{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b780e2d2",
   "metadata": {},
   "source": [
    "**Author:** Dr. Mallarapu  \n",
    "**Created:** 2026-07-27  \n",
    "**Course:** SEAS 8414 \u2014 Security Analytics\n",
    "\n",
    "---\n",
    "\n",
    "### Goal of this notebook\n",
    "\n",
    "Train and audit detectors on NF-BoT-IoT-v2, expressed in the standard 43-field NetFlow schema.\n",
    "\n",
    "### What you will learn\n",
    "\n",
    "1. Read a majority-class baseline before trusting any accuracy figure.\n",
    "2. Find the strongest single feature, then test it by dropping it and refitting.\n",
    "3. Tell duplicate inflation apart from genuine signal.\n",
    "4. Report per-group recall, because the rare classes carry the risk.\n",
    "\n",
    "### Where this connects to the course text\n",
    "\n",
    "The text builds a defence pipeline; this notebook trains a classifier and audits it. The links below are to specific chapter objectives that share an *analytic move*, not to matching subject matter.\n",
    "\n",
    "- **Chapter 3: Vulnerability Assessment** \u2014 Learning objective 1 (section 3.1) frames assessment as **evidence grading**, not output collection. The A-F data-trust grade in section 10 is exactly that move, applied to a model score.\n",
    "- **Chapter 8: Federated Threat Intelligence** \u2014 Learning objective 2 (section 8.1) derives why **non-IID** site distributions produce *client drift*. A model fit on one site does not carry to another. This notebook does **not** run that test: every score here is in-distribution on one corpus. The shared move is scoping a claim to its evidence. Notebook 36 runs the cross-corpus transfer matrix.\n",
    "- **Chapter 11: Formal Protocol Verification** \u2014 Section **11.1.2**, titled *Proved, tested, and hoped*, asks you to separate exactly those three. (Chapter 11 lists its objectives in \u00a711.0, not \u00a711.1 as the other chapters do.) The ablation does that job here: it tests whether the headline survives.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3431d059",
   "metadata": {},
   "source": [
    "# NetFlow-Standardized Intrusion Detection: NF-BoT-IoT-V2\n",
    "### Model comparison + validity audit on NF-BoT-IoT-V2 (\u22651M records, via Kaggle)\n",
    "\n",
    "**Abstract:** NF-BoT-IoT-V2 (Sarhan et al., 2022) re-features Bot-IoT into the standard 43-field NetFlow v2 schema. We compare four learners on its \u22651M flows and audit the scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f2a52c",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Classify standardized NetFlow v2 records (Bot-IoT) as benign or attack. The standard feature set was proposed to enable *cross-dataset* comparison. So it is the right lens for asking whether high scores are feature shortcuts or transferable signal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65aedc45",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Sarhan, Layeghy & Portmann (2022)** \u2014 *Towards a Standard Feature Set for Network Intrusion Detection System Datasets* (Mobile Networks & Applications; arXiv:2101.11315). Defines the **43-field NetFlow v2 schema and the NF-*-v2 datasets used here** (NF-UNSW-NB15-v2 / NF-BoT-IoT-v2 / NF-ToN-IoT-v2 / NF-CSE-CIC-IDS2018-v2), enabling cross-dataset comparison.\n",
    "- **Sarhan, Layeghy, Moustafa & Portmann (2021)** \u2014 *NetFlow Datasets for ML-Based NIDS* (BDTA 2020 conference; proceedings 2021). These are the earlier 8-field NetFlow v1 datasets this v2 set extends.\n",
    "- **Koroniotis et al. (2019)** \u2014 Bot-IoT origin.\n",
    "- **Moustafa & Slay (2015)** \u2014 UNSW-NB15 origin.\n",
    "- **Sommer & Paxson (2010)** \u2014 the closed-world critique.\n",
    "\n",
    "**Related approaches and their known caveats** \u2014 drawn from the wider literature; these are **not** measurements reproduced on this exact corpus:\n",
    "\n",
    "| Reported approach | Known caveat |\n",
    "|---|---|\n",
    "| Sarhan et al. (2021) \u2014 standard NetFlow ML | in-distribution; NetFlow fields can leak |\n",
    "| Cross-dataset NetFlow studies | standardized features expose generalization gap |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6802f5",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `dhoogla/nfbotiotv2` (NetFlow v2) |\n",
    "| Origin | Bot-IoT, re-featured to NetFlow v2 |\n",
    "| Label | `Label` 0/1; family `Attack` |\n",
    "| Access | Kaggle API token required |\n",
    "\n",
    "**Honestly:** `Attack` (family) is dropped from features; NetFlow header fields (ports/protocol) can act as shortcuts, which the audit probes.\n",
    "\n",
    "### Before you run this: getting the data\n",
    "\n",
    "This notebook downloads its own data on the first run, then caches it. You do not fetch anything by hand.\n",
    "\n",
    "**Dataset:** Kaggle `dhoogla/nfbotiotv2` -> `/tmp/kg_nfbotiotv2`. It is about **485 MB** on disk.\n",
    "\n",
    "**One-time setup.** Sign in at kaggle.com, open **Settings**, and under **API** choose **Create New Token**. Kaggle hands you a `kaggle.json` file. This notebook does *not* read that file. It reads a plain key file, so convert it once:\n",
    "\n",
    "```bash\n",
    "mkdir -p ~/.kaggle\n",
    "python3 -c \"import json;print(json.load(open('kaggle.json'))['key'],end='')\" > ~/.kaggle/access_token\n",
    "chmod 600 ~/.kaggle/access_token\n",
    "```\n",
    "\n",
    "Never paste the token into a cell, a commit, or a screenshot. If it leaks, revoke it from the same Settings page.\n",
    "\n",
    "**If the loader fails:**\n",
    "\n",
    "- `FileNotFoundError: ~/.kaggle/access_token` - you created `kaggle.json` but not the key file. Run the command above.\n",
    "- `401 Unauthorized` - the key is wrong, or a trailing newline crept in.\n",
    "- `403 Forbidden` - open the dataset page on Kaggle while signed in, accept its terms, then re-run the cell.\n",
    "\n",
    "The cache sits under `/tmp`, which macOS clears on reboot. To keep it, move the folder somewhere durable and symlink it back. Do **not** edit the path in the code cell below: that changes a code cell and invalidates the stored outputs you are reviewing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c920a4b",
   "metadata": {},
   "source": [
    "## 4. Solution design\n",
    "\n",
    "The methodology is deliberately two-track. We *earn* a headline score with standard modelling, then *interrogate* it with a validity audit. Only a verdict that survives both is reported. The diagram below is the shape of every notebook in this series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b3f8e64",
   "metadata": {},
   "source": [
    "**Figure 4.1 \u2014 Solution design (methodology).**\n",
    "\n",
    "<img 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eAOdhr/evdvJiRPHpWXLLhbovvpq9l3bbdu2Wfr37+y8dhzn3N6X8uWryIIFs2XKlDHubc6cOS29erWRAwf22rzKZ5993qqov/32s/u1Ro7sZ/Mhda6gfqFw8eJ5C2wh0YCsVckuXQZIvXpN7Pz1ywYAQNSjUgcAD9m1a9dk7txpUrny684H7rZ2nw7X02A0a9bkuxpiNGvWUV544WX7WcOKDqs8fvyopE+fMcTXuHHjuuTLV8gqVFqtK136BYmoq1evOuHxWqD7NAS4aPioUOG/oYvp02eyOXY6XFFpFUk/+Ot9Zcu+ZPeVKFFGatcuL19+OVPee699WIcQ5jF4epCvp41lcubMa8Mta9SoZ/frz4ULlwh2aKXuRythFSpUk/BasuQbG7I5YsQUey9V+vSZpXPn9wJtN2fOJ9aYpnfv4Xa7TJkXbcinzhGsVesdC5Faxbt48YKMGTPdva8rVy7bvEf9W9C/NX0trQzr81WVKjVCPb6SJcsGun3kyN/WfAcAEPUIdQDwkGn40EpJ0LlcOqRy5szJcFePUgAAEABJREFUcvnyZYkTJ477fh2a6RIzZiy71g/oobl586Zdv/pqHRk+vI/s2bNLbt8Oftug4eydd1o4QegdmTFjgoUhT0uWrHf/7GqUoq+1Z89O+fnnH5yqYxMZPXqaJE+e0sKnKlSomPs5el45cuSxalN4hHUMnh706+kwSw1UN27csNv+/uulefPOwe5Hw3m0aNGd9/FdCa+tWzdKkiRJ3SFMBe0kqu+1Blf9QsCTVgv1b0f/tnQYqL//OufvJlWgfWk1WMOeHr8O7UybNr2drw5Z1aCn8wJDc+rUvzJ58hg7b22OoyK7kQ0A4N4Q6gDgITt//qxd6zwmT/HjJ7Drkyf/CfRh/H5oJWbq1HEWVIJWWlyCdr9MlSqtXev8q5CeozwbpRQuXNyGiNapU9EJNFOkbdseTlg4Z48Frazp7cOH/5LwCOsYPD3o19NQ9+mnHzsBcZPd1iGwwW2rQ2i1ata378gwO49KoOM/72wfP9Rt9MsAnacX2t+O0ipd0LDuouFMu5sOGDBOvv56rhNav5ZJk0Y5FbvyVhV2LXcR9HXbtXtXUqdOJ127DpA8eQra3D+tAgMAoh6hDgAeMq1iKf0Q78kVShIkCN8crPDQNdKqVHlDZs+eIgULPh3sNiF1v9RW/XoJr6RJk1m158SJY3bbVfnR+X2eQUHPO7znGJFjeNCvp50zkyZNbpUybXSTLdtTdwWgv//+S4YNe19ef72eDf2MCA3X+vzQaFMUrY6F9bejf2M6BLRNm+7BvE5Su9Z1CDXEKa1k6lzNCRM+uGt5C7Vs2Y82ZFPn0+XJU0AAAI8WGqUAwEOWMWNWq7S4Kj4u2qVQg0J4G2uEl86V0jlXD3r+kzYj0YtWc5RWc9SWLRvc2+jQ0t27twcaIhlZHsbrabVOQ51enn762UCP6bl369bCmt80atRaIkqHieqwxlOnTrrvCzq/T+l53v23s8ECvCuc58qV3ypy2bPntvs8L9q5Mqi8eQvacesw3eCcOXPKrl1VXKVr/AEAHg1U6gDgIdNKS61aDaz1vi49kCtXPlm1apl9MO/TZ4RENg2J2mhFG3FEJg0cOr/qypUrFpy0mqNhVVvnq9y58zvBp6SMGzfYlhfQKted4Xp+UqPGWxLZHsbr6Tp0ixcvtJ+1Q6WLvgdduza3OW86hFPnx7losxOdKzdlylgLY/36jb5rrpzSbpc6pFGHy+qyFTdv3rCOlUHVrdtYOnRo5Oynkzz/fKX/L3Q+XapVq+X+QkB/1qGVAwZ0lpo161vVTodZagfLt99uar+3sWMHy4svVna+SMgpAQGnZd26lXfN1XPR5Q7UvHkzrDnMhg2rrbp353e/0wmkuQQAEHUIdQAQBTTUKQ0I2pEwbtx40qZNj7s6X0YWDVqLF38tkUnb5nfu3NQqRNqUQ6tDus6Zztdy6dlzmIwfP8TOUatQ2pxj0KDxYTbluFcP+vV0vTrt7qlhXLthumglyzV0UpcS8DR48IcW6lasWGpDGHWpAw2gQenQSj1WDaW1a79kw0A7dOhjAc6TDn/s33+Mhb8hQ3pYkHz55ddszTgX/XsaNWqaLVvQt29H+yIhZ858trSB0sYquizBsmVLLBDqmnoNGrSQV199M9jzLlbsWQuxunSC/h1pZ9UpU+bbFxPaBZRQBwBRy08AwEdN7LbPv3SN1PlTZIwjQFQbN26IDYH94oufLAjj4Vo85VBAwLGr1VqOybFCAMDH8H8VAAAegt27d0iFCq8Q6AAAkY5GKQAAPGA6p00bi+i6gQAARDa+LgQA4AGLFSuWLFq0VgAAeBCo1AEAAACAFyPUAQAAAIAXI9QBAAAAgBcj1AEAAACAFyPUAQAAAIAXI9QBAAAAgBcj1AEAfMapU//KggVznOuTAgDA44JQBwDwCkeO/C0LF84NdZvvv/9SPv54hPzww1cSXhcunJdFixbKiRPHBQAAb0SoAwB4hWXLlsjEiSND3aZixepSt24jqVSpuoTXgQN7ZfToAfLPP8cEAABvFEMAAPARKVKkkrffbioAADxOqNQBAB47O3ZskZkzJwsAAL6ASh0AwKv89dc++eCD9+Xw4YOSP39RadWqq1Xo1Natm6Rjx8YyfPhkyZevkN3377//yKxZk2XVqmVy9myAxIsXX7JmzSFx48YPtN9Lly5Kjx6tZNu2TZIhQxZp3Lits//CAgDAo45KHQDAa9y6dUvGjx8ilSvXkLZtezoBb69ze2iI22uIa936bTl69LCz3UwZNGi8xIkTV0qXflH69g08P2/SpFFOiCsinTv3dwJfPOfxDnL9+nUBAOBRR6UOAOBVWrfu7lTSMtvP27f7y8qVv4a47e+//yKnT5+SIUMmWDVPL2XKvCgzZkyQqlXfED8/P/e21avXtvvUzZs3ZeDArnL8+FFJnz6jAADwKKNSBwDwGtGiRXMHOhU7dhy5evVKiNvfvn3LrrU656LDLnWopQY3T5kyZXP/HCtWbLu+cuWyAADwqCPUAQB8VrFipSzQff75dLt95sxp+fHHb6R48dISIwaDVQAAvoH/owEAfJYOt2zRorMMH95Hvvtuvt2XJ08BadWqmwAA4CsIdQAAn7ZgwWx56633pF69xgIAgC8i1AEAfNrJkydky5YN8scfuSR27NiSJElySZs2vc3PAwDAFxDqAAA+7d13W8nYsYPE33+9+77MmbPJsGETJUGChAIAgLfzEwDwURO77fMvXSN1/hQZ4wigbty4Ibt2bZPu3VtIpUqvSrNmHQWPh8VTDgUEHLtareWYHCsEAHwMY08AAD5r9OgB8v33X7lva8fLvHkLSqpUaSUg4LQAAOALGH4JAPBZMWI8IfPnfyrJkqWw+XRKh2EePLhf3nzzXQEAwBcQ6gAAPqthw9Zy4cJ5GTCgs1y7dk2SJEkmWbPmkL59R0mJEqUFAABfQKgDAPisOHHiSNeuAwQAAF/GnDoAAAAA8GKEOgAAAADwYoQ6AAAAAPBihDoAAAAA8GKEOgAAAADwYoQ6AIA5depfWbBgjnN9Uh4Fe/bsku+//1Ju3LghAAAgZCxpAACPqKtXr8qECR9IiRJl7PKgaYCaNWuKrev21ltN3PcvWfKNE/Zmy9Gjf0v27LmkWrXaUqbMi/KgTZo0UrZs2Sjp0mWSAgWKCAAACB6hDgAeUdeuXZVFixZKtmw55WGoWLG6XVeqVN193/79e2TkyH7yxhtvS65c+WT7dn+JFSu2RLYTJ45LzJixJFGixO773n67mezatVXy5CkgAAAgZIQ6AIBJkSKVE6SaBrpv48Y14ufnJ+++21KiRYsmzz5bTiLb4cOHpGHD12yR8HLlKrrvz5evkF0AAEDoCHUAgBBdunRRYsSIYYEOAAA8mgh1ABCFbt++LXPmTJWVK391KlZ/SebM2aV69TelbNmXgt1+xYqfZOnS7+TgwX1y7txZG5rYoEELyZ79zhDNa9euyYgRfWXHDn85e/aMpE+fScqUKW/DJzWYrVmzQmbP/sR5/n6JFy++zZGrX7+ZZMmSXbZu3SQdOzaW4cMnW4WsYcPXnWM6aPutUKFooON45pnnpE+fEYGO67vv5suff26T1KnTWUWvbt3GdjwffjhU/v77LzlwYI8kSZLMqnH16jWx49GhnTpnTw0Z0tMuAwaMlaefLmnz+2bOnCSLFq11v47O95s+/SPZvHmd/PvvP5IpU1Zp3bq7ZM2aw72NPu+3336SOnUaOec6RY4dOyz58xeVVq26WjUyOGG9b6Gdoz5+4MBe53Uny7Ztm+TWrVvO+1dY2rTpIQkSJHS/RpcuzSRjxiz2ns+Z84n9rnv1GmZDWj//fJrNH0ycOIm89lpdqVr1DQmJ6/c0atRUmTRplOzfv9uOp2PHvuLvv16++eZzC+O6D/3devrxx29l8eKFsnfvLjvHZs06Sd68Be2xK1euhPq78nxt/RuZNm287Nv3p2TIkEUaN27rvMeFBQAQNfjqFQCi0Ny50+TTTz+2D8SdOvVzPiBntnlkIdEP7xp4mjfvZCHlypXLMmhQVwsS6ssvZ8qqVb9KtWq1pGvXgc5+izi3l8nNmzftg76GqPjxn5QOHXo7oaehXLx43j6YB6dTp77y4ouVrVI3bNjH7kvatBkCbbdt22YZOLCrxI0bz86hZMmy9uFfjyl27NgWYipXfl26dx8iL730ioXKn3/+wZ6roUlfR+nx6P7z5CkY4vnr6/zyyyKb/9e2bU8LG+3bN5STJ08E2k6Dib6vGjZGjpxqIXj8+KEh7je09y2sc7x48YJ069bcAnDDhq3lrbfes3DYvXsLC+2etmzZYKFUA+crr9SSM2dOO8GujYXCJk3aOUHxeSdYDXNC6c8Slj592jv7qCljxsxw9nNKBg/uZmG2b99RUrv2u/Y+//HH7+7t161bZcFVh9O2bdtLUqZMY8eo8xlVWL8rT337dnD+Nqo4oXKeBVe9ff36dQEARA0qdQAQRbRV/7x5n1pAee+99nZfqVLPh/ocrci5qnJKq229e7eXI0cOWeVFKzCJEyeVGjXessc1fLho6Dh7NsCqL67ulVWq1AjxtXLmzCtr1/5uIaBAgf8qdXHixA20nVad0qXL6BzHcNs26Dlo8HApXryUVSXXr18l5ctXsWP287vz/aIGWs/XCWrnzq2yceMfToAa5K5kalfQ2rXLWyhzvYdK39shQz6SZMlSuLcLLSiF9r6FdY5ffjnfqqYffjhLkiZN7j6Xzp2byurVywPtS8PbmDHT7b1Vn302yUKh3qfvhdKgPn/+Z1K69AsSGj3f55+vZD8XLPi0bNiwxgLek08msPA/ZcoYq+Lpe670by1JkqROsJtit8uVqyBNm9Z2KntfSKNGre2+0H5Xnpo16ygvvPCy/ax/v+vXr5bjx48655BRAAAPH6EOAKKIDrvTD/RaFQovHSaow/y0InP06GF3JUircErDy++//yJDh/ayKluhQsXcQ+d06F/atOllxowJNoxRQ4Mr9NwrrWRp0NIP9hp2grNr1zaZOnW8BSc9X6XhIqI0OCg9J5c4ceJIjhx5rJLmSc/Z89y0s+bVq1dC3Hdo71tY56hhSgOZK9ApHX6p9Lg8Q50OF3UFOuXvv86GhLoCnXrqqTw2zFODqVZJQ5IiRWr3z3HjxrcqogY6pVW3WLFiWUBUui+tEuq5uei56Gt5VobD+7vyfG19b5XrtQAADx+hDgCiyPnzZ+06YcJE4X7O4MHd7QO3DissUqSEfQjv3r2l+3H90K4hZNWqZdKvX0dJkCCRDQnUypaGlAEDxsnXX8+VJUu+tvlYOm9Mqy6eSwlEhIZJHYLoChNB7d69Uzp0aCSVKr1qlSWd+6bDJe/FhQvn7FqHj3rS2zof8X6E9r6FdY76e9SKqSd9r/W4Tp78J9D9iRIlCXRbg5MOfww6Z1HpYvApU6aWyHD58iX7AkDnY+rFU6pUaew6Mn9XAICHi1AHAFHEVZsLYxMAABAASURBVEk6f/5cuLbX1v+rVi2TBg2ah7j4t1ZfKlasZpdLly7JxIkjLAhmzpzNKnVp0qSzEKe0iqRzoXSBcx3SeC806GhFyFXVCeqrr2bZunY6XyxmzJhyPzzfL88QqlVHDWH3I6z3LbRzTJ48pQ099OSaaxfWcelzdZH5Nm263/WYDgeNLPp70updsWKl7hpy66q0RebvCgDwcNEoBQCiSKZM2ayas3XrxnBtHxBw2q61wYVLSE1OVNy4caVq1ZohbqddD/PmLSR79uyS+6H70K6LEsIxaxdFV0jQqpfO//PkGmLoakoSElcDFR1G6HL58mWnwrQ90JDM+xXc+xbaOebKld/mK2rTExf9nWplrHDh4hIafa5W5LJnz23zCT0vkR2s9P3T1wr6OrqovArP7woA8GiiUgcAUUQrJzVr1pdp0z60Cknu3PltyQGdG6XdLXVIn37A3rFjixMOSti8K32OtqTXeU5//bXPGmoorbrlyJHbqbi1sA/mGnK0CvTVV7OtsUnu3AUslIwdO9iGGmbLltM+xK9bt9K6Hd4Pbeuvw/T69r3TPGPPnp2yfftmGTJkgr3Opk1rrZW+ns+iRQusdb4euzYX0c6JWoHTaz13PWYNd8GFIX1/tPPnuHGDbTkDncO2cOEc5xE/d4OTe6HhK7T3LaxzrFattg1p7dGjpbz+ej2rHM6dO9U6SerSD6HRbpv63AEDOtvfglbtdGiszr0LuhD8/XKdw6RJo60JigY8bTCjlTkNd+H5XQEAHk1U6gAgCtWq1cA+vK9c+YsMG9ZLjh8/YuufKZ2XpSFBW8pPnDjS5t716zfaGlL07t1OfvrpO5sj17BhK9m5c4sNIezYsY8NTdROhwMGdLFhgyNHfmLzprQhi645pvPwtD2/fqDXNe70Q/390LXy9Lh0PbihQ3tax8yyZStI9OjR7fV0uJ92YtQlBXQ5hPHjP7Nq46FDB+z5Wqnr0WOILUOgHSN1mYeQ9Ow5zAkkpS3M6vDIc+cCZNCg8ffV8CWs9y2sc9TK3qhR0+TJJxPacgQfffSBrd3Wv/+YEJvHuGiA1+fqcgAaGEeP7m8hU5c2iGx6Dvpebdy4xnkfW9uSDzlz5nO+LMhsj4fndwUAeDT5CQD4qInd9vmXrpE6f4qMcQTA423xlEMBAceuVms5JscKAQAfQ6UOAAAAALwYoQ4AAAAAvBihDgAAAAC8GKEOAAAAALwYoQ4AAAAAvBihDgAAAAC8GKEOAAAAALwYoQ4AAAAAvBihDgAAAAC8GKEOAAAvsGfPLvn++y/lxo0bAgCAJ0IdAMAcPLhfhgzpKf/8c0y8yYQJw+WXXxbJ/di9e4f8+utiuXnzpjyqJk0aKWPHDpbt2/0FAABPhDoAgPn7778s2Fy9ekXux7Vr1+TQoQN33X/hwnk5fvyoRLZvvvlCjh07Ivdjx44tFmivXr0aoeedOHFcAgLOSGQLbr9vv91MGjVqLXnyFJDI9scfv0uPHq2kWrXS8s471WXWrCkCAPAehDoAQKQaObKf9O3b8a77mzSpKXPnThNfcfjwIXnrrSqyadMfEplC2m++fIXkjTfelhgxYkhk+uGHBdK/fyfJnbuAdOjQW1KlSiuffvqx/PbbzwIA8A6R+38GAADgVUqVel4yZMgsefMWtNslSpSR1157TrZt2ySlS78gAIBHH6EOALzI1q2bpGPHxjJlynwbIvf77z/LuHGfSebM2eTHH7+VxYsXyt69uyR9+kzSrFkn9wf1K1euyIcfDrUhlgcO7JEkSZJJuXIVpV69JhItWvgHbaxY8ZMsXfqdHDy4T86dO2tDARs0aCHZs+e0IYNaYXKpUKGo5MqVTypXfl2GD+9j9y1atMAutWo1kHffbSn79+9xqndT7biOHDnkhIssUqPGW1K27Et3ve53382XP//cJqlTp5Nnny0ndes2DvbY33+/nb0HEybMkYQJEwV7HnoOur+//tonBQs+baEmqNOnTznv8xhZv36VRI8eXYoXLyMtW3axSplWI5cs+ca202GbehkwYKw8/XRJm5c3ffpHsnbt7zbcVN+jjh37Ou95Uve+dSjq1KnjZfPmtTbMMnfu/FK/fnP59tsvQtyv/r5nzpzkvH9rA+1HX2vz5nXy77//SKZMWaV16+6SNWsO9zZdujSzv4cECRLaeV+8eMF+9++9115ixoxp97v+TtS6dSvl+vXrkixZCgEAeAeGXwKAFxo4sKvEjh1b2rXrZR/Y161bJSNG9BU/Pz9p27aXpEyZRrp3b2FBS+m22bPnsoDVvfsQeemlV2T27E/k559/iMjLWqDSgNG8eSdp1aqrExYvy6BBXeXWrVuSKFESGTbsYylUqJgkT57Sfm7TpocULlzCftaAVbx4afu5SpUatr+kSZNLjhx5nJD3jnTtOtBCybBhveTo0cPu19y2bbOdb9y48aRTp35SsmRZC7f6mkHNmTNVNmxYLX36jAgx0On+NGTq4x079pGcOfNawAuqb98OsnLlr1KtWm07Pg3QEyeOtMd0GGSnTn3t5zp1Gto55clzJxjNmDFBvvhihr3fev5nzpxyzq2Z3L59273vAQO6yPLlP8rLL7/uvI/dbB7iiRPHQt1vcPR90SYxFStWd37vPS3ktm/fUE6ePBFouyVLvrZmMP37j7HtfvjhK5uLGJQGx379OlkQrVTpVQEAeAcqdQDghTQ06Ydzl3nzPrVK0IgRdxpclCtXQZo2rW0f3LW5hnrllZru7YsXL2WBRatQ5ctXCffrakVOLy7x4sWX3r3bW5VNw2WBAkWdStJCqxrpzy5JkyZzKlxPWIXQ834NVjVq1HPfLlSouFWTdD5ZmjTp7L45cz6RdOkyOq8z3EKrDhf0pPcpf//1Fqg0bObIkTvEc/jyy5l2HL17j7AKnNJlArQK5uLvv0F27dpm+3IFUH3OBx+871TU7lS+/PzufC+qVT7XOWlF9Ouv58pzz5W3wKgKFChiFcy1a1fa+75z51bn/NY6QW+AVcyU/r7+O5+79xsc3c/GjX9It26D3JVNHTpZu3Z5O0etxLloyO/TZ6RVGTU4z549Rfbs2Rlof7///ovNpStWrJQT/AdLnDhxBADgHQh1AOCFypev6v5ZA8mWLRvkxRcru+/ToPPUU3mcYLLVfZ+GFB3yp0MTdQie8hwSGB5aUZo1a7L89ttPVk1zVZ8uXboo90pD3IIFs20Ipu7fc386lFGDi1aiXOEtOKdO/WvVr7JlK1g1MjRbt250qofF3YFOPflkgkDb+Puvs+uiRUu679OKnh6fvn86ZDM4+n5rsCtS5Bn3fVqNTJkytQ0d1VC3fv1quz9//pADW3i49qOVURcNYlr51GqkJz0GzwYrsWLFvqvL6YoVSyVt2gxONW+0AAC8C6EOALxQ4sT/hbHLly9ZuNJwpBdPqVKlsevdu3dKhw6NbEidVnB0zpUO04uowYO7W6hp3LitE1xKWFDs3r2l3KuFC+faOnNaEdOhmXpelSuXcD+u4U6HWQYNXUHdunXTCVxXJX78JyUs58+fk7hx44e6jSv01q//yl2PuYa0BkfnuCmdc6eX4J534cI5uw5peGh4ufYT9Jz19uHDf0lEnT0b4P57AQB4F0IdAHg5DTw6Z06HzbmGCrrEjBnLrr/6apZVZ5o0aWfNMe6FttpftWqZNGjQXMqUeVEig84903DoOm6tOnrSc4sVK5Y7ZIUkefJUFjTHjRtiHRtDG7aowygvXw69suhqEtKv32h7bz2lS5dJQj6OlHat75E2Pwn6ukqrZkrDZeLESeReuY5R95MoUWL3/RosEySIeGAcOnSCAAC8E6EOAHyANtPQIYghhZmAgNMWKlyBTitgOg9Oh2iGl+5D6fwsl337/rxrOx3mp5Wz4O6/ffu/5iZaXdR9pkxZ2n2fVgGDypu3kM2XC4sGQ+2SqR0jtTuozvcLjp6zdt30dOPG9bteU2mgDOk9dQ1n1CGiLhkzZrVK2fXr10J8Xr58he1azylol8+Q9hscVwMVHXrrCtmXL192qrLb76nJiXb71PchRYpUAgDwLnS/BAAfoO39t2/3l0mTRltY0I6ILVrUdYehbNly2pw1XfZAG6QMGtTN5n5pS39dmkC5hnRqQw8diheUNgfRqpUum6At9HXo5Pz5n9ljnnO4MmfOLseOHbHXWbbsRydAXrL7s2TJYfPZ9LkavnSOnHaIXLNmhfzxx+82dPSDD3o7rxFHduzY4u5uqeemx64Lmmszj2nTPrRlHVxVPc+ukp0793Ne74KMHTtIQlKtWi05dOiAHb++B/pa2ljEky7FoF0+R43qb9VJPWYdJuo51FQrZbocgB6/vs8690/fn5o169v7oot66/3z52vTklq2dIHnvsePH2KP/frrEue4m8ry5UtD3G9wtBKo+xk3brBz/LPsve7WrbnziJ8tCxERV69elbffruJcqlq4AwB4l+gCAD6qauk2zTLmfjJlvERPiK/QeVkazHRJAm2+4aLVFf2Q//338637onY2zJ+/iJQq9YLEiRPXeayAzcFauHCONdjIl6+Qdc/UIKhr3OnzdeigdmVcvPhrW54g6PBBDSx6n65jpvs5deqEEyIGW5dKbQJSpkx5204D5OnTJ62hyqpVv9oaaLqNHoMuRaD368LWzz9fSZ555jk5cGCvhaC9e3c6gaiBc25VnUD4izWD0aqVHpt2s9TmLLrGnQ431G204qbBUPen55o/f2Fb9kCHHn7++XQnRGYPdv251KnT2nBFfc1PPhkrJ0/+I6+9VtfCW+3a78gTT9z5eylZspyt6afv508/fe/cc1uqV69t4Vbp8gF6XLosxLx5n9kyAtpJVM9XO1h+883nzu/jS7tff19a/XM1Z3nmmbIWLH/9dZGsXr3cOlJqoxsdbhrSfjUQa1WuXr3G7nPRYzx69G/bdunSb62y2KvXMNufy51jl0BdTjWY69+FNpZRely//rrYOfeYtoagZxMZX7F349krVy7cnPvDH+MOCgD4GD8BAB81sds+/9I1UudPkZHW7MDjbvGUQwEBx65WazkmxwoBAB/D8EsAAAAA8GKEOgAAAADwYoQ6AAAAAPBihDoAAAAA8GKEOgAAAADwYoQ6AAAAAPBihDoAAAAA8GKEOgAAAADwYoQ6AAAAAPBihDoAAAAA8GKEOgAAAADwYoQ6AAAAAPBihDoAAAAA8GKEOgAAAADwYoQ6AAAAAPBihDoAAAAA8GKEOgAAAADwYoQ6AAAAAPBihDoAAAAA8GKEOgAAAADwYjEEAHzYgS3n5Z+DlwXA4+3KhZuxBQB8FKEOgM+6dvHm6P3+5zIKHms7jix6J1XCXD8niZ/pkOCx5hcjxkEBAB/kJwAA+LAiRYr85lx127Bhw+8CAIAPYk4dAAAAAHgxQh0AAAAAeDHm1AEAfNrt27f/vX79+k0BAMBHEeoAAD7NCXWxBAAAH0aoAwBUb7fiAAAQAElEQVT4tGjRoiWIGTNmdAEAwEcR6gAAPu3WrVs3nGrdbQEAwEcR6gAAPs2p1On/61jCBwDgs+h+CQAAAABejEodAMCn3bp1a9/NmzevCwAAPopQBwDwadGiRcvqXJ4QAAB8FKEOAODTbt++fenatWu3BAAAH0WoAwD4ND8/v7ixYsViDjkAwGfxPzkAAAAA8GJU6gAAPu3WrVv7b968eUMAAPBRhDoAgE+LFi1alv+vVQcAgE9i+CUAAAAAeDG+uQQA+LRbt27tZp06AIAvI9QBAHxatGjRcrBOHQDAlzH8EgAAAAC8GJU6AIBPu3379p4bN24w/BIA4LMIdQAAn+bn55f9CYcAAOCjGH4JAAAAAF6MSh0AwKex+DgAwNcR6gAAPo3FxwEAvo7hlwAAAADgxfjmEgDg027fvv3v9evXbwoAAD6KUAcA8Gl+fn7JY8aMGV0AAPBRhDoAgE+7devWuRs3blCpAwD4LEIdAMCnRYsWLQGVOgCAL6NRCgAAAAB4MSp1AACfduvWrd03b968LgAA+ChCHQDAp0WLFi2Hc3lCAADwUYQ6AIBPu3379jXHLQEAwEcR6gAAPs3Pzy9mrFixmEMOAPBZ/E8OAAAAALwYlToAgE+7devW4Zs3b94QAAB8FKEOAODTokWLls658P87AIDP4n9yAACf5lTq/naubgoAAD6KUAcA8GlOlS69cxVdAADwUYQ6AICvu3Hz5s3bAgCAj/ITAAB8TKFChSzE+fn997+527dv28W578CmTZuyCAAAPoIlDQAAvmhrtGjRLNS5Lv+/ffHWrVsDBAAAH0KoAwD4HCe4jXcuF4Per1U6f3//qQIAgA8h1AEAfI4T3CZpgPO87/bt25ecoDdGAADwMYQ6AIBPunnz5jgNcq7bGvI2b948RQAA8DGEOgCAT3KqdZOdq336s4Y7J+RRpQMA+CRCHQDAVzlZ7vaE/8+t2+NU6SYLAAA+iCUNAPisca13vyPR/DIKHmvbDn/TOnHcDL+lTVJwk+Cx5hc9+rSWI7IcFADwMSw+DsBnxYwXvW3aHPHyx0/4hODxlU8a6NUr/7/gMbV349krV87f+MX5kVAHwOcQ6gD4tCz5E0iKjHEEwOPt8O4LGuoEAHwRc+oAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIsR6gAAiESnTv0rCxbMca5PCgAADwOhDgDw2Ni+3V+WL18qD9L3338pH388Qn744SsBAOBhINQBAO7ZoUMHpEKForJ48dd3PVapUjGZMmWsRIVbt27J33//JdevXw90/6effvzAw1bFitWlbt1GzvlXFwAAHgZCHQDA52jIbNSohpw7d1YethQpUsnbbzeVZMlSCAAADwOhDgAAAAC8WAwBAOAB++qr2TJx4kiZOfN7SZ48pfv+999vKydPnpCPPpots2ZNkd9++0leeOFl+fnnH+TIkUOSJUsO6dSpn6RLl8H9nAsXzsv06R/J5s3r5N9//5FMmbJK69bdJWvWHPb4O+9Ul6NHD9vPdepUtOtvvlkpsWLFcu/jp5++l88+myjnz5+TcuUqynvvtZeYMWO6H//xx2+dat9C2bt3l6RPn0maNeskefMWdD8+e/Yn8uuvi+X48SN2PvnzF3Uqg60lfvwnZevWTdKxY2MZPnyy5MtXKMzt9XwCAs4EOkcAACKCSh0A4IF77rnydr1q1TL3fdeuXZONG/+QZ555zn2fzoPTQNeyZVcLejdv3pC+fTsE2tfAgV3ll18W2dy1tm17SrRo0aR9+4YWDlXnzv3l9dfr2c89egyxcOUZ6A4d2i/Lli2Rhg1b2zBJnWP3zTdfuB9ft26VjBjRV/z8/Jz995KUKdNI9+4t5MSJ4/a4NluZMWOCFChQ1Ll/sFSoUM25b7OcPRsQ7LmHtX2zZm86x/Ka7N69UwAAuBdU6gAA923UqP52CUnSpMklW7anZP36VVKtWi27b+vWjdbIpGTJcu7tbty44YS28c72yez2a6/Vk6FDe8rOnVslV658dq1BsFu3QVK27Eu2TYkSZaR27fLy5ZczreKm2x04sNcey5OnoHtfLtGiRZdevT5wB71FixbInj3/Bap58z6VJEmSOsFuit0uV66CNG1a24KfVtdc29asWd/mz2korVWrQYjnHtb2GTJktmodc/AAAPeKSh0A4L698cbbMmzYx4EuWunyVKLEcxbIXB0p161baSHHNWxSadXNM4S5Htu370+7Xr9+tV0XKlTMvU2cOHEkR448sm3bZgmPdOkyBqrcxY4dR65evWI/a6jcsmWDFCnyjPtxPY+nnsoju3ZttdvFi5e26yFDeljF8PLly6G+XljbDxw4ThYsWG5BEgCAe0GoAwDcNw1KOrzQ8xI01JUsWdZC04YNa+y2BrRnny0X6n51zplyDVW8cOFcoPs9tzt58h+5X5cvX5Lbt2/L0qXf2VINrsuSJd/Y/D2VOnVaC606LHPy5DHy5psVbHilPi84Ed0eAICIYvglAOCh0KqbVua0QpclS3abP6cNTkLjWpIgceI7VSzXEEVtcJIoUWL3djp8MUGCRHK/nnwygVO5iy3FipWSKlVqBHosZsz/qnuu4Krr4emcvHHjhtixVa78erD7jej2AABEBJU6AMBDU7r0C7J69XJZu/Z3J4QldHeHdNHqlVbzXLZt22TXOXPmtWudI6d0iKSLDmfcvXt7oCGZ0aNHt+tbt25KROlrnDr1712VR52rF5QOF9XwFzduvEDz8kIS3PYaSA8fPiQAANwrKnUAgIdGm6J8+eUsazqiQy+DDtHUUNe/f2d55ZWaFu502YEiRUpYZU/lzp1fnn66pFPpGmzDIbUBy8KFc5xH/KRGjbfc+9GlEJQuTZA9ey5bRiBz5mzhOsa6dRtbN81Jk0ZL8eKlLOBpE5YmTdpZuPvss0lOqFwvpUq9IBkzZrGlFS5duhhoHp6nsLbX7pfaWXPcuM8kR45cAgBARBHqAAAPTZ48BaxCd/DgfnnnnRZ3Pa6VrKJFn7HQdubMaSdEFbF16jz17DlMxo8fIvPnfyanT5+UtGnTy6BB4wN1j8yePac0bdpBPv98mu2nfv1m4Q51eoy6v8mTR8u3335hwVEDWPr0me1xbQoTJ05cWb78R9m3b7e9Vu/ew23OYHDC2p7ulwCA++UnAOCjJnbb51+6Rur8KTLGETw65s+fKV98MV1mz14sMWL8992iLj4+c+YkWbRorQCRbfGUQwEBx65WazkmxwoBAB9DpQ4A8NDoMMP58z+VqlVrBgp0AADg3vF/VADAQ6Fz5dasWWHDDuvWbSQAACByEOoAAA9FiRJlrAGKNhsJzgsvvCx58xYUAAAQMYQ6AMBDUb58lVAfT5UqjV0AAEDEsE4dAAAAAHgxQh0AAAAAeDFCHQAAAAB4MUIdAAAAAHgxQh0AAAAAeDFCHQDggdizZ5d8//2XcuPGDQEAAA8OoQ4AvNCRI3/LwoVzJaqsWrVM/P3Xu28fPLhfhgzpKf/8c8x936RJI2Xs2MGyfbu/PGj6GsuXL5VHQVT/bkKyadNaGTCgiwAAfA+hDgC80LJlS2TixJESVX7++QcntI123/7777/k118Xy9WrV9z3vf12M2nUqLXkyVNAHrRPP/1YfvjhK3kURPXvJiQ7dmyRlSt/FQCA72HxcQDAA5EvXyG7AACAB4tKHQAAAAB4MSp1AODF/vprn3zwwfty+PBByZ+/qLRq1VVSpEjlfvz06VMyZcoYWb9+lUSPHl2KFy8jLVt2kRgx7vzzv2LFT7J06Xdy8OA+OXfurA2VbNCghWTPntO9j1u3bsn06R/Z0L3z589K2bIV5Pr1a2Ee26xZU2TmzEmyaNFa932VKhWTNm16yIYNq2Xjxj8kQYKE8vrr9aRKlRrubW7evGmvt3bt73L8+FE7po4d+0qSJEnDfM25c6fJt9/OkytXLstzz70kLVp0tvN20bl3n38+TbZs2SiJEyeR116rK1WrvhHomH/77SepU6eRzJ49RY4dOxzs+3rhwnmZOnW8bN68VgICzkju3Pmlfv3mgd63sH43+l60a9dL1q1b6VxWyZNPJpB69ZrYNjp8U1+7ePHS9n7Fixc/3L+vLl2aScaMWZz7csmcOZ9I5szZpVevYXe9V7t373Be/13n+c3ljTfeFgCA96JSBwBeSsPW+PFDpHLlGtK2bU8nROx1bg8NtE3fvh0sjFWrVltq1XpHfv/950DzvVKnTidPP11SmjfvZKFDw9CgQV1t3y6ffz7dLrqdBgz1xx+/y7368MOhkjx5KpkwYY6ULv2ijBs3xAkYO92Pz5gxQb74YoaFEn29M2dOSdeuzeT27duh7nfHDn8LbXouFSpUs86bs2d/4n78zJnTTrhpIwcO7JUmTdrJs88+7xzLMCfE/RxoPzo/UOfoNW7cVkaOnGoBKuj7qg1Hli//UV5++XXnfesm165dkxMn/msSE57fjdJt0qXLaO+FhrnJk0fbdhq0Bg/+yBrSeJ5DeH5fasuWDRaMNZy+8kqtu1737NkA6dOngxQp8gyBDgB8AJU6APBirVt3lwwZMtvPGmg8G2H4+2+QXbu22Yd/VyUsSZJkVj2qX7+ZxI//pFV4PKs8WhHq3bu9HDlySNKnz2RVswULZjtVr/LStGkH2+bZZ8tZFerixQtyL1544WUnVLW1n2vUeMsC4549OyVHjlxOSLkiX389116vY8c+tk2BAkXkrbeqOJW7lU7lqlSI+02ZMo1z7MOtCqnHePLkP07V7gsn2DS0at133823Yx4zZrqdm9JQNH/+Z064fMG9H12CYciQjyRZshR2u0SJMoGC386dW62TZNeuA6RcuYp2X7lyFe46ntB+Ny7PP1/Jfhd39lHRAm6vXh/YOascOXI7IXSPe/uwfl8uGlz1PHPmzBvo9fz8/CwAaiiNFSuWcw4DBQDg/Qh1AOClokWL5g4NKnbsOIG6T/r7r7ProkVLuu/TD/laVdq7d5cULPi0/Txr1mQbcnj06GF3NezSpYt2rUsVaFUnf/4igV5bhwrea6jTKp1LrFix7VrDldq1a6sFO60guSRNmtwJbKnlzz+3hRrqdDvXsFKVO3cBW+ZAh3CmTZve3g+thnmGn6eeymNhT4Oc67n6vroCnYoZM1ag93X9+tV2rUMqQxLW7ya498I1xNJziKYGbx3q6RLW78slU6asdwU6F63g7d69XcaO/VTixo0rAADvR6gDAB/lCl31679y12MnThy368GDu1vA06GGRYqUsMpe9+4t3du5AoUrcDxortcbObKfXYI75vDS4KlOnz5poU7fD91HhQp3h7FTp/61CYzoOAAAEABJREFU4Bi+Yzxn1wkTJpKHLazfl0uiRElC3Mfly5fsWoMmAMA3EOoAwEe5qk39+o12PsDHDvRYunSZ5PDhQzZnS+dvlSnzYrD70GYiKmgl6EFJnjylXesxafMRTzp0NCJcAdEV7nTfV69elTZtut+1beLEYTdhcdGKoDp//pz7/XkYwvP7Co9GjdrImjUrZMiQHk5w/sSGZAIAvBuhDgB8VN68d9aI07lTBQrcXZ3atm2zXetcNJd9+/4MtI025tAhgPv37wl0vw4DfBAyZsxqr6fdNYM75tDcuHE90O2tWzdaoHMNg8yVK7/NM8yePfd9DTvMl6+wXfv7r5eyZV+ShyUg4LRdh/b7Cg/9e+jSZYB06NBI5s37VGrWrC8AAO9GqAMAH5UrVz7rlDhqVH957732TpCJJ6tXL7fujoMGjbe5ZVrBW7x4oS0XoM1PtGmI0sCn8810ntkrr9SUb775whqG6JA/nYOm89PSp/9vzpir0qXNTBImTHzPQxP1eDRk6LyxZMlS2rDJPXt2ydKl38rQoR9LokSJQ3yuNluZNGm0HaOepzY30aqWzm9T1arVsiYsAwZ0ttfQqt2SJV/b/LO3324q4eV6X7Vz5cmTJ6xyt2jRAqlc+XVr8PKghOf3FRbXHLy8eQtK9eq1rdOo/l495/8BALwPoQ4AfFjPnsNk7NhBMm7cYLl8+bJkzZrD1oVTGrx0aOYnn4yT3r3bWWgYMGCcrb22c+cWZ4u6tp2unabLCuh8Lm1ooiHv5Zdfcyphm9yvo0Mldc00bcmvatSoJ/eqVq0GFj7mzZthc93Sps1gSxSENa9P18974oknnPDX05q7VKxYzfbloqF21KhpNlevb9+OFpBy5sxnSxtEVI8eQ2X06AFOqPrUqpaFCxe3JRgepPD+vsLr3Xdb2XDOQYO6OQF1ZqAmMwAA78JAegA+a2K3ff6la6TOnyIjDSGAx93iKYcCAo5drdZyTI4VAgA+hsXHAQAAAMCLEeoAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIsR6gAAAADAixHqAAAAAMCLEeoAAAAAwIvFEAAAItmaNStk9uxP5ODB/RIvXnzJnj2X1K/fTLJkyW6PX7hwXqZOHS+bN6+VgIAzkjt3fufx5s52OeX27dvyxRczZOXKX53n75MUKVJLnToNpVy5iu79b926STp2bCxTpsyXWbOmyO+//yzjxn0mmTNnkx9//FYWL14oe/fukvTpM0mzZp0kb96C9rxr167JiBF9ZccOfzl79ow9XqZMeXnjjbclWrS7v+fcv3+PzJ07Vf7++y85cuSQZMiQRWrUeEvKln3JvY2+/m+//SRt2/aUTz4ZJwcO7JH583+RmzdvyvTpH8natb/L8eNHJU+eAs4x95UkSZIKAACRiUodACBSXbp0UUaO7Cfx4z8pHTr0tkB28eJ52bfvT/c2AwZ0keXLf5SXX35dWrXqZmHrxIlj9pgGumnTPnSCWCHp1KmfPPVUHhkypKesXr38rtcaOLCrxI4dW9q162UBbd26VRba/Pz8nJDVS1KmTCPdu7dw9n3ctv/yy5myatWvUq1aLenadaDkz1/Eub3MAlhwkiZNLjly5JFatd6x7TNlyirDhvWSo0cPB9ru9OmTdixFiz5j56xmzJhg56KBtk2bHnLmzClnH80stAIAEJmo1AEAItU//xxzqmABVlkrU+ZFu69KlRrux3fu3CqbNq11As4Ad/WtXLkKdq3hbu7caVK58uvSpElbu69Uqedtn7NmTZZnnnku0GslT57SKmQu8+Z9apWwESOmuPfbtGlt+eabL6RRo9ZWvUucOKlV21TJkmVDPZeECRM529Zz3y5UqLgsXfqdc/x/SJo06dz36/k2bNhKatasb7evXLkiX389V557rrxTnetj9xUoUETeequKU7lbKcWLlxIAACILlToAQKTKmDGLpE2b3ipVCxbMkZMnTwR6fP361XadP3/Ru56rgU8rfQUKBH4sf/7CsmfPLrl8+XKg+8uXr+r++caNG7JlywYpUuQZ931asdNK365dW+12iRJlLCAOHdpLNmxYI7du3ZKwaIhr3ryOVK1aUqpXL2336TEGVaFCNffP+noa7DyPRat+KVOmlj//3CYAAEQmKnUAgEilc9MGDBhnlaolS76WSZNG2by1Zs06SqJEieXChXO2nVbBgjp//qxd6zw8T/HjJ7Drkyf/sWGWLlp1c7l8+ZINbdQQphdPqVKlsesXX6xsQy1XrVom/fp1lAQJEjkVttaB5sh5WrhwrkyYMFxaterqVNdK2+tVrlxCgjtnz/PROYNKh6HqxZNrKCgAAJGFUAcAiHQ6NFFDnNq2bbP07dvBCUcfSLdug6xipc6fP+eEpCSBnqfDKZUrFLm4gqCGsJA8+WQCm19XrFipQMM9VcyYsexaK3cVK1azy6VLl2TixBEyeHB3a7CiFcagdE5ckSIl3PvTamB4uM6jQYPm1gTGU5IkyQQAgMhEqAMAPFDaeVKbnujwSZUvX2G79vdff1eFLGPGrFal27Ztk81Hc9myZaNky/ZUsNU9T3nyFJRTp/69a/hmcOLGjStVq9aUxYu/tiYuQUOdVv0CAk5LypSl3ffpnLzw0PPQRjHXr18L17EAAHA/CHUAgEilYW3s2ME21DFbtpwWjNatW2nNT1SuXPnk6adLyvjxQ2y+nVbuFi1aYI9rkKtVq4EtBRArVmzbdtWqZTZXrk+fEWG+dt26jaV9+4YyadJoa0aiAU87XjZp0s46XXbr1sIqZYUKFbNq2ldfzZY4ceI61bQCd+1Lq3rauVKXZ9C5eOfOBVgTl9ix48iOHVtsPl5wyyAorRhq0xRt7pIsWUqbY6ihdunSb2Xo0I9tGCoAAJGFUAcAiFQanurVayLLli2Rzz+fbvPZGjRoIa+++qZ7mx49hsro0QNk/vxPreNl4cLFLUApDXVK15qbP/8zp6IWz5YECNr5Mji6FtygQeNl8uTR8u23X1hg1GYl6dNntpCmnSi/+mqWdcnUZQg03I0c+Yl7zl1QuozBxx/fGaKpQ0V1aQOdVzdjxkdOFe66EzxjhXgseh5a7Zs3b4aFy7RpM1gzlaDzBQEAuF9+AgA+amK3ff6la6TOnyJjHAHweFs85VBAwLGr1VqOybFCAMDHsKQBAAAAAHgxQh0AAAAAeDFCHQAAAAB4MUIdAAAAAHgxQh0AAAAAeDFCHQAAAAB4MUIdAAAAAHgxQh0AAAAAeDFCHQAAAAB4MUIdADwk165dk+++my/79u0WAACAyBJDAAAPxc6dW2TcuCFSoEBRGTbs4wg9d+vWTTJ9+oeya9c2u50tW06JH/9JGThwnESVVauWSbx48e18QvLbbz9L4sRJJW/egvIg7d69Q44cOSRlypSX6NGjy6PkwoXz9j4UKVJCUqRIJQAARDYqdQDwkOTKlV8aNmwlb7/dNELPu3jxgvTs2VoSJEgkvXp9IH36jJRYsWLLtm2bJKI0YBw/flQiw88//yCTJo0OdZtBg7qJv/96edB27NgiQ4b0lKtXr0boeSdOHJeAgDPyIB04sFdGjx4g//xzTB6kB3Uukfk3AwB4MAh1APCQxIwZU2rWrB/hqtXff/8lV65clurVa0uJEqXl6adLSo4cueVeNGlSU+bOnSYQOXz4kLz1VhXZtOkP8XYP8lz4mwGARx/DLwHgEXf16hW7fuKJmAIAABAUoQ4AHqJKlYpJvXpNpG7dRnZ71qwp8ttvP0mdOo1k9uwpcuzYYcmfv6i0atXV5l+NHj1QFi1aYNu2a/euXY8ZMz3Yfc+e/Yls3rxO9u3706qCRYo8Iw0btpbEiZPI0qXfyfDhfWw73Z9eatVqIO++29Lu+/HHb2Xx4oWyd+8uSZ8+kzRr1ilQRfHWrVsyffpHsnLlr3L+/FkpW7aCXL9+TcJDt9OhkX/88ZvNA6xd+x2pXPl1e6xv347y55/bnWNf5N7+5s2b8sYbL0j58lWc4+gY7D71fLTpzF9/7ZOCBZ+WDBkyB3r8ypUr8uGHQ63KeeDAHkmSJJmUK1fR3vto0aLJyJH9ZMmSb2xbPTa9DBgw1qqgK1b8ZPs/eHCfnDt3VvLkKSANGrSQ7NlzhniOa9assPf/4MH9Ns8we/ZcUr9+M8mSJbt7m0uXLkqPHq1s2GyGDFmkceO2zu+6sPvx27dvyxdfzLD3WF87RYrUzt9FQztuF9ffS9u2PeWTT8bZuZUsWTbEc9F9zpkz1fZ5+PBfkjlzdqfi+6bz+3tJfv/9F+nfv7NMnjzP/f59+eUsmTRplCxYsMJ5zi8h/s38/fdBe3/3798tN27csPPU/ZYq9bwAAB4+hl8CQBTT4PHppx/bh/yRI6faB/rx44faY6+/XleaNGlnP7do0dkarOgH8+BkypTVwlaXLgMsvGzc+IdMmzbeHitcuIQ9N2HCRFK8eGn7uUqVGvbYunWrZMSIvuLn5+eEhV6SMmUa6d69hc3Rcvn88+l20aDQpk0Pu++PP36X8Pjyy5kWLDt27GPHPnbsYPc8uxIlysipU//avDOX7dv9bR6hhpXgbNu22cKGnovuM2fOvBbwPMWOHduClYbH7t2HyEsvvWKhS+cBqjfeeFs6deprP2tw0vcjT547ITZ16nR2ns2bd7JwrUNfBw3qasE2OBrWNCRqYO3Qobft7+LF8xauPWlYyp+/iHTu3F/ixo3nBNoOTuC97n5cA920aR86YbqQc2z95Kmn8lhAW716eaD9nD59UgYO7CpFiz5jrxfaueiwSf3b0vCo+9TwtmvXVgmP0P5mJkz4wPn7OGZht3Xr7pI0aQpZv361AACiBpU6AIhiWukYMuQjSZYshd3WoKPdEpVWzfRDvNKOl7lz5w9xP0FD0JEjf8uvvy62n5MmTWaXGDGesKqVZ8fKefM+de5L6gS7KXa7XLkK0rRpbfnmmy+kUaPWVjlbsGC2PPdceef+DrbNs8+WsyqZhq+wvPDCy/Lee+3t52eeec6pUr5s+9Zj0MqONhFZt26lE/iy2Tbr16+SBAkSWgAKjoZEPYfevUe4O13qezhz5qRA273ySk33z8WLl7Jqle5bK4D6vvr53fleU4OO5/uhFTnPqpxW3nr3bm/dNfV5QWkDlLNnA6yiVqbMi3afK/x40jmRVau+YT/re6rBTBuQpE+f0Za70ACmIbRJk7a2jb43uu9Zsybb++air6UNd3R+pktw56Lvif5uK1as7n7/I1JJC+1vZs+enfZ3+vLLr9ptrfwBAKIOoQ4AopgOB3QFOhUzZiz3PLqI0IrX5MljrArmCoJasQqNfvDfsmWDvPhiZfd9WrHTKpGroqNDCjVIBA1ZTz6ZIFyhLnny/9r467lqMHUtzaCBKV++whbqXCFlwzpaTjAAABAASURBVIbVVhnS4wjO1q0bnSpS8UBLF+ixBKWvMXXqeBtS6jpODa9h0YClQUqHOR49etiGMCqtyAUnY8YskjZtepkxY4J1iixd+oVAv0+XTJmyuX/W7qVKq4Bq586ttv+gy0NohW3mzMly+fJliRMnjvv+ChWqSVhcFc+QwvH90JCpXxgkTZrcOd8XJWvWHAIAiDoMvwQAH6CBQOfcnTlzSrp2HSDff7/G5q6F5fLlSxZadA5ZhQpF3Redo/Xvv//YNhpUlAawyKAB7OTJE+7bGhB0SKXOgztz5rQTwv4MceilOn/+nMSNG/qx7N69Uzp0aGSVqw8+mOScz3qbGxcegwd3l19+WWRDC7/88lcZNGh8qNtrUB0wYJxVL5cs+dq6UA4e3CNCywvoPEUV9D2OH/9OWD158p9Ar6dDIsO7z/BsG1FNm3Z0/r7etfmBLVrUtb+9PXt2CQAgalCpAwAfsGzZjzZUT+fThTe8KA1YWs0rVqzUXUMGtWKodD6cCqlSFVEaEj2Dhg5Z/PjjEdZIRUNmrFix5Omnnw3x+ToU8PLl0I/lq69mWTVM5yNq05jw0qUBVq1a5gS65u6hlOGRJk06d1MXDag6X07nnXXrNihcz0+ePKVduwK0y4UL5+xa1yiMKFe1UENwZIsbN641+9GL/t3pUNL332/rVDh/sNAJAHi4+JcXAHyAVuhUqlRp3ffpsMOgYsSI4VTmAjf80KYaOnRTh/55XnLlymePa+MQbQKyf/+eQM/TYYrhcePGdY+f7wz31CGXLjqET+cL6hBMHXpZtGhJeeKJJ0Lcnw4NDXosnq+hAgJOW/hzBToNpDonzpO+F0rnt3k+T2mzGJegDU/Col1DtdlJRCpXGTNmtSpd0AXlt2zZ6Lw3T4VZbQvuXHS4p/7edLhqcFyhXX8nLq6/o6D7Dvo34yllytTWoEeH/Ab3fADAg0elDgC8UKJESeTq1auyZs1v1gVROz2qefNmWNdCDUeuIY06FDFHjjuPZ8mSwz7k69IH2q5fq1F16zaW9u0byqRJo62hiAY8bUaiVS4Nd/qhXpuOaHMTbY5RpEgJ6zbp779O0qfPHOaxLl78tVPtS2rh7fvvv7T5ea++WifQNjp08dtvv7Bz0i6foalWrZZ07dpcFi6ca01AtK2+Hq8nDYmbNq21pRo0LGk7fn0vtLmLnrc2YtFKll7rcgRaKdNAlDXrU1a51OUddP6dbj9//me2T30/NVAGpXMYtaOnzkvU19VgqAHVtWxDeOhr6nIBumyEVhg1UK9atcwCcJ8+I8J8fnDnovMOdZ6idtTUfepcRn1cO29qZ0/tlqrzFrdv3yzp0mW0Y/7223l37Tvo34wGch3aqn8LGmCjR49hfw+6rIH+jgEADx+VOgDwQtqJUj/E9+7dztZ5K1bsWWnZsousXfu7DBrUTY4e/VumTJlvQWPTpj/cz9MP81qF6tathUyYMNyprpyy4Zo6b2zjxjXSs2dra4GfM2e+QIFNl0jQBiA636xKlWds/y+//Fq4jlWfq8Fn2LBeFlJat+4WaA08pfPq9Fh0+KWGhdAUKlTMzlWDXLVqpazr5bvvtrrrNXU46ZQpY2x5iLRpMzjXn9m5Hzp0wLbRsNqjxxBbUqJz56bWfVIrYv36jbYGJvre/vTTdzZfTrtN7ty5Jdjj0UYk+nramEWHIepx6Xw811IU4aWhTod96vpwun6cLmWgy0d4dr4MSXDn4trn2283tX3q+3/8+BEL0ErXQezSpb8NVa1ataQFbl0iIaigfzM6B1Ofd+5cgIwZM9DWq9OgP3jwRwIAiBp+AgA+amK3ff6la6TOnyJjHMGjT5cN0PlYvXsPFyCyLZ5yKCDg2NVqLcfkWCEA4GOo1AEAotzatSttaKCu5QYAACKGOXUAgCijyyboUEMdQqoLZAddpw0AAISNUAcAiDJPPplQypevYnO5dBFvAAAQcYQ6AECU0a6PVau+IQAA4N4xpw4AAAAAvBihDgAAAAC8GKEOAAAAALwYoQ4AAAAAvBihDgAAAAC8GKEOAAAAALwYoQ4A8Mg5ffqU/PDDAgkIOBPiNqtWLZPhw/tIZNu0aa0MGNBFAADwFoQ6AMAj5+zZMzJmzEDZuXNLiNv4+6+X9etXSWTbsWOLrFz5qwAA4C0IdQAAAADgxQh1AAA8YLdu3ZJfflksS5d+JwAARLYYAgDAPdi/f4/MnTtV/v77Lzly5JBkyJBFatR4S8qWfcm9zaxZU+S3336SOnUayezZU+TYscOSP39RadWqq6RIkcq93c6dW+WzzybK9u3+kjFjFnnppapyL27evCnTp38ka9f+LsePH5U8eQpIx459JUmSpOE+5qB2794h7dq9Kw0aNJc33nhbVqz4ycLZwYP75Ny5s/YaDRq0kOzZc7qfs23bZpk371PZtOkPuXr1qqRMmdou+fIVtsd1zuCUKWNs+Gj06NGlePEy0rJlF4kRg/8tAwAijkodAOCeJE2aXHLkyCO1ar0jXbsOlEyZssqwYb3k6NHDgbbTAPXppx9L48ZtZeTIqRaGxo8f6n780qWL0rt3Ozlx4rgFGw10X301W+7FjBkT5IsvZjgBK5e0adNDzpw55RxbM7l9+3aEjtnl7NkA6dOngxQp8owFOpU6dTp5+umS0rx5JwunV65clkGDulo1Tvn7b5BOnZpI2rQZnPP+Ttq3f1/Onz8n77zTUt5+u6lt07dvB5u3V61abTuW33//WSZOHCkAANwLvhIEANyThAkTOVWueu7bhQoVtwqWVqfSpEnnvv/GjRsyZMhHkixZCrtdokQZp3r3s/vxJUu+sfA0YsQUSZ8+k92XPn1m6dz5PYmIK1euyNdfz5XnnivvVOf62H0FChSRt96q4lTuVjrVsFLhPmY/Pz8LadoFM1asWBYAXbQi51mVixcvvhNK21vlT4//hx++stdp2LCVVeEqVHhFvv32CwubffqMsNC3a9c2C4RVqtSwfSRJkkw++OB9qV+/mcSP/6QAABARhDoAwD3TQLRgwWyrxl27ds3u08qbp2jRorkDnYoZM5ZcvXrFfXvr1o02PNIV6FSCBAklonbt2mrBTqtqLlqZ02GPf/65zUJdeI9Z6TDO3bu3y9ixn0rcuHHd9+tzZs2abMNKtcLnqgK69nH79i0nCMa2QOcSN258uXjxvP3s77/OrosWLel+PGfOvLbfvXt3ScGCTwsAABFBqAMA3JOFC+fKhAnDreJUvHhpSZw4qVSuXEIi6sKF8xZ67pfuR40c2c8unnRoZ0SP+fLlS3YdO3acQPcPHtzdwpcOJy1SpIRV3bp3b+l+vGzZCrJ8+VK7aNVQl0jQ5ReaNGlnj1+8eMGu69d/5a7XdB0nAAARQagDANwTHU6oocY1hFCHWd6LxImTWNXsfiVPntKutaFJ7tz5Az2mwxtVRI65UaM2smbNChkypIcTEj+xIZmHDx+yRc/1NcqUeTHY55UsWdbmBQ4a1M0u6uWXX5Pq1Wvbz66qZb9+o53AGDvQc9OlyyQAAEQUoQ4AEGE65DAg4LSkTFnafZ9Wr+6FNi5ZtuxHOXXqpCRNeid8Xb9+TSIqY8asNh9Nn1ugQFG532PWuXRdugyQDh0aWSfLmjXr2/NVypRp3Nvt2/dnoOcFBJyRX35ZJB98MEny5y98137z5i3k3n9wxwkAQEQR6gAAEaZVK+0wqZUsbXxy7lyAzJ07zYYq6nBDbTKic+nCQ6taupzB1KnjbEjjzZs35MMPh4X5vESJklhXSW2Cot0oteqlwUvnuyVLllLSpk0ve/bskqVLv5WhQz92tk8c7mN2zZPLm7egVdi0q6Y+R+f96essXrzQ5gH+9dc+mT//M9tWlzF46qk8Fvy0Arhs2RI7l+jRY1gTFleFLleufHa8o0b1l/feay9x48aT1auXW7Vy0KDxAgBARBHqAAD3RDtCfvzxCJtjpkMotTW/zlGbMeMjp1p23SpR4fHkkwkszIwbN1hq135JUqdO61TH+liFLDQvvljZul326tXGee5nTsUvl3MMDSyQzZs3w6n8/WvLClSoUM06VN7rMb/7bisbcqlDKcePn2nDJj/5ZJwtw6Ahb8CAcbJ581rZuXOLs3VdW2evUqXq8v33X9rFpVy5ik7lr78F4p49h8nYsYPsnC9fvixZs+aQ11+vJwAA3As/AQAfNbHbPv/SNVLnT5ExjgBR5dKlS/LLLz84AW6IE0CHSalSzwsevsVTDgUEHLtareWYHCsEAHwMi48DABBJdEkFXXhcO2K66HIIpUq9YD+fPXtGAACIbAy/BAAgkuh8O11IffbsKe7hlDq/Tufg6WMFCrAGHQAg8hHqAACIRL17j5CRI/tK585NrfFKqlRp5Kmn8sqoUdMkXboMAgBAZCPUAQAQibTr5ogRUwQAgIeFOXUAAAAA4MUIdQAAAADgxQh1AAAAAODFCHUAAAAA4MUIdQAAAADgxQh1AAAAAODFCHUAAAAA4MUIdQAAAADgxQh1AAAAAODFCHUAAAAA4MUIdQAAAADgxQh1AAAAAODFCHUAAAAA4MUIdQAAAADgxQh1AAAAAODFCHUAAAAA4MViCAD4sANbzss/By8LgMfblQs3YwsA+ChCHQCfde3izdH7/c9lDO/2Zy4cSnskYPPrqRPmX5D0yUx/C4BH0vbD3zWPGzPJn5lTlPw5Is/zixHjoACAD/ITAIAULlx4nHNV9MaNG3W2bNlyQAA80ooUKdLVuWp28+bNOps3b14pAPAYI9QBeKwVKlSokp+f32znx54bN278UAB4jbx586aPFSuW/ve7bcOGDc0EAB5ThDoAjy0n0E2PFi1acufHOs4HwrMCwCs5lfb3nC9nPnCqdq87VbulAgCPGUIdgMeOE+bedMLc7Nu3b9d2qnOfCwCvlzt37vhx4sQZ6Px3ncL577qOc9dtAYDHBKEOwGMjXbp0cZInTz7CCXQJncpcXQHgc5yqXS3navatW7dqO1W7eQIAjwFCHYDHglOde9sJcxOuXbtWbevWrT8JAJ/mhDudI5v+xIkTtQ4fPsy6JgB8WnQBAN/m5wS6GU6gy+xU54o5H/D2CwCfd+zYsR9Sp059Pn78+Ouc673O7e0CAD6KSh0An+V8U1/Fufr65s2blfz9/X8UAI+lIkWKDLl9+3aujRs3Vhfm2gHwQYQ6AD7JCXQTnavUzoe4VwTAY8/5N6Gqc7XQuVRz/l34TgDAhxDqAPgU5xv5DM438pOcH790PrhNFgDw8P+5dnGcfx/eFQDwEYQ6AD7D+bBWz7ka4IS6kps2bToqABAM59+KBn5+foOcHyts2LBhqwCAlyPUAfAJhQoVGuN8SEvsfPv+tgBAGHLnzp0qduzYS5wfP3T+3ZgkAODFogkAeLkiRYpscq42EOgAhNeOHTuOO/9mFHAq+4mdyt03AgBejEodAK/lfBDL5Vxtcz6UFd60aZO/AMCt77eRAAAQAElEQVQ90E65TqV/5o0bN5719/dn6QMAXodQB8Ar/b+T3RDnm/a8QotyAPcpW7ZsCRw/O18STXC+JJoqAOBFCHUAvE6hQoWaOt+qZ3UCXScBgEhUpEiRKU6wO+/8+9JOAMBLMKcOgFdxPnB1ixYtWn4CHYAHYcOGDY2cL43+Kly48FIBAC9BpQ6A13A+ZA3UayfQ9RAAeIAKFSr0ghPu2jj/3rwiAPCII9QB8ArOB6w3nStdf66VAMBDkDlz5pRJkiQ57PyYzKngnRUAeEQR6gA88pwKXR/nG/NbzoeqfgIAD1d059+gk86/Qdmdf4NOCgA8gphTB+CR5lToWumi4gQ6AFHk5saNGxPfvn17TpEiRZIJADyCCHUAHlkFChSo6VxlcgJdGwGAKOQEu/LO1c6cOXMmFQB4xDD8EsAj6f8Li893PkjlEQB4RDj/NmnlLoawPiaARwiVOgCPJD8/v9+uXLlSWgDgEXL69OmkRYoUWSIA8Agh1AF45DgfmBbcvHmz4Y4dO04LADxC/vrrrwDnarJTsftcAOAREV0A4BHiBLr6t2/fvrJp06aJAgCPoGPHju1IkybN86lSpUp//Pjx9QIAUYw5dQAeKcxXAeAtnH+v1jhfQr3mfAl1VAAgCjH8EsAjw6nSjXE+ILUVAh0A7zDQz89vggBAFCPUAXgkON94Z3MCXSXnG+9xAgBeYOPGjd86oe5awYIFywsARCFCHYBHRX/n0ksAwIs4X0aNiBYtWl8BgChEqAMQ5QoVKpTcuXre+dabbnIAvIrz79Yap1oX3anWPS0AEEUIdQCinPOBqI1zNUYAwAvdvHlzkHNVQwAgihDqAES527dvlz137twIAQAvFBAQsCR69OitBACiCKEOQJQqXLhwRadSd27v3r1XBQC80F9//XXF+XLql0KFCr0gABAFCHUAotqrzmWBAIB3WxEtWjS6YAKIEoQ6AFHKqdJVu3HjxtcCAF7M+bdsnVOto1kKgChBqAMQZQoUKFDIuVqyZcuWEwIAXuzMmTMbnWAXSwAgChDqAESZ6NGjl3S+2T4nAODl9u/ff9b596xA/vz54wkAPGSEOgBRqYRzWSMA4AOcSt2KW7dupRIAeMgIdQCijPMBqND169dXCwD4hlTRo0dPIADwkMUQAIgCRYoUeeL27dvZt27dul8AwAc4/6ZdjBEjRkwBgIeMUAcgSjgffrI5V3sFALxYoUKFbuu1n5+f3Xb+bVvj3KfXet+BTZs2ZREAeMAYfgkgqmR1LvsEALzb1mjRolmoc13+f/virVu3BggAPASEOgBRwvkWO4Vz9YcAgBdzgtt453Ix6P1apfP3958qAPAQEOoARAnnm+zMAgBezglukzTAed7nfGl1yQl6YwQAHhJCHYAo4XzoSe5c/SsA4OVu3rw5ToOc67aGvM2bN08RAHhICHUAooR2iXMufwsAeDmnWjdZ/j9HWMOdE/Ko0gF4qAh1AKKE8012jmjaTQAAvJ+T5W5P+P/cuj1OlW6yAMBDxJIGAKKEE+piOR+ArgrgA8a13v2ORPPLKHisbTv8zbXEcTMcfKdMwT6Cx5pf9OjTWo7IclCAh4RQByBKON9qn/GcgwJ4s5jxordNmyNe/vgJnxA8vvJJA7165f8XPKb2bjx75cr5G784PxLq8NAQ6gBEldTRo0dn+CV8Rpb8CSRFxjgC4PF2ePcFDXUCPEyEOgBRwqnS7b5x48Z1AQAAwH0h1AGIEtGiRXvKuTBWDQAA4D4R6gA8NIUKFbqt135+flqp0x9/c+6zn3Vdp02bNmURAAAARAjzWQA8TFt1FQMNda7L/29fvHXr1gABAABAhBHqADw0TnAb//91nALRKp2/v/9UAQAAQIQR6gA8NE5wm6QBzvM+XdbACXpjBAAAAPeEUAfgobp58+Y4z/XpNORt3rx5igAAAOCeEOoAPFROtW6yc7VPf9Zw54Q8qnQAAAD3gVAH4GFzstztCf+fW7fHqdJNFgAAANwzljTAQzeu9e53JJpfRsFjbdvhb64ljpvh4DtlCvYRPNb8okef1nJEloMCAADuCaEOD13MeNHbps0RL3/8hKw7/TjLJw306pX/X/CY2rvx7JUr52/84vxIqAMA4B4R6hAlsuRPICkyxhEAj7fDuy9oqBMAAHDvmFMHAAAAAF6MUAcAAAAAXoxQBwAAAABejFAHAAAAAF6MUAcAABAFfv/9F1mz5jeJqD17dsn3338pN254d5Ohez1/AHcj1AEAgIdu06a1MmBAF3lcHDy4X4YM6Sn//HPMbt+6dUv69+8svXu3C/O5GuAOHTrgvj1p0kgZO3awbN/uL97i9OlT8sMPCyQg4Izdjsj5AwgbSxoAAPCYmjbtQ5k7d1qIj/fpM0KeeeY5eRB27NgiK1f+GqHn1K79kpw5c9p+TpIkmWTIkFmee+4lqVSpuvj5+cmDoOGza9fm0qFDb3nppaqBHvvxx29lxIi+MmXKfEmfPlOo+/n777/k118XS506De12tGjRpGPHPnYdlunTP5Ly5atIkyZ3AtDbbzeTXbu2Sp48BdzbXLhw3i6pUqWRR9HZs2dkzJiBkjhxEvubCun8T5w4LjFjxpJEiRILgPAj1AEA8JiqUKGaFC5c3H5et26VzJv3qXTq1FeSJ09p92XLllMeNUWKlJBatRpY5UoDlwaFf/45Ku+800IehPz5i0js2LFl48Y/7gp1el/q1GnDDHQh0aB2L/LlK2QXT02a1JRixUpJ27Y9xFsEPf/Dhw9Jw4avOSF6gJQrV1EAhB+hDgCAx1SaNOnsoo4dO2LXOXPmk3TpMsijKnHipFKgQFG7VK36hgwc2FUWLVrwwEJd9OjRLSxt2vTHXY/pfWXLVhAAiGrMqQMAACHaunWTU9ErasMHdU5YlSrPyIEDe2X27E+kc+em8vrr5eTNNyvI8OF93EMjXXQ4oM79evfdV+W118pKz56trclHcHbv3iGVK5ewamFERI8eQ65evSoPUtGiJW0u2P79e9z36c96nwY+FZ73I6guXZrZxdPZswH2Pr/xxgtOUK0u33zzxV3PmzVrilSqVMx+Xrr0O/v9nDr1r4Vb/Xnq1PHy1Vez7ed///0n0HPff7+tNG9eJ9jj0XMaNKibNGv2przyyrPSsuVbsmzZj4G2mTBhuA2D9dSnTwdp0aJeoPt27twq3bu3lGrVSkvr1vVl+/bNEtr5jxzZz6p0Ss9fj12rxwDCh0odAAAIk1bEcubMK+3a9bLhhseOHZZEiZJIjRpvOcHhuAWNaNHGS/v277ufo41Q9uzZ6YSchpI0aXILHSdOHJPs2QMP69Qgo8GgSJFnnDDztoTX5s3rZMWKpVK9+pvyIBUvXtqudbhllizZ3T/rsEwdnqkyZcoa5vsRHkOH9rRAVLduY0mWLIUT2r6Vc+fOhrh94cIlZNiwj///+8nnhMq6kjp1OqswTpw4UlatWuYEq1q27bVr1+y4dfhqcPR3lCNHHilZspzEjBnTnjtsWC/nvtzuim54XLp00RqgJEiQyAmGXZzQfcVCZmj0967v5Qcf9LZ5hwULPu38neQWAOFDqAMAAGHSeXZt2/Z03y5Zsmygx48c+dsagbhoMLnTZOS/+VHlygUeqqjNTbQLooa/WLFiOdsOlLD89NP3dnEpXfoFadCgeYjba7UwvGLEeMKCWlDatEMDrQ63rFHjTkVKf9ZA9cQTT9jtsN6P8NBK2YYNa6RVq65ORbSG3VeiRBmpWfOFEJ+TNGkyu+ixa/MYHZbqki3bU7J+/Sp3qNu6daNcv37dQltwEiZM5D4/VahQcasE6rlGJNQtWfKNBfURI6a45xumT5/ZqWS+F+JzdDs/vzsDyLQBjud5AAgboQ4AAISpfPnATUJ0uN/kyWPE33+9nD590u7zDETr16+26/z5Q/9wrp0dd+/eLmPHfipx48aVsLgapVy+fNmG9C1Z8rVVhQYOHBdsJ0kdDhle2rhDOzIGR4dZzpnziXttOD3v5s07ux8P6/0Ijy1bNti1Z6DRfcSKFbH9uJQo8ZzMnTvVgpyGz3XrVkqKFKkka9YcIT5HQ9yCBbNtuK1W9pRW3iJCw2OSJEkDNZBJkCChAHhwCHUAACBM2qDERT/kt2v3rg3z00pcnjwF5bPPJsrChXPc21y4cM6utfoTmsuXL9l17NhxJLzH4Qo9JUqUluzZc9nQw+XLfwy2Y6JWi8IrYcKQ2+hrqPv0049l27ZNdvtOxaus/Rye9yM8XFXFuHHjS2TQ45s5c5JV//S90qD97LMhh9yFC+fanDmtFOqQU32vdZ5jROl5RNY5AAgfQh0AAIgQbZ6hi2h36TIg0FppnnR+ljp//pytTRaSRo3ayJo1K2TIkB4ycuQnEV5vLmvWp+z6+PGjwT6eN29BiQw6D1DPSeek3b5924Y2utZSC8/7ER6u9+nSpQs2pPJ+aUVOK3NaodO5gFp9a926e4jbf/HFDKuEuoZ+uqqSEaXnoa8F4OGh+yUAAIiQM2dO2XWqVGnd9+3dG7irZb58he1ahyOGRufSaRjSxcgj2vlS6dBNlSZNennQtFqnoU4vTz/9rPv+8Lwf4aFNStS+fbvd9928eTNc4SpGjBhO2Lx11/0653D16uWydu3vNgQy6Pp2LhpUAwJOS8qUaUI9B10YPOjxuIabep6H3nfq1H/3X79+TcJzDkrPGUDEEOoA4D7pvJPvvpsf6IMY4Mt0yKOaN2+GExZW2pC9bds2y5UrV5yQtdMey5UrnxN8Ssr48UNk/vyZ8uuvS6zl//LlS9370SChtJpWvXptmTFjgi0qHhoNUBoUf/vtZ3vdDz8cZouka0ORB6148VIWdPbt+9PdEVOF5/1wDV/Vx7WJSHC0Gqjvhe5HK126VMOoUf2df2PCXrIhS5YcNpftTkfQn9z3a1MUne+nSyPo0MuQKqF6v56HVk3/+ON3m1unnSh1WKwGbm1oc+d1slv1VY9Pu3Lq7+yvv/YG2pcu0h4nTlyZOnWcLfugr6+/p7Bot08NnnoM+jvW8AwgfAh1gI/SDxW//LJI8B9dB0u70UXkW+CDB/fbmkk6tMpl1apltgaVy86dW2TcuCHWPhx4HBQr9qy1qtfqj65rdvTo3zJlynx58cXKgRbp7tFjqC1TMH/+p85/I4PtA7srAAX17rutrHuj7i+0ypTOD9NwOHx4bwswr7xS0/l5slX8HjRdr06XCogbN551w3QJz/uRO3d+G5o5efJoC0wh0fcsfvwEtlacrgenrf0zZ84mYWnevJNV2bp1a2H//p8+fad6qK+p77v+W+YZRIOj3Ud1+YLBg7vL7NlTbJmB7t0HO//+HbU5hEoXW3/99Xo2h1DX49P5hK+9VjfQfp58MoHzPoyX/ft32zl07NhYGjduK2HRSl2PHkMsMOrveO7caQIgfCI2cB2IBBO77fMvXSN1/hQZwzcp3lvph5LQJpjrN8sfwYjDwQAAEABJREFUfjhTHhRdmLZevSZSt26jCD1P/0et38rq/4A9W1s/SG+9VUVOnDjufBgZ5Hxg+G9R2x9+WCBjxgy09taTJ8+T++VqArBgwYpwddlTv//+i/Tv39leX49D6T60KcPcuXcW5dVKnTZEyJ27QKTN33lcLJ5yKCDg2NVqLcfkWCFe7HH5dw3eSSulX3wx3Qlqi91DHPHg+Mq/a/Au/JcNPCD6ba4uCOui33xmypTN+WbzXbut38Q+anRIkAa6+PGftIn1DyvUnTsXYK3I9Vt3z1Cn7b31/vPnz8qjTBfprVmzvgDAo0a/MNNKadWqNQl0gA/jv27gAdH5CZ5rDT3xRExJlCjJI72gqgY5pQFF147S+SARXWcponRIj76OVrj8/dcFekxv65AlnZvyuNB5K9pJ7+bNG7ZmFgDcKx1loPPTdGmDiI7aAOBdCHVAFNNhktr57cCBPfLDD185/+NtbPNKdM7FwYP7bCK6zolo0KCFTaJ30QYDc+ZMlZUrf5XDh/+SzJmzS/XqbwaqdHl6//12NsF/woQ5Ia4bpaFOmxtow4GpU8fb/JAyZV6UB0m7rSmd6/HJJ+Ns7lrKlKltToXOCalR420LdRcvXpB48e6se6RrIGno1Mrev//+41RAs1qb7qAL6up7qA1M/vprn81LcQ2f9KSvMWXKGFm/fpVVV4sXL2NzYyL6jXbQ4a6zZk2R3377SerUaWRzU44dO2yLMOv6T9pi3EXPTTv+6bwbbYqg564XV+dAALhX+m+5zjl8lL9MBBA5aJQCPALmzp0q27dvdj7wd7OmArqArXaN04nvGgKuXLksgwZ1dXcfu/OcabYQbv78haVTp34WWHbt2hrs/jX8bdiwWvr0GRFioNPmIevWrbLwkzFjFltnSEPdg+bqAle4cAmrCmoTBKWdz9Kly+i8F2kDbad0oWFtAlOxYnVp27anDdFs376hnDx5wr2NhiVtZqLn27FjH2tqoAEvqL59O1gwrlatttSq9Y78/vvPkdbwRIOp/o50fuLIkVMtpI8fP9T9uL//Bud310TSps3gbPedcw7vW1e5d95pKW+/3VQA4H5otZ9ABzweqNQBjwBdz2fUqGkSJ85/TRY8q3Jaoerdu70cOXJI0qfPZE1YtLqjoea999rbNqVKPR9on6621RqOtOW0hkPtahYSVzWsUKHidrtgwWJhhjqtmIWXztMLjs6nUxq+9DU3b14rL7/8qh23BswECRK5t0uTJp3s3LnV2lx7NlXRb6Nr1y4vX3450/1+6M9a8ezde4RV4JS+bzNnTnK/toaqXbu22XvjWmxXn/PBB+9L/frNQjzm8NLXGzLkI2vT7TpObcPuopVZPe+GDVvZMVao8Ip8++0XtgCwBnAAAIDwINQBj4AyZcoHCnTaTXHWrMk2fO/o0cPutZy0dbTavt3fAlj+/EVC3a+uDTRgQBdrQV258uuhbqsB7oknnrChnkoDlbb/19Dj2brbRUNghw7hn6OhVSgNLUG5KnDx4j0pRYs+I599NtFu63pLLVt2tVbcntutX7/argsVKubeh753utit59w7fX7hwsXdgU5pm21Prjl82qbcRc9V338dqqrvwf3QCqIr0CldtPfq1Svu27pQcKxYsQMdY9y48Z3fbfjDMgAAAKEOeAS4FqV10U6ZGip02F6RIiUsWHXv3tL9uKsbZEhDKV1u3bppi9aGp+Kk8+k0xLjmkj399LN2rWEvuFCn6yaNGDFFwitNmvTB3u+q1OkSAzqvTocn/vLLYjlz5rQNRdWhp57bXbhwzq6DnpPe1rmFLjqMUQNSaDQYq/r17w6b2jHuQdOwrQsx6+W558pb51GtUDZp0k4AAADCi1AHPGIOHz5ki1s3aNA8xCYlruqPBpfQJE+eyoKhLoxduvQLIc6t0GYjujCtXipUCLzNH3/8Huz8Lh0SGhlrsuk5uMKpNhBJmza9NRbRIKkVOF0uQLmGenqee6JEid370cddQzWVDqO8fPliqK/t2le/fqPv6vKZLl0medC0I91LL1W1xYr1ol5++TWpXr22AAAAhBehDnjEuLpBpkyZxn3fvn1/BtpG17vTypQOMQyp26WLzhVbseInGTKkp0yZMt/dQdLT6tXL7bpnz6Hu4Y5Ku0fq5dSpk5I0aTJ5EM6ePRPomHROnzY0qVWrgd3WoYlx48az7VSePHeCpK5h5wq9ly9flt27t0ulSq+69/PUU3lk//49gV7rxo3rgW7nzVvIrmPFihUlzQQCAs5Yw5cPPphkDW8AAADuBaEOeMRoIxStGi1evNCpNiW1dvzz539mj+mcMQ0r+riuJTdt2oc2J0vXctO1iDT8aMdM5ZqHpzp37icNG74mY8cOsgYjQekQS+2eqdU8T9Gjx7BQp49XqlRdHgRdsuHJJ/8LkhrUDh3ab1UsFw2arqqknqt2Bh03brBVGJMmTS4LF85xHvGTGjXecj+nWrVa0rVrc+exudZQZv/+3dY8xZMu36D7GjWqvzVY0fdPA652rRw0aLxt4xoau3btSqeimNiqirreoB6P3qfPdzWliSgN8NpMZdmyJbYunb7f2gzGcx4e4I10Pq9+maTzhR/UF0IAgP+wpAHwiNHQoMMBdS5Z797t5KefvpMBA8ZZh8SdO7e4t9NKlg6LXLnyFxk2rJccP35Enn22XLD71JDQpEl7W9Ra2/d70sW/N21aa50Zg9KmKRp0HuTSBjpXznN+nFbMtHLlOY9PG5y45tSpnj2H2fw7Dbs6/1Af0xDmGYa0kYquN6dBrlq1Utb18t13W931+rovrf5pSOzTp4PNZXR1wlQaIvV9mDx5tAVc9eKLle2YevVqI3v27JJ7pUtHaFj+/vsvLYDq8gZ1675sVVXPUA54G/2b/vjjEdbhNTLpWo6jRw+wL7Eii36R06zZmzb0vF69yrZWJgB4m3v7ehm4DxO77fMvXSN1/hQZ4wiA/1y6dEl++eUHmwPZq9ewu5ap8EWLpxwKCDh2tVrLMTki71N6FODftcC00ZCONtA5opFZedYKeY0azwdahuR+aIB7991XpVy5ivbf2/HjR63Kf6/Vd086z1cvqVKlkag2dep4+fzz6fblXd++gdfh/P33X6R//84yduwMGwniovO7dYRH0PdaOxHrvlavXmYV2fTpM9s0gDfeeFtwh6/8uwbvQqUOAKLAlStXrDKnnU1dtANoqVJ3hsC65hAC3kibHulIgkd9KLHOV9Yh0BpINPBok6LICHSqSZOaMnfuNHkUaHdjHRGh64Dq6Ix7dfLkCWnZsp78+usi5716U7p2HShZsmSXOXOmupedARA1mFMHAFFA50XqhyDt9Pn66/XsPv1wqdUNfaxAgftbIw/wFVpN++qrWdKoURuJbK51I3U+q6/SucfaNEqH8H/yyTgbbl+s2LNyLz799GM5ffqkfPTRbBs+rnT+s3ZZ9myyBeDhI9QBQBTp3XuEjBzZVzp3bmoLleswraeeyiujRk2TdOkyCPAomz37E6dis9jm8yZPnlLy5y/qBK/W/+/Mu0k6dmwsw4dPlnz57nSZ7dKlmTWC0g//Oj9V14nUYY/apMi1dMm1a9ec6tZUa7KiDYuUPj9atOg2pDOk4PDjj9/aFyI6J1Zfo1mzTmEuudKw4ety+PDB///8ml1Pn/61pE6dNsz96blv3rzOKn167LqmZsOGrSVx4iR2bsOH97HtFi1aYBedA/3uuy3dQx0nT55nzanUl1/OkkmTRsmCBSusWq8qVSrmvF8D5MCBPTYvsW7dxlZF3L7dXz7/fJps2bLRXuu11+pK1apvhHqeOidauwhXrVrThk1q1e5eQp2OLtCmThriXIHOhUAHRD1CHQBEEV2TLyILuAOPCg0XM2ZMsEChYeXQoQPy00/fW/XZs/FRUEuWfG3NkPr3H2PP0UZHqVOnkxo17lSrtTmUrpf5/vsfWGffCROGW/OoHj2G2OPBrc25bt0q57+jvha62rbt5QSnn6V79xYyZcqXNgw0JJ069ZU//vjNApr+rMFUtw/P/jJlympdcLXj7r//HpdZs6Y4wXO8tG//vhQuXEKGDftYBg7sKjlz5nMq8XXtHCNKw62+l61adZPMmbPLmTOnrTmTLgHTpEk7OXr0b/nww2F2HEE7F3vStUa106+u+1m06DPWLKtFi84SUX/+ud0a1RQsGPIogo0b/5AhQ3pYx2H9uwDw8BDqAABAhOzZs9OudWkVDTrPPPOce23J0Oj6m336jJQYMWJYMNLhx659/f33QQsF2rXW1f1Wq1N9+3Z0XmeXZM+eM9h9zpv3qS3/4vqCpFy5CtK0aW355psvrHKotGGJJw1L+hrHjh222xq+XNXx8OzPc8kVdeTI31a1VLqEg15ixHjC2U+ye14DU4c5atVew5j67LNJVt0cM2a6VQ+VdknWLsAhhTqdP7dhw2qpXftdu63DurULsi6Vo+9/RJw5c8qu9ZxCokNlNdjr/gE8XIQ6AAAQIbqkiFbRtCqjnRGfeaasO3yERteV1EDnotU417y227dv2XXs2P/tJ168O1W/S5cuBLs/nYe6ZcsGW2bERRudaBfHXbu22m1d/qB37/aBnqeBLbjhmeHZn9Kuj5MnjxF///UWvu4cd2yJTLrGn+d76u+/zgK0K9ApPa7vvptvx+35vrpoSNbHdIkXVaRICbvWIZkRDXXhUbFiNSccZ7xreCaAB49QBwAAIkTnnekQw8WLv7ZwM3bsYHn11Tet4+W9do/UOWbZsj0l3347z6k8vShPPPGErTOplSGdaxqcy5cv2ZqOOo/NtY6ki2spgdy5C9w1zDlz5mz3vL9Lly5Ku3bv2pDKrl0H2DqXn302URYunCORKXHipIFua5VO5xXqenpBachMmTL1XfdreNOw6ap86jb6u9N5dVplvZfj0WGgIdHfvWsOJYCHi1AHAAAiTIcV6uXWrVvWzEPXV9QlDCpXfl3uVffuQ6Rx4xpSrVopu62VPZ1fF1IV7MknE9hjxYqVumvdupgxY9m1NvEIq2lKRPanwxd1mKE2MsmTp4A8LDrnT+e0tWnT/a7HggZAF11YXRucVK5cItD9evw6JFWHoSZIkMju07Dq6eLFO0NWdQ6fypEjtwVtXRbh5ZdfFQCPFkIdAAC4Z9q5VQOQtst3zY+7V99887kTwApZFTC8tFKmlap7nbsW0f255palSpXWfZ92yQxKh0O6hpS6uIKhDokMur+w5MqVX/z9N0j27LndXTJDo/Pa9DzefPNd9/BLpcsbfPzxCGsIo/MFXcM5t27dGGg7va30/VA6FFTX0fztt59k9+4dFvJcXAERQNRh8XEgiuzZs0u+//7LQP9zBwBvoE07OnVqIl9//bm19p8+/SOr9Ghr//uh89O0q+Py5Uttvpr+O6nLHLho1UiXENixY4uz3Z0mJ9ruX7txTpo02p7zyy+LpEWLuvbzvQhrf9mz57Lref5trTsAABAASURBVPNmyNq1K21u4bZtm60itnv3f6E2S5YcFoz0/dElGpTOY9Mhitu3b7bz0k6UOtw0PKpVq2XBasCAzrZP7WrZr18nWzsuONrZU736ah13VVUvGsC1GqlDM5UujaBz4XQBcT2XVauWyZQpY2XatA/ltdfqBOogqh0ttbKnw091W52zp0NP3367qnUt1SDZuPEbNiwXwMNFqAMeEg1w2sLbZdKkkTYPRT88eBsdAjR69ABrQIA7ne8WLpwr90s/FA0Z0tOGRoVm1apl9/yBFYgMb7zxthQvXsYJXz9aE5Jt2zY518NDba0f3v1ev35NBg3qZus3tmxZT2rXfsna6SutCr7+ej35+ecfZOLEkXafDoEcNGi8EzDWSM+erS3kaDfL9Okzy70Ia3+6xpt26NRQpMepIXTKlPnWXGXTpj/c+2nevJN1++zWrYUFoNOnT1lA6tKlvy2mXrVqSfv/QocOvcN1XHHjxrNumNrRUjuCjh7d3+b/Pfvs88Fur8enc+l0SQhPOoRSl13Q0KfPVy1adHHe53dk/frVTmjsIgsWzLY18HTpBE96/LrwuAZD3b+em4ZSfa42R9Hfk/5/bsWKpQLg4bq32czAfZjYbZ9/6Rqp86fIGHantEfJqVMn7RtN/VBxL9544wUpX76K+3+SujivdlPTb1GD61r2KNO1omrUeF5atep617yTx5GuUTVz5iRZtGit3I/gFiYOjm5z/PhR+fDDmeLtFk85FBBw7Gq1lmNyePU3BN7679qjTAPH4cOHZOjQHnLz5k0nGEVuIxJEPm2ioiFcK3rhWeLCV/nKv2vwLlTqgHAaObKvDBv2vkQW7RCm30p7W6ADgAdBKz7abMVFhymmT5/ROl8GBJwRPPq0UqeVQF18HMDDxadJIBx0OIkOS9HhRQCAyJcwYWJZsuRrGzLomselFWld1FvXbMOjb8cOfylbtsJdQz4BPHiEOiAc5s//zFp1P/PMc+Ha/uzZAJtDsWHDausIpkMsg/Icsrdv325p3ryOzdOoWvUN9zYzZ052tpssc+f+aP+T1Pl3n38+TbZs2WhDQXXOg+f2uk/tTNa2bU/rRHfgwB7n2H+xuW+zZ39ic7a00YBO9K9fv5lkyZLdnqdzPaZMGeME11USPXp0myujxxLRKuKPP34rixcvtE5w2lGtWbNO7lbi2kTgww+Hyt9//2XHpWtPlStXUerVa+Ie0qpDUjt2bGzzU/Rcfv/9Z+eb+89sTalKlYpJmzY97D3Vyfnaplzn1ngO/9QhWtqwQed66IdBnRvTsWNf57X+a/et+9FW5HoM2oZdmyK8/PJrMmJEX/tAcvbsGTt2/RCpldSIDLfVJgEffPC+HD58UPLnL2rDUz2bDNzL+6zt4vWctKHC+fNn7QOTzjkCfE2ZMi/KkSOHbK6cDvHWOWTaWKRBg+b23ygefTrsEkDUYPglEAYNaNr9TINZeBfVHTq0p01Cr1XrHSc8Nbefz507G+L2WbPmsA//uiCsJ72dL19hC3Q6V6FXrzZOGNlr8/J0cvyHHw5zQtzPgZ6j3eMGDuwqRYs+YxPwtSPdyJH9LFzq7Tp1Gtr6Q/v2/el+Tt++HSw0VKtW245Zw5SrCUF4aXtsDUb6HrVt28saBHTv3sIWy1XabU3DpK5hpWtRvfTSKxY0teFBUHr8un27dr3c7baVhsLkyVPZ3BpdnFiHanl2m5sxY4J88cUMex0NgNoqvGvXZu5mAC5z50617nOtWnWzbn26wPGqVb9ad7muXQc6gayINSPRkKjvu3Z207AZGg1f48cPcc6vhoXqv/7a69weGmibe3mfP/98ul2efrqknZPSrneAL9L2+/pF1JIl62XBguUyatRUeeWVmgxTB4Aw8K8kEAZdN0mrKpUq3ZkjUKtWeQtqIS2+qmsAbdiwJlATkRIlykjNmqF3hXvuuZes45h2NtM5CTqHZNeubdZBTX333XwnjF2QMWOmu4POlSuXrYro2XFOQ2jDhq2c16tvtzUE6n1aFdNvwpVndUvXPdLX8TxeraJpxUmreeFde2jevE+tIjZixBS7resfNW1a23n/vpBGjVrbffrhzKV48VIWcLRqpQ1kPOkiuxqMgnrhhZedQNvWfq5R4y0LO7ouVo4cuawS+PXXc533sbwTwPrYNgUKFJG33qpibcf19Vw0+GoXOW0PrrSyqIv36j5VyZJl3dtqKNZulKGFcpfWrbu7G5xoVVXPz+Ve3mcNlfo3oefUtGkHu+/ZZ8tZRVD/Fv47xktOqLzpvh09egz3uQEAAN9HpQ4Iha4jpOswvfhiFRu2qDRIhWbLlg127blwrVadYsWKHerzSpV63tas0+GFylW10/uVv/86q+Z5Vq6eeiqPTUwPutZdhQrV3D9rm+m0adNbFWvBgjly8uSJQNvqflXRoiXd9+mcFj334BbUDY6+vp635xpVWrHT49MOny4aarRN+WuvlXWOsagFssuXL921v/Llqwb7Olqlc3G9n67fh76OBjvPY0iaNLlTMUztvEfbAu1Hh1Z6hh4N3Rrchg7tZYFcq24u+t5pZVDbeIdGh2l6dqyMHTuOXL16xX37Xt5nHS6rgVwrh56efDJBoNudO78nr79ezn3RiiAAAHh8UKkDQvHTT9/b3A6du+WiwSE0Fy6ct+u4ceNLROgHfK10aVVJQ4aGOr1Pg4nSyowOZdQwFNSpU/9aeFEaLjwnqevtAQPGWRVLmxBMmjTKQk2zZh0lUaLE7opP/fqv3LVf19DJsGgw0yGOS5d+ZxdPqVKlsWsdJtmhQyOn4vmqvPdeexty2r59w2D3p1WziHK97zrUVC8SynkE3b+uL6VVsVWrlkm/fh1tcd2GDVtL2bIv2eOuuYf3417eZ9c5ub5QCIlWNT2/bAhrewAA4FsIdUAoXFWmd94J3J55zJiBFpB0KGRQ2sBEXbp0wQlkySQitAnG8uVLpUWLztZtUxd0ddEhibrod5s23YN5zdBDUJo06SzEqW3bNlslZ8KED6Rbt0HWAEb16zfaKoqe0qXLJOGhlSN9brFipe5aty5mzFh2rYvtanVN5wPGjBlTIpu+P0qbKuTOnT/QYzrMMTRaVaxYsZpddCjjxIkjZPDg7tagRSudkeFe3uf//pYuSmiyZXtKAADA44tQB4RCux/qPC4XHSrXs2drq9xpAAtOjhx57Fo7WrqGSmoVKOgQyeBo85OvvpotixYtsMqOZxvvXLny27ys7NlzO1XAuHKvtBtl3ryFZM+eXf+/XciuY8WKFWjIaETlyVPQKoYh7SMg4LSFK1eg06Cine50iGZkyJgxq81L086Q93Me+t5WrVpTFi/+2prJRFaou5f3OXXqdHZOOk/Tk/4dAgAAuBDqgFBoKPOcw6aVMpUuXUZrzhGc7NlzWnCaN2/G/7tappZx4wY7H8SvSli0Bb8Gn08//diqRK6hi0o7M+oQygEDOlsTFD0WrRZqy++3324a4j79/dfL2LGDbYhhtmw5LVzp0E7tQqly5cpnnRVHjepvwyK1jfjq1ctt6YFBg8YHu08d3qfhbMeOLVK4cAmrBOrSADqcctKk0daURAOedpXUypyGGH3tTZvW2rIH+nwNrjqUVZt+aBMSXaLgfmj1S98XXQIiWbKUNhdOg+vSpd/K0KEf21DT4Oiw0W7dWtj7XqhQMav4abCOEyeuU/ErIJcvX5bevdvZ77x1625yr8LzPrsqrjoEV9fs0mG02lxGm83okNwiRUpYwxydn5c+fWYBEDn0i5Iff/zGvjzTf7cBwNvQKAV4AHr0GOpUWBJIs2ZvSu3aL0nBgk9bSAuLDgPUzovaHMOzA6PSEKAdG7U7Zt++HWX06P4WSLS6FxptsqFrwWmTEl0qQINWgwYtLGy59Ow5zCptGj779OlgjTuCDqP0pPP0tFqpyxG4WvJrINVwsnHjGqtmajDNmTOfO3zoMeg+dZ02bfWfNm0G5/ozW/pAF3ePDLVqNZA6dRpZoNblH37++XtrGhPaHDN9z7VbpoY+7eA5YEAXq6aNHPmJhWpdt06DsX7gu19hvc86bFTfx8mTR7vnJur7pt1NdTholSrPyNGjf7NmFx4L33//ZaT92xCULn2i3XNddu7cYkukRHQpFwB4VIRv0S0gEk3sts+/dI3U+VNkpOU6vEPjxm9YI5oBA8YKItfiKYcCAo5drdZyTI4V4sX4dy3yvfHGC7bciecXUJFFv3DTURjduw+221qpW7hwjlXndaRFZNEmSDqvOKSRAg+advLVYe6pUqW1pXLwcPjKv2vwLlTqACAUOvdPh0hSHQN8lw4n1+HbkRnoDh8+ZOtkbtr0h0QVnRvcqFGNcK2zCcC7MacOAEKhi4jrshLPPPOcAAAAPIoIdQAQCm1uMmvWDwLgwdF5xBMmDJcNG1Zbx9dXX61z1zb6+PLlP8rcuT+679O5qf/++498+OFMu7116ybp2LGxdO7czxoM7d+/W5InT+VUq1rfNU85qEqVitkc1rp1G7nvW7HiJ2tO9Oef26wb7bPPlrOmUDpc88MPh1oV/8CBPdZoqVy5ivZ8nXOsa2UuWXJnHu6QIT3tosO39d8T7YY8ffpHsnbt73L8+FGbR9uxY19bpzQkXbo0s0682bPnkjlzPpHMmbPbccydO9WOQYdYZsiQRWrUeMu9vqYuxXP06GH7uU6dinb9zTcrbc7w6dOnbH7z+vWrJHr06FK8eBlp2bKLxIjBx0LAW/FfLwAAiFJDh/aUnTu3WlDRNR21a+39DBmcNGmUdZktWLCYTJ06zppETZ/+tXs9y/DQNT31eVql79SpnwVEDY06T0277WrA0kZUCRIkssdmzJhgwU/nAepyOPrYBx/0dgJVQ2uWpcvRKN1OG7XodrVqvWONnbp2bSYTJ35ujZtCsmXLBlm58lcnrLWwoKojCHQJnZIly9nw0VWrlsmwYb2c+3JbR+LOnfvLb7/9bM2xevQYYt11NdApXatUOw/rkFNtJDVz5iSbc6drpKr3329njZxcDaMAPPoIdQAAIMroOowbNqyRVq26urvB6hIeNWu+IPfqvfc6yPPP36lOvfNOS+sm+8svi6xDbnhpRUyXMunde7iFrVKlAnca1uVGXHQZFw1cWvnSsKZNWPz87rQtyJAhs3ttSl3GRZemee658tZ1VxUoUMTm3ulSJrqfkBw4sFfGjJkuOXPmdd9Xo0Y998+FChW389Q5fBrqdBkVfY7SrrtJkyazn3W9U+2G7Pl+a6Xxgw/el/r1m1mlVKuPuiyNBmtCHeAdCHUAACDKaAVKuYKP0kpYrFix5V55VuQ0zGj3yX37/gz383WI5MaNf0jFitVDrJ5pMJo6dbxVtC5evGD3hTaE8s5ztlqwK1LkGY/jS27ddXWIZ2ihTtck9Qx0SkPcggWzbQimDglV2twpNLrOpSpatKT7Pt2vPl/PRauKI0ZMsSGxuu4qAO9AqAMAAFHyXF/HAAAQAElEQVTmwoXzdh03bnx5UOLFe9JCSnhpMNJhlk8+mSDYx3fv3ikdOjSSSpVetWGeumB5+/YNw9yv61x1zp1ePOnyB6FJlChJoNsLF861eYZacStevLQNr6xcuYSExRVA69d/5a7HXMeQIkUquwDwHoQ6AAAQZRInvhNWLl264B4iGNnOnz9rc83CS8Oczj9zBaCgvvpqllUSdQ09nc8WXq4KYoMGzSV37vyBHtMhkBGh8/KKFCnhHkJ548aNcD1P5yyqfv1GW0XUU7p0mQSAdyLUAQCAKKPNPtS+fbttLprS4Y9BQ4ou4h30vtOnTwa7z5s3/9vu4MH9Njcs6NDFsOTNW0j8/dcH+1hAwGkLYa5Ap5U97UD51FN53Nu4OknqubhkzJjV5qxdv34t0HDTiLp9+7YdQ8qUpd336dDJoLSzpbp162ag81IaWu/nGAA8Wgh1AAAgyui8LV30W7tA6jDGFClSy7hxg+XatauBtsuSJbtTcTtn88cSJkxsc8n++muvEwQz37XPDz8cJm+99Z6zXSL59NOPJUGChPLSS1Xdj+tQxUOHDtgwyhw5cgV7XNqJU4dU9u3bUV544WXZs2enbN++WYYMmSDZsuWUTZvWyo8/fmvdIxctWmBz5bSjpAZIfT2tiOn1mjUrrEKn4a5w4eLWcXLWrMnO4yklbdr0zn53WbfPoUM/trl/4aHz/LT7pu5bm8qcOxcgc+dOcypvcWTHji02dFSXVsiSJYdtr8ep2+txaAMVXVph1Kj+NnQ0btx4snr1cntfBw0ab9tPmTJWtm3bZNU8PQcAj75oAgAAEIV69BjqVLASSLNmb0rt2i9Zs47MmbMF2qZs2Qry+uv1pF27d+XNNytYdey11+oGuz/tQPnFF9OlZ8/WFrY0MGl4calWrbaFOl1KIKRhi7p+nIaaY8cO25ILuq6cHoNWv3Q9Oh32qGu9jR8/1AlnGZzrz5zKWRrbr9JKnS4loGGpc+emFrqUduCsU6eRhdhevdrIzz9/LxUqVLNwGBFduw60IaWDB3eX2bOn2DIK3bsPln/+OepUAq/bNhqYmzbtIN9++4W9loY31bPnMOuIqeFZ1/rTKp9rGKdasWKpLTFx+PBBAeAd/AR4yCZ22+dfukbq/CkyxhEgNDppX4dchffba3ifxVMOBQQcu1qt5ZgcK8SL8e/ao8G1+Pjw4ZMlX75CgnszbtwQ+fXXxU4w/okFye+Br/y7Bu9CpQ7AI+nw4UO2dpOuuQQAeHh2797hVA9fIdABXoRQBwAAAHP16lUbjvnqq3UEgPcg1AGIVLoOk84xadSohlSrVlratGlgXe086byZQYO6BbrvtdfKyqRJo+xnXb+pYcPX7OchQ3o63xgXlXXrVgkAhEbn4Q0b9vFd8/EQftoVc9GitaxTB3gZQh2ASDVwYFf55ZdFUrFidWnbtqd1YNMOcidPngj3PnTCf6dOfe3nOnUa2oc0ndQPAKHR5QK0Tb9eA8DjhMHSACKNdkvbuPEP6dZtkJQt+5Ldp+22a9cuL19+OdPaZ4eHrlXl53fnO6cMGTKzlhIAAEAoCHUAIs369avtulChYu774sSJY4sLb9u2WQAAABD5GH4JINJcuHDOroMOfdLbJ0/+IwAAAIh8hDoAkSZZshR2ff78uUD3a/OUBAkSCQAAACIfoQ5ApHE1M9myZYP7vsuXL8vu3dsDDcnUBcVv3Ljhvq2h79q1q4H25Vof6ebNmwIAAICQMacOQKTJnTu/PP10SRk3brD8++8/kjRpclm4cI7ziJ/UqPGWe7vMmbPLxo1rbD2kM2dOyUcfDbMumZ606pcgQUJZs2aFJE+e0sJd4cLFBcCj59q1a/Ljj99Irlz5JWvWHHKvdu3aJmfPBkjx4qUEABB+VOoARKqePYc5H8hKy/z5n8ngwd3l3LkAGTRovHtopnrnnRbOB7+nnKBXTpo3ryOlSr0gOXLkDrQfrdT16DFE/v77L+ncuanMnTtNADyadu7c4nyZM0QmThwp9+Pbb+fJokULBHesWrVMhg/vI1Fh+3Z/Wb58qQDwDlTqAESq2LFjS8eOfULdJmHCRNK79/BA9730UtW7titY8GnnQ+LnAiDynThx3IZCJ0qUWO6XVugaNmzlVOsLyP3QodvVq78Z6L7IPE6XW7duyZEjhyRVqrTyxBNPyKPK33+9rF+/SqLCp59+bNfPPVdeADz6qNQBAPCYOXz4kLz1VhXZtOkPiQwxY8aUmjXrS968BeVeHTt2xAJc/vxF5EEdp8vixV9Lo0Y15Ny5swIAvoBKHQAAiHJalXryyQT3NScPAB5XhDoAAB4jI0f2kyVLvrGfhwzpaZcBA8Zak6OtWzdJx46NZcqU+TJr1hT5/fefZdy4z2T16uWyefM62bfvT6vKFSnyjDRs2FoSJ07i3m+lSsWkXr0mUrduI7utz//tt5+kTp1GMnv2FKcSd9ipwhWVVq26SooUqe46Lm2SonNrXU2TQjtObZw0ffpHsnbt73L8+FHJk6eAc9x9JUmSpLa9Nm4ZMaKv7NjhL2fPnpH06TNJmTLl5Y033naO+zU5evSwbVenTkW7/uablRIrVqxg36+VK3+V776bLzt3brXQ+dxzL8m777a041ywYI58/PEImTTpC8mYMYv7OToPWNft/Oij2Xb7xx+/daqDC2Xv3l12LM2adQpU1ezSpZk9P3v2XDJnzifWTKpXr2F3HcuVK1fkww+H2lzjAwf2OOebTMqVq2jvu+t9c/0OO3fu55zXF7J//25JnjyVU5lsLSVLlpWI0vnMOtfxypXLdu4tWnSW6NGj22P79+9xHp9qx6PDWTNkyGJNscqWfcn9/NmzP5Fff13s/J6OWNMr/RvQY3GtZ6pz9z7/fJps2bLR/p5ee62uVK36hgCIGIZfAgDwGNFg06lTX/u5Tp2GMmzYx+7lSFwGDuxq82PbtetlISRTpqzOB/UKTvgYYAFi48Y/ZNq08WG+ln7Y17lZjRu3dULaVDl4cJ+MHz802G3//HOb5MyZN1zHOWPGBPniixkWgtq06WFddLt2bSa3b9+2x7/8cqasWvWrVKtWy7l/oA3pXLVqmYXBzp37y+uv17PttBnT8OGTQwx027Ztlv79OzvvRRxp3/59KV++ihPkZjuhd4w9rkFRabh00eC1detGJ0CVs9vr1q2ygOnn5ydt2/aSlCnTSPfuLWyoqSedT6hBVUPwK6/UCvZ49Hei51y58uvOPobISy+9YqHp559/uGvbSZNG2fnPmPGtdSbW36l2JVb6+6tZ80WZOjX036GGYg1dzZt3kgoVqsn3339pr+eiHY5z5MgjtWq9Y++z/p0MG9bLHZr1ufq7KlCgqHO8g20f27dvtg6n6syZ0054beME1L3SpEk7efbZ553QOsz5MuBnARAxVOoAAHiMaEjz87vznW6GDJntA3dQWlFp27an+3bQCs+RI39b9SUsuh7lkCEfubvflihRJtgP7PohXz/YN23aIczj1ND09ddzrYGHqylTgQJFbO7d2rUrbTkErYglTpzUvZSK5/HnypXPXktpSEyaNJmERKtm6dJldDd2KlPmRQtnn38+3YKMPleDqL6uhlClAU8bsTz77J1QN2/ep1ZBHDFiit0uV66Cc561rYqmFSsXPaYxY6YHCrbBeeWVmu6f9Vy1kqjNVDRwenrvvQ7y/PN3KpHvvNNSli79Tn75ZZFz3A3kn3+O2Xv+11/7Qn0tDaB67tqNWM/n5Ml/nKrdFxaytVqnTa9q1Kjn3r5QoeL2OjoHMk2adLJnz067X+dbanX2mWees9d30QroxYsX7Lz19620Iqjdk0uXfkEAhB+VOgAAEEj58oG70Z469a8Nf3zzzYpOtaWoVcIuXboQ5n50SKDncibaxfLq1St3baeVLe1CmTNnPgnLrl1bLdjpEFAXrRilTJnaqn1Kw6MGl6FDe8mGDWssZEWUVvW0oqVdeD1p1e/69es2HFNp2Nm2bZNcuHDebq9bt9ICTObM2SzUagXO81g1FD71VB47D09a5Qor0N05/202vPO118ra70KD0+XLl+7aToO5i4ZP7R6qw2dVxYrVrEIZVqdifV810Llod1MNgzrk1UVDnC5NU7VqSalevbTdd+nSRbvW5W3UkCE9LFBevnw50P79/dfZe+UKdErfmz//3G7vHYDwo1IHAAAC0SqXi35Ab9fuXUmdOp107TrAqluffTZRFi6cI5FFQ50GmpCGQXpyhSedc6cXT64hjS++WNlC2apVy6Rfv46SIEEimwPoOdcrLHreGgbjxYsf6P748RPYtVatlA7B/OSTcTbMUqtwf/zxmw2LVBq2dEioBh+9eEqVKk2g24kSJZGw7N69Uzp0aCSVKr3qVOLaW1OZ9u0bSnjEi/eke9ijBst8+QpJROmcQnX69ElJmza98zcwVyZMGG7zJDXA6d9N5col3NunTp3Whs1qt9HJk8fI2LGD5dVX35S3325qx6BVOv2daTgNSr9I0KAOIHwIdQAAIETLlv1oVS+dT6cNSR4ErWbp0LzwcFWgGjRobnPFPGnjEKWBQatRerl06ZJMnDhCBg/ubtUzz4YmodEAo3PYXCHSRRugKA2KSsNZ9uw5bQhk2rQZLDiVKvV8oH0UK1ZKqlSpEWg/WrWMqK++muUE39g2/0wb1kTE+fNnrRHN/XC9F65wp/MaixQp4T634KprOmxWLxqQf/jhK1ukXqu3Oi9Qf5dXr16VNm263/U8zy8WAISNUAcAwGPGNaROq1lh0SYkShfqdtE5a5EluPl0LsEdZ8aMWa1z4vXr14KdDxhU3LhxpWrVmlYt0uGHGupc3Rtv3Qr9/LUqqUMrPWkA1ePyfG1tiqKVS61marD0HEap+9CqU3iONSwBAadt/65Ap9VE7TqpQxaDunnzv4B18OB+W5MvPMM7Pd24cT3Qba2oaqDTOY5agdTjSZmytPvx0P4udCiuhj+tarrm2umi9f7+G5xQnNt+TwDuHaEOeESdOnXSJtzrMCKdawIAkUUrJQkSJJQ1a1ZYtURDU+HCxYPdVrstqnnzZjjblJANG1ZbV0id16bDAXPkyCX3I7T5dCEdpzbemDVrsvN4ShsGuGfPLlm69FsZOvRja97RrVsLCz+FChWz53311WyJEyeuzQlTWbLcWQtPlxrQ89NttIoXVN26jW24Y79+neT55yvZ8gDaJEW7SurruGhTD+3y+M03n1vb/6D70CGSkyaNtsYmGvB0TqJW28IKejok8/z5c9aIRZdyyPY/9u4DvOmqi+P46R7sDYosAdlQ6h4IblwooKK4kKEIIiigLJkCAipLZYMDRUXFCS5AUVy0pYCAIFNkySizpfO950LypqUt3U3S7+d58iTNTtom/9//3Htu7XoSFfW7fd46LHTx4k/s70Ebnmho0/fKQbtIPvTQ4/Z5agdSveymm07PldTrazdM7QKq1cyMaPjSyMwDkwAAEABJREFU563VOF3WQpvcaIXUsXyCvnf6u9E5jEePxtjlD7RT6Pr1a2xlTpe1WLNmlalcXm/DtC6LoUHUMcdQ30dtejNqVH/7O9Wq3ddff2rnF+oQTQBZR6MUwE3pZPvZsyc79ygDWaV75bWphQ6Zc1i5crlMmDBMAKWVJm3nr0sOaNMN3RjPyKWXXiU9ez5ndzKNHj1Adu/+x65jpzuctMthbmU2ny6j56kdFLX1vwZNbYn//fdf2nb5GnR06KU2ANHGINp5ctSo5+x9v/LKbOc8Nh0uqZVB7eSot9fAkh4dbjpy5CS7xp42+3jvvTkmBN1l5+e50kYfWr1yHXrpeh+jR0+VyMhfZfDgXjZgaYC94IKaci76HmtlTJ+jBlddTkKrXbqkgi4NocM9p05923ap3LlzW6rbajfMDz6YZx9Tg58G3tDQYvYybUSi1//xx28zfXxdxkID90svDbZr1WkAdO1eqcsY6JBOHdqqaxFqB1BdumDfvt22mYz+fNllLeSHH76RoUOfsVVP7abp6Gypz+fVV+fa6w4f3lcmThxpK4C6tAGA7PERoIBNH7Al+pr2VZpUrB4i7kI3VH744fSXmw5r0SE0+qVz772PnrWh8dtvP9lhNtqBTPeAXnzxlXL//Y/ZLmFpOa6ra/2UKFFKLrzwIunS5WnzZV79nM9p/PihdhL5sGEvZ3gdbQet8xOU7jnVSeU61KdTpx6pOs516HCTXQ9I6UaPboDo4q66wQDv89NPS+3aWjNnfmg3NJU2M9ANqwULvhF3smTWzpiYPafa9JxU90fxYO74ueYpune/386nozKTNxyLj2t3y4yaoej3gX4v6CLqriENecNbPtfgWRh+CZxRpkxZGTBgtA1SOrTo00/ft3Mnxo59w1kt0wVex417we6t1L2luldWh46sWPGd/QJ1bcusC7TqcJxWrW6xDQZ00VedVL5s2eIsbbzoY9911/2SFdqRTjubaYvsTz55z07YnzHjw1TDg3T4jH55a6cx3TOuoVH3fmsABIDCoEP0tNKjjURQcLRSpxU4rToC8A6EOuCMgIBA5/wGXahWF5ydNOlFG4B0nSJdbPfVV0faIS19+gxx3k73MHfrdo+pikyUESMm2vN2795l53tooNPA5aBzMtK2x07Pnj3/2vCl6yFlhQ7l0dbROkxK20r36vWInbOhe2EdtJOY4/Xpa4iPj7dDmXQ4j6fN2dMNQe3Ip40A0i64C8Bz6AgD189TFAwdPaJDK113/AHwbIQ6IAO1atWxx9pZTEPdN998Zsf9d+jwWKrraYVPw5qu16MT2nX+w3fffWFbO+skdVeONtDnohU3HQaqaxBll3ZBK1u2nO0mlxltCrBsmcjevf+mqjBmxDGkp3//EfLZZx/YhgEVKlSWLl162RDsoI0M5s173c6/0QVqdT5J377D7XNyaN36Ulu93LZts21xrY0E7rqrQ4aPrZVTnRuj83d0Ir0OM9VD48bN7eXPPdfdTsLXSfvvvTfbvLY6MmTIONt+W5+LTs7XSqlOvu/Va2Cq91WHfunr13kgDrqor1ZjtZFBdl57WtokQKu4vXsPth3f9PUuXLhUDh06aOfEaEVVq8A650TnLLku8vv333/ZHQO6U0EbD+jfoD4fbXagVWB9TdrJT/9OtOmAzvHRv0UARZt+tuvacOk1fnFw3eEHwDvQKAXIgA5NVI61cjZsWGvnqVWtWu2s6+o8NvXnn9H2WEOIdl7Trmw5ofP1tHGAo8NYdvn4+NpmAZnRsKr09en8iocfvsMGl3OZMeNV27HszTc/t2tEaQc1DUwOOuRUh5lqwHr66UG2Hfrzz3e3k99dLVgwx7xfq+WppwY4O6GlR9td9+vXzTYEeOutL+SZZ16w4blTp56phrHqcFUNcNo84c4777Pn6XNbunSxHWKkwUrfT+1Cd+DAfsmJc7329OgivXq9iy++Qp59dqg9b/jwZ+Xnn5eZ++og993XSX766XuZPv0V5200jA4c2MM2Z+jeva9tNqBBUrvGKQ2nupddg7FWWiMjf5O5c6cKAOhyDzoqQ48BFB1U6oB06HICOnxRq0vaCEVpONH20ulxNEnRDXjHdR2L4OZEdhbiTUufu7bMvvnmOzO8js6nWLZsia0y6Re/Di3VTonaEvtcHn/8WVOZvMWe1mD17bdf2OCk8/W0w5rOMbz22htt9znVtGm4qVjebltyaztvB32vtOtZSMjpxhJpF/h1bJBoJU+HCHXu/JStaunr0o51Ghxdm8hoZXLSpHnOdZg0hGvY0XmSLVuebjGubbc7dLjR/m4ff/wZya7MXntGdN6lPndt1600pGpof+qp550L9urfyvjxL8gjj3S3r/vLLz+yt3vttfnOhZY1TDqkrQ7q709/nwAAoGgi1AFn6By2m2/+/5pBuh7Q6NGvORd5VRlVv85VFcuOzBbizYwODdV20boMgg7Xu/32e1Jd/t13X9qDgwYvHYqotKL4xhvv2dudiyNkqHLlytu24ToMUOmwUQ12rpU3Dbw6VPKvv9alCnUtWtzoDHSxsbHSrl2rVI+jVbiOHbuYCl+yBAUFp1raITS0uJw4kToEavXKdWHdVat+sce6TpWDPl7dug1tJTUnMnvtmdFW6w7R0X/YY8fOAqXPW+c46sK9OswyIuJXOyTU9fFcaWifOXOSua9Vzh0JwcHBAgAAiiZCHXCGo/ulDrvUZQJ0UVbXOQk6TFGHw6VHN7KVozqn1921a4fkRGYL8Wbk0Uf/Hxo0oL3wwngbOlw5ul8uW/a1XdNJK02uw3MccwizS7tuahBVjmrbK6+MsAdXGppdOYa1Kg1bL788K9XlFStWtsc6zFCXm9CDBlFd1FbDjGO+m0PaKurx40ftcdohSPrzrl3bJS+4vvaM6JBP12YE2l1VPfLI2ZVUx3t07NiRDOdf6hDMPn0es8tuaBMeHfr79tvT7dIZAACgaCLUAWc4ul/qQQOEdrPUYW6OCkj9+o1tmNDmH44FbB20QqYaNWqW6rq7du1Mdw5eZjJbiDcjunFfvnwlWxHT6k56lUNH90utQOocrmnTXpbhw1+R3NIAoi3JlaOy9OijT9o5Z67ONRzV8d6lpb+Dm266w64lqAd1661tM22sohzr9On8O62oOWjwLFkybzq+ub72rHI8L+2Umra6VrVqDed19O8sPdr1U4fK6nw6bUIDAABAoxQgHU8+2c/Oi9MKiMP1199qj7UZh7bUd9BOhjqvSpcTcFSFHNd9883XbTdIB20W4qjUZETn0zm6OmaVVvV0kVmtbp1rKKguqaBdOX/99Uc75yy7dBkBhx07ttp5eI5hj9WrX2jfg4SEeGdAdhyy0mEzPTExh+37O378DPn661X28PTTA1MNx0yPo3mNvp8OOsxz06Y/Uw3JDAwMsp1KHTT0xcefSvc+M3vtWdWo0enFgDW0p32PHNXVBg2a2kY2jgqwK/27VJUrn+88T4dtAgCAootKHZAOHXap65/pkLbbbmsn551XVapVq2nneGmb+t27/5E77rjHzmPTxiAapFyHA7peV4dsapfDkJBQu6C5zjFzXbvOVU7n02WXNujQ5z158miZNesj+zqGDu1j1+br1WtAprd97bVxNhTqkMK33ppmW+xrJU1p5Ukbgmgrfq0c6lDQzZs3yrfffi4vvTQtVcUsq2JiDtnQtXz51zZU+fn529+Ho+KVEa0UXnLJlTJlyhjboVLf99NDFH2kffuHnNfT5Q8iI3+1SyVoYHr99XEZdh3N7LVnlVZx9XnpmofarCU0tJj88ssPdtjv6NGnO1hqUxT9/Qwe3Ms+V602LlmySIYNe8V2FVUffvimNG9+uURE/GLnCOpcxk2bNpjKYX3n0FZtTlOqVBn7fHV4qt6PnqePn5fzQAEAQOEi1AEZ0DlnGiR0AfKXXnrDnqfNO3SjWtcqe/318XZjWRtedOjQ6ayQ4biurl83depYu9Gt1RjXNvxp5WQ+XU5olUvXNRsxop+8//48W1nU4aK6IO25Qp2G3Q8+mGcrVdWq1bJhTYOJg87b04qkhg6tNOlSBNooJCuLrqdH159r3fou2xFSDw66sPtzz43MNJwMHjzOvvcLF75tG4poyNTg5Pq76tSphwnTh014amWH4GqgdiwdkN3XnlX6vDRQa+DU6qGum6dzOB30Pl9+ebYJfiPM9cbY8HjVVa3ssVaEdU27Tz551wS9T22FdtashbaCrOv4aajTQKtDM3UIsWrf/kG54YbbbFAcMuRp87hv2+sBAADvwK5aFLjpA7ZEX9O+SpOK1c/dabGoeeONCXY9Mh1qWNC6dr3HzskbNWpyupc7FuCeMGGmDRKF5eTJk7J06Ve2mY0uMH711ddJfnOX1+6NlszaGROz51SbnpPq/igejM81uNJutnv3/mtHbaDo8ZbPNXgW5tQBbqRSpfPkuutulYKmlSkd/qcNSNyJVjd14XFd180hNDTUBLnr7WmtsAGAu9Huv8OH9xUAKCgMvwTcSNu2D0hh+PPPaDvnLKcLnucXnaOn8wzffXeWc3iizq/T+WV6WdOmlwgAAEBRR6gDYBtnzJ//VabX0eYx48ZNS7V2X0EYOvRls9d7uPTv/4RtYKLLSVx0USN59dW52V4uIqcK67UDKFytW19qlw/Ztm2zfPXVx9KxY1e7PMu3334hO3ZssR1wdf7qo4/2kDp16tm1Jh966Hbn7W+++WLbHGnixLn2Z92B9v77c2XNmki7Nmrbth1t0y0AyC1CHYAs0aUKtNFLQdPmJmkXJi9ohfXaARS+BQvm2M+Ap54aYLvlxsXF2h1ht93W1i5Ro2Fv9OjnZfbsj22XWd0B9N57c2TXrh3Sr99w57qYhw8fso2KtGmUdkvWLsraUVdvc8011wsA5AahDgAAIAPaOVdHBoSE/L8JjlblHDSkDR36jF1bUtfj1B1AixcvskupuO4M+uKLhTYETpo0z7lupwZE7c5LqAOQW4Q6AACADLRocWOqQKedLXUtzhUrvjPVtl12CReV0VIoDtHRf0jFipWdgU5ddFFDG/Z0rrC/P5tkAHKOTxAAAIAMlClTLtXPY8YMlL//3ihdu/aW8PDLbXfegQN7nvN+tEqnc+50nl1auqanLikDADlFqAMAAMiCXbt2ysqVy+XRR580FbwbsnXbChUqyalTp+TppweedVna4AgA2UWoAwAAyIKYmEP2WNcUddiy5a+zrqdDKZOTk1KdV79+E4mOjpA6dRrY9TYBIC+x+DgAAEAW6Hw4XSNT18pcvfoPWbRogW10otatW+28nnbJ3LPnX/n552WyfPk3cvLkSWnT5j47N2/UqP72tr/99pOMGNFP3nprmgBAbhHqAAAAsqBUqdImiE20XSuHDu0j3333hQlpU6Rz56dkw4Y1zuvp2nM333ynjBs3RF5+eZi9LDS0mO2imZCQIMOH95WJE0faJitXXXWdAEBuMbwSGCIAABAASURBVPwSAAAgHYsX/37WebpMweTJb6Y6r27d+ql+DgwMlD59htiDqypVzpfx46cLAOQ1KnUAAAAA4MEIdQAAAADgwQh1AAAAAODBCHUAAAAA4MEIdQAAAADgwQh1AAAAAODBCHUAAAAA4MEIdQAAAADgwVh8HIVi25pjsm9HrAAo2uKOJwULAADIFUIdClz8iaSJW6OPVhcUaev/Xdypcqn635ctXmOnoEjz8fffIQAAIMcIdShwT02uO1dQ5IWHh19vjuZERET8JAAAAMgx5tQBAAAAgAcj1AEAAACAByPUAQAAAIAHI9QBAAAAgAejUQqAwnIgPj4+SQAAAJArhDoAhSIlJaVsQEAAn0EAAAC5xAYVgMLyT3JyMpU6AACAXCLUASgslXx8fEIEAAAAuUKjFACFwgS6UykpKUECAACAXKFSB6BQJCcn7xHAi2xbc0z27YgVAEVb3PGkYAEKGKEOQKHw9fUNMMGuvABeIP5E0sSt0UerC4q09f8u7lS5VP3vyxavsVNQpPn4++8QoAAR6gAUipSUlBgT7EoL4AWemlx3rqDICw8Pv94czYmIiPhJAKAAEeoAFJYYcyDUAQAA5BKNUgAUClOp25mcnOwnAAAAyBVCHYDCcsDX17epAID3OJCQkJAoAFDACHUACoWp0v1jji4QAPAeoUmGAEABI9QBKBSnTp3S7nAVBAC8R3l/f38qdQAKHKEOQKHYuHHjwZSUlBLh4eGlBAC8Q6j5XDspAFDACHUACtMaswHURADAO5ROTk6OEQAoYIQ6AIXGx8cn2hzRLAWAt6i8bt26fQIABYxQB6DQmD3aq8yhlgCAh2vSpEnFlJSUxQIAhYBQB6DQJCUl/ebn59daAMDD+fv7NzJHgQIAhYBQB6DQrFmz5i+zZ7tkeHh4FQEAD2Y+y+qao7UCAIWAUAegsH1vNoauFwDwYD4+PleaowgBgEJAqANQqJKTk5eYjaFLBAA8mPkca5WUlLRMAKAQEOoAFKqYmJiPzVFXAQAPFRYWVj0lJeXP6OjofwUACgGhDkCh2r59e5w5+sJsFLUXAPBAvr6+95tQFykAUEgIdQAKXVJS0jyzUfSoAIAHMoHuIR8fn7cFAAoJoQ5AoVu9evVXZqOofO3atUsKAHiQJk2a6FIGayMjIzcIABQSQh0At2D2cn9YsmTJIQIAHiQgIOAFs1NqoQBAIfIRAHATzZs3P5aYmFh5zZo1JwQA3Jyp0l1kQt2iiIiI+gIAhYhKHQB3MsTf33+kAIAHMJ9XnU2VbpAAQCEj1AFwG5GRkRPNUePzzjsvVADAjTVr1uxGHx+fpuZz62MBgEJGqAPgVsxG0vjKlSuzkQTArfn5+S2Ij4/vIADgBvwEANzInj17tlSpUuUac6hiTrPuEwC307x5cx1y+VF0dPQvAgBugEYpANyS2Wjab6p2DSIiIg4IALgJ89nUyRxdHRkZ2VkAwE0w/BKAWzp16tSNKSkp0wQA3ETTpk0vNTubHifQAXA3hDoAbunPP/+MNkcfmL3iCwQAClnt2rWDfH19X4yIiLhcAMDNMKcOgNvas2fPn1WqVLnovPPOu8mcXiYAUAgaNGgQWKxYsaNRUVEXCQC4IUIdALdmwtwKE+zqVq5c+da9e/f+KABQgGrUqBFcokSJI5GRkcECAG6KUAfA7ZlgF2Gqdd1NsAswwW6dAEABaNq0aV1ToZtrAl19AQA3RvdLAB4jPDz8XXP0RURExLsCAPkoLCzseh8fnzdMoKsrAODmaJQCwGOYMPdASkrKrWZj62EBgHxiKnT3mUD3PIEOgKegUgfA45iK3QRzlGhC3vMCAHnIfL5MTk5OPh4VFTVQAMBDUKkD4HFMmOtrKnaHmjdv/r4AQB4xnyk/m8+WTQQ6AJ6GSh0Aj2X2qN9j9qjfYTbAHjM/JgoA5EBYWNjFvr6+S858nvwiAOBhCHUAPJrZGGtqNsb+MHvXr4+MjFwhAJANZufQ8+bzo62Pj89VERERCQIAHohQB8ArNG/e/AezYbbA7GV/QwDg3HzM58Z35nPjN4ZbAvB0hDoAXsNU7Z43e9tvMBW7m82PSQIA6TBh7m5zNNh8XvQ11bllAgAejlAHwKuYjbXrzIba12bv++0m3H0tAOAiPDx8nvl8KGE+H9oJAHgJQh0Ar2TC3Tsm3MWavfBdBUCRZyr5N2hlzgS6d6Oiot4SAPAihDoAXstsxHU2G3Hjk5KS7oyOjv5JABRJpjo30xxVj42Nbbt+/frjAgBehlAHwKs1a9astK+v7xRz8mRkZOTjAsDrmP/zqNWrV4elc/6t5v//OVOde8tU52YLAHgpQh2AIqF58+Y6DPMVc2jvOtfObPT9a45OxcXFXbdx48btAsCjNG3adI6pyD9sQp2/6/nmf/5Vc36dXbt23bdv374TAgBezFcAoAgwQW5mYmJiZbORd4/Z2FsgZz7//Pz8zjN78muGhIS8KAA8SpMmTS4z/8OtzcEvLCzsmJ5nju8PDw9PMSdXRERE3E6gA1AUUKkDUOSYUHevCXfvJiUlpZhtQbt3Pzk5+V9z6BwdHU3HTMBDmEr7CrNT5mrz/2x/Nv/DJ83Pi0yY6ygAUIRQqQNQ5Jiq3QcpKSn/OgKdMhuC55sNw5ECwCOYKp02QmrmCHTK/F+HEugAFEWEOgBFktmjXy2ds+s1bdr0WQHg7oLNTpn+ZmdMcdczzXk6/PI/AYAihlAHoMhp1qzZSd27b4KdPTiYDcISZiPx8QsvvLCiAHBbZufLOFOVq+l6nv4vm/P0ZHkT7PYIABQhzKkD4LWm9NrUSXx9qqc9f9v+X1omJp8qmZgUVzohKb64r4+Pb4qk+CQnJwWkpCQHBgWU2F7//JsWCAC3FL3j43567OvrlyApkmyinI+fb8Bxf7/A434+AcdrV772y/Ru5+PnN7fny7V2CAB4GUIdAK81fcCW6PPrFmtSvFSAACja/o48Ehd3LPHmnpPq/igA4GX8BQC8WK0mJaVi9RABULTt2nRcQ50AgDdiTh0AAAAAeDBCHQAAAAB4MEIdAAAAAHgwQh0AAAAAeDBCHQAAAAB4MEIdAAAAAHgwQh0AAAAAeDBCHQAAAAB4MEIdAAAAAHgwQh0AAAAAeDBCHQAAAAB4MEIdAAAAAHgwQh0AAAAAeDBCHQAAAAB4MEIdAAAAAHgwQh0AAAAAeDBCHQAgRw4e/E8++eQ9c3xACsrJkyelR48H5fDhQ+LNNm/eKF9++ZEkJiZKQfj333/kmWc6F9jjAQDyFqEOALzEn39Gyw8/fJvpdXbs2Cpjxw6Wffv2SG5p6Jg27WX56quPpaDMnj1ZypevKGXKlM30ell5L/LKpk3rZdmyJZKUlCR5ZcaMV2Ty5DH2dWRGfwc7d26T3Dr//AskJSVF3n13lgAAPA+hDgA8RFxcnNx22+Vy880X20pOWm+9Ne2cAeuff7bbAHLqVJxkx/Hjx2Tv3t2pzrvllrukY8cu0rr1XVIQ1qyJlCVLFsnjjz/jPC85Odm+poSEhFTXzcp7kVfWr19jg/KpU6ckrzz8cHfp0qWXNGzYNNPrzZv3un1P8kL37v1kwYK5sm3b3wIA8CyEOgDwEKtWrbTD44oVKy6///6TFKRu3e61G/yuKlasbMLHE7ZyVhDefPN1ufrq6+W886o6z1uy5FMTftrL0aNHxJs0bhwm99zzsPj7+0tBqVu3vjRrdokNxAAAz0KoAwAP8ccfP5sN7wYSFnZpgYe6wqZz6NatWy3h4ZcL8k94+BX27yw+Pl4AAJ6j4HYBAgBy5ZdffrBDHitUqCRTp74kMTGHpXTpMmddTytqn3/+ocTFxcq1194kPXr0Fz8/vwzv9913Z8vq1X/Ili1/SWBgoN2w79y5l5239u23X8iECcPs9RYv/sQe7rvvUXnssZ6ydm2U9O3b1Vw+01aWlM7L+uCDN+Xnn5fJjh1bTDWvijzwQGdp1eoW5+PNnz9LVqz4zpzfxc7h2rNnlzRpcrE89dTztvqXnj//XG2PL7qoofO8Tp3ukt27d9nTDzxw+v4/++xnCQoKcl7nu+++lLffni7Hjh21z0GHbuprdPjmm8/t8MW//94oF1xQww5BbNSomWRG35Mvvlgo27dvsZWtatVqnnWdQ4cOyqxZk2x1Vd/7yy5rIT17Ppeq8vb333+Z92KmeR8jJTg4xN5Xt259pGTJUvY9euedGeb9/t15/SNHYuSNNyZIRMQvUrx4Cbn77gfSfX7nek3PPdddqlevJXXq1Jf33pstNWvWkSFDxjnfXx3KumHDWmnaNNwOu9W/s6pVqwkAwH1RqQMAD7Bp0wa7Ua8b/npQ6VXr1q+Pts01nnyyn9x8cxvbSENDW2Zq1LhQWra82Wzsj5IHH+wmkZG/ydy5U+1lzZtfLuPGTZNSpUqbYHKNPX377e0zvC8NdHPnvmZCRJj06zfChgSdb6aB1JXOg9Nhfl279pZXXpljA6AG1Yw45nlVqfL/oZf9+4+Udu0etKcHDRprw6VroNu5c6ssX/61Dag6TFTn2H322QfOy//4Y6W8/PJw8fHxkd69h0ilSufJwIE9ZP/+vRk+D60WasjV96Nv32FSr14jG/DSGj78WRts27TpYEJwJ/npp+9l+vRXnJdrWNLH0kDbvXtfO9Ry69ZNcvLkiQwf+6WXBstvv62w9/fII0/a02mHnWb1Na1ZE2Hn42mwvvPO+5znO95f/X2o7t3vN+9fW/v3BwBwX1TqAMAD6AZ8QECADUtaadIqmg6Tu+mmO1JdTzfihw6dYCtCV13VSg4c2Geqdh/YallG1borr2yZ6mdtb6/NVFS5cuXtwd8/QMqWLW+qNxdn+Bx1yJ5WCW+7rZ2pOPW251199XW206ZWpK644lrndXVu4Nixrzvn411+eQtTvfs+w/s+duyIrWa5Vtnq12/sDHsNGzazz9OVr6+fqUCNdwY9rTJu3vz/cPLhh2+Z11TOhKDTHR9btbpZnniigw1+2qQkPR999I59H4YOfdn5fupr0aqaQ3R0hGzcuM5WHh0BWG8zfvwLJox1t1U2Ddsa0l97bb6tvKo2be6TjGzdutlU6H5NdZ/6nt177/WprpfV16Tv26RJ82wodeXoKnrkyGF7rFVIDaAFNW8SAJAzhDoA8AAa4Bo3bu4MNTpEUqtf2kbfNayVK1ch1RC/Bg2a2tb+2rlS29anR9ebmzlzkgkjq+TQodNrzgUHB0t26ZA9rTSlDX5NmjQ3oWemxMbGSkhIiD3P19c3VVAIDAzKtCOnBqfsNg2pWrV6qsqdhkLHY+j9abXqhhtuc16u1S2tLG7cuDbD+9Shks2bX5bqPS9RomSq60RH/2GPL774Sud5Gp409OqQSK20akDToZGOQHcu+lyV63urv6OgoP//nrLzmrQ6mzbQKf29KEc30RdfnCIAAPdHqAMAN6dzmv766087j81Bg4HOF1u3LirT6pmkcJ4gAAAQAElEQVQjcGhYSy/UaQjr0+cxO+zu+edH2YqXzkFbtOg9yS6tpintzumqePHTz0GrhhpkckIDmc4RzCuxsSft/D+dH6cHV5Urn5fh7XRuXmho8czuWk6cOG6PH3nkzrMucwyD1PcqbRjMjFbLVGaPnZ3XVLp0+uv86eLuKu3vEADg3gh1AODmfv31R3s8Z85Ue3ClwzIzC3WOMJBRgFi+/Bs7PFLn051rTbRzcVSdHI/5/+dw1B6XLFlackpDiVaiDh48cNYwy5zQ90MrXZdeevVZcwS1apgRHUYZG3tCMuOoQI4YMfGsimfVqjWc10m77l9mHMMiT548nuHrz+lrcrV//+lF6TNqWAMAcE+EOgBwc9rtUENA//4jUp0/Y8ar8vvvP9uOiQ6JiakX4dbhgrqxn16HRnX48EF7XLny+c7zdIhgWjr0MSUlWTJTvfqFtsKj1cNrr73Reb4uGl679kW2uUhO1avX2B5v3/53qlDjGAaZnJwk2aVVSR16mlkoTkuHMur8Nldp33Od96h06GdG963DYnUIpj6+Dpk9l7p1T3f93LJlk7PaqUNvNei6yslrcqUdPdVFF50emkn3SwDwDHS/BAA3phvu2uVSm2Lohrrr4corW9kuko62/kobgcyYMdEGBu0mqc1HtEOkY65UmTLl7LGGQW3UoW3t1YcfvmnP05b52uExLi4uVcfDWrXq2oCoSx/8+ON36T5XrRLpcgfaDXLWrMm2++P48UPtPC/tqpkbujC2Vo804LrS56W0jb8+f0fjlKzo2LGr7RSq75fOJ1y6dLH06NHRns6INjPZuXObLFq0wL5H69evsc1TXGkDl0suuVJefXWkrFy53L5n+r4OHNgz1f3o0gWDB/eS77//yt6fNjTJqHpXp049uyyB/p70d37q1Cl7//Hxp3L9mlzp3E39m3As8E73SwDwDIQ6AHBjGog0POhyAmlp0FM6BNNBlybQLpna/l7XqrvlljY2aDk0aNDEDrOcOXOinXd16aVX2fXTNDiOHj3ABMR/TCBbaJttREX95rydLpGgnTUHDOhhA4quw5YefaxHH33SBLqlMnJkf9vM5emnB6XqfJlTHTo8ZsObY96X0rDzxBPP2g6fQ4Y8fdbSCZnR92H06KkSGfmrDVe6xIJWBC+4oGaGt9GF3/X90iDXps3VtuvlY489ddb1Bg8eZ6tmU6aMkWHDnrXVT9chkaGhxeTll2fbyubkyWPs/ek8SQ16GRk06CU7P1GDVocON9nr16xZO9evyUGHtuoSEB06dHKepxVefa50vwQA9+YjAOClpg/YEn1N+ypNKlYPEXg+rVp263avCbhXpxpyiryh6+9pFVCXOvBGS2btjInZc6pNz0l1fxQA8DJU6gAAHkHnz/Xo0V8+/fR9W1VC3tEhpdpNtWfP5wUA4HlolAIA8Bi6RtzYsW/kSQdM/J8Os5wwYaYdzgoA8DyEOgCAR2ncOEyQ97QRCwDAMzH8EgAAAAA8GKEOAAAAADwYoQ4AAAAAPBihDgAAAAA8GKEOAAAAADwYoQ4AAAAAPBihDgCAQhAfHy9ffLFQtmzZJPlt8+aN8uWXH0liYqIAALwPoQ4APEhCQoLMmDFRHn74Drn77mtlxIh+smnTBskvx48fkwkThknbti3l5psvlrfemiaFZeXK5RIdvUryy6FDB+Wrrz6RmJjDkh8++OBNef/9ec6fN2xYI1OmjJXp01+R/DZjxisyefIY+fPPaAEAeB8WHwcAD6LVli+/XCidO/eSEiVKSUTEL1KuXIVU19EK0N69/0q1ajUlt2bNmiSrVq2Up556XpKSkqRy5fMlOTlZ/v13pz0dEBAgBeX7778yr2u3vPbaO5Ifjhw5LJMmvShlypSVK664VvLasmVL5IILajh/rl+/ifk9PiUNGjSVvLR//14JDAyS0qXLOM97+OHusnHjWmnYMG8fCwDgHgh1AOBBIiJ+lbp1G8qdd95rf27V6uazrvPKKyNk8+YNMnv2R5Jbf/+9URo3bm4e5xbneVrN0vDz7rtLTKAsL8iZwMBAuffeRyQv7dq10wTFtvL886NS/c4aNw6zBwCAd2L4JQB4kLi4kwVaHYuNjRU/P/b/4f/Wr18j77wzUwAA7oNvagAoYGvXRknfvl1l1qyFMn/+LPnpp+9lypS3pWbN2vLNN5/LkiWLbIVMh+p1795PGjVqJgcPHpAHHvh/5UXnt7nq1q2PXHPN9fLQQ7enuk79+o1l4sS56T6PH3/8Tr799gvZsWOLHD16xA7Ne/TRHlKnTj07VHDs2MH2ert27bA/33DDbWaDPlp2795lz3c8n88++1mCgoLsnDTHcE0/Pz+57LIW0rPnc+Lv73/O152WDvGcN+91+fnnZXLs2BFp2fJmSUiIP+t6Okfs/ffnypo1kXbYZNu2HeWOO+6RrNiwYa28/fZ0ex/Vq9eSm26646zrvPHGBPnhh29kwYJvnOcNG/as/PffPucwUMfr6t9/hHkvPpCtWzdJhQqVpUuXXnLllS0zfQ6tW18qDz7YTTp27OI8T38v2kDlr7/WSZUqVeWqq1qZy7vaYbWvvfaS/PPPdtm2bbOULVveVuP09r6+vrZC+/XXn9n70N+dHkaNmiyXXHKlfb/feWeGLF78e6rHfvrpQXYIb2Tkb1KyZClp1+5Buf329s7r6OucP3+mnc945EiMFCtWXC68sK6EhhYXAID7INQBQCF58cXnpV69RtKnzxAb4P74Y6W8/PJwG+J69x5iQ8/AgT1MCPrIBJZyMm7cNJk27WUTkgJsYFBxcXHywgu97enSpcva67z33hwbxPr1G2421Etn+PgaGHSD/7bb2sqJE8flq68+ltGjn5fZsz+Wpk0vsfc1fvxQqVq1utx//2NSvnwlE/5iZMWK7+Wjj96RQYPG2uelgU4NH/6sbN++xQ4p1I1/DRFaVezRo3+mrzs92lBED3fffb8d/qkNUn777SepXbue8zqHDx+SIUOeto+loXb37n9M6Bln3wcNuJk5efKEDB3ax74/GjxPnYqTjz9+V3JjxoxX5fHHn5FmzS6VOXOm2Nc5b96nJuBVyvJ9rFu32t5O5/T16zfCBkQNjRpyg4ODTeCuL02ahNvnrZe9+eYb9vd44423yz33PGwv09/ZAw90Ns/jEnP9Bpk+nobEO+6414TX92yQ1MYtOry3bt36NsT16vWw+R3VlKlT37FhUoPjNdfc4Bz+CwBwD4Q6ACgkurHfu/dg588ffviWqb6UM8Fulv1Z58s98UQHW/3RENe06cVSvHgJE5QC7Wl18uRJ5+11jpaev3jxIlthcVwnI1qR04ODhqOhQ5+xTVA0bOlzCQoKtiHJcV/nn3+BqRL9bU83bNjMOacuOjpCNm5cZxuqOCo9WkkaP/4FeeSR7vZ5Z/S609KGLJ988q5ce+2N5vU/a8/TapUGRg2fDhpC9OdJk+Y5w2FcXKwsXPi2M9Rp905XjuehFS0NLfpeO26r4aV//8clpx5//Fm57rrT1ctOnXraKujSpYvlvvsezfJ9vPfebBuihw6dID4+PnL11delutw1TF122dW2kqmVUQ11+jp8fE7PqtAmOef6/avrr7/VBOLTOwXat3/IBmmdj6mh7qefltrq69ixb0jFipXtoUWLG2yQ1GqoPj8AgHsg1AFAIbnxxv8P99P1w9asibBDHB10o/miixraroX5QYfz6dC6FSu+s0MqU1JS7Plaxcqu6Og/7PHFF1/pPE+rcfoYOpRUq0YOrq87PTt2bLWBS6tOrkqUKJkq1OljatBwrfbp+6VhT99PXf6hXbtWqe7j4YefsEMd166NtKHV9bY6/DA3XCtyGna1++SWLX9l+fYaZnUY5C233JVhYNLgPGfOVPueOt4LfR05f86Vnac1wCsNxiolJdkeh4SEOq+jwy7170Ofq2NYLQCg8PGJDACFRIcuOsTGnrShSqs7enBVufJ5kh/GjBlow0HXrr0lPPxyGxgGDuwpOeEIGI88cudZl2mLfVeurzs9juqaVg7P9Zh632nnF6qDB/+TSpWqOKueDhoCHY+R3/PCihUrYcNpVmlY0mGWGl7To+sRPvtsF2nd+m47zFPntj3zTGfJL5deerUNdFq90wqsDnf95pvPTIXwGgIdALgZPpUBwA3ohrzOmdINaddGFUrXHMtr2vp+5crl8uijT9ohdblVvnxFezxixET7OlxVrVpDskMbnqhzVQy1Mnbq1Cl5+umB6dzH6eCo8xMzegydI5aftMFL3boNsnx9/RvQ+Ymu1UhXH38831bTdP6gDrXNbxqAdT6kLj6v1U+lzXSeemqAAADcC6EOANyEzlHTClNW5kJlRqsoyclJmV4nJuaQPa5U6f9VwKwOFdTOlsr1MRo1Or0GmoaS3D5/bfyhc9+2bt2c6nwdyulKF+/WuXzaDCQ0NFSyQ5uBLF/+je0q6pgXmF53TQ3UOpTT1aFDB9K9z6Sk/19Ph5BqR1Edgpod+j5qU5j06O9M5yk6Ap2GXp3/qENOHRwVNB0emRd0buNDDz0uDz7YVQAA7ot16gDATWjbem2vP2PGRLthr002evTomOFGfkZq1qwje/b8a5toaHBxbabioHPJtKKmyyesXv2HLFq0wDYYUdqBMTO1atW1x7r8wu+//2wbp+jSCdpJ89VXR9oKoN6nLgeQk+GcGky0Icjy5V/bjqA6JFGbxTjm7Tm0aXOfhISEyKhR/e3jaXfMESP6yVtvTTvnY+jyBTq0ULtUxsQctmFaO2ee/VrrmIrbUVvV05CmTUK2b/873fvU2+tyBPr7mjx5tJ2j57pMglYPd+7cZodRZkT/BvSxhg/vaxuVzJ37ml0uQYOldv7Uy/R919/t6NEDbPdTbSCjz01pxVQf99dff7TPQ+fo5caBA/vtXE99b/X+/vlnh/19KF3DsH//J8xrHSMAgMJFqAMAN6FD20aPnmo2xH+VwYN72XBSr15j25UxO7Qz4c033ynjxg2Rl18eJhs2rDnrOqVKlbZDJbUphrb2/+67L0w4miKdOz+V7vVdacdM7Ur5+ecf2CUFfvnlB3v+4MHjbLVxypQxdi03na+XdihpVunaa9rBUuf93X77FXa5gltvbZvqOqGhxUyInGsbomgImjhxpJ2XeNVV153z/nWoo77XuixAhw432eCkcwvT0vXxdO22Pn0ek/vvv9lWx3QtvPRoB8oPPphnf3catl56aZp9jg5t2nSwoe7557ufVf1z0L8B/b3s2bPL3H6wCc0/2eeg1VF9T/T91LUAp059Sc4/v5o5fttWW/V+lQZiXWpCw58GrgUL5kpuPPbYUybkR9llM/T+unRpJ08++YANkUeOHLZBT+fZAQAKF/2IAXit6QO2RF/TvkqTitVDBMgvjsXHJ0yYKY0bh4m30iB6uplOD9uspXv3viYI32Mb0ugi5+5uyaydMTF7TrXpOanujwIA3niMhwAAEABJREFUXoZKHQAAOMvEiaPkyy8/dv6sVUBtPFO58vl2fp9WLbUimLaCCgAoeDRKAQAAZ/H3D5CFC9+y8/QcHU11uKU2gbn//sfs/M9y5SrIFVdcKwCAwkWoAwAgF2rWrC3jxk2zx96kc+dedj0/bUSjnUe186aujTd8+Kty+eXX2OvMn/+VAAAKH6EOAIBc0OUXcruMgzvSzqLPPz9KAADujzl1AAAAAODBCHUAAAAA4MEIdQAAAADgwQh1AAAAAODBCHUAAAAA4MEIdQAAAADgwQh1AACcsWnTelm2bIkkJSUJAACeglAHAMAZ69evkbFjB8upU6cEAABPQagDAAAAAA9GqAMAAAAAD+YvAAB4kB9//E6+/fYL2bFjixw9ekQaNmwqjz7aQ+rUqee8TuvWl8rTTw+SiIhfJDLyNylZspS0a/eg3H57+1T3pffzxRcLZfv2LdKs2SVSrVpNAQDA01CpAwB4lCpVqsoll1wpTz7ZT5566nmJi4uV0aOfl+Tk5FTXe+21l6RChcryxhvvyTXX3CBTpoyVTZs2OC9ft261TJgwTEqVKi19+w6TevUa2YAHAICnoVIHAPAoWpFzrcoVK1Zchg59Rv79d6dccEEN5/nXX3+rdOvW255u3/4hef/9ebJ58wapW7e+Pe+jj96RsmXLm9u+LH5+fva8xMREeeedGQIAgCch1AEAPEp8fLzMnz9TVqz4Tnbv3iUpKSn2/JMnT6S6nlbpHIKCgu2xVvUc1q6NlObNL3MGOlWiREkBAMDTEOoAAB5lzJiB8vffG6Vr194SHn65bNy4TgYO7CnZdezYUQkNLS4AAHg65tQBADzGrl07ZeXK5XLrrW2lRYsb7NDLnNKhl7GxJwQAAE9HpQ4A4DFiYg7Z40qVznOet2XLX5ITF13UULZu3ZzqvMTEBAEAwNNQqQMAeAxthBIcHCxLliyS1av/kEWLFsjChW/by7SbZXa0aXOf7Ny5zd5HXFycrF+/xjZPAQDA0xDqAAAeQ5cfGDFiom14MnRoH/nuuy9k1Kgp0rnzU7Jhw5ps3VdY2KXSs+dzNsi1aXO17Xr52GNPCQAAnsZHAMBLTR+wJfqa9lWaVKweIgCKtiWzdsbE7DnVpuekuj8KAHgZKnUAAAAA4MEIdQAAAADgwQh1AAAAAODBCHUAAAAA4MEIdQAAAADgwQh1AAAAAODBCHUAAAAA4MEIdQAAAADgwQh1AAAAAODBCHUAAK938OB/8skn75njAwIAgLch1AGAF5g/f5Y89NDtcvPNF8uzz3aRlJQUKQwrVy6X6OhV4m6+/PIjmTbtZfnqq4+lsKxY8b2sW7daAADIa/4CAPBoS5culrfemiZdu/aW886rKvHx8TbU7dq1QypXPl8CAgKkoHz//Veyd+9uee21d8Sd3HLLXfa4deu7JL/p+793779SrVrNVOePHj1AHnywmzRq1EzyUkaPBwAoOgh1AODhNm/eIKVLl5H27R90nvfVV5/IpEkvyrvvLpFy5cpLUVexYmV5+OEnpCC88soI+zuZPfsjKQgF/XgAAPfD8EsA8HCxsbHi58c+uoJy8uRJ+eCDN2XTpvUCAIA7YCsAANzYjz9+J99++4Xs2LFFjh49Ig0bNpVHH+0hderUs00/HnjgFud1dT6dVqT8/f1l9+5d9jzH5Z999rMEBQXJoUMHZdasSbJq1UoTBP3ksstaSM+ez9nbqLVro6Rv367mOgvtPL2ffvpepkx5W2rWrH3Wc0tOTpZ5816Xn39eJseOHZGWLW+WhIT4VNfJ7P70dl98sVA2bFgrJUqUlGuvvUkee6yn+Pr6prpt//4jzPP/QLZu3SQVKlSWLl16yZVXtnQ+xnPPdZcqVapKYmKCREX9LidOHJeLL75C+vUbYV+z631NmDBTGjcOc97uggtqSMmSpex7rLdr1eoWefzxZyQwMNB5/0uWfCrffPOZrF+/xg5rvfDCulKsWHH7mHXrNnBeb//+vXZeo+vvo379xjJx4txU78n06a+Y+/vcPm7bth3ljjvuSXW5XrZkySL5+++N9vl1794v3SGb53q848eP2d/P6tV/yH//7ZMaNS6UXr0G2uefnfcOAOD+/AQAvNQd1zzdvXqDEpWKlS64OWV5TedLacDQwBQWdqkNQBow7rzzXnt+s2aX2LB3/PhRGTlyolx//a028BQrVsJcd40MGjRW2rS5z861U88//6QJOJHSrt2D5v4us6FKO0NeeulV9nINChoq9DqVK59nbttBmjQJdwYtVwsWzDVBbaZ9zDvvvE/27dst33+/WMqWLS+33dY20/vTgDR4cC8b7h54oLMdIrpw4dumCnZcwsOvSHXbP/9cLR07drXhRucJ6vzBm266wwYr9d13X8qvv/5g7/+550ZJ8+aXyYcfvmVf12WXXZ3qvm666U6pVKlKqtvpnMO+fYeZQNRE5s6dau+3QYMm9jrvvTdHZs6cKJ069ZAnnnjWhJ9E2bNnl4wYMVEuuqhhqvcjICDQvKeXyIED+yUpKVGGDXvZBtUyZcray/W9+vffnXL++dVsmFPvvDPTPMdrzOuvYH/+44+V8uKLz9vn+PDD3U0IPyBvvz3dvMe3OV9vVh9v6NBnbHi/++4H5LrrWtvKov7ObrzxdgkNLZbl985b/B15JC7ueNKCr36bskMAwMtQqQMAN6YVOT046Ia9bqxrONAqTtOmF8uyZV+bSluAPe2wbdvf9rhhw2bOOXXR0RGyceM6eeqp5+X229vb8zSAjR//gjzySHcpXryE8/YVKlSS3r0HZ/i8kpKS5JNP3jUh4kYbdtRVV7WS7du32GpPWmnv7733ZkvVqtXNa5lgf27R4gbx8fGR99+fJ/fd10lKlSrtvO7jjz9rQsnpimOnTj1tVU2bw9x336PO65x33gUycOAYW33UANuixY32OvrcMmsUU6nSeSYMvWIrlVrJevfdWXZ+moNWCPV1aQVP6by89u2vs/fdrl3HVPelIVt/B4sXL7KVMdffh4NW0nr06G9P6+9GQ7U+Xt269e15GqjKli0nL788y/7cqtXN5jV0sM9DK5RZfTwN/5GRv8mAAaPNDoGb7HmXX95COnS4UT766B1bjcztewcAcB/MqQMAN6aVurlzX5PHHrtbbrnlEhvo1MmTJyS7oqP/sMcXX3yl87x69RrZx9Chfq5uvPGOTO9rx46tcuRIjK26udJhlOlxvT8NhBo4tMroSu8rISHBBhJXGggdNKBqU5gtW/5KdR0NpxpKHGrVqmPfI62qZUYrZI6hpyooKFhOnYpz/pySkiwhIaHOn/W0Ps6JE8ckJ2rU+P8w1uDgEHscFxdrj7UKuGZNhLNSqTToakVw48a1kh2rVv1ij7W6+//nHmLCY8OzllXI6XsHAHAfVOoAwI2NGTPQBi5driA8/HJbaRs4sKfkhKOC9sgjd551mQ5PdFWmTDnJjM7XUmmHBGbE9f40MOh8vLS3LV78dCA8cGBfpvelQ0s1UGZ+ndP3fa7rnYsOe9Vhm/fe+6ipLFaTjz+eb0PpFVe0lLwWG3vSztnTSqQeXOnwyOzQ4bjKtfrq+HnXru2Z3jav3jsAQMEh1AGAm9q1a6ddzPvRR5+0wxNzq3z5ivZY54MFBwenuqxq1RqSHY55WzmpGGo1Tx/fEQwdHEGkZMnSmd5em7K4NihJj84zPP08Mw+n56Jh+rffVkjnzqfnCGpV7+mnB6UaEptXHO/LpZde7Rwe6xAYmL2mJY7f9bFjR21l00Hf83O9v3n13gEACg6hDgDcVEzMIXus874c0g47zIhjOF1ycpLzvEaNTnd91K6G6c33yg7tmKhVn61bN6c6X4dyZoXOJ1u3LirVeTr0UENT2uemTUAcdNinhg4dNprRdZQ2V9F5edmtcKX1668/2orVxx8vz3JVUl+D6/ueHfq+aJOS7Px+0ns8vR+l76ljh4AufbFp05/SuvXdqa6bX+8dAKDgEOoAwE1pIxSt3Gh7e22eoU1ItEOk0nlRabsvuqpV63Tbeh06WKdOfTsvTZt0XHLJlfLqqyNtowztgPjLLz/IP/9sl9Gjp0p2aJDQDpzawEMbcOjQUG36ofP2Lrig5jlvr90sn322i6ka9rOdGXW5Am2Sop06XZukqNdeGycPPfS4PV87X+pSANr90pUuWTB79hTbjv/ff/+xS0FohdN1vlxOaMDSYavff/+VVK9ey3ac1OPMAl7NmnVsV0ldskHnCGrlLTQ0NEuPp+/LM890lhkzJtruk/r42tikW7c+GQa99B5Pu3fq73rKlDG2iYrOHVy06D1zbR9p3/6hVLfPr/cOAFBw+MQGADelIUaHSuoG99ChfWzIGzVqiqxe/btdrsBEgAxvq8MDtXvh++/PlcOHD9nulrp8wODB42Ty5NF2Y18rN7pmmS5vkBMPPtjN3PdBO+9Pm31oyLv11rY2JJyLrrc3cuQkmTNniowdO8jOU9Pbdu7c66zragv+Dz6YZ6t01arVkpdemuZsye+g67jpc9DlALRKeffd90uHDp0kt3ROnc5v02DpSodg3nrr3eneRted27lzq4wbN8TOHdTumhp6s0LfFw3YuozC559/YMOYNk7JLChn9Hj6u546dazdEaBLI5x//gX2vh1DMx3y670DABQcHwEALzV9wJboa9pXaVKxeojA86S3YHh6dAFt9dJLb0h+08Xb5817zVbG5s9f7Jxb6KkK8r0rbEtm7YyJ2XOqTc9JdX8UAPAyLGkAAEA6/vwzWvr162aXGnDQYbBaOdPKoqOxCwAAhY3hlwAApKNSpSp27uLbb0+X5s0vs+fp/LoFC+bYhdPPP7+aAADgDgh1AAC3pHMAx42bZo8zo8sO5Aede6bz02bMeNUEubkSEBAg1arVNAHvcttsxNfX8we75Nd7BwAoWIQ6AIBb0iUTstLav3btiyS/aAdKPXir/HzvAAAFhzl1AAAAAODBCHUAAAAA4MEIdQAAAADgwQh1AAAAAODBCHUAAAAA4MEIdQAAAADgwVjSAABQ6ObPnyVLliyS/fv3SqNGzWTChJni4+MjAADg3Ah1AIBCtXTpYnnrrWl2Iezzzqsq8fHxeRboNCQGBgZJ6dJlBAAAb8XwSwBAodq8eYMNXe3bPyhXXtlSWra8SfLCrl075aGHbpeoqN8EAABvRqUOAFCoYmNjxc+PryMAAHKKSh0AoFAcPHhAbr75Ylm8+BNz+j97WitrDt9887k880xnufPOq6RHj46ybt1q52VxcXHy8svDpXfvTtKmzdXSqdNddghncnKyvfyVV0ZI585t7emxYwfb+/7jj5X25zfemCAdOqSuBg4b9qx5jAedP69dG2Vv888/2+3tb7/9Ctm27e9zPi8AAAoDoQ4AUChKliwl48ZNk8suu0ZKlSptTw8ZMs5epgFMQ5vOrevde4hUqnSeDBzYw86RU8HBwVKnTn257bZ25vyxcu2ARO0AABAASURBVNNNd8q7786W77//yl5+zz0PS79+w+3pBx7obO+7YcNmkl0vvvi8faw+fYbIBRfUOOfzAgCgMDDeBQBQKAICAqRp04tl2bKvxd//9GmHDz98S8qWLWcC1Cz7c6tWN8sTT3SQzz77QLp06WXPu/POe53Xv+yyq+Xnn5fJqlUr5cYbb7cBzMfn9H7LatVqprrv7KhQoZIJb4Oz9bwAAChohDoAgFtJTEyUNWsi5IYbbnOep5Wxiy5qKBs3rnWet3HjOpkzZ6r8/fdGOXHiuD1PA1deuvHGO7L9vAAAKGiEOgCAW4mNPSkpKSny7bdf2IOrypXPs8ebNm2QZ5/tIq1b3y2PP/6MXHhhXTvPLa+VKVMuW88LAIDCQKgDALiVEiVK2nlsl156tdx+e/tUl+mac+rjj+dLUFCwdOvWx5wXKAUhK88LAIDCQKgDALgdbWqiHTEzmgsXE3NIypYt7wx0J0+ekH//3WmHQjr4+5/+iktKSkp1Ww1gOpTS1aFDByQvnhcAAIWB7pcAALfTsWNX+fPPaJkxY6JER6+SpUsX2+UD9LSqXbueXW5AlxfQBimjRw+wyxxs375Fjh49Yq9TvnxF22Hz119/tLeLjDy9CHmtWnXk2LGj9vZ63TfffMPc7u88eV4AABQGKnUAALfTsGFTE9SmysyZE+Xzzz+QcuUqSHj4FXLBBTXt5Q8+2M3OcZs1a5JduLxFixvs3LrJk8fIzp3bpFGjZrZSN2jQWLsuXf/+T9jqWvPml0nLljfL5s0bpU+fx+x96FDKtm07Otexy83zAgCgMPgIAHip6QO2RF/TvkqTitVDBEDRtmTWzpiYPafa9JxU90cBAC/D8EsAAAAA8GCEOgAAAADwYIQ6AAAAAPBghDoAAAAA8GCEOgAAAADwYIQ6AAAAAPBghDoAAAAA8GCEOgAAAADwYIQ6AAAAAPBghDoAAAAA8GCEOgAAAADwYIQ6AAAAAPBghDoAAAAA8GCEOgAAAADwYIQ6AAAAAPBghDoAAAAA8GCEOgAAAADwYIQ6AAAAAPBghDoAAAAA8GCEOgAAAADwYP4CAF5s25pjsm9HrAAo2uKOJwULAHgpQh0ArxV/Imni1uij1QVF2vp/F3eqXKr+92WL19gpKNJ8/P13CAB4IR8BAMCLhYeHrzBHAyIiIn4SAAC8EHPqAAAAAMCDEeoAAAAAwIMxpw4A4NVSUlL+S0hISBIAALwUoQ4A4NVMqAsSAAC8GKEOAODVfH19SwYGBvoJAABeilAHAPBqycnJiaZalyIAAHgpQh0AwKuZSp1+17GEDwDAa9H9EgAAAAA8GJU6AIBXS05O3pKUlJQgAAB4KUIdAMCr+fr6XmgOAQIAgJci1AEAvFpKSsrJ+Pj4ZAEAwEsR6gAAXs3Hxyc0KCiIOeQAAK/FlxwAAAAAeDAqdQAAr5acnLw1KSkpUQAA8FKEOgCAV/P19a11Zq06AAC8EsMvAQAAAMCDsecSAODVkpOTN7FOHQDAmxHqAABezdfXty7r1AEAvBnDLwEAAADAg1GpAwB4tZSUlM2JiYkMvwQAeC1CHQDAq/n4+NQJMAQAAC/F8EsAAAAA8GBU6gAAXo3FxwEA3o5QBwDwaiw+DgDwdgy/BAAAAAAPxp5LAIBXS0lJ+S8hISFJAADwUoQ6AIBX8/HxqRAYGOgnAAB4KUIdAMCrJScnH01MTKRSBwDwWoQ6AIBX8/X1LUmlDgDgzWiUAgAAAAAejEodAMCrJScnb0pKSkoQAAC8FKEOAODVfH1965pDgAAA4KUIdQAAr5aSkhJvJAsAAF6KUAcA8Go+Pj6BQUFBzCEHAHgtvuQAAAAAwINRqQMAeLXk5ORdSUlJiQIAgJci1AEAvJqvr29Vc+D7DgDgtfiSAwB4NVOp+8ccJQkAAF6KUAcA8GqmSneBOfITAAC8FKEOAODtEpOSklIEAAAv5SMAAHiZsLAwG+J8fP7/NZeSkmIP5rxtUVFRtQQAAC/BkgYAAG+01tfX14Y6x+HMzyeSk5NHCQAAXoRQBwDwOia4TTWHE2nP1ypddHT0HAEAwIsQ6gAAXscEtxka4FzPS0lJOWmC3iQBAMDLEOoAAF4pKSlpigY5x88a8lavXj1LAADwMoQ6AIBXMtW6meZoi57WcGdCHlU6AIBXItQBALyVyXIpb5yZW7fZVOlmCgAAXohQBwDwWlFRUdN8fX331Sh35UcCAICXYp06AIBXmzFgy7qS5YPKnDyc+NBjo2osFQAAvAyVOgCA19JAd80959W+4ZHzzwsI9Vk4Z/D26wQAAC9DpQ4A4JUcga5CteAgx3mLZ+w8nHAypT0VOwCANyHUAQC8TnqBzoFgBwDwNoQ6AIBXySzQORDsAADehFAHAPAaWQl0DgQ7AIC3INQBALxCdgKdA8EOAOANCHUAAI+Xk0DnQLADAHg6Qh0AwKPlJtA5EOwAAJ6MUAcA8Fh5EegcCHYAAE9FqAMAeKS8DHQOBDsAgCci1AEAPE5+BDoHgh0AwNMQ6gAAHiU/A50DwQ4A4EkIdQAAj1EQgc5Bg92pOLmny4jq3wsAAG7MVwAA8AAFGehU627VygQGy4ezXthxvQAA4Mao1AEA3F5BBzpXX5mKXTwVOwCAGyPUAQDcWmEGOgeCHQDAnRHqAABuyx0CnQPBDgDgrgh1AAC35E6BzoFgBwBwR4Q6AIDbccdA50CwAwC4G0IdAMCtuHOgcyDYAQDcCaEOAOA2PCHQORDsAADuglAHAHALnhToHAh2AAB3QKgDABQ6Twx0DgQ7AEBhI9QBAAqVJwc6B4IdAKAwEeoAAIXGGwKdA8EOAFBYCHUAgELhTYHOgWAHACgMhDoAQIHzxkDnQLADABQ0XwEAoAB5c6BTt3arViYwWD6cM3jLDQIAQAGgUgcAKDDeHuhcLTYVu4STifc+NurC7wQAgHxEqAMAFIiiFOgcCHYAgIJAqAMA5LuiGOgcCHYAgPxGqAMA5KuiHOgcCHYAgPxEqAMA5BsC3f8R7AAA+YVQBwDIFwS6sxHsAAD5gVAHAMhzBLqMEewAAHmNUAcAyFMEunMj2AEA8hKhDgCQZwh0WUewAwDkFUIdACBPEOiyj2AHAMgLhDoAQK4R6HKOYAcAyC1CHQAgVwh0uUewAwDkBqEOAJBjBLq8Q7ADAOSUrwAAkAMEurzVulu1MgGh/h/MfGHrjQIAQDZQqQMAZBuBLv8smfXPodiTiR26jqj1rQAAkAWEOgBAthDo8h/BDgCQHYQ6AECWEegKDsEOAJBVhDoAQJYQ6AoewQ4AkBWEOgDAORHoCg/BDgBwLoQ6AECmCHSFj2AHAMgMoQ4AkCECnfsg2AEAMkKoAwCki0Dnfgh2AID0EOoAAGch0Lkvgh0AIC1CHQAgFQKd+yPYAQBcEeoAAE4EOs9BsAMAOBDqAAAWgc7zEOwAAIpQBwAg0Hkwgh0AwFcAAEUagc6z3dLlgrIhof4LZg/eepMAAIokKnUAUIQR6LwHFTsAKLoIdQBQRBHovA/BDgCKJkIdABRBBDrvRbADgKKHUAcARQyBzvsR7ACgaCHUAUARQqArOgh2AFB0EOoAoIgg0BU9BDsAKBoIdQBQBBDoii6CHQB4P0IdAHg5Ah0IdgDg3Qh1AODFCHRwINgBgPci1AGAlyLQIS2CHQB4J0IdAHghAh0yQrADAO9DqAMAL0Ogw7kQ7ADAuxDqAMCLEOiQVQQ7APAehDoA8BIEOmQXwQ4AvAOhDgC8AIEOOUWwAwDP5ysAAI9GoENu3NLlgrLBwX7vzxm85QYBAHgkKnUA4MEIdMgri2fsPJxwMvHex0Zd+J0AADwKoQ4APBSBDnmNYAcAnonhlwDggaY/vyWaQIe81rpbtTIBof4fMBQTADwLoQ4APIwGuqsJdMgnBDsA8DwMvwQAD+IIdJWqB4cKkI8YigkAnoNQBwAegkCHgkawAwDPQKgDAA9AoENhIdgBgPsj1AGAmyPQobAR7ADAvRHqAMCNEejgLgh2AOC+6H4JAG6KQAd34uiKOb3/360EAOBWCHUA4IYIdHBHGuxCSgYQ7ADAzTD8EgDcDIEO7u6raTsPxB5NuPfxcbWXCQCg0BHqAMCNEOjgKQh2AOA+CHUA4CYIdPA0BDsAcA+EOgBwAwQ6eCqCHQAUPkIdABQyAh083VfTdx44cSz5nu5jay4XAECBI9QBQCEi0MFbEOwAoPAQ6gCgkBDo4G0IdgBQOAh1AFAICHTwVgQ7ACh4hDoAKGAEOng7gh0AFCxCHQAUIAIdigqCHQAUHEIdABQQAh2KGoIdABQMQh0AFAACHYoqgh0A5D9CHQDkMwIdijqCHQDkL0IdAOQjAh1wGsEOAPIPoQ4A8gmBDkiNYAcA+cNXAAB5jkCHwnbw4H/yySfvmeMD4i5ufbxa+WIlfD984/ltLQUAkGcIdQCQxwh0px0/fkwWL14k+/fvFRS8L7/8SKZNe1m++upjcScEOwDIewy/BIA8lB+BbufObdK16z32dFBQkJQvX1Fq164nDz30hFxwQXVxBxrg9FC58nnO89aujZK+fbvKhAkzpXHjMEHB0jC9ZMkiufXWtvZvxt0wFBMA8g6VOgDII/ldobvnnodl6NCXpX37hyUxMVEef/xeWblyubiDbt3ulQUL5grcR8WKleXhh59wy0CnqNgBQN4h1AFAHiiIIZdVq1aX8PDLTeXlbnnhhfHStOnFMnnyaDl58qQAnohgBwB5g1AHALlUWHPo7rrrfjl8+JD89tuPzvN0COTUqS9Jly7tpU2ba+Tppx+VLVs2nXXbb775XJ55prPceedV0qNHR1m3bnWqy999d7Yd8nnHHVfKY4/dLRMnvmjvO61vv/1Cbr75YtuUY/HiT+zpOXOmprrOyZMnZNCgp8zzuVqeeuphWbMmMtXlhw4dlHHjXpB7771B7r//ZvtYWonMSHx8vIwZM0geeuh25/N///15kpyc7LzOjz9+J/37P2Ef84knOsjbb093Xr5t298yatRz0qHDTfYxR47sL0ePHkn1GM89111ef328fX36+vU66s8/o02g7m3e+xbSqdNd8vnnH0pm/vlnhzz//JP2cdq2bWmHo/7001Ln5d273y+jRw9IdRu93owZrzp/nj9/ln0NGzeuk379HjeV2utsAxR9r3fs2Jrqtvqan3zyAXtah7/qdfQ4KSnJ3u7FF59PdX39Xeh1fv55meTkd5EXbLAr7vvhjH7brhUAQI4Q6gAgFwqzKUrjxs3t8d69u53n6Ub70qWL5ZZb7pLevQeLr6+vDW8HDux3XuePP1bKyy8PFx8fH3OdIVKp0nkycGAPZ0MTDS5vvvmGrQQOHDjGbPS3MeetliNHYs56Ds2bX25CwDQpVaq0XHbZNfb07be3T3UdDShNmoSbwDFSQkOLyfDhz0pCQoLzcv1ZQ0WbNh3kvvs6mdDzvUyf/kqGr/ujj96RlSvSbhg+AAAQAElEQVT1+veZwPSive+VK5fb4KI0oOr7oI/Vr98IufLKljbYaKg7ceK4DBjwpOzatUM6d+5lguHjsn59tH39KSkpqR5nzZoImTfvdXnggS4mPN5nA/SQIU/bUNitWx+56qrr5LXXxsmKFd9n+FzfeGO8eV/3yKOP9pBevQZKuXIVZdWqXyS7Dh06YF/TxRdfIc8+O1RatLjRnv/77z85rxMXF2deZ6R5va3Our2fn595vq0kIuJX5/vkuL3O07z00qvtz9n9XeSVW5+oVj64lO9Cgh0A5Iy/AABypLC7XIaGhtpgtnfvv/bnDRvWSmTkbya0jJaWLW+y511+eQtTkbrRBqHHH3/Gnvfhh29J2bLlTLCbZX9u1epmWwn67LMPTIWvl2zevMGef++9j9h5WVdcca3ZwH803edQrlx5e/D3DzD3Wd4GwbTuuquDqfidbvSigULDiQZRbfISHR1hK1BPPfW8Mwzq/Ywf/4I88kh3KV68xFn39/ffG6VMmXKm8vSQ/VlDm6v33ptth6oOHTrBvj9XX32d87KPPlpoq3KvvTbfPO8K9rxq1WraCtcvv/yQ6r40vE2aNE/q1Wtkf3777Rk2FOp5F1xQw54XFxcrCxe+Lddcc72kR99L/R3okFnl+L1klwbqzp2fsr8TB31ev//+s51rqTSgaXDV8JYeDXtLlnxqQm+U8/e0atVKG+gCAgJy9LvISxrsvpq2U4Nd+27ja/4gAIAso1IHADngDssWaGVJK3EaXJSjAhQWdqnzOiEhIVK3bkPn8EodSqcVqPDwK5zX0dtfdFFDs0G/1v6sFTc1duwgW/WLjY2V3KhRo7bzdFBQsD3WMKSio/+wxxdffKXzOhpWdIilhrf0aEjat2+PvPTSEFt5ch12qaFRg61W7xzviyu9vgYyR6BTjopn2iGoNWpc6Ax0jueqIdcR6JS+b3/99WeGQxQ1EC9f/rWt+KU3DDY7tGLqSsObBjTHsNg//vjZPr+aNWune3ut8oWEhMpvv52u7v333z4bXB1BNie/i7xGxQ4AcoZKHQBkk7usQ6dD8jTEVKlS1f58/PhRe5y2oqI/79q13Z6OjT1pw6DOFdODK8dyBFWqnG+HUWpVZ+bMSTJ58hi5++77bSfF9IJSbmjlSz3yyJ1nXZbR+nY33HCbfd0rVy6XESP6SsmSpe1QSq2C6fw9DXklSpRM97bHjh2RYsWKpzpPg7G+RwcO7Et1funSZc96rvqcdA5aWjqnsFKlKmed/8QTfe3w1qio32x30Pr1G8uTT/aXOnXqSXboc9Qhrq50CObs2VPscFqttv722wq56aY7M7wPHYKpIVMret269bbX9/f3d4b4nPwu8gMVOwDIPkIdAGSDOy0s7qjMOUKdo3X9sWNHTSAp47yeVnI0+CgNO8HBwXbIXdq5b4GBQc7TOjxPDxqQdPHqKVPG2vu/7bZ2kpccz3nEiIn2ebmqWrVGurfRYHnLLW3sQTt/Tp/+sowZM9BWqKpXr2XniDkCSloVKlRKNQdROebaOd6jjOhtT506JU8/PfCsy3Q4aHp0iGzHjl3sQauLOvRUG63Mn/+VDWq5oSFcw6EOoTz//Gp2iKbrUNP06DBRrb5qlU6HbmpV1xFyc/K7yC8EOwDIHkIdAGSROwU6He63aNF7ds6TY7hcw4bN7LEOr2zR4gZ7WodObtr0p7Rufbfztno9rSylN/8tLQ0eGv60IuSYa5cerfikpCRLdjVqdHpRcg1iWXk+aWlouuOOe21VccuWv2yo0/uMjl6V7vXr129iw7A2PSlT5nQlTpuLaPWyefPLMn0sva3OO6tTp4F93OzSSl7LljfbxiOHDx+0Q0A1SLsO3dQAHh9/Ksv3qfPk9O9Ag73+LbgOF03PJZdcZd/rH374ViIifpEePZ5zXpbb30VeI9gBQNYR6gAgC9wh0GnHxqio32X79i22eqY/62LkjoDRoEETs9F+pamqjbGVGA0NusFvalvOpiKqY8eutiPmjBkT5bLLrrYBTxupaEdH3ZjXhiBr1qwyVZ/rbUhavfoPO6zRdR5eWrVq1bXhSK+rjUgcofJcdDiiPudXXx1pG7lox0ptWPLPP9tl9OipZ11fw9eAAT1sgNEqk1bPPv74XTtXrEGDpqle3/DhfeX662+1YVS7d44d+4bt6vjppwtk0KCe0q7dgzZELVgwxwS1+nZoYma026bedtSo/rZhiVbtvv76Uzv3ToempqXB8dlnu9g5gI0aNRM/P3/54ouF5r2q45zTV7NmHYmM/NXelwa9118fl60KnlbetFPpZ5+9L9dee+4mLNoQRV/nBx/Ms2HStTFMdn8XBYFgBwBZ4ycAgEwVdqDTYXW6Hpq23tehc7o2WfXqF9oFyBs2bJrqulq52b37H/n++6/k228/t1WXIUPG2eDhoM00NAB++eVCG1I09GhjEQ1xGo60+YeGOG1nr4EpKSlRunfvm+nQPg1UumzA/PkzbfOO665rbeev6Xp4Os/LMd/s3393yrJlS+TWW9varpmO57xt22b7XL777ktzTortmOnakMRBh142a3aJvb5e9+uvP7OdLvv2Hea8vr6+unUbyIoV39m183Q46k033WFfV2BgoF2KQMOxVvdWrlxuzm9k30t97Q6nn4fIjTfe7jwvIOD0bbXl/yefvGuHPeqQRa1kajfRtLRJjVbONOx+/PF828BFK2V9+rwgwcEh9jpa/du0ab1MnDjKPJ9F0rZtR4mJOWRDnyNE6+21+vrgg13PegydZ/fjj9/aOW/aHdMxL1LpeWnff6XzEfX1NWnSXO68895U95ed30VBqXNxqdBt647e3jr86d8/XzlphwAAzpK3M94BwMu405BLoCgzFbsDcUeSqdgBQDoIdQCQAQId4F4IdgCQPkIdAKSDQAe4J4IdAJyNUAcAaRDoAPdGsAOA1Ah1AOCCQAd4BoIdAPwfoQ4AziDQAZ6FYAcApxHqAEAIdICnItgBgEjWVzgFAC/1+jNbpl5+R8ULCHSA59EFykPK+E6c+syWugIARRSVOgAwZr+wbUnja8tdW6tpiWAB4DE+enlbrJ+v1O/yYi0WJgdQZBHqAOAMgh3gWQh0AHAawy8B4IzOI2resvaHgz9sjT4WJ0Amfvppqfz66wpB4SHQAcD/EeoAwEVRDXbHjx+TxYsXyf79eyW//PlntPzww7eS1/799x9ZtGjBOa/3wQdvyvvvz5PcSk5OlpEj+8vQoX0EhYNABwCpEeoAII2iGOy2bftbJk4cJfv27ZHc0tDzzz/bJSEhIdX5b701Tb766mPJa8uXfy3Tp79yzustW7ZEtmz5S7Ijvdfi6+srffsOk/79R0hB0+cyevQAuffeG+T++2+RMWMGSWxsrBQlBDoAOBuhDgDSwVDMnFuy5FPp0qW9HD16RDxdRq/lxhtvl+uvv1UK0ubNG6VXr0ckJCTUHA+UK6641gbauXOnSlFBoAOA9PkLACBdGuy0eYo5SfMUFLoLL6wrzz//olx22dX256uvvs5W7tati5KigEAHABkj1AFAJopasNu/f4/06/eG/PXXn3LeeRfI008Pkvr1GzsvT0lJsXPTfv55mezYsUUqVqwiDzzQWVq1usVe3qnTXbJ79y57+oEHTp/32Wc/S1BQkPM+vvvuS3n77ely7NhRe7vHH39GAgMD030+cXFx8tprL9nwsm3bZilbtry9zYMPdrPDIF1FRPwqM2dOlD17dpnn3ESee26UlClTNsPXunXrZlmwYI6973//3SnVqtWS9u0fkpYtbzrna3nuue7255deesN5fzqEdf78mTZk6bDNxo2b2/evZMlSzuu0bn2pPS8i4heJjPzNXtau3YNy++3tndf59dcf5d13Z5v3d6sUK1Zc6tSpL4880l1q1arjDHRKn/euXTvM5fXE2y2csO2kv580INABQPr8BACQqc9+mPTO5TW6XRMY7Fe1TOUgr9wZpg1Svvnmc1mzJsIEj7vt0EI9/cUXC+W229qZ0HU6lGmgmzv3Nbn22pvkrrs6mNAVa+fK1a59kVxwQQ256KKGJoiUkA0b1sigQWOlTZv7TDisam+rYW7nzq1y9GiM3HPPw1K3bgMbqjS4NGjQJN3n5e/vLwcO7DeXN5Ubbrjdhrp33pkhlSufbytXau3aSFm9+g8T0jbJffc9ap5DIzt/bv36aDtM0uHLLz+SUqVKyzXX3GB/9vX1kyNHYuTSS6+2r0dDpt53q1atpUSJkud8Lcpx/ydOHJdevR6W+Ph46dixqzRs2FSWLl0sv/yy3L6fPj6nVxDS0KeBLizsMjsv79SpOJkzZ6oJa9dIuXIV5OTJEzYw6ut7+OEn7Hu0efN6+7odr1dp8NNGLX5+ftKnzxB7ubfSQBd/Iumi7hNq/yMAgHRRqQOALCgqFbsnn+wn113X2p7WylDXrvfYAKMBTgPLggVzbcjr1q23vY4OAdTmKhpWdI6XVvW0YqUaNmxmgkrqsKFBasiQ8c7K3eLFn5jQsiHT53Tnnfc6T2ulSquEq1atTBXY1KhRk6V8+Yr2tFb+pkwZa59LzZq1071fDXjt2z/o/FmD1rfffiFRUb/Z8Hau1+JKw6/Ou3vttfk2nKlq1WpK//5PmGD3g1x5ZUvndTUwO94/rQxqR059D+rWrW/fSw2aWo1s0eJ0+HSt4ikNuaNGPSeVKp0nw4a9YsJ0dfFWjkDXa0qdXQIAyBChDgCyqCgEuwoVKjtPayjR4YEbN66zP2/YsNZWkpo2vTjVbZo0aW4qXDNtF8aQkJBM779q1eqphmIGB4fYalVm9PG1mvX33xttRUyVLVsu1XV0KKYj0CkNYY7nnFGoUxriPvnkXTuUUUOr0teYXTr0UyuVjkCndPilWrdudapQ5/oeBwWd/jPSiqeqXr2WnH/+BfLmm2/YZSauueb6VK9LaZVOu3GOGzc906Dp6Qh0AJB1dL8EgGwoal0xdRji4cMH7eljx053gNThkq6KFy9pjw8c2Cd5bdOmDfLss11swBw/foZ8/fUqO7TxXPR5K8dzT4+ubTdhwjC59da2JjR+Il9++avklL43ad8XDZrFi5fI1vuitxk1aopcdVUr81o/lYceut0uWxATc9h5Ha3kBQQEEOgAAE6EOgDIpqIU7LRa5Gj0UaFCJed5qa9z1B6XLFla8trHH8+31axu3fqkmlN2Ljo/TjnCXXp0fmB4+OV2eKPjteWU3j7t+6LNUrSymN33RYd+du/eV6ZNW2CDbGTkr/LGG+Odl3fs2EW++OIX8VYEOgDIPkIdAOSAtwa7xMT/L7Kt88m0KuQYyli9+oW2GpW2hf6aNZG2UYrOUVPavEMlJydJbsXEHLJNQBzdMXVopHaqTEu7ciYmJro8pwh77BgCmd719b51XpqDDu9MK6uvRbttaifKw4cPOc/TBi76OM2bXyY51ahRM3MIs2vUOWhHUO2M6Y0IdACQcVHS+wAAEABJREFUM8ypA4Ac8sY5drNmTZJ7733UznN7773ZNqjddNMd9rLg4GDbXXLevNdt9UwbiaxcudwGqGHDXnbeR61apytq2k1T2/FrFSuzeW2ZqV27nkRF/W7vSwOlNlbRULN9+xbbmMRRRdTukto8RJu46NIHuiSAVuFcH7dMmXKyc+c2O6RTm5Loc9P5aZdf3sJ25NQmMDrHb/36NbbKpkMhs/pa2rTpIJ9+ukAGDepplyjQqp129tTbaAOZrIqOXiWTJ4+RG264zb52DZ5//PGzfV0OQ4b0skFaO3I6mql4AwIdAOQcoQ4AcsHbgl3Xrr1tUxLtxqghSLsrhoYWc16uoU4tWbJIFi58216m6665BhddN+2JJ56V99+faytXusZaTkOdrkcXG3vShk0/P38bYnRdOw0+GtC0kqUqVz7PdsN8/fVxdm05DZy6Tp0rDV7Dhz8rzz/fXT744Du7kPe0aS/LmDED7Xp2993Xyb7mN9983TYi0YYuWX0toaGh8uqrc2XixFHy2mvj7LDLZs0usY/hWM4gK5o0Cbevefnyr21XTH1djz7aQ+6++37ndapWrWGD5/nnVxNvQaADgNzJ+jcNACBDGuwaX1uuSCxQDuQlAh0A5B6hDgDyCMEOyB4CHQDkDUIdAOQhgh2QNQQ6AMg7hDoAyGMEOyBzBDoAyFuEOgDIBwQ7IH0EOgDIe4Q6AMgnBDsgNQIdAOQPQh0A5COCHXAagQ4A8g+hDgDyGcEORR2BDgDyF6EOAAoAwQ5FFYEOAPIfoQ4ACgjBDkUNgQ4ACgahDgAKEMEORQWBDgAKDqEOAAoYwQ7ejkAHAAXLVwAABarziJq3rP3h4A9bo4/FCeBlFk7YepxABwAFi0odABQSKnbwNqcDXXJ9Ah0AFCxCHQAUIoIdvAWBDgAKD6EOAAoZwQ6ejkAHAIWLUAcAboBgB09FoAOAwkeoAwA3QbCDpyHQAYB7INQBgBsh2MFTEOgAwH0Q6gDAzRDs4O4IdADgXgh1AOCGCHZwVwQ6AHA/hDoAcFMEO7gbAh0AuCdCHQC4MYId3AWBDgDcF6EOANwcwQ6FjUAHAO6NUAcAHoBgh8JCoAMA90eoAwAPQbBDQSPQAYBn8BMAgCfw/ff4T5dUSL46qFjx4mXLVA7yFyAfvf/SX0mLV417bNL7nVYKAMCt+QoAwK2Fh4f3NYf4lJSUTU+Na37J2h8O/rA1+licAPlEK3S///XBtbuPRD3avHnzJebvr54AANwWwy8BwE2Zjen7fHx8Jpow93ZkZGR/18sYion8knbIpQl0N5m/wYnmb/Hn2NjYPuvXrz8uAAC3QqgDADfTrFmzq3x9fV81J7fEx8f3Xrdu3b70rkewQ17LbA5dWFhY5zN/lxMiIiJGCADAbRDqAMBNmIpINVMRedVURComJyf3iYqKWnWu2xDskFey2hTFVJCHmb/R3uZvtY+pIM8VAEChI9QBQOHzNYHuVbOR3Mac1g3lT7JzY4Idciu7XS5r165d0tAdEJeZHRC9zQ6I7wQAUGgIdQBQiEyYe9YcvXSmMjdFcohgh5zKzbIFYWFhDXx9fXXeZ5IJeH0iIiI2CgCgwBHqAKAQnGmCotW5+aYy10/yAMEO2fXh+K1HE04mN8ztOnTm7/lmc6Tz7X5KTEzss2bNmhMCACgwhDoAKEDNmjW70s/PT8Pc1ri4OO0kuFfy0OlgV/bqWk1LFhMgE3kV6FyZynOXM/NCx5mq3UgBABQIQh0AFIBGjRpdEBgYqG3hKyUlJfVZvXr1H5JPZg3a+kWT68q1JNghI/kR6FyFhYUN9/X1fdoEvN6mEj1PAAD5ilAHAPnLp3nz5lq5uPvMBm62mqDkFMEOGcnvQOdwppmK7si45MycUZqpAEA+IdQBQD7RJigmyI0700BishQwgh3SKqhA56pp06YN/f39dchx4pn5dn8JACBPEeoAII+Zyty9crppxLt51QQlpwh2cCiMQOfK/F/ccqY50Apd587s6DgpAIA8QagDgDwSFhZ2xZn27tvOVOf2iBsg2KGwA52rM81U9P9kbFRU1CgBAOQaoQ4AculMExStzFXRhZjzswlKThHsii53CnSuTLgbYYLdU+ZkH5qpAEDuEOoAIBfONEFpazZOdcP0Y3FjBLuix10DnYMJdqXOLIFw8ZlmKt8LACDbCHUAkANhYWHPmA3RCWeGWU4SD0GwKzrcPdC5atKkSSNtpmJOJiQlJfWOjo7eJACALCPUAUA2mMrCPWcqCwtMmOsrHohg5/08KdC5OtNMRefb/XBmhwnNVAAgCwh1AJAFZ5qgaOe+HTrUMioqard4MIKd9/LUQOfKhLuucrqD7JjIyMgXBQCQKUIdAGSiSZMmVQMCAjTMnZeUlNQnOjr6d/ESBDvv4w2BzpWpjI80/3s9zlTt3hQAQLoIdQCQgTNNUNqdaeDwkXghgp338LZA59CsWbPSWiU3/4vhJuD1NpW7pQIASIVQBwBpmDDXx2w83mk2JBd5UhOUnCLYeT5vDXSuzjRTmWjC3XFTNe+3evXqzQIAsAh1AHBGWFhYe23SYA7vmzD3rBQhBDvPVRQCnSuz0+Vm8z862ex4WbZ///4+u3btihUAKOIIdQCKPLOReLk50uFd/+ji4Z7eBCWnCHaep6gFOlfm/7ab+Z/V+a4vRkZGjhYAKMIIdQCKrKZNm56va2OZjcKqZxox/CZFHMHOcywcv/VY/MnkBkUx0Lky4W6U+f/tfmbu61sCAEUQoQ5AkXRmQ/CRMxuCCwVOBDv3Zyp0x02Frn5RD3QOjRs3LhMYGPiq+X8OMDtpZq5evXq5AEARQqgDUKSEh4f3Mht9E83JpyIjI18TpItg574IdBkLCwtr4OvrO9X8jx9PSEjovXbt2q0CAEUAoQ5AkWDC3F1nwtwiE+Z6C86JYOd+CHRZYyrxt2vTI/M/v+TM/3uiAIAXI9QB8GpNmzYN8/Pz0zB30Gzk9Y6IiNgpyDKCnfsg0GWf2ZnzpDnS//+B5n9/ggCAlyLUAfBKOscmICBgsglyDc4sWLxCkCMEu8JHoMsdU7kbZz4LHkxMTOwRHR39iQCAlyHUAfA6Zu/8SBPkws3J+SbMzRfkGsGu8BDo8kaDBg0qBwcHDzfhrumZHT2/CgB4CUIdAK8RFhb2mNlgm2ROjjUbbC8K8hTBruAR6PJe06ZNL9Uh2eazYldRXpcSgHch1AHweKYy18ocTTaH3xISEp5es2bNCUG+INgVHAJd/jI7gdprMxVzWBAREdFXAMCDEeoAeKwmTZrU9Pf318pcsaSkpF7R0dF/CvIdwS7/EegKjgl3z5hgN16HZJqq3RQBAA9EqAPgkUx1Titzt5kNsacjIyO/EBQogl3+IdAVCh/zmaJLILRJTk7usXr16i8FADwIoQ6ARzEbXk+bja62Zs/6hybMTRUUGoJd3iPQFS7z+VLNfL4MNZ8vtc3x0ybcrRYA8ACEOgAewWxs3WaOJps96Z+zeLj7INjlnY8mbDtx6kRSPQJd4QsLC2vh6+s7yXzerD6zvuURAQA3RqgD4NZMmKsnp5ugxBu91q5du1XgVgh2uaeBLjE+uX6PV2r/I3AbzZs3f1SbqZiTr5hgN0IAwE0R6gC4pQYNGgQGBwdrmGtxZhjUtwK3NWvwti+btirbombTksUF2WIDnZ8JdC8S6NyVCXfDzNHTZ6p2bwoAuBlCHQC3Yzag+pmjUebQKzIycrrAIxDsso9A5zmaNWtWWte3Myebnlnf7gcBADdBqAPgNkyYu9scDTWHb0yY6y/wOAS7rCPQeSYT7pr5+vrq+nZrTp06NX7dunX8/gAUOkIdgEIXFhbWwGwkTU1JSTkcGxvbc8OGDXsEHotgd24EOs9nPrdam8+taebkwoiIiGcFAAoRoQ5AYfILDw/XZQmuSUpK6rl69erlAq9AsMsYgc67NG/evI8uXm5O9jLh7nUBgEJAqANQKMyGUG/dEDLVuZ7Mm/NOBLuzEei8lq/5TNPGTjfJ6bnASwQAChChDkCBOjNkSYdafmo2fJ4ReDUNdk1albuuVtMSwVLEEei8nwl2teX0Eiy+SUlJvaKjozcJABQAQh2AAlGvXr0aoaGhr5vqXEpCQkLPNWvWbBMUCXNe2Lak0bXlri3KwY5AV7SEh4drxU7D3dKIiIie5jhZACAfEeoA5Duz9/oVc3S5OYxgWFLRVJSDHYGu6AoLC3vC7Mh6wJz8xHz2vSoAkE8IdQDyjQlznczRG+bwvNmgmSgo0opisCPQQZnK3cspKSltTcDraSp3XwoA5DFCHYA816xZs0t8fX01zEXHxcV1X79+fbwAUrSCHYEOrkzVrroJdVPNIfBMt9/NAgB5hFAHIM9UrVo1pFKlShrm6ptDd7NHOlKANIpCsPv4lW0nE3yS6xHokJbZ6XWjn5+fNov6NjIysqcAQB4g1AHIE+Hh4br47ojk5OTuUVFRbwmQCW8OdhrofH2kQZcXa+0QIAPmM/NJE+w03D1tPjOnCADkAqEOQK40b978Zh8fnzfMhslHZq9zPwGyyBuDHYEO2WXC3STz+Xmr+RztFhERsUwAIAcIdQBypEGDBpWDg4PHmJOVtTq3evXq7QJkkzcFOwIdcsoEuwvNUV9zqJaUlNSDz1MA2UWoA5BtYWFhw319fbuavcudTHXuawFywRuCHYEOecF8trY2n62vmZOfmKrdswIAWUSoA5BlzZo1a2M2OGZoBzezwTFSgDziycGOQIe81rx58z7maKw59DQ7zmYKAJwDoQ7AOZ1pxT3dHGKTk5O7RUVF/SdAHvPEYEegQ34JDw8P0EYq5nP3CnPcw4S7FQIAGSDUAciU2bAYYzYoOpgNi8dNde4bAfKRJwU7Ah0KQpMmTRr5+/u/Zj6D9yUkJPRcs2bNfgGANAh1ANLVvHnze83RdBPoxprK3EsCFBBPCHYEOhS0sLCw9r6+vlPNyZlmB9sQAQAXhDoAqTRu3LhWQECAbjCEnKnOHRGggLlzsCPQoTCZHW6DzGdzO7PD7aXIyMj3BQCEUAfAhQ61NEf3JCUlPbJ69eqfBShE7hjsCHRwB7Vr1y5pTDcnzzef192jo6P/FABFGqEOgA7rudPX13d2cnLyBIZawp24U7Aj0MHdmB1xV5ujN8xhRURExJMCoMgi1AFFWJMmTSr6+/vP9vHxSTpx4kTnjRs3HhTAzbhDsCPQwZ2ZHXNPmB1zGu6eNOHuDQFQ5BDqgCKqefPmA02Y62VOdjYbAV8K4MYKM9gR6OApzOe6dsm8NiUlpTtLIABFC6EOKGLMl/515mi2Ocw3X/qDBfAQhRHsCHTwNKZq18AEO63W7YmJiXl869atNLsCigBCHVBE1KhRI7hs2bJzzckKZi9u56ioKDZS4XEKMtgR6ODJzixL09ccFpkdeKMFgFcj1AFFgPly73GmBfZ0WmDD02mwa9yyXKuaTUoESj4h0NT+GY8AABAASURBVMFbmM//Uebz/5GkpKTHV69e/ZUA8EqEOsCLNW3atKGfn988c/I3E+Z6CuAl5g7b/n2jFmWvzo9gR6CDtzHfBef7+/tPTznt8aioqN0CwKsQ6gAvZfbOjjd7Z1snJyc/ar7AVwngZfIj2BHo4M3Cw8Nv0xEb5rthTkRExAsCwGsQ6gAvY8LcHeYLW+fOjTVf2hME8GJ5GewIdCgqwsLCBp/pfvx4ZGTkJwLA4xHqAC9Ru3btkoaGuYCTJ092Ys05FBV5EewIdChqTNWu/JmqXYmEhITH16xZs00AeCxCHeAFzJez7nEdab6gO5m9rh8LUMTkJtiZQBdnAl09Ah2KIlO1u8HX11fn200z3x/jBYBHItQBHuxMI5TZZk/rbxEREU8LUITlJNhpoCtZ2r9Rh/7VtghQhDVv3ryb+S4Zl5yc3CUqKmqhAPAohDrAQ5nq3FhzdJv5An7QfAFHC4BsBTsCHZCaDuMvVarULFO1K2O+W7quXr16uwDwCIQ6wMOEhYVda/amzjcnJ0dGRo4TAKlkJdgR6ICMme+Z6319fWeak+9HREQMEABuj1AHeBBTnZtlji40e1A7ss4QkLHMgh2BDsgaE+6eM+Guv6ncdaFLJuDeCHWABzBfrO20Ome+WJ80YW6OADin9IIdgQ7InsaNG5cJCAjQHYrF4uPju65bt+4fAeB2CHWAG2vSpEkx82WqQy0TYmNjO65fvz5eAGSZa7Aj0AE5Fx4efpM5mml2Ls41VbthAsCtEOoAN9W8efPHzdEE8wWqQy0/EwA5MmfkX1+mxAdeU7pUSuN2fVm2AMiNM0My+yQlJT22evXqrwSAWyDUAW7G7A2tYo5GmkNCREREdwGQK+Z/aoU5GmD+n34SALnWpEmTigEBAXPMTsf45ORkDXcxAqBQ+QoAt2Gqc73Nl+SqhISEmQQ6AIA7WrNmzX7zHXW7j4/PW6Zqt9XsOHlWABQqfwFQ6MwXYjVz9IEJdCsjIyPPFwAA3JwJdovM0SKzQ3K8+R7boFW7qKioXwRAgaNSBxQy82XYz4S5HxMTE3uZQPeMAADgQcx3V7+EhIS7TOVuwpmldwAUMCp1QCExX3wXyunq3HfmC7GGAADgodasWfOXOboqLCzsMfP9Zr7aUh4z321zBUCBoFIHFAJTnRtovvCWmOqcLuj6nAAA4AV0LdWIiAhtxHe1+a57p3HjxrUEQL4j1AEFqGnTpnXNl1y0ORlqwlyd6OjoKAEAwMuY77jOSUlJkwMDA78xlbsRAiBfEeqAAmK+1Ab4+vpO9fHxedB82Q0WAAC8mNlx+bup2tVOTk6ONzs0t4eFhbUQAPmCUAfkM113znyZ/Z6SklI8KirqJvMFt1YAFBhdS8tUDJIFQKEw332jzA7NFuYwwnwfzhEAeY5QB+QjE+i667pz5vCkqc4NEgAFzmxIBvr5+fF9BxQis0Nzp/kebGlO/mi+GxPN4QEBkGfofgnkg6pVq4ZUqlTpUxPmNrPuHAAAp5nvxHnm6C1TsXvLBLtHY2NjH16/fv1eAZAr7LkE8lhYWFgHE+gOJCcnjzNfXj0EQKEy/4ubEgwB4C6Szffjg/o9GRISEtmsWbMnBUCuUKkD8pDZ8/i+j49PUkRERDEB4BZ8fX3rmkOAAHArUVFR35mj80yoG6CdoVNSUu43560XANlGpQ7IA6Y6d4P5QjplAt1CE+iYJwAAQBatXr16jHaGNjtfPjDfp6MFQLYR6oBcMmFuivky6mgOxU2g+1AAAEC2aGdoc2hkgt3R8PDwHaZ6d6UAyDJCHZBD5kvnQnP425zcGBkZ2cl8GTFnB3BDycnJWxMSEhIFgNsz36VjT506dbWfn9848x37ugDIEkIdkAOmOtcjJSVlSXx8/E0m0L0mANyW2fNfKyAggDnkgIdYt27dPybcXW12yKwxwe6IqdrdKgAyRagDssl8wXxhjuqZMFdn7dq1WwUAAOS5qKioacePH69qqnZPhoWFTRYAGWLPJZBF5gvlerPH/+ukpKQ7V69e/ZUAAIB89ddffx0zR7c3bdr07ubNmyeZ0+3NTtVPBEAqhDogC0x17uWUlJQmERERgebHZAEAAAUmOjpag5y/CXYLzeFeE+zuFwBODL8EMlG/fv0q5svjTxPodpkvkBuFQAd4HPP/+zeLjwNeIcV8F7czx4vMztZEc7hLAFiEOiADYWFh94eEhMyR00M9XhUAHsnHx6d2gCEAvIL5Tn5fR86YHTYPmx2v7wkAhl8C6TFfEtPMhmAJ86XRWgAAgLtJNuGurfm+vk+rdubn9uY7e5EARRSVOsCF+WIobw4bzMlI8+XQUQAAgNtKU7WbKEARRagDzjBfBm3Nl8L6hISEu8yXxAwBAACewFbtfHx8lpsdsye1W7UARYzbDr/cu3fvbgEKyMmTJ0smJyf7FS9e/LD5cZm4ucqVK58nAADASYdf1qhRo6zxuQl3bczPvQQoIqjUoUgzlTmfmJiYCr6+volnAh0AL2N22GwzFfhEAeD1tm/fHqfdqs33+6bmzZtvN+GusQBFAKEORZbZyAs0ga5SiRIlDgcHB58UAF7J7LSpGRAQQGMwoAgxwW6qj49PCxPu3jHhbpAAXo5QhyIpNja2eHx8fEiZMmX2+vn5sQcfAAAvExERsdOEu6Ym3AWbYPd7o0aNKgngpQh1XubHH38MNh9ggeImfvrpp6Du3btXuO2226o8/PDDFffs2eMnhez48eNldNhlsWLFjggAAPBqJtwNMd/7TwYFBa0OCwt7SAAvRKjzMsuWLQuZM2dOSXEDGuDGjRtXtmbNmgkDBw481KZNmxOVK1dOkjxw7Ngxn927d2crIGqQO3LkSMXAwMDY0NDQYwIAAIqEqKioVSbcVTEnr2DBcngjQh3yzebNmwOSkpKkffv2x6+66qpT7dq1O+Hj4yN5wVT/Kr7//vvFs3p9l/lzB02oixMAAFDkmHD3pDlaFB4efqCZIYCXINQh35w6dcomOH9//xQpRLGxscXMoYTOn/P19c2TSiEAz2Gq9DvofgnAQRcsN9sFdf38/OaYql0/AbwA3cAy0b9//3JVq1bVVvfJP/zwQ8jJkyd9r7766lhTJTpiqj3O623ZssV//vz5JTZu3BiolakGDRrE9+7dO6ZUqVL5GmaSk5Nl9uzZJX777bfgEydO+F555ZWxiYmJqUph0dHRgQMHDiz3+uuv73/33XdL/PrrryGvvPLKfxdeeGGieU3BS5YsCd20aVOgvkZTTYvt0qXLMRN8Ut22V69eMV999VWxHTt2BJQrVy7pkUceOdqiRYtMq13mfirs2bPH/n1pVU2PZ86cuf+8885LWrt2bcAHH3xQYv369YHmPUq+8847j991113O7pNvv/128TVr1gRt3749wLzPKU2bNj1l7u9o2bJlk83zDZkyZUppvd53330Xqgdz2+Ndu3Y9pq9n3LhxZaZOnfpfzZo17Qbce++9V/6dd94J+PDDD/c67v+OO+6oYn4/h7dt2xbw7bffht57773HtYp4rucFwDP5+PhUp/slAFfmu/6QOTKZrvlL5vCtLoMggAejUncOS5cuDTWhLeCFF1449OSTT8ZoiFi0aFExx+XHjx/3GTJkSLndu3f7P/zww0fvv//+YxqSBg0aVM7sHZb8ZEJacfNcioeFhZ164oknbNMP86EUlN51x4wZUzYoKChFX0O1atUSTWgKGD9+fBk9r2fPnjHXXntt7BdffFF82rRpZ83HmzdvXsnbbrvthAmQ+y666KL4CRMmlNm/f3+mfzvPPPNMjAlKx/W0hsKRI0cerFixYtKhQ4d8R4wYUW7nzp3+jz766NHLL788dtasWaWWL18e7Lht9erVE01ojDX3cdgErmMa8ObMmVNCL7v44otP6X2VLFky2XwI29MmfJ1I7zkcO3asjAmoCeldZkJeiQ0bNgTq+2buMy4rzwsAAHgXs930nNleGxseHp7SuHHjFgJ4KPZcnkOFChUShw0bdsjs5RWtbr3//vuJf//9d4Dj8s8++6yYCXa+kyZN+s9cN1nP09CkQW/FihXB6VW0TMnfx1TUsvT4WjUrVqzYWelQb//ll18WM+EjrkePHkf1PBPM4kwoCdD7T3v98uXLJ5mQ5Oz2uGDBghJVqlTR12YX3G7VqlWcznfTkGiC6fEyZcokO6772GOPHb3xxhtjHae1aqnh9oEHHrChTZuWuD5WiRIlUky1MuHff/+1f1/169ePN++JHfb4+eefh5qKp48Jhoc0vOl5cXFxvp988knxli1b2vcq7Xumgfnnn38OMSePmNeRbA7xfn5+Yip3SSbYxcvZfExIq1K6dOn92sY4ncvl8OHDvq+++uqBkJAQ+96++eabxc/1vAAAgPeJior63hz5mJ3kb5hwd21ERMRIATwMoe4cNNxooHPQypZjrpgyHwRBGo4cgU41a9bMBo0///wzML1QN3z48DJr164NkizQMGYCx/6052/fvt3/6NGjvk2aNDnler4OozSh7qyukNdff71zGKEGQn18E9RSDS3UYY4LFy4svn79+gBtbOI4XytsLs8nWatkWr3Un03YCho9enRZ1/sxVcGD5nmlF7ZEq276mhzBSdWtWzf+m2++CU1ISBB9r//77z9fUyUrad6/IA1feh0dhilZdOLECe24uSez65jXF+cIdFl9XgAAwHuZbbruJtgNNzuMvzIVvFsF8CCEulzSKl1oaGiy63laXTPnpRw4cCDdlvtdu3Y9qnPgJAsyajKij6vH+jiSBTofzXFaK3k6Hy/t8zYVNvtzRs/bQR/TVOfs4zdq1CheQ5zr5bVq1UrI6LY6L1HvX9etS3uZnq+BsW/fvuUrVaqklcXDjRs3jjehtoRWJeUckpKS7ERHU9k8eK7rmipeUnaelwnuNFhBodqzZ89GU3l2i+VKPI3ZAVbO7MRZZHbOxAuyrXLlyucJ4IZ27949wGxzPSV5LD4+PshspyWXKlXqPxqsFRyzfXqd2Sm/UZAjhLpc0urOvn37UoUgDUwanLTZRnq30WGckkumgmg/ZHTIoGSTDo/UylfaYOkIahqsMru9ziOsXbu2fXxtBpNRVS49+n6ZD0ufHj16xKS9TJuwfP3116Eaokyw00CXkNX7Ne93cfO+57icdq7nJQAAoEgw20inzE6gfUeOHClvdggdDwoKihXAzRHqckkbh0RHR5fQRhuOatjq1asDtUlKs2bNTkk+0S6SWjHTDpGu5yckJGQp5NWrVy9euzy6nmdeR5DOVdPGK67na0dPh61bt/prlbBu3bpZDlyu9P3SYanm8RPSqzI6hlu6VsbMY54V1szzTNHwrMzzKa170kyFTuf4lXV9vjExMVmqiJ7reQHwXObzIdlUOfm/BpBl+plRunTp/3QbIzExMdBsYxwRwI3R/TKX2rZte0LnsQ0ePLicqTKFfPzxx6Evv/xyGR2CeM011+TT+qO6AAAQAElEQVRbgw2d49W6desTP/30U8ivv/4apAHnk08+CdVgkpXbP/DAA8e0kcmwYcPK6FIA2l1Sm4LccsstJ8yHWKqNn2nTppVatmxZcGRkZODUqVNL6es118tRq/+77777RHBwcMrIkSPL6P2tXLkySJ+Do7tlnTp1bFjUhcV/+eWXIPN4JXWpCK2imWNnuKtRo0aCCaVB5vblzetPNkHsmC5joM1e1q1bp9eXM0s2nHPYZlaeFwDPZT4ffc2OtmyPaigK/vrrr4BPP/3Uzh0GcDazzRNjdiQnmHBXRgA3RqjLJa3qTJgw4YAGHW2BP3v27FLnn39+4vDhww9pwMhPDz/88LErrrgiVkPkXXfdVUW7RN5www1ZCls6tNEE0UP79u3zf+WVV8pooNNmKt26dTua9rqtWrU6+dFHHxXXlv8arl588cWD6XXkzAq9nb5fup6eNlh57bXXSmtV0wRgO7TBvJ5TXbt2PWKCVbAunbB3715/Eyr36xIHERERzuYy3bt3P1q+fHnfMWPGBMybNy/k4MGDvpUrV056+umnYz777LPiJqRV0TX4nnrqqZi8eF4AcmbDhg26HmRIVjv+5sSPP/5odzpJAdDRCuZzp/SePXv8xAvMnDmz5IwZM0rpzjABkC6z0/dkSEjIscOHD1fWnUSSB3TJJF1eSvLJrl27/My2W5Z2bLvSNXuXLl2a6+WcTp06JWb7spQ21MvsegXxHVFUuO2eS7Mxv1tQqByLj5uQc7Bp06Zu1WDgyJEjFUuUKHGwqE5gpnFB0eOpjVJ0o8JUvEt++OGHe/NraLN2FP7vv//8TGX/QHqXn2mUciwvGqXoCIBx48aVMY/1n44OEA+nn/O6UdWuXbsTji6/ukyNzrHWYf76M583cFf51SglI1rxN58nFcznyRGddye5cMcdd1S55557jpkd9Mcll8wOel/9/3VtivfWW28V1zV5P//88z3Zua/+/fuX02PzOXfOpnOZ0c+RDh06VDbFgiNt2rTJsODg+h1hvuNa0Sgl56jUwePExMRUMoHuAB2pACB3dIed2fA64bpsS/fu3SvqEHgBkIrOsytVqtR+U4UqFhsbm+0qWH7YuXOn32OPPVbJdTQTiiZCHTyKDn3QD1RtfCAAAAAFzOxYPmSqdn4nTpwoJYCboPslMqTNXkaOHHkws3XnCtKhQ4eqlC1bNlvDCABvosNpVq5cGXLfffcdM5WUErqcSsOGDeN79ux5ROeUOq6nw17mzp1bcu3atYEHDx70u+CCCxLNdWLq1KmT78MFlyxZErJ48eJi//zzj3+jRo1OVa1a9azH1DmwOpcrKioqyN/fXy6++OI4fQ2u1aKMaFOo2bNnl/jtt9+CdVmWK6+8MlbnwrpeJ70hkh9//HGwef/KuQ4D1eFPHTt2PLpmzZqgTZs2BWqzpOuuu+6k2et9TLLpTGOmUL0fnWN91VVXxXbp0uWYrluq81N07vMbb7yxv1q1avb39PDDD1fUIY4fffTRXr3OgQMHfB955JFKAwYMOHT11Vef0vkl2qjJ7H0P1qGl2qG3b9++MeXKlXPu0NLn37t378Pbtm3TOSmh995773Fz3okJEyaU/uuvvwKPHj2qQygTzXOJM9W44/o4abkO0dLf3ZQpU0rr+d99912oHu66665cDw0D8ov5v65jPuuqmMO+ihUrOv83hgwZUlY/+15//fX/9GedJ/bBBx+U0K7futzUnXfeedz8bTuHBOq8L/1M3bVrl79+PpjtnnjtW1C7du0MPzPN9Y6ail2INlAx//OHJQe0Y7nO0dX/82LFiiW3bdv2eNqhipl9tpj/dW1kF6rXmzhxYmk9mNd+6PLLL3cODd2yZYv2TiitfRfS+77IiDb/e++990ro56z5TIo1FXwdcpql55WZrHxHIGeo1CFDup5d8+bN4/VYCpGOYdcKHYEOsHNI/N99990SJngcNcHlgH4xvvbaa6n2Fo8ePbrMjz/+GKKNk3TtRf2Sfe6558rv378/Xz/zTTgK1FCga1326tUrRpc++eabb84aojRixIiyv//+e/Btt912QjdifvnllxATeLI0X9C89uKLFi0qrkuvPPHEE7bFeGRkZI6HHZmNlpIamKZPn76/Xbt2x7QpVHabBGizg/Hjx5cJCgpK0fB87bXXxn7xxRfFp02bZl+Tbkjp8d9//21Tqy6Boxuc+tlmApnduWo2jALOXNfuRDMbqSU+/fTT4hdeeGGCbkzFxMT4mcBXThs4udJAtmHDhkB9LzQcmw3X4ua9Dbn11ltP9OnTJ0YfWwOw61IvGTG3P6U78vT3Zz777Wmz8XtCADdldur8o8c//fST839Wu1/rjppLLrnEdiDX/zdt9LZz507/Rx999KgJPLHa2E4blejlJrT4mB1ApTVUaXM1neemQcbx/5oZXb/OHE6Y/88KkgNmZ0pxDZn6uGaHT6I2LXJt+nSuzxbzXE/oZ62eNp9fx/V/1nXtYN0JZoJtKe1Y/uSTTx5J7/siPdodXb9DzI6mo2aH0DHdwWM+d4tl9XllJKvfEcgZKnVwe9rkoEyZMnsFgF03UjvQOvZK64a82WgPcVz+559/BugGzTPPPHP4+uuvtxs1ZsPn1AMPPFBp4cKFxc0X+9H07lere5JFGe3o0Qnv5n81eejQoYe0Aud4vrqH3HEdU50L1I0l18nzZodN0qRJk8p06tTpmN73yZMnfVxDiN5XSEhIilavvvzyy2JmoyzOhFX7OszGRJzZWAuIjY3N8PlnNv+2ZcuWJ82Gi61G3X333Se/+uqrYroBYyp2WV6SZsGCBSWqVKmSOGzYMLu3vlWrVnHa/VjD5/3333+8UqVKyboRY/aYB+j9amMSXZZF1wXVMGeCW6K+J+XLl0/S9y8uLk70eejr7N+/v91g03VPO3fuXEmXetHfp+OxdW3PV1999YC+P/rzvHnzAsxGYpKp5tow1qJFiyy/DvP4yeYQr89Lfye6U08AN2YqTnH6v6Qds80OIvt5snr16iD9rNDqkv5sglOofqaYqtah6tWr26qQ+R/z1a7f5v8/bu/evX5a1dZgov+7enlmjT3S+azUHTExJghVMv/n+10vCAgISAkOzngfkfn/POn4TNZlsEx1sJJ5vsUc/3vn+mzR1+NYg1NHZKT3P6uVOceIBd0BpDvU5Bx0R+ALL7xwyIQ2+7MJXqGuIfdcz0s/x9K736x8RyDnCHVwW7oXW5uiEOiA/9MvW9dhRrqn9NSpU86NjFWrVtlvYdcvd93gr127doKu+ZjefeoQPB16JFk0c+bM/Y7OiK7MBkNQ48aNTzm+rFXx4sVTBUAdcqnHZi+6M5g0aNAgXtdJ27x5c4A+b1NVLLd161bnBoTe59ixYw9t377dXze+zJ7oVF3ndOiPCXUZLjFg9lbrZeluZJQrVy7V69C95brhI1mkG49r164NuvHGG1NtBDZt2vSUhuj169cHXHXVVafq1asXv2PHDvuadGikVuB0I0iDnjkrVl+v/o70ch0ipsvHhIeHOwNZhQoVNHAl6e/QNdTp0EpHoFOXXXZZnG606ZAuE+pjtfp2ruFQgCe79NJL40xYKKGfITqE+48//gjS/xXHcHPdyaU/OwKdMhWieA0qehsNPDocUUdAHD9+3FeXUHL9jE1LOzpm8nRSXaZB0bFjJj36vByn9f/UPOd4E57s509WP1skE3qfrl16TcBMdv2+yIgGNkegO3M75/dMbp5XVr4jkHOEOrgthlwC2afztPTYVLxSbZSEhoYm7969O92wcvPNN580laAsV2XSC3RKhzFpwMrstrrHXI+7du1aMe1lOkdQj3Uoku5Jd5yvw6L0WDe45PRrybeNAH2sI0eOZDkFaYVQhzjp++t6vuP9P3DggH1NOsRThyfpaQ1yuiGqoW758uW2ymoCa4AOmdTTjt+hrpepB9f7ddyfQ+nSpVP9LszvMlb3fP/6668hJtiV0Qqh2ft/1FG1BbyN2cmhw45LmJ0ZQRomtFKn/1+Oy81njq/+39x2221V0t5WzzcBJmnEiBEHTeXOVunffPPNkroG8BNPPHHUdYkAB/N/pUunZBiMzGdCaROIdG5ZklbNJRv0c0OHi565nyx9thS03DyvrHxHIOcIdXBLGuio0AHZ59jzqxUt1w0S3bDJ6MtUhweaQ66H2umwGkdoO9fzGzRo0CHd++t6mVbJ9NjsRU/M4P7tbc/1GLmhgSqjoUPp0eGigYGBKToHJ+396LGGKj021ciEt99+21eHbmljAW3CoPPjtKGNnqfNULSap9c1VTn7Ou+///5jWsV0vV/XRinp0aBoNl5j9aDvk85VfOWVV8qYvfX/1apVi4YE8DpakdPPlVWrVgVrtVvnHffo0eOI43K9TCvfOr847W0dlfrzzz8/qWfPnnYYpM77evHFF7WxUUnzOXXWbZo0aXKu5nH7zf90WW2k4ufnl61QpzuuHJ/TWf1sKWi5eV5Z+Y5AzhHq4HZ0YXGzd+s/xzhxAFnXqFEjGwJ0UWnH/BDds6rzIbRxiuSjCy+80DnE0EGH6rhq3LixfX4a6LI7Z0srhFql06qW6/naQc71Z93g0GPXeXkxMTHpVt9cO2fqc9Whj/Xr18/W89IwprdzPc+8/0E6N00buujPOtxLA9eyZctCdBiTo6uwPvelS5eG6GXaNEDP0+FS+jp1QzQ389r0PrQbprl/Ox8mq6HOPO8U3RMPeAoddqydgXV+nYYiXX/RcZlWyf/8889A83+akJUqvzYa0c+ArVu3ZnkYdlq65IE2TzHHh83/U4b/d66fPzoUVJ+n646crHy2OIYyFuT/bFaeV3qy8h2BnCPUwa2c+RA8lLaxwV9//RWwceNGHZ50Mittz4GiyoS6BG2qMW3atFJa/dE90dphTUPDPffck6/t6TVADB06tJxOhtfOlhokPvvss1SLWGt3R31+kydPLq0dPHUDzGyMBWu3tTFjxhzK7P71f79169YntB22bsRdeumlpz799NNQ3RAye9qdWwYaivT1rlu3LlCrf7///rvv119/ne4HhzYk0fdImwz8/PPPwbqnXDtyZvQctIGIHutQL60cli5dOuWBBx44NmDAgPLDhg0rY4J0rA6v1IYBt9xyywm9XK+vzRLM80rQ4V264en4HKtevbrzPEdDBZ0jp0sJ6PwUrdrpa9P5hhrOzHt0ML0hYUorf+Z5lDWPmazvccWKFZO0GYQGaEfYzwp9LmaDLUi78GVnKCpQWLQpijZR0oN+Nuj/v8Pdd999Qv/PR44cWUY/A+Pi4nx0Pp1+NujyJfp3rh0iW7ZsGauVPt0BFBUVFXzjjTfmqvOr+T/8z/z/lDefcTEZBTv9n9bPEf2f1uUB9PNHn6/j8qx8tuhnhH6OapdbPa07sy655JJ8bXKUledVrFixFP2c0znKOrdXq6FZ+Y5AzvFh7SXMnl955ZVXSulaK47zzF4m3zkPbwAAEABJREFU3UgqvWfPnkIZd51dOlwhow8/XdNKW/3qRpoAyNTgwYMPh4eHn9IvSx16p8Nihg8ffjCzyf95wXxxx3ft2vWIedxi7dq1qzJ//vwSDz300NH0np/u6Z0+fXqpF198saw2CTFhLUtVRB22qPNddN03E3yq6FCrtBVIbXrw9NNPx+jrNxtIVUxo8uvRo0e6Qe2aa66J1Y2hUaNGldUNOZ3Pl9nwKg3NWnV76623SpqgaNeHMtXHBPOaDu3bt0/XgyqjQer6668/2a1bt1SvXTcYdePHde1PE6AS9fVr4xTX63bs2PF4+/btj+t9mY3RssuXLw/V15l2rqQr3ZB95plnYnT408cff1x87NixZbVq+dJLLx3IaB5kerp3737U/K0k6sbX7NmzWVwZbk//BzXY6M4h1/l0SsPFhAkTDmhVbPTo0WV1nqruANH/fb3cVJbidaizdqLVNv0aTkxoOapz6iSXSpUqdcAEtdKmipbu9va99957TBu56PpyuiNFuwK7LkmQlc8WDU7PPffcYX3tQ4YMKVcQnSSz8ry0SYsJccdXrFgRYj7r7VIHWf2OQM647bjWvXv37hZkmc7J0I5Mrm3C01uA113ph575YDqla76kd7kOJdM24OZD4IRjD7e+Zt1Yzc7GircwG63nCYoUs3Nmo9loz9JabkhNl0Ux1a9j5rMj1d5rXbxb16QyQZEFtjPB5w3cldmpM8CEh6f09Pvvv19MA5nZ4bLP3Ub06GeQ7rTObHkV2KkC15nt1Y2CHKFSh0Jn/omL6wddRoFO6fh4E1pPuH5Qm73JFc2HOGV7AACKMF1r7tNPP7XD/9xxioapnh80wa58RhU7IC8wpw6FKj4+PjgpKSnA7ME6LACQD0yFM5nGS4B3Gj58eJmIiIjgSy65JO6hhx5y26p76dKl9x06dKgKSzUhvxDq3ISO737nnXeK69wOHReti2TqWGTH2kJbtmzx17HHuvCsToLV7ki9e/eOKVWqVLY2VN5+++3iOn5bu8fpXAtdLLJLly6p1mLRIUkdO3Y8qtfT1ts6yf666647qROKHdfRuXu6WPGuXbv8tZtUrVq14nWuS+3ate0wz4MHD/rqPDhdaFg7M1188cVxPXv2POLYg7Zjxw6/119/vYx2QUpMTEwxr7fcnXfeeeLaa69Ndy0lXRz5ww8/LPH555/vWbJkSciUKVPs2k3aYEAP2lSga9euxwQA0jCfr77mcNZ0A51nqPPvBIDH0sYo2oAjN51iC4ou1RQTE1NJA54AeYwysJswga24hiRHWKtatWqCBji97Pjx4z46+VUbAugismcm9AYOGjSonIbB7NCw2KJFi9hnnnnmsGOC7pw5c86aVPvee++V1DbA06dP39+uXbtjH330UfGlS5fa1my6eOTUqVNL6yK92lRA56ToeiXaxchx+xEjRpT9/fffg7W7kXaS++WXX0J0zRfH5Wc68wWY8Hike/fuMdp9LjIyMigrr0G7KI0cOfKgNgMwH+L2tAZCAYBs0I3AojgnF/Amt9xyS6wnBDqlIwbMtos2TykjQB6jUucGdG0SndyrnYOefPJJ2wXItWKlXYK0ze2kSZP+q1Chgq2oaSteDXorVqwINiEtLquPlfa6GhRN1S3EnDzien7Lli1PPvLII3YYw913331S2wFrRcxU7OJ07LoubGyeY6xjHSxHcxZlqnOBGvBcm7ZoG3Dz/Mt06tTpmC5cuXXr1iCt3pm9a/ZyR0UyK8qXL59sDvG6Horer6d8mAMAgKJNewgEBATEaYM4bZ4iQB4h1LkBbdOviwNntI6QDmGsUqVKoiPQqWbNmtnr6vpM2Ql1pjrmO2vWrJLmdkGHDx+2lVrHQr2utHLm+rOGSF1rRE9rJ00dsvTuu++W0LCplT/XVun6fPX4kksucS5AqRVIDa+61lLdunVDta2trgllQlkJvX2dOnVYfRLIhNkQOGgq8/yf5MCJEydKmc+5o2ZDKl8XXwdQsPz9/Y8lJycfEg8TFBSkn0uVdDusTJkyBwRWaGhogiDHCHVuQKteelyqVKl01x/S4GT+0FNdput/6Fy2AwcOZHkNOh022bdv3/KVKlVK0uGXjRs3jn/zzTdL6GKd57qtDrV0LEKrjz1ixIiDn3zyia3emfsoqetG6ZouOjfv5MmTdu5K165dK6a9H1MZDDGhTnr06HHIPI9i0dHRQbq+iQl1Ceb2Ry666CL+oYF0mP+XqwQ5Eh4evsIcDYiIiPhJAHgNs0N5qjmaKh6qefPmi83RxMjIyK8FyCVCnRsoX768rYrpumsZXb5v375U4c3smdKlAHwyCoLpWbp0aYiGQBPsNNBlKzzpenBmb5Lzsc4///yknj172qGia9asCXzxxRfL6Jy5QYMGxThejzl9SJusuDxnX1MBLFm8ePH9+vOZtaGO6+LoY8aMKaML7Or6MhoaAQAAvJkJc61NsNvXoEGDC9evX896mcgVtp7dQK1atRK16rZu3bp0G4Vow5K9e/f6Hzp0yPn7Wr16daA2SWnWrNmpLD6MOIZbVqlSxTm0cuvWreku6JKYmOjjclrMh01g7dq10x0e2qRJk/j69evHm/uywzO1AqjHGuh0vpvjYKpxJatXr34w7e31+VxzzTWx+vy0a6ZkkZ+fX4qGWwAAAE/k4+Nzp9le+laAXKJS5wZCQkJStCX/e++9V0Lnt+n8M13awJyv3SWPtm3b9oQ2Khk8eHC5Nm3aHNdhlB999FEJEwYTTBiy8+mKFSuWossF6Lw37Q6plTRtIqKX/f7770GmypakQxz1Z12wW7tGRkREBGmHzfj4eB9zHFCvXj1n9U4fT+fVXXDBBYk6902HgGoXS73M7FkKfP3110u1bNky1gS9hJiYGN+oqKjgG2+80XagbNiwYYKGzcmTJ5d+7LHHjprKXPJPP/1UylTkUkxFLknDab9+/cproxRz3Xh/f/+UJUuWFNN5e67zBs+lRo0aCSZsBunz0aGhjqYtAAAAnsBsi/0WHh6+2GyXDTPbM8MEyKEsz8cqaH379n1WihCtdun6cytWrAj54YcfQjTc3XbbbSe1iqVh7YorrojTlv9Lly4N1aUCLrzwwoQhQ4Yc1gqf3t7s6bFz5r7++utiu3fv9tOAo81LVq1aZW+jwUrb/urcOHP/oXqehkldDsBUyPx0SQJ9DnpfGi7/x959wDdVfn0AP+yhIhQUWSJI2RsEBGSoCCIbZCPI3nuUZVv2psiUvTcyHAwXAiKrQNmjOBBkyN6zfe/v9L35JyFt0jaFpv19P5+QNuPm5qa53HPP85xjPP/+yZMnk3799dcvG0FbIgRnZcqU0awgiqRgeVgfVO38559/ktSoUeMOhlOaQydLly794MyZM0kQHP7yyy86Z6969eq30FIBr2sEmI+Qmfzmm29eMrKOyYoUKfKgR48eN3Gfo+0TFBSUFAFc48aNLcMTjOzgYxSZWbly5Su4D9U4ze0R140bN268EJFLjP1ouSdPnuy5dOnSeSEiimWMk96/Gvupz9KlS3fu8uXLbE5OUZJAYqmLFy/+K/RCoPk4es/9/5y3aDMycxmMrCF3Um5kBNYZhYhcwkIpRBTbFSpUKGfixIk3GPup3EIUBZxTRzEKDTaNrJ7HlRsmIiIiel6CgoJOGVfrihYt2leIooBBHcWYR48eJTeuQpMmTepyMRciIiKi+MjI0vkYV1WKFCnymhBFEgul0DP8/f2vYt6cRBOydBx2SUREROSyOQkSJJhgXDcTokhgpo6egfYDGTNmjFZQ9//DLq8LEREREblk//79S0SnAhfj3DqKFAZ15HaPHj1KljBhwqdJkyZliwEiIiKiSHj69Gn30NDQLkIUCbF5+CWLa3ioy5cv53zjjTf+NH58LERERETksqCgoC1FixadamTr3g4MDDwjRC6ItUGdERTkF/I4xk6ou3H15v79+3sKEREREUXFJOPSzbh0FSIXcPgluRP6Ho5nQEdEsUloaOijhw8fhggRkYcwjqWmhISEFBAiFzGoI7cxsnRfCs8oEVEskyBBgqTJkiXj/3dE5FESJkx4pUiRInWFyAX8T47comDBgtkkbNjlVCEiIiKiaAkNDV1mnJSqJUQuYFBHbpEkSZJhxs5nuRARERFRtD158mSzccWgjlzCoI6irUCBAtmNq5IHDhxYJkREREQUbYcOHbprXJ0oWrRoMSFygkEdRVvSpEm/CAkJGSJERERE5E7fGZeiQuQEgzqKloIFC2Y2rt43snQLhYiIiIjc6UKCBAkY1JFTDOooWhInTtwtNDR0gBARERGRWz158uSYcZyVTIiciLXNxyn2y5w5cwrj7FHHwMDAl4SIiIiI3CpJkiT/GUFdKSFygpk6irLXX3+9vXE1Q4iIiIjI7e7fv3/ZOIF+W4icYKaOoqODcfaoqhARxWLGfurvJwYhIvIw9wzJkiVLI0ROMKijKClatOj7xtXZ/fv3BwsRUSxmnOXOmiRJEv5/R0QeJ3HixKHGPuxNIXKCwy8pSowz37WNncxEISIiIqIYERwcHGIcb/0uRE4wqKOoSJwwYcJ2gYGB3wkRERERxYgcOXIkNE6kvytETnA4CkVasWLF6hs7mJVCREREREQvHDN1FGlGQNfAuFohRERERET0wjFTR5GSN2/epMZV5f3799cUIiIiInKrQoUKLTZOoDdOnDhxAvxu/IwCdaHm/cYxWAIhssNMHUVK8uTJqyVIkCBAiIiIiMjtHj58OCRRokR/GcdbqN4rCRMmFPNnwzEhcoBBHUWKsUOpERISwh0KERERUQw4ceLEKeN461f7258+fYqWmyxSRw4xqKNICQ0NrWScMfpBiIiIiChGPH78eLRxdc76NiN7d8a4faoQOcCgjlxWtGjRPMbVjcDAwAtCROQhQkJCcCD0WIiIPMThw4dPGPuuLebvxkn1UCNLt/n48eN/C5EDDOooMt4zLquEiMiDJEyY8O0kBiEi8iBPnz4dY8RyZ///1z8ePXo0QYjCwaCOIqOicTkuRERERBSjDh06dNII6n5Als749Vtm6SgibGlAkVE8QYIEg4SIiIhilJ9faMLXbp9ZnDhJgqRC8dbDJ3dfunDt+L2MXnm9232UcrVQvPX0Sei9juNyfBbe/QzqyCW5cuV6xQjoXg8MDDwjREREFNMShjwNbVik6mvsSRavpTMuWfFDVaF4C7naPd9eDjF+ZFBH0ZM0adIiISEhO4SIiIieD+NALnuhVEJE8VtoiAZ1ET6GQR25JEmSJAWNTB2zdEREREREsQwLpZCrcoWGhp4UIiIiIiKKVRjUkUuMLB2DOiIiIiKiWIjDL8klISEhSe7evct2BkTkcZ4+fXra2Iex+TgREcVZDOrIJUamrtypU6f+FSIiD5MoUSJv48Lm40REFGdx+CU5VaxYMdTTvSpah4uIiIiIiGITZurIFRmMywUhIiIiIqJYh5k6ctIzJGYAABAASURBVCokJMTLuPpdiIiIiIgo1mGmjpxKkCABgrrXhIiIiIiIYh1m6sgpI6hLZVzdEiIiIiIiinUY1JFTT548SRgaGvqnEBERERFRrMPhl+RU4sSJvYyg7hUhIiIiIqJYh0EduSKJEdSxcS8ReaSQkJCLT58+fSJERERxFIM6cso4GLqfMGHCh0JE5IGM/dcbxoX/3xERUZzF/+TIKeNgKLVxlUCIiIiIiCjWYaEUIiIiIiIiD8agjpwKCQm5YVzdFCIiIqIX6NGjR/Ltt6vlzJlTQvGDq5/5jh0/y65d2yW+YlBHTv3/8MtXhYiIiCgG7Ny5VcaN83P6uOPHD8nkyaPkq68miCvOnv1TBgzoLJ98Ukpq1iwrv//+q1Dscf78P7Ju3fIIH+PoM9+50/bvxUhAyNChfcXXt4fEV5xTR0REREQua9asmly+fFH69x8hFSp8ZLn9++/XyqRJw+XNN7PJrFmrJDKCgvbJvn07bW5DhubixfO6PFOePAWlVasukjdvIZeW6+fXS159NbUMHDhKrl+/Km+/nUvcwdG6UeRt3bpZFi+eKbVqNQz3MY4+c/u/FyMBIb17++m1NfydJk2aTFKnTiNxHYM6IiKK04wzuNefPHnyVIjILW7duqEHzwcP7rUJ6g4dCtTbb992z4yNCROGyOnTx2XOnDWW25ImTSr16zd36fn37t01MkFnpXbtRlK6dAVxJ0frRjHD1c+8UqVqNr+fO3fWCAbriI/PMKlYsYrEdRx+SUREcZpxkJnGOChIJEQUbY8fP5YHDx4YWZOCRrZkr819+B23X79+TWKD+/fv63WiRMxhUNzHv3IiIiIicsmNG2EBW8mS7xlZqsly6dIFSZ8+g/zzz19y7dpVqVfvMzly5KDcvXtHXnrpZX1shw6NJEuWt2TAgJGW5dSpU0GqVKkpbds+OwcKQ+YwxNNUuXJxyZOngAQEzNPfP/64hDRt2laaNGkd7noGBAyXjRvX6s8YEopLjx6D9TWxnrNnT9Lhe4kSJTLeSznp3LmfJE4cdli8bduP8sMP38rff58xspI3JV++QtKiRSfx9s4d4brdvn3LeP/vS5cuPlKtWj29//TpE8aym4qf33h5993yelu/fh0ka9bsxvLyyLJlcyRbNm8ZPHiM0/WyhyGg48f7y7FjQXLz5nXdxuXKVZJPP/3MMgxxy5ZvZNOmdRIcfELv79Chj+TPX9iyDKxLhgyZ5cmTx3LgwB793IoXf1f69BkiyZIlc7o9rJdj/56aNGkjy5fP1b8NZEzffDO7sX2a2WR3TYGBu2TWrAC5cOGcDrfs12+YpEnjZbnflc8c6wCjR0/XTOrmzRv091GjBull2LAv5fvvv5aTJ4/K0qUbLc97+vSpsc0+0Exfhw695c8/gyVTpjc1Q+hJmKkjIiIiIpfcvHlDr4sWLSXJkyfXg3HAHKfMmbMaAUImm8dFRerUXjJmzAwpUqSEvPZaev25W7eBkVpG3bpNdB5d2M9NdRlmUOXv30t+++0XqVmzoTRo8Lns2PGTTREOBDnvvFNaOnbsowHagwf3ZcQIHy3G4Y51AwxVnT9/mjRu3Fpq1Gjg0nrZW7Nmsezcicc3EB+f4VKwYDEtIIIgBfbu3alBX4IECaR798FG8J3RCKw7aWBq7YcfvjGymvdk7NiZGlwiuJs5c6JL2yOi95Q27WuSM2c+fS9Yv7feetvYXoPl33/P2TwPy0FA16BBC2nevKOcOnVMRo4cINGBwLZPH3/9uXHjVvo55ctXWEqVKidXr/6ngZvp6NEgDWYxRHfTpvXSvn1DIwDsJ56GmToiIiIicgnm0wGKjxQuXEIOHtwjVavW1qCucOF3JFWq1JbHZcyYWaICGZJChYobmbZ18t9/l/TniNy5c9vm95dffkWzUilThmUKEWyaywgKCpQTJ47YZNO8vNIZAc0XRkDRQZ+LDJR1FgoZR1/fnpptwnIjs27hQVAxadJ8yZ07v8vrZQ/ZtzRp0mr2C+znDa5atdBYRlojsJutv1esWFkDlg0bVkrr1l0tj8uYMYtmUZEdxGeGbN/PP280HttLkiRJ4nR7hPeeoF69ppafixQpqRm/Awd2P/O3gSxaunSv68/4/FHtEsvLli2HRAXWK0GCsNwVitmYn1PZsu8bWdVhRsD7m2XZyIymSvWqBsUYqossJ7KOnoZBHRERERG5xMzAvfTSKzpMb9Gir/T3w4f3S+fOPnpwbP24mIZ5c3XrVrS57bPP2oc7TM+cB1i8eGnLbQhCMJQRQRICU/y8ZMks2b79R80qhYaG6uNQeMVdkLWyDn5cWS97yDqhN9vo0YPlww8/0eyhOezyyZMnmjnD7SZk7HLlymcEj4dtloPgEQGdKXt2bx2miKGQCIhc3R727wkQxK1du1SHYGI5jp6HdTYDOkBGDY4fPxzloC48CEgLFCiqQZ1ZfCUw8HcdToztg6GpGzfuEU/EoI6IiIiIXGJm6lKmTKkHwlOmjDayOpu0OEqxYu/q0Dzrx8W0FClSWDJRptdffyPcx2OYHTRvXuOZ+8xhiRj6h0CqTZvuxnsqpRk09LpzJwzjjOx62UPAhqGWO3dulSFDemuWtFWrrjpnDcMpEXwhqMLF2htvZJSImHMhzcDc1e1h/57Qf2769HGafcTfCrKK6BfozCuvpNJrtKCICRiGi+GlKPiD7RQcfFLn/3k6BnXkUOHChc8bZyz0W2+ekSlSpIjv/999/sCBA1EbU0FE9JwZ+7DgJ6gCQETRhmIgGHoJCJ4yZcoiS5fO1gwNAiyzuIT9kMiYZF34wxkzIzRkSIDOCbSWOfNbWgZ/586t0qJFRylX7kN5XpytlyPILKHwCy737t2Tr74arwEYslsYPojllChR1jKc04S+bRFBMRRAEBad7bFy5QINAs3XR/bQFfgbAzO4cze8jxkzxsvu3ds1qENBmHfeKSOejkEdOWQcBK0xdhadjZR4ArvbYa0QEXkIY1+WIwkmhhBRtKHKopnJAcyT+vbb1VrkAjCML2XKl/RxJgQR1gf0CPgePXro9LVQ9TEkxL0tJvPnL6LXOJB3NB8OlTsBRUVMZ86cFFfWzQyWrN/rtWtXxB3r5Qwyp9Wr19dCH1hfBHUYxoiiIM6W9/SpbbB19OhBDdyR0UNWDpxtD3tICKBSavr071luQ7YvvMdim5lVPjFsFDBMMjrM5ZmFY0wo4JIjR24dgvnw4QMd8mr9XwSrX1Kc8ujRo8nG1R8O7vrj8ePHAUJERETxDrI4r7zyquV3ZD0KFixqU6QD8+rMbAugvD0O6B8+fCgXL/6rFRDNuV8mDN3Dc/bs+c0yQgjPu3DhvFaE3Lp1i2ajogvtB1DJceLEoZqBQgN1DBE0hxOiwAYyXGgDgPswhHD16kV6nxnwhbduCMhQlAVBESo6op2BdRXJ6KyXPWwjH5+Oxrb8QodX4vELF84wsqUpJW/eQvoYDClEZceZMwO0kA2Kn3Tq1ER/tnb48AFtT4Hbv/9+rbYwqF27sQZFrm4Pe8gior3Brl3bjIzYDl3HsWN9jWWlkGPHDtlUzsRjUW0S1TqR3Zs3b6pm+CKaT+fo78Uesp/4W8Q64L3t37/bcl+ZMhV1Lh2qt+JnkydXv2RQRw4dP378tPEl2xz67Ddl05EjR84IERERxTuYK2ddiRFZIJTCty6QgWFz1nPqPv+8k7z9di6pV6+idOzYWMqW/UBy5sxrs1zMD8PzBg/upsEQVK/+qVSuXEODwPHj/Yxjk0PiDoMGjdEs1uTJI8XPr5cGnOYQQWSoMAQScwN9fXvIjz9+axzgT5ZWrbrYvH5464bS/ejdh75qI0f2l379hrplvewhEOrd288IbtJolUsEIQgqJ0yYY5kzh35yI0ZMMYKZXcayu2rQlzt3ASNQy2azLAxfxfsdPtxHFi2aYQR0jaRhw88jtT0cwbbA54whoRiiizYDqLJ56dK/2sTehPVFj7hp08ZocImCK+hTFxFHfy/2EJSirQWKtPTt216WL59nuQ/z6tAXEMMvUXDGhKDcU6tfJhCicBQuXNgbgZ3xx63ffuOsyjkjhV3u0KFDfwoRkYcoVqzYduOqf2Bg4A4h8hB+fqGJ014PftRocA4eq1GMsW7YHd+gLQMCOF/fcRLbhRqJzeUjgkO6BHgnCu8xzNRRuA4ePHjauPrJ/N0I8L5nQEdEREREngzDNjEss1athhJXsFAKRcgI5EYbV1Xw8927d0cJEREREZEHQsP4oUP7ysmTR6Vdu55Rbh4fG0UY1E3uHlwrafIETYXitQvXj4WGhIQkyJQ2/1iheO3Rg9DFXQJyrBMiIiLyeOg9F5+gyA/m7/Xq5euR8+Yi4iRTF1r4tTdT1s2c6yWh+KuIWCaQsjddPHb+1F05d/IuahszqCMiIooDcuTIJfEJKnmiyE1c5HT4pdcbySR7oZhp/kdEnuPuzScI6oSIiIiIYhcWSiEiIiIiIvJgDOqIiIiIiIg8GKtfEhFRnFOkSJFQ8+fQUP1xu3Gb/pwgQYKgAwcOFBYiIqI4gpk6IiKKc4zA7YBx0cayuDZ/Ni43nz59OkyIiIjiEAZ1REQU5xiB20Tj6p6Du04cOnRotRBRnHL16n+ydu0y4/qKxEY7dvwsu3ZtF6KYwqCOiIjinKCgoEXG1Wm7m2+GhoaOEyKKdVauXCArVsyXqPruuzUyY8Z4+f77r+V527lzq4wb5xfu/SEhIdrw2te3hxDFFM6pIyKiOMnI1o1PlCjRjAQJEqT8/5tOHjhwgFk6oljol182SZYsb0lUValSS68//riWPG9BQftk376d4d6Pod+9e/vpdXT07NlKjh4NCvf+9et3yOXLF6RNm0+lR4/BxjapaXP/xx+XkLp1m0rr1l3192+/XS2TJ4+SBQs2yBtvZNTbbt++JcuWzZW9e3+T//67JJkyZZFq1T59IduVIodBHRERxUnI1hUrVqyn8WNhI0N3z7iMFSKKk15//Q357LP2EltVqlRNoqtDhz5y794d/RlZzeDgEzJw4Kj/vzeBNtaOjgcPHki3bi3k4cMH8skndTXIPnYsSDOg2bN7S65c+YRiLwZ1REQUZ4WEhEwwrr4ysnXM0hGREaQckv37d0vTpm3E03h757b8vGXLN5I4cRIpVKi4uMuePTvk/PmzMnr0dClc+B297b33PpDGjVvLK6+kEordGNQRUbwyuXtwraTJEzQVijf+/m//k1Qp3njU7qOMDOriiadPQu91HJfjMyG3WrJktmzf/qN07z5I5syZLH/+eVpWr/4ZQ51l/vxpGhRcvPiv5MtXSHr39hcvr7T6vD/+OC3Ll8+Vf/75S4OGN9/MLvXqNZMKFT6KzMvL0qVzdJjmxYvn5bXX0kvBgsV1KOHLL78ihw8fMF6zjYwbN0sKFCiij8fwwSVLZumct5s3b8hLL70sb7+dU1KmfNnm/SBoWbp0tly4cE6X2aU34usmAAAQAElEQVSLj2b+3Lnu/fp10GsETGC9vvPmTZEzZ07qstu06W6sQ1F5Ef6//Ys8fvzY5nZnAR2ehyGbv/32i5w795dky+YttWo1smwj3I6hnsePH9ZllS//kbRs2dlmOCqGhvbrN0z/pjAvskmTNsYyGup2y5Ahszx58lgOHNgjd+/ekeLF35U+fYZIsmTJ9LkoQoM5i7NmrTK2YTa9bc2aJTJz5kRZu3ab8XmnlEePHsn48f6aebx587pmIcuVqySffvpZtIfFxhYM6ogongkt/NqbKetmzvWSUPxQRPTAouT/XyiOw3Hpnm8vhxg/MqiLAdeuXZHhw32kWrV6UqdOY71twYLpWugEQwwbNPhcVq1aID4+HeSrr1ZoO5G0aV+TnDnzSenSFSVp0qQaZI0ZM9i4La9kzJjZpdfFXDK8TvXqn2pAcPbsn/Ljj99psIagzh5u79r1M+PgPZtMmbJYg7IJE4YYmacPpUaN+pbH4faFC2dIhw69JXVqL/H372U8frQMGTJR73fHukcEr9eyZRcNaL78coT+vnz5FkmSJIk8bwUKFJVXX00tY8d+IZ069ZMSJcpKihQpnD5v+fJ5ug1r124kjRq11OD+xInDGtQdOXJQA6533y0vPXt+IX/9FaxDR0NCnkrbtj3sljNXP8suXfprYGj64YdvpFSpcsZ6zdTAe8SI/hqwIfh21Zo1i43P7hdp3ryD8bllMYLq/fpZ1qnThEEdEZGn8nojmWQvxKEkRHFRaIgGdUIxA8FSq1ZdpH795vo75mGtX7/cyL5U0mIgUKhQMWnWrJpxcP+blCxZVgOFevX+N0CiSJGSxoH6t0bmZbfLgdHp08f1Gq+LLBqChAYNWoT7eGRvrl27KqNGTdfH41Ku3IeWwBDBJjx58sR4zDRJl+51/R3Bw/btP1mW4451jwiCyQ8+qKo/o9jLvn2/a7YzS5asEh0TJw7VS2QgszphwlxjewzUwClx4sQ6DBMBXnjvFdtv1aqFuu7t2vXU28qWfd9y/7JlcyRz5qzi6xtWeBifAbY9AjucAMD2NeGEwcSJ854JJBGEDRgwUhIlSqTrgQzbzz9vlPbte7kc/GL+YZo0aTXLCqVLV5C4hi0NiIiIiMhllSv/r6oiMjII7IoVe9dyG7Jb6dNnkJMnj1huQyDUsWNjI6AqLbVqvae33bt3V1xVsmTYcxBw4ID+/v37ET4+FNG9IUWKlJbbMOwSr4nhoiZkacyADpImTaaFQqxFd90j8vrrGWxeGx48uC/RhWGFY8bMsLmYgWxEMmd+08hULpJBg0brZ4r5h507N5VTp447fDwyqBgSWbBgsWfuw3bG8835eSY8FkM8MRzTGoI1R5lBL690GtCZULQF2x9ZO1chWL906YKMHj1YAgN3aZuJuIZBHRERERG5BEGQdXblzp3beo2hjZUrF7dc/v33nFy+fFHvW7duufZxq1q1jsydu1a++26XRFaGDJk0MEmfPqPMmjVJGjWqrFk3cx6YvbChgyktve+uX78mW7Zs0OAQGShXuWPdXwRkx1BExfriSlBnQoEUDEGdMWO5BpkrV853+Ljbt2/qtfXfhAmBF4InzGW09vLLYSNlrly5ZHM7MmmuMJeHrLGrPvzwE23zgAB0yJDe0rx5Ddm6dYvEJRx+SURERERRgoIl0KJFR8mbt6DNfciwAObbFStWSufhAYbsRYUZnCBQQDEN9FhDlg3l9+1huGWnTn01IEORDkABF8zXigx3rbunypo1u5EZy2lkxc47vN/McqK/nT0URUGbBTPwN925E/bYVKlSS1TcuhUWSLoaBAICWvTtw+XevXvy1VfjZeTIAZItWw59j3EBgzoiIiIiipKsWd/W4haPHz9yWF4fmbQbN64ZGbb3LLdhflN0IFuIIAsVOM25do6sXbtUmjVrF+X2BTGx7rEZMqsYemrdBB5DKDHXLX/+wg6f89ZbOf6/+uh+hxVB8+UrLEeOHLC57dChQM2WutqO4elT20D66NGDmhk0G6abw1atA+7r16+GuzxUw6xevb5s2rReq44yqPNwV6/+J9u2/ajjd9OmTSdEREREFDnIxKB4CVoHpEuXXjJlymIEWie0YuHo0TMkdeo04u2dR3bt2qbzmm7duqHVEpMnT6E945B1Q5CGrAsqWmLuVs6ceZ55nUWLZhrBwD4pW/YDPQg/eHCvDu+znstn78qVyxpA7N6dR9fTy+s1XT9Xqx0iu+PKuqNqJjJVKAzzzjulIzXMMTZBwRMMUW3YsKVmXfH+cBveN25zxPz8582bKsmSJdfnYXulTPmSdOzYR1sT9OrVWoYM6SPvv/+x/PHHKR0SW7NmA4dDNh1B+wcE8GhlcP78P3r8jsywOYz2rbfe1m2OYA/DTvfu/U2++WaV5fkIzvv376SZ4yJFSmh2+euvl+rw3Lx5C+n8TF/fHvrcrl0jl8mNTeJtUPfdd2u0PwlSws2atRV3efjwoUyfPla//Li4w++//6qlYtErBX+I48fP1gnI8QEqUGFHH94ZIiIiInqxUIUSB85oZYCT5pkyvanFVMy5Tz4+w2XGjLDhbmnSeGnVQ/zfvmDBNC2YgX5jNWs21HL+aIWwcuWPz8x7Q+EPHIT/+usWI7tyShtxo6JiRFUM0SoAbQKCgvZZbsNwuzFjvpJUqV4VV7iy7pivhQqggwd3k8mTFzkMSj0BKnFiyORPP32v7QUQrObPX8TYhgu1OEl48Pkjo7d583r59ttV2u4BGVLAkNehQyfJ3LmTtcgNHof5ia1adRVX4RgQ8/rQSgMFU9A6oWHDzy33Y6htv35D9VgZ7SgwXLZXL199PCDgQ2XWr79eokEqMo8I7iZMmKPZPlQbxd8Ieth5clAX4amEyd1P+xUo5+Wbv5yXeBqc7UEjxMDA3/XDRCq5RYtOluAAKeZNm9bpH5Z11aPowpmaevXe194Z5vjr6EClnpYta0vFilW0RCz+8HB2wx1ngRDQ4mKmr2MjNKNs2rStcaantcSUmNoOaHSJBqlmI0xPd3jbNTmy7Zp/lwBvP/FgnrxfIyLnUPRw+YjgEGNflUg8mJ9faOK014MfNRqcwzPTPrEMhuadOHFEBgzoZBxb1NYAhmI/+6btMaVNm081YTJs2JcSG7myX4uT1S9xBqdLl2Zy7tzfGsjhTA3K6w4b1tdSAhdR/WeftXdrQBcTMNYXOyKcoULmr1athm5L67dtW1+HEcR3MbUdUAnM35//aRARET1vAQHD5Lvvvrb8jswfTuy/8UYmnSdHZMIwXjShR6LHk8XJ4ZfIwCEQGjdulqbFoXLlGppFc9T/IrqQTUNKt3XrbuJuZq+URIlY04aIiIjIFYkTJ5HVqxfqyXvM+wIMsfv77z+kUaOWQmRCrz0kf9DQ3pPFyUjBnLiKSkxmUAcYJ2zCpMvevdto4FegQBG9DSleDNPEOGs0mkQvCwx7bNeupyRNmlQfgyF1GGeMSZqI6gHPT5gwkQ7pDG+M9pYt32iwiapJeI0OHfo4nSfWqlVdzTaG/Rx29mD+/PXaqwV/gCtWzJNDh/brGO86dZpI9eqfWp67dOkcnUSMTB/WHROJMX4Zj8V7Q4lf2LhxrV4wHrply86yY8fPMnRoX5k1a5Vl2OCaNUtk5syJsnbtNq0YBBgW2a/fMPnzz9NaVhgTYZFFdLZe9v7552+ZOnW0TpxFII4x27VqNdKhpta++mqCbkNsX0fL/O23X7RkMRpZ4nMuX/4jfT/mZGjz8549e7XOpdyx4ydp3Li19rhxtB0AnzGWieapGTJkljJlKur7xN+F/RBbTApHc04/v/Hy9tu5pFmzapZ1Q7+ePHkKGGcNw7KBf/4ZrPMNzL8pIiIici8c82BqBUZp4dgNRTLefjun+PtPlFKl3hPyDG3adJeYhuI2S5Z8L54uTgZ1aJiIg/EBAzrrlxrBk3Un+ohgkidKrGJSJ+blYWIsDujr1Wuq96M6Ds7yfPHFWK3yM336OK3eM3DgKL3fUZ+OvXt3yvjx/roe3bsP1oACY7pnz16jw0DD06ePv+zevV0DNPyMIil4PBpoYjIuJiC3bdtD/v33HyMwGqMTWvHeAZWA8Hu9es3kv/8uaiCTMOEU6dnzCylatJQ28MQE0ty5C0jduk30PUYWgluUsUXPl2zZvF1aL3soKnP58gUdJotKSSgKs2/f7zZBHYItnD3BpNfAwF06CTZXrvyWichHjhzUQBSPwfv7669grawUEvJU18Na2HvOrw0oCxQoqhN4HW0HLBO3Y5l9+gzRoBOBIU4YOIP3i+2LOZ0IyvHZmb1YUD534sSh2vwUTT2JiIjI/TAyy8dnmJBny5Ejl5Br4mRQV7jwOzrREXOa+vZtp8FC+fKVjAP8npZMU3jSp89oZFsm6NhrBEZLl8629EBBVmn//t1GRqafBgaA7BTmTdWvf0IrMTmCSjteXmm1aiVUrFhZ2rdvKBs2rJTWrcOq/9g3ZkSwhNe4cOGc/o6gI3PmN/VnBKzIFk2aNN/SSwRVgVavXmQJnuyrQaEE7C+/bNKf0cIBFwxNwJkrV/uE2EP1oIkT51mGtKLcsLP1sodti7mCVavW1t8d9ThBlgsNRAH9TvD+8TwzqFu2bI6WoUUVLChX7kOdd4jADlWqrEvmIjDu3n2Q5XcEyY62g/UysSz7zGFEkIHDsjZuXGcE1JdslotlInsYV3qiEBEREdGLF2cnaiGVumDBBh0aiGAGB9ioejR27EybYZj2MKbWuowusnHmvLbQ0LAsDfqTmF566RW9vnfvjsPlYUgheqSg3K0JQUKuXPmM9Tmsv6Ofh69vT5vnIQAMb3hmUNBeDUasm0NieQh28HpYf5QUnjVrko4fR/AVtt7JxZ3Q4896jqIr62UPmTB8Ptju7733oQ6NsIfGliZz2yNYBJTGRaD9ySd1bZ5TsGAxWbx4lg7HtB5mUalSdXHGXGaVKrXc3msGn+nGjXuEiIiIiMhd4nT1DWRMkEnDBRWQ0Kvk5583akuAqMAcM6SB0dAQAUiSJElkzZrFmuXBcEBH7t+/p71bMI8NF2tmCX00PjSzeCb0UQkPsmGYv4e5WvYQzCFo7dGjpQ4lxNADZLcWLfpK1q1bJu6EPi2RWS9HvfXat++t2dEDB3ZrBUpk5Tp27Btu1tMeKhZhSKTZC8f08sthgfuVK5ciXOeIlhlR8E9EREREFFvEm5KK6GKPoO7SpX8lOgYMGCVt2tQzAsOy+jsyTJhfF14WDIEB7itRouwzfeuSJg0r4oLiH5Fpro0hhGhy3q3bgGfuQ9Dy44/faUVOFDLBnLHnxdl6OYLhsOhBhwvWGfPYvviiu05YNYucRMTcvvbDV+/cCZvbaM5liwwsEwV2EKQSEREREcV2cTKow5A7DAHEvDSTmbHJmDGLRMeGDSuMAKyIFsJwFTJlyFRFde6avTx5CkpQUKCRzcrrcI7g9etX9Rq9WEyoulSdHAAAEABJREFUumkPwyHNIaUmM9DEcEn75UV3vZxBJq9Chcpa6RKviYDZFdi+R44csLkNQ17x/lzZ5o62Az5jDF11xNE2Moe42i8XxVrssfolEREREblTnGs+jqGOaDjZoUMjHe6IA3OUu0f2J2PGzPLBB59IdODgHVUdf/31B102StmjVK4JwwBxsH7s2CHjcWFFTlAGH6X+Z84M0OdgCGinTk3CDRqcwfBRzGVDmV60Ldi9e4cMGdJHFi4MCzS9vcMKiKxatUD27PlNK3SimuODBw/k1KnjluVkz55TDh/er8tA+X5AcRjMIzt69KC+L2w7DDd1x3rZQ7XMli3r6HbZuXOrPh7z79DWwNWADrB9UZEUr4WWDHg9DOXE+lgXSQmPo+2AZaJlBYrgYJnz5k3VlggI5JDFQ8ETbCMM08TfAFo+2ENF0AsXzus23Lp1i9y7d0+rX6JIzrBh/YQovsB35Lvv1ticCIltsL/D/ufMmVNCRBRX4Zhm167tQnFPnAvqEJBgfhqyN0uWzJJBg7pqBUtUWLSu1BhVn376mfa/GzGiv/Tt2157kzVs+JGcPHlU78eQwbp1m8pPP32vGSfAEMgRI6bI/v27dH0QdKCaZZYs2SQqUM0T7+Xx48cadAQEDNVgtkyZsAqNJUqU0Qqde/bs0PVEEIr+bCjWgrlrpo4d++h8tv79O2ngd+3aVS100q/fUG2mXr16aT0QQysBd6yXPfSxw2vdunVDJk0arv3qihUrJSNHTpPIwPZFCwpUCh01aqC2EkCRE7SzcIWj7YBlDhkSoMscPXqQbktkEc3WGD4+w3W4KPr1jRzZX9+HPfTSQ9P7MWMGG3+TfkYG+RCrX1KchyHYOLGGAlCmmTMnyJdfjtSTW7EVvp+TJ4+y7LcjA3OJcVKpVq1yOqcYJxX79Glr6WX6IuzcuTXKJw6J4hNUB1+3brnEBzgRjRZQaM/lLjipPmrUID0mohcrwtJ+k7uf9itQzss3fzkvoWchYDl37qxx0D9QKyZOn+7eQiREscnhbdfkyLZr/l0CvP3Eg8Wn/Rp6bbZp86n+jAxzunSvS44cuaVZs/bGSaWsEhPQq7NevfelSxcfyzxi9HhEtd/atRs7rIIbFcisXbx4XgtYuWt5KCaFwlWRmeMMyOLfunVTGjduLalTp5Ht23/SrN+XXy7QCsCuwtxgXMwiWtGBA7eLF/81TpYtlvgGo+mXjwgOMfZVrjWojaX8/EITp70e/KjR4BzuLcNMNtDHd/HimW6pTO3u/VJMwCg2nFz+4IOq4g7I/GF/M2vWqlj9vj2dK/u1OJepi0kYhogzuSZkBXFghMqXN25cFyKi2AgjDHx9xxvB1mc6BLJdu/qayXleChQoouvgroAO0IcUIwLcBcPm69dvHumADjBSA7040WcTfVJRGCsq2ratr0PHicgzuXu/FBMqVarmtoCOYpd4U/3SHV59NY1s3rxem4JjmCLgTCj6rKFnGxFRbIRhvxjaDFWr1tahxqgGXLhwiSgVNSJbODufJAkLHxER0YvDTF0klCv3oRbQwJwLzKfz8+slmzatkxYtOuocNiIiT1CrViMtVLR7d9i8NwyZxFwwDBk0obgJbvv9918tt2GYEgr9rFq1UK8x77ZbtxY6DD0ieB7mn1rDMEPMs2vZsrbUqVNB5xvjNQEFiwYP7i6ffVZd56kNHNjFcp/ZCxMn086d+1t/7t79c8tyMW8PhbHwvM8/r+VyoSfAOmJd7d8v1gfXaGWD9cI6ANbB7MuJYkr4ecWK+Q6XvXTpHP1/o27ditKoUWUZN85PPwPAcCg8F1WSN25cqz/PnTvF8twtW76Rnj1bSY0aZbTIFgpfWcM8GTy+Vau6RrbxQ5k2bazO/bbf3s4+J6L4LDBwl+V77uPT0fL9BGf7SGf7JXvYH2BoPPah2AcGBAy3tGbCcEY8H8PnTWvWLNHbUHDNhP0VirBh3/Pppx/I118v1aHvaA1l7dCh/fpcFG2Dfv066AXWrl2m92FenDXsqzp2bGxZ1/D2XRS7MKiLpEaNWsrq1T8bGbt9xpfhV5k4ca7xH219tw4rIiKKSQUKFNVrjDSILBT/QCGozp19jOBhqTx9+kT8/XtJZKEC7K+/bjEyh3WlS5f+mu26fDlson2GDJnlnXdKaxEjzM178OC+jBjho8FL6tRe2lKmSJES2hsTP3frNlCfhwONwYO7aduQtm17aJGmqVPH6By3qML7RXGrNm26y4QJc42DnzMyZcpova9QoXcs7W0+/riW/vzhh9UcLgeVhVFsCf1DmzZtK/v37zYOxsICt6JFS+lzUa23ZMn39GdzPuLevTtl/Hh/He7fvftgLeo0YEAnS2AJCCRxwTYztwWqCVtD8ZZWrerYVEAmojDYt8yaFSANGrSQ5s07Gt+TYzJy5ACXnx/RfskeTjwtWDBdWy4NGDDSCKpqajXtmzdvSGQtXz5Xn4t96DvvlDH2eRU1OEWdBxMKvWFOtaNh4eYoMzzGhErpqAheunRF/T2ifRfFLoxEiIjiGQy5RJCACf2RhTl5w4dPkbRp0+nvdeo01Qqx6A+aJ08Bl5aBxx44sMc4Gz5MKlasordVrFjZcr+3d269mNAqxte3p5w/f1Z7kOJgaOPGdfLff5dselHiLPrdu3dk0qT5+jhAQLh69SKd8wbm2XCTdT/T8N7vqFHTtMgMoJKyGSR6eaXVC6AvqLkuwcHPLqd06Qo2v6PiHs7qA7YlLokTJzGWl87mPSEritdAVWdzOyGbsGHDSmnduqsevK1du1TKl69k3B4WXOPA7q+/zui2MKGAAd67+T6IyNawYV9avh+YY4saCjhBlC1bDqfPxePD2y/ZO3067MQK5vBiKs+775bXYDIq0GbLurI7AjG0TkLvXnMd9u3bqQFdkiRJnnk+9juYUoT2V5j3DAjwEORiPxK2zAo2z7Hed1HswqCOiCieQeVeVD9DYBdZeJ4Z0MHbb+fU6zNnTroc1O3b97teFyzo+MAHWTu0pNm+/Uft94n1hXv37ka0WAkK2qsHSWZAB6g+iWAPwRnarWAIkbXPPmsvTZq0DneZeL/WgVDSpMnk4cMHElkYWjlr1iRtM4ADMUiePHmEz8E6HzoUqO1oTPjM8J5QTRQwbApn+AsWLGbz3FdeSWUT1A0fPlmIyDH77znaYgFOQLkS1EUGsvFon4QWTMjIv/tuhSi320Kmzfq5xYu/a/yeUjP1COoQYCIwRQAZHgRvGMKJkz44ybV372+6HzXfd1T2XfRiMKgjIopn8B8zMjwY5hhdZqYrMkOH7ty5pdcYbugIhj0FB5/QIY8o8HLixBEZMKCzOIMgxpzbYg8HJunTZ7BkvExm0auYhGC0R4+Wur2RncQB46JFX2kLhYjcv39PA1rMucPFmtn2wMw8IptJRO6BkyJw/fpVcbcMGTLp8Exk1BAsYW5x7dqN9ARTZE+0pUmT1uZ39NJF5g/ZtrZtuxvB3XadHoRAMjwIDOfMmaxDvTESAM/56KMael9U9130YjCoIyKKZ8xMmTuCOvRnA/uDi4ikTfuaXqP4QJo0tv0CUcxj586tWoAKxakiA3NZ0Py8W7dn58KY6xeVlgXRhWIGaMyLOSn58hVy+Xk4sMQZcQydMufYmZAxBHP7OctiEpHrsG8CM7hzN2TRcMEwx++//1qHeiJT+MkndSW6MNT85583apYOwyoxzy+ikz44QYTh7himmSnTm3qCrmzZ9/W+qO676MVgUEdEFI9gSB/OsmLuVvHipfU2M0DAfSZzmI09ZI7wOLM4FOZuAOZluMos1ILhPOjtZu3GjbCqaigIYsLQTnt4/ZCQpza35clT0FhmoHGAkjdWtWowz/Zj3p0JmUh7eE+h6DBrBWfGkWUMb44OAnNkS//447TN7RjCag0ZPfRTzZz5TSEiW/b7NQx7BnNf5eo+0tF+KSIY9okTNsiUmXPtHL1WZDKGKJiCwii//vqDBAb+Lp06Oa/Ojrl4+H8B+xP832Duz13dd1HswOqX8QgODFC+9urVK0JE8QdKbKMwCb7/qIKIIhqozGYGPjgAQC87VFHDmWOU6p45c6LDZeHgZ+jQvlphDfM2MBQHQySzZ/fW+3FGGEUDjh07pPPhHMHcO1RqnDJllKxevVh++WWzlszGQQjmwyE7hXYxBw/uNQ40lmuhE7Au5Z8tm7dcuHBey3TjbDJKfdes2UDnlwwb1lefi/UbMqSPVq98nnBQBFgHBFLe3nn091WrFuiZc8ynwXtBlTnrapTZs+fUqnN4HtooANrooFrezJkBGgTjDDzaGuBnwEEkKjBv3bpZh0/h80MRFcwvtMbql0Thw7BHVOTFd2jlygU6xwz7NXNemav7SEf7JXuLFs2UPn3ayvr1K/S7Pn/+NM20Fyv2rt6PapNYH7wWTs5gWZFpzYKCKBiCuXLlfA0M7QudOILsHjJ0GzassGTpwJV9lzkKAvdHpYInuU+8C+pQtQcHCe6Gs6CoemRdZvp5QvldVCMyy9hiCFJAwDDZtWub5THffbdGZswYr6l+Ioo/UEERc9K+/nqJDq/56qsVUqqU7RwLH5/hOswGvY9Gjuwv/foNdbgsnFnGZPzJk0fKiBH99Yxu//4jbO6vW7eptj1AT8/wDBw4Wg9iVq9eqMtKlepVPYDAPLshQwK0aqWvbw/58cdvjYOtyUZA0kWOHz9keX716p9K5co1ZMyYwTJ+vJ/elzLlS1oJDgVR/P17G/vAoRqEorXB85QjRy7Jm7eg9o7bvHm9lChRRnuZYp4Lttm///4js2ev1gIoBw7stjwPLRyQoURzeBw8Xbt2VYc8jRgxRfbv36W9/BCg5s5dwAh+s1mehzLjOCjDXMRq1d7V5VetWsdmnVD9EtuH1S+JnoUhiJUqVZNp08Zo1gyBFYYcWnNlH+lov2QPVSZLliynLV1Q1RejHXx9x1kq9GKeL5aN/TX62OHYrVcvX4kMFF8JK6BUVFKnTuP08TiZhn2E9dBLcGXfhX0d9lNoCWE/95eerwhnZE7uftqvQDkv3/zlvMQTIcBCGtv6DxrNZBcvnmkEYHvEnQ4fPiC9e7eRceNmGen6IvK8IVDFQcDatdv07DvGg6MJJXo8mXMxsD1w9hv/2fM/doqsw9uuyZFt1/y7BHj7iQfz9P3aixRT+08id8II1uUjgkOMfVUi8WB+fqGJ014PftRocI7Il6klojjFlf1anM3UYbJ9s2bVbM6Cxnc4+4PqSgzoiIiIiIjiDs6pIyIiIiIi8mBxsvrlhAlDZPPmDfrzqFGD9DJs2Jc6Md+EQgFjx36hBQTQABfDFK37FWFi+ooV8+TQof1aMrpOnSY6VtoZTHYdOLCLjpF+883s2mcJY5pNmPOGSbEYn3zx4r86Drl3b3/x8gqbaIoKZsuXz5V//vlLzp8/q8uoV6/ZMxXiMG4ZDXXxPgoXfkfHQjvjaIhov34ddLLgbq0AABAASURBVCw15rNgmejzVLFiFWnXrqcWOzBt2fKNDt1E1SM8vkOHPhGWBscwLTQObtasnY5Pv3r1slamw5h0zLPB+8fcmZYtu9iM33bH9sE8wqVL52hTXhRtwDyd5s07aCGHHTt+1iIPs2atsmyzNWuW6IRnc+iquV2yZs2uz122bI5Ofh48eIzTv4uIXpvI033wQdUX0hKAiIiIIhYnM3WYhNqnj7/+3LhxK23yiLLQJlQuQtW1Tz6pJ927DzICo2Dj99GW+69fv2YcwHeTP/8MlrZte+gk+6lTxxhByk9OXxvBQcGCxaRv36E6Kd3fv5dO2jctWDBdKyvhYB/V51Au1seng07mB/RvypkznzRo8LkGQJisiwm31lXkUHlo3Dg/DYp69/bTQgUI8KIKE/lRaGXo0Em6PVBIBdXTTKgGNX68v1Zj6t59sE7kHzCgk9OiMAi8EKBhHfE8VN/r2bOlNsecOnWJboPRowfZVEuK7vZBUI2gHiW+MbEYn//du7cdlkR3BiWNsf6NG7eWGjUaOP27cOdrE8VGKCYQXml9IiIienHiZKYOmaQECcLiVWRjHB2EdO06wJKpQfYFJWNNCJCQsZo0ab4uC1CJDWW1zepE4alVq6Elc4Os0/DhPppxypIlq5aAXb9+uZQvX0kDHShUqJjO/UMp2JIly2qgVq9eU8vyihQpqRk0zA3MmDGsUfCaNYu1ZLav73gNkABla1HAICoQpPn5TdDS2AiSli6dbemXAqich0zZ+PGz9feKFStL+/YNNfBr3bpruMvFOo0cOVXn8KE60owZ44yf02vAgwDxo49qaMlfZEvxvt2xfVCZCkEiso1m42L7pr2uQvCGvwGzXwvKEEf0d+HO1yYiIiIiclW8bD6OktvWwxWTJ08hDx8+sPyO/j4YimkeuEOuXPk02LNuTunIW2/lsPycLFlyvcaBP5w4cVgDF7MXCSDzlD59Bjl58ogGLYAgZe3apZrpMhvIIgtkQh+jokVLWgI6eOWVVBJVWAfr94T1NrcH3i8yVihfa0JAhu2B9xMRbGfroiwvv5xKXnstvT4/7PdX9Nqd2wdDJjNlyqIZP7SZQLAV1cIwCHCtGyo7+7tw52sTEREREbkqXgZ1ziAbg6GFlSs/m+FDA28EGVGBA33AED1crJlDGc3WBJjjV7Lke9rU8ZNPStk8Fu0KUqZ8WZ6H+/fv6dBHBFL2/UcwFMud3LF9EEiipxUyfhhWiuGw5cpVkg4dervUq8Va6tS2Je9d+btw12sTEREREbmKQZ0DyCaheXe3bgOeuQ9BRHSWCy1adNThiNYwnBIwn6xYsVKWYXvIANnDY+/fvyvPAzKAyZMnlxIlyj4zlBA9AN3JXdsHwzARSAHmH2Je4/TpY20aJEd1/Zz9XcTUaxMRERERhSfOBnXmcELMa4ssVGkMCgoUb++8lmqI7pA169s65PDx40cO5/khI3bjxjUj4/Oe5TZUm7SHIX+oAmntyZPHElNQZAaZqJgukOCu7WMNlfry5y8ip0+HPc4MRK2DQRRjcUVk/y7sX5uIiIiIKCbE2T51mMuEMv0oMR8UtE/273e9CXnNmg0kRYoUMmxYXzl4cK/s3r1DhgzpIwsXzpDoQMarfv3mWljj++/X6nqtXr1Y2rVrYAQr13WuGao+Yp3xmhjuOHasr875O3bskFbtNNfv7Nk/dSgi5qDhPhRPsYZy+mhJgPusK2dGRZMmbbSYzMyZAbrOP/+8UTp1aqI/u5M7tg+e06pVXVm2bK5W7cRj9u79zdLOAvPksJyjRw/qfDwUyPnmm1UurZ+zvwtnr01EMQcnT777bo3D7D25F/admEt85swpIYotcPJ57dplxvUVIYqP4nSmbuDAUTr/qm/f9pr5QXERV6AVwcSJ83Rel79/bw02cucuoCXso6tBgxaacVq1aoHugDJlelMqV66pQRigTP+MGeNl5MgB2gcNpfsxtG/BgmnaGiFZsmRSpEgJ6dy5nw5FxPvDcET0e0ObAxPmltWt21QDDMwF8/efIFGFXnEjRkyRWbMCjABopRYvQTGTLFmc98aLrOhuH7STaNq0rWzdullWrJiv8/5atOgktWs30uej0Em/fkM1EEMbC2w7VONElVJnnP1dOHttoojgxMmVK5e1+mtMQwsT9HnEnE/rgkvutHPnVv3exkSGH30gsW/7/PNOljnOM2dO0P6RmTO/pVVzY5vnsc2fl+PHD8nkyaP0s0XLIKLYACd10CMX8/ObNWsrFH1IIOCYCyetcVyNY7B33y0vUeHOZZFjCSK6c3L3034Fynn55i/nJUQUvx3edk2ObLvm3yXA20882IveryGjjIP7N97IJEmSJLHcjob3MHr0dIlpZsGhtWu3uXWIubWhQ/tqO5epUxeLu+3Y8bMuf9asVZZKxocPH9AKurVrN46wQnFk4OAQF3cUhXoe2/x5QaZu3bplkjdvoVjZjD40RGT5iOAQY1/l0dGzn19o4rTXgx81GpwjgZBTKGS2adM6qVq1TpQrTzds+JH2pPXzG28TcAQEDJdjxzBiaaXN4zFiB9+FEyeOaMul4sVLS6NGLfXkN0YN2Be6s5YjR+4Y2T+6U8uWdfR9of8zpqq8804ZPTn+opcVH7myX2OhFCKi52jTpvUyadJwWbp0k/Effzoh9yhQoIhe3Klt2/paJKp794FC/4Oh/RgqTxSbIED47LP2El0Y6YSTMM6ySD/99L2Rqf5CqlSpqcXb0KcW1a+3b/9Rxo2bJZkzZ7XJZGOEEdpeIegDtHmKzdAqCicgMdqodOkKEh3uXBaFj0EdEREREcV7mHNfvPi7smfPbzpc0LqnsbXz5/+RiROHSqVK1aRHj8GW2xEItm37qU5XGTIkwGb4eZIkSbVVUkwXnXOX+/fDeggnShT9UMGdy6LwcesSET0nn39ey1K4qHHjKnq9YcNvOlfW9OOP38miRV9pP8qKFatIu3Y9NTNiwty7FSvm6fwxzCutU6eJVK/+aYSvi6I9KGzx119npHDhdxweqKBS8Pz504yDmR06bBJzaXv39hcvL+dtXDCkFM9F4aHbt29KhQqVtYqtNbyfevXe1x6TZksSFDfp3LmpzVAnDEPNkCGzVvQ9cGCPzgnGQVafPkNstpM9zKVZvHimbNy4x3Ibhk7OnTtFDh7co8WW0CqlefOO4u2dW7Zt+1G3y99/n5Fbt27q+8UcWNyH2805yhs3rtUL5vu2bNlZXP0MXNnm9pYunSO//LLJ2P7ntYVKwYLFpXXrrloVGIKDTxrvc5YcPrxfC0RhuW3b9tCiYHiv+AxQxOm//y5pUaiuXQfI22/ntCwf2zZr1uxacGrZsjmSLZu3DB48Rq5duyqzZ0+Sfft26ny/kiXL6bztiIaxfvxxCZ1D3KRJa/0dBayw/pjviLmUeI3mzTtI9uzeQvQ8YAh2795tNEtmZu2dfaccKVy4hLFvOq51C3r39nP4mC1bNug8/oYNW9rcjv3B++9/rJk+7PPQFiqq76Nv3yHG/w8r5Y8/Thnr/oaut3WWK7zvsyv7AsD+Gvuo48cP63qWL/+R7uOQqcRwU+z3ACNLcEHwiqykPdRBwLbC8rA/ff31DMb/b630/y+IzLIoeuJs9Usiotimb9+hWsAIUMgJBx/WgcrZs39ooZ1WrbrqMKLvv/9a/1M3Ya7H4MHd5M8/g/VgHkV6pk4dI9u3/xTua6JfIgIUzGXAAUru3Pn1P3J7CxZM1/+YcYDQrdtAnfPg49NB/8N2BoWBcEGlVzwXMNckqn744RvjzO49GTt2ph6kILibOXOiRNawYf3k11+3SNWqdY1gsr/OBbt8+YLeh8AR69uxYx8NNB88uC8jRvhogFq0aCkdNoVtVrLke/qzGYi68hm4us2tIVDEZ4Cz+AMGjNQCUajSiyFdgAO1AQM6yYUL57QXJual4GAPw5oAxZ5QmbhKlVrSvfsgPTDr2bOVFt+xduhQoB7wNW7cWmrUaKC3oZ8mDshq1myoxad27PhJvvrK9eJaWAcUkMKBMgpP4YDu7t3bcubMSSF6UZx9p8KDfU/16vX1+4QTHo4gEMK8vcyZ33zmPrSBMl8/OrDPQ9XtBQu+0RNS+I4jSLPm6Pvsyr4A+yjMS8bJoZ49v9CM49q1S/XkDtSt20T/jwr7uanuA8Mbjor/N+bNm6otnHDyDW23Ro0aJL///mukl0XRw0wdEdFzkidPAQ0GAP/x28+pS5gwkREwjLUEeji7iTPGJgQGyFxNmjRfsmR5S29DMII2IO+994HD10S7Ey+vdOLrO95SdRET+JHVMqE1CuaCoPKmeWYaFSSbNaumw5BKliwrDx8+fCb7hoN4ZPhwMIDntm/fS28vU6aiZqiwrlGRMWMWPQjD+mbMmFkrRuIgBcu3Li4TERx0IRj08RlmOWNcsWJly/3IyOFiQnbJ17enzvvAtsVnkzhxEt121sOlXPkMXNnm9szPGXPVMDcIBz3IDppQ2Q8Ho1OnLtGMA+CAz3yvaNvTv/8II0v6kd5WqlQ5I4tQSdcF2V4T/v6w7gg0Ab03UeTBOoOKdR879gvNtEWU0TBdunRB1w3buVy5D/U2c1lEL4qz71R4sJ9D9h3ZL1T8xvfAHk56YSilIyiSAteuRa+1Qrt2vYysX9i+6/PPO2v2H/tB6/dg/312dV+A94Y5f76+4/R3fG8x9BQn53BiB/u2lCnDqo7jceENGcWJsuXL58knn9Q1TnJ119vKln1f9wkYVYBt7uqyKPqYqSMiiiXwH5515g5nUR8+fGD5PShorx6cmMEE4KzoyZNHw+3PhqF6GIpkXUbffkgQqkYisEOrEhMOTNAu4OTJI/o7znjXrVvR5gIYbocDerT0sBaVYUcmBBXW64shfMgGIUvlqn37ftdrDLdyBAcjOLvcsmVt44z2OxrQgZn5Co8rn4Er29weMoIwatRAPXAz56CYAgN36WuaAZ2j94p2Nyb01MyZM5+ekbeGoVjmAaD5fgBV+0y4H9snOPiEuAJDwDJlyqJ/I+gTZp8dJHoRnH2nHMHIBGTrkWXHMEqcTMF3AQGPPUe3RXR7ZFl/13GSKXXqNM9kv+2/z67sC3AiDoEfhm9bwz4cQ0oRGLoKj8U+0z5QK1iwqA6vd2Wbk/swU0dE5CGQIULZ7sqVnw1U0NfR7NlmDfM6zLOk4cHQPsAQOlys4fUAc8YcVS0zn2v2kowJ5rKdDZuydufOLb3GwZkjqESHoKVNm+7arxLZqgEDOjtbrEufgSvb3F6GDJl0WBKqo86aNUm+/HKkVorDMFwcJGKuYniBofle7bNq+P3cub9sbrPPLpjZ1ObNazyzXPOzdwbDu4YNm6zZ3s2b1+uwMWRXMUwUB6JEL4Kz71R4zCHnaI+yefMGnedsP78U/XHDO8mE/QDg5JQ7vfTSK8/sA+2/z67sCxCEIXC132eb1TivXLkkrsJ+KWzdwl8jDaPkAAAQAElEQVSW9QkwilkM6oiIPATO3GIYZLduA565DwcZjuDA4v79u06XCy1adNS5G/bPBxwg4fLs64YdVDjLcEUHCpmEvZbzoi0mcwgUAixzHU3nzp3V5uh4v+ZwQVe58hm4ss0dwdluXHDAhfmUaPCNeTsY2oRrFLBxxOzJhfdqHUQh4E6VKnWEr2k+F5X6kidPbnMfGrm7CsNkEcQBMgKYpzd9+lgdBkb0okT0nXImW7YcmvFCHzoMYbSGofRooo3vpH0fyyNHDui1u3s4IoDKmTNvhI9xZV+Ak0P4rpsn5P73mLCA0Nk+w5r5f4c7lkXRx+GXRETPkTkkLyTkqURWnjwF9Sywt3dey8GKebGukGkNQwP/+OO0zW2oLGkta9a39Uwu5pLYL9fZWVYUHMFz7V8DQ5asJU2a7P9f+3/DRMObc/L0qe1QUhQ3QMYtMk3ACxQoqtc48LJ348Y1vU6f/n/Lc1TUA2fnQ9Hx1Yorn4Er2zwiyHxhTlrKlC9Z5gWh0Tfm+5lZAGtmYQYUTTBh2NOpU0dthmE5guIGgGG/9u8nqn0UcTCL5WL4FVFs4Og75YpatRrpEHMMr7b2wQdV9RpFShAwmlBYBUM9S5Qo49J81IhY7wexDji5ZT3U0hFX9wV4nBl8mvAc7PMiM+cN/3cgS/fssvZLjhy5wh0pQTGDmToioucoe/awstJbtnyjlSZxphNnhF2BwhgY4jZsWF+d/I+MEYa7YV5FeE138Rwfn45aYhvV0FAxERPmreGsLZaHie3p0qXX+VE4IEcVytGjZ0Q4hA4HATVq1NcqnTibjaGMKCaCuVpZsvyvjD+CBswZRICGx585cyrcipYo6T1nzmRtZYB+UGg/gKyaOQTKzIihiMurr6ZxeOCAM+mobjllyiid44XMHQrP4Aw95pLgPW/atE5bNqCoCwqdALJMCMoAnxXmx6E0OA6okNVz5TNwZZvbW7RopnEgtE/Klv1A56jhNZH9NOc5mq87aFBXqVevmZ6Jx/r7+U3Q7Cre6+TJI7U6Ht4rsgsiCfSxETG3E3puoYgCDnpRte6ff/6SESOmiCsQOGNo24cffmIcyOXWoHnv3t9cyoYQxRRn3ylXoEgU9of4G7duS4Kf0c4DrVT+/fcfHZ6O+Wj4jmJoJyrjRheq6jZr1k73bwsXztDWJR99VD3C57i6L2jSpI306tXayND30bmD2EehSAr2M5EJxLAfReEWBLfJkiXX/cnOnVs1QESrGnq+GNRRlKGMLcaeu1JNiojCoOIiqjiizxnK46OymqtBHQ64J06cp/Pe/P1763+ouXMX0LL64cHZWfQcw/d1+vRxGnS1bNnF0ofNhO8xvs+rVi3QbFCmTG9qCXBX5sqhXxmqwWGeGipBImirWrWOBmfWfHyG68EGepzhQAm/d+ny2TPLQ6YHy0FpbmQ2MQ+mYcPPLffjwAV95dDgF+rVa+pwvQYOHC0BAcOMgG2hZg6LFi2pgTQOWjDcEIGjr28PzUZiThj62R0/fsh4ZhN9PtodINjp37+Tzl1B9glBoLPPwNVtbg0tClKkSKktGBDw4u8ElenMeYz47MePn2O89hANoHCAhyqjuIZBg8ZoAIvgFBlQbF8EZeZwrIjguV9+OUI/G5zVRz8rs/WGK1BgAX8DaMeBA0NkVNHzD58b0Yvi7DvlCgRoaBWA77E9nMTB/gQntKZNG6v7FRQcwr7Kle+dM2gzsHLlfM3Svflmdj3Bhv2AM67sC7D/HDp0ksydO1kLyaB4CvbZaKcTWeYxIE4y4TWxjmhtw7YFz1+EJXomdz/tV6Ccl2/+cl5CZK9Dh0Z6MITS4xT3Hd52TY5su+bfJcDbTzwY92uxGxrqwujR04UoKjBidvmI4BBjX5VIPJifX2jitNeDHzUanMM95RTJIzhqok7kyn6Nc+qIiIiIiIg8GIM6IiIiIiIiD8Y5dS/Qn38Ga2ECVA1C9SRUa8M4ZHOOBGAoEoY44rYffvhWewpVrFhFJ7RbV7tD0QWMZ0bfJTy+Q4c+TsvpovgAChqguTAq2GF+BibPokqUK+tmDxP369V7X7p08dEqU4BiC507N9UJs+b4arwnTDJGWd2NG9dpxT28J4x/x5wTrA8KFOD1UHzAhHk4uC0w8HdtnIl1wbwP87WIyPOhbxwRUXyFOdbor+fqXGsiEzN1LwiCs/79O8q5c3/rxFRUODp2LEgGDOhkaXxpQmW1U6eO6aTW7t0Haa8VTMw17d27U8aP99cJvd27D9Yy3VhORI1jUeENRQgwobVPnyE6cRjjuBHARWbdogrvCSW/x46dqaWB8X7Q+BeB37Rpy3Qd7Jsgw9Spo+W1196Q6dOXyXvvfag9Z06dcr08MRHFbiiDjQsRUXyEVghoKxDdlggU/zBT94IgQ4YS2VOnLrE0yUX2qm/f9lpO2ro6E4I0lK1GOW9krpYunW3TZ2XVqoVakW38+Nn6e8WKlaV9+4YaKLVu7biS0bJlc7S8OCpBIRgsW/Z/ldvWrHF93aLq9dczyKBBo/U9oew1Sn+XL/+RpVoagjtUUUIQifUzIQBs2zbsTD7K86LSGrZFzpx5hIiIiIgoPmKm7gUJDNylwyTNoAnMZrnIolnDY8z+TIBeIA8fPtCf0cgX/UCs+64gCEKfpRMnDjt8bZSuxfBFlKG2Dpiism5RZf2eXnop7GwU+nWZcIYK7w3rag1ZOhO2A6D0ORERERFRfMVM3Qty+/bNZ/o/YS4bgpkrVy65uhi5f/+eZrMw3w4Xa+gV5Aiab2KYJea0xeS6ERERERFRzGNQ94IgK3Xx4r82t5nz2VKlSu3qYjQwQ/PbEiXKPlMwJGnSZOE+J1myZPpaMbluREREREQU8xjUvSB58hSUfft+l+vXr0maNGFNkA8f3q9Zt6JFS0ZmUZIvX2G5evU/nVjrqvz5i0hQ0D5x57qZQSSGTZquXbsiREREREQUczin7gWpWbOhluQfOLCz/PTT97J+/QoZNWqgeHvnsZT+dxXaEBw9GiQzZwZooPbzzxulU6cm4QZt5nP++ecv8ffvLTt2/Czz5k2V3r3baEDm6rqlSZNWzp7901J9Etk/FF85evSgZvbQzmDmzInyvPz11xlp0+ZT2bRpvRARERHFVzjZv3btMuP6SrQeQ56DQd0LkjJlSpk4cZ688sqrMnXqGJk2bay8+WZ2bVvgqHhJRPLlKyQjRkyR/ft3yaBBXWXhwhmSO3cByZIlW4TPGTIkQC5cOCejRw+SPXt2SIUKlSVRokQurxuCPwR1Pj4dLNk5H5/hcunSBe0pN3Jkf+nXb6g8LydPHtX12bbtByGiZ50//49WmnW3O3dua8/JiNqoUNTE1GdGFF/s3Lk1wpPcUfXw4UMJCBgmu3Ztk+cNJ/J//TXiY53vvlsjM2aM1zZYppUrF2jV8IgeQ56Lwy9foAwZMhkB1fQIH+Po/kmT5j9zW7FipYxL5P7jL1myrF6ium547vff77a5zds7t3z55QKb2zZvtt2Z2i83bdp0zzwGjchxsbZx4x6b35EZtH4e5hVCZIahEsVVCLAwJDp16jSW27Zu3SyLF8+UWrUaijv9+WewHtyMGzdLXn/9DfEECERxCa+gVGwRU58ZUXyBEUeoEzB16mJxp0ePHurJrBw5csvzhpP3UL58pXAfU6VKLb3++ONaltt++WWTVjeP6DGPHj0yttd5bWVFnoWZOoozkKlLkiSJZSdFFF+dO3dWmjWrJgcO7BZyrG3b+rJ8+TwhIoqLcILts8/aS7p0r0fqMRMmDNGpOeR5mKmjOOPYsSAdQvrqq6zQSURERETxBzN1FGe0bNlZevf2E6L4DGdZW7Wqoz+PGjVIKlcuLnv37rR5DIoKoZhSzZplZfDg7s/MhcN8jS++6C61apWTzz+vJd98s8qVl9YemAMHdtHldunymRw6tN/mfgx3nDJltLRuXc94zHvSrVsLOXPmlM1jliyZLe3bN5Rt237U66iuI+a5dO3aXF+nceOPxde3p/zxx2nt54ltggIBGzeu1Z/nzp0S7nvC/GAUXxo+3Efq1q2or/Xtt6ttHjN9+jhp2PAjm9v8/HoZ27ip5ffDhw/oax07dki6d/9catQoI+3aNdCCUqtXLzbOlleXevXelwULHA97DwzcZdkePj4dtTqxtWvXrsqYMV9I/fofSqNGlSUgYLhNJWLz9VEgC38X1aq9q8NmieISFGnD97lVq7r6XUBNgMePHz3zOGf7D3M/tGrVQr2uXr207q8wCsKZ3377Rfr376TLxoiJ2bO/1PWCr79eqt/D//6z7fmLdenYsbH+jH0f9nnYJ2AZ2KdiP+EIRhs0aVJV901ffjlSnj59arnP/M7jOjzWj8E+Fj9jiOa5c3/rz9hXBQef1J8x1NQatg32jzdv3hCKHRjUERHFIZiL2qePv/7cuHEr40B/hrY9MeHgYsqUUfLJJ/WM/7AHGQFesAZaJgQLgwd30wP+tm17SJky72vBpO3bf3L62qh2W7BgMenbd6ikTPmS+Pv3Mg6oHlvuR2CE6rwYIo3XTpgwofTs2UquXLlssxwEHpgz0qZNdyNInSt//30mUuuI4BLB7csvvyK9evnqdrh797YRQJ6UokVL6TZBRr9kyff0Z/sen/amTh0tr732hhG8LZP33vtQJk8eZan6G1l+fj2NgK6+TJq0wHgfV7Wg1PbtPxrbaqIRGLaUpUvnyO7dO2yeg89s1qwAadCghTRv3tF47WPG8wbYPAbbGgeTKGDVoMHnsmPHT/LVVxOeeX18Buht2qPHYJu5NURxAYqA4PLOOwjCBupt9t8nV/dx2A9hPl7nzj5GcLjUCJie6PcsIkeOHJShQ/sa37EUxr7tC6lUqZqsXbvUCOwm6f3mHLidO7danoM5bPv377ZUF8+QIbOuf8eOfYyTYz7y4MF9GTHCxxIYmjA6CcEpHle5ck0teoL9R1SlTu2l+8MiRUpov2L8jG2YI0cuHaYZGPi7zeNxshDtsTg6Kvbg8EsiojgEB+oJEoSdr8NEd0eFg7p2HWCZBI+DAgQDJmSh7t69owWZzIN+HFSsXr3ICGg+iPC1UcyjevVP9WecMUYAgQIFWbJklePHD+uBS//+I6RChbCsVqlS5YxAppKsWbPYyFr1tCwHGaZRo6ZZ5nngcdYHXM7WERV4cfa4YsUqUq7ch3q/deCG4kyJEycRL690LhVW+uCDqsbBX3f9uV69ZnrQePr0ccmZM49EFt7n++9/rD8XLvyOZuAQ4L3ySio9mMPB3x9/nHqmiNWwYV9atkfSpEk1sMRBabZsOSQoKFBOnDiiB4Dm+8R7Gzv2CyMI7KDBrQkHawioieIa7HMQQCFwat8+LPgqU6aijkzA/sLk6j4O+6Hhw6fo/gLq1Gmq1cKxL8uTp4DDdVi2bI62dvL1Hae/Y/+DquHYZ+BkS9q0r2mQtG/fTuMETAN9DPoA4+RX6cQd9AAAEABJREFU6dIV9XcUnMPF9NJLL+tIg/Pnz9qciEmfPqO+TuLEifV9Xrlyycg4rtSTWKhkHlnYr2B/iIwcMonW+0YsHyMbsI2x7AcPHuh6Iyh25P79+xoEuwIn93ASkKKPQR0RUTyC/0Ctq5rhjPLDhw8svwcF7dWzstYHD7ly5dMDIRzk4AAiPG+9lcPyc7JkyfUaB0uwb1/YWV6cBTalSJHCCIzy6dlt+3W0nriPKp6RWcesWbNLpkxZdCgjhnziQC2iYgHOIEsX3vuKrNdfz2D5OWXKl/VgBgEdIIOGqr72y7bfHmbmFQeXYUHdXv29ePHSlsfkzp1fMwDBwSc0eDRVqlRdiOKiv//+Q0/mYLSANXy/rIM6V/dx+N6ZAR28/XZOvUbG31FQh4AHJ64++aSuze1Yn8WLZ+n3tVSp94xLeVm+fK4Gcijutnfvb7o+5vLxvV2yZJZm8P/995yEhobq7RiBYA0BovX+OG/eQtrmACfSsP9zp3ffraD97I4cOaDBHlpoIXNonjSzh4zmgQN7XFp2+vQZZOHCb4Sij0EdERFZ4ODHnFthD/PQ8B9wVNy5c0uvrbNG5u/nzv0lkeHKOg4bNlnWr18umzev12Gh5cpVkg4detu0ePBUZhCI4ZtgHrA2b17jmcfaz0VMkyatEMVFOIEDyGxFJKr7OHPfFd4cMgRdCHTsX//ll8O+r8ikQenSFbRNCbL0CPJwwguZMBOGVuNkDIafo10VsvADBnQWZ8z9wrVrV9we1BUsWFRSpXpVh7IiqMPQS2QcEVg6ghEJ1oF0RBDYknswqCMiIgsMz0NT3W7dBjxzX3QCAjPTdPv2LZvACgdiqVJFbk6GK+uYMWNmDeIAmUCcOZ4+fawO//R02IZgHsSZ23bIkADN9lnLnPktIYoP0qTx0mv7jJa9qO7jbt26GeFj8H3E988MLk3mCS1zP4eMHDJzyNBlz+6tc/cwJB5QiGXnzq3SokXHcLNg4TFf19wvuBOGkGIYPIaNYij6nj075OOPa4f7eIwgoOePQR0RURxjDsmxroTmqjx5CuocLW/vvJIyZUpxF3PI4KFDgZaDFcy7OHXqaIQHB+5Yx/z5C+uEfusKcthGoaEh4g4YHmpdaRJwttxdMPzKelgYtiEUKFBUr/HeAEM3XZkjSBQXYU4qsmmocmsNwxmtubr/sP/eYeghYGhzeLCfMx9nwvcVy7D+bmJI+NatWzT4QQasQIGw7/CNG2FVbTFfzoThno48efLY5nfMcUNAF92m4VjXkJBn/+9AhnHLlm90iCmKW1lnFyl2YPVLinV27PhZdu3aLkQUNcjc4EABZf2Dgvbpf8KuwuR9zHUbNqyvHDy4V4fbDBnSR6tRRkfevAW1otvkySNlzZolekDTv39H454EWnwkMpytI94zSpovWzZXhwmhjQHOiuP1Tdmz59SDIDwfJcSjA2fbkT3DGXeczcdcPlQVdRecJR82rJ++l5UrF8i8eVN1WJZ5Nhzze/DeJk4cqmf58Z7QZsGVIVtEcQWCEVSW3bp1s35XMBRyw4aVljmnJlf3cQjqUMkSwyTxmEWLvtLvHb7vgGGWKC6CNiWY+wZNmrTRuX1YHo5lsEy0HcBrWleJRFEUDPXE+iE4wnccMM8P2b5Nm9bpuq1bt1wLuID93GMUa5o5M0DXD9WBUUyqbt2mOhcwPMgynj37Z4TVe7Nl85YLF85rAS3sp+/du6e3Fyv2rs7BnjFjvI6EYDYu9mFQF8/s3LlVD3hiK+yEsRP19e0hRBQ1OLgZOHCUBhl9+7bXgwpXoXDHxInzdBK/v39vCQgYqgc3KPsdXYMGjdE2AjhIwbyRW7duyIgRUyJdxMTZOqIwQdOmbXUuCipworpmixadbCq1oQw4zoajnxQCIPR5i6oKFSrrwVSPHi21RxyGf9Wp00Tc5Y03Mmpp9GnTxsicOZPlrbfeln79htk8BtsWWQIEzeiRhzk5zlo1EMU1+N4jC4b9C3ox/vvvP1K1ah2bx7i6j0NwVLz4u/qdGjGiv2borIdv435879H2wGwfki9fIeMYZpIRFJ2TUaMG6okltHBp1aqrzbLxOJx4QwCIfaIJgR+GUaNYEo6DfvzxW50f3KpVFzl+/JDNMrDfwXw0VOREn70qVWpq25OIoOUJgjofnw7PjC4woYJx5co1ZMyYwTJ+vJ/ldRHAYntgnc32CxS7JIjozsndT/sVKOflm7+cl1DsgaEEFy+ej1KKHQETKiNNnbrY5ecg0EIp3TfeyOTWCa3hLRdn1bGzRBlxij0Ob7smR7Zd8+8S4O0nHoz7NaK4DaNql48IDjH2VZGv6x6L+PmFJk57PfhRo8E5Egg9V2g+jmImGze6VsExKlavXmxk3ufL0qWbIqwqHJtgCHvnzk21b59ZrZOeD1f2a8zUeSA01cXZpedl06b10rp1Pcsk4ZheLs5IM6AjIiKiuAjVN1evXmhkxep7TECH4jKzZgXo3EAGdLETC6UQERERET0HGDGF+c4oPNKkSWvxBMhcokUM2jOMGRO9+dUUcxjUPSf4QqCRZOPGrY1U+2wdb12wYHHp0sVHS9uaUJJ2/vxpOkH2v/8u6dwJlLrFWRGc2WnWrJrlseixggnyAQGO58tgeCOWhcmut2/f1PHXjx8/euZxR48GyYoV8+TQof1aEhhzQTCmGj7/vJZlAnDjxlX0esOG37TKGuagzJ49SUvcJkqUSEqWLGek5fvZnHUKDj6pTTRRkAATbNEEF/NaunVrHu5y+/XroL+PHj3dspw//wzW5aCqFN4Xqr516zZQx6SbPv64hN4WGPi7FobAfRjvznklRERE5CqMFkLV3JiA1gAo6OJJlWoxnzBr1gE6/4995WIvBnXPEYoWoBJSWANcL+2bhIpFQ4ZMtDwGk/pPnjyqwR+aOm7YsEJ69mwlc+as0efgDAkm3p4797f06eMfYX+nFSvm66V27UYaBKFACio45ciR2/KY69evyeDB3bSKE4ItTCqeOnWMvhYmG/ftO1QrKqHQAAovoHISAi/A+v/11xmpX7+5Ph/jz/Fl79Spr96PAHXAgE7i5ZVO3zN+RyNgFBGIaLn20MASVfKwTphsjAnECIyx7MmTF1mqRsHUqaN1OMP06cvk229XG/ePkpw58xmXPEJERETkDIoT4RITMMXE06DqJ8V+DOqeI1QaGjVqmqXSG87WILAxHT9+WDNMqK5UocJHlsc0bFhJg5927XrqmZ2NG9dpFi+iszzoT7V27VIpX76StG/fS29D2VwEYQiSTAh88PukSfO1lC4gaEJ1OgR1yAQiSwaorJY2bTr9GT1eUFkOmUYzE4bgbezYL6R58w7aK+a779bIzZs3jEBriTb7BJT1BewsHS3XEawj5t1hOQh0AUViUNXv999/1SEMJpxdQ2NMQJl0BLUo+8ugjoiIiIjiKhZKeY5Q0dG6dDca1j58+MDy+759v+t1kSIlLLehlwoyTfb9SZxByVkEVCjtbQ2NKa2hfwuGf5oBHeTKlU+zheGVuzWfB8WL/6/vE9LzqMyJUtqA3ilYrhnQRZW5HDOgA7Pprv12ee21/w1lTZYsuV4jSCUiIiIiiquYqYtF7ty5pdfIclnD7+fO/SWRgaGOgGGREUGWDnP1MD/PHhpjpk+fIdznQfPmNZ65D8sDzOOzDyKjAsuxfx8IkLFdrly5JERERERE8RmDuljEzOLdvn1LUqdOY7kdAVpEc+ccQcETwPy1iCCLhjK13boNcLCMtOJsXdEkM3ny5Db3Zc78luUx6IkXXVhH++WgWAoCy8huFyIiIiKiuIZBXSyCuWVw6FCglCv3of58//59OXXqqHz8cW3L41BdMiTkaYTLypAhs2ay/vjjtM3tGB5pLU+egjo/zts7r6RMmdLhslDZEqxfM3/+InqN4ibhze3Lm7eQDp1Exs966GREy3UE64ihqSjqYgarqKYZGhoqRYuWFCIiIiKi+Ixz6mKRvHkLyjvvlJbJk0fKmjVLZOvWLVr1USSBFv0wZcvmLRcunNdWBXjMvXv3nlkWAj+UzN26dbPs3btTM1sbNqy0zIUzoXAJ5u0NG9ZX2yigOuaQIX20Sqcpe/awJpNbtnwje/b8pgVOUEAF6zpx4lDZuXOrPnf69HEyYEBnm2WjrcCgQV3lp5++l3Xrlkv79g0tWTdHy3WkZs2GupyBAzvrctavXyGjRg00AtE88u675YWIiIiIIoYT+yg+d+bMKYkJOIm/du0y4/qKPC8x/Z48CYO6WGbQoDHaBwTVJ0eOHCC3bt2QESOm2BRYQQ+5ypVryJgxg2X8eD85fvyQw2U1bdpWK1hiOdWqvavtCqpWrWPzmJQpXzICs3ny+PFj8ffvLQEBQzUDVqbM+5bHeHvn1gqa33yzUtsfoOKkua7ILiII9fPrpQVSrHvCYdnjx8/R+XBffjlSK3iiT53ZWy685dpDBhHr+Morr2q7hWnTxsqbb2aXoUMn2bQzICIiorhn586t2paJImfnTtvthuNFtHr66qsJEhNQ9XzGjPHy/fdfW25DL+Rff/1BYkpMvydPEuER8eTup/0KlPPyzV/OS4gofju87Zoc2XbNv0uAt594MO7XiOK20BCR5SOCQ4x9VSLxYH5+oYnTXg9+1Ghwjnh/9nLo0L46ymfq1MVCrrPfbshqrVu3TKfHuNpcHXUdcHGlbx8K5W3atE4TCGYyol+/Dno9evR0iS6s/8WL57WtlfVtkX1PnsiV/Rrn1BERERERxXFJkyaV+vWbR+o5bdvWlxIlykr37gOdPhYtsj77rL3ElAkThmjv4Tlz1lhui8p7iqs4/JKIiIiIiMiDMVNHREREROFasmS2bN/+ozRu3FqWLp0tFy6ck4IFi0uXLj6anTFdu3ZVZs+eJPv27dQK1yVLlpPOnftp8TYT5litWDFPDh3arxWt69RporUCTCjsNn/+NC0Ghz61FSpUlsePbSt3Y8jd+PH+cuxYkNy8eV2yZHlLypWrJJ9++pn2sbWHSuDLl8+Vf/75S86fP6vz8lGArkKFjyJ839u2/ahFOE6ePKJVxcuUqShNmrTR18CwwqxZs2vRtmXL5mgRu8GDx+hQRaw/Csj9998leeutt6Vr1wHy9ts5LctdunSO/PLLJh1KiLZN2JatW3fVquW7dm3T+//++w+tSYDlN2/eQbJn945wXV3ZbvDxxyW05kKTJq319/Be78yZkzJunJ8+ZuPGtXpp0KCFtGzZWQ4fPiC9e7cxPuvV+rexY8dPMnnyIn3vuH3cuFlSoEARm9ddvnyefPPNKnnw4L6UL/+RdOrU11IFfceOn3Wo6KxZqyxDK1EwcObMibJ27Tbt49ysWTXLstBbGQX7AgLmOXxPgO2Az+748cPaMxmviXU3/z7M94B1nTdvir5f/F20adPd+DyKiidipo6IiIiIIoSACJWxcdA7YcJcIwg4I1OmjLZ5jL9/L2237zIAAA1pSURBVD2YRtXqBg0+14N96wIWaE2Ewmiodt22bQ8tyoYCaNu3/2R5zIoV8/WCCtvduoUN+UNlbmsovLZz5y9aZdvHZ7hxEF5Mi4I8feq4RRLaKuXMmU/XCY9HoIVic//+e07Cc+TIQRk+3EeLvvXpM0RKl66ggQCCJxNaUCGQQrBbo0YDvQ3P+fnnjVKlSi3p3n2QBhE9e7aSK1cu6/0IahcsmK7toAYMGGkEKDWN2w4awekN7S2MIYYI7nr18jWW20ru3r2tAYczrmw3exG9XtGipYxtNENefTW1FvDDz9bF8Mz3il7FPXoM1sA6PAi+8b47duyj7xcFVRBIuip1ai99/SJFSmgQjJ/N9+gIPjsEicmTpzC2/RdSqVI1Izhcqicc7OFv9sMPqxkB5Cot5IffUTzQEzFTR0REREQRevLkiYwaNc1SAKNUqXI2wRh63p44cUSzd+bBv5dXOhk79gvN/CBwQObk7t07MmnSfEsQgMwNKn6jWjeCMhx8ly9fSatjA7Jjf/11Rp9nQrXtNGnSWto9IeCKCAKTevWaWn4vUqSk/PDDt3LgwG7JmDGzw+cg+5Y5c1bx9R2nlbbLln3/mccgOMV7yZ07v/6OrND+/bulf/8RliwgtlPDhpU0EG3XrqfOCQPMA0OWE62ZkAEzl4fgrmLFKpZ+xdaB1MOHD5/JvmG7urrd7F26dCHC10ubNp2RZU2in6OjnsQIsBC4OpM+fUbdjsjYYr2uXLmklc8RRJrZuohg3hxef+PGdZr9DK8/ssn6swO8N3yGCHoR2OPvwdShQ2/54IOq+jMCcfRFRnGZLFmyiqdhpo6IiIiIIoSMk3V7paRJkxlBxgPL72Yf3OLFS1tuQ7CDoZIIwszHIJCxzurkypVPTp48qkEjhgAiyEDmzRqGz1lDoISAZPTowRIYuMsmexYeBHEdOzaW6tVLS61a7+ltyFQ5giAJwRnWI6LWScj4mQEdICAAZJRM6AWMLCGyR4CsF6DfLjJ69+/ftzwWwzkzZcqimTz0ezOzeybcXrduRZsLuLrd7Dl7PWcqVaru0uOQKbUegotKlVhfs2+xO5mfHVpoWcO2QQYOgbe111/PYPkZf9OAEw2eiJk6IiIiIooWMyPUvHmNZ+5DqXvzMfgZc6LsoXE15mQB5nZF5MMPP9GD9507t8qQIb0lVarU0qpV13DnyK1bt1ymTx+nWUQEVcjyffJJqXCXj2APgaKzoAjDAq1h7hcge2YNv58795f+nCFDJh0+uGnTepk1a5L28a1du5FWjUTgPGzYZFm/frls3rxe55RhriCySalTp9G5h46ykq5uN3vOXs8ZbMeoMLfrtWtXNKh0J/Ozs98WL78c9prIEsZVDOqIiIiIKFrMLN6QIQE6z8pa5sxv6TWG62EIYbduA555PgKER48e6s/hZdBMyJ5VqVJTL/fu3ZOvvhovI0cOkGzZcmj2yd7KlQukWLFSlqGFyApGBEFHsmTJIhy66Ii5DW7fvmUTFCHoQuBpwvBBXBB8oFE3mmfjuZ98UleHgyKoAmT3MMdr+vSxOqQTASEu9lBwBpxtN0cier2YYgahzoLmqMAy8fdnvsb/XjMs4Lb+HOIaBnVEFO+cPX770c2rjx4JEcU9oYLxcimEnqv8+cOqHSIYCm/OU548BXXunbd3XkmZMuUz96PCJLJaqFZp7VEEu2ssp3r1+pr5QoEP+6AuNDRUbty4JunTv2e5zRwO6uz9BAXtk8jIly+s+TUKqJhz1DC88tSpo/Lxx7WfeTwyZQg058yZbJlrZ7sOhXU9Tp+OeH2jst0ccfR6GDYZGup8eGtEnjyxLTxy+PB+Db7MSpfmsEfrYPv69avPLAfrEhLiuBiONXwOR44csLkNnwme72w+nidjUEdE8UpCCV13479HfxkXIaI4KjRh9I5CKdJQYh6VFydOHKoFQVA18vfff9WqmSNGTNHHoFolhvoNG9ZXC4Uga4dhf5ibhuGHOOiuUaO+bNiwUufNIbuG4iqYi5clS1gAgCCtf/9OWrzDrIb49ddLJUWKlDpXyx6yeijTj9L9WOatWze0vD4qIx47dkizZY7aIKB1AapW+vuHFdJA0IUqlaNGTbeZH2Ytb96Cug0mTx6pBT0wl2zdumVYC0tRl0WLZhoBxj4pW/YDDUDR+gAZtmLF3tUgEsMxMbw0R47cGozu3fubZvAi4sp2c8SV18uePacGYVjPW7duWoLVyMC2mzkzQNcLfxMosNOiRUfLdsfnj88J2xcFTrAOaH9gD20jfvzxO62wivlxaIru6OQAPrtevVobWeM+8v77HxvB7iktkoK/P+siKXENgzoiilc6BeTEbPWDQkREbjVo0BgjSBihQQ0yVOjNVrfu/6pOItCbOHGeltFHsIRhcrlzF9DWBib0G0OWBsMpUbACwUrVqnW0nQDg4L93bz8jkFsiq1Yt1HlZCO4mTJgjb7yR0eF6oY3BjBlhQzQxVBEVEDHcc8GCaRocILtoL1++QjqUFD3MRo8epMEGgh1n1RqxDaZMGaUVPc05YwhqzaGZ6KWHAPTXX7cYmcVTRsCZW6s0Yq4cAla8/61bN2sQgvfTokUnnXPnjLPt5giKhzh7PbQhQKCOQBpzCM2MbGSgZ16SJEl0O6JACobNmhU/AcVz+vUbqi0z0CYDwR9aLKBlgjXMKTx79g9tR4Fg3M9vgj7WHj67oUMnydy5k7UgDeZfYltg3mVcliCiOyd3P+1XoJyXb/5yXkJE8dvhbdfkyLZr/l0CvP2EiIhilJ9faOK014MfNRqcI4EQUbyGEbDLRwSHGMdg4Z5VYEsDIiIiIiIiD8agjoiIiIiIyIMxqCMiIiIiIvJgDOqIiIiIiIg8GIM6IiIiIiIiD8agjoiIiIiIyIMxqCMiIiIiIvJgDOqIiIiIiIg8WGJnDzh7/Pajm1cfPRIiitduXn6YVIiIiIgo1okwqEsooetu/PfoL+MiRESJRA4KEREREcUqEQZ1nQJy4gCOB3FERERERESxFOfUERERERE9J48ePZKzZ/8Ud7tz57ZcvPivUPzEoI6IiIiI6DmZMGGI+Pv3Fndr27a+LF8+Tyh+YlBHRERERETkwZxWvyQiIiIioui5fPmiNGtWzfJ75crFJU+eAhIQEJZdO3o0SFasmCeHDu2XNGm8pE6dJlK9+qeWx+/atU2WLp0jf//9h7z00svi7Z1HmjfvIGfOnJRx4/z0MRs3rtVLgwYtpGXLzkLxB4M6IiIiIqIYljq1l4wZM0OWLZsr5879LX36+EuqVKn1vuvXr8ngwd00WGvbtof8++8/MnXqGH3Oe+99IPfu3dVhmzly5JZevXzl1q0bsnXrZg3oihYtpcsdPtxHcucuIHXrNpEMGTILxS8M6oiIiIiIYljSpEmlUKHiRiZtnfz33yX92fTtt6vl7t07MmnSfMmS5S297cGD+7J69SIN6i5duiA3b96QihWrSLlyH+r91arVszw/bdp0kjhxEvHySmezXIo/OKeOiIiIiOgFCgraK6+//oYloINcufLJyZNH5cmTJ5I1a3bJlCmLLFgwXdauXSZXrlwWImvM1BERERERvUDI0mHOHebZ2bt69T9Jnz6DDBs2WdavXy6bN6+XmTMnGhm7StKhQ29JnTqNEDGoIyIiIiJ6gV57Lb08fPhQunUb8Mx9adKk1euMGTNrEAdHjhwUf/9eMn36WOnff4QQMagjIiIiInpOEidOLCEhT21uy5OnoAQFBYq3d15JmTKl02Xkz1/YuBSR06dP2Cw3NDREKH7inDoiIiIiouckWzZvuXDhvPz22y+ydesWuXfvntSs2UBSpEghw4b1lYMH98ru3TtkyJA+snDhDH1OUNA+adWqrlbO3Lt3p/zww7fG9W/yzjulLcvNnj2nHD68X5+/bduPQvFLIiEiIiKiWKVCBb+EKR9cG1ygvFcCoTgFbQmuXbsiS5bMkp07f9GsGwqhlCnzvgZ6a9culX37dkq6dK9rhUsvr7Q6pw7tD/bt+11WrVoo//zzp9Sp01SaNm0jCROG5Wjy5i1kBHUHdLlHjhyQ99//2AgUnWf9yAOEihzZfi10467JQ8J7CHcURERERLGMn19o4rTXgx81GpyDx2pE8RxG1S4fERzSJcA73IQch18SERERERF5MAZ1REREREREHoxBHRERERERkQdjUEdEREREROTBGNQRERERERF5MAZ1REREREREHoxBHRERERERkQdjUEdEREREROTBGNQRERERERF5MAZ1REREREREHoxBHRERERERkQdjUEdEREREROTBGNQRERERERF5MAZ1REREREREHoxBHRERERERkQdjUEdEREREROTBGNQRERERERF5MAZ1REREREREHoxBHRERERERkQdjUEdEREREROTBEgsRERERxUq/rb14R4gofguVBMa/KSJ6CIM6IiIioljG10+eTu4e2vLvo7eFiEhCE4ZEdHcCISIiIiIiIo/FOXVEREREREQejEEdERERERGRB2NQR0RERERE5MEY1BEREREREXkwBnVEREREREQejEEdERERERGRB/s/AAAA//+erN/gAAAABklEQVQDAPs4Gi6hhrn0AAAAAElFTkSuQmCC\" style=\"max-width:100%;height:auto;\" alt=\"Figure 4.1 \u2014 Solution design (methodology).\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a9a109",
   "metadata": {},
   "source": [
    "## 5. Implementation architecture\n",
    "\n",
    "Five stages \u2014 ingestion, preprocessing, modelling, evaluation, and a parallel validity-audit path \u2014 feed a single graded results ledger. Leakage defences (dropping label-derived and identifier columns) live in preprocessing, before any model sees the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a26782",
   "metadata": {},
   "source": [
    "**Figure 5.1 \u2014 Implementation architecture.**\n",
    "\n",
    "<img 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style=\"max-width:100%;height:auto;\" alt=\"Figure 5.1 \u2014 Implementation architecture.\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c103fd7",
   "metadata": {},
   "source": [
    "## 6. Data acquisition & preparation\n",
    "\n",
    "Every line below is commented so a student can re-run and modify each step. The cell ends by producing the standard analysis variables: `df`, `X` (clean numeric features), `y` (binary label), `feat` (feature names), and `family`. `family` is the per-group label used for the recall breakdown. It is an attack family on the intrusion corpora, but a transaction type, merchant category or malware category on the fraud/malware ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7610009",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import time, warnings; warnings.filterwarnings('ignore')   # keep output clean\n",
    "import numpy as np, pandas as pd                            # numerics + dataframes\n",
    "import matplotlib.pyplot as plt                             # static plots (embed in HTML+PDF)\n",
    "plt.rcParams['figure.dpi'] = 120                            # crisp figures\n",
    "RANDOM_STATE = 0                                            # single seed used everywhere\n",
    "np.random.seed(RANDOM_STATE)                                # reproducible sampling\n",
    "NEG_WORD, POS_WORD = 'benign', 'attack'                      # class names (overridden by some loaders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1613fbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 30,420,086 rows x 41 features; positive rate 0.9957\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "# Kaggle auth: token read from ~/.kaggle/access_token (students supply their own).\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "import kaggle; kaggle.api.authenticate()\n",
    "REF = 'dhoogla/nfbotiotv2'; DEST = '/tmp/kg_' + REF.split('/')[-1]\n",
    "if not os.path.exists(DEST):                                   # download + unzip once (cached)\n",
    "    kaggle.api.dataset_download_files(REF, path=DEST, unzip=True, quiet=True)\n",
    "NROWS = 1_500_000                                              # per-file read cap (memory bound)\n",
    "files = sorted(glob.glob(DEST + '/**/*.parquet', recursive=True))\n",
    "df = pd.concat([pd.read_parquet(f) for f in files], ignore_index=True)  # combine day/part files\n",
    "df.columns = [str(c).strip() for c in df.columns]             # strip header whitespace\n",
    "LABEL = 'Label'; FAMILY = 'Attack'\n",
    "df['y'] = (df[LABEL].astype(str).str.strip().str.lower() != '0').astype(int)  # benign=0\n",
    "df['family'] = df[FAMILY].astype(str).str.strip()             # descriptive attack family\n",
    "df = df.reset_index(drop=True)\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'    # honesty gate: >= 1M rows\n",
    "DROP = list({LABEL, FAMILY, 'y', 'family'} | set([]))  # never leak label cols\n",
    "feat = [c for c in df.columns if c not in DROP]\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X = df[feat].copy()\n",
    "idlike = [c for c in X.select_dtypes(include='object').columns if X[c].nunique() > 0.5*len(X)]\n",
    "X = X.drop(columns=idlike)                                     # drop ID/timestamp-like leaky columns\n",
    "for c in X.select_dtypes(include='object').columns:           # encode remaining categoricals\n",
    "    X[c] = LabelEncoder().fit_transform(X[c].astype(str))\n",
    "X = X.apply(pd.to_numeric, errors='coerce').replace([np.inf,-np.inf],np.nan).fillna(0.0)\n",
    "X = X.clip(-1e15, 1e15)                                        # clip huge NetFlow counts (float32-safe)\n",
    "X = X.loc[:, X.nunique() > 1]                                  # drop constants\n",
    "import re                                                      # LightGBM rejects special chars in names\n",
    "_seen, _cols = {}, []\n",
    "for _c in X.columns:                                           # sanitize to unique, safe names\n",
    "    _c = re.sub(r'[^0-9A-Za-z_]+', '_', str(_c)).strip('_') or 'f'\n",
    "    _seen[_c] = _seen.get(_c, -1) + 1\n",
    "    _cols.append(_c if _seen[_c] == 0 else f'{_c}_{_seen[_c]}')\n",
    "X.columns = _cols; feat = list(X.columns)\n",
    "y = df['y'].to_numpy()                                         # STANDARD CONTRACT\n",
    "NEG_WORD, POS_WORD = 'benign', 'attack'               # class names for plots\n",
    "print(f'loaded {len(df):,} rows x {len(feat)} features; positive rate {y.mean():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "712cc512",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2c6b264",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1320x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- EDA 1: class balance and the attack-family mix ---\n",
    "fig, ax = plt.subplots(1, 2, figsize=(11, 4))\n",
    "df['y'].map({0:NEG_WORD,1:POS_WORD}).value_counts().plot.bar(               # counts per class\n",
    "    ax=ax[0], color=['#2a9d8f','#e76f51']); ax[0].set_yscale('log')\n",
    "ax[0].set_title(f'Class balance ({NEG_WORD} vs {POS_WORD})'); ax[0].set_ylabel('records (log)')\n",
    "df.loc[df.y==1,'family'].value_counts().head(8).plot.barh(                  # top attack families\n",
    "    ax=ax[1], color='#e76f51'); ax[1].invert_yaxis(); ax[1].set_title('Top attack families')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ab40266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SOomSZrMuXbp0upnLkRzz5s2z66+/3tq2bWu9evUKa2GQVZuDzIKhw4cPt4EDB+bacQMAACQTs8kDAAAkegBWNCWmJTcpk1BBtb179wbXde3a1ZVef/DBB8F1mslas1x37tw500AaEuPff/+1M888003q89577wUn2PJaGOSkzYH6S2p2+tBFbRoAAAAKAjJDAQAAEixFmaHF/AU5Uyz7wVBNULhp0yY3cZN88skntmLFCvfvG2+80QXS7r77bnvttdds8eLFruegFww95phj3AzXf/75p1WtWtVlC2pCGDIGk08zlZ9++unu2iqDN7RtgVcen1Gbg8qVK2cZzNaEP1oAAAAKIoKhQAHUeP6EpO17bsPTk7LfJvPST0qQCJv3V7RkqVBkU1L2u9dnn8O47zfgb7KZ3JCSkn5CikQovj85M3bvKZK8yXHSUookdn9JKpJJLeI/4zM1LfvBUE3KohmsPcr09LI9Ncu0gqHReDNX/+9//7NnnnnGlVUfffTRNmrUKGvYsGG2jwc5p+xOZecuWLDAzWh++OGHh91fq1YtN7HWzz//nO6xP/74o2uBAAAAUJhRJg8AAJBo/5cZ6mfRttml2a41u3S0xcsCVYAz9LanUqVKNmLECFcev337dje7dbQZypE4yszt1q2bzZgxw7UsUK/QaM4//3w3c/ny5cuD67766isXQL3gggsSeMQAAAB5D5mhAAAACRZLL1A3mzxgZrfffrt9/PHHLjNUs8KPGTMm7Lwo21f69evngqUnnnii3XzzzbZt2zZ79NFHrVmzZq71QW5RUB0Fh65nShImcAMAILcRDAUAAEiwlFSzlCI+e4ZSx4P/M2vWrGDvVy2RvGDowQcfbFOnTrXbbrvN7rrrLitevLibbOnxxx/PtcmvUlNTbc+ePQTQClAgVJnIeu8AAFDQEAwFAABIRs9Qn8FQv9uh4FOrAr+aNGlin3/+uSWKgqzqLbtmzRo3+RIZhfk7EKrrqGBobgXPAQBIJoKhAAAAiZaSYimpPoOclKkiH6hevbrt3r3ble9rtntNwkVANP9mhGopVaqUu64AABQ0BEMBAAASLKVIqlv8bgvkdSqTr127tq1evdoFRdPS0pJ9SMgGBbBVGq+MUAVCdV0BAChoCIYCAAAkWGpqDGXyfjNIgSRT4OzAAw9M9mEAAABkimAoAABAMiZQ8hnkZAIlAAAAIH4IhgIAACSYZpL3mxnqd9Z5AAAAAFkjGAoAAJBgygr1G+T0PdESAAAAgCwRDAUAAEiwlNRUt/jdFgAAAEB8EAwFAABIwozNvnuGppAZCgAAAMQLqQYJVLZs2eCiDzZlypQJ3l62bJktXbrULrroIqtatapVrFjRWrVqZa+++qp77JQpU9wMndq2XLly1qxZM/voo48y3NeePXusVKlSNnfu3OC6+++/30qUKGG7du0KrrvwwgvtwQcfdP+uU6eOTZs2zf17wIAB7hg///zz4Lbff/+92yYretyhhx5qgUAguE6vTcffoUOHsG11LBUqVLDzzjsvbL22r1Spkv3+++/BdZMnT7YaNWrYxo0bM93/jh077NZbb7WaNWta6dKlrWXLlvbuu+8G71+yZIkVLZr+ewAd25gxY+zaa68NXpdixYpZ8eLFg7cHDx6c5esHACArqUW0pPhcOJ8AAABAvBAMTaBt27YFFwUl58yZE7xdsmRJa9u2rQve/fbbb7Zp0yZ77bXX7Isvvgg+vm7dum7bzZs323XXXWfdu3e3DRs2RN2XAnhHH310MLgp+reCmT/99FNw3XfffWfHH3981OdQMHLgwIHZeq0KIk6fPj14+80337R69eql2+7jjz+2IkWK2Pjx491r9hxyyCF277332tVXX21paWkuaHrNNdfYk08+6Y4rIwrAKrA6c+ZM++abb9xz6jXoed566y1fx/7CCy8Er8sVV1xh/fr1C97WvwEAyDH1DPW5aFsAAAAA8UEwNI9QkK969eo2YsQIq1WrllvXtGnTqAE8ZVj27t3bBQj//vvvDJ/zhBNOCAZD9+3bZ/Pnz7crr7wyuE6PXbt2rbVp0ybq488++2xbvXq1TZo0KebXowzXN954I3hb/+7Ro0e67ZSJ2adPHzvssMNs7NixYffdcssttnv3bnv++edt0KBBLpiq582MgsdTp051maDaXkHhc845x2W69u3bNyxbNZ7WrFnjgtuhy6JFi3JlXwCA/C8l5b+eob6WFIZrAAAAQLwwus4jvv76axe089MXbP/+/TZy5EhXZh8t29KjjE8v8PnLL79Y8+bNwwKk+nnkkUe6bNRoVEp+zz33ZCs7tGvXrq6Mf+/evTZr1ixXst+wYcOwbdavX28TJ060bt26uSxXBUZDKWP0pZdechmizz33nA0fPtzXeTzmmGNcYDlUly5dbMWKFbZgwQLLDTo2Ba9DF+0TAIBo/GaFBrNDAQAAAMQFEyjlEQoMqh9mZhYvXux6iSpIqEzK999/393OyLHHHuv6Y/7zzz8u8Hnccce54Oevv/7qMiS1TsHRzPTs2dNlZSrjUr1K/VIpe+vWrV3PUfU7jZYV+s4777hgrvqfqk1A//79Xa9Qlch7GjVq5LI7tY2ffqXr1q2Leh694Kju9zJv40nZrRdccEHYOmWGEhAFAESTmvpfP1C/2wIAAACIDzJD84gqVarYv//+m+k2mpRIPTAVOP3xxx/t1FNPzXR7TUykIKKCnl4wVIFFBSD/+OMPty6jfqGh2aHqk5md7FAFQJXtqZJ1ZX5G0n3KCpX69evbEUccEVZaL9q3jlsl/hMmTMj2eVS5v2hyKr0m9SHVEkpZrOp1mh3VqlWzJk2ahC2ZZe0CAAo3Vb77zwxN9tECAAAABQfD6zzipJNOcpMJxbunpVcW/8MPP7hMTVFwUSXsCjBmFQyVXr16uRLzL7/8MqZ9n3XWWa4MXtmdkdma6lc6Y8YMe+qpp9x9WjTzfWgwVAFf9UxVz9Bnn33WZV9qpviszqNmvfeCn55x48a5jNAGDRrYgQce6LJrly1bFraNbteuXTum1wgAQLb47Reamuq2BQAAABAfjK7ziFtvvdVlNGrG9FWrVrl1Cg5GKy+PhYKdb7/9ttWsWdP1GPWCoQouqoenMimzomxJZWhqkqdYqPT9q6++csHMaFmhCs7qNaqnqBbNAL9w4ULX31QTPl111VU2ZMgQV+KucvMWLVrYAw88kOk+O3Xq5F7zhRdeaH/99Zft2bPHBZk1gdLQoUNdT1YFQjXjvPqhbt682U3SpNdWokQJa9WqVUyvEQCA7NDfI9+ZoT76iQMAAADwh2BoHqEy6+nTp9vWrVtdabt6gV5yySXWsWPHHGeGaqZz9Q/16N+aRd5PVqhHs9eXLVs25v0ruFi3bt1065UBet111wWzQrU0btzYBSkVKH388cddmf8VV1wRfMywYcPchEq///57hvvTB0ZlgbZs2dK9Pj2HJmB64YUX3PkMnfBIQV5lrSpQrBL8Tz75xLURAAAgtzGBEgAAAJAcKYF412UDcObMmeNmlR8/foIrzy8s5jY8PSn7bTLv06Tsd1NaJUuWCkU2WWGyN5C8LyuK2+6k7Ldo2p6k7HdPkVJWWCxYsMDOOON010tb/Z4T9bfh09OPs/oV/H3JuHDzNjtrwncJO0Ygs/cu70MAAJDfxynMJg8AAJCUMnl/BTqUyQMAAADxQ5l8PvfII4+48vXIRf0wc4t6mkbbZ506dSwRNPFStP23a9cuIfsHACCnUoqkWKrPRdsCAAAAiA8yQ/O5vn37uiWR1GNz27Ztlixt27ZN6v4BAIjXBEp+twUAAAAQHwRDAQAAEkwl8r7L5H1uBwAAACBrBEMBAAASLCX1vxnl/W4LAAAAID4IhgIAACQYZfIAAABAchAMBQAASDDK5AEAAIDkIBgKFEBFAvuStu8m8z5Nyn7nNDorKfttOu8TS5b9hex/4RX3rE7avvcWLZWU/e4uUjop+0UCpPqfQEnbAgAAAIiPwvVJGgAAIM+UyfucQInZ5AEAAIC4IRgKAACQaApw+g1yEgwFAAAA4oZgKAAAQIKpRN7/bPKUyQMAAADxQjAUAAAgwZhACQAAAEgOgqEAAABJ6RnqMzOUMnkAAAAgbgiGAgAAJKVM3ucESpTJAwAAAHFDMBQAACDRYugZqm0BAAAAxAfBUAAAgASjTB4AAABIDoKhAAAAiaZsT59l8mSGAgAAAPFDMBQAACAZmaE+J0ZiAiUAAAAgfgiGAgAAJFhKSqr/CZRSfGaQAgAAAMgSwVAAAICkzCbvMzOUCZQAAACAuCHVANlWp04dmzZtmvv37NmzrXPnzlaxYkWrUqWKHX/88fbRRx+5+0aNGuVK/Pr37x/2+AceeMCt1/3edkWLFrWyZcta+fLlrU2bNsHnj+baa69122opVqyYFS9ePHh78ODBVrlyZVu1alVw+23btlnt2rXt888/tylTplhqaqrbtly5ctasWbPg8YbeF7ps3ryZdwsAIL49Q30tzCYPAAAAxAvBUOTYvHnzXPCzRYsWtnDhQlu3bp09+uijNn78+OA2hx12mL399tthj3vzzTetXr16Yes6dOjggpYbNmywk046ybp27WqBQCDqfl944QW3rZYrrrjC+vXrF7ytf19++eV20003Bbe/99577YQTTrBTTz3V3a5bt67bVkHO6667zrp37+72G3pf6FKhQgXeLQCAuM4m72vx2VsUAAAAQNYok0eODRw40AUuBw0aFFzXtm1bt4Rmke7cudN+/PFHa926tf30009WqVKlDAOMyhC99NJLbejQobZ+/XqrWrVqzMelzNOmTZvaJ598YjVr1nTB1zlz5qTbTlmgvXv3tuuvv97+/vtvy441a9bY2rVrw9YtWrQoW88FACgE1DPUby9QeoYCAAAAcUMwFDn29ddf28MPP5zldj169LA33njDBUP185JLLnGBymj27Nljr732mtWqVStbgVApXbq0yx696qqrXOm+jvGAAw5It93+/ftt5MiRVqZMGZepOmvWrJj3NXz4cBcUBgDAf5m8z4xPyuQBAACAuCEYihxT5maNGjWy3O7CCy+0I444wh555BF7//337ZdffkkXDJ06darrO6oeoE2aNLEPP/wwR8fWqVMn13t05cqVdtlll4Xdt3jxYrevIkWKuDJ+HZNuh97nUVA2Wlapp0+fPnbBBRekywzt0qVLjo4fAFAw/VcC73M2eYKhAAAAQNwQDEWOKevy33//zXI7ZXiqr+jdd99tzZs3j5ql2b59e/vyyy/jelUOP/xwNyFTpEMPPTTDUvbM7oumWrVqbgEAIJaeoX63BQAAABAfTKCEHFO/UG8m9qxcfPHF9tRTT7kSeQAACi31AY1lAQAAABAXjK6RY/fff7999dVX1r9/fzeTvGZ/10RJ1157bbptzzvvPJs0aZKde+65nHkAQKHleyb5/1sAAAAAxAfBUORY48aNbdq0aTZz5kzXe1Pl8LfddpudeeaZ6bYtWbKkdezY0f3MyzSrfNmyZcOWuXPnJvuwAAAFhUrf1TPUz0KZPAAAABA3BEORbUuWLLHjjz/e/Vs9QD/99FPbvHmzm1BJwdHOnTu7+3r37p1hH9CJEye6+7PaLiuaNX7AgAFR79P6ESNGhK3r0KFDhj1BdV9aWppt27YtbFHQFwCAuPUMjWHJjt27d9udd95pNWvWtFKlSrkJBb/44ossH6e/m9GOIa9/kVlYaEyiapzTTjvNKleu7K7NqFGjom6rL3K1nb7U1baXXnqprV27NuHHDAAAkJcwgRIAAECieVmffrfNBn3J+N5779ktt9xi9evXdwGzM844wyZPnhz8MjMzzz//vAuieYoUKZKt40B8qSXRAw88YLVr13YTU06ZMiXqditWrLB27dpZhQoVbPDgwS6I+thjj9nvv//u2hkVL16cSwMAAAolgqHI82644YaoGQ9PPPGEXX311Uk5JgAAciKWXqDZ6RmqYNfbb79tjz76qN1xxx1uXc+ePa1p06bWt29fmz59epbP0bVrV9f6BnnLgQceaP/884/VqFHDfv75Zzv66KOjbqcA6Pbt210bIwVOpXXr1nbKKae4cRVjKAAAUFhRJo88b9iwYelK1rUwiAcA5F8pMcwkH3swVBmhyuQM/VupMvcrrrjCZsyYYcuXL8/yOTQh4pYtW9xP5B0lSpRwgdCsvP/++3bWWWcFA6Givu0NGjSwsWPH5vJRAgAA5F1khgIAACSasj39ZnxmIzP0119/dUGv8uXLh61XZqDMmjXLDj744Eyfo27duu7LxzJlyliXLl3s8ccft+rVq8d8LEi8lStX2po1a+yoo45Kd5/eA+PHj8/08XpsZG/RjHqtAwAA5DcEQwEAABItJdVSXNanv20zCkYdcMABVq1atXTrVUatcupI3rpVq1ZluLtKlSq5FjVt27Z1WYjffvutPffcc670XmXZkQFW5D26/pLRe2DDhg1ugi1d32iGDx9uAwcOzPXjBAAASAaCoQAAAPkgM1TZmZE0q7hmf4+0c+fOqIEub0Z43Z+Rm2++Oez2+eef77IJe/To4YJkd911l7/jRtJ41zer90BGwdA+ffrYBRdcELZOwfho70EAAID8hmAoAABAgqWkaAKlVN/byrhx46xevXrpMkOjKVWqlMv8i7Rr167g/bG4+OKL7fbbb7cvv/ySYGg+4F3f7L4HlG0cLeMYAACgICAYChRAaX5LL3PB5v0Vk7LfpvM+Scp+/2jU2ZLl8PmfWWFSYveWpO17e4lKSdlvIBsT58RDqu23wiLFkjQ5kAKc/xfk9LWtmQuENmnSxNdDVAqtvpEZlU/XrFnTYqUeoyqvRt7nlcd71zuU1lWuXDnDrFAAAICCjtnkAQAAEj4CS41tiVHLli1twYIFbjb4UD/88EPw/lhoRvklS5ZkmImKvKVWrVruWqnHayT1fo31+gMAABQkBEMBAACSlRnqd4lR165dbf/+/fbSSy8F16lkeuTIkdamTZvgTPLLli2zefPmhT02chZxef7559360047LVsvF4mnXq+ffvqpLV++PLjuq6++ckHyyH6gAAAAhQll8gAAAPmgZ2gsFPBUwOvuu++2NWvWuBL71157zWV3vvLKK8HtevbsaVOnTnWZn55DDjnEunXrZs2aNXOT7UybNs3efvttl014zTXXxHwsiL9hw4bZpk2bbNWqVe72J598YitWrHD/vvHGG61ChQrWr18/e/fdd+3EE090k2Jt27bNHn30UXddL7vsMi4LAAAotAiGAgAAJJp6O/vt75zNPtCvv/663XfffTZ69GjbuHGjNW/e3GUKtmvXLtPHadb46dOn2/vvv+8m21FwtG/fvnbPPfdY6dKls3UsiK/HHnvMli5dGrz9wQcfuEUuueQSFwxV9q8C3bfddpub9Kp48eJ25pln2uOPP06/UAAAUKgRDAUAAEi01JT/Fr/bZoOyOpUJqCUjU6ZMSbfu5Zdfztb+kDjK8PVDE259/vnnuX48AAAA+QnBUAAAgARLSUl1i99tAQAAAMQHwVAAAIBES4kh4zN7iaEAAAAAoiAYCgAAUAB7hgIAAABIj2AoAABAommGeL+zxGdjNnkAAAAA0REMBQAASLTU1P8Wv9sCAAAAiAuCoQAAAAmnzFC/QU4yQwEAAIB4IRgKAACQaJo8ye8ESn63AwAAAJAl6q4KsTp16ti0adOi3rdv3z5r1qyZ1atXL8vnCQQC9uCDD9ohhxxiZcuWdT9vueWWsP2ULl3a3VezZk276aabbP/+/cH7ly5dahdddJFVrVrVKlasaK1atbJXX301032OGjXKihYt6p6zfPny1qZNm7DXsnjxYuvatat7vnLlylmnTp1s1qxZwfunTJliqamp7vG6X6/1448/tmXLlrl1WsqUKWMpKSnB21oAAIjrBEp+FwAAAABxwegaUQ0bNswqVKjg6+y89tpr9u6779rUqVNt27Zt9u2337qAZqhJkyYF73v//fftlVdecevXrFljbdu2dcHS3377zTZt2uSe74svvshyvx06dHDPuWHDBjvppJNc8FOB2XXr1tkJJ5zgAq9//fWXrVy50t3fvn17W7BgQfDxdevWdY/fvHmz3XDDDS4gq8Cq1mnRY8W7rQUAgLhOoOR3AQAAABAXBEORzurVq+2ll16yu+++29fZ+emnn+yMM85wGaBSu3Ztu/TSS6Nue9hhh9lxxx0XzNJ88sknrXr16jZixAirVauWW9e0aVN76623fF8ZZYhqfzru9evX2xNPPOGe85lnnrEqVaq4AOddd93lAqIDBw5M/0uQmuoev2PHjrBgaSwU1J0zZ07YsmjRomw9FwCgEFCA05tEKauFYCgAAAAQN/QMRTp33nmn9evXz5WJ+6ES9dtvv90FIE888URr3ry5CzBGo2CjskPvuOMOd/vrr7+2c845x5WjZ9eePXtcNqmCqSq113OeffbZ6bbr0qWLC4pGUsn+yJEjXVBVJf7ZMXz48KiBVgAAoool45NgKAAAABA3BEMRZsaMGbZw4UIXHFTZux89e/Z05el6jIKoysQcMmSIXX755cFtTj/9dLfN9u3b7bzzzrPrr7/erVcmZ40aNbJ1FXR86glarFgxa9KkiX344Yduvcrkoz2ngrW6L7SvqB6vjFCvD6m2yY4+ffrYBRdcELZOmaEKwAIAkE4svUDpGQoAAADEDcFQBKWlpbnJjZTlGGumZq9evdyya9cu1w/0qquusmOOOcYOP/xwd/+ECRNcebwmKdI+1H+zZMmSroz933//zdZVUA/QL7/8Mt36jJ5TZfTKHPUceuihLmCpYOg111xj33zzjfXo0SNbx1KtWjW3AADgfzZ5n8FQZpMHAAAA4oaeoQjasmWL/fLLL9a5c2eXWakMziVLlrh/6z4/FOBU1melSpXszz//DLtPAVaVxHfs2NEGDRrk1qmPpwKkyhqNF+85I40bN86V8UfS5E2aMOq9996zX3/9NW7HAQBAxmKZPIkJlAAAAIB4IRhayKnfprI5tZQoUcKWL1/uJjfSokmNDj74YPfvcuXKZfgc6tc5ceJEVwKv/ptjxoxxwdMjjjgi6vbqF6rnVsn6rbfe6rI4lZm5atUqd//cuXOznaEpes5//vnHZaCqDF/HMnToUPvqq6/s/vvvj/qYChUquGxWlfcDAJCwMnm/CwAAAIC4YHRdyJ188slWqlQptyhDUpMPKRNUS+XKla1IkSLu35mVzStQqsmDNIGRHqMZ4seOHetmjo+mcePGrsT96aefdqXl06dPt61bt1qzZs1cD89LLrnEZY9ml55z2rRptmLFCqtbt67VrFnTldNPmTLFGjVqlOHjbrzxRvvkk0+yPaM8AAC++c0KjWWiJQAAAABZomdoIaYS+Mx06NDB9dTMisrptcSyn88++yz47zp16thbb71lsejdu7dbMqIg6AcffBDTa1Mwd+fOncHbCgLHs3wfAIAgeoYCAAAASUEwFAAAIMECKSlu8bstAAAAgPigTB6+vPPOO1a2bNl0y6WXXpprZ/CRRx6Jus977rmHqwYAyOdi6RfKcA0AAACIFzJD4Uu3bt3ckkh9+/Z1CwAABU4sEyMxgRIAAAAQNwRDAQAAEiyQ4r/8XdsCAAAAiA+CoQAAAIlGZigAAACQFARDAQAAEk1ZoX4nRmICJQAAACBuCIYCAAAkIzM0lZ6hAAAAQKIRDAUAAEgweoYCAAAAyUEwFCiAAuYz2ygXVCiyKSn73Z+k/50dPv8zS5Y/G56ZlP12/OCmpOx3VZNTLVkO/OubpOx32zffJmW/aRf3sWTZW7RUQvdXLG13QvcXRM9QAAAAICkIhgIAACRaSqoFFBD1uS0AAACA+CAYCgAAkGhMoAQAAAAkBcFQAACABAukpPjODNW2AAAAAOKDYCgAAEDCpfyXHep3WwAAAABxQTAUAAAg0ZhACQAAAEgKgqEAAAAJFkjxX/6ubQEAAADEB8FQAACARCMzFAAAAEgKgqEAAAAJFrAUt/jdFgAAAEB8EAwFAABIMM0k7382eX/bAfnZv7ecl25djac+SMqxAACAgo1gKAAAQKKpX6jfIKfvWecBAAAAZIVgKAAAQDLK5P1OoESZPAAAABA31F0hJsOHD7dGjRpZqVKlrE6dOvbAAw/Y/v373X26PW3atLDtBwwYYFdeeaW98cYbVrZsWbeULFnSihQpErx9+umnZ7i/KVOmWGpqanDbQw45xIYNG+buO/fcc+2OO+4I2/6pp56y4447zpo0aRJ8jB6v4/Vuf/vtt9a7d28rUaJEcJ2Wiy66yD3H7t277eabb7ZatWpZuXLlrH79+jZ06FDeKQCA+Pm/Mnk/i+8MUgAAAABZYnQN34YMGWKDBw+2l156ybZu3WofffSRjR071q6//vosH9ujRw/btm2bW0aNGmUnnHBC8PaECRMyfWzdunWD244fP97uu+8+++2331xQdOTIkTZr1iy33YoVK+zBBx90xzdnzpzgYxo0aOD24d3WvkXP463T8tZbbwVf559//mm//vqrbdmyxT777DMXEAUAIL5l8jEsAAAAAOKCMnn4snnzZhdofPvtt61du3ZuXYsWLWzMmDHWqlUru+222xJyJpXx2axZM5s3b55169bNBg0aZFdffbV9//33dsMNN1ifPn3cNjnx008/2fnnn2/VqlVztxVM1ZKZNWvW2Nq1a8PWLVq0KEfHAQAouFQi738CJYKhAAAAQLwQDIUvM2bMsL1799qZZ54Ztr5ly5ZWu3Ztmzx5ckLOpDJClbV55JFHutvXXnutK8E/77zzbO7cufbOO+/keB9t2rSxhx9+2JXXK4u0cePGvtoHDBw4MMf7BgAUop6hPnuB0jMUAAAAiB/K5OHLunXrrGrVqq7XZ6Tq1au7+3PL4sWLrWLFiq5/p4Kvl1xySbBsPSUlxZXFq2RfZfPqA+rXQw895J7XW3Rb+vXrZ7fccou98sor1rx5czv00EPt008/zfS5lJH6xx9/hC3jxo3L4SsHABRUfvuFBvuGAgAAAIgLRtfwpUqVKi7g6U2WFGr16tUuUFq0aFGXPRpKt4sVK5ajs6xg5KZNm1yf0mXLltnXX39tzz77bPD+ww8/3P30k8EZ6p577nHP6y26LXodmkDphx9+sA0bNtjFF1/sSvLXr1+f4XOppF7l+aFLvXr1sv2aAQAFHD1DgTBlzu6V6W0AAIB4IRgKX9q2beuCmppMKJQmL1q6dKl16NDBDj74YPfvULqtMvp40T5OPfVU+/zzzxNy5ZSNevfdd9uOHTtchioAAPErk0/1udAzFAVfuZPOsRpPfRBcdBsAACA3EAyFLyojV/m4ysG/+eYb27dvn82ePduVrF9++eXWsGFDu/DCC+2JJ56wv//+29LS0mzKlCmuvPzcc8+N21n+559/bNKkSTFngcbi6aeftmnTptmuXbtsz549rvy+QoUK7jUCABC3YGiKz4VgaNKoGuW5555zPcm3bNkSdRtN4qixEAAAAPIHJlCCb/fee68Lil555ZWuXF29Qi+77DK3XjSru0rJO3bs6ErqDzvsMBs9erQ1atQoR2dZwdWyZcu6f5cuXdrOOuss69+/f46v3IMPPmhDhw4N3takTAr0lixZ0m688Ua3X02i1LRpU/v4449dligAAHERSy9QeoYm3O7du+2MM85wX+wGAgG3Tl+MaoJFjXdC/fXXX/baa6/Zq6++mvgDLUC27dltM1YttzXbt1q1MuWsbc2DrWxx/73gAQAA/CIYipjccMMNbolGkyspMOoFRzPSvXt3t/ih8ntlmWbF+6ASzbx589KtGzVqlFuiueaaa9wCAEBuSUtJtbSUIr63RWI99thjNnXqVBswYICrcFFlyiOPPGLXXXedzZw5055//nn3hWl+COref//97svpjRs3uokhBw0aZKeccorlNQqEfr9qmfv335s3up+n1KH/OgAAiL+8P4oDAAAoYHyXyP/fgsR6++23rXfv3nbfffe5ChEFD7/44gtXVTJixAg777zzXKAxr9NrUAujHj16uDZA+uJaGa9qB5TXKCNUGlY+IOw2AABAvBEMRdKpD5fK4COXSy+9NNmHBgBALk6g5H9BYmnSRE0eGUn90998802bOHGiC5Bu3rw5z16aH3/80QV1hwwZYo8++qgr71cP1EMOOcT69u1reU2NosXskBV/WfHpn9tRv31njf+caTt/+97Sdu1I9qEBAIAChjJ5JF23bt3cAgBAYRGIoWeo796iiJvKlSvbmjVrot6nMYvuV3Zou3bt3GSSedF7773nMkFDe5yqL/oVV1zhgrrLly+3gw8+2PKKI7dttOrbNllg/Roru3OblQnstz0p/7VKKtXimGQfHgAAKEAYXQMAACRBbmeFqoz7zjvvtJo1a1qpUqWsTZs2rtTbj5UrV9qFF17oJk4sX768nXPOOW5iwcLiiCOOsE8//TTD+5UV+uWXX9qqVatcYDEv+vXXX61Bgwbu+oVq3bq1+zlr1izLS4pv32I10vbaAauXW4kNq233X3/aqpXLbOeG1ck+NAAAUMAQDAUAAEhSZqjfJZH9Irdt22Ynnniim0BIgb6BAwe6wFr79u1t/fr1Vhho0qQZM2bY999/n+E2Ci5/8803duCBB1pepEmfoh2bt06B3IwoK3bOnDlhy6JFi3L1eFMrVLF9C/6wtH173O0i+/dZiaXzbc6e/24DAADEC2XyAAAACRZL1md2skO9fpHqFXnHHXe4dT179nSTAalf5PTp0zN87PDhw23hwoXuOY4++mi37vTTT3ePffzxx23w4MFW0PXq1csuuOACK168eKbbNW7c2P788888GSTeuXOnlShRIt16lcp792f2HlAQPJFKNGxulrbfAmam4vgiWtL223dWxE5I6JEAAICCjsxQAACABAtYDJmh2RiuZdYvUhmP6heZ2WMVBPUCodKoUSM7+eSTbezYsVYYpKSkWJkyZaxYsWJZbqtJHzUpUV6j1gjRZrzftWtX8P6M9OnTx/7444+wZdy4cbl6vKklS5uVr+JC/wqEigKjNdf+k6v7BQAAhQ/BUAAAgARTkMf/bPL/UZlyZOlyRpP8ZLdfZFpams2ePduOOuqodPfpsX/99Zdt3brVCjrNEn/aaadlmQX70EMPuaxZtRbIa1QOr1L5SN469ZLNSLVq1axJkyZhS7169Sy3VTjvsuD7XZSX2/bPH3N9vwAAoHChTB7IZf99lP1vNtREKbY/fSZIouwtkr4kD7mj4wc3JeXUfnneM0nZ7+HzT7Vk+XPwiKTs9/D7rkvKfjeneHlZiVdiT2KDSsX37rBkCKSkuMXvttKlS5d09/Xv398GDBgQt36RGzZscNmEWT22YcOGVpANGzbMtRIYPXp0pttdddVV9sgjj9hzzz3nJqvKS1q2bGmTJ0+2LVu2hAXFf/jhh+D9ec7+/emaQpTZUfCD7wAAILHIDAUAAEi0QIoFfC7aVlSmHFm6rHLmePaL9NZnt9dkQfHhhx9a9+7d7YADDsh0O2VQXnTRRfb+++9bXtO1a1fbv3+/vfTSS8F1CnSPHDnSTf508MEHW16zfx0l8QAAIPeRGQoAAJCMnqE+v5P2tlOZssqVc7NfpLc+u70mC4p58+aF9VvNzJFHHmlvvPGG5TUKeGoSqLvvvtu1U9D757XXXrMlS5bYK6+8YnnRtk8yz8QFAACIB4KhAAAABWw2eZW0r1y5MuZ+kZUrV3ZZodntNVlQBAKhnSuzpl6redHrr79u9913nyv337hxozVv3tw+/fRTa9euXbIPDQAAIGkIhgIAACRpAiW/2yaqX2Rqaqo1a9bMfv7553T36bF169a1cuXKWUFXu3Ztmzlzpq9ttZ22z4vU2uDRRx91CwAAAP5Dz1AAAIAE8z+TvP8M0uz0i1y2bJkrCY987E8//RQWEJ0/f759/fXXruy6MDjzzDNtzJgxtnDhwky30/3aTtsjfh9OIhcAAIB4YnwBAABQwIKhof0i+/bt64KiJ510kusXqdnPPT179rTGjRuHPVaTMh122GEuwKeMwqeeespOOeUUq169ut1+++1WGOiclS5d2tq3b2/vvPOO7du3L+x+3db6E0880W33v//9L2nHCgAAgNhQJg8AAJBw/zdTvM9tE9kvUmXwU6ZMsVtvvdUGDRrk+mF26NDBnnzyySxnVy8oNEv8+PHj7dxzz7WLL77YTRrVoEEDd262bt1qCxYssJ07d1qNGjXss88+c4Fi5J6df/xkpZoezSkGAABxQTAUAACggE2g5LdfpIKe0Rx00EH27rvvWmF29NFH25w5c+zFF1+0jz/+2ObOnRvswdqiRQvr3LmzXXvttVaxYsVkH2rBUayE2d7d6VZvHjHESj31QVIOCQAAFDwEQwEAAApgMBQ5s2vXLps4caLLjO3Vq5edddZZduCBB3Jac1H5i663La8/wTkGAAC5imAoAABAAZtNHjmzZs0aO/bYY23x4sUWCAQsJSXF9Qb98MMPrWPHjpzeXFL6yOMJhgIAgFzHBEoAAAAJpn6hsSxIrAcffNBNNqW+qeqzqn6pajtwzTXXcClyW6myUVen7drBuQcAAHFBZigAAECCKSs0jTL5PGvSpEnWs2dPe+yxx4LrNEmSJlOaP3++NWzYMKnHV5DVGPK6/XvLeenW754/20q1OCYpxwQAAAoWMkORa+rUqWPTpk2zAQMGuPKyzz//PHjf999/7+7Pih5XpkwZK1u2rJvZ9eqrr7Y9e/bY008/bW3atHF9vDwzZ850s9xqMgNtr6VYsWJWvHjx4O3BgwfbqFGjrGjRosF1WjRRhEcTJWjGWK2vWbOmXXjhhblwdgAAhZnXM9TvgsRatmyZHX/88WHrdFsl86tXr+ZyJEBayOJub17PeQcAAHFBMBQJUalSJRs4cGC2HqsMjG3btrlZXGfPnm0vvPCC3Xjjje6+4cOHu5/79+93pWsKdup+ba/liiuusH79+gVv69/SoUOH4DotK1asCM6q+8ADD9gHH3zg1s+aNcs6deoUt/MAAIBQJp+37d6925XFh/Ju79u3L0lHVXikRbmdUjp6+TwAAECsKJNHQpx99tn27bffurKz7AYXq1Sp4h6roGhqaqq9/PLLduKJJ9q5555r7777rssgvfLKK3N0nD/99JOdcMIJ1rRpU3db2ah+nlMTLaxduzZs3aJFi3J0LACAgovZ5PM+9Qz95Zdfgrc3b97sfi5cuNAqVqyYbvsjjzwyocdX6DCTGAAAiBOCoUgIlaXfc889Ljs0u8FQBRwnTpxoffr0cbebN2/uskHV0+u3335zJfkqq88Jld6rrH/QoEF2yimnuA82KrXPijJUs5v5CgAofAKB/7JD/W6LxLvvvvvcEskbh3i82eZVpYL42GQpVjEk+rlH2aFbNnB6AQBAXBAMRcIoaKkg4xdffGHlypXz/bgmTZq4DxnKyGjbtm1YD8/777/fTWKgoGijRo18P+fUqVPDsjr0vBMmTLB27drZ22+/7YKbQ4cOdYHQ22+/3e69995Mn08fjC644IJ0maFdunTxfUwAgMKDzNC8beTIkck+hELts8Na2BmLf7eyaftdSHRH0eK2f9vWZB8WAAAoIAiGInFvtqJFXc9OZVCGzs6alTlz5rgJjtTD87rrrrNLL73U3n///WD/rkMPPdQaN24c07G0b9/evvzyy6j3de7c2S3qCTZu3Dg3c+xRRx1lp512WobPp3J6LQAA+BJI8Z0Zqm2RWL169eKUJ0narh22p0gRW1u8pAX27bM1xYpboHhxq13W/xfpAAAAmWECJST8w4UmK8ooEJkZze7evXv3sFnpczt427VrV1eO/8cffyRknwCAwjdTtp8FKCzW3HWJXbZgpjXYtd0q79ttNXZutR0Vq1qRqgcm+9AAAEABQTAUCaWyc2WHPvnkkzE/dufOnTZ27NiYs0Bj8dFHH7nJmFSSrx5gmvBJmamtW7fOtX0CAAofZpMH/FFTo2ZtTrISDZtzygAAQFwQDEXC9e7d22V5+qWeoNq+Zs2a9s8//9jo0aNzfAxTpkxxzxm6bN++3SpVqmTPP/+8K72vUKGC3XrrrfbMM8+4XqIAAMS7Z6jfBShsH1BClzrHnmKpJUsn+7AAAEABQc9Q5JolS5a4n8cff3zY+uLFi9vSpUt9PYeyM/0ENjPywgsvRA3GaolGQc+vv/7a17EBAJBdLsjpdzZ5gqEo5Hb+8ZOVanp0sg8DAAAUEGSGAgAAJAFZoYA/m195mFMFAADihsxQJNWqVausQYMG6dZXrVo1mFkKAEBBkxb4b/G7LVCoBZhGDAAAxA/BUCSV+oBu27aNqwAAKFRi6QVKmTwKq9AQ6L/btlqNsuWSeDQAAKCgoEweAAAgwZhNHshcZC7oi99P5ZQBAIC4IBgKAACQYJofMJYFKMz0K7Bl7sxkHwYAACggCIYCAAAkmErf03wulMmjUClfKerqKjtpqwQAAOKDYCgAAECCUSYPRFf1tvQzx6u7bu19ezllAAAgLphACchl+6yY7Q0UT+x5LmJJk/DX+n8q7lmdlP2W2L3FkmVVk1OTst/D5ydnv382PNOSpfH8CUnZ77JAcv5MF0vZY4XF9hIbk7LfWMrfKZNHYVK0YtWo61vv3m5pu3ZYasnSCT8mAABQsJAZCgAAkKTZ5P0uQGH/kFJ++xbb+cu0JB0NAAAoSAiGAgAAJJiSQtMC/hbmT0Jh/qDiLZa233b++l2yDwkAABQABEMBAACSUiaf4nPh8qCQKVsx/brUIma7dyXjaAAAQAFDMBQAACBJPUP9LkBhUvTgumZFInomp+23/fv2275N65J1WAAAoIAgGAoAAJBgaZYS0wIUJuXP6WVFGzQ3q1D5v4xQ/Q6kFrXA1o227auPkn14AAAgn2M2eQAAgESLJeOTzFAUMsVrHGxVr7nX/XvtY/+z/WtWWEqxEhbYvcP2Lp6X7MMDAAD5HMFQAACABPP6gfrdFiiM/t221dZv3WTl9+4127/PilmKBfbtS/ZhAQCAfI4yeQAAgATzO5O8twCF0Yu//WhzUorY9tRU224ptqNIESta46BkHxYAAMjnyAwFAABIMMU3/ZbJEwtFYfXDqmW2qXxlK7N/nxXfv8/SihW3E5oclezDAgAA+RzBUAAAgAQLqNzX58RIfrcDChp9EfBDhaqWVrS4Vdi9w/aXrWinNDs62YcFAADyOYKhAAAACRaIofzd90RLQAFzzIG17fPdu2x6sRJWLDXVTq3TwFJLlk72YQEAgHyOYCgAAECCKcDpu0yeYCgKqWtatrYyxYvbiq2b7KByFe2Sw1sm+5AAAEBhnkCpTp06Vrp0aStbtqxbatSoYVOmTLF69epF3X7dunV2+eWXW9WqVd32xx57rE2ePDl4/0knnWTPP/988PbXX39tKSkpYdsMHz7cTj755EyPq0OHDlayZEkrV66cVaxY0Y4//ngbNWpU2DZ63hUrVoSt6927tw0aNMj9W9sXLVrUHWf58uWtdevW9t1336XbV6dOnax69eq27/9mtTz99NOD50OP13F4t9944w0bMGCAXXnllcHH79ixw2699VarWbOmO5ctW7a0d999N92xtm3bNmzdaaedlu41RdK+9NiRI0eGrdc10HpdK28775iWLFnijjuj8zpmzJh063UcHTt2DN7+8MMPrXnz5u68VatWzc444wzbunVr8ByXKFEieE60XHTRRcHHLl261N3We0TXrlWrVvbqq6/6fg/p+PXaQp9TGjVqFPZ6ixUr5t4fWpo2bWr9+/d318KzbNky69Kli1WpUsUdx5FHHmnjx4/P9HwDAJCdYKjfBSiMapQtZ3e0PsGeOrmz+6nbAAAASZ1NftKkSbZt2za3/Pvvvxlut2vXLhfEVFBs9uzZtnbtWheA69y5s33zzTduGwUtp02bFnyM/t2gQYN067RdVkaMGOH2peDaLbfcYv369bO77rorptem4J9e14YNG+yUU06xCy+8MOz+f/75xwXi9uzZ486DTJgwIXg+FCB84YUXgrd79OgR9vhAIGDnnXeezZw5052DTZs22cCBA+3qq6+2t956K2zb+fPnB/cRi/r169ubb74Zdh0mTpxotWrVstywcOFCF6x87rnnbPPmzbZgwYJ0gcn77rsveE60eK91zZo1LuiroPBvv/3mzsdrr71mX3zxhe/3kKSmprpzNW/evAyPs1evXu559Bwvv/yyOye6xvv373f39+zZ0xo2bOgC5jouvR4FdwEAiJe0QEpMCwAAAIA8EAz16/XXX3eBJ2UWKguyVKlSLmh21VVX2d133+22OeGEE9IFPm+//fZ067SdXxUqVLCuXbu6oOQTTzzhjiFWypRUIHPVqlUuMOZREK9NmzZ28cUXR82YzIqCfFOnTnWZoMqmLV68uJ1zzjkuc7Fv374uWOpR9qgCpbHSuVKAUoFb+eSTT6x9+/YuqzI3zJo1ywURtV9laCqr8tJLL3UZmFl58sknXZatAtlesFZZm16w1M97SLTfG264wR544IEs96nMXQVgx40b5wKwn376qVv/008/2WWXXeb2oeuibbIKwuu9MWfOnLBl0aJFWR4DAKCQiiUrlMxQFGJpu3bY9umTbN1z/W3dk3fZpg9esX2b1iX7sAAAQD6WkGCoSt5PPfVUVyIdSqXI33//vStRVsBp5cqVLhtPGXp//PGHy+D7/fff3W2VLisgecwxx8S8f5Vqp6WluSBXrJT5qUBcpUqV3OJRUK5bt27WvXt3++ijj1yWY6znRK9FAcDIc6JzoKxKjwKKCmh6WZJ+KTCojNa3337b3Vap/iWXXGK5ReXkCogqmKtszZ07d8Z0PhQM1jFn9z3kUTawsj0zyw4NdeCBB9pRRx0VbIWgIPe1117rAtV6z/mhFg4K3oYuOjYAAKJxMU6/AVFOIQqx3fNn2/ZpE23/isW2b+0q2zP7e9s+lfZFAAAgScFQ9chU9p+W2267LcPt1OtRPUUjKRCoIOXGjRtdtqJ6Zn777bcuS+/www93ga/GjRu7smhlhR5xxBHZympUdqf6TGo/filrU69LZdvqZTp27FjXa1Lmzp3rjlFZp8oYrFy5sn3wwQcxHVNm58S7P/T477nnnmxlhyqrVUFQlfsrGKyAYm457LDD7KuvvnIZkQoE6pzfdNNNwZ6q8tBDDwXfM1p0W9avXx/1fMTyHvIoaH399dfbgw8+6PvY9dzecygIquCozvlBBx1kxx13nGtVkJk+ffq4AH7oooxTAACi0UzysSxAYZW2eb0Fdu801y1i105L27Teds6YZLuXLkz2oQEAgMIYDFWPTPV21KIy9IxoIppoPUVXr17tejx6GZde31AtCkCJfnrr/PQLjUbBOAXTvP0UKVLE9u7dG7aNbnvBTlE5uV6XSrM1gVJoVuno0aOtXbt2LqPQy76MtVQ+s3MiCiSGUh9LZc5++eWXMe2nRYsWrt+mJodSgDKjCZLiRddLgWEFN1WWr/Oi0nePAozee0aLbmd2PmJ9D4W2FtCkR1kFMT3KvPWeQ/t67LHHXHbu8uXLXb9QZSlnRpNFNWnSJGzJaDIxAAACgZSYFqCwSq1QxVJKlDLbsc0s7b/+7rZ7p21+9+VkHxoAAMinElImr5niP//8c9u9e3fYemXOqSRZ2ZehfUMzCobG0i80MmirgNnRRx/tbh988MFucqVQul27du10j1WA7KWXXrIhQ4a4gJl6eWpSoh9//NFlE2p55ZVXXBm315vT7zlRebcX/Aw9J+qZqcmjQilQq4mgspMdqr6mTz31VK6WyEdSkFivUYuyJLOi7T7++OOwXqnZeQ95lK2rbE0/2aEKsmoiK+89F0rXQlmmfl4DAAB+MZs84E+Jhs2tdJuTNLr8b0XRYmYpqRbYsp5TCAAA8kYwVMEsZSJ6i4JXympUtp2CcerBqF6Sr776qpvJe/DgwcHHKvNTQSeVqHu9QfVzypQpbkKaWDNDt2zZ4rIU1f/x5ptvtgMOOMCtVyangmQKRCprVGXRf/75p3Xq1Cnq8xx66KGu76gm+VEZvybLUZm8+mNqUW9KZQJGzgKfGe1Lr0fH8tdff7nepAoGagKloUOHRu2d2bt3b5epGGvv0+uuu871G1VfVj9Cr5+W0OzZ0PXe7OseBax1Xb0SfwUY1TtUmbVZUSangpLXXHNNsE+n2hGozF/8vodCqXWDJkXKKEit9+YPP/xg5557rjVr1szOOusst14TMumaqvxe7QVGjRrl6zUAABBLMNRviXwG3xMChUJqydJWpGJVs9Jl9W27Sr5c192U8lWSfWgAACCfinsw9O+//3azcHtLmTJl3E9lTurfCjqpBFzZlpp4qEOHDmH9H9V3UtmWKk32ZoTXba1XKbIfV155pestqgzQxx9/3M0s/sgjjwTv79+/vzVv3txliupYFORU0EzBtoxoZnvNSq+ybwUwVQLtZYZqUcAxllJ5BTuV1ag+qQqK6nXee++9bh8ZZXAqO1SBOgXoYqHs1pNPPtnXtgpwhl4/LZrQSTR7e+h6ZcuGUg/Q999/3/V51fnXBFMKQiuQ6VEQWvd5i9oNiK7t9OnTbevWre49oufSeejYsaO73+97KJSup66LguKhXnvtNTfDvbJHr7jiCndu1H5A7RNEx6DAqN6DjRo1ctdKAVEAAOKFzFAgtr6hxes1MatYRQNis3KVrMIFV3EKAQBAtqQEMqpLBpAjymbWrPKfjP/C6tcPb3uQ24ql7LFk2RsonpT9VtwT3nIiUUrsDg+2J9KG8odYYfJnwzOTtu/G8yckZb/7Arnb5zkv/j8k0RYsWGinn3GGq0xRlUei/jY88MofVquOv/2tXDLH7r+iacKOEXmXql2efvppV9ny888/27Zt22zy5MkZfjGsL5r79u1rv/zyi/uSV1/oq6ImOxOSeu/dZLwPd/72ve1ZODt4u3j95laqxX9VZAAAAHNiHKckpGcoAAAA/j9mk0d2aGLIhx9+2E2qqUqZzKiVk6pfduzY4SY6VeWUqmouuOCCfNk3VAHQotVquZ+6DQAAkF3JSTnJIc2Qrj6bkdRLUyXahcUNN9wQtXxbA96rr746KccEAAB8iKUXKDU8+D+tWrWy9evXu1Y/7733XqaBTU28qVZJ6r3vtZ+qU6eOXXXVVTZp0qQMe+Xn1b6hZIICAIB4yZeZoZq8SGVBkUthCoTKsGHDop4HAqEAAORtaWmxLYB4Pc+zon7pmjxT/de9QKioj7tK5MeOHcsJBQAAhVa+zAwFAAAoCBMo+d0WiMXvv/9u+/bts6OOOipsffHixd3knb/++isnFAAAFFoEQwEAABJM8U3fwdDcPhgUyImW5MADD0x3n9Z9++23mT5+zZo1tnbt2rB1ixYtivNRAgAAJEe+LJMHAADIzxQI9TuJUqIzQzdt2uRa7hxwwAFWpkwZO/HEE91s5H707t3bUlJS0i2NGjXK9ePOb9LS0mzXrl2+lkCMb4KdO3e6nyVKlEh3X8mSJYP3Z2T48OFuRtbQpUuXLjG+QgAAgLyJzFAAAIAEU3DLb4Ar1kBYTgN0Z555puvP/r///c+qVq3qAmMdOnSwmTNnWv369bN8DgXgRowYEbauQoUKuXjU+dM333zjAs1+zJ07N6aAcqlSpdzP3bt3p7tPwVXv/oz06dMn3eRMygwlIAoAAAoCgqEAAAAJlld7hmqG8unTp9u7775rXbt2desuvPBCa9CggfXv39/efPPNLJ+jaNGibuIeZE7BzZEjR/o6TdHK3f1s75XLh9K6mjVrZvr4atWquQUAAKAgIhgK5Pov2V4rlrInoee5aNpeS5aUlOR0t9tbNPMsl9yyvUQlS5YD//omKfv9c3B4xleiNJ4/wZJlbsPTk7Lfk9+8Min7XXvEWZYsqYH9Cd1fsbT0mXOJ4Erg0/xvm8hgaPXq1e28884LrlO5vAKiY8aMcZmG0UqvI+3fv9+2b98eNpM5wtWoUcO1FcgNKmtXUPrnn392186zZ88emzVrVtg6AACAwoaeoQAAAEnKDPW7JIpmGT/yyCMtNTV8iNi6dWvbsWOHLViwIMvn0HYKgqo0vnLlynb99dfbtm3bcvGoEUnnvmPHji6AvXXr1uD60aNHu2sRWQIPAABQmJAZCgAAkKQJlPxum9Fs3srajGc5s0qo27Vrl2HZ9apVq6xZs2YZPl7b9e3b1wVU1X904sSJrueoepBOmTLFZSsiZwYNGuR+zpkzJxjgnDZtmvv3vffeG9zuoYcesmOPPdbat2/vJsRasWKFPf7449apUyc77bTT8vVlSNu1w3bPn21pm9dbaoUqVqJhc0stWTrZhwUAAPIJRqQAAAD5oGdotMlr1MdzwIABUR+nYKTKov1Q6btmfdcs4xnNQC5ZzUI+ZMiQsNvdu3d3/UbvueceV4Kv28iZ++67L+z2q6++Gvx3aDBUAekvv/zS7rzzTrv11lutXLlydsUVV6S7RvmRAqF7Fs7+78aale5HqRbHJPegAABAvkEwFAAAIMECaQG3+N1Wxo0bZ/Xq1UuXGRrP2co1y3hGM5BLVrOQR6NAnAJ4CswRDM25QAx9E44//nj77rvvrKBRRqgUq1nH9q5aErwNAADgB8FQAACABNPcSX7L5L15lhQIbdKkSa7OVq6fGc1ALlnNQh6NAqhVqlSxDRs2xPxYIBqVxisjVIHQ4G0AAACfCIYCAAAkWiwTIwUSN1t5y5Yt7dtvv3Ul9qGTKP3www9WunRpV/IeK03gs27dukyzWAG/9m1aZ7v/mmP7lv9tVryElTriONczFAAAwC9mkwcAAEiwtLRATEuidO3a1VavXm0ffPBBcJ0Cme+++6517tw5rJ/oX3/95ZbQUvrQmcs9Dz74oCvtzu+T9iBv2D51vO1d8LsF9uyywNbNtm/tv0yeBAAAYkJmKAAAQD6YQClRwdBjjjnGLrvsMvvzzz+tatWqbjb4/fv328CBA8O2Pfnkk93PJUv+K1X+999/7YgjjrCLLrrIlejL559/buPHj3eB0HPOOSdxLwQFVtr6f81SQvqF6jYAAEAMCIYCAAAkWF4NhhYpUsQFL//3v//ZM88842aPP/roo23UqFHWsGHDTB9bsWJFO+uss+yLL76w1157zQVQ1ed08ODBdscdd4SV3QPZlVqlhtmaVf/1Cw38320AAIAYEAwFAABIsIAFLM1nlFPbJlKlSpVsxIgRbsmMlxEaGgwdPXp0Lh8dCrsy7c9wP5URqkCodxsAAMAvgqEAAAAJFkj7b/G7LYD/FK1Y1Sqc05PTAQAAsi3b9UrffPON6ylVoUIFq1Klip100km2ePFiGzBggBUrVszKli3rMgS0Xj2nPCqZGjJkiJuNtEyZMnbooYfajTfe6JrzR7Ns2TL3XFq0fUpKSvC2Fo/6Wak/ValSpaxOnTr2wAMPuH1lRdtqdlQ9l15Hx44d7eOPPw7b5sMPP7TmzZtb+fLlrVq1anbGGWe4CQJOP/304HEULVrUSpYsGbz9xhtvZLnvq6++2ooXL27r1693t6dPn+7OmSYgCLVt2zb32mfPnu1uL1261C655BJ3LDomla3dddddtnHjxgz3de211waPTddH+/Vuq3xN5W967ZF0vlesWOH+rRlpNXGCd67U+2vVqlVh2//222/WqVMnK1eunHst559/vntfePT+uPLKK9Nlluj8hXrllVfcjLZ63QceeKB7TvUdC71u06ZNC3tM6HPrOUPfK/Xr17eXX37Z3TdlyhRXthfJe86cXlcAALKiCYViWQBkLG3XDtv52/e2/ZvP3E/dBgAAiGswdPPmzS4Q1rdvXxeAU3BOAU31mZJevXq5AJ4a6deuXTss+HXNNde4wNvIkSNt06ZN9ssvv1itWrXsxx9/jLovPV7PpcWbsdS7rUUUXFVA76WXXnJByo8++sjGjh1r119/va/XM2nSJPdc8+bNs27dutmll15qL7zwgrtv4cKFdvnll9tzzz3nXveCBQvcxAAyYcKE4HEokKjHeLd79OiR6T53795t7733nguwvfPOO27dscce64KMn332Wbpg7GGHHeYCsmvWrLG2bdu6oOTPP/9sW7ZsscmTJ7uArhcsjSb02K644grr169f8Lb+7dd9993nHvP333+7PmLqAebRuWnXrp2deOKJtnLlSne9DjroIDvhhBMyDHZHo1lntZ9BgwbZ2rVrbfny5e69NnHiRIuF3o86Vr0nnnzySRcQnjNnTpaPy8l1BQDAj7S02BYAGds9f7btWTjb9q1Z6X7qNgAAQFzL5BX0UjDuvPPOc7cV0Dv33HPTbaeMugsvvNDNTCoKNr766qsuiHfkkUcG+1IpqzG7FKBU8Oztt992gThp0aKFjRkzxlq1amW33Xaby0L144ADDrCrrrrKBfnuvfde9+9Zs2a5zEsF9ETZjgqW5pQCnsp6VBBZx9qnTx+3XsG2N99802VUepSNqExQeeKJJ1ympPp4KfNRatasaffff78lkjKCu3TpYsOGDQvLzNTMsnfffXdw3dNPP+2yiBWMfOihh7J8XgXXFdhWgFiTMHgUlIyWueqHzpOeS4HmuXPnuplx401BagVuQy1atCju+wEAFAzK9fSb8UleKJC5tM3/VVkFZ5j/v9sAAABxywxVcHHPnj0u41Mzhio7MZodO3bYW2+95bIaRRmMyvT0AqHxMGPGDNu7d6+deeaZYetVYq19aZ+xOvvss13p+vz5892xKiCqzEQF9RQojQcFQBUo7t69u/3www8u01IU9NQsrgryyurVq+3rr7+2iy++2N3W69HxeYHQZFHQ8oMPPgheW9FxKmM4koKmus+P77//3vbt25fueuaEPmyq9YGOuVmzZpYb1KahadOmYYteNwAA0f82maX5XKiSBzKXWqGK++lmmA+5DQAAELdgqLICFRhUqbeyJJVRqSCeypFFM4kqg1IZowrevf766269Aow1atSweFL5tTL9vBL9UNWrV4+pPNvjHaOCZwr2ffXVVy7LT8Et7eumm25yAbvsUnsABTxVkq+ArXqvKjjqBZoVsHv//ffdbWW8Hn/88a7cPNo5fOqpp4LnWmXlOTF16lT3XKFLJGV36vpXrlzZ/vnnH3v22WeD9+lcR7u+sVwHvb7I66nn1LEo0ziUenuGHuvQoUPD7lfPWK1XRqgyfdWeQVm+uUGZvX/88UfYMm7cuFzZFwAg/wukBWJaAESn/qCBvbstsHevW4od0tBKNGzO6QIAAPGfQEmZbwp6qi+oJv7R4pVBK0CqgJ96PXqlyaJ/a/t40nMq0BZtsiRlVWanJFpBPq+EX4477jiXBalA3SeffOIClypTzy71M1Vpe+vWrd1tZYeGTsyjwLJK5SNL5EVByNBzeMstt7hzrW1yEqCV9u3bu+cKXSLdc889LmtV11SZv5rgypPR9Q29DpqQSJm8oXRbkzp5ry/yeuo51WJBwffI3p6hxxrZbkEBVa3fsGGD66fq9fuMdgyRxxErTWbVpEmTsCXaJE0AAIiyPWNZAESn/qB7l8y3lGLF/luKl7DUkqU5XQAAIP7B0FDqzan+ocqGC6WJkdRT8vbbb3czpGtiHQXPVHYeL5pMSAGsyEmHtA9N7NShQ4eYn/PTTz91gb3ILEKVpp900kluiXytsVAwVbOwK+NRi3ptqg+rN4mUgqOa1VyZmtqP13NVdA4VkE22Ro0a2QMPPGA333xzcJ3OiyaviqR1Om45+OCD3XUJpdvKkPWup4KVypzNLToGBWhDg6tqf6C+n95xAACQm9ICAUtL87kQDQV89QsNvQ0AABDXYKiy9DQhjgJ6okCeAnRepmMoTTx0yCGHuImTFEDTzOyajV29PpXJqCzDRx99NNvBL5VBazZ0lSmrdF/PqSxAZUpqX7GURSvz85VXXnEzmQ8cONBlFiooqWP3yrxnzpzp9hPttfqhwN93333n2gcoYKtFM5yrR6ZXKq8sQwUWe/bs6Sb+KV++fPDxt956q5up/eqrr3aZt6LAXjIm69F11Hnxrp0mcfryyy9tyJAhro+szqeCpcrs1HHLaaed5l6zMm11rXSfyvvVMsDLxr3zzjvtuuuuc8+rIKWyRNVXNV70flRPWV1nZbdqUcarArGanAoAgFwXCLie1n4WUkOBjNEvFAAAJCQYWq5cOVcWr4xQzYiuWb4VzMtoVnhlhirgqeDXiy++6IJ8WtR7UkEpBfWyG1wU9YPUvjWhk3pndu7c2WVTvvDCC74e36lTJ/e4+vXru/L0kSNH2vXXXx8Mtqp/Z+PGjd02CtopwKfjzw6VvStArMCblxmq5YYbbnAzqHul7grmKos2tERetK0Cydu2bbMjjjjCBUqV/aproedIJGVwqn+qgp+iczRlyhQ3WZKyguvWretegwLK6ivr9Q9V4FzvB5XOt2nTxo466igX0PYoU1aBVc1KrwxdZXKqN2k8s0XVqmDhwoVWp04dtyhIrcm+AABIhEBabAuA6NQftHj95la0Wi33k36hAAAgKykBl3IAIN6U8aveuhPGj7cGDeon9AQXTUvfEzVR9qcUTcp+i6WF95RNlD1Fwif2SqQD//omKfv9c3D2eybnRMVX/sueT4a5DU9Pyn5PfvPKpOx37RFnWbKkBtL3AM9N8xcuso5nneva0qjfc6L+NvS69yerWvNwX49Zt+pPe23Q0Qk7RiCz9y7vQwAAkN/HKcmJWgAAABRiwRJ4n9sCAAAAyEMTKMWD+o+qDD1yUQlzTqnUPdpzb9++3XKLSvWj7XPixImWm1QqH22/L730Uq7uFwAA+Kf4pt8JlIiFAgAAAPGTZzJDa9as6fpg5oZNmzZZoiVrxvdhw4a5BQAA5F2xzItEMBQAAAAogMFQAACAwiKgjM80n2XyPrcDAAAAkDWCoQAAAAmWFgi4xe+2AAAAAOKDYCgAAEAyJlDymxlKMBQAAACIG4KhAAAAiRZDmby2BQAAABAfBEMBAAASTPFNYqEAAABA4hEMBQqgvaklkrbv4vt3JmW/u4uUTsp+A5ZiybLtm2+Tst/D77suKftdFkjen6yT37wyKfv96uIRSdlv87lnWbLsSy2e0P3tTy1myUCZPAAAAJAcBEMBAAASTG1A/fYCpWUoAAAAED8EQwEAABJM/ULT/E6gRM9QAAAAIG4IhgIAACSjTN53ZigTKAEAAADxQjAUAAAgwegZCgAAACQHwVAAAIAEU+m73/J3yuQBAACA+CEYCgAAkGBpgYBb/G4LAAAAID4IhgIAACRaWgwZn2m5fTAAAABA4UEwFAAAIMECFsMESkZmKAAAABAvBEMBAAASLC0t4Ba/2wIAAACID4KhAAAACcYESgAAAEByEAwFAABItID/MnltCwAAACA+UrPzoDp16ljp0qWtbNmyVqVKFevYsaN9/PHHwfs//PBDa968uZUvX96qVatmZ5xxhm3dutVOP/109xgtRYsWtZIlSwZvv/HGG1H35ecxv/32m3Xq1MnKlStnFStWtPPPP98WL16c5evYuHGj9erVy6pXr+6OtUmTJjZq1Ch335IlSywlJSW4r/r169uIESPCHv/NN9/YiSee6O7Xc+gYvv32W1/n8Oqrr7bixYvb+vXr3e3p06e7Y9+1a1fYdtu2bbMyZcrY7Nmz3e2lS5faJZdc4s6rjrlhw4Z21113udeSkWuvvTb4OooVK+b2690ePHiwe826hpH0+lesWOH+3bt3bytRokTwmp9zzjm2atWqsO2zug4DBgywK6+8MuwxOs+6rqFeeeUVa9mypXvdBx54oHvOzz//POz9N23atLDHhD53tGv38ssvu/umTJli9erVS/davefMznsUAIBYKRAaSEvztxAMBQAAAJIbDJVJkya5QN28efOsW7dudumll9oLL7xgCxcutMsvv9yee+4527x5sy1YsMAuuugi95gJEya4x2hR8E3be7d79OgRdT9ZPUbP365dOxeUXLlypf3111920EEH2QknnGDr1q3L9DXceuutlpaW5p5DwcQ333zTBTU9RYoUCe7rmWeecUFFvV4vqKbA2bnnnusChgoM3n777fbpp59mee52795t7733nguwvfPOO27dscce64KMn332Wdi2CiwfdthhLri8Zs0aa9u2rQtK/vzzz7ZlyxabPHmyC0x7wdJoQs/ZFVdcYf369Qve1r/9uu+++9xj/v77b9u5c6fdcccdwftych1CPfjgg24/gwYNsrVr19ry5cutb9++NnHiRIuFd+0UhH/yySfdtZszZ06Wj8vOexQAgOz2DPW7AAAAAMgjZfIHHHCAXXXVVS44du+991qlSpVctqKCYKIMQQVKc4syAk8++WS7++67g+uefvppl7WpINhDDz2U4WN/+ukne/zxx61ChQrudosWLdwSjQKflStXdkHHRo0auf3pdd90003BbU499VS3ZEUBT2U93njjjTZmzBjr06ePW69gmwKyyqj0KBtRmaDyxBNPuExJZagq81Fq1qxp999/vyWSzleXLl1s2LBhcbkOHgWklamqAPFZZ50VXK+gZLTMVT90nvRcCjTPnTvXqlatarlBgWoFb0MtWrQoV/YFACggmaF+Z5MnMxQAAABIfmZopLPPPtuVfDdt2tRmzZrlsvkUCFOQNDd9/fXXrmQ7koJ1ui8zbdq0cSXmo0ePzrSsXh9C1AZAGY7K0ty+fbv98MMPbh/ZoQDohRdeaN27d3fPo0xLUdBz/PjxLqNWVq9e7V7DxRdf7G4rC1Tn2QuEJouClh988IE7F/G4Dp7vv//e9u3bZ2eeeWbcjtW7djrmZs2aWW4ZPny4e++HLtl9fwAACkuZvM+FYCgAAACQ94KhNWrUcD83bdpkX331lcuKUzBImXjKnlSQKzcoQOntO5TK3bMqz1Zm43nnnWePPfaY6yOpYJkCcp79+/e7zNZSpUq5QN/QoUOtVatWLrCmDybR9psVnR8FPNVaoHbt2nbMMce44Kg0aNDAHcP777/vbr/99tt2/PHHu3JzUbA5dJ9PPfWUOz6V26usPCemTp3qnit0iaTsTmWFKkP2n3/+sWeffTYu18Gj16f3i0rcPXpOHYt6d0Zm6oYeq65NKO/aKSNUGcvqi6qM5dyi7N4//vgjbBk3blyu7Q8AkM/5DYSqRJ4yeQAAACDvBUMVHBOVyR933HEuc1DBrU8++cQF+yInH4oXBbv+/fffdOuVVZlVSbR6barEXBP/qMz5qKOOcsFR9REVBeUUvFTfyTvvvDOY4ajXqOzMaPvNytixY11pe+vWrd1tZYeGTsyj7FCVykeWyIuCkKH7vOWWW9zxaZucBpvbt2/vnit0iXTPPfe4rFWVm+/YscOWLVsW03XQhER79+4Nu1+3NamT9/oUOFUg06PnVJ9W9VmN7O0ZeqzK8A3lXbsNGza41gZev89oxxB5HNmhCa00AVfoEm2iJgAAJC0QsLRAms+FnqEAAABAnguGauIgBcRCs+8UMDzppJPcoky53KDn/uijj9Kt1zpN5uOXjl0TICmoqwBaKAXJlBWpUnpl+6nfp0rso+03KwoMa7IlZTxqUa9NTT70448/BoOjmtVcmZo6Z127dg0+Vq9HweVkU8/UBx54wG6++eaYrsPBBx9sS5cuDbtft5UhK5ocSsFKZc7mFh2DArShwVW1clAw3DsOAABym+8SeS87FAAAAEDeCIYq+/OVV15xM4APHDjQZsyYYa+++mqwNHrmzJmud6iXCRlvyuz88ssvbciQIW52dR2PgnTKKNRs8ZlRafkvv/zisgI1W/iLL75odevWjZpRqkxDPZ8m+BH9fOmll1ypvbIllU2q9gCRGYqRgb/vvvvO9f5UX1UtmuFcPTK9UnllGCqw2LNnTzfxT/ny5YOP1/41U/vVV1/tZlkXBfaSMVHPRRdd5K6xF7j0cx1OO+0095qVNaxMVt2na6CWAV7GrTJwr7vuOve8ClIqS1R9VePlkEMOsZYtW7r3q7JbtSjjVYFYTU4FAEAi0DMUAAAAyGfB0E6dOrlelfXr13dl3SNHjrTrr7/e9WlUz8vGjRu7+xXoUlBMwb3coP1MmTLFlbDXqlXLBTNVvq3sSs10n9UHEZWYqzy7Tp06tnDhwkz7PPbq1ctlhyroqWxHBezee+89t19leT788MNhs6BHUtn7CSec4AJvXmaolhtuuMHNoO6VuuuY9BpCS+RF2yrYrMDtEUcc4QKlHTp0cH1M9RyJpAxO9YJV8NPvdVD/UGW2Pvrooy7grOxatSbo169f8HmVKavAqmalV7auMjnVmzSe2aJqVaBrrWuuRUHqt956K27PDwCA39nk/S4AAAAA4iMlwAgbyBXK+tWs8hPGj7cGDeon9CwH4tcBI2bF9+9Myn73FCmVlP0GLMWSpcLIB5Ky37InnpSU/S47pL0lS+1f3knKfr+6OHf6bWel+dzkTQCXlvL/J9FLhAULF9qpZ5zlWtOo33Oi/ja07/qRlavs72/D1g0Lbep75yTsGIHM3ru8DwEAQH4fpyQvYgIAAFBIKdnTd89QEkPxf1SddPnll1uDBg3cRKCqxLnyyiuDE5lGmj59uh1//PFuW1UYqapHFUYAAACFWZ4Jhnbu3NmV1UcuEydOzNHzqnw82vOq32d+ey158bUCAIDYBQJpMS2AqLe62hKde+659swzz7iJN9X+R+2T1Is9lPq0n3zyya4/+hNPPOGCphoTXnDBBZxMAABQqBW1PCK3ZknXBEdaEilZM74n47UCAIDYxTJLPLPJw6OgpjI9U1P/fz6DJqhs3769GwNqYkqPerJrckoFT70JOdUr/aqrrrJJkya5/v8AAACFUZ7JDAUAACgsfJfIxxA0RcHXrl27sECot06Tgc6dOze4bsuWLfbFF1+4yTi9QKhoQlNVDSmbFAAAoLDKM5mhAAAAhUXAApbms/xd2wIZUQ9QLVWrVg2u+/33323fvn121FFHhW1bvHhxa9mypf3666+cUAAAUGgRDAUAAEgwyuQRL0899ZTt2bPHunXrFlznTah04IEHptte67799ttMn3PNmjW2du3asHWLFi2K2zEDAAAkE8FQAACABAukpbnF77YoeNLS0lwQ048SJUpYSkpKuvXffPONDRw40C688EI76aSTgut37twZfFykkiVLBu/PyPDhw93zAgAAFEQEQwEAABIsEIhhAqUAZfIFkQKZJ554oq9t1Q+0UaNGYevmzZvnZpVv2rSpjRgxIuy+UqVKuZ+7d+9O91y7du0K3p+RPn36pJt1XpmhXbp08XW8AAAAeRnBUABxtadI5h+wCppU25+0fadd3Ccp+92cUiQp+y2W4i+DKjesPeKspOy3+dzk7Hd24+QFPI4d6C84FC9lV2+wpAVD/fYMJRhaICm4OXLkSF/bRpa7L1++3M0GX6FCBRs/fryVK1cu6vZeuXworatZs2am+6tWrZpbAAAACiKCoQAAAAmmrNA0v5mhCZxNXoGyp59+2n744Qf7+eef3cQ8kydPtg4dOvh+jpUrV9qtt95qkyZNcqXgyn588sknrW7durl67PlNjRo1rHfv3jE/bv369S4QqqzPr776KmpfUGWLFi1a1F1DldB7VJY/a9assHUAAACFTWqyDwAAAKCwUVao1zc0y8VnBmk8zJ8/3x5++GEX0GzWrFnMj1fwVMHPqVOnWr9+/VzfSc1c3r59exfEQ85s377dzjjjDHd9lBFav379qNspY7Rjx442ZswY27p1a3D96NGj3TWKLIEHAAAoTMgMBQAASLC8Opt8q1atXNCycuXK9t5778UcNNPEOwsXLrQff/zRjj76aLfu9NNPd5mKjz/+uA0ePDiXjrxw6NGjhzu3l19+uesjqsVTtmzZsJ6eDz30kB177LEuEH311VfbihUr3DVQVulpp52WpFcAAACQfARDAQAAEiyv9gyN7D0ZKwVQFQT1AqFeb8yTTz7Zxo4dSzA0h1TiLq+++qpbQh1yyCFhwdAjjzzSvvzyS7vzzjtd2wJd2yuuuMKGDBmS08MAAADI1wiGAgAAJFhezQzNCfUHnT17tstajNS6dWvXQ1Ql2zkNuBZmS5YsiWn7448/3r777rtcOx4AAID8iGAoAABAgu3ctlxRTn/bbl/pfi5atCjdfQcccECemfV7w4YNblKfaBP6eOtWrVplDRs2TMLRAQAAAP8hGAoAAJAglSpVcpmR836+N6bHFS9ePKwE2tO/f38bMGBAhpmamj3cjxIlSlhKSorlxM6dO4PPFalkyZJh2wAAAADJQjAUAAAgQWrWrGnz5s2zjRs3xvS4/fv3W5EiRaJmhmbkm2++cTO7+6GJeNTbMydKlSrlfio7NNKuXbvCtgEAAACShWAoAABAggOiWnKbgpsjR470tW200vZYaQZ6ZYX+888/6e7z1iXidQMAAACZIRgKAABQANWoUcN69+6dsP2lpqZas2bN7Oeff0533w8//GB169Zl8iQAAAAkXWqyDwAAAAD5z7Jly1zJf6iuXbvaTz/9FBYQnT9/vn399dd2wQUXJOEoAQAAgHBkhgIAACBo0KBB7uecOXPcz9GjR9u0adPcv++99/9P/NSzZ0+bOnWqBQKB4Lo+ffrYyy+/bGeeeabdcccdVqxYMXviiSesevXqdvvtt3OWAQAAkHR5JjO0Tp06Vrp0aStbtqxVqVLFOnbsaB9//HHw/g8//NCaN29u5cuXt2rVqtkZZ5xhW7dutdNPP909RkvRokXdbKXe7TfeeCPqvvw85rfffrNOnTq5cq6KFSva+eefb4sXL87ydWhChF69erlBv461SZMmNmrUKHffkiVL3Eyt3r7q169vI0aMiDrZge7Xc+gYvv322ywzMzTDrM6bjvXII4+08ePHu/umTJniyta8fXrL5s2bgxMyDBkyxBo0aGBlypSxQw891G688UZbt25d8PmHDx/u+o5p0gNdpwceeMA9ztOhQwd3ntavXx9cN3To0GBpXujr1j4OOugg6969u/3+++9Znk+dO10j77gbNmxo7777rtv/0UcfbcOGDQvb/pZbbrGLLroo7LVq39qvd1vnS8ccet213HnnnVleQwAACrr77rvPLW+//ba7/eqrrwbXZUXjAY092rVr54KqekyLFi1c0DSzyZ4AAACAQhcMlUmTJtm2bdtcyVW3bt3s0ksvtRdeeMEWLlxol19+uT333HMuiLdgwQIX8JIJEya4x2hRAFXbe7d79OgRdT9ZPUbPr0G8gpIrV660v/76ywXwTjjhhLAgYTS33nqrpaWluedQUO3NN990QTWPZoL19vXMM8/YtddeGywx04cHBWrPPfdcW7Fiha1atcplUXz66aeZ7lOZGQoS6jFr1qxx50lBPI96dHn79JYKFSq4+6655hoX6NMEC5s2bbJffvnFatWqZT/++KO7X4HSwYMH20svveSCzx999JGNHTvWrr/++rBjUObH448/nuExhr7u6dOnu/PZpk0b+/XXXy0rClx6j9VxKMi6YcMGl3nSv39/d55k5syZ7nw//fTTYa9Vkzkou8W7Xbt2bbe9AtGh2z388MO+riEAAAWZMj0zWkJp3BK5TvQ3Xl9casymscMnn3xi9erVS+ArAAAAAPJZmbwyB6666irbuXOnK8eqVKmSC/YpGCnKflSgNLcMGDDATj75ZLv77ruD6xRgU9bmk08+aQ899FCGj1WfLAUFvWCjsiG0RKPAp2ZenT17tsu81P70um+66abgNqeeeqpbMqN9KqCrzE1p27atr9epIKyyPdTXS9mkonN91113uX/rQ8yDDz7oMkMUHPZez5gxY6xVq1Z22223uYxSUTapgrsK3ipDNSPK0lQw8rHHHrO///7bZZkq69ev9u3bu+fXYxVMvfLKK92+FaBVYFfBW2UO50Qs19CjIPTatWvD1i1atChHxwEAAAAAAIACnBka6eyzz3al102bNrVZs2ZZ3759XUBSQdLcpCb/55xzTrr1KkXXfZlRgE7BRPXXyqysXpkUagOgTNPDDjvMtm/f7mZa1T5ipX0qw1RZGF6WpB+TJ092gUkvEBppxowZtnfvXtf3K1TLli3d4/R4j0r+O3fu7PqCxXJ9v/vuO9/b65x99dVXLotTwWMvcK2WBmpjoFJ3ZRDnlN9rGEqtBPQ+DV2ycy0BAAAAAABQSIOhNWrUcD9Vvq0gmDLtFGCqWrWqy57ct29fruxXAUpv36FUKp1Vmbx6WJ533nku81ElYc2aNbPvv/8+eL96XSqzVVmcCriqt6ayLFWOrWBftP1mRUHQo446yu655x5Xmnbccce5mVs9Cuhpn96iHpiiQHNm+9Nr1blWibufc6EsXgUFVcLuh/at150V9RnTcSvYecopp7j9eFmbOo/KSFVgWSX0yjz1S5mkoedFwU8/1zAaTRjxxx9/hC3jxo3zfSwAAAAAAAAo5MHQf/75J1i6rQDfBx984AJ46j2lUu3IyYfiRWXY//77b7r1q1evdsHBzGgSqPvvv99lK6p0WkFKBdbUg1IUWFRwVz20NGGPl2mq16hAXrT9+jleBe7U43L58uWuX6gmAPJoUiTt01u82WEzep2hz6uAZ+hkSZmdC5XMK4vUb3aorq9et5/SeB23smd17Hqteg94Dj/8cDdJlFey79eLL74Ydl681gtZXcNoVJqvIHPoQn80AAAAAACAvCVPB0M1cZACcuoX6lHA8KSTTnKLsu9yg55bEwVF0jpNquSXjl09NBX0i8yW1IRD6j2qrE1lEGq2c5VnR9tvLDT5kSY38nNu9Fo0s7paEESj3qM6zs8++yxsvbZfunSpm9gokrI2NYGTn+xQXV8FuWPRuHFj95jPP//cEiGzawgAAAAAAID8JU8GQ5X9+corr9h9991nAwcOdL0rNdGPV5atWcPVO7R169a5sn9lBX755ZduMp4tW7a447n55ptdFqVmGs/MoEGD3Izs6rWp3pbKPtR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tu2nTpvB1n3zyibv9yy+/HL7up59+cue0a9eu4euOHDmSUq9ePfc64tChQ1H4TpL3XO3bty/Nz05Iz5493e3fe++98HWcK3/oOa7fadmzZ0/56KOPMr0dP1OJh23yQIxQX5LPP//cKlSo4N6V0vbiSZMmuXep/JS+Si6ry9F4Z05bi/2kalcNe9Fwnddee81V1TVs2ND8pnWGDRtm7dq1c+uqf6cqQlVl8Z///Me3dYM6p7t27UpzecGCBVG7H0OGDHET1n/44QcrWbJkml5A1157rRsk5Ufbicy2Oj3++OPuZ9sv69evj3i9Gr5ru7raIbz99tsJ8/sj/dTbrC4DiG3qOXzDDTe4avMdO3a4ljFqdbFhwwYbOXJk0HcvKen1if7+0hCl4sWLu90tw4cPd8Mg9XdoyMCBA91ttBPk9ttvt82bN7vXWOoZ7kd/bpj997//dVt5t2zZ4h4O7YLR4x7aKaF+hw8++KD7e79BgwZux9WePXtcD/nq1au7158hGnR47733umNqWXX++efbBx984HaKabdHqGci/DlXeq2sXWrqsa5/+8n06dNd33X9/KhHL+fKXz169HBzFfRvbu3KHDduXMTBv/xMJaCg01gAkSuoolXZFEuVoRs3bky56aabUjp06ODrOv/5z3/SvBvodyVqZuuqOlTvCCfqOQ7yuVW9enVXaRSJrtfxaFJ1aPHixVOC8Ndff6WUKFEioc5x7ty5XWX1G2+84T5UcR36sz5UDQMgvujvw/vvvz+lZMmSKbly5Uo5//zzU6ZNmxb03Upazz33XMoFF1yQUrhwYVcxpepPvUb74YcfMtx27ty5KXXr1nW/m4sVK+aqDCNVu8Ebp5xyiqsajPSxfv368O1WrFiR0rhx45S8efO6HRRt27ZN2bZtW4avd/jw4ZRBgwa5r6vXpmeeeWbKuHHjOF1ROFe7du1yP1eqgtd50u8+Pf46HwcPHuRcRYF2UGV2jtLHZfxMJZYT9J+gA1kAZvny5XMTA0M/kmrgPG3atDSVVXrnPdrr6p19vZvsJVV+pq/cOnLkiHv3WVV7L730khUqVMj8oirM1H3J1Eg+dYPyRFtXvb0effTR8GVVKapKVXSedVkVhNH+ftMf95KqItSXVYOa0lMvMzWlVz+maFClhaqRVfGtCbzRpnNbvnx5X/qHqmpIVc6h3xeqClLlUEinTp1cLzKvqQdrVtWfmQ3QAgAAAJDcCEOBGKGw4mj/uNcxbaeO9rpH24J7vDZu3JjhOjXg16S+E0/0f66bQtfTTjstfFmPa/otzAquEmXd1NuhMpPZRPB/G3pre1bInDlzwsOaFJ599tlnLpj0KwzVVMjjPX48KlWqlOFnSUGoQkg1vX/rrbfcdrRo8juIDSqU1BazaD+WAAAAABIDYSiAQKmno6YlFi1a1IVJ0aAQLit+TJQPat0vvvjCLrzwQos29XvLil/TxlURqp6wkSiIHTt2rB04cMD386uQX9PkTz75ZPNTUEFsUKGkn1XFAAAAABIbYSgQY7R198svv3QBYbFixdxQgZNOOinhQoU333zTevbsGW4iLgpVNPhGTcT9NHPmTGvUqJEli6DOsQYs9O7d24JwLMOR+vbt6/m6K1eudB8aUBCtcD/IIDao55Z+J0arzQEAAACAxEIYCsSQJ5980vr37x/uafjrr7+6CjdNg9aE1UQJFd555x3XW/Cxxx6zq6++2kqUKOEq2N5//333/b/yyit2/fXX+7b+Kaec4iown332WdfHMprWrl3rgrpZs2aFK2K1lVzBnF/hWVDBUbJV740YMcK6dOnifnY1OVSVsa1atYra+kEEsUE9t7SuJhsfre2539W4AAAAAOITYSgQI15++WUbNGiQvfjii3bVVVe53pkaKjRlyhS7++67XYXdnXfemRCBVc2aNV0Q2rx58wzHJk+e7Ib9LF261Lf1//rrLxcwT5w40QXQN998s0XDsmXLXAh76aWXpgmBP/jgAxeOqrrv7LPP9nzdvHnz2vTp048aHF188cUJVb33+eefZ3kbr7/nKlWq2LBhw+zKK6+MyvM4FoLYoEJJ/X7UYLDM1lXLAD8GNwEAAACIf4ShQIw444wzXEVkvXr1MhybO3eu3XbbbbZq1SpfQoXMBqAoaNCxw4cPe7pm/vz5XWCjbbzpqc9hwYIFXWDpt6+++spVqCosrFq1appjqSdie6VJkyauCrRXr14Rt5QrEJ0xY4Yvg5sU1B0tOFKg5bVcuXK50PloUk+591L685n6e926dat7A8Dr53X6NxZU9avq32gIKogNKpRkmzwAAACA45UxiQAQCE1Yz2zITd26dW3Tpk2+rKsgcN26dRZNmuT9008/2amnnprhmK7X8WjYtm2ba0VQsWJFK1OmjO/rLViwwLUIiKRr166uMtgP+fLl8yXszIoCMgWPQdB28fQ0UX3AgAH24YcfRgykvabK7mjR46wgVFRxfeutt0ZlXT23gqj+zWqCPQAAAABkhjAUiBHFixe37777zvX7S0/X67hflV3arh1NLVu2tFtuucUFg6qeC9m5c6d17NjRbrzxRt9DULUe+Oabb9z24oYNG1o06LE+dOhQxGOqiNVxPxxtC7OfVDGo9g9BC4WgU6dOda0mFP770St2z549rsdviM516HKoylr9gKMhWkFsUKFkUM9pAAAAAPGPMBSIEarkatu2rY0fP96qVasWvv7bb7+1m266ybdKryBCBVVAXnPNNW6QUe3ata1kyZIuoPzyyy/toosu8q1CMuTMM890oevYsWMtT548Fi0KXTU86ZlnnslwTIOjLrvsMl/W1eOcjIGVQlA93tOmTXOVtxpeVahQId/WW79+vQUlqCA2qHPcrFkzV9WtHqkAAAAA8E8QhgIxQr0VN2/e7AbolC9fPhwQavu8gtCHH37Yl3VvuOEGF5KkDlL8pgBSAZX6Y86ePdv1VTz//PPd1uXGjRv7vv79999vffr0sWgbOnSo6wk7b948t5U5dI7V41HbnHW9H9SDVf1mTz/9dIumdu3aWVD0xoKeX3fddZe99NJLUWm9EFToHGQQG1QoqWp2vamhXrsdOnSI6toAAAAA4hsDlIAYo2q2UECogEEDdzQcxS+q0NQ2fA0Mql+/vsUa3ac5c+b4OugmmnReNegm9TnWdPnu3btbsWLFfFnz2Weftccff9wFgwrdc+TIYdGg1g4Kq6LVvzI1tRwIDQdLv5Xbr0pJDePKih+DuYKk5+2ECRMCCSU1AK1Lly5umNKrr75qp512WlTXBwAAZqNHj3btr1IP0Dz55JNdgcMjjzySph3X9u3bXXGA+rdrHoJej+nNev17RK9T9Qa+LFq0yH3dhQsX2vLly92Ol6B3HAFILIShQAyZPn26CyZr1KjhArJo+eCDD6xbt25uTQV1fm4ljoXgMuhJ1L/99psvPSuPRlXHepG5evVqe/HFFzNUiZYuXdq3sCp//vwuBKxUqZJFiyqqo13JqS35qQ0ZMiTDoKa+ffuaH4IMYoMMJdUb9brrrrMpU6aEe+5Guz8rAADJLBSGquWThqPu37/f7XZ6/fXX3WutFStWuIGtixcvtqZNm7rWPmoBdu6557r/f8mSJTZx4kQ3MFa7euSxxx5zbbPOOuss95pdxSKEoQC8RBgKxIiBAwfaE0884bZ+KhDVC4B77rknauv/9ddf1qpVK/fiJX2Fol6AJFIYqnesVSF5NI8++qh57YsvvrDrr7/eduzYYWXLlrX333/fatasadGiF5HXXnutTZo0Kc31Co4OHz7sW1j1/PPPu+ezArP0IWybNm3Mb3pRvmvXLhfya6hTtGg9rRsNQQaxQYWSoUFoS5cudc+xM844I2baFgAAkGxhqMLO8847L3x9jx49XJGF5iFcccUVbiaCKjy14yv960FVjOoN1VBbMF3WvwHUWktv5uuNfMJQAF6iZygQI/QCQFun1TtTg4T0oiJaYei+ffvcO7DaiqLQKn2okGj0Yko9OqNNvUp1XtVLUy8cdXnWrFlRWVtbjFQ9qN6wCturVq0alXUVjN14441uO5QGVqliIERBmZ9hqIL93r17u+e1wjrdl1q1ark3HdS7NZEmracPOtUawc/wM1IoqcoPhaHR+P2hfxTp+7vttttc5Uk0Q24AAJC10I4z9TV/5ZVX7Oeff7Y33ngjYg97baVPPR8h9dZ6APADYSgQI7R1WkGoaMK6qgej4eOPP7Y777zTrakwQ30eE52Ck5dffjnq665cudJVhyqUGzBggJUrVy4q6/bs2dOFr1pT1ZnRpNBKQXvnzp3to48+clW50aCqAw33UVimgF8vqlVloGrcK6+80g2tisUeufEWxAYVSo4ZM8Y+/fRTN3AOAADEnnXr1rnP6o+vN8RV5akdUgAQCwhDgRirVgxtAVElW+rLfvV1VDimadsKiGKRH1tigtpmE6pOFFVo/v3331F7Mbps2TJfnj9Ho4BdoZyqX6tXrx7VtVVdoK3TqRv6axDZxRdf7O7Lgw8+aPPnz/d0zfRfT60HFixYkOb5pn5YiSSoUFLV82pBoC15qjBRv1KF7eq7rMpUhe/RrMwFACDZ/f777244qNoT6c1/9RBVAHrVVVdZnz59rHLlyu71LwDEAsJQIEaoZ6f6SKYOTsqUKeN7X8fvv//e8uXLZzt37gz3ClVwpoBDW6nV48ePF0sFChTI9Li2cat3qsycOdPz9dW0/ViNGDHCOnXq5Fk7Ak3WDFED+dSXJdQ43kvvvfeeffvtt+6FqXqUasCNKvpCwZH6o6qxvde0BV7bp4MIpdQWILMt+LpeA8O81rZt2zSXNSQr9X3Q4/Djjz+aH4IKYoMKJVXh2759e1dtot+dCr81nfbCCy+0d9991z3OTz75pOfrAgCAyBo2bJihd7e2xevfM+r/r9cIABArGKAEJLnPPvvMbVnRNn31c1Q/RW151bu32tatLbDqbemlUqVK2XPPPWctW7ZMc73eSdYgGPVP1TvLscDLAU6qosuKAh6vqfJXYZEqInVOFQYr7L788std+Kr2DHrMvabKV/WI0pqqEr3hhhvc2lpTQbu2Vet55gcF+2r7EKnnlCqu9VjEynPMC6l7sUbiVxCr4DGzUFLhuwJiP0JJPX/03FKlb+i5rDdRVP27du1aa9Cggf3000+erwsAACIPUNIb7Xpdlz17dvf6S38nh3ZE6Q3ik08+2Q09/KcYoATAD4ShQByqUKGCZ8GGKgXVU1JDbsaNG+cquTTISeGVqr10vdchir6+tudXqlTJ9e5U78xp06a53qU1atSwF154IU1VbJD0Lvaff/4Z9XUfeeQR1+PTq6BM1Xqq1FPVr875pk2b3GOsYPCcc85xQ3C8pnOs9VT9qvObI0cO99xt1aqVvfnmm66Hp673w6233urCOVUk6EV56oBWgayqoV977TVf1k4mQYWS6d+kyJ8/v6u0DvrnFgCAZJPZNPnUtDtFQeju3bv/8VZ5wlAAfiAMBeKQl//Q13Z1bVsXbalVSLR3715f1krtwIED9vjjj7uqxXPPPddWr17tejy2aNHCYomXlaFBrZv+a2V12SslS5Z0VaGFChVyA8FUEaznkrbkq2WA2kL8+uuv5gd9XU0x1WcFdLovCny1fVtVjOpjqs9eUsWrvv7TTz/tLqtP1sGDB93PlSozNawsfVuEeBdUKJl+XVWcqLo9s+MAACC4MFQ7z9Svffz48da6det/9PUJQwH44f/q1gHEFS978KXuKaivm34atF/9HjVVXOGY1ld/R23VTrThMrE65Cl1paSfFKorCJXixYu7gCrUm1RBoZ8DpBR0Llq0yPVD1f3Qn1UpqssLFy70PAiVYcOGpRlEpp+lLVu2uOpb9W196qmnzC8KYnv06BG+rMc3W7ZsbnuaPvvRizaS9NUefv3+CPXfDX2E+u/qo1GjRq7lBgAAiA3aLaTX/XqtsmbNmgzH9aa5iiQAIFoYoAQkOYUGt99+e/iygqPQZQVyquD0mrbRqi+pquY0IEmDVzRlslq1ajZ48OA0E8CDFtTkeS9DJIWAqXtzqmIudFnfX+pKYC/paysITP0Ypr7s92OrwF3P5dTPbz9pSFX9+vXThKGhnqXNmjWzrl27+ra2gtgHHnggzdobNmwIDzlSEOtHVerRhoLp/PoVSmqw2dGGV/2TIWkAAMBfenNcww+bNm3qWmLp72ntDJOvv/7aJkyYYHXq1AnffuPGja63vCxZssR9DoWlGszUrl07ThmAf4Vt8kAc8nILqAYWZUVDlLykLa3qiamJ3qHG6qLqPQVXBQsWtDlz5pjftK1W9+VoNA1dPUzj+RxrSFZWLrnkEvOazq1C3cxCTx3T1HM/jBo1yj755BPXMzQ9vQBX9aDXw6q0RVxDmdJXV4tCwaJFi6bZQu4l9X9Vb87Qz5OqLxQ8y5EjR9zQgs2bN3u+blBDwYLqvwsAAP75NvkQvTbRoEX1slf/er1uUf/xa6+91m2H1+tf0b8D1Hc8s9es0fh3AoDERhgKxKGghoOoGqtTp07/+uvoxY/CmUgUjqnnYuoqN69pyvX111/vtuQoRNI71aF3p5P9HCu4OpaAK9ap7YK+Dw2NSk99TG+++Wb3ot1LGkQ1aNAgu+KKKzIcU79QVT8fzxTVWA9iYz2UpH8oAAAAgNToGQrEiNA7occiUq+daLjvvvs8+TqZBaGhrcYKKf10//33u3ewV6xY4Zq49+zZ02KNelsGQcFwEDRl3kvr1q2LGISKKhB03GuqaFD7B1U7pG4FoMudO3d2x/1SqVIlmz17dsRjur5ixYoWtOeeey6pWl0AAAAAiE2EoUCM+Cf/YNcW2EQKFbS9V71Cq1evbrVr13ZDZ/ykykD1HVIopko19TCNBm3dTt/bMPXW7dQVmUEFtEEFRzt37vT062lokKbHZ7ZFK3V7Bq907NjRhZ433HCDq9BU1bE+t2zZ0h3zoqo6FoPYZOq/CwAAACD+sU0eiBHxsJXTy/uor/PWW2+55ujatq5m6qtWrXJN0jVQKZrfh/qGqn9oIm7djpfnodfrtmnTxm0dHz58eMSJplpr/Pjx5oddu3a5oUWhnrQaCKA+uH5TsP/EE0+4VhPaFq9t89mzZ3ctJ7zu+5vMzy0AAAAA8Y0wFIgRoWEzmVVU+TlsJtqhgvp1Tp061U477TS78cYbrVWrVm4brypely1bZsWLFzc/5ciRI01TdjVhTz0FXGbMmOH5ulmFrtEKZZMhsFJfWoWQJUuWtBYtWrjPqhSdPHmy+zx//vyjtmuIV0EFsbH83Aqq/y4AAACA2JQ96DsA4P/kzZvXlz6GsbjNdebMmZYnTx675ppr3Ee0+xlqEFRqmW1d91po67aCuWht3U7W/ooKOjWsaNiwYS54//XXX61IkSLWuHFj6969uxUrVsyXkD39Gxq6TvdFgb8GKOXMmdP8VKhQoYgDnGJBUM+toPrvAgAAAIhNhKFAjFAQVqJECYtlHTp08OTraECSKvTGjRtnQ4YMccNfVCF68ODBqPT308T0IDRq1MgFM5G2bvfr188FdX648sorXe/IYzFy5EhLlKBMgae2jUfL2rVrM1z3999/24YNG2zgwIG2d+9e93z3QywEsdEOJVVF3r9/f9dmQxWxCoIvvPBCN7VebTdCYnFAGgAAAIDgsE0eiBFBbeUcO3ZslrdRL0u/aDvvxIkT7Y033rAFCxZYrVq17M4777R27dol3Pcc1NbteOiZqMpYLweDqdpXz6kQhaIKBEPUFkHtEaJF51fnfv369b58/Y0bNx41iL3gggt8C2KPNZT00ooVK9zviiZNmljz5s3dz5KeQ1OmTLFp06a5VgEayAYAAAAA6RGGAjFCE9XLlSuX5joFCwpPqlSpYvny5fNl3cy21KrKTMN8FFZGq1epvlcNVNJgGw1T8rMKV9Wo6lkaqSJR37u2Vvs1NV1bt2fPnh3eun3ppZf6tnU7yDD01FNPPWqlr4751RoiqyFZ0X5M9DOk8xtET1g/g9igQsnWrVtb2bJlbejQoRmO9e7d24XDEyZM8HxdAAAAAPGPMBSIEU8++aQL6NRDU7StWVvHtbVWA1AULqjaKho+/fRTt21bAa2q6W6//XZLJKpiU1CiCjZVoLZs2dKFkkFSSPbcc8+5x91rmih+8cUXH/U2s2bN8nzdhQsXRrz+k08+sWeeecZNe1flYjQqrXWu9eZCUGHoBx984H7GVf0bbX4GsUGFkqqg1mOptdP7+eefXUC7efNmz9cFAAAAEP8IQ4EYoepPBaChYUKqWlTQoDDyhRdesI8//tg+++yzqISgChMefPBB11tTQZrXVAmZFT/CufSWLFnitlK/++67dvbZZ7tg9Oqrr/att6KqUNWTU4N9dJ7VDmDPnj02YMAAN9RJW7cVentNw6pGjx591NsoePebnl+PPfaYe37pea0etOprmSiVoZHeNAhtVddW8mi+oRGtIDaoUDKrtiJMkAcAAACQGcJQIEYUKFDAfv/9d/dnbRFXOKfwRtvjFaho+6m2VftBlXoKqbSdViGo+mX6EYKm3qZeuXJl19dRIXCkrerRCOdCNLhJ4ZwqM2fMmHFMYe3x6NatmwteFYjNnTvXfVYge9lll9n9999vp59+ui/rBt0zNPXzS4+zXyF7arlz504zsOfxxx+3hx9+2P1Zzzdd3rdvn6drRqrq1fep9hfaRl68eHHzS1BBbFChZFbP6aCf8wAAAABiF9PkgRih0CDUQ1JBWc2aNcN9Qo8cOeI+/KCAZPXq1a5npSojFd5o2ntqpUuX9nRNBTTqC6qqzLx587pQVBOv/QyLIlEQqYn277//vp111lmuX6mflXsKQj///HOrUKGCrVmzxoWfkyZNsmbNmlm8TWo/FjNnznQB4fbt2+2hhx5yz69s2bJFZW1VVf/www9pwvXUl/V881rfvn0tKGXKlMlwnX6WGzRo4GsQe7SesMdy/HipojqzCm493/36fQkAAAAg/lEZCsQIVQ2qIvS6666zwYMHu3Dy7rvvdsc0pVl//vrrr32p0kwdXKQPznSdnwOUVLWmQPKdd96xqlWr2ksvvWTly5c3Pymg0wT7okWLuiD2hhtuiErP0PTVatHayqvguU2bNhZtem5pe7rC3sy2ww8fPtwSiaq51Q9VbR5++eUX9xxTpfG9994beF9av85xZlW+oVDSj98f6kWalVNOOcXzdQEAAADEP8JQIEZoq/agQYNctaL67Gm7eqiK7vnnn3fbfhNtkJEoKNHUaQWi+qwQ6ZxzzvE9wFFPVoWumVWuabu811Tpqy3jocD5iiuucN9z6gC6bt26nq972223Zfg+FU6q36MGdqllgR+OZRiUX9WUf/31l/ucurpafVlD088VgntNFc/16tVzw4rUe7ZEiRKuKlbVv/o8b948X4P+IIJYQkkAAAAA8YYwFEAgFixY4AJQBUUKp1S5eNVVV1muXLl8X3vMmDFZ3kZ9Lb12tPBVdOzHH3+MSiipfpIKsjS0S+ehadOmnq97LAN7/Ah/Rc8lbZUPhZ6qvNb3qXBQrQpUea1p515ShbG2qz/77LMZjml9DRJSqwQ/BB3ERtstt9yS5c+ShpUBAAAAQHqEoUAM0fTlYcOGuW3xu3btskKFCrkelgpuIk1r9kKjRo2y7OvndZWkqjJVmaiw6vrrr3ffZ3pe9ylFZHPmzLEePXrYV1995flDdOqpp0ZsveB3+CsaOKa+rGpNsH//fhcSaohQ/fr13feq556Oe0ktAdatWxfx+ax+wBUrVnQ/134IKogNKpQcMmRIptWxWk/D6BT4AwAAAEB6hKFAjNi0aZOdd955buuyeiwqzNm6daur3FMF3+LFi33pgRdElWT6PqWSOjDzu0+pQrGpU6fayy+/nOHYnXfeaVdeeaX7iAb1if3+++/dwKwgKvf0uCu82717tyWSggULhr8nDSRTOwBtHfdz2ri+5pYtWyx//vwZjqk3rMJKvyacBxXExkooqXP99NNP24svvujaTzzyyCNuQBkAAAAApEcYCsQI9XVU39BI4aSqr9Q/VD0Pg6SAYcCAARbvLr74Yhs6dKjbnp/eokWL7P7773dbqb1233332bnnnhveuj1q1Ch33hVgKSx7++23fZ8sn9769eutYcOGLkhLJGeffbYLu7UNv1evXu7705CuUDioYV07duzwdM3LL7/cLrvsMnvggQcihoaffvqpL71ogw5igwwlFfBqPZ1rvYHx0EMPWZUqVXxbDwAAAED8IwwFYoS2jiso0ef01q5da40bN/ZtS/Gx8qOaLisVKlTw/PvWlumdO3ce9/F/s21cVYpqeaCKTLUCUFikalT1s3zuuedcBXA0eneGeoaqLUPLli3dwK5E8v7771vHjh1dELd06VIXRNapUyccQmvL+IcffujpmsuWLbNLLrnEhcstWrRw1d3btm1zfTs1OEstCWrUqGF+CDKIDSKUTL2e3kDQepUqVfJtPQAAAACJgzAUiBEKGrWlNFL/PU3CLlCggKvwCtJJJ50U9fvgx5qqxFTAHGnCtp9bilOHyQrOVLWodXLmzOnOse6PH+sqhA0JPb+yZ8/uQlkFobfffnua1gWJ4ocffnCPs1oQKFQPUU9ebaM/88wzPV9TfUg1sCo00V3nVEObHn30UV8rJIMKYoMKJfWzpJ+bu+++O9P1NJQNAAAAANIjDAViRFZVl0FUZcbCffBjzebNm9s555wTccq6rluyZInrK+o19QRVZWi5cuXcoJvJkye70Ez27t3rwkn1WvQzHJw9e7YL6VT9qmFCVNMljiCC2KBCST13sxrcFPrZAgAAAIDUCEOBGKGeoJG2yIu2VKu346FDhyxIiRKGLl++3OrVq+e2FV999dVpqui0nXjevHlWrVo185r6rWp7dtOmTV3/V1XUtWvXzh2bPn26C7IibWn/t/T86dq1q7366qtuQFfx4sVt+/btbmiXtpKrqi/RKkMbNWp01LBM/Nw2nt60adPsiSeesM8++8wSCaEkAAAAgHiTPeg7AOD/xEMVU+qJ7/HsrLPOcoOSFD6qx6K2xoeq6L788kvfqujUH1RVoao8TR2EisLJnj17+rLu448/7ipSta4GC4Wol6buw8CBA919SyQ33XRT1NdUoK7hW3pc1Wrh+eefd4HzPffc455jqp4Mgp9BrLbfB0ntLkLVzkWLFrUGDRq4xx4AAAAAMkNlKBCH2rdvH3HqvN80gd2vwC6aPUNTV1+mDnhTVxKqn2einGP1y1TVa/Xq1SNWyarHpCqPk4X6sw4ePNjzoVGqMt63b5/7/N5777lwVO0PFLq3bt3aVX8HGcTqeKKEkjqHqaudS5QokfDVzgAAAAC8QRgKxCGvt45r+Er//v3dYBkNRNGAoQsvvNBVC/o1/fpYbd261UqVKuX7QKHUwaiu83qCfZDnOF++fC5QjhQOHT582AXOCu2SxYEDByxv3rzue/eS+rDqeaPHUz9HqjZetWqVVa5c2fwWVBAbVCiplhNvvfWWjRs3LmK1swaDJVq1MwAAAABvEIYCccjLaskVK1ZYrVq1rEmTJm6wkPpnKoDUACFtr9W28UgVhf82jMxq+Mm6dessmXl5jqtWrep6lCrgTk/b5zt16mSrV6+2ZApD8+TJ44I8PwNsvamgUDQaggpigwolqXYGAAAAcLwIQ4E45GXVoKrGNMVcW+DT6927t23cuNEmTJhgXlq4cGHE6z/55BN75plnLH/+/LZhwwZLZl6e45deesltC1eVnoY3KWxWFexHH31kd955pzvP+pws/KoMzZ07t5vcnrpX68MPP5zmNl5vzQ86iA0qlKTaGQAAAMDxYoASkOS0NT6zCebqM6iqUa+l/5qa4P7YY4/Zzz//7Aa9dOjQwfM1k5mCzt9//91V6R06dMj1dFRvxxw5clifPn0SMggdP358pscOHjzoy5qtWrWyH374IXz5xhtvTHM5q+n2/4a+p0GDBqUJfFNf9iuI1Zb4M888M+IxXa/jftCW/AULFkSsdtbvMw0qAwAAAIBIqAwFknwLdVZfy48BRqkrQRWCqr+hQjkNDcqenfdo/HrcVTmoAElBqLZR16lTxwoUKGCJSAN8sqKBP4lCbyBk1Xritdde83zdoFowUO0MAAAA4HiROgAxbs+ePW7beGojR4707OtnVa3mRzXbzJkz3WAXVY099NBDrregn5O2k/0cp95Kffnll1syyCro3LJli+drHsvXLF26tPlh9OjRFgRVj6vVxtFaMPghGaudAQAAAHiDylAgxik80bR3DUjxgyY9Z1aNqVBDQ2a87q2oNQsXLmzNmjVz4UUkw4cPt2Th9zlGdHqG6nkdCgMl9Z9Dl71eMxaCWLW2UH/USKGk3uzwUzJVOwMAAADwBmEoECMym/qs4SPqj6fKyTVr1ni+rgYkZeWUU07xdE1tjc+q4rRv376WaII6x4jONPn0ojlNPsggNohQ8rfffrNFixZZkyZNMhybNm2aXXDBBe4NFwAAAABIj23yQIzIlSuXC09UTaWqNVGYcd1119lTTz3l2z/sswo69+3b5/maCkOTUVDnGBn5OcwommuEpA92oxXEpg4l07dg8DOUVJuNMmXKRAxDV6xY4dZ+9tlnPV8XAAAAQPyjMhSIEaraeuGFF9wwEk19btOmjbu+VKlSbgt18eLFfVlXFYmbNm0KX77yyitdv7/UfSZV9eWlzKbXp1a3bl1LNEGdY0Rnm3x6CgEVFgYhWmt369bNhZIPPPBAhmMK+Ddv3uxLKHnaaafZ119/HbH6VL1Ea9asaevWrfN8XQAAAADxjzAUiDE7duywXr162dq1a12IoL6aS5cu9S0oSz+1PH2I4sdU81NPPTXLiroff/zRElW0z3EyUs/KzCoz/eqFm4xhaFChpH4v7d69O+LgNZ3XggULev57CwAAAEBiYJs8EGMUiI0aNcr137vjjjts586dLrgZP358uJLQS0FMk1ePzGQW7XOcjBQ0R1ujRo3S/Lzs2bPHGjdunOY2M2bMsEQL9vPnzx/xmK7XcT9oGNTixYutdu3aGY7pelVbAwAAAEAkVIYCMUwVbNqirl6T+fLl86WSLf02+PQVZX5skx84cKDVq1fP9RPMnTu3JbNonGNEx5gxY7K8Tfv27aMSxM6ZM8fq16/vexBbpUoV931HCiW//PJLu/nmm30ZCqYJ9mPHjrWJEyfa2WefHb5++fLl1rp1a7euqq8BAAAAID0qQ4EYpnBD20/V4zD1ZGgvaUBS6uq11NVsWnP//v2er6nA4uWXX3aTp7WNVsHoRRdd5D40+CWZROMcJ6Pbb789y9sMHz7c0zW3bt1qvXv3tiDcdNNNaS63bds2Kut26NDBbrnlloihZMeOHd2HHxR0avu9fn9oCFzJkiVt27Ztrv/xrbfeGrGHKQAAAAAIlaFAkg98CbKaTX1Bv/jiC5s3b577UAWZKs0UjiosTSbRGuqTLDRtPCt9+/b1dE0/qqiP1eDBgwMJYtXeQcGz2j5ECiVfeeUVX1pthKxatcpVwf76669WpEgRa9CggfsdAgAAAACZIQwFYsTRJqwrKGvYsKEvQVlQIUp6K1assEmTJrlp66oYPXTokCWaoM4xosOPYWPxEMQGEUrqcdbjnZnVq1cTigIAAACIiDAUiBFZTVj3a/BQECHKwYMH3ZATVYPOnTvXTVKvWLGiXXjhhVa3bl33kYjb5YM6x8no888/z/I2F198sadrqu/rQw89dNTbPProo5ZIQWxQoWT631vVq1e3b7/9NtPjAAAAABBCz1AgRgQVggXRp1JBhYLBTp06uXDonHPOsRw5cliiI+iMHg0PKlu2rJtoHuk5rq3b33//vadrah31DQ2C3mDo379/1IPYMmXKHDWUPP/8830JJdOf082bNx/1OAAAAACEEIYCSS6IEEXDTdQr9JlnnrFZs2a5ilB9aLp8njx5PF0LyUmDfaZPn241atSwdu3a2eWXX24nnniir2vmzp07sF63QQWxQYWS6fuQZnUZAAAAAELYJg8kuZw5c2Y58dmvgEf9Mb/++uvwAKVFixZZ6dKlXTA6bNgwX9ZE8lAf1ilTptgbb7xhS5YssauvvtoFowrd/ZCMPUPTr1u4cGH77bfffL9fQa0LAAAAIP4RhgJJLhZCg+3bt7veoQpEJ06caDt37mSQEDy1a9cue/zxx+25556zmTNnugE/XrvjjjuO+Y2DESNGuDYR8R7EBhVKZsuWzU477bTw5R9//NEqVKgQrkZVS4pEHMIGAAAA4N8jDAWSXBAhioaqhKpB9bFhwwbXa/Ciiy5yH/Xq1bNSpUpF9T4hMSloV8A+btw4279/v7Vp08aFlgULFgz0fnkdEgYVxAYVSn722WdZ3uaSSy7xfF0AAAAA8Y8wFEhyd955p7300ktRXTNv3rxuq3Io/NT0eIVDgFfGjBlj48ePtx9++MFatmxpbdu2dYF7rEiULfWEkgAAAADiDWEokOSC2Cb/999/H/P0+EceecQGDBjg+31CYtGwpMqVK7uwPbPBScOHD7dkbE8RZBDrlWnTprnHUG+kiELv9u3b24oVK6xWrVo2atQoK1u2bNB3EwAAAEAM8ne0LoCY59e056M51iBU1OMR+KceffRRa926tZUrV87KlCkT8SNZeTlpXaHk/Pnzw5cVSoYqvRs1apRhurxXHn74YbdFP+SWW25xrQ8mTJjgPvfo0cOXdQEAAADEPypDgSSnafI33XTTUW/z2muvWVASoYoNsc3rYUbJtE3+vPPOsxdffNFVY4oqcfX1u3btaqNHj3ZVuW+++aZ5TYOaduzYYdmzZ7etW7e60Punn35yvYY1wOmMM86wbdu2eb4uAAAAgPiXPeg7ACBYCiuqVKmSFFVsQCT33Xdf1MPQDh06JEQ1uAYmnXvuue7PCiW//PLLcChZp04dF0r69XvryJEj7s9ffPGFVaxYMTx0rVChQrZ3715f1gUAAAAQ/whDgSSnytBevXoFfTeAuA8Hf//9d1u+fLnVq1fPXR40aFCaSep33XWXq2iUF154wYLiZRAbVCipx/ixxx5z34sqU5s3bx4+tmbNGitRooQv6wIAAACIf/QMBZJcED1DE+n+If55VX08ZMiQNNPVn3jiCVctqY+5c+fa0KFDzS8KYrVGiILY/v37hz+0dTzEyyA2FEoqgIxmKPn888/bggULrGbNmq53aJ8+fcLHxowZY02aNPFlXQAAAADxj56hQJJTYJM6SIiW3bt3uy22qiRTj8HMKEDq2bNnVO8bkotXPTSrVq1qs2fPtpIlS4YrI3ft2uX+vH37drv00kvtu+++Mz88+OCDljdvXjdYKNSTNNQLeO3ata63p37WvaYt8TfffLMtXrzYateubW+//bb7vkP3SX1Rg6yClRkzZljjxo0DvQ8AAAAAYgdhKJDkxo8fn+Vt2rRp4+makydPtlatWtn+/fstf/789t5771nDhg09XQOI9jAjTTFXyB/y3HPPWbdu3TI97qUgg9hYDyW9HBgFAAAAIP4RhgJJrkGDBlluIZ41a5ana5599tnWpUsX1+9Pk+pff/11N3gFCMLdd9/tSfWitoR//fXXVqZMmQzHNm/ebOecc47t3LnT/BBkEBvroaRXYTcAAACAxEAYCiDqChQo4HocyuHDh101m18hEZLT2LFjs7yNtnd7qV27dpYnTx4bPnx4hmMK/xXIvfHGG+aHIIPYWA8lqQwFAAAAkBphKIDAwwlN2E494AX4t6644opMK53V31LPNwXxXtq0aZPVqVPHSpcu7QYJKeTftm2bTZkyxX7++Wc38Ofkk082PwQZxMZ6KEkYCgAAACA1wlAgyWkSc/rp7aHp2qE/ex0aac3TTjstfFmDlCpUqJDmNppEDXjp008/tX79+rmhPxoadvvtt3v+AO/YscOGDRtmc+bMsV9//dUF/WpF0b17d98mqwcdxB4rtskDAAAAiAXZg74DAIJVvnx5y549u5s83bJlS9db0G9e9yAFjiUEVSioCeft27d3z3mvrVixwqpVq2aDBw+O+glR0PnNN9+4IPajjz4KB7EanOR3EHus9OaKVyZMmGCtW7f2pC8yAAAAgORCZSgAN7xo3LhxNmnSJDv//POtbdu21qxZM8uZMyePDuLWJ598Yo899pirkFQIqh6hfoSgIfnz57eKFSu6Lett2rSxUqVKWbSEgthYporVyZMne/K12PoOAAAA4HideNz/J4CEUbt2bfvvf/9rGzZssI4dO9rbb7/tghxVmvlh1KhRLnCNRBWqY8aM8WVdJI8LL7zQWrVq5XqHqhK5SZMmbgv7li1bwh9e2759u/Xo0cOFsKq4btiwoY0ePdr27Nlj0fgZrlGjhj399NO2detWi5Z58+a5lgORKICeP39++LJXQajXVaYAAAAAkguVoQDCli1b5ipE33nnHatcubK98sorLtTxmqpPFXieccYZGY6tXLnSVfBpyA1wvE488f+/16e+t+nDMz964aam4FVbucePH2/fffedXXXVVa5i9Morr/Rlvb/++svee+89t57C33r16rk3Fq6//npXseoXhcwKgBs1ahSxPcHQoUNt2rRpnq8bGhZ1tFBUv0cAAAAAID3CUCDJbdy40QUomjSt0EQVmzfeeKMVL17ctzWzmh7PdHkkks8//9wFcxrc5GcAG0QQqwpyVdmGhq6lpqBSx9WmwGs5cuRwAWxmYajuz9SpUz1fFwAAAED8IwwFkpwq6FQFqmEklSpVingb9T/0UrFixezbb791E6/T0xbf6tWr2y+//OLpmkheP/zwg3s+6Xmnnp7R8Oeff9q7777rAkltJdcQH1VqHuvQn3gJYk866SQ3mEo9PNP7448/rGzZsu6z1+gZCgAAAOB4EYYCSa5+/foRq7pCdMzr6e8KV1WFqm2u6XXp0sWFJwqRgH/jzTfftJ49e9rmzZvD1ymcGzJkiC+h5MGDB90kdz13VZWoUF+V1updqiA2WqIZxGpa/XXXXWddu3bNcOyll15y/Ydnz57tSwir7xMAAAAA/in/xuoCiAtz5syJ+pqDBw+2OnXq2LnnnmstWrRwFaLaSqsBK/qceugKcDzU9/aOO+5w0+SvvvpqK1GihBtw9P7779tdd93ltlmrn6aXtEbRokVd2D9o0KAMldZHjhxJ08s0GkGs2l/4GcTq8W3atKn9+OOPaX6WJ02a5N7s8GurunqiAgAAAMDxoDIUQAYzZsywFStWWK1atdxUbq+pUi9Xrlw2bNgwVzX266+/WpEiRVyVWffu3aNaRYfEVLNmTRfUNW/ePMMxhe6PPvqoLV261NM1FyxY4EL+9JYsWWKvv/66vfXWW75Nei9UqFA4iFUVaDSDWL2hosnxCxcudD08VU2u3x0KhFV57oc9e/a4dfLlyxf+/kaMGBH+vaUgGAAAAAAiIQwFkpy28DZu3NhuvfVWd3ngwIHWv39/O+uss+z777+3F154IXzMK/T7g9/UhmH37t2WPXvGDRB///23FSxY0E1g98uGDRts3LhxrjJzzZo1bniRtpJffvnlvqwXZBAbsnfvXtu1a5cLZvPmzeuu05sd2qbvNQ2F0rb/UOjZrVs393jrDRX1SdWbKr179/Z8XQAAAADxz58yEQBxQz0FQ9Vzhw4dctWamkS9ePFit6X4mWee8XzNzCZAA14pUKCAGxoUia7Xca8pCPzf//7nqqk1qGn69OkuAFU4qKpFv4JQSR2EKoh9/PHHrWrVqq5Kcv369TZ69Gjf1lbYq7YE+lymTBkXhKr6VmvrzRY/KORt1qyZ+/P+/fvttddec31S1aNUW/N1GQAAAAAioTIUSHKpqzQXLVpkDRs2dBV1oS21qqDTZa+Hn6jq9Gih6Mknn+zpmkguqgz85ptvXEin7eMhO3futJYtW1qNGjU8D/rV+kHPbVUk3njjjVauXDl3falSpWzZsmVWvHhx84uCWA2MUhWotqsrHNV9UKsA/az5tfbIkSPd0LPChQu7dhfqB6yBa+ohqnPQvn17y507t+frpv69NHfuXLvmmmvsl19+CR+n+hwAAABAZhigBCQ5BTUrV650VWQzZ850g0lCQahC0kjbjP8tbU+uUqVKpmGoegFqyy1wvNSvUgHZKaecYrVr1w4P9vnyyy/toosucse9puBP1YlqLaFhTaqK1JCwaND3FwpiJ06cGA5i1fbCT08++aRNmTLFmjRp4qpB9Zj36tXLPvzwQ996lIrOqwat1a1b162VujepQlk/AlgAAAAAiYHKUCDJvfLKK25LrUJQhRoKUtTfUFRV9/LLL9unn37q6ZoKbf78809PvyaQ2TAw9a1U1WBoSJd65PolNNVd/Su1XVvbxrds2eKGDF1wwQW+rXv77be7IFa9UlX5Ggpi/a5KTV+BmSdPHjfcKFu2bOYntfDo2LGjnX766W4Qln5HhVoFjBo1yj0WCkkBAAAAID3CUAAuqFEPPvX4Uygaokov9TtMfZ0X2MKKZPD777+7resaovTFF1/YJZdc4vkbC0EHsel/lrVd/rfffrNoWLt2rQtCa9asaRUqVAhfr8da2+jPPPPMqNwPAAAAAPGFMBRA1FEZCr/ddtttrt3C0QwfPjxqJ2Ljxo02fvx469OnT7hi1c8K1WgFsdoKn7qVhoawhS6rDYbOgUJav2iN1atXh6fYq/1GVucdAAAAQHIjDAWS3C233HLU8EDHNCQFiCf9+vXL8jZ9+/a1oESzOtrPIFZf+1j6e/pBYfbDDz/seoSG+g9rWFb//v3dUCcAAAAAiIQwFEhyQ4YMiXi9troqBFWF2d9//x31+wX8GxMmTLDWrVvH7IMYZHV0NIJY/f7Qlnm/qL9xu3btbMCAAda8eXM3QGrr1q3ueoXco0ePdtcDAAAAQHqEoQDS2L17tz399NP24osv2hVXXGGPPPKIG1ICxJNY70sb5P3zMogdO3aslShRwi6//HJ3eeHChXbddde5YFJ9PD/44ANfeneqslWDojp16pThmN7E0SC4mTNner4uAAAAgPh3YtB3AEBsUM89bTk97bTT3NbXBQsWuH6DBKGIR6Ft08jIy56aQ4cOtdKlS6eZaq+gcvny5dakSRPr2bOnL6fgm2++sWuuuSbiMV2v4wAAAAAQCZWhQJJTCKpK0JdfftmaNWtmDz30kFWqVCnouwX8K3ny5HE9JY8Wit58882BPcqJsk1eU9v1O0QB64YNG9zvjh07drhhRnv27HHVoboc7e8h1iuDAQAAAATn/4+ABZCUNNwkZ86cds8997ggY/Hixe4jtTZt2gR2/4Djoanm6huaWRiq8M7rMPSf9Clt0KCBJULVbO7cue2vv/6y/Pnz27x586xatWouCA0dO3DggPnh8OHDrno9s+/lyJEjvqwLAAAAIP5RGQokufr162c5TX7WrFlRvU/AvxVEZWC8VCNqsNDkyZM9+VodOnRwgWTbtm2tV69erl+o2m2ItqrfdNNN9t1335nXypcvn+V2//Xr13u+LgAAAID4RxgKAEg4QWxDD3Lru6oyP/roI3viiScyHHvwwQftqquusrp16/oycK179+6umrxWrVpu8JoqQmXgwIEuKA2Fo0HZt2+fa5sAAAAAAEIYCiCi7du3u22/48aNsyVLlvAoIa40bdrUpk6dGtXndZB9SjWsqEePHtaoUaMMxz799FM36GjatGkWpBEjRkSc/u63eKnYBQAAABAdhKEAwvbu3WvvvfeeC4q0Nf7MM8+0Ll26WOfOnXmUELei9bzOkSOHCyOP1qc0UkDrhVKlStmWLVsibh3X/dHxbdu2WZCCCiWDrNgFAAAAEHsYoAQkOQ0amTFjhr3++us2adIkK1KkiN1www22aNEimz59uhUvXjzouwjExfNalaF+hZ1Z0eR2BX4KHNPT9QqEg+bl4KZ/IqveogAAAACSy4lB3wEAwSpdurQbflK4cGG3jXbjxo321FNPWa5cuTg1iFtBPK+DCvvk/PPPd8FvJKqIPffccy1ohJIAAAAAYgGVoUCSO+uss2zOnDm2bNkyO/30061y5cpUgyLuBfG8rlevngXlsccec31Sf/zxR2vRooWVLFnSbYtXVaz6mAZVsQoAAAAAsYaeoQBcaBIaKrN8+XK7+OKL3XZi/fnUU0/lEUJcivbzWlvVVf2YL1++8FZ9DQ1asWKFm7SuSlU/KfzV5PiFCxe6KlXdF607aNAgq1+/vgUtqN6dhQoVsl27dkV9XQAAAACxiTAUQBorV6504dH48eNt586d1rJlS3vttdd4lBDXovG8vuqqq6x169bh0LNbt25uzUsvvdQ+//xz6969u/Xu3dv8pv6gCv8UAubNm9ddN3v2bGvQoIEF6e6777YXXnjB06+5du1a97398ssvVrRoURf6VqpUydM1AAAAACQWwlAAmZo7d6698cYb9r///Y9HCQnDr+e1tqavWbPGDTHav3+/FStWzKZMmeICuq+++soFpTruF31tVb1WrFjRatSo4a6bPHmyDRw40DZs2GDbt2/3Zd3ffvvNnnnmGZs1a1Y4lFQAfO+997rBVX5Q1W3Xrl3t1VdftZNPPtlKlCjhvr9NmzZZx44d7eWXX7YTT6QtOgAAAICM+JcCAEchxuHDh8OPhoKFDz/80CZOnMgjhLgVzee1AtDQNPfFixe7YU2h7ekaYKRt+34ZOXKknXnmmS4gPO+889ywKPUQfeCBB+zWW291A6T8oJD17LPPto8++sguv/xyu++++9xn9SjV9TruBwW88+bNcyGz+qQuWLDAfV6yZInNnz/fHQcAAACASAhDgSSn4KBs2bKuskofqu4aPHiwGzjz008/2SeffBL0XQTi4nl9yimnuHVFgWvqPp2//vqr5c6d2/zy5JNPuipUhb3vvfee9erVy1WHfv/999a5c2ff1u7Zs6ddd9119vXXX9ujjz7q1tJnhZS6vkePHr6sO2rUKNfyQIFravqeVfVLaw8AAAAAmWGbPJDkateubQ0bNnR9DseMGWOvvPKKXXDBBe5z+fLlg757QNw8r99//323RVvT65cuXWqffvqp1alTJxzevfvuuy4k9YMqUv/444/w5Tx58riBTtmyZTM/FS5c2NatW+f6k6anAFhb9v0YXqQhVRrGFGkrvCqBNaxJvVMBAAAAID3CUCDJFSxY0PX8U6hw8OBBFzJoa3GBAgWCvmtA3D2vNdBHQWjNmjWtQoUK4eu/+OILd5+0lT0aYahCSn3/ftO6W7Zssfz582c4prCyTJkyae6XV6pWrWojRoywCy+8MGJP2E6dOtnq1as9XxcAAABA/GObPJDkNIgkVF2VM2dOV1FFEIp4F9TzWpWQ2h6uAFZ9LFetWmUpKSkutPMrCBVVger7DH3s3r07/OccOXK4z35Q5etLL70U8ZiuV4WuX5PpNZBKvUr1+Io+q/JW1cDdunXzZV0AAAAA8S970HcAQLD27dtnjRs3ThOqpL4sM2bMCOCeAfH3vB4+fLg9/PDDbot4KKTTdPX+/ftbly5dzC/r16+3IKhX6SWXXGKLFi2yFi1aWMmSJd2gqEmTJrm+rHPmzPFl3TvvvNN+//13a9mypR06dMg9xqr8VfDbp08fdxwAAAAAImGbPJDk1E8xK+3bt4/KfQHi+XmtAUbt2rWzAQMGWPPmzV0wuHXrVnd93759bfTo0e76aNJWeW2Z99OaNWusX79+bkiVAskiRYrYpZde6gYpqX+qn7QFXxW4oXVVqUplOwAAAICjIQwFkpwmbPfu3TvouwHEPVWeqlJR/SrTGzlypE2cONFmzpzpy9pjx461EiVK2OWXX+4uL1y40G3XVxir3qUffPCBL9v0J0yY4LarR1v6HqkAAAAAcKwIQ4EkR6iARDRo0KAM12kL9cknn+xCy0jTz/+tYsWKuR6hqlCMVKFZuXJlV8Hoh+rVq9v48ePdZzn77LPt3HPPtR49etj//vc/N/F96tSpCfP7Qz1gNaAJAAAAAP4pwlAgyREqIBHdcsstGa77+++/bePGjbZy5Uo3eKdWrVpRDQb9DA41qX7Xrl12wgkn2IYNG6xSpUq2Y8cOF/qqX6qqQ3U5UX5/8CYOAAAAgOPFACUgyWnqtYa7HI16/wHxZNSoUZkee/vtt13F5Lx58zxd8/Dhw65/ZWhwUqQJ937JnTu3/fXXX5Y/f373fVWrVi1c/apjBw4c8GVdDS96/fXXM/2e5eabb/Z8XQW82bJli3hM90WhsM4HAAAAAKRHGAokOQUH6isIJItrr73WOnfu7Ms2+TZt2hz1uF+aNGliXbt2tbZt29rTTz/t+oWGfPvtt1a2bFnfwlD1Dc0sDFUo6UcYmjdvXrf1HwAAAAD+KbbJA0mO7aZINtoufv7557st80HZt2+f5cmTx7Ovt3v3buvevbstXrzYbf9/8cUXXUWoDBw40IWVDz/8sCXK7w9+bwEAAAA4XoShQJKjZygS0ZYtWzLtGTpgwAA766yzXAVlUIIM80aMGBFx4n08/f7g9xYAAACA43Xicf+fABLCTTfdFPRdADynbQkISXQAABDJSURBVOHlypVzn0MfFStWdIOVNGX98ccfD/RRP1qPTb/dd999nn2tevXqHfNtZ8yY4dm633///THfVsOjAAAAACCEMBRIcppCndobb7yR5nL79u2jfI+Af0/DijRAR59DH6oMVZ/JwYMHe7pF/Xiol2YiBLFTp0495ttef/31nq2roPtY7dy507N1AQAAAMQ/tskDSS79dt3ChQvbb7/9lulxIJ78+uuvro/mrl273HNbvUL1OWhB/lwFtXZQW9v5HQYAAAAgNabJA0kufZVYVpeBeNGnTx8bNmyYqwotWrSo/fLLL5YtWza79957XXUokqcaFgAAAABC2CYPJLn0AUVWl4F4MGbMGBs7dqy98847tn//ftu6dav7/Pbbb9u4ceNs9OjRgd4/hbJB4Q0OAAAAAMmMylAgyamP4oQJE8IBycGDB238+PHuz7ru0KFDAd9D4J8bOXKkvfDCC9asWbM0AaQu6zn/zDPPWIcOHXx5aNeuXWuzZ892laiqSK1fv75VqlQpzW20bT8ofn3fsRrCEv4CAAAASI2eoUCSU1BztOpPHZs1a1ZU7xPwb6kv6MaNG12fyvTUt/Lkk0/2PJDUdvyuXbvaq6++6r5+iRIlbPv27bZp0ybr2LGjvfzyy3biif5tyFCvX4W8+nkNBbGXXnqpawtQpEgRX9bUGymtW7c+pts2b97cJk+e7Nn3umjRImvSpEmGY9OmTbMLLrgg3BtWVcGlSpXyZF0AAAAA8Y8wFMBRh888//zz1q9fPx4lxJUCBQrY77//HtWhOgMGDLC33nrLbcM/++yzw9cvXbrU2rVrZy1btrRHHnnE/LBhwwarV6+eFStWzK6++upwEDtp0iT3ed68eVa+fPmEGU7UrVs3K1OmjD3wwAMZjj311FO2efNme/bZZ6N+vwAAAADEPsJQIMlpC6m2FCuwqVixot155522Z88ee/zxx23EiBF2ySWX2JQpU4K+m8A/kidPHnvttdcy3SLdqVMn27t3r6ePaoUKFVz4WL169QzHli9fbi1atLD169ebH2644QYXDkYKABUcKhx89913E2ZC/GmnnWZff/21C73TUwhes2ZNW7duXdTvFwAAAIDYRxgKJDkFJQpJLrzwQps7d677vGTJErvsssvs/vvvt9NPPz3ouwh43v5B1NfTS/ny5XPBYKSt8IcPH3bBodcBbIi2hCv8K1SoUMQKb73R4UefUoXOw4cPP2pfzptvvtnzdfVY7t69O+IgKj3WBQsWDCSkBQAAABD7GKAEJDkFoZ9//rmraluzZo0LP1XdlnrwDBBv5syZc8y3Xb16tVWpUuVfr6k+oQsWLHBvKKQ3f/58K1eunPlFg85y5MgR8VjOnDldQOjXuqkHsKWnQNqPMLR06dK2ePFiq127doZjup4eoQAAAAAyQxgKJDn1+1MQKpUrV3bVbQShSCbnn3++J30v7777bjdMSIOSmjZt6oJAhYQfffSRaz/Ru3dv80udOnXspZdeithDU9dHCg29qgydOnWqRVuHDh3slltusYkTJ6bpz6p2BBpWpQ8AAAAAiIQwFEhyqhhTNVuosktbfFNflrp16wZ4DwF/HW2L9z+hwFP9KjUoSRWTmuauqe6q2OzTp4877pcnn3zS9ffVhHX1Ji1ZsqRt27bNVXl/8skn/6hSNojH7p/q1auXawug3qCnnHJK+PvdtGmTC0l79uwZyP0CAAAAEPvoGQokOU2YPlpvRR378ccfo3qfgGjyeiK6vpbeUFAQWqRIEVe1GWnQj9fU5qJfv342a9as8NqXXnqpPfroo771/lUFbBCVoSGrVq1yQa/6our7bdCggWt5cOTIkYi9WwEAAACAMBQAkNS8CkO9DlX/CfXt1Bb9aNuzZ497w0TtNUQh5IgRI2zFihVWq1Yta9u2bVTvz8GDB+21116zoUOHMk0eAAAAQESUTQAAEMdbxqVz586BrNuqVSv74IMPwpe7d+/uWgJs3brV7rvvPhs8eLAv665cudINqsqfP7/VqFHDli1b5vqHqtJ97NixNmTIEF/WBQAAABD/qAwFACQ1hWlLly6N68rQk046yf7888+or6tendqer+99//79VqxYMZsyZYrVr1/fvvrqK1etquNea9iwoZUrV871Zx0/frx99tlnbpv8Cy+8YBdddJHn6wEAAABIHIShAICEo7CsXr167kOT1PPmzev7mupRmVn/XVWN6pgGlvk11X348OFHrU69+eabPV+3YMGCtnv3bvfnuXPn2jXXXOP6lfodEBcuXNgNTMqZM6ft3bvXhcGbN2+2UqVKeb4WAAAAgMTCNHkAQMK57rrrbN68ea5/pLZsn3POOa5iUOGoPmvSu9cUuGrCeRA0vV59QzMLQxXE+hGGapL7/PnzrW7duvbhhx+6ifYhGmqUO3du8+v7VRAaetw1oIogFAAAAMCxoDIUAJDQNm3aZF988YULR/Xx/fffW8WKFV3fSS8FuU0+qLXff/9969ixo5tW/80337hJ9nXq1HHHRo0aZe+++64LSb2mILRDhw7hy+oTmj7sVaUsAAAAAKRHZSgAIKGpt+QZZ5zhKhX1sX37dsuVK1dCDVAKam1ti69evboLQqtVq+a2zK9atcqqVKlilStX9m2Q0UMPPZTmsoY2AQAAAMCxoDIUAJBwNFBHVaDqY/ntt9+6MFRbuTWBXD1EVUnptZ9++skFr8eiQoUK9uOPP3q2dtOmTW3q1KnHdNsZM2ZY48aNPVtbFZgKJ3/77bdwKKs2BP3797cuXbqYHzZu3GjZsmWzsmXLust79uyxgQMH2nfffWcXXHCB9e7d27Jn5/1eAAAAABkRhgIAEo6GGak6sUePHta+fXvLkSOHxZKgpr97vaVek+PbtWtnAwYMsObNm7vp8urRquv79u1ro0ePdtf7MSDr7rvvthYtWrjL2iKv3qWqVNXazZo1s6FDh3q+LgAAAID4RxgKAEg46l0Z6hOqakFVhqoqNFQZmj9//kDvX5D9Rb0MYlVh2rJlS+vUqVOGYyNHjrSJEyfazJkzzWvFihVz1aEanqTvRZcXLFjgBmWtWbPG3a8NGzZ4vi4AAACA+EcYCgBIaEeOHLFly5a5YFQBqbbQa/L4119/Hdh9SpRhSwoh1SO0SJEiGY5p27z6hv7yyy/mtYIFC7r+pPLJJ5+4ytAtW7bEROUtAAAAgNh2YtB3AAAAP23bts1VC65evdoFdzt37rS9e/fyoHvgwIEDEYNQKVy4sB08eNCXx7lq1ao2adIk9+c333zTGjVqFD6mbfoKQwEAAAAgEqYLAAASziuvvBIeoPTzzz9bjRo17KKLLrJHH33UfS5evHig9y9RJs8fPnzYbU/P7GuqKtcPmlJ/9dVXW+7cuV3g+vnnn4ePKRzVOQYAAACASNgmDwBIOJdddpkLxOrVq2d16tSxfPny+b6mtoUvWrTImjRpkuHYtGnT3JRzVUuGqhe1Vd8rEyZMsNatWx/TbTXQaPLkyZ6sW758eTvhhBOOepv169ebH7TVXxW/2oqvrf8hqgBWZWjp0qV9WRcAAABAfCMMBQAktP3799uuXbusUKFCrpLQL926dbMyZcrYAw88kOHYU089ZZs3b7Znn3024XqQAgAAAEA8oWcoACAhaZu8KkNVJVi2bFn3WdWi2jrvhw8//NA6d+4c8dhtt91mU6ZMsUTcdg8AAAAA8YTKUABAwpkzZ441a9bMhZDqLVmiRAnbvn27vf/++zZy5Ei3Tbx+/fqerqmwVRPOs2XLFrG3piag+zXhPE+ePDZ8+PCjhqKauA4AAAAAyY4wFACQcFQB2rFjR7vlllsyHHvttddsxIgRNn/+fE/XrFKlio0ZM8Zq166d4diXX37pwkj1uPRDjhw53ET1zMJQ9fWcOnWqL2sDAAAAQDwhDAUAJBz10Ny5c6flypUrYg/RYsWKeV6l+cQTT9jYsWNt4sSJdvbZZ4evX758uRtupDC0V69e5gd6hgIAAADAscl+jLcDACBuKATVlnVtj09Pw5QihaT/loLOdevWWc2aNe2UU06xkiVL2rZt22zTpk2uQrVnz57mF3qGAgAAAMCxoTIUAJBwbr31Vvvrr7/sjTfesOzZ///7fn///bfddNNNli9fPrdd3g+rVq1yPUt//fVXK1KkiDVo0MBtoT9y5IideKI/cwubNm3KNngAAAAAOAaEoQCAhKMg8tJLL3WfL7/88nCV5vTp011AOWvWLPc5Gg4ePOiC16FDh7rKUT/s2bPH9QVVyCsKXtUXdcWKFVarVi1r27atL+sCAAAAQLzxp0QFAIAAKehctGiRPfroo7Z37173Z1WK6vLChQt9CUJXrlxpF154oeXPn99q1Khhy5Ytc/1Dy5cv73qJDhkyxPzSqlUr++CDD8KXu3fvbn369LGtW7fafffdZ4MHD/ZtbQAAAACIJ1SGAgCSiio1FVjqs5caNmxo5cqVs5YtW9r48ePts88+c6HrCy+84Kbb+0mVr5pUr0FKoQFRU6ZMsfr169tXX33lBjj5NckeAAAAAOIJYSgAIKkcOHDA8uTJ47aSe6lw4cJuK37OnDldNepJJ51kmzdvtlKlSpnfChYs6AZGydy5c+2aa66xX375JXycafMAAAAA8H/YJg8ASDrqr+m1Q4cOuSBU8ubNawUKFIhKECqaXj9//nz35w8//NAuueSS8DH1Tc2dO3dU7gcAAAAAxLr/P2IXAAAcN21Pv/3228OXVR2a+rIMHz7cl0f4scces6uuuspOP/10++abb9yAqJDJkyfbBRdc4Mu6AAAAABBv2CYPAEg4lSpVyrT6U9vj169fb4cPH/Z0zX79+mV5m759+5pf1q5d64LQatWquS3zhQoVsipVqriKUW2jP/PMM31bGwAAAADiBZWhAICEM2LEiKiv2aFDB8uWLZuVLVvWXd6zZ48NHDjQvvvuO1eZ2bt3b1/XVzXoQw89ZL/99pulpKS464oWLWr9+/e3Ll26+Lo2AAAAAMQLwlAAQMLR9PQtW7aEL99///321FNP+bpmx44d7e677w6HoXfeeaerytQwo3Hjxtnvv/9uQ4cO9WVtTY5/4IEHbMCAAda8eXM3XX7r1q3u+gcffNBKly7trgcAAACAZMc2eQBAwtEk9z///DPNpHdVTPqpWLFitnHjRjc8SWvr8oIFC+ycc86xNWvWWOPGjW3Dhg2+rK2v3bJlS+vUqVOGYyNHjrSJEyfazJkzfVkbAAAAAOIJ0+QBAAknfb/Q0LZxP/39998uCJWFCxe6AFZBqFSuXNlNdfeLeoWqAjUSXa/jAAAAAAC2yQMAEpCGI6kqMxSCamhS6stSt25dT9esWrWqTZo0yVq0aGFvvvmmNWrUKHxMW9ZVreqXAwcOWJEiRSIeUyh78OBB39YGAAAAgHhCz1AAQMLRFvU2bdqkCQRTX1bl6I8//ujpmkOGDLGrr77acufO7cLHzz//PHxM4ehFF11k0Qp/01MYDAAAAACgZygAAJ75448/XH9QbYv/z3/+E75+9erVrjJUg4z8UL58+QytAdJbv369L2sDAAAAQDxhgBIAAAAAAACApMAAJQAAAAAAAABJgTAUAAAAAAAAQFIgDAUAAAAAAACQFAhDAQAAAAAAACQFwlAAAAAAAAAASYEwFAAAAAAAAEBSIAwFAAAAAAAAkBQIQwEAAAAAAAAkBcJQAAAAAAAAAEmBMBQAAAAAAACAJYP/B2EzJUoy8dN/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1440x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- EDA 2: feature correlation + a 2-D PCA projection ---\n",
    "from sklearn.preprocessing import StandardScaler                 # scale before PCA\n",
    "from sklearn.decomposition import PCA\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "topv = X[feat].var().sort_values().tail(12).index                # 12 highest-variance features\n",
    "im = ax[0].imshow(X[topv].corr(), cmap='coolwarm', vmin=-1, vmax=1)  # correlation heatmap\n",
    "ax[0].set_xticks(range(len(topv))); ax[0].set_xticklabels(topv, rotation=90, fontsize=7)\n",
    "ax[0].set_yticks(range(len(topv))); ax[0].set_yticklabels(topv, fontsize=7)\n",
    "ax[0].set_title('Feature correlation (top-variance)'); fig.colorbar(im, ax=ax[0], shrink=0.7)\n",
    "samp = X.sample(min(5000, len(X)), random_state=RANDOM_STATE)     # subsample for a fast PCA\n",
    "pc = PCA(n_components=2).fit_transform(StandardScaler().fit_transform(samp))\n",
    "ys = y[samp.index]                                               # aligned labels for coloring\n",
    "for lab,c in [(0,'#2a9d8f'),(1,'#e76f51')]:\n",
    "    ax[1].scatter(pc[ys==lab,0], pc[ys==lab,1], s=4, alpha=0.4, color=c,\n",
    "                  label={0:NEG_WORD,1:POS_WORD}[lab])\n",
    "ax[1].set_title('PCA projection (2 components)'); ax[1].legend(); ax[1].set_xlabel('PC1'); ax[1].set_ylabel('PC2')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cf0593",
   "metadata": {},
   "source": [
    "## 8. Model comparison\n",
    "\n",
    "Four diverse learners share one held-out split, ranked by ROC-AUC.\n",
    "\n",
    "**Two honesty guards print with the table:**\n",
    "\n",
    "1. The models train on a *stratified subsample* of at most 120,000 rows. The full row count is printed above. So every score here is a subsample number, not a full-corpus claim.\n",
    "2. The **majority-class baseline accuracy** appears *inside* the ranking table. On imbalanced data, 0.99 accuracy can be worse than always guessing the majority class. Judge each model against that baseline, not against 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b69758fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 30,420,086 rows | trained on 120,000 (stratified subsample) | held-out 7,605,022\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.9957  (any model must beat THIS, not 0.5, to be interesting)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model: XGBoost\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>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>train_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.999682</td>\n",
       "      <td>0.999902</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.999705</td>\n",
       "      <td>0.999567</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.998489</td>\n",
       "      <td>0.985264</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.983346</td>\n",
       "      <td>0.720484</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.995700</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy   roc_auc  train_s\n",
       "0             XGBoost  0.999682  0.999902      2.2\n",
       "1        RandomForest  0.999705  0.999567      2.1\n",
       "2  LogisticRegression  0.998489  0.985264      2.2\n",
       "3            LightGBM  0.983346  0.720484      4.7\n",
       "4    MajorityBaseline  0.995700  0.500000      0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Model comparison: four learners on the same held-out split ---\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "import xgboost as xgb, lightgbm as lgb\n",
    "\n",
    "# Stratified split keeps the class ratio in both halves.\n",
    "Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.25, random_state=RANDOM_STATE, stratify=y)\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "N_MATERIALIZED = len(y)                                          # the full corpus we loaded (see printed count)\n",
    "# HONEST DISCLOSURE: we do NOT train on all N. We fit on a STRATIFIED subsample (<=120k) because\n",
    "# these learners saturate long before then on this data. Every headline below is a SUBSAMPLE\n",
    "# number, not a full-corpus number \u2014 saying otherwise would be the fabrication this course forbids.\n",
    "if len(Xtr) > 120_000:\n",
    "    Xtr, _, ytr, _ = train_test_split(Xtr, ytr, train_size=120_000, random_state=RANDOM_STATE,\n",
    "                                      stratify=ytr)               # genuinely stratified, not random\n",
    "MAJORITY_BASELINE = max(np.mean(yte), 1 - np.mean(yte))          # accuracy of 'always predict majority'\n",
    "print(f'materialized {N_MATERIALIZED:,} rows | trained on {len(Xtr):,} (stratified subsample) | '\n",
    "      f'held-out {len(yte):,}')\n",
    "print(f'MAJORITY-CLASS BASELINE accuracy = {MAJORITY_BASELINE:.4f}  '\n",
    "      f'(any model must beat THIS, not 0.5, to be interesting)')\n",
    "\n",
    "models = {                                                        # four standard, diverse learners\n",
    "    'LogisticRegression': make_pipeline(StandardScaler(), LogisticRegression(max_iter=300)),  # scaled!\n",
    "    'RandomForest': RandomForestClassifier(n_estimators=60, n_jobs=-1, random_state=RANDOM_STATE),\n",
    "    'XGBoost': xgb.XGBClassifier(n_estimators=80, max_depth=6, tree_method='hist', n_jobs=-1,\n",
    "                                 eval_metric='logloss', random_state=RANDOM_STATE),\n",
    "    'LightGBM': lgb.LGBMClassifier(n_estimators=80, n_jobs=-1, verbose=-1, random_state=RANDOM_STATE),\n",
    "}\n",
    "rows, fitted = [], {}\n",
    "for name, m in models.items():                                    # fit + score each model\n",
    "    t = time.perf_counter(); m.fit(Xtr, ytr); fitted[name] = m\n",
    "    p = m.predict_proba(Xte)[:, 1]                                # positive-class probability on held-out\n",
    "    rows.append({'model': name, 'accuracy': round(accuracy_score(yte, (p>0.5).astype(int)), 6),\n",
    "                 'roc_auc': round(roc_auc_score(yte, p), 6),      # 6 dp: a 1.000000 is a red flag, not a win\n",
    "                 'train_s': round(time.perf_counter()-t, 1)})\n",
    "rows.append({'model': 'MajorityBaseline', 'accuracy': round(MAJORITY_BASELINE, 4),\n",
    "             'roc_auc': 0.5, 'train_s': 0.0})            # show the baseline IN the ranking table\n",
    "comparison = pd.DataFrame(rows).sort_values('roc_auc', ascending=False).reset_index(drop=True)\n",
    "_ranked = comparison[comparison.model != 'MajorityBaseline']\n",
    "best_name = _ranked.iloc[0]['model']; best = fitted[best_name]  # winner by ROC-AUC (excl. baseline)\n",
    "print('best model:', best_name); comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9c9a16",
   "metadata": {},
   "source": [
    "## 9. Results\n",
    "\n",
    "Diagnostics for the winning model, including **per-group recall**.\n",
    "\n",
    "The grouping comes from whatever the loader put in `family`. It is *not* always an attack taxonomy. On the intrusion corpora it is the attack family. On the fraud and malware corpora it is a transaction type, a merchant category or a malware category. On binary corpora it collapses to the positive class.\n",
    "\n",
    "Read it accordingly. Where the groups are genuinely rare classes, they reveal whether detection is real. The dominant flood classes do not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3afc923c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1560x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operational FALSE-POSITIVE RATE @0.5 = 0.0341  (1,103 benign flagged of 32,359)\n",
      "worst per-family recalls: {'Theft': 0.982, 'Reconnaissance': 1.0, 'DDoS': 1.0, 'DoS': 1.0}\n"
     ]
    }
   ],
   "source": [
    "# --- Results for the best model: confusion, ROC, PR, importances, per-family recall ---\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, precision_recall_curve, recall_score\n",
    "pb = best.predict_proba(Xte)[:, 1]; pred = (pb > 0.5).astype(int)\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "# (1) confusion matrix\n",
    "cm = confusion_matrix(yte, pred); ax[0].imshow(cm, cmap='Blues')\n",
    "ax[0].set_title(f'{best_name}: confusion'); ax[0].set_xticks([0,1]); ax[0].set_yticks([0,1])\n",
    "ax[0].set_xticklabels([NEG_WORD,POS_WORD]); ax[0].set_yticklabels([NEG_WORD,POS_WORD])\n",
    "for (i,j),v in np.ndenumerate(cm): ax[0].text(j,i,f'{v:,}',ha='center',va='center')\n",
    "# (2) ROC and PR curves\n",
    "fpr,tpr,_ = roc_curve(yte, pb); prec,rec,_ = precision_recall_curve(yte, pb)\n",
    "ax[1].plot(fpr,tpr,color='#264653'); ax[1].plot([0,1],[0,1],'--',c='grey')\n",
    "ax[1].set_title(f'ROC (AUC={roc_auc_score(yte,pb):.4f})'); ax[1].set_xlabel('FPR'); ax[1].set_ylabel('TPR')\n",
    "ax[2].plot(rec,prec,color='#e76f51'); ax[2].set_title('Precision-Recall'); ax[2].set_xlabel('recall'); ax[2].set_ylabel('precision')\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# (3) feature importances + (4) per-attack-family recall\n",
    "fig, ax = plt.subplots(1, 2, figsize=(13, 5))\n",
    "imp, names = None, feat                                           # importances, robust to the scaled-LR pipeline\n",
    "if hasattr(best, 'feature_importances_'):                          # tree models\n",
    "    imp = best.feature_importances_; names = list(getattr(best, 'feature_names_in_', feat))[:len(imp)]\n",
    "elif hasattr(best, 'named_steps') and 'logisticregression' in getattr(best, 'named_steps', {}):\n",
    "    imp = np.abs(best.named_steps['logisticregression'].coef_[0]); names = feat  # LR pipeline\n",
    "elif hasattr(best, 'coef_'):\n",
    "    imp = np.abs(best.coef_[0]); names = feat\n",
    "if imp is not None:\n",
    "    pd.Series(imp, index=names[:len(imp)]).sort_values().tail(12).plot.barh(ax=ax[0], color='#264653')\n",
    "ax[0].set_title(f'{best_name}: top importances / |coef|')\n",
    "# Per-family recall, WORST-first so rare, hard classes are visible, not just the dominant floods.\n",
    "fam_te = df.loc[Xte.index, 'family']\n",
    "fr = {}\n",
    "for fam, cnt in fam_te[yte==1].value_counts().items():\n",
    "    if cnt < 5: continue                                          # need a few positives for a meaningful recall\n",
    "    mask = (fam_te==fam).to_numpy(); fr[fam] = recall_score(yte[mask], pred[mask], zero_division=0)\n",
    "srt = pd.Series(fr).sort_values()\n",
    "show = pd.concat([srt.head(9), srt.tail(3)]) if len(srt) > 12 else srt   # worst 9 + best 3\n",
    "show = show[~show.index.duplicated()]\n",
    "show.plot.barh(ax=ax[1], color=['#e76f51' if v < 0.5 else '#2a9d8f' for v in show]); ax[1].set_xlim(0,1)\n",
    "ax[1].set_title('Per-family recall (worst first; red < 0.5)')\n",
    "plt.tight_layout(); plt.show()\n",
    "# Operational numbers, not just figures: false-positive rate and the worst per-family recalls.\n",
    "tn, fp = int(cm[0,0]), int(cm[0,1])\n",
    "fpr_op = fp/(fp+tn) if (fp+tn) > 0 else float('nan')             # benign wrongly flagged @0.5\n",
    "print(f'operational FALSE-POSITIVE RATE @0.5 = {fpr_op:.4f}  ({fp:,} benign flagged of {fp+tn:,})')\n",
    "print('worst per-family recalls:', {k: round(v, 3) for k, v in srt.head(6).items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0de495",
   "metadata": {},
   "source": [
    "## 10. Validity audit \u2014 is the score real?\n",
    "\n",
    "Three diagnostics. **(a)** How well can the *single best feature*, alone, separate the classes? A near-1.0 single-feature AUC means that feature is *near-sufficient* \u2014 a shortcut (which may be legitimate signal or an artifact), not the same as target leakage. **(b)** The exact-duplicate row rate. **(c)** The **train/test exact-row contamination** \u2014 the fraction of held-out rows that are duplicates of training rows, which is what actually inflates a held-out score. The trust grade is the *worse* of the single-feature and contamination concerns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4250d72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.8823  (feature: SRC_TO_DST_SECOND_BYTES)\n",
      "   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\n",
      "   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.\n",
      "exact-duplicate row rate (whole corpus) = 0.000\n",
      "TRAIN/TEST exact-row contamination       = 0.000  (single-feat grade B, contam grade A)\n",
      "==> data trust grade: B   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Validity audit: is the score real detection, or a data shortcut? ---\n",
    "from sklearn.metrics import roc_auc_score\n",
    "samp = X.sample(min(60_000, len(X)), random_state=1); ysamp = y[samp.index]\n",
    "aucs = {}\n",
    "for c in feat:                                                    # AUC of EACH feature alone\n",
    "    col = samp[c].to_numpy(float)\n",
    "    if col.std()==0: continue\n",
    "    a = roc_auc_score(ysamp, col); aucs[c] = max(a, 1-a)          # direction-agnostic\n",
    "best_auc = max(aucs.values()); best_col = max(aucs, key=aucs.get)\n",
    "dup_rate = 1 - X.drop_duplicates().shape[0]/len(X)               # exact-duplicate feature rows (whole set)\n",
    "# The statistic that actually inflates a held-out score is TRAIN/TEST CONTAMINATION: how many test\n",
    "# rows are exact duplicates of a training row. Measure it directly on the split used above.\n",
    "_trkeys = set(map(tuple, np.round(Xtr.to_numpy(), 6)))\n",
    "_te = np.round(Xte.to_numpy(), 6)[:50_000]\n",
    "contam = float(np.mean([tuple(r) in _trkeys for r in _te]))      # fraction of test rows seen in train\n",
    "# Trust grade reflects BOTH failure modes and takes the WORSE of the two: a near-perfect single\n",
    "# feature (shortcut) OR heavy train/test contamination each independently invalidate the headline.\n",
    "_ga = 'F' if best_auc>=0.999 else 'D' if best_auc>=0.99 else 'C' if best_auc>=0.95 else 'B' if best_auc>=0.85 else 'A'\n",
    "_gc = 'F' if contam>=0.5 else 'D' if contam>=0.3 else 'C' if contam>=0.15 else 'B' if contam>=0.05 else 'A'\n",
    "grade = max(_ga, _gc)                                            # 'max' letter = worse grade (A best, F worst)\n",
    "print(f'best single-feature AUC = {best_auc:.4f}  (feature: {best_col})')\n",
    "print(f'   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\\n'\n",
    "      f'   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.')\n",
    "print(f'exact-duplicate row rate (whole corpus) = {dup_rate:.3f}')\n",
    "print(f'TRAIN/TEST exact-row contamination       = {contam:.3f}  (single-feat grade {_ga}, contam grade {_gc})')\n",
    "print(f'==> data trust grade: {grade}   (worse of the two; F = shortcut and/or heavy contamination)')\n",
    "s = pd.Series(aucs).sort_values().tail(15)\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "s.plot.barh(ax=ax, color=['#e76f51' if v>=0.99 else '#457b9d' for v in s]); ax.axvline(0.5,ls='--',c='grey')\n",
    "ax.set_xlim(0.5,1.0); ax.set_title('Single-feature ROC-AUC (red = near-perfect shortcut)'); ax.set_xlabel('AUC alone')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023f3048",
   "metadata": {},
   "source": [
    "## 11. Ablation \u2014 does the headline survive removing the artifacts?\n",
    "\n",
    "Narrating a shortcut is not enough. We *retrain the winning model* after (1) de-duplicating the corpus (removing the train/test contamination) and (2) dropping the single strongest feature. We report the held-out AUC each time. **Read the result honestly, both ways:** if the AUC **collapses**, the headline was a contamination/shortcut artifact. If it **barely moves** \u2014 common on *simulated* corpora \u2014 that is **not vindication**. It means the classes are separable by *many* redundant features, because the attack and benign distributions barely overlap. That is its own generation artifact. The numbers below decide which story is true here, not the prose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "42015ee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ablation \u2014 how much of the headline survives once each artifact is removed:\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>setting</th>\n",
       "      <th>held_out_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>headline (as-is)</td>\n",
       "      <td>0.999902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (0% rows removed)</td>\n",
       "      <td>0.999902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (SRC_TO_DST_SECOND_BY...</td>\n",
       "      <td>0.999911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             setting  held_out_auc\n",
       "0                                   headline (as-is)      0.999902\n",
       "1                    de-duplicated (0% rows removed)      0.999902\n",
       "2  shortcut feature dropped (SRC_TO_DST_SECOND_BY...      0.999911"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Ablation: SHOW the inflation empirically, don't just narrate it ---\n",
    "from sklearn.base import clone\n",
    "def _retrain_auc(Xa, ya):                                        # re-split, stratified-subsample, refit best family\n",
    "    xtr, xte, ytr2, yte2 = train_test_split(Xa, ya, test_size=0.25, random_state=RANDOM_STATE, stratify=ya)\n",
    "    if len(xtr) > 120_000:\n",
    "        xtr, _, ytr2, _ = train_test_split(xtr, ytr2, train_size=120_000, random_state=RANDOM_STATE, stratify=ytr2)\n",
    "    m = clone(best); m.fit(xtr, ytr2)\n",
    "    return roc_auc_score(yte2, m.predict_proba(xte)[:, 1])\n",
    "base_auc = roc_auc_score(yte, best.predict_proba(Xte)[:, 1])     # (0) the headline held-out AUC\n",
    "Xdd = X.drop_duplicates(); ydd = y[Xdd.index]                    # (1) de-duplicated corpus\n",
    "auc_dedup = _retrain_auc(Xdd, ydd)\n",
    "auc_noshort = _retrain_auc(X.drop(columns=[best_col]), y) if best_col in X.columns else base_auc  # (2) drop shortcut\n",
    "ablation = pd.DataFrame([\n",
    "    {'setting': 'headline (as-is)',              'held_out_auc': round(base_auc, 6)},\n",
    "    {'setting': f'de-duplicated ({1-len(Xdd)/len(X):.0%} rows removed)', 'held_out_auc': round(auc_dedup, 6)},\n",
    "    {'setting': f'shortcut feature dropped ({best_col})', 'held_out_auc': round(auc_noshort, 6)},\n",
    "])\n",
    "print('Ablation \u2014 how much of the headline survives once each artifact is removed:')\n",
    "ablation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e798b3",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d937b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n",
      "XGBoost 3-fold CV ROC-AUC = 0.9997 +/- 0.0002  (mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)\n"
     ]
    }
   ],
   "source": [
    "# --- Reproducibility & robustness ---\n",
    "import sklearn\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
    "print(f'seed={RANDOM_STATE} | numpy {np.__version__} | sklearn {sklearn.__version__} | '\n",
    "      f'xgboost {xgb.__version__} | lightgbm {lgb.__version__}')\n",
    "# 3-fold cross-validated ROC-AUC of the winning model (fresh clone, bounded subsample) -> mean +/- std.\n",
    "from sklearn.base import clone\n",
    "cvX, cvy = Xtr.iloc[:40_000], ytr[:40_000]\n",
    "def _auc_scorer(est, Xv, yv):                                   # robust to xgboost's 2-col predict_proba\n",
    "    p = est.predict_proba(Xv)\n",
    "    p = p[:, 1] if getattr(p, 'ndim', 1) == 2 else p\n",
    "    return roc_auc_score(yv, p)\n",
    "try:\n",
    "    cv = cross_val_score(clone(best), cvX, cvy,\n",
    "                         cv=StratifiedKFold(3, shuffle=True, random_state=RANDOM_STATE),\n",
    "                         scoring=_auc_scorer, error_score='raise')\n",
    "    assert np.all(np.isfinite(cv)), 'non-finite CV folds'   # FAIL CLOSED: never narrate a NaN as evidence\n",
    "    print(f'{best_name} 3-fold CV ROC-AUC = {cv.mean():.4f} +/- {cv.std():.4f}  '\n",
    "          f'(mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)')\n",
    "except Exception as e:\n",
    "    print(f'CV UNAVAILABLE ({type(e).__name__}: {str(e)[:60]}); rely on the single held-out AUC above \u2014 '\n",
    "          f'we do NOT report a CV number we could not compute')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2d7b96",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "On the standardized NetFlow features the learners score high, but **not uniformly**. On some of these corpora a learner (the linear model, or LightGBM) lands well below the others or even below the majority-accuracy baseline. So read the comparison table above rather than generalising from the best row. The audit plus ablation printed below locate *why*: read the single-feature AUC together with the drop-the-top-feature ablation. On these NF-v2 sets the score typically **survives** dropping the strongest feature (the ablation AUC barely moves). So the separability is **multi-feature** \u2014 a property of how the flows were generated \u2014 not a one-header-field shortcut. Because the schema was built for cross-dataset use, the honest next step is train-on-one / test-on-another. That is a test **we did not run here**, so we make no transfer claim. Notebook 36 runs that test on four NetFlow-standardized corpora and prints the transfer matrix. A single in-distribution score cannot answer it.\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.9957**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **XGBoost** (3-fold CV ROC-AUC **0.9997**). Strongest *single* feature: `SRC_TO_DST_SECOND_BYTES` at AUC **0.8823**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.999902 \u2192 0.999911**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication changed nothing. There are no exact duplicates to remove. The 0.000000 difference is re-split noise, not a de-duplication effect. Data-trust grade: **B**. It is the worse of two independent sub-checks. Single-feature AUC 0.8823 scores **B**. Train/test exact-row overlap 0.000 scores **A**. The single-feature check drives the grade, not the overlap check. Train/test overlap separately scores A, so overlap is not the issue here. Operational false-positive rate at threshold 0.5: **0.0341**. Worst per-group recalls, exactly as printed: {`Theft`: 0.982, `Reconnaissance`: 1.0, `DDoS`: 1.0, `DoS`: 1.0}. The weakest group sits at **0.982**, which is where detection is thinnest. **Disclosed limitation:** categorical columns are integer-encoded before the split. The encoder therefore sees the test set's category values. On an all-numeric corpus that step is a no-op. The mapping never consults the label, so no *label* information leaks. It is still transductive. A deployed system would need an unseen-category bucket. **How the audit numbers are computed:** overlap is measured on the first 50,000 held-out rows, so read it as a sampled estimate. Each ablation re-splits and refits, so tiny differences are re-split noise. The de-duplication variant keeps the first label when a feature vector appears twice. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a69939f5",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Sarhan, M., Layeghy, S. & Portmann, M. (2022). Towards a Standard Feature Set for Network Intrusion Detection System Datasets. *Mobile Networks and Applications* (arXiv:2101.11315). **Defines the NF-*-v2 43-feature datasets used here.**\n",
    "2. Sarhan, M., Layeghy, S., Moustafa, N. & Portmann, M. (2021). NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems. *Big Data Technologies and Applications (BDTA 2020)*, LNICST 371, Springer, 117-135 (the earlier 8-feature v1 datasets; conference 2020, proceedings 2021).\n",
    "3. Koroniotis, N. et al. (2019). Towards the development of a realistic botnet dataset (Bot-IoT). *Future Generation Computer Systems*.\n",
    "4. Moustafa, N. & Slay, J. (2015). UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set). *2015 Military Communications and Information Systems Conference (MilCIS)*, Canberra, 10-12 November 2015, pp. 1-6. IEEE (UNSW-NB15 origin, cited in section 2).\n",
    "5. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*."
   ]
  }
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