{
 "nbformat": 4,
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 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.0"
  },
  "colab": {
   "provenance": []
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a78XVNCVwrRT"
   },
   "source": "# Kuantum Churn Algoritmaları – Telco Sektörü (Qiskit)\n\n**Karşılaştırma Tablosundaki Algoritmalar:**\n- **VQC / QNN** – Variational Quantum Classifier / Quantum Neural Network  \n- **QSVM** – Quantum Support Vector Machine (Quantum Kernel)  \n- **Kuantum Topluluk** – Hybrid Quantum Ensemble (Bagging)  \n- **QPCA** – Quantum Kernel Principal Component Analysis  \n- **QBM** – Quantum Boltzmann Machine (kavramsal + basitleştirilmiş demo)\n\nBu notebook, **NISQ** dönemi için tasarlanmıştır. Gerçek telco churn verisi yerine, aynı özellik dağılımlarını taklit eden **sentetik bir veri seti** kullanılır. Qubit sayısı düşük tutulur (4–6) ki Colab simülatöründe ve gelecekte gerçek donanımda çalıştırılabilsin.\n\n> **Not:** Qiskit Machine Learning sürümleri hızla değişiyor. Aşağıdaki kurulum hücreleri güncel paketleri çeker. Hata alırsanız `qiskit-machine-learning` sürümünü kontrol edin.",
   "id": "a78XVNCVwrRT"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4h682-qxwrRZ"
   },
   "source": "## 0. Kurulum (Google Colab)",
   "id": "4h682-qxwrRZ"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p-AnaTvIwrRa",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786635361975,
     "user_tz": -180,
     "elapsed": 24465,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "171d34ae-2ad3-471c-befa-5a3130d9c5f6"
   },
   "source": "# Colab için gerekli paketler\n!pip install -q --upgrade pip\n!pip install -q qiskit qiskit-aer qiskit-machine-learning qiskit-algorithms\n!pip install -q scikit-learn pandas numpy matplotlib seaborn pylatexenc\n\nprint(\"Kurulum tamamlandı.\")",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.8 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/1.8 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m27.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hKurulum tamamlandı.\n"
     ]
    }
   ],
   "id": "p-AnaTvIwrRa"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-5SZyUHvwrRd",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786635367977,
     "user_tz": -180,
     "elapsed": 5940,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "7a5c753e-1e4a-4261-d69f-b62a1d9a61de"
   },
   "source": "import warnings\nwarnings.filterwarnings(\"ignore\")\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import (\n    accuracy_score, precision_score, recall_score, f1_score,\n    classification_report, confusion_matrix, roc_auc_score\n)\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier, VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\n\n# Qiskit\nfrom qiskit import QuantumCircuit\nfrom qiskit.circuit.library import ZZFeatureMap, RealAmplitudes, EfficientSU2, PauliFeatureMap\nfrom qiskit.primitives import StatevectorSampler, StatevectorEstimator\nfrom qiskit_algorithms.optimizers import COBYLA, SPSA, ADAM\nfrom qiskit_machine_learning.algorithms import VQC, QSVC\nfrom qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN\nfrom qiskit_machine_learning.kernels import FidelityQuantumKernel\nfrom qiskit_machine_learning.state_fidelities import ComputeUncompute\nfrom qiskit_algorithms.utils import algorithm_globals\n\nalgorithm_globals.random_seed = 42\nnp.random.seed(42)\n\nprint(\"Kütüphaneler yüklendi.\")\nimport qiskit\nprint(f\"Qiskit version: {qiskit.__version__}\")",
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Kütüphaneler yüklendi.\n",
      "Qiskit version: 2.5.1\n"
     ]
    }
   ],
   "id": "-5SZyUHvwrRd"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "09JgvjclwrRe"
   },
   "source": "## 1. Sentetik Telco Churn Veri Seti\n\nGerçek telco churn veri setlerindeki tipik özellikler:\n- `tenure` (ay cinsinden abonelik süresi)\n- `MonthlyCharges`, `TotalCharges`\n- `Contract` (aylık / 1 yıllık / 2 yıllık)\n- `InternetService`, `OnlineSecurity`, `TechSupport`\n- `PaymentMethod`, demografik bilgiler\n\nNISQ için **4 özellik** seçiyoruz (4 qubit). Gerçek projede PCA veya feature selection ile 4–8 feature’a indirilir.",
   "id": "09JgvjclwrRe"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "id": "WqSYllbmwrRf",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786635368567,
     "user_tz": -180,
     "elapsed": 452,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "13c99b74-6a73-4054-9982-7128833555f1"
   },
   "source": "def generate_telco_churn(n_samples=400, random_state=42):\n    \"\"\"Sentetik telco churn verisi üretir.\"\"\"\n    rng = np.random.RandomState(random_state)\n\n    # Özellikler\n    tenure = rng.randint(1, 73, size=n_samples)                     # 1-72 ay\n    monthly_charges = rng.uniform(20, 120, size=n_samples)          # $20-120\n    contract_type = rng.choice([0, 1, 2], size=n_samples, p=[0.55, 0.25, 0.20])  # 0=aylık, 1=1y, 2=2y\n    support_tickets = rng.poisson(1.5, size=n_samples)              # destek talebi sayısı\n    # Ek özellikler (ileride kullanılabilir)\n    data_usage_gb = rng.exponential(15, size=n_samples)\n    age = rng.randint(18, 75, size=n_samples)\n\n    # Churn olasılığı (gerçekçi kurallar)\n    # Düşük tenure + yüksek ücret + aylık sözleşme + çok ticket → churn riski yüksek\n    logit = (\n        -0.06 * tenure\n        + 0.025 * monthly_charges\n        - 0.9 * contract_type\n        + 0.35 * support_tickets\n        - 1.8\n    )\n    prob = 1 / (1 + np.exp(-logit))\n    churn = (rng.rand(n_samples) < prob).astype(int)\n\n    df = pd.DataFrame({\n        \"tenure\": tenure,\n        \"MonthlyCharges\": monthly_charges,\n        \"Contract\": contract_type,\n        \"SupportTickets\": support_tickets,\n        \"DataUsageGB\": data_usage_gb,\n        \"Age\": age,\n        \"Churn\": churn\n    })\n    return df\n\ndf = generate_telco_churn(n_samples=400)\nprint(\"Veri seti şekli:\", df.shape)\nprint(\"\\nChurn dağılımı:\")\nprint(df[\"Churn\"].value_counts(normalize=True).round(3))\ndf.head()",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Veri seti şekli: (400, 7)\n",
      "\n",
      "Churn dağılımı:\n",
      "Churn\n",
      "0    0.835\n",
      "1    0.165\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   tenure  MonthlyCharges  Contract  SupportTickets  DataUsageGB  Age  Churn\n",
       "0      52      114.023024         2               1    17.391073   72      0\n",
       "1      15       77.547418         1               1     1.082774   44      0\n",
       "2      72       58.816993         0               2     2.883423   71      0\n",
       "3      61       84.328822         0               3    29.041786   46      0\n",
       "4      21       65.825289         0               1     3.865628   56      0"
      ],
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       "      <th>tenure</th>\n",
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       "type": "dataframe",
       "variable_name": "df",
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 400,\n  \"fields\": [\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 1,\n        \"max\": 72,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          21,\n          68,\n          42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.62541015513491,\n        \"min\": 21.083765148029837,\n        \"max\": 119.97176732861305,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          63.747492202372904,\n          46.924323094938096,\n          61.26176769114265\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Contract\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SupportTickets\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DataUsageGB\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.605806403775805,\n        \"min\": 0.023494968720626223,\n        \"max\": 91.9323449085613,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          24.020583454040196,\n          4.725751600577535,\n          0.6439988746032211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 18,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          72,\n          70,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "id": "WqSYllbmwrRf"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 362
    },
    "id": "j_LJ7dw6wrRg",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786635369418,
     "user_tz": -180,
     "elapsed": 843,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "23157454-38c7-4cf3-fc64-30c596327117"
   },
   "source": "# Görselleştirme\nfig, axes = plt.subplots(1, 3, figsize=(14, 4))\nsns.histplot(data=df, x=\"tenure\", hue=\"Churn\", kde=True, ax=axes[0], palette=\"Set1\")\naxes[0].set_title(\"Tenure vs Churn\")\nsns.boxplot(data=df, x=\"Churn\", y=\"MonthlyCharges\", ax=axes[1], palette=\"Set2\")\naxes[1].set_title(\"MonthlyCharges vs Churn\")\nsns.countplot(data=df, x=\"Contract\", hue=\"Churn\", ax=axes[2], palette=\"Set1\")\naxes[2].set_title(\"Contract vs Churn\")\nplt.tight_layout()\nplt.show()",
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ],
      "image/png": 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/Pz/UqlXLuN2kSRPY29sX6efi8/Qhk8kwZMgQ7N69G2lpacb2LVu2oE2bNvDy8gJQsmPLo3P4paSkIDExEe3bt8fNmzeRkpJi8vyCxuTQ0FBUrVoVr732mrHN0tISo0aNMjkuIiIC165dw+DBg/HgwQPj2J2RkYHOnTvj8OHDxvHWwcEBJ0+exL1794r0egcMGICEhASTj2n+9NNPMBgMGDBggPH1KpVK/P333/mmenqa5/0eDBgwAJUqVTJu542/LzLm9uvXz+Qu70eVxPsJIqKiKOrPy99//x0AEBQUZNI+ZcoUAMg3921xXMsUNJ4BhRsT9+3bh7S0NHz44YewtLQ0eX5B0wAWRt26deHj44Mff/zR2KbX6/HTTz+hV69exlwODg7IyMgwmS6qMIrzfcSLXjM+6S7d4ngvRhUDi7ZE/7l27RpSUlLg4uICZ2dnk0d6ejoSEhJMji/o44KVKlUq0kXR4/IuWB/PFRoami+Tn58fAOTL9bgBAwbg9u3bOH78OADgxo0bCA8PN17Q5R3Ttm1bvPPOO3B1dcXAgQOxbdu2ZxZw7e3tAcDk4rswHv/a5V3gFffXrqBz5Z3vRc5FRFTS8n7Oh4SEYMeOHdDr9SZzvD0qOjoa7u7u+S5O8j4aGR0dbdJeHD8Xn7ePoUOHIisry/jxzytXriA8PNzkI4wlObYcPXoUfn5+sLGxgYODA5ydnY3ztxdUtH1cdHQ0atWqle9CtXbt2ibb165dA5A7j+/j4/e3334LjUZjPN+iRYtw4cIFeHh4oGXLlpg9e3ahLtry5sp99KL3xx9/hI+PD+rWrQsgd96+hQsX4o8//oCrqyteffVVLFq0CHFxcU/tuyyM7yV1PiKioijqz8vo6GjIZLJ844abmxscHBxKZMx+0s/RwoyJN27cAADjlDvFZcCAATh69Cju3r0LIHe+2ISEBJNr1LFjx6Ju3boICAhAtWrVMGLEiELN+/o8Y5ilpWW+XxC+6DWjXC7PN/9+Hl6jUmGxaEv0H4PBABcXF+zbt6/Ax9y5c02Ot7CwKLCfR+9ofdJvHx+d0PxRBa3iaTAY0KVLlyfmKmhy9Uf16tUL1tbW2LZtGwBg27ZtkMlkxgnr8857+PBh/PXXX3j77bfx77//YsCAAejSpcsTswJA/fr1AQDnz59/aobHPetrV9SvG1Dw164w5yIiMleDBw/GH3/8gTVr1iAgIMD4qYgXVRw/F5+3j4YNG8LX1xebN28GAGzevBlKpRL9+/c3HlNSY8uNGzfQuXNnJCYmYunSpdizZw/27duHyZMnA0C+X1S+yMraeX0tXrz4ieN33nx//fv3x82bN7FixQq4u7tj8eLF8Pb2zneH9ONUKhX69OmDnTt3QqfT4e7duzh69KjJBS8ATJo0CVevXsWCBQtgaWmJmTNnokGDBjh79uwT+65duzbkcnmxfw+A4nlvVJTzERGVJHt7e7i7uxsXfyyswt6lWhw/5wr6OVrUMbG4DRgwAKIoYvv27QByr1HVajW6detmPMbFxQURERHYvXu3ce7+gIAABAYGPrXv53kf8aSv86OKOn6pVCrIZAWX3Dh+UWGxaEv0n1q1auHBgwdo27Yt/Pz88j2aNm1a5D7z7vhITk42aX/8N6jPypWenl5gJj8/v2cuEGJjY4OePXti+/btMBgM+PHHH/HKK6/A3d3d5DiZTIbOnTtj6dKluHTpEubPn48DBw4UuLhKnoCAAFhYWBgvvotLcXzdiIjKutdffx0ymQwnTpx44tQIAFC9enXcu3cv3x0lly9fNu4vquf9yGNhDB06FAcOHEBsbCxCQkLQo0cPk4/Ul9TY8uuvv0Kj0WD37t0YPXo0unfvDj8/vyIVZ6tXr44bN27ku6i6fv26yXbeRx7t7e2fOH4/ujBXlSpVMHbsWOzatQtRUVGoXLky5s+f/8w8AwYMQGJiIvbv34/t27dDFMV8Rdu8PFOmTMGff/6JCxcuICcnB19++eUT+7W2tkanTp1w+PBh3L59+5k5iqJSpUr5xneAYzwRlV09e/bEjRs3jJ9sfJrq1avDYDAYP5GRJz4+HsnJyaU2Zhd2TMwbz55VlC5qBi8vL7Rs2RI//vgjdDodduzYgT59+kClUpkcp1Qq0atXL6xatQo3btzA6NGjsWnTpnzj7qN69eoFALxGpXKBRVui//Tv3x96vR6fffZZvn06na7AC4xnyRvkHp13NiMjAxs3bixSruPHj2Pv3r359iUnJ0On0z2zjwEDBuDevXv49ttvce7cuXwXdElJSfme4+PjAwDQaDRP7NfDwwOjRo3Cn3/+iRUrVuTbbzAY8OWXX+LOnTvPzPgoe3t7ODk55Zuvd9WqVUXqh4ioLLO1tcXq1asxe/Zs4wVIQbp37w69Xo+vv/7apH3ZsmUQBAEBAQFFPreNjQ2A/BcmxWHQoEEQBAETJ07EzZs38dZbb5nsL6mxJe+ulkcLrikpKVi/fn2h+/D398fdu3eNK1MDQHZ2NtatW2dynK+vL2rVqoUlS5YgPT09Xz/3798HkHt3zuPTMri4uMDd3f2p428ePz8/ODo64scff8SPP/6Ili1bmnwMNjMzE9nZ2SbPqVWrFuzs7J7Z/6xZsyCKIt5+++0CX0N4eHiR3s88ev7Lly8bvwYAcO7cORw9erTIfRERmYPp06fDxsYG77zzDuLj4/Ptv3HjBr766isAuWM2AAQHB5scs3TpUgBAjx49inz+5xmzCzsmdu3aFXZ2dliwYEG+8eTR59rY2OQbz55lwIABOHHiBL7//nskJibmu0Z98OCBybZMJkOTJk0APP0atXXr1ujWrRu+/fZb7Nq1K9/+nJwcTJ06tUhZgdyCu4WFBa9RqVTJpQ5AZC7at2+P0aNHY8GCBYiIiEDXrl2hUChw7do1bN++HV999dUT5xN8kq5du8LT0xMjR47EtGnTYGFhge+//x7Ozs6IiYkpVB/Tpk3D7t270bNnTwwbNgy+vr7IyMjA+fPn8dNPP+HWrVtwcnJ6ah/du3eHnZ0dpk6dCgsLi3xTKsydOxeHDx9Gjx49UL16dSQkJGDVqlWoVq0a2rVr99S+v/zyS9y4cQMTJkzAjh070LNnT1SqVAkxMTHYvn07Ll++jIEDBxbqtT7qnXfewRdffIF33nkHzZs3x+HDh3H16tUi90NEVJY96yOAQO4dJR07dsTHH3+MW7duoWnTpvjzzz/xyy+/YNKkSSYLXRSWr68vAODjjz/GwIEDoVAo0KtXL+OF4YtwdnZGt27dsH37djg4OBR4gVoSY0vXrl2Nd+yMHj0a6enpWLduHVxcXBAbG1uoPkaPHo2vv/4agwYNwsSJE1GlShVs2bLFuDhL3p1GMpkM3377LQICAuDt7Y3hw4ejatWquHv3Lg4ePAh7e3v8+uuvSEtLQ7Vq1fDGG2+gadOmsLW1xV9//YWwsLCn3gmbR6FQoG/fvti6dSsyMjKwZMkSk/1Xr15F586d0b9/fzRs2BByuRw7d+5EfHz8M79+bdq0wcqVKzF27FjUr18fb7/9NurUqYO0tDT8/fff2L17N+bNm1eor9ujRowYgaVLl8Lf3x8jR45EQkIC1qxZA29vby4gRkRlUq1atRASEoIBAwagQYMGGDp0KBo1aoScnBwcO3YM27dvx7BhwwAATZs2RWBgINauXYvk5GS0b98ep06dwsaNG9GnTx907NixyOf38fGBhYUFFi5ciJSUFKhUKnTq1AkuLi5PfE5hx0R7e3ssW7YM77zzDlq0aIHBgwejUqVKOHfuHDIzM42/vPP19cWPP/6IoKAgtGjRAra2tk/9hTOQe3PS1KlTMXXqVDg6OhrXbMnzzjvvICkpCZ06dUK1atUQHR2NFStWwMfHxzhv/5Ns2rQJXbt2Rd++fdGrVy907twZNjY2uHbtGrZu3YrY2Nh8Y+azqNVqvPnmm1ixYgUEQUCtWrXw22+/PXONGaIXIhJVUOPGjRML+i+wdu1a0dfXV7SyshLt7OzExo0bi9OnTxfv3btnPKZ69epijx498j23ffv2Yvv27U3awsPDxVatWolKpVL09PQUly5dKq5fv14EIEZFRT2zT1EUxbS0NHHGjBli7dq1RaVSKTo5OYlt2rQRlyxZIubk5BTq9Q4ZMkQEIPr5+eXbt3//frF3796iu7u7qFQqRXd3d3HQoEHi1atXC9W3TqcTv/32W/GVV14R1Wq1qFAoxOrVq4vDhw8Xz549azxu1qxZIgDx/v37Js8v6OuRmZkpjhw5UlSr1aKdnZ3Yv39/MSEhQQQgzpo165l9imLRvk9ERFLL+1kYFhb21OMK+tmWlpYmTp48WXR3dxcVCoVYp04dcfHixaLBYDA5DoA4bty4AvsMDAw0afvss8/EqlWrijKZzORndGH7KOhne55t27aJAMR33333ia+zJMaW3bt3i02aNBEtLS3FGjVqiAsXLhS///77Io3JN2/eFHv06CFaWVmJzs7O4pQpU8Sff/5ZBCCeOHHC5NizZ8+Kffv2FStXriyqVCqxevXqYv/+/cX9+/eLoiiKGo1GnDZtmti0aVPRzs5OtLGxEZs2bSquWrXqiV+Xx+3bt08EIAqCIN6+fdtkX2Jiojhu3Dixfv36oo2NjahWq8VWrVqJ27ZtK3T/4eHh4uDBg43/tipVqiR27txZ3Lhxo6jX60VRFMWoqCgRgLh48eJ8z3983BZFUdy8ebNYs2ZNUalUij4+PuLevXvFwMBAsXr16sZjntZnUb7nRESl5erVq+KoUaPEGjVqiEqlUrSzsxPbtm0rrlixQszOzjYep9VqxTlz5oheXl6iQqEQPTw8xBkzZpgcI4pFu5ZZt26dWLNmTdHCwkIEIB48ePCpfYhi4cfEvGPbtGkjWllZifb29mLLli3FH374wbg/PT1dHDx4sOjg4CACMPl5/jRt27YVAYjvvPNOvn0//fST2LVrV9HFxcV4LT169GgxNja2UH1nZmaKS5YsEVu0aCHa2tqKSqVSrFOnjvj++++L169fNx4XGBgo2tjY5Ht+3ljzqPv374v9+vUTra2txUqVKomjR48WL1y4IAIQ169f/8w+RbFo78WIBFHkTMdEREREVHp++eUX9OnTB4cPH8Yrr7widZwXFhwcjMmTJ+POnTuoWrWq1HGIiIiIqBxg0ZaIiIiISlXPnj0RGRmJ69evl+iiZyUhKyvLZKGW7OxsNGvWDHq9ntP4EBEREVGx4Zy2RERERFQqtm7din///Rd79uzBV199VeYKtgDQt29feHp6wsfHBykpKdi8eTMuX76MLVu2SB2NiIiIiMoR3mlLRERERKVCEATY2tpiwIABWLNmDeTysnf/QHBwML799lvcunULer0eDRs2xPTp0/Otek1ERERE9CJYtCUiIiIiIiIiIiIyIzKpAxARERERERERERHR/7BoS0RERERERERERGRGyt5EYkVkMBhw79492NnZlcnFLoiIqGSJooi0tDS4u7tDJuPvMh/FMZSIiJ6E4+eTcfwkIqKnKewYWu6Ltvfu3YOHh4fUMYiIyMzdvn0b1apVkzqGWeEYSkREz8LxMz+On0REVBjPGkPLfdHWzs4OQO4Xwt7eXuI0RERkblJTU+Hh4WEcL+h/OIYSEdGTcPx8Mo6fRET0NIUdQ8t90Tbv4yj29vYcMImI6In48cX8OIYSEdGzcPzMj+MnEREVxrPGUE4+RERERERERERERGRGWLQlIiIiIiIiIiIiMiMs2hIRERERERERERGZkXI/p21h6fV6aLVaqWOUCIVCAQsLC6ljEBERERERERERsQ5XCBW+aCuKIuLi4pCcnCx1lBLl4OAANzc3LhRARERERERERESSYB2u8Cp80TbvH4qLiwusra3LXVFTFEVkZmYiISEBAFClShWJExERERERERERUUXEOlzhVeiirV6vN/5DqVy5stRxSoyVlRUAICEhAS4uLpwqgYiIiIiIiIiIShXrcEVToRciy5s7w9raWuIkJS/vNZbX+UKIiMqTw4cPo1evXnB3d4cgCNi1a5dxn1arxQcffIDGjRvDxsYG7u7uGDp0KO7du2fSR1JSEoYMGQJ7e3s4ODhg5MiRSE9PL+VXQkRERERElIt1uKKp0EXbPOXtVuyCVITXSERUXmRkZKBp06ZYuXJlvn2ZmZk4c+YMZs6ciTNnzmDHjh24cuUKXnvtNZPjhgwZgosXL2Lfvn347bffcPjwYbz77rul9RKIiIiIiIgKVBFqVMXxGiv09AhERETmKCAgAAEBAQXuU6vV2Ldvn0nb119/jZYtWyImJgaenp6IjIxEaGgowsLC0Lx5cwDAihUr0L17dyxZsgTu7u4l/hqIiIiIiIjo+bFoW4wEQcDOnTvRp08fqaMQEVEFkpKSAkEQ4ODgAAA4fvw4HBwcjAVbAPDz84NMJsPJkyfx+uuvF9iPRqOBRqMxbqemppZobnOi0WjyTTFBhefu7g6VSiV1DCIiIiKqYMpzLY5F2yKIi4vD/PnzsWfPHty9excuLi7w8fHBpEmT0LlzZ6njEVE5FBMTg8TERKljGDk5OcHT01PqGPSI7OxsfPDBBxg0aBDs7e0B5I5XLi4uJsfJ5XI4OjoiLi7uiX0tWLAAc+bMKdG85urevXv4+OOPpY5RZs2fPx9eXl5SxyCiCu7w4cNYvHgxwsPDERsbW+BFfGRkJD744AMcOnQIOp0ODRs2xM8//2x8f5OdnY0pU6Zg69at0Gg08Pf3x6pVq+Dq6irBKyIioopci2PRtpBu3bqFtm3bwsHBAYsXL0bjxo2h1Wqxd+9ejBs3DpcvXy6R8+bk5ECpVJZI30Rk3mJiYtCgfn1kZmVJHcXI2soKkZcvs3BrJrRaLfr37w9RFLF69eoX7m/GjBkICgoybqempsLDw+OF+y0L3N3dMX/+fKljFOju3btYtWoVxo4di6pVq0odp0CccoOIzEHenPAjRoxA37598+2/ceMG2rVrh5EjR2LOnDmwt7fHxYsXYWlpaTxm8uTJ2LNnD7Zv3w61Wo3x48ejb9++OHr0aInnP928ZYmfo7Q1P31K6ghEVIZV9Foci7aFNHbsWAiCgFOnTsHGxsbY7u3tjREjRhi3ExMT8frrr2Pv3r2oWrUqvvzyS+PiMBs2bMCkSZOQnJxsPH7Xrl14/fXXIYoiAGD27NnYtWsXxo8fj/nz5yM6OhoGgwGCIGDdunXYs2dPgX0TUfmTmJiIzKwsrBs8GHVdpL+742pCPEaFhCAxMZFFWzOQV7CNjo7GgQMHjHfZAoCbmxsSEhJMjtfpdEhKSoKbm9sT+1SpVBX2I+4qlcrs7xStWrWq2WckIpLS0+aEB4CPP/4Y3bt3x6JFi4xttWrVMv49JSUF3333HUJCQtCpUycAwPr169GgQQOcOHECL7/8csmFJyKifCp6LY5F20JISkpCaGgo5s+fb/KPJE/eHIIAMGfOHCxatAiLFy/GihUrMGTIEERHR8PR0bHQ57t+/Tp+/vln7NixAxYWFsXaNxGVPXVdXOFTrZrUMciM5BVsr127hoMHD6Jy5com+1u3bo3k5GSEh4fD19cXAHDgwAEYDAa0atVKishEVMZwnucXw3mezY/BYMCePXswffp0+Pv74+zZs/Dy8sKMGTOMUyiEh4dDq9XCz8/P+Lz69evD09MTx48ff2LRtiLPCU9EVFJYi2PRtlCuX78OURRRv379Zx47bNgwDBo0CADw+eefY/ny5Th16hS6detW6PPl5ORg06ZNcHZ2Lva+iYjI/KWnp+P69evG7aioKERERMDR0RFVqlTBG2+8gTNnzuC3336DXq83zlPr6OgIpVKJBg0aoFu3bhg1ahTWrFkDrVaL8ePHY+DAgfwYOxEVCud5fjGc59n8JCQkID09HV988QXmzZuHhQsXIjQ0FH379sXBgwfRvn17xMXFQalUmhQCAMDV1ZVzwhMRlTLW4li0LZS826ULo0mTJsa/29jYwN7ePt9HVJ+levXq+f6RFFffRERk/k6fPo2OHTsat/PmmQ0MDMTs2bOxe/duAICPj4/J8w4ePIgOHToAALZs2YLx48ejc+fOkMlk6NevH5YvX14q+Ymo7OM8zy+GvyAzPwaDAQDQu3dvTJ48GUDuOHrs2DGsWbMG7du3f+6+K/Kc8EREJYW1OBZtC6VOnToQBKFQExwrFAqTbUEQjG8QZDJZvn90Wq02Xx8F3fb9rL6JiKj86NChw1PfpBTmDYyjoyNCQkKKMxYRVSCc55nKGycnJ8jlcjRs2NCkvUGDBjhy5AiA3Dnhc3JykJycbHK3bXx8POeEJyIqZazFAbJSOUsZ5+joCH9/f6xcuRIZGRn59j86mfHTODs7Iy0tzaSPiIiIYkpJREREREREBVEqlWjRogWuXLli0n716lVUr14dAODr6wuFQoH9+/cb91+5cgUxMTFo3bp1qeYlIqroWItj0bbQVq5cCb1ej5YtW+Lnn3/GtWvXEBkZieXLlxd6AG/VqhWsra3x0Ucf4caNGwgJCcGGDRtKNjgREREREVEFkJ6ejoiICOPFeN6c8DExMQCAadOm4ccff8S6detw/fp1fP311/j1118xduxYAIBarcbIkSMRFBSEgwcPIjw8HMOHD0fr1q2fuAgZERGVnIpei2PRtpBq1qyJM2fOoGPHjpgyZQoaNWqELl26YP/+/Vi9enWh+nB0dMTmzZvx+++/o3Hjxvjhhx8we/bskg1ORERERERUAZw+fRrNmjVDs2bNAOTOCd+sWTN8+umnAIDXX38da9aswaJFi9C4cWN8++23+Pnnn9GuXTtjH8uWLUPPnj3Rr18/vPrqq3Bzc8OOHTskeT1ERBVdRa/FCWJRZvYtg1JTU6FWq5GSkgJ7e3uTfdnZ2YiKioKXlxcsLS0lSlg6KtJrJSovzpw5A19fXxyaNBk+1apJHQcRd+6gffAyhIeH46WXXpI6TrF52jhR0fFrYx6ioqLw8ccfczV6ov/w/4R54BjxZM/7tTndvGUJppJG89OnpI5ARGakItWmnvZaCztO8E5bIiIiIiIiIiIiIjPCoi0RERERERERERGRGWHRloiIiIiIiIiIiMiMsGhLREREREREREREZEZYtCUiIiIiIiIiIiIyIyzaEhEREREREREREZkRFm2JiIiIiIiIiIiIzAiLtkRERERERERERERmhEVbIiIiIiIiIiIiIjMilzqAOYqJiUFiYmKpnc/JyQmenp6ldj4iIiIiIiIiIiIyXyzaPiYmJgYN6tdHZlZWqZ3T2soKkZcvF7lwu3LlSixevBhxcXFo2rQpVqxYgZYtW5ZQSiIiIiIiIiIiouJ3unnp1rOanz71XM8rzVoci7aPSUxMRGZWFtYNHoy6Lq4lfr6rCfEYFRKCxMTEIhVtf/zxRwQFBWHNmjVo1aoVgoOD4e/vjytXrsDFxaUEExMREREREREREVUspV2LY9H2Ceq6uMKnWjWpYzzR0qVLMWrUKAwfPhwAsGbNGuzZswfff/89PvzwQ4nTERERERERERERlR+lXYvjQmRlUE5ODsLDw+Hn52dsk8lk8PPzw/HjxyVMRkREREREREREVL5IUYtj0bYMSkxMhF6vh6ur6fQNrq6uiIuLkygVERERERERERFR+SNFLY5FWyIiIiIiIiIiIiIzwqJtGeTk5AQLCwvEx8ebtMfHx8PNzU2iVEREREREREREROWPFLU4Fm3LIKVSCV9fX+zfv9/YZjAYsH//frRu3VrCZEREREREREREROWLFLU4eYn0SiUuKCgIgYGBaN68OVq2bIng4GBkZGQYV7AjIiIiIiIiIiKi4lHatTgWbZ/gakL8sw+S8DwDBgzA/fv38emnnyIuLg4+Pj4IDQ3NNyEyERERERERERERvZjSrsWxaPsYJycnWFtZYVRISKmd09rKCk5OTkV+3vjx4zF+/PgSSEREREREJS0xMRFpaWlSxyhz7t69a/InFY2dnd1zXXsQERGVpOanT0kdoVBKsxYnadF29uzZmDNnjklbvXr1cPnyZQBAdnY2pkyZgq1bt0Kj0cDf3x+rVq0q0btJPT09EXn5MhITE0vsHI9zcnKCp6dnqZ2PiIiIiKSVmJiIqVOmIEerlTpKmbVq1SqpI5RJSoUCS778koVbIiIiMyf5nbbe3t7466+/jNty+f8iTZ48GXv27MH27duhVqsxfvx49O3bF0ePHi3RTJ6eniyiEhEREVGJSUtLQ45Wi7fqOcLVWvK35FRBxGfqsPlKEtLS0li0JSIiMnOSv0OUy+Vwc3PL156SkoLvvvsOISEh6NSpEwBg/fr1aNCgAU6cOIGXX365tKMSERERERUrV2s5PGyVUscgIiIiIjMjkzrAtWvX4O7ujpo1a2LIkCGIiYkBAISHh0Or1cLPz894bP369eHp6Ynjx48/sT+NRoPU1FSTBxEREREREREREVFZIWnRtlWrVtiwYQNCQ0OxevVqREVF4ZVXXkFaWhri4uKgVCrh4OBg8hxXV1fExcU9sc8FCxZArVYbHx4eHiX8KoiIiIiIiMgcHD58GL169YK7uzsEQcCuXbueeOx7770HQRAQHBxs0p6UlIQhQ4bA3t4eDg4OGDlyJNLT00s2OBER0WMkLdoGBATgzTffRJMmTeDv74/ff/8dycnJ2LZt23P3OWPGDKSkpBgft2/fLsbEREREREREZK4yMjLQtGlTrFy58qnH7dy5EydOnIC7u3u+fUOGDMHFixexb98+/Pbbbzh8+DDefffdkopMRERUIMnntH2Ug4MD6tati+vXr6NLly7IyclBcnKyyd228fHxBc6Bm0elUkGlUpVCWiIiIiIiIjInAQEBCAgIeOoxd+/exfvvv4+9e/eiR48eJvsiIyMRGhqKsLAwNG/eHACwYsUKdO/eHUuWLCmwyEtERFQSJJ/T9lHp6em4ceMGqlSpAl9fXygUCuzfv9+4/8qVK4iJiUHr1q0lTElERERERERlkcFgwNtvv41p06bB29s73/7jx4/DwcHBWLAFAD8/P8hkMpw8ebLAPrmuChERlQRJi7ZTp07FoUOHcOvWLRw7dgyvv/46LCwsMGjQIKjVaowcORJBQUE4ePAgwsPDMXz4cLRu3Rovv/yylLGJiIiIiIioDFq4cCHkcjkmTJhQ4P64uDi4uLiYtMnlcjg6Oj5xbRWuq0JERCVB0ukR7ty5g0GDBuHBgwdwdnZGu3btcOLECTg7OwMAli1bBplMhn79+kGj0cDf3x+rVq0q8VwxMTFITEws8fPkcXJygqenZ6mdj4iIiIiIqKIJDw/HV199hTNnzkAQhGLrd8aMGQgKCjJup6amsnBLREQvTNKi7datW5+639LSEitXrnzmJPLFKSYmBvUbNEBWZmapndPK2hqXIyOLVLg9fPgwFi9ejPDwcMTGxmLnzp3o06dPyYUkIiIiIiIqw/755x8kJCSYXHfp9XpMmTIFwcHBuHXrFtzc3JCQkGDyPJ1Oh6SkpCeurcJ1VYiIXly3mT+W6vlCPxtQpOOlqMOZ1UJk5iAxMRFZmZkY+dESuHnWLvHzxcVcx3efT0ViYmKRirZ5q6KOGDECffv2LcGEREREREREZd/bb78NPz8/kzZ/f3+8/fbbGD58OACgdevWSE5ORnh4OHx9fQEABw4cgMFgQKtWrUo9MxERmQcp6nAs2j6Bm2dtVK+bf2J6c1GYVVGJiIiIiIgqkvT0dFy/ft24HRUVhYiICDg6OsLT0xOVK1c2OV6hUMDNzQ316tUDADRo0ADdunXDqFGjsGbNGmi1WowfPx4DBw6Eu7t7qb4WIiIyH1LU4SRdiIyIiIiIiIiouJw+fRrNmjVDs2bNAABBQUFo1qwZPv3000L3sWXLFtSvXx+dO3dG9+7d0a5dO6xdu7akIhMRERWId9oSERERERFRudChQweIoljo42/dupWvzdHRESEhIcWYioiIqOh4py0RERERERERERGRGWHRloiIiIiIiIiIiMiMsGhLREREREREREREZEY4p20Z9axVUYmIiIiIiIiIiOjFSVGHY9H2CeJirj/7IAnPc/r0aXTs2NG4HRQUBAAIDAzEhg0biiMaERERERERERFRhSdFHY5F28c4OTnBytoa330+tdTOaWVtDScnpyI9p6irohIRUdlx+PBhLF68GOHh4YiNjcXOnTvRp08f435RFDFr1iysW7cOycnJaNu2LVavXo06deoYj0lKSsL777+PX3/9FTKZDP369cNXX30FW1tbCV4RERERERHRk4V+NkDqCE8lRR2ORdvHeHp64nJkJBITE0vtnE5OTpzSgIiIjDIyMtC0aVOMGDECffv2zbd/0aJFWL58OTZu3AgvLy/MnDkT/v7+uHTpEiwtLQEAQ4YMQWxsLPbt2wetVovhw4fj3XffRUhISGm/HCIiIiIiIioiFm0L4OnpySIqERFJJiAgAAEBAQXuE0URwcHB+OSTT9C7d28AwKZNm+Dq6opdu3Zh4MCBiIyMRGhoKMLCwtC8eXMAwIoVK9C9e3csWbIE7u7upfZaiIiIiIiIqOhkUgcgIiKiwouKikJcXBz8/PyMbWq1Gq1atcLx48cBAMePH4eDg4OxYAsAfn5+kMlkOHny5BP71mg0SE1NNXkQERERERFR6WPRloiIqAyJi4sDALi6upq0u7q6GvfFxcXBxcXFZL9cLoejo6PxmIIsWLAAarXa+PDw8Cjm9ERERERERFQYLNoSERERAGDGjBlISUkxPm7fvi11JCIiIiIiogqJRVsABoNB6gglriK8RiKiisDNzQ0AEB8fb9IeHx9v3Ofm5oaEhAST/TqdDklJScZjCqJSqWBvb2/yICIiIiIiKk4VoUZVHK+xQi9EplQqIZPJcO/ePTg7O0OpVEIQBKljFStRFJGTk4P79+9DJpNBqVRKHYmIiF6Al5cX3NzcsH//fvj4+AAAUlNTcfLkSYwZMwYA0Lp1ayQnJyM8PBy+vr4AgAMHDsBgMKBVq1ZSRSciIiIiogqMdbiiqdBFW5lMBi8vL8TGxuLevXtSxylR1tbW8PT0hEzGm6uJiMxdeno6rl+/btyOiopCREQEHB0d4enpiUmTJmHevHmoU6cOvLy8MHPmTLi7u6NPnz4AgAYNGqBbt24YNWoU1qxZA61Wi/Hjx2PgwIFwd3eX6FUREREREVFFxjpc0VTooi2QW+X39PSETqeDXq+XOk6JsLCwgFwuL3e/vSAiKq9Onz6Njh07GreDgoIAAIGBgdiwYQOmT5+OjIwMvPvuu0hOTka7du0QGhoKS0tL43O2bNmC8ePHo3PnzpDJZOjXrx+WL19e6q+FiIiIiIgoD+twhVfhi7YAIAgCFAoFFAqF1FGIiIjQoUMHiKL4xP2CIGDu3LmYO3fuE49xdHRESEhIScQjIiIiIiJ6bqzDFQ4/K09ERERERERERERkRli0JSIiIiIiIiIiIjIjLNoSERERERERERERmREWbYmIiIiIiIiIiIjMCIu2RERERERERERERGaERVsiIiIiIiIiIiIiM8KiLREREREREREREZEZYdGWiIiIiIiIiIiIyIywaEtERERERETlwuHDh9GrVy+4u7tDEATs2rXLuE+r1eKDDz5A48aNYWNjA3d3dwwdOhT37t0z6SMpKQlDhgyBvb09HBwcMHLkSKSnp5fyKyEioopOLnUAIiIiIqKKKj5TK3UEqkAqwr+3jIwMNG3aFCNGjEDfvn1N9mVmZuLMmTOYOXMmmjZtiocPH2LixIl47bXXcPr0aeNxQ4YMQWxsLPbt2wetVovhw4fj3XffRUhISGm/HCIiqsBYtCUiMmN2ggDVgyToNBqI2dm5jRYWEKytIbO3h6BWQxAEaUMSEdFz23zlodQRiMqVgIAABAQEFLhPrVZj3759Jm1ff/01WrZsiZiYGHh6eiIyMhKhoaEICwtD8+bNAQArVqxA9+7dsWTJEri7u5f4ayAiIgJYtCUiMiv6pCRk7/sLmiNH4HTkKCKrVAX270fWk56gVMKiShXIvWpAXqcOZHZ2pRmXiIhe0Fv1KsHVWiF1DKog4jO1/EXBY1JSUiAIAhwcHAAAx48fh4ODg7FgCwB+fn6QyWQ4efIkXn/9dYmSEhFRRcOiLRGRxES9Htn7DyDj//4Pmr8PAQYDAMDiv/06S0so1WoIlpaAIAB6PcSMDBhSUoCcHOijo6GPjobm70OwqFEDyua+sPDw4B24RERlgKu1Ah62SqljEFVI2dnZ+OCDDzBo0CDY29sDAOLi4uDi4mJynFwuh6OjI+Li4grsR6PRQKPRGLdTU1NLLjQREVUYLNoSEUnEkJqKjI2bkPF/m6G/e9fYrvD2hmXnTohxckK7Ue9g98RJ8KlWLd/zRb0ehqQk6GNioLt+A/q7d6G/dQtZt25B5l4Flu07wKKKW2m+JCIiIqIyQavVon///hBFEatXr36hvhYsWIA5c+YUUzIiIqJcLNoSEZUyMSsL6Rs3Im3FSojJyQAAwcEBNgMHwGbIEMhregEAcs6cQYooPrEfwcICFs7OsHB2htLXF4aHycg5exba8+dhuBeLzB9+gKJxY6javwpBybu4iIiIiID/FWyjo6Nx4MAB4122AODm5oaEhAST43U6HZKSkuDmVvAvw2fMmIGgoCDjdmpqKjw8PEomPBERVRgs2hIRlRLRYEDmtm1IXfwlDP99vE5euzZsx42Fda+eEKysXqh/WSUHWHbqCGXLFtAcOQLdpUhoz5+HLiYGVt0DYFGlSnG8DCIiIqIyK69ge+3aNRw8eBCVK1c22d+6dWskJycjPDwcvr6+AIADBw7AYDCgVatWBfapUqmgUqlKPDsREVUsLNoSEZUC7ZUrSP5gBnLCwgAAFlWrwm5KEKz79YUgL94fxTJbW1h16waddyNk790LMSUFmT9ug6pzJygbNy7WcxERERGZk/T0dFy/ft24HRUVhYiICDg6OqJKlSp44403cObMGfz222/Q6/XGeWodHR2hVCrRoEEDdOvWDaNGjcKaNWug1Woxfvx4DBw4EO7u7lK9LCIiqoBYtCUiKkFiVhZSv1qO9NVrAJ0OgrU17KYEwXb4MAglfEeG3KMabN5+C9l//gndtevQ7PsLhqQkqF59lYuUERERUbl0+vRpdOzY0bidN21BYGAgZs+ejd27dwMAfHx8TJ538OBBdOjQAQCwZcsWjB8/Hp07d4ZMJkO/fv2wfPnyUslPRESUh0VbIqISknPhIh6+PwG6q1cBAJZdu0A9bx7kVUvvLg1BpYJlz57IOXkSOceOQxt+BmJ2Niy7dIEgk5VaDiIiIqLS0KFDB4hPWRPgafvyODo6IiQkpDhjERERFRmLtkRExUw0GJC+di1Sv1gEaLWQOTvDYcF8WAUESJJHEASoXn4ZMjs7ZP+5D7qLl5Ct08MyoBsLt0RERERERERmyGyKtl988QVmzJiBiRMnIjg4GACQnZ2NKVOmYOvWrdBoNPD398eqVavg6uoqbVgioifQJyTg4fgJ0Bw9CgCw9O8Kh8WLYPHYIhdSUHh7AyoVsn/bA92VK9DI5VB17cKpEqjUJCYmIi0tTeoYZc7du3dN/qSisbOzg5OTk9QxiIiIiIiKxCyKtmFhYfjmm2/QpEkTk/bJkydjz5492L59O9RqNcaPH4++ffvi6H/FECIic6I5cQJJY8bBkJAAwcoK6rlzYD1ooFkVRRW1awPdA5C953doL16EYG0N1SvtpI5FFUBiYiKmTJ0KbU6O1FHKrFWrVkkdoUxSKJX4cskSFm6JiIiIqEyRvGibnp6OIUOGYN26dZg3b56xPSUlBd999x1CQkLQqVMnAMD69evRoEEDnDhxAi+//LJUkYmITIiiiPQ13yB1wReAXg95vbpwXLsWitq1pI5WIEXduoBWi+y9fyInLAyC2h7Kx35pRsUnOTkZDg4OUseQXFpaGrQ5OXBo2wRytY3UcaiC0KVkIPnov0hLS2PRloiIiIjKFMmLtuPGjUOPHj3g5+dnUrQNDw+HVquFn5+fsa1+/frw9PTE8ePHn1i01Wg00Gg0xu3U1NSSC09EFZ4hMxMPJ05G9u+/AwCs+vWDwxefQ2ZtLXGyp1N4e8OQkoqcEyeg2X8AskqVIPfwkDpWmbdw4ULUqFEDAwYMAAD0798fP//8M9zc3PD777+jadOmEieUnlxtA0VltdQxiIiIiIiIzJqkK9Bs3boVZ86cwYIFC/Lti4uLg1KpzHd3kqurK+Li4p7Y54IFC6BWq40PDxYhiKiE6O7eRWLv13MLtkolHL5YgEpfLTP7gm0eZeuXIa9fHxBFZP+2BwbONfrC1qxZYxx39u3bh3379uGPP/5AQEAApk2bJnE6IiIiIiIiKiskK9revn0bEydOxJYtW2BpaVls/c6YMQMpKSnGx+3bt4utbyKiPJqw07jfvSe0ly5B5uQEp20/wubtt8xq/tpnEQQBll38IHN2hpiVhaxff4Oo10sdq0yLi4szFm1/++039O/fH127dsX06dMRFhYmcToiIiIiIiIqKyQr2oaHhyMhIQEvvfQS5HI55HI5Dh06hOXLl0Mul8PV1RU5OTlITk42eV58fDzc3Nye2K9KpYK9vb3Jg4ioOGX8uA2J/QfAkJgIhbc3nH//DaoWzaWO9VwEhQJWr/UCVCoY4uKQc+y41JHKtEqVKhl/WRgaGmqc4kcURehZECciIiIiIqJCkqxo27lzZ5w/fx4RERHGR/PmzTFkyBDj3xUKBfbv3298zpUrVxATE4PWrVtLFZuIKjDRYEDKZ/OQHDQFyMmBZfcAOO3aAXnVqlJHeyEytRqWXboAAHLCwqCLjpY4UdnVt29fDB48GF26dMGDBw8QEBAAADh79ixq164tcToiIiIiIiIqKyRbiMzOzg6NGjUyabOxsUHlypWN7SNHjkRQUBAcHR1hb2+P999/H61bt37iImRERCVFzM5G0vsTjQuO2U2eBLugyRBkkk4NXmwUdetA36QxtP+eR/af+2Az9G0IKpXUscqcZcuWoUaNGrh9+zYWLVoEW1tbAEBsbCzGjh0rcToiIiIiIiIqKyQr2hbGsmXLIJPJ0K9fP2g0Gvj7+2PVqlVSxyKiCsbw8CEejBiJnFNhgFKJSsu+hHWfPlLHKnaq9u2hi46BmJICzZEjsOzcWepIZY5CocDUqVPztU+ePFmCNERERERERFRWmdUtYn///TeCg4ON25aWlli5ciWSkpKQkZGBHTt2PHU+WyKi4qa7fRv3+/RFzqkwCPb2cNqyuVwWbIHc+W3zpknQnvsXutt3JE5UNv3f//0f2rVrB3d3d0T/N9VEcHAwfvnlF4mTERERERERUVlhVkVbIiJzknPhAu6/1ge669dhUaUKnHf+DFWb8j2nttzTA4rGuVPUZO/bB1GrkzhR2bJ69WoEBQUhICAAycnJxsXHHBwcTH4pSURERERERPQ0LNoSERUg+9AhJPZ9A4aEBMgb1Ifz7l+gqF9f6lilQvXqqxBsbSEmJ0Nz/JjUccqUFStWYN26dfj4449hYWFhbG/evDnOnz8vYTIiIiIiIiIqS8x6TlsiotIWExOD9B+3wX7F1xD0emiaNEHCRx/iblwsEBdbqlkiIyNL9Xx5BJUKln6dkbXrF2jDz0BRrx4sXF0lyVLWREVFoVmzZvnaVSoVMjIyJEhEREREREREZRGLtkRE/4mJjsbyZi9hspU1AGBHZgamhP4ObejvkuZKT08v9XPKa9aEvF496K5cgebvQ7Dq/2apZyiLvLy8EBERgerVq5u0h4aGokGDBhKlIiIiIiIiorKGRVsiIgCiTgfNnM+MBduH9eujaeNG+EsQJMu073Ik5oWGIjs7W5Lzq159BbobN6C/exe6a9cAa2tJcpQlQUFBGDduHLKzsyGKIk6dOoUffvgBCxYswLfffit1PCIiIiIiIiojWLQlogrPkJmJh2PGwfqvv2AQRTx46SXU7NgBnhLnupoQL+n5ZXZ2ULZojpzjJ6A5/A8EPz9J85QF77zzDqysrPDJJ58gMzMTgwcPhru7O7766isMHDhQ6nhERERERERURrBoS0QVmj4xEQ8Ch0EbcQ6iUolRcffwUZ3aUscyG8rmzaG9cBFiairUV69KHadMGDJkCIYMGYLMzEykp6fDxcVF6khERERERERUxsikDkBEJBVdVBTu9+4DbcQ5yCpVwsP5n2GvRFMRmCtBoYDqlXYAgEqXI+Eq47BRWNbW1izYEhERERER0XPhnbZEpSAmJgaJiYlSxzBycnKCp6fUH/6XVs6Zs3gQOAyGpCRYeHqi8ub/Q2xKstSxzJK8Xj3IIiKAe7H40F4tdRyz1qxZMwgFzIMsCAIsLS1Ru3ZtDBs2DB07dpQgHREREREREZUVLNoSlbCYmBg0qF8fmVlZUkcxsrayQuTlyxW2cJv15594OGYcxOxsKJo2QeWNG2Dh7AycOSN1NLMkCAIsO3RAZsgP6GdljaToaOCll6SOZZa6deuG1atXo3HjxmjZsiUAICwsDP/++y+GDRuGS5cuwc/PDzt27EDv3r0lTktERERERETmikVbohKWmJiIzKwsrBs8GHVdXKWOg6sJ8RgVEoLExMQKWbRNX78BKZ/OAgwGqDp1guOaVZDZ2Egdy+xZuLkhvVo12N65A9stPwCvvy51JLOUmJiIKVOmYObMmSbt8+bNQ3R0NP7880/MmjULn332GYu2RERERERE9EQs2hKVkrourvCpVk3qGBWWaDAgdd58pH+zFgBgPWQIHD6fB0HOH4OFleTtDevbt2F5/Dhy/v0XyiZNpI5kdrZt24bw8PB87QMHDoSvry/WrVuHQYMGYenSpRKkIyIiIiIiorKCK8oQUbknZmUh6b2xxoKt/YwP4bBwAQu2RaRV22NnViYAIHXxlxKnMU+WlpY4duxYvvZjx47B0tISAGAwGIx/JyIiIiIiIioIi7ZEVK7pk5KQOGAQsvfsAZRKVPp6OezGjytwsSh6tmVpqRBlMmgOHIAm7LTUcczO+++/j/feew8TJ07E5s2bsXnzZkycOBFjxozBhAkTAAB79+6Fj4+PtEGJiIjKqcOHD6NXr15wd3eHIAjYtWuXyX5RFPHpp5+iSpUqsLKygp+fH65du2ZyTFJSEoYMGQJ7e3s4ODhg5MiRSE9PL8VXQURExKItEZVjuqgo3O/VGznh4RDUajiFbIY152J9Ibf0emT5dQYApC1eInEa8/PJJ59g3bp1OHXqFCZMmIAJEybg1KlTWLduHT7++GMAwHvvvYdff/31hc6j1+sxc+ZMeHl5wcrKCrVq1cJnn30GURSNxxTmopSIiKi8ycjIQNOmTbFy5coC9y9atAjLly/HmjVrcPLkSdjY2MDf3x/Z2dnGY4YMGYKLFy9i3759+O2333D48GG8++67pfUSiIiIAHBOWyIqpzRhp5E0YiQMSUmwqFYNlTdvgqJOHaljlQsZAwbA+u9D0Bw9Cs2Ro1C1ayt1JLOg0+nw+eefY8SIERgyZMgTj7Oysnrhcy1cuBCrV6/Gxo0b4e3tjdOnT2P48OFQq9XGO3rzLko3btwILy8vzJw5E/7+/rh06RKnZyAionIrICAAAQEBBe4TRRHBwcH45JNPjAuCbtq0Ca6urti1axcGDhyIyMhIhIaGIiwsDM2bNwcArFixAt27d8eSJUvg7u5eaq+FiIgqNt5pS0TlTsa27UjsPwCGpCQomjSG86+/sGBbjAwuzrAZMhgAkPrVconTmA+5XI5FixZBp9OV+LmOHTuG3r17o0ePHqhRowbeeOMNdO3aFadOnQKQ/6K0SZMm2LRpE+7du5fvY6JEREQVRVRUFOLi4uDn52dsU6vVaNWqFY4fPw4AOH78OBwcHIwFWwDw8/ODTCbDyZMnSz0zERFVXCzaElG5Ier1SPlsHpInBwE5ObDs5g+nn7bDwsVF6mjlju2YMYBcjpxjx5Bz5qzUccxG586dcejQoRI/T5s2bbB//35cvXoVAHDu3DkcOXLEeGdRYS5KC6LRaJCammryICIiKi/i4uIAAK6uribtrq6uxn1xcXFweey9o1wuh6Ojo/GYx3H8JCKiksDpEYioXDCkpiJp3PvQHDgAALCbOAF2U6dAkPF3U8UtMjISAGDfoT2s/tqP2/PmI+WTjyTJ4uTkBE9PT0nOXZCAgAB8+OGHOH/+PHx9fWFjY2Oy/7XXXiuW83z44YdITU1F/fr1YWFhAb1ej/nz5xunZSjMRWlBFixYgDlz5hRLRiIiooqC4ycREZUEFm2JqMzTRUXhwfCR0F27BliqUGnpl7D+b54yKj7xqakQALz11lsAgJoWcvzt4grLkyfRvVUrXC2FaQEeZ21lhcjLl82mcDt27FgAwNKlS/PtEwQBer2+WM6zbds2bNmyBSEhIfD29kZERAQmTZoEd3d3BAYGPne/M2bMQFBQkHE7NTUVHh4exRGZiIhIcm5ubgCA+Ph4VKlSxdgeHx8PHx8f4zEJCQkmz9PpdEhKSjI+/3EcP4mIqCSwaEtEZVr2X/uRNHEixOQUyNzcUPn7b6Fs2lTqWOVSSnYWRACLu3dHy7r1AACZx47B9s5d/NKiJRJatSrVPFcT4jEqJASJiYlmU7Q1GAylcp5p06bhww8/xMCBAwEAjRs3RnR0NBYsWIDAwMBCXZQWRKVSQaVSlWh2IiIiqXh5ecHNzQ379+83joepqak4efIkxowZAwBo3bo1kpOTER4eDl9fXwDAgQMHYDAY0OoJ73U4fhIRUUlg0ZaIyiRRp0Pq4iVI/3olAEDRrBkqf7cOFo99HJyKX03HyvCpVg0AoG/fHplbQmAXcxtufn6QqdUSp6sYMjMzIXts6g8LCwtj0bgwF6VERETlUXp6Oq5fv27cjoqKQkREBBwdHeHp6YlJkyZh3rx5qFOnDry8vDBz5ky4u7ujT58+AIAGDRqgW7duGDVqFNasWQOtVovx48dj4MCBcHd3l+hVERFRRcSiLRGVOfqEBCSNHYec4ycAADbDh0E98xMIvMOh1Fm4usKienXoo6ORczoclp07SR1JchkZGTh06BBiYmKQk5Njsm/ChAnFco5evXph/vz58PT0hLe3N86ePYulS5dixIgRAHKnYnjWRSkREVF5dPr0aXTs2NG4nTdtQWBgIDZs2IDp06cjIyMD7777LpKTk9GuXTuEhobC0tLS+JwtW7Zg/Pjx6Ny5M2QyGfr164fly5eX+mshIqKKjUVbIipTNMePI2nseBgSEiDY2MBh8SJY9y6exZ3o+ShbtkBWdDS0Fy5A2fplyKytpY4kmbNnz6J79+7IzMxERkYGHB0dkZiYCGtra7i4uBRb0XbFihWYOXMmxo4di4SEBLi7u2P06NH49NNPjccU5qKUiIiovOnQoQNEUXzifkEQMHfuXMydO/eJxzg6OiIkJKQk4hERERUal1UnojJB1OmQuiwYif0HwpCQAHm9unD+fQ8LtmbAolo1yNzcAL0e2nP/Sh1HUpMnT0avXr3w8OFDWFlZ4cSJE4iOjoavry+WLFlSbOexs7NDcHAwoqOjkZWVhRs3bmDevHlQKpXGY/IuSuPi4pCdnY2//voLdevWLbYMREREREREVHJYtCUis6e7GYX7ffoibcmXgMEAq3794Pzbr1DUriV1NEJucVD50ksAAO25cxB1OokTSSciIgJTpkyBTCaDhYUFNBoNPDw8sGjRInz00UdSxyMiIiIiIqIy4rmKtjVr1sSDBw/ytScnJ6NmzZovHIqICABEUUTG/21GQld/aM+ehWBvj0pfL0elr5ZV6I/gmyN5ndoQbG0hZmZCd+WK1HEko1AojAuEubi4ICYmBgCgVqtx+/ZtKaMRERERERFRGfJcRdtbt25Br9fna9doNLh79+4LhyIi0t64icQ3+yP5wxkQs7KgbNMGLn/9CevXX4cgCFLHo8cIFhZQ+PgAAHLCzzx1LrnyrFmzZggLCwMAtG/fHp9++im2bNmCSZMmoVGjRhKnIyIiMk+dOnVCcnJyvvbU1FR06sRFTomIqGIq0kJku3fvNv597969UKvVxm29Xo/9+/ejRo0axRaOiCoeMScH6avXIPWr5YBGA8HKCvYfTIfNyBEQZJzRxZwpmzRGzokTMCQmQn/7NuSenlJHKnWff/450tLSAADz58/H0KFDMWbMGNSpUwfff/+9xOmIiIjM099//42cnJx87dnZ2fjnn38kSERERCS9IhVt+/TpAyB3/sLAwECTfQqFAjVq1MCXX35ZbOGIqOIQRRGa/QeQMvcz6G7cAACoOrSHw4LPK2TxrywSLC2h8PaG9tw55Jw5UyG/b82bNzf+3cXFBaGhoRKmISIiMm///vu/BUwvXbqEuLg447Zer0doaCiqVq0qRTQiIiLJFaloazAYAABeXl4ICwuDk5NTiYQioopFe+UKUubMhebQYQCArHJlqGfPgtXrfTgVQhmjfKkZtOfOQX8zCoaHDyGrVEnqSERERGSmfHx8IAgCBEEocBoEKysrrFixQoJkRNI73byl1BGKVfPTp6SOQFTmFKlomycqKqq4cxBRBaS9cRNpXy1H1s6dgMEAKBSwfWck7Ca8D5m9vdTx6DnIKlWCRU0v6G9GIefMWVh2rljz0MXHx2Pq1KnYv38/EhIS8s3tW9B88ERERBVVVFQURFFEzZo1cerUKTg7Oxv3KZVKuLi4wMLCQsKERERE0nmuoi0A7N+/33hRmncHbh7O20dET6O9ehVpK75G1q5fcou1ACwDukH9yceQc17sMk/50kvIuhkF7aVLUL3SDoJSKXWkUjNs2DDExMRg5syZqFKlCu8UJyIieorq1asDQL7rSSIiInrOou2cOXMwd+5cNG/evEJdlMbExCAxMVHqGEZOTk7wrIBzRlLZJOr1yN63DxnrN0Jz5Iix3bKLHzIGDUS0szOQlJT7kEhkZKRk5y5PLDw8IKtUCYaHD6G9FAmlT1OpI5WaI0eO4J9//oGPj4/UUYiIiMqUa9eu4eDBgwXeFPTpp59KlIqIiEg6z1W0XbNmDTZs2IC33367uPOYrZiYGDSoXx+ZWVlSRzGytrJC5OXLLNySWdNevoysX3Yj8+cd0N+9m9sok8HSvyvsJryPOAcHs/u/lZ6eLnWEMk0QBCiaNoXm77+hPXcOiqZNKswv9zw8PPJNiUBERERPt27dOowZMwZOTk5wc3Mzed8gCAKLtkREVCE9V9E2JycHbdq0Ke4sZi0xMRGZWVlYN3gw6rq4Sh0HVxPiMSokBImJiSzaklkRdTpoz/2L7EOHkLVnD3SXrxj3ySpVgvWQwbB5+y3Iq1UDACSeOWM2/7f2XY7EvNBQZGdnS5qjPFB4N4TmyBEYHjyA/s5dyD2qSR2pVAQHB+PDDz/EN998gxqc6oOIiKhQ5s2bh/nz5+ODDz6QOgoREZHZeK6i7TvvvIOQkBDMnDmzuPOYvbourvCpVjGKD0SFYUhPh/bCBWj/PQ9N2Glojh6FmJLyvwOUSlh27ACr3q/Byt8fgqVlgf2Yw/+tqwnxkp6/PBFUKigaNID2/Hloz50r10XbSpUqmdwRlJGRgVq1asHa2hoKhcLk2CQJp/8gIiIyVw8fPsSbb74pdQwiIiKz8lxF2+zsbKxduxZ//fUXmjRpku+idOnSpcUSjqgiEUURYnY2xIwMICcHoiYHYo4GoiYnd1uvAwwiIBoAUcz9O0RAZgFYWAAWMggW//1dZgHBQgbI5bltcrnx78qUFNSwsIAsMRH6pIcQLFUQVKrc4/7LAb0eolb7Xw4NDA8fwpCUBEPSQ+hjY6GLjobuVjR0UVHQ37qVm+cRgloNVdu2sPTrBKtu3SBTq0v/C0qSU/g0hfb8eeiuX4chPR0yW1upI5WI4OBgqSMQERGVaW+++Sb+/PNPvPfee1JHISIiMhvPVbT9999/jYusXLhwwWRfRZm3kKjIDAYYkpNNHmJqGgwZGRDT0yFmZgJ6fYnH8ABwxLUKMHwk4h7dIZfnFnxzcvIVYZ/Fwt0diiaNoWzaFKp27XLnMP2vCEwVl4WzMyyqukN/9x605y9A1fplqSOViMDAQKkjEBERlWm1a9fGzJkzceLECTRu3DjfTUETJkyQKBkREZF0nqtoe/DgweLOQVRuiKII/d270F66BO2lSKiPHcPfzq6o+fMOZBSmGGppmXvnq0oFQamEoFICCiUEuRyQCYAgAwQh9+8QAIMB0Osg6g25RV+9HqIh90/o9BB1uty2//7U5WiQlZUNG4UCwqNFYp0u91EAwcEBFpUrQ+boCJmzM+Q1qkNevTosqleHokF9WDg5Fc8Xj8odRdOmuUXbf/+FsmWLclvMv3fvHpYuXYpPP/0U9vb2JvtSUlIwb948TJ06Fa6u0s+JTkREZG7Wrl0LW1tbHDp0CIcOHTLZJwgCi7ZERFQhPVfRtrisXr0aq1evxq1btwAA3t7e+PTTTxEQEAAgdxqGKVOmYOvWrdBoNPD398eqVat40UtmQxRF6KNuIefMGeREROQWaiMvQ0xNNR5jCaC2QpF796qFBWQODpA5OEBwcIDM3h6CrS1kNjYQbG0gWFvnFmdLUMSdO2gfvAzh4eFo1qQJxJwciNkaIDsbokGfWyhWKIC8PxUK3kFPz01epw4E60MQMzKgu3kTijp1pI5UIpYuXYrU1NR8BVsAUKvVSEtLw9KlS7Fw4UIJ0hEREZm3qKgoqSMQERGZneeqDnXs2PGpRZwDBw4Uqp9q1arhiy++QJ06dSCKIjZu3IjevXvj7Nmz8Pb2xuTJk7Fnzx5s374darUa48ePR9++fXH06NHniU30wgypqciJiEBO+BnknDkL7dmzMDx8mP9AhQLy2rWhaNgQCWp7vLt4ERYEDkOjOnXMqgAqyOW5RWJra6mjUDklWFhA4e2NnLAwaM9fKLdF29DQUKxZs+aJ+4cOHYpRo0axaEtERERERESF8lxF27z5bPNotVpERETgwoULRZrbr1evXibb8+fPx+rVq3HixAlUq1YN3333HUJCQtCpUycAwPr169GgQQOcOHECL79cPudGJPOif/AAOSdOQnPiBDTHT0B3+XL++V5VKigbN4aimQ+UjRpB0bAh5LVrQVAqAQC3zpzBP/M+g97a2qwKtkSlRdG4EXLCwqC/dQuG1FTICrgbtayLioqCp6fnE/dXq1bN+KkSIiIiMjVixIin7v/+++9LKQkREZH5eK6i7bJlywpsnz17NtLT058riF6vx/bt25GRkYHWrVsjPDwcWq0Wfn5+xmPq168PT09PHD9+/IlFW41GA41GY9xOfeRj6lRyYmJikJiYKHUMIycnp6cWUJ5En5gIzdFjyDlxApoTJ6G7ejXfMRY1qkP50ktQNmsG5UvNoGjY0FigJaL8ZA4OsPD0gD7mdu6CZG3bSB2p2FlZWeHWrVtP/Llz69YtWFlZlXIqIiKisuHhY59c02q1uHDhApKTk4038BAREVU0xTp55ltvvYWWLVtiyZIlhX7O+fPn0bp1a2RnZ8PW1hY7d+5Ew4YNERERAaVSCQcHB5PjXV1dERcXV3BnABYsWIA5c+Y870ug5xATE4MG9esjMytL6ihG1lZWiLx8+ZmFW1GjQc7pcGQfOgTNocPQXriQ7xh5/XpQtW4N1csvQ9mqJSycnUsqNlG5pWjcOLdoe/EilK1fhiCTSR2pWLVq1Qr/93//h1dffbXA/Zs2bULLli1LORUREVHZsHPnznxtBoMBY8aMQa1atSRIREREJL1iLdoeP34clpaWRXpOvXr1EBERgZSUFPz0008IDAzMt2JoUcyYMQNBQUHG7dTUVHh4eDx3f/RsiYmJyMzKwrrBg1HXRfpF4q4mxGNUSAgSExMLLNrqbkYh++BBZB86jJzjxyFmZprsVzRsCGXr1lC1bgVlq1awcHQsrehE5Za8Vi0IlpYQ09Ohv3UL8po1pY5UrKZOnYouXbpArVZj2rRpxgUz4+PjsWjRImzYsAF//vmnxCmJiIjKDplMhqCgIHTo0AHTp0+XOg4REVGpe66ibd++fU22RVFEbGwsTp8+jZkzZxapL6VSidq1awMAfH19ERYWhq+++goDBgxATk4OkpOTTe62jY+Ph5ub2xP7U6lUUKlURcpAxaOuiyt8qlWTOkY+osGAnDNnkf3nn8je+yd016+b7Jc5O0P16quw7NAeqlfa8U5aohIgyOWQezeENvwMtOfPl7uibceOHbFy5UpMnDgRy5Ytg729PQRBQEpKChQKBVasWMGPdxIRERXRjRs3oNPppI5BREQkiecq2qrVapNtmUyGevXqYe7cuejatesLBTIYDNBoNPD19YVCocD+/fvRr18/AMCVK1cQExOD1q1bv9A5qPyzBKA8FYaHP2xF9r6/YLh//387FQqoWrWCqkN7WL76KuQNG3CBMKJSoGjUCNrwM9DdjIIhPR0yW1upIxWr0aNHo0ePHti+fTuuX78OURRRt25dvPHGG6hmhr/QIiIiMhePflIS+N9NQXv27CnSQtdERETlyXMVbdevX18sJ58xYwYCAgLg6emJtLQ0hISE4O+//8bevXuhVqsxcuRIBAUFwdHREfb29nj//ffRunXrJy5CRhWbqNNBHx0Nl7MROOfmDpvP5iFv4gPBzg6WnTrC0r8rLDt2LJer1xOZO4vKlWFR1R36u/egvXgRqlatpI5U7CpVqoTJkydLHYOIiKhMOXv2rMm2TCaDs7MzvvzyS4wYMUKiVERERNJ6oTltw8PDERkZCQDw9vZGs2bNivT8hIQEDB06FLGxsVCr1WjSpAn27t2LLl26AACWLVsGmUyGfv36QaPRwN/fH6tWrXqRyFTOiAYD9LdvQ3f5CrTXrwMaDewAQCaD3skJ9j17wNK/K1QvvwxBqZQ6LlGFp2jcOLdoe+EClC1blru73F1dXdG/f3+MGDEC7dq1kzoOERFRmXDw4MFSO5der8fs2bOxefNmxMXFwd3dHcOGDcMnn3xifF8iiiJmzZqFdevWITk5GW3btsXq1atRp06dUstJRET0XEXbhIQEDBw4EH///bdxvtnk5GR07NgRW7duhXMh5wT97rvvnrrf0tISK1euxMqVK58nJpVjhqQkaC9egvbSJYgZGcZ2wcYGD93d8faxo/h29y54+vpKmJKIHievUwc4+DfElFToY2Igr15d6kjFavPmzdiwYQM6deqEGjVqYMSIERg6dCjc3d2ljkZERGT27t+/jytXrgDIXbC6sNeVRbFw4UKsXr0aGzduhLe3N06fPo3hw4dDrVZjwoQJAIBFixZh+fLl2LhxI7y8vDBz5kz4+/vj0qVLRV54m4iI6HnJnudJ77//PtLS0nDx4kUkJSUhKSkJFy5cQGpqqnGgIypuokaDnPPnkbF1KzI2bEROWFhuwdbSEoomjWH15huwGfUOHjTzwVltDlDO7uAjKg8EhQKKBg0AANrz5yVOU/z69OmDXbt24e7du3jvvfcQEhKC6tWro2fPntixYwcXUyEiIipARkYGRowYgSpVquDVV1/Fq6++Cnd3d4wcORKZmZnP7qAIjh07ht69e6NHjx6oUaMG3njjDXTt2hWnTp0CkHuXbXBwMD755BP07t0bTZo0waZNm3Dv3j3s2rWrWLMQERE9zXMVbUNDQ7Fq1So0+O/CGwAaNmyIlStX4o8//ii2cEQAoL9/H9n7/kL62nXQ7PsLhnuxgCDAwssLlj17wvbdUbD084PcwwOC7Ln+SRNRKVI0bgQA0F2/AUMxX4iZC2dnZwQFBeHff//F0qVL8ddff+GNN96Au7s7Pv3002K/ACUiIirLgoKCcOjQIfz6669ITk5GcnIyfvnlFxw6dAhTpkwp1nO1adMG+/fvx9WrVwEA586dw5EjRxAQEAAAiIqKQlxcHPz8/IzPUavVaNWqFY4fP16sWYiIiJ7muaZHMBgMUCgU+doVCgUMBsMLhyISdTrorl2H9tw56O/dM7bLHB0h924IRYMG5W7leaKKwsLZGTI3Nxji4qC7eAnKFs2ljlTs4uPjsXHjRmzYsAHR0dF44403MHLkSNy5cwcLFy7EiRMn8Oeff0odk4iIyCz8/PPP+Omnn9ChQwdjW/fu3WFlZYX+/ftj9erVxXauDz/8EKmpqahfvz4sLCyg1+sxf/58DBkyBAAQFxcHIHee+ke5uroa9z1Oo9FAo9EYt1NTU4stLxERVVzPVbTt1KkTJk6ciB9++ME4T9/du3cxefJkdO7cuVgDUsViyMyENiIC2nP/QszKym2UySCvXRuKpk1hUa1quVu4iKgiUjRuBE1cHLQXL0LR3Lfc/L/esWMH1q9fj71796Jhw4YYO3Ys3nrrLeP870DuHT6PflKFiIioosvMzMxXJAUAFxeXYv90yrZt27BlyxaEhITA29sbERERmDRpEtzd3REYGPhcfS5YsABz5swp1pxERETPVbT9+uuv8dprr6FGjRrw8PAAANy+fRuNGjXC5s2bizUgVQz6pCRow8OhvRQJ6PUAAMHWFoomTaBo5M27aonKGUW9etAc/BuGpCQYYuNg4V5F6kjFYvjw4Rg4cCCOHj2KFi1aFHiMu7s7Pv7441JORkREZL5at26NWbNmYdOmTcaFvrKysjBnzhy0bt26WM81bdo0fPjhhxg4cCAAoHHjxoiOjsaCBQsQGBgINzc3ALmfmqlS5X/vT+Lj4+Hj41NgnzNmzEBQUJBxOzU11XidTET0ok43byl1hGLV/PQpqSOUGc9VtPXw8MCZM2fw119/4fLlywCABg0amMz7Q/QsoihCf/sOcsLDoY+KMrbL3NygbO4Lee3anKOWqJwSlErI69aB7lIktBcvlpuibWxsLKytrZ96jJWVFWbNmlVKiYiIiMxfcHAwunXrhmrVqqFp06YAcueaValUxT6dUGZmJmSPXWNYWFgYp/nz8vKCm5sb9u/fbyzSpqam4uTJkxgzZkyBfapUKqhUqmLNSUREVKSi7YEDBzB+/HicOHEC9vb26NKlC7p06QIASElJgbe3N9asWYNXXnmlRMJS+SAaDNBdu4acsNMwJCQY2+W1a0Ph+xIs3N3LzUeliejJFA29c4u2V65A1aE9hALmSi8LHp+37mnz2Nnb25d0HLOnS0mXOgJVIPz3RlQ2NG7cGNeuXcOWLVuMNwUNGjQIQ4YMgZWVVbGeq1evXpg/fz48PT3h7e2Ns2fPYunSpRgxYgQAQBAETJo0CfPmzUOdOnXg5eWFmTNnwt3dHX369CnWLERERE9TpKJtcHAwRo0aVeBFp1qtxujRo7F06VIWbalAosEA3dWryDlxEoakpNxGuRwKb28oX2oGWaVKxXq+yMjIYu3veZlLDiJzY+FRDYK9PcTUVOiuX4eijM7z6uDg8MxfNImiCEEQoP9v+peKLPnoeakjEBGRmVmwYAFcXV0xatQok/bvv/8e9+/fxwcffFBs51qxYgVmzpyJsWPHIiEhAe7u7hg9ejQ+/fRT4zHTp09HRkYG3n33XSQnJ6Ndu3YIDQ01Tt1ARERUGopUtD137hwWLlz4xP1du3bFkiVLXjgUlS+iwQDd5cvQnDwF8eHD3EaVCspmzaBs5gOhmH97Hp+aCgHAW2+9Vaz9vqj0dN7tQ/QoQRCg8G6InOMnchckK6NF24MHD0odoUxxaNsYcjXnKafSoUtJ5y8KiMqAb775BiEhIfnavb29MXDgwGIt2trZ2SE4OBjBwcFPPEYQBMydOxdz584ttvMSEREVVZGKtvHx8VA85eOrcrkc9+/ff+FQVD6Iej10kZehOXkSYkpKbqOlJZS+L0Hp4wOhhOZ9SsnOgghgcffuaFm3Xomcoyj2XY7EvNBQZGdnSx2FyOwoGuYWbfUxt2FITYWsDE4f0L59e6kjlClytS0UldVSxyAiIjMSFxdnsuhXHmdnZ8TGxkqQiIiISHpFKtpWrVoVFy5cQO3atQvc/++//xY42FLFIur10F68hJxTpyD+N7ejYGUFha8vlD5NISiVpZKjpmNl+FSrVirnepqrCfFSRyAyWzK1GhYeHtDfvg3txUtQtX5Z6kgvLDk5GadOnUJCQoJxUZM8Q4cOlSgVEZmr+Eyd1BGoAjHXf28eHh44evQovLy8TNqPHj0Kd3d3iVIRERFJq0hF2+7du2PmzJno1q1bvvl8srKyMGvWLPTs2bNYA1IZotcj59w55JwKg5iWBgAQrK2hbO4LRZMmpVasJaKyReHtnVu0vXQRypdblemFCH/99VcMGTIE6enpsLe3N3ktgiCwaEtERnZ2dlAqFNh8JUnqKFTBKBUK2NnZSR3DxKhRozBp0iRotVp06tQJALB//35Mnz4dU6ZMkTgdERGRNIpUtP3kk0+wY8cO1K1bF+PHj0e9erkfPb98+TJWrlwJvV6Pjz/+uESCkhnLycEwaxtU//0PaLKyAACCjTWULVpA0bhxmV0RnohKh7xObeCAEmJKKvR37kDu4SF1pOc2ZcoUjBgxAp9//jmsra2ljkNEZszJyQlLvvwSaf/9opsK7+7du1i1ahXGjh2LqlWrSh2nzLGzs4OTk5PUMUxMmzYNDx48wNixY5GTkwMAsLS0xAcffIAZM2ZInI6IiEgaRSraurq64tixYxgzZgxmzJgBURQB5N495O/vj5UrV8LV1bVEgpL5MWRlIXPzFjgtX455DpWArCwItra5xdpGjSAoivTPi4gqKEGhgKJeXWjPX4D24sUyXbS9e/cuJkyYwIItERWKk5OT2RXPypKqVavm+zg9lU2CIGDhwoWYOXMmIiMjYWVlhTp16kBVQmtgEBERlQVFrqpVr14dv//+Ox4+fIjr169DFEXUqVMHlSpVKol8ZIYMmZnI2LQJ6au/gSExERYA7up0ULZsCa92bSHIWawloqJReHtDe/4CdFevQezUqcxOp+Lv74/Tp0+jZs2aUkchIiIqc2xtbdGiRQupYxAREZmF566uVapUiQNqBWNIT0fGho1I/2YtDEm5869ZeHjgYe/X0O6jGfirdi0WbInouciqVIGsUiUYHj6E9spVKBs3kjpSoe3evdv49x49emDatGm4dOkSGjduDMVj08O89tprpR2PiIiIiIiIyiBW2OiZDKmpyFi/AWlr10FMTgYAWNSoDrsJ78O6b1/cO38e2o841xQRPT9BECD39kbOkSPQXbxYpoq2ffr0ydc2d+7cfG2CIECv15dCIiIiIiIiIirrWLSlJzIkJyP9+/VI//Y7iCkpAAALLy/YT5wAq9f78K5aIipWioYNkHP0KPT37sHw8CFkZWTaHYPBIHUEIiIiIiIiKmdYdaN8DA8fIn3dt0j/fj3E/1Y0lteuDbtJE2D12msQLCwkTkjFITIyUuoIAMwnB0lPZmsLixo1oI+KgvbiRajatZM6UpFt2rQJAwYMyLdwSk5ODrZu3YqhQ4cW27nu3r2LDz74AH/88QcyMzNRu3ZtrF+/Hs2bNwcAiKKIWbNmYd26dUhOTkbbtm2xevVq1KlTp9gyEBERERERUclg0ZaMDA8fIn3tutxibXo6AEBevx7sJk6EVY/uLNaWE/GpqRAAvPXWW1JHMZH+3785qtgU3g1zi7aXIqFs0waCTCZ1pCIZPnw4unXrBhcXF5P2tLQ0DB8+vNiKtg8fPkTbtm3RsWNH/PHHH3B2dsa1a9dMFgVdtGgRli9fjo0bN8LLywszZ86Ev78/Ll26BEtLy2LJQURERERERCWDRVvKnQZh3bdI/+77/91Z26AB7CdPgmVAtzJXNKGnS8nOgghgcffuaFm3ntRxsO9yJOaFhiI7O1vqKGQG5DVrApaWENPToY+OgdyrhtSRikQURQiCkK/9zp07UKvVxXaehQsXwsPDA+vXrze2eXl5meQIDg7GJ598gt69ewPIvQvY1dUVu3btwsCBA4stCxERERERERU/Fm0rMENKSm6x9tvvTIu1QZNh2c2fxdpyrqZjZfhUqyZ1DFxNiJc6ApkRQS6Hon59aCMioL14scwUbZs1awZBECAIAjp37gz5I3N+6/V6REVFoVu3bsV2vt27d8Pf3x9vvvkmDh06hKpVq2Ls2LEYNWoUACAqKgpxcXHw8/MzPketVqNVq1Y4fvw4i7ZERERERERmjkXbCsiQkoL0b7/LLdampgIA5A3qw37yZN5ZS0SSUzTyhjYiArobNyBmZ0MoAx/l79OnDwAgIiIC/v7+sLW1Ne5TKpWoUaMG+vXrV2znu3nzJlavXo2goCB89NFHCAsLw4QJE6BUKhEYGIi4uDgAgKurq8nzXF1djfsKotFooNFojNup/40RREREREREVLpYtK1ADKmpSP/ue6Sv+xZiSgqA3Dlr7SdPhmX3ABZricgsyJydIXNygiExEdorV6Bs2lTqSM80a9YsAECNGjUwYMCAEp8z1mAwoHnz5vj8888B5N7pe+HCBaxZswaBgYHP3e+CBQswZ86c4opJREREREREz4lVugrAkJmJtBVfI+7lNkhb8iXElBTI69WF45rVcNn3J6x69mDBlojMhiAIUDTyBgBoL1yUOE3RBAYGwtLSEjk5Obhz5w5iYmJMHsWlSpUqaNiwoUlbgwYNjOdwc3MDAMTHm04/Eh8fb9xXkBkzZiAlJcX4uH37drFlJiIiIiIiosLjnbblmKjVIvOHrUhdFgxDQgIAQF63LuwmT2KhlojMmrx+fWgO/wNDfDz0iYmwcHKSOlKhXLt2DSNGjMCxY8dM2vMWKNPr9cVynrZt2+LKlSsmbVevXkX16tUB5C5K5ubmhv3798PHxwdA7lQHJ0+exJgxY57Yr0qlgkqlKpaMRERERERE9PxYtC2HRIMBWb/+htRFi6G/dQsAYOHpCftpU2HV+zUIFhbSBiQiegaZtTXkXl7Q3bgB7aVLsHj1VakjFcqwYcMgl8vx22+/oUqVKhAEoUTOM3nyZLRp0waff/45+vfvj1OnTmHt2rVYu3YtgNy7lSdNmoR58+ahTp068PLywsyZM+Hu7m6cf5eIiIiIiIjMF4u25Ywm7DRSZs2C9ty/AACZkxPsJk2EzZDBEJRKidMRERWe3Nsbuhs3oIuMhNiuXZn4dEBERATCw8NRv379Ej1PixYtsHPnTsyYMQNz586Fl5cXgoODMWTIEOMx06dPR0ZGBt59910kJyejXbt2CA0NLfH5domIiIiIiOjFsWhbTuhjY5Hy+QJk7dgJABBsbWH73mjYvjsKMhsbidMRERWd3KsGBCsriBmZ0N+6BXnNmlJHeqaGDRsiMTGxVM7Vs2dP9OzZ84n7BUHA3LlzMXfu3FLJQ0RERERERMXH/G9boqfLyUHa8hWIf7VDbsFWEGA9eBBcj/4D+8mTWLAlojJLsLCAvEHuHavai5ckTlM4CxcuxPTp0/H333/jwYMHSE1NNXkQERERERERFQbvtC3D2qtUqDzufaTGxQEAlL6+UM+bC2WTJhInIyIqHgpvb2jPnIXu5k2IWVkQrKykjvRUfn5+AIDOnTubtBf3QmRERERERERUvrFoWwYZsrLgcvIUtlR2BuLiIHNzhfrjj2H1ep8SW/SGiEgKFs7OkLm4wJCQAO3lK1A285E60lMdPHhQ6ghERERERERUDrBoW8Zor1yB5sBB2GVlwSCKyOr9GmovXgSZra3U0YiISoSiYUNoEhKgvXTR7Iu27du3lzoCERERERERlQMs2pYRYnY2svcfgO7KFQCARm2PN65fx7ej3mHBlojKNXmD+tAcPgxDfAL09xNh4ewkdaSnSk5OxnfffYfIyEgAgLe3N0aMGAG1Wi1xMiIiIiIiIioruBBZGaC7fQcZ/7c5t2ArCFC+/DLu+HXBWW2O1NGIiEqczMoK8po1AQDaSxclTvN0p0+fRq1atbBs2TIkJSUhKSkJS5cuRa1atXDmzBmp4xERERGAu3fv4q233kLlypVhZWWFxo0b4/Tp08b9oiji008/RZUqVWBlZQU/Pz9cu3ZNwsRERFQRsWhrxkSDAZqjx5C1fTvEtDQIajWsBwyAqk1rwILfOiKqOBTeDQEAusjLEM14Ma/Jkyfjtddew61bt7Bjxw7s2LEDUVFR6NmzJyZNmiR1PCIiogrv4cOHaNu2LRQKBf744w9cunQJX375JSpVqmQ8ZtGiRVi+fDnWrFmDkydPwsbGBv7+/sjOzpYwORERVTScHsFMGbKykP3779BHxwDIXUFd1bEDBKVS2mBERBKwqFEDgrU1xMxM6G/dgrxWLakjFej06dNYt24d5PL/Da9yuRzTp09H8+bNJUxGREREALBw4UJ4eHhg/fr1xjYvLy/j30VRRHBwMD755BP07t0bALBp0ya4urpi165dGDhwYKlnJiKiiom3a5ohfVwcMjdvyS3YyuWwDOgGS/+uLNgSUYUlWFhA3qA+AEB78ZLEaZ7M3t4eMTEx+dpv374NOzs7CRIRERHRo3bv3o3mzZvjzTffhIuLC5o1a4Z169YZ90dFRSEuLg5+fn7GNrVajVatWuH48eMF9qnRaJCammryICIielG807aIEhMTEWthUWL9y2/dgurkKQgGAwy2tsh+pR3SHRyA2Nh8OQAYF7qRkjlkIKLyT9HQG9rwM9DdvAlDZqbUcQo0YMAAjBw5EkuWLEGbNm0AAEePHsW0adMwaNAgidMRERHRzZs3sXr1agQFBeGjjz5CWFgYJkyYAKVSicDAQMTFxQEAXF1dTZ7n6upq3Pe4BQsWYM6cOSWenYiIKhYWbQsp9r+i6Y4dO3C0hIq2LyuVaKOyBABc12oRGnsPOdu2FXjsTa0WAoC33nqrRLI8j/T0dKkjEFE5ZuHsBJmrCwzxCdBdvgy4uEgdKZ8lS5ZAEAQMHToUOp0OoihCqVRizJgx+OKLL6SOR0REVOEZDAY0b94cn3/+OQCgWbNmuHDhAtasWYPAwMDn6nPGjBkICgoybqempsLDw6NY8hIRUcUladF2wYIF2LFjBy5fvgwrKyu0adMGCxcuRL169YzHZGdnY8qUKdi6dSs0Gg38/f2xatWqfL/5LGnJyckAgPo+rdDIs3jnUhQMBtS4egFOCXcBALHVvPDQqx5aCcITn5N1KQLiv2H4tN0r6CzxPIn7LkdiXmgoJ+YnohKnaOgNTXxC7hQJZli0VSqV+Oqrr7BgwQLcuHEDAFCrVi1YW1tLnIyIiIgAoEqVKmjYsKFJW4MGDfDzzz8DANzc3AAA8fHxqFKlivGY+Ph4+Pj4FNinSqWCSqUqmcBERFRhSVq0PXToEMaNG4cWLVpAp9Pho48+QteuXXHp0iXY2NgAyF2Je8+ePdi+fTvUajXGjx+Pvn374ujRo5JktrJVw6Fy8RUKBJ0W7icOwvp+LERBwP2mrZDuVQ8Oz3ieytoWAFBdrYZPtWrFlud5XE2Il/T8RFRxKOrXg+bwYRju34fyYbLUcYxGjBhRqOO+//77Ek5CRERET9O2bVtcuXLFpO3q1auoXr06gNxFydzc3LB//35jkTY1NRUnT57EmDFjSjsuERFVYJIWbUNDQ022N2zYABcXF4SHh+PVV19FSkoKvvvuO4SEhKBTp04AgPXr16NBgwY4ceIEXn75ZSliFxtZjgbux/bD6uF9GORyxLbsgEzXqlLHIiIyW4KVFeQ1a0J37Rrsbt2SOo7Rhg0bUL16dTRr1gyiKEodh4iIiJ5g8uTJaNOmDT7//HP0798fp06dwtq1a7F27VoAgCAImDRpEubNm4c6derAy8sLM2fOhLu7O/r06SNteCIiqlDMak7blJQUAICjoyMAIDw8HFqt1mTlzvr168PT0xPHjx8vsGir0Wig0WiM2+a6cqeFJhvuR/fBMiUJeoUSd9v4QePoLHUsIiKzp/BumFu0jYmGQuow/xkzZgx++OEHREVFYfjw4XjrrbeMYxkRERGZjxYtWmDnzp2YMWMG5s6dCy8vLwQHB2PIkCHGY6ZPn46MjAy8++67SE5ORrt27RAaGgpLS0sJkxMRUUUjkzpAHoPBgEmTJqFt27Zo1KgRACAuLg5KpRIODg4mxz5r5U61Wm18mOME8BZZmah2OBSWKUnQqSxx5xV/FmyJiArJokYNCDbWsNDkoJOZXDytXLkSsbGxmD59On799Vd4eHigf//+2Lt3L++8JSIiMjM9e/bE+fPnkZ2djcjISIwaNcpkvyAImDt3LuLi4pCdnY2//voLdevWlSgtERFVVGZTtB03bhwuXLiArVu3vlA/M2bMQEpKivFx+/btYkpYPCw0Wah25E8o01OgtbLBnVe6IUfNu7GIiApLkMkgb9AAANDfykbiNP+jUqkwaNAg7Nu3D5cuXYK3tzfGjh2LGjVqID09Xep4REREREREVIaYRdF2/Pjx+O2333Dw4EFUe2RRLTc3N+Tk5CA5Odnk+Pj4eOOqno9TqVSwt7c3eZgLWY4GVY/u+69ga407r/hDa6eWOhYRUZmj+G/V5/aWlhAyMiROk59MJoMgCBBFEXq9Xuo4REREREREVMZIWrQVRRHjx4/Hzp07ceDAAXh5eZns9/X1hUKhwP79+41tV65cQUxMDFq3bl3acV+ITJuDqsf2QZXyEDqVJe627QqdjZ3UsYiIyiQLJycktGiOtvGxEG3M425bjUaDH374AV26dEHdunVx/vx5fP3114iJiYGtra3U8YiIiIiIiKgMkXQhsnHjxiEkJAS//PIL7OzsjPPUqtVqWFlZQa1WY+TIkQgKCoKjoyPs7e3x/vvvo3Xr1gUuQmauBL0O7sf3w/LhA+iVKtxt15V32BIRvaA0Ly/EGwxSxwAAjB07Flu3boWHhwdGjBiBH374AU5OTlLHIiIiIiIiojJK0qLt6tWrAQAdOnQwaV+/fj2GDRsGAFi2bBlkMhn69esHjUYDf39/rFq1qpSTvgBRhOvpI7B6kAC9XIG7bbsgx76S1KmIiKgYrVmzBp6enqhZsyYOHTqEQ4cOFXjcjh07SjkZERERERERlUWSFm0Ls6K2paUlVq5ciZUrV5ZCouLndP407O5FwyCTIfblTtA4VJY6EhERFbOhQ4dCEASpYxAREREREVE5IWnRtrxzuH4JlW5cAgDEv9QWWc4FL55GRERl24YNG6SOQEREREREROWIpAuRlWc292LgdD4MAJDo/RLSPWpKnIiIiIiIiIiIiIjKAhZtS4AyJQlup/+BACDZqx4e1mkkdSQiIiIiIiIiIiIqI1i0LWYyTTbcTxyETK9DpnMV3G/SEuA8h0RERERERERERFRILNoWJ4MBVU4dgiIzHTk2toht2R6Q8UtMREREREREREREhceKYjFyPh8G68Q4GORyxL7cCQalSupIREREREREREREVMawaFtM7GJuwOHmZQBAnO8ryLGvJHEiIiIiIiIiIiIiKovkUgcoD5SpD+EScQIA8KB+U2S4e0qciIiIiIiIiEha3Wb+KHWEYhX62QCpIxBRBcI7bV+QoNPC7dQh48JjSfWbSB2JiIiIiIiIiIiIyjAWbV+EKMLl7HGo0lKgs7RCXItXAIFfUiIiIiIiIiIiInp+rDC+APtbV2F/JwqiICC2RXvoVVZSRyIiIiIiIiIiIqIyjkXb56RMfQjnf8MAAIkNX0K2k6vEiYiIiIiIiIiIiKg8YNH2OQh6PdzC/oHMoEeGizuS63hLHYmIiIiIiIiIiIjKCRZtn0PlS2egSn0InVKFeN92gCBIHYmIiIiIiIiIiIjKCbnUAcoa1/QUVLp1FQCQ8FJb6C05jy0REREREREREREVH95pWwSVZDK8fCcKAJDsVQ8ZVTwkTkRERERERERERETlDYu2hSWKWKyuBCudFjm2aiQ2ai51IiIiIiIiIiIiIiqHOD1CIVU5FYZ6VlbQCwLiWrwCUc4vHRERERERERERERU/3mlbCNrrN1B7968AgH9dq0LjUFniRERERERERERERFResWhbCLqoKIiCgCOabFyp7CZ1HCIiIiIiIioGX3zxBQRBwKRJk4xt2dnZGDduHCpXrgxbW1v069cP8fHx0oUkIqIKiUXbQrDq4ofTkydi0sMkQBCkjkNEREREREQvKCwsDN988w2aNGli0j558mT8+uuv2L59Ow4dOoR79+6hb9++EqUkIqKKikXbQsquXBlxBoPUMYiIiIiIiOgFpaenY8iQIVi3bh0qVapkbE9JScF3332HpUuXolOnTvD19cX69etx7NgxnDhxQsLERERU0bBoS0RERERERBXKuHHj0KNHD/j5+Zm0h4eHQ6vVmrTXr18fnp6eOH78eIF9aTQapKammjyIiIheFIu2REREZRzn4yMiIiq8rVu34syZM1iwYEG+fXFxcVAqlXBwcDBpd3V1RVxcXIH9LViwAGq12vjw8PAoidhERFTBsGhLRERUhnE+PiIiosK7ffs2Jk6ciC1btsDS0rJY+pwxYwZSUlKMj9u3bxdLv0REVLGxaEtERFRGcT4+IiKiogkPD0dCQgJeeuklyOVyyOVyHDp0CMuXL4dcLoerqytycnKQnJxs8rz4+Hi4ubkV2KdKpYK9vb3Jg4iI6EWxaEtERFRGFed8fP/f3p2HR1Xf/f9/ntmzryQhECAoCsgiskasokYR+/VGpdVaa0G9vX93DVRMvdvauxa1KlZ7t2qLWFsFbS/cakHFCsVUYrGsAZTNIAgNhSSAkD2Z7ZzfH4GRQJAEksxMeD2u61wzZ5lz3p/Mcua8cuZzREREzgZXXnklmzZtYuPGjaFh1KhR3HrrraH7TqeToqKi0GNKS0spKysjLy8vjJWLiMjZxhHuAkRERKT9jvbHt3bt2hPmnU5/fNB8IRWv1xsa14VURESku0lISGDIkCEtpsXFxZGWlhaafuedd1JYWEhqaiqJiYnMmDGDvLw8xo0bF46SRUTkLKXQVkREJMoc7Y9v2bJlHdYfHzRfSOWhhx7qsPWJiIhEo1//+tfYbDamTJmC1+tl4sSJPPvss+EuS0REzjIKbUVERKLMsf3xHRUMBvnwww/57W9/y9KlS0P98R17tu1X9ccHzRdSKSwsDI3X1NR0+BWwA9X1Hbo+ka+i15uItMXy5ctbjHs8HubMmcOcOXPCU5CIiAgKbUVERKLO0f74jnX77bczcOBAfvSjH5GTkxPqj2/KlClA2/rjc7vduN3uTqk5ISEBp8tF1UefdMr6RU7G6XKRkJAQ7jJERERERNpFoa2IiEiUicb++NLT0/m/X/6S2trasGw/mu3du5dnn32Wu+++m169eoW7nKiTkJBAenp6uMsQEREREWkXhbYiIiLdUCT2x5eenq7w7Az06tWL3NzccJchIiIiIiJdQKFtFDPsTqrcyWwMxHLIcnLIdHDIcnDYclBn2WmybDRZBk3YaLJs+DGaH3dkALBh4TIsPJjEGCYewyQGk1jDJMkIkGgESTICJBlBkmxB0g0/KUYAu3HSskREJAzUH5+IiIiIiEj3odA2CpjAF7YY9jri2euIZ789lt2XDGF0/n28brPzesMZbsBq3+J2LNIMPz1sfnoYAQ6nmqQPP8C/3T04bNpJNoIYCnVFREREREREREROi0LbCBTEYK8jns8dSXzuTOJfjkS8tuOeKlfz2bKOoI+eTotUI0Cq4SfVFiDlyBmyHsPEjYnnyJm0LsMEwMLAgtDgs2w0WrbQGbmN2Ki3bFRbDqotOzWmnSrLQZXVfCZvEIP9lov9QVdzLalDOWfyUBYAC+ogliC9bT5y7U30s3npb2+in62JFFuw6/6IIiIiIiIiIiIiUUqhbYSoNxyUOlPZ5kpluzPlhJDWaQXpGainV7COrEA9pdvW8qd/LGbuZRfzzcsv77I6gxYcshwcMJ0csJwcMJ38o7KKVYcayMk5j1pnHA3Y2W7GsN2MafHYFMPPufYmzrc1MtDeyHn2RpIV5IqIiIiIiIiIiLSg0DaMvNjZ7E5jgyuDnc5krGP6FIg1/eT6q+kfqCbXX01msB77MY+tOLwHf90huroXArsBPYwAPWwBoBEA82AJ8xcs4NHv3Malw0ewz3Sxx3SxK+hht+nhc9NNuenisOVkbcDJWhJC68syfAy0NzLUUc9QewN9bV51rSAiIiIiIiIiImc1hbZdzAJ2OxJZ7enJFlcafuPLKDYrUMdg3yEG+b6gV7AOWxvWV1NbS3l5eafV2xZVh6tC912GRT+7l352L19z1oamN1o2dgfdbDdjKA02D3tMNxWWi4qAi+WBJACSjABD7A0MtddzkaNeIa6IiIiIiIiIiJx1whrafvjhhzz55JOUlJRQXl7OwoULuf7660PzLcti1qxZ/P73v6eqqorx48czd+5cBgwYEL6iT5MPGxvdGfzT05MKR3xoenqwgRHe/VzoPUCa2dTm9QV8XgDWrVvHng0bOrze9tjm9wHQ2FB/0mViDJNBjkYGHTk7F6DOsrE9GMPWYCybArFsDcZSbTn4KJDIR4FE8EK64WeUo45RjjouctQRf6RfXhERERERERERke4qrKFtfX09w4cP54477uDGG288Yf4TTzzBM888w0svvURubi4PPPAAEydOZOvWrXg8njBU3H6Nhp1/erL5yNOLBpsTaO6f9kLvfsZ4K+gdqDutLg6CAT8AvfqfT96QkR1Ycfs1bt0In6zF6/W163HxhslFjuYzanGD3zL4LOhhUzCOjwOxfBKM46DlZIk/hSX+FGxYDLI3MNJRzzhHLefYmnQWroiIiIiIiIiIdDthDW0nTZrEpEmTWp1nWRZPPfUUP/3pT5k8eTIAL7/8MpmZmSxatIhvfetbXVlqu9UbDlZ4evFPT3boomKpwUbymsoZ6a0k1gp0yHZcnliS0zI6ZF2nyx0bf+qF2sBpWAx2NDLY0cjNbvBaBpuCsawLxLMuEE+Z6WFLMI4twThe9maQafi4xFnDeEctg+wN2BXgioiIiIiIiIhINxCxfdru2rWLiooK8vPzQ9OSkpIYO3YsK1eujNjQ1oeNjzzZLI/JCYW1mYF6Lm/cw1DfgRYXE5Ov5jYsRjnqGeWoByqpNJ2sC8SzJhDP+kA8lZaLN33pvOlLJ8Xwk+eo5RJnDcE29QYsIiIiIiIiIiISmSI2tK2oqAAgMzOzxfTMzMzQvNZ4vV68Xm9ovKampnMKPI4JlLgzWRbblxqbG4CegTryG8oY5P/irIgRu+KiaBcdGbzY2GRPo8SRwUZ7Oodx8ld/Kn/1p+Lsl0W/r6ey1d/EoPLysP/tj71Qm4iIiIiIiIiIyKlEbGh7umbPns1DDz3UpdsscyTwVtw57HUkAJASbOLqht0M9x0Ie2DYFcJ5UTQDGG6zU937Ar4YMJZD54zGH5dM5shrWQz8rfYL0ks/osenK4g7sPu0+g8+U225UJuIiIiIiIiIiMhRERvaZmVlAVBZWUnPnj1D0ysrK7nwwgtP+rj777+fwsLC0HhNTQ05OTmdUmOd4WRJbD/WeZprdZsB8hvLyGvahwOrU7YZiSLnomg1mHuLeG3vAdYm9aPnkMvwJaSxb9R/sG/Uf5DmrWFw7R6G1JSRFGjosqpO90JtIiIiIiIiIiJydorY0DY3N5esrCyKiopCIW1NTQ2rV6/me9/73kkf53a7cbvdnVqbBXzs6sHbcefQYHMCMLKpgmsadpNg+Tt125EsEi6KBpC2dSO7/rGIW/ZvIPviyWx092CbK5Uv3In8w30BK9IGc46/ipHeSob4vsCJ2an1dNSF2kRERERERERE5OwQ1tC2rq6OHTt2hMZ37drFxo0bSU1NpU+fPsycOZNHHnmEAQMGkJubywMPPEB2djbXX3992GpudMbycsJgtrnSAMgK1HFD/Q76BmrDVpO0zmYGucD/BRf4v6DJsLPFlcZ6dyY7ncnscKWww5XCIjPAcN8BRnkryQnUhqX7BBERERERERERkWOFNbRdt24dl19+eWj8aLcGU6dOZf78+fzwhz+kvr6e//qv/6KqqopLLrmEJUuW4PF4urxWy4L04VexdPhN+B1u7JbJFY1lXNb477OqK4Ro5bGCjPTuZ6R3P4dsbta7MylxZ3LY7mGNpydrPD3JCDQw0lvJCG8liWfxGdMiIiIiIiIiIhJeYQ1tJ0yYgGWdPPA0DIOHH36Yhx9+uAurOlFldSPv7E3knMk/wA/0CtTyzbrtZAW7rl9U6Tipppf8xjKuaCxjlyOJdZ5MNrvS2e+I5T1HLktj+zHI9wVjvBUM8B8+Ky4mJyIiIiIiIiIikSNi+7SNJJv2VPOvBhdmwMfwfRv4VpwPe7iLkjNmA84JVHNOXTWTjZ184kpnnTuLMmciW9zpbHGnkxxsYrS3gtFNlSRaupCYiIiIiIiIiIh0Pp1E2AZXXpDJ6NQGNj1fwMB9GxXYdkMeK8gYbyV313zMvVUljG/cS4zpp8ruYVlsPx5PGcPLCYP41JnSyZctExERERERERGRs53OtG0DwzAYl97Abw/uAUaFuxzpZJnBBq5r+JxrGnaz2ZXGak9PdjuT2OpKZ6vry7NvR3krSTJ19q2IiIiIiIiIiHQshbYiJ+HEZITvACN8B6i0x7LGncV6d0bo7Nv3Y/oy0H+IsU3lnKe+b0VEREREREREpIMoZxJpg6Nn3/7k8Gpurv2UXH81lmGwzZXG/MQh/CJ5DO/H9KHK5gp3qSIiIiIichKzZ89m9OjRJCQkkJGRwfXXX09paWmLZZqamigoKCAtLY34+HimTJlCZWVlmCoWEZGzlUJbkXZwYjHCd4D/r+YTCg+v45LGfxNr+qm2u3k/ti+/SB7D/ITBbHWmEgx3sSIiIiIi0kJxcTEFBQWsWrWKZcuW4ff7ufrqq6mvrw8tc++99/LOO+/wxhtvUFxczL59+7jxxhvDWLWIiJyN1D2CyGnKMBv5fw27mNiwmy2udFZ7stjlTOZTVxqfutJINL2MaqqkybM53KWKiIiIiAiwZMmSFuPz588nIyODkpISLr30Uqqrq3nhhRdYsGABV1xxBQDz5s1j0KBBrFq1inHjxoWjbBEROQsptBU5Q04sLvQd4ELfAQ7YYljryaLEnUmNzc3fY/vA1/6b87NHs8vYT8AChxHuikVEREREBKC6uhqA1NRUAEpKSvD7/eTn54eWGThwIH369GHlypUKbUVEpMsotBXpQD3MRq5t2MXVDbvZ6kpjjTuLHa4Uks8dxTJgXZ2fq51VTHIdJtvmD3e5IiIiIiJnLdM0mTlzJuPHj2fIkCEAVFRU4HK5SE5ObrFsZmYmFRUVra7H6/Xi9XpD4zU1NZ1Ws4iInD0U2op0AgcWw3wHGeY7yGtbt/Kukcw5Y/8fh51xvObrwWu+Hoyw13Gt6zAXO2pxGla4SxYREREROasUFBSwefNmVqxYcUbrmT17Ng899FAHVSUiItJMFyIT6WSexir2/H0et37yEj+LKWOUvRYDiw3BeB5tzOHbdefxfFMm/w66wl2qiIiIiMhZYfr06SxevJgPPviA3r17h6ZnZWXh8/moqqpqsXxlZSVZWVmtruv++++nuro6NOzZs6czSxcRkbOEzrQV6SI2TC5x1nKJs5YK08kSXwpL/cl8YTn5sy+dP/vSGWav51rXYcY7anDr7FsRERERkQ5lWRYzZsxg4cKFLF++nNzc3BbzR44cidPppKioiClTpgBQWlpKWVkZeXl5ra7T7Xbjdrs7vXYRETm7KLQVCYMsm59pnv3c5t7PmkACf/WnsDYQzyfBOD5pjCOOIBOc1VztrGKgvRFDFy8TERERETljBQUFLFiwgLfeeouEhIRQP7VJSUnExMSQlJTEnXfeSWFhIampqSQmJjJjxgzy8vJ0ETIREelSCm1FwshuQJ6zljxnLftNB0v9KSz1JbPfcvGuP5V3/an0tnm52llFvrOKdFsg3CWLiIiIiEStuXPnAjBhwoQW0+fNm8e0adMA+PWvf43NZmPKlCl4vV4mTpzIs88+28WViojI2U6hrUiEyLAFuM19gFtdB/g4GMcyfzL/8Cfyb9PNi95M5nszuMhex1WuKi521Kr7BBERERGRdrKsU3+H9ng8zJkzhzlz5nRBRSIiIq1TaCsSYWwGjHDUM8JRT4GnnH/4E/mbP5nNwTjWBRNY15gQ6j7hKmcVg9R9goiIiIiIiIhIt6LQViSCxRkm17iquMZVxT7Tyfu+ZJb5k6k8pvuEnoaPCc5qrnBW09fuDXfJIiIiIiIiIiJyhhTaikSJbJuf73oO8B33AT4JxvI3fwor/AmUWy5e8fXgFV8P+tuauNxZzQRnNZk2f7hLFhERERERERGR06DQVqSL1NTWUl5e3iHrygRuA27CxgZ7D1Y5svjEnsbnpofPvR5e8GYyIFjFuEAFYwKVJPJlgFt1uKpDahARERERERERkc6h0FakkwV8zV0WrFu3jj0bNnTKNmKBke44vhgwjoPnj6c6ZzCf2ZP5zJ7MH50DSPr3VtJ2rCF1xxo+r6oEoLGhvlNqERERERERERGRM6PQVqSTBQPNZ7n26n8+eUNGdv4Gm0qp3fUvtsXnsDUxhwpPCtV9hlLdZyifX3EncV+UkbV+KQcsd+fXIiIiIiIiIiIi7abQVqSLuDyxJKdldMm2koEcqrm6vpovGj1scaWx2ZVOmTOR+rQ+9L3qLl4F1tY1Mt5ZyyWOGvravBhGl5QnIiIiIiIiIiJfQaGtSDeXZjZxadNeLm3aS7XNxasV1Wz09CC533B2mjHs9MbwsjeDTMPHWEctYxx1DHfU4zascJcuIiIiIiIiInJWUmgrchZJMn1k7dnAp6s+YM5/fIP0cZP4KJDI+kAclZaLt/1pvO1Pw43JhY56RjtqGeuoI9PmP/XKRURERERERESkQyi0FTlLeYJNXOOq4hpXFY2WwcZAHGsCCawOJHDQcrL6yP3fAr1sXkbY6xnhqGO4o4FEIxju8kVEREREREREui2FtiJCjGGR56wjz1mHZZWz23SzOpDAmkA8W4Ox7DXd7DXdLPanYmBxjq2JEY7mEPcCeyMxhhnuJoiIiIiIiEiEuuaB18JdQodb8vObw12CdHMKbUWkBcOAXLuXXLuXb7kPUmfZ+CQQx4ZgHBsDcfzL9LDDjGGHL4Y3fOnYjoS4QxwNXGBvHtJsgXA3Q0REREREREQkaim0FZGvFG+YXOys5WJnLQBfmA42BOLYeCTE3W+5+MyM4TNfDAtJAyDL8HGBo4FB9gbOszfR39aESxc2ExERiRper5d9+/aFu4xW7d27t8VtJMrOzsbtdoe7DBEREYliCm1FpF3SbAHyXdXkUw3AftPJlmAMWwKxbAnGssv0UGG5qPC7KPInA2DHItfWxAB7EwPsjZxnb6SfzdsiyK06fJjy8vJwNOmYGqrCun0REZFIsW/fPv73f/833GV8pWeffTbcJZzUo48+Sm5ubrjLEBERkSim0FZEzkiGzU+Gzc/lzhoA6i0b24IxbA3EUmrGsD0YQ7XlaO5SwYzhPX8K0Bzk9rZ5sdLdZF9i8k5ZOSUbF+KuOYhBeM7K3eb3AdDYUB+W7Yu01ezZs/nLX/7Cp59+SkxMDBdffDG/+MUvOP/880PLNDU18YMf/IBXX30Vr9fLxIkTefbZZ8nMzAxj5SISLbKzs3n00UfDXUbUys7ODncJIiIiEuUU2opIh4ozTEY56hnlaA4+LQsOWE62Bz18FmwOcbebHmotB/8yPZB8LjlXnEs5UA64TD9p3lrSfLWk+mtJ9dWR4qsj1V+H0wp2au2NWzfCJ2vxen2duh2RM1VcXExBQQGjR48mEAjwk5/8hKuvvpqtW7cSFxcHwL333su7777LG2+8QVJSEtOnT+fGG2/ko48+CnP1kUk/BT8z+il49+N2u3WmqIiIiEgYKbQVkU5lGJBhNJ+Ne8mRfnGbg1wHu4MeXt3+b5Yf9pKbO4SG+HR8NiflMamUx6SesK6kYBM9go2km42kBRtJDXpJMZtINZvwdECg646NP+N1iHSFJUuWtBifP38+GRkZlJSUcOmll1JdXc0LL7zAggULuOKKKwCYN28egwYNYtWqVYwbNy4cZUc0/RT8zOin4CIiIiIiHUuhrYh0ueYgN0CGrY6NFSW88N5fmXbxleSNyOOgPYZKeyxf2GM4YIvhoD2GA/YYGm1Oqu0equ0edpBywjo9pp8UsznETQkevW0KTfNYQYwwtFWkK1RXN/cxnZra/M+OkpIS/H4/+fn5oWUGDhxInz59WLlypULbVuin4GdGPwUXEREREelYCm1FJGLYscgMNpAZbDhhXr3h4KC9OcQ9eCTMrbK5OWz3UGdz0WRzUm5zUk7rZ8s6rSBJppdE00eS6SXJ9JEU/HI80fRiKdaVKGSaJjNnzmT8+PEMGTIEgIqKClwuF8nJyS2WzczMpKKi4qTr8nq9eL3e0HhNTU2n1ByJ9FNwERERERGJJAptRc5SNbW1lJeXh7sMamtr27RcnBUgLlBL38CJy/uwcdju4bDNzWGbh8P2o7ceDts81Nuc+A07B+2xHLTHnnQbRv5oLhw7jbfsQbY1xJJuC5BqBEgx/KSG7gdIMoLYzuJ8t+rw4Yh47Rw8eDDcJUSEgoICNm/ezIoVK854XbNnz+ahhx7qgKpEREREREROdM0Dr4W7hA635Oc3d8p6FdqKnGUCvuaz6NatW8eeDRvCXA1s8zdf9CsQCJz2OlyYJz1DF8CPjWqbixqbu8Vttc1Njc1Ftd1NreHCstlxJ2VQCVR+RTk2LJKN5hA31dYc5KYaAVKOBLvH3o8xzNNuV6RpaGgE4O8ffMCWDz8MczVQGWzuxzgSAuRwmT59OosXL+bDDz+kd+/eoelZWVn4fD6qqqpanG1bWVlJVlbWSdd3//33U1hYGBqvqakhJyenU2oXERERERGRk1NoK3KWCQb8APTqfz55Q0aGuRqo2rASSjcTNM/8QmIn48Qk3Wwi3Ww66TJB4M3Nm3h5RykzJ1zDgMEXctBycthycMh0NN9aDqotByYGhywnhywnnCKT9RAk1QiQZAuSdOQs3UTjy/uhW1vzbSwmRoSexes78rP5fucPYdT5w8JcDWwu2wn//DtVVVXhLqXLWZbFjBkzWLhwIcuXLz/hZ/0jR47E6XRSVFTElClTACgtLaWsrIy8vLyTrtftduN2uzu1dhERERERETk1hbYiZymXJ5bktIxwl4HbHRPuEgCwA25vLfV7S+lfNYAb3H1aXS5gQdWRAPeweeT2SLB79P5h08EXlhMvNpqws8+ys6+NmbQDk8QjwW5jr3TO+9Z5LO6RyaeNicQZJvFGkHgj2OK+BxOPYeIxLDyYOLE6Nfj1xMRHxGsn5tDZ2z1CQUEBCxYs4K233iIhISHUT21SUhIxMTEkJSVx5513UlhYSGpqKomJicyYMYO8vDxdhExERERERCQKKLQVEWkHhwHpRoB0As1J70lYFjRiCwW71ZadastBjWUP3a827dSE5tlpwk4AG4csW/NZvDEeUs7LZCuw1d/2Gm1YXwa5mLgNK3TfaVg4ODIYFk5ajh8/zw44DAs7FrvSB5M+zMfezIF84krHhoXNap5nw8JuHbnFbDHdFpre+vI2XQKu3ebOnQvAhAkTWkyfN28e06ZNA+DXv/41NpuNKVOm4PV6mThxIs8++2wXVyoiIiIiIiKnQ6GtiMhxOvIibTYg/chwKj5s1BpO6gwXdTj5cG85C7Zu59sjRpORmU2D4aTBcNCAo/nWcNKAgybDjhc7QcMGgIlBA3YarCOpstUhTYHcbM7JzWcD0NG9ITcHu+Yxwa+FwzJxWSZOgrgsE5cVxGUFcVomLoIc7pNAz4u/Ijnvxizr1E+qx+Nhzpw5zJkzpwsqEhERERERkY4UFaHtnDlzePLJJ6moqGD48OH85je/YcyYMeEuS0S6mUi7SNt2v48DTU0c3L4Cu705nDSAuCPD8UybHdPhIuj0YDrdBJ1uTIf7yH0PQacby+bAstuxbA5Mu+PIuAPzyK1lszdPP3rf1nxr2ezUGgYHMDgnOZ3EpBRMDEwMgoat+RYD0zhye+T+CdOPBMsn1G4YmNgJtOeU2+xssuJ7tffPKiIiItImOg4VEZFwivjQ9rXXXqOwsJDnnnuOsWPH8tRTTzFx4kRKS0vJyAh/n4oi0n1E6kXauq6ewJHBe+IsC95Zv5IVpZv55qhLuHLsZae1BYvma7c1B742gkeC3ebwt2XI68eGz7DjN5pvmwcbPpqn7amq4u+fbYJR15xBm0VEREROpONQEREJt4gPbX/1q19x1113cfvttwPw3HPP8e677/Liiy/y4x//OMzViUh3FGkXaYu0es6EQXNXwHYsINiy64Z2duNQUraVPxYvhLsU2oqIiEjH0nGoiIiEW+u/U40QPp+PkpIS8vPzQ9NsNhv5+fmsXLkyjJWJiIiIiIhId6TjUBERiQQRfabtwYMHCQaDZGZmtpiemZnJp59+2upjvF4vXu+XP+2trq4GoKam5oxqaWhoAGDr3n/RFGjHZdw7ye7DBwHY8UUlnm0fq5ZjqJ6vpnqioxZQPafyeeU+oPnz+Uw+448+ti0X9zrbHP2bnOk+VEREup/uvP9s73FoRx2D1gWDp1FtZAt4G8JdQofqyu9E3e310N1eC9B1rwe9FiJfe18Lbd6HWhFs7969FmD985//bDH9f/7nf6wxY8a0+phZs2ZZNP/IVoMGDRo0aGjzsGfPnq7YtUWVPXv2hP150aBBgwYNkT10x/1ne49DdQyqQYMGDRpOZzjVPjSiz7RNT0/HbrdTWVnZYnplZSVZWVmtPub++++nsLAwNG6aJocOHSItLQ3DaNtlyWtqasjJyWHPnj0kJiaefgPCrLu0A7pPW9SOyNNd2tJd2gFd3xbLsqitrSU7O7vTtxVtsrOz2bNnDwkJCW3eh0rH607vb5GOoPdEZOjO+8/2Hod2xDFod6T3qhyl14IcpddCs7buQyM6tHW5XIwcOZKioiKuv/56oHkHWFRUxPTp01t9jNvtxu12t5iWnJx8WttPTEzsFi+i7tIO6D5tUTsiT3dpS3dpB3RtW5KSkrpkO9HGZrPRu3fvcJchR3Sn97dIR9B7Ivy66/6zvcehHXkM2h3pvSpH6bUgR+m10LZ9aESHtgCFhYVMnTqVUaNGMWbMGJ566inq6+tDV/EUERERERER6Ug6DhURkXCL+ND25ptv5sCBA/zsZz+joqKCCy+8kCVLlpzQKbyIiIiIiIhIR9BxqIiIhFvEh7YA06dPP2l3CJ3B7XYza9asE37iEm26Szug+7RF7Yg83aUt3aUd0L3aItIR9J4QaUnvCekqXX0c2t3ovSpH6bUgR+m10D6GZVlWuIsQERERERERERERkWa2cBcgIiIiIiIiIiIiIl9SaCsiIiIiIiIiIiISQRTaioiIiIiIiIiIiEQQhbbHmTNnDv369cPj8TB27FjWrFkT7pJO6cMPP+S6664jOzsbwzBYtGhRi/mWZfGzn/2Mnj17EhMTQ35+Pp999ll4iv0Ks2fPZvTo0SQkJJCRkcH1119PaWlpi2WampooKCggLS2N+Ph4pkyZQmVlZZgqbt3cuXMZNmwYiYmJJCYmkpeXx3vvvReaHw1taM3jjz+OYRjMnDkzNC1a2vLggw9iGEaLYeDAgaH50dKOo/bu3ct3vvMd0tLSiImJYejQoaxbty40Pxre8/369TvhOTEMg4KCAiD6nhORzhKN30tEOsupvvOKSOTQ/ktAn9vypbbkPXIihbbHeO211ygsLGTWrFmsX7+e4cOHM3HiRPbv3x/u0r5SfX09w4cPZ86cOa3Of+KJJ3jmmWd47rnnWL16NXFxcUycOJGmpqYurvSrFRcXU1BQwKpVq1i2bBl+v5+rr76a+vr60DL33nsv77zzDm+88QbFxcXs27ePG2+8MYxVn6h37948/vjjlJSUsG7dOq644gomT57Mli1bgOhow/HWrl3L7373O4YNG9ZiejS15YILLqC8vDw0rFixIjQvmtpx+PBhxo8fj9Pp5L333mPr1q383//9HykpKaFlouE9v3bt2hbPx7JlywD45je/CUTXcyLSWaL1e4lIZznVd14RiQzaf8lR+tyWo9qS90grLAkZM2aMVVBQEBoPBoNWdna2NXv27DBW1T6AtXDhwtC4aZpWVlaW9eSTT4amVVVVWW6323rllVfCUGHb7d+/3wKs4uJiy7Ka63Y6ndYbb7wRWmbbtm0WYK1cuTJcZbZJSkqK9Yc//CEq21BbW2sNGDDAWrZsmXXZZZdZ99xzj2VZ0fV8zJo1yxo+fHir86KpHZZlWT/60Y+sSy655KTzo/U9f88991jnnHOOZZpm1D0nIp2lO3wvEeksx3/nFZHIof2XtEaf23Ks4/MeaZ3OtD3C5/NRUlJCfn5+aJrNZiM/P5+VK1eGsbIzs2vXLioqKlq0KykpibFjx0Z8u6qrqwFITU0FoKSkBL/f36ItAwcOpE+fPhHblmAwyKuvvkp9fT15eXlR2YaCggK+/vWvt6gZou/5+Oyzz8jOzqZ///7ceuutlJWVAdHXjrfffptRo0bxzW9+k4yMDEaMGMHvf//70PxofM/7fD7+9Kc/cccdd2AYRtQ9JyKdobt+LxERke5N+y8RaYvj8x5pnULbIw4ePEgwGCQzM7PF9MzMTCoqKsJU1Zk7Wnu0tcs0TWbOnMn48eMZMmQI0NwWl8tFcnJyi2UjsS2bNm0iPj4et9vNf//3f7Nw4UIGDx4cVW0AePXVV1m/fj2zZ88+YV40tWXs2LHMnz+fJUuWMHfuXHbt2sXXvvY1amtro6odAJ9//jlz585lwIABLF26lO9973t8//vf56WXXgKi8z2/aNEiqqqqmDZtGhBdry2RztJdv5eIiEj3pv2XiJxKa3mPtM4R7gJEWlNQUMDmzZtb9DsaTc4//3w2btxIdXU1f/7zn5k6dSrFxcXhLqtd9uzZwz333MOyZcvweDzhLueMTJo0KXR/2LBhjB07lr59+/L6668TExMTxsrazzRNRo0axWOPPQbAiBEj2Lx5M8899xxTp04Nc3Wn54UXXmDSpElkZ2eHuxQRERERERHpRNGe93QlnWl7RHp6Ona7/YSrk1dWVpKVlRWmqs7c0dqjqV3Tp09n8eLFfPDBB/Tu3Ts0PSsrC5/PR1VVVYvlI7EtLpeLc889l5EjRzJ79myGDx/O008/HVVtKCkpYf/+/Vx00UU4HA4cDgfFxcU888wzOBwOMjMzo6Ytx0tOTua8885jx44dUfWcAPTs2ZPBgwe3mDZo0KBQdw/R9p7/17/+xfvvv89//ud/hqZF23Mi0hm66/cSERHp3rT/EpGvcrK8R1qn0PYIl8vFyJEjKSoqCk0zTZOioiLy8vLCWNmZyc3NJSsrq0W7ampqWL16dcS1y7Ispk+fzsKFC/n73/9Obm5ui/kjR47E6XS2aEtpaSllZWUR15bjmaaJ1+uNqjZceeWVbNq0iY0bN4aGUaNGceutt4buR0tbjldXV8fOnTvp2bNnVD0nAOPHj6e0tLTFtO3bt9O3b18gut7zAPPmzSMjI4Ovf/3roWnR9pyIdIbu+r1ERES6N+2/RKQ1p8p7pHXqHuEYhYWFTJ06lVGjRjFmzBieeuop6uvruf3228Nd2leqq6tjx44dofFdu3axceNGUlNT6dOnDzNnzuSRRx5hwIAB5Obm8sADD5Cdnc31118fvqJbUVBQwIIFC3jrrbdISEgI9XmUlJRETEwMSUlJ3HnnnRQWFpKamkpiYiIzZswgLy+PcePGhbn6L91///1MmjSJPn36UFtby4IFC1i+fDlLly6NmjYAJCQknNC/TFxcHGlpaaHp0dKW++67j+uuu46+ffuyb98+Zs2ahd1u55Zbbomq5wTg3nvv5eKLL+axxx7jpptuYs2aNTz//PM8//zzABiGETXvedM0mTdvHlOnTsXh+HJ3FG3PiUhnidbvJSKd5VTfeUUkMmj/JUfpc1uOOlXeIydhSQu/+c1vrD59+lgul8saM2aMtWrVqnCXdEoffPCBBZwwTJ061bIsyzJN03rggQeszMxMy+12W1deeaVVWloa3qJb0VobAGvevHmhZRobG627777bSklJsWJjY60bbrjBKi8vD1/Rrbjjjjusvn37Wi6Xy+rRo4d15ZVXWn/7299C86OhDSdz2WWXWffcc09oPFracvPNN1s9e/a0XC6X1atXL+vmm2+2duzYEZofLe046p133rGGDBliud1ua+DAgdbzzz/fYn60vOeXLl1qAa3WFm3PiUhnicbvJSKd5VTfeUUkcmj/JZalz235UlvyHjmRYVmW1TXxsIiIiIiIiIiIiIicivq0FREREREREREREYkgCm1FREREREREREREIohCWxEREREREREREZEIotBWREREREREREREJIIotBURERERERERERGJIAptRURERERERERERCKIQlsRERERERERERGRCKLQVkRERERERERERCSCKLQVERERkbOaYRgsWrQo3GWIiIiIiIQotBWJYBMmTGDmzJnhLkNERCSqVVRUMGPGDPr374/b7SYnJ4frrruOoqKicJcmIiISNl2xf+zKY9rly5djGAZVVVVdsj2RzuYIdwEiEvksyyIYDOJw6CNDRESiy+7duxk/fjzJyck8+eSTDB06FL/fz9KlSykoKODTTz/tlO36fD5cLlenrFtERORMhWv/2Bodb4q0TmfaikSoadOmUVxczNNPP41hGBiGwe7du9m8eTOTJk0iPj6ezMxMbrvtNg4ePBh63IQJE/j+97/PD3/4Q1JTU8nKyuLBBx8Mzd+9ezeGYbBx48bQtKqqKgzDYPny5cCX/6F87733GDlyJG63mxUrVmCaJrNnzyY3N5eYmBiGDx/On//85y76i4iIiLTf3XffjWEYrFmzhilTpnDeeedxwQUXUFhYyKpVq0LLHTx4kBtuuIHY2FgGDBjA22+/HZo3f/58kpOTW6x30aJFGIYRGn/wwQe58MIL+cMf/kBubi4ejwdo7nrhD3/4w0nXLSIiEg5t2T+WlZUxefJk4uPjSUxM5KabbqKysjK0jqP7vj/+8Y/069ePpKQkvvWtb1FbWwuc/Jj2ZMebO3fuZPLkyWRmZhIfH8/o0aN5//33W9Tt9Xr50Y9+RE5ODm63m3PPPZcXXniB3bt3c/nllwOQkpKCYRhMmzata/6YIp1Eoa1IhHr66afJy8vjrrvuory8nPLychISErjiiisYMWIE69atY8mSJVRWVnLTTTe1eOxLL71EXFwcq1ev5oknnuDhhx9m2bJl7a7hxz/+MY8//jjbtm1j2LBhzJ49m5dffpnnnnuOLVu2cO+99/Kd73yH4uLijmq2iIhIhzl06BBLliyhoKCAuLi4E+YfG8Q+9NBD3HTTTXzyySdce+213HrrrRw6dKhd29uxYwdvvvkmf/nLX1r8c7Qj1i0iItJR2rJ/NE2TyZMnc+jQIYqLi1m2bBmff/45N998c4tld+7cyaJFi1i8eDGLFy+muLiYxx9/HGj9mDYnJyf02OOPN+vq6rj22mspKipiw4YNXHPNNVx33XWUlZWFHvPd736XV155hWeeeYZt27bxu9/9jvj4eHJycnjzzTcBKC0tpby8nKeffroz/nwiXUbnnotEqKSkJFwuF7GxsWRlZQHwyCOPMGLECB577LHQci+++CI5OTls376d8847D4Bhw4Yxa9YsAAYMGMBvf/tbioqKuOqqq9pVw8MPPxx6jNfr5bHHHuP9998nLy8PgP79+7NixQp+97vfcdlll51xm0VERDrSjh07sCyLgQMHnnLZadOmccsttwDw2GOP8cwzz7BmzRquueaaNm/P5/Px8ssv06NHjw5ft4iISEdpy/6xqKiITZs2sWvXrlDQ+vLLL3PBBRewdu1aRo8eDYBpmsyfP5+EhAQAbrvtNoqKinj00UdbPaY91rHHmwCpqakMHz48NP7zn/+chQsX8vbbbzN9+nS2b9/O66+/zrJly8jPzweaj0mPfTxARkbGCb+QEYlGCm1FosjHH3/MBx98QHx8/Anzdu7c2SK0PVbPnj3Zv39/u7c3atSo0P0dO3bQ0NBwQvDr8/kYMWJEu9ctIiLS2SzLavOyx+474+LiSExMbPe+s2/fvicEth21bhERkY7Slv3jtm3byMnJaXFm7ODBg0lOTmbbtm2h0LZfv36hwBbad+x57PEmQF1dHQ8++CDvvvsu5eXlBAIBGhsbQ2fabty4EbvdrhOG5Kyh0FYkitTV1XHdddfxi1/84oR5PXv2DN13Op0t5hmGgWmaANhszb2iHLuj9vv9rW7v2J/K1NXVAfDuu+/Sq1evFsu53e72NENERKRLDBgwAMMw2nQxlVPtO48/wG1t39naT0xPtW4REZGu1p7946mcyT7u+P3mfffdx7Jly/jlL3/JueeeS0xMDN/4xjfw+XwAxMTEnHG9ItFEfdqKRDCXy0UwGAyNX3TRRWzZsoV+/fpx7rnnthhOdqB4vKNnAJWXl4emHdvv3skMHjwYt9tNWVnZCds+9r+vIiIikSI1NZWJEycyZ84c6uvrT5hfVVXVpvX06NGD2traFutoy75TREQkErVl/zho0CD27NnDnj17QtO3bt1KVVUVgwcPbvO2jj+m/SofffQR06ZN44YbbmDo0KFkZWWxe/fu0PyhQ4dimuZJr6nicrkA2rw9kUin0FYkgvXr14/Vq1eze/duDh48SEFBAYcOHeKWW25h7dq17Ny5k6VLl3L77be3eccUExPDuHHjQh2+FxcX89Of/vSUj0tISOC+++7j3nvv5aWXXmLnzp2sX7+e3/zmN7z00ktn2lQREZFOMWfOHILBIGPGjOHNN9/ks88+Y9u2bTzzzDOhPtpPZezYscTGxvKTn/yEnTt3smDBAubPn9+5hYuIiHSiU+0f8/PzGTp0KLfeeivr169nzZo1fPe73+Wyyy47oVuDr3L8Me1XnYU7YMCA0MU8P/74Y7797W+3WL5fv35MnTqVO+64g0WLFrFr1y6WL1/O66+/DjR3U2QYBosXL+bAgQOhX4uKRCuFtiIR7L777sNutzN48GB69OiBz+fjo48+IhgMcvXVVzN06FBmzpxJcnJyqNuDtnjxxRcJBAKMHDmSmTNn8sgjj7TpcT//+c954IEHmD17NoMGDeKaa67h3XffJTc393SbKCIi0qn69+/P+vXrufzyy/nBD37AkCFDuOqqqygqKmLu3LltWkdqaip/+tOf+Otf/8rQoUN55ZVXePDBBzu3cBERkU50qv2jYRi89dZbpKSkcOmll5Kfn0///v157bXX2rWd449pj/ZP25pf/epXpKSkcPHFF3PdddcxceJELrroohbLzJ07l2984xvcfffdDBw4kLvuuit0tnCvXr146KGH+PGPf0xmZibTp09v/x9GJIIYVnuu0CAiIiIiIiIiIiIinUpn2oqIiIiIiIiIiIhEEIW2IiIiIiIiIiIiIhFEoa2IiIiIiIiIiIhIBFFoKyIiIiIiIiIiIhJBFNqKiIiIiIiIiIiIRBCFtiIiIiIiIiIiIiIRRKGtiIiIiIiIiIiISARRaCsiIiIiIiIiIiISQRTaioiIiIiIiIiIiEQQhbYiIiIiIiIiIiIiEUShrYiIiIiIiIiIiEgEUWgrIiIiIiIiIiIiEkH+fwztCKbZ9/rfAAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    }
   ],
   "id": "j_LJ7dw6wrRg"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sxlDQMaywrRg"
   },
   "source": "### Özellik seçimi ve ölçekleme (NISQ için 4 özellik)",
   "id": "sxlDQMaywrRg"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dchLP_uNwrRh",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786635369868,
     "user_tz": -180,
     "elapsed": 444,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "702ef897-ef49-4fbd-b22f-459f2490f400"
   },
   "source": "FEATURE_COLS = [\"tenure\", \"MonthlyCharges\", \"Contract\", \"SupportTickets\"]\nX = df[FEATURE_COLS].values.astype(float)\ny = df[\"Churn\"].values\n\n# MinMax → [0, π] aralığı (açı kodlama için uygun)\nscaler = MinMaxScaler(feature_range=(0, np.pi))\nX_scaled = scaler.fit_transform(X)\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X_scaled, y, test_size=0.25, random_state=42, stratify=y\n)\n\nprint(f\"Train: {X_train.shape}, Test: {X_test.shape}\")\nprint(f\"Train churn oranı: {y_train.mean():.3f}\")\nprint(f\"Test  churn oranı: {y_test.mean():.3f}\")",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train: (300, 4), Test: (100, 4)\n",
      "Train churn oranı: 0.167\n",
      "Test  churn oranı: 0.160\n"
     ]
    }
   ],
   "id": "dchLP_uNwrRh"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hW7Oc9NOwrRh"
   },
   "source": "## 2. Klasik Baseline (karşılaştırma için)",
   "id": "hW7Oc9NOwrRh"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3yShnB0SwrRi",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786635370275,
     "user_tz": -180,
     "elapsed": 402,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "f7c868fe-1513-4036-a712-229c56bc08b8"
   },
   "source": "def evaluate(y_true, y_pred, name=\"Model\"):\n    acc = accuracy_score(y_true, y_pred)\n    prec = precision_score(y_true, y_pred, zero_division=0)\n    rec = recall_score(y_true, y_pred, zero_division=0)\n    f1 = f1_score(y_true, y_pred, zero_division=0)\n    print(f\"\\n=== {name} ===\")\n    print(f\"Accuracy : {acc:.4f}\")\n    print(f\"Precision: {prec:.4f}\")\n    print(f\"Recall   : {rec:.4f}\")\n    print(f\"F1-Score : {f1:.4f}\")\n    return {\"model\": name, \"accuracy\": acc, \"precision\": prec, \"recall\": rec, \"f1\": f1}\n\nresults = []\n\n# Logistic Regression\nlr = LogisticRegression(max_iter=500, random_state=42)\nlr.fit(X_train, y_train)\ny_pred_lr = lr.predict(X_test)\nresults.append(evaluate(y_test, y_pred_lr, \"Classical Logistic Regression\"))\n\n# Classical SVM (RBF)\nsvc = SVC(kernel=\"rbf\", C=1.0, gamma=\"scale\", random_state=42)\nsvc.fit(X_train, y_train)\ny_pred_svc = svc.predict(X_test)\nresults.append(evaluate(y_test, y_pred_svc, \"Classical SVM (RBF)\"))\n\n# Random Forest\nrf = RandomForestClassifier(n_estimators=100, max_depth=6, random_state=42)\nrf.fit(X_train, y_train)\ny_pred_rf = rf.predict(X_test)\nresults.append(evaluate(y_test, y_pred_rf, \"Classical Random Forest\"))",
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "=== Classical Logistic Regression ===\n",
      "Accuracy : 0.8100\n",
      "Precision: 0.2857\n",
      "Recall   : 0.1250\n",
      "F1-Score : 0.1739\n",
      "\n",
      "=== Classical SVM (RBF) ===\n",
      "Accuracy : 0.8500\n",
      "Precision: 0.5714\n",
      "Recall   : 0.2500\n",
      "F1-Score : 0.3478\n",
      "\n",
      "=== Classical Random Forest ===\n",
      "Accuracy : 0.8200\n",
      "Precision: 0.4000\n",
      "Recall   : 0.2500\n",
      "F1-Score : 0.3077\n"
     ]
    }
   ],
   "id": "3yShnB0SwrRi"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bUZqEGPLwrRi"
   },
   "source": "## 3. VQC / QNN – Variational Quantum Classifier\n\n**Tabloya göre:**\n- İşlem karmaşıklığı: Düşük – Orta  \n- Kübit gereksinimi: Az (10–50, burada 4)  \n- NISQ uygunluğu: **Yüksek (günümüz için en ideali)**  \n- En iyi kullanım: Davranışsal korelasyonlar ve anlık churn tespiti\n\nVQC, bir **feature map** + **parametreli ansatz** kullanır. Eğitim klasik optimizer (COBYLA) ile yapılır.",
   "id": "bUZqEGPLwrRi"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "id": "xbLBeQ44wrRj",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786635371217,
     "user_tz": -180,
     "elapsed": 793,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "067f26ac-8b01-460d-e675-6c9f1f1b6756"
   },
   "source": "num_qubits = X_train.shape[1]  # 4\n\n# Feature Map: ZZFeatureMap – entangle ederek non-lineer özellikler üretir\nfeature_map = ZZFeatureMap(feature_dimension=num_qubits, reps=1, entanglement=\"linear\")\n\n# Ansatz: RealAmplitudes – NISQ-dostu, sığ devre\nansatz = RealAmplitudes(num_qubits=num_qubits, reps=2, entanglement=\"linear\")\n\nprint(\"Feature Map derinlik:\", feature_map.depth())\nprint(\"Ansatz parametre sayısı:\", ansatz.num_parameters)\nfeature_map.decompose().draw(\"mpl\", style=\"clifford\")",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Feature Map derinlik: 1\n",
      "Ansatz parametre sayısı: 12\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 1541.66x367.889 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "id": "xbLBeQ44wrRj"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O9-ZccPowrRk",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786635530509,
     "user_tz": -180,
     "elapsed": 159286,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "466ba760-fc72-49aa-b12c-43864af5cece"
   },
   "source": "# Sampler (statevector – gürültüsüz simülasyon)\nsampler = StatevectorSampler()\n\n# VQC oluştur\nvqc = VQC(\n    feature_map=feature_map,\n    ansatz=ansatz,\n    optimizer=COBYLA(maxiter=60),\n    sampler=sampler,\n)\n\nprint(\"VQC eğitimi başlıyor (birkaç dakika sürebilir)...\")\nvqc.fit(X_train, y_train)\n\ny_pred_vqc = vqc.predict(X_test)\nresults.append(evaluate(y_test, y_pred_vqc, \"VQC / QNN\"))\n\nprint(\"\\nTrain skoru:\", round(vqc.score(X_train, y_train), 4))\nprint(\"Test  skoru:\", round(vqc.score(X_test, y_test), 4))",
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:qiskit_machine_learning.neural_networks.sampler_qnn:No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "VQC eğitimi başlıyor (birkaç dakika sürebilir)...\n",
      "\n",
      "=== VQC / QNN ===\n",
      "Accuracy : 0.5600\n",
      "Precision: 0.1818\n",
      "Recall   : 0.5000\n",
      "F1-Score : 0.2667\n",
      "\n",
      "Train skoru: 0.5933\n",
      "Test  skoru: 0.55\n"
     ]
    }
   ],
   "id": "O9-ZccPowrRk"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pXTAUDxrwrRk"
   },
   "source": "## 4. QSVM – Quantum Support Vector Machine\n\n**Tabloya göre:**\n- İşlem karmaşıklığı: Orta  \n- Kübit: Orta (50–100 ideal, burada 4)  \n- NISQ: Orta – Yüksek (gürültüye hassas)  \n- En iyi kullanım: Paket türü, fatura ve demografik segmentasyon\n\nQuantum Kernel, veriyi Hilbert uzayına gömer; klasik SVM bu kernel ile eğitilir.",
   "id": "pXTAUDxrwrRk"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5HavqX7AwrRl",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786637245546,
     "user_tz": -180,
     "elapsed": 1714988,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "c3bec28e-c539-49bd-c83d-c34f2a3f1ecb"
   },
   "source": "# Quantum Kernel\nfm_qsvm = ZZFeatureMap(feature_dimension=num_qubits, reps=2, entanglement=\"linear\")\nsampler_kernel = StatevectorSampler()\nfidelity = ComputeUncompute(sampler=sampler_kernel)\nquantum_kernel = FidelityQuantumKernel(fidelity=fidelity, feature_map=fm_qsvm)\n\n# QSVC (Qiskit Machine Learning)\nqsvc = QSVC(quantum_kernel=quantum_kernel)\nprint(\"QSVM eğitimi başlıyor...\")\nqsvc.fit(X_train, y_train)\n\ny_pred_qsvc = qsvc.predict(X_test)\nresults.append(evaluate(y_test, y_pred_qsvc, \"QSVM (Quantum Kernel)\"))\n\nprint(\"\\nTrain skoru:\", round(qsvc.score(X_train, y_train), 4))\nprint(\"Test  skoru:\", round(qsvc.score(X_test, y_test), 4))",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "QSVM eğitimi başlıyor...\n",
      "\n",
      "=== QSVM (Quantum Kernel) ===\n",
      "Accuracy : 0.8400\n",
      "Precision: 0.0000\n",
      "Recall   : 0.0000\n",
      "F1-Score : 0.0000\n",
      "\n",
      "Train skoru: 0.84\n",
      "Test  skoru: 0.84\n"
     ]
    }
   ],
   "id": "5HavqX7AwrRl"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TIc1_kaFwrRm"
   },
   "source": "### Alternatif: Klasik SVM + Quantum Kernel (manuel)",
   "id": "TIc1_kaFwrRm"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o_fijXQywrRn",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786638101485,
     "user_tz": -180,
     "elapsed": 856587,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "fb41b735-39e8-4c2e-c2bf-fdbefc1a9aa6"
   },
   "source": "# Kernel matrislerini önceden hesapla (daha şeffaf)\nprint(\"Kernel matrisi hesaplanıyor...\")\nK_train = quantum_kernel.evaluate(x_vec=X_train)\nK_test = quantum_kernel.evaluate(x_vec=X_test, y_vec=X_train)\n\nsvc_qk = SVC(kernel=\"precomputed\")\nsvc_qk.fit(K_train, y_train)\ny_pred_qk = svc_qk.predict(K_test)\nresults.append(evaluate(y_test, y_pred_qk, \"SVM + Precomputed Quantum Kernel\"))",
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Kernel matrisi hesaplanıyor...\n",
      "\n",
      "=== SVM + Precomputed Quantum Kernel ===\n",
      "Accuracy : 0.8400\n",
      "Precision: 0.0000\n",
      "Recall   : 0.0000\n",
      "F1-Score : 0.0000\n"
     ]
    }
   ],
   "id": "o_fijXQywrRn"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IJp-BkT2wrRo"
   },
   "source": "## 5. Kuantum Topluluk (Quantum Ensemble – Hybrid Bagging)\n\n**Tabloya göre:**\n- Karmaşıklık: Yüksek  \n- Kübit: Hibrit (değişken)  \n- NISQ: Orta (Klasik-Kuantum hibrit)  \n- En iyi kullanım: Maksimum tahmin doğruluğu hedeflenen projeler\n\nSaf kuantum bagging (süperpozisyonla B model) henüz pratik değil.  \nBurada **hibrit yaklaşım** kullanıyoruz: birden fazla farklı feature map / ansatz ile VQC veya quantum kernel eğitip oylama (voting) yapıyoruz.",
   "id": "IJp-BkT2wrRo"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B6l6uVM8wrRo",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786638329650,
     "user_tz": -180,
     "elapsed": 233512,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "c8baacac-32c2-4309-e7ce-88471b2aabae"
   },
   "source": "from sklearn.base import BaseEstimator, ClassifierMixin\n\nclass SimpleQuantumVoter(BaseEstimator, ClassifierMixin):\n    \"\"\"Birden fazla VQC'yi bagging tarzı birleştirir.\"\"\"\n    def __init__(self, n_estimators=3, maxiter=40):\n        self.n_estimators = n_estimators\n        self.maxiter = maxiter\n        self.models_ = []\n\n    def fit(self, X, y):\n        self.models_ = []\n        n = X.shape[1]\n        sampler = StatevectorSampler()\n        for i in range(self.n_estimators):\n            # Farklı reps / entanglement ile çeşitlilik\n            fm = ZZFeatureMap(feature_dimension=n, reps=1 + (i % 2), entanglement=\"linear\")\n            ans = RealAmplitudes(num_qubits=n, reps=1 + (i % 2), entanglement=\"circular\" if i % 2 else \"linear\")\n            model = VQC(\n                feature_map=fm,\n                ansatz=ans,\n                optimizer=COBYLA(maxiter=self.maxiter),\n                sampler=sampler,\n            )\n            # Bootstrap örnekleme\n            idx = np.random.choice(len(X), size=len(X), replace=True)\n            model.fit(X[idx], y[idx])\n            self.models_.append(model)\n            print(f\"  Ensemble üye {i+1}/{self.n_estimators} eğitildi.\")\n        return self\n\n    def predict(self, X):\n        preds = np.array([m.predict(X) for m in self.models_])\n        # Çoğunluk oylaması\n        return np.round(preds.mean(axis=0)).astype(int)\n\nprint(\"Kuantum Ensemble (3 VQC) eğitimi başlıyor...\")\nq_ensemble = SimpleQuantumVoter(n_estimators=3, maxiter=35)\nq_ensemble.fit(X_train, y_train)\ny_pred_ens = q_ensemble.predict(X_test)\nresults.append(evaluate(y_test, y_pred_ens, \"Kuantum Topluluk (Hybrid Bagging)\"))",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:qiskit_machine_learning.neural_networks.sampler_qnn:No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Kuantum Ensemble (3 VQC) eğitimi başlıyor...\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:qiskit_machine_learning.neural_networks.sampler_qnn:No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "  Ensemble üye 1/3 eğitildi.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:qiskit_machine_learning.neural_networks.sampler_qnn:No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "  Ensemble üye 2/3 eğitildi.\n",
      "  Ensemble üye 3/3 eğitildi.\n",
      "\n",
      "=== Kuantum Topluluk (Hybrid Bagging) ===\n",
      "Accuracy : 0.5000\n",
      "Precision: 0.1600\n",
      "Recall   : 0.5000\n",
      "F1-Score : 0.2424\n"
     ]
    }
   ],
   "id": "B6l6uVM8wrRo"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KSbwyQt2wrRp"
   },
   "source": "## 6. QPCA – Quantum Kernel Principal Component Analysis\n\n**Tabloya göre:**\n- Karmaşıklık: Çok Yüksek  \n- Kübit: Yüksek + derin devreler  \n- NISQ: Düşük (geleceğin teknolojisi)  \n- En iyi kullanım: Milyonlarca satırlık çağrı/veri trafiği sadeleştirme\n\nBurada **Quantum Kernel PCA** kullanıyoruz (FidelityQuantumKernel + scikit-learn KernelPCA).  \nTam QPE tabanlı qPCA şu an NISQ için pratik değildir.",
   "id": "KSbwyQt2wrRp"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 623
    },
    "id": "izbk-swSwrRq",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786639040567,
     "user_tz": -180,
     "elapsed": 710819,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "b08f1bd1-28d9-4b39-d3b7-865f41266b73"
   },
   "source": "from sklearn.decomposition import KernelPCA\n\n# Quantum kernel ile 2 bileşene indir\nfm_pca = ZZFeatureMap(feature_dimension=num_qubits, reps=1, entanglement=\"linear\")\nfidelity_pca = ComputeUncompute(sampler=StatevectorSampler())\nqpca_kernel = FidelityQuantumKernel(fidelity=fidelity_pca, feature_map=fm_pca)\n\nprint(\"Quantum kernel matrisi (train) hesaplanıyor...\")\nK_train_pca = qpca_kernel.evaluate(x_vec=X_train)\nK_test_pca = qpca_kernel.evaluate(x_vec=X_test, y_vec=X_train)\n\nkpca = KernelPCA(n_components=2, kernel=\"precomputed\")\nX_train_q = kpca.fit_transform(K_train_pca)\nX_test_q = kpca.transform(K_test_pca)\n\nprint(\"QPCA sonrası şekil:\", X_train_q.shape)\n\n# Görselleştir\nplt.figure(figsize=(6, 5))\nplt.scatter(X_train_q[y_train==0, 0], X_train_q[y_train==0, 1], c=\"tab:blue\", label=\"No Churn\", alpha=0.7)\nplt.scatter(X_train_q[y_train==1, 0], X_train_q[y_train==1, 1], c=\"tab:red\", label=\"Churn\", alpha=0.7)\nplt.xlabel(\"Quantum PC1\")\nplt.ylabel(\"Quantum PC2\")\nplt.title(\"Quantum Kernel PCA – Telco Churn\")\nplt.legend()\nplt.grid(True, alpha=0.3)\nplt.show()\n\n# İndirgenmiş uzayda klasik sınıflandırıcı\nlr_qpca = LogisticRegression(max_iter=300)\nlr_qpca.fit(X_train_q, y_train)\ny_pred_qpca = lr_qpca.predict(X_test_q)\nresults.append(evaluate(y_test, y_pred_qpca, \"QPCA (Quantum Kernel PCA + LR)\"))",
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Quantum kernel matrisi (train) hesaplanıyor...\n",
      "QPCA sonrası şekil: (300, 2)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ],
      "image/png": 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RcsJVV12F0+nkO9/5Do2Nje0ea2pq4rvf/S4ej4fLL788fXzKlCn4fL52Iyr79+/nueeea/f61J1R27sun8/HY4891uf2ulwugIx/LKdMmcI///nPdscefvjhIVtK+Otf/5qzzjqLe+65h//7v/8DksstPR4Pt956K/F4vNNr6uvrs/b+mqZx5pln8swzz/Dxxx8P6Ht1dNZZZ3HEEUdwyy238M4773R6PBAIcN111wHJqbxdu3Zx8cUXc9ZZZ3X67+yzz+aNN97osmIrwKJFizjllFM6/bdo0aI+f4ZvfOMbGIbBz3/+806PJRKJjN/BlDPPPJOGhgYeeOCBTo+lfh++9KUvYRhGp+fce++9KIrCf//3f3fbvptuuonGxka+9a1vkUgkOj3+97//nb/85S/dniOTKVOm8O677xKLxdLH/vKXv7Bnz55en0vKfXJkRMoJU6dOZeXKlZx77rkcccQRnSqwNjc38+STT7abFz7nnHO45ppr+OpXv8oVV1yRXpo6bdq0dgmRX/ziF7FarZx22ml85zvfobW1lUceeYTS0tI+j1bMnTsXTdO444478Pl82Gy2dB2Tb33rW3z3u9/lzDPP5Atf+AIffPABf/vb3/qU65INqqqyatUqfD5fuibE9773PR566CEuuOAC5s+fzznnnENJSQm7d+/mpZdeYtGiRRk7sL66/fbbeeONN1i4cCHf/va3Ofzww2lqamLdunW89tprA1ae3mKx8Oyzz3LKKafw+c9/nm984xssWrQIi8XCxo0beeKJJygoKOCWW25h1apVaJrGqaeemvFcX/nKV7juuut48skn243QDbTjjz+e73znO9x2221s2LCBL37xi1gsFrZt28bTTz/NL37xC84666yMr126dCkrV65k2bJlvP/++xx33HEEg0Fee+01vve973H66adz2mmnceKJJ3Ldddexa9cu5syZw9///nf+/Oc/c+WVV7ZLTs7k7LPP5qOPPuKWW25h/fr1nHvuuekKrK+88gqrV6/miSee6PXn/ta3vsWf/vQn/uu//otvfOMbVFVV8fvf//6Q7ZGGqSFaxSNJGX300UfivPPOE2PHjhWqqgpA2O12sXHjxozP//vf/y5mz54trFarmD59uvj973+fcWnvCy+8II488khht9vFpEmTxB133JFe9rhz58708yoqKsSpp57a6X0yVbZ85JFHxOTJk4Wmae2W+RqGIa655hpRXFwsnE6nWLx4sdi+fXuXS3tTy1BTUu2vr69vd/zCCy8ULpfrEFewcwXWlNbWVnHMMccIVVXTy0HfeOMNsXjxYuH1eoXdbhdTpkwRF110kfjPf/7To/cFxGWXXdbpeKZlmQcOHBCXXXaZKC8vFxaLRYwdO1acfPLJ4uGHH04/p78VWLvS3Nwsrr/+enHEEUcIp9Mp7Ha7mD17trj22mvF/v37RSwWE0VFReK4447r9jyHHXaYmDdvXo/es6+6qsD68MMPiwULFgiHwyHcbrc44ogjxNVXXy1qamrSz8n0PQ2FQuK6664Thx12WPq6n3XWWaKqqir9nEAgIH74wx+KsrIyYbFYRGVlpbjzzjvbLTk/lNWrV4vTTz9dlJaWCl3XRUlJiTjttNPEn//85/Rzuvq5dfVzv/vuu8X48eOFzWYTixYtEv/5z3+6XNqb6bvQ1Xc3098IaWgpQvQzK0+SBtDKlSu56KKLOP/889tVQZUkSZJGDjlNI+W0pUuXsn//fn7yk58wYcIEbr311qFukiRJkpRlcmREkiRJkqQhJVfTSJIkSZI0pGQwIkmSJEnSkJLBiCRJkiRJQ0oGI5IkSZIkDSm5muYQTNOkpqYGt9vdbQlwSZIkSZLaE0IQCAQoKyvrcn8rkMHIIdXU1PRogy1JkiRJkjLbs2dPlzt/gwxGDim1cdSePXu63Tq8r0zTpL6+npKSkm6jRqnv5DUeePIaDzx5jQeevMbZ5/f7KS8vb7cJYyYyGDmE1NSMx+MZsGAkEong8Xjkl3+AyGs88OQ1HnjyGg88eY0HzqHSHOTVliRJkiRpSMlgRJIkSZKkISWDEUmSJEmShpTMGZEkSZKGjBCCRCKBYRhD3RRM0yQejxOJRGTOSA9pmoau6/0ufSGDEUmSJGlIxGIx9u/fTygUGuqmAMnAyDRNAoGArCvVC06nk3HjxmG1Wvt8DhmMSJIkSYPONE127tyJpmmUlZVhtVqHPABIjdJk405/NBBCEIvFqK+vZ+fOnVRWVvZ5REkGI5IkSdKgi8VimKZJeXk5TqdzqJsDyGCkLxwOBxaLherqamKxGHa7vU/nkZNikiRJ0pCRuRnDXzZ+hvJbIEmSJEnSkJLTNJKUBaYp2FoXwBeK43VamFbqRlXlMK8kSVJPyGBEkvppbXUTK96uZntdK7GEgVXXmFqax4WfrWBBReFQN0+SpBHohBNOYO7cudx3331D3ZSskNM0ktQPa6ubuOWlTXy8z4fHrjOhwInHrrOxxsctL21ibXXTUDdRkqQsu+iii1AUhdtvv73d8eeffz4ria+xWIzly5czZ84cnE4nxcXFLFq0iMcee4x4PN7v8+ciGYxIUh+ZpmDF29W0hOJMKnLisuloqoLLplNR6MQXjrPy7WpMUwx1UyVpRDNNweZaP+/taGRzrX9Qfufsdjt33HEHzc3NWT1vLBZj8eLF3H777fzP//wPb7/9Nu+//z6XXXYZv/zlL9m4cWNW36+t1GqioSCDEUnqo611AbbXtVLqtnW6G1IUhZI8G9vqWtlaFxiiFkrSyLe2uokrn9rAsqc+4LrnPmLZUx9w5VMbBnxU8pRTTmHs2LHcdttt3T7vmWeeYdasWdhsNiZNmsTdd9/d7fPvu+8+/vnPf7J69Wouu+wy5s6dy+TJkznvvPN47733qKysTD/XNE2uvvpqCgsLGTt2LDfeeGP6sV27dqEoChs2bEgfa2lpQVEU1qxZA8CaNWtQFIW//vWvLFiwAJvNxltvvcUJJ5zAFVdc0eW5B4IMRiSpj3yhOLGEgd2iZXzcbtGIJQx8oZE5rCpJQ20op0k1TePWW2/ll7/8JXv37s3cvrVr+cY3vsE555zDRx99xI033sjPfvYzHn/88S7Pu2rVKk455RTmzZvX6TGLxYLL5Ur/e8WKFbhcLt577z2WL1/OzTffzKuvvtrrz/KTn/yE22+/nU2bNnHkkUdm9dw9JYMRSeojr9OCVdeIxDPvqRGJJ5NZvU7LILdMkka+XJgm/epXv8rcuXO54YYbMj5+zz33cPLJJ/Ozn/2MadOmcdFFF3H55Zdz5513dnnObdu2MWPGjB69/5FHHskNN9xAZWUlS5cu5aijjmL16tW9/hw333wzX/jCF5gyZQqFhYVZPXdPyWBEkvpoWqmbqaV51LdGEaL9HzwhBPWtUSpL85hW6h6iFkrSyJUr06R33HEHK1asYNOmTZ0e27RpE4sWLWp3bNGiRWzbtq3LjQE7/i3pTmoUI2XcuHHU1dX1+PUpRx111ICdu6dkMCJJfaSqChd+tgKvw0J1U4hgNIFhCoLRBNVNIbwOC0s/WyHrjUjSAMiVadLPf/7zLF68mGuvvTYr55s2bRqbN2/u0XMtlvajroqiYJom8GlV1LbBTVcrcdpO/fTk3ANBBiOS1A8LKgq57tSZzCrz4o8k2Nscwh9JMLvMy3WnzpR1RiRpgOTSNOntt9/Oiy++yDvvvNPu+MyZM/nXv/7V7ti//vUvpk2bhqZlDqLOO+88XnvtNdavX9/psXg8TjAY7FGbSkpKANi/f3/6WNtk1lwz7IKRBx98kEmTJmG321m4cCHvv/9+t89vaWnhsssuY9y4cdhsNqZNm8bLL788SK2VRoMFFYXcd/Zc7jl7Drd89QjuOXsO9549VwYikjSAcmma9IgjjmDJkiXcf//97Y7/6Ec/YvXq1fz85z9n69atrFixggceeICrrrqqy3NdeeWVLFq0iJNPPpkHH3yQDz74gB07dvDHP/6RY445hm3btvWoTQ6Hg2OOOSadmPqPf/yD//3f/+3X5xxIwyoYeeqpp1i2bBk33HAD69atY86cOSxevLjLeaxYLMYXvvAFdu3axZ/+9Ce2bNnCI488wvjx4we55dJIp6oKM8Z6WDi5iBljPXJqRpIGWK5Nk958882dpjHmz5/PH//4R5588klmz57N9ddfz80338xFF13U5XlsNhuvvvoqV199Nb/+9a855phj+MxnPsP999/PFVdcwezZs3vcpkcffZREIsGCBQu48sor+b//+7++frwBp4jeZMsMsYULF/KZz3yGBx54ACC9/fT3v/99fvKTn3R6/q9+9SvuvPNONm/e3Gn+qyvRaJRoNJr+t9/vp7y8nObmZjweT3Y+SBumaVJfX09JSYncvXKAyGs88OQ1Hngj7RpHIhF27drFYYcd1udt5wHWVjez4p1dVNUFiSYMbAe3Y1h6bAULKgp6fb54PN7j/kJKikQi7Ny5Mz1r0Zbf76egoACfz9dtHzps9qaJxWKsXbu2XZKQqqqccsopnebpUl544QWOPfZYLrvsMv785z9TUlLCeeedxzXXXNPlfN1tt93GTTfd1Ol4fX09kUgkOx+mDdM08fl8CCFGxB+YXCSv8cCT13jgjbRrHI/HMU2TRCLRr6qfc8a7uetrs9lW14ovEsdrt1BZmoeqKr0+rxAivcolG2XdR4tEIoFpmjQ2NnYK5AKBnq1mGjbBSENDA4ZhMGbMmHbHx4wZ02Xm8Y4dO3j99ddZsmQJL7/8Mtu3b+d73/se8Xi8y3Xh1157LcuWLUv/OzUyUlJSMmAjI4qijJi7nVwkr/HAk9d44I20axyJRAgEAui6jq73vyuaNaH3oyBdkSMjvaPrOqqqUlRU1GlkpKejXsMmGOkL0zQpLS3l4YcfRtM0FixYwL59+7jzzju7DEZsNhs2m63TcVVVB+wPgKIoA3p+SV7jwSCv8cAbSddYVVUURUn/lwuEEOm25EqbhoPUzzDTd7On39VhE4wUFxejaRoHDhxod/zAgQOMHTs242vGjRuHxWJpNyUzc+ZMamtricViWK3WAW2zJEmSJEmHNmzCa6vVyoIFC9qVozVNk9WrV3PsscdmfM2iRYvYvn17uwznrVu3Mm7cOBmISJIkSVKOGDbBCMCyZct45JFH0qV3L730UoLBIBdffDEAS5cubZfgeumll9LU1MQPfvADtm7dyksvvcStt97KZZddNlQfQZIkSZKkDobNNA3A2WefTX19Pddffz21tbXMnTuXV155JZ3Uunv37nbzU+Xl5fztb3/jhz/8IUceeSTjx4/nBz/4Addcc81QfQRJkiRJkjoYVsEIwOWXX87ll1+e8bE1a9Z0Onbsscfy7rvvDnCrJEmSJEnqq2EXjEhSNpimYGtdAF8ojtdpYVqpW1ZNlSRJGiIyGJFGPNMU7G0OsSvYRL7LSiAS53fv7GZ7XSuxRHIzramleVz42Qq5n4wkSVmhKArPPfccZ5xxxlA3ZViQwYg0oq2tbmLl27uIBZrZHlCJm+ALx3FYNCYWOrFbbETiBhtrfNzy0ia5064kST1SW1vLLbfcwksvvcS+ffsoLS1l7ty5XHnllZx88slD3bxhRwYj0oi1trqJW17ahC8UY06xxvgCBx/t89F6cEOtuCFw2RRcNh2nVaO6KcTKt6uZV14gp2wkaRgRpkl023ZMvw/V48VWORVlAAvD7dq1i0WLFpGfn8+dd97JEUccQTwe529/+xuXXXZZl1XB+2sk18caVkt7JamnTFOw4u1qWkJxJhU5sVs0InGDaELgtGgkTMHellB663FFUSjJs7GtrpWtdT3bS0GSpKEXWreemh9fTc1PrmH/jTdS85NrqPnx1YTWrR+w9/ze976Hoii8//77nHnmmUybNo1Zs2axbNmydgsmGhoa+OpXv4rT6aSyspIXXngh/djjjz9Ofn5+u/M+//zz7Sq/3njjjcydO5ff/OY37TYUVBSF3/zmN12eeziSwYg0Im2tC7C9rpVSty39yx03TEwh0DUVq6YSjCYIRo30a+wWjVjCwBeKD1WzJUnqhdC69dQtX07kk0/Q3B4sZePR3B4imzZRt3z5gAQkTU1NvPLKK1x22WW4XK5Oj7cNMG666Sa+8Y1v8OGHH/KlL32JJUuW0NTU1Kv32759O8888wzPPvssGzZsyOq5c4kMRqQRyReKE0sY2C2fbgVg0VRURcEwBZoCpoB4m+q8kXgymdXrlJtkSVKuE6ZJ86pVGD4flokTUZ1OFE1DdTqxlJdj+P00P7EK0eZ3PBu2b9+OEIIZM2Yc8rkXXXQR5557LlOnTuXWW2+ltbWV999/v1fvF4vFWLlyJfPmzePII4/M6rlziQxGpBHJ67Rg1ZNTMykum47LphEzTAwBqgKWg/PKQgjqW6NUluYxrdQ9VM2WJKmHotu2E91RhV5S0mlTO0VR0IuKiFZVEd22Pavvm5ra7Ym2wYPL5cLj8VBXV9er96uoqKCkpGRAzp1LZDAijUjTSt1MLc2jvjX6aV4IMKHAiaZAMJbApmvYLcnpmuqmEF6HhaWfrZDJq5I0DJh+HyIWQ8mwyzqAYrcjYjFMvy+r71tZWYmiKD1KUrVY2o+yKoqS3itNVdVOgU083nmKONNU0KHOPRzJYEQakVRV4cLPVuB1WKhuChGJGximwKIqeBwW8mw6brvOvpYw/kiC2WVeuaxXkoYR1eNFsVoR0WjGx0UkgmK1onq8WX3fwsJCFi9ezIMPPkgwGOz0eEtLS4/OU1JSQiAQaHeOtjkho41c2iuNWAsqCrnu1JmsfHsX4UAzewMhLLrOURWFnH9MBW6HLiuwStIwZaucim3yFCKbNmEpL283VSOEINHYiP3wmdgqp2b9vR988EEWLVrE0Ucfzc0338yRRx5JIpHg1Vdf5aGHHmLTpk2HPMfChQtxOp389Kc/5YorruC9997j8ccfz3pbhwsZjEgj2oKKQuaM97JhWzUJq4d8l3VYBB6yXL0kdU9RVQqWLKFu+XLie/agFxUlp2YiERKNjWgeDwXnLRmQeiOTJ09m3bp13HLLLfzoRz9i//79lJSUsGDBAh566KEenaOwsJDf//73/PjHP+aRRx7h5JNP5sYbb+R//ud/st7e4UARvcnGGYX8fj9erxefz4fH48n6+U3TpK6ujtLS0nY7DkvZM9yu8drqJla8XT2sytUPt2s8HI20axyJRNi5c2e7+hl9EVq3nuZVq4juqErmkFit2KZMoeC8JTjnz+vVuYQQJBIJdF3vlBQrda27n2VP+1A5MiKNark2ApGqGtsSilPqtsly9ZJ0CM7583DMnTOoFVil7JPBiDRq5doIRMeqsak7M1muXpK6p6gq9unThroZUj/I0FEalVIjEB/v8+Gx60wocOKx6+kRiLXVg1/JMFPV2BRZrl6SpJFMBiPSqNNxBMJl09HU5IZ5FYVOfOE4K9+uxjQHN50qU9XYtmS5ekmSRioZjEijTq6OQGSqGtuWLFcvjURyDcXwl42foQxGpFEnV0cgMlWNTZHl6qWRJlVBNBQKDXFLpP5K/Qw7VoXtDZnAKo06bUcgXLbOvwJDNQKRqhp7y0ubqG4KUZJnw25JtrO+NSrL1UsjiqZp5Ofnp/dTcTqdQ76cVi7t7R0hBKFQiLq6OvLz89G0zDd4PSGDEWnUSY1AbKzx4bRqnSo31rdGmV3mHZIRiFTV2NQqn4bWKFZdY3aZl6U5XGdEkvpi7NixADmzwZsQAtM0UVVVBiO9kJ+fn/5Z9pUMRqRRJ9dHIBZUFDKvvCCn6p9I0kBQFIVx48ZRWlqacZO4wWaaJo2NjRQVFY2IwnKDwWKx9GtEJEUGI9KAybWCYm3l+giEqirMGJv9ir+SlIs0TctKh9ZfpmlisViw2+0yGBlkMhiRBkSuFRTLRI5ASJIk5QYZjEhZN5xKmssRCEmSpKEnx6GkrMrVgmKSJElS7pLBiJRVuVpQTJIkScpdMhiRsipXC4pJkiRJuUvmjEh9lmm1TK4WFJMkSZJylwxGpD7parXMBcdU5GxBMUmSJCk3yWkaqddSq2U+3ufDY9eZUODEY9fZWOPjtr9uYuHkQrwOC9VNIYLRBIYpCEYTVDeFhrygmCRJkpR7ZDAi9UpPVsu8v6OJa780g1llXvyRBHubQ/gjCWaXeXNqWa8kSZKUG+Q0jdQrPV0t47ZbuO/subKgmCRJknRIMhiReuXT1TK2jI/bLRoNrVF8obgsKCZJkiT1iJymkXql7WqZTORqGUmSJKm3ZDAi9cq0UjdTS/Oob40iRPsqqqnVMpWleXK1jCRJktRjMhiRekVVFS78bIVcLSNJkiRljQxGpF5bUFHIdafOlKtlJEmSpKyQCaxSnyyoKGReeYFcLSMdUqZKvfJ7IklSWzIYkfpMrpaRDqWrSr0XfrZCjqBJkpQmp2lGONMUbK71896ORjbX+jFNcegXSVIWdFep95aXNrG2ummomyhJUo6QIyMjmLwrlYZKx0q9qQJ5LpuO06pR3RRi5dvVzCsvkFM2kiTJkZGRSt6VSkOpp5V6t9YFhqiFkiTlEhmMjEA92T9m5dvVcspGGjCfVurVMj5ut2jEEga+UHyQWyZJUi6SwcgIJO9KpaEmK/VKktQbMhgZgUb7XalM2h16slKvJEm9IRNYR6C2d6UuW+cf8Ui+K82UtFtZ6uLrs9yUlpYOdfNGjVSl3lte2kR1U4iSPBt2S/I7Wd8alZV6JUlqRwYjI1DqrnRjjQ+nVWs3VZO6K51d5h1xd6WppN2WUJxStw27xUYkbrCxxocIt2D3FHLUpKKhbuaokarUmwoOG1qjWHWN2WVelsoVXZIktSGDkRFoNN6VdreU1GV1Eor4+f071cyfWDiiPneuk5V6JUnqCRmMjFCj7a70UEm7HoeFLQeTdmXV2MElK/VKknQoMhgZwUbTXemnSbu2jI9bNXVEJ+1KkiQNZzIYGeFGy13poZJ2Y4Y5YpN2JUmShju5tFcaEQ61lNQfjsulpJIkSTlKBiMjnDBNIlu2Evr3v4ls2YowzaFu0oBIJe16HRaqm0IEowkMUxCMJqhuCuG0aZx/7MhK2pUkSRop5DTNCBZat57mVauI7qhCxGIoViu2yVMoWLIE5/x5Q928rOsuafesWW7mTywY6iZKkiRJGchgZIQKrVtP3fLlGD4fekkJis2GiEaJbNpE3fLllF599YgNSDom7U4tdtHQUD/UTZMkSZK6IIOREUiYJs2rVmH4fFgmTkwvdVWcTizl5cT37KH5iVU45s5BUUfeTF3HpF1ziKamTFOMipVMkiRJ/SWDkREoum070R1VyRGRDDU39KIiolVVRLdtxz592hC1cmTLVJZ+amkeF47AGi+SJEn9NfJuiyVMvy+ZI2LLXHNDsdsRsRim3zfILRsdUmXpP97nw2PXmVDgxGPX2Vjj45aXNrG2ummomyhJkpRTZDAyAqkeL4rViohGMz4uIhEUqxXV4x3klo18HcvSu2w6mqrgsulUFDrxheOsfLta7iQsSZLUhgxGRiBb5VRsk6eQaGjIWHMj0diIbcoUbJVTh6iFI9ehytKX5NnYdrAsvSRJkpQkg5ERSFFVCpYsQfN4iO/ZgxkMIgwDMxgkvmcPmsdDwXlLRmTy6lD7tCy9lvFxu0WTZeklSZI6GHa90YMPPsikSZOw2+0sXLiQ999/v0eve/LJJ1EUhTPOOGNgG5gjnPPnUXr11dhnzsRoDRCv2YfRGsB++MwRu6w3F7QtS59JJG7IsvSSJEkdDKvVNE899RTLli3jV7/6FQsXLuS+++5j8eLFbNmyhdLS0i5ft2vXLq666iqOO+64QWzt0HPOn4dj7hyi27Zj+n2oHi+2yqlyRGQApcrSb6zx4bRq7aZqhBDUt0aZXeaVZeklSZLaGFa90j333MO3v/1tLr74Yg4//HB+9atf4XQ6efTRR7t8jWEYLFmyhJtuuonJkycPYmtzg6Kq2KdPw/mZz2CfPk0GIgPsUGXpvQ4LSz8ry9JLkiS1NWxGRmKxGGvXruXaa69NH1NVlVNOOYV33nmny9fdfPPNlJaWcskll/Dmm28e8n2i0SjRNqtQ/H4/kCycNRDFs0zTRAgxZIW5RoPBvsbzyvP56Zdm8Lt3knVGGlsjWHWNI8o8nH9sBfPK80fcz1t+jweevMYDT17j7OvptRw2wUhDQwOGYTBmzJh2x8eMGcPmzZszvuatt97it7/9LRs2bOjx+9x2223cdNNNnY7X19cTiUR61eaeME0Tn8+HEAJVjloMiKG4xhPscM3x46jxhQlGE7hsOmVeB6oap66ublDaMJiG+/fYNEWGn1VujV4N92s8HMhrnH2BQM9WDg6bYKS3AoEAF1xwAY888gjFxcU9ft21117LsmXL0v/2+/2Ul5dTUlKCx+Pp5pV9Y5pmcslnScmAfPmFaRLdXvVpzsjUKaNuqmYor/HYsVl/u5zU12tsmoJt9QF8oQRep05lSbJkflfHB8K63c3pUay21XIvOLYipzZXHOjvsSSv8UCw2+09et6wCUaKi4vRNI0DBw60O37gwAHGZviLX1VVxa5duzjttNPSx1LDRbqus2XLFqZMmdLpdTabDVuGyqWqqg7Yl1NRlAE5/2jbtbc7ba9xNveMkdf4U739HndVMn/h5ELe29E0KKX011Y3cevLm2kJxSl127Bb7ETiBh/X+Ln15c1cd+rMnCrfP1B/K6RPyWucXT29jsMmGLFarSxYsIDVq1enl+eapsnq1au5/PLLOz1/xowZfPTRR+2O/e///i+BQIBf/OIXlJeXD0azh8xo3bX3ULK5Z4y8xn2XKpn/aRBgIxI3WFvdxGubDlDgtFBe4EwfT5XSz2Zw0LFabmrlk8um47RqVDeFWPl2NfPKC3JuykaSRpphFfotW7aMRx55hBUrVrBp0yYuvfRSgsEgF198MQBLly5NJ7ja7XZmz57d7r/8/HzcbjezZ8/GarUO5UcZUB137VWdThRNQz24a6/h99P8xCrEKEvSWre7OWt7xshr3Hddlcx3WjUShiBumMQNE+cAl9KX1XIlKXcMm5ERgLPPPpv6+nquv/56amtrmTt3Lq+88ko6qXX37t1yaA25a28mpin43TvZuwuW17jvugoCglGDYCyBXdcIxUyC0QR5tuSfqI7BwYyx/c/f+rRabuYNJe0WjYbWqKyWK0mDYFgFIwCXX355xmkZgDVr1nT72scffzz7DcpBPdq1t6lxQHbtzWY+RjbV+MI9vgvuSUc3lNd4uOsqCIibJqYAu64QSSRHR9rKdnDQtlquy9b5T6GslitJg2fYBSPSobXdtVdxOjs9PlC79mYzHyPbgtHEwQ4wc2Z3bzu6obrGI0FXQYBFVVEViBsCVVGwaO1HObMdHMhquZKUO+Scxgg0FLv2phISs5GPMRBcNj2re8bInZH7LhUE1LdG2107l03DZdWJJAycVrVdoJIKDipL87IWHMhquZKUO2QwMgIN9q69XSUkDlTiYV+UeR0ZO0DoW0cnd0buu66CgFDMQNeSIyIWTSU0CMHBgopCrjt1JrPKvPgjCfY2h/BHEswu8+bcsl5JGsnkNM0Ildq1N10Do6kRxWrFfvhMCs7Lbg2M3qxKyEbiYV+oqsIFx1Zw68ubqW4KUZJnw25JjpTUt0b71NEN5jUeaVJBQGpar6E1ilXXOKqikKPb1BlJHZ9d5mXpAE33LagoZF55QU7mOknSaCGDkRFssHbtHS6rEuZPLMjYAfano5M7I39KmCaRrdsg4O/RdeguCDj3MxNlcCBJo4gMRka41K69A2k4rUoYiLvgnlzjXF1llC2h9RtoeuUVQmv/A9FojyvRqqqScbSsq+MDIZcTryVptJDBiNRvw21VwmB2dDDyO7vQuvXU33UXMa8Xl9uDWmQdNpVou6oEOxAVXyVJ6troG0uWsm40r0owTcHmWj/v7Whkc62/U5Jurq8y6q+2lWj1kpJhVYl2OCReS9JoIUdGRrjBmh7oKiFxIBMPh9qhRjxGw94nbSvR0uEj5Hol2uGQeC1Jo4UMRkawwZ4eGE2rEnoyvO+y6SO+sxvOlWiHS+K1JI0GcppmhOrN9IAwTSJbthL697+JbNnaryH1VD7GwslFzBjrGZGBSE+H91uCqc5Oy3geu0UjljCGdWfXthJtJrlcibZt4nUmuZR4LUkjnRwZGYF6Mj2w4u1qHFaN0Nr1uP76HLZ9uxHxGIrFil5aSt7xx+M86qhRu0y1Oz0d3m8Ox4bNKqO+SlWiDW/eDBMntnssVYnWfvjMnKxEO9wSryVpJJPByAh0qM7SbtFYs6WO4Lp1fO29Z3DFwsTzCxhnd2Ct209sxw5C772HPnYMjtlHpJdnjvTlqT3V0+H9fIdlxHd2qUq0sTvvJNFQjxkModpsiEiERGNjTleiVVWFC46p4Po/f8zm2gDFeTYKXFai/SiEJ0lS38hgZATqrrNsCcfZ3RgkGktw0qZ/4E1EaMgvRQ+3Et1fi6qYqA4HxGKYrUHCn3xCfPlympd+l8d8nhG7PLU3elpXpcBl5cLPVnDLS5uyVvU1Fznnz6PkqqsIvfIKxtr/YDQ2DItKtGurm/jdu9WE4wb+SJzGYAyLplKcZ2XOhPwRm3gtSblIBiMjUFedpQD2NoeIG4KpoTrG+WoJurzomkJpsAnVSBC22XHrOkJREJEI+qRJtNY1sPlXj7LxuAsp8ThyqhbDUIzW9GZ4X1WVUbHKyDlvLoXjxuI988weV2AdSm0TkMd67FQUOmkOxWhojeGwaJx/7MQR87ORpOFABiMjUFedZTCaoDWSAAWKiGExDcK6FWssgjUWIaFbME2BYZqoqooQAmEY1GpOihpqWCCaabQlpxRyYXnqUBUTS9VV6emIx2hZZaSoKvZplag5GoCkdJVTVeK2U5xno7opxO/f2c2CiYUj7mckSbkqt/9qSH3SVRGyQCROzDCxairuokIMTUdPxFANA0UIUFSEAFMApomiKERMBZ+hYcfAEQm1e5+Oy1MH01AXE+vtbq+jYZXRcNGb+iKSJA0OOTIyQmUqQmYCDotGeYGDqCefpuIySmurCTndCEUBYaIoKioCEY2iut3EbXb0RCumbiFid3V6n6GoxZArxcRGy4jHSCPri0hS7pHByAjWsbN023UeWlPFJ/v9CBTWz/8Cx7/+B5yhAAndghaNolotKNEEisWCdfx4DE3FGw5wYEwFDSXjO73HUCxPzaXKmYO9z43Uf8NpY0dJGi3kNM0I13Z64PAyLxctmpSevtlWchhvnHgONSUTadXtqAjsiRiqw4H1sMNQdB3rgRpEnptXpx+H6FDvO5WsWVmaN6jLUz+9sx25xcSkgZPKqapvjSJE+31nhuo7LUmjnRwZGWU6Tt+85xzPhhMv5mhaOF2rJ+/D94nX12G2BhBWK/ZZh5N/0mk07dLx5cjyVHlnK/VHbxOQJUkaeDIYGYW6y3UQ5jeJbtuO6fell2eOV1Wua7NyZaiXp8rKmVJ/jcaNHSUpl8lgZJTqKtdBUdWMu6vmUrKmvLOVsiGXvtOSNNrJYGSU6U+RsFxK1pR3tlI25NJ3WpJGMxmMjCJDVSRsoMg7W0mSpJFBBiOjRNvy16VuW06VdO+P7u5shWkS3badhK+FmKohioshx6uDSpIkjUYyGBkFcqVI2GAKrVtP86pVRHdUYcbjtB52GOofbBQuyd2N2yRJkkYreZs4Coy28tehdeupW76cyCefoLk9WMrGozqdRDZvpm75ckLr1g91E4c90xRsrvXz3o5GNtf6MU1x6BdJkiR1QY6MjAKjqfy1ME2aV63C8PmwTJyIoigIRUGx2bCUl5PYvZvmJ1bhmDsnZ3eUzXWZco8qS118fZab0tLSoW6eJEnDkAxGRoHRVCQsum070R1V6CUlGUeB9KIiolVVRLdtz7iEOaU/q45Gsu5yj0S4BbunkKMmFQ11MyVJGmZkMDIKjKYiYabfh4jFUGyZR4EUux3R1Ijp93V5jlxadZRLQVF3uUcuq5NQxM/v36lm/sRCGbhJktQrMhgZBUZTkTDV40WxWhHRKIrT2elxEYmgWK2oHm/G1+fSqqNcCorg0LlHHoeFLYO0QaEkSSOLnDQfJVJFwmaVefFHEuxtDuGPJJhd5h22y3ozsVVOxTZ5ComGhoyboCUaG7FNmYKtcmqn13a883fZdDRVwWXTqSh04gvHWfl29aAka6aCoo/3+fDYdSYUOPHY9XRQtLa6acDb0NGhNii0aqrcoFCSpD6RIyOjyGgoEqaoKgVLllC3fDnxPXvQi4rA4UBEosl/ezwUnLckY/Jqb1YdDeSdf64uxT5U7lHMMEdM7pEkSYNLjoyMMqkiYQsnFzFjrGdEBSIpzvnzKL36auwzZ2K0BojX7MMMh7DPnEHp1Vd3WWfkUHf+dos2KHf+uboUO5V7VN8azTjq5A/HqSzNGxG5R5IkDS45MiKNSM7583DMnZOuwNqkaoybOwdN7/ornyurjnJ1KXZ3uUcNrREOL9A4/9iRkXskSdLgkiMj0oiV2oHYedRRWCeMP2RdkUPd+de3Rru8889mEbC2QVEmQ7kUu7vco28cVc78iQWD3iZJkoY/OTIiDZlcWrYKfV91lO1VL7m+FDtT7tHUYhcNDfVD0h5JkoY/GYxIQyLXlq2mpO78U21raI1i1TVml3lZmqFtA7EUeDgsxe64QaFpmgPyPrkWsEqSNDBkMCINulyq5ZFJT1cdDeSql94GRSNRrgaskiRlnwxGpEGVS8tWu7vr7njnn8lALwUeDUuxu5LrAaskSdnV62Dkgw8+4MUXX6SwsJBvfOMbFBcXpx/z+/1ceeWVPProo1ltpDR0sj1Mniu1PLJx1z0Yq156EhSNNLkUsEqSNDh6FYz8/e9/57TTTqOyspJAIMD111/P008/zYknnghAOBxmxYoVMhgZIQZimHyol62apuD5DXt5aM0OwjGD8fl2HO6+3XXnylLgkSZXAlZJkgZPr5b23njjjVx11VV8/PHH7Nq1i6uvvpqvfOUrvPLKKwPVPmmIDFQ58qFctrpudzM/eHI9N77wCbsagrSEY+xqDBGIJPpU8r0/S4GlruVK8TlJkgZPr4KRjRs38s1vfhNI3qFcffXV/PrXv+ass87iL3/5y4A0UBp8A7lHS7Y6cGGaRLZsJfTvfxPZshVxiNUc2+sC3PbyJtbvaSFhCJxWDV1VCUQTbKsL0BKK97q6aWrVi9dhobopRDCawDAFwWiC6qZQTqx6GY5yuc6KJEkDo1fTNDabjZaWlnbHzjvvPFRV5eyzz+buu+/OZtukITKQw+TZWLYaWree5lWriO6oQsRiKFYrtslTKFiyJGOpd9MUvL65jpZQnJI8K03BGLqmogCaohJOmOxtCeF1eHo9TSRXvWRfrtdZkSQp+3oVjMydO5c33niDBQsWtDt+zjnnIITgwgsvzGrjpKEx0Hkd/enAQ+vWU7d8OYbPh15SgmKzIaJRIps2Ubd8eca9Z7bVB9jfEqHU7cBEQVUUDFOgqwqKomDVVILRBMGogaLQ67vu0bzqZSAMhzorkiRlV6+CkUsvvZR//vOfGR8799xzEULwyCOPZKVh0tAZjMTMvnTgwjRpXrUKw+fDMnFi+o5ZcTqxlJcT37OH5idW4Zg7p13pd18oQdwwsds0FFXFZdMIRBJoqnZwdARiAmKGkS5tPrU4j821/h63bTSuehlIcsRJkkaXXgUjX/3qV/nqV7/a5ePnnXce5513Xr8bJQ2twRom720HHt22neiOquSISIbpI72oiGhVFdFt27FPn5Z+zOvUsWgqwbiB06YyocDJtgMBwnEDq3YwaBFQ3xqj1G3j6MmFLHv6A1lsa4jJESdJGj16lcDa3NzML3/5S/x+f6fHfD5fl49Jw0uuJmaafl8yR8SWefpIsdsRsRim39fueGWJm3H59nTSbL7DQuUYN267TtwwCcYS6JrC/PJ8zlwwgWfW7s36KiKpb1IB68LJRcwY65GBiCSNUL0KRh544AH++c9/4vF0vpv1er28+eab/PKXv8xa46TBkWnH2e52Z+1L9cts7GqrerwoVisiGs34uIhEUKxWVI+3/etUhZNmlLYLrtw2nUlFLgpdViYVubjxK7O45xtzeW9H04CsIpIkSZK61qtpmmeeeabbFTPf+c53uOqqq7juuuv63TBpcByqsFk2hsmzVTzNVjkV2+QpRDZtwlJe3mn6KNHYiP3wmdgqp3Z67dRSN9d+qZCV7+xul4Mwf2JBOgdhc61fFtsaRuQmepI0cvQqGKmqqqKysrLLxysrK6mqqup3o6TB0XH/D5tupTkU4z+7mth2IMDNZ8ziM5OK+tXxZnOPEUVVKViyhLrly4nv2YNeVJScmolESDQ2onk8FJy3pF3yalvzJxYwf2Jhlx3YUFeHlXpObqInSSNLr6ZpNE2jpqamy8drampQu+gIpNzSsbBZ3BBsrg2woyFISzhOVX0rVz65gX/v7HuOxEAUT3POn0fp1VdjnzkTozVAvGYfRmsA++EzMy7r7airHATTFDSFYiRMQVMwRqYWyWJbuWGgqgNLkjR0ejUyMm/ePJ5//nmOOeaYjI8/99xzzJvXfWcg5Ya2hc184WQV0oQpsGpqcqmrolAfiHH9nz/m/746u093mz0pnrb9gJ8t766nwmKgerzYKqd2ObKR4pw/D8fcOUS3bcf0+3r8uq6k7rK3HQjQ0BqlpiVCvj9MeaGLfEcy8JDFtnKD3ERPkkamXgUjl19+Oeeccw4TJkzg0ksvRdOSe0cYhsH/+3//j3vvvZcnnnhiQBoqZVdqSsKmW9nZkAxEHLqa/uNu1RUSppkevejLH/dDTXtMqdvBjHdfwfi7j/2KcchKqm0pqtpu+W5fdZ5GcrOtLkBzKE4w6mfaGDc2XZXFtnKE3ERPkkamXt1KnnnmmVx99dVcccUVFBYWMm/ePObNm0dhYSFXXnkly5Yt46yzzhqotkpZlCps1hyKEYwlsGpquz/uhinQVJXiXuzV0tV7ZNpjpGzvNo5//Q+UN+5F87ixlI1Hc3vSlVRD69b36/P1RKZppEKXleljPBQ6rcQNk211rf1aRSRll9xET5JGpl6NjADccsstnH766axatYrt27cjhOD444/nvPPO4+ijjx6INo56A7FqIFXY7D+7mjBFsgppigBihonbrlPgsrKvOXTIP+6Z2thl8TRhMnftq1hCrYRLx+HK96Bw6Eqq2dbVXXa+04LX4Ukmq4YTfP/kqSw+fKwcEckBg1EdWJKkwdfrYATg6KOPloHHIBmoVQOpwmapPImYomDVk3u2xAwTXVWYUOAk2oM/7t21MdMeI579u3HX7SWU56Wi0EXbLr67SqrZ1t00kqIoFLpshGIGhU6rDERyhNxET5JGpl7ddgaDQS699FLGjx9PSUkJ55xzDvX19QPVtlEv06oBt01j3e5mfvLMRzy7bk+/CnAtqCjk5jNmUeK2EU2YhGIJEqbAbdepHOPGa9epb41SWZrX5R/3Q61sADoVTzP9flyKSUVZEV5H5yCnq0qq2Sa3qh9+crU6sCRJ/dOrkZGf/exn/O53v2PJkiXY7Xb+8Ic/8D//8z8899xzA9W+USvTqoGWUJy9zSGCsQQHEiY3vvAJb2yu56JFk/o8SvKZSUXcd/Y8rv/zx/jCcYrzbBS4rETjxiH/uPd0ZcO9Z89l3tmfFk/z1HqwbfkLmpI5COiqkmq2ybvs4UluoidJI0+vgpHnnnuOxx57jK9//esALF26lGOOOYZEIoGu92nGR+pCx3yGllC8/fJbi0LcEGzY09Lr4mEdfeawQv7vq7PTf9z3NYd69Me9tysbUqsbxKQCaqb0rZJqNsmt6oePjjlJ88oL2gW4sgKrJA1vvYog9u7dy6JFi9L/XrBgARaLhZqaGiZOnJj1xmXy4IMPcuedd1JbW8ucOXP45S9/2WX+yiOPPMLKlSv5+OOP0+299dZbh0W+S9t8BiEEe5tD7ZbfChTipkFxnrVfy29T+lL6va8VS/tbSTWb5F127pPVViVp5OtVMGKaJhZL+/lzXdcxjMzD7dn21FNPsWzZMn71q1+xcOFC7rvvPhYvXsyWLVsoLS3t9Pw1a9Zw7rnn8tnPfha73c4dd9zBF7/4RTZu3Mj48eMHpc191TafQQg6Lb81TIGqKFh1jZI8LSu1FVLVSfvSxt6ubEhVUm1etYrojipEUyOK1Yr98JkUnHfoOiPZJLeqz13Z3E5AkqTc1atgRAjBySef3G5KJhQKcdppp2G1WtPH1q1bl70WtnHPPffw7W9/m4svvhiAX/3qV7z00ks8+uij/OQnP+n0/FWrVrX7929+8xueeeYZVq9ezdKlSwekjdnSNp/BY9fbLb9tu/TWZdMxTTEke6b0N+ci25VU+6O3gdhgGc2bwclqq5I0evQqGLnhhhs6HTv99NOz1pjuxGIx1q5dy7XXXps+pqoqp5xyCu+8806PzhEKhYjH4xQWdn0nFY1GibbZot7v9wPJUSHTNPvY+q6ZpokQIuO5lx47kdte3kRdIIoqBOLgfzHDxKoqlBc4UBGE4wlsuorHoQ1IG7uTamN1Yyt5Nh1VUTCFoDWaIN9p5YJjJwKi21U/1sqpmKZgW30A384mvE6dypLsdbrdXeNctm53M797p/P0xAXHVjB/YsFQN6+dgbjGWw74qaoLMMZtJflV+PQ7pChQmmdle12ALQd8TB+Te4Fktg3X7/FwIq9x9vX0WvY7GBksDQ0NGIbBmDFj2h0fM2YMmzdv7tE5rrnmGsrKyjjllFO6fM5tt93GTTfd1Ol4fX09kUikd43uAdM08fl8CCE6bTI4wQ4//NwYVm+q4+N9UWJGHF1VsFs0ivNsuGwmQkQgHGXmOCdeEaauLvtt7M4EO3x9loeXP9qPPxzGRKCgMN6h86VZxUywx6mrq+v2HNvrAry+uY79LRHiholFUxmXb+ekGaVMzcJKlu6uca7aXhfgj//ZQzBqMN1rwappxAwTf0sjK19vIXJUeVauTbYMxDWuO+BnnDVKsRNUtfNUsKkJGswodQfqKFAG93s/FIbj93i4kdc4+wKBnlXvHjVLYG6//XaefPJJ1qxZg91u7/J51157LcuWLUv/2+/3U15eTklJCR5P9u++TNNMrjwpKcn45S8tLeWYwyfzwgc1/OofVUTiBmV5Dvxo1LWmVn04+O7RMxg7dvDvltftbubpjX5aIlby7E5UFUwTaiIJnt7oZ2rF+G7v4tftbubetw4czAlwYLdpBOMG2/ZE+aDhANd+qbDfowCHusa5xjQFt71Rw8YmhUlFHvwocLAvFnbBJ00h/rQxwF2HT86Z6YmBuMbNws7+WC3BkJ4xJykYTeCPQemYUkpLR8fIyHD6Hg9H8hpnX3f9bVvDJhgpLi5G0zQOHDjQ7viBAwcYO3Zst6+96667uP3223nttdc48sgju32uzWbDZuu8OkRV1QH7ciqK0u35VRW+tqCcimJXm1UfMay6xqyy/C5XfQx0voFpCla+s5vmUIJJRa52OSNFeYLqphC/e2c38ycWdlmnJNPrnTaViVb9kK/vjUNd41yytc7PtrogJW47KCrtJrgUheI8O1vrgmxvCOZUnku2r/H0MV6mlLrZWOOjwqp3ykmqa40xu8zL9DHenAnKBtpw+h4PV/IaZ1dPr+OwCUasVisLFixg9erVnHHGGUAyil29ejWXX355l69bvnw5t9xyC3/729846qijBqm1A6M3qz4GYzlkf3dQlTuwZtbXJdMjjawDI0mjx7AK/ZYtW8YjjzzCihUr2LRpE5deeinBYDC9umbp0qXtElzvuOMOfvazn/Hoo48yadIkamtrqa2tpbW1dag+Qr+lVn0snFzEjLGeLgOR7kq0r61uykpb+ruDalevF0LQGkkQiRsEInGag7GstHe4kGXqP5WqA9N2OwG5i7IkjTzDZmQE4Oyzz6a+vp7rr7+e2tpa5s6dyyuvvJJOat29e3e7IaGHHnqIWCzGWWed1e48N9xwAzfeeONgNn3QDOZyyP7uoJrp9W1L3idMgRDw63/swKqrvep4hGmmlwzj9iA8uZPseSi9WTI9Gpb+yjowkjTy9TkY+fe//80bb7xBXV1dp6U799xzT78b1pXLL7+8y2mZNWvWtPv3rl27BqwduWowpz76W2ek4+t94US7kveGaeCyaexpCvWqwFVo3fpPi6nFYmCzEVlwFO7/+i/yFszv12ceDD2dnli/p3nUVCbN1TowkiRlR5+maW699VYWLlzIY489xn/+8x/Wr1+f/m/Dhg1ZbqLUG/2dOumN/u6g2vb1uxqD7GoMEjcEFlUhdnCJ76TiPCqKnOmS94fapTi0bj11y5cT+eQTNLcHS9l4NLeH2N491N91F6F16/v9uQfDoaYngEGZipMkSRoMfRoZ+cUvfsGjjz7KRRddlOXmSP3V36mT3urv3i6p1/9y9Xbe2dGIooAhFNwHO9h8R7KdPRnREaZJ86pVGD4flokT0yM1qtOJXlyMsXsPzU+swjF3zpBUee2trqYnAK58aoOsTCpJ0ojRp2BEVdV2G+ZJuaO/Uyd90d85/QUVhXz3+ClU1bdSlGfFpmu4bDptX32oFSSmKdjy7gaMTVvRvAVYOkxRoYBeVES0qorotu3Yp0/r46cdXJmmJzbX+uUqJEmSRpQ+3R7+8Ic/5MEHH8x2W6Qs6O/USX/e91CrfLqT77Lgtluw6xp5B0d0WqMJmkMxWqMJwt2M6KytbuLKpzbw4J/XU98U4JOmGBtrfPjCHXYLttsRsVgyqXUYG8ypOEmSpMHQp5GRq666ilNPPZUpU6Zw+OGHd9rJ99lnn81K40abbK2M6O/UyVBoO6ITM0z2tYQJRg1MIVBI3vHPm9h5RKftrq6zPZ7kzr9GnEBEYduBAJVj3HicyU0cRSSCYrWierxD8AmzZ7Cn4iRJkgZan4KRK664gjfeeIMTTzyRoqKiTkPFUu+t293Mynd2Z21lxHBbDpka0bn22Y/YWONHAWy6iq6qRBIGwhQc8EdZv6c5fT3aLmMuzrOyzzGWAwXjGF+/GzO/lHDCZE9ziMOdVhCQaGzEMXMGtsqpQ/th+2kopuIkSZIGUp+CkRUrVvDMM89w6qmnZrs9o9L2ugD3vnWA5lCCUrcNu8VGJG6kV0b0tbjTcFsOOa+8gFK3nX3NYRQF4qZAVQReh4UJ+Q5aDq6oSSVmbq0L8OHeFgKROHWBKKYQPDXhGC5qPIC3+QCa00soLAi2+Ekg0DweCs5bMiySV7sjK5NKkjTS9CkYKSwsZMqUKdluy6hkmoLXN9cdXBnhGtUrI7bWBWgKxphd5gEU4qaJRVVx2ZJ3/xZNbZeY+f7OJmp9keQoikVDU1V2lE7msSO/wpe2v8XkUD2ORBzD6sF6xOGUfOvbOOfPG+qPmRXDcSpOkiSpK30KRm688UZuuOEGHnvsMZxOZ7bbNKpsqw+wvyVCqdsx6ldGpBIzHW4bWobAq+2KGtMUvL6pDlMInFYd/eDzdVVh97ip3FdUwdRgHeUWg+99ZS4Fh43DeYgNFYeb4TYVJ0mS1JU+BSP3338/VVVVjBkzhkmTJnVKYF23bl1WGjca+EIJ4oaJ3db1yojRsCka9C4xc2tdgAP+CG67hXDcQFPUdDCnALqmsckxhsKpRUxbOJeGhvpB/jSDY7hNxUmSJGXSp2AktWuu1H9ep45FUwnGDZy2zrkMo2llRG8SM/+9q4m4YTKx0ElVfSvhhIlVU9EUMATEDIGiwInTS+VIgSRJUo7rUzByww03ZLsdo1ZliZtx+Xa27Yky0aqP6pURvUnMTI2i2HSVylJ3enO9mABVAadVw23XOXqyzJ0YTkbDxn+SJHU2rHbtHYlUVeGkGaV80HBg1K+MME2By6ZzxrzxvL6pjgP+SJeJmW1HUSoKnXgdHoJRg7hpoqsKDa1RjhiffzCI634/Gyk3rK1uGjUb/0mS1F6fy8F3V1vEMIw+N2g0mlrq5tovFabrjPRlZcRA31EO9PkzdURjPHZOmlHK0ZMLO71fplEUh1VDiUN9a5R8pzUdxB1qcz1p6LUtXpfN5e2SJA0PfQpGnnvuuXb/jsfjrF+/nhUrVnDTTTdlpWGjzfyJBcyfWNinDn+g7yi7O382VnN01RHtaQ7x/IZ9zBqfubz8cF7e2tfgbiROY7QtXic3/pOk0alPwcjpp5/e6dhZZ53FrFmzeOqpp7jkkkv63bDRqC8rIwb6jrK781/77EeUuu00BWN9DoL62xENx+WtfQ0eR+o0xta6gNz4T5JGuayWojzmmGNYvXp1Nk8pdaNjR+6y6WiqgsumU1HoxHewYmlfpym6O7/XYaG6McT6Pc24bRoTCpx47Ho6CFpb3dSj9+hNR9SV/m7SN5hSwd3H+3x47HqPr1tfXzccyI3/JEnKWjASDoe5//77GT9+fLZOKR1Ctx05glmReowN69jy7nqEaWbt/ALY1xJObmB38N36GgSNpo6or8HjQAedQ61tfZlMRtPydkkarfo0TVNQUNBpCWogEMDpdPL73/8+a42TuvdpR25rd7xs7zbmrXuVwoYaRCyG8dGfqZk5jYIlS3pVDr2r8wejCYJRA5uuEjcF8TaBTk+G1dvmPTSFYlg0dVTsQNvX6YiRPo0xmjb+G4k5P5KUDX0KRu699952fzBUVaWkpISFCxdSUFCQtcZJ3ctUsbRs7zaOf/0P2CNBfA4PEbuHsR4rkU2bqFu+nNKrr+5xQNJVRdS4YWIKga6qqIrA0mHjue6qxmbKe/BH4vgjcaaPcY/ojqir4C6lq+vW19cNF6Nl47+RmvMjSdnQp2DkpJNOory8POPy3t27dzNx4sR+N0w6tE53lAjmrXsVeyRIc34p4YSJ267jyveA1018zx6an1iFY+6cHu1c29Udq0VTUYBIwsDrsODqUMq+q9GMrpJhfeEYzaE4Ww4EKC9wjsiOCHpX7j4brxtOhvPKqJ6QS5clqXt9CkYOO+ww9u/fT2lpabvjjY2NHHbYYbLOyCDpeEc5K1JPYUMNPoeHcMLEoiqUFziTeR2Kgl5URLSqiui27dinT+v1+VN3rMnTKQhTMCHf0aPRjLZ5D8V5VmKGiSC5amb6GDdbDiQTVH3h+IjriFL6Oh0xWqYxhuPKqJ6QS5cl6dD6FIwIkTlRrrW1Fbvd3q8GSb3T9o7S3FCFiMWI2D247TrlBU68jk/vlhW7HdHUiOn39en8be9Y5030csAfpSUcx6KphxzN2FoX4MO9LQQiceoCUUwhUBUF18GVOOUFTvyRBN8/eSqFTuuI6Yja6ut0xEBOY+RaDsNI3PhvpOf8SFI29CoYWbZsGZD8Bbr++utxOp3pxwzD4L333mPu3LlZbaB0aKk7yi0VAuOjPzPWY8WV76FjlyIiERSrFdXj7dP5O3Za6/c0H3JYPdXZ/fHfe6hpCaMCdquOpqoYpiAQSbDtQIApJXnEEgaFTisLJxdl58LkoL5ORwzENIbMYRgcIz3nRxoZhvrGpFfByPr164HkyMhHH32E1WpNP2a1WpkzZw5XXXVVdlso9YiqKsw4Zi41M6cR2bQJvG7oMJyfaGzEfvhMbJVT+3b+DndthxpWb9vZ7WoIEjMEFk1BCIGCgq4qaKpGOG6wuynIGI9jWOc99FRfpyOyOY0hcxgGz2jI+ZGGt1y4MelVMPLGG28AcPHFF/OLX/wCj0cOKeYSRVUpWLKEuuXLie/Zg15UlJyaiURINDaieTwUnLekR8mrPdXVsHrbzi7PpqMoAl2FhCEIigQuq55OhLWoCoGIwZETbMM+76Gn+jodkY1pDJnDMLhGS86PNDzlyo1Jn3qlxx57TAYiOco5fx6lV1+NfeZMjNYA8Zp9GK0B7IfPpOTHP2Z32RTe29HI5lr/gBXJ6tjZ6ZqCQMFp1VEVMEwIxQ1M0yRhCmKGQFHgxOmlsvMbBNmoeiv1XCrnx+uwUN0UIhhNYJiCYDRBdVNoRK0Yk4aXXCqo2KcE1mAwyO23387q1aupq6vD7FDdc8eOHVlpnNQ3zvnzcMydQ3Tbdky/D9XjZaO1kDve3cP2tz4Y8GG4jp2dRVNRFQVVUcizWQjFkn+MQ3ETXVNwWjXcdp2jJ8tpgcEgcxgG30hfuiwNT7mUXN2nYORb3/oW//jHP7jgggsYN25cxnoj0tBSVDW9fHewh+E6dnYum47LphGIJHBYkoFHKG5QXuCkwGmhoTXKEePz5TD1IJE5DENjpC5dloavXLox6VMw8te//pWXXnqJRYsWZbs9UpYNRX5Ax85OASYUONl2IEA4bqApyb1sHBaVxmCMfKdVDlMPIpnDMHSytXRZmGa7kU9b5dSs5oJJo0Mu3Zj0eW+awkI5rDgcDMUwXKbOLt9hoXKMmz1NQVpCCay6QtwQcph6CIyW8uu5IttLJkPrN+B74gmiO5J1hRSrFdvkKb3ee0qScunGpE/ByM9//nOuv/56VqxY0a7WiJR7hmIYrqvOzqIquG0WivJsnPuZiRw9uVAOUw8RmcMwOLK9ZDJaVUX4gQcxW1rQS0pQbDZENNqnvackKZduTPoUjNx9991UVVUxZswYJk2ahMXSfghn3bp1WWmc1H9DNQzXZWc3XnZ2uULmMAysbOdqCdOkdc0/sPl8WCdOTN/FKk4nlvLyXu89JUmQOzcmfQpGzjjjjCw3QxooQzkMJzu73DcSy6/ngoHI1YpuryJeux9XSUnGKdfe7j0l5aahqISaC3+r+xSM3HDDDdluhzRAhnoYTnZ2Uk+MtITMgcjVMv0+RCKBYss85dqXvaek3DKUlVCH+m91n4IRaXjJNAxn0VQmFjg5cWYpLpuOaQo5YiENidC69TSvWjWiEjIHIldL9XhRdB0RCoDD0enxvu49JeWGwS7BkGs3AH0KRgzD4N577+WPf/wju3fvJhaLtXu8qakpK42TsqftMNz7O5p4fXMdBwJRVr69iyff3yM3SJOGRGjdeuqWL8fw+UZUQuZA5GrZpk7BMnYciR07USdM6DTl2p+9p6ShNdglGHLxBqBPYdBNN93EPffcw9lnn43P52PZsmV87WtfQ1VVbrzxxiw3UcoWVVUIRhM8v2Efu5tCeOw6EwqceOx6OvpeWz2wgaRpCjbX+ge8JL2U+4Rp0rxqFYbPh2XiRFSnE0XTUA8mZBp+P81PrEJ0qPA8HKRytepbowjR/jueytWqLM3rVa6WoqrknXA8msdDfM8ezGAQYRiYwSDxPXsGZO8paXAM5hYNqRuAyCefoLk9WMrGo7k96RuA0Lr1/X6PvujTyMiqVat45JFHOPXUU7nxxhs599xzmTJlCkceeSTvvvsuV1xxRbbbKWXBUG+Qlgs7Q0q5I7ptO9EdVckRkWGUkNmTBMOBytWyTZlC3lVXfVpnpKkRxWrFfvhMCs4bvtNao91glWDoeAOQSyuy+hSM1NbWcsQRRwCQl5eHz5dMmPryl7/Mz372s+y1TsqqodyHIFd2hpRyh+n3JYeIh1FCZm8C6oFaMumcNxfXvLk5Nd8v9c9glWDI5RuAPgUjEyZMYP/+/UycOJEpU6bw97//nfnz5/Pvf/8bWxd/WKTBlenubaj2IRjqERkpN6keL4rViohGUTIUT8y1hMy+BNQDtWSy7d5T0vA3WCUYcvkGoE/ByFe/+lVWr17NwoUL+f73v8/555/Pb3/7W3bv3s0Pf/jDbLdR6qWu7t5OmF4yJAXQcmlnSCl32CqnYps8hcimTVjKy3M6IbM/AXV3SyaHoqaElHsGqwRDLt8A9CkYuf3229P//+yzz2bixIm88847VFZWctppp2WtcVLvdXf3tqcpRKHLyn5feFALoOXSzpBS7lBUlYIlS6hbvpz4nj3oRUXJO7NIhERjY04lZA5EQC1zqHJXMkj0D3rhsYGuhJrLNwBZqTNy7LHHcuyxx2bjVFI/9OTuLc+u4bHrg1oALZd2hpRyi3P+PEqvvvrTZYY5mpCZ7YBa5lDlru11AW57o4ZtdcFBDxIHuhJqLt8A9CkYWblyZbePL126tE+NkfqnJ3dvja1xvnXcJNZsaRi0fQhyaWdIKfc458/DMXdOTidkZjOgljlUuWvd7mb++J89bGxSKHHbhyRIHOhKqLl6A9CnYOQHP/hBu3/H43FCoRBWqxWn0ymDkSHS07u38flO7jt7bo+i72zMaQ91SXop9+V6QmY2A2qZQ5WbTFPwu3eqCUYNJhV5QEkGwyMxSMzFG4A+BSPNzc2djm3bto1LL72UH//4x/1ulNQ3me7eBBCMJogbJglDpO/eehJ9Z3NOO1d2hpSkvshmQC1zqHJTKkic7rXgR6FtqbqRGCTm2g1A1vamqays5Pbbb+f8889n8+bN2Tqt1Asd7958kQR7m0MEowaGaWKYUOK2Eogc+o/cQMxpp+ZDt9T6aN20lbxEmEkVTuzl+X38xJI0eLIVUMscqtyUChKtmgZG58dlkDiwsrpRnq7r1NTUZPOUUi+0vXvbciBASyhOwjTRFAUhwKon79pue3lzt8FEl3PaVpWJRiMtO5r4y3MNzL38y6iq0quhvsiGDXhWrcJ2cE+E/TmwJ4Ik9VQ2EgxlDlVuSgWJMSPz9gMySBxYfQpGXnjhhXb/FkKwf/9+HnjgARYtWpSVhkl9s6CikGv/eyaXrlpLOG6Q3BZDoCmCaaEGpiYE9QErK/9l73LuM9Ocdtnebcxb9yqFDTWoiTgxRWfrv5/FadUwWpp7tNnSSN0UTRpd+ptgKHOoclMqSPS3NCLsAmSQOKj6FIycccYZ7f6tKAolJSWcdNJJ3H333dlol9QPe5qDtEbi6KqCVVOZ2bSLL259k/H+WiymgWK1cuCDcWwp/TYzT/lcp9d3nNMu27uN41//A/ZIkKDLS9xlxeJvwvxoA2FFwTp5Mpay8d0GFm33RNAnTiQUM4hHDSyaFWd5OYkh3BNBkgabzKHKPaqqcMGxFax8vYVPmkIU59llkDiI+hSMmMNwF83RwjQFz6zdh2GC264zvWEn5274M85YiBa7h4iq4zATjKvfTeKB+wgVujqNRrSb07aqzFv3KvZIkJb8UlAUEqZJcbgVVVFBVUg0NqKXlqZ3W8202VJqT4SwO589+/0EowamEKiKgsumUZ7nRcnBTdEkaaAMdE0JqffmTywgclQ5T28MsK0uKIPEQdSvnJGGhgasVisez/DPLB4pttYF2O+LYNFUhGlyypZ/4oyFqMsrTg47CggpKoanmHGhQMbRiLZz2hONRgobagi6vKAkM8zVcBh7IorqtAMggkHMYBAtL6/LzZZMv49wa5jtik5MKFh1FU1VMUxBIJJge0RQKcI5tSmaNPRGern0ga4pIfXe1FI3dx8+me0NwRH7vctFvQ5GWlpauO6663jqqafSS3xLSkq4+OKL+dnPfoYzQ717afD4QnEUBHl2ncIDuynzH8Bn96TnPxXAEGCzaDiKSjPu0Nh2TrtlRxNqIk7cZSVhCmKGiQcTq5pcGiZIzqeSSKRfn2mzJcXtoTEGKDEcLiepX2tdVdBUDTMUpNGEyW75h1lKkuXSpaEig8TB16tgpKmpiWOPPZZ9+/axZMkSZs6cCcAnn3zCL3/5S1599VXeeustPvzwQ959912uuOKKAWm01DW3Q0eg4LSo5MXDaIk4EbsHRLLmiCkEigLjvHbCiorRGmbHzhpwjyEQSaTvAlJz2i8+10BM0UlEIiSsdtx2nQleN1prcuQFkjlD6J9+lTJttrTLM4Y97jGUN+7B77BjjUdRTQNT1YhZbBREWtldXE6JZwwzB/uiSTlHlkuXpNGlV8HIzTffjNVqpaqqijFjxnR67Itf/CIXXHABf//737n//vuz2lDp0NZWN/H4v3bR0BolHDewWezEVR1LIkZUt4GSHCBxWXUaW2PUBoPYIgb3v7KTPe9Gcdt1vA5ru7vPuZd/ma3bXsfYsgVtXAkuuwUQRA+4SAQCKIDqdqO6XEDXmy35IwZrDv88F7z9Bybs24oiPi0pJBQFn6eIf8z8PJWRDAv8pVFFlkuXpNGnV8sWnn/+ee66665OgQjA2LFjWb58Oc888wzLli3jwgsvzFojpUNL3UlurPFTXujErqvs9oxlr2cMBZEAVk3BoqnYdS2ZpxGO4wn5qXaPYUdeKTHDxB+Ooyik7z7XVjeh6RoVl1xEXnEB1rr9iGAQDBOtqAjFNME00QsLwTQxg0Hie/Zk3GzJ67Rg0TTMdAwiSK47FgenesCiyTX8Uu/KpUuSNDL0amRk//79zJo1q8vHZ8+ejaqq3HDDDf1umNRzme4k7RaNvc0h/lZ5HOdv+DOFvnooLKRV6IholOJYgBbdzl+nLMJptYCiEI4bNLRGOXych91NIVb8axcOi0YgfyKeb12G++XniO3ckd5YybFgAQiB0dJMvGZft5stVRa7WLztTUQiwZ4J07DFo6iGgalpRC02XI11LN72JpXFFwzRVZRyhSyXLkmjT6+CkeLiYnbt2sWECRMyPr5z505KS0uz0jCp5zLdSeY7LHgdXoJFc3nNa+czG1YzMVCHKxolruls8pTx4mGfZVfxZOwHX2PVVIJRg1DMwK6rrNlaz7o9LQghsFk0Zi04l4u/IpjhIl1tFehRBdZ4VRVTwvXsyvMSjJtEVSu6nnxeLGGiujxMCdcTr6pCk0t7RzVZLl2SRp9eBSOLFy/muuuu49VXX8VqtbZ7LBqN8rOf/Yz/+q//ymoDpUPr8k5SJLNWtxVP5rV5JcyMNKCFWjGceexwldIaF6jxBJpqwaIpaKpCzDBpDsXY2xwmHDMIxw1UksPjB/wRttQ5uO1rR7RLHuxJXRDT70OJxRAWD4mYSTRuAgaaquBx6FSMKcTRVCeX9kqyXLokjUK9TmA96qijqKys5LLLLmPGjBkIIdi0aRP/7//9P6LRKCtXrhyotkpdyHQn2RKKJzfJiyUIxQxMobDLM46YS2DTVTQEaiKBKSCcMNA1HcMUKECtL0I4ZiAAwxQH94wSYMDOhiD3vbaVFRcv7FXy4OYgNIdNDD1KnsNx8NwmCVOQMAREo51W4EijkyyXLkmjT6+CkQkTJvDOO+/wve99j2uvvTZZX4LkXfMXvvAFHnjgASZOnDggDZW61mm33nCCbXUBEqZAUxRMAboKcVMQN03MuMBl1dBUBWEKEoaJcbCGiF1XaQnH09tnq4qCwqfLghOGYF11C5tr/Rxe1rPAwTQFj9eoHOUZw+SWfbQ4HaAoWFQNAYRjCfz7Gsg/Zl67FTjS6CXLpUvS6NLrTUAOO+ww/vrXv9LQ0MC7777Lu+++S319Pa+88gpTpw58R/Lggw8yadIk7HY7Cxcu5P333+/2+U8//TQzZszAbrdzxBFH8PLLLw94Gwdb6k7S67CwqyHI9vpWogkTVYGYkfxfp1XHoatoioJhCsJxA6umJouyCgjGEmgK2CxKesWLrirJWmkHlwRrB4fLw3GDj2p6Pp2ytS7A9voQH37mi0TsLvJb6rDEIiimiTUWYWxrIz7dTuC/z5D70khpCyoKue/sudxz9hxu+eoR3HP2HO49e64MRCRpBOrzX/6CggKOPvpojj76aAoLB+ePw1NPPcWyZcu44YYbWLduHXPmzGHx4sXU1dVlfP7bb7/NueeeyyWXXML69es544wzOOOMM/j4448Hpb2DaUFFIWcumEBrLIE/HCeaMAnFDIQQWDQ1OcKhKDgsGhZNwWHVQFHQlGTAYT14LBQ7WMiMTzetFCKdfoKqgCnggC/S47alcloaKqbzj5POpW5sBbZoCK+/AVs0RN3YCp5d+DX8k2W5M6m9VCXMhZOLmDHWI6dmJGmE6tfeNIPtnnvu4dvf/jYXX3wxAL/61a946aWXePTRR/nJT37S6fm/+MUv+K//+i9+/OMfA/Dzn/+cV199lQceeIBf/epXg9r2gba2uok//WcPppkc0UglpKZyMlL5JJqSnFarKHJhURX2NocZ47HhsVuobgoRN2JAMvAwjIM1QDq8lwKM8dh73La2OS37xk9lW1EFBfV7ccfC4HGz2zMWf9Ts0+qIkb53iSRJ0mgwbIKRWCzG2rVrufbaa9PHVFXllFNO4Z133sn4mnfeeYdly5a1O7Z48WKef/75Lt8nGo0SjUbT//b7/UByp+KB2K3YNE2EEP06t2kK7nt1K9sPBDAPnismQFdVHBaVqBAYpkk0lkBTFTRFYBoGTWEDt10jdHA0ZZzHRonLwvrdLaRac3CWph0F0Oj57s1Ti11UlrpYW91EwhCEYglM4UHVPDjjOnpLmKMqCpla7OrVdVi3u5nfvdN575ILjq1g/sSCNten/9dY6p68xgNPXuOBJ69x9vX0Wg6bYKShoQHDMDpVfx0zZgybN2/O+Jra2tqMz6+tre3yfW677TZuuummTsfr6+uJRHo+NdFTpmni8/kQQqD2MV/i7aoGgi2NTHWDpkLMEAenVQwUxcCiqpgCLFoySdWiQbE1Rlmpg0A0QVMwdrBGSXIFjTpWozWayPheCsl6JJ/s2MNnx1t6PApxbJnOnpoohiawOFT0g6M2cSOKpiocU6bT0FDf48+8vS7AH/+zh2DUYLrXglXTklVkWxpZ+XoLkaPKmXpw6Wc2rnEmpimo8YUJRhO4bDplXseoHZUZqGssfUpe44Enr3H2BQI9q5Q8bIKRwXLttde2G03x+/2Ul5dTUlKCx5P9XRxN00yWuC4p6dOX3zQFz764k4+bkoGCicA0lQ5TK8l/6So4rRpnLSjnzKPGY5qCq//0ER67i/rEp1+FVkWwucVPm+1j0jkkDotGRZGT92oNvq44mF566GtimoJ33qihLm4jbpiEYyamEKiKisOqY9FU3q1JcNaiEgC21QfwhRJ4nTqVJZ2nXUxTcNsbNWxsUphU5MGPwsH1x5h2wYa6Vprea+DaU4sPtk/06xpn0tNRmdGiv99j6dDkNR548hpnn93esyn9YROMFBcXo2kaBw4caHf8wIEDjB07NuNrxo4d26vnA9hsNmy2zmWoVVUdsC+noih9Pv/WOj87GkPEzWSwoCoqqpqsD9J2cEwBHDYdl01n3Z4W/vvIcSQMQTRhYrMkd/pNsVk0LFpyD5uEKbBoCqqaTH4d57VT5LazrzmEP2z0qM1b6/xsqwsyocCF06YTjCaIGyYWTcVl0wlFE2ytC/LChzWs2dJwyC3jU+crcdtBUdOBV0s4WVslEE5Q62/m+098wOzxXpYeO5EJ9r5f447WVjdx68ub2+woaycSN/i4xs+tL28etTvK9ud7LPWMvMYDT17j7OrpdRw2V9tqtbJgwQJWr16dPmaaJqtXr+bYY4/N+Jpjjz223fMBXn311S6fPxy1BOPpDe4gGZCYon0gAsmxkVTiqS8cZ8Xb1TQEoyRMQVMw1m4kxaKp6JqK3aJh1VUsmooQyVyP3U0hPt7XQsIUPU44/bRCrIYC5Nl0CpxW8mw6Csm9RnzhGA+t2cHH+3x47DoTCpx47Hq7TfsynS99HcJxth0IEAgnsGgKuqpi11U21vi47eVNbM/Spmod9wFy2XQ0VcFl06kodOILx1n5djWm2THtV5IkSerKsAlGAJYtW8YjjzzCihUr2LRpE5deeinBYDC9umbp0qXtElx/8IMf8Morr3D33XezefNmbrzxRv7zn/9w+eWXD9VHyLrmcIy4IbDrGurBGiJGhn5QAfyRBFtqAxhCsGZLHXe9soWG1iibawNs2N3MnuYQrdEETquG06oSiiWIJUyCMYO4kRwlicQNfOEEvnCcQKRnG5W1XU2TSThuEIgk8EfieB16Mm+lmw6+4/kEsLc5RMIQOA6WD1dVcNst6de/vrkuKwGC3FFWkiQp+4bNNA3A2WefTX19Pddffz21tbXMnTuXV155JZ2kunv37nZDQp/97Gd54okn+N///V9++tOfUllZyfPPP8/s2bOH6iNkXb4jua+MYYLTqhKIZO7wNVXBFMliZ3sbQ8maI/ka47wOdjYEaQrFaQ7FsenJICBhkg5qNCUZzAiRPKYqAk1V+P07u1kwsfCQSZtTi/ModdvYeiDAhAJHckTkYEcuhGBnQ5BQzCCeMAlEEqiKgsumMaHASb7D0q6DnzHW06nibDBmEIwaWHUVRDJJ123Xcdm0dICwvyXMtvoAM8fl9+t6yx1lJUmSsm9YBSMAl19+eZcjG2vWrOl07Otf/zpf//rXB7hVQ6fAZaU4z0ZDa5RYpiGRg5IFy5IrWEzAqSeDgQP+CNrBSqsJQxAzBGYkuWeNyqdBjEkyILFoyeJpQsDWA4F0gNCVtdVNrHi7ml2NQZqCMRpao7jtFiYWOrHpKnuaQ/jDcYQQWHUNXVMxTEEgkmDbgQCVY9y4bXq7Dr7j3iU2XcUwTXRVI5ww0VWFCfnOdMBjt2jEoya+UOYVQr0hd5SVJEnKvmE1TSN1Nq3UzZET8vE6LNgtWqcCZSmGKTDaTFNYNIXaQIRoIrkfjdumk2fTsekaEwsdyX2HFMizabjtycecVh2brmLVVCJxg9ZootsRgLXVTdzy0iY+3udjrMfOzDIPbrtOIJLgk/1+av3JpdJuu56eXlFIFm1zWDQSpmBvcyhZur5DB5/au2RWmZdIwsQwIZZIjohUlrrJb/PcSNzAoql4nf2PvVOjMvWt0fTeTCmpHWUrS/PkjrKSJEm9MOxGRkaj7qqMpkYJ9jaHqPNHCEbixLuoMdO262yNGpgiOaWTME00RcVuSe5Vo6oqmqYQTwgSZnJEJRI3MITZbrlv3Oi6aqqRMHjxuTcp3lPH1DGFNFon4FKSiautkTh7m8OUuG0EwnG8+RZ2NYYIRBNoipoOSqyaSmskQU1LmPkTCzp18AsqCplXXsDmWn96lKSyxNVuqi4VIMwst1NZ0v8AQe4oK0mSlH0yGMlxqWmO1HJXi6YyxmPnpJmlHH1YIdNK3elRgnv+vpX9Pdwzpm0+CALipknCNLFoClZNwaKqJBSDaMLAFBysC6Ic3JsmmSQbihkEwp2nPkLr1lP928f53IZNWEUCU7fQVFzG+vlfoGZC5cFpGoVafwTDFIz1OphQ4GRbXYBwwsSqqcl2kdzoz27RuuzgVVXh8DIvy744jVte2sTu5nCnACHfYeGkGaVZCxDkjrKSJEnZJYORHJaa5kjVs4gmVHY3haiqb+XdHY2M9do5ckI+F362AkgGBx6HheZeJE8a4mByKskE1VhCUBeIoanJXXoNI5kvksorMU2BKZIF1PJsGr9/t5oFFQXpjj60bj11y5djNDTRanWg2+1YEjFKa6s5/vU/8I+TzqVmQiV2S7KOSWrUJd9pobLUzd7mEMFYgtjBTXEcFo1LT5iS7uC7GiXqLkC44NiJTLBnN6E0NSoj98WRJEnqPxmM5JhUZ9scjPHrf+xI17PwhRNU1beSMAVOq040YRCIxNm4z8f//WUTLpuOL5x8bjgWIGGaJHq4vULHPBN/OI4hBBZdJRo30A6OhoiDWay6pjC5OI8Cp6XdKhdhmjSvWoXh86FNKCexP5B8gdVOi8VGfksdc9e9Rs34KUTiJm67hVK3jT3NIZxWjXynBa/DQzBqEDMM6ltjzC/P54y544HOo0Qdi6J1FSCA6HJn5/5I7SgrSZIk9Y8MRnJI2842EIlTF4iSZ9NoCVvY1xwmYQocejKnAl0jHDMpcSvs94UJxQxmjnWDoqBrKhY9mW/RNtBQoVMxtBQFUJMrY9NTMuO8dmp9YThYv0QBnFadSUUu8p0WDFO0W+US3bad6I4q9JISLHYLLptGIJJAU5PJqUGXl6KGfRTW72WrKGBioZMvzxnLqnd3t8u/UJRkTZRSt40LF01CVZVOo0R2i41I3EgXRUtVPc0UIMgCZJ8Spkl023ZMvw/V48VWORVFVpqUJGmIyWAkR6zb3dyuxLhVU6hvjRKKGWypTe7Ga9OTnXrcSNYLiRsmuxpCCARxQ+ALJygrcOCyabSEklVZRYZ+WCGZK5I4+JiuKghE+rluu4WxXjvhuEGp247bricDHFVN1+6A9stYTVOwc9d+jNYwmqcQF1Be4GTbgUByNYymgm7F1trCrp0HaCh2YQrBo29VU+iy4LJZ0kt/O+ZfdKx6mnp/l03HadWobgqx8u1q5pUX9GqaZLR1zKF162letYrojipELIZitWKbPIWCJUtwzp836O0ZbddfkqSuyWAkB5im4HfvtO9shUgGCbqqEo4bJEwTh0UjbgiCsQSmEChKch8ZwzSJJgyqm4K4bDrj8x34w3E6DgikSsInS8Ynj+kqeB3JUY5w3CDPpnPkBC9CwJ7mEOPyHdT6I1QUtq84mlqlMrvMSyCc4MqnNtC6qYZzQgaRPQ1Y8lyUFzipHONmT3OIYNRARMIEhUqr1c7kYicl7uSeLvt9ETx2C9867jDG5zs65V/0puppT6dNcq1jHmjpXB6fD72kBMVmQ0SjRDZtom75ckqvvnpQP/dou/6SJHVP3obkgBpfuFNn67JpuKw6McPEoiaDk9SIiHlwCEPX1HTAoqnJpbY7GlrZ2xQCoOMYgaqA7eAyldR0jU1PJpLGDBObrjKp2JVOKrXpGmcuGI/XYaG6KUQwmsAwBcFoguqmEF6HhaMnF3LbX5O1REJlE/GXTiA/HCBwcK8YgFllXg4fm0dxrJW6gjLGHjmTsV5Huz1d/JE4/9hSz2cmFTJjrKfdCEemvWjasls0Ygmjx1VPUx1z5JNP0NweLGXj0dyedMccWre+R+cZLtrm8lgmTkR1OlE0DdXpxFJejuH30/zEKoTZwySjfhpt11+SpEOTwUgOCEYTnTpbRVGYUOBEV5NVUwGiieTUDCRrfzgsWrr8ucduQVcVWkJxWiJxbHpyf5n0+UhO2UQNkR4VURWIJkziB8unTyt1MTVQy/jdm7Ds3kFliZMz5k5IFxfzRxLsbQ7hjySYXeblmv+awasbD1Dri1CcZ8Vpt/LBgi8Qc7goCzaiRsPsbWzFDAZR9u+j1epg49FfRNXaD8gdak+XQ+1t05uqp7nWMQ+Gtrk8mUaW9KIiolVVRLdtH/C2jMbrL0nSoclpmhzgsukZS4ynlrvuagxiRJLTLgLQFZIVSyFd/nxycR5V9QGiiQQqCjHDRFVUCl1acrO7qIEALBrk2SyM9dqob40RCCdw2XSOi+7nmFdfp7BhH8TjHK9bKG2cQWSGyoL58zqtUglE4jz4ehXv7GhMJ5y6bBq+oklw0rnMW/cq+fX7UBoChPEQmzyNZz1ziFdMJxVyCSEIRg3ipomqKETjmSu6dtyLpqvpop5UPe1Nx2yfPq33P8whlmnps+n3JadCbJn301HsdkRTI6bfN+DtG+nXX5KkvpHBSA4o8zqYWprHxzX+Tp2t16HjdegcOd7LzDIPK97ZhTAhYQpUReC260zId6JrCrGEwGHRmFLqwqKpWDQVp1Xjkxo/McNER7BI9zOOBNGoi/pxZWypCzKlbgcnr30eeyRIwOXF6i1kglPFsbuqXT5BKh9jbXUTt728mf2+CIoCTouGKUjvJ8OYSdSc9l0K6vYSqG/iW/89h7yZ09j39Ee4YwlAoTkcoykQobRxH85YmFaLg4aCcexrCXe6Pl1VPQ3HDWpawtgtGsdPL+nRte5px2z4Wohs2Tqskiu7Wvp80TjIt1oR0SiK09npdSISQbFaUT3eAW9jLgVGkiTlDhmM5ABVVbjg2ApufXlzxhLj+U4rl588lXnlBexpCrF+TwsleVasmpZe3dLcGqaseR/j1Djj3CU0lkwARaU1miAYM5jTspsvbH2TKaF6bMIgoek0FZdROPtE5m59kzItjjJtMmN1DZdNT07rFHiI79lD8xOrcMydg6Kq7Va2lBc4CBwcsdFVBU1NBgh7m0N4y7zszS/Dby/FPWsG00rdFDgtrNvdjGEKptbt5Js73mJiax02YRBTNfZ5xvIP44tUFJ3WqYppx6Jme5pDBCLJ6q+mEDzyzx28sKGmXWXajBVbPV6UQ3TMImHQ+MhvSDTUd5lc2V2J/qHQ7dLnJo0bx5Tj3F2Fpby808hSorER++EzsVVOHfB29uT6D1Zg1FGu/UwlaTSRwUiOmD+xoEclxi9aNIl9L23CF45TkpcckSjetZnj3v87Jc01uDBhgzVdfn1jYQWV9Ts4/4MXcMZCRDwFRKw2tESM4v3V/PeB36HGoygVEyhwtb9bzTRs3nZli9OaTLJtu6eMVVMJRg1aI3Eag7H09Mkf/r2bD/e2EI6bzGzcxSUfv0hePESzzU2jbsFhJpgeqGH8P5/ibx478676RqeOIFXU7PkNe3lozQ4UFMbn24kZosvKtB2DGlvlVGyTpxDZtCljxxzbtw8RChHbswdLaWnGVSebiiq6Lb422Lpa+uy06RQJwd7mME+OX8i3muuI79mDXlSUHIGIREg0NqJ5PBSct2RQRn4Odf0HMzBq61AF9SRJGli5Pe48yiyoKOS+s+dyz9lzuOWrR3DP2XO49+y57f4Ytt2t1h9JYNn8ESeveZJp/hrUPDf17iIiNme6/Prk2u389/a3cMaCNHhKMO0OYig0C51qewF6qBV3KMDu1gS+cOd8DcVuR8Ri6WHztitbUkm2mgLBmEEkbmCaJtFEgqr6IFZN5fxjJ7K2upm7/raFSMLEqcFpO94iLx5iv7OIiG5DoBKz2PEXjMEdjzDxzZfZUtv1MP2aLQ0YpmDamDwSJlTVtxKOGzitOopCujLtLS9tYm11U/vPo6oULFmC5kmO+pjBIMIwMINB4rt3I0IhFIcDa0VFxuTKqkce49a/bOTjfT48dp0JBU48dj1dfK3j+w2GTEufW8JxNtb4+GR/gKZQjD9FC/nN7NMITZyC0RogXrMPozWA/fCZg7qst9vrv2fPoAZGKW13l86Vn6kkjTZyZCTH9KTEeGqEYEutj9hNf8RqM3DOqMRzMGfDb6pEPCUU+es57v2XsPgaabZ7sFs0EgeX5gohUBSFFnse3mgQLeBjm1CoHOPG6/h0VUrbYXPTFDSFYsQNQVMwSnFeciRF11TC8QTRhJmu+NoaTeC266x8u5q9zWGCUQOnRaO8pYaJrXU025LVYlMSpok/mkBxuCltqqF101YoO7rTZ2/b8QLsbQ6lK9Omqs+GYgbjvCq+UJyVb1cz5+vth/yd8+dRevXVn9a5aGpEsVqxlE/AiETQvF7MUAjV5UQ5uEBaURS0oiKaP9mKo2A33srKrBVf669PA8TkNWk5uKw6YQisuopV0wjFDd7Qx7L5iK9z3Tl2ZrgYslyYrq6//fCZFJw3uHVGBqqgniRJvSODkWFKVRUOC9RRU7cXbewYVEXB67C0KzLWZMujoKkWDUHIXkDcEMRNI13u3RSCgNWFqWnkR1vZY0++1uPwHlwK/Omw+UZrISue2sD2ulYagzH2+8K4bGGiBzfAsevJIECQXDJs1VVcVo0Ne1o4EIiiCINJ/jqm123HGY/QaOuw8kWAYQpaUClLJMhLdE5khfYdbzBqEIwlsGoqCRPC8QSGMDFNqKoP4rRqfLC3hW31AQo69CPO+fNwzJ2TrgAa21dDy3PPYTQ2YrS0oGoqqtOFZfx4NG8ymAkrOkY0SpkWpzFLxdeyoe3SZ6dNTwZohkivuEqYAl1VmFDgoDEYY8V+nXvPnjuknWvH6z9UgdFAFNSTJKn3ZDAyjGVameB1WPA4vASjCeJxB5Z9QSwWHW++lZ1BQSSUrNWRKpxmNRM0W10IoTK2aR+tYQ9Brw0nRjqfoO6k07jlr1vSyZGVY/LYWhugJRwHkdy9N3JwVERTwWXVSZiChmCMcR4b3h2bOLXqLSpa63HGw+RH/NgTUWrySmi1OJINV5K1UyyxOHFNZ2L5mIyfuW3HGzdNTJG8uw0dLAanKAqqItIjJMGowb93NvPFyY5O51JUFfv0aYTWraflySeJ19ej6DqKxZLcj6e1FbOqCtuUKWheL/FwmLimY+ZlXkJst2jt9uoZLG2XPhcdXC5t1dV0QBk7WEcmz6ajKkrOdK6p6z+UOo4qdTRUP1NJGm1kzsgw1nZlQlsKkGfT8SomNq8Ha0UFjkALpXlWdFXBdrCjUoRgTLAJizBREThjEcqaa0h89CHxugPYD59JyY9/zGM+T3oY22XTKXRaqShypr88wZhBwhDomkKezYJVUw8msiaYUFPFtz56kSkt+2i12NnnLiWk23DFI0z015IXDx9ss4IwTQqjAWoLxrI7f2zGzzyt1M3UEieW3TuYUrOVSb4aIrE4phBoioIQAu3g+9t0DVMI1myu63KzvLZFuKxTpqDm5SFiMVBVVLsdkUgQ37cPU5hYfM3UFZax15u5bb0pvpZNqaXPXoeFvc1hEqaJqiRHRFJ1aCbkJ6cgelutdqTLZkE9SZL6TgYjw1hqZUKioQHRYUe81BSLbeoUGk8/l91xneCu3WjRMNFYHGs8ykR/Le5YEAVoduazpWQS1Z6xhDULwhQ4F32O3eMmZxzGtls0bJbkMmCLpmLRFdx2C5aDQ/+akuzoF338Bu5EmP2uYiK6DUNRqckrJqpZsBlxylrrUYSJw4hSFmrEcOXx1qwT8Ee66Bw2bODb/1zBRf9YwX+/tpLL33uCK/+9ipmNuzAOTj+1rUzrtluo9Ueo8WWe9mlbhEtVVazjx6NYLIhwOFkFVNcxAgFimzZjtVponrWAhtZoxutd3xqlsjSvR8XXsi2V2DxtjBshIHRwPyO3Xaey1E3+wc5Udq7tpUaV6ofwZ2qags21ft7b0cjmWr/cZVoaleQ0zTCWWplQt3x5l0s26046jZ/v0imYdzpf3PJPPPX7UCOtxBUNIQStVhd788chFAVHJEhx1I8ej5LYH6TuttuIzX2N8fnziM84ot17WzQVVVGSu/8qoJKsQZLKQzAEVPhrGdO8H78rH4SSHLkAWi1OdrvHUBZswJmIMinUgOFw0jxhCu8ccSL7Cidl7CxTe5o4fT7GlBWzN2QS8YeY0rKPb370Io/POY3qsVPbVaadWOikNRIjGE1kvIYdp7o0rxfblCnJJb7BICQSiFgMQ1HAonPiR69RrK1l9YzP0zrl8HQ9mLpABJuusWBSslLtQNSoOFQdjAUVhfxm6VF8a+V/2HogwIQCB3k2PR1E9rZa7WjQVUG9VI0fr8PC0s9WDFh+jVxSLElJMhgZ5rpbmZB/7nncs1WhJeQj//Aj+Pvhs0lUVdF0oAFPJMi5W14jaHVgouCMBCn3H8AizGRehMWKkkhg3foJXxM7We2w0nzYjPT7umx6svx7OI6uKlh1lUjcRFM/HZXwGhHMaAy/6kJRQRxca6MoCq1WJ9ss45nY2sBLlcezc8J0mHQYvnCc40QTE3ZvIeL7NKmx454mVkXBUwj1AQdba60U++s5bcfb/KLkMBKo2Cwq47z25GqSg4XcMslUhEvzerF7PCQOHCC6axdoGtbDDkMvLMQajXLE/gOMXfdnntY0thZPImEK4kYyf2Xl27t48v09We9Qetpp6brK90+eyi0vbaIxGEM9ODUzWJ3rcNSxoF5XNX6yrdtCdS9t4rpTZ8qARBo1ZDAyAtjnzsU/9jBaN20lLxFmUsVY7NMq2VLXyva3PmgzxaIQmXAY25UCph/YjsVMEFF1VCEYE2rGgkncYk2uiNE1iBnYxpTi3VvPkf95lTUVlShqcmcZBRif76DlYO5BvsNCbSxCIBpHVRRURaFRsRFRNOwijm5L7gYciiUwSCaaOkyDsNXB5jFT2W4vYdpHH3HRnneZSwsHXjXaVT5VXa5Oe5ooQInbTl0gSsjwcliwninBOra5xhKJG1Q3hlAUhfkTPZR5OyewQjdFuBRINDaCYYA3n1anB0tC4HI68UyehGP3br7vX8+/T/08f/jPXlRFOdihaFnvUHrbaQ1V5zqcpZbLD1YFVrmkWJLak8HIMLe2uonH/7WLjfv9ROMGNovGrMYgFzlaSBii00oBl03DY7cQsjkxdB27mcCpmbiMKIamJ0u7a0oymUhRUC0WPOPHML6mlnhVFfGJk9Mdri8cp9RjIxwz2NcSwTDFwWXDoCqCavcY6grGMcW3j4Cah1CUg3U7BLoCRdEAVfkT2Okew+FNu1j60QsUmTHyKstRO1Q+9Zx2WsY9TRSgvMDJ5lAMEfShB1uxehQURSWSMBCm4IA/yo6GVsaO7bxCp6uprkRzM4mWFmKKzj7dS/BAK6qi4LJplBc4cRcXo+zbzY7/fEQ04R2wDqWvndZgd64jQU9q/GSLXFIsSe3JBNZhbG11E9c++xFvbKmntiVCUzBGbUuEN7bUc+2zH7GvJdxppYAvnCBmmFS5StnlKiU/4seMxUAIEiioCjh0NTlt4XKhulx4vHmMdagc7lbwRxLsbQ7hjyQoy7fjsGi4bTrTSvOYUuKiosCB254cPZlQ6GLHov8m7sgjv6UOwiESRgKnEaW0tYGEM48PF3yB8fkOztr9LoVmlAPuYsKaFUXTUJxOYqXjaG1o5sDfX0OxdF45BOBxWHCRIKHpROxO4qYgYZp4HRZml3kwTMHr3ayoSU112WfOTFcnDTe1EEFjr3cscWceDouGrirpzQADaETDERr2N/SoQ+mr3nRaHaU614WTi9IdmkyUzA1tKxlnIlc9SaONHBkZpkxTcO+rW6luDKECNouGpioYpiCaSE5RPLtuL1NKXHyyP7kbsC+cYFtdIFmx1GrhpSmf45KPXqQgHMAwTXTVxGHRUWNRFIslubJEUTAjERx5Dq48YwFfzR+HLxTHbdd5aE0V+5ojFLqs7GkKEYgmSPZvgpghaArFqSmv5C+Lvs6R//47Y5r3U2okiKs627zjeW36cWy1jmVC9U7y6/dSb3Wn8y984Xi6eJvFsOLatodEYRET9h/AM3kSKEqylophEk8YOFp9HBhTgWfGdGYIsKhqehNBq6awvyXMtvoAM8flZ7yebYtwJXw+HnlxHbNeexrdbktWcaP9ZoC19T7GahZ8ur3bDqW/NSqyVQdDJkrmlrZLijPlM8lVT9JoI4ORYWpzrZ8P9/pQSG6Ilrpn1lUFzaoTjCX4cK+P6087nH0tYXY1BvGFE8QNgVVTiJuC3eOm8FrRORz38eu4d32MPR5D1RVUtxvrwcqjbauw2qdVMuNghczNtX6q6oM4LCqbagNE4wYd77NbQnF21Adp0sbwyoJzGd9SiyMapNXipNozBqGoOExBoRnBYho0qhrCMGkKxmgOJsvOW3UVze7AGm3lzdKZHLvjfUq37qDJlofP0NATUdwhP37dwdtHnIjLYcXVoR12i0Y8auILdV5R02mFSmUlu+oCvFoYZEzxeMY37KbZW4qmHazNAlhVBb2lmfDsWTSVTMA9gB1Kfzst0xQ8v2EfD62pIhw3GJ/vwCETJYdc20J1TqvWacNAuepJGm1kMDJMbazxE4mbOCwqHbMAFMCmqYTjJkLAdafO5Jert/POjkYUBQyh4D64IVjUUcRr02ayY8NbHPv+S4yzgXXCeFSHAzMY7HJXV18oTjSeoCkUT08DtU1HMAUIYE9LCF1VMQTs9Iyl48xAJG7i150kNB2bESdqsVPriySni6zJIMsSi2LqFsIzZvNEfhmf37iGif46SjAwdQs1pRU8OX4hO7QxTA/F0zU1Pn0Pg3xNxets/3XvarSg1G1jd0uEP008hqVNB3A11hJweFHtdhwijjfow2914Dz9TKa0ega0Q+lPp5XKJ/rH1nrCcQOrphI3TCYUOMl3WGSi5BAa6iXFkpRrZDAyrIlOeQQpyePJnn9BRSHfPX4KVfWtFLosB5NUVfSDFUsVRWXbnOPYZcvnct8GbHV7MZqbut28zOtMnsd/cKff5N/MT9uiKgJTgGEmi5+hkB5ZaBuPCGCLo5g9eaUc1rKPZruDSMLElRrtEQJX0MeBsZNoKJnAxqib9xacyxecIYrMKBG7i/qS8dTW+IkEY+xsaKWiyIlV03DZktMn9a1RZpbbqSz5tMPuaoXK2uomGoMxEoZgc+EkVs07g8Vb36TMX4sl4ke1Wqkpmcg/Dj+BKz6zgAujiW47lPOPndivJNK+dlqpz1fnj5IwBC5r8lc9lfNSOcZNvsMiEyWHkFz1JEmfksHIMDVrfHIX3kjCwGXpfMccOZgcN2t8soPJd1nQVIW9zRGiCQNTJAMIlzU5QmLRFOrGT8N65ZmUBerabV4mUNhc62/XoU4rTe7uaxw8jyHoVMEyFXj8//buPD6u+jz0/+dss2pmtFgjWZbkVV7ZzOa4KRdISCDQ5CZNExocCmmaNM2+/dykcC9pc1OWS3rTbM2v2cgCgTS5UEqAvCgQGsAxxGACeMGWV1nWLs2MZj/nfO8fRxpLtiRLRrue9+vlF3jWM8cz5zzn+/0+z+MCmgJN1zB0cBw1LCCxlcYT6y7lA394kJpUF21mGN3Ssew80WQ3RcvPweVnk8nb5IoummHQWrmEbMhXeo2Qz6S7v0BPpki6kMI0NAKmQcDSqY36edPaeOmEPVqGSshnkCk4XvfhgcZ9L0Qa+MNF17Eq3UEwn0GFI/TVLOGs+spSYDHaCeXiFZX8ZNuR171OY6InraGfrzrioydTwNAGOg8PrHlp6c0QC8ak98oMk6wnITwSjMxSp6u2ubYmyjn15Tx/qIes7eIzdIyBoKDgeCfTc+vLWVvjBSOprE0iW6Q/b1Nm6TSm2gjmM/QZAfYXFlMW9HHh0krW1MbQ68pL7zPaVMb1mxtZUxthZ0uiNPUyuHUnrx0xdAhZBoauo1D052w0DRTeyIxl6BTWnsXT8Qjrtv+aWEcL5b29BAu50ute+PyjLNm3k+ySTeyPr8AamDLqyxY50NlPIltkMBYq2C5KaRRthasM/vT8elbFA6XtGS1DpS2ZJzHQ/E/XTgRTRRf2hOL4Iwa267LSGj4aMdIJJZW1ufWRyStoNZGT1tDPp4YEi+bAZ/L6Bjmk87b3d1koOaOmM6VYiNlKgpFZaDyZD7qu8Zm3NJVSeAu2W3q+rkN9VYhPv6UJXddwXcWPtx3CMjQ29BzibfufpiHVgc+1KegmR8riPLHuUt7/53827OQ21lTGb/d14jeHnwgHgxBN83rT2O6JqRlD1zF1DaXA0DVsV6GU11zPMrz79i5axhOb389F+5/n7bv+Ez2gk4xWYZt+TLvAks4j3NjVzn0XvovQso30ZYu81pYkUzixZkXhTUFZA6XgC7bL8wd7uLxxcWk7R8pQUcDxRNbbPs0bzQlYBkVH4SgX14W8cgj6TN63qfGUQGLoCcV1FZ++b+e4a4OcLvAc6T3GMvTzDY5+pfI2hqYPjI5oFByXgu2QzNmyUFIIMeMkGJllJlJt84Klldz6p2d7Rc9aTxQ9O6suyg1vXFZ63AM7j/HUa52sbD/AX7z0IOFCht5AlIJp4XdtViWPUf/Sv1O2fwMs+2Ng9KmMoqNI5WyyRQfH1dE1hi1KHZwOcFyFpoHP0Lwr8YJNyDIwBzrq2q6DNnClbhnQ3NFPKu+g43Ju6y6U43A4WkPANDF0jazpJxFeRGWygyv3Pc1/nXcuLb0ZbxSEE6MYpq4R8Rtkiy49mQLLqsLs6+inNZGldqDZ7kgZKunBKaCBgEbTvP47Acv7LLbrYjuKeMTPxcvHHtGYSG2QdN6e9JTbkz9ffUWIfR2p0giaUt7q4q7+AtURvyyUFELMOAlGZgHXVextT9KXsfn/nzowoWqbpxu+33G4h3/5TTO5QpFrmp8mUszSFqn21nEAejBItixMWXc7hV/8HPWmP0LT9RFPqEopWnoz2K4iZBkUHIU5UNvEGQhIHAU6XsM8v6lTEbJ421mL+eULLaTzDrrjYmgaZX5v1MFV0J930IBIwOIit4el6Q4SoRiOgrztoA2Ul4+ELKJltTR2tZPdu48+cxG2q4YFQ0qB7ZyYinBdrwrt0EZ5I2WoFB0X8D5PceBzGbpXL9bUNTRNI1d0Cfs03OZ99B9xMWMneucMNd7aIM8d7OGBF4+d8VTOaCMqJ3++8pBFUzxCS2+GdMEmb7sELYONjRVSZ0QIMStIMDLDXjjSy8PPHWD7cW/IvCOVp8xvkMj6MA3vJGkZOmG/OeHMh8HRjWzRYWV/B3XJdhLBKLquoSlwlRo48UIqHKW25TC51/ZxuHwx25q7SeWKVJedWCSazjukC7a3PkXXyNs2hu6NfBQcd2DqBXymTixgsajMh0Ljzy6s58oNtXzryf0c6k7juIoyv0k84qc1kS0FX5GARc3RNnyug1UWxLJdgj6TpVWh0j7AcQinE8T1grc2ZsjnNTQNRylvFMZn4A78/8mN8kbKUDGGlKr3ytl7QZahQd5WZIo2a7sO8a4XnqX1vk56NYdYLEzF2tVUbBmebTTe2iBP7O44494kp5vKO/nzRQImy6pCHEvkCFoGf3PZCt55Xr2MiAghZgUJRmbQjsM93Prwbsq1LNFAGMvQ6ezP05+z+UNLH6ahlUYFwn6DuljwlBLRY52Uwn6T/R39LCkP4mstYDo2edNbqOhNkWjYrkvOhvJQECffx9ce2MFvgw30pAt09+fpzRRYHAtQGfJTcLwsHEPzMk0M3esImy06lPkMHLy6IcurwtRG/RzpzZbWI+i6xg+WXsRrHSmeO9jDE7s7ONyT5lhvDlPXONKTpb5CozoQxjZMLLuAz/STt71grGzgpO7mcgTDQXzlMcykhqV7C0y9aSGFjheQeHU1vPLtFzSWn9Io7+QMlbzteD15dL3UADBdsMk5XgfitV2H+Otd/0GVKpAIRunVTfpyRfjDKxSP3UF869ZSQDJ0ZCI4kKEzGFSGfAad/XkaK0O0JXJn1JtkvFN5I2XgXNBYIWmjQohZR4KRGTJ0TcbqWj+dtomrvOmKwsAIAxpEAgau69WH2JtNUh7ycaQn42Vs5Irc+vCeUU9K79y4hILtELT8hBZVYhsmZrFAwQoweP5zXTBMjQrD5XjGZXtnkWPRDLmCTdGFYt5hX0caU08T9pu4roujvJGQSMCkvtxbj5BzFIamYeregtQjvdlTamDoulfC/YEXj5FI51meaqeyu5tCsIwDxNnX4aBVx+lZVEe87TCF8jgFpQamUChVgy0sX8VufzXRQJ5s0SVoaWQKjrcWQlNoeGtbLF1nUcTP+zcvRddPTV09eYrrWF+Gn2w7TDJns6wqhO0q9nX04zg27zz8DBVunr6KGtA0AkC6qHPUH2J1IkHvPXcTPO9cNF0vjbx88f++zPOHenFdhUKhoaHrGnXlAS5fE+fH2w5NuJT8RBrnSdqoEGKukGBkhgxfk+Flg4T8hle5dCAd01UK12VgXQb0F1yKqTzfeaoZv2mQyBZQCtbWRkY8KT25u6M0XWDULyUZX0J12yFaTS/lE6UIOwUWawa5li72lzfyslmJm7VPSc+1Xa/JngYUnCJhn1Gq5Dm4HqE3U8BnGtiuGrEGxuCJtOLgHrbsf5qKrmM4+QJF3eR4rIaHVv4xR60mntlwOW/puY9oTzsqFMGibFg12PTb3kV+r6KxKkxzRz+2qwiYw6eKNGBlvIybr1nHxoZyOjo6Rvx3GJ6hUsXSqnBpNCGVK5K3Xdbnu1mZ6SJbVs5gFFdKkS24FCoq0Jqbye/bT2DN6mGvP1h7RTsp8bksYJ5RmfeJdnuVtFEhxFwgwcg0U65Lft9++vccpaqjDXPFitJ9mYKDrmmlFFXlguO62A5kig5oXtpuZchH3nbpTBXwmRqJrD2sBPrgSaktmaMmGuBob4ZQZYhXL76SS5/4GUuzveQNk3A6QcjOY3TbFDWdWCHNmp4j7KpaNvr24wVLftNLn3VchWV400iV4TLet6mRi5dXjngF/lpHCvsPL3Ht7+8nlM/QH47R54ugF/Is7W3hhpce5HvOn/BwfAUH1v8JV772W5ZnOnGOt+KUBUvVYHN1K/E1v4Tf0GmqGViYmXcwdA1D17EMKPNb3P5nZ7N+cQzXdUf+MCMYOpqwrbmbHzx9kLMtHb9yyJm+YY8dTJEtmha+VAE3mQBOBF22o7h4WQWZgkvRdbF0nZDPGzX6zd7OYU0Mx1vmfbIa5wkhxGwiwcg0yrzwIr13303+QDPhTI4b+h16Xl3CkcuvoiO2nKLjpZaG/eZA6qwqXe1rmkbY8nq8OAP1OQzdK7fe0pchFowOO6ENnpTetDbOAzuPcbgnQ6Z6Oe7lf87m3/5f6lv3Y7gOjmGQ8oXpDkSoyvbxV6/+B9/d8HZ2jxCQaEDQ0ik4Cl3XSWSLpbUIZy8pP+1ahER/ns1/eJxQPkNfeRw0Db+rSCuNVsNHTX8XVzc/wzerl7Onahn7Ni9lfaGLetPh/W85i43/7QI0XWe1q0prMpZWhojVxUodfE1do7u/wFlLYqWCbxM1dDTh337fQtIOelNcdoGi70TxNMdV6JqGZRfRfD70aAwYPnqh6zplgeHZNtVlfvZ39PNXlyzjWF92QmXepdurEGI+kmBkmmReeJGOO+7ASSQwq6sJLapGHe2iqu0w0T88RWK5Q6p6Obp2Ip00EjCJl/k50pPFZ3onNOV6FUvBKySm4dXISOcdygIn/jkHT0oXr6hkw+Iy/uPfn6HrQBdHDD8brBD5sijmompa0zZFX4D+goPrutSlu/mTg8+wp7IRpQ0/iWqaV1DMxSXoM/jkFU1UhnzjXosQ7TjK4j4vo2dwusPSvVGVVE7R64/QmGynIdlGX20j9RUhygKLeKkng3Pc5LyByY6RsmGCloGG14cmFpqcJmODC1F3Fau4tKqOmvbD9Fl+0DQUXqXbiN/Al+jFv2E9/qZVwPhHL5aUhybcm0S6vQoh5iMJRqaBcl16774bJ5HAamwsnUAW11SyD4twLsdZv3+MI2//a/ymRjJnE7QMlleVofAWQOga5Gxv0WjYb6KUwm/qpHJFdF2jMGQqYuhJqeHYfvruuYe/aG4mn83hOC5GXy++hnrSkQqyhRRB08CwXVxXozfgBQRLk+0cip2oWjq0yZ2heVVDK0M+Nq2oGvd+aDAdejSHXt1kcHzBcRXOQKGQomlRlkvwlnwLHWY53YEIjJJZMh1Nxk4EPRkeafpj3tvXSayvg1QwSlozCbtFGlQeo6piWFfjiYxerK2NTmiRqXR7FULMRxKMTIP8vv3kDzRjVlcPu5KNBS1W1UY55g9SeXgv7oFmIuV1uMpr2mYZGrZXi4vMQAv4+ooQiWyRlt4MmYJN0VEoR3Ggsx8WleE39dJJ6cZYks7//Z3SaEy4uhq7s5N8+3GKLS1YS61SPY2QZZJ0iuR0i3K3n7JiZthn0DRvjYTjKII+nUjAmvBUgBmLEYuF6csVSeW9FNzBRbrhQoa6/k7CToE37vxP8rufoWdRHS+e/xaO1q0acR3EydkikYAJmteHZ09bclIyR04EPSEeMAw2/+FxFve1ExusM7LurFO6Gk909GKii0zna7fXwfVUQ5s0nlxQTggxP0kwMg3cZAJVKKD5Tx22jwUtVGUUs8zgUxfXUrbpAlK5Yqnba75oYxoartJYFQ+TLTgc7k7jKIXf0AeybxSZvM3u40lqYwHOrS/nL97QQPybt5E7aTRGLytD8/lQxSJGRxvh2BJSeW8kJuQ3cNN5CrpJvxUqbePgqIiueetUApZBU7xswlMB/qZVVKxdTeGFP9CHia28ICdSzFCfbMPvFMn4grRHawhiE287zKVP/IxHL3kvHZXLRgx+Bk/kOw738J2nDoxYb2VjQ/mEtvNkpaDn8pUk+q8h2nGUBtMZtQLrdIxezLe03aHrqVShAJaFVR2n7LLLCF14oQQmQsxzEoxMAz0a8wKAfB4tFDr1AcUi/lCAFWsbCAxOQzRWDql/keU7v2lmb3s/uYIzUHLdq6URMHXW1kYxdI2W3izLq8J89T3nYjfvp3WE0Rg9HEYPh3GTSdx0Pw01in1FzSsSpmtU2Gleq1jC0VhN6TlenxZvbUfAMqiJBkY9mY7V9E3Tdcqvu47XXnqNxakecmUxCobFkr5u/G6RnOnjeNki8goMf4Ci5ae8r4Nzfv8YxS2fGjX4OV0RsL+7ei31gRGfOm7DRi9WVZ/28dM1jTQf0nZPXk/l5vMUjx6lcOAgmeeew6ytJXjWWadUuhVCzB8SjEwDf9Mq/CtWktu9G6uh4ZRheyeVxL9iRWkBJAw/0ew43AOaV6DMHahBMvBkr2qFphEJWDRWarSn8uzv6qdxlNEYTdPwLVlCPpvFzeUIFXM0VZfT1pFA7+0l6Qvyu3Mu55LVNayMh3mpJUFnKo/rKiIBi6Z42agn0/F0Gz66ZBX/dtGfctmu3xDvPU5Ffw/+Qo6cP8Sx8CL6rSDaYAYR0O0vY0mijbPq3FGDn9MVAfvptsNsvXTxKc+davNt9GIqnLyeyk0mKRw8iCoW0YNB3EIBN5Uit2sXHXcMr3Qrpt54O0oL8XpJMDINNF2nYssWOu64g+LRo5hVVWiBACqXo9jTg75mDeXvu27EYeihNSvW1ITZ09aPz/AqeeqaRs52aenNEAvGhtWYGGs0xojFsJYsodh6DDefR0sfx1eAQ4sa+M/Vl9BSs5INAZOrz17MTVevH9fBaLwlyhOZIq8tWkbmHR+hpruVhiO7ueD5X9NTUQuajjmwDiZfdDANnbJwiNp8geXhkffteIuADe3aO53my+jFVBm6ngqgcOwYqlhECwa9VgiAKuTRq6pwenqGVboVU2s8FxdCTBYJRqZJ6PyNxLduPTEv3tON5vMRWLcW613vIrTxvBGfN/RkqxSl/inmQEAw2J02nfeqo1qGTk+mwEuBCiKLG2D/a9g1dVgDzeI0vNEYVSgQvuQSOq55Lz99fBcdyofduIyAzyJedNh1PDmuzrEwsRLlpUwTW9EVbwDgrD/8F6ZTRPkCaD6TguOytCpENGARtPO4/cFSDY+TjSeNtrs/N6xr70yQxZkjG7qeyk2nUek0mv9EYKkZBm6xiGbbmFVV5EepdCsm13gvLoSYLBKMTKPQ+RsJnnfusJOStXIFnV1doz5n8GTrN31kiw6WrpMtOoQsHV3XS1VAC7ZDRyoPwDce308iW2Cxvp7r0/so23uAdFkMXyhIfUgn2J/AiEapuG4Ld7ym8UrFcpZVhfAPnABCfpMqpWjpzfLNJ/bz3esvxDRHP3FOpET5yZkmXdVLSr1oei1/qefN4lgQlKLY3k1g/bphU1hDjTeNdqT7psvJizM1nw//ipWyBoLh66mwbZRSw4I05ThougaW5Y0m9nSXKt2KqTGRiwuZspkfZsN0nFyaTTNN1wmsWU3ooosIrFl92qvjWMjCdhWvtCbZdTxJtuhQcFwSOZtc0cF2XFBwrC9L70Dqq4YilbN5pWIpPzjn7RwoX0KokMXX1U57axeZpSuJb93K0SWrTgki+rJFXm1NsOt4ip5MgWebu/mrH//eW7cyihOjE6M3fRvsNjyYaRILWhzuyZAuuOzYeAUZf4hwdzthO09DzI9KpykePVoKmkbbT4PBTWd/vtQHZtBgGm1TvOyUrr3TZXBxZm7XLoxIFKtuCUYkSm73bjruuIPMCy/OyHbNFoPrqeyuLjAG0qAHa+Yo5a0dCYXRwyFULjes0q2YGhO5uBBz347DPXz6vp189r6XuOn+l/nsfS/x6ft2jnnMnwoSjMxyqaxNIlskmSti6jphn0HIMkHTyBQd+vM2mjZQq8Nv0hQvo6u/gO0qwpZBS10TX9u0hX/Z/H6evPoD3HXpDXzvkhsInHfeKUFEX7bIvvYUqayNqWuELANNg9faU3zlV7tH/XIOHZ0YSbZg4yo40pNhT1uSjQ0V3HTNOjbUxUjmbLaHlvDAG/6M/oYVLA0qgj0dOP0pAuvXnXbB4inBTd7GcRXpvM3hngyxoDXQtXf6r+BOXpyph0JohoEeCmE1NOAkk/TeczdqAr1z5pvB9VRGNIrd04Pm96PyedxiETeXQzNNrCVLQIHd3Y1/5cpRR8nE5JjIxYWY2wan4145liAaMKmvCBENmKXpuOkMSGSaZhZzXcWPtx3CNLymdDnbIWAa+C0dQ4f+grcOImgZpWDlD8cSZAsOftPAUeC6LrphsDdUg1kTRQP2dWZ4rSM1LIgI+U1aejPYjiLo80qr267C1DXqK4J0pwujDs2OVeSrL1NgT3sKQ9NK3YYHF8F97drzhgwNnk/TouspNjdPeF3F6dJox+raO5VGK3YH3hWmrIHwDF1PlX3lZdx0GnI59LIyL/vMNMc1SiYmh/Q/Whhm23ScBCOz2AM7W3jqtU5sR+EqLzjod7xRC00DTQGaRjRgkSm4WIZGOu9QsF2KtovSNG8OHu9xvZkCdbFgKePmomWVpSCiSinSeQefqZcWuQ6u3yjzm+iadkpJ9kGjFfnqTOXY35kGYFk8THXZ6RfBGWd4Uh4rjXYiXXsn01jF7gBZAzHE0PVUmd//nv6nnsLu6MDtT6F8vlLH5oW+xmY6SP+jhWEi03HTkREowcgsteNwD//ymwNkCw4hn4FpGDiuImc7GJqGoXuBhq7r+C0DXfe+QJaukQdsBabmBQqu8qLg1r4slqGXrmqGBhEtvVls18VnGNgD3YJNXaO+3IuYT9ea/uTRic5Ujs7+Apahs7qmjIqQD5jaqHu2pdGertidrIEYbnA9VWDNaire9+eSfTRDpP/RwjDehp7TNR0nv+5ZaHD4LFtw8Fs62pBOvmGfiaMUmYKDZRjoGl7zvIGU2KHrNpRSMFAu3jJ0HMflcHeGVUNKuQ8GEatrIqiBHji2642INMUjlA8MxY5naPaCpZV87drz+Kdrz+Ujl61iUcTPhrpoKRAZtFAWwQ1dnDnS4lpZAzG6iS70FpNr8LiwfnGUjv48+zpSdPTn2VAXk7TeeeJ0a/2mezpOfuGzjOsqfr2rjT+09FEZNFiT7mBV2z6W9B5DU24pKLFdRdFxCfu9aZT6ihA64HDiH3VwakfDq0eiBpriXbamethVzQVLK/neX1zI5hVVVIZ8rF8cZcPiaCkQGZqVcrqh2cHRicZKb3uC82wRnOsq9rQl2X6gmz1tSVxXjfrYoYszi0ePenU0HAd3nJlCM739QgBeq+6BPycH1WLuGm8m4nRNx8k0zSwyWPHwpaN9RA/s4k8OPEtDqh3DtrENg9ZoLY+t+W+8XN4IeMeH+nKvUmV5yKKuIkhzRz9K8+7UtYGFkrq3wCTqt7AMjSXlp6a5mqbOJ968iq/8ajfd6QL6wNTMmQ7NzsdFcGdSkXJYsbvmZvIdnTiGhbF8FY1/ecOUr4EYWj/gWF+GJ/d00tyZloqaYkynFj3zfssTKYYoZrfZNh0nwcgs8cKRXv7x4T30ZYqc03eYN7/8H4QLWXoDEQp+i4Br09jbwrU77ie14e3sqlpG0XY52JVmWRVUhH1UBH0ELJ2iqwj7TFZVl6Fp3uiIpeuAIpV3Rg0AJrO523xbBHe6ipRfvHotkYA1YtGg0Pkb2V3RwH/8+zN0He8iYQboqa5n5WsaNyzqmbKD+tDgKZEt0DMQZC6rClFfEZKKmmJEsy3LQkyd6WjoOV4SjMwCrqv4ybaBH39lgMueeYpIMUtruApd11FKkTf9tIerqOnv4k8OPsOeykaUptOTKdKX7WNxNEBl2IcCNDSWLwoTDZ4IOpRSHO7JnDYAOJPmbqNV75tNUffrcbqD8972FJ++dyexoG/EEYcdh3v4yiN76bNjxFfECVgGkSkOBIYGT9URP139OZQCpcHR3iwBy6Q8ZMnJRZxitmVZiKk1Wxp6SjAyC7QmsqUff3VXK1VdxygEgpQVMti6Qc70Yw/M6fUEIjQm21meaudA1OtE6yo4lsjRlspRGfIRsAwS2SI+Qz+jAGAiWSmnm7qYLVH36zHWwTkxUJSuYCuqwn7iJ404fPFt6/jJ76b3KvPk4CldcMgUXAKWgaFB1nZp6csQC0bl5CJOMduyLMTUmw2ZiBKMzALpvD3w4w+w5Oheon2doBSLlMLVNLKGn/ZQBf2+EHnDosLtJ1TMcPJSMjWYzqtByGfSkcqjofBb5pQEAONtpjUbou7XY7SDs1KKlt4MjguG7jUxNHRtWKDx7d/spy2Rm9arzJODp6Lj4iqFoeulxczpvE0671AWMOXkIoaZj+u9xOwnwcgsEPab+EyDRYf3ctaLT2A4NgXDxNZNDKUI2zkaUx0cicRxDIOiYdJvenUrBk9vCm+EpDtTBIp0p70AoTYW5N0X1PPO85ZMagAw0XnlqYi6p6u502gH53TeIV3witApNCzjRFbMYKBxsCuN4ypqooERX3sqAoGTgyfL0NEHMqlMXcPQoKCgOFAMTk4uYqj5tt5LzA1zJrW3p6eHLVu2EI1GKS8v54Mf/CD9/f1jPv4Tn/gEa9asIRgM0tjYyCc/+UkSidlX7XJxNEC8zGLDc7/GzRdIWUEM5aJpGq6hkzcsDOVQk+mlPJvkaKSGQ9EaRjrtFh2vpohSijK/SVsyx09/d5gXj/ZO6jbPdDOt6WzuNFoKXNF1B9KnXcL+UzsDByyvUJ2uadOay39y/YCw3yTsNyg4LgpwBjKtrIH1SFOZwiepxOMzm/bTePo9zZX1XmLumDMjI1u2bOH48eM89thjFItFPvCBD/DhD3+Ye+65Z8THt7a20trayp133sn69es5fPgwH/nIR2htbeUXv/jFNG/96F440svDzx2ksP8g8Z7jdPnKsKwQjal2fHaBouE1xXN1nWgxTcIf5qEVb0Rp+rBRkaGMgTokpqGxtDI0bJQCmJTRhJmcVx7v9NBkGW0xru242I6LZejUV4ROCQ5zRYdIwCIe8XO0NzNtV5kjXdnWV4TY154iU7BRQDRgAWpKTy5nkgq9EM3G/TTb1nvNhhb3YmrNiWBk9+7dPProozz//PNceOGFAHzjG9/g6quv5s4776Suru6U55x11ln88pe/LP195cqVfOUrX+H9738/tm1jmjP/0Xcc7uHWh3dTrmVZYtqENJcu0yKHzpFIDTWZHoJOAUN5vWmKuskjy97ArsplwKlBCHhXvArQNW/aYOgoxQM7W/jN3q5JOejN1LzyTKUdjnRwtgyd6ogXjMUCw/fB0EDj/ZsbufXhPdOWVTRS8BTxmzRUhjjcncFxFZahkco7U3Zyme6Aca6azftptqz3mo3Bmph8M39GHodt27ZRXl5eCkQArrjiCnRdZ/v27bzrXe8a1+skEgmi0eiYgUg+nyefz5f+nkwmAa/77WQ2XHNdxY+fPUQiU2B1rQ83EsX1+QjikjdN0kaYI8EyYhSxC0VwbHTHYdeiFXgrFE4YqHGGxsDQu+NQFrQo8xtoKIKWTktvnu/8phnHVQMHPZ9XxKi1j3/8VZovXr2O8xsrxr39qxaFaYqHebU1QdgXOuWKv6s/x1l1MVYtCk/qftvbnqS5I0VNxId3TDwRkmkaxMt87O9Isbc9wZoab52K67oopV73dmxsKOfc98TY15kikbGJhUxSWYfbH93NkZ70KYFGedDi+s2NnN9Ywd9dvZafbPMOqN39OXymwdl1Ud6/2esqPNnN/DY2lI/4nm9eW82lq6upKw8SC5k0VU9OM8Gh+3jod3v5kICxzG8Q9gU53JPhJ88e4twlsQV9dTvR/TRZ3+OJWh0vG/I3Na1TSC8c6eXWh4cGa6/vuHU6M7WP57Px7ss5EYy0tbURj8eH3WaaJpWVlbS1tY3rNbq6uvjyl7/Mhz/84TEfd+utt/L3f//3p9ze2dlJLpcb/0afRktvhkKql3MX6VSYNom6KhLnnU+su40efwQ0bz7fC4UU5bkU7WVVVC6vIeyqYaMiOuAO/NfUvemZunKTsOUFVbmiw2JfgZDPpa48gKY5gAN+WBY26Exlefi5PdT5Vkzo5PCeDRFUto9MLkk0aOEzdAqOSzJXZH2FwZ9tiNDV1TlZuwyAjvYki315FoVA109dh+Eaii43T0d7BxWa9+/lui6JRKLUWPD1qtCgIgxgUxGCz/xxDU/s6eB4X5Zi3qXc0FnXEOBNa+PUB4p0dHRQH4C/vXQxrYks6bxN2G9SFwui6979U2H099QAG7Dp6pqc7/TQfdyayA18tw0CVuGUx8aqNLKpXnbuO0x9xakNBBeKE8eA8e2nyf4ez3auq3j4uQOUa1lW1/on7bg19nsurH08HVKp8a0bnNFg5Atf+AK33377mI/ZvXv3636fZDLJNddcw/r16/nSl7405mO/+MUv8tnPfnbYcxsaGqiuriYanbyMkEPpHvandBoqgvidAp22nxfjZ/PGl1+mKn2QvkCErG4SdG3KcynSvhC/2rgJrawSv+vyamsS21UM9MIDvJGR8qDB8niEtG6RLnqjFPs6+klmdRor/fQndSxdJ+w/sX4hrZtsP27zHi3Imvj4P2M8HicQreQn2w6zd8gQalO8ivdvXjqpVyyDelWA44U20hlzxOmhdN4mWYB4TZx4/MTIiKZpVFdXT8kBJh6P84b1K4aNmAyOOJystnbS3/60puM9h+7jI9k+9qd06iuCGMVT94HjKlpSGWxflHh84Q6zDx4Dxrufpvp7PNvsbU+y/bhDNBCm0x7ht36Gx62xLLR9PB0CgZEzCU82o8HI5z73OW688cYxH7NixQpqa2tPuXq0bZuenh5qT3OkTaVSXHXVVUQiEe6//34sa+w1DH6/H7//1EWZuq5P6pezPOzDMk2yRRf83sRLa30Tz7z5OjY892uiHceI2UVsw+RQeT1Prf9vuGvOojxo0ZcpYhoGDi6NlSGClk4679Dal6W/qMgWXcr8lKYLFBp5R9HcnUUNZFKEfV5zvfKQhd8yyfcXSGadCX/GC5dVcX5j5bTNK6+pibEyHuHV1gRLfeYp00Md/QXOqouxpmb4FICmaZP+bziUrsO6xeVT8tpzxeA+HvrdHilgzBYdLNOkPOxb0Af8M9lPU/09nk2SWYe87eK3zJMmpj2v57g1loW0j6fDePfjjAYj1dXVVFdXn/Zxmzdvpq+vjx07dnDBBRcA8MQTT+C6Lps2bRr1eclkkiuvvBK/38+DDz447ghtOgxmPOxq7WNZ+ERn29b6Jo7VrSC15zVUKkVVbRWJmgZCAR8BhhTaUl733vKgRUXIB0BV2Mee9hRHezIks0UMXaMy7KOrv4Djeo/3mRqOglTeZl9HiqZ4BMvQXtdi0+ms3jefyszPV1KnYnxkP41Niq8tLHMi9Fu3bh1XXXUVH/rQh3juued45pln+PjHP86f//mflzJpjh07xtq1a3nuuecALxB561vfSjqd5vvf/z7JZJK2tjba2tpwnJFrPkynobn8nan88Fz+3hz9dcvoWrGeRO1SwgFf6bpgaKEtQ9eHFdoqD/lYWhnCcRW2q7Adl33tKbrTeUI+HUcpGOjiGzR1bNcLbDpSuWltFT1eo9VeGMxs2VAXI5mzaenNkMzZnFUXkyyNWUDqVIyP7KexzbYW92JqzYkFrAB33303H//4x3nzm9+Mruu8+93v5utf/3rp/mKxyN69e8lkMgC88MILbN++HYBVq1YNe62DBw+ybNmyadv20VywtJIvXr2Oh5/bw/bjNvn+QimX//2bG/nJtiOnXDUNFtpSyiUatIZdMfRlixztyVCwXSqCFiG/SU+6iIu3sETDG/r1GTqGrmFoGr2ZApXhsnEf9KYr3/906XyzJe1QjGy21amYrWQ/jU5GQRcWTZ0ccophkskksVislBY82VzXpa2tnYQWJJl1hp1UB2sQJLLF0g+xJ51nT1sKy9BZuzhK+UBnXgW82pogkSliGhobFscoui572lIETJ2c7RIwvZGUdMHBVd4srKZpbL1qDX+xedlpt3W8+f7Kdcnv24+bTKBHY/ibVqFNYP711NoLww9AEx39cF2Xjo4O4vG4zANPkdH2sRSrGp/x7KeF+j0e6bjTFC+bkmBtoe7jqTTec+icGRmZz3RdY008esqXf7yFthTQmcqRyBRQaIR9XvnvdN5brOoqBtJuFatqQmh4zdNsx5vOuXj56X/Q4y3OlHnhRXrvvpv8gWZUoYDm8+FfsZKKLVsInb/xtO8zU0XNxNSYDd1A5wLZT6OTUdCFQYKRWW6kH2Iqa3PrI97QZcAyvEAkWyRvK3RNUXBcElmbWNAk7DNJ5W0Cpo6rFLajqAhZKOWVAh/PArnxBghruw7R+b//N04igVldjeb3o/J5crt303HHHcS3bj1tQDKRnjdy8BZiYZBgbf6Tcag5YPCHuGlFFWtro1y03BsxWRwLcKCzn950EU3zurEGTIO87bKvI0Uia1NfEcLUNTJFBxQYmjbhBXLjCRD2tyc58oMf4SQSWI2N6KEQmmGgh0JYDQ04ySS999yNOk01vhM9b4wR7w9YBgXbkXb3Qggxj0gwMkdtbKigIuSjKuznrCVRzllSTlWZDzQIDGbK9GWIBgzqy4OAVy69O52fcObJeAKEys4WnIMHvRGREQIWs6qKfHMz+X37x3yvkzvOnkzS+cRMmk3ddYWYT2SaZo56rSNFc2ea+opgKaOmoTLMvvYUOdvF0DSS2SIvHO0jW3DQNI1Y2GJxLMi7L6jnnectGfec63jy/RfZOQyniDZCwTgALRBA9XTjJhNjvpfUXhCzlTRsE2LqyMjIHDXSaEV50KKpJkIkYFJ0HLJFl3TeIRIwWV8XpbEiRFsyx09/d5gXj/aO+73Gk+9fvXgRvmCAdCpNb6ZAf94e1j9H5XJoPh96NDbme0ntBTEbDS7gfuVYgmjAq14cDZilBdw7DvfM9CYKMadJMDJHjTadUR60WL84SsAyMXWNtbURzm2ooDLkI+w3WVoZIpEt8uNnD497iHk8AcKqTefwillJ+6FW9hxP8mpr0ks1zhZRSmF3d+NfuRJ/06rTvp8UNROzyckLuMN+E0PXzvj3JIQ4lUzTzFFjTWek8zaZgkMsaHmLToc870wzUsYqznTxikp+uaOFiuV/xLXd7SxOd5MKRul3TA73p1lq5CmrqqDiui3jrjci6XxitpAMLyGmngQjc9RY1QlberNoGjRWnkjDVXhBStHx1pPkzyAjZaQAYdWiMj77by/RlylSvv5sfhsLsvGFx6jsaiXi2GTRaV7cyBX/38fGVWfk5M8oB3cx005MiY68HipgGXT15yXDS0yr+VZQUIKROWy0omhLKoK4SuG4LkopEgNTHem8V3kVBaahcawvA1RN6D1PDhD2tCWHXTW21jfRumQlizqPEcil6TUCNIfjrFmyirWT/PmFmA7SsG3+nfjmuvm4mFqCkTno5APDP73nXPZ39fPcgR6e2NNBezJHf96hJ50i7M+St11Q4DN1dM2rOeIq+Mm2wyytCr+uL++IV42aTle8AQDHVeR7M3LVKOashZ7hNR9PfHPZeKthw9wKIiUYmWNGOzBsWlHJAzuPnfiC+gxea0vRly2CgjK/l3WTs118hk5TvIy+gYV3r6e0ulw1ivluITdsm8iJT0y9ibTLePFo75wKIiWbZg4ZLb3wlWN93PnrvbQnc6XV/pUhH0urwqV/4EzBoeh4ab5N8QjlId+whXdnStp8i4VgIWZ4SRbR7DPexdQP7Dw251LRZWRkjhgrIl6k/LT0ZtG9Nryl5wQsHb+po+s6juuyYlEZ1UO+xJOx8G4hXzWKhWWhZXhJFtHsMzgt7rf89A8kJFiGTthvokGpV9kvd7TMuWajEozMEWMdGGxXoesauaJLOm9TNjBdYhleIGJo4GoalqkPe+5kTaGMlfY7FW2+hZgpCynDS7KIZp9YyMJ2Fa8c6yNvK1yl0DWNsN+gviKEpWsoNI4nc3MuiJRgZI4Y68Bg6TqGruG4iqJzohFd2G8S9hskskVMXcMaUuNjshfeLbSrRiHmO1kPNvukckUS2SL9eZuQZeA3DRxXkcrZvNaWJBq0WFYVpi2ZG7OX2GwMImXNyBwxVgO5sN8gYBq4LphDTv4asKQ8yImlHGpKS6uf3F1YAhEh5i5ZDzZ7uK5i1/EEX3tsH6ahEbQMiq53PDc08Bk6OdslU3B418Yl+Odgs1EZGZkjxkovBG99iKsMuvsL6JpWWreRyBZZWhUiHgnQky7QnS7IFIoQ4rRmy3qwuZSeOhUGMyhfOZagpTeDqev4LW80vOi4FBToGkQDlteHbEl0TqaiSzAyR5zuwFATDfDuC+rZfqBnxHUbMoUihJiomV4PttBrnAxNrQ6YOqauYxkaedvF0KCxMkzA0rF0nYClc6wvSyprz4ogcqIkGJlDxnNgeN9FjaMGHZO9WGmhX7EIsRCMth4MvArMU/X7X+g1Tk7OoEznHXTdW4QatHSyRYfudJ4NdTE0vHYfg9Mva2ujcy6pQIKROeZ0C0Wna7X/Qr9iEWIhOfm4MtW//4kU95qvF0AnZ1CG/QZhn0kqbxPUdHyGTjrvkM7bhH3GKdMvcy2pQBawzkEzvVB0tOJrs7mgjhBickzH738iNU7mqxMZlF5WjKZp1FeEMHWNrO1lTTquSypXHDUhYabPFRMhwYiYEKnKKMTCNV2//5NPxCcLWAaFM+g8PpeMlEFZHrJoikeI+E0Ktovjei0+5kMlYJmmERMiVRmFWLim6/cvNU5Gz6AsD1lEAxH2daZZWhnipmvWzfpRj/GQkRExIXLFIsTCNV2/f6lxciKDMha0ONyTIZ23S3WijvRmiUf8fPatq1lfF5vzgQhIMCImaKzia7AwrliEWKim6/c/1ol4Kgo2zlYLqUGjTNOICRmr+NpsLqgjhHj9pvP3P1opgw2Lo1y2thrbUexpS87qDJHJMNeyYs6UBCNiQmZLVUYhxPSb7t//ySfiY31ZntzTwfd+e2hBlRRYCA0aZZpGTNhCGjoUQgw33b//wROxaWj89HeHebU1KSUF5iEZGRFnZKEMHQohTjXdv38pgjb/STAizthCGDoUQoxsOn//UlJg/pNpGiGEELOalBSY/yQYEUIIMatJSYH5T4IRIYQQs5oUQZv/JBgRQggxq0kRtPlPghEhhBCznpQUmN8km0YIIcScICUF5i8JRoQQQswZuq6xJl5Gft9+3KMJCokY/qZVaLoM9M9lEowIIYSYMzIvvEjv3XeTP9CMKhTQfD78K1ZSsWULofM3zvTmiTMkoaQQQog5IfPCi3TccQe5XbswIlGsuiUYkSi53bvpuOMOMi+8ONObKM6QBCNCCCFmPeW69N59N04igdXYiB4KoRkGeiiE1dCAk0zSe8/dKNed6U0VZ0CCESGEELNeft9+8geaMaurRywJb1ZVkW9uJr9v/wxtoXg9JBgRQggx67nJhLdGxO8f8X4tEEAVCrjJxDRvmZgMEowIIYSY9fRoDM3nQ+XzI96vcjk0nw89GpvmLROTQYIRIYQQs56/aRX+FSuxu7pGLAlvd3fjX7kSf9OqGdpC8XpIMCKEEGLW03Sdii1bMKJRikeP4qbTKMfBTacpHj2KEY1Scd0WqTcyR8m/mhBCiDkhdP5G4lu3Eli3Dqc/RbH1GE5/isD6dcS3bpU6I3OYFD0TQggxZ4TO30jwvHO9CqzJBHpUKrDOBxKMCCGEmFM0XSewZvVMb4aYRBJKCiGEEGJGSTAihBBCiBklwYgQQgghZpQEI0IIIYSYURKMCCGEEGJGSTAihBBCiBklwYgQQgghZpQEI0IIIYSYURKMCCGEEGJGSTAihBBCiBkl5eBPY7BVdTKZnJLXd12XVCpFIBBAl94KU0L28dSTfTz1ZB9PPdnHk2/w3Dl4Lh2NBCOnkUqlAGhoaJjhLRFCCCHmplQqRSwWG/V+TZ0uXFngXNeltbWVSCSCpmmT/vrJZJKGhgaOHj1KNBqd9NcXso+ng+zjqSf7eOrJPp58SilSqRR1dXVjjjbJyMhp6LpOfX39lL9PNBqVL/8Uk3089WQfTz3Zx1NP9vHkGmtEZJBMigkhhBBiRkkwIoQQQogZJcHIDPP7/dxyyy34/f6Z3pR5S/bx1JN9PPVkH0892cczRxawCiGEEGJGyciIEEIIIWaUBCNCCCGEmFESjAghhBBiRkkwIoQQQogZJcHINOvp6WHLli1Eo1HKy8v54Ac/SH9//5iP/8QnPsGaNWsIBoM0NjbyyU9+kkQiMY1bPft961vfYtmyZQQCATZt2sRzzz035uP/7d/+jbVr1xIIBDj77LN5+OGHp2lL566J7OPvfve7XHLJJVRUVFBRUcEVV1xx2n8TMfHv8aB7770XTdN45zvfObUbOA9MdB/39fXxsY99jMWLF+P3+1m9erUcL6aCEtPqqquuUueee6763e9+p37729+qVatWqfe9732jPv7ll19Wf/qnf6oefPBBtX//fvX444+rpqYm9e53v3sat3p2u/fee5XP51M/+MEP1Kuvvqo+9KEPqfLyctXe3j7i45955hllGIa644471K5du9TNN9+sLMtSL7/88jRv+dwx0X183XXXqW9961vqxRdfVLt371Y33nijisViqqWlZZq3fO6Y6D4edPDgQbVkyRJ1ySWXqP/+3//79GzsHDXRfZzP59WFF16orr76avX000+rgwcPqt/85jdq586d07zl858EI9No165dClDPP/986bZHHnlEaZqmjh07Nu7X+fnPf658Pp8qFotTsZlzzsUXX6w+9rGPlf7uOI6qq6tTt95664iPf+9736uuueaaYbdt2rRJ/fVf//WUbudcNtF9fDLbtlUkElE/+tGPpmoT57wz2ce2bas/+qM/Ut/73vfUDTfcIMHIaUx0H//Lv/yLWrFihSoUCtO1iQuWTNNMo23btlFeXs6FF15Yuu2KK65A13W2b98+7tdJJBJEo1FMU1oLFQoFduzYwRVXXFG6Tdd1rrjiCrZt2zbic7Zt2zbs8QBXXnnlqI9f6M5kH58sk8lQLBaprKycqs2c0850H//DP/wD8XicD37wg9OxmXPamezjBx98kM2bN/Oxj32MmpoazjrrLP7xH/8Rx3Gma7MXDDmbTaO2tjbi8fiw20zTpLKykra2tnG9RldXF1/+8pf58Ic/PBWbOOd0dXXhOA41NTXDbq+pqWHPnj0jPqetrW3Ex4/332ChOZN9fLK//du/pa6u7pQgUHjOZB8//fTTfP/732fnzp3TsIVz35ns4wMHDvDEE0+wZcsWHn74Yfbv389HP/pRisUit9xyy3Rs9oIhIyOT4Atf+AKapo35Z7wH7bEkk0muueYa1q9fz5e+9KXXv+FCTIPbbruNe++9l/vvv59AIDDTmzMvpFIprr/+er773e+yaNGimd6cect1XeLxOP/6r//KBRdcwLXXXstNN93Ed77znZnetHlHRkYmwec+9zluvPHGMR+zYsUKamtr6ejoGHa7bdv09PRQW1s75vNTqRRXXXUVkUiE+++/H8uyXu9mzwuLFi3CMAza29uH3d7e3j7qPq2trZ3Q4xe6M9nHg+68805uu+02/vM//5NzzjlnKjdzTpvoPm5ububQoUO8/e1vL93mui7gjbbu3buXlStXTu1GzzFn8j1evHgxlmVhGEbptnXr1tHW1kahUMDn803pNi8kMjIyCaqrq1m7du2Yf3w+H5s3b6avr48dO3aUnvvEE0/gui6bNm0a9fWTySRvfetb8fl8PPjgg3J1OYTP5+OCCy7g8ccfL93mui6PP/44mzdvHvE5mzdvHvZ4gMcee2zUxy90Z7KPAe644w6+/OUv8+ijjw5bJyVONdF9vHbtWl5++WV27txZ+vOOd7yDyy+/nJ07d9LQ0DCdmz8nnMn3+I1vfCP79+8vBXoAr732GosXL5ZAZLLN9Araheaqq65SGzduVNu3b1dPP/20ampqGpba29LSotasWaO2b9+ulFIqkUioTZs2qbPPPlvt379fHT9+vPTHtu2Z+hizyr333qv8fr+666671K5du9SHP/xhVV5ertra2pRSSl1//fXqC1/4QunxzzzzjDJNU915551q9+7d6pZbbpHU3tOY6D6+7bbblM/nU7/4xS+GfWdTqdRMfYRZb6L7+GSSTXN6E93HR44cUZFIRH384x9Xe/fuVQ899JCKx+Pqf/2v/zVTH2HekmBkmnV3d6v3ve99qqysTEWjUfWBD3xg2AH64MGDClBPPvmkUkqpJ598UgEj/jl48ODMfIhZ6Bvf+IZqbGxUPp9PXXzxxep3v/td6b5LL71U3XDDDcMe//Of/1ytXr1a+Xw+tWHDBvWrX/1qmrd47pnIPl66dOmI39lbbrll+jd8Dpno93goCUbGZ6L7+Nlnn1WbNm1Sfr9frVixQn3lK1+RC8EpoCml1MyMyQghhBBCyJoRIYQQQswwCUaEEEIIMaMkGBFCCCHEjJJgRAghhBAzSoIRIYQQQswoCUaEEEIIMaMkGBFCCCHEjJJgRAghhBAzSoIRIYQQQswoCUaEEAAcPXqUv/zLv6Surg6fz8fSpUv51Kc+RXd394xsz2WXXcanP/3pGXlvgC996UtomoamaZimybJly/jMZz5Df3//sMf98pe/5LLLLiMWi1FWVsY555zDP/zDP9DT0wPA8ePHue6661i9ejW6rs/oZxJitpJgRAjBgQMHuPDCC9m3bx8/+9nP2L9/P9/5zndKHU0HT6wLzYYNGzh+/DiHDh3i9ttv51//9V/53Oc+V7r/pptu4tprr+Wiiy7ikUce4ZVXXuGrX/0qL730Ej/5yU8AyOfzVFdXc/PNN3PuuefO1EcRYnab6eY4QoiZd9VVV6n6+nqVyWSG3X78+HEVCoXURz7ykdJtgLr//vuHPS4Wi6kf/vCHpb9v3bpVNTU1qWAwqJYvX65uvvlmVSgUSvffcsst6txzz1U//vGP1dKlS1U0GlXXXnutSiaTSimv6RsjNIb84Q9/qGKx2LD3vv/++9XQQ9nga3//+99XDQ0NKhwOq7/5m79Rtm2r22+/XdXU1Kjq6urTdl4dfJ2hPvShD6na2lqllFLbt29XgPra17424vN7e3tPue3SSy9Vn/rUp8Z8XyEWIhkZEWKB6+np4de//jUf/ehHCQaDw+6rra1ly5Yt3HfffagJ9NSMRCLcdddd7Nq1i3/+53/mu9/9Lv/n//yfYY9pbm7mgQce4KGHHuKhhx7iqaee4rbbbgPgn//5n9m8eTMf+tCHOH78OMePH6ehoWHc79/c3MwjjzzCo48+ys9+9jO+//3vc80119DS0sJTTz3F7bffzs0338z27dvH/ZoAwWCQQqEAwN13301ZWRkf/ehHR3xseXn5hF5biIXMnOkNEELMrH379qGUYt26dSPev27dOnp7e+ns7CQej4/rNW+++ebS/y9btozPf/7z3HvvvWzdurV0u+u63HXXXUQiEQCuv/56Hn/8cb7yla8Qi8Xw+XyEQiFqa2sn/Jlc1+UHP/gBkUiE9evXc/nll7N3714efvhhdF1nzZo13H777Tz55JNs2rRpXK+5Y8cO7rnnHt70pjcB3n5bsWIFlmVNePuEEMNJMCKEADjtyIfP5xv3a9133318/etfp7m5mf7+fmzbJhqNDnvMsmXLSoEIwOLFi+no6JjYRo/i5NeuqanBMAx0XR922+ne7+WXX6asrAzHcSgUClxzzTV885vfBE6/v4QQ4yfTNEIscKtWrULTNHbv3j3i/bt376a6uro07aBp2ikn4mKxWPr/bdu2sWXLFq6++moeeughXnzxRW666abS9Magk0cUNE3Ddd0xt1XX9THfe6zXPpP3W7NmDTt37mT37t1ks1kefPBBampqAFi9ejUHDhwY8f2FEBMjwYgQC1xVVRVvectb+Pa3v002mx12X1tbG3fffTc33nhj6bbq6mqOHz9e+vu+ffvIZDKlvz/77LMsXbqUm266iQsvvJCmpiYOHz484e3y+Xw4jjPsturqalKpFOl0unTbzp07J/zaE9mGVatWsWzZslNGhq677jr6+/v59re/PeJz+/r6pmy7hJhvJBgRQvDNb36TfD7PlVdeyX/9139x9OhRHn30Ud7ylrewevVq/uf//J+lx77pTW/im9/8Ji+++CK///3v+chHPjJs1KGpqYkjR45w77330tzczNe//nXuv//+CW/TsmXL2L59O4cOHaKrqwvXddm0aROhUIi/+7u/o7m5mXvuuYe77rprMnbBhG3atImtW7fyuc99jq1bt7Jt2zYOHz7M448/znve8x5+9KMflR67c+dOdu7cSX9/P52dnezcuZNdu3bNyHYLMRtJMCKEoKmpieeff54VK1bw3ve+l6VLl/K2t72N1atX88wzz1BWVlZ67Fe/+lUaGhq45JJLuO666/j85z9PKBQq3f+Od7yDz3zmM3z84x/nvPPO49lnn+V//I//MeFt+vznP49hGKxfv57q6mqOHDlCZWUlP/3pT3n44Yc5++yz+dnPfsaXvvSlydgFZ+T222/nnnvuYfv27Vx55ZVs2LCBz372s5xzzjnccMMNpcdt3LiRjRs3lhbBbty4kauvvnrGtluI2UZTsgpLCDGCW265hX/6p3/iscce4w1veMNMb44QYh6TYEQIMaof/vCHJBIJPvnJTw7LRBFCiMkkwYgQQgghZpRc6gghhBBiRkkwIoQQQogZJcGIEEIIIWaUBCNCCCGEmFESjAghhBBiRkkwIoQQQogZJcGIEEIIIWaUBCNCCCGEmFESjAghhBBiRv0/ZcgTb9Zdm4cAAAAASUVORK5CYII=\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "=== QPCA (Quantum Kernel PCA + LR) ===\n",
      "Accuracy : 0.8400\n",
      "Precision: 0.0000\n",
      "Recall   : 0.0000\n",
      "F1-Score : 0.0000\n"
     ]
    }
   ],
   "id": "izbk-swSwrRq"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MH1Qf87wwrRq"
   },
   "source": "## 7. QBM – Quantum Boltzmann Machine (Kavramsal + Basitleştirilmiş Demo)\n\n**Tabloya göre:**\n- Karmaşıklık: Çok Yüksek  \n- Kübit: Çok yüksek (hata toleranslı)  \n- NISQ: Düşük (geleceğin teknolojisi)  \n- En iyi kullanım: Uzun vadeli müşteri eğilimleri ve simülasyonlar\n\nQBM, kuantum Hamiltonyen üzerinden Boltzmann dağılımı öğrenir.  \nTam eğitim (VarQITE / Gibbs state hazırlama) şu an simülatörde bile pahalıdır.  \nAşağıda **enerji tabanlı basitleştirilmiş bir yaklaşım** ve teorik çerçeve sunuluyor.",
   "id": "MH1Qf87wwrRq"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pm0muscuwrRr",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786639042464,
     "user_tz": -180,
     "elapsed": 947,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "a3388e58-83d9-47cb-d0c2-19887bab2a85"
   },
   "source": "# --- Basitleştirilmiş Enerji Tabanlı Model (Klasik BM benzeri, kuantum ilhamlı) ---\n# Gerçek QBM için: transverse-field Ising Hamiltonian + Gibbs state sampling gerekir.\n\nfrom scipy.optimize import minimize\n\ndef classical_boltzmann_like(X, y, n_hidden=4, maxiter=80):\n    \"\"\"\n    Basit restricted Boltzmann machine benzeri enerji modeli.\n    QBM'nin klasik limitini gösterir; gerçek QBM için quantum imaginary time evolution gerekir.\n    \"\"\"\n    n_vis = X.shape[1]\n    # Parametreler: W (visible-hidden), b (visible bias), c (hidden bias)\n    n_params = n_vis * n_hidden + n_vis + n_hidden\n    theta0 = np.random.randn(n_params) * 0.1\n\n    def unpack(theta):\n        W = theta[:n_vis*n_hidden].reshape(n_vis, n_hidden)\n        b = theta[n_vis*n_hidden:n_vis*n_hidden+n_vis]\n        c = theta[-n_hidden:]\n        return W, b, c\n\n    def free_energy(x, W, b, c):\n        # F(v) = -b·v - Σ log(1 + exp(c + Wᵀv))\n        visible_term = -np.dot(x, b)\n        hidden_term = -np.sum(np.logaddexp(0, c + x @ W), axis=-1)\n        return visible_term + hidden_term\n\n    def loss(theta):\n        W, b, c = unpack(theta)\n        # İki sınıf için ayrı enerji; softmax benzeri\n        fe0 = free_energy(X[y==0], W, b, c).mean() if np.any(y==0) else 0\n        fe1 = free_energy(X[y==1], W, b, c).mean() if np.any(y==1) else 0\n        # Basit margin: churn sınıfının enerjisi düşük olsun\n        return fe1 - fe0 + 0.01 * np.sum(theta**2)\n\n    res = minimize(loss, theta0, method=\"L-BFGS-B\", options={\"maxiter\": maxiter})\n    W, b, c = unpack(res.x)\n\n    # Tahmin: enerji farkına göre\n    def predict(Xnew):\n        fe = free_energy(Xnew, W, b, c)\n        # Düşük enerji → churn (sınıf 1) varsayımı (eğitimde böyle yönlendirdik)\n        threshold = np.median(free_energy(X, W, b, c))\n        return (fe < threshold).astype(int)\n\n    return predict\n\nprint(\"Basitleştirilmiş enerji modeli eğitiliyor (QBM ilhamlı)...\")\nqbm_predict = classical_boltzmann_like(X_train, y_train, n_hidden=4, maxiter=60)\ny_pred_qbm = qbm_predict(X_test)\nresults.append(evaluate(y_test, y_pred_qbm, \"QBM-inspired (Classical Energy Model)\"))\n\nprint(\"\"\"\nNot: Gerçek Quantum Boltzmann Machine için:\n  - Transverse-field Ising Hamiltonian tanımlanır\n  - Gibbs durumu (e^{-βH}/Z) variational imaginary time evolution (VarQITE) ile hazırlanır\n  - Gradientler quantum expectation değerlerinden alınır\n  - Bu, hata toleranslı kuantum bilgisayarlar gerektirir.\n\"\"\")",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Basitleştirilmiş enerji modeli eğitiliyor (QBM ilhamlı)...\n",
      "\n",
      "=== QBM-inspired (Classical Energy Model) ===\n",
      "Accuracy : 0.6000\n",
      "Precision: 0.2600\n",
      "Recall   : 0.8125\n",
      "F1-Score : 0.3939\n",
      "\n",
      "Not: Gerçek Quantum Boltzmann Machine için:\n",
      "  - Transverse-field Ising Hamiltonian tanımlanır\n",
      "  - Gibbs durumu (e^{-βH}/Z) variational imaginary time evolution (VarQITE) ile hazırlanır\n",
      "  - Gradientler quantum expectation değerlerinden alınır\n",
      "  - Bu, hata toleranslı kuantum bilgisayarlar gerektirir.\n",
      "\n"
     ]
    }
   ],
   "id": "Pm0muscuwrRr"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xfYT69z2wrRr"
   },
   "source": "## 8. Sonuçların Karşılaştırılması",
   "id": "xfYT69z2wrRr"
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 886
    },
    "id": "QWXX-xEdwrRs",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786639046519,
     "user_tz": -180,
     "elapsed": 3854,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "20fd02c6-ad88-4279-9141-6118aa9cf0ed"
   },
   "source": "results_df = pd.DataFrame(results)\nresults_df = results_df.sort_values(\"f1\", ascending=False).reset_index(drop=True)\nprint(\"\\n\" + \"=\"*60)\nprint(\"MODEL KARŞILAŞTIRMA TABLOSU (Test Seti)\")\nprint(\"=\"*60)\ndisplay(results_df.round(4))\n\n# Bar plot\nfig, ax = plt.subplots(figsize=(10, 5))\nx = np.arange(len(results_df))\nwidth = 0.2\nax.bar(x - 1.5*width, results_df[\"accuracy\"], width, label=\"Accuracy\")\nax.bar(x - 0.5*width, results_df[\"precision\"], width, label=\"Precision\")\nax.bar(x + 0.5*width, results_df[\"recall\"], width, label=\"Recall\")\nax.bar(x + 1.5*width, results_df[\"f1\"], width, label=\"F1\")\nax.set_xticks(x)\nax.set_xticklabels(results_df[\"model\"], rotation=25, ha=\"right\")\nax.set_ylabel(\"Skor\")\nax.set_title(\"Telco Churn – Kuantum vs Klasik Modeller\")\nax.legend()\nax.set_ylim(0, 1.05)\nax.grid(axis=\"y\", alpha=0.3)\nplt.tight_layout()\nplt.show()",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "============================================================\n",
      "MODEL KARŞILAŞTIRMA TABLOSU (Test Seti)\n",
      "============================================================\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "                                   model  accuracy  precision  recall      f1\n",
       "0  QBM-inspired (Classical Energy Model)      0.60     0.2600  0.8125  0.3939\n",
       "1                    Classical SVM (RBF)      0.85     0.5714  0.2500  0.3478\n",
       "2                Classical Random Forest      0.82     0.4000  0.2500  0.3077\n",
       "3                              VQC / QNN      0.56     0.1818  0.5000  0.2667\n",
       "4      Kuantum Topluluk (Hybrid Bagging)      0.50     0.1600  0.5000  0.2424\n",
       "5          Classical Logistic Regression      0.81     0.2857  0.1250  0.1739\n",
       "6                  QSVM (Quantum Kernel)      0.84     0.0000  0.0000  0.0000\n",
       "7       SVM + Precomputed Quantum Kernel      0.84     0.0000  0.0000  0.0000\n",
       "8         QPCA (Quantum Kernel PCA + LR)      0.84     0.0000  0.0000  0.0000"
      ],
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       "      <th>model</th>\n",
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       "      <td>QBM-inspired (Classical Energy Model)</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.3939</td>\n",
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       "      <th>1</th>\n",
       "      <td>Classical SVM (RBF)</td>\n",
       "      <td>0.85</td>\n",
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       "      <th>2</th>\n",
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       "      <th>5</th>\n",
       "      <td>Classical Logistic Regression</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.2857</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.1739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>QSVM (Quantum Kernel)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SVM + Precomputed Quantum Kernel</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>QPCA (Quantum Kernel PCA + LR)</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "id": "QWXX-xEdwrRs"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oDZBsoKRwrRs"
   },
   "source": "## 9. Orijinal Karşılaştırma Tablosu ile Özet\n\n| Algoritma Türü | İşlem Karmaşıklığı | Kübit Devre Gereksinimi | Donanım Uygunluğu (NISQ)* | Telco Verisindeki En İyi Kullanım Alanı |\n|----------------|--------------------|--------------------------|---------------------------|------------------------------------------|\n| **VQC / QNN** | Düşük – Orta | Az sayıda kübit (10–50) | **Yüksek (Günümüz için en ideali)** | Davranışsal korelasyonlar ve anlık churn tespiti |\n| **QSVM** | Orta | Orta düzey kübit (50–100) | Orta – Yüksek (Gürültüye hassas) | Paket türü, fatura ve demografik segmentasyon |\n| **Kuantum Topluluk** | Yüksek | Hibrit (Değişken) | Orta (Klasik-Kuantum hibrit) | Maksimum tahmin doğruluğu hedeflenen projeler |\n| **QPCA** | Çok Yüksek | Yüksek kübit ve derin devreler | Düşük (Geleceğin teknolojisi) | Milyonlarca satırlık çağrı/veri trafiği sadeleştirme |\n| **QBM** | Çok Yüksek | Çok yüksek kübit (Hata toleranslı) | Düşük (Geleceğin teknolojisi) | Uzun vadeli müşteri eğilimleri ve simülasyonlar |\n\n### Pratik Öneriler (Telco)\n1. **Hemen başlamak için** → **VQC / QNN** (4–8 feature + PCA). Colab’da simüle edilebilir, IBM Quantum’a da gönderilebilir.\n2. **Segmentasyon & kernel yöntemleri** → **QSVM**. Özellik sayısı arttıkça kernel hesaplama maliyeti artar; batch’leyin.\n3. **Yüksek doğruluk hedefi** → Hibrit ensemble (birkaç VQC + klasik modeller voting).\n4. **Büyük ölçekli özellik indirgeme** → Quantum Kernel PCA (şimdilik), ileride tam qPCA.\n5. **Generative / uzun vadeli simülasyon** → QBM (hata toleranslı donanım gelince).",
   "id": "oDZBsoKRwrRs"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N-BQqGBYwrRt"
   },
   "source": "## 10. Gerçek Donanıma Gönderme (Opsiyonel)\n\nIBM Quantum Runtime kullanmak için:\n```python\nfrom qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2\n\nservice = QiskitRuntimeService(channel=\"ibm_quantum\", token=\"YOUR_TOKEN\")\nbackend = service.least_busy(operational=True, simulator=False)\nsampler = SamplerV2(mode=backend)\n# VQC veya kernel oluştururken sampler=sampler verin\n```\n\nGürültü mitigation (TREX, ZNE, PEC) eklemeyi unutmayın.",
   "id": "N-BQqGBYwrRt"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bc0XZxf2wrRt"
   },
   "source": "## 11. Sonraki Adımlar\n\n- Gerçek Telco Customer Churn (Kaggle) veri setini yükleyip aynı pipeline’ı çalıştırın.\n- Feature sayısını 6–8’e çıkarıp `EfficientSU2` ansatz deneyin.\n- `qiskit-machine-learning` içindeki `NeuralNetworkClassifier` + `TorchConnector` ile hibrit klasik-kuantum ağlar kurun.\n- QBM için `qiskit-algorithms` VarQITE örneklerini inceleyin.\n- Production’da: feature store + model registry + A/B test (kuantum skor vs klasik skor).\n\n**Kaynaklar**\n- [Qiskit Machine Learning Tutorials](https://qiskit-community.github.io/qiskit-machine-learning/)\n- [Quantum Kernel Tutorial](https://qiskit-community.github.io/qiskit-machine-learning/tutorials/03_quantum_kernel.html)\n- [VQC Tutorial](https://qiskit-community.github.io/qiskit-machine-learning/tutorials/02_neural_network_classifier_and_regressor.html)\n\n---\n*Notebook, tablodaki beş algoritmayı NISQ-uyumlu ve eğitim amaçlı şekilde kapsar. Başarılar!*",
   "id": "bc0XZxf2wrRt"
  }
 ]
}