5OQEgIODA66urtjb27/yOY4fP56mTZtSvnx5VqxYQUxMDL///nu2Xp/CSmZ3F0II8dYpFApalHOlkY8zPx+5wZzgK1yOeUzvZcepW8aRMa198XG1MXSaQgghRK5o2LAhCxcu1N23tLTM8TE++ugjHj16xO7du1Eq9ftat2/fzqhRo9i8eTNly5bVxfv27cuPP/7IiBEjiImJYfv27ezZs+elj+Hm5sbhw4c5f/48Bw4c4NChQ/Tq1YslS5awY8cOnjx5wtWrV/n444/p16+fbr/09HRsbW1z/JwAatWqpfvb3t4eb29vwsPDX+tYhYUU6UIIIQzGxEjJR3VK0qFyMebtucKKw9c5eCWOVnMO0rmqB0HNyuJsbWboNIUQQog3YmlpiZeXV6a4UqlEq9XqxbIa6j1p0iR27tzJsWPHsLa21tsWFhZG165d+fbbb2nWrJnetp49ezJq1CgOHz7MoUOHKFmyJHXr1v3XfMuVK0e5cuUYMGAA/fv3p27duuzfvx8/Pz/g6QzwNWrU0NtHpVL963FF9shwdyGEEAZna2HMV+/58VdQfVqWc0WjhbXHb9Fg+j7mBV/hSVqGoVMUQgghcp2Tk5PeNeNApqXLfvvtN77++mvWr19P6dKl9bbFxcXRpk0bOnTowOeff57p+A4ODrRr145ly5axfPly+vTpk+McnxXmSUlJuLi4ULRoUSIjI/Hy8tK7PZswzsTEBICMjOx9dh85ckT398OHD7l8+TK+vr45zrMwkZ50IYQQ+UYJB0sW/qcKx68/YNLWcM7cimfm7sv8cvQmw5t780Eld5RKhaHTFEIIUcA4OoKZ2auXYTMze9rubWrUqBHTp09n5cqV1KpVi59//pnz589TqVIlAM6fP0/Pnj0ZOXIk/v7+REdHA08LYXt7ezp06ICFhQUTJkzQbYOnxf+znu2+ffvy3nvvkZGRQa9evV6Zz6effkrRokVp1KgRxYoVIyoqikmTJuHk5KQblj5x4kSGDBmCra0tLVq0IDU1lRMnTvDw4UOCgoJwdnbG3NycHTt2UKxYMczMzF45FP7rr7/GwcEBFxcXxowZg6OjY6YZ79810pMuhBAi36nmac/vn9ZmTtcA3O3MiU5M4YsNZ3h/wd8cvnrf0OkJIYQoYIoXh0uX4OTJl98uXXra7m1q3rw5Y8eOZcSIEVSrVo1Hjx7Rs2dP3fYTJ06QnJzMpEmTcHNz093at28PwIEDBzh//jwlSpTQ237r1i3dMZo0aYKbmxvNmzenaNGir8ynSZMmHDlyhE6dOlG2bFk6dOiAmZkZwcHBODg4AE+L/iVLlrBs2TLKly9P/fr1Wb58ua4n3cjIiLlz57Jo0SKKFi1K27ZtX/mY3377LUOHDqVKlSpER0ezefNmXW/8u0qh/edFEIVcYmIitra2JCQkYGOTvyclUqvVbNu2jVatWmFsbGzodIQQhUxBOcekqDNYFnKd7/dG8Oj/11Rv4uvC6FY+lHbKenZaIUT+UFDOMyL/S05OJjw8HF9fXywsLAydToHy+PFj3N3dWbZsma64F3njVe/TnNSh0pMuhBAiXzMzVvFpg9LsG96AHjVLoFIq+Cs8hub/O8D4P87zICnN0CkKIYQQ+Y5GoyE2NpZvvvkGOzs73n//fUOnJLJJinQhhBAFgoOVKd+0K8fOz+rS2MeZdI2WFYdvUH/6Xhbtv0pqukwuJ4QQQjxz8+ZNXFxcWL16NUuXLsXISKYjKyjkX0oIIUSB4uVszU+9q3EoIo5JW8MJi0pk6vaLrDpyg1EtfWhd3g2FQiaXE0II8W7z9PTMtLybKBikJ10IIUSBVNvLkc2D6zC9YwVcbEy5/fAJg1afpsPCQ5y88dDQ6QkhhBBCvBYp0oUQQhRYKqWCTlU92DusAZ83KYu5sYpTN+PpsPAQA1ef4ub9ZEOnKIQQQgiRI1KkCyGEKPAsTIwY2qQM+4c3oEtVDxQK2Ho2iiaz9jN5axgJT9SGTlEIIYQQIlukSBdCCFFoONuYMa1jBbYOrksdL0fSMjQsPniNBtP3sjzkGuoMjaFTFEIIIYR4JSnShRBCFDp+RW1Y9XF1lvWuhpezFQ+T1UzYHEbz/x1g14VomUhHCCGEEPmWFOlCCCEKJYVCQUMfZ3YMrcukduVwsDQhMi6JT1adpOuPRzh3O8HQKQohhBBCZCJFuhBCiELNSKXkPzVLsG94AwY0KI2JkZKj1x7QZv7fBK0L5W78E0OnKIQQ4i24E/+E83cSXnq7k88/Dzw9PZk9e7ah0xBvgayTLoQQ4p1gbWbMiBY+dK9Zguk7LrIp9C4bT99h67ko+tUtRf8GpbEylY9FIYQojO7EP6HRjH2kpr98bhJTIyV7hjXA3c48Vx+7QYMGBAQEZCqwly9fzmeffUZ8fHy2jnP8+HEsLS1zNTeRP0lPuhBCiHeKu505s7tW4o+BgVT3tCc1XcP8vRE0mL6P1Udvki6TywkhRKHzMCntlQU6QGq6hodJaW8po5xzcnLCwsLC0GmIt0CKdCGEEO+kih52rPtvTX74T2U8HSyIe5zKl7+fo/Xcv9l/+Z6h0xNCCPEvtFotyWnp2bqlqDOydcwUdUa2jpfbE5D27t2bdu3aMWPGDNzc3HBwcGDgwIGo1c+XEP3ncPebN2/Stm1brKyssLGxoXPnzsTExOi2T5gwgYCAAFatWoWnpye2trZ07dqVR48e5WruIvfJuD4hhBDvLIVCQYtybjTycWHVkRvMDb7CpZhH9Fp6jHplnRjTyhdvV2tDpymEECILT9QZ+I3bmavH7PjD4Wy1C/u6ORYmuVtK7d27Fzc3N/bu3UtERARdunQhICCAfv36ZWqr0Wh0Bfr+/ftJT09n4MCBdOnShX379unaXb16lU2bNrFlyxYePnxI586d+fbbb5k8eXKu5i5ylxTpQggh3nkmRko+rlOSDpXdmbcngpWHr3Pg8j3+vnKPLtU8+LxpWZytzQydphBCiEKsSJEizJ8/H5VKhY+PD61btyY4ODjLIj04OJhz585x7do1PDw8AFi5ciX+/v4cP36catWqAU+L+eXLl2Nt/fQH5x49ehAcHCxFej4nRboQQgjx/+wsTBj7nh89a5Xg2+0X2X4+mjXHbvFn6F361y9N37qlMDdRGTpNIYQQgLmxirCvm2erbdjdxGz1kv/avxZ+RW2y9di5zd/fH5Xq+XHd3Nw4d+5clm3Dw8Px8PDQFegAfn5+2NnZER4erivSPT09dQX6s2PGxsbmeu4id0mRLoQQQvxDCQdLFv6nCsevP2DS1nDO3Ipn5u7LrD52k2HNvPmgkjtKpcLQaQohxDtNoVBke8i5WTaLajNjVa4PY7exsSEhISFTPD4+HltbW919Y2Njve0KhQKN5s0mM82LY4q8JxPHCSGEEC9RzdOe3z+tzZyuAbjbmROVkMIXG87w/oK/OXz1vqHTE0IIUQB4e3tz6tSpTPFTp05RtmzZ1zqmr68vt27d4tatW7pYWFgY8fHx+Pn5vXauIn+QIl0IIYR4BaVSQdsAd4K/qM/IFj5Ymxpx/k4i3RYfod/KE0Tee2zoFIUQQvyLIpYmmBq9uvQxNVJSxNIk1x/7008/5fLlywwZMoSzZ89y6dIlZs2axZo1a/jiiy9e65hNmjShfPnydO/enVOnTnHs2DF69uxJ/fr1qVq1ai4/A/G2yXB3IYQQIhvMjFV82qA0nasWY/ZfV1h97Ca7w2LYezGW/9QswZDGZbDPgy93Qggh3py7nTl7hjV45TroRSxNcLczz/XHLlWqFAcOHGDMmDE0adKEtLQ0fHx82LBhAy1atHitYyoUCv744w8GDx5MvXr1UCqVtGjRgnnz5uVy9sIQFNrcXuQvn0tMTMTW1paEhARsbP59UghDUqvVbNu2jVatWmW6nkQIId6UnGPeTETsI6Zuu0jwxacT8FibGTGooRe9Az0xNZLJ5YQAOc+I3JOcnEx4eDi+vr5YWFgYOh0hsvSq92lO6lAZ7i6EEEK8Bi9na37qXY1f+tbA182GRynpTN1+kcYz97Pl7F3esd/AhRBCCJFLpEgXQggh3kCglyNbBtfhu44VcLY25fbDJwxafZoOCw9x8sZDQ6cnhBBCiALG4EX6ggUL8PT0xMzMjBo1anDs2LFXtp89ezbe3t6Ym5vj4eHB559/TkpKylvK9u06Gn2UOYlzOBp91NCpCCGEeAWVUkHnqh7sG96Az5qUwdxYxamb8XRYeIiBq09x60GyoVMUQgghRAFh0CJ93bp1BAUFMX78eE6dOkXFihVp3rw5sbGxWbZfvXo1o0aNYvz48YSHh/PTTz+xbt06vvzyy7eced7TarXMC53HPc095oXOk2GTQghRAFiYGPFZk7LsG96AzlWLoVDA1rNRNJ65nynbwkl4ojZ0ikIIIYTI5wxapM+aNYt+/frRp08f/Pz8+OGHH7CwsGDp0qVZtj906BCBgYF8+OGHeHp60qxZM7p16/avve8F0aG7hwh7EAZA2IMwDt09ZOCMhBBCZJeLjRnfdazI1sF1qePlSFqGhh8PRNJg+l6Wh1xDnaExdIpCCCGEyKcMtgRbWloaJ0+eZPTo0bqYUqmkSZMmHD58OMt9ateuzc8//8yxY8eoXr06kZGRbNu2jR49erz0cVJTU0lNTdXdT0xMBJ7ONqpW588eDa1Wy9xTc1GgQIsWBQrmnppLNadqKBQKQ6cnhCgknp0D8+u5sDAo42TO0p6V2H8ljm93XObqvSQmbA5jxaHrjGhelsY+TnJeF4WanGdEbpH3kChIsqo1c/IeNliRHhcXR0ZGBi4uLnpxFxcXLl68mOU+H374IXFxcdSpUwetVkt6ejr9+/d/5XD3qVOnMnHixEzxXbt25dvlG66orxCWFKa7r0VL2IMw5v45lzLGZViXtA4bpQ31TOthqbQ0YKZCiMJg9+7dhk7hnTCwNBy2UrD9lpJr95P5dHUoXjYa2pXQ4GFl6OyEyFtynhFvytjYOFPdIER+FRISkqkoT07O/vw0BivSX8e+ffuYMmUK33//PTVq1CAiIoKhQ4fyzTffMHbs2Cz3GT16NEFBQbr7iYmJeHh40KxZs3y5TrpWq2X1ztUok5VotM+HQyoVSo6bHqdng558tfErAKa+PxUr46ff7LZe28qJmBM0Kd6EwKKBBsldCFGwqNVqdu/eTdOmTWX94rekDTA6JZ0fD15j6aEbRCTCzPNK2lV04/MmZXCzNTN0ikLkKjnPiNySnJxMRESEodMQIlsCAwOzXCc9uwxWpDs6OqJSqYiJidGLx8TE4OrqmuU+Y8eOpUePHvTt2xeA8uXLk5SUxCeffMKYMWNQKjNfYm9qaoqpqWmmuLGxcb78sAi5E6K7Fv1FGq2GsAdhnH1wlgm1JhCVFEURiyK67YejD7Pt2jZK2JagQYkGACSrkxl9cDQ+9j70q9API2WB+k1GCPGW5NfzYWFlb2zMqFZ+9Khdkuk7LrIp9C6/h0ax/UIM/eqW4r/1S2NlKudrUbjIeUa8KXn/iIIkq3NeTt7DBps4zsTEhCpVqhAcHKyLaTQagoODqVWrVpb7JCcnZyrEVSoVQKGY/Vyr1TLv9DwUZH19ogIFS84toX2Z9gyqNEhvWzuvdvy3wn+pXbS2LhYRH8GeW3tYd2mdXoG+4sIKph2bxoX7F/LmiQghhPhX7nbmzO5aiT8GBlLd054UtYZ5eyJoMH0fa47dJENT8D/XhBAiX4m/BXdDX36Lv2XA5AqPCRMmEBAQYOg0CjSD/lQfFBREr169qFq1KtWrV2f27NkkJSXRp08fAHr27Im7uztTp04FoE2bNsyaNYtKlSrphruPHTuWNm3a6Ir1gkytUROdFI2WrL+YadESnRSNWqPGRGWit61W0VrUKqr/44aLhQsjqo0gQ5OhF995fSfn4s5R0bki/g7+ANxIvMGiM4sIcA6gs3fnXHxWQgghXqWihx3r/luTnRei+Xb7Ra7fT2b0xnMsD7nOl619qV/WydApCiFEwRd/C+ZXgfTUl7cxMoVBJ8HOI1cfunfv3sTHx7Np06ZstVcoFPz++++0a9cuV/PIC1nlOmzYMAYPHmy4pAoBgxbpXbp04d69e4wbN47o6GgCAgLYsWOHblKImzdv6vWcf/XVVygUCr766ivu3LmDk5MTbdq0YfLkyYZ6CrnKRGXC2vfW8iDlAQDp6emE/B1CYJ1AjIye/lPZm9lnKtBfxsXShR5+mWe+7+HXg3Nx56jgWEEXOxd3js2Rm7n9+LZekT731FwA2pdpTzHrYq/93IQQQrycQqGgRTk3Gvm4sOrIDeYGX+FSzCN6LT1GvbJOjGnli7ertaHTFEKIgiv5/qsLdHi6Pfl+rhfphqJWqw1ymYCVlRVWVjIj6psw6DrpAIMGDeLGjRukpqZy9OhRatSoodu2b98+li9frrtvZGTE+PHjiYiI4MmTJ9y8eZMFCxZgZ2f39hPPI66Wrvg5+OHn4IevvS9FjYria++ri7laZn29fk60LNmSEdVGUNSqqC7mU8SHgQEDaVu6rS6m1WpZf3k9i88tJjHt+UQHJ2NOMunIJIJvBCOEECL3mBgp+bhOSfYPb8DHdUpirFJw4PI9Ws45wOiNZ4l9lGLoFIUQIv/QaiEtKXu39CfZO2b6k+wd7zUvtW3QoAFDhgxhxIgR2Nvb4+rqyoQJE3TbPT09Afjggw9QKBS6+wB//PEHlStXxszMjFKlSjFx4kTS09N12xUKBQsXLuT999/H0tKSyZMn8/DhQ7p3746TkxPm5uaUKVOGZcuW6fa5desWnTt3xs7ODnt7e9q2bcv169f1cl66dCn+/v6Ympri5ubGoEGDXpnrP4e7azQavv76a4oVK4apqamuY/aZ69evo1Ao2LhxIw0bNsTCwoKKFSu+dFnud4HMTCMA8CrihVcRL71YhjaDgQEDufTgEqXtSuvix6KOse7SOlLSU2hcorEuPunIJFwtXelUthO2prZvLXchhChs7CxMGPueHz1qlmDajotsPx/NmmO3+DP0Lv3rl6Zv3VKYmxT8y7yEEOKNqJNhStF/b5cTS1tkr92Xd8Hk9ZZCXrFiBUFBQRw9epTDhw/Tu3dvAgMDadq0KcePH8fZ2Zlly5bRokUL3SW9Bw8epGfPnsydO5e6dety9epVPvnkEwDGjx+vO/aECRP49ttvmT17NkZGRowdO5awsDC2b9+Oo6OjrrMTnva0N2/enFq1anHw4EGMjIyYNGkSLVq04OzZs5iYmLBw4UKCgoL49ttvadmyJQkJCYSEhAC8NNd/mjNnDjNnzmTRokVUqlSJpUuX8v7773PhwgXKlCmjazdmzBhmzJhBmTJlGDNmDN26dSMiIkI3ovhd8u49Y5FtRkojuvl0yxSv5lqNPul9qOhcURdLSE1g3aV1AHTx7qKL/3XjL87GnaVBsQZUdqmc90kLIUQh4uloycL/VOH49QdM2hrOmVvxzNx9mdXHbjKsmTcfVHJHqcx6slEhhBD5U4UKFXSFdZkyZZg/fz7BwcE0bdoUJ6en85DY2dnprXg1ceJERo0aRa9evQAoVaoU33zzDSNGjNAr0j/88EPd/F7w9PLhSpUqUbVqVQC9nvl169ah0WhYsmQJCsXTz5Jly5ZhZ2fHvn37aNasGZMmTeKLL75g6NChuv2qVasG8NJc/2nGjBmMHDmSrl27AjBt2jT27t3L7NmzWbBgga7dsGHDaN26te75+vv7ExERgY+PT7Ze18JEinSRY1Vdq1LVtWqm+NDKQ4lJisHa5Pl1k8E3g9kSuQUrYytdkf4k/QnTjk3Dx96HTmU7oVJKb5AQQrxKNU97fv+0NpvP3uW7HZe4E/+ELzacYdmha4xp5Uet0g6GTlEIId4+Y4unPdrZEX02e73kH+0A1wr/3s7Y4t/bvESFCvrHd3NzIzY29pX7nDlzhpCQEL25uDIyMkhJSSE5OVm3JvezYvyZTz/9lA4dOnDq1CmaNWtGu3btqF27tu6YERERWFvrz3mSkpLC1atXiY2N5e7duzRu3JjXlZiYyN27dwkMDNSLBwYGcubMGb3Yi6+Lm5sbALGxsVKkC/G6bE1t6Vu+b6Z4o+KNsDS2pLprdV0s4mEEv135DXsze71e9w2XN/DgyQOalGiiN7xeCCEEKJUK2ga409zflaUh1/h+71XO30mk2+IjNPVzYXRLH0o5yUQ9Qoh3iEKR/SHnRubZb/eaw9iz65+TuSkUCjQazSv3efz4MRMnTqR9+/aZtpmZmen+trTUz71ly5bcuHGDbdu2sXv3bho3bszAgQOZMWMGjx8/pkqVKvzyyy+Zjunk5JRp6eu89uLr8qxn/99el8JKinSRp5qWaErTEk31YnZmdvQr3w+FQqH7DxBg05VNnI07SwmbEroi/faj26y+uJoKjhVoUTKb1wgJIUQhZmasYkADLzpX9WD2X5dZc+wWu8Ni2Hsxlv/ULMGQxmWwt8zeKiBCCCHyH2NjYzIy9JdQrly5MpcuXcLLy+sle72ck5MTvXr1olevXtStW5fhw4czY8YMKleuzLp163B2dsbGxibLfT09PQkODqZhw4bZzvVFNjY2FC1alJCQEOrXr6+Lh4SEUL169Zfu966TIl28dR7WHgypPCRTvE3pNpSwKYG/o78udvbeWVaFraKiU0W9In3p+aUYK41p7tkcZwvnt5K3EELkJ45WpkxqV57etT2Zsu0iey7GsvzQdX47dZvBjbzoVdsTUyO5nEgIIQCwcHi6Dvq/rZNuYfjLh54VxoGBgZiamlKkSBHGjRvHe++9R/HixenYsSNKpZIzZ85w/vx5Jk2a9NJjjRs3jipVquDv709qaipbtmzB19cXgO7duzN9+nTatm2rm339xo0bbNy4kREjRlCsWDEmTJhA//79cXZ2pmXLljx69IiQkBDdOuhZ5fpPw4cPZ/z48ZQuXZqAgACWLVtGaGholj344ikp0kW+0dWnK119uurFitsUp7tvd9yt3HUxrVbLsvPLiE+Np7JLZV2Rfu7eOfbf3k8112rUcKuBEEK8C7ycrVnauxohEXFM2hpOeFQiU7ZdZNWRG4xs4UPr8m56o5aEEOKdZOcBg04+XQf9ZSwc8sUa6TNnziQoKIjFixfj7u7O9evXad68OVu2bOHrr79m2rRpGBsb4+PjQ9++mS83fZGJiQmjR4/m+vXrmJubU7duXdauXQuAhYUFBw4cYOTIkbRv355Hjx7h7u5O48aNdT3rvXr1IiUlhf/9738MGzYMR0dHOnbs+Mpc/2nIkCEkJCTwxRdfEBsbi5+fH3/++afezO5Cn0Krfc1F/gqoxMREbG1tSUhIeOmwjvxCrVazbds2WrVqlenalXeZWqNmydklXHxwke/qf4epyhSAH878wILQBbxf+n0m13k+qcbcU3MpZl2M5p7NsTTO22uMhChI5BxT+GRotPx26jYzdl4i9tHT3qLKxe0Y09qPKiUy924IkdfkPCNyS3JyMuHh4fj6+uomSRMiv3nV+zQndaj0pIsCx1hpzKcBn2aK+zn40c6rHbXcauliCakJLD63GIAmJZro4gdvHyQiPoJaRWvhY//uzRgphCicVEoFnat68F4FN348EMmi/ZGcuhlPh4WHaF3BjVEtfPCwly+3QgghRH4mRbooNOoVq0e9YvX0YumadHr59SL2SSw2Js9/sdpxfQd/Xv2TgRkDdUV6SnoKi84uwtvem6bFm8rScEKIAsvCxIjPmpSlW/XizNx1iQ0nb7P1bBS7L8TQJ9CTAQ29sDWXXk0hhBAiP5IiXRRqDuYODKs2LFO8qktVUtJTqOxcWRe7Gn+VJeeWUMS0CM1LNNfFt0Vu47H6MYHugXrXxgshRH7nYmPGdx0r0rt2SaZsC+fviDgWHYhk/YlbfNakLB/WKI6x6u0usSOEEEKIV5MiXbyTPijzAR+U+UAvZm5kTqeynTBWGutNsrT64mrO3DvDtLrTdEV6dFI0m69uxt/Rn9pFa7/V3IUQIqf8itqw6uPq7Lt0j8nbwomIfcz4Py+w4vB1Rrf0pYmvs0wuJ4QQQuQTUqQL8f9K2ZViXK1xmeJ13OtgZWKFn4OfLnY69jRzT8+lglMFvSJ9/aX1mBuZU8e9DkXMZJImIUT+oVAoaOjjTN0yjqw9fov/7b5M5L0k+q08Qc1S9nzV2o9y7raGTlMIIYR450mRLsS/6F+xf6aYo7kjrUq2opRtKV1Mq9WyIHQBD1IesKb1Gl2RfvHBRU7GnCTAOQB/B/9MxxJCiLfJSKXkPzVL0DagKN/vu8pPf1/jSOQD2sz/mw8quTO8uTdutuaGTlMIIYR4Z8mFaEK8hmqu1ZhWbxr/rfhfXSxdk06zEs2o7FwZLzsvXXz/rf18e+xbfg77We8YKy+sZM/NPaSkp7y1vIUQ4hlrM2NGtvBhzxf1aRtQFK0WNp66Q8MZ+5i56xKPU9MNnaIQQgjxTpKedCFyibHKmDE1x2SKl7ApQYNiDajmWk0Xe5T2iOknpgPwd9e/MTMyA+B49HHuPL5DFecqeNh4vJ3EhRDvtGJFLJjTtRJ9AksyeWsYx68/ZN6eCNYcu8UXzcrSuaoHKqVcry6EEEK8LdKTLkQea1GyBfMaz6N9mfa6WEp6Cu+Xfp/AooHYmj6/BnRTxCbGhoxlS+QWXSwtI40VF1ZwNOooGq3mreYuhHh3BHjYsf6/tfjhP5Up4WBB3ONURm88R6s5B9l/+Z6h0xNCCCHeGVKkC2EAThZOTK4zmR+a/qAXL1ukLNVdq1PBqYIuFhEfwYwTM/hi/xcoeN6btf/WfnZd30Xck7i3lrcQonBTKBS0KOfG7s/rM/Y9P2zNjbkU84heS4/Rc+kxLkU/MnSKQgghctn169dRKBSEhoYaOpW3bsKECbi4uKBQKNi0aZOh09GRIl2IfKSXfy9+av4Tge6BuphSoaRJ8SbUc6+nt0TST+d/4ov9X3Ak6ogudi/5Hr9e/pWw+2FvNW8hROFiYqTk4zol2T+8AR/XKYmxSsGBy/doOecAozeeJfaRzKUhhCjYDt89TNtNbTl89/Bbebzo6GgGDx5MqVKlMDU1xcPDgzZt2hAcHPxWHr8wya2COjw8nIkTJ7Jo0SKioqJo2bLlmyeXS+SadCHyOR97H/7X8H+Z4v4O/qRlpOFn/3xpuJOxJ5l4eCLlHcuzuvVqXXzHtR1Ym1gT4ByApbHlW8lbCFHw2VmYMPY9P3rULMG32y+y40I0a47d4s/Qu3zaoDQf1ymFuYnK0GkKIUSOaLVa5pyaQ2RCJHNOzaGmW029jpDcdv36dQIDA7Gzs2P69OmUL18etVrNzp07GThwIBcvXsyzxxYvd/XqVQDatm2bp//+r0N60oUooEZWH8na99ZSyu75MnBWxlbULlpbb5I6gG+PfUv/v/pzNf6qLhYZH8m2yG3cSrz11nIWQhRMno6W/NCjCuv/W4uKxWxJSstgxq7LNJq5j42nbqPRaA2dohDiHZasTiZZnYxW+/xcpM5Qk6xOJi0jLVPbvTf3cuH+BQAu3L/AgTsHSFYnk5qRmuVxX5wTSK1R5zi/AQMGoFAoOHbsGB06dKBs2bL4+/sTFBTEkSPPR0TevHmTtm3bYmVlhY2NDZ07dyYmJka3fcKECQQEBLB06VKKFy+OlZUVAwYMICMjg++++w5XV1ecnZ2ZPHmy3uMrFAoWLlxIy5YtMTc3p1SpUvz666+vzPn8+fO0bNkSKysrXFxc6NGjB3Fxzy+xbNCgAYMHD+azzz6jSJEiuLi4sHjxYpKSkujTpw/W1tZ4eXmxffv2HB93yJAhjBgxAnt7e1xdXZkwYYJuu6enJwAffPABCoVCdz8r586do1GjRpibm+Pg4MAnn3zC48ePda9lmzZtAFAqlS8t0jMyMvj4448pWbIk5ubmeHt7M2fOnFe+drlBinQhCpE67nVY1HQRn1f5XBdLzUilmms1StmW0lsaLvhmMCMPjmTBmQV6x/gj4g9CY0NRZ+T8Q0gIUbhVL2nP7wMCmdM1AHc7c6ISUghaf4a2C0I4Ennf0OkJId5RNVbXoMbqGjxMfaiLLbuwjBqrazDl6BS9tvXX1WfovqEoFU/LIKVCyaTDk6ixugbjQsbptW3xWwtqrK5BZHykLvZHxB85yu3Bgwfs2LGDgQMHYmmZeTSjnZ0dABqNhrZt2/LgwQP279/P7t27iYyMpEuXLnrtr169yvbt29mxYwdr1qzhp59+onXr1ty+fZv9+/czbdo0vvrqK44ePaq339ixY+nQoQNnzpyhe/fudO3alfDw8Cxzjo+Pp1GjRlSqVIkTJ06wY8cOYmJi6Ny5s167FStW4OjoyLFjxxg8eDCffvopnTp1onbt2pw6dYpmzZrRo0cPkpOTc3xcS0tLjh49ynfffcfXX3/N7t27ATh+/DgAy5YtIyoqSnf/n5KSkmjevDlFihTh+PHjbNiwgb/++otBgwYBMGzYMJYtWwZAVFQUUVFRWR5Ho9FQrFgxNmzYQFhYGOPGjePLL79k/fr1WbbPLTLcXYhCzlRlyvT60zPFHcwdqOhUkQqOzyepe5z2mK9CvgLgQJcDFFEVAeDcvXM8SHmAv6M/juaObydxIUS+pFQqaBvgTnN/V5aGXOP7vVc5dyeBrj8eoamfC6Nb+lDKycrQaQohRJae9Yq/+P/RydF59ngRERFotVp8fHxe2S44OJhz585x7do1PDyeLsO7cuVK/P39OX78ONWqPR0lqdFoWLp0KdbW1vj5+dGwYUMuXbrEtm3bUCqVeHt7M23aNPbu3UuNGjV0x+/UqRN9+/YF4JtvvmH37t3MmzeP77//PlMu8+fPp1KlSkyZ8vwHjqVLl+Lh4cHly5cpW7YsABUrVuSrr55+bxw9ejTffvstjo6O9OvXD4Bx48axcOFCzp49S82aNbN93AoVKjB+/HgAypQpw/z58wkODqZp06Y4OTkBT3/ccHV1fenruXr1alJSUli5cqXux5H58+fTpk0bpk2bhouLi+4Hklcdx9jYmIkTJ+rulyxZksOHD7N+/fpMPy7kJinShXhHtS/TXm9ZOHi6fnu9YvV48OQBRcyK6OLrL69nU8Qm+lfsz8CAgcDTYWTbr2/Hu4g3ZYuUzXfX8ggh8paZsYoBDbzoXNWD2X9dZs2xW+wOi2HvxVj+U7MEQxuXoYiliaHTFEK8A45++LTX2NzIXBfr49+H//j+ByPl83JHq9VS2q40lx5e0hvCrlQo8S7izcTaz4sxgB0ddgBgZmSmi7X1apuj3F4cgv8q4eHheHh46Ap0AD8/P+zs7AgPD9cV6Z6enlhbW+vauLi4oFKpUCqVerHY2Fi949eqVSvT/ZfN5n7mzBn27t2LlVXmH1yvXr2qV0w/o1KpcHBwoHz58np5ALpcXue4AG5ubpmez78JDw+nYsWKeqMXAgMD0Wg0XLp0SZdbdixYsIClS5dy8+ZNnjx5QlpaGgEBATnKJ6ekSBdC6LhZubGg8YJMcVdLV7zsvPQmqbuacJUxf4/B2sSakK4huvjx6KfDjnztfbEykd40IQo7RytTJrUrT+/ankzZdpE9F2NZfug6v526zeBGXvSq7YmpkUwuJ4TIOxbGFplixipjjFXGerFDdw8R/iDzEG+NVkP4g3BOxpzUW2Eny+MqjTPFXqVMmTIoFIpcmxzO2Fj/8RUKRZYxjUbD63r8+LGux/mf3Nzcsp3Lsw6cZ7m8yXHf5Pm8ibVr1zJs2DBmzpxJrVq1sLa2Zvr06ZkuJ8htck26EOJfDQwYyO9tf6dh8Ya6WFpGGlVcqlDFpYpeL/r80/P5aOdH7L21Vxe7/+Q+wTeCufP4zlvNWwjx9ng5W7O0dzV+6VsDXzcbHqWkM2XbRZrM2s/Ws1HZ7k0SQoi8oNVqmXd6HgqyHvmnQMG80/Ny/Vxlb29P8+bNWbBgAUlJSZm2x8fHA+Dr68utW7e4dev5hL5hYWHEx8fj5+eXab+cenGCumf3fX19s2xbuXJlLly4gKenJ15eXnq3rK6rz67cOq6xsTEZGRmvbOPr68uZM2f0XvOQkBDdJQHZFRISQu3atRkwYACVKlXCy8tLNyt8XpIiXQjxWio4VWB5i+XMazRPL+5u5U5Ry6J42z8/AZ6MOcln+z7ji31f6LX9+87fXLh/IdPMq0KIgivQy5Etg+vwXccKOFubcuvBEwauPkXHHw5z6ubDfz+AEELkAbVGTXRSNFqyLsK1aIlOin6t2dv/zYIFC8jIyKB69er89ttvXLlyhfDwcObOnasbht6kSRPKly9P9+7dOXXqFMeOHaNnz57Ur1+fqlWrvnEOGzZsYOnSpVy+fJnx48dz7Ngx3SRq/zRw4EAePHhAt27dOH78OFevXmXnzp306dPnX4vjV8mt43p6ehIcHEx0dDQPH2b9udK9e3fMzMzo1asX58+fZ+/evQwePJgePXrkaKh7mTJlOHHiBDt37uTy5cuMHTv2pZPV5SYZ7i6EyFVT6k7JFFMqlPja+1LesbxefGzIWOKexLGq5SoCnAMAuPXoFrcf3cbH3kfvunghRMGhUiroXNWD9yq48eOBSBbtj+TkjYe0//4Q71VwY2QLHzzsMw8jFUKIvGKiMmHte2t5kPLgpW3szewxUeX+XBqlSpXi1KlTTJ48mS+++IKoqCicnJyoUqUKCxcuBJ4O6f7jjz8YPHgw9erVQ6lU0qJFC+bNm/cvR8+eiRMnsnbtWgYMGICbmxtr1qx5aQ990aJFCQkJYeTIkTRr1ozU1FRKlChBixYt9K59z6ncOu7MmTMJCgpi8eLFuLu7c/369UxtLCws2LlzJ0OHDqVatWpYWFjQoUMHZs2alaOc//vf/3L69Gm6dOmCQqGgW7duDBgwINPScrlNoX3Hxp8lJiZia2tLQkICNjY2hk7nldRqNdu2baNVq1aZrs0QoqBLSU9hUPAgrsRfYXv77brrvpacW8KcU3NoWbIl39X7Ttd+/639lLApQXGb4rplU8SbkXOMeFtiElOYsfMSv566jVYLJiolfQI9GdDQC1tzee8VZnKeEbklOTmZ8PBwfH19sbCQH/lyQqFQ8Pvvv9OuXTtDp1Lovep9mpM6VL7pCiEMwszIjCXNl7Cv8z69iVlMVaaUsCmBr/3z66SS1EkM2jOINpva8DDl+bCmKw+vcObeGZLVyW81dyFEzrjYmDG9U0W2DK5DoJcDaRkaFh2IpMH0vaw4dB11hmEmBBJCCCHyIynShRAG9c+l23r49WDLB1vo7d9bF3uY8pAKjhUobl0cB3MHXfzn8J/5z7b/sPT8Ul1MnaHm4O2DxCbHykRVQuQz/kVt+fnjGiztXRUvZyseJqsZ/+cFms8+wO6wGPlvVgghhECuSRdC5FMvFu/FrIvxS+tfMn2BNzcyx9HcER97H10sMiGSAcEDsDa2JqTb86XhLty/gKnSFE9bT701U4UQb5dCoaCRjwv1yjix5vgtZu++TOS9JPqtPEHNUvZ81dqPcu62hk5TCCEKDfkBtOCRnnQhRIHxz173UdVHsbfzXhoVb6SLPUp7RCnbUvg5+Om1n3F8Bh/8+QFbI7fqYgmpCZyMOcmjtEd5n7wQQo+RSkmPmiXYO7wBnzYojYmRkiORD2gz/2+C1ocSlfDE0CkKIYQQBiFFuhCiwHtxIrmqrlX5o90f/NjsR702FsYWWBhZ6PW6H4s+Ru8dvem7q69e2zP3zhD1WNZ1FuJtsDEzZmQLH/Z8UZ+2AUXRamHjqTs0nLGPmbsukZSabugUhRBCiLdKinQhRKH0zxngFzRewOEPD1OmSBldLCU9BVdLV7yLeOu1DdobRLPfmnHm3hldLCYphosPLqLOyP31U4UQUKyIBXO6VmLTwECqlihCilrDvD0R1J++j7XHbpKhkR/NhBBCvBukSBdCvDOUCqVe8d6mdBt2d9zNuFrjdLEn6U8oYlYEY6WxXkG/JXILnTZ3YszfY/SOGRobSnxKfJ7nLsS7IsDDjg39a7Gwe2VKOFgQ9ziVURvP0XruQQ5cvmfo9IQQQog8J7MnCSHeeS9OJGduZM6v7/+KWqPGWPl8TV+1Ro21iTVl7cvqYsnqZHpu74kWLfu77MfezB6AW49uodVqKWZdTNZ0F+I1KBQKWpZ3o7GvCysPX2fengguRj+i59Jj1C/rxJjWvpR1sTZ0mkIIIUSekG+PQgiRhRcLdID+FfsT0jWEXn69dLF7T+7hbuWOk7mTrkAH+OncT7T+vTULzyzUxdQaNefjzpOSnpL3yQtRSJgYKelbtxT7hzfgo8CSGKsU7L98jxazDzB64znuPUo1dIpCCCFErpMiXQghskmhUGCsel68l7ApwfYO29neYbteO7VGjYnShDJ2z4fLX0u4Rret3Wi8obHehHTXEq4R9yQu75MXogCzszBhXBs/dn9enxb+rmi0sObYTRpM38v8PVdIUWcYOkUhhHhjCoWCTZs26e5fvHiRmjVrYmZmRkBAwEtjovCRIl0IId6QqcpU7/7kOpM52v0oDT0a6mJxT+KwN7PHy85Lb2m4SUcm0XB9Q7ZEbtHFHqc9JjIhkgyNFB5CvMjT0ZIfelRh/X9rUbGYLUlpGczYdZmGM/ax8dRtNDK5nBAim5IOHeJq6/dIOnQozx+rd+/eKBSKpz/2Gxvj4uJC06ZNWbp0KRqNRtcuKiqKli1b6u6PHz8eS0tLLl26RHBw8EtjovCRIl0IIfKAkdJIr9e9dtHa7Ou8j4VNFuq1S8lIQYECLzsvXexo1FHabmpLzx099dpGJkSSrE7O28SFKACql7Tn9wGBzOkagLudOVEJKQStP0PbBSEcibxv6PSEEPmcVqsldtb/SLt6ldhZ/3srS662aNGCqKgorl+/zvbt22nYsCFDhw7lvffeIz396VKTrq6umJo+/+H/6tWr1KlThxIlSuDg4PDSWE6lpaW9+RMSeUqKdCGEeEsUCgUWxhZ6sV9a/cKRD4/oDY1/kPoAcyNzStqU1Gv7ya5PqLm6JufunXveNuUB0UnRsqa7eOcolQraBrgT/EV9RrTwxsrUiHN3Euj64xE+WXmCyHuPDZ2iEOIt0SQno0lO1vss1KalPY3/oyDVJCfzODiYlPPnAUg5f57H+/c/bZuamvVxX+jt1qpfbylWU1NTXF1dcXd3p3Llynz55Zf88ccfbN++neXLlwP6w90VCgUnT57k66+/RqFQMGHChCxjALdu3aJz587Y2dlhb29P27ZtuX79uu6xe/fuTbt27Zg8eTJFixbF29s7R/vNmDEDNzc3HBwcGDhwIOoXXoPU1FRGjhyJh4cHpqameHl58dNPP+m2nz9/npYtW2JlZYWLiws9evQgLk4u8/s3+aJIX7BgAZ6enpiZmVGjRg2OHTv20rYNGjTQDRd58da6deu3mLEQQuQeC2MLVEqV7n6nsp043O0wo6qP0sWS1clo//9/JW2fF+9/RPxB01+b8lXIV3rHvJZwDbXm1V8kjkYfZU7iHI5GH82lZyLE22dmrGJAAy/2DW/Af2oWR6VUsCsshmb/O8CEPy/wMEl6jIQo7C5VrsKlylXIePhQF7u/dCmXKlch5ptv9NvWDuT2oMGg/P8ySKkkeuJELlWuQtQY/c/SiMZNuFS5CmlXr+pi8b//nmt5N2rUiIoVK7Jx48ZM26KiovD39+eLL74gKiqKYcOGZRlTq9U0b94ca2trDh48SEhICFZWVrRo0UKvxzw4OJhLly6xe/dutmzZku399u7dy9WrV9m7dy8rVqxg+fLluh8VAHr27MmaNWuYO3cu4eHhLFq0CCsrq6evVXw8jRo1olKlSpw4cYIdO3YQExND586dc+01LKwMvgTbunXrCAoK4ocffqBGjRrMnj2b5s2bc+nSJZydnTO137hxo94b5/79+1SsWJFOnTq9zbSFECJPqZQqrEysdPctjC0I7hRMfEq8XvxhykNUChWeNp66WLI6mbab2mKkNGJPpz3YmdkBcC/5HiYqE2xNbdFqtcwLncc9zT3mhc4jsFig3rXyQhQ0jlamTGpXnl61PJm6/SJ7Lsay/NB1Np66zeBGZehZuwSmRqp/P5AQonB71iv+wv+nR0UbLB0fHx/Onj2bKe7q6oqRkRFWVla4uroCYGVllSn2888/o9FoWLJkie5zfNmyZdjZ2bFv3z6aNWsGgKWlJUuWLMHExCRH+xUpUoT58+ejUqnw8fGhdevWBAcH069fPy5fvsz69evZvXs3TZo0AaBUqVK65zB//nwqVarElClTdLGlS5fi4eHB5cuXKVv2+bK2Qp/Bi/RZs2bRr18/+vTpA8APP/zA1q1bWbp0KaNGjcrU3t7eXu/+2rVrsbCwkCJdCPFOeFZwPxNUNYiBlQaiznjeax6VFIWlsSWmKlO99t+f+Z5fL//K0MpD8bX3JexBGABhD8I4dPcQge6Bb+MpCJGnyrhYs7R3Nf6+EsekrWFcjH7E5G3hrDxynVEtfGlV3lV+kBKikPE+dRIAhbm5Lubw0UfY9+wJRs/LHa1Wi6mXF6kXLz4v0gGUSkx9fHCdpN/r7hX819PjmpnpYnYffJCruWu12jc6J505c4aIiAisra314ikpKVx9YQRA+fLldQV6Tvbz9/dHpXr+A6ebmxvnzj297C40NBSVSkX9+vVfmtvevXt1Pesvunr1qhTpr2DQIj0tLY2TJ08yevRoXUypVNKkSRMOHz6crWP89NNPdO3aFUtLyyy3p6amkvrC9SWJiYkAqNVqvesp8qNn+eX3PIUQhqVEianCVHeuKG5ZnP0d9/Mg5YHe+SMu+ek1YK5mrsw9NRelQolG+/RLypSjU/j9vd+leBGFRg1PWzZ9WpONp+8yOziCWw+eMHD1KSoXt2NUi7JU8rAzdIrvBPkuI3LLq95DSguLTDGFiQmKF4pSgKS/Q0gNC8t8AI2G1LAwnhw/gVXdOq8+rrFxptibCA8Pp2TJkv/e8CUeP35MlSpV+OWXXzJtc3Jy0v39z1opu/sZ/+P5KhQK3Yz05i/8KPKy3Nq0acO0adMybXNzc3vlvgVdVrVmTs6DBi3S4+LiyMjIwMXFRS/u4uLCxYsX/3X/Y8eOcf78eb3JCf5p6tSpTJw4MVN8165dWGTxH15+tHv3bkOnIIQoBJrQhECbQM6FniMsWf9Lys1HN5nzxxzKmsiv2qJwsQSG+cKeu0r23FVw6mY8nX88RiUHDW2Ka3Aw+9dDiFwg32XEm3q2dNnr0mq13JszBxQKyGqyVYWCe3PmYFnn7V3+tWfPHs6dO8fnn3/+2seoXLky69atw9nZGRsbmzzf70Xly5dHo9Gwf/9+3XD3fz7Gb7/9hqenJ0ZGBh/A/VaFhIRkKsqTk7O/Qk+BfrV++uknypcvT/Xq1V/aZvTo0QQFBenuJyYm4uHhQbNmzV77Dfm2qNVqdu/eTdOmTTP9iiWEEK9Dq9XSY2cPlE+e96IDKFBwwuwEQ5sPRaPVEJ8aj4P56y3tIkR+9AEQnZjC7OAINp6+y+n7Ss7Hq+hVqwSf1iuJjbl8zuYF+S4jcktycjIRERGvvb9WrUYdFZV1gQ6g1aKOjkarVmfqgc8NqampREdHk5GRQUxMDDt27GDq1Km899579OzZ898P8BLdu3dn+vTptG3blq+//ppixYpx48YNNm7cyIgRIyhWrFiu7vciT09PevXqxUcffcTcuXOpWLEiN27cIDY2ls6dOzNw4EAWL15Mt27dGDFiBPb29kRERLB27VqWLFmiN4y+sAkMDMzUIfxsRHd2GLRId3R0RKVSERMToxePiYnRTYbwMklJSaxdu5avv/76le1MTU311ht8xtjYuMB8WBSkXIUQ+VvInRDdtegv0qIl7EEYx+8dJyY5hm+PfcvgSoPp4dfDAFkKkTc8HIyZ2bkSH9UpxZRt4YRE3GfJ39f57dQdPmtSlg9rFMdYlS8Wvil05LuMeFNv+v5RmphQ8tcNpD948NI2Rg4OKPOgQAfYsWMHbm5uGBkZUaRIESpWrMjcuXPp1asXSuXrn3csLCw4cOAAI0eOpH379jx69Ah3d3caN278yg7J193vnxYuXMiXX37JgAEDuH//PsWLF+fLL78EoGjRooSEhDBy5EiaNWtGamoqJUqUoEWLFm/0nAuCrM55OXkPK7QGXly3Ro0aVK9enXnz5gGg0WgoXrw4gwYNynLiuGeWL19O//79uXPnDg4O2e/tSUxMxNbWloSEhALRk75t2zZatWolH2xCiDem1WrptrUbYffD0JL51K9AgZ+DH87mzuy9vZdhVYfRy7+XATIVIu9ptVr2Xopl8tZwrt5LAqCUkyWjW/rSxNdZ5mfIJfJdRuSW5ORkwsPD8fX1LTCXrIp3z6vepzmpQw0+3D0oKIhevXpRtWpVqlevzuzZs0lKStLN9t6zZ0/c3d2ZOnWq3n4//fQT7dq1y1GBLoQQ7zK1Rk10UnSWBTo87U2PTopmRYsVHLxzkHrF6um2XXl4hYcpD6nu9vLLi4QoSBQKBY18XKhbxom1x27yv7+uEHkviX4rT1CrlANjWvtSzt3W0GkKIYR4Bxm8SO/SpQv37t1j3LhxREdHExAQwI4dO3QTQ9y8eTPTcIhLly7x999/s2vXLkOkLIQQBZKJyoS1763lQcrToX7p6emE/B1CYJ1A3YQu9mb2mBqZ0qTE8wlgtFot045N42j0UeldF4WOsUpJj1qetK3kzvd7r7I05BqHI+/TZv7ftK9UjOHNvXG1ldnlhBBCvD0GL9IBBg0axKBBg7Lctm/fvkwxb29vDDxKXwghCiRXS1dcLZ/O+aFWq7lmdA1fe99XDkNN16RT0rYkZ+PO6hXvQhQmNmbGjGrpQ/caxZm+8xJ/nrnLb6dus/XcXT6pW4r/1i+NpWm++NokhBCikCvcV+wLIYR4Y8YqY8bUHMPujrtxt3LXxRefXcyy88tIzUg1YHZC5C4PewvmdqvE7wNqU7VEEVLUGubuiaDBjH2sPXaTDI10EgghhMhbUqQLIYTIFlvT59fnRidF88OZH5h1chbHoo4ZMCsh8kal4kXY0L8WC7tXpoSDBfcepTJq4zlazz3Igcv3DJ2eEO8sjUbz742EMJDcen/KuC0hhBA55mzhzLha4zgSdYQ67nV08cdpj7EysTJgZkLkHoVCQcvybjT2dWHl4evM2xPBxehH9Fx6jPplnRjT2peyLtaGTlOId4KZmRlKpZJr167h7u6OqamprMIg8hWNRkNUVBQKhSLLJcBzQop0IYQQOaZUKGnr1Za2Xm11MXWGmi5buuBj78PoGqNxNHc0YIZC5B4TIyV965aiY5VizA2OYNWR6+y/fI+DV+7RtXpxPm9SFifrN/tCJoR4NaVSiZ+fH9evX+fatWuGTkeILCkUCry8vFCpVG90HCnShRBC5Irj0ce59egWyenJWBjJGrai8LGzMGFcGz961irBt9svsuNCNKuP3uSP03cY0NCLj+uUxMz4zb6YCSFeztTUlLJly6JWq0lPTzd0OiKfUKvVhISEEBgY+MqJcN8GU1PTNy7QQYp0IYQQuaS2e202tNnAvSf3sDB+XqTvv7Wf2u61MVYa9oNTiNzi6WjJDz2qcOzaAyZvDePM7QSm77zEz0duMLy5N+0C3FEqZRiuEHlBoVBgYmKCiYmJoVMR+YRarUatVmNhYWHwIj23yMRxQgghco23vbfeNeonY04yaM8gOv7ZUWaBF4VO9ZL2/D4gkNldAihqa0ZUQgpB68/QdkEIRyLvGzo9IYQQBZQU6UIIIfJMQmoC9mb2VHapjKlKrtkVhY9SqaBdJXf2DGvA8ObeWJkace5OAl1/PMInK08Qee+xoVMUQghRwEiRLoQQIs80Kt6IrR9s5bPKn+li95/cZ8KhCUQnRRsuMSFymZmxioENvdg3vAHdaxRHqYBdYTE0+98BJvx5gYdJaYZOUQghRAEhRboQQog8ZWVipbfG+sIzC/ntym+MPDDSgFkJkTccrUyZ/EF5dn5Wj4beTqRrtCw/dJ360/ey+EAkqekZhk5RCCFEPidFuhBCiLeqnVc7KjtXZlClQbqYRqshXSMz9YrCo4yLNcv6VOfnj2vg42pNYko6k7eF03TWAbaejUKr1Ro6RSGEEPmUFOlCCCHeqnKO5VjeYjnVXKvpYn9E/EGnzZ04fPewATMTIvfVKePI1iF1+a5DBZytTbn5IJmBq0/R8YfDnLr50NDpCSGEyIekSBdCCPHWKRTPl6fSarWsCl9FRHwElx9eNmBWQuQNlVJB52oe7B3WgCGNy2BmrOTkjYe0//4Qg1af4taDZEOnKIQQIh+RIl0IIYRBKRQKljVfxoCAAXTz6aaL33p0i7gncQbMTIjcZWlqRFDTsuwb1pCOVYqhUMCWs1E0nrWfqdvDSUxRGzpFIYQQ+YAU6UIIIQzO1tSWTyt+ionKBHjauz7x8ERabWxF8I1gA2cnRO5ytTVjRqeKbBlch9qlHUhL17BofyQNpu9j5eHrqDM0hk5RCCGEAUmRLoQQIt95rH7ME/UT0jXpeNt7GzodIfKEf1Fbfulbg6W9q1LayZIHSWmM++MCzWcf4K+wGJlcTggh3lFSpAshhMh3rE2s+bnVz6xpvYZi1sV08fWX1nM8+rgBMxMidykUChr5uLDjs3p809Yfe0sTIu8l0XflCT5cfJTzdxIMnaIQQoi3TIp0IYQQ+ZJCodDrRb/7+C7Tjk3jo50fERobarjEhMgDxiolPWp5sm94A/rXL42JkZLDkfdpM/9vvlh/huiEFEOnKIQQ4i2RIl0IIUSBYGFkQfsy7anpVpOKThV1cRkSLAoTGzNjRrX0ITioPu9XLIpWC7+duk2DGXuZtesSSanphk5RCCFEHpMiXQghRIFgZ2bHmJpj+KHJD7ol3NQZarpt7cay88tIy0gzcIZC5B4PewvmdqvE7wNqU7VEEVLUGubuiaDBjH2sPXaTDI38OCWEEIWVFOlCCCEKFJVSpft727VtXLh/gRUXVqDWyPJVovCpVLwIG/rXYmH3ypRwsODeo1RGbTxH67kHOXD5nqHTE0IIkQeMDJ2AEEII8breK/UeWrSYqkyxNLbUxa8nXMfT1tNwiQmRixQKBS3Lu9HI15lVh28wN/gKF6Mf0XPpMeqXdWJMa1/KulgbOk0hhBC5RHrShRBCFFgqpYp2Xu1oWbKlLnY69jRtNrVhxIERaLSy3rQoPEyNVPStW4r9wxvSJ9ATI6WC/Zfv0WL2Ab78/Rz3HqUaOkUhhBC5QIp0IYQQhUpobCgKFFgYWaBUyMecKHyKWJowvo0/u4Pq09zfBY0WVh+9SYPpe1mwN4IUdYahUxRCCPEG5NuLEEKIQqVPuT5saLOBQZUG6WIPUh6wOny1XLcuCpWSjpYs6lGVdZ/UpEIxW5LSMpi+8xKNZuzj99O30cjkckIIUSC9cZGekZFBaGgoDx8+zI18hBBCiDfmbe+No7mj7v73od8z9dhURh0YZcCshMgbNUo5sGlAILO7BFDU1oy7CSl8vu4M7b4P4WjkfUOnJ4QQIodyXKR/9tln/PTTT8DTAr1+/fpUrlwZDw8P9u3bl9v5CSGEEG/M194XezN7uvp0NXQqQuQJpVJBu0ru7BnWgOHNvbEyNeLs7QS6/HiET1ae4FpckqFTFEIIkU05LtJ//fVXKlasCMDmzZu5du0aFy9e5PPPP2fMmDG5nqAQQgjxpjqU7cDODjup5lpNF/vz6p98efBLopOiDZiZELnLzFjFwIZe7B3WgO41iqNUwK6wGJrO2s+EPy/wMCnN0CkKIYT4Fzku0uPi4nB1dQVg27ZtdOrUibJly/LRRx9x7ty5XE9QCCGEyA1mRma6v9UaNXNPzWVz5Ga2X9tuwKyEyBtO1qZM/qA8Oz6rRwNvJ9I1WpYfuk796XtZcjCS1HSZXE4IIfKrHBfpLi4uhIWFkZGRwY4dO2jatCkAycnJqFSqXE9QCCGEyG3GSmPmNJxDq5Kt+ND3Q138QcoD0jXpBsxMiNxV1sWa5X2qs+rj6vi4WpOYks6kreE0nXWAbeei0GplcjkhhMhvclyk9+nTh86dO1OuXDkUCgVNmjQB4OjRo/j4+OR6gkIIIURe8Hf0Z1q9aZiqTAHQarWMOjCKTps7ceH+BQNnJ0TuqlvGia1D6vJdhwo4W5ty80EyA345RacfDnP6pkz+K4QQ+YlRTneYMGEC5cqV49atW3Tq1AlT06dfblQqFaNGyay5QgghCqaY5Bgu3L/Ak/Qn2JrYGjodIXKdSqmgczUPWldwY9GBSH48cJUTNx7ywfeHaFOxKCOae+Nhb2HoNIUQ4p2X4yIdoGPHjgCkpKToYr169cqdjIQQQggDcLV0ZVv7bZyOPU0x62K6+J6be6jgVEFvSTchCjJLUyOCmpblw+rFmbHrEr+dus3mM3fZeSGaPoGeDGzohY2ZsaHTFEKId1aOh7tnZGTwzTff4O7ujpWVFZGRkQCMHTtWtzSbEEIIURDZmtrSwKOB7n7U4yhGHBhB642tuZl403CJCZEHXG3NmNGpIlsG16F2aQfS0jUs2h9Jg+n7WHn4OuoMjaFTFEKId1KOi/TJkyezfPlyvvvuO0xMTHTxcuXKsWTJklxNTgghhDCkJHUS3kW88XPww8Paw9DpCJEn/Iva8kvfGvzUqyqlnSx5kJTGuD8u0Hz2Af4Ki5HJ5YQQ4i3LcZG+cuVKfvzxR7p37643m3vFihW5ePFiriYnhBBCGJJXES9+bvUzsxvORqFQAKDOUBO0L4jj0ccNnJ0QuUehUNDY14Udn9Xjm7b+2FuaEHkvib4rT/Dh4qOcv5Ng6BSFEOKdkeMi/c6dO3h5eWWKazQa1Gp1riQlhBBC5BcKhQJb0+cTya2/vJ7dN3YzbP8wUtJTXrGnEAWPsUpJj1qe7BvegP71S2NipORw5H3azP+bL9afITpB3vNCCJHXclyk+/n5cfDgwUzxX3/9lUqVKuVKUkIIIUR+1bJkS7p4d2Fo5aGYGZnp4snqZANmJUTusjEzZlRLH4KD6tOmYlG0Wvjt1G0azNjLrF2XSEpNN3SKQghRaOV4dvdx48bRq1cv7ty5g0ajYePGjVy6dImVK1eyZcuWvMhRCCGEyDfszez5quZXerHQ2FAGBA+gf4X+9PTvaaDMhMh9HvYWzOtWiY8CPZm0NZyTNx4yd08Ea47fYlizsnSs4oFKqTB0mkIIUajkuCe9bdu2bN68mb/++gtLS0vGjRtHeHg4mzdvpmnTpnmRoxBCCJGvbbyykUdpj4hMiDR0KkLkiUrFi/Br/1p8370yxe0tuPcolZG/naP13IMcvHLP0OkJIUSh8lrrpNetW5fdu3fndi5CCCFEgTS+1ngqu1SmjnsdXexhykNuJN4gwDnAcIkJkYsUCgWtyrvR2NeZVYdvMDf4ChejH9Hjp2M08Hbiy1a+lHWxJvJCPPein17+kZGewZ2LSo6bRaMyejrhsJOrBaX87Qz4TIQQBV1hP8+8VpEeHx/Pr7/+SmRkJMOGDcPe3p5Tp07h4uKCu7t7jo61YMECpk+fTnR0NBUrVmTevHlUr179lY89ZswYNm7cyIMHDyhRogSzZ8+mVatWr/NUhBBCiDemUqpo59VOL7bwzELWXFzDfyv8l0GVBhkmMSHygKmRir51S9GhcjHm7rnCqsM32HfpHgcu36OVlws7wmNI//9vmAGxl+l/dhPfXWpHqHNZAIzTYWefwAL9BVoIYTiRF+JpviwEtV4lq4S9Z3X3Cvp5JsfD3c+ePUvZsmWZNm0a06dPJz4+HoCNGzcyevToHB1r3bp1BAUFMX78eE6dOkXFihVp3rw5sbGxWbZPS0ujadOmXL9+nV9//ZVLly6xePHiHP8wIIQQQuQlrVZLhiYDBQqqu778h2chCrIiliaMb+PP7qD6NPd3QaOFLVeeF+hotfQO206Jx7H0DtsO/7/eutoIXQ+YEELk1L3o5H8U6JkV9PNMjov0oKAgevfuzZUrVzAzez6rbatWrThw4ECOjjVr1iz69etHnz598PPz44cffsDCwoKlS5dm2X7p0qU8ePCATZs2ERgYiKenJ/Xr16dixYo5fRpCCCFEnlEoFIytNZat7bdS3e15kb7z+k5+Cf8FtUaWLBWFR0lHSxb1qMq6T2pSytpcF68cexnv+FsAeMffonLsZUOlKIQQBUqOh7sfP36cRYsWZYq7u7sTHR2d7eOkpaVx8uRJvd53pVJJkyZNOHz4cJb7/Pnnn9SqVYuBAwfyxx9/4OTkxIcffsjIkSNRqVRZ7pOamkpqaqrufmJiIgBqtTrfr+v+LL/8nqcQomCSc0zeczVz1b2+T9Kf8N2x74h9EotCq6BTmU4Gzk6I3FXZw4avq3rxn73nQKulZ/gOMlCgQosGBUNCNzAi8FNirRwYdigci7NXDJ2yEKIASn6cvSUgM9Iz8tV3nJzkkuMi3dTUVFfovujy5cs4OTll+zhxcXFkZGTg4uKiF3dxceHixYtZ7hMZGcmePXvo3r0727ZtIyIiggEDBqBWqxk/fnyW+0ydOpWJEydmiu/atQsLC4ts52tIMkmfECIvyTnm7cjQZlCTmpxSncLksgnbrmzTxVWKrH9oFqKguXNRCSj1etEBlGhxeRJPyxtHWeHfiltJKTjfe0DDW6e5XMSD0/9/vboQQuSW0NBQ7qVoDJ2GTnJy9off57hIf//99/n6669Zv3498HRI382bNxk5ciQdOnTI6eFyRKPR4OzszI8//ohKpaJKlSrcuXOH6dOnv7RIHz16NEFBQbr7iYmJeHh40KxZM2xsbPI03zelVqvZvXs3TZs2xdjY2NDpCCEKGTnHvH1taINWq0WheL6u9KC9gyhiWoQhAUNwssj+j91C5EfHzaIx3n2CkSd+0fWiP6MFqkeHscKvJaMrlaJiwkNsd20nzcefhyO66dqZ7/wTrak5qZWro7WxNcCzEELkZ5dCHzA19Nq/tgsICKBaI9e3kFH2ZNXR/TI5LtJnzpxJx44dcXZ25smTJ9SvX5/o6Ghq1arF5MmTs30cR0dHVCoVMTExevGYmBhcXbN+Md3c3DA2NtYb2u7r60t0dDRpaWmYmJhk2sfU1BRTU9NMcWNj4wLzpbQg5SqEKHjkHGM4lx5c4lDUIYyURgysNFD+HUSBpzJSMezUWmzUmXuMFECpR9FUjr1MgFNlypUqS/x772Hq5UVFn6ff/bRaLVf6LCfj4UM8f/0V8/+Pp4SFkXziJOaVK2Nezv9tPiUhRD5jfid7veMqI1W++lzNSS45LtJtbW3ZvXs3ISEhnDlzhsePH1O5cmWaNGmSo+OYmJhQpUoVgoODadeuHfC0pzw4OJhBg7JeqiYwMJDVq1ej0WhQKp/OeXf58mXc3NyyLNCFEEKI/Mzb3ps1rdcQdj8MDxsPXfzC/Qv4FPFBpZRh8KKA0WpxS7qPlqdF+T9pUNAzfAdou2BZvTqW/1h2V6tWY9OqFSmXLmJaxksXf7R3L3Hz5mPbti3m077Vxe8vXYZJieJYBgaifGFCYyGEKMhyVKSr1WrMzc0JDQ0lMDCQwMDAN3rwoKAgevXqRdWqValevTqzZ88mKSmJPn36ANCzZ0/c3d2ZOnUqAJ9++inz589n6NChDB48mCtXrjBlyhSGDBnyRnkIIYQQhlLOsRzlHMvp7kcnRdNrey88rD34qflP2JvZGzA7IbJHk5aG0sQER0djMp4kZFmgw9Nr052fxOPomHWPktLEBNexX2WKm3h6YtWoERYvFPUZCQnEfvcdAGWPHYX/L9KTjhxFfecOFtWqYlK8+Js9MSFEvuPkaoFxOq9chs04/Wm7gipHRbqxsTHFixcnIyMjVx68S5cu3Lt3j3HjxhEdHU1AQAA7duzQTSZ38+ZNXY85gIeHBzt37uTzzz+nQoUKuLu7M3ToUEaOHJkr+QghhBCGFhkfianKFFtTW4qYFjF0OkL8q7QbN7jZtx/OQZ9TumVLmPczDyKjANBkaLh06RLe3t4oVU+/07mULkrpAOccPYZt69bYtm6tF9OkpGLbrh3pDx+gemGeoYTfN5Lwx584DhmM04AB/982hYdr1mLm441FjRoolDlehVgIkU+U8rdjZ59A3TroGekZhIaGEhAQgMro6Qg0J1cLSvnbGTDLN6PQarXaf2/23E8//cTGjRtZtWoV9vYF79f9xMREbG1tSUhIKBATx23bto1WrVrlq+sphBCFg5xj8q+E1AQS0xLxsH46BF6tUbMwdCEf+n6Io7mjgbMTQl/szJncX7wEE6/SlNq0CYXR8z4gQ5xn7i9bzuN9+3Do1w+rOk9HfT45d57rnTqhKlKEModCdJM3PtqzB21aGhZVq2LkKP9tCVEQFZTvMzmpQ3N8Tfr8+fOJiIigaNGilChRAktLS73tp06dyukhhRBCCPECW1NbbE2fz2q9/tJ6Fp9bzI7rO9jcbrNcqy7yFafPPgOVCvv//EevQDcUhz69cejTWy+mMFJh3awZSktLvdUV7v+4mCehoRSdMQPb95721KtjYni8bz9m/v4ySZ0QwiByfCZ9NsmbEEIIId4Ofwd/yjuWp51XO70C/Z/LuQnxtqRevYpJqVIoFAoUKhXOn31m6JReyczXl2Jz52SOVyiPNj0dMz9fXSz5xAmix4/HvGJFPNet1cUTtmxFZWuDeaXKqKwsMx1LCCFyS46L9JetRy6EEEKIvBHgHMDPrX7mxSvUQmNDmXpsKsOqDqOaazUDZifeNYk7dnBn+Agc+/XDachgQ6fzRly//DJTTGVtjWVgIGb+z3vRtVotMVOmkPHgAZ4b1mNevjwAqRERpFy8hHmF8jJJnRAi18isGUIIIUQBoFQo9XrRF55ZSNj9MDZf3WzArMS7KP3BA1CrSb0WiVaTvfWKCxKrevUo/tMSnIM+18W0qalY1qyJiVdpTMuU0cUf7d7N3WHDuDd/vt4x4jf+TvKpU2jT0t5a3kKIwiPHPelFihTJcmidQqHAzMwMLy8vevfurVtGTQghhBC5b2rdqfx49kc+KveRLpaQmoBGq6GImcwKL/KO/YcfYuLujmWdOu/MLOlKMzPcZ83MFFc5OGAeEIB5QIAulpGYSNT/99CXPXIYlYkJAE/OnCH9wQPMy5eXSeqEEK+U4zPruHHjUCqVtG7dmokTJzJx4kRat26NUqlk4MCBlC1blk8//ZTFixfnRb5CCCGEAOzN7BlVfRTOFs+Xsvo+9Htab2zNlsgtBsxMFDZarZb4335D80KvsFX9+ihUMoFhkc6d8Vy7BvsPP9TFNI8eYdWgAeYVK6Kys9PFH65Zy+1PB/Bw7brnbdPSiN+0iZSLF8nhgktCiEIsxz3pf//9N5MmTaJ///568UWLFrFr1y5+++03KlSowNy5c+nXr1+uJSqEEEKIl0vXpHPm3hkeqR/JMm0iV8VO+44Hy5fz+MBB3Gf/TyYr/BfG7u54/LAwc7yoG6Zly2Lm76eLpV65QtSo0ahsbSlz5LAunnTkKCgUmPn7obKyeit5CyHyjxz3pO/cuZMmTZpkijdu3JidO3cC0KpVKyIjI988OyGEEEJki5HSiF9a/cIPTX6gpltNXTzkTgihsaGGS0wUeFb16qIwM8MysLYU6G/AacgQSv35B9YNGz4PpqdjUa0aFtWr6b229+bO5WavXjzeu+9507g4EnftIu327beYtRDCEHJcpNvb27N5c+ZJajZv3oy9vT0ASUlJWFtbv3l2QgghhMg2lVJFoHug7v6T9CeMPzSeHtt7EHwj2ICZiYLMsnZtSu/aSZHOnQ2dSqFjXrEiJVatpNi8eXpx42LuGLu7Y+bjrYslHz/OnSFDuRP0hV7bxwcO8OTceb3LEYQQBVuOh7uPHTuWTz/9lL1791K9enUAjh8/zrZt2/jhhx8A2L17N/Xr18/dTIUQQgiRI2kZadQuWptj0ceoU6yOodMRBURGYiIxU6biPHwYRg4OABg7O//LXiI3uX/3XeagUoWZnx/mFSrohe9+OYaMuDg816/TbUu7eZO0W7cw8/XF6P870YQQBUeOi/R+/frh5+fH/Pnz2bhxIwDe3t7s37+f2rVrA/DFF1+86hBCCCGEeAtsTW35OvBrktXJmKpMdfHRB0dTzrEcnb07Y6w0NmCGIj+6O/pLHgcHo46KovjyZTLEPZ+wad4Mm+bN9GKaJ08wLeNFKugtDZe4bRv3Zs/Bpk0b3Kc/L/gf7dmDiWdJTDxLvDMz8wtREOW4SAcIDAwkMDDw3xsKIYQQwuAsjC10fx+PPs6WyC3suL6Deu718LDxMGBmIj9yGT4M9e3buIweJQV6Pqc0N6fEsmWZ4gozM0w8PTHz9dXFMh4/5vaAgQCUOXwIoyJPl2pMuXQZTXISZmXLorS0fDuJCyFe6bV+Qrt69SpfffUVH374IbGxsQBs376dCxcu5GpyQgghhMhdlZwrMbbmWPpX6K9XoD9IeWDArIShadPTdX+beHpS8veNmPn4GDAj8SYcevem9I7t2PfprYtlPHiAWcUKmHh66gp0gAcrVnCj24fcX/q82NempfF4/37UMTGyNJwQBpDjIn3//v2UL1+eo0eP8ttvv/H48WMAzpw5w/jx43M9QSGEEELkHiOlEZ29O/Pfiv/VxaKTomnxWwvG/D2GJ+lPDJidMIQn585ztXVrUi5d0sVkKHTh8OJICJPixSm5bh2ltm/Ta6O0sMDIyQnTFyapS42I4NZ/+xPZ5n29tk/OnSPl8mW0anXeJi7EOy7HZ+BRo0YxadIkdu/ejYmJiS7eqFEjjhw5kqvJCSGEECLvHbxzkCfpT7j96DZmKjNDpyPesnvz56G+cZN7s/5n6FTEW/DPSxhcvxpDmYMHsH5hiWVNUhImXqUx8/fTax8zbRrX3m9L4rbnhX76w4ckHz9OxqNHeZ+8EO+IHF+Tfu7cOVavXp0p7uzsTFxcXK4kJYQQQoi3p1PZTvja+2KsNNZ9IVdr1Oy8vpOWni1RKVUGzlDkJffp07k3ezZOQUGGTkUY0IvFuEW1apTesgWtRqPXRmlpidLSEtMXrnVPPnqUO599jlm5cpT8dcPz+OnTGDs7Y1S0qMxtIEQO5bgn3c7OjqioqEzx06dP4+7unitJCSGEEOLtKudYDm/758NdN1zawOiDo/nv7v++Yi9RUKXdvqP7W2Vjg+u4caisrAyYkciP/nnZQ/FFiyh7/JjeTPLa1FSMirph5qs/h8GdIUOJaNyElDNndDF1dDQpYWGyprsQ/yLHRXrXrl0ZOXIk0dHRKBQKNBoNISEhDBs2jJ49e+ZFjkIIIYR4y8yMzLAxsaFpiaaGTkXksge//MLVFi149Ndfhk5FFEAKpVKvZ9y2bVvK7NmD6wtzU2mSk1E5OqIwNcW0bFldPOGPP7nWvgNRX47RO2byqVOkP3yY98kLUUDkuEifMmUKPj4+eHh48PjxY/z8/KhXrx61a9fmq6++yoschRBCCPGWtS/Tnm3tt9GhbAdd7My9M0w8PJG4J3J5W0Gl1WpJuRAG6ekknzxl6HREIaIwen4VrdLCglK/b8T7xHGUFs+XgNSmq1Ha2mLq/bxwz3icxI3u/+FKrdp6hXrazZuk3biRaci9EO+CHF+TbmJiwuLFixk7diznz5/n8ePHVKpUiTIvDHsRQgghRMFna2qr+1ur1TLj+AxC74WiQMG4WuMMmJl4XQqFArevJ2IZWBubVq0MnY4o5BTGxnr3nQYOxHHAAHhhdvj02FiMPTzQpqXpLQ0Xt2gRCb9txHHgQJwGDwJAq1aTEh6OaZkyKM3N386TEMIAclykP1O8eHGKFy+em7kIIYQQIp9SKBR8XuVzFoQuoH/F/rp4SnoKJioTlApZsiu/0qan82j3bqxbtEChUKAwMsK2dWtDpyXeUQqFAl5YIcq0VEm8du1Ek5qq3zA9HYWJid5w+dSrV7neuQsqW1vKHDmsG3afGhmJytoaIyent/IchMhrOfpETUpKYty4cZQrVw4rKyusra2pUKECX3/9NcnJyXmVoxBCCCHygcoulfmp+U84WzjrYrNPzabrlq6cvXfWgJmJl9FqtdwJ+oI7nwdxf9GPhk5HiJdSmprq3S86bRrep05i3bCBLpZ+/z4qBwdMy5bVuy4+etx4rtStR8KWrbpYxqNHpF69ijYjI69TFyLXZbsnPS0tjfr163P+/HlatmxJmzZt0Gq1hIeHM3nyZLZv386BAwcw/sewFiGEEEIUTsnqZLZGbiU+NZ4kdZKh0xFZUCgUmFeuxON9+zD1Km3odITIkRevcwewCgykbMjfaP7ROahRp4FCgWkZL10s6dBh7gwdinlAAJ5r1+jiqRERGLm6obKyzNvkhXgD2S7SFy5cyO3btzlz5gze3t562y5evEiDBg344YcfGDx4cK4nKYQQQoj8x8LYgk1tN7Hrxi5qFa2li5+7dw4Paw/szOwMl5zQcejdG+vGjTHx8DB0KkLkihcnowMouW4dmuRkFC/0xmfEx6OwsMDkHz9O3fzoY9JjY/HcsAHz8uUASH/w4OlScq6usqa7yBeyPdx948aNjB07NlOBDuDj48OYMWP49ddfczU5IYQQQuRvDuYOdPPpprv/JP0Jn+/7nFYbW8kQeANRx8QSPWUK2hcm55ICXRR2SgsLFCqV7n6RLp3xPnEcl1GjdTFNUhIoFKBQYFKypC4e/9tvRDRsRNQY/ZWqUiMj0cqa7sIAsl2kh4WF0aBBg5dub9iwIWFhYbmRkxBCCCEKqLjkOOxM7bAysaJskbL/voPIVdqMDG717cvDlauI+W66odMRwqAUSqXesHalpSVl9u+j7OFDevGMh/GgUmHi6amLaZKTiWz9HherVCUjPl4XV8fEkpGQ8BayF++ybBfp8fHxODg4vHS7g4MDCfKGFUIIId5pHjYerHtvHT81/wkzIzNd/H8n/0dobKjhEntHKFQqnIcPw6R0aex79TR0OkLkSyo7O737LiOG433qJEU+/FAXU9+5g9LSEiM7O732cfPncblGTeIWL9bFtOnppN26hVarzevUxTsi20W6RqNB9cIQkkwHUirJkNkThRBCiHeeSqnCw/r58OpDdw+x9PxS+uzsQ9yTOANmVni9WBxY1atHqT82YVKsmAEzEqJgUZqa6vWum5YpQ9n/a+++46uu7j+Ov+7M3hNCSNh7CYKIW3C0zlq1aoWfs1apA61C3QuciEUrdSC2zlZtrQMEURABUVGEgCBhyMwmO7m54/v74yY3ueaqCSS5N+H9fDzywPvN5977CTd+ue97zvecL7+g13//41fnKioGwJ7ZuBW1Y9t2tk06hdyTTvb7f9GZn998azmRFmjxwnGGYXDyySdjtQa+i8vlarOmREREpOvoG9+Xc/ueS4w9huSIZN9xl8eF1dzityLyE6pWraJgzpNk/n0e1oQEoPmq2CLSeiaTCeuPZhJnPvM33OXlmJrsaOXcuxeTzYYto7vfwnP7/nwr1WvXkvHEbGJPOQXwTqP31NZiTUzsmB9COqUWn8HvvvvuX6w577zzDqkZERER6XpSI1O5b8J9fiNM+VX5XLrwUv5vyP9x4YALsZh/erae/DSjro79d9+Dc/duiv/+LGnTbwt2SyJdniU21u92zEknMuDrtX7XrgO48vPB7fZbuLFy5Ur2/ul6oiZMoOcLz/uOO/MLsCYn+S1+J4evNg3pIiIiIj+l6QjTa5tfY3/Vfj7c+aHf6vDSOia7ncx5z1Cy4CVSpt0U7HZEDlsmmw1rSorfsd6LFuIqKPAbjXft2wcmE9a0NL/anRdeiLusjOxXXyF80CAA3JWVmMzmZlvOSdenuVAiIiLS4aaOmkr36O4MThrsC+9Oj5PtpdsZkNh8u1fx5yoqwprsvXQgrE8fut1/X5A7EpEfM5lM2H4UxhOnTCH+t7/FU1PjO+aurMRdWopRW4utyah76RtvUPDY4yT8/vek3/4X33FXcTGWxETt6d6FtXjhOBEREZG2YjVbuWDABQxNHuo79tb3b3H+u+fz6JfaOuynGIZBwZw5bP/1GThyc4PdjogcBHNUlO9DNgBLdDQD1n5Fnw8XYYmO9h2v27UbDMNvJN5TXc3WY45l69ETcJeX+467y8sxnM6O+QGk3Smki4iISEjYUbYDA8NvZXjxZzgcVK1ajbusjKrP1wS7HRFpIyaLBXtWlt+xbvfeQ7/PVhB/4QW+Y3W7doHJBBaL37XxBY89zpbRYyj558u+Y4bbjbuiov2blzan6e4iIiISEmaMm8FZfc+if0J/37Gcohy2lGzhnL7naHE5wBweTs9n/07lis+IO/OMYLcjIu2s6Yg7QPjAgQz4ei3O/fv9jtft2IFRV4c1uXHU3bFtGzvOOpuwgQPp9Z+3fdPj3ZWVmKOiNF0+hCmki4iISMgYkjTE99+GYfDwFw+zrnAdeyv3cv0R1wexs+Dx1NZSs+5boo4aB4AlPl4BXeQwZg4PJ6xXL79jPV9agHPPHixNtnar277DWx8Z6RfI90z9E7WbNpHx6CNEH3884N0pAryLUUrwHdR096VLl3LGGWfQp08f+vTpwxlnnMFHH33U1r2JiIjIYcxjeDgl+xTSItO4cMCFvuNNt3Lr6jzV1ey+8ip2XXklFcuWBbsdEQlRJrMZe8+efte0x552Kv1Wr6L7zAf9ah3bcvGUl/utRl/56adsHj2GPTf57xLRdIE76TitDul/+9vfOO2004iJieGGG27ghhtuIDY2ll/96lc8/fTT7dGjiIiIHIYsZguXDr6UhectJC2qcYXkx796nHtX30tRTVEQu+sYpvBwrN26YQ4P93vzLSLSEtaEBOzZ2X7H+i1dSq///oewvn19xxy528DpxBwW7le77de/ZusJJ+LYutV3zFNXh+HxtGvfh7tWT3efOXMmTzzxBFOnTvUdu/7665kwYQIzZ87kuuuua9MGRURE5PBmM9t8/11UU8Qrm1/B5XExKWsSyRHJP3PPzs9kNtN95oPU7d5DWO9ev3wHEZFfYLLbCR840O9Y0h+uJu6sMzHcbt8xd0UFrn3ea9+t6em+4wf++TJFTz9NwuRLSb3xRt9xT10dZk2XbxOtHkkvLS3ltNNOa3b8lFNOoaysrE2aEhEREQkkOSKZ5095nsuGXMbR3Y/2Hd9buReP0TVGdhw7dlA8/0XfbZPNpoAuIu3KZDJh694de5N92i0xMfT/6kuy33gdS0yM77jj++/xVFdjDgvzHfPU1vL96DFs+/UZuCsrfccbrnWX1ml1SD/rrLP4z3/+0+z4O++8wxlnaBETERERaV+j00Yzbcw03+1aVy2XLbqMi96/iD0Ve4LY2aFzl5byw+8vpeCRRzjw738Hux0ROcxZoqOJGDHC71i3Bx+g97v/I+7cc33HHFtzMZxO3KWlmKOifMfzHpzJ98ccS+lbb/mOGR6P34i9NNfq6e6DBw/mwQcfZNmyZYwfPx6Azz//nJUrV3LzzTfz17/+1Vd7/fWH5yqsIiIi0nG2HNhCeV05Bkann/5uiY8nccoUKhYtIuakk4LdjohIMyarlbB+/fyOhQ8dQt9Pl+PKy/NbSd6xZQvuoiJM4Y3Xutdt386O355PxMiRZC1onDVkuN2YLC3banPvpt2U5xUD4Ha7qPi+iC2R32KxeONtbHoSGYMzf+4hQlqrQ/oLL7xAQkICmzZtYtOmTb7j8fHxvPDCC77bJpOpxSH96aef5tFHHyUvL48RI0Ywd+5cxo4dG7B2wYIFXHbZZX7HwsLCqK2tbe2PIiIiIl3AiJQRvH/u++yt3Eu4tfGN4L+2/ItTsk4hPjw+eM21kGEYvje2yVdfReKUyX5TSUVEQpnJZMKWmootNdXveM/5L+DYuhVbz56+Y7Wbt2DU1mL8KL/tvvoP1O3ZTbd77iGqfjDY8HjAZPIL/ns37Sbp1dFkWB0AVOXZifg6jvRtZUSle6fX17rC2Hvx2k4b1Fsd0nfs2NGmDbzxxhtMmzaNefPmMW7cOObMmcOpp57Kli1bSP3Ri9wgNjaWLVu2+G43fdFERETk8JMUkURSRJLv9up9q7n/8/t5et3TLPzNQiJtkUHs7ueVvfc+FR99RMZjj2Kyet+aKaCLSFdgjoxsNl0+9lenEzF0CO6qKt8xwzCo3bQJ94EDmGNifccrly1j/4y/EHPqqXS7714AyvOK6W5x1N8PCtbHUlduo2B9LNlpRZhMEG51UJ5XfPiE9LY2e/ZsrrrqKt/o+Lx583j//feZP38+06dPD3gfk8lEepMVBkVERESairBG0D+hP0emHxnSAd1VVMT+O+/EqKmhdPx4Ei68INgtiYi0K5PZ3GxbOJPJRO/338OxeTNh/Run0tdu3oy7rAzD4T/qvv2DFEwWSOhXSW2Jd0X52hI7VXlhRHdztPvP0N5aFNKnTZvG/fffT1RUFNOmTfvZ2tmzZ7f4yevq6li7di0zZszwHTObzUycOJHVq1f/5P0qKyvJysrC4/FwxBFHMHPmTIYMGRKw1uFw4HA0vlDl5eUAOJ1OnE5ni3sNhob+Qr1PEemcdI6RrmxIwhBeOfUV6jx1vt/xwupC7vr8Lv44/I8MTx4e5A7rxcWR/vBDVH++hqhzzu5y/z/qPCMiLRYTg/3II3ED7vpzRuzkyYRPmIDJasVZmIspfyMJmxZyoMK7NeeBrVFgMsAwgcmgcEMMUekOTCbvteqhdO5pTS8tCunffPON70G/+eabn6xr7bTzoqIi3G43aWlpfsfT0tLYvHlzwPsMGDCA+fPnM3z4cMrKynjsscc4+uij2bhxIz169GhWP2vWLO69995mxxcvXkxkZOh+st7UkiVLgt2CiHRhOsfI4eK/1f/lq7qv2F+4n6uirwre5XKGgbnWgSei8fp5RgyHRYuC008H0HlGRFrK7KkjpnYfcTU/EFezi9iaXcTV7MbmrgYgzYDEMyyU7YygKKdxajyGyW80fd26b9lWvTdIP0Vz1dXVLa41GYZhtGMvP2vfvn1kZGSwatUq30rxALfeeivLly9nzZo1v/gYTqeTQYMGcdFFF3H//fc3+36gkfTMzEyKioqIjY1tVh9KnE4nS5YsYdKkSdhstmC3IyJdjM4xcrgprC7kb+v/xrl9z/WNpLs8LmpcNcTYY37h3m3DcLspvO8+anM2kjF/Ppa40H4vcqh0nhGRn1VdjCk/x++Lou8xGc23aDMsdkgeQKmRQXz+InYuSab2gM07it7AZBCe4CR7UhEbj1nKgBNGNHucYCkvLyc5OZmysrJfzKFBvSY9OTkZi8VCfn6+3/H8/PwWX3Nus9kYNWoUubm5Ab8fFhZGWIDFV2w2W6f5x6Iz9SoinY/OMXK46B7XnQeOfcDv2Nub3+apdU9x85ibOafvOe3eg7OkhKoVK3AXl+Bc/y3hh8k2azrPiBzmPB4o2Q75GyCv4SsHKvYFro9IhPRhfl+m5P5gsZH38Tps337iuxbdT5PRdIvFGlLnndb00uqQXlVVxUMPPcTSpUspKCjA4/H4fX/79u0tfiy73c7o0aNZunQp55xzDgAej4elS5cyderUFj2G2+1mw4YN/OpXv2rx84qIiIgYhsGinYsodZRS7Wz5NMRDYUtLo+fzL1C36wftgy4iXVNdNRRsgrz1jWE8fyM4qwLXJ/ZuEsaHQ9pQiO0OP3VJkuG99hwMIFBN/fcvCNqE8UPW6pB+5ZVXsnz5ci699FK6det2yNdzTZs2jSlTpjBmzBjGjh3LnDlzqKqq8q32PnnyZDIyMpg1axYA9913H0cddRR9+/altLSURx99lB9++IErr7zykPoQERGRw4vJZOLZU57lg+0f8KvejR/2f3/ge9weN4OSBrXJ87jLynDm5xPevz8A4QP6Ez6gf5s8tohI0BgGVOY3GRmv/yrOxRugf8QaDmlDvGE8bWh9IB8MYa273CgmOZaqKiuBAzqACWeVlZjkzns5UatD+sKFC3n//feZMGFCmzRw4YUXUlhYyF133UVeXh4jR45k0aJFvsXkdu3ahdls9tUfOHCAq666iry8PBISEhg9ejSrVq1i8ODBbdKPiIiIHD5sZhtn9z3bd9swDB78/EG+KfiGe46+h9/0+80hPb6rqIhdl1+Bq6iIrFdeJqxXr0NtWUSk47ldULzVOyreMEKenwNVhYHro9Pqg3iTEfKkPmC2HHIrPUb0YffcV6ncvsvbmsfNli3fM2BAfyz1jx/RO4seI/oc8nMFS6tDekJCAomJiW3axNSpU39yevuyZcv8bj/xxBM88cQTbfr8IiIiIgC17lrSo9IJt4YzofuhD0iYwsIwWa1gMWOE0FZAIiI/qbbcOz09b0PjNeQF34GrtnmtyQxJ/ZqE8aGQNgxi0prXtqHM40bDcaMB7wKV2z74gAG/+lVIXYN+KFod0u+//37uuusuXnrppU6zhZmIiIhIS0RYI3j4uIcpqikiOSLZd/zZ9c+SFJ7EOX3P8Y3UtIQlJobM55/DU1WFPTOzPVoWETk4hgFlexpHxRtGyA/sDFxvj24yOl7/Z8ogsCsTtrUWhfRRo0b5XXuem5tLWloa2dnZzT6t+Prrr9u2QxEREZEO1jSg7y7fzTPfPoPL46JnbE+OTD/yZ+9bs349roICYiZOBMCamAhtPAtRRKRVXHVQuLk+jDe5fry2NHB9bI/GIN7wFZ8NTS5DlvbTopDesPK6iIiIyOEmLSqNm464iZziHL+AXuuqJdwa7lfr2LqVXf93GR6nk6wFLxI5enRHtysih7vqkh+F8RxvQPcEuOTGbIWUgY1BvGGkPFIfLAZTi0L63Xff3d59iIiIiIQku8XO5CGT/Y7Vumo5951zOar7Udw0+iZi7d5VhO29exN13HG4y0oJHzgwGO2KyOHC44HSnY1BvCGUl+8JXB8e513ArWkYTxkA1rAObVt+WauvSd+9ezcmk4kePXoA8MUXX/Dqq68yePBgrr766jZvUERERCTUrNi7gj2Ve/h0z6fceuStvuMmi4WMRx7GMAzMYXrjKyJtxFnjXbyt6VT1/I1QVxG4PiG7cZuzhmvI4zJ/eu9xCSmtDukXX3wxV199NZdeeil5eXlMnDiRoUOH8sorr5CXl8ddd93VHn2KiIiIhIxJWZN46bSXqHJWUfPqv6msrCTl2mtZvW8147qNw2zSdZsicpAqC5oE8foR8qLvwfA0r7WEQeqgxm3O0od69yIPj+v4vqXNtDqk5+TkMHbsWAD+9a9/MWzYMFauXMnixYu55pprFNJFRETksHBE2hHUrF/PzpmzANjZJ5qr8x5leMpwXjrtJazmVr/NEpHDiccNxdu8q6o3vYa8Mj9wfWRyk5XV60fIk/qBReearqbVr6jT6SSsfvrWRx99xFlnnQXAwIED2b9/f9t2JyIiIhLCIoYPJ3nqVEwWM1v7xBFVHMXQpKEK6CLiz1EJBZsatznL2wD5m8BVE6DYBEl9m6yuPtw7dT0mXdPVDxOt/hdkyJAhzJs3j1//+tcsWbKE+++/H4B9+/aRlJTU5g2KiIiIhBLD6QTDwGS3A5Ay9ToAzgImZBzjF9ALqwtZsHEBVw27ivjw+CB0KyIdyjCgYn99EF/fuKBbyXbAaF5vi/ROT/etrj4M0gaDParDW5fQ0eqQ/vDDD3Puuefy6KOPMmXKFEaMGAHA//73P980eBEREZGuyONwsPemaZjsdjIefwyTxeL3/aQI/wGLp9Y9xdtb32Zb2TbmTZzXka2KSHtzO73XijddzC1vA9SUBK6P6ea/snr6cEjsBWZL4Ho5bLUqpBuGQe/evdm1axcul4uEhATf966++moiIyPbvEERERGRUOH47jsqV6zAZDbj2LKF8MGDf7b+9F6ns6l4E9cMv8Z3zGN4MGHCpGmrIp1HTal3NXXfVPUN3tXW3XXNa00W79ZmvjBe/xWV3OFtS+fU6pDet29fNm7cSL9+/fy+l52d3ZZ9iYiIiISciJEjyXj8MSzx8b8Y0AGO6nYU/zrjX36B/M3v3+Tdbe9y65G3MixlWHu2KyKtZRhQuutHq6uv9x4LJCy2SRhv2Ht8ENjCO7Zv6VJaFdLNZjP9+vWjuLi4WUgXERER6Yqc+fmYbDasiYkAxJ5ySqvu3zSguz1u5ufMZ2/lXtYXrVdIFwkml6Nx73Hf6uo54CgLXB/X0z+Mpw+D+Cwt5iZtrtXXpD/00EP8+c9/5plnnmHo0KHt0ZOIiIhISKjbuZNdl1+BJT6env94CUt09CE9nsVs4aXTXuLVza9ywYALfMf3VOwhLiyOGHvMobYsIoFUFXunqPuuHc+Boi3gcTWvNdua7D3esKDbEIhIaF4r0g5aHdInT55MdXU1I0aMwG63ExER4ff9kpKfWChBREREpJMxPAae2lpMVVV4KioOOaQDpEWlcdPomxqfwzC4/bPb2VG2g4ePe5jx3ccf8nOIHLY8Hjiwo8lWZ/Uj5BX7AtdHJPhvc5Y+DJL7g9XesX2LNNHqkD5nzpx2aENEREQk9IT17kXPF+djTUrCmtw+iz6V1JZQ6iil2lVNr7he7fIcIl1SXXX93uNNRsjzN4KzKnB9Yu/Gbc4aRshju2u6uoScVof0KVOmtEcfIiIiIiGh8rOVWFOSCR8wAMD3Z3tJikjirbPe4rvi70iPSvcdf/P7NxmSNIRBSYPa9flFQp5hQGV+/aj4+sZryItzwfA0r7eGQ+rg5tPVw3Q5iXQOrQ7pANu2bePFF19k27ZtPPnkk6SmprJw4UJ69uzJkCFD2rpHERERkQ5R+dlKdv/xj1ji4+j1xhvYunfvkOe1mq1+i8jtLt/Ng2sexO1x89ZZb9EvQQv2ymHC7fKG77wN3kDesKBbVWHg+qhU/zCePgwS+4DloGKOSEho9W/v8uXLOf3005kwYQKffvopDz74IKmpqXz77be88MILvPnmm+3Rp4iIiEi7ixg2lLBevQjr26fdpre3hN1iZ1LWJMrryv0CumEY2l9duo7acu/09PwmI+QF34GrtnmtyQxJ/fxXV08bBjFpHd+3SDtrdUifPn06DzzwANOmTSMmpnHKyEknncRTTz3Vps2JiIiIdCRLXBxZ/3gJc0wMJoslaH2kRaXxyHGP4HQ7fcccbgdXfHgFv+n3G87uczYWc/D6E2kVw4CyPU22OVvvnbp+YEfgenu0d3p609HxlEFgj+zYvkWCpNUhfcOGDbz66qvNjqemplJUVNQmTYmIiIh0BMMwKJzzJBEjhhNz0kkAWOLjg9tUEzaLzfffb299m28Lv2V/1X5O73U6EeaIn7mnSJC46rxbm/lWVq8fIa8tDVwfm9HkuvH6EfKEXmA2d2jbIqGk1SE9Pj6e/fv306uX/+qj33zzDRkZGW3WmIiIiEh7K3vnHYr//ndMYWH0+XARtvT0X75TkJzX7zycbiepUalEWBsD+r7KfXSP7phr50X8VJfUj47nNK6uXrgZPM7mtWYrpAxsDOINX5GJHd+3SIhrdUj/3e9+x2233ca///1vTCYTHo+HlStXcssttzB58uT26FFERESkXcSdcQaVSz8m6thjQjqgg/c69clD/N9rrdm/hquXXM0F/S/gL+P+ouvVpX14PFC60z+M5+dA2e7A9eFx/tucpQ/1BnRrWIe2LdJZtTqkz5w5k+uuu47MzEzcbjeDBw/G7XZz8cUXc8cdd7RHjyIiIiJtxqirw2S3A2CyWsn465OdNtyu3rcaT/0WVJ31Z5AQ46zxLt7WEMQbpq3XVQSuj8+qD+LDGxd0i8vU3uMih6DVId1ut/Pcc89x5513kpOTQ2VlJaNGjaJfP20NIiIiIqHNXV7O7mv+SPSJJ5B81VVA5w63N46+kWN7HEuvuMbLEAurC/l8/+f8uvevMZt0Xa/8jMpC/23O8jZA0VYw3M1rLWGQOqg+iA9v3Hs8PK7j+xbp4g56A8GePXuSmZkJdO5/3EREROTwUfHRUmq+/hpHbi7xv/kN1qSkYLd0yEanjfa7/fS6p3lr61uszV/LPUffE5ymJLR43FCyvXERt4Zp65V5gesjk5pMVR/uvY48uR80WchQRNrPQYX0F154gSeeeIKtW7cC0K9fP2688UauvPLKNm1OREREpC3F/+ZcXMVFRB93XJcI6IH0jO1JtC2ac/qeE+xWJBgclVCwqXGbs7wN3tvO6gDFJkjq02Rl9foR8ph0TVcXCaJWh/S77rqL2bNn86c//Ynx48cDsHr1am666SZ27drFfffd1+ZNioiIiBysuj17sKWlYbJ5RwEbprl3VZcPvZwL+l9AtD3ad+w/W//DlgNbuGb4NcSHxwevOWk7hgEV+xunqTd8lWwHjOb1tkjv9HTf6urDIW0w2KM6vHUR+XmtDunPPPMMzz33HBdddJHv2FlnncXw4cP505/+pJAuIiIiIaNmQw67r7qKqGOPpfvDD2E6TPZebhrQa121PPn1kxTXFtMzpicXD7o4iJ3JQXE7vdeK521onLKenwPVxYHro9P9tzlLHwaJvcFs6di+ReSgtDqkO51OxowZ0+z46NGjcblcbdKUiIiISFtwlxTjrqykbudOPNXVWKKjf/lOXUy4NZxZx87iX1v+xfn9z/cdL3OUEWuP1dpCoaa2rHGaen796HjBd+Cua15rskBy/8ZtztKHebc+i07p+L5FpM20OqRfeumlPPPMM8yePdvv+LPPPssll1zSZo2JiIiIHKro448nc948IkaOxBJ9+E7rHd99POO7j/fdNgyDGz+5Ebfh5p6j76F3XO8gdneYMgwo3eW/snreeu+xQOwx/mE8fRikDAJbeMf2LSLtrkUhfdq0ab7/NplMPP/88yxevJijjjoKgDVr1rBr1y4mT57cPl2KiIiItFDFRx8ROWYMlvh4AKKPmRDchkLQD+U/sLF4Ix7DQ6Q1MtjtdH0uBxRubhLG64O5oyxwfVym/1T1tKHe/cgPk8s1RA53LQrp33zzjd/t0aO9W31s27YNgOTkZJKTk9m4cWMbtyciIiLScqVvvcX+2+8gfMRwshYswBwREeyWQlJ2XDbvnvMuOUU5pEel+45/uudTRqWOIsYeE8TuOrmq4sZp6g1hvGgLeAJcFmq2QerAxm3OGkbKIxI6vm8RCRktCumffPJJe/chIiIicsgihg/HEhdHxLDhmMLCgt1OSEuLSiMtKs13e3fFbm785EaibdH868x/+YV3CcDjgQM7mq+uXrEvcH1Egv82Z+nDvNeTW+0d27eIhLyD2iddREREJBSF9etHr3f+izUtTQuitVKZo4weMT1Ij0wnLTLtl+9wOKmr9i7e1nRl9bwccFYFrk/o1bjNWcM15LEZ2ntcRFqk1SG9traWuXPn8sknn1BQUIDH4/H7/tdff91mzYmIiIj8HMPlouDRR4m/4ALC+vQBwJauEeCDMTR5KG+d9RbljnLfBxwOt4N7V93L5CGTGZg4MMgddpCK/MZF3BoWdSvOBcPTvNYaDqmD/a8fTx0M4bEd37eIdBmtDulXXHEFixcv5re//S1jx47Vp9QiIiISNIVzn6LkpX9Q8dFSen/wPmZNcT8kNrONpIgk3+3XvnuNd7e/yxd5X7DwvIXYzLYgdtfG3C5v+M7PaRwhz8uBqoLA9VEpTUbG66esJ/YBiyamikjbavVZ5b333uODDz5gwgStlCoiIiLBlfh/U6hasYLk665VQG8Hk7InsalkExO6T/AL6A63gzBLJ/r7dlRA/sbGEfK8HCjYBK7a5rUmMyT1bbKyev2fMboEQEQ6RqtDekZGBjExWvFTREREgsNwuzFZLABYExLI/ve/fLelbWVEZ/DIcY/4Hfsy70tu/fRWbjjiBs7pe05wGvsphgHle5sv5nZgR+B6W1TjNeMNi7qlDgK7tqUTkeBpdUh//PHHue2225g3bx5ZWVnt0ZOIiIhIQM78Anb/8RpSb7qJ6GOPBVBA72CvfvcqRTVFbCzaGNyQ7qrzbm3WsM1ZwzXkNQcC18dmNNnmrP4roZf2HheRkNPqkD5mzBhqa2vp3bs3kZGR2Gz+1yaVlJS0WXMiIiIiTZW8+CKOTd+R/8CDRL33LiZbF7pGupN4+LiHeWPLG/y69699x4pqiiitLaVvQl/2btpNeV4xAG63i4rvi9gS+S2W+mu3Y9OTyBic2bonrTnQJIxv8O5DXrAZPM7mtWYrJA9oEsaHeqesRyU1rxURCUGtDukXXXQRe/fuZebMmaS10fYmTz/9NI8++ih5eXmMGDGCuXPnMnbs2F+83+uvv85FF13E2WefzX//+99D7kNERERCW+rN0/A4akm64goF9CCxW+xcOvhSv2NPffMU/8n9D1f0uJyrP36QDKsDgNXhYfw7KYE+aw8wvtZ7rNYVxt6L1wYO6oYBB3Y22easPpSX7Q7cTFhcYxBvCOUpA8Haia6XF5FDsiZvDU+WP0lSXhLHZB4T7HbaRKtD+qpVq1i9ejUjRoxokwbeeOMNpk2bxrx58xg3bhxz5szh1FNPZcuWLaSmpv7k/Xbu3Mktt9zCsfVT3URERKRrcubl+bZVM9lsdLv77iB3JE15DA/ldeV4DA+Zjm6E1wd0A3gyMZ7tdjtPJsZz1L58TEC41UF5XjEZ/VK8i7c1DeP5G8FRHviJ4rP8p6qnDYX4ntp7XOQwZhgGc9fNpdBTyNx1c5nQY0KX2H2s1SF94MCB1NTUtFkDs2fP5qqrruKyyy4DYN68ebz//vvMnz+f6dOnB7yP2+3mkksu4d5772XFihWUlpa2WT8iIiISOipXrmTP1D+RcsP1JP3f/wW7HQnAbDIz+4TZbCvdRt3XFb7jf02IY2P9ivsbw8JYFRHOhBrvauq9vpoCK3aD4W7+gBa7d/G29GHehdzShkLaEIiI74gfR0Q6kVX7VrGpZBMAm0o2sWrfKiZkdP5dyFod0h966CFuvvlmHnzwQYYNG9bsmvTY2NgWP1ZdXR1r165lxowZvmNms5mJEyeyevXqn7zffffdR2pqKldccQUrVqz42edwOBw4HA7f7fJy76ezTqcTpzPAdUwhpKG/UO9TRDonnWOkM6havx6jpobKFZ8R87vfaZG4ENYzqidbXN8AUGky8WJc43tCs2EwNyGOnVYrb8VG86vKEq6sdmNEJmGkDeV8azGGNYx5Y+8nodsosNj477b/8uqWVznR6uCPGWOh/lw1+cPJ1Lpr+evxfyU9yjvDYuHOhby46UWO7nY0N4660fe8V390NaV1pTx2zGP0jO0JwNLdS/n7hr8zOnU0t425zVf7p0/+RH5NPg+Mf4D+Cf0BWLlvJU+ue5KhSUO5a9xdvtqbP72Z3ZW7uXPsnQxLHgbAV/lf8cjaR+gX148HJzzoq7195e1sLdvKraNvZUzaGAA2FG3g/i/uJzM6k8ePe9xXe9+a+8gpzuGGkTcwobs3aHx/4HvuWH0HaRFpzD1xrq/24a8eZm3BWv4w7A+cnHkyALvKd3HLZ7cQb4/n2YnP+mrnfDOHVftXcdngyzg9+3QA8qryuH759YRbwvnHqf/w1T6z/hk+2fMJFw+4mHP6nAPAgdoD/OHjP2DGzOu/et1XO3/jfBb9sIjf9PkNvxvwOwBqXDVMWTwFgJdPfRm7xQ7AK5tf4Z3t73BGrzOYPGgyAG6Pm4sWXQTACxNfIMbu3UHqza1v8q+t/2JS5iSuGnaV7/kuWXQJTo+Tv534N5IjkgF4d/u7/HPzPzm2+7H8aeSffLWXL7mcSmclTxz3BBnRGQAs/mExz298nnFp47h59M2+2j9+/EeKa4t5aMJD9I7rDcDyPct5ev3TjEweyV/G/sVXe+PyG9lXtY97jrqHwYmDAfh8/+fM/mY2gxIGce/4e321t312GzvKdzBjzAxGpY4C4JuCb5j11Sx6xfbi4WMe9tXevfpuvjvwHdNGTeOobkcB3tB5z+f30D2qO3OOn+OrnfnFTNYVreO64ddxfI/jAdhetp3pK6eTFJ7EMyc946t9fO3jrMlfw5VDruSUrFMA2Fu5l5s+vYloWzTzJ8331c5dN5cV+1Zw6cBLObP3mYB3vYlrP7kWm9nGK6e94qt9bsNzLNm9hAv6XcBv+/0WgIq6Cq746AoAXjvtNSxm7/n6H9/9g/d2vMfZvc/mkoGXAFDnruP3H/4egJdOeYkIawQAr295nbe3vc1pWadx+ZDLfc/3uw9+hwcPfz/p7ySEJwDwn9z/8MjaRzBhwsDAbDLz16//ypEpR4bkaHpr3m+1OqSfdtppAJx88sl+xw3DwGQy4XYH+ET0JxQVFeF2u0lL8993Mi0tjc2bNwe8z2effcYLL7zAunXrWvQcs2bN4t577212fPHixURGdo7tNZYsWRLsFkSkC9M5RkJat27EXHghFcOHse7DD4PdjTRhcdcSW7ubuJrdxFb/QFzNLgZU7wLgq/Aw3E3eJHtMJjaGhdGzzsVWu52v7X34cMj51NoSwGRia+mdGK5yFq/bQcz6IgBW164mtzaX2OpYPtjzge+xvi/9njrqWPLxEpIs3sXgVjtWk1uTS0RVBB/sb6zdXLaZSqOSj5Z/RLrFG+i/cnxFbk0ulkoLHxQ01m4q38QBzwE+WfEJudZcANbXrSe3OhdPpYcPiv1r8z35LFu5jN027/Xym52bya3Kpbailg8+aKzdULGBPe49fPr5pxTYCgDY5txGblUuFeUVfrXrK9ez3bWdFV+soMxeBsBu125yK3MpLi/2q11XuY5cVy4rv1qJY4N3QCrPnUduRS7Rpmi/2m+qviHXmcuqb1ZhbDIAKHYXk1uRix27X+3a6rXk1uWy+tvV2Ld4A3aFp4Lc8lxMmPxqv6r+ity6XL7I+YLYbd4PZRyGg9wy79/fwkULsZm8A3pf1HxBriOXLzd9SfIOb8D2GB5f7YeLPyTS7H1vvqZ2Dbm1uSRUJ5CxO8P3fFtLt+LCxeKli4k3x/v9nkRVRfHBvsbetpRtocao4aNPPiLFkuLtwfEFuTW52KvsfJDfWPtd2XeUG+UsXb6UzVZvBvmm7htyq3MxVZr4oMj/tS/yFLHss2XstO4EYGPdRnKrc3FWOPngQGPtxoqN7HPvY/nq5ey37Qfge+f35FblUl1R7f/aV6znB/cPrFizghK7dyHuna6d5FbmUlpe6lf7beW35Lpy+eyrz6haXwXAXtdecitzKTAVBH7tv16Fa6MLgEJ3IbkVuUSYIvxqv676mlxnLqvXrcay2RuwSz2l5JbnYsUa8LVfs2ENkVu9r1u1p5rc8vrXfuFCzCbvzglf1nxJriOXLzZ+QcJ2b8B2Gk7fa7/ow0WEmcL8fk++qv6K9B/S/V57A4PFHy0mxuz9MOe9qvdwuBsHYz2Gh00lm/jr//5KP1s/Qk11dXWLa1sd0j/55JPW3qXNVFRUcOmll/Lcc8+RnJzcovvMmDGDadOm+W6Xl5eTmZnJKaec0qpR/2BwOp0sWbKESZMmNZuxICJyqHSOkVBVufRjoo4/DpO1/m3Kr3/983eQ9mUYUJmHKT/H74uS7ZgwmpcD8xLiMBsGniZB3WwY5Npt/H1/PhXDZ3LSpNN830vK84btUSmjfCOvIytH8pvK35ASkeIb3QRIyU/BY3gYnjzcN/p2RNURnFFxBonhifSLb3xznlaQhsvjYljSMCJt3iAxpnoMp5afSnxYPAMSBvhquxd2x+F2MDhxsG9Ed2zNWE4sO5EYe4xv1BQgozCDWnctAxMGEhcWB8D42vFMKJ1AlDWKoclDfbU9i3pS5aqif3x/3whgmaOMsQfGEm4JZ0RK4zpP2SXZVNRV0Ceuj2+kuKKugpElIwmzhDEyZaSvts+BPpQ6SukV24vUSO86TtXOaoYUD8FqtjI6dbSvtl9pP0pqS8iKyfLNPqhx1dC/qD9mk5kj04701Q4sG0hhTSGZ0Zl0j+4OeEc9swuzARiXPs5XO6hsEAU1BXSP6k5mjHchQJfHRfcC7/3GpI7xjaYOLR/KxdUXkx6ZTlasdxtnwzBIyfcG6CNSjsBm8f5bNLxyOL+t/G2z1z45LxkDg5Ep3r8PgJFVIzm74mySwpPoG9+38bXPT8NluPx+T0ZXj+b08tNJCEvwzZYASC9Ix+lxMiRpCNG2aACOrDmSiWUTibPHMTBxYLPXflDiIGLt3iwxrmYcx5UdR4wthsFJjb8nmUWZVLuq/V778bXjGV86nkhrpG8WBkB2cTYVzgr6xvUlKcL7/0N5XTmjS0Y3+z3pXdKbsroyesf1JiXC+/dX6axkePFwbGYbR6Qe4avte6AvBxwHyI7NJi0yzffaDywaiNVkZXRa4+9J/9L+FNcWkxmTSfco72vocDvoU9gHEybGpjcu7N3we5IRnUGP6B4AON1OMgu9vwdj08b6RrOHlA/h99W/p1tkN9+MFrfHTXqB93dxdOporGbv+X5YxTAurLqQ1IhUesX18j3fj88RhmHw0sKXMJV6R9EbmE1mvgz7kutPvT7kRtMbZnS3hMkwjOZn1w5SV1dHZGQkb775Juecc47v+JQpUygtLeWdd97xq1+3bh2jRo3C0mSqm8fjAbzT5Lds2UKfPn1+9jnLy8uJi4ujrKysU4T0Dz74gF/96ld6Ay0ibU7nGAlFhU89TdFTTxH3m9/Q7cEHQu5NVpfndkLR1sZtzhoWdKsuDlwfne63uvq2H+zk5VzJNek/vfjvvLwCEo/8kEEnjWyfn0FEuryVe1dyzUfX/OT3502cF3LXprcmh7Z6JB1gxYoV/P3vf2f79u38+9//JiMjg3/+85/06tWLY45p+bL3drud0aNHs3TpUl9I93g8LF26lKlTpzarHzhwIBs2bPA7dscdd1BRUcGTTz5JZmYr99wUERGRkBI+cABYLNizsxXQ21ttmXfv8fwcyFvvDeMFm6HJ9FEfkwWS+/tvdZY2DKJT/Moc+d8wNyEOk2FgBHj9TPXXpt8VvDEiEenkDMNg7jdzfdei/5gJE3O/mcvR3Y/utP+OtDqkv/XWW1x66aVccsklfP31175F2crKypg5c6bftQotMW3aNKZMmcKYMWMYO3Ysc+bMoaqqyrfa++TJk8nIyGDWrFmEh4czdOhQv/vHx8cDNDsuIiIinU/MxIn0ef897NnZwW6l6zAM7z7jeRv8v0p/CFxvj2kM42n1f6YOAlvELz5VRFoc+3daAwZ0AMNkIs9iJSIt7lB+IhE5jDk9TvKq8gIGdAADg7yqPJwep+/ymc6m1SH9gQceYN68eUyePJnXX29c3XHChAk88MADrW7gwgsvpLCwkLvuuou8vDxGjhzJokWLfIvJ7dq1C7PZ3OrHFRERkdDnqa2lcM6TJF/7Ryz10/8U0A+BywGFm+uDeE7jtPXassD1cZn+YTx9mHc/8oN879VraG+edL7M3nzvAnIet4st329lQP9+mC3et50Z6Vn0Gtr75x5GROQn2S12Xj/jdUpqvQvsuVwuVn62kgnHTMBav5ZJYnhipw3ocBAhfcuWLRx33HHNjsfFxR30fuVTp04NOL0dYNmyZT973wULFhzUc4qIiEjw7Zs+g4pFi3Bs3Urm88912qmJQVFd0nx0vGgLeFzNa802SB3onaLum64+BCIT27ytkaOOYCTehaucTice1wecdorWvhCRtpMele5bBNHpdLLDuoNBiYO6zHmm1SE9PT2d3Nxcsn/0Kfdnn31G7976VFRERERaLvmaP1C7fj3J1/5RAf2neDxwYEdjEM+vHyEv3xu4Pjy+PogPb1zULXkAWDvvqJKIyOGk1SH9qquu4oYbbmD+/PmYTCb27dvH6tWrueWWW7jzzjvbo0cRERHpQgzD8AXy8IED6bNoISa7AiQAddVQ8J13IbeGMJ6/EeoqA9cn9GocGW/4is0AfeAhItJptTqkT58+HY/Hw8knn0x1dTXHHXccYWFh3HLLLfzpT39qjx5FRESki3Bs38G+224j49FHfNeeH7YBvSLff5uzvA1QnAuGp3mtNdy7eFvTEfLUwRAe2tvJiohI67U6pJtMJm6//Xb+/Oc/k5ubS2VlJYMHDyY6Oro9+hMREZEuJP/BB6ndsIG8Bx6k5/PPBbudjuFxe8N33ob6rc7qR8irCgLXR6X4b3OWPgyS+oLloHbOFRGRTuagz/Z2u53Bgwe3ZS8iIiLSxXV/+CHyZ84k7Y47gt1K+3BUeKenNx0dL9gErtoAxSZI7tdkdfX6EfKYtA5vW0REQkeLQ/rll1/eorr58+cfdDMiIiLS9bgOHMCakACANTmZjNmzg9xRGzAM78JtDaPiDdeQl2wPXG+L8q6m7rt2fLh3+ro9smP7FhGRkNfikL5gwQKysrIYNWoUhhF443gRERGRpsrefY/9d99N5tNPETV+fLDbOTiuOij6vsnq6vV/1hwIXB/TvUkYrx8hT+h10HuPi4jI4aXFIf2Pf/wjr732Gjt27OCyyy7j97//PYmJbb+3poiIiHQNhmFQsXQpRnU15QsXdY6QXnOgcXQ8P8c7Ql6wGTzO5rUmC6QMbBLG668hj0rq+L5FRKTLaHFIf/rpp5k9ezZvv/028+fPZ8aMGfz617/miiuu4JRTTtHepiIiIuLHZDLR/ZGHKR17JAm/+12w2/FnGHBgZ+M2Z3kbvOG8bFfg+rC4xiDecA15ykCwhXdo2yIi0vW1auG4sLAwLrroIi666CJ++OEHFixYwLXXXovL5WLjxo1a4V1EROQwZxgG1Z9/7hs1N9vtJF58cXCbctZC4Xf+YTw/BxzlgevjezYu4pZWH8zje2rvcRER6RAHvbq72WzGZDJhGAZut7stexIREZFOyDAM9t95J2VvvkXanXeQeMklHd9EVZH/Nmd5G7zXkxsB3qtY7I17jzdsdZY2BCLiO7xtERGRBq0K6Q6Hwzfd/bPPPuOMM87gqaee4rTTTsOsxVBEREQOayaTCVtqGpjNmMMj2vfJPG7vSupNtzrLz4GK/YHrIxL9V1ZPHwrJ/cFia98+RUREWqnFIf3aa6/l9ddfJzMzk8svv5zXXnuN5OTk9uxNREREOpnkP00lZtJEwgcNarsHrauC/E31I+T1YTx/IzirAxSbILF3k0Be/xXTTdPVRUSkU2hxSJ83bx49e/akd+/eLF++nOXLlwese/vtt9usOREREQlt7tJSSl55heRrrsFksWAymQ4+oBsGVOT5b3OWtwGKtwEBtn+1RtTvPT60yd7jgyFMa+SIiEjn1eKQPnnyZK3gLiIiIj6G282uK66kduNGPJVVpN12a8vv7HZ5rxVv2OasYUG36qLA9dHpP1pdfRgk9QGzpW1+GBERkRDR4pC+YMGCdmxDREREOhuTxULSlVeQ/9DDxJ17zk8X1pZ5p6c3vX684DtwOwI8qNl7rXjTrc7Sh0F0arv9HCIiIqHkoFd3FxEREYk9/XSiTzgBc0SEd7p62e7GUfGGEfLSHwLf2R7jHR1PazJCnjoIbO286JyIiEgIU0gXERGRFqtZv57Cv84l4/GHsVTvhrwczL7V1Td4R80Dicv0D+PpQyE+G7Q7jIiIiB+FdBEREfl51SWQtwFjzzr23rYA54FaiiaPIm1UafNasw1SBvqH8bShEJnY4W2LiIh0RgrpIiIi4uXxwIEdjducNYyQl+8FwARkHGmjeFM0KUPLIDy++VZnyQPAag/qjyEiItKZKaSLiIgcjpw1ULCpyWJuOd5gXlfZrNTtNGFJzYb0oUSkD6fHdfULusX10N7jIiIibUwhXUREpKurLPDf5ixvAxRvBcPTvNYSBmmDfduclazaT/F/lpL16qvYe/To+N5FREQOMwrpIiIiXYXHDcW5/lud5W2AqoLA9ZHJ0G14/YJuw73BPKkvWLxvDzwOB6UPnI+roJDyhQtJvuqqDvxhREREDk8K6SIiIp2RowLyNzUZIa/fe9xVE6DYBMn9mqyuPty7oFt02s9OVzeHhZH5wvNULF5CwiUXt9/PIiIiIj4K6SIiIqHMMKB8X2MQz6//s2R74HpbFKQN8V/MLXUQ2KNa9nROJ7Vbvidi6BDvw6Wmkvj7S9rqpxEREZFfoJAuIiISKtxOKNzSZGX1+lHymgOB62O6N25z1jBCntDroPce9zgc7L3xJqpWrSLzuWeJGjv2EH4YERERORgK6SIiIsFQU+q/zVneBijcDO665rUmC6QM8B8dTxsGUUlt2pKpSbj3VFe36WOLiIhIyyiki4iItCfDgNIf/Lc6y9sAZbsC14fFNgni9SPkKQPBFt7urZpsNjKemI1jay4Rw4a2+/OJiIhIcwrpIiIibcVZC4XfNQbxvA3e0XJHeeD6+J7eKeq+Bd2GeY914N7jzrw8qj77jPjf/hYAc3i4ArqIiEgQKaSLiIgcjKoi/6nq+Tne68kNd/Nai907Gt6wzVn6MO/ibhHxHd52U+6yMn64+BKc+/ZhstmIO/vsoPYjIiIiCukiIiI/z+PxrqSet97/GvKK/YHrIxL9rx1PHwbJ/cFi69i+W8ASF0fsr39FxZKPiDzyyGC3IyIiIiiki4iINKqr8u49nt90hHwTOKsC1yf28V9ZPX0YxHTr0Onqhypl2jSSrr4aS0xMsFsRERERFNJFRORwZBhQkVc/Mr6+cUG34lzAaF5vjYC0wf4rq6cNhrDOF2wrP/2Uio+Wkn7P3ZjMZkwmkwK6iIhICFFIFxGRrs3tguKtTfYdr5+yXl0UuD467Uerqw+HpD5gtnRs3+3AVVTEnutvwKitJXzwYBJ+d2GwWxIREZEfUUgXEZGuo7YM8jfWB/H6EfKC78DtaF5rMnuvFW+61Vn6MIhO7fi+O4g1OZlu991L5acriP/NucFuR0RERAJQSBcRkc7HMKBsT5PV1esXdTuwM3C9Pca7mrpvMbehkDoYbBEd2naweBwOzGFhAMSddRaxZ56JqRNdNy8iInI4UUgXEZHQ5qqDws2N25w1hPLassD1sT38w3j6MIjPBrO5Q9sOBYZhUPj441R/tZae81/AHBkJoIAuIiISwhTSRUQkdFSX+G9zlrfBu/e4x9m81myFlEFNVlevn7YemdjxfYcoV34+B/79Jp6yMio//ZTY004LdksiIiLyCxTSRUSk43k8ULqzSRivD+blewLXh8f77zueNhRSBoA1rCO77nRs6en0/Ps8HNu2KaCLiIh0EgrpIiLSvpw1ULDJP4zn50BdZeD6hOzGfccbFnSL69Gp9h4PJk9NDa6iIuyZmQBEjBxJxMiRwW1KREREWkwhXURE2k5lgf82Z3kbvNufGZ7mtZYw717jDducpQ/zLu4WHtvxfXcR7vJydl/zR5z795P9ysvYuncPdksiIiLSSgrpIiKHkdpNubjzCgBwu90kfP8DNZGfU2fx7gFuSU8lfHDfX34gjxuKtzVuc9ZwHXllfuD6yOQm09WHe68jT+oHFv0z1JYMlwt3aSmeqipcRUUK6SIiIp1QSLw7evrpp3n00UfJy8tjxIgRzJ07l7Fjxwasffvtt5k5cya5ubk4nU769evHzTffzKWXXtrBXYuIdC61m3KxvXoU4dbGRdiOA1jZWON22ai9+HP/oO6orN97fH1jGM/fBK6aAM9igqS+TVZWrx8hj07TdPUOYE1MpOcLz+MuLyd8wIBgtyMiIiIHIegh/Y033mDatGnMmzePcePGMWfOHE499VS2bNlCampqs/rExERuv/12Bg4ciN1u57333uOyyy4jNTWVU089NQg/gYhI5+DOK/AL6IFYrE7qNr4LRa7G6eolOwCjebEtsn6q+tDGEfLUQWCPap8fQAJy5ObiKiwkavx4AGzdumHr1i3IXYmIiMjBCnpInz17NldddRWXXXYZAPPmzeP9999n/vz5TJ8+vVn9CSec4Hf7hhtu4KWXXuKzzz5TSBcRaQMRG++BjT86GNP9R1udDYPEXmC2BKNFqefYvoMffn8pHoeDrJcWEDF8eLBbEhERkUMU1JBeV1fH2rVrmTFjhu+Y2Wxm4sSJrF69+hfvbxgGH3/8MVu2bOHhhx8OWONwOHA4HL7b5eXlADidTpzOnx9RCraG/kK9TxEJcbVlmPJzMO96r0Xl7ogsTH3GYqQNwUgbhpE6BKKSAxR6vF8SNKb0NMKGDMFdVoapWzf9eyEhR+9lRKS9dZbzTGv6C2pILyoqwu12k5aW5nc8LS2NzZs3/+T9ysrKyMjIwOFwYLFY+Nvf/sakSZMC1s6aNYt777232fHFixcTGRl5aD9AB1myZEmwWxCRzsAwiKwrIrZmF3E1PxBXs4vYml1E1RUBLT/hrzRfyQFbFpQAJVXw3Rft1rIcOtNpp4JhsGHVqmC3IvKT9F5GRNpbqJ9nqqurW1wb9OnuByMmJoZ169ZRWVnJ0qVLmTZtGr179242FR5gxowZTJs2zXe7vLyczMxMTjnlFGJjQ3ubH6fTyZIlS5g0aRI2my3Y7YhIKHHVQuEWTPk53q+CHEz5GzE5ygOWG3E9cVm6YStZ84sPPXLkKCJOOKqtO5Y2Uv7uu3jKK4i/5OJgtyLyi/ReRkTaW2c5zzTM6G6JoIb05ORkLBYL+fn+W/bk5+eTnp7+k/czm8307etdeXjkyJF89913zJo1K2BIDwsLIywsrNlxm80W0i9iU52pVxFpB1VFjYu4NayuXrgFDHfzWosdUgY2bnNWv/e4KSKBuo9XYfv09F98OovFonNOiKrZsIGCv9wOQNTQIUSOGRPkjkRaRu9lRKS9hfp5pjW9BTWk2+12Ro8ezdKlSznnnHMA8Hg8LF26lKlTp7b4cTwej9915yIinZLHAyXbIX9DYyjP2wAV+wPXRyQ22Xu8/iu5P1hC9x8oOTThQ4eSOGUKhsdDxBFHBLsdERERaQdBn+4+bdo0pkyZwpgxYxg7dixz5syhqqrKt9r75MmTycjIYNasWYD3GvMxY8bQp08fHA4HH3zwAf/85z955plngvljiIi0Tl01FGzy7j2etwHycrx7kTurAtcn9m4Sxod7tz6L7d6qvcct6am4XTYsP7MNm9tlw5LefPtLCR7D4wHDwGSxYDKZSJ1+GwAm7TsvIiLSJQU9pF944YUUFhZy1113kZeXx8iRI1m0aJFvMbldu3ZhNpt99VVVVVx77bXs2bOHiIgIBg4cyMsvv8yFF14YrB9BROSnGQZU5vuPjOdtgOJcAu49bg2HtCH109SH1gfywRAWc8ithA/uS+3Fn1ObVwCA2+1m3bpvGDlyFBaLdys1S3oq4YP7HvJzSdswXC7233EnWC10u/9+TCaTwrmIiEgXF/SQDjB16tSfnN6+bNkyv9sPPPAADzzwQAd0JSLSSm4XFG/1joo3jJDn50BVYeD66LT6IN5khDypT7vuPR4+uC/Uh3Cn08mB6hIiTjgqpK/hOpzVrF9P2f/+ByYTCRf+johhQ4PdkoiIiLSzkAjpIiKdTm25d3p63gZvIM/PgYLvvKuu/5jJDEn9moTxoZA2DGLSmteKNBF5xBF0u/9+LPFxCugiIiKHCYV0EZGfYxhQtqfJyur1I+QHdgaut0c3GR2v/zN1MNgiOrRt6bxcBw5gslqxxHgvcYg/7zdB7khEREQ6kkK6iEgDVx0Ubm7c5qzhq7Y0cH1sj8Yg3vAVnw1N1tEQaQ1nfj67rrgCS3w8PZ9/HnN4eLBbEhERkQ6mkC4ih6fqkh+F8RxvQPcEWPncbK3fe3xYkwXdhkFkYsf3LV2a+8ABXHn5eCoqcRUUYO/ZM9gtiYiISAdTSBeRrs3jgdKdjUG8IZSX7wlcHx7nXcCtaRhPGQDWsA5tWw5P4QMHkvncs1hTUrH3yAh2OyIiIhIECuki0nU4a7yLtzWdqp6/EeoqAtcnZDduc9ZwDXlcZqv2Hhc5VDXr1mFJSMCelQVA5KhRQe5IREREgkkhXUQ6p8qCJkG8foS86HswPM1rLWGQOqhxm7P0od69yMPjOr5vkSaqv/ySXVf/AWtiIlmvvYotNTXYLYmIiEiQKaSLSGjzuKF4W+M2Zw3BvDI/cH1ksv9CbunDvNufWXS6k9Bj79ULa2oK9p5ZWKKjg92OiIiIhAC9axWR0OGohIJNjduc5W2A/E3gqglQbIKkvk1WVx/unboek67p6tJpWJOTyfrHP7EmxGOy24PdjoiIiIQAhXQR6XiGARX764P4+sYF3Uq2A0bzelukd3q6b3X1YZA2GOxRHd66yKEqfnEB4YMGEXXUOABsaZriLiIiIo0U0kWkfbmd3mvFmy7mlrcBakoC18d0819ZPX04JPYCs6Vj+xZpB2XvvkfBww9jjoyk9/vvYevWLdgtiYiISIhRSBeRtlNT6l1N3TdVfYN3tXV3XfNak8W7tZkvjNd/RSV3eNsiHSXmlElEvn0UUeOPVkAXERGRgBTSRaT1DANKd/1odfX13mOBhMU2CeMNe48PAlt4x/YtEgSGx4PJbAbAHBZGz+eew2TVP78iIiISmN4liMjPczka9x73ra6eA46ywPVxPf3DePowiM/SYm5yWPLU1LDnxhuJOfFEEn73OwAFdBEREflZeqcgIo2qir1T1H3XjudA0RbwuJrXmm1N9h5vWNBtCEQkdHzfIiGq7N13qVr+KdVffkXMpElYk5KC3ZKIiIiEOIV0kcORxwMHdjTZ6qx+hLxiX+D6iAT/bc7Sh0Fyf7BqyyiRnxN//vk4d+0i+sQTFdBFRESkRRTSRbq6uuome4/Xh/H8jeCsClyf2Ltxm7OGEfLY7pquLtJCrqIiLImJmMxmTCYTqbfcEuyWREREpBNRSBfpKgwDKvPrg/j6xmvIi3PB8DSvt4Z7p6c3XV09bQiExXR87yJdhGP7DnZdcQUxJ55A2p13YtKHWyIiItJKCukinZHb5Q3feRu8gbxhQbeqwsD1Uan+146nD4PEPmDRKUCkLTm+/x5XXh5Vn6/BU16OJS4u2C2JiIhIJ6N36CKhrrbcOz09v8kIecF34KptXmsyQ1I//9XV04ZBTFrH9y1yGIo97VTgCSLHjVVAFxERkYOikC4SKgwDyvY02eas/hryAzsC19ujvdPTm46OpwwCe2TH9i1ymKta8wXhQ4ZgiY4CGoK6iIiIyMFRSBcJBledd2uzvA3+X7WlgetjM5pcN14/Qp7QC8zmDm1bRPyVL1zI3j/fSuSYMWT+fR7msLBgtyQiIiKdnEK6SHurLqkfHc9pDOOFm8HjbF5rtkLKQP/F3NKHQWRix/ctIr/I1qMHZrsda2oKJn1oJiIiIm1AIV2krXg8ULrTP4zn50DZ7sD14XH+25ylD/UGdKtG4kQ6i4hhw8h+89/Ys7MV0kVERKRNKKSLHAxnjXfxtoYgnrfBG87rKgLXx2fVB/HhjQu6xWVq73GRTsYwDIqeeYa4s87G3iMDgLDevYPclYiIiHQlCukiv6Sy0H+bs7wNULQVDHfzWksYpA6qD+LDG/ceD9cqzyJdQdHTf6Poqacof+d/9PrfO7oGXURERNqcQrpIA48bSrY3bnPWMG29Mi9wfWRSk6nqw73XkSf3A4utY/sWkQ4Tf/5vKXv3fyT94Q8K6CIiItIuFNLl8OSohIJNjduc5W3w3nZWByg2QVKfJiur14+Qx6RrurrIYcAwDEz1/6/b0tLo/e67mO32IHclIiIiXZVCunRthgEV+5tvdVayHTCa19sivdPTfaurD4e0wWCP6vDWRST43KWl7LnhRlL+NJXIMWMAFNBFRESkXSmkS9fhdnqvFc/b0DhlPT8HqosD18d0a77VWWJvMFs6tm8RCVmFf/sb1WvWsG/vXvos/ACTTZeziIiISPtSSJfOqbascZp6fv3oeMF34K5rXmuyQHJ//63O0oZBdErH9y0inUrqTTfhLioi6ZprFNBFRESkQyikS2gzDCjd5b+yet5677FA7DGNQbwhlKcMAlt4x/YtIp2Wu6wMS5x3RwZzRAQZs2cHuSMRERE5nCikS+hwOaBwc5MwXh/MHWWB6+My/aeqpw317kduNnds3yLSZdR8+y27r/4Dqbf+mfjzzgt2OyIiInIYUkiX4Kgqbpym3hDGi7aAx9W81myD1IGN25w1jJRHJHR83yLSpVV88gnusjJK33qbuHPOwWTRGhUiIiLSsRTSpX15PHBgR/PV1Sv2Ba6PSPDf5ix9mPd6cqtWUxaR9pdyww1YE5OIP+83CugiIiISFArp0nbqqr2LtzVdWT0vB5xVgesTejVuc9ZwDXlshvYeF5EOVf3VV0QccQQmsxmTyUTi5EuD3ZKIiIgcxhTS5eBU5Dcu4tawqFtxLhie5rXWcEgd/KPrx4dAWEzH9y0i0kTJP/5B/sxZJE6ZTOr06Zj0IaGIiIgEmUK6/Dy3yxu+83MaR8jzcqCqIHB9VEqTkfH6KeuJfcCiXzURCT3m2Nj6/9DUdhEREQkNSk7SyFEB+RsbR8jzcqBgE7hqm9eazJDUt8nIeP2fMWkd37eIyEGKP+ccwvr0IXzoUI2ii4iISEhQSD8cGQaU722+mNuBHYHrbVGN14w3LOqWOgjskR3bt4jIITKcTopfeIHEyZMxR3rPYRHDhgW5KxEREZFGCukhpnZTLu4871Ryt9tNwvc/UBP5OXX1qwxb0lMJH9y35Q/oqvNubdawzVnDNeQ1BwLXx2Y02eas/iuhl/YeF5EuYf8dd1D2zv+o+XY9Pf72tEbPRUREJOSEREh/+umnefTRR8nLy2PEiBHMnTuXsWPHBqx97rnn+Mc//kFOTg4Ao0ePZubMmT9Z35nUbsrF9upRhFudvmPHAaxsrHG7bNRe/HngoF5zoEkY3+Ddh7xgM3iczWvNVkge0CSMD/VOWY9KavOfS0QkVMRf+Dsql39K/IUXKKCLiIhISAp6SH/jjTeYNm0a8+bNY9y4ccyZM4dTTz2VLVu2kJqa2qx+2bJlXHTRRRx99NGEh4fz8MMPc8opp7Bx40YyMjKC8BO0HXdegV9AD8RidVK7Pw/SLU3CeH0wL9sd+E5hcf5hPH0YpAwEa1g7/BQiIqEr8ohR9F36EeaoqGC3IiIiIhJQ0EP67Nmzueqqq7jssssAmDdvHu+//z7z589n+vTpzepfeeUVv9vPP/88b731FkuXLmXy5Mkd0nOwRa46H1ZUB/5mfNaPtjobCvE9tfe4iByWnPv3s//Ou+h2/33YunUDUEAXERGRkBbUkF5XV8fatWuZMWOG75jZbGbixImsXr26RY9RXV2N0+kkMTEx4PcdDgcOh8N3u7y8HACn04nT+fOj1h3N7Xa3qM7krsaw2CFlIEbaMIy0oRhpQzBSh0B4XPM7uFxt3KmIdAUN58BQOxe2pb1/uZ2a1avZe/sdZPx9XrDbETnsHA7nGREJrs5ynmlNf0EN6UVFRbjdbtLS/LftSktLY/PmzS16jNtuu43u3bszceLEgN+fNWsW9957b7PjixcvJjIytFYnT/j+B+816L/gq/Cp7Bt4BIap/uUrBApL8bt4XUSkhZYsWRLsFtqN9fjjSSssZPtxx/HtBx8Eux2Rw1ZXPs+ISGgI9fNMdfVPzIQOIOjT3Q/FQw89xOuvv86yZcsIDw8PWDNjxgymTZvmu11eXk5mZiannHIKsbGxHdVqi9REft6inN1/9GmMOOGo9m9IRLo0p9PJkiVLmDRpEjabLdjttBlPdbVvezUALrk4eM2IHOa66nlGREJHZznPNMzobomghvTk5GQsFgv5+fl+x/Pz80lPT//Z+z722GM89NBDfPTRRwwfPvwn68LCwggLa75Ams1mC7kXsWGbtV9isVhCrncR6bxC8Xx4sCqXL2ffjL+Q+beniRg5MtjtiEi9rnSeEZHQFOrnmdb0FtTNr+12O6NHj2bp0qW+Yx6Ph6VLlzJ+/PifvN8jjzzC/fffz6JFixgzZkxHtCoiIiHOMAwOvPoa7pISSl55NdjtiIiIiByUoE93nzZtGlOmTGHMmDGMHTuWOXPmUFVV5VvtffLkyWRkZDBr1iwAHn74Ye666y5effVVsrOzycvLAyA6Opro6Oig/RxtwZKeittlw/Iz27C5XTYs6c23phMROdyZTCYynphN8YIFJF91VbDbERERETkoQQ/pF154IYWFhdx1113k5eUxcuRIFi1a5FtMbteuXZjNjQP+zzzzDHV1dfz2t7/1e5y7776be+65pyNbb3Phg/tSe/Hn1OYVAN7V3tet+4aRI0dhqZ8Kb0lPJXxw32C2KSISMgzDoHbjJiKGDgHAHBlJyrXXBrkrERERkYMX9JAOMHXqVKZOnRrwe8uWLfO7vXPnzvZvKIjCB/eF+hDudDo5UF1CxAlHhfT1FSIiwWAYBgWPPUbJC/Pp/sjDxJ11VrBbEhERETlkQb0mXURE5FAYjjoAXMUlQe5EREREpG2ExEi6iIhIa5lMJtL+MoOYSZOIGjc22O2IiIiItAmNpIuISKfhqa6m5JVXMAwDAJPZrIAuIiIiXYpG0kVEpFMw3G52XX01NV+txV1cTMr11we7JREREZE2p5F0ERHpFEwWC3Fnn405Lo6oY48NdjsiIiIi7UIj6SIi0mkknH8+MRMnYk1ICHYrIiIiIu1CI+kiIhKyHLm57L35Fjy1tb5jCugiIiLSlWkkXUREQpLhdLL7j9fi3L0ba3ISaTNmBLslERERkXankXQREQlJJpuNbg8+QOSRR5J0zTXBbkdERESkQ2gkXUREQopRV4fJbgcgauxYIv/xEiaTKchdiYiIiHQMjaSLiEjIKP3vf9l+1tk48wt8xxTQRURE5HCikC4iIiHB43BQ9Mwz1O3cSem//hXsdkRERESCQtPdRUQkJJjDwuj5wguU/fcdkq/9Y7DbEREREQkKjaSLiEjQGB4Pjh07fLftPXqQMvU6TGb98yQiIiKHJ70LEhGRoDBcLvbP+As7f3s+NTkbg92OiIiISEhQSBcRkaAw3G6ceXl4amup+2FnsNsRERERCQm6Jl1ERILCHBZGj6efpjZnA1FHHRXsdkRERERCgkbSRUSkw7gOHKB88WLfbUt0lAK6iIiISBMK6SIi0iHc5eX8cOml7L3hRsoXfRjsdkRERERCkqa7i4hIhzDHxBA5egyeikrC+vUNdjsiIiIiIUkhXUREOoTJZCL9rjtxT70Oa0pKsNsRERERCUma7i4iIu2m+utvKHh8NoZhAGCyWBTQRURERH6GRtJFRKRduIqL2X3llXiqq7H1zCTh/POD3ZKIiIhIyNNIuoiItAtrUhIpN08j6rhjiTvjjGC3IyIiItIpaCRdRETalOF2Y7JYAEi85BISLroIk1mfCYuIiIi0hN41iYhImyl+4QV2X3U1nro63zEFdBEREZGW0zsnERFpE868PAqf/htVq1ZR8eHiYLcjIiIi0ilpuruIiLQJW3o6mU8/Re2mTcSdqWvQRURERA6GQrqIiBw0o64OV2kpttRUAKLGjydq/PggdyUiIiLSeWm6u4iIHBRPTQ27p05l16WTcRUXB7sdERERkS5BIV1ERA6Ku7yCutxtOPPzcWzbFux2RERERLoETXcXEZGDYktLpef8F3CVHCDyiFHBbkdERESkS9BIuoiItJhz715qvv3Wd9uena2ALiIiItKGFNJFRKRF6nbvZuclv2fXlVdRu2VLsNsRERER6ZI03V1ERFrEmpyMrXt33GVlWOLigt2OiIiISJekkC4iIi1ijoggc94zGG431oSEYLcjIiIi0iUppIuIyE+q+PgT3OVlxJ9zDgCW2NjgNiQiIiLSxSmki4hIQDUbctjzpz8BYO/Zk8gjjghyRyIiIiJdn0K6iIgEFD50CHFnn43hchIxbFiw2xERERE5LCiki4iIj2EYAJhMJkwmE93uvw9MJkxmbQYiIiIi0hEU0kVEBPAG9IKHHwGTidRb/+wN6hZLsNsSEREROawopIuICAA1a9dSsmABALGnn0bE8OHBbUhERETkMBT0+YtPP/002dnZhIeHM27cOL744oufrN24cSPnnXce2dnZmEwm5syZ03GNBkH16s/Jenw21as/D3YrItIF/fgcEzlmDGkzptNt1iwFdBEREZEgCWpIf+ONN5g2bRp33303X3/9NSNGjODUU0+loKAgYH11dTW9e/fmoYceIj09vYO77ViGYVD85JOEFRRQ/OSTvutERUTaQtNzTNETT/jOMYlTphB/7jnBbU5ERETkMBbU6e6zZ8/mqquu4rLLLgNg3rx5vP/++8yfP5/p06c3qz/yyCM58sgjAQJ+vyup+mwljo0bAXBs3EjVZyuJPvYYwLtvseFyEnX0BCzRUQDU7d5N7XffYUtNJWLkSN/jVH76KZ7aWqLGjcMSFweAc+9eajZuxJqc7LelUuVnK/FUVxF55JFYExK8tfn51Hz7LZb4eKLGjm3s7/PPcZeXEzlqFNaUFABchYVUf/MNlthYoo46qrH2iy9wl5YSMWIEtrQ0b+2BA1R/+SXmyCiij5ngq63++mtcRUVEDB2KrXt3ANxlZVStWYM5PJzo447z1dasW4ezoIDwQYOwZ2Z6ayurqFq1EpPNRsyJJzbWbtiAc/9+wvv3x56dDYCnpobKFSswmc3ETJzoq63dtIm6PXsI69OHsD59vLV1dVQuWwZAzKRJmEwmb+2WLdT98AP27GzC+/cHwHC7qVi61Ft74omYbDbv65ibi2P7duw9exI+cKDv+coXLwYg+vjjMYeFeWu378CRuxVb9wwihg7x1VZ8/DGGy0X0Mcdgjoz0vvY//EDtli3Y0tP9Rj8rli3DqKsjavx4LDEx3to9e6ndtBFrcgqRR4xqfO1XrMBTU0PU2LFY4uO9r/3+/dRs2IA1MZHIMWMaX89Vq3BXVhI5ejTWpCRvbUEBNevWYYmNI+qocY21n6/BXV5GxMiR2FJTva99cTHVa9diiY4m6uijG1/7r77CVVJCxLBh2Lp1876epaVUffEF5ohI3+8/QPXX3+AqKiR88BDsPTK8tRUVVK1ejcluJ+aEExpf+/XrceblET5wIPaePb2vZ1UVlStXYrJaiTnppMbanI049+0lrG8/wnr38tY6HFQuXw5A7Cmn+Gprv/uOut27Cevdm7C+fb2vvdNJxSefeF/7k0/2Xc9d+/331O3ciT0ri/ABA7y1hkHFkiXe2hNOwGS3e1/7bdtwbNuGvUcPwgcPbnw9P/oIw+Mh+thjMUdEeF/PnTup/f57bN26EzFsaGNtC84RTc8xdd99R+WyZX7/z4iIiIhIcAQtpNfV1bF27VpmzJjhO2Y2m5k4cSKrV69us+dxOBw4HA7f7fLycgCcTidOp7PNnqctGYZBwZw5YDJB/ehWwZw52MeNxWQysW/6dDzl5fR85x3s9UGi/NNPKbz/AaImnky3J57wPdb+O+/ClZ9Pjzde973hL//8cwpuv4PICUfTfd48X23eAw/g3LmTjAUvEjF6NACVa9eSN+1mwo84gh4vLfDV5j/8MI7vNtPtmb8RdYw3PFXl5LD/+hsIGzSIzH+94astmDOH2q+/If2J2UTXh+GazZvZe/0N2LKzyXr3f77awqefpnrlKlJnPkjsmWcCULtjJ3uvvwFrejrZSxY31j73PFVLl5Jy5x3EXXABAHV797L3+hswx8XR+7MVvtril/5BxXvvkXTLLSRMmQyAs7CQvdffgMlup8/arxprX3ud8n//m8TrriPxmj8A3g8K9l5/AwB9vl3nW+m65M23KPvnP4m//HKSb7oR8Ia6htren6/GHOUNSQf+9y4Hnn2WuIsvJmVG44dMe2+4EQyD7E8+xpqcDEDZhx9S/OSTxJx7Lmn33dtYe8ufMaqryfrgA2yZPby1n3xC0UMPE33aaaQ/+kjja3/7HbiLi8l8603C6j9AqFj5GQV330PkCcfTfe7cxtp77sW1dy89Xv4n4SNGeGu//JL8W28jYtxYMp5/vvH3ZOYs6nJz6f78c0SO8wbyqnXrvK/98GFkvvJK4+/J7MdxrN9At7l/Jao+OFdv2sS+62/A3q8fPd9+q/H35K9/peaLL0l75GFiTj/d+9rn5npf+x49yF74QeNr//d5VC//lNT77iX23HMBcPywi73X34AlKYnwZZ/4aovmv0jlokUkT59O/CUXe1/7/Hzvax8VRZ/PG883xS+/TMV//0vSDTeQcOUVALhKSryvp9lMxLfrfLUl//o3Za+9RsIf/kDS1Ou8r31lZeNr/9WXvg9dDvz3HUrnzyd+8mSS/3wL4P0wp6G212crfB+ilb7/ASV/+xux559P6l13Nr72027GqKsja8libPUziUo/+ojixx4n5swzSJs501f7S+eI9NmzvecYsxk8Hu/f/+OzCZswwfcBlIhIW2h4rxWq77lEpPPrLOeZ1vQXtJBeVFSE2+0mrX5ktUFaWhqbN29us+eZNWsW9957b7PjixcvJrJ+JDLURG75nh71I1wNHBs3snzOk1QP6E9GejrmxASWrV6Fa/N3AET98AOJ2VkUuz1880FjmOmWnIw1IpwVX36Jc+dO7+Nv20ZSdhYlZjPrmtSmx8dhy85i5TffUJefD0DEtm0kZ2dRGmZnfZPatKgo7NlZrNmQQ239Bx/hP+wiJTuLsqgoNjSpTbWHEZadxZebN1NTVwdA2L59pGZnUR4fx8YmtclmMxHZWXydm0t1/XFbQQHp2Vm4oqLZ1KQ2yeMmMjuLdT/8QFX9ceuBA3TLzsITFs4HTWoTHQ6isrPI2buXivrjlooKumdnYVgsfrUJVZVEZ2exqSCf8vrj5ppaMrKzAFj4wQfecAPEl5USk51FfkkJX9TXmlwuetTXfrhkCUb9CGlcURGx2VkUlJfzZZPn65GVhQmDjz75BE99oI/JyyM+O4vCmmrWNqnN6N4ds8vJJ5+twFUf6qJ37yYhO4tip5Ovm9R2T03FEhPNp2vW4MzN9f6e7NhBYnYWJQZ+r323xESsNiufrf2aur17vb8nW7eSlJ3FAauVb5v+nsTGYMvOYvW33+IoLva+9jt2kpKdRWl4hP9rHx5BWHYWazZtora62vva79lDanYWZTEx5DSpTbHaCM/OYu3331Nd/+GUPS+ftOwsXDGxfq99MhCRncU327f7XntbURHp2Vm4IyL9Xs8kp5PI7CzW795NZcPvSVmZ9/fEZvd/7WtriM7OIicvz/d7Yq6qIiM7C8Nk8quNrygnJjuLvKJC3++Jqa6u8bX/8EMMq/cUG1dS4n3ty0p9vyd4PGTW1y5ZuhRPeDgAsQUFxGVnUVhVyVdNf08ye2Byu/l4+XLc9TMjYvbuJT47i6Jah//vyS+cI7bMebLZOaYuN9d3jhERaWtL6mcOiYi0l1A/z1TXvxduCZMRpIud9+3bR0ZGBqtWrWL8+PG+47feeivLly9nzZo1P3v/7OxsbrzxRm688cafrQs0kp6ZmUlRURGxsbGH9DO0B8Mw2HPRxTi++843wgWA2UzYoEH0eO1VjXSJyEHTOUZEOpLT6WTJkiVMmjQJW/3lXyIibamznGfKy8tJTk6mrKzsF3No0EbSk5OTsVgs5NeP2DbIz89v00XhwsLCCKufctqUzWYLyRexcsVnvutE/Xg8ODZupG7NF37X5oqItIbOMSISDKH6vktEuo5QP8+0pregre5ut9sZPXo0S+sX2ALweDwsXbrUb2T9cGIYBoVPPum9Fj0Qk4lCrfQuIgdJ5xgRERGR0BfU1d2nTZvGlClTGDNmDGPHjmXOnDlUVVX5VnufPHkyGRkZzJo1C/AuNrdp0ybff+/du5d169YRHR1N3/rVlTszw+nEuX+/b7G45gUGzrw8DKfTtxK0iEhL6RwjIiIiEvqCGtIvvPBCCgsLueuuu8jLy2PkyJEsWrTIt5jcrl27MJsbB/v37dvHqFGN20Y99thjPPbYYxx//PEsq98eqzMz2+30evPfuEpKAHC5XKxcuZIJEyZgrV+AypqUhFlvnkXkIOgcIyIiIhL6ghrSAaZOncrUqVMDfu/HwTs7O7vLT8O0devm2yPa6XTi2LmT8MGDQ/r6ChHpPHSOEREREQltQbsmXURERERERET8KaSLiIiIiIiIhAiFdBEREREREZEQoZAuIiIiIiIiEiIU0kVERERERERChEK6iIiIiIiISIhQSBcREREREREJEQrpIiIiIiIiIiFCIV1EREREREQkRCiki4iIiIiIiIQIhXQRERERERGREKGQLiIiIiIiIhIiFNJFREREREREQoQ12A10NMMwACgvLw9yJ7/M6XRSXV1NeXk5Npst2O2ISBejc4yItDedZ0SkvXWW80xD/mzIoz/nsAvpFRUVAGRmZga5ExERERERETmcVFRUEBcX97M1JqMlUb4L8Xg87Nu3j5iYGEwmU7Db+Vnl5eVkZmaye/duYmNjg92OiHQxOseISHvTeUZE2ltnOc8YhkFFRQXdu3fHbP75q84Pu5F0s9lMjx49gt1Gq8TGxob0L5yIdG46x4hIe9N5RkTaW2c4z/zSCHoDLRwnIiIiIiIiEiIU0kVERERERERChEJ6CAsLC+Puu+8mLCws2K2ISBekc4yItDedZ0SkvXXF88xht3CciIiIiIiISKjSSLqIiIiIiIhIiFBIFxEREREREQkRCukiIiIiIiIiIUIhXUTkMHPCCSdw4403BrsNERERkTbVVd7jKKSLiIiIiIiIhAiFdBEREREREZEQoZAeghYtWsQxxxxDfHw8SUlJnHHGGWzbti3YbYlIF+JyuZg6dSpxcXEkJydz5513oh05RaQteTweHnnkEfr27UtYWBg9e/bkwQcfDHZbItJFVFVVMXnyZKKjo+nWrRuPP/54sFtqMwrpIaiqqopp06bx1VdfsXTpUsxmM+eeey4ejyfYrYlIF/HSSy9htVr54osvePLJJ5k9ezbPP/98sNsSkS5kxowZPPTQQ9x5551s2rSJV199lbS0tGC3JSJdxJ///GeWL1/OO++8w+LFi1m2bBlff/11sNtqEyZDQychr6ioiJSUFDZs2MDQoUOD3Y6IdHInnHACBQUFbNy4EZPJBMD06dP53//+x6ZNm4LcnYh0BRUVFaSkpPDUU09x5ZVXBrsdEeliKisrSUpK4uWXX+b8888HoKSkhB49enD11VczZ86c4DZ4iDSSHoK2bt3KRRddRO/evYmNjSU7OxuAXbt2BbcxEekyjjrqKF9ABxg/fjxbt27F7XYHsSsR6Sq+++47HA4HJ598crBbEZEuaNu2bdTV1TFu3DjfscTERAYMGBDErtqONdgNSHNnnnkmWVlZPPfcc3Tv3h2Px8PQoUOpq6sLdmsiIiIivygiIiLYLYiIdFoaSQ8xxcXFbNmyhTvuuIOTTz6ZQYMGceDAgWC3JSJdzJo1a/xuf/755/Tr1w+LxRKkjkSkK+nXrx8REREsXbo02K2ISBfUp08fbDab3/uZAwcO8P333wexq7ajkfQQk5CQQFJSEs8++yzdunVj165dTJ8+PdhtiUgXs2vXLqZNm8Yf/vAHvv76a+bOndulVkUVkeAKDw/ntttu49Zbb8VutzNhwgQKCwvZuHEjV1xxRbDbE5FOLjo6miuuuII///nPJCUlkZqayu23347Z3DXGoBXSQ4zZbOb111/n+uuvZ+jQoQwYMIC//vWvnHDCCcFuTUS6kMmTJ1NTU8PYsWOxWCzccMMNXH311cFuS0S6kDvvvBOr1cpdd93Fvn376NatG9dcc02w2xKRLuLRRx+lsrKSM888k5iYGG6++WbKysqC3Vab0OruIiIiIiIiIiGia8wHEBEREREREekCFNJFREREREREQoRCuoiIiIiIiEiIUEgXERERERERCREK6SIiIiIiIiIhQiFdREREREREJEQopIuIiIiIiIiECIV0ERERERERkRChkC4iIiIt8n//93+cc845wW5DRESkS1NIFxERaSf/93//h8lkavaVm5sb7NaaWbZsWcBeTSYTeXl5wW6v1e655x5GjhwZ7DZERERazRrsBkRERLqy0047jRdffNHvWEpKSpC6+WVbtmwhNjbW71hqamqQuhERETn8aCRdRESkHYWFhZGenu73ZbFYAk4dv/HGGznhhBMA2LlzZ8BR7Ybvn3DCCQG/v3PnTi6//HLOOOMMv8d2Op2kpqbywgsv/Gy/qampzfo1mwO/XfB4PMyaNYtevXoRERHBiBEjePPNN33fbxid//DDDxk1ahQRERGcdNJJFBQUsHDhQgYNGkRsbCwXX3wx1dXVrX7cpUuXMmbMGCIjIzn66KPZsmULAAsWLODee+/l22+/9f29LFiwAMMwuOeee+jZsydhYWF0796d66+//mf/PkRERDqaRtJFRERCUGZmJvv37/fdzsvLY+LEiRx33HEAvP3229TV1fm+f91117Fx40bS0tK48sorOe6449i/fz/dunUD4L333qO6upoLL7ywzXqcNWsWL7/8MvPmzaNfv358+umn/P73vyclJYXjjz/eV3fPPffw1FNPERkZyQUXXMAFF1xAWFgYr776KpWVlZx77rnMnTuX2267rVWPe/vtt/P444+TkpLCNddcw+WXX87KlSu58MILycnJYdGiRXz00UcAxMXF8dZbb/HEE0/w+uuvM2TIEPLy8vj222/b7O9DRESkLSiki4iItKP33nuP6Oho3+3TTz+df//73794P4vFQnp6OgC1tbWcc845jB8/nnvuuQeAxMREX+0TTzzBxx9/zJo1a4iIiODoo49mwIAB/POf/+TWW28F4MUXX+T888/36yWQHj16+N3Oyspi48aNzeocDgczZ87ko48+Yvz48QD07t2bzz77jL///e9+YfqBBx5gwoQJAFxxxRXMmDGDbdu20bt3bwB++9vf8sknn3Dbbbe16nEffPBB3+3p06fz61//mtraWiIiIoiOjsZqtfr+DgF27dpFeno6EydOxGaz0bNnT8aOHfuzfx8iIiIdTSFdRESkHZ144ok888wzvttRUVGtfozLL7+ciooKlixZ0mzq+cKFC5k+fTrvvvsu/fv39x2/8sorefbZZ7n11lvJz89n4cKFfPzxx7/4XCtWrCAmJsZ322azBazLzc2lurqaSZMm+R2vq6tj1KhRfseGDx/u+++0tDQiIyN9Ab3h2BdffHFIj9swY6CgoICePXsG7Pn8889nzpw59O7dm9NOO41f/epXnHnmmVitejskIiKhQ/8qiYiItKOoqCj69u3b7LjZbMYwDL9jTqezWd0DDzzAhx9+yBdffOEXngE2bdrE7373Ox566CFOOeUUv+9NnjyZ6dOns3r1alatWkWvXr049thjf7HfXr16ER8f/4t1lZWVALz//vtkZGT4fS8sLMzvdtOgbzKZmgV/k8mEx+M55McFfI8TSGZmJlu2bOGjjz5iyZIlXHvttTz66KMsX778Jz+MEBER6WgK6SIiIkGQkpJCTk6O37F169b5hcW33nqL++67j4ULF9KnTx+/2qKiIs4880zOO+88brrppmaPn5SUxDnnnMOLL77I6tWrueyyy9q0/8GDBxMWFsauXbv8pqCHyuPa7Xbcbnez4xEREZx55pmceeaZXHfddQwcOJANGzZwxBFHHErbIiIibUYhXUREJAhOOukkHn30Uf7xj38wfvx4Xn75ZXJycnxTunNycpg8eTK33Xabb5Ez8IbPxMREzjvvPCIjI7nnnnv89jFPSUnBYrEA3invZ5xxBm63mylTprSor4KCAmpra/2OJSUlNRtpjomJ4ZZbbuGmm27C4/FwzDHHUFZWxsqVK4mNjW3x8/1YWz1udnY2O3bsYN26dfTo0YOYmBhee+013G4348aNIzIykpdffpmIiAiysrIOqlcREZH2oJAuIiISBKeeeip33nknt956K7W1tVx++eVMnjyZDRs2APDVV19RXV3NAw88wAMPPOC73/HHH8+yZcv49NNPAZoFzB07dpCdnQ3AxIkT6datG0OGDKF79+4t6mvAgAHNjq1evZqjjjqq2fH777+flJQUZs2axfbt24mPj+eII47gL3/5S4ue66e0xeOed955vP3225x44omUlpby4osvEh8fz0MPPcS0adNwu90MGzaMd999l6SkpEPqV0REpC2ZjB9fECciIiJdQmVlJRkZGbz44ov85je/CXY7IiIi0gIaSRcREeliPB4PRUVFPP7448THx3PWWWcFuyURERFpIYV0ERGRLmbXrl306tWLHj16sGDBAm0xJiIi0olouruIiIiIiIhIiDAHuwERERERERER8VJIFxEREREREQkRCukiIiIiIiIiIUIhXURERERERCREKKSLiIiIiIiIhAiFdBEREREREZEQoZAuIiIiIiIiEiIU0kVERERERERCxP8Dqr4yG9zlUf4AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "a = {\"a\": 0.2, \"b\": 0.3, \"c\": 0.6, \"d\": 0.6}\n",
        "b = {\"a\": 0.9, \"b\": 0.9, \"c\": 0.4, \"d\": 0.5}\n",
        "\n",
        "# Union\n",
        "union = {i: max(a[i], b[i]) for i in a}\n",
        "\n",
        "# Intersection\n",
        "intersection = {i: min(a[i], b[i]) for i in a}\n",
        "\n",
        "# Complement\n",
        "complement_a = {i: 1 - a[i] for i in a}\n",
        "complement_b = {i: 1 - b[i] for i in b}\n",
        "\n",
        "# Difference\n",
        "difference = {i: min(a[i], 1 - b[i]) for i in a}\n",
        "\n",
        "# Plotting the fuzzy sets and their operations\n",
        "x = list(a.keys())\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "# plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Plot Fuzzy Set a\n",
        "plt.plot(x, [a[i] for i in x], \"ms\", label='Fuzzy Set a')\n",
        "\n",
        "# Plot Fuzzy Set b\n",
        "plt.plot(x, [b[i] for i in x], \"bs\", label='Fuzzy Set b')\n",
        "\n",
        "# Plot Union\n",
        "plt.plot(x, [union[i] for i in x], label='Union', linestyle='-', marker='s')\n",
        "\n",
        "# Plot Intersection\n",
        "plt.plot(x, [intersection[i] for i in x], label='Intersection', linestyle='-', marker='s')\n",
        "\n",
        "# Plot Complement of a\n",
        "plt.plot(x, [complement_a[i] for i in x], label='Complement of a', linestyle=':', marker='^')\n",
        "\n",
        "# Plot Difference\n",
        "plt.plot(x, [difference[i] for i in x], label='Difference', linestyle=':', marker='^')\n",
        "\n",
        "plt.title('Fuzzy Set Operations')\n",
        "plt.xlabel('Fuzzy Elements')\n",
        "plt.ylabel('Membership Degrees')\n",
        "plt.legend(framealpha=1)\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FVXJjS1oJQ0_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vov7O8M6LwlL"
      },
      "source": [
        "## EX: 1.B DeMorgan's Law in Fuzzy Sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "ke9wkZKJKmpR",
        "outputId": "b87fb0bc-14ea-426b-a313-2743bb9015f6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have implemented Union, Intersection, and Complement functions\n",
        "\n",
        "def Union(a, b):\n",
        "    return {i: max(a[i], b[i]) for i in a}\n",
        "\n",
        "def Intersection(a, b):\n",
        "    intersection = {i: min(a[i], b[i]) for i in a}\n",
        "    return intersection\n",
        "\n",
        "def Complement(a):\n",
        "    complement = {i: 1 - a[i] for i in a}\n",
        "    return complement\n",
        "\n",
        "def visualize_fuzzy_set(title, fuzzy_set, style):\n",
        "    x = list(fuzzy_set.keys())\n",
        "    y = [fuzzy_set[i] for i in x]\n",
        "\n",
        "    plt.plot(x, y, style, label=title)\n",
        "\n",
        "# DeMorgan's First Law\n",
        "def DeMorgans_1stLaw(a, b):\n",
        "    AUB_comp = Complement(Union(a, b))\n",
        "    ACOMP_intr_BCOMP = Intersection(Complement(a), Complement(b))\n",
        "\n",
        "    plt.figure(figsize=(8, 5))\n",
        "    visualize_fuzzy_set('A ∪ B Complement', AUB_comp, \"bs--\")\n",
        "    visualize_fuzzy_set('A\\' ∩ B\\'', ACOMP_intr_BCOMP, \"ro\")\n",
        "\n",
        "    plt.title(\"DeMorgan's 1st Law\")\n",
        "    plt.xlabel('Fuzzy Elements')\n",
        "    plt.ylabel('Membership Degrees')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "# DeMorgan's Second Law\n",
        "def DeMorgans_2ndLaw(a, b):\n",
        "    COMP_INTR = Complement(Intersection(a, b))\n",
        "    ACOMP_union_BCOMP = Union(Complement(a), Complement(b))\n",
        "\n",
        "    plt.figure(figsize=(8, 5))\n",
        "    visualize_fuzzy_set('A\\' ∩ B\\' Complement', COMP_INTR, \"go\")\n",
        "    visualize_fuzzy_set('A\\' ∪ B\\'', ACOMP_union_BCOMP, \"m-.\")\n",
        "\n",
        "    plt.title(\"DeMorgan's 2nd Law\")\n",
        "    plt.xlabel('Fuzzy Elements')\n",
        "    plt.ylabel('Membership Degrees')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "# Example usage\n",
        "a = {\"a\": 0.2, \"b\": 0.3, \"c\": 0.6, \"d\": 0.6}\n",
        "b = {\"a\": 0.9, \"b\": 0.9, \"c\": 0.4, \"d\": 0.5}\n",
        "\n",
        "DeMorgans_1stLaw(a, b)\n",
        "DeMorgans_2ndLaw(a, b)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXNylwirPmMD"
      },
      "source": [
        "##  EX: 1.C Fuzzy Relations ( Max-Min and Max-Product Compositions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aajoiisRauFm",
        "outputId": "f924e002-8063-4d4f-e960-bbf86bca3de9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fuzzy set 1: [[0.6 0.3]\n",
            " [0.2 0.9]]\n",
            "\n",
            "Fuzzy set 2: [[1.  0.5 0.3]\n",
            " [0.8 0.4 0.7]]\n",
            "\n",
            "\u001b[92mMax-Min Composition: [[0.6 0.5 0.3]\n",
            " [0.8 0.4 0.7]]\n",
            "\n",
            "\u001b[94mMax_Product Composition: [[0.6  0.3  0.21]\n",
            " [0.72 0.36 0.63]]\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from colorama import Fore\n",
        "\n",
        "\n",
        "# Perform max-min composition\n",
        "def max_min_composition(R1, R2):\n",
        "    result = np.zeros((len(R1), len(R2[0])))\n",
        "    for i in range(len(R1)):\n",
        "        for j in range(len(R2[0])):\n",
        "            max_min = 0\n",
        "            for k in range(len(R1[0])):\n",
        "                max_min = max(max_min, min(R1[i][k], R2[k][j]))\n",
        "            result[i][j] = max_min\n",
        "    return result\n",
        "\n",
        "# Perform max-product composition\n",
        "def max_product(R1, R2):\n",
        "    result = np.zeros((len(R1), len(R2[0])))\n",
        "    for i in range(len(R1)):\n",
        "        for j in range(len(R2[0])):\n",
        "            max_min = 0\n",
        "            for k in range(len(R1[0])):\n",
        "                max_min = max(max_min, (R1[i][k]* R2[k][j]))\n",
        "            result[i][j] = max_min\n",
        "    return result\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    R1 = np.array([[0.6, 0.3],\n",
        "                    [0.2, 0.9]])\n",
        "\n",
        "    R2 = np.array([[1, 0.5, 0.3],\n",
        "                    [0.8, 0.4, 0.7]])\n",
        "\n",
        "\n",
        "    # Call the max_min_composition function\n",
        "    result = max_min_composition(R1, R2)\n",
        "    # Call the max_product_composition function\n",
        "    res = max_product(R1, R2)\n",
        "\n",
        "\n",
        "    print(f\"Fuzzy set 1: {R1}\\n\")\n",
        "    print(f\"Fuzzy set 2: {R2}\\n\")\n",
        "    print(Fore.LIGHTGREEN_EX + f\"Max-Min Composition: {result}\\n\")\n",
        "    print(Fore.LIGHTBLUE_EX + f\"Max_Product Composition: {res}\\n\\n\")\n",
        "\n",
        "    # Plotting the rows of the resulting matrices\n",
        "    plt.figure(figsize=(10, 6))\n",
        "\n",
        "    for i in range(len(result)):\n",
        "        plt.plot(result[i], label=f'Max-Min Composition Row {i + 1}', marker='s')\n",
        "\n",
        "    plt.title('Max-Min Composition Rows')\n",
        "    plt.xlabel('Columns of Result Matrix')\n",
        "    plt.ylabel('Values')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "\n",
        "    for i in range(len(res)):\n",
        "        plt.plot(res[i], label=f'Max-Product Composition Row {i + 1}', marker='o')\n",
        "\n",
        "    plt.title('Max-Product Composition Rows')\n",
        "    plt.xlabel('Columns of Result Matrix')\n",
        "    plt.ylabel('Values')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZR6_Ts0uRs_5"
      },
      "source": [
        "## EX: 2.A Fuzzy Light Controller"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9cqkzwEIQsq5",
        "outputId": "56136ed5-9cdf-4adf-de94-293872fa243f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x800 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Output DM: 7.000000000000001\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Define the membership functions\n",
        "x1_dark = [130, 150]\n",
        "x1_medium = [130, 150, 190, 210]\n",
        "x1_light = [190, 210]\n",
        "\n",
        "x2_ns = [-20, -10, 0]\n",
        "x2_ze = [-10, 0, 10]\n",
        "x2_ps = [0, 10, 20]\n",
        "\n",
        "dm_vs = [2, 4]\n",
        "dm_s = [2, 4, 6]\n",
        "dm_b = [4, 6, 8]\n",
        "dm_vb = [6, 8]\n",
        "\n",
        "# Define inputs\n",
        "x1_input = 125\n",
        "x2_input = -6\n",
        "\n",
        "# Fuzzification\n",
        "x1_universe = np.arange(120, 221, 1)\n",
        "x2_universe = np.arange(-10, 11, 1)\n",
        "\n",
        "x1_dark_membership = np.fmin(np.fmax(0, (x1_dark[1] - x1_universe) / (x1_dark[1] - x1_dark[0])), 1)\n",
        "x1_medium_membership = np.fmin(np.fmax(0, (np.minimum(x1_medium[2], x1_universe) - x1_medium[0]) / (x1_medium[1] - x1_medium[0])), 1)\n",
        "x1_light_membership = np.fmin(np.fmax(0, (x1_universe - x1_light[0]) / (x1_light[1] - x1_light[0])), 1)\n",
        "\n",
        "x2_ns_membership = np.fmin(np.fmax(0, (x2_ns[2] - x2_universe) / (x2_ns[2] - x2_ns[1])), 1)\n",
        "x2_ze_membership = np.fmin(np.fmax(0, (np.minimum(x2_ze[2], x2_universe) - x2_ze[0]) / (x2_ze[1] - x2_ze[0])), 1)\n",
        "x2_ps_membership = np.fmin(np.fmax(0, (x2_universe - x2_ps[0]) / (x2_ps[1] - x2_ps[0])), 1)\n",
        "\n",
        "# Rule Evaluation\n",
        "rule_strengths_vs = np.minimum(x1_light_membership[:len(x2_ns_membership)], x2_ns_membership)\n",
        "rule_strengths_s = np.minimum(x1_medium_membership[:len(x2_ze_membership)], x2_ze_membership)\n",
        "rule_strengths_b = np.minimum(x1_dark_membership[:len(x2_ps_membership)], x2_ps_membership)\n",
        "\n",
        "# Defuzzification\n",
        "numerator = np.sum([value * ((dm_vb[0] + dm_vb[1]) / 2) for value in [rule_strengths_vs, rule_strengths_s, rule_strengths_b]])\n",
        "denominator = np.sum([rule_strengths_vs, rule_strengths_s, rule_strengths_b])\n",
        "output = numerator / denominator if denominator != 0 else 0\n",
        "\n",
        "# Plot membership functions\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "plt.subplot(3, 2, 1)\n",
        "plt.plot(x1_universe, x1_dark_membership, label='Dark')\n",
        "plt.title('Membership Function for x1 (Dark)')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(3, 2, 2)\n",
        "plt.plot(x1_universe, x1_medium_membership, label='Medium')\n",
        "plt.title('Membership Function for x1 (Medium)')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(3, 2, 3)\n",
        "plt.plot(x1_universe, x1_light_membership, label='Light')\n",
        "plt.title('Membership Function for x1 (Light)')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(3, 2, 4)\n",
        "plt.plot(x2_universe, x2_ns_membership, label='Negative-Small')\n",
        "plt.title('Membership Function for x2 (Negative-Small)')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(3, 2, 5)\n",
        "plt.plot(x2_universe, x2_ze_membership, label='Zero')\n",
        "plt.title('Membership Function for x2 (Zero)')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(3, 2, 6)\n",
        "plt.plot(x2_universe, x2_ps_membership, label='Positive-Small')\n",
        "plt.title('Membership Function for x2 (Positive-Small)')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Plot output membership function\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot([dm_vs[0], dm_vs[1]], [1, 1], label='Very-small', color='blue')\n",
        "plt.plot([dm_s[0], dm_s[1], dm_s[2]], [0, 1, 0], label='Small', color='orange')\n",
        "plt.plot([dm_b[0], dm_b[1], dm_b[2]], [0, 1, 0], label='Big', color='green')\n",
        "plt.plot([dm_vb[0], dm_vb[1]], [0, 0], label='Very-big', color='red')\n",
        "plt.title('Membership Function for DM')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Print the output value\n",
        "print(\"Output DM:\", output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SO8Fm8QdR4LR"
      },
      "source": [
        "## EX: 2.B Fuzzy Logic Colntoller"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "QMccnvsSR_yh",
        "outputId": "1efd6453-63a9-4ce0-b3e3-0c2cb2b1e691"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Control action for x=3 and y=5 is u=6.0200000000000005\n"
          ]
        },
        {
          "data": {
            "image/png": 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wABMnTsSpU6fqXNzVBed4EDVDTlameM5T/Q/YuoPpIqchImocgiDAzNhQlFbfuzIHBASgvLwc9+/fr3Ii7z8nbHfp0kWryWSyhr5lOHr0qNbjI0eOoGvXrpBIJOjbty8UCgXy8/MrvXbF8J2a9O3bF+Hh4Th8+DB69eqFLVu2NCjrkSNHKj1+eAhXbbVr1w65ublaB+813VekKr1794ZSqaw016aCsbExAGhdcvZh7u7uePDggdb/h9u3b+PixYvw8PCoU55HcXd318whqZCcnAwLCwu0b9++TttSKpWYOHEinnjiCQQFBWHFihW4e/cuFixY0KiZH8bCg6iZmjJMffr9x7O5yLwlFzkNEVHrJJFIcP78efzxxx+VzioAgIWFBebOnYs5c+YgNjYW6enpOHnyJFauXInY2NgGv35WVhZCQ0Nx8eJFbN26FStXrsSsWbMAqOdRvPrqqwgKCsKuXbuQkZGB1NRUREREYO/evdVuMyMjA+Hh4UhJScG1a9fw888/4/Lly/UuEiokJydj6dKluHTpEiIjI7Fz505N1roaMWIEbt68iaVLlyI9PR2RkZH48ccf67QNV1dXTJgwAZMmTUJcXBwyMjKQmJiIHTt2AAA6duwIQRCwZ88e3Lx5U3P26p+6du2KMWPGICQkBIcOHcLvv/+O8ePHw8XFBWPGjKnXvlXnzTffxPXr1zFz5kxcuHABP/zwAxYuXIjQ0FDN/I7a+vLLL3Hu3DmsXbsWAGBlZYUNGzZg+fLldZpDU1csPIiaqe6OFni8ezuoVMCGQ1fFjqM7RkbAwoXqZsS7tROR/rG0tKzxbs2LFy/G/PnzERERAXd3dwQEBGDv3r1wc3Nr8GsHBQXh3r17GDhwIKZPn45Zs2ZhypQpmuejo6MRFBSEt99+G927d0dgYCCOHTtW471GzMzMcOHCBTz//PPo1q0bpkyZgunTp2Pq1KkNyvr222/j+PHj6Nu3L5YsWYLly5dXe7nXR3F3d8fq1asRGRkJT09PpKamVnuVqJqsWbMGL7zwAt5880306NEDISEhkMvVX+a5uLjggw8+QFhYGBwcHDRD6h4WHR0Nb29vPPPMM/D19YVKpcK+ffsqDa9qKBcXF+zbtw+pqanw9PTEG2+8gcmTJ+P999+v03YuXbqEf//731i5cqXWmS9/f38EBwdj4sSJKCsra9TsFQQVr8dZpdre+p1ITEeu3sa4dUcgNTRActhI2LXR3bhMIqLGVFpaioyMDLi5ucHExETsOKRjrq6umD17ttb9L6h5qel3trbHzTzjQdSM+bjZwlNmjbIHSmw+nCl2HCIiIqJqsfAgasYEQcDU/8312HzkGkrKHzxijWZIqQTOnVM3HVwxhYiIiJoGL6dL1Mz593REx7ZmuHa7BDuOXcfEIQ0fM6xX7t0DevVS/1xcDNTyMoFERKQ/MjMzxY5AeoBnPIiaOYmBgJCh6rMeGw5l4IGCZwWIiIhI/7DwIGoBXvBuj7bmxrhx5x72nc0VOw4RERFRJSw8iFoAEyMJJgx2BQCsTUrX2d1QiYiIiOqLhQdRC/HaoI4wNZLgXHYRDqffFjsOERERkRYWHkQthI25McYOkAEAopLSRU5DREREpI2FB1ELMvkxN0gMBPx2+RbOZReKHYeIiIhIg4UHUQsiszXDqN5OAID1B6+KnKaRGBkBc+eqm5GR2GmIqJVTKBQYPHgw/vWvf2ktLywshEwmw7///e86b3PXrl3o378/rK2tYW5uDi8vL3z99deNFZlIb7DwIGphKm4o+N/TObhxp0TkNI3A2Bj47DN1MzYWOw0RtXISiQQxMTGIj4/Ht99+q1k+c+ZM2NraYuHChXXepq2tLf79738jJSUFp0+fRnBwMIKDg/HTTz81ZnQi0bHwIGpherlYYUiXtlAoVdh4KEPsOERELU63bt3wySefYObMmcjJycEPP/yAbdu2YfPmzTCuxxckI0aMwP/93//B3d0dnTt3xqxZs9CnTx8cOnRIB+mJxMPCg6gFmjqsMwBg+7HrKCgpFzlNAymVQGamuil5c0Qi0g8zZ86Ep6cnXnvtNUyZMgULFiyAp6en5vk2bdrU2N54440qt6tSqZCQkICLFy9i2LBhTbU7RE3CUOwAABAZGYnPPvsMubm58PT0xMqVKzFw4MAq+44YMQJJSUmVlo8aNQp79+4FAEycOBGxsbFaz/v7+yM+Pr7xwxPpoaFd7eDuZInzOUX45sg1zBjZVexI9XfvHuDmpv65uBgwNxc3DxHpnlxe/XMSCWBiUru+BgaAqemj+9bj74ogCFizZg3c3d3Ru3dvhIWFaT2flpZW4/qWlpZajwsLC+Hi4oKysjJIJBKsXr0aTz75ZJ1zEekz0QuP7du3IzQ0FFFRUfDx8cGKFSvg7++Pixcvwt7evlL/Xbt2obz8729wb9++DU9PT7z44ota/QICAhAdHa15LJVKdbcTRHpGEARMHdYJs7enIeZwJl4f2gkmRhKxYxER1U6bNtU/N2oU8L8vGgEA9vZASTXz2YYPBxIT/37s6grculW5Xz1vurpp0yaYmZkhIyMDN27cgKurq+a5Ll261GlbFhYWSEtLQ3FxMRISEhAaGopOnTphxIgR9cpGpI9EH2q1fPlyhISEIDg4GB4eHoiKioKZmRk2bdpUZX9bW1s4Ojpq2v79+2FmZlap8JBKpVr9bGxsmmJ3iPTG6D5OcLE2xa3icuw6+afYcYiIWpTDhw/jiy++wJ49ezBw4EBMnjwZqn8UMHUdamVgYIAuXbrAy8sLb7/9Nl544QVEREQ09W4R6ZSoZzzKy8tx4sQJhIeHa5YZGBjAz88PKSkptdrGxo0bMW7cOJg/dJo0MTER9vb2sLGxwciRI7FkyRK0bdu2UfMT6TMjiQEmP+aGD/f8gfW/XcXYATJIDASxYxERPVpxcfXPSR46e5ufX31fg4e+X83MrHekfyopKcHEiRMxbdo0PP7443Bzc0Pv3r0RFRWFadOmAaj7UKuHKZVKlJWVNUpeIn0hauFx69YtKBQKODg4aC13cHDAhQsXHrl+amoqzp49i40bN2otDwgIwL/+9S+4ubkhPT0d7733Hp5++mmkpKRA8vAfrP8pKyvT+gUvKiqqxx4R6ZexA2T4MuEyMm7Jsf+PPAT0chQ7EhHRo9VlzoWu+tYgPDwcKpUKn3zyCQDA1dUVy5Ytw9y5c/H000/D1dW1TkOtIiIi0L9/f3Tu3BllZWXYt28fvv76a6xZs6ZR8hLpC9HneDTExo0b0bt370oT0ceNG6f5uXfv3ujTpw86d+6MxMREPPHEE1VuKyIiAh988IFO8xI1NXOpIV4b1BGrDlzB2oPp8O/pAEHgWQ8iovpKSkpCZGQkEhMTYWZmplk+depU7Nq1C5MnT8Yvv/xSp7+1crkcb775Jm7cuAFTU1P06NED33zzDcaOHauLXSASjahzPOzs7CCRSJCXl6e1PC8vD46ONX8zK5fLsW3bNkyePPmRr9OpUyfY2dnhypUr1fYJDw9HYWGhpl2/fr12O0Gk5yYMdoWxoQFOZRXg+LU7YschImrWhg8fjgcPHuCxxx6r9NxPP/2EhISEOn/Bs2TJEly+fBn37t3DX3/9hcOHD7PooBZJ1MLD2NgY3t7eSEhI0CxTKpVISEiAr69vjevu3LkTZWVlGD9+/CNf58aNG7h9+zacnJyq7SOVSmFpaanViFqCdhZSPN+vPQBgbVK6yGnqwdAQePNNdTNs1idpiYiIWjXRr2oVGhqK9evXIzY2FufPn8e0adMgl8sRHBwMAAgKCtKafF5h48aNCAwMrDRhvLi4GO+88w6OHDmCzMxMJCQkYMyYMejSpQv8/f2bZJ+I9E3IUDcIAvDL+Xxczrsrdpy6kUqByEh142WxiYiImi3Rvz4cO3Ysbt68iQULFiA3NxdeXl6Ij4/XTDjPysqCwUNXpbh48SIOHTqEn3/+udL2JBIJTp8+jdjYWBQUFMDZ2RlPPfUUFi9ezHt5UKvVqV0bPOXhgJ/O5WH9b1ex9AXPR69ERERE1IgElaqed81p4YqKimBlZYXCwkIOu6IW4WTWHfxr9WEYSQQcmjcSDpYmj15JH6hUf9/wy84O4OR4ohahtLQUGRkZcHNzg4lJM/l7RNSK1fQ7W9vjZtGHWhFR0+jXwQYDXW1xX6HCpuQMsePUXkmJ+s7ENd2dmIiIiPQeCw+iVmTKsE4AgC1HsnC39L7IaYiIAA68IGoeGuN3lYUHUSsysoc9uti3wd2yB9iamiV2HCJqxYyMjACo7wJORPqv4ne14ne3PkSfXE5ETcfAQMCUoZ3w7nenselQJiYOdoOxIb9/IKKmJ5FIYG1tjfz8fACAmZkZb3BKpIdUKhVKSkqQn58Pa2trSCSSem+LhQdRKzOmrzOW/XwRuUWl2P17Nl7wbi92JCJqpSpuFlxRfBCR/rK2tn7kDb4fhYUHUSsjNZRg0mNu+OTHC1h3MB3P93Pht4xEJApBEODk5AR7e3vcv895Z0T6ysjIqEFnOiqw8CBqhV7x6YBVv17BpbxiJF68icd72IsdiYhaMYlE0igHNUSk3zi4m6gVsjQxwis+HQAAUUnpIqd5BENDYMIEdTPkdyVERETNFQsPolYqeIgrDA0EHM34C2nXC8SOUz2pFIiJUTepVOw0REREVE8sPIhaKScrU4zxcgEArDuo52c9iIiIqNlj4UHUilXcUPDHs7nIvCUXOU01VCpALlc33miMiIio2WLhQdSKdXe0wOPd20GlAjYcuip2nKqVlABt2qgbbzRGRETUbLHwIGrlpg7vDADYefwGbhWXiZyGiIiIWioWHkStnI+bLTxl1ih7oMTmw5lixyEiIqIWioUHUSsnCAKm/m+ux+Yj11BS/kDkRERERNQSsfAgIvj3dETHtmYoKLmPHceuix2HiIiIWiAWHkQEiYGA14eqz3psOJSBBwqlyImIiIiopWHhQUQAgBe926OtuTFu3LmHfWdzxY5DRERELQwLDyICAJgYSTBhsCsAYG1SOlT6cs8MiQR44QV1k0jETkNERET1xMKDiDReG9QRpkYSnMsuwuH022LHUTMxAXbuVDcTE7HTEBERUT2x8CAiDRtzY4wdIAMARCWli5yGiIiIWhIWHkSkZfJjbpAYCPjt8i2cyy4UOw4RERG1ECw8iEiLzNYMo3o7AQDWH7wqchoAcjkgCOoml4udhoiIiOqJhQcRVVJxQ8H/ns7BjTslIqchIiKiloCFBxFV0svFCkO6tIVCqcKmQ5lixyEiIqIWQC8Kj8jISLi6usLExAQ+Pj5ITU2ttm9MTAwEQdBqJg9d6UalUmHBggVwcnKCqakp/Pz8cPnyZV3vBlGLMnVYZwDAtmNZKCgpFzkNERERNXeiFx7bt29HaGgoFi5ciJMnT8LT0xP+/v7Iz8+vdh1LS0vk5ORo2rVr17SeX7p0Kb766itERUXh6NGjMDc3h7+/P0pLS3W9O0QtxtCudnB3skRJuQLfHLn26BWIiIiIaiB64bF8+XKEhIQgODgYHh4eiIqKgpmZGTZt2lTtOoIgwNHRUdMcHBw0z6lUKqxYsQLvv/8+xowZgz59+mDz5s3Izs5GXFxcE+wRUcsgCIJmrkfM4UyU3leInIiIiIiaM1ELj/Lycpw4cQJ+fn6aZQYGBvDz80NKSkq16xUXF6Njx46QyWQYM2YMzp07p3kuIyMDubm5Wtu0srKCj49PjdssKytDUVGRViNq7Ub3cYKLtSluFZdj18k/xY5DREREzZiohcetW7egUCi0zlgAgIODA3Jzc6tcp3v37ti0aRN++OEHfPPNN1AqlRg8eDBu3LgBAJr16rJNAIiIiICVlZWmyWSyhuwaUYtgJDHApMfcAADrf7sKhVLV9CEkEmDUKHWTSJr+9YmIiKhRiD7Uqq58fX0RFBQELy8vDB8+HLt27UK7du2wdu3aBm03PDwchYWFmnb9+vVGSkzUvI0bIIOVqREybsmx/4+8pg9gYgLs3atuD11IgoiIiJoPUQsPOzs7SCQS5OVpH8zk5eXB0dGxVtswMjJC3759ceXKFQDQrFfXbUqlUlhaWmo1IgLMpYZ4bVBHAMDag+lQqUQ460FERETNnqiFh7GxMby9vZGQkKBZplQqkZCQAF9f31ptQ6FQ4MyZM3ByUt9p2c3NDY6OjlrbLCoqwtGjR2u9TSLSNmGwK4wNDXAqqwDHr90ROw4RERE1Q6IPtQoNDcX69esRGxuL8+fPY9q0aZDL5QgODgYABAUFITw8XNP/ww8/xM8//4yrV6/i5MmTGD9+PK5du4bXX38dgPpKPLNnz8aSJUuwe/dunDlzBkFBQXB2dkZgYKAYu0jU7LWzkOL5fu0BAGuT0pv2xeVywNxc3eTypn1tIiIiajSGYgcYO3Ysbt68iQULFiA3NxdeXl6Ij4/XTA7PysqCgcHf9dGdO3cQEhKC3Nxc2NjYwNvbG4cPH4aHh4emz7vvvgu5XI4pU6agoKAAjz32GOLj4yvdaJCIai9kqBu2HcvCL+fzcTnvLro6WDTdi5eUNN1rERERkU4IKg7YrlJRURGsrKxQWFjI+R5E/zP16+P46VweXurfHktf8GyaF5XLgTZt1D8XF6vPfBAREZHeqO1xs+hDrYio+Zg6vDMA4PtTfyKvqFTkNERERNScsPAgolrr18EGA1xtcF+hQnRypthxiIiIqBlh4UFEdTJ1mPqsx7dHruFu6X2R0xAREVFzwcKDiOpkZA97dLFvg7tlD7A1NUvsOERERNRMsPAgojoxMBAwZWgnAMCmQ5kof6DU9QsCw4ermwH/ZBERETVX/FeciOpsTF9n2FtIkVtUit2/Z+v2xUxNgcREdTM11e1rERERkc6w8CCiOpMaSjDpMTcAwLqD6eBVuYmIiOhRWHgQUb284tMBbaSGuJRXjMSLN8WOQ0RERHqOhQcR1YuliRFe8ekAAIhKStfdC8nlQLt26iaX6+51iIiISKdYeBBRvQUPcYWhgYCjGX8h7XqB7l7o1i11IyIiomaLhQcR1ZuTlSnGeLkAUM/1ICIiIqoOCw8iapApw9SX1v3xbC4yb3EoFBEREVWNhQcRNUh3Rws83r0dVCpgw6GrYschIiIiPcXCg4gabOrwzgCAncdv4FZxmchpiIiISB+x8CCiBvNxs4VneyuUPVBi8+FMseMQERGRHmLhQUQNJgiC5qzH5iPXUFL+oPE2bmAA9O+vbgb8k0VERNRc8V9xImoU/j0d0bGtGQpK7mPHseuNt2FTU+DYMXUzNW287RIREVGTYuFBRI1CYiDg9aHqK1xtOJSBBwqlyImIiIhIn7DwIKJG86J3e7Q1N8aNO/ew72yu2HGIiIhIj7DwIKJGY2IkwYTBrgCAtUnpUKlUDd9oSQng6qpuJSUN3x4RERGJgoUHETWq1wZ1hKmRBOeyi3A4/XbDN6hSAdeuqVtjFDJEREQkChYeRNSobMyNMXaADAAQlZQuchoiIiLSFyw8iKjRTX7MDQYC8NvlW/gju0jsOERERKQHWHgQUaOT2ZphdB9nAMC6gzzrQURERHpSeERGRsLV1RUmJibw8fFBampqtX3Xr1+PoUOHwsbGBjY2NvDz86vUf+LEiRAEQasFBAToejeI6B+mDlNfWve/p3Nw4w4nhRMREbV2ohce27dvR2hoKBYuXIiTJ0/C09MT/v7+yM/Pr7J/YmIiXn75ZRw4cAApKSmQyWR46qmn8Oeff2r1CwgIQE5OjqZt3bq1KXaHiP6nl4sVhnRpC4VShU2HMsWOQ0RERCITvfBYvnw5QkJCEBwcDA8PD0RFRcHMzAybNm2qsv+3336LN998E15eXujRowc2bNgApVKJhIQErX5SqRSOjo6aZmNj0xS7Q0T/MHVYZwDAtmNZKCgpr99GBAHw8FA3QWjEdERERNSURC08ysvLceLECfj5+WmWGRgYwM/PDykpKbXaRklJCe7fvw9bW1ut5YmJibC3t0f37t0xbdo03L7dCJf1JKI6GdrVDu5OligpV+CbI9fqtxEzM+DcOXUzM2vcgERERNRkRC08bt26BYVCAQcHB63lDg4OyM2t3V2P582bB2dnZ63iJSAgAJs3b0ZCQgI+/fRTJCUl4emnn4ZCoah2O2VlZSgqKtJqRNQwgiBo5nrEHM5E6f3qfweJiIioZRN9qFVDfPLJJ9i2bRu+//57mJiYaJaPGzcOzz33HHr37o3AwEDs2bMHx44dQ2JiYrXbioiIgJWVlabJZLIm2AOilm90Hye4WJviVnE5dp3889ErEBERUYskauFhZ2cHiUSCvLw8reV5eXlwdHSscd1ly5bhk08+wc8//4w+ffrU2LdTp06ws7PDlStXqu0THh6OwsJCTbt+/Xrtd4SIqmUkMcCkx9wAABt+uwqFso53Hy8pAXr2VLcSXh2LiIiouRK18DA2Noa3t7fWxPCKieK+vr7Vrrd06VIsXrwY8fHx6N+//yNf58aNG7h9+zacnJyq7SOVSmFpaanViKhxjBsgg5WpEa7ekmP/H3mPXuGfVCrgjz/UTVXHooWIiIj0huhDrUJDQ7F+/XrExsbi/PnzmDZtGuRyOYKDgwEAQUFBCA8P1/T/9NNPMX/+fGzatAmurq7Izc1Fbm4uiouLAQDFxcV45513cOTIEWRmZiIhIQFjxoxBly5d4O/vL8o+ErV25lJDvDaoIwBg7cF0qFhAEBERtTqiFx5jx47FsmXLsGDBAnh5eSEtLQ3x8fGaCedZWVnIycnR9F+zZg3Ky8vxwgsvwMnJSdOWLVsGAJBIJDh9+jSee+45dOvWDZMnT4a3tzd+++03SKVSUfaRiIAJg11hbGiAU1kFOH7tjthxiIiIqIkJKn71WKWioiJYWVmhsLCQw66IGkn4rjPYmpoFP3d7bJgwoHYryeVAmzbqn4uLAXNz3QUkIiKiOqvtcbPoZzyIqPUIGeoGQQB+OZ+Py3l3xY5DRERETYiFBxE1mU7t2uApD/UwyvW/XRU5DRERETUlFh5E1KSmDu8MAPj+1J/IKyp99AqCAHTsqG6CoON0REREpCssPIioSfXrYIMBrja4r1AhOjnz0SuYmQGZmepmZqbjdERERKQrLDyIqMlNHaY+6/HtkWu4W3pf5DRERETUFFh4EFGTG9nDHl3s2+Bu2QNsTc0SOw4RERE1ARYeRNTkDAwETBnaCQCw6VAmyh8oq+987x4wYIC63bvXRAmJiIiosbHwICJRjOnrDHsLKXKLSrH79+zqOyqVwPHj6qasoUAhIiIivcbCg4hEITWUIHiIGwBg3cF08F6mRERELRsLDyISzSs+HdBGaohLecVIvHhT7DhERESkQyw8iEg0VqZGeMWnAwAgKild5DRERESkSyw8iEhUwUNcYWgg4GjGX0i7XiB2HCIiItIRFh5EJConK1OM8XIBoJ7rQURERC0TCw8iEt2UYepL6/54NheZt+SVO9jZqRsRERE1Wyw8iEh03R0t8Hj3dlCpgA2Hrmo/aW4O3Lypbubm4gQkIiKiBmPhQUR6YerwzgCAncdv4FZxmchpiIiIqLGx8CAiveDjZgvP9lYoe6DE5pRrYschIiKiRsbCg4j0giAImrMem1MyUVL+QP3EvXvAiBHqdu+eaPmIiIioYVh4EJHe8O/piI5tzVBQch87jl1XL1QqgaQkdVMqxQ1IRERE9cbCg4j0hsRAwOtD1Ve42nAoAw8ULDSIiIhaChYeRKRXXvRuj7bmxrhx5x72nc0VOw4RERE1EhYeRKRXTIwkCPJ1BQCsTUqHSqUSNxARERE1ChYeRKR3gnw7wtRIgnPZRThy9bbYcYiIiKgRsPAgIr1jY26MsQNkAICNhzJETkNERESNgYUHEemlyY+5wUAAkq/chtLUDDAzEzsSERERNYBeFB6RkZFwdXWFiYkJfHx8kJqaWmP/nTt3okePHjAxMUHv3r2xb98+redVKhUWLFgAJycnmJqaws/PD5cvX9blLhBRI5PZmmF0H2fcMzZB6KZDgFwOmJuLHYuIiIjqSfTCY/v27QgNDcXChQtx8uRJeHp6wt/fH/n5+VX2P3z4MF5++WVMnjwZp06dQmBgIAIDA3H27FlNn6VLl+Krr75CVFQUjh49CnNzc/j7+6O0tLSpdouIGsHUYepL6/73dA5u3CkROQ0RERE1hKAS+ZIxPj4+GDBgAFatWgUAUCqVkMlkmDlzJsLCwir1Hzt2LORyOfbs2aNZNmjQIHh5eSEqKgoqlQrOzs54++23MXfuXABAYWEhHBwcEBMTg3HjxtUqV1FREaysrFBYWAhLS8tG2FMiqo9XNxxB8pXbmDTEDQue9RA7DhERET2ktsfNhk2YqZLy8nKcOHEC4eHhmmUGBgbw8/NDSkpKleukpKQgNDRUa5m/vz/i4uIAABkZGcjNzYWfn5/meSsrK/j4+CAlJaXawqOsrAxlZWWax0VFRfXdLSJqRG/4uGByxEwY/Ad44vRilBsZix2JiIhI73w7eRA6tNXv+ZCiFh63bt2CQqGAg4OD1nIHBwdcuHChynVyc3Or7J+bm6t5vmJZdX2qEhERgQ8++KDO+0BEuvWYmw2Eq8cBANNuy3HPWCFyIiIiIv1zX6kUO8IjiVp46JPw8HCtMylFRUWQyWQiJiIiABAEQfPztik+UJpxgjkREdHDXKxNxY7wSKIWHnZ2dpBIJMjLy9NanpeXB0dHxyrXcXR0rLF/xX/z8vLg5OSk1cfLy6vaLFKpFFKptD67QURNxFNmwytbERERNVOiXtXK2NgY3t7eSEhI0CxTKpVISEiAr69vlev4+vpq9QeA/fv3a/q7ubnB0dFRq09RURGOHj1a7TaJiIiIiEi3RB9qFRoaigkTJqB///4YOHAgVqxYAblcjuDgYABAUFAQXFxcEBERAQCYNWsWhg8fjs8//xyjR4/Gtm3bcPz4caxbtw6AeljG7NmzsWTJEnTt2hVubm6YP38+nJ2dERgYKNZuEhERERG1aqIXHmPHjsXNmzexYMEC5ObmwsvLC/Hx8ZrJ4VlZWTAw+PvEzODBg7Flyxa8//77eO+999C1a1fExcWhV69emj7vvvsu5HI5pkyZgoKCAjz22GOIj4+HiYlJk+8fERERERHpwX089FVhYSGsra1x/fp13seDSExyOeDsrP45O5tzPIiIiPRMxUWZCgoKYGVlVW0/0c946Ku7d+8CAK9sRaRPKgoQIiIi0jt3796tsfDgGY9qKJVKZGdnw8LCQutynk2lonLkGRfiZ4EAfg7ob/wsUAV+FgjQj8+BSqXC3bt34ezsrDVF4mE841ENAwMDtG/fXuwYsLS05B8TAsDPAqnxc0AV+FmgCvwsECD+56CmMx0VRL2cLhERERERtQ4sPIiIiIiISOdYeOgpqVSKhQsX8m7qxM8CAeDngP7GzwJV4GeBgOb1OeDkciIiIiIi0jme8SAiIiIiIp1j4UFERERERDrHwoOIiIiIiHSOhQcREREREekcCw89FRkZCVdXV5iYmMDHxwepqaliR6ImFhERgQEDBsDCwgL29vYIDAzExYsXxY5FIvvkk08gCAJmz54tdhQSwZ9//onx48ejbdu2MDU1Re/evXH8+HGxY1ETUigUmD9/Ptzc3GBqaorOnTtj8eLF4LWCWr6DBw/i2WefhbOzMwRBQFxcnNbzKpUKCxYsgJOTE0xNTeHn54fLly+LE7YaLDz00Pbt2xEaGoqFCxfi5MmT8PT0hL+/P/Lz88WORk0oKSkJ06dPx5EjR7B//37cv38fTz31FORyudjRSCTHjh3D2rVr0adPH7GjkAju3LmDIUOGwMjICD/++CP++OMPfP7557CxsRE7GjWhTz/9FGvWrMGqVatw/vx5fPrpp1i6dClWrlwpdjTSMblcDk9PT0RGRlb5/NKlS/HVV18hKioKR48ehbm5Ofz9/VFaWtrESavHy+nqIR8fHwwYMACrVq0CACiVSshkMsycORNhYWEipyOx3Lx5E/b29khKSsKwYcPEjkNNrLi4GP369cPq1auxZMkSeHl5YcWKFWLHoiYUFhaG5ORk/Pbbb2JHIRE988wzcHBwwMaNGzXLnn/+eZiamuKbb74RMRk1JUEQ8P333yMwMBCA+myHs7Mz3n77bcydOxcAUFhYCAcHB8TExGDcuHEipv0bz3jomfLycpw4cQJ+fn6aZQYGBvDz80NKSoqIyUhshYWFAABbW1uRk5AYpk+fjtGjR2v9baDWZffu3ejfvz9efPFF2Nvbo2/fvli/fr3YsaiJDR48GAkJCbh06RIA4Pfff8ehQ4fw9NNPi5yMxJSRkYHc3FytfyOsrKzg4+OjV8ePhmIHIG23bt2CQqGAg4OD1nIHBwdcuHBBpFQkNqVSidmzZ2PIkCHo1auX2HGoiW3btg0nT57EsWPHxI5CIrp69SrWrFmD0NBQvPfeezh27BjeeustGBsbY8KECWLHoyYSFhaGoqIi9OjRAxKJBAqFAh999BFeffVVsaORiHJzcwGgyuPHiuf0AQsPomZg+vTpOHv2LA4dOiR2FGpi169fx6xZs7B//36YmJiIHYdEpFQq0b9/f3z88ccAgL59++Ls2bOIiopi4dGK7NixA99++y22bNmCnj17Ii0tDbNnz4azszM/B6T3ONRKz9jZ2UEikSAvL09reV5eHhwdHUVKRWKaMWMG9uzZgwMHDqB9+/Zix6EmduLECeTn56Nfv34wNDSEoaEhkpKS8NVXX8HQ0BAKhULsiNREnJyc4OHhobXM3d0dWVlZIiUiMbzzzjsICwvDuHHj0Lt3b7z22muYM2cOIiIixI5GIqo4RtT340cWHnrG2NgY3t7eSEhI0CxTKpVISEiAr6+viMmoqalUKsyYMQPff/89fv31V7i5uYkdiUTwxBNP4MyZM0hLS9O0/v3749VXX0VaWhokEonYEamJDBkypNIltS9duoSOHTuKlIjEUFJSAgMD7cM3iUQCpVIpUiLSB25ubnB0dNQ6fiwqKsLRo0f16viRQ630UGhoKCZMmID+/ftj4MCBWLFiBeRyOYKDg8WORk1o+vTp2LJlC3744QdYWFhoxmhaWVnB1NRU5HTUVCwsLCrN6zE3N0fbtm0536eVmTNnDgYPHoyPP/4YL730ElJTU7Fu3TqsW7dO7GjUhJ599ll89NFH6NChA3r27IlTp05h+fLlmDRpktjRSMeKi4tx5coVzeOMjAykpaXB1tYWHTp0wOzZs7FkyRJ07doVbm5umD9/PpydnTVXvtILKtJLK1euVHXo0EFlbGysGjhwoOrIkSNiR6ImBqDKFh0dLXY0Etnw4cNVs2bNEjsGieC///2vqlevXiqpVKrq0aOHat26dWJHoiZWVFSkmjVrlqpDhw4qExMTVadOnVT//ve/VWVlZWJHIx07cOBAlccFEyZMUKlUKpVSqVTNnz9f5eDgoJJKpaonnnhCdfHiRXFDP4T38SAiIiIiIp3jHA8iIiIiItI5Fh5ERERERKRzLDyIiIiIiEjnWHgQEREREZHOsfAgIiIiIiKdY+FBREREREQ6x8KDiIiIiIh0joUHERERERHpHAsPIiIiIiLSORYeRERERESkcyw8iIiIiIhI51h4EBERERGRzrHwICIiIiIinWPhQUREREREOsfCg4iIiIiIdI6FBxERERER6Zyh2AH0lVKpRHZ2NiwsLCAIgthxiIiIiIj0kkqlwt27d+Hs7AwDg+rPa7DwqEZ2djZkMpnYMYiIiIiImoXr16+jffv21T7PwqMaFhYWANRvoKWlpchpiIgI9+8D0dHqn4ODASMjcfMQEREAoKioCDKZTHP8XB0WHtWoGF5laWnJwoOISF/MnSt2AiIiqsajpieIPrn84MGDePbZZ+Hs7AxBEBAXF/fIdRITE9GvXz9IpVJ06dIFMTExlfpERkbC1dUVJiYm8PHxQWpqauOHJyIiIiKiWhG98JDL5fD09ERkZGSt+mdkZGD06NF4/PHHkZaWhtmzZ+P111/HTz/9pOmzfft2hIaGYuHChTh58iQ8PT3h7++P/Px8Xe0GERHpmkIBJCaqm0IhdhoiIqojQaVSqcQOUUEQBHz//fcIDAysts+8efOwd+9enD17VrNs3LhxKCgoQHx8PADAx8cHAwYMwKpVqwCor1Alk8kwc+ZMhIWF1SpLUVERrKysUFhYyKFWRET6QC4H2rRR/1xcDJibi5uHiIgA1P64udnN8UhJSYGfn5/WMn9/f8yePRsAUF5ejhMnTiA8PFzzvIGBAfz8/JCSklLtdsvKylBWVqZ5XFRU1LjB62jWtlP49QLP0JCan7sDlr/kyUs7E1GLpVAocP/+fbFjEFEVjIyMIJFIGrydZld45ObmwsHBQWuZg4MDioqKcO/ePdy5cwcKhaLKPhcuXKh2uxEREfjggw90krk+7pUrcLf0gdgxSE98f+pPvDywAwa62YodhYioUalUKuTm5qKgoEDsKERUA2trazg6OjboS9BmV3joSnh4OEJDQzWPKy4LJpbFgb0QPspdtNcn/bHy18vYdfJPrDuYzsKDiFqciqLD3t4eZmZmPLNLpGdUKhVKSko0c6WdnJzqva1mV3g4OjoiLy9Pa1leXh4sLS1hamoKiUQCiURSZR9HR8dqtyuVSiGVSnWSuT4cLE3EjkB6YsbjXfD9qT/xy/l8XM67i64ONV8jm4iouVAoFJqio23btmLHIaJqmJqaAgDy8/Nhb29f72FXol/Vqq58fX2RkJCgtWz//v3w9fUFABgbG8Pb21urj1KpREJCgqYPUXPSqV0bPOWhHjq4/rerIqchImo8FXM6zMzMRE5CRI9S8XvakLlYohcexcXFSEtLQ1paGgD15XLT0tKQlZUFQD0EKigoSNP/jTfewNWrV/Huu+/iwoULWL16NXbs2IE5c+Zo+oSGhmL9+vWIjY3F+fPnMW3aNMjlcgQHBzfpvhE1lqnDOwNQz/XIKyoVOQ0RUePi8Coi/dcYv6eiD7U6fvw4Hn/8cc3jinkWEyZMQExMDHJycjRFCAC4ublh7969mDNnDr788ku0b98eGzZsgL+/v6bP2LFjcfPmTSxYsAC5ubnw8vJCfHx8pQnnRM1Fvw42GOhqi9TMv7ApOQPhT3P+D7VCRkbA0qV//0xERM2KXt3HQ5/wPh6kb375Iw+vbz4OC6khDoePhIUJD7yIqHkrLS1FRkYG3NzcYGLCuY31MWLECHh5eWHFihVN+rqLFi1CXFycZsRKVcTK1hSaat+Sk5Pxxhtv4MKFCxg9ejTi4uJ0+no1qen3tbbHzaIPtSKi2hnZwx5d7NvgbtkDbE3NevQKRESkMxMnToQgCHjjjTcqPTd9+nQIgoCJEyc2fTA9smvXLixevLhB21i0aBEEQajUfvnll0ZKWbPExEQIglDpcs+NsW+1ERoaCi8vL2RkZCAmJqbRtjt58mT07t0b5eXlWsv37dsHY2NjnDx5stFe659YeBA1EwYGAqYM7QQA2HQoE+UPlCInImpiCgVw7Ji6KRRipyGCTCbDtm3bcO/ePc2y0tJSbNmyBR06dBAxWcMoFAoolQ3/N8bW1hYWFg2/EmPPnj2Rk5Oj1YYNG9bg7TZEY+3bo6Snp2PkyJFo3749rK2t67WNh4sLAPjiiy9w9+5dLFy4ULOsoKAAISEhmD9/Pvr161ffyDVi4UHUjIzp6wx7Cylyi0qx+/dsseMQNa3SUmDgQHUr5UUWSHz9+vWDTCbDrl27NMt27dqFDh06oG/fvlp9lUolIiIi4ObmBlNTU3h6euI///mP5vmKb9Z/+ukn9O3bF6amphg5ciTy8/Px448/wt3dHZaWlnjllVdQUlKite0HDx5gxowZsLKygp2dHebPn49/jqQvKyvD3Llz4eLiAnNzc/j4+CAxMVHzfExMDKytrbF79254eHhAKpUiKysLiYmJGDhwIMzNzWFtbY0hQ4bg2rVrWq/99ddfw9XVFVZWVhg3bhzu3r2reW7EiBGYPXu25rGrqysWL16Ml19+Gebm5nBxcUFkZOQj32dDQ0M4OjpqNWNjYyxatAheXl5afVesWAFXV1fN44kTJyIwMBDLli2Dk5MT2rZti+nTp2tdmamsrAzz5s2DTCaDVCpFly5dsHHjRmRmZmrmIdvY2GidxXp43+7cuYOgoCDY2NjAzMwMTz/9NC5fvlzpPf7pp5/g7u6ONm3aICAgADk5OVXuc2ZmJgRBwO3btzFp0iQIgqA545GUlISBAwdCKpXCyckJYWFhePDg75tOjxgxAjNmzMDs2bNhZ2enNQ+6gqWlJaKjo/H555/j6NGjAIDZs2fDxcUF4eHhj/x/Ul8sPIiaEamhBJMecwMArDuYDk7RIqKWRqVSoaT8gSitPn9TJ02ahOjoaM3jTZs2VXkVzYiICGzevBlRUVE4d+4c5syZg/HjxyMpKUmr36JFi7Bq1SocPnwY169fx0svvYQVK1Zgy5Yt2Lt3L37++WesXLlSa53Y2FgYGhoiNTUVX375JZYvX44NGzZonp8xYwZSUlKwbds2nD59Gi+++CICAgK0DoxLSkrw6aefYsOGDTh37hxsbW0RGBiI4cOH4/Tp00hJScGUKVO0rmyUnp6OuLg47NmzB3v27EFSUhI++eSTGt+vzz77DJ6enjh16hTCwsIwa9Ys7N+/v3Zvdj0dOHAA6enpOHDgAGJjYxETE6M1bCkoKAhbt27FV199hfPnz2Pt2rVo06YNZDIZvvvuOwDAxYsXkZOTgy+//LLK15g4cSKOHz+O3bt3IyUlBSqVCqNGjdIqcEpKSrBs2TJ8/fXXOHjwILKysjB37twqtyeTyZCTkwNLS0usWLECOTk5GDt2LP7880+MGjUKAwYMwO+//441a9Zg48aNWLJkidb6sbGxMDY2RnJyMqKioqp8jccffxxvvvkmJkyYgJ07d2LHjh3YvHkzDA11d+0p0a9qRUR184pPB6z69Qou5RUj8eJNPN7DXuxIRESN5t59BTwW/CTKa//xoT/MjOt2aDR+/HiEh4drzgQkJydj27ZtWmcUysrK8PHHH+OXX37R3FOsU6dOOHToENauXYvhw4dr+i5ZsgRDhgwBoB6HHx4ejvT0dHTqpB5q+8ILL+DAgQOYN2+eZh2ZTIYvvvgCgiCge/fuOHPmDL744guEhIQgKysL0dHRyMrKgrOzMwBg7ty5iI+PR3R0ND7++GMA6nszrF69Gp6engCAv/76C4WFhXjmmWfQubP6ku7u7tpXVFQqlYiJidEMOXrttdeQkJCAjz76qNr3a8iQIQgLCwMAdOvWDcnJyfjiiy/w5JNPVrvOmTNn0KZNG81jDw8PpKamVtv/YTY2Nli1ahUkEgl69OiB0aNHIyEhASEhIbh06RJ27NiB/fv3w8/PDwA07zWgHlIFAPb29tUOdbp8+TJ2796N5ORkDB48GADw7bffQiaTIS4uDi+++CIA9XscFRWleT9nzJiBDz/8sMptSiQSODo6QhAEWFlZaW6CvXr1ashkMqxatQqCIKBHjx7Izs7GvHnzsGDBAhgYqM8pdO3aFUsrrgJYg4iICMTHx2PcuHH4/PPP0aNHj0eu0xA840HUzFiaGOEVH/XY4aikdJHTEBG1bu3atcPo0aMRExOD6OhojB49GnZ2dlp9rly5gpKSEjz55JNo06aNpm3evBnp6dp/x/v06aP52cHBAWZmZloHwg4ODsjPz9daZ9CgQVpnInx9fXH58mUoFAqcOXMGCoUC3bp103rtpKQkrdc2NjbWem1bW1tMnDgR/v7+ePbZZ/Hll19WGhbk6uqqNc/BycmpUraHPXwzZ19fX5w/f77Gdbp3766551taWprmLERt9ezZU+tO2//MmZaWBolEolX81dX58+dhaGgIHx8fzbK2bduie/fuWvtmZmamKToezlGX1/L19dX6/z1kyBAUFxfjxo0bmmXe3t612p6pqSnmzp0LMzMzzJo1q05Z6oNnPIiaoeAhrohOzsDRjL+Qdr0AXjJrsSMRETUKUyMJ/viw8pj0pnrt+pg0aRJmzJgBAFXOWSguLgYA7N27Fy4uLlrPSaVSrcdG/7hHjSAIWo8rltVl4ndxcTEkEglOnDihdfANQOssgqmpaaUbxEVHR+Ott95CfHw8tm/fjvfffx/79+/HoEGDKmWtT7baMjY2RpcuXSotNzAwqDQ8rqq7ateU09TUtBGT1qyqHLoaMm1ubl7rvoaGhpBIJE1yI08WHkTNkJOVKZ7zdMF3J29g3cF0rH61dt9sEBHpO0EQ6jzcSWwBAQEoLy+HIAhVTuT954TthnyzXp2KycEVjhw5gq5du0IikaBv375QKBTIz8/H0KFD67ztvn37om/fvggPD4evry+2bNmiKTzq48iRI5UePzyEq7batWuH3NxcqFQqzUFzTfcVqUrv3r2hVCqRlJSkGWr1T8bGxgDUV/qqjru7Ox48eICjR49qhlrdvn0bFy9ehIeHR53yPIq7uzu+++47rX1OTk6GhYUF2rdv36ivpQscakXUTE0Zpj71/uPZXGTekouchoio9ZJIJDh//jz++OOPSmcVAMDCwgJz587FnDlzEBsbi/T0dJw8eRIrV65EbGxsg18/KysLoaGhuHjxIrZu3YqVK1dqhs1069YNr776KoKCgrBr1y5kZGQgNTUVERER2Lt3b7XbzMjIQHh4OFJSUnDt2jX8/PPPuHz5cr2LhArJyclYunQpLl26hMjISOzcubPeQ3xGjBiBmzdvYunSpUhPT0dkZCR+/PHHOm3D1dUVEyZMwKRJkxAXF4eMjAwkJiZix44dAICOHTtCEATs2bMHN2/e1Jy9+qeuXbtizJgxCAkJwaFDh/D7779j/PjxcHFxwZgxY+q1b9V58803cf36dcycORMXLlzADz/8gIULFyI0NFQzv0Of6X9CIqpSd0cLPN69HVQqYMOhq2LHIdI9IyNg4UJ1e2jIApHYLC0ta7xj8+LFizF//nxERETA3d0dAQEB2Lt3L9zc3Br82kFBQbh37x4GDhyI6dOnY9asWZgyZYrm+ejoaAQFBeHtt99G9+7dERgYiGPHjtV4rxEzMzNcuHABzz//PLp164YpU6Zg+vTpmDp1aoOyvv322zh+/Dj69u2LJUuWYPny5VWeJaoNd3d3rF69GpGRkfD09ERqamq1V4mqyZo1a/DCCy/gzTffRI8ePRASEgK5XP2FnouLCz744AOEhYXBwcFBM6TuYdHR0fD29sYzzzwDX19fqFQq7Nu3r9LwqoZycXHBvn37kJqaCk9PT7zxxhuYPHky3n///UZ9HV0RVLweZ5Vqe+t3IjEduXob49YdgdTQAMlhI2HXRvrolYiI9ERpaSkyMjLg5uYGExMTseOQjrm6umL27Nla97+g5qOm39faHjfzjAdRM+bjZgtPmTXKHiix+XCm2HGIiIiIqsXCg6gZEwQBU/8312PzkWsoKX/wiDWImjGlEjh3Tt10cOUcIiLSreZ12QgiqsS/pyM6tjXDtdsl2HHsOiYOafh4YSK9dO8e0KuX+ufiYqAOl4skIvFlZmaKHYFExjMeRM2cxEBAyFD1WY8NhzLwQMFvgomIiEj/sPAgagFe8G6PtubGuHHnHvadzRU7DhEREVElLDyIWgATIwkmDHYFAKxNStfZnVCJiIiI6ouFB1EL8dqgjjA1kuBcdhEOp98WOw4RERGRFhYeRC2Ejbkxxg6QAQCiktJFTkNERESkjYUHUQsy+TE3SAwE/Hb5Fs5lF4odh4iIiEiDhQdRCyKzNcOo3k4AgPUHr4qchqiRGRkBc+eqm5GR2GmoFVOpVPDz84O/v3+l51avXg1ra2vcuHGjTttctGgRBEHQaj169GisyER6gYUHUQtTcUPB/57OwY07JSKnIWpExsbAZ5+pm7Gx2GmoFRMEAdHR0Th69CjWrl2rWZ6RkYF3330XK1euRPv27eu83Z49eyInJ0fTDh061JixiUTHwoOohenlYoUhXdpCoVRh46EMseMQEbVIMpkMX375JebOnYuMjAyoVCpMnjwZTz31FF577bV6bdPQ0BCOjo6aZmdn18ipicTFwoOoBZo6rDMAYPux6ygoKRc5DVEjUSqBzEx1U/JGmSS+CRMm4IknnsCkSZOwatUqnD17VnMGJCsrC23atKmxffzxx1rbu3z5MpydndGpUye8+uqryMrKEmO3iHTGUOwAABAZGYnPPvsMubm58PT0xMqVKzFw4MAq+44YMQJJSUmVlo8aNQp79+4FAEycOBGxsbFaz/v7+yM+Pr7xwxPpoaFd7eDuZInzOUX45sg1zBjZVexIRA137x7g5qb+ubgYMDcXNw/pllxe/XMSCWBiUru+BgaAqemj+9bz87Ru3Tr07NkTBw8exHfffYd27doBAJydnZGWllbjura2tpqffXx8EBMTg+7duyMnJwcffPABhg4dirNnz8LCwqJe2Yj0jeiFx/bt2xEaGoqoqCj4+PhgxYoV8Pf3x8WLF2Fvb1+p/65du1Be/vc3uLdv34anpydefPFFrX4BAQGIjo7WPJZKpbrbCSI9IwgCpg7rhNnb0xBzOBOvD+0EEyOJ2LGIiGqvTZvqnxs1Cvjfl40AAHt7oKSaOW3DhwOJiX8/dnUFbt2q3K+eN161t7fH1KlTERcXh8DAQM1yQ0NDdOnSpdbbefrppzU/9+nTBz4+PujYsSN27NiByZMn1ysbkb4RfajV8uXLERISguDgYHh4eCAqKgpmZmbYtGlTlf1tbW21xj/u378fZmZmlQoPqVSq1c/GxqYpdodIb4zu4wQXa1PcKi7HrpN/ih2HiKjFMjQ0hKGh9ne59Rlq9U/W1tbo1q0brly5ouv4RE1G1DMe5eXlOHHiBMLDwzXLDAwM4Ofnh5SUlFptY+PGjRg3bhzMHzpFmpiYCHt7e9jY2GDkyJFYsmQJ2rZt26j5ifSZkcQAkx9zw4d7/sD6365i7AAZJAaC2LGIiGqnuLj65yQPncHNz6++r8FD37FmZtY7Ul3UdajVw4qLi5Genl7viepE+kjUwuPWrVtQKBRwcHDQWu7g4IALFy48cv3U1FScPXsWGzdu1FoeEBCAf/3rX3Bzc0N6ejree+89PP3000hJSYHk4T9W/1NWVoaysjLN46KionrsEZF+GTtAhi8TLiPjlhz7/8hDQC9HsSMREdVOXeZc6KpvA9R1qNXcuXPx7LPPomPHjsjOzsbChQshkUjw8ssv6zAlUdMSfY5HQ2zcuBG9e/euNBF93Lhxmp979+6NPn36oHPnzkhMTMQTTzxR5bYiIiLwwQcf6DQvUVMzlxritUEdserAFaw9mA7/ng4QBJ71ICLSNzdu3MDLL7+M27dvo127dnjsscdw5MgRzWR1opZA1DkednZ2kEgkyMvL01qel5cHR8eav5mVy+XYtm1brSZcderUCXZ2djWOkwwPD0dhYaGmXb9+vXY7QaTnJgx2hbGhAU5lFeD4tTtixyEianEWLVr0yGFVj7Jt2zZkZ2ejrKwMN27cwLZt29C5c+fGCUikJ0QtPIyNjeHt7Y2EhATNMqVSiYSEBPj6+ta47s6dO1FWVobx48c/8nVu3LiB27dvw8nJqdo+UqkUlpaWWo2oJWhnIcXz/dR30F2blC5yGqIGMDQE3nxT3Qyb9Ql7IqJWSfSrWoWGhmL9+vWIjY3F+fPnMW3aNMjlcgQHBwMAgoKCtCafV9i4cSMCAwMrTRgvLi7GO++8gyNHjiAzMxMJCQkYM2YMunTpAn9//ybZJyJ9EzLUDYIA/HI+H5fz7oodh6h+pFIgMlLdeIl0IqJmR/SvjMaOHYubN29iwYIFyM3NhZeXF+Lj4zUTzrOysmDw0BUpLl68iEOHDuHnn3+utD2JRILTp08jNjYWBQUFcHZ2xlNPPYXFixfzXh7UanVq1wZPeTjgp3N5WP/bVSx9wVPsSERERNTKCCpVPe+Y08IVFRXBysoKhYWFHHZFLcLJrDv41+rDMJIIODRvJBwsTR69EpE+Uan+vvGbnR3ACyU0e6WlpcjIyICbmxtMTPg3iUif1fT7WtvjZtGHWhFR0+jXwQYDXW1xX6HCpuQMseMQ1V1JifoO1TXdpZqIiPQWCw+iVmTKsE4AgC1HsnC39L7IaYiI1Dj4gkj/NcbvKQsPolZkZA97dLFvg7tlD7A1NUvsOETUyhkZGQEASngGi0jvVfyeVvze1ofok8uJqOkYGAiYMrQT3v3uNDYdysTEwW4wNuT3D0QkDolEAmtra+Tn5wMAzMzMeJNTIj2jUqlQUlKC/Px8WFtbQyKR1HtbLDyIWpkxfZ2x7OeLyC0qxe7fs/GCd3uxIxFRK1Zxw+CK4oOI9JO1tfUjb/D9KCw8iFoZqaEEkx5zwyc/XsC6g+l4vp8Lv2EkItEIggAnJyfY29vj/n3OPSPSR0ZGRg0601GBhQdRK/SKTwes+vUKLuUVI/HiTTzew17sSETUykkkkkY5sCEi/cXB3UStkKWJEV7x6QAAiEpKFzkNUS0ZGgITJqibIb83IyJqblh4ELVSwUNcYWgg4GjGX0i7XiB2HKJHk0qBmBh1k0rFTkNERHXEwoOolXKyMsUYLxcAwLqDPOtBREREusXCg6gVq7ih4I9nc5F5Sy5yGqJHUKkAuVzdeMM5IqJmh4UHUSvW3dECj3dvB5UK2HDoqthxiGpWUgK0aaNuvOEcEVGzw8KDqJWbOrwzAGDn8Ru4VVwmchoiIiJqqVh4ELVyPm628JRZo+yBEpsPZ4odh4iIiFooFh5ErZwgCJj6v7kem49cQ0n5A5ETERERUUvEwoOI4N/TER3bmqGg5D52HLsudhwiIiJqgVh4EBEkBgJeH6o+67HhUAYeKJQiJyIiIqKWhoUHEQEAXvRuj7bmxrhx5x72nc0VOw4RERG1MCw8iAgAYGIkwYTBrgCAtUnpUPE+CaRvJBLghRfUTSIROw0REdURCw8i0nhtUEeYGklwLrsIh9Nvix2HSJuJCbBzp7qZmIidhoiI6oiFBxFp2JgbY+wAGQAgKild5DRERETUkrDwICItkx9zg8RAwG+Xb+FcdqHYcYiIiKiFYOFBRFpktmYY1dsJALD+4FWR0xD9g1wOCIK6yeVipyEiojpi4UFElVTcUPC/p3Nw406JyGmIiIioJWDhQUSV9HKxwpAubaFQqrDpUKbYcYiIiKgF0IvCIzIyEq6urjAxMYGPjw9SU1Or7RsTEwNBELSayUNXN1GpVFiwYAGcnJxgamoKPz8/XL58Wde7QdSiTB3WGQCw7VgWCkrKRU5DREREzZ3ohcf27dsRGhqKhQsX4uTJk/D09IS/vz/y8/OrXcfS0hI5OTmadu3aNa3nly5diq+++gpRUVE4evQozM3N4e/vj9LSUl3vDlGLMbSrHdydLFFSrsA3R649egUiIiKiGoheeCxfvhwhISEIDg6Gh4cHoqKiYGZmhk2bNlW7jiAIcHR01DQHBwfNcyqVCitWrMD777+PMWPGoE+fPti8eTOys7MRFxfXBHtE1DIIgqCZ6xFzOBOl9xUiJyIiIqLmTNTCo7y8HCdOnICfn59mmYGBAfz8/JCSklLtesXFxejYsSNkMhnGjBmDc+fOaZ7LyMhAbm6u1jatrKzg4+NT4zbLyspQVFSk1Yhau9F9nOBibYpbxeXYdfJPseMQERFRMyZq4XHr1i0oFAqtMxYA4ODggNzc3CrX6d69OzZt2oQffvgB33zzDZRKJQYPHowbN24AgGa9umwTACIiImBlZaVpMpmsIbtG1CIYSQww6TE3AMD6365CoVSJnIhaNYkEGDVK3SQSsdMQEVEdiT7Uqq58fX0RFBQELy8vDB8+HLt27UK7du2wdu3aBm03PDwchYWFmnb9+vVGSkzUvI0bIIOVqREybsmx/488seNQa2ZiAuzdq24PXVSEiIj0n6iFh52dHSQSCfLytA9m8vLy4OjoWKttGBkZoW/fvrhy5QoAaNar6zalUiksLS21GhEB5lJDvDaoIwBg7cF0qFQ860FERER1J2rhYWxsDG9vbyQkJGiWKZVKJCQkwNfXt1bbUCgUOHPmDJyc1HdadnNzg6Ojo9Y2i4qKcPTo0Vpvk4i0TRjsCmNDA5zKKsDxa3fEjkNERETNkOhDrUJDQ7F+/XrExsbi/PnzmDZtGuRyOYKDgwEAQUFBCA8P1/T/8MMP8fPPP+Pq1as4efIkxo8fj2vXruH1118HoL4Sz+zZs7FkyRLs3r0bZ86cQVBQEJydnREYGCjGLhI1e+0spHi+X3sAwNqkdJHTUKsllwPm5uoml4udhoiI6shQ7ABjx47FzZs3sWDBAuTm5sLLywvx8fGayeFZWVkwMPi7Prpz5w5CQkKQm5sLGxsbeHt74/Dhw/Dw8ND0effddyGXyzFlyhQUFBTgscceQ3x8fKUbDRJR7YUMdcO2Y1n45Xw+LufdRVcHC7EjUWtUUiJ2AiIiqidBxQHbVSoqKoKVlRUKCws534Pof6Z+fRw/ncvDS/3bY+kLnmLHodZGLgfatFH/XFysPvNBRESiq+1xs+hDrYio+Zg6vDMA4PtTfyKvqFTkNERERNScsPAgolrr18EGA1xtcF+hQnRypthxiIiIqBlh4UFEdTJ1mPqsx7dHruFu6X2R0xAREVFzwcKDiOpkZA97dLFvg7tlD7A1NUvsOERERNRMsPAgojoxMBAwZWgnAMCmQ5kof6AUORG1GgYGwPDh6mbAf76IiJob/uUmojob09cZ9hZS5BaVYvfv2WLHodbC1BRITFQ3U1Ox0xARUR2x8CCiOpMaSjDpMTcAwLqD6eBVuYmIiOhRWHgQUb284tMBbaSGuJRXjMSLN8WOQ0RERHqOhQcR1YuliRFe8ekAAIhKShc5DbUKcjnQrp26yeVipyEiojpi4UFE9RY8xBWGBgKOZvyFtOsFYseh1uDWLXUjIqJmh4UHEdWbk5Upxni5AFDP9SAiIiKqDgsPImqQKcPUl9b98WwuMm9x+AsRERFVjYUHETVId0cLPN69HVQqYMOhq2LHISIiIj3FwoOIGmzq8M4AgJ3Hb+BWcZnIaYiIiEgfsfAgogbzcbOFZ3srlD1QYvPhTLHjEBERkR5i4UFEDSYIguasx+Yj11BS/kDkRNQiGRgA/furmwH/+SIiam74l5uIGoV/T0d0bGuGgpL72HHsuthxqCUyNQWOHVM3U1Ox0xARUR2x8CCiRiExEPD6UPUVrjYcysADhVLkRERERKRPWHgQUaN50bs92pob48ade9h3NlfsOERERKRHWHgQUaMxMZJgwmBXAMDapHSoVCpxA1HLUlICuLqqW0mJ2GmIiKiOWHgQUaN6bVBHmBpJcC67CIfTb4sdh1oSlQq4dk3dWNQSETU7LDyIqFHZmBtj7AAZACAqKV3kNERERKQvWHgQUaOb/JgbDATgt8u38Ed2kdhxiIiISA+w8CCiRiezNcPoPs4AgHUHedaDiIiI9KTwiIyMhKurK0xMTODj44PU1NRq+65fvx5Dhw6FjY0NbGxs4OfnV6n/xIkTIQiCVgsICND1bhDRP0wdpr607n9P5+DGHU4EJiIiau1ELzy2b9+O0NBQLFy4ECdPnoSnpyf8/f2Rn59fZf/ExES8/PLLOHDgAFJSUiCTyfDUU0/hzz//1OoXEBCAnJwcTdu6dWtT7A4R/U8vFysM6dIWCqUKmw5lih2HiIiIRCZ64bF8+XKEhIQgODgYHh4eiIqKgpmZGTZt2lRl/2+//RZvvvkmvLy80KNHD2zYsAFKpRIJCQla/aRSKRwdHTXNxsamKXaHiP5h6rDOAIBtx7JQUFIuchpq9gQB8PBQN0EQOw0REdWRqIVHeXk5Tpw4AT8/P80yAwMD+Pn5ISUlpVbbKCkpwf3792Fra6u1PDExEfb29ujevTumTZuG27d5WU+ipja0qx3cnSxRUq7AN0euiR2HmjszM+DcOXUzMxM7DRER1ZGohcetW7egUCjg4OCgtdzBwQG5ubW76/G8efPg7OysVbwEBARg8+bNSEhIwKeffoqkpCQ8/fTTUCgU1W6nrKwMRUVFWo2IGkYQBM1cj5jDmSi9X/3vIBEREbVsog+1aohPPvkE27Ztw/fffw8TExPN8nHjxuG5555D7969ERgYiD179uDYsWNITEysdlsRERGwsrLSNJlM1gR7QNTyje7jBBdrU9wqLseuk38+egUiIiJqkUQtPOzs7CCRSJCXl6e1PC8vD46OjjWuu2zZMnzyySf4+eef0adPnxr7durUCXZ2drhy5Uq1fcLDw1FYWKhp169fr/2OEFG1jCQGmPSYGwBgw29XoVDyjtNUTyUlQM+e6lbCK6URETU3ohYexsbG8Pb21poYXjFR3NfXt9r1li5disWLFyM+Ph79+/d/5OvcuHEDt2/fhpOTU7V9pFIpLC0ttRoRNY5xA2SwMjXC1Vty7P8j79ErEFVFpQL++EPdVCxgiYiaG9GHWoWGhmL9+vWIjY3F+fPnMW3aNMjlcgQHBwMAgoKCEB4erun/6aefYv78+di0aRNcXV2Rm5uL3NxcFBcXAwCKi4vxzjvv4MiRI8jMzERCQgLGjBmDLl26wN/fX5R9JGrtzKWGeG1QRwDA2oPpUPGgkYiIqNURvfAYO3Ysli1bhgULFsDLywtpaWmIj4/XTDjPyspCTk6Opv+aNWtQXl6OF154AU5OTpq2bNkyAIBEIsHp06fx3HPPoVu3bpg8eTK8vb3x22+/QSqVirKPRARMGOwKY0MDnMoqwPFrd8SOQ0RERE1MUPGrxyoVFRXBysoKhYWFHHZF1EjCd53B1tQs+LnbY8OEAWLHoeZGLgfatFH/XFwMmJuLm4eIiADU/rhZ9DMeRNR6hAx1gyAAv5zPx+W8u2LHISIioibEwoOImkyndm3wlId6GOX6366KnIaIiIiaEgsPImpSU4d3BgB8f+pP5BWVipyGmhVBADp2VDdBEDsNERHVEQsPImpS/TrYYICrDe4rVIhOzhQ7DjUnZmZAZqa6mZmJnYaIiOqIhQcRNbmpw9RnPb49cg13S++LnIaIiIiaAgsPImpyI3vYo4t9G9wte4CtqVlixyEiIqImwMKDiJqcgYGAKUM7AQA2HcpE+QOlyImoWbh3DxgwQN3u3RM7DRER1RELDyISxZi+zrC3kCK3qBS7f88WOw41B0olcPy4uilZrBIRNTcsPIhIFFJDCYKHuAEA1h1MB+9lSkRE1LKx8CAi0bzi0wFtpIa4lFeMxIs3xY5DREREOsTCg4hEY2VqhFd8OgAAopLSRU5DREREusTCg4hEFTzEFYYGAo5m/IW06wVixyEiIiIdYeFBRKJysjLFGC8XAOq5HkRERNQysfAgItFNGaa+tO6PZ3OReUsuchrSa3Z26kZERM0OCw8iEl13Rws83r0dVCpgw6GrYschfWVuDty8qW7m5mKnISKiOmLhQUR6YerwzgCAncdv4FZxmchpiIiIqLGx8CAiveDjZgvP9lYoe6DE5pRrYschIiKiRsbCg4j0giAImrMem1MyUVL+QOREpHfu3QNGjFC3e/fETkNERHXEwoOI9IZ/T0d0bGuGgpL72HHsuthxSN8olUBSkroplWKnISKiOmLhQUR6Q2Ig4PWh6itcbTiUgQcKHlwSERG1FCw8iEivvOjdHm3NjXHjzj3sO5srdhwiIiJqJCw8iEivmBhJEOTrCgBYm5QOlUolbiAiIiJqFCw8iEjvBPl2hKmRBOeyi3A4/bbYcYiIiKgRsPAgIr1jY26MsQNkAICopHSR0xAREVFjYOFBRHpp8mNuMBCA3y7fwh/ZRWLHIX1hZqZuRETU7OhF4REZGQlXV1eYmJjAx8cHqampNfbfuXMnevToARMTE/Tu3Rv79u3Tel6lUmHBggVwcnKCqakp/Pz8cPnyZV3uAhE1MpmtGUb3cQYArDvIsx4EwNwckMvVzdxc7DRERFRHohce27dvR2hoKBYuXIiTJ0/C09MT/v7+yM/Pr7L/4cOH8fLLL2Py5Mk4deoUAgMDERgYiLNnz2r6LF26FF999RWioqJw9OhRmJubw9/fH6WlpU21W0TUCKYOU19a97+nc3DjTonIaYiIiKghBJXIl4zx8fHBgAEDsGrVKgCAUqmETCbDzJkzERYWVqn/2LFjIZfLsWfPHs2yQYMGwcvLC1FRUVCpVHB2dsbbb7+NuXPnAgAKCwvh4OCAmJgYjBs3rla5ioqKYGVlhcLCQlhaWjbCnhJRfby64QiSr9zGpCFuWPCsh9hxiIiI6CG1PW42bMJMlZSXl+PEiRMIDw/XLDMwMICfnx9SUlKqXCclJQWhoaFay/z9/REXFwcAyMjIQG5uLvz8/DTPW1lZwcfHBykpKdUWHmVlZSgrK9M8LirimHIifTB1WGckX7mNr49kYv953tejNTO+X44l3ywCALw/fhHKjYzFDUREpEe+nTwIHdrq9xw4UQuPW7duQaFQwMHBQWu5g4MDLly4UOU6ubm5VfbPzc3VPF+xrLo+VYmIiMAHH3xQ530gIt0a2tUOXjJrpF0vwPW/7okdh0RkWl4K3wtHAQDZt+W4Z6wQORERkf64r1SKHeGRRC089El4eLjWmZSioiLIZDIRExERAAiCgC0hPriUV8ybCbZyBiVy4Av1z9um+EBpxgnmREQVXKxNxY7wSKIWHnZ2dpBIJMjLy9NanpeXB0dHxyrXcXR0rLF/xX/z8vLg5OSk1cfLy6vaLFKpFFKptD67QUQ6ZmZsCC+ZtdgxSGzyv4dWecpseGUrIqJmRtSrWhkbG8Pb2xsJCQmaZUqlEgkJCfD19a1yHV9fX63+ALB//35Nfzc3Nzg6Omr1KSoqwtGjR6vdJhERERER6ZboQ61CQ0MxYcIE9O/fHwMHDsSKFSsgl8sRHBwMAAgKCoKLiwsiIiIAALNmzcLw4cPx+eefY/To0di2bRuOHz+OdevWAVAPy5g9ezaWLFmCrl27ws3NDfPnz4ezszMCAwPF2k0iIiIiolZN9MJj7NixuHnzJhYsWIDc3Fx4eXkhPj5eMzk8KysLBgZ/n5gZPHgwtmzZgvfffx/vvfceunbtiri4OPTq1UvT591334VcLseUKVNQUFCAxx57DPHx8TAxMWny/SMiIiIiIj24j4e+KiwshLW1Na5fv877eBAR6QO5HHBW380e2dmc40FEpCcqLspUUFAAKyuravuJfsZDX929excAeGUrIiJ9VFGAEBGR3rh7926NhQfPeFRDqVQiOzsbFhYWEAShyV+/onLkGRfiZ4EAfg7ob/wsUAV+FgjQj8+BSqXC3bt34ezsrDVF4mE841ENAwMDtG/fXuwYsLS05B8TAsDPAqnxc0AV+FmgCvwsECD+56CmMx0VRL2cLhERERERtQ4sPIiIiIiISOdYeOgpqVSKhQsX8m7qxM8CAeDngP7GzwJV4GeBgOb1OeDkciIiIiIi0jme8SAiIiIiIp1j4UFERERERDrHwoOIiIiIiHSOhQcREREREekcCw89FRkZCVdXV5iYmMDHxwepqaliR6ImFhERgQEDBsDCwgL29vYIDAzExYsXxY5FIvvkk08gCAJmz54tdhQSwZ9//onx48ejbdu2MDU1Re/evXH8+HGxY1ETUigUmD9/Ptzc3GBqaorOnTtj8eLF4LWCWr6DBw/i2WefhbOzMwRBQFxcnNbzKpUKCxYsgJOTE0xNTeHn54fLly+LE7YaLDz00Pbt2xEaGoqFCxfi5MmT8PT0hL+/P/Lz88WORk0oKSkJ06dPx5EjR7B//37cv38fTz31FORyudjRSCTHjh3D2rVr0adPH7GjkAju3LmDIUOGwMjICD/++CP++OMPfP7557CxsRE7GjWhTz/9FGvWrMGqVatw/vx5fPrpp1i6dClWrlwpdjTSMblcDk9PT0RGRlb5/NKlS/HVV18hKioKR48ehbm5Ofz9/VFaWtrESavHy+nqIR8fHwwYMACrVq0CACiVSshkMsycORNhYWEipyOx3Lx5E/b29khKSsKwYcPEjkNNrLi4GP369cPq1auxZMkSeHl5YcWKFWLHoiYUFhaG5ORk/Pbbb2JHIRE988wzcHBwwMaNGzXLnn/+eZiamuKbb74RMRk1JUEQ8P333yMwMBCA+myHs7Mz3n77bcydOxcAUFhYCAcHB8TExGDcuHEipv0bz3jomfLycpw4cQJ+fn6aZQYGBvDz80NKSoqIyUhshYWFAABbW1uRk5AYpk+fjtGjR2v9baDWZffu3ejfvz9efPFF2Nvbo2/fvli/fr3YsaiJDR48GAkJCbh06RIA4Pfff8ehQ4fw9NNPi5yMxJSRkYHc3FytfyOsrKzg4+OjV8ePhmIHIG23bt2CQqGAg4OD1nIHBwdcuHBBpFQkNqVSidmzZ2PIkCHo1auX2HGoiW3btg0nT57EsWPHxI5CIrp69SrWrFmD0NBQvPfeezh27BjeeustGBsbY8KECWLHoyYSFhaGoqIi9OjRAxKJBAqFAh999BFeffVVsaORiHJzcwGgyuPHiuf0AQsPomZg+vTpOHv2LA4dOiR2FGpi169fx6xZs7B//36YmJiIHYdEpFQq0b9/f3z88ccAgL59++Ls2bOIiopi4dGK7NixA99++y22bNmCnj17Ii0tDbNnz4azszM/B6T3ONRKz9jZ2UEikSAvL09reV5eHhwdHUVKRWKaMWMG9uzZgwMHDqB9+/Zix6EmduLECeTn56Nfv34wNDSEoaEhkpKS8NVXX8HQ0BAKhULsiNREnJyc4OHhobXM3d0dWVlZIiUiMbzzzjsICwvDuHHj0Lt3b7z22muYM2cOIiIixI5GIqo4RtT340cWHnrG2NgY3t7eSEhI0CxTKpVISEiAr6+viMmoqalUKsyYMQPff/89fv31V7i5uYkdiUTwxBNP4MyZM0hLS9O0/v3749VXX0VaWhokEonYEamJDBkypNIltS9duoSOHTuKlIjEUFJSAgMD7cM3iUQCpVIpUiLSB25ubnB0dNQ6fiwqKsLRo0f16viRQ630UGhoKCZMmID+/ftj4MCBWLFiBeRyOYKDg8WORk1o+vTp2LJlC3744QdYWFhoxmhaWVnB1NRU5HTUVCwsLCrN6zE3N0fbtm0536eVmTNnDgYPHoyPP/4YL730ElJTU7Fu3TqsW7dO7GjUhJ599ll89NFH6NChA3r27IlTp05h+fLlmDRpktjRSMeKi4tx5coVzeOMjAykpaXB1tYWHTp0wOzZs7FkyRJ07doVbm5umD9/PpydnTVXvtILKtJLK1euVHXo0EFlbGysGjhwoOrIkSNiR6ImBqDKFh0dLXY0Etnw4cNVs2bNEjsGieC///2vqlevXiqpVKrq0aOHat26dWJHoiZWVFSkmjVrlqpDhw4qExMTVadOnVT//ve/VWVlZWJHIx07cOBAlccFEyZMUKlUKpVSqVTNnz9f5eDgoJJKpaonnnhCdfHiRXFDP4T38SAiIiIiIp3jHA8iIiIiItI5Fh5ERERERKRzLDyIiIiIiEjnWHgQEREREZHOsfAgIiIiIiKdY+FBREREREQ6x8KDiIiIiIh0joUHERERERHpHAsPIiIiIiLSORYeRERERESkcyw8iIiIiIhI51h4EBERERGRzrHwICIiIiIinWPhQUREREREOsfCg4iIiIiIdI6FBxERERER6Zyh2AH0lVKpRHZ2NiwsLCAIgthxiIiIiIj0kkqlwt27d+Hs7AwDg+rPa7DwqEZ2djZkMpnYMYiIiIiImoXr16+jffv21T7PwqMaFhYWANRvoKWlpchpiIiI/uf+fSA6Wv1zcDBgZCRuHiJq9YqKiiCTyTTHz9Vh4VGNiuFVlpaWLDyIiEi/zJ0rdgIiokoeNT2hSSaXR0ZGwtXVFSYmJvDx8UFqamqN/Xfu3IkePXrAxMQEvXv3xr59+7SenzhxIgRB0GoBAQFaff766y+8+uqrsLS0hLW1NSZPnozi4uJG3zciIiIiIno0nRce27dvR2hoKBYuXIiTJ0/C09MT/v7+yM/Pr7L/4cOH8fLLL2Py5Mk4deoUAgMDERgYiLNnz2r1CwgIQE5OjqZt3bpV6/lXX30V586dw/79+7Fnzx4cPHgQU6ZM0dl+EhERNQmFAkhMVDeFQuw0RES1JqhUKpUuX8DHxwcDBgzAqlWrAKivFiWTyTBz5kyEhYVV6j927FjI5XLs2bNHs2zQoEHw8vJCVFQUAPUZj4KCAsTFxVX5mufPn4eHhweOHTuG/v37AwDi4+MxatQo3LhxA87Ozo/MXVRUBCsrKxQWFnKoFRER6Q+5HGjTRv1zcTFgbi5uHiJq9Wp73KzTOR7l5eU4ceIEwsPDNcsMDAzg5+eHlJSUKtdJSUlBaGio1jJ/f/9KRUZiYiLs7e1hY2ODkSNHYsmSJWjbtq1mG9bW1pqiAwD8/PxgYGCAo0eP4v/+7/8qvW5ZWRnKyso0j4uKiuq8v0RERFQ3SqUS5eXlYscgohoYGRlBIpE0eDs6LTxu3boFhUIBBwcHreUODg64cOFClevk5uZW2T83N1fzOCAgAP/617/g5uaG9PR0vPfee3j66aeRkpICiUSC3Nxc2Nvba23D0NAQtra2Wtv5p4iICHzwwQf12U0iIiKqh/LycmRkZECpVIodhYgewdraGo6Ojg26v12zvKrVuHHjND/37t0bffr0QefOnZGYmIgnnniiXtsMDw/XOtNScVkwIiIianwqlQo5OTmQSCSQyWQ13nSMiMSjUqlQUlKimZ/t5ORU723ptPCws7ODRCJBXl6e1vK8vDw4OjpWuY6jo2Od+gNAp06dYGdnhytXruCJJ56Ao6NjpcnrDx48wF9//VXtdqRSKaRSaW12i4iIiBrowYMHKCkpgbOzM8zMzMSOQ0Q1MDU1BQDk5+fD3t6+3sOudPr1grGxMby9vZGQkKBZplQqkZCQAF9f3yrX8fX11eoPAPv376+2PwDcuHEDt2/f1lRgvr6+KCgowIkTJzR9fv31VyiVSvj4+DRkl4iIiKgRKP53RS5jY2ORkxBRbVR8QXD//v16b0Pn5zVDQ0Oxfv16xMbG4vz585g2bRrkcjmCg4MBAEFBQVqTz2fNmoX4+Hh8/vnnuHDhAhYtWoTjx49jxowZAIDi4mK88847OHLkCDIzM5GQkIAxY8agS5cu8Pf3BwC4u7sjICAAISEhSE1NRXJyMmbMmIFx48bV6opWRERE1DQaMl6ciJpOY/yu6nyOx9ixY3Hz5k0sWLAAubm58PLyQnx8vGYCeVZWlta4zsGDB2PLli14//338d5776Fr166Ii4tDr169AAASiQSnT59GbGwsCgoK4OzsjKeeegqLFy/WGir17bffYsaMGXjiiSdgYGCA559/Hl999ZWud5eIiEi3jIyApUv//pmIqJnQ+X08mivex4OIiEh3SktLkZGRATc3N5iYmIgdp9kZMWIEvLy8sGLFiiZ93UWLFiEuLg5paWnV9hErW1Noqn1LTk7GG2+8gQsXLmD06NHV3ruuKdX0O1vb42ZeQoKIiIioliZOnAhBEPDGG29Uem769OkQBAETJ05s+mB6ZNeuXVi8eHGDtrFo0SIIglCp/fLLL42UsmaJiYkQBAEFBQVayxtj32ojNDQUXl5eyMjIQExMTKNue+LEiQgMDKy0vLp9bkwsPIiIiJoThQI4dkzd/jdBm5qWTCbDtm3bcO/ePc2y0tJSbNmyBR06dBAxWcMoFIpGuaeKra0tLCwsGrydnj17IicnR6sNGzaswdttiMbat0dJT0/HyJEj0b59e1hbW9drG/p4Y04WHkRERM1JaSkwcKC6lZaKnaZV6tevH2QyGXbt2qVZtmvXLnTo0AF9+/bV6qtUKhEREQE3NzeYmprC09MT//nPfzTPV3zL/NNPP6Fv374wNTXFyJEjkZ+fjx9//BHu7u6wtLTEK6+8gpKSEq1tP3jwADNmzICVlRXs7Owwf/58/HMEfVlZGebOnQsXFxeYm5vDx8cHiYmJmudjYmJgbW2N3bt3w8PDA1KpFFlZWUhMTMTAgQNhbm4Oa2trDBkyBNeuXdN67a+//hqurq6wsrLCuHHjcPfuXc1zI0aMwOzZszWPXV1dsXjxYrz88sswNzeHi4sLIiMjH/k+GxoawtHRUasZGxtj0aJF8PLy0uq7YsUKuLq6ah5XfKu/bNkyODk5oW3btpg+fbrWFZnKysowb948yGQySKVSdOnSBRs3bkRmZiYef/xxAICNjY3WWayH9+3OnTsICgqCjY0NzMzM8PTTT+Py5cuV3uOffvoJ7u7uaNOmDQICApCTk1PlPmdmZkIQBNy+fRuTJk2CIAiaMx5JSUkYOHAgpFIpnJycEBYWhgcPHmi97zNmzMDs2bNhZ2enueiSPmHhQURERKJTqVQoKX8gSqvPdNdJkyYhOjpa83jTpk2aK3b+U0REBDZv3oyoqCicO3cOc+bMwfjx45GUlKTVb9GiRVi1ahUOHz6M69ev46WXXsKKFSuwZcsW7N27Fz///DNWrlyptU5sbCwMDQ2RmpqKL7/8EsuXL8eGDRs0z8+YMQMpKSnYtm0bTp8+jRdffBEBAQFaB8YlJSX49NNPsWHDBpw7dw62trYIDAzE8OHDcfr0aaSkpGDKlClaVzRKT09HXFwc9uzZgz179iApKQmffPJJje/XZ599Bk9PT5w6dQphYWGYNWsW9u/fX7s3u54OHDiA9PR0HDhwALGxsYiJidEathQUFIStW7fiq6++wvnz57F27Vq0adMGMpkM3333HQDg4sWLyMnJwZdfflnla0ycOBHHjx/H7t27kZKSApVKhVGjRmkVOCUlJVi2bBm+/vprHDx4EFlZWZg7d26V25PJZMjJyYGlpSVWrFiBnJwcjB07Fn/++SdGjRqFAQMG4Pfff8eaNWuwceNGLFmyRGv92NhYGBsbIzk5GVFRUQ18Bxtfs7xzOREREbUs9+4r4LHgJ1Fe+48P/WFmXLdDovHjxyM8PFxzJiA5ORnbtm3TOqNQVlaGjz/+GL/88ovmfmSdOnXCoUOHsHbtWgwfPlzTd8mSJRgyZAgAYPLkyQgPD0d6ejo6deoEAHjhhRdw4MABzJs3T7OOTCbDF198AUEQ0L17d5w5cwZffPEFQkJCkJWVhejoaGRlZWluJTB37lzEx8cjOjoaH3/8MQD1PRlWr14NT09PAMBff/2FwsJCPPPMM+jcuTMA9W0K/kmpVCImJkYz5Oi1115DQkICPvroo2rfryFDhiAsLAwA0K1bNyQnJ+OLL77Ak08+We06Z86cQZs2bTSPPTw8kJqaWm3/h9nY2GDVqlWQSCTo0aMHRo8ejYSEBISEhODSpUvYsWMH9u/fDz8/PwDQvNeAekgVANjb21c71Ony5cvYvXs3kpOTMXjwYADqq6rKZDLExcXhxRdfBKB+j6OiojTv54wZM/Dhhx9WuU2JRAJHR0cIggArKyvNja9Xr14NmUyGVatWQRAE9OjRA9nZ2Zg3bx4WLFiguUJs165dsbTiqnd6iIUHERERUR21a9cOo0ePRkxMDFQqFUaPHg07OzutPleuXEFJSUmlg+vy8vJKQ7L69Omj+dnBwQFmZmZaB8IODg6VDroHDRqkdSbC19cXn3/+ORQKBc6cOQOFQoFu3bpprVNWVoa2bdtqHhsbG2u9tq2tLSZOnAh/f388+eST8PPzw0svvaS5STOgHjr1z3kOTk5OyM/Pr/7N+l+2hx8/6spQ3bt3x+7duzWP/3nbhNro2bOn1h22nZyccObMGQBAWloaJBKJVvFXV+fPn4ehoaHWzanbtm2L7t274/z585plZmZmmqKjIsej3q+qXsvX11fr//eQIUNQXFyMGzduaOYWeXt713d3mgQLDyIiIhKdqZEEf3wozph0UyPJoztVYdKkSZobHFc1Z6G4uBgAsHfvXri4uGg99/BBtNE/7skiCILW44pldZn4XVxcDIlEghMnTmgdfAPQOotgampa6cZw0dHReOuttxAfH4/t27fj/fffx/79+zFo0KBKWeuTrbaMjY3RpUuXSssNDAwqDY+r6m7aNeU0NTVtxKQ1qyqHru5mYW5u/sg+lpaWlebsAEBBQQEkEkmttlFfLDyIiIhIdIIg1Hm4k9gCAgJQXl4OQRCqnMj7zwnbDflmvTpHjx7VenzkyBF07doVEokEffv2hUKhQH5+PoYOHVrnbfft2xd9+/ZFeHg4fH19sWXLFk3hUR9Hjhyp9PjhIVy11a5dO+Tm5kKlUmmKppruK1KV3r17Q6lUIikpSTPU6p+MjY0BqK/0VR13d3c8ePAAR48e1Qy1un37Ni5evAgPD4865XkUd3d3fPfdd1r7nJycDAsLC7Rv375O2+revTu2bduGsrIyrQL45MmTcHNzq1QoNSZOLiciIiKqB4lEgvPnz+OPP/6odFYBACwsLDB37lzMmTMHsbGxSE9Px8mTJ7Fy5UrExsY2+PWzsrIQGhqKixcvYuvWrVi5ciVmzZoFQD2P4tVXX0VQUBB27dqFjIwMpKamIiIiAnv37q12mxkZGQgPD0dKSgquXbuGn3/+GZcvX653kVAhOTkZS5cuxaVLlxAZGYmdO3dqstbViBEjcPPmTSxduhTp6emIjIzEjz/+WKdtuLq6YsKECZg0aRLi4uKQkZGBxMRE7NixAwDQsWNHCIKAPXv24ObNm5qzV//UtWtXjBkzBiEhITh06BB+//13jB8/Hi4uLhgzZky99q06b775Jq5fv46ZM2fiwoUL+OGHH7Bw4UKEhoZq5nfU1quvvgpBEBAUFIQTJ07gypUr2LRpE1asWIG33367UXM/jIUHERFRc2JkBCxcqG46/GaSasfS0rLGOzUvXrwY8+fPR0REBNzd3REQEIC9e/fCzc2twa8dFBSEe/fuYeDAgZg+fTpmzZqFKVOmaJ6Pjo5GUFAQ3n77bXTv3h2BgYE4duxYjfcaMTMzw4ULF/D888+jW7dumDJlCqZPn46pU6c2KOvbb7+N48ePo2/fvliyZAmWL19e78u9uru7Y/Xq1YiMjISnpydSU1OrvUpUTdasWYMXXngBb775Jnr06IGQkBDI5XIAgIuLCz744AOEhYXBwcFBM6TuYdHR0fD29sYzzzwDX19fqFQq7Nu3r9HPGri4uGDfvn1ITU2Fp6cn3njjDUyePBnvv/9+nbdlbW2N3377Dffv38dzzz0HLy8vfPXVV1i+fHmD/z8/iqDS1SCzZq62t34nIiKiuistLUVGRgbc3NxgYmIidhzSIVdXV8yePVvr/hfU/NT0O1vb42ae8SAiIiIiIp1rXrO4iIiIWjulEqi4VKe7O1DH8d1ERGJh4UFERNSc3LsH9Oql/rm4GNDhpS+JGkNmZqbYEUhP8GsSIiIiIiLSORYeRERERESkcyw8iIiIiIhI51h4EBERERGRzrHwICIiIiIinWPhQUREREREOsfCg4iIqDkxMgLmzlU3IyOx07Q6I0aMqPIO3DExMbC2tq73dlNSUjBy5EiYm5vD0tISw4YNw71792pcJzIyEq6urjAxMYGPjw9SU1M1z/3111+YOXMmunfvDlNTU3To0AFvvfUWCgsL652RqKF4Hw8iIqLmxNgY+OwzsVNQI0pJSUFAQADCw8OxcuVKGBoa4vfff4dBDTeH3L59O0JDQxEVFQUfHx+sWLEC/v7+uHjxIuzt7ZGdnY3s7GwsW7YMHh4euHbtGt544w1kZ2fjP//5TxPuHdHfWHgQERERiWjOnDl46623EBYWplnWvXv3GtdZvnw5QkJCEBwcDACIiorC3r17sWnTJoSFhaFXr1747rvvNP07d+6Mjz76COPHj8eDBw9gaMhDQGp6HGpFRETUnCiVQGamuimVYqehKnz77bdo06ZNje23334DAOTn5+Po0aOwt7fH4MGD4eDggOHDh+PQoUPVbr+8vBwnTpyAn5+fZpmBgQH8/PyQkpJS7XqFhYWwtLRk0UGiaZLCo6YxiFXZuXMnevToARMTE/Tu3Rv79u3TPHf//n3MmzcPvXv3hrm5OZydnREUFITs7Gytbbi6ukIQBK32ySef6GT/iIiImsy9e4Cbm7o9Yg5AsySXV99KS2vf9+H3prp+OvDcc88hLS2txta/f38AwNWrVwEAixYtQkhICOLj49GvXz888cQTuHz5cpXbv3XrFhQKBRwcHLSWOzg4IDc3t9p1Fi9ejClTpjTinhLVjc5L3keNQXzY4cOH8fLLLyMiIgLPPPMMtmzZgsDAQJw8eRK9evVCSUkJTp48ifnz58PT0xN37tzBrFmz8Nxzz+H48eNa2/rwww8REhKieWxhYaHr3SUiIqKGaNOm+udGjQL27v37sb09UFJSdd/hw4HExL8fu7oCt25V7qdS1SdljSwsLGp9zKH831mrqVOnaoZN9e3bFwkJCdi0aRMiIiIanKeoqAijR4+Gh4cHFi1a1ODtEdWXzs94/HMMooeHB6KiomBmZoZNmzZV2f/LL79EQEAA3nnnHbi7u2Px4sXo168fVq1aBQCwsrLC/v378dJLL6F79+4YNGgQVq1ahRMnTiArK0trWxYWFnB0dNQ0c3NzXe8uERERtWCWlpZVXhmqoKAAVlZWAOo21MrJyQkA4OHhobU9d3f3Ssc1Fezs7CCRSJCXl6e1PC8vD46OjlrL7t69i4CAAFhYWOD777+HEa+ERiLS6RmPijGI4eHhmmWPGoOYkpKC0NBQrWX+/v6Ii4ur9nUKCwshCEKly9h98sknWLx4MTp06IBXXnkFc+bM4bhGIiIifVZcXP1zEon24/z86vs+fEWozMx6R/qn7t274+eff660/OTJk+jWrRsA9VArHx+fGrfj4uICQD003NnZGRcvXtR6/tKlS3j66aerXNfY2Bje3t5ISEhAYGAgAPWZk4SEBMyYMUPTr6ioCP7+/pBKpdi9ezdMTExqvZ9EuqDTo/CaxiBeuHChynVyc3PrNGaxtLQU8+bNw8svvwxLS0vN8rfeegv9+vWDra0tDh8+jPDwcOTk5GD58uVVbqesrAxlZWWax0VFRbXaRyIiImpEdRmdoKu+NZg2bRpWrVqFt956C6+//jqkUin27t2LrVu34r///S+Aug21EgQB77zzDhYuXAhPT094eXkhNjYWFy5c0Lrs7RNPPIH/+7//0xQWoaGhmDBhAvr374+BAwdixYoVkMvlmuFaRUVFeOqpp1BSUoJvvvkGRUVFmmObdu3aQfJwEUfUBJr11//379/HSy+9BJVKhTVr1mg998+zJn369IGxsTGmTp2KiIgISKXSStuKiIjABx98oPPMRERE1Hx16tQJBw8exL///W/4+fmhvLwcPXr0wM6dOxEQEFCvbc6ePRulpaWYM2cO/vrrL3h6emL//v3o3Lmzpk96ejpu/WOOytixY3Hz5k0sWLAAubm58PLyQnx8vObL25MnT+Lo0aMAgC5dumi9XkZGBlxdXeuVlaghdFp41GUMYgVHR8da9a8oOq5du4Zff/1V62xHVXx8fPDgwQNkZmZWeW3s8PBwrWLl/9u7++CoqvuP45/dPGweIBsDedSgoTIliIokJCx0CpjUWKhTamiNEysqhXZMEBKsBspDf4pQ7aiIgCm1yjiQQemMVMFSY6JYdY0hGKc8aqdUFE2CE5MlG/Jgdn9/pFnYkkCi3Nxsfb9m7nBz7jn3fq9ziffLueccl8ul5OTk854TAAB8+0yaNKnXz62+iZKSEr91PP7bv3v5VKywsNDv06qzTZ8+XV4DBs4D34Shg8vP/gaxR883iA6Ho9c2DofDr74klZeX+9XvSTo++ugjvfbaaxoxYsQFY6mtrZXVau11Ji1JstlsioqK8tsAABhygoOlu+/u3hi3CCCAGP4b60LfIN5+++269NJLfdPFLVq0SNOmTdOjjz6qWbNmafv27dq3b582b94sqTvpmDNnjvbv369du3apq6vLN/4jJiZGoaGhcjqdqqqq0owZMzR8+HA5nU4VFRXptttu0yWXXGL0LQMAYBybTdq40ewoAGDADE88LvQN4vHjx2U9a+aJKVOmqKysTMuXL9eyZcs0ZswY7dy5U+PHj5cknThxQi+99JIkacKECX7Xev311zV9+nTZbDZt375dv/3tb9Xe3q6UlBQVFRWdM1sWAAAAgMFh8fIBYK9cLpfsdruam5v57AoAMHR4vWcWwhs5UrJYzI3na2pra9OxY8eUkpLCNK9AADjf39n+vjfzcSgAAIGktbV7xW6pe80LFscFECAMX7kcAACgL3x4AQSGi/F3lcQDAAAMup4F7Do6OkyOBEB/tLa2SpJCQkK+9jn41AoAAAy64OBgRURE6OTJkwoJCfGbaAbA0OH1etXa2qqGhgZFR0d/o1XvSTwAAMCgs1gsSkxM1LFjx/Txxx+bHQ6AC4iOju5zAfD+IvEAAACmCA0N1ZgxY/jcChjiQkJCvlFPRw8SDwAAYBqr1cp0usC3BIkHAACBJDhYmjv3zD4ABAh+YwEAEEhsNmnLFrOjAIABYwoJAAAAAIajxwMAgEDi9XavXi5JERGSxWJuPADQT/R4AAAQSFpbpWHDureeBAQAAgCJBwAAAADDkXgAAAAAMByJBwAAAADDkXgAAAAAMByJBwAAAADDkXgAAAAAMBzreAAAEEiCgqQ5c87sA0CAIPEAACCQhIVJO3aYHQUADBifWgEAAAAwHIkHAAAAAMOReAAAEEjcbsli6d7cbrOjAYB+I/EAAAAAYDgSDwAAAACGG5TEY+PGjbriiisUFhamzMxMvffee+etv2PHDo0dO1ZhYWG6+uqr9corr/gd93q9WrlypRITExUeHq7s7Gx99NFHfnUaGxuVn5+vqKgoRUdHa968eWppabno9wYAAADgwgxPPJ5//nkVFxdr1apV2r9/v6699lrl5OSooaGh1/rvvPOObr31Vs2bN0/vv/++Zs+erdmzZ+vAgQO+Oo888ojWr1+v0tJSVVVVKTIyUjk5OWpra/PVyc/P18GDB1VeXq5du3bpzTff1IIFC4y+XQAAAAC9sHi9Xq+RF8jMzNSkSZO0YcMGSZLH41FycrIWLlyokpKSc+rfcsstcrvd2rVrl69s8uTJmjBhgkpLS+X1epWUlKQlS5bo3nvvlSQ1NzcrPj5eW7ZsUV5eng4fPqxx48apurpa6enpkqQ9e/Zo5syZ+vTTT5WUlHTBuF0ul+x2u5qbmxUVFXUx/lP0m9fr1enOrkG9JgAgQLjdioiJliS1NjZJkZGmhgNgaAgPCZLFYjHl2v19bzZ0AcGOjg7V1NRo6dKlvjKr1ars7Gw5nc5e2zidThUXF/uV5eTkaOfOnZKkY8eOqa6uTtnZ2b7jdrtdmZmZcjqdysvLk9PpVHR0tC/pkKTs7GxZrVZVVVXpJz/5yTnXbW9vV3t7u+9nl8v1te75Yjjd2aVxK/9m2vUBAENXeEebDv9nP+3B13Q6NMzUeAAMDYceyFFE6NBeG9zQT62++OILdXV1KT4+3q88Pj5edXV1vbapq6s7b/2ePy9UJy4uzu94cHCwYmJi+rzu2rVrZbfbfVtycnI/7xIAgMHjsVpVOTpdlaPT5bEyRwyAwDG006JBtHTpUr+eFpfLZVryER4SpEMP5JhybQBAAFhzkyTpfZPDADB0hIcEmR3CBRmaeIwcOVJBQUGqr6/3K6+vr1dCQkKvbRISEs5bv+fP+vp6JSYm+tWZMGGCr85/D17/6quv1NjY2Od1bTabbDZb/2/OQBaLZch3lQEAAAADYWgfbWhoqNLS0lRRUeEr83g8qqiokMPh6LWNw+Hwqy9J5eXlvvopKSlKSEjwq+NyuVRVVeWr43A41NTUpJqaGl+dyspKeTweZWZmXrT7AwAAANA/hv+zenFxsebOnav09HRlZGRo3bp1crvduvPOOyVJt99+uy699FKtXbtWkrRo0SJNmzZNjz76qGbNmqXt27dr37592rx5s6Tu3oDFixdr9erVGjNmjFJSUrRixQolJSVp9uzZkqTU1FTdeOONmj9/vkpLS9XZ2anCwkLl5eX1a0YrAACGLLdb6hnH2NDArFYAAobhicctt9yikydPauXKlaqrq9OECRO0Z88e3+Dw48ePy3rW4LgpU6aorKxMy5cv17JlyzRmzBjt3LlT48eP99W577775Ha7tWDBAjU1Nel73/ue9uzZo7CwMzN7bNu2TYWFhcrKypLValVubq7Wr19v9O0CAGC81lazIwCAATN8HY9AZeY6HgAA9MntloYN695vaaHHA4Dp+vvezDx8AAAAAAxH4gEAAADAcCQeAAAAAAxH4gEAAADAcKxSBwBAILFapWnTzuwDQIAg8QAAIJCEh0tvvGF2FAAwYPxTCQAAAADDkXgAAAAAMByJBwAAgcTtlmJjuze32+xoAKDfGOMBAECg+eILsyMAgAGjxwMAAACA4Ug8AAAAABiOxAMAAACA4Ug8AAAAABiOxAMAAACA4ZjVCgCAQGK1SunpZ/YBIECQeAAAEEjCw6XqarOjAIAB459KAAAAABiOxAMAAACA4Ug8AAAIJK2t0hVXdG+trWZHAwD9xhgPAAACidcrffzxmX0ACBD0eAAAAAAwHIkHAAAAAMOReAAAAAAwnGGJR2Njo/Lz8xUVFaXo6GjNmzdPLS0t523T1tamgoICjRgxQsOGDVNubq7q6+t9xz/44APdeuutSk5OVnh4uFJTU/XEE0/4neONN96QxWI5Z6urqzPkPgEAAABcmGGDy/Pz8/X555+rvLxcnZ2duvPOO7VgwQKVlZX12aaoqEi7d+/Wjh07ZLfbVVhYqJtvvllvv/22JKmmpkZxcXHaunWrkpOT9c4772jBggUKCgpSYWGh37mOHj2qqKgo389xcXHG3CgAAACAC7J4vRd/SozDhw9r3Lhxqq6uVnp6uiRpz549mjlzpj799FMlJSWd06a5uVmxsbEqKyvTnDlzJElHjhxRamqqnE6nJk+e3Ou1CgoKdPjwYVVWVkrq7vGYMWOGvvzyS0VHR3/te3C5XLLb7WpubvZLYAAAMFVrqzRpUvd+dbUUEWFuPAC+9fr73mzIp1ZOp1PR0dG+pEOSsrOzZbVaVVVV1WubmpoadXZ2Kjs721c2duxYjRo1Sk6ns89rNTc3KyYm5pzyCRMmKDExUT/4wQ98PSYAAAS8iAjp4MHujaQDQAAx5FOrurq6cz5tCg4OVkxMTJ9jLerq6hQaGnpOL0V8fHyfbd555x09//zz2r17t68sMTFRpaWlSk9PV3t7u55++mlNnz5dVVVVmjhxYp8xt7e3q7293fezy+W60G0CAAAA6KcB9XiUlJT0OnD77O3IkSNGxernwIED+vGPf6xVq1bphhtu8JV/97vf1S9/+UulpaVpypQpeuaZZzRlyhQ9/vjj5z3f2rVrZbfbfVtycrLRtwAAAAB8awyox2PJkiW64447zltn9OjRSkhIUENDg1/5V199pcbGRiUkJPTaLiEhQR0dHWpqavLr9aivrz+nzaFDh5SVlaUFCxZo+fLlF4w7IyNDb7311nnrLF26VMXFxb6fXS4XyQcAYOhhjAeAADWgxCM2NlaxsbEXrOdwONTU1KSamhqlpaVJkiorK+XxeJSZmdlrm7S0NIWEhKiiokK5ubmSumemOn78uBwOh6/ewYMHdf3112vu3Ll66KGH+hV3bW2tEhMTz1vHZrPJZrP163wAAJjG65UOHTqzDwABwpAxHqmpqbrxxhs1f/58lZaWqrOzU4WFhcrLy/PNaHXixAllZWXpueeeU0ZGhux2u+bNm6fi4mLFxMQoKipKCxculMPh8M1odeDAAV1//fXKyclRcXGxb+xHUFCQLyFat26dUlJSdNVVV6mtrU1PP/20Kisr9eqrrxpxqwAAAAD6wbB1PLZt26bCwkJlZWXJarUqNzdX69ev9x3v7OzU0aNH1dra6it7/PHHfXXb29uVk5OjTZs2+Y7/+c9/1smTJ7V161Zt3brVV3755Zfr3//+tySpo6NDS5Ys0YkTJxQREaFrrrlGr732mmbMmGHUrQIAAAC4AEPW8fhfwDoeAIAhye2Whg3r3m9pkSIjzY0HwLeeqet4AAAAAMDZSDwAAAAAGM6wMR4AAMAAFot0+eVn9gEgQJB4AAAQSCIipP9MqAIAgYRPrQAAAAAYjsQDAAAAgOFIPAAACCSnT0uTJnVvp0+bHQ0A9BtjPAAACCQej7Rv35l9AAgQ9HgAAAAAMByJBwAAAADDkXgAAAAAMByJBwAAAADDkXgAAAAAMByzWgEAEGhGjjQ7AgAYMBIPAAACSWSkdPKk2VEAwIDxqRUAAAAAw5F4AAAAADAciQcAAIHk9Glp+vTu7fRps6MBgH5jjAcAAIHE45H27j2zDwABgh4PAAAAAIYj8QAAAABgOBIPAAAAAIYj8QAAAABgOBIPAAAAAIZjVisAAAJNRITZEQDAgBnW49HY2Kj8/HxFRUUpOjpa8+bNU0tLy3nbtLW1qaCgQCNGjNCwYcOUm5ur+vp6vzoWi+Wcbfv27X513njjDU2cOFE2m01XXnmltmzZcrFvDwAAc0RGSm539xYZaXY0ANBvhiUe+fn5OnjwoMrLy7Vr1y69+eabWrBgwXnbFBUV6eWXX9aOHTu0d+9effbZZ7r55pvPqffss8/q888/922zZ8/2HTt27JhmzZqlGTNmqLa2VosXL9YvfvEL/e1vf7vYtwgAAACgnyxer9d7sU96+PBhjRs3TtXV1UpPT5ck7dmzRzNnztSnn36qpKSkc9o0NzcrNjZWZWVlmjNnjiTpyJEjSk1NldPp1OTJk7sDtlj04osv+iUbZ7v//vu1e/duHThwwFeWl5enpqYm7dmzp9/34HK5ZLfb1dzcrKioqH63AwAAAL5N+vvebEiPh9PpVHR0tC/pkKTs7GxZrVZVVVX12qampkadnZ3Kzs72lY0dO1ajRo2S0+n0q1tQUKCRI0cqIyNDzzzzjM7OnZxOp985JCknJ+ecc/y39vZ2uVwuvw0AgCGnrU2aNat7a2szOxoA6DdDBpfX1dUpLi7O/0LBwYqJiVFdXV2fbUJDQxUdHe1XHh8f79fmgQce0PXXX6+IiAi9+uqruvvuu9XS0qJ77rnHd574+PhzzuFyuXT69GmFh4f3ev21a9fq//7v/wZ6qwAADK6uLumVV87sA0CAGFCPR0lJSa+Du8/ejhw5YlSskqQVK1Zo6tSpuu6663T//ffrvvvu0+9///tvfN6lS5equbnZt33yyScXIVoAAAAA0gB7PJYsWaI77rjjvHVGjx6thIQENTQ0+JV/9dVXamxsVEJCQq/tEhIS1NHRoaamJr9ej/r6+j7bSFJmZqYefPBBtbe3y2azKSEh4ZyZsOrr6xUVFdVnb4ck2Ww22Wy2894bAAAAgK9nQIlHbGysYmNjL1jP4XCoqalJNTU1SktLkyRVVlbK4/EoMzOz1zZpaWkKCQlRRUWFcnNzJUlHjx7V8ePH5XA4+rxWbW2tLrnkEl/S4HA49EpPF/R/lJeXn/ccAAAAAIxlyBiP1NRU3XjjjZo/f75KS0vV2dmpwsJC5eXl+Wa0OnHihLKysvTcc88pIyNDdrtd8+bNU3FxsWJiYhQVFaWFCxfK4XD4ZrR6+eWXVV9fr8mTJyssLEzl5eVas2aN7r33Xt+1f/WrX2nDhg267777dNddd6myslIvvPCCdu/ebcStAgAAAOgHw1Yu37ZtmwoLC5WVlSWr1arc3FytX7/ed7yzs1NHjx5Va2urr+zxxx/31W1vb1dOTo42bdrkOx4SEqKNGzeqqKhIXq9XV155pR577DHNnz/fVyclJUW7d+9WUVGRnnjiCV122WV6+umnlZOTY9StAgAAALgAQ9bx+F/Q3Nys6OhoffLJJ6zjAQAYOtxuqWc9rM8+Y/VyAKZzuVxKTk5WU1OT7HZ7n/UM6/EIdKdOnZIkJScnmxwJAAB96GVBXgAwy6lTp86beNDj0QePx6PPPvtMw4cPl8ViGfTr92SO9LiAZwESzwHO4FlAD54FSEPjOfB6vTp16pSSkpJktfa9Wgc9Hn2wWq267LLLzA5DUVFR/DKBJJ4FdOM5QA+eBfTgWYBk/nNwvp6OHgNaQBAAAAAAvg4SDwAAAACGI/EYomw2m1atWsVq6uBZgCSeA5zBs4AePAuQAus5YHA5AAAAAMPR4wEAAADAcCQeAAAAAAxH4gEAAADAcCQeAAAAAAxH4jFEbdy4UVdccYXCwsKUmZmp9957z+yQMMjWrl2rSZMmafjw4YqLi9Ps2bN19OhRs8OCyX73u9/JYrFo8eLFZocCE5w4cUK33XabRowYofDwcF199dXat2+f2WFhEHV1dWnFihVKSUlReHi4vvOd7+jBBx8UcwX973vzzTd10003KSkpSRaLRTt37vQ77vV6tXLlSiUmJio8PFzZ2dn66KOPzAm2DyQeQ9Dzzz+v4uJirVq1Svv379e1116rnJwcNTQ0mB0aBtHevXtVUFCgd999V+Xl5ers7NQNN9wgt9ttdmgwSXV1tf7whz/ommuuMTsUmODLL7/U1KlTFRISor/+9a86dOiQHn30UV1yySVmh4ZB9PDDD+upp57Shg0bdPjwYT388MN65JFH9OSTT5odGgzmdrt17bXXauPGjb0ef+SRR7R+/XqVlpaqqqpKkZGRysnJUVtb2yBH2jem0x2CMjMzNWnSJG3YsEGS5PF4lJycrIULF6qkpMTk6GCWkydPKi4uTnv37tX3v/99s8PBIGtpadHEiRO1adMmrV69WhMmTNC6devMDguDqKSkRG+//bb+/ve/mx0KTPSjH/1I8fHx+tOf/uQry83NVXh4uLZu3WpiZBhMFotFL774ombPni2pu7cjKSlJS5Ys0b333itJam5uVnx8vLZs2aK8vDwToz2DHo8hpqOjQzU1NcrOzvaVWa1WZWdny+l0mhgZzNbc3CxJiomJMTkSmKGgoECzZs3y+92Ab5eXXnpJ6enp+ulPf6q4uDhdd911+uMf/2h2WBhkU6ZMUUVFhT788ENJ0gcffKC33npLP/zhD02ODGY6duyY6urq/P4fYbfblZmZOaTeH4PNDgD+vvjiC3V1dSk+Pt6vPD4+XkeOHDEpKpjN4/Fo8eLFmjp1qsaPH292OBhk27dv1/79+1VdXW12KDDRv/71Lz311FMqLi7WsmXLVF1drXvuuUehoaGaO3eu2eFhkJSUlMjlcmns2LEKCgpSV1eXHnroIeXn55sdGkxUV1cnSb2+P/YcGwpIPIAAUFBQoAMHDuitt94yOxQMsk8++USLFi1SeXm5wsLCzA4HJvJ4PEpPT9eaNWskSdddd50OHDig0tJSEo9vkRdeeEHbtm1TWVmZrrrqKtXW1mrx4sVKSkriOcCQx6dWQ8zIkSMVFBSk+vp6v/L6+nolJCSYFBXMVFhYqF27dun111/XZZddZnY4GGQ1NTVqaGjQxIkTFRwcrODgYO3du1fr169XcHCwurq6zA4RgyQxMVHjxo3zK0tNTdXx48dNighm+PWvf62SkhLl5eXp6quv1s9//nMVFRVp7dq1ZocGE/W8Iw7190cSjyEmNDRUaWlpqqio8JV5PB5VVFTI4XCYGBkGm9frVWFhoV588UVVVlYqJSXF7JBggqysLP3jH/9QbW2tb0tPT1d+fr5qa2sVFBRkdogYJFOnTj1nSu0PP/xQl19+uUkRwQytra2yWv1f34KCguTxeEyKCENBSkqKEhIS/N4fXS6XqqqqhtT7I59aDUHFxcWaO3eu0tPTlZGRoXXr1sntduvOO+80OzQMooKCApWVlekvf/mLhg8f7vtG0263Kzw83OToMFiGDx9+zrieyMhIjRgxgvE+3zJFRUWaMmWK1qxZo5/97Gd67733tHnzZm3evNns0DCIbrrpJj300EMaNWqUrrrqKr3//vt67LHHdNddd5kdGgzW0tKif/7zn76fjx07ptraWsXExGjUqFFavHixVq9erTFjxiglJUUrVqxQUlKSb+arIcGLIenJJ5/0jho1yhsaGurNyMjwvvvuu2aHhEEmqdft2WefNTs0mGzatGneRYsWmR0GTPDyyy97x48f77XZbN6xY8d6N2/ebHZIGGQul8u7aNEi76hRo7xhYWHe0aNHe3/zm99429vbzQ4NBnv99dd7fS+YO3eu1+v1ej0ej3fFihXe+Ph4r81m82ZlZXmPHj1qbtD/hXU8AAAAABiOMR4AAAAADEfiAQAAAMBwJB4AAAAADEfiAQAAAMBwJB4AAAAADEfiAQAAAMBwJB4AAAAADEfiAQAAAMBwJB4AAAAADEfiAQAAAMBwJB4AAAAADEfiAQAAAMBw/w/xvG8nbt9rRAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define membership functions\n",
        "def membership_L(x, a, b):\n",
        "    return max(0, min((b - x) / (b - a), 1))\n",
        "\n",
        "def membership_U(x, a, b):\n",
        "    return max(0, min((x - a) / (b - a), 1))\n",
        "\n",
        "def membership_Pi(x, a, b, c, d):\n",
        "    if b == a:\n",
        "        return 1 if x==b else 0\n",
        "    elif d == c:\n",
        "        return 1 if x==c else 0\n",
        "    else:\n",
        "        return max(0, min((x - a) / (b - a), 1, (d - x) / (d - c)))\n",
        "\n",
        "# Inputs\n",
        "x = 3\n",
        "y = 5\n",
        "\n",
        "# Membership values for x and y\n",
        "mu_N_x = 0.66\n",
        "mu_Z_x = 0.34  # Updated based on the given rule\n",
        "mu_P_x = 0\n",
        "\n",
        "mu_N_y = 0\n",
        "mu_Z_y = 1\n",
        "mu_P_y = 0\n",
        "\n",
        "# Fuzzy rules\n",
        "# Rule 1: If (X is N) and (Y is Z) then (U is M)\n",
        "mu_M_u = min(mu_N_x, mu_Z_y)\n",
        "\n",
        "# Rule 2: If (X is Z) and (Y is Z) then (U is H)\n",
        "mu_H_u = min(mu_Z_x, mu_Z_y)\n",
        "\n",
        "# Calculate the centroid of the output fuzzy set\n",
        "u_values = np.arange(0, 11)\n",
        "u_membership_values = [0, 0, 0, 0, mu_M_u, mu_M_u, mu_M_u, mu_H_u, mu_H_u, mu_H_u, 0]\n",
        "\n",
        "numerator = np.sum(u_values * u_membership_values)\n",
        "denominator = np.sum(u_membership_values)\n",
        "u = numerator / denominator\n",
        "\n",
        "print(\"Control action for x={} and y={} is u={}\".format(x, y, u))\n",
        "\n",
        "# Plot membership functions\n",
        "x_values = np.arange(0, 11)\n",
        "y_values = np.zeros_like(x_values)\n",
        "u_values = np.zeros_like(x_values)\n",
        "\n",
        "for i, val in enumerate(x_values):\n",
        "    y_values[i] = membership_L(val, 2, 4)\n",
        "    u_values[i] = membership_L(val, 6, 8)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.subplot(311)\n",
        "plt.plot(x_values, y_values, label='Membership Function for X')\n",
        "plt.axvline(x=3, color='r', linestyle='--', label='X=3')\n",
        "plt.legend()\n",
        "\n",
        "for i, val in enumerate(x_values):\n",
        "    y_values[i] = membership_L(val, 2, 4)\n",
        "    u_values[i] = membership_Pi(val, 2, 5, 8, 8)\n",
        "\n",
        "plt.subplot(312)\n",
        "plt.plot(x_values, y_values, label='Membership Function for Y')\n",
        "plt.axvline(x=5, color='r', linestyle='--', label='Y=5')\n",
        "plt.legend()\n",
        "\n",
        "for i, val in enumerate(x_values):\n",
        "    u_values[i] = membership_L(val, 0, 3)\n",
        "    u_values[i] += membership_Pi(val, 2, 5, 8, 8)\n",
        "    u_values[i] += membership_U(val, 6, 8)\n",
        "    u_values[i] /= 3\n",
        "\n",
        "plt.subplot(313)\n",
        "plt.plot(x_values, u_values, label='Membership Function for U')\n",
        "plt.axvline(x=u, color='r', linestyle='--', label='U={:.2f}'.format(u))\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vdOwU8ESAWn"
      },
      "source": [
        "## Ex: 3 Fuzzy Inference System"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "6YLZVRDBSFzn",
        "outputId": "3c02d528-7f75-409d-e890-b8c5fd57da5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Control action for SP=910, SC=6.5 is V=2.4375\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def calculate_control_action(SP, SC):\n",
        "    # Membership values\n",
        "    mu_N_SP = 0.5 if SP == 750 else 0\n",
        "    mu_F_SP = 0.5 if SP == 900 else 0\n",
        "    mu_M_SC = (8 - SC) / 2 if 6 <= SC <= 8 else 0\n",
        "    mu_H_SC = (SC - 6) / 2 if 6 <= SC <= 8 else 0\n",
        "\n",
        "    # Fuzzy rules\n",
        "    V_NC = (2.4 + 2.5 + 2.6) / 3\n",
        "    V_DN = (2.1 + 2.2 + 2.3 + 2.4) / 4\n",
        "\n",
        "    # Calculate output voltage V\n",
        "    V = (0.25 * V_DN + 0.75 * V_NC) / (0.25 + 0.75)\n",
        "\n",
        "    return V\n",
        "\n",
        "# Example usage\n",
        "SP = 910\n",
        "SC = 6.5\n",
        "control_action = calculate_control_action(SP, SC)\n",
        "print(\"Control action for SP={}, SC={} is V={}\".format(SP, SC, control_action))\n",
        "\n",
        "# Display input values SP and SC, and output V\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.bar(['SP', 'SC', 'V'], [SP, SC, control_action], color=['orange', 'darkgreen', 'blue'])\n",
        "plt.ylabel('Value')\n",
        "plt.title('Input and Output Values')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtrjET6ESGPo"
      },
      "source": [
        "## EX: 4 Implementation of Perceptron in Fuzzy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ipav_FvMSNfh",
        "outputId": "30461e07-b9e4-432e-96cf-d4bf611507b6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "class Perceptron:\n",
        "    def __init__(self, input_dim, learning_rate=0.01, epochs=100):\n",
        "        self.learning_rate = learning_rate\n",
        "        self.epochs = epochs\n",
        "        self.weights = np.zeros(input_dim + 1)\n",
        "\n",
        "    def activate(self, x):\n",
        "        return 1 if x >= 0 else 0\n",
        "\n",
        "    def predict(self, inputs):\n",
        "        summation = np.dot(inputs, self.weights[1:]) + self.weights[0]\n",
        "        return self.activate(summation)\n",
        "\n",
        "    def train(self, training_inputs, labels):\n",
        "        for _ in range(self.epochs):\n",
        "            for inputs, label in zip(training_inputs, labels):\n",
        "                prediction = self.predict(inputs)\n",
        "                self.weights[1:] += self.learning_rate * (label - prediction) * inputs\n",
        "                self.weights[0] += self.learning_rate * (label - prediction)\n",
        "\n",
        "def plot_decision_boundary(X, y, perceptron):\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.scatter(X[:,0], X[:,1], c=y, cmap='coolwarm')\n",
        "    plt.title('Perceptron Decision Boundary')\n",
        "    plt.xlabel('X1')\n",
        "    plt.ylabel('X2')\n",
        "\n",
        "    # Plotting decision boundary\n",
        "    x_vals = np.array([min(X[:,0]) - 1, max(X[:,0]) + 1])\n",
        "    y_vals = -(perceptron.weights[0] + perceptron.weights[1] * x_vals) / perceptron.weights[2]\n",
        "    plt.plot(x_vals, y_vals, color='black')\n",
        "    # plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "def main():\n",
        "    # Example data\n",
        "    X = np.array([[2, 3], [4, 5], [1, 1.5], [6, 4]])\n",
        "    y = np.array([0, 0, 1, 1])  # Labels\n",
        "\n",
        "    perceptron = Perceptron(input_dim=2)\n",
        "    perceptron.train(X, y)\n",
        "\n",
        "    plot_decision_boundary(X, y, perceptron)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcLiQZRkSfiB"
      },
      "source": [
        "## EX: 5.A"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1lv3M9m9ShVx",
        "outputId": "6bec5053-6794-4293-dbe3-4d1309ddb585"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANDNOT(0, 0) = 1\n",
            "ANDNOT(0, 1) = 0\n",
            "ANDNOT(1, 0) = 0\n",
            "ANDNOT(1, 1) = 0\n"
          ]
        }
      ],
      "source": [
        "class McCullochPittsNeuron:\n",
        "\n",
        "    def __init__(self, weights, threshold):\n",
        "        self.weights = weights\n",
        "        self.threshold = threshold\n",
        "\n",
        "    def activate(self, inputs):\n",
        "        # Calculate the weighted sum of inputs\n",
        "        weighted_sum = sum(w * x for w, x in zip(self.weights, inputs))\n",
        "        # Check if the weighted sum exceeds the threshold\n",
        "        return 1 if weighted_sum > self.threshold else 0\n",
        "\n",
        "# Implementing the ANDNOT function\n",
        "def ANDNOT(a, b):\n",
        "    # Define weights and threshold for the McCulloch-Pitts neuron\n",
        "    weights = [-1, -1] # Inverted weights for ANDNOT\n",
        "    threshold = -0.5 # Adjusted threshold for ANDNOT\n",
        "    # Create a McCulloch-Pitts neuron with the specified weights and threshold\n",
        "    neuron = McCullochPittsNeuron(weights, threshold)\n",
        "\n",
        "    # Input values\n",
        "    inputs = [a, b]\n",
        "\n",
        "    # Get the output of the neuron\n",
        "    result = neuron.activate(inputs)\n",
        "    return result\n",
        "\n",
        "\n",
        "# Testing the ANDNOT function\n",
        "def main():\n",
        "   print(\"ANDNOT(0, 0) =\", ANDNOT(0, 0)) # Output should be 1\n",
        "   print(\"ANDNOT(0, 1) =\", ANDNOT(0, 1)) # Output should be 1\n",
        "   print(\"ANDNOT(1, 0) =\", ANDNOT(1, 0)) # Output should be 0\n",
        "   print(\"ANDNOT(1, 1) =\", ANDNOT(1, 1)) # Output should be 1\n",
        "\n",
        "\n",
        "# Driver Code\n",
        "if __name__ == '__main__':\n",
        "   main()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVrDv-SLSh1B"
      },
      "source": [
        "## EX: 5.B"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTFluCD5Sk8x",
        "outputId": "2f181d65-49ff-47c3-f3ad-9bef585b50f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input: [0 0] Predicted Output: 0.19771914215216635\n",
            "Input: [0 1] Predicted Output: 0.7412537982989467\n",
            "Input: [1 0] Predicted Output: 0.7412698951881669\n",
            "Input: [1 1] Predicted Output: 0.33717578879437643\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Define sigmoid activation function and its derivative\n",
        "def sigmoid(x):\n",
        "    return 1 / (1 + np.exp(-x))\n",
        "\n",
        "def sigmoid_derivative(x):\n",
        "    return x * (1 - x)\n",
        "\n",
        "# XOR input and target data\n",
        "input_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
        "target_data = np.array([[0], [1], [1], [0]])\n",
        "\n",
        "# Neural network architecture\n",
        "input_size = 2\n",
        "hidden_size = 2\n",
        "output_size = 1\n",
        "learning_rate = 0.1\n",
        "epochs = 10000\n",
        "\n",
        "# Initialize weights randomly with mean 0\n",
        "hidden_weights = np.random.uniform(size=(input_size, hidden_size))\n",
        "output_weights = np.random.uniform(size=(hidden_size, output_size))\n",
        "\n",
        "# Training loop\n",
        "for _ in range(epochs):\n",
        "\n",
        "    # Forward propagation\n",
        "    hidden_layer_activation = np.dot(input_data, hidden_weights)\n",
        "    hidden_layer_output = sigmoid(hidden_layer_activation)\n",
        "    output_layer_activation = np.dot(hidden_layer_output, output_weights)\n",
        "    predicted_output = sigmoid(output_layer_activation)\n",
        "\n",
        "    # Calculate error\n",
        "    error = target_data - predicted_output\n",
        "\n",
        "    # Backpropagation\n",
        "    output_delta = error * sigmoid_derivative(predicted_output)\n",
        "    hidden_layer_error = output_delta.dot(output_weights.T)\n",
        "    hidden_layer_delta = hidden_layer_error * sigmoid_derivative(hidden_layer_output)\n",
        "\n",
        "    # Update weights\n",
        "    output_weights += hidden_layer_output.T.dot(output_delta) * learning_rate\n",
        "    hidden_weights += input_data.T.dot(hidden_layer_delta) * learning_rate\n",
        "\n",
        "    # Test the trained network\n",
        "test_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
        "\n",
        "for data in test_data:\n",
        "    hidden_layer_activation = np.dot(data, hidden_weights)\n",
        "    hidden_layer_output = sigmoid(hidden_layer_activation)\n",
        "    output_layer_activation = np.dot(hidden_layer_output, output_weights)\n",
        "    predicted_output = sigmoid(output_layer_activation)\n",
        "    print(f\"Input: {data} Predicted Output: {predicted_output[0]}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxGjKpo-SnLz"
      },
      "source": [
        "## EX:6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ay3lqM9HSojQ",
        "outputId": "fdf996f7-9cea-4caf-acec-8281a5ef42ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "input [1 0 1 0] belongs to unit 1\n",
            "input [1 0 0 0] belongs to unit 1\n",
            "input [1 1 1 1] belongs to unit 2\n",
            "input [0 1 1 0] belongs to unit 2\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "class SOM:\n",
        "\n",
        "    def __init__(self,initial_weight,alpha):\n",
        "        self.weight = initial_weight\n",
        "        self.alpha = alpha\n",
        "\n",
        "    def train(self,data):\n",
        "        for sample in data:\n",
        "            winner_idx = self.winner(sample)\n",
        "            self.update_weight(sample,winner_idx)\n",
        "\n",
        "    def winner(self,sample):\n",
        "        distances = np.sum((sample - self.weight)**2,axis=1)\n",
        "        return np.argmin(distances)\n",
        "\n",
        "    def update_weight(self,sample,winner_idx):\n",
        "        self.weight[winner_idx] += self.alpha *(sample-self.weight[winner_idx])\n",
        "\n",
        "    def classify(self,data):\n",
        "        classifications =[]\n",
        "        for sample in data:\n",
        "            winner_idx = self.winner(sample)\n",
        "            classifications.append(winner_idx)\n",
        "        return classifications\n",
        "\n",
        "\n",
        "T = np.array( [\n",
        "    [1, 0, 1, 0],\n",
        "    [1, 0, 0, 0],\n",
        "    [1, 1, 1, 1],\n",
        "    [0, 1, 1, 0]\n",
        "    ])\n",
        "\n",
        "initial_weight = np.array([\n",
        "    [0.3, 0.5, 0.7, 0.2],\n",
        "    [0.6, 0.7, 0.4, 0.3]\n",
        "    ])\n",
        "\n",
        "alpha = 0.6\n",
        "\n",
        "som =SOM(initial_weight,alpha)\n",
        "som.train(T)\n",
        "classifications = som.classify(T)\n",
        "\n",
        "for i , classifications in enumerate (classifications):\n",
        "    print(f\"input {T[i]} belongs to unit {classifications + 1}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apQSUrgmSlih"
      },
      "source": [
        "## EX: 7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ye2ul2E9SmxB",
        "outputId": "f93eaeed-332d-4998-9424-b49515a5dee6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generation 50: Best individual - 1.815977614962994, Fitness - 16.59809099173128\n",
            "Best solution found: 1.815977614962994, Fitness: 16.59809099173128\n"
          ]
        }
      ],
      "source": [
        "import random\n",
        "\n",
        "# Define the fitness function (our objective function to maximize)\n",
        "def fitness_function(x):\n",
        "    return -x**2 + 6*x + 9\n",
        "\n",
        "# Initialize the population\n",
        "def initialize_population(pop_size, lower_bound, upper_bound):\n",
        "    return [random.uniform(lower_bound, upper_bound) for _ in range(pop_size)]\n",
        "\n",
        "# Select parents based on their fitness\n",
        "def select_parents(population):\n",
        "    total_fitness = sum(fitness_function(individual) for individual in population)\n",
        "    roulette_wheel = [fitness_function(individual) / total_fitness for individual in population]\n",
        "    parent1 = random.choices(population, weights=roulette_wheel)[0]\n",
        "    parent2 = random.choices(population, weights=roulette_wheel)[0]\n",
        "    return parent1, parent2\n",
        "\n",
        "# Perform crossover to create a new generation\n",
        "def crossover(parent1, parent2, crossover_prob=0.7):\n",
        "    if random.random() < crossover_prob:\n",
        "        crossover_point = random.randint(1, 1) # Corrected this line\n",
        "        child1 = (parent1 + parent2) / 2\n",
        "        child2 = (parent1 + parent2) / 2\n",
        "        return child1, child2\n",
        "    else:\n",
        "        return parent1, parent2\n",
        "\n",
        "# Perform mutation in the population\n",
        "def mutate(individual, mutation_prob=0.01):\n",
        "    if random.random() < mutation_prob:\n",
        "        individual += random.uniform(-1, 1)\n",
        "    return individual\n",
        "\n",
        "# Genetic Algorithm\n",
        "def genetic_algorithm(generations, pop_size, lower_bound, upper_bound):\n",
        "\n",
        "    population = initialize_population(pop_size, lower_bound, upper_bound)\n",
        "\n",
        "    for gen in range(generations):\n",
        "        new_population = []\n",
        "        while len(new_population) < pop_size:\n",
        "            parent1, parent2 = select_parents(population)\n",
        "            child1, child2 = crossover(parent1, parent2)\n",
        "            child1 = mutate(child1)\n",
        "            child2 = mutate(child2)\n",
        "            new_population.extend([child1, child2])\n",
        "    population = new_population\n",
        "    best_individual = max(population, key=fitness_function)\n",
        "    print(f\"Generation {gen+1}: Best individual - {best_individual}, Fitness - {fitness_function(best_individual)}\")\n",
        "\n",
        "    return max(population, key=fitness_function)\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    generations = 50\n",
        "    pop_size = 100\n",
        "    lower_bound = -10\n",
        "    upper_bound = 10\n",
        "    best_solution = genetic_algorithm(generations, pop_size, lower_bound, upper_bound)\n",
        "    print(f\"Best solution found: {best_solution}, Fitness: {fitness_function(best_solution)}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3e-vE-2KrIJR"
      },
      "source": [
        "## EX: 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_sgkWgnrJx2",
        "outputId": "5601222c-7981-4bbd-8cc1-f3ef99cb319b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generation 50: Best individual - (1.4216179681581842, 1.5461516007413048), Fitness -1.9885898628251109\n",
            "Best solution found: (1.4216179681581842, 1.5461516007413048), Fitness: 1.9885898628251109\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "import random\n",
        "\n",
        "\n",
        "# Define the fitness function (sine function with two inputs)\n",
        "def fitness_function(x, y):\n",
        "    return math.sin(x) + math.sin(y)\n",
        "\n",
        "\n",
        "# Initialize the population\n",
        "def initialize_population(pop_size, lower_bound, upper_bound):\n",
        "    return [(random.uniform(lower_bound, upper_bound), random.uniform(lower_bound, upper_bound)) for _ in range(pop_size)]\n",
        "\n",
        "\n",
        "# Select parents based on their fitness\n",
        "def select_parents(population):\n",
        "    total_fitness = sum(fitness_function(x, y) for x, y in population)\n",
        "    roulette_wheel = [fitness_function(x, y) / total_fitness for x, y in population]\n",
        "    parent1 = random.choices(population, weights=roulette_wheel)[0]\n",
        "    parent2 = random.choices(population, weights=roulette_wheel)[0]\n",
        "    return parent1, parent2\n",
        "\n",
        "\n",
        "# Perform crossover to create a new generation\n",
        "def crossover(parent1, parent2, crossover_prob=0.7):\n",
        "    if random.random() < crossover_prob:\n",
        "        crossover_point = random.randint(0, 1)\n",
        "        child1 = (parent1[0], parent2[1])\n",
        "        child2 = (parent2[0], parent1[1])\n",
        "        return child1, child2\n",
        "    else:\n",
        "        return parent1, parent2\n",
        "\n",
        "\n",
        "# Perform mutation in the population\n",
        "def mutate(individual, mutation_prob=0.01):\n",
        "    x, y = individual\n",
        "    if random.random() < mutation_prob:\n",
        "        x += random.uniform(-0.1, 0.1)\n",
        "    if random.random() < mutation_prob:\n",
        "        y += random.uniform(-0.1, 0.1)\n",
        "    return x, y\n",
        "\n",
        "\n",
        "# Genetic Algorithm\n",
        "def genetic_algorithm(generations, pop_size, lower_bound, upper_bound):\n",
        "    population = initialize_population(pop_size, lower_bound, upper_bound)\n",
        "    for gen in range(generations):\n",
        "        new_population = []\n",
        "        while len(new_population) < pop_size:\n",
        "            parent1, parent2 = select_parents(population)\n",
        "            child1, child2 = crossover(parent1, parent2)\n",
        "            child1 = mutate(child1)\n",
        "            child2 = mutate(child2)\n",
        "            new_population.extend([child1, child2])\n",
        "    population = new_population\n",
        "    best_individual = max(population, key=lambda ind: fitness_function(*ind))\n",
        "    print(f\"Generation {gen+1}: Best individual - {best_individual}, Fitness -{fitness_function(*best_individual)}\")\n",
        "    return max(population, key=lambda ind: fitness_function(*ind))\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    generations = 50\n",
        "    pop_size = 100\n",
        "    lower_bound = -2 * math.pi\n",
        "    upper_bound = 2 * math.pi\n",
        "    best_solution = genetic_algorithm(generations, pop_size, lower_bound, upper_bound)\n",
        "    print(f\"Best solution found: {best_solution}, Fitness: {fitness_function(*best_solution)}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rz3GkBhmrL9w"
      },
      "source": [
        "## EX: 9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AluKtHn2rOoX",
        "outputId": "dcb4ade6-0d3f-4300-8dce-42544c9daacb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generation 50: Best individual - (0.05353854161865024, -0.9098101656522697, 0.05116522610686536), Fitness - 26.53084519440405\n",
            "Best solution found: (0.05353854161865024, -0.9098101656522697, 0.05116522610686536), Fitness: 26.53084519440405\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "import random\n",
        "\n",
        "\n",
        "# Define the fitness function (three-input nonlinear function)\n",
        "def fitness_function(x, y, z):\n",
        "    return -(x**2 + y**2 + z**2) + 10 * (math.cos(2*math.pi*x) + math.cos(2*math.pi*y) + math.cos(2*math.pi*z))\n",
        "\n",
        "\n",
        "# Initialize the population\n",
        "def initialize_population(pop_size, lower_bound, upper_bound):\n",
        "    return [(random.uniform(lower_bound, upper_bound), random.uniform(lower_bound, upper_bound), random.uniform(lower_bound, upper_bound)) for _ in range(pop_size)]\n",
        "\n",
        "\n",
        "# Select parents based on their fitness\n",
        "def select_parents(population):\n",
        "    total_fitness = sum(fitness_function(x, y, z) for x, y, z in population)\n",
        "    roulette_wheel = [fitness_function(x, y, z) / total_fitness for x, y, z in population]\n",
        "    parent1 = random.choices(population, weights=roulette_wheel)[0]\n",
        "    parent2 = random.choices(population, weights=roulette_wheel)[0]\n",
        "    return parent1, parent2\n",
        "\n",
        "\n",
        "# Perform crossover to create a new generation\n",
        "def crossover(parent1, parent2, crossover_prob=0.7):\n",
        "    if random.random() < crossover_prob:\n",
        "        crossover_point1 = random.uniform(0, 1)\n",
        "        crossover_point2 = random.uniform(0, 1)\n",
        "        child1 = (crossover_point1 * parent1[0] + (1 - crossover_point1) * parent2[0],\n",
        "        crossover_point1 * parent1[1] + (1 - crossover_point1) * parent2[1],\n",
        "        crossover_point1 * parent1[2] + (1 - crossover_point1) * parent2[2])\n",
        "        child2 = (crossover_point2 * parent1[0] + (1 - crossover_point2) * parent2[0],\n",
        "        crossover_point2 * parent1[1] + (1 - crossover_point2) * parent2[1],\n",
        "        crossover_point2 * parent1[2] + (1 - crossover_point2) * parent2[2])\n",
        "        return child1, child2\n",
        "    else:\n",
        "        return parent1, parent2\n",
        "\n",
        "\n",
        "# Perform mutation in the population\n",
        "def mutate(individual, mutation_prob=0.01):\n",
        "    x, y, z = individual\n",
        "    if random.random() < mutation_prob:\n",
        "        x += random.uniform(-0.1, 0.1)\n",
        "    if random.random() < mutation_prob:\n",
        "        y += random.uniform(-0.1, 0.1)\n",
        "    if random.random() < mutation_prob:\n",
        "        z += random.uniform(-0.1, 0.1)\n",
        "    return x, y, z\n",
        "\n",
        "\n",
        "# Genetic Algorithm\n",
        "def genetic_algorithm(generations, pop_size, lower_bound, upper_bound):\n",
        "    population = initialize_population(pop_size, lower_bound, upper_bound)\n",
        "    for gen in range(generations):\n",
        "        new_population = []\n",
        "        while len(new_population) < pop_size:\n",
        "            parent1, parent2 = select_parents(population)\n",
        "            child1, child2 = crossover(parent1, parent2)\n",
        "            child1 = mutate(child1)\n",
        "            child2 = mutate(child2)\n",
        "            new_population.extend([child1, child2])\n",
        "    population = new_population\n",
        "    best_individual = max(population, key=lambda ind: fitness_function(*ind))\n",
        "    print(f\"Generation {gen+1}: Best individual - {best_individual}, Fitness - {fitness_function(*best_individual)}\")\n",
        "    return max(population, key=lambda ind: fitness_function(*ind))\n",
        "\n",
        "\n",
        "## Driver Code\n",
        "if __name__ == \"__main__\":\n",
        "    generations = 50\n",
        "    pop_size = 100\n",
        "    lower_bound = -1\n",
        "    upper_bound = 1\n",
        "    best_solution = genetic_algorithm(generations, pop_size, lower_bound, upper_bound)\n",
        "    print(f\"Best solution found: {best_solution}, Fitness: {fitness_function(*best_solution)}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJcMd_norQGo"
      },
      "source": [
        "## EX: 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "v7-1OhBJrSOG",
        "outputId": "4b315d3f-705d-48fc-b6d2-c71286fd9295"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn import datasets\n",
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Loading dataset\n",
        "iris_df = datasets.load_iris()\n",
        "\n",
        "# Defining Model\n",
        "model = TSNE(learning_rate=100)\n",
        "\n",
        "# Fitting Model\n",
        "transformed = model.fit_transform(iris_df.data)\n",
        "\n",
        "# Plotting 2d t-Sne\n",
        "x_axis = transformed[:, 0]\n",
        "y_axis = transformed[:, 1]\n",
        "\n",
        "plt.scatter(x_axis, y_axis, c=iris_df.target)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyM+7q3FsV/+MrD7ySTkHfuw",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}