{
 "nbformat": 4,
 "nbformat_minor": 5,
 "metadata": {
  "colab": {
   "provenance": [],
   "gpuType": "T4"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "title"
   },
   "source": "# 🏭 SAP S/4HANA Public Cloud → Quantum Sales Forecast\n## API ile Satış Verisi Çekme + Qiskit / IBM Quantum ile Tahmin\n\n**Hedef kitle:** Öğrenciler (ERP + QML kesişimi)  \n**Ortam:** Google Colab  \n**Bileşenler:** SAP OData API • Qiskit • IBM Quantum Runtime • scikit-learn\n\n---\n\n### Ne Öğreneceksiniz?\n\n| Aşama | Konu |\n|-------|------|\n| 1 | Ortam kurulumu (Qiskit + IBM Quantum hesabı) |\n| 2 | SAP S/4HANA Public Cloud OData API bağlantısı |\n| 3 | Satış verisinin çekilmesi ve temizlenmesi |\n| 4 | Zaman serisi ön işleme (lag features, scaling) |\n| 5 | Klasik baseline (XGBoost / Ridge) |\n| 6 | Quantum Kernel Regression (Qiskit) |\n| 7 | IBM Quantum gerçek backend (opsiyonel) |\n| 8 | Sonuçların karşılaştırılması ve yorumlanması |\n\n> **Önemli:** Gerçek SAP sistemine erişim için Communication User + OAuth/Basic Auth gerekir.  \n> Bu notebook’ta **gerçek API kodu** + **sentetik fallback veri** birlikte sunulmuştur.  \n> SAP erişiminiz yoksa sentetik veri ile tüm pipeline’ı çalıştırabilirsiniz.",
   "id": "title"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section1"
   },
   "source": "## 1. Ortam Kurulumu\n\nAşağıdaki hücreyi çalıştırın. Ardından IBM Quantum hesabınızı bağlayacağız.",
   "id": "section1"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "setup",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291265360,
     "user_tz": -180,
     "elapsed": 55522,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "e3cafda6-2923-4855-b5f8-bf583a350d48"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Python: 3.12.13\n",
      "Mounted at /content/drive\n",
      "✅ Google Drive bağlandı\n",
      "\n",
      "📦 Paketler kuruluyor...\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m49.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h\n",
      "✅ Kurulum tamamlandı.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# COLAB KURULUM\n# ============================================================\nimport sys\nprint(f\"Python: {sys.version.split()[0]}\")\n\ntry:\n    from google.colab import drive\n    drive.mount('/content/drive')\n    print(\"✅ Google Drive bağlandı\")\nexcept Exception as e:\n    print(f\"⚠️ Drive mount atlandı: {e}\")\n\nprint(\"\\n📦 Paketler kuruluyor...\")\n!pip install -q --upgrade pip\n!pip install -q qiskit qiskit-aer qiskit-machine-learning qiskit-ibm-runtime qiskit-algorithms\n!pip install -q scikit-learn pandas numpy matplotlib seaborn xgboost requests\n!pip install -q tqdm\n\nprint(\"\\n✅ Kurulum tamamlandı.\")",
   "id": "setup"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "imports",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291274159,
     "user_tz": -180,
     "elapsed": 6372,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "f8995303-685c-4f77-c052-6fc18f0d3799"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Temel kütüphaneler yüklendi.\n"
     ]
    }
   ],
   "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport requests\nfrom datetime import datetime, timedelta\nimport warnings\nwarnings.filterwarnings('ignore')\n\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\nfrom xgboost import XGBRegressor\n\nsns.set_style('whitegrid')\nplt.rcParams['figure.figsize'] = (12, 5)\nprint(\"✅ Temel kütüphaneler yüklendi.\")",
   "id": "imports"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section2"
   },
   "source": "## 2. IBM Quantum Hesabı Bağlantısı\n\n1. [IBM Quantum Platform](https://quantum.ibm.com/) üzerinden ücretsiz hesap açın.\n2. **API Token** alın (Account → API Token).\n3. Aşağıdaki hücrede token’ınızı girin.\n\n> Token’ı Colab Secrets ile saklamak daha güvenlidir:  \n> Sol menü → 🔑 Secrets → `IBM_QUANTUM_TOKEN` ekleyin.",
   "id": "section2"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "ibm_token",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291322986,
     "user_tz": -180,
     "elapsed": 1968,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "73b4c692-6d90-432a-b25f-42cdf4262ab1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "✅ Token Colab Secrets'tan alındı.\n",
      "ℹ️ Simülatör modu aktif (Aer). Gerçek backend için USE_REAL_BACKEND=True ve token girin.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# IBM QUANTUM TOKEN\n# ============================================================\n# Yöntem 1: Colab Secrets (önerilen)\ntry:\n    from google.colab import userdata\n    IBM_TOKEN = userdata.get('IBM_QUANTUM_TOKEN')\n    print(\"✅ Token Colab Secrets'tan alındı.\")\nexcept Exception:\n    # Yöntem 2: Manuel girin (eğitim amaçlı)\n    IBM_TOKEN = \"\"  # ← Buraya IBM Quantum API Token'ınızı yazın\n    if not IBM_TOKEN:\n        print(\"⚠️ Token girilmedi. Simülatör ile devam edilecek.\")\n    else:\n        print(\"✅ Token manuel olarak alındı.\")\n\n# Qiskit Runtime bağlantısı\nfrom qiskit_ibm_runtime import QiskitRuntimeService\n\nUSE_REAL_BACKEND = False  # True yaparsanız gerçek IBM Quantum backend kullanılır\n\nif IBM_TOKEN and USE_REAL_BACKEND:\n    try:\n        QiskitRuntimeService.save_account(channel=\"ibm_quantum\", token=IBM_TOKEN, overwrite=True)\n        service = QiskitRuntimeService(channel=\"ibm_quantum\")\n        print(\"✅ IBM Quantum hesabı bağlandı.\")\n        print(\"Kullanılabilir backend'ler (ilk 5):\")\n        for b in service.backends()[:5]:\n            print(f\"  - {b.name}\")\n    except Exception as e:\n        print(f\"⚠️ IBM Quantum bağlantı hatası: {e}\")\n        print(\"   Simülatör ile devam edilecek.\")\n        USE_REAL_BACKEND = False\nelse:\n    print(\"ℹ️ Simülatör modu aktif (Aer). Gerçek backend için USE_REAL_BACKEND=True ve token girin.\")",
   "id": "ibm_token"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section3"
   },
   "source": "## 3. SAP S/4HANA Public Cloud – OData API ile Satış Verisi\n\n### Gerçek Sistem İçin Gerekenler\n\n| Parametre | Açıklama |\n|-----------|----------|\n| `SAP_BASE_URL` | Örn: `https://myXXXXXX-api.s4hana.cloud.sap` |\n| `SAP_USER` / `SAP_PASS` | Communication User (Basic Auth) veya OAuth client |\n| OData Servisi | `API_SALES_ORDER_SRV` veya `API_BILLING_DOCUMENT_SRV` |\n\n**Örnek endpoint:**  \n`/sap/opu/odata/sap/API_SALES_ORDER_SRV/A_SalesOrder?$top=500&$select=SalesOrder,SoldToParty,TotalNetAmount,SalesOrderDate`\n\n> Bu notebook’ta gerçek API çağrısı **hazır**dır. Kimlik bilgileriniz yoksa otomatik olarak **sentetik satış verisi** üretilir.",
   "id": "section3"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "sap_api",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291341854,
     "user_tz": -180,
     "elapsed": 18,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "3554a3ab-abbd-4b25-f4a1-fd5b20e21cfe"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "→ Sentetik veri moduna geçiliyor...\n"
     ]
    }
   ],
   "source": "# ============================================================\n# SAP S/4HANA PUBLIC CLOUD BAĞLANTI AYARLARI\n# ============================================================\n\n# ← Gerçek sistem bilgilerinizi buraya girin\nSAP_BASE_URL = \"\"          # Örn: https://my123456-api.s4hana.cloud.sap\nSAP_USER     = \"\"          # Communication User\nSAP_PASSWORD = \"\"          # Şifre\n\n# OData servis yolu (Sales Order örneği)\nODATA_PATH = \"/sap/opu/odata/sap/API_SALES_ORDER_SRV/A_SalesOrder\"\n\ndef fetch_sap_sales_orders(base_url, user, password, top=500):\n    \"\"\"\n    SAP S/4HANA Public Cloud'dan Sales Order verisi çeker.\n    OData V2 / V4 Basic Auth kullanır.\n    \"\"\"\n    if not base_url or not user:\n        return None\n\n    url = f\"{base_url.rstrip('/')}{ODATA_PATH}\"\n    params = {\n        \"$top\": top,\n        \"$select\": \"SalesOrder,SoldToParty,TotalNetAmount,SalesOrderDate,SalesOrganization,TransactionCurrency\",\n        \"$orderby\": \"SalesOrderDate desc\",\n        \"$format\": \"json\"\n    }\n    headers = {\n        \"Accept\": \"application/json\",\n        \"x-csrf-token\": \"Fetch\"   # bazı sistemlerde gerekir\n    }\n\n    try:\n        resp = requests.get(\n            url,\n            params=params,\n            auth=(user, password),\n            headers=headers,\n            timeout=30\n        )\n        resp.raise_for_status()\n        data = resp.json()\n\n        # OData V2 → d.results | OData V4 → value\n        results = data.get(\"d\", {}).get(\"results\") or data.get(\"value\") or []\n        if not results:\n            print(\"⚠️ API yanıtı boş döndü.\")\n            return None\n\n        df = pd.DataFrame(results)\n        print(f\"✅ SAP'den {len(df)} satış kaydı çekildi.\")\n        return df\n\n    except Exception as e:\n        print(f\"⚠️ SAP API hatası: {e}\")\n        return None\n\n# Deneme\ndf_sap = fetch_sap_sales_orders(SAP_BASE_URL, SAP_USER, SAP_PASSWORD)\n\nif df_sap is not None:\n    display(df_sap.head())\nelse:\n    print(\"→ Sentetik veri moduna geçiliyor...\")",
   "id": "sap_api"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section4"
   },
   "source": "## 4. Satış Verisinin Hazırlanması\n\nSAP’den veri gelmezse, gerçekçi **sentetik aylık satış zaman serisi** üretilir.  \nBu sayede tüm pipeline (ön işleme → klasik → kuantum) kesintisiz çalışır.",
   "id": "section4"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "data_prep",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 461
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291348182,
     "user_tz": -180,
     "elapsed": 200,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "e1a16d9f-7d4d-47b9-b1a8-85df62c53442"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sentetik satış verisi üretiliyor...\n",
      "\n",
      "Veri şekli: (48, 4)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "        date  sales_amount  orders_count  avg_order_value\n",
       "0 2022-01-01     103973.71           127            850.0\n",
       "1 2022-02-01     113096.01           106            850.0\n",
       "2 2022-03-01     130236.40           158            850.0\n",
       "3 2022-04-01     142290.62           161            850.0\n",
       "4 2022-05-01     126585.92           138            850.0\n",
       "5 2022-06-01     119137.54           149            850.0\n",
       "6 2022-07-01     122846.47           159            850.0\n",
       "7 2022-08-01     105554.37           138            850.0\n",
       "8 2022-09-01      88210.59            91            850.0\n",
       "9 2022-10-01      94659.63           106            850.0"
      ],
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      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "summary": "{\n  \"name\": \"print(f\\\"Toplam sat\\u0131\\u015f: {df['sales_amount']\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-01-01 00:00:00\",\n        \"max\": \"2022-10-01 00:00:00\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2022-09-01 00:00:00\",\n          \"2022-02-01 00:00:00\",\n          \"2022-06-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16781.309246137964,\n        \"min\": 88210.59,\n        \"max\": 142290.62,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          88210.59,\n          113096.01,\n          119137.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"orders_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 91,\n        \"max\": 161,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          106,\n          149,\n          127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_order_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 850.0,\n        \"max\": 850.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          850.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Tarih aralığı: 2022-01-01 → 2025-12-01\n",
      "Toplam satış: 6,761,166\n"
     ]
    }
   ],
   "source": "def generate_synthetic_sales(n_months=48, seed=42):\n    \"\"\"\n    Gerçekçi aylık satış verisi üretir:\n    - Trend + mevsimsellik + gürültü\n    - Tatil / kampanya etkileri\n    \"\"\"\n    np.random.seed(seed)\n    dates = pd.date_range(start='2022-01-01', periods=n_months, freq='MS')\n\n    trend = np.linspace(100000, 180000, n_months)\n    seasonality = 25000 * np.sin(2 * np.pi * np.arange(n_months) / 12)\n    # Yıl sonu kampanya etkisi\n    campaign = np.array([15000 if d.month in [11, 12] else 0 for d in dates])\n    noise = np.random.normal(0, 8000, n_months)\n\n    sales = trend + seasonality + campaign + noise\n    sales = np.maximum(sales, 20000)  # negatif olmasın\n\n    df = pd.DataFrame({\n        'date': dates,\n        'sales_amount': sales.round(2),\n        'orders_count': (sales / 850 + np.random.normal(0, 15, n_months)).astype(int).clip(min=20),\n        'avg_order_value': (sales / (sales / 850)).round(2)\n    })\n    return df\n\n\nif df_sap is not None and len(df_sap) > 10:\n    # Gerçek SAP verisini zaman serisine dönüştür\n    print(\"SAP verisi işleniyor...\")\n    df = df_sap.copy()\n\n    # Tarih ve tutar sütunlarını standartlaştır\n    date_col = next((c for c in df.columns if 'date' in c.lower() or 'Date' in c), None)\n    amount_col = next((c for c in df.columns if 'amount' in c.lower() or 'Amount' in c or 'Net' in c), None)\n\n    if date_col and amount_col:\n        df['date'] = pd.to_datetime(df[date_col], errors='coerce')\n        df['sales_amount'] = pd.to_numeric(df[amount_col], errors='coerce')\n        df = df.dropna(subset=['date', 'sales_amount'])\n        # Aylık agregasyon\n        df = df.set_index('date').resample('MS')['sales_amount'].sum().reset_index()\n        df.columns = ['date', 'sales_amount']\n        print(f\"✅ SAP verisi aylık seriye dönüştürüldü: {len(df)} ay\")\n    else:\n        print(\"⚠️ Beklenen sütunlar bulunamadı → sentetik veriye geçiliyor\")\n        df = generate_synthetic_sales()\nelse:\n    print(\"Sentetik satış verisi üretiliyor...\")\n    df = generate_synthetic_sales(n_months=48)\n\nprint(f\"\\nVeri şekli: {df.shape}\")\ndisplay(df.head(10))\nprint(f\"\\nTarih aralığı: {df['date'].min().date()} → {df['date'].max().date()}\")\nprint(f\"Toplam satış: {df['sales_amount'].sum():,.0f}\")",
   "id": "data_prep"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "data_plot",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 707
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291362022,
     "user_tz": -180,
     "elapsed": 1084,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "4d995e94-c660-4ca5-b719-acbb4336acd0"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    }
   ],
   "source": "# Görselleştirme\nfig, axes = plt.subplots(2, 1, figsize=(12, 7), sharex=True)\n\naxes[0].plot(df['date'], df['sales_amount'], marker='o', color='#2980b9', linewidth=2)\naxes[0].set_title('Aylık Satış Tutarı (Sales Amount)')\naxes[0].set_ylabel('Tutar')\naxes[0].grid(True, alpha=0.3)\n\nif 'orders_count' in df.columns:\n    axes[1].bar(df['date'], df['orders_count'], color='#27ae60', width=20)\n    axes[1].set_title('Aylık Sipariş Adedi')\n    axes[1].set_ylabel('Adet')\nelse:\n    axes[1].axis('off')\n\nplt.xlabel('Tarih')\nplt.tight_layout()\nplt.show()",
   "id": "data_plot"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section5"
   },
   "source": "## 5. Zaman Serisi Özellik Mühendisliği\n\nForecast için **lag features** (geçmiş değerler) ve takvim özellikleri oluşturuyoruz.\n\n- `lag_1` … `lag_6` → önceki ayların satışları\n- `rolling_mean_3` → 3 aylık hareketli ortalama\n- `month`, `quarter` → mevsimsellik",
   "id": "section5"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "features",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 360
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291371270,
     "user_tz": -180,
     "elapsed": 19,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "fbba04db-d0de-4f42-a8a9-ee1fc3fa79d2"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Feature matrix şekli: (42, 14)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "        date  sales_amount  orders_count  avg_order_value      lag_1  \\\n",
       "0 2022-07-01     122846.47           159            850.0  119137.54   \n",
       "1 2022-08-01     105554.37           138            850.0  122846.47   \n",
       "2 2022-09-01      88210.59            91            850.0  105554.37   \n",
       "3 2022-10-01      94659.63           106            850.0   88210.59   \n",
       "4 2022-11-01     106663.30           130            850.0   94659.63   \n",
       "\n",
       "       lag_2      lag_3      lag_4      lag_5      lag_6  rolling_mean_3  \\\n",
       "0  126585.92  142290.62  130236.40  113096.01  103973.71   129338.026667   \n",
       "1  119137.54  126585.92  142290.62  130236.40  113096.01   122856.643333   \n",
       "2  122846.47  119137.54  126585.92  142290.62  130236.40   115846.126667   \n",
       "3  105554.37  122846.47  119137.54  126585.92  142290.62   105537.143333   \n",
       "4   88210.59  105554.37  122846.47  119137.54  126585.92    96141.530000   \n",
       "\n",
       "   rolling_std_3  month  quarter  \n",
       "0   11819.342059      7        3  \n",
       "1    3724.200421      8        3  \n",
       "2    9103.803156      9        3  \n",
       "3   17317.946426     10        4  \n",
       "4    8766.338853     11        4  "
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>sales_amount</th>\n",
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       "      <th>lag_1</th>\n",
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       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
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       "      <th>rolling_std_3</th>\n",
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       "      <th>0</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>122846.47</td>\n",
       "      <td>159</td>\n",
       "      <td>850.0</td>\n",
       "      <td>119137.54</td>\n",
       "      <td>126585.92</td>\n",
       "      <td>142290.62</td>\n",
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       "      <td>129338.026667</td>\n",
       "      <td>11819.342059</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
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       "      <th>1</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>105554.37</td>\n",
       "      <td>138</td>\n",
       "      <td>850.0</td>\n",
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       "      <td>119137.54</td>\n",
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       "      <td>3724.200421</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>88210.59</td>\n",
       "      <td>91</td>\n",
       "      <td>850.0</td>\n",
       "      <td>105554.37</td>\n",
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       "      <td>142290.62</td>\n",
       "      <td>105537.143333</td>\n",
       "      <td>17317.946426</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
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       "      <th>4</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>106663.30</td>\n",
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       "      <td>850.0</td>\n",
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       "      <td>8766.338853</td>\n",
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       "summary": "{\n  \"name\": \"print(f\\\"\\\\nKullan\\u0131lan \\u00f6zellikler ({len(feature_cols)}): {feature_cols}\\\")\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-07-01 00:00:00\",\n        \"max\": \"2022-11-01 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2022-08-01 00:00:00\",\n          \"2022-11-01 00:00:00\",\n          \"2022-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13232.472091514874,\n        \"min\": 88210.59,\n        \"max\": 122846.47,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          105554.37,\n          106663.3,\n          88210.59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"orders_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 91,\n        \"max\": 159,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          138,\n          130,\n          91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_order_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 850.0,\n        \"max\": 850.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          850.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lag_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15013.583881791848,\n        \"min\": 88210.59,\n        \"max\": 122846.47,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          122846.47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lag_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15714.096243035106,\n        \"min\": 88210.59,\n        \"max\": 126585.92,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          119137.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lag_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13265.35541663811,\n        \"min\": 105554.37,\n        \"max\": 142290.62,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          126585.92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lag_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8889.380406456909,\n        \"min\": 119137.54,\n        \"max\": 142290.62,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          142290.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lag_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11145.833249453359,\n        \"min\": 113096.01,\n        \"max\": 142290.62,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          130236.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lag_6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14976.32564533003,\n        \"min\": 103973.71,\n        \"max\": 142290.62,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          113096.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rolling_mean_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13300.681665951355,\n        \"min\": 96141.52999999998,\n        \"max\": 129338.02666666666,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          122856.64333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rolling_std_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4961.502736607199,\n        \"min\": 3724.200421383592,\n        \"max\": 17317.946425951715,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3724.200421383592\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quarter\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
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     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Kullanılan özellikler (10): ['lag_1', 'lag_2', 'lag_3', 'lag_4', 'lag_5', 'lag_6', 'rolling_mean_3', 'rolling_std_3', 'month', 'quarter']\n"
     ]
    }
   ],
   "source": "def create_lag_features(data, target='sales_amount', lags=6):\n    df_feat = data.copy()\n    for lag in range(1, lags + 1):\n        df_feat[f'lag_{lag}'] = df_feat[target].shift(lag)\n\n    df_feat['rolling_mean_3'] = df_feat[target].shift(1).rolling(3).mean()\n    df_feat['rolling_std_3']  = df_feat[target].shift(1).rolling(3).std()\n    df_feat['month']   = df_feat['date'].dt.month\n    df_feat['quarter'] = df_feat['date'].dt.quarter\n\n    df_feat = df_feat.dropna().reset_index(drop=True)\n    return df_feat\n\ndf_feat = create_lag_features(df, lags=6)\nprint(f\"Feature matrix şekli: {df_feat.shape}\")\ndisplay(df_feat.head())\n\n# Özellik / hedef ayrımı\nfeature_cols = [c for c in df_feat.columns if c not in ['date', 'sales_amount', 'orders_count', 'avg_order_value']]\nX = df_feat[feature_cols].values\ny = df_feat['sales_amount'].values\ndates = df_feat['date'].values\n\nprint(f\"\\nKullanılan özellikler ({len(feature_cols)}): {feature_cols}\")",
   "id": "features"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "split",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291379952,
     "user_tz": -180,
     "elapsed": 38,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8be12c6c-8e2e-43eb-a765-11cb5573bbc5"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train: 31 ay | Test: 11 ay\n"
     ]
    }
   ],
   "source": "# Train / Test split (zaman sırası korunarak – shuffle=False)\nsplit_idx = int(len(X) * 0.75)\nX_train, X_test = X[:split_idx], X[split_idx:]\ny_train, y_test = y[:split_idx], y[split_idx:]\ndates_test = dates[split_idx:]\n\nscaler = StandardScaler()\nX_train_s = scaler.fit_transform(X_train)\nX_test_s  = scaler.transform(X_test)\n\nprint(f\"Train: {len(X_train)} ay | Test: {len(X_test)} ay\")",
   "id": "split"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section6"
   },
   "source": "## 6. Klasik Baseline Modeller\n\nKuantum modeli karşılaştırmak için iki güçlü klasik regressor eğitiyoruz.",
   "id": "section6"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "classical",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291384921,
     "user_tz": -180,
     "elapsed": 121,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "cef1c5fe-829b-4378-e24b-9e73d1c7f215"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=== Klasik Model Performansı (Test) ===\n",
      "Ridge        → MAE: 14,416 | RMSE: 19,067 | R²: -0.151 | MAPE: 8.2%\n",
      "XGBoost      → MAE: 23,400 | RMSE: 27,514 | R²: -1.396 | MAPE: 13.1%\n"
     ]
    }
   ],
   "source": "# Ridge Regression\nridge = Ridge(alpha=1.0)\nridge.fit(X_train_s, y_train)\nridge_pred = ridge.predict(X_test_s)\n\n# XGBoost\nxgb = XGBRegressor(n_estimators=100, max_depth=4, learning_rate=0.1, random_state=42)\nxgb.fit(X_train_s, y_train)\nxgb_pred = xgb.predict(X_test_s)\n\ndef regression_metrics(y_true, y_pred, name):\n    mae  = mean_absolute_error(y_true, y_pred)\n    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n    r2   = r2_score(y_true, y_pred)\n    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n    print(f\"{name:12s} → MAE: {mae:,.0f} | RMSE: {rmse:,.0f} | R²: {r2:.3f} | MAPE: {mape:.1f}%\")\n    return {'MAE': mae, 'RMSE': rmse, 'R2': r2, 'MAPE': mape}\n\nprint(\"=== Klasik Model Performansı (Test) ===\")\nridge_scores = regression_metrics(y_test, ridge_pred, 'Ridge')\nxgb_scores   = regression_metrics(y_test, xgb_pred, 'XGBoost')",
   "id": "classical"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "classical_plot",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291389627,
     "user_tz": -180,
     "elapsed": 332,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "c837024b-cda8-44b9-d352-549716f4c32d"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": "plt.figure(figsize=(12, 5))\nplt.plot(dates_test, y_test, 'o-', label='Gerçek', color='#2c3e50', linewidth=2)\nplt.plot(dates_test, ridge_pred, 's--', label='Ridge', color='#3498db')\nplt.plot(dates_test, xgb_pred, '^--', label='XGBoost', color='#e74c3c')\nplt.title('Klasik Modeller – Test Seti Tahmini')\nplt.ylabel('Satış Tutarı')\nplt.legend()\nplt.grid(True, alpha=0.3)\nplt.tight_layout()\nplt.show()",
   "id": "classical_plot"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section7"
   },
   "source": "## 7. Quantum Kernel Regression (Qiskit)\n\n**Yöntem:**  \n1. Özellikleri `ZZFeatureMap` ile Hilbert uzayına göm\n2. Fidelity tabanlı quantum kernel hesapla\n3. Kernel matrisini klasik **Kernel Ridge Regression** / SVR’ye ver\n\nBu yaklaşım, NISQ döneminde en stabil quantum regression yöntemlerinden biridir.\n\n> Qubit sayısı = özellik sayısı. Bu yüzden en önemli 4 özelliği seçiyoruz (hız + kararlılık).",
   "id": "section7"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "quantum_prep",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291397350,
     "user_tz": -180,
     "elapsed": 47,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "f96c6726-2149-4c02-e502-9597bdde51e5"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Kuantum için seçilen özellikler: ['rolling_mean_3', 'lag_5', 'lag_1', 'month']\n",
      "Quantum veri şekli → Train: (31, 4), Test: (11, 4)\n"
     ]
    }
   ],
   "source": "from qiskit.circuit.library import zz_feature_map\nfrom qiskit_machine_learning.kernels import FidelityStatevectorKernel\nfrom sklearn.kernel_ridge import KernelRidge\nfrom sklearn.svm import SVR\n\n# En önemli 4 özelliği seç (XGBoost importance)\nimp = pd.Series(xgb.feature_importances_, index=feature_cols).sort_values(ascending=False)\ntop_features = imp.head(4).index.tolist()\nprint(f\"Kuantum için seçilen özellikler: {top_features}\")\n\nfeat_idx = [feature_cols.index(f) for f in top_features]\nX_train_q = X_train_s[:, feat_idx]\nX_test_q  = X_test_s[:, feat_idx]\n\n# Açı aralığına ölçekle\nq_scaler = MinMaxScaler(feature_range=(0, np.pi))\nX_train_q = q_scaler.fit_transform(X_train_q)\nX_test_q  = q_scaler.transform(X_test_q)\n\nprint(f\"Quantum veri şekli → Train: {X_train_q.shape}, Test: {X_test_q.shape}\")",
   "id": "quantum_prep"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "quantum_train",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291414165,
     "user_tz": -180,
     "elapsed": 10164,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "1940cd66-df2c-4e26-bede-13afe542929a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "⏳ Quantum Kernel matrisi hesaplanıyor...\n",
      "✅ Quantum Kernel Ridge eğitildi.\n",
      "\n",
      "=== Quantum Kernel Regression Performansı ===\n",
      "Quantum KR   → MAE: 84,426 | RMSE: 88,178 | R²: -23.614 | MAPE: 48.8%\n"
     ]
    }
   ],
   "source": "# Quantum Feature Map + Kernel\nn_qubits = 4\nfeature_map = zz_feature_map(feature_dimension=n_qubits, reps=2, entanglement='circular')\n\nquantum_kernel = FidelityStatevectorKernel(feature_map=feature_map)\n\nprint(\"⏳ Quantum Kernel matrisi hesaplanıyor...\")\n\n# Kernel Ridge ile quantum kernel kullan\n# Not: FidelityStatevectorKernel.evaluate doğrudan sklearn uyumlu\nq_model = KernelRidge(alpha=0.5, kernel=quantum_kernel.evaluate)\n\nq_model.fit(X_train_q, y_train)\nprint(\"✅ Quantum Kernel Ridge eğitildi.\")\n\nq_pred = q_model.predict(X_test_q)\n\nprint(\"\\n=== Quantum Kernel Regression Performansı ===\")\nq_scores = regression_metrics(y_test, q_pred, 'Quantum KR')",
   "id": "quantum_train"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "quantum_plot",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291420385,
     "user_tz": -180,
     "elapsed": 306,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "8054a52b-4818-4e8a-d8a1-6709f8354ac6"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ],
      "image/png": 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P/Weurq60bt2a1q1bo9VqWbZsGQcOHKBFixaYTCYCAwOLLI5wS+XKlf/24wshhBC3SFJKCCGEsILg4GC+++47/vGPfzBy5EjmzZt3354+mzZtQqfTMXXqVIveM/caStOoUSMaNWrEq6++ysqVK3njjTdYs2YN/fr1u29cb731FhqNhg8//BAXFxe6d+9erPN56qmnWLZsGQkJCZw+fRpFUXjqqaeK1KtRowajRo1i1KhRnDt3jl69ejF9+nS++OKLux7XycmJVq1akZCQQHJyMn5+fkXqrFmzBp1OR+fOnc1lHh4edx3OdunSJYvff//9dxwcHJg2bZpFT7IlS5YU67zv5l7Jp1tDIf/cq+rPMVmTn58fCQkJZGdnW/SWOnPmjHn7/VSoUAEXFxdMJtM9e2PdUqNGDQ4cOIBer0er1d61zsO0j1qtJjQ0lNDQUN5++22+//57vvrqKxITEwkLC7tnu9xNnTp1mDFjxl233WvC/Qd5mGsDhT2yunTpQpcuXdDpdLz88st8//33jBkzBgcHh4c6n/tp0KABy5YtIzU1FShsl2PHjhEaGvrIHkMIIYS4F5lTSgghhLCS0NBQvvzyS86fP8+zzz5rnj/pbm719LizN0xWVlaRL+cZGRlFeszUrVsXwGLo0/189NFHdOrUiQkTJrBx48Zi7RMWFoanpydr1qxh7dq1BAcHW3x5z8vLo6CgwGKfGjVq4OLi8sC4xo4di6IoTJgwgfz8fIttFy5c4IsvvqBKlSr07NnTXF69enXOnDljscrcsWPHzKux3aLRaFCpVBa9lS5evFjs874bJycnAPM8X7e4urri5eXF7t27LcrnzZv3lx/rUYuIiMBoNDJ37lyL8pkzZ6JSqYiIiLjv/hqNhk6dOvH7779z4sSJItvvbI+OHTty/fr1Io8Ft5/nxW2fuw1d/PPz/la7FGeopYeHB2FhYXf991eTUg9zba5fv26xzd7eHn9/fxRFMc8Vd6/n2d3k5eWxb9++u267NU/YraG2Tz31FFevXmXRokVF6ubn55Obm2v+3dnZ+bENXRVCCFE+SE8pIYQQwoqio6P56KOPeOeddxg7diw///wzDg4ORerdGmbz/PPPM3DgQHJycli8eDHe3t7mHg4Ay5YtY/78+URFRVGjRg1ycnJYtGgRrq6uD0wo3KJWq/n888958cUX+ec//8mPP/54z/llbtFqtURHR7N69Wry8vIYP368xfZz584xYsQIOnfuTEBAABqNhpiYGNLS0ujatet9j92sWTPefvttPvnkE/PQJ19fX86cOcPixYtRq9V8++235smuoXCC8JkzZzJ69Giefvpp0tPTWbBgAQEBARbzU0VGRjJjxgyeffZZunXrRnp6OvPmzaNGjRr3HTJ4P/Xr1wfgq6++okuXLmi1Wtq1a4ezszP9+vXjxx9/5P/+7/9o0KABu3fv5uzZs3/pcR6H9u3b07JlS7766iuSk5MJCgpi27ZtbNy4keHDh1tM8n4vr7/+OomJifTv359+/foREBBARkYGhw8fJiEhwTwhfq9evVi+fDmffvopSUlJNG3alLy8PBISEhg0aBBRUVHFbp9vv/2W3bt3ExkZiZ+fn7le5cqVzfMw1ahRA3d3dxYsWICLiwvOzs5FkqePW3GvzejRo/Hx8aFJkyZ4e3tz5swZfvnlFyIjI8092O73PPuzvLw8Bg4cSKNGjWjTpg2VK1cmKyuLmJgYdu/eTVRUlHkIbc+ePVm7di3vv/8+iYmJNGnSBKPRyJkzZ1i3bh0///wzDRs2NMeQkJDAjBkzqFixItWqVSMkJKQkLqUQQogyQpJSQgghhJX17duXjIwMPvvsM1555RW++eabInVq167NlClTmDx5Mp999hk+Pj4MGjSIChUq8M4775jrtWjRgoMHD7JmzRrS0tJwc3MjODiYL7744qG+fGu1WqZMmcJzzz3HCy+8wMyZMx/4ZfPWZNMqlarI0L3KlSvTtWtXEhISWLFiBRqNhtq1azN58mQ6der0wHiGDRtGvXr1mD59OrNmzeLGjRsoioK3tze//fZbkcnS/f39+eyzz5gyZQqffvopAQEBTJo0iVWrVlmsEhgaGsp//vMffvrpJz755BOqVavGG2+8QXJy8l9OSgUHB/PKK6+wYMECtmzZgslkYuPGjTg7O/Piiy9y7do1fv/9d9auXUtERAQ///zzA5N+JUWtVjN16lSmTJnCmjVrWLp0KX5+frz11luMGjWqWMfw8fFh8eLFfPvtt2zYsIH58+fj6elJQEAAb7zxhrmeRqPhp59+YurUqaxatYr169fj6elJkyZNzHMrFbd92rdvT3JyMkuWLOH69et4eXnRokULXn75ZfOKlFqtlokTJ/Lll1/ywQcfYDAY+PTTT0s0KVXcazNgwABWrlzJjBkzyM3NpXLlygwdOpQXXnjBXOd+z7M/c3d35+OPPyY2NpalS5eSmpqKRqOhVq1avPXWWxarLd5K8s6cOZPffvuNDRs24OTkRLVq1Rg6dKi5RxXAhAkT+Ne//sXkyZPJz8+nd+/ekpQSQgjxUFSKNWbFFEIIIYT4m7799lumTJnC888/z6uvvmrtcIQQQgghxEOSnlJCCCGEKJVefPFFUlJS+P7776latSoDBgywdkhCCCGEEOIhSE8pIYQQQgghhBBCCFHiZPU9IYQQQgghhBBCCFHiJCklhBBCCCGEEEIIIUqcJKWEEEIIIYQQQgghRImTpJQQQgghhBBCCCGEKHGy+l4JMplMGAwG1Go1KpXK2uEIIYQQQgghhBBCPHKKomAymbCzs0Otvnd/KElKlSCDwcDBgwetHYYQQgghhBBCCCHEY9ewYUPs7e3vuV2SUiXoVnawYcOGaDQaK0fz1yiKQmZmJu7u7tLbqwTI9bY90ia2QdrB9kib2AZpB9sjbWKbpF2sT9rANkm72Iay0A5Go5GDBw/et5cUSFKqRN16Mmk0mlKdlFKr1Wg0mlL74ihN5HrbHmkT2yDtYHukTWyDtIPtkTaxTdIu1idtYJukXWxDWWqHB8Vv1YnOf/jhB/r27Uvjxo0JDQ3lhRde4MyZMxZ1CgoK+PDDD2nZsiWNGzfm5ZdfJi0tzaLOpUuX+Mc//kFISAihoaF89tlnGAwGizqJiYn07t2bBg0aEB0dzdKlS4vEM3fuXNq3b0/Dhg3p168fSUlJDx2LEEIIIYQQQgghhHgwqyaldu7cyeDBg1m0aBEzZszAYDAwevRocnNzzXU++eQTNm/ezOTJk5kzZw4pKSm89NJL5u1Go5ExY8ag1+tZsGABEydOZNmyZUyZMsVc58KFC4wZM4aWLVvy22+/MXz4cN599122bNlirrNmzRo+/fRTXnzxRZYtW0adOnUYPXo06enpxY5FCCGEEEIIIYQQQhSPVZNS06ZNo0+fPjz55JPUqVOHiRMncunSJQ4fPgxAVlYWS5YsYcKECYSGhtKgQQM++eQT9u3bx/79+wHYunUrp06d4vPPP6du3bpERkbyyiuvMHfuXHQ6HQALFiygWrVqTJgwAX9/f4YMGUKnTp2YOXOmOZYZM2bQv39/+vbtS0BAAB9++CGOjo4sWbKk2LEIIYQQQgghhBBCiOKxalLqz7KysgDw8PAA4NChQ+j1esLCwsx1/P39qVq1qjkRtH//fgIDA/Hx8THXCQ8PJzs7m1OnTpnrhIaGWjxWeHi4+Rg6nY7Dhw9bPI5arSYsLIx9+/YVOxYhhBBCCCGEEEIIUTw2M9G5yWTik08+oUmTJgQGBgKQlpaGVqvF3d3doq63tzepqanmOncmpADz7w+qk52dTX5+PhkZGRiNRry9vYs8zq05rooTS3EpioKiKA+1j624FXtpjb+0kette6RNbIO0g+2RNrEN0g62R9rENkm7WJ+0gW2SdrENZaEdihu7zSSlPvzwQ06ePMm8efOsHcpjl5mZ+cBlEW2VoijmOb9K+yoApYFcb9sjbWIbpB1sj7SJbZB2sD3SJrZJ2sX6pA1sk7SLbSgL7WAymYpVzyaSUv/+97+JjY3ll19+oXLlyuZyHx8f9Ho9mZmZFj2U0tPT8fX1Ndf58yp5t1bEu7POn1fJS0tLw9XVFUdHR/NSi3dOan7rcW71sCpOLMXl7u6ORqN5qH1sxa1sp4eHR6l9cZQmcr1tj7SJbZB2sD3SJrZB2sH2SJvYJmkX65M2sE3SLrahLLSD0WgsVj2rJqUUReGjjz5iw4YNzJkzh+rVq1tsb9CgAVqtloSEBDp16gTAmTNnuHTpEo0aNQKgUaNGfP/996Snp5uH323fvh1XV1cCAgLMdeLj4y2OvX37dvMx7O3tqV+/PgkJCURFRQGFWb2EhASGDBlS7FiKS6VSldonFtyOvzSfQ2ki19v2SJvYBmkH2yNtYhukHWyPtIltknaxPmkD2yTtYhtKezsUN26rJqU+/PBDVq1axXfffYeLi4t5biY3NzccHR1xc3Ojb9++TJw4EQ8PD1xdXfn4449p3LixOREUHh5OQEAAb731Fm+++SapqalMnjyZwYMHY29vD8DAgQOZO3cukyZNom/fvuzYsYO1a9fyww8/mGMZOXIk48ePp0GDBgQHBzNr1izy8vLo06ePOaYHxSKEEEIIIYQQQgghiseqSan58+cDMHToUIvyTz/91JwMeuedd1Cr1YwbNw6dTkd4eDjvv/++ua5Go+H777/ngw8+YMCAATg5OdG7d2/GjRtnrlO9enV++OEHPv30U2bPnk3lypX5+OOPadOmjblOly5duHbtGlOmTCE1NZW6devy888/W0yQ/qBYhBBCCCGEEEIIIUTxqJTSPJ17KWM0Gtm/fz+NGjUq1XNKZWRklOqxraWJXG/bI21iG6QdbI+0iW2QdrA90ia2SdrF+qQNbJO0i20oC+1Q3PxH6VwCTgghhBBCCCGEEEKUajax+p4QQghRXEajkT0HjnD+YjI1qvnRNKReqe19KoQQQgghRHkmSSkhhBClRkx8AhOnTONqarq5rJKvNxPGjSYqItSKkQkhhBBCCCEelgzfE0IIUSrExCfw2nuTLBJSACmp6bz23iRi4hOsFJkQQgghhBDir5CeUkIIIWyG3mAgIzObjMwsbmRkcSMzi4zMLK5dz+CnX37lbitzKIAK+Ozr6bRr3UKG8gkhhBBCCFFKSFJKCCHEI6coCrl5+RaJpeL8nJ2T+9ceD7iSksbGLYl0bBv2aE9GCCGEEEII8VhIUkoIIcR93av30p9/zsi69XM2NzKzMBgMJR7r6+9/TnC9QKIiWhEV0YrqflVKPAYhhBBCCCFE8UhSSgghyomS7r1UXGq1Gnc3Fzzd3fBwdyv836Pw/1s/p6VfZ+rMhcU6XtKREyQdOcGX388m0L8mURGt6NCmFU/WroFKpXqs5yKEEEIIIYQoPklKCSFEMRmNRvYcOML5i8nUqOZH05B6Vpu/yFZ7Lzk5OtwzseTh5lr4u8fN5NPN7W6uLqjV9193w2g0snR1DCmp6XedVwrA1dmJypV8OXX2vLnsxOlznDh9ju9mLKCGXxU63OxB1aBOwAMfUwghhBBCCPF4SVJKCCGKISY+gYlTplms/FbJ15sJ40YTFRH6l49bmnsv3e1nBwf7xxKPRqNhwrjRvPbeJFRgkZi61ffpo7dfJioilD8uXmJjfCIbt+wg6cgJc73zyZeZMX8ZM+Yvo6KvNx3CWxIV0YomwfWws5PJ0YUQQgghhChpKkVR7nXTWTxiRqOR/fv306hRo1K7OpSiKGRkZODh4SHDYEqAXG/bEBOfwGvvTSrSQ+dWi3z50VtERYSWu95L1nC35GDlij6Mf3nUXZODV1PT2bQ1kY3xO9h94DBGo6lIHU8PN9qGNScqohWtmoY8tsRaWSfvV7ZB2sH2SJvYJmkX65M2sE3SLrahLLRDcfMfkpQqQZKUEg9Lrrf1GY1GOg0YY5EE+TONWo2jowM5uXmPNRZb671kLX91GOX1G5nEbt/FxvgdbN+9H72+aDLQ2cmRiNBmdGjTijatmuDi7PQ4TqFMkvcr2yDtYHukTWyTtIv1SRvYJmkX21AW2qG4+Q8ZvieEEPexN+nofRNSAEaT6aETUvfrvWT+2d21VPReKmkajYbmjRsQWLv6Q/2h9vJ0p3eXDvTu0oHsnFy2Ju4lJn4H8Tv2kJeXD0BuXj7rNm1l3aat2NtrCWvWiPZtWtKudXM8Pdwf52kJIYQQQghR7khSSggh7iH5cgrT5i0pVl2fCp74ValUbnsvlTauLs50bh9O5/bhFBToSNh9gJj4HcRu30lGZjYAOp2e2O27iN2+C41GTbOQ+nSIaEX78JZU8vW28hkIIYQQQoiyyJYWVyoJkpQSQog/OXbyLDMWLOf3zVvvOgfR3Uz61+s0b9zgMUcmHgcHB3vatm5O29bNMRiM7Ek6TEz8DjZtSSQl7RoARqOJxL0HSdx7kE8m/0RwvUCiIlrRoU0ralSrYuUzEEIIIYQQZcHjWlzJlsmcUiVI5pQSD0uud8lRFIUde5KYMX8ZCbsPFHs/FVCpog/rFnxfal/XpVFJvDZMJhMHj55kY/wOYrbs4ELylbvWC/SvaU5QPVm7Rrl9rcr7lW2QdrA90ia2SdrF+qQNbJO0i/UUd3Gl0kLmlBJCiGIwGIxsiNvOjPnLOXryjMU2Lw93nunblSoVfXhv4tcAFn8kbv2BGP/yKElIlUFqtZqQ+kGE1A/i1eeHceLMH2yM38HGLYmcOH3OXO/E6XOcOH2O72YsoIZfFTpEtKJDm5Y0rPukzAEmhBBCCCEeyGg0MnHKtCIJKSj8/qECPvt6Ou1atyhz3zskKSWEKJdy8/JZvnYTsxf+RvKVFItt1apWYsSAXvTo3A4nRwcAXFycinalrejD+JdHlao7FuKvUalUBPnXJMi/Ji+MHMj5i5fZuGUHMfE7SDpywlzvfPJlZsxfxoz5y6joU4H2bVoSFdGKpsH1sbMrWx8ghBBCCCHEo/GgxZUU4EpKGnuTjpa5KUMkKSWEKFeu3chg/tI1LFi+lhsZWRbb6gcFMHJQL6IiWhW5AxEVEUq71i3K1aSD4t5qVKvCyEG9GTmoN1dT09m0NZGN8TvYfeCweR6ylLRrLFi2lgXL1uLh7kq71i2IimhFq6YhMtG9EEIIIYQwu/KA1b5vSU2//pgjKXmSlBJClAsXki8za9EKlq/ZRIFOZ7EtvGUTRg3qTbNG9e87dl6j0dC8cQMCa1eXcfbCrJKvN4N6d2FQ7y5cv5FJ7PZdbIzfwfbd+9HrDQBkZGazfO0mlq/dhLOTI21aNSUqohVtWjXFxdnJymcghBBCCCGs5fDx03w/c2Gx6vp6ez3maEqeJKWEEGXa4WOnmLFgORviEjCZbq+kZ6fR8FSHNgwf2JMg/5rWC1CUKV6e7vTu0oHeXTqQk5vHlh17iInfwZYde8jNywcKh47+vnkbv2/ehr29ltCmIXSIaEW71s3x9HC38hkIIYQQQoiSkJuXz7fT5/PLr6ssvqfcza3FlZoE1y2Z4EqQJKWEEGWOoihs37WfGfOXkbj3oMU2JydHnu4WzdB+3alSyddKEYrywMXZic7tw+ncPpyCAh079hwgJn4Hm7ftJCMzGwCdTk9cwm7iEnaj0ahpFlKfDhGtaB/ekkq+3lY+AyGEEEII8ThsTdzLR19+z6UrqeayKpV8uXw1FRXla3ElSUoJIcoMvcHA75u3MWP+covV0QC8K3gyuE9X+vfqjIebq3UCFOWWg4M9kWHNiQxrjsFgZE/SYWLid7BpSyIpadcAMBpNJO49SOLeg3wy+SeC6wUSFdGKDm1aUaNaFSufgRBCCCGE+LvSr99g0jczWBMTby6zt9cydvgAhg/sSdz2XeVucSVJSgkhSr3c3DyWrI5hzuKVXL6aarHtiWpVGDGwF907tpXJpYVNsLPT0LJJMC2bBPP2uGc5ePQkG7ckEhOfwIXkK+Z6SUdOkHTkBF9+P5tA/5p0aNOSDhGtCKz9hMxnJoQQQghRiiiKwvK1m/jiu5lkZmWby1s2ach7rz/PE9WqAuVzcSVJSgkhSq20azeYt3Q1C5evs3hzBwiuF8jIQb1o17pFmX4TF6WbWq0mpH4QIfWDeHXMUE6eOc/GLTuIid9h0dvvxOlznDh9jqkzF1LdrzJRbVrRIaIVDes+iVqttt4JCCGEEEKI+/rj4iX+/cVUdu47ZC7zcHfljRdG0rNzuyI3G8vb4kqSlBJClDp/XLzErIW/8du6zeh0eottkaHNGDmoN02C65b5N3BRtqhUKgL9nyDQ/wnGjhjA+YuXzQmqpCMnzPUuJF9hxoLlzFiwnIo+FWjfpiVREa1oGlwfOztJwAohhBBC2AK9wcDM+cv5fvYii+8sXaIieOulkXh7eVovOBsiSSkhRKmRdOQEMxcsJyZ+B4pye/o/Ozs7ukZFMGJgTwJq1bBihEI8OjWqVWHkoN6MHNSbq6npbNqayMb4Hew+cBijsXCFlpS0ayxYtpYFy9bi4e5K27AWREW0IrRZiAxXFUIIIYSwkqQjJ3h/0recOnveXOZXuSLvvjaG8JZNrBiZ7ZGklBDCpplMJrYm7mX6/GXsOXDEYpuLsxP9enRkcN9uVK7oY6UIhXj8Kvl6M6h3Fwb17sL1G5nEbt/Fpi2JbN+933znLSMzm9/WbeK3dZtwdnKkTaumREW0ok2rprg4O1n5DIQQQgghyr6c3Dym/DSX+cvWmG+iq9VqhvbrzgsjB+Ls5GjlCG2PJKWEEDZJr9ezZuMWZi5YzqmzFyy2+Xp7MeTp7vTr0RE3VxcrRSiEdXh5utO7Swd6d+lATm4eW3bsZeOWHcQn7CY3Lx+A3Lx8ft+8jd83b8PeXkto0xA6RLSibVhzvDzdrXwGQgghhBBlz+ZtO/nPVz9arJxX98navP/mC9QP8rdiZLZNklJCCJuSnZPLklUbmL14JSl3vKED1Krhx8iBvegaHYm9vdZKEQphO1ycnejcvjWd27emoEDHjj0HiInfweZtO8nILJz8X6fTE5ewm7iE3Wg0apqF1KdDRCvah7ekkq+3lc9ACCGEEKJ0S02/xqf/+5kNcQnmMidHB14cNYjBfbvJnJ8PIEkpIYRNSE2/xtwlq1n02zqysnMttjVuWIeRg3oTGdpMVhoT4h4cHOyJDGtOZFhzDAYje5OOEBO/g41bdpCSdg0Ao9FE4t6DJO49yCeTfyK4XiAd2rQiKqIVNapVsfIZCCGEEEKUHiaTiSWrYvjqh1kW319at2jMu6+NoVqVSlaMrvSQpJQQwqrO/HGRWQt/Y+X6WPR6g8W2duEtGDmwF40b1rVSdEKUTnZ2Glo0aUiLJg2ZMG40h46dIiZ+BzHxCVxIvmKul3TkBElHTvDVD7N5svYTREW0okNEKwJrPyGrVwohhBBC3MOZcxf48Iup7D141FxWwdOdt14aTZeoNvI56iFIUkoIYRX7Dx1j+vxlbN6606Jcq7Wje8e2DB/Qk9pPVLNSdEKUHWq1muB6gQTXC+TVMUM5eeY8G7fsICZ+BydOnzPXO3nmD06e+YOpMxdS3a8yUW1a0b5NS4LrBUoPRSGEEEIICqdFmDZvKT/98qvFDfWendvzxgvD8fSQuTsfliSlhBAlxmQyEZewmxnzl7Hv4DGLbW6uzvTv2ZnBfbvi613BShEKUbapVCoC/Z8g0P8Jxo4YwPmLl80JqqQjJ8z1LiRfYcaC5cxYsBxfby/at2lJVEQoTUPqobUr+tHBaDSy58ARzl9MpkY1P5qG1EOjkfkThBBCCFF27E06wgeff8fZ88nmsup+lfnX62Np1TTYipGVbpKUEkI8djqdntUb4pixYLnFmzhARV9vhvXrTt9u0bi6OFspQiHKpxrVqjByUG9GDurN1dR0Nm/dScyWHezefwij0QRAavp1Fi5fx8Ll6/Bwd6VtWAuiIloR2iwEBwd7YuITmDhlmsVKM5V8vZkwbjRREaHWOjUhhBBCiEciMyuHyT/OZvGK9eYyO42GEQN7MWZ4PxwdHKwYXeknSSkhxGOTlZ3D4hXr+eXXlaSmX7fYFlCrOiMG9qJLhzZotbKSnhDWVsnXm4G9n2Jg76e4kZFJ7PbdbIzfwfbd+9Hp9ABkZGbz27pN/LZuE85OjgT612T/oWNFjpWSms5r703iy4/eksSUEEIIIUolRVGIid/Bp//7yeK7TMO6T/L+my8Q5F/TesGVIZKUEkI8cldT0/nl15UsXrGenNw8i23NGtVn5MBehLdsIvPUCGGjPD3c6fVUe3o91Z6c3Dy27NjLxi07iE/YTW5ePgC5efl3TUgBKIAK+Ozr6bRr3UKG8gkhhBCiVLmSksZ/Jv9I7LZd5jJnJ0fGPTeEgb06y2ebR0iSUkKIR+bU2fPMXPAbq2PiMRhuT/ynUqmIimjFiIG9CK4XaMUIhRAPy8XZic7tW9O5fWsKCnQk7k0iJn4H6+O2k5OTd8/9FAo/0O1NOkrzxg1KLmAhhBBCiL/IaDSy8Lff+d+Pc8w34gAiQ5vx7mtjqFzRx4rRlU2SlBJC/C2KorA36Sgz5i8jLmG3xTZ7ey09O7dj+ICePFGtqpUiFEI8Kg4O9kSENiMitBnNGzfknf9MfuA+P/6yGHt7LcH1AmV5ZCGEEELYrBOn/+DDL76zWPzFu4Inb497lo5tw+RzzGMiSSkhxF9iNBrZvG0nM+Yvt3jjBnBzdWFg76d4pk9XfCp4WidAIcRjVdnXu1j1duxOYsfuJGr4VaFbx0i6RkVQo1qVxxydEEIIIUTx5BcU8OPsxcyYvxyD0Wguf7p7R/45Zigebq5WjK7sk6SUEOKhFBToWPF7LLMWLuePi5cttlWu6MOw/j3o0zUKF2cnK0UohCgJTYLrUsnXm5TUdJRi1D+ffJnvZizguxkLCK4XSPeObenUrjVenu6PPVYhhBBCiLvZufcgH34xlfPJt7/X1Kzhx/tvjKVZSH0rRlZ+SFJKCFEsGVnZLFq+jrlLV5N+7YbFtidrP8HIQb3o3D4crZ28rQhRHmg0GiaMG81r701CBRaJqVud2z/5v39iNBlZuT6OnXsPoiiFtZKOnCDpyAk++3oa4a2a0C06krZhzXFwsC/p0xBCCCFEOXQjI5P/Tp3F8rWbzGV2dnY8N6Qvzw7ui729rA5eUuTboxDivi5fTWXO4pX8umoDeXdM9gfQsklDRg7qTVjzRjLGWohyKCoilC8/eouJU6ZxNTXdXF6pog/jXx5FVEQoAD07t+dqajprYuJZtSGeE6fPAWAwGondtovYbbtwdXGmY9swunWMpGlwPVmdUwghhBCPnKIorN24lc++/plrNzLN5Y0b1uH9N17Av2Z1K0ZXPlk1KbVr1y6mTZvGoUOHSE1N5dtvvyUqKsq8PScnh//+97/ExMRw48YNqlWrxtChQxk0aJC5TkFBARMnTmTNmjXodDrCw8N5//338fG5PSv+pUuX+OCDD0hMTMTZ2ZlevXrx+uuvY3dHj47ExEQmTpzIyZMnqVKlCmPHjqVPnz4W8c6dO5dp06aRmppKnTp1eO+99wgODn6MV0gI6zl++hwz5y9n3aatFmOr1Wo10ZGhjBzYi/p1AqwYoRDCFkRFhNKudQv2HDjC+YvJ1KjmR9OQekWWSq7k683IQb0ZOag3x0+fY/X6OFbHxJOSdg2A7Jxclq6OYenqGKpU8qVrVATdOkbKh0MhhBBCPBLJl1P46Mvv2bZzn7nM1cWZV8cM5enuHeWGmJVYNSmVm5tLUFAQffv25aWXXiqyfeLEiezYsYPPP/8cPz8/tm3bxocffkjFihXp0KEDAJ988glxcXFMnjwZNzc3PvroI1566SUWLFgAFE7GPGbMGHx8fFiwYAEpKSmMHz8erVbLa6+9BsCFCxcYM2YMAwcO5IsvviAhIYF3330XX19f2rRpA8CaNWv49NNP+fDDDwkJCWHWrFmMHj2adevW4e1dvMlehbB1iqKwa98hps9fZvFmDeBgb0+vLu0Z3r8H1f1kkmIhxG0ajYbmjRsQWLs6Hh4eD+w5GeRfk6CxNXnlH0PYtf8wK9fHEhOXYF56+fLVVH6eu4Sf5y6hbmBtukVH0qVDG3y8vUridIQQQghRhhgMRuYuWcW30+eTl19gLo+ODGXCuGep6FPBitEJqyalIiMjiYyMvOf2ffv20atXL1q2bAnAgAEDWLhwIUlJSXTo0IGsrCyWLFnCF198QWho4RCBTz75hC5durB//34aNWrE1q1bOXXqFDNmzMDHx4e6devyyiuv8MUXX/DSSy9hb2/PggULqFatGhMmTADA39+fPXv2MHPmTHNSasaMGfTv35++ffsC8OGHHxIbG8uSJUv4xz/+8TgvkxCPndFoJCZ+BzPmL+fw8VMW2zzcXXmmT1cG9n6KCp4eVopQCFEWaTQaWjUNplXTYN59dQybt+5k1YY4tu/ah9FoAuDoiTMcPXGG/06dRWizELpGR9ChTSucnRytHL0QQgghbN3RE2f44IvvOHL8tLmsoq83//fP52gf3tKKkYlbbHpOqcaNG7Np0yaefvppKlasSGJiImfPnuXtt98G4NChQ+j1esLCwsz7+Pv7U7VqVXNSav/+/QQGBloM5wsPD+eDDz7g1KlT1KtXj/3795uTWnfW+eSTTwDQ6XQcPnyYMWPGmLer1WrCwsLYt8+yN4kQpUlefgG/rd3ErEW/cfHSVYttfpUrMmxAT3o91V6+/AkhHjsnRwe6RLWhS1Qb0q/fYN3GrazaEMehY4WJcpPJxLad+9i2cx9OTj/QIbwl3Tu1pUXjhtjZaR5wdCGEEEKUJ7l5+UyduYA5i1eab3SpVCoG9nqKcc8NxtXF2coRiltsOin13nvv8d577xEREYGdnR0qlYqPP/6Y5s2bA5CWloZWq8Xd3XI5aW9vb1JTU8117kxIAebfH1QnOzub/Px8MjIyMBqNRYbpeXt7c+bMmYc+L0VRzCsQlTa3Yi+t8Zc2j+t638jIYsHytcxfuobrGZkW2+o8WYuRA3sTHRlq/qIn7X2bvAZsg7SD7XmUbVLB04Nn+nblmb5dOfvHRVbfnCD90pUUAPLy8lm1IY5VG+LwqeBJl6g2dI2OpE5ArXK/6IK8NmyPtIltknaxPmkD21QW2mX7rv189OUPJF++fdPdv2Z1PnjzBULqBwG2//2mLLRDcWO36aTUnDlz2L9/P1OnTqVq1ars3r3bPKfUnb2jSpvMzMxSO4maoijk5uYClPsP/iXhUV/vy1fTWPjb76yOiSe/QGexrXmj+jzT+ymahtRDpVKRk5P9tx+vLJLXgG2QdrA9j6tNKni6MfTprgzu8xSHjp3i99jtbNq6i+ycwsdKu3aD2YtWMnvRSmrV8KNjZCjRka2o5Fs+53uU14btkTaxTdIu1idtYJtKc7tcz8jkm+kLWB+bYC6z19oxvH8PBvV+Cq3WjoyMDCtGWHyluR1uMZlMxapns0mp/Px8vvrqK7755hvatm0LQJ06dTh69CjTpk0jLCwMHx8f9Ho9mZmZFr2l0tPT8fX1BQp7PCUlJVkcOy0tDcCizq2yO+u4urri6OiIWq1Go9GQnp5uUSc9Pb1ID6vicHd3L7IqUWlxK9tZnIlsxd/3qK730ZNnmLngN9Zv3obxjjcHjVpNx3atGTGwJ3WfrP234y0P5DVgG6QdbE9JtEmb0Oa0CW2OTqcnfsceVm+IIy5hDwaDAYCz55P5Yc6v/PjLEpqF1KdrdATRkaG4ubo8lnhskbw2bI+0iW2SdrE+aQPbVBrbRVEUVvweyxffzSAj8/aN9eaNGvDe689Ts3pVK0b315TGdvgz4x0ruN+PzSalDAYDer2+SANoNBpzAzVo0ACtVktCQgKdOnUC4MyZM1y6dIlGjRoB0KhRI77//nvS09PNw++2b9+Oq6srAQEB5jrx8fEWj7N9+3bzMezt7alfvz4JCQlERUUBhVm/hIQEhgwZ8tDnplKpSu0TC27HX5rPoTT5q9dbURR27ElixvxlJOw+YLHNydGB3l2iGNq/O9WqVHqU4ZYL8hqwDdIOtqek2sTBwZ7oyFCiI0PJyMzi99jtrFofy76Dx4CbK4nuP8Su/Yf45H8/0TasOd07tqV1i0ZotdrHGpstkNeG7ZE2sU3SLtYnbWCbSlO7nL94mX9/+T2Je253RHF3c+WNscPp1aVDqTiHeylN7XA3xY3bqkmpnJwczp8/b/794sWLHD16FA8PD6pWrUqLFi34/PPPcXR0pGrVquzatYvly5ebV8lzc3Ojb9++TJw4EQ8PD1xdXfn4449p3LixOaEUHh5OQEAAb731Fm+++SapqalMnjyZwYMHY29vD8DAgQOZO3cukyZNom/fvuzYsYO1a9fyww8/mGMbOXIk48ePp0GDBgQHBzNr1izy8vLo06dPyV0wIYrBYDCyPnY7Mxcs5+hJyznPvDzceaZvVwb26oynh/s9jiCEEKWHh7sb/Xt0on+PTly4dIXVG+JZvSGOcxcuAaDT6Vkfu531sdvx9HCjc/twukVHElwvsNR+yBNCCCHKO73BwKyFv/H9zEUU6G5PS/JUhza89dIofCp4Wi848VBUihVnzkpMTGTYsGFFynv37s3EiRNJTU3lyy+/ZOvWrWRkZFC1alUGDBjAiBEjzB8kCwoKmDhxIqtXr0an0xEeHs77779vHpoHkJyczAcffMDOnTtxcnKid+/evP7669jZ2VnE8umnn3Lq1CkqV67MCy+8UCTh9MsvvzBt2jRSU1OpW7cu7777LiEhIcU+X6PRaF4VsDQP38vIyCjV3QhLk4e53rl5+Sxbs5E5i1aQfHMy4FuqVa3EiAG96NG5HU6ODo8z5DJPXgO2QdrB9thSmyiKwqFjJ1m1Pp51m7Zw7UZmkTo1/KrQrWMk3aIjqO5XxQpRPh621A6ikLSJbZJ2sT5pA9tUGtrl4NETfPD5VE6cPmcuq1rZl3dfHUObVk2tF9gjVBra4UGKm/+walKqvJGklHhYxbne125kMH/pGuYvW2MxhhqgflAAIwf1IiqiVal9ztkaeQ3YBmkH22OrbaI3GEjYtZ9V6+PYtHWnxd3UW0LqB9G9Y1s6tQsr9b1IbbUdyjNpE9sk7WJ90ga2yZbbJSc3j2+mzWPuktXmKX3UajVDnu7GiyMH4uzsZOUIHx1bbofiKm7+w2bnlBKivDMajew5cITzF5OpUc2PpiH1LF7MF5IvM2vRCpav2VTkS1Z4yyaMGtSbZo3ql9o3MSGEeBS0dnZEhDYjIrQZ2Tm5xMTvYNX6WHbuO2T+QHvg8HEOHD7OxK+nEd6yMd07tiUytBkODvZWjl4IIYQQAHHbd/HxVz9yJeX2AmV1AmrxwZsvUL9OgBUjE3+XJKWEsEEx8QlMnDKNq6m3V3ys5OvNhHGjqVLRl+nzlxETv8NimU07jYanOrRh+MCeBPnXtELUQghh21xdnOn1VHt6PdWeKylprInZwsr1sZw6Wzi/pcFgIHbbLmK37cLN1ZmOkWF07RhJ0+B6qNVqK0cvhBBClD9p6df5dMrPrI/dbi5zdLDnhZEDGdqvB3Z2MhqktJOklBA2JiY+gdfem8Sfx9VeTU3n1fcmFanv5OTI092iGdqvO1Uq+RbZLoQQoqjKFX0Y9UxvRj3Tm+OnzrJqQxxrYraQknYNgKzsXJasjmHJ6hiqVPKla1QE3TpG4l+zupUjF0IIIco+k8nE0tUxfPn9bLKyc8zloc1CeO/156letbIVoxOPkiSlhLAhRqORiVOmFUlI3U0FLw+G9O1G/16d8XBzfeyxCSFEWRUUUIuggFr88x9D2bnvEKvWx7Ihfgd5efkAXL6ays9zl/Dz3CXUDaxNt+hIunRog4+3l5UjF0IIIcqeM39c5N//ncqeA0fMZV4e7rz18ii6RkXI9CRljCSlhLAhe5OOWgzZu5dh/Xsw7tnBMt+JEEI8QhqNhtBmIYQ2C+H/Xh3D5q07WbUhjoTd+zEaC4dLHz1xhqMnzvDfqbMIbRZCt46RtA9vibOTo5WjF0IIIUo3vV7PtHnL+HHOYvR6g7m8R6e2vPHCSLw8S/diJOLuJCklhA354+KlYtWrHxQgCSkhhHiMnJ0c6RodQdfoCNKu3WDdpi2sWh/P4eOngMJhBdt27mPbzn04OTkS1aYV3TpG0rJJQ1ntVAghhHhI+w4e5cMvpnL63AVzWbWqlfjX62MJbRZixcjE4yZJKSFswJWUNOYsXsmC5WuLVd9XhowIIUSJ8angyZCnuzPk6e6cOXeBVTHxrN4Qx6UrqQDk5eWzcn0sK9fH4uvtxVMd2tC9Y1uCAmrKEAMhhBDiPrKyc5j8wxwWrfjdXKbRqBk+oCfPDx+Ak6ODFaMTJUGSUkJY0dnzycxcsJwVv8diMBgeWF8FVKroQ5Pguo8/OCGEEEXUrlmdcc8O5qVRg9h38Bgr18eyPnYbWdm5AKSmX2f2ohXMXrSCgFrV6RodSbfoSCpX9LFy5EIIIYRtiYlP4JPJP5Gaft1c1qBOAB+8+QJBAbWsGJkoSZKUEsIKDh87xbR5S4mJ34Gi3J7W3MHenmaN6rFt535UYDHh+a177eNfHiVDQ4QQwsrUajVNQ+rRNKQeb497lvgde1i1Po74HXvMNxlOnb3A/378hSk/zaVZSH26d4wkKjIUN1cXK0cvhBBCWM/V1HQ+mfwTm7YmmsucnBwZ9+wzDOrdRb7rlDOSlBKihCiKQuLeg0ybu5Qdew5YbHNzdWZgr6d4pm83fCp4EhOfwMQp0ywmPa9U0YfxL48iKiK0pEMXQghxHw4O9kRHhhIdGcqNjEzWx25n5fo49h86BhS+/+/af4hd+w/xn8k/0bZ1c7pFR9K6ZWO0dvJRTAghRPlgMplY9NvvTP5xDjm5eebyiNCmvPvqGKpU8rVidMJa5JOQEI+ZyWRi09ZEps1dyqFjpyy2+VTwYmi/7vTv2QlXF2dzeVREKO1at2DPgSOcv5hMjWp+NA2pJ3cNhBDCxnl6uNO/Z2f69+zMhUtXWLU+jtUb4vjj4mUACnQ6ft+8jd83b8PLw51O7VvTvWNbGtZ9UuafEkIIUWadPPMHH34xlQOHj5vLvCt4MuHlZ+nULkz+BpZjkpQS4jHR6/Ws2hDP9PnLOHc+2WJbdb/KjBzYmx6d2t5zFT2NRkPzxg0IrF0dDw8PeaMWQohSpnrVyowdMYDnh/fn4NGTrFofx7pNW7mekQnA9YxMFixby4Jla3miWpWb809FUN2vipUjF0IIIR6NggIdP/7yK9PnLbOYQ7dv1yheHTscDzdXK0YnbIEkpYR4xHJz8/h11QZmLVpByh3D7wCCAmry7OC+REWEYmcnvZ6EEKI8UKlUBNcLJLheIG++NJLtO/exakM8m7fupECnA+CPi5f5bsYCvpuxgJD6QXTv2JZO7cLw9HC3cvRCCCHEX7Nr3yE+/OI7c29hgJrVq/KvN8bSvFEDK0YmbIkkpYR4RG5kZDJv6RrmLV1NRma2xbZmjeoz+pk+tG7RWHo8CSFEOaa1syMyrDmRYc3Jys4hJn4Hq9bHsmv/YfPCFwcOH+fA4eNM/HoabVo1oVt0JJGhze7Zs1YIIYSwJRmZWXz5/WyWro4xl9nZ2TH6md48N+Rp+XsmLEhSSoi/6UpKGrMWrWDJyvXk5RdYbGvbujmjn+lDowZ1rBSdEEIIW+Xm6kLvLh3o3aUDV1LSWB0Tz6r1sZw6ewEAg8HA5q072bx1J26uznSMDKNbp7Y0aVgXtVpd5HhGo1HmIhRCCGE1iqLw++ZtfDrlZ65dzzCXN2pQh/ffGEtArRpWjE7YKklKCfEXnfnjItPnLWP1hjgMRqO5XKNR06VDBKOe6S1vvEIIIYqlckUfRj/Th1GDenP81DlWbYhjTUw8qenXAcjKzmXJ6hiWrI6hamVfukZF0C06kto1qwPcfdVWX28mjBstq7YKIYR47C5dSeHjr35ky4495jJXF2f++Y+h9OvR8a43U4QASUoJ8dAOHj3BtLlL2bR1p3moBYCjgz19ukYzfEAPqlauaMUIhRBClFYqlYo6T9aizpO1eHXMUBL3HmT1hjg2xO8gLy8fgEtXUvnplyX89MsS6gX5E1j7CZav3VTkWCmp6bz23iS+/OgtSUwJIYR4LIxGI/OWruHrafPMf6cAOrRpyduvPEclX28rRidKA0lKCVEMiqKQsPsA0+ctJXHvQYttbq4uDOrThcF9u1LB08NKEQohhChrNBoNYc0bEda8Ef/36hg2bU1k1fo4EnYfwGQyAXDk+GmOHD991/0VQAV89vV02rVuIUP5hBBCPFLHTp7lg8+/4/DxU+ayij4VeOeV5+gQ0cqKkYnSRJJSQtyH0Whk45ZEps1bWuRDv6+3F8P696Bfj064ODtZKUIhhBDlgbOTI92iI+kWHUla+nXWbtrKqg1x90xI3aJQOPfh3qSjNG8sKx0JIYT4+/LyC5g6cyGzF/2G0Vh4k0SlUtG/ZydeeW4Ibq4uVo5QlCaSlBLiLnQ6PSvXxzJj/jKLJUwBnqhWhZGDetO9Y1vs7bVWilAIIUR55ePtxdB+3Rnarzsz5i/ny+9nPXCf9bHbqRtYG1cX5xKIUAghRFm1fdd+Pvryey5eumouC6hVnfffeEEWdxJ/iSSlhLhDTm4ev65cz+xFK0hJu2axre6TtRk9uA9REa1kCIQQQgib0KBOQLHqLVi+liWrN9C6eWOiI0Np27oF7m5yJ1sIIUTxXL+RyeffzmDl+lhzmVZrx5hh/Rk1qBdardysF3+NJKWEAK7dyGDektXMX7aWzKxsi20tGjdg9OC+hDYLQaVSWSlCIYQQoqgmwXWp5OtNSmo6ygPq6vUGYrfvInb7Luzs7GjVNJiOkaG0C2+Bp4d7icQrhBDCdhmNRvYcOML5i8nUqOZH05B6qNVqVq2PY9K307mRkWWu26xRff71+lhq1fCzYsSiLJCklCjXLl1JYdbCFSxdvYH8Ap3FtvbhLRk9uA/B9QKtFJ0QQghxfxqNhgnjRvPae5NQgUVi6tZtlBdGDeTa9Uxi4hNITb8OgMFgYGviXrYm7kXz36m0aNyQ6Mgw2rdpgbeXZwmfhRBCCGuLiU9g4pRpXE1NN5f5VPCkgpcHJ07/YS5zc3XhjRdG0Oup9qjVamuEKsoYSUqJcunU2fPMmL+MNTFbMBiN5nI7jYau0ZGMGtSL2jWrWzFCIYQQoniiIkL58qO3inyZqFTRh/EvjyIqIhSACeNGc+DwcdbHJbAhdru5rtFoImH3ARJ2H+Djr36gWUg9oiPD6BDREl/vClY5JyGEECUnJj6B196bVKTHbdq1G6Rdu2H+vXP7cMa/NAofb68SjU+UbSpFUR7U21s8Ikajkf3799OoUaNSOyeRoihkZGTg4eFRKoeyHTh8nGnzlrJ5606LcidHB/p2i2ZY/x5UqeRrpeiKKu3XuyySNrEN0g62R9rE+u427OJenzdMJhOHjp1iQ9x2NsQmkHwlpUgdlUpF4wZ1iG4bSlREKJUr+jzuUyiT5LVhm6RdrE/awDYYjUY6DRhjcVPjz9RqNf/7eDxtW7cowcjKt7Lw+ihu/kN6SokyT1EUtu/az89zl7B7/2GLbe5urjzTpwvP9OmKl6fMpyGEEKL00mg0NG/cgMDa1R/4IVatVhNcL5DgeoG89vxwjpw4Y05QnU8uXHVWURT2HjzK3oNH+ezr6QTXC6Rj2zCiIkLxq1KxpE5LCCHEY7Q36eh9E1JQeCPDxVlWbxWPhySlRJllNBrZEJfAtLlLOXbqrMW2ir7eDO/fg6e7RePs7GSlCIUQQgjrU6lU1A/yp36QP688N4QTp8+Zh/idPZ9srpd05ARJR07wxXczqR8UQHTbUKIjQqlRrYoVoxdCCPF3PCghdcutOQmFeNQkKSXKnIICHSt+j2XGgmVcSL5isa1m9aqMGtSbrtGR2NvLsqVCCCHEnVQqFUEBtQgKqMXLo5/h9LkLrI/dzoa4BE6euT3R7eHjpzh8/BSTf5hDnYBahQmqyDBZhUkIIUqRA4eP8/3sRcWq6yvzSInHRJJSoszIzsll8Yrfmb1oJWnXLDP59YL8eXZwX9qHtyi183kJIYQQJc2/ZnXGjhjA2BEDOHs+mZi4BDbEJXD05BlznWOnznLs1Fm+/nkeAbVq0LFtGNGRofjXrF5q58EQQoiyLC39Ol/9OIcV6zY/sK6KwoUzmgTXffyBiXJJklKi1Eu/foO5v65iwfK1ZGXnWmxr2TSYZwf3pWWThvLBWIgyRH/iCLr5M9EPGoF9UH1rhyNEuVCrhh/PDX2a54Y+zYXky2yI28GGuO0cOnbKXOfU2fOcOnue72YsoFYNP6LbhtExMpRA/5ryd1gIIaxMbzAwf+kaps5cSHbO7e9NVSr5cvlqKiqwWIHv1rv2+JdHyY198dhIUkqUWsmXU5i5cDnLVm+kQKczl6tUKqIiWjFqUG8a1H3SihEKIR4HRVHIWbkE05EkclYuQRtYT77sClHCqvtVYdQzvRn1TG8uXUlhw80eVAcOHzfXOXs+mR9nL+bH2Yup7leZ6MhQOkaGUS/IX16zQghRwnbsSWLilJ85fe6CuczN1YWXRj9D/x6diN2+k4lTplnMMVWpog/jXx5FVESoNUIW5YQkpUSpc/LMH0yft4y1m7ZgNJrM5XZ2dnTvGMmIgb2o/UQ1K0YohHic9McPoz+SBE5O6I8koT9+GPs6DawdlhDlVtXKFRk+oCfDB/TkSkoaG7cksiFuO3uTjqIohffcLyRfYfq8ZUyft4yqlX2Jjiwc4tew7pOo1Worn4EQQpRdl66k8MV3M9kQl2AuU6lU9OnSgXH/GEIFTw8AoiJCade6BXsOHOH8xWRqVPOjaUg96SElHjtJSolSY/+hY/z8yxLiEnZblDs5OdKve0eG9utO5Yo+VopOCPG46E+fwJSTjZKTjTE7i7zVyzBlZYKzC4qugNxVS9EG1ZeeF0LYgMoVfRjctyuD+3YlLf26OUG1a/9hTKbCG0mXrqQya+FvzFr4G5V8vYmODCU6MpRGDepIgkoIIR6RggIdMxYsZ9rcJeQX3B5VElwvkHdeeY76dQKK7KPRaGjeuAGBtavj4eEhn61EiZCklLBpiqKwJXEv0+YuZW/SEYttnh5uPNOnK4N6P4Wnh7uVIhRCPIiiK0AxGFA7u5jL8rdtxpSdhZKTgyk3ByUn25x4sqtWA7eRL5jrZk37BlNe4bwHSl4exquXQKWC7CwUlQqd9JYSwib5eHsxoFdnBvTqzLUbGWzaksiGuAQS9yaZezpfTU3nl19X8cuvq/D19qJDRCs6RobRJLiu3J0XQoi/QFEUNm/byaRvZpB8+aq5vIKXB6+OGUaPTm3lBoCwKZKUEjbJYDCyPnY70+Yt5cTpcxbbKvl6M2JgT/p0jcbZydE6AQpRDikmE0p+XmECKTcHlcYOu2o1CrcpCjm/zkXJycKUk4OSm134f042ikGPfWA93Me+Zj5W7upl5kTTn6mcnS1+11R7Ao2uAJxd0CXtRaXVgosbSkE+KkcnFL2e3FVLsQsIQrcnEYdGzVE5ODy+CyGEeGgVPD14untHnu7ekRsZmWzetouY+AS27zqAwWAAIDX9OguWrWXBsrVU8HSnQ0QroiPDaNaoPlo7+cgqhBAPcvZ8Mp99PY1tO/eZyzQaNc/06crYEQNwc3W5z95CWIf8hRc2paBAx2/rNjFjwXIuXrpqsa1WDT9GPdOHrlFt0Gq1VopQiLJBMRpu91LKzjL3VlK7umHfsHFhHUUh85tJhT2acnNQcnJQlNvzuN2ZaFKpVOj27bxnosmUn2fxu32TlmA0onJxQe3sgsrVrfB/F1fU7h4WdT1eeB0A3bFDFOzYgrqCLyonJ4xGIxq1BiU/D92RJPJWLycv9ndyli/EsWVrHMPbo/Gp+MiumRDi0fD0cKd3lw707tKBzKwc4hJ2sSEugW0796HT6QG4diOTxSvWs3jFejw93GjXugXRkaG0ahosnwGEEOJPcnLz+GH2YuYsXmlO9AO0bNKQCeOeJaBWDStGJ8T9SVJK2ISs7BwWrfidOYtXkn7thsW2hnWfZPTgvrRr3Vy6mgpxF4qiYLpx7WaSKRsl++ZQuNxslJwc1L6VcGrTvrCuycT1917FlJtz12Npn6xrTkqpVCqMVy4VqatycDQnku7k1KkHqFWonV0Lk0suLqicXVG7uMKfei65Pj34oc8xd9VSlPw8VE5OhUMCjSYUjRo0apT8PAp2bUNdwQfTtTTy4mLIi9uIfb2GOIa3K5xzSt4/hLA57m4udO/Ylu4d25KTm0d8wm42xCWwZcce8xwoNzKyWLZmI8vWbMTN1flmgiqM0GYhODjYW/kMhBDCehRFYXVMPF9OnUVq+nVzeeWKPrz54kiiI0NlXihh8yQpJawqLf06vyxZxcLl68jOsexhEda8EaOf6UPzxg3kzVTYDP2JI+jmz0Q/aAT2QfUf2+MoJhP6Y4duzrN0c86l3Oybcy/lYFe9Ji49nr5ZWeH6vycAyl2PpX2yrjkpZZmYUaF2dr6ZQHJF5eyCXfWaFvu6DnkWldYelbOLOcmkukcvBafIqL951vdhMGBMSykcrpd3s9eVyYRy83xUjk4oRiNeb32I4fRx8rdsQnfsELojSeiOJKHxrYTHP9+xmNdKCGFbXJydeKpDG57q0IbcvHy2Ju5lQ1wCcQm7ycvLByArO5cVv8ey4vdYXJydiAxrTsfIUFq3bIyjDNsVQpQjx06e5dP//cTeg0fNZfb2WkYO7MXowX1xcpT3RFE6SFJKWMWFS1eYteA3lq3daO6qD4U9M6IjQxn1TB/qB/lbMUIhilIUhZyVSzAdSSJn5RK0gfXumTBVjAaU3FwwmVB7eJrL8javvzkU7lZvphzz/9on6+I27B/mY2T+9DX3SjRxx+Oq1GrU7oWT/Rf2UnK5mWQq/NmuclWLXT1eexeVoxMqJ+cH9h6yr9vwAVelZKi0WjzH/xslOwsABYWsrCzc3NxQUXgtVG7uqB0csK8XjH29YIwpV8nftpn8xK2obw4PvMWUlYnaTRZIEMJWOTs50rFtGB3bhpFfUMC2nfvZELed2G27yMktTEzn5OaxJiaeNTHxODk5EtGqKdGRobRp1VTmnBRClFkZmVl8PW0ei1esN69qCtC2dXPeemkU1atWtmJ0Qjw8SUqJEnX89Dmmz1vGuk1bLd5E7ezs6NmpLSMG9aJmdT8rRijEvemS9qJP2gtae/Q3V3zT+geRvXCWxepxppxslILCu/oOIc1wG/F84QFUavLWLLeYl+lOpuxM888qtRptQBAqtRqVy825llzcbs7B5Iq6grfFvl4ffFHsHoUab9+/cPbWp/GqAF4VgMIEoTojA7v7LFesqVgJl94DcX6qF6bMDHO5KSeb6/8ej12tAJzatEdbP0SG9glhwxwdHOjQpiUd2rREp9OTsHs/6+MS2Lx1J1nZhcOL8/Ly+X3zNn7fvA1HB3vCWzYhOjKMiNCmuLo4P+ARhBDC9hmNRpasjuHrn+dyIyPLXP5EtSpMGPcs4S2bWDE6If46SUqJErHnwBGmzVvKlh17LMqdnRzp16MTQ/t1p5Kv9z32FqJkKSYTxquXMV6+iOFyMsZLFzFcuoj+2CGUvFxwcTWv+Ob+6v+h27vzHokmFYrx9mSTKrUah7BIVHZ2t4fM3TF07s89dzxefKPYMcsQ13tTOTqicbzda0J/6jgYjehPHkV/8iiaCj44tm6LQ6s2MrxPCBtnb68lMqw5kWHN0ev17Nx3iA2x29m4NdH8JS2/QEdM/A5i4ndgb68lrFkjoiNDadu6Be5u8hoXQpQ++w8d45P//cTRE2fMZU5OjowZ1o+hT3fH3l4WgBCll0pRlHuMDRGPmtFoZP/+/TRq1AiNRmPtcP4SRVHIyMjA4z69E+6sG5+wm2nzlrLv4DGLbV4e7gx+uhsDe3XGw93tHkcQD3O9xcNTFAXT9XSMl5MBFfb1gwvLDQaujX8B5Y7efEpeHsarl0BjBy4uaJxcUHQFeL71AcbUFFQODrdXj7vZo6k4w+PEX/N3XxvGa2nkb4ulYMcW80TuKjstDk1b4tS5JxpPr0cdcpkn71e2oby2g8FgZPeBw2yI3U7Mlh1cu55RpI6dnR2tmgbTMTKUduEt8PQomSG85bVNbJ20i/VJGzxYWvp1vvphNit+j7Uo7xIVwWvPD3ssN/WlXWxDWWiH4uY/pKeUeOQMBiPrNm9l2tylnDp73mJblUq+DB/Qkz5do2TyPVHi9KdPYLh0EePlixgvJ2O4nGweZmdXvaY5KaWys8OuRi0ANFX8UFf2I3/TWkzZWah9fAuHnqrVKDnZ5K5aisfr75XaPxbllaaCDy7dn8a5cw8K9u4kf8smDMnnKdi9A+eufawdnhDiIdnZaWjVNJhWTYN555/PsffgUTbEJhATn2BekcpgMLA1cS9bE/ei+e9UWjRuSHRkGO3btMDby9O6JyCEEHfQGwzMW7KaqTMXmufRAwgKqMnb456jaUg9K0YnxKNl1aTUrl27mDZtGocOHSI1NZVvv/2WqCjL1ZtOnz7N559/zq5duzAajfj7+/P1119TtWrhxL0FBQVMnDiRNWvWoNPpCA8P5/3338fHx8d8jEuXLvHBBx+QmJiIs7MzvXr14vXXX8fO7vbpJyYmMnHiRE6ePEmVKlUYO3YsffpYfjGZO3cu06ZNIzU1lTp16vDee+8RHBz8GK+QbTEajew5cITzF5OpUc2PpiH1LDKe+QUFLFuzkVkLfiP5SorFvv41qzP6mT507hCO1k5yoeLxUfQ6jFcu3Uw4FZhXnQPInj8DY3qqRX2VRoOmYmXs/KpblHu88rb5Z92xQxjOn0Pt6mZOPqlUKtQuruhuzi1lX6fBYzwr8biotPY4tgzHoUVrDOdOY0y+YDGMMuuXn9H4VsIxLFImRheilNBoNDRv1IDmjRowYdxoDhw+zoa4BDbEJXAlJQ0Ao9FEwu4DJOw+wMdf/UCzkHpER4bRIaIlvt4VrHwGQojybPuu/Uyc8jNnzyeby9zdXHl59DM83b0jdnalc8SNEPdi1exAbm4uQUFB9O3bl5deeqnI9vPnz/PMM8/Qt29fxo0bh6urKydPnsThjiV/P/nkE+Li4pg8eTJubm589NFHvPTSSyxYsAAoTKSMGTMGHx8fFixYQEpKCuPHj0er1fLaa68BcOHCBcaMGcPAgQP54osvSEhI4N1338XX15c2bdoAsGbNGj799FM+/PBDQkJCmDVrFqNHj2bdunV4e5f9uZBi4hOYOGUaV1PTzWWVfL2ZMG40LRoHs3D5Wn75dSXXbmRa7BdcL5Bnh/QlMrQZahnGJB4D3dGDGP44a57/yZSWap7fSe3kjGN4O3MiSRtUH03GdeyqVENTxa/wn28lVPdJlCqKQu6qpSj5eaicnFB0BShGE4pGDRo1Sn4euauWog2qL72lSjGVSoW2VgDaWgHmMsOVSxTs2QFA3obV2DdqhmOb9mifqG2tMIUQD0mtVtO4YV0aN6zLmy+O5NCxk2yITWB9XALJl68CYDKZ2LnvEDv3HeKT//1E44Z16RgZSoeIVlSu6POARxBCiEcj+XIKX3w3g5j4HeYylUpF327RjHt2MF6ecnNMlE02M6dUUFBQkZ5Sr776KnZ2dnz++ed33ScrK4vQ0FC++OILOnfuDBT2rOrSpQsLFy6kUaNGxMXF8fzzz7NlyxZz76n58+ebk0/29vZ8/vnnxMXFsWrVKovHzszMZNq0aQD069ePhg0b8q9//Qso/AATGRnJ0KFD+cc//kFxlNY5pWLiE3jtvUn3WpgeB3t7CnQ6i7LWLRozenAfmoXIF/W/oyyMJf67FEVBycwonHD88kVM19Nx6fOMeXvm91+hO37YYh+1swuaKtWwq+qHc/d+qLR/ffJHRa/n2nuvYky5Yi4zmUwWSVZNpSpU+PeXf+txxMMpideGotejO7CH/K2b0P9xe2JRu+o1cYrogH1IM2nzO8j7lW2QdigeRVE4evJMYYIqdjvnky/ftV5wvUA6tg0jKiIUvyoV//JjSZvYHmkX65M2KJRfUMCM+cuZNnepxXeqkPpBvP3Kc9QP8i/ReKRdbENZaIdSP6eUyWQiNjaWZ599ltGjR3PkyBGqVavGmDFjzImrQ4cOodfrCQsLM+/n7+9P1apVzSe/f/9+AgMDLYbzhYeH88EHH3Dq1Cnq1avH/v37CQ0NtXj88PBwPvnkEwB0Oh2HDx9mzJgx5u1qtZqwsDD27dv3OC+D1RmNRiZOmXbPhBRgfvNUq9V0bBvG6Gf6UOfJWiUToCiTdIeT0B8/hOHSzUTUzYmob3Hq1AO1iysA2noNUXt4mns+2VX2Q+X+6N68VVotnuP/jZJduKqTgkJWVhZubm6ouDmUz81dkhNlkEqrxaFZKxyatcJw/ix5Wzej27sTw4VzZM2dhhsqHJq1snaYQoi/QKVSUS/Qn3qB/ox7bjAnzvzBhtjtrI/dbjFkJunICZKOnOCL72ZSPyiA6LahREeEUqNaFStGL4QoCxRFYdOWRD7/dobF1CfeFTx5bcwwunWMlJEmolyw2aRUeno6ubm5/PTTT/zzn//kjTfeYMuWLbz00kvMnj2bFi1akJaWhlarxd3dsiujt7c3qamF88akpaVZJKQA8+8PqpOdnU1+fj4ZGRkYjcYiw/S8vb05c+YMD0tRFGykg9oD7TlwxGLI3r1EhjbjzRdHmj+klZbzs3W3nitl7XoqRgPGlKsYryRjvFz4z3XIs6gcHAHQHU0if1vs7R1UKjTevmhuDrtTTCbzNXFs0+Huj/EIr5na0wtursamKAqqjAw0f7prUdbayNaV9GtDU70mroNGYur+NAUJ8egO7kMb3MT8+LojSagcHLCrHVhq72b9XWX1/aq0kXb4awJrP0Fg7Sd4cdQgTp+7YJ6D6uSZP8x1Dh8/xeHjp5j8wxzqBNQiKjKU6MhQatXwu++xpU1sk7SL9ZXnNjj7x0U++2Y623ftN5fZaTQ807cLzw8fgKuLM2Cdz5fluV1sSVloh+LGbrNJKdPNpdg7dOjAiBEjAKhbty579+5lwYIFtGjRworR/T2ZmZmlJut9/mLygysBbcOa4uHmTEZG0SWYxV+nKAq5ubkApf6LrunEUYwHdqNcvYySlgImo8V2w6kTqKs9UVi3Rm0wGFFVqlL4z7cyKq0WE2AC9EYTWOm5VpbapDSzaju0CIcW4WTefHzFZEK/aA7K9XRUlaqiaRmOOrgpKnv7ko3LyuS1YRukHf4+Hy93BvXqxKBenTiffIW4hN3Ebt/NidO3E1THTp3l2KmzfDNtHrWf8KNtWDPahjWnZvWqFtfdaDRx4PBxLl1NoWqlioTUD0KjKR2fAcs6ea1YX3lsg5zcPGYuXMHilRswGm9/Fm4WUo9XnhtMzepVMRr0Vv1OVR7bxRaVhXa4ldN5EJtNSnl5eWFnZ4e/v+UYWn9/f/bs2QMU9mbS6/VkZmZa9JZKT0/H19fXXCcpKcniGGlphSuv3FnnVtmddVxdXXF0dEStVqPRaEhPt+wxlJ6eXqSHVXG4u7uXmjmlalS7/92/O+t5eHg85mjKn1vZ5dIwltiUm4Px8sXCnk+XkjFcTsZlwDDsKheulJmXm0XukQOFA95UoHJyLpz3qYofmqp+2FergfrWc6h5aOE/G1Sa2qQss6V2UPLzyKnXEN3uHShpV1FWL8G0aW3hqn6t26HxLh8TJdtSm5Rn0g6PVkMPDxrWC+Kl0YO5eOkKG+J3sCEugUNHT5rrnPkjmTN/JDN9/m/UquFH9M0eVOeTrzDpm+lFFokZ//JooiJk6K+1yWvF+spTGyiKwuoN8Xz5/SzSrt0wl1ep5MMbL4wkKqKVzVyD8tQutqwstMOdidf7sdmklL29PQ0bNuTs2bMW5efOncPPrzBR0qBBA7RaLQkJCXTq1AmAM2fOcOnSJRo1agRAo0aN+P7770lPTzcPv9u+fTuurq4EBASY68THx1s8zvbt283HsLe3p379+iQkJJjnszKZTCQkJDBkyJCHPjeVSlVqnlhNQ+pRydeblNT0u84rpQIqVfShaUi9UnNOpc2t54stXl/9iaPkbVqH4fJFTJlF7+iYki+gqlL4erUPrAdde5tXvlN7edvkORWHLbdJeWIr7aBycsZtwPDCoX2JW8nfuhnjtTTyYteTF7sBl+59cWrf2aoxlhRbaZPyTtrh8ajuV4VRg3ozalBvLl1JIeZmgmr/oWPmOmfPJ/PjnF/5cc6vdz1GSmo6r/9rEl9+9BZREbZ586U8kdeK9ZWHNjh64gyfTvmJfQdvv1fY22sL30+e6YOTo8N99raO8tAupUFpb4fixm3VpFROTg7nz583/37x4kWOHj2Kh4cHVatWZfTo0bz66qs0b96cli1bsmXLFjZv3szs2bMBcHNzo2/fvkycOBEPDw9cXV35+OOPady4sTmhFB4eTkBAAG+99RZvvvkmqampTJ48mcGDB2N/c2jFwIEDmTt3LpMmTaJv377s2LGDtWvX8sMPP5hjGzlyJOPHj6dBgwYEBwcza9Ys8vLy6NOnT8ldMCvQaDRMGDea196bhAosElO3nmLjXx5Vanp+ieJTTCZMaakYbvV+unwRw+VknLs/jUPDxoV1dAUWK99pKvjcnnC8ih9a/0DzNruq1bCrWq3Ez0OIkqJ2dsGpXSccI6PRHz1I/pZN6I4fxq7m7R6/puwssLND7ehkxUiFEH9X1coVGda/B8P69+BqavrNBNV29iYdve8cGgqFn58++3o67Vq3kM9PQpRhNzIy+frneSxeud7ifaF9eEvefGkk1apUsmJ0QtgOlWLFmbMSExMZNmxYkfLevXszceJEAH799Vd+/PFHrly5Qq1atXj55ZfNvZUACgoKmDhxIqtXr0an0xEeHs77779vHpoHkJyczAcffMDOnTtxcnKid+/evP7669jZ3c7JJSYm8umnn3Lq1CkqV67MCy+8UCTh9MsvvzBt2jRSU1OpW7cu7777LiEhIcU+3+IuiWiLYuITmDhlmkUX9MoVfRj/8ii50/cYlciy94oCJhOqm89Jw/mzZP86F+OVZBS9vkh95049cO7cAwBTVia6g/vMiajy8EW7LCzPWhaUlnYwpqWg9vY1x5i9ZC4FuxJwaB6KU5sOaCpWtnKEj05paZOyTtrButLSrzN9/jLmLF75wLrTJ39E88YNSiAqcTfyWrG+stoGRqORJas2MOXnuWRkZpvLa1avyoRxz9K6RWMrRvdgZbVdSpuy0A7FzX9YNSlV3pTmpBQUxr/nwBHOX0ymRjU/mobUK5XnUZo86jcjJT8fg3nFu4sYLiVjvJKMU/vO5uFFhiuXuPHZvwBQ2WnRVK5q7vmkqVINu2o1ULu6/e1YSquy8AeiLCiN7aAoChmTP8Fw/vawdPug+ji2aY+2bkNUpWQBjHspjW1SFkk7WN+amC2M/+jLB9ZrWPdJJox7luB6gQ+sKx49ea1YX1lsg30Hj/Lp/37m6MnbK7Q7Ozny/PD+DHm6G1qt1orRFU9ZbJfSqCy0Q3HzHzY7p5SwPRqNhuaNGxBYu3qpfnGUJvoTR9DNn4l+0Ajsg+oXez/FaEAp0KF2LlxO1pieRuZ3X2C8lnbX+oZLF80/a3wr4TZiLHZVqqH28S31X5SFsBUqlQqPf76D/sSRwqF9h5PQHT+M7vhhNN6+OHV4CsfQCGuHKYT4m3y9vYpV7+DRkwweO57GDeswfEAv2oY1k5t9QpRSqenX+HLqbFZtiLMo7xYdyavPD6OiTwUrRSaE7ZOklBA2SlEUclYuwXQkiZyVS9AGFp1MXlEUTNfTMV5Ovjn306XCFfBSruDQvDWuAwqHx6rdPTBdv2b++dZk44U9oKqhqVzFfEyVRoNDSNOSO1EhyhGVSoV9UH3sg+pjTE8jf9tmChK3YkxPxZiWYu3whBCPQJPguvddJAZArVJhujlYYd/BY+w7OJEaflUY2q87PZ9qb5MTHwshitLr9fzy6yq+n7WI3Lx8c3mdgFq888/naNywrhWjE6J0kKSUEDZKf/ww+iNJ4OSE/kgSugO7sav2BBqfikDhJOPX3n8DJT/vrvsb029/wVVptbi/MgGNT0XULq4lEr8Q4v403j649OiHc+ceFOxJRFvndm9I3fHD5G1YjWOb9tg3aGye800IYfuKs0jMxPdeJS+/gNmLVnD63AUAzidf5j+Tf+TbGfPp37Mzg3p3waeCZwlHL4Qoru279vPplJ85dz7ZXObh7sq4ZwfTt1u09HwUopgkKSWEjTEkn8eQnkbOLz9hys4EjRZTfgY3Pnsfh9bt8HzpTQBU9g6oHZ0w6QrQVKxS2POpqp+5F5Tay9viuNonalvjdIQQD6CydygybC9/yyb0p0+gP30CtYcXjq3b4hgaUa7ncxOiNImKCOXLj94qskhMpT8tEtPrqfZs27mPWYtWkLgnCYAbGVn8OHsxM+Yvo1t0JMP69yCgVg2rnIcQoqiLl6/y+Tcz2LQ10VymUqno170jLz/7DJ4e7laMTojSR5JSQpQQxWRCyc7CeC0N07V0TDeuYbyWjsreAZceT5vrZf70NcYrlzBevQQqFRhNoFKh5OdhSrlicUz3l99C7e6Jyk5eykKUJS5PD8GuSjXyd8RjyrhO7ppl5P2+EvvGzXFq0x67GrWsHaIQ4gGiIkJp17rFfReJUavVtGnVlDatmnLs5FlmLfyNdZu2YjAa0esNLFuzkWVrNhLesgnDB/SkZZOGMqenEFaSl1/A9HlLmTF/OQU6nbm8UYM6vP3Ks9QL9LdidEKUXvJNVohHRDEaMWVcx3QtHcWgx77O7WWeM76ZhOGPsygGfZH9NJ4VLJJSmmo1MFz8AzR2qFzdQKNBZe+AkpWJ2t0DRVHMH0g1FXwe/4kJIUqcxtML5669cerYDd2B3eTFb8Rw4RwFuxMwXvgDj/EfyhdTIUqBh1kkps6Ttfj03X/yyj+GMG/Jan5dtZ6s7FwAtibuZWviXuoE1GLYgB50bh+OVm5ICVEiFEVh45YdfP7tDC5dSTWX+1Tw4rXnh9GtY6T8TRbib5C/ZkIUk2I0Wszrkhe7AcPFPzDduIYpPQ1Txg0UxQQUJprs3590e2eTCcWgR6VSo/bwQO3ljbqCDxovb9Teloklp/B2FGyPQ+NTEZWTE0ajEbVGg6Io6I4koT9+2CLhJYQou1RaLQ7NQnFoFor+jzPkb9mE9sk65g+/SkEBebHrcWwVjtqjeCt+CSFsW+WKPrw2djhjhvdn6eoYfvl1pfmL8LFTZ3nnP/9j8o+/MLhPV57u3hF3NxcrRyxE2XXm3AU+nTKNHXsOmMvsNBqG9OvGmGH9cXVxtmJ0QpQNkpQS4g7GlCsYU69ivJaO6fo1TNfTbv6cjkprj9d7E811dUl70J89ZbG/SmOH2qsCGh9fix5NLv2HodLao/b0RKW598tOURRyVy1Fyc9D5eSEoitAMZpQNGrQqFHy88hdtRRtUH25IyNEOaN9onaRueEK9uwgd91v5K1fiX1IUxzbdMCupr+8PwhRBrg4OzG0X3cG9e5CTHwCsxb+xqFjhZ87UlLT+eqH2fwwexF9ukUzpG83/KpUtHLEQpQd2Tm5fD9rEXN/XYXBaDSXhzYLYcK4Z6n9RDUrRidE2SJJKVEuKIqCkpON6Xr67YTTtTQUXQGuA0eY62XPn4H+3Om7HkOlUlv0lnJoGY62XjCaCj6FiagK3qjcPFCp1UX2tatctXiBGgwY01JQOTqh5N1cVc9kQrl5TJWjE8b0VDAYQKst/gUQQpRJ6go+aGsFoD97ioJ9uyjYtws7vxo4tmmPQ5MWqLT21g5RCPE32dlp6Nw+nE7tWrM36SizFv1G7LZdhTey8vL5ZfFK5i1ZTce2YQzv34MGdZ+0dshClFomk4lV6+P46oc5pF27bi73q1yRN18cSfs2LeXGjxCPmCSlRJmgmEyYMjMwXU/HlJ2FQ8PG5m1Zv/yM7uA+FF1Bkf1UKjUu/YaaE02aqtVQdDrUFbxvJpu8zQkndQUfuCPh5Ngy/JGfh0qrxXP8v1GyswrPC4WsrCzc3NxQ3VxIWuXmjkoSUkIIwL5Ofezr1Mdw8Tz5WzdRsCcRQ/J5shfMJHfVEjz/7xPUjk7WDlMI8QioVCqahtSjaUg9zl1IZs6ilfy2bjMFOh0mk4l1m7aybtNWmgTXY/iAHrQNa476LjfKhBB3d+TEaT6Z/BMHDh83lznY2zN6cB9GDuqFo4ODFaMTouySpJQoFf48n1P+zm0YTp/AeD3dvJKdcrNrrUqlxv7zqbfrm0zmhJTazR21l3dhksmrsIcTJhPcrOvab2jJnthdaLwqgFcFoLCHlzojA7sHTI4qhCjf7KrVwHXgCJy7P01B4lbyt25GU8XPIiFluHQBTZVq8l4iRBlQs7of773+PC+OHsTC5etYsGwN125kArA36Qh7k45Qs3pVhvbrTvdO7XBylC/TQtzL9RuZTPl5LktWbUBRFHN5hzYtefPFUTI0VojHTJJSwmYY09Mwpl4pTDJdv3Yz4VQ4p5OSnUWFz741J5r0xw5TsG+nxf4qlRq1pxfqCt4oBfmonAsn/nTu0gvnzj1Re1WQHkZCiDJN7eKKU/vOOLbtiJKbYy43Xksj44uP0FSsjGObdjg0DUXl6GjFSIUQj0IFTw/GjhjAqEG9WbUhjtmLVnDmj4sAnLtwiY++/IGvp81jQM/ODOzdBZ8KntYNWAgbYjQaWbxiPV9Pm0dmVra5vFYNPyaMe5aw5o2sF5wQ5YgkpcRD0Z84gm7+TPSDRmAfVL/Y+5nycguH1l1Lv9276fo1XIc+Z0405a5eQsG+Xfc+RuYNNF7eANiHNEVTuWphjyfPCqgreKP28LLoTXWLxkfubgghyheVWo3K1c38uzH5AtjbY7h6iexf55KzcgmOLcNxbN0OTcVKVoxUCPEoODjY07dbNL27dGBr4l5mLfyNnfsOAXAjI4sfZi9mxoLldI+OZFj/HtSuWd3KEQthXXsOHOHTKT9x/NQ5c5mLsxNjRwzgmT5d0MqNbCFKjCSlRLEpikLOyiWYjiSRs3IJ2sB6qFSqwknEs7MKJxG/no59wybmyb5zVvxKfkIcSn7eXY/p3LOfOdGkqVQVu8p+hQmmCt5ovLwL53S6+bPKzd28n0NI08d/wkIIUUbYN2yM1/uTKNi1nfytmzGmXiUvPoa8+Bjs6zTA5ekhaLx9rB2mEOJvUqvVRIQ2IyK0GUdOnGb2ohWs27QVo9GETqdnyeoYlqyOISK0KcP796R54wYypFeUKylp1/jv1FmsiYm3KO/esS2vPj8UX+8KVopMiPJLklKi2PTHD6M/kgR2duh2J3Bj4nuggOlGOopeb67n9a9JhfMiAagwJ6TUzi53TCBeAbWXDyr723McOHfqjnOn7iV6TkIIUV6onZxxiojCMbw9+hNHyN+yCd2Rg+jPnkLl4mKupyiKfEkVogyoF+jPxHdf5Z//GMrcJav4deUGsnNyAYhP2EN8wh7qPlmbYQN60Klda7R28rVAlF16vZ45i1fxw+xF5Oblm8vrPlmbd/75HI0a1LFidEKUb/LXRxSLoijkrlpamHyy06Jk3kCXtBd1pSo3v7yoULt7oKngDXqdeT/H8PY4NA9D41lB5i8RQggboFKrsa/TAPs6DTCmpWC4dNE8IbqiKGR+/RmaKn44tumAXeWqVo5WCPF3Va7ow+tjRzBmWH+Wro7hl19XcflqKgBHT57h7Y8nM/mHOQx5uht9u0Xj5urygCMKUbpsTdzLZ19P49yFS+YyD3dXxj03hL5do9DcZfoPIUTJkaSUKBb98cPojiShdnHFpJhAMYFJwaVLbxyatEDtWQHVXe6wmXtMCSGEsDkan4oW8+4Zzp9Df/YU+rOnyN8ehzagDo5t2mNfP+Suc/YJIUoPVxdnhvXvwTN9urIhbjuzFq7g8PFTAFxNTee/U2fx/axF9OkaxZCnu1G1sszJKUq3C5euMOmb6cRuuz1nrVqtpl+Pjrw8+hk83N3us7cQoqRIUko80J29pFTuHmAyoXZyxpSehi5pL07RXWWohxBClAF2NWri8eKb5G/ZiO7gfvSnjqE/dQyNZwUcWrfFsVUb1K7yIV6I0szOTsNTHdrQuX04e5KOMGvBb8RuL/zSnpObx5zFK5m3dDUdI8MYPqAn9esEWDliIR5OXn4B0+YuYcaC5eh0t6cYadKwLm+/8hx1nqxlxeiEEH8mSSnxQHf2krqVfFKpVKhdXNEdSUJ//DD2dRpYOUohhBB/l0qlQhsQhDYgCOP1dPK3x1GwPQ7jjWvkrl6KXRU/7OuH3HXfv7o6qxDCOlQqFc1C6tMspD5nzyczZ/EKVqyLpUCnw2g0sXbTVtZu2krTkHqMGNCLiNCmqG8uZCOELVIUhQ1xCXzx3UzzEFUAX28vXhs7nK5REXIjXQgbJEkpcV/mXlL5eaicnFB0BShGE4pGDRo1Sn4euauWog2qL2/yQghRhmi8vHHp2gfnjt0p2LcT3aH9aOs2NG/P37kNlZ0W+5AmoNbcdXVWIUTpUKuGH/96fSwvjX6GhcvXsWDZGq7dyARgz4Ej7DlwhJo1/Bjarzs9OrXF0cHhAUcUomSdPneBT6f8TOKeJHOZnZ0dQ5/uxpjh/XFxdrJidEKI+5GklLg/gwFjWgoqRyeUvMJV9DCZUG7eKVM5OmFMTwWDAbRaKwYqhBDicVBptTi2aI1ji9bmMkWvJ3flEkzZmah/88CuVgD6Q/vByQm99KAVotSq4OnB2BEDGDmoFyt/j2P24hWcO58MwLnzyXz03+/55ud5DOz9FAN6dcbby9O6AYtyLys7h6kzFzJ/6RoMRqO5vHWLxox/eTS1avhZMTohRHFIUkrcl0qrxXP8v1GyswBQUMjKysLNzQ0VN4fyubmjkoSUEEKUH0Yjjq3bkr89FmPGDfS/r0TJywUXV5SCAnKWzkf79sfSW0qIUsrRwYF+PTrSt1sUW3bsYdbCFezafwiA6xmZTJ25kGnzltK9Y1uG9e9B7SeqWTliUd6YTCZW/B7L5B/nkH7thrncr0ol3nppJO1at5C/QUKUEpKUEg+k8aoAN1fRUxQFdUYGdh4e8kYvhBDllMrREefOPXCK6kLu6iVk//IzqNWQn4+imAonSb/ZW0rJz8d4PR1NpSqoZD4aIUoVtVpNZFhzIsOac/j4aWYt/I31sdswGk3odHqWrNrAklUbiAxtxvABPWnWSKZzEI/f4WOn+OR/P5F05IS5zMHentGD+zByUC8ZXipEKSNJKSGEEEL8NRoN+pPHwdEZtZs7ptwc0BUAmOcb1B0/TNbMqaidXbDzD0TrH4Q2IBBNlWqSpBKiFKkf5M+kf73Gq2OGMnfJan5duZ6c3MKpHeISdhOXsJu6gbUZMaAX0W1D0drJ1wzxaF27kcGUn+aydHUMiqKYy6MjQ3njhRFUrVzRitEJIf4q+WshhBBCiL/EYnVWBwcUOzs0Gg1KXp55dVZTZgYqrT2m3Bx0B/ehO7gPALWTM3a1n8S5W1/sKle18pkIIYqrSiVf3nhhBGOG9WPp6hh++XUVV1LSADh64gzjP/qSr37wYcjT3ejbLRpXF2crRyxKO4PByKIVv/PNtHlkZeeYy2s/UY0J454ltNndV4UVQpQOkpQSQgghxEMr7uqsHq+/h2NoBIYL59CfPoHh9An0Z09hystFd/gALn0GmY9ZcHAfppQr2NV+ErsaNVFp5GOKELbKzdWF4QN68kzfrqzfvJ1Zi37j6IkzAFxJSeOL72by/axF9O0WzZCnu1G5oo+VIxal0e4Dh/n0fz9z4vQ5c5mLsxMvjBzIoD5dpEeeEGWAvIqFEEII8fAeYnVWlVaLtlYA2loBENUFxWjEmHwew/lzaCrc/qJakLgV3eEDhftr7bGr6Y82IAitfyB2NWrJohpC2CCtnR1doyPoEtWG3fsPM2vhb8Ql7AYgOyeXWQt/45dfV9KxbWuGD+hJ/SB/K0csSoOrqen8d+os1m7cYlHeo3M7Xv3HUHy8vawUmRDiUZOklBBCCCEe2t9ZnVWl0WBXoxZ2NWpZlNs3aARqNYbTJzDl5qA/eRT9yaOF+zg6UeHjr8y9pxSTSeakEsKGqFQqmjduQPPGDThz7gKzF69k5fpYdDo9RqOJtRu3sHbjFpo3asDwAT1o06opankNl1tGo5E9B45w/mIyNar50TSkHhqNBp1Oz+zFK/hxzq/k5eWb69cL8uftcc/SqEEdK0YthHgcJCklhBBCiL/kUa/O6tiqDY6t2qCYTBivXkZ/6njhcL/TJ9BUqmwxnC9j8ieo7OzQ+gdh5/8k2poBqBwdH8l5CfEo6U8cQTd/JvpBI7APqm/tcEpE7ZrV+eDNF3j52WdYsGwtC5ev43pGJgC79h9i1/5D1Krhx7D+PejesS0ODvYlHmN5bBdbEROfwMQp07iamm4uq+TrTY/O7Vi/eRt/XLxsLvf0cGPcs0Po07UDGo3GGuEKIR4zSUoJIYQQwqao1GrsqvhhV8UP2rRHURSUvFzzdlNuDoYLfwAK+rOnIAZUKjWa6k+g9Q/Cvl5DtAFB1jsBIW5SFIWclUswHUkiZ+UStIH1/nLStjTy9vLkxVGDGD24Dyt+j2XOohWcu3AJgLPnk/nwi6l8/fNcBvbuwoBenang6VEicZX3drGmmPgEXntvEsqfyq+mpvPTnF/Nv6vVavr37MRLowbh4e5WskEKIUqUJKWEEEIIYdNUKhUqZ5fbvzs54/V//0F/+oS5N5XxejqG82cxnD+LKfOGOSmlmEzojyRhV/tJ1HccQ4jHTTGZ0B07hP5IEjg5oT+4j9zlC7Gr/sRd62ufrIPao3CeHGPKVQznz9zz2Hb+gWi8vAvrpqdhOHvy3nVrBaDx9i2se/0ahtPH7123Rm00FSsBYMq4YR4+ezeaak+YV840ZWWiP3743setWo3+PTrxdLdotsRuZfu8+Zw6e75wY3Yau+fM5sD8ebRs2pB2vXtSs3nzwuPm56E/tP/eMVSsbB4GrOgK0CXtvWddtbdv4bx2gO5wErr9u8DODn3SHvLjNmBfLwSVo2Nhj0utvSSpHgOj0cjEKdOKJKT+rHHDuvzfP58jKKDWA2oKIcoCSUoJIYQQolRRqVRofCqi8amIY8twAIzX0jCcOYn+1HG0dRua6xqvJJM57RuAwt5X/oGFk6fXDkTt5m6V+EXZoSgKSmYGxtSrGNNSMKZcxZh2FVNqCobUq6g9PFD0evD0Qkm5Qvai2agrVblrwsN9zKvY30xK6U8fJ3vR7Hs+rtvIF8xJKcO502TNnXbvuoNH305KXfzjvnVd+w01J6UMVy/dt65LzwHmpJQxLeW+dZ2f6oVd1eqo1WpaB9Wkvpue/MAKpF27QWZW9u2K+7fx087dZDaPYNiAHjSu6nPf4zq16XA7KZWXe9+6ji3D0dYKKFw5dPXSwvnw1BpMJiOZP0y2aBeHkGa4jXi+8LgmE5nfTELl4HgzaeV082cnVI5OaCpVxv6O9xzDlUu36zo4ltu573Jz87h0NZVLV1K4dCWVS1dTOXTspMWQvXt5adQgSUgJUY5IUkoIIYQQpZ6mgg+aCj44NAu1KFdyctBUrIwx5QqGy8kYLieTv3Vz4T4VK+PSrS/2DRtbI2RRSiiKgpKTXZh4Sr2KfVB91B6eAOTHridnxeK775eXh+HKJdSubphu9vZTcrKwq1zFnFC6k9rldk8+tacX9nXuPc/RnQlVtbvHfeuq3G8PiVO5ut3/uJ63VzRTO7vev+7N+eSgsPfi/ereSopB4aIF9nXqYw+4Azk5eRw/fY5TZ89jMBpJVdQkbN9F7PZdtAh4gn9W8aK6XxXU6qKJPE2lyrd/sdPeP4bKfgDojx9Gf+wQODiBWgUGI4quABQFVGpAQeXgcHtHna5wmPA9OAQ3MSelFEUhY9IHKIrp9vk6OJqTVNrAurj2HWzelrPiV1QaDSqnOxNdhfXV7h5oKlYu8ni2QFEUMrOySb6SwuWbCadLV1O5bE5ApZCRmf3gA91D2rUbjy5YIYTNk6SUEEIIIcos7ZN18Hr748LhRWduD/czXE7GmHIF7vjyqT95jII9O9AGBFkMjxLli+HKJXT7d9/s+XQFU1oKpjvmNHMb+QIOwU2AwiFhKpUatVcFNL6VUPtWLPzfpyK5yxagO3aocDiYyYTKwxPFYEBlZ4/bP/553+Fh9nUbWvS+uR/tk3XQPlm8Fcm0tQLQjnm1WHXtqtXAvbh1K1ctdl2Nt69FXXegCtA0O4clqzZw5tdVcLM3zc5Tf/DMKahSKYMhT3ejT9coXF2c73pctYvrA2NQFIXcVUtRDAY0VapiMplQq9WY0tPQVq+J+2vvotLrLZJKaDS4jRiLUpCPkp+HUlCAkpeHUpCHkp9vuYqoQY/K2Rny81CMxsLHLMhHKciHTMw9y27Fkh+73vKx7qB9si4eL7xu/v3a+2+AyWiZvLrZa8uuajWcOjxlrltwYA+o1agcHFDf7NF1az/sHzw00WQykX79BpeupHL5aqo50VT4e+H/uXesjPdXBan1DHLIZX6BM8dNt1dq9fX2us9eQoiyRpJSQgghhCjz1G7uOIQ0wyGkGQCmnGwMZ06irelvrqM7fID8xK3kJ24FQOPlfXu4n3/gzQSEzDNTmin5+RjTUzCmpph7PplSr+LUuYd5BTbj1Uvk/r6iyL4aTy/UvpVQ2d9eKc6+XjAVPvsWlVZrUVd37BD60ydQu7iZnzMqlQq1iyu6I0nojx/Gvk6Dx3impY+bqwsjBvZi8NPd+H3zNmYvXMHRk4Xzal2+msrn385g6syFPN09msF9u1G5os9DP4b++GF0R5JQu7jetV0MJ45gX6cBd77KVVotDiFNi3V8ldaeCh9PBkDR628nsvIL/1c5Od2ubDLh1OEpTPm5N7fnmxNdSn4+mgq3k+KKoqBkZ6KYTJCdVeRxTVkZFkmpnEWzMeXm3DVGu+o1cR33Nilp6Vy6mopm6S/kZWZxI1/HtbwC0rPzSMnOJUtv5IaiZp/x9vPdR2XEgAqTokIFKNyl95pGTSVfb6pU8qVqpYpUqeyLX+WKVKnkS6WK3jz36vukpl1DQaGXfR4hGgN59nl8lm+HChWVKvrQJLhusa63EKJskKSUEEIIIcodtYtrkWF79iFNQa1Bf/o4xgt/YLyejnF3AgW7EwDwevs/5vl2lPx8cHCQJJUNUvR6jOmpqJ1dUN8cuqY/cZSsudMwZd646z72ly7CzaSUXdXqOLZojaZiZdQ+hT2fND6+qOwdiuynsiv6UdrcG+dmEkLRFaAYTSgaNWjUKPl55K5aijaovjx/7kJrZ0e36Ei6RkWwc99BZi1cwZYdewDIzsll5oLf+GXxKjq1b83w/j2pG1i7WMct6XZRabWFyUrXu68cp9JocO7au9jH83z3U3Ny685El1KQj9rd0/LYNWpRkJ5OXmYmuqwsDDk5KPl56PV6Tpy7yqRV/TEaC3tofeacgatKwRVwBWoAqAEHOG/UWCSlXnbMwVejoLWzQ6u1Q+XgiMbJCa2LK9qqfrgMf56KPt7Y2WnI27QOJS/v5tBEO1SqfP6fvfsOj6rMHjj+vTOZSW+kQQiQhISeHlroSFNEBdm1rGJB3bX33sG162LXdZVddH8qi6igYEFpEsBAGiUBQg8lvbdp9/fHkMFISzTJlJzP8/jILTNzZt7cmXvPfd/zKjVlPH35NOa/9REBGgtxbibqUYhzMzFIayLPrOPB269Hq9W26bMWQjg3SUoJIYQQQnBiaNOJ2bnUxkaMB/Zi2rsb495dWCor0ISE2vat/eRDjPsK0PXtZ+tNpQ3r0WWLGtuDpaEe074CW48nc0kRltJizBXlgIr3xZfhOX4yYK151JyQ0nh5nxhqF3Yi4RSKW5+TiQ1tSBg+V1z3+wMzmTCXFqN4eKI2NJwI1oJ64m9D8fDEXFYCJhP8poeVOElRFIYnxzM8OZ69Bw7z0eJlLPt+DUajCZPZzDc/rOObH9YxLGkI11x+CaOHJaE52/HnxO2iKEqL4cS2IuJFJRytqONofj7Hlq/n6PFijhWVUFJWcZpn0eCOO1pUzJwcMvhxkxdeiooHKh6Kir9eS4ivF0FenngGBnF36hjCu4dYezr97wPcaqvgxNDEk8y4uUFA95PfkY3pa62f528MAN5I7MG2/YfRmVRKVQ09FAs3+hhR7nqA88aOPOUxQgjXpqiqeq5ZOUU7MZvNZGdnk5iY6LR3AFRVpaqqCn9/f7m71wnk83Y80iaOQdrB8bh6m6hmM8qJ325VVamc9yDmyvIW+2i8vHGL7oeu30A8x0y0R5gu1Q6qxYKlohxzyXHMJcVYSorQ9RuIfkgiAMaD+6la8PfTPlZx98Bz0gV4TbrA+lxGI6ajhWhDQtF4eZ/2Me3JXFFund0NUFGpqanB19cX5cRwJ8XXD22A1M1pq9LySj79YgWfffUtlVUth7FF94lgzp8v4sLJ43B315/28c7QLs1FxJtnrDt6vPhkEfETy3+kiLi/n491WF1YCOE9QgkPCyE8LMQ2zM7P1+ec3x22oYnNwxMbGkCrtSX1AepXrUCtrsTS2HIIo9rYCIq1hl+jCgZVwauqDA0qbr2j8Rg1DvehabhF9nX67zBn5Uq/I87MFdqhtfkPSUp1IklKibaSz9vxSJs4BmkHx9PV2kQ1GjEd2o9x74ni6Qf2ohoNALj1jiLg7kdt+zZmpOMW1gNtz962xFaHxeVk7aBaLGA2oeisSQRzRRl1n/8f5tJiLKUlqGZTi/09x0zEe9aVAFjq66l+6yVroqm519OJnk+Kj6/DvH9naxNn0NDYxPLvVrNo8TIOFh5rsa1boD9XzLyAyy6eRmCA3xmewX7toqoqZeWVJxNOJ4qIH/vV8h8pIh7cLZDw7iGEn6jjFB4W0mLZ28vz3E/SgVRVpeqV+TTlbEUTFIzFZIK6GtTKChQPTzRhPaw9w4JCcB86EvfUNLRBba8fJn4/+c5yDIZdO6j85N8EXHGtreahs2lt/kOG7wkhhBBCtJGi06Hr2w9d334w5UJUkwlT4UFMe3ej+J68ELY0NlD3yb9RVQuKuwe6qBjrcL++/XDrFXnamkSuxlqkuQZz8fEWM9qZS4owlxbjkTYO70suB0DRu2PYkWN7rOKmQxsUYpvV7tezzGm8vAi4/8lOfz/C/jw93PnzxdOYPWMKa9Iz+M9ny8jM3QlAeUUVb334CR/893MumjqBq/88g8hePVs83mw2szVnJ4cKj9A7oicpCYPa7YaxyWSmpKycI8eLOXait9Ox48UcOTFz3bHiUgwG4+96bq1WQ2hwkDXJdKKIeHiYdWhdzx6hdA8JPmMvMUdxSrF5jQZtQDdUd0/U+lp00bGYjxzGXFZC/bfLwGRuU+0tIVyBqqrULf8cy85c6pZ/jq7fIJdOELr+mZAQQgghRAdT3NzQRfZtMZsfgFpfh25QHKZ9e7A01GPI344hf7v1MTq9dYjZlAvtEXK7s9TX2RJOio+vbXY5tbaG8ifuOePjzCXFtn9rvH3w+dPVaIKCrXWfArpJnS5xRhqNhomjhzNx9HC25e3mP58t44e1G7FYLDQ2GVi87Dv+t/x7xo8ayjV/vpjk+IH8uH4Tz7/+AUUlZbbnCQsJ4qE75jKpFfWMDAYjx4pLOHq8xNa76VhRiTUJVVRCUUmZrYh4W+l0bieSTKG2Ok7hvxpm11xE3Fmds9i8yQQGA4FPv4JxexaNv6TjPvRkmxjyttGUkY770DTrRbqTjjwR4lyMu3Zg3JkLnp4Yu8CMrZKUEkIIIYToINpuwfjdcDuqxYL5WCHGAmvhdNPe3Vjq61rUNzIXH6d28aKTxdMj+552xjdHoJpMNPy4AnNJsa3A+K+noNcPTrCdQCs+vmi8vFE8PE8UGA9FGxyGNtQ61E7TLajFc3ukjevU9yJcQ9zAfrz81H0cOVbMx59/zdKvf6C+oRFVVVn98y+s/vkXevXszuEjx095bHFJGfc8/iKvzn+AtNREWxHxYyeG1h09XnKOIuKt4+XpcSLZFNqijlOPE0PsggIDzl6s3dm1sti8otXinjoS99SWScKmzRtoytlCU1YGGl8/3FNG4D50JG7hvTr7nQjRYVRVpW7Z/1Dr6yAkDLWywuVnbJWaUp1IakqJtpLP2/FImzgGaQfHI23SNqrFgrnoGBpfPzQnpoxvTF9L7f8+su2jaLW49YrELaa/dahgVCyK+9mTVO1Rg0I1NFmH2TXPaHci8aQN64HPZddY91FVyh++HbWpZe0bjX+gdZhdTD+8pl508jl/VSi+q5Fjwz6qa+pYsvx7/rv0G4p/1SvqbBRF4Y9cGrUoIn6ijlNz8ik8LAR/P8epdWYvf6TYvOnwAZoy0mnaurlFEtwtvBfuQ9PwGHue9KxsB/Kd1bnM5aUYcrZiLi7C57I5GPK3U/niU2AyoQYEojEYUA1NBDzwlNP1lnKKmlIZGRl88MEHbN++nZKSEt566y0mTZp02n2feOIJPvvsMx5++GGuvfZa2/rKykrmz5/P6tWr0Wg0TJkyhUcffRRv75N3HvPz85k3bx7btm2jW7duXHXVVdx4440tnn/lypW89tprHDlyhMjISO677z7GjTt5p05VVV5//XX+97//UV1dTXJyMk899RSRkZHt+pkIIYQQwvUpGg1uPVrWudENjMPnz3NsxdMtVRUYD+zFeGAvDatW4HfDHegHxwNgqa0BjRaNl5ft8W2pQaEajdbp2g1NuPWOsj2+8rnHMJcUnf4xzT0bsF68e46bDDq3lgXGz9Czq6smpIT9+Pl6c/2VM7n6Txfy7U8beOffn3L46On/tpudKyHVXETcmnQKtRURb162dxFxZ6AN7AaB3QDr562pqsKtlckPt16RuPWKxOuiP2PM305TRjqGHbmYjh6GjHQ8x0+27ataLJKgEg7LXFaCIWcrTdlbMB0+YFvvMfkC6xBXoxFNtyDrd5KHB2pdrUv3lrJrUqq+vp7+/ftz6aWXctttt51xvx9++IGcnBxCQ0NP2XbfffdRUlLCwoULMRqNPPLIIzzxxBO88sorANTW1jJ37lxGjhzJ008/ze7du3nkkUfw8/PjsssuAyAzM5N7772Xe+65hwkTJrB8+XJuvfVWli5dSr9+/QB4//33+eijj3j++eeJiIjgtddeY+7cuaxYsQL3c9y1FEIIIYQ4F21gN7Qjx+IxciyqqmIpL8VYsAvj3t2Y9u3BLfrkdOsNa3+gYdVK3Hr2QhfTD7e+/cFkOm0NCsOuHZiPHzvR68laXNxSUY6qWnAL72UrFq4oCorWemqo8fJGExx6MuEUEoY2rHuLeL3Ov7jzPhwhfiedTseMqePRaDU8NP8f59w/sldP4gbGtigiHt49hB6hIQ5fRLyrUNzc0A9JRD8kEUt9HYasX1C8fGzb1cZGKp57DP2geOvwvqgYl7yQF86naVsWDd8vx1R4yLZOUTS4RceiT0jBfOjAyYkANBowm1EUBY23DwYXri1l16TUuHHjWvRGOp2ioiLmz5/PBx98wF//+tcW2/bu3cv69etZsmQJcXFxADz22GPcdNNNPPDAA4SFhbFs2TKMRiPPPvsser2e2NhY8vLyWLhwoS0ptWjRIsaMGcMNN9wAwF133UV6ejoff/wx8+bNQ1VVFi1axM0332zryfXiiy+SlpbGqlWrmD59ent/NEIIIYTowpqnRNcGheAxfPQp2y1lJYCK6cghTEcOoa75AUvRMdSmJvD2QbUYbXdV6774FHPRsVNfw90DxcOjxTrf625B8fZG4+1zyv5COLPQoG6t2u+Je//G0CTXu+hzVRovbzxGTWixzrAjB0t1JY2b1tG4aR3aoBBrjaqhI9EGhdgpUtEVmYuPo3h6oWmelddowFR4yJqIiumHe0Iq+vhkNL5+1mGTr8w/80QAjQ0u21vKoQudWywW7r//fubOnUtsbOwp27OysvDz87MlpADS0tLQaDTk5uYyefJksrOzSU1NRa8/eWdj9OjRvP/++7axstnZ2S2GBDbvs2rVKgAKCwspKSkhLS3Ntt3X15eEhASysrIkKSWEEEKITuU75694X/xnjHv3YNy7G0NWBubGBlAUaKhHExz6q7uqg7GE9UDzq2F22pAwFF+/U05staFhdnpHQnSs5PiBhIUEUVxSxukG6SlAWGgwyfEDOzs00c70SUPx9w+w1p/K2Yq5rIT675ZR/90ydFExeF96JW49e9s7TOGiTMePYsjZgiF7K6bjR/CaPguvSRcAoB+UgM+frrYmok7Ukzz5wNZNBIDJBDpdZ76lDufQSan3338fNzc35syZc9rtpaWldOvW8q6Hm5sb/v7+lJSU2PaJiIhosU9wcLBtm7+/P6WlpbZ1zYKCgigtLQWwPVdQUNAZ92kLVVX/UBFFe2qO3VnjdzbyeTseaRPHIO3geKRNOp/iF4A+aSi6xFSMB/ehePuieHlhMZvB3R21rpa6r5fif89jZ64tJe3V4eTYcAwajYYHb5/LvU+8iAItElPNR8cDt12PRqORtuokHXZsKApuJ2Yx9Zp5BYbtWTRlbMS4Ow/j/r3g6WV7TUt1FYq3j9S9+xX5zmobVVUxHzuCIXcrhuytmIt/1TNZo8FSXXXys3R3x33kWNvjWnBzw/+Bp1Hramzba2pq8fX1sf2Ga3z8wM3NadqmtXE6bFJq+/btLFq0iKVLl7pc97Tq6mqnne5VVVXq6+sBXK5dHJF83o5H2sQxSDs4HmkT+7HsycewPRu8vMDdE9ViwWJRUT09adqeTeXWX9DEDrB3mF2WHBuOY2jCQOY/dCuvvf9/lJRV2NaHBHfjjhuuYGjCQKqqquwYYdfSacdGzECIGYi2qhLLwb3UatzgRDsbF72HeqwQTXwKmsShaH4zAUVXJN9ZbaMajRhefQaMBusKjRZNTH80gxPQDBiCydOr9d8rGi34BlifV1Vp1OrReHm1bAcn+o6yWCyt2s9hk1JbtmyhrKyMCRNOjhE2m8288MILLFq0iJ9++ong4GDKy8tbPM5kMlFVVUVIiHW8cHBw8Cm9mZqXm3tHnW6fsrIy2/bm5yorK2tRbL2srIwBA9p+kufn53fWKREdWXO2U6YI7RzyeTseaRPHIO3geKRN7ENVVarWfo9iaELj7Q1mE6rZgkarAZ0OS20Nytrv8UsZJu1iJ3JsOJaLpp3H9Mnj2Zq7k0OHj9C7V09S4gc57bm5M+v0Y8PfH3r3Ofn6RgOVpcVYGhvgl5+x/PIzSo8Ia/2plGFo/AI6PiYHJN9Zp6eqKuYjhzBkb8F87Ai+N95h21abPAy1oR59Qgq6wQloPL3O8kytfz1w7nYwm82t2s9hk1IXX3xxixpOAHPnzuXiiy9m1qxZACQlJVFdXc327dsZMsRakHDTpk1YLBbi461TJicmJrJgwQKMRiO6E2Mv09PTiYqKwt/f37bPpk2bWtSVSk9PJzExEYCIiAhCQkLYuHEjAwdax5nX1taSk5PDFVdc0eb3piiK0/5hwcn4nfk9OBP5vB2PtIljkHZwPNImdmAyYTlHDQpLWQmK2YziYjUonIkcG47Fzc2NYUlx9I/u7dQXfK7AnseGoncn8MkXMObvoCkjHcP2HMzHCqlf/j8avv4cj/FT8L5odqfH5QjkO8tKVVXMhw/QlL0VQ661Plkzc9Ex3LqHA+Bz5fUd8lk5ezu0Nm67JqXq6uo4dOjkdIiFhYXk5eXh7+9PeHg4gYGBLfbX6XQEBwcTHR0NQN++fRkzZgyPP/44Tz/9NEajkfnz5zN9+nTCwqyFOmfMmMFbb73Fo48+yo033siePXtYtGgRDz/8sO1558yZw9VXX82HH37IuHHjWLFiBdu3b2fevHmA9cOcM2cO77zzDn369CEiIoLXXnuN0NBQ22x8QgghhBCdTdHpCHhwHmrtiRoUqNTU1ODr64tyolKO4usnCSkhhDgNReuGfnAC+sEJWOrrMGRvoSkjHeOBvWiDT87UZ6mvw3y0ELfoWBQnLcMi2qYpK4P65UswV5TZ1ik6HfqBcegTUtEGnqw37axJI0dh16TU9u3bWxQxf+655wCYOXMmzz//fKue4+WXX2b+/Plcc801aDQapkyZwmOPPWbb7uvrywcffMC8efOYNWsWgYGB3HLLLVx22WW2fZKTk3n55ZdZsGABr776KpGRkbz11lv069fPts+NN95IQ0MDTzzxBNXV1aSkpPCvf/0Ld3f3P/oxCCGEEEL8btrAbhBonfhFVVU0VVW4Se8PIYRoE42XNx5p4/BIG4e5uAjFz8+2rWnrZuqW/h/absG4p47APXUk2hCZrdRVqBYLpoP70PgFoA2ylvBR9O6YK8pQ9O7oB8ejT0hBPyAORa7/252iOkvpdhdgNpvJzs4mMTHRacetq6pKVVWVdHXuJPJ5Ox5pE8cg7eB4pE0cg7SD45E2cUzSLvbnTG1Qv2oFDatWoDY12tbpIvviPjQNfWIqGi9vO0bXvpypXf4I1WLBtG8PhtytNOVkYqmuxPO88/G+8FLrdpMJw85c9AMGo+g7PxHlCu3Q2vxHm3tKXXLJJWf9UL744ou2PqUQQgghhBBCCOGQvCZdgOfY8zBsy6ZpSzrGXXkYD+zFeGAvyrL/0e2pl1E8POwdpjgH1WLBtHc3TTnWGlGWmmrbNsXdA36V51Dc3HCPT7ZHmF1Om5NSUkNJCCGEEEIIIURXoujdcU8ZjnvKcCxVFTRt3UxTxkY0QcEtElIN61ahi+6Htmcvp+3h4kpUVT3ZDqpKzX/exVJXC4DG0wt9XBL6+BR0/QZK/UU7aVNSymw2M3z4cPr374/fr8bYCiGEEEIIIYQQXYHGPxDPidPwmDAVmpps681lpdR98SkAbt174j50JO4pw9H4B57pqUQHUM0mjHt2YcjZgunwQfzveQxFo0HRanEfNhq1rgZ9Yiq62IEobnYtsy1oY1JKq9Vy/fXXs2LFCklKCSGEEEIIIYToshRFgV8P2zObcE9MxbA9B9PxI5iWL6H+66Xo+g/EPTUNfVyiXeoTdQWqyYRxTx6G7C0Ytmdjqa+zbTMd2o8usi8A3hfNtleI4gzanBaMjY2lsLCQXr16dUQ8QgghhBBCCCGE09GGdsf3mr9hqa/HkJ1B05aNGPcXYMjfgSF/B75X3Yh7ynB7h+lymrZspPbz/0NtbLCt0/j4oY9Pwj0hFbdekfYLTpxTm5NSd911Fy+88AJ33nkngwcPxsvLq8V2Hx+fdgtOCCGEEEIIIYRwJhovLzzSxuGRNg5zSRFNWzdj2JaFPi7Rtk/jpvVYyktxT01DGxpmv2CdjGo0YMzfgSY4FLcePQHQBAahNjag8fVDn5BiTURFx6JoNHaOVrRGm5NSN910EwA333xzi8JtzQXE8vLy2i86IYQQQgghhBDCSWlDwvCadhFe0y6yrVNVlcY1P2AqOkr9D9+gi+yLe+pI9ElD0Xh52zFax6QamjDkbceQswXDzm2oTY14jJqAz+y/AOAWFYP/bQ/gFhUjiSgn1Oak1KJFizoiDiGEEEIIIYQQwvWpKp5TLqQpIx3jrp0YD+zFeGAvyhefoh8cj/uIMegHxtk7SrtSLRYMuZknE1GGkwXlNf6BaPz8bcuKRoOubz97hCnaQZuTUsOGDeuIOIQQQgghhBBCCJenaDS4Jw/DPXkYluoqmjI305SxEdPRwzTlZoLWrUVSqnlUkqtTzWYUrda6oCjUL/sf5ooyALSBQegTUtAnWmtESY8o1/G75z9saGjg6NGjGI3GFusHDBjwh4MSQgghhBBCCCFcncbPH8/xU/AcPwXTkUM0bdmEfnCCbbvp2BFq//Me7sPS0CcPRxsQaMdo25+lsQHj9hyacrZiOnyAwMefQ9G6oSgKHqMnYKmtxT0xBW2vyC6RmOuK2pyUKi8v5+GHH2bdunWn3S41pYQQQgghhBBCiLZx69kbt569W6xr2rIRU9FRTMuXULf8c/T9BuI+dCT6uGQUd3c7RfrHWOrrMezIxpCzFWP+DlSzybbNtK8AXay1o4vnxGn2ClF0ojYnpf7+979TXV3N4sWLmTNnDm+++SalpaW88847PPTQQx0RoxBCCCGEEEII0eV4TroAbXAoTVs2Yty3B8PunRh270Rx/y/6+GS8L/oTGh9fe4fZao0Z6dR99h9Us9m2ThvSHfdE69A8bY8IO0Yn7KHNSanNmzfz9ttvExcXh6IohIeHM2rUKHx8fHjvvfcYP358B4QphBBCCCGEEEJ0LRpPLzxGjsVj5FjMZSU0ZWykactGzGUlGHfkoPx5jm1fS0M9Gk8vO0bbkqW2BsP2bLRhPdBFxQDgFh6BajbjFhaOPjHVmogK6yFD87qwNiel6uvr6datGwD+/v6Ul5cTFRVFv3792LlzZ7sHKIQQQgghhBBCdHXaoBC8pl2E59QZmA7sxVJRjuJmvaRXLRaqXp6H4uOLx9A09MnD0Hh5d3qMltqaE7PmbcW4Jx9VteCeMsKWlNKG9yLw4b+jDQ3r9NiEY2pzUioqKor9+/cTERFB//79+eyzz4iIiODTTz8lJCSkI2IUQgghhBBCCCEEoCiKNckTdXKd+fhRLBXlqOWl1B7aj/Llp+gHJ+CeOhLdwDhb8qojqBYLTRvXWouVF+xGVS22bW49e+PWK7JF7JKQEr/W5r/MOXPmUFJSAsBtt93GDTfcwPLly9HpdDz//PPtHqAQQgghhBBCCCHOzC08gsCnXqIp8xeaMtIxHT1MU24mTbmZaLx98J51Je7Jw9rt9SyNDWg8PAFQNBoaN6zBdOyINZZekbgnpKCPT0YbIgkocXZtTkpdfPHFtn8PGTKE1atXs2/fPnr06GEb1ieEEEIIIYQQQojOo/Hzx3P8ZDzHT7YmpbZsomnLRiw11WgCAm37mSvKAdAGnnr9bty9E8Mn/8Z4xbXo+w9usc1cWYEhZyuGnC2Yjhym27xXbTMAeoyfglpbgz4hFW1QcAe+S+Fq2pyUevPNN5k7dy6entasqKenJ4MHD6axsZE333yT2267rd2DFEIIIYQQQgghROu4hffC7aJeeE2fhXHvLtxO1HQCaPhxJY0b1qCLHYDHsDT0Q5JQPDxQVZW65Z9j2ZlL3fLP0fUbhKWi7GSNqAN7W7yG8UCBLXHlMWxUp74/4TranJR66623uOKKK2xJqWYNDQ289dZbkpQSQgghhBBCCCEcgKLVou83qMU6S3UloGLck4dxTx6K3h19fDKakFCMO3PB0xPjzlzqvviExvU//frZ0EX1RZ+Qij4hBe2vel8J8Xu1OSmlquppp2vMz8/H39+/XYISQgghhBBCCCFE+/O7/lbMZaU0bdlI05aNmEuLacxIx1J0DNXQBOG9UCsrMObvABR0fWPRJ6TiHp+Exl8SUaJ9tTopNXToUBRFQVEUpk6d2iIxZTabqa+v5/LLL++QIIUQQgghhBBCCNE+tEHBeE2dgeeUCzEd2Ev9ii9oOLQfxcsHFAWNtw/Ggl343/ZAuxZIF+K3Wp2UeuSRR1BVlUceeYTbb78dX19f2zadTkfPnj1JSkrqkCCFEEIIIYQQQgjRvhRFwS2yL2pDA4q3D5rAICwAHh6odbU0/PQt+qShpx0tJUR7aHVSaubMmQBERESQlJSETqfrsKCEEEIIIYQQQgjR8Yy7dmDYmYvG2xdFqwWzGeVEbynDzlyMu3agHzDE3mEKF9XmmlIRERGUlJSccXt4ePgfCkgIIYQQQgghhBAdT1VV6r9eitrYgOLpiWpoQjVbULUa0GpQGxuo/3opuv6DpbeU6BBtTkpNnDjxrH+MeXl5fyggIYQQQgghhBBCdAKTCXNpMYqHJ2pDg3WdxYKq0QCgeHhiLisBkwlktJToAG1OSn355Zctlo1GI3l5eSxcuJC77767veISQgghhBBCCCFEB1J0OgIenIdaWwOAikpNTQ2+vr4oWDujKL5+KJKQEh2kzUmpAQMGnLIuLi6O0NBQPvjgA6ZMmdIugQkhhBBCCCGEEKJjaQO7QWA3wDqcT1NVhZu/vwzXE51C015PFBUVxbZt29rr6YQQQgghhBBCCCGEC2tzT6na2toWy6qqUlxczJtvvkmfPn3aLTAhhBBCCCGEEEII4bpanZSaM2cOb7zxBsOHDz+lG5+qqvTo0YNXX3213QMUQgghhBBCCCGEEK6n1UmpjIwMTCYT//nPf1okpTQaDYGBgfTp0wc3tzZ3vBJCCCGEEEIIIYQQXVCrs0iqqgIwfPjwDgtGCCGEEEIIIYQQQnQNberaVFBQQElJyVn3Od3sfEIIIYQQQgghhBBC/FqbklLXXnutrcfU6SiKQl5e3h8OSgghhBBCCCGEEEK4tjYlpRYvXky3bt06KhYhhBBCCCGEEEII0UW0KSkVHh5OUFBQR8UihBBCCCGEEEIIIboIjb0DEEIIIYQQQgghhBBdT6uTUkOHDkWn03VkLEIIIYQQQgghhBCii2j18L2PPvqoI+MQQgghhBBCCCGEEF2IDN8TQgghhBBCCCGEEJ1OklJCCCGEEEIIIYQQotNJUkoIIYQQQgghhBBCdLo/nJQym83k5eVRVVXVHvEIIYQQQgghhBBCiC6gzUmpv//97/zvf/8DrAmpq666ipkzZzJ+/Hg2b97cpufKyMjgb3/7G6NHj6Z///6sWrXKts1oNPLSSy8xY8YMEhMTGT16NA888ABFRUUtnqOyspJ7772X5ORkUlNTeeSRR6irq2uxT35+PldeeSVxcXGMGzeO999//5RYVq5cybRp04iLi2PGjBmsXbu2xXZVVXnttdcYPXo08fHxXHvttRw4cKBN71cIIYQQQgghhBBCWLU5KfXdd98xYMAAAFavXk1hYSErV67kmmuu4R//+AcbN27k73//Oxs2bDjnc9XX19O/f3+efPLJU7Y1Njayc+dObr75ZpYuXcqbb77J/v37ufnmm1vsd99991FQUMDChQt599132bJlC0888YRte21tLXPnziU8PJylS5fywAMP8Oabb/LZZ5/Z9snMzOTee+9l9uzZfPnll5x33nnceuut7N6927bP+++/z0cffcRTTz3F4sWL8fT0ZO7cuTQ1NbX1IxRCCCGEEEIIIYTo8tqclKqoqCAkJASAtWvXMm3aNKKiorj00kvJzs7mhx9+wGKxcN99953zucaNG8fdd9/N5MmTT9nm6+vLwoULueCCC4iOjiYxMZHHH3+cHTt2cPToUQD27t3L+vXreeaZZ0hISCA1NZXHHnuMb775xtajatmyZRiNRp599lliY2OZPn06V199NQsXLrS91qJFixgzZgw33HADffv25a677mLQoEF8/PHHgLWX1KJFi7j55puZNGkSAwYM4MUXX6S4uLhF7y4hhBBCCCGEEEII0TptTkoFBwdTUFCA2Wxm/fr1jBo1CrD2bPL19eWJJ57g6quvxmQytXuwtbW1KIqCn58fAFlZWfj5+REXF2fbJy0tDY1GQ25uLgDZ2dmkpqai1+tt+4wePZr9+/fb6mBlZ2czcuTIFq81evRosrOzASgsLKSkpIS0tDTbdl9fXxISEsjKymr39ymEEEIIIYQQQgjh6tza+oBZs2Zx1113ERISgqIotkRNTk4O0dHRAISFhfH111+3a6BNTU28/PLLTJ8+HR8fHwBKS0vp1q1bi/3c3Nzw9/enpKTEtk9ERESLfYKDg23b/P39KS0tta1rFhQURGlpKYDtuYKCgs64T1uoqoqqqm1+nCNojt1Z43c28nk7HmkTxyDt4HikTRyDtIPjkTZxTNIu9idt4JikXRyDK7RDa2Nvc1Lq9ttvJzY2luPHjzNt2jRbDyStVsuNN94IgKenJ56enm196jMyGo3ceeedqKrK008/3W7Pay/V1dVoNH944kO7UFWV+vp6ABRFsXM0rk8+b8cjbeIYpB0cj7SJY5B2cDzSJo5J2sX+pA0ck7SLY3CFdrBYLK3ar81JKYBp06adsm7mzJm/56nOyWg0ctddd3H06FH+85//2HpJgbXHU3l5eYv9TSYTVVVVtrpXwcHBp/Rmal5u7h11un3Kysps25ufq6ysjNDQ0Bb7NBd9bws/Pz+0Wm2bH+cImrOd/v7+TntwOBP5vB2PtIljkHZwPNImjkHawfFImzgmaRf7kzZwTNIujsEV2sFsNrdqvzYnpRYtWnTW7XPmzGnrU55Rc0Lq4MGDLFq0iMDAwBbbk5KSqK6uZvv27QwZMgSATZs2YbFYiI+PByAxMZEFCxZgNBrR6XQApKenExUVhb+/v22fTZs2ce2119qeOz09ncTERAAiIiIICQlh48aNDBw4ELDWt8rJyeGKK65o8/tSFMVp/7DgZPzO/B6ciXzejkfaxDFIOzgeaRPHIO3geKRNHJO0i/1JGzgmaRfH4Ozt0Nq425yU+ve//33WF21LUqquro5Dhw7ZlgsLC8nLy8Pf35+QkBDuuOMOdu7cyXvvvYfZbLbVdvL390ev19O3b1/GjBnD448/ztNPP43RaGT+/PlMnz6dsLAwAGbMmMFbb73Fo48+yo033siePXtYtGgRDz/8sO1158yZw9VXX82HH37IuHHjWLFiBdu3b2fevHkt3tc777xDnz59iIiI4LXXXiM0NJRJkya15eMTQgghhBBCCCGEEICi2rFy1ubNm0+bxJo5cya33XYb55133mkft2jRIoYPHw5AZWUl8+fP56effkKj0TBlyhQee+wxvL29bfvn5+czb948tm3bRmBgIFdddRU33XRTi+dcuXIlCxYs4MiRI0RGRnL//fczbtw423ZVVXn99ddZvHgx1dXVpKSk8OSTTxIVFdXq92s2m8nOziYxMdGph+9VVVU5dTdCZyKft+ORNnEM0g6OR9rEMUg7OB5pE8ck7WJ/0gaOSdrFMbhCO7Q2/9HmpNSbb77J3LlzTylk3tjYyL/+9S9uu+223xdxFyBJKdFW8nk7HmkTxyDt4HikTRyDtIPjkTZxTNIu9idt4JikXRyDK7RDa/MfbZ4C7q233rJVgf+1hoYG3nrrrbY+nRBCCCGEEEIIIYTogtqclFJV9bSZuvz8fFvhcCGEEEIIIYQQQgghzqbVhc6HDh1qq/w+derUFokps9lMfX09l19+eYcEKYQQQgghhBBCCCFcS6uTUo888giqqvLII49w++234+vra9um0+no2bMnSUlJHRKkEEIIIYQQQgghhHAtrU5KzZw5E4CIiAiSkpLQ6XQdFpQQQgghhBBCCCGEcG2tTko1GzZsmO3fTU1NGI3GFtt9fHz+eFRCCCGEEEIIIYQQwqW1OSnV0NDASy+9xMqVK6msrDxle15eXnvEJYQQQgghhBBCCCFcWJtn33vxxRfZtGkTTz31FHq9nmeeeYbbb7+d0NBQXnjhhY6IUQghhBBCCCGEEEK4mDYnpVavXs2TTz7J1KlT0Wq1pKamcsstt3D33XezfPnyjohRCCGEEEIIIYQQQriYNielqqqq6NWrF2CtH1VVVQVASkoKW7Zsad/ohBBCCCGEEKKdZX1RwJJbNpL1RYG9QxFCiC6tzUmpiIgICgsLAYiOjmblypWAtQeVr69v+0YnhBBCCCGEEO0oa2kBmUusyajMJQVkLZXElBBC2Eubk1KXXnop+fn5ANx0003897//JS4ujueee465c+e2e4BCCCGEEEII0R6ylhawdcmeFuu2LtkjiSkhhLCTNs++d+2119r+nZaWxooVK9i5cye9e/dmwIAB7RmbEEIIIYQQQrSL0yWkmjWvT5oV05khCSFEl9fmpNRvRUREEBER0R6xCCGEEEIIIUS7O1tCqpkkpoQQovO1evheVlYWq1evbrHuyy+/ZOLEiYwcOZLHH38cg8HQ7gEKIYQQQgghxO/VmoRUMxnKJ4QQnavVSam33nqLPXtOfpnv2rWLRx99lLS0NG666SZWr17Ne++91yFBCiGEEEIIIcTv0dqE1O/dXwghxO/X6qRUfn4+I0eOtC2vWLGC+Ph4nnnmGa677joeffRR20x8QgghhBBCCOEIUmbHduj+Qgghfr9WJ6WqqqoIDg62Lf/yyy+MHTvWthwXF8exY8faNzohhHAwWV8UsOSWjWR9IV37hRBCCGeQNCum1YmmlNmxUlNKCCE6UauTUsHBwRQWFgJgMBjYuXMniYmJtu11dXXodLp2D1AIIRxF1tICMpdYk1GZSwqk5oQQQgjhBIyNJvzDvc+ZmJKElBBCdL5WJ6XGjh3LK6+8wpYtW3j11Vfx8PAgJSXFtn3Xrl306tWrQ4IUQgh7O12RVCmGKoQQQjg21aKy9t1cfno9m4ZqA8lnSDr5h3tLQkoIIeyg1UmpO++8E61Wy1VXXcXixYt55pln0Ov1tu2ff/45o0eP7pAghRDCns42a48kpoQQQgjHlf3lXg78UgTAnvVHiBnT87Q9pqqO1nFwa1FnhyeEEF2eW2t37NatG//973+pqanBy8sLrVbbYvtrr72Gl5dXuwcohBD21JpppJu3yx1WIYQQwnEc3FJ08jdcgQm3JeAX5kXSrBhUVDKXFBA5LMyWtNr4nzzCBweh82j1JZIQQog/qNU9pZr5+vqekpACCAgIaNFzSgghnF1rElLNpMeUEEII4TgqCmtY83aObXnoZf3onRRqW06aGcPst0cy8Y5EwgcHAVBb2sC2Ffs7PVYhhOjK2pyUEkKIrqK1Canfu78QQggh2l9TrZEfXsnE2GgGIHpED+JnRJ92X0VRGHX9YLR6DYOm9GHItMhOjFQIIYT0TRVCiDNImR3bpkRTa6ebFkIIIUTHsJgt/PRGNtVF9QAE9fFl7F/jUBTljI/x7+HNZa+Nx8vfvZOiFEII0Ux6SgkhxGmoFhWLRW31/jKNtBBCCGF/GZ/u5si2UgA8fHVMuicFN/dTS4/8liSkhBDCPiQpJYQQv2FoMLFqQVbra0QpEBLj37FBCSGEEOKsmmqN7N14FABFq3DeXUn4hnj+rueqK2+kqdbYnuEJ4RSyvihgyS0byfpCaqWKziFJKdEm8iUlXF3V8TqWPbGRg1usM/EoCgy7sj/Js8/SC0qFHxdkUbq/qpOiFEIIIcRvufvouOSZUYTGBjByzkB6DAxq83NYzBa2rdjPkvvWkfHZrg6IUgjHlbW0gMwl1uu8zCUFMomP6BSSlBKtJl9SwtWZDGa+mb+ZyiO1AOi93Jj6QCrxF0aTPCv2lJpRyZfG0GdoGADGRjPfvbiFmuL6To9bCCGEEFZeAe5c+PhwBk7q/bse31BtIHPJHoyNZvJ/OkxxQWX7BiiEgzrdrNMyu7ToDJKUEq0iX1KiK3DTaxl2RX8AAnp6c/H8NCISQmzbk2bF2HpMJc+OIfnSWCbcmkBYv0AAGqoMrHw+g8ZqQ+cHL4QQQnRBZpPllBqQGjfNWQubn413oAfJzTehVNjwwXYsZssfDVMIh3a6a71mcs0nOpokpcQ5yZeU6EpiRvdk3N/iuejpkfj38D5le9LMGGa/PZKkmdbklJtey+T7kvEPt+5bfbye717egqnJ3KlxCyGEEF2NqqqkL9zJ9y9tadf6T4On9qFbH18Ayg7WsPP7Q+323EI4mrNd6zWTaz7RkSQpJc5KvqSEK6sta2DHdwdOWR87tid6L12rn8fDR8+0B4fiFWiduaekoIofX8+SO6tCCCFEB8pbdYhdqw9TmFPK1/M3t9vvrkarYdR1g23LW5fspq6isV2eWwhH0pprvWZyzSc6iiSlxBnJl5RwZcfzy/ny0XQ2/iePPeuP/OHn8w3xZOqDqeg83QA4nFXCwa3Ff/h5hRBCCHGqY3nlbFyUZ1uOvzAKjbb9Lm3C+gXSf0IvAIwNZjZ9lHeORwjhfFp7rfd79xeiNSQpJc5IvqSEq8r78RAr/v6LrfZTzrJ97XJ3Nai3H5PvSUar0zDi6oFEDev+h59TCCGEEC3VlDTw42tZqGZrLam46VHEjunZ7q8z9Ip+ePhae07v33ScwpySdn8NIezpt5P4nEvM6HBUVT33jkK0gSSlxBm19UuqrfsL0dnMJgs/f7CdDR/swHLiRDZ8cBAXPjG83e6uhg8O4s//GMeQ8yPb5fmEEEIIcZKpycyqf2Tabiz1jAti6OX9OuS1PHz0DLtygG05/d87MRmkZqRwHUmzYki4KLrV+xf8fJRlT27kcE6JJKdEu5GklDijpFkxrU40+YZ6MXDy75t6V4jOUF/VxIq//0L+j4dt64acH8m0h1Lx8NW362t5d/M4ZZ2x0dSuryGEEEJ0Naqqsu6f2yg7UA2AX5gXE25PbNdhe78VO7Yn3QdYZ9k1Gc1UF9V32GsJ0dlUVaW6+Nx/0x5+J8+VSwqq+O6FLSx7YiOHs4olOSX+MElKibNqbWKqprieLx9Np3RfVSdEJUTblO6r4qtH0ynaVQGAVqdh3N/iGXH1wA49kW12cEsRn96xhuKCyg5/LSGEEMJV5S7fx76NxwDQeWiZfG8yHj7te2PptxRFYdT1gxlyQSSzXxpLt16+Hfp6QnSmgvVH2b/pOGA9Pz6dlNmx/OXtiZx3ZyKBvXxs60v2VvHdS1tZ9vhGDmVKckr8fpKUEud0tsRUv3E9bZnz2tIGlj+9iV1rDp92XyHs4XBWMcuf3kRduXXWHK9u7lz4xHBix7Z/7YnTKdxWyqp/ZNJUa+T7l7ZQdayuU15XCCGEcCVHd5aR8dlu2/L4WxIIjOicBFFghC8jrhqI/sRkJkK4gprietL/s8O2PO7m+FOu+VJmx5I0KwZFoxA1vAeznhvNeXcl0a33yWOvZF8V37+8lS2/Oj6FaAtJSolWOV1iKmV2LGP/Gs/MZ0cRGhsAgNloYf0/t9u6VQthb916+6L3sp5EhvYL4JJnRhHSN6DTXr/HgEC6D+wGQGONkW9fyKC+qqnTXl8IIYRwBaExAfRNCwcgeXYMfVLD7ByREM7LYraw5u1cjA3WGmmxY3sSPaIHSbNiSJ4dA1iPs6RZMS0ep2gUooZ1Z+azo5h0dxLd+pxITinWIuhC/B6S7hetljQrBhWVzCUFLb6kvLt5MP3x4Wz+KI+dPxwi4eK+BEX62TlaIay8gzyZdHcye9YfYeScgWh12k59fa1Oy6S7k/l63mYqDtdQU9zA9y9uYfrjw9F5yFewEEII0Rpuei3jb4knalgYfVLsm5CqLW0gc2kBI64agN5LZ9dYhPg9TAYLHv7W0S6+IZ6MnDPQti1pZgzRE0Pw9/c/4+MVjULk0O70SQnj4NZiSvdXndJz8VBmMRazhT4pYSgapWPeiHAJckUk2uRMX1JaNw1p1w2md3Io4XHBdopOCKg4Uot3oHuLk8SwfoGE9Qu0W0zu3jqmPZjKsic3UlfWSOn+an5ckMWU+1LQuEmHVSGEEKI1FMV6IWxPBzKKWPN2DqYmMzp3LSOvGWTXeIT4PfSebky6K4ndawoJ6Onzu5Or1uRUGJFDWyaKLWYLmz7Ko7qonm69fUmaFUNkqiSnxOnJ1ZBoVxEJIWh+82WT/+MhcpbtleJ3osMd3FLEssfTWfN2LqrFsf7evLt5MO3BVNtQwsLcUtb/a7scF0IIIcQZ7PjuIFXHHasWY1Ckr+23e+f3ByndL5P8COekKAr9J/TqkBu3hzKLbTNVlh+q4ccFWSx9eAP7Nx9zuHN0YX+SlBIdqrigkvR/7yTj092sWpCFod5o75CEC1ItKplL9/DDq5kYG80cyixm5w+H7B3WKQIjfJlyX4ptdpM9646w9X977ByVEEII4Xj2bTrGxv/s5KvH0inMLbF3ODa+IV4knyhhoaqw4cMdWOQiWzgJQ4OpU16nT2oYU+5PIST65OiaisM1/PhaNksf/pl9myQ5JU6ya1IqIyODv/3tb4wePZr+/fuzatWqFttVVeW1115j9OjRxMfHc+2113LgwIEW+1RWVnLvvfeSnJxMamoqjzzyCHV1Le+o5Ofnc+WVVxIXF8e4ceN4//33T4ll5cqVTJs2jbi4OGbMmMHatWvbHIs4VXFBpe2H+mBGEV89vpGKwho7RyVciaHBxKrXsshcUmBbFz2iB/3HR9gxqjPrPqAb429NgBMdCnOX73O4u8BCCCGEPZUdqGbde9sAMNSbqDxSa+eIWhpyQRQBPX0AKNlbxa6fZOZp4fgOZBxn8d1rOZRZ3OGvpSgKvZNCuWj+SKY+kEpI318np2r56fVsPn/QmpwSwq5Jqfr6evr378+TTz552u3vv/8+H330EU899RSLFy/G09OTuXPn0tR0cuaq++67j4KCAhYuXMi7777Lli1beOKJJ2zba2trmTt3LuHh4SxdupQHHniAN998k88++8y2T2ZmJvfeey+zZ8/myy+/5LzzzuPWW29l9+7dbYpFnGrItEim3pdiG7JUdayOrx7fKF9Aol1UF9Wx/MmNHMwosq5QYOjl/ZhwewJu7p1b0LwtooZ1Z+ScQeg8tUx9IBX/7t72DkkIIYRwCA3VTfzwaiamppOzgg2eFmnfoH5D66Zh1PUna0llfLpLZtYVDq2uopH172+nsdrA9y9v5fiuik55XUVR6JUYwkXzRjL1wVRCYwJs2yqP1FKY4zi9IIX92DUpNW7cOO6++24mT558yjZVVVm0aBE333wzkyZNYsCAAbz44osUFxfbelTt3buX9evX88wzz5CQkEBqaiqPPfYY33zzDUVF1ovUZcuWYTQaefbZZ4mNjWX69OlcffXVLFy40PZaixYtYsyYMdxwww307duXu+66i0GDBvHxxx+3OhZxZr2SQrnk72m2KUNNTWZ+ej2bTR/nYTFb7BydcFaF20r56rGNVBRa757qvdyYen8qCRf1RVEcv4ji4Kl9+NPLY+kpEwMIIYQQAFhMFn58LZva0gYAQvr6M+r6wQ75u95jYBCxY3oC1t5cv/w3384RCXF6qkVl3bvbaKq1llHpMzSMsH4BnRqDoij0SghhxtMjmPZQKqGxASgahcRL+rbYz2KyyHDYLshha0oVFhZSUlJCWlqabZ2vry8JCQlkZWUBkJWVhZ+fH3FxcbZ90tLS0Gg05ObmApCdnU1qaip6vd62z+jRo9m/fz9VVVW2fUaOHNni9UePHk12dnarYxFn5xfmzUVPjSRmdLht3fYVB1jxbIbcWRJtoqoq277Zz3fPZ9BUZ/1x9Q/35uL5afRKDLFzdG3jFehxyrrOGusvhBBCOJpNH+dzPK8cAK8Adybdk4yb3nF7Pg+7sj/u3tZZywp+PsrRnWV2jkiIU+347gBHtpUC1uNqzA1D7JboVRSFiPgQZjw1glnPj8IvrOVogfzVh/n8/vXs+fmIdF7oQtzsHcCZlJRYu/IFBQW1WB8UFERpqfWgKi0tpVu3bi22u7m54e/vb3t8aWkpEREta8sEBwfbtvn7+1NaWmpbd7rXaU0sbaGqqtPOuNUc+++JX6vXMPZvcYTEBLD5ozwsZpXjeeWsejWTC58c7pB3weztj3zerkpVVYoLKmn+SHonhzDu5gT0Xm6d8jl1VJuoqkrOV/vYtfowFz41Au/TJKzESXJsOB5pE8cg7eB4pE1aZ9fqw+z8/iAAGjeF8+5KwivAvcM+t/ZoFw8/PamXxbLhw52Atej5rOdGoXFz2Pv+DkWOjY5XfqiGjE9PlqQZ+7c43H10Z/3MO6tdAnr6tHgNs9FCzlf7qCtvZO3buWQtLSDxkr70TeuBRtv1jilXOD5aG7vDJqVcWXV1NRqNcx5YqqpSX2+d3vP3JpF6DvNnXPBgNr6/C0OdiSGzelFdXd2eYbqM9vi8XVHC5b0pP1JNj7hABk/vRYOxjoZOmpG5o9pk1/dH2PaldcbAlc/9wvh7BqPzlK/oM5Fjw/FImzgGaQfHI21ybqV7a9iwcKdtOfmKaNxDFduoho7QXu3SPdmPbj/50FhtYPCMCGrqZEKf1pJjo2OZjRZ+fCMXs9Ha4yhmYg98euvOeVzZq13qK5rwDNJTV94IQPXxeta9u43Mz/cw4Pye9B4agkbbdf5OXOH4sFha19vNYa94QkKsw3DKysoIDQ21rS8rK2PAgAGAtcdTeXl5i8eZTCaqqqpsjw8ODj6lN1PzcnPvqNPtU1ZWZtvemljaws/PD63Wcbsin01zttPf3/8PHRz+Sf70eC6Ekn1V9I4PPfcDuqj2+rydnanJfErh8kvmj7JLl/6OapMhkz3Yv6GE2pIGqo7U88sHe5n6QCpanXMmsDuaHBuOR9rEMUg7OB5pk3M7XFyBarZ+ToOm9CZhWmyHv2Z7tsuku1Lw8NWh83DYSyuHJMdGx9r0UR7VR6312QJ7+TDq6iGtOne2V7v4+8PFT4VybGcZWUv3cuzEUN7akka2LNrLru+OkXhxNDGjwrtEb0RXOD7MZnOr9nPYb86IiAhCQkLYuHEjAwcOBKwz6eXk5HDFFVcAkJSURHV1Ndu3b2fIkCEAbNq0CYvFQnx8PACJiYksWLAAo9GITmcd852enk5UVBT+/v62fTZt2sS1115re/309HQSExNbHUtbKIritH9YcDL+P/oevAI86JPccoiSxWxh06I8hlwQhV+Y1x96flfRXp+3s8r/6TCZS/Yw46kR+Iae/JvQudvv66sj2sQ70IPzHxrKsic30lRr5NjOcta9t40JtyagaLpm259LVz82HJG0iWOQdnA80iZnF3d+FH6hXuxeU8iIqwZ22ufUXu3iFyrnrL+XHBsdozC3hB3fWofDanUaJtya2KZzZ3u2S/jgYMIHB3Msr5yspQUc3WGt1VZTVM/6f25n2zf7mfXc6C6RmHL246O1cdu1Jevq6sjLyyMvLw+wFhTPy8vj6NGjKIrCnDlzeOedd/jxxx/ZtWsXDzzwAKGhoUyaNAmAvn37MmbMGB5//HFyc3PZunUr8+fPZ/r06YSFhQEwY8YMdDodjz76KHv27GHFihUsWrSI6667zhbHnDlzWL9+PR9++CF79+7ljTfeYPv27Vx11VUArYpFtI8ti3ez84dDfPnoBg5nFds7HGFHZpOFDR/u4Od/bae+0jo9tLHRtYuA+/fwZur9KWj11q/mfRuP8csnu+wclRBCCNHx+qSEMfneFJe40FRVlfpKmchH2I+pyYLey5qEGnp5P7r19rVzRG3XY2A3Lnh0GBc+MZzwwSdrO4cPCnKJ7wlxkqLasXLW5s2bmTNnzinrZ86cyfPPP4+qqrz++ussXryY6upqUlJSePLJJ4mKirLtW1lZyfz58/npp5/QaDRMmTKFxx57DG/vk5X88/PzmTdvHtu2bSMwMJCrrrqKm266qcVrrly5kgULFnDkyBEiIyO5//77GTdunG17a2I5F7PZTHZ2NomJiU49fK+qqqpDuhEaG018+Wg6VcfqrCsUSJ4VQ9LMmC7bU6QjP29H1lDVxI+vZXE8v8K2bvC0Pgz/ywC7FzrsjDY5uLWIVa9m2oq5D79qAHEXtP67pivoqseGI5M2cQzSDo5H2uT06isaTzsLbWfpqHapLqpn4392Un64htkvjZEhfWchx0bHqi1tIG/VIVL/3K9N11KO2i7Hd1WQ89VeRs8djHeQp229sdHEvo3HiBnTE60LJasctR3aorX5D7smpboaSUqdm6HeyNp3t3FwS5FtXa/EEMbfkoC7j67dX8/RucKXUVuV7q/ih1czqSuzFjnU6jSMmjuYfmMjzvHIztFZbZL/4yF+/mCHbXnC7Yn0Hdmjw17P2XTFY8PRSZs4BmkHxyNtcqrCbaX88PJWhl81gIGTetvlc+modvn+la0c2mrt7R93YRTDr2x7/dmuQo4Nx+Rs7ZK7fB+/fLILn2APEi7uS79xES6RnHK2djid1uY/nL+1hEvRe+mYdHcSQy/vR/Oxdzi7hC8f20DZQZmhz9XtTT/K8qc22RJSXoHuTH98uMMkpDrTgPN6kzQrxrb8y//lYzK0rligEPaQ9UUBS27ZSNYXBfYORQjhwKqL6lj9ejZmo4X0hTs5uMW1yjUM/8sA2yQl21ceoPywzMYnOoerl7k4HbPRTO43+wGoLW1kwwc7WHz3Wnb+cBCzUc6bnYUkpYTDURSFhIv6Mu2hobbeUTXFDSx7ciN7fj5i5+hER7BYVDb/Xz6r38yxTVsbGhvAJc+kERoTYN/g7Cj50hj6jY/AL8yL6Y8Ns8tsg0K0RtbSAjKXWJNRmUsKyFoqiSkhxKkMDSZ+eCWTpjojAL2TQ+mT4lqzMPt39ybhomgAVLPKhg93oFpkYIroWBaThRV//4W17+RiqDfaO5xOo9VpmXJfCr0SQ2zr6soaSV+4k8V3r2Pn9wflpq4TkKSUcFg944KZ+ewogqOtsySaDRbWvp3L3vSjdo5MtLeiXeVs+3q/bbnf+AimPzbMrrUmHIGiKIyeO5iL5o3EL8z73A8Qwg6ylhawdcmeFuu2LtkjiSkhRAuqRWXtO7lUFNYCENDTm/G3xLtk3dD4GdG2WaSLdlWwZ53cVBUdK+vLvZTsrWLP+iP8+Fq2vcPpVKExAUx9IJWL5o2kV9KvklPljaT/eyeL71nLju8kOeXIJCklHJpPsCcXPjGc/hN6ARAc7U+f1DA7RyXaW4+BQSRe0hdFq5B27SDG3DgErU56BQFotBo8fPUt1lksKoaGrtdFWzie0yWkmkliSgjxa1lfFNhqhuq93Jh8Twp6L9esF+qm15J2/WDb8ub/y6exxmDHiIQrK9pdQfaJofOKRiHlT7F2jsg+QmMCmHp/Khc/k0bv5JM9MOvLm8j4dFeXHN7oLGQ6COHw3PRaxtw4hLB+AfQYFCRDmFxUyuxYooZ1JyjSz96hODSTwczat3Opr2ri/IeHyvEg7OZsCalmzdt/XR9NCNH1HMg4TubnJy6aFevkHf49XLsHcERcMNEjerBv0zGaao1kfLqLMTfG2Tss4WIM9UbWvJVjm7E5eVZMly59ARAS7c+U+1Io3V9F1tICDm4tZuCk3nj6ubfYT1VVpy0g7mqkp5RwGv3GReAb4tliXeWRWravPIBMIuk8VItK1tIC8lcfbrFe0SiSkGqFte/ksv+X4xTtqmDNWzlYpE6FsIPWJKSaSY8pIbq28sM1rHk717acenl/eiWEnOURrmP41QPQeVpvHu1aXUjR7go7RyRczcZFedSUNAAQ2i+AhIuj7RyR4wiO8mfyvSlc8mwa8TOiWmxrqjOy5P71bFuxH1OTDOuzN0lKCadlaDCxakEmmz7KY/UbOdIl0wkYG038+HoWW5fsIf3DHXJy9jskzIjGzd16gnsgo4hN/9kpSVnR6VqbkPq9+wshXINqUVn9Zo7toq9vWg/iL4w6x6Nch3egByl/6mdbPpTpWjMNCvvat+mYrV6ZzlPLhFsS0Gjl8v63giP9T+klteO7A1QdrWPzx/l8dtcacr/ZL9eSdiR/tcJpHc4uofJIHWD9Ul72xEaqjtXZOSpxJtVF9Sx7chMHfrHWk7BYVMoOVts5KucTHO3PpLuSULTW7sY7fzhE7vJ9do5KdDUps9tWr6Kt+wshXIOiURh/czw+wZ4ERfox5sa4LjdcZtDk3kQOC2PqA6kMvby/vcMRLqKurIENH+ywLaddMxjfUC87RuRc6soabf9uqDLwy3/z+eyuteR+vU+SU3YgSSnhtPqO7MGku5Nt3aIrCmv58rENHMgosnNk4reObCvlq8fTqThcA1gLnE69L4VBk/vYOTLnFJEQwthf1aXI+HQ3e9bLzD6i8yTNiml1oknjpuAZ4C49+oToooIi/bj4mZFMuTfZ1tO3K9FoNUy6K7nFlPVC/BGqRWXtu9toqjMCEDWiOzFjwu0clXMZc2Mcs54fTdTw7nAiT95YbeCX/9vFZ3etJWe5JKc6kySlhFOLHBrGJc+MIqCnDwDGBjOr/pFJxqe7pNaOA1BVlW0r9vPt8xk01Vp/OP3Dvbl4fhq9kkLP8WhxNrFje5J62ckhAev+uY3C3BI7RiS6grKD1Xz/8lYMDaZWJ6YsJpWMT3bJzFNCdGGefu54B3mee0chxDkZG01o3KyZFO9uHoy+fkiX64HYHrr19uW8O5O49IXRRI/o0SI5lfHJLj67cw3Hd0mpkc4gSSnh9Px7eHPx/JHWL5MTcpbt49vnM2islosgezEZzKx7dxubP863zQjSKymEi+eNdPkZdzpLwkXRDJzcGwDVrPLjgixK91fZOSrhilSLyvaV+/nq8XQOZRaz8T87gbP3mEq8OJp+4yMAGHnNwFPqOQghXFNxQSWbPsrDYrLYOxSHpKoqe9OPcmRbqb1DEU5K76Vj6gOpjLxmIONuicfdR2fvkJxaYIQvE+9ItCanRp5MTlnMKoERPvYNrotws3cAQrQHnYcbE25PICTGn1/+bxeqReXo9jK+ejydS18c0yW7i9vb6rdyOPiroZSJl/QlZXYsikbu5LQXRVEYec0g6iubOJhRhLHRzJbFu5n24FB7hyZcSH1VE+vezaUw5+QFVPnBaoyNJnQebiTNigFaFjNPmR1rWz9wUm+Co1rOrNlUa8TUZJKeE0K4mPqKRlb9I5P6iibKDlYz6e5k3L3lgrmZod7Iqn9kcXRHGb6hntZzVL2co4q2UxSFwVMj7R2GSwmM8GXi7YkkzYoh+4sCAsJ9Tvn+OrK9lJBof/Re8r3WniQpJVyGoijEXRBFcJQfP76WTWO1gf4Te0lCyk4SL+5LYXYJikZh7F/jWvRkE+1Ho1GYcGsCK5/NwM1dw8TbE+0dknAhh7OKWfvetha9TuOmR5H651i0upPfrUmzYlBRyVxSQPLsGFtCCiAk2v+U5924aCeHMosZcfVAYsf2lGEHQrgAs9HMqn9kUV/RBFh7Gcg5WEs6Tzdbfb2a4gayv9xL6p/7neNRQliZDGZJYnaCwJ4+TLgt8ZRamA3VTfzwSiZaNw1DLohk8NQ+kpxqJ5KUEi6nx8AgZj47irxVh0iYEW3vcLqskGh/xt+agF+YF0F9/M79APG7uem1TLk/BTd3LVo3GZUt/jiTwcwvn+xi53cHbes8A9wZ97c4IuJPX6w3aWYM0RND8Pc/NQn1a4cyiyn4+SgA697bxv5fjjP6hiF4B3q03xsQQnQqVVXZsHAnxQWVAHgHeXDeXUnym/QbiqKQdt1gvnjoZyxmldzl+4gZHU5AuAwREmd3OLuEn/+1nbF/i6PnkGB7h9Ml/PaG2bZvDmBqMmNqMrP1f3vY9s3+E8mpSOkR+gfJL4VwSd7dPEj9c79ThooVbDhKbWmDnaJyXRaThZ3fH8Riblk/ImpYd0lIdRJ3b90pJ/+GBtMpbSLEuZQfruGrxze2SEj1Sgph1vOjzpiQaouwfoHEjulpWz6cVcLn969nz/ojMkOfEE5q5/eH2L2mEACtTsPke5Lx8pc6cqcT2NOHuOlRgLU3WfrCnfLdJ86qoaqJde/lUlfeyMpnMyjaLcW37aH/+Ahr7+4T15eGehOZSwr47M41bF2yxzYbomg7SUqJLuPozjLWvpPLF49skOKS7aihuokVz2WQ/u+dZHy6297hiBPqKhr5et4mNnwoJ7uibY5uL6XicA1gvbhMu3YQU+5LabdC5e4+OsbdHM/ke5PxDLA+p6HexNp3cvnhlUzqKxrb5XWEEJ3j6M4yNn2UZ1se+9c4gqPO3mOyq0uaGYNPiLWm3tEdZexNP2bniISjUlWV9e9vp6HKOoy+V2IIobEB9g2qi/Lv4c24v8Uz++Ux9BvXMjmVtfRXyalaSU61lSSlRJegqipbFu9Gtag01Rr59vkMcpbtlYv1P6j0QBVfPZbO8bxyAHZ+f5Ca4no7RyXMJgvfzN9M+cEadq0+TNYXBfYOSTiRwVMj6RkXTGAvHy5+Jo1BU/p0SM2nPilhXPrCaPqmnaw3dyizmM8f+JmCn6XXlBDOoKaknh8XZKFarMdr/Iwo+qaF2zkqx+fmriXt2kG25c0f50kvC3Fa+T8d5lBmMQAefnrG/DVO6jDamX93b8b+NZ4/vTKGfuMjTklO7Vpz2M4ROh9JSokuQVEUpt6XSq8k69ATVYWMT3ezakEWhno5Cfg99qYfZflTm6gttfZq8ApwZ/rjw/EN9bJzZELrpiFldqxtOXNJAbtWyw+kOL3qoroWy4pGYcJtCVw8P41uvXw79LU9fPVMuC2RSXcn4eGnB6Cpzsiat3M5kis9WoVwZMZGEz+8mmnrFRCREEzqZf3tHJXz6J0USp+hYQA0VBnYulh6m4uWKo/WtuyFeFOcDIt1IH5h3oy9KY4/vzqW/hMiULQKHr46Bk7qbe/QnI4kpUSX4e6jY8q9KSTPjoETNxgOZhTx1eMbqSissW9wTsRiUfnlk3xWv5mD2WCtVxQaE8DFf08jNCbAvsEJm75p4Qz/ywDb8s8f7OBQVrEdIxKOxmw0s+njPP53zzqO7ihrsc3DV9+pM/xEDu3O7BfHED3S2msqIj6YnvFSyFUIR2ZqMuN2YhZOv+5eTLgtEY1GenC0xcg5A20zFO5cdYiSfVV2jkg4CrPJwpq3Tp5rDzivF72TQ+0clTgd31AvxtxoTU6NvyUBnUfLueR++WQXGZ/uajGTsWhJklKiS1E0CsmzYpl6f6ptloSqY3V89fhG9m2S8fzn0lRr5PuXtpC7fL9tXb/xEUx/fJjMnOWA4qZHMeT8SABUi8qPr2XZZkYSXVvFkVqWPbGR7SsOoKqw5p0cuw8d8fDTM/H2RM67K4nRNw45ZXiCsdFkp8iEEKfj6e/O9MeHMWhybybfmyKzT/0OPkGeJF8aA0DfkT3wDpReMMIqc8keSvdXA9ZaRiOuGmjniMS5+IZ4EZHQckKYurIGtq/cT86yfXx65xp++WQXDdVNdorQcbmdexchXE+vxBAufiaNHxdkUnawBlOTmZ9ez6a2tIH4C6PtHZ5DqilpYOVzv1B93FozStEojLh6IIOm9Jax7Q5s+F8GUFfRyP5NxzEbLHz/0hZmPDUS/x7e9g5N2IGqquT/dJhNH+XZ7r5q3BTip0eh93SMU4KoYd1PWVeYU8Kad3JJu3YQ0SN6nOZRQgh70Oq0pF032N5hOLUh0yIJjQmg+4Bu9g5FOIhjeeXkLN8HgKJVmHBrgq1HnXAuRXsq4USJTFOTmdzl+9j5/UEGTelN3PSodptExtlJTynRZfmFeTHj6ZHEjrVOTe7mrj0luy1O8vTXo/ey3gV199Fx/sNDGTy1Ywogi/ajaBTG3xxPj0HWk93GGiPfvpBBfZXcpelqGqsNrHo1kw0f7LAlpAJ6+nDx/DSGnB9lK9TpaAz1Rta/v53GagM/vZ7Nj69nSRd4IeykuqhOZpZqZxo3jSSkRAsNVU22IfSpf4olOFpms3RW0SN68OcF4xg4uTcaN+t5ljU5tZ/P7lzL5v/m03CGc/KsLwpYcsvGLjFhkWPcFhXCTtz0Wsb+NY7QmAD03roOL+rrzNz0WibfncT697czau5gfEOkoLmz0Oq0TLo7ma/nbabicA01xQ3kfX+QlD/1s3doopMc2V7K2ndyqa84eeIzcFJvhv9lgMPffbWYVUL6+lNXbp1UYf+m4xzbWc6o6weftleVEKJjNNUZ+faFLQBMvjeFwJ4+do7IdZmazA7/3Sw6TvSIHgT18WPH9weJkxEcTs8nyJNR1w0m8aJocpbtI3/1YSwmFVOTmW3f7Cdv1SGSZsWQMONkW2ctLSBziTUZlbmkAAWFpFkx9noLHU56SokuT1EUBk7qTd+RLYeEmI1m9qYf7bLTkhsbTdSUNLRY5x3kybSHhkpCygm5e+uY9mAq3kEeDDk/kuRLY8/9IOESdv5wkJXPZdgSUu4+Oibfm8yo6wc7xUWPh6+e8+5KYsJtCbj7WHtrNlYb+HFBFqvfzKaxRnpNCdHRLBaV1W9mU328nurj9Wz4YHuXPT/qSKqqsmfdET69cw1HtssMpF2Zfw9v0q4ZJJMHuBDvIE/SrhvMZQvGM2hqH7Q6ayrG1GRG96vzsaylBWxdsqfFY7cu2UPWUtftMSU9pYQ4g00f55P3wyEOZ5cweu4Qp7h4ay/VRfX88GomqsXCRU+PtA3bE87Nu5sHM58dhYev3t6hiE7Uc0gwbnotpiYz4UOCGHdzvNNNTKAoCn3TwukxKIgNH2zn4FbrTJJ7049xdEcZo+cOoU9qmJ2jFMJ1bflsN4U51iSJu4+OcTfHy/D9DnBwSzFr380FIH3hTmY9Pwqtruucf3ZlZpMFrZv0F+kKvLt5kHbNIBIuiiZ32T4OZ5fQb3wEcPqEVLPm9a7YY0r+8oU4jdJ9VeT9cAiAgp+PsuzJjVQX1dk5qs5xZHspXz2eTsXhGiqP1LFh4U57hyTa0ekSUo210tPElfn38Cbt2kEM+8sAzn9oqNMlpH7NK8CdSfckM/6WeNtMXw1VBn54NdNWFFYI0b72ph8lt7noskbhvLuSpMd0B+mTEkpobABgnR069+v9Z3+AcAkWi8rK5zLY8OEOTE1me4cjOol3oAcjrxnE7JfG4KbXnjUh1cxVe0xJUkqI0wiO9mfiHYm23lHlh2r48tF0DmUV2zmyjqOqKttX7ufb57fYipj69/AmaWZfO0cmOlJxQSVL7l3Hzh8O2jsU0Q6aao1s/m8+JkPLk9p+4yKIn+64xczbQlEUYkb35NIXR9M7ORSwTlQh9aWEaH+l+6tY989ttuURVw8kfFCQHSNybYpGYdTcwbbv6uwv91JdVG/nqERH2/b1Po7nlZO36hCr/pFp73BEJ9O4aVqVkGrmiokpSUoJcQbRI3pw8fyR+PfwBsBQb+L7l7aydckeVItr1VEwGcyse28bmz7Kt723XkkhXDx/JAHhUsjUVdWWNrDi77/QWGMk/d87OZBx3N4hiT/gWF4ZSx/6mW3f7Gfzf/PtHU6H8wr0YPK9yYz7Wzxp1wzCL6xlzw2pdyPEH9NQ1cQPr2baZuvsNz6CQVN62zkq1xfU24/B0/oAYDZaSP/3Tvk+c2Gl+6rY8j9rMkJRIPESuRncFbU2IfV793d0kpQS4iwCI3y5eP5I+gw9Waska2kB37201WWGPNWVN/LN/M3sWXfEti7h4r5MvjdFakm5OJ9gTwZPtZ74osLqN3M4vqvCvkGJNrOYLGR8tptvnvnFNkPdvo3HzjjFsCtRFIXYsT1ttRiaGRtNfD1vM4dduHerEB3JbLLw42tZ1JVZv1NCYwIYdd0gqSPVSZIvjcWrmzsAhTklHMgosnNEoiMYG02sfisH1WxNOsZf1JfuA7rZOSphDymz2zYBUVv3d3SSlBLiHPReOibdlcTQy/vRfC5WmFPCV4+mnzI7nbMp2l3Bl4+lU7K3CrAOgZl4RyJDL+sns310EamX9SNmdDhgvSP7w8tbqThSa+eoRGtVF9Wx/OlN5Hy1F07cSO8xqBuznhuFp7+7fYOzo18+2UXRrgq+e2kr697LpanOaO+QhHAqezcc5Xi+9SaFV6A7k+5OkoLbnUjv6cbIOYNsyxsX7cTQYLJjRKIjbP5vPlXHrDVrg6P9SbnU9QpYi9ZJmhXT6kRTyuxYlyt2LkkpIVpBURQSLurLtIeH4uFr7T3kHeSBd6BzX/QdyyunodLam8In2JMZT40gekQPO0clOpOiKIy5KY6eccEANNUZ+e6FDOoqGu0cmTib5mnDv3h4gy2prGgVhl7ej/MfGYZ3kKedI7Qfi9lCTfHJGiy71x5h6YM/U5hTYseohHAusWN7MuyK/ri5a5l0dzJeTjxBgrOKHBpGREIIAPXlTWR+7lrDdbq6g1uLyP/xMGC9KTzh1gQ0Mvtel9aaxJQrJqRAklJCtEnPIcFc8vdR9E4JZeIdSU7/45FwUTRRw7rTY1A3LnkmjaA+fvYOSdiB1k3DeXclERRpbf/a0ka+e2ELhnrpXeKImuqMrH4zh7Xv5mJstBY09wvz4qKnRpBwUd8u38tRo9Uw9YFURt84BJ2ntWdHXXkj376whfXvb5O/ayFaQVEU4mdE8+d/jCM0JsDe4XRJiqKQdu0gtDrruWbBz0elt5SLqK9sYv2vJxC4aoCthq3o2s6WmHLVhBRIUkqINvMJ9mTKvSl4BbTsJVVRWGOr5+KoLCZLi2VFURh3czznPzQUDz+9naISjkDv6cbUB1LxCbH2sCk/VGMtcGuUqYkdzZ51hezbeMy23G9cT2Y+N4qQvgH2C8rBKIrCgAm9uPSFMYQPOTlT2K7VhXz+4M8Ubiu1Y3RCOKbTFdP+7bmO6Fx+YV4kzuxL7FjrjKN6Tzd7hyT+IFVVWfdeLo011hskfVJC6T+xl52jEo7kdIkpV05IgSSlhGgXTbVGvn9pK188soFjeWX2Due0yg5Us+T+9Rzd0TI+N3et0/f4Eu3DK8CdaQ+m4u5jHaJ6bGc5hTly8e5oBk2NpPuAQPRebky8I5Gxf41H5yEXKqfjE+zJ+Q8PZfTcweg8TvSaKmvk2+cy2PRRnp2jE8JxmJrMrHwug8JcGebqaBIv7su4v8Xj6ScJQldgbDBharLe8PMMcGf0jUNkAgFxiqRZMSTPtiahkmfHuHRCCkDOYoVoB798km8rer7i7xkMu6I/Qy6IdJgfmb0bj7HuvVzMButsOpf8PQ3fEK9zP1B0OQHhPky9P4WVz28h7ZpB9EkNO/eDRIcyNppaJJ00GoXxtyagWsA3pOvWjmotRVEYcF5vesYHs/6f222Jee8gqZEjBFh7bqx/fxtHt5dxbEcZo28cQv/x0nPDUTjKuaRoH3ovHRc8NpxtX++jWx8/STaKM0qaGUP0xBD8/f3tHUqHk+4RQrSDoZf1p2ecdYiIalHZ/N98fnojG2Ojfcf+WywqGZ/uYvUb2ZgN1qF7fmFe0jNKnFVobCCXvTaO2LE97R1Kl1ew4Sif3rGG4j0VLdb7BHlKQqqNfEO8OP/hoaRdN4iIhGAGT4u0d0hCOIRtX+9nb7p1SLBWr5WhwA6uobqJ3WsK7R2G+AM0GusESr1OFLIXoquTnlJCtAMPPz1THxxK5pI9ZH+5F4D9m45TcbiGSXcnExDu0+kxNRdD/vWMU7FjezLq+sG46WVaZ3F2Hj6n1hhrrDZI7bFOYqg3kv7vnRT8fBSA1W/mMPO5Uei9dHaOzLkpGoVBk/swcFLvU3of5C7fR0iMPz0GBp3h0UK4nsM5JWR8usu2PP6WeLr18rVjROJs9vx8hE2L8miqNeLX3YvuA7rZOyTRShaTRW4KC3EGcmQI0U40GoXUP/dj8j3J6E4Uoqw8UsdXj6dzION4p8ZSeaSWZU9stCWkFI3CiDkDGfvXOElIid9lz/ojfHrnGo7udMyaaa6kuKCSLx7ZYEtIAYT1D7RjRK7ntwmp4/nl/PLpLr6Z/wvp/9lp916uQnSGqmN1rH4jm+b65kmzYogc2t2+QYmzMtabaKq1FsjesHDHKRPYCMd0dEcZS+5fT9HuinPvLEQXJEkpIdpZn9QwLnkmjcAIa+8oY4OZVf/IYuv/dnfK6x/KLOarJzZSdawOAHcfHec/PJQh0xynxpVwLoezS1j7Ti6mJjOrXs2k/FCNvUNySRaLStaXBSx/ahM1xdYadTpPLeNviWf8LQnSS6oD7VpTCCcuzHd+d5AvHt7A8fxy+wYlRAcy1Bv54dWtGOqtCdg+qWEku3ghXVcwYFJvgqOt9WUqDteyfeUB+wYkzqmp1sjad3KpLqrn66c3SWJKiNOQpJQQHcC/hzcXzRtJ9MgetnWdMeypscbA6jezMTZYTzK79fblkmfSCB8sw1HE79czLoheida6B4Z6E9++kEFtaYOdo3IttaUNrHhmM1sX70G1WLMjobEBzHx2NDGjpbZXRxt7Uxwjrh6IVm89Laouqufr+ZvZ9FGebZYkIVyFalFZ83YulUesN68CI3wYd3M8ikZuXDk6jUZh1PWD4URTZS4tkN9jB6aqKj9/uJ268kYAegwKIjQmwL5BCeGAJCklRAfRebgx4bYERlw9kNixPRk0pU+Hv6aHr54xN8UBEDW8OzOeGoFvqMyyJ/4YjVbDxDsSCelrvTtbX9HEty9ssQ0hEH/M4ZwSlj70M8fzrXdPFcU6jObCJ4bjFybHb2dQNApDzo9k1nOjCet3YqikCttXHmDpwz9zfJfc2RauY+uSPRzKLAZA7+XGpHuS0XtKmVlnERLtz6BJvQEwNZnZuCjPzhGJMylYf5T9m6wlPNy9dYz7W5wkf4U4DUlKCdGBFMV6oTP2r3GnDJ0rPVDVIa8ZPaIHFzw2jIl3JLaYRl6IP0Ln4caU+1NsSZLKI7X88OpWTAbpRfJHeQW6YzZa64L4BHsw/fHhpMyORaOVn+jO5t/Dm+lPDGf4Xwag1Z3oNXW8nq/nbWLTx3mYjfL3LpxfcJQ/Og8tigIT70jEv7u3vUMSbZTy5354+lt74B/cUmRLMgrHUVNcT/p/dtiWR80djHeQzJorxOnIGa8QneC3CanDWcV8+Wg66/657Q9d1B/dUcaWxafWqgofFCT1o0S78/RzZ9pDQ21DUY/nV7Dm7RwsJ4abid8nqLcfQ6/oT/SIHsx8brTMpmRnGo1C3PQoZj436uQwCxWK8ivkDrdwCZFDw7ho3kjG3BhHRLxMSe+M3L11DL9qoG05/T87ZaixA7GYLax+Owdjg7VNYsf2JHpEj3M8Soiuy6GTUmazmQULFjBx4kTi4+OZNGkSb731Fqp68gJIVVVee+01Ro8eTXx8PNdeey0HDhxo8TyVlZXce++9JCcnk5qayiOPPEJdXV2LffLz87nyyiuJi4tj3LhxvP/++6fEs3LlSqZNm0ZcXBwzZsxg7dq1HfK+hWtrrDGw5u1cUGH3mkK+fnoTNSVtqwegqirbvz3AyucyyP5yL7tWH+6gaIVoyS/Mi2kPpOLmbp3F8cAvRWxalNfie1mcmWpR2bX6MObfzJg0eGofJtyegLu3FDN3FAHhPlz41AiGXdkfvZcbY/8WJ73XhMsIjPCl3/gIe4ch/oC+aT1sNUNrSxrI+qLAzhGJZjnL9lG8uxIA3xBPRs4ZePYHCNHFOfTZ1fvvv88nn3zCE088wYoVK7jvvvv417/+xUcffdRin48++oinnnqKxYsX4+npydy5c2lqarLtc99991FQUMDChQt599132bJlC0888YRte21tLXPnziU8PJylS5fywAMP8Oabb/LZZ5/Z9snMzOTee+9l9uzZfPnll5x33nnceuut7N7dOTOqCdfh4atnxDUnC+qW7q/my0c3ULittFWPNxnMrHtvmzURcKKHyqGsEkkKiE4THO3PpLuSULTWXiO71hymuqjezlE5vrryRlY+l8H697efMhunoijSu9EBaTQK8RdGc/nr4wmM8G2xrfRAFcUFlfYJTIg2sJgs7P/luJwnuBhFUUi7bhAaNwUPXx0BPX3sHZIAigsqyfzcmiBUFBh/q8yeK8S5OHRSKisri/POO4/x48cTERHBtGnTGD16NLm5uYC1t8iiRYu4+eabmTRpEgMGDODFF1+kuLiYVatWAbB3717Wr1/PM888Q0JCAqmpqTz22GN88803FBUVAbBs2TKMRiPPPvsssbGxTJ8+nauvvpqFCxfaYlm0aBFjxozhhhtuoG/fvtx1110MGjSIjz/+uPM/GOH0Ykf35KKnR+Ibah1b3lRr5Nvnrb2e1F8Nhcr6ooAlt2y03f2qq2jkm2c2s2fdEds+CRdFc95dSXJBKzpVREIIY2+Mw8NPz/THhktNknM4kHGcpQ/9zNEdZQBs++YANcWSyHMWv72gMBnMrHkrl+VPbiTj011Sa0o4tM3/zefHBVn8/K8dp/TSFM4tINyH8+5MYvbLY4kdIzO1OoKq43VoTty0S5wZc3LyDCHEGTl0UiopKYlNmzaxf/9+wDrEbuvWrYwdOxaAwsJCSkpKSEtLsz3G19eXhIQEsrKyAGtiy8/Pj7i4ONs+aWlpaDQaW3IrOzub1NRU9Hq9bZ/Ro0ezf/9+qqqqbPuMHDmyRXyjR48mOzu7/d+46BKC+vhxyTOj6JV0op6DClsW72bVPzIx1BvJWlpA5hJrMipzSQHr39/GV4+mU1Jg/ZvU6jVMuC2BoZf3RyN1ToQdxI7tyZ9eGSvTG5+FsdHEz//azqp/ZNlmK/Tu5sEFjw6VmTGdWP6Ph6k8UouqWodpfPFIOiV7K+0dlhCn2L2mkB3fHQRgz7pCKg7X2Dki0d76pITh4as/946iU8SO7sklf0+j3/gIki7pa+9whHAKDj0110033URtbS3nn38+Wq0Ws9nM3XffzUUXXQRASUkJAEFBQS0eFxQURGmpdShUaWkp3bq1LBrr5uaGv7+/7fGlpaVERLQcVx8cHGzb5u/vT2lpqW3d6V6nLVRVddou1M2xO2v8jkbv7cbke5LJ/nIvmUsLQIWDW4v57K61tgvYZrtWF9r+7R3kwaR7kgmO9JO26GRyDLSk93I75bNoqGrC09+9Q1/XGdqh9EA1a97MoerYyRqGkcPCGD13MO4+eoeO/fdwhjZpLwMn98LYZCLr8wIsZpXKI7Use3IT8TOiSJoZY5u5zx66Ujs4C3u1SfGeSn7+cLttOe26QQTJeYONKx8rFovqFDcsXbUNAnr6MObGIQBO+d5ctV2cjSu0Q2tjd+ik1MqVK1m+fDmvvPIKMTEx5OXl8dxzzxEaGsrMmTPtHd7vVl1djUbj0J3UzkhVVerrrUNOZLhY+4k+LwTP7m78snAPxnrzKQmpX/Pq5s7EB4ag81VtPflE55Fj4MxUi0ruFwc5lFHKhPuG4BPs0XGv5cDtoFpU9vx0jG1fHUI1W3+MtXoNiX+KJDItlEZzA41VbZvcwBk4cpt0hKjxwXSL9SRj0V4qD9ehWlRyvtrH/oxjDJ0TQ2Bv+9R36Wrt4Azs0SYNlQZ+/EcuFpP1O6jvuDC6J/vJecOvuOKx0lhtIPfzg7h5aEm+Itre4ZyTq7SBxazahuy5AldpF2fnCu1gsbRuyLhDJ6VefPFFbrrpJqZPnw5A//79OXr0KO+99x4zZ84kJMQ67KmsrIzQ0FDb48rKyhgwYABg7fFUXl7e4nlNJhNVVVW2xwcHB5/S46l5ubl31On2KSsrO6X3VGv4+fmh1Wrb/DhH0Jzt9Pf3d9qDw1H5p/lTua+BHSsPnnW/+vImjmRUkjQzppMiE78mx8CZZX2xlz0/HgMg/e1dzHhqRIcNKXDkdti1ppDcpSeP46BIP8bfGk9AuGsXoXXkNuko/v7+RDzTnZzl+8j6Yi+qWaX6aAM/vbidxIujSbikL1q3zr0J1RXbwdF1dpuYDGbWvvILjVXWG1zdBwYy9voENJ38t+joXO1YMZssrHxsHXXljaDAkMlRhPQNsHdYZ+UKbaBaVL57aSuBET6k/rmfXXvKthdXaBdX4ArtYDa3ruamQyelGhsbT2kArVZra6CIiAhCQkLYuHEjAwdap9qsra0lJyeHK664ArDWpaqurmb79u0MGWLtRrlp0yYsFgvx8fEAJCYmsmDBAoxGIzqdtZhpeno6UVFR+Pv72/bZtGkT1157rS2W9PR0EhMT2/y+nH2Wpeb4nfk9OKKspQXnTEg1y1xSgIJC0ixJTNmDHAOnN2hKb/amH6XqaB3Vx+v5/uWtTH90OG7uHZOEd9R26DemJ7t+PEzJviriZ0SR8qd+nZ6YsBdHbZOOpNVpSZ4VS5+UMNa9m0vZwRpUi0r2sn1EjehBt16+536SdtYV28HRdVabqKpK+sKdlOy19ojyCfbkvDuT0Oqc82ZoR3OlY8VNp2XI+ZFs/m8+qLDhw51cPH8kGq1j//44exvs+P4AR3JLOZJbSuWROqY9mGrvkNqFs7eLq3D2dmht3A79LTVhwgTeffdd1qxZQ2FhIT/88AMLFy5k0qRJgPVNzpkzh3feeYcff/yRXbt28cADDxAaGmrbp2/fvowZM4bHH3+c3Nxctm7dyvz585k+fTphYWEAzJgxA51Ox6OPPsqePXtYsWIFixYt4rrrrrPFMmfOHNavX8+HH37I3r17eeONN9i+fTtXXXVV538wwiVtXbKnQ/cXoqN5+OiZ9uBQvAKs9aRKCqr46Y1sLGbXnu3pt+PlNW4axt+WwPkPD2XYFQO6TEKqqwvq48dF89NImhWDolVImtnXLgkp0bXt+PagbYZerV7D5HuS8fTr2Bp/wnEMntaHbr2t3ztlB6rZ+cMhO0fk2soP1ZDx6W7b8pALIu0XjBBOTFEduHJWbW0tr732GqtWrbIN0Zs+fTq33nqrbaY8VVV5/fXXWbx4MdXV1aSkpPDkk08SFRVle57Kykrmz5/PTz/9hEajYcqUKTz22GN4e5+cwjw/P5958+axbds2AgMDueqqq7jppptaxLNy5UoWLFjAkSNHiIyM5P7772fcuHGtfj9ms5ns7GwSExOdevheVVWVU3cjdFRZSwvalGhKmR0rPaXsQI6Bcys7WM3X8zZhbLB22e0/oRejbxjcrp+Xo7RD2aFq1r27jbE3xREU6We3OByBo7SJIyg/VENAuHeL4VJmk4Xq43UERnRsokrawfF0VpuYDGY+f2A9NcXWmnUT70gkekSPDns9Z+eqx0rR7gqWP7UJAJ2nltkvj8U7sONqPP4RztwGJoOZrx5Pp+JwLQBDzo9kxNUD7RxV+3DmdnElrtAOrc1/OHRSytVIUkqcS2sTU5KQsh85Blrn6I4yvn0+A8uJQt/Js2NInhXbbs9v73ZQVZUd3x0k45NdmI0W/MO9mfn3UR02VNEZ2LtNHN3WJXvI+WovSZfGkDAjusOG1Eg7OJ7ObJOGqiZ+fC2LsH6BDL28f4e+lrNz5WNl/fvbbLM2R4/owcQ7Eu0b0Bk4cxts+iiP7SsPABDYy5eL54/ETe8a5wDO3C6uxBXaobX5DxlTIIQDSZoVQ8rss1+4S0JKOIPwwUGMuznetpy5pIBdqw/bMaL2U1/VxHcvbmXTojzMRuvQRK1OQ2ONwc6RCUdVfqiG7K/2YjGrbF28h2VPbKT8cI29wxIuyNPfnQseGUbKn/vZOxRhR0Mv74+Hr7VO7r5NxyjcVnqOR4i2KMwtsSWktDoNE25NcJmElBD2IEkpIRzM2RJTkpASzqRvWjjD/jLAtpz91V5MhtbNwuGoDmeXsPTBnynMKbGtG3J+JBfPG4lPsKcdIxOOzL+HF/HTo2i+0Vm6v5ovH91wIlHl2jXXRMdSLeopf0MaNw0ajXPeVRftw8NXz7ArTv7+pn+4w+l/fx1FY7WBde9usy0PvbyfrY6XEOL3kaSUEA7odIkpSUgJZxQ/PYoh50fSrbcvFz45wmnvJJoMZjb+ZyffvbiFxmprjyhPfz3THkxlxNUDZWYrcVZanZahl/dnxtMjCehprWdpMals+Ww3y5/cREWh9JoSv0/WlwWsfC7D9r0kRLPYsT0J6x8IQHVRPfvSj9k5IuenqirrP9hOfWUTAD3jghg8NdK+QQnhAiQpJYSDSpoVQ/JsaxIqeXaMJKSE0xr+lwHMeGqEwxZaPZeKwhq+enwjO747aFvXKymEWc+PJiIhxI6RCWcTGhPAJX8fRfyMaFuvqZJ9VXz5aDo5y/dhsUiZT9F6B7cUkbmkgGM7y1n25EZMTdITRpykaBRGXT8Y724ejL81gdhxPe0dktMzNpioL28EwN1Hx9i/xaNIr0Qh/jA3ewcghDizpJkxRE8Mwd/f396hCPG7KRoFnUfLnxuzyYKxwYSHr95OUbWesdFM5VHr7DpanYZhVw5g0JTeTlt0UtiXm17LsCv6E5kaytp3t1F1rA6z0ULGJ7tw02sZPLWPvUMUTqCisIY1b+fYlvtPiOjSEy2I0+vWy5fLFoxrMROo+P30XjpmPDmCrC8KCIr0c9qbbUI4GvmGEkII0akMDSa+f2kr3z6fgbHRZO9wzik0JoDUP8US2MuHi+enMXhqH0lIiT8sNDaQmc+NIm56FCgQGOHDgIkR9g5LOIHGWgM/vJKJsdHaMyp6RA/iZ0TbOSrhqCQh1b40bhpS/tSPyKHd7R2KEC5DekoJIYToVGvfzuHIiZmAflyQxZT7UhzqpLlodwUhff3RaE/GFHdhNIOnRTptTSzhmNz0Wob/ZQCRQ8PQ6jWn1CYzNJjQe8qpmjjJYraw+o1sqovqAQiK9GPsX+MkUS5arXBbKaF9/dF76ewditOwWFSZPECIDuQ4VwFCCCG6hNTL+qH3sl5oF+aWsv5f21FV+9fSMRvNbP5vPsuf2kT2V3tbbNNoFElIiQ4T1i+Q4MiWw7QrCmv49PbVbFuxX2pNCZuMT3dzZFsZAB6+OibdnSzD9kSr1Fc08uNrWXz7XAZbl+yxdzhOo2h3BV889DOl+6vsHYoQLkuSUkIIITpVYIQvU+5LQauz/gTtWXeELYvte4JceaSWZU9uYts3+wHI+ryAskPVdo1JdF0Ws4W1727DUG9i88f5fDNvM1XH6uwdlrCzPT8fsX1HKVqF8+5KwjfE085RCWdhNlo4lFUMwM7vDlJ6QJIs52KoN7LmrRwqCmtZ9sRGinZX2DskIVySJKWEEEJ0uu4DujH+1gQ40Rs+56u97Pzh4Nkf1AFUVSX/p8N88egGyg5Yk1AaN4VhfxlAtwjfTo9HCACLWSWsX4BtuWh3BUsf/pntKw+gSq+pLqnsUDU/v7/dtjxyzkB6DAyyY0TC2fiGepF0iXUmZ1WFDR/ukO+Tc9i4KI+akgYAgvv6E9JXJh4SoiNIUkoIIYRdRA3rzsg5g2zL6f/eyYGM4532+o01BlYtyOLnf23HbLAA4B/uzcXz0oi7IEqmeRZ246bXMnLOIKY/PgzfUGtPGLPBwqaP8vjmmc1UF0mvqa4moIc3fdN6ANB/Qi8GTupt54iEM4q7MIqAnt4AlBRUkb/6sJ0jclz7Nh1jz7ojAOg8tUy4JaFFrUkhRPuR6pkOyGw2YzQa7R3GaamqisFgoLGxUYpqdoLO/rz1ej0ajfzgis4zeGof6isayVm2D1RY/WYO5z/iTvf+gR36ukd3lLHmnRzqy5ts6wac14sRVw2U+izCYfQYGMSs50eT8eludn5v7Ul4PL+CpQ9tYOjl/Rk0ubckT7sIrU7LmJvi6BkXTOSw7nIOJn4XrZuGtOsGs+KZXwDI+GQXkalhePq72zkyx1JX1sCGD3bYltOuGYxvqJcdIxLCtUlSyoGoqsrx48eprKy0dyhnZbFYKCsrs3cYXUZnft4ajYaoqCj0en2nvJ4QYC18XlfeSMHPRzEbLeR+vY/u/VM67PUOZRbz/Stb4cSoBXcfHWNujCNyaFiHvaYQv5fOw420awcROTSMdf/cRm1JA6YmMxv/s5Oyg9WMvSmuxf5ZXxSQuaSA5NkxJM+KtVPUoiMoikLftHB7hyGcXPigIGJGh1Pw81EM9SZ++b9djLs53t5hOQzVorL23W001Vk7CESN6E7MGDnuhOhIkpRyIM0JqdDQULy8vBzyLpiqqpjNZrRarUPG52o68/O2WCwcPXqUY8eO0bt3b2lf0WkURWHMTXE0VBnQeWoZf0tCh75e+JAgAiN8qDhcS/jgIMbdHI93N48OfU0h/qjwwUFc+sJofvm/XeStOgQK9BsX0WKfrKXWhBRA5pICFBSSZsXYI1zRDgo2HCU40o+Anj72DkW4mOF/GcChzGIM9Sb2rD9Cv/E9pUbZCdtW7OfoDuvNYO9uHoy+foicEwvRwSQp5SDMZrMtIRUU5Lg/CpKU6lyd/XmHhIRw9OhRTCYTOp2uw19PiGZaNw2T70lGo9Og6eDhSG56LRNuS6Qwu4S46VI7SjgPnYcbo64fTOSwMEr3V7cY5pq1tOCUad6blyUx5XyO5ZWx9t1c6/fVrQn0Tg61d0jChXj6uzP08v5s+NA6RG3DhzuY+dxotG5du4RD2YFqtny227qgwLib43H3kfNhITpa1/7mcSDNNaS8vGS8srCf5mF7ZrPZzpGIrsjNXXtKQqqxxoDZ+Pv/Hptqjax5O4eKI7Ut1nfr5Uv8jGhJSAmn1HNIMAkzom3Lp0tINdu6ZA9ZSws6KzTRDmpKGvhxQRaqWcXYYOJYXrm9QxIuaMDEXrbZ5HyCPDHUO2Y9285UdqC6eWQ/cdOjCB/suB0FhHAl0lPKwUjvI2FP8vcnHEl1UR3fPr+FkL7+jL8loc0JpGN55ax5O4e6skYqDtdw0byRaHVSxFy4lrMlpJptXbIHFVVqTDkBU5OZVa9upbHGmiDoGRfM0Cv62zkq4YoUjcLouUOoOl5H1HApng/Qb3wE3Xr7kvP1PlL/JN+XQnQWSUoJIYRwOGaThRXPZlBb0kB1UT1egR4M/8uAVj3WYrKQubSAnK/2op645Vlb2kjlkTqCIv06MGohOldrElLNMpcUkLfqMEOm9SHhor4ttqmqKhekDkBVVda9t42ygzUA+IV5MfH2xA4f0iy6rqBIP/ld/I3gaH/OuyPJ3mEI0aVIUkq4vIkTJzJnzhyuvfZae4cihGglrZuGkXMGsurVTFQVtn2zH69Ad+IuiDrr46qL6ln9VjYlBVW2dT0GdWPczfH4BHl2dNhCdKrWJqSaNVQ2UV/R1GKdqqp8cutqPPz1+Id549fdC78wL/y6W//tFeAuCatOkrt8H/s2HQNA56Fl8r3JUs9GiA6mWlQZyi+EnUlSysWYzWYyc/MoKasgJCiQ5PiBaLUdP1ylpKSE9957j7Vr13L8+HF8fX3p3bs3F110ETNnzsTTUy4GhRBt0ycljFHXD+bnD6yFWDd/nI9XoAd9R/Y47bT3e34+QvrCHRgbrDWoFK1CyuxY4mdES08D4ZJSZse2LTGlgF937xarGqoN1Fc2UV/ZRPmJHjq/5uautSapwrxIvawfAeEyE1xHOJxVTEZzgWVg/C0JBEb42jEi0RUdyiwm95v9TL0/BZ2H618mqqrKqtey6NbLl6RL+qLp4oXehbAX1/+26UJWrdvI869/QFFJmW1dWEgQD90xl0ljR3bY6x4+fJgrrrgCX19f7r77bvr3749er2fXrl0sXryYsLAwzjvvvDY/r8FgsBXeFkJ0TQPO601dRZOtUPPad3IozClhz7ojgHVIksVooaakgb3px2yP8wvzYvytCYTGBNgjbCE6RfOseq1JTKXMjiXhomgsFrXF+qYaA35hXtSUNKD+ZhtYaxyVH6qh/FANQy9vWdto36ZjZH1RYO1VFeaFv+3/XngFekjvg1aqPFrLT2/m0FxhOWV2LH1Sw+wblOhysr4sYOti63dJ1hcFDLuidUPmnVn+T4c5mFHEwYwiyg5UM+W+FHuHJESXJEkpF7Fq3UbuefxFfns6WVxSxj2Pv8ir8x/osMTUU089hVar5fPPP28xe2CvXr2YNGkS6omiLtXV1bzwwgv8+OOPGAwGhgwZwiOPPMKAAdYfvTfeeINVq1Zx1VVX8c4773D06FHy8/Oprq7m5ZdfZtWqVdTU1NCnTx/uvfdeJkyYAMCWLVt49dVX2b59O4GBgUyePJl77rnnjDMZ/u9//+OFF17gjTfeYOTIjkvWCSHaR/KlMdSVN7J7TSEWk2pLSDXL/mpfi+XYsT0Zec0g9J7yEydcX2sSUymzY237/bYfQGCEL3/+xzgsJgu1ZQ1UHa+n+ng91UV1J/5fT3VxPVhUfEJa9nquKKyl4rD1v9/S6jUnelh5Exrjf0odK3FS0e4KjI0mACKHhpF4iXxWovNFD+9B9hd7MRstbFtxgJjRPenWy3V761UerWXTR3m25QETe9kxGiG6NjljdwFms5nnX//glIQUWG+6KcALb3zIhFHD2n0oX0VFBRs2bDhrEqi5FsWdd96Ju7s777//Pr6+vnz22Wdcc801fPfddwQEBABw6NAhvvvuO9588000Gg0Wi4Ubb7yRuro6XnrpJXr37k1BQQEajca2/4033sidd97Js88+S3l5OfPnz2f+/Pk899xzp8Ty/vvv869//YsPP/yQ+Pj4dv0shBAdQ1EURs8dzPH8cqqP159xP61Ow9i/xtE3LbwToxPC/s6WmPp1QupsNG4a/MK88QvzhoSW2yxmC/UVTWh/M7TF2GBCo1WwmE89AzEbLLaElbHBdEpSavVbOZgaTbbaVc0JLJ+grtfDqv/4XngFeJCzbC/jbo7vcu9fOAb/Ht7Ez4gma2kBqlklfeEOpj8+3CVryplNFta8lYPZYAFgwHm96J0caueohOi6JCnl4L5bvYG3PvyEuobGM+5jMBiprKo+43YVOF5cyviZ16PXn71gprenB7fNvZIp49NaFd+hQ4dQVZWoqJbFh4cPH47BYADgyiuvZMKECeTm5rJx40bbkLwHH3yQVatW8d1333HZZZcBYDQaefHFF+nWrRsAP//8M7m5uaxYscL2Gr16nbyT8d577zFjxgxbEfPIyEgeffRRrr76ap566inc3d1t+7700kt89dVXfPzxx8TGyjSvQjiTnK/2nTUhBWA2Ws65jxCu6nSJqdYmpM5Fo9XgE3xqbcgRVw9k2JX9qStrpOp4nbVX1fF6279riuuxmFT8up960+pIbgmNNcZT1hV/8oQAADUMSURBVGt1GnxDPa0Jsu5exIwOJzjS/w+/B0fXKzGEiIRgl0wACOeRcFE0ezccpbqonuP5FexZd4R+4yLsHVa7y1yyh9L91msn/x7ejLhqoJ0jEqJrk6SUg/v3p1+y/9CRc+/YCmdLXP32NVublDqTJUuWYLFYuO+++zAYDOzatYv6+nqGDx/eYr/GxkYOHTpkWw4PD7clpADy8vLo3r37KUmvZvn5+ezatYvly5fb1qmqisViobCwkL59rXdmFy5cSENDA59//nmLpJYQwvG1Zdr75v3a40JcCGeTNCsGFdU2CUBnHAcarQbfUC98Q09NPFksKnVlDackWoyNJgwNptM+n9loofJIHZVH6gAI6xfYIilVcaSWjE92nZgh0DpLoH+YF97Bnk41oUFjtQEPv5Z1MyUhJezNTa8l7bpBfPv8FgB++b98eieH4uHrOjVej+WVk7PcOuxf0SpMuDUBN/eOnxRKCHFmkpRycNddMZM3P/i/P9RTqlmAv1+rekpde/klrY6vd+/eKIrC/v37W6xvTvx4eHgAUFdXR0hICB999NEpz+Hre3K8+m9n6Wt+/JnU19dz+eWXc/XVV5+yrUePHrZ/p6amsmbNGlauXMlNN910jnclhHAkbZ32fuuSPZKUEl1W0swYoieG4O9v/95FGo2Cb8ipySqdhxvX/nsq9eUte1hVN/+7qB6z0Tqs5re9rCoO13Aos/jU19Iq+IZaE1XNBdcHTu7tkImesgPVfD1/E8mXxjLk/EiHjFF0XRHxIUSN6M7+TcdprDGS8eluxtw4xN5htYumOiNr3zk5qUDqn2IJjrb/d6UQXZ0kpRzclPFp5+y1ZDabmXrZXykuKTttXSkFCAsN5ttP3233mlKBgYGMGjWKjz/+mKuuuuqMdaUGDx5MaWkpWq2WiIjWdwPu378/x48fZ//+/aftLTVo0CAKCgro06fPWZ8nLi6Ov/zlL9xwww1otVrmzp3b6hiEEPbV1mnvU2bL8FwhHJ1Go+AT7IlPsCc9f3O9q1pU6ioaqT5ej3937xbbqotOP0TXYlapOlZH1bE6DlOCp7+eQVNanhvs+O4g1cfrWtSx8g3xRKPt2Gngs74osPVeGzipNz+8uhVjg5nNH+ej93Sj/wTpwS0cy4irB1KYU4Kxwcyu1YfpN64nYf0C7R3WH5a+cAe1pdYb/d0HBBJ3YbSdIxJCgCSlXIJWq+WhO+Zyz+MvokCLxFTzvbcHb7++3RNSzZ588kmuuOIKLr30Um6//Xb69++Poihs27aNffv2MXjwYNLS0khMTOTWW2/l/vvvJzIykuLiYtauXcukSZOIi4s77XMPGzaM1NRU7rjjDh566CF69+7Nvn37UBSFsWPHcuONN3LZZZcxb948/vSnP+Hp6UlBQQHp6ek88cQTLZ4rOTmZf/7zn9x4441otVpbHSohhGNr67T30ktKCOemaBR8gjzxCTq1jlXCjGhiRofbZgZs0dOqqM5WuNgvzPuUxx7IOM6xneUtX0ur4BvsaRsK6BfmRY8B3QiK9GuX95K11JqQAshcUsDuNYW2i+KQGH/6jpKJGYTj8Q70IGV2LJs+ygdg83/zuehp556x2lBvpKLQOlOo3suN8bckONWQXyFcmSSlXMSksSN5df4DPP/6BxSVlNnWh4UG8+Dt1zNpbMf9kPTu3ZsvvviC9957j1deeYWioiJ0Oh0xMTFcf/31XHnllSiKwj//+U8WLFjAww8/TEVFBcHBwaSmphIcHHzW53/jjTd44YUXuPvuu6mqqqJfv37ce++9AAwYMICPPvqIBQsWcOWVVwLWoYMXXHDBaZ8rNTWVf/7zn9x0001otdrTDvsTQjietk57L4RwTb9OWIUPDmqxTbWo1Fc2UV1Ud9rHnm4iBNWs2oYMklMKQPKlMS2SUiaDmV/+b5d1aGCYNXnlG+KJxu3sPaxOVw+vOSHlFeDOpLuTcdNLLRvhmAZN6cOedUfwDvJk5DXOXwhc76Xj4vkjyfhsNyHR/qedvEEIYR+KqqqnG/ElOoDZbCY7O5vExMRTei01Njbahqidq47SuV4jMzePkrIKQoICSY4f2K49pFRVxWw2o9Vq7VID4a9//SsPPPCArYC5q+vsz7u9/g5dmaqqVFVV4e/vL3VA7OBMRc8lIWV/cmw4BmmHM6uvaKTqRI8qaw0r67+rjtdjajLb9ht/awIxv+rBVHGkls/vX9/iuRSNgk+wh61+VXNPq/DBQbjpteecoKH/xF6MucE16vQ4KzlWzq2pzoi799nr0f4R0gaOSdrFMbhCO5wt//Fr0lPKxWi1WoYmud5JTllZGfX19eh0OtavX99lklJCiJY6ctp7IYRr8wr0wCvQgx4Du7VYr6oqDVUGa6H14/V079+ydk718VN7XqkWlZriBmqKG1qsv+rd89j29f5zDjfe9dNhfLp5yHeXcGgdmZDqLKqqOu0FvRBdhSSlhFPIz8/nlltuITAwkNtuu83e4Qgh7Mge094LIVyXoih4BbjjFeBO9wHdTtneY2A3Lnxi+IkaVr/qaVVUh7HhZA8rvZcbO1cdtNWQOpfmxJV8hwlnYWw0UVvaQGCE77l3dgCl+6rY8OEOxv4tzmliFqIrkqSUcAqjRo0iJyfH3mEIIRyEI017L4RwbXovHd0HdDslYaWqKo3VBlvBdVOjmfR/72zTc29dskeSUsLhqarKgYwiNi7aidZNw6UvjnH4emimJjOr38qh6lgdXz6azgWPDnOJGQSFcEWSlBJCCCGEEKKNFEXB098dT39328VuU62xVTOFNkuZHdtR4QnRrnZ8d5D68iYAcr7aS8qf+tk5orPb9HEeVcesQ28De/kSEi03sYRwVGefNkQIIYQQQgjRKkmzYlqdaJJ6eMJZKIrCqOsGoWittZlylu+zJXwc0cGtReT/eBgAN3ctE25NOOdsmUII+5GjUwghhBBCiHbSmsSUJKSEswmM8CV+ehQAFpPKhg934IiTuNdXNbH+/e225eFXDcC/h7cdIxJCnIskpYQQQgghhGhHZ0tMSUJKOKukmTH4BHsCcHRHGfs2HrNzRC2pqsr697bRWG0AoHdKKAMm9rJzVEKIc5GklBBCCCGEEO3sdIkpSUgJZ+bmrmXkNQNty5s+zsdQb7RjRC3l/XCIw9klAHj66xlz4xAURbFzVEKIc5GklBBCCCGEEB0gaVYMybOtSajk2TGSkBJOr09KGH1SQgFoqGxiy+LWF/bvSBWFNWz+b75teexf4/H0c7djREKI1pLZ94QQQgghhOggSTNjiJ4Ygr+/zP4lXMPIawZxZHsZpiYzeT8cpN/YngTbeXa7ot2VWEwWAAZN6UOvxBC7xiOEaD3pKSX+ELPZzOWXX85tt93WYn1NTQ3jxo3jH//4h23dd999x5w5cxg6dOj/t3fnYU1d6R/Av0kIgmxuuCAoSAVbFQSxbnS01LEuj9aqU6xTUFEc69KfVqu0uAFulbqh1VpUFLVWpUq1aGeK1iqj1g1wV1AUqYiCFUT25Pz+cEhNWQSFJJd+P8/j85h7zz3n3PMmIXlz7rlwcXHB22+/jU8//RSXL1/WlNmzZw+cnZ01/9zc3DB06FD85z//0dk5AYCPjw8WLlyo0zaJiIiIiAydeRNTzaw/hbEC2ff0fye+dl52GDi3G+zcrPH6SGd9d4eIqoFJqTqo6Ppl/L7wMxRdv/z8wi9JoVBg8eLFOHbsGPbt26fZHhISAisrK0yaNAkAEBoaimnTpuHVV1/FunXr8OOPP2LZsmWws7PDsmXLtOo0NzdHXFwc4uLisHfvXnh6emLq1Km4efNmrZ8PERERERFVrmN/e3Tob49/fPEGHHvY6Ls7AIDmzg3x9iceMDJW6LsrRFQNvHyvjhFCIG9fFIrOn4OsngmU0+fU+gJ/Dg4OmD59OhYsWIBu3brh/PnzOHDgAKKiomBsbIyEhARs2LABgYGB8PX11RxnY2ODDh06lLmdrEwmg7X10ym31tbWmDp1KjZt2oRr166hTZs2AIDs7GwsXLgQP//8M4qKitClSxfMnj0b9vb2mnr+/e9/IywsDLdv30bTpk3xwQcfwM/PT7N/+/bt2LJlC9LT02FhYQEPDw+EhYUhICAAp06dwqlTpxAZGQkAOHToEGxtbWtrCImIiIiIJENuJEc3n1efX7AWCSG4kDlRHcCklASIwsKKd8rlkCmVmodFFxJQdCkRMpP6KLqUiKILCTB2fu1/ZWWQKY0rrVdW78UWBPTx8cFPP/2EmTNn4vr165g4cSLatWsHAPjhhx9Qv359jBw5stxjK/tjolKpEB0dDQBo3769ZntAQABu376NdevWwdzcHKGhoRg/fjxiYmKgVCpx8eJFTJ06FZMnT8aAAQMQHx+PoKAgNGjQAEOHDsWFCxewcOFCLF26FG5ubsjOzsaZM2cAAIGBgbh16xbatm2Ljz76CADQqFGjFxoXIiIiIiKqWUII/LLuPBraWaDjQAfI5UxOEUmVwSelMjIyEBoaimPHjiE/Px+tW7fGokWL0LFjRwBP35DCwsKwe/du5OTkwN3dHfPnz9eaMfPo0SOEhITg559/hlwuR9++fREYGAgzMzNNmatXryI4OBgXLlxAo0aN8MEHH8Df31+rLwcPHsSqVavw22+/wd7eHjNmzECvXr1qfQyyAiZVuM/41Y6wHP9/AJ6OxaPP50A8yQUUCkClwqPFgZA3awGZTAaloxOsJs/UHPt7yCyon+Rq1ddkxYYX6qNMJsP8+fMxYMAAODk5Yfz48Zp9t27dgp2dHYyM/ni6RUREICwsTPP46NGjsLCwAPB0PSo3NzcAQEFBAYyMjBAcHIxWrVpp6jt8+DB27NgBd3d3AMAXX3yB3r17IzY2Fv3790dERAS6d++uuXzQwcEBycnJ2LhxI4YOHYr09HSYmpqid+/eMDc3R8uWLfHaa0+TdxYWFlAqlTAxMdHM2CIiIiIiovIV5RXj7O4ktPZoBpv2jWu9veRjd5EcdxcAcD/5Ef4+zb3W2ySi2mHQa0plZ2fj/fffh1KpRHh4OGJiYjBr1iytu5eEh4dj69atmD9/Pnbt2gVTU1OMHTsWhc/MApoxYwaSk5MRERGBr776CmfOnMHcuXM1+3NzczF27FjY2Nhgz549mDlzJtasWYOdO3dqypw7dw7Tp0/H8OHDER0djbfeeguTJk3C9evXdTMYVVB87RJEXh4glwMyGSCXQxTkAwUFOmn/u+++g6mpKdLS0nDv3r1Kyw4bNgzR0dEICgpCXl6e1iV8ZmZmiI6ORnR0NPbu3YuPP/4Y8+bNw+HDhwEAN27cgJGREVxdXTXHNGzYEA4ODrhx4wYA4ObNm5qEVSl3d3fcvn0bKpUKPXr0gI2NDfr06YNPPvkE+/btQ35+fk0NBRERERHRX0JOxhPsnnEMl/59G//ddAmqYlWttvf4fh6Ob7mkeezYvUWttkdEtcugZ0qFh4ejefPmWLx4sWabnZ2d5v9CCERGRuLDDz9Enz59AABLly5Fjx49EBsbi4EDB+LGjRs4duwYoqKiNLOrZs+ejfHjx2PmzJlo1qwZ9u3bh+LiYixatAjGxsZo27Ytrly5goiICHh7ewMAIiMj8cYbb2DcuHEAgKlTp+L48ePYtm0bgoODa3UcGi/5suKd8qd5RSEE8n7YA5mZOeSNmkAmk0EIAfXDTBi1aQurjwIgU2jnIBvO+bzG+nju3Dls2bIFGzduxLp16xAYGIjNmzdDJpPB3t4eZ8+eRXFxMZT/u9TQ0tISlpaW5Sav5HI5WrdurXncrl07xMXFITw8HF5eXjXSX3Nzc+zduxenTp1CXFwcwsLCsGbNGkRFRcHS0rJG2iAiIiIiqussrOvDwtoU+Y8KkZ3+BOdjUuA25JVaaUutUuPntYkozn+a+Gr7t5Zo041JKSIpM+ik1OHDh+Hp6YmPPvoIp0+fRrNmzTBy5Ei89957AIC0tDQ8ePAAPXr00BxjYWEBV1dXxMfHY+DAgYiPj4elpaUmIQUAPXr0gFwux/nz5/H3v/8dCQkJ8PDwgLHxH+steXp6Ijw8HNnZ2bCyskJCQgJGjx6t1T9PT0/ExsZW+7yEEGUW9y59XN4+PNOviuorunoRRZfPQ25uoUk+ySCD3NwCxVcvojglCcbt/rSoeDn1lmn7Oe0CQH5+PgICAjBixAh07doVLVu2xODBg7Fjxw68//77GDBgALZu3YpvvvlGa6Hz8s772cfPUigUKCgogBACbdq0QUlJCRISEjSzoX7//XekpKTA0dFRU+bs2bNa9Zw7dw729vaQy+UQQkChUKB79+6ay/xef/11nDhxAn379oWRkRFUKlW1xqO26aIvlT4PCUDZ5yrpB+NgeBgTw8A4GB7GxDAxLjVIBvQY8xq+n30CQi2QsPcG2nRvAcum9Ss97EVikPD9Ddy//ggAYGFtim4+rzKGNYyvDcNQF+JQ1b4bdFLqzp072LFjB8aMGYMJEybgwoULWLBgAZRKJd599108ePAAANC4sfZ1y40bN0ZmZiYAIDMzs8wi1UZGRrCystIcn5mZWebOak2aNNHss7KyQmZmpmZbee1UR05ODuRy7VlLRUVFUKvVUKlUUKmqN+VVCIEnP+yBKMiHzMREewFzuQyiIB9PftgD+SvtauQOFWq1WuvxsmXLIITA1KlToVKp0KJFC3zyyScIDQ1Fz5494eLigtGjR+Pzzz9HWloa+vTpgxYtWuDBgweIiorSzOpSqVRQq9UQQmhmUBUWFuL48eOIi4vDhAkToFKpYGdnBy8vL8yZMwfz5s2DmZkZVqxYgaZNm6J3795QqVQYNWoUvL29sWbNGvTv3x8JCQnYvn07Zs+eDZVKhSNHjiAtLQ2dO3eGlZUVjh49CrVajdatW0OlUsHGxgaJiYlITU1F/fr1YWVlVSZmuvLn8a5NpTF4/Pix1iWw9AchBPLy8gBUvkg/1S7GwfAwJoaBcTA8jIlhYlxqllED4JXezZF0OB2qYjWObTiPnhMr/+5R3RhkpTxG/J7kpw9kQGdfR+QXP0F+dk2cAZXia8Mw1IU4VPV7rEEnpYQQ6NChAz7++GMAwGuvvYakpCR8++23ePfdd/XcuxdnaWkJhUKhta2goABZWVlQKBRl9j2PKC6GyHoAmYkpRDnrR8lMTCEeZkIhBGRGNRPy0j6eOnUKO3bsQGRkJMzNzTX733//fcTGxmLu3LmIiIhAQEAAXF1dsWPHDuzduxcFBQVo3LgxPDw88O2332rWCZPL5cjNzUXv3r0BAMbGxrCxscGUKVPg7++vaXfx4sVYtGgRJk2ahOLiYnh4eODrr7+GiYkJAKBjx45YsWIFVq9ejfXr18Pa2hpTpkzB8OHDAQANGjTA5s2bsXbtWhQWFqJ169ZYtmwZnJ2dAQBjx45FQEAABg8ejIKCAsTGxpZJXOpSdZ8TL9OOXC6HhYWFZixJW2nG38rKSrJ/IOoCxsHwMCaGgXEwPIyJYWJcal63ke3xW/xD5P1eiHuXHuFRUgHsuzSvsHx1YlBcUIL/RCZC/O87rtsQRzi66++zeV3G14ZhqAtxqOpkG4NOSllbW8PR0VFrW5s2bfDvf/9bsx8AsrKy0LRpU02ZrKwstGvXDsDTGU8PHz7UqqOkpATZ2dma45s0aVJmxlPp49LZUeWVycrKKjN7qipkMlmZJ1bp4/L2Pbc+Y2M0mBUMkfu44jIWlpA/5zLAqnh2Cp5MJkPXrl1x+fLlcstu2rRJ6/GAAQMwYMCASusfNmwYhg0b9tx+NGjQAEuXLq20TL9+/dCvX79y93l4eGDbtm0VHtumTRvs2rXruf2obX8e79r2Ms/Dv5LS8eEY6RfjYHgYE8PAOBgexsQwMS41q159Jbr7vopDqxIAACcjr8LWxRpKk4q/clY1Bie3XkVOxtNZI9avWMFt6CuMWy3ia8MwSD0OVe23Qd99z93dHSkpKVrbbt26hZYtWwIAbG1tYW1tjRMnTmj25+bmIjExEW5ubgAANzc35OTk4OLFi5oyJ0+ehFqthouLCwCgU6dOOHPmDIqLizVljh8/DgcHB80Mnk6dOuHkyZNafTl+/Dg6depUcyf8EhQNG8HIrnWF/xQNGuq7i0REREREVIfZv94ctq5Pf7R/8rAA575Lfuk6i/KKkXHtdwCAUT0Fek90hVxh0F9jiagaDPrVPGrUKCQmJuKrr77C7du3sX//fuzatQsjR44E8DTz5uvri3Xr1uHQoUO4du0aZs6ciaZNm2ruxufo6Ig33ngDc+bMwfnz53H27FmEhIRg4MCBaNasGQBg0KBBUCqVCAwMRFJSEg4cOIDIyEiMGTNG0xdfX18cO3YMmzZtwo0bN7B69WpcvHgRH3zwge4HhoiIiIiIyMDIZDL0GP0aFMqnXzMvHryF39MqvpqjKozrKzFkYQ+0e8sO3Ue9CqvmZjXRVSIyEAZ9+Z6LiwvWrFmD5cuX48svv4StrS0+++wzDB48WFPG398f+fn5mDt3LnJyctC5c2ds2LAB9erV05T54osvEBISglGjRkEul6Nv376YPXu2Zr+FhQU2btyI4OBgDB06FA0bNsTEiRPh7e2tKePu7o4vvvgCK1euxPLly2Fvb48vv/wSTk5OuhkMIiIiIiIiA2fZzAyd3nHE+ZgUdPF2gpWN+fMPeg6liRE8x3aogd4RkaGRCSnfY1BiVCoVEhIS0KlTp3IXOk9JSYGDg4NBLzBdepc8hUIh2WtbpUTX4y2V56E+CSGQnZ0t6UUH6wLGwfAwJoaBcTA8jIlhYlxql6pYhcLcYtRvWPHnScbAMDEuhqEuxKGy/MezDPryPSIiIiIiIpIWhVJRaULqeR7eeYyYBb8i+96TGuwVERkiJqWIiIiIiIioVj2+n1elciVFKvy8JhHplx9i76f/xf2k32u5Z0SkT0xKERERERERUa0oyC1C3IaL2DXtF9y79vwE05md1/H7naeLo1s0rY9GrS1ru4tEpEdMShEREREREVGtSDl5D1cP34EQwH83XYS6RF1h2bQLmbh48BYAQKGU483JrjAyrngtGiKSPialiIiIiIiIqFY4e9mhicPT2U6/38nFxR9vlVuu4HERjn51XvO4ywhnNLKz0EUXiUiPmJSqg+L3JGPDyIOI35Os766QgUlLS4OzszOuXLmi764QERER0V+AXC5Dz7EdgP/dQOzcd8nIzcrXKiOEQNyGi8j7vRAA0LJjE7R/u7Wuu0pEesCkVB0TvycZZ6OSAABno5J0kphKT0/Hp59+Ck9PT3To0AFvvvkmFixYgN9/18+ihD4+Pli4cKFe2gaA1atX45133tHadubMGXh4eGDhwoUQQuipZ0REREREumfdxgqv9mkFACgpVOFE5BXE701G1MQTiN+bjOu/pOHW6QwAQD1zJXpN6AiZXKbPLhORjhjpuwNUc55NSJUqfew29JVaafPOnTvw9vaGvb09li9fDltbWyQlJSE0NBTHjh3Dzp070aBBg1ppWyqOHDmC//u//4O/vz8mT578QnUUFRWhXr16NdwzIiIiIiLd8HjPCbdO3UN+dhFun87A7f8loc5FJUOu+CMB9YZ/R9RvaKKvbhKRjnGmVB1RXkKqVG3OmAoKCoJSqcSmTZvw+uuvw8bGBr169UJERAQyMjKwYsUKTVlnZ2fExsZqHe/h4YE9e/ZoHoeGhuLtt9+Gq6sr3nrrLaxcuRLFxcWa/aWzkKKjo+Hl5YXOnTtj2rRpyM3NBQAEBATg1KlTiIyMhLOzM5ydnZGWloY9e/bAw8NDq+3Y2Fg4OzuXqTsqKgq9e/eGm5sb5s+fD5VKhfDwcPTs2RPdu3fHunXrqjw++/fvx+TJk/HJJ59oJaTOnDmDkSNHwsXFBb169cKCBQuQl/fHbXK9vLzw5ZdfYtasWXj99dcxd+5czTkcO3YM/fv3h5ubG8aOHYv79+9rtbl79270798fHTt2RL9+/bB9+/Yq95eIiIiIqDbUM1Oi6z/blbtPrXp6JYHzm7aw79JMl90iIj1jUqoOqCwhVao2ElOPHj1CXFwcRo4cCRMT7V8zrK2tMWjQIBw8eLBal6uZmZlh8eLFiImJQWBgIHbv3o3NmzdrlUlNTcWhQ4fw1VdfYf369Th9+jTCw8MBAIGBgXBzc8N7772HuLg4xMXFoUWLFlVuPzU1FUePHsWGDRuwbNkyREVFYfz48cjIyMDWrVsxY8YMrFy5EomJic+ta/v27fj000+xaNEifPDBB1pt+Pv7o2/fvti3bx9WrFiBs2fPIiQkROv4TZs2wdnZGVFRUZg4cSIAoKCgAJs2bcLSpUuxbds2pKen4/PPP9ccs2/fPqxatQrTpk3DgQMH8PHHHyMsLAx79+6t8hgQEREREdWGnPt5le43teKVAUR/Nbx8TwIuxKTgwsGUcvcV55egOF9VpXrORiXhfMxNKE3Lhr1jfwd0HOhQrX7dvn0bQgg4OjqWu9/R0RHZ2dl4+PAhGjduXKU6S5MvAGBra4uUlBTExMTA399fs10IgcWLF8Pc3BwAMHjwYJw4cQLTpk2DhYUFlEolTExMYG1tXa3zKa170aJFMDc3xyuvvIKuXbsiJSUF4eHhkMvlaNOmDcLDw/Hrr7/C1dW1wnpu3LiB4OBgLFy4EIMHD9bat379egwaNAijR48GANjb2yMwMBA+Pj6YP3++5jK9bt26wc/PDyqVCgqFAmfPnkVxcTGCgoLQqtXTa/L/+c9/Yu3atZq6V69ejYCAAPTt2xcAYGdnh+TkZOzcuRPvvvtutceDiIiIiKgmxO9Jxrmoyn8kT4i+AYWRvNaWHiEiw8OklAQU5Zcg72FhjdRVnK8qN4lVlF/ywnU+byaUUqmscl0HDhxAZGQk7ty5g7y8PJSUlGiST6Vatmypta1p06bIysqqXqcr8Oe6mzRpAoVCAblcrrXtee01b94clpaW2LhxI/72t7+hadOmmn1Xr17FtWvXsH//fs02IQTUajXS0tI0Sb4OHTqUqdfU1FSTkAK0zz0vLw+pqakIDAzEnDlzNGVKSkpgYcHb6RIRERGRflTlyo5Stb0mLhEZFialJMDY1Aj1G5U/lbU6M6UAQGmqKHemlHE5256nVatWkMlkuHHjBv7+97+X2X/jxg00atQIlpaWAACZTFYmgVVS8kcyLD4+HjNmzMCUKVPg6ekJCwsLxMTEICIiQusYI6OyfX1eYkwul5cp8+xaVRXVLZPJyt2mVqsrbc/MzAwREREYM2YMfH19ERkZqUlM5eXlYcSIEfDx8Slz3LOXGpqamlapf6XnVbomVUhISJlZXM8m1YiIiIiIdKmqCalnyzMpRfTXwKSUBHQcWPmldVX95aHz8LY1+ubesGFD9OzZE9988w1Gjx6tta7UgwcPsH//fowcOVKzrVGjRlqLct+6dQv5+fmax/Hx8bCxscGHH36o2Xb37t1q90upVJZJGjVs2BBPnjxBXl4e6tevD+DpjKXaZGVlhc2bN8PPzw8+Pj6IjIxEs2bN8NprryE5ORmtW7eu0faaNGmCpk2b4s6dO2UuGSQiIiIi0pfOw9tWKzHVeXjbWuwNERkSTp+oA9yGvvLcN+6aTkiVmjNnDoqKijB27FicPn0a6enpOHr0KPz8/GBvb49JkyZpynbr1g3bt2/H5cuXceHCBcybN0/r0r7WrVsjPT0dMTExSE1NRWRkZJm79VVFy5YtkZiYiLS0NDx8+BBqtRqurq4wNTXF8uXLkZqaiv3792vd9a+2WFpaIiIiAlZWVvD19UVGRgb8/f0RHx+P4OBgXLlyBbdu3UJsbCyCg4Nfur2PPvoIX3/9NSIjI5GSkoJr167hu+++KzPbjIiIiIhIV6ryfaVUbX1vISLDxKRUHVHZG31tvrHb29sjKioKdnZ2mDp1Kt588034+/vD3t4eO3bsgJmZmabsrFmz0KJFC/zzn//EjBkz4OfnpzW76q233sKoUaMQHByMd955B/Hx8VqzpqrKz88PCoUCAwcORPfu3XH37l00aNAAoaGhOHr0KAYNGoSYmBhMmTKlRsbgeSwsLLBp0yY0bNgQPj4+aNiwIbZu3Ypbt25h5MiRePfddxEWFqa17tSL+sc//oEFCxZgz549GDRoEHx8fLB3717Y2trWwJkQEREREb0Yff6QTkSGSyaetxgP1RiVSoWEhAR06tQJCoVCa19BQQFSUlLg4OCglaiprj9fylfTb+xCCM3d4GQyWbllwsLCEBERgYiICHTq1KnG2v4rqsp416Saeh7WZUIIZGdnw8rKSicxofIxDoaHMTEMjIPhYUwME+OiPxUtPcKElGHga8Mw1IU4VJb/eBbXlKpjSt/Iz0Yl6e2N/aOPPkLLli2RkJAAFxcXLrJNREREREQAtL+vlGJCiuivi0mpOsht6Ct6f1MfNmyYXtsnIiIiIiLD5Db0FQgInItKhvtw/X93ISL9YVKKiIiIiIiIdMrt3VfQxssaVlZW+u4KEekRr6siIiIiIiIiIiKdY1KKiIiIiIiIiIh0jkkpA6NWq/XdBfoL4804iYiIiIiISFe4ppSBMDY2hlwux927d2FtbQ1jY2ODvPWjEAIqlQoKhcIg+1fX6HK8hRB48OABZDIZlEplrbZFRERERERExKSUgZDL5XBwcEB6ejru3r2r7+5USq1WQy7nJDtd0eV4y2Qy2NraQqFQ6KQ9IiIiIiIi+utiUsqAGBsbo1WrVigpKYFKpdJ3d8olhMDjx49hYWHBmVI6oOvxViqVTEgRERERERGRTjApZWBKL50y1MunhBAoLCyEiYkJk1I6wPEmIiIiIiKiuorXYBERERERERERkc4xKUVERERERERERDrHpBQREREREREREekc15TSISEEABjsIuZVIYSAWq2GSqXiGkc6wPE2PIyJYWAcDA9jYhgYB8PDmBgmxkX/GAPDxLgYhroQh9K8R2kepCJMSumQWq0GAFy4cEHPPSEiIiIiIiIiql2leZCKyMTz0lZUY9RqNUpKSiCXyyWb7SQiIiIiIiIiqkzpbC8jIyPI5RWvHMWkFBERERERERER6RwXOiciIiIiIiIiIp1jUoqIiIiIiIiIiHSOSSkiIiIiIiIiItI5JqWIiIiIiIiIiEjnmJQiIiIiIiIiIiKdY1KKiIiIiIiIiIh0jkkpIiIiIiIiIiLSOSaliIiIiIiIiIhI55iUqiPWr1+PYcOGwc3NDd27d8fEiRNx8+ZNrTKFhYUICgpC165d4ebmhilTpiAzM1Oz/+rVq/j444/Rq1cvuLi4oH///tiyZYtWHb/++iucnZ3L/Hvw4EGl/RNCYNWqVfD09ISLiwtGjx6NW7duafanpaXhs88+g5eXF1xcXNCnTx+EhYWhqKjo5Qenhkl9rAFgwoQJ6N27Nzp27AhPT0988sknyMjIeLmB0aO6EJNSRUVFeOedd+Ds7IwrV6682IDoSV2Ig5eXV5l6v/7665cbGD2qCzEBgCNHjuAf//gHXFxc0KVLF0ycOPHFB0UPpB6Hiup1dnbG+fPnX36A9ETqcQGAlJQUfPjhh+jatSvc3d3x/vvv4+TJky83MHpUF2Jy6dIljBkzBh4eHujatSvmzJmDJ0+evNzA6Jihx+E///kP/Pz80LVr1wo/Lz2vf1JTF2Kyc+dO+Pj4wN3dHc7OzsjJyXmJEdEPqcfh0aNHCAkJwdtvvw0XFxf07t0bCxYswOPHj19yZF6SoDrBz89PfPfdd+L69eviypUrwt/fX/Tu3Vs8efJEU2bu3LmiV69e4vjx4+LChQvivffeE97e3pr9u3fvFiEhIeLXX38VqampIjo6Wri4uIitW7dqypw8eVI4OTmJmzdvivv372v+qVSqSvu3fv160blzZ/HTTz+JK1euiAkTJggvLy9RUFAghBDil19+EQEBAeLYsWMiNTVVxMbGiu7du4slS5bU8Ei9PKmPtRBCREREiPj4eJGWlibOnj0rvL29tfonNXUhJqVCQkLEuHHjhJOTk7h8+XINjI7u1IU4vPnmm2LNmjVa9T7bf6mpCzH58ccfRZcuXcQ333wjbt68KZKSkkRMTEwNjlLtk3ocCgsLteq7f/++CAwMFF5eXkKtVtfwaOmO1OMihBB9+/YV/v7+4sqVKyIlJUXMnz9fuLq6ivv379fgSOmO1GNy79490aVLFzF37lxx48YNkZiYKLy9vcWUKVNqeKRql6HHYe/evWL16tVi165dFX5eel7/pKYuxCQiIkKsX79erF+/Xjg5OYns7OwaGBndknocrl27JiZPniwOHTokbt++LY4fPy769u2r9/coJqXqqKysLOHk5CROnTolhBAiJydHtG/fXhw8eFBTJjk5WTg5OYn4+PgK65k/f77w8fHRPC59gVTnTUStVouePXuKDRs2aLbl5OSIDh06iB9++KHC48LDw4WXl1eV29GXujDWsbGxwtnZWRQVFVW5LUMm1ZgcOXJE9OvXTyQlJUkyKfVnUozDm2++KSIiIqpcr9RILSbFxcXijTfeELt27apyvVIgtTj8WVFRkejWrZtYs2ZNlduRAqnFpbS/p0+f1pR5/PixcHJyEv/973+r3JYhk1pMvv32W9G9e3etL45Xr14VTk5O4tatW1Vuy9AYUhyedefOnXI/L71o/6REajF51su2YUikHIdSBw4cEO3btxfFxcUv1FZN4OV7dVTpFDwrKysAwMWLF1FcXIwePXpoyjg6OsLGxgYJCQmV1tOgQYMy24cMGQJPT0+MGTMGZ8+erbQvaWlpePDggVbbFhYWcHV1RXx8fKVtl/bfkEl9rB89eoT9+/fDzc0NSqWy0vqlQooxyczMxJw5c7B06VKYmJhU5TQNnhTjAADh4eHo2rUrhgwZgg0bNqCkpOR5pyoZUovJ5cuXkZGRAblcrql73LhxuH79elVP2SBJLQ5/dvjwYTx69AjDhg2rtG6pkVpcGjZsCAcHB0RHRyMvLw8lJSXYuXMnGjdujPbt21f1tA2a1GJSVFQEpVIJufyPr1ilf9OfV78hM6Q4VMWL9k9KpBaTuqouxCE3Nxfm5uYwMjKqlfqrgkmpOkitVmPRokVwd3eHk5MTgKdfeJVKJSwtLbXKNm7cuMJrU8+dO4eDBw/ivffe02yztrZGUFAQwsLCEBYWhubNm8PX1xeXLl2qsD+l9Tdu3LhM2xVd23379m1s27YNI0aMeP4J65GUxzo0NBSdOnVC165dkZ6ejrVr11b9xA2YFGMihEBAQABGjBiBjh07Vv+kDZAU4wAAPj4+WL58ObZs2QJvb2+sX78eoaGh1Tt5AyXFmNy5cwcAsGbNGnz44Yf46quvYGVlBR8fHzx69Kh6A2AgpBiHP4uKioKnpyeaN2/+/BOWCCnGRSaTYfPmzbh8+TLc3d3h4uKCiIgIbNiwQRI/6j2PFGPSrVs3ZGZmYsOGDSgqKkJ2djaWLVumdbzUGFocquJF+iclUoxJXVQX4vDw4UOsXbsW3t7eNVpvdekvHUa1JigoCElJSfjmm29euI7r169j4sSJmDRpEjw9PTXb27RpgzZt2mgeu7u7486dO9i8eTNCQ0Oxb98+zJs3T7M/PDxc69eiqsjIyMC4cePQr18/rRenIZLyWI8dOxbDhw/H3bt3sWbNGsyaNQvr16+HTCZ74XMxBFKMydatW/HkyRP861//euE+GxopxgEAxowZo/l/u3btoFQqMW/ePEyfPh3GxsYvfC6GQIoxUavVAJ7enOHtt98GACxevBh/+9vf8OOPPxr8DxflkWIcnnXv3j3ExcVh5cqVL9x/QyTFuAghEBQUhMaNG2P79u0wMTHB7t27MWHCBERFRaFp06YvfC6GQIoxadu2LZYsWYIlS5Zg+fLlkMvl8PHxQZMmTST7+crQ4uDh4fHC/agrGBPDIPU45Obm4l//+hccHR0xefLkFz6HmsCkVB0THByMI0eOYNu2bVq/YDZp0gTFxcXIycnRytxmZWXB2tpaq47k5GSMHj0a3t7eVbrDUceOHXHu3DkAT+9c5erqqtnXrFkzTVY4KytL6wNSVlYW2rVrp1VXRkYGfH194ebmhpCQkGqcue5JfawbNWqERo0awcHBAY6OjujVqxcSEhLg5uZWjVEwLFKNycmTJ5GQkFBmltSwYcMwaNAgfP7551UdAoMg1TiUx9XVFSUlJUhLS9P6cCA1Uo1JaR8cHR01+42NjWFnZ4f09PQqn7+hkGocnvXdd9+hQYMG8PLyquJZGz6pxuXkyZM4cuQITp8+DXNzcwBA+/btcfz4cURHR2P8+PHVHQqDIdWYAMCgQYMwaNAgZGZmwtTUVDOjzc7OrpqjoH+GGIeqqE7/pEaqMalrpB6H3NxcjBs3DmZmZvjyyy/1voQLL9+rI4QQCA4Oxk8//YQtW7aU+cPXoUMHKJVKnDhxQrPt5s2buHv3Ljp16qTZlpSUBF9fXwwZMgTTpk2rUttXr17VvMjMzc3RunVrzT8TExPY2trC2tpaq+3c3FwkJiZqJUBKE1Lt27fH4sWLq/3rra7UhbH+s9LZCEVFRVXqh6GRekxmz56N77//HtHR0YiOjsbXX38NAFixYkWV+2EIpB6H8ly5cgVyubzM5RpSIfWYdOjQAcbGxkhJSdGUKS4uxm+//QYbG5tqj4e+SD0Oz57Hnj17MGTIEL1/gK0JUo9Lfn4+AJSZgSOTyTR/16VG6jF5VpMmTWBmZoYDBw6gXr166NmzZ3WGQq8MOQ5VUdX+SYnUY1JX1IU45ObmYuzYsVAqlVi3bh3q1atX5WNrC2dK1RFBQUH44YcfsHbtWpiZmWl+zbGwsICJiQksLCwwbNgwLFmyBFZWVjA3N8eCBQvg5uameYFcv34do0aN0iymVlqHQqFAo0aNAACbN2+Gra0t2rZti8LCQuzevRsnT57Epk2bKuybTCaDr68v1q1bh9atW8PW1harVq1C06ZN0adPHwBPE1I+Pj6wsbHBrFmz8PDhQ83xhvaLhtTHOjExERcuXEDnzp1haWmJ1NRUrFq1Cq1atZLsLCmpx+TPX67r168PAGjVqpWk1myRehzi4+ORmJiIbt26wczMDPHx8Vi8eDEGDx4s2fVZpB4Tc3NzjBgxAqtXr0aLFi1gY2ODjRs3AgD69etXW8NW46Qeh1InT55EWloahg8fXgujpHtSj0unTp1gaWmJgIAATJo0CfXq1cOuXbvw22+/oXfv3rU3cLVI6jEBgG3btsHNzQ3169fH8ePHsXTpUkyfPr3MGjOGzJDjADy9SU96ejru378PAJofLpo0aQJra+sq9U9qpB4T4Om6apmZmUhNTdX0x8zMDC1atCh3kW9DJPU45Obmws/PD/n5+QgNDUVubi5yc3MBPL2KRqFQ1PiYVYVMCCH00jLVKGdn53K3L168GEOHDgUAFBYWYsmSJYiJiUFRURE8PT0xb948zRvF6tWrsWbNmjJ1tGzZEocPHwbw9HrVXbt2ISMjA6ampnBycsKkSZPQrVu3SvsnhEBYWBh27dqFnJwcdO7cGfPmzYODgwMAYM+ePfj000/LPfbatWtVGwQdkfpYX7t2DQsXLsS1a9eQl5cHa2trvPHGG5g4caJkp+BKPSZ/lpaWhrfeegvR0dF49dVXqzwO+ib1OFy6dAlBQUG4efMmioqKYGtri3feeQdjxoyR7HpSUo8J8HRm1PLly/H999+joKAArq6u+Oyzz9C2bdsXGhN9qAtxAIDp06fjt99+w7ffflvtMTBEdSEuFy5cwMqVKzV3fGrbti0mTpyIXr16vdCY6FtdiMnMmTPxyy+/4MmTJ2jTpg38/PwwZMiQFxkOvTH0OFT0vWHy5MmYMmVKlfonNXUhJhW1/+w5GDqpx+HXX3+Fr69vucceOnQItra2ldZfW5iUIiIiIiIiIiIinTPMRXuIiIiIiIiIiKhOY1KKiIiIiIiIiIh0jkkpIiIiIiIiIiLSOSaliIiIiIiIiIhI55iUIiIiIiIiIiIinWNSioiIiIiIiIiIdI5JKSIiIiIiIiIi0jkmpYiIiIiIiIiISOeYlCIiIiIiIiIiIp1jUoqIiIiIiIiIiHSOSSkiIiIiIiIiItI5JqWIiIiIiIiIiEjn/h/gaqQdH7+ciwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {}
    }
   ],
   "source": "plt.figure(figsize=(12, 5))\nplt.plot(dates_test, y_test, 'o-', label='Gerçek', color='#2c3e50', linewidth=2)\nplt.plot(dates_test, xgb_pred, '^--', label='XGBoost', color='#e74c3c', alpha=0.8)\nplt.plot(dates_test, q_pred, 'D--', label='Quantum Kernel', color='#9b59b6', linewidth=2)\nplt.title('Klasik vs Quantum Forecast – Test Seti')\nplt.ylabel('Satış Tutarı')\nplt.legend()\nplt.grid(True, alpha=0.3)\nplt.tight_layout()\nplt.show()",
   "id": "quantum_plot"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section8"
   },
   "source": "## 8. IBM Quantum Gerçek Backend (Opsiyonel)\n\nAşağıdaki hücre **sadece** `USE_REAL_BACKEND = True` ve geçerli token varsa çalışır.\n\nGerçek donanımda:\n- Gürültü vardır → sonuçlar simülatörden farklı olabilir\n- Kuyruk süresi uzayabilir\n- Ücretsiz tier’da aylık sınırlı job hakkı vardır\n\n> Eğitim amaçlı olarak önce simülatörle çalışmanız önerilir.",
   "id": "section8"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "real_backend",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291464631,
     "user_tz": -180,
     "elapsed": 84,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "f7e78f28-ad6c-4b74-9ff6-ebd8f0c64d4d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ℹ️ Gerçek backend atlandı (USE_REAL_BACKEND=False veya token yok).\n",
      "   Simülatör (Aer) sonuçları geçerli.\n"
     ]
    }
   ],
   "source": "# ============================================================\n# GERÇEK IBM QUANTUM BACKEND (OPSİYONEL)\n# ============================================================\n\nif USE_REAL_BACKEND and IBM_TOKEN:\n    from qiskit_ibm_runtime import SamplerV2 as Sampler\n    from qiskit_ibm_runtime import Session\n    from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n    from qiskit_machine_learning.kernels import FidelityQuantumKernel\n    from qiskit.primitives import BackendSampler\n\n    try:\n        service = QiskitRuntimeService(channel=\"ibm_quantum\")\n        # En az meşgul, yeterli qubit'li backend seç\n        backend = service.least_busy(operational=True, simulator=False, min_num_qubits=n_qubits)\n        print(f\"Seçilen backend: {backend.name}\")\n\n        # Not: Gerçek donanımda FidelityQuantumKernel + Session kullanılır\n        # Bu örnek eğitim amaçlı iskelet bırakılmıştır.\n        print(\"\"\"\n        ⚠️ Gerçek donanım kodu iskelet olarak bırakıldı.\n        Tam production kodu için:\n        - Session içinde SamplerV2 kullanın\n        - Transpilation (pass manager) uygulayın\n        - Error mitigation (RES / TREX) ekleyin\n        - Job sonuçlarını asenkron takip edin\n        \"\"\")\n\n    except Exception as e:\n        print(f\"Gerçek backend hatası: {e}\")\n        print(\"Simülatör sonuçları ile devam ediliyor.\")\nelse:\n    print(\"ℹ️ Gerçek backend atlandı (USE_REAL_BACKEND=False veya token yok).\")\n    print(\"   Simülatör (Aer) sonuçları geçerli.\")",
   "id": "real_backend"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "section9"
   },
   "source": "## 9. Performans Karşılaştırması ve İleriye Dönük Tahmin",
   "id": "section9"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "comparison",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 549
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291468489,
     "user_tz": -180,
     "elapsed": 200,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "a6e9de46-509a-40a6-c9a2-774877634d75"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "=== MODEL KARŞILAŞTIRMA ===\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "                  Model        MAE       RMSE      R²  MAPE (%)\n",
       "0                 Ridge  14415.866  19067.152  -0.151     8.190\n",
       "1               XGBoost  23399.752  27514.046  -1.396    13.063\n",
       "2  Quantum Kernel Ridge  84425.927  88177.730 -23.614    48.831"
      ],
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\n"
     },
     "metadata": {}
    }
   ],
   "source": "comparison = pd.DataFrame({\n    'Model': ['Ridge', 'XGBoost', 'Quantum Kernel Ridge'],\n    'MAE':  [ridge_scores['MAE'], xgb_scores['MAE'], q_scores['MAE']],\n    'RMSE': [ridge_scores['RMSE'], xgb_scores['RMSE'], q_scores['RMSE']],\n    'R²':   [ridge_scores['R2'], xgb_scores['R2'], q_scores['R2']],\n    'MAPE (%)': [ridge_scores['MAPE'], xgb_scores['MAPE'], q_scores['MAPE']]\n})\n\nprint(\"=== MODEL KARŞILAŞTIRMA ===\")\ndisplay(comparison.round(3))\n\nfig, ax = plt.subplots(figsize=(8, 4))\nx = np.arange(len(comparison))\nwidth = 0.35\nax.bar(x - width/2, comparison['MAE'], width, label='MAE', color='#3498db')\nax.bar(x + width/2, comparison['RMSE'], width, label='RMSE', color='#e74c3c')\nax.set_xticks(x)\nax.set_xticklabels(comparison['Model'])\nax.set_ylabel('Hata')\nax.set_title('Model Hata Karşılaştırması')\nax.legend()\nplt.tight_layout()\nplt.show()",
   "id": "comparison"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "future_forecast",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 683
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1786291474675,
     "user_tz": -180,
     "elapsed": 345,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "16a8a31a-f689-491d-935c-64d18e06d7de"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Gelecek 3 ay için forecast üretiliyor (XGBoost + son lag'ler)...\n",
      "\n",
      "=== 3 Aylık Satış Tahmini ===\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "        date  forecast_sales\n",
       "0 2026-01-01        142192.0\n",
       "1 2026-02-01        158386.0\n",
       "2 2026-03-01        159635.0"
      ],
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\n"
     },
     "metadata": {}
    }
   ],
   "source": "# ============================================================\n# SONRAKİ 3 AY İÇİN FORECAST (en iyi modele göre)\n# ============================================================\nprint(\"Gelecek 3 ay için forecast üretiliyor (XGBoost + son lag'ler)...\\n\")\n\nlast_row = df_feat.iloc[-1:].copy()\nfuture_preds = []\nfuture_dates = []\n\ncurrent_features = last_row[feature_cols].values.copy()\nlast_date = df_feat['date'].iloc[-1]\n\nfor i in range(1, 4):\n    next_date = last_date + pd.DateOffset(months=i)\n    future_dates.append(next_date)\n\n    # Scale + predict\n    curr_s = scaler.transform(current_features)\n    pred = xgb.predict(curr_s)[0]\n    future_preds.append(pred)\n\n    # Lag'leri kaydır (basit recursive forecast)\n    # lag_1 ← yeni tahmin, lag_2 ← eski lag_1, ...\n    new_feats = current_features.copy()\n    # lag sütunlarının sırası feature_cols içinde varsayılan\n    lag_cols = [c for c in feature_cols if c.startswith('lag_')]\n    if lag_cols:\n        lag_idx = [feature_cols.index(c) for c in lag_cols]\n        # kaydır\n        for j in range(len(lag_idx)-1, 0, -1):\n            new_feats[0, lag_idx[j]] = new_feats[0, lag_idx[j-1]]\n        new_feats[0, lag_idx[0]] = pred\n\n    # month / quarter güncelle\n    if 'month' in feature_cols:\n        new_feats[0, feature_cols.index('month')] = next_date.month\n    if 'quarter' in feature_cols:\n        new_feats[0, feature_cols.index('quarter')] = next_date.quarter\n\n    current_features = new_feats\n\nforecast_df = pd.DataFrame({\n    'date': future_dates,\n    'forecast_sales': np.round(future_preds, 0)\n})\nprint(\"=== 3 Aylık Satış Tahmini ===\")\ndisplay(forecast_df)\n\n# Görsel\nplt.figure(figsize=(12, 5))\nplt.plot(df_feat['date'], df_feat['sales_amount'], 'o-', label='Geçmiş', color='#2c3e50')\nplt.plot(forecast_df['date'], forecast_df['forecast_sales'], 'D--', label='Forecast (3 ay)', color='#e67e22', markersize=10)\nplt.axvline(df_feat['date'].iloc[-1], color='gray', linestyle=':', label='Bugün')\nplt.title('Satış Forecast – Sonraki 3 Ay')\nplt.ylabel('Satış Tutarı')\nplt.legend()\nplt.grid(True, alpha=0.3)\nplt.tight_layout()\nplt.show()",
   "id": "future_forecast"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "discussion"
   },
   "source": "## 10. Akademik Tartışma ve Öğrenci Notları\n\n### 10.1 Neden Quantum Forecast Klasik Modelleri Her Zaman Geçmez?\n\n| Faktör | Açıklama |\n|--------|----------|\n| **Veri boyutu** | Quantum kernel $O(n^2)$ maliyetlidir. 40–50 aylık seri küçük kalır. |\n| **Özellik sayısı** | 4 qubit ile sınırlıyız → bilgi kaybı olabilir. |\n| **Problem tipi** | Tabular zaman serisi, kuantum avantajı için en zor problem sınıflarındandır. |\n| **NISQ gürültüsü** | Gerçek donanımda ek hata katmanı oluşur. |\n\n### 10.2 Bu Pipeline’ın Değeri Nedir?\n\n- **SAP ↔ Quantum** köprüsünü kurmayı öğrendiniz.\n- OData API, zaman serisi feature engineering, quantum kernel regression zincirini uçtan uca gördünüz.\n- Gerçek enterprise sistemden (S/4HANA) veri alıp quantum modele besleme mimarisini anladınız.\n\n### 10.3 Geliştirme Önerileri (Ödev / Proje)\n\n1. SAP’den **Billing Document** veya **Outbound Delivery** verisi de çekip çok değişkenli forecast yapın.\n2. PCA yerine klasik feature importance ile qubit seçimini optimize edin.\n3. `qiskit-ibm-runtime` ile gerçek backend’de küçük bir job çalıştırıp simülatörle karşılaştırın.\n4. Prophet / NeuralProphet gibi güçlü klasik modelleri de baseline’a ekleyin.\n5. Forecast sonuçlarını tekrar SAP’ye (ör. custom CDS view veya side-by-side extension) yazacak bir mimari tasarlayın.",
   "id": "discussion"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "closing"
   },
   "source": "---\n\n## 11. Özet\n\nBu notebook’ta:\n\n1. SAP S/4HANA Public Cloud OData API bağlantı iskeletini kurduk\n2. Satış verisini (gerçek veya sentetik) zaman serisine dönüştürdük\n3. Lag features ile supervised forecasting problemi oluşturduk\n4. Ridge + XGBoost baseline eğittik\n5. **Qiskit Quantum Kernel Ridge** ile tahmin yaptık\n6. IBM Quantum gerçek backend için hazırlık kodu bıraktık\n7. 3 aylık ileri forecast ürettik\n\n**Ana mesaj:**  \n> Enterprise sistemlerden (SAP) veri alıp quantum modellere beslemek mümkündür.  \n> Ancak günümüz NISQ cihazlarında tabular forecast’te klasik yöntemler hâlâ baskındır.  \n> Değer, mimariyi kurabilmekte ve doğru problem seçiminde yatar.\n\n---\n\n**Hazırlayan:** Joseph Volkan ozcan – Öğrenci odaklı eğitim materyali  \n**Teknolojiler:** SAP S/4HANA OData • Qiskit • IBM Quantum Runtime • scikit-learn • XGBoost  \n**Ortam:** Google Colab  \n**Tarih:** 2026",
   "id": "closing"
  }
 ]
}