Logistic_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Logistic Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "eX-8EjD3gJyL",
        "mX0XP5lDgNeh",
        "9BnvDh-FgRFW",
        "cukH-Y9Fm9A-",
        "HTHdeAGf2ixA",
        "XBEdK_lV3CaV",
        "XqtO81eZlJsm"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eX-8EjD3gJyL",
        "colab_type": "text"
      },
      "source": [
        "# Necessary Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4W9ZUqJG0Bki",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mX0XP5lDgNeh",
        "colab_type": "text"
      },
      "source": [
        "#Download Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUgyGl2PLVin",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "just run the cell to download the dataset \n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0CreCMydppX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "800cee8b-30b2-420f-9277-af5126f23b5b"
      },
      "source": [
        "!pip install gdown\n",
        "!gdown --id 1xIYCrCOo3cPIgslEzI11_cQ_2QFROTHv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: gdown in /usr/local/lib/python3.6/dist-packages (3.6.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from gdown) (4.41.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gdown) (1.12.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gdown) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2.9)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (3.0.4)\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1xIYCrCOo3cPIgslEzI11_cQ_2QFROTHv\n",
            "To: /content/data.csv\n",
            "20.4MB [00:00, 43.9MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BnvDh-FgRFW",
        "colab_type": "text"
      },
      "source": [
        "#Read Dataset and create multiple datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLdxZr3F0ls_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv('/content/data.csv',header=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DacjZNd05CdE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "D0 = data.copy();D1 = data.copy();D2 = data.copy();D3 = data.copy();D4 = data.copy();D5 =data.copy();D6 = data.copy();D7 = data.copy();D8 = data.copy();D9 = data.copy()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QUFxe058Ukx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "datasets = [D0,D1,D2,D3,D4,D5,D6,D7,D8,D9]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLuoDlHhI70v",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "this cell creates individual binary datasets \n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJKCkoBhCdfZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " for i in range(10):\n",
        "  for j in range(5000):\n",
        "    if datasets[i].at[j,400] == i:\n",
        "      datasets[i].at[j,400] = 1\n",
        "    else:\n",
        "      datasets[i].at[j,400] = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_iW8x_sJQIC",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "dictionary holding dataset names as keys and their input and output splits as value pairs\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxePZwAaGUmJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#TrainTest_datadict['key'][0] ==> X && TrainTest_datadict['key'][1] ==> y\n",
        "TrainTest_datadict = {}  \n",
        " #399 --> X 400 --> y \n",
        "for i in range(10):\n",
        "  X = datasets[i].iloc[:,:399]\n",
        "  X = X.T\n",
        "  y = datasets[i].iloc[:,-1]\n",
        "  y = np.array([y])\n",
        "  TrainTest_datadict['D'+str(i)] = [X,y]  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cukH-Y9Fm9A-",
        "colab_type": "text"
      },
      "source": [
        "#1 vs All classifier "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FIVr91NWbfy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "174dfa67-bced-4cf5-daeb-e658a8e1527a"
      },
      "source": [
        "itterations = 10000000   #random big value \n",
        "alpha = 0.160 \n",
        "m = 5000\n",
        "cost_values = []\n",
        "trained_parameters = []\n",
        "\n",
        "for trainsets in range(10):\n",
        "  X = TrainTest_datadict['D'+str(trainsets)][0] \n",
        "  y = TrainTest_datadict['D'+str(trainsets)][1]\n",
        "  weights = np.random.randn(1,400)\n",
        "  bias = 0 \n",
        "  costfunc_values = []\n",
        "  k = 0\n",
        "  print('Training for dataset '+str(trainsets))\n",
        "  for i in range(1,itterations+1):\n",
        "    #logistic function\n",
        "    z = np.dot(weights,X) + bias \n",
        "    hypothesis = 1/(1 + np.exp(-z)) \n",
        "\n",
        "    #cost function\n",
        "    j = 1/m*(-1*(np.sum(y*np.log(hypothesis) + (1-y)*np.log(1-hypothesis))))\n",
        "    costfunc_values.append(j)\n",
        "    k+=1\n",
        "\n",
        "    #gradient decent\n",
        "    dw =  1/m * np.dot(hypothesis-y,X.T)\n",
        "    db =  1/m * np.sum(hypothesis-y)\n",
        "    weights = weights - alpha*dw\n",
        "    bias = bias - alpha*db\n",
        "\n",
        "    #stop training\n",
        "    if i%2000 == 0:\n",
        "      print('running @ ',j)\n",
        "    if i%2 == 0:\n",
        "      if abs(j-costfunc_values[-2])<0.000001:\n",
        "        if abs(j-costfunc_values[-3])<0.000001:\n",
        "          break \n",
        "  cost_values.append(costfunc_values)  \n",
        "  trained_parameters.append([weights,bias])      \n",
        "  print('itteration number:',k)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training for dataset 0\n",
            "running @  0.03733691876660016\n",
            "running @  0.026799257023805822\n",
            "running @  0.022566730194620903\n",
            "running @  0.019988195091991647\n",
            "running @  0.01814724099003655\n",
            "running @  0.01673750037989655\n",
            "running @  0.015614973177285848\n",
            "itteration number: 14098\n",
            "Training for dataset 1\n",
            "running @  0.04637565110276773\n",
            "running @  0.038044884951326984\n",
            "running @  0.0337818893378054\n",
            "running @  0.030954153334241934\n",
            "running @  0.02883680063556638\n",
            "running @  0.02714652758988379\n",
            "running @  0.025748575725093127\n",
            "running @  0.024567133774878704\n",
            "itteration number: 17174\n",
            "Training for dataset 2\n",
            "running @  0.09451461093457657\n",
            "running @  0.08056621001901286\n",
            "running @  0.07446566843234666\n",
            "running @  0.07080273344000455\n",
            "running @  0.06823881593924119\n",
            "running @  0.06627880573695093\n",
            "running @  0.0646957954523923\n",
            "running @  0.06337005463524538\n",
            "running @  0.06223149911019144\n",
            "itteration number: 18916\n",
            "Training for dataset 3\n",
            "running @  0.0984247823158101\n",
            "running @  0.0859713643828655\n",
            "running @  0.08008996860568429\n",
            "running @  0.07646294469280149\n",
            "running @  0.07391874515320247\n",
            "running @  0.07199717600350318\n",
            "running @  0.07047474245985076\n",
            "running @  0.06922727896788657\n",
            "itteration number: 17566\n",
            "Training for dataset 4\n",
            "running @  0.08136410688135302\n",
            "running @  0.06551281942525346\n",
            "running @  0.058594275627383535\n",
            "running @  0.05443190384369066\n",
            "running @  0.05154621151883408\n",
            "running @  0.04937348302541822\n",
            "running @  0.047648726781163796\n",
            "running @  0.046229022298587545\n",
            "running @  0.045029100739869016\n",
            "itteration number: 19482\n",
            "Training for dataset 5\n",
            "running @  0.10554976726932815\n",
            "running @  0.08861119705934094\n",
            "running @  0.08147796913564428\n",
            "running @  0.07716690656808753\n",
            "running @  0.07413456282567286\n",
            "running @  0.07181655416528021\n",
            "running @  0.06994886356884324\n",
            "running @  0.06838961491900948\n",
            "running @  0.06705479132954605\n",
            "running @  0.06589071147449355\n",
            "itteration number: 21494\n",
            "Training for dataset 6\n",
            "running @  0.05173470316817965\n",
            "running @  0.04213791492601112\n",
            "running @  0.03803242968573393\n",
            "running @  0.03547038358520775\n",
            "running @  0.033615001991878106\n",
            "running @  0.03215876621446171\n",
            "running @  0.030956591740391583\n",
            "itteration number: 15354\n",
            "Training for dataset 7\n",
            "running @  0.07224080489836782\n",
            "running @  0.05959852561143496\n",
            "running @  0.053960222002072566\n",
            "running @  0.05041554445302673\n",
            "running @  0.04787547421057222\n",
            "running @  0.045940124801835375\n",
            "running @  0.04440606191151501\n",
            "running @  0.043150649949649116\n",
            "itteration number: 17664\n",
            "Training for dataset 8\n",
            "running @  0.13104148899326956\n",
            "running @  0.11088366365023197\n",
            "running @  0.10263531762691715\n",
            "running @  0.09796305071363343\n",
            "running @  0.09485199108986674\n",
            "running @  0.09257892448820985\n",
            "running @  0.09081786393316434\n",
            "running @  0.08939834886708432\n",
            "running @  0.08822110830827894\n",
            "itteration number: 18950\n",
            "Training for dataset 9\n",
            "running @  0.11727395877637928\n",
            "running @  0.10278390286500526\n",
            "running @  0.09579012217985165\n",
            "running @  0.09147435023517372\n",
            "running @  0.08848795862813402\n",
            "running @  0.08626001654069344\n",
            "running @  0.08450622084093302\n",
            "running @  0.08306968935966583\n",
            "running @  0.08185758237475371\n",
            "itteration number: 19640\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HTHdeAGf2ixA",
        "colab_type": "text"
      },
      "source": [
        "#prediction accuracy for each classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cn9KNNhwjXZW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "e9adae29-7b69-4d5f-a1e0-98b5db00f712"
      },
      "source": [
        "for datasetnum in range(10):\n",
        "  X = TrainTest_datadict['D'+str(datasetnum)][0]\n",
        "  y = TrainTest_datadict['D'+str(datasetnum)][1]\n",
        "  weights = trained_parameters[datasetnum][0]\n",
        "  bias = trained_parameters[datasetnum][1]\n",
        "  correct_predictions = 0 \n",
        "  for i in range(5000):\n",
        "    z = np.dot(weights,X.T.iloc[i,:])+bias\n",
        "    hypothesis = 1/(1 + np.exp(-z))\n",
        "    if np.logical_and(hypothesis >= 0.5,y.T[i,0] == 1):\n",
        "        correct_predictions+=1\n",
        "    if np.logical_and(hypothesis < 0.5,y.T[i,0] == 0):\n",
        "        correct_predictions+=1    \n",
        "  #print(correct_predictions)      \n",
        "  acc = (correct_predictions/5000)*100\n",
        "  print('accuracy for dataset '+str(datasetnum),\" = \",acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy for dataset 0  =  99.5\n",
            "accuracy for dataset 1  =  99.33999999999999\n",
            "accuracy for dataset 2  =  98.1\n",
            "accuracy for dataset 3  =  98.02\n",
            "accuracy for dataset 4  =  98.64\n",
            "accuracy for dataset 5  =  98.02\n",
            "accuracy for dataset 6  =  99.22\n",
            "accuracy for dataset 7  =  98.68\n",
            "accuracy for dataset 8  =  96.8\n",
            "accuracy for dataset 9  =  97.22\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBEdK_lV3CaV",
        "colab_type": "text"
      },
      "source": [
        "#1 V All classfier prediction accuracy "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "al9Tk_Pmje3W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f0e268dd-be75-4528-f264-9742b22e6ccc"
      },
      "source": [
        "inputs = data.iloc[:,:400]\n",
        "inputs = inputs.T\n",
        "outputs = data.iloc[:,-1] \n",
        "outputs = np.array(outputs)\n",
        "accuratepredicts = 0                \n",
        "for i in range(5000):\n",
        "  probabilities = []\n",
        "  for j in range(10):\n",
        "    weights = trained_parameters[j][0]\n",
        "    bias = trained_parameters[j][1]\n",
        "    z = np.dot(weights,inputs.iloc[:,i].T)+bias\n",
        "    hypothesis = 1/(1 + np.exp(-line)) \n",
        "    probabilities.append(hypothesis)\n",
        "  predict = probabilities.index(max(probabilities)) \n",
        "  if outputs.T[i] == predict:\n",
        "    accuratepredicts+=1\n",
        "print(accuratepredicts)    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4693\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAr8AgjGJsTm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8aefe853-4139-4e3e-9f82-c71e792925fa"
      },
      "source": [
        "(accuratepredicts/5000)*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.86"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqtO81eZlJsm",
        "colab_type": "text"
      },
      "source": [
        "#Cost function plots for all the classifiers \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "heEOhKfjLZCR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "7e7651d6-08e2-4c4c-f293-1e6c952003c5"
      },
      "source": [
        "fig,a =  plt.subplots(2,5,figsize=(35,15))\n",
        "a[0][0].plot(cost_values[0])\n",
        "a[0][0].set_title(\"0's dataset\")\n",
        "a[0][1].plot(cost_values[1])\n",
        "a[0][1].set_title(\"1's dataset\")\n",
        "a[0][2].plot(cost_values[2])\n",
        "a[0][2].set_title(\"2's dataset\")\n",
        "a[0][3].plot(cost_values[3])\n",
        "a[0][3].set_title(\"3's dataset\")\n",
        "a[0][4].plot(cost_values[4])\n",
        "a[0][4].set_title(\"4's dataset\")\n",
        "a[1][0].plot(cost_values[5])\n",
        "a[1][0].set_title(\"5's dataset\")\n",
        "a[1][1].plot(cost_values[6])\n",
        "a[1][1].set_title(\"6's dataset\")\n",
        "a[1][2].plot(cost_values[7])\n",
        "a[1][2].set_title(\"7's dataset\")\n",
        "a[1][3].plot(cost_values[8])\n",
        "a[1][3].set_title(\"8's dataset\")\n",
        "a[1][4].plot(cost_values[9])\n",
        "a[1][4].set_title(\"9's dataset\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8YAAANeCAYAAABgfWRtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde5xldXnn++937V2XruqqvlBFN/TFxggqUUGs4HhiFJMJgiYweenJAZN4ecXTYyKJk4wZ8UwGJjiZOTlmiOPRyHQMw+QYIEYlw4lEcOILSSYxQ7US7pfmIt1NQ1df6Et1d3VdnvljrV1dFFUN1Fqrdq29P+/Xq17Ve62113r0j/qx1rOe53FECAAAAAAAAAAAAACAVpU0OwAAAAAAAAAAAAAAAMpEYhwAAAAAAAAAAAAA0NJIjAMAAAAAAAAAAAAAWhqJcQAAAAAAAAAAAABASyMxDgAAAAAAAAAAAABoaSTGAQAAAAAAAAAAAAAtjcQ42oLtC2zvaHYcAIBqs73JdtiuNzsWAEB1sZ4AAIrAegIAKAL5E7QTEuOoDNurbd9ie9T2D21/oKTrfNj235Zx7mZcBwDwQravsD1se8z2DSVeZ1FuKrh5AYDFZ7vL9h9n9yWHbN9j++KSrsV6AgAtzPZXbO+yfdD2o7Y/WtJ1WE8AoA3YPtP2MdtfKen85E9QabxNiCr5oqTjktZIOlfSN23/Y0Q80NywAAAV84ykfyfp3ZKWNTkWAEA11SVtl/ROSU9Leo+kr9p+Y0Q81czAAACV8x8k/XJEjNl+naQ7bf8gIrY2OzAAQCV9UdLdzQ4CWKqoGEcl2O6V9D5J/yYiDkfE30q6VdIvzXP8Mts32N5v+0FJPzZr/5W2H8+qOx60/XPZ9tdLuk7S22wftv18tv29tn+Qvb273fa/nXGu7uzt3r22n7d9t+012b4VWSXJLts7bf8727X5rgMAKF9EfCMi/kLS3pc6Nvub/fu299h+QtJ7Z+3/iO2HsvXkCdv/PNveK+mvJJ2e/Z0/bPt02+fb/vtsvdhl+wu2O7Pv2PYf2N6drTf32X5Dtq8ri+Np28/Zvi5b6+a8TrH/jwEAZouI0Yj4txHxVERMRcRfSnpS0lvmOp71BAAwn4h4ICLGGh+znx+Z61jWEwDAydi+TNLzkv76JY4jf4K2RWIcVXGWpImIeHTGtn+U9KPzHH+10puIH1FaEfihWfsfl/QTklZI+h1JX7F9WkQ8JOljkv4+IpZHxMrs+FFJH5S0UulNx6/Y/mfZvg9l59kg6ZTs+0ezfTdImpD0GklvlnShpI+e5DoAgKXl/5T0M0r/hg9Jev+s/buz/f2SPiLpD2yfFxGjki6W9Ez2d355RDwjaVLSb0gakPQ2ST8l6Vezc10o6R1K17wVkn5eJ5L3/3e2/Vyla8o6SVed5DoAgEWUPdg5S9J83axYTwAA87L9h7aPSHpY0i5Jt81zKOsJAGBOtvslXSPpN1/G4eRP0LZIjKMqlks6OGvbAUl98xz/85J+NyL2RcR2SZ+fuTMi/jwinsmqO/5M0mOSzp/v4hFxZ0Tclx1/r6SblLZNlKRxpX/QXxMRkxGxNSIOZg/H3iPpX2QVJbsl/YGky17R/3IAQDP9vKTPRcT2iNintM3htIj4ZkQ8HqnvSrpD6Y3DnLI14nsRMZG12v3PeuF60ifpdZIcEQ9FxC7blrRZ0m9k69ohSf9erCcAsCTY7pD0p5L+a0Q8PM9hrCcAgHlFxK8q/dv9E5K+IWlsnkNZTwAA8/mMpD+OiB0v41jyJ2hbzBhHVRxW+rbrTP2SDs1z/OlKZ/41/HDmTtsfVPrm1KZs03Klb8fOyfZblb4N+wZJnZK6JP15tvv/U/q20822V0r6iqR/LelVkjok7UrvGSSlL6PMjAsAsLS91HpysdK3bM9S+je+R9J9853M9lmSrlVa3dGj9L/FtkpSRHzH9heUzoJ6le1vSPqkpO7s2K0z1hNLquX83wYAyMl2ovR+4LikK05yKOsJAOCkImJS0t/a/kVJv6JZSYoM6wkA4EVsnyvpnyqtun45yJ+gbVExjqp4VFLd9pkztp2j+VsV7lL6x7ZhY+Mftl8l6Y+UPrg6JWvDcb/S/4iX0llOs92odKb5hohYoXS+hSUpIsYj4nci4mxJ/5vSllUfVPoHfEzSQESszH76I6LR/n2u6wAAlpaTrSddkr4u6fclrcnWk9t08vXkS0rbI54ZEf2S/q8ZxysiPh8Rb5F0ttKHWb8laY/SFlM/OmM9WRERy09yHQBAybKKuT+WtEbS+yJi/CSHs54AAF6uuuaZMS7WEwDA3C5QmsR+2vazSl9kep/t789zPPkTtC0S46iESGcUfUPSNbZ7bf+4pEuVvm00l69K+rTtVbbXS/q1Gft6lf5RHZEk2x9R+iZTw3OS1tvunLGtT9K+iDhm+3xJH2jssP0u22+0XVPa7n1c0lRE7FLasuo/2u63ndj+EdvvPMl1AAAls1233a20oqFmu9v2fF10virp122vt71K0pUz9jXegB2RNJFVZ1w4Y/9zkk6xvWLGtj6la8Vh269TWgnSiOvHbL81a8k7KumY0vVkSukNyR/YPjU7dp3td5/kOgCA8n1J0usl/WxEHH2JY1lPAAAvYvtU25fZXm67lv1NvlzSX8/zFdYTAMBctih9qerc7Oc6Sd9UOj98LuRP0LZIjKNKflXSMkm7lc6o+JWImK9i/HeUtv94Uukf1+kEekQ8KOk/Svp7pX9c3yjpf8z47neUVqI/a3vPjGtfY/uQpKuULhwNayV9Tekf9YckfXfG9T6o9MbkQUn7s+NOO8l1AADl+22lFQ5XSvrF7N+/Pc+xfyTpdkn/KOn7Sl/SkiRls/R+XemasF/pf/TfOmP/w0rXqydsP2/7dKVv7H5A6SiQP5L0ZzOu1Z9t2690Ddsr6bPZvk9J2ibpe7YPSvrvkl57kusAAEqUVVH8c6UPnZ61fTj7+YV5vsJ6AgCYSyhNRu9Q+nf795XOWr11nuNZTwAALxIRRyLi2caP0tG0xyJiZJ6vkD9B23IE3QgAAAAAAAAAAAAAAK2LinEAAAAAAAAAAAAAQEsjMQ4AAAAAAAAAAAAAaGkkxgEAAAAAAAAAAAAALY3EOAAAAAAAAAAAAACgpdWbdeGBgYHYtGlTsy4PAJW2devWPREx2Ow4lgLWEwBYONaTE1hPAGDhWE9OYD0BgIVjPTmB9QQAFu5k60nTEuObNm3S8PBwsy4PAJVm+4fNjmGpYD0BgIVjPTmB9QQAFo715ATWEwBYONaTE1hPAGDhTrae0EodAAAAAAAAAAAAANDSSIwDAAAAAAAAAAAAAFoaiXEAAAAAAAAAAAAAQEsjMQ4AAAAAAAAAAAAAaGkkxgEAAAAAAAAAAAAALY3EOAAAAAAAAAAAAACgpZEYBwAAAAAAAAAAAAC0NBLjAAAAAAAAAAAAAICWRmIcAAAAAAAAbcf29bZ3277/JMdcYPse2w/Y/u5ixgcAAACgWIUmxm2vtP012w/bfsj224o8PwAAAAAAAFCQGyRdNN9O2ysl/aGkSyLiRyX974sUFwAAAIAS1As+33+S9K2IeL/tTkk9BZ8fAAAAAAAAyC0i7rK96SSHfEDSNyLi6ez43YsRFwAAAIByFFYxbnuFpHdI+mNJiojjEfF8UecHAAAAAAAAFtFZklbZvtP2VtsfbHZAAAAAABauyFbqZ0gakfRfbP/A9pdt9848wPZm28O2h0dGRgq8NAAAAAAAAFCouqS3SHqvpHdL+je2z5p9EM+7AAAAgGooMjFel3SepC9FxJsljUq6cuYBEbElIoYiYmhwcLDASwMAAAAAAACF2iHp9ogYjYg9ku6SdM7sg3jeBQAAAFRDkYnxHZJ2RMQ/ZJ+/pjRRXqjt+47o2jse0dN7jxR9agBAG9l96JiuveMRPfbcoWaHAgCosIPHxnXtHY/ovh0Hmh0KAKB4/03S223XbfdIequkh8q40PGJKV377Ud191P7yjg9AKBNRIQ+998f1d88RgcTAJhLYYnxiHhW0nbbr802/ZSkB4s6f8PO54/q89/Zph37SYwDABZu3+hxff4727Rt9+FmhwIAqLDRsQl9/jvbdP8zJMYBoGps3yTp7yW91vYO279s+2O2PyZJEfGQpG9JulfS/5T05Yi4v4xYJqdCn//rxzT81P4yTg8AaBO2dd13H9ffPLan2aEAwJJUL/h8vybpT213SnpC0kcKPr8SW5IURZ8YANBWrHQ9mWJBAQDkMH1/wnoCAJUTEZe/jGM+K+mzZceSLSeaYkEBAOTU21nX6NhEs8MAgCWp0MR4RNwjaajIc87GjQIAoAhJtp4Er1oBAHLIlhPuTwAAudSyG5Qp3twFAOTU01XTkeOTzQ4DAJakImeML4rGgyeeOwEA8jjxolVz4wAAVNz0i1YAACxcLbtBmeSBFwAgJyrGAWB+1UuM8+AJAFAAT7e+ZUUBACycpzPjrCcAgIXjxV0AQFF6OqkYB4D5VDAxTiIDAJAfM2EBYGmzfb3t3bbvn2f/b9m+J/u53/ak7dXZvqds35ftGy43zvQ3ywkAIA/bSkwrdQBYymxfZPsR29tsXznH/g/bHplxn/LRGfs+ZPux7OdDZcbZ21XX6HEqxgFgLoXOGF8MtFIHABSBmbAAsOTdIOkLkv5krp0R8VlJn5Uk2z8r6TciYt+MQ94VEXvKDpIXrQAARakl5v4EAJYo2zVJX5T005J2SLrb9q0R8eCsQ/8sIq6Y9d3Vkq6WNKT0ndqt2Xf3lxFrT2dNuw+OlXFqAKi86laMU5MBAMiBRAYALG0RcZekfS95YOpySTeVGM68eNEKAFAU28wYB4Cl63xJ2yLiiYg4LulmSZe+zO++W9K3I2Jflgz/tqSLSooznTFOxTgAzKl6ifHsN/cJAIA8TszwY0EBgCqz3aP0odLXZ2wOSXfY3mp780m+u9n2sO3hkZGRBV4/uyDLCQAgp5pNK3UAWLrWSdo+4/OObNts77N9r+2v2d7wSr5bxP2JJPV0MWMcAOZTucQ4FX4AgCIwExYAWsbPSvofs9qovz0izpN0saSP237HXF+MiC0RMRQRQ4ODgwu6uNXoaAUAQD5pK/VmRwEAyOH/l7QpIt6ktCr8v76SLxdxfyJlFeNjVIwDwFwqlxinwg8AUITp0RysJwBQdZdpVhv1iNiZ/d4t6RalbQ9L4WT6mmVdAgDQJmxpksw4ACxVOyVtmPF5fbZtWkTsjYjGcO8vS3rLy/1ukXo66xqbmNLE5FRZlwCAyqpcYryB2wQAQB4JrW8BoPJsr5D0Tkn/bca2Xtt9jX9LulDS/aXFkP1mPQEA5JVWjLOgAMASdbekM22fYbtT6Qu6t848wPZpMz5eIumh7N+3S7rQ9irbq5Teo9xeVqC9XTVJ0pFx2qkDwGz1ZgfwSjHDDwBQhEbrWwoyAGBpsn2TpAskDdjeIelqSR2SFBHXZYf9nKQ7ImJ0xlfXSLol6wxSl3RjRHyrxDglScGruwCAnGomMQ4AS1VETNi+QmlCuybp+oh4wPY1koYj4lZJv277EkkTkvZJ+nD23X22P6M0uS5J18waBVWo3q407XNkbFL93R1lXQYAKqlyifHGjHFqxgEAeUxXjLOeAMCSFBGXv4xjbpB0w6xtT0g6p5yoXoyKcQBAUWyLrrcAsHRFxG2Sbpu17aoZ//60pE/P893rJV1faoCZns60Ynz0OHPGAWC2yrVSPzFjvLlxAACqrVHhx3oCAMhjuqNVc8MAALSAWiJNcYMCAMipt/NExTgA4IWqlxjPajKoyAAA5HFiNAcLCgBg4RodrVhOAAB50UodAFCEni4qxgFgPtVLjNP6FgBQABIZAIAikcgAAORlW5OsJwCAnBoV46NjJMYBYLbKJcanZ8JynwAAyKExE5ZEBgAgj8aLuwAA5FVLTCt1AEBufd1pYvzQMRLjADBb5RLjjVQGiQwAQB5UjAMAinBi1BMLCgAgn1pikRcHAOS1YlmHJOngsfEmRwIAS0/lEuNUZAAACpGtJ7xoBQDIg45WAICi2KKVOgAgt77uLDF+lMQ4AMxWvcR49pv7BABAHgkvWgEACmA3Olo1ORAAQOXVTCt1AEB+nfVEyzpqOkBiHABepHKJ8enWt+JGAQCwcCcSGawnAICFm35xl/sTAEBOaSt11hMAQH79y+o6eJQZ4wAwW+US441W6lNTzY0DAFBttL4FABTBrCcAgILY1iTPuwAABejv7mDGOADMoXqJcTUqxgEAWLiE1rcAgALY3J8AAIpRS+hoBQAoRv8yEuMAMJfqJcanKzK4UQCAVmT7tbbvmfFz0Pa/KOt6PHgCAORli5JxAEBuNdNKHQBQjP5uWqkDwFzqzQ5gobhNAIDWFBGPSDpXkmzXJO2UdEvR12lUjAMAkJdFBxIAQH5pK3UWFABAfv3LOvT4yGizwwCAJadyFeNJNhSWinEAaAs/JenxiPhh0Sdu5MWnePAEAMjJtoJXdwEAOdUSKsYBAMVYQSt1AJhT5RLjjfo+7hMAoC1cJumm2Rttb7Y9bHt4ZGRkQSdOmAkLACgIndQBAEWo2ZqaanYUAIBW0N/doYNHxykwBIBZqpcYb8wYb24YAICS2e6UdImkP5+9LyK2RMRQRAwNDg4u7PzZbyoyAAB5JTb3JwCA3GxpkvsTAEAB+pfVNRXS6PHJZocCAEtK9RLjarRSb3IgAICyXSzp+xHxXBknn37RivUEAJCXedEKAJBfLTGjngAAhejv7pAkHThKO3UAmKlyifFkumKcGwUAaHGXa4426kVxo5U6iQwAQE6WaGkFAMiNGeMAgKL0L0sT4wdJjAPAC1QuMd7ofcsLtADQumz3SvppSd8o8zqJyWMAAPIz6wkAoAC2NcmCAgAoQKNinMQ4ALxQvdkBvFKNVur0vgWA1hURo5JOKfs6ianIAADkl9h0IAEA5FazaKUOAChE/7I09XPw2ESTIwGApaVyFePTM2GbGwYAoAXYdCABAORnsZ4AAPKjlToAoChUjAPA3CqXGE+mZ8I2ORAAQOXZZj0BAOTGegIAKIJtTfKmFQCgACuyGeMHSIwDwAtULjGeFYzzBi0AIDdLtL4FAORmSUFPKwBATjVGPQEACtK/rEO29PyR480OBQCWlOolxhkxDgAoSGKTxgAA5GfuTwAA+aWt1JsdBQCgFdQSa+WyDu0jMQ4AL1C9xHhWM859AgAgL1ua4skTACCnxrgnAADy4P4EAFCkVb2d2j9KK3UAmKl6ifEsYlrfAgDyomIcAFAEm1FPAID8aok1yXoCACjI6p5O7R0da3YYALCkVC8xnv3mPgEAkJdFIgMAkJ/F/QkAID9mjAMAirSainEAeJHqJcbdaKXOjQIAIB8zExYAUADb3J8AQAXZvt72btv3v8RxP2Z7wvb7S45HU1NlXgEA0E5W93YyYxwAZqleYjz7TSIDAJBXkpjRHACA3BJetAKAqrpB0kUnO8B2TdLvSbqj7GBqiTTJjHEAQEHSGePHefYFADNULjGeTFeMAwCQT9pKvdlRAACqz6wnAFBBEXGXpH0vcdivSfq6pN1lx1NLaKUOACjO6p5OTUyFDo1NNDsUAFgyKpcYz/Li3CgAAHJLaH0LAChAeo/CegIArcb2Okk/J+lLL3HcZtvDtodHRkbyXI/nXQCAwqzq7ZQk7R+lnToANFQuMd7AfQIAIC+binEAQH4W9ycA0KI+J+lTEXHSyd8RsSUihiJiaHBwcMEXq9m0UgcAFGZ1b4ckaR+JcQCYVm92AK9Uo2IcAIC8bJPIAADkZmaMA0CrGpJ0s9OHUQOS3mN7IiL+ooyLpa3UyzgzAKAdrerJKsaPkBgHgIbKVYxPzxjnyRMAIKe0wo/1BACQD6M5AKA1RcQZEbEpIjZJ+pqkXy0rKS5lHa3IjAPAkmX7ItuP2N5m+8qTHPc+22F7KPu8yfZR2/dkP9ctRryrs1bq+0bHF+NyAFAJ1asYz35znwAAyCuhYhwAUACL+xMAqCLbN0m6QNKA7R2SrpbUIUkRsShJi5lqtia5QQGAJcl2TdIXJf20pB2S7rZ9a0Q8OOu4PkmfkPQPs07xeEScuyjBZlYzYxwAXqR6ifHpivEmBwIAqLx0xjgLCgAgH0ZzAEA1RcTlr+DYD5cYiqRGK3UWFABYos6XtC0inpAk2zdLulTSg7OO+4yk35P0W4sb3ost76qro2bto5U6AEyrXCv1RsU4rQoBAHmlrW8BAMiP+xMAQF62NTXV7CgAAPNYJ2n7jM87sm3TbJ8naUNEfHOO759h+we2v2v7J+a6gO3NtodtD4+MjOQO2LZW9XRq7+Gx3OcCgFZRvcR4lhnnBVoAQF5UjAMAipAkEnlxAEBetUS0UgeAirKdSLpW0r+cY/cuSRsj4s2SflPSjbb7Zx8UEVsiYigihgYHBwuJa7CvS3sOUzEOAA2FtlK3/ZSkQ5ImJU1ExFCR58+uIUkKbhQAADnZvGgFAMjPovUtACC/xKwnALCE7ZS0Ycbn9dm2hj5Jb5B0Z5bDWCvpVtuXRMSwpDFJioitth+XdJak4bKDHljepZFDVIwDQEMZM8bfFRF7SjjvNJuCDABAfonNi1YAgNy4PwEAFCG9P0mLQRqFIQCAJeNuSWfaPkNpQvwySR9o7IyIA5IGGp9t3ynpkxExbHtQ0r6ImLT9aklnSnpiMYIe7OvSo88dWoxLAUAllJEYL51FhR8AID9LmmI9AQDkxP0JAKAItSRNhk9Oheo1EuMAsJRExITtKyTdLqkm6fqIeMD2NZKGI+LWk3z9HZKusT0uaUrSxyJiX/lRpxXjew6P8dIVAGSKToyHpDtsh6T/HBFbZu60vVnSZknauHHjgi+S2ApqMgAAOaXrCQAA+Zj1BABQgCwvzsu7ALBERcRtkm6bte2qeY69YMa/vy7p66UGN4+B5Z0anwwdODqulT2dzQgBAJaUpODzvT0izpN0saSP237HzJ0RsSUihiJiaHBwcMEXsblJAAAUwGKGHwAgN1uM5gAA5JZkmXHuUQAARRns65Ik7TnMnHEAkApOjEfEzuz3bkm3SDq/yPM3WKZVIQAgt4ShsACwZNm+3vZu2/fPs/8C2wds35P9XDVj30W2H7G9zfaVpccqWqkDAPKr+UQrdQAAijC4PE2Mjxw63uRIAGBpKCwxbrvXdl/j35IulDTnQ6z8FxOt1AEAuaUzxllPAGCJukHSRS9xzN9ExLnZzzWSZLsm6YtKu1idLely22eXGagZ9QQAKEBiKsYBAMVqVIyPUDEOAJKKnTG+RtItTv8jvi7pxoj4VoHnn5ZYVPgBAHJLTAcSAFiqIuIu25sW8NXzJW2LiCckyfbNki6V9GBx0b0QFeMAgCJMt1KfanIgAICWMZBVjO85RGIcAKQCE+PZg6dzijrfyVjm7VkAQG5mxjgAVN3bbP+jpGckfTIiHpC0TtL2GcfskPTWub5se7OkzZK0cePGBQfBi1YAgCLU0rw49ygAgMKsWNahemIqxgEgU+iM8cViU5EBAMjPthjfBwCV9X1Jr4qIcyT9v5L+4pWeICK2RMRQRAwNDg4uOBBetAIAFKFRMT7JmgIAKEiSWAPLu6gYB4BMNRPjopM6ACC/9LkTKwoAVFFEHIyIw9m/b5PUYXtA0k5JG2Ycuj7bVm48ZV8AANDypmeM8/YuAKBAA32d2kPFOABIqmhinFaFAIAipBV+zY4CALAQttfaaQbB9vlK7232Srpb0pm2z7DdKekySbeWHAv3JwCA3GqNGeOsKQCAAg0u76KVOgBkCpsxvqhoVQgAKED6ohXrCQAsRbZvknSBpAHbOyRdLalDkiLiOknvl/QrtickHZV0WaR/1CdsXyHpdkk1Sddns8fLi1USNeMAgLyyvDit1AEAhRpY3qWHdh1qdhgAsCRUMjHuZgcAAGgJFtUYALBURcTlL7H/C5K+MM++2yTdVkZcc0kSUTEOAMiNVuoAgDIM9HVp7+iYpqZCSUJ2BUB7q2QrdVPhBwAogG3q+wAAuVmmoxUAILcTrdRZUwAAxRlc3qXxydCBo+PNDgUAmq6SifHENCoEAORnixetAAC5mfsTAEABGhXjk1SMAwAKdGp/lyRp9yHmjANAJRPjNhUZAID80hnjzY4CAFB1Fq3UAQD5JVSMAwBKsKa/W5L07MFjTY4EAJqvmolx8eAJAJBfOmOcBQUAkA+jOQAARag1ZoyzqAAACrQ2S4w/d4DEOABUMzFOq0IAaGm2V9r+mu2HbT9k+21lXCehAwkAoACM5gAAFCErGKeVOgCgUI1W6s9RMQ4Aqjc7gIUwrW8BoNX9J0nfioj32+6U1FPGRdJERhlnBgC0EzpaAQCK0GilTmIcAFCkrnpNq3o6aKUOAKpqYlxUZABAq7K9QtI7JH1YkiLiuKTj5VxLmpoq48wAgHaStlLn/gQAkE9HLU2MT5AYBwAUbE1/NxXjAKAqt1LnHgEAWtUZkkYk/RfbP7D9Zdu9Mw+wvdn2sO3hkZGRBV8oIZEBACgAFeMAgCLUk/Qx3cQkb+8CAIq1dkW3njs41uwwAKDpqpkYF4kMAGhhdUnnSfpSRLxZ0qikK2ceEBFbImIoIoYGBwcXfCFbohgDAJBXYmuKzDgAIKd6VjE+PsmaAgAo1pq+blqpA4CqmhinYhwAWtkOSTsi4h+yz19TmigvXGIzmgMAkFuS8KIVACC/RsU4M8YBAEVbs6Jbew6PaZyuJADaXCUT42lFRrOjAACUISKelbTd9muzTT8l6cGyrsd6AgDIixetAABFmK4YnyJpAQAo1pr+LkVIew7TTh1Ae6s3O4CFopU6ALS0X5P0p7Y7JT0h6SNlXCSdMQ4AQD68uAsAKELH9IxxFhUAQLHW9ndLkp49cEynrVjW5GgAoHkqmRi3JTIZANC6IuIeSUNlXycdzcGCAgDIxxYzxgEAuTUqxidocwsAKNiaLDH+HHPGAbS5SrZSt8mLAwDyS1vfNjsKAEDVUTEOAChCx3QrdRYVAECxTiTGaaUOoL1VMjHODD8AQBESKvwAAAVI6EACAChAfbqVOhXjAIBinQ+K0F8AACAASURBVNLbqY6a9SwV4wDaXCUT45aoyAAAFIAKPwBAfmnFOAsKACCfE63UWVMAAMVKEuvUvm49d4DEOID2Vs3EuE0rdQBAblT4AQCKYFtTFPcBAHLqqKWP6cZZVAAAJTi1v0vPHSIxDqC9VTMxLhIZAID8bDFjHACQG6M5AABFqCdUjAMAyrO2v1vPUjEOoM1VMzFuUTEOAMgtsRWsKACAnBKbF60AALnVGxXjzBgHAJRgTX+3dh8ca3YYANBUFU2Mm4pxAEButpgxDgDILUmkSe5PAAA5NSrGJ7lJAQCUYO2Kbh0am9ChY+PNDgUAmqaaiXHR+hYAkB8vWgEAipDYtFIHAORWr2Wt1EmMAwBKcPrKZZKkXbRTB9DGqpkYZyYsAKAAvGgFACgCrdQBoJpsX297t+3759n/C7bvtX2f7b+zfU6Z8XQktFIHAJRn3cpuSdLO5482ORIAaJ5KJsaZCQsAKEK6ngAAkE9iUTEOANV0g6SLTrL/SUnvjIg3SvqMpC1lBpMkVmJpYpI1BQBQvEbF+DMkxgG0sUomxiVmwgIA8iORAQAoAq3UAaCaIuIuSftOsv/vImJ/9vF7ktaXHVO9lmh8iopxAFiKbF9k+xHb22xfeZLj3mc7bA/N2Pbp7HuP2H734kT8Qqf2dauWmMQ4gLZWycS4aVUIACiASWQAAApgW+QwAKDl/bKkv5prh+3NtodtD4+MjOS6SEdiKsYBYAmyXZP0RUkXSzpb0uW2z57juD5Jn5D0DzO2nS3pMkk/qrRTyR9m51tUtcRa29+tZ55nxjiA9lXNxLgk0fwWAJCTzYxxAEB+iaVgQQGAlmX7XUoT45+aa39EbImIoYgYGhwczHWtei3RBDPGAWApOl/Stoh4IiKOS7pZ0qVzHPcZSb8naWb2+VJJN0fEWEQ8KWlbdr5Ft27lMmaMA2hrlUyMJwmJDABAfhYdSAAA+aWt1JsdBQCgDLbfJOnLki6NiL1lX6+jZo2zqADAUrRO0vYZn3dk26bZPk/Shoj45iv9bvb9wjqQzOf0ld20UgfQ1iqZGLdofQsAyI8KPwBAEZJE3J8AQAuyvVHSNyT9UkQ8uhjXrCdUjANAFdlOJF0r6V8u9BxFdiCZz+krl+nZA8c0yUtYANpUvdkBLIRNI3UAQH62qPADAORmKsYBoJJs3yTpAkkDtndIulpShyRFxHWSrpJ0itJZsJI0ERFDZcZUY8Y4ACxVOyVtmPF5fbatoU/SGyTdma0ZayXdavuSl/HdRXP6ymWamAqNHBrT2hXdzQgBAJqqmolx0UodAJBfYit41QoAkBMdSACgmiLi8pfY/1FJH12kcCSlrdQneNsKAJaiuyWdafsMpUntyyR9oLEzIg5IGmh8tn2npE9GxLDto5JutH2tpNMlnSnpfy5i7NPWrVomSdr5/FES4wDaUjVbqdukMQAAuVExDgAoQmJrksQ4AKAA9VqiiSlaqQPAUhMRE5KukHS7pIckfTUiHrB9TVYVfrLvPiDpq5IelPQtSR+PiMmyY57LupVpYpw54wDaVTUrxqnIAAAUwDYdSAAAuSW2pnjTCgBQgHpijdNKHQCWpIi4TdJts7ZdNc+xF8z6/LuSfre04F6m07IqcRLjANpVNSvGRSt1AEB+tL4FABQh4UUrAEBBOmqJJiapGAcAlKOvu0P93XUS4wDaVjUT48yEBQAUwLKmyGQAAHJKLNYTAEAh6swYBwCU7PSVy7Tz+WPNDgMAmqKSifG0wq/ZUQAAqi6xeM0KAJBbkljkMAAARehIEo1TMQ4AKNG6lcuoGAfQtiqZGKfCDwBQBDMTFgBQAFMxDgAoSL1mTTBjHABQorRinMQ4gPZUycS4qBgHABTAVIwDAArAjHEAQFHqtUTjvLwLACjRhtXLdODouA4cHW92KACw6CqZGLdIZAAA8rNIZAAA8mPGOACgKB2JNUErdQBAiTas6pEkbd93pMmRAMDiq2RiPKHEDwBQgMRSkMgAAOSUmFFPAIBi1BJaqQMAyrVhdZoY37GfxDiA9lPJxDgz/AAARUjXk2ZHAQCoOtusJwCAQnTUEk1MUTEOAChPIzH+NBXjANpQZRPjPHcCAOSV2ApWFABATonT31NkxwEAOdVr1gTrCQCgRCuWdWjFsg5t33e02aEAwKKrZmJcpvUtACA3KvwAAEVInGbG6WoFAMirniS0UgcAlG7D6mVUjANoS9VMjFMxDgAogJkxDgBLlu3rbe+2ff88+3/B9r2277P9d7bPmbHvqWz7PbaHy461ljQS42VfCQDQ6jpq1vgkrdQBAOXasKpH25kxDqANFZoYt12z/QPbf1nkeee4Dg+dAAC5JcwYB4Cl7AZJF51k/5OS3hkRb5T0GUlbZu1/V0ScGxFDJcU3zY1W6rxsBQDIiVbqAIDFsHF1j3bsP8o4KABtp+iK8U9Ieqjgc76IJYmHTgCAnBIzmgMAlqqIuEvSvpPs/7uI2J99/J6k9YsS2BwardRZUgAAeXXVazo+QcU4AKBc61f36PjElHYfGmt2KACwqApLjNteL+m9kr5c1Dnnvxat1AEA+VlUjANAi/hlSX8143NIusP2Vtub5/uS7c22h20Pj4yMLPjiCRXjAICCdNUTjU1MNjsMAECL27i6R5Jopw6g7RRZMf45Sf9KUumvtaYVfmVfBQDQ6jxd4ceiAgBVZftdShPjn5qx+e0RcZ6kiyV93PY75vpuRGyJiKGIGBocHFxwDI2KcRLjAIC8uuo1jU+GJnmDFwBQog2rlkmSnt5LYhxAeykkMW77ZyTtjoitL3FcIRUZaYUfNwgAgHxofQsA1Wb7TUo7Vl0aEXsb2yNiZ/Z7t6RbJJ1fchyS6EICAMivqyN9VEfVOACgTOtWLZNNxTiA9lNUxfiPS7rE9lOSbpb0k7a/MvugoioybJIYANDKbD9l+z7b99geLus6tL4FgOqyvVHSNyT9UkQ8OmN7r+2+xr8lXSjp/jJjaawndCABAOTVVc8S4+PMGQcAlKerXtPa/m49vY/EOID2Ui/iJBHxaUmfliTbF0j6ZET8YhHnnpuZMQ4Are9dEbGnzAskCRV+ALBU2b5J0gWSBmzvkHS1pA5JiojrJF0l6RRJf5hVbE9ExJCkNZJuybbVJd0YEd8qM9aEinEAQEG6O2qSpLEJEuMAgHJtWN2j7STGAbSZQhLjiy0x1RgAgPxMxTgALFkRcflL7P+opI/Osf0JSeeUFddcGhXjzIMFAOQ1XTFOK3UAQMk2ru7RXY8ufOQtAFRRUa3Up0XEnRHxM0WfdyZaqQNAywtJd9jeantzWRdhxjgAoAgnOpCwoAAA8umqUzEOAFgcZwz0avehMY2OTTQ7FABYNIUnxheDZQXN1AGglb09Is6TdLGkj9t+x8ydtjfbHrY9PDKy8DdbswI/EhkAgFzqWWKcinEAQF6NivFj41SMAwDKdcZAryTpqb2jTY4EABZPNRPjVIwDQEuLiJ3Z792SbpF0/qz9WyJiKCKGBgcHF3yd6YrxhYcKAMD0ekJiHACQV1dHo5U6FeMAgHJtOiVNjD+5h8Q4gPZRycR4YpPEAIAWZbvXdl/j35IulHR/OddKf1MxDgDIo0bFOACgINOt1MdJjAMAyrVpoEeS9BSJcQBtpN7sABbEJDEAoIWtkXSL06x1XdKNEfGtMi40XTHOMycAQA7TiXHuUQAAOXVPV4zTSh0AUK6ezrrW9HfpyT1Hmh0KACyaSibGLdH3FgBaVEQ8IemcxbhWQsU4AKAAVIwDAIoyXTFOK3UAwCI4Y6BXT+453OwwAGDRVLKVummlDgAoQJIlMkiMAwDyqDFjHABQkK46FeMAgMVzxkCvntpLxTiA9lHJxHhiKUhiAAByytq1izwGACAPKsYBAEXpylqpH2PGOABgEWw6pVf7Ro/rwNHxZocCAIuikolxiyQGACC/rJM6L1sBAHIhMQ4AKMp0K/VxKsYBAOXbNNArSXpqz2iTIwGAxVHNxLitoJk6ACCnJKsYZ0UBAOQxnRjnRSsAQE4nWqlTMQ4AKN+rs8T4kyTGAbSJaibGJfHMCQCQV5bHYMY4ACAXKsYBAEUhMQ4AWEwbVvfIJjEOoH1UMjEukxgHAOSXMGMcAFCAmkmMAwCKUa8lqifW2ASt1AEA5evuqOn0Fcv01F4S4wDaQyUT441EBgAAeTSWkykSGQCAHBoV46wnAIAidNUTjY1TMQ4AWByvHuzV4yOHmx0GACyKSibGLdreAgDym54xzpICAMihkRifIDEOAChAV0dNx6gYB4Alx/ZFth+xvc32lXPs/5jt+2zfY/tvbZ+dbd9k+2i2/R7b1y1+9PN7zanL9fjuUV70BdAW6s0OYCFMK3UAQAGS7PUwXrYCAOSRNGaMs54AAApAxTgALD22a5K+KOmnJe2QdLftWyPiwRmH3RgR12XHXyLpWkkXZfsej4hzFzPml+s1py7X0fFJPXPgqNav6ml2OABQqopWjFshHjoBAPKxGjPGWVMAAAtXp5U6AKBAXfVExyZIjAPAEnO+pG0R8UREHJd0s6RLZx4QEQdnfOyVqpHEOPPUPknStt20UwfQ+iqZGE8SKsYBAPk1ZoyzpAAA8miM5qCVOgCgCD2ddR09PtHsMAAAL7RO0vYZn3dk217A9sdtPy7p/5H06zN2nWH7B7a/a/snyg31lXnNqcslkRgH0B4qmRiXLJ45AQDyOjFjnEUFALBwNSrGAaCSbF9ve7ft++fZb9ufz2bJ3mv7vMWIq7erptExZowDQBVFxBcj4kckfUrSb2ebd0naGBFvlvSbkm603T/7u7Y32x62PTwyMrJoMa/u7dTq3k4S4wDaQiUT42keg4dOAIB8Golx8hgAgDzqzBgHgKq6QSdmv87lYklnZj+bJX1pEWJST2ddR6gYB4ClZqekDTM+r8+2zedmSf9MkiJiLCL2Zv/eKulxSWfN/kJEbImIoYgYGhwcLCzwl+M1py4nMQ6gLVQzMS5aqQMA8svyGMwYBwDkkjQS47xpBQCVEhF3Sdp3kkMulfQnkfqepJW2Tys7ruVddR0eIzEOAEvM3ZLOtH2G7U5Jl0m6deYBts+c8fG9kh7Ltg/armX/frXSF66eWJSoX6bXnLpcj+0+TFdFAC2v3uwAFiKxqRcHAOTmRsX4VJMDAQBUWs0kxgGgRc03T3bXzINsb1ZaUa6NGzfmvmhPZ01HjtNKHQCWkoiYsH2FpNsl1SRdHxEP2L5G0nBE3CrpCtv/VNK4pP2SPpR9/R2SrrE9LmlK0sci4mQvZi26M09drgNHx7Xn8HEN9nU1OxwAKE0lE+M21X0AgPyoGAcAFKExY3yCxDgAtKWI2CJpiyQNDQ3lXgx6u+oapWIcAJaciLhN0m2ztl0149+fmOd7X5f09XKjy+c1py6XJG3bfZjEOICWRit1AEDbaswYZ00BAOTRSIxPkRgHgFbzSufJFqK3K60Yp50tAGCxnHlqnyRp2+5DTY4EAMpVzcS4zc0BACA3UzEOAChAIzE+yXoCAK3mVkkfdOqfSDoQEbte6kt59XTWNTEVGptg5hMAYHGs6e/S8q66tu0+3OxQAKBUlW2lziMnAEBe0xXjTY4DAFBt04lxKsYBoFJs3yTpAkkDtndIulpShyRFxHVK2+W+R9I2SUckfWQx4urtrEmSjhyfVHdHbTEuCQBoc7Z11prleuQ5KsYBtLZqJsZl2t4CAHKjYhwAUISaSYwDQBVFxOUvsT8kfXyRwpnW25U+rhsdm9Dq3s7FvjwAoE297rR+ffPeXYoIufHQDABaTEVbqYtW6gCA3E7MGGdNAQAsXL2WricTk6wnAID8phPjxyeaHAkAoJ28fm2fDhwd17MHjzU7FAAoTTUT46LtLQAgv0ZinAI/AEAeHbX0tmp8ilmwAID8erJW6qNjk02OBADQTl53Wr8k6eFdtFMH0LoqmRhPElqpAwDySxqt1MmMAwBymE6MT7CeAADyW55VjB+hYhwAsIheu7ZPkvTgroNNjgQAylPJxLjFPFgAQH6mYhwAUIBaYtUSa3ySinEAQH49nY0Z41SMAwAWT393h9avWqaHn6ViHEDrqmRiXKaVOgAgvywvzoxxAEBuHTUS4wCAYvR2NVqpUzEOAFhcr1vbr4epGAfQwiqZGDeZcQBAARozxllSAAB5ddQSHScxDgAoQF93hyTp4LHxJkcCAGg3rz+tT0/sGdWxcbqWAGhNlUyMJ5aCNAYAIKfpGeNUjAMAcuqsJVSMAwAK0d+dtlI/cJTEOABgcb3+tH5NToW27T7c7FAAoBSVTIzbzIMFAOTHjHEAQFE6aonGJ1hQAAD51WuJlnfVSYwDABbd69b2SZIeop06gBZVzcS4zDxYAEBuVIwDAIrSUWfGOACgOCuWdZAYBwAsuled0qvujkQPP3uo2aEAQCmqmRhnxDgAoADTM8ZJjAMAcmLGOACgSP3LOnSQxDgAYJHVEuu1a/qoGAfQsiqaGLfIYQAA8mokxqfIYwAAcmLGOACgSCuW0UodANAcrz+tXw/tOkghCYCWVM3EePabP8wAgDxMK3UAQEE6aonGJ1lPAADFoJU6AKBZ3rBuhfYfGdfO5482OxQAKFw1E+NZIoM8BgAgj+n1pLlhAABaQEeNGeMAgOKQGAcANMub1q+QJN2740CTIwGA4lUzMZ7VjJPIAADkwYxxAEBROmqJjk+QGAcAFGNlTyeJcQBAU7x2bZ86aiYxDqAlVTIxnkxXjJPIAAAs3PSMcZYTAEBOnXVmjAMAirNiWYeOjU9pbGKy2aEAANpMV72m163t1307n292KABQuEomxk/MhG1uHACAakuYMQ4AKAgzxgEARepf1iFJOnCEqnEAwOJ74/oVunfHAYoTAbSciibGG63U+aMMAK3Ids32D2z/ZcnXkcSLVgCwFNm+3vZu2/fPs9+2P297m+17bZ83Y9+HbD+W/XxoMeLtrFExDgAozim9nZKkvaPHmxwJAKAdvWndCh06NqEf7j3S7FAAoFCVTIw38LISALSsT0h6qOyLMJoDAJa0GyRddJL9F0s6M/vZLOlLkmR7taSrJb1V0vmSrra9qtRIJXXUEx0nMQ4AKMjA8i5J0p7DY02OBADQjt64foUk6d6dzBkH0FoqmRhvzIQFALQe2+slvVfSlxfhWpJopQ4AS1FE3CVp30kOuVTSn0Tqe5JW2j5N0rslfTsi9kXEfknf1skT7IXoqJmKcQBAYQaWpxXjJMYBAM1w1po+ddYT3budOeMAWkslE+NmJiwAtLLPSfpXkubNLtjebHvY9vDIyMiCL3SiYnzBpwAANM86SdtnfN6RbZtv+4sUtZ5IWSv1CRYUAEAxBvqyivFDtFIHACy+jlqis0/rp2IcQMupZmI8+00iAwBai+2fkbQ7Irae7LiI2BIRQxExNDg4uODrJcwYB4C2VtR6IqUPjqgYBwAUpa+rrs56QsU4AKBp3rR+hR7YeUCTPDgD0EKqmRhvVPg1NwwAQPF+XNIltp+SdLOkn7T9lbIuRgcSAKi0nZI2zPi8Pts23/ZSddSYMQ4AKI5tDS7v0giJcQBAk7xx3QqNHp/UEyOHmx0KABSmkonxRoVfkMgAgJYSEZ+OiPURsUnSZZK+ExG/WNb1WE8AoNJulfRBp/6JpAMRsUvS7ZIutL3K9ipJF2bbStVRZ8Y4AKBYA8s7tecwrdQBAM3x5o2rJEnff3p/kyMBgOLUmx1AHnTwAADkQSt1AFi6bN8k6QJJA7Z3SLpaUockRcR1km6T9B5J2yQdkfSRbN8+25+RdHd2qmsiYl/Z8XbWEo1PsqAAAIozsLxLuw4ca3YYAIA29eqBXq3s6dDWH+7X//FjG5sdDgAUorDEuO1uSXdJ6srO+7WIuLqo88+6VvoPnjsBQMuKiDsl3VnmNRJaqQPAkhURl7/E/pD08Xn2XS/p+jLimk9HLdHkVGhyKlRrLDAAAOQwsLxL9+080OwwAABtKkmst2xcpa0/pGIcQOsospX6mKSfjIhzJJ0r6aKspWHhGo+Zgsw4ACCP6cR4c8MAAFRfRy29taKdOgCgKAN9ndo7elxT3LAAAJrkvFet0uMjo9o/ymgPAK2hsMR4pA5nHzuyn1L+y71RgEGBHwAgD2aMAwCK0lFL1xQS4wCAogws79LkVOj5o+PNDgUA0KaGXsWccQCtpciKcfl/sXfn8VHVZ///39fMZE9IIBnCFvawBJAtsikgrmgt2lpbsG5tLbXV3l21+m1r79qfd71tb9va4oJ7bd21Fi1qXQBFRIgKKPsOASRhX7N/fn9kQlOKisxMzpzJ6/l4zCMzZ86Z8+4f9TBznev6mAXNbJGkCkmvOOfeOer9qWZWZmZllZWV0ZxHEqNvAQDR+Vdh3OMgAADfSw01dYxzUQEAxEZBdpokqXJ/tcdJAACt1Uld8hQKmMoYpw4gScS0MO6cq3fODZHURdIIMxt41PvTnXOlzrnScDh8wudhiXEAQCywxjgAIFYYpQ4AiLXCNumSpO37qjxOAgBorTJSgxrQOZd1xgEkjZgWxps45/ZImiVpYjw+/8ga49QxAABR+NcEEo+DAAB8r6kwXl1LYRwAEBud8hoL41v3HPY4CQBAksxsopmtNLM1ZnbDMd6/2sw+MLNFZjbXzEqavXdj5LiVZnZOyyaPzvCubbV48x7V1PFdB4D/xawwbmZhM8uLPM+QdJakFbH6/KPOJUly9IwDAKLQ1DHOGuMAgGhlpgYlSYdr6z1OAgBIFoVt0hUwCuMAkAjMLChpmqRzJZVImtK88B3xqHNuUGSq7m2Sbo8cWyJpsqQBamwmvDPyeb5Q2r2tqusatGzbPq+jAEDUYtkx3lHSLDNbImmhGtcYfyGGn3/EkVHq1DEAAFEIHOkY54ICAIhOU2H8YE2dx0kAAMkiJRhQ+5x0bd3LKHUASAAjJK1xzq1zztVIelzSBc13cM41rxxn6V+rwV4g6XHnXLVzbr2kNZHP84Xh3dpKkso27PI4CQBELxSrD3LOLZE0NFaf90ksMkydOgYAIBoBRqkDAGIkM7Xxq9XhGjrGAQCx0ykvnY5xAEgMnSVtbva6XNLIo3cys2sk/VBSqqTTmx07/6hjOx/j2KmSpkpS165dYxI6FgrbpKtbfqbmr9ulq8b29DoOAEQlLmuMx9uRjnFGqQMAotB0PaFjHAAQrSMd49V0jAMAYqdTXgaFcQDwEefcNOdcL0k/kfSzz3jsdOdcqXOuNBwOxyfgCRrdM1/vrN+perpLAPicPwvjkb/UMQAA0WBpDgBArGSlNXaMH6JjHAAQQ53yMrR1b5UcX1oAwGtbJBU1e90lsu3jPC7pwhM8NuGM7pWv/VV1WraVdcYB+JsvC+OsCQsAiIWm6wk/MgEAopXFGuMAgDjolJuumroG7TxY43UUAGjtFkoqNrMeZpYqabKkGc13MLPiZi8/J2l15PkMSZPNLM3MekgqlrSgBTLHzOie+ZKkt9ft8DgJAETHl4Vx0eEHAIgB1hgHAMRKRqQwzhrjAIBY6piXIUnatqfK4yQA0Lo55+okXSvpZUnLJT3pnFtqZjeb2aTIbtea2VIzW6TGdcaviBy7VNKTkpZJeknSNc45X31xaN8mXb3CWZq3dqfXUQAgKiGvA5yIf3X4eRwEAOBrAdYYBwDESGZq41erg9W++n0LAFo1M5so6Q+SgpLuc87detT7XSU9LCkvss8NzrmZLZmxc6QwXr77kAZ1yW3JUwMAjhK5Bsw8attNzZ5/7xOOvUXSLfFLF39jehXo2ffKVVvfoJSgP3suAcCX//Vq+m8uhQwAQDSMjnEAQIwEA6b0lIAOMUodAHzBzIKSpkk6V1KJpClmVnLUbj9TY0fgUDWOzL2zZVNK3fIzJUkbdx1q6VMDAPBvRvfK18Gaen2wZa/XUQDghPmyMM4a4wCAWAkYa4wDAGIjKzXEGuMA4B8jJK1xzq1zztVIelzSBUft4yS1iTzPlbS1BfNJknLSU1SQnaqNOw+29KkBAPg3o5rWGWecOgAf82VhnA4/AECsmBk3WgEAYiIjNahDrDEOAH7RWdLmZq/LI9ua+29Jl5pZuRpH5373WB9kZlPNrMzMyiorK2MetFt+ljbsoGMcAOCtdlmp6tchR/PW7vA6CgCcMF8WxpvWhKXDDwAQrcaOca9TAACSQVZqSIdYYxwAkskUSQ8557pIOk/SI2b2H7+lOeemO+dKnXOl4XA45iG65WdqAx3jAIAEMLa4QAvX72YJKQC+5dPCeGNlvJ5KBgAgSo0d416nAAAkg8y0IKPUAcA/tkgqava6S2Rbc9+Q9KQkOefelpQuqaBF0jXTPT9L2/ZWqaqWm68AAN4a36e9auobNH8d49QB+JNPC+ONfxsavM0BAPA/1hgHAMRKJqPUAcBPFkoqNrMeZpYqabKkGUfts0nSGZJkZv3VWBiP/az0T9EtP7MxzC7GqQMAvHVyj7bKSAlq9soWvxwCQEz4tDDetMY4hQwAQHQCrDEOAIiRzNSQDlbTMQ4AfuCcq5N0raSXJS2X9KRzbqmZ3WxmkyK7/UjSN81ssaTHJF3pPLirtnt+liRpww7GqQMAvJUWCmpMr3zNXllJowkAXwp5HeBENBXG+e8uACBaAUapAwBiJDcjRR8ervU6BgDgODnnZkqaedS2m5o9XybplJbOdbSmwvjGnXSMAwC8d1rfsF5bUaH1Ow6qZzjb6zgA8Jn4s2M8kpoOPwBAtMy4ngAAYqNdVqp2H6rxOgYAIMnkZqYoLzNFG3bSMQ4A8N74Pu0lSXNWMU4dgP/4sjBujFIHAMSIiQkkAIDYyMtMUVVtgw6zzjgAIMa652dRGAcAJISu+ZnqWZDFOuMAfMmXhXHWGAcAxEogYKyJBACIiXaZqZJE1zgAIOZ6hbO1puKA1zEAAJAkje8b1vx1O7kpSRLUcwAAIABJREFUGIDv+LIwHjxSGPc4CADA91hjHAAQK3mRwviugxTGAQCxVVyYre37qrX3cK3XUQAA0Bn9ClVd16A3V9M1DsBffFkYDzTWxdVAJQMAEKUAa4wDAGKkXRYd4wCA+Chuny1JWlOx3+MkAABII3u2U5v0kP65bLvXUQDgM/FlYdzoGAcAxIjRMQ4AiJG2mSmSpN2H6OYDAMRWn8IcSdLq7YxTBwB4LyUY0Bn9C/Xa8u2qq2/wOg4AHDdfFsabOsZZExYAEK2AcT0BAMRG26aOcUapAwBirHNehtJTAlrNOuMAgARxdkmhdh+q1cINu72OAgDHzZ+F8UhlvJ5CBgAgSo1rjHM9AQBELy+jsWOcNcYBALEWCJh6t8/Wqu2MUgcAJIZxfcJKDQX0z2UfeR0FAI6bPwvjjFIHAMSIiesJACA2QsGA8rNSVbG/2usoAIAkVNw+R2voGAcAJIistJDG9i7QP5duZxojAN/waWG88S8dfgCAaJmZuJwAAGKlQ266Ptp72OsYAIAkVFyYrW17q7SvqtbrKAAASJLOGdBBW/Yc1tKt+7yOAgDHxaeF8cbKOHchAQCiFQhwPQEAxE7H3Axt21vldQwAQBIqbp8jSXSNAwASxhn92ysYMM38YJvXUQDguPi6MN7Q4HEQAIDvBcxUT2EcABAjHXPTKYwDAOKiuH22JGk164wDABJEfnaaxvTK1/NLttJ4AsAXfFkYj9TFKWQAAKIWNFM9i4wDAGKkQ2669h6u1aGaOq+jAACSTFG7TKWFAlq1nY5xAEDimDS4kzbvOqxFm/d4HQUAPpUvC+PBAKPUAQCxEQiYGrieAABipFNeuiTpI7rGAQAxFgyY+nbI0YqPWMcVAJA4zh7QQanBgJ5fzDh1AInPl4XxI6PUqWMAAKIUNGNpDgBAzHTOy5Qkbdp1yOMkAIBkVNKxjZZu3UezCAAgYeRmpOi0vmG9sGQrUxkBJDyfFsYb/9LhBwDJx8zSzWyBmS02s6Vm9sv4no+lOQAAsdOjIEuStGHHQY+TAACSUUmnNtpzqFbbmEwCAEggnx/cSRX7q7Vg/S6vowDAJ/JlYdzoGAeAZFYt6XTn3GBJQyRNNLNR8TpZMGBq4IICAIiRguxU5aSFtJ7COAAgDko6tpEkLdvKOHUAQOI4o397ZaYGNWPxFq+jAMAn8mVh/EjHOIUMAEg6rtGByMuUyCNu/8EPBoyOcQBAzJiZeoSztI7COAAgDvp1bCMzadk2CuMAgMSRmRrSxIEd9MLibTpcU+91HAD4WD4tjDd1jFPIAIBkZGZBM1skqULSK865d456f6qZlZlZWWVlZVTnCpix/hEAIKZ6FGTRMQ4AiIvstJC652fRMQ4ASDhfLi3S/uo6vbR0m9dRAOBj+bIwHgwwSh0Akplzrt45N0RSF0kjzGzgUe9Pd86VOudKw+FwVOcKBowbrQAAMdWzIFtb9hxWVS2dEgCA2Cvp2IaOcQBAwhnZo5265WfqyYXlXkcBgI/ly8K4NY1Sp5ABAEnNObdH0ixJE+N1jiAd4wCAGOsRzpJz0sadh7yOAgBIQiWd2mjTrkPaV1XrdRQAAI4wM108vIveXrdTm/guBCBB+bIw3jRK3VEYB4CkY2ZhM8uLPM+QdJakFfE6XyDABBIASFRmNtHMVprZGjO74Rjv/87MFkUeq8xsT7P36pu9N6Mlc/cOZ0uSVm3f35KnBQC0EiUd20iSljNOHQCQYC4a3kVm0tPvbvY6CgAck68L4xQyACApdZQ0y8yWSFqoxjXGX4jXyQJmauCCAgAJx8yCkqZJOldSiaQpZlbSfB/n3A+cc0Miy2/8UdKzzd4+3PSec25SiwWX1Lt9tlKCpqUULAAAcTCgU2Nh/EOuMwCABNMxN0PjisN6+t1yJjQCSEg+LYw3/uU/rACQfJxzS5xzQ51zJznnBjrnbo7n+YIBUz0TSAAgEY2QtMY5t845VyPpcUkXfML+UyQ91iLJPkVqKKA+hTlaunWv11EAAEmofZt0dWiTriXlez59ZwAAWtiXS4u0dW+V3lxd6XUUAPgP/iyMBxilDgCIDTrGASBhdZbUfP5eeWTbfzCzbpJ6SHq92eZ0Myszs/lmduHHHDc1sk9ZZWVsf7Qp6dhGy7bu4zsLACAuBhflavFmCuMAgMRzVkmhCrLT9MjbG72OAgD/wZ+FcUapAwBihI5xAEgKkyU97Zyrb7atm3OuVNIlkn5vZr2OPsg5N905V+qcKw2HwzENNKBTG+08WKOK/dUx/VwAACRpcFGeNuw8pD2HaryOAgCthplNNLOVZrbGzG44xvs/NLNlZrbEzF6L3MDb9F69mS2KPGa0bPKWlRoK6JIRRXp9ZYU27TzkdRwA+Dc+LYw3/m2gkAEAiFLATPUNXqcAABzDFklFzV53iWw7lsk6aoy6c25L5O86SbMlDY19xI83oHOuJDFOHQAQF0OK8iRJi+gaB4AWYWZBSdMknSupRNIUMys5arf3JZU6506S9LSk25q9d9g5NyTymNQioT10ychuCprpL+/QNQ4gsfiyMG50jAMAYiQYEKPUASAxLZRUbGY9zCxVjcXv/+isMLN+ktpKervZtrZmlhZ5XiDpFEnLWiR1RL8OOZKkpVv2teRpAQCtxKDOuTKTFm/mBiwAaCEjJK1xzq1zztVIelzSBc13cM7Ncs41tUjPV+PNva1Sh9x0nTOwg55YuFmHa+o//QAAaCG+LIwf6RinkAEAiFIwYEwgAYAE5Jyrk3StpJclLZf0pHNuqZndbGbNOywmS3rc/fti3v0llZnZYkmzJN3qnGvRwnhOeop6FGTpgy0ULAAAsZeTnqLe4WwtLqdjHABaSGdJm5u9Lo9s+zjfkPRis9fpZlZmZvPN7MJjHWBmUyP7lFVWVkaf2GNXjO6uvYdr9fdFHzf4CwBaXsjrACciGGjqGKeQAQCIjhlrjANAonLOzZQ086htNx31+r+Pcdw8SYPiGu44DCnK09w1O+ScOzL1CgCAWBlSlKfXV1RwnQGABGNml0oqlTS+2eZuzrktZtZT0utm9oFzbm3z45xz0yVNl6TS0lLf/1h1cve26t+xjR6at0FfObmIaxWAhODTjnFGqQMAYiNoxgQSAEBcDOuap8r91SrffdjrKACAJDS4KE87D9ZwnQGAlrFFUlGz110i2/6NmZ0p6aeSJjnnqpu2O+e2RP6ukzRb0tB4hk0EZqavndJdKz7ar7lrdngdBwAk+bQw3nRjER3jAIBoBQN0jAMA4mNo17aSpPc27fY4CQAgGQ0pypMkxqkDQMtYKKnYzHqYWaoal3Sa0XwHMxsq6R41FsUrmm1va2ZpkecFkk6R1KJLPXnlgiGdVNgmTXfNXvvpOwNAC/BlYbypY9xRyAAARClgpoYGr1MAAJJRvw45ykwN6v1NFCwAALHXt0OO0kIBLeI6AwBx55yrk3StpJclLZf0pHNuqZndbGaTIrv9RlK2pKfMbJGZNRXO+0sqM7PFkmZJutU51yoK42mhoK46tafmrd2pxZu5XgHwni/XGG8qjNdTyAAARCkYkOoZpQ4AiINQMKCTuuTSMQ4AiIuUYECDu+Rp4UauMwDQEpxzMyXNPGrbTc2en/kxx82TNCi+6RLXlJFd9cfXV+vuOWt116XDvY4DoJXzacd4419GqQMAosUodQBAPA3r2lbLtu5TVW2911EAAEno5B5ttXTLXh2qqfM6CgAAx5SdFtLlo7vrpaUfaW3lAa/jAGjlYlYYN7MiM5tlZsvMbKmZfS9Wn32Mc8mMUeoAgOgFzLieAADiZljXtqprcPpgy16vowAAktDJ3duprsGxbAcAIKFdeUp3pQYDupu1xgF4LJYd43WSfuScK5E0StI1ZlYSw8//NwEzMfkWABCtgBmj1AEAcTOsW1tJ0oL1uzxOAgBIRsO7tVXApHe4zgAAElhBdpqmjOiqZ9/fog07DnodB0ArFrPCuHNum3Puvcjz/ZKWS+ocq88/WsAYpQ4AiF4wQGEcABA/7bJS1bcwh4IFACAuctJTVNKpjRZynQEAJLjvTOillKDpjtdWex0FQCsWlzXGzay7pKGS3jlq+1QzKzOzssrKymjPwZqwAICoMYEEABBvI3u207sbdqm2vsHrKACAZsxsopmtNLM1ZnbDx+zz5WbLBj7a0hmPx8nd2+n9zbtVU8d1BgCQuNrnpOvy0d313KItWlPBWuMAvBHzwriZZUt6RtL3nXP7mr/nnJvunCt1zpWGw+GozhM0E3VxAEC0ggHRMQ4AiKtRPfN1sKZeH7LOOAAkDDMLSpom6VxJJZKmHL0koJkVS7pR0inOuQGSvt/iQY/DiO7tVFXboA+3cp0BACS2b43rqfSUoH7/6iqvowBopWJaGDezFDUWxf/qnHs2lp99tIBJDRQyAABRCgSYQAIAiK8RPdpJkuavY8wtACSQEZLWOOfWOedqJD0u6YKj9vmmpGnOud2S5JyraOGMx6W0e+N1ZgHj1AEACS4/O01fO6W7XliyTcu37fv0AwAgxmJWGDczk3S/pOXOudtj9bkfJ8AodQBADATNuNEKABBXBdlpKm6frfnrdnodBQDwL50lbW72ujyyrbk+kvqY2VtmNt/MJh7rg2K5dOCJCOekqWdBFoVxAIAvTB3bS7kZKfqfmcvlqPEAaGGx7Bg/RdJlkk43s0WRx3kx/Px/EwgwSh0AEL1gwNTABQUAEGejeuarbMMu1bHOOAD4SUhSsaTTJE2RdK+Z5R29UyyXDjxRI3vma8H6XarlOgMASHC5mSn6rzOK9ebqHZq9quVvKAPQusWsMO6cm+ucM+fcSc65IZHHzFh9/tGCAVNdA//YBwBEx8zU4MQdqgCAuDqyzvhWxgUCQILYIqmo2esukW3NlUua4Zyrdc6tl7RKjYXyhDOuuEAHquu0ePMer6MAAPCpLhvVTd3zM3XLP5Zz8zCAFhXTNcZbUjBgqmf0LQAgSkEzSRKXFABAPI3s2bj+69trGacOAAlioaRiM+thZqmSJkuacdQ+z6mxW1xmVqDG0errWjLk8RrTq0ABk95cvcPrKAAAfKrUUEA3ntdfayoO6PGFmz/9AACIEd8WxkMUxgEAMRCMXAm5pgAA4qkgO039OuTozdWMCgSAROCcq5N0raSXJS2X9KRzbqmZ3WxmkyK7vSxpp5ktkzRL0nXOuYS8wyk3M0WDuuRp7hoK4wAAfzi7pFAje7TT715ZpX1VtV7HAdBK+LYwHjBTHUUMAECUAoGmjnGuKQCA+BrXJ6yyDbt1qKbO6ygAAEnOuZnOuT7OuV7OuVsi225yzs2IPHfOuR8650qcc4Occ497m/iTje1doEWb91BcAAD4gpnpZ58r0a5DNfrdK6u8jgOglfBtYTwUNDVQGAcARKlplDod4wCAeBtXHFZNfYPeWbfL6ygAgCR0anGB6hscy3YAAHxjUJdcXTKiqx6et0EfbtnrdRwArYBvC+NBOsYBADEQjHSM19MxDgCIs9LubZWeEtAbjFMHAMTBsK5tlZka1FzWGQcA+Mj1E/upXVaqfvq3D2hcARB3/i2MB4yxtwCAqFmkY9w1eBwEAJD00lOCGtkjX2+sojAOAIi91FBAI3u0Y51xAICv5Gak6Ofnl2hx+V49umCT13EAJDlfF8br6imMAwCiE2ysi9MxDgBoEWOLC7S28qC27DnsdRQAQBIa3yes9TsOav2Og15HAQDguE0a3Emn9M7XbS+tUMX+Kq/jAEhivi6M0zEOAIjWkVHqjGoCALSA8X3CkqQ36RoHAMTBGf0LJUmvLd/ucRIAAI6fmelXFwxUdV2Dfv7ch3LUfgDEia8L46wxDgCIViBSGOdmKwBAS+jdPlsd2qTrTdZ/BQDEQVG7TPXrkKNXllEYBwD4S89wtn50Vh+9vHS7Zize6nUcAEnK14VxuvsAANEKGh3jAICWY2Ya16dAb66uVG19g9dxAABJ6KySQpVt3K3dB2u8jgIAwGdy1dieGtY1Tzf9fam272OkOoDY829h3CiMAwCiF2CUOgCghZ3Rv1D7quq0cMMur6MAAJLQGf0LVd/gNHtVhddRAAD4TIIB028vHqyq2nr9v2c/YKQ6gJjzb2GcjnEAQAw0dYwzSh0A0FLGFhcoNRTQq8soWAAAYu+kzrkK56RxnQEA+FLPcLaun9hPr62o0FNl5V7HAZBkKIwDAFq1QORKyCUFANBSMlNDOrV3gV5Z/hEdEACAmAsETGf2b685qypVXVfvdRwAAD6zr43prtE98/WLGUu1puKA13EAJBF/F8b5EQkAEKUAa4wDADxwZv9Cbd51WKv5kQcAEAdnlRTqQHWd5q7e4XUUAAA+s0DA9PvJQ5SRGtS1j76nqlpu9AIQG/4ujFPEAABEKRhglDoAoOWd2b+9JOmVZds9TgIASEan9g6rTXpILyzZ5nUUAABOSGGbdP3fxYO14qP9+p+Zy72OAyBJ+LYwHqIwDgBJycyKzGyWmS0zs6Vm9r14ni9IxzgAwAPt26RrcFEehXEAQFykhgI6d2BHvbJsO112AADfmtCvvb45tof+/PZGvfgBN3sBiJ5vC+MBozAOAEmqTtKPnHMlkkZJusbMSuJ1skCAwjgAwBtn9W+vRZv3qGJ/lddRAABJ6PzBHXWguk6zV1Z4HQUAgBN23Tn9NKQoTz9+arHWVOz3Og4An/NtYTwUNNVRxACApOOc2+acey/yfL+k5ZI6x+t8TR3jjFIHALS0M0sKJUn/XErXOAAg9kb3zFd+VqqeX0yHHQDAv1JDAd116TBlpAY19c/val9VrdeRAPiYbwvjwUBADRTGASCpmVl3SUMlvROvcwTpGAcAeKRvYY56hrP0D9Z/BQDEQSgY0HmDOuq1Fdt1sLrO6zgAAJywjrkZuvOrw7Vp1yH98IlF1IYAnDD/FsZNdIwDQBIzs2xJz0j6vnNu31HvTTWzMjMrq6ysjPI8jX+5pAAAWpqZ6fyTOumd9TsZpw4AiIvPD+6kqtoG/XPZR15HAQAgKiN6tNNNny/Rq8sr9IfXVnsdB4BP+bcwHgjQ3QcAScrMUtRYFP+rc+7Zo993zk13zpU650rD4XBU52rqGGeUOgDAC58/qaManPTiBxQsAACxV9qtrYraZeipsnKvowAAELXLRnXTl4Z30R9eW60Zi7d6HQeAD/m4MM7YWwBIRmZmku6XtNw5d3u8z9e0xjjXFACAF4oLc9S3MEcvLOFHHQBA7AUCpouHF2ne2p3avOuQ13EAAIiKmemWLwzUiB7t9OMnF2vhhl1eRwLgMz4ujAdUT3cfACSjUyRdJul0M1sUeZwXr5MFmjrGKYwDADxy/kkdtXDDbm3be9jrKACAJPSl4V1kJj1VttnrKADga2Y20cxWmtkaM7vhGO//0MyWmdkSM3vNzLo1e+8KM1sdeVzRssmTS1ooqOmXDVeXdhn65p/LtK7ygNeRAPiIjwvjdPcBQDJyzs11zplz7iTn3JDIY2a8ztc0Sp2brQAAXvncSR0lSf9Yss3jJACAZNQpL0Nji8N6+t1yfksDgBNkZkFJ0ySdK6lE0hQzKzlqt/cllTrnTpL0tKTbIse2k/QLSSMljZD0CzNr21LZk1FeZqoeunKEgma68sGF2nGg2utIAHzCt4XxEGuMAwBiIMAodQCAx3qGs1XSsY2eZ408AECcfKW0SFv3Vmnumh1eRwEAvxohaY1zbp1zrkbS45IuaL6Dc26Wc65p3Yr5krpEnp8j6RXn3C7n3G5Jr0ia2EK5k1bX/Ezde0WpKvZX6YoHFmhfVa3XkQD4gG8L4wEzihgAgKg1dYw30DEOAPDQF4d11uLyvVq9fb/XUQAASejMkvZqm5miv87f6HUUAPCrzpKar0lRHtn2cb4h6cXPcqyZTTWzMjMrq6ysjDJu6zCsa1vdfelwrdq+X19/cKEO1dR5HQlAgvNtYTwUpDAOAIhe8EjHuMdBAACt2oVDOysUMD31brnXUQAASSgtFNSUEV316vLt2rzr0KcfAAA4YWZ2qaRSSb/5LMc556Y750qdc6XhcDg+4ZLQaX3b6w+Th+q9Tbv1rUfeVXVdvdeRACQw3xbG6RgHAMRCKNhYGK+jMg4A8FBBdpom9GuvZ9/bwjUJABAXl47qJjPTX+gaB4ATsUVSUbPXXSLb/o2ZnSnpp5ImOeeqP8uxOHHnDeqoWy86SW+u3qHvPbaI71QAPpZvC+OhgKmesbcAgCilBBsvhTX8gxkAEo6ZTTSzlWa2xsxuOMb7V5pZpZktijyuavbeFWa2OvK4omWTn5iLh3fRjgPVmrOKsYkAgNjrlJehiQM66LEFmxg1CwCf3UJJxWbWw8xSJU2WNKP5DmY2VNI9aiyKVzR762VJZ5tZWzNrK+nsyDbE0JdLi3TT+SV6aelH+u5j76uW3/oAHINvC+PBQGPHuKM4DgCIQsqRjnGuJwCQSMwsKGmapHMllUiaYmYlx9j1CefckMjjvsix7ST9QtJISSMk/SLyA1RCm9CvvQqyU/U049QBAHFy5Sndta+qTs+9v9XrKADgK865OknXqrGgvVzSk865pWZ2s5lNiuz2G0nZkp6K3Lg7I3LsLkm/UmNxfaGkmyPbEGNfP7WHfva5/nrxw4/0nb++x1h1AP/Bt4Xx1FBj9FoKGQCAKDR1jHMXKQAknBGS1jjn1jnnaiQ9LumC4zz2HEmvOOd2Oed2S3pF0sQ45YyZlGBAFw7prFeXb9eugzVexwEAJKHSbm01sHMb3Td3HUsUAsBn5Jyb6Zzr45zr5Zy7JbLtJudcUwH8TOdcYbMbdyc1O/YB51zvyONBr/43tAZXje2pmy8YoFeWbdfVj7yrqlqK4wD+xbeF8aYOPwoZAIBoNK0xXsuPQgCQaDpL2tzsdXlk29EuMrMlZva0mTWt23dcx5rZVDMrM7OyysrEGF/+lZOLVFvv9GTZ5k/fGQCAz8jMdPX4XlpXeVAvL/3I6zgAAMTF5aO763++MEizVlbqm38u08FqlhAB0MjHhXE6/AAA0Uttup7UcT0BAB96XlJ359xJauwKf/izHOycm+6cK3XOlYbD4bgE/KyKC3M0qmc7PfL2Rjr5AABxce7AjupZkKVps9awRCEAIGldMrKrfvOlk/TWmh265N752nmg2utIABKA7wvjNRTGAQBRCEWuJ3UNXE8AIMFskVTU7HWXyLYjnHM7nXNNv27cJ2n48R6byK4Y3V1b9hzW6ysqvI4CAEhCwYDp6tN6aenWfZq9KjEmpgAAEA8XlxbpnstKteKj/frS3W9r865DXkcC4DHfFsaPdPixxjgAIAr/WpqD6wkAJJiFkorNrIeZpUqaLGlG8x3MrGOzl5MkLY88f1nS2WbW1szaSjo7ss0XziopVMfcdP357Q1eRwEAJKkLh3RWp9x0/el1usYBAMntrJJC/fWqkdp1sEZfvGuelm7d63UkAB7ybWE8JRQpZDD6FgAQhZQAS3MAQCJyztVJulaNBe3lkp50zi01s5vNbFJkt/8ys6VmtljSf0m6MnLsLkm/UmNxfaGkmyPbfCEUDOiSEV315uodWlt5wOs4AIAklBoK6Nun9dK7G3fTNQ4ASHql3dvp6atHKxQwfeWe+Zq9kulcQGvl38I4a4wDAGIgEDAFA8b1BAASkHNupnOuj3Oul3Pulsi2m5xzMyLPb3TODXDODXbOTXDOrWh27APOud6Rx4Ne/W84UZNHdFVK0PTneRu8jgIASFJfObmrurbL1G0vrVRDA13jAIDkVlyYo2e/M0ZF7TL19YcW6oG565maArRCvi+Ms8Y4ACBaoYCpjlHqAIAEEs5J0wVDOuuJss3adbDG6zgAgCSUGgroR2f30fJt+/T8kq1exwEAIO465mbo6atH66ySQt38wjLd+OwHqmEqMdCq+LYwzhrjAIBYSQ0GuNEKAJBwrh7fU1W1DXqIrnEAQJx8/qRO6tchR//3z1UUBgAArUJWWkh3fXW4rp3QW48v3KxL739HOw9Uex0LQAvxbWGcUeoAgFgJBekYBwAknt7tc3R2SaEenrdBB6vrvI4DAEhCgYDphnP7adOuQ3rwrfVexwEAoEUEAqYfn9NXf5g8RIs379H5f5yrdzfu9joWgBbg48K4SZJquZsVABCllGCAG60AAAnp6tN6ae/hWj22YJPXUQAg6ZjZRDNbaWZrzOyGT9jvIjNzZlbakvlayml92+uMfu11x2urtX1flddxAABoMRcM6axnvj1GKcGAvnLP26w7DrQC/i2Mh1hjHAAQG42Fcf7RCwBIPMO6ttWonu1075vrVF1X73UcAEgaZhaUNE3SuZJKJE0xs5Jj7Jcj6XuS3mnZhC3r5+eXqLbe6dYXV3gdBQCAFjWwc66e/+6pOq1ve938wjJd++j72l9V63UsAHHi28I4a4wDAGIlJWh0jAMAEta1E4q1fV+1Hn2HrnEAiKERktY459Y552okPS7pgmPs9ytJ/yspqVupuxdk6Zvjeuhv729R2YZdXscBAKBF5Wak6N7Lh+uGc/vppaUfadKf3tIH5Xu9jgUgDnxbGGeNcQBArKQEA6pr4HoCAEhMp/TO1+ie+Zo2aw1rjQNA7HSWtLnZ6/LItiPMbJikIufcPz7pg8xsqpmVmVlZZWVl7JO2kGsm9FbH3HT99G8fMqUEANDqmJmuHt9Lj141UlW19frCnW/pztlrVN9AcyaQTHxcGI+sMU5hHAAQpVAwoJo6/pELAEhMZqbrJvbVjgM1evCt9V7HAYBWwcwCkm6X9KNP29c5N905V+qcKw2Hw/EPFyeZqSHd8oWBWrl9v6a9vsbrOAAAeGJkz3y99L1xOmdAB9320kpdcu98bdlz2OtYAGLEx4XxyBrjdRTGAQDRSQ0aHeMAgIQ2rGtbndm/UPe8sU57DtV4HQcAksEWSUXNXneJbGuSI2npPSjOAAAgAElEQVSgpNlmtkHSKEkzzKy0xRJ64PR+hfri0M66c/ZaLd3KCFkAQOuUm5miP10yVL+9eLA+3LJXE3//hv6+aIuco7EG8DvfF8ZZYxwAEK1QMMAEEgBAwvvxOX10oLpO02bRxQcAMbBQUrGZ9TCzVEmTJc1oetM5t9c5V+Cc6+6c6y5pvqRJzrkyb+K2nJs+X6K8zFRd99QSvicBAFotM9OXhnfRzO+NVe/22fre44t09V/eVcW+Kq+jAYiCjwvjjaPUa1jzCAAQpZSgcaMVACDh9evQRhcP76KH5m3Q2soDXscBAF9zztVJulbSy5KWS3rSObfUzG42s0nepvNWXmaqbvnCQC3btk+3v7LK6zgAAHiqW36WnvrWaN14bj/NXlmpM2+fo6ffLad7HPAp3xbG01KCkqQa7lwFAEQphY5xAIBPXHdOP6WHgrr5+WX8EAMAUXLOzXTO9XHO9XLO3RLZdpNzbsYx9j2tNXSLNzlnQAdNGVGku2av1ZurK72OAwCAp0LBgL41vpde/N5Y9e2Qox8/tVhXPriQtccBH4pZYdzMHjCzCjP7MFaf+UnSQ43Rq2opZAAAopMSDKiOjnEAgA+Ec9L0vTOLNWdVpV5fUeF1HABAErvp/AEqbp+tHzyxWJX7q72OAwCA53qGs/XE1NH65aQBWrhhl866fY7umbOWhhvAR2LZMf6QpIkx/LxPFAoGlBI0Ha5llDoAIDqhgPEPWACAb1wxprt6hbN08wvLVMX3IQBAnGSkBvWnS4Zpf1WtfvDEItXxnQkAAAUCpivGdNfL3x+nMb0K9OsXV+hzd7yp+et2eh0NwHGIWWHcOfeGpF2x+rzjkZ4S1OEafggCAEQnJcQodQCAf6QEA/rVhQO1cech/Y61XwEAcdS3Q45+deFAzV2zQ7e+uMLrOAAAJIyidpm674pS3Xd5qQ7V1Gvy9Pn6wROLVLG/yutoAD5Bi64xbmZTzazMzMoqK6NfnygjJUiHBAAgaikBUy2j1AEAPjKmV4GmjCjSvW+u05LyPV7HAQAksS+XFunKMd1139z1eubdcq/jAACQUM4sKdQrPxivayf01gtLtuqM387R9DfWqrqO2hWQiFq0MO6cm+6cK3XOlYbD4ag/L53COAAgBhrXGKdjHADgLzee11/tc9J1/dNLVFPHdQwAED8//Vx/jemVrxuf/UDvb9rtdRwAABJKRmpQPz6nr17+/jgN795W/zNzhc68fY7+sWSbnKMZB0gkLVoYj7WMlCBrjAMAohYKBlRDxzgAwGfapKfoli8M1IqP9uv3rzJSHQAQPynBgKZdMkyFuWm66uEyrd9x0OtIAAAknJ7hbD30tRF65BsjlJUa0jWPvqcv3f02N5UBCcTXhfH01KAO19IZAQCITmrQVNfA9QQA4D9n9C/U5JOLdNectZq3ZofXcQAASaxtVqoe/toIOUmX3f+OKvaxhioAAMcytjisf/zXWN36xUHauPOQvnDnPH33sfe5sQxIADErjJvZY5LeltTXzMrN7Bux+uyPk5ESUFUNHeMAgOiEggHVMoIWAOBTN32+RD0LsvSDJxdp18Ear+MAAJJYz3C2HrzyZO06WKPLH1igfVW1XkcCACAhBQOmySO6avZ1p+m7p/fWq8u268zb5+gnTy9R+e5DXscDWq2YFcadc1Occx2dcynOuS7Ouftj9dkfJz0lqKo6CuMAgOikpwRUVdfAmj8AAF/KTA3pjilDtftgrX781GI1NHA9AwDEz+CiPN196XCtqTigKx9YoP0UxwEA+FjZaSH96Oy+euP6Cbp8dDf97f0tOv23c/SLv3+oiv1MXwFamq9HqWekBHWYjnEAQJQyU0Oqb3CqpmscAOBTAzrl6mfn99frKyr0O9YbBwDE2bg+Yf1xylAtKd9L5zgAAMchnJOmX3x+gGZfd5ouGt5Zf3lnk8bdNku/nrlclfurvY4HtBq+L4wfojAOAIhSVmpQkrjZCgDga5eN6qavlBbpj6+v0cwPtnkdBwCQ5M4d1FF/umSYPijfq8vuX6C9hymOAwDwaTrlZejXXzxJr/1wvM4d2FHT31ynU//3df3i7x9qy57DXscDkp6vC+M56SHGNQFAkjGzB8yswsw+bKlzZqaGJEkHa+pa6pQAAMScmenmCwdoWNc8/ejJxVq2dZ/XkQAASW7iwA6686vDtGzrXk2ePl8V+xgJCwDA8ehekKXffWWIXvvheE0a3El/fWeTTvvNLF3/9GKt33HQ63hA0vJ1YbxNRooOVNexhh4AJJeHJE1syRNmpjV2jDOFBADgd2mhoO6+dLhyM1L0tYcWqHz3Ia8jAQCS3NkDOui+K07Wxp0H9YU752lNxQGvIwEA4Bs9w9n6zcWDNef6CbpkRFf9fdFWnfF/s/Xdx97X0q17vY4HJB1fF8Zz0kNqcHT4AUAycc69IWlXS54zK9IxTmEcAJAM2rdJ10NfP1mHa+p1+QMLtOtgjdeRAABJbnyfsJ6YOlrVdfX60t3z9O7GFv1KBwCA73XOy9AvLxiouT85XVPH9dLry7frc3fM1ZTp8/Xqsu00iAIx4uvCeJv0FEnS/ioK4wDQmpjZVDMrM7OyysrKqD8vI7LG+KFqricAgOTQr0Mb3X/lydqy+7C+9uACHeQaBwCIs0FdcvXMt8coLyNFU+59R0+WbfY6EgAAvhPOSdMN5/bTvBvP0P87r5827jyoq/5cpjNvn6NH5m/UIRpFgaj4ujCeEymM72OdcQBoVZxz051zpc650nA4HPXnZR1ZY5yOcQBA8ji5eztNu2SYPty6T1c8sED7+d4EAIizbvlZ+tt3TtHJ3dvq+qeX6Bd//1C19Q1exwKAmDCziWa20szWmNkNx3h/nJm9Z2Z1Zvalo96rN7NFkceMlksNv8rNSNHUcb005/oJumPKUOWkh/Tz5z7U6F+/rv99aYW27jnsdUTAl3xdGG+T0VjIoGMcABCNIx3j3HEJAEgyZ5YU6o9ThmrR5j26/IEF2nuY4jgAIL7aZqXq4a+N0DfH9tDDb2/UV+97R9v3VXkdCwCiYmZBSdMknSupRNIUMys5ardNkq6U9OgxPuKwc25I5DEprmGRVFKCAU0a3EnPXXOKnr56tMb0ytc9c9bq1P99XVc9XKbZKysYsw58BiGvA0SjaZT63kP8uAMAOHFZaU2FcTrGAQDJ57xBHRUMmK599D1ddv87evDKk5WfneZ1LABAEgsFA/rp50o0oFOubnz2A038/Rv6zZcG68ySQq+jAcCJGiFpjXNunSSZ2eOSLpC0rGkH59yGyHuMykDMmZlKu7dTafd22rzrkB5bsElPlm3Wq8u3q2u7TF0ysqu+XFqkdlmpXkcFEpqvO8ab/g++62CNx0kAALFiZo9JeltSXzMrN7NvxPucmZFR6hTGAQDJ6pwBHXT3pcO18qP9uuiuedqw46DXkQAArcCFQzvr+e+eqo65Gbrqz2X67xlLVVXL9y4AvtRZ0uZmr8sj245XupmVmdl8M7vwWDuY2dTIPmWVlZXRZEWSK2qXqesn9tO8G87QHVOGqkNuum59cYVG/c9r+v7j72vB+l1yji5y4Fh8XRgP5zR2OVQeqPY4CQAgVpxzU5xzHZ1zKc65Ls65++N9zsymUerVjFIHACSvM/oX6tFvjtLew7X64l3z9O7G3V5HAgC0Ar3bZ+tv14zRN07toYfmbdDn/zhX72/iGgSg1enmnCuVdImk35tZr6N3cM5Nd86VOudKw+FwyyeE76SGGsesP/mt0frnD8Zpyogivba8Ql++521N+O1s/en11dq2l7XIgeZ8XRhPTwkqJy2kyv0UxgEAJy4lGFBqMKCDdIwDAJLc8G5t9ex3TlFOekhT7p2vJxZu8joSAKAVSAsF9fPzS/Tw10foYHWdvnjXPP3qhWU6VMPNyQB8Y4ukomavu0S2HRfn3JbI33WSZksaGstwQJ/CHP3ygoF656dn6P8uHqwOuen67T9Xacytr+uy+9/R84u3MrUFkM8L41Jj1zgd4wCAaGWmBXWYH2UAAK1Aj4Is/e07p2hkj3b6yTMf6IZnlvADCQCgRYzvE9bLPxinr47sqvvnrtfE37+p2SsrvI4FAMdjoaRiM+thZqmSJkuacTwHmllbM0uLPC+QdIqarU0OxFJmakgXDe+ix6eO1hvXTdB3J/TWusqD+u5j72vELa/q5899qHc37mbUOlqtpCiMb99b5XUMAIDPZaWGtL+KwjgAoHVol5Wqh742QtdO6K3HF27Wl+6ep7WVB7yOBQBoBXLSU/T/XThIT0wdpVDAdOWDC3XVwwu1YcdBr6MBwMdyztVJulbSy5KWS3rSObfUzG42s0mSZGYnm1m5pIsl3WNmSyOH95dUZmaLJc2SdKtzjsI44q5rfqZ+eHZfvXn9BP3lGyM1oV97PVm2WRfdNU9jb5ul215aoZUf7fc6JtCiQl4HiFZRu0y9sarS6xgAAJ8ryE7VjoM1XscAAKDFBAOmH5/TV4OL8nTd04v1uTve1E/P669LR3WTmXkdDwCQ5Eb2zNdL3x+nB99arzteW62zf/eGvjmuh759Wm9lp/n+J0sAScg5N1PSzKO23dTs+UI1jlg/+rh5kgbFPSDwMQIB06nFBTq1uED7q2r1z6Xb9ffFW3XPG+t05+y16tchR5OGdNLnT+qkonaZXscF4sr3HePd2mWqYn+1DrMuLAAgCgXZadqxn6U5AACtz1klhXr5++M0oke+fv73pbriwYUq333I61gAgFYgNRTQt8b30qwfn6bzB3fUtFlrNf62Wbp/7nqW+QAAIA5y0lN00fAu+vPXR2j+jWfol5MGKCstpNteWqmxt83SRXfN04Nvrde2vYe9jgrEhe8L413zG+9e2bCTcUsAgBNXkJ2mHQcojAMAWqfCNul6+Gsn61cXDtTC9bt01u1v6O45a1Vb3+B1NABAK9C+Tbpu//IQ/f2aU9S/Yxv96oVlmvDb2Xp8wSbVcS0CACAuwjlpumJMdz3z7TF68/oJun5iXx2srtMvn1+m0b9+XRdOe0t3z1mrjdTfkER8Xxgf0ClXkvRB+V6PkwAA/KwgJ1U7D9aoocF5HQUAAE+YmS4b1U2v/mi8Ti0u0K0vrtD5d8zV/HU7vY4GAGglBhfl6S9XjdSjV41U+zbpuuHZD3TG7XP013c20kEOAEAcFbXL1HdO662Xvj9Or/9ovK47p6/qG5xufXGFxv9mts79w5u647XVWr2dNcnhb74vjPcsyFKb9JDe37zH6ygAAB8ryE5TfYPT7kOsMw4AaN0652Xo3stLNf2y4TpQXafJ0+frqocX8gMIAKDFjOldoOe+M0bTLxuu3IwU/fRvH2rsbbN0z5y12l9V63U8AACSWs9wtq6Z0FvPf/dUzf3JBP3sc/2VlRrU715dpbN+94bO+L/Z+vWLy7Vwwy4mu8B3Ql4HiFYgYBpclKf3N+32OgoAwMcKstMkSTsO1Cg/8hwAgNbs7AEdNK5PWA+8tV53zVqrc37/hi4eXqT/OrNYnfMyvI4HAEhyZqazB3TQWSWFenvtTt05e61+/eIKTZu1RlNGdNWlo7qpqF2m1zEBAEhqXdpm6qqxPXXV2J6q2Fell5dt18sffqQH5q7XPXPWKS8zRaf1Cev0/oUa3yes3IwUryMDn8j3hXFJGtq1rf70+mrtOVSjvMxUr+MAAHwonNNYDK/cX62+HXI8TgMAkCQzmyjpD5KCku5zzt161Ps/lHSVpDpJlZK+7pzbGHmvXtIHkV03OecmtVjwJJKeEtR3TuutKSd31Z9mrdGf396gZ94r1xeHdda3T+utHgVZXkcEACQ5M9OY3gUa07tAS8r36J4563Tf3PWa/uY6nd63vS4b3U3jisMKBMzrqAAAJLX2bdJ12ahuumxUN+2vqtWbq3foteUVmrWyQs8t2qpgwHRy97Y6o1+hTu/fXr3C2V5HBv5DUhTGzxlQqDteW63nF2/VZaO7ex0HAOBD3fIbOw3W7zigU4sLPE4DADCzoKRpks6SVC5poZnNcM4ta7bb+5JKnXOHzOzbkm6T9JXIe4edc0NaNHQSa5uVqp+fX6Kvn9pD976xTo8t2KSn3y3XeYM66urxvTSwc67XEQEArcBJXfI07avDtG3vYT32ziY9umCzXntwobrnZ2ryiK76wtDOKmyT7nVMAACSXk56is4b1FHnDeqo+ganRZv36PUV2/Xa8grdMnO5bpm5XN3yMzWuOKyxxQUa3StfOel0k8N7SVEYL+nYRv065Oipd8t16ahuMuMOUQDAZ9OhTbqy00JaU3HA6ygAgEYjJK1xzq2TJDN7XNIFko4Uxp1zs5rtP1/SpS2asBXqnJeh/540QNdM6K0H3lqvR97eqBeWbNPQrnm6fHQ3nTeoo9JCQa9jAgCSXMfcDP3w7L669vRivfjhNj3y9kbd+uIK3fbSCo3rE9ZFw7rorJJCpadwTQIAIN6CAdPwbm01vFtbXXdOP23Zc1ivL9+u2Ssr9cx75Xpk/kYFA6ZhXfM0tjiscX3CGtQ5V0GmvcADSVEYNzNdOqqbfvbch3pteYXOLCn0OhIAwGfMTL3CWVpTSWEcABJEZ0mbm70ulzTyE/b/hqQXm71ON7MyNY5Zv9U591zsI7Ze4Zw0/WRiP337tF565t1yPfL2/8/efcdJclb3/v+e7p6enMPubNYGabXSahVWQkIECVkokOxLkmxAxvZPvhiwja9trJ9tMNi+zgbb2CBhY2GwRRRYgA3IgBAo7yqu8ua8MzthZ3bydJ/7R1X39MRN09Pp836pX11dVV19prb1nK46Tz21Rx/68lP6k28/r3dculxvu2QZw+YBALIuHovoLRcu1VsuXKqdncf19cf36+7HD+iDdz2h2oqY3njBEr3pgnZddlaTYtFIrsMFAKAkLG2o1LuvWKV3X7FKo+NJPb63R/e/1KmfvHxUn/ifl/S3976k+soyvWpti169rkVXrm3R8qaqXIeNElEUhXFJeuely/W5B3bpj771rC49q0n1lQzJAAA4NWvaavSTl4/K3Rl9BAAKiJm9S9JmSa/NmL3S3Q+Y2WpJPzSzZ9x9x5T33SrpVklasWLFgsVbTOoqyvTeK8/SLVes0gM7jurfHtqj23+8Q5++b4cuXN6gt168VG+8YIkaq+O5DhUAUORWt9bod65br9+69hw9tKNLX9u6T9984oDuenSvmqvjuu78xXrDxna9giI5AAALJh6L6PLVzbp8dbN+93qp6/iIHtjRpZ+81Kn7X+7Ud545JEla1lipK8L1rljTrCUNlTmOHMWqaArjZdGI/uptm/TO2x/SB+96Qp99zyUM4QcAOCUXLW/Q3Y8f0N7uQa1srs51OABQ6g5IWp7xelk4bxIz+xlJvy/pte4+kprv7gfC551mdp+kiyRNKoy7+x2S7pCkzZs3+zzHX1IiEdOr17Xq1etadaRvWP/55AHd/fgB/eF/PquPf/s5vfbsNt24cbGuWb9I9VV0YgYAZE80YnrVuha9al2LBkfHdd+LwUn3bzx+QP/xSFAkv3bDIl1z7iJdubZZVfGiOT0KAEDea64p15s3LdGbNy2Ru+vljuN6YPtRPbyzS/c+f0Rf3bpfkrSiqSoolK9p0hWrW7S4viLHkaNYFNUvv0tWNupPf+58ffjrz+h9X3xc/3DzRaouL6o/EQCQRVesaZEkPbiji8I4AOTeY5LWmdlZCgriN0n6+cwVzOwiSbdLut7dOzLmN0oadPcRM2uRdKWkv1ywyEvcoroK3fqaNbr1NWv03ME+feOJ/frWU4f0P88fUSxiumJNs647b7Fef94itdVycgMAkD1V8Zhu3NiuGze2a2g0ofte7NB3njmkbz11UF96bJ/isYiuWN2sa85t09XntDGMKwAAC8jMdPaiWp29qFbvvfIsJZOuFw736+GdXXpoZ5f+e9shfXlLcIe1s1qqdfnqJl26qkmbVzZpeVMlI37itBRd1fidl67QeNL1h9/cprd++kHd/u5LKG4AAE7KmtZqtddX6N7njujmyxhSFwByyd3HzewDkr4nKSrpc+7+rJl9XNIWd79H0l9JqpH01fCAeK+7v1nSuZJuN7OkpIiCe4w/l5M/pMRtWFKnDUs26LYbztXTB47pu9sO63vPHtYffHOb/uCb23T+0jq9Zl2rXnN2qy5e0ah4jKFtAQDZURmP6oaN7bphY7tGx5N6bHe3fvB8h370Yoc+8p/PSnpW69pqdOXa4F6nr1jdpLoKRjkBAGChRCIWHkPW6ZdedZYSSdfzh/r08M4uPbyzS99++pDuejQolLfVlmvzqkZdsrJJm1c2asOSOpVxqxScBHPPzYiBmzdv9i1btmRt+/e/1KkP3vWExhNJ3Xbjufr5y1YoEqH3CIDiYGZb3X1zruPIB/OdT/7yuy/oMz/eoQd+73Vqr+deNgCKG/lkQraPTzAhNVze9589rPtfOqqte3uUSLqq41FdsaZFrzm7RZevbtba1hqO4YACQT6ZQD4pTDs7j+uHL3Toxy916rHd3RoeSypi0sZlDbpyTbOuXNuiS1Y2qqKM2zYC2UQ+mUA+AaZLJF0vHenXlj092rq7W4/t7tGB3iFJUmVZVJuW1+vSVU26ZGWjLl7ZSAe3EjZXPinawrgk7e8Z1G13P6OfvHxUrzirSX/4xg06f2l9Vj8TABYCBwoT5juf7Ose1NV/fZ/eeely/enPbZy37QJAPiKfTODEU+70DY/pwe1duv/lTt3/Uqf29wQnNhqryrR5VZNecVaTLjurSRva6xTjCgAgLxVqPjGz6yX9nYKRSf7Z3f98yvLfkvQrksYldUr6JXffM9c2ySeFb2Q8oSf29urB7Uf1wI4uPbmvV4mkKx6N6PylddocnnC/ZGWjWmrKcx0uUFQKNZ9kA/kEODmHjw1ry55ubdndo617evTcoT4lki4zaV1bjTYta9Cm5Q26cHmDzllcy1XlJWKufFJ0Q6lnWtZYpX/7pcv05cf26S+++4Le9Kmf6q0XL9OHrj1bSxu4ChAAMN3ypiq96/KV+vxDu/UzGxbp6nPach0SAABFra6iTNefv1jXn79Y7q693YN6dFd38NjdrXufOyJJqo5HdeGKBl2wrEGbltXrgmUNaq+v4L5yAE6LmUUl/aOkayXtl/SYmd0z5dYbT0ja7O6DZvY+SX8p6Z0LHy0WUnksqstXN+vy1c36LUnHR8b12K5uPbyzS1v29OjOB3brjvt3SpJWNVfpkpVBofyiFQ1a11ZDJy4AABbQ4voKvfGCJXrjBUskSQMj43pyX6+27O7Rk/t69IMXOvTVrfslSeWxiM5fWq9Nyxp04YoGXbisgXuVl6CiLoxLkpnppstW6IaN7fqnH23Xvz6wW9984oDecuFSve+q1VrbVpvrEAEAeebD16/Xwzu79JtfelJfuvVyndtel+uQAAAoCWamlc3VWtlcrbdvXi5JOtI3nC6UP7GvR5+9f6fGk8HIZ6215eki+cZl9drQXqe22nJObAA4GZdJ2u7uOyXJzL4k6S2S0oVxd/9RxvoPS3rXgkaIvFBTHtPV69t09fqg0/TwWELbDhzT1j092rKnR/e92KGvPx6ccK8oi+jc9jptXFofPJbVa20rxXIAABZKdXlMV65t0ZVrWyQFt/La3zOkJ/f16sl9vXpqX6/+/ZE9+twDuyRJTdVxbVpWr03LG7Rxab3OX1rPMWWRK/rCeEp9ZZluu/Fc3fLKVfrsT3bqrkf36u4n9uu1Z7fq5stW6HXr2xhCAQAgSaqMR3X7uy/RO29/WG//zEP6m3ds0nXnLc51WAAAlKRFdRV606YletOm4AqA4bGEnjvUp6f39erp/cf01P5e/eCFDqXuEtZYVab1i+u0vr1W54bP69pqVRnnvrAAJlkqaV/G6/2SXjHH+r8s6b9nWmBmt0q6VZJWrFgxX/EhT1WURbV5VZM2r2rSryo44b7r6ICe3n9MzxwIHl/ful//9tCecP2INrTX6fyl9Vq/uE7nLK7V2YtqVMt9TwEAyDoz0/KmKi1vqkofU44lknrpSH+6UP7UvmO676WX08eULTVxnbekXuctCfL3+UvqubK8iJRMYTxlSUOlPvqm8/TB163T5x/crS89tle/+oWtaq0t19suWaY3XtCuDe11fMEBoMStbK7WN97/Sv3K57foV7+wVW+4oF1/+IYNWlxfkevQAAAoaRVlUV28olEXr2hMz+sfHtOzB/v04uF+vXC4T88f6teXHt2nobGEJCli0qrmaq1pq9Ga1hqtbq3WmtZqrWmtUUNVPFd/CoACYWbvkrRZ0mtnWu7ud0i6QwruCbuAoSEPmJlWt9ZodWuNfvaipZKkZNK18+iAtoWF8mf2B8XygdFE+n1LGyq1fnGtzl5cq/WLa3XO4lqtbqlRPMaFOwAAZFNZNBIWvuv1C69YKSkYgv35Q33aduCYnj3Yp20H+/RAxmhltRWxoFC+JLiq/LwldVrdWqNohFpioSm5wnhKU3VcH7r2bH3wdWt134uduuvRvbr9xzv06ft2aFVzlW7Y2K7rzlusjUvr+WIDQIlqr6/UN37tSt1x/w79/Q+2697njuimS5frfVetUXt9Za7DAwAAodqKsvT9YFOSyeB+5alC+QuH+7Szc0D3vdihscRE3aqpOq41rdVa3RIUzM9qqdaK5iotb6xSdXnJHjIDpeCApOUZr5eF8yYxs5+R9PuSXuvuIwsUGwpcJGJa21ajtW0TxfLUUK4vHu7Xi0f6g+fD/frxS53pk+6xiGlFU5VWt1ZrdWuNzmqp1uqWYLqlJs6FPAAAZEl1eSw9IkzK8FhCLx3pDwrlB45p28E+feHhPRoZT0oKRoVZ1zbRwe3c9mBkmJaa8lz9GTgJ5p6bjqybN2/2LVu25OSzZ9N1fET3PndE33nmkB7c0aVE0kMLCqEAACAASURBVNVQVaYr17To1eta9Kp1LVrWWJXrMAFAZrbV3TfnOo58sFD5ZF/3oP7pvu366pb9MpOu3bBIN126Qq9a26IIHagAFCjyyYR8PD5BdownktrfM6Qdnce1s3NAO48e146O4Pno8dFJ67bUxINh9xqrtKIpeCxvqtKK5iotrqugEzUQKsR8YmYxSS9JukZBQfwxST/v7s9mrHORpK9Jut7dXz6Z7ZJPcKpGx5PaefS4Xjzcr5eO9Ae5qXNAu7oGNBqeeJeCK9VSRfKzWqrTOWl5U6Vaa7gXKopDIeaTbCGfAPlpPJHUjs5gVJjnDk2MWpZ5LNlSE0/fPuWcxcEtvtYtqlFFGbf3Wihz5RO6v2dorinXTZet0E2XrVDPwKjuf7lTP3n5qH7ycqe+88whSVJ7fYUuXtGoi1Y06OKVjTpvSZ3KY3yZAaDYLW+q0p/9rwv0/qvX6s4Hduvrj+/Xfz1zWMsaK/XmTUt0w/ntOn8pt+IAACDfxaIRrWqp1qqWal1z7uRlxwbHtKd7QHu7B7W3e1D7wucn9vXoO88cUiI50bG8LGpaVFehJfWVam+o0OL6cLq+Qksaguemaq7uA/KVu4+b2QckfU9SVNLn3P1ZM/u4pC3ufo+kv5JUI+mr4f/Le939zTkLGkUpHoto/eI6rV9cN2l+Iuk62DuknUcHtDOjM9fDO7v0jScmD25QWRbVssbKjGJ5qjNXJSOgAAAwj2LRSLrg/daM+Z39I+kiefDcry9mXF0eMWlVS7XWL67V2rZarW2r0bq2oLMbBfOFxRXjJ8Hdtb3juH66/age39urx/f06EDvkKTgZMia1hptaK/T+vZgqIT1i+vUWstQCQCyhx60E3KVT0bGE/r+s0f0lS370qOMLG2o1PXnL9bV57Rp86pGftQAyHvkkwmFdHyC3BhLJHWod1j7egbThfNDvUM6eGxYh44N6fCx4UlDtEtSeSyi9voKtYfF80V1FWqrLVdrbbnaaiemKVig0JFPJpBPsBCGxxLan8pHXYPa1zOU7tC1r3tw0r3MJamuIpbutLW4vlJL6ivU3jDx3F5fwfEr8gL5ZAL5BCh8iaRrT9eAXggL5S8e7tMLh/u1r3tQqT7XEQsuyFrXVqM1bTVa21qTvh1LbUVZbv+AAsYV42fIzLRuUa3WLarVe68M5nX0DevxvT16av8xPX+oTw/u6NLdGb01G6vKdFZ4FcLq8Pmslmqtaq7mpAcAFIHyWFRv2rREb9q0RD0Do/qf54/ou9sO6wsP7dG//HSX4rGILl3VqFeuadHmlY3auKxeVXHafwAAClVZNKIVzcEw6lfOsDyZdB0dGNGh3qBQfjD1fGxYh3qH9NCOLnX2j6TvI5upOh5VW12FWmvK1VpXrrawcB4U0MvVVB1Xc01cTdVxRiwDAKiiLBpebVY7bZm7q2dwLF0o398zNCkvPbX/mLoHRqe9r7GqTO31lVrSUJHOSW115eFz0JmrpaZc8VhkIf5EAAAKXjRiWt1ao9WtNbpxY3t6/vBYQruODujljuPa3nFcOzqO6+WOfv34pc5Jna0X11Wki+Rr22q0prVGq1ur1VbLLVTOBGfoT1NbXYWuP79d158/8WXuHhjVC4f69NyhPu3oHNCuo8f14PYu3f345OGNmqrjWtIQDLO3tLFSSxsqtaQheG5vqFBzdTn3qgOAAtJYHdfbNy/X2zcv1+DouB7d1a0Hth/VT7d36a++96Kk4IfQOYtqdeGKBl24rEHnLK7VukU1FMsBACgSkYiFV4FXaNPyhhnXSSZdvUNj6ugfVmf/iDr6RtTRPxJM9w+ro39Ezx/s04/7R3R8ZHzGbdSUx9RUHRTJm8PnpprUdPnEvLCYzm8NACgtZpbOAxfOko+GxxI6fGxYB48NTXToOjasw8eGtb9nSE/s7VXXDMVzKSigt07pwNUaPpqrJzpzNVbFKaIDADCDirKozm2v07ntk2+jMp5Iam/34JSC+XF9Zcs+DWaMBlMVj2pVc3AxbuqRuki3sTq+0H9OweEIeR41Vcf1yrUteuXalknzB0fHtfvooHYdHdDurgEd6B3Swd4h7e4a0APbj04b3ihiwf3OW2smfli21k5+3VwdV0NVXA1VZSqL8iMTAPJFVTymq85p01XntEkKOk09ua9HT+zt1ZP7evWtJw/qPx7ZK0kyk1Y0VensRbU6Z1GtVjZXaWVztVY2V6m1plwROkkBAFBUIpGJYsX6xXOvOzg6HhbMR9R1fFTdA6PqHhhR10BqelSHjg3r2YN96h4Y1WgiOeN2KsoiaqgMjh3rKsvUUFmmhqoyNVTFVV9ZpvrU68p4erq+qky15TGuQgCAIlVRFtWq8CT6bMYSSXUdH53ozJXRkSv1evfuAXX0j2h0fOYcVFseU1PNRGeuxqrpnbkaU8uq46qOR8k9AICSFYtG0leYX3fexPxk0nWob1g7O49r19GB9OPZg8f03WcPK5ExKll9ZTCadeZI1qnCeQ2jWUuiML4gquIxbVhSpw1L6qYtc3f1DY/rQE9QLD90bEid/SPqPB782OzsH9FLR/pnHXJPkmorYmqsiqsxPLkx8RxXY3UwXVcRU21FWfq5tiKmKn5sAkDWNVXH9br1i/S69YskBT9k9nQP6sXD/XrpSL9ePNyvF4/064cvdEz6EVNRFtHyxiqtbK7SssYqLaqr0KK6ci2uq9Ci+gotrqvg1hwAABSxqnhMK5tjWtk8e9Eixd11fGRc3QOjQeE8LKR3hcX0Y0Nj6h0c07GhYGjdp/ePqXdoVMNjMxcypGC0m7qKWLqA3lBVprrwWLKmIpaerq2IqbY8NR0811WUqaYixkhoAFDAyqIRLa6v0OL6ijnXS53b7OwfVtfxUfUMTuSizM5cB3uHte3A3J25YhGb1JEryD+TO3JlPteHHb/qK7lwCABQvCIR09Jw1OlXr2udtGx0PKn9PYOTCua7uwb08M7Jt3+WpJaauJY3VWlF+EhNr2yu0qLaipK5SIsz6jlmZukfdzMVzlOSSdexoTF1Hg+G2+saGFHv4Jh6BkfTzz3h886jx9U7MKb+WYbeS4lGTDXlMdWUx9InL9InNjJOeFSVRVUVj6mqPKqqeDid8Vwdj6kyHmV4JAA4CZGIpXvqXX/+xKViY4mkDvQMaU/3oPZ2DWhv96D2dA1qb/egHtnVrf7h6W16TXlMbXXlaqqaGDK1sTqupqrwubos6JFfHU+365wsAACg+JhZmOvLTqqQnjI8llDf0Jh6MwrnvYOjkwrpveG87oFR7T46oP7hcfUPj89a1MhUHY+mf4NkHmdOdNoOjkerMx/xqKrD49SqcLo8FqFTNwDkqcxzm2vbTrz+1M5cPRnPqbxzLMxBncdHtL3zuHoHx2Y8Js5UHY+qvjIoqk86t1kem5KLgg5dNRWTz4fWlMcU43gZAFBg4rGJq8ynGhpNaE/3gHZ1DmhX14D2dQfnmrfu6dG3njqozGtx49GIljVVpovmmYXz5U1VRXW1efH8JUUuEjE1hgWPsxfVntR7xhJJ9Q4GJzH6hsfVPzyWPokxMR089w2P6/jImA73Devljon5s12lPpNYxCYK5lOK6BWxqMrLIqqIRVVRFlF5WVQVseC5PBZRRVlUFZOmIyoP1500P9wOJ0YAFJuyaCRjKLvWacsHRsZ1pG9YR/pGdKRvWIf7gvu/dfaPqGdwVHu7B/Xkvl71DI5qLDF7211RFlFN+cTJ6NqKsnQHqcwRRariQbucbsfLgnmV8agqy4LnVPteKr0JAQAoNqnjsLa6ua8GnMnwWGLKsWXm8eUMx54jY+nfLP3DY+obHp916N2pohFLF8ynFtAnF9Jjqi6PTlunsiw67fcMv2EAIDdOtzNXIunqG5rcaetY+PrYYFhQDx/9w2Pq6B/Wjs6JXDTXcXJKZVk0faFQqhNX6qKi6owOW9XxqKqmdOKqikcn5aLKMkbqBADkVmU8qvWL67R+8fSLcscSSR3sHdLesFi+t3twUuF8aoe05urgavOljZXpq9eXNlQGrxsrVVdRtlB/1hmjMF7EyqKR9D3JT4e7a2Q8qcHRhAZHxzU0mtBAOD04ktDgWEKDI+Pp5cFzMD0wmgjWD3uADo8lNDKe1PBYQsNjSY2MJ+Yctu9kxKMRxWMRlUUtfA5ex6MT08GyqOIZ62Sul14nGlVZzDLmhduJRRSPmqKRiGJRU1kkomjEVBY1xaIRxSKmWNQUi0yZTq0bNcUiprJoRBETP4gBnLbq8tisvf8ypXrf9wyMqXsw6HXfPTCacVJ6egepjv7h9Mnr4ycYbWQmFWURVcVj6Q5P8WhE5WUT7Wx5LGx3Y9H0dPo5OvN74rGIYpFIur0ti4Ttbti+xqJhW5yeTrXDkfT8sqjR7gIAkCWpovrpHm9K0sh4QseHg2PJ4yPjGhwd1/GR4Djz+Mjk+QMjwfHlQMY63QODGhydmH+qx5ip3zCpTn+Zz1UzvK6IR1WVmhefoeAePpeHnb3jUTp0A8B8iWZcNHQ6Mjt0HZ92XDw+bVlfOH3o2HCQZ0aC852Jk7yIyEyqjmcUzMuDjucTHbuCeTXlsYkcNEM+mtpJPXUBEfkFAHAmyqIRrWyunrWT2rHBsUlF873dwQinzx3s073PHZnWybm2Ija5WJ753FCplpryvOmYTGEcszKz9MmOptP80TmXVOF9ZDypkSkF8+HxhEbGwkJ6anp88jqj48FjLBE8RseTGk1MzBtNJDU2HvQmnTwvcz3XaCJ50j9qz1TZ1CJ6RnE9VXRPFXWikYniTzQSPswUCZ+j0fA5YopYUICPREzRiML5EUUjSq+fXh5uIxaZeG80kjHPMrYTiYTbUvAZ0Yn1U58dzdhudNI2g+9QxEwRC94fiUxMmwVxRsKHRSZeW7hONFyfH/vAycvsfb+iueqU359MuobGEsEj7PA0NBZ0ehoeS6Q7QU2fHtfQaKp9DTpDpdrp/vCKsNFE0N4Hz0mNhG1xNkUjEx2UYhkF88x2N92OztnGaaItzmg7M9dNtaOTlk1tu1Nta2SinZvaPme2m6n2MDXPJrWp09vZzPVnaofn3F5qeWSW7YVt9Ym2BwDAySqPRVVeE1XzPG1vPJHU4FhYKM8opA+lf89M/IZJzRua8ntmcDSh3sFRHeydWJ76beSneNhopmmjp6UKGuXhsXaqY2HFbKOnTRpBbcqIarOMtkY+BoDp5qNDV+ZFRKkcMzCSmNSBK3XB0GBYSE8V1AfD9Y8eH9We7kENjiTC94/rVE9LRkzp4nlFRkE9s6NWZVnQoSuzE9fkgnuQP8rLJo/SOTU3Mbw8AJSm+qoybayq18Zl9dOWJZOuowMjOtAzpAO9QzrYO5Se3t8zpMd2d6tvyhXn8VhES+ortLSxUkvqK9XeUKkl9RVaXF+hJQ2VWlxfsWBXnVMYR85kFt5VmdthFhJJTxfO08X2cddoIqGxhGs84RpLJtPrjSd8YjrpwSOcH7wOiu6J8Hk8NZ30SdtIvy9jG6n3jYfrjSWSGhx1JT14byIZTI8nXcmkK+GuZFIaTyaVSCq9XjLcZrDcT2lY/Hw0U3E9XZyJTC7KRDMLPrMU3CcXg2bZRiRV+AmKYqnpSMZ2ZFOKRJpcaDKb/jr1/LMXLtWm5Q253rXAJJGIpYcfXQjunm57R8POUqki+kRbmZzUFo8nwvYyo+1NzU+3y2F7Op6en7mdiTY2s11NhG1lZns7Mp5QwoMffKl5qXY1kbHetG1MaZ8LvAk+JZPaPwXtpClzXtAuTp2eaEMlKdUeB9tILVe4PDU/1WaHb5m0jdT7LGNeZgxKtfmaWCdiE/GmPyMysS3N8HdEwg9PzZto+2eOYfLnZG4rc/3wD8p4j01bf6Locd15i3XFmvkqKwFA4YpFI6qLRrJyQiWzGBIUzINOgYOj45MK6Kki+8SIaZNHT8t8fWxoTB0zrTN+6kX4TPFYZNpIPuVTRu5JvQ6K9MHV7Ved06ar15/EDYIBoERl4yKiVH4ZHpvII0NhLpnUQSujo9bQ6OTXmZ2+egZGdTAjHw2NBqN+nm5eiUVs2m0wy9OF9SlF9bCj1uWrm3XDxvZ52T8AgPwTiZjaaivUVluhi1Y0zrhO//CYDmQUzDOn73+5Ux39I9NyU015TIvrK9QePq5Y06yfu2jZvMdPYRxQ6irAsEhfxDILOZOK61MKPMmkwtdBsX22otHU4vtEcSgo0Cfd5T7xfk/PD7bv4fsy109PT5ofHCiktu2eikHhdqduU5O3NW27mduZHl/q7xhLhJ+T8ZkT2w7WcUnyyZ/nGdtOerCOT1medNeFyxsojM/CzK6X9HeSopL+2d3/PMchIUvMLDwxW9ztb2Z7k9nmTi2wT20rM9u4zDbIp7Rvk9aZ0lbNub0Z2rep609tk09me6n20V1yucL/lJwyP9VepuYlM6alIKb0egqWp7aVel8yYxvKiDH1vmS4IHMbnrmNSbFkxJCUXEl5YnJcM70vFYOm/R0Z75v6d/rEd2PaPkl/bybHnrlu5nvXtNVQGAeALJvUsTvL3INjkWDUtGAEtfToalNHW5tplLWw0B48wumxidF7+ofH1TU+ml6W6pzYVldBYRwAFlhmfsnWGaKpxfeg8J6clD9Gxma+DeZst8ccCd870y00y8uiRVsYP9H5KjN7jaRPSrpA0k3u/rWMZbdI+oPw5Z+4++cXJmoAWHi1FWVav7hsxvubS8E9zjv6R3Sod0gHjw3r8LEhHewd1uFjwzp0bEgvHO5XxCz/C+MUMoD8FomYIjIVef0fBc7MopL+UdK1kvZLeszM7nH353IbGXD6UiNPRPPkXjoAACB/mZniMVM8lp2r3wEApWUhiu+l4CTPV+2V9IuSfnvKe5skfVTSZgX9nreG7+1ZiNgBIN+URSPp+4/PJnURynybt5uEZCSGGyRtkHSzmW2Yr+0DAErGZZK2u/tOdx+V9CVJb8lxTAAAAAAAAChdJzxf5e673f1pSckp771O0r3u3h0Ww++VdP1CBA0AhSrzNoLzad4K46KQAQCYH0sl7ct4vT+cl2Zmt5rZFjPb0tnZuaDBAQAAAAAAoOSc8HzVmb6X810AkH3zWRinkAEAWBDufoe7b3b3za2trbkOBwAAAAAAADgjnO8CgOybz8L4CdGwAwBOwgFJyzNeLwvnAQAAAAAAALlwJuerONcFAHliPgvjNO4AgPnwmKR1ZnaWmcUl3STpnhzHBAAAAAAAgNJ1Juervifp9WbWaGaNkl4fzgMALLD5LIxTyAAAnDF3H5f0AQUHCM9L+oq7P5vbqAAAAAAAAFCqZjtfZWYfN7M3S5KZXWpm+yW9XdLtZvZs+N5uSX+soIbymKSPh/MAAAssNl8bcvdxM0slhqikz1HIAACcDnf/L0n/les4AAAAAAAAAGnm81Xu/pGM6ccUjKQ703s/J+lzWQ0QAHBC81YYlyhkAAAAAAAAAAAAAADyz3wOpQ4AAAAAAAAAAAAAQN6hMA4AAAAAAAAAAAAAKGoUxgEAAAAAAAAAAAAARY3COAAAAAAAAAAAAACgqFEYBwAAAAAAAAAAAAAUNQrjAAAAAAAAAAAAAICiRmEcAAAAAAAAAAAAAFDUzN1z88FmnZL2nMEmWiQdnadwsq2QYpUKK95CilUqrHgLKVapsOKdj1hXunvrfART6Eosn2QL+yDAfgiwHwKlsh/IJyHySVawT2bGfpmOfTKzQtov5JMQ+WRBsI9OjH10YuyjE8vFPiKfhIo4nxDXqSGuU5OvcUn5G1uxxjVrPslZYfxMmdkWd9+c6zhORiHFKhVWvIUUq1RY8RZSrFJhxVtIsZYC/j3YBynshwD7IcB+wKniOzMd+2Rm7Jfp2CczY7+UJv7dT4x9dGLsoxNjH50Y+6iw5eu/H3GdGuI6Nfkal5S/sZViXAylDgAAAAAAAAAAAAAoahTGAQAAAAAAAAAAAABFrZAL43fkOoBTUEixSoUVbyHFKhVWvIUUq1RY8RZSrKWAfw/2QQr7IcB+CLAfcKr4zkzHPpkZ+2U69snM2C+liX/3E2MfnRj76MTYRyfGPips+frvR1ynhrhOTb7GJeVvbCUXV8HeYxwAAAAAAAAAAAAAgJNRyFeMAwAAAAAAAAAAAABwQhTGAQAAAAAAAAAAAABFreAK42Z2vZm9aGbbzez3ch2PJJnZ58ysw8y2ZcxrMrN7zezl8LkxnG9m9vdh/E+b2cULHOtyM/uRmT1nZs+a2W/ka7xmVmFmj5rZU2GsHwvnn2Vmj4QxfdnM4uH88vD19nD5qoWKdUrcUTN7wsy+ne/xmtluM3vGzJ40sy3hvLz7LoSf32BmXzOzF8zseTO7Io9jPSfcp6lHn5n9Zr7GW6ryMZ9kSyHlqWwqpByYLYWaW7OlkHI28lcp5ZNMtKlzo32ZzArot/xCMrMPhf//bDOzu8I8XdLflVJWqvlkKvLLySPXzI3cc2LkoeKU63xieXgOKp9zi+XxeZp8zTOWpzWNfMw7lsd1CsthDiqowriZRSX9o6QbJG2QdLOZbchtVJKkOyVdP2Xe70n6gbuvk/SD8LUUxL4ufNwq6dMLFGPKuKT/4+4bJF0u6f3hPszHeEckvc7dN0m6UNL1Zna5pL+Q9Al3XyupR9Ivh+v/sqSecP4nwvVy4TckPZ/xOt/jvdrdL3T3zeHrfPwuSNLfSfquu6+XtEnBPs7LWN39xXCfXijpEkmDkr6Rr/GWojzOJ9lypwonT2VTIeXAbCnU3JothZazkWdKMJ9kok2dG+3LZAXzW36hmNlSSb8uabO7ny8pKukm8V0pSSWeT6Yiv5w8cs3cyD1zIA8VpzzJJ3cq/85B5XNuyefzNPmcZ/KxppF3eSdf6xQ5z0HuXjAPSVdI+l7G69sk3ZbruMJYVknalvH6RUnt4XS7pBfD6dsl3TzTejmK+z8lXZvv8UqqkvS4pFdIOiopNvU7Iel7kq4Ip2PherbAcS5T0JC8TtK3JVmex7tbUsuUeXn3XZBUL2nX1P2Tj7HOEPvrJT1QKPGWyiOf80kW/+aCzFNZ3icFkQOz+PcXRG7N4t9fUDmbR34+SjGfzLEvSrpNnbIvaF8m74+C/S2f5f2yVNI+SU3hv/23JV1Xyt+VUn6QT+bcN+SXmfcLuWbu/UPuOfE+Ig8V4SNf8ony/BxUvuYW5dF5mnzOM8rDmkYh5B3lUZ1COc5BBXXFuCZ2Vsr+cF4+WuTuh8Lpw5IWhdN58zeEww1cJOkR5Wm84XAdT0rqkHSvpB2Set19fIZ40rGGy49Jal6oWEOflPS7kpLh62bld7wu6ftmttXMbg3n5eN34SxJnZL+NRy+5Z/NrDpPY53qJkl3hdOFEG+pYJ+X+PexEHJgthRgbs2WQsvZyE9F2U6cqlJuU2dB+zJZIf+Wzxp3PyDpryXtlXRIwb/9VpX2d6WUlcT3/lSRX+ZErpkbuecEyENFK1+/y3nz/14+5pY8PU+Tz3kmH2sahZB38qZOkescVGiF8YLkQTcGz3UcmcysRtLXJf2mu/dlLsuneN094cEwD8skXSZpfY5DmpWZvVFSh7tvzXUsp+BV7n6xgiEy3m9mr8lcmEffhZikiyV92t0vkjSgieE9JOVVrGnhPTDeLOmrU5flY7woXaX2fSyUHJgthZRbs6VAczaQl0q9TZ2K9mVGBflbPtvC+/i9RcFJtCWSqjV92FGgZJFfZkeuOSnknhMgDyFXcvn/Xr7mlnw7T1MAeSYfaxp5nXfyrU6R6xxUaIXxA5KWZ7xeFs7LR0fMrF2SwueOcH7O/wYzK1OQAP7d3e8OZ+dtvJLk7r2SfqRg+IQGM4vNEE861nB5vaSuBQzzSklvNrPdkr6kYJiRv8vjeFM9c+TuHQruLXGZ8vO7sF/Sfnd/JHz9NQWJJh9jzXSDpMfd/Uj4Ot/jLSXs8xL9PhZiDsyWAsmt2VJwORt5q6jbiROhTZ0R7ct0hfpbPtt+RtIud+909zFJdyv4/pTyd6WUlcr3/qSQX06IXHNi5J4TIw8Vp3z9Luf8/71CyC15dJ4mr/NMntY08j3v5FudIqc5qNAK449JWmdmZ4U9HG6SdE+OY5rNPZJuCadvUXDfitT891jgcknHMoYsyDozM0n/Iul5d//bfI7XzFrNrCGcrlRw34/nFSSHt80Sa+pveJukH4a9XRaEu9/m7svcfZWC7+YP3f0X8jVeM6s2s9rUtIJ7TGxTHn4X3P2wpH1mdk446xpJz+VjrFPcrInhSVJx5XO8paSQ8km2lNz3sZByYLYUWm7NlkLL2chrJZtPaFNnRvsyXQH/ls+2vZIuN7Oq8P+n1H4p2e9KiSvZfDIV+eXEyDUnRu45KeSh4pSv+SSn/+/lc27Jx/M0+Zxn8rWmUQB5J9/qFLnNQZ7Fm7ln4yHpRkkvKbjPwu/nOp4wprsUjIM/pqBnyC8rGN/+B5JelvQ/kprCdU3SP4bxPyNp8wLH+ioFwyI8LenJ8HFjPsYr6QJJT4SxbpP0kXD+akmPStquYOiH8nB+Rfh6e7h8dQ6/E1dJ+nY+xxvG9VT4eDb1/1M+fhfCz79Q0pbw+/BNSY35GmsYQ7WCXkv1GfPyNt5SfORjPsni31oweSrL+6FgcmAW90HB5tYs7pO8z9k88vtRSvlkyt9d8m3qSewj2peJfVFQv+UXcL98TNILYU7+gqTyUv+ulPKjVPPJDPuB/HJq+4tcM/u+IfeceB+Rh4rwket8ojw8B5XPuUV5fp4m3/KM8rimka95R3lap1AOc5CFGwUAAAAAAAAAAAAAoCgV2lDqAAAAAAAAAAAAAACcEgrjAAAAAAAAAAAAAICiRmEcAAAAAAAAAAAAAFDUKIwDAAAAAAAAAAAAAIoahXEAAAAAAAAAAAAAQFGjMA4AAAAAAAAAAAAAKGoUxgEAAAAAAAAAAAAARY3CTNCsdgAAIABJREFUOAAAAAAAAAAAAACgqFEYBwAAAAAAAAAAAAAUNQrjAAAAAAAAAAAAAICiRmEcAAAAAAAAAAAAAFDUKIwDAAAAAAAAAAAAAIoahXEAAAAAAAAAAAAAQFGjMA4AAAAAAAAAAAAAKGoUxgEAAAAAAAAAAAAARY3COAAAAAAAAAAAAACgqFEYBwAAAAAAAAAAAAAUNQrjAAAAAAAAAAAAAICiRmEcAAAAAAAAAAAAAFDUKIwDAAAAAAAAAAAAAIoahXEAAAAAAAAAAAAAQFGjMA4AAAAAAAAAAAAAKGoUxgEAAAAAAAAAAAAARY3COAAAAAAAAAAAAACgqFEYBwAAAAAAAAAAAAAUNQrjAAAAAAAAAAAAAICiRmEcAAAAAAAAAAAAAFDUKIwDAAAAAAAAAAAAAIoahXEAAAAAAAAAAAAAQFGjMA4AAAAAAAAAAAAAKGoUxgEAAAAAAAAAAAAARY3COAAAAAAAAAAAAACgqFEYBwAAAAAAAAAAAAAUNQrjAAAAAAAAAAAAAICiRmEcAAAAAAAAAAAAAFDUKIwDAAAAAAAAAAAAAIoahXEAAAAAAAAAAAAAQFGjMA4AAAAAAAAAAAAAKGoUxgEAAAAAAAAAAAAARY3COAAAAAAAAAAAAACgqFEYBwAAAAAAAAAAAAAUNQrjAAAAAAAAAAAAAICiRmEcJcHMrjKz/bmOAwBQ2MxslZm5mcVyHQsAoHCRTwAA8yXMJ2tzHQcAoHBxfIJSQmEcBcPM7jOzYTM7Hj5ezNLn/KKZ/TQb287F5wAApjOzm8zseTMbMLMdZvbqLHzGgnTKovMXAORGxnFJ6pEws3/IwueQTwCgiIXFiP8ysx4zO2xmn8pGYWKhih4UVwAgN8zsXDP7oZkdM7PtZvZzWfocjk9Q0CiMo9B8wN1rwsc5uQ4GAFB4zOxaSX8h6b2SaiW9RtLOnAYFACg4GcclNZIWSxqS9NUchwUAKDz/JKlDUrukCyW9VtKv5TQiAEBBCTsj/aekb0tqknSrpC+a2dk5DQzIQxTGUZTMrNLM7gx72z4n6dIpy38vvEKw38yeS/WeMrNzJX1G0hXhVR+94fw3mNkTZtZnZvvM7I8ytlVhZl80sy4z6zWzx8xsUbis3sz+xcwOmdkBM/sTM4vO9jkAgAXxMUkfd/eH3T3p7gfc/cBMK4Zt9l+b2VEz2ynpDVOWvze88rzfzHaa2a+G86sl/bekJRlXEi4xs8vM7KEwXxwKrwaJh+8xM/uEmXWE+eYZMzs/XFYexrHXzI6Y2WfCXDfj52Rv1wEAZvFWBUWNn8y0kHwCAJjDWZK+4u7D7n5Y0nclnTfbymb2O2Hbf9DMfmnKslnPX0m6P3zuDdv5K8xsjQVXF3aFOerfzawhY3sfDs9n9ZvZi2Z2TTg/YhPn1rrM7Ctm1jTb55zZ7gEAnIT1kpZI+oS7J9z9h5IekPTumVbm+ASljMI4Cs2fhY31A2Z21RzrfVTSmvBxnaRbpizfIenVkuoVFEi+aGbt7v68pP8t6aHw6o/UwcCApPdIalCQJN5nZj8bLrsl3M5ySc3h+4fCZXdKGpe0VtJFkl4v6Vfm+BwAQBaZWVTSZkmtFgwrtT/8sV45y1v+P0lvVNCGb5b0tinLO8LldQquQP+EmV3s7gOSbpB0MOOKwoOSEpI+JKlF0hWSrtHE1SCvV3D1+tkK8so7JHWFy/48nH+hgpyyVNJH5vgcAMDCukXSv7m7z7KcfAIAmM0nJd1kZlVmtlRBe/zdmVY0s+sl/bakayWtk/QzU1aZ6/zVa8LnhrCdf0iSSfozBcWUcxWc2/qj8LPOkfQBSZe6e62C82u7w218UNLPKri6fYmkHkn/OMfnAAAWnkk6f5ZlHJ+gZFEYRyH5sKTVChrHOyR9y8zWzLLuOyT9qbt3u/s+SX+fudDdv+ruB8MrBb8s6WVJl832we5+n7s/E67/tKS7FPz4l6QxBQXxtWFvrK3u3mfBVeM3SvpNdx9w9w5Jn5B00+nuAADAGVskqUzBD/5XK/jhfZGkP5hl/XdI+qS773P3bgUnjdLc/TvuvsMDP5b0/XC7MwpzxMPuPu7uuyXdrsn5pFZBL19z9+fd/ZCZmYIhsD4U5rV+Sf9X5BMAyAtmtlJBW/75OVYjnwAAZnO/givE+yTtl7RF0jdnWfcdkv7V3beFRYM/ylx4gvNX07j7dne/191H3L1T0t9mrJ+QVC5pg5mVuftud98RLvvfkn7f3fe7+0gYx9uM+4oDQK68qKCY/TtmVmZmr1fQnlfNsj7HJyhZFMZRMNz9EXfvD3+sf17BUCA3zrL6Ekn7Ml7vyVxoZu8xsyfDoT16FfScapnts83sFWb2IzPrNLNjCg4AUut/QdL3JH0pHMbqL82sTNJKBcWXQxmfc7uktlP+4wEA8yU1osc/uPshdz+q4OTP6eaTG8zsYTPrDtv5GzV3PjnbzL5tZofNrE/BD/4WSQqHufqUgistOszsDjOrk9Sq4EBma0Y++W44HwCQe++W9FN33zXHOuQTAMA0ZhZR0BbfLalaQVveKOkvZnnLifLJXOevZvr8RWb2pXC49D5JX9REPtku6TcVFL07wvVSw9iulPSNjHzyvIJC+qKT/+sBAPPF3ccUjOTxBkmHJf0fSV9R0OFqJhyfoGRRGEchcwXDgczkkILhn1JWpCbCKzo+q2A4qOZwGPNtGduaafjD/5B0j6Tl7l6v4P7gJgVJx90/5u4bJL1SwRAj71GQWEYktbh7Q/ioc/fUfaJmG2YRAJAl7t6j4KAgsw2eqz2eK5+US/q6pL+WtCjMJ/+lufPJpyW9IGmdu9dJ+v8z1pe7/727XyJpg4KhpH5H0lEFBf3zMvJJvbvXnET8AIDse4/mvlpcIp8AAGbWpCAnfCq8EKRL0r9q9o67s+aT0KznrzRzO/9/w/kbw3zyLk3OJ//h7q9SUAh3TRTs90m6ISOfNLh7hbsfmOVzAABZ5u5Pu/tr3b3Z3a9TMPruo7OszvEJShaFcRQEM2sws+vMrMLMYmb2CwruSzHjPZcU9Ia6zcwazWyZgnsfpVQraFQ7w22/V5PvtXFE0jIzi2fMq5XU7e7DZnaZpJ/PiO1qM9sY3re2T8HQIEl3P6RgiJG/MbM6M4uY2Roze+0cnwMAyL5/lfRBM2szs0YF90D69izrfkXSr5vZsnDd38tYFlcwtGCnpHEzu0HBfZNSjkhqNrP6jHm1CnLFcTNbL+l9qQVmdml4hUeZgnsDDivIJ0kFHbo+YWZt4bpLzey6OT4HALAAzOyVCm719NUTrEo+AQBME45gtUvBvcBjZtYg6RZJT8/ylq9I+kUz22BmVZI+OmX5rOevFOSZpIJCSeb6xyUds+D+5r+TWmBm55jZ68ICybCC4kUyXPwZSX8aXnwiM2s1s7fM8TkAgCwzswvC+kmVmf22pHZJd86yOscnKFkUxlEoyiT9iYLG+KiCQvfPuvtLs6z/MQXDf+xSUJz+QmqBuz8n6W8kPaSgcd2oYFj2lB9KelbSYTM7Gs77NUkfN7N+SR9RkDhSFkv6moKG/3lJP874vPcoSCTPSeoJ12uf43MAANn3x5Iek/SSgnb7CUl/Osu6n1Vwu4ynJD2uYIhDSVJ476NfV5ATehScdLonY/kLCu7ptzMcEmqJpN8O1+sPt/3ljM+qC+f1KMhhXZL+Klz2YUnbJT0cDkn1P5LOmeNzAAAL4xZJd4c5YS7kEwDAbP6XpOsVnPParuCCiw/NtKK7/7ekTyo4p7Q9fM406/krdx9UcNzzQNjOX67g/NnFko5J+o4y8pOCosifKzgPd1jBrQFvC5f9nYJc9f3wsx6W9Io5PgcAkH3vVnAleIekayRd6+4js6zL8QlKlrkzGgEAAAAAAAAAAAAAoHhxxTgAAAAAAAAAAAAAoKhRGAcAAAAAAAAAAAAAFLVYrgMAAAAAgGwws90K7nGWkDTu7ptzGxEAAAAAAAByhcI4AAAAgGJ2tbsfzXUQAAAAAAAAyK2cFcZbWlp81apVufp4AChoW7duPerurbmO43SYWVTSFkkH3P2NU5aVS/o3SZdI6pL0TnffPdf2yCcAcPoKOZ/MN/IJAJw+8skE8gkAnD7yyQTyCQCcvrnySc4K46tWrdKWLVty9fEAUNDMbE+uYzgDvyHpeUl1Myz7ZUk97r7WzG6S9BeS3jnXxsgnAHD6CjyfnAyX9H0zc0m3u/sds61IPgGA01cC+eSkkU8A4PSRTyaQTwDg9M2VTyILGQgAoLSZ2TJJb5D0z7Os8hZJnw+nvybpGjOzhYgNAFCUXuXuF0u6QdL7zew1mQvN7FYz22JmWzo7O3MTIQAAAAAAABYEhXEAwEL6pKTflZScZflSSfskyd3HJR2T1Dx1JQoZAICT4e4HwucOSd+QdNmU5Xe4+2Z339zayoiNAAAAAAAAxYzCOABgQZjZGyV1uPvWM90WhQwAwImYWbWZ1aamJb1e0rbcRgUAAAAAAIBcydk9xgEAJedKSW82sxslVUiqM7Mvuvu7MtY5IGm5pP1mFpNUL6lr4UMFABSBRZK+Ed6RIybpP9z9u7kNCQAAAAAAALlCYRwAsCDc/TZJt0mSmV0l6benFMUl6R5Jt0h6SNLbJP3Q3X0h4wQAFAd33ylpU67jAAAAAAAAQH6gMA4AyCkz+7ikLe5+j6R/kfQFM9suqVvSTTkNDgAAAAAAAAAAFAUK4wCABefu90m6L5z+SMb8YUlvz01UAAAAAAAAAACgWEVyHQAAAAAAAAAAAAAAANlEYRwAAAAAAAAAAAAAUNQojAMAAAAAAAAAAAAAihqFcQAAAAAAAJQcM/ucmXWY2bZZlteb2bfM7Ckze9bM3rvQMQIAAACYPxTGAQAAAAAAUIrulHT9HMvfL+k5d98k6SpJf2Nm8QWICwAAAEAWUBgHAAAAAABAyXH3+yV1z7WKpFozM0k14brjCxEbAAAAgPlHYRwAAAAAAACY7lOSzpV0UNIzkn7D3ZNTVzKzW81si5lt6ezsXOgYAQAAAJykgiuMP3ewTzff8bC2HTiW61AAAAVsb9egbr7jYT26a64LRAAAmFvX8RHdfMfD+uELR3IdCgBg/l0n6UlJSyRdKOlTZlY3dSV3v8PdN7v75tbW1tP6oKHRhH7+sw/rnqcOnlHAAAC8+18e0V2P7s11GACQlwquMN43PKaHdnapb2gs16EAAArYwOi4HtrZpa7jI7kOBQBQwEYTST20s0tH+sgnAFCE3ivpbg9sl7RL0vpsfJCZ9OCOLu3rHszG5gEAJeSRXd3a00U+AYCZFFxh3MJnz2kUAIBCZ2FCIZ8AAM6EhUcoTkIBgGK0V9I1kmRmiySdI2lnNj4oHg1O0Y2OTxupHQCAUxIxKckBCgDMKJbrAE6VGSeeAABnLkI+AQDMg4mOViQUACg0ZnaXpKsktZjZfkkflVQmSe7+GUl/LOlOM3tGwbUaH3b3o9mIJRIxlUVNowkK4wCAMxMxUzLJ8QkAzKQAC+O5jgAAUAxS6YQetACAM5Ee0Yp0AgAFx91vPsHyg5Jev0DhKB6NcMU4AOCMRc1EXRwAZnbCodTNrMLMHjWzp8zsWTP72Azr/KKZdZrZk+HjV7IT7gSuyAAAnAmGUgcAzAvyCQBgnsRjFMYBAGfOGEodAGZ1MleMj0h6nbsfN7MyST81s/9294enrPdld//A/Ic4GVdkAADmR2oodRIKAOD0WboyTj4BAJyZsmhEYwylDgA4Q5GIcb4LAGZxwsK4By3o8fBlWfjIWavKFX4AgPnArTkAAPOB4xMAwHzhinEAwHyIMJQ6AMzqhEOpS5KZRc3sSUkdku5190dmWO2tZva0mX3NzJbPa5STo5HEFX4AgDPDCCQAgPlAPgEAzJd4LKIRrhgHgIJjZp8zsw4z2zbL8l8IayfPmNmDZrYpm/FETEpwgAIAMzqpwri7J9z9QknLJF1mZudPWeVbkla5+wWS7pX0+Zm2Y2a3mtkWM9vS2dl5WgFzRQYAYD5YmFCcjAIAOAPpfMKJJwDAGYpHuWIcAArUnZKun2P5LkmvdfeNkv5Y0h3ZDCZiDKUOALM5qcJ4irv3SvqRpjTy7t7l7iPhy3+WdMks77/D3Te7++bW1tbTiVeMfAsAmA9c4QcAmA/pfJLTKAAAxaCcodQBoCC5+/2SuudY/qC794QvH1ZwAWLWRMyUJJ0AwIxOWBg3s1YzawinKyVdK+mFKeu0Z7x8s6Tn5zPIGXHmCQBwBtIjkJBPAABnIBImFO7hBwA4U2XRiMYYSh3/j727D7LsPusD/33u7e6Znne9tCVZI0vGL2CZ2BaZGHudKgyERDiUXaklW/ZuIKRMVCSQwIbdTUhlTeKt/SOVKpIlBlwCXIYsa3CBi1Uos+CtmBhvbOPByMKSsCMbg8eW0VjvoxnNTE//9o97uzXumdZM97m375yez6eqa273PXPP89f91Tnf8zw/YKd7e5Lfvtgbk5i4m4xGqa+44QVwUXOXccxNSX6pqoYZBenvb639VlW9M8nR1to9Sf5xVb05yXJGT0b9wLQKNvoWgEmorK4nANDB2oNWVhQAulnQMQ6wo1XVt2cUjP/Vi73fWrs74zHrR44c2fIFRlV5cBdgA5cMxltr9yW54yJ/f8d5r38iyU9MtrSLM/oWgEkoQQYAE1D2egJgQhbmBjlxennWZQAwBVX1qoy2of3u1tqj0zzXYKBjHGAjm9pj/Epg9C0Ak2A9AWASPLgLwKQsDHWMA+xEVfWiJB9I8n2ttc9N+3zDKsE4wAYuZ5T6FaWiJQOA7mzNAcAkWE8AmBSj1AH6qarel+SNSa6vqmNJfjLJfJK01t6d5B1Jrkvys+Prh+XW2pFp1TMwSh1gQ70Lxlf5XgegCx1+AEyC9QSASVkYDnLmnGAcoG9aa2+7xPs/mOQHt6mcVBmlDrCRHo9S98UOwNatrSezLQOAnrOeADApOsYBmIRBlfwEYAO9C8ZX+VoHoIvVrTlcJwDQhfUEgElZmNMxDkB3g6qcM0sd4KJ6F4w/1zE+2zoA6LfnOvwsKABsnfUEgElZGOoYB6C7wcAe4wAb6V8w/twufjOtA4B+sycsAJNkPQGgq3mj1AGYgEHZihZgI/0LxuvSxwDAJdkTFoAJcH0CwKQsDAdZXmlZ0eYHQAeD0jEOsJHeBeOrPPAEQBdrE0gsKAB08Nwe49YTALpZmBvdprPPOABdDCpZcX0CcFG9C8ZLhx8AE2A9AWASynNWAEzILsE4ABNQVTmnZRzgovoXjK91ZMy4EAB6bVDWEwC6W52kbjkBoKu1jnH7jAPQwXBQ7ncBbKB/wfhah59vdgC2bjXIMFoKgC7Kg1YATMj8UDAOQHdGqQNsrH/B+Phf3+sAdGH0LQCT8FzHuAUFgG52z49u050WjAPQQVUJxgE20L9gvC59DABcytrWHDOuA4B+86AVAJOyOD9Mkpw6c27GlQDQZ6OO8VlXAXBl6l0wvsr3OgCdrAUZVhQAtm5tlPqM6wCg/3avBuNnBeMAbN2gKiuScYCL6mEwvrqHny92gD6pqt1V9QdV9emqur+q/tVFjvmBqjpeVfeOf35wevVM65MBuCq5PgGgo9WO8WcF4wB0MBwYpQ6wkblZF7BZggyA3jqd5Dtaayeqaj7JR6vqt1trH1933K+11n5k2sWs7QnrOgGAjqp0jAPQ3eKCUeoAdDfaY3zWVQBcmfoXjI//FWQA9Esbjfo4Mf51fvwzs2/z50bfWlAA6Kbi+gSA7tY6xpcF4wBs3aBM3AXYSO9GqZeWcYDeqqphVd2b5JEkH2qtfeIih/23VXVfVf16Vd2ywefcVVVHq+ro8ePHt1bL+F/XCQB0VVUetAKgs7U9xnWMA9DBQMc4wIZ6F4yvcuMJoH9aa+daa69JcjjJa6vqm9cd8h+T3NZae1WSDyX5pQ0+5+7W2pHW2pGlpaUt1bL6nJXVBICudIwDMAmro9TtMQ5AF4NKzknGAS6qd8G4Dj+A/mutPZHkw0nuXPf3R1trp8e//kKSvzytGmq8olhPAOjKHuMATMLqKPVTgnEAOhjtMe4KBeBi+heMr3b4+V4H6JWqWqqqQ+PXi0m+K8mfrDvmpvN+fXOSB6dXz+hfE0gA6KpSrk8A6Oy5UeorM64EgD4blusTgI3MzbqAzVrr8JtxHQBs2k1Jfqmqhhk9mPX+1tpvVdU7kxxtrd2T5B9X1ZuTLCd5LMkPTKsYD1oBMDHlQSsAuhsOKgtzAx3jAHQyGETHOMAG+heMrwUZvtgB+qS1dl+SOy7y93ec9/onkvzEdtTz3Ch16wkA3VTiyV2AHqqq9yT5niSPtNa+eYNj3pjk3yWZT/K11tq3TbOmxfmhPcYB6MQodYCN9W6UOgBMgo5xACbFHuMAvfXeJHdu9OZ4K6ifTfLm1tork/ztaRe0OD/MqTOCcQC2bmCUOsCGehuM+14HoItxLm49AaCz0R7jVhSAvmmtfSSjLZw28t8n+UBr7c/Hxz8y7ZoWF4ZGqQPQyaCSc65PAC6qd8F4STIAmICq1VHqMy4EgN6rsp4A7FAvT3JNVf1eVf1hVX3/xQ6qqruq6mhVHT1+/HinE+6eF4wD0M3QKHWADfUwGB8HGZJxADp47jkr6wkA3VQ8twuwQ80l+ctJ/maSv5Hkf62ql68/qLV2d2vtSGvtyNLSUqcTLs4P7DEOQCdVlZWVWVcBcGWam3UBm7UWZLjzBEAH9hgHYFLKHn4AO9WxJI+21p5J8kxVfSTJq5N8blonXFywxzgA3QwqtnoC2EAPO8ZH//paB6CL5yaQAEA3o45xKwrADvR/J/mrVTVXVXuSfGuSB6d5wkWj1AHoaFCVFZcnABfVw47xuvRBAHC5PEELQFf2GAfopap6X5I3Jrm+qo4l+ckk80nSWnt3a+3Bqvp/ktyXZCXJL7TWPjPNmuwxDkBXg0FyzgUKwEX1Lhhf5XsdgK6qdIwD0J1HdwH6qbX2tss45t8k+TfbUE6SUcf4s0apA9DBoMoodYAN9HiUui92ALoZ2BMWgAkoN54AmJDFBR3jAHRjlDrAxvoXjI//dd8JgK4qyYoFBYCOTCABYFLsMQ5AV4NyvwtgI70LxrPWMQ4A3QgyAJiEigd3AZiM3fPDPHt2JSta/QDYoqqyjgBsoHfBeNnBD4AJqRilDkB3VWWrJwAmYt+uuSTJSV3jAGyRUeoAG+tdML5GkgFAVxVBBgCd6RgHYFL2joPxE88uz7gSAPpqODBKHWAjvQvGyyh1ACakEgsKAJ2NOsYBoLt9u8fB+GnBOABbM+oYd4UCcDH9C8bH//peB6Are4wDMAlVSXOBAsAE7Ns1TCIYB2Dryih1gA31Lxgft4y78QRAV6M9xq0nAHRjlDoAk7J3YdQx/oxgHIAtGnhwF2BD/QvGx//6Wgegq1GH36yrAGCaqmpYVX9UVb81vXNYTwCYDKPUAehqUJVzWsYBLuqSwXhV7a6qP6iqT1fV/VX1ry5yzK6q+rWqeqiqPlFVt02j2NG5pvXJAFxtKh60ArgK/GiSB6d5gkqlWVEAmIB9u8bB+LOCcQC2ZjAwSh1gI5fTMX46yXe01l6d5DVJ7qyq16075u1JHm+tvTTJv03yrydb5oV0ZADQ1aDKegKwg1XV4SR/M8kvTPc8rk8AmIzVYPyZM4JxALZmMG4uNE4d4EKXDMbbyInxr/Pjn/XfqG9J8kvj17+e5DurptPbXeNh6r7SAeiskhUXCQA72b9L8r8kWbnYm1V1V1Udraqjx48f3/JJTCABYFL2joPxp3WMA7BFg3E0o2sc4EKXtcf4eF++e5M8kuRDrbVPrDvk5iRfSpLW2nKSJ5Ncd5HP6X7jydNOAEyI3TkAdq6q+p4kj7TW/nCjY1prd7fWjrTWjiwtLXU5l45xACZi19wgc4PKM/YYB2CLVjvGNYMAXOiygvHW2rnW2muSHE7y2qr65q2cbBI3nuwxDsCkjIIMFwkAO9Qbkry5qr6Y5FeTfEdV/Z/TOpk9xgGYhKrKvt1zgnEAtmx1mO85LeMAF7isYHxVa+2JJB9Ocue6t76c5JYkqaq5JAeTPDqJAtdbzcXlGAB0VWX0LcBO1Vr7idba4dbabUnemuQ/tdb+zjTOVWapAzBBexfm8rRgHIAtGo5bxmUoABe6ZDBeVUtVdWj8ejHJdyX5k3WH3ZPk745ff29GN52m8rU7pa3LAbgKVVwkANCdB60AmKR9u3SMA7B1RqkDbGzuMo65KckvVdUwoyD9/a2136qqdyY52lq7J8kvJvkPVfVQkscy6siYKqMKAeiqqqwnAFeB1trvJfm9aX1+xdYcAEzOvt1zOSEYB2CLBuPmQsE4wIUuGYy31u5LcsdF/v6O814/m+RvT7a0izNKHYBJ0TEOwCToGAdgkvbumsuTp87OugwAeqrWgvEZFwJwBdrUHuNXgtVJ6r7TAehKkAHAJHjQCoBJ2m+UOgAdrI1Sl4wDXKB/wfi4Z9yNJwC6K+sJAJ2NtuYAgMnYu2uYE88KxgHYGqPUATbWv2C8Ln0MAFyO0ZriIgGAbkYd49YTACZjr45xADoYDIxSB9hI74LxVU2QAUBHgzKBBIAJsDUHABO0f9dcTpxZ9tAVAFuyNkrdOgJwgf4G477TAeioUi4SAOjMABIAJmnvrrm0lpw8c27WpQDQQ/ODUeyzrGUc4AK9C8aNUgdgUkrHOAATMNpj3IICwGTs2z2XJDlhnDoAWzAct4yfO+caBWC9/gXjo34M46QA6KyiwQ+A7kZ7jM+6CgB2iv2755MkTz8rGAdg8+aGowzl7MrKjCsBuPL0Lxgfd4y78QRAV1VlPQGgMxNIAJikA+OO8SdPnZ1xJQBcjqp6T1U9UlWf2eD9qqqfrqqHquq+qvqWadYzNx6lfs4odYAL9C8Yn3UBAOwW8q3TAAAgAElEQVQoRt8C0FXFKHUAJufA4qhj/KlnBeMAPfHeJHc+z/vfneRl45+7kvzcNItZHaW+bJQ6wAV6F4yv8pUO0C9Vtbuq/qCqPl1V91fVv7rIMbuq6tfGT9B+oqpum25NsaAA0JmOcQAm6cB4lPpTOsYBeqG19pEkjz3PIW9J8stt5ONJDlXVTdOqZ241GDdKHeACvQvGq1b3GJ9xIQBs1ukk39Fae3WS1yS5s6pet+6Ytyd5vLX20iT/Nsm/nmZBVXJxACbDegLApBxYHI1Sf8oe4wA7xc1JvnTe78fGf/s6VXVXVR2tqqPHjx/f8slW9xhfNkod4AL9C8bH/xpVCNAv46diT4x/nR//rP8yf0uSXxq//vUk31mrT0RNQaXSPGkFQEdV5cFdACZGxzjA1am1dndr7Uhr7cjS0tKWP2d1j3Gj1AEu1L9gfByPuPEE0D9VNayqe5M8kuRDrbVPrDtk7Qna1tpykieTXHeRz5nIE7Q6xgGYhPFMqxlXAcBOsXt+mF1zA3uMA+wcX05yy3m/Hx7/bSqGRqkDbKiHwbjbTgB91Vo711p7TUYXAK+tqm/e4udM5AnaigetAOjOHuMATNqBxfk8dcoodYAd4p4k318jr0vyZGvt4WmdbH48Sv2cUeoAF+hdMA5A/7XWnkjy4SR3rntr7QnaqppLcjDJo9OqY1DlQSsAOjOBBKCfquo9VfVIVX3mEsf9laparqrv3a7aDuyeM0odoCeq6n1JPpbkG6vqWFW9vap+qKp+aHzIB5N8IclDSX4+yT+cZj1rHeNGqQNcYG7WBWyZlgyAXqmqpSRnW2tPVNViku9K8q/XHXZPkr+b0cXE9yb5T22am4BXsmI9AaCjSmWayxUAU/PeJO9K8ssbHVBVw4yuW353m2pKMu4YN0odoBdaa2+7xPstyQ9vUznP7TGuYxzgAr3sGNeRAdBLNyX5cFXdl+STGe0x/ltV9c6qevP4mF9Mcl1VPZTknyT5Z9MsqBILCgCduT4B6KfW2keSPHaJw/5Rkt9I8sj0K3rOgd3zOsYB2JK5tVHq9hgHWK+XHeP2hAXon9bafUnuuMjf33He62eT/O3tqqmq0kQZAHTk+gRgZ6qqm5P8rSTfnuSvbOe5DyzO588fO7mdpwRgh5gbj1I/a5Q6wAV62jEuyACgO0EGABNR5eoEYGf6d0n+aWvteVvuququqjpaVUePHz8+kRPbYxyArZobjmKfc0apA1ygtx3jANBVlWAcgO5GD1pZUAB2oCNJfrWqkuT6JG+qquXW2m+ef1Br7e4kdyfJkSNHJrIgHBzvMd5ay/j8AHBZVjvG7TEOcKFeBuOJIAOA7iomkADQnbwCYGdqrb149XVVvTfJb60PxaflwOJ8zp5refbsShYXhttxSgB2iOFqMH7OHuMA6/UyGK+KGAOAznSMAzAJtuYA6Keqel+SNya5vqqOJfnJJPNJ0lp79wxLy4Hd80mSp549KxgHYFPmhjrGATbSz2A85cYTABNhOQGgqyoTSAD6qLX2tk0c+wNTLOUCBxZHt+yePHU2NxzYvZ2nBqDn5gb2GAfYyGDWBWxJxY0nADqr8qAVAN3pGAdg0tY6xk+dnXElAPTN6ij1s0apA1ygl8F4JVr8AOhsdJlgQQGgG1tzADBpBxafG6UOAJsxPx6lrmMc4EL9DMZr1hUAsBMMBolrBAC6qhilDsBkHdg9GqX+1KnlGVcCQN+sdozbYxzgQr0MxhP9fQB0V6k0LX4AdKVjHIAJ0zEOwFat7jG+fM5FCsB6vQzGBRkATEKVB60A6K5iPQFgsuwxDsBWDQeVquTcij3GAdbrZzCuIwOACahYTwDobuBJKwAmbGFukMX5YZ561ih1ADZvblA5a5Q6wAX6GYzHfScAJqDKegJAZ1XJiietAJiwA4tzOsYB2JLhoHJOMA5wgX4G41U6/ADobNQxbkEBoBsN4wBMw4Hd83nipGAcgM2bHwzsMQ5wEf0MxmddAAA7QllQAJiASnnQCoCJO7RnPk+cOjPrMgDooeGwsmyPcYAL9DIYT5KmJwOAjuwxDsAk6BgHYBoO7VnQMQ7AlswNBlk2Sh3gAv0MxkuQAUB3VeVBKwAmwvUJAJN2aHE+T9pjHIAtmBtUzhmlDnCBXgbjJt8CMAk6xgGYhNGDVgAwWdfsXcjjJ41SB2DzhoPKWaPUAS7Qz2C87OEHQHeDKsE4AJ1V4kkrACbu4OJ8nj27kmfPnpt1KQD0zPywcs4odYAL9DQYt4cfABNQyYogA4COXJ8AMA2H9swniX3GAdi0ueEgZ8/pGAdYr5fBOABMQkWQAUB3tuYAYBqu2bOQJHnilHHqAGzOwnCQM8uCcYD1ehmMu/EEwCSUZByACRjtMW5BAWCyDi3qGAdga3bND3JaMA5wgX4G4248ATABFesJAN15cBeAaTi02jF+Usc4AJuza04wDnAxlwzGq+qWqvpwVT1QVfdX1Y9e5Jg3VtWTVXXv+Ocd0yl3fL648QRAd1XWEwC6s54AMA32GAdgq3bNDQXjABcxdxnHLCf58dbap6pqf5I/rKoPtdYeWHfc77fWvmfyJV6oyuRbALqzngAwGWU9AWDiVoPxxwXjAGzSwtwgp8+em3UZAFecS3aMt9Yebq19avz66SQPJrl52oU9v5rt6QHYESqVpsUPgI5GHePWEwAma3F+mIW5QZ44ZZQ6AJuza26QM+d0jAOst6k9xqvqtiR3JPnERd5+fVV9uqp+u6peucH/v6uqjlbV0ePHj2+62PO57wRAVzrGAZgEj+0CMA1VlUOL83niGR3jAGzOrrlhTp8VjAOsd9nBeFXtS/IbSX6stfbUurc/leTW1tqrk/z7JL95sc9ord3dWjvSWjuytLS01ZpTlYgyAJgED1oB0JU9xgGYlmv2LOgYB2DTds0P7DEOcBGXFYxX1XxGofivtNY+sP791tpTrbUT49cfTDJfVddPtNLz64kbTwB0Nyij1AHorlJpHtwFYAoO7pnPE/YYB2CTFoaDnF62xzjAepcMxquqkvxikgdbaz+1wTE3jo9LVb12/LmPTrLQrz+fYByA7gZGqQMwAa5PAJiWQ4uCcQA2b9f8IGd0jANcYO4yjnlDku9L8sdVde/4b/88yYuSpLX27iTfm+QfVNVyklNJ3tqm2IKnIwOASRhUZUWSAUBH5UErAKbkmj0L+fSxJ2ZdBgA9s2tumNPLK2mtZdzTCEAuIxhvrX00o+nlz3fMu5K8a1JFXYrvcQAmoaqy4uFZADqq2JoDgOk4tGc+j588K9gAYFN2zY2GBZ85t5Jdc8MZVwNw5bisPcavRO47AdDVoKJjHIDudIwDMCWH9izkzPJKnj3riV4ALt9aMG6cOsDX6WUwXnHjCYDu7AkLwCRU4gIFgKk4tGc+SfLEqTMzrgSAPlkNxk8LxgG+Tj+D8SpBBgCdDarSJBkAdFRVVhMApuLQ4igYf/yZszOuBIA+WR2fLhgH+Hq9DMaTCDIA6GxQlRXLCQAdVWKPcQCm4tCehSQ6xgHYnF3z447xs+dmXAnAlaWXwXiZpQ7ABJQ9xgGYgNF6MusqANiJ1kapn9QxDsDlWxiO9xg/p2Mc4Hz9DcYB6JWquqWqPlxVD1TV/VX1oxc55o1V9WRV3Tv+ecc0axrYmgOACbA1BwDTcs1qx7hgHIBNWO0Yf/asYBzgfHOzLmCr3HYC6J3lJD/eWvtUVe1P8odV9aHW2gPrjvv91tr3bEdBAx3jAExAVTxoBcBUrHaMP37SKHUALt/u+dEe488apQ7wdfrZMZ6yhx9Az7TWHm6tfWr8+ukkDya5eZY1jfYYt54A0M3o+mTWVQCwWVX1nqp6pKo+s8H7/0NV3VdVf1xV/6WqXr3dNe6eH2bX3CBPntIxDsDl27sw6ok8eWZ5xpUAXFn6GYyXjnGAPquq25LckeQTF3n79VX16ar67ap65Qb//66qOlpVR48fP96hkGTFRCkAOhpUPLgL0E/vTXLn87z/p0m+rbX2l5L8b0nu3o6i1rtmz0Ief0bHOACXb++uUTB+4rSOcYDz9TMYj1GFAH1VVfuS/EaSH2utPbXu7U8lubW19uok/z7Jb17sM1prd7fWjrTWjiwtLW25lkHVlv8vAKwaTSCZdRUAbFZr7SNJHnue9/9La+3x8a8fT3J4Wwpb59Ce+Txuj3EANmHvrtEo9ZOndYwDnK+fwbggA6CXqmo+o1D8V1prH1j/fmvtqdbaifHrDyaZr6rrp1WPPcYBmISyngBcDd6e5Lcv9sbEJlpt4Lp9C3nsmdMT/1wAdq4941Hqz5zRMQ5wvl4G44lR6gB9U6Onmn4xyYOttZ/a4Jgbx8elql6b0Tr16LRqssc4AJNQVa5PAHawqvr2jILxf3qx9yc10Woj1+7dlceMUgdgE/YsjDrGn9ExDvB15mZdwFaMRqm79QTQM29I8n1J/riq7h3/7Z8neVGStNbeneR7k/yDqlpOcirJW9sUv/DL6FsAJsAe4wA7V1W9KskvJPnu1trUHtp9PtftXcijgnEANmF+OMjC3CDPnBGMA5yvl8F4Ssc4QN+01j6a0bNNz3fMu5K8a3sqEmQA7GRVtTvJR5Lsyui659dbaz85nXPFg1YAO1BVvSjJB5J8X2vtc7Oq49q9C3n62eWcWV7Jwlxvhz8CsM327ZrTMQ6wTi+D8Uok4wB0NtAxDrCTnU7yHa21E1U1n+SjVfXbrbWPT/pEgyoPWgH0UFW9L8kbk1xfVceS/GSS+WRtotU7klyX5GfHOz4tt9aObHed1+5dSJI8fvJMbjiwe7tPD0BP7VkY5uRpe4wDnK+fwXhVmmQcgI5GHX7WE4CdaLwVx4nxr/Pjn6l86Vd0jAP0UWvtbZd4/weT/OA2lbOh1WD80ROCcQAu396FOaPUAdbp5fyl553DCwCXadThN+sqAJiWqhpW1b1JHknyodbaJ9a9f1dVHa2qo8ePH+9yHh3jAEzN+R3jAHC59uwa5hkd4wBfp5fBeBJBBgCd6RgH2Nlaa+daa69JcjjJa6vqm9e9f3dr7Uhr7cjS0tKWz+NBKwCm6brVjvFnBOMAXL59u+Zywh7jAF+nl8F4lWAcgO4EGQBXh9baE0k+nOTOaXy+B60AmKbVjvHHTpyecSUA9Mn+3XN5+tmzsy4D4IrSz2A89hgHoLuBIANgx6qqpao6NH69mOS7kvzJNM41qCltXg4ASQ7tWUhV8piOcQA24eDiQp48JRgHOF8/g3Ed4wBMwKBKMA6wc92U5MNVdV+ST2a0x/hvTeNE1hMApmk4qFyzZ8EodYArVFXdWVWfraqHquqfXeT9F1XVh6vqj6rqvqp603bUdWjPfJ44eTbNtQrAmrlZF7BVvsoB6KqqsmJBAdiRWmv3JbljW05WsZ4AMFXX7l3QMQ5wBaqqYZKfyWhC1bEkn6yqe1prD5x32L9I8v7W2s9V1e1JPpjktmnXdnBxPssrLSfPnMveXb2NggAmqqcd4zXrEgDYAUYTSCQZAHQzKLPUAZiua/fqGAe4Qr02yUOttS+01s4k+dUkb1l3TEtyYPz6YJKvbEdhhxbnkyRPGKcOsKaXwXhilDoA3Q1szQHABFRilDoAU3Xd3oU8LhgHuBLdnORL5/1+bPy38/3LJH+nqo5l1C3+jy72QVV1V1Udraqjx48f71zYoT2jYPzJk4JxgFW9DMZH/eJuPAHQjT1hAZiEQZWrEwCmyih1gF57W5L3ttYOJ3lTkv9QVRdkM621u1trR1prR5aWljqf9MBax7j1A2BVP4NxHX4ATIA9xgGYhEHpGAdguq7du5DHT57JigsYgCvNl5Pcct7vh8d/O9/bk7w/SVprH0uyO8n10y7s0OJCEh3jAOfrbzA+6yIA6L3BaASJfcYB6KbKg7sATNW1exey0uwTC3AF+mSSl1XVi6tqIclbk9yz7pg/T/KdSVJVr8goGO8+K/0Srts3CsYfNXEEYE0/g/HxMHUA6GJQo/VE0wUAXXjQCoBpu3bvKNx47JnTM64EgPO11paT/EiS30nyYJL3t9bur6p3VtWbx4f9eJK/X1WfTvK+JD/QtuHi4dq9C6lKjj9t7QBYNTfrArbKTScAuloNMlZay9BDVwBs0fkPWg0tJwBMwXV7dyVJHj1xJi99wYyLAeDrtNY+mOSD6/72jvNeP5DkDdtd1/xwkGv3LOQRwTjAmn52jBulDsAE1FqQYVUBYOtWs3DrCQDT8lzHuHG4AFy+pf27dIwDnKefwXhiDz8AOqu10bezrQOAfhuMR5BYTwCYFvvEArAVS/t35fgJwTjAql4G46nSMQ5AZ6ujbwUZAEyCjnEApuWaPaNg/HHBOACbsLR/V76mYxxgTS+D8VHHuJtOAHRz/h7jALBVqw9aAcC0LMwNsn/3nI5xADZldZS6PAVgpJ/BuPtOAEzAwB7jAEyAB60A2A7X7V0QjAOwKS/Yvztnzq3kyVNnZ10KwBWhl8E4AExCrQXjMy4EgF6rtWB8tnUAsLMZhwvAZi3t35UkecT6AZCkp8H4aJT6rKsAoO9WO/yMkwKgi9UJJNYTAKbp+n27cvyEYAOAy3fTwd1Jkq88cWrGlQBcGfoZjFelxU0nALpZ3ZlDhx8AXZhAAsB2WNq/K18TjAOwCYevWUySHHtcMA6Q9DUYj45xALobDOwxDkB3qw9a6RgHYJqW9u3KEyfP5vTyuVmXAkBP3LB/d+aHJRgHGOtnMF6CcQC6q7XRtzMuBIBee25rjtnWAcDOdv14n9hHT5yZcSUA9MVgULn50GKOPX5y1qUAXBH6GYyv9WQAwNbZYxyASXhulLr1BIDpWdo3CsaPP22cOgCX7/A1e3SMA4xdMhivqluq6sNV9UBV3V9VP3qRY6qqfrqqHqqq+6rqW6ZT7nPsMQ5AVwN7wgIwAWsPWs22DAB2uKVxx7h9xgHYjMPXLArGAcYup2N8OcmPt9ZuT/K6JD9cVbevO+a7k7xs/HNXkp+baJXrGaUOwASsBhk6/ADoQsc4ANthdZS6jnEANuOWa/fkaydO59SZc7MuBWDmLhmMt9Yebq19avz66SQPJrl53WFvSfLLbeTjSQ5V1U0Tr3asohsDgO4EGQBMQtljHIBtcP2+hSSCcQA25/A1i0mSLz9hn3GATe0xXlW3JbkjySfWvXVzki+d9/uxXBiep6ruqqqjVXX0+PHjm6v06z4nknEAOlsdpS7IAKAL6wkA22HX3DAHF+dz3Ch1ADZhNRj/0mPGqQNcdjBeVfuS/EaSH2utPbWVk7XW7m6tHWmtHVlaWtrKR4xqSW35/wLAqtXVRMc4AF3YmgOA7XL9vgV7jAOwKS+6dm+S5IuPPjPjSgBm77KC8aqazygU/5XW2gcucsiXk9xy3u+Hx3+bmqZlHICOBuNVUI4BQBerD+4KxgGYtqX9u4xSB2BTrt+3kP275/KF44JxgEsG4zXagPUXkzzYWvupDQ67J8n318jrkjzZWnt4gnWuq0mIAUB3A3uMAzAB9hgHYLss7d8tGAdgU6oq37C0L1/42olZlwIwc3OXccwbknxfkj+uqnvHf/vnSV6UJK21dyf5YJI3JXkoyckkf2/ypT6nyhbjAHRXa8H4jAsBoNfKHuMAbJPr9y0IxgHYtJdcvzcf+8Kjsy4DYOYuGYy31j6aPP+m3q21luSHJ1XUpVQqzV0nADoarHX4WVMA2Lq19cTjuwC9UlXvSfI9SR5prX3zRd6vJP9HRs0gJ5P8QGvtU9tb5ddb2r8rz5w5l5NnlrNn4XL6XQAgefH1e/OBP/qy9QO46l3WHuNXGh3jAEzCQMc4ABNgPQHorfcmufN53v/uJC8b/9yV5Oe2oabndf2+XUmSrz19ZsaVANAn37C0L0nsMw5c9XoZjAPQP1V1S1V9uKoeqKr7q+pHL3JMVdVPV9VDVXVfVX3LNGta7fCzxzgAXZT1BKCXWmsfSfLY8xzyliS/3EY+nuRQVd20PdVd3NL+UTB+/MSzsywDgJ75hqW9SZIvfE0wDlzdehmMV5VuDID+WU7y462125O8LskPV9Xt647Z5o6M1Q4/iwoAW2ePcYAd6+YkXzrv92Pjv32dqrqrqo5W1dHjx49PtaClcce4fcYB2IwXX783VckXjp+YdSkAM9XLYHxQ9oMF6JvW2sOr+/G11p5O8mAuvKm0rR0Zz+0xPq0zAHA1eG49saAAXI1aa3e31o601o4sLS1N9Vwv2C8YB2Dzds8P88KDi0apA1e9XgbjwyrdfQA9VlW3JbkjySfWvbWtHRkDHX4ATEDFHuMAO9SXk9xy3u+Hx3+bmev27cpwUPnqU0apA7A5L3nBvjz0iI5x4OrWy2C8qrKyMusqANiKqtqX5DeS/Fhr7amtfMakOjIG41XQw1YAdLHWMR7rCcAOc0+S76+R1yV5srX28CwLGg4qS/t25atP6hgHYHO+8YZ9eej4iSyfE64AV6+5WRewFYMSYgD0UVXNZxSK/0pr7QMXOWRbOzJW94S1pgDQxXg58fAuQM9U1fuSvDHJ9VV1LMlPJplPktbau5N8MMmbkjyU5GSSvzebSr/eDQd35y90jAOwSS+/YX/OLK/kzx47mZcs7Zt1OQAz0dNgvIy9BeiZGqXQv5jkwdbaT21w2D1JfqSqfjXJt2bKHRmDMvoWgO5WH7TSMQ7QL621t13i/Zbkh7epnMt244Fd+bw9YgHYpG+8cX+S5HNffVowDly1ejlKfTBIzknGAfrmDUm+L8l3VNW94583VdUPVdUPjY/5YJIvZNSR8fNJ/uE0C1obfWtNAaCD1QetLCcAbIcbD+zOXzypYxyAzXnZC/anKvnsXzw961IAZqa3HePG3gL0S2vto0nqEsdsa0dGRcc4AN2tLm6uUQDYDjceXMzTp5fzzOnl7N3Vy1t7AMzA4sIwt167J58TjANXsX52jBulDsAEDMar4DnJOAAdrK4nrlEA2A43HtyVJPmqfcYB2KSX37A/n/2qYBy4evU0GNeNAUB3w7U9xq0pAGxdWU8A2EY3HNidJMapA7Bp33jj/nzx0ZN59uy5WZcCMBM9DcaNUgegu7nhKMjQMQ5AF8+NUp9pGQBcJW4cB+MPC8YB2KSX37A/51ZavnD8mVmXAjATvQzGqyorK7OuAoC+G5RgHIDuVteTxHoCwPTdeHAUjBulDsBmfeON+5PEPuPAVauXwbhR6gBMwnAgGAegu9Vc3HICwHbYszCX/bvn8heCcQA26cXX7838sPJZwThwleplMD4cGKUOQHerwfiyJAOADlY7xl2iALBdbjywO181Sh2ATZofDvKSpX35k4efmnUpADPRy2C8qnRjANDZajDuYSsAuniuY9x6AsD2uPHgbh3jAGzJK246kAcf1jEOXJ16GYwPKmluOgHQ0ZxR6gBMQMWDVgBsrxsO7LbHOABbcvtNB/LVp57NoydOz7oUgG3X02BcxzgA3a2OvhWMA9DF+DmrxHICwDa58cDuHH/6dJbPrcy6FAB65pUvPJAkuf8rxqkDV5+eBuNCDAC6mxuMlkFrCgBdDNa25phxIQBcNW44uDsrLfnaiTOzLgWAnrl9HIw/YJ9x4CrUy2B8tMe4u04AdDPOxQXjAHSy2jDuGgWA7fLCg7uTJF958tSMKwGgbw7tWcjNhxZ1jANXpV4G48NBxT0nALoaru4xblEBoIMab81hNQFgu9x8zWKS5MuPC8YB2LzbX3gg93/lyVmXAbDtehmMD0o3BgDdrQbjyzrGAehgnIu7RgFg29x8aByMPyEYB2DzXvnCA/nTrz2Tk2eWZ10KwLbqaTBulDoA3Q3HScaKYByADgarybjlBIBtsn/3fA7sntMxDsCW3H7TgbSWPPjw07MuBWBb9TIYH+0xPusqAOi7ufEm4/YYB6CLgY5xAGbg8DV7cuzxk7MuA4AeeuXNB5MkDxinDlxlehmMD0p3HwDdjXNxwTgAnVTGE0gsJwBso5uvWTRKHYAteeHB3Tm0Zz73f+WpWZcCsK16GYwPB0apA9DdWse4NQWADtYmqVtPANhGNx9azJcfP2X9AWDTqiq333QgDzwsGAeuLr0Mxo1SB2ASdIwDMAmre4xbTgDYToevWcwzZ87liZNnZ10KAD30yhceyJ989emcWV6ZdSkA26aXwfhARwYAEzAcBxmCcQC60DEOwCwcvmYxSYxTB2BLXnX4UM4sr+Rzf/H0rEsB2DY9DcZ1ZADQ3XD8pNWyBQWADlbXE1tzALCdbj60J0ly7HHBOACb9+rDh5Iknz72xIwrAdg+PQ3GR//q8AOgi6rKoJIV6wkAHQxMIAFgBm7WMQ5AB7dcu5hr9szn018SjANXj14G47XWMe7GEwDdzA0GOvwA6GS1Y9z1CQDb6Zo989mzMMyxx0/OuhQAeqiq8qrDh3LfsSdnXQrAtullML5648l9JwC6Ggx0+AHQzXCtY3zGhQBwVamq3HxoMV82Sh2ALXr1LYfyub94OifPLM+6FIBt0ctgfHWUuo4MALqaGwwE4wB0MhhfVbk+AWC73XzNolHqADNWVXdW1Wer6qGq+mcbHPPfVdUDVXV/Vf1f213jRl59+GBWWvKZLz8161IAtkVPg3GjCgGYjEHpGAegm7VR6tYTALbZ4WsWc0zHOMDMVNUwyc8k+e4ktyd5W1Xdvu6YlyX5iSRvaK29MsmPbXuhG3jV4UNJYp9x4KrRy2D8uT3GZ1wIAL03HJRgHIBOVh/cPefBXQC22a3X7s2Tp87miZNnZl0KwNXqtUkeaq19obV2JsmvJnnLumP+fpKfaa09niSttUe2ucYNLe3flZsPLebTxwTjwNWhl8H42ih1QQYAHQ0HgyxbTwB2nKq6pao+fN64wh+d1rnWJlpZTwDYZrdetydJ8mePnpxxJQBXrZuTfOm834+N/3a+lyd5eVX9f1X18aq682IfVHSRMSEAACAASURBVFV3VdXRqjp6/PjxKZV7oVffclAwDlw1ehmMr40q1JEBQEfDgSADYIdaTvLjrbXbk7wuyQ+vH2k4KavXJyaQALDdbrt+b5Lki48+M+NKAHgec0leluSNSd6W5Oer6tD6g1prd7fWjrTWjiwtLW1bca86fChfeuxUHnvG9BFg5+tlMG6UOgCTMjcYGH0LsAO11h5urX1q/PrpJA/mws6NiRiujVKfxqcDwMZedK2OcYAZ+3KSW877/fD4b+c7luSe1trZ1tqfJvlcRkH5FeE1t4wy+nu/9PiMKwGYvl4G46uj1JsgA4COBgMdfgA7XVXdluSOJJ9Y9/eJjCocjK+qTCABYLvtnh/mpoO7dYwDzM4nk7ysql5cVQtJ3prknnXH/GZG3eKpquszGq3+he0s8vm8+vChDAeVP/wzwTiw810yGK+q91TVI1X1mQ3ef2NVPVlV945/3jH5Mr/eQMc4ABMyNxgIxgF2sKral+Q3kvxYa+2p89+b1KjCtVHqHtwFYAZuvW6PjnGAGWmtLSf5kSS/k9GUqve31u6vqndW1ZvHh/1Okker6oEkH07yP7fWHp1NxRdaXBjmlS88kKNfFIwDO9/ldIy/N8mdlzjm91trrxn/vLN7Wc9vtWPcHuMAdDUoHeMAO1VVzWcUiv9Ka+0D0zrP6oO71hOAfqmqO6vqs1X1UFX9s4u8/6Kq+nBV/VFV3VdVb5pFnZdy23V782c6xgFmprX2wdbay1trL2mt/e/jv72jtXbP+HVrrf2T1trtrbW/1Fr71dlWfKG/fOs1+fSxJ3L23MqsSwGYqksG4621jyR5bBtquWzlxhMAEzIclPUEYAeq0UXDLyZ5sLX2U9M812rHuFHqAP1RVcMkP5Pku5PcnuRtVXX7usP+RUadf3dkNBr3Z7e3ystz63V787UTZ/L0s2dnXQoAPXXk1mvz7NmV3P+Vpy59MECPTWqP8ddX1aer6rer6pUbHTSpPfyG42BcwzgAXQ0HgywLMgB2ojck+b4k33Hetk9T6fRbvT4xSh2gV16b5KHW2hdaa2eS/GqSt6w7piU5MH59MMlXtrG+y3bbdXuSxDh1ALbsyG3XJEmOfvGK6pEEmLi5CXzGp5Lc2lo7Mb7R9JtJXnaxA1trdye5O0mOHDmy5btGg3Gcb5Q6AF3NDSrnVoyJAthpWmsfTVLbca6BjnGAPro5yZfO+/1Ykm9dd8y/TPK7VfWPkuxN8tcu9kFVdVeSu5LkRS960cQLvZRbr9ubJPnio8/km28+uO3nB6D/bjiwOzcfWsyn/tw+48DO1rljvLX2VGvtxPj1B5PMV9X1nSt7Hqt7+AnGAfqjqt5TVY9U1Wc2eP+NVfXkeV1979iOuuaHpWMcgM6Gg9IxDrDzvC3Je1trh5O8Kcl/qKoL7qW11u5urR1prR1ZWlra9iJvu37UMf7Fr9lnHICtO3LbNTn6xcfTXNcAO1jnYLyqbhzv35eqeu34Mx/t+rmXOGeSRI4B0CvvTXLnJY75/dbaa8Y/79yGmjI/HOTMso5xALoZVrk+AeiXLye55bzfD4//dr63J3l/krTWPpZkd5KpNoNsxZ6Fudx0cHc+f1wwDsDWHbn1mjzy9Okce/zUrEsBmJpLBuNV9b4kH0vyjVV1rKreXlU/VFU/ND7ke5N8pqo+neSnk7y1TfmRovGkQh3jAD3SWvtIkituo6KFuUHOnhOMA9DNYGCUOkDPfDLJy6rqxVW1kOStSe5Zd8yfJ/nOJKmqV2QUjB/f1iov00tfsC8PPXJi1mUA0GPfcut4n/E/u+Ju3wFMzCX3GG+tve0S778rybsmVtFlGBqlDrBTvX78oNVXkvxPrbX7L3bQJPfwmx8Ocvac9QSAboZVOScYB+iN1tpyVf1Ikt9JMkzyntba/VX1ziRHW2v3JPnxJD9fVf9jkpbkB6bdDLJVL33BvvzaJ7+UlZWWwWpHCQBswjfdeCD7d83lD/70sfytOw7PuhyAqbhkMH4lWhulrsEPYCf5VJJbW2snqupNSX4zycsudmBr7e4kdyfJkSNHOt2Ymh+WjnEAOhvYYxygd1prH0zywXV/e8d5rx9I8obtrmsrXvqCfTl55ly+8uSpHL5mz6zLAaCHhoPKa198bT72+anulAswU533GJ8Fo9QBdp7W2lOttRPj1x9MMl9VU9+/b344yBnBOAAdDQdllDoAM/PSpX1JYpw6AJ28/iXX5YuPnszDT9pnHNiZehqMj5JxuTjAzlFVN9Z4JEhVvTajNWrqj6guDO0xDkB3w9IxDsDsvPQFgnEAunvdN1yXJPn4F3SNAztTL0epD8Zxvo5xgP6oqvcleWOS66vqWJKfTDKfJK21dyf53iT/oKqWk5xK8tbt2L9vfjjImWXBOADdDAYVz1kBMCvX7duVa/cu5PPHBeMAbN3tNx3IwcX5fOzzj9pnHNiRehmMr+4xriMDoD9aa2+7xPvvSvKubSpnzfxc5ew56wkA3QzLKHUAZuulS/t0jAPQyWBQ+dYXX5uP6RgHdqhejlIfro1Sd+MJgG7mh4Oc1TEOQEfDgVHqAMzWS16wL//1kRPulwHQyetfcl2+9NipHHv85KxLAZi4Xgbjq3uMa8gAoKuF4SBnzL4FoKOq6BgHYKZefsO+PHHybI6fOD3rUgDosde/ZLTP+Mc+r2sc2Hl6GoyP/nXjCYCuFuYGOSsYB6AjHeMAzNorbjqQJHnw4adnXAkAffbyF+zPtXsXjFMHdqReBuOlYxyACZkfDrLSknMWFQA6GFZZSwCYqVfcOArGH/jKUzOuBIA+Gwwqr3/Jdfnof/2a7TmAHaeXwfhax7gvZQA6mh+OlkJd4wB0MRiU6xMAZurgnvncfGgxDz4sGAegm297+VIeefp0/uSrppAAO0svg/HhYLVj3I0nALqZH47WFPuMA9CFjnEArgSvuOlAHhCMA9DRt718KUnynz93fMaVAExWL4Nxo9QBmJSFuXHH+LJgHICtG3WMz7oKAK52t9+0P184fiLPnj0361IA6LEbDuzON924P//5s4JxYGfpZTBulDoAk/LcKHVrCgBbNxwkK5JxAGbs9hceyEpLPmv0LQAdfds3LuXonz2WE6eXZ10KwMT0NBgfJeNNMA5AR/YYB2AShlU55/oEgBl7xU0HksQ4dQA6+7aXL+XsuZaPff7RWZcCMDG9DsZlGAB0tbrH+Gmj1AHoYDCwxzgAs3fLNXuyb9dcHhSMA9DRkVuvzZ6FYf7z5x6ZdSkAE9PLYHxuHGIsS8YB6Gj3/DBJ7MEHQCfDKls9ATBzg0Hlm27cn/u/IhgHoJuFuUH+m5dcn9/77HHTe4Edo5fB+Gp331kdGQB0tCgYB2ACdIwDcKX4S4cP5v6vPGm7KAA6+/ZvWsqxx0/lvz5yYtalAExEL4PxucGobB3jAHS1uDAKxk8JxgHoYFiVFZcnAFwBXnPLoTx7diWf/erTsy4FgJ77a6+4IUnyu/d/dcaVAExGP4PxtVHqOjIA6Ga1Y/zUGcE4AFs3HFTOGS8IwBXgjluuSZLc+6UnZlwJAH13w4Hdec0th/KhB/5i1qUATEQvg/H54ajss1oyAOhIxzgAk2CUOgBXiluuXcy1excE4wBMxF9/5Q359LEn8/CTp2ZdCkBnvQzG5wY6xgGYDHuMAzAJc4JxAK4QVZU7bjmUP/rzx2ddCgA7wF+/fTRO/f/VNQ7sAP0Mxlc7xu0xDkBHRqkDMAnzw3J9AsAV4zW3HMrnjz+TJ0+dnXUpAPTcS5b25Ruu35vfFYwDO0Avg/H51T3GdWQA0NFzo9SFGQBs3dxwIBgH4IrxmhcdSpLcd8w4dQC6qap81ytvyMc+/6gHroDe62kwPu4YX3bjCYBuds0NUmWPcQC6WRgOPLgLwBXjVYdHwfi9fy4YB6C7v/HKG7O80oxTB3qvl8H46h7jZ914AqCjqsri/DCnzizPuhQAemxuUB7cBeCKcXBxPi+/YV8++Wf2GQeguztuOZSbDy3mP973lVmXAvD/s3ffcXLV9f7HX9/p23vfzaY3CCEFEnpoCkjRi4WiiIoo6rVdvbar1+ttei33Z0EEBFFRAUUFLtIRQiAhCYGQ3jZle++7s7Oz8/39MbMlIY1kd8/O7Pv5eOzjnDlzdvbDAzifOefz/X6+JyUuC+PGGDwuQ1itCkVEZBQked2aMS4iIifF43Zp4K6IiEwoy6fnsH5fi5b6EBGRk2aM4cqFRaza1URLd8jpcERETlhcFsYBPG6jVoUiIjIqknxuevpUGBcRkRPncxsVHkREZEJZPj2HntAAm6rbnQ5FREQSwNULiwlHLH/bVOt0KCIiJyxuC+Nel0sPnkREZFSkBbx0BNVKXURETpzH7SI8oIG7IiIycZw5LRuAVytaHI5EREQSwfyidGbkpfDYRrVTF5H4FbeFcY/b6MGTiIiMivSAh85gv9NhiIhIHPO6XYQ0cFdERCaQ3FQ/s/JTWVPR7HQoIiKSAIwxXL2whLX7WqhrDzodjojICYnjwriLcEQPnkRE5ORpxriIiJwsr9sQVmFcREQmmMF1xpWjRERkNFy1sAhr0axxEYlbcVsY97oM/ZoxLiIioyA9yUNHr2aMi4jIifO4XEQsDER0jyIiIhPHsunZdGudcRERGSXT81JZWJbJH1+rxFrd+4hI/Inbwnh0DT+NdhURkZOXHvDSoVbqIiJyErweA0C/7lFERGQCWT49B4BVu5ocjkRERBLFB5aWsbO+i41VGnQlIvEnjgvjhn7NxhARkVGQHvDQ1RcmorwiIiInyOuK3lqFlUtERGQCyU31c1ppBi/sbHQ6FBERSRBXLSwi4HXx4LpKp0MREXnb4rYw7nW56A9rNoaIiJy89CQv1kJnn9YZFxGRE+Nxx2aM6x5FRCRuGGMuM8bsMMbsNsZ89QjnvN8Ys9UYs8UY8/vxjnE0rJidx+sHWmnrCTkdioiIJIC0gJcrFhTx2MYaekJ6liYi8SV+C+Meo9kYIiIyKjKTfQB6UCQiIifM647eWvVHVBgXEYkHxhg3cDtwOTAfuN4YM/+Qc2YBXwPOsdaeAnx+3AMdBRfMySdi4SW1UxcRkVHygaVldPWFeWJTndOhiIi8LXFbGPe4XFq/T0RERkVuarQw3tSlwriIiJwY7+CM8QEN3hURiRNnAruttRXW2hDwAHDNIed8HLjdWtsKYK1tGOcYR8XpZZlkJnt5YYfaqYuIyOg4c1o2U3OSeWDdAadDERF5W+K2MO51G8J66CQiIqMgN9UPQFNXn8ORiIhIvBqcMR7W4F0RkXhRAoxcHLUqdmyk2cBsY8zLxpg1xpjLDvdBxphbjTHrjTHrGxsnXvHZ7TKcNyuPF3c2ElH3RRERGQXGGG5cVs66fa1sqWl3OhwRkeMWt4Vxj8tFWG0KRURkFAwWxps1Y1xERE6QZ7CVugrjIiKJxAPMAlYA1wN3G2MyDz3JWnuXtXaptXZpXl7eOId4fC6am0dTVx8bq9qcDkVERBLE+5eWkeR18+tX9jkdiojIcYvfwrjbqE2hiIiMiuyUaCv1Zs0YFxGRE+R1qZW6iEicqQbKRrwujR0bqQp41Frbb63dC+wkWiiPOxfNKcDrNjy5WWvBiojI6MhI9vKexSX89Y0aWro12URE4kPcFsa9bs0YFxGR0eHzuMhJ8VHTHnQ6FBERiVPDrdRVGBcRiRPrgFnGmGnGGB9wHfDoIef8lehscYwxuURbq1eMZ5CjJSPZy9kzcnlicx3WKleJiMjouPnsqYTCEa01LiJx45iFcWPMvcaYBmPM5iO8b4wxPzHG7DbGvGmMWTz6Yb6Vx2XoD+uLvIiIjI6y7GQqW3qcDkNEROKUxx2dMR5SK3URkbhgrQ0DnwGeArYBD1lrtxhjvmOMuTp22lNAszFmK/B34MvW2mZnIj55l59ayIGWHrbUdDgdioiIJIjZBWmcMzOH367er2WlRCQuHM+M8fuAy47y/uVE20jNAm4F7jj5sI4t4HXTFx4Yjz8lIiKjZKIOtgKYkp3MARXGRUTkBPm0xriISNyx1v7NWjvbWjvDWvufsWPfstY+Gtu31tovWmvnW2sXWGsfcDbik3Pp/AJcBrVTFxGRUfWxc6dR2x7k0TdqnA5FROSYjlkYt9auBFqOcso1wG9iNwtrgExjTNFoBXgkSV43vf0qjIuIxJn7mICDrSBaGK9u6yWsgoaIiJwAvzd6a9UXVh4REZGJKSfVz/LpOfxtU63aqYuIyKi5cE4+cwvTuOPFPUQiyi8iMrGNxhrjJUDliNdVsWNvYYy51Riz3hizvrGx8aT+aJLPTW9IhXERkXgyUQdbQbQwPhCx1LRpnXEREXn7/B43AEEN3hURkQns6oXFVDR180Zlm9OhiIhIgjDGcNuKGexu6OLprfVOhyMiclSjURg/btbau6y1S621S/Py8k7qs5J8boL9mo0hIpJgjmuw1WgOtBpUlp0MoHbqIiJyQpJ8KoyLiMjEd8VpRfg9Lv68odrpUEREJIG8a0ER5TnJ3PHCbnUlEZEJbTQK49VA2YjXpbFjYyrJ6yY0EFHLWxGRSWg0B1oNKs+JFsb3t3SPyueJiMjkEvCqMC4iIhNfesDLO08p5NGNNfSFlbNERGR0eNwuPnH+DDZWtbNyV5PT4YiIHNFoFMYfBW4yUcuBdmtt7Sh87lElxR48aZ1xEZGE4shgK4DC9ACpfg/bazvH48+JiEiCGbo/0XJPIiIywV27pJT23n6e39bgdCgiIgnBGHOZMWaHMWa3MearRznvWmOMNcYsHc/4xsu1S0ooyUziB0/t0KxxEZmwjlkYN8b8AVgNzDHGVBljPmaM+aQx5pOxU/4GVAC7gbuBT41ZtCMMtipUYVxEJKE4MtgKwOUynFqSzpvV7ePx50REJMEEvNFbq2BYHa1ERGRiO3dmLgXpfv70WpXToYiIxD1jjBu4HbgcmA9cb4yZf5jz0oDPAa+Ob4Tjx+9x84VLZ7Opup0nNtc5HY6IyGF5jnWCtfb6Y7xvgU+PWkTHSTMyRETiT2yw1Qog1xhTBfwr4AWw1v6C6GCrK4gOtuoBPjKe8Z1Wmsl9r+wjFI7g84xGUxUREZksAh7dn4iISHxwuwzXLi7lFy/uobqtl5LMJKdDEhGJZ2cCu621FQDGmAeAa4Cth5z378D3gC+Pb3jj6z2LSrjzxT384OkdvGN+AR63nq+JyMQSt1clzRgXEYk/1trrrbVF1lqvtbbUWnuPtfYXsaI4NurT1toZ1toF1tr14xnfgpIMQuEIO+vVTl1ERN4el8vg97gIar1WERGJAzcsmwLA/Wv2OxyJiEjcKwEqR7yuih0bYoxZDJRZax8fz8Cc4HYZvvTOOVQ0dvPwBnUmEZGJJ/4L45qRISIio+T0skwAXj/Q6nAkIiISjwJeN0Hdn4iISBwozUrm0vkFPLD2AEFNOhERGTPGGBfwI+CfjuPcW40x640x6xsbG8c+uDHyjvkFnF6WyY+e2Ul3X9jpcEREDhK/hXGvZoyLiMjoKs1KoiQziVW7m5wORURE4lCS102wX2uMi4hIfPjwWVNp7ennsY01TociIhLPqoGyEa9LY8cGpQGnAi8YY/YBy4FHjTFLD/0ga+1d1tql1tqleXl5Yxjy2DLG8M0r51Pf0cdPn9/tdDgiIgeJ/8K4ZmSIiMgoMcZw7sxcXtnTTHhAhQ0REXl7Al61UhcRkfhx1owcZuWnct8r+7DWOh2OiEi8WgfMMsZMM8b4gOuARwfftNa2W2tzrbVTrbVTgTXA1eO9fOB4W1KexbWLS7lnVQUVjV1OhyMiMiRuC+PJsVbqPSqMi4jIKDp3Vi6dwTAbq9qdDkVEROJMwOvWwF0REYkbxhhuOW8aW2o6WLlLXbNERE6EtTYMfAZ4CtgGPGSt3WKM+Y4x5mpno3PWVy6fg9/j5jv/t1UDsERkwojbwnhawAtAR7Df4UhERCSRnDcrF4/L8MzWeqdDERGROJPkc2upJxERiSvvWVRKUUaA29XqVkTkhFlr/2atnW2tnWGt/c/YsW9Zax89zLkrEn22+KD8tACfv2QWL+xo5Kktes4mIhND3BbGM5OjhfG2HhXGRURk9GQm+zh7Zi5PbK7VaFYREXlb0gJeOoJhp8MQERE5bj6Pi1vPn87afS2s3dvidDgiIpJgPnz2VOYVpfPNRzbTrlqOiEwAcVsYD3jdJHndtHaHnA5FREQSzBWnFrK/uYettR1OhyIiInEkLeChUx2tREQkzlx3xhRyUnz89PldTociIiIJxut28f33nkZLd4h/f3yr0+GIiMRvYRwgK9lLW68ePImIyOh6xymFuF2Gv22qdToUERGJI+kBD52aMS4iInEmyefmkxfM4KVdTbyyW2uNi4jI6Dq1JIPbLpjBn16r4u87GpwOR0QmubgujGcm+2jr0YxxEREZXdkpPs6ekcNfX68hElE7dREROT7pAS8dGrgrIiJx6ENnlVOSmcR/P7Fd90AiIjLq/vHimczKT+VrD29SF2ARcVRcF8azUry0al0KEREZA+9bWkZ1Wy+rK5qdDkVERE6AMeZeY0yDMWbzeP3NtICHvnCEvvDAeP1JERGRURHwuvnipbPZVN3O/6lzloiIjDK/x82P3n86zd19fOXhN7FWg7BExBlxXRjPTPLRqhnjIiIyBt4xv4D0gIeH1lc6HYqIiJyY+4DLxvMPpgW8AGqnLiIicendi0qYW5jG95/aTrBfg7xERGR0LSjN4CuXzeXprfXc/+oBp8MRkUkqrgvjeWl+6tuDGl0kIiKjLuB18+5FJTyxuY52dScREYk71tqVQMt4/s30JA+gwriIiMQnt8vwL++aT2VLL3e+WOF0OCIikoA+es40Lpidx7//31a21XY4HY6ITEJxXRgvzUqiOzRAu9bxExGRMfD+pWWEwhEe2VjtdCgiIjIGjDG3GmPWG2PWNzY2nvTnZSRFZ4yrq5WIiMSrc2flctXCYm5/YTf7mrqdDkdERBKMy2X4wfsWkpnk5RO/fY023TuJyDiL68J4SWYSAFWtvQ5HIiIiieiU4nQWlGTwm9X71Z1ERCQBWWvvstYutdYuzcvLO+nPy031A9DU2XfSnyUiIuKUb75rHn63i28+sln3QSIiMury0vz84kNLqGsP8o9/eJ3wQMTpkERkEonvwnhWtDBe3abCuIiIjD5jDB8+eyq7G7p4eXez0+GIiMgEN1gYb+7WrAcREYlf+ekBvvTOOby0q4k/b1D3LBERGX2Lp2Tx7+8+hZd2NfG9J7c7HY6ITCJxXRifkp0MwP5mtXYSEZGxceVpReSk+LjvlX1OhyIiIhNcTqoP0IxxERGJfx9cXs6ZU7P59qNbNCFFRETGxAfOmMJNZ5Vz90t7eXDdAafDEZFJIq4L45nJPgrS/Wyv7XQ6FBERSVABr5sblk3hue31HGjucTocERE5TsaYPwCrgTnGmCpjzMfG+m/6PW7SAx6aulQYFxGR+OZ2GX74/oVErOVLD20kElFLdRERGX3fvHI+F8zO4+t/2cxz2+qdDkdEJoG4LowDzCtKZ2tth9NhiIhIArtxWTluY/jN6n1OhyIiIsfJWnu9tbbIWuu11pZaa+8Zj7+bnx6griM4Hn9KRERkTJVlJ/OvV53C6opm7nqpwulwREQkAXndLn5+42JOKU7n07/fwIYDrU6HJCIJLu4L46eVZLCroYuOYL/ToYiISIIqzAhw+YIiHlxXSXuv8o2IiBxZaVYSVa1qOSsiIonhfUtLedeCIv7nye2s3tPsdDgiIpKAUvwe7r35DArSA3zsvnVsr9NESBEZO3FfGD9nZi4DEasv5yIiMqY+cf50OvvC3L9mv9OhiIjIBKbCuIiIJBJjDN9772lMy03hH/+wgbp2dUUREZHRl5vq57cfXYbf4+aGu19lR52WzxWRsRH3hfFFU7JI9rlZubPR6VBERCSBnVqSwQWz87h31V56QwNOhyMiIhNUWVYy7b396jAiIiIJI9Xv4c4PLaEnNMBtv3uNYL/uh0REZPRNyUnmD7cux+s23HD3GnbWqzguIqMv7gvjPo+LC2bn8eTmOvoHIk6HIyIiCexTK2bQ3B3iofWVTociIiIT1PS8VAB2N+ghjoiIJI6Z+Wn88H0Lef1AG//00EYiEet0SCIikoCm5abwh48vx+2KFse31qituoiMrrgvjEN0vaPm7hDPbWtwOhQREUlgZ07LZkl5FnetrNBgLBEROaxTitMB2KIHOCIikmAuX1DEN66Yx+Obavnuk9udDkdERBLU9LzU2MxxFx+4czWvVmgZXREZPQlRGD9/Vh4lmUncuXIP1mrEqoiIjA1jDJ9aMYPqtl4eeaPG6XBERGQCKsoIkJXs1cwGERFJSLecN40Pn1XOXSsr+OVLFU6HIyIiCWpGXioP33Y2+el+PnTvWp7aUud0SCKSIBKiMO5xu7htxQxeP9DGyl1NTocjIiIJ7KK5+cwvSucnz+0iFNascREROZgxhvnF6ZoxLiISB4wxlxljdhhjdhtjvnqU8641xlhjzNLxjG8iMsbwratO4fJTC/mPx7fx29X7nA5JREQSVHFmEn/65NnML0rntvtf41cv79XESBE5aQlRGIdoO/WSzCT+8/Gtam8rIiJjxhjDl985hwMtPTyotcZFROQwTinOYEd9pwZQiYhMYMYYN3A7cDkwH7jeGDP/MOelAZ8DXh3fCCcut8vw4+sWccm8fL75yBZ+/+oBp0MSEZEElZXi4/cfX8ZFcwv4t8e28rU/b9J9loiclIQpjPs9br599SnsrO/i3lV7nQ5HREQS2Io5eSwtz+Knz+2iNzTgdDgiIjLBLCnPIhSOsOFAq9OhiIjIkZ0J7LbWVlhrQ8ADwDWHOe/fge8BwfEMbqLzeVzcfuNiLpyTx9f/solfvaxncSIiMjaST5V+1QAAIABJREFUfR7u+tASPn3hDB5YV8mNv1xDU1ef02GJSJxKmMI4wKXzC7hkXgH/79ld7G/udjocERFJUMYY/vmyuTR09vHr1fucDkdERCaYs2fk4HEZXtzZ6HQoIiJyZCXAyBZQVbFjQ4wxi4Eya+3j4xlYvPB73NzxwSW8Y350Ft8Pn96hFrciIjImXC7Dl985lx9fdzpvVrVz5U9WsXZvi9NhiUgcSqjCOMB3rjkFr9vw2QfeUEt1EREZM2dOy+aC2Xnc8cIe2nv7nQ5HREQmkLSAlyXlWby4Q4VxEZF4ZYxxAT8C/uk4zr3VGLPeGLO+sXFyXfsDXjc/v3Ex151Rxk+f383X/7JJz+NERGTMXHN6CQ/fdjZ+r4vr717D7X/fTSSiQVkicvwSrjBenJnEd689jY2VbfzomZ1OhyMiIgnsy++cQ0ewn589v8vpUEREZIK5aG4+W2s71MlKRGTiqgbKRrwujR0blAacCrxgjNkHLAceNcYsPfSDrLV3WWuXWmuX5uXljWHIE5PH7eK//2EBn7lwJn9YW8mH711La3fI6bBERCRBnVqSwf/947lcfmoh339qBx/+1VoaOrXiiYgcn4QrjANcsaCI688s444X9vDk5lqnwxERkQR1akkG719Sxq9e3sfuhk6nwxERkQnkqoXFADzyRo3DkYiIyBGsA2YZY6YZY3zAdcCjg29aa9uttbnW2qnW2qnAGuBqa+16Z8Kd2IwxfOmdc/jh+xayfl8r19z+MjvqdI8kIiJjIy3g5afXL+K/3rOAtXtbeMf/ruTRjTVa0kNEjikhC+MA/3rVKZxelskXHtzI1poOp8MREZEE9eXL5pDkc/Nvj23Vl28RERlSnJnE8unZ/HlDlVr7iYhMQNbaMPAZ4ClgG/CQtXaLMeY7xpirnY0ufl27pJQHPrGc3v4B3vPzl/nr69XH/iUREZETYIzhhmVTePyz51Gek8Jn//A6n/79Bpq7+pwOTUQmsIQtjAe8bu760BIykrx8/DfraezUxVBEREZfbqqfL146m5d2NfHUlnqnwxERkQnk+jOnsK+5hxd3Ta71ZkVE4oW19m/W2tnW2hnW2v+MHfuWtfbRw5y7QrPFj8/iKVk89plzOaU4nc8/+AZf+uNGuvvCToclIiIJamZ+Kg9/8iz++bI5PLu1gXf870r+8nqVJrCIyGElbGEcID89wN03LaWlO8RN966lvaff6ZBERCQBfWh5OXMK0viPx7fSE9IDHxERibr81CLy0/zcu2qv06GIiIiMq8KMAH/4+HI+e/EsHt5QxVU/W8WmqnanwxIRkQTlcbv41IqZPPaP51KalcQXHtzI9Xev0dKHIvIWCV0YB1hQmsGdH1rC7oZOPnLfWhUsRERk1HncLr5zzSlUtfbyw6d3Oh2OiIhMED6Pi5vOKuelXU3srNcDGRERmVw8bhdfvHQ2v7tlGd19Yd7985f5nye3E+wfcDo0ERFJUHMK0/jzp87hP99zKltrOrjs/73E957crrqQiAxJ+MI4wPmz8/jJdYt4o7KNj/9mPb0hfQEXEZHRtWx6Dh9cPoV7X97LhgOtTocjIiITxA3Lyknxufnxc7ucDkVERMQRZ8/I5ekvXMC1i0v4+Qt7uPKnq3TPJCIiY8btMty4rJznv7SCdy8q4Y4X9rDi+y/w4LoDDETUXl1kspsUhXGAyxcU8T/vXcgre5q56d5X6QiqrbqIyHgzxlxmjNlhjNltjPnqYd6/2RjTaIx5I/ZzixNxnqivXj6P4owk/vlPb2oWhIiIAJCd4uMj50zj8Tdr2Vbb4XQ4IiIijshI8vI/713Ibz56Jr2hAa694xW+/pdNtHSHnA5NREQSVG6qnx+8byEP33YWJVlJfOXhTVzx45d4YUeD1h8XmcQmTWEc4L1LSvnJdYt4/UAbN979qr58i4iMI2OMG7gduByYD1xvjJl/mFMftNaeHvv55bgGeZJS/R7+6x8WsLuhi/95cofT4YiIyATx8fOmkxbw8L/PaLkNERGZ3M6fncdTXzifj5w9jQfXVXLhD17gN6v3ER6IOB2aiIgkqCXl2fz5trP5+Y2LCYYHuPlX6/jgPa/y2n51LxGZjCZVYRzgqoXF3HXTEnbWd/LeX7zCvqZup0MSEZkszgR2W2srrLUh4AHgGodjGnUXzM7j5rOncu/Le3l+e73T4YiIyASQkezllnOn8/TWej18ERGRSS/V7+FbV83nyc+dx6kl6XzrkS1c+dNVmsEnIiJjxhjDFQuKeOYLF/CtK+ezvbaTa+94hZvuXat7NJFJ5rgK44nW+vaiuQX89mPLaO0Occ3tL/PKnianQxIRmQxKgMoRr6tixw51rTHmTWPMn4wxZYf7IGPMrcaY9caY9Y2NjWMR60n56uVzmVeUzpf++CZ17UGnwxERkQnglvOmUZge4NuPbtG6diIiIsCsgjTu/9gy7rhxMd2hMDf/ah0fuGsNr+1vcTo0ERFJUD6Pi4+eO42XvnIhX7t8Lpur20cUyJV/RCaDYxbGE7X17ZnTsnnk0+eSn+bnpnvW8vtXD2hUqoiI8x4DplprTwOeAX59uJOstXdZa5daa5fm5eWNa4DHI+B187MbFhHsH+DzD76utoAiIkKK38PXrpjLpup2HlxXeexfEBERmQSMMVy+oIjnvriC71xzChWN3Vx7x2pu+fU6ttS0Ox2eiIgkqGSfh09cMIOX/nlkgXw1197xCk9tqdNgZpEEdjwzxhO29e2UnGQe/tTZnDMzl6//ZRP/9NBGuvvCToclIpKoqoGRM8BLY8eGWGubrbV9sZe/BJaMU2yjbkZeKt+55lTWVLTw309sdzocERGZAK5eWMyZ07L5/lPbaesJOR2OiIjIhOHzuLjprKms/OcVfPmdc3h1bwvv+skqPvKrtazbpxl8IiIyNlL80QL5qq9cyLevmk99R5BP/PY1LvnRi9y/Zj/B/gGnQxSRUXY8hfGEbn2bHvBy781n8IVLZvPXN6q56mer2Fbb4XRYIiKJaB0wyxgzzRjjA64DHh15gjGmaMTLq4Ft4xjfqHvvklI+cs5U7lm1lwfXHXA6HBERcZgxhn+7+hTae/v5r7/FdYoTEREZE8k+D5++cCarvnIRX3rHbDZWtfO+X6zmfb94hb9v1xrkIiIyNpJ9Hm4+ZxovfGkFP7thEWkBD//y182c/d3n+e4T26ls6XE6RBEZJce1xvhxiOvWt26X4XOXzOJ3tyynKxjmmttf5p5Ve4moXYaIyKix1oaBzwBPES14P2St3WKM+Y4x5urYaZ81xmwxxmwEPgvc7Ey0o+cbV8zjvFm5/MtfN/NqRbPT4YiIiMPmFaXziQtm8ND6Kv6+vcHpcERERCakjCQvn7loFi9/5SL+9ar5VLf28pH71vGO/13Jb9fsV8dHEREZEx63iytPK+aRT5/Dg7cuZ2l5Fnet3MP53/87N/9qLc9urVebdZE4Z4410tIYcxbwbWvtO2OvvwZgrf3vI5zvBlqstRlH+9ylS5fa9evXn1DQY6mpq4+v/OlNntvewNLyLP7nvacxPS/V6bBERA5ijHnNWrvU6TgmgomaT0Zq7+3nPbe/TEtPiAdvPYs5hWlOhyQiAiifjDSe+aQvPMBVP11Fe28/T3/+AjKSvePyd0VExoryybB4uD+JR6FwhEc31nDfK3vZXN1BWsDD+5aUcdNZ5UzNTXE6PBEZJconw5RPJo7a9l7+sLaSB9YeoKGzj5LMJK47o4xrl5RSnJnkdHgichhHyyfHM2N8UrW+zU3188sPL+VH71/IzvpOLv/xS9y9soLwQMTp0EREJE5lJHm57yNn4nO7+NA9r3KgWe2XREQmM7/HzQ/fdzpNXSG++chmtYUVERE5Bp/HxXuXlPLYZ87l4dvO5sI5+fxm9T5W/OAFbrp3LY+/WUtfWOvAiojI6CvKSOKLl87m5a9exB03LmZqbjI/fGYn53zveT74y1f5y+tV9ITUyUQkXhyzMD4ZW98aY/iHxaU8+8ULOG9WHv/5t2286yerWL1HLXBFROTETMlJ5v5blhEaiHDjPWuo7wg6HZKIiDhoQWkGn794Fo9urOF3rx5wOhwREZG4YIxhSXkWP7l+Ea989SI+d/EsdtV38unfb2DZfz3Htx/dwtaaDqfDFBGRBOR1u7h8QRG/u2U5L355BZ+9aBb7W7r5woMbOeM/nuXLf9zImopmLdErMsEds5X6WImXViDWWp7eWs93HttKdVsvVy0s5utXzKUoQy0yRMQ5ai01LF7yyaCNlW3ccPca8tL83H/LMkqzkp0OSUQmMeWTYU7kk0jE8tFfr+OV3c089MmzOL0sc1z/vojIaFE+GRZv9yeJYCBieXl3Ew+tr+TpLfWEBiKcWpLOexaVcuVpRRSkB5wOUUSOk/LJMOWT+BCJWNbta+HhDVU8/mYt3aEBCtMDXLGgiHedVsSiskxcLuN0mCKTztHyiQrjxynYP8AdL+zhFy/uwRj4yDnT+OT5M7QeoIg4QjcKw+ItnwC8tr+Vj/xqLal+D7+9ZRkz8lKdDklEJinlk2FO5ZPW7hBX/nQV1lr++plzyE/Tw3sRiT/KJ8Pi8f4kkbT1hHjkjRoeWl/JlpoOjIEzp2Zz1cJiLj+1kJxUv9MhishRKJ8MUz6JP72hAZ7eWsfjb9byws5GQuEIxRnDRfLTyzIxRkVykfGgwvgoqmzp4QdP7+CRN2pID3i4bcVMbj57Kkk+t9OhicgkohuFYfGaT7bWdHDTva9iLfzqI2dwWqlmCYrI+FM+GeZkPtlU1c7771zNjPwUHrj1LFL9HkfiEBE5Uconw+L1/iQR7Wns4rGNNTy2sYY9jd24XYZzZuZy5WlFXDKvgOwUn9MhisghlE+GKZ/Et85gP89uq+fxN2tZubOJ0ECEkswkLp1fwCXzCjhzWjY+zzFXOhaRE6TC+BjYWtPBD57ewfPbG8hL8/Px86Zxw7JyPcQSkXGhG4Vh8ZxPKhq7+NA9a2nu7uMH71vIlacVOx2SiEwyyifDnM4nz2+v5+O/eY1zZuZyz4eX4nXrIYmIxA/lk2FO5xN5K2st22o7eezNaJG8qrUXl4Gl5dlcOr+AS+cXMDU3xekwRQTlk5GUTxJHe28/z26t54nNtaza3USwP0Ka38MFc/K4ZF4BK+bkkZmswVoio0mF8TG0dm8L/+/Znbyyp5mMJC8fPqucm8+ZplGnIjKmdKMwLN7zSVNXH5/87Wus39/KZy+execvnqW1h0Rk3CifDJsI+eSBtQf46p83ccWCQn583SIVx0UkbiifDJsI+USOzFrLlpoOnt5az9Nb6the1wnA7IJULp1fwMXzClhYmolb92QijlA+GaZ8kph6QwO8vLuJZ7fV8+y2Bpq6+nC7DGdMzWLFnHzOm5XLvMJ0PRsUOUkqjI+D1w+08osX9/DUlnqSvG7eu6SUm84qZ1ZBmtOhiUgC0o3CsETIJ33hAb7xl8386bUqzpuVyw/fv1BrzIrIuFA+GTZR8skvX6rgPx7fxqXzC/jZDYvwe7Rkk4hMfMonwyZKPpHjU9nSwzNb63lmaz1r97UwELFkJHk5d2YuF8zO47zZuRRlJDkdpsikoXwyTPkk8UUilo1VbTy7rZ7ntjUMDdbKTfVz3qxczpuVy7mzcvWMUOQEqDA+jnY3dHLnixU8srGGUDjCWdNz+PDZ5VwyrwCPZnyIyCjRjcKwRMkn1loeWFfJtx/dQlrAww/ffzoXzM5zOiwRSXDKJ8MmUj75zep9fOuRLZw3K5fbb1xMesDrdEgiIkelfDJsIuUTeXvaekK8tKuJlTsbWbmrkfqOPiA6m/z8WXmcPzuPM6Zmk+TToDWRsaJ8Mkz5ZPKp7wgO5aFVu5to6Q4BMK8onfNn5XLWjByWTs3Wcr4ix0GFcQe0dId4cF0l96/ZT3VbL0UZAa5dXMo/LC5hel6q0+GJSJzTjcKwRMsnO+s7+czvN7Czvov3LSnlG++ap3WGRGTMKJ8Mm2j55KH1lXz9z5uYnpfCPR8+g7LsZKdDEhE5IuWTYRMtn8iJsdays76LF3c2sHJnE2v3tRAKR/C6DQtLM1k+PYfl03NYXJ5Jsk8FCpHRonwyTPlkcotELFtrO1i5q5GVOxt5bX8r/QMWt8twanE6y6fnsGx6NkunZmsgtchhqDDuoIGI5fntDdy/Zj8v7WokYmHxlEyuXVLKlacVk5Gki5aIvH26URiWiPkk2D/AT57bxZ0rK8hK9vKvV53ClacVYYzWFxKR0aV8Mmwi5pNXdjfxyftfw+N28aP3L2TFnHynQxIROSzlk2ETMZ/IyesNDfDq3mbWVLSwpqKZTdXtDETsUKF82fTsaKF8ShYpmskncsKUT4Ypn8hIPaEwG/a3xXJRM29UttE/YHEZOKU4g+XTszljajaLy7PITfU7Ha6I41QYnyDqO4L89fVqHt5Qxc76LnweF+fPyuOKBYVcPK9ARXIROW66URiWyPlka00HX3n4TTZVt3Pm1Gy+8a55LCzLdDosEUkgyifDJmo+2dPYxafu38CO+k4+ccF0/unSOfg8WqJJRCYW5ZNhEzWfyOjq6guzfl/LWwrlLgNzCtNZUp7J4ilZLJ6SRXlOsgY5ixwn5ZNhyidyNL2hAV4/0MqavdE89MaBNkIDEQCmZCezeEomi8ujeWhuYZqW+ZVJR4XxCcZay6bqdv7yejVPbq6jtj2I1204d2Yuly8o4pJ5BWSnqG2uiByZbhSGJXo+CQ9EeHB9Jf/7zE6aukJcc3oxn7t4lpblEJFRoXwybCLnk2D/AP/+f1v53asHmFOQxn9fu4DFU7KcDktEZIjyybCJnE9k7HT1hXltfyuv7Wthw4E23qhso6svDEBOio9FIwoUC0oyNKtc5AiUT4Ypn8jbEewfYFN1Oxv2t7LhQCsbDrTR2NkHQJLXzWmlGUN56LTSDArSAw5HLDK2VBifwCIRy8aqNp7YXMffNtVS1dqLMXB6WSYrZudz4dw8Ti3OwOXSyFIRGaYbhWGTJZ90Bvu588UK7n6pgv6BCFcsKOLTF85kXlG606GJSBxTPhkWD/nkuW31/MtfN1PXEeSDy8r5p3fMJjNZA2pFxHnKJ8PiIZ/I2BuIWHY1dPLa/lY27G/j9QOtVDR1A2AMTM9NYUFJBqeWZHBaaSanFKerWC6C8slIyidyMqy1VLX2suFAK68faGPDgVa21nQQjkTrgXlp/qE8tKAkQ8VySTgqjMcJay2bqzt4bns9f9/RyJtVbVgLuak+zp+Vx7mzclk+PYfizCSnQxURh+lGYdhkyyeNnX3cs2ov96/ZT1dfmIvm5nPTWeWcPytPg6hE5G1TPhkWL/mkqy/MD5/ewa9f2Ueq38OnLpzJzWdPJeB1Ox2aiExiyifD4iWfyPhr7Q7xemUrm6o62FTdxqbqduo7orP5Di2WLyjJYG5hOhnJWnZRJpd4zSfGmMuAHwNu4JfW2u8e8v4XgVuAMNAIfNRau/9on6l8IqOtNzTAlpp2NlW3s6kqut3T2EWsVv6WYvm8ojRKMpO0HIjEJRXG41RzVx8rdzXywo5GVu5spLWnH4DynGSWT8th+Yxslk/PoShDhXKRySZebxTGwmTNJ+09/dz3yj5+u2YfTV0hynOSuXHZFK5dXEpOqt/p8EQkTiifDIu3fLKjrpPvPbmd57c3UJDu55Zzp3P9simkaraZiDhA+WRYvOUTcVZDZ5DN1e28WdXO5ur2g4rlAMUZAeYUpjG3KJ25hWnMLUxnel4KXq0VKwkqHvOJMcYN7AQuBaqAdcD11tqtI865EHjVWttjjLkNWGGt/cDRPlf5RMZDTyjM1pqOg/LQyGJ5mt/D3KJo/hnczilM032nTHgqjCeASMSyva6T1RXNrKlo5tWKZjqC0bWKyrKTOL0si0VlmZw+Jdp+ye/RjBGRRBaPNwpjZbLnk1A4wpNb6rh/9X7W7mvB7TKcNyuXd59ewqXzC9SOT0SOSvlkWLzmk9V7mvnJc7tYXdFMWsDDh5aXc8OyKZRmJTsdmohMIsonw+I1n8jE0dARZEtNB9vrOtle18GOuk52N3QNtb/1ug0z8lKZVxQtTswpSGNGXiolWUm41UVM4lw85hNjzFnAt62174y9/hqAtfa/j3D+IuBn1tpzjva5yifilJ5QmG21HWyrjeah7bWd7KjrpLMvPHROWXYScwvTmVeYxpzCdGYVpDI1JwWfRwO3ZGI4Wj7R0/I44XIZ5henM784nY+dO42BiGV7XQer9zSz4UAr6/e18NjGGgB8bhfzitNZVJbJwrIMTinOYHpuCh6NJhURSTg+j4urFxZz9cJidtZ38ucN1Tz6RjWff/ANAl4XF88t4OJ5+Vw4J5+sFK1DKyKSaM6akcNZM3LYWNnGnSv3cMeL0Z/zZuVx3RllXDKvQA8nRERE4kh+eoD89AAXzs0fOhYKR6ho6mJ7bSfbYsXy1Xua+cvr1UPn+D0upuWmMDM/lRl5qUPb6XkpWnJFZGyVAJUjXlcBy45y/seAJw73hjHmVuBWgClTpoxWfCJvS7LPw5LybJaUZw8ds9ZS3dbL9sFieV0n2+s6eW5b/dDscrfLUJ6dzPRYDhr8mZGXQlpAS4PIxKHCeJxyuwynFEeL3oPq2oO8UdnK65VtvHGgjQfXVXLfK/uAaOFkTkEa84vShwrscwvTdEESEUkgswvS+Orlc/nnd85h/f5W/vpGNc9srefxTbW4DCwpz+LieQWcOzOX+UXpWpNcRCSBLCzL5Oc3LqGqtYc/rq/ij+sr+dTvNpCZ7OXSeQVcsaCIs2fmqLOUiIhIHPJ5XNE2toXpvJuSoeNtPSF2N3Sxu6GLPY3R7ZtV7Ty+qZbBJqHGQGlWEjPzooXy8twUpuYkMzUnhaKMgCbSiIwjY8wHgaXABYd731p7F3AXRGeMj2NoIkdljKE0K5nSrGQumV8wdDzYP3BQDhr8eXFnA/0Dw/8JF6T7o4XyvFRm5Ednl0/LTaE4U91OZPypMJ5ACjMCXJZRxGWnFgEQHohQ0dTN1poOttZ2sLWmg2e21fPg+uEBbMUZAWaMGL0zeGHKSfFhjC5IIiLxyOUynDktmzOnZfMf15zKpup2nttWz7PbGvjuE9sByEjysmxaNmfNyGH59BzmFKSpUC4ikgBKs5L5wqWz+ezFs1i5q5FH36jhyc11/PG1KtL8Hi6al88Fs/M4d2Yu+ekBp8MVERGRk5CZ7GPp1GyWTs0+6Hiwf4C9Td0HFSv2NHbzyp5m+sKRofO87mihozxWKC/PSY79pFCWlayuMyLHpxooG/G6NHbsIMaYS4BvABdYa/vGKTaRMRXwujm1JINTSzIOOt4/EOFAS89Bg7f2NHTxp9eq6A4NDJ3ndRvKsqM5aGpOClNzozloWk4KxZkavCVjQ4XxBOZxu5hdkMbsgjTevSg6mtRaS0Nn31CxfFd9J7sbu3hgbSW9/cMXpMxkLzNj7ZbKc1Ioy06mPDv65TgjyauiuYhInHC5DAvLMllYlskX3zGHuvYgqyuaWL2nmdUVzTy9tR6AVL+H00ozWFiWyemxnwIVTERE4pbbZbhwTnQpjb7wAK/sbuaJzbU8u62BR96ILsE0pyCNc2flcvaMHBZNySJbS26IiIgkhIDXzbyidOYVpR90PBKx1HcG2d/cw/7mbvbFtvube1i/r5WuEevHugwUZyYxJTuZkswkSrOSKclKojQriZLMJM02Fxm2DphljJlGtCB+HXDDyBNi64rfCVxmrW0Y/xBFxpfX7WJGrFPJO08ZPm6tpb6jj33N3exv7mZv03A+WlPRTM+hRfOs4QFbpVlJlGUnU5oVzUkZSeqGLCdGhfFJxhhDQXqAgkPWKopELLUdwYPaXexp6OLvOxpp7Kw66DPSAh7Kc5KZkp3MlOyU6BfkrCSKMwIUZSaR6td/ViIiE1VhRoD3LCrlPYtKAahq7eHVihZer2xlY2U7d6+sIBxbHKgwPcD84nTmFKYxtzCNuYXpTM9LwauHHyIiccXvcXPh3HwunJtPJGLZWtvBqt1NrNrVxG/X7OeeVXsBmJabwqIpmSyeksXpZZnMzE/VmqQiktCMMZcBPwbcwC+ttd895P0vArcAYaAR+Ki1dv+4ByoySlwuQ1FGEkUZSSyfnnPQe9ZamrtDQ4Xyfc097Gvqpqq1hxd3NtLQefAEV7fLUJgeiBbLM2MF81ixoiQzicKMgL5HyKRgrQ0bYz4DPEU0n9xrrd1ijPkOsN5a+yjwfSAV+GNswtkBa+3VjgUt4hBjDIUZAQozAofNQ42dfUP5Z18sH+1t6mbt3paDZpoDpAc8sfbu0dxTlp009LosO1l1KjkiY60zS1UsXbrUrl+/3pG/LW9PTyhMZUsv+5u7OdDSw4GWHvY3R7dVrT0HrRUB0cJ5cUYSxZnRQnlxRiD6pTszus1P85Oii5LISTHGvGatXep0HBOB8snoCvYPsKWmg42VbWysamN7bSd7GruGiuVet2FGXiqzC9KYmpvC9NzomkBTc1M0UlMkDimfDJus+STYP8CbVe28tr+VDQdaef1AK01dISD6wHtGXgrziqJrms4rig6SKkj3q4OUiBwkHvOJMcYN7AQuBaqIzvi73lq7dcQ5FwKvWmt7jDG3ASustR842udO1nwiiS/YP0Bte5Cq1h6qW3upau2luq03tt9DXUeQyCGPmbNTfBSkByiKFUEK04e3g8fSArqPlGHxmE/GivKJyDBrLe29/VS19lLZ0kNVLPdUDm5beg/qiAzRrsilWUmxAWGBEdvofkGGH79HA7gS1dHyiaqTckzJPg9zCtOYU5j2lvcGIpa6jiA1bb3UtPVS2x6ktq2X6rYgte29bKxqp6U79JbfS/G5yUvzk58WIC/Nf9BP/oj9nBQ/bq15KyIybgJeN0vKs1hSnjV0LBSOUNHAt4VtAAAeu0lEQVTUxfbaTrbXdbKjroPXK1t57M0aRo6vy0nxDRXJSzKThmYOFGdGB0fpy6aIyMQT8Lo5c1o2Z06Lrk1qraWypZc3q9vYVtvB9tpO1u1tGWq/DpDsczM1JzowaugnL4Xy7GSyU3wqmotIvDgT2G2trQAwxjwAXAMMFcattX8fcf4a4IPjGqHIBBLwuofy/uH0D0Soaw8OFSvq2oPUdgSpbw9S2x7kjcq2Iz4jLBwsUsQK5vnpfvJS/bFt9Nlhkk/3kyIik5UxhsxkH5nJvresZw7R+9iW7lAsBw0WzXuGCumvVjTTEQy/5fdyU30UZUS7nBRnBCiMTfgsTA9QnJlEfrqK54lIhXE5KW6XiRY/MpOOeM7giNLBwnlTVx8NHX00dvXR0BFkW10HK3f10XmYC5PLQFayj6wUH9nJPrJSvGSn+MlO8ZKV7CM7Zfhn8HWyz62HcSIio8jncTG3MDpbcKS+8ACVLT1UNEbbG+1t6qaisZtVu5qo7wweVDQ3BvJS/ZSMWI8uPy32wCM2UCo/3U+a36NruIiIg4wxTMlJZkpOMleeVjx0vK0nxPa6TnbVd7K3qYe9TV1sqWnnyS11DIyYHhbwuiiO3R+UxAZHDb4uyog+2Fb3KBGZIEqAyhGvq4BlRzn/Y8ATh3vDGHMrcCvAlClTRis+kbjidbsoy06mLDsZyDnsOcH+ARo6+qjriE6oqWsPUtcRHNq+sqeJhs6+g75bDEr1e8hP85M7OKEm9a0TbDTJRkRkcjLGkJPqJyfVz8KyzMOe090XprY9mnNqYjmotr2XmrYgB2JrnB+uRpWZ7CV/8Nllmp+89OH9/DQ/BenRZ5rJPt3nxgv9m5Ixd6wRpYN6QwPRonlnH42dQRo7+2js7KO5O0RrT4iW7hD7mnp4bX8brT2hw35JBvB7XGSn+MhI8pKe5I1uA9Ft9JhnxP6I4wEvAa9LBRkRkePk97iZmZ/GzPy3dhQJhWOzBdp6qGkLUt3aS3VbD9VtvWyp6eCZrfX0hSNv+b2A1zX85TI2SyDrkAFQg9vMZK/WrBMRGSeZyT6WT895yzpw/QMRKlt6htZ/q2mLtVVtC7JtWwNNXX1v+azkWPeowQfahz7Uzkr2Df2kBTy49HBbRBxmjPkgsBS44HDvW2vvAu6CaOvbcQxNJK4EvO6hAXhHMhCJzvpr7OyjYfD5YFff0HPCxs4+ttV0sLKzj86+w0+yyU7xkzM4mSbVN7Qf3fqj+6nD95cqpIuIJL4Uv4eZ+anMzE894jldfWHq2gc7Iwep7wjSEMtH9R197G3qprGzj9DAW59pDg7gykvzk58+XDjPS4sW7HNSfOSmRnOQz+May39UOQYVxmXCSPK5R4wsPTprLR3BMC3d0YJ5a3eIlp7h/ebuEO29/XT09lPZ0kNHbz8dwTBdh/nCPJLP7SJ9RPE81e8Z+knxe0gLHH4/NfZ6cN/vUYFdRCY3n8d11Aceg9fxxs4gDR19Q18yR+5vr+tkVWfTYVsdDUrxuQ8qnGcmRwc6pSd5SAsM76cHvKQFPNFrfOyYWiGJiJw8r9vF9LxUpucd/uFCsH+AuvYg1W3REfmHPtjeWd/Jy7uPfK13uwwZSV4yk72xYrmXzIO20eJ5asBDeiB67R/6bu5TUV1EjqoaKBvxujR27CDGmEuAbwAXWGvfOtpHREaV22WGBsvNJ/2o5x48yWbERJuuPpq7os8Jt9V0DD0nPBxjIDPJGyuc+99STB+cfDP0/SNJA/dERBJVqt9zxElAg6y1tPX0H/ZZZkNntEvym1VtNHT0vWXd80HpAQ+5qX5yUqO5JyfVR06qn9wRrwf3M5K8yjmjTIVxiUvGmKGZ3seaiT5SeCBCRzBMR28/7bGfjuCI/d7wUEG9I9gfGyEUpLsvTGdftLBuj2Pst9dtDiqap/o9JPs9JHvdJPvcJPkGtx6SfW5SRuwn+dyx8zxD50V/PBpJJCIJY+R1/GhfNiF67W7r7Y8OghrqItI/1E1kcHBUa3eIvU3ddAajg6GO1FlkkM/jOqiInuqPXmtTfG5SYtfwZJ87eg33eUjxu0nxeUiObaPnDP+Ox61rtIjIoQJeN1NzU5h6jO/swf7hB9ut3SFae/pp6wnR1hO93g9uq9uCbKnpoLUnRLD/raP0RzIm+mAjzR+9zg8W0AeL5ymHfCcf/M49+J08Rd/HRRLdOmCWMWYa0YL4dcANI08wxiwC7gQus9Y2jH+IInI0b2eSTf9AZOgesqUrOqmmpXtw2xfd7wqxp7GLtfui951HegZoDGQkRQftDQ7gy4wVz0fuZ8T2BwdxpwW8mp0uIhLnjDFkpUSX/51TePQCeldfmOauEM3dfTR1RfNMc1e0S3JTbBBXRVMX6/ZFn20eLu+4XWao60lWbLnhzOTo0sNDA8hHHFPntWNTYVwmFY/bNTTa80RYa+ntH6ArGCuUB8PDRfPYjPShn0Pea+/tp7atl57QAL39A/SEwsd8mPeW+F1mxMM5D0mxQnvA6ybgdeH3ugl4ovuDx6KvR7zvdRPwDL4/4lzPyHNc+Nya9S4iE4PH7SI31U9uqv+4f2fwet3RG6YjODzgqTM2OKpjaDu83xMaoLmrh57QAN19Ybrf5nXa53GRNOK6muR14/e6SRpxnU3yjbxGDx4beU2OXqOTYtd2v8eFz+PC73Hj80SvzdHX0X19yRWRRBHwuinNSqY069gPtgcF+wdo6+mnM9hPZ1+YzmCYzmB/9Lt6bH+wa1RnLAe0dIfY39xDZzBMbyhMT//AcQ18HXTo93F/7Hv1sbYBb/RaPrj1H/J65Hbk9d7rHvwx+m4uMsqstWFjzGeApwA3cK+1dosx5jvAemvto8D3gVTgj7H/Bw9Ya692LGgROWFe9+CyXYHjOn8gYqOD9HqHB+u19fTT1ttPe09sEF/svZbuEBWN3bT1hI7a8cwYhgdn+4e7m6UnxTqcvWU/to3tp/o9GpAtIhInjDGxAdreYw4Uh2jeae0ZLp43dceK6LHCemNniLaeEDvqOocGjh9pTpDLMNTpJDo4K7ofXZry4E5sI5cbTva5J8V9pwrjIm+DMSY2i8RD/ih8XiRiY0XyAXpDA/T0h+kJDdDTFy2cD74XfT88Yn+A7lA4+jux/ebuCH39AwT7BwiGI9Ft/8ARL47H/mflkCL78IO9kUUZn2dEkcbjwud2H3Rs+PjIc99a4Bk675BzVaAXkRMx8npdmHF8Dz4OZyBi6QmF6e6LXmt7Ytto4XyAnth2sJDe1x+9/vbGrsG9sdct3aHYdTky9F6wf4D+gZNbgtLjMoe9dg5eZ/3uI1xbY68Hr71etwuP2+Bzu/C4DB539H2PO7rvdZmhc7yxc7weF17X4LHB96PnemJFnMFz3S4VdERk9AW8bgoz3Cd1nbfWEuyP0DPiu3ZP7Ht29yH7vYec0xMaoC/2vbsvHKGtJ3TQ65HbE/1OPpIvdm0dWTAfvJ57PdFr7siCevR49Hf8h/xO9P3o9drjMkPXfq/b4HYNbg2eEftetyu2jZ7jecux4eu+J5YfRv6eyERkrf0b8LdDjn1rxP4l4x6UiEwIbpeJrgn7NgZow3C3ysGievuI7jeDRfWOwQF8vWEOxJaA7IxNwjmWFJ97aImw4eXCYkuJJXlI9Ue7oaXGlpVJDXgOWioyNRCdaKP7MxGRicXtMiMmBh29uyZEa0udwTCtPaHhn+6DO64NHqtq7WFzdT8tPSFC4SNPAvK4zFChPD2WYwaL5gftB0Yejy5LHE9dUVQYF3GQy2WG2vWOBWst/QOWYDhagBks2AT7I0PHgv3DRfRgeERxffB4eOQ5EfrCA4TCEbr6woTCEULhCH2xbWggMnQsNPD2ZsMfzVuKO4NFHJc54r439pBw5L53sMAz4kHgofuDhZzBfd9goeeQ4wftx4pDgw8EdXMhkjjcruHRnWMhPBAZGszUGxqgLzxcPO+NFVyi19SBg6+5I6+3sdd9/cPHh8+JFm/aeg9z/ojr93gYKrTHrtce18GFdrfLRIsoZrgYM3TMNeL4iIKMy0Q/zx37XHesuONyDb52HXTcfdC+66BjI8/xuAc/23XQ68E4XC6GXh/P8f/f3v3HSlafdRz/PHfu/gK2uwusuO4iu7SIbqICrgixwUbKzzSsGmoXa6SKIdWa2FZjQBKSNjERNdqaEFvSYrDBQsVWNwSDte1f/kFZKL9h4Uqp7Aa6WwS6QPfunZnHP873zJw7e+/embl35pznzPuVTPbM93zvvc/3nMPzDOd75hybUif+7npRL4AKMMu+Ab5udUOnjehvuLuabZ83UZ5/rs7/nS28z/P0XMrpc622jrV83vu5lMfnWq65nv5vzTazPk0v9Cv+Pl/Rz+r9MFN3wjzl1c6kfKObb7Nc2s3lDdO8PD1lhVyf59M8fx/3s7bAz0qNqam0Tt3+PX2Lv6vRu77wdxuW5fOp1Oe4ZUvLqQ5Yas9/p/Uu5/0LNeW4ZeoHAFTacu5W2Wq73jqa7nqWJs7n3/ksaztSWH/oyFHNHOreKa2fi/GmTJ1J8+JjIPPHQq5fmz02LJ9kP7lnYj1/n99BJ8pECADUydSUacNJq7ThpFXarv4eN5zfYfP1d7JHVnYfMdz76OHuI4cPvv6jzrrmEkVm/ZruZPr6/KKttdPzHmuWt69P9abbll3QNY67YzIxDtSYmWn1tHWeoztO7XZ2wq138qY4yTO7YHuxLZsImm3N79dsdU/oFZffmm2qmZ80bLU7y3OpT3F5VOad9Ct+s7IwIZQvf+L9P6VLf+aMkcUCoPqmG1M6pTGlU0Z0gVQ/3F2tdjZpk+fOZqutuXY22dJsZ3mzWci7S/dNebfd7XMs/dtsd/Nxs+Waa7taqV8eR9u983622eq0twqvZufftlptZb+jZ91Sz5kvW2cSpDjRkiZVetvzyZNi+1Tqf+MlZ2v3eVvLHg6ARZh176yx9HX/45FfQJvn7Va7m6ObqT3P1622z+uT5+28Lc/nrXaW9/P+xd/Xamc/N/9vZO+zn8v6ZLlbWR1ou9p5PnfX3Fy2vu3z60Gr8L7d8z7rLzXbbbXb6qyLztKkeyNNqE8VakXvBViNwmR6p84Uakhx8n3PL/6kfvuis8oeHgBMrEZhkmMY+Z1wjszO6e3Z1rzHPr49O3/5yNHj2w4dOaq3Z1s6cnRObx9r9V0z161q6OQ1jXTXtoZ+7fyt+uivvHuoMQAARqd4h82tG9cN9LP5pHo2Yd48waT6XOcxlgff+JGeSxd4HTm69MVbZtIpq7sT5pftPEN/esW5yxjxwpgYBzASU1OmtVPZLdirJv/WznET5s3sZN685WZ3cidfbra737ZZaOI9P8mX980m6IvLWZ8qbhsAk8cs/+aeapeX3LsTIp1J93mT6q5Wys/5JExxgn7+RHw7TcZkEzbFyZeF2ruTOd2LD7rtqe8S7d3Jn/Q7etrbnl2Itq5m+w3A6HUuoNXkPac0rw3FfNqZhD9uUv3E64q/xxdZbns3t+d5e6HllnsWW6F2FOuL9y4Xf3d7kWV3tdPPtFJc+ZiLy+3098q8UA8AsHzFO+Es92o8d9dss73gBHr2b/eRYu/kjxdLjxpbv5Z6AgB1U5xU37Jh8J93d71zrKW3ZvM7nzQ7E+ZHjjb11tHj29+1bjT1hCoFYOIUv7UDAKiv7JtyUmOKiWMAQKZbG7jtKwAAizEzrV2VfeFl8/rBnrMOAEAvs+5jhc9419pSY2FWCAAAAAAAAAAAAABQa0yMAwDGxsyuNLP9ZjZjZjctsH6Nmd2b1j9kZtvHHyUAAAAAAAAAAKgbJsYBAGNhZg1Jt0u6StJOSdeZ2c6ebjdIet3d3yPp7yTdNt4oAQB1stQFWQAAAAAAAJgcTIwDAMblQkkz7v6iux+TdI+k3T19dku6Ky3fJ+lSM+MBkACAgfV5QRYAAAAAAAAmBBPjAIBx2Srp5cL7A6ltwT7u3pT0pqTTen+Rmd1oZvvMbN/hw4dHFC4AILh+LsgCAAAAAADAhGBiHAAQjrvf4e673H3X5s2byw4HAFBNS16QxYVWAAAAAAAAk4OJcQDAuByUdGbh/bbUtmAfM5uWtEHSa2OJDgAwcbjQCgAAAAAAYHIwMQ4AGJeHJZ1jZjvMbLWkPZL29vTZK+n6tHytpG+6u48xRgBAffRzQRYAAAAAAAAmBBPjAICxSM8M/yNJD0p6VtJX3P1pM/u0mV2Tun1R0mlmNiPpk5JuKidaAEAN9HNBFgAAAAAAACbEdD+dzOxKSZ+V1JD0BXf/y571ayT9k6RfUHbL2w+5+0srGyoAIDp3f0DSAz1ttxaWj0r64LjjAgDUj7s3zSy/IKsh6U53f7rksAAAAAAAAFCSJSfGzawh6XZJl0k6IOlhM9vr7s8Uut0g6XV3f4+Z7ZF0m6QPjSJgAAAAAOjHQhdkAQAAAAAAYDL1cyv1CyXNuPuL7n5M0j2Sdvf02S3prrR8n6RLzcxWLkwAAAAAAAAAAAAAAIbTz8T4VkkvF94fSG0L9knPkH1T0mm9v8jMbjSzfWa27/Dhw8NFDAAAAAAAAAAAAADAAPqZGF8x7n6Hu+9y912bN28e558GAAAAAAAAAAAAAEyofibGD0o6s/B+W2pbsI+ZTUvaIOm1lQgQAAAAAAAAAAAAAIDlMHc/cYdsovt5SZcqmwB/WNJvufvThT4fk/Sz7v5RM9sj6Tfc/TeX+L2HJX1vGbGfLukHy/j5qmE81Ve3MTGe6jvRmM5yd269oYmtJxFjloh73Ih7vKLGfa67ry87iCqY0HoixY1bihs7cY9f1Ngjxc3/nyQTWE+Id7SixSvFi5l4R2vQeKknyQTWk0HVfXxS/cfI+OKr8hgXrSdLToxLkpldLekzkhqS7nT3vzCzT0va5+57zWytpC9JOl/S/0na4+4vrlj4C8e0z913jfJvjBPjqb66jYnxVF8dx1RFEbdzxJgl4h434h4v4kbUbRk1bilu7MQ9flFjjxo3lifafife0YoWrxQvZuIdrWjx1kndt33dxyfVf4yML76oY5zup5O7PyDpgZ62WwvLRyV9cGVDAwAAAAAAAAAAAABg+fp5xjgAAAAAAAAAAAAAAGFFnhi/o+wAVhjjqb66jYnxVF8dx1RFEbdzxJgl4h434h4v4kbUbRk1bilu7MQ9flFjjxo3lifafife0YoWrxQvZuIdrWjx1kndt33dxyfVf4yML76QY+zrGeMAAAAAAAAAAAAAAEQV+RvjAAAAAAAAAAAAAAAsiYlxAAAAAAAAAAAAAECthZsYN7MrzWy/mc2Y2U1lx9MPMzvTzL5lZs+Y2dNm9sep/VQz+7qZvZD+3ZTazcz+Po3xCTO7oNwRLMzMGmb2HTO7P73fYWYPpbjvNbPVqX1Nej+T1m8vM+7FmNlGM7vPzJ4zs2fN7OLI+8jMPpGOt6fM7MtmtjbaPjKzO83skJk9VWgbeJ+Y2fWp/wtmdn0ZY0lxLDSev07H3BNm9jUz21hYd3Maz34zu6LQHi4PVlGVt+NKHfslxB2y3qX8+G0zezzF/anUXvmcaQFrsZm9ZGZPmtljZrYvtVX6GEmxhPucYGbnpu2cv35oZh+vetzRVLmeSNSUEuIOW1Ny1Jaxxx6uvmA0qlhPAufiUHksWh6wip9vsmDnkhaJt9LnihaKubDuT8zMzez09L70bTxpqlhPhhG1Bg0qWs0aRLT6Noyq18RBRauhQ3P3MC9JDUn/I+lsSaslPS5pZ9lx9RH3FkkXpOX1kp6XtFPSX0m6KbXfJOm2tHy1pP+QZJIukvRQ2WNYZFyflPTPku5P778iaU9a/pykP0jLfyjpc2l5j6R7y459kfHcJen30/JqSRuj7iNJWyV9V9K6wr75SLR9JOkSSRdIeqrQNtA+kXSqpBfTv5vS8qYKjedySdNp+bbCeHamHLdG0o6U+xpR82DVXlXfjitx7JcUd8h6l/7+KWl5laSHUjyVz5kKWIslvSTp9J62Sh8jKZbQnxNS3ntV0lmR4q76q+r1JMVITRlv3GFrSmEM1Jbxxh66vvBaseOgkvUkcC4Olcci5QEFON+kYOeSFom30ueKFoo5tZ8p6UFJ31Oqy1XYxpP0GvexMOKxhKxBQ4wzVM0acGxh6tuQ46t8TRxiTKFq6NDjLDuAAXfKxZIeLLy/WdLNZcc1xDj+XdJlkvZL2pLatkjan5Y/L+m6Qv9Ov6q8JG2T9A1Jvyrp/nTw/0DdD22dfaXsA9HFaXk69bOyx9Azng0piVlPe8h9lJLyyyn5TKd9dEXEfSRpe08iHmifSLpO0ucL7fP6lT2ennW/LunutDwvv+X7qC55sOxXhO243GO/7PhTLOHqnaSTJD0q6ZeqnjMVtBZr4cmLSh8jqsHnBGUn1/47WtxVf0WoJykuako5MYepKYWYqS3jjTt8feG1YsdClHpS+VwcLY9FywMKcr5Jwc4l9cbbs66S54oWilnSfZJ+XoW6XJVtPCmvMo6FMY6t8jVoiDGFqlkDji1UfRtyjCFq4hDjmpffB91nEfJ7tFup5wda7kBqCyPdHuF8Zd8cOMPdX0mrXpV0RlqOMM7PSPozSe30/jRJb7h7M70vxtwZT1r/ZupfJTskHZb0j+nWJV8ws5MVdB+5+0FJfyPpfyW9omybP6LY+yg36D6p9L7q8XvKrrKS6jGeKou4HUPlo2j1Lt266jFJhyR9XdkV1lXPmVFrsUv6TzN7xMxuTG1VP0bq8Dlhj6Qvp+VIcVdd1G0W6higpowVtWW86lBfsDIqv28D5eJoeSxUHgh8vinyuaQQ54rMbLekg+7+eM+qysZcU7XcroFq0KCi1axBhKpvwwhcEwcVuYYuKNrEeGhmdoqkf5X0cXf/YXGdZ5dOeCmBDcjMPiDpkLs/UnYsK2ha2S0i/sHdz5f0trLbQnQE20ebJO1WVoB+QtLJkq4sNagRiLRPlmJmt0hqSrq77FhQfVU/9iPWO3dvuft5yq7WvVDST5cc0gkFr8XvdfcLJF0l6WNmdklxZUWPkdCfE9LzrK6R9C+966ocN8aj6scANWV8qC2lCF1fMDmi5OKgeSxUHqjD+aYqbc+lRDlXZGYnSfpzSbeWHQvqJ0oNGlTQmjWIUPVtGHWoiYOKvs9y0SbGDyp7VkluW2qrPDNbpSyB3+3uX03N3zezLWn9FmXfKJCqP85flnSNmb0k6R5lt/r4rKSNZjad+hRj7ownrd8g6bVxBtyHA5IOuPtD6f19yhJ31H30fknfdffD7j4n6avK9lvkfZQbdJ9UfV/JzD4i6QOSPpyKixR4PEFE3I4h8lH0eufub0j6lrJbHVU5Z4atxemKWrn7IUlfUzZpVPVjJPrnhKskPeru30/vo8QdQdRtFuIYoKaMHbVl/KLXF6ycyu7bYLk4Yh6Llgeinm8Kdy4p2LmidyubGHo8/fe3TdKjZvbjJ4it7JjrqlbbNVgNGlTEmjWIaPVtGFFr4qDC1dClRJsYf1jSOWa2I33zZY+kvSXHtCQzM0lflPSsu/9tYdVeSden5euVPScjb/8dy1wk6c3CrQpK5+43u/s2d9+ubB98090/rOykz7WpW+948nFem/pX6qoSd39V0stmdm5qulTSMwq6j5TdvuMiMzspHX/5eMLuo4JB98mDki43s03pKq7LU1slmNmVym6Zc427v1NYtVfSHjNbY2Y7JJ0j6dsKmgcrKOJ2rHw+ilrvzGyzmW1My+uUPbPqWVU4Z0atxWZ2spmtz5eV5eSnVPFjpAafE65T9zbqUpy4I4hYT6QAxwA1ZfyoLeNXg/qClVPJehItF0fMYwHzQNTzTaHOJUU7V+TuT7r7j7n79vTf3wFJF6Tju5LbuMYqWU+GEa0GDSpizRpEwPo2jKg1cVChamhfvAIPOh/kJelqSc8re07bLWXH02fM71V2e4EnJD2WXlcre37ANyS9IOm/JJ2a+puk29MYn5S0q+wxnGBs75N0f1o+W9mHsRllt+pck9rXpvczaf3ZZce9yFjOk7Qv7ad/k7Qp8j6S9ClJzyk7KfQlSWui7SNlJ/FfkTSn7EP1DcPsE2XPY5pJr9+t2HhmlD1zI88Nnyv0vyWNZ7+kqwrt4fJgFV9V3o4rdeyXEHfIeifp5yR9J8X9lKRbU3uInKlAtTjF93h6PZ3/t1f1YyTFEvJzgrJbeb0maUOhrfJxR3pVuZ6k+Kgp4407dE0pjIPaMr74Q9YXXiM5FipXT6Lm4hRLpDwWKg+o4uebFOxc0iLxVvpc0UIx96x/SdLpVdnGk/Ya57Ew4nGErUFDjPV9ClKzBhxXqPo25BgrXROHGE+oGjrsy1KQAAAAAAAAAAAAAADUUrRbqQMAAAAAAAAAAAAAMBAmxgEAAAAAAAAAAAAAtcbEOAAAAAAAAAAAAACg1pgYBwAAAAAAAAAAAADUGhPjAAAAAAAAAAAAAIBaY2IcAAAAAAAAAAAAAFBrTIwDAAAAAAAAAAAAAGrt/wGIqGh6zjwdVwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 2520x1080 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}