AutoEvaluation.ipynb

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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8eb76307-d7cb-4770-aa39-e071a8064755",
   "metadata": {},
   "source": [
    "The dataset used in this project can be downloaded from [here](https://www.kaggle.com/thevirusx3/automated-essay-scoring-dataset?select=valid_set.tsv) on Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "210ff153-b74a-422e-ba5c-36307b64d6c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "\n",
    "import my_utils\n",
    "from my_utils import essay_to_wordlist, essay_to_sentences, makeFeatureVec, getAvgFeatureVecs, get_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c27d292-888f-4c05-9aad-8b02649ee969",
   "metadata": {
    "id": "241wSQ58GChW"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/long.tsv', delimiter='\\t', encoding='ISO-8859-1',\n",
    "                      usecols=['essay_set', 'essay', 'domain1_score'])\n",
    "\n",
    "y = df['domain1_score']\n",
    "X = df['essay']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "480dc65d-5f2e-48f5-b1f7-30943b6c5790",
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>essay_set</th>\n",
       "      <th>essay</th>\n",
       "      <th>domain1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Dear local newspaper, I think effects computer...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dear @CAPS1 @CAPS2, I believe that using compu...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Dear Local Newspaper, @CAPS1 I have found that...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Dear @LOCATION1, I know having computers has a...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Dear @LOCATION1, I think that computers have a...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Did you know that more and more people these d...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>@PERCENT1 of people agree that computers make ...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Dear reader, @ORGANIZATION1 has had a dramatic...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>In the @LOCATION1 we have the technology of a ...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   essay_set                                              essay  domain1_score\n",
       "0          1  Dear local newspaper, I think effects computer...              8\n",
       "1          1  Dear @CAPS1 @CAPS2, I believe that using compu...              9\n",
       "2          1  Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...              7\n",
       "3          1  Dear Local Newspaper, @CAPS1 I have found that...             10\n",
       "4          1  Dear @LOCATION1, I know having computers has a...              8\n",
       "5          1  Dear @LOCATION1, I think that computers have a...              8\n",
       "6          1  Did you know that more and more people these d...             10\n",
       "7          1  @PERCENT1 of people agree that computers make ...             10\n",
       "8          1  Dear reader, @ORGANIZATION1 has had a dramatic...              9\n",
       "9          1  In the @LOCATION1 we have the technology of a ...              9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
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   "id": "d172fe0b-5b25-4fef-b37f-88578178e47b",
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    {
     "data": {
      "text/plain": [
       "0    Dear local newspaper, I think effects computer...\n",
       "1    Dear @CAPS1 @CAPS2, I believe that using compu...\n",
       "2    Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...\n",
       "3    Dear Local Newspaper, @CAPS1 I have found that...\n",
       "4    Dear @LOCATION1, I know having computers has a...\n",
       "Name: essay, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4606277b-8c3c-4c09-a7b3-1ec617e9a585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12971    35\n",
       "12972    32\n",
       "12973    40\n",
       "12974    40\n",
       "12975    40\n",
       "Name: domain1_score, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66edf0e-6661-437f-87a2-11fa7c2c4b1e",
   "metadata": {},
   "source": [
    "### 5-Fold Cross Validation Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45179122-8eed-4ad8-b519-93a7dde1572a",
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 836
    },
    "executionInfo": {
     "elapsed": 107446,
     "status": "error",
     "timestamp": 1619766846141,
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    "id": "pL1ABArP3MdH",
    "outputId": "4374689a-3b65-46c5-f041-d572cbecd6fd",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bidirectional (Bidirectional (None, 1, 600)            1083600   \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 128)               255744    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 1,339,473\n",
      "Trainable params: 1,339,473\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "--------Fold 1--------\n",
      "\n",
      "Training Word2Vec Model...\n",
      "Finished Word2Vec training\n",
      "\n",
      "Training the Sequential Model...\n",
      "Epoch 1/5\n",
      "325/325 [==============================] - 12s 20ms/step - loss: 28.0532 - mae: 3.0787\n",
      "Epoch 2/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 11.3246 - mae: 2.1496\n",
      "Epoch 3/5\n",
      "325/325 [==============================] - 7s 20ms/step - loss: 9.8382 - mae: 2.0432\n",
      "Epoch 4/5\n",
      "325/325 [==============================] - 7s 20ms/step - loss: 9.2562 - mae: 1.9908\n",
      "Epoch 5/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 8.7092 - mae: 1.9240\n",
      "Kappa Score: 0.9572111746451858\n",
      "\n",
      "--------Fold 2--------\n",
      "\n",
      "Training Word2Vec Model...\n",
      "Finished Word2Vec training\n",
      "\n",
      "Training the Sequential Model...\n",
      "Epoch 1/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 19.3273 - mae: 2.6378\n",
      "Epoch 2/5\n",
      "325/325 [==============================] - 6s 18ms/step - loss: 10.3102 - mae: 1.9676\n",
      "Epoch 3/5\n",
      "325/325 [==============================] - 6s 17ms/step - loss: 8.3930 - mae: 1.6988\n",
      "Epoch 4/5\n",
      "325/325 [==============================] - 6s 18ms/step - loss: 7.4426 - mae: 1.5541\n",
      "Epoch 5/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 6.9556 - mae: 1.4769\n",
      "Kappa Score: 0.9640188462854267\n",
      "\n",
      "--------Fold 3--------\n",
      "\n",
      "Training Word2Vec Model...\n",
      "Finished Word2Vec training\n",
      "\n",
      "Training the Sequential Model...\n",
      "Epoch 1/5\n",
      "325/325 [==============================] - 6s 18ms/step - loss: 14.9277 - mae: 2.0629\n",
      "Epoch 2/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 9.4211 - mae: 1.6395\n",
      "Epoch 3/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 7.8993 - mae: 1.5274\n",
      "Epoch 4/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 6.9603 - mae: 1.4686\n",
      "Epoch 5/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 7.1801 - mae: 1.4536\n",
      "Kappa Score: 0.9636205887263255\n",
      "\n",
      "--------Fold 4--------\n",
      "\n",
      "Training Word2Vec Model...\n",
      "Finished Word2Vec training\n",
      "\n",
      "Training the Sequential Model...\n",
      "Epoch 1/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 13.5326 - mae: 1.8934\n",
      "Epoch 2/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 8.6848 - mae: 1.5758\n",
      "Epoch 3/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 8.1700 - mae: 1.5286\n",
      "Epoch 4/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 7.1818 - mae: 1.4504\n",
      "Epoch 5/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 7.0245 - mae: 1.4198\n",
      "Kappa Score: 0.9619483342514199\n",
      "\n",
      "--------Fold 5--------\n",
      "\n",
      "Training Word2Vec Model...\n",
      "Finished Word2Vec training\n",
      "\n",
      "Training the Sequential Model...\n",
      "Epoch 1/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 19.4778 - mae: 2.2704\n",
      "Epoch 2/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 9.1213 - mae: 1.5912\n",
      "Epoch 3/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 7.8906 - mae: 1.5095\n",
      "Epoch 4/5\n",
      "325/325 [==============================] - 6s 20ms/step - loss: 7.1774 - mae: 1.4565\n",
      "Epoch 5/5\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 6.7803 - mae: 1.4313\n",
      "Kappa Score: 0.9645825531903511\n"
     ]
    }
   ],
   "source": [
    "cv = KFold(n_splits = 5, shuffle = True)\n",
    "results = []\n",
    "y_pred_list = []\n",
    "\n",
    "clf_model = my_utils.get_model()\n",
    "\n",
    "for count, (traincv, testcv) in enumerate(cv.split(X)):\n",
    "    print(\"\\n--------Fold {}--------\\n\".format(count+1))\n",
    "    X_test, X_train, y_test, y_train = X.iloc[testcv], X.iloc[traincv], y.iloc[testcv], y.iloc[traincv]\n",
    "\n",
    "    sentences = []\n",
    "    \n",
    "    for essay in X_train:\n",
    "            # Obtaining all sentences from the training essays.\n",
    "            sentences += my_utils.essay_to_sentences(essay, remove_stopwords=True)\n",
    "            \n",
    "    # Initializing variables for word2vec model.\n",
    "    num_features = 300\n",
    "    min_word_count = 40\n",
    "    num_workers = 8\n",
    "    context = 10\n",
    "    downsampling = 1e-3\n",
    "\n",
    "    print(\"Training Word2Vec Model...\")\n",
    "    w2v = Word2Vec(sentences, workers=num_workers, vector_size=num_features, min_count=min_word_count, window=context, sample=downsampling)\n",
    "\n",
    "    clean_train_essays = []\n",
    "    \n",
    "    # Generate training and testing data word vectors.\n",
    "    for essay_v in X_train:\n",
    "        clean_train_essays.append(my_utils.essay_to_wordlist(essay_v, remove_stopwords=True))\n",
    "    trainDataVecs = my_utils.getAvgFeatureVecs(clean_train_essays, w2v, num_features)\n",
    "    \n",
    "    clean_test_essays = []\n",
    "    for essay_v in X_test:\n",
    "        clean_test_essays.append(my_utils.essay_to_wordlist(essay_v, remove_stopwords=True))\n",
    "    testDataVecs = my_utils.getAvgFeatureVecs(clean_test_essays, w2v, num_features)\n",
    "    \n",
    "    trainDataVecs = np.array(trainDataVecs)\n",
    "    testDataVecs = np.array(testDataVecs)\n",
    "\n",
    "    # Reshaping train and test vectors to 3 dimensions. (1 represnts one timestep)\n",
    "    trainDataVecs = np.reshape(trainDataVecs, (trainDataVecs.shape[0], 1, trainDataVecs.shape[1]))\n",
    "    testDataVecs = np.reshape(testDataVecs, (testDataVecs.shape[0], 1, testDataVecs.shape[1]))\n",
    "    print('Finished Word2Vec training\\n')\n",
    "    \n",
    "    print('Training the Sequential Model...')\n",
    "    clf_model.fit(trainDataVecs, y_train, batch_size=32, epochs=5)\n",
    "    y_pred = clf_model.predict(testDataVecs)\n",
    "    \n",
    "#     Save any one of the 5 models.\n",
    "#     if count + 1 == 5:\n",
    "#         clf_model.save('./saved_model/aes_model.h5')\n",
    "    \n",
    "    # Round y_pred to the nearest integer.\n",
    "    y_pred = np.around(y_pred)\n",
    "    \n",
    "    # Evaluate the model on the evaluation metric. \"Quadratic mean averaged Kappa\"\n",
    "    result = cohen_kappa_score(y_test.values, y_pred, weights='quadratic')\n",
    "    print(f\"Kappa Score: {result}\")\n",
    "    results.append(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c000f55-eef9-4776-bab8-8f917e2d75e9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1146,
     "status": "ok",
     "timestamp": 1619693290100,
     "user": {
      "displayName": "Hunter Bamel",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Kappa score after a 5-fold cross validation:  0.9623\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Kappa score after a 5-fold cross validation: \",np.around(np.array(results).mean(),decimals=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739dba4b-b5b1-4060-99eb-6d1c041bd1f1",
   "metadata": {},
   "source": [
    "### Random Plot of Comparison (Manual Evaluated vs Model Predicted Scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4982e751-378d-4108-84af-79a819757448",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot():\n",
    "    y_test.reset_index(drop=True, inplace=True)\n",
    "    indices = np.random.choice(y_test.index, size=30)\n",
    "    test, pred = y_test[indices], y_pred.flatten().astype(int)[indices]\n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.plot(range(30), test)\n",
    "    plt.plot(range(30), pred)\n",
    "    plt.xticks(range(30))\n",
    "    plt.legend(['Human Score', 'Predicted Score'])\n",
    "    plt.xlabel('Questions')\n",
    "    plt.ylabel('Marks')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c1469add-fe47-4db9-ae55-95832a1dfec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HP8nmzVvIZDJ8/vOfobf3LM4773xuvvlL3HTT19my5Xweeuh3/Nd//Wfe3+dtb3snX/jC/2T79ufYtOkctm59HZdd9gYAfvvbe3j88Uf5p3/6AdXVNcRiUVRV5amnnuD+++/lttt+iNtdzTe+cTM/+tH3+aM/+l8AvPjidn74wzvweDwEAsP86Ec/4O/+7v9SXV3D4cOHuOGG/8UvfvGbAj8zlS2dMQjFU1zqdWMZOjND+1Cd7fg8LtniUgghhBBCCLGsBMIJmrUY2F0ornpsDVVYKDQfm5MkhaTiWhaFJHvPxfNaNVQMX/nK/8HhcFJdXc0tt9xKbW0tAG94w5W4XNkWpSeeeJSDBw/wqU99FADLspiczFZZt29/gTe/+VpsNhs2m41rrnkzL7/84gnneeqpJ3jTm95KY2MTkF0hlE8ymWTHjheIRqOzlyUS0xw9epSmpiaqqqrYsuV8AK688o38zd/ckvc4q1ev4Wc/u5uXXtrByy+/yN///d/wzDNP8qd/+iWeeOIx3vnO91BdXQNAfb0HgOeff5Yrr7x69vK3v/3d/MM/fGv2mNu2XTzb9vf0008xPDzEZz/7qdnrDcMgHA7R0NCYN9NSNHZs1ZG/wU1m/+OMPPYjqt//N7Q0uGevE0IIIYQQQojlIBBOcIFjEtXbiqIoqDYHSk0jzam4dGyUwLIoJFWC3Iyk13K7XbP/bVnw1re+nU9+8tMn3G6uW7zP/XYmiqLw/e//Gzbb8X8Mjhw5caj2qTidTi688CIuvPAitm27hP/9vz/Ln/7pl4CTZbFm5/zkvPpnl8t93G23bt3Gn//51+aVaanJ7djW0uDGPDQEgBkewu+tZdeRMKZloSoyT0oIIYQQQgix9AXCCfxaDNWzcvYyzdtKS3yYp2UzoqKTXdsqyMUXX8p99/2GsbEgkF15s2/fXgDOP/8C7rvvXnRdJ5Wa4Xe/y78zWu4Y4XAIgEQiQTqdprq6mpmZmWNDvcHtruacc87l9tt/NHvfYDBAKDRBd/cKUqkUL764HYDf//5Bpqam8p7vpZd2zJ4LYP/+fbS2th3Lchl33fVzEolpAGKx6LHfZSsPPfQAicQ0lmVxzz13cf75F+Y9/oUXbuOZZ57i8OFDs5ft3bv75A/iEhU89mLob3BhRkcBMKIjNDe4yOgmkXiqnPGEEEIIIYQQoiQsyyISilJtTaN6Xhn3ota34FPjBEPTZUy3PMiKpAqyefMWPvWpP+LGG7+AYZjoeobLL7+K9et7efvb383Bgwf50IfeR3Ozn82bz2N0dPiEY5x77nl86EMf5Y//+I9QFBWHw86tt36bhoZGrr76zXzkI++ntraO2277ITfd9HW+852/48Mfvg7IFpf+7M9uwu9v5qtfvWV22PaWLRfg97fkzTw6OsLf//3fkMnoaJqKx9PATTd9HYA3vemtjI+P8alPfQxN03C73Xz3u//Ctm0Xc+jQAf7wDz8GwPr1Z/GRj3wi7/G7urq46aav89d//XVSqRS6nmHTpnPo7d1QiId80QiEEnhqHFQ5bEwdKySZkRH8q16XvT6SoLG+qpwRhRBCCCGEEKLoJhMZavUwwPGFJE8rdnSS0cLsIi5OTrHm2gtVwUKhKUzzlV8jEOinpaW7jInmz2ZT0XWz3DGAxZ2lmM+9z1fL+PhkUY59Orf85HnsmsqfvreXqR99BgDVt5LUlf+HG773JB+6uofLt3SUJVs5H5fXkiz5SZb8JEt+kiU/yZKfZMlPsuQnWfKTLPlJlvwkC/QNRHj4v/6LD9Y8QfX7/grV04rPV8voS8+SvOdWvhe/ij/+/B/gtGslzwaV9RwthKoqNDbW5L+uxFmEEGcgGE7ib3BjxrJtj1ptI2Z0lPoaBw67KgPlhBBCCCGEEMtCbj6Spagodb7Zy9X6bBdNsxaXDYmKTApJQlS4qWSGqWQGv9eNGR0BoHrdVsjMoCQiNHvcBMMyUE4IIYQQQgix9AXCCVpscdQ6P4r6yrQexe3B0pw0azH5fFRkS7aQtAQ69sQ8WZYJLL2dy4Kv3rEtOgqKinvt+QCY0VH8DS5ZkSSEEEIIIYRYFgKhBK32STRv63GXK4qC4mmhWYvPblYkimNJFpJsNgfT03EpJi0TlmWh6xmi0QkcjqU3cDoQPn7HNqWuGad/BQBmZBi/1814NIlhVsZcKyGEEEIIIYQolrHwJA3EUD1tJ1xn87bRYpuUL9qLrGS7tv3RH/0RQ0NDqKqK2+3mz//8z+nt7eWKK67A4XDgdDoBuOGGG7j00ksXdC6v10ckMs7UVLQAyUtDVVXMCikELMYsqqrhctVQU1NfglSlFQgnUBUFn8dFKjqK5mlFddehOGswI6P4vZswTItQbIZmr7vccYUQQgghhBCiKHTDxIxPoNabx+3YlqN6WvAoTxEKxcqQbvkoWSHp1ltvpba2FoAHH3yQL33pS/zyl78E4Dvf+Q49PT0FO5em2WhqOvEPVSWrpMnukqWyBMMJfJ4qNMXCjAWwdZ2Doiio3jbM6Aj+ldniUTCSlEKSEEIIIYQQYskaiyTxqVGA/IWk+uxlxrFNikRxlKy1LVdEApiamkJRlt4sGyGKIXBsxzZrchxMY/YFU/W0YkSG8XtdADJQTgghhBBCCLGkZXdsiwPZ1UevlbusOj1BMqWXNNtyUrIVSQBf/vKXeeKJJ7Asi+9///uzl99www1YlsV5553HF77wBerq6koZS4iKZVoWY5EEZ63wZgdtA6q37ZX/3zdNjTpDlUOTPmAhhBBCCCHEkpYtJMXA5UFxnNiNodb7AWhW44xFknS31J5wG7FwJS0k3XLLLQDcddddfPOb3+Rf/uVfuOOOO2htbSWdTnPLLbfwta99jW9961vzOm5jY00x4pacz1c5f8glS36lzjIeSZLWTdZ0eXFlBkgCvtVrAfB2rybwFNQTo725hsh0umyP1XJ+jk5FsuQnWfKTLPlJlvwkS36SJT/Jkp9kyU+y5CdZ8it1luh0hnPtk1T5O084d/bnWqaqG2lOxUnqlnw+KpKSFpJy3vnOd3LTTTcRiURobc226TgcDq6//no+85nPzPt4odAUprm4d2irpFlAkiW/cmTZczQMQLVdJX70KIqrnvCkia8KJlUvAOGjB2msbebIaLwsj9Vyf45ORrLkJ1nykyz5SZb8JEt+kiU/yZKfZMlPsuQnWfIrR5ajw1GuVqMY7o3HnfvVWZT6FvyxUfb3h1jfUfpup0p6jhZCVZWTLtopyYyk6elpRkdHZ39++OGHqa+vx+l0MjmZfYAty+Lee++lt7e3FJGEWBRyc49aGqsxY6PHDZRTqhvAXoUZGaHZ62YiNoNuVMZue0IIIYQQQghRaFPhCZykUT1tJ72NzdtKszZJMCQzZIulJCuSkskkn//850kmk6iqSn19PbfddhuhUIjPfe5zGIaBaZqsXr2am2++uRSRhFgURsMJHHaV+mo709FR7Ku3zl6nKAqqpxUzOoq/y4VlwXg0SWtjdRkTCyGEEEIIIUThTSbS1OjZjo18O7blqJ4WnEqG6ch4qaItOyUpJDU1NfGzn/0s73V33XVXKSIIsSgFw0lavG6YmYTU9AkvmKqnDWN4Ny2bs4PmghEpJAkhhBBCCCGWntlB25ymkFR/7Lp4sBSxlqWStLYJIc5MMJzA3+B+Zce21xaSvK1YiSjNx1pXx8KyfFMIIYQQQgix9ARC2UKSZXOiVHtPejvV0wJArR5meiZTqnjLihSShKhQumEyHkuespCkedoBcM+MU11lIxhJljynEEIIIYQQQhRbIJygRYuheVpRFOWkt1OqvZiag2YtTjAsn4+KQQpJQlSo8WgSy4KWBle2kJSn8q56s4UlMzpKs9dNQFYkCSGEEEIIIZag0VCCVvvkKdvaABRFxarx41djs5sXicKSQpIQFSpXFMquSBpB9bSgKMf/lVVqfaDZMCIj+BtcjEXkhVIIIYQQQgix9ITCMeqYOm0hCcDe0JpdkSSfj4pCCklCVKjcMsyWY61t+V4wFVVDrW/BjI7g97oJx1NkdKPUUYUQQgghhBCiaHTDRJnMDs+eSyHJ5m2lQZ1iIhwvdrRlSQpJQlSoQHiaWrcdt83Emgqhetry3k71tGEeW5FkAWMyJ0kIIYQQQgixhEzEZvApUQBUb/7PRa+melpRFEiFAkVOtjxJIUmIChUI5wZtZ1/8TlZ5V71tWJMT+OtsADJwWwghhBBCCLGkzO7Ypqiodc2nvX3us5M6FcSyrGLHW3akkCREhQqGE7R4T75jW052pZJFs5pdtil9wEIIIYQQQoilJBBO0KzFUWqaUDT7aW+v1rcA4DUjTCYzxY637EghSYgKlEzpxKbT+HM7tikKar0/721zSzsdiTFq3XbZmUAIIYQQQgixpIyGpmmzxbA1tM/p9ordie6szw7cls9HBSeFJCEqUG5VUW7QtlLbfNLKu1rvB0XBjAzj97pnh3QLIYQQQgghxFIQDE3RpMbnNGg7R6lvoVmLyeejIpBCkhAVKBB+dSFpBNXTctLbKpodpc6PGR3F73VJa5sQQgghhBBiSUlFx9Aw51VIcjS2HVuRNF3EZMuTFJKEqECBUAIF8NU7MWOB075gap7WYzu3uYlOpUmljdIEFUIIIYQQQogimp7JUJMOASefG5uPzduGS8kQC4WKFW3ZkkKSEBUoGEnSWF+FbSYCho7mOfUWl6q3HTMWxO9xHru/rEoSQgghhBBCLH7ZHduiwPwKSbnbmtGRYsRa1qSQJEQFCoQT+BtOv2NbjuppBcug1TEFZAtRQgghhBBCCLHYjYYS+LU4prMOxVk95/vldm6zTY9hWVax4i1LUkgSosJYlkUwnKDF656tnp+2kHRs57YGKwwgOxMIIYQQQgghloRAOIFfi2Hzzn01EoBS04Ch2GkgSnQqXaR0y5MUkoSoMPHpNDNpA3+DK7tjW1UtSlXNKe+TKzRpk0HqaxzS2iaEEEIIIYRYEgKhaVpscbT5FpIUFaO6iWY1zph8PiooKSQJUWGO37EtMLva6FQUexVKTSNmdIQWr1ta24QQQgghhBBLQjwcwkUK9TRzY/PRPK3Zndvk81FBSSFJiAqTe5FrOTYjSa2fW+Vd9bZhRkbxN7gYk9Y2IYQQQgghxCJnmhbqZACY36DtnCpfB43qFGMT8UJHW9akkCREhQmEEtg0Fa8jgzUzOecXTNXThhkdxe+pIp7IkJjRi5xUCCGEEEIIIYpnIpakUYkBZ1ZI0jwtqIrFTGi00NGWNSkkCVFhAuEEfq8LKz6/yrvqbQMjTZsrO0hO5iQJIYQQQgghFrPsjm0xTM2BUtMw7/vnPkuZsUChoy1rUkgSosIEIwn8x9raANQ5DpXL9Qz7tcjscYQQQgghhBBisQqEE/jVGGqdH0WZf/lCrW8BwJkcx7SsQsdbtqSQJEQFMUyTsUgyu2NbZAQ0O0pN45zuqx2rttdlQgCMhWWgnBBCCCGEEGLxCoQTtNji2Braz+j+isNF2l5LoxIjEk8VON3yZSvVif7oj/6IoaEhVFXF7Xbz53/+5/T29nLkyBFuvPFGotEoHo+HW2+9lRUrVpQqlhAVJRSbwTAtWrxuzJFRVE/LnCvvSlUNiqsOdTJAY916WZEkhBBCCCGEWNQmJmJ41akzmo+UY9Y005yMEYwkaKyvKmC65atkK5JuvfVWfvWrX3HXXXfx8Y9/nC996UsA3HzzzVx//fXcf//9XH/99dx0002liiRExQkcW0XU0nhsx7Z5bnGpetowIiM0e92yxaUQQgghhBBiUdOjARTmPu4jH3tjO341TlB2ti6YkhWSamtrZ/97amoKRVEIhULs2bOHa6+9FoBrr72WPXv2EA6HSxVLiIqSe3Hz19uxJifmXXlXvW2Y0RH8Xpe8UAohhBBCCCEWrcSMjjs9AZzZjm05rqZ23GqayPhEoaIteyVrbQP48pe/zBNPPIFlWXz/+99ndHQUv9+PpmkAaJpGc3Mzo6OjNDTMfyK7EItdIJzA7bThToVIYs2/kORpg3SSjlqdR2Z0ppIZalz2IqUVQgghhBBCiOIIhLM7tlkoqHX+Mz5ObpZsOjQMbC5MuGWupIWkW265BYC77rqLb37zm3z+858vyHEbG2sKcpxy8/lqT3+jEpEs+RU7S3gqRYe/hlorShJoXLkG50nOmS9LcsVqRp+EHk92kFzKgpUlePyW03M0H5IlP8mSn2TJT7LkJ1nykyz5SZb8JEt+kiU/yZJfMbPs7I/iV+MotU00t55+A6KTZcnYVjMIaNPjJXvsKuk5KoaSFpJy3vnOd3LTTTfR0tJCMBjEMAw0TcMwDMbGxmhtnd8qjFBoCtNc3Fv5+Xy1jI9PljsGIFlOphRZBoOTrOv0EB04CCjEzFqUPOc8WRZT9QJQNRUAXPQdnqDRXdwVScvtOZoryZKfZMlPsuQnWfKTLPlJlvwkS36SJT/Jkp9kya/YWQ70hzhLi6F6Ok97nlNlsUwXhqLhTI4TCMbQ1OJO+Kmk52ghVFU56aKdksxImp6eZnR0dPbnhx9+mPr6ehobG+nt7eWee+4B4J577qG3t1fa2sSylMoYhOMp/A3ZQdtKbROKzTGvYyiuenC4cKcmUBQIhmXgthBCCCGEEGLxCU5M0azF0bzz24DotRRVJV3VRJMaIxRPFSjd8laSFUnJZJLPf/7zJJNJVFWlvr6e2267DUVR+OpXv8qNN97I9773Perq6rj11ltLEUmIijN2bJe1lgY35p6RMxoopygKqrcdYqM01a8lGJGB20IIIYQQQojFJxEew64YCxq0PauuBf/UYcbCCZo9roUfb5krSSGpqamJn/3sZ3mvW716NXfeeWcpYghR0XK7rLV4qzCjAextvWd0HM3Tij7wEn6vm2BEViQJIYQQQgghFhfTtFAmg1C9sB3bcqqa2nAEXmZ7aBJWnX7ekji1krS2CSFOb/RYIcnnmAEjfcYvmKq3DSsZp6MuW5yyrMU9P0wIIYQQQgixvITiMzQpUQA0z8Ja2wCqmtrRFIvJsZEFH0tIIUmIihEMJ/DWOrFPB4Ezr7yrx15ou1xTzKQN4olMwTIKIYQQQgghRLEFwgn8agzDXo1StfBd2jVv9rOVHh09zS3FXEghSYgKEQwn8HtdmMde3BZaSPKrsdnjCiGEEEIIIcRiEQglaNZihZmPBKj1LQBok2MFOd5yJ4UkISpEIJzIDtqOjqI4a1BddWd0HKW2ETQHHiMEIAO3hRBCCCGEEItKIJygxRbH3thekOMpzmpSWjU1mRC6YRbkmMuZFJKEqABTyQzTMzr+Y4WkhVTeFUVF9bTiSI6hqcrsbnBCCCGEEEIIsRhEJiaoUWbQCrQiCSDjbsanxpiIzRTsmMuVFJKEqACB3I5tBSgkwbGB29FRmjyu2WMLIYQQQgghxGJgLHDcRz6qp4VmLS6fjwpACklCVIDcHKOWGgsrGV94IcnTijUVot2jEQzLiiQhhBBCCCHE4pBM6VSnsmM6CllIcvvaqVFThMcmCnbM5UoKSUJUgEA4gaYqeMwIAKp3oSuSsr3Eq6oTjEUTWJa14IxCCCGEEEIIUWzBSAK/FsNUbSg1TQU7blVT9jNScny4YMdcrqSQJEQFCIQTNHlcqJNB4JWd185UrhDVYY+TzphEp9ILziiEEEIIIYQQxRYIZQtJVo0fRS1cyULzZj9jmbHRgh1zuZJCkhAVIBhO0OJ1YURGoACVd7WuGRSNBiUye3whhBBCCCGEqHSBcIJmLY69cWFfrr+WUtuEgYo9MV7Q4y5HUkgSosxMyyIYSb6yY1t9y4Ir74pqQ/X4qUlne4uDESkkCSGEEEIIISpfcCJOozqFzVvgQpKqMeNooE4Pk9GNgh57uZFCkhBlFomnyOgmLY1uzNjogucj5aieNmyTAWyaKgO3hRBCCCGEEItCOjyKqlgFHbSdY9Q049PijEXk89FCSCFJiDILHFst1FLvwIqPFewFU/W2YU2O0epxyIokIYQQQgghRMUzLQttdm5s4QtJtoY2mtRJgqGpgh97OZFCkhBllptf5LdPgVW4yrvqaQPLYk1dkqBU3IUQQgghhBAVLhJP0UAUCwXV01Lw41c3d2BTTGJjIwU/9nIihSQhyiwQSuC0a9SkJ4CF79iWox7rKe52TTMWSWJaVkGOK4QQQgghhBDFEAgnaNGiGC4vis1Z8OO7fB0ApCeGC37s5UQKSUKUWSCSwN/gwoxmt6FU6wtTec8eR6FFjaIbJuH4TEGOK4QQQgghhBDFMBqaplmLoxWhrQ1e9VkrHizK8ZcLKSQJUWbBcIKWYzu2KTWNKPbCVN4VmwOltol6M5w9j7S3CSGEEEIIISpYIDSNX4vjaGwvyvGVqhpmVBfOmfGiHH+5kEKSEGWU0U0mYjP4vdlCUqEHyqneNlzJ7ItkbhaTEEIIIYQQQlSiqVAQh6IXZdB2TqqqiXojQiptFO0cS50UkoQoo/FoEsuClmOtbQUvJHnaUCaDVNkhGJYVSUIIIYQQQojKZcWOjfvwFmZubN5z1Prxa3HZ2XoBpJAkRBnlVgm1udOgpwpeSNK8bWDqrKnX5YVSCCGEEEIIUbFSaQP3TAigqCuSHI3t1KozTIyFinaOpU4KSUKUUeBYIamJKFD4F8xcJX9NzbTMSBJCCCGEEEJUrGAkQbMWw7C5UKpqi3aeGn8nAFNjg0U7x1InhSQhyigQTlDntmNPjAFFKCR5soWkdsckE9EkhmkW9PhCCCGEEEIIUQijoQR+LYZV14KiKEU7j8uXHeSdCY0W7RxLna0UJ4lEInzxi19kYGAAh8NBd3c3X/va12hoaOCKK67A4XDgdGZ3qrrhhhu49NJLSxFLiLILhhP4G9yY0SPgcKO46gt6fMXhQqn20kQEw+yYHewthBBCCCGEEJUkEE5wvhbD0bi2qOdR6nwYqKhTwaKeZykrSSFJURQ++clPsnXrVgBuvfVWvvWtb/GXf/mXAHznO9+hp6enFFGEqCiBSJKzVzfODtouRuVd9bRRMzUBZAduSyFJCCGEEEIIUWlCE2Hq1BnsDcUbtA2gqDYSNg+ulMxIOlMlaW3zeDyzRSSAzZs3MzIyUopTC1GxEjM68ek0rQ3uouzYlqN627BPjwGWDNwWQgghhBBCVCQ9lK0RFHPQdk7a7aPBipJM6UU/11JU8hlJpmny05/+lCuuuGL2shtuuIG3ve1tfPWrXyUej5c6khBlkSvqtNYpWIno7DyjQlM9rSh6ihbnDGNhGbgthBBCCCGEqCyWZc22mpWikKTUteDT4gTDU0U/11JUkta2V/v617+O2+3mgx/8IAB33HEHra2tpNNpbrnlFr72ta/xrW99a17HbGysKUbUkvP5ijeZfr4kS36FzLJ7MAZAT0MGA/B2r6J6Hsefa5bkyjWMPg5nNaSITKeL8ngu1edooSRLfpIlP8mSn2TJT7LkJ1nykyz5SZb8JEt+kiW/QmWZiCZpJIKpaDSvWoWiakXNMrViFdbQf5OOh/Cd3TnvcxUyy2JU0kLSrbfeSn9/P7fddhuqml0M1dqarTY6HA6uv/56PvOZz8z7uKHQFKZpFTRrqfl8tYyPT5Y7BiBZTqbQWQ4cDaEAamQEA5hSPCTmePz5ZDHxANBhj7MzGC/447mUn6OFkCz5SZb8JEt+kiU/yZKfZMlPsuQnWfKTLPlJlvwKmWXP0TB+LYbh9jERmv84jvlmsdX5yQBjRw4xvm71vM9XyCyVSlWVky7aKVlr27e//W127drFd7/7XRwOBwCJRILJyewDbFkW9957L729vaWKJERZBcIJGuurUCYDoGoodb6inEd11aE4a/BrcSZiM+iGWZTzCCGEEEIIIcSZCIQTNGtxtCIP2s5xNGbPY0RGS3K+paYkK5IOHDjAbbfdxooVK3j/+98PQEdHBzfeeCOf+9znMAwD0zRZvXo1N998cykiCVF2wXCSltyg7Xr/GS3fnCvV24Z3OoxlwXg0SWtjddHOJYQQQgghhBDzEZyYZIs6ibOpvSTnU6pqmaEKe2KsJOdbakpSSFq7di19fX15r7vrrrtKEUGIimJZFoFIgjUdrZiR0aIN2s5RPW24Qs8CFsGwFJKEEEIIIYQQlSM5MYKmWGglGLQNoCgK045GqpOhkpxvqSn5rm1CCIhNp0mlDVq9TszYWNF3JlC9baiZBDXKzOxucUIIIebmgWcH+PRfP4hpLe55jEIIIUSlsmIBgKJ/wf5qenUzjUqMqWSmZOdcKqSQJEQZBMPZYk57VQIsoySFJIAVrimCkWRRzyWEEEvN8/vHGR6fZmhMtggWQgghCi2dMXCnJgBQPS0lO6/mbaFeTTI2Fi7ZOZcKKSQJUQaBY4UknxIFXin0FEuusr+6JjFbxBJCCHF6qYzBkZE4AHv7I2VOI4QQQiw9wUgSvxYj46xHsVeV7LzVzR0AxEcGSnbOpUIKSUKUQSCcwKapuNPZnly1vriVd6XaC/YqOhxxaW0TQoh5ODwcwzAtVEUKSUIIIUQxBMIJ/FoM6kozHynH09YFQHJiuKTnXQqkkCREGQTDSfwNLqzoKEq1F8XhKur5FEVB9bTSpEQJx1OkM0ZRzyeEEEtF32AURYFLN3fQNxhFN8xyRxJCCCGWlMDEFM1arGQ7tuXYvS2YloIVD5T0vEuBFJKEKINAOEGL140ZLf6ObTmqt43aTHYF1FhU5iQJIcRc9A1E6fbXctGmFlJpg/7AZLkjCSGEEEtKbGKMKkXH3lC6QdsAimZnUqvHmRgv6XmXAikkCVFihmkyHk3S0uA6VkgqzUA51dOGPR2nSkkTDEshSQghTiejGxwaibOuy8Om1U2AtLcJIYQQhaaHR4Diz43NJ+lspNYIY8nOrPMihSQhSmwiNoNhWrTXGJBJFn3Hthzt2AuzX40xJnOShBDitA6PxNENk3WdXuprnHT4aqSQJIQQQhSQZVlo00GAkn0uejWz1k+jEmdyOlXycy9mUkgSosRyu6a12rK7AJWste3YeVa6p2TgthBCzEHfQBQF6OmsB6C328vB4RgZXeYkCSGEEIUQm07TYEXRVSeKq77k53c0tOFQDCZGZOD2fEghSYgSC4SyRZwGK/utdqkq70qtDzQb3a4pAtLaJoQQp9U3GKWzuQZ3lR3IFpIyusnhkViZkwkhhBBLQyCUoEWLYdT6URSl5OevaekEIB4YLPm5FzMpJAlRYoFIkuoqG/bEGNirUNyekpxXUVXU+lZatJisSBJCiNPQDZNDwzF6ujyzl/V0elAUmZMkhBBCFEognKBZi2Erw3wkAG9bFwCZY3OaxNzMuZAUDoeZnp4GwDAMfv7zn3PXXXdhmrK8W4j5CIYT+BtyO7a1lrTyrnpa8ZphYlNpZtJ6yc4rhBCLzZHROGk9Ox8px11lY0VLrRSShBBCiAIZH4/gUZNU+TrKcn6t2sOM5UCJB8ty/sVqzoWkP/zDP6S/vx+Ab3/72/zwhz/kX//1X/nrv/7rooUTYikKhBP4va8UkkpJ9bZTlYlhR2csIu1tQghxMn0DUeCV+Ug567u9HB6Jk0obZUglxOKlD7xI9JlflzuGEKLCzExkZxNp3tIP2gZQFIW4rYGq1ERZzr9YzbmQdPToUXp7ewH41a9+xb/8y7/w4x//mHvvvbdo4YRYalJpg8hkinaPhjUdLtmg7RzV24qCRbMWIyiFJCGEOKm+wSjtvmpq3Y7jLu/t9mKYFgeGouUJJsQilXrhbsIP346VlvcfQohXKPFRALQSfy56tbTLR70RwbKssmVYbOZcSFJVlUwmQ19fH7W1tbS1tVFXVzfb7iaEOL3cbKKOquzfm5KvSPK0A9CixQiEZU6SEELkoxsmB4dirOv0nHDd2nYPmqpIe5sQ82DNTGGOHwVTxxjZV+44QogKkdFN3KkJTFSUOl/5gtT58agJopF4+TIsMra53vCyyy7j85//PNFolLe85S0AHDx4EL/fX7RwQiw1uVVAzUoUKEMhqd4Pikq3a4qgFJKEECKv/uAkqYzBui7vCdc5HRqr2+qkkCTEPOgje4HsN/364MvYVpxb3kBCiIowFknQrMXJuBpR1DmXJgrO2dQOIxAe7sfbcHbZciwmc16RdMstt/CGN7yB9773vXz6058GIBKJ8LnPfa5o4YRYagKh7EqkWj0EioZa31zS8yuaDbWumQ7HpLS2CSHESeyfnY/kyXv9+m4v/cFJpmcypQslxCJmDO0GexWu1VvQh3ZK+4gQAjg2O1aLodSXZz5STn1rJwDTwaGy5lhM5lxIGhgY4LrrruM973kPmqYBsHXrVurq6ooWToilJhBO4q11ok4GUet8Zam8q942fEp0ts1OCCHE8foGo7Q2uqmvduS9vrfbi2W9UnASQpyaPrwbW1sv1WvPx5qcwIyNljuSEKICBCYm8alxnGXasS3H09qFaYEeldemuZrXrm2Dg4PHXfbwww/zZ3/2ZwUPJcRSFYwkaGlwY0ZHSt7WlqN62qjRI0wnUiRm9LJkEEKISmUeG6Sdbz5Szqq2ehw2VdrbhJgDMz6GNTmO1r4B1+psS5sxuLPMqYQQlWBqfARNsXA2tpc1h+ZwEFdqsU2NlTXHYjLnQtIXv/hFPvnJTzI2ln1wH3jgAW666SZuu+22ooUTYimxLItAKEGrtwozFixfIcnbhoqJT43LqiQhhHiNgbFJkimDni7PSW9jt6ms7ahn34AUkoQ4HX1oFwC2jg3YPc2onlZ0KSQJIQAjkl0BVK7PRa82ZW/EnQ6VO8aiMee+mmuuuYapqSk+/vGPc/311/O9732P73//+6xfv76Y+YRYMqaSGRIpna7qFJgGqrc8W1yqx7bWbNFiBMMJVrZKe6oQQuT0HWtXW9d54qDtV1vf7eXn/32Y+HSaupO0wAkhsvORlOoGlPoWALTOs8nseQhLT6HYnGVOJ4QoF8uysE8HwQGqp6Xccci4m/FFhzBMA03Vyh2n4p1yRZJpmsf9713vehfvfve7+d73vscPfvADenp6ME2zVFmFWNSC4exw61Z7DChf5T13Xr8Wk4HbQgjxGn0DUZq9Lry1p/6Au747W2iSVUlCnJxlmugje7F1bEBRFABsnZvA0DFG+sqcTghRTpOJDF4rStpei+JwlzsOqrcFp6ITCQTKHWVROOWKpLPOOmv2RT8nt8vCO9/5TizLQlEU9u7de8qTRCIRvvjFLzIwMIDD4aC7u5uvfe1rNDQ0cOTIEW688Uai0Sgej4dbb72VFStWLOy3EqICBcLZNrIGK/uhQ60vT+VdsTtRahrpMifZJa1tQggxy7Sy85G29PhOe9sVLbVUOTT29Ue4sNdfgnRCLD7mxBFIJ9DaN8xeprX0gOZAH3wZW5dssy3EcjUamsavxTBqKuPfUHdTBxyF2OgATW3lndm0GJyykPTQQw8V5CSKovDJT36SrVu3AnDrrbfyrW99i7/8y7/k5ptv5vrrr+cd73gHd999NzfddBP/9m//VpDzClFJAuEEmqrgSk1guupRnNVly6J622lNjPJQWFYkCSFEztDYFNMzOutOMR8pR1NV1nV6ZOC2EKegD+0GFLSOVwpJis2B1rYefUjmJAmxnAVC06xXY9gbzip3FAA87V3wPCTHh8sdZVE4ZWtbe3s77e3ttLS0cOONN+Lz+WYve/X/Tsfj8cwWkQA2b97MyMgIoVCIPXv2cO211wJw7bXXsmfPHsLh8AJ/LSEqTzCcwOdxYcUCZZuPlKN6WvFaEcYjU2XNIYQQlaRvMAqcfj5STm+3l2AkSTg+U8RUQixexvBu1KYu1KpaYlMpBoOTANg6z8aKBTHjskOSEMtVZHwMt5rB3dxZ7igA1PuambFsWFFpbZuLOQ3b1jSNoaGhgsxDMk2Tn/70p1xxxRWMjo7i9/vRNG32PM3NzYyOjtLQ0DDnYzY21iw4VyXw+WrLHWGWZMlvIVkm4jN0+WshFqD6rItpWuDvtZAs8c5VZHbejzMdw+l2LnhQ7FJ5jgpNsuQnWfKTLPmVMsvR4BTNDW7Wr8nf2vbaLK87t4P/ePggw5Ek61afvh2ukJbrc3Q6kiW/cmQx00kmg4eo33otjb5a/t+v99DXH+FHN12Nec5FDD55O1WR/dSvXl3ybDnL/Tk6GcmSn2TJ70yzmLEgAA0rV+Mu0O+z0MflsNqAY2a8II9vJT1HxTDnXds++9nP8tWvfpXPfe5ztLS0HDc7SVVPubDpOF//+tdxu9188IMfZM+ePfNLexKh0BSmaRXkWOXi89UyPj5Z7hiAZDmZhWQxLYvh8WnOabNjBqdIVTUt6Pda6ONi2LKFWr8WY/eBMda015ctSyFJlvwkS36SJb/lmsWyLHYenOCc1Y15z5kvi9umUOOy8+zOUTZ1z20VUyEs1+fodCRLfuXKog+8CKZOuqGHkdEYO/aPkc6YPPXiEOu6vCh1fqJ7nyPdfUnJs4E8RycjWfKTLPktJIsRHgYFJvEwXYDfpxCPS8LRSGNqcMHHqaTnaCFUVTnpop05F5K+8pWvAHD33XfPXjbXYds5t956K/39/dx2222oqkprayvBYBDDMNA0DcMwGBsbo7W1PLtZCVEs4fgMumHSWZVtJSvXjm05uda6Fi1KMJxYUCFJCCGWgpGJaaaSGXrmMB8pR1UU1nd52DsQmX1PJITI0od2g2ZH869hz1CUdCbb2bB9/wTrurzYOjeS6XsMS0+j2Ba2MloIsbjohok7FUJ3O1CqS/dFzOkYNc3Up/aip5LYnK5yx6locy4kLXTw9re//W127drFP//zP+NwZP+xaGxspLe3l3vuuYd3vOMd3HPPPfT29s6rrU2IxSB4bKh1sxoDyl9IUpzV4KqnJRUjGJGB20IIMTsfqWt+b2h7u7083zfOWDSJ31v+7YuFqBTG8G601nUoNge7DoewaQpnrWxk+/5x3n/lGmydm8jsfggjsB9bx8ZyxxVClNBYJEmzFiXt8lXUlzB2byuEIDIygG/lunLHqWhzLiTNZaj2yRw4cIDbbruNFStW8P73vx+Ajo4Ovvvd7/LVr36VG2+8ke9973vU1dVx6623nvF5hKhUgXACgDojBDZnRVTeNW8bHYkQj0QS5Y4ihBBl1zcQxVvrxFdfNa/7rT/W0ravPyKFJCGOMacjmJERnD2XArDzcJieTg+v39LB//3ZiwyOTdHZ2guaDX1wpxSShFhmAuEEfjWG6qmMHdtyqv2dcBDio4NSSDqNOReSILsq6bnnniMSyS7hzvnmN795yvutXbuWvr6+vNetXr2aO++8cz4xhFh0AuEEToeGfXoMy9OCosx9rlixqJ5WfCOHCIakkCSEWN4sy6JvMMpZK7zz/ma0pcGNp8bB3v4Ir9985l+6CbGUGMO7AdA6NhCKzTAyMc0lm1rZuqGFf1Rg+/5xui5dhdayDmNwJ2z7gzInFkKU0th4hLVaArW5o9xRjtPY0QVAOjRS5iSVb86fZv/xH/+Rm2++GdM0ue+++/B4PDz++OPU1dUVM58QS0IwnKDF68aMjqJ62sodB8jOSXKQJhELHVcYFkKI5SYQThCfTrOu0zPv+yqKwvpuL/v6I/JaKsQx+tBuFFcdakMHO4+EANi0upH6GidrOzxs3z8BgK3zbMzoCObkRDnjCiFKLDE2DICjsbK+gKmrqyFs1kA8WO4oFW/OhaSf//zn/PCHP+RLX/oSdrudL33pS9x2220MDQ0VM58QS0IgnKC9wY41FSr7fKScXEHLa4SIT6fLnEYIIcrnTOcj5fR2eYknMoxMTBcwlRCLk2WZ2flI7WehKCq7DodpqHPS1pht/dyytomh8SnGokm0zk0A6IM7yxlZCFFiZnQUeGUDoEqhKApxzYszOV7uKBVvzoWkeDxOT08PAHa7nUwmw9lnn81zzz1XtHBCLAUZ3SQUm2GFO/sBo2IKScdeuP2aDNwWQixv+wei1Fc78HvPbIeW3mNzkvb2RwoZS4hFyQwPYSXj2No3oBsme46G2bSqcbZt9NweHwA79o+jelpRahoxhqSQJMRyYk8EMVFQ65rLHeUEM1VN1Bmyyvh05lxI6urq4sCBA0B25tFPf/pT7rrrLurrZdtwIU5lLJrEAtrsk0DlFJIUVz2W3UWLFiMYljlJQojlKTcfaV2X54x3jmnyuGiqr5JCkhCAMZSbj7SRQ8MxZtIGG1c2zl7v87jobK5h+/5xFEXB1rkJfXgPlqGXK7IQooQmE2m8VpSUswFFs5c7zgmsuhacSgZ9MlzuKBVtzoWkP/7jPyYajQLwJ3/yJ/zkJz/hb/7mb7jxxhuLlU2IJSFXpGkkCoqCWu8vb6BjFEVB87bTYpMVSUKI5WssmiQymTqj+Uiv1tvtpW8gimnKN5hiedOHd6N62lCrvbx8OISmKpy14vi20S09Pg4OxYhPp9E6z4bMDEbwQJkSCyFKKRBO0KLGMGtbyh0lL2dDtmsjMtxf5iSV7bSFpJGREUZGRli7di3t7e2MjIzg8/n48Y9/zJ133snGjbJdpxCnEjhWSKpOTaDUNldU5V3zttFqixGMyIokIcTy1DcQBc58PlJOb7eXREpnYGyyAKmEWJwsPY0x2ofWsQGAXYfDrGmvx+U8fqPoc9c2YQEvHpzA1tYLqpbdvU0IseQFJibxaXEcjZU1HymnrjW7c9tUUGZBn4rtdDe44oorZpd6v7pPUFEULMtCURT27t1bvIRCLHKBcIK6agfKZADFU1mVd9XbSjUzxEKydFMIsTz1DUSpc9tpPTYIOJ/0rgcZOvwUjmu/jKLm/w5u/avmJK1okR1txfJkBA+CkcHWvoHIZIrBsSne+4bVJ9yus7mGpvoqtu8f57Jz2tBaetAHd+Lc+r4ypBZClFI8OIpNMXH4O8sdJa+mtlZSlo1MeKTcUSraaQtJ69atI5VK8a53vYu3v/3tNDdX3kAsISpZMJyg1ePEjAWwd2wqd5zjqJ7slpvqZADTslDPcD6IEEIsVvsHI/R0nno+UmbPw5jREbSRvdiOrbR4LU+Nk9ZGN3v7I7x5a3ex4gpR0YyhXaBoaK3r2LUvBMDGlQ0n3E5RFLb0+Hh4+xDJlI7WsYn0sz/DnI6gVi9sdaAQorKljxVobBW2Y1tOrdvBsFWPbSpY7igV7bStbXfffTff+c53iEajXH/99XzqU5/iN7/5DZlMBk3T0DStFDmFWLSC4QSrPDoYOlqFDNrOUb3ZPI1EiE6mypxGCCFKayKaJBRPnbKtzQgPY0azb3ozB5865fF6u70cGIyhG2ZBcwqxWOjDu9H8q1EcLnYeDlNf46CzuSbvbbf0+NANi52HQ9i6sl+0SXubEEufOhnI/n+FfS56tSlbA65UqNwxKtqchm339PTwf/7P/+Ghhx7iox/9KI888giXXHIJu3fvLnY+IRa1xEyGeCJDZ9UUUHkvmEpNI6bqyO7cJgO3hRDLTN9gFOCUg7b1w88CCq7V56IfeR5LT5/0tr3dXlIZg6OjMidJLD/mzCTmxABaxwYM02TPkTCbVjaedLXfmvZ6at12dhyYQPV2oFR70QdfLnFqIUQp6YZJdWqClFaN4qwud5yTSrt91JrxU/6bv9zNedc2gKNHj/Lcc8/x4osv0tvbS12dzAAQ4lRyxRm/GgMqsJCkqFDfgl+LysBtIcSy0zcQpbrKRpvv5G9m9cPPobX2UL/1bZCZQR946aS3XdflRQH29svcObH8GMN7AAtb+wYOj8RJpHQ2rjqxrS1HVRU2r2ni5UMTGKaFrWMT+vBuLNMoXWghRElNxGbwqTEy1ZU9Lkepb0VRIB0eLXeUinXaQlI0GuWOO+7gve99L5/97Gdxu93cfvvt/OQnP6GzszIHZAlRKXI7ttUZYRRXHUpV/uXd5WRvbKdFizMWlhVJQojlpe/YfKSTzYfLtbXZVl2Aq3sjituDfor2thqXnc7mGvb2R4oVWYiKZQztBocL1beSnYfDKApsyDMf6dW29PhIpgz29UfQOjdBOokxdqhEiYUQpRaYmMavxSpu3MdruXzZ+U3RkYEyJ6lcpx22femll9LR0cE73vEOzjnnHAD6+/vp7++fvc22bduKl1CIRSwQSqAo4EyOV9xqpBzN24ZXfYqJULTcUYQQomTC8RnGozNced7JvxTLtbXZVp6PomrYVm8ls/shrNT0SZfkr+/28vD2YdIZA4dd5kiK5cGyLPTh3djazkJRNXYeDrG6rZ7qKvsp73fWCi9Oh8b2/eNsuPwsUFSMwZ3YWnpKlFwIUUqhsXFWq2mUCt2xLae+tQtehMTYULmjVKzTFpJ8Ph+pVIqf/exn/OxnPzvhekVReOihh4oSTojFLhhJ0FRfBdFR1JXnlztOXqonW3E3IrLFpRBi+ZjTfKQj2bY21Z29jX3NRWR23k/myPM41r8+7316u7088Nwgh4Zj9K449WoMIZYKKxbEmgqhbX4r8ek0/YFJ3nXpytPez27T2LSqkR0HJvjgNevQ/GvQB1/GecF7SpBaCFFqifFsYaaqqaPMSU7N7/MyYbgxo9LadjKnLSQ9/PDDpcghxJIUCCfo9qhY8amKXpEE4EiMYZoWqnryLbCFEGKp6BuI4nLaTrqjlBEexoyM4Lz4g7OXqU0rUOpb0A8+fdJCUq5Vbu9ARApJYtnQh3cBYGvfwK4j2Z2ONq5qnNN9t6xt4vl9YxweidPZuYn0cz/HTMRQ3fVFyyuEKA8rli3MVOrnohx3lY0wHryJ8XJHqVjzGrYthJg7y7IIhpOsqpkGQPVW5gumUteMqWg0KxHC8ZlyxxFCiJLoG4zS01F/0uL5q9vachRFwb7mIoyRfZjT+ecguZw2VrbWypwksawYQ7tRaptQ6prZdThMrdtOd0vtnO579uomNFVhx/5xbJ2bjh1vVzHjCiHKxJEYQ1fsKDWV/0XLtKOR6nQIy7LKHaUiSSFJiCKJTqVJZQza7NltoCu18q6oGka1D78Wm91lTgghlrLoVIpgOMG6Lu9Jb/PatrYc+5ptgIV+8OmT3nd9t5cjI5MkU3qBEgtRuSzTQB/Zh619A5YFu46E2biy4aRD7F/LXWVjfbeX7fvHURo6UVx16IM7i5xaCFFq0zMZvGaEmaqm7M7RFU6vacZJGisZK3eUilT5z6AQi1Tw2I5tjURAc6DUzG2Jdzlo3jZatBjBSKLcUYQQouj25+YjdXnyXp9ra7OtuuCE69R6P6pvFZlTFJJ6u72YlsWBoWgB0gpR2cyxw5BJonVs4Ghgkqlkhk1zbGvL2dLjIxhJMhpKonVsQh/aiWWaRUoshCiHQCiBX4tj1baUO8qc2I4tApgZl4Hb+UghSVQscypM+Pe3YxmZckc5I4FjhaSadAjV01LRlXenr4NGdYqxiXi5owghRNH1DUSpcmh0+fPPR8rX1vZq9jUXYYb6T7pJwZr2emyaIu1tYlnQh3cDCra2s9h1OIQCbFg5v7aVzWuaANh+YCLb3paaxpw4UviwQoiyCY5H8apTOJvayx1lTtzHdpaLBwbLnKQyVe4nW7Hspbf/iuiTv0Q/8kK5o5yRQDiBw6aiTQUrtq0tR/O0oSoWqbDsTCCEWPr6BqOs6ahHU/O/DTpZW1uObfWFoCjoB5/Ke73DrrGmvZ59/dECJRaichlDu1F9K1Cqath5OMSK1jpq3Y55HcNb62R1Wx3b949j69iY/fs18HKREgshymEyMISqQE1LZ7mjzEljSwtpSyM1ITtb5yOFJFGRrJkpMgeeBCCz//EypzkzwXCCNo8Da3Ki4gtJ6rGd25SYvFAKIZa2eCLNyMQ06zo9ea8/VVtbjur2oLVvIHPw6ZMO4Vzf7WUgmG3zEWKpstJJjLFD2No3MJXMcHg0zqZVZzZE99weH/2BSSJpG6pvFfqQzEkSYinJHFvFa2toK3OSuWluqGbcqMOKBcodpSJJIUlUpPS+R8BI4163FWNoN+ZUqNyR5i0QSbKmfgawKr+QVN+ChYI7NYEhMwmEEEvY/oEowEkHbZ+urS3HvuYirMlxzLFDea9f3+XFIttGJ8RSZYzsA8tE69jA7iNhLIt5z0fK2dLjA8iuSurchDl2BHNmspBxhRBlpE0GsQC1zl/uKHNS5bARVjw4kuPljlKRSlJIuvXWW7niiitYt24d+/fvn738iiuu4E1vehPveMc7eMc73sFjjz1WijiiwlmmTmb3w2htvTRe+WHAml2dtFjohslENElX1RQAqqeyK++KzUHa6aVZiTIRmyl3HCGEKJq+wSgOu8qKk2xNfrq2thzbivNAs5M5SXvbqrY6HHaVfTInSSxh+vAusDnQ/GvYdThEdZWNla11Z3SslgY3bU3V7Dgwga3zbMDCGNpd2MBCiLIwTYvq9ARJuwfFNr/W13KaqfJRbcQW7czeYipJIenKK6/kjjvuoL39xMFa3/nOd7j77ru5++67ufTSS0sRR1Q4/cgLWNNhHJuuxu5tQWtdR6bv8ZO2D1SiUGwGw7Twa3FAQa2v/Mq7Vd+KX4vN7jYnhBBLUd9A9Ngw7BPfAs2lrS1HcbiwdW9GP/QslqmfcL1NU+np8LB3QApJYukyhnajta7HUm3sPBJmw8oGVFU54+Odu7aJvoEoiep2FGcN+qDMSRJiKZiIJWlWY2SqK/8z0atZtc2oWJixsXJHqTglKSSdf/75tLZWdmuPqBzpXb9DqWtG6zoHAPu6S7HiQYzggTInm7vcjm0eM4xS27QoKu/Opg6atTjB0FS5owghRFFMJTMMj0+ddD7SXNvacmxrtmHNTGIM7cl7fW+3l5GJaWJTqTNMLETlMqdCmLEAtvYNDAaniE+nz7itLWdLjw/Tsnj5cBitcyPG0C4sS1ruhVjsRiem8GlxbN7K7tJ4LcexeU4z40NlTlJ5bOUOcMMNN2BZFueddx5f+MIXqKub/3LYxsb82/cuNj5f/mX25VCuLDPDB5gMHqTx6o9T31wPQMsFb6D/idux9T+Db9N5ZcmVM9fHZWpPEIDqTAhHc0dRHs9CH9O5ai0Te+5Hj0/g820qa5aFkCz5SZb8JEt+SzXLoV2jWMDWs9vzHndw4AWqunrxd+ffUea197EaXkf/oz9EG3oe33kXn3D7bZvbufORQwxHZlizsqkgv8PJspSTZMlvqWeJDz/DNODbdCFPvJz9Eur153fhras64yxNTTU01Vexuz/CledfwPjBp6k3JnC2ri5k9DllKTXJkp9kyW+xZUlt34tDMfCuWlPU7IU+dtvaNXAQ9Hhw3seupOeoGMpaSLrjjjtobW0lnU5zyy238LWvfY1vfetb8z5OKDSFaS6etqd8fL5axscrY6BgObMkH78L7FWk2i5gfHwSn6+WUExHW3kBk3uewNryPhS7syzZ5vO4HBqIUFOlYYRH0VvWF/zxLMZzZNiyu6xMB/rndWz5s5ufZMlPsuQnWfIrdJbndo1it6l4XbYTjmtEhslMDKG+7oN5z3myLNqK85ja9zRcMHHCv091Dg2X08azu0Y4q7O+YL/HUn6OFkKy5FesLMm9L6C4PUTx8MzO7XT5a9BTGcbHTz5LZC5ZzlndxGMvjxC9LLsyffzlp3Hamguafa5ZSkWy5CdZ8luMWcL9hwEwq31Fy16Mx8VZ5SZmujCHjy7az0cLoarKSRftlHXXtly7m8Ph4Prrr2f79u3ljCPKzJyOoB96Dvu6y1AcruOus6+7BDIz6EeeL1O6+QmEE6zxGmCkK37HtpxcTvuUbHEphFia+gairG6rw2478e2Pfvg5QMG2am5tbTm2NdtAT6H37zjhOlVVWN/lYa8M3BZLjGWZGCN70drPIpnSOTgcX3BbW865PU2kdZM9AR21aQXG4M6CHFcIUUaxUQC0Ct+A6LWaPS6CRj3qpMxIeq2yFZISiQSTk9kqnWVZ3HvvvfT29pYrjqgAmT0Pg2Xi2HjVCddpLT0odc1k9j9ehmTzF4wkWV2dnZO0WApJisPFjK2O2kyIjC7zCIQQS0tiRmdgbJKeU8xHmstuba+ltfagVDecdPe29d1exqMzTMSS80wsROUyQwNYM5PY2jew52gE07IKVkjq6fRQXWVj+/5xbJ2bMMYOYqWmC3JsIUR5OJPjpFQXStXiGknjsGvENS+umfFFtfFTKZSkkPSNb3yDyy67jEAgwMc+9jHe+ta3EgqF+NCHPsTb3vY2rr32Wo4cOcLNN99cijiiAll6msye32Pr3oxad+LyZUVRsPdcgjGyFzM+XoaEczeT1olMpmhzZAul6iIaKqdX+/FrMcaj8oFHCLG0HBiKYlmwrst7wnVG5NhubStPv1vbaymKim31VozBXZgzJy5j7+3Onm9ff3TexxaiUulDuwHQOjaw83AIl9PG6vb5zznNx6apnL26iZcOTqC0bwTLQh/eXZBjCyFKLzGj4zEjzFT5yh3ljKRcPhxWCivPv/HLWUlmJH3lK1/hK1/5ygmX33XXXaU4vVgEMgefwkpNYd909exlpmlxZCRGjT1b77T3XEz6+V+S2f84zvPfVa6opzUWyRZhmpQoirMGtWrxDFpTG9rwR48wGJ6iram63HGEEKJg+gaj2DSF1W0nftg907a2HPvabWRe/i364edwnHXFcde1N1VT67aztz/CJWcvjhWqQpyOMbwb1duB4qpn15HdnLXCi6YW7vvpLT0+ntod4NBMAx0ON8bgTuyrLizY8YUQpRMIJ2jRYhj1K8sd5Ywo9S0wBmZ0FNVVmIL5UlDWGUlCQLa1MbPzd6gNnWit62cvv//ZAf7X3z5C30B2toRa04jWfhaZA09U9FawgXC2pa0mHVo0bW057uZOnIpONBgsdxQhhCiovoEoK1vrcNi1E64707a2HLWhE9Xbhn7w6ROuUxSF9V1e9g1EZFm8WBIsPY0R2I/WsYHh8Wkik6mCtbXlbFzZgN2msv1QBFvHBvTBnfL3R4hFajw4Ro2aoqqpvdxRzoizMdtdkhwfLnOSyiKFJFF2xshezMgQjk1XoygKkF0Cee/T/QD85tj/Q3botjU5gTHaV5asc5ErJNmmxxZdIcnVnN3yOjUxVOYkQghROMmUTn9gknVdnhOuW0hbW46iKNjWbMMI7MecnDjh+t5uL5HJFMGItA2Lxc8I7AdDx9a+gZ1HQkC28FNITofGxpUN7DgwjtaxESsRxQzLexMhFqOpYPbvbm1rV5mTnBmPv42MpTI9Jq9BryaFJFF26Z0PoFTVYlu9dfay3z0/yPSMziXntLHrcJj+QLYn1bbiPLC7yPRV7tDtYDhBe50FM3FU7+IqJOXmOSnx0TInEUKIwjk4HMO0LNZ1njgfaaFtbTn21RcBkDl04qqk3Jwk2b1NLAX60C5QbWit69h5KESHr5qGuqqCn2dLj49wPMVo1arseWX3NiEWJSM8AoC9YfHMjX01f2M140YdRmSk3FEqihSSRFmZsSDGwEvYz7ocxeYAYCqZ4YHnBjivx8dn/7/NVDk0fvtMdlWSYnNgX70V/chzWOnK/GY3EE7SU5cCFs+ObTlqVS0zqouqpGxxKYRYOvoGomiqwpr2+hOu0w8/t6C2thy1zofqX4N+4MRCUrPXhbfWKYUksSQYw7vR/GuYMTUODMXYWOC2tpxz1jShKgovDKRRGzoxBl8uynmEEMVlmw6io6HUNJU7yhnxeVyMmXVoU/L56NWkkCTKKr37QVBV7K8aTnrfMwPMpAzecelKalx2Lt/SznP7xghGsi1j9nWXgJ4mc/jZcsU+KcuyCIYTdLmmAFA9i6/yPlPVjMcIk8oY5Y4ihBAF0TcYYUVLLU7H8fORsm1twwtqa3s1+5ptmJEhjPDgcZcrikJvt5d9/dlt0oVYrMxEDDM0iNaxgX39EQzTKvh8pJwal52eznq275/A1rkJI3CgYr9EFELkZ5oW1ekQCUcjSgEH8peSTVOZsjfgykSwDL3ccSrG4nw2xZJgpRNk+h7DturC2W+CY9NpHnxhkK1n+enw1QBw9fmdaKrKb58eAEBtXo3qaUXf/0S5op/UZDJDIqXTosVBsy3KyrtZ14JfizF+bNaTEEIsZqm0wdHRSXryzEcqVFtbjm3VBaCo6AeeOuG63m4vU8kMw+PTBTmXqFyGbpCemSl3jKIwRvYAHJuPFMbp0FjbceJKv0I5t8fHyMQ0sfoesAz0Y+cXQiwOofgMPjWGUeMvd5QF0d3NqFiYk7IqKUcKSaJsMn2PQWYGx6arZy/77dP9ZHSTt1/yyvaQ9TVOLjm7lSd3jRKZTGWHmvZckh1qGguUI/pJBY8VX+rNMGp9y6KsvDubOqhW00yMyQulEGLxOzgSwzBPPh+pEG1tOaqrDq1jI5mDT5+wu+j6LpmTtFzs+s9/ZOfffoZMOl3uKAWnD+0GZzVKYzc7D4U4q9uLTSvee50ta30AbA/Xgr0KQ+YkCbGoBMdjNKpT2BbpfKScXJeJGZE5sjmL71OuWBIs0yS960E0/1o0X7ZoFJlM8fD2YS7e2EpLg/u4279paxeGafHAc9lVSfa1rwNFqbih24FQtpDkmplYdPORcnI7KkwHB09zSyGEqHx9A1FURTlh1USh29py7Gu3YU2HMQIHjru8sb6KZq+LfVJIWtJGB4bonnoRL3EOP/FAueMUlGVZGMO7sbX1EozOEIrPFG0+Uk5jfRXd/lpeOBTB1n4W+uBOLGkPFWLRiI4OoioWNf7OckdZEFdzOwCJ8eEyJ6kcUkgSZaEPvIg1OY590xtnL7vnyaNYlsXbL15xwu2bPS629vp55MURppIZ1GovWscmMgeexDLNE25fLoFIAodqok6PL9pCksuXfaHXw/JCKYRY/PYPROhuqcHltB13eaHb2nJs3eeCzYF+MH97W99gBKOC/t0ShTX4378AIGTVU3XwwYp6j7JQZnQUazqC1rGRnYdCAGxa2VD0827paeLQcJx0cy/WVAgzKisChFgsUhPZzxPuRV5I8vkaiJtVzIwPlTtKxZBCkiiLzM4HUKobsK04D4DxaJJHXxrhsnPaaPK48t7nLRd1k0obPLw9+xfYvu6S7Le+FdQvHwwnWedJg2UtykHbAEq1lzR2bFPBckcRQogFSWcMDo/GT97W1rK2YG1tOYq9CtuKLWQOP3fCUM7ebi/JlMFAcKqg5xSVYXBglFWJlwnWb2K69600WFGGd1TWyumFMIZ3A6/MR2ptdJ/0PVshnduTbW/bNZN9XyW7twmxiMSzY0g0T0uZgyyM3+tizKivuLEq5SSFJFFyRmgAY3Qf9g1XoajZHXR+/cRRFEXh2tetOOn9OpprOGd1Iw8+P0QqbWS/9XVWZ2ctVYhgOMHqmmx722JdkaQoCpP2JmrSE+WOIoQQC3J4JI5uWCcM2p5ta1t1YVHOa19zEaSmMYaOn+eyTuYkLWlHH7kLOzrtb3gvF7z5WibMWvSXfrNkWrH0oV0odc1kXA30DUSLtlvba7U3VdPsdfFMfwbV24Yuc5KEWDSqkuNMa3UoNme5oyxIY30V42YdjsR4uaNUDCkkiZJL7/wd2Bw41l8GQCCc4MldAS4/tx1v7alfZN66bQVTyQyPvjSCotmxr74I/egLWKny74JjmhbBSJJ25yQAav3irbxnavw0EiGZki0uhRCLV99gFAXoec18pGK1teVoHRtRqmrJvGb3tvpqB+1N1VJIWoKODo7Rk9hBqG49NS1dVLudjDRfSqMeJHbgxXLHWzDL1DFG+7C1b6BvIIJumGxcVfy2Nsh+wbVlrY+9/RGs1g0Yo31YmVRJzi2EOHPJlI7XipByNZc7yoJpqsq0swmHmcSakVXFIIUkUWJmMo5+6Cnsay9GqaoB4FePH8FmU3jLtu7T3n9NRz09HfXc9+wAumFiX3cpGDqZQ88UO/ppheMz6IZJEzGUmkYU++KtvGueNurVJONjoXJHEUKIM9Y3EKHTX4O7yn7c5cVqa8tRVBu2VReg97+IlU4ed936bi8HBqPoxtKZnSPgwO9/jVtN03Lpu2cvW/v6NxEzXcSevbuMyQrDCB6CzAxaxwZ2HgrjsKms6/SU7PxbenwYpsURtQtMHWN0b8nOLcpj5un/ZPye75U7hliAYHiKZi2Osoi/XH81syZbEJM5bVlSSBIlldn7ezB07BuzQ7aHxqd4Zk+Qq87rpL7aMadjvGXbCiKTKZ7aHUBt6kZt6KiI3dsCkWxLW01m8e7YluM6NhAvNtJf5iRCCHFmMrrJoZET5yMZkZGitrXl2NZsAyONfnT7cZf3dntJ6yaHR+JFPb8onf1HJ+hNPE+0ZiXujp7Zy5sb69hfcwENiaPMjBw4xREqnzG8GxQFW1svO4+EWN/txW7TSnb+Ve111FU7eDJYmx1mPyDtbUuZPrSLzMu/ZfKlh9BH9pU7jjhDodEATkWnytdR7igFYfMem9MWHSlzksoghSRRMpahk9n9MFrnJrRjfxHvfuwIVU6NN23tmvNxNq1qoKu5ht8+PYAF2HsuwRw/jBEp7y5jgVACsLAnFu+ObTne9hUAzEzIzgRCiMXpyGicjG6y7jXzkYrd1paj+deg1DaROfT0cZev6/KgIHOSlgrLstj3yL3Uq0maLn73Cde3b3sz06aD8Sd+WYZ0haMP7Ub1rWQsoTAWSZZsPlKOqiicu7aJF4/EUFt70YekkLRUWXqamcf/DaW+Ba2mgfTzv1gyc8aWm+ngAAD1bafvOlkM6ppb0C2V5LjsbA1SSBIlpB9+FisZw3FsNdLRQJwX9o9z9QVd1Ljsp7n3KxQl2wYXCCfYsX8c29rXgaKVfVVSMJykpWoGRU8t+kKS0+tHtzSQpZtCiEWqbzAKQM9r2m/0w88Wta0tR1EU7KsvwhjajZl8ZfVRdZWdrpZaKSQtEXuOhtiYfI4pdztVXRtPuL5nVQsvaWfjiezBCC/OL2es1DTm+GFsHRvZdTgMULL5SK+2pcdHKm0w5lqFFR+T3ZOWqPT2X2HFx6i69CN4Ln4PRmD/7I6BYnHRI9nPEfbG9jInKYzmxlomzFpSIVmRBFJIEiViWRbpXb9D9bSidWTfaN312BGqq2y88fzOeR/v/HXNNHtd/OapfpSqWmxdZ6MfeBLLNAodfc4CkQQ9ddnhj4u9kKSoKlHNizM5Vu4oQghxRvYPROjwVR/3RUWp2tpybGu2gWWiv2aOX2+3l0PDMVKZ8v2bJRbOsix2P/I7fNok3ovegaIoJ9xGURRqt1xDyrIx/sRdpQ9ZAPrIPrAstPYN7Dwcotnrwu91lzzH+i4vVQ6N5yezc0pk97alxwgPkn7pt9h6LsXW1kvd5itRahpJPfdzWZW0CNmnx5hRnChVteWOUhB+r4sxow7iwXJHqQhSSBIlYQQPYo4fwb7xjSiKysHhGC8fCvGmrV24q2zzPp6qKrx5axdHA5Ps6Y9gW3cJVjJ2wlbLpRQMJ+h2ZXePUz1tZctRKMkqH/W6DNsWQiw+umFycPjE+UilamvL0RraURs7yRw8vr2tt9uLYVocHIqVJIcojpcPTrAp+SwzVU04V5/8z9QF56ziBWMdrtHtmJMTJUxYGMbwbrBXYTauYF9/hE0rS9vWlmO3qZy9upHHj+godX4pJC0xlmUy8+iPUJxuqi66DgDFZsex5e2Y40cw+l8sb0AxL6ZlUZMJkXQ05S2yL0YNdVWMmx6cM6GyLl6oFFJIEiWR2fUAONzY114MwC8fPUyd285V581/NVLO6za2Ul/j4N6n+rF1nZPdarlM7W0Z3SAUm6HFFgOHG8VVV5YchWTWteBRppielC0uhRCLS39gklTGyDMfqTRtba9mW70Nc+wQZvyVFZ5rO+rRVEXa2xYx07LY8eh/02GLUHvB21CUk7+ltts0jHVXYVkWsefuKWHKwtCHdqG1rmP/8BRp3WTT6tK3teVs6fExmcgw5V2HMbIPS0+XLYsorMye32OOHcK57Q9Qqmp47OURfvnIQew9F6PU+Uk9/wssS3a7XCwi8RTNahSjdmns2AbZWW0zriZUTKzJ8XLHKTspJImiM6dC6EdewL7+9Sh2J3v7I+ztj/CWbStwOs58xw+7TeWaC7rY2x/hSCCBbe3r0Pt3YM5MFjD93IxFkliAxwyjelqXROXd0dCBqkBoSHZuE0IsLvnmI73S1nZBSbPY12wFIHPwqdnLqhw2VrbVsW9ACkmL1fa+cc6ZeY60ox5nz8Wnvf3rtp7FC+lVKIceP25mVqUz4+NY8bHZ+Ug2TWVdl/f0dyySTasasWkKu1OtYKQxRvvKlkUUjjkdIfXsf6G1b8C2ZhtDY1P82319/PDXuzkaTOA87x2Y4UH0I8+XO6qYo2Bwgjp1BnvD4u/SOE6tHwBT5shKIUkUX2b3QwA4Nma/jfvlY4fx1jq5/NyFv7C8fnMb1VU27n26H3vPJWAa6K9pISiFQDgBgHtmYkm0tQHUHtthYTowUOYkQggxP30DUVob3dRVO2Yvm21rW1matrYctaYRrXUd+oGnjpvxsb7Ly5HROIkZvaR5xMKZpsVzjz3FGnuQmi1vQdFO36LvqXEy3vF6FFMn8eL9JUhZGPqxIce5+Ujrujw47Wf+JeBCuZw2ersbeHikGjS7tLctEakn7wBTp+rSj2ABP75vHy6nDU+Nk58+dABt1VZUbxvp53+JZcqqpMUgPjoIQE3LmXefVCJnU3ZwuBGRQpIUkkRRWZkU6b2PYFuxBbWmkV1HwhwcinHt61Zgty38jYjLaeOKLR1s3z9O0GpAbeouS3tbIJygSkmjpuKLftB2TmN7J4alkAnLFpdCiMXDME0ODEVPWDWhH34u29ZWXfrVFLY12zBjAczQKys8e7u9WBbsH4qWPI9YmGf3Btmceg7d5sbR+4Y532/rRZvZmekis+chrHSyeAELyBjahVLtJYKH0VCCTSvL19aWs6WnidGoTqZxDcbgy+WOIxZI79+BfuR5HFvegVrXzCM7hjk0EucPrlzLB960noNDMV44EMJx3rswo6Por1rdKSpXeiK7S2XtEiskNTQ1MGU6SY7L56OSFJJuvfVWrrjiCtatW8f+/ftnLz9y5AjXXXcd11xzDddddx1Hjx4tRRxRQpkDT0A6gX3TNdnVSI8epqm+ikvPLlyx5arzO3DYVH77dD/2nksxQ/0YodKuogmGk6yuya5K0pZIIcnhdBKhDtuU7EwghFg8BoJTzKQN1p3Q1jZU8ra2HPvK80HVjhu6vaa9Dpumsk/mJC0qhmnyxOPPs9ExhOuca1Dszjnfd2VrHftqL8JmzJDa/XARUxaGZZroI3vR2jew62j2z+mm1eUZtP1qm9f6UICjSle2QBuXWSWLlZWZYebxn6B6O3Cc8yYikyl+/t+H2LDCy0Ub/Lxxazcdvmru/P1BrM7NqI1dpF64C8uUlZyVTpkMoKOi1jWXO0pB+b1uxow6MrIiqTSFpCuvvJI77riD9vb24y6/+eabuf7667n//vu5/vrruemmm0oRR5SIZZlkdv0OtWkFmn8NLx6Y4GhgkrddvAKbVrg/erVuB5ed08bTe4JMNm8G1VbyVUmBSILV1dlvF5fKiiSASXsT1enFt8OMEGL56huIAhw3aLtcbW05SlUNts6z0Q8+PduWYbdprO2ol4Hbi8yTOwNsSb+AqTlxbrxq3vc/Z+v59GVaSb50X8UPijYnjkJqGltHtq2tsa6KlgZ3uWNRX+1gdUc9j01kV0fpZdyxVyxM6rlfYE1HqLrsoyiqjX9/cD+6YfGha9ahKAqaqnDdlWuZiM3w4AsjOM9/N9bkeNk21xFzVzUzwZStAUUtXytsMfgb3ASNejT5or00haTzzz+f1tbjP1yHQiH27NnDtddeC8C1117Lnj17CIfDpYgkSsAY2o0ZHcWx6Wos4JePHcbvdfG6jYWf3n/NhV0A3PdSGFv3ZvSDT2EZpfu2IhhO0O6cBFVDqfOV7LzFlnb7qTejmHqm3FGEEGJO9g9G8XtdeGpeWSlSzra2HNuabViJKMbovtnL1nd7GRybYjJR2QUFkZXRTR594iW2OI/iPOsNKM7qeR9jS08TT6vnYktPktn/RBFSFk5uPhItvezpj7BpdWPFbCayZa2Pl8ZtmNWNGDInaVEyxo+S2f077GddPvuF8wt947z94hU0e18pWG5Y0cA5qxu558mjTDf2ojavIr39V1iGvDetVKm0gdeKkHEvnc9EOZ4aByE82PVprNR0ueOU1emnAxbJ6Ogofr8fTctWKTVNo7m5mdHRURoa5td/3dhYU4yIJefz1ZY7wqxCZBl98GG0ag+tW6/g8ZfHGBqf5oYPnEeLv77gWXy+Wt5wXgePvTjCBz5wOZNHnqc6up/q9VvPNP6cs0wl0kwmMrTYJ7E3tNLs9xT0nPPJUmju1i60+JPY0hEaW1eXNctcSZb8JEt+kiW/xZrFMC0ODMe4+Oy22fulJ4aYjAzRePUnqF/g77WQx8X0XEL/Yz/ENvQCvs3Zf5ted047v3z0MKPRFKu659cytFifo2IrZpbfPHGELfoOFIdG6+XvxVZ76nOdLMvZF19K/+PP0P3SvbRd+taSfGN/Jo/LyNg+HM0rmDCqSKUNLt7cXpDHtxDHuOqiFfzs9weJ1PXQNLKDJm8Vis1eliyFspyyWKbB8N0/Rqv20P7mj5JSnPz7Qwfobqnlg2/dcFzngs9Xy6ffew7/829+z33PD/Hxqz5I4N+/hnPwaeoveEtRc77WcnqO5uO1WQ4PhGhSJ0n4u0qesxTnM2v9kIE6JU6V7+QLJCrpOSqGshWSCikUmsI0rdPfsIL5fLWMj5d+2/p8CpHFiI6QPLwDx/nvIjiR4N/u3UO7r5r1HXXzOvZ8slyxuY2Hnxvkl31O3uj2EHr+dyQazzrTX2HOWQ6PZLfxdc+MYTV3leR5LNWfF6Uu++LYv2cvpjt/j/NS+7NbKJIlP8mSn2TJb75ZBoKTTCczdPuqZ++XeuERQGHGt5H0An6vQjwuWvd5TO59Euu861BsDuqrNJwOjWd2jtDTNvc3nIv5OSqmYmZJZwzueWA7/7vqEPaeS4jM2GHm5Oc6VZYta5v40e/P5iOTv2f0mYexr7moKJnnkuVkrEyKmcF92De+kce3D6GpCm2eqgU/voV6jmxAh6+ap8ONvDUzQ2DXdmzt83vPt1z+7M5XKbKkX76PdPAIVVd9ltCkyU8ffJlQNMmnPnQekfArqzxyWZwKXH5uOw8808/FvRfQ0LqO8GP/RarjQhTb3OeULcRye47mKl+Wg7v3sUaxUOtaSpqzVI+LVdMMEQgfPYTdkX+kSSU9RwuhqspJF+2Ubde21tZWgsEghmEAYBgGY2NjJ7TAicUps+tBUG3Yey/n6d1BAuEE77xkJWoRl0S3NlazpcfHQ9tHYNVF6AMvYSZiRTtfTiA8jYqJPRlaUvORALztKzAtmBkfLHcUIYQ4rZPNRyp3W1uOfe02SCfRj+00ZdNU1nV6ZE7SIvDIjmHONV5Cw8R5zsJWQdS47NSt20rAqCe5/R4sq/K+DDVG+8A0sHVsZOfhEGs76nE5K+v753PX+vjvQC2Wqs3+nRKVz5wcJ/X8L9C6NmNbeT5HA3EefGGQN5zbzpr2k3ctvP2SlbidNv7j9wdxnPdurGSMzCIYWr8cJceyO7Z52rrKnKQ43L5WDEtBX+YDt8tWSGpsbKS3t5d77rkHgHvuuYfe3t55t7WJymOlpsnsfzy73bGjhrsfP0KXv4YtPcXvk33Ltm4SKZ3nM2vAMtEPPln0cwbCSZq1KRTLRPW2Ff18pdTUWEfErMGKLe8XSiHE4rBvIEJTfRUNdVVA+Xdrey2trRfFVYf+qt3b1nd5CYQTRCZTZUwmTmUmrfPQ0/u5zHUA+6oLUOv9Cz7mVed38mByI0p0CGPwpQKkLCx9eDdoNiaruxkan66I3dpea0uPj5RlZ6qmG2NwV7njiDmwLIuZx38CKFRd8iFMy+JHv91HXbWD97z+5CMUIFuAffvFK9lzNMKuRCNax0bSL/4GK50sTXgxZ0Y0+7nB2dR+mlsuTr6GGibMWmYmhssdpaxKUkj6xje+wWWXXUYgEOBjH/sYb33rWwH46le/yu23384111zD7bffzl/8xV+UIo4ossy+/wY9jWPTG3l85ygTsRnefdmqkgxoXNlaR2+3l7tfnkHxrSLT90TRv+kLhhOsqUsAS2vHNsh+Wx5RvTgTY+WOIoQQp2RaFvsHoxW1W9trKaqGbfVW9IEXsdLZfzd6u7MrpfYNyKqkSvXQC0OcY+7GQRrH5rcW5JhtTdXMtG0hatWQ2vGbghyzkIyh3WgtPewcyLZmbFpZeYWkLn8NjXVV7Mt0YEaGMKdC5Y4kTkM//BzG4Ms4L3g3ak0jDz4/xEBwig9c1YO76vQr3i7f0o6/wc3PHj6Ibcs7sVJTpHf9rgTJxXw4poNMKbUo9qpyRykKv9fNmFGHGV3eX7SXpJD0la98hUcffZQ9e/bwxBNP8JvfZP/BXL16NXfeeSf3338/d955J6tWrSpFHFFElmmQ3v0QWut6zPp2fv3EUVa317FpVenegLx1Wzex6TRH3Zuybywmjhb1fMFwghWubD+3Wl/4HenKLVHlo1YPz25ZLYQQlWhkfJrpGZ11na+0sFVSW1uOfc02MHT0Iy8A0OmvobrKJu1tFSoxo/PgM4e5qroPrXMTWlN3wY591YUreDDRixk8gB7YX7DjLpSZiGJGhtDaN7DrcAhvrZN23/x3qCs2RVE4t6eJR8ayf7/1IVmVVMms1DSpJ+9AbVqBfcMbmYgm+eVjh9m8ponz1s2ta8GmqVx3+RoC4QT/PeTE1n0u6Zd/u6x2zxoNTdM/Gi93jJOyLItaPUzS2VTuKEXjb3AzZtRjS0ws689HZWttE0uTfnQ71lQI+6areeTFESKTKd59aWlWI+X0dntZ2VrLz454QLOT6XusaOeyLItAJEGLLY5S3YDicBXtXOVi1rZgx8CcHC93FCGEOKm+wSjwynwkI1pZbW05qm8lSl0zmYNPZX9WFNZ1edknhaSK9MBzA2yy9uGyEjg2X1vQY29Y2cCR6rNJUEV6xz0FPfZCGEO7AVDbzmL30QgbVzaU9H3cfGxZ62MoU0fGUY8xIHOSKlnq2TuxZuJUXfZRUBRu/91+FBQ+8Maeef35OmdNI73dXn71+BH0jW+DdJL0y/cVL3gFiU2l+Kvbt/OFf3iU/cf+zas00ckUPjWKWbfwFuBKVee2E1E9qJaBNTVR7jhlI4UkUVCZXb9DqfVhtG7iN0/1s77LQ++K0s69UhSFt1y0gqGoRaxhA5lDz2Dp6aKcKzqVJp0x8VqRJdfWluNo7ABgMjBQ5iRCCHFyfQMRGuqcNNVnl9JXWltbjqIo2NdswxjeizmdLR71dnuZiM0wHpVZH5VkKpnhwef6eXPtPlT/GrSWnoIeX1UULj9/Jb9PrMcYfBkjVBn/zurDu1GqajkyU08ypZd0Vfl8re2sp8bloF/rRh/eg2Xq5Y4k8tADB8jsfQT7xqvRmlbw3L4xXj4U4l2XraKxfn7tT4qi8P4r15JI6fx6r45t1QWkd/0OM1m5q3QKwbIsfnDvXlIZg8a6Kv7hv16iP1B5u4KNjYxQpeg4GpbmfCTI/hnUq7O7WS/n9jYpJImCMcaPYgT249h4Fb9/cYT4dJp3XlqedsVze5pobXTzQKgTUtPo/S8W5TyBcAKwcM+Mo3qWXlsbQE1LdseFKSkkCSEqlJWbj9Tpmf1mWz9UeW1tOdnt3i30Q88CsP7YnCRpb6ssv32mnw0cpMaM49x8bVFW5bxuYyvPWxvIKHbSL5Z/VpJlWRjDe9Daetl5JIKqKJy1ovL+DuVoqso5axp5KtIEmSRG8FC5I4nXsAyd1GP/ilLTiPP8d5GYyfDTBw/Q3VLLVed1nNExO5truPTsNn6/fZjY6jeBniL90r0FTl5ZHnphiF2Hw1x3xRpu+czFuJ02/vY/X2R4orLa+nJfPNe1Fa4NuBLZvNkFBGY0UOYk5SOFJFEw6V0PgL0KfcU2fvv0ABtXNdDT6SlLFlVReMtF3TwxUY/u9JDZX5z2tkA4QZ2SRDVSS3ZFks/fSMx0oYeW984EQojKNRpKEE9kWNeV/cBbqW1tOaqnFbVpxWx7W1ujm7pqh7S3VZDYVIqHnx/kbZ59qN4OtK6zi3Iep0Pjws0reSzZQ+bws5ixYFHOM1dmZBgrEUXr2MCuw2HWtNfhrrKXNdPpbOnxsSvZjKWoGIM7yx1HvEb6pXsxIyNUXfIhFHsV//XIIeKJNB9903pU9cyLs++6bBV2m8rPXpjGtmYbmd0PYyaihQteQYbHp/jZ7w9x9upGLj+3HZ/XxQ1/cC6aqvC3/7GDsQpazZoOjQBQ29pZ5iTF5WlsYtp0oMuKJCEWxkxE0Q89g33dpTz4coipZIZ3lWk1Us7Ws/x461zsMNZgDO2abSEopGA4Qbszu6xU9bQV/PiVoLHOSdCoR5sq75tbIYQ4mdn5SMe+vKjUtrZXs6/ZhjlxFDM6iqIo9HZ72dsfKfpOo2JufvN0Pz3qAPV6CMfmt6AoxXvLfOWWDv575iwsVNIv/7Zo55mL3HykpLeH/uAkGyu4rS1nw4oGTFsVIUc7+qDMSaokZixAesevsK26AFvXZg4MRXnkxRHeeH4n3S21Czp2fbWDt27r5sWDEwz43wCmXlGzxgolo5v8v1/tweXU+NhbemdXRvq9bv7k/ZvJ6Cbf+ukOIpOpMifNUieDpHCguj3ljlJU/gYXY0Y96Ynl+0W7FJJEQWT2PAymSWb1G7j/2QHOXdvEyta6smayaSrXXNjFfRMdYFlkDjxR8HMEwgnW1GS/BViqK5I0VWXS3og7NS4fcIQQFalvIEJ9jYNmb3bDg0pua8uxrb4QUMgcfBrIzkmKTacZDSXKG0wQjs/wyI5h3uXtQ6n1YVu9tajna6yvYs3aLp5PryHT93hZV1Xow7tR6lvYNZb9uZLnI+U47BqbVjby4rQfMzSwZFelLDaWZTHz2I9Bs+N83QfQDZMf39dHY52Td166siDnuPqCTprqq7jjqSi2nkvJ7H0EcypUkGNXil88eoih8Sk+9pZe6qsdWOkERnIKgA5fDV+4bjOTyQzf+o8dTCaKMxN2PtypcSbtjRU7oL9Q/F43Y2YdVlxa24Q4Y5aeJrPn92hd5/DAvhmSKaNss5Fe67Jz2phxNhKwtZPpe7zghZBgOEGHcxLsVShLuPKedvtxWGkseXMmhKgwlmXR96r5SJXe1pajVnvR2nvJHHwKy7JkTlIFueepflZpARozIzjOeTOKqhX9nG+8oJP7ps/CMg3SL99f9PPlYxkZjNF92No3sPNwiLpqB53+mrJkma8tPT62T2dnVUp7W2XQDzyJMbIX54X/H6rbw2+fGWBkYpoPXr2OKoetIOew2zTe+4bVDI1P8aIrW/BNb/9VQY5dCXYfDXP/s4NcvqWdzWuasGammP7FXzB42+cwxg4DsLK1js+/52wmYjP83X++RGKmfAPn0xkDrxUh424uW4ZS8Te4GTPqsKUnsdKV01pYSlJIEgumH3oGa2YSfe3lPPj8EBf2NtPZXBlvPJx2jTee38HD0S6sWABzrHBDGHXDZDw6g0+Jonpal3TlXTm22soIL9/lm0KIyjQWSRKbSs/OR1oMbW059tUXYcXHMMeP4KuvorGuSuYkldl4NMljL43w3qb9KK467D2XlOS8a9rrqWluYy+ryez9PVaq9AN0jeBB0NOo7Wex+0iYTSsbUBfJe5uz1zQSsBpIaTXoUkgqO3NmktRTP0X1r8He+waC4QS/fuIo569v5pw1TQU91wXrm1nTUc9/PhNG6bmMTN9jmPGxgp6jHKaSGX5wzx5aG9287/I1WKZB8uHbsKYmUG0OEr/+a/T+HUB2w4bPvmsjQ+NT/MN/vUQqY5Qlc3AsjEdNonmXZpfGq9W47MS07PuO5bpzmxSSxIJYlkV61wOo3g5+e9RFWjd4xyWFWa5aKFec18FeVqMrdjJ9hRu6PRGbwbQsavXwkp2PlFPVnN25bTooO7cJISpLvvlIld7WlmNbdT5oNjIHn5qdk7RvIIIpbcRl86snjtCphfDPHMG+6RoUm6Mk51UUhavP7+TXsfWQmSG9+6GSnPfVjKHdoKgMKe1Mz+iLYj5STnWVnXVdXvqMdvTh3VhmeT5Ii6zUU/+BlUlSdenHAIV/u78Pu03l+qvWFvxciqLwB1euJT6d5veZs0HVSL1wd8HPU0qWZfHj+/YxmcjwqbdtwGnXSD17J8bQLpyXfJi2j92K6m0j+cB3SO/5PQBnr27if7ztLA4Ox/jHX+wko5slzx0dyX5OcDef2W58i41Zm10FacaWZ3ubFJLEghij+zBDg2TWXs7DO0Z43YYWWhuryx3rONVVdl63eQXbZ7pIH3wGSy/MMLpAKIGTDI50bMnOR8pp9GV3JkiODZU7ihBCHKdvIEKd205rozvb1hau/La2HMXhxta1Obuy1zTo7fYyPaMzNDZV7mjLUiCc4MldAa5rOQR2F46zLi/p+S/obWa6qoUB2woyu35XsPcrc6UP70ZtXsXLgwkUBTasbCjp+Rfq3LU+tk81Q2oac/xIueMsW/rwHvQDT+A45y1oDe08uSvA3v4I733Dajw1zqKcc2VrHds2+Pn19ij6mtejH3wSIzJSlHOVwuM7R3mhb5x3X7aK7pZaMgeeJPPyfdjPuhLH+tdjq/HgftuNaB2bSD3+Y1LP/heWZXFhr5+Pvmk9u4+E+edf7cYwS1tMSo4NAuBtr6xFBcVS1dCCaSmyIkmIM5HZ9TsUZw33BvyYpsXbKmw1Us4bL+jk+cwaFH0G/cgLBTlmIJygWYsBS3fQdo6/oZqgUY8VW7z/KAshlp7cfKSeLi+KoiyqtrYc25qLsJJxjJG9MiepzO5+/Ait9klap/fh2HAlisNd0vPbNJXLt7Tzy/A6rJlJMvseLdm5rZkpzPGjx+YjhVnVWkeNy16y8xfCuWub6Mu0YaFIe1uZWHqamcd+jFLnx3Hu25hMpPnPhw+ypr2e128u7ur997x+NaoCd4V7wOYk/cJdRT1fsQQjCf79dwdY3+Xhmgu7MMaPMvPov6K1rsP5uj+YvZ1ir8J1zeexr3896RfvYeaRf8EydC49p433X7mWF/aP86/37ivpClcrOophKTgb/SU7Zzn5GmoJmTXoi7houRBSSBJnzIyPoR/dQWbVJfz+5XEuObuVZo+r3LHy8tY6aendTMisIbmnMG/MgpEEXa7sDAN1ifcCe+ucjFkeHInF33MuhFg6JmIzhOOpRdnWlmPrPBscLjIHnsJb68Tf4JZCUhkMjU3x7J4gH2g7gqLZsG+6uiw53rC5nQGrhZCzg/TL92GZpRmcq4/sBSzSvvUcHY0vit3aXquhrgp/SxOjih998OVyx1mW0jt+jRUPUnXpR1BsDv7z4YMkUzofedO6os/baqir4k1bu3isb4qprsvQDz+LEVpcIxl0w+Rffr0HTVX45LVnwUyc5APfQXHVUXXVZ1HU44eUK6qG89KP4jj/3egHniR5399hpRNcfUEn77xkJU/uCvDT3x0o2a7L9sQ4cc1zQs6lqrnBxZhRRyYsK5KEmJf07odAUbk/ugpFgbe9bkW5I53Smy/q5tnUagjuw5ycWPDxguEEK93ToGiodUt7dwJVUUg4fTiNBObMZLnjCCEEAH0DUQDWdXkWXVtbjmJzYF95PvrRF7D0NL3dXvoGo+hG6edbLGd3PX6E5qoZOqZ2Yl93Gaqrriw56qodXHRWC3dF1mFNhdAPPl2S8xpDu8DuYvdkHRYsqvlIr7alx8eO6RaM8aOYyXi54ywrRniY9Iv3Ylt7Mbb2s9hzNMyTuwK8+aIu2n2l2YTnzVu78dQ4+MlgFzhcpJ//ZUnOWyj3PHmUwyNxPvymdXirbcw8+F2smSlcV/+vk74mKYqCc8vbqXr9JzBG+kj86q8wpyO87eIVXHNhJw9tH+IXjx4uenbLsqgzQsxU+Yp+rkrR0uBmzKhHmRrDspbfv9lSSBJnxEonyex7FL1zCw/umeYNm9tpqKsqd6xTava6SXVeiAIk9i586HYgnKDVFket8y2LyruRGyi3TJdvCiEqT99ghBqXnbam6kXZ1pZjW7MNMjPo/S/S2+0llTboD0jRvlSOBuJs3z/OBzsGwLJwnPOmsua56vwOXp5pY7rKT/rFe4v+AcWyLPTh3dja1rPzSIwal50VrbVFPWexnLvWx75MGwpWtjgmSsKyTGYe+1cUhwvntveTzhj82/19NHtdXLttRclyOB0a73n9avYF0gRbLkHv34ExVvwiSiEcHIrx6yeP8rqNLVzY6yf15B0Ygf1Uvf7jaE3dp72/fd2luN78vzEnx0nc9XXMyDDvu3wNl53Txm+e6ufep/uLmj82maRJiWPVtRT1PJXE73UTNOtQTR1rKlTuOCUnhSRxRjL7H4dMkoen12HTFN667fQvcJXg9Refzf5MC4ndjy7ojdlMWic6lcZrRZb8fKQce0O2t90ID5c5iRBCZPUNROnp9KAem4+02NracrTW9ShuD/rBp1jX5QFkTlIp3fXYEZqqdLriO7Ct3opaW95v1Lv8tazv8nLf9FmY0RH0ozuKej4rPoY1OYHavoFdR0JsXNlQ9DakYmlrqiZd30lSccmcpBLK7P1vzOBBnNvej1pVyz1PHWUskuTD16zDYddKmmXbxha6W2r5waE2cNaQev4XJT3/mUimdP7517tprKviA2/sIb33ETJ7f4/jnLfw/7d33/Ft1ff+x1/naEte8p7xzF52dkIGmQQIhNFSZge9HZeyS2/puL8OSildQOmg+0KhjAIJkAQIkIQkkG2ypx3He++peX5/OM50EiexdWT783w8+ih2JJ23NT7S+eg7TFnTenw7xuQx2K/7Hmh+2t5+HF/5Qb541XCmjIzl9XX5rMntu01zakqKMSgalqikPjtGsLFZjLQYOzcl8DcMvp3bpJEkLpqm+XHv/RBvZDqrjqjMn5hMeB/twtDbhsSFUhY+Hpunno6Sg5d8O5V17aj4sbvrUJ19u3hgsAiPjcelGWk7viODEELoqa6pg5rGDoan9N9pbV0UVcWYORVv8W5CVTfJMSHSSAqQvJJGdufX8sX0MvC6MGcv0TsSAAsmpbCxMQm3NRL3zpV9usaJt3QfAJXWNJrbPP1yfaRT5QyLYb8rAW/J3kE53STQ/G0NuLa+hiFxJMahV1Ba3cK7m4uYMSaeUWmB3/lPVRRumz+UymaNvIjp+Er24i0/FPAcF+OlDw5T29TB164bham+ANcn/8KQMhbz5M9d9G0ZolOxL/0hqt1J+6rf4Du6hf9aMorsrGheXH2YTXv7puHRXNG5HlVYwpA+uf2gdXxAwWDcuU0aSeKi+Yp2ozVV8qlnFGazgcVT+1fBGD5zPh2aifItH1zybVTUtRGltqBovkEzIqlr5zaPjEgSQgSBU9dH6s/T2rqYhk4Hvw9PwXZGpjrJK23E4/XpHWvAW7bhKNF2SG3YhjE1B0NkcHybnp0VTWS4nU984/FXH8VXdqDPjuUr2YcSEsWuis7TgtHpgT/5700ThsWwz50IHc34a/p2Oo8A16f/Bp8H66wvoQHPv3cIm8XIF+Zl6ZZpWEoEE4fH8I+8ODRrGO7tbwZswemLtfVAJZ/ureC6GWlkRvjp+OBZlNBobPO+iaJe2qm6GhqNfekPMMRl0rHmOfx73+ObS0cxYkgEf195gNzD1b38V4C3rnPpi/Ck/nVeeLnCnJG0a2b8jTIiSYgLcu9djd8awbJCJ4smpRBqN+sd6aIMS4sl3ziU8No9eDraLuk2KuvaiDM0AgyiRpKdSl84atPgK5RCiOBzqLgeu8VIckxIv57W1kWNSkWNSMCbt4mRqU48Xj/5pbJYcF86cKyOA4X13JVZDa5WzNnX6h3pBFVVWDAxmRWVifgtYbh3ruyT42h+H96y/RiTRrPnWD1p8aGEOfrX57ozpSeEUW5ORQPZva2PeYt24j26FXPO9ajh8azfWUZeaSNfmJel+/nB5+dm4fIb+MwyGV/5IXyl+3XN0526pg5eeO8QGYlhLJmaSPsHz6J53dgWPYBicVzWbSsWB7arv40xYwquLa/h3/oy9944hrSEUJ57ay/7Cup66a/opDZX0owDw2Xm7m/ioxxU+sLwDsI1ZKWRJC6Kr64EX+l+cpUxWC1mrpqSonekSxIxfi5mxcuRT9Zc0vUr6o/v2MbgaSSFO8zU4MTiaUJzt+sd5ywbdpXxvT9upKYx+LIJIXpf1/pINFX062ltXRRFwZg1DV/5IYZG+lEUOFgk09v6iqZpLNtQQHSokbT6zRgSRmCI028ERXdmjkvEYDaz25yDr3QfvuqCXj+Gv7oA3O14Y0eQX9rYb3drO5WqKAwfOoRiXzSeIlknqa9ong46Nv4L1ZmIefw1NLS4+M+6fEamOpkxRv8Fl2MjbCyYlMJLBfH4bE5cQTYqye/X+NuK/fj8Gl9bMhLPxufxVxdgm/sNDL20bIZiNGOd/01M4xbj2fcRyobneODGkcRH2nn2zd0cKWnoleMA2N01tJr7f/24WHFOO1W+MHz1g++LdmkkiYvi2bsaTTXxRlkii6cOwW416R3pkgybMJE6ItDyP8F/CW8qlXVtpFhbUOwRKGZ7HyQMPoqi4LZ1LkAaTPOANU1j+Yaj/PPdg+zNr+XXr+ykscWldywhRB9qaHFRWd/OsJQIPEe3AfTraW1dTFnTATAWbyctPkzWSepDewvqyCtt5K6h9dBWH1SjkbrYrUZmjk3g1eJENJMd92crev0YnesjKRz2JqBpMDajf09r6zJhWAz73Yn4q/LROlr0jjMgubYvQ2upxTLryygGI//+8Ager58vXjUcJUgWa18yPQ2b3cp6Xw7+qnx8xbv0jnTC+1uLOFjUwO0LhuIs2YA3bxPmSTdiTMvp1eMoiop12q1Ypt+O99hnqGue4ttLs3CGWHj6P7t7ZYdQt8dHlFaP1xHbC4n7l1injSpfOGpHQ1B+0d6XpJEkeszf0YznyCYOGoajWkNZMClZ70iXTFVVPKlTGUI5+3Zf3KLbmqZRUddOjNo4aEYjddEiOr8h8TcEx/BNr8/PP1Yd4O1PjjFzbAJP3HMFjS1ufv3qTlraPXrHE0L0kdPXR9qKIX5Yv57W1kUNi0WNzcCbv4kRqREcLWvC5ZZ1knqbpmm8uf4oMeFm0us/RY1KxZA8Ru9Y3VowMZl2v4ljYRPwHsvF18vvv76SfajRQ9hZ1IHdYiQjMaxXb18vw4dEcJQhKGh4g3BKU3/nqzmGZ+9qTCOuxBg/jF15NWw/WMV1M1KJiwyeL1jtViM3zMrg7YpE3NZIXNuWBcUC7IUVzby5/igTh8UwLbIO15ZXMaZNxJxzXZ8d0zx2EdYF9+CvOYbho1/yyJJkbBYDv3l1J2U1rZd12zUVFdhUT6+NpOpPukYkAfgbK3VOE1jSSBI95jmwDnwellVncO30VKxmo96RLkvqjIX4NYWK7Wsuaqhrc5uHdpeHMG/toGskOaIT8Goq3lr9F9xud3l55vXdfLKnguuvSOMr14xgTGY09908lsq6Np56bRftLq/eMYUQfeBQcQNWs4FkS/OAmNZ2KlPWdPy1xYyLcuHza7069UB0+uxIDYUVzdwxohWtsRJzzrVBM4LiTHGRdsZlRvFy6RAwmHDvfLfXbltzt+OrzMeQNJo9BbWMSo/EcImL+wYbo0ElMn04bZoZT1HwjEIZCDS/j471/4diDcMy9fN0uL28uPoQidEOrp6Wqne8s8wen0BCdCjvto3DX1uIt2CHrnlcHh9/eWcfoXYTX5wZSceaP6FGJGGd+zUUpW9ff6aMydiu/S5aRwuWNb/ifxY5URX4zas7qWm49NE0DaWdi9o74vrnkieXw2I20G7tnNLnbwyeGRuBEBTvFvPmzWPx4sUsXbqUpUuXsmHDBr0jiTNofi+efR9RpKbQYY9lbk5w7GpyOUyh0TSHZzHMvZ+DhT1fcK6iro1QpQOjr2PQNZJiI0Oo8oXRUVOia46GFhdP/juXA8fq+fLVI7hhVsaJk4BRaZH899IxFFY08+wbu3F75Nt8IQaaQ0X1DE2OwH+s84RgIExr62LMmAKKSnLLPgyqItPbeplf01i24ShxTisZdZ+ihMdjTAvu58/CySlUthmpjpmI98in+Ftqe+V2feWHQPNR58igscU9YKa1dckZFsdBdyLuwt1BtTZOf+fZ9yH+mmNYZtyBYnGwfEMBtU0uvnjVcIyGoDi1PI1BVfnC/CzWNiTTZonGvWMZml+/UUmvrc2jvLaN/1qcifrxnwCwXXU/iskakOMb44diX/oDMJqxrX+a715pwuX28atXPqO++dKWhmiv7vyC2ZkUfI3EQDCGx6MB/obBtU5S0Lzaf/e73/HWW2/x1ltvMWvWLL3jiDN4j25Ha2vg3cahLJmehtlk0DtSr4ieMI9IQyu7Nm7s8XUq6tqIHWQ7tnWJP75zm6bjGkllNa08/sIOKuvauf9z45g9/uxhtDnDYvjqkpEcKmrgj8v34vXpP4xZCNE7mlrdlNe2DbhpbV1UeziGpFFoBVvITAiVRlIv23agitLqVm4f40WrLcQ8/upL3mI7UEamOkmKcbCsunMxcPfu93rldr2l+8BgYldTBABj0gfWQrljMiI57EvC4G7GX1ukd5wBwd9Si2vbmxhSxmHMmExhRTMfbC9mTnZi5+YHQWpMehRjM2NY1jgGf30Z3qNbdMmxM6+GtbmlLJqUTPrR1/E3lGKbfw9qWGDXFjJEJGJf+kPUiARCtvyZ701rpanVw29e3Ulzm/vib7CpApdmwuaM6f2w/UB0VBj1/tCgWkM2EIL7nVMEDffe1dQpEVTaMro9ce+vLBkT8RqsxNXv5FhFz7ZZrqxrI8HUednB1kiKjbRR6Q/H2F6L5r2EN5rLdLi4gSde3IHH5+e7d+QwLrPzQ6+m+fEc+ZTqFX/Ec3QbmtfN9NHx3HXVcHbn1/LXd/bj98u3kUIMBIeKGwAY5XQNuGltXUxZ09Faapka20JhZTOtHbLmW2/w+f28tbGApBhH52gkhxPT0Bl6x7ogRVFYOCmFfdUKbQk5eA5+jL/j8hfI9ZXsw5AwnN0FTaTEhuAMtfRC2uBhNRvREkYB4C2W3dsul6ZpdGz8F6BhnXkXfk3j/947SKjdzOeuzNQ73gXdMjeLHe1DaDTF4tq+HM0f2BHrja1u/rnqAMkxISyN2I/32A4sU2/FmDw6oDm6qPYI7Nd9D0PyGML2vMoPx5dSVd/Gby9haQhLexWNBmfQThHua3GRNiq8YXjrB1cjKWgWuXnkkUfQNI2JEyfy8MMPExbW88X+oqJC+jBZ4MTEhOod4YRTs3SUHqa56igftU7h9qWjSEwI1y1LX/CNmcX4nWt4Z/tRJn/l/KPhYmJCqW91M8zRhmKwEpuWqlvR1OP5Eq1p1KuRKGiEq81YYtIClmXjrlJ+++pOYp12fvy1acRHOdA0jfa8XOrWvYS7qhCX0Yzm/QjFYscxfCrXjpmFwTiCf648SESYlftuyQ7o4xWsr2m9SZbuSZbunZmlaEMBVrOBDP9RGoG4iXMwhgUmb6DuF3/YHAo3Ps94UwH/0lKpaHQxLeX0aUfB/Bjp6XxZPtpWREVdGz+6Phr/xoNELvgSEfF9N52rN++XJXOyeOPjo6z1jONa73ZMRz8mcs5tl5zF21RLc0MZoePmkvd2IzfMyQzY4xjI58uUySMpec9JUtFukhedfX/1l+duoHWXpeXgJlqKdhI5/0tEZGTw1vp8Ciua+Z87J5GWEvyvo5iYUK6ekc7r20bxVc86rOU7CMueH5Asmqbxx7e24HL7+O4SA94PlxMydg4x826+5M+lvXO/hKLd8UNq3v0z7FrN42Om8sM9w/jjW/v48dem9Wg9XE3TCPfW0RqRERTPYT0yDE2N4sCWMEY25RMd7Tix1lUw3B99KSgaSS+99BIJCQm43W4ef/xxfvrTn/LrX/+6x9evrW3p96MNYmJCqa6+/G+XesOZWdo2LKcDMwX2MXwxNSKgOQNxvyjp0zHt+gDPkc3sPjiUhCjHebMUVTQz29CIEh5PTY0+W8rq+XzxOuLAC7VHj2BSowKSZfXWIl5dk0dmcjj33zwOg99P+e7tuLe+jq/yCEpYLNZ53yRhylwqdm/Hk7eZlgObadm9lhxbOM7MEby+o4ZnfX5uXTA0IM2kYH5N60mydE+ydK+7LDsPV5GZGEbzvg8xxA2l3mWGAOQN9P1iGJKNqWQHVmMqW3aXkRl38kuzYH+M9HK+LF6fnxffPUBqXCgxxWvxWRy4U6b3Wfa+uF9mj09g1aZCFo8dT8O2d/FmzUcx2y4pi+dQ59SevW2x+Py1ZCUE5nEM9PMlIz6EDz1JJFbup6q0EsV8ckex/vLcDbTusmjuNlrf/RtqVCru9NkczKvmX6sOMC4ziuFJfZe9t++XRZOSeXR7OtWGAygfv0pHfA6KwdTnWdbklrD9QCVfvSIc/8d/RY1Jh8l3XPJ5RG/fL9qUuzAbw2DHcn6cVsdjRyfyk79u4v6bx11w3Suj6idCbaXJEav7c1iv15HNAFW+MBSfm6pjRaghgTk/CgRVVc45aCcoprYlJHRODzKbzdx+++3k5ubqnEh08bfU4c3fxqftWVxzxbCgXETvcqkx6WjhCUy15PPu5vPPoff7Narq24jUGgbdtLYupqhE/JoSkHnAfk3j5Q+P8MqaPCYMj+GRL2Rjayun7d3f0v7OE/ibqrDM/CKOW36OKWsaisGIMXk0tiu/Sshdz2Bd8C0McVmkNu7g2+GrmJL3LLuX/XPQLYYnxEDR3OamtLqV8XE+/HXFGDOn6B2pz5iGToeOFq6Ma+BAkayTdLk27i6nprGDWybY8BV+hnn0goAtbttb5k1IRlUVNpMDrlY8B9dd8m15S/eh2MLYUWnGajaQmRTY0eaBEmY30+wcjoofb+l+veP0W66tr6O1N2Kd/WVQVF5cfQgNjTsXDutX05lCbCauvyKd1xvGoLXU4jm4vs+PWVbTyqtr8piQZmd86asoRjO2hfehGM19fuyeUhQFy8QbsM6+m5DGfH6U/DFFBSX85e19+C6wMHl5QT4A1ujkQEQNSrFOG1W+zho6mNZJ0r0r0NbWRnNzZ7dO0zRWrVrFyJEjdU4lurj3fYSGxiFbNlNHxekdp08oioJ1xGzSjNXkHzhEXVPHOS9b29SB6nNj8zYO2kZSTGQotf4QvHWlfXocj9fHc8v38sH2YhZMSuYbc2PxbfgrbW/8P3yVeZinfB7Hbb/EPGoeinr24ErFaO7c5nTRfYTc9Qzm2V9BsztJq15P62uP0rrsJ7h3v4+/VU7QhOgvDhd3bnQwiqPAwNqt7UyG5LFgcTDRXEBpdStNrYFfl26g8Hh9vPPpMTKTwkir/RSMFsxjFuod66I5Qy1MGhHLO4cVlPgRuHe/j+a7+PWzNM2Pr3Q/hsRR7C2oY1Ra5ID8orBL4oixtPtNtOR9pneUfslXmYdn/1pMoxdgiElnx6FqduXXcsPMDKIjLjwiLtjMm5hMfUgmxcTj+uydPl3z0+P185e392E1KXzRsQGtpRbrwvtQQ4Jzh0TTiNnYFj+I3V3HD2M/oPjIEf7v3YP4z7PrYUNJAQDhiYNzxzYAk9GA2x4NDK6d23R/16itreWuu+7iuuuuY8mSJRQUFPCjH/1I71gC0Lwu2vetZY87hXmzs1HV/vONw8UyDp2OpqhMNufx/tbic16uoq6NGMPgXGi7S1yknQpfOJ4+bCS1tHv49Ss72X6omrtmxXGTdTPt//k+3oJczNlLCLntV1iyr0Ux9mxhUMXiwDJiDul3/Zg3o77BsrZJNLe6cG1+mdaXHqZt5S/xHFyP5m7rs79JCHH5DhXXYzKqhNXsxhA3dEDt1nYmxWDElDGZmJZDmPFwUEYlXbJ1O8uob3bx+UkRePM3Yxp5JYq1f66vuXBSCu0uH/tDpqK1NeA5/MlF34a/rgStvYnmiCxqm1yMyQjOk9rekjMsjsPeBHwle9DOc0Iszqb5vXSs/z8UhxPLpJto6/Dy0oeHGRIXwsLJ/XMEitGgcsvcoSxrGgdtDXj2r+2zYy3bcJSiqha+PbwApXwflivuwhg/tM+O1xuMKeOwX/89rEaN70SupuLALl758Mg5XzsdVSX4NAVnYv98PvQWhzMaFyb8jTIiKWBSUlJYvnw577zzDitXruR3v/sdsbGB3QJRdM916FOM3jYO2CYwYfjA3s5RtUdgTBnLdPsx1u8qPufWlxV1bcSdaCQNnN3rLkac006lLxy1uapPdryoaWjniRd3UFFezf+OK2LSwWfwHPgY08g5OG77JZYpn0OxdL+O1YUYVJW7bpxKTeJMflCygLwJ38Y84Xr8zTV0rP8HLf+6n/bVz57Y+U0IEVwOFzUwMd6HVl8yoKe1dTFmTUfxuZlgL+VgoTSSLoXL7WPlpkJGDIlgSO0mUBTMY6/SO9Yly0gMIzMxjDePWFCj03DvehftAlNPzuQr2QfAvvZ4AMamR/V6zmASHWGj0pqBxdOEv75M7zj9invXe/jrS7BecReK2cYbH+fT1OrmS4tHYFB1P428ZNlDozEljiDPl4hr5wo0z7lnI1yqA4X1vL+liDuHNuAsWodp1DzMI6/s9eP0BUN0GvYb/hdzaATfCvuQmj0bWb6hoNvLKk0VNCphGEzBM1VPD3GRDqp94TIiSQhN02jKfZdibySTZ1+B2o/mP18q0/BZ2P0tZFDKRztKur1MZV0bSeZmUBTU8MHZ8IyLtFHhi0DRfGhNVb1624UVzfzyX1vIdm3jp1FvEV3yMcb0CTi+8ATWmV9EtUdc9jGMBpVv3TiWocnh/HFNDQciZuH4wpPYb/h/mEbOxVd5hI4P/0DLvx6gfd3f8Zbuv+gP6UKI3tfa4aG4qoWpjs76PJCntXUxxA9FcUQyM6SYA9JIuiRrcktoanVz89QYPIfWYxo6I2inlfTUwskpVNV3UBY/G62pEm/Btou6vrd0H2pEIjtKPCRGO4gK719rRV0KR2Y2AC35Mr2tp/xNVbhz38KYNhFjWg55pY2s+6yU+ROTSU/o+e7awUhRFG6dP5QVreOhoxn33g979fZb2j38bcV+xka2MbnhPQzxw7BMv71Xj9HX1NAYHEt/iCkug6+ErKc5dxXvbj521uXsrmpazQO7Gd0TcU4bFd5QfIOoWS2NJNEtV9FebO1V7LdNYHxWtN5xAsI4JBvFEsJVUcV8tKOEdpf3rMtU1rUxxNqMEhrb410eBhqH1USTsfMNw9fQe8VyT14V6177Nw+aX2OxeQemxGHYb/4JtnnfRA3r3aadxWTggc+PJyU2hD8u28vBogYMsRlYZ9yB446nsF3zCMb0iXgLttG+8pe0vvQQHZ/+G191gQyLF0InR4ob0YCU9kMDflpbF0VRMWVNI9lbSEtD/XnX8BNna3d5WbW5kDEZkSTXbgGfF/P4a/SOddkmDIvBGWph+bEI1PB43DtX9vi9SfO68ZUfgoSRHC5uYOwAn9bWZfToLMq9EdJI6iFN0+jY8DyoBixX3InX5+f59w4SEWrhxlkZesfrFUPiQkkeNY59nmRcO1f12vIGmqbxwvuH8Lc18mXbRyjWEKwL70UxBMVm6RdFsYZgv/Y7GNImcaN9O96tr7Iu9+QSIB6PB6fWiDdkYK6jezFiIztnbNBWj+Zx6R0nIKSRJLpVvXkFTX4rI2cv6le7MVwOxWDEOHQ6ad58NFcr63ed3SSpqGsnVh28C213UcI7h8P3xhBxTfOz56N3cXzwGDdZNxESk4Dt+u9jX/wQhqghl33752KzGHn4C9nEOW387o3d5Jd1LuKrqAaMyWOwXflfhNz1uxM7v3n2r6Ft2U9ofe17uHYsx984eIauChEMDhXXk2BqwtRSNiimtXUxZk1HwU+O+ZiMSrpIH2wrprXDy03TEnDv/whj+sQB8f5tNKjMm5DEgaIGWjLn468twleyp0fX9VXmgc9DqSkVr09jTMbgGEmQHOOgQB2Co+lYn0xjGmi8eZvwle7DMuVzqA4n728torS6lTsXDcNm6X8NkXO5aXYGH3pyUDxtuHe/3yu3+eneCnIPVvBw4hYM7hZsi+5HtfXfEVydu8zdg2H0Qq60HsDw6V/ZsruzmVRTWoJR8WNyDs7lPk4V57RR5e98nAfLOYI0ksRZWiqLiWg8xEHLeEZlDOy1kc5kGjYTxe/j6rgK3t9ahMd7ckqTy+OjvqmNUF/9gPggejkioyJo1ByX1UjSNA1P4U7KX/geafmvoposqPPvJ/TGH2KMH9aLac8txGbi27dmE2Y38fRruyiuajnt38/c+c0y+yuoDifuHW/R+urxnd/2vI+/rSEgeYUYzA4VNXBlZOeHs8Ewra2LISoFxZnEFOsxWSfpIrS0e3h/WxE5Q6NJqNsO7nbM2Uv0jtVr5mQnYTaqvFeVgOKIxL1zZY+u5yvZC4qB3IYIzCaVYckRfRs0SCiKgpo0BgM+2gr36h0nqPnamnFtehk1NgPTyHlU1bfx9ifHmDgshpyhA+u8IDzEQvaUiex0D6Fj13toHS0XvtJ5VDW08+IHh7k7dhcRrYVY59yNISatd8LqSFFU7FfcgWHKFxhvLsK68Vl27TtGU1kRAI74vvvit7+IibBR4w8HpJEkBrH9K1/Dq6kMmXntoBmN1MUQnYoaNYSplnwaWtxs2neyEFTUtOJUWzFoPgyDvJEU57RR5g275HnA3oojtL39BB3vP42rrZXNziUkffkXODInBPw5FxFi4ZFbczCbDPzm1Z1U1nU/tFmxODCPmIN9yXdx3PFbLNO+AJof16aXaX3pIdpW/grPoQ2y85sQfaDd5aWwspnRasGgmdZ2KtPQ6aQaqigvKpTptT30/tYiOlw+bpyRjGfP+xiSRg+IE7ouITYT08fE8+n+GrQRC/CVH8JXceSC1/OW7kONy+SzYy2MHOLEZBw8pwKpYyfg0ozUHtiud5SgVvvRC2iuNqyzvgKKwgvvH8KgKty+MDBf8gXaoskpbFIno/hcuHauuuTb8fn9/PWdfUw1HWasdw+mcYsxZU3vxaT6s2dfjWHONxlirCVk/W9pPJILQFRymr7BgoDRoOIPiUGDQbPg9sAZm9iP+fw+dq1dQ2Od/t80KppGdNk28s3DmTR0cHaXTcNm4t/0bybGunl3cyEzxyagqgol1S3EGTqnPw32EUlxkXYqfRGMaMhH03q+ELWvthjXttfxFe2iTbGzonUq0RMXcP2sLF2bljERNh65NZsnXszl1698xqN3TDzv4qOqw4l53NWYx12Nr74Mb/5mPHmb6fj477DxeYxDsmkePRVPu/4nfB1uL0XbPbS2Bcd87crYaGKyxqIaB+caY8FMc7fjry+lvdWIZktGUYPnI8KRkkailSZCXZUYM+brHSfgTJnTcG99nUz3Yaoa5hAbq/80Cc3dhq+ulPZ2C5olGSWIdnBqanXz4fYSJo+MJbZ+J672Jsw5A2c0UpcFE5P5eGcZG9qzmG0JwbVzBfbFD53z8v72Jvw1hbhHLaF6fwdXTRlcn/OyhkSx3Z9ISuX+oGnItrR7KK1uobiunRCTSkSIWbfPQ1pHC97i3XTsXoM5+1oMUSl8urec/cfquWPhMJyhFl1y9TWzycCcK6ew46NcJuz9APO4RZe0ucuKTwvxVeZxY/hmDEljsEy5pffDBgHH8Gn4LaGErv4dsa5dtGg2EsLD9Y4VFCIjw2hqCCWqoVzvKAERPJ8SB7HyI4cI//QPhOod5Dg/CjHTrtM7hm6MQ6fj2vIqS+JK+ckeM9sPVTFlZBxl0kg6Ic5pZ5cvHMXnxttUA9jOe3l/UxWu7cvw5m1GM1nZqE5jRW0Gty4ew+zxwTGvOiHKwbe/kM0vX/6ss5l050TCHRfeytTgTMQw6SbME2/EX30UT95mvPlbqC4Inm88ncf/FxQOQu16C96kHGJy5mBIGI6iBM8J6GCg+X34Gyvx1xXjryvBX1eCr64ErbkagDZAsYRgzJyCMWs6hrhM3R+jQ8X1TLAUAmDMmKxrFj2oodH4ojKZ6D3KgWN1jBkWuIVNNb8Xf0PF8edKMb7jzxmtpRaAdkCxhWHMnIopazpqTLruo5lXbS7E7fWxdMYQ3B8+jxqbgSFhhK6Z+kJSTAij05x8uLOKudMW4M1djq+uGENkSreX95XuB+CwLxHoGDTrI3VRVYX2qBGENKzGXVcGAWzIerx+ymtbKaluoaTq+P9Xt9DQ4j7tciE2E8kxDpJiQkiOcZAcG0JStAOrufdO2TSvG39D+fHaf/J9QDs+Td8UmYh5wvU0t7l55aM8MhLDmJuT1GvHD0ZTRsbyx20zyOn4N207VhAy686Lun5+aSPrP93HdyM3YAiJxjb/v4Oqud7bQtNG47v2ezSs+DVN1gQG91nRSXFOOxU1oTgbpZEkAiR5+Ch8KU9RVVGrdxQA4hJiCLcN3s6yag3FOCSbyIpdJESOYNWmQiaPiKW0uoUUSwuKLQzFGqJ3TF3FOm2dOxMAnuoSCB/a7eX8bQ24c9/Gc+BjUFXcwxbwzIEEqtpU7vncGMZlBteOgKnxoTz4+XH85tWd/OaVnXz3jhwc1p6NnFEUBUNsJobYTLRptxJhaKGu7vLm2l8Kr9fP9oNVrNtZSku7l5GpTq6ZmY6nm10I9dBcU0F17jqGl2ynvXQzPms41mHTO09Ao4bofgI6kGiahtbWcFYDwN9QBr7jzwdFRQ2PxxCTjjp8FobIFMLCLNTmrsNzaCOe/WtQQqIwZU3rbCpFJuvytxwuauA2exGG2ME3ra2LfcQMEmr/RW7+YWBkr9++pmlorXUnGosnGo0N5eD3dV5IMaBGJHROLxw5F0NkMqEOI3W5a/HsX4tn7wcoYXGYsqZhypqmy5cu9c0u1n5WyozR8UQ37qOjuRrL9NsGbG1ZMCmFZ17fzV7TOEaY3sO9cxW2ed/o9rK+0n1gtrOlwkZcpEpsxPm/BBqIokdOhE2rKd+zleQRvd9c9GsatY0dxxtFrZRUdTaMKuva8R8fBWU0KCRGORiZGklKbGfDKDoqhL1HqiipbqW0uoWNu8txeXwnbjcmwkpyTAhJMSEnrhPrtGE4T7NC0/xozTUna3/X67qxErpGkxuMqBFJnVM/o5JRI1OIG5VNbZOX197fT7vLy5cWj0BVB+brp4uiKCxeOIVtb2xh8sG1+HOuQQ3p2Y6G7S4vf39nF18L/xib6sW26H4Ui6OPE+svIikN25d/y6goO80twfEZU29xThsV3jCGNQyOXZ6lkRQk4lOHYLAHx4fjmJhQqqub9Y6hK9PwmXiP7eCWYR08s9nFvoI6yqpbmWxqGvSjkaBzx7NWS+eCi+7asxtJmqsV9653ce9dDT4fphGzKYm/kmdWFmJQFb57+3jSE/SfmtGdockR3HfTOJ55fRdPvbaLb38h+6J3KFFUA+boZAxa4F5Hfr/Gpn0VLN9QSG1TByOGJPNfczLJTAoPqtf0uKk5lE2YwfrtBRzdvoEx7jxG7V6NZ/d7qBGJGLtOQMNi9Y7ar2iejrMaAL66EnC1nriMYo9AjUzGlDQKQ2QKamQyakQCivH0kXeOmFDaIkehudvxHsvFk7+58/W8cyVqZDLGrOmdj1FIYEYzuNw+WqtKiAmrxZhxVUCOGYyMmVNo/+Qlwqo+Q9Ouv6zb6pqWduaoNE5Z401xRKJGJmNOGdf5XIlKQQ1POGsL65CYUNqjx6C5WvEW7MCTtwl37tu4c99CjU7rbEJmTg1YA3DlpmP4/RrXXZGG+6OfozoTMaZmB+TYehibGUWc08b7u+oYO/JKPHtW4590E2rY6Ysia5qGt2QfasIIDuxpZE6QjAYOtGEjsyj6JBytaPdl31bXtLSS6q6RRi2U1LTicp9sAEWHdzaAJg6P7RxhFBNCXOTZDaCYmFDiw09OHfNrGjWNHZQeb0R1HWNnXg1d56dGg0pitJ3kmBDSnCqp1kZiqcPcWtH52q4vg1N2qFNCYzBEJmNMn9T5eo5MRg2LQ1ENp2VRLTYOFBbxyZ4Krp42hJTYwfHlaWZiOJuT5qFV/42mLcuJmH93j6738geHme/5mCRLNdZ592OIHNijt05lsZix2mw0twTHZ0y9xUXa2eYLQ/G68DXXARee2dCfSSNJiG4YUsah2MLIcu3FGTqJlZsKKa9rI9LWgBo+XO94QSHc6aStzUZodQlkdP5O87pw7/0I966V4GrFmDkNy6QbyS1X+Mvy/USFWXjoC9lB/y3o6PRIvrl0DH9ctpdn39jNQ7eMx2Q0XPiKOtA0jZ1Hanhz/VFKa1pJjQvlS1cPZ3RaZNB+A28yqsyflsmMnFQ+2FbMY1sPM1I5ylxzKTHb38S9/U3U2ExMWdMxZk7p19vm9raT09JObxh1TUsDwGRFdSZhSp/UeaIQmYIhMvmiR1IqZhumYVdgGnYF/vYmvPlbO5tKW/+De+t/MMQP62z8ZUzp01GaeaWNjDUeAwbntLYuqjWU5ohhjKnNp7CiEUcPatLp09JOTmPpmpYGgMmGITIZU+bU48+X5M7ny0V+o65YHJhGzMY0Yjb+1vrO50veJlybX8G1+VUMiSM6ny/pk/rs2/qqujY+3lnGrHEJRDYdpr2uBOuVX9N9amZfUhWFBZNSeOmDw5RfMZ1o5QPcu9/FOvOLp11Oa6xAa62jJmUuHq+fsZmDa1pbF5PRQK0jk6y2XXhdHRe+AmdMSzulaXTqtDSH1UhyTAgzxySQHNvZMEqMdlz0F1FdVEUhNsJGbISNnGEnm4Lujg6qCo/SWFqAt6YYU0s54aXVhJWdbAI3ahYajNG4HGMwJqcQkphGbFomNkfP6rTb4+OF9w4SE2Hl+ivSLyl/f7V4/gS2/mso0/I34p983VkN2TNtP1iF4chapjjyMU+8AVPahAAlFcEoLtJOVdeMjboycKTpG6iPSSNJiG4oqgHj0Bl49nzAtTmLeHF9OQ6lA4ulHdUpI5IAYiPtVLaE46wtweT34jm4AXfuW2htDRhSxmGZfDOG6M5GwSsfHSEjKYz7bx5HqL1/dOcnDIvhq9eO5K8r9vOn5fu458YxGA3BdTJyqKie19flk1/WRJzTxjeXjmbSiFjUIG0gnclmMXL9zHSunJDEqk0Z/CK3hAilhc+l1THCfQT/py/i2vRvDEmjOptKaRNQzMHdhOwtp09LO2Wk0QWmpamRySihUb1+4qzawjCPWYB5zAL8TVWda4Hlbca18QVcn7yEIWVM52OUmoNi6t0FWQ8V15NjLoTYrEE7ra2LfcQMTJsPkr99G+OmTTvx+85pafWnT2HsmsZ4nmlpalQKiqP3m86dGxJchXncVfgbyvHkdW5I4Fr/T1wb/4VxyHiMWdMwDhl/1oi4y/HKB4dQFIUl01NxrfsNSkgUxqypvXb7weqKsfG8uf4o7+9t5svDrsBzaD3mCdeftmCwt2QfADtbYzEZ2xmeEtH9jQ0CtoxsTPtyyd++hchhJ0/8z5yW1jXaqKK2rdtpacmxDlKOTzfr7UWyzzctLVzzEw4npqWpkTl4QxOoUaIocodTUK9QUtNKaWkrrmM+2FEL1J6YHpccE0LyeabHvfbRYSrr2/n2F7KxmILzS7S+EhVuxTvyanx5R6jZ+B9ir7nnnJeta+pg4+o1fMWxHTU1B/OEyxspKvq/qDALNdrxRlJtqTSShBisTMNm4dn9HtNshSy3OYj1VAKy0HaXOKeN0rwwMiqP4X7tB2hNlahxWVjn/zfGhOH4NY1XPjrC6m3FTBgWw9evG4W5n30gmT4mng63l3+tPszfVx7ga0tGBcU6AYUVzbzxcT57C+pwhlr40uLhXDE2IegaXT0VZjdz6/yhLJyUwlufFPDnPeWYTWncPM7KNEch/oItdKz7KxjMGFOzMWVNx5Ay9qwpNv2Vp6ONwj15tBYfRW0qRW0sw9BUhuI+OS3Nbw3HH5aIP2MOvrAk/OGJ+EPjwXDGGl5uoLb9svJoBgOapp2zIamGxWKZcD3mnOvw1xadWGC+o2gXGC0Y0yZ0PkbJo3pl57fKYwUsNNZjybz6sm+rv3OOnErdphdQD35EtdqA2lSG2lja+XzxnHzc/TYn/rBEfFkjOp834Un4Q+PgzMejA+ho43J0+KGurvU8lwiDtEWQuhC1vghT8Tb8JdvxHtuBZrTiTczGM2QyvphhcBkN0KZWNx9tL2ZeThJhbUW0V+ZhmXFnUO0+2FesZiOzxiXw4fYSbrlzPqZDG/DsWY1l6sldo3yl+1BCY9harDF8SES/ez/uTRnjJ+Ha+zxl29ZxoAoq69uoqGujqq79tHWJnKEWhkbamZVqIy7STrzTQVS4BfW0xosLvC78DZeXqb3Ng/vo4ZNfHNSXnntaWtfU5PDTp6WFAunAnOM/nzk9rvh4c6y76XGnNsTeWHOEaaPjGJ3eszWCBpr5s8bw8eFRTCvZhq++DIPz7Gmgfk3jtXc2c6tlLYTFY5/79QE98lH0jEFVMYdH4cGEu7YUBvjGmAP/3VWIS2SITEKNSUfL/4QFE79C5bbOb/OkkdQpzmlnpy8SzXMYNdSE9aoHMQwZj6IoeLw+/rbiANsOVjF/QjK3LRgaFA2YSzF3QjLtbh+vr8vHYjLwpcXDdZsyVlHXxrL1R9l2sAqH1cgtc7OYNyFpwJwQRIVbufuakSyeMoRlG47y7+3VvG1LYMn0B5iT1IZWsBXv0W14j24FiwNT+mSMWdMwJAzrFx/gTp2W5qsrprm0AF9dCQ5vAwBWoEMzUuqLoNybSJkvgnKfkzJfBG2aFcpOvbUyzvhFr7KYDSRHO45/ax1yYhehENvJxpWiKBiiUzFEp6JN/Ty+8sN48zbjKdiGN28TijUUY8bkzoXU47Iu6XXj8viIbNgHVjCmT+rNP7FfUkwWyh3DSW3bBzuP0O43UeKLoMyXQrkvgjKfk3JfBO11FijtupYGlBz/XzBIQSWJocYKJloKGF+4A3vRZhr9Nj5zp7HDlU6RLwq4+OeL2WTg2umpuDf8AcUaimnErN6PH6TmT0zmg+3FrDni5dr0ybj3r8WcswTFbEfzefGWHcCbMomKwrYBvwPXhThC7BwzDSG1dTfsP2WtpO5mXDYe/19B5489mwx38brauYolpHMtu2EzT0wzVZ1JlzQa91zT4zxeH2U1bZRUt1Ba3UpxdQt7j9Xxyd4KoHPnuFvndb+JymBgNRsJm3I9nu0HKVv3Kik3PnTWZT7clMfcxuVYLAqhVz84aEZLiwuLc9qpqo/E2dZ8Ce9i/Ys0koQ4D9PwWbg2vsDiWQpt/lA4YkYJ0AKzwS4u0s4WVybz5k8lfcz4E9uctrR7+P0buzlc0sgtc7O4akpK0K7V01PXTEul3eVl5aZCbBYDt8y9tJPiS1Xf7OLtTwrYsKsco1FhyYw0Fk8Zgt06MEt4YrSDb904lqNlTbzxcT6vrMnjgzAL189cwPTbb4WyA8enymzCc3AdiiPy+Nbj04Ji5zdN09DaGzunItSeulta6YlpaX5NocEfRoXfieIcQ/zQYTQbY/BYIkBRcQJOYLQO+VWTkUNHaympbmH7wSo+3nmyYRURYj6tuZQcE0JClAOTUcWYOAJj4ggsV9yBr3hv5+NzaEPnzm+h0Zgyu3Z+6/lJ7KHCOsYZj9ERnkZoD3fQGehSl/wXNQWHqCcCryUCFIUoIAoYo0OesFAbTc2XMgpuLADHfB5C6g4QVr2T2XUHudJ6AJctmqaYbJpisnHbz79GyalGZETjaC6hrXg35sk3oxh7d5plMIuJsJEzNIaPd5Zy7a2L4ehW3PvWYMlZgqssDzwdFNK56+JgXR/pVElLvklr+WGsBgW71aj7+0ZETBTNhigUe0SfZzEZDaTGh5IaH3ra71vaPZRUtZCW4sQa/N/N9KmpE4ay5rNxTKnKpaOyEGtc6ol/K6powv7Zi8SbGrEvehg1PE7HpCLYxEXa+b+iWfxp7iIa3Re+fH82MM9ChOglpsypuDb9G3/eJ4R46nBFxPeLkQ+BEOu04cPAMX8cGcebSDWN7Tz12i6qG9r5xvWjmTpq4Ly53jQ7gw6Xj/e3Fneu7ROABShb2j2s2lzIRztK8Ps15k5IYsmMNMId/WOdqcuVkRjGd27LYd+xOt5Yl88/Vx3kvS12bpqdwYS5XwevG2/hZ50Niz2r8ex+t3OHpsxpnaNgLrBIZm/QPB3460s7m0W1J3fA0lwtJy9jDafeGE2efzSHW0Ko8DuJSkln8pgkJg+Nxmo2BtXOejExoUzK6jzR1DSNhhb38TVDWiip6lxo9mBhMV5f59wIVVGIj7KfGLXU2WAaQdT8bKyejs6d3/I24d61CvfOFaiRKcd3fpt6wZ3f8vYdIttYjzJs8O7WdqbQCCcZixYG1fPl8rMkAwvRXK14CrZjyNuMpegjYoo+RI1J72xCZk654BpZMTGhFL38ZzBZMY+ad5mZ+p+Fk5LJPVzNlgoLU1LG4tm7GvPYRbQV7AIUNtc5iQ73EeeU0RMRsXHEjM4KmteRPSaUVp2zhNhMjEh1BtX7kV5URWHIlTfR8eEeata+TMatjwKdC5HvfvtfzDEVwoTPYUoZq3NSEWzinDaqPHaaNDvg1TtOn5JGkhDnoVgcGFMn4MnbhN9iQ43J1DtS0LCYDDhDLZTVdK6NUVjRzNP/2YXb6+fhW7IZkTqwFsVVFIXbFg6lw+1l+YYCrGYjiyan9MmxXG4fq7cX896WIjpcXqaNjueGWenEBPlud31ldFoko77kJPdwNW+uP8oflu0lPSGMz83JYGTWNExZ0/B3NHdOe8vbjLtr57e4rM6txzMuf+c3ze/D31R5ctHT4yONTtstzWhBjUzCmD4BT0gih5rsrC9W2Vva+ZVUVlI40ybGcceIWML6yaLziqLgDLXgDLUwNuNk08fr81NZ3358MdrOBtPRsia2Hqg6cRmr2UBSjIPkmCiS425lyFA/8S37oXAb7q2v4d76GoaE4Z2Nv4zJ3e785s3bCoB96JS+/2OF7hSLA/OIOZhHzDm+89uW4zu/vYxr8ysYkkZ2NpUyJqGY7Wdd31NXjrdgG+ZxV/fZznDBbFhKBENiQ/hwewkzr7mG9hVP4jm0Hk/hLpToNHYVdDBjbLzuo2+E6A+GZSWzYfMksps20VB0mJiYiaxf+R6ztG20JE4kfuK1ekcUQSgusvO9qaymhYRwq85p+pY0koS4ANPwWXiPbsXnasU8dKbecYJKnNNGaXULewtq+cOyvTisRr5/6wSSYvpuK3A9qYrCl68ZQYfbxysfHcFmNjBr/NmLMF4qr8/PxzvLeOfTYzS1usnOiuamORkkD9D782IoisLE4bFkD43m0z0VvPVJAb96ZSej0pzcPCeT9IQwzKPmYR41D39zDZ7847uKffIirk//jSF59Mldxc6zlsFp09K6dsCqPX1aGopyfLe0NNThM1EjUzBEJuO2ONmZV8vm/ZXs21yHz+8lMdrBTbOTmToqbkA1Ao0GlaRoB0nRDqaMPDnysN3lpbTm5PbYJdWtZ0yPM+MMnceIyFlMMBcwpGE/1o3P4/r0RQzJYzsbf2k5KEYLHq+fxNb91DmSZVrbINS589tizOMW42so61x/K28zHev/AZ+8gDFlPMah0zGmjDux81vDpuWgGjCNXaRveJ0oisLCySn8feUBDnVkkRaXhXvnKrS2BprS5uHy+BibLtPahOipYYs+R+ubuTSte4Vmn5HxlctpssSTfPU3pCEruhXn7GwklVa3SiNJiMHOkDQaxeFEa61Hjei9psFAEBdp59O9FTzzn90kRDl46JbxOEMH9poUBlXl69eP5tk3dvN/7x3EYjacdiJ9Kfx+jS37K1m24Sg1jR0MS4ng3pvGkpUU3kupBw6DqjJrfCLTRsexNreUFZsKeez57UwcHsNNszNIiHKghkZjyV6CJXsJvrrikyega//SufNbWg6mrGn4Qibgq8o/Y3vlErSOk0P6FXtE58KnSQs6Fz2NTEaNSDxx4ur1+dl/rI7N6yrJPbIft8dPZJiFRZNTmDY6nuQYx6D6sGmzGMlKCj/tuXva9LjjuweVVLeyrSQNr28ISYY6JlmOMbnoCKFFO/GpJtpixtIclk6ioZ7q5MGzYLLoniEiEcOkmzBPvBF/dQGevE1487fgPbYDTDaM6ZMwDhlHx551mIbNOm3b+8Fmysg4/rM2jw92lHDvlGtpf/8ZAA644jEaFEakRugbUIh+JC42im0xMxhRu5am1b/GpxiJu+HbJz4DCHEmZ5gFk1GlrLoFsgZ2414aSUJcgKKqmIZe0bm2h+zYdpr4SDser59RaU6+deNYbJbBUVJMRpVv3TSW3766k7++sx+r2cC4zOiLvh1N09iVV8sb6/MprW5lSFwID101njHpkYOq+XApTEYDi6YMYdb4RN7fWsT724rJPVzNzLEJLJ2ZTmRY57dAhsgUDFNSME++GV9lHt68zXjzt+LN30Lh+6fcoNGMGpmMMS3n5NbKkcmo1tCzju3XNPJKGti8r5JtB6toaffgsBqZMTqeqaPiGJoSgSqP3wnnnR5X10ZJdSsl1RN4o6oZpSaPoR2HyC7fS2JlLgAx467QK7oIMoqiYIjNwBCbgTbtVnxlBzqbSgXb8B7eAIqKefzVesfUlcmocmVOEm9/coyaeVMIdSajtVSzsdzO0GQbVvPgeJ8WoreMWnQzTf/ehIN22mc+gDmi79dfFP2XqijEOW3UNFzKJhT9i7ybCNED5vFXE5aYQkdkst5Rgsrs8YnERYcwJjUCo2FwLUJuMRl44HPj+dXLn/GHZXt5+JbxDB/S83WhDhXV88bHR8krbSTOaeObS0czaUSsNCAuks1i5IZZGcybmMzKTwtZ+1kJm/ZVMm9CEtdOTyX0+FpEiqJijB+GMX4Y2ozb8ZXsxeaqps0UiSEyBSU0+oIL6ZdUt7B5XyVb9ldS29SB2aiSPTSaaaPiGZMROeheA5fLaFBJigkhKSaEqXSN6svunB5X0UhZfi4RIUbSowfOov2i9yiqAWPyGIzJY9Bmfglv0U7Cwx20hcXqHU13c3OSWLmpkI92lHHrvG+gtTdQ9GI1t4yTzzBCXCxHaAi1M7+FwayRMHSc3nFEP/DFxSOIiR74y1JII0mIHlAsDsJyFuAa5LtYnMlmMbJwauqg3d3DbjXy8BfG84uXcnn69d1859YcMhLPv6hzUWUzb3x8lD1Ha4kIMfPFxcOZOTZBmhCXKcxu5rYFQ1k4OZm3NhbwwfZi1u8qY/GUISycnHLaaDlFNWIckk1ETCieCzx3axrb2Xqgis37KiipbkVVFEalO7lpdgbZQ6MHzSi8QLJZjGSlRkHqQtk9SPSIYjRjypiCIyaUNnm+EB5iYeqoODbuKefG2TM42GAHqhmbIWuNCXEphoweK+9HoseyksIHxfNFPgELIcRlCLWbeeTWHJ54cQdPvbaT794xodvFsSvr21i2/ihbD1ThsBr5/NxM5k9Ixmwy6JB64IoOt/HVa0exeGoqy9YfZfnGAj7KLWHJ9DSuzEnCZLxww66l3cO2g53NoyMljQBkJoVxx8JhTB4RS5hD1kYQQgS3hZNS+HRvBRt2l1NU3UpkmIXE6MG3k50QQoi+ERSNpIKCAh599FEaGhqIiIjgySefJC0tTe9YQgjRI85QC4/clsMvXtzBb17ZyaN3TiAmpnNtnfpmF+98UsCG3eUYDArXTk/l6qlDsFtNOqce2JKiHdx701jyyxp5Y10+L390hNXbilg6M4MZY+JR1dOnELrcPj7Lq2bLvkr2FtTh82skRNm5cXYGU0fFETuAdlwTQgx8qfGhDEsO58PtJbS7vUwaHitr7wkhhOg1QdFI+tGPfsTtt9/O0qVLeeutt/h//+//8cILL+gdSwgheiw2wsa3b83hyZdy+fXLO/nR16bx7idH+Wh7CT6/xpzsRK6bkUZ4yMDe1S7YZCaG853bcth/rJ7XP87nH6sO8N7WIm6ancH8SAe782vZvL+Czw7X4PL4cIZaWDg5hWmj4kiJDZETLyFEv7Vwcgp/WLYX4LSF7oUQQojLpXsjqba2lv379/PPf/4TgCVLlvDYY49RV1dHZKTM5RZC9B9J0Q4e/kLnAtz3/2YdCjBtdBxLZ2XIiBYdKYrC6PRIRqU52XGomjfWH+X3b+7hL+/sx+3x4bAamTY6jmmy45oQYgDJGRpDVJiVhhYXo9J6vhmEEEIIcSGKpmmangH27t3Ld7/7XVauXHnid9dccw2/+tWvGD16tI7JhBDi0hwsrOPDrUUsmZlBWsL5F98Wgefz+floezGHi+qZPDKOCSPierR2khBC9DfbD1RSUtXMDXOy9I4ihBBiANF9RFJvqK1twe/XtR922YJpZXfJ0j3J0j3JcrYou4l7P59NdXVzUOQJlvsFgidLTkYki47vONhQ36p3nKC5X0CynItk6Z5k6V6wZEmNtjNpZFxQZIHguV9AspyLZOmeZOmeZOleMGW5HKqqEBV19iZCALp/BZuQkEBlZSU+nw8An89HVVUVCQkJOicTQgghhBBCCCGEEKfSvZEUFRXFyJEjWbFiBQArVqxg5MiRsj6SEEIIIYQQQgghRJAJiqltP/7xj3n00Uf54x//SFhYGE8++aTekYQQQgghhBBCCCHEGYKikZSZmcl//vMfvWMIIYQQQgghhBBCiPPQfWqbEEIIIYQQQgghhOgfpJEkhBBCCCGEEEIIIXpEGklCCCGEEEIIIYQQokekkSSEEEIIIYQQQgghekQaSUIIIYQQQgghhBCiR4Ji17bLpaqK3hF6RTD9HZKle5Kle5Kle5Kle5Kle5Kle5Kle5Kle5Kle5Kle5Kle5Kle5Kle5Kle8GU5VKd729QNE3TAphFCCGEEEIIIYQQQvRTMrVNCCGEEEIIIYQQQvSINJKEEEIIIYQQQgghRI9II0kIIYQQQgghhBBC9Ig0koQQQgghhBBCCCFEj0gjSQghhBBCCCGEEEL0iDSShBBCCCGEEEIIIUSPSCNJCCGEEEIIIYQQQvSINJKEEEIIIYQQQgghRI9II0kIIYQQQgghhBBC9IhR7wACCgoKePTRR2loaCAiIoInn3yStLS0gOd48sknef/99yktLeWdd95h2LBhAc/Qpb6+nv/5n/+hqKgIs9lMamoqP/3pT4mMjNQlzz333ENJSQmqqmK32/nf//1fRo4cqUsWgN///vc8++yzuj9O8+bNw2w2Y7FYAHjkkUeYNWuWLllcLhc///nP2bRpExaLhezsbB577LGA5ygpKeFb3/rWiZ+bm5tpaWlh69atAc8CsHbtWp555hk0TcPv93PfffexaNEiXbKsW7eOZ555Bq/XS3h4OE888QQpKSl9ftxz1TY9au+5suhRf7s7pl6191x/vx6190KPRSDr77my6FF7z5VFj9rbXRa9au+57hc9au+5suhRe89XSwJde8+XJdC191xZFEUJeO093/0S6Nrbk/eeQNXe82UJdO09X5ZA195zZWlrawt47T3f/RLo2nu+LHp97g0YTejurrvu0pYvX65pmqYtX75cu+uuu3TJsW3bNq2srEybO3eudujQIV0ydKmvr9c2b9584udf/OIX2ve+9z3d8jQ1NZ347w8++EC74YYbdMuyd+9e7atf/ap25ZVX6v44BcNzpctjjz2mPf7445rf79c0TdOqq6t1TtTpZz/7mfaTn/xEl2P7/X5t0qRJJx6jAwcOaNnZ2ZrP5wt4loaGBm3KlCna0aNHNU3rrHV33313QI59rtqmR+09VxY96m93x9Sr9p7r79ej9p7vsQh0/T1XFj1q77my6FF7e/J6CVTt7S6LXrW3uyx61d7z1ZJA197zZQl07T1XFj1q7/mOGejae6G/P5C193xZAl17z5cl0LW3p8/RQNTec2XRo/aeK4uen3sDRaa26ay2tpb9+/ezZMkSAJYsWcL+/fupq6sLeJZJkyaRkJAQ8ON2JyIigqlTp574OTs7m7KyMt3yhIaGnvjvlpYWFEXRJYfb7eanP/0pP/rRj3TLEIxaW1tZvnw5DzzwwIn7JTo6WudUnY/XO++8w80336xbBlVVaW5uBjq/JYqNjUVVA1/6CwsLiY6OJj09HYA5c+awcePGgNS67mqbXrX3XHVWj/rb3TH1qr3n+vv1qL3nyqJH/Q2m9+XusuhVey90vwSy9p4rix61t7ssetXec9USPWrv+epaoF9j58qiR+093zEDXXvPlyXQtTeYzkHOlUWP2tuT+yVQtfd8WQJde8+VRc/PvYEiU9t0Vl5eTlxcHAaDAQCDwUBsbCzl5eW6TeMKNn6/n5dffpl58+bpmuMHP/gBn3zyCZqm8be//U2XDM888wzXX399UA2LfOSRR9A0jYkTJ/Lwww8TFhYW8AzFxcVERETw+9//ni1btuBwOHjggQeYNGlSwLOcas2aNcTFxTF69Ghdjq8oCk8//TT33HMPdrud1tZW/vznP+uSJT09nZqaGnbv3s24ceN45513AHSrdVJ7L0xq7+mCrf5K7T03qb0nBUPtPbWW6F17g6WunS+LHhm7O6ZetffMLHrW3u7uF71q76lZ9K6953qO6lF7T82id+09NUsw1N6+JiOSRNB77LHHsNvt3HnnnbrmePzxx1m3bh0PPfQQv/zlLwN+/M8++4w9e/Zw++23B/zY5/LSSy/x9ttv88Ybb6BpGj/96U91yeH1eikuLmbUqFG8+eabPPLII9x33320tLTokqfLG2+8oetoJK/Xy5///Gf++Mc/snbtWv70pz/x0EMP0draGvAsoaGhPPXUUzzxxBPcdNNN1NbWEhYWhtEo32cEK6m9JwVb/ZXae35Se08KhtobLLWkv2TRI2N3x9Sr9p6aRe/ae+b9omftPTWL3rX3XM9RPWrvmfeLnrX31CzBUHv7mjSSdJaQkEBlZSU+nw8An89HVVVV0Axl19uTTz5JYWEhTz/9tC7Tcbpzww03sGXLFurr6wN63G3btnH06FHmz5/PvHnzqKio4Ktf/SobN24MaI5TdT1PzWYzt99+O7m5ubrkSExMxGg0nhgqP378eJxOJwUFBbrkAaisrGTbtm1cd911umU4cOAAVVVVTJw4EYCJEydis9nIz8/XJc+MGTN4+eWXefPNN7nzzjvp6OjQbXSH1N7zk9p7umCrv1J7z01q79n0rL1n1hI9a28w1bVzZdEj44WOGcjae2YWPWtvd/eLXrX3zCx61t5zPV/0qL1nZtGz9nZ3vwTT596+EByfDgexqKgoRo4cyYoVKwBYsWIFI0eOHDBD3i7HU089xd69e/nDH/6A2WzWLUdrayvl5eUnfl6zZg3h4eFEREQENMfXv/51Nm7cyJo1a1izZg3x8fH8/e9/Z+bMmQHN0aWtre3EHGRN01i1apVuO9lFRkYydepUPvnkE6BzN67a2lpSU1N1yQOwbNky5syZg9Pp1C1DfHw8FRUVHD16FID8/HxqamoYMmSILnmqq6uBzqG/v/3tb7n11lux2+26ZJHae25Se88WTPVXau/5Se09m161t7taolftDZa6dr4semTs7ph61d7usuhVe7vLolft7S6LXrX3fM/RQNfe7rLoVXvPdb8E0+fevqBomqbpHWKwy8/P59FHH6WpqYmwsDCefPJJMjIyAp7jZz/7GatXr6ampgan00lERAQrV64MeA6AI0eOsGTJEtLS0rBarQAkJyfzhz/8IeBZampquOeee2hvb0dVVcLDw/nud7+r29oLXebNm8dzzz0XkG3Cu1NcXMx9992Hz+fD7/eTmZnJD3/4Q2JjY3XL8/3vf5+GhgaMRiMPPvggc+bM0SULwFVXXcUPfvADZs+erVsGgLfffpu//vWvJxZjvP/++1mwYIEuWX7wgx+Qm5uLx+Phiiuu4Pvf//6JLXT70rlqmx6191xZ9Ki/3R3z6aef1qX2dpfl+eef16X29uSxCFT97S7Lc889p0vtPdf9okftPd9jFOjae64setTec2XRo/ae73NcoGvv+bIEuvaeK8uDDz4Y8Np7riw/+clPAl57e/q5PxC191xZHn300YDX3vPdL4GuvRd6jAJZe8+XJdC193xZ9PrcGyjSSBJCCCGEEEIIIYQQPSJT24QQQgghhBBCCCFEj0gjSQghhBBCCCGEEEL0iDSShBBCCCGEEEIIIUSPSCNJCCGEEEIIIYQQQvSINJKEEEIIIYQQQgghRI9II0kIIYQQQgghhBBC9Ig0koQQQgghdPT2229z99136x1DCCGEEKJHFE3TNL1DCCGEEELo4c033+Sf//wnRUVFhISEsHDhQr797W8TGhraJ8crKSlh/vz57Nu3D6PR2CfHEEIIIYToSzIiSQghhBCD0j/+8Q9+/etf853vfIft27fz6quvUlpayt13343H49E7nhBCCCFEUJJGkhBCCCEGnZaWFp599ll++MMfMnv2bEwmE8nJyTz99NOUlJSwYsUKHn30UZ566qkT19myZQuzZ88+8XNlZSX33Xcf06ZNY968ebzwwgsn/m337t3cdNNNTJgwgRkzZvDEE08AcOeddwIwefJkcnJy+Oyzz3jzzTe57bbbTlw3NzeXm2++mYkTJ3LzzTeTm5t74t/uuusunn76aW699VZycnK4++67qaurA8DlcvHII48wdepUJk2axM0330xNTU3f3IFCCCGEGLSkkSSEEEKIQSc3NxeXy8WiRYtO+73D4WD27Nls3LjxvNf3+/3893//N8OHD2f9+vU8//zzPP/882zYsAGAxx9/nC9+8Yvk5ubywQcfcPXVVwPw4osvArBt2zY+++wzcnJyTrvdhoYGvvGNb3DXXXexZcsWvvKVr/CNb3yD+vr6E5dZsWIFTzzxBJs2bcLj8fCPf/wDgGXLltHS0sK6devYsmULP/nJT7BarZd3RwkhhBBCnEEaSUIIIYQYdOrr63E6nd2uUxQTE3Na46Y7e/bsoa6ujnvvvRez2UxKSgq33HILq1atAsBoNFJUVERdXR0Oh4Ps7Owe5Vq3bh2pqanccMMNGI1GlixZQkZGBmvXrj1xmZtuuon09HSsViuLFy/mwIEDJ47Z0NBAYWEhBoOBMWPGEBIS0sN7RAghhBCiZ2SVRyGEEEIMOk6nk/r6erxe71nNpOrqapxO53mvX1paSlVVFZMmTTrxO5/Pd+Lnxx9/nN/97ndcffXVJCcnc++99zJ37twL5qqqqiIxMfG03yUmJlJZWXni55iYmBP/bbPZaGtrA2Dp0qVUVFTw8MMP09TUxPXXX89DDz2EyWS64HGFEEIIIXpKGklCCCGEGHRycnIwm82sXr2aa6655sTv29raWL9+PQ8++CAHDx6ko6PjxL+dut5QQkICycnJrF69utvbT0tL47e//S1+v5/Vq1dz//33s2XLFhRFOW+u2NhYysrKTvtdeXk5s2bNuuDfZDKZuPfee7n33nspKSnh61//Ounp6Xz+85+/4HWFEEIIIXpKprYJIYQQYtAJDQ3lW9/6Fj/72c9Yv349Ho+HkpISHnjgAZxOJ9dddx0jR47k448/pqGhgerqap5//vkT1x83bhwhISH85S9/oaOjA5/Px+HDh9m9ezcAb731FnV1daiqSlhYGAAGg4HIyEhUVaW4uLjbXHPmzOHYsWO88847eL1eVq1aRV5eHldeeeUF/6bNmzdz6NAhfD4fISEhGI1GDAbD5d9ZQgghhBCnkBFJQgghhBiUvva1rxEREcEvf/lLCgsLcbvdTJkyhX/+85/Y7XaWLl3Kp59+yrx580hKSuLmm28+sbC1wWDgT3/6E08++STz58/H7XaTnp7Ogw8+CMCGDRv4xS9+QUdHB4mJiTz11FNYLBYAvvnNb3Lbbbfh9Xr529/+dlomp9PJc889x89//nN+/OMfk5qaynPPPUdkZOQF/56amhp+9KMfUVlZid1u55prruH666/v3TtNCCGEEIOeommapncIIYQQQgi9vf766zz77LO8/PLLZ61TJIQQQgghOkkjSQghhBDiuOXLl2Mymbj22mv1jiKEEEIIEZSkkSSEEEIIIYQQQgghekQW2xZCCCGEEEIIIYQQPSKNJCGEEEIIIYQQQgjRI9JIEkIIIYQQQgghhBA9Io0kIYQQQgghhBBCCNEj0kgSQgghhBBCCCGEED0ijSQhhBBCCCGEEEII0SP/H4CUqLeUb3SzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
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