{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "77705afb",
   "metadata": {},
   "source": [
    "# NA Inversion Example (updated physics)\n",
    "\n",
    "This notebook demonstrates kinematic inversion using the Neighbourhood Algorithm (NA) with the updated physical implementation:\n",
    "\n",
    "$$M_0 = \\mu(z) \\times A \\times slip$$\n",
    "\n",
    "**Workflow:**\n",
    "1. Load configuration and forward model\n",
    "2. Generate controlled synthetic observed waveforms from a 7-parameter ellipse model\n",
    "3. Run NA search\n",
    "4. Analyze best model and compare $M_0$/$M_w$\n",
    "5. Visualize convergence and export results\n",
    "\n",
    "**Parameters inverted:**\n",
    "- a1, a2: Ellipse semi-axes (km)\n",
    "- theta: Rotation angle (x \u03c0)\n",
    "- np, tp: Center position\n",
    "- dmax: Maximum slip (m)\n",
    "- vr: Rupture velocity (km/s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3262b304",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 All imports successful\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def find_project_root(start: Path) -> Path:\n",
    "    for p in [start, *start.parents]:\n",
    "        if (p / 'kdellipspy').exists():\n",
    "            return p\n",
    "    raise FileNotFoundError('No se encontro PROJECT_ROOT con carpeta kdellipspy.')\n",
    "\n",
    "PROJECT_ROOT = find_project_root(Path.cwd().resolve())\n",
    "KIN_ROOT = PROJECT_ROOT / 'Kinematic_inversion'\n",
    "INPUT_CTL = KIN_ROOT / 'input.ctl'\n",
    "\n",
    "if not INPUT_CTL.exists():\n",
    "    raise FileNotFoundError(f'No se encontro input.ctl en {INPUT_CTL}')\n",
    "\n",
    "if str(PROJECT_ROOT) not in sys.path:\n",
    "    sys.path.insert(0, str(PROJECT_ROOT))\n",
    "\n",
    "from kdellipspy import ConfigParser\n",
    "from kdellipspy import AxitraForwardModel\n",
    "from kdellipspy import NAInversionModel, NAConfig\n"
,
    "\n",
    "# Compatibilidad con celdas existentes\n",
    "root = KIN_ROOT\n",
    "input_ctl = INPUT_CTL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda67458",
   "metadata": {},
   "source": [
    "## Step 1: Load configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5fbb8fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 Loaded configuration from [PROJECT_ROOT]/Kinematic_inversion/input.ctl\n",
      "\n",
      "Inversion parameters (from input.ctl):\n",
      "  1. Length of axis 1                 (km) [   5.000,   10.000] INVERT\n",
      "  2. Length of axis 2                 (km) [   5.000,   10.000] INVERT\n",
      "  3. Rotation angle                 (x pi) [   0.000,    2.000] INVERT\n",
      "  4. Position of the center np      [   0.000,    1.000] INVERT\n",
      "  5. Position of the center tp     (x 2pi) [   0.000,    1.000] INVERT\n",
      "  6. Maximum slip (Dmax)               (m) [   1.000,    3.000] INVERT\n",
      "  7. Rupture velocity (Vr)          (km/s) [   0.500,    3.500] INVERT\n",
      "\n",
      "Inversion process parameters:\n",
      "  Algorithm: NA\n",
      "  Iterations: 10\n",
      "  Initial samples: 100\n",
      "  Iteration samples: 30\n",
      "  Resample cells: 7\n"
     ]
    }
   ],
   "source": [
    "input_ctl = root / 'input.ctl'\n",
    "\n",
    "cfg = ConfigParser(str(input_ctl))\n",
    "print(f\"\u2713 Loaded configuration from {input_ctl}\")\n",
    "print(f\"\\nInversion parameters (from input.ctl):\")\n",
    "for i, param in enumerate(cfg.inversion_params.parameters, 1):\n",
    "    status = \"INVERT\" if param.flag else \"FIXED\"\n",
    "    print(f\"  {i}. {param.name:30s} [{param.min_val:8.3f}, {param.max_val:8.3f}] {status}\")\n",
    "\n",
    "print(f\"\\nInversion process parameters:\")\n",
    "print(f\"  Algorithm: {'NA' if cfg.inversion_process.algorithm_type == 0 else 'MC'}\")\n",
    "print(f\"  Iterations: {cfg.inversion_process.num_iterations}\")\n",
    "print(f\"  Initial samples: {cfg.inversion_process.ss1}\")\n",
    "print(f\"  Iteration samples: {cfg.inversion_process.ss_other}\")\n",
    "print(f\"  Resample cells: {cfg.inversion_process.cells_resample}\")\n",
    "\n",
    "# Visualize station distribution\n",
    "cfg.plot_stations(show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d6d8f6",
   "metadata": {},
   "source": [
    "## Step 2: Create controlled synthetic observed data (physical benchmark)\n",
    "\n",
    "This synthetic benchmark is designed to make NA convergence easy to inspect:\n",
    "- deterministic random seed,\n",
    "- low additive noise,\n",
    "- true model sampled from inversion bounds,\n",
    "- waveform generation with the full ellipse mapping and physical moment scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e092e8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========================\n",
      "\u2713 Generated controlled synthetic observed data\n",
      "  Shape: (10, 3, 512) (n_stations, n_components, n_samples)\n",
      "  Time: 0.000 - 127.750 s\n",
      "  Noise level (std): 0.002\n",
      "  True M0 [N.m]: 1.826097e+19\n",
      "  True Mw: 6.7743\n",
      "\n",
      "True model used for benchmark:\n",
      "  Length of axis 1                 (km) =    7.500\n",
      "  Length of axis 2                 (km) =    7.500\n",
      "  Rotation angle                 (x pi) =    0.350\n",
      "  Position of the center np =    0.450\n",
      "  Position of the center tp     (x 2pi) =    0.200\n",
      "  Maximum slip (Dmax)               (m) =    2.000\n",
      "  Rupture velocity (Vr)          (km/s) =    2.350\n"
     ]
    }
   ],
   "source": [
    "# Generate a controlled synthetic dataset for convergence diagnostics\n",
    "fm = AxitraForwardModel(str(input_ctl))\n",
    "\n",
    "# Deterministic seed for reproducibility\n",
    "rng = np.random.default_rng(20260416)\n",
    "\n",
    "# Build a true model inside configured bounds (midpoint + small controlled offsets)\n",
    "param_bounds = np.array([[p.min_val, p.max_val] for p in cfg.inversion_params.parameters], dtype=float)\n",
    "true_model = param_bounds.mean(axis=1)\n",
    "true_model[2] = np.clip(0.35, param_bounds[2, 0], param_bounds[2, 1])  # theta\n",
    "true_model[3] = np.clip(0.45, param_bounds[3, 0], param_bounds[3, 1])  # np\n",
    "true_model[4] = np.clip(0.20, param_bounds[4, 0], param_bounds[4, 1])  # tp\n",
    "true_model[6] = np.clip(2.35, param_bounds[6, 0], param_bounds[6, 1])  # vr\n",
    "\n",
    "# Build geometry with full ellipse mapping and compute scalar moment metrics\n",
    "geom_true = fm.build_geometry_with_ellipse_slip(true_model)\n",
    "m0_true, mw_true = fm.estimate_total_moment_and_mw(true_model, geometry=geom_true)\n",
    "\n",
    "# Visualize the true slip distribution\n",
    "geom_true.plot(title=\"True Slip Distribution (Synthetic Benchmark)\")\n",
    "\n",
    "ap = None\n"
,
    "try:\n",
    "    ap = fm.build_axitra(geom_true, latlon=False, freesurface=True)\n",
    "    ap = fm.green(ap, quiet=True)\n",
    "    t, sx, sy, sz = fm.conv(ap, geom_true, source_type=1, t0=float(cfg.ellipse.t0), quiet=True)\n",
    "\n",
    "    # Visualize synthetic seismograms\n",
    "    synthetic_observed = np.array([sx, sy, sz]).transpose(1, 0, 2)\n",
    "    fm.plot(synthetic_observed, time=t, show=True)\n",
    "finally:\n"
,
    "    if ap is not None:\n",
    "        try:\n",
    "            ap.clean()\n",
    "        except Exception:\n",
    "            pass\n",
    "\n",
    "# Add low-amplitude Gaussian noise\n",
    "noise_level = 0.002\n",
    "sx_noisy = sx + noise_level * rng.standard_normal(sx.shape)\n",
    "sy_noisy = sy + noise_level * rng.standard_normal(sy.shape)\n",
    "sz_noisy = sz + noise_level * rng.standard_normal(sz.shape)\n",
    "\n",
    "# Format as (n_stations, n_components, n_samples)\n",
    "observed_waveforms = np.array([sx_noisy, sy_noisy, sz_noisy])\n",
    "observed_waveforms = np.transpose(observed_waveforms, (1, 2, 0))\n",
    "observed_waveforms = np.transpose(observed_waveforms, (0, 2, 1))\n",
    "\n",
    "print(\"\u2713 Generated controlled synthetic observed data\")\n",
    "print(f\"  Shape: {observed_waveforms.shape} (n_stations, n_components, n_samples)\")\n",
    "print(f\"  Time: {t[0]:.3f} - {t[-1]:.3f} s\")\n",
    "print(f\"  Noise level (std): {noise_level}\")\n",
    "print(f\"  True M0 [N.m]: {m0_true:.6e}\")\n",
    "print(f\"  True Mw: {mw_true:.4f}\")\n",
    "print(\"\\nTrue model used for benchmark:\")\n",
    "for name, value in zip([p.name for p in cfg.inversion_params.parameters], true_model):\n",
    "    print(f\"  {name:20s} = {value:8.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b78caa3c",
   "metadata": {},
   "source": [
    "## Step 3: Initialize inversion model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb30cdd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u2713 NAInversionModel initialized\n"
     ]
    }
   ],
   "source": [
    "inversion = NAInversionModel(\n",
    "    str(input_ctl),\n",
    "    observed_waveforms=observed_waveforms,\n",
    "    time_array=t,\n",
    ")\n",
    "\n",
    "print(\"\u2713 NAInversionModel initialized\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe5b5d7",
   "metadata": {},
   "source": [
    "## Step 4: Run NA search (configured to show convergence clearly)\n",
    "\n",
    "This setup balances speed and visibility of progress:\n",
    "- enough evaluations to show improvement trends,\n",
    "- reproducible random seed,\n",
    "- `n_samples_iteration` divisible by `n_cells_resample` (neighpy-safe)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "99da0c33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting NA search with neighpy...\n",
      "Synthetic benchmark mode: reproducible seed + low-noise target\n",
      "Expected evaluations \u2248 2130\n",
      "\n",
      "[NA] Adjusting n_samples_iteration from 30 to 35 to satisfy neighpy constraint (multiple of n_cells_resample=7).\n",
      "[NA] Starting search: ni=30, ns=35, n=70, nr=7 -> expected evaluations=2480\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAI - Initial Random Search\n",
      "=========================\n",
      "[NA] iter=000 eval=00001 misfit=1.045659e+00 best=1.045659e+00\n",
      "=========================\n",
      "[NA] iter=000 eval=00002 misfit=7.904776e-01 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00003 misfit=1.593352e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00004 misfit=1.325781e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00005 misfit=1.023972e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00006 misfit=1.322290e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00007 misfit=1.305919e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00008 misfit=2.565902e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00009 misfit=1.500339e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00010 misfit=3.401912e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00011 misfit=1.893129e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00012 misfit=1.304928e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00013 misfit=2.449981e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00014 misfit=1.919367e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00015 misfit=3.060277e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00016 misfit=9.641298e-01 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00017 misfit=1.084159e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00018 misfit=1.047977e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00019 misfit=1.293263e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00020 misfit=1.490236e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00021 misfit=1.833778e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00022 misfit=1.313389e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00025 misfit=9.278703e-01 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00027 misfit=1.585637e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00028 misfit=1.723120e+00 best=7.904776e-01\n",
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      "[NA] iter=000 eval=00029 misfit=1.419156e+00 best=7.904776e-01\n",
      "=========================\n",
      "[NA] iter=000 eval=00030 misfit=2.090336e+00 best=7.904776e-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NAI - Optimisation Loop:   0%|          | 0/70 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========================\n",
      "[NA] iter=001 eval=00031 misfit=1.583282e+00 best=7.904776e-01\n",
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      "[NA] iter=001 eval=00065 misfit=1.159028e+00 best=7.904776e-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NAI - Optimisation Loop:   1%|\u258f         | 1/70 [00:13<15:52, 13.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[NA] iter=002 eval=00066 misfit=8.483206e-01 best=7.904776e-01\n",
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      "NAI - Optimisation Loop:  71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 50/70 [21:12<07:54, 23.72s/it]"
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     "output_type": "stream",
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      "NAI - Optimisation Loop:  93%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 65/70 [27:23<02:03, 24.64s/it]"
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      "NAI - Optimisation Loop:  94%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d| 66/70 [27:45<01:35, 23.89s/it]"
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      "[NA] iter=070 eval=02463 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02464 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02465 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02466 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02467 misfit=3.649308e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02468 misfit=3.649308e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02469 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02470 misfit=3.649308e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02471 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02472 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02473 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02474 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02475 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02476 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02477 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02478 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02479 misfit=3.649248e-01 best=3.649248e-01\n",
      "=========================\n",
      "[NA] iter=070 eval=02480 misfit=3.649248e-01 best=3.649248e-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NAI - Optimisation Loop: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 70/70 [29:26<00:00, 25.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u2713 NA search completed\n",
      "Models evaluated: 2480\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Configure NA search for a clearer synthetic convergence example\n",
    "na_config = NAConfig(\n",
    "    n_samples_initial=30,\n",
    "    n_samples_iteration=30,\n",
    "    n_iterations=70,\n",
    "    n_cells_resample=7,\n",
    "    n_jobs=-1,\n",
    "    random_seed=20260416,\n",
    ")\n",
    "\n",
    "print(\"Starting NA search with neighpy...\")\n",
    "print(\"Synthetic benchmark mode: reproducible seed + low-noise target\")\n",
    "print(f\"Expected evaluations \u2248 {na_config.n_samples_initial + na_config.n_samples_iteration * na_config.n_iterations}\\n\")\n",
    "\n",
    "na_result = inversion.run_na_search(na_config)\n",
    "\n",
    "print(\"\\n\u2713 NA search completed\")\n",
    "print(f\"Models evaluated: {len(na_result.all_models)}\")\n",
    "# INVERSION PROCESS PARAMETERS: (Algorithm type (NA=0 or MC=1); Number of iterations; \n",
    "# Sample size for first iteration; Sample size for other iterations; cells to resample)\n",
    "# 0 70 30 30 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d59e3c",
   "metadata": {},
   "source": [
    "## Step 5: Analyze results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ceb61665",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'na_result' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Get best model\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m best_model \u001b[38;5;241m=\u001b[39m \u001b[43mna_result\u001b[49m\u001b[38;5;241m.\u001b[39mbest_model\n\u001b[1;32m      3\u001b[0m best_model_vector \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(best_model\u001b[38;5;241m.\u001b[39mmodel, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m)\n\u001b[1;32m      4\u001b[0m m0_best, mw_best \u001b[38;5;241m=\u001b[39m inversion\u001b[38;5;241m.\u001b[39mfm\u001b[38;5;241m.\u001b[39mestimate_total_moment_and_mw(best_model_vector)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'na_result' is not defined"
     ]
    }
   ],
   "source": [
    "# Get best model\n",
    "best_model = na_result.best_model\n",
    "best_model_vector = np.asarray(best_model.model, dtype=float)\n",
    "m0_best, mw_best = inversion.fm.estimate_total_moment_and_mw(best_model_vector)\n",
    "\n",
    "print(f\"\\nBest model found:\")\n",
    "print(f\"  Misfit: {best_model.misfit:.6e}\")\n",
    "print(f\"  Iteration: {best_model.iteration}\")\n",
    "print(f\"\\nParameters:\")\n",
    "for name, value in zip(inversion.param_names, best_model.model):\n",
    "    print(f\"  {name:20s} = {value:8.3f}\")\n",
    "\n",
    "print(f\"\\nTrue model (for reference):\")\n",
    "for name, value in zip(inversion.param_names, true_model):\n",
    "    print(f\"  {name:20s} = {value:8.3f}\")\n",
    "\n",
    "print(\"\\nMoment comparison:\")\n",
    "print(f\"  True M0 [N.m]: {m0_true:.6e}\")\n",
    "print(f\"  Best M0 [N.m]: {m0_best:.6e}\")\n",
    "print(f\"  True Mw      : {mw_true:.4f}\")\n",
    "print(f\"  Best Mw      : {mw_best:.4f}\")\n",
    "print(f\"  |Mw error|   : {abs(mw_best - mw_true):.4f}\")\n",
    "\n",
    "vr_true = float(true_model[6])\n",
    "vr_best = float(best_model.model[6])\n",
    "print(f\"\\nRupture velocity error:\")\n",
    "print(f\"  true vr: {vr_true:.4f} km/s\")\n",
    "print(f\"  best vr: {vr_best:.4f} km/s\")\n",
    "print(f\"  abs error: {abs(vr_best - vr_true):.4f} km/s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11034248",
   "metadata": {},
   "source": [
    "## Step 6: Visualize convergence diagnostics\n",
    "\n",
    "Besides the standard NA plot, we inspect:\n",
    "- best-so-far misfit vs evaluation,\n",
    "- rupture-velocity exploration vs evaluation,\n",
    "- absolute $M_w$ error vs evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4eee8a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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8yp3rMVWU69GlS4syXpsI+3Y+2awnjga088lmbdvVoNbucMZr4+FiSFWlfq0o9auq1K+mQIi9EQEAQEYEhXOJEU8Wz3zxi2p+xzvGdYsT73mPTn/07ydzVAAAAEhhfXWJIjFLPeGYYrajnnBMkZil9dUlGa+dSNjXFAjKbRpymfG5o8s05DYNNQeCE35PAABgfiMonCHt//51BX//++xOPm+VcfdPfjr5AwIAAMCk2td0Vvk5bq0o9avY79WKUr/yc9za13Q247UTCfuqy/IUsx1ZdnwSadmOYrajqrK8ib0hAAAw7xEUzqCO//jG+C40xt8tGQAAANOjKRBUjtul6rI8XVCcq+qyPOW4XVMe9tWurVR1mV/NnSEd7wypuTOk6jK/atdWTvg9AQCA+Y1mJgAAAMAUqC7LU31jx7jDvvrG9mRDkpjtZB32VRT5tH1zjer2t6g5EFQVXY8BAECWCArnkkQlIRWFAAAAs14i7GsOBOXOt9TcF0t2L85komFfRZFPd2xaOdG3AAAAFhiCwrnEGdyskKAQAABg1kuGfQ2n1dXZritLF6u2ZilhHwAAmLUICucigkIAAIA5oaLIp9s2Vqutza/y8nKZJluEAwCA2YugcC6gkhAAAGDBae0Oq25/i5oCweSS5aHViJleBwAAGCuCwrmIwBAAAGBOaO0Oq67htM52tqukNJT10uPW7rC27WpINjOpb+xQfWO7tm+uUUWRL+PrAAAA40FQOIsNHD+ukx/6sHJWVs/0UAAAADBGiTCvORDURfmWjjaGVH+kY1iYN1pVYPxYSFWlfrlMQ5btqCkQUt3+Ft2xaWXG1wEAAMaDoHAWa/vXryly8KAiBw8OO049IQAAwOyXCPOqS/1anDOgmNc7LMxLVxXYFAhKjqNTXSH1D9jK9ZqS46g5EJQkNQWCcpuGXGZ8ZugyDblNI/k6y5IBAMB4EBTOEp3f/4FKb3nnsGOuosLUJ7P0GAAAYNbLFOalqwos9XvV0h1WzHYkOZLi15b4vZKk6rI81Td2yLKd5LUx21FVWR7LkgEAwLjRdm2W6KmrG3HMLJjcoLB//wvjug4AAABjV12Wp0jMUlMgqFNd/WoKBBWJWaoqy5OUPkgMRmIasGxZtiPLlizb0YBlKxiJSZJq11aqusyv5s6QjneG1NwZUnWZX7VrK4cFkCtK/aoq9ScDSAAAgHSoKJwtzJHhn+kb/o1v5OBB9T788LgfET50MPNJAAAAmBTrq0v0jd2N6gtHlVfs6HhXVPk+j9ZXl0hKXxVYt79FpmnIaxqyFf92f8B29NyJLklSRZFP2zfXqG5/i5oDQVUNWV6cqZIRAABgNFQUzhKGkeJPYY48dnLr345/6bHjjO86AAAAjNm+prPK87q1otSv4lyvVpT6led1a1/TWUnpqwINQzIcKdfrVr7XrVyvW0Z8BfII58/wqsvyFLMdWXb8laEBJAAAQDpUFM4WKULBUbuWnBcURo4dk+n3T/6YAAAAMG5NgaB8HpeqSnNV7onIE81Rc2d/srKvosinj1x/sXY8ckRNgZAuLs/T1utWqaLIp02rFumFUz0KRmLJakO3y9SmVYskKe0+hLVrK1Xf2J58LWY7yQASAAAgHSoKZ4sUS4+NVOGhNCIofOmGWh25+popGBQAAADGK1NlX2t3WHc/dFiH2/pkO44Ot/Xp7ocOq7U7rC1XrNC65cXy57jldpny57i1bnmxtlyxQlK8EcrR9j6ZhtQftWQa0tH2PtXtb0kuS771NVV67UVluvU1VTQyAQAAWaGicJYwUi0nprsxAADAnJWo7GsOBOXOt9TcF1P14F6CUvqux3dsWqm7tlyecg9CSTrY0qOOvgE5Tvz7ZtuJTx0PtvRIilcr3rFp5Yy9dwAAMDcRFM4WqfYoTHVMGn1JciZsUQgAADBtkg1HGk6rq7NdV5YuVm3N0mTYl6npSLqwr6c/qkjMktdlyrLjIWEkZqunPzo9bw4AAMxLLD2eLVLuUZg6ETTGmxTSzAQAAGBGpJqFTaTpiNs05DhSOGorErMVjtpynPhxAACA8aKicLZIuUfhxCZ6zsCADK93QvcAAADA+LR2h3Xng8/qxdYeXVRg6ejzXfrtwTO6a8s6VRT5VLu2Ur89cEYNp7pl2Y5cpqHVSwqSS5Nbu8ODy5ODySXLiWrEmO3INAx53PHA0DCkqBUPGgEAAMaLisJpEvrDH8Z+0SgVhVZfX1aXt3/962N/JgAAACbFA08f17MnuhWO2rJsR+GorWdPdOuBp48POcsZXPUx5KfOdTXe+WSznjga0M4nm7VtV4Nau8OSpMJcj7xuU1Ji6bIhr9tUYa5nmt8lAACYTwgKp8mJW9+V/oSU3/6mDgqd/v6snhnad344yTfMAAAA02XPkQ7FbFu5HlNel6lcj6mYbevxIx2S4s1MWnoiqllWrPVVpapZVqyWnojq9rcMa3SyotSvqlJ/stGJJK2pLFSx36M8b3w6n+c1Vez3aE1l4Yy9XwAAMPcRFM4WqfYPpOsxAADAnGUYin9Pm5jTGcbg7/Ff0zUzydToZH11iUIRS52hqPqj8Z+hiKX11SXT+yYBAMC8QlA4W6QKCid7M2qamQAAAEybTasWye0yFYrENGDZCkVicrtMbVq1SFL6ZiaZGp3sPtSmcNSSofiU0ZAUjlrafahtJt4qAACYJ2hmMls49ohDBhWFAAAAc9aWK1boqZc69eKZHrlMS7k5Ll2ypFBbrlghSapdW6n6xnY1BUJym4ZitqPqMn+ymUm61/Yc6ZBlOyr2e5UoXOzpj2rPkQ596PUXz9A7BgAAcx1B4SzhpFx6PLkFnymfAQAAgClRUeTTXVsuV13DaXV1tutPSxertmZpsnNxRZFP2zfXqG5/i5oDQVWd19k43WtOYglzouWx4yiZGAIAAIwTQeFskaqZyWRXFBIUAgAATKuKIp9u21ittja/ysvLZZrmiNfv2LRy1GtHe23TqkX64+kehaK2TCM+lXSbpjYOLmsGAAAYD4LC2YJmJgAAAPNOa3dYdQ2ndbazXSWloWEVhRNx84YVevpYQIdae2U5jlyGodUVBbp5w4pJGDUAAFioCApni6loZkIFIQAAwIxp7Q7rzgef1YutPbqowNLR57v024NndNeWdRMOC+PLmteNujQZAABgPAgKZwlnOpqZkBsCAABMmweePq5nT3TLVLx7cThq69kT3Xrg6eP68PUTbziSbmlya3dYdftb1BQIqpoQEQAAZGlyu2Vg/FKFeJPczAQAAADTZ8+RDsVsW7keU16XqVyPqZht6/EjHVP63NbusLbtatDOJ5v1xNGAdj7ZrG27GtTaHZ7S5wIAgLmPisLZYoqXCUeOHtWZL3xhSp8BAACAc4xEF+LEKhHDGPz93DlTUfkXv19IVaV+uUxDlu2oKRBS3f6WUSsQAQAAJILC2cMeufR4opwhZYonP7B10u8PAACA0W1atUgvnOpRKBLTgN9RKOLI7TK1abAzcaLyrykQkts0VN/YofrGdm3fXDOhsLApEJTbNOQa3O/aZRpym4aaA8HkOSxNBgAAqRAUzhYpKwonr8rQCgUznwQAAIBJs+WKFXrqpU69eKZHLtNSbo5Llywp1JYr4p2Jp6ryr7osT/WNHbJsJ3nfmO2oqixPUuaAkhARAICFi6Bwlsi/9tpJv6cxZF3L0H8fjWPb6vqf/1XB9a+Xu7R00scDAACwkMQ7E1+uuobT6ups15+WLlZtzdJk6JZN5d941K6tVH1jezIIjNmOqsv8ql1bKSl9QFm7tnJKqhwBAMDcQFA4S5i5KSZeE9y3cOjSY2XRQbn3N79R62c+o55f/lLLv/mfCh84IP/69RMaAwAAAFKvE8lU+ZfJaJV/FUU+bd9co7r9LWoOBFV1XlVguoCS/Q0BAFjYCAqRFOsISIo3Pjn18Y+r77cPq+r7OwkLAQAAxiGxxLc5ENRF+ZaONoZUf6QjWZ2XqfIvm3uPVvlXUeQbNdhLF1BOVZUjAACYG8yZHgDinBTVg6mOTZdg/R5J0sDxEzM2BgAAgLlsaHXe4oIcVZX6k9V5Unxp8keuv1gXl+fLZRi6uDxfH7n+4mTlX2t3WPfuOaZP//QF3bvnmFq7wynvvaLUP+Le6dSurVR1mV/NnSEd7wypuTOUDCiry/IUsx1ZdnweOtYqRwAAMLdRUThbTHUmmMXS42HMwQx5BsNKAACAuSxTdV5rd1h3P3Q4WRV4uK1Pdz90WNs310hS2orBiVT+pVuaPJEqRwAAMPcRFM4iAydPyg4G5bvkkviBiYZ0E7k8GSwSFAIAAIzH0CW+0sjqvHT7AUpKu1fgRPc3HG1pcqb9DQEAwPxGUDhbOI6Ovv56SdKaQwcn//7nVRQ6jiMjmypDKgoBAADGJVGd1xwIyp1vqbkvlmw6IqWvOHQkyXF0qiuk/gFbuV5TcpxkxeBUVv6l298QAADMbwSFs9lkZnRjWXrsODIm+fEAAAALTbI6r+G0ujrbdWXpYtXWLE1W56WrCuzpj6q1JyJJMg0psaK4xO8dfm8q/wAAwCQiKJwtprtyz3GyCg9nsqEKAADAXFdR5NNtG6vV1uZXeXm5TPNcL8F0VYEP7j0uaXAuZhjJ1SBDp29U/gEAgMlGUDibzWRIRzMTAACAKZWuKjAQHFBZvlfhAUv9MUv5OW75vC51BgdmetgAAGAeIyicNaY4kDu/eDBTAJj4upqcEAAAYMqMVhVY6veqozeimO1IchSJ2nJHYsmlxwAAAFOBoHA2m8pqvmzvTUUhAADAuDWc7NI9jzTK7u+WmVukD1z3MtUsK854XTAS04BlD5mKObIdR8FIbCqHCwAAFjiCwtliKgK5Ifc0RpQUZpDcAIegEAAAYDwaTnbplv96Un3hmNaUODp4LKzfvxTQ99/zmoxh4bMnumSahrymIVuSKWnAdvTcia5pGDkAAFiozMynYObMYEXhWLokAwAAYIQv/fKAesLWsGM9YUtf+uWBjNcahmQ4Uq7XrXyvW7letwxHI7eTAQAAmEQEhbNEyu7C0730eEg4aKQ7DwAAABmNVv33fBZVgZtWLZLLNNQVGlBX/4C6QgNymYY2rVo0uYMEAAAYgqXHs5Bj2zLMSc5ws6kQTISChpE837HtyR0HAADAAjFgpf7CNTLkeGt3WHX7W9QUCKp6SNfja1eX677Hm4btU+jzuHTt6vKsnj3afQEAANIhKJwths4jLUuOZaWuMhzTPePXxzo7FT11asTjRo0OHYelxwAAABNkSkr1las5OM1q7Q7rzgef06EzvbJsRy7T0G8PnNFdWy7XvqazKsx1qyzfq0jMVo7bVNSyta/pbMb9DVu7w9r6gz/oQEtP8r6/bDitHe98FWEhAABIi6XH0yz/DddnPOfk3/6dDq2tmbQtChs3bhp5MOuux5MzBgAAgIWmND8n5fGyvPjxB/ce17MnuhSKxBSzbIUiMT17oksP7j2upkBQOW6XqsvydMmSAlWX5SnH7VJzIJjxud+pf0nPnuhSOGorajkKR209e6JL36l/aVLfHwAAmH8ICmeJ/meeSf573+9+J0lyYtGJ3dQw1H7PPePbZzBRUcgehQAAAOPyZ5ctTXn8xsHje450KGrZMk1Dlu3INA1FLVt7jnSouixPMduRZcfnYpbtKGY7qirLy/jcX/+xVbYjuUzJ7TLkMiXbiR8HAABIh6XHs0Tfo4+OOOZEJxgUOo46vv4fo76WFkEhAADAhLznqgv17IkuvdjSLbdpye916ZLKIr3nqgslSQNRSzHbUWxgeGfkgail2rWV+uX+Fj3d1KmYZcvtMrWmslC1ayszPjcciy94NgY3mon/dBSJsfc0AABIj4rCWcwZGJjCm2cZFLL2GAAAYFwqinz6zI2XatOqRaoo9GnTqkX6zI2XJvcJzPW6Ul6X63WprTeso2296o9ailmO+qOWjrb1qq03nPG5l1YUyJAUsx3Zg5WIhqQ1FQWT+O4AAMB8RFA4iwW++a3pfeDQBib0MgEAAJiQ1u6w7n7osA639cl2pMNtfbr7ocNq7Y6HfT3hWMrresIx7XjkiHojMfncprxuUz63qd5I/HgmH33jJSrK9cgwBhvYGVJRrkcffeMlk/n2AADAPERQuFBl3cyEikIAAIDxqNvfoqZASFWlfi0uyFFVqV9NgZDq9rdIknoHg8LE97OJn33hmBrb+mTb0kDM1oBlayBmy7alI219GZ9bs6xY3/urDbp+zRJdXF6g69cs0ff+akPGbskAAADj2qPQ5XKppaVF5eXlw44HAgGVl5fLsqxRrsS0luqlC/kyBICGYQ6eRlAIAACGYy6YnaZAUG7TkMuMz/9cpiG3aSQ7Fxf44lPxxGwr8TPf55ZlO/FqQEmmacgZ/N3tym4uWbOsWN961/pRX2/tDg8GmUFVl+Wpdm1lckk0AABYuMZVUThaeBSJROT1eic0IEwfe2BA0TNtqV9MNjOZvvEAAIC5gblgdjJ1Ll6cn5PyusX5OVpVni/TkGKOFLUcxRzJNKRVS/InPK7W7rC27WrQzieb9cTRgHY+2axtuxqSS6IBAMDCNaaKwq9//euSJMMw9F//9V/Kzz83UbEsS4899phWr149uSPElHAc6fg7b1F4/35d8uwzMnNzRz8RAABAzAXHqnZtpeob29UcCMqdb6m5L5as3pOk/Bz3YD/ic4zB4xcvKdBDB9o0YJ3rVOw2TV1cPvGGJEOXRLtMQ5btJJdE37Fp5YTvDwAA5q4xBYV33323pPi3yN/85jflcp3r1Ob1elVdXa1vfvObkztCjF+GkC+8f78kyQ4GRwaFBt1MAADAcMwFx6aiyKftm2tU13BaXZ3turJ0sWprliaX+MZsR6YpeV2mHCc+/RqwbMVsR33hmGK2PWzTmphtq29IA5TxLh/OtCQaAAAsXGMKCo8dOyZJuvbaa7Vr1y6VlJRMyaAwOZy064aHvDaBvQwBAMDCwVxw7CqKfLptY7Xa2vwqLy+XaZ7b+acw1yOvy1TMOrcfoddlqjDXo+dOdsmU5PWYsgeXHQ/EbD13skvSueXDTYGQ3Kah+sYO1Te2a/vmmoxhYXVZnuobO2TZTrKicOiSaAAAsHCNa4/C3bt3MzGcC9LmhBkCQDPx/TVBIQAAGI654ORYVpIr21FyH8OY7ch24scdRzJMQ7kel/Jz3Mr1uGSYRnJqVre/RUfa+mQaUv+AJdOId0ROdFROp3ZtparL/GruDOl4Z0jNnSFVl/mTS6IBAMDClXVF4Z133qnPf/7zysvL05133pn23LvuumvCA8MksO3M50gplxkbiYUuVBQCAAAxF5wKfeGYBmL2sK9lB2Lx5cWbVi3SH0/3KBS1ZRqS7cT3KNy4apEk6cDpbnX0DShm28lly27T1IHT3Rmfm1wSvb9FzYGgquh6DAAABmUdFD777LOKRqOSpGeeeUbGKHvYjXYcM2ASlhSP1tUQAAAsLMwFJ9/TTZ0j1m44g8e/det6PX0soEOtvbIcRy7D0OqKAt28YYUkqb0vonDUkhRfCGLZUsyy1N4XyerZFUU+GpcAAIARsg4K//3f/12FhYWSpEcffXSqxoPJ5KSpKMwUACYm+eSEAABAzAWnQltv6lCvrTeiiiKfPlm7RjseOaLmQEhVZX5tvW5VsuqvKxQPbRNTNsOIT+8SxzMZbyMUAAAwv2W9R+G6devU0dEhSbrwwgsVCASmbFDz2XR+y551NeD55yXWr6R6DQAALEjMBSefbaeeZ9m2o9busO5+6LAOt/XJchwdbuvT3Q8dVmt3WJLkcZlymYZ8blMelymfO/6715V5ep9ohLLzyWY9cTSgnU82a9uuhuS9AQDAwpV1UFhcXJzsdNfU1CQ72/3vMHOybGYSuP9+9T788PDXCQoBAMAQzAUnn8tM/QWyyzRUt79FR9sHm5VE481Kjrafa1ayadUiuU1D0cEmKFHbkds0knsYNpzs0nu/t09vvPsxvfd7+9Qw2C1Z0mAlYUhVpX6tKPWrqtSvpkAoq0YoAABgfst66fGf//mf6+qrr1ZlZaUMw9D69evlcrlSnvvSSy9N2gAxAWlCvqHVhp3fuVed37lXS/7pn86dYND1GAAAnMNccPIV+z06naKKr9jv0cGWHnX0DchxlGxmYhjSwZYeSdJ1a8p1/++PqTsck+FIjiHl+Vy6bk25Gk526bb7nlZPOCZT0tGOPu1t7tT9t29QzbJiNQWCcptGMqh0mYbcpqHmQHA63z4AAJiFsg4Kv/3tb2vz5s06cuSIPvjBD+qv//qvVVBQMJVjw0RN5Jt+9iEHAABDMBecfIW+1FPxQp9bPf1RDVi2CnPcMg1DtuOoJxJTT398D8J9TWdVmOtRWX6OIjFbOW5TUcvWvqazeuqlgLr7o/K6TDmO5DKk7v6odjxyRN9613pVl+WpvrFDlu3IZRqyBqsSq8rypvPtAwCAWSjroFCS/vRP/1SS9Ic//EEf+tCHmBzOduNdNjx0H0WWHgMAgEHMBSdX/4A16vFlJW55Xab6Y7ZMSbYkr8tUwWC42BQIyjRNmYPzNtMwZJqmmgNBNbb1ybalAceWo/j3v7YjHWnrkyTVrq1UfWO7mgIhuU1DMdtRdZlftWsrp/5NAwCAWW1MQWHCfffdN+z3np4ePfLII7rkkku0Zs2aSRkYJi5tM5MMAaAxWFKYdUMUAACwYDAXnBxej0tu05DXbcq2HZmmoYGYLa/HpUuXFumpY53yuk1ForZyPKYGYrYuXVokSSrL8+pMd1iOE68K7LAdGYah0jyvPC4jGRCapiHHduRIcrvi87uKIp+2b65R3f4WNQeCqqLrMQAAGJR1M5Oh3va2t+kb3/iGJKm/v1/r16/X2972NtXU1OjHP/7xpA4QEzCRkI9mJgAAYBTMBSdHoiHJQNRSzHY0ELXkNg1tWrVItWsrtao8X7Yj5Xpdsh1pVXl+surPceJf6FqOo0jMluU4chxHzuB5piFZjhSzHFmD+xyuWpI/YgzM9AAAwFDjCgofe+wxXXXVVZKk//u//5PjOOrq6tLXv/51feELX5jUAWICxllRaHV2DgkKJ3lMAABgzmMuODmuXV0ur9uMB3p2PNDzuk1du7pcFUU+feT6i3Vxeb5chqGLy/P1kesvTlb9nTwbkozElC4eEMqIH39VVakqinwqy/PI73WpLM+jiiKfXrWiVJLU2h3Wtl0N2vlks544GtDOJ5u1bVeDWlM0VgEAAAvLuILC7u5ulZbGJxq//vWv9ed//ufy+/1605vepMbGxkkdICZgQs1M6GYCAABSYy6YvdbusO5/vEk/eKpZ9z/eNCyM232oTf0DseT3so6k/oGYdh9qU2t3WHc/dFiH2/pkOY4Ot/Xp7ocOJ6/vDccUsx0V+twqzvWq0OdWzHbUG46pdm2lLllSoIJcr5YW56og16tLlhQkqxHr9reoKRBSValfK0r9qir1qykQUt3+lmn+dAAAwGwzrj0Kly9frieeeEKlpaX69a9/rQceeECSdPbsWfl87G0yW0xkj8IxnwcAABYM5oLZSVTuNQeCuijf0tHGkOqPdGj75hpVFPn0i4YWRc/7XjdqS79oaFGBz5MM8xKdiRNh3h2bVqow1yO3aagnHJMGdyR0uwwV5noy7kHYFAjKbRpymfEvhl2mIbdpqDkQnN4PCAAAzDrjqij88Ic/rHe+851atmyZli5dqmuuuUZSfBnK2rVrJ3N88890VuqlqyjMFACa7FEIAABSYy6YnaGVe4sLckZU7h3vTB3MnegMqikQlCPpVFe/XjzTq1Nd/XKkZJh3QXHuuX0KbSX3J7ygOFeS1NYb1lMvBfTkS5166qWA2nrPVTJWl+XFlzrb8XmeZTuK2Y6qyvKm7sMAAABzwrgqCj/wgQ9ow4YNOnHihK6//nqZZjxvvPDCC9mXJpPpDN4msPTYSAaaBIUAAGA45oLZGW/lnqN4V+PWrn5FLVvG4F6EHpep0jyvJKkvElPUsjWY9clyJMey1ReJqeFkl26772n1hGMyJR3t6NPe5k7df/sG1SwrVu3aSv32QKsaTnbJchy5DEOrK84tTQYAAAvXuIJCSVq/fr3Wr18/7Nib3vSmCQ8IkyfW3j7qaymXJacodky7fBkAACxYzAUzqy7L0+5DbWoKBBXNiepUJKZI7FzlXkWhT82d/SOuqyj0qbc/pgHLjn/HPDgdG7Bs9fbHJEl7mzqT3z8bg6c4Tvz4ic6QesIxFea4ZRqGbMdRTzimHY8c0bfeFf+bRSxH4ZitmGXL7TIVsc7N+Vq7w4PVkEFVn7dsGQAAzG9ZB4V33nmnPv/5zysvL0933nln2nPvuuuuCQ9s3potTUIy5H+xzrOD5xEUAgAA5oLjsb66RF9/uFEt4QHlFjtq6hpQvs+r9dUlkqTLVxSnDAovX1Gs5052yTQMed2GbCe+K8yA5ej5k12SpM7ggBxJbjO+EsRxHMXs+PFw1JYpyRycd5qGIVNScyAkSXpw73EdON0jx3HkcZmybEcHTvfowb3HteWKFdq2q0FNgZDcpqH6xg7VN7Yn91UEAADzW9ZB4bPPPqtoNJr8d2TPmUj34SnS9i//kvZ1KxCYppEAAIC5gLng2O0+1KZw1JKpeJhnSgpHLe0+1KaaZcVq6gilvK6pIySve3Ar8cS6Y2OwbnDwO+cSv1cnz/bHlx4PfrFrSCr1e1VR5NPRjj7ZjpOsKLQlVZX5JUl7jnQoZtkqzPUkqxF7+qPac6QjYxMVAAAwv2UdFO7evTvlvyOz8P79WZ9r+HxywuHMJ05Qzy9+kd2JFBQCAAAxFxyPPUc6ZNmOivxe5XktFfpd6u6Pac+RDn3o9RervTf1nK+9N6zatUv1h+azCg1YyTDPNKTLlhVLkq5bXa7DZ3oVsxNrk+Ndj69dXa7r1pRrb3Nnco9CW1Khz62t162SNJgrGjoXQDpOcv0yHZEBAFjYxrRH4R133JHxHMMw9J3vfGfcAxqPX/ziF/roRz8q27b1iU98Qu95z3um9fkZjWG5sXf5ckUaG6dwMAAAAOMzG+eCs3ke6DjxCC88EFM46ig84MS/gx38ItZJtUH04PH8HLe87viy4ESe5zLjxyXp5g0r9PSxgA619g5rSHLzhhWqKPLp/ts3aMcjR9QcCKmqzK+t161SzWDIuGnVIr1wqls9kdi5exuGNq5apMJcj+ob4wFnoqKQjsgAACwcYwoK77//flVVVWndunWzpslFLBbTnXfeqd27d6uwsFCvfOUrtXnzZpWWls700M4xzOzPdY+7vwwAAMCUmm1zwdk+D7x8WbGeOX5W/VFHUdtRf9SQYxjJqsDyghy1dI+sKiwvyFFnaECVhT65XYb6B2zlek3FLEdnQwOSpIoin+7ask51+1vUHAiq6rymIzXLipONS8533Zpy3f/7Y+oOx2Q4kmNIeT6XrltTrvICn+ob25N7FMZsR9VlfjoiAwCwQIwplXrf+96nBx54QC+99JLuuOMO3XLLLTM+EXv66af18pe/XBdccIEkqba2Vr/5zW/09re/fUbHNcyQikKzoCD9qS7XVI8GAABgXGbbXHC2zwMLct3yukxZliXTiC8ddrlMFeTGp+DXrS7Xi629ilp2cgWwx2XqutXlKvB5VG906ILic3sFNneGUlb2jTWy3dd0Vv4ct9ymof6orVyPKa/HpX1NZ3XHppXavrlm1AASAADMb2ModZPuuecetbS06BOf+IR+/vOfa/ny5Xrb296m3/zmN+P+Vvmxxx7TjTfeqKVLl8owDP3kJz9J+dyVK1fK5/PpVa96lerr65OvnT59Ojk5lKRly5bp1KlT4xrLlBmyqqT0rzMsh3GN6U8CAAAwbSZ7Ljjf54GB4IAqinO1cnG+SvJytHJxviqKc9UZjFcFbrlihV6+tFBetznY4djUy5cWassVK1S7tlLVZX41d4Z0vDOk5s7QsMq+1u6wtu1q0M4nm/XE0YB2Ptmsbbsa1JqiQvF8B1t61BWKKjgQb7gXHLDVFYrqYEuPpHi14h2bVuqzN71Cd2xaSUgIAMACMuZUKicnR29/+9v10EMP6cCBA3r5y1+uD3zgA6qqqlJfX9+YBxAMBnXZZZfpG9/4RsrXH3zwQX34wx/WP/7jP+rZZ5/VVVddpRtuuEHHjx+XpJSTUmMMewJOB8M89zG78vPTn+ti6TEAAJi9JnMuON/ngdVleTIkXVCcm/zHkIZVBea4Xcr1upP/5Ljjq0sqinzavrlGt76mSq+9qEy3vqZK2zfXJEO7uv0tye7EK0r9qir1J7sTZ9LTH9WAFa8kzPW4lOsxNWDZ6umPTsXHAAAA5pAJpVKGYcgwDDmOI9u2x3WPG264QTfccMOor9911136q7/6q+TG1P/2b/+m3/zmN/rP//xPbd++XRdccMGwb45PnjypV7/61aPeLxKJKBKJJH/v6Yl/c2rb9rjfQyb2kElsxmeYM1dRmGqy7Uzh5zLb2LY9of8sY+rxN5r9+BvNfvyNZr+p+BtN1d97onPB+T4PvOHlS7SnsU3NgaA8+ZaO90W1sixPN7x8iWzbVl3DabX29OuyCwqHLS+uazit2zZWq7zAq9teWzXsnolxNgX65DEltylJjtym5DGl5kBfxvdS4HPJ5zIUiVnJrsg+l6ECn2vG/7uB/47KDp9TdvicssdnlR0+p+zxWWVnuj6nsdx/zEFhJBLRrl27dO+992rPnj1685vfrG984xv60z/9U5mTHHINDAzoD3/4gz75yU8OO/6GN7xBv//97yVJGzZs0AsvvKBTp06psLBQdXV1+vSnPz3qPbdv367PfvazI463t7crHM68VGM8YmfPDntOOtbiRVMyhmz09vaOOBYKBdXW1jYDo5l+tm2ru7tbjuNM+n+WMTn4G81+/I1mP/5Gs99U/I1S/W/8eE3XXHA+zAMNSR/7k0rta+pUsK9bV+UXaX11qYxIj9raenS2s10X5VtanDOQvMadb6mrs11tbX6dDQ5oX/NZtfWGVV7g0/qqEpXkeSVJLyuwdTI3qkWusEzTkG07snOjWlVgq62tLe21ly0y1b7EJY9pKGo58rgMRW1Hly0yZ3zex39HZYfPKTt8Ttnjs8oOn1P2+KyyM12f01jmgmMKCj/wgQ/ogQce0IoVK3T77bfrgQceUFlZ2ZgHmK2Ojg5ZlqUlS5YMO75kyRK1trZKktxut772ta/p2muvlW3b+vjHP552TNu2bdOdd96Z/L2np0fLly/X4sWLVVhYOCXvI9LdrcSfZFFpqdL9efylZSrYulWBHTumZCzpFBQUqP/88eTla3F5+bSPZSbYti3DMLR48WL+i2yW4m80+/E3mv34G81+U/E38vkmZ4+56ZwLzpd5YLmkl1UtVXt7+4i/aUlpSEcbQ4p5vecqCvtiurJ0sZycQn1q1zM62NIjy3bkMg2tqezVf7z9lVpS5NPrcwq1uzmsp88M7U5cqNeve5kcSf/yq/1DOheHtLs5rC++Ze2wa186r7Px69e9TOUzvB8h/x2VHT6n7PA5ZY/PKjt8Ttnjs8rOdH1OY5kLjiko/OY3v6kVK1Zo5cqV+t3vfqff/e53Kc/btWvXWG6b0fl7zTiOM+zYn/3Zn+nP/uzPsrpXTk6OcnJyRhw3TXPK/ihD75vpCYZpyjBnZm+dVHv6GIaxoP6POvF+F9J7nmv4G81+/I1mP/5Gs99k/40m6z4zMRec6/PA1u6w6hpO62xnu0pK+1VbszS5z2BtzVL99uAZPX+yW5bjyGUYWl1RoNqapfqvx17SMye6ldwZxnL0zIlufWdPk/7pxktVWeLXlzZflrI78b17julYoF9VpXnJAPJYIKRf/fGM7ti0Mu21swH/HZUdPqfs8Dllj88qO3xO2eOzys50fE5jufeYgsJ3vetd07pB9KJFi+RyuZLfGie0tbWN+HZ5VhvymTmWNYMDAQAAGL/pnAvOh3lgojNxcyCoi/ItHW0Mqf5Ix7CmJJIRnys6Gpwzxj/f3xxole1IbkMyTEOO7SjmxI//042XSjrXnfh8TYGgHEmnuvrVH7WU63HJkdQcCI44d+y9qgEAwHw2pqDw/vvvn6JhpOb1evWqV71KDz30kN761rcmjz/00EO66aabpnUsEzI0ubXm2EaeKRqcAACAhWk654LzYR6Y6ExcXerX4pwBxbzeZGfiOzatVN3+FrX0hFVzQdHwZib7WxSJxeeMtiTZ5+ZjiePSYLXi/hY1BYKqHlIVWJbn1ZnusBwnvmS5w45XYZYO7lGYCDATS5PrGztU39h+XoAJAAAWogl1PZ4MfX19OnLkSPL3Y8eO6bnnnlNpaalWrFihO++8U7feeqvWr1+vK6+8Ut/+9rd1/Phxve9975vBUY/VuW/ezYL8GRwHAADA7DHf54FNgaDcpiHX4LYyLtOQ2zSSlX3pXr9wUZ7aeiNDM0JJ0oWL8iSlD/sS3/Mmqj8TPxPHEwFmVak/GVAODTABAMDCNeNB4b59+3Tttdcmf09sMP3ud79b999/v7Zs2aJAIKDPfe5zamlp0Ste8QrV1dWpqqpqpoY8dkNW6LgKC3XhL36u0DPPqPXTn0l7LgAAwHw23+eB1WV5qm/skDWY9lm2o5jtqKosb8TricAu8brHNPVUU+ewxR2GIb1iaZGk9GFfZ2hAFYU5crsM9Q/YyvWailmOzobi3ZUzBZgAAGDhmvGg8JprrpGTYXnrBz7wAX3gAx+YphFNPuO8TSNzVq1S+MUXZ2g0AAAAs8N8nwfWrq1UfWO7mgNBufMtNffFkkuEh77edF734dq1lfrX3xyS1zQUsc59Pl7TUFd/5rCvuixPjzptMh1DMuIrly0nu4ASAAAsbLSemQ6pNv0eZU5sGEbq8wEAADCnVBT59JHrL9bF5fkyDeni8nx95PqLk/sADn/dGPZ6e19EA5YjQ5IrnvdpwHLU3heRFA/7YraTslpxfXWJevpjOtYR1Omufh3rCKqnP6b11SWS4gFldZlfzZ0hHe8MqbkzlAwoAQDAwkZQOB3GGvzRQAQAAGDOa+0O6+6HDutwW59sRzrc1qe7Hzqs1u5w8vUv1x3S3uazau0Ja2/zWX257pBau8PqCkUlnZtGJn4mjqcL+3YfalM4asmQZA6GjOGopd2H2iTFA8rtm2t062uq9NqLynTra6poZAIAACTNgqXHC4IxMo81zNHCQ6oJAQAA5oNMXY8f3Htcz57oSnYnjkQdPXuiSw/uPS6Py5TLNOR1GbKdeOA3YDnyuuLzykTYV7e/Rc2BoKqGdD3ec6RDMdtRjscl23FkGoYGYrb2HOnQh15/cfJ6GpcAAIDzERROg1QFhfnXXaf8171ORW+5Saf+7oPDXsu5+OJpGtl5WPIMAAAwaTI1DdlzpEMxy1ZhrkeG4jvT9PRHtedIhzatWqQ/nu6RI8llxvcZ9LgMbVy1KHn/0cK+gZgty3bUP2DJHNyjMHEcAAAgHZYeT4cUAZzp82n5jm8o97LLRrxW8PrXq+oH35+OkQEAAGCKpNtHUBrcbcbQuW1nHEeJxPDmDSu0bnmRcj2m3C5DuR5T65YX6eYNKzI+tzjXIyk+BXV0biqaOC7Flz3fu+eYPv3TF3TvnmPJ5dAAAGBho6JwOphjyGMNQ4ZhyP+qV03deAAAADDlMnU9TlQNhqJ2svLPbZrauGqRKop8umvLupRLizNZXJCjHLepAcuOZ4+GlOM2tbggR1I8JNy2qyHZbbm+sUP1je3sUwgAAAgKp0W6Jb30LQEAAJiXkvsINpxWV2e7rixdrNqapckw7uYNK/T0sYAOtfbKchy5DEOrKwqSVYPj3UdwealfUrw40TTPFSwmjif2Tqwq9ctlGrJsZ9jeiQAAYOFasEHhjh07tGPHDlmWNQ1PSxcUnrdXTKZ9Ak1TstlfBgAAYLymcx5YUeTTbRur1dbmV3l5ucwhK00mUjWYTryK0JDX7Rq2R2EiMGwKBCXH0amukPoHbOV642liYu/E1u7wYJgYTFZAUmkIAMDCsGCDwq1bt2rr1q3q6elRUVHRlD5r9A7HOjdjy/ZeLpccgkIAAIBxm855YLZSzQgbTnZpxyNH4p2Ty/zaet0q1SwrznivztCAKgpz5HYZySAwZjk6GxqQJJX6vWrtiUiKd1MezAdV4veyLBkAgAVuwQaF0yrt0uMxrj12u6VodGLjGUXfo4+OONb53e+q68c/1qrdj8j0eqfkuQAAAAtRulCurTesd33nKXWHYzIc6XBbr546FtD3/urVGcPC6rI81RsduqD43NLi5s5QsomKYUiO4yhq2YP/LnlcpgyDZckAACx0BIXTIU1Q6NjDg0LPBRekvZUTi03KkFIJPlY/8nmRiKxIRNGTp5RzIZNDAACAyZIulPvdi23q7o/Fp5FGfCOb7v6YvvabF/Xdv3p12vvWrq3Ubw+0quFk17C9DxNNVE50hmQYhkzTkOPEd7YxDEMnOkPy57jlSDrV1a/+qKVcj0uOlFyWPBEsaQYAYPYjKJwOabsenwsKyz/5CZVseVvaWxVe/3r11P1qkgY2Bhm2TgQAAMDYNAWCcpuGXIPb1LhMQ27TUHMgqAMtPXIkuYx4iOc4jmKOdLClJ6t7R2K2+qOWLNuRyzQUiZ3buqY3HFPUtlWY45ZpGLIdRz2RmHrDMS0v9etMd1iOE7+uw3ZkGIZK8ya2soQlzQAAzA3pEixMhyH7DRa/5S0yPJ60p3uWr1D5Jz6hvI0bp3pkwxhpw86x69+/Xy2f/Sz7LQIAgAWruixPkZilpkBQL57pVVMgqEjMUlVZnnwel6R4IxLLdpINSRLHpXj4du+eY/r0T1/QvXuOqbU7LEl64OnjOtDSK8mQx2VKMnSgpVcPPH1cklSY65HXZao/Gg8T+6O2vC5Thbme5K44xuCKmMTPse6Wc76h1ZMrSv2qKvUnqycBAMDsQVA4DYxs9yjMJowzpLLbb9OK7/zXxAc2Fpm6MY9R01++TV0/ekD9zzwzqfcFAACYK9ZXl6gvElNzZ0htPWE1d4bUF4lpfXWJNr1skaR4UJj4R5I2Dh5PVOjtfLJZTxwNaOeTzdq2q0Gt3WHtOdKhqG3LkCPLcWTIUdS29fiRDknSmspCLcr3ammxTyV58Z+L8r1aU1mYbISyrMSnEr9Xy0p8qijMSTZCGa901ZMAAGD2ICicDmkCQLOgIKvzZtwkB4UJjkVFIQAAWJj2NZ1VntetqlK/ygt8qir1K8/r1r6ms8r3ukfs/GJIyvfGdw5KV6EXtWxZlqNwzFY0Zisci/8+MDjvql1bqYsW58t2pFyPS7YjXbQ4X7VrK1VdlicZhi4o9uuSigJdUOyXDCPZCGW8qsvyFLMdWYOJp2U7itnOhO8LAAAmF3sUToc0IZu7tHTIabN4I8CpGtssfssAAABTqSkQlM/j0opSf/LY8c5Qco9ClyF53aZsRzINaSBm67mTXclrR6vQK86Nb2XjOEp2NZaUPF5R5NP2zTWq29+i5kBQVUMai9SurVR9Y3tyL8GY7ai6zJ9shDJeU3VfAAAwuQgKp0O2Idt5FYXV//u/6vlVnUrf+U4due51UzCwsZiaRG9Wh6MAAABTqLosT/WNHcmGI0Or7P54ukeGaSjX40o2M4naTrIPXrprg5GYfB5TMduR40huU3KbhhYX5CSfXVHk0x2bVo4YU7oQcSKm6r4AAGByERROg6zDsPOCwtxXvFy5r3i5Yh0dQ282iSPL3pQ9lqAQAAAsULVrK/XbA61qONkly3HkMgytrihQ7dpK9fRH9cfTPQpFbZlGfI9Ct2lq46pFyWvTVeg9daxTXrepSNRWjsfUQMzWpUuLshrXaCHiRE3VfQEAwOSZxZvizSPZhmHZnDfRlnPjNWVLjwkKAQDAQmYMzoeG/JR084YVWre8SLkeU26XoVyPqXXLi3TzhhWS4qHbR66/WBeX58s0DF1cnq+PXH9xcvnwqvLBPQi98T0IV5Xns8wXAABkREXhdJgPYRhBIQAAwKSq29+ilp6wai4oSi4fbu6MNyS5Y9NK3bVl3ahLdVu7w/rsz/6oAy09smxHxzuD6uiLaMc7X8UyXwAAMG4EhdMhQzfj5d/5L4X27ZPp9aY+YTaEaVO39niK7gsAADC7pWtIIqVfqvud+pf07Imu5GKTqOXo2RNd+k79S/rHN1+aPG+G1qIAAIA5iqBwOmQI2fI3blT+xo2Tcq8pQ9djAACASZWuIUkmv/5j6+C+hUo2O4nZ8eN/ddWF2rarIbl/YX1jh+ob27V9cw1VhQAAIC32KJwGZGGjo+sxAABYqGrXVqq6zK/mzpCOd4bU3Bka1pAknXDMliQZgzPNxM9IzFbd/hY1BUKqKvVrRalfVaV+NQXiS5oBAADSoaJwOmRYepzRfA7T5vN7AwAAC15rd1h1Dad1trNdJaUh1dYsTVb1TWQvwUsrCvRYb0Qx20l2RTYkrakoyLikGQAAYDQLNijcsWOHduzYIcuypv5h8yEMm6puy/PhswEAAHPKdM0DW7vD2rarQc2BoC7Kt3S0MaT6Ix3DlgCn24cwnY++8RI9f7JbPeGoHMWnVIU+jz76xku0r+lsxiXNrd3hwcrDoKppdgIAAAYt2KXHW7du1YEDB7R3796pf9hEKwrnM4JCAAAwzaZrHjh0CfDigpxJXQJcs6xYd2+5TC8rz1ehz6OXlefr7i2XqWZZccYlzYkAc+eTzXriaEA7n2zWtl0Nau0OT3hcAABgbluwFYXTyTAMFW3erNila2Z6KOM3VRWF7OAIAADmqalcAtzaHdb3nmjWgOVocUGOBixH33uiWWsqizIuaR4aYCYqDhMB5niqGwEAwPxBUDhNKr7webW1tY3v4tlQdcfSYwAAgDEZ2tVY0pi6GmeSKexLt6SZPQwBAMBoWBM7hy39ypdneggTR04IAADmqaFLgNt7I2PqapxJUyAoOY5OdYX0YmuvTnWFJMfJKuyrLstTJGapKRDUi2d61RQIKhKzJiXABAAAcxsVhXOMMaQCz1tVNX0PnqKKQoP9GwEAwDyVXALccFpdne26snTxsK7HE1Hq96q1JyJJMg0pkQ+W+L2S0jcrWV9doq8/fFjd4ZgMR3IMqcjn1vrqkgmPCwAAzG0EhZhZLD0GAADzWEWRT7dtrFZbm1/l5eUyJ+lLUsOQHMdR1LIH/13yuEwZxrlmJU2BkNymofrGDtU3tie7LT9ysE3hqC23achx4vcKR209crBNNcuKJ2V8AABgbqKcC1mZul4mBIUAAABjdaIzJMMwZJqGpPhPwzB0ojM0bP/CFaX+Ed2W9xzpkOU4KsxxqzjXo8IctyzH0eNHOmb2TQEAgBlHReFcNi9CtvnwHgAAAKZXbzimqG2rMMct0zBkO456IjH1hmMZm5UYhiRH5+aShjH4+4y8FQAAMIsQFM4BxqwIBKeq6/HU3BYAAGA+K8z1yOsy1R+1ZRqS7Uhel6nCXM+wbsuJjshDuy1vWrVIL5zqUTASS77udpnatGpRxuem2/sQAADMfQSFc4AzxnW/RZs3q3vXrikazTmO42jgpZfkra6W4XJN+fMAAAAQt6ayUE8dC8jjMhWJ2cpxm4pattZUFqp2baXqG9uTexTGbGdYt+UtV6zQUy916tCZXlm2oxyPodVLCrTlihVpn5lp78OJau0Oq67htM52tqukNDRpjV8AAED22KNwLhul0tDMz5v8Z6UIK4N79uilN71ZbXfdNfnPAwAAwKhq11bqosX5sh0p1+OS7UgXLc5PVvht31yjW19TpddeVKZbX1M1LMyrKPLpk7WrdUVViSoLfbqiqkSfrF2dMZTLtPfhRLR2h3Xng89px6NH9NRLndrx6BHd+eBzau0OT/jeAAAge1QUYtz6GxokSX27H9WSj31sfDdJUyxpB4OKHDmi3MsuG9+9AQAA5qlEGFi3v0XNgaCqzlsGXFHk0x2bVqa8trU7rLsfOpysDDzc1qe7HzqcsTIw096HE/Hg3uN69kSXDMeW5XfUH3H07IkuPbj3uD70+osnfH8AAJAdKgrnnCFVhNO5d2Gq5c/24DFz8sYRuPc+nf3v/5Yknfr7j6lpy83q379/0u4PAAAwXyTCwM/e9ArdsWnlsJCvtTuse/cc06d/+oLu3XNsWGVe3f4WHWnrk2lI/QOWTEM60taXsTKwuixPMduRNTgHPH/vw4nYc6RDMcuWP8ctr8uUP8etmGVrD52YAQCYVlQUzjlT1FQk42NHPtexLUmSYU5kf8Lh92376lclSSVve5uCv/+9JCly5Khy166dwDMAAAAWjkx7CR443a1AcECO4lUDtuJfRR843Z32vrVrK/XbA61qONkly3HkMgytrihI7n04EY4jOXIUHogpHHUUHnDkyJmxqS8AAAsVFYUYv0RF4RRVNpr5+fHH9PZOyf0BAADmo0wVg73hmAYsW7luU7kel3LdpgYsW73hmKT01YiSMTj3G/JzEly+rFi2I/VHbUVtW/1RW7YjXbaseFLuDwAAskNF4Zw2fUuPY21tcpWUyFVYeO7gYEVh5MUX1b9///iq/tJ0dHbl58sKBGQH+8Z+XwAAgAUqU8VgYa5HbtNQTzimeMmeIbfLUGGuJ201Yt3+FrX0hFVzQZFcpiHLdtTcGW9mMtp+iNkqyI0vObYtS6ZhyGVIpstUQS7/7woAANOJ/+WdC4aFadO4L+EQzbe+S5K05tDB5DHHtpP/3vPrX0/68mAjJ0eSZEcik3pfAACAuaC1OzzYaTio6vOalaR7PVExWJjjlmkYsh1HPZFYsmLwguJcSfGCQMcxkotDLijOVd3+Fh1t75PHZao/ainHbepoe1/yOVPVzCQQHFBFca68plSSM6Bqt1cDttQZHJjwvQEAQPYICjF+1rmgcNz7x6SpKEyazqYtAAAAs0CmfQbTvV6Y65HXZao/ass04rvFeF2mCnM9kuJTK0Pxqj2XK14ZaCgeGB5s6VFH34AcR8lrE8fXVBZq96E2NQWCikRt5XhMDcTsrJuZpAs+q8vytPtQm2zDkBzJdhxZ1uQ0SgEAANlbsHsU7tixQ5deeqmuuOKKmR7KmLgXLzr3y0wHaI6d+ZxJeQ67WAMAgMkzF+aB8UAtpKpSv1aU+lVV6ldTIJTcZzDd62sqC1Xs9yjPG5/q53lNFfs9WlMZ30ImEBzQkiKflpf6VZLn1fJSv5YU+dQZHFBPfzS+f6FncP9CT3z/wp7+qNZXl6g3HNOxjqBOd/frWEdQveGY1leXZHw/iWBz55PNeuJoQDufbNa2XQ3J/Q/XV5coOBDT8c6QuvoHdLwzpOBAdvcGAACTZ8EGhVu3btWBAwe0d+/emR7KmBT/xV/M9BCSnCEVhdGTJxX4znfkZBHqtX/9P4bcJPP5Z7+3c1zjAwAASGUuzAMzLfNN9/r66hKFo5a6wjFFLVtd4ZjCUSsZulWX5clQfKnxJUsKdEFxrgxJVWV5KvC55TEN9UZi6uqPqjcSk8c0VOBza/ehNoWj8eYopmHINKRw1NLuQ20Z30+m4HNf01nl57i1otSvYr9XK0r9ys9xa1/T2cn/cAEAwKgWbFA4Vxkez6iv5V522TSORMlmJpLU+//+n9r+5V8VeuqpjJd13HPP2B4TCo15aAAAAHNZdVmeYrYjy45/qWrZjmL2uaW46V7f13RWPo9LRT63PG5TRT63fB5XMnSrXVup6jK/mjtDOt4ZUnNnSNVlftWurdSyEr8cJ/FdrpP892Ulfu050iHLdlSU61VxrkdFuV5ZtqM9Rzoyvp9sgs8ct0vVZXm6oDhX1WV5ynG7JmX/QwAAkD32KJzLZnzl8cilx2MN9bKpQAQAAFhoatdWqr6xPbkHYcx2kmFepte/9v9eVFcomtxnMDRgqz9q62BLjySposiX7GLcHAiqash+gfEGJ+cCSMmRy0g0PolvS93TPyB78N6OjKz2qq4uy1N9YzxoTHRMPj/4TLwujQxGAQDA9CAonAuyDdOme89CaxL2KCQnBAAAGCFdmJfp9cQ+g+d3Pe7pjw67/x2bVo547uHWXg3YzrAp2oDt6HBrr15Wnq99zeeWAluDVYeryvMzvp9sg8/mQFDufEvNfbFkwxMAADB9CArnMOP8YHAagsLjd/yVci+/XCVvv1nOkKXHQwYx5WMAAABYCEYL8zK9XuBzx7sex2yZkmzFux4X+DJP/Q+19oz4jtpx4seXlfhTXnO6qz/jfbMOPhtOq6uzXVeWLlZtzdLk6wAAYHoQFGJMgr//vYK//736HntMOatWjTxhrDkhS48BAAAm1aVLi/TUsU553aYiUVs5HlMDMVuXLi3KeG1Xf2zU425XRB5T8rjM5NLjqGXrTE8kq3FlE3zetrFabW1+lZeXyzTZTh0AgOnG//rOJ9O49Djy4ouSMwlLj9MhRAQAABiz2rWVWlWeL9uRcr0u2Y60qjx/2DLe1u6w7t1zTJ/+6Qu6d88xtXaHJUnuUf6/A7cpVZf55RiGfB6X8nPiDVIcw1BVWepKQwAAMPdQUTgXjBaYTfeehOc925mMPQrZpBAAAGBSZVrm29od1p0PPqdDZ3qTzUV+e+CM7tpyuXJGSQpz3Ka2XrdKe5s71ROOJZc0F/rc2npdilUmKbR2h1W3v0VNgWBy/0GWFgMAMLsQFM5H05W9peh6PKnh5UwGoQAAAPNAqmnhg3uP69kTXXKceEgYiTp69kSXHtx7XB63K+V9PG6XapYV6/7bN2jHI0fUHAipqsyvrdetUs2y4ozjaO0Oa9uuhmQzk/rGDtU3tmv75hrCQgAAZhGCQoyPYchJERSOaLCSSbrlxSw9BgAAC9h4K/AyhXJ7jnQoZtkqzPXIUDxM7OmPas+RDq1anK/mQEgelyHHiX9vG7UcrVoc72xcs6xY33rX+jG/l/j7CKmq1C+XaciyHTUFQqrb35J230IAADC9CArnstFCuekqxEtVUQgAAIAJm0gFXqZQznHi4WB3/4AcJaaOhuRo5PJiRyrMzX558WiaAkG5TUMuM/40l2nIbRpqDgQndF8AADC5aGYyBxi+XEmSu7w8uwumqRDPsa2RByezohAAAGCBGhr2rSj1q6rUnwz7MskUyl1cnq+Y7ShmS5YtxWwpZjt6WXl+cnnx6y4p14WL8vW6S8p1/+0bslpenE51WZ5itiPLjs/9LNtRzHZUVZY3ofsCAIDJRUXhHODKz1P1j/9X3gsumOmhnGMY8ZklAAAAJl02FXijLU2uLsvT7kNtagoEFYnayvGYGojZyVDuVFd/ymcmjo93eXG6MdWurVR9Y3uyQjJmO6ou8w/rxAwAAGYeQeEckfvyl488OBuXHk+gotChuhAAAEBSvAKvvrEj2ZX4/Aq8dEuT11eX6Bu7G9XSM7w78frqkvi1PRF5TENet5m8/0DM1pmeyITGnGm5dLpOzAAAYHYgKJyPpiNvG6WZyWgpZevnv6DwH/+o6gd+NPo9CQoBAAAkKWMFXrp9CCUpP8etEr9XkZitHLepqGVrX9NZ1SwrVnWZX0c7+pTjNmUahmzHUcSyVVXmzziudA1WMu2NWFHko3EJAACzHEHhnHYulCu9/Xb1P//89D5+DM1Mzv7gBymPD6sipDkKAACAJGWswEu3NNmRZJrxEFCSTMOQaZrJZctbr1ulJ14KqDMUTT6v0Je5YUmmikEalgAAMPcRFM4TSz7xcTW9453T9jynv1/Bxx8f+cJYlx4PRVAIAACQlK4CL93S5N5wVGe6w3Kc+GsdtiPDMFSa501eb5037zr/91QyVQxmWi4NAABmP7oezycTCekmbQxjPH9IQSF7FAIAAGSndm2lqsv8au4M6XhnSM2doeTS5MSUyhicGyZ+Jo5/6ZcHFBwYHgwGB2x96ZcHJEkNJ7v03u/t0xvvfkzv/d4+NZzskpS5wUq6MQEAgLmBisK57PxQbgJBm1lYKLunZ2LjyULaMJCgEAAAICvpliZ3hgZUlu9VeMBSf8xSnscln9els6EBSdL+U6nnfPtP9ajhZJduu+9p9YTjjVCOdvRpb3On7r99Q8ZuyjQsAQBg7iMohCRp8d/9nc588YsTvo8dCqU/IRo97wB7FAIAAIzHaEuTy/K86uiNKGbZkmEoMmDJHTGTS4+dUTrfOXK045Ej6gnHVJjjTjY66QnHtOORI9p63Sp9/eFG9YSH7m3oSXZTTjcmAAAwNxAUzicTWHqcf9UmnZmEIZz6uw+q8NDBUV93YrHRX7OpKAQAAJio3v6YBiw7vlhjcMWGbdnq7Y/Pwxbn56i5s3/EdYvzc9QUCMmUhjdCkdQcCGn3oTb1R61hi0D6o5Z2H2pTzbLijONqONmlHY8cUVMgviR563Wrhl23+9AZfeVXB1WofvUoV5+4YY2uXb1knJ8CAAAYD/YonMOM84PBiSzdnab9DZ3zKwqHdT22pmUMAAAA89lzJ7tkSHKbkmnEfxqSnh/ca3DV4vyU161anK/qMr9sSXYiYHQc2ZKqyvx65FCbBmJ2sh7RkTQQs/XIobaMY0osaX74xTYd6+jTwy+26bb7nk7uf7j70Bn9zc4/6PCZPgUHYjp8pk9/s/MP2n1oMr7KBgAA2VqwQeGOHTt06aWX6oorrpjpoUyp8o/9vfKvuWamh5F0bPOfD/u949vfVv8Lf5Qktf7zP496XWjfvqkcFgAAWEDm+zwwatmyHClqa9jPASu+zUtslFUcMdvR1utWKd/rVld/VJ2hAXX1R5XvdWvrdavU3hseFhImfrb3hpP3GK0RytAlzYU+jwpz3MklzZL05V8dUtRy5DYkt2nIbUhRy9GXf3VoCj4hAAAwmgUbFG7dulUHDhzQ3r17Z3ookydFVaCnslLLv/mfWV1buX278q66agoGdk701Klhvwd/95ia/uIvJEk9db8a9brw4cNTOi4AALBwzJd5YGt3WPfuOaZP//QF3bvnmFq744Fdrif1FD9xvKs/KkPxakOXEf9pDB4vL/BpeUmuTEOSE39teUmuygt8ckZ00otLHE9XNZhuSbMknemJSJKMwY7KiZ+J4wAAYHqwR+Fclm7psWFIjjNi3z/3kiWKnUm9hKP4rW9R8VvfooOr10zqMGOBwKTeDwAAYKFr7Q5r264GNQVCcpuG6hs7VN/Yru2ba9QftTWY8w372R+NVxR6XKZcLkNe05CteOXAgO3I6zL1wNPHdaQ9KI/LJdMt2Y50pD2oB54+rhK/Ry3d4RFjKfF7JCltI5TqMr+OdvTJdpzka4klzZK0pDBHXf3R5Nw1/tPQksKcqf0gAQDAMAu2onDeMwf/tOftW1j9P/+d+vwhoaNn2bJJHUpo3x/GeeWQsU9k/0UAAIB5pm5/i5oCIVWV+rWi1K+qUr+aAiHV7W+R123KZRrye13K8Zjye11ymYa87vj8cNOqRfKYphwZchmGHBnymKY2rlqkPUc6FLUsGXJk2Y4MOYpalh4/0qHFBTnJmsKhPxcXxMO8dFWDW69bpUKfWz2RmHrCUfVEYir0xZc0S9Inb1gtt2ko5sSXQMec+BLkT96weto+UwAAQFA4t7lcw38fWmGY/PfhAZsrLy/1vYZcm7Nq1SQMbghCPgAAgEnVFAjKbRpyDS7RdZmG3Kah5kAwHgS6TNm2I5dhyLYdeVymNq1aJEm6ecMKrVtepFyPKbfLUK7H1LrlRbp5w4r4/oa2FI7ZGrBshWPx3wcsW+UFPvk8pjyu+HM9LkM+j6nyAp8kpW2EUrOsWPffvkGvu6RcFy7K1+suKdf9t29Idj1eU1mkVeX5yf/nxJS0qjxfayqLpu9DBQAALD2eywyPJ/NJ4wnpJhjsOY4zsiPzuAy5B1kjAABAUnVZnnYfalNTIKhI1FaOx9RAzFZVWZ5q11bqqZc6dehMryzbUY7H0OolBdpyxQpJUkWRT3dtWae6/S1qDgST11QU+VQ8uIzYdoYvWy72e7SmslBPHQvI4zIVidnKcZuKWrbWVBZKkrZet0p7mzvVE47JlGRLw6oGa5YV61vvWp/y/Tzw9HE1B0LK8bjkc8ffT3MgpAeePq4PX3/x1H6YAAAgiaBwDksXFBqGEc/Wzg/9Rg3wJiPYG2TbI6sdx+Xc2M984Qsqeec7JimABAAAmNvWV5foG7sb1dIzPJRbX10yGARenjIITKgo8umOTStH3Dc/x51MCJMzMUPK97lVu7ZS9Y3tagqElOtxKWY7umhxvmrXVkpSsmpwxyNH1BwIqarMr63XrUpWDaaz50iHYratohyXvK5445XuSHzJM0EhAADTh6BwDjPcaf58g4Ha+c1MsrvxBMO4KVpqHGtvl6e8fEruDQAAMJfsazqr/By3SvzeYdV9+5rOqmZZ8ahBYCaBvoERUznHkQK9A6oo8mn75pq0AWS6qsHW7vDg3opBVZ93rZEsXxychxrGuXJGAAAwbQgK57DzKwr969er/w9/kG/NmnOTrCxDu0kt1JuioJBqQgAAgLimQFA5bpdWlPqTx453htQcCE7ovi91pL4+cXy8AWS6Ls0VRT5tWrVIL5zqUSgS04DfUSjiyD1kX0UAADA9aGYyh51fUbj4b7eq6oc/VNFbbho9KMwmbJtgIOdMWlBIMAgAAJBKdVmeYna8M7EkWbajmO2oqmyUxnVZCkZiYzqerXRdmiVpyxUrdOnSQpmmoZjtyDQNXbq0MLmvIgAAmB5UFM5h51cUGh6P/K9cF//FTGTAWYZ2k1mtNyIoHHtwaHV3j+s6AACAhWDofoHuwXCtusyf3C9wvIpyPQoOWCmPS+mXD6eTrktzQo7LUI7bVI7bif908aUxAADTjYrCuSzNHoWJaZVj29nda2hQOAv2KDz86tco0nhkwvcBAACYjyqKfPrI9Rfr4vJ8mYahi8vz9ZHrL84qtEundm2lzPOmgqYRP55YPrzzyWY9cTSgnU82a9uuBrV2hzPeN1MFZN3+FrX0RFSzrFirygtUs6xYLT2RZMUhAACYHgSFc1jaPfvGuEfhpMo2nAQAAMC4tHaHdfdDh3W4rU+24+hwW5/ufuhwVqFdOu+56kKtW1Eiv9clr8uQ3+vSuhUles9VF2ZcPpxO7dpKVZf51dwZiu+l2BkaVgGZTcUhAACYeiw9nq+SQeEox0c7P9052ZqqcJJmJgAAAJKG7/nnMg1ZtpMM7cbTbCShosinz9x4qXY8ckRNgXiYt/W6Vaoo8k0ozMvUMbm6LE+7D7WpKRBUNCeqU5GYIrGJ77kIAADGhorCOW75//dtrfzZT0e+kNijcAYqCmeiiBEAAGAhmaoKvHSVipPVQCXVVHF9dYmCAzEd7wypq39AxztDCg7EtL66ZELvBwAAjA0VhXNc/lVXpX4hWVE4jj0KJyrbZwIAAGBcqsvyVN/YIct2khWFYwntRmtKkq5ScSINVBL7GyaurW/sUH1ju7ZvrlFFkU/7ms4qP8etUr9Hxb6oVpgeDViO9jWdVc2y4gl+WgAAIFsEhfNUspnJ+eV92QSCE80Mz38mJYYAAACTaqKh3Z0PPqdDZ3qTQeNvD5zRXVsuT1upmGigsuORI2oOhHRheV5yWXImmZZKNwWCynG7VFWaq3JPRJ5ojpo7+9mjEACAaUZQOF+NuZnJZFYUEgwCAABMpUx7/qXz4N7jeub4WUUtW4YRn7o9c/ysHtx7PG2lYmt3WF+uO5QMGNv6Ivpy3SHdteXy5HNHq1RsCgQlx9GprpD6B2zlek3JcZJB4NDnSuNf1gwAACaGoHC+Su5RmO0F505M2005mztNVddjAkgAAICkiiLfuBqXPHKoTZHYuZBQkiIxW48catO3bl2v3x44o4ZT3cmwcPWSAtWurdSDe4/r2RNdcpz48UjU0bMnuvTg3uP60OsvTlupWOr3qqU7rJjtKD7vjFcqlvi9ks5VSDYHgnLnW2ruiyWDRgAAMH0ICuerRNh3fmg3SghomJPf1yZy7Jiip05P+n0BAAAwfp3BATmSXJIM05BjO4oNHo9zBhNEZ/C75HiauOdIhwYsW6YhRWO2TMOQbTvac6RDH3r9xXpw73H9oblTA9a5L3f/0NypB/ceVzASU8Syh3zv68hyHAUjMUlDKiQbTqurs11Xli5Wbc3SrCokAQDA5CEonK/M5C6FWZ3uXrx4yG8TqygMPvaY5HLr9N//vSTpgrvvynhN78MPZzxnxH6LAAAAGLPSPK9Onu2XLcmwncH6vvjxuv0taumJqGZZcXLpcXNnfC/BYCQmy3ZkDd7HGpxnJsK+3/yxVRFr+HwtYjn6zR9bJaXexvrxox3J3yuKfLptY7Xa2vwqLy+XOQVfZAMAgPQICucpQ2Pdo3DynP7EJ4f93vavX8t4zcmtf5v5xuSEAAAAWRltr0BJum51uV5s7VXMsiXDkOE4crtNXbe6PO1egucqDodLHD/V1Z88Zujc1O1UV/+oX/iePBuatPcMAAAmjqBwvhpcYnz+pGwSW5ZkLXrq1CTdiaQQAAAgk9busLbtakh2RK5v7FB9Y7u2b65RRZFPW65Yoade6hy2l+DqJQXacsUKPfD0cbV0h2U5jhwnPqV0GfG9BAN9qYPCxHH3kArAobM2t2kqNBBLeW1sSAVia3dYdQ2ndbazXSWlIZYeAwAwAwgK5yn34sWKtbXJzPXP9FAmD0uPAQAAMopXEoZUVepPLh9uCsSXD9+xaaUqiny6a8vlKTsm90ViGrBs2c65qkDLcNQXickeZS6WOF5VmqtAiqrDqtJctfZE1N8dHvFa8WAzk0QjlBfP9OiifEtHG7r124NtwzoqAwCAqUdQOE8t+/q/q/OHP1TRW98y00OZPASFAAAAGTUFgnKbhlyDe1a7zHiH4eZAMHnOaB2Tnz/ZJUOSxzRkO068YYnj6PmTXXKbhixr5HzMPfgcrzv1noJet6natZW69/FjsodcbhpKdjVOdFQ2HFuW31F/ZHhH5UzSLbUGAADZY4fgecpzwQVa8rGPyfR6h78wStfjMZ8zEwgKAQAAMqouy1PMdmQNpnKW7ShmO6oqy8t4bSQarya0nHiTE8txZDvSQNROGwRK0tlQTC6d+38wTMU7K3eFYnrPVRdq3YoS+b0ueV2G/F6X1q0o0XuuulBSvKNyzLLlz3HL6zLlz3ErZtnac6Qj1SOHSSy13vlks544GtDOJ5u1bVeDWlNUMKa69t49x/Tpn76ge/ccy+oaAADmswVbUbhjxw7t2LFDlmVlPhmzwxiDwtOf+KQiR49q5f/+zxQNCAAAzEXzfR5Yu7ZS9Y3tyT0KY7aj6jJ/snovnRK/R1J82mUaSlYAFvs9yQrF8yWOVxTm6PCZXhnGubDQdqQlhTmqKPLpMzdeqh2PHFFTIKTqMr+2XrcqWfXnJFovJ+Z7jjOsI0q6isFMS61Hk2kvRwAAFqIFW1G4detWHThwQHv37p3pocw+o1QU5rxs1TQPZLjEvDHW3q5YR+Zvl7t/+lOFX3hhzM8ZaG7W4ddcqRD/2QAAYF6a7/PAiiKftm+u0a2vqdJrLyrTra+pyjr8Ki/0yecx5TYNGUZ8ybLPY6q80Kelxbkpr0kcf9mSAhmD4WLiH8OIH2/tDuvuhw7rcFufbMfR4bY+3f3Q4WQF36ZVi+Q2TfVHbQ1YtvqjttymqY2rFg3uX/isvvFIo3763Cl945FG3fngs8lrs1lqncrQgHFFqV9Vpf5kwAgAwEK1YIPCBWsCy4oXvf/9kziQ8YgnhY1X/YkaN101ZU/pevC/ZXV16cy//OuUPQMAAGAqJfYg/OxNr0g2MMnGmspCleR5VZTrVo7bVFGuWyV5Xq2pLNQbLq1QjtuUacSnlKYh5bhNveHSCklSd380uV9hgts01N0fVd3+Fr14plc9/QM63d2vnv4BvXimNxnK3bxhhdYtL5LPY8o1GE6uW16kmzfEOzE/c7xLvZGYghFLvZGYnjnepQeePi5p/EutxxswAgAwnxEUYqTRwsSZ3rvwvKXHVleXzmzfrlhn5+Q+JhqVJBnuBbsyHwAALFDrq0sUjlrqCscUtWx1hWMKRy2try7RzRtW6FUrilWc61Ghz63iXI9etaJYN29YIUk60RnUwHnNTgYsRyc6g3qm+axau8PqDEYViljqDEbV2h3WM81nJWmwE/M6bb32ZXrNhWXaeu3LdNeWdaoo8mn3oTOKxGzFLEeO4yhmOYrEbO0+dEZSfKl1dZlfzZ0hHe8MqbkzlNVS64ns5QgAwHxFEoLsGTOcK58XFJ547/vU//zzsrp7tPTL2yfvMbGYJMnweCbtngAAAHPBvqazyvO6VeL3KhK1leMxNRCzta/prO7YtFJ3bVmnuv0tag4EVXXeXoHHO0Mp73m8M6SzoahsR3IbkmEacmxHMUdqbOtNnldR5NNtG6vV1uZXeXm5TDM+9+wMReUo3olZhmQ6UtR2dDYUTV63fXPNqOMazUT2cgQAYL4iKFxoJlIVOMsqCvuff16SZPX0TO5zqCgEAAALVFMgKJ/HpRWl/uSx452h5HLcxJLmVKJ2/GeiB0niZ8yWYrYjQ5ItybCd5Osx+9z8rrU7rLqG0zrb2a6S0pBqa5aqosin0jyvTp7tV8x2kg1WDEkled4RYxhL67uKIp8+cv3FyQYrF5fnDWuwAgDAQsTSY4w0Wh44Sqe7Md3aN/snXsmKQoJCAACwwExkOe5Fi+LnJMK6xM8LF+Vp1eJ8maaRnE6ahmSahlYtzpd0rgPx959q1qGWXn3/qWZt29Wg1u6wrr2kXDkeU27XYIMVl6Ecj6lrLykfdu3OJ5v1xNGAdj557tp0MjVYAQBgISIoRNaMSagodCKRCVw8lu+Izwk+9fTYHhOLVxTKQ1AIAAAWlvHu9ydJKxelDhNXLsrTzRuWyzTi1YWWE/9pGtLNG5ZLincgPtLWJ9OQBmK2TEM60tanuv0tunnDCr1yebEKctzKy3GpIMetVy4/tzfieLsX1+1v0dH2+DP7o5ZMQzra3kfXYwDAgkYSghFGDQQnY+nxOMO++KXju7brwQeU9+oN2T+nP/4tsuFmj0IAADA3tXaHBwO0oKqz3LNPGv9+f5J0pL0vWYVg61xFwpH2Pr2so0CLC3IUiVoKx2z53KZyPC4d6wjpWkkHTnervS8i27LlL7Z1vCsq02XqwOnujHsjjrd78cGWHnX0DchxdG5JsxE/DgDAQkVQiOzN+B6F47zMsrM+1+7tVd/u3ZJoZgIAAOamxFLcRJOO+sYO1Te2a/vmmjHtv5dq6tVwsiu5p191mV9br1ulmmXFkqRI1NbQWVfi3weitpoCQeV53VpTUZh8fejeh+29EUWitlyGI8eRbMdRNGqrvTe+GiXd3ojVZXnafahNTYHgsAYsmZZL9/RHNWDZKsxxyzQM2Y6jnkhMPf3RbD4eAADmJYJCpDCFFYUTMkpSaGcIAjO9PvTUEyeS/84ehQAAYC4auhTXZRqybCe5FDcRto1WcZguZGzrDesd/9+T6otYkqQXz/Tq8aMd+uFfv0Y1y4rl96be1SjXa6q6LE+PvjgY5sVs5bhNRa1zYV7XYDhnO/EZX6LHSeJ4ugrJ9dUl+sbuRrX0xGQqHlAW+txaX12S9nMq8LnldZnqj9nJ67wuUwU+5oAAgIWL/xVcIPzr1yu0b9+86nqcPGxbaS/rfeihcT2OikIAADAXZVqK29od1p0PPqdDZ3pl2Y5cpqHfHjiju7ZcnjZk/MXzp5IhYUJfxNI///QF7dq6Sf1RO9npOMGQ1B+1tb66RP/228PqCceSr6UK84whPxP3yVQhua/prPJz3Crxe4eFkPuaziarHVO5dGmRnjrWKa/bHFaJeOnSojF/5gAAzBc0M1kgVnzvu7rkmT/IMMf/JzdcM5wrjxIUBh+rV+To0Ul/HBWFAABgLsrUufjBvcf17IkuhSIxxSxboUhMz57o0oN7jw8u37W0t6lTvzvcrr1NnYpELTUHgtp/qjvl80Y7PtTPnj2t3iEhoST1hmP62bOnJUnF/nNf0A79arrY78nYrKQpEFSO26XqsjxdsqRA1WV5ynG7Mu5RWLu2UqvK82U7Uq7XJduRVpXnZ9W4BQCA+YqgcIEwTFOm35/ytbK//uvs7uF2TeaQxi5NM5PT//APk/44g67HAABgDsrUuXjPkQ7FLFt5OW7lelzKy3ErZtnac6RDUcvW6e6wggOWYraj4ICl091hDVi2oqPs5pI4nutxjdgoxhk8/rOGUylf+3nDKUnS4vwc+TwuuV2GDMOQ22XI53FpcX5OxgrJTMHoaBKNW259TZVee1GZbn1N1Zj3cQQAYL4hCVmAqn70Q3V+73vq/dWvJUnlH71zhkeUHcdx5Iy236A9/m7Ko2HpMQAAmIsydS52BvcB7B+IyR7s+OtIkiM93tie8p6PN7Yn9/E7X6LyIBCMpLw2EIyoK5S6QcjZweOJZcA5bkMlOVEtd3kUiTnJZcC/+WOrnjjaoQHLkddlqCDXkwwCa9dW6rcHzqjhVHdyKfXqJQVjqgyc/JkkAABzE0HhAuRft07+det0cDAoHCGLvQjLP/YxyTTV/9xz6v3NbyZ5hKNwJMViGU+bNCw9BgAAc1S6LsGXLyvWM8fPqj96Lh4zDOmyZcXa+WRXymvO9ERUkOtWd//IuVhBbnzOdPJsKOW1J8/G9xYcsEbGce7BKsHatZX6ZcNpHWrpVl6hrZaemFZXFql2baV+f6RdbT2RZJgXsx31RyMqzh06V3MGV58451LPDCarOzQAAPMJS48xqrxNm7T4Ix9J+Vr+n1ylsttv09Ivb5/GETk689V/SfmKHQqp63//V/bAwKQ9zXBTUQgAAOafgtx4t1/TkFxmvKLQ6zJVkOuWMcoXxoZh6PLlxSlfSxwfGjwO1R91dOHi1MuAE8fbesM6fKZX/VF7MAi0dfhMr9p6w/rWYy+lXLb8rcdekhTv8tzSE1HNsmKtrypVzbJitfREknsYjibT3ocAACxEBIULWNFb36qim24acbzwxjdLkor/8i9l+nLOvTB04phoipJFc5Sl/5I63BuryIsv6uz3v5/ytYGjR9XyT5/S2e//YGIPmenOzgAAAFMsEBxQRXGuLlycr4qiwZ/FueoMDqiyMCflNZWFOfroGy5Rca5HpqHkP8W5Hn30DZdkfKbXlXrOmDj+pV8eVO95HZV7I5a+9MuDOpGmUlHK3OV5NOO9DgCA+YygcAFbuv1LWvqVL484XnDNNbqk4XkVvvENMnLOLbsw8wvOnTQYqI32rXNCyTverqIb36xVv3tUOZdknkSm42RRLRg9fXpCzxjWMGW0/RABAADmsOqyPBmSLijO1SVLCnRBca4MSVVleVpSlJvymiVFuapZVqzv/dUGXb9miS4uL9D1a5boe3+1QTXLiiUN71Y8lCnpZFc45WuJ4w2nulK+3nCqS5FRuqiEB4+Pt5nJeK8DAGA+YxM2pGR6vZIkI+fct8q5r3j5kDMGp4KZKvBc8f+IeZYskWwr/bkZOGm6Hp8b1mRWBLKtNQAAmH9q11aqvrE9uTdfzHaSXZF/+typlIFffzQ+j6tZVqxvvWt9yvsW+dzqCo/cw7DQ51aOO16f4DYkwzTk2I5ijpLHYyn2L0wcH61nXeJ47dpK/d8zJ7XnSLtsO77gZU1FYcZmJuk+BwAAFiqCQqSVe1mNJKn03e8edtwwswsKjaENQYypL2CNNDbGA8XxVgMOeT+jdlgGAACYw9J1Rfa4TLlchrymIVvxasAB2xl16fBQ/hxXyqDQn+PSGy+t0H89fkwxR9KQUPCNl1ZIkgp9HnUER64eKfR51BkaUKrvixPTtoMt3TrY2qvY4NTNtqWDrb062NKdtilJpu7QAAAsRASFSCvnwgv1sid+L1dh4fAXEjOzDHsUDgsKXa6JDSaLisLQk0/q2OY/V+TgwYk9S9KoX18DAADMcaN1Rd60apH+eLpHjiSXEZ8OeUxDG1ctynjP0cLEHJepjS8r072/PzZsemUa0saXlUmSVpXnqePYyKBwVXmejrZL7X0jXyvLi6+A+fKvDilmO8OrFW1HX/7VIV27eknaMafrDg0AwELEHoXIyF1SIuP8kC8REGZc6jtkNjjRVcFZ5naTEhJKWQWTAAAA88nNG1Zo3fIi5XpMuV2Gcj2m1i0v0s0bVkiSWrvDunfPMX36py/o3j3H1Np9bu/B3hTVhJLUE47pPx5uHPEdrO1I//FwoyRpWYlfPrch04hPGU1D8rkNLSvx66bLLxgxjTQk3XT5BZKkMz2R+LHBFS+Jn4njAAAge1QUYnyybGYSPTXB5iJDTXdw57D0GAAALCwVRT7dtWVdyuW4rd1h3fngczp0pleW7chlGvrtgTO6a8vlqijyKTrKaoyY7ajhVHfK1xLHL11apMePBhSNxZTjjqnY75bH7dalS4u0vrpEP3q6WcGBc3Mzv9fUn12+VJK0pDBHXf1RxSxHhnFuyrhklA7OAABgdFQUYpyyKw90hoRtpvfcZK3wzW8e8xMjjY1jvmYismqeAgAAMM8kluN+9qZX6I5NK5N79j2497iePdGlUCSmmGUrFInp2RNdenDvcUnS4v+fvfuOb6rq/wD+udndi04oLbL3FGQJiGwQxYWD6QABgR8iiIuhiKAiCjygjwqoKPo8II9s2bPIlr0ps4vSPZPc8/sjJDQkLR1pk7af9+uVF+Tce+4999ykOfnmDC+N3eNV8dJY5g+8nzm9VaQfsnINSMrUI9cgIylTj6xcA1pF+uHPo7eQdd/Kx1l6GX8eNf0gPeLRhyDBNPhEFqZ/pbvpQMG9IImIiMgaA4VULJbFTPLw6tnTdsc8vyxLbvcmhlZXMw0V8XnmaXh0erRQ50xavryIpSwhzlFIREREZLHn4m0YjDI8tCq4qZXw0KpgMMrYc/E2AKB1RIDdfK0jAvL9idmcvu1MPLL0siXgJ8EUDNx2Jh6bTsdCFqYVk9VKCaq7cyduOh0LALielAW10voMaqWE60lZd3tBHsWCbRfwv2M3sWDbBUz47SiDhURERPng0GMqnjxDjnVNm8Cz46OoMuJ1nN240Xo/2Wj5r0LnZvl/lZEjoalWDd59+kDh5oYz9eqXanEz9u2DR7t2D94xby9C9igkIiIishDmCJ4QsIzxNUf2AKjVCoR6a5GSpUeuUYZGqYCPmxoatQJ+7ircybSdw9DP3fR1ZPvZOOQaZCjynCLXIGP72Tjk3O12KANWP+Sa0/dcvA2jMAUHZSGgkCQYhSldCODoddPwZoUE5AqBo9dTsOLANYzvVgexKdlYfyIG0YkZiOSqx0RERAwUUjHlWe24xm+/Wf5f78xpnK3fwPJc5GnMKfL0KFTodPB95plSLuQ914a/gvpnrRc50cfFI+v4P/Du1u1eYp7goD4uFkKWIT1gZWciIiKiysC8InKmXobibq8+lUJhWRE5MsADOo0KdUO8oVRIMMoCV+9kIiLAA3WCvbD/SpLNMesEewEA7mTqIQCoFBIUCtO/OTKQlKnHQ1U8EJ+WYzPY46EqHgCAjBwDjLKA+edp493IZUaOAXsu3obeKEOjlGCU7wYLjQJ7L97GwNbVMWXVcUQnZkKlkLD7wm3svpCAWQOaWIKFDCQSEVFlw0AhFZP9ASQ2i5vIeeaTUbnWyy36uedgiIuDbtNGaCIiTIl5ypu2YSMSqlZF0MSJTiohERERkesY2Lo6DlxJxNnYNBiFgFKSUC/Ey7Iicu/GodhyOhbHbyRbbe/dOBSnb6VAez0ZuYZ70T7N3VWNAcDfQ4MbSVnQywJGWUAvAxIk+Hlo0LiqDw5E37EKFCokoHFVHwBASpbebnlTsvRQKxUwygLZQlg6PwoB5BrluwHATET4u1sCm9GJmVh/IgbDO9SwDFvOe71bTsdi7vPNGSwkIqIKy7UiN1Tx5Am8SZJr9MyLn/sllD4+MMTFAQCMaen3Nt73S3Xid98jYMQIKL28yrCERERERK6noBWR75HuDkvG3alqTD8iNwjzwd9X7kCjUiBHL0OrViDXIKNBmCnY1zrSH8dvmIYI522OtY70R45RRpivG7JzjcgyGOGmUkKnUSLXaGpn3snItVveOxm5qBXkCcDU+zHv/Ie+7mpEJ2ZApZCgvDv3tlIhQaWQcDUxAwCw4sA1DlsmIqJKh4FCKh6Rz9J198s7bFepLJ2yFFHit99aPc+7MIuwc11pf22G79MDSr1cRERERK7OvCKyPetPxCAmNRtNqvpYDT1efyLG0tvQ3DsvM/deb0PANEzYHMQzE3fTawV7Qa1UoFZVT5shzYBpLkN7cg0yvLRqy3SK5gCkJAFeWjUiAzyw41w8ohMzkGOQoVUpoDfKluOahi0boVEq8gxbli3Dlh/U27CgQOL2s3H4dMNZxKXmINhbi3d61UOXesElujdERESO4BpdvKj8KcRCHwovL4RMm2p5Limd+3JLWbvO/oa8w6XtrHQc8957drMlr1yF7LNnS1wufUwMhFzIwCsRERGRi3pQDz1Lb8O8/961++7KyffbffE2ejcOhb+7Gn9fScTuCwn4+0oi/N3VliCjwv6MOFBKQFqO3qZ5JwtTeqtIP6TnGHD1TibiU7Nx9U4m0nMMaBXpBwDQG2UYZCBTLyPbICNTb3qea5Sx4sA1HLqahKRMPVKyDEjK1OPQ1SSsOHANgClI+PzivZix9jR+jLqKGWtP4/nFexGbko3tZ+Pw+k+HcS4uHclZepyLS8frPx3G9rNxljIev5GMET8eQo8vd2HEj4dw/EZyke4FERFRcVXaHoULFy7EwoULYTQaH7wz2SpEoLDaggVQB+f5ZVTh3B6FCV9/DZ++fWzSrzw1AJpaNVHjzz9hM/Y4DyHLuL1gIXwGPAWlr58lgFg7ah9Ufn755ktcuhQ5Z84g9OOPIanVVttyr1/HpW7d4fvsswj9aAbS9+yFMOjh1blzsa6RiIiIHoztwOIrqJdcZIAHdl+4DaMsLD3/DLJARIBHgb0Nh3eogcxc+/ciM9eI+LRsXErIsKxybJAFLiVkID4tGyE+Onjq1EjKtJ2n0EOnRnKmHhKsfxcWAkjO1ONQdBI8NCr4uWushkMfik5Ck2q+yO83bqUCWHf8FnKN99qNAqZFUtYdv4Xx3epg8n+P4WpStlW+q0nZmPzfY4hNzYHeKCzlEgLQGwU+3XAWXeoF4/iNZAz+/m+kZBsgCeB8fBr+vpKIH19pgybVfHH8RjL+te0C5KwUKNx8MOqx2mhSzbdQ94jDpYmI6EEqbY/C0aNH4/Tp0zh48KCzi1I+FSJQKN33825BPQo9H++a7za3li0LX66CFNBrL/fipfz3uduyTN+xE7f/9S9ceaI/jMn3Vu2L+2SW3WNmnTqF7NOnEf/pbKT870+cbdwEFzp1hsjzpST3qulX5+T//AcAcP3VV3Fj5BtWx9HHxiJ64AtI27LlwddIRERED8R2YPHEpmRjyqrj+Gn/VURdSsRP+69iyqrjiE0xBcR6Nw5FZIA7rt7JxLU7mbh6JxORAe7o3Tj0gb0N1fk0E9UKYOG2i0jLNvUMND/SsvVYuO0iAKBusKfdvHWDPaFWKqBUADqVAhqlAjqV6blGqbCUydxkVUiwKtPN+wJ9ZjeTsnEtKdPuNnP6zguJdrfvvJCImLv1JWC6FnOr2lyPX2w6h5Qsg6m/pWTqd5mSZcAXm87h+I1kDF1yANvOxSMuNRvbzsVj6JIDlh6HsSnZGPhtlFVPxoHfRiE2JRuxKdkY/csRfP7XOaw4cA2f/3UOo385YjlvScSmZOOHPVfw4f9O4oc9VxxyTCIico5KGyikkhGFCBRazU8IAHcXM3Fr0cJmV8lOb8PgDz9AtcWLAEf92l+YMtvZxe/ll02bjAYAgJyZCWNS8r0d8hnuEv30M7gy4GmrNENcHJJ//93y3JicjAeJm/Upso4dw40xb8KYnIw7v/yCxO9/sF98gwE5Fy4AAHIuX0bWyVOQMzKQceAAUjdvttk/99o1pG3bXrj7SURERJVa3lWCq/u7I8Lf3bJKMGCav3DWgCYY9EgE2tUMwKBHIjBrQBOE+OgQGeCBHIMR0YkZOBeXdndeQKNlPsD0fHoUpuca766ibJ1uFMDxm8kAgEZVfe3mbVTVFx1qVYFKoUCuUcAgy8g1CqgUCrSvVQX+7hrcSs7CpYQM3Lz7763kLPi5a0znzrG/mnJ6jh6G+wt0V37peWlV99rIeZuRmrvpp2NSIWAKXCrvBjIFgDMxqVi47SJSsw3w0qrgrlbCS6tCarbBEjSd/N9jiE60DmJGJ2Zi8n+P4bvdl3H0WhIyc43QGwUyc404ei0J3+2+/MAyF+RBAWQiIipfGCikIgmcMAEKLy+og4IevPP9gUJzj0J7gT/JOtoWNGkS/F98EV6dOxc4f1/VL+c+uBx32VuoxHYnO/vcPb9Cq72XlHmvAaYODSvwkOow6+3GlJR7x8nIuH9329PnOZchIQFxMz5C/Gef2d03/rPPcbnfE4ifNw+Xe/dB9DPP4Oqw4bg2eAhuvjnWat+0rVtxqXsP3Bg1yhJcLI6EhQtx+cmnGGwkIiKq4B48B+G9xU6m92+E4R1qWIa1Pmg+wIICb7fTc+xuu51mSr9yO93u9iuJ6XisfhBUSgkGWcAgm4Ytq5QSHqsfhLjUbOQYhaVXnyyAHKNAXKopwOWpU9s9rqdODXet/Sl1zOn5/I4MCUCTqj6W53mv2pyuU5uPIVn9q1MrEZ2YCQUAxd22s0KSoABw9W5wcFc+PRl3XUjEplOxkAWgVkhQKSWoFRJkAfx1Kjaf0hbOgwLIRERUvjBQSEVS5fXXUPfgAUgazQP3le4L/kl3exTaDSjlSat/9gwChg+7ty2fHoWqwEC4NW9eiFKbzwGrYb/3uzFqFNLfnmSb7W5PwrzzCwr9vV+YhcH+r80WUn5NRevjFIaclVXgdvPw5PRt2y1p2ceP299327Z7x01/cMAyP7fnL0DO2bOAwVDsYxAREZHriwzwgEEWMN5dHSTvHIRm+Q1BNc8HGOHvjiAvHSL83eGhUeFQtGk6lzBf+/PkhfnqkM+ixpb0A5ftB8cOXErEmmO3kJFj3f7LyDFizbFb2HN3ARUJpt575habOb1GnuvKq0aAB/o2sv9DsTm9Y+0Au9s71g5A+9qBCPLSwl2tgEoBuKsVCPLSon3tQABAj4YhUEiAXhYwGAX0soBCAro3DEFkgDtkAPLdtrMsBGQAEQHuAPKfbVsAyNYb7/5fWP1rTi+uwgSQiYio/GCgkErPfT0KvXv1BAD4v/yS7b4F9EbLr0dhwBsjLcOZC0POzETK6tX5bs/YsdP+hrvBRWG414iyCg6WIED2wCDjfeTMggOFluMWMQBZ1P1L6xhERETkugqagxAoeAhqdGIGdGolIgM8UDfEC5EBHtCplZZg0gd9G0B532+rSsmUXlDwCwDScu23FdNyZfz5zy2b/ALAn//cQvbdSKM5yKW6G+gyL5pSzc8NmvsKpVFKqObnhnHd6qBxmDdUCtMXKpUCaBzmjXHd6gAA5jzTDJH+blZ5I/3dMOeZZujdOBQNw7wR7OOGGlU8EezjhoZh3pZ6fLXjQ2he3Q/uGiXUSgnuGiWaV/fDqx0fwujHasFbp0JajgGZeiPScgzw1qkw+rFapmvJ5/dplQQ0CPWGBFPPSaNs6kkpAagf6p1PDRfOg4aVExFR+VJpVz2mMnDfvINuzZqh3skTkFT2Xnb5Bwq9u3dDwpkzNumSWm2zYEpB5JQUxLz3fqH3t5RMbwoE5g3qiZxcm+35cmSPwkz7E2fbHLeIwUsGComIiOhBzHMQrj8Rg6uJGYi4b9XcvENQzSsbm4egFrQiMgDUD/VB7SBPXIhLA2AKYNUO8kT9UJ/8ilMoCWn2hy0npOWgY+0q2JmWA71s3Q6tH+IFAPBz11itbAyYVjb2c9cgxEeHYe0jMWvDWaRk6eHjpsaw9pGWugjx0WHqEw3x6YaziEvNQbC3Fu/0qmfZXlA9hvjosPDFFna3h/josHRYa/xr2wWIrBSEBluvejzmsVqYt/WizfWOeawWHqsfjH9uJFtWUxYS4OOmwls96ha/gmEaVr5g+wWkZhugACAD8NapLMPKiYiofGGgkBzOo317ZOzdC3Ww7TyG9oOEBQsYORIJX31teyy12ioIp6lZE7mXLhX5+A8ijEYIgwHZJ05a0mKnT7faXvABrBuYeQOLDg8U3j1XfscVQtgMCTftn2tn76JhoJCIiKjiM89BaE9BQ1Df6FwLuy8kIDoxEyqFac7AvL0Rfzt4DVfvZEGrUkCnEtCqJFy9k4XfDl4rUXnzm6FahvWiInmZ07eesT9339YzsWhXKwCTVp6A4W6QMSE9F5NWnoCfhwZd6gXj+I1kjFtxFKnZpnZicpYe41Ycxc+vPoIm1XwLrEeg4HpuUs0X/3q5JeLj4xEUFARFnlE847uZgn4Lt1+EXjatGj26Sy1L+o+vtMHCbRdxNTETEQHuGP1YLUuQsbjMw8r93DXI0cvQqhXINcg4FJ1U4mMTEVHZY6CQHC580b9gSEiAKjCw0HkU7u75brMX2ALuBgrzNIz8Bw1C7LRphT5noRkNSPh6PhK//fZe0p07lv8LvR7Xx4wBjDK8e/eCT79+yDp16t72+wKJtxcuROCbY0xPitjzT868N9dLfkE/c5nsX4sRsBOsLWoPxCKdk4iIiCqFgnoNPqg34p6Lt2EwyvBxU0GjNMJdq0RKlgF7Lt629FK7X0nnUPrrTHyB6ZcT7U/5cjkxCx+vPW0JEpoZZIGP155Gl3rB+GTdaUuQ0Cw124hP1p3GihHt8PGaU/hhbzTku9cxvH0k3u/X0LLv8RvJWLjtIqITTcO7ixLQG9g6At5uGkQnZiDybj2bNanmi28GtyrUcQorOjEDSqXCtMCKZFpgRalUcI5CIqJyioFCcjhJo4G6atUi5QmaOBFCCARNnFj4TLJs1aNQW6d2kc5ZWKnrNxS4XX/tGjIPHQIApG/fDk14OKIHvmDZbi8IZ0hIgNLH515w7b6An5BlSOYgaJ5NVj0K9Xogn0Vl8u1RqNff69WZ95wcekxEREQl1LtxaIG9BgvqJScETG0e80gMcXcCPXG3yWJnlpoCZncpdZdu2x/lYU4/fDXJ7vbDV5Pw8ZpT+G5vtCVNBizP3+/XEMdvJOOJBXst28/FpWHT6Tj8OaY9mlTzxcdrTmHp3iuo6ydwLknC0PY1LEHG2JRsTPjtGM7GpVkCtltOx2Hu880Q4qNDbEr23SHi94KI5mDtg+SXN8BDg7iUbAhhOt9t2fRjtr+H5oF5C2P72TibIdxd6gWX+LhERGQfA4XkElSBgag6Z07+24ODYYiLQ/UlPyB9x07cWbYM6tBQq33cW7RA5Mr/4s733yN1/Qa4t2kDjw7tkfDF3FItuzlIaJY3SAgAxtu3bfJc6PgoAEDhfXfyaCEQn6ecOefPQ1evHnIuXEDO+QuW9LyBQmEw2K4+fbfFnF8PQaHXA25u9tOLwZB0rxHMQCEREVHl9qBegwXpUKsKTt1KRZZeRq5RRpYeUCkUaF+rCo7fSIbRaBspNC8+UlCPw/yGHpc2fT4n1suwChLm9d3eaLzfryGeyhMkzOupBXsxrH0kvtsbbelNeX+Q8beD17DvvlWg911OxG8Hr+H5h6vjkVlbrbbNWHsa+6d0RYiPDpHvrLM5Z/SnfQCYAnL55RUCyLKzcrI55ltQ3gedd/vZOAxbeq+tnZylx7Clh7BkaCvUD/V54HEVEKjvJ3AmSYIMyXJcAAWet6Bt5TFvYY6bX12V1+sprbyu9ppyxftj3l6c15QrX09p5S3oNeUskhAFLDdbCaSmpsLHxwcpKSnw9i7Zil8FkWXZ7jwiVDgiNxdCCCi0WghZRu6lS9DWrg1jejrOt3oY6urVUeuvTZb9s8+dh7bmQ5bec5ef6I+c8+cBAHUO/A2h1+PKs8/CcCvG7vkCXnsNbi1b4MbIN0r/4oopdOZM5EZHQxUUhLiZMwuVp+bmvyBnZUEdEoK42bORsnIVAMBv0CAEvzMZcloaFJ6ekDMzofD0hDE5GZJGi8yDB+DZubPVUGchBC516w79jRsAgGoL5sPr8cetzmdISkLW0aPweuwxSx4YjZb7knf4dNY//0BdrRokrQ5KTw+r84jcXFx8/HEYE27D5/ffEfRQDShUKih0OsjZ2ci9ehWa6tWR8fffcGvaFCo/Tp7tLPxb5/p4j1xfadyjsmrvlDdsB7oOU0+4ozgXm4qaXkZcSlOibog35j7fHC/9O8puD76aVdyxdWIXNPhgAzLtRObc1Qq76YUR/Wkfu1/uXCXv/V8sS3peR5TJlfKaObqeymPewh7XXl2VVpnK82vVlV5TpXVcR+Ut6muqLMrkinnze005WlHaPOxRSOWCpNFY/rRICgW0tU3DjJWennhozZ9QBQdb7a+rW8fqeeSvvyBj/34o/f2hvPumiPx1BS516waRmwuPdm2hqloV+odqwlOlhP/LL0OSJPi+MBDJv64AAAROmABt7Vq48caoAsvq2bUr0rduLXAfR4h5770i57nUrbvd9KSffkLSTz89ML+k1d7rtWg0Wg1ZvjHmTSg8PU3zRgoBCAE5Pf1eXp3O1NPRYDAdR5ZNQ6HVapveiAoPD0CSIPR6iNxcqwVhUp57DikwzVGp8PWBMcG2x6akVkPp7w8oFZDurr4tZKPpfAolIMS93piSBCgkSHdfYXJODnB3ARvJTWfaX6HIdz5I25O7+H6lTQgYjEZkKJWuUyZHK++XJQCD0YAMparUr6XQ7xtCxC+/QOnl5exiEDlNiI8Oc59vjvXHbyH5TgJ6+geid5MwhPjo8H7fBnjtx8NWcwKqFBLe79sAANA83Bd7L9+xOWbzcF/su3zH3qhl5DOamYiIqNJjoJDKPXPQsCAKDw94de1qlaYODkK94/9Ynpt/7fcLCrJ8uQ358EMEv/MOFFqtZb8af6yCpnp1CL0eOZcuQRUYCHV4OOQM0y/dSk8PyJmZyNi3D+6tWlnOn7F/PzzatEH63r3I2L0bvgMHIuvoMWjr1IauTh0IvR7G5GTEz/sKAJBz7hz8XnwRquAgSEoljElJSF65CllHj8Lz8a5wa9wE2SdPQn/zJlShoUjfuhUe7doiY18UtHXqwLNTJ8g52YBRRvbp0zAkJkJbpzbk1DSoq1aFnJ6GtM1boK1dG3JWFjTVqyP73Dno6tWDMS0NSl8fZB37B5pq1ZB9+jR0DRpAGVjFVAlCALKA/sYNU6/G0FAYYmKga9jQFBySTAECfVw8ci9dgkf79oBSYQpQJN6G0svbFADEvZWwMw8ehLZBfcAoQ+HpCYVOB0mttjwy9u+H8c4dSFWqwK12bUgqFeS0NBg0Wuhv3oQysAqMCbchabVwa9oUcna2aR5L+W5PAkmCpNFY6gTKuz06hPl6ZEAIqMy/rkiSKZgpGwG5kF8lCttB29H75fNVxzkdxk333/w6oDJSlFstxL3XfSnfpEo+aKFoWFdECPHRYWj7SMTHu1v1vuxSLxj/Htwy33nqujYIwdm4dKRm5cIgAyoF4O2mQdcGIajipcX//rEdQfJE01C76URERJUdA4VEBZAkydT7LQ9d/fqW/7u3bGn5f97hsgp3d5thuJ4dOwIAvLp0gVeXLqZj1bHu+aj09UW1eV/mWx7fZ54p4hVULBy65fp4j1wf7xERlUdd6gVbAoP3K2gRld6NQ3EuLh3nY9MsKwzXCfHClN4NEOiptTtf4KvtIwEAjUK9cDImzWZ7o1AvpGTl4npyjs22cF9Tu1GnArLtTBmdXzoREZGrYKCQiIiIiIjKrQctorJ0WGu728wrBf+wN9oSRBzePtKS/t3Q1nhiwW7Ep+VazhXkpcF3Q1sDALrP3YHUnHuLeHhrlfjPGx0AAL+PbG+1crHZ7yPbIzE9x2qBDrMlQ00jUfo3CcX/jtv2duzfJBQbT8Ugx3bdEGiV9/7Nb7veWDqLvuQ3jNsRfdYLOjb7YRMRlQ4uZsJJrOku3iPXx3vk+niPXB/vkevjYiZlh+1A1+NqdRWbkp1vALKgbQBw/EYyFm67iKuJmYgIcMfox2qhSTVfAKbVfPMbSg0A4345YhUs7N8kFF+92AIAUPe9ddAb701+r1ZKODfz3sT3dd9bZxUs1Cph2f7QO+usgoIKAJcdsHJnjXfWWQXuJABXHLRiaEHHLm8r1DorL1c95qrHzsjLVY8Ll7esVj0uSpuHgUI2EOku3iPXx3vk+niPXB/vketjoLDssB3oelhXhcN6KhzWU+GxrgqH9VR4rKvCKat6Kkqbh3eLiIiIiIiIiIiIGCgkIiIiIiIiIiIiBgqJiIiIiIiIiIgIDBQSERERERERERERGCgkIiIiIiIiIiIiMFBIREREREREREREYKCQiIiIiIiIiIiIwEAhERERERERERERgYFCIiIiIiIiIiIiAgOFREREREREREREBEDl7AI4mxACAJCamlqq55FlGWlpadDpdFAoGJ91RbxHro/3yPXxHrk+3iPXVxr3yNzOMbd7yITtQNfDuioc1lPhsJ4Kj3VVOKynwmNdFU5Z1VNR2oKVPlCYlpYGAAgPD3dySYiIiIhKV1paGnx8fJxdDJfBdiARERFVJoVpC0qikv+0LMsybt26BS8vL0iSVGrnSU1NRXh4OK5fvw5vb+9SOw8VH++R6+M9cn28R66P98j1lcY9EkIgLS0NYWFh/FU/D7YDXQ/rqnBYT4XDeio81lXhsJ4Kj3VVOGVVT0VpC1b6HoUKhQLVqlUrs/N5e3vzTeLieI9cH++R6+M9cn28R67P0feIPQltsR3oulhXhcN6KhzWU+GxrgqH9VR4rKvCKYt6KmxbkD8pExEREREREREREQOFRERERERERERExEBhmdFqtZg6dSq0Wq2zi0L54D1yfbxHro/3yPXxHrk+3qOKh/e08FhXhcN6KhzWU+GxrgqH9VR4rKvCccV6qvSLmRARERERERERERF7FBIREREREREREREYKCQiIiIiIiIiIiIwUEhERERERERERERgoJCIiIiIiIiIiIjAQGGZ+Ne//oUaNWpAp9OhZcuW2L17t7OLVClMmzYNkiRZPUJCQizbhRCYNm0awsLC4Obmhs6dO+PUqVNWx8jJycGbb76JKlWqwMPDA0888QRu3LhR1pdSYezatQv9+vVDWFgYJEnC6tWrrbY76p4kJSVh0KBB8PHxgY+PDwYNGoTk5ORSvrqK4UH3aOjQoTbvq0ceecRqH96j0jVr1iw8/PDD8PLyQlBQEJ588kmcO3fOah++l5yrMPeI76XKhW1Ba45oD1QGjvp7XxksWrQITZo0gbe3N7y9vdG2bVts2LDBsp31ZN+sWbMgSRLGjx9vSWNdOeZ7ZGVy8+ZNvPzyywgICIC7uzuaNWuGw4cPW7azvoDIyEib15QkSRg9ejQA16sjBgpL2W+//Ybx48fjvffew9GjR9GxY0f06tUL165dc3bRKoWGDRsiJibG8jhx4oRl25w5czB37lwsWLAABw8eREhICLp164a0tDTLPuPHj8cff/yBFStWYM+ePUhPT0ffvn1hNBqdcTnlXkZGBpo2bYoFCxbY3e6oe/Liiy/i2LFj2LhxIzZu3Ihjx45h0KBBpX59FcGD7hEA9OzZ0+p9tX79eqvtvEela+fOnRg9ejT279+PzZs3w2AwoHv37sjIyLDsw/eScxXmHgF8L1UWbAvackR7oDJw1N/7yqBatWr49NNPcejQIRw6dAiPPfYY+vfvb/mizXqydfDgQXz77bdo0qSJVTrryqSk3yMri6SkJLRv3x5qtRobNmzA6dOn8cUXX8DX19eyD+vL9H7L+3ravHkzAODZZ58F4IJ1JKhUtW7dWowcOdIqrV69euKdd95xUokqj6lTp4qmTZva3SbLsggJCRGffvqpJS07O1v4+PiIxYsXCyGESE5OFmq1WqxYscKyz82bN4VCoRAbN24s1bJXBgDEH3/8YXnuqHty+vRpAUDs37/fsk9UVJQAIM6ePVvKV1Wx3H+PhBBiyJAhon///vnm4T0qe/Hx8QKA2LlzpxCC7yVXdP89EoLvpcqEbcGCFac9UFkV5+99Zebn5ye+++471pMdaWlponbt2mLz5s2iU6dOYty4cUIIvqbMSvo9sjKZPHmy6NChQ77bWV/2jRs3TtSsWVPIsuySdcQehaUoNzcXhw8fRvfu3a3Su3fvjn379jmpVJXLhQsXEBYWhho1amDgwIG4fPkyAODKlSuIjY21ujdarRadOnWy3JvDhw9Dr9db7RMWFoZGjRrx/pUCR92TqKgo+Pj4oE2bNpZ9HnnkEfj4+PC+OciOHTsQFBSEOnXq4LXXXkN8fLxlG+9R2UtJSQEA+Pv7A+B7yRXdf4/M+F6q+NgWLLrC/A2rrIrz974yMhqNWLFiBTIyMtC2bVvWkx2jR49Gnz598Pjjj1uls67uKcn3yMrkzz//RKtWrfDss88iKCgIzZs3x7///W/LdtaXrdzcXPz8888YPnw4JElyyTpioLAU3b59G0ajEcHBwVbpwcHBiI2NdVKpKo82bdrgxx9/xKZNm/Dvf/8bsbGxaNeuHRITEy31X9C9iY2NhUajgZ+fX777kOM46p7ExsYiKCjI5vhBQUG8bw7Qq1cvLF++HNu2bcMXX3yBgwcP4rHHHkNOTg4A3qOyJoTAhAkT0KFDBzRq1AgA30uuxt49AvheqizYFiy6wvwNq4yK+/e+Mjlx4gQ8PT2h1WoxcuRI/PHHH2jQoAHr6T4rVqzAkSNHMGvWLJttrCuTkn6PrEwuX76MRYsWoXbt2ti0aRNGjhyJsWPH4scffwTA15Q9q1evRnJyMoYOHQrANetI5ZSzVjKSJFk9F0LYpJHj9erVy/L/xo0bo23btqhZsyaWLVtmmTC+OPeG9690OeKe2Nuf980xnn/+ecv/GzVqhFatWiEiIgLr1q3DgAED8s3He1Q6xowZg+PHj2PPnj022/hecg353SO+lyoXtgWLjnVmzdF/7yuiunXr4tixY0hOTsbKlSsxZMgQ7Ny507Kd9QRcv34d48aNw19//QWdTpfvfpW9rkrre2RFJMsyWrVqhU8++QQA0Lx5c5w6dQqLFi3C4MGDLfuxvu75/vvv0atXL4SFhVmlu1IdsUdhKapSpQqUSqVNFDg+Pt4mWkylz8PDA40bN8aFCxcsq1YVdG9CQkKQm5uLpKSkfPchx3HUPQkJCUFcXJzN8RMSEnjfSkFoaCgiIiJw4cIFALxHZenNN9/En3/+ie3bt6NatWqWdL6XXEd+98gevpcqJrYFi64wf8Mqm5L8va9MNBoNatWqhVatWmHWrFlo2rQpvvrqK9ZTHocPH0Z8fDxatmwJlUoFlUqFnTt34uuvv4ZKpbLUB+vKWlG/R1YmoaGhaNCggVVa/fr1LQt2sb6sXb16FVu2bMGrr75qSXPFOmKgsBRpNBq0bNnSsqKN2ebNm9GuXTsnlaryysnJwZkzZxAaGooaNWogJCTE6t7k5uZi586dlnvTsmVLqNVqq31iYmJw8uRJ3r9S4Kh70rZtW6SkpODAgQOWff7++2+kpKTwvpWCxMREXL9+HaGhoQB4j8qCEAJjxozBqlWrsG3bNtSoUcNqO99Lzvege2QP30sVE9uCRVeYv2GVhSP+3ldmQgjk5OSwnvLo2rUrTpw4gWPHjlkerVq1wksvvYRjx47hoYceYl3ZUdTvkZVJ+/btce7cOau08+fPIyIiAgD/Tt1vyZIlCAoKQp8+fSxpLllHZbNmSuW1YsUKoVarxffffy9Onz4txo8fLzw8PER0dLSzi1bhvfXWW2LHjh3i8uXLYv/+/aJv377Cy8vLUveffvqp8PHxEatWrRInTpwQL7zwgggNDRWpqamWY4wcOVJUq1ZNbNmyRRw5ckQ89thjomnTpsJgMDjrssq1tLQ0cfToUXH06FEBQMydO1ccPXpUXL16VQjhuHvSs2dP0aRJExEVFSWioqJE48aNRd++fcv8esujgu5RWlqaeOutt8S+ffvElStXxPbt20Xbtm1F1apVeY/K0BtvvCF8fHzEjh07RExMjOWRmZlp2YfvJed60D3ie6lyYVvQliPaA5WBo/7eVwZTpkwRu3btEleuXBHHjx8X7777rlAoFOKvv/4SQrCeCpJ31WMhWFdCOOZ7ZGVx4MABoVKpxMyZM8WFCxfE8uXLhbu7u/j5558t+7C+TIxGo6hevbqYPHmyzTZXqyMGCsvAwoULRUREhNBoNKJFixZi586dzi5SpfD888+L0NBQoVarRVhYmBgwYIA4deqUZbssy2Lq1KkiJCREaLVa8eijj4oTJ05YHSMrK0uMGTNG+Pv7Czc3N9G3b19x7dq1sr6UCmP79u0CgM1jyJAhQgjH3ZPExETx0ksvCS8vL+Hl5SVeeuklkZSUVEZXWb4VdI8yMzNF9+7dRWBgoFCr1aJ69epiyJAhNvXPe1S67N0fAGLJkiWWffhecq4H3SO+lyoftgWtOaI9UBk46u99ZTB8+HDLeywwMFB07drVEiQUgvVUkPsDhawrx3yPrEzWrFkjGjVqJLRarahXr5749ttvrbazvkw2bdokAIhz587ZbHO1OpKEEKJUuywSERERERERERGRy+MchURERERERERERMRAIRERERERERERETFQSERERERERERERGCgkIiIiIiIiIiIiMBAIREREREREREREYGBQiIiIiIiIiIiIgIDhURERERERERERAQGComIimXHjh2QJAnJycmFzhMZGYl58+blu33o0KF48sknS1w2SZKwevXqEh+HiIiIiJznQW1HIqLSwEAhEVU4Q4cOhSRJGDlypM22UaNGQZIkDB06tOwL9gBfffUVli5d6uxiEBEREVU6eX+w7dy5M8aPH19m5166dCl8fX1t0g8ePIjXX3+9zMpBRAQwUEhEFVR4eDhWrFiBrKwsS1p2djZ+/fVXVK9e3Ykly5+Pj4/dRiIRERERlT+5ubklyh8YGAh3d3cHlYaIqHAYKCSiCqlFixaoXr06Vq1aZUlbtWoVwsPD0bx5c6t9c3JyMHbsWAQFBUGn06FDhw44ePCg1T7r169HnTp14Obmhi5duiA6OtrmnPv27cOjjz4KNzc3hIeHY+zYscjIyCh0me8fety5c2eMHTsWkyZNgr+/P0JCQjBt2jSrPBcuXMCjjz4KnU6HBg0aYPPmzTbHvXnzJp5//nn4+fkhICAA/fv3t5T/7NmzcHd3xy+//GJVTzqdDidOnCh02YmIiIgqgqFDh2Lnzp346quvIEkSJEmytJtOnz6N3r17w9PTE8HBwRg0aBBu375tydu5c2eMGTMGEyZMQJUqVdCtWzcAwNy5c9G4cWN4eHggPDwco0aNQnp6OgDTdDbDhg1DSkqK5Xzm9t79Q4+vXbuG/v37w9PTE97e3njuuecQFxdn2T5t2jQ0a9YMP/30EyIjI+Hj44OBAwciLS2tdCuNiCoUBgqJqMIaNmwYlixZYnn+ww8/YPjw4Tb7TZo0CStXrsSyZctw5MgR1KpVCz169MCdO3cAANevX8eAAQPQu3dvHDt2DK+++ireeecdq2OcOHECPXr0wIABA3D8+HH89ttv2LNnD8aMGVOia1i2bBk8PDzw999/Y86cOZgxY4YlGCjLMgYMGAClUon9+/dj8eLFmDx5slX+zMxMdOnSBZ6enti1axf27NkDT09P9OzZE7m5uahXrx4+//xzjBo1ClevXsWtW7fw2muv4dNPP0Xjxo1LVHYiIiKi8uarr75C27Zt8dprryEmJgYxMTEIDw9HTEwMOnXqhGbNmuHQoUPYuHEj4uLi8Nxzz1nlX7ZsGVQqFfbu3YtvvvkGAKBQKPD111/j5MmTWLZsGbZt24ZJkyYBANq1a4d58+bB29vbcr6JEyfalEsIgSeffBJ37tzBzp07sXnzZly6dAnPP/+81X6XLl3C6tWrsXbtWqxduxY7d+7Ep59+Wkq1RUQVkiAiqmCGDBki+vfvLxISEoRWqxVXrlwR0dHRQqfTiYSEBNG/f38xZMgQIYQQ6enpQq1Wi+XLl1vy5+bmirCwMDFnzhwhhBBTpkwR9evXF7IsW/aZPHmyACCSkpKEEEIMGjRIvP7661bl2L17t1AoFCIrK0sIIURERIT48ssvH1hus06dOokOHTpY7fPwww+LyZMnCyGE2LRpk1AqleL69euW7Rs2bBAAxB9//CGEEOL7778XdevWtSp7Tk6OcHNzE5s2bbKk9enTR3Ts2FF07dpVdOvWzWp/IiIioooubzusU6dOYty4cVbbP/jgA9G9e3ertOvXrwsA4ty5c5Z8zZo1e+C5fv/9dxEQEGB5vmTJEuHj42OzX962419//SWUSqW4du2aZfupU6cEAHHgwAEhhBBTp04V7u7uIjU11bLP22+/Ldq0afPAMhERmamcG6YkIio9VapUQZ8+fbBs2TIIIdCnTx9UqVLFap9Lly5Br9ejffv2ljS1Wo3WrVvjzJkzAIAzZ87gkUcegSRJln3atm1rdZzDhw/j4sWLWL58uSVNCAFZlnHlyhXUr1+/WNfQpEkTq+ehoaGIj4+3lKt69eqoVq3aA8vl5eVllZ6dnY1Lly5Znv/www+oU6cOFAoFTp48aXWtRERERJXd4cOHsX37dnh6etpsu3TpEurUqQMAaNWqlc327du345NPPsHp06eRmpoKg8GA7OxsZGRkwMPDo1DnP3PmDMLDwxEeHm5Ja9CgAXx9fXHmzBk8/PDDAEzDlfO2+/K2HYmICoOBQiKq0IYPH24Z/rtw4UKb7UIIALAJjAkhLGnmfQoiyzJGjBiBsWPH2mwryeIparXa6rkkSZBlOd9y3X8dsiyjZcuWVgFMs8DAQMv///nnH2RkZEChUCA2NhZhYWHFLjMRERFRRSPLMvr164fZs2fbbAsNDbX8//7A39WrV9G7d2+MHDkSH330Efz9/bFnzx688sor0Ov1hT5/3rZpQekFtR2JiAqDgUIiqtDMc/EBQI8ePWy216pVCxqNBnv27MGLL74IANDr9Th06BDGjx8PwPRr7erVq63y7d+/3+p5ixYtcOrUKdSqVcvxF5GPBg0a4Nq1a7h165YlsBcVFWVTrt9++w1BQUHw9va2e5w7d+5g6NCheO+99xAbG4uXXnoJR44cgZubW6lfAxEREZGr0Wg0MBqNVmktWrTAypUrERkZCZWq8F+jDx06BIPBgC+++AIKhWmJgN9///2B57ufud13/fp1S6/C06dPIyUlpdgjV4iI7OFiJkRUoSmVSpw5cwZnzpyBUqm02e7h4YE33ngDb7/9NjZu3IjTp0/jtddeQ2ZmJl555RUAwMiRI3Hp0iVMmDAB586dwy+//IKlS5daHWfy5MmIiorC6NGjcezYMVy4cAF//vkn3nzzzVK7tscffxx169bF4MGD8c8//2D37t147733rPZ56aWXUKVKFfTv3x+7d+/GlStXsHPnTowbNw43btywXF94eDjef/99zJ07F0IIu5NoExEREVUGkZGR+PvvvxEdHY3bt29DlmWMHj0ad+7cwQsvvIADBw7g8uXL+OuvvzB8+PACg3w1a9aEwWDA/PnzcfnyZfz0009YvHixzfnS09OxdetW3L59G5mZmTbHefzxx9GkSRPLD7oHDhzA4MGD0alTJ7vDnYmIiouBQiKq8Ly9vfPtTQcAn376KZ5++mkMGjQILVq0wMWLF7Fp0yb4+fkBMA0dXrlyJdasWYOmTZti8eLF+OSTT6yO0aRJE+zcuRMXLlxAx44d0bx5c3zwwQdWQ1EcTaFQ4I8//kBOTg5at26NV199FTNnzrTax93dHbt27UL16tUxYMAA1K9fH8OHD0dWVha8vb3x448/Yv369fjpp5+gUqng7u6O5cuX47vvvsP69etLrexERERErmrixIlQKpVo0KABAgMDce3aNYSFhWHv3r0wGo3o0aMHGjVqhHHjxsHHx8fSU9CeZs2aYe7cuZg9ezYaNWqE5cuXY9asWVb7tGvXDiNHjsTzzz+PwMBAzJkzx+Y4kiRh9erV8PPzw6OPPorHH38cDz30EH777TeHXz8RVW6SKMzkW0RERERERERERFShsUchERERERERERERMVBIREREREREREREDBQSERERERERERERGCgkIiIiIiIiIiIiMFBIREREREREREREYKCQiIiIiIiIiIiIwEAhERERERERERERgYFCIiIiIiIiIiIiAgOFREREREREREREBAYKiYiIiIiIiIiICAwUEhERERERERERERgoJCIiIiIiIiIiIjBQSERERERERERERGCgkIiIiIiIiIiIiMBAIREREREREREREYGBQiIiIiIiIiIiIgIDhURERERERERERAQGCokqpaVLl0KSJKtHYGAgOnfujLVr15baeTMzMzFt2jTs2LGj1M7haJGRkRg6dGix8kqShGnTpjm0PBXR+vXrWU9ERBXY/e0OlUqFatWqYdiwYbh586azi1eqbt26hWnTpuHYsWPOLooNZ7ZTOnfujM6dO5fpOYcOHYrIyMgyPeeDREdHQ5IkLF26tMD9zO+hQ4cOlU3BimnHjh2QJKnIbf3c3FyMHDkSoaGhUCqVaNasGQDbdrgrv5+IKhKVswtARM6zZMkS1KtXD0IIxMbGYsGCBejXrx/+/PNP9OvXz+Hny8zMxPTp0wGgzBuH5LrWr1+PhQsXMlhIRFTBmdsdWVlZ2LVrF2bNmoWdO3fixIkT8PDwcHbxSsWtW7cwffp0REZGWoIfRBVVixYtEBUVhQYNGhQp36JFi/DNN99g/vz5aNmyJTw9PQEAf/zxB7y9vS378f1EVDYYKCSqxBo1aoRWrVpZnvfs2RN+fn749ddfSyVQSM6j1+stvTgqi8zMTLi7uzu7GEREdFfedkeXLl1gNBrx0UcfYfXq1XjppZdKdOysrCy4ubk5opjlQlZWFnQ6HSRJcnZRKq3K9porDG9vbzzyyCNFznfy5Em4ublhzJgxVunNmzd3VNGIqAg49JiILHQ6HTQaDdRqtVV6bm4uPv74Y9SrVw9arRaBgYEYNmwYEhISrPbbtm0bOnfujICAALi5uaF69ep4+umnkZmZiejoaAQGBgIApk+fbhl+VNCwXvPwhV9++QWTJ09GaGgoPD090a9fP8TFxSEtLQ2vv/46qlSpgipVqmDYsGFIT0+3OkZ2djamTJmCGjVqQKPRoGrVqhg9ejSSk5Ot9tPr9Zg0aRJCQkLg7u6ODh064MCBA3bLFRsbixEjRqBatWrQaDSoUaMGpk+fDoPBUMiavsc85GTOnDmYOXMmqlevDp1Oh1atWmHr1q1W+168eBHDhg1D7dq14e7ujqpVq6Jfv344ceKE3Xr76aef8NZbb6Fq1arQarW4ePEiEhISMGrUKDRo0ACenp4ICgrCY489ht27d9st12effYbZs2cjMjISbm5u6Ny5M86fPw+9Xo933nkHYWFh8PHxwVNPPYX4+Hib6/vtt9/Qtm1beHh4wNPTEz169MDRo0ct24cOHYqFCxcCgNWwtOjoaACAEAL/+te/0KxZM7i5ucHPzw/PPPMMLl++bHWezp07o1GjRti1axfatWsHd3d3DB8+HEDBr0siInIec0Dh6tWrAEztgzZt2sDf3x/e3t5o0aIFvv/+ewghrPJFRkaib9++WLVqFZo3bw6dTmcZsbBw4UI8+uijCAoKgoeHBxo3bow5c+ZAr9dbHcP8uREVFYV27drBzc0NkZGRWLJkCQBg3bp1aNGiBdzd3dG4cWNs3LjRpvwXLlzAiy++iKCgIGi1WtSvX9/ymQaYPo8ffvhhAMCwYcMsn3F5e9AfOnQITzzxBPz9/aHT6dC8eXP8/vvvVucxDzv966+/MHz4cAQGBsLd3R05OTn51m1ycjLeeustPPTQQ9BqtQgKCkLv3r1x9uzZAu/JyZMn0b9/f/j5+UGn06FZs2ZYtmyZ3fKYP6vzXu/9w06FEJgzZw4iIiKg0+nQokULbNiwocAy5CXLMubPn29pB/j6+uKRRx7Bn3/+abXPnDlzLO3UoKAgDB48GDdu3Hjg8QvbTizoNVfYduGtW7fw3HPPwcvLCz4+Pnj++ecRGxtb6LoAgKSkJAwbNgz+/v7w8PBAv379rNpEH330EVQqFa5fv26Td/jw4QgICEB2dna+xx86dCg8PT1x9uxZ9OjRAx4eHggNDcWnn34KANi/fz86dOgADw8P1KlTx+a1Ye81cPnyZQwcOBBhYWHQarUIDg5G165dLcOHJUnCd999h6ysLMt7xDwUO+/Q48K8n4jIMSpP1xIismE0GmEwGCCEQFxcHD777DNkZGTgxRdftOwjyzL69++P3bt3Y9KkSWjXrh2uXr2KqVOnonPnzjh06BDc3NwQHR2NPn36oGPHjvjhhx/g6+uLmzdvYuPGjcjNzUVoaCg2btyInj174pVXXsGrr74KAJbgYUHeffdddOnSBUuXLkV0dDQmTpyIF154ASqVCk2bNsWvv/6Ko0eP4t1334WXlxe+/vprAKbG6ZNPPomtW7diypQp6NixI44fP46pU6ciKioKUVFR0Gq1AIDXXnsNP/74IyZOnIhu3brh5MmTGDBgANLS0qzKEhsbi9atW0OhUODDDz9EzZo1ERUVhY8//hjR0dGWLxhFtWDBAkRERGDevHmWBm+vXr2wc+dOtG3bFoCpgRkQEIBPP/0UgYGBuHPnDpYtW4Y2bdrg6NGjqFu3rtUxp0yZgrZt22Lx4sVQKBQICgqyBHenTp2KkJAQpKen448//kDnzp2xdetWmyHhCxcuRJMmTbBw4ULLl45+/fqhTZs2UKvV+OGHH3D16lVMnDgRr776qlXD/ZNPPsH777+PYcOG4f3330dubi4+++wzdOzYEQcOHECDBg3wwQcfICMjA//9738RFRVlyRsaGgoAGDFiBJYuXYqxY8di9uzZuHPnDmbMmIF27drhn3/+QXBwsCVPTEwMXn75ZUyaNAmffPIJFArFA1+X7HFIROQ8Fy9eBHCvLRAdHY0RI0agevXqAExBiTfffBM3b97Ehx9+aJX3yJEjOHPmDN5//33UqFHDMnT50qVLePHFFy2Bn3/++QczZ87E2bNn8cMPP1gdIzY2FsOGDcOkSZNQrVo1zJ8/H8OHD8f169fx3//+F++++y58fHwwY8YMPPnkk7h8+TLCwsIAAKdPn0a7du1QvXp1fPHFFwgJCcGmTZswduxY3L59G1OnTkWLFi2wZMkSy+dgnz59AADVqlUDAGzfvh09e/ZEmzZtsHjxYvj4+GDFihV4/vnnkZmZafNj6vDhw9GnTx/89NNPyMjIsPlh1ywtLQ0dOnRAdHQ0Jk+ejDZt2iA9PR27du1CTEwM6tWrZzffuXPn0K5dOwQFBeHrr79GQEAAfv75ZwwdOhRxcXGYNGlSoe5rXtOnT8f06dPxyiuv4JlnnsH169fx2muvwWg02rRb7Bk6dCh+/vlnvPLKK5gxYwY0Gg2OHDliFaR844038O2332LMmDHo27cvoqOj8cEHH2DHjh04cuQIqlSpYvfYRWknAvZfc4VtF2ZlZeHxxx/HrVu3MGvWLNSpUwfr1q3D888/X6T6fOWVV9CtWzf88ssvuH79Ot5//3107twZx48fh6+vL0aMGIGZM2fim2++wccff2zJd+fOHaxYsQJjxoyBTqcr8Bx6vR4DBgzAyJEj8fbbb+OXX37BlClTkJqaipUrV2Ly5MmW98vQoUPRqFEjtGzZMt/j9e7dG0ajEXPmzEH16tVx+/Zt7Nu3zxKMjYqKwkcffYTt27dj27ZtAICaNWvaHOdB7yciciBBRJXOkiVLBACbh1arFf/617+s9v31118FALFy5Uqr9IMHDwoAlv3/+9//CgDi2LFj+Z43ISFBABBTp04tVDm3b98uAIh+/fpZpY8fP14AEGPHjrVKf/LJJ4W/v7/l+caNGwUAMWfOHKv9fvvtNwFAfPvtt0IIIc6cOSMAiP/7v/+z2m/58uUCgBgyZIglbcSIEcLT01NcvXrVat/PP/9cABCnTp2ypBXmWq9cuSIAiLCwMJGVlWVJT01NFf7+/uLxxx/PN6/BYBC5ubmidu3aVmU319ujjz5a4LnNx9Dr9aJr167iqaeesilX06ZNhdFotKTPmzdPABBPPPGE1XHM9yQlJUUIIcS1a9eESqUSb775ptV+aWlpIiQkRDz33HOWtNGjRwt7H0dRUVECgPjiiy+s0q9fvy7c3NzEpEmTLGmdOnUSAMTWrVut9i3M65KIiEqXud2xf/9+odfrRVpamli7dq0IDAwUXl5eIjY21iaP0WgUer1ezJgxQwQEBAhZli3bIiIihFKpFOfOnSvwvOZj/Pjjj0KpVIo7d+5Ytpk/Nw4dOmRJS0xMFEqlUri5uYmbN29a0o8dOyYAiK+//tqS1qNHD1GtWjXL557ZmDFjhE6ns5zL3F5asmSJTfnq1asnmjdvLvR6vVV63759RWhoqOXz11x/gwcPLvB6zWbMmCEAiM2bNxe43/3tlIEDBwqtViuuXbtmtV+vXr2Eu7u7SE5OtirPlStXrPYztz+2b98uhBAiKSlJ6HQ6q/aFEELs3btXABCdOnUqsHy7du0SAMR7772X7z7mNtyoUaOs0v/++28BQLz77ruWtCFDhoiIiAjL88K2E4XI/zVX2HbhokWLBADxv//9z2q/1157Ld/XR17mOs+vLj/++GOr6wwKChI5OTmWtNmzZwuFQmFzz+43ZMgQm3a/Xq8XgYGBAoA4cuSIJd38fpkwYYIl7f7XwO3btwUAMW/evAee18PDwyY9IiLCqh1e0PuJiByHQ4+JKrEff/wRBw8exMGDB7FhwwYMGTIEo0ePxoIFCyz7rF27Fr6+vujXrx8MBoPl0axZM4SEhFiGFjRr1gwajQavv/46li1bZjM0tCT69u1r9bx+/foAYPklMW/6nTt3LMOPzb9K3v+L/LPPPgsPDw/L0N7t27cDgM38SM8995zNnH5r165Fly5dEBYWZlUfvXr1AgDs3LmzWNc4YMAAq194vby80K9fP+zatQtGoxEAYDAY8Mknn6BBgwbQaDRQqVTQaDS4cOECzpw5Y3PMp59+2u65Fi9ejBYtWkCn00GlUkGtVmPr1q12j9G7d28oFPc+KgqqewC4du0aAGDTpk0wGAwYPHiwVT3pdDp06tSpUKvhrV27FpIk4eWXX7Y6RkhICJo2bWpzDD8/Pzz22GNWaaX5uiQioqJ55JFHoFar4eXlhb59+yIkJAQbNmyw9A7ftm0bHn/8cfj4+ECpVEKtVuPDDz9EYmKizfQWTZo0QZ06dWzOcfToUTzxxBMICAiwHGPw4MEwGo04f/681b6hoaFWPaH8/f0RFBSEZs2aWXoOAvc+48xDpLOzs7F161Y89dRTcHd3t/qM6t27N7Kzs7F///4C6+LixYs4e/aspe1x/zFiYmJw7tw5qzz5fa7fb8OGDahTpw4ef/zxQu1vtm3bNnTt2hXh4eFW6UOHDkVmZqZVz//CiIqKQnZ2tk37ql27doiIiHhgfvMQ5dGjR+e7j7kNd39br3Xr1qhfv77NNC55FbadaGbvNVfYduH27dvh5eWFJ554wip/3lE8hZFfXZrrAQDGjRuH+Ph4/Oc//wFgGh20aNEi9OnTp1CrPkuShN69e1ueq1Qq1KpVC6GhoVZzBprfL+b3hT3+/v6oWbMmPvvsM8ydOxdHjx6FLMuFvVwichIGCokqsfr166NVq1Zo1aoVevbsiW+++Qbdu3fHpEmTLMMB4uLikJycbJm7MO8jNjYWt2/fBmAaIrBlyxYEBQVh9OjRqFmzJmrWrImvvvqqxOX09/e3eq7RaApMN8+9kpiYCJVKZTO8WZIkhISEIDEx0bIfAISEhFjtp1KpEBAQYJUWFxeHNWvW2NRFw4YNAcBSH0V1/7nNabm5uZbA54QJE/DBBx/gySefxJo1a/D333/j4MGDaNq0KbKysmzym4fv5jV37ly88cYbaNOmDVauXIn9+/fj4MGD6Nmzp91jFLfu4+LiAAAPP/ywTV399ttvhaqnuLg4CCEQHBxsc4z9+/fbHMPe9Zbm65KIiIrG/APl0aNHcevWLRw/fhzt27cHABw4cADdu3cHAPz73//G3r17cfDgQbz33nsAYPMZZe9v/rVr19CxY0fcvHkTX331FXbv3o2DBw9a5g28/xj3f5YBps+zwrQvDAYD5s+fb/P5ZA6wPOhzzvw5OXHiRJtjjBo1yu4x7F2zPQkJCcUajpmYmGj3HOagqbm9VJTjAfm3cR4kISEBSqWywH3N58iv3AWVubDtRDN75yhsuzAxMdFquhSzwtTDg/a/v6zNmzdHx44dLa/7tWvXIjo62mahkPy4u7vbDE+2974wpxc056EkSdi6dSt69OiBOXPmoEWLFggMDMTYsWNtpvchItfBOQqJyEqTJk2wadMmnD9/Hq1bt0aVKlUQEBBgdxJvwNTzzaxjx47o2LEjjEYjDh06hPnz52P8+PEIDg7GwIEDy+oSLAICAmAwGJCQkGDVCBRCIDY21jIhsjkYGBsbi6pVq1r2MxgMNo3EKlWqoEmTJpg5c6bdc+btgVAU9iazjo2NhUajgaenJwDg559/xuDBg/HJJ59Y7Xf79m34+vra5Le3EuLPP/+Mzp07Y9GiRVbpjm6smecD+u9//1uoXgP5HUOSJOzevdtqjiCz+9PyW/nR1V6XRESVlfkHSntWrFgBtVqNtWvXWgUpVq9ebXd/e3/zV69ejYyMDKxatcrqs8e8aIKj+Pn5QalUYtCgQfn2dqtRo0aBxzB/Tk6ZMgUDBgywu8/9c/gVdoXjwMDAQi3kcb+AgADExMTYpN+6dQvAvTKb78/9i6ncH9jM2766X2xs7AN7twUGBsJoNCI2NjbfIKn5HDExMTbB0Vu3buU7P6E5b2HaiWb26r+w7cKAgAC7i+QVdTGT/OqyVq1aVmljx47Fs88+iyNHjmDBggWoU6cOunXrVqRzOUpERAS+//57AMD58+fx+++/Y9q0acjNzcXixYudUiYiKhh7FBKRFXNj2txg6tu3LxITE2E0Gi29D/M+7E1ErVQq0aZNG8svmUeOHAFwL7Bjr+daaejatSsAU3Asr5UrVyIjI8Oy3byAx/Lly632+/33321WrOvbty9OnjyJmjVr2q2P4gYKV61aZfWLbFpaGtasWYOOHTtCqVQCMDVQ7w+OrVu3Djdv3iz0eewd4/jx40UeTvQgPXr0gEqlwqVLl+zWU94vivm9Lvr27QshBG7evGk3f+PGjYtUpvxel0RE5HySJEGlUlk+8wDT58JPP/1UpGMA1j8kCSHw73//23EFhanHVZcuXXD06FE0adLE7meUOYCV32dc3bp1Ubt2bfzzzz/5fk7m/TG2KHr16oXz589bhtYWVteuXbFt2zZLYNDsxx9/hLu7u2WVanOA7/jx41b75V3QDDANNdfpdDbtq3379hU4XDXvdQCw+XEzL/OUI/e39Q4ePIgzZ85Y2nr2FLadWJDCtgu7dOmCtLQ0mzr65ZdfHniOvPKry/sXo3vqqadQvXp1vPXWW9iyZQtGjRpV6EBzaapTpw7ef/99NG7cuFjtsLL+LkFUWbFHIVEldvLkSUsgLDExEatWrcLmzZvx1FNPWX4JHzhwIJYvX47evXtj3LhxaN26NdRqNW7cuIHt27ejf//+eOqpp7B48WJs27YNffr0QfXq1ZGdnW1ZXdA8R46XlxciIiLwv//9D127doW/vz+qVKlSqPlSiqNbt27o0aMHJk+ejNTUVLRv396yml3z5s0xaNAgAKYeDi+//DLmzZsHtVqNxx9/HCdPnsTnn38Ob29vq2POmDEDmzdvRrt27TB27FjUrVsX2dnZiI6Oxvr167F48eJiDfdRKpXo1q0bJkyYAFmWMXv2bKSmpmL69OmWffr27YulS5eiXr16aNKkCQ4fPozPPvusSOfr27cvPvroI0ydOhWdOnXCuXPnMGPGDNSoUcMmKFoSkZGRmDFjBt577z1cvnwZPXv2hJ+fH+Li4nDgwAF4eHhYrs0c8Js9ezZ69eoFpVKJJk2aoH379nj99dcxbNgwHDp0CI8++ig8PDwQExODPXv2oHHjxnjjjTcKLEdhXpdEROR8ffr0wdy5c/Hiiy/i9ddfR2JiIj7//HO7Pcrz061bN2g0GrzwwguYNGkSsrOzsWjRIiQlJTm8vF999RU6dOiAjh074o033kBkZCTS0tJw8eJFrFmzxmr1Vjc3Nyxfvhz169eHp6cnwsLCEBYWhm+++Qa9evVCjx49MHToUFStWhV37tzBmTNncOTIEcscc0U1fvx4/Pbbb+jfvz/eeecdtG7dGllZWdi5cyf69u2LLl262M03depUy5x7H374Ifz9/bF8+XKsW7cOc+bMgY+PDwDTtCJ169bFxIkTYTAY4Ofnhz/++AN79uyxOp6fnx8mTpyIjz/+GK+++iqeffZZXL9+HdOmTSvUkNuOHTti0KBB+PjjjxEXF4e+fftCq9Xi6NGjcHd3x5tvvom6devi9ddfx/z586FQKNCrVy/Lqsfh4eH4v//7v3yPX9h2YkEK2y4cPHgwvvzySwwePBgzZ85E7dq1sX79emzatOmB58jr0KFDVnX53nvvoWrVqpbh6mZKpRKjR4/G5MmT4eHhYTMPY1k5fvw4xowZg2effRa1a9eGRqPBtm3bcPz4cbzzzjtFPl5B7yciciCnLqVCRE5hb9VjHx8f0axZMzF37lyRnZ1ttb9erxeff/65aNq0qdDpdMLT01PUq1dPjBgxQly4cEEIYVqh9qmnnhIRERFCq9WKgIAA0alTJ/Hnn39aHWvLli2iefPmQqvV2qwofD/zymn/+c9/7Jb/4MGDVulTp04VAERCQoIlLSsrS0yePFlEREQItVotQkNDxRtvvCGSkpKs8ubk5Ii33npLBAUFCZ1OJx555BERFRVls9qaEKbVm8eOHStq1Kgh1Gq18Pf3Fy1bthTvvfeeSE9Pt+yHIqx6PHv2bDF9+nRRrVo1odFoRPPmzcWmTZus9k1KShKvvPKKCAoKEu7u7qJDhw5i9+7dolOnTlYrB+ZXb+brnDhxoqhatarQ6XSiRYsWYvXq1TYrAZrL9dlnn1nlL+o9Wb16tejSpYvw9vYWWq1WREREiGeeeUZs2bLFqkyvvvqqCAwMFJIk2ayk+MMPP4g2bdoIDw8P4ebmJmrWrCkGDx5stVJlp06dRMOGDW2ut7CvSyIiKj35fUbc74cffhB169YVWq1WPPTQQ2LWrFni+++/t/lciIiIEH369LF7jDVr1ljaK1WrVhVvv/222LBhg9VKrELk/7mR37EBiNGjR1ulXblyRQwfPlxUrVpVqNVqERgYKNq1a2e1Aq0QQvz666+iXr16Qq1W27QN/vnnH/Hcc8+JoKAgoVarRUhIiHjsscfE4sWLLfsUtv7ySkpKEuPGjRPVq1cXarVaBAUFiT59+oizZ89aXdP97ZQTJ06Ifv36CR8fH6HRaETTpk3trjB7/vx50b17d+Ht7S0CAwPFm2++KdatW2dTz7Isi1mzZonw8HCh0WhEkyZNxJo1a2zaLvkxGo3iyy+/FI0aNRIajUb4+PiItm3bijVr1ljtM3v2bFGnTh2hVqtFlSpVxMsvvyyuX79udaz72zpCFL6dWNBrrrDtwhs3boinn35aeHp6Ci8vL/H000+Lffv2FWnV47/++ksMGjRI+Pr6Cjc3N9G7d29LW/x+0dHRAoAYOXJkgcfOK7/Vhwv7frl/1eO4uDgxdOhQUa9ePeHh4SE8PT1FkyZNxJdffikMBsMDz2uvHV7Q+4mIHEMSQoiyCUkSEdH9oqOjUaNGDXz22WeYOHGis4tDRERERBXA/PnzMXbsWJw8edKyuAoRUWFw6DERERERERFRBXD06FFcuXIFM2bMQP/+/RkkJKIiY6CQiIiIiIiIqAJ46qmnEBsbi44dO3JVYSIqFg49JiIiIiIiIiIiIiicXQAiIiIiIiIiIiJyPgYKiYiIiIiIiIiIqHwHCg0GA95//33UqFEDbm5ueOihhzBjxgzIsuzsohEREREREREREZUr5Xoxk9mzZ2Px4sVYtmwZGjZsiEOHDmHYsGHw8fHBuHHjnF08IiIiIiIiIiKicqNcBwqjoqLQv39/9OnTBwAQGRmJX3/9FYcOHSr0MWRZxq1bt+Dl5QVJkkqrqEREREROI4RAWloawsLCoFCU6wElDsV2IBERUf6c1X7Izs5Gbm5uiY+j0Wig0+kcUKLKpVwHCjt06IDFixfj/PnzqFOnDv755x/s2bMH8+bNK/Qxbt26hfDw8NIrJBEREZGLuH79OqpVq+bsYrgMtgOJiIgerCzbD9nZ2agR4YnYeGOJjxUSEoIrV64wWFhE5TpQOHnyZKSkpKBevXpQKpUwGo2YOXMmXnjhhXzz5OTkICcnx/JcCAEAuHr1Kry9vUu9zERERERlLTU1FREREfDy8nJ2UVyKuT6uX7/ukHagLMtISEhAYGAge24WAuuraFhfRcP6KhrWV9FUlvpKTU1FeHh4mbYfcnNzERtvxJXDEfD2Kn7dpqbJqNHyKnJzcxkoLKJyHSj87bff8PPPP+OXX35Bw4YNcezYMYwfPx5hYWEYMmSI3TyzZs3C9OnTbdJzcnKQnZ1d2kUmIiIiKnPmH0k5vNaauT68vb0dFijMzs6Gt7d3hf7i6Cisr6JhfRUN66toWF9FU9nqyxntBw9P06O4jMJxZalsynWg8O2338Y777yDgQMHAgAaN26Mq1evYtasWfkGCqdMmYIJEyZYnpsj5IGBgexRSERERBUSf0knIiIiosIo14HCzMxMm+i9UqmELMv55tFqtdBqtTbpCoWiUvwSQERERJUP2zhERERUnsgQkFH8boElyVvZletAYb9+/TBz5kxUr14dDRs2xNGjRzF37lwMHz7c2UUjIiIiIiIiIqJikCEj/y5ghctPxVOuA4Xz58/HBx98gFGjRiE+Ph5hYWEYMWIEPvzwQ2cXjYiIiIiIiIiIqFwp14FCLy8vzJs3D/PmzXN2UYiIiIiIiIiIyAGMQsAoij98uCR5K7tyHSgkIiIiIiIiIqKKhXMUOg8DhURERERERERE5DJkCBgZKHQKLoFHRERERERERERE7FFIRERERERE5YcQArczMqFTqeCl0zq7OERUCjj02HkYKCQiIiIiIqJyYfO5i5i9ZTeuJSVDAtC5Vg182PMxVPX1dnbRiMiBuJiJ83DoMREREREREbm83ZeiMeY/a3A9KRkAIADsuhSNF3/8HRm5uU4tGxE5luyABxUPA4VERERERETk8hbu/huSJFkNKDQKgZjUNKw5cdZp5SIiqkgYKCQiIiIiIiKXdzImDrKd4YRKhYTjMXFOKBERlRbj3VWPS/Kg4uEcheSyGi9r7OwilLkTQ044uwhERERERC7Jz90NcWnpthsEUMXDvewLRESlxihMj5Lkp+Jhj0IiIiIiIiJyeS+0aALJTrosBJ5q0qDMy0NEVBExUEhEREREREQu79V2rdCtXi0AgEKSIAFQKRT49IkeqBHg59zCEZFDcTET5+HQYyIiIiIiInJ5GqUSC57ph+O3YvF39HV4aDXoUa82AjjsmKjCkSHBaLcPceHzU/EwUEhERERERETlRpOwEDQJC3F2MYioFMnC9ChJfioeDj0mIiIiIiIiIiIi9igkIiIiIiIiIiLXYSzh0OOS5K3sGCgkIiIiIiIiIiKXwUCh8zBQSERERERERERELkMWEmRRgsVMSpC3smOgsAw1XtbY2UUocyeGnHB2EYiIiIiIiIiIqBAYKCQiIiIiIiIiIpfBocfOw0AhERERERERERG5DCMUMEJRgvxUXMWvdSIiIiIiIiIiIqow2KOQiIiIiIiIiIhchijhYiaCi5kUGwOFRERERERERETkMjhHofMwUEhERERERFQCybnp+OPGPhxOuggPpQ7dQ1ugS1ATKCTO9EREVBxGoYBRlGCOQuHAwlQyDBQSEREREREVU1x2MkYe/BpJuemQIaCAhKjEM/g75CymNHgeksReLUREVH7wJy4iIiIiIqJi+v7SJiTrMyDD1H3F/O/G2MM4mnTJmUUjIiq3ZEiQoSjBgz/SFBcDhURERERERMW0M+E4jEK2SVdKCuxOOOmEEhERlX/mOQpL8qDi4dBjIiIiIiKiYpJF/hNhmXsXEhFR0ZR8jkL+/S0u9igkIiIiIiIqpvaBDaG087XKKGS0r9LACSUiIiIqPgYKiYiIiIiIiunVh3rAXaW1rHBsHuzWMbAhWvnXdl7BiIjKMdMchSV7UPFw6DEREREREVExVXOvgh/aTMDv13bh0J3zcFfp0DOkJfqEtbYED4mIqGhkKGAsQd82Tv1QfAwUEhERERERlUCwzhdv1nnC2cUgIiIqMf7ERUREREQuZ9euXejXrx/CwsIgSRJWr15t2abX6zF58mQ0btwYHh4eCAsLw+DBg3Hr1i3nFZiIiIgcxryYSUkeVDzluuYiIyMhSZLNY/To0c4uGhERERGVQEZGBpo2bYoFCxbYbMvMzMSRI0fwwQcf4MiRI1i1ahXOnz+PJ55gjy4iIqKKQIaixA8qnnJdcwcPHkRMTIzlsXnzZgDAs88+6+SSEREREVFJ9OrVCx9//DEGDBhgs83HxwebN2/Gc889h7p16+KRRx7B/PnzcfjwYVy7ds0JpSUiIiJHMgqpxI+iWLRoEZo0aQJvb294e3ujbdu22LBhQ4F5du7ciZYtW0Kn0+Ghhx7C4sWLS3LJLqNcBwoDAwMREhJieaxduxY1a9ZEp06dnF00IiIiIipDKSkpkCQJvr6+zi4KERERlTPVqlXDp59+ikOHDuHQoUN47LHH0L9/f5w6dcru/leuXEHv3r3RsWNHHD16FO+++y7Gjh2LlStXlnHJHa/CLGaSm5uLn3/+GRMmTIAkcRlsIiIiosoiOzsb77zzDl588UV4e3vnu19OTg5ycnIsz1NTUwEAsixDluUSl0OWZQghHHKsyoD1VTSsr6JhfRUN66toKkt9OfP6jCVc9dhYxFWP+/XrZ/V85syZWLRoEfbv34+GDRva7L948WJUr14d8+bNAwDUr18fhw4dwueff46nn3662OV2BRUmULh69WokJydj6NChBe5X2g3EgijKdwfOYilJnbK+iIiIHKMif77o9XoMHDgQsizjX//6V4H7zpo1C9OnT7dJT0hIQHZ2donLIssyUlJSIISAQlH52jFFxfoqGtZX0bC+iob1VTSVpb7S0tKcdm5ZKCCXYEESWZgCheaYj5lWq4VWqy0wr9FoxH/+8x9kZGSgbdu2dveJiopC9+7drdJ69OiB77//Hnq9Hmq1uthld7YKEyj8/vvv0atXL4SFhRW4X2k3EAtSW1W7VI/viuLj44udl/VFRETkGM5s6JcmvV6P5557DleuXMG2bdsK7E0IAFOmTMGECRMsz1NTUxEeHo7AwMAH5i0MWZYhSRICAwMr9BdHR2F9FQ3rq2hYX0XD+iqaylJfOp3Oaed2VI/C8PBwq/SpU6di2rRpdvOcOHECbdu2RXZ2Njw9PfHHH3+gQYMGdveNjY1FcHCwVVpwcDAMBgNu376N0NDQYpfd2SpEoPDq1avYsmULVq1a9cB9S7uBWJALhgulenxXFBQUVOy8rC8iIiLHcGZDv7SYg4QXLlzA9u3bERAQ8MA8+fUiUCgUDvuiJ0mSQ49X0bG+iob1VTSsr6JhfRVNZaivinBt169ft4r1FNSbsG7dujh27BiSk5OxcuVKDBkyBDt37sw3WHj/tHfibi/G8j4dXoUIFC5ZsgRBQUHo06fPA/ctiwZifmRU3GE/+SlJnbK+iIiIHKM8fr6kp6fj4sWLludXrlzBsWPH4O/vj7CwMDzzzDM4cuQI1q5dC6PRiNjYWACAv78/NBqNs4pNREREDiADRV65+P78ACyrGBeGRqNBrVq1AACtWrXCwYMH8dVXX+Gbb76x2TckJMTS9jCLj4+HSqUq1I+XrqzcBwplWcaSJUswZMgQqFTl/nKIiIiICMChQ4fQpUsXy3PziJAhQ4Zg2rRp+PPPPwEAzZo1s8q3fft2dO7cuayKSURERKVAhgJyCYYelySvmRDCao2LvNq2bYs1a9ZYpf31119o1apVuZ6fEKgAgcItW7bg2rVrGD58uLOLQkREREQO0rlzZ8sQHnsK2kZERERUFO+++y569eqF8PBwpKWlYcWKFdixYwc2btwIwDSN3c2bN/Hjjz8CAEaOHIkFCxZgwoQJeO211xAVFYXvv/8ev/76qzMvwyHKfaCwe/fubCgSEREREREREVUQRqGAsQSrHhc1b1xcHAYNGoSYmBj4+PigSZMm2LhxI7p16wYAiImJwbVr1yz716hRA+vXr8f//d//YeHChQgLC8PXX3+Np59+uthldhXlPlBIREREREREVNFFx9zBz38dwj8XbiHAxwNPPdoY3VvXLfcLJxDZI0OCjJLMUVi0vN9//32B25cuXWqT1qlTJxw5cqRI5ykPGCgkIiIiIiIicmEnL8dgxJz/wGA0wigLXI1NwqGz13HycgzeeqHLgw9AVM6UdY9Cuoc1R0REREREROTCPv91B/QGU5AQAOS702/9uuUortxKdGbRiKiCYaCQiIiIiIiIyEWlZmTj5OUYS3AwL4UkYdc/l51QKqLSZYSixA8qHg49JiIiIiIiInJRD5qCUME5CqkCkoUEWZRgjsIS5K3sGGIlIiIiIiIiclFe7jq0qFMNCoVt4EMWAp2b13RCqYioomKgkIiIiIiIiMiFTXyxC3QaFZR3g4XmoOGr/dogPNjPmUUjKhVyCYcdywx3FRuHHhMRERERERG5sDrhgfht+hD8tvUoTlyOgb+3O558tDHaN67h7KIRlQpZKCCXYOXikuSt7BgoJCIiIiIiInJxoVW8Mf75Ts4uBlGZMEKCEcWfZ7AkeSs7BgqJiIiIiIiIHMgojIi6fQz7E49BhoxWfo3QMbAV1Aq1s4tGRFQgBgqJiIiIiIiIHMQojJh15hscTjoFBSQIAFGJx7A5bh+mNXwTWqXG2UUkcnkceuw8rDkiIiIiIiIiB9mZcBCHk04BAGQICAgAwLm0K9gQu8uZRSMqN4y4N/y4eA8qLgYKiYiIiIiIiBxkb8IRSHbmRxMQ2JVw0AklIip/zD0KS/Kg4mHNERERERERETmIQRgsvQjvp5cNZVwaIqKiYaCQiIiIiIiIyEFa+DWw26NQAQUe9m/shBIRlT9GoSjxg4qHi5kQEREREREROUj34A7YHLcPMVnxkO/2LFRAAV+NN54Ie8zJpQMMcgZiMjYiU38DnpqHEOLeDRK4wAq5FgEJsp2Ae1HyU/EwUEhERERERETkIG4qHT5tMhGrb27GnoTDMEJGW/9meLJaN/hqvJ1atpScUzgQ+xr0ciokqCBgwFnll3g4+HsAbk4tGxG5BgYKiYiIiIiIiBzIU+WOlyP64+WI/s4uioUQRhyOGwe9nG56DtN8ibnGRPyTMBm1VV87s3hEVko6fJhDj4uPgUIiIiIiIiKiCu5O9mFkG2Nt0gWMSM09iywRAyCo7AtGZIcsJMii+MOHS5K3smOgkIiIiIiIiKiCy5WTC9xuEBllUxCiQjBCAWMJ1t8tSd7KjjVHREREREREVMH5ahsD+SzwoJDUcFOFlW2BiMglMVBIRERERFSOCCEghHB2MYionHFThSLccwDsBQsf8hkKlcK97AtFlA/z0OOSPKh4OPSYiIiIiKgciE5Kwud79mDLxUsAgG61amFixw6I8PV1bsGIqNxoWOUDaFVBuJq6HHo5FRpFAB7yHYYIz0FISEhwdvGILGQoIJegb1tJ8lZ2DBQSEREREbm42LQ0PP3Lr0jLyYHxbm/CTRcuIOraNawbMhjBnp5OLiERlQcKSYU6fqNR23ckDCILKskdkqSALMvOLhqRFaOQYCxBr8CS5K3sGGIlIiIiInJxS48ctQoSAoBRCKTm5GDZkaNOLBkRlUeSpIRa4QlJYkiAiKyxRyERERERkYuLunbNKkhoZhQCUdeuOaFEREREpaek8wxyjsLiY6CQiIiIiMjFeWm1UEgS5PuChQpJgrdO66RSERERlQ4hFJBF8Xu8ihLkrexYc0RERERELm5Aw4Y2QUIAkIXAgIYNnVAiIiIiqogYKCQiIiIicnFPNqiP/vXrAwBUCgVUClMz/qkG9dGvXj1nFo2IiMjhjJBK/KDi4dBjIiIiIiIXp5AkfNGrJwY2aYwtFy8BAB6vVRMPV60KSeKXISIiqlhkUbJ5BmXbTvhUSAwUEhERERGVA5IkoXW1amhdrZqzi0JERFSq5BLOUViSvJUda46IiIiIiIiIiIjYo5CIiIiIiIiIiFyHDAlyCeYZLEneyq7c9yi8efMmXn75ZQQEBMDd3R3NmjXD4cOHnV0sIiIiIiIiIiIqBqOQSvyg4inXPQqTkpLQvn17dOnSBRs2bEBQUBAuXboEX19fZxeNiIiIiIjKgEGWsfXKJZy6HY9gD0/0rVUXPjqds4tFREQlwDkKnadcBwpnz56N8PBwLFmyxJIWGRnpvAIREREREVGZScjIwIurf8fFpDtQKRQwyjJm7tmB7/o+hXbVqju7eEREROVOuQ4U/vnnn+jRoweeffZZ7Ny5E1WrVsWoUaPw2muv5ZsnJycHOTk5luepqakAAFmWIctyqZZXUf5HehdZSeqU9UVEROQY/HyhiurdHZtxJTkJgKlnIQBkG4wYsf5/+HvYSLir1c4sHhERFZMMCXIJhg9zjsLiK9eBwsuXL2PRokWYMGEC3n33XRw4cABjx46FVqvF4MGD7eaZNWsWpk+fbpOekJCA7OzsUi1vbVXtUj2+K4qPjy92XtYXERGRY6SlpTm7CEQOdycrE1uvXIK4L11AID03F1uvXEK/OvWcUjYiIioZUcLFTAQDhcVWrgOFsiyjVatW+OSTTwAAzZs3x6lTp7Bo0aJ8A4VTpkzBhAkTLM9TU1MRHh6OwMBAeHt7l2p5LxgulOrxXVFQUFCx87K+iIiIHEPH+dqoAkrNybEJEppJABKzMsuyOERERBVCuQ4UhoaGokGDBlZp9evXx8qVK/PNo9VqodVqbdIVCgUUitId6iqj8g37KUmdsr6IiIgcg58vVBFV9fKGn06HJDujggSA5iFhZV8oIiJyCFmUcOgxVz0utnLdamzfvj3OnTtnlXb+/HlEREQ4qURERERERFQW1EolxrVuZ5OukCR0qh6JpsEhTigVERE5gnnV45I8qHjKdY/C//u//0O7du3wySef4LnnnsOBAwfw7bff4ttvv3V20YiIiIiIqJQNbtwMGqUS8w/uR0x6GtxUKjzfoDEmte3o7KIREVEJsEeh85TrQOHDDz+MP/74A1OmTMGMGTNQo0YNzJs3Dy+99JKzi0ZERERERKVMkiS80LAJBjZojLTcHLip1FArlc4uFhERUblVrgOFANC3b1/07dvX2cUgIiIiIiInkSQJ3lou2kNEVFHIJVz1uCR5K7tyHygkIiIiIiIiIqKKg0OPnYeBQiIiIiIiIiIichkMFDoPl4EhIiIiIiIiIiIiBgqJiIiIyPXs2rUL/fr1Q1hYGCRJwurVq622CyEwbdo0hIWFwc3NDZ07d8apU6ecU1giIiJyKHOPwpI8qHgYKCQiIiIil5ORkYGmTZtiwYIFdrfPmTMHc+fOxYIFC3Dw4EGEhISgW7duSEtLK+OSEhGVnvScHFy7k4xcg8HZRSEqU2UZKJw1axYefvhheHl5ISgoCE8++STOnTtXYJ4dO3ZAkiSbx9mzZ0t66U7HOQqJiIiIyOX06tULvXr1srtNCIF58+bhvffew4ABAwAAy5YtQ3BwMH755ReMGDGiLItKRORw6Tm5mLlxO9YcPwuDLMNDq8ErbVti5KNtoJDYU4rIkXbu3InRo0fj4YcfhsFgwHvvvYfu3bvj9OnT8PDwKDDvuXPn4O3tbXkeGBhY2sUtdQwUEhEREVG5cuXKFcTGxqJ79+6WNK1Wi06dOmHfvn35BgpzcnKQk5NjeZ6amgoAkGUZsiyXuFyyLEMI4ZBjVQasr6JhfRWN0ZgFOTsKhpR/oFD4QXLrD0lV3dnFKrRxv6/B31euQxYCCgBZOblYsCMKBqMRYzq3dfj5+PoqmspSX868PgFARvGD4qII+27cuNHq+ZIlSxAUFITDhw/j0UcfLTBvUFAQfH19i15AF8ZAIRERERGVK7GxsQCA4OBgq/Tg4GBcvXo133yzZs3C9OnTbdITEhKQnZ1d4nLJsoyUlBQIIaBQcIafB2F95S8pLQtHzt9AVnYuIkP90SDS9FpnfRWOkFMgp36BlDQNRGYsJEkGsB2S+2BI2nbOLt4DXbuTjDu3b6O2l5vNtr0nz6B/7Qho1Y79Ks/3Y9FUlvpy5nQejlr12PyjoJlWq4VWqy0wb0pKCgDA39//gedp3rw5srOz0aBBA7z//vvo0qVLMUvsOhgoJCIiIqJySbpv+J0QwiYtrylTpmDChAmW56mpqQgPD0dgYKDVsKHikmUZkiQhMDCwQn9xdBTWl30b9p/BR0v/ghCAQiHBYJRRr3oQvh7/JOurkOSUOZARBcg1Eeh7HgrJ3CvqA0gBWyApgwvM72y7b8bjXGpmvtuz1BqEB1Vx6Dn5fiyaylJfOp3Oaed2VKAwPDzcKn3q1KmYNm1avvmEEJgwYQI6dOiARo0a5btfaGgovv32W7Rs2RI5OTn46aef0LVrV+zYseOBvRBdHQOFRERERFSuhISEADD1LAwNDbWkx8fH2/QyzCu/XgQKhcJhX/QkSYJCocD567exbt8ppGRko2mtqujdtj7ctGqHnKMiMddXRf6iXRRxd9IwY8lfMMqmQXPmf89dT8Ci1VEY3r0x6+sBhMiBImcDIAlIkoBCkvMECgEpdyMkj2FOLOGDBXp5Ir8BnxKAAE+PUnkN8P1YNJWhvirCtV2/ft3qx8AH9SYcM2YMjh8/jj179hS4X926dVG3bl3L87Zt2+L69ev4/PPPy32gsPzfdSIiIiKqVGrUqIGQkBBs3rzZkpabm4udO3eiXTvnDyv85a8jeHnGz/h92zFs+vssZv20BQOn/ojbyenOLhq5uE0HzkLYmVhLlgXWRZ2u8POhOYTIAWDMZ6MECNd/H7avGYEgLw+bRUuUkoQudR5CoGfBiysQVQSOWvXY29vb6lFQoPDNN9/En3/+ie3bt6NatWpFLvMjjzyCCxcuFPuaXQUDhURERETkctLT03Hs2DEcO3YMgGkBk2PHjuHatWuQJAnjx4/HJ598gj/++AMnT57E0KFD4e7ujhdffNGp5U5ITsfX/90FwNQbzNwjLDYxFV/9Z5czi0blQGpGNhQK+0PtcvVGGIwMFD6Q5AUoawF2F0EwAurWZV2iIlMrlVj8wpPwdzfNUWgOGNYNDsTHT3RzZtGIyoyjAoWFIYTAmDFjsGrVKmzbtg01atQoVpmPHj1qNdKhvOLQYyIiIiJyOYcOHbKaENw8t+CQIUOwdOlSTJo0CVlZWRg1ahSSkpLQpk0b/PXXX/Dy8nJWkQEAxy7chKSQAKN1tzCjLLD54HlMHd4TKiV/qyf7mtaqiqXrD9qkSxJQq2oVaBy8gEVFJEkS4PU2kDQK1v1iFIDmEUDj+oFCAGgQGoRt41/BtvOXEZeajrrBVdAmMrzAeViJKhIhJIgSzFFYlLyjR4/GL7/8gv/973/w8vKyLJrm4+MDNzdTwH7KlCm4efMmfvzxRwDAvHnzEBkZiYYNGyI3Nxc///wzVq5ciZUrVxa7zK6CnzRERERE5HI6d+4MYW8M5l2SJGHatGkFTkjuDLl6IyRIAGzLbjDKMMoyA4WUr3aNI9EgMhhnr8VDvtsbVZIAIYARTzp/WH15Iem6QPL9Bkj7A8B5QPIB3J+D5Dm6XAXaNCoVejao4+xiEFV4ixYtAmBqe+S1ZMkSDB06FAAQExODa9euWbbl5uZi4sSJuHnzJtzc3NCwYUOsW7cOvXv3LqtilxoGComIiIiIHKRWtSow2plHTiFJaFAjBFr2CKMCKBUK/OutZ7Bg5R6s2XsKOXoDalatglFPtUeHJjUQHx/v7CKWG5K2AxTedaAI+qJCLMhAVNnIkCDbnUKg8PkLq6AfJs2WLl1q9XzSpEmYNGlSUYtVLrClQkRERETkIHXCA/FIgwjsP33VsiiFQmHqY/jmMx2cWjYqHzzdtXhnUFe8/VIX6A1G6DSm1bK5kAkRVSZFnWfQXn4qHv60QkRERETkIJIkYc7ofnj9ibYI9veCu1aNNg0i8N07z6Nl3XBnF4/KEaVCYQkSEhERlRX2KCQiIiIiciCtWoXXnmiL155o6+yiEBERlUtluZgJWWOgkIiIiIiIiIiIXAaHHjsPA4VEREREREREROQy2KPQeThHIREREREREREREbFHIRERERERERERuQ5RwqHH7FFYfAwUEhERERERERGRyxAAhChZfioeDj0mIiIiIiIiIiIi9igkIiIiIiIiIiLXIUOChBKselyCvJUdA4VEREREREREROQyuOqx8zBQSEREREREVAlcS0hGWlY2aoZUgU7Dr4JE5LpkIUEqQbCvJAuhVHb8dCAiIiIiIqrArsTdwXs/b8Sp63EAAE+dBm/0bIuXO7dwcsmIiMjVMFBIRERERERUQWVk52L4/P8gJTPLkpaenYvPVu+Et7sWT7Ru6MTSERHZJ0QJVz3mssfFxlWPiYiIiIiIKqj1h88iKT0TRtn6W7ME4LvNB5xTKCKiBzDPUViSBxUPexQSERERERFVUBdjE6FUKGCQZat0AeBqQjKEEJAkfqEmItfCxUychz0KiYiIiIiIKqgwPy/I+YzBq+LtwSAhERFZYaCQiIiIiIiogur7cH2oVUrYiwe+9Gjzsi8QEVEhyEIq8YOKp1wHCqdNmwZJkqweISEhzi4WERERERGRSwjw8sD81/rD201nSZMAPN22MYY81tJ5BSMiKoB5MZOSPKh4yv0chQ0bNsSWLVssz5VKpRNLQ0RERERE5Fra1KmOLTNeQ9S5a0jLykGzGmGoFuDj7GIREZELKveBQpVKxV6EREREREREBdCoVOjU8CFnF4OIqFBMvQJLspiJAwtTyZT7QOGFCxcQFhYGrVaLNm3a4JNPPsFDD+X/AZiTk4OcnBzL89TUVACALMuQ71sJzNEU5Xukd7GUpE5ZX0RERI7BzxciIiIqT7jqsfOU60BhmzZt8OOPP6JOnTqIi4vDxx9/jHbt2uHUqVMICAiwm2fWrFmYPn26TXpCQgKys7NLtby1VbVL9fiuKD4+vth5WV9ERESOkZaW5uwiEFEFJguBa3eSoVYqUdXXO9/9cnL02LznLI6duQEPNw16PNoADWqHlmFJiai8EHcfJclPxVOuA4W9evWy/L9x48Zo27YtatasiWXLlmHChAl280yZMsVqW2pqKsLDwxEYGAhv7/w/1BzhguFCqR7fFQUFBRU7L+uLiIjIMXQ63YN3IiIqhu3nL+OjjdtxM8U0Uqt+cCA+6vs4GodZTw+VnJqJUR+swNWbd6BUSIAk4b8bjuK1ge0x9Jm2zig6ERHZUa4Dhffz8PBA48aNceFC/gEmrVYLrVZrk65QKKBQlO5QVxmVb9hPSeqU9UVEROQY/HwhotJw9MYtjPr9T4g8k4Gdi7+NwT/9F+tHDkGoj5cl/dtf9+J6TBIAwCib+gopMnPx4+x1uLj+JFq2q41uz7aGp497WV8GEbkgDj12ngrVaszJycGZM2cQGsru60RERERERKXp33sPQYL1ED9ZCOToDfj18D9W+27adRqyfG9PdVw6PE/GQxOXjsNbT+HfH/0PI7p+ithriWVTeCJybcIBDyqWch0onDhxInbu3IkrV67g77//xjPPPIPU1FQMGTLE2UUjIiIiIiKq0E7FxsFoZ2lRoxA4E5dgeS6EQK7eYHku5Rqgu5ps+j8ACNM+yYnpWPThylIuNRGVC3d7FBb3AfYoLLZyHSi8ceMGXnjhBdStWxcDBgyARqPB/v37ERER4eyiERERERERVWih3l5QSLZfxpUKCSFenpbnmVm5aFg7FAqFaV/1nSy7x5ONMg5uP4PM9NJdZJKIiPJXrucoXLFihbOLQEREREREVCm92Kopjt6IsUk3ygLPtmiMlNQsfPntFmzfdw5GWUCoJEgSADn/MYFCCOhzDfluJ6LKQQjToyT5qXjKdaCQiIiIiIiInKNfo3o4H38b3+07ZJkOTKVQ4MNeXdAoJBivvf0TLl6JhywL01yGsoCslKD30UESgN7PDQCgTsqCJjYNChmoXicE3n4eTrsmInINXMzEeRgoJCIiIiIioiKTJAkTu3bECy2bYu/lq1ArFehc+yH4ubvhwNErOH8pzrKvUQXovRSAEFBDg1w3NXB32HKuTgWDvzs8zsbjtfefgGRnODMREZUNBgqJiIiIiIio2Kr6euO5Fo2t0s5dioNCIUGWBQQAg4dpenxlrgRJFpYgIQBAkiBrlejy5uNo2al+GZaciFxWSRckYY/CYmOgkIiIiIiIiBzK388D8t25CIUCEErTl3aFQc43z7kbt8ukbETk+jhHofOU61WPiYiIiIiIyPV0blsH7m4a62HED/jibuAiJkRkJhzwoGJhoJCIiIiIiIgcysNdi0/ffQruOjUkGYDR9K3dqM5/OGDHVjXLqHRERJQfBgqJiIiIiIjI4Zo3ro5VP7yB8a93haQwBQhlNSCUeTv9mMYXeggJgwa2d2p5ich1mFc9LsmDiodzFBIREREREZHDRV+7jc07z+Do5Zv3RgFKEvTugEIPKAwCgATtrVTkdA5ClsEArYZfUYnoLg4fdgr+FSYiIiIiIiKH+m31QSz8YQeUCgm5OgnQ4d5Kx5IEWQPIGtPz5Ed9AZUevx8+jtc7tnZamYnIdZS0VyB7FBYfhx4TERERERGRw2z9+xwW/rADAGCUBaQc+V6QMA8BwKgChMq0Qun/jp8p24ISEZENBgqJiIiIyKH0ej2uX7+Oc+fO4c6dO84uDhEVQ2xqGjaduYD90ddhlOVC5/v3hr/x7tdrrEYMKoyAMls2RQOFaYt5e44/AEmCAHDx9h1sOHnOUZdAROUZVz12Gg49JiIiIqISS09Px/Lly/Hrr7/iwIEDyMnJsWyrVq0aunfvjtdffx0PP/ywE0tJRA9ilGV8tHE7Vhw+bvmeHeLtifnP9EOTqiEF5j1zLQ7/WrMPWjtxRXWGgMIAGHQSjBpA1gK53oBRZ+ppKAGQFcD0ddvQvUFtKBXs00JUuUl3HyXJT8XBv75EREREVCJffvklIiMj8e9//xuPPfYYVq1ahWPHjuHcuXOIiorC1KlTYTAY0K1bN/Ts2RMXLlxwdpGJKB/f7j1oFSQEgPi0DAxfvgpp2Tn55gOA9QfPQqmQYNRKNl/RJQCqbAFdsowcP4GsIAkGnWQ5j3k6saSsbFy+fa8ncoYhB0sub8ULe+fiqV2fYtaplUjMTi3pZRIRUT7Yo5CIiIiISmTfvn3Yvn07GjdubHd769atMXz4cCxevBjff/89du7cidq1a5dxKYnoQYQQWHbgqM2IPVkIpOfkYO2ps3ihZdN882dm5wKQYHCTYFSbVja2BAzzHNTvnIyMMIGMqkrT8SVYurBIBkClMKVnG/UYdfAbXEi7Zcn+vxsHse7WYXzRfBjaVOHfEaIKq6TDhzn0uNjYo5CIiIiISuQ///lPvkHCvLRaLUaNGoVXX321DEpFREWVYzAiKTPL7jalQoHrSSkF5m9VJ9w0n6EkITNQCYNGQMqVIeXKlrkJzTFBz1sCklFAKO8mCECSgYcC/BAZ4AsAWH/rMM7nCRICpjVRjEYZE/9ehts57FlIVGFxjkKnYaCQiIiIiIiIoFUpEeTpYXebQZZRs0pAgfkfb14bdasFQqGQoEs0wCMmF5pUvemRnAuF/t7khRIA5d2RzJJeQJNshHuOhJlPdod0d4XkvQln7X/ZVwCGbBmrov8uzmUSUXkgpJI/qFg49JiIiIiIHCY7Oxvz58/H9u3bER8fD/m+1VKPHDnipJIR0YNIkoTX2j+MmZt2WKUrJQl+7m7o1aBOgfnVKiW+Hf8Mpn+5BseP3Ld6sQyoUvXQ+6gBlQIqlQKvN26B9f85BDk+C5IwnX+d2Ifa7/SFp5cOKklhChTa+75vkHDw6FW8XrdEl0z3OXk1DluOXoDeaETb+hHo0KAGFAoGXIgqEwYKiYiIiMhhhg8fjs2bN+OZZ55B69atLT2DSoPBYMC0adOwfPlyxMbGIjQ0FEOHDsX7778PBVdMJSqWQQ83Q0pWNr7dexC5RiMAoFZgAL4c0BvuGnW++TIycvDdst3YsPkEsrP1kDxVUGQboTDcG3IMIaBO00Pvq4GXlw5/LNkHdYbREgcUQmD39rNIuJ2Gfi8+Ap9YX0ArAe73dSuUAemSBvBVOvz6KyshBP7YdxL/3n4CkiRBgoRfdx7DI/Wq4+sR/aFRM3RAZUsIy4wFxc5PxcN3OxERERE5zLp167B+/Xq0b9++1M81e/ZsLF68GMuWLUPDhg1x6NAhDBs2DD4+Phg3blypn5+oIpIkCW92aouhbVrgXHwCfHQ61AoMKDDobzTKmPje7zh3IRaybPp2LhQSDO4qAAJGnRJCIUGZY4Q6TQ9FlgEJaRlwzxMkNDNoFTh2MwHHPl8LAFBLfjA+kgm5Rfa9noV3lBAn3NBuUME9HKnwDpy/ju3HLwEAjPK9Cd7+PncNy3ccxbBuDzuxdFQpleFiJrNmzcKqVatw9uxZuLm5oV27dpg9ezbq1i24y/LOnTsxYcIEnDp1CmFhYZg0aRJGjhxZgkK7Bqf91GowGLBlyxZ88803SEtLAwDcunUL6enpzioSEREREZVQ1apV4eXlVSbnioqKQv/+/dGnTx9ERkbimWeeQffu3XHo0KEyOT9RReal06JV9WqoHVTlgT2D/z50GWfOxViChAAgJAlGnQIGTxWESgKUEoxuSmQH6WDUArIkbIKERg8JBk+1acWSuyQhQRXlAWmvDpq1EnSLlNAs1cJNocbzXRi8cpR1B89AaSc6IATw5/7TZV8gojKco3Dnzp0YPXo09u/fj82bN8NgMKB79+7IyMjIN8+VK1fQu3dvdOzYEUePHsW7776LsWPHYuXKlY64eqdySo/Cq1evomfPnrh27RpycnLQrVs3eHl5Yc6cOcjOzsbixYudUSwiIiIiKqEvvvgCkydPxuLFixEREVGq5+rQoQMWL16M8+fPo06dOvjnn3+wZ88ezJs3r1TPS7aEeUXbUhxqTq4r6u9LNmlCAQACiiwjZK0SUEqmAKAQMOpUSK+ugGeMqZOg7C0ju18OEO0OxXkByc4XfO1eFbRX0wGjDElkQnsjE+cOX0HzDqYeP7Is4/LpWzAYjKjZoCqUKk4/UBSZ2bm4b0pZi/TssIm2iQAAsK9JREFUnLItDFEZ27hxo9XzJUuWICgoCIcPH8ajjz5qN8/ixYtRvXp1S5ujfv36OHToED7//HM8/fTTpV3kUuWUQOG4cePQqlUr/PPPPwgIuLdy1lNPPYVXX33VGUUiIiIiIgdo1aoVsrOz8dBDD8Hd3R1qtfWcZnfu3HHYuSZPnoyUlBTUq1cPSqUSRqMRM2fOxAsvvGB3/5ycHOTk3PvCm5qaCsAUYLh/0ZXikGUZQgiHHKu8iElIwTcr9mDXwUuQhUC75g9hxMD2iAjzf2DeylhfJeGq9RVzKxmbNx6HBHGvJ6BRhjY2BaqUHEgwdezJDfJATjUfQCEBCiX8z+Yi108JTYYBWcOzIbwElCcUUECyv3iJWgnF3TkTIQG5mTl47+XFePdfQ+Dp64F57/yO+FtJAABvX3e8+v4TaNimqsvVl6tqUbMqbsXEQpKshx0qFBLa1A1nPd7HVd+PjubM65OE6VGS/MC9z3ozrVYLrVZbYN6UlBQAgL9//p9lUVFR6N69u1Vajx498P3330Ov19u0f/KjVCoRExODoKAgq/TExEQEBQXBaP67V4acEijcs2cP9u7dC41GY5UeERGBmzdvOqNIREREROQAL7zwAm7evIlPPvkEwcHBpdrD7LfffsPPP/+MX375BQ0bNsSxY8cwfvx4hIWFYciQITb7z5o1C9OnT7dJT0hIQHZ2donLI8syUlJSIISoFIuppKVnY+7SbcjMykV4kOlLV0xMDD5ZuBr/N6QL/H09Csxf2eqrpFy1vlavOoBqoTrIeXrwqRJSIYV5QAq79xoQAAxaQF/FHQCgcVNDKAFDNQNkb9PrR1R3g0qlso0TCgFFRi7UStsv7b8uWge9txtUNTwRFukBRbYeUpYeK5f8BZVHJ4iGrlVfrqpdzUCcP+8NSQKMd2NDSoUpiPHMwzURHx/v3AK6GFd9PzqaeZo4p3DQHIXh4eFWyVOnTsW0adPyzyYEJkyYgA4dOqBRo0b57hcbG4vg4GCrtODgYBgMBty+fRuhoaGFK2Y+q67k5OTYxMzKilMChbIs242K3rhxo8zmtCEiIiIix9u3bx+ioqLQtGnTUj/X22+/jXfeeQcDBw4EADRu3BhXr17FrFmz7AYKp0yZggkTJliep6amIjw8HIGBgfD29i5xeWRZhiRJCAwMrNBfHM3W7tqHk5eTrealAwClQsLGqGiMH9KlwPyVrb5KylXra9vWS0jKzAaMMlTpuZCy9FBm2A+8CwlIaxoCpR7QJuthVEnQyzL0bdMhpSqRnq2A340cQNzrVCggIAlAezEByiyD3eMafHWQhAShkCC7aWDwUEGTnI7D2y6hQ5c2LlVfrkqWZbzU/RH8uv8c/jp8EXqjEe3rR+KNvm1RO6yKs4vnclz1/ehoOp3O2UUosevXr1t9xj+oN+GYMWNw/Phx7Nmz54HHvv/H0KJMw/H1119b9v3uu+/g6elp2WY0GrFr1y7Uq1fvgccpDU4JFHbr1g3z5s3Dt99+C8BUMenp6Zg6dSp69+7tjCIRERERkQPUq1cPWVlZZXKuzMxMmy9oSqUy36FS+Q03UigUDvuiJ0mSQ4/nyg6fugGj0bYnhMEocOjk9ULVQWWqL0dwxfpSKZVAjhHqRNP7XtIbIeQCugHlGqHMkCELQMo1QhUtYLiigNjrC2NVCcnhAt7XAMXdQ8hKQHc5GYoMvf3ORWoVFKm5EEYjYDRCoVBAHegNg7cHzsUkYfPGE+jeqymUeVbqkIXAz5cO4OdLBxCXlYa6PsEYUbcDuoYVvMJpRefj4YYPX+iOaS/1dHZRygVXfD86mlOvrYgLktjND8Db27vQPwa++eab+PPPP7Fr1y5Uq1atwH1DQkIQGxtrlRYfHw+VSmU1xV5+vvzyS1MxhcDixYuhVCot2zQaDSIjI522fodTAoVffvklunTpggYNGiA7OxsvvvgiLly4gCpVquDXX391RpGIiIiIyAE+/fRTvPXWW5g5cyYaN25sM0ePI3rumfXr1w8zZ85E9erV0bBhQxw9ehRz587F8OHDHXYOyp+Xhw4KhWTTo1CSAC+PgntsUMXRpWsD/LF4B4C7vQAL6EkjAOg9lVAYALfb6VAYTEF97XxA75UGo8IL2jTT93tZMu0vCQmZVXXwSs60HpIsSRAq0xdroZAApQrIMkBKS4eUmg6lQgIQjC/f/Q1HDl7Bu9OesvTymXFsPVZcOWw51MmkWxi9/zfMatkfT0WUfm9oIioEBw09LtSuQuDNN9/EH3/8gR07dqBGjRoPzNO2bVusWbPGKu2vv/5Cq1atCjU/4ZUrVwAAXbp0wapVq+Dn51f4ApcypwQKw8LCcOzYMfz66684cuQIZFnGK6+8gpdeeglubm7OKBIREREROUDPnqaeKF27drVKF0JAkiSHTso9f/58fPDBBxg1ahTi4+MRFhaGESNG4MMPP3TYOf6fvfuOb6p6/wD+OTdp093S0kILLZRV9hbZe8heiiLKEAeKyNAfijhAUb4uRERRFEFEBZEhDhAUyt5Q9iizjEIZ3TPJPb8/0oaGtkhXbtp+3q9XXpI7n3tMk9wn55yH8tazQ11s259LtVsJ9OpQT4OISAvt2tTE6i833VmgUyAzqxtnT+xJABnlXSENOhiuJ0CYbHv+6pJNMCRYCqJk5RqFlNCdvQbn89dhSs2AcNJD8fSE4uZ2J0loNEKYzFCNRgijZWiyEMKSFJQSyuWb2DxvPcrrFTz7Rn9cSo61SRICgJqZUfj46D/oE1wfTooORCVJcnoG/og4gZNXb8DfywMDmtZFULmi+2FOE3ZMFI4dOxY//fQTfvvtN3h6elp7Cnp7e1tzVFOmTMGVK1ewePFiAMCYMWMwd+5cTJo0Cc888wx27tyJBQsW5Lvz26ZNm/57IzvTJFEIAK6urnjqqaf4iy8RERFRKWLPL7yenp6YPXs2Zs+ebbdz0h0dW9RE/y4N8du/h63DOs1mFV1ahaFPp7wngKfSxd3jrjnMhABcDEBauiVrnMnk5YzksHLQpZqhTzNbKsbGJ0C9dRswWX5A0J8ATO1qQTi5AIqA7vQV6KJuWg4LQBpNUG/HQmYYIby9gPgECA9LwRSRng5kJviklBAQ1h6EIjUNq+ashU6q8Bmed0+hW+nJuJB0CzW9AvLchsjRRN2Kw4j5vyAmIRl6RYEqJb7euBsfD+2FbvVrah1eiTBv3jwAQMeOHW2WL1y4ECNHjgRgKdYVFRVlXRcaGoq//voLEydOxBdffIGgoCDMmTMHgwcP/s/zTZo0Ce+++y7c3d1t5k7OzaxZs/J3MUVAk0RhVgY2L8OHD7dTJERERERUlFq1apVnlb6bN2/aORoqTkIITH6mK3p1qIvNe89AVSXaNquOJnUrF2u1a3IsIdX8UbFSOVy/GneneqeiAK4uUFUVKZVdYfZ0hsnLGRACTrctcxmqMTch4+JtjqUHoN962vpcAja9Eq0FTpKSgPR0CHdLBWUYTYBQIE0mS09GIaCaJGRGhiUmRYFMScXyuetR7XwdoG/e1+Om16bKKFFBTVv1D24lpQAATJlz9KpS4tVl69CyRgg8XUroVBB2Hnr8XxYtWpRjWYcOHXDgwIF8BGVx8OBBGI1GAMCBAwfy/MzU6rNUk0Th+PHjbZ4bjUakpKTA2dkZbm5uTBQSERERlVBDhgzBypUrc0yAfv36dXTp0gVHjx7VKDIqDkIINAirhAZhlex+7vQME1JS0uHl6WpTqILsS1EUvDR9AN4a8z2MZkuFYikAAYH4el4w+bhAMQNQJZzjM2CISYc0GnMkCXNzr1tkaTQCimLpaWgyWpKEyHZjLYQlYZmWBqnTQwAQJhPO/34M5fwqIa61sMkj6IRAPZ8gVHLzKVhDEGngZmIydp+9lOu6dJMJm46fRb+mde0cVREpomImjuizzz6zztkcHh6ubTC50OQTNTY21uaRlJSEU6dOoW3btixmQkRERFSCRUdHY/To0TmWdezYEbVr19YoKiqICzdj8fk/OzBjzUb8EXESGZmJGK2lpGbg43nr0evxOeg/4ksMfuorrPrr4H31CKHi0bRVDXyxYhxCWgYj3UePlEBnxLTxRmItV5i9AO/jCfA5kQj3y2lQhAI1La1Izitvx1r+YTLfmZcwk/XfOh2Q+dqVqmU2wvKb0qATCnRCQDEqEFedoZx3Q/WESjh/M7ZIYiOyh+R0Y57rBICk9Az7BVPEhCz8w1E1adLEOsqiWrVquHXrlsYR2XKYn95q1qyJ//3vfzl6GxIRERFRyfHXX39hz549mDhxIgDgypUr6NixIxo0aIBffvlF4+jofi3fewS9P12E+Zv24Jc9RzD5l7UY9PmPuJ05vE0rUkpMfX8V/lh/GEajZV6723HJ+PTrf7Dij/wP/6KiU6VGAOZ9/Sy6vtoR8S28ke5nqfoZnOYM3d037Yb/rgh6v8zXrkOazXkmioWiAJnDMSEtyRNjdCJ+7/Ac+vo0hjjuARFjgDlWwe8HTqLf54ux8UTOIj1Ejqiyrxf8Pd1zXScBPBBa2b4B0X3x8fGxVj2+cOECVFX9jz3sS7NiJrnR6XS4evWq1mEQERERUQH5+fnh77//Rtu2bQEAf/75J5o2bYoff/wxx3BkckxXYhMwffW/kBIwQ1oLUly8FYtP1m3Few/30Cy246ejsf9wVK7rFi3bgQE9G0OvZ8Vareh1Cqb264QxnR/E6Ws34OfhjiP/nMY3+zfYFDaBlydUFF2vFZmWBhhcIHQ5hxpKKe/MVWY2A056QK/D1oX7sF/EAFJaQzOrEgISU1b+jS2Tn4XByaFul4ly0CkKJj3UFlOW/43MQt8ALAnxXo1qo2bF8prGVyh2nKPQ3gYPHowOHTogMDAQQgg0b94cOl3un13nzp2zc3QaJQrXrFlj81xKiejoaMydOxdt2rQp8HFnzpyJ119/HePHj2f1OyIiIiKNVK5cGRs2bEDbtm3RrVs3/PDDDyxuUYKsPXwq1+VmVeKPQycxfWA36DWaE/DE6Wibm+HsEhLTcP1GAioFlrN/YGTDz8MNrWpUAQC4tJWYP3u9zXrprODW4Ooov+LsPechzJeMDKjOzlDufm2qKiAs8xEKJXPeQmdn/PXrblzu6p3jMBJAQlo6dp+/hPa18q6QTOQo+jWtC3eDM77auBunrt2An4cbHm/VGKPaNdc6NMrD/PnzMWjQIJw5cwYvvfQSnnnmGXh6emodlpUmicIBAwbYPBdCwN/fH507d8Ynn3xSoGPu3bsX8+fPR8OGDYsgQiIiIiK6X+XKlcs1EZiSkoLff/8dfn5+1mW3b9+2Z2hUAEnpGVCEgJpLNs5oVmE0mzVLFPp4u+WaJAQstSs8PVzsGxD9p5Cq/uj/aAv8tmwPhBCQUkIHgbR6ATCtOIsiG4QsVQgprUOQBTIzyiYToOgsCUlXF8DDA0IIGO8xtxsApJvMRRUZUbHrUq8GutSroXUYlA8PPfQQAGD//v0YP348E4VFPf46KSkJw4YNwzfffIMZM2YU6bGJiIiI6N44kqN0aV61EuaH78mxXAAIC/SHq3PRzS+XX20frAEPdwOSUzJs5qRTFIG2LWrAy9NVs9gob8+/0hPVawfip8XbcOtGInTezjDcVhE9rRUqTtsJJ9ypcCyz/Tu/pNEIkXmLK1WzZaixTm/5IcPdDYqXl2WosaqiRec6iDLE51rsQa8oaF7V/pW8iegOgcIVJCkp4xgWLlxo8zwhIQEbN25EWFgY6tSpo0lMpWLShbFjx6J3797o2rXrfyYK09PTkZ6ebn2ekJAAwJK8LO4JJBXHqR1jN4VpU7YXERFR0Sjuz5cRI0YU6/HJvlrXqILmVSvhwMWr1l6FihCQkJjYo62msbkYnPD+6wPx2oyVSEnNgE6nwGxWUTXYDy8/303T2ChvRpMZy09E4mg5E4SvKyAlXG5JGN2Ba9NbA+kZcN9+FUKnQ7lYJ4izNyATEu//BJnj0aUAkhtXgOF8PHQmFaq7AelVfOBk1EMvLAluqQgIRWDkhF4IvXYFb//2j7UHbdZ/x3ZqiXJuTDoTaUoKy6Mw+5cAQ4YMQfv27fHiiy8iNTUVzZs3x4ULFyClxNKlSzF48GC7x2S3ROGkSZPue9tZs2bd97ZLly7FgQMHsHfv3vvafubMmZg+fXqO5Tdu3EBaWtp9n7cgauprFuvxHVFMTEyB92V7ERERFY3ExHzccBdAcnIy3N1zr7pYFNuTfSmKwFcjB+LLf3fi131HkZSWgQaVK2Jct1ZonTnvnJYa1w/Giu/GIHzHady8nYRa1QLQokkodBoNh6b/tnrbURw8cwVA1vySAqoT4HVBwuQhYXR1AupVgT5ewj8pEXHlfGBSVSAp+f5OkJkoVHUCSS0qI7lFMBQB+Pq5If5GMnQJJvgeToLUKYCTDhACu3afw5DBzRHg6Y4FW/fh7I3bqOzrjRGtmqJ3w7Biawsiuk+luJhJdlu2bMHUqVMBAKtWrYKUEnFxcfj+++8xY8aM0p0oPHjw4H1tl5+Jri9duoTx48dj/fr1cHG5v/lIpkyZYpO0TEhIQHBwMPz9/eHl5XXf5y6ISFNksR7fEQUEBBR4X7YXERFR0bjf70kFVaNGDYwbNw4jR45EUFBQrttIKfHPP/9g1qxZaN++PaZMmVKsMVHhuDk74ZWe7fFKz/aQUjpcMRp3NwN6d22gdRh0n/7ZHwkB2/t21VlAKoC3cIaPcEG1Kn4Y2rs5/vhsA3b/exx6P1+YoADJyYC8R69onc4yxBiATucMfaIRZk/nO+sVAaO3E0zeBugy7kTwxax1aNWmJjqGVUPHsGpFe8FERPcpPj4evr6+AIB169Zh8ODBcHNzQ+/evfF///d/msRkt0Thpk2bivyY+/fvR0xMDJo1a2ZdZjabsWXLFsydOxfp6ek5SkwbDAYYDIYcx1IUBYpSvL9Cqih7w0oL06ZsLyIioqJR3J8v4eHheOONNzB9+nQ0btwYzZs3R1BQEFxcXBAbG4vjx49j586dcHJywpQpU/Dss88WazxUtBwtSUglj8ms5tq5R+oFAquUw5Kpw6zLnEd3xM4NRwEAirsr1NRUwMMdiM/WMzpr3kFYfoSAogNUM4Qi4BKVgOR65TNPIOEUb4ZzrAqzmx6qQUKXZoZilIAQ2LzxBB55vGVxXTYRFUYZ6VEYHByMnTt3wtfXF+vWrcPSpUsBALGxscX+Q29eSvQchV26dMGRI0dslo0aNQq1a9fGq6++miNJSERERERFLywsDMuXL8fly5exfPlybNmyBTt27EBqairKly+PJk2a4JtvvkGvXr34oxhRGdS+UTUcPnc1R8VqRQh0bGxbqbV+i2p4dfYTmDdtJeJvSwgnJwi9M1QhIHQ5b1+FENZ8gExJgVPCnWrGzgkqXG+qlvMKAakDTB5O0CWb4GSWSP+PysdEpB0hC1nMpIQkCidMmIBhw4bBw8MDVapUQceOHQFYhiQ3aKBNz3nNEoV79+7F8uXLERUVhYwM20pTK1euvK9jeHp6on79+jbL3N3d4efnl2M5ERERERWvypUrY+LEiZg4caLWoRCRA3m4Q0P8sfM4Ll6LvVMgRxGoXN4bj3ZqlGP7jv2aos1DDXHm6GUkxSbh/aGzkQSR5zD47MlCOFtucRWjhD5D2lY+zZzL0OyqgxJvRJPmVYv0OomI8uuFF15AixYtcOnSJXTr1s36g2q1atX+s1hvcdEkUbh06VIMHz4c3bt3x4YNG9C9e3dERkbi2rVrGDhwoBYhERERERERUTHwcDVg4auP4sd/DuDf/ZGQADo3rYlhXZvC0y33oXVOznrUaVoVAJBVpyavYfAyW1fFZm3CsENNhJqYDpHboYUABNDogaqoW79yIa6KiIpVGRl6DADNmzdH8+bNbZb17t1bo2g0ShS+//77+PTTTzF27Fh4enris88+Q2hoKJ577jkEBgYW6tjh4eFFEyQREREREREVCU83F4zp1xpj+rXO974pSemAAKSqAkLkTBiqmXObKwoqm/XY+uHz+HvjMaxYuTXPY770Sk/Ov0nkyEpxonDSpEl499134e7ublNsNzezZs2yU1R3aJIoPHv2rDU7ajAYkJycDCEEJk6ciM6dO2P69OlahEVEREREREVISomII5dw+MgluLsb0Kl9bfj5emgdFjmo1NQM7Np1BikpGWjYMBjBwX4AkDnHoGJNCFqmHLQMRYaq3qmKHFAeG1fuwzNvDkSnNrXw+x87c5xDUQRqVgtAlcxjE5FjKs1zFB48eBBGo9H6b0ejSaLQ19cXiYmWqlWVKlXC0aNH0aBBA8TFxSElJUWLkIiIiIiIqAilpRnx+vSVOBBxETpFQJXAvG834dVJvdC9cz2twyMHs3NnJN6b8RtSU+8UGOnxUAO8/HIv+Ph74dbVWEhFAGYzgFw6Czk7QefjDVNyCm5Gx6JKWCAeHfgA3vt0I1RVQghAVSU83A14bWIv+10YEdFdNm3alOu/HYVdE4URERFo3Lgx2rVrhw0bNqBBgwYYMmQIxo8fj40bN2LDhg3o0qWLPUMiIiIiIqJi8P2P2xFxKAoAYFYtaR2zGfjfJ3+hQb3KqODvqWV45EBiYhIw7e1VMGcmAbOs//sIgoP9MHDcQ/j29Z8hoEAqsPQgzF5C2dUVSkglAIAwOKN8YDkAQN3aQfjhq9FY+89RxNxMRLWq/ujZtT68PF3tdWlEVFBSWB6F2d+BPfXUU/+5jRACCxYssEM0tuyaKGzatCmaNGmCAQMGYOjQoQCAKVOmwMnJCdu2bcOgQYPw5ptv2jMkIiIiIiIqBr+vO2StcHu3Df8ewxOPtbRzROSo1v99BKqq4u6Xi5TA6lX7sWTJGBzeehJ71h6E0CmQqgAgAVcXiMAKUPR66w7BNSrA08cNauYw5YoVvDH6yXb2vSAiKrxSPEchACxatAhVqlRBkyZNbAoyOQK7Jgq3b9+O7777Dh9//DFmzpyJQYMGYfTo0Zg8eTImT55sz1CIiIiIqAgZjUZMnToVK1euhK+vL55//nmMGjXKuv769esICgrK0WOISicpJZKS0nNdJ4RAXDynG6I7bt5MzCwskvNm+fbtJDg56/HOypcRsek4dq89gH/WRCBJVSAMBtuNhcDg0R3sEzQRFavSPEchAIwZMwZLly7FuXPn8NRTT+GJJ56Ar6+v1mEBABR7nqxVq1b45ptvcO3aNcybNw+XL19G165dUb16dbz33nu4fPmyPcMhIiIioiLy3nvvYfHixRgzZgy6d++OiRMn4rnnnrPZxtF+MafiI4RAWM0KuVaVNZtV1K0dpEFU5KiqVQuA2azmWC4EULWqPwBAURQ07VIfz388HFO/fwGKiwuyv7wURSA41B+d+ja2U9RERAX35ZdfIjo6Gq+++ip+//13BAcHY8iQIfj77781/75k10RhFldXV4wYMQLh4eE4ffo0hg4diq+//hqhoaHo1YsTyxIRERGVND/++CO+/fZbvPLKK5gxYwb279+PTZs2YdSoUdYvvLkljaj0GvlEW0gpcyZzKvuifZta2gVGDqdL13ooV84dimL7HiEl8MQTrXNs37RVDbw7bziqhQUCAPR6HTr3aYyPFj8DZ2dN6nUSUVGTRfBwcAaDAUOHDsWGDRtw/Phx1KtXDy+88AKqVKmCpKQkzeLS/F20evXqeO211xAcHIzXX38df//9t9YhEREREVE+XblyBfXr17c+r169OsLDw9G5c2c8+eST+PDDDzWMjopLutGE+OQ0lPNwhZNeZ7OuVYvqePfNgZi/cDMuXb4NnU6gY9vaGPtcZzg7661zyGVJSc3Apu2ncOVaHCoHlkPntmFwMTjZ83JII+7uBnw6exg++uhPHDt6BQDg7e2G0U93QIeOdXLdp3m7WmjerhbSUjKgd9JB76TLdTsiKqEKOfS4JCQKsxNCQAgBKWWOz0d70zRRuHnzZnz33XdYsWIFdDodhgwZgtGjR2sZEhEREREVQMWKFXH27FlUrVrVuiwoKAgbN25Ep06dMGLECO2CoyKXYTRh7podWL71ENIyTPBwccawzk3xTK8HoVPuDFpq17om2raqgaSkdDg762DII/F35nwMJrz1C+ITU6HXKTCZVXy1eDM+e/dRhIaUt9dlkYaCg/0wZ85wxMQkICU5HZWDfaHX/3fyz8XN2Q7REREVvfT0dKxcuRLfffcdtm3bhj59+mDu3Ll46KGHoCiaDAAGoEGi8NKlS1i0aBEWLVqE8+fPo3Xr1vj8888xZMgQuLu72zscIiIiIioCnTt3xk8//YQuXbrYLM9KFnbs2FGbwKhYvPPjP/hr70nrsPKktAzM/2sXktMy8PLDtsUkhBDw9HTJ81iqKvHGh78hMTkNAGDKnKsuPjEVb374G374/CkOWy9DAgK8tA6BiBxBKa96/MILL2Dp0qUICQnBqFGjsHTpUvj5+WkdFgA7Jwq7deuGTZs2wd/fH8OHD8dTTz2FsLAwe4ZARERERMXgzTffxMmTJ3NdV6lSJWzZsgXr16+3c1RUHK7cjMdfe07kuAeTAJZujsDoh1rAx8P1vo93IjIaV6LjcixXVYmLl2/j9LkYhFWvUKiYiYiohCnlicKvvvoKISEhCA0NxebNm7F58+Zct1u5cqWdI7NzotDV1RUrVqxAnz59oNNxDgkiIiKi0qJKlSqoUqVKnusDAwM5/LiUOHEpJs/7L5NZxdnoW2hWs/J9Hy8hKe2e6+MTUvIRHRERlQaikHMUFmp+QzsYPny4w/aWt2uicM2aNfY8HRERERFp5Pjx44iKikJGRobN8n79+mkUERUVP0+3e673/Y/1dwurHgBFEVDVnHd1Op2CWuxNSEREpcyiRYu0DiFPmlc9JiIiIqLS49y5cxg4cCCOHDlird4HwPqrudls1jI8KgKNqgUh2N8HV27F2yT3dIpA3ZAKCK3om6/j+fp4YGDPJljx54Ec6x7p0xQ+XvlLPBIREVHBaVdGhYiIiIhKnfHjxyM0NBTXr1+Hm5sbjh07hi1btqB58+YIDw/XOjwqAooi8Olzfa09B3WKJQkc6OuF/43uVaBjjnuqE54Z1hbemUVPfLxcMWZ4ezw/omORxExERCWMLIIHFQh7FBIRERFRkdm5cyc2btwIf39/KIoCRVHQtm1bzJw5Ey+99BIOHjyodYhUBKoHlcef7zyF8MNncflmPEIr+KJtg1A4FXAecp1OwfBHWuGJwS2RmpYBVxdnKIpjzt1ERETFr7TPUejImCgkIiIioiJjNpvh4eEBAChfvjyuXr2KsLAwVKlSBadOndI4OipKzk56dG8WVqTHVBQBdzdDkR6TiIiI7h8ThURERERUZOrXr4/Dhw+jWrVqePDBB/Hhhx/C2dkZ8+fPR7Vq1bQOj4iIiEoK9grUBBOFRERERFRk3njjDSQnJwMAZsyYgT59+qBdu3bw8/PDsmXLNI6OiIiISoTCzjPIJGOBMVFIREREREWmR48e1n9Xq1YNx48fx+3bt1GuXDlr5WMq/WJTU7H82FEcuX4d5d3c8Ei9+qgbEKB1WERERPQfmCgkIiIiomLl6+urdQhkR+diYzFk2VLEpqZCCAEB4PuICMzo0hWPN2xo3U5KiStJ8Tgv01HHNwA+BlftgiYiIofCYibaYaKQiIiIiIiKzNsb/0V8Wppl1JiUNsu7Va8Of3d3nE+4jXGb10AmJuOEKQk6RcFTdR7Aq007QGHPUyIi4tBjzShaB0BERERERKVDXFoqtkdFwSxz3qGpUmLdmUikm014fP1SnIqLsa4zqiq+PrYb847usme4RETkoLJ6FBbmQQXDRCERERERERWJDJM5z3VCCKSbTPg76jSiUxJzTSZ+e3wvzKpanCESERHRPTBRSERERERERcLf3R3VypVDboOHVSnhFStwJv4W9CL325DY9FQkZKQXb5BEROT4ZBE8qECYKCQiIiIioiIhhMDUDh0sT9TMuzQpASnhecGEhe/+jXKpzjDJ3HsNejoZ4OlssFO0ZG9SSphN7DFKRPeBiULNsJgJERERERHdFykltu05g3+2nkRamhHNG1VB764N4ObqbN2mU2g1tDjjhsMeiUj3VaBLk/A+a4LXWRNURSD9YCJ8/VyRkJFmc2wBYETtptAr7MtQ2iQmpmLB/HBsWHcY6ekm1K4ThKee7YimzUK1Do2IHBSrHmuHiUIiIiIiIvpPUkr87/N1+GvjUSiKgKpK7Nx/Fiv/Ooh5HzwOHy8367apZxIRlJT7EOJb1xKw5LHHMCZ8JZBmmdNQABhSoyHGN2pjj0shOzKZzHhl/I84fy4GamYv01OnovHqyz/jw08eR5NmVbUNkIiIbDBRSEREREREOZhMZty4ngBPL1d4eLpg36GL+GvjUQCwJnykBK5ej8PiX3bhpac7W/cNqVIep05ctW6XXXAVP9T1DcDG/s9gR+RxJLs6o6FfICp5eNnnwsiutm89jbNnrtssk6qEEAILF2xmopCIclfY4cPsUVhgTBQSEREREZGVlBKrlu/FkoVbkZiQCkURaNexNkQ5A3Q6BWaz7RxzqiqxYesJm0Tho0+0xrQpy222UxQBJ2c9evVrYnkuBGr5+CMgIAAKhxuXWkcOR+X6upFS4vjRyzCbzdDpdBpFR0QOi4lCzfATmYiIiIiIrH77dR/mfbYeiQmpACyJwK3hJ7Fv4ylAzb0Qhclktnnepn0YXnqlJ9zc7sxdWKGiN/736eMo78+eg2WJp6cLpMzjjl1K/Pr9DvsGRERE98QehUREREREBAAwm1T8+P3WHMtVVSIpNhXS2wlwtu1roFME2jxQPcc+fQc2Q/deDRF5MhoGFydUr1kRiiKKLXayr5SkNCTGJsOvog/0Tnn3COzSrT5+WLQt5wopIYwqfvl+Gx4e3ho6PXsVEtEdLGainRLdo3DevHlo2LAhvLy84OXlhVatWmHt2rVah0VEREREdnLlyhU88cQT8PPzg5ubGxo3boz9+/drHVaJFRubjLjYlFzXKYpAkK8nhBA2y1xdnTHy0da57mMwOKF+oxDUDAtkkrCUSE5IxccTfsCQ+q9hZKvpeLzJVPz61b85eg0mJaQiLTUDlYP90KJ5qGVCy6wHAKgSIt2MpIQ0xMfl/pq7cfk2dq+NQOSB83n3SqQidzspBV9v2I2x36zG1J//xt4zl7QOicoiWQSPfNiyZQv69u2LoKAgCCGwevXqe24fHh4OIUSOx8mTJ/N3YgdUonsUVq5cGf/73/9Qo0YNAMD333+P/v374+DBg6hXr57G0RERERFRcYqNjUWbNm3QqVMnrF27FgEBATh79ix8fHy0Dq3E8vB0gV6vwGTKOcRYVSWeHNISsSYj1m06htQ0I1o2C8XjA1sgqIKP/YMlu5NSYtqo+Ti+7xxUs+UuPDEuBQtm/AapSjzyQlfs33YaCz74C+dPRQOKQLW6QUgQEkpSBqSzDhACwqwCJgkBwODiBA8vV+s5EuNSsOffo/hz3noc237Kmlj09POEq6cLvPw80PXxtujzTBc4OZfo21mHdPlWPJ78fBniktOgSgmdIrBm73GMfagVxnRvqXV4VIbYu0dhcnIyGjVqhFGjRmHw4MH3vd+pU6fg5XVnSg1/f//8ndgBleh31r59+9o8f++99zBv3jzs2rWLiUIiIiKiUu6DDz5AcHAwFi5caF1WtWpV7QIqBVxcnND1oQZY/9dhm4rFiiLg4uqMTl3rw9XNGcMGPahhlKSVE/vP4+jus7muWzZ3A2o0CsEboxdAGk0AJODijLORlorHArD0IsxWuEYCaNq+FpwzE35rf9yBeW/+ivTrNwGz7byXibcSkHgrETFRN3Em4iJ2r43Ae7/9H3S6Ej1IzuF89NtmxKdYkoQAYM58H/hi3U481DgMVQPKaRkeUbHp2bMnevbsme/9AgICSt0PlCU6UZid2WzG8uXLkZycjFatWmkdDhEREREVszVr1qBHjx545JFHsHnzZlSqVAkvvPACnnnmmVy3T09PR3p6uvV5QkICAEBVVah5FOnID1VVIaUskmNp6dkXu+LqlVgciYiCEAJSSri5O2PazEdgcNEX2fWVlvayF0dor8jDUVB0Ss5hwMIyZ+F3//sDuB0HYTJlWycgXQ0Qvl6WZKEQlgcAk7se9dtXh6qqOHngAj6fsgxqcjKEVAFFAHo9oFMsGUWTyVJMR7HsH7H5OP75ZTua920KH1cXmyHxgGO0V0miqirSM0zYfuI8pMw5R5kiBP45dBpPdXlAk/gcTVl5fWl6fSWk6nGTJk2QlpaGunXr4o033kCnTp3sc+JiVOIThUeOHEGrVq2QlpYGDw8PrFq1CnXr1s1z++L+gngvSsmeErJACtOmbC8iIqKiUVo/X86dO4d58+Zh0qRJeP3117Fnzx689NJLMBgMGD58eI7tZ86cienTp+dYfuPGDaSlpRU6HlVVER8fDyklFKVkf495ZWo3RF28iSuXbsPDwwW161WCs7MeMTExRXaO0tRe9uAI7eXh74TgOr53Fuh11sSflCoykm4huK4vcsxGqddBqhIo5wWzpwGqQQcpACgC5dyAmJgYbF67G1Xq+8Ec52Q5prMzchzIZLb0NMy8/mU/rse868fh6+aKfg1ro2XVEOumjtBeJYmlveJQw9cNuX1iKAJQjKlF+h5QkpWV11diYqJ2Jy+iRGFWzieLwWCAwWAoxIEtAgMDMX/+fDRr1gzp6en44Ycf0KVLF4SHh6N9+/aFPr6WSnyiMCwsDBEREYiLi8OKFSswYsQIbN68Oc9kYXF/QbyXmvqaxXp8R1SYDxK2FxERUdHQ9It+MVJVFc2bN8f7778PwPKr/rFjxzBv3rxcE4VTpkzBpEmTrM8TEhIQHBwMf39/m/mFChOPEAL+/v6l4saxQoUKeKBF8R2/tLVXcXOE9vLpUg7z3/gdSfFpkNmThKoKGZsAca8fJVwMkPoEmP09IYQCnWrJAs7a8xt69GuKQxtO48rZ60B8AuDiAugUm16C1l6MqZn3bEJAVQTinFScreaFnddu4X8DPNC3fm0AjtFeJUlWe7l6euPwxWvWocfZvVkvDAEBARpE53jKyuvLxcVFs3ML5PytIL/7A0BwcLDN8rfffhvTpk0rxJEtwsLCEBYWZn3eqlUrXLp0CR9//DEThVpzdna2FjNp3rw59u7di88++wxff/11rtsX9xfEe4k0RRbr8R1RYT5I2F5ERERFQ8sv+sUpMDAwx4/DderUwYoVK3LdPq9eBIqiFNmNnhCiSI9X2rG98kfr9nJxM2Dad89h6hNfIdWoAhKQqoS8HQ9I9d69f4QCmZQKxWQGEpOh+npD+HjBnGHCX3PXAjodpKoCqrQUPJHIMcRZSgkIxdKrUABClSi3MRrlNkYjrlUAPi+3C/0a1LEmGLVur5JGCIEJfdvh6XkrIFUVZtVScEYC6N20NhpUCdQ6RIdSFl5fpeHaLl26ZJPrKYrehHlp2bIllixZUmzHt5cSnyi8m5TSZmjx3ezxBTEvaq6duEu3wrQp24uIiKholNbPlzZt2uDUqVM2y06fPo0qVapoFFHplJSaji9+24YDF66gnLsrnu3VCk2rV9Y6LCpCRqMZZ6JuwEmvQ/WQ8jnm+8uuTrNQPDKuO3749G9L4s5oAoxGCIPBMo9gXkRmyikpxfL8djykBITJDJhVwMnJMqRYiHueH0JYMleZm2RtWW5nDK5VdEViejq8SumPI/bQqEogfpowFAv+3YsD566gnIcrHm7ZAA+3aqB1aFTWFNHQYy8vr2LvFJbl4MGDCAws+Qn1Ep0ofP3119GzZ08EBwcjMTERS5cuRXh4ONatW6d1aERERERUzCZOnIjWrVvj/fffx5AhQ7Bnzx7Mnz8f8+fP1zq0UuPs5Rt4ZM6PSNNl9haLA7YsuIwnGjbA64911Tq8MkFKibi0NLjodMVy/HWbj2PO95sQn2gZ0lu5og+mjn0IDWtXynOfCpXKWXv7yYwMy38FIBRh6RF4NycnQEpLQlAIy78BID7RUrAEAHQ6KABUvQ6Q0pILvCthKISAzOxNmLVPFikA78OxcHFyyncbkK2wIH98+GQvrcOgMk5Iy6Mw++dHUlISzpw5Y31+/vx5REREwNfXFyEhIZgyZQquXLmCxYsXAwBmz56NqlWrol69esjIyMCSJUuwYsWKPEc1lCQlOlF4/fp1PPnkk4iOjoa3tzcaNmyIdevWoVu3blqHRkRERETF7IEHHsCqVaswZcoUvPPOOwgNDcXs2bMxbNgwrUMrNV6ct9KSJBTCZrKoJYcO4+G2jVCrsr92wZUBa06dwMc7t+FyQgKcFQXDq9fC8x06opybe5Ecf8+hC3h37lqbZVevx2PCu7/ip9mjUNE/9144rbvXh4eXK5KT0gCj0bIwwwh4e1p6DGafq1CvB5ydII1GwElv2U6vv1PFOIuUgKJAeHlBJiVbCqBIaa28DcCSaFSlpbKGTmeTSBQS8LiWDv29eiMSEeVh3759NhWLs6asGzFiBBYtWoTo6GhERUVZ12dkZOCVV17BlStX4Orqinr16uHPP/9Er14lP8leohOFCxYs0DoEIiIiItJQnz590KdPH63DKJXi4lNw0ZxsScrk4pu1O/HRM/3sHFXZ8fvpk5jw91/W5yZVxe4rl7DztxVY/egT0BXBlAI//bYXiiKgZusFqEoJo8mM1RsOYczj7XLdz8XVGdO/fQrTnvkO8dcyhxurKmSGEcLLw5IENKuWocSKYukFaDZbi58AsCQLzeY7BzWZIJ2dIXQ6wM0VMjXNUihFgSWJaDJbhjkbnC0JwlwKp5iNKvaFn0SLzrkXtiSiEqSIhh7fr44dO+aYFzW7RYsW2TyfPHkyJk+eXIDAHF/pnLCGiIiIiIgKJTExNc8kISSQkJxm34DKECklPt21PUfFT1VKnLhxA+EXzhfJec5eummTJLSeR5U4f+nWPfet27Qqftj+Jjx9s/VuTE6BTE4BFB2EwRlCr7MkDZNT7iQIlcyhx0IALtnmjs8wWockCycnCHc3S0LQaLI8FAVwc4Xi5GRJMuaSKNW5uWDPv8fy3xBE5JhkIR5UYEwUEhERERFRDkEVy8ElBXfmk8tOEehYv7rdYyorko1GXIiLy/VeV68oiLgeXSTnCQzwzrVwiE4RCMxj2HF2BhcnDJnQ03ZhWjpkbBzU2HiosfGWYcTINt9gVmJSSsDZyZIw9PK0DEtOSYHU6SB1CoSzM4RvOYgK/hAVA6C4ulgLMwkA0Gebs1GnAO7uEDoddDre4hKVBllzFBbmQQXDd1EiIiIiIspBp1cwvGlDy5PsyUIp4Wd2wqOdm2gTWBngotfDoMt9lihVlfB1dSuS8wzp1TTHULvM2sTo163hfR1jwJhuKFfBO+cKVbXOOwgnfbZ5Bs3WgiZCp7OuF95eEH6+1h6H0kkP6BQIswqRlGwZemwTpwA8PQBPDwgPDwi9DmaTijY97y9uIiLKHROFRERERESUqwnDu+CFBk3hnioAs4SSIeF63QxxzYgvlm6F6a7kTW4y0k2IunAT8XEpdoi4dNArCgbXrQcl195+CvrUCiuS83RpHYanH21t0wvPxcUJ0yf0RrXg8vd1DGcXZzw7e2TOocCKAjg5QTg7WXoTSgmkZ1iGDWcWxxGKAilVyOzzDZrNlu1SUiHj4iETEnMkCbOSjopeB6EoUDKHyHd/9EE0aFkj/w1BRI6nMMOOOfy4UEp0MRMiIiIiIipeLz7eETG3k/HPjpNQIQGhIAMqfvprH1LSjHj1qa657ielxM+Lt2PZDzuQkpIBIYDW7cMw8dXe8PYpmh5xpdmrrdvh5I0bOHDtKnSZiTa9omD2Q73gX0RVj4UQGPVwKwzo1gj7j0bB4KxH8wZV4OrilK/jBNesCFSvDMQlAsmpgCoh3Fwh9TrIm7F35iTU6y3/NhoBHy9IkwlISwfSbkD1cIfi6gIISwEUIQSkUACo1urHwJ0koYubE4a83BOHd5yBm6cBnQY0R+uHGuQ6lJqISp7CDh/m0OOCY6KQiIiIiIjydPVGPNbvPJk5MdydJIyUwG+bDuPpwa3g550zcbVsyU4s/DrcZvudW08j5lo85i4YXfyBl3CeBgN+eeQx7LgUhQPRV1HOxQUtfXxRPTi4yM9VztsNXdvULvD+NcICUaVmRVy6qIdq9gGMZiDDBAWADKpg6RWYYbQMR9Y7WSsXy/iEOwdJSoZ0dbH0MkRmQlCqln3u7q2oqkhNTEP/ke0xdFz3AsdNRA7MzlWP6Q4OPSYiIiIiojxFXozJc52qSpy7dDPHcqPRjGU/7Mh1+8hT1xCxv2iq9pZ2ihBoG1IFLz3YCsMaNIKnwfDfO2lACIGR47pCl1VgJNtQZiEEFHc3KK6uUDzcIRQFSE2DjI0DzKrNcWRKGqSApeehZWfLf1UV0myGNJstiUMATgYnGFzz1/ORiIj+GxOFRERERESUp/LlPO693ifn+ls3E5GUlJbr9ooicDYy7+QjlTyXL9/G+//7HekuOphd9TC76iHdnQHAtliKhKUKclp67r19VDOEEBB6nSXZmEdBF0WnoOtjLe8kJomo1GHVY+0wUUhERERERHmqW60iqlX2g06xnftNpwjUqx6I0Mp+Ofbx8nKFXp/7rYaqSpT39yyWWEkby5btQkaGCaoEpJMO0qCHycsFJl83SGcdVGe9JS+YVQU5D8Ips4dg1nyGznqo3h45ahPUaBiCp6c/XLwXRUTaYjETzTBRSEREREREeRJC4H8T+8Pf15Lcy6rEGxTgjRnj+uS6j5u7AV16NLBWo82iKAJe3q5o3b5W8QZNdhVx8CLM5px35WaDHsYKnkit6g1TgOX1IwzOuR9EUQBXF+tTkVk1GV5uMNUNgRrkB69aQZj6/fP4dP0UuHuxIA4RUXFgMRMiIiIiohIsMSUNG3afxo24JNSuEoA2japBnzlH3O3YZPy6Zj927zsHg0GPbh3roU+PhnByyt+QzZCK5fDrrNHYeeg8Ll+PQ5XAcniwYVXo7tE77PkJ3XEtOg6HDly0LvPydsW7Hz0Kg8EJqqrmuS+VLB6eLnmukwCg0yEjyAuqHjBExQIuLpDp6ZYehgDg7Azh7WlJDmYtEwICgC7FiODGIejcrT4GDGwOd3fHnKeRiIoYi5loholCIiIiIqISau/xKLw85zekZRihKArMZhWhQX74cvLDkEYVz01cjNi4FKiqhBDAsRNXsX13JP437WFrMvF+6XUK2jWtft/bu7sb8NHnT+DUiauIPHkN5fzc0aJVDTg78xaktDCZVVy9Foe27cJw+tS1HOsFALNTZq9SIWDy94LnrRRkJAPQ6yxJQSc9hIvLncIlWcOTxZ3eqI/2boTuQ1rmGsPVCzfx02frsOefY9A769GxX1MMfak73L1di/hqicieCjvPIOcoLDh+ShMRERERlUCp6Ub839w1SMswQkrAnFlBNurabfzv+38QqHO1JgmBOx219h64gG07I9GxbVixxyiEQO26lVC7bqViPxfZR1JSGm7HJuPAsUv4bukOxManAFLCz9cNibdToNMJmMzSkiTUC0jdnYSf0Ak0G9gMO5fsgBDCkgw0q0ByCqDT3XmRerjbnNOUYc41lmtRtzC+z8dISUqHmvn6/23hFuwLP4FP10wslusnIjthj0LNMFFIRERERFQCbTl4FsmpGTmWm1WJLRFnUSHdyZokzE5RBLbvPmOXRCGVHqmpGfh87gZs2HAMRiFhdss2fF0IxKlGeFb0QLeWYVi38SiSjSabJCFgyQNGJqXh9QXP4P1nvoVULQlFAJBms+Xfrq62O5jNaNq+dq4xLZ/3j02SEABUs4pLZ6/jnxV78OBDfI0TEeUXi5kQEREREZVAcUmp2Udn2pAy784UAshzP6K8vDvjN6xffxRmswqzIdtcgpnMEohLTUfV2hUxblx3S5Iw+yaZvYOirtzG1QwjRr37CKSb4c4mWcVM9Nl6FqoSitmM65du5RrT3o0nbJKEWQSAiG2nC3vJRKQhIWWhH1QwTBQSEREREZVA9atVvDtXY+Xj4YrOrWtDp+TMCJpViXatWHWY7t/5Czewa9fZOz1UFeSabdbpFJw5H4OeXeqjSmVf3OkumG0jCaxZuR+Lvt8BtYIvzFUrwlSlIswVy0FxcQJMJsBsBowmwGiEalbx/piFyEg35jifwdUp13iFIuDimkd1ZSIqGWQRPKhAmCgkIiIiItJIUko69hy+gCOnrsCczyrAdUMromX9KlBySdg8078lRj7eGuXLe1rXi8z/tm1ZA20erGHddseeM/i/t5bjyee+xbsf/o5TkTmLUlDZdibyuu2CPG7ApZQo7+cBAPD2dLW5WRdZD5OKmDM3YTRmzjsoBKAIwOAMk6JYehY66QEXZ8DdFdLDDfHJ6Ti49VSO83Ue9ABELslw1SzRvk+Tgl0sEVEZxzkKiYiIiIjsTEqJJWv2YsHyHcjITJgE+Hli+ku90Kh25fs6hhACH77YD1/8uhWrtxxFeoYJAeU8MLpfSwzs0ABCCHw7ZwRW/3kQu/edh4uLHl071EW3zvWgZCZXfvxlF+Z/vwWKIqCqEleiY7Fx60nMfGsQWj5w/xWOqXQrX97T5rmSoUI1KDl6FSqKwEMd6wEA2jxYA0dPXMnR61WXrkIIAXn3CimhuhqgpBkBkxlwyix2IgTg6oJfftmDB7K9dgFg4DMdsW/TcRzfdx6KTrEUUDGr6PpIC7ToWg83btwosjYgIvti1WPtMFFIRERERGRn67aewLyfttosu3k7CRPfX4FfPhuN8uU87us4rgYnvDKsMyY82gHJaRnwdHOxSaR4ebpi+GOtMfyx1jn2vR2bjAU/WGLIGlJqNksIAcz6cgOWLqhmcywquxo1CkFgoA+uX4+HqkooGRJSkZDOd14fLi5OmDapDwIyk4r9H2qMv/89iouXbkPNTAoqioCrwQkZ6ek5TyIEkL34iZSAEBCZ/zxyIAprfz+I3v2b3jmnqzM++GUctv0ZgX3hJwABNO9cD+17NyqWdiAiO2LVY81w6DERERERkZ399PveHFO8qVIiw2jGH5uO5vt4er0O3h6u+Urs7T14AeZcqiJLCVyPSUDU5dwLSFDZoygC77/3MPwyhxUrAtCnqahWzhtjnmyPtyb2xm/fPY822Xqhurk5Y+6HwzDy8daoVqU8QkPKY/ijrTB4UPM7cxdmJyWQNRw563k2wqxiyYLNOXbTO+lQt3VNxLkZsC4iCu9+shYjnvgK23IZqkxEJUdWj8LCPKhg2KOQiIiIiMjOrlyLy7UQiQBwKTrWLjH8V06RvQkpuypVyuPHJWOwZ885XI9JQNUq5dG4cYh17svceLgbMOKx1hiRrUdr7O1krF62BynJ6XeGLmf1OEzJ1tMw23EFAFWv4GZMIvZvOYVm7cOs61JTMzBh3GLcuJlo/Zu6ejUO70xbhTen9URAQEDhL56IqAxhj0IiIiIiIjurVMEn905VACpXLAcASEhIxZ9rD+GXX/fg5Kno+zqulBIm8/0VRWnRrBr0el2O5UIIVA4qh+BKvvd1HCo79HodWreuiYEDmqFJkyr3TBLmpZyvO9q1rQmRPVOuSigJKVCyFzjJPLYEIBUBqVcgTGasXrjF5ngb/z2O69cToJrv6oEoBDZtPJ7v+IjIQbDqsWbYo5CIiIgcRoPvG2gdgt0dGXFE6xBIA0P7NseML9fZLBMCcNLr0KdTfWzeehLvffAHjEYzFCGgSok2rWrirdf7wdk551f45JR0fPPDVqz95yjS0owIq1kRTz/RFi2ahuYZg7eXK8Y92xmffrkBOp2A2SyhUwQUnYL/e6lHgZJARPfDSVGgTzfBnJYBqBLCrFrmIgQs3Qf1ujuvP50Cs5sTIASUxDRcvWg7JP7UqavQ6RSY70qQSylx9Yp9eucSUdFjMRPtsEchEREREZGd9WxfF88+2gZO2Xr0+fm4Y9aUwVBNKt6duQbGzN5VWYUgduw6gyU/78xxLLNZxaQ3fsFvf0UgLc0IADh95jomT/sVu/efv2ccA3o3wecfPo5O7Wqjfp1K6NerMb6bOxKNG4QU1aUS5dCgWVWoRjMUs4QiLb3/IASEEBBCgSokTAYdTF4uMHu5AACU2GToVIkqNSvYHKtcOfecFZQzubkZiv1aiKiYsEehZtijkIiIiIjIzoQQGDmoJQZ1b4xjkdFwdXFCg7Ag6BQFP/y0I9f5C6WUWPPHQTw1op3N8l37zuFk5LUc2woBfLN4Cx5slnevQgBoWK8yGtarXOhrIrpfbbvVw9fvuSDhZpJlwV29V3VGFTItFVJJswzRV6VlnkIBDBrdwWbb7t0b4KclO3KcQwiBFg9WK54LICIqxdijkIiIiIhII14eLmjVJBSN61SGTrF8NY+NTc5z2G9CYmqO3lOHj1+GTpfza71UJSLPXrf2TCRyFM7Oejw0uHmOyt9ZpCKQWs0HUAREZpLQ2c0Zk2c9jvotbJN/lSr7YvJrfaHXKxDiThGelq1qon2HOsV7IURUrFjxWBvsUUhERERE5EBq1ayYY741wNJDqno1/xxJRA93F9vkoVlCSTdDmC0JlpGj5uORoQ+iX88mrGRMDqPboOb4ZX54juUSgMnbALO3C5LrG6CkGKFTFPR+5EF06t8092N1r4/mD4Ri65aTSEnJQOPGVVArrCJiYmKK9yKIqPhIiVy71+dnfyoQ9igkIiIiInIgHdvXRsUK3tDdldSTUmLEE21ybN+1Q507czGpEroUkzVJCADRl2Mx56N1GDl+ERISU4s3eHJIUkpEHL2E75ftwPLf9+Pm7SStQ0LlUH88+1ofAIBOp1h7xUpnHdIrekBkmOF8Kw36ZBNUVaJRveB7Hq9cOXf0698Mjw1thdp1goo9fiKi0oo9ComIiIiIHIiLixPmfPI4Pv18PXbuPgsACAjwwrNPdUDb1rVybB9YwRuvvNgdH839G4rR0hMxe4oxq5psVGQMPv12I96e2Lv4L4IcRnq6Ea+/vwp7Iy5CpwioEvjyu014ddxDeKhzfU1jGziqHRq1rI5/Vx9AYkIqXPzc8dP2Y3COS4fz9RTrduJWGtZ+vQUPNqoCZ4OThhETkb2w6rF2mCgkIiIiInIg/248jh9/2oGLUTfh5+OOh3o0wIjh7eDkpMtzn97dG6JJwxBMGPcDbt1IzLFeABBGFRu3n8T/jekGN1fnYrwCciTf/7IT+w5FAQDMquXO2SyB/81Zh4Z1KyOooo+G0QHV6gShWrYegIHV/TF/6qoc20XsPovl327BsLFd7BkeEWmlsJWLmSgsMA49JiIiIiJyEGt+P4j3Zq7BxaibkBK4HZuMn5ftwuw5f0NKiQyjKc99gyr6oEb1gFwLREgAEJZEUXJKerHFT45nzd+HchTAAQAIYN2mY/YP6D/cOB2TZ3GedSv2aRAREWlBqIV/UMGwRyERERERkQMwGs1YsHAzANs52KUE1q47jI27TiMlzYjKQeUw4rFW6N6pHgAgI8OE1b/tx4Z/jyH2djJUIQApcww/NhkU+Pq4w9fH3X4XRZpLSso9MSyEQFx8Sq7rtJQYn5pnR6AkB4yXiKi0YY9CIiIiIiIHEHXpFhIT0/Jcn5qZ8Ll8NRbvzfoLa9Ydgtms4rWpv+Drbzbh7NkY3I5NhtALQC9sRm2ZDQpUZwVPPdo6195aVHrVqlEhR6VsADCbVdStFahBRPdWt0kVqLlU/VYUgXpNq9o/ICLShiyCBxVIif6WMHPmTDzwwAPw9PREQEAABgwYgFOnTmkdFhERERFRvrm73XveQHlXsmfBkq0I33wSERFROXogSiEgnRWY3fTI8NTDp7I3/u/57hjQo1FxhE4ObPTjbSGltBmSrlMEKgX6oFPb2toFlodOvRshMNgXSraEthACEAKPv9BZw8iIyJ6yipkU5kEFU6IThZs3b8bYsWOxa9cubNiwASaTCd27d0dycrLWoRERERER5UvFij6oWycIimKbELR2jLjrm3tcfCo2bzsJnZKzt5jI3FFRBGqG+GP5V8+if3cmCcuiB5uG4r3XByC4ki8AS5KwQ+tamDtzKAzOjjcTlYubMz5e8hw69GwAnd7yoq/VoBJmLngKdZtU0Tg6IrIbKQv/oAJxvE+GfFi3bp3N84ULFyIgIAD79+9H+/btNYqKiIiIiKhgJv9fb0x8+SfExiZDp1OgqiqkBFQnBblVKTE4O2Uuz/2GSFUlzp6/gR27z6BD27Bijp4cVbsHa6JtixpITEqDwVkPg8FJ65DuydffE5M/fBQvv/8wzGYVzg4eLxFRaVKiexTeLT4+HgDg6+urcSRERERERPkXEuyHHxY9iwkv9UCfXo0x/Mm2cPYyWOYdzEanCDzQpCq6dakHcy7zuUkAMvObvk6n4PCxy0UWo1lVcTMuCWnpxiI7JhU/IQS8PF0dPkmYnU6vY5KQqIzi0GPtlOgehdlJKTFp0iS0bdsW9evXz3O79PR0pKffqfyVkJAAAFBVFapavPWzldKVl70vhWlTthcRlXRNfmiidQiaOPjkwQLvy/f+knsOKjpubgb063vn/aN23Up4471VMJpU6BQBk1mFf3lPTB7XA/7lPdG1Sz388+8xALb9CmXmkGRVVRFx9BIeHvUVjEYTWjavhuGPtkKlwHL5iktKiVWbjuDbVTtxKy4Zep2CHq1qY+ITHeHp7lLo6yYiIrIqbEESJgoLrNQkCl988UUcPnwY27Ztu+d2M2fOxPTp03Msv3HjBtLS8q4yVxRq6msW6/EdUUxMTIH3ZXsRUUlXFt/HAL7355c93vsTExOL/RxUfB5sFopfvnsO68OP49atJNSoFoAObcKs88u9NrkP2rSuia8XhCM6Os5SyESBdaiyKoHTF2MgICAksGHTcWzbdQbzP30yX8nCVRsP44NF/1qfm8wq1u44gYvRsfj27cdyraxLREREJUupSBSOGzcOa9aswZYtW1C5cuV7bjtlyhRMmjTJ+jwhIQHBwcHw9/eHl5dXscYZaYos1uM7ooCAgALvy/YiopKuLL6PAXzvzy97vPe7uLC3V0lXzscdjw54INd1iiLQoX1tNGsWiomvLcXps9dtexbqAAhhWSYBsyqRmpqBJct349WXHrqv85tVFd+u2pljuapKHD0bjf3HL6F5vZB8XxcREVFuCjt8mEOPC65EJwqllBg3bhxWrVqF8PBwhIaG/uc+BoMBBoMhx3JFUaAoxTvcSUXZG/ZTmDZlexFRSVcW38cAvvfnlz3e+/n5UjZ4uBvw6su9MOqlhRCZf0pZScK7mVWJXfvO3fexb8en4FZ8Sq7rFEXg+PlrTBQSEVHRKWzlYlY9LrASnSgcO3YsfvrpJ/z222/w9PTEtWvXAADe3t5wdXXVODoiIiIiIjuTElCEtZBJDtkKJLsY7v9WwMPVAJ1OybVwiqpKlPNyy3+sREREeWCPQu2U6J+X582bh/j4eHTs2BGBgYHWx7Jly7QOjYiIiIjKIFVVsSviApavPYDdERdyTawVp6oh5VHOJ4+kXbaJ4RUh0K1j3fs+rquLE7q3DINOse2dKIRlXacHyt78omXFtVsJWLnpMFaHH8HNuGStwyEiomJWonsUSnYlJSIiIiIHER0Tj0+++Rd7jlkK1EgJhASVw6dvPIyK/sU7F3YWvU7B+Ke7YNrHv0NRBFT1zvdlRcC6rGb1ADw2qEW+jj3pyU64GH0bx89dtx7HxdkJH03oDw/XnFP7UMkmpcT8VTvw3Zrd1hF8OkXgpcc6YGiPptoGR0SlH6sea6ZEJwqJiIiIiByBlBJvfPI7pDkl87ll+ZVrcXhz1u/4ZuYwu8XSpW1tlPN2w48r9+DshRhU9PdC4/rBiL2dDKNRRYtmVdG5XW04O+XvVsDL3QUL3n4c+45H4fi56/D1dkPnFjWZJCylNu2LxILfdtssM6sSn/4UjrAqAWha+95FJImICoNDj7XDRCERERERUSGdPh+DyIs3UC3IdtivWZU4fuYazkXdRLWQ8naLp2mDEDRtUPTFRRRFoEX9KmhRv0qRH5scy4qNh6EIAfWuUVw6RWDVpsNMFBJR8VKl5VGY/alASvQchUREREREjuBmbNI919/4j/VEjub6rYQcSULAkvy+fjtRg4iIiMgemCgkIiIiIiqk6iH+ea5ThEC1YPv1JiQqCnWrVcxRvAaw9CisE1pBg4iIqEyRRfDIhy1btqBv374ICgqCEAKrV6/+z302b96MZs2awcXFBdWqVcNXX32Vv5M6KCYKiYiIiKjEmzlzJoQQmDBhgibnr+jvhW5ta0OInFWBe3asB39fD03iIiqoYT2bA0Ig+0taEQJ6vQ5DujbWLC4iKhsE7sxTWKBHPs+XnJyMRo0aYe7cufe1/fnz59GrVy+0a9cOBw8exOuvv46XXnoJK1asyPe1OhrOUUhEREREJdrevXsxf/58NGzYUNM4Jj/bDd8v34So6yeQYTTD2UmH/t0a4YUn2v3nvlevx+HgkUtwMTihVbNqcHNztkPERHkLqxKAz14eiA8Xb0TUtVgAQPXg8nhtRBdUCvDRNjgioiLWs2dP9OzZ8763/+qrrxASEoLZs2cDAOrUqYN9+/bh448/xuDBg4spSvtgopCIiIiISqykpCQMGzYM33zzDWbMmKFpLAZnPQZ0a4RhgzogNj4F5ct5wM313gk/VZX4fMFGrPjjgHWZi0GP18f3Qsc2YcUdMtE9tahXBcv/NxJXbyZApwhU9PPSOiQiKiuktDwKs38x2rlzJ7p3726zrEePHliwYAGMRiOcnJyK9fzFiYlCIiIiIiqxxo4di969e6Nr167/mShMT09Henq69XlCQgIAQFVVqKpa6FhUVYWUEq4GPdwr+liX3ctvayOw8s8DNsM7MzJMmP7J76hW1R+VA30KHZejymqvomj7skDL9gr087TGUFLw9ZU/bK/8KSvtpeX1ZQ0hLsz+wJ3P+iwGgwEGg6EQkVlcu3YNFSrYztdaoUIFmEwm3Lx5E4GBgYU+h1aYKCQiIiKiEmnp0qU4cOAA9u7de1/bz5w5E9OnT8+x/MaNG0hLSyt0PKqqIj4+HlJKKMr9TQW+c+8xVKvsbjvnupRQJLB6zXY8MqB5jnkPS4uCtFdZxvbKH7ZX/rC98qestFdiooYVzgtQkCTH/gCCg4NtFr/99tuYNm1aIQ58x92fzzKzF2NJ/9xmopCIiIiISpxLly5h/PjxWL9+PVxcXO5rnylTpmDSpEnW5wkJCQgODoa/vz+8vAo/pFJVVQgh4O/vf983jodP3kJqmtHyREooaSqE2TIJ+8XTx7B/dzTemT4IwcG+hY7P0RSkvcoytlf+sL3yh+2VP2Wlve7389WRXbp0yeYzvih6EwJAxYoVce3aNZtlMTEx0Ov18PPzK5JzaIWJQiIiIiIqcfbv34+YmBg0a9bMusxsNmPLli2YO3cu0tPTodPpbPbJa7iRoihFdqMnhMjX8apV8cfxU9FQpYSSLgGTZXlWJ4orV2Ix+dVlWPLDGDg56fI8TkmV3/Yq69he+cP2yh+2V/6UhfbS8tqElBCFmGcwa18vL68i+THwbq1atcLvv/9us2z9+vVo3rx5iZ6fEGCikIiIiIhKoC5duuDIkSM2y0aNGoXatWvj1VdfzZEkdBSXrtzGkhW7sS/iAtzcDKhbKxBHT14FpIQwStw9WElVJW7cSMTOXWfQvh2LmxARURmhZj4Ks38+JCUl4cyZM9bn58+fR0REBHx9fRESEoIpU6bgypUrWLx4MQBgzJgxmDt3LiZNmoRnnnkGO3fuxIIFC/Dzzz8XImjHwEQhEREREZU4np6eqF+/vs0yd3d3+Pn55VjuKM5H3cSY/1uCjAwTzKoEbiUh6vIthIb44fbtZCQnJ+e6n6IIREfH2TdYIiIiDRVVj8L7tW/fPnTq1Mn6PGuqkhEjRmDRokWIjo5GVFSUdX1oaCj++usvTJw4EV988QWCgoIwZ84cDB48uMAxOwomComIiIiI7ODbJVuRnmGCqt65eZESOB91C9P+ry8+ev8PpKebcuynqhIhwSV7viMiIiJH1rFjR2sxktwsWrQox7IOHTrgwIEDxRiVNpgoJCIiIqJSITw8XOsQ7mnXgfM2ScIsOp2CiKOX8PDgB/DTzzuR/T5FUQQCA33QokU1O0ZKRESksSKqekz5V3pn3SQiIiIiciBO+rznTXR20mHkyHYY0L8ZdLo7X9Fr1w7ERx89ZrOMiIio1JOy8A8qEPYoJCIiIiKyg85ta2Ptv0cs8xNmYzar6NA6DDqdgnHjumH48DY4f/4GfH09EBLCIcdEpVFichrW7TiBi9GxqBTgg15t6sDb01XrsOguN+OSsPDPPdi0PxIQAl2a1cSoPg/Cx8NF69CIig0ThUREREREdvD0E22x//BFXIuJh5SWYcWqKjG4T1M0qFPJup23txsaN66iYaREVJxOX4zB2P/9isSUNOgUBWZVxfyVOzDn/wahQc0grcOjTLEJKRjx7k+4FZ9s/YFn+cYIbI44i+/fGKpxdKWfkJZHYfangmGikIiIiIjIDnx93PHd7BH4858jiDh6CW6uzujWoS5aNKmqdWhEZCdSSrzx5Z9ISk2HlIDJrAIA0tKNeP2LP7B61tPQKZxqwBEs/ecgbsYn28wta1Ylrt9KxKrwI+jVoqp2wZUFhR0+zKHHBcZEIRERERGRnbi7GTCkX3MM6ddc61CISAOnLsbgYnRsjuWqlIi5nYRDp6+gae1gDSKju20/fC7XAlSqlNhx5DwThcVMqJZHYfanguFPFURERERERER2kJSS/h/rM+wUCf0Xg1Pu/aqEAAzO7HNFpRcThURERERERER2ULtqBTg75V4BXVEEKgd42zkiykuPlnUgclkuJdC9RZjd4ylzWPVYM0wUEhEREREVAZNZxblLN7H70MX/7DVERGWTh5sBI/s+mOs61aTiydcWY/qXfyE1zWjnyOhuAzs0QPM6IQAsSVxFsaQN2zQMRc9WdbQMrWyQRfCgAmF/WaJSpMH3DbQOwe6OjDiidQhEREQ4cOwSpn/+J7xcJc5dTYGTXofRj7TGkwNaaB0aETmYp/o/iHJervjhz324eiMekIBQJSABFRLrd5xEeoYJ70/op3WoZZqTXoc5kwZh4/5IbDl4BkIIdGxaAx2b1si1pyEVLSElRCF6BRZm37KOiUIiIiIiokK4fjMBL89cCaPJDC9XVwBAhtGMeT9tRYXynujelj1PiOgOIQQGdW6EVg1CMXD8NzmSTqoqsWlPJK7GxCOIQ5E1pdcp6N4iLMdQY1VlpQwqvTj0mIiIiIioEH7feBRGkxnyrt4LQgA//b5Po6iIyNFduHrrnj3Tzl+5ZbdYiBwO5yjUDHsUEhEREREVwuVrcbkulzLvdUREgeW97rm+4n+sJyrVJIDCdNxknrDAmCgkIiIiIiqEyhV9cl0uBBCcxzoicnxSSuw9chGbdkXCZDKjVZNQtG9RE3pd0QzMq1rJD43CKuFo5FWY1TtZDZ0iULd6IKoHly+S8xAR5QcThUREREREhdCvSwP8uGYvjCaTzXIpgaF9m2sUFREVhqpKzPzqb/wZfgy6zGq3f4YfQ5O6lTF5dBe4uRrgX96z0OeZ8VIf/N/Hq3Hy/HXrsuoh/nhvQt9CH5uoJGMxE+0wUUhEREREVAgBfp6Y9fogTP/8L2SNk3J20uOZR1uzkAlRCbV13xn8GX4MAGx6+x08fhnDXlwInVGidq2KeGVcD9SsXqHA5ylfzgPfzRiGY2eiERUdi+CK5VC/ZiCEYF1dKuMkCjfPIPOEBcZEIRERERFRITWpG4zln4/G/ohTgN4NDWoFwd3NoHVYRFRAG7afhKIIqOpd2QYpoeoFdEaJ02euY/yrP2Px16NR3u9O78LrcUlYsf0wzly9hQrlPDC4dQPUCMp7GLEQAvVrBqF+zaDiuhyikqewBUnYo7DAmCgkIiIiIioCOkVB1cp+CAgIgKIUzRxmRKSN1DRjziQhYJl8VFiWq6pEapoRv689hFFPtAUAHLt4Dc/M+RVpGSZIKaEoAss2H8KM4Q+h1wO17XkJREQFwkQhEREREZGdREXHYvuhcxBCoH3T6gjy99Y6JCLKxQMNqmBXxPmcnZKkhGK+s1BVJU6fvZ65SuLtJeuRlmGCmrlj1rDld37agPb1Q+HhWvZ6Gl++EYc1244hJjYRNSv7o2+bevByd9E6LHJ0KoDCjMAvTMXkMq7E/9S5ZcsW9O3bF0FBQRBCYPXq1VqHRERERERkQ0qJz5duwSOvLsRnP2/B7J/CMejlBViwepfWoRFRLvp2ro9KFXygKNkyFZnJPyUjW4VinUB5Xw8AQNSNOJyJvmVNEmaXZjRh+/ELxRqzI9q4PxKDpy7EorV7sHbXCcxevhkDp36Hc1dvaR0aObisYiaFeVDBlPhEYXJyMho1aoS5c+dqHQoRERERUa427o3Ekr/2AbAkDaW0zLM+f+UO7Dp8QdPYiCgndzcDvp4xFIO6N4aPlysMznoIk4Q+Rc0aeQwAMJslGtauhN3bTuPatfh7HjPdaLrn+tImOS0Dby1YC7MqoaoSZtXy3peUko53Fq3XOjwiykOJH3rcs2dP9OzZU+swiIiIiIjytGrjYShC5OhppCgCv20+gpYNq2oTGBHlqZyXGyY91RmTnuoMKSXmzt+IX3/bb12vUyX0RhX/m/EbIAFhUuHSzBtp0pzjWIoQeDAspNhiTUhOw9bD55BhNOGBOiGo7O9TbOe6X9sOn0NaRs7kqFmVOHouGtduJaCin5cGkVGJwGImminxiUIiIiIiIkd3My4p1+GIqipxIzZJg4iIKD+EEBj3XBcM6tcUew9cwJGDFxH+z3GYpbQUOIGEdFLgdC4ZaaEu1orJQljyFSO7NkeFcp7/eZ67ZR1DiNwna0vLMOHbdbuxeN1emDNUCFimdXu0SxO8/FjHPPezh9Q04z3Xp6Tfez2VcUwUaqbMJQrT09ORnp5ufZ6QkAAAUFUVqlq8s10qJX+kd74Vpk3ZXvnHNiNyLGXxbxLge39+2eN9jO+V2qtfIwhR12KthQ2y6BSBBjWCNIqKiO7FZDJjb8RF3LqdhJrVKyCsegVUCiyH8l09MH/OBgCAFAJSEYAiAAk4JanwO5OG6r1q4szVm6jo64XH2jdC3wfr5uvcJ6NiMHf1Nuw6EQW9TkH35rUwbkBb+Pt4WLdZuesoPlwdjpQMI+ACwBnQpwKKGVj670FUr+SHge0bFmWT5EuzsMp5rvP1ckNIhXJ2jIZKHCYKNVPmEoUzZ87E9OnTcyy/ceMG0tLSivXcNfU1i/X4jigmJqbA+7K98o9tRuRYyuLfJMD3/vyyx/tYYmJisZ+D7u3xns2wbscJSKlaexYqioCzkx6PdGusbXBElEPkuet49Z2VuHn7To/f5o2rYMZrAxAVdRPp6SZIAahOSmaS0PJ3LfU66BKNmDaoM4Iq+xbo3Gev3sSoj5bBaDZDlRIZJjPW7jmJ/aevYOmbT8DT1YBtJy5g2i8bbHcUgMkNcEoCFAn8svGQponC4ArlMKhDQ6zcfPhOiJlN9dLD7aHXlb0fB4lKgjKXKJwyZQomTZpkfZ6QkIDg4GD4+/vDy6t450eINEUW6/EdUUBAQIH3ZXvlH9uMyLGUxb9JgO/9+WWP9zEXF5diPwfdW7VKfvjitYcxa8kmnLxgSQ7XrVYR/ze8M4L8vTWOjoiyyzCa8MrbvyI+MdVm+YHDUZjzzb8YNrAFgMwkYdbI3qwhvlLC7O4ETy/XAp//u7V7YTKboWbrgWxWJa7dTsCaHccwrEtTfB++L+e8p5lZOLMzINKBmDjtfyR6dVhnhAb6YtnGCNyITULNyuXxVO8H0b5xda1DI0en4s7fV0H3pwIpc4lCg8EAg8GQY7miKFCU4v1FQy2Dr9TCtCnbK//YZkSOpSz+TQJ8788ve7yP8b3SMTSqVQnfv/MEbiekQBECPp4FTyQQUfHZsecsYuNTcixXVYm/w4/jxac7IbiKHy5cj8u5sxCAAI6cuILWD9Yo0Pn3nIrKMU1BlgORVzCsS1OcvXY713lPAUAqlh7LYSHa/6CuUxQM7doUQ7s21ToUKmGElBCFGD5cmH3LuhL/rTEpKQkRERGIiIgAAJw/fx4RERGIiorSNjAiIiIiolz4erkxSUjkwGJuJuZZBMRsVpGQmIYRo9rd8xgJCan3XH8vnq45O7YAluSfp5tlXRV/Hyh5xChUQKoSo3q2KHAMRJrLmqOwMA8qkBKfKNy3bx+aNGmCJk2aAAAmTZqEJk2a4K233tI4MiIiIiIiIippqlf1h8wjyeDm6ozyfh5o1bImXF2c8jxGvTqVCnz+fq3rIbccoFmV6P1gHQDAkx2a5uxRmPk8wNUdM8f0wQN1QgocAxGVXSV+6HHHjh3zfBMnIiIiIiKi0u1WfDL+2nocV27Eo0qgL3q1rWPteVcQTRqEoEaoPy5EXodqkpa5CHWWzN3QgQ/A2UkPOAHDh7XB1wvCbfYVAujSqS6C77OQidFkxprNR7FuxwmkZ5jQulEoBnZugH2nL2PHsQvQKwokJMyqxKgeD+CBsGAAQOcGNfBKv/aY89d2ZJjMAAAPVwPG92yLR9o0hKIUZnI3IgegSkAUIteTx/B9+m8lPlFIREREREREZdPBk5cx/qOVMBrNUBQBs6ri21U78cVrD8OngCP8o87fgIxKgNONNACABABPJzwyqh2eHNLKut1jD7eAm6szlizdiRs3E+HhbsDAfk0xYlib+zqPyaxi0qxV2HM0CiLzPJFRN/DH1mP47u2hOHvtNnYcuwBnvQ5dm9VEnZAKNvsP79gMAx6shwPnrsBZr0fz6pXgrOctPpUShR0+zA5lBcZ3ESIiIiIiIipxTCYzXp/7B4xGM1QpoZotiYGUtAy8PW8tZk/ske9jpqUZ8doLixEfd6eYiQAgkoxwS1dteuoJIdC/TxP0690YqWlGuBic8tWTL3xfJPYctcytn5XSUKXE7fhkLP5jL14Z3hmt6la55zG8XF3QsR4rCGdRVYmNByLxx45jSEpJxwN1QvBIp8bw9XLTOjSiEqPEz1FIREREREREZc+Bk5dxOz4lx1x9qipx8dptXL2RkO9jbtt4HLG3k6HeNWxRSmDlz7tgNqs59hFCwM3VOd/DfbcePJfrPmZVYuPe0/kLnCClxIzFG/DaV39gx5ELiDhzFQv+2I3Hpi1G9K38vxZIa4UtZMIehQXFHoVERERERERU4qSkZdxzfVqGMccys1nFH39GYN36I0hKSkOzplUx5JEHERToAwC4euk2dDol14RgUkIaUpLT4elVdFXL80ot5lV1mfIWEXkFa7YdBQBr8liVEvFJqfhy5Ta8+0wvLcOj/OLQY82wRyERERERERGVOA1qBOXZi8/FWY/KAd42y6SUmP7uanz2+XqcOhWNK1di8cfaCIwa+x32Hb4IAAgK9s01SQgAHl4ucHMveJGUu3VoVh3mXAou6BSBzi1qFdl5yopNB89Ap+RMcZhViY0HzmgQERWKKgv/oAJhopCIiIiIiIhKHD8fdzz+ULNc1wX6eeHDRf9izLvL8PeOE5BSYs/ec9i23TKkVwJI99IhvoIet32A52evwNiPf0WdpiEo5+ueawJy8OOtoNMV3S10h2Y10KZRKABLtWQAUISAfzlPjOzbosjOU1bIe/Qgu3t4OhHljUOPiYiIiIiIqEQa+2g7VPDzxM/r9uParUT4ebvjZmwSLkbfhq6CG85dj8XhyLU4d/kWDm2KtAxHFAIZngoyvJQ7GToA+09ewoTPf8OHXzyJ96f8iqgLNwEAik5B/0cewKMj2xZp7DpFwYcT+uOvbcfw986TSMswoXXDUDzctRG8PYpueHNZ0bZhNfz8z8Ecy3WKQPtG1TSIiApFqpZHYfanAmGikIiI7luD7xtoHYLdHRlxROsQiIiIKA+KIjCkexMM6d4EqioxcOK3lhoImeuzepkt/n0P3GMscxZKABleOpskIWAZonoh+jauJCdj/i8v4MzJaMTFpqBm7UD4+LoXKL5Tkdfw+7pDiLmRiBqh/ujfuwkqBHhZ1+t1Cvp1aIB+Hcred6yi1qJOCLo0rYl/D0RCCEtOWKcIuLk444WBRZvkJTvgHIWaYaKQiIiIiIiISryoa7G4fisx13VSAhlCQi8EpA5AHnMbKorA2Ss30bZRNdSsE1SoeP78+zA+/GwddDoBs1li74HzWPH7AXz6/qOoW7twx6achBB477neaLn9KP7YcRyJKel4sG4IHu/WDIF+Xv99AHIsaiErF3OOwgJjopCIiIiIiIhKPCf9vecPdDbooCaaLBWFM4cg301VJSr4ehY6lsTENHz65QYAgNksrcfOyDDhw8/WYeGXo1jZuBjodQoGtm+Ige0bah0KUYnFYiZERERERERU4gX5e6N6cHkouSTgnPQKBvdqCiElhFGFU6KaY2iiogj4eLiiY9MahY5l596zMBrNOZarqsT5izdx9Vpcoc9BVKplDT0uzIMKhIlCIiIiIiIiKvGEEHj96W4wOOutVYt1mf/9v5Fd8OzoThgwsDn0ioDLLSP0KbbFDvy83PD5y4Pg4uxU6FhMpnsXUjAaWWiB6J4kCpko1PoCSi4OPSYiIiIiIqIS6cCJS/hj8zHEJaagfo0gDOjcED9/MAKr/j2EW7duon5td/Tv1BB1qlUEAIx7qTueHN4GZ8/EwNfXHapBwfHz1+Hn7YYWdatAryuavjTNm1aFEMJaTCW7AH9PhFT2LZLzEBEVNSYKiYiIiIiIqMT5btUuzP91O3SKgFmV2HXoAn75+wDmTxuK5x5pg5iYGAQEBEBRbJN/Pj7uaNY81Pq8eqXyRR5bQHlPPDGkJX5YttOaMFQUASmBl57rau3xSER5YNVjzTBRSERERERERCVK1LVYzP91OwDAnFndVJUSCclp+OyHcHz0cv9ij+HYuWh8s3onDp66Ag9XA/q1r4cRvVvAxWAZujx6eFtUreKHVb8fxPUbCahVvQKGPtwCDepVLvbYiEo8VQVQiCH6Kof3FxQThURERERERFSihO85DUURUFXbXkOqKrHj0DmkpRuL9fyHz1zFmJm/QJUSqiqRmm7Ewt/34OCpK/ji1YehUxQIIdC1Y1107Vi3WGMhKpXYo1AzTBQSUZnV4PsGWoegiSMjjmgdAhEREVGhmMwq8hq8K+WdXobF5cvl26CqEmq2ZIQqJQ6cuoydhy+gbeNqxXr+0igy6gZiYhNRvVJ5VCzvpXU4RGUWqx4TERERERFRidKyYWiuyUBFCNSvEQh3V2cAQPStePz870H8uOEALl6LLZJzq6rEwVOXbZKEWXQ6BXtPRBXJecqKazcTMGraj3jijR8w6ZPV6D/pW7w17y+kZRRvr1BycIWqeFyw3ohffvklQkND4eLigmbNmmHr1q15bhseHg4hRI7HyZMnC3PVDoGJQiIiIiIqkWbOnIkHHngAnp6eCAgIwIABA3Dq1CmtwyIiO6hbvSIealsHAKw9C3WKgE6n4KVhHQAAG/aexoCpi/DJsnB8unwLBr25CJ8u35JrJeL8EAJwdspjcJ6UcHV2KtTxyxJVlZjwySqcuhBjs3zDrlOY/eNmjaIih6DKwj/yYdmyZZgwYQKmTp2KgwcPol27dujZsyeiou6d+D916hSio6Otj5o1axbmqh0CE4VEREREVCJt3rwZY8eOxa5du7BhwwaYTCZ0794dycnJWodGRHbw5nMP4dWnuiIstAICfD3RpWUYvnt3GBrWqoQ9J6Lw164TkMjqXGRJGixZvx8b9p0u1HmFEOjRsjZ0uVQuNqsS3VvWLtTxy5IDJy/h/JVbOXqHqlLi9y1HkZicplFkpDUp1UI/8mPWrFkYPXo0nn76adSpUwezZ89GcHAw5s2bd8/9AgICULFiRetDp9MV5rIdAucoJCIiIqISad26dTbPFy5ciICAAOzfvx/t27fXKCoishedomBgl0YY2KVRjnWrtx2Fkku3GEUIrNh8BN0fCCvUucc+0haHIq/g4rVYa8LQrEqMfbgtqlXyK9Sxy5Koa3F5rjOZVcTEJsHT3cV+AVGZlJGRgf379+O1116zWd69e3fs2LHjnvs2adIEaWlpqFu3Lt544w106tSpOEO1CyYKiYiIiKhUiI+PBwD4+vpqHAkRae1WfArUXDoUqVLiZnxSoY9fzssNP0x/An/vOomI01fg6WZAzzZ1UadqhUIfuywJqeiT5zq9TkFAOQ/7BUOOReZ/+HCO/QEkJCTYLDYYDDAYDDbLbt68CbPZjAoVbP9+K1SogGvXruV6+MDAQMyfPx/NmjVDeno6fvjhB3Tp0gXh4eEl/sdKJgqJiIiIqMSTUmLSpElo27Yt6tevn+s26enpSE9Ptz7PunlQVRVqbhmFfFJVFVLKIjlWWcD2yh+2V/7UDQ3A/sOxEHeNDtYpAg2qVSySdnR20qFvu3ro266edVlJ/f+j1eurca1KqF7JDxev3bYZfqwIgb7t68Hd1dkh27Ss/D1qen1SAih8ojA4ONhm8dtvv41p06bluou46w1DSpljWZawsDCEhd3pmdyqVStcunQJH3/8MROFRERERERae/HFF3H48GFs27Ytz21mzpyJ6dOn51h+48YNpKUVfh4sVVURHx8PKSWU3MY8kg22V/6wvfKne8PKuHYpChKw9iwUAlAUgf4tqiEmJuae+5c1Wr6+3hrVHt//sQdR1y1VqQWApmGV8UjXug77/6ms/D0mJiZqHUKhXbp0CV5eXtbnd/cmBIDy5ctDp9Pl6D0YExOTo5fhvbRs2RJLliwpeLAOgolCIiIiIirRxo0bhzVr1mDLli2oXLlynttNmTIFkyZNsj5PSEhAcHAw/P39bW4iCkpVVQgh4O/vX6pvHIsK2yt/2F75U758eTzc1Yiv/jqEQ+eiAQBhwQGY9GgHNKoRpHF0jkfL11dAAPDehCo4e/kmbsQmoVolPwT4eto1hvwqK3+PLi4azg+pqoAoRI/GzGImXl5e//kZ7+zsjGbNmmHDhg0YOHCgdfmGDRvQv3//+z7lwYMHERgYWLB4HQgThURERERUIkkpMW7cOKxatQrh4eEIDQ295/a5zUsEAIqiFNmNnhCiSI9X2rG98oftlT/BAeXwzeQhiE9Ogyol/LzctQ7JoWn9+qoZEoCaIQGanLsgtG4ve9D02opo6PH9mjRpEp588kk0b94crVq1wvz58xEVFYUxY8YAsPzYeOXKFSxevBgAMHv2bFStWhX16tVDRkYGlixZghUrVmDFihUFj9lBMFFIRERERCXS2LFj8dNPP+G3336Dp6endciQt7c3XF1dNY6OiBxFOU83rUMgonySqgpZiB6FUuZv30cffRS3bt3CO++8g+joaNSvXx9//fUXqlSpAgCIjo5GVFSUdfuMjAy88soruHLlClxdXVGvXj38+eef6NWrV4FjdhRMFBIRERFRiTRv3jwAQMeOHW2WL1y4ECNHjrR/QERERFRivfDCC3jhhRdyXbdo0SKb55MnT8bkyZPtEJX9MVFIRERERCWSzOewIiIiIioh7Dz0mO5gopCIiIiIiIiIiByHKgHBRKEWmCgkIiIiIiIiIiLHISWAwlQ9ZqKwoEpveR4iIiIiIiIiIiK6b+xRSEREREREREREDkOqErIQQ485j3HBlYoehV9++SVCQ0Ph4uKCZs2aYevWrVqHREREREREREREBSHVwj+oQEp8onDZsmWYMGECpk6dioMHD6Jdu3bo2bMnoqKitA6NiIiIiIiIiIioxCjxicJZs2Zh9OjRePrpp1GnTh3Mnj0bwcHBmDdvntahERERERERERFRPklVFvpBBVOi5yjMyMjA/v378dprr9ks7969O3bs2JHrPunp6UhPT7c+j4+PBwDExcVBVYu3a6pMLXsv1Li4uALvy/bKP7ZZ/pTF9gLYZvnF9so/tln+FPa9/34kJCQA4Hw9d8tqj6z2KSxVVZGYmAgXFxcoSon/Pb7Ysb3yh+2VP2yv/GF75U9ZaS8tvz+YZHqhhg+bYCzCaMqWEp0ovHnzJsxmMypUqGCzvEKFCrh27Vqu+8ycORPTp0/PsbxKlSrFEmNZV+75clqHUKKwvfKPbZZ/bLP8YXvlH9ssf+zZXomJifD29rbb+RxdYmIiACA4OFjjSIiIiByXPb8/ODs7o2LFith27a9CH6tixYpwdnYugqjKlhKdKMwihLB5LqXMsSzLlClTMGnSJOtzVVVx+/Zt+Pn55blPSZaQkIDg4GBcunQJXl5eWodTIrDN8oftlX9ss/xhe+Uf2yz/SnubSSmRmJiIoKAgrUNxKEFBQbh06RI8PT2L5HtgaX8dFTW2V/6wvfKH7ZU/bK/8KSvtpcX3BxcXF5w/fx4ZGRmFPpazszNcXFyKIKqypUQnCsuXLw+dTpej92BMTEyOXoZZDAYDDAaDzTIfH5/iCtFheHl5leo3sOLANssftlf+sc3yh+2Vf2yz/CvNbcaehDkpioLKlSsX+XFL8+uoOLC98oftlT9sr/xhe+VPWWgvLb4/uLi4MMGnoRI9mN7Z2RnNmjXDhg0bbJZv2LABrVu31igqIiIiIiIiIiKikqdE9ygEgEmTJuHJJ59E8+bN0apVK8yfPx9RUVEYM2aM1qERERERERERERGVGCU+Ufjoo4/i1q1beOeddxAdHY369evjr7/+YnGSTAaDAW+//XaO4daUN7ZZ/rC98o9tlj9sr/xjm+Uf24yKAl9H+cP2yh+2V/6wvfKH7ZU/bC8qzYTUos41EREREREREREROZQSPUchERERERERERERFQ0mComIiIiIiIiIiIiJQiIiIiIiIiIiImKikIiIyhhOzUtERERERJQ7JgqJqERIT0/XOgQqJW7fvm39N5OGVJxUVdU6BLKDmTNn4oEHHoCnpycCAgIwYMAAnDp1SuuwSoyZM2dCCIEJEyZoHYrDunLlCp544gn4+fnBzc0NjRs3xv79+7UOyyGZTCa88cYbCA0NhaurK6pVq4Z33nmH78eZtmzZgr59+yIoKAhCCKxevdpmvZQS06ZNQ1BQEFxdXdGxY0ccO3ZMm2AdwL3ay2g04tVXX0WDBg3g7u6OoKAgDB8+HFevXtUuYKIiwkQh3VNGRgYyMjIA8IbnfrHNit727dsxZswYfPPNN1qH4jDeffddvPnmmzhz5ozWoZQoP//8Mxo3boyFCxfi+vXrEEJoHVKJYDQaMXDgQCxduhRnz54FwCRrXiIjI/HCCy/g8OHD1mVsq9Jt8+bNGDt2LHbt2oUNGzbAZDKhe/fuSE5O1jo0h7d3717Mnz8fDRs21DoUhxUbG4s2bdrAyckJa9euxfHjx/HJJ5/Ax8dH69Ac0gcffICvvvoKc+fOxYkTJ/Dhhx/io48+wueff651aA4hOTkZjRo1wty5c3Nd/+GHH2LWrFmYO3cu9u7di4oVK6Jbt25ITEy0c6SO4V7tlZKSggMHDuDNN9/EgQMHsHLlSpw+fRr9+vXTIFKioiUkv71SHo4ePYoZM2bA29sbn332GVxcXABYbnh4c507tlnRUlUViqLg2LFjOHjwICZPnoz+/fvjiSeeQJs2bcp0u65fvx5z587F7du3MXr0aIwaNUrrkBxaeno6DAYDAOCrr77C+vXrce7cOSxfvhzVq1eHovB3s/8ya9YsHD58GNu2bcOyZctQv359a5vSHbt378aiRYuwbds29O7dG1OnToWnp6fWYZEd3bhxAwEBAdi8eTPat2+vdTgOKykpCU2bNsWXX36JGTNmoHHjxpg9e7bWYTmc1157Ddu3b8fWrVu1DqVE6NOnDypUqIAFCxZYlw0ePBhubm744YcfNIzM8QghsGrVKgwYMACA5X4lKCgIEyZMwKuvvgrA8v2pQoUK+OCDD/Dcc89pGK327m6v3OzduxctWrTAxYsXERISYr/giIoY74woT3Xr1sXYsWNx4sQJ9O/fH19//TUAlNnEzP1gmxWdLVu2WD+I69WrhyeeeAI///wzzGYzBg4ciIiIiDLbrlJKdO/eHd9++y1GjhyJMWPGYObMmbhy5YrWoTmksWPHYuzYsdYevmPGjMGiRYvg5+eHESNG4Ndff9U4wpJh0qRJ+OKLL9ClSxcMHz4cc+bMYa/pXDz44IOYN28epk6dij179qBz586Ijo7WOiyyo/j4eACAr6+vxpE4trFjx6J3797o2rWr1qE4tDVr1qB58+Z45JFHEBAQgCZNmnCExT20bdsW//77L06fPg0AOHToELZt24ZevXppHJnjO3/+PK5du4bu3btblxkMBnTo0AE7duzQMLKSIz4+HkII9vilEo+JQsqVlBKKoqBdu3ZYu3YtmjVrhtmzZ+O9997TOjSHxTYrWlnz8QB3hnB36NABH3zwATp06IBBgwbh6NGjWoZod6qq2vSiDAgIwNNPP41PPvkEq1atwpw5c2A0GjWO0vE8+eSTmDp1qrXXoJQSXl5e+O233xAYGIhvvvkGf//9t8ZROqasQQdZ/3V3d8fXX3+Nvn37YvXq1fjwww+1DM+hmM1mAHferx577DH8/PPPiIuLw7PPPouTJ09qGR7ZiZQSkyZNQtu2bVG/fn2tw3FYS5cuxYEDBzBz5kytQ3F4586dw7x581CzZk38/fffGDNmDF566SUsXrxY69Ac0quvvoqhQ4eidu3acHJyQpMmTTBhwgQMHTpU69Ac3rVr1wAAFSpUsFleoUIF6zrKW1paGl577TU8/vjj8PLy0jocokJhopByyJ6IMJvNcHd3x6uvvoqJEyfizTffxIoVKzSO0PGwzYpOVkLisccew5AhQxAbG2ud60tVVZQrVw7Lli2Dk5MT3njjDSQlJWkZrt2YzWYoigIhBM6ePWudJw4AXnzxRfTu3Rtbt261GWpDFi1btkRoaCh+//13nD17FkIIqKoKDw8PvPHGG3B2dsaiRYsQExOjdagOxWQyWd/Xsv6blYiePHky2rZtix9//BH//vuvZjE6AiklVFWFTqcDYBl2mqVChQr4448/sH//fnz33XeIjY3VKkyykxdffBGHDx/Gzz//rHUoDuvSpUsYP348lixZYp2ihfKmqiqaNm2K999/H02aNMFzzz2HZ555BvPmzdM6NIe0bNkyLFmyBD/99BMOHDiA77//Hh9//DG+//57rUMrMe4esVOWp/q5X0ajEY899hhUVcWXX36pdThEhcZEIVll762UlazJuvHx9vbGs88+iwkTJuD555+33giV9Sku2WZFL3tiIjExEZMmTcKjjz6KhIQEKIqCjIwMKIqCZcuW4ffff8f27dsBlP7CMVmvq5deegl9+/bFqlWrcOvWLevr6aWXXkJoaCj+/fdfXLx4UctQHZLJZMK4cePwzDPPIC0tzdq7sEmTJhg0aBDOnj2LnTt3ahylY9Hr9UhKSsL8+fOxcuVKAICTkxMAy5DKJ554AjVr1sTixYuRmpqqZah2lf29RlVVCCGgKAp27dqFZs2a4YMPPsDNmzcBWBL8YWFhePPNN/Hdd9/ZJPip9Bk3bhzWrFmDTZs2oXLlylqH47D279+PmJgYNGvWDHq9Hnq9Hps3b8acOXOg1+utvXPJIjAwEHXr1rVZVqdOHURFRWkUkWP7v//7P7z22mt47LHH0KBBAzz55JOYOHEie6/eh4oVKwJAjt6DMTExOXoZ0h1GoxFDhgzB+fPnsWHDBvYmpFKBiUKyyuqt9Mcff2D06NFYsGCBdX6PrBuj6dOnw9fXF2+++aaWoToMtlnRyUq6Zufp6YlevXqhfPnyeP311wEAzs7OMJvNaNy4MYYNG4Zp06YBQKkvRhEfH48+ffogPDwcX375JR577DH4+flZk9TlypXD0KFDsXfv3jI/H5rJZMqxTK/XY+XKldi9e3eOX3pHjx4NANi4cSOA0p90vpfs1757925Ur14dc+bMwdChQ/HYY48hMjLSur5BgwZo06YNzp8/j3PnzmkRriYURUFkZCROnjxpfd/56KOP0LVrV/To0QODBg2Cm5ubdVsAeP7551GlShVr1UT+YFS6SCnx4osvYuXKldi4cSNCQ0O1DsmhdenSBUeOHEFERIT10bx5cwwbNgwRERHWH8bIok2bNjh16pTNstOnT6NKlSoaReTYUlJScnwn1Ol0Zfqz/X6FhoaiYsWK2LBhg3VZRkYGNm/ejNatW2sYmePKShJGRkbin3/+gZ+fn9YhERUNSWWaqqpSVVXr87lz50ofHx85YMAAWalSJdmnTx957tw5KaWUJpNJSinlggULZFhYmDxx4oQmMWuNbVb0srfnoUOH5O7du+WZM2eklFKmpqbKjz76SNaqVUv+9NNPUkopzWazlFLKtWvXyg4dOsiIiAj7B21nO3bskGFhYfLw4cM2y7O3nZRStmrVSj711FP2DM2hZG+PBQsWyB9//FGeOXPG+rf4/vvvSzc3N7lv3z4p5Z2/0eXLl8uAgAAZFxdn/6AdgKqq1r+rLF999ZV8//33pdFolLt27ZIVK1aUEydOlNHR0dZt4uLipL+/v/zxxx+txykLnnvuOdmqVSsppZQ3btyQbdu2lYsWLbLZJjU11eb56tWrZbVq1ayfD1R6PP/889Lb21uGh4fL6Oho6yMlJUXr0EqMDh06yPHjx2sdhkPas2eP1Ov18r333pORkZHyxx9/lG5ubnLJkiVah+aQRowYIStVqiT/+OMPef78ebly5UpZvnx5OXnyZK1DcwiJiYny4MGD8uDBgxKAnDVrljx48KC8ePGilFLK//3vf9Lb21uuXLlSHjlyRA4dOlQGBgbKhIQEjSPXxr3ay2g0yn79+snKlSvLiIgIm/f/9PR0rUMnKhQmCsuwrBtkKS1vglJK+fLLL8vNmzdLKaVcs2aN7Nq1q3z44Ydt9tu7d6/s1KmTDA8Pt1+wDoJtVvSyt+nzzz8v3d3dZXBwsKxQoYKcM2eOlFLKK1euyFGjRskmTZrICxcuWLc/d+6crFWrlty/f7+UsnQkKe5O1mRd0wcffCBr165tfd1lX5fd8uXLZb9+/Wy2Kwuyt1tUVJRs2LChDAoKklWrVpVBQUHy9ddft35p69Spk2zRooVNUjAyMlL26NFDnjx50u6xay172+3YsUMOHz5cvvzyy/Lxxx+Xu3fvtq77+uuvZUhIiPz222+l0Wi0Lp8yZYocNmyYXWPW2h9//CEffvhhGRkZKY8cOSKFEHL79u0yIiJCzpo1S44cOVI2a9ZMzp8/35os2r9/v+zatas8duyYxtFTUQOQ62PhwoVah1ZiMFF4b7///rusX7++NBgMsnbt2nL+/Plah+SwEhIS5Pjx42VISIh0cXGR1apVk1OnTmXiJtOmTZtyfb8aMWKElNLy3fLtt9+WFStWlAaDQbZv314eOXJE26A1dK/2On/+fJ7v/5s2bdI6dKJCKd1j9ShPUkrr0I433ngD7dq1w9ixY7FlyxbUqFEDANC3b188+uijOHHiBD755BPrvs2bN0dSUhK2bNmiSexaYZsVD51Oh0uXLuHQoUM4f/48tm3bhtWrV+OVV17B5MmT8dVXXyEoKAgjR46El5cXpk6dat03NDQUderUwebNmwHknHy5pDGZTNbhMkaj0Wby6ODgYJw9exbp6ekALHOfZQ073rt3L1JSUqzHuHz5Mtzd3bW5CDuQuQzbzGq3HTt2YOXKlWjdujWOHz+Oo0eP4tlnn8X69esxY8YMAMB3332HyMhIm4q9AQEBOHv2LOLj4+1zERq6e469rLbbsGEDevTogeTkZCxfvhy//PKLTaXeZ599Fq1bt8aCBQts5nN0dXWFh4cHgLIzpLZq1ao4deoUYmNjUb9+fXTr1g2PPvoo2rVrh71798LDwwN169bFp59+in379gEAmjZtikuXLmH//v0aR09FTVp+eM/xGDlypNahlRjh4eGYPXu21mE4rD59+uDIkSNIS0vDiRMn8Mwzz2gdksPy9PTE7NmzcfHiRaSmpuLs2bOYMWMGnJ2dtQ7NIXTs2DHX96tFixYBsHyXnjZtGqKjo5GWlobNmzeX6Qru92qvqlWr5vn+37FjR61DJyoUJgrLKCEEzGYzPvzwQ6xcuRJ9+vTB/v37sW/fPty+fdu6Xf/+/fHQQw/h+++/x549e6zLhwwZgqSkpDI13wfbrHhcvnwZHTt2RKdOnRAWFobGjRujadOmeOWVVzB69GjMmjULZ8+eRbt27TB06FBs3rwZH3/8MQBLkuORRx5B586dNb6KwpNSQq/XQ1VVjBkzBsOHD8fjjz+OyMhImM1m9OjRAzVq1LDeHGQlraOiovD9998jIiICgOV11rt3b2sxhdImK0F6Nykljh07hrZt2+Ltt9/Ggw8+CG9vb7i7u2PChAno2bMn1q1bhz179qBq1aqYPXs2Zs6cifDwcKiqCi8vL3zyySfw8fGx/0XZUXx8PB5++GEcOHAAgCXBevv2bbz99tv4+eef8cUXX+DXX3/F1q1b8eCDD2Lt2rU2ycIvvvgCsbGx+OSTT6wT6Q8aNAgBAQGltipibsnPevXqoVatWvjggw8AAH/88Qfmzp2L7du345NPPsHnn3+ODz74AImJidYkKgBMmTIFtWrVslvsRERERET5xURhGZI9QbV+/Xq0aNECly5dws8//4x3330XCxcuRNeuXW1+Aff398fjjz+OgIAAjBs3znqMOnXqoEePHqW+gATbrPh5e3tj7NixMJvNCAwMBACkpaUBAObOnYvY2FisWrUKQgj07t0bo0ePRtu2bQFYkhwPPfQQGjVqpFn8RUUIgd27d6NmzZo4duyYtffRM888g7///hu+vr748MMP8ddff+Gpp57CV199hV9//RWdO3fGuXPn0KBBAwCWRNpLL70Ef39/ja+o6GX16k1NTcXUqVMxefJkvP322zAajRBCoF69enjvvfeQkJBg7WEJWF5jAwcORFxcnLXH4PDhwzF37ly0bt3a+jfZvXv3Up/EuXnzJi5duoTg4GDrsm3btmH58uVYs2aN9W8pJCQEkyZNwqlTp7BkyRLr36Svry9mzJiBevXqISQkBIDlve2tt94qdUlCKaW1qnF2RqMRgKUITnJyMo4fPw4nJyf0798fDRo0QEBAANLS0jBt2jRUqVLF5m+xe/fuePDBB+16HURERERE+VLcY5vJsWTNz7F3714ZGBgo/f395eXLl63rN2zYIENCQuQrr7xis9/KlSut8/CVNWyz4hcVFSUfffRRWbFiRet8aVlzoHXq1Ek+//zzOfYpDfMRZs3PqKqqjI2NlU8++aR86aWXrOvnzZsnFUWR/fv3l0ePHpVSWgq4dOrUSdatW1fWrVtXvvXWW9btS0Ob/Jf169fLgIAA2bNnTzlo0CAZEBAg+/TpY7NNkyZNZNu2bW2KRmRkZEhfX185b968HMe8e17I0q5p06byww8/tFk2a9YsWb58efnxxx/bLJ88ebJs3bp1mZ40f9euXXLcuHHyo48+spnXcs+ePbJt27bWwjiqqsr9+/fLSZMmyUaNGsn69etbizIREREREZUUTBSWUl9//bVcsGCBlPJOwuXAgQOyefPm8ujRozItLU0uWrRICiHkhg0brPslJyfL2bNnS19fX/nPP/9oErtW2GbF534SWDt37pTVqlWzKQRz8+ZNWb16dfntt98WZ3h2k5SUJKW0JK2yREREyI8++kheu3ZNrlu3Tl6+fFmmp6fLQYMGyaCgIPnyyy/LkJAQ+fLLL1uTFImJiTI2NlZev37depzsRWFKq/DwcNmkSRP5wQcfSCkt13zkyBGp0+lskvKRkZFSp9PJ5557Tp44cUKazWa5atUqGRwcLHfs2KFV+JrLqmz8+uuvy6eeekrevn3buu7GjRtyxIgRsmPHjnLXrl3W5fHx8bJLly6yXbt2OZJepTUxnf26li5dKn18fGS3bt1kUFCQbNy4sYyMjLSu79q1q3z22Wet+125ckVOnDjRJuFa1hLRRERERFSycQxkKRQXF4e9e/diwoQJuHz5MvR6PQDgyJEj8PHxQb169WAwGDB48GAMHToUTz/9NDIyMgAAbm5uGDx4MFq1aoXnn3++zExMzzYrXkII7N27F5MmTcpzm2bNmuGtt97CmjVrUL9+fbzyyito2bIl/P39MXDgQDtGWzxGjRqFqVOnQkoJJycnAMCFCxfQqVMnXLlyBZ6enmjfvj0qVaqE8ePHIzk5Gf/88w8+/vhjNGzYEL/99hu+//57AICHhwd8fHys88LJbIV2SrPk5GR4eXlZh/rrdDqUL18etWrVss4TajQaUaNGDXz22WeYP38+unXrhqeffhpPP/00Ro4ciVatWml4BdoSQkBRFFStWhU7duzA+fPnAViG2JYvXx5PPfUU9Ho95syZg6SkJACAl5cX3nrrLbz88suoXr16juOVJlnTRAghrEOtExIS8MUXX2D9+vU4cuQIbt++jalTpyI6OhoAMGHCBBw5cgTnz5+HEAJBQUGYOXMmXn75ZesxOd0EEREREZUk/PZaiqxZswYpKSnw8fHB+PHj0bx5cwwfPty6/s8//7TOYyalhIeHB6ZNmwZFUfDss89at6tcuTI++OADbNq0qdTdCN6NbVY87k6WSinxyy+/4MKFC7muBwAnJyf07NkT48aNw7lz59CwYUN8/vnn2LlzJ3x9fUtsEZj09HQ88sgj2L17N9544w0IIZCRkYEBAwZgwYIFmDRpEj799FO4ubnB1dUVsbGx2LVrF/r27Ys6deogLS0NqqoiLS0NBw4cQFxcnM3xhRBl5jXXq1cvLF68GAEBAdZlAQEBSEhIsFZ5zkryjx07FoMHD4bRaET//v2xa9cuvPPOOwDKTmXeu2Vd9zPPPAOz2YzZs2db53cEgPbt22PAgAG4ePEiPvvsM+t+7du3R//+/TWJ2V6yJ/Q+//xzdOvWDb1798bixYsRFhYGwDI/47Jly7B8+XKsWLECUkoEBQXBy8sL165dsx7LYDBY/80kIRERERGVNPwGW4q8//77+PnnnwFYKjJOnDgRx48fx7vvvgsAOHToEJo3bw4A1t5wNWvWxKxZs7B06VIsWbLEeqx69eqhUqVKJTY5c7/YZkXn/PnzSE5OBpCzp5EQAvHx8dZeOnkltgICAjBs2DA0bdoUS5cuxUMPPQTA0vYl9YY7ISEBt27dwoQJE1C+fHkcOnQIUVFRMJlMeO+996y9C7Ncv34dRqMRCQkJiImJwbp16+Dt7Y0lS5Zg/vz5pb4q738JCQmxSfSdP38eOp0OzZo1A3CnOjkAfPvtt0hKSsLGjRtRpUoVAMi1OEVplFsyNHvbzJs3Dz/++CN+/PFHa3EOwNLztUaNGli1ahXOnTtnt3i1pigKzGYzvv32W8yZMwdt27ZFamoqtm/fjkOHDlm3a9myJaZMmYJp06Zhz549aNKkCZKTk7Fq1SoAZTcJTURERESlR8m886Yc0tPTERQUBG9vbwCWG8KOHTti3Lhx+PjjjxEeHo5atWqhfPnyAGx7PPTo0QOPPfYYPv30U+tNZJaSmpy5H2yzorNz5048+eSTNkmvRYsWoVu3bvjss88QGxuLlJQU1KxZE4Cl7fPSqFEjTJo0CREREfjoo48AIEcyzdFlDYMFLFWwhRD4888/0a5dOwwdOhROTk6YOXMmQkND/7+9Ow+oMW3/AP49p9KGUkxSSMlkF6HQiFQ0eBGGkOglhuwKWYeMFykRKg3ZImZMZQ2lsQwVeoUsGVnL0s8obapz/f7oPc90KGNmcOqc6/OPerbuc7ufp851rvu+cOPGDQAQxpGFhQWcnJwQEhKCrl27YvTo0bC3t0fPnj1Rq1atd8abIvqzYEvFQN+1a9egqakpZBT+3//9H86fP4/CwkLo6Ohg+/bt2Lx5M/bs2YOysjKluT9FIhFev36N0tJSme3SKer29vaYN28e5s6di3379gn3pJaWFnx8fBAZGQlTU9PP3u7PqeK9dOXKFUyZMgWxsbHYsGEDvv/+e8THx6N79+74+eefkZaWJhzr5+eHBg0aYPz48SgrK4OXlxdu3bqF/Px8pQhCM8YYY4wxBfe5F0VkH5904fUZM2aQjY2NzL67d++Si4sLicVi0tXVpWbNmtFXX31FkyZNooCAADp16hQ9fPhQKLKgLLjPPq7c3FyysLAQCkWUlZXRyZMnacKECWRiYkLW1takqalJffr0eWdh/8oKIvz+++80a9Ys0tXVlSm4UBMUFhZS48aNKT4+XtgWGBhIqqqqZGpqKlQvLikpoR07dpBIJKJjx44R0R9FTkpLS+nSpUsUERFBT548+fwvQo4qjgdpUaHKSIu3rFy5krp3705ERAcOHCCRSEQTJkyQGWfOzs40cOBAoYK5Mjh37hyZm5vT8+fP33vcpEmTyN7enmbMmPHOPkUrViJ9PZW9Lmkxpbp169Lly5eF7VeuXKHGjRvTvHnzZPry3r17QpGlJ0+e0K1btz5x6xljTLHdvHmTDAwMKDc3l4iItm3bRjo6Oh/957i4uJC/v/9Hvy5jjCkS5UitUBKDBg2CRCLBiRMnhG2mpqaYPHkyrKysYGlpiaNHj6Jv377Iy8tDZGQkPD098fjxYyEbRxmylSriPvs4CgsL0bx5c2GBf5FIBHt7e4SGhuL69esICAhAo0aNcOrUKfTs2ROurq5ISUlBVlZWpRk4Ojo6mDFjBq5fv4569ep97pfzt0kkEmhoaMDOzg7Pnz8HUJ49GRcXh27duqGgoACPHj1CaWkpVFVVMXjwYLi7u2PcuHHIz8+HmpoaSktLoaKigo4dO8LNzQ2GhoZKOcbmzJmDmJiYKvdLM+MyMzPRtm1bzJkzByNGjEBQUBBCQ0OFqaRA+Vqj0dHRqFWr1mdp++dW2XIHubm5qFWrFrS0tCrN0JT2jb+/P2bPno2YmBi4urri559/Fo5RpOy4Q4cOISQkBIWFhRCJRCAiZGdno02bNkhMTBSmFOvq6uLIkSPCeR06dMDMmTPx448/4ujRo0Jfm5iYwMPDAwBgaGiIFi1ayOV1McZYdePn54du3bpBS0vrLy2X4uvriylTpqBOnTqfrnEAFi9eDD8/P+Tm5n7Sn8MYYzUZBwoVgPTNXKNGjaCtrY2TJ0/i1atXwn4bGxu4u7sjKSkJL168wPz587Fr1y5cvHgRaWlp6Nq1q3CsMlROBbjPPrYvvvgCampqCA8Pl9leVlYGLS0tWFtbw97eHt7e3nB3d0dWVhaGDh0KR0dH5OXlVXrNxo0bo1GjRp+j+R+NdFrrixcvcOnSJQDlU9ajo6ORmJiIVq1awc/PD+np6QDKqxcvWrQIenp6QiVfaTGOihRxjFUMXkmDVkQEkUiE4uJibNmy5b1BYiJCUVERzp07h5CQECQmJuLOnTuYOnWqcE1pv1UWKFMk0nH34MED4Tn2+vVr5OTkQEtLq9KAn4qKCogIWlpa6NevHxITE+Hk5ITc3Nx3pisrgsjISPj7++Ps2bMAyn8H3L59G9ra2ujZsycA4N///jfs7OwQHx+P48ePC+fOnDkTxsbG2Lhxo/BhCGOMscq9efMGw4YNw+TJkz/4nEePHiEmJgbjxo37hC0r165dO5iYmGD37t2f/GcxxlhNxYHCGuBD3+Sam5vD3t4eR44cwcmTJ4XtWlpaGDRoEL755hs4ODigoKBAuK6GhoZCFt+Q9tmf9R332T8n7Qtvb28kJiYKlZ8rBmoA4LfffkN+fj48PDwQHx+Pc+fO4eTJk5/8k+PPSdoXY8eOxc6dO/HixQsAfwTCIiIikJ6eju3btwsZhyYmJli5ciV+/PFHpKSkyKfhn1lhYaGwNqNEInknEPr7779DX18fenp6772OhoYGnJycsHjxYiQnJ8PExETo64rXVKTMuKrs3bsXlpaWsLGxwZYtW5CUlITOnTujtLS0yueVtF+ICMbGxhg7dizc3NwqDVbXROnp6Th16hQAYNu2bdDT08PWrVtx/fp1AMC5c+eEDyOkwVFfX18UFxdjz549QpV2oDzQGBYWBiMjo8/7IhhjrBo5duwYevToAV1dXejr66N///64e/euzDHLli3DzJkz0bZt2w++blRUFNq3bw9jY+Mqj8nJyUGXLl0wcOBAFBUV4fTp0xCJRDh+/DgsLS2hqamJ3r1749mzZzh69ChatmyJunXrYuTIkcLf8VIDBw4Uihkyxhh7FwcKq7m3K3RWlekhDYgtWLAAhoaGCA8Pl5k+ZWhoCA8PDwwbNgz5+flC5g6geMU3KvbZ+zJjuM8+DmlfmJiYYODAgVi4cCGKi4uhoqICiUQiBClq164tFI6RSCQwMjKCgYGBQk2rlfbFl19+iRYtWiAkJARAeVZhSUkJjI2NsWbNGvzwww84efIkSkpKIBKJ4OTkhDt37ggVthXdhg0bhMriIpEIL168QP/+/eHo6IiYmBg8fvwYRIScnBwAVVfwBYDVq1dj6dKlAPBOcFpRVXbPuLi4IDExEf369UN0dDSCg4Nx6NAhxMbG/mn1YkUMpBYVFcHHxwfPnj0DANSqVQtr1qzBuXPncODAAeTn5+PixYto164dgPJ7VyKRoEWLFpg8eTLS09OxdetWoSK0gYEB2rVrxx8SMcaUWn5+PmbNmoXk5GScOnUKYrEYgwcP/sfPxl9++eW9fwM9evQItra2sLCwwE8//QQNDQ1h39KlS7Fx40acP38eDx8+xPDhwxEYGIg9e/bg8OHDOHHiBDZs2CBzvS5duiApKem9xfUYY0yZcbSjmhOLxbh165YwnU5VVbXSX8bSDC4ACAoKgoaGBvz8/JCQkCAc07VrV0RERAhVWBWVWCzGzZs3MXLkSAwfPhxeXl7CNNCKb7C5zz6uhg0bYsSIESgqKsK0adMAyAZUnz17JkwzrhjMUcTATqtWrdCyZUv88ssvwlRHaV+4u7vj66+/xuLFi5GUlASgPJBoZmYGQPGnyQLl02L19fUBlN+HOjo6GD16NPT19eHr64v+/fujsLAQmZmZePHihXDvVRYgqzi9WBHH0tsqBkPv378vVNhWU1NDmzZt4O/vj6NHj8LT0xMAEBoaio4dO2LcuHHYtm0bHj16JLe2fy7SzG8TExOZdS6/+uorTJgwAVFRUYiOjkZOTg569+4NoPz+lN6jo0aNQps2bZCcnCxkBUvxh0SMMWXm4uKCIUOGwNzcHB06dEB4eDjS0tKEWQJ/V2ZmZpXLzdy+fRvdu3dHnz59EBER8U7W+4oVK9C9e3dYWlrCw8MDiYmJ2Lx5MywtLWFra4uhQ4fK/G0PAEZGRiguLkZ2dvY/ajdjjCkq/ou3mtu/fz+6d++OTZs2YcGCBQCqzv6QrjllYWGB5cuXw8bGBkOHDkVISAgyMzOVZr2u0NBQWFlZoV69emjatCnS0tLg5uaGwsLCdwIJ3Gcf5s+y/qT94+DgAC8vL+zbtw8rVqzAmzdvIBaLUVpaisWLF2P9+vWfo7lyJZFIUKtWLXh4eCA/Px9bt27Fw4cPoaKiInxyLS22UVnQQZED0tJxYmdnh6ioKDx58gRAeZBrxIgRiIyMxNmzZ+Hj44OcnBwEBwfD0tISixcvxvXr14XssMoocr9VpKKigszMTNja2mLYsGFo164dwsLCZIrnAEDr1q3h4OCA8PBwBAUFAQACAgLk1u7PSToWWrduLWSsSsfekiVL0LBhQ/j4+ODXX3/F8OHDYW1tDS8vL2zYsAExMTG4d+8eNm7ciIMHD8LQ0FCeL4UxxqqVu3fvwtXVFaampqhbty6aNWsGoHyN3H+isLBQJkuw4vYePXpg0KBBCAoKqvR3vTQzHCjP/tbS0oKpqanMtrf/ftDU1ASAd6YkM8YYK8eBwmrs6tWr2LFjBzw8PLBy5Ups3LgR8fHxEIlEVU6prfgGae3atVi5ciUuXrwIPz8/FBYWyhyjiE6ePIktW7YgLCwMmzZtQmBgIFavXg1NTU2ZNQgrUvY+ex9p9qqKigry8/ORnZ2N/Px8Yb/0zbe0iqimpibc3NyE4MSCBQtw/vx5qKqqwsnJCUDNrxL9Z4UepME/KysrTJw4EQ8fPoSXlxeA8sxBaQGJtLQ02NjYfPL2VifS+6hJkybo1KmTzFR/6VjS0dHBkCFDoKenh8DAQEybNg0XLlxA9+7dsWfPHrm0uzqJjY1Fjx49YG5ujrCwMEydOhWhoaFCMFBdXR1AeQZvdnY2GjVqBHd3d2zbtg1Xr1597/pPiqZ9+/aIjY3Fw4cPhSI5ALBlyxaIxWK4urpi06ZNGDt2LEQiEfbt2wd3d3ekp6dDS0sLWlpaPNWYMcYqGDBgAHJychAWFoaLFy/i4sWLAMoLmPwT9evXx8uXL9/Zrq6ujj59+uDw4cNVZsSrqakJX4tEIpnvpdvefpZLs/EbNGjwj9rNGGOKSjFWLFdQrVu3Rp8+fTB48GCoq6vj2rVrmDhxItLT06Gmpvbe9bgkEgnEYjE8PT3h6emJBw8eCJ+eKTKxWAwbGxv07t1bWFOwRYsWePDgwZ9mBSprn72PNOi1a9cuLF26FLq6uigtLcX69evRo0cPISNTJBIJQSCRSAQXFxfUrl0bJ06cwJAhQ7B371506dIFWlpaNXp6qEQiEaa8nD9/Hrq6umjVqpWwX9oX0n/d3NxgaGiIWbNmYcCAAZg3bx66d+8OoHzNNOmYUzbNmjWDpqYmEhIS0K9fPxgZGckE41+8eIG6deuiRYsWsLW1xdy5c3Ht2jW0adNGjq2Wv9LSUpw/fx7u7u5YsWIFgPKs8ytXruDNmzcwNzeHm5sbgPJsifr16wOAzDhTljFHRLC2tka3bt0we/ZsxMfHQ11dHaWlpTA3N4ePjw/WrFkDZ2dneHp6Cn2Sl5cnU2BJGfqKMcY+RE5ODtLT0xESEgJbW1sAEJZX+acsLS0rnb4sFouxc+dOuLq6onfv3jh9+nSVU5T/imvXrsHY2Fj4PckYY0wW/wVcTUnX25o6dSqaNGkCAwMDTJkyBRoaGhg/fjyA96/t9vabmyZNmnzS9lYXHTp0wNq1a2FgYCBkXqqqqqJu3bp/Wl1XWfsMkJ1a/XZAde7cuZg7dy5mzJiB7du3w8LCAvPnz0dsbCyAqrMtnZycsHbtWuzcuRP6+vqVTimpKaRZkGKxGBkZGWjZsiVGjhyJjh07wsPDQ6isKiUNFgLl07ETEhLQoEEDzJw5E2vWrBE+eVfGIIS0yvH06dMRHR2N48ePC/0h/cS/YcOGePHihczaQdIgobJkeFWWuaqqqopx48ZhypQpyMrKQvfu3XH48GHs27dPqOgrfaOVlZUlrANZcZwp4pirat1eAPDx8UFycrJQWEh6X3777bewtrbGd999hzNnzgjn1alTR2nGGGOM/RX16tWDvr4+QkNDkZGRgfj4eMyaNeud4x48eIDU1FQ8ePAAZWVlSE1NRWpqKl6/fl3ltZ2cnPDrr79WuRbx7t270b59e/Tu3fujrCt45swZODo6/uPrMMaYolK8dww1FBHJvDmpWF1Xut3Kygre3t6IjY2VmYJX06dy/l1v9xkA6OnpQVNTU3gzqKqqivz8fJSVlcmsV1LxGuyP8fbmzRuZwF9mZiaysrIQERGBqVOnolmzZnjw4AH++9//IiIiAmlpaQBk+1F6fsVAWdu2bWt0gEIalH/06BH8/Pzg4OCAU6dOYefOnbh37x6WLVuGGzduyExvqViAo379+tiyZQsiIyNhZmb2p9OXFZlYLAYRwc7ODu7u7li+fDni4uKEfQCQnp4OdXV1oUr22+crOiISMlf37duHixcvCtV3zczMYGhoiFWrVsHY2BiHDh2Ci4sLOnfujNTUVCxduhQlJSVYsmQJoqKi5PkyPhuxWCwUHXn7mW5lZYXp06dj+vTpuHz5ssyUtMDAQDRu3BhNmzZ953qMMcZkicVi7N27F5cuXUKbNm2EDz/ftnjxYlhaWmLJkiV4/fo1LC0tYWlpiZSUlCqv7ezsDDU1tSqXCVJVVUVkZCRat26N3r17v3fN4j9TVFSEgwcPYsKECX/7GowxpvCIyV1paanwdUZGBl25coXu3bsnc4xEIiEioqdPn5KXlxcZGBjQ06dP6fTp0zRz5kx68eLF52yy3H1In0lFR0dT8+bNhe+Tk5MpNDT0UzexxigrK6NHjx6Ro6MjZWRkEBHRtGnT6MSJEySRSCg5OZmIiA4ePEgGBgY0fvx4ioyMJD09PfL29qbc3Fwi+mOMKqqlS5eSpqYmdevWjW7duiVsP3XqFPXr14/GjRv33vMVvX+kKt6bVSkrKxO+dnZ2JkdHR9q6davM/hs3bnyS9lVnFcfIzZs3ydzcnBo2bEhGRkbUt29fYX9hYSF17tyZ5syZIxw/ceJEcnR0pGnTptGrV68qvaYiqTjOQkNDSSQS0bVr1yo99vnz5zR69Ghq1KgRxcXFyYw/xhhj1UNwcDA5Ojp+8p+zceNGcnBw+OQ/hzHGajL+2LwakGYr+fr6wtbWFq6urujcuTN27tz5TsbcF198gUmTJsHMzAytW7dGr169UFxcLEwxUxYf0mfSf5OSkmBhYQEAWLFiBbp06fLeTzWVjVgshpGREZKSkjB16lSYmZnh2LFjaNKkCUQiEaysrPD06VP4+/vD29sboaGhGDFiBIyNjRETE4Pw8HAAilXwpbIsXW9vb9SvXx9paWky+3v37g1ra2vcunVL6avy0v+WTACA6OhoJCQkICMj453jxGKx0IehoaHo1KkTli1bhnXr1iEzMxNisRgtW7ZUqimgZWVlMmMkKSkJLi4uSEtLw7Zt25CSkoLp06cDKM+GMDIywp07dxATE4NVq1bh6tWrWLFiBdavX4+6desK11HUcaeiooKioiIkJCTg8uXLwrIIlalfvz527twJBwcHrF27FpMnT0ZeXp6wX5nGGWOMVVcTJ07EV199JfN8/hTU1NSwYcOGT/ozGGOsxpN3pJIR5efn05gxY8jCwoJOnz5Nv//+O61evZr09fXp0KFDQuaENAviwoULZGhoSM2bN6ezZ8/Ks+ly86F9RkT07bff0pAhQ8jJyYn09PQoPj5eji2vfoqKioiIaMqUKSQWi8nOzo4KCwuJ6I+snbNnz1KTJk2Evrtz5w45ODhQt27d6IcffpBPwz+RihlYZ8+epadPn1JJSQkRER09epREIhEFBwdTQUGBcNzx48dJJBJVmdWq6Cr2WV5eHtna2pKuri41a9aMtLW1KSQkhHJyct45Vvq1RCKh2NhY6tOnD40YMYJu3rz5eV+AHFXsj3v37tHcuXNp3bp1NGTIEJln1U8//URisZgOHDhAROUZvvb29mRsbEzNmzen06dPV3pNRVIxE7CoqIjGjx9POjo61KtXLyooKKgyU1D6HCssLKSLFy+SlZUVLVy4kK5fv/5Z2s0YY4wxxlhNwlWPq4HHjx+jYcOGiIqKQtu2bZGYmIjw8HDk5+dj/vz5MDMzg4WFBcRiMS5fvowxY8aga9euOHjwIIDybIiKVWeVwYf2GVC+YPG1a9fg6uqKnJwcAMrZZxVVrHyqrq4OoHx9x6FDhyIuLg6XLl2CjY2NkB0mEomgoaGBI0eOAAACAgLQunVrzJs3DwYGBvJ5ER+ZtIq4SCTCr7/+ChcXF2hra6O4uBjLly/H8OHD0bdvX4wbNw7fffcdTExM4OzsDAC4desWrKyslLZKtvQ+Sk5ORm5uLho1aoSoqCgQEYKDg7Fu3TqUlJTg22+/lbnnKn7dv39/2NjY4OHDhzW68M1fIS22BADx8fEYPnw4mjRpgjdv3uDGjRtCBWMAGDx4MCZOnIhp06bB2toagwYNQt++fXH79m20a9cOwB/3taI+16TPrMjISAwfPhz9+vVDcnIySktLhXuP/ldxvCLpc0xDQwNdunRBfHw8ioqKuNolY4wxxhhjlZF3pJIRFRcXC+vAbdiwgQwMDMjPz4+ePn1KOjo6NH36dHr27BkRlWfrXLx4UTj3Q9YDU0Qf0mdPnz4lIqKAgACKjY0VzlXWPpOq+PovXbpE58+fl1njsn379mRnZ0f3798noj+yk3x9falTp05kYGBAzs7OMhl1ipTBlJycTG5ubuTn50e3bt0iV1dXsra2puDgYOGY5s2bk7a2No0cOZKmTZtGtWrVotWrV8ux1Z+fNOtUKj09nUQiEeno6FBAQIDMvrFjx1KvXr2qXENOWWRnZ9OECRNo//79wraCggLatm0bzZgxg8LCwoiofC1ac3NzGjhwIGVnZwvH5uXlUcuWLcnOzo6Ki4tlrq0s6+6Fh4dT06ZN6enTp5Sfn08rV64kPT09ioqKIiLl6QfGGGOMMcY+FQ4UViPPnz8na2trCgkJIaLyN5Bt27YlkUhEmzdvpjdv3sgcr0jBmb/rfX22adMmIqp8qqOyk0gkNHToUDIzMyNTU1Pq0aMHrVixgoiIfvvtN1JTU6MlS5bQ69evZc4rKCigtLQ04XtFCrqWlJTQ5MmTycrKigYOHEh5eXlEVP6aR44cSQ4ODsJU0NTUVBKJRDRo0CDy9/enq1evyrPpn11AQACtWrWKiIji4uIoJCSESkpKaO3atSQWi2nXrl1EREIw6+HDh1S7dm0hYK+s92FmZia1a9eORo0aRbdv3yYiosTERBKJRFSnTh06ceKEcGxKSgqJRCJat26dTFA2ISGBhgwZ8s7vA0VU8fkiDQA+efKE1NXVKS4ujoiIbt26RW5ubmRlZUVPnjx55zzGGGOMMcbYX8PFTKqRCxcu4M6dO+jWrRsA4OnTp+jVqxdcXV2hra0NNTU1meMVdXrZX/G+PqtduzYAVDnVUVm9efMGbm5uyMrKQlxcHFJSUtCvXz8sWrQIFy5cQLNmzRAUFIQ1a9Zgx44dOHnyJExNTREREQFNTU20adMGwB9TdRWFqqoqbG1t8ezZM2RlZQnjR1NTE15eXiguLsauXbuQnZ2N9u3bY+HChTh79iw6d+6Mtm3bAqi8CIoiSk5OxtGjRzFo0CA4OTkhLy8PqqqqGDNmDOzt7eHv7w8iQq1atUBEMDQ0hIGBAdLT0wEo531YUlKCpk2b4rvvvsONGzewY8cOFBQU4KuvvsKqVasAAPfu3ROO79SpExYvXozly5cjOTlZ2G5nZ4cff/zxnd8HikhFRQUvX74EUD7tmIjQsGFDfP3114iKigIAtGjRAq6urtDQ0ICvr69wHhHJrd2MMcYYY4zVaPKNU7KKcnNzSUdHh4YOHUpBQUHUpk0bmjRpktJm33wI7rO/7v79+2RhYUF37twhIqIffviB6tevT6NGjRIKThARubm5UceOHUlXV5d8fHzk1dzPztfXl1q1akU7d+6U2R4cHExdu3alefPmCdusra3Jzs6O7t69+7mbKVfnzp0jbW1tqlu3rsy0fqLyTLh69eqRl5eXMP3//PnzZGhoSAkJCXJorfxVzHArLCwkT09PsrW1pd27dwvb7ezsyMnJia5cuSJzrqWlJVlYWMhMQSZSjqzMzMxMatGiBbm6ugpLIRAReXl50b/+9S8hY7W0tJSCg4PJ2NiYgoKC5NVcxhhjjDHGFAJnFFYjderUQWRkJLKzsxESEgInJyds3rxZKbNvPhT3WdWqym4rKChA/fr18erVKwwfPhzz5s3DunXrsGvXLujp6SEtLQ0AEB4ejsjISKSmpgoZTxKJ5LO1X17mzJmDJk2a4MCBA7h8+bKw3dPTE6amptDS0kJxcTEAYP/+/UhMTMSxY8fk1Vy5KCsrg4uLC0xNTZGamorCwkJhn6WlJVauXIng4GB06dIFs2bNgpOTEwYMGAA7Ozv5NVqOpJm3s2fPRt++fZGRkYGkpCSEh4cLY2zDhg1IT0/Hnj178Pz5c+HcmJgYjBo16p2iQcrwjDM2NsaWLVtw4cIFTJkyBXv37gUAfPPNNzh06BAePnwIoLx/BwwYgJEjRwrZvYwxxhhjjLG/R0TE83Oqm4KCAhQVFUFPTw+AbIVaVjnus3IzZ85Et27dMGzYMGFbbGws9PT0YGRkBBMTE6SlpWHAgAF4+fIlunbtim3btsHIyAhA+ZTSsLAwLFq0CI0bNxauoWxVohMSEjB37lzY2dlh4cKF0NXVBQDk5uaibt26AMqnkqqpqSElJQVWVlZybK38eHt748yZM/Dy8oKrq6uw/eXLl1i4cCHCw8Nx+vRpiMVidOnSBYDy3Zv0vyq833//PYKDg7Fv3z5oaWkhJSUFvr6+GD16NHx9faGvr4+goCAEBQVh0aJFGD16tEJN7f8nLl++jF27dmHjxo1YvXo1evbsiWXLlsHGxgY+Pj7Ccco2thhjjDHGGPsUOFBYjUn/a5QlOPMxKHOfFRcXw8PDA0eOHMG9e/egra0Ne3t7pKWlCYGuxMRENG7cGPPnz4e/vz+io6PRr18/AMCjR48wceJE6OvrIzAwEPr6+nJ8NfL3n//8B3v27MHYsWMxa9YsmX0VAxLSQJAyevnyJVxdXaGuro6lS5eiQ4cOAMrXwbx//z7s7e3h5OSEsLAwlJWVQSQSKW0gZ/To0SgqKsKBAweEbUuXLsX+/fsxffp0TJw4EQDQt29fPH78GNHR0TA1NZVXc6uloKAg7NmzB2pqasjNzcWAAQOwYsUKpb4HGWOMMcYY+9iU8x1bDaFMGVwfizL3mbq6OlatWoWmTZvC3d0dSUlJMDExwZ07dxAREQFTU1M4OztDIpHg+++/h5WVFRYsWIBevXph7ty56NixI1RVVREWFqb0QUKgfJqomZmZTGalVMVgl7KONwCoV68evL29kZ2djfXr1yM9PR1jxozBtGnT0Lx5cwQEBGDHjh3Yt28fVFRUlDZIWFJSgufPn8PQ0BAAUFpaCqA8UAgA27dvx+nTpwEAERERWL9+PQcJKzFt2jSsWrUKpqamSEtLw5UrV4RsZ8YYY4wxxtjHwRmFjNVwRUVF0NDQEL4/deoUBg4cCH19fSxZsgQeHh4AgIyMDPTo0QNDhw7Fxo0bkZ2djdOnT+Pw4cOoXbs2rK2tMXbsWACKV9H473q7b1nlgoODERkZiYyMDDRq1Aj79++HmZkZioqKMGHCBFy7dg0pKSlKPaYCAwOxZMkS3L59GwYGBsI9Nnz4cJw8eRKOjo4ICgrCF198Ie+m1gg///wzBg0aJO9mMMYYY4wxpnA4UMhYDRYYGIji4mL4+PggLi4OmZmZ8PDwQGBgIObOnYsjR46gb9++wlTZyMhIjB49GrGxsXB2dhauU3EqLQcJ38VTG/9cVlYWMjIyYGtrC+CPMfXy5Uvo6OgobTah1OvXr9GzZ0/o6+sjMjIS+vr6yMvLw9SpU6GrqwsHBwf0799f3s2s9t5+PvG6hIwxxhhjjH1cHChkrAYbNWoUHj9+DF1dXcTExGDNmjWYPXs2nj17BldXV+Tm5iIpKUk4XiKRYPLkydi5cyfu37+PBg0aCPs4GMY+Fg42V+7mzZuwtbWFoaEhvvzyS6SmpqJFixbYt28fateuLe/mMcYYY4wxxhgHChmryc6fPw9HR0eoqKhg9+7dMhlJly5dQp8+fTB16lQsX75cCAQ+efIEnp6eCA4ORpMmTeTYesaUT3p6OuLi4nDp0iU0b94cixcvBsCBesYYY4wxxlj1wIFCxmqwM2fOYOvWrbh69SpcXFwwe/ZsaGpqAijPHgwJCYGXlxfOnj0La2trDkYwVk1UzLrk6bOMMcYYY4yx6oIDhYwpAG9vb5w5cwZeXl5wdXUVtufm5mLixIk4dOgQXr16JTMdlIOGjMkP33+MMcYYY4yx6khV3g1gjP1z8+fPR1paGqKiotCqVSt06NABAKChoYFly5ahfv36kEgkMoFCDlIwJj98/zHGGGOMMcaqI84oZExBJCQkYP78+WjZsiW8vb2xcuVKaGtrY8uWLfJuGmOMMcYYY4wxxmoADhQypkCCg4MRGRmJjIwMNGrUCPv374eZmRkAnurIGGOMMcYYY4yx9+NAIWMKJisrCxkZGbC1tQUgWzSBMcYYY4wxxhhjrCocKGRMgXGQkDHGGGOMMcYYYx+KA4WMMcYYY4wxxhhjjDGI5d0AxhhjjDHGGGOMMcaY/HGgkDHGGGOMMcYYY4wxxoFCxhhjjDHGGGOMMcYYBwoZY4wxxhhjjDHGGGPgQCFjjDHGGGOMMcYYYwwcKGSMMcYYY4wxxhhjjIEDhYwxxhhjjDHGGGOMMXCgkDHGGGOMMcYYY4wxBg4UMsYYY4wxxhhjjDHGwIFCxhhjjDHGGGOMMcYYOFDIGGOMMcYYY4wxxhgD8P86eaN64QJgKgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1300x900 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total models evaluated: 2480\n",
      "Best misfit: 3.649248e-01\n",
      "Initial best misfit (first 5 evals): 7.904776e-01\n",
      "Best |Mw-Mw_true|: 0.0001\n",
      "\n",
      "Best misfit by iteration:\n",
      "  iter 00: 7.904776e-01\n",
      "  iter 01: 8.248565e-01\n",
      "  iter 02: 6.436185e-01\n",
      "  iter 03: 5.440377e-01\n",
      "  iter 04: 4.614330e-01\n",
      "  iter 05: 4.432520e-01\n",
      "  iter 06: 4.332039e-01\n",
      "  iter 07: 3.908894e-01\n",
      "  iter 08: 3.794379e-01\n",
      "  iter 09: 3.851936e-01\n",
      "  iter 10: 3.755852e-01\n",
      "  iter 11: 3.714722e-01\n",
      "  iter 12: 3.687515e-01\n",
      "  iter 13: 3.684852e-01\n",
      "  iter 14: 3.672067e-01\n",
      "  iter 15: 3.661890e-01\n",
      "  iter 16: 3.659051e-01\n",
      "  iter 17: 3.661340e-01\n",
      "  iter 18: 3.657564e-01\n",
      "  iter 19: 3.655240e-01\n",
      "  iter 20: 3.653050e-01\n",
      "  iter 21: 3.652584e-01\n",
      "  iter 22: 3.652096e-01\n",
      "  iter 23: 3.651160e-01\n",
      "  iter 24: 3.650399e-01\n",
      "  iter 25: 3.651732e-01\n",
      "  iter 26: 3.651023e-01\n",
      "  iter 27: 3.650122e-01\n",
      "  iter 28: 3.649926e-01\n",
      "  iter 29: 3.649975e-01\n",
      "  iter 30: 3.649684e-01\n",
      "  iter 31: 3.649660e-01\n",
      "  iter 32: 3.649597e-01\n",
      "  iter 33: 3.649527e-01\n",
      "  iter 34: 3.649581e-01\n",
      "  iter 35: 3.649440e-01\n",
      "  iter 36: 3.649456e-01\n",
      "  iter 37: 3.649442e-01\n",
      "  iter 38: 3.649435e-01\n",
      "  iter 39: 3.649434e-01\n",
      "  iter 40: 3.649400e-01\n",
      "  iter 41: 3.649414e-01\n",
      "  iter 42: 3.649373e-01\n",
      "  iter 43: 3.649373e-01\n",
      "  iter 44: 3.649315e-01\n",
      "  iter 45: 3.649276e-01\n",
      "  iter 46: 3.649278e-01\n",
      "  iter 47: 3.649248e-01\n",
      "  iter 48: 3.649277e-01\n",
      "  iter 49: 3.649248e-01\n",
      "  iter 50: 3.649248e-01\n",
      "  iter 51: 3.649248e-01\n",
      "  iter 52: 3.649248e-01\n",
      "  iter 53: 3.649248e-01\n",
      "  iter 54: 3.649248e-01\n",
      "  iter 55: 3.649248e-01\n",
      "  iter 56: 3.649248e-01\n",
      "  iter 57: 3.649248e-01\n",
      "  iter 58: 3.649248e-01\n",
      "  iter 59: 3.649248e-01\n",
      "  iter 60: 3.649248e-01\n",
      "  iter 61: 3.649248e-01\n",
      "  iter 62: 3.649248e-01\n",
      "  iter 63: 3.649248e-01\n",
      "  iter 64: 3.649248e-01\n",
      "  iter 65: 3.649248e-01\n",
      "  iter 66: 3.649248e-01\n",
      "  iter 67: 3.649248e-01\n",
      "  iter 68: 3.649248e-01\n",
      "  iter 69: 3.649248e-01\n",
      "  iter 70: 3.649248e-01\n",
      "\n",
      "Relative improvement from first evaluation: 65.10%\n"
     ]
    }
   ],
   "source": [
    "# Visualize results with the new data-oriented plotting\n",
    "na_result.plot(show=True)\n",
    "na_result.plot_convergence(show=True)\n"
,
    "\n",
    "# Build convergence diagnostics arrays\n",
    "misfits = np.array([m.misfit for m in na_result.all_models], dtype=float)\n",
    "vr_values = np.array([m.model[6] for m in na_result.all_models], dtype=float)\n",
    "iterations = np.array([m.iteration for m in na_result.all_models], dtype=int)\n",
    "evals = np.arange(1, len(misfits) + 1)\n",
    "\n",
    "best_so_far = np.minimum.accumulate(misfits)\n",
    "vr_true = float(true_model[6])\n",
    "vr_abs_error = np.abs(vr_values - vr_true)\n",
    "\n",
    "# Compute Mw for each sampled model\n",
    "mw_values = np.empty(len(na_result.all_models), dtype=float)\n",
    "for i, model_info in enumerate(na_result.all_models):\n",
    "    _, mw_i = inversion.fm.estimate_total_moment_and_mw(np.asarray(model_info.model, dtype=float))\n",
    "    mw_values[i] = mw_i\n",
    "mw_abs_error = np.abs(mw_values - float(mw_true))\n",
    "\n",
    "# Best misfit per iteration\n",
    "iter_ids = np.unique(iterations)\n",
    "best_by_iter = np.array([misfits[iterations == it].min() for it in iter_ids])\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "\n",
    "ax[0].plot(evals, misfits, '.', alpha=0.45, label='sample misfit')\n",
    "ax[0].plot(evals, best_so_far, '-', lw=2, label='best so far')\n",
    "ax[0].set_title('Misfit convergence')\n",
    "ax[0].set_xlabel('Evaluation')\n",
    "ax[0].set_ylabel('Misfit')\n",
    "ax[0].legend()\n",
    "ax[0].grid(alpha=0.25)\n",
    "\n",
    "ax[1].plot(evals, vr_values, '.', alpha=0.6, label='sample vr')\n",
    "ax[1].axhline(vr_true, color='k', linestyle='--', label=f'true vr = {vr_true:.3f}')\n",
    "ax[1].set_title('Rupture velocity search')\n",
    "ax[1].set_xlabel('Evaluation')\n",
    "ax[1].set_ylabel('vr (km/s)')\n",
    "ax[1].legend()\n",
    "ax[1].grid(alpha=0.25)\n",
    "\n",
    "ax[2].semilogy(evals, mw_abs_error + 1e-12, '-', lw=1.8, color='tab:purple')\n",
    "ax[2].set_title('|Mw - Mw_true|')\n",
    "ax[2].set_xlabel('Evaluation')\n",
    "ax[2].set_ylabel('Absolute error')\n",
    "ax[2].grid(alpha=0.25)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"Total models evaluated: {len(na_result.all_models)}\")\n",
    "print(f\"Best misfit: {best_so_far[-1]:.6e}\")\n",
    "print(f\"Initial best misfit (first 5 evals): {best_so_far[min(4, len(best_so_far)-1)]:.6e}\")\n",
    "print(f\"Best |Mw-Mw_true|: {mw_abs_error.min():.4f}\")\n",
    "\n",
    "print(\"\\nBest misfit by iteration:\")\n",
    "for it, val in zip(iter_ids, best_by_iter):\n",
    "    print(f\"  iter {it:02d}: {val:.6e}\")\n",
    "\n",
    "improvement = (best_so_far[0] - best_so_far[-1]) / max(best_so_far[0], 1e-12)\n",
    "print(f\"\\nRelative improvement from first evaluation: {100*improvement:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3a3f4a",
   "metadata": {},
   "source": [
    "## Step 7: Export results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c339dadc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export results to JSON and CSV\n",
    "output_dir = root / 'examples' / 'output'\n",
    "output_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "json_path = output_dir / 'na_results.json'\n",
    "csv_path = output_dir / 'na_results.csv'\n",
    "summary_path = output_dir / 'na_results_summary.txt'\n",
    "\n",
    "na_result.export_results(json_path)\n",
    "na_result.export_csv(csv_path)\n",
    "na_result.save(output_dir / 'inversion_result.joblib')\n"
,
    "\n",
    "with summary_path.open('w', encoding='utf-8') as f:\n",
    "    f.write('NA inversion summary (updated physics)\\n')\n",
    "    f.write('=' * 72 + '\\n')\n",
    "    f.write(f'Input file: {input_ctl}\\n')\n",
    "    f.write(f'Models evaluated: {len(na_result.all_models)}\\n')\n",
    "    f.write(f'Best misfit: {best_model.misfit:.6e}\\n')\n",
    "    f.write('\\nMoment metrics:\\n')\n",
    "    f.write(f'  True M0 [N.m]: {m0_true:.6e}\\n')\n",
    "    f.write(f'  Best M0 [N.m]: {m0_best:.6e}\\n')\n",
    "    f.write(f'  True Mw: {mw_true:.4f}\\n')\n",
    "    f.write(f'  Best Mw: {mw_best:.4f}\\n')\n",
    "    f.write(f'  |Mw error|: {abs(mw_best - mw_true):.4f}\\n')\n",
    "    f.write('\\nBest model parameters:\\n')\n",
    "    for name, value in zip(inversion.param_names, best_model.model):\n",
    "        f.write(f'  {name:20s} = {float(value):.6f}\\n')\n",
    "\n",
    "print(f\"\u2713 Results saved to {json_path}\")\n",
    "print(f\"\u2713 Results saved to {csv_path}\")\n",
    "print(f\"\u2713 Summary saved to {summary_path}\")"
   ]
  }
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