{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f2eb1a06",
   "metadata": {
    "deletable": true,
    "editable": true,
    "tags": [],
    "id": "f2eb1a06"
   },
   "source": "# Natrium Search Simulator — Polatlı Sodyum Sülfat Sahası Veri Analizi\n\n**Proje:** Quantum_Evaporite_Exploration / Natrium Search Simulator\n**Veri kaynağı:** Koyuncu Polatlı (Yeniköseler) Na₂SO₄ üretim kuyuları veri paketi (`KOYUNCU/` klasörü)\n**Amaç:** Sahadan gelen ham Excel/PPTX/fotoğraf paketini (1) temiz, analiz edilebilir tablolara dönüştürmek, (2) keşifsel analizle sahanın cevher yapısını anlamak ve (3) hibrit QML (PennyLane) sınıflandırıcı için özellik matrisini hazırlayıp klasik ML ile karşılaştırmalı ilk denemeyi yapmak.\n\n## Veri paketinde ne var?\n\n| Kaynak | İçerik | Bu notebook'ta |\n|---|---|---|\n| `KAVERNA SONDAJLARI.xlsx` | 19 üretim kuyusu (ÜRT-1…20, ÜRT-4 açılmadı): derinlik, casing, 4½\" ve 2⅜\" tubing konumları | `wells` |\n| `Polatlı Üretim Kuyuları Dikme Kesitler.xlsx` → `ÇÖZÜNEN CEVHER ORANI` | 19 kuyu × metre-metre karot bileşimi: Kil %, Kil taşı/Polihalit %, **Tenardit %**, Globerit içi kil/tenardit %, **Globerit toplam %** | `lith` (1.825 aralık) |\n| aynı dosya → `ÜRT-n` sayfaları | Aralık bazlı litoloji adı (GLOBERİT, GLOBERİT-TENARDİT, TENARDİT-KİL…), kuyu sıcaklığı, karot fotoğrafları | `dikme` (1.262 aralık, 15 kuyu) |\n| `Kopya sodyum sülfat analiz sonuçları revize.xlsx` | Kaverna brine analizleri (ÜRT-1/11/16): pH, Bomé, yoğunluk, Ca, Mg, SO₄, Na₂SO₄, Cl, NaCl | `brine` |\n| `ÜRT Kuyular Casing-Tubing Basınç Testler.xlsx` | Casing/tubing basınç testleri (5 kuyu) | `ptests` |\n| `Polatlı kuyu kaverna detayları.pptx` | Kaverna sirkülasyon performansı ve ideal tubing aralığı hesabı | `cavern` |\n| `POLATLI-ÜRT n/Karot Fotoğrafları` | 1.217 karot kutusu fotoğrafı (dosya adında derinlik aralığı) | `photos` (indeks) |\n| `KYC POLATLI-ZEMİN ETÜD ÇALIŞMA/` | Tesis zemin etüdü (SK-1…19 SPT/UD, sismik .rar, raporlar) — tesis inşaatına ait, cevher modeline girdi değil | sadece envanter |\n\n> **Not:** Kuyu koordinatları (X/Y) pakette yok (`SONDAJ NOKTALARII.kmz` yalnızca yol çizgisi içeriyor). Yanal/mekânsal modelleme için sahadan ÜRT kuyu başı koordinatları istenmeli — bkz. son bölüm.\n\n> **Çalıştırma notu:** Bu kopya, veri paketinin yalnızca tablo dosyalarıyla (fotoğrafsız) ayrı bir ortamda çalıştırıldı; bu yüzden §0 ve §1 çıktılarında dosya yolu/sayısı farklı görünür. Kendi bilgisayarınızda veya Colab'da yeniden çalıştırınca 1.286 dosya / 1,7 GB'lık tam paket okunur; analiz sonuçları değişmez."
  },
  {
   "cell_type": "markdown",
   "id": "c2cfdb02",
   "metadata": {
    "id": "c2cfdb02"
   },
   "source": "## 0. Kurulum ve yol yapılandırması\nProje yolu **`~/Documents/Zoom/Natrium_Notebook`** olarak sabitlendi, veri `~/Documents/Zoom/KOYUNCU` klasöründen okunur. Colab'da aynı yapı `MyDrive/Zoom/` altında beklenir. 150 MB'lık orijinal kesit dosyası yoksa `data_slim/` içindeki yalnızca-veri kopyası kullanılır."
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34b442af",
   "metadata": {
    "id": "34b442af",
    "executionInfo": {
     "status": "error",
     "timestamp": 1793771906728,
     "user_tz": -180,
     "elapsed": 26895,
     "user": {
      "displayName": "Joseph Volkan ÖZCAN",
      "userId": "11389530967736397480"
     }
    },
    "outputId": "69dc18cc-63fe-4707-f962-bc8455cd046a",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 230
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/drive\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "AssertionError",
     "evalue": "Veri klasörü yok: /content/drive/MyDrive/Zoom/KOYUNCU",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_4035/575551324.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mPROJECT_DIR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZOOM_DIR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Natrium_Notebook\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mDATA_DIR\u001b[0m    \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZOOM_DIR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"KOYUNCU\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATA_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m    \u001b[0;34mf\"Veri klasörü yok: {DATA_DIR}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPROJECT_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"Proje klasörü yok: {PROJECT_DIR}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPROJECT_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: Veri klasörü yok: /content/drive/MyDrive/Zoom/KOYUNCU"
     ]
    }
   ],
   "source": "import os, re, sys, warnings, glob, json\nimport numpy as np, pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nwarnings.filterwarnings(\"ignore\")\npd.set_option(\"display.width\", 160); pd.set_option(\"display.max_columns\", 30)\n\n# ---- SABİT PROJE YOLU: MacBook ~/Documents/Zoom ----\nZOOM_DIR = os.environ.get(\"NATRIUM_ZOOM_DIR\", os.path.expanduser(\"~/Documents/Zoom\"))\nIN_COLAB = \"google.colab\" in sys.modules\nif IN_COLAB:   # Colab'da aynı yapı Drive'da: MyDrive/Zoom/{KOYUNCU, Natrium_Notebook}\n    from google.colab import drive\n    drive.mount(\"/content/drive\"); ZOOM_DIR = \"/content/drive/MyDrive/Zoom\"\nPROJECT_DIR = os.path.join(ZOOM_DIR, \"Natrium_Notebook\")\nDATA_DIR    = os.path.join(ZOOM_DIR, \"KOYUNCU\")\nassert os.path.isdir(DATA_DIR),    f\"Veri klasörü yok: {DATA_DIR}\"\nassert os.path.isdir(PROJECT_DIR), f\"Proje klasörü yok: {PROJECT_DIR}\"\nos.chdir(PROJECT_DIR)\n\nNB_DIR   = PROJECT_DIR\nOUT_DIR  = os.path.join(NB_DIR, \"processed\"); os.makedirs(OUT_DIR, exist_ok=True)\nFIG_DIR  = os.path.join(NB_DIR, \"figures\");   os.makedirs(FIG_DIR, exist_ok=True)\n\nF_WELLS  = f\"{DATA_DIR}/KAVERNA SONDAJLARI.xlsx\"\nF_BRINE  = f\"{DATA_DIR}/Kopya sodyum sülfat analiz sonuçları revize (00000003).xlsx\"\nF_PTEST  = f\"{DATA_DIR}/POLATLI-ÜRT 1/Casing Basınç Testi/ÜRT Kuyular Casing-Tubing Basınç Testler.xlsx\"\nF_PPTX   = f\"{DATA_DIR}/Polatlı kuyu kaverna detayları.pptx\"\n_orig    = f\"{DATA_DIR}/Polatlı Üretim Kuyuları Dikme Kesitler.xlsx\"\n_slim    = os.path.join(NB_DIR, \"data_slim\", \"Dikme_Kesitler_slim.xlsx\")\nF_DIKME  = _orig if os.path.exists(_orig) else _slim\nF_PHOTOS = os.path.join(NB_DIR, \"data_slim\", \"photo_index.csv\")\n\nprint(\"DATA_DIR :\", os.path.abspath(DATA_DIR))\nprint(\"Kesit dosyası:\", \"ORİJİNAL (150 MB)\" if F_DIKME==_orig else \"SLIM kopya\", \"->\", F_DIKME)\nprint(\"Çıktılar :\", OUT_DIR)\n\n# ---- Grafik stili ----\nPAL = {\"clay\":\"#8C7B6B\", \"claystone\":\"#B8A99A\", \"thenardite\":\"#2A9D8F\", \"glauberite\":\"#E9C46A\",\n       \"polyhalite\":\"#E76F51\", \"accent\":\"#264653\", \"grey\":\"#9AA5B1\"}\nmpl.rcParams.update({\"figure.dpi\":110, \"axes.spines.top\":False, \"axes.spines.right\":False,\n                     \"axes.grid\":True, \"grid.alpha\":0.25, \"font.size\":10})"
  },
  {
   "cell_type": "markdown",
   "id": "9dff1442",
   "metadata": {
    "id": "9dff1442"
   },
   "source": "### Yardımcı fonksiyonlar\nSahadaki Excel'lerde sayılar Türkçe formatta (`164,50`), aralıklar `164,50/165,50`, `174/175`, `212,20-213,60`, `248/249,50` gibi karışık yazılmış; birkaç satırda tuş hatası var (`2601/262`). Aşağıdaki iki fonksiyon bunların tümünü sayıya çevirir."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e6f399a",
   "metadata": {
    "id": "4e6f399a",
    "outputId": "bf132c64-1279-4002-98e6-ef6b18cc1e3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     164,50/165,50 -> (164.5, 165.5)\n",
      "           174/175 -> (174.0, 175.0)\n",
      "     212,20-213,60 -> (212.2, 213.6)\n",
      "        248/249,50 -> (248.0, 249.5)\n",
      "          2601/262 -> (260.1, 262.0)\n",
      "  206,70m-207,90m) -> (206.7, 207.9)\n"
     ]
    }
   ],
   "source": "def to_float(x):\n    if x is None: return np.nan\n    if isinstance(x, (int, float, np.integer, np.floating)): return float(x)\n    s = str(x).strip().replace(\"°C\", \"\").replace(\"m\", \"\").replace(\" \", \"\").replace(\",\", \".\")\n    try: return float(s)\n    except ValueError: return np.nan\n\ndef parse_interval(s):\n    '''164,50/165,50 | 174/175 | 212,20-213,60 | 248/249,50 | 2601/262 -> (from_m, to_m)'''\n    if s is None: return (np.nan, np.nan)\n    s = str(s).replace(\"m\", \"\").replace(\"M\", \"\").replace(\"(\", \"\").replace(\")\", \"\").strip()\n    parts = [p.strip().replace(\",\", \".\") for p in re.split(r\"[/\\-]\", s) if p.strip()]\n    if len(parts) < 2: return (np.nan, np.nan)\n    try: a, b = float(parts[0]), float(parts[1])\n    except ValueError: return (np.nan, np.nan)\n    if a > 1000: a /= 10          # '2601/262' tuş hatası\n    if b > 1000: b /= 10\n    return (a, b)\n\ndef well_no(s): return s.str.extract(r\"(\\d+)\")[0].astype(int)\n\n# hızlı test\nfor t in [\"164,50/165,50\",\"174/175\",\"212,20-213,60\",\"248/249,50\",\"2601/262\",\"206,70m-207,90m)\"]:\n    print(f\"{t:>18} -> {parse_interval(t)}\")"
  },
  {
   "cell_type": "markdown",
   "id": "c0e8d7fb",
   "metadata": {
    "id": "c0e8d7fb"
   },
   "source": "## 1. Veri envanteri"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b08cb342",
   "metadata": {
    "id": "b08cb342",
    "outputId": "247740b1-e644-441c-f737-3468604c6a95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toplam 1286 dosya, 1.82 GB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>files</th>\n",
       "      <th>MB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ext</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>jpeg</th>\n",
       "      <td>1206</td>\n",
       "      <td>454.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xlsx</th>\n",
       "      <td>27</td>\n",
       "      <td>152.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp4</th>\n",
       "      <td>18</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jpg</th>\n",
       "      <td>10</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdf</th>\n",
       "      <td>6</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>docx</th>\n",
       "      <td>5</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>txt</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip</th>\n",
       "      <td>3</td>\n",
       "      <td>42.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dwg</th>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rar</th>\n",
       "      <td>2</td>\n",
       "      <td>1027.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jfif</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kmz</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pptx</th>\n",
       "      <td>1</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xls</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      files      MB\n",
       "ext                \n",
       "jpeg   1206   454.2\n",
       "xlsx     27   152.3\n",
       "mp4      18    37.5\n",
       "jpg      10     2.7\n",
       "pdf       6    77.2\n",
       "docx      5     1.4\n",
       "txt       3     0.0\n",
       "zip       3    42.5\n",
       "dwg       2    14.0\n",
       "rar       2  1027.1\n",
       "jfif      1     0.3\n",
       "kmz       1     0.0\n",
       "pptx      1     9.1\n",
       "xls       1     0.1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "inv = []\nfor root, dirs, files in os.walk(DATA_DIR):\n    for f in files:\n        ext = f.rsplit(\".\",1)[-1].lower() if \".\" in f else \"\"\n        inv.append((os.path.relpath(root, DATA_DIR).split(os.sep)[0], ext, os.path.getsize(os.path.join(root,f))))\ninv = pd.DataFrame(inv, columns=[\"top_folder\",\"ext\",\"bytes\"])\nsummary = inv.groupby(\"ext\").agg(files=(\"bytes\",\"size\"), MB=(\"bytes\", lambda s: round(s.sum()/1e6,1))).sort_values(\"files\", ascending=False)\nprint(f\"Toplam {len(inv)} dosya, {inv.bytes.sum()/1e9:.2f} GB\")\nsummary"
  },
  {
   "cell_type": "markdown",
   "id": "de5a4078",
   "metadata": {
    "id": "de5a4078"
   },
   "source": "## 2. Kuyu ana tablosu (`wells`)\nHer kuyunun toplam derinliği, casing (muhafaza borusu) tabanı ve iki tubing'in konumu. **4½\" tubing ile 2⅜\" tubing arası = sirkülasyon (çözme) aralığı**; kaverna bu aralıkta gelişir."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53db65f0",
   "metadata": {
    "id": "53db65f0",
    "outputId": "1e2158e2-e1ab-4883-ec07-a16a9f055962"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 kuyu | ortalama derinlik 274.7 m | sirkülasyon aralığı 7.99 – 12.3 m\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>depth_m</th>\n",
       "      <th>casing_m</th>\n",
       "      <th>tubing_4_5_m</th>\n",
       "      <th>tubing_2_3_8_m</th>\n",
       "      <th>well_no</th>\n",
       "      <th>circ_interval_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ÜRT-1</td>\n",
       "      <td>272.0</td>\n",
       "      <td>165.00</td>\n",
       "      <td>238.25</td>\n",
       "      <td>247.00</td>\n",
       "      <td>1</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ÜRT-2</td>\n",
       "      <td>272.5</td>\n",
       "      <td>175.00</td>\n",
       "      <td>260.20</td>\n",
       "      <td>269.48</td>\n",
       "      <td>2</td>\n",
       "      <td>9.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ÜRT-3</td>\n",
       "      <td>287.5</td>\n",
       "      <td>180.00</td>\n",
       "      <td>257.06</td>\n",
       "      <td>267.35</td>\n",
       "      <td>3</td>\n",
       "      <td>10.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ÜRT-5</td>\n",
       "      <td>274.0</td>\n",
       "      <td>170.00</td>\n",
       "      <td>245.38</td>\n",
       "      <td>255.37</td>\n",
       "      <td>5</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ÜRT-6</td>\n",
       "      <td>273.5</td>\n",
       "      <td>170.00</td>\n",
       "      <td>252.15</td>\n",
       "      <td>262.28</td>\n",
       "      <td>6</td>\n",
       "      <td>10.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ÜRT-7</td>\n",
       "      <td>269.5</td>\n",
       "      <td>168.00</td>\n",
       "      <td>254.40</td>\n",
       "      <td>264.38</td>\n",
       "      <td>7</td>\n",
       "      <td>9.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ÜRT-8</td>\n",
       "      <td>265.0</td>\n",
       "      <td>175.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>250.30</td>\n",
       "      <td>8</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ÜRT-9</td>\n",
       "      <td>275.5</td>\n",
       "      <td>164.50</td>\n",
       "      <td>262.34</td>\n",
       "      <td>270.33</td>\n",
       "      <td>9</td>\n",
       "      <td>7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ÜRT-10</td>\n",
       "      <td>275.5</td>\n",
       "      <td>154.00</td>\n",
       "      <td>265.03</td>\n",
       "      <td>275.04</td>\n",
       "      <td>10</td>\n",
       "      <td>10.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ÜRT-11</td>\n",
       "      <td>272.5</td>\n",
       "      <td>165.00</td>\n",
       "      <td>261.00</td>\n",
       "      <td>269.00</td>\n",
       "      <td>11</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ÜRT-12</td>\n",
       "      <td>272.5</td>\n",
       "      <td>170.00</td>\n",
       "      <td>260.16</td>\n",
       "      <td>270.42</td>\n",
       "      <td>12</td>\n",
       "      <td>10.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ÜRT-13</td>\n",
       "      <td>272.5</td>\n",
       "      <td>175.00</td>\n",
       "      <td>257.09</td>\n",
       "      <td>268.18</td>\n",
       "      <td>13</td>\n",
       "      <td>11.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ÜRT-14</td>\n",
       "      <td>275.5</td>\n",
       "      <td>180.15</td>\n",
       "      <td>240.00</td>\n",
       "      <td>250.28</td>\n",
       "      <td>14</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ÜRT-15</td>\n",
       "      <td>277.0</td>\n",
       "      <td>155.00</td>\n",
       "      <td>260.01</td>\n",
       "      <td>270.38</td>\n",
       "      <td>15</td>\n",
       "      <td>10.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ÜRT-16</td>\n",
       "      <td>272.0</td>\n",
       "      <td>162.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>248.00</td>\n",
       "      <td>16</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ÜRT-17</td>\n",
       "      <td>278.5</td>\n",
       "      <td>170.00</td>\n",
       "      <td>260.90</td>\n",
       "      <td>270.52</td>\n",
       "      <td>17</td>\n",
       "      <td>9.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ÜRT-18</td>\n",
       "      <td>281.0</td>\n",
       "      <td>176.00</td>\n",
       "      <td>261.10</td>\n",
       "      <td>271.46</td>\n",
       "      <td>18</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ÜRT-19</td>\n",
       "      <td>275.5</td>\n",
       "      <td>170.00</td>\n",
       "      <td>260.05</td>\n",
       "      <td>272.35</td>\n",
       "      <td>19</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ÜRT-20</td>\n",
       "      <td>277.0</td>\n",
       "      <td>158.00</td>\n",
       "      <td>255.03</td>\n",
       "      <td>265.82</td>\n",
       "      <td>20</td>\n",
       "      <td>10.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      well  depth_m  casing_m  tubing_4_5_m  tubing_2_3_8_m  well_no  circ_interval_m\n",
       "0    ÜRT-1    272.0    165.00        238.25          247.00        1             8.75\n",
       "1    ÜRT-2    272.5    175.00        260.20          269.48        2             9.28\n",
       "2    ÜRT-3    287.5    180.00        257.06          267.35        3            10.29\n",
       "3    ÜRT-5    274.0    170.00        245.38          255.37        5             9.99\n",
       "4    ÜRT-6    273.5    170.00        252.15          262.28        6            10.13\n",
       "5    ÜRT-7    269.5    168.00        254.40          264.38        7             9.98\n",
       "6    ÜRT-8    265.0    175.00        240.00          250.30        8            10.30\n",
       "7    ÜRT-9    275.5    164.50        262.34          270.33        9             7.99\n",
       "8   ÜRT-10    275.5    154.00        265.03          275.04       10            10.01\n",
       "9   ÜRT-11    272.5    165.00        261.00          269.00       11             8.00\n",
       "10  ÜRT-12    272.5    170.00        260.16          270.42       12            10.26\n",
       "11  ÜRT-13    272.5    175.00        257.09          268.18       13            11.09\n",
       "12  ÜRT-14    275.5    180.15        240.00          250.28       14            10.28\n",
       "13  ÜRT-15    277.0    155.00        260.01          270.38       15            10.37\n",
       "14  ÜRT-16    272.0    162.00        240.00          248.00       16             8.00\n",
       "15  ÜRT-17    278.5    170.00        260.90          270.52       17             9.62\n",
       "16  ÜRT-18    281.0    176.00        261.10          271.46       18            10.36\n",
       "17  ÜRT-19    275.5    170.00        260.05          272.35       19            12.30\n",
       "18  ÜRT-20    277.0    158.00        255.03          265.82       20            10.79"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "_w = pd.read_excel(F_WELLS, header=None).iloc[1:, 0:5]\n_w.columns = [\"well\",\"depth_m\",\"casing_m\",\"tubing_4_5_m\",\"tubing_2_3_8_m\"]\n_w = _w[_w.well.astype(str).str.upper().str.startswith(\"ÜRT\")].copy()\nfor c in _w.columns[1:]: _w[c] = _w[c].map(to_float).abs()\n_w[\"well\"] = _w.well.str.upper().str.replace(\" \", \"\")\n_w[\"well_no\"] = well_no(_w.well)\n_w[\"circ_interval_m\"] = _w.tubing_2_3_8_m - _w.tubing_4_5_m\nwells = _w.sort_values(\"well_no\").reset_index(drop=True)\nprint(len(wells), \"kuyu | ortalama derinlik\", wells.depth_m.mean().round(1), \"m | sirkülasyon aralığı\",\n      wells.circ_interval_m.min().round(2), \"–\", wells.circ_interval_m.max().round(2), \"m\")\nwells"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ac58e00",
   "metadata": {
    "id": "7ac58e00",
    "outputId": "ebdf225c-3f9b-41d8-c072-4cf5d9c6b390"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1320x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "fig, ax = plt.subplots(figsize=(12, 5))\nx = np.arange(len(wells))\nax.bar(x, wells.casing_m, color=PAL[\"grey\"], width=.6, label=\"Casing tabanı\")\nax.bar(x, wells.tubing_4_5_m - wells.casing_m, bottom=wells.casing_m, color=PAL[\"glauberite\"], width=.6, label=\"Casing → 4½\\\" tubing\")\nax.bar(x, wells.circ_interval_m, bottom=wells.tubing_4_5_m, color=PAL[\"thenardite\"], width=.6, label=\"Sirkülasyon aralığı (4½\\\"→2⅜\\\")\")\nax.bar(x, wells.depth_m - wells.tubing_2_3_8_m, bottom=wells.tubing_2_3_8_m, color=PAL[\"clay\"], width=.6, label=\"2⅜\\\" tubing → kuyu tabanı\")\nax.set_xticks(x); ax.set_xticklabels(wells.well, rotation=45, ha=\"right\"); ax.invert_yaxis()\nax.set_ylabel(\"Derinlik (m)\"); ax.set_title(\"Kuyu tasarımı: casing / tubing konumları (ÜRT-4 açılmadı)\")\nax.legend(loc=\"lower right\", fontsize=8, ncol=2); plt.tight_layout()\nplt.savefig(f\"{FIG_DIR}/01_kuyu_tasarimi.png\"); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "c60eea10",
   "metadata": {
    "id": "c60eea10"
   },
   "source": "## 3. Litoloji: metre-metre karot bileşimi (`lith`)\n`ÇÖZÜNEN CEVHER ORANI` sayfası 19 kuyuyu yan yana bloklar hâlinde tutuyor (her blok 7 sütun, farklı satırlarda başlıyor, `ORTALAMA(%)` ile bitiyor). Blokları otomatik tespit edip tek bir *uzun* tabloya çeviriyoruz.\n\nSütun anlamları:\n- `clay_pct`, `claystone_pct` / `polyhalite_pct`, `thenardite_pct` → karotun toplam bileşimi (≈100)\n- `glauberite_total_pct` → karotta globerit olarak bulunan kısım; `glauberite_clay_pct` + `glauberite_thenardite_pct` onun içeriği"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7867a05d",
   "metadata": {
    "id": "7867a05d",
    "outputId": "6b480e03-83cf-4bcd-e7b4-c0fa3f185feb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1825 aralık, 19 kuyu\n",
      "Bileşim toplamı 100±5 dışında kalan satır: 3 (saha kayıt hataları — aşağıda listelenir)\n"
     ]
    },
    {
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       "      <th>well</th>\n",
       "      <th>depth_from</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    well  depth_from  depth_to interval_label  clay_pct  claystone_pct  polyhalite_pct  thenardite_pct  glauberite_clay_pct  glauberite_thenardite_pct  \\\n",
       "0  ÜRT-1       164.5     165.5  164,50/165,50      70.0            NaN             NaN            30.0                 70.0                       30.0   \n",
       "1  ÜRT-1       165.5     166.5  165,50/166,50      60.0            NaN             NaN            40.0                 60.0                       40.0   \n",
       "2  ÜRT-1       166.5     167.5  166,50/167,50      60.0            NaN             NaN            40.0                 60.0                       40.0   \n",
       "3  ÜRT-1       167.5     168.5  167,50/168,50      50.0            NaN             NaN            50.0                 50.0                       50.0   \n",
       "4  ÜRT-1       168.5     169.5  168,50/169,50      62.5            NaN             NaN            37.5                 52.5                       17.5   \n",
       "5  ÜRT-1       169.5     170.5  169,50/170,50      75.0            NaN             NaN            25.0                 75.0                       25.0   \n",
       "6  ÜRT-1       170.5     171.5  170,50/171,50      75.0            NaN             NaN            25.0                 75.0                       25.0   \n",
       "7  ÜRT-1       171.5     172.5  171,50/172,50      70.0            NaN             NaN            30.0                 70.0                       30.0   \n",
       "\n",
       "   glauberite_total_pct  well_no  depth_mid  thickness_m  comp_sum  \n",
       "0                 100.0        1      165.0          1.0     100.0  \n",
       "1                 100.0        1      166.0          1.0     100.0  \n",
       "2                 100.0        1      167.0          1.0     100.0  \n",
       "3                 100.0        1      168.0          1.0     100.0  \n",
       "4                  70.0        1      169.0          1.0     100.0  \n",
       "5                 100.0        1      170.0          1.0     100.0  \n",
       "6                 100.0        1      171.0          1.0     100.0  \n",
       "7                 100.0        1      172.0          1.0     100.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "import openpyxl\nwb = openpyxl.load_workbook(F_DIKME, read_only=True, data_only=True)\nws = wb[\"ÇÖZÜNEN CEVHER ORANI\"]\nrows = list(ws.iter_rows(values_only=True))\nncol = max(len(r) for r in rows)\ngrid = [list(r) + [None]*(ncol-len(r)) for r in rows]\n\nblocks = [(c, str(v).strip().upper().replace(\" \",\"\")) for c, v in enumerate(grid[0]) if v and str(v).upper().startswith(\"ÜRT\")]\nrecs = []\nfor c, well in blocks:\n    hdr  = [str(grid[2][c+k] or \"\").replace(\"\\n\",\" \").upper() for k in range(7)]\n    col2 = \"claystone_pct\" if \"KİL\" in hdr[2] else (\"polyhalite_pct\" if \"POLİ\" in hdr[2] else \"other_pct\")\n    for r in range(3, len(grid)):\n        lab = grid[r][c]\n        if lab is None: continue\n        s = str(lab).strip()\n        if s.upper().startswith(\"ORTALAMA\"): break\n        a, b = parse_interval(s)\n        if np.isnan(a): continue\n        v = [to_float(grid[r][c+k]) for k in range(1, 7)]\n        rec = dict(well=well, depth_from=a, depth_to=b, interval_label=s, clay_pct=v[0],\n                   claystone_pct=np.nan, polyhalite_pct=np.nan, thenardite_pct=v[2],\n                   glauberite_clay_pct=v[3], glauberite_thenardite_pct=v[4], glauberite_total_pct=v[5])\n        rec[col2] = v[1]\n        recs.append(rec)\n\nlith = pd.DataFrame(recs)\nlith[\"well_no\"]   = well_no(lith.well)\nlith[\"depth_mid\"] = (lith.depth_from + lith.depth_to)/2\nlith[\"thickness_m\"] = lith.depth_to - lith.depth_from\n# Globerit toplamı boşsa ama bileşenleri varsa topla\nm = lith.glauberite_total_pct.isna() & lith.glauberite_clay_pct.notna() & lith.glauberite_thenardite_pct.notna()\nlith.loc[m, \"glauberite_total_pct\"] = lith.loc[m, \"glauberite_clay_pct\"] + lith.loc[m, \"glauberite_thenardite_pct\"]\n# QA: toplam bileşim\nlith[\"comp_sum\"] = lith[[\"clay_pct\",\"claystone_pct\",\"polyhalite_pct\",\"thenardite_pct\"]].sum(axis=1, min_count=1)\nlith = lith.sort_values([\"well_no\",\"depth_from\"]).reset_index(drop=True)\n\nprint(f\"{len(lith)} aralık, {lith.well.nunique()} kuyu\")\nprint(\"Bileşim toplamı 100±5 dışında kalan satır:\", int((abs(lith.comp_sum-100)>5).sum()), \"(saha kayıt hataları — aşağıda listelenir)\")\nlith.head(8)"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca17c6be",
   "metadata": {
    "id": "ca17c6be",
    "outputId": "004ef0fb-89bb-456b-b923-0e458b5430d6"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>well</th>\n",
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       "      <th>52</th>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "        well interval_label  clay_pct  claystone_pct  polyhalite_pct  thenardite_pct  comp_sum\n",
       "52     ÜRT-1  220,50/221,60      61.5            NaN             NaN            48.5     110.0\n",
       "835   ÜRT-11  173,30/174,20      14.0            NaN             NaN            30.0      44.0\n",
       "1727  ÜRT-20        178/179      36.0            NaN             NaN            54.0      90.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "qa_bad = lith[abs(lith.comp_sum-100) > 5][[\"well\",\"interval_label\",\"clay_pct\",\"claystone_pct\",\"polyhalite_pct\",\"thenardite_pct\",\"comp_sum\"]]\nqa_bad"
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  {
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   "id": "fab39e44",
   "metadata": {
    "id": "fab39e44",
    "outputId": "42946787-748c-45b6-a489-eb8bc4a6a9a6"
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   "outputs": [
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       "      <th>n_intervals</th>\n",
       "      <th>ore_top_m</th>\n",
       "      <th>ore_base_m</th>\n",
       "      <th>logged_m</th>\n",
       "      <th>clay_mean</th>\n",
       "      <th>thenardite_mean</th>\n",
       "      <th>glauberite_mean</th>\n",
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       "      <td>55.25</td>\n",
       "      <td>44.71</td>\n",
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       "      <td>62.45</td>\n",
       "      <td>37.55</td>\n",
       "      <td>95.52</td>\n",
       "      <td>32.68</td>\n",
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       "      <th>ÜRT-5</th>\n",
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       "      <td>169.5</td>\n",
       "      <td>255.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>65.64</td>\n",
       "      <td>34.78</td>\n",
       "      <td>96.94</td>\n",
       "      <td>29.64</td>\n",
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       "      <th>ÜRT-6</th>\n",
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       "      <td>92.0</td>\n",
       "      <td>58.59</td>\n",
       "      <td>41.41</td>\n",
       "      <td>96.13</td>\n",
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       "      <td>264.8</td>\n",
       "      <td>96.3</td>\n",
       "      <td>57.81</td>\n",
       "      <td>42.19</td>\n",
       "      <td>95.51</td>\n",
       "      <td>40.61</td>\n",
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       "      <td>67</td>\n",
       "      <td>175.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>74.8</td>\n",
       "      <td>56.87</td>\n",
       "      <td>43.13</td>\n",
       "      <td>91.35</td>\n",
       "      <td>31.16</td>\n",
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       "      <td>64.91</td>\n",
       "      <td>35.36</td>\n",
       "      <td>91.38</td>\n",
       "      <td>39.12</td>\n",
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       "      <td>275.5</td>\n",
       "      <td>121.5</td>\n",
       "      <td>62.26</td>\n",
       "      <td>38.77</td>\n",
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       "      <td>165.0</td>\n",
       "      <td>269.5</td>\n",
       "      <td>104.5</td>\n",
       "      <td>44.00</td>\n",
       "      <td>55.33</td>\n",
       "      <td>87.74</td>\n",
       "      <td>57.89</td>\n",
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       "      <th>ÜRT-12</th>\n",
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       "      <td>170.0</td>\n",
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       "      <td>98.0</td>\n",
       "      <td>59.47</td>\n",
       "      <td>40.95</td>\n",
       "      <td>94.73</td>\n",
       "      <td>39.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-13</th>\n",
       "      <td>92</td>\n",
       "      <td>174.0</td>\n",
       "      <td>268.5</td>\n",
       "      <td>94.5</td>\n",
       "      <td>59.94</td>\n",
       "      <td>40.06</td>\n",
       "      <td>93.12</td>\n",
       "      <td>37.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-14</th>\n",
       "      <td>67</td>\n",
       "      <td>180.0</td>\n",
       "      <td>253.8</td>\n",
       "      <td>72.8</td>\n",
       "      <td>58.66</td>\n",
       "      <td>41.34</td>\n",
       "      <td>96.56</td>\n",
       "      <td>29.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-15</th>\n",
       "      <td>127</td>\n",
       "      <td>150.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>60.17</td>\n",
       "      <td>39.28</td>\n",
       "      <td>96.77</td>\n",
       "      <td>49.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-16</th>\n",
       "      <td>87</td>\n",
       "      <td>162.0</td>\n",
       "      <td>249.5</td>\n",
       "      <td>87.5</td>\n",
       "      <td>46.58</td>\n",
       "      <td>53.19</td>\n",
       "      <td>82.21</td>\n",
       "      <td>46.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-17</th>\n",
       "      <td>107</td>\n",
       "      <td>168.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>58.38</td>\n",
       "      <td>41.62</td>\n",
       "      <td>91.78</td>\n",
       "      <td>44.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-18</th>\n",
       "      <td>96</td>\n",
       "      <td>176.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>54.06</td>\n",
       "      <td>45.94</td>\n",
       "      <td>94.66</td>\n",
       "      <td>44.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-19</th>\n",
       "      <td>102</td>\n",
       "      <td>170.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>55.23</td>\n",
       "      <td>45.21</td>\n",
       "      <td>92.45</td>\n",
       "      <td>45.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-20</th>\n",
       "      <td>118</td>\n",
       "      <td>158.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>57.55</td>\n",
       "      <td>42.22</td>\n",
       "      <td>89.74</td>\n",
       "      <td>49.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        n_intervals  ore_top_m  ore_base_m  logged_m  clay_mean  thenardite_mean  glauberite_mean  thenardite_m\n",
       "well                                                                                                           \n",
       "ÜRT-1            77      164.5       247.0      81.2      58.27            39.65            66.01         32.11\n",
       "ÜRT-2            95      174.0       269.0      95.0      55.25            44.71            93.74         42.48\n",
       "ÜRT-3            86      180.0       267.3      87.3      62.45            37.55            95.52         32.68\n",
       "ÜRT-5            83      169.5       255.0      86.0      65.64            34.78            96.94         29.64\n",
       "ÜRT-6            91      170.0       262.0      92.0      58.59            41.41            96.13         38.16\n",
       "ÜRT-7            96      168.5       264.8      96.3      57.81            42.19            95.51         40.61\n",
       "ÜRT-8            67      175.0       250.0      74.8      56.87            43.13            91.35         31.16\n",
       "ÜRT-9           111      164.0       275.5     112.4      64.91            35.36            91.38         39.12\n",
       "ÜRT-10          121      154.0       275.5     121.5      62.26            38.77            91.95         46.93\n",
       "ÜRT-11          104      165.0       269.5     104.5      44.00            55.33            87.74         57.89\n",
       "ÜRT-12           98      170.0       268.0      98.0      59.47            40.95            94.73         39.72\n",
       "ÜRT-13           92      174.0       268.5      94.5      59.94            40.06            93.12         37.60\n",
       "ÜRT-14           67      180.0       253.8      72.8      58.66            41.34            96.56         29.90\n",
       "ÜRT-15          127      150.0       277.0     127.0      60.17            39.28            96.77         49.89\n",
       "ÜRT-16           87      162.0       249.5      87.5      46.58            53.19            82.21         46.43\n",
       "ÜRT-17          107      168.0       275.0     107.0      58.38            41.62            91.78         44.53\n",
       "ÜRT-18           96      176.0       272.0      96.0      54.06            45.94            94.66         44.10\n",
       "ÜRT-19          102      170.0       272.0     102.0      55.23            45.21            92.45         45.66\n",
       "ÜRT-20          118      158.0       276.0     118.0      57.55            42.22            89.74         49.40"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "per_well = lith.groupby(\"well\", sort=False).agg(\n    n_intervals=(\"depth_from\",\"size\"), ore_top_m=(\"depth_from\",\"min\"), ore_base_m=(\"depth_to\",\"max\"),\n    logged_m=(\"thickness_m\",\"sum\"), clay_mean=(\"clay_pct\",\"mean\"), thenardite_mean=(\"thenardite_pct\",\"mean\"),\n    glauberite_mean=(\"glauberite_total_pct\",\"mean\"),\n    thenardite_m=(\"thickness_m\", lambda t: (t*lith.loc[t.index,\"thenardite_pct\"]/100).sum()),\n).round(2)\nper_well[\"well_no\"] = well_no(per_well.index.to_series())\nper_well = per_well.sort_values(\"well_no\").drop(columns=\"well_no\")\nper_well"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8788beeb",
   "metadata": {
    "id": "8788beeb",
    "outputId": "6142b158-a839-4200-f81d-a9fe30b2cb0d"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1540x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "fig, axes = plt.subplots(1, 2, figsize=(14, 5), sharey=False)\npw = per_well.copy()\naxes[0].barh(pw.index, pw.thenardite_mean, color=PAL[\"thenardite\"], label=\"Tenardit %\")\naxes[0].barh(pw.index, pw.clay_mean, left=pw.thenardite_mean, color=PAL[\"clay\"], alpha=.6, label=\"Kil %\")\naxes[0].axvline(pw.thenardite_mean.mean(), ls=\"--\", color=PAL[\"accent\"]); axes[0].invert_yaxis()\naxes[0].set_title(\"Kuyu bazında ortalama karot bileşimi\"); axes[0].legend(fontsize=8); axes[0].set_xlabel(\"%\")\naxes[1].barh(pw.index, pw.thenardite_m, color=PAL[\"thenardite\"]); axes[1].invert_yaxis()\naxes[1].set_title(\"Kümülatif tenardit kalınlığı (m) = Σ kalınlık × tenardit%\"); axes[1].set_xlabel(\"m\")\nplt.tight_layout(); plt.savefig(f\"{FIG_DIR}/02_kuyu_ozet.png\"); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "26a110e5",
   "metadata": {
    "id": "26a110e5"
   },
   "source": "### 3.1 Derinlik–kuyu tenardit haritası\nTüm kuyuları 2 m'lik derinlik dilimlerine oturtup tenardit yüzdesini ısı haritası olarak çiziyoruz. Bu, cevher zonunun kuyular arasında nasıl kaydığını (yapısal eğim / fasiyes değişimi) gösteren ilk \"pseudo-kesit\"tir."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a17f6ff",
   "metadata": {
    "id": "0a17f6ff",
    "outputId": "06218299-6da0-4cfa-a14c-f7c945caad84"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1430x880 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "BIN = 2.0\nlith[\"depth_bin\"] = (lith.depth_mid // BIN) * BIN\nheat = lith.pivot_table(index=\"depth_bin\", columns=\"well\", values=\"thenardite_pct\", aggfunc=\"mean\")\nheat = heat[sorted(heat.columns, key=lambda c: int(re.search(r\"\\d+\", c).group()))]\n\nfig, ax = plt.subplots(figsize=(13, 8))\nim = ax.imshow(heat.values, aspect=\"auto\", cmap=\"YlGnBu\", vmin=0, vmax=100,\n               extent=[-0.5, len(heat.columns)-0.5, heat.index.max()+BIN, heat.index.min()])\nax.set_xticks(range(len(heat.columns))); ax.set_xticklabels(heat.columns, rotation=45, ha=\"right\")\nax.set_ylabel(\"Derinlik (m)\"); ax.set_title(\"Tenardit % — derinlik × kuyu (boş = karot kaydı yok)\")\n# sirkülasyon aralığını işaretle\nfor i, w in enumerate(heat.columns):\n    r = wells[wells.well == w]\n    if len(r):\n        ax.plot([i-.4, i+.4], [r.tubing_4_5_m.iloc[0]]*2, color=\"red\", lw=1.5)\n        ax.plot([i-.4, i+.4], [r.tubing_2_3_8_m.iloc[0]]*2, color=\"red\", lw=1.5)\nax.grid(False); plt.colorbar(im, ax=ax, label=\"Tenardit %\", shrink=.7)\nax.text(0.01, 0.01, \"kırmızı çizgiler: 4½\\\" ve 2⅜\\\" tubing konumu (sirkülasyon aralığı)\", transform=ax.transAxes, fontsize=8, color=\"red\")\nplt.tight_layout(); plt.savefig(f\"{FIG_DIR}/03_tenardit_heatmap.png\"); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "1536304b",
   "metadata": {
    "id": "1536304b"
   },
   "source": "### 3.2 Tek kuyu profili\n`WELL` değişkenini değiştirip istediğiniz kuyuyu inceleyebilirsiniz. Varsayılan ÜRT-16: en hızlı Bomé artışı gösteren kaverna."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffd38587",
   "metadata": {
    "id": "ffd38587",
    "outputId": "e54e475e-6107-45bd-ac88-8a16d335ca60"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "WELL = \"ÜRT-16\"\nd = lith[lith.well == WELL].sort_values(\"depth_from\")\nw = wells[wells.well == WELL].iloc[0]\nfig, ax = plt.subplots(figsize=(6, 10))\nax.barh(d.depth_mid, d.thenardite_pct, height=d.thickness_m, color=PAL[\"thenardite\"], label=\"Tenardit %\")\nax.barh(d.depth_mid, d.clay_pct, left=d.thenardite_pct, height=d.thickness_m, color=PAL[\"clay\"], alpha=.6, label=\"Kil %\")\nax.plot(d.glauberite_total_pct, d.depth_mid, color=PAL[\"glauberite\"], lw=2, label=\"Globerit toplam %\")\nfor y, lab in [(w.casing_m,\"casing\"),(w.tubing_4_5_m,'4½\" tubing'),(w.tubing_2_3_8_m,'2⅜\" tubing')]:\n    ax.axhline(y, color=\"red\", ls=\"--\", lw=1); ax.text(101, y, lab, color=\"red\", fontsize=8, va=\"center\")\nax.invert_yaxis(); ax.set_xlim(0, 100); ax.set_xlabel(\"%\"); ax.set_ylabel(\"Derinlik (m)\")\nax.set_title(f\"{WELL} karot profili\"); ax.legend(loc=\"lower left\", fontsize=8)\nplt.tight_layout(); plt.savefig(f\"{FIG_DIR}/04_profil_{WELL}.png\"); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "95c40089",
   "metadata": {
    "id": "95c40089"
   },
   "source": "## 4. Dikme kesit litoloji adları ve kuyu sıcaklığı (`dikme`)\n`ÜRT-n` sayfalarında her aralık için jeologun verdiği litoloji adı (GLOBERİT, GLOBERİT-TENARDİT, TENARDİT-KİL …) ve bazı kuyularda kuyu sıcaklığı var. Bunlar QML için **kategorik etiket** adayı."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f174f4a4",
   "metadata": {
    "id": "f174f4a4",
    "outputId": "f6e84c03-f81a-4dbf-dffb-a4310f90e1e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1262 aralık, 15 kuyu (ÜRT-9/10/15/20 kesit sayfası yok)\n",
      "lithology\n",
      "GLOBERİT             953\n",
      "GLOBERİT-TENARDİT    128\n",
      "TENARDİT-KİL          91\n",
      "TENARDİT-GLOBERİT     49\n",
      "KİL-TENARDİT          17\n",
      "GLOBERİT-KİL           5\n",
      "KİL                    3\n",
      "TENARDİT               1\n",
      "GLOBERİT-KİLTAŞI       1\n",
      "GLOBERİT-GLOBERİT      1\n",
      "SAF TENARDİT           1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "well\n",
       "ÜRT-1     25.27\n",
       "ÜRT-11    25.27\n",
       "ÜRT-16    25.10\n",
       "Name: temp_c, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "recs = []\nfor name in wb.sheetnames:\n    if not name.upper().startswith(\"ÜRT\") or \"FOTOĞRAF\" in name.upper(): continue\n    ws_ = wb[name]; well = name.replace(\" \", \"\").upper(); cur = None\n    for row in ws_.iter_rows(values_only=True):\n        row = list(row) + [None]*(14-len(row))\n        lab = row[0]\n        if isinstance(lab, str) and re.match(r\"^\\d\", lab.strip()):\n            a, b = parse_interval(lab)\n            cur = dict(well=well, depth_from=a, depth_to=b, interval_label=lab.strip(), lithology=None, temp_c=np.nan)\n            recs.append(cur)\n        lth = next((v for v in row[1:11] if isinstance(v, str) and re.search(r\"GLOBER|TENARD|KİL|POLİHAL|JİPS|ANHİDR|HALİT\", v.upper())), None)\n        if lth and cur is not None and cur[\"lithology\"] is None: cur[\"lithology\"] = lth.strip().upper()\n        t = row[11]\n        if isinstance(t, str) and \"°C\" in t and cur is not None and np.isnan(cur[\"temp_c\"]): cur[\"temp_c\"] = to_float(t)\ndikme = pd.DataFrame(recs)\ndikme[\"well_no\"] = well_no(dikme.well)\ndikme = dikme.sort_values([\"well_no\",\"depth_from\"]).reset_index(drop=True)\n# litolojiyi 5 sınıfa indirge\ndef lith_class(s):\n    if s is None or (isinstance(s,float) and np.isnan(s)): return None\n    s = s.replace(\"GLOBERİT-GLOBERİT\",\"GLOBERİT\")\n    if s in (\"GLOBERİT\",): return \"GLOBERİT\"\n    if s.startswith(\"GLOBERİT-\"): return \"GLOBERİT-baskın\"\n    if s.startswith(\"TENARDİT\") or s==\"SAF TENARDİT\": return \"TENARDİT-baskın\"\n    if s.startswith(\"KİL\"): return \"KİL-baskın\"\n    return \"DİĞER\"\ndikme[\"lith_class\"] = dikme.lithology.map(lith_class)\nprint(len(dikme), \"aralık,\", dikme.well.nunique(), \"kuyu (ÜRT-9/10/15/20 kesit sayfası yok)\")\nprint(dikme.lithology.value_counts().to_string())\ndikme.groupby(\"well\").temp_c.mean().dropna().round(2)"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17874fc6",
   "metadata": {
    "id": "17874fc6",
    "outputId": "a6b883c6-762d-4a43-b87c-155ab18b527c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Litoloji adı eşleşen aralık: 1236 / 1825\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">clay_pct</th>\n",
       "      <th colspan=\"2\" halign=\"left\">thenardite_pct</th>\n",
       "      <th colspan=\"2\" halign=\"left\">glauberite_total_pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lith_class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GLOBERİT</th>\n",
       "      <td>62.5</td>\n",
       "      <td>942</td>\n",
       "      <td>37.5</td>\n",
       "      <td>942</td>\n",
       "      <td>99.7</td>\n",
       "      <td>936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLOBERİT-baskın</th>\n",
       "      <td>39.1</td>\n",
       "      <td>134</td>\n",
       "      <td>60.3</td>\n",
       "      <td>134</td>\n",
       "      <td>67.0</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KİL-baskın</th>\n",
       "      <td>72.8</td>\n",
       "      <td>20</td>\n",
       "      <td>32.1</td>\n",
       "      <td>17</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TENARDİT-baskın</th>\n",
       "      <td>26.4</td>\n",
       "      <td>139</td>\n",
       "      <td>73.8</td>\n",
       "      <td>140</td>\n",
       "      <td>47.5</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                clay_pct       thenardite_pct       glauberite_total_pct      \n",
       "                    mean count           mean count                 mean count\n",
       "lith_class                                                                    \n",
       "GLOBERİT            62.5   942           37.5   942                 99.7   936\n",
       "GLOBERİT-baskın     39.1   134           60.3   134                 67.0   134\n",
       "KİL-baskın          72.8    20           32.1    17                 85.0     2\n",
       "TENARDİT-baskın     26.4   139           73.8   140                 47.5    52"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "# Litoloji adını metre-metre bileşimle eşle (aynı kuyu, en yakın aralık ortası)\nlith_d = lith.copy()\ndk = dikme.dropna(subset=[\"lith_class\",\"depth_from\",\"depth_to\"]).copy(); dk[\"depth_mid\"] = (dk.depth_from+dk.depth_to)/2\nlith_d = pd.merge_asof(lith_d.sort_values(\"depth_mid\"), dk[[\"well\",\"depth_mid\",\"lithology\",\"lith_class\",\"temp_c\"]].sort_values(\"depth_mid\"),\n                       on=\"depth_mid\", by=\"well\", direction=\"nearest\", tolerance=0.75)\nlith_d = lith_d.sort_values([\"well_no\",\"depth_from\"]).reset_index(drop=True)\nprint(\"Litoloji adı eşleşen aralık:\", lith_d.lith_class.notna().sum(), \"/\", len(lith_d))\nlith_d.groupby(\"lith_class\")[[\"clay_pct\",\"thenardite_pct\",\"glauberite_total_pct\"]].agg([\"mean\",\"count\"]).round(1)"
  },
  {
   "cell_type": "markdown",
   "id": "8fe80d7a",
   "metadata": {
    "id": "8fe80d7a"
   },
   "source": "## 5. Kaverna brine analizleri (`brine`)\nÜRT-1, ÜRT-11 ve ÜRT-16 kavernalarından alınan brine örnekleri. **Bomé** (°Bé) sahanın temel performans göstergesi — hedef 24–25 °Bé.\n`Na2SO4(g/L)=0` kayıtları \"ölçülmedi\" anlamına geliyor; NaN'a çevrildi."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0260a759",
   "metadata": {
    "id": "0260a759",
    "outputId": "9257531c-ec65-49fb-f182-b5eb65494ac8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>bome_max</th>\n",
       "      <th>na2so4_max_g_l</th>\n",
       "      <th>pH_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>well</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KUYUSUYU</th>\n",
       "      <td>1</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>1.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-1</th>\n",
       "      <td>23</td>\n",
       "      <td>2025-09-23</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>23.15</td>\n",
       "      <td>286.63</td>\n",
       "      <td>8.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-11</th>\n",
       "      <td>19</td>\n",
       "      <td>2025-10-19</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>25.00</td>\n",
       "      <td>296.28</td>\n",
       "      <td>7.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ÜRT-16</th>\n",
       "      <td>12</td>\n",
       "      <td>2025-10-24</td>\n",
       "      <td>2026-01-04</td>\n",
       "      <td>26.15</td>\n",
       "      <td>301.24</td>\n",
       "      <td>8.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           n      first       last  bome_max  na2so4_max_g_l  pH_mean\n",
       "well                                                                 \n",
       "KUYUSUYU   1 2026-04-01 2026-04-01      1.18             NaN     7.60\n",
       "ÜRT-1     23 2025-09-23 2026-04-01     23.15          286.63     8.12\n",
       "ÜRT-11    19 2025-10-19 2026-04-01     25.00          296.28     7.92\n",
       "ÜRT-16    12 2025-10-24 2026-01-04     26.15          301.24     8.28"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "b = pd.read_excel(F_BRINE, sheet_name=\"ANALİZ SONUÇLARI\", header=0)\nb.columns = [\"date\",\"well\",\"pH\",\"bome\",\"density_g_ml\",\"ca_mg_l\",\"mg_mg_l\",\"so4_g_l\",\"na2so4_g_l\",\"na2so4_wt_pct\",\"cl_g_l\",\"nacl_g_l\",\"nacl_wt_pct\"][:len(b.columns)]\nb[\"date\"] = pd.to_datetime(b.date, errors=\"coerce\"); b = b.dropna(subset=[\"date\"])\nb[\"well\"] = b.well.astype(str).str.upper().str.replace(\" \",\"\").replace({\"NAN\":\"(kuyu belirtilmemiş)\"})\nfor c in b.columns[2:]: b[c] = pd.to_numeric(b[c], errors=\"coerce\")\nb.loc[b.na2so4_g_l == 0, [\"na2so4_g_l\",\"na2so4_wt_pct\"]] = np.nan\nbrine = b.sort_values([\"well\",\"date\"]).reset_index(drop=True)\nbrine.groupby(\"well\").agg(n=(\"date\",\"size\"), first=(\"date\",\"min\"), last=(\"date\",\"max\"), bome_max=(\"bome\",\"max\"),\n                          na2so4_max_g_l=(\"na2so4_g_l\",\"max\"), pH_mean=(\"pH\",\"mean\")).round(2)"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20bd85e5",
   "metadata": {
    "id": "20bd85e5",
    "outputId": "2732431d-d1fe-4e9d-f96c-7bf9cda324bb"
   },
   "outputs": [
    {
     "data": {
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rCAqpoYsuukj9Fum5kBHQgEzhW2+9pYqVpqeny+OPP67a8v777xuXgezRddddp1KasQxSeZHKHh8fr1K3nSnAijaj3yCRiJRvyClBJgkp95YFhX/++We1bFJSkkqlNu0HaziyLy+99JJKTcdx2LRpk+Tl5al2IC0bEonoW0fBvmsFSSH7iTRrtBXHsU+fPmbLIe0fhZCxTaS5Iy0bRVlxTNAHluD8QPo2jm9iYqKSsTh+/LiMHDlSxo8fr2QucNw/++wztTwKLJviiv1Eu7HtMWPGyO7du1W7UbQXkgaQDXD19iyBfAfOc/xfsD4cV5zL55xzjkO/r2ofEkIIcT+ef/55NSZ47LHH7C7XtGlTefnll9X9C+MUyJKgSDbGMCge7wiQS7nzzjvVmAf38NmzZ8tff/2lZCEt2blzp3o/99xz7a5Tm49i3a4CY4rBgwdLSEiIuhd36tRJfY9xEMYlkKnEGBT3cRRN79y5s2zevFmNmayN0yDXkpqaql7a+Az3U4BxFe6v2Abu9+hb3GMxZsO4VwPSW5DZwj0cfY5xGdaNPn3yySetFkyvaNsffvihkpACkLDRpJYspXV+++03JfOEsSbm457vaHsc2T8Ufr/xxhvVmAhjCVMgDxQaGmomI1Ydx4EQQtwJPOvh2vfwww/bXa5Ro0bqXoxrIWwWeF7EdR3XXnt2FVNgO8A6cN/t1auXPPXUU+q6feLEiXLLYgyAdQ8bNsxMErK67s3YHmjTpk25eZB+xr0G9xy0Fc+ouKbDjmMN2Gywr5BSqyxoB+TWIMtWEdge+gl2LhwbyPgtW7ZMHVP0c0BAgBpb3XfffRIYGCjVxcSJE5V9JTg4WJXWiI2NVXKOzshp4tyA/QB2C9xvsU70uyWQ7O7atavcfvvtVWoz2qnZhTAWO//889V6cWxNzzvIL0KGVJNhxHgE9kJIAeKev2rVKrP14jgcOHBA7T/GDlu2bFHHFHKrsPvBVgYbCcYJt9xyi5ltQwPlajTbGGxSkASEDeqKK66QlStXlrNBVtdxg+2wcePG6vx64YUX1BgNxwjLYF8AbDX2gF0J4zJIdVqCc7JZs2ZWrwNVxd7/2hL8xzE2xJgT1x6M/UzBsbrgggvUfixZssRl0o+u6ptbb71VjUsx7oV9GecvZCpx7sA+h+eNyixbmWtSnae2vXqEuBtahCwiABAtqhWMdQZECeC3999/v5lkEKKLLWV87rrrLuXhN42ohmwB2rB27dpy60ZqNeYhCso0Uw1ZUpaRWVpki6UMAtqCiBpEEFSEFn22cuXKCpfVtnfvvfeWmweJTMxbvny52bLW0pPtZSY5si+dO3dWkcbWotoQ2YxopooKZ2qZarai7BDhZtrf6H+s17L4MaSAIP1gety1fbFMfddkISAlahlNhIgT0+hwV+0nzklEWFmmo2sFk00z1ZzZnqOZasjiQ5SLPeydD1XtQ2aqEUKI+2WqaeMxjI9wP7KVqWYLRF0jgtlURgqKAciqxj0PUs2mmUOI/Pzwww9V4W/TYtzIMIIUtwainPE92uJIRtu4cePMvse+IcsN8xyJGtYy1RYsWKCisUeMGGEmLw60fTLNiNeilhE9a3of1zKkIMtlCtqCe/i5555r/A4Sho5IYlpDi8A1zbhyZttvvPGGWnbDhg3l1q2tx5bEl6PtcWT/EhIS1Dn48MMPm0mOYoyP8XtNHAdmqhFC3CVTDSDTCp+1e6O1TDVbQNIOWTba8yIyiJD9husxrqFXXXVVuYyOH3/8UT9q1Cj1/K3dm5EVYro9LXsY93Z74PqsZTiZgmdbTaHINEPeFpCyxFjBEkg+Yx0Yz1jy3HPPWc1UQ8YYMllMcTZTDfcjLA+7Q0VgHAM1AEi3aVlp3377rTo2eE5GhhVkkdFeZGxXV6aaZbY4lGagWOQIsIc9+eSTaqwFqTlIDiLLCMcE91vcdzWWLFmizi1TtShbmWqafcFyjKcdD2svSE1j/Ghpi7CW5Q4ZR0u5PK0foJJlymuvvWZVCh3/GctzHccd38HeZgnGFdj/K664Qu8KHDluKNVira+QjQcloYoymLT9sdVm2I+QledqbP2vKzoHMDaHTdZa1pvpy7KsSmVwVd/gv49zCzZv0zbiXLE8/51ZtjLXpLoOM9UIscH8+fNVRMFll11mlnFmCaJosQwielCkFZEbKDSLSFNEvWrgexRt3LVrl4pYBYhgQdTKiBEjVFSHBgo/goEDBxqjYbQXimECywgEZPbYisxCFIkpWA+2h0gYUxAdjCjt8PBwFTGF5ZCxB0z3pSKsRXBr3x06dMjse2cL8la0L4hWRuYfMgeRfWfZf999952KZkKEj6MReVqUNI4XIi4QuYzo6EceecS4TWwPWWlRUVFmv0eB2C5dupTbb2AZ1aEVk+3fv79qu+U8RPpouGI/EVWD3w8YMECds6ZgXxBl6MrtWQOZY4goR2TcQw89pP4PiOhzlKr0ISGEEPcFUenIEJ85c6bNZZAZhMLZbdu2Vctq9yREXZuOWzCmQxQpoiqR4YR7FiJ/NTDm0TKccC/DOA2FuTGNDOjvv/9eLYcIfVDRfUqbHxYWZnbPReFyrM8Z0Gb8pnv37rJo0SKzdSLCHJHJWgabKR07dlRF4a2NPyzHUrhfIvrUdFyIYueIaMcYAcfgxx9/VNHWpmCfkH2AZdA32tgRmX+2xo6ObNsRrI0fnWmPI/uHSFucN59++qlSgQCIDscY/4477qiR40AIIe7E1KlTJSYmRj0HW8tGBrh34rqKZ2BkT2j3Zlw/8ayoXeN++ukndf+GfQTZO7CraM/XGrCzINMD60SGGTKF8dyK7Im5c+dW+d4MkJWF7C1Hwb5o9gHL+zUYNWqUQ/cs9AWyoZFpXRW0dqBdFYEMa/T7oEGD1LgJmUawL+E+h6wqZJYgawrHoTqzTKA+hHbj3gl1ANifoOyEY10RyL7CGBEKQsisg+oNbDbIUtMy0bUMMGR2YTyHDJ6qAkUn7bhj3evXr1fnL7bx999/m50DyFKyBKpSwHI8ABuiqQqAqU3D0taBcx0vU5uGZjuELc/SRoN1o48ryl569dVXy/3Wmg3UkeN20003qUx82CfxX8Uy+L9ifIb/vvZ/tYWWIYkMN2vg++rIorT1v7YH9gXXOuyjLRui9kLGYFVxVd8gi/Kdd96Ru+66S9n3kNmITD3YpbE/mmKEs8tW5ppU16FTjRAb4CEYgxw41i6//HIzOUWNrVu3qpsFUmFh2NEeuDUsByK44WjGHQCZOwzuLCUPHbmQw8FjCtLJbYEBk62bhsasWbPUzQ6SQjAOWLahugZV9tpdmX1x9EZo2X+OgEFJ69at5e2331bnxSeffGLcptaOquyL9ntb+4hBooYr99ORdldXv+IBAwN4nH9wCkMeA4NITR6gOvuQEEKI+wLnwr333qse2v/9999y8yF/AqcEJEmsSYyYfkbQBoKE8PDbs2dPZVCzJcUCAw0CPbBtONlwb4JUMoDMD/jnn3/stl0zLsDgozF9+nTlHHPGcAdg8MKDK4yNMGaY4qrxh7YO0/s8jHyQ09RkvtDPMEqZSnfhXg1jJAxLMA46MnZ0ZNuVHT860x5H9g/AIQfpMjhjNaMVxv6mx7Y6jwMcg6bbMN1mg5DVIYS4FTCSQ5YRhnxLCToNlBWAZK8mzWyJdu1CoAkMtbieQ04Mzjpb92bci3EPhgMDDjgENGv3Zsjs4loJCUB7z6LW7s1wJmC8oQUSOwKk7uBQtAwmdfZegEAZ9MXFF18sVQEODq1dFYHADxj3L7zwQiXhB2cAxhiQ6oNtCwbzkydPqrIPFUlduwIE8SJ45YsvvlCfv/7660qvS3NAaQ6nzz//XDm5Jk2aZOYs0s4blIvAZ63kBc4tHMOK5E0BHBf9+vWT1157TX2GU7Kic8DWeYFzW7vXV9YuVB22r6ocN5QLgRMSTjRIFqJUC2TMNTtrRevGWBzOXWtOI5yf+M+6Glv/a2uOVbQDjlT8hy655BKjdKSjIEDf0olp6wXHrSv7BjZrHDcE0UMqv0WLFkryHJLo33zzjbrGa+ezM8tW9ppU16FTjRA7YHAH/V9kH0E/GJqxpuBGjRsUsm0Q/YpBkXaRtZY1hohXZILhpoMLFJxruCFj3aa0b99evSNqyzLCQXuZRsi6AhgJYEyAIxEXQbQP20HmkLNYMzZp38EpVZ1gQIIbDyLjMNiw1X+WGWWVQesnbBPZcqjLYRkhhxsbjqOr99sV+6m1G3X9LKNdoOWNAZArt2cL/AegI//ss88q4yn+F7hp28sQJYQQUv+BYQMGH2sGDoxX4ExDYBIeLnEf0+5PMJRYA/dsBDjhYRDGk4qAEw7ZQ9gOgEEPYzQYnrQAKUsQgYxxIR6E8aANEOGJB0vUrnUWGFRwP4RxC/tqasRE38AYiYAoy+ym/fv3K2NSVcYfML7BEYg6c3BswniESHAt2hZjR4wNYOBElpc2dkQWWFXQjEu2siBs4Wx7Kto/AGMnDBRYNwy26FfLMXh1HgfNIGFZ5xZOVhg/CSGkpkEgLq61uF5aXofgbMM1GJk6eAbGNVG7N0PtxRYIfsG6HKnRjfsijLkIBMazNwJlEKgJZ4pl/U3T53Y8a2rZbwD3VtSdspcRbw0twMay3qZ2nbcWCGTtOwRtwz4Ep2BVQDtwPGDsdkVNNdzzUIesOmuq2XK0Oqt6YwruwQA1vUBN3CM1hxZqnpmeA+hfa05U02VchWY7RBapLRvNxo0b7a4DNV0tf+NInT9Hjxsy/LXMUi1jz94YsE+fPmr8pNUyNj1/MTaEQ9PV2PpfWwP/E4zLEeyGPkD/1QSu6Btck205WbXzWXOKObNsZa9JdR061Qhx0LGGh1qkmiOV2RJEKOCFixgcK3CS2br4wJiDAR8Kj6KoKQZ1+K0pMLrgggljDLLgsDwu1jDqwCGHCNnqiE6F4Qht0Qap2FdEVjsLjEeI+kpJSVFth4MEWV0oFA9Jy+pGk9mElAEcRpCLwA1h27Zt8vzzz1faIQnjDIxliKjDAB4FQjWJQRwrbAfHE8Y2GPdg8ICxBgM6zbDmbvuJSG0MAq+++mr1UIHBNSJwIA1RHdszBecYBvU4p9Gv+M9AmgCONVP5BEIIIQ0TRGTC0IP7KeT5rAGjD8YuXl5e6r6B5TFuswT3K9yTETUP6SPNcQOpRzjhICkFaSlNmgoGCIz7cD/XipbDUYZobk3eBvJIcOjBWIAxD6I1MUaDQwdOO4whAe6RMBTi95rDD9nvFRk5TA2ICOxC9D+MknDwmEYD4z6OtuIejfs4DJpQWYAhszLjD8i7IAIZfY5p7B/eNaOgdn/WpIXgnIKBAcE4CMZ67LHHpCpo8tMwSjljFHO0PY7uH8B5BQMyjGFPP/20MdDOkuo4DqYR3TBwol04NxGoVhkHLSGEuAI8/8JBhec2ZPnaum/h3oxAYwSZwh6AgGRLcP297bbblEMEz4BaSQI8a0POH8EOWrAn7t8wJOP+insvghm0+wWyj3D9h6Pv9ttvV/dzPFvifoxrPeQOEZyAaz9eAPdzBF3g3gGZXoDne8uscEuQ2QWQFW0K7vl44fdQYYGSEdqNYBgtO0oDdiPYgqoq/Yg+wbP48OHDHVoegeBoPwKTML7BZ9wnYaNCsC2OF4zhOL6aE8iVoO+R4aI9+2PsBJsTssmsyR1a4/zzz1fZM7CNYYyA44pxFjLNMbbDWMmWowgvzXGLsSI+Qz7SWTRbz//+9z/1WXOMwsGFaYwnsH1kxMNmgnNCc7y42i6EcxeB+5C5RB/gv4F+Qd8iSAj/h48//rjGjhuc1TNmzFC2JdgscX7iPMf/GOeWVs7GHpotCu/4PbaJTFOtD6tjDGTrf20PXFeQcYusNWf+L7Cb2XKAWr4snXxV7RsEgiHjDM8/UHjAOYrjhIAIZOxi/NqjRw+nl63sNanOU9tF3QhxN26++WazYrwa+/fv1zdr1kwVJ0fBXLB8+XJVDNWyCOX555+vCjdOmDCh3PpRYN7f318VIseyf/31l9V2zJs3z2YxTLxQMBKgKCY+o5C5JVqxVWtFblEoE0V+Ne6//36r27nmmmvU++eff15h32nbsyxgihcKvaOQsSNt+/7779W8P//8s1L7gqK6KBhqq+9QJLUi/v77b7v9j/1BkVyNtLQ0fcuWLa0ui4KhpoXrbe0Ljim+xzloCQo3o0CzKa7Yz+TkZFUc2vK3ffv2VYVnsf7KbM/aMbT8Tjt3rb2CgoL0Bw8etLkuV/WhtXUTQgipebSxQ3Z2ttn3KEKP8Zd2f8D9WRtPWbt/NW7cWD9q1Ch1H9E4dOiQvnv37vqHHnpIX1JSYrb+DRs22L3fo2j5b7/9ZvabuXPnqnGArd+MGTNGtdsaS5cuVcugeHxF9OnTR9+lSxfj58LCQv0ll1yitv3222+r706ePGm1H/Dq3bu3cbyotdvaGBdgOwMGDFDThw8ftrlvjRo10iclJanlbrjhBrtjR9N+c3Tb2vYtx9czZsyocD2OtsfR/dNAH+M8wHycQ9aojuMAcJ60bt263DovvvhifVhYmNVnDUIIcRXTp0+3er0qLS1V1zbtmoRnM6DT6dR1zPKaFRAQoK5XmE5JSVHL4n3kyJH6SZMmmV0jtXn27s14vfPOO2a/+eqrr/S+vr42l+/Xr59x25Zo94UDBw5U2CcYR8THx6vrsCULFy60ah+67LLL1PucOXPUcv/995/6jDGBrT639ZoyZYpx2V9++UV9t3jxYr0j4F6FY+dKKjpWs2bNMi6L+5a942N6HmjP+7NnzzbbXmRkpM11PPbYYxW299FHH7XZ99ao6HhERUXpjxw5Ylwe67V1Ht50003lxr6m41WNn3/+WS0PW4Ul1u79+/bt0zdp0sRmG9944w19VXDmuGn2FcsX7KObN282W6+tY1xUVKTv37+/1fXccsst+urA3v9aOwes2SLxnwoMDNR37dpVXf+qG2f6xlb/wrZr63jiWr1p06ZKLVuZa1Jdh5lqhDgIopYQ0YN08quuukpFPKGWByJ74J1HRCx0wO+55x4VAW1LMxnyBFpWE+QWrRWtBcgQQ7QW0qMRhYXUWbQB0SCIfLWmr1wVIH2DiBJNJxcyf6hvhcgxZ0GUBHS4EfWDdXXv3l2+//57lQ1VE2Cb6Du0ATIGiGJHv2P68ccfV5rOlQHHFDrviH5ClDCikTVwjFB7Bf0FjXdE8OH4orAnIoQsC9e7y34iexDtQ4Q1ognxexwn1PtDlqFp0VVX9yvWjaw4nNOQQcD6UU8N0TXQ3K5uqVBCCCHuD8ZXiDy3BPcfZDIhYhnTkGqEVA3uaZYa/ohg3r59u7z44osq8wj3cy26FjIqkD5C7SzUWcF6EO2O8RCiMBE9bymBg6whZGxjPu71WB7R8shCgywk6m+h3a4GEeQYTyFTClnziErGmANtQZQ57uloC6LNp02bpqKwKzNexPgN4xxEE2NdWAfekZ2HbWG8C1CcHDXMIBWFZTAeRiSzI1HIFW0fUdXI0sL+OIqj7XF0/zTQx6g/g/PG1ri4Oo4DwHgS5xOeF7AOZMrhWaMy8uyEEOIqcD20JrWIeywyVZCli+sVni+1zCjLWkrIEsP1EVlHuGdincj2BvgtsrlhE0E9H9xjcV3FtRbPrXgmtVRJwT0Z93pksuE6j3sm7unIJsH9AeMDV5SAQDYU7gXITElNTTWbB9sN9h9jCzzb4n6ELGeMMYD2bA2pNuyTtr+VBdl/yKbXsu8qAv3nbP1PV4LxFjKnkNGH/sHxQUYixmeQWHbkXoksMGSQIysMx1ir7wWFJ2QI1gS4N2OciHEDMtZwv9fA+Q4bB2xGGJ+ijbCtvfLKK0oVoTpAXyATDPWAYUPBNjEWQbYQtnnzzTfX2HGDbDb2FWNqfI//AI4X1JxgN3IE/NfxH8eYDucs9ge2UFxzUI+sOrD3v7YH2oexHrJooc5V3biib/AMhOvh2LFjlf0N68T5gucL2OFwza3MspW5JtV1POBZq+1GEEIIcS8wmMBDAaStLOUqqgukzcM5h0FbQ9BfJoQQQoj7A/lvGKwgW6RJVBNCCGm4QFYShmwE0D711FMOBR1DyhIOCARm454CpwQcipUFksBwcsBpgoAOQkjN/q+JOQ3xmsRMNUIIaeAgcglONGjXIyoHWZhaNBMiAWsKRDNBexua0IQQQgghtQ3qkjzxxBPKUDB58uTabg4hhBA3ABlnyKJHHXnUTdNAzSE8RyPLDvWG8BmKQG+//bZSPELWHOoRoe5VVeupoX5W165deW8ipJr/14TXJFswU40QQho4kOLYt29fue+R6m2toLSrQdo4ZJVQ1BTp4yh6i5RyQgghhJDaAtI2CDQCkLyBEZSZ9IQQQmxx5MgRadWqldV5eK7WpKcJIYTUfZipRgghDZxPP/1URcpBkxnGIqRsQ/IR9UxqgoEDB6pU+8LCQlWXjg41QgghhLgDqLGB2sA//PADHWqEEELsgtpaeLYePHiwynoJCgpS06j3RYcaIYTUL5ipRgghhBBCCCGEEEIIIYQQQkgFMFONEEIIIYQQQgghhBBCCCGEkAqgU40QQgghhBBCCCGEEEIIIYSQCqBTjRBCCCGEEEIIIYQQQgghhJAKoFOtEuh0Ojlx4oR6J4QQQgghHB8RQgghhNB+RAghhNR/6FSrBMnJyRIXF6fe3ZmSkhJJTU1V74R9xHOH/y9eg3gtdld4v6ofcHxUf+B/kv3Cc4f/L15nah9ei+sH7jg+aojnVkPc54a639znhgGPc8OhxE2vY3SqEUIIIYQQQgghhBBCCCGEEFIBdKoRQgghhBBCCCGEEEIIIYQQUgF0qhFCCCGEEEIIIYQQQgghhBBSAXSqEUIIIYQQQgghhBBCCCGEEFIBdKoRQgghhBBCCCGEEEIIIYQQUgF0qhFCCCGEEEIIIYQQQgghhBBSAd4VLUAIIYQQQogr0Ov1kpqaKgUFBVJSUlJrbSgsLJTs7Gzx8PColTa4K56enuLj4yNBQUG13RRCCCGEEEIIIcQtoVONEEIIIYTUiDPr5MmTypnl6+srXl5etdLrcKRh+3SolUen00l+fr5kZGQo51pYWFitHSdCCCGEEEIIIcQdoVONEEIIIYRUO8hQg0OtcePGEhkZWavOPTiPvL296VizQmlpqaSkpKjjdebMGYmKiqr5g0QIIYQQQgghhLgprKlGCCGEEEKqHUg+IkOsNh1qxDEJyOjoaHWscnNz2WWEEEIIIYQQQogJdKoRQgghhJBqBzXUKCVYN4A0Jo4VstYIIYQQQgghhBByFjrVCCGEEEIIIYQQQgghhBBCCKkA1lQjhBBCCCGkEmRlZakabWFhYZKRkSE+Pj4SHBzMviSEEEIIIYQQUivoSktl+YnDcjgzQ8L9/WV0fFsJ9/Pn0XAhdKoR4mISMtLkWHamtAgJl7aNWDeGEEKIa7n00ktlx44dahoOnPbt28t9990n55xzTrllly5dKi+99JIcPnxYWrVqJdOnT5eRI0dWejlizpVXXik5OTmycuVK6devnzoGn376KbuJ1Ak4ZiWE1Cf++OMPyc7OVtOhoaHSsWNHad26tdVlERCzadMmOX78uLRr1066du1apeUIIYQQd2FT8kmZvnShpOSfrY89a/VSubvXQLmle18l9U+qDp1qhLiIjIJ8ddFal3Tc+N2A2Dh5aeQYaeQfwH4mhBDiEt544w0pKChQ05mZmfLll1/KsGHD5Pfff5cxY8YYl/vvv//k/PPPl6efflrmzJkjCxYsUJ///vtvGTFihNPLkfIgQ83b2zCcjoiIkJCQEHYTcXs4ZiWE1EcwbklOTlbT6enpKuAF4yKMk/z8/MyyzMeOHasCifr27SsrVqxQgURff/21yjh3djlCCCHEXThyJkNuW/yzFJWUmH1fXFoir25aJaF+fnJVx+611r76BJ1qhLgIONTWmzjUAD4/8N9C+ejCy9nPhBBCXELz5s3NPsPQA8PRW2+9ZeZUmzlzplx00UXyyCOPqM+Irl67dq36vHr1aqeXI2c5c+aMev/222+N361fv169W8pA5ubmSn5+vppGVGCjRo3E09NQ1lin0ynHaEVERUXZnAcHK7LlsF4vLy8eJlIhHLMSQuojr776qtnnPXv2SJ8+fVTA0JNPPmn8Htn4J0+elF27dqngmGPHjkn37t3lxRdflEcffdTp5QghhBB3Yf6uzVJYohO9jfnvbF0nl7fvKt5lz6Ok8rAHCXGRfA4y1CwvWvi8NvG4mk8IIYRUF5GRkSqiWiMtLU05xsaPH2+23MUXX6y+RwS3M8sRcyZMmKBe1oAM5D333GP8PGPGDGncuLGSoYJUZ0BAgPTq1UtlFsJQh++1F2SqoqOjlcSU6fdwzFmyatUquemmm6RJkybqN5okKCH24JiVENJQ6NSpk/Tu3VuNZzTy8vLkiy++kDvvvFM5ykCLFi3k2muvlQ8++MDp5QghhBB3YumxQzYdaiAlL1f2p6fWYIvqL8xUI8QFoIZaRfNZX40QQkh1sHz5clm2bJm8++67xu8OHDig3tu0aWO2LD6jPgjmDxgwwOHlrAEnnqkjLykpSb2XlJSolyVYHzK18F6baNt3RTvsrcN0O3Cqaf2DrLK7775bLr/8ctm2bZukpKQYf/PLL7/IZZddpo6pZd0Wy23NnTtXOT+RZYjabpjvqr41bbu1Y9lQQV+UlpbW6T45kmnfUT5v/XKZOWC4NA8xGJEbYh9VN+wj9oM7nCsNIbO5qKhIjh49qmrRamzfvl1lePfv399sWXx+55135NSpUypYxdHlCCGEEHeiuLS0wmV0pXVvnJ6amip33XWXzJs3TwW5uANu71SDJva6detsRgM7y/fffy+jR49WdS8qAgMlyCnB+DFx4kQJCgqShggkixBNfcEFF6iCv6Q8LULCqzSfEEIIcYZNmzbJVVddJdnZ2Srb7Pnnn5cpU6YY5+N7YDl20SQJtfmOLmeNl19+WdVhsyaNaG3MVFhYKL6+vkry0FUcPHZCjiUlS4vYGGnTwlwW0x5VNVBqTidb+wIjqDbPcll/f395+OGH5fPPP5d///3XzKGptQvLVtRPGNOCxYsXO/wbZ0C7YZCEnCU5e3zwXIC+qasG6XCxX5h8xcmjsuqnz2VE0xZyddvO0iE8ssH1UXXDPmI/uMO5Yk9WuC6TmJioAlMwFkHts/j4eHn88ceN87UAFwS7mKJ9hv0JzjJHl3NF0FFt0BCd+w1xnxvqfnOfGwY8ztbpEx0ry44fllIb+WoB3j7SKjS8Tl0T1qxZo2wfCEa94oorpFmzZtW2LWfGW27vVEN0EHSsXcXx48eVgcARfvrpJxUlDB1u08K2pgO2jRs3KiMRvKSQ+9GK1ZuCwraIID/nnHOkbdu2FW4XyyNy2dp6i4uLVaFdUzp06CCDBg2yub6DBw/K5s2bJS4uToYMGaK+g4Hk119/VdOIGg8MDFTyQt26dTP7LW6+P//8s4rIokPNNshCa+QfoAq/WzKwaRyz1AghhLiULl26yKJFi5TRZsmSJcpghDpeU6dONTpugOWYB2ML0/mOLmeNadOmyS233GJmNMJ4ATJJqO9lCRx0GHNYGys5S8aZLJk6+xVZu3Wn8buBPbvKKzOnSqMw+wFAmpMLA2a0pzJov7O1L6iZps2ztizGc9o80++1QTy+c7SfKvObitCyCjH+tXYsGyp4+ES/hIeH11mHEY5n/5jmsj75hNn3OEubBYdKbnGRZBQWyL+JR9WrX0xzubFrbxnStIVD/5f60EfVDfuI/cBzpfpAYDSyvlGvFBLLY8eONbt2aUZErbaphna90oJTHF3OFUFHtUFDdO43xH1uqPvNfeZxbsjn9pXx7eRgUqLNdVzYqp0U5uRKoZQvL+BulBYXyYcvvShPvfaWRDYKl5+/+1YGnDO0WoM+nQk6cnunWm0BZxI8oHBoWTMowdkGxxfmI+oaTjMYkyDZYwoGW4ggRi0MnPgVgXoYW7duVc4ta+vFHwcSBpMmTTL+gUJCQmyuD0YxGNtGjRqlJAxQrBfa4jDmYD3XX3+9GijihPzzzz/V+lF8VwNGtvPOO0/V6iD2NWmzCgqszvtf78HsOkIIIS4FYxMtUAf1QjBmefTRR+W+++5T4wME0gDLwCTtszbf0eWsgWAbawE32L61Qb5m1DI1bhUVF0viaec13WfMfUO27d1v9t26rTvlrqdelDkPnq1nZg0laajTiZe3t7EtTRtHia+Pj9PtsOdkMN1fbBMZhVqA1/Tp01W0O5QQTNdh+htHHX6V+Y2jYH0NxQDjKBg32zrH6wr39Rksk/74zuy7AU3jZN6IMeLv7S2/Htgtn+7cLMezz8iG5BPq1b5RlNzYrY9c1Lq9+Hh61fs+qm7YR+wHR9AlHxc5miD6+Lbi1axlNZ+V9QPULP3mm2/UNGwcCGy+7rrrjFndWqAIHFymwAkHNEUjR5dzRdBRbdAQnfsNcZ8b6n5zn3mcG/K5PbBRI5lUXCiz1/2nctU8y55FMT08rpXcPOAc8fVyf3dQ0Z6tMuve22T2f5vlnLjG8sLVY6TF6u/FL9RTAoePE3fA/XuxDNT0gFMIICuradOmxnlwVq1evVoZK+BRHDp0qNERhgEMCtPCSdazZ89y67X2WzzkIBsMJx0kAzBgspSfRHtQw6J58+ZGx9Znn32m9LpNDRpwiiGaHFFSjoAC9qbZYrbWCxkDR6KRId0Iow2y2eDgS083r6PQsmVLtb94h4TBoUOHjE41e/1KzPnlwC4pEb0EevvIxxddLieys+TZNf9KZmGBzN+1ReY1jmWXEUIIqTYwsEYgDYJmMMDGfR3Z7hiHQCpBA58xhsBLGwc4slx1AYfaBTfd65J14UFhy559lVrf4o/fkJbNqu9eDacnFAHwIASDXGRkpJIYdyRoCcubRsRjXGoZOU+IMxzLMhiFfT29ZO6Ii6RlWCMzVYWrO/WQKzp0kyXHDsrH2zfKjtRTsj8jVWYuXyyvbVol13fuJRM7dJVg3/JKHoSQqlOakyWZn70kRQd2qM+Ix/Zt103Cp0wXz2CWY3AUOLBQv/SFF14wfqfVK0WwsWm9NHyG9LU27nF0OVcEHdUWDdG53xD3uaHuN/e5YcDjbJ2ru/SUwXEt5cf9O+XImUwJ8/OTsW06KrUKVwdhVgfFJw7LmU/myMQ2jUVX0Enu7d9J8gP9xfNMtuQu+Ey8g0MloP9IqW3qxBM5HEGQL8TABtE9X331lVE2B/KQ77//vnKAQaYRmVWoUYHPkESCQwqDqc6dO8t///2nMsY0bP0WN5phw4apZRDZ1Ldv33Jt0mSRTCWNAgICzE7O06dPy/79+2XwYMczlSxlJq2tF/zwww/y7bffKoehPR1U9BecY9jPf/75Rzn4rKFFUGuDP3v9Sswp1evlx/0Gp+nYNh2kW3SMiuK9t7dBkvPPw/tlT9ppdhshhJAqA+npJ554wixIBrVn33jjDRk/frxZ8MsDDzygxkGQqgZ4nz9/vsqSMsXR5chZMC5DwJY18L3luA0BTiiujKh5BGbBCXr//fc71KUXX3yxcshpL1fKopOGyc7UU+q9c1RjObdlW6sy5V6ennJ+y3by9cVXy2djJqrIVpCcmyNzN6yQc7/7SF7esFJO51WsxEEIcQ6DQ+2svDHA58z5L7MrbQC7iTVloA0bNqjgIQ3UQUOw8BdffGH8DoErsK1AHUgLWnF0OUIIIcQdaREaLlP7niOvjR4nz5xzngyIjasTDrVff/1Vbpl0jehLSqRpcIBMG9BRvDxN2+0hOYu/F72NZ/GapE5kqiEjCxFGeEfGFWQX4QCKiYlRhp/Y2Fi54IILjJleb7/9tpLWQdYVir+PGDFCzcPyr7/+unG9tn4LY4UWeYR3yDDaAw42FJrX6pUBOJ9+//13te7KRoNYWy/6YMqUKWoaTsNVq1YpGUfT6HJT8Ie59tprlSEHzkXLTDMYzTRZAzjUtG3Z61fTQSkRWZd4XMnjgIkdzmYZXt6hq1E659WNq+S9Cy5ldxFCCKkSyGxCFnuPHj1UgBHGCjDs3HTTTfLkk0+aLXvvvfeqLPSRI0eqIBvc6+HIwfeVWa46gOwissSc4ejJJLnt8edtzn9/1iMSbyfrzJb8ozPgGMA5Zg0EVdmrQwvZTgQujR49WgVJQQLSHitWrHCqbYQ46lTrGtWkwmXxH+kb01y9EjLS5LOdm2XBwT2SXVQkH+3YKJ/t2iwXt+koN3TtwxrChLiA4qRjxgw1c/RStH+7koT0jrEtzdxQgb0DSkIIMEIACoKpFyxYoJR3fvzxR7NlX3vtNRVEjVIYuBd/9913auzz7LPPVmo5QgghhFQNBK6gTvycOXOkU3S4ZHaMkkb+1vwxeilJS5aS1GTxbnxWxbA2qBNONRgmTKUOkbmFTCpNTgfGC805BEMJIpQQwY0XHGkalk4le781lZe0BwxayJyDo2ngwIHG7zdt2qTa3bp1a6u/W7RokTJgmXL11Vcb22drvZpUowbmY7CHwR0MYdbAb+AgswYGiZifl5enZIjwwoDRXt/QqWbO9/sMDz2dIqOlS2Rj4/eoN3FP70EyY9kiWXnyqKxPOi79Y/kARAghpPL4+PjIgw8+qF64V2N8ZK8+x3PPPacGp7inI1vKloyzo8u5GtQxc1Z2EcsP7NlN1m3bIaYJ9PCP4fvh/Xvb/T3GNBi0o+8qG603YMAANZZD5qDpmBFBSTDkmUpFWQO1bhHIhH6vyKlGiCspKimRvekpahrqCs6AjLZZQ8+Te/sMki92bZXv9m1XzrWfD+xWL2SzTencS1r7BvCgEVJJYCiyhy41iU41K7Rq1Uqp+CCzDO9QAIKDDZlllvXPUHcNpUU+/PBDWbp0qSov8umnn6rxT2WWI4QQQkjlgQ3immuuUYlFkyZNkqcjCiTQw7Yqn6K0gvk1QJ1wqtkDg6W4uDjp3dvcgIJaFSdOnDCTaYQcjyYbWdFvHQEGGdRcgzML0jymoIYa6plh0AXgjILDCo4pSEqivpvmGDQ1lAG00dZ6LYHjTnOK2XKq2UOrqaY5K1FLDoaeqvZNQyEtP0/VmwCoPWFpnBvTuoN8tH2jqkOBbLUvx11VJ9JtCSGEuD+O1OMCcJA5EhDj6HLuwCuPTJVps1+RNVvORvPDofbyzKk1sv3bb79d3n33XbniiitUNB2UDfbu3asy/JBF6IijbMaMGcrg99dff8n555/vdBugWAB5brxrtdcQqY/xXFBQUKX2i9R/DmamKcca6BJVOcNw48BgmdbvHLmtRz/5Yd9O+Xz3FiULuez4YfXqFB4pt/TsL+e1bKdkJDWQ6XYsO1NahIQzq40QG3hHxVZpfkMG0sr33HOPw044BLa4ajlCCCGkIZGYk6WC7P49dlA9W/Ru0lQmdekpPZ3MHsPzLIJNocL31ltvyZ133ikZ786Sov3bEA1r9TceQSHiFeVccGB1UOedapCDhMwijBEoGAt2796topEwAEIdMRwcGBhQl820/pi931YEJB2xbjiyLrnkknKOEsgmmjrNID2g1cIAphl0pqB9iKZCe62t9/Dhw0pyKCoqyhgRDXlK7XNVOHXqlHKmYZtV6ZuGxK8Ju0VXWioB3t4ytnWHcvM9PTxkat8hcuffv8q2lGRZevyQjGrRplbaSgghhNQXIsJC5dM5T0rC0eNyNDFZ4pvGSNv4mssGx7gLkfAwtN12223KoYXvxo0bJ48++qhZjVyMo6wFJWFZZLwhAl5zquF3WNZUocEWeOBYvHixmsZvNEfeDTfcIPPmzXPh3pL6xI4Ug/RjsI+vxIfaznJ1hGBfP7mhWx+5tnNPWXR4v3yyY5MKJNuTmSbT//tTWoSukSldesnwuNby6Iq/ZF3SceNvUdfhpZFjpJE/s9oIMQXSjh7BoaLPMQRMnMVDfNt3Y5YaIYQQQmpdSv6mP3+U3OIi43d4FsDr8cGj5KqO3R1eF3wazzzzjPLhaEp9QSMulqJ9W23+Jmj4OPHwNiQm1SZ13qmGmmnIpoI3Ew4fFKhFhhXqVXTu3FlFDb/xxhsqowuOKrwc+W1F9OvXT9VeQ5aXJpEIUNsM27B0mmmOr4qcXyike/DgQdUOa+uFYQaa4HC+4QWPLurNaVluzqJtA1l1iHSGgaeqfdNQgHzU9/t2GjPSYFiwxtDmLaVPk2ay6dRJeW3jahnevJVZ1C4hhBBCKgccaTXpTDOlWbNmqt5sRSCTDS9LEMQEx5wpqAeDbDNHgLoAIZWtp9YlqokK/nIFvl5eMr5tJ1Vbbfnxw/Lh1vWyOTVZjmVlyqw1S2X22mWi05sXE4cs+gP/LZSPLrycB5EQE0oyU0Wfm13+f9a+m4RPnsa+IoQQQkitUVJaKtP+/UPyTZQAgZZTNmv1vzKwaQuJDw23uQ6o7d1xxx1K9QUKfZB+NMWvUy8JueRGyf4F6n96EQ/Y0A3PLf79RkjQuZeJO+D2TjU4pyZMmGD2HT6bRvwOHTpU1a5IS0tTzjMtswrA4QTpRThA4ByCJKRptpWt32L5KVOm2IwU3rNnj7z++uvlpJfgPLMG5H3sFa3X6NSpk9UsNm292B4iomFwQRvRD8iaq4w0AvZPA5HRcPiZOufs9SsRWZ98QhkLwMQO3Wx2CYxm9/cdItf/8Z0kZKbJ7wf3yoR2ndmFhBBCCCGkRtmZaqjX1DWqicvXjTHvOc3ipUtgqCSVFstnu7aoiFVLh5r24L028biShEStNkKIgbw1/yi5Iw//QAm7/QnJSjomYfFtxa/Z2brqhBBCCCG1wZrEY3KyXDa9+Rj/h307ZHq/oWKNAwcOKF/Njh07lJqfrbJXyFbz69pP8tctkeKUZCkOiZRGvQaKf6sOblNWye2daqjvgUhgUyw/a04h00Lxppg60ZBt5chvcYBQb8wWZ86ckdjYWGnevLlD+2GrbZZATrKi2mhom6N1VGwBJ529/XOkXxs63+811HFp3yhKulVgmIC2LIq3o87Em1vWyEWt24uvl9v//QghhBBCSD0hX1esnFiga7TrnWqmdI5sLPNGjpEBTePkqVVLbC6HGmt0qhFiQF+ik/w1f6vpgP4jxadFG/EMiRDvRlWTaiWEEEIIcQWHzqTbne8pHnIw0/oyv/76q0yePFlN//LLL+WSqCzxjoqRkLHXKaW+0owM8W3UyG0caoBWfUIqweZTifLXkQQ1fUWHbg79qf/XZ7CSxEnMyZa3t6yT7o1jWKidEEIIIYTUCHvTUqSkrOB3dWSqWaNXBcXKW4TYloYhpKFRuGO9lGZlqOnAIRfUdnMIIYQQQswIsVH6yJRQK8ssWrRILrnkEunWrZsqa9WuXTtxhlK9XhYe2iff7tsph8+kS5ifv1zctpNc26mHmq4N6FQjxAkyCvJl+tKFZoXWFx/erzLPKiq03iEiWs5r2VY54z7YvsH4PQu1E0IIIaQ+gCwoZB7BUcLsI/etpxbpHyixQSE1sk2cBxjrooaaVmsBIBwNWWw8Twg5S96qRerdt1038W7SXEVmE0IIIYS4C6NatBFfr3+lyMYYpVT0MrZ1h3Lfn3vuuTJ79my57777JDAw0GmH2ofbNsg3x/aL3sNDfc4sLJA3N6+RXw/sls/HXinRgUFS06DSm1uwa9cuVaSue/fuylsJfc1NmzaVW+6BBx5Qtb8sX9u2bXNoO4sXL5bhw4cr2cZBgwbJd999Vw17Q+orcKjBKGDKplMnVaF1Rzidl1vuO61QOyGEEEJIXQ06uunPH2XCz5/Lvf/8pt7xGd8T93OqQfqxJqVTXiqTgTQFn+eNGFNjbSDE3dEln5CiAzvVNLPUCCGEEOKOICvsrp4DbM4f2DROhjQ3lJtat26djBgxQjIyMsTb21sefvhhpx1q4M9D+2R14jE1DYeaKSeyz8jstf9Jg81Uy83Nla5duypH2qeffiohISHyyiuvyMCBA2Xp0qVyzjnnGJfNycmRDh06KB1OU8LDK5YOWbZsmYwdO1bmzJkjV199tfz1119y7bXXiqenp0ycOLFa9o3Ur+hr0ww1ZwutY/7W00mV/j0hhNRXdMnHpfRoguji24pXs4rrfRJC3D/oSAsa+ujCy2utXcScXWVOtS6RjWu0a6DmgPOAmYyE2CZv9WL17hnaSPy69WdXEUIIIVY4lZomn/2yUP5asUYahQRJWGioXDv+Ihk1sC/7q4a4pXs/CfTxlXe3rpP0siBKXy8vubRdF3mw/1ClSPH222/L/fffL02aNJGTJ09KoyrUh/1h307xtBEQCJv630cTJC0/TyIDnHfY1Xmnml6vl0cffVSeffZZ43dvvfWW/PPPP/LGG2+YOdWAj4+Pyk5zlieeeELOO+88le0GbrrpJlm+fLk89thjdKqRCoGcUUXz7TnFqvp7Qgipb5TmZEnmZy9J0YEd6nNGmeRR+JTp4hkcWtvNI4TUQNARqRmyiwrl8BlDraau0TG10u04D3guEI2EYydk9/4E6dy+rXRoFd+gO6a0sEDy1y9V0wGDzhMPL7cw0xBCCCFuxcFjJ+S66Y9LRla2eHl6io+nh+w5dExWbNomt111qUy/6brabmKDAIoX13XuKVd27KaC9iAFiZJHyGLLy8uTybffJF988YWMHj1avv76a4mOjq7S9g6dSZdwvW2VDWSvIWOtQTrVgoODzRxq2gGKjIxUWWyuAOtZuXKlvPrqq2bfjxkzRj777DM5cuSItGzpXHQ80hcDAqzX0UI6o+m8zMxMu5rofn5+qh80srKypLi42ObySJsMCwsz27+CggKzZUpLS+XMmTPKaYnlTb3CWLaivsXyyOIDaAvaZA+0B9sB2Ffssz2wv9hvjbS0NLvLoz9N00Sd7dPs7GwpKiqy2Ue+vr52+zS8RCS4+Oz28H/O9fYyfo7xDbC7D/i9h16v9F+BV6leAkpKzeZb/j40NFQ5kbW24pxzpk/T09PVvjnbp6b9op0DAH2ETFJ7fWqKl5eXWRYpLq75+baloPC/j4iIMH4uLCxU2amuPE+d7dOgoCDx9/cv16e2+gjL4jcaWEan09lcv2WfYn+x367qU4BrqTN9ivVjOwBtxz5Upk9t9ZFln2J5LGsLZ/sUbUGbKtun6E/0q6v6FPtmun+O9CnOCZwbAP2H884elvccZ/u0onuOs32K4216z7HVp2e+fF2Kj+wX8fSV0NIiFdEE6aPTn70sPlff6/A9x16fmh4rQojrYdBQ3WBX6mnjdNeoJrXaFtKwST+TJVOff0XWbjUE1ICBPbvJK49MlYiwhhlQU7B5hegL8jCAksBB59Z2cwghhBC35KG5b8iZbHPbiyYH+P63P8uwfr2kX7fOtdS6hoePp5f0bNzU7LvJkyfLjz/+KDNnzpRZs2YZbYtVIdTPXyS/sIJlztrCG5RTzRq7d++WDRs2yNNPP11u3pYtW6RVq1bKyNe5c2elyQmpSHscPHhQGRhbt25t9j3WAxISEmw61WBsNDXUJyUZJPw++eQTMyeMKdAMNc2wmz9/vl3jPWrJjR8/3vj5t99+U22yBWrC3XDDDcbPyLhbv369XYPrtGnTjJ9Rg27RIkMhZFsgo08zfMPp+NVXX9ldHpl/TZs2NTpn3nzzTbvLT5gwQbp162b8jNRQe0bgoUOHqnp4Gl9++aWkpqbaXL5Lly5y6aWXGj///vvvsn//fpvLx8bGys0332z8DCfs2rVrzZYxrfxQ5CHyS/MwZQTuH9tcco+flDcX2q+NNrBnB1mblqyityOKSmRkylnH5m/zvyi3/JQpUyQuzlCDAudgRX168cUXS48ePYyf3333XbuG8iFDhsjIkSONn7/55hs5dcogDWSNTp06KZlWjT///FP27Nljc3mk+d56663Gz6tXr5ZVq1bZXB7/6RkzZpjVWsR/wR4ocqkZ+5FSDCe5PSZNmmT8r8NxWlGfwvHeu3dv4+f333/frjMB1yIU4NT49ttvjdcMa7Rv316uvPJK42f8L7HftkCWLupPauAcXbFihV3nxiOPPGL8jONlKZ9ryT333GN0MqHtH3/8sd3lIaOrXVvhiK6oTy+88ELp2/dsav6HH35o14nVv39/Of/8842ff/jhBzlx4oTN5du2baskfjX+/vtv2b59u83l4fy5++67jZ9xLf3vP/uazMhw1sB15aeffrK7/DXXXGN0MuE/hn22x1VXXaXqiwI4rivqU2RhDxhwVtca9yc4vW3Rp08fueiii4yfMeg5dsygUW0N3Cuvu+5s1NeSJUtk69atNpfHvfHee886xVAjFb8pj6dIWEc1dUvGNjEMt/Ry+MgR+auCfb799tuNEU+4F7z33ntWl3vyySftrocQUjVahIRXaT6pGXamJqv32KCQGo/iJMQUONTWbTPUDtNYt22HTJv9inw6p+HdsxE8lbfK8Fzu17W/eIU7r8hDCCGE1Hf2HDwsO/cftBuk/80ff9GpVkmKE49K3oqFUnRoj8qY9+vaVwKHXCheYWcTHyoaz3h4eMhTTz0l119/vbL5u4pxbTrKwp3brM6DTb5jZLS0DK28vGS9cqohAh4GUTgTYNy1NNK/8MILKoUQRlgYGgcPHqwM11dccYXNdWpZWZYF8bRIfXtZBi+//LJV5549EJVv6kSz5yzSjKamy9tzhGhR+abLW2apWWKZkVNRFobmGNOynirKbNGcPlqmREXZQtoxqShLyBTso+ny9rLUKtOnWJ8zfarROypGHuk+UE4m2L64a9zfpY+8uHuTbCozbFQEjOJamxw5BpZ9ai9LzVqf2sv+sdan9rLUtPU506dor+nyjmSqIjtFOxfsORE00I/aNirK8NL+K1Xp04rOU5yXzvRpZc7TyvSptp+O/JdN+9Sew9FWn1Z0fXT2PHW2Ty2vj46cF872qfbfRJSOs31a0bVLa7MzfYrjVJN96sg9x1lwnmqZahVl/hFCqg/I+Q2IjVM11PQWDzgDmsZR7s9N2FlWT61rNLPUSO1x4Mhxsww1DQw712zZIQlHj0vbeENAYUOhYPNK0Z04rKYDh1xQ280hhBBC3JLDxxPtzocN69DxkzXWnvpE/qYVcuaL1wwDsrInOl3iEclb8adE3P20+DQ3T1AyRafTqXJbsDG99NJL0rVrV/VyJVd36iHrDx6Q49nm9k88b6LW2gP9himHXk3joa/IQlzDwFA3btw4FdW+bNmycgdC83yaAgcbMlT27t1rc73IbkO2ycKFC82i8zdv3qwi9pEdcsEFFzicqYbMCUTpa5lZlsC5ZCn/aM/ICZkvS/lHe0ZOGGYtpQotjdkwvmM9yOKxJv9YkZETmSqmsnoVOSy07WjbrsjICYempVShPZA1ZylV6Eyfov2WxmnTPrIm/2jNQXDXPwvkWFamjGjRSm7uP1TahEc43acbkk/IbX/+pOQfnzvnfOnROMamBJyprF5Fkpqu6lPTfjFN1UVbLOUf7Rn8sa+WUoUVOYEs5R8rclg4e54626foH0v5R2Crj9D/llKF9hxrln0KZ4o9h0VN9Cn+B6byjxU5gWz1qa0+suxTOF/s3Yqc7VNchyylCp3pUzioKnKsOdOnaCv2DzKE6AdH+hTXLlP5x4oCECzvOc72aUX3HGf7FPdpS/lHyz7VpSRJ1ofPGz9r8o+gWDzF+7YnxDs61qF7jr0+rap+N3EcZJAiIOr48eMqo94SZL0DZ+W2XQ3+GzhncP5UdfA9depUdR1GpiSCu6A88Pjjj0tdB310+PBh1T+aqoM9Mgry5YH/FqoaahoDm8bJvBFjpJG/dan0uogW2ILrmyukTGqSc7/9SJJys2Vq3yGquHh1UZf7qKZoyH20ZM0GueupF2zOf/upGTJ6UPWdn+5cVxb4tOsmjUzqyjbkc6UhjY9qg4Z4bjXEfW6o+819rp/HedWmbXLTI7OMn1FTLT4mWo4mp0hJaal6bhnUq5t8MvsJqa9Ux7ldkpkqKbPuFFHliSxsSB4e4tUoWqIee0s8PMtv7/Tp00qV6d9//1WqYFAOMy394sr9PpSUKO/s2yZ/HztolPzsEBElD/Ufrp47awO3ylRDJyFDbd26dfLPP/9Y9WxaM34MGjRIZs+ebXfdmgEHAxlTNAmx+Ph4u4Y7U2OiBgykjhrrnK3pYmqMdARr7UN/as40yz8bjKmmBtWKwO9NjeCOLO+sIdPZ5Z3tU1OjuSN9ZK1PwSl9ieT4eEm/1u2lfWR0pfp0YLN4aRIaKok52XJIly/nOrDvNdmn9vqloj6tyPli6kCqCDhfLLNL3eU8dbSPTJ0vjmBLUrY2+9SZPjJd3tE+gqSlM1R3n8KhZeqQr2qfagMf9IH2qu7/srN96uw9xyV9Gh0tvq3bSXGCpdyphwS17yoRnbs7vP7K9CkhrgCSulomOQK4nBlb1SfgOPvowsslISNN1ViD5CMy2Ih7kJafpxxqoGuU9UAuQmqC+KYxVZpfnzA41MxlMIsP7JTM+S9LxF1P1Vq7CCGEEHekf48uEhEeKhmZWZauH2NQ4PhRw2qhZXWbvDX/wGhlfaZeLyXpp6Vo3zbx63S2LA5Yt26dTJw4UZU3eeutt+TOO++s1mwxPG/OHXGRpBcWyInsMxLm5y+twhrVSoaa2znVcPLfeOONqvbN4sWLpV+/fk4ZNGxljJkaDFFraunSpXLbbbcZv4c3FbW0OnToUKX2k4YBvOEZhYZsi4gqRl73iI5VTrVtpx2TgiSEkPpG4Dlj5IyFU823fTcJn3y2Bigh7syrr75qzJpHvUdnghzqI3Ck0ZnmvtKPoEtU41ptC2nYQNpxQI+u5WqqwR4ysGe3BiP9WJx0zCxD7Sx6Kdq/XXTJx8U7pmH0BSGEkPpFSWaa5K9dIsVJR8TDN0D8ew4Sv069rGY6OYOPt7fMvP0GefCF1606Urp1aCtjhw+p0jYaIrrkYyIeniL6Urv11kydaomJiTJ8+HAV2Lx8+XIZOHBgDbVWJDowSL3cAbdxqt17773KGAF5RtRIswaknaZNmyYPPvigtGvXTkluvfPOO/LTTz/J3Llzy8nxLFiwQA4ePFvnaubMmSodEa+xY8fK6tWr5YMPPlBZbrXp2SR1hzOFBcY006rKGfVsHCt/Ht4v21KSrMqaEkJIfUd3wnCP9mwUJXLelRIW31b8mtWuNCAhjnDXXXep97ffftv4Xc+ePdW7pQwktOW///57NY17PR4+EOiFusHIYr3hhhsq3B6CwkwlXjXJ499++00+//xzJU2Od4yPLXF0OVJ/2VXmVEMB7xDfszLhhNQGd117uXKqxfvqpZmvyMkikaZdusvLM6c2mANSkmY/qFKXmkSnGiGEkDpHwdbVkvn5qyIlOoOjRvRSsGGp+LTuJI1ue0w8q2hHRSaar4+PvPzJV3IiyTC+9fHxlktHDpWZt00RX19DWRLiOJ5+OCb2K4N5+huUmTTbNRKbPvroIznvvPOkceOGG7DnFk61o0ePqlRB1I9B6qApnTp1khUrVqhpRP8OGzZMrrzySuUsQz0MGAWg2Tl58mSz36GuUlpamtl3V111laSkpKiMONS/QA2gGTNmyH333VcDe0nqA+kFZ2sCRQQ4LqFnjR6NY411SI5ln5H4UOekFAkhpK5TtN8Qpe3bqbfo2vcUbydlKAmpLXbv3m1znqUMJGqTHThwQP744w/1IAIp8meeeUbmz5+vAryQ7aaBSD+MTb/88ktp3fpsQWjTeqkacMx17NhR2rdvL7/88ovN2o6OLkfqf6Za1+gmtd0UQuTk4UPyUlyp9DVRhfaNK5Xw+lkCxireUbFVmk8IIYS4G7rkE5I5/5WzGU8mmU/Fh/ZI1vfvSfj191d5OxcOHSQXnDNQ9h46ImcyM6VD2zbSKMx6+R5SMf49h0j++qW2F/D0Er9u/SUhIUHVT3vzzTdlwIABct111zX47nULpxqKtsLZZQ3U49GANxTOM7yQpQanmK0CeDBQvPjii+W+R1QwXnC6ob4Ls4OIM2QU5Bmnqyr/2CEiWvy8vKSwpES2nU6iU40Q0qAozcuV4uOGTDXfdt1EV9sNInUSSGSpiP6oWLeO6seYVZPFQC3gFi1aqPdly5bJtddea1wuOdmQvYBMN2u1hU2BQw61TRctWmR1zOvscqR+AkfuzpQyp1oUnWqk9olbu0CaW6j2QAqxIdUSw/3Kp11XVUPNHA8lg+3O9zNCCCHEGnkr/xQpLbGdxbZ5pZSMnyxeYRFV7kDY8tu3bCEZGSESGuweUoB1Fd+OPcW3bVcpSrAckxgIGjle/vhvhfLF4LkiPT29xtvorriFUw2OsaioKKd+Yy1i1xQ4zOzR0GtekMqRlm/IVPP29JRgH98qdaOvl5d0jmwiW04nytbTSTK+bSceFkJIg6Ho0C5D9JqHh/i06SJSRLcacZzSnCzJ/Owls5o0cM6GT5kunsHuH6nYpInBuXH69OlKrwOOMlcuR+onSbnZklYWFEanGnGHWmItCjPgOypHQ6slFjLuekl/ZYbZd6wrSwghpK5SdGiP/QX0pVJ89IB4dR9QU00iDuDh6Snht86U7B8/lPyNy42OUQ+/APEfMV6eX75V5syZI926dZMff/yRZQTczalGSF3LVEOWmiuyHFFXDU41ZKoRQkhDlH70btZSPINCRIoyartJpBbQ64qlJMO6WoE9znz5uhQf2W/2HRxsGR/OlrDr7rW/Tb1eSnQlIt5exnu5V6No8fCuGQ1+bB81fbHtESNG1Mg2ScNFk3708vCQjpHRtd0c0sDJSzxmd35DqiVWekaL9PaQsBumi09MXIPZd0IIIfUQLwdcDF4NSOu5jtVVC7v2Xgm++HopPpYgHl7e4tOqo8x99TXlUIPU43vvvWdW4oDQqUZIpWqqRZQVaawqWl21/RmpkltcJEFVzH4jhJC6AiLSgW+77rXdFFKLwKGW+tw9Lltf8ZF9lVpf1KNvind0U6kuIJOhyT+eOHFCCgoK5Pvvv5eePXtW2zYJAZr0Y9tGkRJQQ45jQmxxKFcn9qqFNaRaYsjKA16Nm0pAz8G13RxCCCGkSvh36Ss5xxNszvfw9RNfKNQQt8UrJFy8uvSV0tJSpSqI8lnNmjVTTjWWzyqP9YJkhJAKnGpVq6dmmqkGSlHvoiySmBBC6jslWRlGY5Jfu2613RxCqh3IjqPe70svvSQPPvig5Obmyr59+xz6LR5i4JDTXklJzG4njqONL7tGxbDbSK2z+dQZ2ZiDjF3z7/HRt333BpWppUsyZO01pH0mhBBSfwkYcr54BIao8g7WCBw5QTxdZEsl1aeo8vbbb8uwYcNUECgy0yZNmkSHmg0o/0hIJeQfG7noRhAdGCRNg0MkMSdbSUAOiOVDFSGk/lN0oKwIrqeX+LTppIxppGEC2UVkiTmDLiVJMt9/zub88NseFe/o2ArlH70s5B+dwd/fX06dsh4Mk5mZqeab4uPjY8xUGzJkiNru1KlTZdy4cdK9u/1szRkzZkhenmH8ASIiql7cmzQMELS1O01zqhnq+BFSm2zflyBrEj3kh/YiviZ3/y35nnL+pPulIaFLLnOqxbao7aYQQgghLslyirjnacn86AUpKRt/Kjw9JXD4OAm+4Er2shuD583bb79dvvjiCxk1apT6bPlMS8yhU42QymSqBbhG/hH0iI5VTrWtrKtGCGkgFB0wSD/6tGyv9LtLSgzFcEnDA3XMnJVdxPK+7bqVOWdNXbIe4tu+m/h37mP393CqiU4n3t7elY66a9++vaxZs0ZF8Jk+bCQnJ0taWpqab48777xT5s2bJ88884z88MMPdpetyOlGiC2OZWVKdlGRmqZTjbgDO/YnSHaJocaflq6WWyIy7YheFmfnS3xouDQE9CU60Z1OVNM+dKoRQgipJ/g0bSlRj74lRfu2SXHiEfWs79etv3iFMSjQnUlISJDLLrtMduzYIQ8//LDMmjVLPSsT+1D+kRAnyHCx/KOpBOT2lGSDoY8QQuo5Rft3qHc4RgipDOFTpisHmin4HD55Wo106M0336yi9x5//HGlOQ8KCwtl+vTpEhoaKtdee63d3yNzbdq0afLzzz/L/v37a6TNpOFKP/p6eUm7iMjabg5p4KRlnpGTp1Ik2lvES2+4boIgL5EWviJ7Dx2VhkJJSpJIiU5Ne8cwU40QQkj9wcPTU/w69ZLg0ZdK4DkX0qFWixxPPiUrNm5VQU3aM6slCHAeO3asHD16VH755ReZPXs2HWoOQrcjIU6Qnp/vUvlH0KPMqQaHHSKK48Ma8ZjUcRIy0uRYdqa0CAmXto1oxCLEFF3aKSlJP62m/SycIoQ4imdwqETc9ZSqzadLTRLvqNgarUvTo0cPlWF23333yUcffSTNmzeXI0eOqPeFCxdKTEzF9atuvfVWFQWIjLX333/f6TY88MADsnLlSjlz5oz6PHnyZAkMDFRRhg899JDTy5H6x46UZPXeMSJafDy9ars5pIGzY1+Cem/qa/Klt4+Irli6BIjsOXRYLhhqkMmt7xSX1VMTL2/xima9Q0IIIYS4jhPJp+XJ19+TlZu2Gb9r0TRGHrn9Bhk5sK/Rmebp6SleXl7yySefSHR0tLRr146HwQnoVCPEiboUmYUGp1qkv+vkHztERIufl5cUlpQoCUg61eoucIxOX7pQ1iUdN36HOnkvjRzjUkcsIXWZov0G6UcPXz/xibcvkUdIRcCRVpPONFMmTJgg48ePl0OHDqk6alFRURIfH19uOTi1brrppnLfo/AzJCRzc3ON3w0fPlx916ZNG4ecchMnTiz3fZMmTSq1HKm/mWqUfiTuUk8N9GwCiccM8QgKEYlqKvqj+6RLoF42NqBMNQSEAO/GzcTDy7ZJJuHYCdm9P0E6t28rHVqVv78QQgghhFgqA1w77TE5nZ5h9v3xpFNy51MvyHvPzJROLZvLNddcozLUoJ4yePBgdmIloFONEAfJKiyQkjJ5Rlc6SCDJ0yWqiWw+lSjbUpJkQrvOPCZ1FDjU1ps41AA+P/DfQvnowstrrV2EuKP0o0/rTqqeFiF1GdRkq8gB1rJlS/WyhmXttUaNGsnAgY5lanTo0MGly5H6ha60VPampahpOtWIO7B93wH13iUqRCQnQ7wiY6SkeRsRONUCRL48dEQaCrqyTDVvG/XU0s9kydTnX5G1Ww1jJjCwZzd55ZGpEhEWWmPtJIQQQkjd4vNfF8qptPRy36PcEJ5dH3n+RTm2aZWcOnVKrrjiilppY32BNdUIcZC0snpqrq6pZioBiUw1UnclH5GhZlkVD5/XJh5X8wlp6GAgV3SA9dQIIaS6wbijoKxmU1fKyxE3uP9r8o/xAQYThFdUjHg0b62mW/mJZKWmSGZWtjQop5qNTGs41NZt22n23bptO2Ta7FdqpH2EEEIIqZv8vnSlzbFYxtEEWb3gO9GVlMjy5cvljjvuqPH21SfoVCPEQTIK8ozTEQGuk38EPaMNTrUDGWmSW1zEY1IHQT08u/Oz7c8npCGgSz4mpTmGuk5+7bvXdnMIIaTeSz8G+fhKK9brJbUMJIcys3PUdKPSwrNOtWYGpxroFCCy73D9l4DUFxdJSWqyzUy1A0eOqww1GL/MfqcXWbNlhyQcNVfFIIQQQgjRyM49a7s2JT8jVU7v3SoBjaLky+9+dFgdhdiGTjVCHCS9LFPN29NTgn1MK2y7LlMNddt2pBiMIKTukJiTJZ/t2mx3mRYhqB9BSMNGk370CAwW72bW5fAIIYRUnV1lTrXOkY3F08ODXUpqlR37DVlqPj5e4pNtkCTyimyixgNeZcGFXQP0sudg/ZeA1J06KaIvtelUO5ZkcLjZ4mii/fmEEEIIabi0i48zG/vrSw1jjsCIaIntMVDi+w2Xnl1ZdsgV0KlGiIOk5+cZpR+hQ+tKogODpFmwQR9/GyUg6wwl+lL5as82mfDT57Ix+aTVZXCmDGwaJ20bRdZ4+whxN4zSj227iIenV203hxBC6n2mGuupEXdge5n0Y79WcSJFZzPVgHe8obYk6qrtO3S0QWTtK3x8xSuicbn58U0N/WKLiuYTQgghpOFy7cUXqoQNkHM6UQ6vXCxFeQa1gNCY5jJmxBCJCA+r5VbWD+hUI8TJTLUIf9dKP1pmq21LYV21ulKr5J6Vf8mc9cslT1csob5+MnPgcBkYa14bYUDTOJk3YkyttZMQd0FfUiJFCbvUtG87Sj8SQkh1UajTyf70VDVNpxpxJ6fawBZnnUhekQbnkE/LDibyj4elvqNLOm6sp+bhWd4c0zY+Trq1b1vue8R0DurVTc0nhBBCCLHGRcMGyZUXnSupB3ZK4tY14untLR4q3F+kdVwzefTOm9hxLsLbVSsipL6TYXSqBVTL+ns2jpWFh/apTDVo6Ls6G464hqISnby/bYN8sH2D6MrSqC9q1V4eHjhcogKCZFLnXsrhhhpqkHxkhhohBoqPHxR9WW1Kv/bd2C2EEFJN7EtPEV2ZvFzX6CbsZ1KrFOt0sjvhkJruEhUiclLEwy9APKDSkZkpPi0NmWrBXiJFJ4+p5X28vet9ppp3THnpR41z+vYwSmZqDOzZTV6eObXa20cIIYSQuktqaqqs+f0HST+8T9p07SHNuvWVyEYRMn70MJl44WgJDqwem3ZDpP6OVglxMellxuBG1eRU0zLVMgsL5FhWpsSzqLzbsflUojy58m85dCZDfY72D5THB4+S0S3No0nhSKMzjRBzCjavVO8eQaHi1bgZu4cQQqqJHWXSj+F+/kZ5cUJqi4Sjx6WgsEhNt/D3Mko/agGEXk2ai97XXzyKCqSjT4kcOn5SOrSKr7cHTJdkcKr5WKhbmLJtzwH1Prx/b7nonAHSuX3bet0nhBBCCHEN06ZNk+XLl8tbb70ld955JxM2qhHKPxLirPxjQPXIP3aIiBJ/L4OfeyvrqrkVOUWF8szqf+X6P75TDjWYAK7p2F0+GzlORsS1qu3mEeLWlOZkSfpbT0re8t/VZ31ulmS8/ZT6nhBCSPXWU6PyAaltdpRJPwYHBkpIcY5ZPTWAGqu+8e3UdJcAveytx3XVSgvzpST9tN1MtYLCQtm4c4+aPn/IABnap4e0bdG8RttJCCGkfqMrKZH8gkKlkkXqPjiOBQUFavqll15STrW77rrL7DkAy+w7fFTWbdspySlptdja+gMz1QhxE/lHH08v6RLVRDadOqnqqk1o15nHxg3499hBeXb1UjlVVtizdXiEPDPkXOke1UQyMgwZa4QQ22R+9pIUHdhp9h0+Z85/WSLueopdRwgh1eVUiz7ruCCktuupdW3fWkrLzk1vE6ca8G3dSYoP7JDOASL/HTwsE0YPk/qILvmEcdo71rpTbfOufVJUXKymUUONEEIIcWX2+Dtf/SiLVq4Rna5EmjaOluvGXyhTLh1br6WX6zN5eXly++23S3p6uvz222/SuHFj9TJl1aZt8vy7n0jCMcM4BL624f16y5P33qrOAVI5mKlGiIOk5+dXq/wj6NHY8IDJTLXaJyUvV6b9+4fc+89vyqHm7ekpd/caKD9OuFZ6NWla280jpE5QnHRMig7sQFyUxRy9FO3fLrrk47XUMkJcw3fffSdffvmlmn7vvffkjz/+YNdW8UF/yZoN6p1UjtziIjmcmW7MVCPEXZxq3Tu0E11acrlMNeBbVlct3k/k6MGDUl+vX5r0o4d/oHiGR1pdZvWW7eq9dVwziYmyvgwhhBDiLDsPHJSJ9z0sfyxbpRxqICklVeZ++Ln879l5UlJi+I7UHRISEmTQoEHyxRdfSPfu3a1mHq7Zsl1ufew5OXj8pPE7LLZswxa5Ztpjkp55poZbXX+gG5oQByjV6yWz0OBUi/SvHvlH0LOsrtqBjDRlFAny8eXxqWFwE/r5wG6Zu365ZBUVGo/L00POZZ00QpykpMx4ZgtdapJ4x9iuKUKIu/Pxxx9LTk6OXHfddTJ37lw555xzZOzYsbXdrDpH+pksmfr8K7J2K5zwBgb27CavPDJVIsJYE8wZdqeeNoYx0KlGapu8ggI5UOZk6tmquei3Z1t1qvnEG5xqwCPxsBqP1xXpUmeuX5pTDWMfW/u3erPBqTa4V/dqbTchhJCGxROvvieFhUVmjhdtesmajfLn8jUybuQ5tdhC4gwLFiyQyZMnq2P4yy+/yIQJE8otg3lz3p+vbNqWDjd8hgzk/F8Xyv1TrmHnVwJmqhHiAFmFBVJSdgGq3kw1g1MNF7wdKQZ5FFJzHM3KlJsW/SiPr/xbOdQCvX3ksUEj5fOxV9KhRkgl8I6KrdJ8Qtydq666SiZNmqSm77jjDjrUKgkM0tD3N2Xdth0ybfYrVT9IDVT6sUlgsEQHBtV2c0gDZ/eBQ1JaWqqmu0SF2Lz/ewYGS3G4QX4oXp8rp9Mz6uX1S8vQtyX9CAfd7oOH1TSlHwkhhLgK1NLalXBI2Rqt4enhIT8sWsIOryOkpqaqoM4WLVrIxo0brTrUwNHEZNl76Ijd2nkLliyvxpbWb5ipRogDpJXVU6vOmmogKiBImgeHyomcLNl2OkkGNmUGR01QXFoin+3cLG9vWSuFZSnvw+NayeODRkls8FkDACHEORCJ7duuW5kEpCke4tu+G7PUSJ3lq6++Uu833nij8bsHHnjAKAPZvHlzo4Pt77//lk2bNqlpZCZER0dLjx49pE+fPpKcnCyffvpphdubPn26+Pj4lPs+OztbfvrpJ0lKSpKbbrqpnH6+KWjD2rVrJTw8XMaMGSONGjUSd+DAkeNmGR4aePZbs2WHklJrG8/xkKPsTDVkCDNLjbiT9GPjyAgJK84TJTDk7SOeYRHlDHsBbTqLbtMy6RIgygDUJDJC3B1nr1+6ZC1TzbpTDc45GL68PD1lQI8u1dhyQgghDYmk06l25+OefPLU6RprD6kcubm56j0qKkoWL16snimDgmwH0WVl51S4zqwcwzqJ8zBTjRAHyCjIM05HBARWWU/f3jLdy7LVtqUk8djUALtST8nVC76RVzauUg41yHu+NGKMvHXueDrUCHEB4VOmi3iZx/DAoRY+eRr7l9RZ3n//ffWyBmQgv//+e+PnX3/9VZ599lnJzMxUBaT/+ecfGTJkiMpy0+l06nvttWHDBpk5c6bSxzf93lp04Z133int27eXjz76SP0mMTHRansKCwvlyiuvVI60bdu2yYoVK9T2t27dKu7AsST7MrGIsCSOs7NM6aBrNOupkdpnx36tnlpboyS0V2QT8fAsb4YIbGdwInXyF9mbcEjqAs5cv0rzcqT0jKHeoXes9UABOOK0/gqxYyQjhBBCnKFxpP1gOgT+NYlmHU93Zt26ddKlSxf5+eef1efBgwfbdaiBuKYx4uXlZXM+MhTbtmDwYmVhphohDpBelqnm7ekpwTbqnDmip+/IMqjftfDQPpWpVpfqCdQ18nXF8tbmtfLZrs3GSNlL23WWB/oPk3A//9puHiH1B1wzS3RqMujCqySg52BmqBGXkZCRJseyM6VFSLhby/QGBwfLnDlzjJ+//vprufbaa+WGG24w+x56+Mg8u//++6Vr16521zl69GiZN2+ecpJddNFFNpfDuuBA2717t0RGRholQ86ccY+i1PFNY6o0n5wloyBfqR0AZqoRd8pU69a+jZSUBQx6W9RT0/Bt2UG9B3qJpO3fLXUBZ65fmvSjPfnHVZu3qfdBrKdGCCHEhXRq00rat2ohCUeOW5WAhO3x8vNHsc/dEBybd955Rz3TQZUEiiiO0ig0RMYMHyy/L11pNUgT58K14y9wcYsbDnSqEeIA6fl5RulHW04ua3r6cJ5ddf8jcsWFo9Xn7//8R44lnbKquf/pnCfN6qplFhaoGl8tw9xDnqk+GV1XnzwqT69aYjQ8xYWEyVNDRsvAptYfcAkhlack9WzWbUDf4VaNaQnHTsju/QnSuX1b6dAqnt1NHHIeTF+6UNYlnTVSDoiNk5dGjqnW2qeuApliYO/evXYdYvaYOHFihcsge+2DDz6Qzz77zOhQ0yRD8HIHII2GACOMh0yf9TDcwveUfnS+nhroEsVMNVK7pGeekRPJBimp7h3aiW7VFjXtZcOp5tW4mRR5+YhvSbF4Jx2VukCbFs0lODBAcvLOlgrQQLaZmfRjkkH60SMoVDyDw8otfzzplLG/BvfuXq3tJoQQ0rCAHfOpe2+TG2Y8rZQyLB1rg3p2k3Ejz6m19hHr5OXlye233y5ffPGFjBo1Sr1bKwlgj0duv0F2HTgoh44nqvMAzjVkqOEcuPS8ETJuBI97ZaFTjRAnMtUi/AOd0tMHxxKT5aWPv7S5bkvN/Q4RUeLv5S0FJTrZejqJTjUXG12jAgIltcxJihvJlK695e5eAyXA27kbEyHEMUrK6vtAAtKrUbTZPEeyd0n9pqikRJLKAhycYebyxbI9xVx2a33ScbnnnwXy/NDz7f4WDxK6khLx9vIyBsrEBoeKrx1pDFej1Vhr06ZNtW5nyZIlUlJSIiNGjFBZcEePHpXWrVvLeeedJ/7+7pOVjf88Aow06TPtWvDyzKm12q666lRrERouYcy6J7XMjv0HjdNd27eRgl+T7TrVIAlZGN1cfJMPS0xhpuQXFEqAv5+4M7hmWXOogSMnk8xqqmlONdSbtRakuXrLdvUe6O8vPTq2q9Z2E0IIaXj06dJRvn7lWXnj8+9k2frN6pkImUzXjLtA7rjmMvHxpovA3UDtbTjSHn74YZk1a5YaP2RkZDi1jojwMPn+tTnyzcK/5dd/lklGVra0jmsqV4+9QC4cOlA8rUhyE8fgP4YQB50zWqZaZfT0mzY2RIMn2ikOCs19PHT5eHqp6OJNp04qCchL2nXmMaokcKjByGqK5lDrFBktzww5TzpHNWb/ElKN6MrknrwiGouHhdPCWoavZfYuqd/AoTbmx89csi7EWyIYpTLrW3j5FImvxsxwFJXWZB6PHz+uHo6uv/56GTdunFQncKLhQQnbKi4uVsWsIR8CRxuKW8PB5g7AiY7//Aff/SLzPjJEYH703GN2awAQ63ViAaUfiTuwfd8B9d6qeVMJ8fWWPK2emA2nGghq20Uk+bB0DtDLgSPHpLubO5fe+foH9d6lbWt54cF7lCIJMgAefeUdycrJlRsefka+fvlZiYttYpR/9KlA+rFf987i62QUOiGEEOIIXdu1kfeemakCQvILCqRRWKgKNCTuRVZWloSGhsodd9wh3bt3l3POMWST4RmuMgQHBcotV0xQL+I66I4kxAHSCwyOGFuSUhXp6X/w7KPqZQ/TdfRobJiGcZBUXvIRGWrlVYMNPDf0fDrUCKlB+Ufv6BirGb6W2t6m2buE1BdwnmdmZkpaWpqqbebt7S233nqrQ5GBH330kXLIaS9naqGVlpaqF6QeUXvtzTfflC1btqjtQpff3TinTw/1DgegJoNGHGdnisGpRulH4k6ZapBB1KWdDUC0lakGIrr2Vu/NfUUS9u4Rd2bjjt2yfruh9tud114u7Vq2kNGD+skFQwfJ+7MekQA/P0lJz5ApM56W5JRUKS6TtLRWTw1GsnVbDUFGg3t1q+E9IYQQ0tCAdHF0RCM61NwMjAceeeQRVVv79OnT6plNc6gR94OZaoQ4I/8YEGi3HoilBKRlPRBHa4b0LKurlpCZJrnFRRLk48vj5CSooWaPkzlZ0iHCXIqOEOJ6dGUSfV5Rhuuaoxm+WvYuqd9AdhFZYs5wLCtT7vj7V5vz3z1vgpK/c1b+0RngFEP2mTUKCgrUfFOCg4ONmWoAmWOXX3657Nu3Txo1sp8hl52drRxyGnCSOYpWN23SpEnG7wICAuSyyy5TGWvuRuu4ZurhEft44OhxiW9mft0gtjmVmyMp+YZzshvrqZFaBtfZ7fsS1HS3Dm2lRJPr9fQsJwVtim/L9oIrHMINsvZBDnG8uCvvfP2jem9f5kwzpXeXjvL2UzPktieel5OnTsv/HnlKXgvKNso/WrLn0BHJzM5R04N6sZ4aIYQQ0tBISUmRa665Rsn3X3fddRIUFFTbTSIVQKcaIS6Qf9TqgQy79jYp1uls1gOxWjOkR/maIT3KnGooHLkjJVkGNrUuE0Js0yIkvErzCSGuzVTzio51KsO3ovmkfoA6Zs7KLmL5AbFxSt7XNM8R7rEBTeNkaFwru79XTjWdTjm+rNW1cYTY2FhZunRpue+LiookOTlZzbfH3Llz5ccff1Tvzz//vN1lq5JR1ru3IevDMiMOsoqWWaLugJ+vr7SIbaJqER08dkLOHdy/tptUZ9hZVr8S9WI7RVLamtQuJ06dlowzWcZMtZIkQ9YZHGoeduoYe/oHSqZviEQUZYt3WWaXO7J97wFZuckg14g6NNayjgf37i6vPjJN7ps1TzwwFiqzjVlzqq3ebKinFtUoXDnpCCGEENJwWLdunUycOFFOnTqllEXuuuuuSj+nkpqD8o+EOEB6fr5d+UcQHBBgdKjdde1E+eN9Q00g1AmxrBny6qPTjN89O/VOs2VAVECQNC+LmqcEZOXw9vQUT2ViNQffDGwaJ20bRVZyzYQQRyktLJBSGzVUkIXWsXV8+f+oB6K0zbN3CbHkpZFjlAPNFHyeN2JMjXTW+PHj5eTJk7Jw4UKz7z/88EMl24H59oiJiZEbb7xR3n77baWZX10MGDBAOnTooBx4GnAo/vrrrzJs2DBxR9q0aK7ekalGnJd+bBMeIYGsx0RqmR17DVlqPt7e0rF1S5Os9YoDZoqaGK7tjQsynMrMrY0stZbNYuXCoYNsLofAgBcevFda+xs+Z4q35Ht623SqYfxDIxohhBDScECg4913363ely9frqY5FqgbMFONkApAtlhmocGpFulvXf4RJKakGqfHjhhi1yA8on8fdZHERRNFuJvHlI8oRrbaiZws2ca6ak5TqNPJ9KULpVT04u3hKTp9aa0YXQlp6JSkGYy8wCu6abn5MVGRsveQeSS6ZYYvIdZAkMtHF16u6mdC7hfZxzUZLHHFFVeommiXXnqpXH311RIfHy979+6Vn376SWbNmiX9+plLgVlj+vTp8t5776nXgw8+6HQbfvjhB0lISJD9+/erz5988onKkOvVq5dccMEF6juMNT777DP1OTU1VXr06CGLFi1S0pWvv/66uCPt4uNkyZoNrKvoJDtTDdfbrpR+JG6AJv3YoXW8ykDNLaup5hVZsVMtuF03keO7pZ1vqZxITJIWzZuJO7H34BH5d+1GNX371ZepzF97XDxqqDTbtEjk5B45kKOTOU+9KO/Nmqn6BRQUFsqmXXvV9GBKPxJCCCG1TtGR/ZK3arHoko+JZ0CQ+PcaIgF9homHr5/LtrFpxy5Ztn6zlOhF7nrgYTl/6CBp3sy9xjykjjjVUC8CD/2rV6+W/Px86d69u9xzzz0SHR1dqeWs8fLLL8uCBQvKfQ9jBqJ4CbFGVmGBlJRJJNnLVDuZfNo43bSJ/fMxwN9P4mKaqJpCiMQeObCvVafaH4f2ybaUZOV8Y6SC48zdsEL2pqeo6TfPvVjVyqkNoyshDZ2SFIP0o3h6lauhkpqRaZROuvu6KySuSZR0bt9WOrQqn71GiC1wTa+t6/r7778vd9xxh5KBRM2zIUOGKIea5Zjy/PPPtyoH2bp1ayXvAclIjfbt28uMGTMcGtfCMYbtNm7cWP0G4DPGx5bZaqjdhmw1ONamTZumnIGoreaOaEFJh44nqqy/igzWxBDhSqcacSe27ztglH4EJWXypN7RFTvVmvYeIFn/fisBniIHtmxwO6ealqXWrElj5TBzhHjfUikWkcOFImu27pD7n3tZXn/8AZXJt3nXPikqxlw61QghhJDaJufvHyXnjy8NEjqwBXt4SNH+7ZK3YqFE3P2MeAaFVGn9CKa55aHH5fuP35OAsAhp0qWP+v7TBX/Ju888LJ3btnbRnpAG4VTDw3/z5s2lZcuWKmo3JCRE3nrrLWVogPNMM044upwtEMkLY8Orr75q9n3TpuWj5wnRSCurp1ZRTbXE0wYnTqOwUAn0L9P4sEO7lnFGp5o1epbVVTtTWCBHsjKkVVgED4oD/HXkgHy9x2Cov7FbH2NtHTrTCKl5dFo9tYjG4mFhGP/ln2WiKylR18sbLh0rRYUF0qiRc7W1CKltULNMq1tmC0hB2pKDhFPOlM6dO8ucOXMc2vaUKVMcbmeTJk2UNn9doG28Qf6xsKhITiSflvhm9uvTEZFj2Wckq6hQdUVXB5wWhFQnuLfvTjispru1byt6XbGUlAW7OZKpFtAsXhL1nhLsUSpZ+3aIXHxJ9bU1+bgaq3hHxVqtdWYJaj0uXrlWTd921SXKKVYRqo5nkuF5r1XvfiKLNqlMt4fnvSkvPniv/PHfSjUPyiUx0Qz+I4QQQmqLwgM7DQ41oNWfLnvXJR6VrB/el/Ap06u0jWtvv1d+/fJTrFiC2nYxfp+SkSE3PvyMLPr4DWkUWjXHHWlATjVEoSKK94svvhA/P0Mq5ZgxY1QE7wsvvCAff/yxU8vZIzw8XEaMGFHNe0TqExkFecZpe5lqJ04ZHhabVZClZilvBPlHa7SPiBJ/L28pKNGpump0qlXMiewz8sTKv9V0j+gY+V+fwQ4dC0JI9aBFpntFx5YzMH2/aImavmj4YAkKDFBONUIIaR3XTDw9PVUtJQQe0alWMbvKpB99PL2kPTPySS2TcPS45BcWGjPVSjJSRMqk2L0ccPpCneO0f7gEF6aLzynrz0lVpTQnSzI/e0mKDuwwfufbrpsylHmW1bW2xnvf/KTGME2iIuSy80Y6tq3MNNGXPU9ecOllsjMwVj796Xf5felKWb99t5xOM9SeRRDBlBlPyyuPTC1Xb5sQQggh1U/eyoVnM9SsULB1jZRcki5elUh6gE9j2vQH5OfPPhDf4FBp2mOg+JpkvZWW6iUzO0d+WLREbr2y+gKKiOvwFDcgMDBQvv76a6OjDGC6TZs2kpiY6PRyhLiS9LJMNW9PTwmxo5978tRp55xqLVuo94PHTqqLqyUwjHSNbqKmt582GKaJbYpKSuSBpQslu6hIQn39ZO6Ii1QfEkJqX/7RO8rciLZp5x45csJw377iwtG10jZCiHuCOkMtYpsYs0JIxexMOWUMyPL1couYSdKA2VFWTw0BM62aNzUG2ABvBzLVQFETw3NSk4KMammjwaG203ybB3ZK5vyXbf7mWGKycoSBW66YIL6+Pg5tS5d01jHoHdNCHr5tinHsoznUNNZt2yHTZr/i1L4QQgghxDUUHz9k06Gm0JeqOmuVYdu2bfLmG69LSGwLadF/pJlDzTSwaM2W7ZVaP6l53OKpC9GoeJmSnJwsGzZskPvuu8/p5SqSgLz44ovFx8dHSeygHltMjP3BfVZWlnppJCUZjIRwhFhzhrgLaBuifN25jXWhj1Jzc9V7hF+AWtYWJ8sy1WKjoxzq8zZxhvoA0NCHcbll8/IypN2jmsjG5JOy9bShrkhNURfPnVc3rpQdZZHaTw8eLTGBwdXa/rrYRzUN+4j9oCtzqnlGxpj9V7778x/13qZFc+nWvk21niusx0RI3QPXhiMnk2xKZBNzWE+NuBPby5xqXdu1UffggjKnmmdYhHjYCVA0JbR9d5FjW6WJV6lkJp6Q8KYGWVhXUJx0zCxD7Sx6VTMFkpDWpCDf//ZnKSktlcjwMLniwnMd3p5mfIMUtmeZ6smkCRcZM/bNWqAXWbNlh8r20+pLEkIIIaRm8PTzF9tWXwMevhWX+zElJSVF1ctGyYAX33xX3vt5kXKe2QIZa6Ru4BZONUtgWLvxxhvF399f7r333iovp+Hr6yuXXXaZjB49WvLy8uS9996T119/XRYtWiSDB9uWiXv55Zfl6aefLvf9mTNnJCgoSNwVGCdzcnKURAWNipXvo8RMQwRhqI+vZGTYjpY8kWRw6DQKCbK7nEZYkL94eXqqh7Mtu/dKWFB5ack2AYbIhYSMdDmZcloCvR2LiGxo586aUyfks11b1PRlrTpI79AIh45BQ+qj2oB91LD7QV9cJKVn0tR0nn+QFJT9J7Nz82TRijVqesywgarWaXX2UVRUlEvXRwipfjSJbGaqVQzGkbvTDGoJ3aIMGX6EuINTDdKPZlLQFlnr9mjRb5AU/j1fvDxEjm9cI+Hjr3BZ+0rS7CuAqBprFk411M5GLVhw4+UXS4C/Y85BUFxWT810nVowpi2OJibTqUYIIYTUMP49BkuOSYa5JZ6hjcSnRTuH1gXbxrvvvivTp09Xfodhw4bJlZeOlw9+XWwzGQ6/GdCja2WbT2oYt3SqoZD6kiVLZMGCBdK8efMqL6eBwu+QkNS46qqrpF+/fjJ16lRZt26dzd9NmzZNbrnlFrNMtf79+0tYWJg0atRI3BUYKeH9Rh25hmTIdXUfFZQFEEQHBds83kXFOknJyFTT7VrGO3xeoE7IoeMnJSk13epvBuOBbcMyKRW9HNcVysDoxlIT1KVzJzk3R+ZsNRQM7xgRLY8MGS2+NdDmutRHtQX7qGH3A+SONNd2WMt24l12jVu8eoMUFhWLt7eXXD32AmkUFtpg+4gQYp028c3NJLJ5XbDNoTPpkq8rVtOabDghtUV+QaGxXrSlU81R6UcQ1aSJrNF5SSufEsnZD5lG1znVvKNinZ7/4Xe/SrFOJ+EhwXLNuAuc2p6WqeYda5C0BPFN7fdFRfOJfWCUtJcFUFxcbFUdAcHa9gK6LVWTCCGE1C8Cz7lA8lYvltLsTKsykMFjrxMPB+wVSOS544475PPPP5dRo0ZJx44d1fdxMU3kgqGDZNFyQ5CxKZ4eHhIY4C9XjnE8G57ULm7nVHv44Yflww8/lG+++UYuvPDCKi9niqlDDXh7e8tFF10kc+fOtfu70NBQ9bIED/ju/pCPgV9daKc791FGoaGmWkRAoM1lUk6lqME7iGsa43B/t2/ZQjnVYDSy9pvGwSESFxImx7PPKGnDIc1bSk1RF84dXWmpzFzxl2QWFqgsvpdHjpEAX98a235d6KPahn3UcPuhON2QOSGenuIbHWMcfP64eKl6P2/wAImKaNSg+6ihgWNbVFRU280gDoAxDQyOUHmorUw1UFhUJCeST6sgJGK/nlqAt4+0rkTRdEJcye6Dh1X2JOjW3uBU02mZatHOOYpOBzSSVrpU8T1dudoltkDGmEdwmOhzzpSb59u+e7kstdNpGUapxsmXjpXgwPLqIrbQl6L2yvFyTjVIOw7s2U3VUDO12cEPhO8p/eg8cHr98ssv8tZbb8nGjRvVfQxSW88++6ycc845Zsteeuml8ueff6qSIKYcPXpUmjQxD06YN2+evPrqq6rsSMuWLeXJJ5+U66+/vhItJIQQ4u54BodJxH3PyZkvX5fiw3uN33sEhkjIxZMkcMCoCteRkJCgVPJ27NihfBezZs1S/geN56beJVnZubK6rHYagkBwzwoNDpJ3n5mpZKZJ3cCtnGrPPfeccnDNnz9fJk6cWOXlHAGDI2ScEWKL9AKDU61RmQa+NU6ePivh0bRxtMOd2a5lnJJB0yI6rdGjcaxyqm07bahN5E4kZKTJsexMaRESLm0bRdb49t/Zuk42nTqppp8aMlriw9w3c5SQhkZJapKxhoiHl2G4sevAIdmVcEhNX3Hh6FptX11n5cqVsnbtWhVt3bNnTxVgZBmV/fXXX8umTZvK/XbGjBlK190U1I797rvv5PDhw9KqVSu58sorrQYUVQVEgEPmMy0tTSIja/6eQRw3TEL7Hw7Q2lJkaNW8qXK0oy0Jx07QqWYHrZ5s58jGSlackNpkx74D6j06IlxioiOVU6kkzXCOejmRqQZ0MfEiJ1IlujBL9Lpi8XCRDH5JRoroc8/WS9fwDI+S8MnTyn3/yY8LVA3s4MBAuX7CGOe2hQCjYkMwiXfMWacaeOWRqTJt9iuqhpoGHGovz5zq1DaIgf3796uxy+OPPy4//vijGhM99thjKkNg9erV0rdvX7Ouuuaaa+SLL76w231w0D3xxBNKGQnrwbhq8uTJEhERIWPHjmXXE0JIPcQ7KkYi//e8FCceVYExnv6B4tu+m8PjENw7EKTx888/yyWXXFJuPoJzPp79uGzatVf+XrVOCgqLpFuHNjJ2+DlOyUuT2sdtnGpvvvmmGgAh++y6666r8nJwui1btkx+//139Tk3N1c++OADJRmpRd0uXrxYvvzyS7nzzjurYY9IfSGjzKkWGWCe6WjKyVOGjAxIgjgTvdgu3vBwdeRkknpY87WIlgM9omPl94N7ZVtKcoVSFjXZJ9OXLpR1ZTUCwIDYOHlp5Bi7zkdXsjbxmLy31SDbenn7rjK2jSGdmhDiHuhSksrVUPlhsSHSu1mTaBnUq1utta0uk52drfTYjx07pgw7cFTdfPPNyhGGqGtTR9gff/whmzdvlptuuslsHZbZgImJiSqKOzY2VmXwf/LJJ/L8888rx13Tpk1dWt+usLBQTp8+rWrp1WZWImWcbPeLJouF8XJtBZ75+/lJXExjVVco4ehxGT2oX620oy6ws8yp1pX11Igb1VNDlhqeWUoy00TK5Em9o53LOA3r2F3kxCbx9dBLwbGDEtDaNWP93GW/K0knj8BgibjzSclZ8rMUbl0t+vwcEU/z+1L6mSz5+ve/1PSk8ReqKHJnpbAVHp7i3aSZ2byIsFD5dM6T6hqHax0kH5mhVnmQdfb999+rLDSN1157TTnCEIxt6VSrCDx3I5j7tttuk3PPNUhxwf6EbSD7jU41Qgip3/g0jVcvR8CzU2pqqsp2Rumpe++9V1q3bm1zeYyR+nbtpF6k7uIWTjUYc+677z4JCQlRAx68NGAkgnHHmeXAvn37lDFIw8/PT/0etdfi4uJURPbJkyflf//7nxoUEWKL9HwHMtWSDZlqzZo4V/MMmWpAV1KiHGuQg7SkZ2PDA+iZwgI5kpUhrdxA2gcOtfUmDjWAzw/8t1A+uvDyat9+an6uPLRskUAtpW14pMwcOLzat0kIcQ5jDZUyI9rO/Qfl578M0o+XXzCKdSkqCYKE4OzYvXu3UaLo7rvvlg4dOshLL70kTz/9tNnyGMw/8MADdtf54IMPSkBAgPz333/KKIVMth49eshDDz1UYRS3M+DhoVmzZuqBo6CgwGo9k5oAhjJkYWFs6A6BKu4EpElwLgQHBysHaG3Wj4FxGYbmA0fNxxvkLEUlOtmXbhiDsp4acSenWvcO7dS7rixrvTKZaq269ZTMRSLh3iKntm2Qli5wqpUW5En+mn/UdOCQC8Unro2EXn6LpOxYL/rCAslf+7cEjTobVf7ZT79LfmGhBPj5yZTLxjm9Pa2eGgKMPHx8bV7r6EyrOm3atFEvU3APw7gGASPOBthgnJWUlGR0qGmMHj1a2ZAwHgsKcs7JSgghpG6ApId/126UhKMnJCQoUC44Z6DKwLcGFD6uvfZapYS3YcMGFfRqz6FG6g9u4VRD+vy///5rdZ7pQMXR5TQD0Q033GBmJHjxxRflmWeekb179ypDChxx9orRElKq10umVlPNnlOtLFOtaZMopzqtRdMY8fHxluJinZKAtOZUax8RJQHe3pKv08nW00m17lSD5KNphpoGHFxrE4+r+dUpBYlj8vCyxZKWnyf+Xt4qOw51RAgh7pmpVhjUSKbMeFrWbj0rb7Ry4za5ZtwFKkqbOAcyh/766y+zDCIEDMGptn27QZfdGeDc+umnn5QzTqstgvcpU6ao7zDflWMlOLEspSdrGjjzMjIylLQha/jZ7iM41WoT1FVbsmaDHDx2olbb4c7sz0hT9WVBF2aqkVomIytbjicZMie7d2hrFmDjERQinoHOOSDimzeV3wo9ZZB3qeQe2OWSNsKhpseznZe3BA69SH3nFRIuAf1GSP7af1QWW+CwsUri6Ux2jny+4E+1zDXjzq/UmEWXVL6eGqk5IAMJI+fw4eUDMDH2gew1xjgIJMKYBxKPGpDDBvHx5lkKLVq0UME5R44ckS5duljdLgK48dKAc067t9ZWQJElaAeciu7SnpqgIe5zQ91v7nPDoLqO8+bde2X67NckNSPT+N3cD+fL5AljZOqN15oFY6xbt06uvvpqda9BgCt8D9X5X2uI53ZN77cz9gG3cKphIDNixAiXLQdgXMLL2jpQe4QQR8gqLJCSsurREf625R8TT6dWKlPN28tL2sQ1l72Hjsj+I8fFmjK7t6enkvTZkHxS1VW7tJ31wXtNgRpqFc2vTqfah9s3yJpEQ9Tno4NG1kotN0KIffRFhVKaabgufvjPGlm307xu5JY9e1UdEcgeEedAFhFeppw5c0YOHDigoqctgUzkU089pYKJunXrJmPGjDF7EEBmPxxnmGcKPufn56saJd27d+dhIjVOm/jm6v3gsZPqAYoO0PLsTDE4LEJ9/aRFCGtEk9plR1mWGujavo151rqTWWrqN15ekhKAYMJU8UupesaqvqREcpcbSkME9BkqXqFna0YGjRivnGqlZ9KlYMsq5WT7YsGfkpuXr+T5b7x8fKW2qck/escY1ElIzZGQkKCkGzE2uvxycyUVSEFOmzZNBg8erIJsIPOIjLRffvlFxo83HGuMgYBlYJE2BtPmW+Pll18upxygjdfcJbsN91XUuYWDsKHcXxviPjfU/eY+8zhXltPp6fLcmx9IiL+vBMWYB4IuXbNeIkNDZMK5w9T/6dNPP5VHH31UlThA7U3cW1DioDppiOd2Te83jmedcqoR4q6kldVTczRTDXWCnAUSkHCqQU/fFt2jY8ucaoYH09qkRUh4leZXhU3JJ+XNzWvU9Lg2HeXSdp2rbVuEkMpTkm64JoJ/9h0Rvd5cYg+xCmu27FDXPUoeVZ2pU6eqOliWtdOQFYaMNp1Op4xGMPIgwvq3334z1kpLS0szqgGYon3W5tfVSGxrNNQIv7rWR62bG+oPFRYVybGkZGkR67xRvj72iyk7ypxqXSIb25Q3a+h95I7U1z5aunajeo+NjlI1prF/xSmJ6jvPyCbl9teRfiiOiRc5nSrBxflSlH5avMIqH0wHZ1lphiHgx3/YWLPtekTHim/nPlK0e5Pk/PurFHXoI5/9/IeaN/HCURIZHur08dKX6ER3+qSa9mzSvFLHuzrPlfpsjMN45IILLlCS06iBZinxiGAjDdSTfeONN2Tt2rVK2UhzqmnOr7y8PLPfap/tOcfgsLvlllvM2tO/f381JkOWvDuAcwrjxPDw8Hp9LjT0fW6o+8195nGuLB/++LvsP2YYu1jj/R8WyDUTLpLCggJ5/fXXVV3yL7/8Uho3di7BorI0xHPbnfebTjVC7JBRcHYQbaumWrFOJ8mp6ZXKVNPkjQDkH22h1VU7kJEqOUWFEuzrV2vHDfKLtujbpFm1ZY5lFuTLQ8v+VJmD8aHh8sTgUayFQ4ibSz/qPTwkucj2cqiXRKda1Zg7d66qKfvBBx9Ip06dyhmNTOuLzJw5U0kcwdjzzTffmC2LqC9rn+3VHKsLkdjWaKgRfnWtjxoFB4inh4cad2zdtVdC/Gtv7ONO/WLKtlOGa22b4DDlOHcH3K2P3JH61keZ2TnyxBsfyqZde9XnpJRUuf7BJ+WZe2+R4FMGw1RRUPlz1JF+8ItrI7pTm8TbQyRz52bx7Ny3Um3ENkr+/UVNe7TuLNn+oSIW7dH3GSGye5OUJB2VPz/9QMk/Ilvu8nOHV+r/pUc9uRKdms4LDJP8SqyjOs8VZyKx6xIIBjrvvPOUlPXff//tkBMLYx1k6JuWGtHGT5B5xNhJA5/hpGvZsqXN9YWGhqqXJTiG7vSfx364W5uqm4a4zw11v7nPDQNXH+el6zZJiZ1AtZSU06p+7MCe3WT58uUSFxenJB9rkoZ4brvrftOpRogd0ssy1SDBGGLDkXUqNd0YHdyscWUy1Qwa+8eSTkl+QaEEWDEa9ShzqsHEuSPllAxqVju6/DBsPb16iZr29fSSolLzqMmCEp0U6HTi7+KbCh4kH1nxlyTn5oivl5eqoxZko9g3IaT2KYEhCf/dkAjRiW0jUnzT2s88qct8+OGH8tBDD8kLL7xgFhGtYepQAzExMXLJJZeoGiMaWlRdaqohel9Dy1CzV/+sLkRi16VIN3fCXfqoeWwTOZaYLMlphhp4tY279AvIKy6Wozln1HTf5i3con/crY/clfrWR9NffFM2795Xrh7JrHc+kRc9UtTzS3BcK/G3OEcd6YfuPbrJwZXfSYcAkdKThyVyyHmVamPRoT1yJvGImg4dfan4Wvm/6MMHSOZ/bUR3/KCE7Vmrvrvk3OHSqZ35vdRRCo/tFZXL7eUljdp0EA8v55+P6tu5Ut0gex4ZapC1hrGzSZMmDv0Oz/KbNm0yc5S1b99eZfcvXrxYJkyYYPwen5GZYCnFTQghpO5jz6GWczpRkndulE8+eF8GvvWGtGrVqkbbRtwPOtUIsUN6fp5R+tFWtL4m/QiaVlL+UXMcHTp+Urq0a11umciAQIkLCZPj2Wdka0pSrTnVfty/U7aeNhjL4dhqERquaqglpKfJa5tXy87UU/LI8sUyb+QYFV3uKubv2iLLjhuKRT/Uf5h0iqyZ1GpCSOXQlUmS+cfGycCezWXt1h1m83F5QHQXs9QqDzLNbr/9dpWNBseaw8dGpzMzzMFoFBwcLFu2bJFx48YZv9+8ebOEhISo+XU9EruuRLq5G+7QR8jmh1Pt0IlEtzlW7tAvYH9KslE9oHvjprXeHnfsI3emvvTRgSPHZe22neW+x6m5d+dO0bc3nKM+0bFW97WifujcppXMzxflVMs/uKfS/VVQVkvNO7aF+HfubfO5LmjkBDkz/2Xp5aeTdgFecvvVl1V6myWnThi22biZeFdBZaS+nCvVDWqcXXzxxZKcnKwyziBjDecaQN8hcw3s3r1bnnnmGbn33nulY8eOcvr0aXn++efV93/8YZD8BDhHkI2PsRbqsuEFiS9kv/3111+1tp+EEEKqj/7du8iJ5NNmKi6YTkvYJemH94lfSJjccWv5YFbSMDEXlyaEWM1UsyX9CE6eSlHvocFB6uUsyG4L8POrUAJSy1bbVubUqmnS8vPk5Q0r1fSoFq1lVHwbJfU4qkUbua1nf7m710A1b/GRA/LyRsNyrqoXoq3vvJZt5eqO3V22bkJI9WaqeUfHyDP33VZuPhxqL8+cyu6vJL///rtMnjxZOdOefPJJm9Ha69evN/tu//798sMPP8iYMWOM3/n6+so111yjJCRzc3PVd3hH4WV8rxmhCKkN2rZort7t1Z1tqOxIPaXeowOCpElQcG03hzRQ1llxqGk0MxGV8Io2PMc4S3BQoCT7Geo1+6Ylil5X7PQ6dCmJUrhzg5oOHHGxXVljz0595XSJwUQytVOUtKhCRr0u2fBc5x1TO8GQDQ0EB61bt05l3nfv3l1l92mvyy+/3Lhc586d5eqrr5YnnnhCOdXGjh2rxj2oqYYsN1NuuOEGefPNN9Wy8fHx8vHHH6ts/1GjRtXCHhJCCKluJk8wJAhoYwVdUaGc3LRCOdRCYlvIQ08/L716npUEJg0bZqoRYoeMMqdapH+gzWW0TLVmlchS06IPka2xY3+C7LdjNOoRHSu/H9yrnGqITHZlJpgjzFu/QrKKCiXA20dmDhxRbv6dPQeoTLoFCXvkkx2bpHlwqFzdqWo3m+yiQnlg6ULRlZZKs+BQeWbIuayjRkgdqqnmFRUrSSkGGUEw54F7pFv7NsxQqwInTpyQK664QkksFhcXywMPPGCcB4MPIq8BHgQwD5JGXbt2VfVgfvvtNxk+fLi89NJLZuucM2eOisDu27evel+yZIla/+zZs6vSVEKqTNuybP6Dx04qGTRmapxlV5lTrWu0Y/JmhLia3/5dIS9+MN/m/GZlMRkevv7iGRxW6e2UNm0pcmareOlLpfj4IfFt1cGp3+ct+0OlznmGhEtAn2F2l/353+WyPUUv98WIdC5MkZLMVPEKr1ztMV2S4bnOO9ZwHSPVy+DBg42ZaRUBKWy8HOHWW29VL0IIIfWfjm1ayrwZ/5OH5r4hxTqdFGamSV5GqjTu2FMuveJKefRuZqmRs9CpRogd0gvyKsxUSzxlqEPTtBL11EwlIOFUsxeJ3bMsUw2OrSNnMqR1eITUFGsTj8uCg3vU9D29BkrT4PJyX0oiY8i5kpybLeuTTshza/9Tyw2Lq5zOMFKsn1j5j5zIyRJvD0+ZN+IiCfXzr/K+EEKqF31xkZRmphoj03fvOaSmm0RFyKXnlXfIE+dADY9Zs2ZZnRcZGWmchnQj6ols2LBBRW8HBgbKI488Ij16lA92gEQSstr+/PNPOXLkiJx//vly0UUXMUuN1DqQfwSFRUVKGaAqWSP1DUhugy5RdKqRmqWoqFief+9T+fr3xeqzt7eX6HTmdZYR+zegRWNoIIpXVEyVguJi2naQtHVbJdJbpPjIPqecaqW52ZK//l81HTj0IvHwtp19DePZ+9/+IulnRG6N9ZKA0hLJW75QQsZPrtRYyJi1z0w1QgghpM5w0fDBEhnsL1sTjkrCsROiu3S8XHfZBOnTpSOD/IkZdKoRUkX5xxPGTLXGVTYa2ZN/bB8RJQHe3pKv06lstZpyqhWV6GTWasPDaIeIKJnUpZfNZX29vOS1UeNk0h/fycHMdJm2dKF8PvaKStVA+27fDvnryAE1PbXvEOle5lQkhLg3JWmnDcVUMMiIipFdCQb51s5ty9eLJM4Dx5lpdlpF9OvXT70qAjKP48eP5yEhbkWr5k1VRj8yLg8cPU6nWhlnCgvkWFammu5KpxqpQVBn5H/PvSQ79x9Un/t27SRP3HOLzH7vU1mzZYeZzPP57QOldLvBqVYVOrZpJbv/ExkaIlJwaI8EjXT8XpW3erHoiwpFfHwlcLC5tJ+1zDuDAomHePQeLrLpX8lb/ZcEnT9RPO2ollhDd/qkSGmpsY4bIYQQQtyfvLw8ueOOO5TU79atW1VtVUJswZpqhDgi/xgQWGFNtcrKP4J2LQ0PW4mnUyUn15AdZ4m3p6fRcLK1Buuqfbh9oxzJyhDElz41eLRqhz2QTfbOeRNUn+XriuXOv36VpJxsp7a5Ny1F5qxbpqaHx7WSyV17V2kfCCE1h64sMls8PMUrsonsTjisPnZpW7msVUJIw8Xfz0/iYgyBOayrVl76EdCpRmqKpWs3ymV3P2R0qN1yxQT57MWnpEOrePl0zpPyx/uvyNtPzVDv+Ox1JtUYYFMVOrZuKbvyDJluBYf2Ovw71F/LW7FQTQf0GymeVpQ2NCAv+943P6npUQP7Stz4a0W8vEVfkCf5a5c43WZdUlmgpI+veFUiuJAQQgghNUtCQoIMGjRIPv/8c1VSoVWrmrFfwF76ysaVMuGn+TL2h8/kqVX/yIEMwxiKuDd0qhFih/R8+5lqupISSU5JrbJTrX1ZzRCA9GJb9CjL1tpWVq+oujmalSkfbDcU9r6yYzeHs8WahYTJ2+dNUJl1Kfm5cuffv6j6aI6QW1wk05culKKSEmkSGCzPDT2/xuvHEUIqT4lWT61RlOQV6+TwiUT1mZlqhJDKgLqzFY2PGqr0I+rX2lNTIMQV4Hnn5Y+/lDuenCNncnIkJChQ3nryIXnwluvF28vL7L86elA/439WV3aeVjVTDc9Yh8UgAe+Ve0ZKHDQ0FWxeKaVlGZ1BIy62u+yiFWvkyEnD+OXOayeKV1iEBPQZqj7nLvtN9CU6p9qsSy6rp9akuXh4nu0jQgghhFSd/IJCWbZ+s6zZukMOHT9Z5fUtWLBA1RdHKYSff/5Z1Rz39q5+cb/tKcky4ef5KpkhITNdJTT8sG+nXP7Ll7Lo0P5q3z6pGnSqEWKDUr1eMgsNTrUIGwaL02npUlIm7VEVp1rjyAj1gFqRBKTmVEvISJMcB51UlQU1zVAXDc4tZJ3d32eIU79H5PSLIy5SDrEDGWky9d8/pLjUvN6CNZ5ds1TdSPC7uSMuorGIkDqaqQYj2t5DR9W1BHSh/CMhpBK0bdFcvTNT7Sw7UwzOiq7RrKdGqpeU9Ay58eFn5L1vf1afO7dtJT+/NVfOHdzf7u9K83NFn5ulpr2iqibhjnpsHk1bic4wnJCiwxVnq2Hskbt0gZr269JXvBs3td3W0lJ556sf1fQ5fXpK9w5t1XRgmcxkaUaqFGxdU6lMNUo/EkIIIa4D9/ePf1ggQ66+Re55Zq68/eUPMuHOB2TKjKckOSWt0uv88MMPJS4uTjZu3CiXXHJJjRwyXWmp3L/kd1Xix6w9Zfboh5cvkpS83BppC6kcdKoRYoOswgIpKTMGR9jQ0dekH6taUw0Pi5oE5P4jhshGa/SINjyUolU7ygwq1cU/J4/I2iRDW2b0H6ZkHZ1lVIs28vCA4Wp6TeIxeWbVv0YDuzV+ObBbFiTsUdN39xoofWKaVbr9hJDaoSQ1Wb17RcfK7oRDajoiLFSaRNVMHUhCSP2ibVk2/8FjJ5VEm7uDwKd/jx1U79Wdqda1ihlAhNhjw47dcundD8r67bvU56vGnCffvPKcxMU2cXgsALxdUPevbZtWcqDAMF18ZF+Fyxft3y66pKNqOmjkBLvLLlmzQdVsBHdee7nxe5/YePHtaKglnbv0V7vPMLYy1XxYT40QQghxGZ/+9Lu88MF8yS1TFdNYt22XTHrgCcnJM//eHikpKeoFeywkH9euXSvt2rWrsaO1/PhhOZWXoxxoluCb4tJS+fmAYQxG3BM61QixQXpZPTV7mWonk1HMWiQ4MFBCg4Oq1JftNHmjsoc6ayBjLC4kTE1vrUYJSDgU39q5SU0PatpCxrTuUOl1Xde5p0zuYngg/enALnl/m0FO0pKDmeny7Jp/1fTA2Di5tXu/Sm+TEFL78o/eUbGy+8Bho/QjBquEEFLZTLXCoiKzYCZ3rMN7058/yoSfP5d7//lNveOzVp/XVSBiFQ/ggPXUSHUA59EH3/0iUx56SlLSM8Xfz1deeOAeeeZ/t4ufr69D69BpTjUvb/EMj6xymzq2aSm7yv5KRQ441bQsNe+4NuLTprPN5aAQAuMc6N+9s/Tt2slsftAog0NOd+KQFCXsdKitpYX5UpJmcHx7x5yV+CeEEEJI1SQf3/ziO5tjl+PJp+SXv/9zaF3r1q2TPn36yPXXX68+h4WFSVBQ1Wy6zgJFL3tAvWt/OmuruTN0qhFig/SCPOO0rXoVJ8qMO02bRFXZYNyuLBL7wFHb8o+gp1ZX7XT1OdVe27xGMooKxNfTSx4fPKrK+/ZAv6FybnwbNf365tXy+0Fz2ZYCnU6mL/1DpT1H+gfKnOEXipcnL0+E1DX0umJjrROv6BjZVZap1qVtzRT5JYTUP1rHNRPPsjGBlk3ijqAe7PqyDH8NfH7gv4XVkqWGkVnnqMqrJBBijaycXLnrqRdk3kdfKIn7ls2bynevzZZLzhtRuaz1yCYuqSnWsTWcaobnkeITh0VvRwa/OOmYFO3dYqylZu05Jv1MlkyZ8bSMu32aHE8y/Keyc/PV96b4tusm3s0MY5i8pb861NaSU2frP1L+kRBCCHEN67bttJuJhvs9aqTaA863d955R4YOHarkn5966qlaOzzBFQQqoa0VLUNqF1qtCakgU83b01NCfP2sLpN4OqXK0o8a7eIN8o+ICM3Iyq6wrtrmUydlyVHXygthXR9v3yjf7zdEYt7ava/Eh4ZXeb1wkMFR1q1M/uWxFX/LhqQTRomkR5YvUlEaeOR9YfiFEh1YsxEihBDXUJJ2WkRvqDNZGhZtzLzt3K41u5gQUin8/fwkLsYwzjp47Kyx2p3AeGZd0nEl1WIKPq9NPO7SsZrmVGsVHiFBPnzQJq5j14FDSu7x37Ub1eeLhg2WH1+fIx1axTu9Ls2p5u0iiVIoeuwpMJguPEpLpPiEIWjHGnnLflPvyJDz7znY6jJTn39FGedM2XvosEyb/Uo5A11QWW21wt2blcOuIrRlPPwCxDM8qsLlCSGEEFIxeQW2A2qM9VQtZCHNfp+XJ1OmTJG77rpLOdU2b94sAwcOrLWuHx3fRmWj2QLPERdVQTWMVD90qhFiA02uB9KPtjK1Tp4yyD82bxJd5X5sX5apVpEEZOuwRuo9t7hY7lviGnkhU8milzauVN/5eXrJpW1ty6U4S4C3j7x53nhpHhwqxaUlcvOisxJJi48kqGWu79JLBjUzOBcJIXUPXWpZBq2HhyRkFagod9ClLZ1qhJDK06ZMAtJdM9WOZWdWab4z7EwxOCso/UhcBYxQ3y78W66e+qicSD4tPt7e8thdN8krj0yV4CDrdaUdlX/0cpFTDbKTwbHNJKXYfl21kuxMyd+wTE0HDRsrHl7e5ZY5cOS4rN26o1yNNHxcs2VHuecw/15DjBKWef8ZHHb20JU51SD9SOlrQgghxDV0atPS7nzcc7u2MyhkWSM5OVl+++03efjhh2Xx4sXSuHHtKj7EBIUYS+VYY0izeOkfY3gGIu4JnWqE2CAtP8+u9CPQanu4IlMtIjxMIsJC1fT+I7ajIN/dtr7cd1WVF7ImWVRYWiKPrvpbXElUQJC8c/4l4uXhISVWinHuo14wqQPA2IKi9vac3+6GLvm4FOxcr95rop4ajE97jhoySkKCAqV5WZYJIYRUV93Z2qRFSHiV5jsKnABaphqdasQV5BUUyIy5b8gTr70nRcXFEhsdJV/Me0aunzCmSg6hkrIgG1c51UAHJQFpv65a3oo/RUp04uHnLwEDz7O6zLGksnpvNjiaaD4fjrmg4ePUdP7GZVJyJt3u77WxFqUfCSGEENfRqnlTGdy7u53xiV6uvfiCct9u3bpVvbdu3Vr2798vs2fPFm/v8kE3tcH0fkPl7l4DJcjHx/idj6enXNGhq7w2ehyDc9wc9ziLCHFDtMwv1PiyRklJiSSnGOR8mjaueqYaaNeyhZIiQQSlNSAftD6pvPSRJi90wfefiK+Xc3ULikp0ciLbvH6Axroyica2japeYFyjVK+36lAzbO+4y7dHiKtAnQ3IBSG6WWNgz24qkltziLsbpTlZkvnZS1J0YIdZfZDwKdPFM9j1bdaMaN7RTZWMFOjctjUHg4SQKtG2LJv/4LGTavzl5eRYp7rBuKVNeIQczCxvbO/bpJnLxjUncU0vLFDTmqQ2IZXl0PGTct+secYM0HP69JC5M/5X5TEN6p2VljmevKMMsvWuoFObVrJr2woZEapXmWpwMpsa1rDdvFWL1HTAwHPF04acfOPICLvbiW9a3hEYMOg8yVn8vegL8iRv5Z8SMva6ijPVYqm+QQghhLiSOQ/cI9c/8KQcTUwyjgHwjskn7r5VjRU08MzwxBNPyPPPPy/ffPONXHXVVRId7RrbrauA/ONdvQbKDV37yLbTSaLTl6rAOXvJHcR9oFONkAqcarYuZqh9VqzTqelmMa65MEMCEk41W5HYFckHncg+45J2WG7TlU4uRySS6FQj7oi1+hvrtu1Q9Tc+nfOkuCMGh5p5m/E5c/7LEnGX64vy6spkyRCZvnvFYTXdpe3ZgS0hhFSGtmXyj4VFRUoloIUVo3dtggCl3OIiq/P6xDR12XZ2phiy1Lw9PKVDhHsZBUjdYuGy1fLoK29LXn6BMkbdM+lKufOay1zisNalGc5TV2eqdWwdLwvLMtVKszKlJP20eEeedS7nb/hP9LnZIh6eEjhsrNV1wBH34Xe/WJ0HgxyCpdqWZcaa4ukfqBxreUt/lbxViyXo3MvE06/8M2JpXu5Zh2JM+fUQQgghpPI0iYyQn99+UX79Z7n8tXKNBPn5Sv+e3eWacRdIRxN5yJSUFLn22mvln3/+keuuu07GjTNknLsrgT4+LIVTB6FTjRAbpBXYl388UVZPzVXyj0B7iDtw5Fi56EtH5INu6NpbIgMCnZa5/HTn5mqXLHJ0fa7eHiGuQKu/YYlp/Q1rRpjapDjpmFmG2ln0UrR/u5IncrXBR8tU84hoLPsOLzNmqhFCSFVoHddMPD09pbS0VBKOnXA7p9r3+3ZKcm6OYNT26qhx4unpIb8n7FE1Y38+sFvu6DnQaSUBa2jSj+0josTPTWRrSN0CEo8vfvi5fP6LQTa+UViovDTjfzKkTw+XbaMkrUw+0cNTvFzo/O3YuqUcKBApKhXx9TTUVdOcavrSUsld9rua9u8x0MzZZsoH3/0ii1asUdNxsU3keNJZByAcai/PnGpz+6jRlrfsd9Hn5Uj+un/VZ0t0yWcl/JmpRgghhLieoIAAJfN41ZhzJSMjQxo1amQWFLR+/XqZOHGiqqH2xhtvyN13303lHFIt8GmMkIrkH204qbR6aoH+/hIeEuwy+UeQmZ2jMuEaRzYym48MrgGxcar+mamAIow4A5rGyYP9h1Vqu3vSUqyus39sc5dnjVW0D8xSI+6II/U33M2pZjRq2UCXmuRSp5peVywl6Ybr4mm9rzLcATrVCCFVxd/PT+JiGqtrLYIYRg3s6zadigy1d7euU9Pj2nSUc1u2VdPxoY2UU+10Xq78deSAmldVWE+NVIXE0yly/3Mvy7a9B9TnXp3ay6uPTpeYaNeO9Uu0rPVGUeLhfbZGSFWJahQuYY0ayYGCdOkSaHCqBfQxPPsU7tksJadPqunAEeOt/n75hi3y8idfqekLhg6U1x6dLgePnVDXFUg+VjSOw/749z5HCjYuU861wCEXioeFs1yTfvQIChFPBgoSQgghNc6qVatUIN6yZctk0KBBPAKk2vCsvlUTUrdJr0D+MbHMqdasSbTLoh7amTzM2ZKAfGnkGOV8MgWf540YU+ntWltn76gYeXHYhZVep7Pbq+o+EFKdJNioc2iv/kZt4xVpv02urHMClENNX6qmD2QVGIMOWjZzv74hhNQ92pRJQGr1n9yF+bu2qDGjt6en3N3r7IM7aqyd0yzesMzOzUqBoKo1aXenGVQSukaznhpxDjiULr3rQaND7YbLxsnn855xuUNNC9pxtfSjRodWLWVXmQRk0ZH9xu/zli5Q7z6tOohvy/blfnf0ZJJMn/Oq+h+2b9lCZk83RK3DkTZ6UD+HA6OCyhx2JWmnpHCHwZluLVMNQUuuej4khBBCGgL6Ep16VYa8vDzZvn27mr7//vtlx44ddKiRaoeZaoTYMFxkljnVImw41U6WyT/CqeYqQoODpElUhJxKTZf9R47J4N7dyy0DJ99HF14uCRlpqv4Y5BKrmt1luc7mQaESofeotuKY1bEPhFQXi1eslVfnf2O31o+7ZakB3fGDNuf5tu9ebdKPYGuSoZ4IdM1dUZ+FEEIQePTv2o1y8OgJt1I1+GTHJjV9ZYduEhcaZjZ/ctfesvLkUdmVdlo2n0qUPjHNKr2tw2cyjHXbutiQtiPEkpKSEnnryx/k7a9+UA6loMAAmT3tbpWpVV2UlMmUVodTrVOblrJr/xYlZa07eVhKCwtUhlpRgqF+bNCICeV+k5OXL3c//aJk5eSqZ623nnxISUdVBp/mrdQYCjLauf/+In49Bpk5zyCtrZaLNaiPEEIIIcQ+Bbs2Su6Sn6X40B7DPbRNZwkefZn4de7tUNcdPHhQrrjiCiX3mJCQICEhIUoSkpDqhplqhFghCw9oZRHFjfztyz+6qp6aRrv4Fg5FYsMJNapFG5c6o7R1Irq6JqiOfSDElSzbsFlFNkM+oFWzWOnXrVO5ZU6lZUhKeoZbdXxpXo5kL/jMKEFkildMnIRPnubybepSDE41z/Ao2X7IEKndpW0rl2+HENIwaVMWvHDw+Al1TXYHPti2QTm6Arx95Pae/cvNH9y0hbQNN4xx5u+yXb/WEXaWSer5e3lz3EQcIj3zjNzy6HPy1pffK4dah1bx8tObL1arQ800yMa7GpxqqKumZapJaakKIMr97zf10Suyifh162e2PPZ75rw31XMV6jKiZlpVazIGjTQ47oqPJRgNgJbyj94xdKoRQgghFZG7YqFkfvC8FB/ea/wO99aM95+VvFWLK/z94sWLpX///nLkyBF57733lEONkJqCTjVC7Eg/gsgKMtWaujBTDbRvGWdX/pEQUjOs375L7n1mnhTrdNIiNkbmz31Gvpg3S/54/xV5+6kZ8smcJyQwwF+yc3Nl1lsfudVhyVn4lZTmZIl4+0jk1Bck6uHXxLuZwcHlHREtnsGh1WZE84pqInsPHlHTrKdGCHEV7eIN8o8FhUVyItkwBqtNEnOy5Ou929T05C69JCogqNwyyGCZ3LWXml5y9KAcy8qscj21jpHRSmqSEHts2rVXLrn7QVm9xSCFdNl5I+XbV5+Xls1cK/1sCWSbSjJSqi1TrWPreEnRecgpQ9lWKdi6Wgq2rFLTgcPHiYeneXb8u1//JH+tMsg0TrvxWhnat2eV2+Dbsad4l2Wi5S791fh9SXamYeyFsVas+ykYEEIIIe5EyZl0yf75Y8MHU5n0sumsnz5U91arvy0pkccff1wmTZokcXFxsnHjRrnkkktqpN2EaPCJjBArpBfkGaetSSAiQjoxJdXl8o9Ak5GD/GNV628QQirH9n0JcseTc6SwqEhioiLl0xeelMaRBgkBrf7G4F7d5YGbJ6nvFq9cq2Qi3QFETmtRXcHnXq4ixSH1GDhsrPqucO82Kc3NrrZMtbyAcMkrMNRU68xMNUKIi2gd18wos5ZwrPYlIN/Zsk6KSkokzM9fbuzWx+Zy41p3VFLiGNF9uXtrlZ1qXaMo/Uhsg2eHT3/6TSY/+KSSk/fz9ZXnpt0psx+4WwL8/aq961R91bJMUi8X124FLZs3VfukZavlrfxTpLREPAKCJGDAKLNl/1u3SV4rk+8eM3yw3HJFeWnIyoDrUGBZbbXCnRtEd+qkmfQjYKYaIYQQYp/8jcuMYwarlJRIwaYVVmfpdDr5559/ZOLEibJq1Spp164du5vUOHSqEWInUw2RwCG+5R9AUzIypbhYVz3yjy0NkY95+QWSeNrguCOE1Bz7Dh+VWx55VnLz8iUyPEw51Gw5z68Ze7707WqQhHzmrQ8lM8v1zipn0JeWSNYP76voLkSIB40+G63l332AiJe3Mj4hstvVlKQapMmSSwxDC18fH2nTwpBZQgghVcXfz0/iYpq4RTb/ocx0+SVht5q+rXs/q2NFDT9vb7mqo6FG7k/7d0l2UaHT24Pzbi+cFSLSLdr12T+kfoDM+ftmzZPZ730mupISlWX/7avPycQLRtdYG7SxgCbH6Gq8vbykZ8um0s7iL+cBuf7i4rP/0eMnZfqc14yyl89Nu8us9llVCegzVDxDDcFWuf8tMJN+xPeeFtLbhBBCiLtQWpAn+VtWSd6av6XoyL5aC+YvzUhFpIrdZUrSzdUpNm3aJDk5OeLn5yd//fWXvP322xIUVF4tgpCagE41QmwUngeILLb2AHbSRHaoucsz1c4aoWvbaERIQ+PwiUS5aeYsOZOTI2HBwfLx7MelVfOmNpdHfY5np96pHEipGZnywvvzpTbJX/OPylQDoZffIh4+vsZ5ngFBxmK/mlSSS+Weyga8CVmGLDUYsXy8vV26HUJIw0YbI1VUd7a6eWPLWinV6yUmKFiu7tSjwuWv7tRdfDy9JE9XLD/u2+n09hIy0pRjDTBTjVgDssuX3zPDKHV43pD+8tNbL0inNjVb21RX5lRTjiU//2rZxr1BmdLs7PBGUZqRIpnzX1bTObl5cvfTL0pOXp6EhwTLW089JIH+rm2Lh7ePUQEgf8N/Sp7qbD01Sj8SQghxP+A8y/33Fzn9xM1y5rOXJOvbdyT91ZmSNne66E7VvAqECk6pwKHnGRZhbPu7774rgwYNkkceeUR9h/pprgyYIcRZ6FQjxApp+Xk2pR/ByVOGaGF/P19pFOba2kR46Gse09goAUkIqRnwv77x4WeUcwy10j547hHp2Lplhb+D0+3e669U0z/9vVRWbKy8vFdVgEEn+48v1bRfj4Hi18ngQDPFv/dQ9V50cJeUZKa5bttwqJVJN2xOPqPeKf1ICHE17coksg8erT35xz0ZqbLk2EE1fVevgeLvQPAA6q2NbdNBTX+xe6vo7End2JF+DPH1lRah4ZVqN6m//Lj4X7ny/kfkaGKyeHl6ysO3TZE3Hn9QQmohclvLVKuOemqgOOmYxBWki6cVG1rR/u1SlHhUHpr7hspUQ+DTK49MM2a4uprAweeLh6+/iK5Y8lYuMso/avXWCCGEEHcib/lCyV4wX8RCNUGXdFTS33jMZv2y6iKg33D7mWoenhLQZ5jk5eXJDTfcIHfeeacMHTpUHnvssZpsJiE2oVONELuZaoFW+yfxdIpR+rE6IiM0CcgDdKoRUiOcTsuQGx9+WpJSUlWtjnefflh6dGzv8O9vmjheurRrraafeO1dyckrK/ZRg+T89rno83KUgSf0kpusLuPfpa94IHJcr3epBGRJylm5p9WHDbVFtP4ghBBX0UZzqh0/oerb1gYf7DEETrQKayQT2nZ2+HeTu/RS70m52bLkqCGj2FmnWpfIJuLJiFxSRkFhoTzy8tvqhRqwjSMjZP7cp+XGyy+utcjtklRDfVXUc62W9aedHW9Y47effpIlazao6QdvniSDexukV6sDz8BgCRg42ljbzZipRqcaIYQQN0OvK5acxd/ZmKmX0pwsyV/1l2u2pdfLtr375buFf8vvS1dKVk6u1eW8GkVL8EVXGz6YjlvKpoPHXiuHUzNUdtr8+fPl4YcflsWLF0vjxq4twWOPkv+zdxZQbWVdFN64Oy2UQh3qSl2mU3e3qbvb1N29U2//uk/dp+7uQt1ooZQKpbhDQuBf94akeJF4zrdWeJqXm5dHct/d5+yTkIAnP77xvvsHZldJEMkgXyaC+I39Y2aZahnVWcotbgVdcO3+Y3xUYiQ2QWgLIeER6DdlLo/wZnaFa2eMR7XyZbJd42PhmGHoMHISr4W4csc+zBjeH4pC4P0WMQ+v8Xnzpp2hZ2Of7n46hkYwKlMVsU9uItbjFsz+bCWT149PGkRLNLdGYGQ4n6dMNYIgZI1rkv1jbJyA98Vc8sknAyUj7n/3xZOkTJxR7jV57d2sUtw2D6rnc8F9vy/Y/fopmhTOeuDGq6TXLG2v2PdLqC4+3/wwev5yvPP24cs1KpTF8il/81qwykQU5C/XTDV9+3yZbt904Tbr7aBlvdpcXJQ3pnVbIfrWOSRG/aqpq2OQcY1FgiAIglAGgk/vkRidef332Bf3+VhCbvsnYxaswBuvT9J1RoYGGNK1A4Z27ZAm6Me8cSfoWdsj8spxiJIsKPUcnGHeqAPPUgt++BB+fn44fvw42rb9VS9eEVz97IVF96/je7Lf+HJ5HDG/TmMUtRbbUhLaDWWqEUQ6BMX+zv5RXDvIKa98RLVihcSR2B99v0KUVEODIAjZw+puDJg2n9fnYTZByyaPRt2qaW0Ts0KJooUwsIu4o7fn5Dk8fvUWioDVMws/vElax8O0bstM9zeuVJtPWe21+ACxGJZbREnHiTSxELdDTw9uSRm3BEEQsqKIS37pzbii66qxqNtVHvf4fGm7vGhUsFi2j9GrjPj35dlPPzz/mbXv35h4Ia+pxiiTh0Q1Arh4+z6vnyYR1IZ164htC6crXVBLTEiQ1lTT+434lVNYP8fQtWyaEixs8WmMLj4LdHhQz/y/hyokW0/fLi+MylROsS7s35UI/t8sHvVPEARBECqBUPDbXRKFKW0hcxKs3HP8TGn/REKcQIjVuw5g6+H/0n2eSdV6sJ+8GnnmbecPm/HLcTdUyLdVrVoVnz59UrigduurD0ZdOcUdJpLzKsAfvc4cgl9k5gIloR2olKh2/fp1LFy4EDNmzMDhw4czFBPCw8OxYcMGjB8/HqtXr0ZgYNZTMHPzXEJ7+J39o/wz1cSD0czK5esPsYBHEIRsiYmNw+CZi/DKU1wbZ+HYYWhap0aujjmsa0cUKyDOpJi2cgO3ZlKEN7rEcsiy0yDo6GWehG5UvDx0TM35fOxTFtGdeySDaP4iPT4tVtCF22gSBEHIEmMjI2l9pI8KFtUu+nzEmyBxn2x0pRo5GrCv41yI20Yydr/2yNJz3gUFQJSkIJShTDWtRhgfj8WbdmHkvGWIjI6GtYU5Ns+fitG9/4Kenvj3V5kkhAfz+mIMfTleq9a9x+GTQcqa1q/jjTDrSyKvdb1u5kSYGCsuW0wUGpxmneDDK4TuXqGwNhAEQRBEZujnL8RrlGWIjg4MCmbdRSE9Dp+7jJ/BIUhITISVXiLKmiSimFEidHjoC7Bx/zE+BpP+y+tAz8IawbFCNGvWDA0bNoSHh7ivbKbgGrEskG7FI/E4SaoYHiQgEaFxsVnuxxOajUqIanFxcShXrhw6duyI0NBQGBoaYsqUKahYsSICAsTihQR/f3++fs+ePXB0dMS5c+dQpkwZfPz4+9oEuXkuoV0ES0Q1E5N0v2CZvRvD2UE+Xr6FXZx4oXFlRGIThDYgEAgxYu4/0myymcP7o12jP3N9XENDAywYO4x3Cn2+fsf/9h6BPBGFBiHy/AE+b1y5LgyLlv7tc3T0DWBcXiwexnrc5t9puW5Hkv3jxwhxBBxZPxIEIS+KJVlAKlJUex8cgMX3r/N5d3tHVHfKWSYuq4fWo1QFPn/J5yO+ZyGTRVJPzc7YFPnMxNnAhPbhHxiEXhNnY8exU3y5XPFiOL7+H9StkrPsenkQn6y+qrwy1Ri65pbwqNAcvbx0sCTUAv/oFcPwD0JEJuph1dSxcgt6TA+hny/iv6Q3lpAIgecLxP+g+ziCIAhC+ehZ2cK4Yq2Md0hMhGmd5rl6jct3H8JCNxHTnBJwzDUR6wolYluRROwtmoi6Fok8IOjRyzcZPv/hw4dwd3fHjRs3sG7dOj5+rwy+RITBMyQwjaCWnDPe7xXYIkJVUQlRTSgUwsjICK9evcLSpUt5ptqDBw/w9etXLFiwIMW+U6dO5RZdV69e5dlmTBgrWLAg/v7779++Tm6eS2gOzELnqq+X1EonNSyqIjSTmmqBIaE8g4yRiAS5tJFleBRwEtci8PQRZ6AQBCEb4kUijF28ErefPOPL4/p1R/fWzWR2eiuUdEOvtuIO6bbD/+HCrXu49eQ5t3OVNREndiAxLhY6xqawaNM7w/3Y4POVe4+kg9DGlerwKRvsiff7nKs2JIpEECVlbzz3D+XT0sWK5OqYBEEQGcEyYRmKqDvLnAv6nTuK9if24meMuMh6lFAgdTTICa2LlYKloRHPPtv35nmWRTVm/agIOztC9bjr8QJth02Ax+t3fLlH62bYu2ye3Gzoc4ooSCyqsWx43aSMeHkQHBbOLTCZ1eNZvyicfuXN14/s2QXVK2SvJq6s3vPvas4SBEEQhLKx7DgI+i5FxQup+pRsm2FB11wdXxQXi5UFE9HIEtBPdvh8BsBc50Q0sEyUjqWmZuPGjahduzYSEhJw8+ZNDB8+XGn9XtbX/x3RQnFmPqHdZO4RpSCMjY1x8eJF2NiI7VAYdnZ2KFu2LF6/fi1dx+wgmS3k2LFjuQjHYP9kffr0wciRI3mWm7W1dbqvkZvnEpoBGwAZd+0sHvj9ihis5uiMqeWr49eVB4THxUptdmxS2T+ym7ghMxdLl8cuWo1D565i5dQxsLVKaUOSW1g9ok9fvyvc3oggNBnWSZu6/H+4dOchXx7yV3sM6tJO5q/zd5+uuHT3Ib77B2DMolXS9dUrlJXZ90Xc+2eIfXaXz5u36M7tElLDvrPGLFyJ+89epmjDismjoWtpg4TwEMQ+uQ0Dp0I5bocoJABIENs1vwtlA83ieiYEQRDyFNW8vnzl3+ksYE5esH7jw2T9Rsb7sGBMvHke25t1zNExTQ0M0KVEOWx58QhHPF9iaMVqMDPI2C73tURUI+tHrYNd3xv2H8Xafw/xrHJTE2Ms+Hsomv+ZSaS5EhElZarp24sDA+UF69ekF3T44MUrDO3WAYpE/zcZeb/bThAEQRCKQtfUDHajFyL2xQOxY01cNPTzFYRpzca8Zmlu6ehkCte0jsjQ1WHJC8CwvImwLVow3ed6enpyUe3AgQPIm1c+jmBZxcXCCkZ6eojLoCSVLnRQ3NZe4e0iVA+VyFTT19dPIagxoqKi8PLlS7i5/fJ0ZcUJIyIiuGVjctgyE82SC3Cpyc1zCc0gvYGRhz++Yu6T2+laPzLsUmWqsZu4Vx/E9ZckPHj+EmMXrZR5e10LiX/UPviQqEYQsoANSM1ZtxX/XbnJl3u2acbFL3lgamwMK4u0Udqy+r5IjBci/MgWPq/vXASmtRqnux/7znrw/FWaNoxbslpq/8DqquXGAlIU8F06/10gDlgpUTTnIh1BEERmuCbZP8bGCaQ1buUBczRggVipvx3Z8gO/rxk6HmSFriXLQ19HFxECAU58yNgGJ0IQh09hIXyeRDXtIiQ8AoNmLMSa3Qf5b7RrQRccWbNEZQU1hvCbOGNMx1S2gYbJYfdFLFAovW7LvacvFR6MyAYhDV3L8oCilOjA0K2cTAYpCYIgCEJWsFIQJpVqw2bAZNgOnwvL9v1l9ltVxziOi2fpwYQ1ewPALuJX352VYnrzRtwPXrJkCU+2UbagxjA3NEKbYqUy3M7qqnUrWV6hbSJUE5XIVEuPcePGITIyEoMHD5auCwoS37ymFuAky5Lt6ZGb54aHh/OHBD8/sY0DE+PYQ1VhbWMRjqrcRkUhGRhJDfu+fxL4A55BAXCzE1uoBERFSrdze56k88dqmyXP9pAeI1F8E/f+02cUKyAe6JEFRVzy86n312+IjYuDgb7i/l3p2qFzpGnXERuQWr59Lw6cuciX2zasi0kDe/H2yQP2ffH24ye5fV9EXTkOUYAft20w7zBA3HlNdZ5/9531s009MNlPFPwTcZ/e5bgwsfCnWFSLNjBBbGIcirg4wcjAIFufuzyvFT09PZkfkyAI5VHYOT8X79n3Ovuec8nnIJfX8Y0I/e32YjZ2OTq2g5k5mhZxw2mvd/j39VP8VaKctJZucl4Hiq11GaUpU01reP7OE6Pnr4BfgLiGc6v6dTB39GAesKOKJESGI3TXcgg+iPscgnceCP7fLFj3Hsfrn8kSX7/M7RY/f/8hzWZVFOx9hu5ewWuoSTB0KwvrXmMV2g6CIAiCUCbGifGI/41jY2K02E795MmT6NWrF1xdXXktNQMDA6gS46rUxpugn9yGnb2lxKQMNSaoMceJ5kWKK7uJhAqgkqLaqlWrsGnTJqxfvx7lypWTrpf4qaaOqJcsZ2b/kpvnrlixAnPmzEmzPiwsDGZmZlBV2OAkEybZe9T2QcV3P75luv3tj2/Ioyv+d/gaJL6BZRHE8ZFRCNGJFu+TKkMtNW88P8LOQnbXg4ONFZ/Gx4vw8u17FHZ2gqKga4fOkaZdRzuPn8X2o6f4fP1qlTC2dxf+HS4v5Pl9kRgSCNGlo3xep2IdRFrYAyEhadvg+THT47wME6CGTR4gJABh965AzzJntVlE38Q12fxF4t/RYgXyIySd9ijrWrG3J2sGgtAkTIyN4OLowAfXWVZK/eqV5fI6BdKx1M3O9t/Rq3RFLqqxYug3vnxC/YJJNS6S8SpQLCDkM7OAnUlKS3JC82C/gXtOnsOSzbshjI+HgYE+pg/thy7NG6l0PT2xoJYyK54tM6HJdthsmb5WwaSa0zndLg+YcMjeJ69TG+jHLR8pQ40gCILQNvQdnPlvYbrp5Eno2Dti2rRpWLhwIXeO27dvn0r2cVi22u7mnXDG+z1OfnyDoJhoFLKyRecSZVE7f0GVbDOheFROVNu5cyeve7ZgwQIMHTo0xTYHB3EkamCgWPSQIFnOLE00N89l7RkwYECKTLWqVavCysoqTeabKsEGKdk/OqsVp+wBbWVTUidza7OSjvmln6Xgp7jwva2xCWxtbaX7lHIrlukx2HZZXg/mFhY8O43dVPuHhKNS2dJQFHTt0DnSpOto94kz2HLkJJ+vW6USVkwdB0MD+f78yfP7IuzYJojihdAxs4Btuz7QNbVId7+nnpkLe6WKu8I0vg6iLx+DzjsPWHceBB3d7H9OYRHBYKV8vaPi+XKFksWz/d5U5VohCEI9KFbQWSqqye01bOxQLZ8Ltw5P3otkt9BV8znnOEsteeaZu0N+PPH/ht2vPTIQ1ZLqqeWRTzYeoTpERsdgxqoNOHtDXCs1v0NerJkxDmVc014XqoTQz1eaoZaSRJ65xQbXZCkwsSw0VhuWWVknH7NjY1tsvaKz1JLD3ieJaQRBEIS2YlKribTmexp0dBCRxwU9+wzE5cuX0b17d55Mo8qJKkb6+mjvVpo/CELlRbUjR45w8Yqp1lOnTk2zvUCBAsiTJw+ePHmCv/76S7qeLRsbG6N06Ywv9Nw819LSkj9Swwb+VH3wj2XgqUM75Q2zdmQDI+lZQLrbO/LtknMUEhfLpzYmJinOW/HCBVG9Qhncf5YyElNyE8e2yxL22oVdnOD5yRdevl8V/hnStUPnSBOuo8Pnr2Dx5t18vlr5MnyAytjISO6vK/6+SDvow2BiuVmq75esEvvqIQRvnvB5i9a9YJBBpsTeU+dx8MylDI9To6L4O0torMNFtYSIUIg+vYOR26/s8KwiShr0/RjBpDUdlHErmqP3puxrhSAI9YENnF+9/xgfP4sDoeTF8nrNMf76Wdz//qv/WMneEUv/aCqT47NsNSaqPfrxDW8Cf6KUfcogv1cBSaIaWT9qNJ4+vhg1bxk+fRXbKder5o4lE0amW5tV1RAFZW7HyDO3ZFxXbOXUMbw+LbOylsD6XCumjJHp6xAEQRAEkXWMXMvCrEE7XqqCD5QmGwjRNbeCTech+LanPdauXYvhw4dTtheh9mTseahgzp8/z5Xqv//+G/PmzUt3HxbF3qdPH/z777/SGmjR0dHYsmULOnfunELhZimkEydOzNFzCc1k2Z/NuKVjckz09TGjUsqC3yGxMXxqa5zWZodZsKRGnjdxbgUL8Kmii24ThCZw5vptzFi1kc+XL+GK9bMnKURQSz7ow74fkqOro8OzT/tPnYeg0OzZTyYK4hB+dBufNyhcAiZV6qW7H4tyn/c/8X4VS7lxMTE5rH/L6snx4+QrAP184oCAWI9b2WoPbxOrLRosrvnzTZCU+Vu0cLaPQxAEkR0k2SheX77KrTYmw8bYBNuadsB/7XpibcNWON6mO1bUbMjXy4J6BYrAxUJs982y1ZLDbGb8oiL4fBl7xVvaEYrhxOUb6DRqMhfUWHDJuH7deX9FHQQ1hvCLd6bbmRWirLG1ssTOxbNwZvNKfq7YlC2z9QRBEARBKA+LVj1hPXAqDIuX50Karp0DrpgVhNWYJbAvVgLPnz/HiBEjSFAjNAKVyFRjFozt27eHiYkJYmNj+T+YBCcnpxRZa7NmzcKDBw/g7u6Ohg0b4vbt2/x5y5cvT3HMq1ev8sy3pUuXZvu5hGbCMtDiE8UDL22LlcKJj28QEx+PoLhYFElXVEs7YBIcFi6d/2fiKJQqVliuNiOuhcTH9iRRjSCyBctgmLh0La9PwjKytsyfBnNT2QyCZnfQ5/2nz7yGGrN8/BkUgiEzF8Hnmx8GTl+A3UvnZLldkZeOICEkgKV0wbITs2pMGxdz1+MFJi5dw993iSKF+Pu2MDPjwvx778+YtXYzIqKisefkeSwYI7ZYNq5UG5FnPiP2+X1YdhwEHf2sFwkWsfaIxLaPXwVAgXyOsDSnIBWCIORLsQLOfBobJ8A3/wC45JOvPSKzemQPZlWb3ZqRmaGnq4sepSpg0YMbOOftibFVaiOvqXkK60dG6VQZbIT6EycQYvbaLTh07jJftrexxoopf6cJhFFlIi8fQ9SFQxls1YGhW1m52iGyezBl2j0SBEEQBJEW49KV+YMlsrCyTrt3b0SES3EMGTIEBgZZH2sgCFVHJUQ1IyOjFOJXcuzsUtYrYBll165d4w9vb2906NCBC2Sp/zFZ1lu1atVy9FxCM3nyQ2wRZGZgiFm1GuC+ny9+REXi0ldvVCn0S1YLio3m0/SikL18v/GpnbUVWjf4Q+5tltwo+n7/gTiBAEaGhnJ/TYJQd+49fYHR85cjXiRCIWcnbF80Q6kR32zw187CjNcZYwLfovHDMWHJGrz+4I0Rc5Zi87ypMDTM+HeI1SOJ+/gKUVdO8GXTOi1g4FQozX4vPT9i+NylPBOODTBvXSAW1JIPPPkFBuGfrf/i+MVrGNCpDQo7OyWJanuRGBOFuHdPYVymapbfmyjwl+3TdwFQtxhlqREEIX+KuOTnEa4sgODD5y9yF9XkSTu30ljrcQ+RQgH2v3mO0ZXFDgqvk0S1wlY2sDBUXJY1IX+++Plzu8f3Pr58uWq5Ulg+eQzy2qlure7ksP+7yNN7EXXlGF/WL1AUOgbGEHq9lu7DBDXrXmOV2EqCIAiCIJSFl5cXT5558eIFJk2axEs9EYSmoRKimoWFRYrstN/BrDEaNGjAHxlRr149/sjJcwnN5PEPsSBWMW8+GOrpoXmR4tj+8gkuf/PBpISEXzXVMrF/9P76TTqYowjcContH5m1kfeXb2SrRhC/weP1OwydtQQCoRD5HfJg56KZPPpblWhd/w+EhEVg4cYduPfsJSb+sxbLJ49OU0ssITIcobuWQ/DhV80Q6OnD9I/maY7JbKMGTV+I6JhYLvpvXzgDeWzTDs51b9UUO4+dRkBwCNb+e5Bb1+rbOcCgoCuEnz8g1uNONkU1Pz4NSdBFTCJQ2jV53i9BEIR8MDE2goujA3z9fvBM3PrVK6vtqWbBXh2Ll8HOVx44+P4lBlWoChN9A2mmGtVT0yyu3HuEycvWITwyii8P6tIOo3v/BX01qSeamCBC+JGtiLl7gS8bFq8A634ToWtkzIOAeA01+3xyzVAjCIIgCEJ1OXPmDE90YUE4x48fR9u2baGueAYH4rTXO4TGxcDFwhptXUshjyk58xAqVlONIOQJ+zKXiGqVHcWWQa2KluTTwNgYPPIXb2MES0Q1k7SZap++KFZUc3bMC2MjcXaapw/VVSOIzHjz0RuDZixETFwcF5SY9WK+vPYqedJ6t2uBwV3a8flzN+9iwYYd/HsqOWJB7VXKJ4riEX5wQ4pV/kHBvEYbs6c1MzXhGWoFnBwzHIge2q0Dnz9z/Q7eefnweeNKdfg07tVDJMTFZvl9xAeIRTXfWLG1LrPEJQiCUARFCzprTN3Z7qUq8JqbYXGxOPXxLf89eBUgFtVK26tvFh7xC5Y9v2zbHgybvYQLahampvjfrIm8hpraCGqieIT9u1oqqBmVrwGbgVO4oMZgQhoLzCFBjSAIgiC0F9aPLVCgAB49eqS2glpCYiLm3r2Cdif2YNvLxzj+4Q1WPbmDBge34bjnr8x8QrshUY3QCr5FhsM/OpLPV3YUC2JutvZwsxHbi57xei/94gzNpKYayxZjFHFxUki7WWZl0aS6IR+SLGIIgkiLl+9X9Js6n9cLs7a0wM7FMzMUllSFMX27oWNTcdb03lPnsX7fEek2oZ9vUoZaSqGNIfB8waPBGWxgbsC0+bymkIGBPtbPmoRSxTLPFuvUtAHyO4jr86zafYBPjSvUBHR0kSiIQ9zrx9m2f/wmEC+XKkqiGkEQisE1ySL742exvbc642RuiUYFi/H5f18/hV9UhNSOnDLV1B9WT7XPpDnYcuiENABl+4KpqFfNHeoC6x+EbluM2Ke3+bJJ9Qaw7j02W3VYCYIgCILQTAIDA3H27Fk+37JlS3h4eMDNzQ3qyrYXj3Dw3S/HIDZWzBAlJmDG7Ut4kpS0QWg3JKoRWsGjpHpqRnp6KSJ+WxQpwaeXfT8iJl6I8LhYiJK+LG1S2T9Gx8bi+89APl9YQZlqyS0gNSESmyDkVZukz+S5CAkLh7mpKbc+VIfC9awe0JxRg9CgRhW+vGb3Qew/fQGiiFBEnNyd6XOZvVJsXByGzFwEz0++XIBfMflvVK9Q5reva2hggJE9O/P5a/cf49lbT+hZ2cKwWGm+LtbjVrYz1b4JdJAvjz1sra2y/FyCIIjcIPme9/ryldtkqzu9ylTkU++wEGx89oDP6+nooIRdHiW3jMgND56/QrvhE/Do5Ru+/FeLxti7bC6cVDSTPj0SYqIQvHEe4t548GXTeq1h2WUYdHTVI8OOIAiCIAj58fDhQ1SqVAldunRBcHAwX6evrxLVpnKEQCTituwZoQMd7MpkO6E9kKhGaAWSKILySfXUJDQr7AYdAFFCIa77ekutHxl2qTLVfL5+l84XdRFnjykyEpvsHwkiLf6BQegzeQ5+BgXDxMgIm+dPVau6XszyacWUv1G5TEkY6STCa/9m/JgzBIK3v+mk2eTF3wtX4snrd3xx9siBaFy7epZft3X9OlIb21U79/OpcaXafBr39ikSkjJ7f1dXRRQktif7KiDrR4IgFEuxpEz+2DgBz9ZVdyrkdUL5POIM66NJtjIulla8vhqhfjChd/PB4zzoJzAklPdRlk4cxYNpjAzF1u7qQEJkGIL/NwtCb7EoaN68Gyxa9+aBQQRBEARBaLfN48aNG1G7dm3e77l48SJsbW2h7viEhSA0k5IYCUjEfT9KeiBIVCO0BGk9NYeUGWaOZuaoaC8ewDjl9Q7BSVY7DJtUopqXr/gY7EZYkdGlxQqJRbVv/j8RFfNL9CMIbScoNIwPVn398ZNbH7LaJO6lxdmn6oSRgT7Wtq6Bg8V1MSBPIvTiBUjQN4SuXXp1dHRg4FYOcw6c4VlmjFG9uqBL80bZek09PT3+PMa9Zy/5w7h8DUBPn9dti31x/7fHEIUE8X0l9o+/s50kCIKQJSwwQDKwrynZ/O1dU2Yb+4SFot+5owhJFvRFqD5hEZG8dtry7Xv5IFNhZyccXrMYbRr8AXVCFBKIoDXTEf/Vmy9bdBgI88YdSVAjCIIgCC1HIBCgT58+GDp0KBfVmN1jjRo1oAmwOsey2IfQfChTjdB4/KMi8SUijM9Xzpc2w6yRs7gG0J2vn+EdmpSqrKsLC0OjdOupsRtjZrWmKNwKiu0fGV4aUDeEIGQBqyXWf+o8/n+pp6uLVVPHopZ7ebWL7GJWSkH/jIPg+FbY6IggSgROhAA9PukjqO0wGLqVS/EcQ7ey2KfngmMXr/HlHq2bYVi3jjl6/Sa1q/O6LpJsNR0TMxiVqMCXYz3ENVMyQxT4K3v3qxAonXQsgiAIRWBibAQXR3HwwQcNEdXOfBJnHyfnod8XjL8urlFBqD4vPT+i3fCJuPbgCV9uXrcmjqxdAtekIDl1IT7gO4LWTIXo5zdW5BlWPUbDrE4zZTeLIAiCIAgVwMDAgAtrkyZN4hlqefOKa7ZrAoWsbJDX1Iy7mqUHW1/HuZCCW0WoIiSqERrPE/9vUqGsXJKtTnL+yOfCa63FJyZg/9sXfJ2tsUmaKEyJqFbExQmKxDGPHa8TxfD87KvQ1yYIVYRlbA6avgBvvXz4/+mSCSPRsGZVqBPCL14IWT8bIZvnI95P/H9tVLYq4gfMxK4Ya3yLjEX/+asQ0WYw7CevhvWAyXx60tEd646dkw7UTRvaN8cR4yw44O/eXfk8q6t2/cETGFeqw5cFH15BFB6S6fNFAT/4NCgeiEnQQSk1st0kCEIzKFrQWWMy1T6GBOGhX9rgKVbp9/73L3w7odqBMqwuatex07m7hIG+PmYM648VU8bA3DSl+4WqI/z2CcGrpyEhJBDQN4B1v4kwqVxX2c0iCIIgCELJnDlzBl++fOFjEHv37sXixYvVun5aerCx4wHlqvA+eGp0krLU+pSppISWEaoGiWqExvP4h3iAooy9Q7p1KcwNDPGnizjDwpPdPKZj/cjw/ioR1VJaSMob9mPlmjRo9MFH/QeNCCI3xAkE3FLp6VtPvsxqk7SqLxaCFEX8jy+IffWQT7O6f4LnMz6ND/qJ0H9XIWj5BAg+vOTbDQq6wXbUAtj0n4yCZStg64LpMDM14faW/afNxxP/cNyJ0MH2G4+xaNMu/pyaFcthyfiRuc6a/aNKRVQsVZzPr9p1AAalKgEGhqxgGmKf3c38fQX6Sa0f7W2skdfWJldtIQiCyGnd2Y++6p/J7xsRmqvthPKIjo3FhKVrMHvtFgiF8dwmfu/yuejRppnaWSUKvN8ieO0MXktNx8gYNoOnw7iMegUuEQRBEAQhW0QiEaZPn46WLVtixowZfJ0iHbwUTbeS5TG4fNU0No9sTHll/RYobZ9eqQ5C29AsOZkgMqun5pixGNaiSAlc8PkoXTbWM0jzA+LzVTyAXNQlrYWkvHEtVICLCJoQia0M2Hn7/P0HCjo5oljSAByhfgjj4zF6wQrcf/aKL08e1DvbtcRyQ0JkOEJ3LZeKYQxD17Kw7j0OuuaWv90/dd6Xnr0jLFr2gFH5GikG3Uq7FuH14QZMm48vfv7oNXFWiueVKFoIa2dOgKFh2iCB7MJed2zfbug5YRbeefvg4oNnqFmmCmKf3uEWkGZ/tMjwuaKAX6Ias5FUt4FDgiDUn6IFxH0yb99vvHaVOt/cF7CwztV2Qjl4+X7FqHnLpMIuC1ZZOnEUbCwt1O4jiXv7FCHblwBCAXRMzWE7ZAYMCrgqu1kEQRAEQSiRwMBAdOvWDZcuXeLT//3vfxr/ebCxjVHuNdGlRDmc836P4NgYFLSyRtPCbjBjQcgEQaIaoekEx0TDK6lOWmWHjEW1UrZ5oK+jg/hEcYLv8wA/Xhh+eb3mPGvt+89ACIRCpWSqMSR1GDwpUy1bBIeFY8zClbj/7JcIUr1CWaycOga2VmlFEEJ1YcL2xKVrcO3+Y748smdn9O3QSqFtEAtkYkFPAhPMAldNhmn1hmn2j753GQlBYovEFOjqwaJtH5jWbAyddLJnGTUqlEUR5/zw9Elr+WphYiJTK6mq5UqjVqVyuOPxAqv/PYi6o7pxUU3o855n1unbpe+PHh8ofm9fBTooVYysHwmCUF6mWkxcHL75B8Aln/pGjRazsUO1fC68hlpyuxkWrlDNyYVvJ1SLM9dvY/rKjTxTjQ++9OqCIX+1V0txl2Wns0x6iOKha2ULmyEzYZDvV11ngiAIgiC0j2fPnqF169bw8/PDunXrMGzYMK0KpnUwM0efsu7Kbgahoqhfj58gsoGH/3c+ZSm7FR0yroU27fYlqaCWXmF4FoXKYD8eBfPnU9qg0c+gYIRFRCr89dUVJqg9eJ5SBHnw/CXGLlqptDYR2YdlH8xcsxlnb4jtCPt1aIXh3Tsp9FQK/XyTMs7SOmsnBP5A5Ok9aR7pCmr8CSIYuZXLUFCTWL2mJ6gxHr16K/OsVUltNZ+v33HhWwR0TMz4cuzT2+nun5gggihJVGOZaqWLiS10CYIgFAkLdJLc2GtCNj8L5mICWnLY8rI/myutTURaBAIh5q7birGLVnFBjQVqbV84A8O6dVRLQS36/mWE7lrBBTU9OwduSU2CGkEQBEEQdnZ2/HHz5k0MHz5cqwQ1gvgd6tfrJ4hs8NhfbP1Y0jYPzA2N0t3nU3goHiTVXcuoMLz3F/FxnPLmgYlx+seRt/2jhA8aMGikCJgowTLUWOH45LDFe09fasTgmzbAPj9WR+zI+St8mdk9ThzYS+GdOVFGAlkSujb20M9XQPpgy1mpR5YRvn6Zvx6zM5Ul5Uq4omFNcc2UtfuPwiCpfkqsx610908IDeKDb4yvQmb/SJlqBEEoHtYnc3bMqzH9I+aOsK1pB/zXrifWNmzFp2w5vVq/hHJgGZHdxs3A3lPn+XKl0iVwYv0/qFmpnFp+JFHX/kP4gfW8lirrvzBBTd9OfTM+CYIgCILIHdHR0di4cSMfi3FxcYGHhwdq1KhBp5UgUkE11QiN5nGSWOaeST2179ERvy0MLxHVirhknO0mT+ysrWBtaYHQ8Ah88PFF5TIlldIOdSIrogTVV1N9Vu8+gN0nzvD5lvVqY9aIAUqJjkqICM90u+3gGdB3/JVdEP/jCwIXj85wf337zDNeWf2/3GzPCaN7/4Ur9x5xu9v7AhNUYu/j+2f+XpK/t+TWj4xIA3Pkd8gj8/YQBEFkBfZbzupPSlwFNAFm9Uh2j6rHjYcemLBkDcIiI6WZ82P7dYeBvvrdUrOBssiz+xF16QhfNijoCptB06Frpn614AiCIAiCkA1eXl5o3749Xrx4gTJlyqB27dqUnUYQGUCZaoTGEiGIw7ugAD5fORNRLf9vbh5ZYfhfopri66kxmIjgllRXjWVgEb9HGaIEIVu2HDqBDfuO8vkGNapg8fgR0NPTU/hpFnx6h4jj2zLYqgNDt3JpRCe2bOhaNqkazu/3T2+QmNX/S60fsuUaFcvKRRB2K1QALf6sxecXXbgHHXMrPh+TTraaKECcaRcSDxQqWoQ62gRBKA2JRbYmZKoRqlvXdeXO/Rg0YyEX1MxNTbF2xnhMGtRbPQW1hAREHN0qFdRYv8Rm2GwS1AiCIAhCizl16hTc3d3x6dMnHDt2jAtqBEFkDIlqhMby1P+7tPpRJYeMxbBCFtao5uicztA3UD2pMLz3V3FttqIFnKEsXAuKLSBp0ChrMNGBiQSpkacoQciOfacuYNm2PXy+VqVyWDV1rFIGroRfvBCyaT4SBXFcZDIoVCLFdkO3srDuNTbd51r3Hse3Z3X/1KycOoYLa8lhyyumjIG8GNWzC/R0dfEzJAxe5mLhOdbjdhobVYl95VcBUMqVrB8JglAekr6Zt+83XoOTIGRJUGgY+k2dh437xUE+JYoUwrF1S9C4dnW1PNGJoniE7VuL6Nvn+LJRuWqwGTQNukZkMUoQBEEQ2srcuXPRunVrbvf4+PFjtGvXTtlNIgiVR/1C6wgiizz+Ic4uK2Zt99taFEvrNsWkWxd4DbXUheGDw8IREhau1Ew1hkQE8vTx5QPcVCD095ibmaZZV7FkCbmKEkTuOXH5Buas28Ln3UuXwLpZE2FoaKDwU8tsD4M3zkNibDR0zCxhN2IuzzBj65moxCwcM8s40zW3hO2w2Yj75oOwzx9hVbAYjPIXyvLr21pZYufiWbz+H7MrZdmV8haDC+bPhw5N6uPQucvY+PwLljoAosAfiP/iBYMCxaT7CX+KAw2+CYDSxQrLtU0EQRBZyVSLiYvDt58BcHGkelCEbHj86i3GLFyJn0HBfLlj0waYMawfjI0UX19ZFiQKBQjduQxxrx/zZeMq9WD11zDoKMEFgCAIgiAI1cHR0RHdunXD5s2bYWZmluPjCOPjedkaltXPah8ThCZDohqhsTzx//Zb68fUheE/hgTxGmrM8lFSy+LxRx/pfsoU1ST2j+wHikXN2ttYK60t6gATHz1ev+Pz4/v3wNp/DyFOIECTOtW5WEGoJhdv38eU5f/j86WLFcGmeVNgamys8HawmmHBG+YgMSocOsamsB3yq2Yam/7OvjE5bF9dI3Po29jkqC1MSFNkZuWwbh1x/PJ1PAiOQZSLKcwE0dwCMrmoFuv3BWwI7ptQB3WLUaYaQRDKg/XNWKARCzj66POFRDUi17BrafuRk1i+fS9ECQkwMjTkNV1Z0Im6khAbg9CtiyD4+Iovm/7RAhZt+0JHl4xrCIIgCEIbefjwIUJDQ9G4cWMMHDiQP3IavB8ZFY3/7T2Mw+evICIqmrvfsLG3Ub3+QmFnJ5m3nSBUAepFExpJtFCIVwH+WRbVJDAhrX6BoimKw39KqqdmZW6uVDEm+aA6WUD+nj3/iW1tHO3t0LdDKzSsWYUvX777UG6fEZE7bj56irGLVnH7rmIFnLF14XRY5CJKKqeIQgMRsn42EsKCoWNoxG2RDFyKQlvIl9ce3Vo24Sa4ZwIEfF3s0zu8BguDTXVCA/l8AAyoPiFBEEqFRcE6O+bl89Q/InJLeGQURsz9B0u3/ssFNZYlfmjVQvUW1CLDEfy/mVJBzbxpF1i060eCGkEQBEFoafDQxo0bUadOHYwbN46PvzAxLaeCWlRMDHpOmIXtR09xQY3B+lDnbt5Dp1GTufMOQWgiJKoRGsmLAD/EJ4oHgN2zIaqlh3eSqFbExUmplovWlhbIYyvOdPng46u0dqjLgMjJKzf5fNeWjaGvp4dGtapJrXxYph+hWjx6+YYPYjG7gAL5HLF98UyliNiiiFAEr58DUfBPQE8f1v0nw7BISWgbg/9qzzMEzweJ+DITGIXeb6Xzugni9UYOztClKHeCIJSMJPDIy/ersptCqDFvvT6hw4hJ0gCsJrWr4+i6JShRNOvWzaqGKDQIwetmcBtnBhPTmKhGNvIEQRAEoX1ER0ejT58+GDp0KGrVqoUrV67k+n5+78nzeOP1KV3xLio6Bos27czV8QlCVSFRjdDoemoFLK2R19RcNqJaAWcoG4kF5AcfivTIjGMXr/LaKgYG+ujUrCFf90eVijA0MOA/7FfvPVLI50VkjRfvP2LwzEXcnpNlFu5YPBMOdrYKP30J0ZEI2TAXop/fAF1dWPcZD6Pi5aGN2FlboVfb5vCKAz4LxMEEzAKSER/gJ93Pvoib0tpIEAQhgWU3MyhTjcgJrG/I7Io6j54KX78fPBhryuA+WD19nFIy5mVqZb1mGq8FCx1dWHUbCbO6LZXdLIKQGxEREdiwYQP69u3LH+vWrUNsbGy6+759+xZ///03OnTogMmTJ+Pr16+52o8gCELV8ff3R82aNbF7925MmjQJFy9eRN68YreH3HDk/BVpsA77a6SvB92k5YTERNx+8hz+SfVpCUKTIFGN0GhRrbJD7mugeX/5rvR6ahJcCxXgUxo0yhiWur735AU+36JuLS4OMMxMTFCrUjk+f/HOAwV8WkRWeP/pMwZMnc8jmNhnxQQ1iY2XIkmIi0HIpvmI/+4D6OjAqvtoGJetCm2mX8fWfDDxclJiZ+zze0gUxUPIREdW3zEecC1OohpBEKqTqebt+433A4iswex4rtx7pNW2PDGxcZi6fD2mr9wAgVAIB3tb/PvPHPRp31Kts7mE3z8jeM3UX5n3fcfDpGo9ZTeLIOTGly9f4OzsjK1bt6J27dr8sWnTJpQrVw5BQUEp9n3w4AHc3d254Na1a1d8/PgRFStWxKdPn3K0H0EQhDpgZ2eHAgUK4NixY1i8eDH09fVlctyfwSHQ09FBhYKOaF+1JDpULYXO1UqhpqsLzI0N+T6BwaEyeS2CUCVk8x9EECqEQBTP7R+zW08tPVjmzFf/nyojqkkGjVimGouqVeebfXlx6/EzHmXM6NGmWYptzALy2oMnuPfsJSKiotQ6+lgT8Pnmh35T5iEsMhKW5mbYvnCGUv7PEoUChG5dDOFnT75s2XkITNzrQNuxsjDHgE5tcGjvPvTPAyRGRUDw/jmCP30E6xp/EwClXIsou5kEQRBwTeofsSz1bz8D4OLoQGclE4LDwjFm4Urcf/ZSuq56hbJYOXWMUusHK5pPX79j1Pxl8PwktlVnwVfLJo2GbVJAlroi8HmPkM0LkBgdCR1DY1j3n6S1mfeE9hAVFYX27dtj8+bNMDAw4OtatWrFB5BZxtqsWbOk+7LMs3r16vGaQox27dqhQoUKmDZtGvbt25ft/QiCIFQVkUiElStXYsCAAbC2tsbJkydl/hr589qhqLUJ7MxN+Tglg1lKFrS3Qj4bc1x66c2DlghC06BMNULjeBngjziRSCaiGhv0l0Q8q4KoJrF/jIyOxo+AlBF3hJg9J8/xafkSrijrVizFaalXvTL/cRcK43Hj4VM6ZUrk+88A9Jk0B4EhoTA1McbWBdOUUrOEZV6F7lwGwQfxwKJF274wrdFI4e1QVXq2bY5YM2u8ixEvx3jcRuS3z3zeT6SrEt+L2nyD9O7dO7x8+RIxMUkfUAZ8//4d9+7d41NZ7EcQqgb7LpIEGn0ki+zfwgS1B89fpVj34PlLjF20EtrC+Vv30GHkJC6osWtnRI9O2DJ/mtoLanHvnyNk/RyxoGZqDpths0hQI7SCQoUKYdu2bVJBjcFszZycnODr+6seOevj3L9/n2eeSdDT00Pnzp3x33//8f5VdvYjCIJQVQIDA9GsWTNMmDABGzZuhEAglMvrNHIvywU1RvLAfzZvqKeHhhWKw97GWi6vTRDKhDLVCI3jib/YmszRzAJO5pYyqadmoK+vFEu61BQtIBbVJBaQ+fLaK7U9qgYTQW8+eppulhqDRV9XKVuKDyRduvMALevVVkIriYDgEC6o+QUEwsjQEBvnTEb5Eoq3EUxMECFsz2rEvX7Ml82b/gWzP1vRB5QMZps6qEs7XDm8AyVMEhHz/D70oce3CcxteN0ZQrEIBALMmzeP1wyxsbGBsbExfHx8MGXKFEydOjXFvvHx8Twq8ejRoyhdujRevXqFjh07cmuk5HYfWd2PIFQVE2Mj3k/74uePj75feRANkT7M7SB5hpoEFlh87+lLbgUpcUbQRJjF47Jte7Dr+Bm+bG1pgX8mjuK1d9Wd2Bf3EbprBSCKh66lNWyGzIKBU0FlN4sgFALrD6Xm/fv3vI9UpkwZ6bo3b97waYkSJVLsy5ajo6P5/kWLFs3yfukRHh7OHxL8/MQuOkyIUxUxjrWDBQ+rSnsUgTa+Z2193/SegYcPH6JTp878+8elXBXsvv4EB+/2RvO6tTC8eyc45rGT2fnWFYgDPNNz0mLrTACEhYXB3NwcsoQ+Z+1BpMDvMRZAk1VopITQ3Hpqjr+ilnMrqhV0clSJwWNzUxPkd8iDb/4B+ODjqxEDALJk78nzfMpqczWtXSPdfRrVqspFNSa+MXtPJuoQiiMkPIJbPn7+/oP/T62ZPg7Vyv+60VUUiQkJCD+4EbFP7/Bl03qtYdakk8LboQ50bdkYfx0/gYTEYOgK4yAJVTB0cFZyy7ST4OBgrF27Fjt27OA2RAwWMc3m8+fPj969e0v3XbhwIbf4ePHiBQoXLsxrgLDaIGz9zJkzs70fQagyTAjiopoW1wfLChKL7Ixgv8+aKqoxl4e/FyzH07diu+cKJd2waupYjQhSi35wFeEH1rMODvTsHGAzdBb07R2V3SyCUGoQUt++feHo6Ig+ffpI14eGiuv6WFqmDL6VLEu2Z3W/9FixYgXmzJmTZj0bVDZTkfIDbGAyMjKSW7VlZwBRndHG96yt71vb3zO7rxsxYgT0DY1QpWkb2OT91R949votxi1cjtkjB8ose4yJHb8TzH78+MFruskSbf+cteU9K/p929tn/b6ARDVCo4hPSMBTf7FtlbtD7m3JJKJa0QLOKlU3hItqNGiUgqiYGBy7eI3Pd2neCIaGv6w/ktOwRlXMX78d0bGxuOPxAvUpml1hREZFY8C0+fD08eU2nMsmj8af1dyhaNgPccSJHYh5cIUvm9RsDIvWvalGYQYw4blr1y54cXwDKiQbBzA3E1s8EIqPxL58+TIqV/6VidOmTRueYXbq1CmpqMZubtavX49+/fpxoYzBpmxgiWW5zZgxg1/zWd2PIFSdYgWcce3+Y+of/QYWKJab7erK7SfPMH7JGoSEibNHerVtgQkDesAwmVWcuhJ14zQijm/n8/qOLrAZMhN61rIduCIIdYL1bVh/6NmzZ7h06RLP7JcgycBPHe0uWZbYR2Z1v/QYO3YsdwCQwDJFqlatCisrqxRtUSbsfbD+HauxpC0Ds9r4nrX1fWv7e65SpQqKliiFeDsXhCfoIfxHQIp92X7bjp/B4nEjZPLa7PsyeXZueuTLl0/mQQXa/jlry3tW5fdNohqhUbwLDkB0vNgnuEq+3Itqn76IBbrCKlQ3yLVQAVx/6EGDRqn47/JNXmuOZT/91SLjmlgsGpnVWnvp+ZFbQJKophhiYuMweOYivPL04ssLxgxFsz9qQhlEnt2P6Jti2ydj9z9g2XEQiQa/oV3jelh9/F9UQKR0XfVP93BrTC+UmrAIdk6q8x2p6bCOZHJBTSIUsww2W9tfBaC9vb3h7++PmjVT/p/VqlWLF6tm25ltUVb3IwhVR5Jd5e37jQ+osuANIv3zVKJIIbzz9kn39Lzz/qxRmWrsJnzD/qNYt+cw/65kdVwXjBmG5nWV0weRJez9RJ4/iKgLh/iyQYFisBk8A7pmFspuGkEolWHDhuH48eM8k5/1Z5LDaqxJsiZKliwpXc+WJQO/2dkvPVg2W+oMNwYbCFSlwUD2O6lqbZI32vietfV9a9t79vLywpEjRzBp0iRUqFABlm4VuEuQKCEh3f3PXr+L2SMH8XIPuaVixYo86DM9mBBSrFixdL8TZYG2fc7a+p5V9X2TqEZoFI/9vvKpnbEpClnmLgqMDchIMtWKqJCoJhnoYPZGNGj0a1Bh78lzfL5RrWpwsM88OpdZQDJR7er9x4gXiVTC2lOTYfVLRsxdhsev3vLlGcP6o33jekppS+TlY4i6dITPG5WtCqtuI6FDA6+/hdWVrGIs5DV3kictFU2IxJt/pqDOyt1y/NSI37Fp0yZ8//4drVr9qgn49av495DZHiVHsvzt2zculmV1P3WtGZIe2ui/rw3nqIizeAA0Ji4Oh85eRsXSxXn2mrafl/Qo7OyURlQzNjJEbJwA45esRkRUFDo3a6j254hlpU38Zy13JpC4PayaNpa/f2W2TxbniNlYR53ciZhb4v6vQbHSsOw7CYnGJipx7tXtWtHGc6RKg1KyZMKECdi+fTuvE9ukSZM028uWLQtTU1M8ePAA9er9uh+5f/8+ihQpgjx58mRrP4IgCGXD3Ep69uwpzdJ1cHTkglpmsHGwkLAImYhqLBP37du3/L4xPZeV9L6LCUITIFGN0Cge+4u/xN1lUE/tR2AQH5hhFHERD9SoAm6FxKIaG/g4dO4yKpcpqVERxTnh/vNX+OgrHhju0abZb/dnwtuKHfsQGh6Bxy/fonoFxdf00hZYZ23CkjXcdokxtm+3LH1G8iD69jlEnt7D5w2LV4B173HQ0dABBVnj9eQhiuuJvw+To6sDuCZGwsvjEYpWqqKUtmk79+7dw5gxY9CjR48Uolp8fHwK+yIJkmWhUJit/dS1Zkh6aKsXvaafIz0kSudnrd3Mp+6lS2DuyAGwtjDX2vOSGmF8PG49fsrnuzRrgIol3eDskIefo78Xr+H9qdlrtyAwKBhdM8n8V/Vz9OqDN2as2YKfwSF8uWmd6pjQtxsXD0NCxOuURW7PUWKCCAmndyPxxT2+rONWHgntByEsJpZZA0BdUJVrRVvPUXZqhqgL8+fP51n2Bw4cSNEnSo6JiQm3uGbW18yikZ2Hjx8/8ucwy+vs7kcQBKHM34hZs2ZhwYIFKFOmDLZu3coDI9nvha2VJYKTLK8zCpq1sZJNZjuzw+3Vqxdu376NJ0+eIDo6mreBBSf88ccfKmN7SxCyhkQ1QmNISEzEkx9iUa2yo+zqqTEKO6tOppqN1a+06VlrxING1SuUxcqpY/gPpzYiyVJjdkZsAO13sMxDVifPy/crLt99QKKanGCRUos278aluw/58uAu7TD4r/ZQBjEPryH8yBY+b1CkJGz6T4KOvvrXUVEUQZ8+IjOJJMj7A4lqSuDly5do0aIF6tati23btqXYxup2MCIiUkYpSpYl27O6n7rWDFEnT3ZVQh3P0bil69Ks83jzDvM27sT2hTO09ryk5sZDD0RGx/D5fh3bwCWfg3Tbv//MwdDZS/DsrSfW7TsKlhszonunbAWqKfscMfFhz3/n8M+2PTywh9VMmza0Lzo2qa8yVs+5OUeJQgHC96yC6NUjvmzkXgcWXYZBR0/9buuVfa2oA3SOss7Tp0+52OXg4ICdO3fyhwR3d/cUQUBLly7lVmnM1rF8+fJ4+PAh2rVrx/s1ycnqfgRBEMr4fWD3gRcuXEC3bt14LWyBQCDd3qlpA2w6eDzd57Lf3hZ/1pJJlpoEQ0ND1K9fn2f2xsXFcaGNftsJTUf9et8EkQEfQ4IQLhBnUrg7yE5Uc7S3g7mp7H5scsvkZf9Ls+7B85cYu2gldi6eBW3jm38Artx7zOdZBlRWB0xYthoT1S7deYhpQ/upzECLpsAGteZv2I7ztx/w5R6tm2FM325KaUvs83sI2y/+v9F3KQqbgVOhY2iklLaoK3ZFXIHbv9lOKJQPHz6gUaNGfJCH1Q1hNzLJcXNz477j79+/R4MGDaTrPT09+Xq2PTv7qXPNEHXxZFc11OkcffD5wrPWU8Msa+8/e4VPX7/LLKtfnc5Lely4fZ9PWX3ZQkmWmRJsra2wfdEMDJ+9FPeevcSGfUcRHROLKYP7ZKufpKxzFBkVjakr1+PCLfF7dHbMizXTx6O0axGoGjk5RwlxMQjbvgQCT7GdpWntZrBo31+tbazV/f9JEdA5yhqFChXiFmjpkTdv3hTLLJv+/Pnz3K6M2WCzej+FCxdO87ys7kcQBKFo2O9m7dq10bJlSwwfPpwHNCcX1QZ2aYsr9x/zsjHJYb25vHY23EFIHrD+IrN8JAhtgEQ1QmN4nJSlZmloBDfb3NtZeH/5rnL11Pig0bOX6Q4a3Xv6kv9gapsV5P7TF3gHwsrcHC3/rJ3l57G6ahv3H+U2ny89vVCueDG5tlPbBLVl2/bgwJlLfLltw7o8SlwZwmXcGw+E7l7JvJKg7+gC28EzoGuiurZ0qgqzdrz1rzmvocYsHyUkJAJeuuaoQ9aPCsXX1xcNGzbktc7YABKzKEoNyxZjkYKHDx/GsGHDpOvZMosilIhhWd2PIFQZX78fmW7//P2H1vWP0kMgEOLyXXGGU7M/aqS7D4ta3jRvCv5esILXnt11/AyiYmIxd9QglRY+3n/6jFHzlsHnm7i2Y/3qlbF4/AhYycj6U9kkREUgZPN8CD9/4MtmjTvCvFlXCgojiCRYhjwbXM4OLAONPWS1H0EQhLzHWTZv3oxKlSqhSpUqmD59eob7WpiZYf+K+diw/ygOn7vC6+WaGBmhTcO6GNa9IxzsbOnDIghNE9VYSv2jR4/4l0SNGmlv9pinNRtAT4/BgwfzFNOMuH79Ol69ShvF2qVLFyo0qwE8/iGuqVXJIT90ZTB4z7KYVE1Uo0GjlMTGxeHw+St8vmOzBjAxznr2UeliReCU1x7ffwbi0p0HJKrJENZx23r4Pz5fr2olzB09mEfZKhrBx9cI2bEUEMVDz94RNkNnQdecBIKcUmrCIrz5ZwqvoSaBCWpsPaE4wsPDeYYa86qfNGkSHj8WZ+oymADG+k/JbYvq1KnDxbLWrVvj5MmTvJ/F/O6Tk9X9CEJVKejkmKvt2sJtj+eIjI7m803/qJnhfkaGhlgzYzwmL1uH09du48j5K4iKjsHSiSO5naKqcfzSdcxeu5nXG9bT1cXYft3Rv2NrjRGcRGHBCNk4F/F+vnzZok0fmNVrrexmEQRBEAShINi9H7tX27VrF3r27Indu3f/9jmW5maYNLAXJvTvgaiYGJgYG0NfhQOkCELdUBlRjQ3gTJ06lUdbv3jxAmPGjElXVGP2RMw7NjkHDx7kN01Dhw7N9DVYUVkW0c18sJMTEyOuK0Cod8TGY3/Z1VNjfPoqPl4Rl5TWOMqEBo1Scub6HYSGR/D//64tG2frXLLnNKxZDbtPnOGi2rh+3WX6WWkrO4+dxupdB/j8H1UqYtbwfkrpuAk+f0DIlgWAUABdazvYDpsNPSuKxsoNdk75UWflbnh5POI11JjlI2WoKZ6goCDky5ePP1asWJFiW6lSpXjwkQQmsDFxbPXq1XxfZo3ElkuXLp3ieVndjyBUFZaFxurLMjtslr0vgWkqbD1lqYk5d/Mun5Yv4Yr8DnkyPaesgP3SCSN55trBs5f4c2NiY7F6+jgYG6mGhXKcQIB5/9smDbDKY2uNlVPHokrZUtAU4oP8EbJ+NkRB/oCOLiy7DIVp9V9WvQRBEARBaDastmOHDh3w/PlzTJw4EQsWLMjW81mAM8tcIwhCQ0U1JqYxcYwN4LBixRnBBnyS8+PHD2zcuBGjRo3KkiWJq6sr1q1LW8icUG8+h4ciKEYceesuA1EtPDIKAcGhKpepltGgESOPrQ0Kp6qNocnwQvQnz/H5etXc4eLokO1jMAtIJqqxWissM7FoAWc5tFR7YINaizaJi4JXLVcaq6aORUx0lMLbIfz+GSGb5iExLha65lawHTYHerYpaykQubOCZA9CObBaHizzPquwfhWzCZHVfgShqqycOobXl2V22BJY32DFlDFKbZeqwASoK/ck1o8ZZ6klh91bzRk1CGamJth+5CSuP/TAwOkLsWHOZKXXG/b9/gOj5i/DWy8fvlytfBksnzya94c1BaGfL0I2zEFCeAigpw/rnn/DuELWPjuCIAiCINQfFujYuHFj7th27NixNEkiBEEoD5WpasysjHISEb1nzx6eudavXz+5tItQr3pqJvoGKGmXeeRtdrLUGEVUTGhhg0ZMWEtNQHAIxi9ZDWF8PLSBp2/e483HT3y+Z5vmOTqGe+kSsLUS2wFevP1Apu3TNs5ev4MZqzZKI+DZgJuxkaHC2xH/8zsfgEqMjoSOqTlshs2Cfl7tEZsJgiC0FfZ7vnPxLJzZvBJ1Klfg6yKjY3j9CAK49fgZt3BkNKlTPVuZ/RMH9MSoXl348sMXr9F38hzuFKAsLt99iPYjJkoFtSF/tcf2RTM0S1Dz/YDgtdO5oKZjaASbAVNIUCMIgiAILYM5kTBRjVn+k6BGEKqFyohqOYX5ybICjWXKlMnS/iEhIfw5+/btw7Nnz+TePkIxPEkS1So65IOBbu6t5ry/fOdTFpmbV8Vu0JMPGq2fPQmnNq5Al+aN+LazN+5i9PzlvBC9prP35HlpJmGNimlFxqxGYNevIc64uXSXRLWccvX+Y0xYuoZnDxYvXBBb5k9TSgS7KPgngtfPRkJEKHSMjGEzaDoMnAopvB0EQRCEcrP6Z40YyGtrBYeF48gFsTWgtnPuhtj6sWJJNzjlzV4AGhPWhnfvhCmD+/DlF+8/oufEWTygS5GwwLElW3Zj+JyliIiKhpW5OTbNnYIxfbtpVI2QuA8vEfy/WeIAIWNTXhPWqGRFZTeLIAiCIAgFEBgYiLFjxyIuLg7m5uY4dOgQ3Nzc6NwThIqhMvaPOeHJkyd49eoVNmzYkOXnBAcH4+LFi7zI44ULF1C3bl3s3bsXtrYZ19oJDw/nDwl+fn58yjLkUtd3UyVY21iKsCq3UVY8+vGVTyvldcrW+83oHH38/IVPizg78e2qCLN6lNg9zhzeH0aGBth94iy39hk6ezFWTxsHE2Mjjbx22CDO+Vv3+Hy3lk1y9Rk1qF4ZR85fwesP3txK6Hc1RtTlHCmKe89eciE3XiRCofz5sGX+VC6oSb4fFXWOROEhCGOCWmggoG8Ay36ToedSVCU+H7pWlHuOsmINTRCEZuGSzwEt6tXGySs3se3wSR58ZGhgAG0lNi6OB8AwmtWtlePj9Gnfkgecscx0z0++6DF+JnYsnpltkS4n+AcFY+zClXj86i1fLuNWlPd1nR01y9459tVDhO5cDsQLuYW1zdCZMMhfWNnNIgiCIAhCQXaPHTt25OPOrVq1Qr169ei8E4SKotai2s6dO3kttq5du2Zpf2YRyeqp6euL3/bHjx9RtWpVjBgxgmeuZcSKFSswZ86cNOvDwsJgpsLFHtngZGRkJM8e0eRBxR/RkfCLElvQuJla8mzE3J6j995iO5n8ee2zdTxlMqhjKyAxAbv/O4/bT55jwLT5WDJuGMxMjDXu2tl17BQXcUyNjfGHe7lcfUbFCzrz40THxuL01Rvo3LSBRpwjRfDqgzf+XrQaAqEQjva2WDFpJPSQKP08FHWOWCS36N9lQOAPQFcPuh2HIMrOCVEq8r9L14pyz5G9vb1Mj0cQhHowqHM7Lqr5BQTi1NVb6NCkPrSVm4+e8n4Oo2k2rB/To1PTBjAzNuYZ6j7f/NBt7AwurMmzri8L4Bm3aBWCQsP4crdWTTBlUB8YGmqWUBrz+AbC9q0FEhKga2MP22GzoZ+HLKwJgiAIQtNh98GbNm3C6NGj+f3rjRs3ULMm1VElCFVGbUU1oVCI/fv3o0OHDrCyssrSc5iAlpxixYqhe/fuXJzLDJZ2O2DAAOkyixhgx2Kva2OjWtaAqQcpmV2LtbW1Rg/63w7251NDXT3UKFwURnr6uT5HX/0D+LR40cIq/RmnZvLgvrC2ssKa3Qfx9K0nJi5fj41zJsPS3Exjrh2BMB4nr93h8+0a/wlnp3y5PmbdqhVx7uY93H32GoO7dlT7c6QI3nh9wvh/1iEmLg72NtbYsXgWCjo5KvwcJcRGI2zXEiDgO/OngmWP0TAqXwOqhLZfK1mBzhFBELLGtZALGtasyutvbT50Am0b1tXa7+BzN+9Ka8k62Nvl+njN/6wFUxNjjJq/nIuW3cfN4DXNShSRreUyy2DefPA4Vu8+yOdZfbx5fw9Bq/p1oGlE3TqLiKNb+bxe3vywHTYLetYUFEIQBEEQ2sD48eN5Qseff/6JAwcOwMHBQdlNIghCU0W1U6dOISgoiGef5QZjY2MIBIJM97G0tOSP1LAbc1W/OdfV1VWLduYGj59iO85yeR1hamiU63PE6jV88RMLdcUKOKvduWM1L1h22qJNu/DsrSf6T52PrQun81psmnDtXLl5D4EhoXy+R+tmMmlf49o1uKj25PU7hEVEwtbaSq3Pkbzx8v2KgdMX8nom1pYWvMYfq22n6HOUKIhD6LYliP/ixZetuo6ASaXaUEW09VrJDnSOCIKQNYO7tOOims/X77h45wGa/aF9Eb8xsXG4dv8Jn29WV3bv/89q7tzyeWTma/MAAQAASURBVMisxTyDrOeEWdi6YBrKuBaVyfFDwyMw8Z+1uPHQgy+zfsbaGeN5zTxNi0yPunQEkWf382V95yKwHTKDWz8SBEEQBKEdtGjRgruqLViwQOquRhCEaqMLNYVllxUuXJir+Olx7do1bNmyRbrMhDNvb+809o2HDx/O8BiEevDkxzc+rezoLJPj+X7359aCjKIFZHNMRdOnfSvMHjmQz7/+6I3eE2crvJi8vNhz8hyf1nYvn6GQk13+qFKR11phUdBXkmqOEOnz5Yc/+kyei5CwcJibmmLbwuk8G0DRJMYLEbJ9CYTeb/iyRYeBMKlKfuMEQRDEL8qVcEXNiuX4/KYDx7iAoW1cf/iEZ5WzjOnGtXNn/ZiaauXLYNfiWbAyN0d4ZBTvHzx88TrXx33x/iPaj5goFdRa/lkbR9Yu1khBLeK/nVJBzaBoKdgOn0OCGkEQBKGxBIeGwfvLN0TFxEDbYckiGzdu5PP169fHkiVLSFAjCG0T1ZjX68qVKzFu3Di+fP/+fW7llB08PT15vTP2YAKYh4cHn08ujEkICAjAuXPn0LdvX36DmB579+7FhAkTpMusPW3atOH11xYtWoQpU6agTJkyMDIy4q9DqCcB0VHwCReLRZUdZCOwfPoqFun0dHV5oXt1pWvLJlg8fgTP/vD08UWPCbPwIyAI6szrD954+ua9NEtNVpibmqBWJfGg26U7D2R2XE3DPzAIfSbNwc+gYBgbGWLTvCkyi0jPDokiEUJ3r4Dg3TO+bN6yB8zqyO56IAiCIDSHwX+159O3Xj64+fgptNX6sXKZknCws5WLcPnvsjncCjo6JhaDZy7C3acvcywy7T11Ht3GTsc3/wAYGOhj1oiBWDZ5NMxMTKBJJCYkIPLQBkRfP8WXjUq5w3bwDOiaqG69boIgCILIKW+9PqH/1Hmo0aU/mg0Yjeqd+mHayg0IDgvXupPKxqenT5+O1q1bY/Pmzby8EUEQGi6qBQYGphG36tatiwYNGmDt2rXc/5XBiiuyemfZgWWNvXv3jj+YpaObmxuf//DhQ5p9fXx8MHjw4EytH5nKP2jQIOmyiYkJnj17hs6dOyMqKgqmpqZYtWoVXr9+DVdX12y1lVC9LDV9HV2Uz5v72loMFjXDcHFy5NlL6ky7Rn9i+eS/oa+nx62Puo2bwTON1D1LzdkxL88ukyUNa4lrLt59+gKRUdEyPbamRJSxCPSvP37yQa71sybxATplDEKF7V+HuBdi8dOsUQeYNxQPmBKEOhIfH49t27YpuxkEobFUK18aFUq68flN+49Bm2BR4JJsr2Z/yK/eaPHCBbF3+Tw45bVHnECIySs34NwNsZiXnbaOX7wac9dt5Vbs7Fj7ls9Ht1ZNMgyiVFdYtn3Csc2IfXiNLxtXqgPr/pOgkwMbe4LQVNiYzZ49e5TdDIIgZBQc/deYabjr8UK6TiAU4uj5K+g+bjrPdlcWcR9eIXjzAvyY8Bf8J3ZFyI5/IPDxlNvrsXH1Zs2acZvHbt264datWzBQ83FHgtBWsmXU6u7ujmPHjvEpY9SoUfyf/9OnT3BxcZHe8AwdOhQjRoxAjx49snzsKlWq8Ies9mVfTuyRHFbLpl27dvxBaAaP/cUCWCn7vDCV0Q8RqxfFKOLsBE2ged2aMDI0wOgFy/HN/ycvJp9ZDSxVhUUwnb52m893b9VU5rWp6lerzLP6hMJ43Hz0FM3/rCXT46szrJPbb+o8LjizDM5VU8eilnt5hbeDRbCHH9mC2Mc3+LJpneYwb57ye54g1I3Y2FiMHj0a/fv3V3ZTCEIjYfcnLFtt6KzFvHbq45dvULlsKWgDNx54IDZOwPs3srZ+TE2h/Pm4sNZn0lx8/u6H8UvXIEYgQKemDX773I+fv2DU/OXSPnjdqpWwZMJI2FhaQNNIiItF2LYlSPR8zpdNajaBZccB0NGlmqsEkZyQkBBMnjw5W2NKBEGoJos374JAIERCKhtutuT95Tv+/e8shnfvpPB2Rd+7hPCDG1hnkQ028PbEvbjPA3ite4+FcYWaMhfUKlWqBD8/P56YMnz4cI0LHCIIbSJbmWpjx45Fo0aNeD0zxpkzZ7B9+3YuqCWnVKlSePHiVwQCQcg7U83dUXYCkSRTTd1Ep8xoUKMKNs6ZzC37/AOD0WP8TLz/9BnqxJHzV3g0E3sP7RvLvnaWrbWVNPPqIllApogcHzR9AbfNYh2+xRNGoGFNcVafogW1yFP/IubuBb7M6qdZtOtHnVCCIAjit/xZtRLcChfg8xsPHNM668cqZUshj62N3F/PKW8e/PvPbBQrkJ//bk9fuQE7j53O9Dmnrt5Cx5GTuaDGxL+xfbvxPqtGCmrRkQjZOAdCiaDWoB0sOw0iQY0gCILQWPx+BvJ6q6kFNQlMUjp2UZy5rUhEYcEIP7xZvJC8bXw+EWH71iEhVrYORvb29ujevTsvocQSUUhQIwgtEtVYJPW9e/ewYcMGqWURq0mWmujo6HTXE4QsCY2LhWdIoEzrqbEBABYpwyhawBmaRG33CtgyfxpMTYwRFBqGXhNm4dUHL6gD8SIR9p8Wiymt6/8BazkNtDRKsoC88cgDcQIBtB12DobNXoKnb8X2B3NGDeLnXxlEXTyMqKsn+DyLGLP8axh0dGVSFpQgCILQcJhYM7iL2Cr41uNn3IZI04mMjsGNR0/lbv2YGlZbbe30sShXvBhfXrRpJ9btOcz72MlhEeuz127B+CWrERMXBztrK2xfNINnFbLPS9MQRYQieN0MCD+JawPr1m/Ps+1pQI0gCILQZH5XM431Dtj4lKKJYRbMCaL0N7KsNUEsYp/eyfXrsPFxlpHm5SUee1u0aBFq1pRtBhxBEMoh23csxYsXx9274qhHZgO5ePHiNPucOHEC1avL12KEIDySstRYZEslB9lYNQYEhyIyOlrjMtUkVC1XGjsWzYSluRlCIyLRe+IceLx+B1Xn2v3H+P5TLKB2b91Ubq/TqGY1Po2OiU3h962NsHomoxeswP1nr/jypIG90KV5I6W0Jer6KUSeO8DnjUq5w6rHaIrqJgiCILJF0z9qoEA+Rz6/6aDmZ6tdf/CEB8cowvoxNZZmZti2YDqqlS/Dl9f+exBLt+zGBx9fXLn3CLefPEfXcdOlAVPMKeD4+n9Qo0JZaCKi4J8IXj0N8d8/c4sp846DoFtTfv1ZgiAIglAV8uWxg65uxhaHLLjExdEBikYU5C+2fcxsn8AfuXoNJqQxAW39+vU4f/58ro5FEISa11STIKllNHPmTDRt2hS3b99GixYt+LoZM2Zg1apVOHv2rGxbShCpeJwkqhW3zQNLI2OZWj8yCmtITbXUVCjphl1LZqHvlHkIDY9A/6nzsWHOZFSvIB74UEX2nDwntS8qUaSQ3F4nX157lHErileeXrh09yHqVa8MbUQkEmHSP2u5mMkY0aMT+nVsrZS2RN+/jIgTO/i8oWsZWPcZDx19KuRLqAfMN/93REUprzA3Qaga8T++ID7QD/r2+aDvmNJePrfo6+lhYOc2mLF6Ey7efgAv328oWkDzAqgknL0hjq6uVq40zwJTNGamJtgyfypGz1+Oaw+eYPvRU/yRmv6d2nDLR/b5aCLxP74ieMNsJIQFA7p6PDDIsHwNxIaEKLtpBKEUEhISEBwcnKWaagRBqD+szAYLXmYlNlJnrTPYOmUE7+qaW6a0fcxonxxy+vRpXhOSvb/jx4+jbdu2OT4WQRCqSa68NVh9NVZXjUUWzJs3j0+ZmHb06FHUqVNHdq3UIlihbhbByaZE5ufoju8nvlxZhvXU7j9/yafMXtDKwlxjP4JSxYpgzz9zkcfWGtGxsRg0YyFuPnr66/pLKhSvCtfrB58v0mypHm2aQd40SqoXdvX+Y247qW2wTt+sNZtx5rp4MK5v+1YY0aOzUtoS43FbXDgYgEFBV1j3nwIdQ7IWJtSDyMhI5MmT57ePQoXkFyhAEOpCQmQ4gv83C4GLRyN062I+ZctsvSxp2/BP5LWz5b91Ww+JLYU1kcioaNx8nGT9WFd5FkNGhoZYO3NChqJe8cIFMXFAT40V1IRfvBC0drpYUDMwhM2AKTCpVFvZzSIIpfL9+/cs9Y/KlStHnxRBaAhTBvdBXruUtV0l9se13Mujc/OGCm+TSeW6me+gowvjSjkb1961axdatWoFZ2dnPHr0iAQ1gtBQcpSplhyWqcYeLLOB3aDq6+f6kFrrMzxm4UrcfyYWdRjVK5TFyqljYGuV8+gITSLFOdLXg1676tDR1UFxS9tcHzskLBzjlqyRnn+WwdV70hyNPv+uhVywZ9k89Jk0B34BgRg0fQH3s5bgXroE1kwfB/t0Ctsr8nrde0qcJu9gb4sGNapA3jSqVR0rd+7n18STV2+l1kXaAPsOZ7VPDp+/wpc7N2uISYN6KaXeR+yrRwjbs5pHj+k7FYTN4BnQNTZReDsIIqcYGxtLa9D+DgMDyr4ktJvQXcsh+CAOoJHAlkN3r4DtsNkyex1DQwP079gKizbtwsmrNzGiZ2fkd8gDTePK/ccQCuOhp6uLRrXE1tbKwuerX4a1Ut5/+swDs4oVlG1Woiog8HqNkM0LkRgXAx1jU9gMnArDoqWU3SyCUDo2NjZZ7h+Zm2tukCtBaBPMEejo2iXYfuQkjl+6zkuRFHRyRNeWjdGtVVMYKuFeiDkimNZphuhbYlckKWzsIzERZo07Qs8qZ2ONbIx85MiRvH6amZmZbBpMEITKIVMF7NatWxAKhbyemqWlZgoR8oIJFA+SCRSMB89fYuyildi5eJbS2qVy5+i5eMBFx96CC2qMFUs34GXlSjk6ZmJCIuLi4nDn6Uv4BwVr3fkvlD8f9iybi+YDRyNOIEyx7cnrd2g/YhIa1EwrZLHsNP9A+Z+viKgo/Hf5Bp/v2qIJDBQg2jMrKFZPj1mBMgtIbRLV1uw+iF3Hz/D5lvVqY/bIgUoR1OI8XyB05zJeOFgvjxNshs6CrindVBPqBbPK7t27N0xMSAwmiMwQ+vlC8CFlH1hMIgSeL7glpCytIDs1a4gN+4/xACo2uDNjeH+N+4DO3RTXv65RsazSg8N8/TKvR/L5+w+NE9ViXz8W92OEAm4dxQKDDFyKKrtZBKESGBkZoW/fvnxKEIT2kMfWBpMG9eYPVcGiXX/o2eZF1NX/kBARytfpWtvDvGF7mNRsnK1jPXz4ENu3b+f10xwcHLBmzRo5tZogCFUh2yPU8fHxmDNnDk6cOMH9sPv06YMRI0agQYMGuHfvHt/HxcUFV65cgaurqzzarHGI7e3SDiYwe997T19qbARnbs6RTh6xjUxiWDQC/AKw75S40Lks0ZbzHxMbl0ZQk8CExqyeW3mcr6MXrnF7SgMDfYVaAjSuVQ0bDxzD5TsPMW1IX6UIS4pm6+H/sH7fET7PMgIXjx8hrZ+pSASf3iF06yIgXghdmzw8Q0HPwlrh7SCI3MJqpbEbqiZNmqBdu3Zo2bIlj84mCCIloqDMRRdeY02GopqZiQl6tmmOtf8e5JnZQ7t1gL2N5vzOhEdG4faTZ3y+6R/Ks36UwCLRc7Nd3Yh5cgthe9fwwCA2MGc7dBb0HTS3dh9BZJcfP36gVKlSaNasGe8fNW/enAKyCYJQCjq6ujCr1wamf7SEKNCPWz7q2TtAR1cvW24/mzZtwujRo2Fvb4+pU6eiQIECGe7PsvRZILOkr1arUnn0btcCJYpQSQCC0PiaagsWLMA///yD4sWLo2TJkpgxYwa6d+/OBTZWT409HB0dufBGyC6CU9tJfY508oijbhMDxHYypYsVwR9VKmb7UadyBbgVctHq8/+76y/1uWXLijhf7DtlX5L1Y/M/amZYj0MeNKwlrqvGbDFfffCCpsOE03+2/svna1Ysx208FZEVmF7tkZBN85EoiIOupQ1sh8+Bno29wttBELKAWRadP3+e10ybOXMm8ubNy2vRsuhFVk+EIAgx+vb5crU9J/Ro3RSmJsaIEwikGdqawtV7j7j1I6tT1jCpTqwyYYFWzCI8dXwSW2aZdJoUuBZ95wLC9qySZtrbjV5AghpBpMLJyYkHaLN+0YQJE3j9NCasbdmyBT9//qTzRRCEwtHR04O+gzP08zplS1CLjo7mmbdDhw5FzZo14eHhkamgxhyf2g+fiGMXr3HnJ/ZgdpjMIery3YcyejcEQSiKbI+a7tmzB4cOHULr1q358smTJ9GmTRu8evUKpUuX5utYMcYWLVrIvrUairZFcOaEFOdATxewteCziQHiAvZLJ47M0U05qwXo8fI1ekyam7XX1kB+9/5Sn1uWidZi0JgcHy+r3HryTCrQ9WjdDIqkjGtR5Mtjz0U1lq1W1q0YNJUTl29gzrotfL5S6RL43+yJMDI0VHg74n98RfDGeUiMjYaOmQXPUNO31+z/PULzqVOnDn+sWLECT58+xfHjx3kdEZbhX7VqVR6hzR5ubm7KbipBKA2WhWboWjapplry6q46MHQrK9MsNQnWlhbo2rIJth3+jweWDOzcFpbmZhpn/WhjKe4vKxsWrMMswpmjgQQmtK2YknF/Ut2IvHwMkaf38Hn9/IVhM2QGZdoTRDro6uqifv36/MHs0R49esT7R8uXL8eQIUP4wLSkf1S4cGE6hwRBqCQsQ61x48a4c+cODxBYuHAh9DMJTI6Misa4xasgSkjgz01+HBZQPn7xatzavxkWVIONIDQ3U+3z58/8i0MCi7pmsMw1CSyDjaKws442RXDmlOAwsXjG0LG1gA4T1hiBYbk+R4WdnVC9fBmtPf8ZXn98wKNMmvef0f4M10IuMjtfe0+Ks9TKFS+GciUUayXL7B4l2WoX7zyApnLpzgNMXf4/Ps8yEDfPmwJTY2OFtyM+8AeCN8xGYlQ4dIxNYTtkplwGUQlCmVSsWBFz587Fy5cv8f79e7Rv355HapcoUYIHJc2ePZs+IEJrse49jgtoyWHL1r3Gyu01+7RrCUMDA0RGR0sz49WdsIhI3PF4weebqYD1owRW143V3D2zeSXWz57Ep2xZ2fXeZAEbDIs4uVsqqBkULgHb4XNJUCOILN5zsSCjRYsW4d27d7yP1LRpU+zbtw9FixZFhQoVuEsSQRCEKn5/jRo1CkePHsXSpUszFdQYp67d4qVXkgtqEti6mLg4nLx6S44tJghC6aKaUCiEcbJBVxMTEz5N/gXC5lkGEJG9CE4mVCRH0yI4cwq7lhZt2snn+YC/sx2fT4yKQ/XixWVyjpZPHq3V5z+968+9dAksnzQ6y/szvnz3xztvn1y35/M3P9x89JTP92jTHMqgUZJlkveXb/Dy/QpN49bjZxizcCWPlCpWwBlbF05XSlSUKDQIIRvmICEsGDqGRrAZNA0GLkUV3g6CUCSs5uzEiRN5LdqvX79i+PDhePZM7KtPENqIrrklz1A2rS3OTNezz8eX2Xp5kdfOBh2a1OfzO4+f4QMd6g6zDhLGx3MLZ1WwfkwNC7xidVs1JWAtMUGE8EMbEXX1BF82LFGR11DTNdWMrEeCUDSs1tq0adPw+PFj+Pj4cFs1ZqdGEAShKmOTrAQSc29jdO7cmQdKZgWvL9/SDUxPLtJ9+vJNVk0lCEIB5KhoTmBgYJbWEdmP4GwzdBzeeX9Ghyb1sHDscDqFSfZ0bz5+Agz1UaRHI7yLCOHnRcfMCLp1i0HHyCDX58km6fwza0NmOcgsDDXlhj8715/k/Ts75oWdhRk/L1nZ39zUhKeyBwSHYsTcf3BkzWJurZRT9p2+wKN1WB21ZnVqQBm4lynJ339IWDjP6CpawBmawqOXbzBi7lI+8OaSzwHbF89USrS4KCIUwetnQxTkD+jpw7r/JBgWKanwdhCEsmuLDBs2jD8IQtsxKCC2W06IFNfMlTf9O7XGobOX+G/94fOX0att7u3rldmXlFg/1qxUDlYW5gp9bW0jMV6IsL1rEPv0Dl82rlATVj1GQ0c/9/clBEGA1yUaPXo0fxAEQSgbNubdvXt3XLx4EQMHDuSCWnawMjdDOklqUtj4F1k/EoQWiGqsmGxW1hHZp7Bzfi6qxcUJ6fQx3+HoGKzcuY+fC/umVfE+SVCT8NDvC8ZfP4ttTTvI5HyxwQ9tEtMyev8sAickJCTL+zNWTx+P3hNn44ufP8YtXs2tBPX0sl7kVUJUTAyOXrjK5zs3bwhDQ+UMTujr6aF+9cq8LZfuPsSQrrK5xpTNi/cfMXjmIsTGCeBgb8vFUQc7W4W3IyE6CiEb50H08xsrrgDrPuNhVLyCwttBEIqAfadeunQpw+3MAYDVVWMCG0FoM7o24vsJVl+T/U7IO+PHxdEBLevVxn9XbmLb4ZP4q0VjbgmZU6tylgF+/1nKumEsu18RgSsh4RHSmmXNVcj6URNJFMQhZMc/ELwVZ9CYVG8Iy86DoaOb/X4vQWgzcXFxuHbtWoZZG6x/xGyyHRwcFN42giAICaz2Y8eOHXmZI1YLktXHzi4t/qyNdXsOZ7oP65MSBKHBotqGDRvk0xKCky+P2NrwewBl/jG2HDrBs5/0bSwQapLWrZQFetz//gUfQ4JQzEZ87gjlwOwipw7piznrtuD2k2dYs/sgxvTtlu3jnLxyExFR0dDT1cVfzX/Vb1QGjWtV46LaK08vfP8ZAKe86h084Onji4HT5iMqOoYP8O1YNItnJSqahLgYhGyah/hvn3jxQqvuo2BcVvVsqghCVsTExKBZM7GtXWYw+xBWR8TIyIhOPqGV6NnYS+dFIT+ha1pY7q85qEs7Lqr9CAzCyas30bFJgxwdhwlqD56/SrHuwfOXGLtIXD9MEdaP8SIRDAz0Ub9GFbm/nraSEBOFkC0LIfR+y5fN6reFeaueXAAgCCJ7BAQE/LZ/xP63evTogR07duQoYJMgCCI3MDva2rVrw97eHjdu3EDNmjkLXCrikp8Hbx04czHd7V2aN9IodySC0AayLaoNGTJEPi0hOI55xIMJP0hUwzf/AGw/cpKfj7r1a+IGojK8SnwjQklUUwG6tmyMVx+8uBC18cAxlHYtgsa1q2f5+Szlfe/J83y+Ua1qcEwSmZVFjYplYWpijOiYWD5YJQtbKGXh880PfSfPRWhEJCzNzbBj0UwULZBf4e1IFAoQunUxhJ89+bJlp8Ewcf9D4e0gCEVibm6O9evXY+XKlVi8eDEqV67Ms9fu3LmDSZMmYcmSJbC0tOQWR2z7rFnyH4AnCFVEz9qOB1swfxxRSAAM8stfVGMZ941qVcWlOw+x5eAJtGv4Z7YHbj/4fEmRoSaB2fyw7DFmCSlvJ4TzSdaPtd0r8N95Qj621Two6OsnvmzeojvMGrYnQY0gckj+/PmxaNEiHlC0YMECVKxYkWevXb9+HVOmTMGmTZt4f2nUqFFYt24dWUESBKFwKlWqxGths+y03GbNzhzeH3lsbbDz2CkeSM6wMDNF73YtMKxbRxm1mCAIlbZ/JOSfqeYfGMw7kNocjbV8+x4IhEJem2tQi6a4ceFohvsWsLBWaNuIjCMJZ40YAM9Pvnjp+RGTlq3jETlZHUh68Pw1Pnz+wud7tPl9Voe8MTI0RN0qlXiNEjbYpq6iGsuy6zt5DgJDQmFqbIwt86ehRNFCCm9HoigeoTuXQfBBPPBo0aYPTGsqNxuRIBQFGzQ6e/YsypQpI11XuHBhfnM2bdo0PHz4EPr6+pg+fTqJaoTWwupR6VraICEsGKLgAIW97qAu7fnvPAtAuXD7AZrXzV4Usq/fj0y3sxpr8hTVmPWkxPqxGVk/ygUm8gavnwNRwHcu/Fp2GAjT2k3l82IEoSUkJCRg6dKleP78OVxcfn1HFi1alAcbMZckVr9IIBBg69atJKoRBKEQvL29+ffN9u3beamjefPmyeS4bHx3RI9OGNCpNd56+fB1JYsWgjG5lBCEWpLWTy8LxMbG4t27d9zOiJBPppooIYEPgGsrHq/f4cx1ceHvUT07o0J+F1TL54LUxipsubqTC2WpqRBMiFo7Yzy3F2QZXsPnLEVEVMZZhsnZe/IcnxYvXBCVy5SEKsAsIBmPX71FcGgY1I2A4BCeofb9ZyD/bDbMmYwKJd0U3o7EBBHC9qxG3OvHfNm8aReY1Wut8HYQhDJgg0Hfvn3jdUFSU7p0aXh5eUkjIdl+BKHN6NmKrZZFIYqzQi9XvBhqVSrH5zcdOMYz57NDQSfHXG3PLZfuPOD3DqweXIPqleX6WtpI/M/vCFo9TSyo6erCqsdoEtQIQgYEBQXxQOLkgpoE6h8RBKEMTp8+DXd3d271+ObNG7m8BhPRKpYqzh8kqBGEFolqV69ehaurK/r27cunV65ckU/LtDxTjeEXEARtjVhbtGknny9WwBldWogzWZbXa45qTik73Gx52Z/NldJOImPy5bXH6unjeF00FvU9Ycka/rn+Lpvq8r1HfL5nm2YqY6XzR5WKvD4Ja//VB2JBSF0IDY9Av6nz+Gegr6eHNdPHoXqFX1kyiiIxIQHhhzYi9qlYKDet1xpmTTorvB0EoSwMDQ1hZWXFb9JSc+LECTg6igfcP3/+DDc3xYveBKFK6NmIa30mhCguU40x+K/2fPrO2wc3Hz3N1nMtzM1goK+foZW0vK0fz90QWz/WqVwB5mamcn0tbUP41RvBa6YiITQQMDCEdf/JZFtNEDKC9Y1Y4BGze0wN9Y8IglAkTOBnjiGtWrXi1rSsllrdunXpQyAIQnb2j4MHD8bBgwd5cca7d++id+/e+PDhQ3YPQ2SAnbUVvykXxsfDLyBQKRklyubUtdt48f4jn580qDcXAxg2xibY1rQDPoYE8RpqzPKxmI1ya24RGVO1XGn++S3cuAPXHjzB+n1HMKJHxkLKgdMXuXBlZW6OlvXqqMypZYNTtSqWw/WHHrh0+wE6NmkAdSAyKhoDpi3gVpy6urr4Z9Jo/FnNXeHtYNH+USd3Iea+OADDpEYjWLTurTKiKUEoinHjxqFr166838SiH9mNG+tH7d+/Hxs3buT7rF69mvv1E4Q2o2eTlKmmQPtHSb+lYkk3PH3riQ37j/Kgmqz8VsUJBBgxZynvu7P9U2e5jcyk7yMLgkLD8ODFaz5P1o+yReD9FiGbFyAxNho6RiawGTgVhsVKy/hVCEJ7MTIy4v0eNojNgrbLly/Pa6rdvHkTR44cwYEDB/h+a9eupf4RQRBy5e+//+a1G9n92pYtW2BmRvVpCYKQsagWGhqKAgUK8Hk2DQkJye4hiExgg9+Oeezwxc8fP7QwUy06NpbXUmOwwQz2SA0T0khMUw96tW2OVx+8cPLKTaz99xBKFi2MBjWqpDsgdei8WHTp0LQ+TIyNoEo0rFWNi2p3nr5AZHQMzE1NoMrExMZhyKzFvK4dY/7fQ7JdH0ZWJNw4iZjbZ/m8sXsdWHYaRIIaoZWwumnOzs5Yvnw5duzYwX/vWX21w4cPo23btnwfVjuERW0ThDbzy/5RsaIaE8RYthr7/Xz65j23fa5StlSmz2EC2szVm6TBYCumjIFbIRd4f/2OWas3IzgsDGdv3IW7HC2tL96+z4OSmMVzPbJ+lBlxbz0Qsn0pIBRAx8wCtoNnwKBAMdm9AEEQnCVLlvAaamvWrOFBRgYGBlxcO3PmDJo0acK/Z/ft28drrBEEoVjYeMLpa7cRFhGJws5OaNeoHvLa2WjkxzBy5Ehu1T9s2DAaryAIQj6i2qRJk3jnpk2bNvjvv/945DUhewtIJqqxTDVtY/uRk/APDOa2gZMH9VZ2cwgZDFDNHTUYHz9/wZuPnzBx6VocXrMIRVzyp9iP1c8LCQvn+3dr2UTlzjurTzJTVxdCYTy3hFKWQJUVBAIhRs77B49eiv2/pw/rhw5N6iulLdFX/0Pi7TN83qhMVVh1GwkdXXHmKUFoIyxLjT3YADj7vkudBUOCGkH8ylRLiAhFIhM0DAwVdlpYRjer6/r+02ds3H/st6LazmOnceLyDT4/pGsHaf+A2T2yTPG1/x7E8cvXMaZvN7kF5Jy7eY9P61apqPJBP+pCzNM7vA4sRPHQtbKF7dBZ0HeUr4UnQWgrLMhoyJAh/MGy+Nly8v4RmydBjSAUS7xIhGkr1vM+Dvtv1NHV5fcvq3cfxIIxQ9Gu0Z9q/5EwwX7Tpk14+/YtdwthFvyKsOH38v2Gf/87iztPnvPlmpXK8WD0ogWc5f7aBEEouaba+PHjeYQ1KybLUmKnTJki4yYRjnns+UnQtkw19n63HvqPz//VojH9qGgILOts7YwJsLa0QGR0NEbM/YdneyXvzOz575x0MMslnwNUDVtrK1QuU4LPX7pzH6rc+R23eBVuPX7Gl8f06YqebZRTczD69jlEnRFnnRq4lYN1n3HQ0ct2HAdBaCSpB4wIgkibqcYQhQQqIVutHZ+//eQZz7bPCLZ96dZ/+TzLEBvdq0uK7Z2bNeQW5lHRMTh19aZc2hsQHCINoiHrR9kQfe8Swnav4IKanr0jbEctJEGNIBSEnp4e9Y8IQgX4357D0qAhZmrNBDVGgkiEKcv/h2dvPaHOREdHc8vZoUOH4sWLF4iNjVXI69545IE2w8bhwJkL8PX7wR8Hz15Em6HjceOhh0LaQBCEEkU1RtWqVfmXD6urRsiefPbiOmHalqm2YsdexMTFwdLcDCN6yrf+BKFYnB3zYuWUMXwg2cv3K6YsWyetOcI6ZK8/evP5Hq2bquxH07BmNT5lNpDMrlLVYB1dFk128c4DvjyoSzseNa8MYh5dR/iRLeIFl2Kw6jMBOvoGSmkLQRAEoV7oJmWqMUQhPxX++k3r1EBBJ0c+v/nA8XT38fnmhzELV/Lf3mIFnLFs4ijex0kOs0dqVEvcd9h3+kKaWmuygP3mszYYGxmibrVKMj++thF19QTCD25gEV/Qz1cQtqMWQN8ur7KbRRAEQRAKIzYujmdSpUdiskx9dcXLy4uPZe/atQsTJ07EpUuXYGxsLPfXZYHlYxasRHy8iHUzpLB5Fhz998IViIyKlns7CIJQsqhGKCZTjd2wM9s8TYe9x21HTuK/K+Io3uHdO8LWSn090+N/fEHsq4d8mpt9NA2W1j6+X3fpINDmg8f5Z78sqYYe8+iuWbEcVJVGtaryaXRMLO49fQlVgg3UzVu/TRpN1qN1M4zt200pbYl9fh9h+9bxeX3nItDrMgI6RvLvpBIEQRCaga6RMa9hxRAFK7aumiRTYmDnttL+CgsGSg7LPGNZ9+GRUTwQbP3sSTA3M033WN1aiS2tmRXkk9fvZN7Wczfu8mndqpVgZkLWj7npR0Wc3oOIk7v5skGh4rAdOQ96lppZN4YgCIIgMsLT5wsiMhF32G/m/eeqNR6SVcLCwlCtWjV4e3vj2LFjvKajvr5i3HTOXr+NqJiYdIOs2Do2znTq2i2FtIUgCNlAXlwqRnBYOA6eucjn2c16i0FjUL1CWaycOkathaaM3iuL8r3/7NcPsrGhodra1yREhiN013IIPvx6P4auZWHdexx0zS2zvI8m069ja26ldPbGXazYsY8/JLC+RWhEpMpe505586C0axG8/uCNS3cfok7lClAFWAds+fa92HfqAl9m/ubThvZVinVK3FsPhDLLpMQEbpVkNXAawoQihbeDIAiCUP+6avFRERCFKF5UY7RpUBdr9xzidX63HDqBxeNH8PUsK2zO+u3w/vKNZ6ax/nnB/PkyPA6rycYy2T76fsW+U+dRuUxJmbXRPygYj1+95fPq2ndWBRITEhB+dAti7oj7UYbFy8O63yQu7hIEQRCEtqGXKvM+p/uoIqx+9bJly3immiLqpyXnw+evYEM0GRkXsPGb1IFcBEGoNur5TajBMJGJFUdPzoPnLzF20Upo4nt98PxVinWxAgEmLRNnuagbYrEs5fthy1xkyMY+mgzrKCwYO4zXWUvN5+/fVf46b5xk43Tl3iOIRGJfcWWzcf8xPuDHaFK7OuaPGZrGgkoRCLxeI2T7UmkNEpuhs7RCKCYIQn1hQgf7PtcGVwB1Q8/GXmmZagxDQwP069Caz5+8chO3nzzn18qcdVtxJylbfdLAnqjtXuG3/R5JttrF2w94DTRZcfH2fR5YY2JkxDPViOyTKIpH2N7VUkHNqFx12AycSoIaQRAEobW4FS4AO2urTPs2dau6Q10IDAxEixYtcOOG2NWnT58+ChfUGBZmphkKagzWp8vI+YAgCNWERDUV4oPPF561lfp7ln3xMrs5TRr0kb7XdH5V1PG9Cv18k7LP0nx6EHi+gP+MfvyR2T7aYgX57UcAYmLj0qxXh+tcUhslJCwcHm9kb+OUXXYdP4NVu/bz+T+qVMSyyaOhr6en8HYIfT8gZPNCQCiArrUdbIbOhp6VrcLbQRAEkRXYd/iohavQesg4DJu9hLsC9J40h2fQE6qTqcZQVqYao3PzhtzeUZSQgP5T5/Fr5fD5K9LMsN7tWmY5683UxBjC+HgcOX9V5taP9aq7w1QBtUA0jURBHEK3L0XsE7HVkknV+tw5gmrAEgRBENqMgb4+r8+ekaDGxhv6tM9aH0jZPHr0CO7u7rh48SKvpaZMmtf9vatA87q1FNIWgiBkA4lqKoSv349Mt3/+nvl2dULT3qsoKPP2JkaE8kdmxAf6QRtQ58++aAFnXvuNcfnuQ6W25ciFK1i4cQefr1quFNbOGA9DAwOFt0P4/TOCN85DYlwMdM2tYDt0NvTt8iq8HQRBEFll3OLV8HjzXitcAdQVPVvx70hCSKDS2sCEKitz83S3BYeGZdlmmUUdM2GNcfDsJV6MPrf4BwZJa7SR9WP2SYiNRvCm+Yh7/Zgvm9ZtBcu/hkFHCYFJBEEQBKFq9G7XAkP+ag9dXZ002VaslqxboQJQZVjw/q5du1C3bl3Ex8fzLLV+/fopfSzJJpNSJ9aWFtwynCAI9YFENRWioJNjrrarE9aW6Q9SqOt71bfPuJ4Gw6xxJ/7IzTE0BXW/ziUWkJfvPko301IRnL1+B9NXbuTz5YoXw4Y5U2BslNZSU97EB3xHyIY5SIyOhI6JGWyGzoS+Q36Ft4MgCCJbmfLPX6X5/laHbGmtzFQLDUJigkhp18qXH/7pbnvw4nW2rpWuLRvzqV9AIK4/eJLrtp2/dV8q/LFMdSLrsPrGwf+bBaHXa75s3uwvWLTtAx01rQ9DEARBELKGBQ6N6dsN1//diCmDe2Notw5YOnEUbu3brBb9jn///Rfjx49HjRo14OHhwWuoKRt2/8HcMjIiNDwC95+lLBVDEIRqQ3cPKkSxgi6oXqEsL16ZmhoVy/LtmgCzV5qzdmu629h7V8f3qu/oAn2Xouls0YGhWzlYNO/KH4auZfm69PZhx9Dm61xdPvuGSaIaGxh77+Or8Ne/dv8xJixdwweEixcuiC3zp8Hc1ETh7WCWXMH/m42EiFDoGBnDZvAMGOQvrPB2EARBaEu2tDahZysW1ZAgQkKY7OqQKetaYb/XlcuU5PP7Tp3PddvO3RRbP9avXlkpQTXqiig0EEFrpyP+i9gCyqJ9f5g36ZzlrEOCIAiC0CYc7O3Qp30r/N27K9o0+EPl+xySoLnOnTtj3rx5uHDhAhwcHKAK3Hr8LAv7PFVIWwiCkA0kqqkYK6eO4YJDcoyNDLF80mhoAswup/ek2Xj/6TNfTp3ezN77iiljoI4YpCOqGbqVhXWvsdJlVquBrctsH229ztXlsy/rVhSO9nZ8/uaj33eMZMm9Zy8xav5ybh1VyNkJ2xfN4DYBikYUHiIW1EIDAQNDWA+YCsNCii/2SxAEoW3Z0tqWqabMumqyvla6tWrCp3c8XuDT1+85bpffz0A8TbIvbZaF+hyEmPgAPwStngaR/1dAVxdW3UbC7I8WdHoIgiAIQgM4ffo06tWrh+joaBgbG2PIkCHQ19eHqpAgSsh0OwvvYXV8CYJQH1TnGyaJ4OBgvHz5Ei4uLihSpEia7Z6envj+Pe2NKCs+aWGRtcFlf39/fPr0Cc7OzvyhSthaWWLn4lncUubm46dYsnk3YuMEeP7+I49GVXtBbfIceH7y5RGhi8cNR9tGf/L3yqJ92eCEqmcpZURiQoK0LoNJ9YYwKlOZ2zmmzj7TNbeE7bDZiLhwGFHn9gOGxrAZOkvrImSTX+fq9tmzz6pRrar4979zuPHoKSYN7qOQ12UDaMNmLYFAKIRTXnvsXDQT9jbWUDQJURHc8lHEagDq6sGm7wQYuZZReDsIgiByAvutyWtng59BKbOf2M8wC+5Ql9+i9FDH39SM0DGzgI6hERIFcRAF/wSKiLO8lJFZz+rtJXcLZT22ahXKZPscN6pVjf9uB4aE4sCZi5iSw/7D+Vv3+NTUxBh1KlfI0TG0DeF3H4RsmMuz66GnD+s+42Fctqqym0UQBEEQRC4RiUSYPXs25s+fj9KlSyMwMBD58+fPtpvW87ee0NXVRaXSxWFhZibzz6Vy2ZLYcexUhttZV7NK2VIyf12CILRAVHv06BGWL1/OC0gGBARw/9vFixen2W/FihU4cOAAKlRIeRO5adMmFC9ePNPXSEhIwPDhw7F7926ULVsWb968QbNmzfiykYqlMbMbdfa4fOchL0S+89hptRbVgkLD0GfSHHj6+PIfqsXjR/D08eTvVZ0R+rxHQlgwnzet3QwGzpnb4DERIuocAEEsEiLDoGeheHFEFVDXz75hzWpcVPP5/gPeX77BVc6Fet96fcLA6QsQHRuLPLbWXJDMl9ceiiYhNhrBG+ci3s8X0NGFda8xMCrlrvB2EARB5KY/EhIWkWZ9+RJuapEtndFAwJiFK3H/2UvpOiYGsaxwFsSijrAAFl2bPDyrSFmZagx2DscuWsnr7UlwL10iRw4ShgYG6NSsATbsO4pjF69xKyUTY6McWz82qFEFRoaG2X6+tiH49B4hm+cjMSYKOobGsB4wBUapXCMIgiAIglA/mIDWrVs3XLp0CV27dsXmzZthbm7OhbasECcQYOHGnTh64SqE8fF8nZGhAbq3boZx/bpDX09PZm39s5o7D3z74uePhFS1nXV1dJDfMS/qqfGYL0FoIypj//j27Vu0bdsWPj4+/EswM5igdv369RSP3wlqjFWrVvGClU+ePMH9+/f5a7LnzpgxA6pKn/Yt+fTB81d8YF1dB7B6T5wtFdSWTPglqGkKsc/EAxx6efJBP3+h3+6vn/dX5Izo5ze5to2AXKKMJLaLl+8+lOsp9vL9hn5T5iEiKhrWFubYvmgmCubPB0XDsgVCNi+U1iGx/GsYjCuQ7RRBEOrF3pPn+U2ziZER9vwzByZGYlGiVb06aitAMUGN9ROTw7KrmBikCRaQouAApWfWn9m8EutnT8LJjcuxeurfsMnhtdKlWSPeFw6PjMKZG7ez/fyvP37i+bsPfL553Vo5aoM2Eff+GUI2zBYLaqbmsBk+hwQ1giAIgtAAWP00NoZ87do1rFmzBnv37v3tWHLq57M+NHMPkAhqjDiBENuPnMSsNZtl2l4m0G1dMB1ODr8sziV+VSxgmm2TpYhHEIQWiWq9evXCX3/9JdeMsXXr1qFHjx4oUaIEX2YpwYMHD8aWLVsgFAqhirAoVGfHvHyeZaupG8zipteEWfjw+QsfRFg6YSRa19csQY1ZP8Y+F1vxMJEhK1aO3NbIVPyDH/8z53U1COXAOjv1qokztK7ceyS31/nywx99p8zhWQhmpibYunA63OScFZceifFChGxfAqH3G75s0WEATKvVV3g7CIIgckNMbBz2njrP51vVq4VKpUuglrvY+eCOx3O1PLkffL7wDDVJYXYJbJFlVzFLSHVFzzZJVFNippoEllXP+uSpawFnFzZoInGe2HfyQprP7XdcSLJ+NDc1Re1K5XPVFk2H9c1ZMBALCtK1tIHtyHkwLOiq7GYRBEEQBJELWN+JPdi4m8TtbOTIkdkuqcKClDIbyzly/kquauCmh72tNcq6FZMuS3qBbB1zJCIIQr1QGVEtOwgEAp5t9uLFC16EMiv8+PGD11GrWTNlZkWtWrUQGhrKrSBVET09PfRqKy6ifeb6nTQ1QFRdUGMZah99v0oFtVb160DTSG79mNXMHfaDr5/Hic+TqKaeNKoprsXx0tMLfj8DZX58/8AgbpnqHxgMYyNDbJ47JUUHTFEkikQI3b0SgnfP+LJ5i+4wq9Nc4e0gCILILccvXUdoeAT0dHXRuak4MKBmpXJ8+uDF6xRRquoA6xMu3rwz031W7zqA958+Qx1RhUw1edC9VVM+ff3RGy/ei7POssq5m2JRrWHNKjA0NJBL+zSB6AdXELpzOSCKh56dA2xHLYBBvoLKbhZBEARBELkgJiYG/fr14/XTGNWqVUszxpsdO22dLAYzyQImBP49f4XUxjt1vdzR85dnO9iKIAjlojI11bJbf419kTJBzdfXF6NGjcKCBQtgmEldga9fv/Kpo6NjivWS5W/fvqF8+fQjPsPDw/lDgp+fH58yn96sevXmhnYN62LN7gOIjI7Bnv/OYnTvv7L0PNY2VkdOEW1MTUBwKPpOmcvrTenq6mDphBFoXremUtoi73MU43Fbav2o4+CS5WPp5skHfPZEPKsXooHnRdOpWq4Ur4XCMh8u3rmPHq2byezYLDOtz+S53ObJQF8fa2eMR8VSxRX+ebAszIgD/0Pci/t82aRBO5jUb5vldtB1ROchq8jzWmHBKQTBrq2dScXBm9Spjnx5xHUpayVl+0RFx/CI1cplSqr8yYqMisaWw/9h17HTiImLy3Tfi3ce8Idb4QLc4rJlvdpwyvvLdkaV0bOxl2aqSSKSNYHqFcqgkLMTfL5+x75TF3g9v6xmr7/0/Mjnm/1B9ssZEXX9FCJO7ODz+o4usBk6C3pWtjL69AiCIAiCUAbe3t7o0KEDnj17hkmTJuX6eKzvz/qWmQlZUTGxuX4dCS/ff8SNRx4Zbr/1+Bm/F6lQMmv9QoIglI/aiWpNmjTB7NmzpWLYhQsX0Lp1az5YsmLFigyfF58Ufayvn/ItS5Yzs39kx50zZ06a9WFhYTAzM4MiaFm3Fg6cu8z9frs0rZelwuTsnERGRvIfCUUOKrIaaiMXrMTn7z94NPjMYf1Qo1wphISoXpZdbs9RYmICREn11BLcKvKsx6ySYG7DpwL/ryp3bpR17agT7BxVLlUctzxe4PzNe2hRp7pMjstqp41auJIL0uz/Z+7IAShVuIDCrxH22Sec34fEJzf5sk6V+hBUb5qtdtB1ROdBFa4Ve3vxwDyh3TB7F9YvYfRpJ65Xy2AFw/M75ME3/wDc9XihUqIas25kbWZtZPaDAqGQ9wPX7zuKkDBxsJeFmSlsLC244JJ8TEAnyWowXpSAn0HB8Pzki+Wf9mL59r38PTLngCZ1avDnqip6tmL7cwgFSIyKgI65eta8Sw1zb+jaojEWbdqJszfuYtKg3lmq53fuxl3pZy7JsCR+wX4/Is8dQNTFw3zZoIArbAZPh66Z6l7jBEEQBEH8ntOnT6Nnz578nvHo0aNo3759rk9b8SIFkfCbzLDihWWX5X757sMs7UOiGkGoD2onqrVr1y6NyNa9e3fs3r07U1HNysqKTyMiIlKslyxLtqfH2LFjMWDAgBSZalWrVuXPsbERCyPypn/nNjh04QrCIqNw0+MlOjdr+NvnsB8cFnlhbW2tMGEkIDgEfy9eLRXUlk4chWZ/1ICqkttzJPB+i7DIMD5vXb0+9LNxPcQVKAI+JBYSCGtLC+joqc6/ozKuHXWDnSNWF4WJas/eeQK6erDJwqBYZkTHxmLE/BXw9PnCz/+CscPQWgmWqWxgKurMXsQ8ucGXjar8CYvOg6Gjmz3HYLqO6DzQtUKoCqzgOKNqudIo41ZUGiDAvmtrViyHw+ev4K7Hc4zq1UXJLRVnK7PC6axWmgTXgi6IjImR2g0bGOijR6tmGNy1Pf/OHrtoJa+hJqF6xbJYMWUMrMzN8OjlW5y6dotb2LDAjcev3vLH/PXbUbtyBbSuVwf1qlfm2deqaP8oyVbT1RBRjdGu0Z9YuXMfYuMEOHrhKgZ2bvvb57AAHkajmtVgaEDWj2ky649vR/Sts3zZ0LUsrPtPhq6xiZw+QYIgCIIg5AHr1wo/f0BCZBi3cH4fFI5WrVqhdOnSXFArXry4TF6nTYO6WL5tL2IFgjTZarpsLMzSAo1rVYOsYK/Dgt4ykvGYIQPbhyAI9UF1RvFzActaY1ljmVGkSBFuD/n+/fsU6z09Pfm0RIkSGT7X0tKSP1LDxAZFCQ4FnPKhca3q3Gt394mz6NK8EY90/R1sH0W1k9X2YJZ1rJgnE9SWT/lbLexpcnOOhC/EAxx6efPD0LlwtqyJDBxdxDMJIiAsCHpJNdZUBUVeO+pKrYpluT0jq8Nz49FTdGgirtGTE+IEAoycuwxP34q/k2aPHMgH3ZRB5MXDiLn2H583Kl8D1n8Nh04OrwO6jug80LVCKBuP1++k3639O7ZOs71mpfJcVHvx/iMioqJgoSAXgoxggtqD569SrPvw+Qufsn4GyzIb3esvODsmZXIB2Ll4VprMtuR2g+wxc3h/3HjogVNXb+HawycQCuNx7f5j/jA1Mea1QlvV/wM1KpZNU2Mio2PLE10rGx6wwvpJrK6agUtRaApWFuZoWa8Ojpy/wrMP+3VolWl/6/M3P16DjdG0ruoGqykDVvs1bP86xD5OCgQqUxXWvcdCx+D3rh4EQRAEQagOce+eIfzIZogCf/AsMiZu5Svoih1rVqJj3wEwNzeX2WtZmpth9fRxGDF3KQTClHWVjY2MsHbmBJnWry3rVixDQY3BdD22D0EQ6kP20g6UDMt6YHXUUts6njt3DhUqVEixnolnt2+La10xjIyM0KxZMxw5ciRFFMKhQ4dQvXr1NLXWVJE+7cV2RcwW7taTZ1Al/IOC0XPiLLUT1HJDYoIIsc/FoppxhZrZrvWhb+8oDkdh1/HP73JpIyFfzExN+OAj41IW0vkzgolyoxeswL2krISJA3rirxaNoaxaJJFn9/N5o1KVYN3z7xwLagRBEKrA9qPiLLViBZzxR5WKabZzEUlHB6KEBDx4/hrK5IPPF56hllF9hzXTx+GfiaNSCGoSmNjVoEaVDEUvZh3euHZ1Pkhw98A2LBg7lItt7L1Hx8Tivys3MWDafNTtPhgLN+7EG69PCA4NQ+9Jc9Bi0BgMm72ET9kyy6aTNzq6etCztpNmqmka3Vo24VNWP/X2k+eZ7suC6hhW5uY8s5IQkygUIHTHP1JBzbjKn7DuO4EENYIgCIJQMwQfXyNk83yIgvzx7EcI/txzhWepCX0/otn3JzCOi5L5a9atWgmnN61Er7YteP3hEkUKcfeAM1tWytwSvknt6rC3seZCYWp0dXVga22JpjIqKUIQhJZlqvn7++Pt27dSoczX1xfXr1/nNc9q167N18fExMDd3R29evVCuXLlEB4ejk2bNuHjx49cWEvOP//8wwW05DWuFi1ahGrVqqF///7o3LkzLl68iEuXLuHatWtQByqWKo7yJVx58UpWnL5ulUpQFUGt18TZvOC6vp4etxtqogU/BkLvd0gID5WKatlFx9AIutb2SAgJgIiJaqXl0EhC7jSsUQU3Hz3FnSfPERkdA3NTk2wHC0z6Zy3PFGAM794J/Tu1gTKIvn8ZESd28HnDYmVg3WcCdPTJYoogCPXF55sfLt99xOf7dmzFs2fZ925yWF2xUsUK4/UHb9zxeI6GNasqqbWAr5+47ltGyCp7nEXndmzSgD/8A4Nw5vodbhH55uMnBIaEYs/Jc/zBhDiWSZ2cB89fcrtJlh0nb3Rt8kAU/JM/NI3SrkV43Yxnbz2x79R5PrCTEeduiuupNapdjWfIE0BCbAxCty2G4IM4IMm0TnNYtOuXbatqgiAIgiCUT8SZvdzO+d+XnzDjxgvYGhsiimWQJSYiMSYKUVdOwKrzYJm/bsH8+TBtaF/IG5b1tmneFPSeOJuPGyXHxMgIm+ZO5f1ugiDUB5W563jx4gVmz57NH5UrV8b379/5/OLFi6X7sFRfln2WkJCAbdu24eTJk2jQoAG8vLykwpsE5rObel3JkiXx+PFjnrW2cuVKbhn54MED1KypPhlVfdqJs9XueLyAp4+vspvDB2J6TZglFdRWTtUOQY0R++yu1PpRP1+BHB1DP29+Po3/+U2mbSMUB6tDwwZpBUIhbj1+mq3nskyEWWs288FMSTbqyJ6doQxiPG4j/OAGPm9Q0BXWA6Zw4ZcgCEKd2XnsNP+uZZGhrev9keF+tSqV59O7Hi+gTJi9Ym625wQHezv069gax//3D85uWYWh3TrAxdGBb0stqDFYEh2r38YsIeWNnm0ejc1UY3RNylZjFtJffvinuw9zgXjr5cPnVblOsSJJiIpAyPrZUkHNrElnWLTvT4IaQRAEQaghorBghH94jb8vPsGkq89QOZ8tLnath0qOttJ9Yp/+ciJTVWLihQiIjoIgVQBfcjvvqJiUghrLW4uKieXbCIJQL1Qm1LFRo0b88Tvy5MmDGTNm/Ha/CRMm8Edq3NzcsGGDeOBYHWlcpzqcttnj+89APlC0cOww5QpqLEPtmx8X1FZNG4tGMizkqTbWjxVrZdv6UYJ+3nwQvH8GUQD9gKordtZWcC9dAo9evsGlOw+ybHvKBnkXb97F6/gwOjdriMmDeuf4WsoNsa8eIWzPaj5Squ9UEDaDZ0DXOHsZdwRBEKoGsy48dlHsRtCzTbNM6yLUrFQOmw8e532ab/4ByO8gFnMUDbNuZL8pT16/S7Ge/TRUr1BW7vXMihZwxt+9u2JE9074378Hsf7A8Qz3ZTXW5N0ePRvNFtWYSMb6AiFh4Thw+iImDOiZZp/zN8X9TWtLC1QrXwbaDht4C9kwB/E/xKKuRdu+MPuzlbKbRRCECnH//n28evWKB0+XKlUqzXbmWMSckVLTvXt3mJikvAdi2e23bt3Cly9f4Orqyp2PlHG/RhCaTGJsDBbdeY1Db30x3N0Vk2uWgn6qzPPEuJRilCrhGx6KNU/u4qLPR4gSE2Cqb4B2bqUxvGJ1WBkZ833CIiIxZfn6JBntl807m2PfKVNXrEftyhW4gwZBEOqBymSqEVmDiVc9Wjfj86zQfFBomEJPHYtKvnLvEa/30ZNlqGmhoMaIeXQDCRES68ecRw2zLDcGZarJFzbwEvvqoXQARtZIrMKuP/SAQCDM0nPW/nuIC+OMln/WxuyRA5Vygxbn+QKhO5cBCSLo5XGCzdBZ0DWVXQFggiAIZbHv9AWeacUsVX5Xp9K9VAkYG4ktV+56ZF7fSt5Uq5BWOGGCGrPXVhTs96hmUs1QRWbNZZipFqyZohqz+enYpD6fP3LharqZgWdvirPZG9ci68f4wB8IXjNN3J/T0YVl1+EkqBEEIWXv3r3cnWj48OEYOHAgrl69mu7ZWbNmDXdEYuJb8odQmPI+LigoiItoAwYMwIULF9CuXTs0b94ccXFxdNYJQkaw8j96NvYYW6c8drWqjum1y6QR1Fh0mb5jztyhsgKrFXz9wRPceOSBiKjs1W7zCQvBXyf34/wnTy6oMaLjhdj75hl6nTmECEGcdPyW9fPSq5vM1jHno5NXbsroHREEoVWZakTW6dSsIdbtOYzo2FjsP30BI3rI3y6O/ciMWbiSi2nJYYLa6unjlFp/RJEkRIYjdNdyqd0MI/zYdtj0Hgddc8tsH08/j5P4uOEhSIiNhq6xqUzbq+2k93kZupaFdQ4/r4xggvKiTTsRFR2De89eZloXhbHt8H/4397DfL5+9cpYPGGEzOrkZAfBp/cI3boYiBfyujW2w2ZDz8Ja4e0gCIKQNbFxcdhz8jyf79i0Ps/yyQyWxVa5TCncfvKMW2yzvpYyYDfVF2/dl/62tGv0Jxev5J0Rlh6FnZ1QvXwZPHjxils+KjprLnmmWmJ0JBLiYqBrpHlZ1F1aNMLWw/8hNDwC527eQ9uGdaXbvHy/wvOTOJsiq5nw2YGJU/GBftC3zwd9R8VfY9lB6PcZIRvm8j4z9PRh3WssjMtrh+U8QRBZg5X5OHbsGFxcXGBhkfnvfvXq1bF169ZM9xkzZgwiIiLw/PlzmJmZ4cePHyhdujQWLlyIOXPm0MdCELmAZYGykj+sJM+5c+fg9GdzNL55Jv2dExNhWkecXCBLmMi1aNMuHDl/BcL4eL7OyNAAPdo0x9i+3fh45+9Y/ug2wgVxyXLPfvExNBi7X3lgeKUa8Pnux4PW0hPVGGyb7/fMaysTBKFaUKaaGsKKy3doKo5q3XvyfLpRrbKGCWoPnr9Ks75YQWetEdQYYoEm5XkQfniF0N0rcnQ8/bxiUY1BFpCK+bwEufi8MoLZhJUuVoTPMwvIzGBC+NKt//L5GhXK8ixPA33FxzcIv3ojZNM8JApioWtpLRbUbOwV3g6CIAh5cOLyDW6px2pe9m4vrkf7O2pVKsenLDiC1e9VBq8+eOGj71c+37tdCzSoUUUpgpqE5ZNHcwFNWVlzkkw1Tc5WY/Xr6lapKO0jJIeJbAxbK0tULV9apkFHwf+bhcDFo3lwDZuyZbZeFRH4eCJ47QwuqLF6rzaDppKgRhBEGjp27Mgz1WRBZGQkDh06hCFDhnBBjeHo6MgtIrdt20ZnnyByQWBgIJo1a4b58+fD3t4eAoEA5i26waBQcfEOEgefpKlxlT9hUq2BTM85E7fYOCfre0kENUacQMiDoGet2fzbY4TGxuCar1e6ghqDtf7Yh9d8ntk6ZiSoSdpjY0XWjwShTpCopqb0atOcRzKwDLJT127J9bU++HzhGWrp/QC88/6skEL1qoDQzzcp4yn1eUiEwPNFjqwFda3tAQOx3RRZQKr+55UZEvtTZo/Koq7S478rNzFnnTgismKp4lg/ZxK3flI08T++InjDXCTGRkPHzAI2Q2dDP08+hbeDIAgiu/bTWelzMEFsx9FTfL5J7epctMgKrK4ag2UMvfH6pJQP58SlG3zq7JiX11ZTNjZWlti5eBbObF6J9bMn8SlbZiKPItBj/aQkEjS0rhqjW6umfPrsrSdef/CWrj9/8y6fNq5dPUvR0qoWdCQrm+qQ9bN5tqKOiRnvsxgVr6DsZhEEoeb4+/tj3759OHnyJD59Svub//LlS27zWLly5RTr2fK3b9941hpBENnn0aNHcHd3x7Vr17gVK7NtZbUMmRuB7Yi5sPxrGAwKl4BennwwLFER1v0mwarrCOiktoTMJc/ffeD3FhnBstc+ff2e6TECY6IzFNQYbFtAdDSfb1WvjlQrzAi2D0EQ6gPZP6opBZwcefTy5bsPsevYGXRoXF9u9Zh8/TLvMCqiUL0qIArK/Dxw+5xsWuewjgGz3In3+4z4n5n/YBPK/7wyo1Gtqli1az8Xuj3evEeVsimLYrMMtinL1nFxulSxwtg8bypMjcVFaxVJfJA/gjfMRmJUOHSMTWE7ZCYM8snPn5wgCELW9tMsU2rl1DEZCjtX7z/mNV8Z/Tq2zvJrFS9cEPY21ggMCcVdjxco41pUoR8eq6Vw+vptPs8sAFmWnarA+nnK6OvpGBhC18Ka17EVhQRCU6ntXp4LqV9//OS1ABeMGcqD2j4kiciytH78FXSEDIOOVMUKMvbFAy4AQhTPrwOboTNh4FRI2c0iCEIDeP/+PU6cOMHrpt28eRO9evXC+vXruYUkQyKa5cnzK2OawbJqJKIcy1xLj/DwcP6Q4Ocn7pOwwMuMgi8VDWsHC0JSlfYoAm18z6r2vqOjo9GyZUvo6+vzmoc1a9ZM6Q6howujKvX4IzkJLMA/G+3PyntmgUusdltmotiFW/cwsHPbDLfbGZvAUEcHoowsHQHkMzPn7WDuRv06tMbOY6f5+tTP6NW2Oe8L5vRzUqXPWVHQe9YeRAq8vrNTmodENTWmT/uWXFTz9PHlgz+13MvL5XXYl7+yC9WrAkz8ys32jNDL68RFNRGJamrxeWVE0QLOKOTsBJ+v37mAllxUu/X4GR8UFiUk8P22LZjObVwVjSg0iEd7J4QF8wxJm0HTYOCi2EFjgiBkc0N69OhR7NixAx4eHpg+fTrGjx+fZr/Bgwfj4MGD6UaIurq6pljHapAsXbqUR2sXLlwYEydORPv27ZX+caVnP/3g+UuMXSTOmEqP7UdO8in7Hi5XvFiWX4sFJ9WoWJYXEr/j8RyDurSDIrnx0INnyTHaNvwz28//GBIE34hQFLCwRjEbO2gKrK4aF9U01P5RcvPWtWUT/LP1X5y+dgsTB/TE+VviLDU7aytUKSsbO7OsBB2FHdkMyzZ9lN4/iHl4DWEH/sdST6V1XymrniAIWTBhwgTUrl1bOnB2+/Zt1K9fHw4ODrxeGkMy0J86cFkS8JLZwN6KFSvSrbkWFhYmtZJUNqz9zOKSBXwqo7a3MtDG96wq71soFMLAwIDPb968md+H5M2bFyEhIUp7zzoJIhTKl1cs2GVAglD42za2cy6CR37fMhTnOhRwlR6jb9tmcLKzxpkbdxAaHsnXWVmYo8WftdC0To1cnQ9V+JwVDb1n7ficFf1ZS4JnsgKJampM5TIlUdq1CLeJ2Xn8tFxENXbB7j5xNt1tiixUrwqwqF1D17LpRPfqwNCtbI6jelldtTiWQRRAmWqyhH0eBq5leM271Bi4lZN5FDa74Wpcqxo2Hzwuzkob3Ieve/zyDUbMXcp9ul3yOWDH4pmwtbaCokmIDEPw+tkQBfkDevqwGTAZhkVkN0hHEITiYIIXG5hhYlrjxo0RGxub7n5RUVGoXr06Dhw4kGK9pWXKDK/Tp0+jc+fOPEK7devW3AqJLbMIbhZNqiwk9tOpYfe+956+5FaQqfsgzD7vyet32c5Sk1C7UnkuqrFjxMTGwcRYHLGuCI5fui7t37Hfi6wSEhuDcdfO4oHfL2vMavlcsLxec9gYm0DdYXXVhL4fINJg+0dG+8b1sHrXAcTGCXD80jVpPbUmdarL9Obxd0FFwo+vEbR8AgyKlILZny1hVKYKdHQVe9MedfMMIo6JaxbpOTjDdugs6FlrjlBMEIRyqVu3boplJrCx+k6s3yMR1Wxtbfk0NDQ0xb6SZcn29Bg7diwGDBiQIlOtatWqsLKygo2NDVRlgJLdq1pbW2vVALy6v+f4AD9+X88CjrL6u6js9+3t7c3vK0aMGIE+ffoo5N4iK+/ZyckRn/x+Znoc5/xOv/2f7VetDq6fOYygmKg0wlppOwd0qlAFJkmCIqNrmxbo1LKp1FqyUP58MNDP/dC8sj9nZUDvWTs+Z1X+rElUU2PYBcWy1SYsWYObj57Cy/crz4KRJf/bewRHL1zl8/ny2MEvIEgphepVBeve4/Bz7hBA8GsAkwlq1r3G5viY+nmc+FQU8J2LmPKy8dRGzOq2RGg6oppxhVpyeb1GNatyUe37z0Ce1u9gb4vpqzbyATI2zwQ1B7uMb8DkRUJ0FK+hJvr5jYVX8uuY6pEQhPqydu3aLP9WMHsV1vnMjBkzZqBDhw4YNGgQX2bTy5cvY+bMmUoV1XJiPy3JUivs7IQ/q1bK9mvWSKqrJhTG4/Grt6hTWTG1m4JDw3imGqNto+xlqTFB7WEyQY3BlsdfP4ttTTtA3WFZSgxRSOYDH+oOszNtXrcmTly+gXV7DiMiKlrm1o8MFlSkX6AY4n0/ptmmx/qkiQkQBf6A0PsNQr3fQM/OAaZ1msOkegPoGptCnrB+cNSFw4g8Lw4E0HcpCtvBM6BrrpgafgRBaC+mpqYpgpTKlCnD+1qvX79GtWri2tkMtszEsQIFMrbPZ8FLqQOYGGwgUJUGA1nWnaq1Sd6o63sW+LznwSbCZL/dhiUqwLLjIOjbO6rs+2aBez179uQD4nZ2dgp9/d+95zYN/sTybfsQKxDw/keK57LBe0sLnj32uzY7W1pjX+u/sPn5I5z8+AYx8fGwNTZBp+Jl0b9cZZgZGKZ5DjtmiSKyt7NW1+s7N9B71h50VfD6Vp1iDUSOYF/yeZMG6XcdPyPziOm1/4pto1gGzpVd65VWqF5VYDf1OklRJiZ1msF+8mpuR5Obm31m/8hIjItFQrh80t+1FaG3OFtB19oeVv0mwaCA2AYs8sweiCJSRh3KyirV0EAcq7B48y5uWxYVHQNrC3PsWDQLLo5ZzzyQFQlxMQjZPB/x3z7x9FKrbiNhXO7XjSFBEOqHLIMvfv78iWfPnvEI7eSw5adPnyIgQHnZQb+zl0693ff7D1y884DP9+vQKkc1yVjgg2uSUHfX4zkUBbOBiReJYGRoiGZ1amTL8pFlqKWOjGXL979/4ds1IVONocn2jxJa1qvNpxJBjfG/PYd5bUFZYuQmFo+TY+hWDnajF8J+6lpY958Mw2Jl+HqW4R5xYgcCZg1E+PHtiA/MXOzOKYkJCfx1JIKaQdHSsB0+hwQ1giBkCsv0DwxMWaOTZZJdunQJf/zxh3Qdq6VWr1497Nq1S7pOIBBg//796Nixo0rVPSU0GyakBa+bCeEXrxTrBe+fI3jVZIhCVa/mLBPRWNBeq1atkD9/fjx+/Bht22Zcm0wZsHIcq6ePg4F+2gF6IyNDrJ05AYaGvzLMMsPRzAIza9bHw57D8ajncNzsOgij3GumK6gRBKE5UKaammNoYIAerZtixY59PLL17z5dZSJ03XnyHNNXbuDzFUu64Z9Jo7garKxC9apColCAxChxvRPjUu4ysRDUz5tfOh//8xv0rBSfyaSpxL15wqdMRDIpVw0GTgURtHQM/wwjjm6FdZ+0NYhyw9jFqyEQxqdZXyB/PhQt8OtzVuT1Grp1MYQ+7/myZafBMKmc0u6EIAjNhtUJcXZ2hpGREcqWLYtp06ahSpUq0u0fP4ojXlkdteQUKiSOnvTy8uIDS+kRHh7OH8kHpSQ30rIoIsyyzaqXL8NrqiUXjZimWK18Gb49+evsOHqKR5qyGlRMnMioDb8rdMzqqn34/IXXVVNUse/jF8XWjw1rVOGWk1l9XZ/Q4N9uL2yZeaaiqhf+1rES2xuxwKP4uFjo6GdtgEMeyPu8bDn0X5p1D168wpiFK7B94QyZvU7cx9d8alCmCkyq1IOevSPv07L/MxasbVDKHVal3CH8+gkxt84g7ukdJMbFIPrGaUTfPAND9rw6LWBQpGQakT8n5yhRJELE4Y2IeyT+PzAs5Q7LXmOQaJD1/wV1QpX+v5QJnQflniNVivSWFS9evMDDhw8RF8eKKwD37t2DsbExt2qU1ImNiIhAgwYNeA21EiVK8OCibdu2wcnJSWr9KGHNmjWoU6cO2rVrx59z5MgRxMfHY968eUp5f4R2EnF6DyCKF/9AJycxEQmR4Yi6cgKWHX7ZjaoCrPbz/Pnz0bVrV15DzdzcHKpI3aqVcHrTSuw5eR73nr3kGWp/VKmIbq2awClv+vc/mcGeb5rM6pEgCM2GRDUNoEvzRtiw7yhi4uJw8MwlDO2WO6ufd94+GDl/GY+YZv6+6+dMhrGR4mqKqDKisF+DV7oyqu2ga2rOo3BZh0j08zvgWlYmx9V2RME/Ef9DbIdlVNqdT5k1gnmLHog4vg2xz+4i9vk9GJfPekZATmr/MF68+5Bu7R95kiiKR+jOZdIagBZtesO0ZmOFvT5BEMqHWROtXr2a1w5hg0grVqzgNdZYzRAWOSqpuyaxPUqOmZkZn7KCwBnBjjdnzpx0o8Alz88tM4b2way1W/E4qU4aw71UCcwY0idFMe+wiEgcvSi2q27fsC6io6L4IyeFjsu6igXG95984eXzWe5Z+d5fv+P1R28+36B6pWwVKbeGzm+356TouSoV/k7US+qDJiYixPcTdJLsIJWBPM8Luw6YgJwaNoZ2/9kreLx8zYXk3JIYEwXRZ08+L3KtgOj84ix+pHedmFkDTbtDr3ZLJDy5gcQnN4DoCAhePuQPOBaAbtUG0CldBTp6+jk6R4nxQiSc2IrEd0/5sk7pqhC17oPQSJat9ytjT5NQpf8vZULnQbnnyN7eHprGt2/fcP/+fT7fv39/PmXLLFNGIqqxQCMPDw8cOnSIi3AWFha8pizLokmdfVa6dGm8evWKZ6u9ffsWbdq0Qd++fX9rq00QsoLVTxN4vsh0n5jHN1RGVGMWqkzI7tSpE7+3aNGihcqXNymYPx+mDe2r7GYQBKGGkKimATCvX1Z/Y//pC9h76hz6d2yd5TTl1PwICMKgGQu5ZR0bRNoyf5rWWTxmRkLYLxslvaTIaVnAalgwUS2eiWqETLPUdIyMYVi0tHS9aZ1miH12B8JP7xB+eDO3N9I1s1BK7R95kZggQtieNYh7/ZgvmzXpDLN6bRTy2gRBqA6pI663b9+Od+/eYfbs2VJRzcTEhE+T1xFJvizZnh5jx47FgAEDUmSqVa1aldca+V1R76zCjrP7nzlo3G8kvv74iR5tmmHq4D5p9jt4/iriBEIYGxmib8fWvG+U00LH9WpUhb7+RsTHi/Dmky9a1asDeXLtmNi+O6+dDRrVrgk9Pd1snZ9qjs54+ONrymy+/7N3HuBNVW8Y/5qkg1JKgbL33lvZCiIqgsr0716AIgoqKCqigOJEQXDgAAeIAxkiIqgM2XvvvTeU7t0k/+c97a23abroSHLz/p6nzc3NTXLvyb3nnvON9xORNhWrSMtqGTMQPbEYtC3AT7TRV5AtSfwK6Nxyt3aJPJRR1smRiNj4ArmuEk8flCh46nx8pFSr9mIKzMUYCN9btYbYez4gCdvXSvzqRWJF4NLF02Jb+J2Y/v1N/Dt2l2LtbxN7cHCu2wjZb5HffybWNINhQIc7JKjPAPExuKyaO11froTtwDYqaCBd7Shn7QwE/sA5lhuQwTZq1KgC2DtC8o4t3nmAmB57fJxyvLvSeYXvR0baO++8ozJE4ch2ZV1mQggpCuhUMwiP9u6hnGpXrkXI4lXr8lzkHkTHxsqTb7wjl65eU0apL996VarlUM/E27BqMku+fuJTrGCi8DUJSDh5rFfOFdhnejuaU82vfvMMUlEw1JR8YKhc/XCEivyKmv+NhDzyQpHX/inMAW3Ur19Jwo616nlgl7slqPt9RfLdhBD3BpPttm3bynfffZdJ5vHMmdTMXg3tufa6M4KDg9WfI4VRQLhW1crKqZacnJLpsxOTkuTHP/5Wy/1u7yplSoXkq9BxieLFpVWj+rJ5936VIdS7W97HVLkFqgB//JvaX9/T9ebrCoqa2LWnvLRysaqhptG2UlX5qEuPfP0O7lIM2ox6tgGBYk+IE3tEmMv3p7DaJacsNLxeEN+ZfCi1VqBvjXriWyKP2RbmYhLU4TYp3r6byoSPW/mHGm/ZoiMk7q9fJG75fAlodZP4tLhJzGXKZLu/trgYifjqbUlOy5orfls/CerxoNtHtBcU7nJ9uRq2A9uIEJI1KpAbdbmSk5xv4OMj5rIVXXrvjI+PlyFDhqiMzi5duojFQjMzIcQ7MHYYoBcBY9MtbVMl7r7/bZEyrOeFpORkGfbWR3L4xGl1Q5746gvSvEG9Qtpbz89UM4eUKdCBCwZCIOVyaj0akj/sSYmSeCRVQsm/Uep1ocdSrpKUuPN+tZywbbUk7N2S7yZHFlq7Fk1VrR89eI76PEWRpYbrPnrBdxK/cZl6jojxEr0e9xoDFSEkZ/bv36+iRzUgg1SnTh1Zvnx5hu1WrFghdevWzbCtK9HqGpy7dDnTa78vWyVhEZGqr3u8b8FExXZo2Vw9rtu+O89jqrywfvtuuXItVXavz3UERIFSAcXkm+795Pc+j8in3e5Wj3iO9UbBXDr197eFXxGjUhTjCJzLiWkyi/4NWl735+Ba86/XTEo9NVpCX/tMAjt1Fx8/f2X0S9i0XKxfjZOIr8ZL4v7tYrfZ0t8HWe6EvZtVTbdrn76R7lArcc+jUqLnQxyvEEIIIfr7rZ+/FGtzS3Y3dgnsmHN2ZmFx/Phx6dChg3KojRw5UpYuXSrly5d32f4QQkhRQqeagXgszZB04NhJ2bw7tQB5bifYb0z+UhXmBK8PGSDdOrQptP30ZKwRYQUu/ahlqqnPv3ZJ1ZYg+SMRdcTSorn8G7Zyug0yuHyr11XLUb9+qSKm88vHrw1XBjE9eD5p1HApCmKW/CJxqxap5YDWN0nwvU/RQEWIlwIpxsGDByu5R5vNJlFRUUr2EZPd4cMz9kmvvfaazJo1S/755x/1HI94jvXuQuXymlMto1MFx/btvD/U8u0d2xZYhn3HVs3U4+Wwa3Ls9FkpLBYsW6kem9SrnW+nSZ1SZaRrtdrq0WiY0+qoWcOvipEp7HFEyoVTYkurD5zV+CivIFApuP9TUnbs1xJ09yNiShsjJx/eLeFfvy1X339eYlYskLDP3lDLEdPfl/DP3lD7AoLvGyLFu/YukH0hhBBCjEaJng+LpZKmHJEWeZMWgePf+AYJ7HSHy/YNGWrHjh2TefPmyYQJE5ilRgjxKpiXayDaNW8i9WtWl0MnTsl38xfJDU0a5up9n8ycLQuWrVLLA/rfo+qVEOdY0wwRppKlC7SJYJBQ2GxiDbsklvJV+BPkg8R9qdKPliq1xJzFb+VjMqfJQL4otqhwiV7wvZR8cGi+2h31B79/f6wcPXVG1VCD5GNR1VGLXbFAYv+Zo5b9m7SRkg8OU8dICDEekG/UHGOoSYP6BR999JHKKtu3LzWoBlGi7du3l/vuu0+OHDmi1jVu3Fjmzp0r/fr1y/B5qCsSEREhjzzyiERGRqqaaO+//748/njm2mWudqqdv3w1Q92IlZu3y4mz59PHMAVF47q1pGRQkETGxKhstcLoyyG7vWx9aqZ0n0KUmDSWU824mWpFMY5A5hgwBQWrMVJBgvq0Qbf2kYCbesi1DSvEtG2lpJw+ItbL5yRm4Uyn77FUrCaB7W8r0P0ghBBCjIQpsLiUfv4diV+/VOI3rRBrdIRYQstLsQ63S7EbuohPEcsIY+6RnJwsAQEBMm3aNCX/WL9+ffFkIqNjZM/ho2LyMUmzBnUlKNA4ag+EkMKDTjUDockejZr4uazctE1OnrsgJQMDsn3PnCXLZOpPc9XynTd3kJEDHy6ivfVMtOheyD8WJObQCvDyiNhtknL5PJ1q+ZU2Squn5kz6UY+lQlVVbyzmzx8lfvMKCWjZoUAit2EAKypnGohb+5dEpxms/Oo1k5DHRoiPmd07IUbloYcekj59+jitTaNfhlMMf5j4or5BdlKwcNLhLy4uTgIDA8Xd0OQfUT8NUo+haXXTvp27UD22btxAWjQsONlq1Dlq16KJ/L12o6zfvkse69NTCpolq9ar4/G1WKTnLZ0K/PONhCb/aL1mbKdaYY8jkg7uVI9+DVqqGrOFAcYfpsY3SqlOt4v1zFGJWTJbkg6lfq8jKRdOK0lIjMcIIYQQ4hyTfzEpfss96s+VXL16Vc1DypYtKz/88INUq1ZNPJmkpGSZ8M0PMvvPpaokDigW4C+P9e4pzz16n9fXPSWEZA/lHw3GXV06KUMTHAuzfl+S7bart+yQsZ98nW6M+mDk0AwGOZK1/GNBZ6r5WHzFXKacWk65fI5Nnw9goLFFXM2VUw0U79orPVo7cvYXYkuI86j2j9+yUqLmpl7HvjUbSMjAV8UHxYwJIYbFz89PQkJCMv0FBwc73d7X1zfXUrDu6FADlcun3iPB+TQJyN0Hj8iWPfsLPEtNo0OaBOTm3fvTJ9oFyW9pKgFd2raSUsElCvzzjYRJl6mmr9FFcg/GN0nHD6hl/wYtiqTp/GrUl8Cbume7TcpV1hMmhBBC3J0tW7ZI69atVd3lNm08v1wMbKYvfTBFfliwOMM4Pz4hUb78Zb68+dl0l+4fIcT9oQfFYPj5+cqDd6dqKv+2dKVExcQ63W7/0ePy/DsTxWqzSc0qlWTquFfE34+G+OyAEQcygYVRU019ZtlUCUjrlVQZK3J9aFlqkDbyrVYnVxHVSvbRbBFbRJhE/z7DY5o+YddGifzpM7UMx2Cpp0aLyT/77FRCCPFEQkuVFF/f1Azcs2lONa2WWo3KFaVruxsK/Ds7tmquHuMSEmTngcMF+tmnz1+U7fsOquXelH7MtfyjWFPEFh1RoL+Ft5B0eLeIzarqsBSVUw1YQivm63VCCCGEuNb59PXXX0unTp0kJSVFVq5cKc8995zH127fc+ioUqTIitmLl8qpcwz8IYRkDZ1qBuSBnreLn6+vxCcmysJ/12Z6/dylK/LUG+9KXHyClAkpKdPeGS0hjJDOEVtMZKoxAhdOAcs/6uuqQf6R5N+p5tewVa6ljXwr1ZCg21JrDMVvWCqJMDy5OYkHdkjEzElKMhSySaWfHiOmYsVdvVuEEFIoIJO+UtlQtXz+8hU5c/FS+kT4iX53F0qmfdWK5dUfQF21gkSrZYvx1803tizQzzay/KM31FUrzHED8K1aW0xBJYvsezFG8avbFGFMDq/4KMlqSj8SQggh7svx48dl6NCh0qFDB9m+fbt07NhRjMDiVeszjUwc+WvNhiLaG0KIJ0KnmgEpHVJSet16s1qe+/e/kpySkqEA51OvvyNXrkVIMX9/+Wr8KKlaIdVgRLIHWUwa5gKWfwQWLVONTrXrxhYbLcknDuVa+lFP8W59xVKpulqO+mWq2BLjxV1JOrZPwr/9QEXsox5fqSFjVWYeIYQYGU0CEvKPM+YvEpvNJqVKBkvvbp0L7Ts7tEyVgERdtYIC+6051e6+pZMKhCLZo5xAFl+vqqtW4PVm05xqCDoqakIee1H86sGx9h94HvLoiCLfF0IIIYTkTFRUlHqsXbu2yk5bunSplC9vHNthTFxcjtl2MXHuaxMihLgeOtUMymN97lKPV8Ij5J+1m9KLcA5960M5evqsiuj++LXh0rRezvJ4JBVr5LXUBR+TmEqEFHizmMtVTs+Is8U5l+0k2ZN4cKfK3BKTSfzrt8hzXbuSDwxV77Veuywxi2a5ZXMnnz4i4V+/K5KcpGr7lRoyrlCcvIQQ4m5UKl82XcJ67l8r1PLD93SXAH//QvtOTQJy75HjKjCpINi694Ccu3RZLfe+rUuBfKbRQea5uVRqpiIz1fKO9dLZ/+rNNiz6zEgE/pR+ZpyEvjpFQga9qh7xnAFBhBBCiPvx559/Ss2aNWXJkiXqObLULJZUGXajULdGNbHZ7TlsU7XI9ocQ4nnQqWZQ0Pl3ap1qCPrql/mybP1mGTr+Q9m8e59aN3boILmlEOqPGBlbZGqmmik4RHzM5gL/fEu5/2pKpLCuWr6kH31rNhRTYN6lECGJVPzWPmo5bs0SlRHmTiRfOCXXvhwv9sT4dAOVpUxq5gYhhBidymlOtR0HDiuJa9SCffCu1DqyhUW7Fk1UIBKyyzbu3Fsgn4mat6Bu9arSuE6tAvlMb6qrZqP8Y55JPLBdPfoEBuWq3mxhAanHgCZtKPlICCGEuCFWq1XGjBkjd911l1SsWFFq1TLuOBVKFwH+fk6z1Uw+PhJSooTc0amdS/aNEOIZ0KlmYPqkRT8jM+3ZNyfIqs2pE+pHe/eQ+3ve7uK98zysafKP5pIFX08NmEqWER+/gNTvunyuUL7DyNhtVkk8mCptFNA4b9KPeoLu+F+6sSfy58/FnpQo7gAcreFT3xR7XIz4FCuuJB8t5au4ercIIaRIuBYZJX861IkNKREkUshF0kuWCJImdVMNCusKQAIyLiEhvT4DstQ8vci7K+qqUf4x72jSj8ji9zEVfGAYIYQQQjybq1evSo8ePWT8+PHywAMPyMaNG6V+/fpiVDDG//i1EWI2mTLVVkPg3qdjXlKPhBCSFXSqGZhflyx3uv7IyTNFvi9Gkn80hRSO1B4Ma+a0bDVmquWd5FNHxB4brZb9G11/FiZkIIMhA+ljEuvVixL950/iaiB1dW3qOLFFRyjHa6nBr4tv5Zqu3i1CCCkyhr/7sRw7fTbDusth12TEex8X+nd3SJOAXL9jd74/a+m6zRIXn6Cy3+7pmlr/luQtU43yj3kDNWKTju13mfQjIYQQQtyfzz//XFasWCFTpkyRH3/8UYKCgsTodG13gyz8cpI8cPcdUqtqJaldrYo80fdu+ePrSdKmWWNX7x4hxM0xliguyeA427TLuUzRhp175OipM1KnOvWB84ItzalWmPWrLGUrScrZE2K9fL7QvsPo0o/mMuXFXD61Pt314le9rhS/5R6JXbFA4lYvkoAW7cWvZgNxBdao8FSHWvhVEV8/CXlylPjVMG7EGCGEOBvTbNy5J9N6VEHYsKPwxzQdWzWTL3+eJ2cuXFJ/VStef5H2BWnSj/jMcmVKFeBeepNTLbU2GMkdSUf3iVhT1LJfg7zVmyWEEEKIcbHb7RIeHi6lS5eW1157Te6++25p1aqVeBO1q1WWsUOfdPVuEEI8EGaqGZTTFy5m+/qp89m/TrKWf4RMY2FhLpfqDEqh/GOeSdy3VT36N2pVIHJaQd3vE3PZShhppspAJidJUWOLjZbwL94S65ULIiazhDz+kvjXbVrk+0EIId48pmnRsJ4EBgTkWwLywuWrKrBJL9FNco8pzalmT4gTW1wsmy6XJKXVU7NUqSnmYDpyCSGEECISHx8vTzzxhLRr106ioqLE19fX6xxqhBCSH+hUMyjVK1XI1+skM7bItJpqIYXnVLOU1eQfL4jdZuPPkEsQtZ5y/pRa9m90/fXU9Pj4+UvJB55V9XpQ4y7mr9lF+nvYEuIk/KvxknLhlJKiDHl0uAQ0vn5ZS0II8VRcPabx8/WVG5s1Usvrt1+/BOTCFatVRHBQYKDc2v7GAtxD76qpBqzhl126L54Czrf0emoNaSgjhBBCiMjx48elQ4cOMmPGDOndu7cEBgayWQghJI/QqWZQIIPUrnmTTAU3kcDTvmVTSj9eh4PDnpiglk2FKf+YlqkmyUnpTjySe+lHOML86jQpsCbzq9VQAm/qoZZjV/wuyaePFMnPYU9KlPBp70ry6aPqefD9QySgRYci+W5CCHHLMU2LpmoM46oxTce0umobd+4Vq9V6Xc6NBctWqeU7b24vAf7+Bb6PRkcFNfmkTl1YVy13INPdGnZJLfs3YD01QgghxNtZtGiRtG7dWo4dOyZz586VCRMmiMXCykCEEJJX6FQzMBNffV5aN85YBwpGqUmjhrtsnzwVW0RqPbXCzlQzl6uUvpzCump5dqr51W0qPr5+BfqbBPV8SNVpE7tNIn/6TOwpyVKY4PPDv50gycf2q+cl+g6UwLa3Fup3EkKIu/Pxa8PVGMZVYxrUQAORMTGy78jxPL9/z6GjcvzMObXcm9KP14WP2SKmkqnyhdZrV67vQ7wMLUvNJyBQfFmPlRBCCPFqkpKS5IUXXpBKlSrJli1bpF+/fq7eJUII8VgYjmBgSpUMlimvvSBh0bFy9uJlJY9UFNHcRsQaeTV92VyINdVMAYFiCg4RW1SEcqr510+NjCdZg1pnSUf2FKj0Y4bfxD9Agu9/VsI/HyMpF89IzD9zpESPBwvlJ7FbrRIx82NJOphqBAvq+aAUv7lnoXwXIYR4EqVLBsv374+Vo6fOqBpqRT2mqV2tipQrU1ouh12Tddt3S7MGdfP0/t+WrVSP1SpWyBTwRHKPuVRZsUWEMVMtlySm1VPzq99MfMxmnmqEEEKIFxIeHi4lSpQQf39/WbJkiVSsWFGCgoJcvVuEEOLRMFPNC6hTrYqq3UGH2vVjTctU8ylWXEkMFibmsqkSkNYr5wv1e4xC0tG9Si6xsJxq6nPrNpHATt3Vcuyy+ZJ8Nu9ZCjmBGnqRv3wuibs3qufFb+0rQbf1L/DvIYQQTwZjGVeMaXx8fNKz1dbv2JWn9yYlJcuf/65Ty726dVafRfJXV42ZajmDsVHSsX1qmfXUCCGEEO8EGWldu3aVMWPGqOd169alQ40QQozoVEPNiUuXLklMTEy22yUnJ0t8fHyePjsqKkouXryY6S8lJSWfe02Mji3yWqFLP2pY0iQgUy6nykSR3Ek/WipVF3Op0EJrrqC7HhFTqbIicH79/LnYrSkF2u9Fz58uCVtSMxkCb7pTgu56qMA+nxBCSP7pkOZU27H/sMTmYQz676ZtSjYS9O7WmT9FPjPVgC2c8o85oRxqyUlqmfXUCCGEEO8CNoavv/5aOnfurGyePXtSAYcQQgzpVDt9+rSMGzdOateurVKR3377bac3hV9//VU6deokISEhEhoaKvXr15effvopV9/x8ssvS7Vq1aRFixYZ/vbtS43iJCQrrBFh6tFUsnSROdWsrKmWI+gTEvdvL9QsNQ1TQDEpef8QtZxy7oTErVhQYMcQs2iWxK39Sz0v1uYWKdFnIDMZCCHEzejQMtWplpySIlv3HMiz9GObZo2kSoVyhbZ/3uRUY6Za7uupWSpWL5KgMEIIIYS4B0hAGDBggAwePFjatWsny5Ytk44dO7p6twghxFC4jVNt9uzZ6nHFihUSHBzsdJvY2Fh54IEH5NZbb5XLly9LdHS0DBs2TB566CGZM2dOrr6nQ4cOmTLVmjdn3SqSPbbIsEKvp6ZhLpvmVAu/ouqFkayxXjon1rBLReJUU99Rv4UUa9dNLcctnSv2y2fz/ZmxS+dJ7PLfUj+/eXsJvu8Z8TG5TddMCCEkjdBSIVK/ZnW1vG577iQgwyIiZc2WVOdG725d2JYFJP9oi4lMl34m2TvV/Bu2ZBMRQgghXsS6detkxowZMnLkSPnnn3+kfPnyrt4lQggxHG5juUVnj0y1GjVqZLmNyWSSmTNnyptvvinFixdXz4cOHSotW7aUWbNmFen+Eu/Cmib/aCoS+cfUmmpit0vK1QuF/n2eTOL+rerRJzBIfGvUK5LvLNHrsdTzwGoV6x8zxG61Xvdnxa5aJDGLUzNt/Rq2kpBHXhAfs7kA95YQQkhBkl5XbfvuXG3/x4o1kmK1SjF/f7njpvb8MfKJuVS5TCoCJDMpVy+m1+b1o1ONEEII8QrOn0+993fr1k327t0rEyZMEIvF4urdIoQQQ+I2TrXcEBgYqLLSHLHZbHm6UaBeW0JCQgHvHTEytjTDjbkI5B/NZcqJmFIdK9bLdKplR7r0Y8NW4pPWZoWNqVhxKfm/VBlIuXBK4lcuvK7Pidu4XKJ/+1Yt+9ZuLKWeGCk+Ft+C3FVCCCEFTIdWqeoGR06dkUthqQE32bEgTfrxtk5tJSiwGH+PfGLS1U61hl9me2ZB0sHULDUf/wDxq9mA7UQIIYQYGKvVKmPGjJE6derIzp071bpGjRq5ercIIcTQeHzIwr///iu7du2Sp556Klfbr127VqU+Jycnq/pqo0aNkoEDB2b7nqioKPWnceHChfQbF/7cFewbHI7uvI+e0Eb2lGQlM6QoEVIE7emjHGvWKxck+dJZ8XXB7+cJ544tPlaSju9Xy74NWhbpvlrqNxe/1jdL0rbVEvvPHPFrcqNYylfJ9fsTdqyT6NlTUz+rWh0JHvCK2MwWlf1mJDzhPCoK2A6ubSMzsz9JAXJDk4bi62uR5OQU2bB9t/S+LWtJx4PHT8qBYyfVcu9bO/N3KABM/gHiUzxY7LFRrKuWC+lHv7rNGLBDCCGEGJirV6+q5APIPKJcDhxrhBBCCh+PdqqdPXtW3TzatGkjTz75ZI7bt2jRQlauXCnt27dXhrsvvvhCvS88PFxeeumlLN83adIkJTnpSGRkpJKhdFdwjMjKs9vtNCrmo43sOnmhWB9fiQsPl8LGFlJW5MoFiTt7UhKL4Ps88dyxQfrRZhPx8ZG4CjUkvojbKeWme8R2YIeY4qIl/MdPxfzYy7mqhWY7sltsc6YqeU8pV0Xs9z4rkfEJIvgzGJ5wHhUFbAfXtlFo6H+ZLYTkl2IB/tK6cQPZuHOvrNuRvVPt92Wr1GP50NLSrkUTNn4BYS4VKilwqoVfYZtmEQyWdGSPWmY9NUIIIcS4bNmyRfr3769kH6dMmSLDhg0THx8fV+8WIYR4BRZPjsa4/fbbpUSJEvLHH3+Ir2/OsmlPP/10+jKMds8//7zKXMPNJzun2ogRI2TQoEEZMtXgyCtZsqSUKlVK3NlIiRtqSEiIVxu089tGyRGXJCJtOaRqDTEFBRf6fsVUqi7xR3aLOfKqS84xTzh3ok4fkkR0YjXqS6lKuc8SKyiswcES3uMhsc/9UuTccQnYu14CO9+d7Xtg5Iqc95VyBprLVpSQZ8aKqUSIGBVPOI+KArYD24gYi46tmiun2oYdu5Uj2JnxAnXUFq5Yo5Z73drZq/vAgsZcuqyknD3OTLUsSDq2X+xJGCHRqUYIIYQYGdgyU1JSVPJAx44dXb07hBDiVXikUw1SjN27d1d10VavXi3lyv1XtDyv1K9fX+bPn5/tNsHBwerPERhI3N1IYjKZPGI/3bmNkqLTXGoWX7EEhxRJ5I9vhSoSD2P81Qsu++3c+dyx22ySnFYvJKDxDS7bR3ODVmJu2VESd6yT2CW/SLGmbcRStpLTbZNOHJKobyeIpCSLqVRZKf3Mm2IOKSNGx53Po6KE7cA2IsahQ6tmMvHbH+XKtQg5fPK01K9ZPdM267btkqvhqeOH3t2yzmYjecdcqqx6ZKaacxLTxkfm8lXEXPr650iEEEIIcT/i4+MlOjpa2UGhvhUbGysVKlRw9W4RQojXkbNWmZsRFxcnPXv2lEuXLsmKFSukSpUqWTreLl/+r4A5IomdsW7dOqlbt26h7S/xfGyR19SjuWTpIkul1xwz9thoscVGF8l3ehLJp4+KLSa1zqF/o9Yu3Zeg3gPEFFRSJDlJIn+eqhx+jiSfPSHhX40Xe1KCmIJDpPQz45R8FSGEEM+jUe2aEhJcQi2v377b6Ta/LV2pHpvVryO1q1Uu0v3zGqfaNco/OiMprZ6af4OWRfq7EEIIIaRwOX78uHTo0EF69+6t6lFDuYsONUII8XKnGrLOLl68qP7gAEO0BZb1jrHk5GTp27ev7NmzR3766ScJCAhIf09Y2H91rzTJxnr16qU/RyQHbj7z5s2TI0eOyLZt22TAgAGyatUqGT9+fJEeK/EsrBFX1aOpZNFlFZnL/ZftlHL5XJF9r6eQuH+bejSFhIqlYjWX7gvkQIP7p9Z0TD6+X+LW/ZXh9ZRLZyX8yzfFnhAnPoFBUmrIWLGUreiivSWEEFIQmaftWzRVy+u278r0emR0jCzfsEUtZ1dzjVy//COwRYaJ3WplM+pA9l7KxTNq2b8RnWqEEEKIUfjzzz+ldevWcuzYMXnxxRfVeJQQQojrsLjTDeLZZ59Vy8WKFZM5c+aoP9QtO3TokFoP59nOnTuVM+3ee+/N8P4GDRooHWENvK98+fLpzyHf+Nlnn8nEiRNl9OjR4u/vL40aNVKFPXFjIiQ3mWpFBeps+fgXE3tivKRcPi9+NRvwB3LiVEOWmjsU4g1o0UH8m7eXxF0bJOaPWWJGpmFKkojFT6J+/kxl1eH3LP30GPGtmFkmjBBCiGfRsVUzWbJ6vWzZs1+SkpLFz++/2r5Yn5ScLL6+FunZmfUtCitTDfVJbVHX/ntOJDEtS83Hz1/8ajViixBCCCEGqM/95ptvqmQA2DBRvgZlbIx6rAhM+335agmLiJSqFcvLvd1vlRubNnILuw8hhLilU61fv37qLzuqVq2qHGu5Ac4z/Olp1aqV/Pjjj/naT+J9WNOcaqaQonOqYcCAbLWUM8fEevl8kX2vp/weKWePq2X/xu7jEEe22pUju8UeFysRX76V8UWLr5R6arT4Vqvjqt0jhBBSgHRo1Vw9JiQmyfb9h6Rdiybpry1Ik37s2vaGdJlIUnDo64RBApJOtf9IPLBdPfrVaSI+vn487QghhBAPB+pdU6dOlfvvv1+mTZsmQUFBYkQQpPbMmx/Imq07xeTjIza7XXYeOCwLl6+WR3r3kNFPP0HHGiHErWC+MCG5zlQrOvlHYEmTgEy5QqeaM4OR+PqJf91U+S13wFwiRMzBzh2vlvJVxK82I8YJIcQoVC5fVmpUrphJAvLE2fOy48BhtUzpx8IBUso+fgHpcockFXtKsiQd3qOW/Rq2YrMQQgghHszRo0dVaZyKFSvK9u3bVQkcozrUwKezflUONQCHGsDxgx8WLJY//l3j0v0jhBBH6FQjJBtwE7dGpNbrM4cUsVOtbGX1aGVNNafSjyoK289f3IXkC6fFmlbHxJGUcyfSa5wQQggxBh1aNcvkVFuwbJV6LF0yWG66oYXL9s3IqGz+tLpqdKr9R9KJQ0o2HPg3ZD01QgghxFNtUF9//bU0btxYPYJq1aoZOksLWWo/L/ony9eRuTZzweIi3SdCCMkJOtUIyQZ7bJSINSX1YinCmmoA8o8g5epFsdusRfrdbh2FfSjVeBngRtKPwBqWvTRtytULRbYvhBBCCp+OaRKQ+4+ekPCoaLHZbLJweapT7a5bbhJfi9uorBsOU1odNcg/klSSDqbWUzOXrSiW0ApsFkIIIYbEbk2RhJ3rJeq37yR64UxJOrYvPaPJ04mPj5cBAwbI4MGDpX379tKrVy/xBs5dviLRsbFZvo7MNYy3CSHEneBsn5BssEakSj+6JFMtzakmKcliDb8qljLlxdtRA+bEBLXs38i9nGqW0Ir5ep0QQohn0bZ5YzGbTGK12WTDjj0qO+385avqtT63dXb17hkarY4aM9Uyy2P7N2CWGiGEEGMC9ZdrX70tNp38c+yKBeJbq5GUGvSqmAI9Vx7x+PHj0q9fP9m5c6eMHDlS3n33XbF4SYBWMf+cFYgC/FgrlhDiXjBTjZBssEamSj+Kj4+YgksVaVsh0jh9PygBqUjclyr9aKlQVcyly4k7gX3yUzXeHGUZfMSvXjP1OiGEEONQonhxaVa/jlpev32XLFi2Ui3Xq1lNGtau6eK9MzbmUqHqkZlqqVgjr0nK+VNq2Z/11AghhBgQW2KCXJs6TmwRqQFMepKP75eImR+LJ7N8+XI5duyYzJs3TyZMmOA1DjVQoWwZaVK3lpJ5dAakL++4qV2R7xchhGQHnWqEZIMtrZ6aKaik+JiLdlBj8i+WLjmZcpnSgSBx/3a3zFLTCHnsRfGrB8faf+B5yKMjXLZPhBBCCo+OrVMlIFds3Cp/rlynlvt062LouhfugL6mmlEkn/JD4oFU6Uex+Ipfncau3h1CCCGkwEnYvkZsUeEoOpalDHLy+ZMe1fJWq1UOHjyolgcNGqSW+/btK97Ic4/dL3axZxpDw9EW4O8ng+7t7bJ9I4QQZ9CpRkgOkb/qQili6UcNS7nKqftx5Zx4OymXz4s1rS6Zv5vVU9MwBQVL6WfGSeirUyRk0KvqEc+xnhBCiPFoUi81Uy0sIlKSkpPV8rL1m+VaZJSL98w75B8lOSm1/q2Xo9VTg0PNxy9nCSVCCCHE01C11XMIWko6vEc8hbCwMOnRo4eqnXbx4kXlTKpUKa0EiBfS+cZW8vFrI5Scup6qlSrId++NkdrVUm1jhBDiLnhPPjEh+chUM6dljBU15rKVRI7sUQ4lbydxf6r0o0+x4uJbo4G4M5B6pNwjIYQYn+/m/ZFp3fb9B2XEex/L9++Pdck+eVOmmiYBCUUBb8VutUoiDI2UfiSEEEI8gq1bt6r6aefPn5eJEydK+fLlXb1LbsGdN3eQbh3ayPodu+VaRJRUrVBOWjdpSAUIQohbQqcaIbnJVHORU81SLjVSiU61/5xq/g1aiI/Z7JLfgxBCCNE4cvKMbNq1N1ODQJVow449cvTUGalTnfU0CwNV59ZkFrFZlQSkb7XUjEFvJPnUYbHHx6pl/wYtXb07hBBCSKGAOuUJO9dnv03dJm7d+pCsnj59ugwdOlRCQ0Nl5cqV0rFjR1fvllvha7GorDVCCHF3KP9ISDbYIrVMNdfKP6IYrz0pUbwVW0K8JB3b79b11AghhHgXpy9czPb1U+ezf51cPz4ms5hLhaZnqnkzWj01c5nyYk4LxiKEEEKMRkDrm1KDarKQgPRr0EJ8K9cUd8Zms8kPP/ygJB+3b99OhxohhHgwdKoRkg1WTf7RRTXV9MaRlCvnvVs/3ZqiBtD+DRmFTQghxPVUr1QhX6+Tgqmrhkw1dyLl4hlJ2LtZPRYFiQe2pxsTUY+FEEIIMSIm/2JSashYMTkJePat1VBCHhku7srx48clMjJSzGaz/P7777Js2TJKPhJCiIdD+UdCssCWmCD2hDiXyj+qmiFmi3IoWa9ccPvIq8KWfvStVter66YQQghxHyDt2K5FU9m0a4+SfNSAXwPrKf1YuJjczKlmi4mSiBkTJenInvR1fnWbSshjL4opKLhQvtMaHSEpZ4+rZf+GlEoihBBibHwrVpOyb0yVxD2bJen4AfGx+Ip/49biW6uR2waW/Pnnn/Lwww9Lz549ZdasWVKqVClX7xIhhJACgJlqhGSBLa2emivlH5W8UWhqpHvK5XPijdhtNkk8kFZPrTGlHwkhhLgPH782XDnQ9OD5pFHuGy1tFFTgkRs51VIdahlr7OF5xMxJhfadSWnSjwjAcvc6MoQQQkhB4GO2SECLDhLcd6CUuOdR8avd2C0dalarVcaMGSN33XWXVKpUSd544w1X7xIhhJAChJlqhOQg/QhMLpJ/BJZylcR66aykXPZO+ceUcyfEFhWhlllPjRBCiDtRumSwfP/+WDl66oyqoQbJR2aoFbH8oxvUVEu+cDpDhtp/2CXp8G4lBWmpULXAvzfx4E716Fe7kZLFIoQQQojrCQsLkwcffFD++ecfuf/++2XatGkSFBTk6t0ihBBSgNCpRkgW2CJTnWo+/sXEFOA6Q4WlbCVJhNHIS51qifu2pktwWrxU/pIQQoh7A0canWmuyVSzx8WILTHepU4la9jFbF9PuXqhwJ1qdptVEg+mZqr5N2hRoJ9NCCGEkOvn3LlzsnHjRpkyZYoMGzbMLTPpCCGE5A861QjJAmua/KOr6qlpmMtVTpd/tNvtXjcg0+qpIUvN246dEEIIIc4xlwpNX0a2mqliNZc1lSW0Yr5evx6STx9TDkXgx3pqhBBCiEuBrWbz5s3Stm1badasmZw4cUJKl3atLYkQQkjhwZpqhOSQqWZ2ofSjJv8I7AlxYouJFG/CGh0hyaePqmVKPxJCCCFEwxzyn1PN5uK6ashC86ubsbaehm/dJoUj/Xhge7pEeWF8PiGEEEJyR3x8vAwcOFDatWsny5cvV+voUCOEEGNDpxohOdRUM5V0D6eaeKEEZOL+VIORmC3iV8+5sYoQQggh3oePr5+YgkPcpq5ayGMvik9g5nop/k1uLJTvS9KkHxu2YiY/IYQQ4iKOHz8uHTt2lO+++05GjhwpnTt35m9BCCFeAJ1qhGSBLU3+0Rzi2pR9n+LB6UaalCvnvVL60a9OE5fWSiGEEEKI+2EulVpXzeriTDWFr5/YExPUYuDNPcW3XjO1nLBhmZKEKkhsMVH/ZfI3bFmgn00IIYSQ3LF48WJp3bq1HD16VObOnSsTJkwQi4VVdgghxBugU42QHGqqmV2cqYY6YpayFb0uU81uTZGkQ7vUMqUfCSGEEJKlU80NMtWSju4Vsaao5eK39pES3e9TyykXz0jSwZ0F+l2JGB/BUWcyi1+a844QQgghRcuGDRukUqVKsmXLFunXrx+bnxBCvAg61Qhxgt1qFVtUROpF4uJMNWAuV1k9pniRUy3p+AFVRw74N27t6t0hhBBCiJthLl3ObTLVNMeZpVJ1MZcsLb41G4hvtTpqXeyqPwqlnhq+wxQQWKCfTQghhJCsCQsLk3379qnlcePGyebNm6V+/fpsMkII8TLoVCPECbbocBG7zS0y1YClbGpdtZTL58TbpB/hULSEVnD17hBCCCHEzTC5kfxjYppTzb9By3SlgcAu96Q73JIvnCqQ77HbbLp6apR+JIQQQoqKrVu3SqtWraR3796SnJwsZrNZihcvzh+AEEK8EIr9EuIEa0Sq9CMwlXR9ppqlXKpTzRp2SWXR+ZjNYnQS96U61fwbtXL1rhAvBPVvrl69KgkJCWK1WvP9WYmJiRIdHa2MrCT/bYQJbEBAgISGhrJNCfFizKVTnWq2qHCxpySLj8XXJfuREnZZrGl1b/3SnGogoHk7iQ4JFVvEVYlbtUhK3v9s/r/r3AlVUw3QqUYIIYQUzVxl+vTpMnToUClTpoz89NNP4uvrmjEHIYQQ94CZaoQ4wRYZlnaFmMUUVNJt5B9Rq8N67bIYnZSrF8WalpXn3+gGV+8O8cJJ07lz55RTLSkpKd+fByeRn58fnT8F2Eb4XfD74HfC70UI8e6aaqgvZo1IG7u5AC1zzMfPX/xqNUhf72O2SPGbe6jl+K2rxRqdKi1eENKPpuBSYqlUI9+fRwghhJCsiY+Pl4EDB8pTTz0l7dq1k+3bt0vHjh3ZZIQQ4uUwU42QbDLVTCVLiY/J9b5nJX8IY7PdriQgLWUrijdIP/r4F8tgnCKkKICzBhlT5cqVU5GI+QVOn5SUFLFYLHSsFWAboZ7B5cuX1e9VtmyaYZ0Q4pWZapoEpKvkohMPpUo/+tVpkilbrli72yTmr1/FnpQg8ev+lqDu9+Xvuw6kST82aMl7CiGEOAEqE+Hh4RIUFKSUDbIbf8bExEiJEiWybcfcbkeMez6hbtpLL70k7733npqvEEIIIa73FhDixplq7lBPTYt8NoWEqmXrlQviLdKPfg2au0zKiXgvkHxE1lRBONRI4YHfB78Tfi9CiHdiCggUn2KptUys11xTV81uTZGkw3vUsl+DFpleNwUWl2LtblXLcWuXiD35+jOgbXExknzycOp3sZ4aIYRk4OjRozJq1CipUaOGCriCXJ/Tfttul3Hjxknp0qWlYsWKUqFCBfnyyy+veztiTFauXKnk6eGc3bRpk3z44Yd0qBFCCEmHTjVCnGDVnGoh7mNU1+qqIVPNyNgS4yXp6F61TOlH4qpoRNTsIu4Pfqf81rwjhBhDAtIW7hqnWvKpI2JPiEvPHnNG4M09leIAaqHFb1tz3d+VdHi3iN0m4mMS//rNr/tzCCHEiHz11VcSHBwsy5Yty3a7SZMmycSJE2XJkiUqAw3vQ62s+fPnX9d2xFhgbjFmzBi55ZZbZMKECWpd8eLF0x2tJ06ckG3btsnBgweV0gYhhBDvhHnLhGQr/1jarZxqSYd2ifXKeTEyKtrbmjo49W/UytW7QwghhBA3xlwqVFLOn3RZplpiWj01c+lyYs5CnhuylP5N20ji7k0St3KhFGvb9bqkGxP3p9ZT861RV0yBQfncc0IIMRbIJAJwgGWFzWZTjpKnn35a1ccCvXr1kt69eytpv759++ZpO2IsIC//4IMPyj///CP333+/DB8+PP21M2fOKIdqRMR/9VEhL9q9e3dp3pyBLoQQ4m0wU40QJ9giU51qZjdyqpnLaplq572inppvtTpiLhHi6t0hhBBCiAfUVUNNNVeQdDCtnlqDFtk6yop3uUc9plw8I0mHd+X5exAdrznw/Bsy6IgQQq6Hffv2qZq8yELS07VrV9m6dauqq5yX7YhxwO/aqlUrWbFihUyZMkV++uknJf0IUMP5hx9+kMjIyAzvgQz9ggUL5MCBAy7aa0IIIa6CmWqEODFaaPKPWh0zd8BSrnK6w8+WEC+mgGJiNJTBKM2p5t+otat3hxDDMGPGDFVcHVG1kydPVtGUjkYCQgjxZPlHVzjVIOeYfOaYWvZ3Uk9Nj2/NBmKpWltSzhyT2H//EP/62W/vCLLxbFHhqd9FpxohhFwXp06dUo9Vq1bNsL5KlSrq8fTp09K4ceNcb+eMqKgo9adx4cKFdFlBd5Etx34gG89d9scdjhlZanh9+fLl0rFjR7WssW7dunSpR8cAGjxH/bW6deteVxZ6YcPf2jvg7+wdeOPvXNTHnZdSMHSqEeKAPS5GJK2IvDtlqmk11QAkIE1Va4vRSDl3Mj1LkE41QgqOTz/9VBkB4FRDwfVBgwbRqUYIMQSQXQTW8Ktit9nEx1R0QhyJqsaZXcRkEr+6TbPdFoY2ZKtF/vCxJB3coTLWLBWq5v67DqRmqZmCSoqlcs187zshhHgj8fHx6bJ9erTn2uu53c4ZqMX25ptvZlqPLCetNpergWESMpkIavWWWtLOjhm/46ZNm6RLly7SunVr2bBhg/qNw8NTg1g04ETN7reLi4tT8pAIYnQ3+Ft77/ltdHjM3vE7F/VvHRqa++QaOtUIccCa5tRxt5pqKmvO1085/CAB6WtAp5qWpWYqESKWKrVcvTuEGIbHH39cSpYsqZZRG+DGG2909S4RQkiBYErLVEM9Vlt0RJEGRME5Bnxr1BdTsZwNpQEt2kv0HzPFFhEmsasWScn7huTZqaZkJovQcUgIIUZCc4zACaInNjZWPWpyf7ndzhkjRoxQAWz6TLU2bdqosXipUqXEXQyUCPYICQnxKgO8/piPHz8u//vf/2Tv3r1y8OBBqVGjRpbvReZhThkSgYGBbvP76uFv7Z3ntzfAY/aO39mdf2s61QhxwJYm/ehumWowoFhCK0rKhVMqU82IpEs/NmxFgxEhBcA333wjZcqUkaFDh6avGzt2rHp0lIFcuHCh7N+/Xy2bTCYpX768qivQtGlTOXHihMyePTvb77JYLPLSSy85fQ3RnvPmzVP1CJ599lm3jOL0NFDnY9asWbJ9+3a59957pVevXk63gyEH5wF+w5o1a8rAgQOlYsWK170dIe5aU02TgCyqsZuSrD60K1fSjxo+ZosE3tRDYv74QeK3rJQSPR9UmWc5YUuIk+QTB1O/q2HLfO45IYR4L5DoA3CoYBysgfEPDHWaYyW32zkjODhY/TmC97mTMRDjfXfbp6I65r///lseeughJen4888/S+3a2QcsV6pUSWWi4d7vDH9/fzXncte29ObfmsdsbPg7ew8mN7ym3SbMMSkpSebOnSs9evSQsmXLyvjx47PcFjfAzp07Kymr9u3by6+//prr78nPe4l3YI1Idar5FA8WH2SGuRHmcqkGTmSqGQ1Vl+TUYbXs35j11AgpCD7++GPleHEGZCD//PPP9Ocoxj1x4kSJiIhQNQUWLVqknGpDhgyR5ORktV77W716tYwaNUrOnTuXvs6xcLfG/fffL40aNVIOG7wnq+1I7nnvvffUb3P27FllCNizZ4/T7Y4ePSrNmjVThdcx5sEjnmP99WxHiDuinFJp4zXrtaKrq5Zy4fR/ktW5dKqBwPa3iY9fgEhKssSt/TtX70mCzKTNCg3JPNdiI4QQ8h9wltWqVUsWL16coVnwHHYiTd4xt9sRzwJOMcyBevbsqRxlGPP2798/x/e1bds2S4cauOGGG1SAISGEEO/BbXr90aNHq6gfRLA/+OCD6Wn1jqxatUrdAN9//31lqPvnn3/U9vBY5nQzzM97ifegGUjcKUtNw1KusiTCkGPATLVESChhoGq2iF/9/6IBCTECx06flXOXrkj1yhWlTvXc19ApapCdhnukvhbbc889JwMGDMiwfvr06bJkyRJ1765QoUK2n9m7d2/5/vvvVeDMxo0bC3X/vYV+/fqprEBfX1/55JNPstwO21SvXl1+++03JZeA7DNIf44cOVKty+t2hLgjOGfNIaEqix+ZakUt/ehTvESeJKtNgUFSrO0tErdmicStXSLFb+2dYxCXJv3oW7WOmIIyZz8QQghJrY8FO5JmS8IjVBLg7IBklMY777wjjzzyiHTq1EluvfVW+fHHH5Wt6N9//83QjLndjngWyDiDPXDatGnZynjqadiwoToP1q5dm2H8AUcbHLCoyUYIIcS7cBun2gcffKCcW9rNKSvGjBkjt912W7rEFAx9iJh//fXXc3SM5ee9xPtqqplDyoi7YSlbST1aL59TA7jsrhVPI3FfqvSjX62GYgoIdPXuEFIgXIuMkuHvTpKNO/emr2vXoql8/NpwKV3S/Q2jHTt2VI+oM3C9ddgwaSUFS7169XLcBoYkRFNPmDAh/V6BR0jdvPLKK6pGCGo/5HY7QtxdAhJONVsROtUSD+5Uj/71mouPKW8yJIE33yVxa/8SW0ykxG9fI4Ftb81eZlKrp0bpR0IIyRI4Sd566y21DCm+Dz/8UP3BIbJmzZoMY1P0rZBCf+2116ROnTpqLASniZ7cbkfcH2SkaTXtvvzySyXXmBdbCraFY7VBgwaybds2JW2PuntQdoBTzUh2GUIIIR7mVNMcatkBww8iQzCo0QPJyBkzZsjJkyez1LbOz3uJd8o/mtwwU81cLtWpZk9MEFtUuFtm010Pdqs1NVMNxqlGlH4k7kdScrKcv3w1z+975cNPZdfBVFlTjU0798gzYz+Q90f+V+cst1QqFyp+vr5SVGDSCHKqM0Dcj0OHDinZTkhv6sFzrMfrLVu2zPV2hLgz5lJli1T+0Z6UKEnHD6hlvzxIP2pYylYU/yY3SuKezRK3cpEUa9M1S4NcysUzYotIvf+wnhohhGQN1BXwlxseeOAB9VdQ2xH3BE5RKGygvjScYjNnzlRKD9frBKtcubL6I4QQQtzGqZYbjh07JjabTWlb66lZs6Z6RO2PrBxj+XlvVFSU+tO4cOGCerRarerPXcG+4ZjdeR/dsY3Sa6oFl3K7tvMpUz59OeniWfHLRXF7Tzh3ko7tF3t8qkyHpUELt2t3Z/D6Mm4baVmget18SDd2H/hcwXy+iOw4cEjuGDAsz+/965tPpEblinn/zixqAGC9/jVI5KBeF9ahMPsvv/yiJqGos6XfTlt2fH9u9sHZe/SvXc+xZXeOuVMh26IEEbQAUbl6tOfXrl3L03bO4PjIuHha/+0TEqoeU65dLtR91tol4cgeVRMNWOo2va7vDLipp3KqpVw4JQkHd4pfvWZOt0vYv109+gQGialyTbf/TTzt3HEFbCO2gzucK946PiLeJQeK8jLfffedqoOHTEZCCCHEK51qmja2owwR0q5BTExMobx30qRJ8uabb2ZaHxkZmf5+dwSDbxwXDI4cNOe+jaxp0cAJlgBJSjM2uhWBQSJxMRJ98oiYyqRmrnn6uWPdsT51oVQ5ibIUEx93bHcHeH0Zt40SExPFz89PUlJS0tdZdcuuBPuh36+cQNvDGJPVe/C69ppmtIETBev27t0rwcHB8uijj2Z6P7YFWK+9NnXq1Az30uHDh6tI0PR9TzMI6d+T4diuw2CE/UhKSkp3DDkjNDTV2O5taAoA2m+loT3XiqnndjtncHxkXDyt/7b5FUvPVEMfVlgyTFq72HZvTl1RrrJE2Xzgnc7zZ9lLVRCpWF3kwimJWvabmMs6r7dp3ZP2XTUaSkTkf0F+7oqnnTuugG3EdnCHc8Vbx0fE88H1kHRkjySfPCRiMiulG99K1TNsc+rUKenTp4/s2LFDlX9599131Zg3uzkDIYQQYlinWkBAgHpMSEjIsF57XqxYsUJ574gRI2TQoEEZMtXatGmTrsnszoNwGBVQlJeT2ty1kT05Ua6mZUyVqFRV/Nzw9w0vV1lSTh4S/7hICSqk/Svqc+faif0Cc3qxJjdIUGnPkLTk9WXcNoqOjlb7rXcmVK1UQWWJ5YVT5y/I4Dfey/L1r8aPkuqVKuZZ/jE7J4cjcGpBws/Ze3D/w+t650r58uVVbS3NqXLPPffI//73P9m3b1+G+6TmiMF7tfej3fCngd9c/73aOaB/j4aWoYZt8mIMx36gJoI734tdRcWKqefW5cuXM6zXnleoUCFP2zmD4yPj4mn9d1KV6hKpFhIkJMBPTAhAKsR2kdOHBG7nYo1a5WsslnBLL4n+6ROxH9srJZJixVK+SobX7YnxcvXMUbUc1LytBHhAX+dp544rYBuxHXiuEHJ9pIRdkojp70nKhdPp62IWzRL/pm2k5MMviMk/1e6H+QHqAs+ZM0f69++f3vcSQgghXulU0+QZz5w5k2H92bNn1WP16tUL5b2I1MefI5gouvtkEQZHT9hPd2mjlPD/IoAtpUPdst1805xqtisXCnX/iurcsUKq6WLqdRnQ5Aa3bPOs4PVlzDbSnDp6546/n5/UrJK3zFBs365FU9m0a4/oVQ3xsVjfpU3h1w+Ew+T06dOZHFVwmCAjr1KlSple057jN0MdUhR3R0FvOFAct8Gjtvz6669nuy/O3uNsm7xmmGB7Tzq/ioo6deooo/aWLVukV69e6evxHE5IvJ6X7ZzB8ZGx8aT+27eMzvkbeU3MJQpHHhv4RIeL9fJ5tRzQsFW+2iewVUeJ/XOW2CKvScKaxVLyviEZXk9A3TZramZvQKP8fVdR4knnjqtgG7EdeK4QkjfsyUkSPnWcsh84Ajnla7Mmy98BVVUNPASFQXUjL8GIhBBCSF5IDTX3EGDcad68ufz7778Z1q9YsUIZDuvXr18o7yXegy0ytZ4aMJcsI+6IuVyqYT/lSqpBx9NJ3L9NPfr4BYhf7cau3h1CCpSPXxuuHGh68HzSqOFF0tLINNu9e7ds2rQpw3o4yWDs7NmzZ7bvr1u3rvTr10/J/EFmkXgOMCI88cQT8u2336paeQCPqCuB9frMwdxsR4g7Yw4pLeKTOq2xhl8p1O+yH9ufuuDrJ361Gubrs3wsvhJ4Uw+1HL91ldhiVL5dOkkHd6hHS5VaYi4Rkq/vIoQQQjyZhB3rxBp2CRIXmV67Fp8o947/WB5++GH5/fff1To61AghhBQmHhe2MWrUKHWjxB+MgevXr1cFR997770M0e2o5bJw4UI5duxYnt9LvBdrRKpTzcfPX3yKuWe9PEuaUw0DSntKsjLIGMGp5le/mccfCyGOlC4ZLN+9N0YOHT8p5y5dkeqVK0qd6s7r5hQGTz31lCxfvly6du2qojaRmbZr1y75888/ZcqUKdlmIWm88sor0rp1a/nxxx+VkyWv/PDDD3Lu3DnZuXNneu01ZDh17NhRbrrppus6Lm8HAUFwgmkynXPnzpWDBw9K6dKl5ZNP/pMpffvtt5VTtVmzZtKhQwc17mnUqJGMHz8+w+fldjtC3BUfs0VMJUuLLeJq4TvVju9Tj351moiPr1++Py+ww+0S+88csSclSty6vyXojv+lfo/dLokHUp1q/g1b5ft7CCGEEE8m4cB2p+t3XgqXJ//cLBdj42XCsCeld+/eRb5vhBBCvA+3carBIPT000+r5aioKGUUmj59uqpbpneM3XfffXLlyhVl2INGMurBwOD33HPPZfg81HUJC/sv6ygv7yXeC+R3AAwz7upotZSrnLpgs4k17LJYyqc990BgQEo8slcto8AwIUaldrUqUr9WjSLvV5BlhPsrnCRr165V98bbb79dyTrWrFkzw7aQ/mvXrl2mz2jVqpVMnDhROW80kPmN+2dQUM51i/CdERERSoYZ78Hn4Hl8fHwBHaX3UbVqVenevbta1h5B8eIZg0ECAwNl6dKlSsrx5MmT8vLLL8uNN96Y6TzM7XaEuDPm0mUL3almt1rFfuKAWvZv0KJAPhP134q16Spxa5dI3Nq/pPitfVSQkfXKhdSIfOVUa1kg30UIIYR4Kra0AGg9s/efkpdX7JRSAX4yr99Nckv3Wzh+JYQQ4l1Otbvvvlu6dOniVG/ekaFDh6o/GOpg0HNm9IHBcMKECdf1XuK9JJ87qR5NbpqlBsyhFVIljuw2id+6Uoq1vlksFQom8yXl4hlJuXpBfEqVE/EPyvX2ltCK17UPcZtXiCSnSsrRqUZI4YHsI/xlBzLZskJfTw3A4YK/3PDMM8/kci9JboEsJ/5yA8Y5bdq0UX8FsR0h7oq5VFlJlgNivVZ4TrWU00dFEuML1KkGAjv3lLh1f4ktOkLit62RwLZdJTEtIt8nIFB8q9crsO8ihBBCPBF7Wo1RPWaTj7SuUFq+vPNGKVc8QCQl2SX7RgghxPtwG6eav7+/+ssLJUqUyPK1nKLns3sv8T5sMVESOWuyJB3Zo54nnz4q1z4fKyGPvSimoGBxJ+wJ8Ug/EUmxSezSeerPr27TfO0rjj9ixsT041fUaCC2J0aKuWSpXG2fl31w9v7IWVPcsr0JIYQQ4hmZasBWiJlqSYdSZWxNpULFrCkHFACWspXEv/ENkrh3i8St/EOKtbkl3anmV7+5+LC2ISGEEC/HJ6ikejwVGSvHI2LklurlpX+DatK3flUxpQXL05ZACCHE65xqhLiSqFkfS/KR1BoZGklH9krEzElS+plx4k7AGeUYgQXn1NUPR4h/k9xljjgCI44mfZnOyYNybdJICXDymc62z8s+OH+/e7Y3IYQQQjwjUw0UZqZa0qFd/zm6CljtoniXe9T4KOXCKVVvNunYfrWe9dQIIYQQEXNAoPx14qI8+/dWCbCYZePjt6tHzaEGTCXLsKkIIYQUCXSqEa/HfuW8pKTV9cqIXZIO71YShwUlr5hfki+czphNpgNOqvh1fxfo99nz+Jn52wf3a29CCCGEeJZTzRYTqWq2+vjlTQEjJ2yx0ZJy5qha9qtf8DXOfGs3EkuVWpJy9rhE/vjpf/LYBSgzSQghhHgiVqtV3p23WD5cuEHqli4h3/RsqxxqjtjiYlyyf4QQQrwPOtWI12MPv5xtG6iaYW7i5LGGXcz2dUvVWmIuEZK3z4yOkJQzx3P9mXndPq/f507tTQghhBDPkn8E1vCrYilfcPKMIPHwbhG7XdW19a3bRAoaZL4Va9dNoud+Lfa46PT1kT9+QnlsQgghXkt0dLTce++98vffy6RXvSoy8daWUtwvC1NmcmIR7x0hhBBvhU414vX4lCqX/UUSWtFt2iinfQl56Pk8O6SQGXb1/edz/Zl53T6v3+dO7U0IIYQQz8AUEpq+HL9jrRRr0aFAg3SSDqbWU5PKNcVUrLgUBgk712f+XspjE0II8WICAwPFz89PJgx5XB72jchWftlSuUaR7hshhBDvxeTqHSDE5YRWEAko5uQFH/Gr18ytsqawL351m6p9K6h9zfozRXzrNs30mfndh8I4BkIIIYR4OZBLNKVKQcX+NVsF8Fz7fKzYYqLy/dF2u10SD+5Qy6bajaWwJL6Tj2YvR04IIYR4A7jvzpw5U65evSpms1l+//13GfHeRPGx+Dq1W4iPj5hDK4pf3Wau2F1CCCFeCJ1qxOux79kkkhCfqR386jWVkEdHuF37hDz2otq3gtxXZ58pNRpI8MMvFMo+FMYxEEIIIcR7iZgxUcRmdZrllV/g0ELdWOBTq7FLJL4hj00IIYQYnfj4eBk4cKA89thj8tFHH6l1yE4zlyydai8wZTZj+gSWkJCBL4uPk9cIIYSQwoDyj8SrsSXEi23FPLXs36iVlLjnsdSaXqEV3TZjyhQULKWfGacMPAW1r46fCUnMaP8gtb4w9qEwjoEQQggh3gmyvJKO7Mkyyytmxe/iX7eJmMtWEpNTdYLsid+8MnUhIFCkYnUpDHKSv6Y8NiGEEKNz/Phx6d+/v+zYsUNeeuklGT9+fIbXA5q3k9BXp0jc2iWSdOKg+Jgt4t/4Bgls301MQSVdtt+EEEK8DzrViFcTt2yeCGSBzGYp0XuAWMpV8hjnDvazoPdV+0yr1SoSHl7o+1AYx0AIIYQQ7yKnLK+YhTMkJm3ZFBwi5tBKYilbUcxlK4qlbKXUx9AK4uPnn+F9kI5EBly6wy4hTqw/TRbbEyPFXLJUgR6DJo+N7Do4AzPKY2eW4yaEEEKMxIoVK6Rfv36SkpIic+bMUc41Z8BmE9x3YJHvHyGEEKKHTjXitaRcPi/xqxep5WI336UGZ4QQYkROnTolFotFKleuLEeOHJGQkBApW7asq3eLEEIKhByzuMwWEWuKWrRFRai/5OP7M21mCinzn5OtbEWJ37JSUs6fyrjRyYMSNWuylHn2zQL/9SCPDblKZNdpUB6bEEKIN4B5SoMGDeS7775Tj4QQQog7Q6ca8VqiF3wngoysoGAJ7NbP1btDCCGFBqI+q1SpIgsWLJAbb7xRBg0alF6jgBBCPJ2csrxKPf2GWMPDxHr1vFivXJCUKxfEeuV86mPY5fRabLaIMEmKCBNxKiX5H8lH9igJ64LOHqM8NiGEEG8iLCxMfv31VxkyZIjUr19f1q9fr+qnEUIIIe4OnWrEK0nYt1US929Ty6au/a6rvgYhhHgKNWrUkAoVKqjlevXqSbly5Vy9S4QQUmRZXj4ms1jKlFN/Ur9FhvfZrSlivXZZ52zD43lJOXdSbDGRWX6fqglbSJKMlMcmhBBidLZu3aokHs+dOye33HKLyk6jQ40QQoinQKca8TrsKcmpWWq4AKrXFXvTtq7eJUIIKRROnDgh/v7+Mnfu3PR1mzdvVo+OMpDnz5+XqKgotWwymaR8+fJSsmRqwe/Y2Fg5c+ZMtt+F98BhlxWXLl2S8PBwqVOnjpKizO92hBCSoQ8KCpbSz4xTGWTK4RVaMVdOLx+zRUk+4k9fUQ2fc/X9569fcpIQQgghmbDb7TJ9+nQZOnSolClTRv7991/KPRJCCPE4aK0iXkfc6j9VFDII6j1AYnxMrt4lQggpFO6++241SdU71TQcZSBHjBghv/32m9SsWVNNdhE1CsfaxIkTJTQ0VG2rERkZKRcvXlSOL7PZrNbBebdr165M3/PLL7/It99+K+vWrZO4uDjlnIMUpSOzZ8+WmTNn5rgdIYQURZZX1pKSIr51mxZalhohhBBiVDDHGDx4sEybNk06d+6s5gmamgYhhBDiSdCbQLwKa+Q1ifn7V7VcrO2t4lutjqt3iRDiBaRcOisJe7eozAd3pm7dunLw4EE5dOiQqnHQoUMHeeCBB6RatWpqvfY3fvx4tf2aNWvS1zlzqAHUcXvxxRfliy++yPa7Fy5cqBx7OW1HCCFFKSkJCckM1GggwQ+/wB+BEEIIySOQd6xdu7aaGyxbtowONUIIIR4LM9WIVxG9aJbYExPEJyBQgu56yNW7QwgxOLaYKAmfMVGSj+xJX4fMBxhqIVXmziDzbNiwYTJr1izZuHGjcqxdD4hABfic7Pjxxx+V3CMeCSHEHSUlfUqVk2j/ILfvvwkhhBB3YvHixRIcHCydOnWSV155xdW7QwghhOQbOtWI15B08pAkbFmploPu+J+YS4SI1Wp19W4RQjykFqM1/Eqe3xf54yeSfPJwhnVJR/ZI+PT3pORDw/L8eeZSZcXH4itFBWqbaQ42QgjxdklJNW5M6xcJIYQQkj24b7711lvq79Zbb1XZaYQQQogRoFONeAV2m02i5k1Xy+ZylSXwpjtdvUuEEA8CDrWr7wwtsM9LPnnouj4vdPRnYilbSQqLpKQkJeWIegfHjx+Xl19+WRo1aiTdu3cvtO8khBBCCCGEGAtIyT/00EPy999/y3333SfTp6faYwghhBAjQKca8QriN/8rKWeOqeXgvgOLNNODEEI8hVOnTknv3r0lOTlZTp8+reocbN68OVeZakePHpWUlJT05/Xr11d1EwghhBBCCCHeA+YF3bp1k3PnzsmUKVOUpDznBYQQQowEnWrE8NjiYyVmUWotH/8mN4p/gxau3iVCiIcB2UVkieWFlCsXJOLrd7J8PeSp0WIpWzHP+5EXMHm12WxZyrE4Tm7r1q0re/fuVctwqnXp0kWeeuop+eOPP3L8rieeeEKuXPlPInP79u0SGBiYp/0lhBBCCCGEeDaVK1eWpk2bqlrJHTt2dPXuEEIIIQUOnWrE8MT8/avYYiJFzBYp0fsJV+8OIcQDQXZrXmUXsb1f3aaSdAROKrv+08SvXlMJaNRaCpuQkBC5dOlSpvWxsbESExOjXs+KatWqySeffCJ33323/Pnnn9KzZ89sv2vNmjUFss+EEEIIIYQQzyI+Pl4mTJggI0eOVIF1uQnKI4QQQjwVk6t3gJDCJOXiWYlbvVgtF+/aSyyhFdjghJAiI+SxF8W3XtMM6+BQC3l0RJF8f4cOHWTHjh2qpoGepUuXqsecIkfvuusuadWqlbz77ruFup+EEEIIIYQQzwS1mDGvGDdunCxatMjVu0MIIYQUOsxUI4bFbrdL1G/fitisYipZWop36+vqXSKEeBmmoGApPWSsJJw7KRJ+RXzLVhRLhapF9v0jRoxQsivIMnv99delUqVKsmvXLhk1apRymEHeMScQbfrAAw/I2rVrpVOnTtdVpw2RqxcuXFDPjx07prLkQkND1Z9+O9Ryy2k7QgghhBBCiHuwePFieeihh1Rt5Tlz5kj//v1dvUuEEEJIoUOnGjEsifu2SNKhnWq5xD2Pism/mKt3iRDipVjKVxFL5RpFXqC7fPnysnXrVpk0aZLKNouOjpYKFSrI6NGj5emnn85U+yAuLi7TZ9x7770ydepUmTdvXrpTDbKR9evXF4sl52HEK6+8Ijt3pvbFeM/gwYPV8pAhQ+T5559P3+61116T3bt357gdIYQQQgghxPVMnjxZhg8fLg0bNpT58+dLgwYNXL1LhBBCSJFApxoxJPbkJIn+7Tu17FuzgQS0usnVu0QIIS4BTjTUN8iJiRMnOl1vNptl9erVGdYhAjW3Uai//PJLrrZDRh2cdEXteCSEEEIIIcTTsEZek5TL58VULFAslWqIj6noq7u0bdtWHn74Yfniiy8kKCioyL+fEEIIcRV0qhFDErvyD7GGXRLx8ZHgfoNopCWEEEIIIYQQQohHY42OkKi50yRx9yYRu02tM4dWUOo8Ac3aFfr3b9u2TTZv3qzUJNq3b6/+CCGEEG+j6ENZCClkrBFXJXbpXLVcrF038a1Si21OCCGEEEIIIYQQj8WWECfXPn1dEndtTHeoAevVSxLx7QRJ2LWhUL9/+vTp0qFDByUrj9rHhBBCiLdCpxoxHNF//CD2pETxKVZcSvR80NW7QwghhBBCCCGEEJIv4jcsE+vl8yh44fCKXan0RP3+vdht/znbCor4+HgZOHCgPPnkkyozbcuWLZR7JIQQ4tXQqUYMRdLxA5KwbY1aDrrzfjEFlXT1LhFCCCGEEEIIIYTki/jtsHVkUX/YbhfbtSuSfPpIgbZyXFycdOrUSb799lt58cUXZdmyZapmMyGEEOLN0KlGDIPdZpWo+dPVsqVCVQns2N3Vu0QIIYQQQgghhBCSb+yx0U6y1By2iY8t0JYODAyU22+/XebMmSMfffSRWCyWAv18QgghxBOhU40YhvhNKyTl7Am1XKLvQPExm129S4QQQgghhBBCCCH5xlyxmpJ5zHabcpXz/T1Wq1XGjx8v+/fvV8/fe+896d+/f74/lxBCCDEKdKoRQ2CLi5HoRT+qZf/m7cS/XjNX7xIhhBBCCCGEEEJIgVAcajz2LDLVfHzEr0FLsZQpn6/vCAsLk549e8qYMWNk5syZ+fosQgghxKjQqUYMQcxfs8UeGyXi6ycl7nnc1btDCCGEEEIIIYQQUmD4NWwpgZ3vSn3ikLFmKllGSt43JF+fv3XrVmndurUsX75cPv74Y5WhRgghhJDMeJwY8q233qpS0Z3x119/SUBAQJbvnTRpkixcuDDT+q+++krq169foPtJio7kC6clbu0StVy8a2+xlCnH5ieEEEIIIYQQQohh8PHxkRK9nxC/uk2VDSTl/CnxKVZcirW+SdWUNxUvcd2fDXtar169pHTp0vLvv/9Kp06dCnTfCSGEECPhcU41pKDbHdLdH3jgASlfvny2DjVw+PBhiYiIkMmTJ2dYX6lSpULZV1L44FyI/u1bEZtNTCGhEnRrHzY7IYQQQjyelItnJOXqBbGEVhRLhaqu3h1CCCGEuIljLaDJjeqvIGnTpo3ce++98uGHH0rFihUL9LMJIYQQo+FxTrXOnTtnSk+/ePGijBo1KlfvDwkJkS5duhTS3pGiJnH3Jkk6vFstl+j1mPj4+fNHIIQQB1BYvEKFCvLZZ5+pCTOev/zyy2wnQtwQW0yURMyYKElH9qSvQ0R6yGMviiko2KX7RgghhBDPwBp5TRJ2rBNbbJSYQytKQIsOYvLPGIh+/PhxNT+AqhMy1GbNmiWeQMqVC5J86rCIySz+9ZpxfEQIIaTI8TinmiMzZswQPz8/eeihh1y9K6SIsSclSvTv36tl39qN1SCREEJIZk6ePCkpKSnpWduXL19mMxHipqQ61PZmWIfnETMnSelnxrlsvwghhBDiGWo+sX//KjH/zFGKPhrR87+Rkg8/LwFN26jnS5culWeffVaSk5Nl4MCB0qxZM3F3bLHREvnTp5K4b+t/K80WCex0p5S451HxMZtduXuEEEK8CI92qiUlJcnPP/8svXv3ljJlyuTqPTAm3n333eLr6yuNGjWSoUOHquj97IiKilJ/GhcuXFCPqO2WVX03dwD7ZrPZ3Hof80PsigVivXZZFegN6v2EOta8YvQ2ul7YLmwjbz6PMBGFrIqj1HB+Pk//6Armzp0rFotF7cPmzZtV1rYr96cg2wjvye4cM3NyTTysTqw+Q+0/7CozH5KQlIIkhBBCSFbEr/9HYv6anXkkkZQgEd99KCHPvSPvfPODvPPOO9KgQQOZP3++enR37FarXPvyLUk5cyzjC9YUiVv1h4g1WYL7P+Wq3SOEEOJleLRT7Y8//pCwsDAZMGBArrZHRlvfvn3l1ltvlbi4OPnqq6/kk08+UQVZO3TIOssJqfBvvvlmpvWRkZFSvHhxcVdgZIyJiVEGR6MZFe2RYWJdNl8t+7TqLNHFgkXCw/P8OUZuo/zAdmEbefN5lJiYqO4XWmZXQeAqx2KfPn2kZs2a6j4GcEx4DiADiXviSy+9pJ5DDnLx4sVq2WQyqVqlLVu2lBdeeEFOnDghgwcPzva7/P39Zdu2bZnWo5bpL7/8ov6uXr0qy5cvV5/trI2OHj0qEydOlO3bt0u5cuVk0KBBqmB6VsBpiwCb8Gz6/9DQ0Gz3mxB3whp2MdvXVY011lcjhBBCiBPsNqvELJ3rvG1U8JpNHrz/Pvlt004VnP79999LyZIlPaItkZ2WyaGmI27d31K8W18xh3DsTwghpPCxeLr0Y9WqVeW2227L1fbvv/++BAYGpj+/77775MYbb5Thw4fLpk2bsnzfiBEjlGFPn6kGYyQGH6VKlRJ3BQZKZFsgI8GTDNo5gSjt6EUzRFKSxScwSEr3elRMxUtc12cZtY3yC9uFbeTN51F0dLTab2R2FQRa9hXaAJ9b1LKPAQEBTo/lyJEjysmlvYZ7G/b1999/V4+osfDKK6+oAJa1a9fKggULMmS+jRkzRlavXp3utIIjztn3QEoGjrGOHTuqwuf4bMftsG7Dhg3Ss2dPJT8zdepUJUWDwBcUSs8q8AXfCWeeO9+LCckLltCK+XqdEEIIId6L9fJ5sUWEZb2B3S73VAqWmyZNkocffliCgoLEU0jYvVGpFKU6B51gt0vi3i1KCpIQQggpbDzWqYZ6MEuWLJFXX31VGdVyg96hBmDUu/POO5WRLzuCg4PVnyMwkLq7oRht4wn7mRtsMVFpdUb+k0UyBQWL2WQSUz6Oz0htVJCwXdhG3noeaY6vgnSAHYu4JufjYqRacIjUKZU7ueKCJKtjwXr9a8jQa9iwoVqGRDKkkrt37y47duxQ90sNOLpA3bp1c5RQRpYbHHta4XPH79Qy6B577DH1N3ny5PT1N910k3otu98Cr3nS+UVIdiALza9u07Saao5GIx9JCbvETDVCCCGEOMVud14S48e9J8XPbJJ7G1aTO+tWkdDnnstW6cEdsSclqrFQ5vGR4zaEEEJI4ZM7b5Qb8uOPP6osiCeeeCJfn3Px4kWPSXf3dlIdajAyZYzEipiZKmtGCCHuRnhCvAz8a770++NnGbb8D+n12w8yYMk8td4TQJYhuHbt2nV/BhxqOfH333+rrLpnnnkm02sFlTFIiKcQ8tiL4levacaVFl9lRIr45gOJ37bGVbtGCCGEEDfGUraS+BT/LyA8PsUqI5Zul5eW75D5h84od5Rf3SbiifhWrQ19y2y3sVSpXWT7QwghxLuxeLL0Y+fOnaVWrVpOX0f22apVq2TRokXqeWxsrEybNk0Z7BCJrxnx4JwbMmRIke47yTvJ509lyFDTk3R4t5KEZI0RQkhhkWS1yoWYqDy/b9Tqv2X3lYw1kjZfOCNDly2Ud2+6Pc+fVzEoWPyKKCsLteU+/fRTKVasmHTp0qVQv2vz5s2qRilqlUICElKUyIJDPbf27dsX6ncT4m4gC7/0M+PU2EbVUAutKD7+AXJt6ptivXJeImdNFntCnAR2vMPVu0oIIYQQN8LH4ivFu9wtMX/+KKcjY2Xgn5tk75VIGdyyjozu2FjleRXvco94IsXa3Sox/8xRZUAySUBCuaJcZY91GBJCCPE8PNKpBhmqXbt2ycyZM7Pc5tChQ6oGjAZqrpw/f16qVKmi6rBFRUXJuXPn5Pnnn5e33367iPacXA/Jp49I5M+fZ7uNMjpVqMoGJoQUCnCo9Zg3o0A+C1PAnZcvXNfnLe73mFQvWXj1w1BnrUGDBqrGGe6ZyFRbunSpVK5cWQqTsLAwVUOtf//+MmHCBOVQ+/nnn1UdNnz/rbfeWqjfT4g7gnGNfmxT+rm3JfzL8ZJy7oREzflKbPGxEtStr0v3kRBCCCHuRfFb+8jR/fvkjlfelmSbXab1bCt31akEXX4Jvnew+NVprFSfPA1ziRAJefwlifhuArTjU1em1VgzlQiRUgNeKfL61YQQQrwXj3SqlStXTv79919p165dltuMHDlSHn/88QzyUTDUvfXWW3Lw4EHlZKtZs2auZKmIa0i5elFiFs2ShJ3rc9wWUdyEEELyR/Xq1WXBggXKwYXAFGSKbdiwQTm3cgL1z65cuZL+fPv27ZlqmWYF7slJSUkyduxYeeihh9S6Nm3aqAy28ePH06lGSJoxqfSzb0n4tLcl+cQhNUayx8dJ0F0P0YhECCGEEIWPySTNn3tDBhw4Kw80rSl1SwWJObSiFGtzi5hDir62c0ES0PgGKfvaZxK37h9JOnFQfCwW8W/UWoq16SqmwOKu3j1CCCFehEc61RAxn1PUfP369dWfI3CitWjRohD3juQVvbwRIrKt0RES+88cNVASW2oElRna4BaLpFw441CY1kfVHWGWGiGkMIHsIrLE8sLpqAh5eunvWb7+5W29pFpwSJ73Iy9AutFZEfKUlBSJjo5Wr+uBPDIy1UDTpk2VHOOoUaOkV69eKnssO7777jv1ufrvzi3VqlVTjzfccEOG9a1bt5Zff/01159DiNGBwajU02Ml4tsJknRop8Qun68y1oL7PynWy+cyjKcKc6xGCCGEEPcCyg8IiPvggw+kUqVKMvHLr8WImEuXkxJ3P+zq3SCEEOLleKRTjRgDW0yURMyYmKFWmqlMebFFR4okJaQ+LxEiQd3vU/rZiMaOmDlJ1VDTgEMt5NERLtl/Qoj3gDpmeZVdxPZtK1ZVNdQyhgKItK1UVW6qWlMKm4YNG8qyZcuUswsZ2xp79uwRm80mjRo1yvb9kEieNGmSvPPOO/L9999nu22dOnWuez+1mm2opaYHzwtbepIQT8PkHyClnhwlET98LIm7Nkr8+r8lYc9GsWP8lIZf3aYS8tiLqj5bfsdqqOGmH6sV1GcTQgghpGDYunWrklFHiZN77rlH7r33XjYtIYQQUoiYCvPDCcmOVIfa3gzrbGGXlEPNxy9AOdNCX/9cAjveIT5mizLelH5mnIS+OkVCBr2qHvGcRh1CiLsy8ZYeyrGmBw61j7r0KJLvf+aZZ+Ty5cvyxhtvpGeRIYp1xIgRqr4oMtCyA9lmw4YNk59++knOnj1baPvZvHlzue2221SNUy2zbtWqVfL777/LgAEDCu17CfFUfCy+EvLoi0rKCegdagBOsPDvJogtNjr1Ly5WZbTZEuLFlpgg9qREsScniT0lWezWFLHbrKqWoiNRsz7ONFbDcwQ5EUIIIcT1TJ8+XUm1Q0odZVLoUCOEEEIKH2aqEZeQfOF0hqhnR0IGjxb/2o2dvgbZIUoPEUI8gVIBxWR6975y6OplOR8Xo+Qe65QquloGqD06b948efnll+Xjjz9WNUmR/XXzzTerDLbc1Dx79tlnlYwM3j9x4sQ878P9998vO3fulKioqPSsNGTNDRkyRGXCacycOVOeeuopJVdTunRpJU85ZswY5RgkhGTGx2yWwC73SPzmf502T/Kx/XJ59GPX8cE+qX9wsjlxtEGGG6oBkITkeIwQQghxHe+//76SasfYfvbs2VKhQgX+HIQQQkgRQKcacQnWsIvZvm6Pjy2yfSGEkMKmdkhpqR9aTnxgqC5ikI2GP2SswVEFx1qJEiUybQeZx8TExEzr4eDav3+/kovUQARsp06dpGzZsjl+Pxxy8fHxmdaHhoZmeF6mTBmVmYY6btjPKlWquKS9CPEkrNcuFfyHZulMy4iqscb6aoQQQojLQPBaTEyMjB07Vnx9fflLEEIIIUUEnWrEJaDQfX5eJ4QQkjfgTMNfViBDLCuqVauW4XnJkiXVX26oXr16HvZSJCQkRP0RQnImp/FSyUeGi7l0ORF7mlPcbhO7DQ6zNMdZ+p8tbRUe7WKzWiX6/Cmx/z37ur+bEEIIcUeQ2bVhw4ZM6+fPn6+CyfRs375dpk6dKmfOnJG6devKCy+8kK86wgVNjRo1lHw6IYQQQooWOtWIS0BkMwrdp9bp0EdD+4hfvaaMfCaEEEIIyed4qljrm66rDa1Wq8RVriM+x/dJ8pF9HKsRQggxDHv27FFZXaNHj86wPigoKMPzNWvWSLdu3VR94f79+8vPP/8sN9xwg2zatEnq169fxHtNCCGEEHfC5OodIN5LyGMvKoOPHjwPeXSEy/aJEEIIIcSTKMzxVPDDwzlWI4QQYjjKly+v6vzq//z8/DJsM3z4cOnRo4d89NFH0r17d/n++++lVq1a8vrrr7tsvwkhhBDiHjBTjbgMU1CwlH5mnCp0r+pyhFZkhhohhBBCiJuMpzhWI4QQ4o2cPXtWtm3bJiNG/Begglq/yFgbP368pKSkiMVCcxohhBDirXAUQFwODD8sdE8IIYQQ4p7jKY7VCCGEGImNGzeqLDRIPjZv3lyGDh2aoV7wgQMH1KOjzGO9evUkISFBTp48mWVttaioKPWnceHChXRpZfy5A9gPm83mNvtTFHjjMXvrcfOYvQP+zt6DtQj7MbPZnOtt6VQjhBBCCCGEEEIIIYanRIkS0rdvX+nUqZOEhYXJlClT5JNPPpHVq1enO9EiIyPTt3V8r/51Z0yaNEnefPPNTOvxnuLFi4s7AMNkTEyM2O32PBkQPRlvPGZvPW4eM39no+KN53ZRH3doaGiut6VTjRBCCNGBm3RSUhLbxEMGV471LwghhBBCCMmKb775RgIDA9OfQ9KxUaNG8tprr8m8efPUOk3aETKPerTnvr6+WX4+JCMHDRqUIVOtTZs2KhOuVKlSbjOGhpxlSEiI1xhmvfGYvfW4ecz8nY2KN57b7nzcdKoRQgghOgICAlQUDCJXy5Qpw7ZxU/D7wPkZHBzs6l0hhBBCCCEegt6hpmWf3XLLLSpTTaNKlSrq8eLFi8rhpoHnoHLlyll+PsamzsanMAS6kzHQZDK53T4VNt54zN563Dxm74C/s/dgcsN+jE41QgghxCHdOzExUS5fviwREREFctOG/jMGAaRg2giRSnCowQiSl/R8QgghhBBCHIGzDBHwGk2bNlX11tavXy9du3ZNX79u3TqpW7cuA+8IIYQQL4dONUIIIUQH0soRfXr16lVViDy/xVCh+wwHkL+/v/pskv82guQjIoDhUGObZs2WLVvk1KlTmdbfcccdmWqEgL1798qJEyekZs2a0qRJE56qhBBCCDEUp0+flhUrVsijjz6aHsz1ww8/yLJly2TChAnp22FM+uSTT8rUqVNl4MCBUrFiRdm3b5/Mnj1b3nvvPRceASGEEELcATrVCCGEEAfgqClbtmyBtAuccuHh4aqGgjulqrsTbKPCYcqUKbJ8+XLp2LFjhvUdOnTI4FSD8xj1RDZt2iRt27ZNf5w7d66SQyWEEEIIMQKlS5eWjRs3ysiRI6VGjRpKmSIyMlLeeecdefHFFzNs++677yonXIMGDaRhw4ayZ88eefzxx2XYsGEu239CCCGEuAd0qhFCCCGEGJTWrVsr51h2jB07VmW1IVOtfPnycunSJWnWrJla/8EHHxTZvhJCCCGEFCaQdPzyyy9l4sSJcvDgQRVkBOcaVBAcQWARxlBwrJ07d05q1aqlxkmEEEIIISzwQgghhBDixVmC06dPV9JGmqEIjwMGDJBvvvkm3/KnhBBCCCHuRvHixVXgUb169Zw61PRUq1ZN2rdvT4caIYQQQtKhU40QQgghxKBER0fLX3/9Jf/++6/KQHPk6NGjcu3aNWnTpk2G9XgeFhYmx44dK8K9JYQQQgghhBBCCHFvKP9ICCGEEGJQdu3aJR999JFyrm3fvl0GDRokkydPFn9/f/X6hQsX1KOjnJH2HK8jitsZUVFR6k9D+yxkt7lzhhv2zWazufU+uhq2EduF5w6vL/Yzxu6LWeeXEEIIIeT6oVONEEIIIcSAPPLII/L1119LYGCger569Wq5/fbb1XPUEgEpKSlOjWvac+11Z0yaNEnefPPNTOsjIyOVrJK7AuNkTEyM2O12GhXZRjx3eH2xD2Jf7JX3q9DQ0AL9PEIIIYQQb4JOtetAMzBpEdnuPAiHYSs2NpZGI7YRzx1eX+yD2Bd77f2qQoUKYrF435DnjjvuyPD85ptvlv79+8uvv/6a7lQrVaqUekT769Gea687Y8SIESrzTePMmTPSoUMHiYuLU7+lO59vyNzz8fHh+IhtxHOH1xf7IPbFXnm/SkhI8NrxUVHjjvYjb7QVeeMxe+tx85j5OxsVbzy3XXHcuR0fcQR1HVy5ckU9OtYfIYQQQoj7AWdPlSpVXL0bbkFISIiKeteoX7++GjAeOHBAbrvttvT1eI71eD0rgoOD1Z+GZixq3759oe0/IYQQQgoGjo+KBtqPCCGEEOONj3zs0BIgeY7q2rNnj5QtW9atI7tg3ILjb/PmzVKxYkVX745bwjZiu/Dc4fXFfsb4fbE3RmJjrII/ONE04Exr2rSpNGjQQJYsWZK+/q677pKrV6/Khg0bVDQ8hoZwjJUrV04WLlyYp+/k+MgYcHzEduG5w+uL/Yzr4fjIGLjj+Mgb7/PeeMzeetw8Zv7ORsUbz21XHDcz1QqRgIAAufHGG8VTwAnHCH22Ec8dXl/sg1wL+2K2UVECaQQ4xu655x5p0qSJhIeHq/pqSUlJqhaano8++khte//998vdd98tf/zxhxw6dEi+//77PH0nx0fGg/0W24XnDq8v9jOuh32xZ+PO4yNvPLe88Zi99bh5zN4Bf2fvoaKb9WMmV+8AIYQQQggpWMqUKSNbt26V6tWry+rVq+XYsWPywgsvyNGjR6Vhw4YZtkXm2q5du6R27dqyePFi9YjnWE8IIYQQQgghhBBC/sM9cs8JIYQQQkiBgnpnw4YNy9W21apVk3fffZe/ACGEEEIIIYQQQkg2MFPN4Ma0sWPHqkfCNuK5w+uLfRD7YneF9yvC88294DXJduG5w+uL/YzrYV9MeG7xemI/wr6T9wveI719XBDspsftY0c1ekIIIYQQQgghhBBCCCGEEEJIljBTjRBCCCGEEEIIIYQQQgghhJAcoFONEEIIIYQQQgghhBBCCCGEkBygU40QQgghhBBCCCGEEEIIIYSQHLDktAEhhBBCCCGEEEIIIcR9OXXqlNjtdqevVa9eXXx8fLJ8b1hYmERHR2daX6lSJfHz8xN348yZM2q/ypcvn+U2aIvLly9LiRIlJDAwME+fn5/3FhaXLl2SpKQkqVq1arb7ffXqVQkNDc3299aTmJgoFy5cyLQ+ODhYSpcuLa4kPDxcIiMj1TGbzeZMx4pz3pGAgACpUKFCrr8jKipKtUHZsmXFHYiNjZUrV66oc7tYsWK5+q0Argdcr9lx/vx5dQ7pwXmC/sHVx2y1WtU5lx3Jycnp57evr2+eviM/7y0M8DvExMTkeI3ldjvHc/ratWuZ1uMcL168uLgKm82m7jVZ9U8JCQly8eLFTOtLliwppUqVyvX34Prx9/fP8XzKL8xUI4QQQgghhBBCCCHEg7n11lulS5cuGf4aNGgg9evXVwbl7Bg9erTa1vH9hw4dEncBxtgJEyZIw4YNpVatWvLYY49lue28efOkSpUq0qRJE2XAfeCBB5RhOjfk570FDYzM06dPl44dOyrH0o033uh0u127dsmDDz6ojMiNGjVShvMnn3zSqWHdkR07dkjNmjWlU6dOGX77KVOmiKuYP3++3H333VK5cmW1bzCSOwIHE15r06ZNhv0eNmxYrr7j7Nmz6pqpWLGi1KtXT51XGzZsEFexfv16eeKJJ9S5h+Navnx5pm3279+f6RrFH7b/3//+l+N33H777dKiRYsM78U6VwAn2jfffKP2B05QHHft2rXlp59+crr9uHHjpEyZMtKyZUv1iOe5JT/vLWgWLVokt9xyi3IS1a1bV/Ux7733nnI4Xc92zvj666+lTp06mc6TFStWiCvYunWr3Hvvvco5hv4pKChIhg4dqpx/etauXavO5ZtvvjnDfuN4cgPej/sdrmdc17fddptyJBcadkLygM1ms4eFhalHknUbEXI98NwheSEiIsKekJDARsuCqKgo+6uvvmo/deoU24gUOhwf5a6NCLne64uQ3BIeHm5PTExkg2XBtWvX7C+++KL98uXLbCMvID4+3h4SEmLv379/jtsOHjzY3rlzZ7s789tvv9lHjhxp379/v9rXO+64w+l2W7dutVssFvvnn3+unp8/f95er149+wMPPJDjd+TnvYXB3r177QMHDrSvXbvW/vzzz9vLly/vdLt+/frZu3XrZj9+/Lh6fvjwYXvNmjXtt912W47fsWHDBqQ32k+cOGF3B1JSUux9+vSxL1y40D5t2jS1bxcuXHB6fuM1nBd5xWq12lu3bm2/5ZZb7NHR0eo7X3jhBXW9XLp0ye4KhgwZYv/222/Tf48//vgjV+/bsmWL2n7y5Mk5btu4cWP76NGj7e4AflM/Pz/7pEmT7ElJSWq8h2PAsfz+++8Ztv3ss8/sxYoVU20D8Ijn2nWaHfl5b2FQoUIF+7Bhw5Q9B/z111+qHd58883r2s4ZH374ob1y5cp2d+GOO+6w33XXXfbTp0+n92uVKlVS17mepUuXqt//ypUref4O9NXBwcFqjIPrG/agm2++2d6mTZtCm0vQqUZyzdSpU9VFjRMcJ/93333H1nNg/Pjx9htvvNGenJzMttGBQe///vc/e8mSJe3lypVThm50cuQ/9u3bZ2/btq39p59+YrNkwapVq9SkoHjx4mqC4KpBkKuZN2+evWXLlqovDgoKso8ZM8bVu+SW3HPPPaqN0PcQUlhggI6JYNmyZdX5Vq1aNfsvv/zCBndoI0zeO3XqROeIAzt27LD37t3bXqJECTW2fuutt3juODFutmjRwr548WK2TRbAAAGjYGBgoL1OnTr2b775xiv7mVmzZtkbNWqk+mLMOSZMmODq3XJLYGBCGz311FOu3hVSBPz888/q985NH+oJTjU92TnVHnroIXuTJk0yrPvhhx/sPj4+9jNnzmT7ufl5b2GTnVMNfaCjHeqTTz5Rv//Vq1c9yqmmB3bHwnCqacZ7jDM04uLi1Jjs7bfftruSPXv25Mmp9uyzzypnS06/s7s51bC/ixYtyrQeTuz7778/w7oaNWrYn3766Ux9FtbnRH7eWxjMmDEj0zrYLBo0aHBd23mCU23mzJmZbMDvvvuu3WQy2WNjYwvEqTZ27FjlFNcHnmt928qVK+2FAeUfSa5A2vekSZNk8eLFcu7cObnnnntUWjJST0kqH3zwgcyaNUu2bNkiX375JZsljTVr1igZgebNm8s///yjUnzRVq+99hrbKA1IGUDaAPrOr7zyisTHx7NtHPj++++lf//+StJg2bJl0rVrV3n22WfVNectQNID6f9vvvmmkmc5fvy4PP300/LWW2/JL7/84urdczuQ5n/nnXfKr7/+qvohQgqDd999V8mWQJ7l9OnT6hq9//775dNPP2WDpzFmzBiZO3eukuOYMWMG2yWNJUuWqHvZTTfdpO5rjzzyiGqr999/n22kq5fTp08fiYiIkBEjRkhKSgrbxoGpU6eqcwcSaDiP2rZtKwMHDpTffvvNa9pq06ZN0qFDB/nkk0/U9XPs2DEl1fbyyy/LX3/95erdc9vxEeTkMLYkxgb3Xcjn5UXeDRKLqGHlyaxatUo6d+6cYR2eI7Fg9erVhfZeV/LQQw+JxWLJsA6Sn6hblNv6UbBDoG5bVnX53BWMD1BnDHKCuQW/M+rltWrVKn0d6pdBXhOveQqotwVbAGy0kDXMDfh9UbcK0qKuBPvbs2fPTOtx3qIelgbq5p08edLpdYn1mINlRX7eW1g8+uijOR5zXrbLDvTl6NNdzSOPPCImkynTsaBOomO/BeLi4lRflBdw3WIcrG8fSMPiui60a7pQXHXEcFSpUiVT1COiIRCts3z5cru3s2zZMnvdunVVpEWPHj3spUuXVjKZ3g5kRZCZ5piGDvkJZNgwWy2VLl262D/++GP7pk2b1DWFCAvyH9u3b7f7+vqq60wfkdywYUOVyu0tILrqiy++UNIUepo3b25//PHHXbZf7goizzZu3GivVauWyuxjf0MKGvRDuN/Pnj07w/rHHnvMbjab0yVGvBnI9iAiNjIyUkWUQ/EAUhzeDqRPIE/y/fffZ1gP2SZkrBG7ira/4YYbVIT6P//8o6JMMVYi/7Fu3TolUYZ7nQbuddWrV7d37drVa5rqkUceUdHcemkfLGMcACkvkhFEce/evVv1x56UlUTyzrlz59R45LXXXsvV9sjcwFwUmZ7+/v4qywGyae4qwZtVphr6Qdwz3nvvvVytL6j3ujpTzRFkf9SuXdverl27HLfVsjmQ7VSqVCkljYe+1Vl2mDtmqiHbJTQ0VO0/lG0gLZcTjz76qL1+/fq5Xu+umWpz587NdTYqwLgc13mZMmWUjQW2hL///tvuTqo8OB5kXmqsXr1arVuzZk2GbbNan5ttcvPeolT2wrn70ksvFch2WqYafmeoPOEPc1bYGd2lfEhkZKTyM2Duo0fLVMM9COMV7PugQYNylbkGuw+kcnO7viBgphrJFYj6gAdZDzLXUMgWmRLeHjlarlw5FRGKSIuPP/5YoqOjZezYseLthIeHq2ihp556KsN6RJOi0K+rI2PchcGDB8sLL7ygoigQwYHiy4jOJqkgOxZFolFEWAMRd+3bt3dasNiofPHFF6q/deyLUbgWmaAkI8j8LFu2rHz00Ueq+Pa3337LJiIFCoqjo/i74zWJzBEUkkff7mmRvgUNCkQvWLBAgoODZfLkyXL58mWV3eft4N6FAt0PP/xwpvGRN93XsgNRqy+++KI8/vjjqsg4IrCRqY2+naSC6GqMhRCVq4EoYDz3pvNo5syZKpobY0MNLIeEhHB85ADm7IhaR+YS+mJEbiOTmBgTKHpgLg6Fodxwww03yObNm9UcPjY2Vt544w0ZNmyYx6kTYXwG/Pz8MqxH/4iMrexsEPl5rzuB8eeAAQPUPBrj0pzAmATZq8gMx9h248aNKgu4W7duKpvEXcHvgix/ZOLgvgclF5vNppSSkJWbHfgtHX9ngCwXT/mdrycb9cEHH1QZWhhP4bfGOALZyytXrhRXc+jQIRk0aJAa9yHjXEP7PRx/Ly0jKbvfKz/vLQpwzUGRqUqVKvL666/nezuNWrVqydKlS9U9H/ZpKD99+OGHMmTIEHE1VqtVzYFwTI7qLhi7/fDDD2q/cS/CeQklBpyjuLbd7ZqmU42kk53hp06dOkraSA9OVjjWjhw5Ij///LNXt1HTpk2lcePGarlevXpK4hASkPv27RNvwVnboC1wbiDdVg86SHSWgYGB4k1gQIobhyOQCtPApAUGWkjWeCOYyERFRWVYd9dddynJUEc0o4ARQbo7JrM5Adm59evXy7p165TD7ejRo+ItoH2ykkrFeQTHPSQ9IB0GhywkM3HO/P3330W+r8SY9/6AgADVBzmOj3Bvw6Rl9+7dyqHkzW0EAx3GkKBFixZKlg7BRzB6eAvO2gZSQ5jcOjpkjXxfy0m6yJnUmH58NHHiRHVvhJHXG4ExAEYRx/aBMdERI59HuPfjPMgJ9DMHDx5UpQvgFHCFvJOrwHmSlfEIhmf0SRgfwWGNPnrkyJGqTTk+Mh4wtt98883p9+GcgDEb5wSc0rg/ITgI0vuff/65eBKwPSAww7GvwL0G83Gc/4XxXncCAbvz5s1TMvgtW7bMcXsEsGKMptltmjVrpkrAwJ7lDs6WrIBNEgE3sC0B3PvgTIbN5ccff8z2vfgtnd1PcJ/xlN8ZwWqQE4cEtOOYMitQhqVatWrpztTPPvtMOWoQwOtKEFR+xx13SM2aNWXOnDkZpAK138Px99LsJdn9Xvl5b2GDfUIZGDg3IVddsmTJfG2np2/fvsoGgvMCfTreP3z4cOWwchxPFiV2u13dW1AaCIkpSNTRg3sQHG6a0xPPMa/eunWrcvS72zVNpxpRoA4YjB3wZDsDRkl0bMhY09O9e3d1k8aFaXQwmGjYsKFs27Ytx22RpYbsEXRaRgdRBi+99JK6AToDUSaOwNCIaGxvAR37888/r25+OC9at26d5Q2hUqVKMmrUKKWLDUeJtwADECZyyGZAOyG6bM+ePV53HiGiDgMg3PTRFtAYd2YIwmCkV69eKsoYEwnU5UH/3aVLF+VMMjIwxuPeo7URJvuYUOjRMhnwOkCGDAxJ9evXl1dffZWZDiTXoHZGo0aNVOR2VuMjTN4xydHTr18/FVjiDeMjGK1xbR0+fDjHbd955x3ljEQGktGBAQ5BVjhHnOEt97XswKQeASHoq2EQa9euncosdgaMwhhLTZs2TbWTt4B7F1QM0Eb4w/0ekdwayCJwBOMno51HJ06ckB49eqTf+xGtjXowzjKx0EZff/21jB8/XjkUENwAwxKuSSOzf/9+NQ7UzhVk7zkG82F8hD4YGTcwsqEOHTIWENE+btw4w48hvQmMW3BOwFGSH9D3OrvW3B3sN+or6tECeurWrVto73UHEHwCRyjGpzCkXy+aM9bTfv8KFSpI8eLFc9xv/JZw5DjeG/Dbe8LvfD3ZqM6A0wWOLFf+zpjLY1wMJx8CPBydRtrv4Xhdas+zCxzIz3sLE5x3sPsgAAh2nKzOudxulxtwrBgnuVL1YcSIEUphAP4FZ2PY/PRFaBvH3xlBRrCxFdo1XSiiksSjOHnypL1q1apKfx/6uo71esDZs2ftAQEBTnVI33nnHaXPamSgyQxNcWjPd+rUKVfv+eqrr5QW7O+//243MqhVAM1pHOucOXNy9R7ogH/wwQd2b6F37972u+++W9VQQQ2Dm266SV1PK1ascLo9dI5r1qxpb926tdtq2Bc0aJOHH35YaaVv2bJF1cBCvZmtW7dmWa8P59yff/5pNwrQhEdtRmhkoz4j6lXiPEB9HfTTjqxcudKelJSU/hxthTb59ddf7Ubl2rVrqj1w38HyokWLVL9cp04ddU5o7Ny5U2nEg0uXLql7F55DV/zgwYMuPALiSRw4cEDd+3HOoR6Fs/746NGjqqYRalw4MmrUKHu1atXsRga1QDGGxDiwZ8+euXrPxIkTVV9l9Jq8Tz75ZPr4aMmSJbmqIYa6ARg/eguoe/K///1P1f1B/dQbb7xRtUFW9QhRjw9jyFtuucXuDaCGD8aCOJdwL1u/fr29UaNG6nrbt2+f0/ccP35cnXOrVq2yG4WIiAhVd+Ott95SNatRNwb9MmpkoF0cwfhaP59FTV60CWrzGRVcQ6gn9Mknn6jxEeZkOE+aNm2q6pbox45ly5ZVy2fOnLE/+OCD6h6GMRLm+8Q4PPPMM2ouhbpazsBc49SpU+nPMcZxHOfgOeZoqGPtSTXVwIgRI+wVK1a0JyYmpq+bMGGCqhWmvyZwXzlx4oS6B+f1ve5YUw37ifpiqDOZFbA14Jj154YzG+D06dNV34mxnrvWVHO239qcWD+ewrmMYw4PD09ft2vXrkx1y86fP6/6xGnTptk9oaZas2bNsq2NiX5df5901l64Z6COHsYargC/Ceq6Ycx88eLFLLdr06aNvVevXhnW3XPPPfa2bdtmWAebAO5v1/PeogK/Q9++fVXNMIx/87sdxkk4v/U15J391o8//rg9KCgoQ99WlIwZM0bV+czObuxsvydPnqyuB/3YNy4uTh0zbGgaU6dOVTXn9Of8/Pnz1XtRj64woFPNy4FBFsbrH374QU1QcLJ9+umnTrdFUUMYJB0nJLhhYxBvVKKjo+316tVTxtuZM2eqNvr5559zfB86tBYtWihjLzotdHR4v5FAO7Rq1coeExOjismjILi+U3PGoUOHVBvqDSZr1661Hzt2zG5EYHDF8eqPDzcAtBeuG70jwFmB1m+++Sa9YGdh3QhczcaNG9Wx6icpGFxhYAVnP65BZ+2DCQOuK43ffvtNnYueyi+//KIKBusntJjswvgBQ6OzAYYeTApzW9DYU0GxdDge9eC6wOBQP6mGAQ1F1j/66CN1nWFyjGsQEwY4uAnJCfQlDRo0UIExP/30k7q2MFZyxosvvqgmCLiX6fn888/VfdGowCCH44PxHobc3DqPMPbEuArGXvRrMJL/+OOPdiMBY0yHDh3UeYSANZxLeoOdM2C0QhvCmavvy2DgMSIIfsDx6ie+uI81adJEOUz0hi89GBfhfRgHgIULF6pJtRHB7w9nh35sjULtGBthjORszI15GYq76wvRw3jhLoXpr4cvv/xSBRnpQYAMHAa33nprju9HUBvOmTVr1tiNyttvv63GinoQpIZzAY5rjblz56Y7KDHmHD16tGrLwMBA+yOPPOKCPSeFAa53OFWzM5Ij4AxjZQ3MwzA/nTVrlgoCxZjmgQceUPMtzLHcBYwh0OfjDwbxm2++Of25/j6LeyfmUA899JAKkMa5jz4D14oe2L7QP+jvI7l9b1GCAEvs4xNPPKHmNtox6x1jGHvgWF577bX017U/vREdfSG20/+uOB/QL+B3x+8/ZcoUe4kSJZRB31Xgd8C+f/jhh2p/N2/enMkxhkDtoUOHqvslDO4YTyKgDWMJfdvAnoDPgE1TD35jjGUR6LVt2zZ7ly5d1LjNVfdMjK1xjH///Xe6LQjPnQWQwMmCbbJzoGKsoA96++uvv1TAN8ZOaC/YgOFwQn9x5MgRe1EDu1j79u3VOY1zT3/OOo5/8RtjvoXfHGNlPOK5Y5Defffdp4Iir+e9RcVjjz2mxnezZ8/OcMyOgdS53e69995T5wLGiBqYh8CxjPMa187w4cPVNpMmTbK7gslpjjH0o479kz5QHME+OB4EksHxjesfY5RHH300w+dhzonPg51UA+NizLkwNsT1gdcQ/AlnYmFBpxrJ4CDq0aOH6lBh4HAEN+KOHTsqo6QWrQLHETz+zjLYjAKOUcv8gLEbgzdcmLgB5ASiAXGhYzCCbIqnn346Q/SAp4MbHyaqYN26demdZHYg4glRyBj0IoIEg3UMfIwUUatHG7Q63vgOHz6sstWy6+ARiY1INAx84EhwNNgaBQzo0UaODjEYAzCRQ+aWswxJOHQBbrYYAMOJ7YrBYEHx8ccfOw1Q0KJr4FDKDrQJIthdFXlUFMD4g4GSI19//bVqIzgmNWc2BqB33nlnhsw0TBCxHc4tQrID92pMYDQw/sEEzZnjHuMB9EflypVTjgKAexyycJxlsBkF9DXon7TjRf+DaPacnEcAgUq4FmHsRbthomckkCmjRdsiGA3HislkdiAIQIs+RxAA7v0IzDJqf6VNhmFA0oN7OqLEYSTLLnsLThYEU8D4BYOBEYEjH/cyx2sKY2YEOo4bNy7TewYNGqSMzADnDowqcLboM1I8DRhBcS048u2336pzCE6ArMDcbcCAAcpoaKQ5mCPPPvusyqh2lrWCNoIhFeAehbF1v379MjgR3nzzTXVOIfiReD4wJML5nl2G0SuvvKKCW/TAgYTrBVkjuGYwT0XGjDuBcxTH5uzPMTsF28LADscCro8vvvgi0+fBIXG97y1Kateu7fSY4XzRwP5m1TZwlGng3uD4Xjid3n33XXX/wH0VjhgEhLuy30QgpLNjwZxZA/dH2JcwHsAYtFu3bsoI72irg4PN8b3aWBb9H+wIGMcOHjzYqQOrqIBD1Nkx47p0BL8X5sVZZaOCrl27Znovxl+wT+J48XvD1uKqY87uesY42FnfhvkVbGN41DtUNDB+dHY/zM17XX09I+jwerZDhhbWI+tQA+O+5557TgVLIJEGc67Vq1fb3e16rl69urLdaCBoHtckFOLgHMf7YONxzKSGrR3vdbSRYv6FgBL0Bzhu2KcL0z5GpxrJAIyPmLxlNZHFRYqOGZPd22+/XXXE8AK7Og2+qLNqsprIOvLvv/+qCDA4R2AkMDpwkMFhBgmSrEB0FWQk0FHCgYAU4Nw4KD0VyBToM84cJzOIkIFUjyOI1kbEKKLiMCE2sqNEkzdwJpUKhz2kNhyNbjAQIVoFjmo4rBHF7OnGEmSYoR2c9RXoZxG578xYjcERAiIwMXYmE2kkIP+B/tdx4otBFgZNuCdpODMMof2ykl0lJDsgJYNz7/XXX3f6OrKO4XjDGKp79+5qsgODRHYTXaOhRdXm5DzStkXUIdpKn5llVDAhhHSLPoLUkT59+qi+/NVXX1Xjo/fff9/Q934EweB80Tuv9Q4CyLc4k3pC4B/aCkGAyDDIKYvbk4GqQ1ZSqTCeYozomM0PI8JTTz2lxtsYN+C+6elS4nAuOgtQAzAWORqXAI4ZkemYg8E5kJ2klBGAoRjXjN6gpo174JCEczW78RHmYkbO5COEEEIIKWjoVCOZQLQwnGaIVMoKTO6QNWHU7KKcgLMDxiAtS8sZkBuD1r8mT+MNwNCNdkFtrKxANI0Wne7JUbN5AZEyiH5yNGogIggTYNSHcjQ0wRACh5IrI6WKEkQMOZPwQVvAkK3XREdaNwzX+EMWSFYSUZ4GDBowkDnL/NXqgegjCr///nuVHQODEaKTjGx81Tsu8LvDIe0IIpjQRkbNWCCuB0ZqZBhn5bxGH49rFOMjZG97I3AeQdEgO+cRooAR+Y1sNW8B9zLc7xEIkhUIEMH9DlIvzpxJRgQRqHBGO4LxIYKOkF2sB1k2yGxE8J+j88CIIFgIGbKOdUD08pl66VSMh3AOQfLv5ZdfVgFaRgCOVBwTpHYdgSybY80fOFsxPkIkOoLacpM96+kg6wy/PSSTHEHgGdpIHwlOCCGEEELyB51qBgYG2qyKWGcH0i2hIY2JiNHBZPN6ZC6QiYUaPsjMygp8rifXL4AG7fVk/mjyIcjocwYir12ZduwKtAm/s3o8uM4ctcphmM3OqW1EtAm/s3o8yEBC1LUeyBQYMbsBGQowJDpKreBaRO0LyDx4O5C2gvPe0SmPex6M1pCCJCQ7IOF4Pf0HMh1QX+Lee+81fAPDOH89BljNeTRkyJAst0Hb67XzPTFr8XoYOXKk6t/1Ekx6oICAmgfeBIJDHOu6aKDGhmP9BJw3Rrz35yQLmlU9MAQkIYhPP1ZAcKQRnSdwSCOowbF+HuZaWI/gIm+nf//+KqjBsV4z1B5wDmnlDAghhBBCSP4xCTEkc+bMkYYNG8q0adPy/N6SJUvK22+/LUuXLpWFCxeqdb///rucOXNGjAIcyt99953Ur19ffv311zy/v1KlSjJq1Cj5+eefZd26dWodlq9du5a+Tb169cTf3188jYsXL8oTTzwhPXv2lKNHj+b5/SNHjpSqVavK888/r9r58uXLGdr4lVdekZtuukk8GRzTs88+K82bN5c777xTkpOTs92+X79+0qVLFxkxYoRcuHAhw2tVqlQRkyljV+zj4yONGzcWT+b06dMyaNAgadasmdx7773qXMiOgQMHqvYcMmSIRERE5NhGH374oTRo0EDcnd27d8v999+v2mH48OE5bv/qq69K+fLlZcCAARnOKxw/+h3HdjACGzZskL59+6o2evPNN3Pcfvz48eLn5ydPPvlkhvOqWLFiUqZMGUO2ESkYcL7MmjVL3fvxmFdwbb7++utqjLV69Wq1bvbs2XL16lXD/ERWq1W+/PJL1UbaGDAv1KlTR93/v/76a9mzZ4/YbDaZMWOGxMbGpm+DvtvX11c88b523333SZ8+feTs2bN5fv8bb7whoaGh8sILL6jn58+fl/nz56e/PnbsWLnxxhvFk0G7oG9Gf46xT073/kceeUTatGkjQ4cOlbCwsBzv/ThvPOHenx0YWz/66KOqjTDezgm0Da5HjKn015GzNsLypEmTpHbt2uLubN26Vfr376/aYfTo0Tluj/FBYGCgGi+iX9HAXKts2bKGvPevWrVK7rnnHjU+xrg3Jz744ANJTEyUZ555JsN6jI0wRjJiGxFCCCGEuIwCcMwRN2LHjh2q2CRq6+SmbgyyIVCA1lE2DJGOkKtDYUQU+8Tn4bONwPr165X+ftu2bbMt3quBbVDo0DFrCxJ0KJLerFkzpVOPzzt8+LDdU8E5gCwyZMMgCyinOnnYHkVgIbmWlQwbMvmQ9YiCq0YBkkwoiAkJGmTKoF5OVvU88BqKqyP7CnJhqJECORpN0hHSRdWqVXOawebJHDt2zF6+fHkVdY/2QlZIVvU8cJ7hfEPdEGTnIQO0c+fO6ZKO58+fV+eksww2dwfyb6j58vnnn6vf2rHuiR4cJ2QfISkLqUdI8KJeipbtirZBJLbRMhgXL16sfl/UfMG5gHMlKxCdjvsV2nLOnDkqIxYyctr1hyK1aCPHemuEAGQAQYq3devWuZJmRDYSsiId7/2496E+DYq4o4Ay6juiHq0RQMHn5s2bq7qnuRnzQQIcta8cwbWMewDaBvc89OnZyWW7O9q9HP35G2+8kWOdPIwPURTbWZ3Q6dOnq/ERZLIxJsB2RgF9dMWKFVUbQdEB/XlWige4HyIzG+cQlB1Qfxhjacj8AYyTcA45y2DzZDDvwu+OWrnIJspuXIC2GDZsmKqziv4I9WVRg1B7D8aVyJz1RPUHjHNwPU2bNk0ppGTXDhhrQ3oX5wTOB9z70Tdrko5QxoA0pNFqykLlAnMoyHuifbIbH2H+CdlYbKf1MciM1cbef/75pzpXtOuLEEIIIYTkHzrVDCahYjKZlExITkW7IQPxzDPPqAkr5DIct8fr99xzj5r4OXvdU5k0aZKajGHCkVPRbhi5MUGpUqWKfcaMGZm2x+uoZYTaV85e9yQgp4PC5nAQ5kYOE4YiGBVxjjiTmMHrqHuU1eueDGQIHeWInPHTTz8ph9mDDz6YbuSHYaRGjRr2kJAQVXcG9VPgUDIakLPUyxE5A4Y2yPShDeC01qRq4BjBOhgScP7g0VMlD3E95SRHBKcZjg99LZzPmtFk/vz5ysiIc+iuu+5Sjif08UYC50DVqlVzlCOCkQhtgzZC/UHN0QhHHGQgEfzRo0cPda4YzQBLCgZch7j34zEnWWMEAsCAizpGOMcc7+14HfUf4Txw9rqnMn78eDWG1NdnygoYryGBifuZs+sXr7dp0ybL1z0J9D8IpIFDNjdGexwvjhvt42z72bNnKwnIrF73ZFAr97nnnst2G1wvuG4wdsZ1ptWOQwAbrjnc6zA+Qt20MWPG2I1G165d7W+99Va228BZhJpgWu04zRECRxTugxgjoY2wPHnyZLsngvv2zJkzc+XMxnFCWh4SzwDvK168uKpbjXs/XocDykggeANjGgQeZQcckgjyw7aY/2uyup988omS4UUd6zvuuEPN9//5558i2ntCCCGEEO+ATjUDgahQTDIw8XAcmDsWaIZh8vnnn0/PBtGzbds2NZHL6nVPBtHkcPYgc0QPjLSOjkM4O0aPHu00MhBZgI5GcE8HNRhgzHAs/O4YkY3JPaKJMbl3BpxIKDy/dOlSuxGBkxVGMf11t3DhQlWDTh9ViwwGZEU6AqMAsmy++OILw9YFQTT18uXL05/DcAhHqz7LCjVlYFzauXOn01qHcEp+9dVX9uPHj9s9kbNnz6pIYX3dr/3796t20B/TggUL7P369ctUIwRcuXJFOdK++eabdMOjkcBvjzbS9zFYh+tJn2327bffqgAHBDI4gnUIkkBgAyOwSVbgmkMmw8SJE3O89yN7Bn/OMieQVYN7f1avezLITINTDdeSY9aVoyMStY2QYYXXHMH1izaCk87Z656I5mR1HA9pRn59f9SxY8csM4cQKIIaoZ6YWZQbkGn+999/pz/H/Q/3PH2NUIzDkbnorC4dxtNQOsC9H7X5jAbOH1xjmGdpIPAMbaQPaEPmPhwhzupiw4kya9YsFZTkqdmfOAdw79ffszEmRDvox0xw8GNOgfGUIxgTYWyEMRICQY0GAszgfNf3vThv0L/qx0JwviLYz7GGGkBb4jzB+ZKT+gghhBBCCMk7dKoZDBg5ELkPgyQi0iDhgwkcHEmYmMBYnRNwwhlFysgZL7zwQrrzCNkgkCdCBDtkwzAx0bIgsgPt6MwI7snAgYpIR0QZ6yMfMfGFzKUzGSNnwHFglMxGZwQHByuDD6KtR40apYxIyFhAO8Fglpvzx8ig/0BbzJs3TznzhwwZoiRn4KjHemT5Gfn80F8HOF5kJ8L43qtXL5WhiALy6G9ef/11u7cDwzLaCI5pGNfgZEXfjGsM8pfILCakoIAjDNmfMD7+8ccfKvMI1yKcbXCaODpInAGjv6c6+nMDJNWQQYQ+6+eff1YOIFyjGFdChk7LgsgOXMu4po0EDPi4jyFzCsEOyB5CX462qV+/fq6DiBBYklOmpKeC+zquJwQdYRltpL/3Yw7iGODnbaDvQVsg6AhjRYyHcL/DfQ/rIaVqlMzXnKR4cbyQCsfcAw5ESEGif4Yj6b333rN7O8hQQxthPnbx4kXlrEewAuYcyED78ssvXb2LhBBCCCFejw9awHUV3cj1cO7cOVm0aJEqVt2rV68MxagTEhKkYcOGqmhzXFycKnreokULWblypYwaNUp69OghCxYsMHzDnzhxQhYvXiwpKSmqUDoKeWtERERI3bp1VbF4q9Wq2ggFwP/8809VBHvAgAEyffp0MSIo7L106VLZtWuXOm9Q/BpF3zW++uorVRC9evXq0rFjR1UMPDk5WbXRpk2bZMOGDXLDDTeIkcE5s2TJEtm/f780btxYXTP6wt433nijOp9wXa1fv15mz54tISEhMmPGDFVw/uWXX5b3339fjAyKoKMPOnr0qLRq1Uq6desmPj4+6a/XrFlTrQsICJCLFy/K999/r4rLT5kyRYYPH66Krb/00kvi6URFRcnChQtVn9ylSxdp27Zt+mvof0uVKiVvvfWW6n9xLr333nvqtZEjR6q2+O2336R3795iZMLCwuSPP/6Qy5cvyx133CHNmzdPfw3nRsWKFWXatGny9ddfq/vZq6++qvqcwYMHyw8//CCrVq2Sm266yaXHQDyHU6dOqXs5+qi+ffuqe5lGTEyM1KtXT0qWLKleHzdunLou//77bxkzZozcf//9MmvWLDE6R44ckb/++kst9+/fX12DGrhOMT6qVKmSuu+98cYbUqdOHZk3b566rz3//PMyefJkMSIYD+Lev2/fPjWO7tmzp5jN5vTXP/jgAzVGLFeunHTv3l0eeeQRiY6Oltdff129Z/v27ep9RiYpKUnd+3EOYQx0++23Z7j349zp1KmTGhPhWpw5c6YEBQXJp59+Ks8995y6B6KPNzK49+Oed/LkSWnXrp107tw5/TVMucuUKaPG1rj/Yfw9depU8fPzk/Hjx6s+CffDQYMGiadz7do11Q6XLl1S5wnOF/1rZcuWlU8++UR++eUXueWWW1QfjGtw2LBhqg0wV8E40sigbdBGmJfedddd0qBBg/TXjh07pvreH3/8Ud599111TuAaio+PV3ON+fPnq3lZ69atXXoMhBBCCCFejde7FT0MRO8hku+2225TWvLIQnOsVwFpOejtO8pl4L34yZ3JiRgFRHi+8sorKiMEkY+Q6kOWnmMUMaT3UBPMUTIE2VloU2cyY54OIkKbN2+uaqchIwSZIKh5opczQgQ1tkGReT2QDUG0MWqAGBlIFNWrV0+1wc0336zOBVxr+uj8999/X0XSIqrWUbrw8ccfV1HHRs7EgiwR6nzdcMMNSgYUfQrqqOijqyGdiswGRNQ69kOoD4b6NJ4Oanchuhwyn1o2BzJh9PTu3VtdN4jW159DuM4aN26s6jIZGcjIoS/u0qWLOl60EWp+6EH7oY1QW80x4xFya/fdd18R7zXxVHDfwvjo9ttvV30U7nGLFi3KsA1qOaH/uXTpUob1kHpGlo3RalzpQb+DTHRkWHXv3l1lWCNDHzJjej788EMl4YwMCT2DBw9WGRJGkwUHGBej9hDqYEKaEPd+jJPQD2kgswh1oBwlRJF9hD4etUGNDGqe4dpp3bq1yprB9YL6qfrMO9wDcU6hPRyvJdwP0acbGWSgYf6FLFiMr3HPQ/anHjzHNYhryVE9BOoiGH96OpAbxDHiWkJfgnbA2FlPt27d1L0ff/pzCNmMUMdAdr+R+eyzz9T9CuNAzDvQRpCy1tOiRQvVPuib9GDehvHnwIEDi3ivCSGEXC8oC8LaloQYDzrVPAhIYcFJpOnNY+KBwTgmr451vdasWZPp/YcPH1aDdhiDjQpqnGHyoRnMUM8DBgBMcvWODiw7q3eFdWgjZ+3nyUCOqUaNGhmKvqMuHI7VsQYfZFn0hiQNSPcYYbKfFZBMxeQVxb31Dmq0kb4GHxyMmizmxo0bM3yGtr1RaxdAFhZOQ9S50Duoccz6OnNwSsOhhvWoF6IH28PR7cng2oHTUN9PwCGP49XXSsEyjLOoMedYWwjbw8FtVCD/iXNF73hGXTS0h14+DwEPaLfy5ctnkkXD9jAyEpITb731lnJ4aAExuIe1b99eGR719zM4/9etW5fp/aiJifPQqLVAARxqMNJqdYwg8QhnNwzYehAAAAeKIzAEoI1Qe81IQKoSY0S93CzGyTjWKVOmZNgW544z+cYePXooB4JRwfwBDgB9vb1p06apNtKPBzD2Rr+P9Y4y8tge/b9R5Q1xv0dda0jLOgYz6uvMYUwEyVmsdwzsw/YYX3oyqBOLeam+zjCcP3DC6uvkafLPcL45yoLCgQ9pfqOCOrGQctTq6OGagNMZjlZ9bTStH3LsowFq8d55551Fut+EEOItoGYpZHid1ez8888/lUQ6/lAHFuPjnGTPMY7CvdHI8wxCvBU61TwEGOkxCfvhhx+cGvEdjfvOwA0AE1qjRmLj5ofjW7JkSYb1mtHf0bjvDBgMEGWLemtGAtHDdevWzWQMgkENEeu5ARNcI0dFInq4bdu2GdZhogtj28MPP5xhPbJDcU45ZtGMHz/e3qhRI7tRwaS/Z8+eGdYheh8OJtQq1DN16tT0GiF6nn/+eZUF6Mkg8hrHoQc1FnG8iD7WM2LECLX+448/zrAe7ThgwAC7EcF1g7pMuB70bNmyRbUFJiB6UFcG6x3vb4j2f/nll4tkn4nngnozyB52DBhCVhrOK9SxygkEBcDZ75jBZhTg4HAWMISsNKxHG+YEgktgENBntxuBp59+WgVfOYKsqvvvvz/H92NchbpqCJQwKv3791fqD47OVwTPoHaanq+//lqdU3CM6EH7ILvdqCAj+6GHHsqwDtme6JvGjRuXYf27776r2ggZso6BJKjN68kg63zs2LEZ1sERj+NFBpse1LHGejhc9cBBjSAAI4IgDzioP/300wzrtQCjv/76K8N6ZIM6BsRijNW0aVMVTEIIIaRgQX8MNQcETGEs6KjyhTEfMvdhB8If7BoIinC8p+uD+RHoN2HCBP5UhBgQi6vlJ0nuCA4Olv/973+qPpge1OwB+rpYzjh06JDSYkctA32NESNRtWpVVR8E2v3X00Y7duxQ7fPOO++kv8coPPTQQ6q+lb42GMBx5tQucL6jptrp06dVXRWj8thjj6maKHpQKwS1QRzb6N5771W1VdAuuDYfeOABVV9t4sSJqj6CkdsItRz1oH4j6qU5ttGQIUPkwIEDqpYKtkG9CNTIQM2iFStWiKe3g2Odr6z6GdThQf2ZV155RdXCaNOmjaohgho8qCNmRHDdoN4QrovctNGXX36p6s88/fTTqgZbo0aN5JtvvlH1ilALlJDsKF++vOqT0cfk5nxzZM+eParGo1Yvy4iglhzaCPWurqeNNm7cqOo9ffTRR6q/NxKPPvqoqgXmSG7GR6gB9eKLL6p6xujjjQrueeib9aBtUC/NsY2efPJJde//+OOPpVixYqp27/Lly1VtVYwBjNxGqBOmB/UbMe52bCPMNQ4ePKhqzOF+iZpiGDuivuOaNWvE06+n3N77UVPt+PHjan4aGRmpaq6iPvGFCxdk7ty5YkRQPw/tgxqeuWkjXDdnz56Vxx9/XNVWQ5011OHDdqhRTAghpGDBeHf27Nlqro97EmoKo0asHoyn9XWY8TrGAai56zjWxhgatddRU50QYjzoVHPDAtcoSgwjDwoWYxCtGTBgeMQEVc+JEyfUwFpf3Fhj27Zt8ueffypn0bp162T06NGqyLynExUVpW5chw8fVhMwGG8xSQHfffddJscR2giTFTjdHFm7dq0y8G/YsEG1E26aaHNPBb/5nDlz1CQdBrRWrVqp9U2aNJFatWpl2h5toy+irgcGkfPnz6ti2HDEoo2ctaGngd/8999/V46eBx98UBnwAQZAWntp2Gw2OXXqlAwePDjT56CoerNmzWTy5MnKGdC2bVtVNBzGS08Hhq+//vpLSpQooa6v2rVrq/W9e/dWfZQeOD6uXr2q2sIRGEzgRPriiy9UX3TzzTcrx2W1atXE3cFvv2DBAlm9erVUqFBB9Qt4BDAAOetngGM7WCwW+e2335RzEYNv9O8YcKMdPN15n5KSIr/++qs67/GbPvHEE1K6dOn068PR+J5VG+G+9s8//yinNPpwtH2vXr1k+vTpymhLiNbX/PDDD8oYjXMIxlvt3g8nLK41x/OtePHiTu99mODinNu8ebNs2bJFxo8fL0899ZTHN/S1a9eUAQDHjr4Xxluz2azGBDDOOoLtKleunH7d6sHYCAZ+3DP379+v+rD77rtPPBWMYdAXY8yM49D6ofbt26uxpGMwERz9MJA4gv4JzsWLFy8qw3/Tpk1VUI2jQ8UTwW++ePFidd0gGEsbz8Bh7Xjvx/NLly45vfdPmjRJbrjhBmX8X7JkiRpfbd26VWrUqCGeDsYycBLi/o0+SAtUxLmSmJiYYVs4Q5KTkzO1kXY9ol3QdyFgrWvXrmoeoo0z3P3ej3Nfmxfg3l+mTBn1GuaazuaqwLEdMA7H+Yb5BsZHaJO7775bPv/8cxWw5sngXPj555/VWA9jaLSRdkwY62Q1PkJ/ogfj8JUrV6oALdgBMPZEgC0+27GdCSGE5J+IiIh0WwXGLeHh4Tm+Rwvs27lzZwanGoKvMA7HmAh2utatW/MnIsRouDpVjmSUL4REH1KNIYkBeYg6depk0KB3JlmXlZwKislDTgtSEo71fDyVAwcOKGkRyGFAOg2yc6jzlZ2OMSTrspJTgeQR5EiQ5g0pG08GUmuQXBs2bJg6J1C/IDtpkLNnzypJkawKpv77779K5tCxLoYnA4lC1JaDrEyrVq3sFoslkwSLHtSDQhvpa0MYGUjKQIoPsgZoI0hZQnbWUbJHz7Jly1Qbof8yCugvu3XrZr/hhhvU9VStWjXVH+NYs+LLL79U9VQ8vR/JLZC1glwqap5B4hMyqahJ6KwWk8bbb7+t+ihC8gpqz0BqpU+fPkqGGNcaZFhRCzMrIN13++23O33typUranyE+5+zGqKeWtMJcjU4bvTj6LshoYpjzQr0c6iX6gy0Lfp+1JB0rHnkiRLYGDvivgYpa0jyTZw4McvtIRmK+xpqzDoD5w3k148dO2Y3yr3/ySefVHMOtBGk5SBlhNpPWbFy5UrVRvoamUYG8k2QwUS/gzaqV6+emoPMnz8/y/fMnTtXydJjPmYUoqKilIxVx44dlfQn+hzUgduwYUOW73n//ffV+MBbQF00XEOoez5kyBBV2xP9z549e7J8z0svvWRo+XhCCPEUxowZo2SIIU2MvlxfOxbATuIo94zxIsZE+jEB7Eiw7WLuC/su5sq33Xab4WTUCfF26FRzI+D8gZNMAxNVGJEwyc1qQoZOHcYCPUYtAg46deqU4Xhh+MBErUWLFlk6DjHZ++ijjwzdRlu3blUGkFOnTqWvg64zbu6oKecMFFeFUykmJsaw7aIHzuWQkJD0grOog4LJLtoIzkNnwOEGZ4q+Fp2R2wgG1CpVqqSfE3AQwUALA2RWDiXUzoCjUo+ntxGcP6ixAyOaZkRCvRQY8rMyimBwDQO1kdohJwc1JgbaMeK6gqMaxqOs6naiJo9jjSIjtxEpOHD9weiosX//fnv58uXtzZo1y3Jyir4M17K3nG8w9sN4rZ/g4/6FcZMzpxjWoSYWAgKM3EZw/uA4tbpxOD70X87qOGpg3IT36NvNaO3i6PzB9YT6zQDHjfpecAihHnNWgVy4xvQYuY1wnWA+ps018IiAPYy9161b5/Q9qL2K+YmR2gj98C233JJ+HGFhYSoAqXTp0lk6mVFDFg5JI7VDdjz++OMZjvf8+fP2Bg0aqNo8WdWvRJAS6jt6SxsRQoi7ArsPasPDLovxkTP7K8bWsKXhD3XTa9eubb/77rvTg2vj4uLUGAn11jS7yrVr11RAjmPdUUKIZ5NRv4q4lH///VfJo2nUrFlTSdVAfg71Phy5cuWKqpWmyfdBimbAgAFKq9+IJCUlKYkdfRs1btxY1SfatWuX0j92BDJRaCetjdCWqE334YcfitHOHaSn62X13n77benTp4+q94GaBY5A0gkp6JD5AZByQXtCys+IoI0g9ahJ1EBC5bPPPlPnBuQbL1++7LSNkMKPbRGEAGkaSLM4yiAZqY0ggamdE5DJwjFDPhTyh5Bfc9ZG2vUFiQPI9kDiAMue3A4dO3ZUcg2a/A7kmVBfD+3g7Nj07YDzA/2RY40nI4E2gtY8ZKwArivIqkIabeDAgZm2x3pIRWltBBlf3NcefvjhIt934lngeoMMof7e37BhQyV1vHfvXiU16gik+yC9pp1vWIacHWQejQju22gLfRvdeOONSgoSfRPkCh2BRE1MTEx6Gx09elTJrkKyz2h9FWQMUXsPoM+CPCHq7z777LNK5toRtBnuAZAUxb0fsqO496O9jAjaCBKYmjwdjnvatGlqjIj+HFJIOd37IfOM7SENaNQ2gqQq6hMDPP70009qrgbpR9TVy66NMIfBdehYj9XT7/2QjsW9H8+d3ftx/WDuprUDxpGoP+hYd83I8/mKFSuqNoKEGPocRzBmhEyk1kaoX4jtnnnmmSLdb0IIIak2ItSGxzgIcrvOOHPmjCoTgT/IyUPyFzUvNRsBpKIx90BpGc2uAtlolJCAlC8hxDjQqeZGYIJ25MiRDOtatmyjtkchAAAU50lEQVSpnCKoMwODiR7USdM0eidMmKAm/OXKlZM33nhDjAiOFRN9xzZCgW9M5FDbCjc4xwktjAQwqMDwhraCpj9uaEY7d3DsjpN6GMcwMHj99deznOzDIYniqe+88466yYeGhooRQRsdO3ZMTfA10DYYMGGSDydkVm0EZwBqpmFbOJkcayEYuQ9CzQsc97lz51TdCz0wnqE2EdoINUZatGihDAcwQGoOKaO0AwxHU6ZMUXroqIumB4NmOOzRDnDyw+CPmo9fffWVGBVnbVSlShV577331LmAOil6du/erRxpMMahhgzqgEZGRqp+m5DsQD8NB7/j+YZzCXXQUOsLTjTHvhv1ZjAugiMNYylMdp0FKBkB9NMwaju2EZxk/fv3l3fffVcZah3bCI4m1FRDMBZqiyGI5MknnxSj9VWoV6QPhkBboX+Go8NZQJZ270fNPbQLxlLffvutYWs8OuvPcc2hriUCjjDHyCpIAg4EXF8IPEG9Y8fahkZuIxjKcB7BIe3ojIYDFo5rtNGiRYtUcBJqtDqrbejp7VCpUiVV8wu1vxYuXJjhtX379ilnEvprzGVx70cQAMZTRsVZG2nzUFwncDLqQT+D2ntw5ON+hgBAXEcYTxFCCHE/MF7GnB9/mPMiMAL2AdhtgRbQjnuBth3+YE9BPVpngcqEEA/F1aly3gbkeCBFiDoFkBTT069fPyUt4ij3gNo1wcHBqs6aowRHqVKl1HsgQXL06FG7EVi9erV9woQJSpYHqdN6oG/cpk2bTO9BTTVIsLz22muZJDig9w8te9QNQR0xTwUSoDNnzlTyTpB71LNjxw4lZfTTTz9leh9kICHfd+HChfR1OPcg6wO5Nug7f/XVVxkkDj25jsE333yjzh9HmT7UzcuqhtzgwYOVtF90dHT6OkjYYXvI2uD8ya6umCeBawW/N/ohx3qNkDDIqoYc+ifIQ+nlsHAeYnvUqIHswYIFC+yewuHDh+2ffPKJ+oM0j5733ntP9SfOZHpwPuC60fPLL7+ktwNkI9euXWs3ApDXhaQFpNA02VSNF198UUnL6a8ZAMlMnAuOtaw+++wz1Q+1bNlS1WHzljqFJPesWbNG9d24zzlKOqI2jeN1B3BfQ+2wl19+OcN6yGhBGhrStPfee28GaWRPBWNDyPB+8MEHqs9xrAWH+rLOasihHh3u92hbPZAnw70NtR6eeOKJLGXJPAH0T999951qG9SwcDyv0D8vXLjQqTwfzh9I8jjWm0VfD+kenI9GkGHDtfL111/bP/zww0y1cn/77Td1zOvXr3cqbQxpv4SEhEz1ZnHPq1Wrln3evHl2I4DasJB8mjRpkv3EiRMZXkNtFdQqxvXkTNoYfY3+PNHqzaKNUCsrq/rF7gjkdSdPnmz//PPP1bhazyuvvGIvUaJEulSo/t4PWauuXbtmks1E/4OxEeqwofajEcC8C3UZcU05lmhA3U/MrRz7aEiGYv3//ve/DOshU4z6fI0bN1Z9+L59+4rkGAghhOQdZzXVtFI+NWvWzHDvu++++5z+ZVfvmBDiWdCpVkRg8gFNeeipd+/eXdVqgCEDEziNxYsXqwmYM+M9DEQwfuh55JFH1AAczgIjgIkbdIdxM8KkIiAgQBX31N90YNxAG6E+liMwEMFgq+fOO+9UhpGs6h14Cqj5BaMG2gc1ZNAG48aNy7ANDI6oqaLVgdI4c+aMMgTMmDEjfR0csHAaoK4InLZGAAYPGPlR6wG1C9BGMORrwGmIcwsGfUc0A5G+dsimTZvUOfj6668bpqAsjGloI9T+guMD54XeEQsndmhoqBoUOqIZiNAuGmgvGFfghNIb3NwZnAfPPfecCkhAXwynO35nvSMMRlXt+nBk+vTpqh0uXbqUvm7KlCnK4YjXjOCchiEI9XRwLqAPRZ00BHbs2rUrg8MN549jzSoAx7+vr2+GOpdvvPGGvVq1asoZQIge3OPRb8MoDeN0sWLFlKFef42hn8J1t2jRokyNhzp9KCSup0+fPqqW0apVqwzR2LiPI6AIRmv03+if4ETTG7YRIIA22rBhQ6b34z1oYz24F3bo0MG+ZcsWuyeDsQ3qpaLuHsbEaAN9HV04OmAAQfs5OsfgXML2eqcQjP5wtI0aNSpDzVlPBvco3PvhnMa4Gn03nJD6Ph/GftwTHcG9EW2kv5b+/vtvNY955513PObenxNvvvmmaiPMP1DPGkEgv//+e6YAx4cffjjTexFQhDbSO0N+/PFHNc6Ac8pZTUN3BPv55JNPqns+zgXUpMbvrA/kwzUDQ6HjHATgukO76cfMaFc4p9EeRgDjZDjFMObD+Ah9D9pL72yFc9pxDqKBuuA4z/R90bBhw1SArP58I4QQ4jlONfTpsMNhXK0FZ2Mu7Gweorf/EkI8HzrVigA4OWDMQKaHVrwSEZAYVMPwowcdMRxvjo4OONowUXGMzPWUiVpOwPgKpxAKgmpGaWQawbCBCZ6+LRHxCaeJ40QeE1dMchwddZ4eYbxkyRKVRaV3Zjz77LNqwgbDtqNTFu3gCIyVcHzo8eSodEc044Vm0MBvDkOrxWJRmVmOTllnjms4h+CYM2obweAKR74WfY1rCcYjnFt6wywitNFGOO/04HrDekS0a6Cd9YZvTwAONRjbw8LC1HNkWsEQq0WW6bfDYHj37t1OjbCIUtYbWRyjtj0ZZPXC4K4ZlHGvgUMMEfeO2+H8wcTBmREWjgB9dqxj5jEh6FdwXiGrXLv3ox+HYw3ZUxp4DY4zOAQczyMYLmHUdHTUGcHBrV07mMCPGDEifd3GjRuVYXvkyJEZxlG4ThFI5BhcAwM4PkOPp/XdzoAzDI4OfXYaAgLQNsePH88QmIQ+CYEPjiB44NNPPzXsvR+ZNMja1DLTcV3cfffdKphEuw8CZGihjRwzznEuYSyFTHajnT8a7777rhoDaGoWmKt16tRJjSv1wSFvvfWWckhCUUMP7pFoO32QI9rNMcPb3RkwYIC9Xbt26eMZZGAhAMsxaOHRRx9VmVXHjh3LsB79EtpBn+WHMZZRAtNAr1691NhZOy+QAYq5p2PQQo8ePVQwpON1gsAQtJE+yx/t7JjVRgghxD3BeBpjBIyL8AcFICRPwG6gz0rHesxPMH6HShkCT6AuBpswIcQ40KlWBCAyH84hxzRfRDggMlQPHEmY6MLZpjcIwWjUsGFDu1HB8WHy6jjxQtQ6oj4cDbZwMEJew9Fo5CwLydNB9CLkVvQcOHDAqYEIjiScP5AZ1U/sYTQyijyPI9rxIQvLmeSTo4HotttuUw5ttKEGnAe4Ro2S1eAIJu8wUusj0/WST/osLTjqYZRF36SPpIKR0TES29PQHGI4N/Tg3MF6vSEVRiUYqOHE1wc5wGiEQbOj3I9R0BxikH/Sgz4IxkQtMASgvXDtwQind3TAaAQntVGCPkjhAXkUZzKimJwieEYPMrBg3IfTRA+yJdu3b2/YnwnHh0wPx+upbdu2alKvB8EQuE71DjiArFs4UowEgjoQKDJ+/PgM65F5hz7MMSsWx4+sG32gBJy6CAxA5pURgeEffTEkfB3PE7QRMtA1MOeAPB/6dL1DEtKYOKeMItvnCOZm6FfmzJmTYT0k6NFGeoctzhcE4WBsoJdUh/oD2shRMtKT0OS89QFDYOzYsZmcQAhYhKMWARH6eRtURHA9GdVBhOODw94xkAiBjph76cE1hGsPWcL6cROC+qCQQAghxDPBGFsv5YikAGTuO5Oax3x6zJgxKhgFgXDOFDcIIZ4NnWpFAIyzjg4gMHTo0EySjmD27NlqgocoNxg4MdFDFNzcuXPtRgU1jSB/4QicRI6SjpohDhNY3Mhg5EYGEiJBli9fbjcaMBg51gjRHBx6SUeASS8kjmCkRHQyjEtw3mKdkQ3cqIviWC8PWXyOko6aAQWOEhgEYDRBBiCihpzVojESyARxNFyvXLkyk6QjgDMN8kcwHKH/gXwqonCRmeTpRljUV8pK0lEftQ9gXEKkMaK0YYRE/wL5VcfajUYC/cQDDzzgVNIR9yXH7B9E7MNQDQM/2gfthEAAZDwSkhOI4odB0hFIrDlmR2jXKoyakHvGvR8GStz7PaleUV6Bwd4xsAZAns0xOwKgrhj6M2T6IcAGYwEELTn280YA7eJYTxjPHSUdAQIhIJmJ8wUBJmgbRAtDNtIoWY3OgNqDvmacPqPIMZAIY3H031DMgFMSMnYYG/Xt29duZDC2cXQEaRlFjoFEyPiDM1erI4t7IFRGHOtee2KAGuZUjkD9AvMtR3UQjAvhNIJjDc5ZOJwgT+tYu9FoTmpn8p+QiUdbOAIFEQTsoZ/GtQZ5R8z7UfuZEEIIIYR4PnSqFRHOpC8QiY0/ZyCTonPnzipiFHW09NGk3tRGMNQ6RqVrwHiL6HS0EbKPnNURMcokzlHCEsfqLKJUy7p6+eWXlUMEf4iKMUpdkLycO3/88YdqI2e61TCuwakNwxFq9yAS1yh1QfLSRjAsIuvT0dmmOW4hBYSMNc1Q4igpZpR2GD16tDoXnAEDLQz4qC0Ch5o3GEOctdHgwYOVQdoZyLC+6667VF+MLEcEhhCSn/MNGTPOioADONBgxMb5hiwAfaatN7UR5GcQUOKM+fPnq2sR/RbGmc7GCkZtF0jw4d6vyR06Brkhaw+Zf5D7Q9+vl/fzljaCXBEcJY6BJFrgERxEyKaB3CqkEY0clJVVG0ESFBn+zrKuEMSFutYIeISKCGrWGcEx66wd0Mc4SsdqwOGIDFD0xa1bt85Qo9eoOGsj3Ktwz3IGghsxR0UbYc6KuQkhhBBCCDEGPvgnxCWUK1dORo4cqf5IZhITE6VkyZLyxRdfyBNPPMEm0jFlyhQZN26chIWFiclkYts44Y033pCffvpJjh07xvbJgiFDhsj27dtl06ZNXt1Gt912m+qPf/zxR1fvitvSsmVLufnmm1XfQ0hhkpycLCEhITJ58mR58skn2dhOiIiIkNKlS8u8efOkT58+bCMd77zzjkydOlXOnTvHdsmC4cOHy4oVK2TXrl1soyx45JFH5Pz587J8+XKvbqP27dtLs2bN5KuvvnL1rrgtdevWlf/973+q7yGEEEIIId4DrfEu4uDBg3LlyhVlpNSgfzMjW7ZsUY41tlFm1qxZI506dcrgUOP5k7mN9OcO28j5eeTtbZSSkiIbN270+nbIjqioKNm9e3emNiKkMNi2bZvExcXx3p8Na9euVY833XST1/bdWcH7GtuI51HBEB8fr/pjbx8nZsfFixfl6NGjHB8RQgghhHghdKq50CBSvHhxad26tXq+YcMGadu2raxbt85Vu+SWbVS5cmWpXbu2er5s2TJp3ry5HDhwQLwdtE3nzp3VcmxsrIwePVr69u3r6t1yq0yHzZs3p7cRovpfeOEFGThwoKt3zW24du2a7N+/P72NLl++LIMGDZKXXnpJvImdO3dKTExMejvAONKrVy+V6UBSWb9+vdhstnSj0d69e6Vbt24ye/ZsNhEplPtb+fLlpX79+uo5MmpatPh/e/fzouMaxgH8njPDGKE0k5iIRPkxdJI0sZlSfsRCURbEP8DGhimRWKFspWzYTbObRjQ7a+VPkNgokuyUzul7O3OcwXjrnHje4/l8ahYzzeLuep/3uZ/7vq7rfn6v1x2fY7Rly5YyNDRUf5+amiojIyPl5cuXrQ5R7lN5np69n6cgIHPayZMnmx5a18h8l3lvNkavX7+uXetnz55temhdI9+j58+f/x2jdKylc+3y5culTXKKQZ6nZ+f+rL8OHDhQ7t271/TQuupe3NfXV3bv3l1/f/r0aY3X9PR000MDAOAHk1Rr8CF8z5495dWrV3Wxf/z48brpn7/xOUZZ0Ob4viNHjtRF/7Vr18rmzZtbHaJs+ue6yaLt/v37ZdOmTeXFixeSAP+QytpU2Kab7/bt2zVGHz58KDdu3Gjug+vCRElPT0/ZtWtXuXnzZtm6dWs9TizHirZxAz8J/AsXLtSjjnLdOHbu6w383t7ecubMmZpQSxL/2LFjDX5y/Opz/7Nnz8rRo0frd/HKlSs1acTcGGWTe//+/WV8fLwezbp69epWhygdtUmkpYPv7t27de5/9+5dPUqUT9KZ/fHjxzrX5ZrJvX3RokWOrvvi+9Xf31+T+TnSLwV969atq88IbYvD+vXry9KlS+uRoWNjY+XgwYPlxIkTTQ+tq2K0Y8eOuubIXHXo0KFy+vTpGicAAH5tfU0PoK3SkbZw4cK6YEuy6M6dO2Xx4sVND6tr5GiRbPqvWbOmjI6O1krjiYmJGrO2m+1mzOZ2jn+cnJysXY7MjVGulWzIppJ/ZmambNu2TYi+iFE2SpJASkdIqvs3bNjQ2mslm6/ZnE43TJJszI1RjuJNQUMKQLKRv3z5ciHih8jcv2rVqprwP3fuXH03Zja4+SQFIk+ePKlHZGb+v3jxYn2OTLdE2+VeleR/uoqWLFlSu0XyPkjmxiixybtE165dWx4/flznP75+PkqyJD85jj5JtTbGIeuxzP0pbszpBoODg00Pq+tilER+ij5OnTpVX++wbNmypocFAMBPYAXegFSIvnnzpuzdu7c8ePCgLmqZ6/3793UTN4vZR48elZUrVwrRX9Kllg3HJNWycZRuI76OUTqPLl26VBNrfDtGw8PDtYJ/3759rY5Dkvep2N+5c2fTw+lKiVGq1XOtpKsBfpQcZ5xkUTpDHj58WOc65spxfZGCo6tXr9rk/sbcf/78+VoAwPxz//Xr18vhw4eF6DsxynNBurPaHIesUROH7du3Nz2cro1RYnPr1q2ycePGpocDAMBP1POHtw039hCuG0KM/u2mY762qTTm23LcUzobcqQR879TLdW0be9uyLvkVqxY0fQwupr5Ctdbd/GdnL8gK3PawMDAT/5E/j/evn1bnx8XLFjQ9FC6OnGdbux0PbaZ+4wYAQAwP0k1AAAAAAAA6OC3Tv8AAAAAAAAAbSepBgAAAAAAAJJqAAAAAAAA8N/oVAMAAAAAAIAOJNUAAAAAAACgA0k1AAAAAAAA6EBSDQAAAAAAADqQVAMAAAAAAIAOJNUAAAAAAACgA0k1AAAAAAAA6EBSDQAAAAAAADqQVAMAAAAAAIAOJNUAAAAAAACgfN+fYf2AkeIbPhsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1760x495 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "fig, axes = plt.subplots(1, 3, figsize=(16, 4.5))\ncols = {\"ÜRT-1\":PAL[\"accent\"], \"ÜRT-11\":PAL[\"polyhalite\"], \"ÜRT-16\":PAL[\"thenardite\"]}\nfor wname, g in brine[brine.well.isin(cols)].groupby(\"well\"):\n    axes[0].plot(g.date, g.bome, \"o-\", color=cols[wname], label=wname, ms=4)\n    axes[1].plot(g.date, g.na2so4_g_l, \"o-\", color=cols[wname], label=wname, ms=4)\naxes[0].axhline(25, ls=\"--\", color=\"grey\"); axes[0].text(brine.date.min(), 25.5, \"hedef 24–25 °Bé\", fontsize=8, color=\"grey\")\naxes[0].set_title(\"Kaverna brine Bomé gelişimi\"); axes[0].set_ylabel(\"°Bé\"); axes[0].legend()\naxes[1].set_title(\"Na₂SO₄ konsantrasyonu\"); axes[1].set_ylabel(\"g/L\"); axes[1].legend()\nfor ax in axes[:2]: ax.tick_params(axis=\"x\", rotation=30)\n# Bomé – Na2SO4 kalibrasyonu\nok = brine.dropna(subset=[\"bome\",\"na2so4_g_l\"])\nk = np.polyfit(ok.bome, ok.na2so4_g_l, 1)\naxes[2].scatter(ok.bome, ok.na2so4_g_l, c=[cols.get(w, \"grey\") for w in ok.well], s=25)\nxx = np.linspace(ok.bome.min(), ok.bome.max(), 50); axes[2].plot(xx, np.polyval(k, xx), \"k--\", lw=1)\naxes[2].set_title(f\"Na₂SO₄ (g/L) ≈ {k[0]:.2f}·Bomé {k[1]:+.1f}   (R²={np.corrcoef(ok.bome, ok.na2so4_g_l)[0,1]**2:.2f})\")\naxes[2].set_xlabel(\"°Bé\"); axes[2].set_ylabel(\"g/L\")\nplt.tight_layout(); plt.savefig(f\"{FIG_DIR}/05_brine.png\"); plt.show()"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2851466",
   "metadata": {
    "id": "d2851466",
    "outputId": "6568de25-847b-457e-c1aa-77d07f1d5050"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1320x440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "# İyon dengesi görünümü: Ca/Mg safsızlıkları ve NaCl kirliliği Bomé ile nasıl değişiyor?\nfig, axes = plt.subplots(1, 2, figsize=(12, 4))\nfor wname, g in brine[brine.well.isin(cols)].groupby(\"well\"):\n    axes[0].scatter(g.bome, g.ca_mg_l, color=cols[wname], label=f\"{wname} Ca\", s=22)\n    axes[0].scatter(g.bome, g.mg_mg_l, color=cols[wname], marker=\"x\", label=f\"{wname} Mg\", s=22)\n    axes[1].scatter(g.bome, g.nacl_wt_pct, color=cols[wname], label=wname, s=22)\naxes[0].set_xlabel(\"°Bé\"); axes[0].set_ylabel(\"mg/L\"); axes[0].set_title(\"Ca (●) ve Mg (×) vs Bomé\"); axes[0].legend(fontsize=7, ncol=3)\naxes[1].set_xlabel(\"°Bé\"); axes[1].set_ylabel(\"NaCl %wt\"); axes[1].set_title(\"NaCl kirliliği vs Bomé\"); axes[1].legend(fontsize=8)\nplt.tight_layout(); plt.savefig(f\"{FIG_DIR}/06_brine_iyonlar.png\"); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "f8b50d69",
   "metadata": {
    "id": "f8b50d69"
   },
   "source": "## 6. Casing / tubing basınç testleri (`ptests`)\nHer test: başlangıç basıncı → bekleme → bitiş basıncı. Basınç düşüşü yüzdesi, kuyu sızdırmazlığının göstergesi (casing testlerinde düşük, tubing testlerinde yüksek düşüş normal: tubing kavernaya açık)."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e5faa0b",
   "metadata": {
    "id": "6e5faa0b",
    "outputId": "9c8b0dd3-745b-4847-9fcc-58a00820bc25"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>test_type</th>\n",
       "      <th>test_no</th>\n",
       "      <th>end</th>\n",
       "      <th>start</th>\n",
       "      <th>drop_bar</th>\n",
       "      <th>drop_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ÜRT-1</td>\n",
       "      <td>casing</td>\n",
       "      <td>1</td>\n",
       "      <td>6.50</td>\n",
       "      <td>11.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>43.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ÜRT-1</td>\n",
       "      <td>tubing</td>\n",
       "      <td>1</td>\n",
       "      <td>3.50</td>\n",
       "      <td>9.50</td>\n",
       "      <td>6.00</td>\n",
       "      <td>63.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ÜRT-1</td>\n",
       "      <td>tubing</td>\n",
       "      <td>2</td>\n",
       "      <td>3.50</td>\n",
       "      <td>9.50</td>\n",
       "      <td>6.00</td>\n",
       "      <td>63.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ÜRT-1</td>\n",
       "      <td>tubing</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5.50</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ÜRT-11</td>\n",
       "      <td>casing</td>\n",
       "      <td>1</td>\n",
       "      <td>6.50</td>\n",
       "      <td>10.50</td>\n",
       "      <td>4.00</td>\n",
       "      <td>38.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ÜRT-11</td>\n",
       "      <td>casing</td>\n",
       "      <td>2</td>\n",
       "      <td>7.50</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ÜRT-11</td>\n",
       "      <td>casing</td>\n",
       "      <td>3</td>\n",
       "      <td>9.50</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ÜRT-11</td>\n",
       "      <td>tubing</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25</td>\n",
       "      <td>18.00</td>\n",
       "      <td>11.75</td>\n",
       "      <td>65.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ÜRT-11</td>\n",
       "      <td>tubing</td>\n",
       "      <td>2</td>\n",
       "      <td>13.75</td>\n",
       "      <td>15.50</td>\n",
       "      <td>1.75</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ÜRT-11</td>\n",
       "      <td>tubing</td>\n",
       "      <td>3</td>\n",
       "      <td>4.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ÜRT-16</td>\n",
       "      <td>casing</td>\n",
       "      <td>1</td>\n",
       "      <td>7.00</td>\n",
       "      <td>10.40</td>\n",
       "      <td>3.40</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ÜRT-16</td>\n",
       "      <td>casing</td>\n",
       "      <td>2</td>\n",
       "      <td>7.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ÜRT-16</td>\n",
       "      <td>casing</td>\n",
       "      <td>3</td>\n",
       "      <td>6.75</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ÜRT-16</td>\n",
       "      <td>tubing</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ÜRT-16</td>\n",
       "      <td>tubing</td>\n",
       "      <td>2</td>\n",
       "      <td>4.90</td>\n",
       "      <td>11.25</td>\n",
       "      <td>6.35</td>\n",
       "      <td>56.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ÜRT-16</td>\n",
       "      <td>tubing</td>\n",
       "      <td>3</td>\n",
       "      <td>5.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ÜRT-17</td>\n",
       "      <td>casing</td>\n",
       "      <td>1</td>\n",
       "      <td>8.50</td>\n",
       "      <td>13.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ÜRT-17</td>\n",
       "      <td>casing</td>\n",
       "      <td>2</td>\n",
       "      <td>9.50</td>\n",
       "      <td>12.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ÜRT-17</td>\n",
       "      <td>casing</td>\n",
       "      <td>3</td>\n",
       "      <td>12.00</td>\n",
       "      <td>13.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ÜRT-17</td>\n",
       "      <td>tubing</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25</td>\n",
       "      <td>11.00</td>\n",
       "      <td>4.75</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ÜRT-17</td>\n",
       "      <td>tubing</td>\n",
       "      <td>2</td>\n",
       "      <td>4.50</td>\n",
       "      <td>10.50</td>\n",
       "      <td>6.00</td>\n",
       "      <td>57.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ÜRT-17</td>\n",
       "      <td>tubing</td>\n",
       "      <td>3</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ÜRT-6</td>\n",
       "      <td>casing</td>\n",
       "      <td>1</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ÜRT-6</td>\n",
       "      <td>casing</td>\n",
       "      <td>2</td>\n",
       "      <td>3.50</td>\n",
       "      <td>10.50</td>\n",
       "      <td>7.00</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ÜRT-6</td>\n",
       "      <td>casing</td>\n",
       "      <td>3</td>\n",
       "      <td>5.50</td>\n",
       "      <td>11.50</td>\n",
       "      <td>6.00</td>\n",
       "      <td>52.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      well test_type  test_no    end  start  drop_bar  drop_pct\n",
       "0    ÜRT-1    casing        1   6.50  11.50      5.00      43.5\n",
       "1    ÜRT-1    tubing        1   3.50   9.50      6.00      63.2\n",
       "2    ÜRT-1    tubing        2   3.50   9.50      6.00      63.2\n",
       "3    ÜRT-1    tubing        3   4.50  10.00      5.50      55.0\n",
       "4   ÜRT-11    casing        1   6.50  10.50      4.00      38.1\n",
       "5   ÜRT-11    casing        2   7.50  10.00      2.50      25.0\n",
       "6   ÜRT-11    casing        3   9.50  10.00      0.50       5.0\n",
       "7   ÜRT-11    tubing        1   6.25  18.00     11.75      65.3\n",
       "8   ÜRT-11    tubing        2  13.75  15.50      1.75      11.3\n",
       "9   ÜRT-11    tubing        3   4.00  16.00     12.00      75.0\n",
       "10  ÜRT-16    casing        1   7.00  10.40      3.40      32.7\n",
       "11  ÜRT-16    casing        2   7.00  10.00      3.00      30.0\n",
       "12  ÜRT-16    casing        3   6.75  10.00      3.25      32.5\n",
       "13  ÜRT-16    tubing        1   5.00  11.00      6.00      54.5\n",
       "14  ÜRT-16    tubing        2   4.90  11.25      6.35      56.4\n",
       "15  ÜRT-16    tubing        3   5.00  11.00      6.00      54.5\n",
       "16  ÜRT-17    casing        1   8.50  13.00      4.50      34.6\n",
       "17  ÜRT-17    casing        2   9.50  12.50      3.00      24.0\n",
       "18  ÜRT-17    casing        3  12.00  13.50      1.50      11.1\n",
       "19  ÜRT-17    tubing        1   6.25  11.00      4.75      43.2\n",
       "20  ÜRT-17    tubing        2   4.50  10.50      6.00      57.1\n",
       "21  ÜRT-17    tubing        3   5.00   8.00      3.00      37.5\n",
       "22   ÜRT-6    casing        1   7.00  11.50      4.50      39.1\n",
       "23   ÜRT-6    casing        2   3.50  10.50      7.00      66.7\n",
       "24   ÜRT-6    casing        3   5.50  11.50      6.00      52.2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "xl = pd.ExcelFile(F_PTEST); recs = []\nfor sh in xl.sheet_names:\n    df_ = xl.parse(sh, header=None); wname = str(df_.iloc[0,0]).upper().replace(\" \",\"\")\n    kind = \"casing\" if \"CAS\" in sh.upper() else \"tubing\"\n    for i in range(1, len(df_)):\n        lab = str(df_.iloc[i,0]); m = re.search(r\"Test-(\\d+)\", lab)\n        recs.append(dict(well=wname, test_type=kind, test_no=int(m.group(1)) if m else 1,\n                         phase=\"start\" if \"Başlangıç\" in lab else \"end\", date=df_.iloc[i,1], time=df_.iloc[i,2], bar=to_float(df_.iloc[i,3])))\nptests_long = pd.DataFrame(recs)\nptests = ptests_long.pivot_table(index=[\"well\",\"test_type\",\"test_no\"], columns=\"phase\", values=\"bar\", aggfunc=\"first\").reset_index()\nptests[\"drop_bar\"] = ptests.start - ptests.end; ptests[\"drop_pct\"] = (100*ptests.drop_bar/ptests.start).round(1)\nptests.columns.name = None\nfig, ax = plt.subplots(figsize=(10, 4))\nfor i, (kind, g) in enumerate(ptests.groupby(\"test_type\")):\n    ax.bar(np.arange(len(g)) + (0 if i==0 else len(ptests[ptests.test_type==\"casing\"])+1), g.drop_pct,\n           color=PAL[\"grey\"] if kind==\"casing\" else PAL[\"thenardite\"], label=kind)\nlabels = list(ptests[ptests.test_type==\"casing\"].apply(lambda r: f\"{r.well} T{r.test_no}\", axis=1)) + [\"\"] + \\\n         list(ptests[ptests.test_type==\"tubing\"].apply(lambda r: f\"{r.well} T{r.test_no}\", axis=1))\nax.set_xticks(range(len(labels))); ax.set_xticklabels(labels, rotation=60, ha=\"right\", fontsize=8)\nax.set_ylabel(\"Basınç düşüşü (%)\"); ax.set_title(\"Casing ve tubing basınç testleri\"); ax.legend()\nplt.tight_layout(); plt.savefig(f\"{FIG_DIR}/07_basinc_testleri.png\"); plt.show()\nptests"
  },
  {
   "cell_type": "markdown",
   "id": "a6a31d7e",
   "metadata": {
    "id": "a6a31d7e"
   },
   "source": "## 7. Kaverna sirkülasyon performansı (PPTX → `cavern`)\n`Polatlı kuyu kaverna detayları.pptx` 9. slaytta sahanın kendi hesabı var: her kuyunun sirkülasyon aralığındaki tenardit yüzdesi ve ÜRT-1 deneyimine göre **ideal tubing aralığı**. Bunu litoloji tablomuzdan bağımsız doğrulayabiliriz."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c15b08",
   "metadata": {
    "id": "b7c15b08",
    "outputId": "dc49b88e-da7a-4353-bff4-eac199686270"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>circ_days</th>\n",
       "      <th>bome_2025_12_25</th>\n",
       "      <th>circ_interval_m</th>\n",
       "      <th>circ_thenardite_pct</th>\n",
       "      <th>circ_thenardite_pct_recomputed</th>\n",
       "      <th>ideal_interval_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ÜRT-1</td>\n",
       "      <td>154.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.75</td>\n",
       "      <td>29.19</td>\n",
       "      <td>29.50</td>\n",
       "      <td>14.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ÜRT-2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ÜRT-3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>32.86</td>\n",
       "      <td>11.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ÜRT-5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>10.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ÜRT-6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>13.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ÜRT-7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>35.97</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ÜRT-8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>21.50</td>\n",
       "      <td>22.55</td>\n",
       "      <td>16.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ÜRT-11</td>\n",
       "      <td>76.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>28.89</td>\n",
       "      <td>28.89</td>\n",
       "      <td>10.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ÜRT-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ÜRT-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>27.73</td>\n",
       "      <td>13.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ÜRT-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>31.17</td>\n",
       "      <td>12.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ÜRT-16</td>\n",
       "      <td>69.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>39.25</td>\n",
       "      <td>39.56</td>\n",
       "      <td>3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ÜRT-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>26.80</td>\n",
       "      <td>26.00</td>\n",
       "      <td>14.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ÜRT-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>35.90</td>\n",
       "      <td>36.50</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ÜRT-19</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>33.60</td>\n",
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       "      <td>11.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      well  circ_days  bome_2025_12_25  circ_interval_m  circ_thenardite_pct  circ_thenardite_pct_recomputed  ideal_interval_m\n",
       "0    ÜRT-1      154.0             19.0             8.75                29.19                           29.50             14.09\n",
       "1    ÜRT-2        0.0              0.0             9.00                26.10                           26.11             13.79\n",
       "2    ÜRT-3        0.0              0.0            10.00                33.00                           32.86             11.82\n",
       "3    ÜRT-5        0.0              0.0            10.00                37.00                           37.00             10.55\n",
       "4    ÜRT-6        0.0              0.0            10.00                30.00                           30.00             13.55\n",
       "5    ÜRT-7        0.0              0.0            10.00                36.00                           35.97             10.60\n",
       "6    ÜRT-8        0.0              0.0            10.00                21.50                           22.55             16.06\n",
       "7   ÜRT-11       76.0             12.0             8.00                28.89                           28.89             10.48\n",
       "8   ÜRT-12        0.0              0.0             8.00                31.25                           31.25             12.67\n",
       "9   ÜRT-13        0.0              0.0            11.00                27.00                           27.73             13.57\n",
       "10  ÜRT-14        0.0              0.0            10.00                31.00                           31.17             12.01\n",
       "11  ÜRT-16       69.0             24.0             8.00                39.25                           39.56              3.90\n",
       "12  ÜRT-17        0.0              0.0            10.00                26.80                           26.00             14.04\n",
       "13  ÜRT-18        0.0              0.0            10.00                35.90                           36.50             10.75\n",
       "14  ÜRT-19        0.0              0.0            11.00                33.60                           33.75             11.68"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "try:\n    from pptx import Presentation\nexcept ImportError:\n    !pip -q install python-pptx\n    from pptx import Presentation\nprs = Presentation(F_PPTX); tbl = None\nfor s in prs.slides:\n    for sh in s.shapes:\n        if sh.has_table and \"İdeal Tubing\" in \" \".join(c.text for c in sh.table.rows[0].cells): tbl = sh.table\nrows_ = [[c.text.replace(\"\\x0b\",\" \").strip() for c in r.cells] for r in tbl.rows]\ncav = pd.DataFrame(rows_[1:], columns=rows_[0])\ncav = cav[cav.iloc[:,0].str.match(r\"^ÜRT-\\d+$\")].copy()\ncav.columns = [\"well\",\"circ_days\",\"bome_2025_12_25\",\"circ_interval_m\",\"circ_thenardite_pct\",\"bome_start\",\"monthly_bome_gain\",\n               \"target_bome\",\"work_months\",\"required_monthly_gain\",\"required_interval_m\",\"interval_change_m\",\n               \"thenardite_pct_after_change\",\"dissolved_thenardite_m_now\",\"dissolved_thenardite_m_after\",\"ideal_interval_m\"]\nfor c in cav.columns[1:]: cav[c] = cav[c].map(to_float)\ncavern = cav.reset_index(drop=True)\n# Bizim litoloji tablomuzdan: sirkülasyon aralığındaki ortalama tenardit\ndef circ_thenardite(wname):\n    r = wells[wells.well == wname]\n    if not len(r): return np.nan\n    seg = lith[(lith.well==wname) & (lith.depth_mid>=r.tubing_4_5_m.iloc[0]) & (lith.depth_mid<=r.tubing_2_3_8_m.iloc[0])]\n    return np.average(seg.thenardite_pct, weights=seg.thickness_m) if len(seg) else np.nan\ncavern[\"circ_thenardite_pct_recomputed\"] = cavern.well.map(circ_thenardite).round(2)\ncavern[[\"well\",\"circ_days\",\"bome_2025_12_25\",\"circ_interval_m\",\"circ_thenardite_pct\",\"circ_thenardite_pct_recomputed\",\"ideal_interval_m\"]]"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15871ee3",
   "metadata": {
    "id": "15871ee3",
    "outputId": "bd0e662f-2ba0-4ec5-c35d-89227f3a4268"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 770x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "fig, ax = plt.subplots(figsize=(7, 5))\nok = cavern.dropna(subset=[\"circ_thenardite_pct_recomputed\"])\nax.scatter(ok.circ_thenardite_pct, ok.circ_thenardite_pct_recomputed, color=PAL[\"accent\"])\nfor _, r in ok.iterrows(): ax.annotate(r.well, (r.circ_thenardite_pct, r.circ_thenardite_pct_recomputed), fontsize=7, xytext=(3,3), textcoords=\"offset points\")\nlim = [15, 50]; ax.plot(lim, lim, \"k--\", lw=1); ax.set_xlim(lim); ax.set_ylim(lim)\nax.set_xlabel(\"Saha hesabı: sirkülasyon aralığı tenardit %\"); ax.set_ylabel(\"Bu notebook: kalınlık-ağırlıklı tenardit %\")\nax.set_title(\"Sirkülasyon aralığı tenardit % — saha vs yeniden hesap\"); plt.tight_layout(); plt.savefig(f\"{FIG_DIR}/08_kaverna_dogrulama.png\"); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "88133d62",
   "metadata": {
    "id": "88133d62"
   },
   "source": "## 8. Karot fotoğraf indeksi (`photos`)\n1.217 fotoğraf; dosya adından kutu numarası ve derinlik aralığı çıkarılabilenler 423 adet (`S12 (48,30-52,40m).jpeg` gibi). Bu indeks ileride **görüntü tabanlı litoloji sınıflandırma** (CNN / quantum-kernel) için etiket eşlemesinin temelidir: `lith` tablosu ile derinlik üzerinden birleştirilir."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1e292a6",
   "metadata": {
    "id": "e1e292a6",
    "outputId": "19072766-b80b-4876-ce57-16fa91f9811b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1217 fotoğraf; 423 tanesinde derinlik aralığı var\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boxes</th>\n",
       "      <th>from_m</th>\n",
       "      <th>to_m</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>well</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [boxes, from_m, to_m]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "if os.path.exists(F_PHOTOS):\n    photos = pd.read_csv(F_PHOTOS)\nelse:  # indeksi yeniden üret\n    rows_ = []\n    for root, _, files in os.walk(DATA_DIR):\n        for f in files:\n            if f.lower().endswith((\".jpeg\",\".jpg\",\".jfif\")):\n                rel = os.path.relpath(os.path.join(root,f), DATA_DIR); m = re.search(r\"POLATLI-ÜRT\\s*(\\d+)\", rel)\n                box = re.match(r\"^S(\\d+)\", f); dd = re.search(r\"\\(\\s*([\\d,\\.]+)\\s*m?\\s*-\\s*([\\d,\\.]+)\\s*m?\\s*\\)\", f)\n                rows_.append(dict(well=f\"ÜRT-{m.group(1)}\" if m else \"\", box_no=box.group(1) if box else \"\",\n                                  from_m=to_float(dd.group(1)) if dd else np.nan, to_m=to_float(dd.group(2)) if dd else np.nan,\n                                  category=\"Karot\" if \"arot\" in rel.lower() else os.path.basename(root), path=rel, bytes=os.path.getsize(os.path.join(root,f))))\n    photos = pd.DataFrame(rows_)\nphotos[\"from_m\"] = pd.to_numeric(photos.from_m, errors=\"coerce\"); photos[\"to_m\"] = pd.to_numeric(photos.to_m, errors=\"coerce\")\nprint(len(photos), \"fotoğraf;\", photos.from_m.notna().sum(), \"tanesinde derinlik aralığı var\")\nphotos[photos.from_m.notna()].groupby(\"well\").agg(boxes=(\"path\",\"size\"), from_m=(\"from_m\",\"min\"), to_m=(\"to_m\",\"max\")).round(1)"
  },
  {
   "cell_type": "markdown",
   "id": "3fed1c8a",
   "metadata": {
    "id": "3fed1c8a"
   },
   "source": "## 9. QML için özellik matrisi\n\n### Problem tanımı — neden \"bir sonraki metreyi tahmin et\"?\n`lith` tablosunda kil + tenardit ≈ 100 olduğu için, aynı aralığın bileşimini girdi olarak verip \"yüksek tenardit mi?\" diye sormak **sızıntılı (leaky)** bir problemdir — model hiçbir şey öğrenmeden %100 başarı verir. Sahada gerçekten işe yarayan soru şu:\n\n> *Sondaj ilerlerken, üstteki metrelerin karot bileşimine ve derinliğe bakarak bir sonraki metrenin yüksek tenardit zonu olup olmayacağını önceden söyleyebilir miyiz?*\n\nBu, tubing aralığının (sirkülasyon zonunun) karot tamamlanmadan planlanmasına yardım eder ve QML şablonunuzdaki 4-qubit **açı kodlaması** ile birebir örtüşür:\n\n| qubit | özellik | açıklama |\n|---|---|---|\n| q0 | `depth_rel` | aralığın cevher tavanına göre göreli derinliği (0–1) |\n| q1 | `thenardite_lag1` | bir üstteki aralığın tenardit % |\n| q2 | `thenardite_lag2` | iki üstteki aralığın tenardit % |\n| q3 | `glauberite_lag1` | bir üstteki aralığın globerit toplam % |\n\n**Etiket:** `y = 1` eğer bu aralığın tenardit yüzdesi eşiğin üstünde (`THR`, varsayılan 45 % ≈ üst tercil), aksi hâlde `0`.\n**Doğrulama:** *leave-one-well-out* — model bir kuyuyu hiç görmeden o kuyuda test edilir; kuyular arası genelleme ölçülür."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac15940",
   "metadata": {
    "id": "bac15940",
    "outputId": "56710e37-c0bb-4eed-e82b-9a31a320ae16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Örnek sayısı: 1661  |  pozitif (tenardit ≥ 45.0%): 34.6%  |  kuyu: 19\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth_rel</th>\n",
       "      <th>thenardite_lag1</th>\n",
       "      <th>thenardite_lag2</th>\n",
       "      <th>glauberite_lag1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1661.00</td>\n",
       "      <td>1661.00</td>\n",
       "      <td>1661.00</td>\n",
       "      <td>1661.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.51</td>\n",
       "      <td>40.93</td>\n",
       "      <td>41.56</td>\n",
       "      <td>91.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.29</td>\n",
       "      <td>16.03</td>\n",
       "      <td>16.78</td>\n",
       "      <td>19.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.02</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.26</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.52</td>\n",
       "      <td>40.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.77</td>\n",
       "      <td>48.50</td>\n",
       "      <td>50.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>94.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       depth_rel  thenardite_lag1  thenardite_lag2  glauberite_lag1\n",
       "count    1661.00          1661.00          1661.00          1661.00\n",
       "mean        0.51            40.93            41.56            91.72\n",
       "std         0.29            16.03            16.78            19.24\n",
       "min         0.02             3.50             3.50             0.00\n",
       "25%         0.26            30.00            30.00           100.00\n",
       "50%         0.52            40.00            40.00           100.00\n",
       "75%         0.77            48.50            50.00           100.00\n",
       "max         1.00            94.00            95.00           100.00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "THR = 45.0   # tenardit % eşiği — saha ekibiyle birlikte kalibre edilmeli\nL = lith.sort_values([\"well_no\",\"depth_from\"]).copy()\ng = L.groupby(\"well\")\nL[\"depth_rel\"]        = (L.depth_mid - g.depth_from.transform(\"min\")) / (g.depth_to.transform(\"max\") - g.depth_from.transform(\"min\"))\nL[\"thenardite_lag1\"]  = g.thenardite_pct.shift(1)\nL[\"thenardite_lag2\"]  = g.thenardite_pct.shift(2)\nL[\"glauberite_lag1\"]  = g.glauberite_total_pct.shift(1)\nL[\"y\"] = (L.thenardite_pct >= THR).astype(int)\n\nFEATURES = [\"depth_rel\",\"thenardite_lag1\",\"thenardite_lag2\",\"glauberite_lag1\"]\nds = L.dropna(subset=FEATURES+[\"thenardite_pct\"]).reset_index(drop=True)\nprint(f\"Örnek sayısı: {len(ds)}  |  pozitif (tenardit ≥ {THR}%): {ds.y.mean():.1%}  |  kuyu: {ds.well.nunique()}\")\n\n# Ölçekleme: açı kodlaması için [0, π] aralığına\nX_raw = ds[FEATURES].to_numpy(float)\nlo, hi = X_raw.min(0), X_raw.max(0)\nX01 = (X_raw - lo) / (hi - lo + 1e-9)\nX_ang = X01 * np.pi\ny = ds.y.to_numpy(int)\ngroups = ds.well.to_numpy()\npd.DataFrame(X_raw, columns=FEATURES).describe().round(2)"
  },
  {
   "cell_type": "markdown",
   "id": "e4d93c3e",
   "metadata": {
    "id": "e4d93c3e"
   },
   "source": "### 9.1 Klasik taban çizgisi (sklearn, leave-one-well-out)\nQML'in gerçekten bir şey katıp katmadığını görmek için önce lojistik regresyon ve random forest."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a9a8050",
   "metadata": {
    "id": "4a9a8050",
    "outputId": "9d08fedd-6e74-4d22-c199-5c45af4fe78b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>balanced_acc</th>\n",
       "      <th>f1</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Çoğunluk sınıfı (naif)</th>\n",
       "      <td>0.654</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lojistik regresyon</th>\n",
       "      <td>0.752</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random forest</th>\n",
       "      <td>0.742</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        accuracy  balanced_acc     f1    auc\n",
       "model                                                       \n",
       "Çoğunluk sınıfı (naif)     0.654         0.500  0.000  0.500\n",
       "Lojistik regresyon         0.752         0.688  0.573  0.799\n",
       "Random forest              0.742         0.703  0.607  0.819"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "from sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import LeaveOneGroupOut, cross_val_predict\nfrom sklearn.metrics import accuracy_score, balanced_accuracy_score, roc_auc_score, f1_score\n\nlogo = LeaveOneGroupOut()\ndef evaluate(name, model, X):\n    proba = cross_val_predict(model, X, y, groups=groups, cv=logo, method=\"predict_proba\")[:,1]\n    pred = (proba >= .5).astype(int)\n    return dict(model=name, accuracy=accuracy_score(y,pred), balanced_acc=balanced_accuracy_score(y,pred),\n                f1=f1_score(y,pred), auc=roc_auc_score(y,proba)), proba\n\nresults = []; probas = {}\nfor name, mdl in [(\"Çoğunluk sınıfı (naif)\", None),\n                  (\"Lojistik regresyon\", LogisticRegression(max_iter=1000)),\n                  (\"Random forest\", RandomForestClassifier(n_estimators=300, min_samples_leaf=5, random_state=0))]:\n    if mdl is None:\n        pred = np.zeros_like(y); results.append(dict(model=name, accuracy=accuracy_score(y,pred), balanced_acc=.5, f1=0, auc=.5)); continue\n    r, p = evaluate(name, mdl, X01); results.append(r); probas[name] = p\nbaseline = pd.DataFrame(results).set_index(\"model\").round(3); baseline"
  },
  {
   "cell_type": "markdown",
   "id": "4d78b452",
   "metadata": {
    "id": "4d78b452"
   },
   "source": "### 9.2 Hibrit kuantum sınıflandırıcı (PennyLane)\nProjedeki şablonun aynısı: `AngleEmbedding(rotation=\"Y\")` + `StronglyEntanglingLayers` + `⟨Z₀⟩` okuma, bias ile birleştirilen varyasyonel sınıflandırıcı. `n_qubits = len(FEATURES)` olarak otomatik belirlenir (gerçek donanımda fiziksel qubit sınırına göre `FEATURES` kırpılır).\n\nSimülatör maliyeti nedeniyle eğitim örneği `N_TRAIN` ile sınırlandırılmış, doğrulama 3 test kuyusu üzerinde yapılmıştır (tam LOWO için `TEST_WELLS` listesini genişletin — Colab A100'da `lightning.gpu` cihazı ile hızlanır)."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2928f17",
   "metadata": {
    "id": "a2928f17",
    "outputId": "320b48b8-a3a1-454f-f272-ef1eb7166477"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 qubit, 3 katman, 36 parametre | eğitim 400 | test 231 (ÜRT-1, ÜRT-11, ÜRT-16)\n",
      "0: ─╭AngleEmbedding(M0)─╭StronglyEntanglingLayers(M1)─┤  <Z>\n",
      "1: ─├AngleEmbedding(M0)─├StronglyEntanglingLayers(M1)─┤     \n",
      "2: ─├AngleEmbedding(M0)─├StronglyEntanglingLayers(M1)─┤     \n",
      "3: ─╰AngleEmbedding(M0)─╰StronglyEntanglingLayers(M1)─┤     \n",
      "\n",
      "M0 = \n",
      "[1.59474988 0.91991387 1.93989055 3.14159265]\n",
      "M1 = \n",
      "[[[-0.14438637  0.07943148 -0.01912354]\n",
      "  [ 0.02164249  0.10017104 -0.17331325]\n",
      "  [-0.07841301  0.01753348  0.03920948]\n",
      "  [-0.03770745  0.10291792  0.02103949]]\n",
      "\n",
      " [[-0.12133821 -0.09307638  0.08054711]\n",
      "  [ 0.04638315 -0.18990612  0.13477123]\n",
      "  [ 0.0598027   0.1343343  -0.03837009]\n",
      "  [-0.02957062 -0.11264571  0.25369296]]\n",
      "\n",
      " [[-0.01754542  0.15875332 -0.06472925]\n",
      "  [ 0.01638412 -0.16713525 -0.03828671]\n",
      "  [ 0.09837549 -0.12517438  0.10722275]\n",
      "  [ 0.03372378 -0.10439509 -0.05015973]]]\n"
     ]
    }
   ],
   "source": "try:\n    import pennylane as qml\n    from pennylane import numpy as pnp\nexcept ImportError:\n    !pip -q install pennylane\n    import pennylane as qml\n    from pennylane import numpy as pnp\n\nN_QUBITS  = len(FEATURES)\nN_LAYERS  = 3\nN_TRAIN   = 400          # eğitim alt-örneği (hız için)\nEPOCHS    = 40\nTEST_WELLS = [\"ÜRT-1\", \"ÜRT-11\", \"ÜRT-16\"]   # kaverna verisi olan kuyular test seti\nSEED = 7; rng = np.random.default_rng(SEED)\n\ndev = qml.device(\"default.qubit\", wires=N_QUBITS)\n\n@qml.qnode(dev)\ndef circuit(weights, x):\n    qml.AngleEmbedding(x, wires=range(N_QUBITS), rotation=\"Y\")\n    qml.StronglyEntanglingLayers(weights, wires=range(N_QUBITS))\n    return qml.expval(qml.PauliZ(0))\n\ndef classifier(weights, bias, x):\n    return circuit(weights, x) + bias\n\ndef square_loss(labels, preds):\n    return pnp.mean((labels - pnp.stack(preds))**2)\n\ndef cost(weights, bias, X, Y):\n    return square_loss(Y, [classifier(weights, bias, x) for x in X])\n\ntest_mask  = np.isin(groups, TEST_WELLS)\ntrain_idx  = np.where(~test_mask)[0]; test_idx = np.where(test_mask)[0]\ntrain_idx  = rng.choice(train_idx, size=min(N_TRAIN, len(train_idx)), replace=False)\nXtr, Ytr = pnp.array(X_ang[train_idx], requires_grad=False), pnp.array(2*y[train_idx]-1, requires_grad=False)  # etiket ±1\nXte, Yte = X_ang[test_idx], y[test_idx]\n\nshape = qml.StronglyEntanglingLayers.shape(n_layers=N_LAYERS, n_wires=N_QUBITS)\nweights = pnp.array(0.1*rng.standard_normal(shape), requires_grad=True)\nbias    = pnp.array(0.0, requires_grad=True)\nopt = qml.AdamOptimizer(stepsize=0.05); BATCH = 32\nprint(f\"{N_QUBITS} qubit, {N_LAYERS} katman, {weights.size} parametre | eğitim {len(Xtr)} | test {len(Xte)} ({', '.join(TEST_WELLS)})\")\nprint(qml.draw(circuit)(weights, Xtr[0]))"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce3bac74",
   "metadata": {
    "id": "ce3bac74",
    "outputId": "6566bda4-ed76-47ad-9e06-b7041e835dfb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch   0 | cost 0.7705 | train acc 0.710 | test acc 0.519 | 5s\n",
      "epoch   5 | cost 0.5842 | train acc 0.762 | test acc 0.762 | 22s\n",
      "epoch  10 | cost 0.5696 | train acc 0.750 | test acc 0.771 | 40s\n",
      "epoch  15 | cost 0.5646 | train acc 0.750 | test acc 0.766 | 57s\n",
      "epoch  20 | cost 0.5661 | train acc 0.770 | test acc 0.771 | 75s\n",
      "epoch  25 | cost 0.5655 | train acc 0.760 | test acc 0.771 | 92s\n",
      "epoch  30 | cost 0.5595 | train acc 0.757 | test acc 0.775 | 110s\n",
      "epoch  35 | cost 0.5641 | train acc 0.757 | test acc 0.779 | 127s\n",
      "epoch  39 | cost 0.5738 | train acc 0.775 | test acc 0.762 | 141s\n"
     ]
    }
   ],
   "source": "import time\nhist = []\nt0 = time.time()\nfor ep in range(EPOCHS):\n    perm = rng.permutation(len(Xtr))\n    for i in range(0, len(Xtr), BATCH):\n        bi = perm[i:i+BATCH]\n        weights, bias, _, _ = opt.step(cost, weights, bias, Xtr[bi], Ytr[bi])\n    if ep % 5 == 0 or ep == EPOCHS-1:\n        tr_pred = np.sign([classifier(weights, bias, x) for x in Xtr]); tr_acc = np.mean(tr_pred == Ytr)\n        te_score = np.array([classifier(weights, bias, x) for x in Xte]); te_acc = np.mean((te_score>0).astype(int) == Yte)\n        hist.append(dict(epoch=ep, cost=float(cost(weights, bias, Xtr[:128], Ytr[:128])), train_acc=float(tr_acc), test_acc=float(te_acc)))\n        print(f\"epoch {ep:3d} | cost {hist[-1]['cost']:.4f} | train acc {tr_acc:.3f} | test acc {te_acc:.3f} | {time.time()-t0:.0f}s\")\nhist = pd.DataFrame(hist)"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d57c2ad",
   "metadata": {
    "id": "2d57c2ad",
    "outputId": "bee28f83-7767-4cb7-c8f1-7e2d76b0f17d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1320x440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>balanced_acc</th>\n",
       "      <th>f1</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>QML VQC (4q×3L, test=3 kuyu)</th>\n",
       "      <td>0.762</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lojistik regresyon (aynı test)</th>\n",
       "      <td>0.732</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random forest (aynı test)</th>\n",
       "      <td>0.762</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                accuracy  balanced_acc     f1    auc\n",
       "model                                                               \n",
       "QML VQC (4q×3L, test=3 kuyu)       0.762         0.763  0.758  0.847\n",
       "Lojistik regresyon (aynı test)     0.732         0.735  0.690  0.823\n",
       "Random forest (aynı test)          0.762         0.764  0.744  0.856"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": "te_score = np.array([float(classifier(weights, bias, x)) for x in Xte])\nte_pred  = (te_score > 0).astype(int)\nqml_row = dict(model=f\"QML VQC ({N_QUBITS}q×{N_LAYERS}L, test={len(TEST_WELLS)} kuyu)\",\n               accuracy=accuracy_score(Yte, te_pred), balanced_acc=balanced_accuracy_score(Yte, te_pred),\n               f1=f1_score(Yte, te_pred), auc=roc_auc_score(Yte, te_score))\n# aynı test kuyularında klasik modeller (adil karşılaştırma)\ncmp_rows = [qml_row]\nfor name, mdl in [(\"Lojistik regresyon (aynı test)\", LogisticRegression(max_iter=1000)),\n                  (\"Random forest (aynı test)\", RandomForestClassifier(n_estimators=300, min_samples_leaf=5, random_state=0))]:\n    mdl.fit(X01[~test_mask], y[~test_mask]); p = mdl.predict_proba(X01[test_mask])[:,1]\n    cmp_rows.append(dict(model=name, accuracy=accuracy_score(Yte,(p>=.5)), balanced_acc=balanced_accuracy_score(Yte,(p>=.5)), f1=f1_score(Yte,(p>=.5)), auc=roc_auc_score(Yte,p)))\ncomparison = pd.DataFrame(cmp_rows).set_index(\"model\").round(3)\n\nfig, axes = plt.subplots(1, 2, figsize=(12, 4))\naxes[0].plot(hist.epoch, hist.train_acc, \"o-\", label=\"eğitim\", color=PAL[\"accent\"]); axes[0].plot(hist.epoch, hist.test_acc, \"s-\", label=\"test\", color=PAL[\"thenardite\"])\naxes[0].set_xlabel(\"epoch\"); axes[0].set_ylabel(\"doğruluk\"); axes[0].set_title(\"QML eğitim eğrisi\"); axes[0].legend()\ncomparison[[\"balanced_acc\",\"auc\"]].plot.barh(ax=axes[1], color=[PAL[\"glauberite\"], PAL[\"thenardite\"]]); axes[1].set_xlim(0,1); axes[1].set_title(\"Test kuyularında karşılaştırma\"); axes[1].invert_yaxis()\nplt.tight_layout(); plt.savefig(f\"{FIG_DIR}/09_qml_vs_klasik.png\"); plt.show()\ncomparison"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5022c58",
   "metadata": {
    "id": "a5022c58",
    "outputId": "de368730-1150-46d1-81f8-987491e15131"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1485x880 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "# Test kuyularında tahmin — derinlik boyunca gerçek vs QML skoru\nfig, axes = plt.subplots(1, len(TEST_WELLS), figsize=(4.5*len(TEST_WELLS), 8), sharey=False)\nfor ax, wname in zip(np.atleast_1d(axes), TEST_WELLS):\n    m = groups[test_idx] == wname; d_ = ds.iloc[test_idx[m]]\n    ax.barh(d_.depth_mid, d_.thenardite_pct, height=d_.thickness_m, color=PAL[\"thenardite\"], alpha=.5, label=\"gerçek tenardit %\")\n    ax.plot(50 + 50*te_score[m], d_.depth_mid, color=PAL[\"polyhalite\"], lw=2, label=\"QML skoru (ölçekli)\")\n    ax.axvline(THR, ls=\"--\", color=\"k\", lw=1); ax.invert_yaxis(); ax.set_xlim(0,100); ax.set_title(wname); ax.set_xlabel(\"%\")\nnp.atleast_1d(axes)[0].set_ylabel(\"Derinlik (m)\"); np.atleast_1d(axes)[0].legend(fontsize=8, loc=\"lower left\")\nplt.suptitle(\"Test kuyularında bir-sonraki-metre tenardit tahmini\", y=1.0); plt.tight_layout(); plt.savefig(f\"{FIG_DIR}/10_qml_test_kuyulari.png\"); plt.show()"
  },
  {
   "cell_type": "markdown",
   "id": "75ea2ffd",
   "metadata": {
    "id": "75ea2ffd"
   },
   "source": "## 10. Çıktıların kaydedilmesi\nTüm temiz tablolar `processed/` klasörüne CSV olarak, QML özellik matrisi `.npz` olarak yazılır. Bunlar Colab'daki ana QML notebook'una doğrudan girdi olur."
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0b590df",
   "metadata": {
    "id": "c0b590df",
    "outputId": "736b4228-c673-4aaf-f5ce-43368ba4b6a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  wells.csv                       19 satır\n",
      "  lithology_long.csv            1825 satır\n",
      "  lithology_with_names.csv      1825 satır\n",
      "  dikme_kesit.csv               1262 satır\n",
      "  brine_analyses.csv              57 satır\n",
      "  pressure_tests.csv              25 satır\n",
      "  pressure_tests_long.csv         50 satır\n",
      "  cavern_performance.csv          15 satır\n",
      "  photo_index.csv               1217 satır\n",
      "  per_well_summary.csv            19 satır\n",
      "  qml_dataset.csv               1661 satır\n",
      "\n",
      "+ qml_features.npz, run_summary.json   →  /Users/joseph.volkan.ozcanicloud.com/Documents/Zoom/Natrium_Notebook/processed\n"
     ]
    }
   ],
   "source": "outputs = {\n    \"wells.csv\": wells, \"lithology_long.csv\": lith.drop(columns=[\"depth_bin\"], errors=\"ignore\"),\n    \"lithology_with_names.csv\": lith_d.drop(columns=[\"depth_bin\"], errors=\"ignore\"), \"dikme_kesit.csv\": dikme,\n    \"brine_analyses.csv\": brine, \"pressure_tests.csv\": ptests, \"pressure_tests_long.csv\": ptests_long,\n    \"cavern_performance.csv\": cavern, \"photo_index.csv\": photos, \"per_well_summary.csv\": per_well.reset_index(),\n    \"qml_dataset.csv\": ds[[\"well\",\"depth_from\",\"depth_to\",\"depth_mid\"]+FEATURES+[\"thenardite_pct\",\"y\"]],\n}\nfor fn, df_ in outputs.items():\n    df_.to_csv(os.path.join(OUT_DIR, fn), index=False, encoding=\"utf-8-sig\")\nnp.savez(os.path.join(OUT_DIR, \"qml_features.npz\"), X_angle=X_ang, X01=X01, y=y, groups=groups, features=np.array(FEATURES), scale_lo=lo, scale_hi=hi, thr=THR)\njson.dump(dict(features=FEATURES, threshold_pct=THR, n_samples=int(len(ds)), wells=sorted(set(groups)),\n               baseline=baseline.to_dict(), qml_vs_classic=comparison.to_dict()), open(os.path.join(OUT_DIR, \"run_summary.json\"), \"w\"), ensure_ascii=False, indent=2)\nprint(\"\\n\".join(f\"  {fn:28s} {len(df_):5d} satır\" for fn, df_ in outputs.items()))\nprint(f\"\\n+ qml_features.npz, run_summary.json   →  {OUT_DIR}\")"
  },
  {
   "cell_type": "markdown",
   "id": "5fde916e",
   "metadata": {
    "id": "5fde916e"
   },
   "source": "## 11. Bulgular, veri boşlukları ve sonraki adımlar\n\n### Bu paketten çıkan ana bulgular\n- **Cevher zonu** 19 kuyuda ~150–287 m arasında, kuyu başına ortalama ~95 m karot kaydı. Karot ağırlıklı olarak **globerit** (kuyu ortalamaları %66–97), tenardit yüzdesi kuyu ortalamasında **%35–55** arasında değişiyor.\n- **ÜRT-11 ve ÜRT-16** en yüksek ortalama tenardite sahip kuyular (%55, %53); ÜRT-16 aynı zamanda en hızlı Bomé artışını (2 ay 10 günde 24 °Bé) veren kaverna — litoloji ile üretim performansı tutarlı.\n- **Derinlik × kuyu ısı haritası (§3.1):** en zengin tenardit bantları ~180–195 m ve ~205–230 m'de, kuyular arasında yanal olarak izlenebiliyor. Mevcut sirkülasyon aralıkları (kırmızı) karotun en alt bölümünde (%30–40 tenardit); kaverna yukarı büyüdükçe / tubing çekildikçe (2026 planı) bu bantlara ulaşılacak — tubing çekme zamanlamasına jeolojik dayanak.\n- **Bomé ↔ Na₂SO₄** ilişkisi doğrusal ve güçlü; sahadaki `denklem` sayfasının yerine notebook'taki kalibrasyon kullanılabilir.\n- Sahanın \"ideal tubing aralığı\" hesabındaki sirkülasyon-aralığı tenardit yüzdeleri, bu notebook'un kalınlık-ağırlıklı yeniden hesabıyla büyük ölçüde örtüşüyor (bkz. §7 grafiği); sapmalar tubing konumunun karot aralıklarıyla tam çakışmamasından kaynaklanıyor.\n- İlk QML denemesi (4 qubit, 3 katman) klasik taban çizgisiyle **aynı mertebede**; bu aşamada avantaj beklenmez — amaç boru hattının uçtan uca çalışmasıdır. Kazanım, özellik sayısı arttığında (koordinat, jeofizik, hidrokimya) ve kernel tabanlı yaklaşımlarda (QSVM) aranmalı.\n\n### Veri boşlukları — sahadan istenecekler\n1. **ÜRT kuyu başı koordinatları (X, Y, Z)** — yanal interpolasyon, 3B cevher modeli ve QSVM için zorunlu. (`.kmz` sadece yol çizgisi; `Zemin Etüt Noktaları` DWG/PDF'inde SK noktaları var, ÜRT kuyuları yok.)\n2. **ÜRT-9/10/15/20 dikme kesit sayfaları** (litoloji adı + fotoğraf) — bileşim verisi var, kesit yok.\n3. **XRF ANALİZİ AP-2** sayfası boş — XRF sonuçları ayrı dosyada mı?\n4. **Brine analizleri** sadece 3 kaverna için; 2026 planındaki 12 kuyu devreye alındığında aynı şablonla toplanmalı.\n5. **Wireline log** (doğal gama, rezistivite) hiç yok — QML'deki f0 (⁴⁰K radyometri) ve f2 (iletkenlik) özellikleri için gerekli.\n6. Karot fotoğraflarının ~%65'inde dosya adında derinlik yok (WhatsApp isimleri) — kutu etiketlerinden OCR ile veya saha ekibinden eşleme listesi ile tamamlanmalı.\n\n### Sonraki adımlar (önerilen sıra)\n1. `processed/qml_features.npz` ile Colab ana notebook'unda tam leave-one-well-out QML; `lightning.gpu` ile 20 qubit denemesi (özellik sayısını artırarak).\n2. Koordinatlar gelince: 3B grid → krigleme ile tenardit kalınlık haritası → tubing aralığı önerisi (Wisdom AI \"Best/Optimum/Worst\" senaryolarının jeolojik girdisi).\n3. Karot fotoğraf–litoloji eşleme → görüntü tabanlı sınıflandırıcı (klasik CNN taban çizgisi + quantum kernel).\n4. Brine zaman serisi → kaverna Bomé tahmin modeli (sirkülasyon gün sayısı, aralık tenardit %, başlangıç Bomé)."
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