CNN_Covid_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN_Covid_detection.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUJSITdkLeAk"
      },
      "source": [
        "## **Covid_Detection using VGG19 architecture**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ymGM_Z46lSTz",
        "outputId": "6d3fb5b4-68b8-49b1-97cb-93f1b8d06c1d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8KSTWLrryaW"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.layers import Activation\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.models import load_model\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1I_7Bt0IDuzP"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from imutils import paths\n",
        "import numpy as np\n",
        "from numpy import asarray\n",
        "import argparse\n",
        "import random\n",
        "import tensorflow as tf\n",
        "import pickle\n",
        "import imutils\n",
        "import cv2\n",
        "import csv\n",
        "import os\n",
        "import gc\n",
        "import keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Phfnw7g8Fawz"
      },
      "source": [
        "train_datagen= ImageDataGenerator(rescale=1./255,rotation_range=25, width_shift_range=0.1,\n",
        "\theight_shift_range=0.1, shear_range=0.2, zoom_range=0.2,\n",
        "\thorizontal_flip=True, fill_mode=\"nearest\")\n",
        "\n",
        "test_datagen=ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6U6NgXCGELef",
        "outputId": "5328ab51-b77d-47bb-9445-a989b7f03673"
      },
      "source": [
        "train_generator=train_datagen.flow_from_directory('/content/gdrive/MyDrive/Train',\n",
        "                                                  target_size=(224,224),\n",
        "                                                  batch_size=32,\n",
        "                                                  class_mode='categorical')\n",
        "\n",
        "validation_generator=test_datagen.flow_from_directory('/content/gdrive/MyDrive/Test',\n",
        "                                                  target_size=(224,224),\n",
        "                                                  batch_size=32,\n",
        "                                                  class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 4148 images belonging to 3 classes.\n",
            "Found 1209 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QckISVk_7lQ2"
      },
      "source": [
        "model=Sequential()\n",
        "model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(Conv2D(filters=64,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters=128,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(Conv2D(filters=128,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters=256,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(Conv2D(filters=256,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(Conv2D(filters=256,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters=512,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(Conv2D(filters=512,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(Conv2D(filters=512,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters=512,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(Conv2D(filters=512,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(Conv2D(filters=512,kernel_size=(3,3),padding=\"same\",activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=256,activation=\"relu\"))\n",
        "model.add(Dense(units=256,activation=\"relu\"))\n",
        "model.add(Dense(units=3,activation=\"softmax\"))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sO_Vj-54bTdc"
      },
      "source": [
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNqV_3Q2bwvU",
        "outputId": "5c68b306-47a2-4a5d-c674-783951321774"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               6422784   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 256)               65792     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 3)                 771       \n",
            "=================================================================\n",
            "Total params: 21,204,035\n",
            "Trainable params: 21,204,035\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Xfz6XcPKqru"
      },
      "source": [
        "model.load_weights('/content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jkb5uWTg1IxZ"
      },
      "source": [
        "filename = \"/content/gdrive/MyDrive/VGG16_cp/logs_VGG.csv\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thFu1mP_cuTw",
        "outputId": "42224ef4-0971-467d-ebdd-136f77a15dda"
      },
      "source": [
        "H = model.fit(train_generator,\n",
        "                             validation_data = validation_generator,\n",
        "                             steps_per_epoch = len(train_generator),\n",
        "                             initial_epoch=73,\n",
        "                             epochs = 100,\n",
        "                             validation_steps = len(validation_generator),\n",
        "                             callbacks=[checkpoint,myCallback()])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 74/100\n",
            "130/130 [==============================] - 1750s 13s/step - loss: 0.1374 - accuracy: 0.9505 - val_loss: 0.3103 - val_accuracy: 0.9032\n",
            "\n",
            "Epoch 00074: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 75/100\n",
            "130/130 [==============================] - 64s 491ms/step - loss: 0.1294 - accuracy: 0.9581 - val_loss: 0.3353 - val_accuracy: 0.8999\n",
            "\n",
            "Epoch 00075: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 76/100\n",
            "130/130 [==============================] - 64s 496ms/step - loss: 0.1040 - accuracy: 0.9602 - val_loss: 0.2391 - val_accuracy: 0.9214\n",
            "\n",
            "Epoch 00076: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 77/100\n",
            "130/130 [==============================] - 64s 491ms/step - loss: 0.1093 - accuracy: 0.9558 - val_loss: 0.2626 - val_accuracy: 0.9123\n",
            "\n",
            "Epoch 00077: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 78/100\n",
            "130/130 [==============================] - 64s 495ms/step - loss: 0.1231 - accuracy: 0.9522 - val_loss: 0.2535 - val_accuracy: 0.9148\n",
            "\n",
            "Epoch 00078: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 79/100\n",
            "130/130 [==============================] - 64s 490ms/step - loss: 0.1156 - accuracy: 0.9488 - val_loss: 0.3346 - val_accuracy: 0.8991\n",
            "\n",
            "Epoch 00079: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 80/100\n",
            "130/130 [==============================] - 64s 494ms/step - loss: 0.0976 - accuracy: 0.9610 - val_loss: 0.3034 - val_accuracy: 0.9057\n",
            "\n",
            "Epoch 00080: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 81/100\n",
            "130/130 [==============================] - 64s 494ms/step - loss: 0.1141 - accuracy: 0.9592 - val_loss: 0.5564 - val_accuracy: 0.8296\n",
            "\n",
            "Epoch 00081: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 82/100\n",
            "130/130 [==============================] - 64s 496ms/step - loss: 0.1116 - accuracy: 0.9565 - val_loss: 0.3423 - val_accuracy: 0.8958\n",
            "\n",
            "Epoch 00082: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 83/100\n",
            "130/130 [==============================] - 64s 493ms/step - loss: 0.1289 - accuracy: 0.9514 - val_loss: 0.3610 - val_accuracy: 0.8850\n",
            "\n",
            "Epoch 00083: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 84/100\n",
            "130/130 [==============================] - 64s 491ms/step - loss: 0.1304 - accuracy: 0.9533 - val_loss: 0.3182 - val_accuracy: 0.9016\n",
            "\n",
            "Epoch 00084: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 85/100\n",
            "130/130 [==============================] - 64s 495ms/step - loss: 0.0912 - accuracy: 0.9624 - val_loss: 0.3414 - val_accuracy: 0.8941\n",
            "\n",
            "Epoch 00085: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 86/100\n",
            "130/130 [==============================] - 64s 493ms/step - loss: 0.1179 - accuracy: 0.9539 - val_loss: 0.4029 - val_accuracy: 0.8734\n",
            "\n",
            "Epoch 00086: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 87/100\n",
            "130/130 [==============================] - 63s 488ms/step - loss: 0.1132 - accuracy: 0.9584 - val_loss: 0.3221 - val_accuracy: 0.8991\n",
            "\n",
            "Epoch 00087: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 88/100\n",
            "130/130 [==============================] - 65s 497ms/step - loss: 0.1022 - accuracy: 0.9618 - val_loss: 0.2202 - val_accuracy: 0.9272\n",
            "\n",
            "Epoch 00088: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 89/100\n",
            "130/130 [==============================] - 64s 490ms/step - loss: 0.1076 - accuracy: 0.9580 - val_loss: 0.3787 - val_accuracy: 0.8759\n",
            "\n",
            "Epoch 00089: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 90/100\n",
            "130/130 [==============================] - 64s 493ms/step - loss: 0.0925 - accuracy: 0.9619 - val_loss: 0.4131 - val_accuracy: 0.8817\n",
            "\n",
            "Epoch 00090: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 91/100\n",
            "130/130 [==============================] - 63s 488ms/step - loss: 0.0948 - accuracy: 0.9637 - val_loss: 0.3093 - val_accuracy: 0.9107\n",
            "\n",
            "Epoch 00091: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 92/100\n",
            "130/130 [==============================] - 66s 510ms/step - loss: 0.0918 - accuracy: 0.9644 - val_loss: 0.3091 - val_accuracy: 0.9016\n",
            "\n",
            "Epoch 00092: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 93/100\n",
            "130/130 [==============================] - 64s 490ms/step - loss: 0.1059 - accuracy: 0.9545 - val_loss: 0.3802 - val_accuracy: 0.8867\n",
            "\n",
            "Epoch 00093: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 94/100\n",
            "130/130 [==============================] - 64s 496ms/step - loss: 0.1139 - accuracy: 0.9585 - val_loss: 0.3695 - val_accuracy: 0.8809\n",
            "\n",
            "Epoch 00094: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 95/100\n",
            "130/130 [==============================] - 64s 493ms/step - loss: 0.0989 - accuracy: 0.9631 - val_loss: 0.2989 - val_accuracy: 0.9016\n",
            "\n",
            "Epoch 00095: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 96/100\n",
            "130/130 [==============================] - 64s 493ms/step - loss: 0.0907 - accuracy: 0.9681 - val_loss: 0.3754 - val_accuracy: 0.8883\n",
            "\n",
            "Epoch 00096: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 97/100\n",
            "130/130 [==============================] - 64s 491ms/step - loss: 0.1078 - accuracy: 0.9548 - val_loss: 0.2695 - val_accuracy: 0.9140\n",
            "\n",
            "Epoch 00097: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 98/100\n",
            "130/130 [==============================] - 63s 482ms/step - loss: 0.0994 - accuracy: 0.9609 - val_loss: 0.3906 - val_accuracy: 0.8842\n",
            "\n",
            "Epoch 00098: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 99/100\n",
            "130/130 [==============================] - 63s 487ms/step - loss: 0.1048 - accuracy: 0.9613 - val_loss: 0.2510 - val_accuracy: 0.9181\n",
            "\n",
            "Epoch 00099: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n",
            "Epoch 100/100\n",
            "130/130 [==============================] - 63s 486ms/step - loss: 0.0963 - accuracy: 0.9665 - val_loss: 0.4273 - val_accuracy: 0.8768\n",
            "\n",
            "Epoch 00100: saving model to /content/gdrive/MyDrive/VGG16_cp/check_point_VGG.hdf5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "9Uen_q53dTez",
        "outputId": "66005f26-a2a7-485a-9fa4-12b717626cf1"
      },
      "source": [
        "# plot the training loss and accuracy\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.figure(figsize=(10,10))\n",
        "N = 100\n",
        "plt.plot(np.arange(0, N), df[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(np.arange(0, N), df[\"val_loss\"], label=\"val_loss\")\n",
        "plt.plot(np.arange(0, N), df[\"acc\"], label=\"train_acc\")\n",
        "plt.plot(np.arange(0, N), df[\"val_acc\"], label=\"val_acc\")\n",
        "plt.title(\"Training Loss and Accuracy\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Loss/Accuracy\")\n",
        "plt.legend(loc=\"upper left\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd21e91ca90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tCvx9+egHm81GUFAQXq93UPqm0+kGra3rDUVRsFqtjNRiDnJk0shEnpeRizw3IxN5XkYuQznKckTkIZs2bRq7du1CCMHp06exWCxX5K4EpHttkJHHUyKRSCSSa8+QWMhefvllTp48SXt7Oz/4wQ/4xje+EbA6LVmyhMmTJ5OXl8cTTzyBwWDgRz/60VB0SyKRSCQSiWREMCSC7J//+Z97na8oCo8//vhQdOWa09raykcffcSjjz46oPUefvhh/vd//5ewsLABrffP//zPLFq0iGXLlg1oPYlEIpFIJCOHEeGyvJFoa2vj3Xff7TK9rzi0tWvXDliMSSQSiUQiuTEYEUH9NxKrV6+moqKCxYsXo9frMRqNhIWFUVJSwp49e3jssceorq7G5XKxatUqHnroIQBmzpzJ5s2bsdlsPPTQQ8yYMYPDhw8THx/PW2+9hdls7nPbu3fv5uc//zk+n4+JEyfywgsvYDQaWb16NZ9//jk6nY558+bx//7f/2Pjxo38+te/RqPREBoayoYNG671oZFIJBKJRNIDN7QgU99/HVFZfnVtKEqnUYbKqDQ0D3y3x+WffvppiouL2bJlC/v27ePb3/4227ZtIyUlBYCXXnqJiIgIHA4Hd955J3fccUeXEaXl5eW8+uqr/OpXv+L73/8+n376Kffee2+v/XQ6nfz4xz/mgw8+ID09nSeeeIJ3332Xe++9l82bN7Nr1y4URaG1tRXwx/WtW7eOhISEwDSJRCKRSCTDg3RZXmMmTZoUEGMAb731FosWLWL58uVUV1dTXt5VMI4aNYrc3FwAJkyYQGVlZZ/bKS0tJSUlhfT0dADuu+8+Dhw4QGhoKEajkZ/85Cd8+umnAUvbtGnT+PGPf8y6devw+XyDsasSiUQikUiukBvaQtabJau/XG0eMovFEvj/vn372L17Nxs3bsRsNrNy5cpuqwBcmohWq9XidDqvePs6nY5NmzaxZ88eNm3axNtvv82HH37IL3/5S/Ly8ti6dStLly5l8+bNPeZ+k0gkEolEcm25oQXZcBAUFITVau12Xnt7O2FhYZjNZkpKSsjLyxu07aanp1NZWUl5eTlpaWmsX7+eWbNmYbPZcDgcLFy4kOnTp3PzzTcDcPbsWaZMmcKUKVPYvn071dXVUpBJJBKJRDJMSEE2yERGRjJ9+nRuvfVWTCZTp5ILCxYsYO3atcyfP5/09HSmTJkyaNs1mUz8z//8D9///vcDQf0PP/wwFy5c4LHHHsPlciGE4NlnnwXg+eefp7y8HCEEc+fOZfz48YPWF4lEIpFIJANDESO1Lk4/qa6u7vS33W7v5Ca8Wr7KpZNg8I/nYCLLjYxM5HkZuchzMzKR52Xk8pUrnSSRSCQSiUTyVUa6LK8Tnn76aQ4dOtRp2uOPP879998/TD2SSCQSiUQyWEhBdp2wevXq4e6CRCKRSCSSa4R0WUokEolEIpEMM1KQSSQSiUQikQwzUpBJJBKJRCK5KlRVcK7Mxd6t7dRVe67ptqrPuTl+2I7demNVmZExZBKJRCKRSK4IVRWcr/Bw+qQTu1VFq4NDe2xMmmkhOdUw6Ns6ddxJWbG/wk1luZu0TCMZ44zoDde/fen634PrnIyMjB7nVVZWcuuttw5hbyQSiURyvdPU4MXafm2tR0IVVJ11s2NzO0cP2tHpFKbPDWLxijAiY3Tkf2nn7JmupQGvFJdL5cBOG2XFLtIyDCxcFkJSioHSIhdbN7VTftqFql5ZWlWPW1BU4KC1ZXgtbtJCJpFIJJIbEiEEXg/oDUqfyzrsKm6XiqqC6vNbY4SAyBgdOl3f648EfD7BqWMOys+4URQYk2kkY7wJvb7//ff5BF6PwGjq3l4jhKCu2kvRcQftbSqh4RqmzbEQn6RHUfzbmTkviCP7bBTkOXB7BBnjjIF5l+P1CprqvdRVe2io9Sdhj47TEROvIzpWh8Go4UKzl8N7bbicgkkzLIxK81veJs20kJZp4ORRJ4X5DspOu0gYpSc2QU9ktBaNpvf99nkFZ0tcnDnlwuMW6PUKYRHafh+rwUYKskFm9erVJCYm8uijjwLw0ksvodVq2bdvH62trXi9Xn76059y2223Dahdp9PJU089xfHjx9FqtTz77LPMmTOH4uJi/uVf/gW3240Qgtdee434+Hi+//3vU1NTg6qq/NM//RN33XXXNdhbiUTyVcXlVKmv9ZKcokfp48U31KiqoLrSQ8kpJ+2tKqPSDIybYOpWZDgdKqeOOaiq6D7uKTRcw8x5wZjMPQuUqrMe3C6VpFRDj8sNBLdbpb1VJTJK2+9ja7P6OLLPTmuLj7QMAz4vlBa7qKpwM26CmeTR+p5FkUdQX+uhpspDfbUHrxfCIrQkJOuJT9YTEuoXKU31Xk4dd9DS5CMoRMPU2RYSkru2q9UqTJsTxNGDdooLnHhcgtEZBtxOgdstcDlVXC5Bc4OXxnovqg+0Wr8QQ4HqSjfnytwAhIZrsbb7MBgV5iwMJjyys2wJi9Axa0EQddVeyk+7KCt2UVrkQqeH2Hg9MfE6gkK0mMwKJrMGrVZBvWjdKy504nQIYuJ1ZN9k6tL2UHNDC7I3DtdR3uK8qjYUReHS6lJpESYenxbX4/IrVqzg2WefDQiyjRs3sm7dOlatWkVISAjNzc0sX76cJUuW9HhzdMc777yDoihs3bqVkpISvvnNb7J7927Wrl3LqlWr+PrXv47b7cbn87Ft2zbi4+NZu3YtAG1tbVe28xKJRNINLqfKvu1WrG0qF5oM5E4xD+h5dq3w+fwv2pIiF3arSkiohpQxBirPuqmpcpM53kTaWCMarYLPJyg/7eL0SSdChbHZRsKj/FYVjRY0GgWnXeXYYTt7tlqZNT+I4JDO1hOPR3DsoJ2aKr+YO3XcSXySntR0A9Fxuk7HxOcV2G0qXq8gOETTJeZJVQUNtV6qzrqpPe9BVSEmXsfUm4P6tPBVn3Nz7JAdRfG7DeOT9ACkphsozHdw9KCdsyVaRo02oApQfcJvCVQFbRd8NNR6UVUwGBUSUwyYgzTUnfdQVOCkqMBJcKgGo0lDU70Xk1lhwjQzo9IMvVqgNBqFyTMt6PV+y1XZ6a7uS0uQhtQxBmIT9ETF6tBqlcCxaG320VDnpbHOQ2yCnglTzT1a7RRFIT5JT3ySHo9H0Fjnoa7aS32Nh+rKzkLbYFRQFHA5BeGRWibPNBEdp+/1+A4VN7QgGw5yc3NpbGyktraWpqYmwsLCiI2N5bnnnuPAgQMoikJtbS0NDQ3Exsb2u91Dhw7xne98B4CxY8eSnJxMWVkZU6dO5be//S01NTUsXbqUMWPGkJ2dzX/913/x3//93yxatIiZM2deq92VSCQ94LCrAJgt1zZU1+1S0euVIbNSuZwq+3dYsdtUEkfpOVvixmjWkJljGlA7QohBFXE1VW4K8xw4HYKwCG0nN1p6tpET+Q5OHnVSUepm9FgjZ8+4sFlV4pJ0jJ9kJii4e1dVULCGA7tt7N1qZea8oIAVpe2Cj8P7bNitKjkTTcQm6jlX5qay3E1NlQdLkIaIKC12u4rdquJydo5vMpoUgkO1BIdoCApqoPR0Gy6nQG9QSE33W9qKCpzs+aKd6V/rKgbBb90rLnRyrsxNRJSWKTcHYQn6+/UWHqVjzsJgqs56OHXcQUGeo3MDCpjN/u0lJBuIjP67RS4zx4TDrlJ73kNtlQer1ce4iX5Bq+2nC1dRFHKnmImJ1+NxCwxGBaNRwWBUMBg16HpwpWo0ChHROiKidWSOH9h1pdcrJCT790cIgbVdxWlXcdhVnA6B06HidguSUvSd3KwjgRtakPVmyeovV1JcfNmyZWzatIn6+npWrFjBhg0baGpqYvPmzej1embOnInLNTjBjvfccw+TJ09m69atPPzww/zyl79k7ty5fPbZZ2zbto0XX3yRuXPn8uMf/3hQtieRSPrGZvWx5wsrXq9g/CQzqemGAT/4+xIsbpfKqeP+l7HBeNFCkKwn+hJLg8upUl/jtxQ01HmJiHKSlau7YteMy6Xy5Q4rNqvKzK8FERWrQ9H43VImk0LKGGOXdWxWH+WnXdhtKm6X8P/cAo9HkJCs56apZozGKxetQghKi1ycOu4kLELLpBmmLtap4BAtM+cFU1ft4US+gxP5DoJCNMycF0RsQu/WkQ5R8+VOG/u2W5k2Jwi3U3D8sB2dXuHmBcFExfqP5/hJZrJvMlF73kNFqZvmRi+WYC2xCXoswRqCgjRodQrWdh/WNhVrm4/z59z4fG5iE3SMGm0gLkGP5uL5C4/ScXivjT1fWJl6s4WYeH9f7VYfJUUuKsvdqALSs41k32Tq1mKlKAqj0gwkpvhFkUYDGq3i/7cPEW+2aEjLMJKW0fW89pcO69VwoCgKIaHagMt1pHNDC7LhYsWKFfzbv/0bzc3NrF+/no0bNxIdHY1er2fv3r1UVVUNuM0ZM2bw0UcfMXfuXEpLSzl//jzp6elUVFSQmprKqlWrOH/+PKdOnWLs2LGEh4dz7733EhoaynvvvXcN9lIikXSHy+kfDSYEREbpKDjioKHWy8TpZgz9EB6qT3DiqINz5W5i4nWkphuJjdMFLBdCCCrL3Zw67sTjFowea8DtFlSf88fd6PQQE6fHYVe50OwfNWY0KcQm6Giu97B7i5OUNAPZPcRU9YT7ohizWlVmzA0KuHkmTbfgcto4ftiB0aQhLtE/3e1WOXPS5R9pp0BwiAaDUUNYkAbDRRdcRZmb5gYvE6dbAusNBNUnOH7EQWW5m8QUPZNmWAJitDviEvVEx+lobfERHqENCJ++CA7RMndhMAd2WjmwywYCImO0TL05qEvMmFarkJRiICmlt5QPf99XIQSRkdG0tDR1WSo6Vse8xcEc3GPjwC4bmbkmbG0+zp/zoCiQPNrA2HHGHq17l/dLax451iBJV6QguwZkZWVhs9mIj48nLi6Or3/96zzyyCMsXLiQCRMmMHbs2AG3+cgjj/DUU0+xcOFCtFotv/71rzEajWzcuJH169ej0+mIjY3lH//xHzl27BjPP/88iqKg1+t54YUXrsFeSiTXPzarLxBrcqHJR2i4huh4PTFxOsIi+h6ldTlej+DgbhsOh8rsBcGER2kpO+233uz8m5fJMy29xqs4HSqH99loafQRl6SjpdFH3XkbZovf+hQZo6WowElLo4+IaC0TploIDfe/jH0+QWOdl9oqD/W1HkxmDVm5JmIT/PuiKAqhoZF8ues8ZaddVHfEVGUY+9xPt0tl/w4b1jaV6V8LClhqwG9tmT4niH3brRzZZ2PW/GAutPg4fcIvGEelGcjKNXXruk0ZYyD/gJ2Du22kphvImWgOuLFUVWBtU2m74EPRQHikFkuQJmD5crtVDu+101TvJXO8kczxpn5ZIbVahcjogb/6TGYNs28N4fhhO0EhGjLHd2+RGiiKovQqIi3BWuYuDCHvSxvFBU60WkjLMDImy3jN3eGSoUURl0asX4dUV1d3+ttut2OxWAat/StxWd5IDPbxHEyio6NpbGwc7m58JVBV/1D4/lh4Lj8vLpdKaZGL+EQ9kTHD/w3Y2uKlqsJDXbUHW7s/zisoRENktN9y0nbBb1XS6SE61j98PjxSR1ikttf0B6oqOLTHRn2tl+lzgjq5aVpbvBzZb8fWrpKabiB5tIGIKG0nAdHS6OXwPhset2DiDAtJKQZUn6C22u/+aqzzP4f0BoWciSZGpQ3cDdpxbqxtPgrz/Za74FAN4yebiY3vKhSFENRUeSjMc+BxC6bP7dnF53So7N3qjy0D/4i5nIkmwiJ6P+c+n6C40ElpkQtLsIaoGB1tF3y0t/pQ1c7LGowK4ZFawiO1nD/nwWFTmTDdwqjRg5uAdKjpz7NMCEF9jZfwKO1VuXglA2Ow3zOJiYk9zpOCrA+kIJOC7KuOqgr277DS3qqy4PaQPof1X3pevF7B/u3WgOssMkZLRo6JmMtifK41Xo/g/EWX3oVmHxoNRMXqiE3QE5fgHxbfgcup0lTvvTjCyxsQGIoCIWEavziL0BIWriUk3C/ShBAcO+ig8qybCdPMpKZ3jbnxegUnL7oihep3IyYk+wOL7TaVwjwHJrOGaXOCus2FZLP6aKr3Eu/MXZ4AACAASURBVJ+k75cw7o5Lz03HC74w34HdqhKfpCdnking/nLYVQry7NSd9xIarmXidHOfsWfWdh/FBU6SRxuITRjYOW6q93LskB2Pxx+UHxru/4WFa1FVwYVmHy1NPi40e2lvVdEb/CMKo0aAyL9a5LNs5CIF2QC4EQTZqVOneOKJJzpNMxqN/PWvf72m2+0PUpDd2Hg9IjDEvycK8x2Un3ahKBCboGP63KBeX7Qd50VVBYf32qir8TJlpgWXS1Ba5AyMgsvIMRITr+/V6iSEwOnwj87qza3Tgcftz28UCB53+eOozle48XohJFRDarqRpNF6DP0steJyqhfFgJcLzT4uNPvwuP/+2AwK0WA0KTQ3+MgcbyIrt/dRYR63Sl21l9rzHuprPPguJgePidcxZZblisVWf+junvH5BGWnXZy5mP4hPduIyaThVIEDVYXsXBNpmX27NQeL/oy+9HoEikK/R/uNdOSzbOQylILs+v+0uAEYN24cW7ZsGe5uSK4zhBC0NPqor/UwarShk5WnPzgdKru3tKPXK8yYF9xpuHwH58+5KT/tL1ViDtJw8qiTqrOeQKbs3vpWcNhBXbWXm6aaSbpY0y413RDIE3V4rx0As+Xvw/+DQ7V43IL2touj0Np9+LxgDtIwabq5x/gra5uP44ftNDV0LX2i0ULiKD2p6cYubsL+4A9U/3uwuhACh92fv6nDxdne6iM920jm+L5Ho+kNGpJH+92WXq+godaD1wPJqcOTYFWrVcgYZyI51cCp4w7OnPSPAI+O0zFhWs/pIK4V/Tk/PaVLkEiuZ6Qgk0iuM4QqqDnvoazYRUuTX4CUn3YxYbqlj5Fdf0dVBUf223C7BV6vYM8X7cycF9Qp3qe91cexg3YiorXkTDKjALVVHgrz7UTH6XoNKD59wsm5cjcZOUZGj/27SNFqFVLTjYxKM9BQ66X1gg/rRfF1rsEdsBaZzH6RlpJmwBKk4WyJm/07ugn89glKivzWHa1O8QePB2kuyXWkYDRp+mVd6y+KomAJUrAEaa56OL9O58+ZNBIwWzRMmRVE2lgvTqc64nI0SSQ3OlKQSSRDREfZEZ9PMCbTRFKqvl9uIFUVuJz+hIYXmnyUXczpZAnWkDvFTHScjmMH7eTt9484Gz/J3Kcr59QxJ80NPibP9I/SO7DLyt5tVqbN9gdte9z+AHWdXmHa7KBAPyfNsLDjb+0cP2xnxte6d10WFbZy+oQrMLquOzQahbhEfadUBx3uSb1e6WIBSUk3UlzgpOy0i/paL5Omm9FqFY4dttPe6k9Qmjul50zekv4TcQUjECUSydUj7zyJ5Bpjbfdx6piT2vMeTGYFvUHx13g7oWFstt9a1GHBsdtUmhu8NDV4abvgw+lQcToFXBLpGRGlJWeShfjEv7u4Zt8aTHGBk5IiFy2NXqbODiK4h2SI58+5Kbvohky+ODpt7qIQDu6ycnC3jQnTzNRWe7DbVG6+pXMNv6AQLTkTzBTm+3M/XZoI1G7zUVbs4myJP8nlhGkDK6ejKApmS/fL63QK4yebSUjWc/Sgnf07bIDfkjbja0FXlMNKIpFIRhJSkEkkg0BDnQeXQ6DTK+h0/hgXjUbhXJlfoGi0kHWTiTGZRrRaqKv2cuakk4IjDk6fcBIVo6OlyYvD7ldeOj2ER+iIidcHiuKaLRosQRpCwroKLY1GYdxEM5GxOo4esLNrSztjMo2kpnfOVdTe6uPYIbtf1E00B6abLRpmLwzh8F4bxw75y6uMn2zudgTb6AwDNVVuThx1EBOvx+VQKS12UV3lQQHSs0LIGK+5JkHgkTE65t0WwpmTTlQVssabZDyRRCK5IZCjLPtgoKMsW1tb+eijjwLFxfvLww8/zP/+7/8SFhY2wB5eW+Qoy97pyKp+tsTd/QIK/qzoN3XNii6EoKney5lTLqxtPiKidETG6IiK0RIapr3iAG+H3Z9Cofa8P5t3QrKe0RlGQsO07P6iHa9HMG9J9+krVNWfmkFRFHIm9Zxo02b1sfNv7Wg0Ch63QKeH1HR/iZVRKbHDfl4k3TMS7hlJV+R5GbnIUZbXMW1tbbz77rtdBJnX60Wn6/lwr1279hr3TNJBTZU/H5XJpMESrAnUmDNZNPh8/gSoXo8/b5SqCqJidd2mSLg0q3p6lpGUdIN/Xe/F9T2CsMie66gpikJ0nL7XzO1XgtmiYfrcIGxWH2dL3FSWuamu9KDXK3i9oosb8lI0GoXcKX0L8KBgLTdNsVBa5CQzx0jKGKO0VEkkEslVIAXZILN69WoqKipYvHgxer0eo9FIWFgYJSUl7Nmzh8cee4zq6mpcLherVq3ioYceAmDmzJls3rwZm83GQw89xIwZMzh8+DDx8fG89dZbmM3mbre3bt061q1bh9vtJi0tjd/+9reYzWYaGhp48sknqaioAOCFF15g+vTpfPjhh6xZswbwp9t45ZVXhubAjACEEJScclFU4MRkUWht8eFy9m0g7i5tQkdWdbdbMOXm/o9uHEqCgrWMn2QmK9fE+Qo3leVuRqUZBi2R5qg0Q5/pLyQSiUTSP25ol2Vhnj1QBuVKURR/Fu4OQsO1vVoQKisreeSRR9i2bRv79u3j29/+Ntu2bSMlJQWAlpYWIiIicDgc3Hnnnfz5z38mMjKykyCbM2cOn376Kbm5uXz/+99nyZIl3Hvvvd1ur7m5mcjISAB++ctfEhMTw2OPPcYPfvADpk6dyne/+118Ph82m42amhpWrVrFJ598QmRkZKAvvXGjuCxVn+D4YX8m9aQUPRMvFiH2egV2q4rdpuJ0qGh1/hiwjpF+qg+qKtydEotGx+moKHX3mlX9q4x0v4xc5LkZmcjzMnKRLssbiEmTJgXEGMBbb73F5s2bAb+YLC8vDwiqDkaNGkVubi4AEyZMoLKyssf2i4uLefHFF2lra8NmszF//nwA9u7dy29+8xsAtFotoaGh/PnPf2bZsmWB7fUlxoYaIQQtTT6CQzSDmq3c7VI5vNdG08VM6pnjjYHYKJ1OCZRo6YnIGB05E81UV7qpKHVTfsY9JFnVr0fcbjdnzpwhPDxc5rCSSK5DTp8+TVFREcuXL5f38BBzQwuy/sTC9MXVlk661Lq0b98+du/ezcaNGzGbzaxcuRKXy9VlHaPx0kSaWpxOZ4/t//jHP+bNN99k/PjxfPDBB+zfv/+K+zpQmi6mZ8gYZ7zqG9fa7uP4YQdN9V70BoWMHCNpY41oBpjQ0+f7e8kct8ufv+v0CScOu8rkmZZAmoeBotMrpIzxx0q5nCoGo3JF+1xXV4fX6yUpKemK+jGSEUKwZcsWSktLufvuuzt9iFyv9KeMj0RyI3H06FFqa2ux2WwEBwcPd3e+UsjP+0EmKCgIq9Xa7bz29nbCwsIwm82UlJSQl5d31duzWq3ExcXh8Xj46KOPAtPnzp3Lu+++C4DP56OtrY05c+bw17/+lebmZsDvPr1SfD5B/pc2igucnDjq5Eo936oqOHPSyc7P2mlt8ZIz0UR4pJaTR51s/6yd6kp3v9q2WX3s3dbOp39u5YuNbez63MqXO23kH/AXK561IPiKxdjlGE2aK35J7969m08//RSf7+pc6dcSVVVRVXXA6xUWFlJaWgpAUVHRYHdryGlqauL111/n7Nmzw90ViWRIsFqt1NbWAv7rXzK03NAWsuEgMjKS6dOnc+utt2IymYiOjg7MW7BgAWvXrmX+/Pmkp6czZcqUq97ev/3bv7Fs2TKioqKYPHlyQAz+13/9Fz/96U95//330Wg0vPDCC0ybNo0nnniClStXotFoyM3N5eWXX76i7Z4rc+OwC6LjdJSfdmE2K6Rn91xU2eNWufwdb7OqFBy209aqkpDsz7RuMmtIz4b6Gg8njzo4ss9OZLSWzPEmouN0XYSQEIJzZf6cWIoCmeONmMyai2Vz/P+aLZpeC1gPJc3NzTidTsrKysjIyBju7nTC4XBQUFDAsWPHiI6O5p577un3uk1NTezatYuUlBSio6MpKCjA4/Gg11+fCVt9Ph9btmzB6XRSXl7O6NGjh7tLkiFGCEF+fj5JSUnExcUNd3eGhI4PKvDf06mpqcPYm68eN3RQ/2BwtS7L653ujqfXK9i2qY2gEA2zbwnmyH47NZWebl2CTofKyaMOzp/zdNu+yayQO8XcbT0/VRVUlrspLnTicgosQRpS0g2kpBkwmjRYzOFs/1sV9TVeouN0TJph6bW+4nDjcDh4/fXXAUhJSeHuu+8e5h75aW1t5ejRo5w4cQKv10toaChtbW088sgj/cqL5/F4+OCDD3A6nTz44IOoqspbb73FkiVLyM7OHoI9GHwOHDjAgQMHsFgsmM1mvvWtbw13lwaFywOUO8IhTKaeP6Z6oq2tjfz8fObOnYtWe+MNbDl//jzr169Hr9dz11139RqMfbWMlKD+DRs2YLfbcblcjBo1iiVLlgx3l4YdGdQvGdGcPePC5RRMne0vjTN5pgW308rRQ3aMJoWYeD1CFZwtcVNU6ED1QXqWEXNQZ7Gk0UDiKAN6Q/fWK43GX4g6ebSB2ioPFaUuio47KS5wEpuo40JTGx6PyvjJZtIyDN26EV0uFw6Hg/Dw8GtyLAZCh4s4Li6Oc+fO0dbWRmho6LD2ad++fRw5cgRFUcjKymLKlCno9XreeecdTp8+zfTp0/tsY/fu3TQ3N3PXXXdhsViIjIwkNDSUoqKi61KQ1dXVcfDgQbKysoiIiODLL7/E6XRekWgZyQgh2LBhA6qq8uCDD6LRDOxj5vTp0xw7doyxY8fekDGRx48fx2g0Yjab+ctf/sLdd99NQkLCcHfrmmG32zl//jzTpk2jrq5uRAjEa4Hb7Wb79u3MmjVrxCViH7nmBEknnn76aRYvXtzp98EHHwx5PzxuQUmRi9gEXSCflVarMH1uEMEhGg7ttVFZ7mb3F1YK8x1EROmYf3sIOZPMpGUYO/1S0409irFL0WoVklINzL41hFuWhpCWaaSl0UdomJ55S0IYk9nzoIJdu3bx/vvvdzt4YqjpEGSzZ88G4OTJk8PZHTweD3l5eaSmpvLoo4+yePFioqKiCA0NJSEhgeLi4j7j90pKSigsLGTq1KkB94ZGoyE7O5vKysoe4ylHKl6vl88//xyLxcKCBQsCL+COuJobifLychobG2lubr6ia7EjxuhyL8WNgNVqpbS0lPHjx/P1r3+doKAgPv74Y2pqaoa7a9eM8vJyhBCMHTuW6OhoWlpariiWdKRTUVFBcXExZ86cGe6udEEKsuuE1atXs2XLlk6/+++/f8j7UXbaicctyMrtbC3QGzTMnBeM4WLhbKdDZerNFmbOCyI4ZPDcGcGh/mSnS+4KZdnKUT1mwQd/HFBpaSlut5uCgoJB68OV0tLSgkajISkpiZSUFE6ePDmsD7zq6mpUVWXChAldRlNlZWXR3Nzc61dyW1sbW7duJS4ujlmzZnWal52djRCC06dPX5O+Xyv2799PS0sLixYtwmg0Eh8fj0ajGdGio7y8fMBhFUIIDh06RGhoKPHx8Rw4cACPp/uwgp7oGBw0ko/NlVJYWIiqqtx0000EBwd3K8p8Ph/nz59n//79fPDBB+zevXuYe311lJSUEBoaSnR0NFFRUfh8Pi5cuDDc3eo3breb8vLyPpfrSCM1Ej+ypCCT9BuXy19EOiFZT3hkV2+32aLh5gXB5Ew0ccsdoSSmdO9GHAz6025lZSVutxuz2cyxY8eGPRawpaWF8PDwwIAKq9XKuXPnhq0/VVVVaDSabmMaMjIy0Gg0vQqqPXv2oKoqt99+e5cYovDwcOLj4zl16tQVj8AdaqqqqsjPz+emm24KWPv0ej0xMTEjVnRcuHCBjRs3cvTo0QGtV1VVRV1dHVOmTOFrX/saNpuN/Pz8fq+vqmpAkNXU1Fw357g/+Hw+CgsLGT16dMCl1SHKLBYLH3/8MZ988gmvvfYa69ev5/DhwzgcDvLz869bC5rL5aKyspKxY8f6S7pdHIx2PbktDx8+zMaNG/vsc8czt7a2dsRdt1KQSfpNySkXPh9k3dRzLE1t/Vl27vsjZ8+WDGHPuqe0tBS9Xs/ChQux2WzDbq25tDJCWloaZrOZEydODFt/KisriY+Px2DoOqDCbDaTkpLSo9uyvr6ekpISJk+e3GMcRnZ2Nk1NTdfFQ93tdvPFF18QFhbG3LlzO81LSEigrq5uRKYq6XCD98cycCmHDx/GYrGQk5NDQkIC6enpHDlyBLvd3q/129ra8Pl8JCUl4Xa7b6gUCSUlJdjtdiZMmNBpeocoi4iIoLm5mezsbO68806+973v8eCDDxIUFMSuXbtG3Eu+P5SXl6OqKunp6YA/abiiKAM+r6qqUlBQMOShCpda43u7F1pbW2lrayMyMhK73U57e/tQdbFfSEEm6RcOu8rZMy5GpRp6dRNWVVVhtVrZvHkz27ZtG7AbZLBQVZXS0lLS0tJIS0sjOjqavLy8YXtYduSC6xBkWq2WcePGUV5e3u+X4GDidDqpr68nOTm5x2WysrKwWq3dWof279+P0Whk8uTJPa7fYWW7HnKSFRcX09bWxsKFC7uk6khMTMTn81FfXz9MveuZDpdSTU1Nv6+j2tpaKisrmTx5Mjqd39I9e/ZsfD4fBw4c6FcbHS/qm266Cbix3JbHjx8nLCys25QPISEhPPDAAzz66KPccsstpKenYzQaMRgMzJkzh7q6uuvier+c0tJSgoKCiI+PB/zZBcLDwwf8MXXu3Dm2b9/O+vXrh1SU1dbW0tbWhkajoaysrMflOtyVHYOVRprbUgoySSdUVWCz+mi74P/VVLnZtqmNXZ+3I4DMXGOv67e2thIVFcXUqVMpLCzkgw8+GJav5+rqapxOZ8AEP2XKFJqbm3tM8tnc3MyOHTuumVuztbUVVVU7lavKyclBVVVOnTp1TbbZG+fPnwf8Zbp6Ii0tDZ1OR3Fxcafp1dXVVFRUMG3atE5VJS7HbDYzevRoiouLR3xw8MmTJ4mKiup2tGCHS/dq3VEXLlzgyy+/HJSE0Je22UF/E9gePnwYo9EYEFPgt4jk5uZSWFjYr4TRHe7K0aNHExQUNGSuOiEEDoeDpqYmbDbboLdfX19PTU0NEyZMGHC4RVZWFnFxcezduxe32z3ofbtWeDweKioqSE9P77TP0dHRA352l5aWotPpsNvtbNiwYchEWVFRETqdjsmTJ1NXV9fjtXHu3DmCgoIYO3YsOp1OCjJJZ4YyOajPJ3q1EPl8Amu7isct0OpAqwODQSE8UktMnI4JU81YgnoP0L9w4QIRERHMmTOHu+66C4fDwfvvv09hYeFg706vlJSUoNPpAl+5GRkZBAcHd/sybG9v56OPPuL48ePX7Eu/4yV3qSCLjIwkMTGREydODLnlrrKyEp1OF/gi7g6DwUB6ejolJSUBd50Qgn379mGxWLq4dLpj3Lhx2O32YY2V64umpibq6uoYN25cty9hi8VCWFjYFV0bDoeD48eP86c//Yl3332XgwcPsmfPnkELlr5w4QKxsbEEBwf3ahnooL6+nrKyMiZOnNjFVT1jxgx0Oh379u3rs52mpiZCQkIwGAwkJCRcUwvZkSNHeO+993jzzTd59dVXef3111m3bh1vv/02O3fuxOFwDNq2jh8/jk6nIycnZ8DrKorC/PnzsdvtHD58uMt8IQQnTpzo1/EdSioqKvB6vQF3ZQfR0dG0tbX1W1yqqkpZWRlpaWncfffd2Gy2IRFlPp+PM2fOkJaWRlZWFtC921IIQVVVFSkpKWi1WmJjY0dczJ8UZCMUj0fFYVcH7UXtcqq0t/pob/XhcnZt1+NRsbb5EKogOERLULD/FxWrZ8rNQRhCyqlrOt7rNlRVpa2tLRBTlJqayre+9S2SkpLYtm3bkLl8hBCUlpaSmpoacD9ptVomTZrE+fPnqaurCyzrcDj4+OOPAw+da9XH7gQZwPjx47lw4cKQu3wqKytJTEzsM6FnZmYmTqeTiooKwP+FWV1dzfTp0/uVhT81NRWj0Tii3TinTp0KpOroicTERKqrq/t9Pwoh2LNnD2+++SY7duzA7XYzZ84c7r//fjQazaCN+m1tbSU8PJy0tDTOnTvXp4V39+7d6PV6Jk6c2GWexWJh6tSplJaW9nk9Njc3ExkZCfiPTXt7+zWJxykqKmLv3r1otVpSU1OZMmUK8+bN4/bbbycnJ4fjx4/zf//3fxw6dKjTvjudTs6cOcPWrVv54IMP+Mtf/sKWLVvYu3cv+fn5nDlzpksqHIfDQXFxMdnZ2b1afnsjPj6e7Oxs8vLyaG1tDUx3uVxs3ryZrVu3cvjw4RFlQSstLcVkMnWxDkdFRQH9L6FUV1eHw+FgzJgxJCQkDJkoq6ysxOl0kpWVFUjb093HSUNDA06nM+AVSEhIoKGhYdgHe13KDZ0YdteuXTQ0NFxVG4qidHoIx8TEMG/evB6XX716NYmJiTz66KMAvPTSS2i1Wvbt20drayter5ef/vSn3HbbbT224fMJ7FYVq83GE//0OFZrW5f1PvzwQ9asWQP4rRCvvPIKDQ0NPPnkk4GXZ0e5JJdT4HSo6PQKQvjjwVxOBaNZwWBQcLsEDruKVqtgCdag7aag94kTJ7Barb0mCrVaraiq2inI22KxsGTJEt544w3OnTtHbGxsj+v3xKZNm0hISOh3qamOwriXf/Hl5uZy8OBB8vLyWLp0KW63m08++YS2tjbuvvtutmzZcs0E2YULF7BYLF0e9GPHjmXnzp2cOHFiyJJrWq1WWlpa+mUFSElJwWQyUVxcTFpaGvv37yc0NJTc3Nx+bUun05GZmcmpU6dwuVxX/KK7VqiqSlFREaNHj+61wkdiYiKnTp0KWID7Ij8/n7y8PLKyspg8eTIxMTEB61t6ejonT55k1qxZV1Vayufz0d7eTnZ2NgkJCRQUFFBVVdVjmafW1lYKCgqYMGECZrO522UmT55MQUEB+/fv59577+12mY4Rlh3F4y916YaEhFzx/lxOY2Mj27ZtIzExkXvuuafLx0NmZiYTJ05k37597N+/n4KCAsaOHUtNTU3go8tgMBAbGxtwc9rt9oD7XKvVkpaWxrhx4wJpaHw+X78sv70xe/ZsSktL2bNnD3feeSc1NTV89tln2Gw2UlJSOHfuHO3t7QHBc7VUVlYSFRV1RRVqvF4v5eXljB07tkti4EsFWX8S4paWlqLRaALXX0JCAnfddRd/+ctf2LBhA/feey9BQUED7mN7eztOp5OYmJhu5xcXF2M0GklNTUVRFNLS0igsLOxSuq3DSt9x3cbHx6OqKg0NDSMm4e8NLciGgxUrVvDss88GBNnGjRtZt24dq1atIiQkhObmZpYvX86SJUu6dY8IIbDb/A+MsHAzv/n1a4SGhuLyXODuu1ewZMkSTp8+zW9+8xs++eQTIiMjA9aXn/3sZ8yaNYs333wTn8+H1WrF6RC4nCp6g4LlYqZ8r0fgdAgcNhWnHYQAvd4/X9F0HzfR2tqK0+ns9aXa8UV4+ag7i8VCVFQUlZWVTJs2bUDH0+12U1paSmlpKT6fr1+Z4zseDGlpaZ2mGwwGbrrpJvLy8mhpaWHnzp3U19dzxx13kJSURExMzDUTZM3Nzd2+yPV6PZmZmRQVFXHLLbcMSe3HqqoqoPf4sQ60Wi0ZGRmcOnWKoqIi6uvrWbRo0YBK5WRnZ1NQUMDZs2cDLoWRQkVFBXa7nXHjxvW6XMcDu7q6uk9BVlZWxp49exg7dmy39/mECRM4c+YMxcXF/Ra23dHW1oYQgrCwMJKSktDr9ZSVlfUoyPLy8gLxlD2h1+vJzc3lwIEDPd7rHfGQHS/s6Oho9Ho91dXVZGZmXvH+XIrL5WLTpk0YjUaWLl3a4/UWFRXF8uXLOX/+PHv27OHYsWPEx8czc+ZMUlJSiIuL6yQ0hBC4XC5aWlo4ffo0xcXFlJSUYDabEUKQmJjYqf7wlRAcHMy0adPYv38/X3zxBadOnSIkJISVK1f6a+8OoiCrqanho48+IigoiDvvvLPXEITuqKqqwu12M3bs2C7zQkND0ev1/Qrs7/BKJCcnd7pmEhMTueuuu/j444/Ztm0by5cvH1D/hBBs2rSJpqYmHnzwwS73nsfjoaysjKysrMA1MmbMGI4dO8a5c+c6fZRfLlw76pPW1tZKQTYU9GbJ6i8DrWWZm5tLY2MjtbW1NDU1ERYWRmxsLM899xwHDhxAURRqa2tpaGjo1lrkcgp8XnFRHAle/f9+xZf7D6BoNNTU1NDQ0MDevXtZtmwZ4WERuF0qZlMYbrfK3r17A8XCNRoNel0wLqcaKLDd8WLQGxR0eoHXI3A5BTq9gtGk9BjE6nK5AjXvWlpaerzpO+JiuitTlJycHKiV2DGyqz90uEHCwsLYv38/QK+iTAhBSUkJo0aN6vZlMnHiRPLz8/nwww9xOp0sXLgwcNPGxsZSWlo66GVyhBC0tLT0GC+YkZFBYWFhlwfItaKqqgqTydTjF+flZGVlUVBQwLZt24iIiBhwOaS4uDhMJtOIFGQnT54MDD7ojYiICEwmEzU1NYwfP77H5RoaGvjb3/5GbGwsixcv7vae6njpHz9+nPHjx19xrr5L77eOeMmObOuXt9nS0sKJEyeYMmVKlyTAl9NhqT1//jxjxozpMr/DhdXhstRoNMTHxw+a210Iweeff057e3u/rSpJSUl84xvfQFXVXj8WFEXBZDKRkJBAQkICc+fOpaKigqKiosBAlcFg8uTJnDhxgpMnT5KZmcktt9yC0WgMPM8Gw73b4Ra3WCxotVrWr1/PwoULB3R/lpSUYDAYuh1trSgKUVFR/XJZNjc309ra2q3YT0xMZNasWezZs4eysrJur6meqK6uDnwkf/7559x3332dBHZZWRkej6fTcyUxMRGDwUB5eXngeer1eqmuru40de84hwAAIABJREFUkCU4OJiQkJARFdgvY8iuAcuWLWPTpk188sknrFixgg0bNtDU1MTmzZvZsmUL0dHR3Zby8VuuVAwGBYNRw4YNG2hpaWbzZ5v5y0efERUVQ+sFB26XitslaGv1YbepOB0qdquKENDe5sPW7sNm9S9jNGk6ibEOFEVBb9AQHKrFZO46/1IujYXobQRWa2srWq222wf+qFGj8Hq9A77429raAFi8eDFZWVns37+/24DZDhobG2lra+tR2AQHB5OdnY3T6WT27NmdXq4dAvlq3dyX43A4cLlcPVpWkpKSMJlMlJRc+9xtQggqKytJSkrqtxBISEggJCQEn8/HrFmzBlzzUKPRkJqaSkVFxYjK0eRwOCgvL+/0dd0TiqIE4sh6wmazsXHjRoxGI8uXL+/R2qkoChMmTKCxsfGqRMzlH0BpaWnYbLZurby7d+9Gq9Vyyy239NlufHw8Wq02MBL3ci4XZOB/CTY1NQ1KibJDhw5RXl7O1772tQFZLhRFGXCRc61Wy5gxY7jjjjv44Q9/2Kcw7y86nY5ly5Zx5513cttttwU+DoOCgtBoNP0SZH3FmZWVlVFTU8OsWbO4//77iY+P5/PPP2fv3r39GtXs9XopKSkhPT29x4/kjsLafd23HTFbl3slOpg4cSKRkZHs3LlzQKmQjh49islkYtGiRdTV1XHkyJFO84uLiwkODu6U3Loj3rDj4wT8ws7n8wXclR3E///s3Xl0W/WZP/73vbpavMibvMdOnDghIYEQ0kBIgJBAWIYlUFqgQylT6LdnWubX6bdnzsw57bSHdlpm6DYzTNvp9MyhTDudthkKXyjQslPCFpqSEEhCiJfES+zE8SJZtrXd5feHfG8kW5aubF1Jlt6vczghjmN/IslXz32e5/N8GhsZkBW6Xbt24cknn8QzzzyDG2+8EX6/30jrv/HGG0bJKJaqapiaVCCK0WwWAOPvuVwOHHz/LQwM9CMcUrFp01Y899wzCIR8KK+wQcU4yitsuHTrpfjf//0FFBUIBWVElIm4YExRlHm9IcbuCEsVkFVUVCR8o9cDgET/9mT0gKyystIIyt588805g7Kuri4IgpD0Luzyyy/Hrl278JGPfCTu43pAlumypf74zRWQiaKIFStW4Pjx45YPH/X5fPD7/abKlTpBELBp0ya0t7cnLG2Y0dbWhmAwGLehIhtOnDiB119/PeGbgD6Ow+yOuqamJni93oTzvmRZxtNPP41QKISbbropZVZn9erVcDqdeO+95BtlkvH5fHA4HEY2t62tDYIgzNph1tPTgxMnTuDiiy821eOl776dKyAbHR01ylm6pqYmaJq24De3np4e7N27F6tXr15wL1eu1dbWzholIYoiysvLUwZkp0+fxn/8x3/MORJHURS88cYbqK6uxtq1a1FSUoJbbrkF559/Pt555x3jtZhMT08PwuFw0jKzx+NBKBRKOWKkq6sLDQ0Nc2Zf9ZsBv9+Pffv2Jf1aOp/Ph+7ubpx33nlYu3YtVq1ahbffftu4PgcCAfT29mL16tWz3nNWrFiBQCBgvB77+vqMY+tiNTY2wu/3WzJCZT4YkFlg9erVmJycRGNjIxoaGnDrrbfi4MGDuOqqq/Doo4/OelPTtGhTvaoCpeVn+7hi/95jjz2GlStXotxtw0c2nYsv/t+/xic/eRuuu+5q/MM//AMkScC3Hvgm/vTOXnz01mvwiTtvRE/P2YyLqqoYGRmBz+dLOyjTM2Tl5eUpA7K5prY7nU7U19cbg/nMGh8fh81mQ2lpKURRxNVXX41zzjkHb775Jp5++ulZF7bOzk40NzcnbXB1Op3Gm1eskpISuN3ujAdk+symZL1H7e3tCIfDaT8+6dK/fjoBGRAdAHrDDTfMu7ym35manZW1ULIsY8+ePfjtb3+L/fv344knnjDK7rojR46grq7OdM/QXPPIFEXBc889h9OnT+Oaa64xVQq22+1Yu3Yturq65r0Dzev1oqqqynhOSkpK0NTUFLfDTFEU7NmzB5WVlQl3Vs6lpaUFZ86cSfimPjIyEpcdA6JvbIIgLGiMgKZpePnll+HxeHDllVdaduxarrnd7pQBmX4NeumllxIGxocPH4bX68Wll15qZKz1oGf79u3o7e3FH/7wh6Tf4+jRoygtLU16LTBzhJLf78fQ0FDKdoslS5YYO1D1a2Iy7733HgRBMMqM27dvh8vlwgsvvABZltHR0QFVVRMGlHqDv35z0tvbi6ampllZa739Jl+yZAXdQ5ZLL730kvH/NTU1eOqpp6AoCkZHR2G3240yQ0dHB0LB6OwvV4kISRJm/b1Ebr/9dtx+++1xH6urq8MjjzyS8PNlWTYaWsfHx+fMZCXi8/lQUlKC+vr6OX+QNE2Dz+dLOvm9tbUV+/fvRzgcTnhcTyIz1yqKIq655ho4nU786U9/wp/+9Cecc8452LhxI0RRxOjoKK644gpTXzuR+vp6SzJkNpstaXaitbUVdrsdnZ2dGSubJNLX14eysrKEfX5WKikpQWNjI3p6emYdRJ5pY2NjePbZZ3HmzBlccMEFaGxsxAsvvIDf/OY3uPnmm+F2u3HmzBkMDw+n9Vqpq6uDzWbDwMCA8eYTiUTwzDPPoLe3F9u2bUurB/D888/HgQMHcOjQoXk9Jl6vd1Y/54oVK/D666/D7/fD7Xbj/fffx9jYGG688ca0ejeXLFkCTdMwMDAQV4bSD5xOtGFmoWd+jo+Pw+/344orrsjK5pZccbvdc2YfdT6fD6IooqKiAs888wxuv/1242c2HA7j7bffRnNzc8IS4fr16+Hz+XDw4EFs3bo14XUnFArhxIkTOO+885K2IMTutJzruqTfAJh57V922WU4fvw4/vCHP+CjH/3onO9B4XAYhw8fxsqVK431l5SUYOfOnfjtb3+Lt99+G4ODg6ipqUl4Q6WP8eju7saFF16IM2fOJPwZq6urgyiKOHXqVFb6d1NhhixL9IBFVVWEw2Gjxi/L0eyY3lg/n68bCoVSZr30jQllZWUIBoPGDi0z9MxXTU0NfD5fwrLa1NQUIpHInBkyIBp0qKqa8mIUy+/3o6KiIu5joihix44d+OIXv4j169ejq6sLv/zlL/Hkk08CQFpNozPV19fD5/NlpBdGNzo6ahwqPhdJkrB8+XJ0d3dbNtVeH4zY2tqak+xDW1sbTp8+bdlRUZqm4ciRI/j1r38Nv9+PG2+8EVdccQVWr16Nm2++GX6/H7/5zW8wNjZmzB5LZ5OBJEloaGgwgo5AIIDHH38cfX19uOqqq7Bhw4a01ltVVYW2tjYcOnQo7VK1PvJiZmCtv0F3d3cjEAjg7bffRmtr65y9PXPR+8hmthh4vd64HZaxmpqacOrUqXmX3fXsWqLD7guJ2+02RgTNRb/m7tq1C5qm4amnnjKuSfv370cgEMBll10258/x+vXroWnanCVxfdd6qte/y+VCWVlZ0gxZd3c3qqurTY2DKS0txZYtW9Df35/0bOEjR44gHA7P+plqa2vDunXr8M4772BgYCBhuVK3fPlyjI6OGkPJZ/aPAdGf6bq6urwZEMuALEv8fj8ikQhKS0uhadp0UKZhakJBZ+dRfPTW63DNNdfg6quvxtVXX40bb7zR1NcNBALwer0pd4LKsgxRFFFWVga3251WUKZfHKqrq43hr4k+B5g98iJWU1NTwot8MnqGLJHq6mps27YN99xzD7Zs2QJFUdDa2rqgWUip+shCoRB++ctfYvfu3XjppZdw8OBBnDx5MmkAZ3Z2VXt7O4LBYFoBazqGh4fjBiNmm36Hrc/Jy7R33nkHL774Iurr63HnnXfGBeatra342Mc+BlmW8eijj+KDDz7AihUr0t5N29zcjDNnzmBsbAy/+c1vMDw8jOuvvz7pzstk1q9fj6mpKXR1daX192JHXsTS3xiPHz+OvXv3IhwOY9u2bWkH4HP1kekZ8pklSyD62MiyPOvNW5ZlfPjhhymbuQcGBuBwODI2nytfud1uaJqWtG9Jv+ZWVVXhhhtugM/nw+9//3v4/X7s378fq1atSjriorKyEitWrDDmcc304YcforKy0hj9kEyyI5SCwSD6+/vTugk+77zzUF9fj9deey3hdVNVVRw8eBBNTU0J/42XX3658Z6QrP9Nvwn505/+ZMyjS6SpqQlDQ0N5cbwbA7IsCAQCCAQCKCsrQ3l5OQRBQDgcxtRktG9sw8Z1eOGFF+L+e/rpp019bf0FnepiJ8sybDYbBEFAaWkpysvLTQVlqqpiYmLCCMiAxI39ekCWrBQmSRKamppM90np4zbmCsh0LpcLF110Ee69917cfPPNpr72XFIFZN3d3RgeHoYgCOjq6sKrr76Kxx57DD/5yU8S3o0qigKfz2cqIGtra4PNZpvzzbmrqws//elP592Aqj/uycrKVqqrq0NpaaklAZmeDWhtbcVHP/rRhM3F9fX1uO222+BwOBAKheZ1PE5zczNUVcWvf/1rTE5O4pZbbllQqWPZsmWorKzEwYMH0/p7yX7eli9fjv7+fhw6dAjnn3/+vAOcJUuWzOojGxkZgSAICQOy2FltOq/Xi0cffRTPPfdcyg0MAwMDaGxsTHsX72Kj3zDO1UemV1P0YLulpQU7duxAb28vdu/eDVVVsWXLlpTfZ8OGDQiFQrNOyZicnER/f3/S7FKs2tpajI6OJsx8njhxApqmpfUzoFc4pqam8Mwzz8y6wT9x4gR8Pt+cGWeHw4Ebb7wRV155ZdIEQFVVFWpqahCJRNDS0jLn66qxsTHhjUQuFNwrP5+21QPRQMnv98PhcKCsrAyCIMDpdCIUDEGOaCgpje8bS4de/gSQNEOmaRoURYnrIdGDw2AwmLSEpH/9VAGZ1+uFIAgps1Otra0YHh42VbbSL1hmM16iKC74Yp6qsb+jowNutxu33XYbPvvZz+Lee+/Frl270NjYiD/+8Y+zngd9E4WZgMxut2PZsmXo6uqa9TqenJzESy+9hImJCVNnFurBdux/PT09qKqqyug09XQIgmCMv8j03ejg4CAmJiZw7rnnJn0NVFVV4bbbbsPVV19tnHOaDr153W6342Mf+9iCT1fQR2AMDg6m1Vis79xN9Ia0YsUKqKoKh8OBzZs3z3ttsX1kupGREVRUVCTsRysvL0dFRYXx+R988AF+9atfwe/3o6ysLGkgHgwGMTo6WvDlSuDs9SxRpQGI3sDPbP9Yt24dNm7ciKmpKaxfv95UD2hzczPq6+vx7rvvxl1Pjh07Bk3TTJfrPR4PVFVNeP5qV1cXysrKTGXaYjU0NODKK6/E6dOn8Ytf/AIHDhwwrgkHDhxAeXl50iCvtrbW1FBlPXOXqFypy6fG/oJr6hdFMe3ho/OlKApkWYYsy1AUBaIoQpIkSJIEm81m3OnozZn63YjN5oCqBWGTFDic6c3NiaXfuer/5rmoqgpVVWc9Jno/WTgcTrhNX5Zl4+DeqqoqOJ1OlJWVzZkhc7vdKecA6dmZ/v7+lFO99QtWqgxZps3V2K9vs96wYYPxXJaXlxtZzyeffBIdHR1xU9/nOsNyLitXrkR3d3fc9GhN0/DSSy8ZJe/jx4/HDTicaWJiAj/72c8S3tEm+3vZ0NbWhg8++ACnTp2a9earaRr27duHpqamtMuqx44dM47CSaWsrCzlZP65uFwufPSjH0VlZWXGAtt169bh7bffxv79+3H99deb+jterxcOhyPhEUiNjY1oaWnBueeeO+cRSWY0NTVBFEWcPHnSeFxHR0eTZtyam5vR09OD559/HkePHkVzczOuvfZaHDx4EO++++6cG3qKpX8MSJ0hm6v9Y+vWrWhqakoaXMQSBAEbNmzA888/j97eXuMG5MMPP0R9fb3pa1JsY3/scy/LMnp7e7FmzZp59aSed955WLZsGV555RW89tprOHbsGC644AKcPHkybvfoQqxZswY9PT1JS6putxulpaU4depUzketFFxA5nK5jCN+MtG47HQ641L2iqJgcHAQgUAgLgiSJCluzpc+pFBRFCxbtiymtKji9MkQ/IF+eDxB2CRz09IT6evrQygUgtvtxtjYGBwOR8IX8cTEhHHK/UxerxcTExNwOBxxj5emaRBF0RiSql8cqqurE+60TDbyIlZDQwMcDkfeB2RdXV2zjo7p6uqac5v10qVLUVNTg3fffTfuAqUHZGZ3NS5fvhyiKKKrq8sIyI4cOYITJ05g27Zt8Pl8Cc9pi9XZ2QlFUXD55ZfHrV8QBEt3cJqxdOlSCIKAEydOzHrz3b9/P/bu3WuMNzF7B6+qqrE7NRtnZWa65Bt7pJfZn6OZIy9iiaKIW2+9dcHrmtlHJssyvF5v0sxFc3Mzjh49ig8//BCbN2/GRRddZAwG3r9//5z9RgMDAxBFMe1My2Jkt9vhcrnSDshEUUy7PL5q1Sq8/vrrOHDgAJYtW4axsTEMDQ3hsssuM/01qqurIQgChoeHjWvf+Pg4XnnlFUQikQWV7N1uN2666SYcO3YMe/bswfPPPw9JkubdkzlTTU0N/vzP/zzp5wiCkDcDYgsuIBMEYUF3hTPpk4p1AwMDePbZZ9HW1obW1lbU1taitrYWJSUlkGUZo6OjGBkZwZkzZzA6Oopzzz3XuKvQNA3vvDGByQkJ3lAXOjo68IlPfGJe6wqHw3jhhRewfv162O12vP7661i2bFnCu9ejR4/ijTfeMAYIxpqYmMCrr76K9vb2hFkyn88Hu91u/L3q6moj5R37ZuDz+eY8GiiWPpzPTB/Z+Pg4JEnK6PNphj5HamhoKC5T09HRgaqqqoRzpvS70ZdffhkDAwNGKWtsbAxlZWWmAwWn04nW1lZ0dnbi0ksvhd/vx549e9DS0oILLrgAvb29OHjwIPr7++fMBnV2dsLj8eDCCy9M959uOafTiaamJvT09GDr1q3Gx0+dOoW33noLy5cvRzgcxnPPPYdAIGBq5+LJkycxNTWVsXMUc0E/0uvAgQPYvn17ys/3+XxzNilnUktLC/bt24dQKAS/3w9N05JmyFasWIHe3l5ccMEFceVcfQbUXNmKwcFB1NXVFfS4i1gVFRUpA7JM3IjabDasX78ee/fuxcjICDo6OgAkb4afSZIkVFdXY2RkBKqq4r333jOOsdu2bduCNwkJgoDVq1dj6dKlePvtt41jyrKpsbHR2Jmc7febWAXXQ2Y1vYR3ySWX4MILL0Rra6vxBEqShPr6epx77rnYtm0bbrnllri7/JEhGd5RBWvOd6G9fTmGhobm3aB94sQJqKqK9vb2lEf+jI6OorS0NOELTQ8u5vq7+h27HnxVV1cjFAoZjwMQ7f8IBoOm7uyB6EVenxifjD7yItsjGhI9nlNTU0ZWb671rFmzBi6XCwcOHDA+NjY2Zro0oGtvb8f4+DjOnDmDF154AQCwc+dOCIJgHCI914DViYkJDAwMzHuifja0tbXhzJkzxkDUUCiEZ599FmVlZbj66qtx8803Y8WKFdizZw/eeuutlH2hx44dg91uz3n2byH0I72OHDkS97OViKIoGB8fz8osOb2PbHBw0MiMJwvISktLcf3118/qrZMkCS0tLQmPz5JlGadPny6KcqUu2XBYn8+H8vLyjLXdnHfeebDZbDh48CCOHTuGlpaWlOeZzlRbW4vTp0/j0UcfxZ49e9Dc3IxPfvKTce0bC1VSUoLt27enNcA4U/Q+smyfJDITA7I06RfL+UTRXR+G4HAKaG1zGNmN+U4u7+zsNCZzV1VVwWazzblLZGbtP5Y+VC9VQKbTg4vYsqWZkRex9DuqVFmyZCMvrKTvQo3tI+vo6ICmaUmzgJIk4bzzzkN3d7fRzD+fgGzFihUQBAHPPvssTp48iW3bthmPgyRJWLp0adw5bbH0HZpmspW5Ejv+QtM0vPLKK/D7/bjuuuvgcrkgSZIxSmLfvn14+eWX59wEoCgKurq6sGLFikWfXbnwwgshyzLef//9pJ+nZ6qyEZDpux5Pnjxp7LCc7/ddtmwZxsfH487GBaLXHkVR0jq3crHTA7JEP8N6OTpTSktLsWbNGmO6fzqz93QejwdTU1MYHx/Htddei127duXk2myVhoYGCIKQ87IlA7I0zTcg8/sUDA3KWL7KCZskwOPxoLy8fF4BmSzL6OnpQXt7O0RRhM1mg8fjSRhUaZqWNCBzOByoqqpK2MSuquqsgEzf7h7b2G9m5EUsj8eDkpISUwFZrnYEzmzs7+jogMfjSTlCYP369RBFEQcPHkx5qPhcSktL0dzcDK/Xi7a2tlnjGdra2jAxMZEwAO/o6EBNTU3CsQT5wuPxoKysDCdOnMAHH3yAY8eO4ZJLLol7QxZFEVdeeSUuuugiHD58GC+//HLCr9XX14dgMJjXAahZHo8HbW1tOHjwYNJNOsl2WGaa3W5HY2Mj+vv7MTIygqqqqnlnbvSm8pm7LfVdmcWWIYtEIgnncJntI0zHhg0bjL7g+WTP161bh61bt+Kuu+4yPS5jMbHb7bjrrrtw8cUX53QdDMjSFAgEYLfb074odX0YgmgDlq2M7jASBAHLly9Hb29vyqGuM/X09CASicT9YNXV1eHMmTOz7rjGx8chy3LSN2j97840OTkJVVXjLg56Kj1RQGb2jkkQBLS2tqK/v3/OclQoFEIoFMrKm04i9fX18Hq9Ru/MwMCAqb6L8vJyrFq1CocPHzbS3+kGZEC0zFBVVYWrrrpq1sVPzzDNDOYnJycxMDCQ98GJvrlAP2+vpaVl1kHv+udt2bIFmzZtwpEjR2bNUwKiAajD4TC98yzfbdy4EYFAYM5DpYGzAVm2jr9asmQJhoaGcPr06QUF+vqg00QBWVVVVdLzZwvNXDstw+EwAoFAxq97Ho8H55xzDtatWzevjS+lpaXYtGlTTvurrFZdXZ3zGXgMyNI0n6a/YEDFyZ4wWtsccDrPPuRtbW2IRCJpT2bv6uqC0+mM69Oora1FMBicdVCxPmE5WWanrq4Ofr9/1uHLie7EBUFAdXV1XEDm9XpRVlaWVsmopaUFk5OTcx5Wru+wzGWGDIiWU/RGWLOBzoYNGxCJRIzG1/kEZKtXr8bdd9+dcKNFWVkZ6uvrjYNzdZ2d0cPk87l/TKe/9iVJwjXXXJP0QnjJJZegubkZr7zyStwsJFmW0dXVhZUrV2ZlzE02LFmyBPX19XFzmWaaudHGai0tLcZk+YVO0V+2bBn6+/uNm1C9P62YypXA3LPI0m3/SMd1112HHTt2ZPzrUuYwIEvT1NRU2hfC4x0hqCqwYnX8nUlrayskSUqrbKkoCo4fP44VK1bEzfyaqzlfD8iS3dnOtSlgrovDzIBsPil2fXTAXMForkZe6GIn9nd0dKC+vt50RqKhoQFNTU0YHh5Oeaj4fC1fvhynTp2KG7CrlysXw9EzS5cuRUtLC6677rqUDcaiKOLaa6+FKIp49tlnjflqPT09CIfDeZ8RTIcgCNi4cSO8Xu+sgFuXbOSFFWKn5y+0FL5s2TLIsmyUKcfGxhAMBouqXAmcDchm3kBbGZBR/mNAlqZ0M2SyrKGnK4zGJXaUu+OHpuo7j+Zq0E6kv78foVBo1uwXvTl/Zl/R6Ogo3G530jT1XMGcPtR2ZkBRXV2N8fFx47gmn8+XdvmksrISJSUlcx7qmuuATG/s7+jowOnTp9MeqaCPa9Bn+GSavilEL//o5crFkB0Doj0bt956q+lSo9vtxs6dOzE0NGRkHjs6OuByuXJ2FJRVVq5ciYqKCuzfvz/hn2e66TsVu91uzAdbaLC/ZMkSiKJovG6LsX8MiPYg22y2rGbIKP8xIEtTIBBIq9eh73gYkbCG9jWJA6Lly5djfHx8ztLdTF1dXbDb7bPeyPTm/EQZslQX0ZKSEpSXlycMyNxu96xykl6C83q9iEQimJycTPsCIggCmpqa5gzI/H6/MUAxV+rr640+sHSzMO3t7aisrLRsVlRdXR3KysqMLIperiykbNFM7e3txgDVzs5OdHd3Y9WqVSlPh1hsRFHEhRdeiMHBwVn9VoqiwO/3ZzUgA2AM3V3o97Xb7ViyZInx7xocHITL5cr6vyfX9GPmZvaQ+Xw+uFyurAw4pvzDgCwNmqallSHTVA3dH4ZQ7bGhpjZxj4veoN3V1YWJiQkMDQ3hxIkTOHLkCI4cOYLTp08b/RaqqqKrqwttbW0Je2Zqa2vjgipVVVMedaKrq6ubtdNyrsxX7E7LhdzRNTc3w+fzJTzXUh95kcvdPHow1dTUlHbZURRFfOITnzA15HM+YhvjFUVBZ2cnqqurF0W5ciEuv/xyeDwe/P73v4csywUbgK5btw6VlZXYs2dP3BFYfr9/1kabbPjIRz6Cu+++OyPB77JlyzA6Ompslmlubi64XXtmzBWQMTtWvAqjEzZLwuEwVFVNGJCdOhmBpmkoK7ehtEyEZBcweDKCqUkVazfMnVFzu92ora3FW2+9ZZRiZtJn/7jdbgQCgTmPqqirq0NnZ6dx5I/X64Wqqqb6Purq6nDixAnjSB79HM5EzbZ6kDY2NmZcoOdzEdG/9uDg4Kx/Uy5HXuj0gGy+E+Ctvstta2vD4cOH0dnZiZMnT+Z8y3Y2SJKE6667Drt374bT6SzYUpckSbj88svx9NNP4/333zdK4NneYakTRTFjmwiWLVuG119/HUePHoXP58v5+aq54na7Z/UP+3w+Y0gpFR8GZGmYawbZ6YEI9r0eP3Hf4RSgaUBpuYjG5uS7D3fs2IHe3l6UlpbG/adpGoaHh+P+c7vdc04k13vBhoeHsWTJElM7LGP/rv79mpqajEPHEwVakiShoqICY2Njxs7K+QRkdXV1EEVxVkCmaRrGx8dz/ma7dOlSXHHFFbPmgOWL1tZWiKKI1157DUBhlytjeTwe3HTTTQCQ823qVlq+fDlaW1vx9ttvY/Xq1SgpKUl75l8+qqks9PWpAAAgAElEQVSpQXl5udEjV2w7LHUVFRWYmpoyKiB6OXo+g1upMDAgS0OigExRNBw+EECZW8TGzaWYmlIxNaFickJFYEpF20onBDF5Or6pqWnOi1JVVZXpRu3Y5nw9IBMEwVSGLHanZVNTU8pSZE1NDUZHR+F0OuFyuebV6yVJEhoaGozGXl0oFEI4HM75JGhRFHNyjIdZDocDLS0t6O3tRXV1dV4Pg820hZ6ftxgIgoBt27bhl7/8Jfbu3YsdO3bA6/VmdeSFFQRBwLJly3D48GHYbLaEZ8MWg5k7LfXJ/SxZFq/Cvb20QKKArOvDECYnVJy/sQRVHgnNrQ6sPNeFCy4qxSVXlKNxSfaOcykrK0NpaanRRzYyMoLKykpTM5rKy8vhcrmMv5sqIKuurobX68XY2NiCLiBNTU0YGhqKG46b6x2Wi4m+23LVqlVF2YdT6DweD84//3wcOnQIw8PDRl/nYn+u9an9DQ0NBTNDLl0zh8NyhyUxIEvDzIBsalJFx5EgmlrsqGvMj3P0Yhv7zTb0A9G71tiJ/WYCMlmWcerUqQUHZKqqxm0o0C9QDMhSW7VqVcLjlahwXHLJJXA6ndizZw+8Xm9BvGG3trbCZrMV3MiSdMwMyLJ5JBblJwZkaZgZkB1+NwABwNoN+VM+qKurw+joKEKhELxeb1q77urq6jA8PAxFUeDz+VBWVjbn3as++kKW5QX1s8Q29uuYITOvtLS04A76pXgulwubN29Gf39/1meQWcXpdOKTn/wkNm3alOul5Iw+EFm/3vl8PkiSlPB0DioODMjSEAgEIEkS7HY7hk5FcKo/glVrXSgty5+Hsa6uDqqqoru7G5qmpdVXpP9dfZxFsju12OOAFnJHV1paisrKylkBmcPh4Cweomnnn3++8bNcCAEZgAUdVF4IbDYbysrKjB4yn8+X81E/lFv5E0ksAvoMMlXRcGh/AGXl4qzjkHJNb5DVD2JON0MGRBv7UwVkJSUlRiP/QlPs+oBY/bSCfJhBRpRPRFHEFVdcAZvNZkzNp8XP7XbHZchYrixuDMjSoAdk3cdCmPSrWLexBDZbfgUNehN/X18fRFFM625av2MdGBhIOX1fP2Rc/54L0dzcjEAgYPSt5cMMMqJ809rais9//vMFP/y3mOjDYfVRP4WS/aT5YUCWhkAgAKezBMeOBNGwREJDU3408scSRdE417K6ujqtydqiKKKurg7d3d0AUgdaNTU1cDgcaR0llUhsH5l+YWJPFNFshTx3rRi53W5MTEzA7/dDlmVmyIocf7rTEAgEINlcUGSgrT2/SpWx9NLjfO6k6+rqjM0LqS4Omzdvxs0337zg0qIe2A0ODiIYDCISiTAgI6KC53a7oSgK+vr6AHCHZbEr3o7KeQgEArDbXYgEopP489VCAzJdqvR5eXm5sVNoIfSDxgcGBjjygoiKht6aYbYqQYWNGTKTIpEIZFmGZIs2sudzQKaXAOfT/KsHZPoE/mxpamrC6OioMQeNARkRFTr9Onf8+HEIgsDe2SLHDJlJehnPCMgc+RvLejwe3HPPPfP64a6pqYEoilm/U9ODyGPHjgFgQEZEhU+/Rg8PD6OioiKtnl8qPAzITJqamgIAiIITggjY8vyRm++dliRJWLJkSdycsWxoaGiAIAjo6+uD0+nkDDIiKnhOpxMOhwPhcJjlSmJAZpaeIRPggsMhFPSMrEw06qfL4XAYxz4xbU9ExcLtdhvnDlNxy9+6W57RAzLACbujcIMxILq1PhcBp162ZLmSiIqFfgPKgIwYkJlkBGSqK68b+hez5uZmAAzIiKh4MCAjHQMykwKBAGw2G+SImNcN/YtZU1MTBEFI6/xNIqLFjAEZ6dhDZpJ+bJIcARwFXrLMFbfbjTvvvJPHhxBR0VixYgWmpqZ4I0rMkJmlB2ThkAY7S5aW8Xg83PpNREWjpqYGt956K697xIDMrEAgAJerBKrKDBkRERFlFgMykwKBAJyO6FDYQt9lSURERNnFgMwk/RxLIL+PTSIiIqLFhwGZCbIsIxKJnA3IuMuSiIiIMoiRhQn6DDK7rST6K0uWRERElEEMyEzQAzLRxpIlERERZR4DMhNiDxYHuMuSiIiIMosBmQlGhgwu2CRAtDEgIyIiosxhQGaCcY6l5mB2jIiIiDKORyeZEAgEIIoiFNkOuyPXqyEiIqJCwwyZCXHnWLKhn4iIiDIsaxmyd999F4888ghUVcVVV12FW265Je7Ph4eH8aMf/QiTk5NQVRV33nknNm7cmK3lJWWcYxnWUFnKGJaIiIgyKysBmaqqePjhh/HVr34VHo8HX/7yl7Fp0ya0tLQYn/PYY49hy5YtuOaaa9Df349/+qd/yr+ALKQxQ0ZEREQZl5V0T2dnJxobG9HQ0ABJkrB161bs27cv7nMEQTDGS0xNTaG6ujobSzMlerC4C5GIxqGwRERElHFZyZCNjo7C4/EYv/d4POjo6Ij7nNtuuw3f+ta38OyzzyIUCuFrX/taNpZmSiAQgNNZAnmcM8iIiIgo8/Jml+Ubb7yB7du346abbsKxY8fwgx/8AN///vchivFJvBdffBEvvvgiAODBBx9EbW2t5WsLh8OoqKjG5BmgprYCtbUVln9PSk2SpKw8/5QePi/5i89NfuLzkr+y+dxkJSCrqanByMiI8fuRkRHU1NTEfc7LL7+Mr3zlKwCAc845B5FIBH6/H5WVlXGft3PnTuzcudP4/fDwsIUrBxyO6JwLRY7+PhSaxPBw2NLvSebU1tZa/vxT+vi85C8+N/mJz0v+yvRz09zcPOefZaWHrL29HYODgxgaGoIsy3jzzTexadOmuM+pra3FoUOHAAD9/f2IRCKoqMh9JmpychIAIPEcSyIiIrJIVjJkNpsN9957Lx544AGoqoodO3agtbUVu3fvRnt7OzZt2oS7774bP/nJT/DMM88AAO677z4IQu6DHz0gE8VoQMamfiIiIsq0rPWQbdy4cdYYizvuuMP4/5aWFnzzm9/M1nJM03d+2sCDxYmIiMganHKagp4hg+AEBGbIiIiIKPMYkKUwOTkZLZ2qDtjtQl6UUYmIiKiwMCBLYWpqKjoUNsyGfiIiIrIGA7IUJicnjXMs2T9GREREVmBAloIRkIV4bBIRERFZgwFZCnpAFgmrLFkSERGRJRiQpTA1NRVTsuTDRURERJnHCCMJRVEQCATgcpVAkQE7M2RERERkAQZkSQSDQQCAw1Ey/SsDMiIiIso8BmRJBAIBAIBdP8eSARkRERFZgAFZEnpAZpsOyFiyJCIiIiswIEsiHA5DFEXYbDzHkoiIiKzDgCyJ9vZ23H///XA5qgAADicfLiIiIso8RhgpCIIAORL9fw6GJSIiIiswIDMhHNYgioDNluuVEBERUSFiQGZCOKTB4RQgCMyQERERUeYxIDMhEuY5lkRERGQdBmQmhMMqd1gSERGRZRiQmRAOabBzhyURERFZhFGGCZGwxgwZERERWYYBWQqaphlN/URERERWYECWghzRoGmc0k9ERETWYUCWQjCoAOBQWCIiIrIOA7IUQtMBGY9NIiIiIqswykghFFQBMENGRERE1mFAlsLZDBkDMiIiIrIGA7IUQqHpgIwZMiIiIrIIA7IUWLIkIiIiqzEgSyEYVCDZAVFkQEZERETWYECWQiiowOHgw0RERETWYaSRQiioslxJRERElmJAlkIoqHCHJREREVmKAVkKoaDKHZZERERkKQZkKYRCCkuWREREZCkGZEmoqoZwSGXJkoiIiCzFgCyJSFgDAO6yJCIiIksx0khCD8jszJARERGRhRiQJRE2MmQMyIiIiMg6DMiSCIcYkBEREZH1GJAlUV4h4iNbPCgp58NERERE1mGkkUS524b1G6vhdPJhIiIiIusw0iAiIiLKMQZkRERERDnGgIyIiIgoxxiQEREREeUYA7IktP7j8P/0IWjekVwvhYiIiAoYA7Jkhk9j6qndgM+b65UQERFRAWNAloxkj/4qR3K7DiIiIipoDMiSYUBGREREWcCALBk9IIswICMiIiLrMCBLxs4MGREREVmPAVkyLFkSERFRFjAgS2Y6INMYkBEREZGFGJAlI0nRX9lDRkRERBZiQJaMUbKUc7sOIiIiKmgMyJLRAzKFGTIiIiKyDgOyZIyxF8yQERERkXUYkCVjn+4hY1M/ERERWYgBWRKCaANEGwMyIiIishQDslTsdgZkREREZCkGZCkIdjvHXhAREZGlGJClINgdzJARERGRpRiQpSKxZElERETWYkCWgiDZORiWiIiILMWALAXBbudZlkRERGQpBmSp2B3MkBEREZGlGJClIHDsBREREVmMAVkqEsdeEBERkbUYkKXAsRdERERkNQZkKbBkSURERFZjQJYKM2RERERkMQZkKfDoJCIiIrIaA7JUOBiWiIiILMaALAWBRycRERGRxRiQpRBt6meGjIiIiKzDgCwVNvUTERGRxRiQpSDY7YAiQ1PVXC+FiIiIChQDshQEuyP6PwrLlkRERGQNBmSpSPborxx9QURERBZhQJaCkSFjHxkRERFZhAFZKvbpDBkDMiIiIrIIA7IUBAZkREREZDEGZCkIRg8Zm/qJiIjIGgzIUtEzZAozZERERGQNBmQpCNJ0Uz93WRIREZFFGJClcLaHjCVLIiIisgYDslQ49oKIiIgsxoAsBe6yJCIiIqsxIEuFGTIiIiKyGAOyFPSxFxqb+omIiMgiDMhSYMmSiIiIrMaALBWJARkRERFZiwFZCjxcnIiIiKzGgCwFziEjIiIiqzEgS4U9ZERERGQxBmQpCDYJEEQeLk5ERESWYUBmhl1ihoyIiIgsw4DMDMnOgIyIiIgsw4DMDAZkREREZCEGZGZIdoCT+omIiMgiDMjMYIaMiIiILMSAzAxJgsaAjIiIiCzCgMwMyc7BsERERGQZBmRm2FmyJCIiIutI2fpG7777Lh555BGoqoqrrroKt9xyy6zPefPNN/Hoo49CEAQsW7YMX/ziF7O1vOTYQ0ZEREQWykpApqoqHn74YXz1q1+Fx+PBl7/8ZWzatAktLS3G5wwODuKJJ57AN7/5TZSXl8Pn82VjaeZIEjAVyvUqiIiIqEBlpWTZ2dmJxsZGNDQ0QJIkbN26Ffv27Yv7nJdeegnXXnstysvLAQCVlZXZWJo5HHtBREREFspKhmx0dBQej8f4vcfjQUdHR9znDAwMAAC+9rWvQVVV3HbbbdiwYUM2lpeSINm5y5KIiIgsk7UeslRUVcXg4CDuv/9+jI6O4v7778f3vvc9lJWVxX3eiy++iBdffBEA8OCDD6K2ttbSdUmSBGd5OSKaavn3ovRIksTnJA/xeclffG7yE5+X/JXN58Z0QPZf//Vf2L59O9ra2tL+JjU1NRgZGTF+PzIygpqamlmfs2rVKkiShPr6ejQ1NWFwcBArV66M+7ydO3di586dxu+Hh4fTXk86amtrEVJUaKGg5d+L0lNbW8vnJA/xeclffG7yE5+X/JXp56a5uXnOPzPdQ6aqKh544AH8zd/8DZ544om4ACuV9vZ2DA4OYmhoCLIs480338SmTZviPufiiy/G4cOHAQDj4+MYHBxEQ0OD6e9hKe6yJCIiIguZzpDde++9+PSnP40DBw7gtddew+OPP45Vq1Zh27Zt2Lx5M1wu15x/12az4d5778UDDzwAVVWxY8cOtLa2Yvfu3Whvb8emTZtwwQUX4ODBg/jSl74EURRx1113we12Z+QfuWCSBEQ4GJaIiIisIWiaps3nL/b19eHf/u3f0NvbC4fDgUsvvRS33377rFKk1fTNAFapra3F0E++D+3FJ2H78eOWfi9KD9P8+YnPS/7ic5Of+Lzkr2yWLNNq6p+amsLevXvx2muvoaenB5s3b8ZnPvMZ1NbW4umnn8Y//uM/4nvf+96CF5x3po9O0jQNgiDkejVERERUYEwHZN///vdx8OBBnHvuubj66qtx0UUXwW63G39+991349Of/rQVa8w9afphkuXoMUpEREREGWQ6IFu1ahU+85nPoKqqKuGfi6KI//zP/8zYwvKKHoTJEQZkRERElHGmd1muX78eshzf2D48PIwTJ04Yv3c6nRlbWF6RYgIyIiIiogwzHZD94Ac/gKIocR+TZRk//OEPM76ovKMHZDw+iYiIiCxgOiAbHh6eNRessbERZ86cyfii8g4zZERERGQh0wFZTU0Nuru74z7W3d2N6urqjC8q79gZkBEREZF1TDf133DDDfjud7+LXbt2oaGhAadPn8ZTTz2FW2+91cr15QXBJkEDGJARERGRJUwHZDt37kRZWRlefvlljIyMwOPx4O6778Yll1xi5fryg5Eh47R+IiIiyry0BsNu2bIFW7ZssWot+Ys9ZERERGShtAIyr9eLzs5O+P1+xJ64dOWVV2Z8YXmFARkRERFZyHRA9sc//hE/+MEP0NTUhL6+PrS2tqKvrw9r1qwpnoCMB4wTERGRBUwHZLt378Z9992HLVu24J577sF3vvMdvPLKK+jr67NyffnBrh+dxAwZERERZV5ac8hm9o9dccUV2LNnT8YXlXemM2QaAzIiIiKygOmArKKiAl6vFwBQV1eHY8eO4fTp01BV1bLF5Q32kBEREZGFTJcsr7rqKhw9ehSXXHIJbrjhBnzjG9+AIAi48cYbrVxffuDRSURERGQh0wHZrl27IIrRhNoVV1yBdevWIRgMoqWlxbLF5Q1O6iciIiILmSpZqqqKT33qU4jEZIhqa2uLIxgDABub+omIiMg6pgIyURTR3NwMv99v9Xryk8RJ/URElHmKquGkL5jrZVAeMF2yvOyyy/Dtb38bf/ZnfwaPxwNBEIw/O++88yxZXN6w2QBBYIaMiIgy6s1eP/71rWN45NaVqHDacr0cyiHTAdnzzz8PAHj00UfjPi4IAn74wx9mdlV5RhCEaJaMARkREWWQNyhDVjX4QwoDsiJnOiD70Y9+ZOU68p9kZ8mSiIgyKiRr078WwQgpSsr0HLKiJ0kce0FERBkVUqKBWJABWdEznSH7/Oc/P+ef/fjHP87IYvKanSVLIiLKrLASzZAxICPTAdkXvvCFuN+PjY3hd7/7HS699NKMLyovsYeMiIgyLDydIdNLl1S8TAdka9eunfWxdevW4YEHHsD111+f0UXlJcnOsyyJiCij9AyZXrqk4rWgHjJJkjA0NJSpteQ39pAREVGG6c38LFmS6QzZ7t27434fCoVw4MABXHjhhRlfVF5iyZKIiDLMyJCxZFn0TAdkIyMjcb93Op248cYbsW3btowvKi9JdkDh2AsiIsqcEJv6aZrpgOy+++6zch35T7IDgclcr4KIiApImCVLmma6h+yJJ55AZ2dn3Mc6Ozvx5JNPZnxReYljL4iIKMPOliwZkBU70wHZ7373O7S0tMR9rKWlBb/73e8yvqi8JEmc1E9ERBlljL1Q2ENW7EwHZLIsQ5LiK5ySJCEcDmd8UflIYFM/ERFlGHvISGc6IFuxYgWee+65uI89//zzWLFiRcYXlZckO8deEBFRRuk9ZCxZkumm/r/4i7/At771LezZswcNDQ04ffo0vF4vvva1r1m5vpzqHg3iv97rxE0ry1DNHjIiIsqws0cnsWRZ7EwHZK2trXjooYfwzjvvYGRkBJs3b8ZHPvIRuFwuK9eXU2emIvh/75/CpUuWoZolSyIiyjCWLElnOiAbHR2Fw+GIO7tyYmICo6OjqKmpsWRxueaSohXdsKwBNokBGRERZYyiapBV7rKkKNM9ZN/97ncxOjoa97HR0VF873vfy/ii8oUekAVldXrshQxNY1qZiIgWLqKefT/hLksyHZANDAxg6dKlcR9bunQpTp48mfFF5QunTQAABBU12tQPcFo/ERFlRGxWjCVLMh2QVVRU4NSpU3EfO3XqFNxud8YXlS+c0xmykKydDchYtiQiogzQG/rLHTaWLMl8D9mOHTvw/e9/H5/4xCfQ0NCAU6dOYffu3bjyyiutXF9OuYyALCZDFpGBwt3HQEREWRKaHgpbVWLHSV8QmqZBEIQcr4pyxXRAdsstt0CSJPz3f/83RkZG4PF4cOWVV+Kmm26ycn055ZSmS5ayCtinHypmyIiIKAPC06MuKkrs6PcFEVY0432Hio/pgEwURezatQu7du0yPqaqKg4cOICNGzdasrhcc9pYsiQiImvoJcsqV/StOCirRqsMFR/TAVmsnp4evPrqq3j99dehKAoefvjhTK8rL9hEAQ6bEE0rMyAjIqIM0s+xrCyJvr+EOBy2qJkOyHw+H1577TXs2bMHPT09EAQB99xzD3bs2GHl+nLOZbchKKsQJDs0gMcnWey1E+PY0FQGt9OW66UQEVlKD8CqpgOyoMLG/mKWMjf61ltv4cEHH8TnPvc5/OEPf8DWrVvxwx/+EBUVFbjkkkvgcDiysc6ccUli9EgLiT1kVvMGZHzvjQHsOTGe66UQEVnOyJBNlyy507K4pcyQ/eu//ivKy8vxpS99CRdffHE21pRXXHZb/C5LBmSWGQ8rADiPh4iKgz4M1siQ8dpX1FIGZJ///Ofx6quv4p//+Z/R3t6Oyy67DFu3bi2arbkldnFGQMbBsFaZDEUDshDT9kRUBNhDRrFSBmTbt2/H9u3bcebMGbz66qt49tln8fOf/xwAcODAAWzbtg2iWLi7QpySDUFFjh6dBDBDZqHJSPTiFOZFiYiKgL7LsjJmlyUVL9NN/XV1dfj4xz+Oj3/84zh69CheffVV/OxnP8OvfvUr/OQnP7FyjTlVYhcxGmLJMhsmwsyQEVHxCM9o6mcPWXFLGZC99957WLt2LSTp7KeuWbMGa9aswb333ot9+/ZZusBcc0l6D1l084IWiaA4irXZNxmezpDxkF0iKgIhRYUAoMLIkPHaV8xSBmRPPfUUHnroIaxevRobN27Exo0bUVNTAwCw2+3YunWr5YvMJZd95i5L9pBZZVLPkPEukYiKQFjR4LAJcNmjY3547StuKQOyv//7v0coFML777+PAwcO4PHHH0dZWRkuvPBCbNy4Eeecc05B95C5JFu0hMYeMsvpJUtmyIioGIRkFQ5JNKbzcw5ZcTPVQ+Z0OrFp0yZs2rQJANDb24sDBw7g17/+NU6ePIl169bhhhtuwKpVqyxdbC7M3mXJgMwqelM/7xKJqBjoGTJREOC0CdxlWeTmdXTS0qVLsXTpUtx8882YmprCwYMHEQgEMr22vOC02xCSNWg2Doa12tmmfl6UiKjwhRUVTlu0K9kpidxlWeRMB2SHDh1CfX096uvrMTY2hv/5n/+BKIq48847sWXLFivXmFMlkggNQFiwwQ7w6CQLnW3q50WJiApfSNHgsEXLlS5JYHWgyJlu/nr44YeNXrGf//znUBQFgiAU9MgLAGebLbXph0phU79VjAwZ0/ZEVATCsgpHXIaM175iZjpDNjo6itraWiiKgoMHD+Lf//3fIUkS/vIv/9LK9eWca7rZMqQA5ZKdGTILMUNGRMUkrGhGQ79LEpkhK3KmA7KSkhJ4vV709fWhpaUFLpcLsixDLvAxECV6hkzfackeMstMRpghI6LiEVI0VNmjARl7yMh0QHbdddfhy1/+MmRZxqc//WkAwNGjR7FkyRKr1pYXXNM/LEF9pyUDMkuomoap6QwZm/qJqBiEFRUOW3QHv8smYGz6ppSKk+mA7JZbbsHFF18MURTR2NgIAKipqcHnPvc5yxaXD4wMmawxILPQVESFhmhja1hRoWla0RxgT0TFKaxoM3ZZ8ma0mKU19qK5udn4/0OHDkEURaxduzbji8onxsA+WY1O648Udok2V/Qp/TUlEgb8EciqBruNARkRFa7oYNizARl7yIqb6V2W999/P44ePQoAeOKJJ/DQQw/hoYcewuOPP27Z4vJBXA+ZZIfGDJkl9Ib+mpLoPQLLlkRU6MIce0ExTAdkfX19OOeccwAAL730Eu6//3488MADeOGFFyxbXD5wxZYs2dRvmQkjQxbtp+CFiYgKXexgWBdLlkXPdMlS06IvlFOnTgEAWlpaAACTk5MWLCt/uCQ29WeDkSErjb4keZ4lERUyRdUgq4BDOrvLMqJqUFQNNpHtGsXIdEC2evVq/PSnP8XY2BguuugiANHgzO12W7a4fKDvsgzpPWQMyCwxEdNDBjBDRkSFTb/pdBgZsuivIUVFqWjL2bood0yXLP/qr/4KpaWlWLZsGW6//XYAwMDAAK6//nrLFpcPXNLMXZZs6reCPoOsuoQZMiIqfPoAbOd0D5n+K8uWxct0hsztduPOO++M+9jGjRszvqB8YxMF2EUhpmQ5nuslFaTJsApRAKpcMZsoiIgKlD4AO/bopOjHee0rVqYDMlmW8fjjj2PPnj0YGxtDdXU1tm3bhltvvRWSlNb0jEXHJQnGLksenWSNibCCMrsYc1HiXSIRFS4jQyad3WUJMCArZqYjqV/84hfo6urCZz/7WdTV1eHMmTN47LHHMDU1ZUzuL1T6wD6BYy8sMxlWUeawGTuOeJ4lERWy2T1kLFkWO9MB2d69e/Hd737XaOJvbm7G8uXL8bd/+7dFEZCFZBWwS+whs8hEWEGZgxkyIioOelvGzJIlz7MsXqab+vWxF8XIGNjHsReW0TNkDiNDVryvNyIqfPo1zmkMhmUPWbEznSHbsmULvv3tb+PjH/84amtrMTw8jMceewxbtmyxcn15wWkTEVR4lqWVJsIKWkucZzNkLFkSUQEL6039xtFJ0V+ZIStepgOyu+66C4899hgefvhhjI2NoaamBlu3boVcBCU8pyRG52Sxqd8ykxEV5Q7xbA8ZS5ZEVMDOlizjx17w2LjiZTogkyQJd9xxB+644w7jY+FwGJ/61Kdw1113WbK4fOGSBIxMnR0Mq2kaBIGTlDNpMqygzGGDJAoQBWbIiKiwnS1Zxjf1swTIDcUAACAASURBVGRZvEz3kCVSLEGJvssSUvScRShKbhdUYMKKirCiodwhQhAEOGw8ZJeICpt+jXPMGHvBkmXxWlBAVixcxi7L6YCMfWQZNTV9jmWZIzoU1mkT2dRPRAVt5tgLvTrAsRfFK2XJ8tChQ3P+WTH0jwHRlLIxqR+YDshKcrqmQqKfY1k2fW6owyawZElEBe3s0UnRgEwQhLM3/1SUUgZkP/7xj5P+eW1tbcYWk6+ckoiQokGzMUNmhclI9AJUrmfIJJFzyIgK3P8eGsbFS8rRVu3K9VJyIqxoEBDNjOmi7TEMyIpVyoDsRz/6UTbWkdf0ZsuwzQEHwJ2WGTYRms6QGQGZwEn9afCHFLidtlwvg8i0kKzifw4OIxhRizYgC8kqHDYhrhfbaRO4y7KIsYfMBGM+jG06fi2SUm22nM2Q6SVLkRclk/rHQ7j7sQ50jARyvRQi0/zTbQr6r8UorGjG3EUdS5bFjQGZCbEZMgAsWWaY0UNmNPULLFmadMofgaoBg36+Jmnx0LPi/lDxBh8hRTMa+nUsWRY3BmQmGAP7BPaQWWHSCMimM2SSyJKlSePTb2wTRZxpoMVncnpndXFnyFRjKKzOJQncZVnEGJCZYJQshemSJXvIMmoyHO2lODuxmhkys/x6QBYq3jc2Wnz0QKyYX7fRkmV8howly+LGgMwEY4KyMN04zQxZRk1MT+nXRXvIeFEyQw/IijnTQIuPntH1F3NANt3UH4sly+LGgMwEvfHSyJApbOrPpMmIaswgA/RdlsyQmaG/sbFkSYsJS5Z6D1n8WzB3WRY3BmQm6IP7QmDJ0gozM2ROG9P2Zo2zOZoWIT0zFla0ov1ZDyuq8d6iY8myuDEgM+FsyTL6q8aSZUZNhlVj5AUAOKYzZJrGO8VU/MyQ0SIU+3ot1ixZSNaMcyx1esmS177ixIDMBD0gC2rsIbPCZIIMGQCWLU0wesiKuBeHFh+9ZAkUb2N/OMHYC5ckQNUAWeW1rxgxIDNB7yEL6Q8XA7KMmgwr8RkyvUTMgCwlP8de0CIUmxUbL9qATDVuPnXGzT93mRclBmQmGAGCNv1wRdjUnymqpk039cdkyPRBvNxpmVJsUz/LHLRYTIQVVJdEe3KLumSZYJclAO60LFIMyEywiQIcNgFB/f2OGbKMCcoqVA0odybIkPEuMamIoiIoayizi5BV3lWTdSKKhrFA5m5EJ8MKmsqjg7YninRDSlhRZx2dZGwg481oVmmahv0DEzgzmdv3dgZkJjklESF1+m6GAVnG6L0kzJClTy/1NLmjR3qxbFk4QrKKdwcn8ybr+btjY/j/nu7O2Hr8YRWN06/bYux/VFQNioYEPWTT7TG8ucqqkKLhG6/047UT4zldBwMyk5w2AUH9usGALGP0IKI8rqmfGTIz9Dey5iJ+YytUb/T6cf/LfXhk/1BeBGWnJsKYCKsZ2WijaRomwwo8JRIcNqEoS5Z6Bowly/wwFYk+3iX23IZEDMhMckli9GIkSYDMHrJMMTJkMU39Z3dZ8qKUzMT0Y9dUMV36KcI3tkI1PBW96Xvy6Bh+cXA450GZXlYMZCBQCMS0KbgdtqK8kdAD29lnWeoZMl77simQJwGZlNPvvogYR1pIdmbIMmjCOFg85ugkiRkyM2ZlyBiQFQxvUEGJJGJbWwV+c3gEdlHAJ9bX5mw949OvrUwECvrrttxhg9tpK8rXbXj62jb7LMvpc5MZkGWVEZBJDMgWheiB1wzIMm3SKFnOzpCxsTU5/Y3M6CEr0uboQuQNyKgukfC5ixsgqxp+9f4wJJuAj6/z5GQ9+qywTGwc0bPi5Q4byp22opxDdrZkOXswLMANOtkWkKOvQWbIFgmXJEbfACU7j07KoImETf3Ru0QOhk1uZlN/MWYaCpU3KKPKZYMoCPirzY2IqBr++90zsIsCbj63Juvr0TPZmcjcxPaNuh029PlCC/6ai41+bZt5dJKTJcucyJeSJXvITDJKlnZmyDJpMqJAAFAaNxiWFyUzJkIKHDYBFU4bHDahKDMNhWoscHZOl00U8H+3NGHrUjd+un8IJ8aCWV+PP5S5gMwfkxWvcNqKsvcxPP04zjw6SS9ZsjqQXQzIFhmndLZkybMsM2cirKLULkIUzt4pMkNmjj+swD3de1fuKM5enELlm86Q6WyigFvXRjNjZyazu6lIUaPDm4HMBGRGydJpQ7lDhD9UfEONQ0ZT/4wMmW1xlSxzPbcrU6bypIeMAZlJLkmMNplzl2VGzTzHEmCGzCx/SIHbGX3s3I7izDQUorCiYjKioqokvqNEf7PIxE7HdMS+roKRDJQsZzT1K1r2/025Fp5j7IVNFGAXhUVx7Xt3cBL/54ku9HgXf8lZf/0xQ7ZIuGJ3WbKHLGMmw2rcyAsAkEQBNoFnWabiDykonw7Iyp0iS5YFwhuIPo9VrhkB2fSbRSADQVE6YjOvmfiZ9IcVSKIAp00wbiiKbfTF2R6y2W/BLklYFLss952cAACc8odzvJKF03+mXMyQLQ4Om4CQokFlD1lGRQ8Wt836uFMS2UeRwnhoZsmSj1ch8AajGfjqGQGZy8iQZTd4iQ2WMlWyLHeIEATBeP36i2yHsJ4Bm5khA/R+5fy/GX3v1CQAwFcAwXRAVuGS4ltncoEBmUn6xTBicxZ8QBZRNChqdi4IiTJkQPRCFV4EF6VcmggrqNBLlkU6PqAQjU0HZFUl8Tcq+jUoGMnuz0XsOJWMlCxjbsKMDFmRlduNDFmCjIxTEvO+ZDkakNHri2bGvBk84zRXAhE15+VKIIsB2bvvvosvfvGL+MIXvoAnnnhizs/bu3cvbr/9dnR1dWVraaYYF0N7ScH3kH3rD334zz+dzsr3mggrcSMvdMyQJadpWlwPGZv6C4cvmLhkaZsu82W73yr2dZWpXZZ6QFZe5CXLRBkyl5T/PWR6dgwAvAXw3AUi0c1luZaVFaiqiocffhhf+cpX8C//8i9444030N/fP+vzAoEAfv/732PVqlXZWFZa9J1/Ibur4DNkfeNhDGapL2AyosQNhdU5bAIn9ScRkFUoGuB2Rh87t8OGsKLl/YWcUhubzjjE7rLUldjF7PeQTb/higIQzEAPWbRNIfq6rXAUZ0A212BYINpXlonHeabOkWDGRqYcPDUFt0NEY7kdvuDiT1AEImrOd1gCWQrIOjs70djYiIaGBkiShK1bt2Lfvn2zPm/37t24+eabYbfbs7GstBjnK0qFX7KcCCnGNmAryaqGoKzN2mUJRB9vnmU5t9jjZ4BoUz/A8ywLgTcoo8whwp6w4Tv7AdlEWIEoRHvaMlOyVGdlyIrtdRuWNYgCkCgGcFlUsvzh24P4z3eGFvx1NE3De6cmcX5jGWpKJHiDi/+5C8hFVLIcHR2Fx3P2yA+Px4PR0dG4z+nu7sbw8DA2btyYjSWlzShZFnhAFlFUhBQtKwHZZDg+qIilb6LItZGp/Hyu9Sbo2LEXwNmTD2jx8gaVWQ39uhK7mLJkqWkavvWHPuzrn8jIevyh6Ggal13MzKT+mN3BkiigRBKLLkMWVlQ4bAKEBE3kxhDyDFI1DSfHwxmZGzbgj2B4Ssb6hlJUumyFkyHLg4AsL45OUlUVP//5z3Hfffel/NwXX3wRL774IgDgwQcfRG2ttQfuSpKE2tpa1AckAP3QStwQFcXy75srI5PRUmVQgeX/xqmxAACg0VM563u5S0/BH5STrkF/bqzSMzqFe//ffvz4tvVY31xh2feZj67JMQBAa70HtbUVWDIlARiAraQctbWVOV2b1c9LoZuQB1DndiV8DCtKBiALQtLHNyQr2HfyQ7R6KvBnG+I/bz7PTVgYRlWJA6UOGzRxYc+tPmS2vqrc+DpVpccRForrNSPYvXDZbca/OfZ5qSwfxXFvOKOPx5A/hLCiYWRKRo3Hs6DdhK8NDAIAdqxtwangSRw5M7zon7uwehxVZSUJ/x3ZvJ5lJSCrqanByMiI8fuRkRHU1Jw9jy0YDKKvrw/f+MY3AABerxff+c538Hd/93dob2+P+1o7d+7Ezp07jd8PDw9buvba2loMDw8jOBkNHsZVQA2HLf++udI7fa7cREi2/N/YPxx9TLXQ1KzvJagyJkORpGvQnxurfDA9Z+dQ7xCaHen31PX6Qii1i6gtzXwJ/uSZcQCAGvBjeDgMNRjtDekfGkWLM7dZPaufl0I37A9gRY0r4WMoQYF3Skn6+Oq73vpH/bM+bz7Pzcj4FEolDRIUjKf43qkY/WhyyPg6pRIwPD77GlDIxienYBfOvn/FPi+CHEYgnNnr76HpJnxZ1dDVf9o4lms+3ug8jbpSCc7IBFyIwBeUcXroDGxibkdGLMRESIaoJn6/yfT1rLm5ec4/y0pA1t7ejsHBQQwNDaGmpgZvvvkm/vqv/9r489LSUjz88MPG77/+9a/jU5/61KxgLJf0kmVILOw5ZBMxZ9YpqmbpD5l+HEtZglSxw5b7rd96X8vo1PxS8t997SRWVLvwpUvn/gGcL6OHLGaXJVB8vTiFaCygzNphqYv2kCW//ujtBsMZOtbGHz67Ht8C+4UmErQpuB22oitZhmRt1jmWOitKlifHz95QDk9F5h2QKaqG909P4ZJWNwRBQOX062I8pCwoyMu1fNllmZVH0Gaz4d5778UDDzwAVVWxY8cOtLa2Yvfu3Whvb8emTZuysYwFMQ59FR2AHIGmaQnr/4vdxIwt7oka7jP2vaYvwmXORE39Qs7PstTfJEbnOWdnZEpGXZk1bzT62twzmvqL7Y2t0IRkFQFZTbjDEjDXQ6YHZCPzvJGYyR9S0VppQ0TRcHqBN6MJAzKnDUMFciaiWWFFSzjyApg+N1nRoGpaxgaVDvhjAzIZqzxJPjmJ7rEgJsIq1jeUAgAqp1+n3qC8aAOyiKIhomp5scsya4/gxo0bZzXs33HHHQk/9+tf/3oWVpQe49BX0Q5oGqAo0XMtC0xsU/hUJHlANhVRMBlWUVc2v5LcZGQ6IEuUIcuDOWT6/KX5NPZHps8jtGpHnD+soMwuGhnMEkmETWBT/2JnTOmf482txC6m3Ok4Nf1z5Qsp083jC3uj+f/Ze+8wWa77OvDcququTjM9eebl9+bhvYecSRCMAERSYrCEz7Ko9UoyvV5ztbKVPq+8XoXVUt8n2StxLa1WYW3JWlmWvFqZFoMkkiJIggABAQRB5Jxenpw6h0p3/7h1q6urb6Xunp7uwZy/gHmdpqfq3t89v/M7p2wnQlR1q2fmhl+fbqub3NuQIQv6u6T4RL9JHSKgV6yUNUymFezUjZ6Y0+dXawCAWxayANA35nS3oJsUD18s4r7FvG9xOyw5lsCBU39kqE7L0l4o92nb0r0whk1afvbFLfzC1y51/V6txdmHIdtjH7JeGLKSq/W7G3CbwgIAIQQ59e23se03FHxMYTnSdjuLUv97w30I6JUlMyyKumFhTJX7YljqbbUDdsqEZg0sHWQYoJkUqi9DZh/++7h2LJV0XDuTRkIi2OzhmnhutYoTedUJvnczZMOIZ1Yq+J1vr+L1TX//tbp9gDkoyEYIjjEssdkgczgvwF7hblnyk7YfNmsGtutG4OYQhKodMizMc5Ml6NbgIpxE6KUg4yfG3cqk8xZkAGtfHmjIRhsFxxTWnyGjCL6u3AepzR5tWyouv7tUHzIWRVY3Y6oMivAD4H5C07B8W5aOPKZPBZlpUaxVNBwZT2I6o3Rt5aOZFl7ZqOPmhYzzs2FnyPjnCloX+QFmGFqWe/8JRgQSYYVDg9gLSYiwdi+xUdXxw3/xGs5vx3dldl+4Ye22mmbCsAC9y6KJ51iKtHhJe1HaSx0ZD+suNkwYMX/H4m4zZIJQ9oP4pNGHX44lR9oJGPe/rmp9ZMj49cQYst4PSS0NWWvrGXsbuvVrJhXmWAKuAbI+HebWqzpMChweS2Amm+iaIXt1ow7NpE67EmByE0UaXoaMX1OBBdlBy3I0oSoSmmT4W5aXCk00DIpLhWbs57qDhMNOrHxKstuTbUVQVHA4yQh7qCPjNzNFK84mKrhZ4qBalgCLUdqPAeO//8Qq/uiptbdFS4u3LPOqP0MGBId8u5ntzWpvGyW/nnhBBvR2TVc0C6pM2lII3o4B49wYVoR+tyz5hOXh8SRm0krXGrLnVmuQCHDDfNr5GSEEeVUZWoaMX1ORGLKDgmy0kJIJmrA3wSEuyHiLrZsFrqK1xpdDCzL79bsVrld1SyjoB1wt4j3UkVU0E1n7JB+3benWkHXb0g1CWessyHL7tGX56KUS/urVHfybb13dtQJ3WFCoGxhLSkj4bNZRGTJFIsglpZ5bliUXo9WfgqzzEDb2NgwYb5rUV9TP175+Xet8wvLIWBIz2QS26kZXh5vnVqs4O51GJtH+95tIy0PLkPF1uBow7OQwZActy9GCqkijVZB1scCVNRNz9tRkmIaMX+RdF2QBDFlySBiykxMqgPheZPzEaNHuW7p+MC2KqmY5wcwcTNS/vwqWqmaiqls4N5PGU8tV/OLXLjs6q/2IQsNwBNMi8FN80D3HPZVmMt23pzg4Qzauyq5CobeWZUdB9nZsWRrUkWV40e+W5XJJQzYpYUyVMZNRYNH4LcaKZuKt7UabfoyjW4bsb17bxrMr1djPi4NILcsDhmw0oSoSmtT+yvTh3RR48dDNAlfRTMxmIzJkPbYsqy4Gygs+gbRXeZaGxfI8T/CCrMuWJRDcXuoGjg5Hbf/uxpIy6oYVW+82zODZe99/7SR+4f1HcaXYxL/86iVcLcZvx48CgkxhgdZmHVSQ1eyCrBcBN0fZJcJvFQo9MGRNs/O6fRsGjDdNy5FleNHvluVyWcPhsSQIIZjOsGsrbqH+0loNFgVudenHOPKp+AwZpRR/8swGfu3hq3hjqx7ruXHQKsiCDzAAkE7snudmVBwUZDGQUggasE81Q82Qsc/WzQJX0SyMqzLSihRYaJkWdRaMMCYt6L38fM74oqTtUYuKf3eHx5KQSXwvspKrGO73pKXXFJZjlNz6m4aFf/GVi3hloxb4OL5xzGYTeMfRHH7tQ8fRNC38qwcuhT53FFFoGL6msICLIQtpWab7xJCVmxYkAmQS/WpZWh0MWSYhgaD9ntnPMCwKi8LX9oL7kPXLh3G5pOHIWBIAnBi3uK3sNftgdNw+oLoxkWIMWRxpRqFhQjMpNJPi1x5e6rm17odWy/JgynLfIaVIaFr2VzbUBVl3DJlFqdNGzCQk1AJOFVVXsdYNQ0Zd7yVCco8ZMv7d5VMKJtNKFwxZe+JBXz+ba/LNDYdpGIGNbbWi463tBp5bDS6quIM7Nx8+M53GZ773BDIJGX/01Pquf85BI2rLMuiaquum3bJUULLNYbsFN4UlhPRNQ+Y9hMm23u3t0rLkfw//lmX/9LNNw8JGzcChcbsgs++juMMefC8QxQvlUzJ0268uKvh9/Y9unUVDt/CrD12NJX155GIJv/rQldDH8bUysCAzmObST7c5SBwUZDGQlCU06fAzZHzUvRzTtb2mW7AoY1rSiWCGzH2Bd6MhaxgUJhW79AMuI969Yshc02XTGQVbcQuypuEsXn0vyJrigoxbCYzCtBpv6a5VgkPbN6o6FAltrNF8Lom7j+VwqdDcV5OXDcNCw6CBLct0jJYl33x7sb6oaKZj4prqg9i8opkYE8gUcur+HEgRgRte+4v6+7durNiC/sM2QzaWlJCUSWzGv6qbSClEmG3M8ywL9eh/v7UKe/93HM3h5957GJcKTfzmY8uR7+enVyp4cqkKPeCwQSl11vFqiOZyGPRjwEFBFgsphaBhF2R0azhP54ZFHXYm7omz6mJesknJcTAWwV2sdVOQ8diknCDHEth7DZl7umwqrcQW9ZcaJuZzbEPcrYJs3JchG35hP7d34AuzHzarBmYyiY7YkxMTKjSTYiWkoBsl8GGFyYCWZVQNWTohu/RC3R8ey5rptMZbDFl396RhUTQMKmTF304B47wV6Wd7kZQJCPpVkLG//RGbISOEYCajxG5l82tKBH5YKsbQka3b9/1cNoE7j+TwT26fw3euVvCnz25Eev6GzfAVA66Zmm6Bbx9BXYNhCRYHDgqyWEhxUf+RE6CPfn2vP44QO3UDFKx4jHvi5BN6uaSEdEKOzJB107J0gsV9boS9nrJ0T5dNZRKxfMh0k6KqW860ahwqPwrcQms3+P+PFkMWXCxs1HRhVurJyRQAdOW1N6xwTGEDGDLZTrYIuqbcU5ZAb15kzO+O3Yu9tiz5eiTSjY6pcmxGf1TBza79GDJCCAsY78O6sWQzZIfGWvcQ0xbGK9JrARZF/HotxCio16oa8qnWoMjHz03iI2cm8PlXtvHg+WLo8/mwT9B0J9ePZZNSoKi/ZlhDoR8DDgqyWFDtHDny/u8FLr0Jeumtvf5IHeBap2N5FTU93sRdxbXRZ0Jbli4NWRcLh1NU+DFke+xD5i56ptJKrGDlUpP9DThD1u/fody0IJNOPcfYCIn6+UK6VTMC2w7rVd2Z+nXj6HgSEtlfBRlnDf2CxTnSCSlay9JmyHpqWboMiHttpVV8Wu3A24whs9cDP1E/wPea3teN5ZKGyZTc5h02k+2CIdNMXxbJybOMcWhdq+iYdx20CCH41J3zODGh4oE3C4HPtSh1CsogVo5fT4fHkqgb/lmpBy3LEYWqEGgmBb3rA0AyCfqtr+71R+oAL8i4XUOczdkd/BtakNktR4l017IsOY7kwT5k/Zo0igv3dNmUvUFGbVvyYmM3W5Y5Ve6InMok2bTaKGxsfEyeotV+8MKwKHbqhsP0uKEqEg6NJfdXQWbfu/mAliXAdGR+DJluWtAtinRCgqpIGOvRHNYd0aX2mLHIWYqcQEM2pr59CjLO+vtFJwF2N6ZPGrLDdruSYzrNGP84+staQFtv3E6VCGoferFe1TGXa7+vZYng9JQaypoXGiyyj/+3H/j1tGC/j5+OrK4fMGQjCT6OrKtZkDvfB/rEw6CN3fNQ6Qa8aOCGpnEWOV68jcVgyCbTSqDWzA/8RvJrz3B9xV5lWbqny7gWZ6sebWPjVLnTsuyzD5lb1+OGRNi02kgwZK7rcs0nymW7ZsCire/RixMT6v4qyBq8IOueIeM/55tnL9mFusmGDDijJRECVSZdMzdBLcucuv889PzQaln6M2QpWerLYXTJ9iBzYybLzGHjTI7XdAsZn4n4hMzWnagaMtOi2Ki2M2Qc89kktutGoFRlw7Ve8G6ECLzLccj+/f0mLevGAUM2knBT9uT93ws066Df+dYef6p2bNcNKFJLxBnHAsEd/JtJsPas3ymqqpsgAGYySlcaMn4jidoXADstKVJ/dBTdwB1NFJ8h4y1L9jfo9+8gyrHkyKnySIj6iw0DR+1rdLUsFuZveCwvvDgxoWK1rO+bOKWduolxVYYimGRzI4ghqzkFGbs+ptNK1wwZZ7TcxX/Klm1093pi/zz3z0bBsqVXtET9/ttvUum+8OWoaCaKDbOzIOvCiyyIIQPYwTqIrXJju27AsNDBkAGtn/mx5uzfWp87SEPGyQhekPkdVA9aliOKNsp+8RwT9w+wbWla1AmK9cNWTcdkSnE27FIMtsQd/MsXdL+Fv6qxizgTIv73Q7HBigrRGDWHqpA9m7J0a2daDFnEgsxeCKbSChQpWIDdDQILsqQ8IqJ+E6cmVShSy5PIiw17w5gRaMgAVpBRAFf2iWt/mCksRzoh+aY/8Hsx7WLIutWQORIGd0GW6L0g82tZAqMxkNIrHNsLHx8yQNyyrDRN/OuHr0ZOqXAsL8a9BVl8bWFN99eQAazNHpUh4/c7P7C6wWUeQXY4vCDLJqTAIrDUZKQBZ9j98iwPCrIRhTtjjBAC8r7Bivv/6Ol1/PSXLgS2pLbrBqYyimOJEJch4yL7sMy8mm4im2BMWjctuWLT9NWPcaiytGdO/W7tTFqRkFJIZIq/2DAhETbdk1bIrhjDilgGgDENo9CyLDRYiP1cNuGrGXEYMoGGDGi15fdL27LQMANNYTlSAQyZt2U5bZvDdsPSigyIU3J4QeZ3aOTMrd+UJTAa+sde4WjIglqWgnXjr1/bxhNXK3jscjnS+yyX/AqyeAyZaduVZAOihfIxGDJ+vwtblk5B5v/ZNmrM43FhLBkq6s+psnNtiVqWFmWJMwcashEEzx7jNwq5+x4gMRhx/8WdBr7y+g4Mizo3mgjbdQNTaaUrC4Rys1WE8AXdj/2q2rFHYRNffig2jFDxclLeO4bMzUIRQjCVTkRuWZaaBsZVmWlu+jQt5ffZvMiNgDi6abCJ1XxKwVwuiVXfgsxAXpV9xc/zuQRUmeDizn4pyIxAywuOoHuu5tWQ2Ztv3KQJQGxArIa00t7YquOf/fV5vLTemcBQsSf1RKz42ylgvBliewF0TlnWdBN//doOAOD1rUak91kuayAADnlag9kkO2BGtUNxinyf3GGAeZHFYcgIIJye5l0FP9YcYAe12WzCfs9ghmwsKTuMrMj6omFYoBiOYHHgoCCLBadlaZ9wSCYHcud7d13cTynFHz61Dtmeqlv20dwATOc0lUkgk5AgkZa3WBS4XbRbBZn4gufB4GHifz8UG2aoeFlVpD3zIWOi/tbtMZWJHp/k/t160dyI0DQsaCb1LcjGRkDUX3D8tmTMZxNY92lPbFR1x21eBIkQHN8nwn5KKQp1I9AUliOtBBVk7G/vtCzt9tRGwAbnB5HmK5UInv7j7/PimrggE7UrATheZ2+LlqUzZRm9Zfnl1wuoahYWJ1W8sVWPlBu5XGKFS8JT+DFz2OheZHyiPrhlqaCiWdAjHKDXKjqm0krH5wLYPT2XVYIZsqqO2YwS2iblOuBsgB3QMOVYAgcFWSy4W5Ycjrj/yUd27X0fu1zGi2s1fPK2WUjEvyBrGBaquoWptAJCSGxvn4pmOS1LR0PmCX2woAAAIABJREFUx5DpLYasYViwYgTLAtFalkmZ7IkPmWZaaHqKnqkYeZbFRut3C9L7dINygDAaYAxZVfMfxhgGFF0Ttgu5BMqaJWwnMFPY4KJ9v0xa1g12zUVlyBqGJdyUvaJ+zpB1oyPj08I5tbVNhB0wOAvx2mbnAbUSkF37tmpZhkQnAewwyg/+DcPCF1/Zxu2HsvjQNRMoNMxIk7PLAssLjukYbv1e1lUEvt4FTT1yrFc0pzUpQpCMAQA2bYZsXFVQbPqHmpebJsZVCapMoEjiliVv/R8wZCMIoTHi6WuZuP/hv92V92waFv6fp9dxalLFR89OYi6b8G1Z8pbatK1DYe7XMQoyQcvSTwhZ1djUTSYhgSKe15ZpsYyxsJblXjFkIjHztB2fFOVkWmoaGE+1zDT7yZC1zDV9mIakDIru0hMGBV6Q5VOyr2aEUuq0JoJwYkJFsWnGMqXsFQ+8WQgdrokLp0iNoCFLK+yeE7XzRRoyoLv4pErThCK1swdhGjLOQry22cniVDTLtyBLKxJkIm4r7Tc0TQqJIHCa1m0v8sCbBZSaJj5x4zTOTLOEitcFBa8blFIslzUcGRPfP4whi1uQ+a/X/LoNaiFyrFd1XysbgIn9/axw6rqFsmZhNptAPiVDM/1Dzbm0gxCCbFIWXlt1/aAgG1mkBMaIbeL+i2/0/T0/9/IWNmsGPnXnPGSJ4NBYEstlH98me1OashfhXFKOJepvE7JzUb/flKVuIpuUQ5k04fs0TVC0DAX9oO4RQybKipzKKNAtGinexd2y7LeovyTQ9biRC6DnhwVF+xSdVxVn0sq7AFc05oHlJ+jn4AbIFwfEkukmxe89sYqvvL7T19fl0VxRpywB8T1X05mhMReMq4qEMVXuiiHjBZTbgDiVCNaQ8QNcRbOc2J7W65lCQT/A1tFR0D/2A5ppBbJjAGMiWfanhc+/vI0b5zO4bi6DkxMpJCQSqiMrNkzUdMuxfPBiJqugUDcitRhrWjhDNmGvR4UQHZlhUWzWDKHlBcdcLoFy0xTKZfjkNdOQBReB3EsSALIJ8bCT9wCz1xiOTzEiaDFk7Rcxedc9QCYH6w8+A7qz1dVri5iXtYqGz728jfedGMMNcxkAbGJmuaQJH+8UZF0wZJrJtElc45FN+mvILEpR01i2GT89x2Fk3BqiICT7ZI4YF47/ktrOkAHAdgjTwHMsOYXfbw1ZWMtyFFo/hXonQ7buYchaHmThLUtgcJOWvO2x2udQ80KEHEuOwILMNrl0F1EsTDo+Q1ZqdrYYVSW4Be/e9F7fbC8aKq5cTBHGkrJz4NjP0EwaOGEJtOQxX3ptB9t1A5+4cRoAM2FdnGI6siDwYviIT8tyJpMABSJl9DoasgBRPz+Ahk1ablZ1WBSBLUs+feldE/jzATANmcpDzTvfs2lLAPihP5eUhE79TstSCT8IDQIHBVkMtDRk7X9Yks1B+pn/DSgXYf3mL4GW4p2e/+C7a/iR//oG/o9Hl/DwhaLDav3x0xsgAD5525zz2CN2Lpfowt+yF13epogTR9KKNWkVEoC40KrrbDIlZ2vI+M+igi+646EtS7InTv2ilqVjDhuygHENBWfX+j1lKZp8c2MUGLJC00BaYdE+3ITYW+C4T8JBmEgxce+gCrKKU5B1H0ckwk49Wo4l0Lo3Rex1XTeR8QiUZ2LohdyoaGYbSwy0HOSDdDuHxthQ0asb7UVDUMsSYPfMMF+3/ULTsAJd+oGW4P9zL2/h3EwKN89nnH87M53Gm1uNQJ2o40Hmx5DFaGXXI7UseXEUfJ3x6cnglqUtYxC0Lblh7Ew24RSBovf0WrbkkrJYQ3bQshxd8JtIxNqQxXOQfuqXge0NWL/5y6CVUuTXfXm9hqRE8PxaDb/52Ap+7C/fwP/81Yt4/EoZ/+DG6bZN6ZCtCRAJ+7frBlIKcVirsaQUvSDzbPQSYa9TE7To3CLPMHsMEQqOhiisZdmfPLe4EPkv8TZweEHWHgmV7jdDFlaQ8Wm1IWYaWEu3ZSkyn+sU8Ya59LsxSGE/vzbWKnokPWFUFBoGCNBRAInANw8RU8Uc1dtfYzqGXsgN7uPkRiohwaKA7pfgYRdxZ6dTeN3F4jQNlrHp17IERsOypR/QTBqYYwm0iu6KZuETN860MZ5nplNomjTQEHmppEGR/O+flhdZFIbM9o8LKFrSioSEREI1ZI4HWRBD5sOaA6ygkwg7IPM1RJShWfZobbNJ6aAg22+QCEEyIMuNnL0B0j//JWBtGdb/+WnQWjX0NSmlWCnreO+JcfzHv38NfuN7T+AHr59G06BYnFRx/3VTbY/nFLRI2M89yPjNO6bKaJo0kjC+5aLdWjD9LC34hc1tL4C4DBnXEIW0LPeYIfNOWQLh02p8QeLsH29Z9mvz5mkKfhoUJ4JmiMXRzIOuVYyLCzIDSZmEXiMAK8guF5sDmSzl5qaaSbvy9vJDsWFiPBWcXMGRDmDIagLX8ZmMgnIX5rAiA2Kuo/VrW3IW7OxMGpcKTUfyEBSbxBF3KnxUwTRkYQwZ+xuemlRxx+Fs27+dnU4DAN4I0JEtlzUs5JK+1xNPv9iMYIdS1y3IJDh7kxCCfEoO1ZCtVVhBNROgDR1XZaQUIpy03KzqmMkokCXSKshEDFmzkyELFPUf2F6MJkSRFm6Q62+F9BP/C3D1Iqz/61dC/cmKDRMNw8LCWAISITg3k8aP3jqL3/7YKfzWR091bLwzmQQUiYgZMtuDjMMxh42wyJVFBVnSryBrOW6nHYYs+kLKneyD2hfA3jFkFc1EQiJtOo+EzMTRYZswXxzcGrIgRiEuSgGmsAC6MgQeNAoNs00/OJ9NYL3azjhtVHXMZBJtzIAfTk6o0EwaOCrfL7hbav18v52IprAAXPeceIPxCpSnu7S+qAiutZSPjtZ5ji3cv3YmDYsCb9pFQ0sSEaAhizkVPqpomjRU1M/vjx/2sGMA65Jkk1JwQVbyt7wAWPsxrUjYjKIhsw19w+7FiZQSypCt2/d10MGDEMKsL0Qty5ruFHNJmRECovd0CjIu6rcTTLwH47phgaB10NhrHBRkMcHGkYOLBHLzOyB96ueA86+Dfum/BD52xdbOLAhyvUSQJYKFXMK3ZTnl0qA48UkR2BKRnQKLReq82Cv2z7IJGemQzEsRouRYAuy7NimbzBkkeNHjXYCmI3iRtfRxtjFsIphRiIsgl36AXR+ZhDTUIc1FT/Exn0tCMyl2XAsrs7yIVqAMUtjvLsj6qSOLagoLuIsin5ZlspMhA+JZXzhefB0Mmf3ePqx71TZ/PTvDWBzuR1ZxWPVghkwz6Z4cwgYJzaCBOZYAcO1MGr/z8VO4+/hYx78RQnBmOt3WEnZjtazhclFzLDL8MJNVIjFk7JoKvzbzKdmZoPbDWkUPnLDkELHmAGPO3fqzvI9bv3caPZtkB2PvQYLnWEY5+A0CBwVZTDDDvvACgdzxbuD0OdA3Xgp83KptYeE3niwCn7R0g1KKrZrhTAMCcPQfURiyiov14kgnZOFkSs15bHdTlsWmEakVxRetQXuRucel3ZhKK6EsQ8Fh/9j3EsYo9OuzuTHMAeMWpSh5POhEgcIbNSOSfgwAjudVEAymIOPfq0T6O2kZNTYJCLe98DJkcfRCHM5gi2cq0rmeBe9tUTZhnEuy/MAj40m8Zk9aejWqIow5B8jhvHb7Bc20QqcsCSE4nld9//3sdAqXCk1hUf71t4qQCHDfYj7wPaJ6kYmuKREmUoozQe2H9aouzLD0Yi6X7NBpmhbFVq09vSOvKigIikB+n467WpZA57VVH6IcS+CgIIuNsJalG+TkWeDKeVDD/6JfqWiQSPDUiReHx5JYKett7vgVjYlmufgccOXDRVjgKhorJNw3nl9weNVhyCQkZKari6UhixCbBLSyQwftRVbRxOP5UeKT3DmWQDCb0f1nCy7IxtThZcgqTRMWhU9Bxg4numlhp26EepBxqIqEhbEELhaiZfz1gkqTRYbNZBTnMNUrKKWRg8WBYA1ZXTcFon6uf4z+eb0tHw4+/Se6nuu6BYu2Nr9zMynHILalUfXfcvZ6IGW9osdOHOkGUVqWYTgznYJFgfPb7de8aVF843wRtx/KBuq0gOh2KDXdjFSQcYbMTy+rmRa260agoJ9jPptAw7DafB93GgZM2m6FE8SQpRTJiWfiNk5eYb9Ic7mXGJ5PMiJQ4xh9njoDaBqwfNn3Iatl1hNPhJyY3DgynoRu0bZwWK8HGRDPk6rcZNoPyUXd+ov6edisbSKrxMuzLDQ6x+lF4CLSvWDIvNNlAPtuiw0jUDzOYpNaf4Mgi4JuP1tYQcYYsuFs+xTsa9H9HfHDCC/I+Kk9assSGNykZUWzMJaUMR8Qih4XNZ15AEYxhQVYW1p0CDItioZBOzYYbg4bhyETefEB4vi41nNawz4AcG4mjVLTxGpFFzLwXvDiby+8yC7uNPDjf/UWnrha6dtrfuX1Hfz+E6sdP9cMKzDHMgr8hP1PL1exXTfwoWsmQl9jJpNAsWFCD1lfRZO7IkykFBiWf7pLFMsLDiFrLng+062JRf3jrkM1PyR4P1v9oCAbbTCGLNopipw8AwCgF1/3fcxKWcOCT7yFH0TWF44HmaAgi8KWVDWr4/QaNGWZUogT/ZH2YdL8UGoakTYfNWDx302U7U3Xi6m0AosGu1G7LR2AFpvRD10MpVQ4+eZFLjm8fk484sj9HamKhMl0K1A4juUFx4kJFStlfdf1RzyPcSGXaNssekHBle0ZFaKAcV70i9iMmYzSFUPmHbwJOmBUPV6G51w6MhED78X4HrYsv3mhBIu2/Lv6gSeuVvDAm4WOgkHrA0M2kVYwl1U6dGQPvFXARErGnUdyoa/BJy3DZBg13Qq0vODg97SohQi0bCwiMWQC6wu3BxnHuMrMhL3Mpvfg6tuy1A9aliONWNmEswtAdgy4+KbvQ1YrOg5FFPRzcLM/d0HmjU0CYIeqkkgtS3dsEkfGDjH2MkJVz4nJr3ATQTcpKprliN6D4LQsB8iQUUp9WajpCF5k7hxLoFVU9oMhq9otofCWZbzIrEHCL7PRXeC0GLJ4BRkFcDnAm6kfKDeZaH0hl0ShYcY6iPiBF/hRTGE50gmp45oKioGJk10IiL34AH9zbKDTOud4XkVKIU5Blk1IbQy8Fy3N62DZXdOiePgi843sZybqZk0HBWOt3GAty95F5Gem020M2VZNx3eXKrhvMR+Yk8kRVVtY08xILFJYlFEUDzIOL2sOtA5qM5n2lqVFO0kHr9aWF5ReTXTdOGDIRhosXzGihowQ4NQZ0AtihqyqmSg1zdgM2VRaQUohbcJ+HizublkSQiK79VcEzEvGZ4KyqllOW4I9TjyNKUJUDzLAJeofIEPWNCkMq3O6DACm0uH2AcWm2fa79XPKMoowGmiJ+vtpXNov8CmsCc/vMJ9tTVWtCxbeMAxq0rKisXY2v2f7wZIVYuRYcqQTnQdDfigSbTBx3fr9rrUgTaRXJyZLbBqQFWRWYLsSiKd57SeeX6s5EUI7EcKxo4JLSp5abm+DRsmyjIIz0ymsVXSHgfvmecbyfeh0eLsSiBY8TyllDFnEKUvAv4OwXtWhSCTSwSOblJFLSm3WFxtV3U72aH0WJ7LJW5BpMRiyg4JsdJGKOGXJQU6eAZYugzY7BcdcgxKXISOEh4y3M2RjquyIGDnGklI0hkyQW+c3zVXVTWRdN0U6BkPG9SH5KC3LPWDIgpzww9z6dZOiqlltAwsOo9AHg9uwHEuOMZWNePdLt9ZPOB50nu93LpfAlh12vFHVMZnqvJaDcCiXRFImAyjIuIaMFWT90JEVfFjDIIhaltwLUKT3mcmwwOZGxINTWeDFB7iMYYUFWadO7NxMGhd2mtiq6aEHCVWRkJTJwEX9D10oIpuQcHoq1TeGrKabqBvMUPWZlarTZTAsCosidMoyCtw6MotSfO2tAm6cSwf6j7kRhSHTTAqTRnOyj8KQzWWVQJbUDa/1xWZN72DNJ3zMYZmGrH2PAg6mLPcdYrUsYU9aUgu4fL7j31btgupQTIYMYG1Lb0E2JVjQo7avqprZMeLOaV5vseVlyNIJOfLmX4wYmwS4o6oGx/S0xv07N4+8KkMi/gyZN8cScGlu+sCQhcUmcYgMgQt1A//hu2t4fTPYqHi3UbQHOryL8kIuAYuyRXez2j7aHgWyRHAsn9zVgsyypwWzSdnxDeyHOexWTYdMwgttN0S6zaCWJWdDNiIyenywxevPpEgEEhHbuFQEurNzM2wa8LXNejSWRZUHYvDL0TAsfPtKGe85MYa5bKJv6QucHXvn0TFUNMvxY+PdlbDopChYnEpBIsDrW3W8uFbDakWPJObnSCckZJNSoBdZlNgkDr7u+eVZrlf1WG4Cc9lk27WwXu20wuH7SMlVBJoWk8W410lZIsgmpANR/34DD7yOPB596hoAELYtV8q8px6PIQNYQbZW0R3TVK8HGUcuKYdqMthG0xn86+fCX/MwZHE0ZI6TfSxR/wAZMu5fI9g8ZIlgMuVvfSFi//ple9E0LHzxlW0QhE8feuOTHr1Uwk9+6QL++rUdPHi+2NPn6BUFT2wSx7yrwInjQebGyYkUzu80d826oO5o+NjUYjYp9UUEvlLRMR8QcyNCSunUkPHNRtiytK+Z9Uq0gpUNj3S+DrEzbv00ZDJpdz3nwn7DCra84LjzSA5PLVdiJX/0gm9fKaNhUNxzMo/JdHj0T1TwNuAHT+chE+ApW0fGo+D6oSFLJyQcy6t4Y7OBr71VRDYp4e5jnUayQQjTFrZY1/C/nSwxiUzBhyFbt6/zqJjPJbBRbVmRbFZ1zHpkDK02aes9Kz76x6xn2Ek3LRhW51TyXmJ4PsmIIGW3UaJmLJLxSWBqFrj4Rse/rVQ0TKbkri6Iw+NJWLR1Qt+uG22Cfo4xVUYppGVZ0yxQdE5U8daHN2BcrCGLWJAJbA/80LK9GA6GDLC9yHw0Fw775/rdFIkNVvRSkNV0E59+8AqeX6vhp+8+5ETh+IF/9qWSht94ZAmfeXQZC7kEZjMKtvooWu4G3tgkjlYLUMNGzJM0x43zGZSbJi7u7A5L5hWtL/i4icfFSlmLzZILGbLAKUs+tRbtuxHFJnGogmIQaOVYulm1fErBgv23jcIA3ruYh2ZSPHa5HOlz9opvXihhLqvgurk0JlMK83Psg0SCFznH8yqum8s4OjJu4dOPggxgOrLXNut4/HIZ95wcj828hXmR8bU/iu0FwFqIIoasrlsoNs1ILv0c87kEdItip26gqpmo6lbHQW0sKYMAbQkBfh563oDxYcuxBA4KsthQu2E8Tp0BFRRkq2UNCzEc+t1wJi1LGkyLotDwaVkmWcsySODtd6LICFqWlFJU7bYNR1qRoNli+DAUG+wUnY1wWt4ThiykLTgVEJ/kx/6l43jXCT7PL3/jCl7brON/es/hUPdtoLUQ/dZjy3jiahk/essMfv3DJ3B8Qo1lfbAb8AaLc0ylFSgS08NoJo0l6Oe4ZSEDAHh2tRryyO7geHM5BVnvXmSUUqyU9VhJHQC75zpF/f5sBm9Zrkdk9MrNTsacI6WIB5v4wIMX19osWZSW5dnpFA6PJfvG5K5XdPzHp9eFB8btuoHnV6v4wMk8JEIcDZ8fwxMHmzUdBOwAd8fhLC7sNLFZ0x35RT9E/QBjIKs6MwWP067kmEwrgbo5vvZ747j8kPfJs4zjQcbBHf3XK7rv5LUsEYyr7eawfmt4Lim3tSz5oeKAIRthcDo+TpFATp4BNlZBy6W2n69UdOf0GBdcuLlc1lBoGLAofDVkukUDdVhlz3QUB78J3adhLvJ0awqColy8KDYMoYZIBHUvGDJHOC++NYIKMm+OJUcqpu6Qo1A38Itfv4yLO038/PuP4r0nxiM9bzKtICERnJxQ8W+/7yR+6MYZyBLBdCY8+mm34fVp45AlgtlsAi+u1QDEs7zgmM4kcCyfxHMru1OQeb255nMJrFe1QKPgMBQbJhqG1RVD1jCstoMW3zxTghN/UpYwrspYK8doWfocStj13Pk78xxLL3iuZZSWJSEE9y2O46X1el8mWL91sYTPv7KN//1bVzuYr0cusqnEe06x+2rSvm93+sAib9UMTKQVKBLBnYeZJ9jTy1VnYjwsyzIqeF7lNVMpnJoMzq4UYSKloCjw8eKouVJZoiCvitu+cTzIOBxz2Koe6E2Y97ByJR+CIZeU2lqWQZrLvcLwfJIRQTdmpeTUWfYfLpasaVjYqhmxT8Yc4yobC14ua0IPMo4obv0Vj6EjR0agIeMXdMbTsvQ+zg8sxzAa+9ES9Q+OIas0memt34TfdIa1NUQFedGTY8mh+mxgQdipG/iFr1/GalnDL91zFO84Gm70yDGmyvj3P7CIz3zfSZx0LdLTmQQKDRP6AAtcN5qGhbphYcKnXT2fTTiMUzctSwC4ZSGLlzfqu5Lu4LQs1RZDZljBvnRh4Bq0uJPWKYVN0roPWjXb5NLvsDOdUbBZjVbkiGxw3O/tN2UpYtWum2UFWdT7/p5TeRCwdmKvuFRoIikTPLtaw289ttJWPH/zQhHXTKVw1M6MnEizz77TBx3ZZlV3WN5j+STmsgq+u1Rx1jK1TwzZ8byKG+bS+Ac3Tnf1/Anbx8tvfwiyUhG+XlrMkK3Z112cgmzW5UUm8iDj8LJy/HfxpsFkvQyZ87tFH6bZbRwUZDHRlUj7xGmAkLa2JfdX6ZYhA+xJy5LmeJBNpztfa0wwcedFxUc3xX9X90XcmrrpHCmOwpAVGmabcWoQCGERMYP0IQtzwucspGgTLjXFE4TphBTbh+xv39jBSlnDp+87hlsPZWM9F2DFl9cccsax7dibtmVrwlb8/boFv3GnLDluXchCMyle2ej/NKnXZ4t7kfUi7F/h1jdxW5aCe64eEgI9lY5WkDUNFuUUpCHz8yETtSUXp1L4+fcfwXuORxOcz2YTuGk+g2+eL/bspXex0MAtC1n8d7fP4u8ul/Hvn1wDpRSXCk1c2Gk67BjQMuYNC8iOgs2a4dxvhBDccTiH51arjiarH7YXAGOW//WHTsQW83M4Pl4+bVq+9mejashUGVXd6jgQrVV0qDKJ5D/JoSoSJlOyU5Apktg8OZ9qHyQoBbQs2xgy3rI80JCNLoLCdf1AUhlg4WjbpGXL8qI7hgxgbcswhoxbWQTFkVR8/K0ke6LKvehzUWS7qN82kI1QdJSahi9DIoIqk4H7kPkJ+gE4gvptQeuv0DCEwwrdtCyvFDXMZRO4fi4T63lB4J99r9qWjimsD1PCT8+qTHxbxmG4YT4NmWBX2paVZjuTvOAJRe8GyyUNEkEssTPQ2kTc11VYUHLUgszPpZ8j5TNl6deyBIB3HRuLJTi/dzGP1YreU2GtmxauljScmFBx/3XT+MHrp/DVNwv4z89t4qELRUgEeN/JVkHGr8teGTJKKTZrelu49x2Hc2gYFM+sMHF/ckiKAD8fL456FwwZe732/Wa9qmMul+iwUQnDXC5ptywNzGQSQvY3n1I6RP2K1FloZRPMQ5R3COKyf4PA8HySEQGnmuNusOTUWeDiG86Jj1tedCvqBxhDtlkzsFJmi7ro9BGFIfPTkAGsNekW9dcE5o9pgfjfD8UYDBnAFq5BZlmWm1agzxdnyETTiqWm+HdLdSHqXy5rOBLR4DEqWs7ce1SQhTBkvMCZzcZfuDkyCRnnZtJ4drXW3YcMQFkzkZSJU1jMZBKQSW/msCsVVnhHibpxQ8SQ1cIYsoyCnVq45q2llRO/luh6tihFVfcfBIiLu4+NIaUQfPNC9+L+qyUNFgVO2ikOP3brLD58TR6ffWkLf/PaDm47lG07HCi2QLxXc9iqbqFhUOd+A4CbFzJISASPX7ELsj4xZL0ibJChqjMJR1RLFr4HeQsyZgobn/WezyWwXtGxUfP3JsyrrBXJCy0em+RdQ/ieVbWlNQdTlvsAvICKPVp/6gxQLgLbGwDYeH82KXXNBACtScuX1uuYTCnCm8bRkAUwZFXNgiqLdVNejzGRUaBoGlME3bRQ061IHmQcqkx2RQ/kh7CW5VwugYRE8MpG54bvJ1iPy5BZlGK5pEV23I4K3kLZq0nLQogH3ZyrIOsFtxzK4vx2w2ld9As8WJyDDyKs9iA+72bCEhAbDtd1M7RlaVH/aBuOsEljkaife7T1qyBLJ5in1qOXyl1PWfM1+uQkK8gIIfgf37GAdx8fg2ZS3HOqc2I5yGcwKjgD7WbIVEXCTfMZ57WHpiBzWpbi37nmyS0OA2+Bfu7lLfzpsxv4/57fxGdf3MRKWY+lH+OYzyawWdOxWtYw5+O/yNcTbsztN5DCDxi8I3QwZbkPMK7KODWp4oW1eCdwcvIM+w+7bblS1rGQS3bNBACtScvzOw1huxKIJuoPatOxgqz13FbLUqAhC1k4uQeZX8tKBDVmVFWvCPJfAthmdNexHB65WOqY2io2DSFL6TeV5oetmoGmSXGkB/ZUhExCRlqR9qxlWQhJaeAasjDj2zDcspABBfBCgP1FN5ORIqH7wliy65YlpRSrXXiQAeJ7jrUsu9M/uuEnYeAQHTAqAilDr7hvMY+abuGJq5XwBwtwqdBEQiLOwRVgRfS/ePdh/Mp9x/C+E526q4k+mMNy5/sZz3V8x5GWFrRfov5ekUtKkIl/qHoY6+rFkXE2wPDkUgWfe3kLf/7CJv7suU00DMuJeoqDeTvBY6dhthW4buQ9kU1lnzXcYcjsLk/cduwg0NvK9zbFjfMZfPWNQryQ2KMnAUUBvfAGyJ3vxWpFw2IXY8pu8IXcz/ICYOPuqkycSUoRvCd/N9IJuc1MzxF5CqYswzRk/IbxTr8EISlL0AbkQ0YpDWXIAOC+U3k8eqmM7y5VcbctVDYslmPptbwA4jNkS3ZofL9g6XDDAAAgAElEQVRbloA9addFQfbdpQre2m7gh2+a6fq9iw0DKYUIbRkAZjVy19GcYxPQLc5Op5FWJDy3WsN7BFYhD10o4v/+zir+3fefjhR0zFFpmh0Fx0Iugb+71J3OqdxkZpfdMGRdtSztoZ+wgownewQxZJpJYVrUYeX9JrV7wY3zGcxkFDx0oYj3n4xm+eLGxUITx/KdCQgJmfgOykymFLzc40DIpoAhA4A7D+fwh1gH0D/bi14hEYJx2/pChJoWzLp6MabK+MP7r3H+36LsOrFod3FRblbNjzmf4G3SZqsgE62dOacga7UskzKJLRfYTQxPaThCuHk+A82kTj5ZFBAlARxbBL34BkyLYr3SXavCjUxCxqRN1/oVZACbngxq31R8YlIA1ppsE/XrLHTYXYjyDTbM9iJObBIHE/UPhiGrOdE4wZ/v1kNZTKZkPOjStzixSb4MmRV5YoxnlO5GQTaTUbpqWT54vojPvbzd03sXG2YgO0oIwS984Cju6nJijEOWCG5ayAgNYmu6iT9+eh0NI979CwBlrVNfOJ9LoKxZgUMzfnAmLLuITuO6FzdDFjplyadsQwrylqbUb8qy047Gm2LQD0iE4J5TeTyzUu2qjXix0MQJWz8WFRNpBYWG0dN052ZNh0Q6JwIXxpLOPd0vY9h+YCLlr5ur6RYyPfxNJcKkMN1md7oLMj8NWoshs1uWPgxZq2VpM2RDFiwOHBRkXeGGuQwkgu7alpfexHqlCZN2FyruBW9b+rUsAcZIBU5ZNi3flmXaqyHTrA7XZj6NGaYhE0ULhYFlhw6GIYsa3i1LBB84lcdTSxVnEQgqNlMJ5hmlR2yTLZU0pBQpsMjuFtOZRFcty526gYZh9RSSzlz6B+P5c+tCFmsVvcOS4r++uIVCwwQBa/XHQaXZySQf6iFk3PEg66Vlaf89LEpDg5LzqgyJRGHI2ocXvOAHMPewjdcSpF+4d3EcFgUejinuLzUM7NQNRz8WFZNpGZpJI2fzirBZMzCRUoTMy7uO5jCWlIaKlZlIKb6i/ppuRTaF3Q1MZxLgX5W3BcyRdyZFWSJNqSnucmQ9DFnYVPJeYLg+zYggm5SxOJnCCyGTXBtVvV2Qeuos0Gxg9dIygO5Oxl5wfYQoWJyDBYwHT1n6nWw7NGSeYHEOb+Emgih8OwxJeXBTlkHTpl7ct5iHSZkTOBBcbPJ0h6heZEslDUfGu580DMJ0RsFOw4itoeJWAL24mBdjmAL3Ch6j9JyLJVuraPjiqzu459Q4juaTOL8dbzCnIhALuzM442KlrIEgnlkmh5chaxgsjzaIIZMlgqlMMlJBFtS2F3kxVgXT1/3A0XEVZ6ZT+LuY2ZYXC7agfyKeLKQf1hfM8kJ8nf/Dm2fwWx891fVr7wYmUv66ueoeFy2KRJzvctZHQ5ZJsAK30DBQNyyYPl2OrFfUf1CQ7R/cvJDB61t13wmghmHhZ758Ab//xKrzMy7sX7nKfrbQD4ZsjDNk/q81pgYXZEEaskyCCdL5Bu4NFnc/Low9KTQMKFK8qArVJzdvNxCVIQOAExMqTk+pTuZeKzZJ3LIEwoceOJbLWpsQuZ+YzkSbtHODUuq0uXopyAp18dDDbuDIeBLTGQXPrrQOTX/89AZkAvyjW2exOJnC+e3oDJlmWmiatFNDZt/Dq+X4DNlyWcdsVvFNhQiCLDHTZH7PtWJggr/fmWwy9G+4UdPbLBu8EBVkjrl0nwsyALj9cBZvbTditYUv2QVZ3JZlP8xht2qGr0VDQpZ6niLuN7jTvahNW9fNPWXIAOZFNq7KvowtIcSOTzID1/CkLCHp0lMftCz3EW6az8Cw4Gtc+NjlMqqahYcvlnC1aJ/E5w8D6QxWN8tIyiSWoNgP52bTUCSCowF6o7Gk7Gt74bhy+xZktumrvfjWdLEbdzpCQVZqmsirSizmJylLwizL33tiBZ958M2ecgS9cG7miJvKvafyOL/TxMWdhqtlKRb1A9HitjTTwnpF3xX9GNASGscR9tcNy9HxdWsJYFE6UIaMEIJbF7J4Ya0K06J4ca2Gx6+U8YM3TGM6k8DilIqtuuFriOmFN1icI5OQWUZkly3LXnSk7oDxqCaXM7lwhmy9EmxRkEoICjLNhExabHA/cct8FhYFXlqPLhG5WGgir8qO8WlU9JpnSSnFZjW4oB02TKRY3rG3w2FaFA2DxrK92A184OQ4Pni606LEjQk7z9LPpZ+DxScdMGT7DtfNZiAH6Mi+/lYBc1kFqkLwFy9sAQCIJIHceAdWCjUsZKIFbIfhhrkM/vwTZwJPXWOqjEpTfAIKG1f3TlBWNbGmwOtXJkKxYcQyhQXETv2FuoGvvVnEF15YxWceXe5bNqOz6UZkcd5/chwyYZl7fjmWQDyGbKWsgwI4Mh7vZB8V0114kbk38G5bORWNDUzE3SB7wS0LGVQ0C29uN/AfnlrDTEbB/ddNAYAz4Xw+op9gkGh9PtedF9lqrwWZ6xBUcxiykIIsmwwU9ZsWc5kPMvFMyTytxK0hYzrU3Wizn51JISmTUImIG5dsQX/cz+MwZF1e51WNHV782mvDiAmf+CSHde2zLjAuPnzNBD5521zgY/IqmxT1y7HkYAHjLVb5oCDbJ0gnJJyZTuN5wSTXSlnDS+t1fPiaCXzs7CQeuVTCFZslIx/7BFaTE5ivbfTts4RN7OSSEkwqLgiqIUWI1/S1qnWO/gPRGDJmnBrv5KjaI/buYvI7SxVQAPfftIDHr5Tx648sdXiCdYNyzLZLPqXgziM5PHShiJ2GgTFBjiUgjrnxw7JtebF7Lcv48UnuDbxb5iCIQdwt3LLArA1+/4lVXNhp4pO3zTltD6cgi9i2rAScvA/lkrHd+stNE2XN6mmwJ6VIzj1dj1GQFZv+AfPbdQOGFRzlpIpalgGyh16RkCVcP5vG8xELMtOiuFxo4kRMQT/A1kpF6p4J3qz5h2APKyZ8ilDuaB9HYrJXyNsMWZjsJJuQW079By3L/YWb5jN4c7vRYffw4HmWk3bvYh73XzcFVSH4LzZLhsPHsZqdxcKVV0ArpYF8ziBz2LARdycWyX5cVbeEov5MQkLdCLG9aJqOZ0xUJGUCizKfL44nrpQxn0vg5+49jR9/xzyeXKrg1x5eiqw1axoWLhc6mZGyxvQSUWNCACbuLzRMPHGl7JvRKdrA/MA9yA6P784JeyzJdBRxWpa8CCPopSDjpsCDY8gm0gpOTqi4WGji2pl0mxFoTpUxl03grYgFWTmASZ7PJbBR1duu0TBwRq2XwZ52hiza5jmTZe/n93dct01N5wM+F18Tmm2ifv8cy37gpoUsLhWbkWKN1io6miZ1IpPigBBiTx12W5Cx502PFEPG7knv7xyVdR0G5O1JUUfL67Of5ZLSQctyv+KmhQwsCry83tKRmRbFg+eLuGUhi5lMAuMppY0l264b0IiCheoa6Ne+OJDP2cqz7CwIwsS4XD9Q0y3oJtObiRkyeXdallx/ZZ/o67qF51ZruOtoDoQQfPTsJH7yrgU8u1LFrz50NVLR86XXd/AzX76Aix7bAz//miDccTjHhiY0y/d3SyWiT1kulZuYSiu7ptsghGA6phcZb1MeGQ8XhPuhsAcMGQDcZhuA/tM75zraV4tTKi5EtL6oBOgLF8aYm/hGtfWdbtV0/MYjS/jTZ8VMOM+y3QsNGeDPAHEtXFDLkt+Tbka8ovUvx1KEm+fZ1GwUq6GLBfY3jSvo55hMK9jpUtTvMGQ9pk0MEl6ne45axEGRYUBeZXYlmzUDBP7TvrmkbMsnKBrGQUG2r3DtDBPUuxeJF9Zq2KwZbSJExpJJ+IsXNp3WxuGjC6Df+JuBsGRBeZZOTIrqoyFLtlqWTo6l4GLP2D5kfoaKTYMF7sbdkHnmGz+NP7NSgW5R3HW0xXZ86JoJ/Oy7D+HF9Rp+99sroa95aacJiwJ/9txm28/LAp+pMCRk4riI++kWWi3LcAZlqbR7gn6OuF5k2zUDSZnYBVl3G1VYsPhu4YdunMavf/gEzghiW05PprBc1kMNjQGXE73gb7zg8iKjlOLrbxXwU39zAX93uYy/enVbeEjgHmTdWF5wuBmyOFOWALBdFxfknCHzyw0EWsJ9rzFsvy0v3Dg9lUI2IUUqyC4VmpAIcDzfXUHWE0NWNZgp7IAPHr0gr8ogEDBkglSWYQVfV64Um8gm/bscWZsh4/fkQctyH0FVJFw7k8ILay0d2TfeKiKXlPDOo634l/GUgo+fm8Sjl8p40s5lW7jnXkBrDIQlywW0LMMiTxxRv2E5ejMRhZ22zU9FE5GA26crrqifvRd/3SeuVDCmyrhutn2DvedUHvecykfSmSyVNUgEeHKp0hYS7hdKG4b77JBiv2IjTstyudTcNf0Yx0w6XnzSTt3EVFrBZFrBdpcbVaHBNqqoE6z9QjYp49pZcYbe4hTTkV2IIOyvaMxMVnTtc+uLF9dq+JVvXsXvfHsVJydV/ORdC9BMiqeWO7MYV8oapjNK1w7mgFjUH8qQZVmR4seQrVd0TKWDrTj4Pek+YOx2y1KWCG6Yz+D5Nf98Uo6LhSYWcsmuv9vJtNw1E7xZ0zGZVmLJHvYaskQwpsodVh9cazVsLJII/KB/taQFruFsytIaWvZv+L/pIcdN81mc326i0jRR0Ux8+2oZ7z853iG0/4FrJ6EqEr746jYkAswtngC5872MJSvvLks2nvQvyMpNNh3od9PxDaiqmQ6TICrewvIsi834sUlAK/OtaVgwLIonlyt4x5GscME7McEEy0ExUZRSLJU03HMqj4mUjD97dsNh9cIMMf1wekrF379+Cu8T5CYCYt8mEUoNA2XNGgBDpmC7rsOKGA+z3TCcgqwcIAgPQrHBit1h2qhO2aLvKMJ+xp5KwqGNqTRzZf/sS1t4ZaOG/+HOefzqB4/jvsU88qqMxwWmpstlvefC2y3qr0XM5cunFSZa9ynI16rBE5ZAywONt+AtSne9ZQmwtuVKWW9rDYtwcacZ26HfjYmUglLT7MpSZ6tm+IZgDzNE5rB8LRdphocNfF9Zr+iBWcm5pAyK1lDTsBWbw/VpRhA3zWdAwTxyHrlYgmZSfM/iRMfjOEtmUabPUCQC8vEftlmyL+zqZ+StBJGxIm81+FlwpBQJBHbLklPYPgwZAF8dWatlFXPKUm5pyF5er6GqWW3tSjeO2lYRju+bADsNEzXdwukpFZ+4cQYvrtfxzAo7dTMn9vi3BCEEn7xtDtfPZYT/rkgECYmEFmRLu5hh6cZ0JgHDAko+cSlebNcMTKYVJ8qpm3ZOsWn4Dj3sFabSCiZSciTri4pm+saLSYTgjsNZ3Hooi9/+6Cl87NwkJEIgSwR3HcvhyaVqR/wXs7zobePmGjJqxyZFEV9LhGAypQQyZFHaqKpHv0axO6awbtxk68hEk+0cdd3CWkXvWj8GMA2ZRRF4sPNDkEv/MIObw7pRHSFRP7fuoAhm4Xn7ddMu6g9alvsM3CPn+bUavnG+6Di4i/AD104ipUjOyZgcPs5Ysge/tKssWUJmWZPilqV/sDhg51TarZGqM2kmNoYF/AuyoPDtIPAgY8208O2rFSRl4gi1vTiWZ9/r1ZK/J9RSiW2+R8ZVfPiaCcxlE/iz5zZgWhRVQXh0v5BSIhRkpcEUZHzDiNq23KnbDJm96HVjCVComwPXj4WBEILFyVQkYX8YA/QLHziKX7nvGBY8rNfdx8bQMCyn6AcY21xsmj1Hp7llAjXdjLxxTmXEBVkUDzLnvZWWP2A1xMuwXzgxoSKvyng+QEd2udgEBbqasOTo1hyWUiYqH8WCTMSQ1WyzX67jHWa4WbGgNZzfwxv28MUBQ7bPkJAlXDebxiMXS3hjq4HvWcz7mhGOpxT88j1H8cnbZp2fOSzZA5/f1c85pkpCUX9Zs0LFuDyn0jkx+UQnAfC1vuA3e9wpS976bRoUT1wp49ZDWV9tyGw2gaRMHM83EXjRc3Q8iYRM8A9vnsFb20088GZhV0/5KRejEPTZFCl4wq0fcLzIfITdbtR1C3XDwqTdsgQQyXrAi2JzcMHicbA4lcLlQjPUx65bn62b5rPIJqW2tiUf7OllwhJoDxhnQcnRPt9UWlyQbdZ0WDTaoIGqSKjrrKUXpkPtFwghuGkhgxdWa77DQ5ecDMveGDIgfkFW1tgU+ihZXnCIAsZruoVMcnfMfvsNVZEctitYQ8Yes1E9aFnuW9w0n0GxyU4T95wS64g4bpjP4ORkK/CWHD4O3PJO0Cce3tXP6JdnWYmgm+IB485JWOhD1rLHEKHUMJG0mbo4UO3T2SsbdWzWDNzlGpbwQiJsEvBq0Z8hu1rSkJSJ41j/gZPjOJ5P4s+eY/YEu8aQ2ZmgQVgua1jIJXddZ9Vy6w/fcPimxAoy9t10w5AVG6bTVhgmLE6qMClwqRDstN+tvjAhE7zzSA7fWao42js+Ydlry9KdABG1ZQn4F2TOhGWEgiylSM7kc1CKQb9x83wWW3XDae97cbHQREohkX4HP/DrPG4qBW+DjZLlBcdESkHDsNq85Wq6OJVlWMEPfFEYMm5PctCy3Ie42XYEf8fRXFc+S+TcTcDOJujOVr8/mgPmvyJuWfppYzh4LFJVsyD55NVFEfXnu4hW4WzYI5dKkAjwjiP+BRkAHBtXcbXkz5AtlzQcGU86mjlZIviRW2Z9swr7hZQihfqQLdmfbbeRT8mQSbyCjOmtFGYOG3Oj0kzG4AwrQwYA50Paluw+6W65vPv4GKqa5Uxj84LM296MCy9DFr0gS7CIHw9jG8WDjMPN+LYKst3fTm5esP3IfKapL+00cGJC7SmWzokSimnxwu+nkRT1pzvNYWtDaJwaBL73Bq3hTsvygCHbv7hmKoXvWczjh26Y6er5ZPEc+48Lr/XxU7VDxJBppoVCI3xcPWObvlbtYHFRUcVPGkGi/vEuilWuX1ir6Lh2Jh1a8B7NJ7FeNXzbg6Ki566jOZydZhvz7mnIgluWpkWxUt59DzKAMYnTGcU5JQZh21WQyRLBeCq+JUC3Ax2DwHwugUxCCpy0tCjTF3bLAN12KIuUIuHxK6xtuVJm1gipHk/naRdDFqsgy4i1gOtVHRKJVlC4NZHOsM8AGLKFXAIzGUWoI6OUOhmWvUBVJGQSUnyGbARjkzjyameeZU03R4oh44kDQVOWXlH/sA0sDNenGVHIEsFP330I10ynwh8swrFFQFFAz+9iQZZkbvJu/L/PbaJhWHjXMfHUIkfGEfX7U9jpMIasYXYVm6O67EPCPifACjKgpRVzQzMtrFc7ix5CCP77O+ZxzVQKh3epIAoryHj0ziAKMiC6Oey2q2UJsMIsbkHmuPTvUrHbCyRCcGpSDWTIalpvU4RJWcKdR7J44krFLrw1HO6xXQm033P1OKL+tLggW3M8yMLZJTZlaWvIYmbA9gJCCG5eyOKFtVqHbct2ndnGnJzoch12YSIV/zrfrDGvvWFszYfBnyEbvnvWD1FalmlFgkRYjJ80hAMLBwXZEIAkEsDx07tbkKkyqprpLGKvbtTxhVe28b3XTDghzH5IJyRUdYudmHzYtKRMIJMghswIPLn4QXW1R98ZoB/jOGZbX4iE/StlJlo+ImgVXTubxr/9yMldFPUHT1k6E5a7bArLETU+aaduICERh0VllgnxWjk8ML3XFt1uYXEqhYs7TV/fqbKTZtH9tfHuY2MoNk28vFHDSlnrWdAPuBiyLkT9QKcXWVTLC6BTQ6ZIYinDbuDm+QzKTdMR8HP0Q9DPMZnunDoMw2ZNd5jkUcOEID5p5DRkNssXtM8QQpzfKa1IQzewMDrf9j4HWTwHXHoT1OjOIToMY6oMi7LTftOw8NuPr2Amo+Af3z4b+lzGkJk2Qya+2AkhzuO8oJSi2DS7alkpEoFEgBN5NdImdmgsCYlAKOznlhdHu4xU6QXMxNNf1M9FyrvF0Hkxk0lgs2b4Tqtx7NSZBxlfuCa7YMguFzXIBLueQNAtFidTaJoUyz5C8X5opG4/nENSJnjwfAk7jd4tL4AWQ1ZsGjBp9PaLH0O2HsEUlqNdQ2b5Shl2AzctcD8yxpK9td3A517acnJDe21ZApwhi3fw2BxRU1igxS65J6hrmimcqB9WcBY/bJ/hrfXUEBabo8et7lcsngO+/lfA0iXgxOm+vzxnfsqaib99o4DlsoZfue9YpOiIbEJGw6AoNU3H60sEbo/hRcOg0EzaVcuKEIJTkyncGzK9ypGQCRZySaGwn7NQe1EYpBJSh4jajeWShlxS6opF7AbTGQWayRzWg5ifbbsg45hMs5w/i9LIwulLhSaO2DYjw4hFl2P/MUGx3g9bh3RCwm2HsvjWxSKA3icsgRZDxpmuqAVZNikhKZO2gkw3KbZqRuTpRK+ofxDtSo6ZTAKHx5L4/Mtb+OxLW4429kRexY/eMhM6pBQFk2mlzTsuCrZqOk5P9d4u3QskZaabK9jfJaUUVd0aumihINy7OI7ZrNK2XonArlV96CYsgYOCbGhAFs+BAqDnXwPZhYKMb/RPLlXwxVe28X1nJnCrj8GqF/wkvlkzcG5GnAvIHic7US5ulLqMTeL4zY+cjPX4Y/kkrggYsqslDdNpZU8ma1Jyy1VdxCTwYYNBsQwt6ws9tCBzFymTadlxMY+qlblcbDpDE8OIo3kVCYng/E4THzjV+e98w+91o3/38TE8YWfZ9qVlaV/HW/V4E2OEkA7ri82aDgpgPjJDRmBSVsjtdo6lCPctjuOBNwu4bT6DWxayuGUhG7oRx8FkSkFNZ92EKJmYlLKC9q6jo8mQAbY5rH1NaCaFFYN1HQZkEjLe6ZPi4gaX3QzbhCVw0LIcHkzNAvlJYJd0ZPwE+yfPbGA2q7SZ04aB35QNwwp04874MGSFAU/ZHR1PYqWswfBogpZKGo4EMHy7iVRI+PpSSRsoc8dbK2Fu/TsNA1PpViES1zSTR9kc70MbabegSAQnJ1XfSUvesuzVEuUdR3Lge/tCHxgynikZlyEDOr3I4niQAe35rOUB5Fh68UM3zuAP778GP3P3YdxzKt/XYgxweZEJrvOGYXUMFJSaJjSTjuSEJcdESkHR1s2NUmxSXPBrdRh/t+H7RG9TEEKAU+d2TdjPWRDDovipdx2KRUW7L9yg0XY+jekFv8kH1Y47mmdmn6suTRCllHmQ7ZGOKeUKSfeirlvYqhsDm7AEopnDNg02Weve7KZixsrw4YoTe6Dbi4PFyRTO7zSEmrp++WxlkzJuXchiKq30rRWUUiSHIYvzmpNppU3UH8eDDGj5AzYMFqk2CMuLQcI5eHiE/aZF8bNfvoCff+Ayai697Ch7kHHkXW79/HcbxqKlV/CC7IAhO0AgyOI5YH0ZtBKea0nLRdDXX4T18N/C+toXQZvBAckTKRmKBHzkzIRjZBsV7gs3aOompYgZsov29NOgGCCuc7visr4oNExUdWugRY8bbld1L1YGFCruxmRKgUSC45PcprDO83wE4X64bBdkw8yQAcCpSRUVzXIMI92oNE2oMkFC7n25/Gd3LeB/vedoz6/DkU5IzrRsnM1z2pNnuV6J7kEGtK7npmHZGrL9tZX4mcM+vVzFSlnHq5t1fPrBK07hwrMRp0eaIZOdw3NtgN5yg4bTsjzQkB0gCFxHhgtvADfd0fHv9OpFWH/+B8DyJaBSbv+3V5+H9BM/D6KI/6TZpIzf/fhiVzmJ7puyG4bstY06jo4n+yK2jQJe2FwtNgHbu8zJsNwjpibtMAqdDMzVPRg2kCWCyZQSyJDteDzI3P8dlSG7VGgiKZPIdgp7Be4h+MZWvaNtV9asvl2705lEX7MO04qENfuainPin0qzqBwWSi5jrapjJpOIbNnAGd+6zaIOumW52/BjyL5+voB8Ssan7pjHbz22jE8/eAWfvu8YNu1CfmaXc2h3ExMpBWXNgmFR52C9Hxmy7BAzZAcF2TDh5DUAkZiwX1CQWZ/9Y+DqRZA73wMcOgqycAw4dAz0hSdB//O/A/1Pvwv8458GkcQXmkhITE0TkIL9WKIyZCINGaUUr201AjMo+41MQsZ0Rmmzvhi0z5cX7haPF8slDQT9EXrHwVRGCdSQbQsYMlWRkE1IkQuyy4Umjud7i7IZBE5NppCUCV7ZqOM9J9oneitadzmWg4D73oyrIQPYhGYmL8fyIANaDNl23ejJNHdYMa7KkEj7waNQN/Dk1Qr+3rVTeN/JcSgSwWceXcKnH7yCxckUZDKc5sdRwYeuig1jX7cs3T5kw4bh+0RvYxA1BRw9IdSR0aXLwMvPgHz4fkg/9s8hffAHQG68HWR6FtI9HwX5/v8W9PEHQf/yTyK/HzUMWL/446Bf+ovAx0XVkKUTbJLQbbC5UtZRbpqB05m7gWPjybaW5VKJMTV7FfybDijIlsoaZrOJSNNc/cRMiDmsqCADbC+yRjSPpktFbejblQAT9p+bSePljXrHv1Wa4XmvewV3/FKsgswTnxTHg8z9vpxhDRr2GUXIEsG42m4O+9DFIkwKfPB0HgDLKP2X7z2CN7ca+MobBUxnRtMUlmMi3YpPajFkw3nd94IDDdkBIoMsngMuvA5qeZimb/wVkEiCvP/7xM/7+A+D3Psx0Ac+D+tv/zLam738DLC1DvrYg4EGoe0FWRBDxi50d9Hx6ibb4K4dcEF2NK9iqdR0pqGuekLFBw1uQigKGB9UqLgXYfFJO3UDitTpUD8R0Ry21DSxUzdwYmI4DWG9uG42jQs7jTaxNoCh1kjxTUWR4mncptKs+NquG9BMC9v16B5kQKsg45mA+40hA7gJcsuX62tvFnFuJt1mA8OLMjmG/m5YMeFiyHg+6SgZw0YFP1wNY7G5/77tUcepc0C9CqwtOT+i5RLotx8CuftekDGxQSohBOS/+RTIO94H+pd/AuvRr4W+FX3iW+w/NrbhF2wAACAASURBVFaBKxd8H5dSJPAyJugU7mTruQqy1zbryCQkJ2NyUDg6nkTDoE7BMWhbCS94BJSXIaOUss+2JwUZ81ryFiAc23UDEymlo509FTHn74o9zHF8yCcsOa6fy8CiwOub7fYXe2HrEBWceY3bWuK2Dts1wxlkiOpBBrSuZ35/Dev30wsmUorDkL2+1cDVkuawY27cfXwMv/bB4/ind84P+iP2Fc4gQ8N0tMDD2NbrFU7L8oAhO0AYyOI5AGhrW9KHvwLoGsgHvz/4uZIE8k9+Frj+NtD/9Hugb73q+1jabIA++23g9ruZbu3px3wfKxHiXLxhon6gPc/ytc06zk6nBs5M8VPslWITuh0qPuii0A0/Uf923UDDsPZE28ZP9H4s2U7d6GhXAmwz366Hxy7x6dp+RNkMAudmUpAI8MpGre3nlebwa8jiFmSZhIy0ImG7bmCtwlr7cRgyfj1v1nlBtv+2ksm07Bw8vvZmAapM8N4TYuPR6+YyI+vSz+GOT6rqJlKKNNItWD8cGU/i7HTKGeQZJuy/u2jUMX8YyGQdg1iq66APfRm48XaQQ8dCn06UBKSf+FdANhvYuqTPPgFoTUjf8/eAszeAPv144OumE4wlC2TIXGHHAPOyuVRo4tzsYNuVAJzi62pJCwwVHxRSPhoyZ9hgjxgyIKggM4WGm5NpFrvkFyTPcbnYRDYpCYu6YUQmIePUpIqX11s6sqZhQbfo0DJA/Lrq5rQ/ZVtfcFPYOKJ+1dOy3I/2CJM2Q1bXLTxyqYz3nBgfyjZXv5BWWKRWoWGgplv7UtAPsGv1M993ciiZ+/35jY8wiCQBp846DBn97qNAcQfS9wSzY22vkcqAfOAjwHPfAV1dEj6GfudbwOQMcM31IHe8G1i5Arp82fc1MwmWdRbEdHkZsje3GrDo4PVjAJt2GktKuFrUXEXP3t2Aw1iQcVfxTR9h/3Zd92HIollfXC40cSKvDiwOqh+4bjaD1zbrTsqDYwqrDudS2S1DBrTc+tcrOhSJFSBRkZQJCFrFfFD81qhiMq3AsICvvrmDhmHhQ4J25X4CIcR26zf3dUE2zDj4xocQZPEcsHQZtFED/foXgUPHgBtui/ca934MkGX2fA9ouQS89DTIO9/H2py3vYv9PKBtmUnIoZNUjobM1iRxQf/Z6cEXZIQQHM2ruFJs7mnRwyFLBAmJdBZkZQ2qTPbEUJIXWyKGTDdZJI6oIJuKYA5LKcWlYnMkJizduH42jaZJcWGH6ch4juXQtiy71JABrYIsrgcZwO6vlCJBtygUCVCHNDi+F3BN1Rde2cHhsSSu2wOmf9DIp9hkaU0z993k7Cjg4BsfQpDFcwC1QB/4AnD5PMgHvz82y0DykyB33cMmKMvtzv/0qb8DTBPknR9gj52YBk5fC/qUf9syl5RC2xKczucM2aANYb04Op7E1ZKGq6XmnoWKu5GybUHcWLYF/Xsx/ZmQJeRTspAh49Nlfi1LoJVRKsJ2nU1qDWNbIAjX2psub1vyabNhtb1IOwLl+J9vyo5PWovpQcbBzWGzSXmkWNCocDPBHzyd35e/oxcTdnxSTbe6uqYO0BsOCrJhxKmzAAD65c8CuTGQd93T1cuQD90P6Brow19u+zn9zsOMdTt2qvXYO94DXL0Aur4ifK0fuWUWn7ojeIqoxZBZjiHstXt4qjyWV1Fqmnhlo76n7BhHShYwZHtkecFxckLFi2v1DoH+tsCln2MyQp7lpRET9HNMZxJYyCUcYX/ZybEczs2pp5ZlRoFuUVwqNLtK8OA6smH9bnrFhD2JKhHg3sX93a7kmOAMmW4FmoAfYHdw8I0PIUh2DFg4wlis938EJNndpkaOHAduvAP0wS+B6qxtR7c2gDdeBrnrA20nPnL73ezffdqWp6dSuGE+E/h+7oJseY8MYd04ahc6qxV9OAqyhIS63ip8+PTnXn62u4+NYbms4bIr1QAQ51hyZJMSEhIJbFk6GZZ7ONnaLa6bZQaxlNK+BYvvFnptWQKAZtJYE5bOeyd4QTac302v4AePOw7nRmYwpVdMpBSUmiYqmnmgIdsDHHzjQwpy+lpAVkDu/WhPryN9+H6gXAT99kMAAPok8x4j73x/+/tNzwEnrgmdtgyCIhEkZYKabuE1Wz+2pwWZqxgYioJMkdB0MWQrFTb9uZf+aO86NgYC4PHL7dmofi79ANMPTYaYw14qaJhMyRiPIRQfFlw/l0GxYTopE8DwitbTPXgquf+2cTzIOFR5fzNk2aSMH71lBj926+xef5SBYSItw6JMjnBQkA0eB9/4kILc/6OQfu5XQSamenuha28Gjp4C/doXQSllZrCL50BmFzrf8453s5SArY2u347nWXJD2GN7yJDMZhOO2HhYCjJ3y3IYhg0m0wqun0vjMUFBJhFgPCXebMMKssuF0RP0c3Dx9ssbNVQ0CxIZXoPMfjBkQDwPMg63hmy/4odunBm5tnsvyKutayKzj/+uw4rhXGUOADIxDXLN9b2/DiEgH76f2Vo88AXg6gVHzN/x2NvfDQCgz3S2LelLz4C+/lLo+6UTEup2QbYXhrBuSIQ4xc7RPbS84EgpUluKwTAUZABrW14qNnG11HR+tlM3MJlSfP9+U7Y5rAgWpbhcbI7sRvb/t3ff4VVVWQOHf/vc9F4JSehNaYIQQCkCgoCAggVEREUYULGiooiOMAMqFgRRHBQZUJHPhqAgxaGroFKl914DCSVACsne3x8HAjG9kHuTrPd5fCS55+yz7j2csLLL2hUCPPD3dLD1ROKlbZNcd9J6iI8b3m5Wgf5+Xz0/MMIv/38HvUr5kGVZdHneHCBzyJxAPvEyQDVpCUEh9sbjykI1aZH1cRFRUKFKhtWWJv4EaRPeQI8bjp48Jtfq7D7uFvGJF51WEPbvKgZ6OnVT8at5uakMQ5aHz6YQ7O3m9GKTN1eyq49fPWx5KjE1ywn9l9kbjGedkB05k0RKmimxCZlSyp5HFptIQnKaSw/J+Xk4+Oq+WjSM9M33uZ5uFn6X5gMGZdMTmhOvUj5kWRYFXTXFwNmr0ssi+cTLAOXmjmp3BxgNtRugAoKzP7ZRc9i9FXMqDr3wR/RrT8KWtfbQZ/xJiIvN8Vre7g62nXReQdi/u7deKM+1iHJqT91ldg/ZlYTW2SssLwvzcee6MC9WHrySkMXnISE7n6JJSctcrX9vnL1CsaSVvLha7XBvjiTYRYX9XbQobFEI8XYj3Ne9QM9HaV9lWRZdnZD5StmLYld6f9KIDNQtHSG6Mla7rjkf16g5GIP+9zOYrz+FmrWxRnyIdd8/AHIdtvR2s9KrnDujIOzfVQr05OaKWe8/V9y83C2Srtpu6EhCilO3c7razRX92R2fzLEEexg1u30sL8up9MWeSwlZxRKckNUJt1cU7zudXKoTjkZRfjSt4Fegc6/sbyv/jJQWvh4Wl2v8+sh9LXbyiZcRyscPx4gPUDc0yfnAqEv1yRwO1MAXsZ4ebi8AiKoEPn6wa0uOp1+eXOzMgrCuysthkZxm12g7m5xGQnKaS/SQATS/PGx5MIFUbTiTnJZzQpZeNDNzcdg9ceeJ8HMv0UMe1UPsoW4o3T1AjzQqxyONyhXoXE+30v/5lDWWUgRe+mVLVlkWP+dPrBEuRSmF9eKbYDky1D9TlgU16+TaQ3b5IXZmuQtX5eVuoY1d9+nwpQn0rpKQRfh5UD3EkxUHEmhZOQDIuijsZSHeOfeQleThSrB3MagZ6sXm2ET5xSIbMoesdAryshfsOHtua1kkKbDIRHn5ZFmMVtWqC8cPY86cyvbcy70izqzQ76q8r9pg3FVWWF6tecUAdsQlsSvO3scxLz1kf19peTHNsP9UYomd0H+12peGLf1l6CZLssqydAqSHjKnkU9c5JmqWc/+w87se8m8pYcsW5eHeC4nZG4WBdqy5lq5vNrypx12wp1TD1mApwNLZe4hO5qQQpo2JbJC/9/VufRLhfQAZa1SoCdBXg7CXejvsCi8y6UvJCErfjJkKfKuUjXw9MLs2IyKaZnlIc0q+HM+RTu1IKyrutJDZjh8NoXyfh44LOev/rwsOsCDykGebDxuT8oP9s4+EXFY9lyTq0tfXEzTzNgSB0CVYK9rG2wxqFPOhxvK+1CnXM5bhpVV9SJ8+Oyems4OQxSxCF8PfD2s9DmUovhIQibyTDkcUP16TA49ZJWDPAs8Sbi08/rbkKUrDVde1ryiP/tPJ2OpjEvgsxLs5UjvITuVmMro5YfZdjKRvk0rloohS293i5HtKjk7DCGKVbfaIbSuGuCyxZBLM+mTFPmiataFw/sx5xNyP1hkcDkhu3BRc+yciyZkl4YtAz0dufbeXd4+aWdcIs/P28feU0m82DKKATdXLo5QhRDXgLe7RaSLlOMpayQhE/miatUFY2DXVmeHUuJcngR94HQyqdq1JvRfVjHQg+gAD0J8cp8XFOztxqGzKbz88wEcluKtjpVpcWmFphBCiPyRhEzkT9Va4OaWbfkLk5KM2bM912bMlvWkvfIo5uDeoo7QZV3uIdsdb69idMWETCnF8y2ieKxJRK7Hhvq4kZJmuD7cmzGdKlO1FMwbE0IIZ5E5ZCJflLsHVK2V7TwyM+0/mJWLUd0ewOp6X9bH7N2J/ugNSE6yj63Y/1qG7DK8Lq2yTE/IXHRYoHpI3hKrTjWDCfdxp221QNxcaHGCEEKURNJDJvJN1awH+3dhkhIzfN/s34VZuRhCwjA/fIme+UWmzcjN0UPo8SPAPxCqXYdZ/0euG5aXFpd7yA6fTcHfwyIgl0nzri7E243bagRJMiaEEEVAEjKRb6pWXdAa9mxL/54xBv3Vp+AfiDV8PKpVB8zcbzHfTE5PuEz8CfS418ByYA3+F+rmW+HEMTh2yFlvpVhdTsgMEBVQ8lchCiGEKDrF9iv6+vXrmTJlClpr2rVrR/fu3TO8PmfOHBYtWoTD4SAgIIDHH3+c8PDw4gpP5Ef168Cy7HpkdW60v7d2BezagnpwEMrHDx58Ajw8MQt/hJQU6P4AeuxwSLyA9cIbqHJR4OaB+fI/mPV/oiIrOvc9FQOHpXC3FBe1ccn5Y0IIIZynWBIyrTWTJ0/m1VdfJTQ0lJdffpmYmBgqVKiQfkyVKlUYPXo0np6e/Pzzz0ybNo3BgwcXR3gin5SXD1Sqnj6PzFxMQX87BaIro1reZh+jFNz3D/DwwMybgVn9C1y8iPXsCFSlavYxIWFQuQbmrz/g9nvyFYM5tI+0Eti/6+VucdGFNhUXQgjhGorln7Rdu3ZRvnx5IiIicHNzo3nz5qxatSrDMfXq1cPT0x7GqVmzJvHx8cURmiggVasu7NmBuZiCWTgb4mKxevZHWVequyulUHc9hOr2AKSmYj36IqpWvYztNGgKe7Zjzma/P+bVzIE9pH0wEv2vpzk9emiJm3/mfWlivyRkQgghrlYsCVl8fDyhoaHpX4eGhuaYcC1evJiGDRsWR2iigFTNupB6ETaswsz9Bho0RdXJfM+UUlhd78N6///s5OvvrzdoCsZgNqzO8Xrm6CH0xLfQI5+FXVugQVNSd26B7RuL7D0VB89L88gkIRNCCHE1l1vmtXz5cvbs2cOIESOyfH3hwoUsXLgQgNGjRxMWFnZN43Fzc7vm1yiJdLNWnJgA5vMJcDGF0AHP4VaAz8mEhnIyLAL3resJ6n5/5teNIeHTsSTO/x7l4YVvj0fw6dYL5e7ByUfvwW3xbIJb3loUb6lY+HsdwjqbQr3KkXi4lcAx1zyQZ8Z1yb1xTXJfXFdx3ptiSchCQkKIi4tL/zouLo6QkJBMx23YsIGZM2cyYsQI3N2zrhTevn172rdvn/71yZMniz7gq4SFhV3za5RY0ZUxh/ej2t/JaU8fKODnZOo3Jvm3hZw4chjlkXH1oV71C2bud6hbOqK69yHJP5CkxGRITMana0/OTZvIibV/oCpVz7rtC+fgzGlUZIUsXy9uDjTlfN05e7r0DsnLM+O65N64Jrkvrquo701UVFS2rxXLr+jVq1fn6NGjxMbGkpqayooVK4iJiclwzN69e5k0aRIvvvgigYGBxRGWKCRVtxH4B6K69ipcOw2a2Ssxt27I8H2TdAHzzWSoXAP1wGMo/4x/L7w73Q3ePpj532fZrrmYgn73FfSowZizpwsVY1G5vWYQPeqF5n6gEEKIMqVYesgcDgf9+vXj9ddfR2tN27ZtqVixIl9//TXVq1cnJiaGadOmkZSUxHvvvQfYWelLL71UHOGJAlJ3PYjq0hPl41u4hmrVAy9vzF9/oBo0Sf+2mf0VnDmFNWhYhsUCl1m+fqjWt2MWzMR0f8AupXEV8+0UOLgXlMIs/BF190OFi7MIyF6PQgghslJsc8gaNWpEo0aNMnzvvvuubK3zz3/+s7hCEUVEubmBW+H/Cil3d1TdRpgNqzBaoywLc3i/nUS1vA1VtVb257a7A7PwR8yCWagHB6V/36z7HbPkJ1T7bnA6DrPkJ0zHu1G+foWOVwghhChqpXNWsSh5GjaFM6fsLZmMQU//GLx9UXfl3KulgkJQzW/FrFiIuTQvy8SdQE8dbw913vMQqnMPSErELJlTHO9ECCGEyDdJyIRLUPVj7Or/6//E/LkcdmxC3f0gyj/3IT7V8S5I05hFszFpaehJ74BOwxo4BOXmjqpYFW5oglk4O9P+m0IIIYQrkIRMuATl6w816mDW/Ib59r9QpWZ61f9czy0XhWrcHLNsnr0IYPc21INPoMpFph9jde4B5xMwy+Zfq7cghBBCFJjL1SETZZdq0NROxpTCevLVLCfyZ3tup3swq3/FLJ6DatUBq+ktGV+vfj3UboD53yzMrV1Q7rkXZjUH92Lmfgtu7hAUAkEhqMBgCC1nJ4xK5fs9CiGEEFmRhEy4DNWwKea7KahWHVFVaubv3MrVodHNcPI46r4BWR5jde6BHvMq5teFqLads23LGGMPf86YCp7e4OUNp+MhLZXLGzWpHv1QHbrnK0YhhBAiO5KQCZehykVhvfwuVKhSoPOtR1+028muZ+26+lD9esz8GZhWHexVon9jzp5CTxkPm9ZAg6ZYDz+F8g+098w8lwBn4tBffYpZ8D2mbec89bQJIYQQuZE5ZMKlqKo1Udns0pDruZYjx2FOpRRWl54QfwLzx9IMrxljMJvWoP/1DGzfiOr9GNYTr6QXo1VKofwDUBWqYnW9D86exqxcXKA4hRBCiL+THjJRttRrDJWqYb6bQtrSeXDhPFw4B4nnIS0NoitjPTcSFV05+zauqw9VatoFaVvelq+5bkIIIURWJCETZYpSCuveR9A/TgdPL1R4efDxBR8/CA5FtWifaT/NLNu4/R70f0bD2pUQ07KYohdCCFFaSUImyhxVuwGO2g0K10jDZhARjZ43A6txC1lxKYQQolBkDpkQBaAsh12Q9sBu2PqXs8MRQghRwklCJkQBqZvaQlAIev6MQrdlkpMxOq0IosrhGocPyE4FQgjhomTIUogCUu7uqPZ3Yr6bitm3k7/XTrMTLIWycv69R3/5H8zSefYXDge4e9j/RVbEGjSsUBuiG2Ng81r0vBmwYxOUr2AX3Y2IKnCbQgghip70kAlRCOqWTuDtm95LZrTGbN+I/vxD9LN90B+OwqRl3/Ol/1iGWToP1fQW1J29UR3uQrW8DdWwGezehp463k6q8smkpaH/XI7+97Po9/8FsUdRnXvCuTPoN17AFMEwq/5jGWnDBmK2rC90W0IIUdZJD5kQhaC8fVBtbsfMn4H+v08w63+H+JPg6QU1asPG1ZhvJqPuH5jpXHPiGGbaR1CjNqrfYJQjY/kMHVkR881kezuodnfkOSZzPgH91lA4ehDKV0D1fQbV7BaUmzumZXv0hNfR44aj7vsHqm2XAi9IMEt+ghPH7LbuvB/VuWeuvYFCCCGyJgmZEIWk2t+BWfQjZulcqNsIdffDqIbNUJ5e6K8nYxb+gI6sgNXmynZNJjUVPeldUBbWP57PlIzZ7d6J2b4R8+0UTPXryet2UuaH6XDsMGrgi6jGzTMkSSq8PNbQt9Cfvof5v0/g8H64fyDKLX/FeE3cCXsT9849Ie445ofpmN3bsPo/B2Fh+WpLCCGEJGRCFJoKCMYa/gF4eaMCgjK+1qMv5vhhzP99gikXharTEAAz+yvYu8NOmkLLZd2uUliPPIP+9zPoT97BenUsysc3x1jM4f2YZfNQrTthNcm6Ppry8sEaNAwzaxpm3neYTWtRMS1RTVpC5Rp56jEzq3+122rRDsLLQ826mK8+QY98losvvQkhEbm2IYQQ4goZXxCiCKhykZmSMbDLY1gDXoDIiuiP38IcO2T3es37FtWifbZJU/r5vv5YA4ZAXCzmiwk5ziczxqC/mgRePqhuvXNu17Kw7n4I68l/QnRlzKLZ6NefR7/yKPr7zzFxsTmeb1b/aidv5SLtxLF1J6yhb4OyiH9lEObwgRzPF0IIkZEkZEJcY8rbB+vJV8Hhhv5gJPrT96BcFKrXgLydX6M2qvuDmNW/YpYvyP7Adb/Dtg2obr1RfgF5a7tBExxPv4Y15jPUQ09CeHnMgu/RY17FpKZmeY45cQz27bR71K5uq3INrGHvotzd0bOm5en6QgghbJKQCVEMVFgE1qBhEH8CEs5gDXgB5eWd9/M73gX1GmG+moT+Y1mm183FFPS3/4XoyqjWt+c/Pl9/rFYdcAz+N9bjL8OJY5gsrgNgVv9mn5PFllEqIAif7g/A+t8xe7bnOw4hhCirJCETopioGrWxnv0X1pOvoCpXz9+5loXV7zl7U/NPx6Cnf4xJvZj+uvl5Fpw8jnXfP7JcIJAvDZpCxaqYud9kWbLDrP4FqtbKdu6bzx09wT8QPfOLwsWRA718Pnr+jAKVBBFCCFckCZkQxUhdVx9Vr3HBzvUPwHp+FKpDd8ySn9Bvv4yJO4GJP4mZ+y3ceBOqsHt0cmkxQddeEHsUs+qXDK+Z40fgwB5Uk1bZnm95+6I694BtG65JjTKzfzdm2kTMjM8wM6ZKUiaEKBVklaUQJYhyc0P16Iepdj166vvoUc9CZEXQGqtHv6K7UMNm9mT/n77BNG2Fsuxet/TVlY1b5Bxn69sx//sBPfMLrNoNsly5aZKT4cRROHkcc/K4/f/TcVi3dETVuTHLdk1qKvqz8RAQiKofg1kwE9zcUd37FPINCyGEc0lCJkQJpBo3x4qujJ44GnZusYuyhpcvuvYtC9XlPswnb2PWrEjvETOrfrEL2YbkXGtMubuj7rwfM3U8rFsJjZqnv2aMwSybj/l2MqSkXDnJ0xvc3dAb12C9+Caqco1M7ZqFP8DBvViPD4WGN4FSmJ++QTvcsO7olfn4tDRITsq1XIjIyGgtRX6FKGaSkAlRQqny0Vgvv4NZ/weq0c1F337jmzGRFe1essYt4PhhOLwf1SvzrgNZnn9TW8z879GzvsRq2AxlOTAJZ9GffwDr/4A6N6JatkeFlYewCPDzh4TT6DeGoD8YhTXsHVRIeHp7JvYI5sf/s4dmLyd4fQZBWhrmx+lohwOrcw/MxYuw7S/M2pWY9X/AxYtYQ0ejKlQt8s+oKJjYI3YtuObt8rXQ41rRX3yEObAba+jbhZ+PKITIM8eIESNGODuIwkhISLim7fv4+HDhwoVreg1RMHJvLg1hVqhyTf7hVMoCHz9YNg9VsQpm20bYuRnr4adyTBwu3xdlWajAYHsHg7DycD4BPW44HNyH6vEI1v0DsSpUQQWHojw9UUqhPL1RtRtils/DbFyDuqm1veWTMeiJb9krVJ9+DeXlcylGBQ2a2KtCF/6I2bUF8/UkzG+L7P076zWGs6fsXr6b2qA8PIv8cyoMc+Ec+u2X4Y9lmBWLwNsXKlaxP/trILdnxvy1CvPtf+F0vL3tVoUq1yQOkZH8LHNdRX1v/P39s31N+qSFENlSTVpCRDR69tf2/LGadVFBIXlvoNHNULkG5utJ6LGvgZc31rB3sG7rlu2QmIquhPXoS3DkAPqTdzFpaZjfFsL2jah7+6KCQjMebzlQjzyLatYaDu1DxbTEevo1rPemYQ14wS7jcToO/fHbOW70XtyM1uj/joP4E6g+gyAsAvP5h+iRg52yYbu5cB497SOIrgyRFe1dHLQu9jiEKKukhywX8puL65J7c+0pZYGXNyyfD+fOojrdTW57al59X5RSqPDymN8WolrehjVoWLblMjJct1wkBATBwh8g7gRm8RyoWgur18AsFwgoy0I1ao7V8W57H9GIqPReQxUcBsFhsPBHSEpE1WuU/w8iG+bsKcyiOeipH8CpE9kuRsjy3Lnf2r2P9w3AatsZ1aI9RFayN6RfPAezfxeqfmOUu0eRxZvTM2O++hR2bLaLGEdVgmXzUZWqoSIrFNn1RdbkZ5nrKs4eMplDJoTIkWraGjPnazgZe2XuVn7Or90Aa/xXKE+vfJ1nte6Ejj1i11hzc8d68IkCTzS3WrRDH9yDWfgjumI1rOa3FqgdsBclsGOTvTBh7UpIS4WQcMzPszAxLcnLJvBm8zrMD1+imrZG3doFuJS8NmmJadgMs3g2ZuY09NjhWIP/hfLxK3C8eXpP2zdils9HdeiOqloLKlW35+X99I09/y8P+5sWWSxpaZCUCInnIfGC/V/Fqihvn2KLQQhnkIRMCJEj5eaG9fDTmCMHstyvM09t5DMZSz/vnofBGKhQBVW+cD016t5HMIf22XuCRlZEVc09cfo7c+4setwI2L8LfHxRbTujbukEgcHo1wahv5yI9fI7OSaOJi4W/em7EFUJ9dATmZId5e6O6ng3pnwF9H9GX/OkzCQnoz//EMLLo+58wI7B4UDdfi/m8w9h81ooYO28fMVhDPrdV2DHpswvRkRjDX+/SHsLhXA1ModMCJErdV09rLadi/+6lgOrZ3+s5u0K35abmz03sGbF+wAAG3VJREFULTAY/dHrmL0783W+SU5GfzASjhxAPfQk1jtT7Z0RIiugfHxR9z4C+3Zifv1f9m1cTEH/ZzSkpWE9/nKOiapq0NQu73FwL3rscMyFc/mKN6/M7OkQexTroSdRnlcWPaib20JIGPqnb/JcfNckJdrHnz2V/0C2bYAdm1A334q6rz/q4aewHhtqz687fhjz0zf5b1OIEkQSMiFEmaH8A7CefAWUhR49BP3D9Gw3Ub+aSUtDT3oH9u7A+sfzWK06ZFqxqZq1hlr1MN9/jjl3NnMbF1PQk9+D/buw+j2LiojKPd6rk7JxI/KclOU5gdq3E/PzD6hbOqKuvyHjtd3snjp2bYUdm/PW3q//w8yahh79Eib2aJ7OuUwv/gn8AlAPDsJq3w2r5W12vb3WnVA3t8XMn4E5fCBfbQpRkkhCJoQoU1SFqlgjxqOatcbM+Qr95pAc/6E3xmCmT4S//kTd/2i2Nd+UUli9H4PE85i/7eNpLpf8WLMC1bM/quFNeY+3QVOsx16CA3vspCwp5wnG5swp9Ev9SftwFOZ0fPbHbVxt9/gFBqHu6Zv1tVveBgFB6J++zlOsZuUSCC8PiefRb72EObA7b+fFxdqfb6sOWQ5Lqh79wdsH/cWHsvJTlFqSkAkhyhzl44fVb7BdEiP+BHrUYPSC7zEJZzIda+Z8jVm+ANW5R67Dtiq6Eqr9nZhffsbs3WGfHxeLfmso7NmOGvAC1m3d8h9vw2Z2UrZvF+bLj7M9zhhjzwdLOANb1qOHP4n+Y1mGHjOTnISe9hF6/L/BPxDr2X9lu5OB8vBEdbgLtv6F2bM9xxjN4f1wYDeq3R1YL74Fbm7od4Zhtm3I9f2ZJXNBgWpze9Zx+AegevSD3dswyxfk2p4QJZGUvciFLEd2XXJvXFNJui8qsiKqeVvMsUOw+CfMgpmY35fA3h2YM6cw2zZiZk2z5zXdn3XJjUyqX4dZsRizYzOqUjX0e/+EC+ewnhqO1aBJwWMtXwGMwSyebU/Ar5h55wGzYjEs+B517yNYPfthdm2BRbMxh/ahrquPx4mjXBg9FLauR3W4C2vgC5nqumVSoQpm+QLMyeNYzVpne5j5eRbs24HV92lUWDlU4xaYDaswi2fb8+yiKmV9XkoyZvJYqNcY65aOOcRRFbNzC/y+1L4fLrCrQVEpSc9MWVOcZS8kIcuFPCiuS+6Naypp90V5eqOatELVbQTlo+0yFju3wOpfYetfUPdGrAFD8rwbgnJzh6AQWDzHLmjr44f1/KgCrerMpGYdzI5N8Mv/UI1boPyu/HA38ScwE0ZBtVpYDzyO8g9EtWgHnl6wfAFm+QISF8wCD0+sJ4bZm7jn4T0pN3dITYXl81H1Y1DBmRM4o9PsfUtr1cNq1cE+z9sH1bQVZvsmzMIfoVxUlpX/zYrFsPpXO+aw7GvUKaVQ1a+3a9KdPI6KaZmHD6xkKGnPTFkilfqFEKIYXf7H3up4N45Bw+wVlKM/xRr8L7uYrVv+KgSpJq2gQVOoXAPr5bez7R3Kd5yWA6v/c+BwoCe9i0m9CFwaqpw6HrTG6vtMetkNZTmwOt2D9c9xUKEKXm06YQ0fj7qufv6u2/4O8AtAz5qW9QHbNsDpeKyb22Y8z9cfa/BIO5Gc+r7dY3cVYwxm8U/27gC16uYeR0QUqut9mDW/Yf5ala/3kH7NXVtJe7Ef+velBTrfWYzWmPW/Y85fm9W2wvkkIRNCiL9RSqFCy6Hq3Fig/S+VUlhPvIJj2Lu5Dwnmt+2QcKy+T8P+XemLB8yyebD1L1SPfqjw8pnPiaqEY8gbBD71SrbzxXK8ppcP6vZ7Ycs6zPaNmV43K5eAjy/ckHlIVnl62nP1QsqhJ7yBOXHsyos7t8Chvahbu+a5+KzqeBdEVUL/38eYlOR8vQ+j09BfToRTJzGT30P/74d8nQ9gUlPzvIq1KJmlc9ET3kC//pw9X0+UOpKQCSHENXAtq9urG29CtemM+XkWeslPmG+nQJ0bUTnNwSrsNdvcDkEh6JlfZFwkkHQBs3YlKqZVtoVblV8A1tOvgTHo8f9O7+Uxi+eAjx+qWZu8x+HmjtX7UYiLxeQzoTK//M9OAPsNhsbNMd9MRn83NX911kY8hZn8XpEmZebixZxfjz2CmfEZVL8eUpLtlcFrVhTZ9YVrkIRMCCFKINXjEYiujJn+MTjcsB5+8tomgR6eqK69YPc22Lg6/ftm7UpISbYLyeZ0fkQU1qCX4cQx9MTRmBPHMOtWolrelqEgbZ5iua4+NLzJ3gA9h9IeVzPnz2FmfQG16qFuaoM1cIid1C74HjNlXN7q0f043S5S+8eyHAsA54deMJPYBztiNq7J+po6DT3lfbhU2Nh69T2IroyeONpOjnVakcQhnE8SMiGEKIGUhyfWwCEQWg7V53FUSPi1v2aL9hBeHj1rWno9sPTaY9Wvz/38WvVQDz0J2zag3x4KxmRb6iI3Vo++kJqKyW5e29+YH6fD+fNYvQbYQ9KWA9X7UVS33piVS9ATXsckJ2V//oE9mEWz7dps19+A+WoS5tjhAsWe3uauLZjvPwOt0R+9gdmyPvMxC3+EXVtRvQaigkNRQaFYL7yBatUBM/db9AejMBfOFyoO4RokIRNCiBJKRVXCenMSVtNbiud6bm6oO3vDwb2YNSswcSdg+0a7DEUee+es5reiuvSE0/FwQ5Ms57zlKZZyUah2XTErFuVagNYc3o9ZOhd1S4cM5UKUUlhde6EefAI2r0P/5830hRIZztdp6Gkfga8/6t6+WP0Gg7sH+tMxWR6fF+Z8AnrSuxBajrAPpkNEFHrCKMz2K3t5mqMHMTOnQcNmqJvaXInb3d3e6qrPINi63k7qriG96lf05PcwyfmbsyfyRxIyIYQowa7lMGWW12vaCqIqYX74ErNykd3LdVWykKc2uj1gb5HUa0DhYunSE3z90V9PznZOlzEG/fWn4OWD6tYny2OsWzqiHhwEm9dh/jsu0zCgWf4z7N2B6tkP5euPCg7Fevgpe2HFD9PzHbcxxh6GPHMaa+AQHBFRWM+NhNAI9Af/xuzaam/XNeV98PTC6jMoy/tste6EatEe89sizJkC7B+aW5xJF9BT3sd88jbm96WY34pmmFZkTRIyIYQQeaYsB1b3Plc2/K5ZJ9+9XEoprFs6ocIiCheLjx+qW2/YsQnW/Z71Qet+t1egduuN8g/Iti2rVQfUvX0xq37BTP84PcEzZ05hvv8crr8hw+IDdeNNqFs62nPQtv6VqT1z9hTmdFyW1zKLZttbRd37MKqKXZ9OBQTZSVlgCPr9EZjPP7STwAceQwUGZ/8ZdLwL0tLsoc08MmlpmIQzmKOHMHGxWSazZu9O9MjBmJWL7cS3+vWYn2flaa6dKJj8FdcRQgghGjaDKjVh307Uzbc6NRTVqiNmyVz0d1Ow6seg3N3TXzMXzqG//S9EV0a1zn2umtXxbvS5BMz8GfZG5937YL75L1xMxnrgsUy9VKpnf8yOTej/jsV6biTm0H7YsdEedjx6EJSCOg2xWnWEBk1Rbm72hu7fTbW/bndnxvaCQrCeH4V+dxhmxSJU4xZYTVrl/P7LRaFiWmCWzsXcfg/Kxy/L4/T3n2NW/wrnEiDxb3PO/AOh2nWoqrVQ1a7D7N9lz80LCMZ6/nXUdfUwf61CfzgSs+qXXBdwFIQ5HWdvi+XhiWpzO8rLp8iv4eokIRNCCJEvSimsXgPQP3zp9Ir5yuHA6tkfPW64PYxaLhL2bLf3Ej16EIzBem5k3ndauPshOJ+A+ekbdNwJzJ/LUF172VtX/f1YTy+sAS+g3xiCfu0J+5te3lCjDqr5rZCSjPl1IXriaPAPRDVvh1m7AgKDsB55JsthSBUShvX8KMziOahO9+Yt5k732D17S+ehOvfI9Lpe9Qtm3ndQuwGqfgz4+tv/+fnbydmeHZi92zF//Ul6X1mjm+15ar6XKsvXb2yv6p0/A9OsdXrx4bww586CuwfK0yvza4f32z1vfywDnWZvD7ZgJqpzDzsxy6aUSmEYY4p9qD8vlHFGhbsidOTIkWvaflhYGCdPnrym1xAFI/fGNcl9cV2l+d6kjf/3lXIcvv5QtZbd41OnIapG7Xy1ZXQa+uN3YO0KKBeFNWJ8jomBWf8H5vhhVK36UKlahuTP6DTYtBb9y8+wwd5dwBryBqpGnfRjiuK+pL0/Avbvxhr9aYZixuZUHHrEUxARhfXSWzkmpub8Odi7AzBQt1GmpEX/vhQz+T2sJ19FNWiap7jMiWP29VOSISQcyldARVaA8EjMptWwaa3dK9aiPeq2bnDurL0jxJb1EBSKuuM+VPP2+d4tA+zEi4N77UK6Rw9gjhy0k/Qzp1B39EJ1uCvXxKyon5moqKhsX5OELBel+QdYSSf3xjXJfXFdpfnemIQzsH0jVKoG4ZGF7gExFy9iZk9HNW6Jqly9aGI8HQfnzqIqZNwYvijui9mxCf3OMFTvR7HadrG/pzX6/RGwayvWP8ehykcX7hppaehXHoWgEDu5y8NnnPbRG7B5HarTPfa8w6OH4NghO0HzD0S1uwPVuhPKL+P8PrN9I3rmF3bdu+rXYw15M8+9nGD/fdBfTLgyt9DhBhFRqMiKmKQLdky3dkXd1x9lZd9ucSZkMmQphBCixFP+gVCEw6fK3R1198NF1h5gb6NVxFtppatZ1554v2AmplVHe77a0rmwZT3qgccLnYyBPTysOt5lFyPeuRlq1cvxeLP1L1j3O6p7H6wuPa98X2u77Il/YIY5fxmudV19rJfewvyyAPPFR3bZknZ35ClOs2EV+rMP4MI5VPc+qEbNIbx8ei+b0RozY6o9VHomHqv/c9dkaDS/ZJWlEEIIUcIppbA63WNvKbX6F3sF5YypUK8xqnWnortO8/bgH4ieNyPH40xaGvqrSXYi1KF7xjYsCxUSlm0yln6cUqhWHaHujZhZ07JdtZp+zaRE9BcfoT8YCf6BWK+MwerSExVZIcOQp7IsrB79UD37w5oV6LGvucSm7ZKQCSGEEKXBDU3sGnHzZqAnvwfunlgPP1WkE9iVp6fdU7VpDebg3myPM0vnwZEDduJTiN4npRRW78fsXRm+npz99Q7tQ498FvPLAlSHu7BeGZNpaPjvrNu6oQYOgb070G+9hIk/UeA4i4IkZEIIIUQpoCzLnqt15ADs34X14BOooJCiv07bzuDlba/czIJJOIv58Uuo3cAukVLY65WLRHXpgVn9K2bT2szX278L/e4rkJKM9fworB6P5DkJtJq0wnpmBJyOs7cBcyJJyIQQQohSQjVpZa/0bNMZ1bj5tbmGjx+qdSfM6t/QC2ZikhIzvG5+mAZJien7hhbJNTveAxHR6OkTMSlXtnAyu7ehx/wTvLyxXhxtbzyf37avvwHrtfdRt+etzMi1IgmZEEIIUUooNzesV97DeuCxa3udTvdA7Rsw301Bv/wP9NxvMYkXMAf3Ypb/jGrbBRVVqeiu5+5uv6cTxzCX5q+ZHZvQY4eDf4C9CrOA+6ICqLCIfNVWuxZklaUQQghRihRHYqH8AnAM/rfdQzXna8zMLzALZoJfAPj6oe64v+ivWbsBqmlrzPzv0EEhmG8+hZByWM+PtFewlnDSQyaEEEKIAlHVr8fxzHCsYWOgZh2IPYK652GUb9ZbOBX6ej37gbsnZtpHEB5pF9ktBckYSA+ZEEIIIQpJVa2J48lXMRfOZbufZpFcJzAY9eATmFXL7a2d/LLfML6kkYRMCCGEEEXiWiZjl1lNWkIT5+6hei3IkKUQQgghhJNJQiaEEEII4WSSkAkhhBBCOJkkZEIIIYQQTiYJmRBCCCGEk0lCJoQQQgjhZJKQCSGEEEI4mSRkQgghhBBOJgmZEEIIIYSTSUImhBBCCOFkkpAJIYQQQjiZJGRCCCGEEE4mCZkQQgghhJNJQiaEEEII4WSSkAkhhBBCOJkkZEIIIYQQTiYJmRBCCCGEk0lCJoQQQgjhZJKQCSGEEEI4mSRkQgghhBBOJgmZEEIIIYSTSUImhBBCCOFkkpAJIYQQQjiZMsYYZwchhBBCCFGWSQ9ZLoYOHersEEQ25N64JrkvrkvujWuS++K6ivPeSEImhBBCCOFkkpAJIYQQQjiZY8SIESOcHYSrq1atmrNDENmQe+Oa5L64Lrk3rknui+sqrnsjk/qFEEIIIZxMhiyFEEIIIZzMzdkBuLL169czZcoUtNa0a9eO7t27OzukMunkyZNMmDCB06dPo5Siffv2dO7cmXPnzjF27FhOnDhBeHg4gwcPxs/Pz9nhlklaa4YOHUpISAhDhw4lNjaWcePGkZCQQLVq1Xjqqadwc5MfN8Xp/PnzTJw4kYMHD6KU4vHHHycqKkqeGRcwZ84cFi9ejFKKihUrMmjQIE6fPi3PjBN89NFHrF27lsDAQMaMGQOQ7b8txhimTJnCunXr8PT0ZNCgQUU6nCk9ZNnQWjN58mSGDRvG2LFj+e233zh06JCzwyqTHA4HDz74IGPHjuX1119nwYIFHDp0iFmzZlG/fn3Gjx9P/fr1mTVrlrNDLbPmzp1LdHR0+tfTpk2jS5cufPDBB/j6+rJ48WInRlc2TZkyhYYNGzJu3DjeeecdoqOj5ZlxAfHx8cybN4/Ro0czZswYtNasWLFCnhknadOmDcOGDcvwveyek3Xr1nHs2DHGjx/PwIED+fTTT4s0FknIsrFr1y7Kly9PREQEbm5uNG/enFWrVjk7rDIpODg4/bcQb29voqOjiY+PZ9WqVbRu3RqA1q1by/1xkri4ONauXUu7du0AMMawefNmbrrpJsD+gSf3pnhduHCBrVu3cuuttwLg5uaGr6+vPDMuQmtNSkoKaWlppKSkEBQUJM+Mk9SpUydTL3F2z8nq1au55ZZbUEpRq1Ytzp8/z6lTp4osFukPzUZ8fDyhoaHpX4eGhrJz504nRiQAYmNj2bt3LzVq1ODMmTMEBwcDEBQUxJkzZ5wcXdk0depU+vTpQ2JiIgAJCQn4+PjgcDgACAkJIT4+3pkhljmxsbEEBATw0UcfsX//fqpVq0bfvn3lmXEBISEh3HHHHTz++ON4eHjQoEEDqlWrJs+MC8nuOYmPjycsLCz9uNDQUOLj49OPLSzpIRMlRlJSEmPGjKFv3774+PhkeE0phVLKSZGVXWvWrCEwMFCW7LuYtLQ09u7dS4cOHXj77bfx9PTMNDwpz4xznDt3jlWrVjFhwgQ+/vhjkpKSWL9+vbPDEtkozudEesiyERISQlxcXPrXcXFxhISEODGisi01NZUxY8bQqlUrmjVrBkBgYCCnTp0iODiYU6dOERAQ4OQoy57t27ezevVq1q1bR0pKComJiUydOpULFy6QlpaGw+EgPj5enp1iFhoaSmhoKDVr1gTgpptuYtasWfLMuICNGzdSrly59M++WbNmbN++XZ4ZF5LdcxISEsLJkyfTjyvqvEB6yLJRvXp1jh49SmxsLKmpqaxYsYKYmBhnh1UmGWOYOHEi0dHRdO3aNf37MTExLFu2DIBly5bRpEkTZ4VYZvXu3ZuJEycyYcIEnn32WerVq8fTTz9N3bp1+f333wFYunSpPDvFLCgoiNDQUI4cOQLYSUCFChXkmXEBYWFh7Ny5k+TkZIwx6fdGnhnXkd1zEhMTw/LlyzHGsGPHDnx8fIpsuBKkMGyO1q5dy2effYbWmrZt23L33Xc7O6Qyadu2bbz22mtUqlQpvev4/vvvp2bNmowdO5aTJ0/KEn4XsHnzZmbPns3QoUM5fvw448aN49y5c1StWpWnnnoKd3d3Z4dYpuzbt4+JEyeSmppKuXLlGDRoEMYYeWZcwDfffMOKFStwOBxUqVKFxx57jPj4eHlmnGDcuHFs2bKFhIQEAgMD6dmzJ02aNMnyOTHGMHnyZP766y88PDwYNGgQ1atXL7JYJCETQgghhHAyGbIUQgghhHAySciEEEIIIZxMEjIhhBBCCCeThEwIIYQQwskkIRNCCCGEcDJJyIQQIg969uzJsWPHnB2GEKKUkkr9QogS54knnuD06dNY1pXfKdu0aUP//v2dGFXWFixYQFxcHL1792b48OH069ePypUrOzssIYSLkYRMCFEivfTSS9xwww3ODiNXe/bsoVGjRmitOXz4MBUqVHB2SEIIFyQJmRCiVFm6dCmLFi2iSpUqLF++nODgYPr370/9+vUBiI+PZ9KkSWzbtg0/Pz+6detG+/btAdBaM2vWLJYsWcKZM2eIjIxkyJAhhIWFAbBhwwbeeOMNzp49S8uWLenfv3+uGw/v2bOHe++9lyNHjhAeHo7D4bi2H4AQokSShEwIUers3LmTZs2aMXnyZP7880/effddJkyYgJ+fH++//z4VK1bk448/5siRI4wcOZLy5ctTr1495syZw2+//cbLL79MZGQk+/fvx9PTM73dtWvX8uabb5KYmMhLL71ETEwMDRs2zHT9ixcvMmDAAIwxJCUlMWTIEFJTU9Fa07dvX+68807Zik0IkYEkZEKIEumdd97J0NvUp0+f9J6uwMBAunTpglKK5s2bM3v2bNauXUudOnXYtm0bQ4cOxcPDgypVqtCuXTuWLVtGvXr1WLRoEX369CEqKgqAKlWqZLhm9+7d8fX1xdfXl7p167Jv374sEzJ3d3emTp3KokWLOHjwIH379mXUqFH06tWLGjVqXLsPRQhRYklCJoQokYYMGZLtHLKQkJAMQ4nh4eHEx8dz6tQp/Pz88Pb2Tn8tLCyM3bt3AxAXF0dERES21wwKCkr/s6enJ0lJSVkeN27cONavX09ycjLu7u4sWbKEpKQkdu3aRWRkJG+++Wa+3qsQovSThEwIUerEx8djjElPyk6ePElMTAzBwcGcO3eOxMTE9KTs5MmThISEABAaGsrx48epVKlSoa7/7LPPorVm4MCBfPLJJ6xZs4aVK1fy9NNPF+6NCSFKLalDJoQodc6cOcO8efNITU1l5cqVHD58mBtvvJGwsDCuu+46pk+fTkpKCvv372fJkiW0atUKgHbt2vH1119z9OhRjDHs37+fhISEAsVw+PBhIiIisCyLvXv3Ur169aJ8i0KIUkZ6yIQQJdJbb72VoQ7ZDTfcwJAhQwCoWbMmR48epX///gQFBfHcc8/h7+8PwDPPPMOkSZN49NFH8fPzo0ePHulDn127duXixYuMGjWKhIQEoqOjeeGFFwoU3549e6hatWr6n7t161aYtyuEKOWUMcY4OwghhCgql8tejBw50tmhCCFEnsmQpRBCCCGEk0lCJoQQQgjhZDJkKYQQQgjhZNJDJoQQQgjhZJKQCSGEEEI4mSRkQgghhBBOJgmZEEIIIYSTSUImhBBCCOFkkpAJIYQQQjjZ/wOWTkhjFCYhKQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrunYNysyp1j"
      },
      "source": [
        "\n",
        "model.save('/content/gdrive/MyDrive/VGG16_cp/model_VGG16.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgQ1Jjw-0-De"
      },
      "source": [
        "# load the image\n",
        "image = cv2.imread('/content/gdrive/MyDrive/COVID-19_Radiography_Dataset/Viral Pneumonia/Viral Pneumonia-667.png')\n",
        "output = image.copy()\n",
        " \n",
        "# pre-process the image for classification\n",
        "image = cv2.resize(image, (224, 224))\n",
        "image = image.astype(\"float\") / 255.0\n",
        "image = img_to_array(image)\n",
        "image = np.expand_dims(image, axis=0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnrjK2Kl3tqX"
      },
      "source": [
        "modelp = load_model('/content/gdrive/MyDrive/VGG16_cp/model_VGG16.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXVgP2pv7gTE",
        "outputId": "cca6b0a0-f06a-4cde-feaa-1f0f1fc102be"
      },
      "source": [
        "# classify the input image\n",
        "print(\"[INFO] classifying image...\")\n",
        "proba = modelp.predict(image)[0]\n",
        "idx = np.argmax(proba)\n",
        "arr=['Covid','Normal','Pneumonia']\n",
        "label=arr[idx]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] classifying image...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "I31C2Hc53gM_",
        "outputId": "8214bbca-70f6-47b0-bde3-52ee06cbb243"
      },
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "cv2_imshow(output)\n",
        "label = \"{}: {:.2f}% \".format(label, proba[idx] * 100)\n",
        "output = imutils.resize(output, width=400)\n",
        "cv2.putText(output, label, (10, 25),  cv2.FONT_HERSHEY_SIMPLEX,\n",
        "\t0.7, (0, 255, 0), 2)\n",
        "# show the output image\n",
        "print(\"[INFO] {}\".format(label))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=299x299 at 0x7F89CC6140D0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[INFO] Pneumonia: 99.87% \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcsb5vrxj_Y3",
        "outputId": "3323ebe6-450e-4f62-99a1-4be85157c9ef"
      },
      "source": [
        "train_generator.class_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'COVID': 0, 'Normal': 1, 'Viral Pneumonia': 2}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsyfYbahj_ut"
      },
      "source": [
        "y_actual=[]\n",
        "y_test=[]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJTyXvgRj_67"
      },
      "source": [
        "import os\n",
        "from keras.preprocessing import image\n",
        "\n",
        "for i in os.listdir(\"/content/gdrive/MyDrive/Test/COVID/\"):\n",
        "  img=image.load_img(\"/content/gdrive/MyDrive/Test/COVID/\"+i,target_size=(224,224))\n",
        "  img=image.img_to_array(img)\n",
        "  img=np.expand_dims(img,axis=0)\n",
        "  idx=np.argmax(modelp.predict(img)[0])\n",
        "  print(idx)\n",
        "  y_test.append(idx)\n",
        "  y_actual.append(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9DqQpnhkC9e"
      },
      "source": [
        "for i in os.listdir(\"/content/gdrive/MyDrive/Test/Normal/\"):\n",
        "  img=image.load_img(\"/content/gdrive/MyDrive/Test/Normal/\"+i,target_size=(224,224))\n",
        "  img=image.img_to_array(img)\n",
        "  img=np.expand_dims(img,axis=0)\n",
        "  idx=np.argmax(modelp.predict(img)[0])\n",
        "  y_test.append(idx)\n",
        "  y_actual.append(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0OdOGpskGHR"
      },
      "source": [
        "for i in os.listdir(\"/content/gdrive/MyDrive/Test/Viral Pneumonia/\"):\n",
        "  img=image.load_img(\"/content/gdrive/MyDrive/Test/Viral Pneumonia/\"+i,target_size=(224,224))\n",
        "  img=image.img_to_array(img)\n",
        "  img=np.expand_dims(img,axis=0)\n",
        "  idx=np.argmax(modelp.predict(img)[0])\n",
        "  y_test.append(idx)\n",
        "  y_actual.append(2)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}