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{
"cells": [
{
"cell_type": "markdown",
"id": "54aac251",
"metadata": {
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"exception": false,
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"status": "completed"
}
},
"source": [
"# Uncertainty Features\n",
"\n",
"**Docker image**: `ml4t`\n",
"\n",
"This notebook demonstrates Bayesian and frequentist approaches to extracting\n",
"**uncertainty features** — posterior distributions and prediction intervals\n",
"become ML inputs, not just diagnostics.\n",
"\n",
"**Learning Objectives**:\n",
"- Understand the uncertainty-as-feature principle\n",
"- Implement a walk-forward stochastic volatility (SV) model with filtered posteriors\n",
"- Extract `vol_posterior_std`, `vol_ci_width`, `vol_of_vol`, and `vol_persistence`\n",
"- Compute ARIMA forecast uncertainty on log-volatility with proper back-transform\n",
"\n",
"**Book Reference**: Chapter 9, Section 9.4 (Uncertainty Features)\n",
"\n",
"**Prerequisites**: `08_garch_volatility` for frequentist volatility modeling\n",
"(GARCH provides point estimates; this notebook adds uncertainty quantification).\n",
"`09_har_rough_volatility` for realized volatility estimators."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "71c532e1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:36:30.914665Z",
"iopub.status.busy": "2026-06-13T03:36:30.914544Z",
"iopub.status.idle": "2026-06-13T03:36:33.670373Z",
"shell.execute_reply": "2026-06-13T03:36:33.669778Z"
},
"papermill": {
"duration": 2.758955,
"end_time": "2026-06-13T03:36:33.670882+00:00",
"exception": false,
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"status": "completed"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
".venv/lib/python3.14/site-packages/ml4t/engineer/features/ml/__init__.py:9: UserWarning: Feature 'cyclical_encode': lookback=0 but has period/window parameter. Consider using lookback='period' or specifying the actual lookback.\n",
" from ml4t.engineer.features.ml.cyclical_encode import * # noqa: F403\n"
]
}
],
"source": [
"\"\"\"Uncertainty Features — walk-forward SV posteriors and ARIMA forecast intervals.\"\"\"\n",
"\n",
"from datetime import datetime\n",
"\n",
"import arviz as az\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import polars as pl\n",
"import pymc as pm\n",
"from IPython.display import display\n",
"from ml4t.diagnostic.evaluation.autocorrelation import analyze_autocorrelation\n",
"from ml4t.diagnostic.evaluation.stationarity import analyze_stationarity\n",
"from ml4t.engineer.features.volatility import garman_klass_volatility\n",
"from statsforecast import StatsForecast\n",
"from statsforecast.models import AutoARIMA\n",
"\n",
"from data import load_etfs\n",
"from utils.paths import get_case_study_dir\n",
"from utils.reproducibility import set_global_seeds"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d8c35f52",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:36:33.674943Z",
"iopub.status.busy": "2026-06-13T03:36:33.674837Z",
"iopub.status.idle": "2026-06-13T03:36:33.676802Z",
"shell.execute_reply": "2026-06-13T03:36:33.676464Z"
},
"papermill": {
"duration": 0.004686,
"end_time": "2026-06-13T03:36:33.677258+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.672572+00:00",
"status": "completed"
},
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"# Production defaults — Papermill injects overrides for CI\n",
"N_DRAWS = 1000\n",
"N_TUNE = 2000\n",
"N_CHAINS = 2\n",
"REFIT_INTERVAL = 63 # Quarterly (~252/4 trading days)\n",
"ARIMA_WINDOW = 252\n",
"TRAIN_DAYS = 252\n",
"SEED = 42"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "011b2cff",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:36:33.680667Z",
"iopub.status.busy": "2026-06-13T03:36:33.680596Z",
"iopub.status.idle": "2026-06-13T03:36:33.701272Z",
"shell.execute_reply": "2026-06-13T03:36:33.700846Z"
},
"papermill": {
"duration": 0.023076,
"end_time": "2026-06-13T03:36:33.701809+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.678733+00:00",
"status": "completed"
}
},
"outputs": [],
"source": [
"set_global_seeds(SEED)"
]
},
{
"cell_type": "markdown",
"id": "993cefb7",
"metadata": {
"papermill": {
"duration": 0.00147,
"end_time": "2026-06-13T03:36:33.704928+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.703458+00:00",
"status": "completed"
}
},
"source": [
"## Load Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6333e3e8",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:36:33.708503Z",
"iopub.status.busy": "2026-06-13T03:36:33.708414Z",
"iopub.status.idle": "2026-06-13T03:36:33.724602Z",
"shell.execute_reply": "2026-06-13T03:36:33.724149Z"
},
"papermill": {
"duration": 0.018543,
"end_time": "2026-06-13T03:36:33.724921+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.706378+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Returns: 2515 observations\n",
"Date range: 2015-01-05 00:00:00 to 2024-12-31 00:00:00\n",
"Sample mean: 0.0548%, Sample std: 1.1099%\n"
]
}
],
"source": [
"etfs = load_etfs(symbols=[\"SPY\"])\n",
"sp500 = etfs.select([\"timestamp\", \"open\", \"high\", \"low\", \"close\"]).sort(\"timestamp\")\n",
"\n",
"start_date = datetime(2015, 1, 1)\n",
"end_date = datetime(2024, 12, 31)\n",
"\n",
"sp500 = sp500.filter((pl.col(\"timestamp\") >= start_date) & (pl.col(\"timestamp\") <= end_date))\n",
"\n",
"sp500 = sp500.with_columns(\n",
" returns=pl.col(\"close\").pct_change() * 100,\n",
").drop_nulls()\n",
"\n",
"returns = sp500.to_pandas().set_index(\"timestamp\")[\"returns\"]\n",
"\n",
"print(f\"Returns: {len(returns)} observations\")\n",
"print(f\"Date range: {returns.index.min()} to {returns.index.max()}\")\n",
"print(f\"Sample mean: {returns.mean():.4f}%, Sample std: {returns.std():.4f}%\")"
]
},
{
"cell_type": "markdown",
"id": "36a2aace",
"metadata": {
"papermill": {
"duration": 0.001515,
"end_time": "2026-06-13T03:36:33.728120+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.726605+00:00",
"status": "completed"
}
},
"source": [
"## Garman-Klass Realized Volatility\n",
"\n",
"ARIMA forecast uncertainty requires a volatility target series. We use the\n",
"Garman-Klass estimator (Garman and Klass, 1980) rather than close-to-close\n",
"rolling standard deviation. GK incorporates open, high, low, and close prices,\n",
"producing a more efficient estimator — see `09_har_rough_volatility` for the\n",
"full comparison of range-based estimators."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d5c2f247",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:36:33.731712Z",
"iopub.status.busy": "2026-06-13T03:36:33.731627Z",
"iopub.status.idle": "2026-06-13T03:36:33.737421Z",
"shell.execute_reply": "2026-06-13T03:36:33.736900Z"
},
"papermill": {
"duration": 0.008277,
"end_time": "2026-06-13T03:36:33.737868+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.729591+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Garman-Klass RV (21-day): 2495 observations\n",
"Mean: 0.1189, Std: 0.0687\n"
]
}
],
"source": [
"sp500_gk = sp500.with_columns(\n",
" rv_gk_21=garman_klass_volatility(\"open\", \"high\", \"low\", \"close\", period=21),\n",
").drop_nulls(subset=[\"rv_gk_21\"])\n",
"\n",
"rv_gk = sp500_gk.select([\"timestamp\", \"rv_gk_21\"]).to_pandas().set_index(\"timestamp\")[\"rv_gk_21\"]\n",
"\n",
"print(f\"Garman-Klass RV (21-day): {len(rv_gk)} observations\")\n",
"print(f\"Mean: {rv_gk.mean():.4f}, Std: {rv_gk.std():.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b74f08a1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:36:33.744361Z",
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"shell.execute_reply": "2026-06-13T03:36:33.828998Z"
},
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"end_time": "2026-06-13T03:36:33.830164+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.740945+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_1848554/4056149403.py:6: UserWarning: The figure layout has changed to tight\n",
" plt.tight_layout()\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1400x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(14, 4))\n",
"ax.plot(rv_gk.index, rv_gk.values, linewidth=0.5, alpha=0.8)\n",
"ax.set_title(\"21-Day Garman-Klass Realized Volatility (SPY)\")\n",
"ax.set_ylabel(\"Annualized Volatility\")\n",
"ax.set_xlabel(\"Date\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3a67d89b",
"metadata": {
"papermill": {
"duration": 0.001771,
"end_time": "2026-06-13T03:36:33.833886+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.832115+00:00",
"status": "completed"
}
},
"source": [
"## The Uncertainty-as-Feature Principle\n",
"\n",
"Any point estimate becomes a distributional feature under Bayesian inference.\n",
"A brief example: the Sharpe ratio. The frequentist estimate is a single number;\n",
"the Bayesian posterior reveals how uncertain that number is. The same principle\n",
"applies to volatility, hedge ratios, and any model parameter — the posterior\n",
"width is itself a feature. Chapter 17 develops Bayesian Sharpe estimation fully;\n",
"here we apply the principle to volatility and forecasts."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bf42f082",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:36:33.838076Z",
"iopub.status.busy": "2026-06-13T03:36:33.837939Z",
"iopub.status.idle": "2026-06-13T03:36:33.840954Z",
"shell.execute_reply": "2026-06-13T03:36:33.840468Z"
},
"papermill": {
"duration": 0.005605,
"end_time": "2026-06-13T03:36:33.841199+00:00",
"exception": false,
"start_time": "2026-06-13T03:36:33.835594+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Frequentist Sharpe (annualized): 0.784\n",
"A single number with no uncertainty quantification.\n",
"The Bayesian version (Chapter 17) produces a full posterior distribution,\n",
"yielding credible intervals and probability statements like P(SR > 0).\n"
]
}
],
"source": [
"freq_sharpe = (returns.mean() / returns.std()) * np.sqrt(252)\n",
"print(f\"Frequentist Sharpe (annualized): {freq_sharpe:.3f}\")\n",
"print(\"A single number with no uncertainty quantification.\")\n",
"print(\"The Bayesian version (Chapter 17) produces a full posterior distribution,\")\n",
"print(\"yielding credible intervals and probability statements like P(SR > 0).\")"
]
},
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"source": [
"## Stochastic Volatility Model — Walk-Forward\n",
"\n",
"The canonical SV model specifies an AR(1) process in log-volatility:\n",
"\n",
"$$\\log(\\sigma_t) = \\mu_h + \\phi \\cdot (\\log(\\sigma_{t-1}) - \\mu_h) + \\sigma_\\eta \\cdot \\eta_t$$\n",
"\n",
"We implement this using `pm.AR` with the centered parameterization:\n",
"$\\tilde{h}_t = \\phi \\cdot \\tilde{h}_{t-1} + \\sigma_\\eta \\cdot \\eta_t$\n",
"where $\\tilde{h}_t = h_t - \\mu_h$. The observation model uses Student-t\n",
"rather than Gaussian to handle fat tails in financial returns.\n",
"\n",
"### Walk-Forward Protocol\n",
"\n",
"To maintain point-in-time integrity, we refit the SV model at quarterly\n",
"boundaries on a trailing training window. At each refit, we extract the\n",
"**filtered final state** — the posterior at the last time step, conditioned\n",
"only on data up to that point. Between refits, features carry forward the\n",
"most recent posterior state. This mirrors the GARCH walk-forward in\n",
"`08_garch_volatility` but with quarterly cadence due to MCMC cost."
]
},
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},
"source": [
"### SV Model Specification\n",
"\n",
"Define the model as a function for reuse across walk-forward folds."
]
},
{
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"source": [
"def fit_sv_model(returns_array, n_draws, n_tune, n_chains):\n",
" \"\"\"Fit AR(1) stochastic volatility with Student-t observations.\n",
"\n",
" Returns the MCMC trace (InferenceData object).\n",
" \"\"\"\n",
" with pm.Model() as sv_model:\n",
" mu_h = pm.Normal(\"mu_h\", mu=0, sigma=5)\n",
" phi = pm.Uniform(\"phi\", lower=0, upper=1)\n",
" sigma_eta = pm.Exponential(\"sigma_eta\", lam=2)\n",
" nu = pm.Deterministic(\"nu\", pm.Gamma(\"nu_minus2\", alpha=2, beta=0.1) + 2)\n",
"\n",
" h_centered = pm.AR(\n",
" \"h_centered\",\n",
" rho=[phi],\n",
" sigma=sigma_eta,\n",
" init_dist=pm.Normal.dist(0, 1),\n",
" shape=len(returns_array),\n",
" )\n",
" h = pm.Deterministic(\"h\", h_centered + mu_h)\n",
" volatility = pm.Deterministic(\"volatility\", pm.math.exp(h / 2))\n",
"\n",
" pm.StudentT(\"obs\", nu=nu, mu=0, sigma=volatility, observed=returns_array)\n",
"\n",
" with sv_model:\n",
" trace = pm.sample(\n",
" n_draws,\n",
" tune=n_tune,\n",
" chains=n_chains,\n",
" cores=1,\n",
" progressbar=False,\n",
" random_seed=SEED,\n",
" target_accept=0.99,\n",
" )\n",
" return trace"
]
},
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"source": [
"### Walk-Forward Execution\n",
"\n",
"Refit every `REFIT_INTERVAL` trading days (~quarterly). Each refit trains\n",
"on the preceding `TRAIN_DAYS` observations. Between refits, we carry forward\n",
"the filtered posterior from the training window's final state."
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Walk-forward: 4 refits, interval=63 days\n",
"Training window: 252 days, forecast horizon: 252 days\n"
]
}
],
"source": [
"forecast_start = len(returns) - TRAIN_DAYS\n",
"refit_points = list(range(forecast_start, len(returns), REFIT_INTERVAL))\n",
"n_refits = len(refit_points)\n",
"\n",
"print(f\"Walk-forward: {n_refits} refits, interval={REFIT_INTERVAL} days\")\n",
"print(f\"Training window: {TRAIN_DAYS} days, forecast horizon: {len(returns) - forecast_start} days\")\n",
"\n",
"sv_features_list = []\n",
"last_trace = None"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Refit 1/4: train [2011:2263] (252 obs), carry [2263:2326] (63 days)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Initializing NUTS using jitter+adapt_diag...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
".venv/lib/python3.14/site-packages/pytensor/link/c/cmodule.py:2986: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.\n",
"This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.\n",
"Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.\n",
"For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library\n",
" warnings.warn(\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sequential sampling (2 chains in 1 job)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"NUTS: [mu_h, phi, sigma_eta, nu_minus2, h_centered]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 48 seconds.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Initializing NUTS using jitter+adapt_diag...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" phi=0.606, sigma_eta=0.214, nu=29.7, divergences=0\n",
" Refit 2/4: train [2074:2326] (252 obs), carry [2326:2389] (63 days)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sequential sampling (2 chains in 1 job)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"NUTS: [mu_h, phi, sigma_eta, nu_minus2, h_centered]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 22 seconds.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Initializing NUTS using jitter+adapt_diag...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" phi=0.363, sigma_eta=0.198, nu=26.5, divergences=0\n",
" Refit 3/4: train [2137:2389] (252 obs), carry [2389:2452] (63 days)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sequential sampling (2 chains in 1 job)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"NUTS: [mu_h, phi, sigma_eta, nu_minus2, h_centered]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 22 seconds.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Initializing NUTS using jitter+adapt_diag...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" phi=0.431, sigma_eta=0.271, nu=27.0, divergences=0\n",
" Refit 4/4: train [2200:2452] (252 obs), carry [2452:2515] (63 days)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sequential sampling (2 chains in 1 job)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"NUTS: [mu_h, phi, sigma_eta, nu_minus2, h_centered]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 39 seconds.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" phi=0.756, sigma_eta=0.330, nu=25.2, divergences=0\n",
"\n",
"SV features: 252 daily observations (4 refit points)\n"
]
}
],
"source": [
"for refit_idx, t in enumerate(refit_points):\n",
" train_start = max(0, t - TRAIN_DAYS)\n",
" train_data = returns.iloc[train_start:t].values\n",
"\n",
" carry_end = min(t + REFIT_INTERVAL, len(returns))\n",
" carry_dates = returns.index[t:carry_end]\n",
"\n",
" print(\n",
" f\" Refit {refit_idx + 1}/{n_refits}: train [{train_start}:{t}] ({len(train_data)} obs), \"\n",
" f\"carry [{t}:{carry_end}] ({len(carry_dates)} days)\"\n",
" )\n",
"\n",
" trace = fit_sv_model(train_data, N_DRAWS, N_TUNE, N_CHAINS)\n",
" last_trace = trace\n",
"\n",
" # Extract FILTERED final state — posterior at the last training time step\n",
" vol_final = trace.posterior[\"volatility\"].values[:, :, -1].flatten()\n",
" phi_post = trace.posterior[\"phi\"].values.flatten()\n",
" sigma_eta_post = trace.posterior[\"sigma_eta\"].values.flatten()\n",
" nu_post = trace.posterior[\"nu\"].values.flatten()\n",
"\n",
" features = {\n",
" \"vol_posterior_mean\": vol_final.mean(),\n",
" \"vol_posterior_std\": vol_final.std(),\n",
" \"vol_ci_width\": np.percentile(vol_final, 97.5) - np.percentile(vol_final, 2.5),\n",
" \"vol_of_vol\": sigma_eta_post.mean(),\n",
" \"vol_persistence\": phi_post.mean(),\n",
" }\n",
"\n",
" # Carry forward to each day until next refit\n",
" for dt in carry_dates:\n",
" row = {\"timestamp\": dt, **features}\n",
" sv_features_list.append(row)\n",
"\n",
" # Per-refit diagnostics\n",
" n_div = int(trace.sample_stats[\"diverging\"].values.sum())\n",
" print(\n",
" f\" phi={phi_post.mean():.3f}, sigma_eta={sigma_eta_post.mean():.3f}, \"\n",
" f\"nu={nu_post.mean():.1f}, divergences={n_div}\"\n",
" )\n",
"\n",
"sv_features_df = pd.DataFrame(sv_features_list).set_index(\"timestamp\")\n",
"print(f\"\\nSV features: {len(sv_features_df)} daily observations ({n_refits} refit points)\")"
]
},
{
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"id": "743d2b0c",
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},
"source": [
"### MCMC Diagnostics\n",
"\n",
"Beyond divergence counts, we check $\\hat{R}$ (convergence across chains) and\n",
"effective sample size (ESS). $\\hat{R} > 1.05$ or ESS below 100 indicate the\n",
"sampler has not explored the posterior adequately.\n",
"\n",
"SV models are notoriously difficult for NUTS because the latent AR(1) path\n",
"creates a funnel geometry between $\\phi$, $\\sigma_\\eta$, and the 252 latent\n",
"states. Expect lower ESS for these parameters than for $\\mu_h$ and $\\nu$,\n",
"which are better identified. This is a genuine operational consideration:\n",
"production SV implementations often use particle MCMC or sequential Monte\n",
"Carlo rather than NUTS to handle this geometry."
]
},
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>sd</th>\n",
" <th>hdi_3%</th>\n",
" <th>hdi_97%</th>\n",
" <th>mcse_mean</th>\n",
" <th>mcse_sd</th>\n",
" <th>ess_bulk</th>\n",
" <th>ess_tail</th>\n",
" <th>r_hat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>phi</th>\n",
" <td>0.756</td>\n",
" <td>0.224</td>\n",
" <td>0.237</td>\n",
" <td>0.990</td>\n",
" <td>0.052</td>\n",
" <td>0.043</td>\n",
" <td>21.0</td>\n",
" <td>36.0</td>\n",
" <td>1.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sigma_eta</th>\n",
" <td>0.330</td>\n",
" <td>0.155</td>\n",
" <td>0.099</td>\n",
" <td>0.641</td>\n",
" <td>0.037</td>\n",
" <td>0.034</td>\n",
" <td>17.0</td>\n",
" <td>23.0</td>\n",
" <td>1.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mu_h</th>\n",
" <td>-0.712</td>\n",
" <td>0.206</td>\n",
" <td>-1.100</td>\n",
" <td>-0.341</td>\n",
" <td>0.011</td>\n",
" <td>0.012</td>\n",
" <td>400.0</td>\n",
" <td>402.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>nu</th>\n",
" <td>25.245</td>\n",
" <td>14.195</td>\n",
" <td>6.265</td>\n",
" <td>51.237</td>\n",
" <td>0.281</td>\n",
" <td>0.426</td>\n",
" <td>2504.0</td>\n",
" <td>1506.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \\\n",
"phi 0.756 0.224 0.237 0.990 0.052 0.043 21.0 \n",
"sigma_eta 0.330 0.155 0.099 0.641 0.037 0.034 17.0 \n",
"mu_h -0.712 0.206 -1.100 -0.341 0.011 0.012 400.0 \n",
"nu 25.245 14.195 6.265 51.237 0.281 0.426 2504.0 \n",
"\n",
" ess_tail r_hat \n",
"phi 36.0 1.04 \n",
"sigma_eta 23.0 1.10 \n",
"mu_h 402.0 1.00 \n",
"nu 1506.0 1.00 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Divergences: 0\n"
]
}
],
"source": [
"# Diagnostics from the last refit\n",
"diag_vars = [\"phi\", \"sigma_eta\", \"mu_h\", \"nu\"]\n",
"summary = az.summary(last_trace, var_names=diag_vars)\n",
"display(summary)\n",
"\n",
"n_divergences = int(last_trace.sample_stats[\"diverging\"].values.sum())\n",
"print(f\"\\nDivergences: {n_divergences}\")"
]
},
{
"cell_type": "markdown",
"id": "b6c41400",
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"papermill": {
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},
"source": [
"### Walk-Forward SV Features\n",
"\n",
"The plot shows piecewise-constant features that update at each quarterly\n",
"refit. This honestly reflects the production protocol: features carry the\n",
"last filtered state forward between re-estimations."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "5e0e7ba9",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:38:56.763521Z",
"iopub.status.busy": "2026-06-13T03:38:56.763423Z",
"iopub.status.idle": "2026-06-13T03:38:56.920105Z",
"shell.execute_reply": "2026-06-13T03:38:56.919569Z"
},
"papermill": {
"duration": 0.160163,
"end_time": "2026-06-13T03:38:56.920657+00:00",
"exception": false,
"start_time": "2026-06-13T03:38:56.760494+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_1848554/1413289411.py:47: UserWarning: The figure layout has changed to tight\n",
" plt.tight_layout()\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1400x800 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)\n",
"\n",
"ax = axes[0]\n",
"wf_dates = sv_features_df.index\n",
"ax.plot(\n",
" returns.loc[wf_dates].index,\n",
" returns.loc[wf_dates].values,\n",
" linewidth=0.4,\n",
" alpha=0.6,\n",
" label=\"Returns\",\n",
")\n",
"ax.fill_between(\n",
" wf_dates,\n",
" -2 * sv_features_df[\"vol_posterior_mean\"],\n",
" 2 * sv_features_df[\"vol_posterior_mean\"],\n",
" alpha=0.3,\n",
" color=\"red\",\n",
" label=\"±2σ (filtered)\",\n",
")\n",
"ax.set_title(\"Returns with Walk-Forward Volatility Bands\")\n",
"ax.set_ylabel(\"Return (%)\")\n",
"ax.legend()\n",
"\n",
"ax = axes[1]\n",
"ax.plot(\n",
" wf_dates,\n",
" sv_features_df[\"vol_posterior_mean\"],\n",
" linewidth=1.5,\n",
" label=\"Posterior mean (filtered)\",\n",
" drawstyle=\"steps-post\",\n",
")\n",
"ax.fill_between(\n",
" wf_dates,\n",
" sv_features_df[\"vol_posterior_mean\"] - sv_features_df[\"vol_ci_width\"] / 2,\n",
" sv_features_df[\"vol_posterior_mean\"] + sv_features_df[\"vol_ci_width\"] / 2,\n",
" alpha=0.3,\n",
" label=\"95% CI width\",\n",
")\n",
"for rp in refit_points:\n",
" if rp < len(returns):\n",
" ax.axvline(returns.index[rp], color=\"gray\", linestyle=\":\", linewidth=0.5)\n",
"ax.set_title(\"Filtered Posterior Volatility (Step-Function at Refit Points)\")\n",
"ax.set_ylabel(\"Volatility (%)\")\n",
"ax.set_xlabel(\"Date\")\n",
"ax.legend()\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3bb3e61d",
"metadata": {
"papermill": {
"duration": 0.002527,
"end_time": "2026-06-13T03:38:56.925928+00:00",
"exception": false,
"start_time": "2026-06-13T03:38:56.923401+00:00",
"status": "completed"
}
},
"source": [
"**Interpretation**: The step-function updates show how SV features evolve at\n",
"quarterly refits. Between refits, features are constant — reflecting the\n",
"production reality that MCMC is too expensive to run daily. The CI width\n",
"varies across refits, widening during uncertain periods."
]
},
{
"cell_type": "markdown",
"id": "e6a9adba",
"metadata": {
"papermill": {
"duration": 0.002483,
"end_time": "2026-06-13T03:38:56.930874+00:00",
"exception": false,
"start_time": "2026-06-13T03:38:56.928391+00:00",
"status": "completed"
}
},
"source": [
"### Parameter Posteriors (Last Refit)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "fa866cb4",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:38:56.936633Z",
"iopub.status.busy": "2026-06-13T03:38:56.936538Z",
"iopub.status.idle": "2026-06-13T03:38:57.155602Z",
"shell.execute_reply": "2026-06-13T03:38:57.155153Z"
},
"papermill": {
"duration": 0.222749,
"end_time": "2026-06-13T03:38:57.156118+00:00",
"exception": false,
"start_time": "2026-06-13T03:38:56.933369+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== SV Parameter Posteriors (Last Refit) ===\n",
"phi (persistence): mean=0.756, std=0.224\n",
"sigma_eta (vol-of-vol): mean=0.330, std=0.155\n",
"mu_h (mean log-vol): mean=-0.712, std=0.206\n",
"nu (Student-t df): mean=25.2, std=14.2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_1848554/2262438674.py:29: UserWarning: The figure layout has changed to tight\n",
" plt.tight_layout()\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1600x400 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"phi_samples = last_trace.posterior[\"phi\"].values.flatten()\n",
"sigma_eta_samples = last_trace.posterior[\"sigma_eta\"].values.flatten()\n",
"mu_h_samples = last_trace.posterior[\"mu_h\"].values.flatten()\n",
"nu_samples = last_trace.posterior[\"nu\"].values.flatten()\n",
"\n",
"print(\"=== SV Parameter Posteriors (Last Refit) ===\")\n",
"print(f\"phi (persistence): mean={phi_samples.mean():.3f}, std={phi_samples.std():.3f}\")\n",
"print(\n",
" f\"sigma_eta (vol-of-vol): mean={sigma_eta_samples.mean():.3f}, std={sigma_eta_samples.std():.3f}\"\n",
")\n",
"print(f\"mu_h (mean log-vol): mean={mu_h_samples.mean():.3f}, std={mu_h_samples.std():.3f}\")\n",
"print(f\"nu (Student-t df): mean={nu_samples.mean():.1f}, std={nu_samples.std():.1f}\")\n",
"\n",
"fig, axes = plt.subplots(1, 4, figsize=(16, 4))\n",
"\n",
"for ax, samples, label, title in zip(\n",
" axes,\n",
" [phi_samples, sigma_eta_samples, mu_h_samples, nu_samples],\n",
" [\"φ (persistence)\", \"σ_η (vol-of-vol)\", \"μ_h (mean log-vol)\", \"ν (Student-t df)\"],\n",
" [\"Volatility Persistence\", \"Volatility of Volatility\", \"Mean Log-Volatility\", \"Tail Thickness\"],\n",
" strict=False,\n",
"):\n",
" ax.hist(samples, bins=30, density=True, alpha=0.7, edgecolor=\"white\")\n",
" ax.axvline(samples.mean(), color=\"red\", linestyle=\"--\", label=f\"Mean: {samples.mean():.2f}\")\n",
" ax.set_xlabel(label)\n",
" ax.set_title(title)\n",
" ax.legend(fontsize=8)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "b0bf70bf",
"metadata": {
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"duration": 0.002664,
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},
"source": [
"**Interpretation**: The $\\phi$ posterior captures volatility persistence — values\n",
"near 1 mean shocks decay slowly (clustering), lower values indicate faster\n",
"mean-reversion. The $\\nu$ posterior indicates tail thickness: values near 46\n",
"confirm that Student-t is substantially better than Gaussian ($\\nu \\to \\infty$);\n",
"values above 30 suggest Gaussian is adequate. Both $\\sigma_\\eta$ (vol-of-vol)\n",
"and $\\phi$ are re-estimated quarterly and serve as slowly varying features."
]
},
{
"cell_type": "markdown",
"id": "3670be9b",
"metadata": {
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"source": [
"### Scalability\n",
"\n",
"Full MCMC-based SV is practical for small universes (tens of assets). For\n",
"large panels (1000+ assets), pragmatic alternatives include:\n",
"- **Sector aggregates**: Run SV on sector ETFs, broadcast uncertainty features\n",
"- **Variational inference**: `pm.fit()` gives faster but less accurate posteriors\n",
"- **GARCH baseline with SV overlay**: Reserve SV for portfolio-level risk\n",
"\n",
"See the chapter text (§9.4) for universe-size guidance."
]
},
{
"cell_type": "markdown",
"id": "95994dbf",
"metadata": {
"papermill": {
"duration": 0.002604,
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},
"source": [
"## ARIMA Forecast Uncertainty on Log-Volatility\n",
"\n",
"ARIMA produces prediction intervals alongside point forecasts. We apply it\n",
"to **log-volatility** rather than raw volatility for two reasons:\n",
"\n",
"1. Log-transform ensures forecasts stay positive (volatility cannot be negative)\n",
"2. ARIMA residuals on log-vol are more Gaussian, improving interval coverage\n",
"\n",
"The back-transform uses the log-normal property: $\\exp(\\mu_f)$ gives the\n",
"**median** forecast on the original scale (not the mean, which is\n",
"$\\exp(\\mu_f + \\sigma_f^2/2)$). The prediction interval is\n",
"$[\\exp(\\mu_f - z \\cdot \\sigma_f),\\; \\exp(\\mu_f + z \\cdot \\sigma_f)]$.\n",
"Both the median and the interval are exact, not approximations."
]
},
{
"cell_type": "markdown",
"id": "b5c55bff",
"metadata": {
"papermill": {
"duration": 0.00261,
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"exception": false,
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"status": "completed"
}
},
"source": [
"### Pre-ARIMA Diagnostics\n",
"\n",
"Before fitting, verify that log-volatility is stationary (confirming $d=0$)\n",
"and examine autocorrelation structure."
]
},
{
"cell_type": "code",
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"status": "completed"
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,193 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - Running comprehensive stationarity analysis (n_obs=2495 tests=['adf', 'kpss', 'pp'] alpha=0.05)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,194 - ml4t.diagnostic.evaluation.stationarity.augmented_dickey_fuller - INFO - Running ADF test (n_obs=2495 maxlag=None regression=c autolag=AIC)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,221 - ml4t.diagnostic.evaluation.stationarity.augmented_dickey_fuller - INFO - ADF test completed (statistic=-3.9179267205663377 p_value=0.001906806644747079 lags_used=24 n_obs=2470 stationary=True)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,221 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - ADF test completed (stationary=True)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,222 - ml4t.diagnostic.evaluation.stationarity.kpss_test - INFO - Running KPSS test (n_obs=2495 regression=c nlags=auto)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
".venv/lib/python3.14/site-packages/ml4t/diagnostic/evaluation/stationarity/kpss_test.py:269: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
"look-up table. The actual p-value is smaller than the p-value returned.\n",
"\n",
" result = kpss(arr, regression=regression, nlags=nlags_param)\n",
"2026-06-12 23:38:57,222 - ml4t.diagnostic.evaluation.stationarity.kpss_test - INFO - KPSS test completed (statistic=0.7942157387428047 p_value=0.01 lags_used=30 n_obs=2495 stationary=False)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,222 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - KPSS test completed (stationary=False)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,223 - ml4t.diagnostic.evaluation.stationarity.phillips_perron - INFO - Running PP test (n_obs=2495 lags=None regression=c test_type=tau)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,227 - ml4t.diagnostic.evaluation.stationarity.phillips_perron - INFO - PP test completed (statistic=-4.583754026095919 p_value=0.00013837485362322503 lags_used=27 n_obs=2494 stationary=True)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,227 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - PP test completed (stationary=True)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,228 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - Stationarity analysis completed (n_tests_run=3 consensus=likely_stationary agreement=0.6666666666666666)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,229 - ml4t.diagnostic.evaluation.autocorrelation - INFO - Starting autocorrelation analysis\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,230 - ml4t.diagnostic.evaluation.autocorrelation - INFO - ACF computed (n_obs=2495 nlags=33 significant=33)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,237 - ml4t.diagnostic.evaluation.autocorrelation - INFO - PACF computed (n_obs=2495 nlags=33 significant=14)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-06-12 23:38:57,238 - ml4t.diagnostic.evaluation.autocorrelation - INFO - Autocorrelation analysis completed (arima_order=(5, 0, 33) white_noise=False)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== Pre-ARIMA Diagnostics (log Garman-Klass RV) ===\n",
"Stationarity: likely_stationary (agreement: 0.67)\n",
"Suggested ARIMA order: (5, 0, 33)\n"
]
}
],
"source": [
"log_rv = np.log(rv_gk.clip(lower=1e-8))\n",
"\n",
"stat_check = analyze_stationarity(log_rv.dropna().values)\n",
"acf_analysis = analyze_autocorrelation(log_rv.dropna().values)\n",
"\n",
"print(\"=== Pre-ARIMA Diagnostics (log Garman-Klass RV) ===\")\n",
"print(f\"Stationarity: {stat_check.consensus} (agreement: {stat_check.agreement_score:.2f})\")\n",
"print(f\"Suggested ARIMA order: {acf_analysis.suggested_arima_order}\")"
]
},
{
"cell_type": "markdown",
"id": "43cd6967",
"metadata": {
"papermill": {
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"end_time": "2026-06-13T03:38:57.246458+00:00",
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},
"source": [
"### Order Selection and Rolling Forecast via Nixtla\n",
"\n",
"We use Nixtla's `AutoARIMA` (compiled C, AIC-based order selection) on the\n",
"first training window to choose (p, d, q), then roll forward with that fixed\n",
"order using Nixtla's `ARIMA` for speed. The model operates on\n",
"$\\log(\\text{RV}_{GK})$; we back-transform to the original scale afterward.\n",
"\n",
"$\\exp(\\hat{\\mu}_f)$ gives the **median** forecast on the original scale\n",
"(not the mean $\\exp(\\hat{\\mu}_f + \\hat{\\sigma}_f^2/2)$), which is more\n",
"robust for position sizing."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "94ef8894",
"metadata": {
"execution": {
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AutoARIMA selected: ARIMA(1,1,1)\n"
]
}
],
"source": [
"from statsforecast.models import ARIMA as NixtlaARIMA\n",
"\n",
"log_rv_clean = log_rv.dropna()\n",
"\n",
"# Step 1: AutoARIMA order selection on first training window\n",
"first_window_df = pd.DataFrame(\n",
" {\n",
" \"unique_id\": \"SPY\",\n",
" \"ds\": log_rv_clean.index[:ARIMA_WINDOW],\n",
" \"y\": log_rv_clean.values[:ARIMA_WINDOW],\n",
" }\n",
")\n",
"\n",
"sf_auto = StatsForecast(models=[AutoARIMA(season_length=1)], freq=\"B\", n_jobs=1)\n",
"sf_auto.fit(first_window_df)\n",
"selected_order = sf_auto.fitted_[0, 0].model_[\"arma\"]\n",
"p, q, P, Q, s, d, D = selected_order\n",
"print(f\"AutoARIMA selected: ARIMA({p},{d},{q})\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "0ee47d76",
"metadata": {
"execution": {
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ARIMA(1,1,1) forecast features: 2243 observations\n"
]
}
],
"source": [
"# Step 2: Rolling forecast with fixed order (fast)\n",
"n_total = len(log_rv_clean)\n",
"n_windows = n_total - ARIMA_WINDOW\n",
"\n",
"arima_input = pd.DataFrame(\n",
" {\n",
" \"unique_id\": \"SPY\",\n",
" \"ds\": log_rv_clean.index,\n",
" \"y\": log_rv_clean.values,\n",
" }\n",
")\n",
"\n",
"sf = StatsForecast(\n",
" models=[NixtlaARIMA(order=(p, d, q), season_length=1)],\n",
" freq=\"B\",\n",
" n_jobs=1,\n",
")\n",
"cv_result = sf.cross_validation(\n",
" df=arima_input,\n",
" h=1,\n",
" step_size=1,\n",
" n_windows=n_windows,\n",
" level=[95],\n",
")\n",
"\n",
"# Back-transform from log scale to original scale\n",
"col = \"ARIMA\"\n",
"arima_df = pd.DataFrame(\n",
" {\n",
" \"timestamp\": cv_result[\"ds\"].values,\n",
" \"arima_forecast\": np.exp(cv_result[col].values),\n",
" \"arima_lo_95\": np.exp(cv_result[f\"{col}-lo-95\"].values),\n",
" \"arima_hi_95\": np.exp(cv_result[f\"{col}-hi-95\"].values),\n",
" \"actual\": np.exp(cv_result[\"y\"].values),\n",
" }\n",
")\n",
"arima_df[\"arima_ci_width\"] = arima_df[\"arima_hi_95\"] - arima_df[\"arima_lo_95\"]\n",
"arima_df[\"forecast_uncertainty_ratio\"] = arima_df[\"arima_ci_width\"] / arima_df[\"arima_forecast\"]\n",
"\n",
"# Forecast std from the CI width on original scale (robust to negative log-CIs)\n",
"arima_df[\"arima_forecast_std\"] = arima_df[\"arima_ci_width\"] / (2 * 1.96)\n",
"\n",
"arima_df = arima_df.set_index(\"timestamp\")\n",
"\n",
"print(f\"ARIMA({p},{d},{q}) forecast features: {len(arima_df)} observations\")"
]
},
{
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"outputs": [
{
"name": "stderr",
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"text": [
"/tmp/ipykernel_1848554/3430856232.py:41: UserWarning: The figure layout has changed to tight\n",
" plt.tight_layout()\n"
]
},
{
"data": {
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qRVNO6348K1etO2Rg9q0PviIsNIQPZzzlfywczP65Q9nvb9d56fywcLk/MJuRncMlF5zhD4ruD6JC2a3jA3qfQrvWqf6f3aF88c0PzP5+AS63G7fbQ1CQhQvOGurfP3J4P16c8SG3XDeWsNAQSq02fl68gonXXVZln4dzLZj30y84nC7uv32CP2g66fpx3PbAFK6/8mLiYqP5dNY8xl40kv69TvHv/2XFqmqPcd6Zg/nkq7lV1vn84LNvGTGkrz+jtkXzJvy1YQsffD6nXGB26IDTOPOAxyiA1+vjnluuIjwslNYtm3FS12PYtSeDqZNvw2g00jK1KTM/+YbfV6/3B2YPrLXdJCWRCeMv4Knn36xxYPb+x1/CZDTi8Xhxulz0OvVExl080r+/fduW5a4jE8ZdwKKlv7P4lz+44Kyh7NqTzo+LfuW5x+/0Z9FW9tjJzSvkvsdeJLVZMg/def1hl2ep7Od3pH+DmVm5hIQE0+vUEwkPC6VJcgId27UCICI8jLDQUIwmo//aZ7Pb+eKbH7l30gR/VutdN1/Jb3/8xey5CxhzwZnl5nak15n9qvPcIyIiIiIiUt8UmAV27t7L+o3beOKBmwEwm0wM6tuD2XMXVgjMPnL3/2iV2pStO3bz0usfce+tV1crY9ZsNjN8YC/mzF3I3vQsWjRrQrs2LSq0Ky4pZcHSFUyfer9/27CBvZj9/QJ/YNbhKMvMrI8FZ2oqOCgI+775ViUsLIS3X5zs/35/QHLH7r0kJcaXy3xs3bIZkRFh7Ni11x+YTUlKKBeI27xtFza7neEXXFNuHIfTSdrerH1tdnLW6f2rnNPfm7fz+swv2LJtF8UlpXi9Zbf/Z2bl0rplM849czB3PfIcm7bsoHu3LvTt2a3GNRV37N5Ll2Palctg7dq5A1abnaycPH+GXdt/PU4S4mLIKyiq0VhPPHAzLpebl9/4GIfDVe3z3K9Th4p1Wg+cl8lkJDoqgratm/u37S83kV9Q6N/22az5fDN3IZnZuTgcTlxuN+3blF8grU3LZphMxn/ONz6Grdv3APD2h1/z7kez/Ps+mPGk/+fU/aQurPjzL975aBY3XzOGQ/lhwXI++XouaelZ2Gx2PB4v4WGh/v2jzz2dx599g+9/WMopJx3LwD6n0rxpMgAXnjWUp154m19/X8spJx5XFqSt5O/5QMMGnMa40WdRXFLK6zO/oEvn9uUeM0P79+S56e/z46JfGTmsHz8uXI7RaCgXMPu3w7kW7NidRvs2LcplsnY9tgNer4+de9IJCrKQl19I505t/ftNJiMd27fGt+/xMfenpTz53Jv+/VMn31ZuEcSgIAtXXXYeU19+l3PPHFxxDrv2Vgi0dz22A598NbfctmMqedw1SU4o93uKi43GZDL6rxv7t+Uf8Dfy2x9/8e7Hs9i5Ox2r1Ybb48HpdGG3O2qUiXrThDGccuKxeL3esrIFr73PQ0+9yiN3l33QYbXZeX3mFyz7bRW5eQV4PB4cTieZ2bkAbNq6E5PRyEldOx1sGG666wk6d2zLI3ffUO5vYeI9T/mzU1OSEvhgxpMH7aeyn9+R/g2ectJxpCQncP74W+hxcld6nNyVfqedXOXPMW1vFm63p9xj3Ww207ljG3bs2luubW1dZ6B6zz0iIiIiIiL1TYFZYPbchXg8HkaNvsG/zYcPi8XCpOvHEXHAQlTJifGkNkshtVkKHo+XOx+axvvTn6hWYOTMYf34v5seYNvOPZw5rG+lbeb9vAyn08X/3fhAubl4vT527UmnRfMmREdFYjAYKK5mPdj6VFRcQrMmyQdtYzQYy2Uj1lTovwICNpud+LgYXnrqngpt998eGxwUVGV/Nrudm+9+klO7deXBO64lJjqKzOxcbr77SVz7FtvpecrxfPnuNJatWMWKP/7ihjsf57yRQ7jx6ksO+zyq8u/FawwGQ7k6sQeKiY4kMiKMXbvTy23fH7wMCwvx1xGuznnu9++fcWXzwmDAbDIf8G1ZwNm7b67zF/zCCzM+4MarL+G4Y9oTFhrC+5/NYf3fW//V778vSwa8Pi8A54wYxKC+p/r3JBxQruHkE47lgrOGcvuDU/H5vEy8dmylPyOAtes38+CTL/N/l53HqSd3ISI8jPkLfuHDz7/zt/m/y85j6IDTWPrbKpavWM3rM7/g4buup3+vUxh1+gBO7daVpb+t4rc/1vLux7O48epLy2XA/lt4eJj/cT75nhu44PJJHNupnT9zMjw8jAG9uzNn7kJGDuvHN/MWMbDvqQetg1pf14LePU7yfzACkJgQV6HN8EG9+ODzb3nrg69okpxYYX91VBbsM5sr/j38+zFjMOD/kCE9I5vb7p/COWcO4prxFxIVGc7qdZt4bOoMXG43IVQ/MBsfF+3/HbZMbYrVZuf+x1/i6nHnk9oshRde+4AVf/7F/64aTfOmKQQHWbhn8vO4XGV/T8HBVV93DnRa9xNYsHQF23el+bOyAe6a+H/+YHx1PhD898+vNv4Gw8NCefulyfyxegO//bGWGe9+zuszv+DNFx4uVwP3cNTGdWa/6jz3iIiIiIiI1DfjoZs0bm6Ph+9+WMKNV1/CO6886v/37iuPkRAXw7yff6ny2IF9umMymfj8mx+qNVabVs1p3bI5W3fsqbK+7OzvFzL6vDMqzOWE4zryzb6yBxaLmdYtmrF9Z1qlfRxNtu3YQ4d2LQ/dsBKtUpuSlZ1LZlauf9v2nWkUl1jLZXP+W8d2rcjLK8RsMvmD6Pv/xURHAmXlAVauWlfp8Tt3p1NYVMJ1V1zECV060apF0wrZWFC2qNuIIX158I7ruPmaMXz93U8AmPfdduz1eg95fms3bCkXZF2zfhNhYSEkVRLoqg6j0cjAvqcy96elZOfmH7Rtdc+ztqxZt4kundtz3sghdGzXitRmKf4M5uqKjooo9/v8d9Dm1G5dePqhSXz93QKmvvxulf2sXb+ZlOQExl9yFsd0aENqsxQysire9tyieRNGn3s6zz1+J/17nVxusbvkpHjOPXMQT9x/M6PPO4Ovv/u52ucRFhrChWcP48UZH5T7/Y8c3o/V6zaxZPmfrF2/uVypicoczrWgVWozf1b5fmvWbcJoNNCyeRMiwsOIi41mw8Zt/v0ej5eNByxyFR4WWu73EFJJwNFoNHLt5Rfy5ZwfSc/MLj+HFk1Zu25zuW1r1m2iVYuq/64P19+bt+P1efcFI9vRonkTcg7xt1Fd+7N0HfsW31qzfhNnDOlD/15lZS7i42JIP+B2+natUvH6fPyx5u+D9nvdlRdx+uA+3HjH4+V+t0kJcf6feZPkhBrPtzb+BqEsWNr9pOP43/+NZuarj5GemcPvq9ZX2rZZ0yQsFjNr1m3yb3O73WzYtI3WAfh973ckzz0iIiIiIiJ15T8fmF36658Ul5Qycnh/2rZKLfdvQO/ufDN3QZXHGgwGLjxrKDM/no3d7qjWeC8+dRfffPhipZlFm7buZOOWHYyqZC5D+vfk2x8W4/Z4gLIA1Jp1G8sd73K52bR1J5u27sTtcpOdm8emrTvZnZZx0Dlt35nGpq07KSouoaTU6u/jYDKycti0dScZWbl4vV7/MVbbP8Ge9IxssnPzD3vxlVNOOo42rVN58MmX2bh5O+v+3srDT7/KiV07+Wu7VnXccce0446HnuXX39eSnpHNmnWbePWtT9iwqSzYdOWYc5i/4BdmvPs5O3alsWX7bmZ+PBsoy4q2WMx8OmseaelZLP7ld9764KtyY7z2zmcsWvY7u9My2LZjD0t/XUWr1LIgQ2xMFMHBQSxfsYa8/EJKSivPZjx35GCysvOY8tK77Ni1l0XLfuf1mV8w+tzTy92WXVPXXn4hiQmxXHljWQ3jLdt2sWdvJguWruCvDZv9fVfnPGtTarMU/t60neUr17BrTzrT3/nU//uoTd1POo5nHp7E7O8X8syL71Qxl2QysnKZv+AX9uzN5JOv5rJw6e/+/XaHk2defIc/Vq8nPTOH1es2sX7jNv/v+NlXZrJ85Rr2ZmSxcfN2/li9nlapVS/QVZmzzxjIrrQMfl6ywr/txC6daN40mYeffpWWqU2rVR6jsmsBlJXu2P93uf/fnr2ZDBt4GsFBFh55ejpbd+zm91Xrmfryuwwf1Nt/W/gFo4by7sezWLTsd3bu3suzr8ykuLgUKl8LsUq9Tj2RYzu25atvfyq3/dILRjBn/iK+mP0Du9My+PDzb1m4dCWXnn9GzQaohuZNk3G7PXz6ddnj/LsflvDlnB8Pq6+SEiu5eQVk5+bzx5oNvPn+l7RonlKuDvGCpSvZtHUnm7fu5IEnXvJnmkJZfdszBvfmsakzWLhsJXszsvhj9Xp+WLi8wlg3Xn0JQweexv/ueKzCLf+Hqzb+Bpcs/5NPvprLpq07Sc/M4bv5S/D5vLRo3qTS9qEhIZwzYhAvvv4hv6xYzfadaTw+7Q3sdicjh/evhbOq6Eife0REREREROrKf76UwezvF3LKiceWK1ewX//ep/Dep9+wZduucjUND3TGkD68+vanfDZrvn/RoIOpbIXyf+aygNYtmlW6Anu/Xicz5eV3+OW3VfTp2Y2Rw/tz+Q33UVJq9c89Jzefcdf9c/v+B599yweffcuJXTvx8tP3AjBn3iImT3mNX+a+5293y31Pl1skZX8f+9ukZ2Rz7riJvPTU3f6auzPe/Zxv5y+ucMyBbeYt+IXuJx13WJldUBb4furBiUx96V2uvXUyBqOBHt26Mun6cYc8bsrk25j+9qdMnvIaBYVFxMfGcEKXjsTFlAWeTjq+M4/ecyNvfvAVMz+ZTXhYKCccV1b3MTYminsnXc2rb33Kp1/No0O7Vvzvqku4/YGp/jEsFjOvvPUx6Zk5BAcFccJxHXnk7uuBsmyyW669jDff/4oZMz/j+OM6+n/+B0pKiGPK5Ft5ccaHjL3uZ6Iiwxk5rB/jD1iU6nBER0XyxvMPM/Pj2bz/6RzSM7IxGA2kNk1hUN8eXHTO8GqfZ206+4yBbNqyg/seexGDAYb078m5IwezfMXqWh/r5BOOZcojk7j1/in48HHrvx4zfXp24+JzhzPlpXdwudyc1v0ELr/kbN547wsATEYjRcXFPPz0dPIKComOiqR/r5P5v7Fli7B5vV6eefFtsnPyCQ8L5dSTu3LzNZfWaI7RURGcPqg3b8z8gv69TsZoNGIwGDhzWD9efesTxh6wqNTBVHYtANi1J6Pc9WD/z+WFJ+/i2cfuYNorM7nihvsJCQ5mQO9TuHHCP/Mfc9GZ5OYX8PDTr2I0Gjn7jAGc2q1Lubqj1XXdlRdz9cSHym3rd9rJTLzmMj74/FuefXUmTVMSuWfS1RVqeteG9m1bcuOES3nvk2945a1POOG4jlx7+UU8/PSrNe5r8pTXgLJrTHxsNCd06cQ1l1/oz9y+6epLeXTqDK6e+BAxUZGMufBMSq22cn3cduPlvPrWJzzzwtsUFpeQnBjPuItHVTrezdeMwev1csMdj/HS0/dUGfysrtr4G4yMCGPB0hW8PvMLnC4XqU1TeOjO62nTqnmVx1x35UX4fD4efvpVrFY7nTq0ZtpjtxMVeWSlD6pypM89IiIiIiIidcXgdDoqL1YpR727Jz9Px3atqnxTX5kZ737On2s3VBoorMrvq9Zz1yPT+OztZ6v9RtrlcnPhFZN48M7rOb6Gi2KJSM0czrWgJrxeL6Ovup2BfU9lwrgLAjKGSG3Qc4+IiIiIiDQk//lSBg3ZDf83+qALA1XmlxWruf7K0TU6ZtmKVYy9eFSNspsysnIYe/EovTEWqQOHcy04mPTMHL7+9md27Ulny/bdPPXCW+zNyGZYFbWxRY4Weu4REREREZGGRBmzIiJSTmZWLvc9/iLbduzBh482LVO57sqLOLFLp/qemoiIiIiIiEijocCsiIiIiIiIiIiISB1TKQMRERERERERERGROqbArIiIiIiIiIiIiEgdU2BWREREREREREREpI79pwOzPp8Pt9uNz6cyuyIiIiIiIiIiIlJ3zPU9gYPZnZbBw09Pp7ComIjwUO6dNIE2rZqXa/PN3IV88tVc//dZOXmc0KUTT9x/8yH793g89BkxnsVz3sZsPqp/FCIiIiIiIiIiItKIHNXRyCefe5OzzxjAiKF9+Wnxb0yeMp03X3ikXJszh/XjzGH9/N9fevWdDBtwWl1PVURERERERERERKTajtpSBnkFhWzYvI1hg3oBMKD3KWRm57E7LaPKY9b9vYX8giL69DyprqYpIiIiIiIiIiIiUmNHbWA2KzuPhLgYzCYTAAaDgeTEeDKzc6s8Zvb3Cxk+qJfKEoiIiIiIiIiIiMhRrdFEMG12O/MX/sLr0x46aLvPZs3n89nzAbTol4iIiIiIiIiIiNSLozYwm5QYR05eAW6PB7PJhM/nIzM7l+TE+Erb/7ToN9q0bE7rls0O2u/5o4Zw/qghALjdbvqMGF/bUxcRERERERERERE5qKO2lEFcTDQd27Vi7o9LAfh5yQqSEuJIbZZSafvZ3y9g5AGLgImIiIiIiIiIiIgcrY7awCzAHTdewVff/sSFV9zKzI9nc8+kqwF47NkZLP7ld3+7nbv3snnbLgb161FfUxURERERERERERGpNoPT6fjPFlrdX8pg8Zy3tWCYiIiIiIiIiIiI1JmjOmNWREREREREREREpDFSYFZERERERERERESkjikwKyIiIiIiIiIiR4VSq42BZ13Jo1NnlNv+x+r19Bt5OWOvvZsx19zFlTc+wF8btvj3vz7zc559Zaa/bc9hY3j2lXfL9fHw06/Sc9gYNm3dWW77tbdO5vzxt+Dzla/26fP5uOaWh0nPzAFg9tyFXDrhTnqfPpaPvvi+ynOw2uzcfPeTDL/gGoace/VBz/dgfT7/2gfM/WlZlcf+tWELl064k7HX3cPylWsOOk59KC4p5d2PZx20jd3u4PL/3Uep1RaweVhtdnoOG+P/fuy1dx/xeHdPfp416zYd6dQUmBURERERERERkaPDjwuX07F9axYuXYHVZi+3r2XzJrz7ymO89+rjDB/Ui0envlZlP6nNUliy/E9cLjcApaVW1qzbRGJCbLl2u9My2JOWQZDFwp9rNpSfy6JfSW3ehCbJCQB0ateKR++5gSEDTjvoOZhNJsZceCbPP3HXIc/3YH2OuXAEb7z3BR6Pt9Jjv52/mKEDTuPdlx+lx8ldDznWfm6Pp9ptj0RJiZWZH39z0DafzZpPn54nER4WWidzAnj3lceOeLzxo8/ilTc/PuK5aMUrERERETlqeTweTCZTfU9DRESk0fP5fBSXlAZ0jMiIcAwGw0HbzJ67kMsvOZuv5vzEDwuXM2p4/0rbnXLisbz8RtWBsZDgII7p0IZFv/zOoL6nMn/hcgb07s4Pi5ZXGG/YoF7Ex8Uwe+5CTjq+s3/fV9/+xLiLR/m/b9+2JQBG48HPISjIwsknHEt6RvZB2x2qz7iYaJo1SeK3P9bS85Tjy+179+NZ/LBwOcHBQfy4cDkvPX0Pe/ZmMvXlmdjsdoIsFm66ZgzHH9uB9Ixsxl53D2efMZDf/lzL6YP6MKR/D6a+/C7pmTk4nE769uzGhPEXALBjVxrTXn2PnLwCAM49czDnnjmIDz//lvkLfsHt9mA2m5h47Vi6dG6P1+tl6sszWblqHRazGZPJyPRn7+fJ59/EarMx9tq7MZlMvPXiIxXO8atvf2LaY3f4vz9n7M0MH9iLlavWk5Wdy7jRZxFkMfP1dz+Tk1fAdVdcxJD+PQFYv3ErL7/xMaVWGx6vl3EXj2JQ31MB+HLOj3zw2beEhobQv9fJ5cbsOWwM8z6fTmREOM+/9gGr1m7A7fYQHhbKnTdfScvUpv52E8ZfwKJlv1NQWMwVl57NmcP6AdChbUvyC4vZsSuNVi2aHfL3XBUFZkVERETkqOVwOgkLrbsMChERkf+q4pJSWhzTK6Bj7NqwlKjIiCr3b9+ZRmZ2Lqd264rH4+Hdj7+pMjD70+LfGNy/x0HHGzG0L2998BWD+p7KnHmLuHfS1eUCsx6Pl+/mL+aFJ+8mJjqSN9/7kpJSKxHhYbjdbtau28yxndod1rnWluOOac+KP9dVCMyOvWgUO3en075NSy4+dzgul5u7Hn6OO2++kh4nd2X1Xxu5+5Hn+PStKQCUlFpp3bIZ1//fxQDcfPeTjL14FCd1PQa3x8Ot9z3Dj4t+pV+vk7n9wWf5v8vOZei+LN6CwmIAhg/qzejzzgDKyig88sx0Pn7jaTZv28XKVev44LUnMBqNlJRasZjN3HHjFYy97h7efeWxSs8tMyuXklIrzZsml9tuszuYMe0BdqdlMOaauxg/+ixmTHuQ9Ru3Mum+ZxjSvyfFJaU88dybTH3kVhLiYykoLGb89ffSpXN7iktKeX3mF7zz0mQS4mMPmtl62YVncuPVlwAwf8EvPPvKzHKB4iCLhTdfeJgdu/Zy5Y33M3xwb8z7kgaOO6YdK/5cp8CsiIiIiDROTodLgVkREZE6EBkRzq4NSwM+xsHM/n4Bpw/qjclkpGf3E3jy+TfLZSTu3JPO2GvvJje/EI/Hw+vPPXTQ/roe24HMrFyWr1yD0Wj0Z0Lut2zFKlKSE2nVomz7KScey7yflnHuyMEUFBZjNBoJCw05gjM+cvFx0ezYlXbIdjv3pGMwGvwlDY4/riNxMdFs3rqTpIQ4zGYTwweVBd5tdjsr/1xHXn6h/3irzc6uPens2p2O0+nyB2UBYqIjAdi0dSdvf/g1RUUlmExGdu1Jx+5w0qxJEh6Ph0enzuCk44+hV/cTMRoPXT01KyePuJjoCtsH9ysLuKc2SyE4yMKA3t0BOKZDG4qKSyguKWXt+s3sTc/ilnufLnfsrj3pbNuxh54ndyUhvqxsxblnDubdj2dXOoff/viLz2bNw2q14/V5KSounzU+bGDZz6FVi6aYTEby8gpISowHID42muycvEOe58EoMCsiIiIiRy2H01nfUxAREflPMBgMB81mDTS32833Py7FZDYxb8EvANjtTmZ9v9Cf0bi/xqzb7ebpF97mwSdfZsa0Bw9aHuH0wb156KlXuPaKiyrs++b7hexOS+ecsTcD4HA4Sc/M4dyRgwkJCcblcuHz+Q5ZfiGQnE4XwUGWwzr2wGmHBAf7g6X71zib8dyDBAcFlTtm2449lfZVlpE7jRefupvOHdtSWmpl8LlX43K5iIwI5/3pT/Dn2r/5ffV6Xn3zE15+5l5/ZmlVQoKDcLpcFbYHHXC+RqPRf/4GgwEDBjweLz6fj9YtmzNj2gMVjv/3OVT168vIymHKS+/w5gsP07xpMlu27eLaWycfdC4H1vt1ulxEhIcd9BwPRYt/iYiIiMhRq7IX6yIiItL4LP7lD5o2SWT2By/w5bvT+PLdabz+3IN8/+MS3G53ubZms5mJ115GVk4eC5etPGi/I4b1ZfR5p/uzMPfLyy9k5ap1fPrWFP9433z4Ijm5+WzeupOI8DASE2LZszezWvP/9Ot5vFzNxaAuuvI2sqqZablj117atWl5yHYtmzfB5/Xx2+9rAVizbhO5+YX+GrYHCgsN4aTjOzPzgCzS7Nx8srJzaZHahODgIOb9vMy/r6CwGKfTicvtJiWpbCG0T7+e79+fX1CEze7g1G5duPbyC0lJTmTHrjTCwkJxOJ3+Bdj+rUVqE/ILirA7av5BfJfOHUjPzOK3P/7yb9u0dScul5tuJ3Rm+e9ryN1XI/fLOT9V2kdJqQ2z2URCXAw+n4/PZs2vtF1Vyn43LWo89wMpY1ZEREREjloupwKzIiIi/wWz5y5k2MDyNW5btWhGYkIsS5b/SVRk+TIIISHBTBh/AW/M/JJ+p5Vf3OlAcTHRjL1oVIXt385fTPeTupQrr2A0Ghncvwez5y7kluvGMqB3d379fS2pzVIAmDNvEdPf+ZTiYiuLlv3OB5/P4emHJtGxXSt27EqjaUqSv68x19xFQWERpVYboy69gW7Hd+aB268lr6CQouISf3bywfr0+XysXLWOyy4aecifn8Vi5vH7b2LqyzN5fsYHBFksPHbvjYSFhlC4r0bsgR6881qen/4+l159JxggNCSYO268gqTEeJ56cCJTX36Xdz6ahdFg5NyRgzhnxCAmjLuAK2+8n+ioyHL1fbOyc3l82hu4PR68Hi9dj+1Az1OOx2w2c/qg3oy55i7CQkMqLP4VHBRE95O68PuqdfQ69cRDnuOBoiLDmfLwrbww40NeeO0D3B43yYnxPPngRNq2SuXKMedyzaRHKl38a792rVMZ0r8Hl1x9J9FREfQ9rVu1x7fZ7WzdsZtTTjyuRvP+N4PT6fAdUQ8NmNvtps+I8Sye8zZms2LUIiIiIkebvzdtpVOHtvU9DREREfkPysjK4e5HnueN5x86ZDmDCbc8zNTJtxEedvDa+D8u+pVde9K5/JKzDzn+LytWM/enpTx4x3U1mXaDsu7vrbz5/pdMeeTW+p5KjXzxzY9k5+QxYfwFR9SPopEiIiIictTyeLx4vd5qLSAhIiIiUptSkhK47MIzyc7J8y/4VJXpU++vVp+D+p5a7fFLS21c/3+jq92+ITq2U1v69z6FUqvtkEHto4nRaGDsxYfOZD4UZcwqY1ZERETkqODz+UjPyKJpk2T/trXrN9I0JYn4uNh6nJmIiIiISO1T6oGIiIiIHBU8Hg9Z2bnk5xf6t3m9XvbszcBms9fjzEREREREap8CsyIiIiJyVPB6y27kKi4t9W/z+cDn9ZGTl19f0xIRERERCQjdvy8iIiIiRwWv1wuAzWanqLiE/ILCssgsZWUOREREREQaEwVmRUREROSo4PWVBWbtDge5ufkUFhX/s29f0FZEREREpLFQKQMREREROSrsL2Xg8/rKlTPYv01EREQav1KrjYFnXcmjU2dUq/0fq9fzy4rVRzxuekY2Q869usr9GzZtY+I9T3Hu2ImMv/5exl53D2998JV//3W3TWbhspVA2QfKTz3/FtdOeoSSUmuFvnoOG8OlE+7ksmvuZvRVt/PN3IUA3HLv03z69bwK7S+75m4WLFlxhGcoRyNlzIqIiIjIUcHj8fi/9nrKZ8iqlIGIiMh/w48Ll9OxfWsWLl3BxGsvIyw05KDt/1izgeISKz1POT5gc9qyfTcT73mKeydNoHePEwEoLCph5iezK7R1u908/PSrWG0Onn3sDkKCgyrt89Up9xEZEc7mrTu54sb76XFyV0YO68c7H83igrOG+ttt2LSN3LwC/7jSuCgwKyIiIiJHBavVVuW+/WUOREREpHGbPXchl19yNl/N+YkfFi5n1PD+AJSUWnn+tfdZt2ErRqORju1bceHZw/hyzk94vV7+XLOBfr1O4YzBvRl73T3M/+I1AKw2O4PO/j9+mfseAA888TK79qTjcrtJTozj7olXER8Xc9A5vffJbEYN718uOBodFcH//m90uXYOh5M7HnqWyIhwnnjgZswm0yHPt33blkRGhJOVk0efnifx9Atvs2XbLtq1aQHAN3MXMnxwb8xmhfAaI/1WRUREROSoUGqteKvffl6VMhAREWn0tu9MIzM7l1O7dcXj8fDux9/4A7PTXplJcHAQM199DKPRSH5BEbExUZwzYiDFJVYmXnsZUFaS4GBuvmYMsTFRALz78Sxen/kFd9x0xUGP2bhlB/17n3LI+U99+V1O7daVB26/FoPBUI0zLsv4jYmKpH2blpjNZoYP6sXsuQuZeO1lOJxO5i/4hdeefaBafUnDo8CsiIiIiNQ7l8uF3e6scr9KGYiIiAReSfpKvO6q72A5EkZzKBFNTj5om9nfL+D0Qb0xmYz07H4CTz7/Jjt2pdGqRTOW/raK1597CKOxbLmk/cHVmpr38zK+/3EpTqcLh9NJTHRkjft4YcYHrPjjL/ILi3nusTto06o5AKd268rvq9exdftuf8ZrVa6Z9AgOh5P0zGwevfdGLJayEN3I4f257rbJ/O//RrNgyUpapjajVYtmNT9RaRAUmBURERGReuXz+diTloHb7a6yjderUgYiIiKBdqjAaSC53W6+/3EpJrOJeQt+AcBudzLr+4XcePUl1e7HZDKWe93gdLr8X6/+ayOffj2P16Y9QFxMNIt/+Z0Z735+yD47tG3F+o3b6N+rLGv2hqvK5nPO2JtxH1Ajf2Df7vTpeRI33f0kzz56Ox3atqyyz/01ZufMW8TkZ16ja+cOxMVG07plM5o3TWbJ8j/4Zu5CRg7vV+1zl4bHWN8TEBEREZH/tuKSUgqLig/aRqUMREREGrfFv/xB0yaJzP7gBb58dxpfvjuN1597kO9/XILb7aZ3j5P44LM5/qBrfkERAOFhoZSW/lMOKS4uBp/Px/adaQB898Ni/76iklLCQkOIjozE5XLz1bc/VWtuYy4YwazvfmbZb6v821wud7mFS/cb3K8Hk64fx8R7nmLjlh2H7HvE0L6cfOKxvPPR1/5t+xcBW79pK4P79ajWHKVhUmBWREREROpVdbJhVcpARESkcZs9dyHDBvYqt61Vi2YkJsSyZPmf3DzhUpwuN2Mm3MXYa+/m1bc+AaBfr5PZtG0XY6+9mzfe+xKzycQt143l1vuf4Yob7sPt/id42vPkrrRo3oSLrryVayY9Qvs2VWe0Hqh925ZMeeQ2PvlqLueOncgVN9zP9bc/yrkjBtOiWUqF9gP7dOe2/41n4j1P8ffm7Yfs//orL+abeYvIyskDYFC/Huzak87APqcSFhpSrTlKw2RwOh3/2Ve5brebPiPGs3jO21rdTkRERKSe5OUXsGv33oO2MZqMdD22Ux3NSEREREQk8JQxKyIiIiL1qrLbAAHSM7IYO2ESoIxZEREREWl8FJgVERERkXpVVf3YDRu3smv3XtweD4rLioiIiEhjo8CsiIiIiNSrqjJmoyNMRIUZKSgoRJFZEREREWlsFJgVERERkXrlqWLxrxhjBr2PDSM3rwBQOQMRERERaVyO6hWvdqdl8PDT0yksKiYiPJR7J02gTavmFdpt2b6bqS+/Q15+EQDXjL+A/r1PqevpioiIiMhh8FYRmHW6vFjMBnLz8oGywKzBYKjLqYmIiIiIBMxRHZh98rk3OfuMAYwY2pefFv/G5CnTefOFR8q1sdsd3PHgVO6/7RqOP64jHo+XouKSepqxiIiIiNRUVZmwHo8du8tHTm7+QduJiIiIiDRER21gNq+gkA2btzHt8TsAGND7FKa89A670zJIbZbibzfv52Uc26kdxx/XEQCTyUhsTFS9zFlEREREDkMV8Va3yw0+ymXMioiISOP1y4rVvPbOZ7jcbkKCg7jjxito37YlANfdNpmMzFwiwkMBOH1IH0afezoATzz3BmvXbyY2OoonHriZiPAwfD4ft9z7NJOuH0fzpsmVjvfIM9P57Y+/iI2OxOF0cWLXTtx6/TjMZjPnjL2ZJx+YSId944sEwlEbmM3KziMhLgazyQSAwWAgOTGezOzccoHZ7bvSCLKYmXTfM2Tn5NG2dQtuvPoSBWdFREREGoj9AVer1caqtes57dRu4PPidntwun3k5eaUayciIiKBsWrN+oD1fULXzgfdX1RcyoNPvsIrz9xLm1bNWbX2bx588hXef+0Jf5ubrrmUfqedXO64rTt2syctk/enP8Eb733Jdz8s4YKzhjLruwV0O75zlUHZ/S49fwQXnzsch9PJdbc+ypdzfuKCs4Ye/omK1ECDX/zL4/Gy4s913HHTFbzz8qMkJsTy9AtvVdn+s1nzGX3V7Yy+6nbGXHNXHc5URERERCqzP+D6waezuPuhZ5j93Y/gdVDq8OE1RbJu7Ur+Wr8Jr1eBWRERkcYqLT2T6KgI/9pCJ3TpREZ2Dhs3bz/ocWaTCafLhdfrxW53YLGYycnNZ/6CX7j4vNOrPX5wUBAndu3Ezt3pR3QeIjVx1AZmkxLjyMkrwO3xAGUv2DOzc0lOjC/XLjkxnpOO70xSQhwGg4HhA3vx199bquz3/FFD+HDGU3w44ynee/XxgJ6DiIiIiBza/sBsVlY2J59wLM+/8jYuexHFNi9NW3TgiouHct+jUykpKa3nmYqIiEigpDZLobCohDXrNgGw+JffsVrtpGfm+Nu8/MbHXDrhTu599AXS0rMAaJnalG7Hd2b89feSlpHF8IG9eG76+/zvqtH+u7Cro6i4lOUr19CpfataPS+RgzlqSxnExUTTsV0r5v64lBFD+/LzkhUkJcSVK2MAMKjfqcyeu5DSUivh4WEsW7GK9m1a1NOsRURERKSmfPuKzDaJKmXkqU14aOcOXnr1VQqKnSQ3a0/fUzvz0AtzSM/MJj4+tp5nKyIiIoEQER7GY/feyCtvfYLNZue4Y9rTukUzTKaynMIHbruW5KR4fD4fn82az633P8OHM54CYML4C5gw/gIAFi37naTEOJokJzD5memUWu0M6nsqg/v3qHTc9z+bw7fzF2EwGhjQuzsjhvatmxMW4SgOzALcceMVTJ7yGu98NIvwsFDumXQ1AI89O4M+PU6iT89upCQlMO7iUVw98WEMRgOJ8bHcedOV9TxzEREREamu/aVjQ8xOspwpXNQ/Fqc9kxxDC84ZNQIDaQQHB2Gz2+t3oiIiIhJQ3U7oTLcTymrROp0uzhx9Pa1aNAMgOansDmqDwcAFZw3lxRkfUlhUTHRUpP/40lIrH3w2h2mP3cE7H8/ixK7HMGxQL8Zecze9e55ESHBQhTH315gVqQ9HdWC2ZWpTZkx7sML2uydeVe770wf35vTBvetoViIiIiJSm/aXMnC7XPhMMYQFm/h6cTHvvXtnWYPidEJDLArMioiINHI5ufkk7Ls75q0PvqLb8ceS2iwFt8dDUVEJcbHRAPy8+DfiYqPKBWUBXn7zY6649BxCQoKx2x1gAAMG3B4PbrcbKgnMitSnozowKyIiIiKNn8/nIzcvn9170mnVNYi2va9kTNO0f/abwkiIDsZmc9TjLEVERCTQZrz7Oav+2ojH46FL5/bcfcv/AeByuZh03zM4XS6MBiPR0RE89eAt5Y5dvW4TDoeT7t26AHDeyCHc//hLvPfJNwwf1IuI8LA6Px+RQzE4nY7/7PK2brebPiPGs3jO25jNilGLiIiI1Ie/N23lrgefxlKylqHn3sDJJ3Utt9/gyODeyVO58qobOfvMofU0SxERERGR2mWs7wmIiIiIyH+bz+fD6XQCEBISUnG/KZyEKAs2uzJmRURERKTxUGBWREREROqVz+fD43ED4PV5KzYwhREXacauGrMiIiIi0ogoMCsiIiIi9Wruj4v5a93feLw+4mJjKjYwmAgKMqvGrIiIiIg0KiqsKiIiIiL16r2PvmTqrQPo3KEF7riUStuYzcqYFREREZHGRRmzIiIiIlKvSkqtxMRE4QuuPCgLYDGZVGNWRERERBoVBWZFREREpF65HFYMQbF4wo+pso3JbMbuUGBWRERERBoPBWZFREREpN74fD6iQtyYQhIO2s5kMuF0KjArIiIiIo2HArMiIiIiUm9KSq0kx5oICk86aDuj0YTb5aqjWYmIiIiIBJ4CsyIiIiJSb4qLS4gMNRIaHlvpfoPRAJQFZp1OZ11OTUREREQkoBSYFREREZF6U1hUTEiQBZPZUun+4KAgAIwmM263ArMiIiIi0ngoMCsiIiIi9cbhcGKymKvcHxISDIDRaFYpAxERERFpVBSYFREREZF643A4MBmqfkkaErwvMGsyKWNWRERERBoVBWZFREREpN44HC5MJlOV+/8pZWDB43HX1bRERERERAJOgVkRERERqTculxMOkjEbFFxWe9ZkUikDEREREWlcFJgVERERkXrjcNoxGKt+SWoxWzAYDZjMJjwqZSAiIiIijYgCsyIiIiJSb9wuJ/6XpAYDzZomg8Hg3280GmiakoTBaMGrUgYiIiIi0ogoMCsiIiIi9cbpdIKxrMasyWQkMSGexPg4/36jsWxbVFQkHrcCsyIiIiLSeCgwKyIiIiL1xu12YthXY9a0r6RBREQYJrOJ8PAwjPu2hYaGafEvEREREWlUzPU9ARERERH573I7HRgMZRmz+4Ow4WGhNGuSTFxsjL+dxRKkwKyIiIiINCrKmBURERGReuNyOWFfxuz+wKzZbCYqMqJcO4slGK9XgVkRERERaTwUmBURERGReuN2uzCayjJmzfv+h7Lg7IEswUF4PJ46nZuIiIiISCApMCsiIiIi9cbtdoKhLAgbGRleZbsgSzAoY1ZEREREGhEFZkVERESk3ng9bowGIwajgYjwgwRmg4OVMSsiIiIijYoCsyIiIiJSb9wuFwajmdiYaEJCgqtsFxwcis+nwKyIiIiINB4KzIqIiIhIvfF4XFiCgmjRvCkGg6HKdkHBQfi8Hnw+Xx3OTkREREQkcBSYFREREZF643a7MJsth2wXEhKK0QBut+rMioiIiEjjoMCsiIiIiNQbr8eNsRqB2eDgYEwmcLpcdTArEREREZHAU2BWREREROqNx+3CbKpOxmwYRoMBp0OBWRERERFpHAISmH36xbfZuHl7ILoWERERkUbE43FjCQo6ZLuQkBBMRrA7HHUwKxERERGRwDMHolOvx8sNdz5OUmI8Zw7tx/BBvYiJjqxxP7vTMnj46ekUFhUTER7KvZMm0KZV83Jt/li9non3Pk3L5k38216b9iAhwYd+gS8iIiIi9cvjdhMUcujXbWZzECYjOBSYFREREZFGIiCB2TtuuoKJ117GgqUr+Hb+Yl5562NOO+UEzhzWl56nHI/RWL1E3Sefe5OzzxjAiKF9+Wnxb0yeMp03X3ikQruWzZvw7iuP1fZpiIiIiEiAeT0ugqrxgbrRZCLIYsZmU2BWRERERBqHgNWYDQqyMHTAaUx77A5mvvIYaRlZ3P7gVM669EZen/k5Vpv9oMfnFRSyYfM2hg3qBcCA3qeQmZ3H7rSMQE1ZREREROqYx+PGbK7GnU4GI8FBJmz2g7+GFBERERFpKAKSMbvfn2v/Zs68RSxYsoIO7Vpy76QJNElO4MPPv2PSvU/zypT7qjw2KzuPhLgYzCYTAAaDgeTEeDKzc0ltllKubVp6FuOuvweT0ciIoX05b+SQQJ6WiIiIiNQSr9eNxRJcjZZGzGYTdocz4HMSEREREakLAQnMvvHeF3z3wxJcLjfDB/XirRcfKRdMPbZTO4adP6FWxurYrhVfv/88EeFhZGXncst9zxAdFcngfj0qbf/ZrPl8Pns+AD6fr1bmICIiIiKHx+vxYLZYDtnOYDBgNpm0+JeIiIiINBoBCcxu3raLiddeVmU9WYvFzJTJtx20j6TEOHLyCnB7PJhNJnw+H5nZuSQnxpdrFx4edsAx8Qzp35PVf22sMjB7/qghnD+qLKPW7XbTZ8T4Gp6diIiIiNQWr9dNUFB1MmbBZDZjtyswKyIiIiKNQ0BqzHbu2IZep55YISj73iff+L8+sUung/YRFxNNx3atmPvjUgB+XrKCpIS4CmUMcnLz8Xq9AJRabSz99U86tGtVC2chIiIiIoHm83qwBIVUq63ZbMZ2iHUKREREREQaioBkzL7z0SzGXjSqwvaZn3zDmAvPrHY/d9x4BZOnvMY7H80iPCyUeyZdDcBjz86gT4+T6NOzGz8vWcGX3/yIyWTC4/EwsG93zhzat9bORUREREQCx+t1YwmqxuJfgNlkwqFSBiIiIiLSSNRqYHbLtl0AeD0+tmzfDQfUcN2Tnklw8KHrhx2oZWpTZkx7sML2uyde5f/6grOGcsFZQw9vwiIiIiJSr7weD8HB1QzMmk3YVMpARERERBqJWg3Mjr3uHgyGfV9fe7d/u8EAcbExXD3u/NocTkREREQaOLfHS3A1a8xazGYFZkVERESk0ajVwOyy72cCcOWND/DG8w/VZtciIiIi0gh53G6Cq1vKwKxSBiIiIiLSeARk8S8FZUVERESkOjweD8FB1St3ZTabsStjVkREREQaiVrLmH3giZd56M7rALjz4WlVtnvi/ptra0gRERERaeDcHg9B1cyYtVjMOBzOAM9IRERERKRu1Fpgtk2r5v6v27dpUVvdioiIiEgj5qnR4l9mClXKQEREREQaiVoLzI67eJT/6yvHnFtb3YqIiIhII+b2eGsUmHWWKDArIiIiIo1DrQVmt2zbVa127ZRNKyIiIiL7eDxuQoKDq9XWZFYpAxERERFpPGotMDv2unswGMDnq7qNwQBLv5tZW0OKiIiISAPm8XjweSGouot/mSw4ncqYFREREZHGodYCs8u+V8BVRERERKrP6XQBEBJSvYxZS1AQDkdhIKckIiIiIlJnjPU9ARERERH5b7Lb7fgAi6V6GbMhIaHYrDZ8B7tFS0RERESkgai1jNkHnniZh+68DoA7HnoWg8FQabsn7r+5toYUERERkQbM7rDj9foIqmZgNjQ0DLvNisfjwWyutZexIiIiIiL1otZe0bZp1dz/dYe2LWurWxERERFppOw2K54a1JgNDQ3FZi8L5oqIiIiINHS1Fpgdd/Eo/9cjh/UjKTG+Qpus7NzaGk5EREREGjin3YbHZ6jyTqt/CwsLx2az4fV6AzwzEREREZHAC0iN2dFX31Hp9jHX3B2I4URERESkAbLbS/F4q/9yNCwsHIfdhtenwKyIiIiINHyBWfyrkrvLHE5ntbMhRERERKTxczmseDBVu31YeBhOh0oZiIiIiEjjUKurJoy97h4MBnA4XYy7/p5y+3LzCjihS8faHE5EREREGjCn04avBoHZiIhIvD4PVpuNiPCwAM5MRERERCTwajUwe9E5w8AHT73wFheePcy/3WgwEhcbTbcTOtfmcCIiIiLSgDkdNrw1eDkaHh6OyWigsLCYpISK6xmIiIiIiDQktRqYHTGkLwDt27akQ9uWtdm1iIiIiDQyHpcNn6/6GbPBISEEWUwUFZUEcFYiIiIiInWjVgOz+3Vo2xKb3c6WbbspKCoG3z91wPr07BaIIUVERESkgXE6bBjNQdVubzSYiQwPpbhUgVkRERERafgCEpj9Y/V67p78PF6vD6vVRlhYKDa7naSEOAVmRURERAQAp8OKyRRc7fZGk5nw8FCKiooDOCsRERERkbphDESnL77+IWMvGsW8z6cTFhbCvM+nM3702Zw3ckgghhMRERGRBsjlsGGy1CQwayEqPJhClTIQERERkUYgIIHZXXsyuOic4cA/VQwuu/BMPv7y+0AMJyIiIiINkMvpwBIcUu325qAwYiKDKSpWYFZEREREGr6ABGZDQ0NwOJ0AxMZEsWdvJnaHkxKrNRDDiYiIiEgD5HA6CAsNrXZ7U1A4MZFBKmUgIiIiIo1CQGrMnnxCZ35e8hsjhvRlYJ/u3Hz3k5jNZrqf2CUQw4mIiIhIA+R0uQkNrX7GrNEURERYkEoZiIiIiEijEJDA7AO3X+v/esL4C2jVoilWq50zhvQJxHAiIiIi0gA5HU7CQqOq3d5oNBIcHExavjJmRURERKThC0hg9kAGg4Hhg3oHehgRERERaUB8Ph9Op5PQGpQyAAgJDlKNWRERERFpFGotMPvc9Peq1e6mCWNqa0gRERERaah8HmwON6Fh1S9lAGAOCsVuywnQpERERERE6k6tBWaLi0trqysRERERaeS8HidWm4uo+JoGZiPwuXfj8/kwGAwBmp2IiIiISODVWmD23lsn1FZXIiIiItLI+bwuSu0uUmqw+BeAOSgMo8+Jx+PBbA54VS4RERERkYAJ2KvZ9Mwc5i9YRlZ2PkmJsQzpfxpNkhMCNZyIiIiINCQ+L3aHk7CwmtWYNQeHY8SFx+NFcVkRERERaciMgeh0xZ9/cclVd/DH6g14vV7+XPM3l159B7/98VcghhMRERGRhsbnw+5w1TgwGxwSicngwuP1BGhiIiIiIiJ1IyB5Bi+/8TH33zaBAX26+7ctWLKCl974kO4nPVrtfnanZfDw09MpLComIjyUeydNoE2r5pW29fl83HDH42zcsoP5X7x2xOcgIiIiIoHj83lxOJyEhtQsMBsSGgk+N4WFxYSF1uxYEREREZGjSUAyZtPSM+nX6+Ry2/r07Mbe9Owa9fPkc29y9hkD+OTNZxhz4UgmT5leZduPvviOZk2SDmu+IiIiIlLXfNgdTsJrmDEbEh6B0efC4XAGaF4iIiIiInUjIIHZJimJLFn+Z7lty1asoklKYrX7yCsoZMPmbQwb1AuAAb1PITM7j91pGRXabtuxh0XLfueyi0Ye2cRFREREpG74fNjtNa8xGx4WAT4Pbo87QBMTEREREakbASllcN0VF3PHQ8/S7fhjSElOJCMzmz9W/83j999U7T6ysvNIiIvBbDIBYDAYSE6MJzM7l9RmKf52brebx6e9wT23/B8mY0DizCIiIiJSy3x4sTtdhIWF1Oi4iIhw3G4XbrdqzIqIiIhIw1argdk58xYxqF8PTu3WhfdefZwfFi4nOyePLp07MPHasTRvmlybwwHwxntf0r/XybRq0Yz0jEOXSvhs1nw+nz0fKKtLKyIiIiJ1z+vxYHe4iIgIr9FxERHh+HxgtzsCNDMRERERkbpRq4HZ12d+zrRX32NQv1MZNbw/40efddh9JSXGkZNXgNvjwWwy4fP5yMzOJTkxvly7P9dsIDM7l89mz8fj8VBqtXHO2Jt58/mHiY2JqtDv+aOGcP6oIUBZtm2fEeMPe44iIiIicnjsdjs+n4+IsLAaHRcVFYnPBwVFRQGamYiIiIhI3ajVwOwX707j19/X8u38RVx762SaN01m1PD+DB/Ui+ioyBr1FRcTTcd2rZj741JGDO3Lz0tWkJQQV66MAcCrU+/3f52ekc3Y6+7hy3en1cbpiIiIiEiA2O02vD4Ir2FgNjw0lLCwUDIysgI0MxERERGRulGrgVmDwUCPk7vS4+SuFJeU8v2PS/l2/mJefuNjevc4kVGnD+DUbl2q3d8dN17B5Cmv8c5HswgPC+WeSVcD8NizM+jT4yT69OxWm9MXERERkTpis9kwYCQ0NLhGx5lMJsLDI8jKysbj8WDatx6BiIiIiEhDE5DFvwAiI8K54KyhXHDWUNau38yDT77MgqUrWPrdzGr30TK1KTOmPVhh+90Tr6q0fZOUROZ/8drhTllERERE6ojNZsdisdQ4sGo2mwiLiCI/Lwe3W4FZEREREWm4AhaYBVi7fjNz5i3ix0W/EhUZzpVjzg3kcCIiIiLSQDjsdiyWIIxGY42OMxqNBAWHYbcV4/F6AjQ7EREREZHAq/XAbF5+Id/+sJg58xaxNyObfqedzGP33cgpJx5X20OJiIiISANls9sICrIc1rFGSwQuZzEej7eWZyUiIiIiUndqNTB76/1T+PX3NbRu2ZxzzxzMsIG9iIoMr80hRERERKQRsDvsWIKCDutYQ1A0JvsOPG5lzIqIiIhIw1WrgdnkpHhen/YgHdu3rs1uRURERKSRsdvsBAXVbOGv/cIjYsjOsauUgYiIiIg0aLUamL3tf+NrszsRERERaaTKMmYPr5RBTHQ067OzsdkdtTwrEREREZG6U7PVFkREREREaoHH7cZkOrzAbK8eJ2Oz2fh745ZanpWIiIiISN1RYFZERERE6pzH7cZsMh3WsWHhEcTFRpFXUFjLsxIRERERqTsKzIqIiIhInXN73JhMh1dVy2gyEhoSQn5BUS3PSkRERESk7igwKyIiIiJ1zuN2YzQfZmDWaCQ0NJgCZcyKiIiISANWa4t/nTb8MgyGQ7db+t3M2hpSRERERBoozxFkzJqMJkJDQsjLV2BWRERERBquWgvMvvPSZP/Xq/7ayHc/LOaS80fQJDmB9MwcPvjsW04f3Lu2hhMRERGRBszj8WA+7IxZA6GhIezJVWBWRERERBquWgvMtm/b0v/1/U+8xItP3k18XAwAx3Zqx/HHdeTGOx/ngrOG1taQIiIiItJAeT1uzIeZMWuxmAkNCaawsASPx4PpMBcRExERERGpTwGpMZubV0BISHC5baEhweTkFgRiOBERERFpYNxuNyaz5bCODQoKIjQ0hOKSElwudy3PTERERESkbgQkMHvKicdxz+Tn+XvzdvILitiwaRv3P/4Sp5x0XCCGExEREZEGxu12HXZgNjgoiNCQEIqKS3B7PLU8MxERERGRulFrpQwOdNfE/2PKS+8yYeLDuNxuLGYzA/t255brxgZiOBERERFpYDwe92HXmDWbTYSEBFNcXILbrYxZEREREWmYAhKYjQgP44Hbr+G+W6+moKiYmKhIjMaAJOeKiIiISAPkdbswH2bGrMlkIjQ0lKLiUjzKmBURERGRBipg0dKi4lLmL/iFb+cvxmg0kp2bT1Z2bqCGExEREZEGxOvzYLEEHfbx4eHhOOyl2O2OWpyViIiIiEjdCUhgdu36zVx4xSS++OZH3nr/KwB27ErjmRffCcRwIiIiItLAeD0ezJbDy5gFCI1uRnI0ZOXog38RERERaZgCEpid9up73DXx/5g+9X5MprIhuhzTnvWbtgZiOBERERFpYLxez2HXmAWIS2lHamIQWVm5OJ2uWpyZiIiIiEjdCEhgdndaOv1OO3nfdwYAQkKCcTq1OIOIiIiIgNfjPaJSBnFxycRFh1JUVIzdoXIGIiIiItLwBCQwm5yYwOatO8tt27h5O01TEgMxnIiIiIg0MB6vF8sRZMyazCYwBVNclI/LpYxZEREREWl4Dv/V8EGMvXgktz0wlUsvGIHH4+Grb3/i/U/ncN0VFwViOBERERFpYDweD5YjqDFrNpkIC4skLy9bpQxEREREpEEKSGB2SP+ehIeF8vnsH0hJSmDh0pXcNGEMvXucGIjhRERERKSB8Xi9BFkO/6Wo2WwiIiqW/NwcXG6VyxIRERGRhicggVmA07qfwGndTwhU9yIiIiLSgHncHixHFJg1ExkdS0ZWDi5lzIqIiIhIAxSQGrPnjZvIG+99UWH7pRPuDMRwIiIiItLAlFqtxMfHHvbxBoOBiKhYbNZinKoxKyIiIiINUEACszl5BSz65XceeOJlXK5/bi3LyMwJxHAiIiIi0sAUlZTSrEnKEfUREZWIwV2qwKyIiIiINEgBCcyazSZenXIfpVYb/7vjMQqList2GAIxmoiIiIg0JHa7A5vVTpOUpCPqJyYuGbw2fF4fHo+nlmYnIiIiIlI3AhKYBQgNCeHph26hU/vWXHnjA+zYtTdQQ4mIiIhIA5KTlw9AwhGUMgBITkygtNSGy+nEqTqzIiIiItLABGbxL1/ZfwaDgYnXXkbL5k249tZHypU1EBEREZH/JrvNjtlswmg8shyB1NSm+IyhrF3zJ61apRIaGlJLMxQRERERCbyABGb/b+x55b4/d+RgmjVNZv7PywIxnIiIiIg0IE6HFY/vyG/cspgtdOrUkd9W/k6ffgNITIivhdmJiIiIiNSNgARmR597eoVtp3brwqndugRiOBERERFpQBz2Ujw+0xH3YzabaNO6NSvWLsLhcNbCzERERERE6k6tBWYfeOJlHrrzOgDufHhale2euP/mave5Oy2Dh5+eTmFRMRHhodw7aQJtWjUv12bt+s08/cJbALjdHroe14Fbrh1LUJClxucgIiIiIoHntJfg9h55xqzZbCImNo78/Dzcbjc+nw+DQavNioiIiEjDUGuB2QMDpu3btKiVPp987k3OPmMAI4b25afFvzF5ynTefOGRcm3at2nBmy88jNlsxuv1ctcjz/H5Nz9UmrUrIiIiIvXPYbeC4cg/RDeZTMTHJ+Cw27BabQrMioiIiEiDUmuB2XEXj/J/feWYc4+4v7yCQjZs3sa0x+8AYEDvU5jy0jvsTssgtVmKv11ISLD/a5fbjcPhxIBekIuIiIgcrRwOOybTkZcyCAqyEB4eQXhYEBlZObUwMxERERGRulNrgdkt23ZVq127ambTZmXnkRAXg3nfi3aDwUByYjyZ2bnlArMA6RnZ3P7gs6SlZ3Ja9xM4b+Tgmk1eREREROqMw27HYDryjNngoCAMRjPJibFkZmXj8/lqYXYiIiIiInWj1gKzY6+7B4MBDvZ62GCApd/NrK0h/ZqkJDLz1cew2uw89OQrLFi6giH9e1ba9rNZ8/l89nwAvXgXERERqQculx1TLQRmTSYTRnMQyfHRZGTm6LWdiIiIiDQotRaYXfZ97QZckxLjyMkrwO3xYDaZ8Pl8ZGbnkpwYX+UxYaEhDO7fg7k/LasyMHv+qCGcP2oIAG63mz4jxtfqvEVERETk4JwOByZz7SzUagmOpHXTCLZlZ9ZKfyIiIiIideXIl8MNkLiYaDq2a8XcH5cC8POSFSQlxFUoY7A7LQO32w2Ay+Vm4dKVtGudWufzFREREZHqsVlLsViCaqUvS1AYJYZmRBn2KmNWRERERBqUWsuY/bd5Py/jjzUbKCwswcc/L5KfuP/mavdxx41XMHnKa7zz0SzCw0K5Z9LVADz27Az69DiJPj278fvq9Xz61TyMRiMej4eTTzyWyy89u5bPRkRERERqg9frpbi0BLO5dgKzZouZqLhmZGxbjtvtwWKpnUxcEflv8ng8tbI4oYiISHUEJDD72jufMev7nxncryffr1zK2WcMZN7PyxjSv0eN+mmZ2pQZ0x6ssP3uiVf5vz77jIGcfcbAI52yiIiIiNSBwsJiPC4n5lrKmA0OCuLkE7uwavH7LF/xJwP6Vl7OSkSkOgoKi4iPi63vaYiIyH9EQEoZfPfjEqY9egc3XzMGi9nMzdeM4emHbiEjMzcQw4mIiIhIA2Gz23G5nISEhtVKf9FRkURGRtCkSTIbN2+tlT5F5L8rMyuHwqLi+p6GiIj8RwQkMFtUXEK7Ni0AMJtNuD0eju3Ujj/WbAjEcCIiIlJDefkF5OTmAZCfX4jX663nGcl/hcPhxOW0E1pLgdnQ0BBCQoKJi41hx47dtdKniPw3uVwunE4Xe9Iy6nsqIiLyHxGQUgYpSQnsTssgtVkKqc1S+GHBL0RFRhASEhyI4URERKSGsnPycLs9AOxJy6C1KZXoqMh6npU0dj6fjxKrFZfLSXhURK31GxIcjNlswWlz1VqfIvLf43CWXUNcLhdFxSVERdbedUpERKQyAQnMjr1oJGnpWaQ2S+GKS8/h7keew+lyc+v/xgViOBEREakhj8eDy/VPVpDT6aznGcl/gdPpwuP24PU4CA+vvYBHUJAFA0Y8bgVmReTwHfhcmJubr8CsiIgEXEACs8MG9vJ/3ePkrsz7fDpOl5uw0JBADCciIiI15PZ4yn3vdLrraSbyX+L1esHnw+l0EVGLgdng4CAMRiMer+fQjUVEquBw/BOYLSwuweVyYbFY6nFGIiLS2AWkxuy/mc1mBWVFRESOIl5P+ZqyypiVuuDDBz4nRaVOomqxlEFQUBAGgxGvR4FZETl8jgOfC30+Sktt9TeZACkpteLRtVJE5KgRkIzZ04ZfhsFQ+b6l380MxJAiIiJSTZW9IXO5lTErgef1+jB4SkjPsREZEV5r/QYHBWE0GvEqY1ZEjoDTWb4cis1uJ4aoeppN7XK73WRl51JUXEJKUiIxMY3jvEREGrqABGbfeWlyue9z8gp4/9M5DO7fIxDDiYiISA14/pUtCxXfjIoEgtfrZcfWjWxPy+ek44+rtX6DgiwYjCY8HmV+i8jh8Xq95OUXsuavDXTvdjwANpsdu93RKBaxLimxkpWTBz4fVptNgVkRkaNEQEoZtG/bsty/nqccz8N3X89ns+YHYjgRERGpgcoyZt1uNz6frx5mI/8lPp+PpUsX0KtXfxLiY2u1b6PJjNejzG8ROTylVhvfzVvA7fc9gXvfXSSlNhtZObn1PLPa4XS5YN/zvM1mr+fZiIjIfnVSYxYg2GIhIzOnroYTERGRKnh9FTNmQVmzEngOh5NdO3cwfOhgjMYq6l4dppDg4Cof2yIih2K12vA4SwG464En8Xq9eNweiopK6nlmtcN9QMkiq12BWRGRo0VAShl88tXcct/bHQ4WLFnJSV2PCcRwIiIiUgMej5c1f/3N1u27OGfkUFb+uZbkxATatG5BcHBQfU9PGrEdO/dgNplo2iQZQ1ULEhym0NBQLWgjIofFZrOTnbGbJPM2Lj+9KTvTt/PrylX07H4Sbrcbh8PZ4J8f3QdcHz1uD06ni6AgSz3OSEREIECB2YVLV5b7PjQ0hJ6nHM/F5w4PxHAiIiJSA0uX/87t9z2B1+elNHMV6zZsJCT+WE464TiiIiPqe3rSiP2xajXx8fEYjUZMJlOt9h0UFIxPi3+JNEilVivhYWH1MnZ2Ti5pezMxuGys3V5Cm84jad9hC59//T09u5/kn19DD8y6/nVXTE5uHslJCbV+LRYRkZoJSGD2pafvCUS3IiIiUgv27N7JJcPa4DLFsn3bakaeeSaffDWf7JxcDAZo2iS5vqcojdTy5cs5sX17DEZDrWfMGk0mvMqYFWlwsnNysdkd+HwQEV73wdnSUlvZFz4Paek59BmSQoeEEB5+9WW279xN65apOBwNe2HBouISiktKy23Lys7FYDDQJCWpnmYlIiIQoMAsgM1uZ9eeDKz/Kix+YpdOgRpSREREqsFZko4xKIzx5w2l1DAG3FY+/XIWVpudrOxcQkKCiYuNqe9pSmPktWEOborJWPsZWiaTGZ9qzIo0KDabnbS9mQAYDIZ6Ccw6XWWZpA5HKVm5RTRvmkJYcBAXnNGdz7/+nltvvKpcGYCGKCMz2/91emY20ZERhIWF4nA27ICziEhjEJDA7A8Ll/P4tNdxuzwEBx9Yt8bAvM+nB2JIERERqSanrRCbJxJTdEeiAJ8nnMSYEDIysmjTugVFxSUKzEpAGH0ODKYwTKbaX3/WaDKpxqxIA+H1ejEajeTmF/i3FRYWkxgfR0hIcJ3OxeUqWxQrOysLszmI2JhovEQyvFdr7poyB5/Ph8fdsK8tbrebTVu2s/jnb1m2/DcSmnbiyUfu8mcCW202QoKDMRrrbG1wERHZJzClDN74iFuvH8/pg3sHonsRERE5AtbSIoJDY/zfG0xBJCfGsHdfYNb5rzp0IrXFbHCCJSwgNQ1NJpNqzIo0AD6fjy3bdhIRHkZxcYl/u9vtJjs3j9RmTep0Pm5PWWA2KyuTuISkfWVWTMS3Oo0uqV/z488/c9bIM+t0TrXN4/Xy4QczaRebQWKMiZL8nfh8Pmx2ByWlVnLz8mmakqTArIhIPQjIlbe01MrwQb0C0bWIiIgcAY/Hg81aRFh4ZLntsTFRpGeU3U6qwKwEitngxmQOxRyAwKzRYMKrUgYiR7WSUivbd+zGarVhs9lx/Ov5xlnHtVy9Xi8+rw+APXt20LJla/++kLBYmsab+f7r9ygsLK7TedU2r9dLsDeTpq26cMPN95MQ4SVtbwb4fOzclUZBYREej66fIiL1ISCB2WEDe7FgyYpAdC0iIiJHoKTUitvpJCS0fB2/8IgYsrMzgLKspdV/bSA3L78+piiNmdeNOSiU+PjYWu/aaDKBV4EFkaPZ9p27KdqXJVtitYLPV26/D19lhwWM+4ASBTmZe+jY6dhy+1fticDr8/HH6r8a9IeWdpsdg8/J8b1H0yy1FW1bJrF+4xYAXC4XPq8Pt8eDy9Vwz1FEpKEKSCmDjKwcHnzyFT74/Dvi46LL7Xvi/psDMaSIiIhUg81mx+lyEfbvwGxUAsWbt/i/93l9/rp7IrXF5XYTHRVJdFTkoRvXkMlsVo1ZkaNYqdVarlbr/kzVA3kr2RZI+xf+2rplE7v35nDWpccQGRFOcUkpAJMfvI/Zn03nz9XrOHfUMBLi4+p0frXB4XBSlLeH7EIP0VFRAMQnNWHrlo0wsI+/XUlJKQ6Lmfi42v/gTEREqhaQwGyn9q3p1L71oRtKvXC5XFgslkM3FBGRRsdud+BwOgkLCym3PSo2Baf113LbvMo+lFrmcrmIi40+dMPDYDKacbuV7SVytCoqKqly3xezvuOErsfRuVO7OpwRuF1uHnxsKt7C9fToPZS42BiaNEnCl55JSYmV4JBwju3QgmffX0VJibVBBmYLCotwFO0hzxriX3gxLrkdq/6ax7btu2jTugUAWTm5hIeGKjArIlLHAhKYvXLMuYHoVmpBekYWWTm5tGrRPCDZKiIicnSz2azY7S7CQkMxGA1lGUsGA01bdCI25CPmzPqYM0ZeiMFgwOur28wlafxcbjdRkREB6dtoMuHzefH5fPsW7xGRo8n+LNQDZWXn8sEnn5PoXcWTPy7h7VefruM5lRBi/5vLJtxAszbHY7FYCAsNpWVqM9Zv3ILPayS1eXN27fqa/IIiWrUsO+7vTVuJiY4iJTmxTudbUx6Ph/z8Alb8tpSWbTr6t3c9sQ/Zu9cy8Y77+PT91wmyWPB6vFjt9nqcrYjIf1NAArMAezOy2LR1J1Zr+Yv7GUP6VHGEBFqp1UpmVg4Au/bspXPHdgFZFVlE/lvsdgelVqsyLBoAr9eLw15CQbGdtm1a0jK1GWl7M2ndqjkhwUEMO38isz9+gez8EsaPu1IZs1KrfD4fbrcnYBlnRpMFA14cDichIcEBGUNEDo/P58Nq+1fQz+tg3S8fElzyF4QZ2b0nnYI6XGTL6XSxbt1arE4fTVp1ASAkOAgAi8VCXGwMubn5hMW2oHmCmeycXP+dh3aHk4zMbOJiYwgKOnrvRCwoLGLJkgWs25zBzbc+6t8eFh7Gmeddyco1d5G2N4OoyLKEnfi4GDwej94jiojUoYAEZj/+8ntefP1DmiQnlnthbDAoMFufHAescupxe0jPzKZ505R6nJGINHSlVitbtu0kNiZagdkGwO5wgNtGXpGD+NgYYqKjCA8LLcsQCgulfbt2XDx+ItNfeJTcvHOJiY6q7ylLI+J0li0wExYWGpD+jZZIIkK82O2ORhmYVSawNGQOh7PcQl87du3B4Mxm1o9ruXTcNXRp7ia96CMevPtanpz6Jid07RzwOe1JSyc7Mw1zcBRGY9kt/sEHXDuSExPIzSvAGJJIx5ZR5OUXUFpqIyrK5D8Xm91+VAdmrTY7pQXpRMQ0qXC3pCEolvYt4ti29kd+Wb2bqPhUbrxmPE6ni9BQBWZFROpKQAKzMz+ZzXOP38lJXY8JRPdymP69iEtObj7JifGqNysih81ms+Pz+pRZ2UA47E4c9iKKSt1ER5e9Qdv/HBASXPZmtGnTljRJjGb7zj20bplab3OVxsdqLcHj9REaGnLoxofBFBROWBBYbTZiYqIoKi4hIjzMH3BpyAqLisnOyaVdm1b1PRWRw+Jw/pMgsumv5bzz9gxaJlsodpjoduIJGJ1ZxCa2wlZaiK2ObqcvKimlKD+L0LAY/7bQAwKzQUEWoiLCKSr2ERcdTn5BASVWK+Hh/3y45PUc3a9/bDY7Bo8Vj6FszkaTkcjwcEqtNtxuN0ZzMBnbf6Mk147PHAOU/a4CdZ0WEZGKAvJK1ev1cfyxHQ/dUOqUy/2v1bV9PhxOLZIhIofPue8aUtnKylK73P++hh8Gh9PJn3+sJC4hpUJdvOB9t29iMBAXG832bdtwupyV9CJyeAoLC3B7fIQHKGPWZDLj8/mw2x0AZGXnsGdvRkDGqkt2u4P0jCxs+85LpCHyerw4nE4ef+Ylvvr0DQYMHkVYsBGXNwSDwYAvOJkLLx6PyWjwv7YIJLfbDT4fxYU5REQn+LcHB5fPtt8foDRaIsjL2UtpqRXPAcFYr+8oD8za7bhddsxBoYSEBNOqRXNSmzchKqqs1ndCm4H8tKqU2EgTRcUlrF67vk5+/iIi8o+ABGYvOGson349LxBdyxHweDwVttXGG30R+e/aHwBRxmzg2Gx2XC4Xm7ZsL1eS5nA4nE7W/7WKQYMHV7glOuiAuydCY9rw8w9fcfq5l+PTAmBSSwoKCjCZLQHLYDWZzXi9XkptZZlgbreHwqKK9SqP9O+oLmVm5bB52w7sdke5YJBIQ+Pxevj6m/k4Cv6m98BRDB5+Fsv/ttKqVQt/m8ioSJKT4impZJGw2uZ2l70vspXmERVT9kGlyWyq8MFReHgYYWGhxKe0Zu/OjdhsdpyufwKXR/PrH7vdgcft4a/16wkKDiUsLJSoyAjMZjMR4WEADB3Uhxdfms5ZZ57BcQm7+PrDqezNyKrnmYuI/LcEpJTBz0tWsH3nHt7/bA5xseXr073z0qNVHCX14Wh+MSEiRy+bzY7RaKRo35snr4J3AVNUXEL6vjdJm7Zu57hjOhx2nUl7SQ670zI586ITK+zzZ8wCI8++gEGndeCMK6bidLrK7RM5XIWFBZjNgav9GhYagtPpJD+/kOycPDweLx63p1zN2Y2bt2E0GmnftlXA5lGbsnPy8OwLIOHzqc6sNFgej5cVK3/jkuE9ObHvSAAemvws4WFh5dqZzWbyCgoDPp/9dxKWlhSR1D4ZgIiwsAp/XxHhYaQkJ9Ku4wlsXLscr9dLXl6Bf7/3KL5jKC+/gL83bcVuLcEX5cFi/uetf0R4uP/r0JAQvJZ4zCYDFpOBbdt2ckIXlSQUEakrAQnMXnTOsEB0KwGw/9NiEZHqstpsbNq8vWxFx30BWX3IEzgH/mw9bs8RBUrTt69k/W4PLVKbVdhnNBqxWCy4XC4wBhESVPbmtKi4mMTg+MObvMgBCguLMAcFLsjfvm0rUpITWfLLCpo1ScbtKQu87A/Mejyesg+VTA2j5qzP56twZ5PH48FsDsjLd5GAysrMICk4g1adJ/i3VbZoqNlsrjTTvbbtv5OwsKiYrvtK+8TGRFdoZzQaiYwIp9Mxx/Cdwc2OnXvYun0Xn3/9PYP7n8YZwwYEfK6Hw+fzkZ2bR0HWFpolWNhWVFJukbKgIMs/z/lAVFIHPllYyHGtQti6Y1d9TVtE5D8pIK/sRgzpW+n2P1avD8RwUgNFxSXs3JXGMZ3aYTaZyhXiFxGpDqvVVvbFAVmyCswGzr9LCdhsdoKDg3C5XJhMJrxeb7UDNelpe0hp2gKzufLVlkP29QtgtoQQGmwkv6CIxAQFZuXIWUsLMZoClzFrMBhIbd6Mtdu2Y7M7/LWvi0pKiImJ8tdNPNzFenw+H0XFJURFRtRJ1mpldR49Hi+Ky0pD9NvyBfiCEolNaFrpfqPJiNfjxWI2U1xcUmG/1+slMyuH5KQESq02IiPCK+ml+jweD3v2pFFQWEKHtq3o2L5NlQteGQwGYqOjad6sKX+s/otIUz4U/MnjU38nLT2LB+66sdL+TSZTuYz9umTfdw1c+dsy4oPB6XJVqJ/bJCWRjMxsnE4XzZum8PWn7/DNpy+yY9eeOp+viMh/WcBTBnJy83nno1lccPkkJt03JdDDyUEUF2bz9isP8+m7T/Hk01NwezwUFgb+E2kRafgOrFFdXPxP7Tefz8e3c2Yx6c4HtFhEgPy7TESJ1QpAcUkp23bsYtfuvdXua29GJh3atcFkrDwwm5AQh8G4L+BkiaFFchjFJRXfIIscDkdpPh5DYFf6Tk5pRkbaznKZpnl5BWTn5JarLfvvD5Ps1VhYq6TUyvYdu+skmw8O+BBsH5/PV+l6ASINQVbGHpJSmle6LzQ0hMT4OIKDgzCbzZSUWss91p1OF39v2kpWTi6FhcVs3bbziP8Oi0tKef7FaZx40sl0bN+2yqDsgXNMbd6CdevWYfYW0aTNyTx4bT9WrPzN32b/daWgsIi16zaSnpHFjl17sNnsRzTXw2Gz27EX78VekkmLrucz+b5b/HVl94uLjSEhPu6ALSaS4mPYvSdd65CIiNShgARmPR4vC5et5Nb7p3D2ZTfz2jufMnTAaXz13rQa9bM7LYOrbn6IC6+4lStuuI9tOyp+erdy1TquuOF+Rl91O5dcdQcvvv6hMreq8O4bL+A0xHDpuBuIs2Tx0Wez9y2OoSdeETm4Xbv3UlBQxMbN28q9Gfpk5jQ2rvoBb8lW0hrB6udHo38/p+0P1pRabZSUWCkqLvFnuR7K3vQs2rdrVeWt3NFRkXRq35agIAteczTNk0LLBeJFjoTDmo/BHBHQMZJanUxicDalJQXM/nYeJaVlH2SkpWeRnZvnb/fvAGdOXr7/a7fb7d9vtdlIz8giOyeX0n191dXrpv3XWp/Px4/ff8G5l05g1nc/1snYIrXNVlpEaFjFUgHBwUF0aNea5KQE4uNiMVtMOBwOrAcEM602G06nC5/XR3Fp2XPSkQY7P/v0Y45tEczQkeMIDjl0iRWLxUzLVm3YvGUTdrsVrzmW8Lh22IrS/W0ys3LYuHkbGZnZ/u/tdgd70zOPaK6Hw2qzk7t7FSu3WejTbyAd27ettN3+bF6jyQhGI/Fx0exJSy+3wJmIiARWrQZmd+7ey4uvf8ioS2/g4adeJTIinKmTbyMmOorzRg0mOiqyRv09+dybnH3GAD558xnGXDiSyVOmV2gTGRHOI3f/jw9nPMVbLz3C2vWb+e6HJbV1So2Gz+clY+9uTj/zPDp2PoGzRwzmvY++YveedOwNaHViEakfxaWlFbM+fB7Sdm9lxIX/IyYi1J/JKbVrf2DW5XLj8/mw2ux4vd5yv4vSf2XWVcbn85GZnUubVi0wmSrPmIWyN8lJifFgCicpJtgf2BI5UiVFucQnpgR0jFat2rE908n2P78k++8v+d/Nt5YFeHw+f2AVyi/Y43A4/avAezwe1v29mfR9gZXsnDwys3JIz8wmIytnX5vAJwD4fD6KSkpIS8/kueeeJX/XItqnxugDMGmwHPYiwiP/qSlrNpsxGA0kJcZjMBgwGo3ExUbjNYRi9NrKZYwf+OFjUVHZXRxFlZQ7qAlbUTrmyNaYLSHlFsWqitlspmnztiRGGdm+czeREeEEB4fi9ZTNzePxUFRcgs1mr5CBb7XXfcZsaamVtPQsOnYoC8iaqvhANjwsFIvFQlJCPCaTkbjYGNIzMqp1F4GIiNSOWg3Mjr7qDubMW8Q1l1/INx+9yAO3X0P3k47jcMpw5RUUsmHzNoYN6gXAgN6nkJmdx+608i9IO7ZrRbMmSQAEBwXRvk1L/4tp+YfL5WH2siwSExMAaNaiLeedcRpvvvcpLt1+LCIH4fP5Kq3JWFKUQ0aOlaZNmhERHkRxkW55D4RPvpjDrp07GHr2Zcz69gfw+SgptZbLJqrOG9TsnGzsdgfNmiYTfMACIJUJCQkBg5GgIAulJQrMSu3Izy+gZWpqQMcwW0xYwlPISNvOnuJEBpwQzeJlv/r35+UX8PWc+dgd/wQdiktK8Hi8FBWXUFBYhM/rIy+/gPV/b6agsAjYV5d2X1mRuignYLXZKC0p5qXnHseaswGruR0ndW6uElTSYDntViKjYoCyhac6tm9N86YpREb8k0VvNpsxBcdi8BRjOyCY6XL9k6W+P2PdeoQZs15nIeaQGP+4hxJksWAITaHfyS0pLi4lKjIC877AbH5+IQ6ns8os3rr4MOdAdrsDm83O3r2ZdPIHZiv/QNZkMtG2dQsSE+Iwm0xEJ7ShWZyBHTtVZ1ZEpK7UamD2jCF9cDpdTH/7E16f+QVbd+w+7L6ysvNIiIvBvO9JxGAwkJwYT2Z2bpXH5OYV8POS3+h16omHPW5jlZ6ZhdFoID627JNqb1ATTjs+hfSMLN2qIiIHVVUQYuf2TXgNoUREhBNksRxx9opU5PP5eOyZl/nwzUc567RYlsz/kMXLVrB7T3q5xdcKCosqLBL2b7u2b8NgCSM8PIy42JiDtg0JDiIkJJig4GBy86t+3hWpLp/XQ05+IS1SK1/4pzaNHDGCzOwcih1mmqW2Zd1fqwFYtPBHPnj1Dj788D1+XbHK397pdOHxesjNzSc7p6zcgdfj9d86/W/uOgjM5uYV8OuSeTRJCOXq62/j/IvGkxhjpqi4ZoFZj8fDho1bWP3XBjL3ZfyK1IfSUiuxsTGkNm9Cx/ZtsFgsRISHE/SvDwqPPe5YNm/aVC5j02qr5K4Qn6/aZXz+zeVyYbOWEB4ZXZa5W40sotDQEEwWC5bYY1iz3U5yUgLBwSEY8bJ7bzpOx0Hm4vPVaam93LwCNm/Zyl9/b/YHZo3Gqt/2h4QEYzKZMJvNhMS0pFunBDZt2V5X0xUR+c+r1cDsvZOuZvaHL3L12PNZs24zY6+9m3HX34PVaqe09NC3WR6J0lIrtz0whTEXjOCYDm2qbPfZrPmMvup2Rl91O2OuuSugczqaBFksXH356H9uYzFHEBNhprCwUIFZETkot7t8EGLxkiXcec/9LP7xS044pT8AFouFktLq1yL1+Xz+F/26Xa5qdruD+EgTF55/Hv+741k6t45l+W8rKrwZ9e7L9juYkuJcfAQTFRFx0FIGUJY91KlDW2LjUti7Z9cRn4eIy17I7vR8WresfPGf2mIymejXuwcAaRm5pDRrT3522WPYmvMXvqiuXDaiM7/9vtp/jNPlwuv14XK7q3U9quwOgtrkdrvJLyjEWphBSEx7Epp0ICw8grCQoBoHZm12Bw6HE5/XR35BYYBmLHJwvn13eiQmxBMfF+t/DgoOrljb9dTup1JSXMCevRk4nS527dlLyb47N/bszSj3IaTrMOs9u90ebDYb0VHRFQLDBxMTHUXHzieRnufm2GPaYwkKxWwyUFJcWq6GdVVj1gWbzU5ufj4b1izDYwzn+C7HEP6vRb+qkhgfR1x8InFxMWzcslXrtoiI1JFD37dRQ2GhIYw6fQCjTh/A9p1pzP5+Adk5+Vx27d0MHdCTuydeVa1+khLjyMkrwO3xYDaZ/LXxkhPjK7Qttdq4+Z6n6dOzG6PPO+Og/Z4/agjnjxoClL3w7TNifI3PsSFqkpLENVdeSn7+Py/KzWHJRFisuF1a/EtEqubxln8zsfOv7zjlmERcpraMOnskUHZbYnENbnm32x1YrTbWrt+Ix+3huM4dqnUr4X+N1WbjpPahNGnbC7M5mBO6D+TTb5YCsGjpb6QkJ7Bq1RqGDR1EcUnpQWu5O2xFuHwWwsJCqz1+UlISS/9UYFaOXGb6bnILnbRIbRbQcRLiYsnNzWfGd/mEBAfTtNUxRJreY81ff1NYUETL1j0JcqxnR2bZYjxFxSWUltrYuGkrHdq1Pmjm3PJflvLlN/Pp27cf99x2fcDOITsnD5/Xh7W0gNiE4//ZYQ7DaateuS6fz8eevRnlSh/Y7Q4yMrNJSow/aPbc0cZms+P1eQkPq15wSY4+BYWFOF3uan0wEx0TR6vmiaxbv4mkxHi8Hi9Wm53F8z9hyuvfc/5ZgyksdjDyjKG0TG1GWGj1n9P2c7ndWK12oqMiaxSYjYqMoF2bliz49sOyoLLHhsVswO5wlKthvWr1Gn5ftZ62LVP4/MsvmDTpdjq2bwNUf6zDVVxSitfjpSR3Byecejpmk4m42IqLrlUmJiYKh8NJZFQce9LTyMnNL6s5LyIiARXQV2WtWzbjxgmXMuuDF3jgtmvIzSuo9rFxMdF0bNeKuT+WvQH9eckKkhLiSG1WftEIq83OxHueosfJXbn8krNrcfaNX2hsK1okGMhTBoWIHITb7aF438I4NmsR23Zl0XvoGC4ePY6w0BCgLDCbmVX9+t6OfYsOevZlkChzv3KlxbnYnF6CQ8tq8LVq3Y7srL2UlpaSt/FTXn3+EVzp81iweDmOKjL99q8OXZCfi8FoITy8+m9iI6NiKC7Wc4QcuT179hAeHlntzK3DFRoagsFo4N7b/sfk+ycRF59E3z79+OGrF/lzQzqpzZtitoRQWlLCpi3b2bZ9F4UFedx0+8P8/ufaCv15vV7SM7P5fdVaNq+aQ/cOFr7/9quAzb+4pJSsnFzcHg9709Np17aVf58lOByXo3p3JtjtDnJz8/31OPfLyMxm245dlDaQxRq379jNxs3bqqzdKQ1DdlYGHq+R1OZNDtnWZA6hbasU1v292Z+dPvPtV1nzxxIuGdoCR+avxJl28dS06RSVHF4JJZfLhdVmIzoqksiI8GofFxEexv7FU9q3bcUxxxxDsMXkf00z460P+Oa7H1g+/w0iXH/zx6IPOD7VxZNTX6X4MOdaUw6nE4/HS0ZGBp2P6URUZASxMdULzELZ67nImEQKcjPIys5V1qyISB2ok/Qks8nEgD7dGdCne42Ou+PGK5g85TXe+WgW4WGh3DPpagAee3YGfXqcRJ+e3fjky7ms37gNu93BwqUrABjY51TGX3JWrZ9HYxMZFUdyUhyLli6n2wnH1fd0ROQotXHTNkZe+H9cMfp0TPbteMzxFTIoLBYL+fkF5OUXHLJ+KUDJv4ICaXszMRjKsvuVFfWP0sJsSu1GfxZfXHwizVNi+W35YjbsKGDUqHPYtn4Ze7dsx+F0Vjje5/ORkZlNSWkpxcVFmEyWGmUXhYZF4LBbyc3LJz4u9tAHiFRhb0Y6cXHxmOogUzM8LJTBA3r5v+/R/2zW7XYy9/eFPNSpHb+mr8Rhz/Ov+r5zzSw8HgclpaVsXPcH0994h6i4ZO669SaefexmfD7IyHcTGmRg3OUT+PnX6fh8vmrVpayp7OxcfF4ff69fTYnNR5fOnfz7LMHhOB3VC6ge7BbvkhIrDofzqLzWllqtFBQUExsbhcloonBfiRanFqpt0HJysggKCavWY85kMpPSpBk//bGmbIPPh9G+m8FnX09B2h/MnruImy+8kL+3vUDa3kxaHkYWfl5eDqU2Fy1bNqvRc5vJZCI8LJRSq61sobLwcJLjgrHbbeTnpBFWsoTsLdHsLQzh8hvuYsWvi4gJtvLbOz/x0effcM7IYTRrkhzQjHW73cGunVspdXhp3SqViIjwGo1nMBhISmmJtehX3G43Nrv9qLxWiIg0Jkf1faMtU5syY9qDFbYfWA5h/CVnKQhbQyazCY/bg8FgIDW1NZs2rAvYGwwRafh+XvwLFjNkbl1Cq+OGcv3ZA8pdLyIjwgkNiyC/xIrN5oBqvMc58Ja/A7/PyMimbZuWtTr/hqykKBeH55+nap8xmOM6tmLxwu+xuhPoP3gUqfE+Hnl1IZ5Kslr2Z8uVlFhxOmxYgkNrdK0PDY/E63ZQXFKqwKwckeysLOLi4jCbD17fuDaEhYZSUmLFYrGU1WM2RzB+7HhOHz6C4KAggoJCcDv3rX3g87Frx0aSY8y8/dZrnNmvA1HBDuKNO3njpUfYmeniqWemsfjn7wiLaUGL1JYEmcvqZdYk06463G43pfsWOdr690oSmnbyrw0QFhZK06ZNcDqqt2aD5xD1LEutNiIjwrFYAn9rdU1kZuZQVFxCbn5+WS3RffVEc/LyiYqKLMtYlAYnPzeXkNDq/b0YjQYSW3Qj3Dcfp8uFCTdpWUWMSE4hPyeJXVkuUtscz3HtU1i09FdO6HpMjcsZZKSnYXf66NyxXY3PJSoyAruj7A4Vg8HElj2lGOZ9R7wli29+LebD9172t+3ddwgGZxa9/trBjp17yM0rwGI2ExERHpDHstfrpbTUSua2X/CFNMNsMtXoLpn9Ult1wOsqxO1243K6oRam6nA4cbpctX7dFBFpDI7qwKzULoPBQPNmKcTHxZKRmU1mVg4JSc347a/fcTpdlRbgFxHJzcnllstOodtpI0hq1rHcvuioSFq3SiU8PApbVjHOSrI2/83r9WKr4rZ7q123qx7IXpqP03tA4MQYQse2KezesY6E5icB0Lz1MRjcX7Nl2066dC7/+zkwa87jdhESWrN3V+HhEXjczkpXphepCbvNRkhoWJ0EAvfXUW6R2hSbzc7e9ExMJiNNmyQDYAkKw+Muu9a4XaVs3pFJz87hxIQb+fqnDTz++FOs+v1XCgtyGHncECIjYzhj1GgAvG4rYSFG8vLyaz3AsHNXGh63B4+zhMzdGzmmx0VgMBAaEkyL5k2xFqTgcR46MOvxeCjetxjj3B8X8tnH79EkJYm2Hbsy5uILMJmM5ObmEx4WWq07HOrS/rI2/15gzevxUlBYpMBsA5Wfn0tYeFS12hqNRpo0a010ZDCbt2yjSZyFghI3iQnxJA0YTnKz9hiMRpqkNGX7jh0UF5fWKDDr8XiY+f7HWB1egoJq/t4nMjKcvPwC//c5RW48a3+lfcs4xl9ecS0VnzmGpnEWNmaXgM9HYVExDqcTu91OXGxMrWbP2ux2vPZcfl62il79zsdgNBxWDd4WLdsQFW5mb0ZWubrgNpsdk8l00Lq8Xq8Xn8+Hy+UmJCQYKLt7Z+v2nQC0bd1S7zlFRP6l4VT+lyMWFRFBQnwcBoOBJilJREdFkpCQSG5ezmGvaioijZ/Zk4XTmFAhKAv/rKgcFhaJ3VqC23PoVYdLSq143B4ys3Iq1C7zuD3lVlz+r3PaCvFQ9sYmJiYKjGaaHzOcuStLOOXkEwAIjmzG8L7H8v38hRWOP3AVaI/bSXBwzd6gRUTG4PM6VWNOjpjb7cQSFFwnY+3PBDWbTMTHxfhrQkLZXUNBkU2JCSkLzObnpLMtw01SXCi7slwcc1w3mjdN4cyRZ/0/e2cdJld59uH7jLuvu288xNAQJLi7fVBci5fiUFpaaIFCKVBcg7sTLBBX4r7uOu76/THZIUs22Z3NJtmQua+LC5g558w7s0fe9/c8z+/hggsv4/hjjux1bJFYjlGnpMtqG9IxN7W0Jby8N6+YyYINQQ6YPIG8nEzKS4tQKOTo9SbC4UC/98jOLis2uwNiUTzN88g0wCFTRtJZM4eVq9clthuO9gB9WbL0sHUjsxR7FzabFbVm+80pt0YkEiGVSjGYsmivXsDSuZ8j0eQilUqQiMWMGlEOgEJtwmnvwuFM7rxwe7w4bN0UFRUn/T0gnpGv3OKvD/DxRx+z/34VNLTYyM3pw0NXJEOjkiY8Zn0+Pzabg6bmtiG/Bn0+P9/O/IymrhDTDp6CWpVclUwPapUKoymNNWvW9KpwCm7x5t0egUCQVWs2sHrtRlpa4w0W7XYn3VYbwWCIYDBE9xDfO1OkSJHi90BKmN2HkMl7RzfzcrMwp6Xhcbtxuz3YbKkGLylSpOhNLBZFKdiJSNL6fF+piC9OlBotAb9nQAJeU3Mrb7/3Pg8+8CdeevklXnnpGeYvWpZ4/7fNavZl/H4/KpWSgrwcCvNzyc/NwmDQ8+VHM5g8YWx8I5GKAyeUMevnOdtkLG/9W3rcLpRJZswajBkopbFUc7YUO014EIGBwSIWi8nMsCCRiBGLxYkmhQCjR5RzwIGHEAm4qKtayeKfP6LLHqLTHmbeOi933Xbjjg8uiNGolHR1Da244NripRoJR5i3cAmnnHwSUqkEo0GfEFZ0eiNScdyGYHvEYjE6u62sXbeZ/z7+N35cVMtZf7iVo0+8gJKifN5/52XqG5uJRKLDLigfDof7zM5vqK/nq29/JBQKpZqA7aV43A40moE3oDIYdEgMFbzx0Y98MreTq6+4fJttVLp0Qr4uvF4fTtfAG2t5vT7SdBEOOfTI/jfeDpnpv86JFHI5Bxx5AStqw2Rl9D1XEqRahNC2AvJQP1u7um0sX7aQE084GYlEQn5u8v67AHKFjIoR4/jpp++w2p3U1jUC8WzjHYnJW88BnW4PkUiEbquNtvauxOtWmyMVgE+RIkWK35ASZvch5L8p15FIJGSkZ6CUi2lsasXucA7L7IkUKVLsOUKeDurb/Gh1vTNdFAo5Mpk0nsUJqHUZiGOefifbDqeLYCCIIrCZSaPycHRswiRuYt78hYltwv14I+4rxGIxHC43RoMeozG+oDUZDUil0t6lj4JAbtmBFKdHqaqp73WM5pZ2nnr+dRy2Tqztm1m7sSapMZjT0pDLBLqt9p39Oin2caLhEArF7hFmAZRKBRJJ3LGrx9pAKpUiCAKlxQUImlKeevoZPvlxHcefdDqNDiNuX/+BJbFEjEKhoHurUuahIByJEI1Gefb5Z7A6wxx39GFIJJJe17pWb0AuFXDuIEMwGAwRCUf4duanuKxN/OGyPzJ2VLyB2PiDzqIyT8pnb/yVy66+lnBoeAmzvxWpnE4Hzz/1IHM++yfvvvUqEG8OlmLvIxYJIpYq+t9wCxq1imOPOoLzL7ySpx77K6VbvOe3tkIpKB1N2BfPmE1GmA2FggT8HgyW7IF/gd/QU6IP8QzfwoI8PpjxbJ/WIIJIICjORE07XV2dvd7z7iDIkixuj5e21iaCoQiHTzsUuVy2Q8uBHSGXyTjgoCOwqCPM/P4nHE4XDqeLSCSyw6z2Xl73W9k2bB0kDofDhHbxvSdV5ZMiRYq9jZQwuw8hFm/bcEMmU5Bm1lPf2BwvTxnCCUKKFCn2fmJhH21dHvRbCbNqtYrK8hJGVpYlMrlGjJmIDDcffz5zh8fr7OqGYAerNrUzfur5/OmuR8gr25/2umV8NfMHnnvhRZzugS+wfs/Ewn7aOp3blEb2NPKQSqVIJBJkMikiuYURxWmsWbep17afff41vyz8ng9m/JtgKEZZko3V1CoVFaXFPPnsKzv3ZfZRIgOw9thXiESS9zjeGVTKX0t4szPT0WhUKLeIKYIgcMZZ53HymZfx5H+f49wzT+WvD9zHZ+++sONjqpSMGVmBRqOiu9tGINC/p/ZAiUSi1NQ24GxbxennXolcJkOt6i1kS2VKNCoZnV3W7R7Ht8WnWy+2csixlzJ5wrjEe0XFRVx0zd+RGyuQxnzDLhM+GAixZt0mnnj6JeYvWsaKhV/RYfUy8oBzSTdIcLncu1zQSbGLiAYRSwZuZaLVqDGbTRx1+CGIRCIEkUBmRhqlxQWo1SoqyorJyysgLzuNRUtWEEoiscTraGRDvZ28nMELs1sjEsXvMxq1KmGb0jNPMpkMFBfmM2nK/phUfv798B38smINq9ZuIBaLYXc4h2QMAI1NLbQ01RIRqVAo5ImA1GAQi8XINRaOPnQ/fvxpLgANTS3YHfHg+vb4rSBqtzsJbnXNdnR2AxAK79p7T119U8IaJkWKFCn2BlLC7D6OWCYn3WKgsamVQDCIO5WJkCJFiq2IxaI4nG50Wk3itb6ar+Tn5nLSCSczY8br2xWj/P4AHq8Pe9sGFq93UFlRAoA+owyAplXvo/Qs46fZi1J2BkAk5KGpzUF+bm9htudvoddrKS8toqSoAEQitFptwtOtB2fXRk6cPhGNuQxp1lE8+ve7kh7HKScdx/wFS9hUVTv4L7MPEgyGqG9o3tPDGDZEwiGUqj3TjVskElFUkIfFYkq8VlSQx6EHT0nYsRTm5ySuLblclsi2VatV5GRnkGYxY9DHKwRUShVWm5XW9o4huVdFIhGIxdiwej46Yx4lpXEPTaOhd+m3IAgolXKaf3Odb02PMOLxeDAaTdu8L4gknHnWeYwpUvDMCzN2euxDybqNVfztH/+gTFfNl+8/w9zZP3L40acxZvxkxpcouPiaW3tn5KXYaxBiYURJCLO/tSDRqtVkZqQhl8vQ67QolQp0Wg1lpUXMW7Rsh1mcv6V63VI8QTUFeUMlzP66nM5Mt1BWWkhudiaKLU37tBo1Y0ZV8skiP3q1iGefeYQZL/6LTVW1SY27PwLBEBvXryKvID6n2tnmhHK5HJWpCGk0LqZGwhFqaht2KCb/Vph1uuINz1wuJ3fedRf/fug25sxfkgiw7Crx1B8IxJsppoKjKVKk2EtICbP7OHK5AotJT11DE9FINJUxmyJFil5EIyEcTg/5+TlIJBJMJgNplm0X+2KxiILKAyjLEvcpGkSjUeoamohFY9TVNzKioixhr1JSOpK//+tZRh94Du32CPUNTVhTnteEQ35aO2zk5/ZePPYstjRqFTKZFLlchkqpQCaV9PKeDIVCiCNOxLpKzjn/Ui46//Q+Kyf6Q2/MICddG28mlGLAOF3x8trQMMtK3FNEo2HUe0iYhbjQs3WASSz+dQqsVCrQajQgCEilUirLS8jJykChkJOeZibNYiYnO4P0NDMAMoWG9rYW7HYnXp9/pzNnXW4Pb737EVVr5nLo0WcB8fJnnU6zzbYKuZyW1vbt2sZEo1Ee/c+z2J1uTCZDn9sYzZmY0zJ57a0Ph5XX4/qNGzjp4BxOOO8uLr/mds67/F4OPmAyEqkSfeZYIkH3Dm0cUgxjYmEksoFbGUDvILBM/qsdm3GLhZLBoKOstJTaqjU4XW6aW9oHVMJeXb2ZEaPGIJEk/zzsC7FIjCASkEjintBqlapXczAAlVLBs08+xlU3PcSYQgXjihU8+Z9HeOWN94ck874nuOO2NpBfNBoEYaeFWYVCji6tBI3EkwhA3XnnrVx05S3bvW/8+PN8/vvsazzy+FP89aF/Ew6H+eSTT/nl5zcoNlkZWaDgy5k/4vcHCIfDNLe0JcTTobwXhcJhwuEwGzbVpDJnU/wuqaqpG9KM+xR7npQwu49jNJqxGLW0d8RN2b0+P9W/8ShMkSLFvovb7SEYjjKivISKsiLyc7MTmWRbIxaLkch1ZKXr+7yHNDa34vcHIBajrr6Z/caN6vW+XC7joEMOZ9TISppb24Y0i2RvJeD30tZh3aZ5h1QqRaGQ91p06XVaZFIZHu+vCxCv14Pb7cFk+lVI7ym5TAaRTIVRJ0s13UkS/5bF9o4aNe1LBEMh5IrkhJldydZBCq1GjVgsIiPNTJrFhCAIGI16KstLetm49KBQawn44pYrXV3WRLf1wfLVtz8x67tPGT3hMMaMGgHEr+leXtI9n62Q43A4t+up2dnZyeJFc3H7o5i2eFMbjXrS08zIt4hbgiBw2iknoZAJgx673x+gqaVtyLwcY7EIgn0lnQELSDQUlZRTVJifeP+kk05iXGUOs2YvGJLPS7F7iUQiyJMUZlXKX0vx5Vt5pfb4zCoVCtJLpnLgSB1LFi+ks6ublraOfo9bVVPPpIljUSqHxvNap9OQnZmOQiFLXGO/RSwWYzabMBgt7H/MNdS4S5g+JY+Nm2sS9iM7QzgcIeTpoKq+g9LSYswmQy8/3sGgUatIT89AKlPy2WcfEQ6FOHK8Gr+ne7s9Sd778BPw1XPYyCje7g38+ON3eBpn8tw7C5k4/Y+ccOKZLFu+hqXLV7NhUw1+fyBxDwmFwkOS4RqJRBJNBEOhENU19TQ2twJDK/6mSLGnCIfDuN1eWlrbcXu8qfP6d0JKmN3Hkco1ZFo0tHd0EggGiUYiuNwerEPc1CJFihR7Jza7FUEkxqDX7XCSLwgCYokYpVLRZwTX7w8AsHrVcpav2cR+Y0dtsw2AQqnB6bDt0MNsX8HptGO1uynI37arckFeTi9hyWwyIJLI8G1lR+NztlLd7EqIM0CfQk9/iKUajFpZSmBMksCWcz6V0RAnHAwnPF6HA5Ktrh+5XIZYLEajUSeyYneEVmtMCLNOlxu3e+dsoGx2ByV5eg6ffkL8BUHo1fV9awSxAr/PSXe3rc/3v/r0bc49ppK/3PcXlAoFCoWcgrwcsrMyyNsq+16t0aNTSalvbNnh2LYnlDS1tNLVZWXths3xhkCBIN3Wvsc0ECIhP83tLrTGvsvLY/JMjptazpw5s1NWN8OAnmf6QIlEIr0aZg2Erf3U9TrdNu8LgoBcrqR0zOHM/uETIpEoXV3WHZ4fTqeDptZOJu83tk9bpsEgEolIs5j7DOJsTc89Z8rE8dx7561Ujp6MEOzEbt/5Z0QkGmHN0pl0eA1UlpeQkWbZ6WPqdVrEEjGTp55K7fr5fPblNwDIpUJC6PwtRUYn48dPYvL0K5g+dSKrFn1Jmy3MfpMOYsL40aRnZDJ92kS++PqHxN8pEolu+XdkSDyk+zpGd7eNFavWbXfcKVLsTfQkrwSDIaqq6xLezTvzDE6x50kJs/s4giCgN2UQcDXzxN+v46tvfwJIqrtpihQpfr/YbDZUKnWiic6OkIjFiEXiPsvywuEIkUiUOT+8T1CcxuiR5Wg02y6K5EodXreNcMoXjNbWVsRSWZ/lyL8tk5RIJCiVagL+XwUiW3sN62p6WyEMRpiVyNQYNNJUxmyS9GTMOpyuVIdo4qWl28sm2xOkbRFgFQo5KqUSkUiUsFfpD73RjFrionHjTzQ1t+Ly7FypbHtHF2NHFCCWxu+JZpNhuyJWRKQlGrDj8fn6FE1bmmtRpo9Hoc8nPc1M3lYe1WqVEovFhCASEMQKsjONNDbtWKjYXmZcz+uRcIS1GzazfmNVovpqMPi9Hto6rWRnpff5viCWkF4wCXvHZlat2bCNn3aK3YfD6aK+sRmvb+DBukgkiizJDE6pVEpBXg4jK0uRyfreVyGXc9jh0wn7nXR1WxOftT3aWpoIhAQy0vsPwCSLxbytzdPWbG2fAmDOHYte5mL12o1srq7DNggLp7iYGWLdug18/9MCzj7jFAx6/XZ/r2QQi8VkpFk4/PAjOPnEE3DVfQXEhdn1G6sS20WjUWKxGA1NLfh8PqJSM4gUTBmTTX2bG4doBLfddE18W0UOh03IpG4r//VINH4fC0ciQ9KQcNXaDZx36Y0s/WXVNu9Fwqm5ZYq9n98GH9o6Oum22mjayhokxd5HSphNgSmrgvGFkGWSsmz5aoCU12yKFCkAcNgdaLQ7zgLpQSKRIJaI+1zIR6IRqqs34nZ0cellVyGVSiktLiQ7K4PSkkKELSX2Co2JoNeWmlgAbW1tGI2mAS9mVWo1wa2E2abGahQaM0ajAYiLuQMR2H+LTKlDp5bgSTWHHDDRaJRuq41rbryd2XMX43CkfDHD4fCAhc/dgUatoqS4gIqy4kSgY6BihsGST3u3m7feepO/P/JfIuFI0hmEW2Pt6kChVIMgoNGoyM7sW5wEKCmtoGrTeiLhCJur69hUVdvrfumwtpOVW4hSqSA7KwO16tcAmCAI5GZnUlZSiFKtIzvdQHNLP8JsH0JJLBYjtFVWYrQn462PAMRAs1v9Pg8t7VayszJ6vW7Q6ygsyEWv05KZlQuReBPJnfm9UwyeSCRCY1MrPp+fTZtrqa1r7HefWCxKOBJGOgix0GjU7/C5pdNpkMqUpJl1tLTFxfoeoa8v6uprEEnkO13m3xf9PV+1Wk1irgOQnp5BUX4OS5avxOPx4g8kf067PV7Wb6rmiy8+JRCVcejB+6NQDN19NiPdgkQioWzcUTR5c9jUFKAwN4OausZEED4YDNHU0obVaicQDKFSKUEQMGTvh0/IJDPz1+z/mESPWSfD5bRDLMrMb7+jta0TiJ9bNXWNbK6uG/R4/f4AVRtWk2cK88hjT7BuQ1Wv90OpbPsUvwPC4XCvJLpYNEZjUyuxaIzOLuseHFmKnSElzKYgt2gEvkCMdluYVWvWEYvFCAZDqVKxFClSYLPb0Gi2bUDTF0aDDrFYvM3iIhaLEQ35aFr/HUFFKWUlRahV8RLF9DQzGrUKhTyeHVZYOgaVxMO7H305tF9kL6SzswOz2TJgsUip0hAM/hpUa2puo7y0iPLSIoxGPVk7EHt2hMVsRqGQpQJ2SRAIBPm/S65iv1wHS35ZSXtn1z6fNRsKh5ENI2FW2NIcZzDBioyMHH76pZNOe4RiQxfvfvT5oESVHkK+LpAZ48dOT9thk76jjj4aj7ODDZuq8fsDeL0+urayNQgGA+h1eqR9eIH3oFIqkSm1ZKbpaGpu2+HYgn34fXdbbQkPx63p6xz39pNpH4vFCIfDeL0uOroc5OVmJ0qoRWIRuTmZGPQ65DIZSpUOpVyMy+UmGtu3r6c9RVe3rdf6wOF09du8KhYJEQjGdnhODha9TguCgEimZ+2KhUQiUaJ9nJs9OGxdiCXKIbMxSAaLyUhJUQEymTQh0OpNmbQ01AIMqgmYx+MlGomiiHWSWTAesViERj10TRYFQUhY0Nx1591cdtXNFORl0tjUkmgIGgqH49YqsSiBQBDVlkBXVJHLn264gpOPn544nlgiBnUBedpO2jd/R9WyD3jsyefZVFXL/EW/UF1Th8fjJRgM0dnVHfeLHaB/Zm1dIxurarB3bGLciAJuuOgg3nn9P8z84efEMVJr2xS/B7769idOPucK7v7rY9sET9vaO+no7E75zu6FpITZFOi0Oj6e70SlNUPUT8MWv7GUn2CKFClcLhe6Przd+sJiNiGXy7Zp3BWJRFg5/2Ne/Gg1F114IQBabe+FQ35uNuVlRRQVlXHOaccz46239vms2ZaWFgoLCgYsHCmUagKBuAgSi8Voam6joqwYmSxeDrp1R/pkEAQBlUKZeiYkgbVtM9PGqiksrsDl6MLvD2y3JHxfIBaL4fX6MPdhy7E3UlZayN23Xcepp59DtlnCa2+8vVOd1UVhO1JVOlqNul/ByGzOYuJ+Y/jp+08Tr/X4GMdiEYLBuAAu68c2Ii0tk+x0Q7/NkgJ9nLfhcASrzc4Hn3xN01aejbForNdiMBgMEeqnNNnhcNHU3EZTczPRmIj9xo6kqDCPitJiKstKEs0m0ywm1Fo9eq0Sp8u9Q/Etxa6jr2Zx/fWliEUCeIMRpNKhF2alUilatYppx5yDt2sdf33wgR2ec26nHbFMhW6AlUBDiUIhR6NWkZmRhsloQCQWYU7Px97dFB+bJ/mqFIczXo0R9HtRagzI5dtvQDZY5FuEWUEQKCkpZ0SBBre1ITHXC/eUVccieHzBRFM1kVhEZXkJudmZABQV5lGYn0taTjlt1jDX3fcGnc4oLqcNr9fHDTfdwmXX3QFAR1c3zS3ttLZ30tVt7Tew6fcHcDhdxKIxnA4rzmgaBx11KaeedCybln7M7bdeydLlq2ho2rGndooUewNz587juvMOxGtv5OJLL2XpL6vilSxbrsWWto7EvSHF3kNKmE2BXCbj9puv5vyLrubMIwqZ8fxf2Fwdj1imSJFi38br8aDX6/vfcAtymTTR9KiHX5avYeb3P3HbLTcl/E61v8nCVSoVqJRKsjLTySk/iFF50kSQaF/F2tVOaWnFgLdXKtWEQ1t++1iYhuY2Ro0oH5KxSGXSVMbsAAiFQnR0dlO3cSGzVocYNf4Q6ms34/F69+kmYHa7g2AwtE2Z+t6KWCzmmOmHMm7Cways8aNRiqipbRj8AcMe0jPzKSrM6zcQI5VJGX/Qmbg6N9HaHi8B9vn8hEIhwgEfHn8Yi9lIumXHHpoKlZ6sNB2tOxBmHU4Xo6ccg8vV20N39dqNPPTALWz85Qv++fBfWLehiq7ONv779NO4t/LbDYZC/Qoq4UiYjq5uNm7ahN5gRLVF1JHJpL2qBSQSCWaTAaNOgdPeuc9noO8pAoF4Rd0rMz5IZB/292yIRoL4/JGEyD7UGI16Ksor+MN1D5KjdfL8y29ud1u3y4ZSrR0SD9bBYjToycpIY/SIcvKKRyEEu7B3txDwB5Lu8dFTmu/z+dHrdHEbgSFGs5UdikJtJKodg61lNes2bI4HX7aMwWrtwukOMH7MCEaPLKcgL964VK/TYjIZ0Ou0aDVqKstKuPev/+bOO+7gmKOOxO+L3zNOO1hHjw1vTxWAze6guaWdVWs3Eg6Htxuw39oDP+B1YTSaEUQiJhx4AhdccQ9arZqP3vwP5158A00tO64SSJFiuCMNtZKdV8QNlxzL8Qdm8vBjT/LJ+y9x1VWXxp/psVhKmN0LSQmzKZDJpBx39GEY0woYe8gF+AJRPnj/rVQDsBQpUuB0OshMH7iYIpVumzFbs3k5Kl0G+40bBbDDjA65XIZYpic3y8jGzTX79OLb7XGTk5PV/4ZbUKp+FWb9Xg8tbTYqy4qGZCxSmRyff+caHO0L1Dc209LaTkNjC3m5ueTmFVJakMGs2Qvp6rbtVFbl3kxzSytSqXTQWdvDEalEglar4ZGHHmS/UUXMmrMAIGkBPhL20213M6KibEDN+SRiMVlZGZQUZDLr2w9xuePXpcPpwu/3EorEKMjL6Vd4EomlpJl1dHRuv2FXz7HXbdzc6/W2xnVkZVi4/a6HOP6wsXz/0b956al7idpW8fW3Pye2CwaDO2zEBPFGTU/+71VefvVN5ArlDrP9jAY9n81t4aN3nuf+fzyRVPOpFIOn577V4y386ivP0bjuG775fjaRSBSvvx+7ikgATyCSdPOvgWLQ65DJpMjlSipHjGPVqlVsqqrdZv7g9nixdbeTmzs0z8XBIggCEokEkUhEcUkZeQWlPP34/TzwwN1xS4AB4HC6cHu8LFm2ivmLluFwuTAadKRZdtyAbDDo9VpKigtAEJBIJUw/8kgQYPWaDWyuruOx/77Iiy8+x7NP3IfLFyMrMx2JRIJOq0EikZCWZiZnq6CcxWzEYjExcfxoZDIl4a0smIwaMVVVVQkLlUSzrliMrm5bIhj1W7b2jnW6PVjMvwamTCYL9z7wBA/fdwOZ6fp9PuifYu8n7LehMhZRNOZYLrzyHq49ezzN1UsozpLx1vufAfFG7ik7g72LlDCbAkEQkG0pLxo1opxb/nQPrY2b94qI4vYm5fuymJMixVBid9gpyM8d8PZSqWQb8cnraCXIr3YIO/I6FYlEiCVi1EolbR1dtHd07ZMTi0g4hNPlJTPDMuB9FEoVkVAAt8fLxk3r8AajpKen9b/jAJBI5QRTIsgOcbrcuN3xSpPOLitFBbkgknPkIaP5/oeftvho7pu/YUtLC0qVdlB+rsOVnuy/mMzCtEn5LPllFX5/gKbmtqQa5Tk662ls95OdNTAP6J5y8PUtUjasWcK/HvwTP/08F5fLQ8DvIxKJN0QaCHpLPrFA/41CVq/b1KvMuqVxMzF5BhKFjiNOuoIDjrmKq255jIKCYppb2hPb/bJyLXc/8Oh2jxv3l40gDTZh1kmQyVU7PEdEIhEPPnA/l19wEgsXL2fl6vUD+p4pdo6W1nYCgSBen4+VC7/C2baG0ZXF1Cz/lPvvuYma2oYd2geEgl483tAusTKA+HlhMccFSblSg9/vwuv1JZpK9bB+YxVr162hckTlLhnHYNDrdJx/yc0UVB6Cz9VBR2d3vwEHvz9AQ1MLTc2t/PztO3z67tNUN1iZNvWARMb5UNLjx52ZbkEuk2E2GyksHU/NhsWEQiFctV9ia1qGMW8Kz/zvlUQzRUEQMJsMyGXSXr7ZYrE4IdLLFCoMygBr167B7YsysVzJJ2/+i29/mL3NODq7rXR12/oMfvn9AVavXc8TT79EQ1MrBXnZ8Jt7SUyZx5RKA63tO7ZvSZFiuOP2eDGZ4r70MYmeqcdfTcmk8/jD+WfhalnCrJ9mEQlHBmWPkmLPkRJmUwBxO4OeRYbJkkFpURYrV6/v1xtsT+PxeHuN0eX2sH5jFRs31+yTYk6KFENJvLO8ncLCvAHvIxZL8fniE4GekjOX04ZCbQBAq1Fj0O/Ys1YqkaBQyGluaaNzAN5iv0es3R24fRGyMweeraxUaQiFglRV19HWVI8vKMbYz289UKRSBYHAvikqDpStfRY7uqwU5ucilhsYO3YixYZ2vvvhJ9xJCHa/J9rbWlBpdr+n465EKpGg0ahALMdoycfnaKauoYlwONyrGVd/dLXV0OEEk9EwoO0lEgkqlZJTTzuLMQeezsknncK3X75Jt82O1+chFI4lmin2R1b+aMzq0HaF5J5S9ba2dmrrGxOCyIL58whE4nNGuUzGlInj4lmLchlO16/lky+89DqffPHNdj8/HA5TW9dAcXqUvDQpcmX/gvJhUw+korwYg17bq3w5xa7D5/ezsaqGmpp65vz0DWMP+T9Ov+gurr3tMfYbkcs333y9wyZv1u5O3N7QLrMyABKZ1jK5inAwPhar3Z54PxaLsX7dasRiKScec+QuG0ey6HVaJBIJF5x/ARkmJU0trXR0dO9wn8bmViLhCJ0dHUgjXVx3/W3ced+jjCgv2aVjzcxII81iQqGQM+XAw6irXk8wFCLNqKbBpuCKyy5Bo+ndP8BiNiLtI1O6R5iVqsw4bJ188+GTNHeHOOOiuxg5chTzvnuLG265nb888BcWLvkF2JI9G4vR0NRCR2fv3+jt9z9m5gePI3WvwOWLMHb0CIp/M3eNSYzkpavo6Nh+lUCKFMMdr89HOBzGuNWcQSqVcNxRh5E36himH38OTave5/mX3+jlAZ9i+JMSZlMAIJPLyM5KRyQWgUhGRXE2q9du7FUaMhyJRKK0tHXQ0NSCx+ulrb2TQCBIIBCM++Tuo4vgFCmGgpWr1xONxdhv7KgB7yMWS/F6PUSjURqbWuns6sblsKLVGgDIyOg/g1Or0SCXy+juthKNRPsthf29EQqFWLV6BZGoeMBiDYBKpSEWjWcrB302QjHFkPnNyeUKgsGUCLIjepp7RSMh2ju7qSgvobSkCKWlktyiMfz045d0dVv3yaBhZ2cbGq1xTw9jSDEa9RTk5SCXSTFkj0Itsie8WF0uz4ADSrbuVqQK3YBsDHpITzMzakQ5Z516ApMOPJr8DCWLly6no70DBPGAj5WdV4xJJ6O+obnP93vKiFvbOomEI7R3dNHS3IBaIeLSC8/bZnu5XJqwP/D7A4zL6qYiV75dATUSiXLfX+5n5cZWFDKBQLD/a0MiERMTqTDrZTsUA1MMHaFwmGgkysoVS3D64IhpBwLxIMGEg46nZsMirFb7dvePRYJ4/OFd6uuqVCgQiUXIFGrCoXgQceuMsVAoTNhZg1fI6rcx3u5ELpchEosQRBKys9JZsWo1Lo9nu7Y30Wg00Yhz5fJ5+GJm8otGYDAae2Wl7irEYjEKuZyi4grSDDJaWlqo7wxz9dV/jNsziHvfe/oSZSHulY0gUFI+llkrPRxx8rV0erVkZqRhzihiVIGcg0fKKdK38+4b/+W5l99M2BhEI1G6untn+rdULaRs1EFcdsPDXHPDA5SWFKDTaiguykexpXkZgoBMoaars287hBRx9sU5yt5Ed3c34QgcMGkcOdnbJm8cMvUI1JZKvv5mJm63t8+/Z31jcyqwOQxJCbMpgHjGg1wuQ6fRAGJKCzJYu24DwcDwzpiNRqPYbA6sVjtVNfW9GpZ5vT6czpRPbooUg6W5uRmtVodSoRjwPmKpDK/HQ1VNPXani+aWdrxeL3qDHqNR32/HcYCc7AyUSg2+LQ0httfs4fdKZ5eVzvY2xHJ1Ut2VlSoNAlHCkQjhgAPEqiFbCMvkSkKB1CRuR4SCIZ574RWuvOpy6pqs7D9pHAqFHIVcRnlZCSMzPfzn6Vfo7Oq/dPz3ht3ahc4w9N6HexK9TotUKsViNlJRXolJr2bV6lWsXrsRr2/g8w+7w4U5iQAMxD01e+4NgkhEfvEIli2eR2dnB4Jo4OKMRCJBp9NQvZ3GZZEt4nJP6a/P56d24zLWNfgxbukqvzVymQyXO/69HU4XkbCfcHT7TUhC4RAHlIYw508mGI7RZXX0P2axhJhES4ZRllpY7gbC4TChLUEnR1cdgiK9l/BfWDyCvHQ1S5ev3u4xopEA3kAY6S7MmJXJpIysKEVnysKicLJm7WoikWgiyzsUDuL3dCNTmZFIdr2AmQyqLaX/JeWjmTv3Z0LBEB1dfWfNutwe2CK0hNytSDXxhqq78rf9LSKRCIVSid6Yxvvvvk5zuwvzlrLqgYrDeTlZVJQWMXnCWL795HX2Gz+eF599GoNBx+iJR/LlcoG88ql8vtDF6aeciDa8iYf++meefWkGEA+E9gS/IpEIbnsrpaMPQqFUU1RckBiHTquhsryEvNwspFIpMoUWpz2VMbs9gsEQ9Y19B+pSDA9s1g5iggyZTIbFbKK0pDCRhNEz5z/91FPIzTLyy4o1icqXQCBIQ1MLsVgMl8tDQ1PKa3m4kRJmUwCgUilRKhRotWoQBNIKJpGtsdLU0jasm5VEtspIiUW3jQh5vH1HilKkSNE/Xo+VSEyW1CJGECsQE4x7acZiEAvj8frIzckkPzd7wMeRqfTIYi7Wb9jEhs01gxn+XkswGMLrsqNQJlf6rdYasegkBD3deL0edLqhKx2XK5R4fZ5tslRS/MqC2V/g6VjG6ccfyJSpJ5C5xd/XZDQgqAvpdISpa2zuVeq9rxAJ+5HJ1f1vuBeSZjFjNOjJzs3H0/AdM176J59/9T2OAf6d7Q4XlkE07MnK+NWTduSYA2ioXYvb40IkTi4bUKfV0tjU90I8HAohlwosXb460Xcg5O2g3RajsCCX0SPKe3mGKxXKhM+y1+PC4fIjlwh0Wfu+b/g9TtbUujnrzLNY3xDYpnFkX4glYmISHWl6Sar511bsKuuxJb+sYvrJF2K12fE6O9Hq4xlaPaK8IBKTm5PFkmWrtnuMaDiA1xtGsos8ZnuQSCSUVYzh59UeZn32HG+88RoOR/w6DDhbmbesjrGjK3dZE7LBYjbGRc0Jkw4kSxtg0ZJldFvtfa6/3G4vGzdt4g+XXs6CBQuRq+J2RbtbbM7MSOOcC68jS23H5YtgNhnjGcsD/G0FQUh40fZ4Ykskce9ZtVrN2688zYFTp/PcM/9l6pFnMOXQU9m/UsmKxd8xd/4iAPyBeLPTzs5OHC5v4pnb1xjMJiOjRpRhTs/C5dyxVcS+jNfrw253smLVOmq2E7BLsWexd3cgksaFWEEQ0KhVFObnolIpKS0uRBAJCBIlxxwyimWLfya8pfIlEo1gtdrZuLmGcDiMz+dPBTeHGSlhNgUAGrUKkUiETquJ/2Mpoqwom0+++Jb1G6sSJZrDjWtvvpfWtu2buLvdXjZsqqauoWnYfocUKYYrfo+DqEiRVHmcIFYhFX5dTAjBLjY2uCkuyk+q8Y9ck0mupo3XXv4vb2/pMLqv4A8E8PscqDTJlX5rNFrmrfPw5uvP8d83fub4ow8fsjEpFCpCAT/NW5rA7E3sDo9in8/HonnfMeaAMzn+9Ks564wzEotOk1HPyJGVTJw4mbr6hn2yGYNACLF06JvSDBd0Og1acyEbNtfi9kVZMveLHZZ29xCLxXA6XYPqpK7RxDPiBZFAQWEp6SY169ZtRBAnJ36pNAY6O/r2oQsHrFxxQgYnHj2V2+55iHUbNhAIBJApVOi0GkQiERnpFoqL8hlZWYbZbExYGdjb1rNskwuFTER7RzeBQBC3x5vI3gG47e6/4PXH0Om0XHn9/bzyv3/1O16FXE5BURkmnXSfbabXF63tnVTX1FPf2Dyk95iqmnrkUoH7//E4mzdvpLCoGJVKSUFeTiI7Ky8nm5Vr1ve5yI/FYrz70RdxATc7c8jGtT0qyop59OF/cNTpf6S1Zj6r1qzH6XKzaf0Klm9s5egjpu5Sr9vBYDTqqSgrJi2nkjETpjL/+7fx+/x9NqlqaWvn2ace4qj9c9AoRfj9QbQaNRr17g18aTVqtIYMJHIdTk8UlUpJSVE+GekDb1jaQ88cUyKO2zf1ZP/Fm4fF50HFZWP5ZL6TY488iIXfvcS1V19MQ2M846++agXN3WEMej3QtzDbg95gwePpPzN/X6TbaqO989dsYqfLvc9VrO0N2KwdSOW9/dhlMinlpUXIZFLSLWZiEj1jxowjYF3LX//5JADRLQlsfn+AcDhMbItfc1NL2z7Zy2M4khJmU/RCKpVSVJiHRqPCbEmnpakRYEBZDHuCOfOX8PHnMxP/v3rlYl569l/ceP1VCcPrQCCI3e7E509FhVKkSIaQ34UgSU5MUejSUYrdOLtqeeedt4j521hdZSMzfWAdx3uYOu1olq9vg4inzw68v1dCoRA+n5/29hZ0uuSEWbFYjN0dpd6m4uJLruIPF5wxZOOSKXXIxCFi0dhe8/eorWukqqaONes37fJJ57yF8e7Uhx82FQBBJCRKzSUSCSajgUMOPoR8C1RX1/USp/YFRLEwEunALVH2NsRiMQcccgzv/OTgplvvI8csZvW6DQPI6Ixid7hIT0tezJBIJJSVFDKqsgylWscB4wpwty6mpTW5Ml2lJg2Pva3P92JhP/UdUa4+fSQXHZvHJzMeo77Nj1qt6mWzotNqkMmkqFSKhCi4dtV8wrIc8tKk/Dx3IU3NrVTV1LNm3abEtRlzrMMXjCKTSpk4fjSF+f03mpTJpBgNepQqZaqPwFZ4PF5cbg82m4OauoYhu087Oqq54pQyTjrAgCdq4OgjplJcmIdep2VkZRnFRfnk5WWzbuOmXkJ5IBAkEongcLpYsWotb7zw76Q80weLIAiMGVVBZUUlo0dUMHv+YmrrG5k3by7jxk2gtKRgl49hMCiVCtQqJcccfwZqRYzFS5fi8fS+f4TDYZbM+ZzibB3HnXkT0066lpuuu5SS4oI+vSZ3JVKpFINBR1sgixZrmPLSQtSq/q2q+kIiFmM06pFIJaSnmSktLgBBQLxVFrBILOb9t2dw6tlXMXridEqyZSxeugIAW9smHAElJqOewoLcbRqQbY3ZnIbX7ehlhZAiTlt7F16vr1fwvSfQlgLah0nTuK6O5m2E2a3JykwnN6+AwpEH4fRGcTntQNzyIxqN0t3dxTVXX8Zrb32Iz+enq8tKVU19Knt2GJASZlNsgyAIWEwm1FozDns7AMFhKMz2lG299/FXvPjauyxYMI9vPnkZvTGT8RUWnvrv43z7w5zE9ilLgxQpkiPg9yBXJJeFMWr0OGrrm/juy7ew1v3M3x95mpzcPArzc5I6TklxIVOOuozDDxqL3b7vZDe0d3Tz3NOP0tHaxLlnJy+sfvPxq/ztnls4cMqEIS1tlKosWDQRiEWH/eTN4/XicLpwOF243V6ikWii5HFX0VK3jpDIiGRL5o9K2TugkZOVgcQ4gv1GFTH7h08JhfYxYVYII/2dWhn0kJ+XzQ9fvEVZaTGFJRV889VX2O07Fsdi0Sg2h5vMjOSFWYiLIxKJhPT0NMy5Y3l5po3DjjwuqWNYMvOxdfftNRcJBWi2xojq9+O4M28kJzuN+x97i+Ki/D4bjCmVCtxuD9FIkOWrNjFt6sGUVoxh3tz5vbwxe67NwiwtwXAMsUSMXC5DLB74skQikeNPWRkA8fnw1mJKNBKlrr5pSLLdXLZ2bJEsjj7jZm67/S8UFeb2yjjVaTXk5xcil8TYsLk68XptfSNd3Ta8rm48viiZg8ikHCw951FFRSULFiwgEo5gt3VRWlKalG/77kalUiISiUgvmMSsme/h9wd6VQVam1awadNmDIXTMJuNjBs7Li5i7iHUKiVXXXYRH7zxzHabfA2ErMx0CvJyElYEIpGINLOJMSMrsFhMaDS9Bd9jTziLstIy1m6oIhT00dLSRkZmDkWFeRj0OtLTzNv9rIzMbII+N+s2bE40UUsRz44NhUK8++7bnHXBJTQ2tRKORGhoaqFzO37H+xqt7Z173M4rEAjid3ciUe64ysZiNmEyGph6yMFEI/H575/u/gfHn3EpP333IUfsp+XTz79k1doNQNzCIhXo3POkhNkUfaLRqNCbMvFs8eFxueMdQneVh9Vg8Hh8CAIcNlZNxLqMuTNfo9Gh4cxzLuKcS+/mtCMr+O+zLxJOlWGkSDEoQgEfWq0uqX1GVpSiz57Iq19sZl1HGnkjpvOff933a1fcASIWizjskAPIzszE5do7MjR3FrvDycaN67G1b+Kcy+5n7OgRSR8jLyc7YRkxlB2aVSo1a2u6+eGHrwkOo+fA1vh8fqpr6tlcVUdtXWOv91pbO3bZ4qKuoYnu9kakSgMQ915Uq3oLsxKJBLVKw7GnXkp70/peAsY+QTSCTJbcPWBvQ6tWJ669Aw44hA0b1tDVbdvxTrEwdoc7IUgMFoNeR+mYqZhMJv5045VJ7Ttp4kRaWpr6tCiJRkPEEMXtEURSzr38L3z8ziu88OQ/+jyWUqHE5/PR0ljHmo2NHHvUoRx//HE0NdVhtdm32d4RUHD1dX+itKhgizA78HuWVCrD70stJAF8/gAut4dYLNYrCWFnA0DhcJjurlYsaVmJ19R9NPA0ZVVw8qGFLF62csvnhvD7A3R0deN2WfGFhN1qHyDZYudRWlaJiCBr1m3C6/Nh0OuGnb/s1qhV8Uz0k089mwyDhK+/m0V7RxfRaBS/P0BXex0f/bCBKRPHJ/bpaRy2J9Co1Wg1GkZUlO7UcXrmh1uL5lmZ8XtiRpqZ0uJCRo0oQywRx72NBYGMdAsbNlVTX72eucuqOPqIQwb0WRmZWQjEAxmBXRyw3Vvw+fx0dnZDNESgezWHjNYx4/n7eOCeG1i+Yu12mzfuS4RCIYjFsNmdezTRKxgKEfD7MRr7r6gzGfVI5Wp8nvgaShVp5LD9TNRsWIYm70huuXAKX3zxZUKQtTucKdvHPczwMtlJMWyQSCRk5RSSqXGwaMlSxo8bh9MV77Sr02goLMjdwyMEp9vNtDFq7r/vfgRlFrXVm1Bo4hF5lUrF5EPP5JC5S1mybAWTJ46HVMJsihRJEQj4UaqSy3KRSqXcctN1nHfOWcjlsvhCSJb8QkgsFoMggMyAEHaxuboOg15LmmX7mRB7O93dNjpaavFHNRgM+kE1ShGJfvXxHUphdkRlKXJNFj99+xFGSwHlpUVDduyhIBgMUVPX2GfwMBwOx0ULYrvk/HG63Hi9dtQ6E1mZ6aRZTH2WSGZmpuHx+cjLzmDWz/MZN3pEn1mHv0ei0SiSYSyIDAU9DWy0GjVCRi6lhRnMmr2AsaMrt7tPLBrF7nSTnZmc1ctvEYlEZKRZeP/1pzFu8VkcKIWFhUhFMbq6bduUQ0fDQaKIUaviTb3ksrhwsr3MVoVSgdfnoapqA1KFjuzMDGQxGSccUsDCBXMZP34S6emWuEi3JfveZDKiVCoQBCEpH3KpVE7Ansp4A7DZ7Zx3yXVMGVtMXW0Vp5x+AaeceAyhcBgFgw+IeLw+urva2G/q6YnXFPJtj6e3FJKVncfm9csBEsG7SDhCl70TQRT3Qt5dGAw6lEoFbcEOzj9uJCvmfUhnRzvjppp3KrNzd5CVkU5dQxNHHTGNOx57h4MPmERDYwvBUIjaunpEYillJYXotBoi0ege9ctVKhWUlxbtEnujnmdjz99LKpWSmW5BKpFS19BEepqZ6ppVtLbUs2JdPTfcetiAjmsyZ6BVSrDa7GR60/gdTykHRDQapa6hifb2Tj6c8RjfL2zi348+hslk4INXH2LhkmXsP3n8nh7mHicQDNLU0kZudiZ2uxOjMbnn7FAR9jtp7nBhKexfmFWrVKg0BgLe9QDIRQEOP+okMjOyySkopau9mTVr/sE9d1zH9BMuZPrhh+Dx+hg9onxI1w8pBs6+sSJIMShKykayviHAB28+w1PPvUY0EiUaiWJ3OIdF5qy3YzV2rwhBlQeChKLSkb26A4vkeirGHMjib5/nkb/eyNPPvrgHR5sixd5HOBRAmaSVAcTtUDLSLRj08WzbwQizEF/8y9VGRDEvHo+X1vZOrDY73dZ+stD2QqLRKC63h6bGGnSmuDgiHcSCq2cxo1QqUCaZpbwjVEolTz7xOMcdeyyffvDKsGsI0dbR2eu51NbaxH333Mr/Hr+H+++4ilmz5xMI7JrnVjQSxe3opqCgmIx0CyKRqM/FskqpRKtWY07LYMPGjbS2de6S8QxHwpHooM7nvQmxWIxaraIgPweNIZMjDyjl59lzdrhPNBLC7nCTnbXz/pBGQ3yhmKwAplLr0GkUfWaURyJBojERhfm5vbLZJNtpMKZSqPB7fbQ116HUmJDL5Ug1OWSWHISt+iveefE+Hv3bH7n9lkt57JEHaO6wY97iO5psVYVMriAYHN62KruLBQvmc/7hZsaXG6nIk7P4p/d4+913aWvfuXtM1eYNuD1+Dp96MBKJBIVC3mcwSS6XkZ1XQktTPQDhcIT5i5axZt0mrFYrgliKXqfdqbEkg0GvIyPdQnruSLIrj2Lh8s0oZSIK8pKzVNoTGAw6tBo1JWMO56hJGSxetgKb3YHX66O6poHJE8ai12kpyM8hPzd7Tw8XmUya9LU7WCxmEwaDjrzcLLIyM/C5u1i9ajkZWbnk5gyssZxEIsEZUrFk9sd8+8Oc3+V8MhmcLjeBQJC1y2fR7Qhy5x13k5WZjlwmo7hiItaWNftkw9Lf8vPP87n82ltZuGQ5Ls+e891dvXwhC36p5sxTjx/Q9rm5BQT8bqLRKIFgCCRGCoorkIjFZGbnc9L/3c85px1P48r3uP3OO4lFY7vc+ivF9kkJsym2S3ZWJkcefwEXnXUUDfV1vd4bDmUNfncnjdYdR3ROPfMizrn8bxx66DTmzpm1m0aWIsXvhFgYqTz5MjnxbxZug81Q0arVlJePIhJ0E9kSGGpobKGxqTWRwf97IRqN0m21M3/uLMbtNwVgUB6xWq2akZVllJcWDXnEWyQSmHDgCSgENxs31wzpsXeGSCSC3REvL/P6/Mye/RNLZr1FJBKjsPIgJowfzYIF84lEh15MjkQiOLrq2VjTzqT9xva7vU6nQa0x4XZ043DGs4z6Cnb+npqDxWIxopEI0kFkgO9tFBfmIZFIsKRnklt5GLg37/BeZbfbCYQiZGbunJUBxDN1ZTJp0oEwqVQazy7s6EPEi4SIIkYikVBaXJAQ10TbzZhVEolGaG2uR2tIR6/XIpVKOPro4/honhNF9qGcd+ldFBaVIRJJsBRMYdyY5C1bAKRyOQF/KmMWYP3qRYSUJZx8zvXc/sALXHbJJXRV/8g/H39mp45bu3YW1pAFs8nIiIoSCvO3Xy2XlpaJ3+ciGo2ycOEiFn/3HDff+SAOhw2JVJEI1O5O0tPMjBo1ipv+dD81rtxBn2u7m/R0C0i0SBQa1s1/k+9/mseKFcuZt3ApkyeOJTcnC7FYPGz8cjV92FvsCnoy6s0mIyMmn8xh443UbVpGXkFZUpnDkw8+nsa6jbz73gf92838jgmHw7S2dRCNRtm0dhEV4w7vVeGx35Tp5JhEnHnhNTRuaai9rxEMhujqtrL+l6+58OgcHnzkaZqaWveYH+tXX33KIYceSdEAK5ezc/MI+t3U1jXgDwRRbrE+yc/LRq1WkZuTxQGHn8X0k69ACNnjAm4ftkYpdg8pYTbFdlEo5Jx8wlFo0kegFnX38lRxOPa8MBsIBLfxuhJEAnqdFpPJkHgtMzOd9PSMXbIoT5Hi90wkEkEmS37ir1TEH/xKpQIEAb12cJkyeblZjBg5BrNWRPdvDPetVvugjjlcicViPPz4M8ilcMjBB1OQlzMoYVWlVCKTSZMqCR4oIkGEQqnCYtazcPFyWlrbh/wzBkNdfRPRSJS333qD/zx0PYt//pDvlnZx8eXXc9xxx1MxegomoYbz/nDtkE+mf/x5Ac8+9Q/kphFMnjim3+11Wg06UyYBb3diwl/X0NyrCUk0Gt2mNHRv9v2KRUP4Q3u25HZ30XPN6nVaSkpHYtQrWbNu03a3d7tdxBC2aRY3GARBICc7c1DH0qiVfXacjkZCxIh/J6lUSk52ZnxBp+r7M5RKJWIR2GwdGIwWNGoVJqMBpVLBjFdf5KpLzqegIJ9rrr+Tm/90L7ffcv2gMynlciXBVGYP0WgUa3sduYW/io7Fow5hwpRDcTt2zle7sbGBvPxSZDIpYrF4h5mR6RnZxCIB/P4AKxd+yehRIyjONTN33kIkkj0jIIrFYvQ6LeWlRTz5yAN7RBweDFqNGpPJwLgDTsTpjdJQu4G1c14AAfafNH7YCLI97AlLHp3eRHbFEXy/tIORIyrQagZe3XX5H87lhNMvQ0kHPv++m3Xf1NKGx+XgnnvvYHNdB0cdcShSqZT8vGzEEjEKpYI/XHIVIwr0fD9rHjW1Df02tPy9YbM7aGpuw+3oJrNwP046wMhzL71G2x6qeAoFXOQVlAx4fZCVlYuYEB2dHbi9AcrLipDLZeh12nhT5i1rhcKS0eSlybjz3gdYsGR5n3ZcKXY9v/9ZcopBIwgCcpmU7PwRZBmi/POBG/DHtKh1Fm668UYKI5E96kHiDwTRqFQY9DrCkTAer4+8nCxMPWVxcjld3VaCwRCCSEwsJcymSJEUkUhkUAuAnOxMJFIJ2ZnpRKPRQWfMisVixGIljpCBrz74D25RIVdcciFyuex31013c3Ud9rYq7v3r9RQX5u8x/6odIZaIEUQCRqOB6rp6Ojq7kctltLR1oFWr95j3uMfn46evZ1C9YRHjJkxn8oGH97K1qRi1P1a7B3fwC5YsW8VhUw8Yss9euXI5CqWG62++LhGQ2BESiYQx4w/kh6/fZfbcBRxx2FSIxXplzIbDEZxONyajgY7Objq6uolGY+RkpaNQKJJagA4HYpEAPv++kTG7NSaTEY1a3aur+m8JBv2IhKHzvBysyKnVaGjto+w9Gg2B8KvoIpNJMckM2z2OTCZHLpXS3W2jZMKvZdYGvQ6fb1sBZGfEerlcSSi074oqPbg9HlwOKwUFxb1eV6mN+NzrsTucuD1eMnu8fZOgqbmVcWNPGFCgLyc3H61SoKvbht3aSk7JZG64qJDX3vkcpaEwqc8dSswmI3a7c9h7y/6WrIw0pkwah6P7TL7/+n3cvihqc8mw83ffUwiCwPnnns2oUWOYMnFcUutRiURCdn4ZFq2IxYsWUllWstvsGIYTHo+PWV+/jk4e4bizrkOlUqJWKzEZDZiMBrw+H5s2+Kgs0NLS2o7T5UYkFqHXa3dJ8H+4EYvF6Nhi8eNwudAXFDJynIoPP/kCh9OF3x/Y7eeN3x9ArVYN+Pc3WzLQqiR0d3Xi9gSxmI2UFhcC8TWWxWxMJNtd9se/8N1nr/LSa+9y5KEH7ZPXxJ5m35olp0gahVxOQB6kbOLprF2/nlxdlMba9Xz7w2wy0s3kZGXsMXHWt+XmVJCfgyAIBIOhXgu/9DQz0WiUtvZORGIJsVhKmE2RIhnCkcigFjMymZS8nHgX56G4P9x974PMnvUt9Rt+4tGH7yYtbxxnn3kyULbTxx4ufPnpOxxz6Ah0mWOG7WQoPzcbo0GPWmemvqMFgMameHmb3ekisgeCdeFwmI72dhYtnMO5F99BWcm2i1apVMKhhx1Ne+1i1m+sGlJhFn8TxowRSKSSAYseY8aM5rBjzuHLT1+lsrIctUqJ3eFKNCYLhUM4XC4am1ux2RyJ/Zpb2pHJpFSUFe9VjRmi4QDeQASpZO8SRnYWg16HVqOirX1HwmwA0TDIJM7MSGfRpqptXo9GY8mJeYKIA0frcDgcZGf9GhwxGvR9CtSSnTiP5QoloVCq5LJ63Tw2Nvq4d9wopFIJkXAEj9eHQpeJJOqktq4RQRCQSaWkpw2801HA76e5pZPTLxw3oO0taRnIpRJWLF9AY5ubgrFZjBidS9mcmczbuP1rYFej1aiRy2XDLsu0P6RSKTlZGRw6bTpFpaMwGQ3oddpEk8EUoFarqCwv6VUlORAkEjEymQxV+jg+/+AFxo+fyJRJAzvPfy+EQiEaGptZtmwpR51+I+PGj6YgLwe9/tfgnkqpRKrQYdYrcTjilg92u5M0iwm1avfYV+xJvD4fLS3ttLW30t5p5bCifLIz9mP54m/54ed55Odl79b5eiwWIxAIkpUx8GahgiCgNRfy/eev4vWHtqmoyc3OJCcrg9a2DsQSMaNGjmTJhvlEY6mM2T1BysogxQ5RKhXotBqOPfZYbr3pJv7v0ls4+oipfDdrLlarHZ//1zIyvz9ewrS78PsDaLaKGvVVvmvQ61CplEilMqKR1E0mRYpkiEaiyIZBlklGmpmjjz2RacdfjlYtJ+ZYxb8ee6qXvcreTsDbTUSWCyLpdsuE9zSCIKDTajCnF2Bt3YDLvVUDhNieaRjgDwR49+1XURvztxFlszLTycnOoKy0EAQBlVJB1xA3+gj5HMSkOlTKgXsxS6VSJh14BGXF2cz55nUefegu/vbP/ybe/9+Lb/Lwv5/tJcr2EAyGCAT3LjEqFgng8UWQyva8ALk7kcmkaLQ62tu3b/kRCgQQbaeR1u4kOyeXmpqabbJao5HoNp7hO0IQRCjEQb5Y5GREeWnidZlMSnZWBhVlxRQV5mE2G0mzmNFoBr+4l8kVRAYhzMZiMZwuNzabg7qGpr3eT692w1IEZTZZmWnk52ZTVJhHWUkho8dNRq+G5SvXAtDa3pHUHH3tujVEYmJGjijtf2Piz4e8gmJWL/mODfUuzCYjEnU6o6dezp9uuWFQ322oKC8tIjN9532cdzdajRqJVEpRQR56nRaJRLJPCGIDpee5m2wViU6rISPdwrXXXEt5cS5Ll6/aFcPb5YTDYWKx2KDmwt1WOy89/wT+sJzRI8uRSqUYDLptbClysjNwk06gfTEfv/cqL7/xPm73vtEMzO8P8ML/HmHWp//DkDmKE445HKPJxBFHHM2Mt97a/f7EsQgef5CC/OSaGF5/y33UNXfT1BVC08e1IggC2VkZcautrHFY5N0sW756qEadFL+n/gqDYVgLs43NbVxx0wOcfemfuPT6e6mpa9pmm9a2Tq697UGmn3YFF11z1x4Y5e+b9DQzxUX5KLaKNI8cUcbqtRvp6OzG7/cTjUaJxWJsrqnD5d59DXn8fv82HrO/RaGQU15aRFFhHqSiPylSDJhYLLolY3bPiwY9Prfl5ZXc8KcHOfP861BH6lm9dsMeHtnQIUQDIFZgNOiGfTZk5eiDsVpt/OXu6/n2u+94572PmDN/CX7f7hdmZ34/B4+1lhNOOS/xmk6rwWCId+VOs5hRq1SolApkchVdnUObuRXwe9FotVhMpqT2k8tkmHQqVq5eQ0lBJo01vy4MBedq5i1YRCAQZP3albz22gs8/Pe7+eHneTS1tBHw711CUjQSwBMI73MZswBavZmuzlY6u7r7XDwHQ0HEoj1/jy0oKKS5uYn6xuZer0eiEcRJZsyuq4/fB37baCk9zYxSqUCv05KXk0VOdkYiS3wwyJUaiCZ/z7HbndTUNlDf2Izd7oz/2+EkEtn7qqpi0TBVdS2MHlnZKxNKLpcxZmQFeTk5NNWt55dFP7K5qo7mlrYBH3vDhg1Y0jOSauxUUlaJUrBT3exg/NiRZKRZOHD/SZx8/PSkvtdQIxaLk26KNxyQSqWUlRQA8R4a5iQzQ3/vGA16DIOYM0kkErIy01Gr1WRlpLF6Bz7gvyUYDBEOhwmFQjS37Dmf/UAgyIZNNdTVN7F2/WbWbdic1P5zZv+AWeXj9nsfQaVSkpFu7rM83qDXccWV19Hq0eNz1DH3py+2eU78Xpk3bx7iqIPLb3qEa6+7EYlEQkZ6GqMnHMph49N58bV3dutzw+tx4fIE0CVpWWTQ6zjhjCs56tiT0Wm3n3FvMhooLill1JgJvPr6Gzs73AHTExyNRqP49/JA6c4yrIXZf/7nZU49/nDee/lR/u/sk3jwsee22UalUnLVH87igTuu3QMj/P3TEzlTyH9N1ddoDBx+0Ai+mvkDDqeLqpp6Vq/bSCQcIRTafZEOvz/QZ+SnLyQS6bC2MohEInvloiDF75dYJIQvEBkWDXvk8t4LKnNGARWl+cyevwTgd5E5G4sEUGt0FOQlFwnfE0ycOIZH//Myh0+bRt2KD/G2LeSdt17io89nJn0sn8+P2zP47Ivq9YspLC6jqKgYg17HyMoyiovyt+keXpifi95gxm7rHtKIvNfnp6ykCIMhuaYyCoWc9nAxs1d7Ofq40wkHXInzWBGzohCHeeDem/nsvWeQRt2YNDFWzX6N+++7M6lF5HAgnjEbRjoM7iW7G73RgsPWSXNLO3a7s5eXMEA4NDwyZgtKRrF/hZKa6t52BtFYLCm7AYWpjA1NuydAI1eakItD1Dc2J3UP+W3Gudfro66+ierahl3a8MTvD+BwDm3jXJ/HyabqZibt13fjQbG+jLk/z2TmF2/zzjtvEwwNvImg192NWKpJys5Ib8pg9i/tCAhkZ6WTnmbe0jhsWC83hzVqlQqdVoPFZCIj3bKnhzOsEIvF2zzrk0GtVpKZaWH9xm1tXCB+zbZ3dBEKhQgEgrg9Xlpa21mzbhNr12+ms6u7z3vPrs768/p8NDa3EA6HcThdhMNhgsFQ7yqmHe3vcdJVOx+frAyVWkVxYR4W8/aDy9mZ6dx1x52c/YfbGVek4MGHHsXr+331efgtoVCIhT9/SmHFgWi22KFAPDvbmF7IxMkHsnnNfLp3UyPibquNtevWEQzFkgqWQdzOa/KEsdx245U73E6rUVNZXkJmZiZu1+5r8tba1kEwGKK9o2ufbzo2bJ+UVruD9ZtrOObIgwE4/JDJtHdaaWzuHe3V6zSMG12Bcph68v1e0GhUyGRSpFIpEXUppx1ehtT2Ezfedj9ery9hExDeSXExGXHSHwgM+OYklkiG9cXuDwRobG7d08NIkSJBLBLAF4gOiywTg15Hbk5mr47KKpUy4Vm4o+Y6ewvBYIiMvaTUsidQd9QJ5zHxqD9y5sX3cO7x41mwYEHSx6qtb6Squm5Q5cSRkJewq56wvCjeYbYgd7vnq0wmRa1Lg5CTmrrGIRHza2obaWxu5cApE5LeNy8ni2suv5CvP3oFhVKNRBzD7w/g9njx+nxMn6DD5vRw1sV3c/4lN3PpH//Ghdf8nWkTs3nh5Zdp7+ja6fHvLiIhHx5faFhk3+9uDKYsYqG4GNfQ3LJNg61QKIBoGGQS69NKaHVrWLHoq8S1EYtFiUSjSYlqIrGc3RUnU6p0SEVhbDYHNXUNA76HhCMRvF4ftXUNvPTqDDq7rEBcoF23oYrmlnYamlp2amx+fyAxHq/PR3NLO7X1jXRuaSQzVDQ11lPT0L5df8xzzj6Pi6+6g+knX4bYX8/ipSsHfOxIwIkgSy4zKz2nkp9WWLnj1mtQKZUIgkB6mhmRMGyXm3sFxUX55GRnbFNmnmLnMBr0FBXmsWlzTa81YkdnNw1NLfh8flrbOmjv7Kato5Oq6jrsjt6CVX1DMz6fPxF0icVibNhUs1MB5/6w2Zy43V6aW9tpam5NfPZAM+Kd9m5WbGyjvLwSvVbbrz1Gz7NbIlNyxHEXEPG20NbWOazX1TtDLBajqqae7o5GykfG53dbW4yZjAaKK/cnFrSysaoGj3fXWzt0dlmx26yDEmaNBj0ajWpA92FBEFAo1IR3k397IBDE7nBSU9fAl199xa03XbVbPne4MmxnyR2dViwmQyJSLwgCGWlm2ju7ycvJ3MOj2/ewmE2YjAYAfH4/Xt9JzHh/JvZAY/w1n4dPPvmEdluQV599pM9j+Hx+JBJxn9H3zdV1xGIxZFIpZrOxX7+gWCyGL8mM2Wh092WkRqNRIkk0TgoGQtjtTla7N2I2GkizmPa6DrIpfl9EIwE8/uGR5SaRSLCYTRgNeqRSKYIAcoWK6uY26hqacLk9qNWqQXckHw4Ew+G9Zvxi8a/38YnjRwOgsxTSNu+npI4TjUYJbqmyCIXDSTdnifhtLN/YxaHTDyUzo39RO7toNNGvP8XpcuP2eJGIxYQjkaT96SCeETNn7lzUag1lJYVJ7y8SicjOzsDt8aJWmJFJBFat3YBcKmVTg4vJR1zG0WenU1yYB8SbJFksaYyZcBiffvUDrW0dpFlMe8VCvaurA5cnOCyy73c3adllRP3dzJ75FqVjjkAkEhGLxQiFwshkUkKBIBLxnn/WC4JASfkYVi6fw9JfVpOVlU52holIRNipBl27koKCXBzuAJ3tTaRl5FLX0ERpcUG/Zc2PPP4Uyxd+R45FgkEt5pY7FnHtFX9g3JhRqFTKhHianZk+6HPW7w8QDIUwGfW0tnYkMtkCgSCNza2J5piDxe3x0tDYzJpVK5Ap1JQUF/S5ndlsZGRlKVCKPFDNX/75H44/5rABWUiEAh4UyuQqAQ7afyJvvPBvxoys+HUMJmNSx0iRYnchkUgoKylDIopSXduQeJa3d3YRCUdobGpFEATsdicx4hGnn+YuxKTXII66mPH2+1x17S0IQnwO07N/OBymqrqO3JzMHWaiDpRwOEw4HEEmk7JhUzXBYIiFP3/Cm+99Ro5ZjDcQZXktXHflRZx64tGUFOXvcA3p8djZVN3M6eePJT8ve0C/k0IhJxKJMmr8Qeg/f5tlK9aQlmYe1PxpuONwunB111PdaOeykZVUlBX3avKl1agpKi4l3aThh+9/IDM9bVDzwGQIhcLM+nEmpeUjkn4u9WSWu1wDy6jWGwyEw7un8mXegqWcf/lNfPPhy/g7VyDV7NyzcW9n+M/oh5gPPvuO8674M+dd8Wf+7+o79/Rw9ipEIhEikQi1SkVampnLrriWkbkCP377MZ+/+1+w/8IX3/zYZyZSNBp/6LVsJ7PN5/fj9fqwO5wDy36LRfH7g+g0A+tOKpHIEIjt0uheMBjC4XTh9niprm3AZh94GYB3S8ONSDhCR2c3GzbXsKmqlrb2zpTFQYo9QiwSwOsfeHBhdyAWi8nJziA7KwO11oDf58RudxIJR/b6Ji6hYGi3dnfdWTIzLL2E5MysQrzOTmz2bRtWbY/Orm5WrV7P5upa/H5//zv8hnDIT0NzB/tPHodyAM23Dp06FaVcxIJFy3A4XbR3dtHQ2DKo50I0GsPvasYbGbyYrlQoMJkMjKgoRykT09Vto6u9nrpWF4dOPTghyiIICdE6LbuEaMBKKBTG6dp9nu47QyTox+MfHn7Vu5uJ+43FJhTx7ax5vP/mf1m0aDF19U1sqqrF7nASCgcQD5N77H133EhuThaz5syn22qju6uTcARESQqzIytLOfrIqbtolL+i1agJyfJZ+P0Mli1dgM/np7Fpx5VHsViMts0/c+P1f+SG2x/jsusf5PCxShZ99zw3//kuOjp/zWitqqkf9NiCoRCt7R2sWb85Icp+8P4M7v/7Y3R327axtEgWp9NFMBiirnodRkvedhtG6rS/3p/GTTiQ8nwTP/48sMqGcNCPRqtPalwymYySonwkW13rgiD06V2ZIsVwQGvKorI4jcYtWfLhcJiamgYA/vn3ezjt3Mv4ZcUaZs+Zz+eff0Ltktf47J0neOWVV8jQuPng4y8IBkPEojEam1rZtLmW+YuWAdDU0o5jy7W6MzhdbjZsqmbdhiqCwRAvvvQCs374kuOmH8KqWj8XX3INV50xhp++fo2Nm2sI9PN5/378KRzuAAftP2HAwd2KsmJGjSgjPSOdsaPKee+jLwnvRvvCweJ0uZOqkIrFYjQ0NrF26bfUdsWbHyqVim3uYVmZaRizx7Js4bfY7M5BV2FFIpF+M27bO7r40+130d2yiQsvunpQnyORSDAaB3Y/NxiMg2qsORh+nLMAvz/Alx+/xuez1rD//gfuls8drgzbWXJ6mokuq51wJIJELCYWi9He2U1G2uAbBQCcefJRnHnyUUD85jv1hIuHYLT7HmaTgdyCCtSyEG+//wmlRfmcddIxvDvrNRoaW7bpGOgPBAiH4yVn6RZzr0W03e5MWCGEQmE8Hi8Op6uf7LEoXl8A8wA7+kokUkQCWyKOQxeP6Oq2EolE41kR7R3YHc54GV8sRigUIn2A5+vTL7xGMBjm/845FYgLtN6wD6/Xh8fjpbgoPzWxTbFbiYYD+PzhYSum6E25SKIbsLet5cFHnuGue/6OL8+fdNblcCEUCqNU9C8uDhfMJiMmo4GGphbSLCay0nWY9RJWrl7PYVMP6Hd/vz/Ap599yrefvowvKOKXFSfy0AN3JDWGlpZmfIEIY0dVDmh7sVjM+MlTeffdV8jLzaa0uJBwOExnlzVp775QKITP3kREPPhsGEEQyMnKQCTAiEIVzvYNfPfdN4TFRnKyMmhobIlP/g16BEGg22pDp9OQYVTw32ee5bxzzua4ow8b9OfvLtraOwkFQ8Mi+353I5FIeOLh+2hra+PVp+7g+eeepKysHL1OS1e3lXAoiEQyPO5ZapUStdaIu7sep9ONFB9efzhpf9DvPntjt81Xrrvmat548x02Vr1Cc5uNU085cYfb27o6CAQjZBWMSsxDT/m/uyBkY8X8T3n1qTtIM+tZ0SDjgvPO6TfzbHtcdOWtKOQy/nLXTcxfMJ+2+lW01y7H2hKgo6sbdYOKrMz0pEtSe3B7vISCATasWcq06ZdtV1yRyaRYLCbUSiXNDS7K8nR0WwfWSTwQ8KGxDCz54befqRpAoCxFiuGARGEiJ02VCHTOnjOXvz5wF0qNmQkFPtIONPH4v//B+GIlSm06aCdy902XxZ/JzWt47dVnef6ZR2lu6+Liy66jbu13/G/Gjyw5+HAuvvBsausakUgkjKwsHXSFS0//llAoRP3GRbTVLKJiv2M5/rQzOf60SwGortpIeY6cJUtXcNCUCUjE4j6D/eFwGE/3Rk48+XQ06oFnu/bc07Mz0zno0KNYs+5J3v/wI668/JJBfafdhc3uQCwW9WvX0IPb42X2D5/zxVeL+Ou9f97ubyQSibjqmut5/rFbeeAfj/Hua/9DMwBNIhAI0tLajtvrZfSIcmx2B+FwZIfjmzP7B8ozQ1xw+f0UFpUM6HvsDHKFikgkLszGYrFd9jz3ue3oQ6s4/+h8qtYtxBFM47STjtkln7W3MGwzZk0GPRWlhcz8YR4As+YuId1iStkYDBMMeh0qjY6QYSq33nYft9/1N0oqpzCyQMPyVWupa2jC6XLT0NRCdU09HR3ddHR209TSRlVtfa9sgRWr1vLA3/7Oo/95jvvuuIonnnmJti0+bD6fv89splgsis8fHHBnQolUgkgEofDORS17CIfD+P0BurpttLZ10NFpxePxEovG6DFYCwZDA5oAh8NhjIFFfPzxB7z+xps0/cZr1uX2YE8i+zZFiqEgFgni9g1fMeWgacehE3Xz0qtvYlEHee+jz5PKLo9EIjS3tOP1+XC5PXs8+zAU3rsyZiE+US/Iy0GlVJKekUN+loVlK9bg8+04+zUWi1Fd24CrYwPG7HGceeoxrFuzktokvV9r6+owmy0okhC0Dz/6bE4+YhQz3niNTz/7mNff/oiubuuA9+/hLw/9m6W/rOTII6Ylve/WiMViBJGYcCjEm2++jsPWiSktD61GTXFhHiajITEpNpuMFBXkMeXw89AJrXz+9Q/DvqKiubmV/734BodNPYDMzL3DQ3mokUolGI0mLr7mPsZW5PHN11/wzZcfsWjJCsKhEOJh4DEL8QxulbEIW9NS7r/rBurr6+m2uZLueC4WiXfbc2PkiDJuu+V68ioOY83iz1i9ZsMOs9Pa2xvxhUQJ4VAkFmE2GTBnFHHEqTcy6Yg/0NjuodTiZt6CRXi8g2twU7N5FT/PW8T8eT8z95tXcPlEjDz4D5x42Fjuvv8hGhpbqKquo6qmLulje7xeHnv8Pzz84G202EScfPxRO9w+NzsTo1FPdl452WYZXQMUZv3+AFpN8hUBOVmZCeuzFCmGO2K5gUyzAqczPgfsbFzFxDIlZx8zgg57mJPOuZHJFRrWtiq48sa/ceXll//6TM4exfFnXIu9fSOKWDevvvE+XnszV5x9EOnSOh76+/10W+1xa4Oa+m38aQfKKzPe56XX3+OdGc/xxozXEJv248wzzkAQbRHMBIFDpp+BR5THvB8/4OY7H2TDpupe95eeudX6tavodEQ5/9xzBiW4iUQiSkZNw5BWykeffDhklagerxe/P5AY61A0UHN7vKzfUEV9QzOdXd3YbP1XdK1bv4llC2Yy/fhzmXrQ5B1ua9DrOOXca8hQtPHtj3N2uK3D6cLpcrOxqgaH00UkHMHhcNHe2Y2/n2o/v7MJa8iCQmXcLesEuVJJJByKV5j8xhd/IHPOzq5u1m+sin/PHWwfCrqYv6KVKYedxynn3cSjf78L2TCpINpTDM8V9xZuv+FSHnzseV575zPUKiV33xrvJvePx19g6gETmHrgRPz+AGdf9idCoTBuj5eTL7ieY488hGsvPWcPj/73jUgkojA/l2uvvJBIOH7RxSR68rO0dHVZsdudvcTElvo1/PeZZ1lTZeOl555Eq1ZTWJBLZ1c361bMQiO0U2wUCHaKWLFkDs0tZxKNRgkEgqhUStItZrRaNaFQmFgshlwaw5dE8y+JRIZYJBAOD80itqd7b0/ptNMVL1UJh8NEg24+/PA91jd4+OvdN+Nye8jJytgm6yIWi+Hx+ujo6KClw8n1Fx1Fc0sDM55/AH1GOZa0HAqKK5k8YWxSnXRTpBgKEh6zw6D5V18U5OdxwgX38OJr75Odm8GqlXN5/e0ybr7u0n73DYfD1NY34fF46bbZEhn7ep2WnOxMRCJht/phxmIxgsHQXp1lJAgClRUlfPrTXM4/62SUSgVeny9e7dLeRW5OVqIxl9vjxefz0VRfRW7hYchVEvxeJw6ni5raBvJyswmGQtvN+OihrraG7Oyc7b7fF2qNhtH7n0pz7d/Z/Es9YZS896GCO279I2KxeMDN7vyOFiZMnsq5Z56U1Odvjxe+joslX7z/UiJ7ty97BkEQOPLIowg46vnws5l0dN1A1gD8dXc1Xp+PtrZOtFo1GrUau8NJIBhk9S9z6fKIee6Ju1Ep+y63/r2jUau3VNJkcvCkcj74/HtGFsh5+d0fuP/6o4dNxqxSqeC0M87lxj9vJD/fyxcfPEtG/ljESWZ5SSTipMXcwaJSKrACZ59zHktnRfjrPx7BbDaSl5NNTnZGr4aRgUCQzZs3IpFrKcjLQSwRo1IqaG3vpLvbhiAIHDHtEA49+ACWLp7NrK/fZtbsBUln8MRiMU45UIc/GGP+D2+hypjAHy6Jr18igRG4PM/w7gcfc+2VlyRsrJLBbncQ7FrBsceeSk7h6AH3WjCZjOj0eqoG2IDMHwig1SafMTsQW5kUKYYLIrEUnUZBt83GyjXraWyoJqybyKHHX0F2xVEUFhVyzhX/4MwtfUqkUmkii9BsMoAgIL7oLrRCG1989DrrN0c47uxzOPKkYvQfPM5jD/2ZilFTOPf8i5CIxb3uSQPB7w+wav6HWIwa6jptnHzO9YwbMxK5XEZJUQFenw+lQkEsFuP6G+9k45IP+M878SZ/breXmtoGwpEIFrMRjVrNmtXLQKLFaEjOpmRrjEYdhx9+OFWvvMa6DVUUFeYOOCN1e9Q3NBMMhSkvLaS1tQODQbeNP3U4HE5qbt7Y2Myfb7+VQ6Ydx5WXnAeCgEwu3WasVps9HiQXBL75/G1sHjjkwP37bSyv1WgwphVQUlTE3HnzmTRhDPm5vT17Y7EYXd1Wmls7IBYjEokyZ85sWtvamHbI/sz+8Qs+nLmcTasW9CmUB4MhPA4rMrkGg0GX9PkzGBQKFdFI3A/d5/OTlZkOQLfVRnNrO+UlRdudm8diMaw2B4FAkNq6xnjw02gkJztjm21nfvsDzR029pswnlg0fm3tbQkqQ82wFmYL8rJ54Ym/bPP6XTdfkfhvhULOZ2/+dzeOKkUPOq0GjUqV6AaZl5dNRnoGixfNZcL40b1KNpbO+5qSwjwqMiO8+e7H3PzHKwiFQrS2d9JUuw5l+n6ceuHFnBKw8sqrL/LKjA+49frLgXin3LqGJsQSMRaTkVA4TE6GHq8vMGDTccmWhkFDEYGLRqOJjLAffp5PXk4WBXmZvPr8v9i4uY7SbBkhNNhabImM2UAgSFlJIW6PF5VSgUQiodtqo6mlnaa69Wxo8HHuVechl8tob63nx+++JOqpY/63c/nkfTF/f/iJpEttU6TYGWKRAG7v8M2YBcjNyeGeP/8RYmE2LPmUf776Ub/CbEdnPHu/517QI8pCPKLtcLoQiUWMqizbbeICsTD+QDipzM/hyJQpBzLjk0dYt2FTwpOroaklbs3i8yVET6fDxmfvPEmbPco50w+jvXEdSsFGQ2MT+Xm5bKyqIRKJIpNKGFlZtt3P6+zsIL+g76Y32yPNbMKalUdjd4z5a93cfdMpfPjRx6w8+kjS08wUF+UP6DgaqYegqHTIro9bb7ic0SPK0ahV8cXeDlCplJSMPpyMOT/w48/zuODsU4dkDDuD1ebA6XJvk3lu7WxBLNPvFU3KdhWZGWlkZqRRV99E5f7nUFyv4LTjKvl25dOsWrUOiTR9Tw8xQWF+Lg8/cDuBYJBv3/4LX3y/iP0mH5rUMXZnUMug12F3OPH5A0w+9DROqWngpzmLOPeME7fxHG9pbaelqR6t3tzLay8nK4N0ixmrzU57RxcSiYQDDjoCR/tGvv5mZtLCbMDnobo1yAWX3Ul+fkHCDkgsESOXZ1A0ciq1X83gqac9HLj/ZPKyswbs/Qfwy5K5OPwyJh14FGKJeLv+sn2h1Jjx1Q2wc7vLgzmV+ZpiH0AmV9HW2kosGsNh7yY7Zz8ARo+qwGKOZymKRCIUCjlGg77X2tPvD1BWUgQUUTTOyhtvvcufR1USjUY5+rRraHS8RWf9Mt57V8LxJ55CUWEeHq8PmVTabyDY5/PTUFeD0+3hsuvux6A3IJaI0Wk1ZGdlIJP1PobJbESuyyFP8y3PPf0wmYUTOPHY6YjFIjxKBR6vj/amzRgtOVjMg2/Kp1IqyS0oQyb48fv92O2uAQuzwWBom+8di8XijWC3VFNFwhFUv7mvdXVbcXu8FObnbnPMrcvte/7b5/OzYN4sjpmoYena5XRbj0MkEqitE5ObnUlrewcyqZS83GwamlrJzkzH0VVDffVaDjrybLKy0vttXCiXy8jOykBnymTNL9VYrXYEQSA3OxNBEAiHw9Q1NOF2xy0aWxo388XHryMiSppRyaI5nSz7ZTnZxnii1m+TzeL9eeqx2brIzC7u87vvCpRqEyY1bP7lS977cj7PPvsygiDQ2tZJNBLFZnckxNrfjreqpp6PP/2K1g4rp5wwHZPRQGdXN2kW0zZ/93//5xn8gRgjysvo6OreLaLzcGf4rrhT7BWoVErUahUyqRSDQUdW2TSqv3mRv/79YfYbkUN3dzsSETg6qxl/2OXo5D7effs15i0Yj16roXHTYtZvrueK6+N+PYJcz5nH788Dj82gpuoQWto6ycjKo6ykkEg4wubqOiQSMTJRCI/XP+CMWalEikjEkGTMerw+otEoa1b/QvXi11nsiuD1R0Gs4uQTj2F9vY/Lzjub7z76D//93wvcc8et+Hx+1m2oIhwOb8nmyCIQCEEsRs36pejSShLemBlZBZx30bUAuFxu3nv+Ni648GK++uwjCvJz9nqv2Z6Hpt8f2OcjY8MZn9eN2xca1mUlMpmUcDiMQqnGbDZit+24JD3uVR7vtrtsyVxmzvyWmMRASb6BcFTG1EMPpyA/h2gkSjAYQqncPcJsOBTE7Qmg0yWfnTScKBhxOGMqP+Ct158nP++v1DU0xa93BMQSMeFwGJFIxBcfvc7CVY3cfsf9jB1dgTVDz3sznuHxf93LWRdcw0EHTAHiE/j2jq4+g1KxWIzOjjZGHZicaKRUKiguzOPaP/2L8+0OCvOyWb30R5avWsshB+64bK0XkSAypX7AGbb98ccrLqK+qQW1Stmv75vRoGfc2JGMHj2GZcuWDQthdu36zWzcVM3kiWNRKhQ4XW5s3W0sXTQLtS4dbRJedr9XCvJzMOh1XHbR2QghG6dMH8e61b+QP2r7wYfdjVwuQxAJKBRyDGWnMpnaePBrmCKRSCgtLsTr81Fd20B6RhZLVsxhcX4mU6ZM7nXv8AcCOG1tZGT1Dr6IRCLkclnc81WjjgfnBIGc/DK+X/BV0h57Lkc7VmeEgoJCJBIxcrkMnVaLTqchEokwYfJUtIZ0Vi78nNlfv4TVFeK2G6/s97g9pcg/zPyEwvLxCIJARpolqQCizmDB41zX73bhcBCrzU5hwe4RAlKk2KNINIhca/nsAyvNLe2cOHUUIypKsdkdWMwmxCJxQhD9LQX5OYhb4tn3Rx59IuMnHczY0ZV0dnXT1iHmqiuvZP2GDcz95kUuvOJn5n77Pj5fgHAkjNlkRK1Ubjcw09beyceffYLTE8VoNCISidDrtKSlmftcP+VkZdBRMon5a5/mkpFGWtd/w4sNG8jKziWEiuMOyuPHOYs5/5JbdjrxYOy4/dDptLzw7BOce+GVfWZEbk1nVzftHd2EIxGyMtIQiQQsZhOxWIyvv/2RB//xEIJUy7WXnkVbwxqWb7Ly5ivPEQ6HcThdNLe2E4vFq3NUSiU2myOR/dre0YXT5SY/N5uaugYy0i34HS0smf0RafkHMV68nucfvw1fMMaZF/4pkZgRCATZsGkzy5Yt55OWLnJVzSyvjXHL3YdTkDewaiyDXkdR6Sga1v/E2+99yLlnnY5CLsNsMmK1OXC5PCxZPJ8fv3yVSBRanUr++McbsbdvonrF53hjJowqB11d1m00DavNjs3mwOvzkl6w+5Kz1BoDNW1BZnz0E25rHUuWrWTs6BGsXjGfl9/8jIsuvJALzz0tkQ0sEUsIR8I4nW6aGuqoXf4eKq2Fpx75BrGmgPETDybNYiInu7cd6YETiskt3R+ZTEpudsqqFFLCbIqdJM1i6pUJc9YZpzB2ZBEfv/cSc+b8jCWrhIqyIhYsW8c55cWkW8wooh3887nnKM3V8vlnH2MuPIiKsmIi0SiCICAqPJxDDq7jrVceRanUEIkEMGSOoXzUBFbPf4+5a0O8+/LD+HxB1AO1MpBKEQsCoSHImP1l+UpuuOlWJpdL2NCm4h8P3M3nb9zHsmYThx19Lodt2a541DQaG1/n0Uce4uyzL6S4OJ7ZZbM7EAkiHE4nK5bNp6ZqNftNPJ6szHRa2zp6fZZWq2HCoeexbPPLWG12pFJpvw+/4UgoFCIYCiESRDS1tJGfm017Zxf5udl7vdD8e6XbaiUUivTqrjzcKMjLoaW1ncyMNOytBRiUQXw+/3ZLKV1uD16Pj7k/fsKCeT8yef+pbFq3DIcthEkrZsYLD1I66mAaGxs4/az/4/STj90t38Nht+IPRfrNlBzuaHVajjv5Qj547R/MmbeAmR8+RSAUo90WJqd0Evfc9kekUikb1i7l8KNOIS3NhFKhIDu3gIrJZ3KkKcicH15l8/LPsbvD2D1SbrjpVuRy2TaR9JWr19PR2cX10w5Kepw6rYaC3Gzyc7KQSCVk55WwYfEHvD/jvzz74tuUlxb3e4xgKEhFWfGQZVVLpBIqy4oHFKzqydLIzC1j+RYf/j2J02Hj1ecfIejpYuanIorzzNis3XgCAp0eOU/951YMhlQmhCAI6PVaFAo5SmMB+ekqNooFcnOSs+PY1RTm5ybO66kHTdkr/EJVSiXFhXlUTjiO2YvWMvubV3n4iZeoWf0zEK+U+N8zj+NqX8PhJ9+43eNoNepENlzliJE4HTOoa2iiIC9nwFnfHkc7VneUrMw03B4vOdmZvRbchfm5CIJAeXkln8z454B9ousbm4lFwngcHZx0/MWMrCxDIknu/pORlYfH1b+n9rp1a7C5guTlZCV1/BQp9kZk2lzmfvk9lvQwWn0R06buj1wuI3OLTdCOMtoFQUhcJ6FQGP0W8TbNYkatVlFd28CIykpGFd5Mq/1JPvr0K4oLCpAp5MSiMYJazTbH77GB+ua7WaxZ/BURsZ6S4gL8/sAOqyfFYjGTJo7n60/fIRyOUF21ibde/heSQD0/r3Tga8vCkDORY6cftpO/GCiVSm687Z9888F/uOHmP7Ns/vfIZDIcThcatWqbuVFnlzUhiPasdYPBMF6fj7p1s5lYYaKs0ML8b56j2S7Fao9Xy/r9AfxeJ19+9i6zFm7iXw/9jcx0Cz6fH5fbg0qlJBQKEwqF2FhVQywao7m1nUXfz2B5g5yX7riYYDCE1WaneuU3PPXU41xw7hlUV1fjCSk5eKyFn77/gsXVMKVUwjVXXEFRYV5Sv8XkA6bx2TtP0rDwSwJeJxdccCHNLe3YrB3M+vIVqqurGT3lZAqKysjOysBsMhItLSImS+OAEy18+e4TNLe09QqEhUIh6qrXcfU1N1GeK+P2827byb/YwNHpNOSXTeKnpWuYNlrBD7MXULNpJZ+8+wKTyvJ5a8bLHDB5PLEYCRtHALe9nS8++B/WcDZXXHknS5cuwdtdxYZVn/NiOMyfbryyV2a1EAuRkV20277X3sDwXXGn2Cv47URVKpWi0qZx/qW3c8pWafmnnXE+GekWPF4vFZNO5ZRN1bz6wqO0uM088vD55OZkIZfLiEQiWG12TjvrDxx65MkY9DqqqjaxefVsNi95F5MygMftYPkvywiFgwP21hKJxEgkImx2ByajPtEBfTDZTmuXfc/R+2fgkpTzxG3/h0QiYe5aL0cdf2BiG0EkMGH/QykuLuS9N1/gnZcf5IgT/kAMgZzcQqLhEP+492q8gSiWgslcd8pxZKRbMBn1OF1uuq12fH4/sWiMUWPGY9ZJ6OyyIpFKyMpM22vKQv3+AIFgMP6dun9tOLF+YxVOtwetRr1XLPr2NdweLzabg2g0ilw2PPwP+0IulyUmUOMmHkJempz2jq7tZvksX/ELLz55H6GonFPPv5nRIys45qQLEu//snQ+61YuIEvRxOP/fX63CbPNTQ1EopK93oNTKpUyZsxY3hRn8OPnLxATxFhdAS468whmfLYEl8tNV2crdY1tHH/2WDLT0+KVEoLAReefid3hJC13P95583liEgsmRQ1ffPMDRQV52wizna2biEkNFBUkN4HuwWIxAfFO9MefdgXvfPAJ6R2d/Dx3MRKxhKLCvB0GjQKBUFIlxP2hUauSDlJNmDiRV2e8v8NgxK4mFovxy9yPqG7o5JGHH8ba1cLCFbUccuwYykuLEQSBkuLk7CZ+zwiCQFlJIWKxGJM5HYVUID8vu/8ddyP6LU1ViwryqKlrRCTaO4KnapWKww49kJzsR/G3L+b7q/+TuDaWLppNR9MGzvvDbRx55BEDOt6okaMoLczi5zmLOPaoaahUyj4z5npwOF2IRCK62urxh6XbZAf1YDTq6ey24vX6UKmUONxx+4/+qoicLjcRTyvr65z8sSBnUPPXzJxCYiEX0Wh0u/NIl9vDql/mo9RYUn6xKfYJDjp4Kvc+9Bzv3PdH8vOy+y1h3x6ZGWm9mp+qlErKS4oIBIPU1EQ56sBSNq3/muU/d9PQGeTEU85DoTZww9UXJ57/0WiU2romgu42qpa+Qygc47LLrkCv0ybuzf2hUatxut2UlJZzyz1PIhFFUbzwZ+b80sAT/7lnyK7rrOxMjjvzGtZs/BNzFyxl2iH709DUgoBAVmZa4neMRqN8/PGHrFv9Cy1t7Rx25Cnk5GQxdsxIouEwVRtXEFGNZtLhJ7KqYQY3XXI6/3zoHqpr6lEo5FSv+o6ZP8wm1yTw3Q8/ceJxR/HVzB+oKC8jLzuDx/79MIGYhkMmj+KLb35g0pSD8Las56zTTiQrM53OLmvcE3jaafgCYWZ+8S6VlSMxxJr4buY8Ro49GLl0AeuaItxzyP5J++VmpKczcfrVHKeN8MUnb/HUU09w80238s1Hz7CmPsptf3o0MYdVKORkpqehVCoQicWYTQbmfq2hpr6RKZPGIZVKCQSCVNXU88nbTzFhTBH7H3w0kyeMHZK/2UAQi8X895G/0NnVzRfvPUF7awtKvwuZZT9OPf9CNjxwKxs31aDXKXn+xdc47PDDqa9eg615BbaghZtvuQGpTMYBBxwEHIRCbeDH2bNoaT1li+1HnGAwhMVs2m3fa28gJcymGFJ6FqqCIPTKEJDJpGRlpsctANZvYtyBp1Bf9zjj95uIQa9LlPGLxWLSLGbsDlfiIVVZOZLKypG0tbWyZP63XH5qKz/PnEGHU0zaQC9oQYJCJqaurgmVQoHL40WjVlJaXJj0d2xtqkKatj9Xn3FK4rVn/vcKCoUclUpJLBYjPc2M0+kmEo5w8TX38b/H7+abT1/FaNDT2O7k9j/fTTQW45Jr7qWkuDCRKSeVSjGbjImHmc/np629kxHlhbz0zAMcf/qVFOTlDPjhvKfpstro6vo1OyMaifDZl18yeVwFf/nbP3jgwUcoLy3EoNelbs47gc/nx+f3YzTohyQDubOjjVff+oSTjj9yr/E2VqgNpJs1rFq3kbzcrD4zGZfN+5qYsoDbb/1zn7/ThEkHMWHSQSye/SFV9vXYbI6kvP8GS119DRL5zjVOGC5YLCb+/Oc7+fLNB/jfJ43859G/k5udxvrVC3jtqVtp6Q7Tag0xZlQF6WnmxH5KpYJQOExeXg633fEAALVrf+Tef82grKSIqy+7oNfnuKzthAXNoM/3rQWWwsJ8Ljj3LD58vYY1a9YyecJYXG7PDkWYQDC4080utmYw32PUiJHoNHJWrFrHgftPGLKxJEMwGOKX5b+QXzoevTENvTGNorJxifdFYtGwDu7sCXruTXLLKL5dNoMHHh2zh0fUN1qNmlGVpbvVM3ZnUauUCCIRCkMBpVlyGptaUKvVfP3lhwSENAoKi/tt6NKDTKFlbGUhcxcs5aADJmJ3OBGLRX1e96FQaIt1C3R1tYF4x8JHeWkRDqcLpVJNS3dcmG1sbqW0uKDPe4HN7uTRfz/JiOwoNW0BCgcZkMrNLUIhg7r6pj79tIPBENWb11G7cRnp+btPCEiRYk8ycfwYPnv3BXRazU4/r34resrlsngSQVEBtkknM+OT2ykacRDHTdBjq5vDm9/Wc+z0aVSUxSt1uq02lq9cS1v1AqraxRhNZRyc5PM9Pd1MYUEuDqeL2vqmxOu1bSGystITa+6dxaDXYbcbyM3OZO68BchlEr7+9ieOnn4YMWKYTcZ45u/XX1K16jumTDkKIRpg/dqfaNvkZ8GPcnQqMS02MZdeeSyZmTlcf90fEYmgIMvAvJmv0trSRJfTz4mnXoRZHeK7r99n/pwfyFB2MfOzIMFwjPw0KWMrKmla/RHF6XpqVnyJxxfipPNGYjIaUCmVOF1uQqEw0469kLEHnIzRoMdqtTJn/mKOO3Y6n35u5PjzJmzTvGugHHX4VNxeLxdmFPHik/fy1L9upLrZzR33/AuDXpeY72Zm/JpclZluwWjQk52VwbwFS5g8YSyV5SXU1jeyaOF8Vqyv5+Y7Hktkbu9ORCIBkVhEVtFENs98h/XLA5xwxpWYLRkU5aXz+dv/RBzzIw3GWPBtLV1WN7qscdxw49UU5MU9jDu7rLg9HsZPmc7yxd/z+lvvc95Zp21JRDMQCIbQ6/cOPWN3sffMtlLsFYjFYsaMqqC1vRO324NWo0EqlSQyLkQiERWlxYgEEfljT+eQg6f0WZqfl5PFpupatGo1Xp+fUChEZmYWJ51xMV6vj3UbNnPvoQcO+OEiEktRysVEwgFcbg8Q71bpdLl3uPj+LZFIhObmZo487QwAsrMykMtlW4ytzb0EU4VcTseW7rdX3PAADY3NlBblM+O5+/n3o3+jyxmmuKgABGG7QqtSqSAvN4vLb/g7X372Hp+8/QwT9xvHmFGVAx7zniIajeL3+yEW4/VXnsbjtqOShvA42phZN5vyXDkzv/2B7Mzz8PuDSKXSvUZwHk60d3QlyoL8/gBmk3GnJ11ff/YWnW4Zjz94615jNSEIAnqdhpqaeqpq6snPzUYul+Hx+oiEI3i8HtavWcr4yWfFA0caFbnZWdgdzkTmUWeXFZ/Pj0afTcz3IzX1jUzcxcKs3e6kubEeper3UeqtUauwpGeTVXIw3sDbpKdbyM7OYb/pf0QuEfjyvSdYURPcZqKZnmYmPc1Mc2s7XV1WxBIxRSMO5IEbbdzy8BOcdtIxvYIEDkcXWt3gm1dsjUGvo6Wtg3GTp/HpB68zY0aIU087g4MPmNTn+R+LRvD7g9s0p9jdSGQqyotzWLJ81R4TZn+eu4hFS1dw8jm3kpuTicfrw+v1odNqkckkKJXKIfPh/b0x7dBp3HbzH4etj6cgCEiHscd4XwiCQH5uFsGAkcpCPes3VZObacFnr2fk6JPQ67QDth8RSVWMqcjj01k/8sjfbkCXcwAXnnc6I8pLkMmkRKPRxLFa2zv5/Kvv0aulOJpXDyibXq/TojcY8TdZaWhqobGpJWGjkGYxJY4dDoeZ+dUnGKki5JYSRTbouZJSqSA7K52f5i5Ap9Og12l7/Y3dHg+NG+by6mdref/NWwf1GSlS7I1kZaZTUpSf8HIeavQ6LZMmT+HOex4iOysDkUiEED6OTsffWbNuY0KYXbnoGz54/Wl0KhFHHXcFx0yfRllJYVKf1VN9pddpKS8tRCqRsNlZxLSp6iG1J9GoVYysLKWw8hBWLv6G9coWmtcu5u/zvqB8zGH8/S+3UdfQzKbVc4iqSpl25MkAHHrUWXg8Du74841kFE7iz7dfxfgxI5HJpDQ2t5KRZuGMP9zBq6+/TX7h4Vx+6vHI5FIUcjlKmcDs79/ju1/cvPrMw3zz9WeEZdmcdNapLF+xgoyMLNqba/hi5o8cMDnexE2hkCeqEWLE/74qlRK5PJNTTjyW/LxsLrvkQsRi8aCzidPTLWSJRFQLIv7vqgewO5ycZdBTWJBLdlZGn83Ke+bBI8ZMYtV7H7BxUxVyqYils99nyU9f0dIdpay0cEirswZKZkYaJqOB8pIiRDITVpudC889DaNRz7QTr2H+oqUEnI0olVlceP4Zif20GjVmkwGxWLzl+6WRl5NN+0nn8PATr3DA5IkEQyGs1m48vmCqKuM3pITZFEOOWBzveLi9hglyuYyC/BxOPekYcnMy+8w+UCjkjN0iPkYiEVxuDy63h0AggNGgQ6VUJC0+zV/rIOuH1/jUrcZjb6Js3FGcc8ZJjBlZ0ec4u7qtyGSyXsLtxvWr6LQHOWj/CSgVCizmuBl7LBbb5qYrEokYUV5CS1sHdoczkb5/2oW3I37vf7z/zSpkMlniBrY9JBIJGrWKw6efREf9Ct5+72PGPHDngL5zOBxGLBbj9fmGNLtrR/h8fhQKOd1WG3+59zYCXht+n4cJBxyNWKrgwCPLWfbLSgzyDpo2zuaH77QYLLlIJPsPqTC79fkXDIbw+f2/S+HX7nASi8VoaGrB6XLT0dmNcsv1YTGbCIfCif8fCLFYjHWrFnLokacjEe9djwiJVMbcWZ+w/+Tx+PwBJGIx4UgEYjFWL/6Khi648ejDSLeYf41eK34VCI2GuJVIVmY6yxd9w3Mvv8XTjz3QpzgRDoexO5wolYqdurYa6jayculsTj71kkEfY7iRlZHGwUeeyZ0BA5P3G4tKpWTawfvjdLnRXv4Af7RsK6j2XKu52ZlYTEakUgmhUBhiUU48aBHf/zQ30eQqGo1it3ahNwxNFoFYLGb0iHLysrOoanTRVTuHV197jYL8XLRqNWq1stc5EI0EcXkDe2SyvDWCWE5ZcQ4Llq5MukHRUPHZR6/T5Qhz7PRDsZhNWMz975Mijkat4uwzTtzTw/jd0WOPZDQa+fKzd8nNMLC5OcxDT15GWtrAT1BBEDFy8kmM++IzNDIRn33+EeNHlxOLRhFEIlRKBTnZGSjkchZ9/wY/f/Mx+5UqcXoFVKqBfY5CqcHpqGXdL7N47rmnOeSYSzjysIORSMSJKqKOzi6aqhbhk1cy4YgjuVi/MenfZGssaVmsXr2ayRPHx/9/y+fUNTTx4ftvMvfHTzn1lFMoTVIMSpFib6Ywf+A+0oNFq1FTWJCHyagnGovR1QW5mSYWLV3O2NEjCIcCfPbJexSNnIpep+OEY4/Y6aq1HpH2lWefQBAJQ/4dRSIR5533f2xYv5KHX5jFUccczx9OyGH2j5/xxtsfU1max9Il8zEWHYnRqEcuk9HW0YVareexJ15AJpWiUMgTAdwe4Xi/cWNIvzU9/iGCQEFeDlKJBO/EI3j9zRnYXBG0plzOuuBaVColErGY/caPx2QyUJCfy6GHTUej2XZunpudidGgQ61SEY1G6eq2YTIaEAminRIJe6qEK8uLkcmkeL0+1GrVgET1A6adxitvfslrz/6Ngw+eygef/8yRR53A41dPH3QG784iCAJyuYxYLMZ+40cjCCR6BUw9eDIF+XFvfLlcRiAYQiIWo9Wq++wdI5fLGD9pGsdOW8xbz9+Ph3Tuv/duvL7gXm/hNtTsXavuFHsVO1okajVq9DrtgMrXxWIxBr0Og15HKBQiGo3h9wewJOkBVDlqImvquym31CCRRXj/vbcpzI+Xg+l1WkxGA7FYLCHE2uxxwcvt9pCdFc/qrdqwHKkqbZtGEL/1P+xBKpVSkJeD0aCnrqEJjUqFRCLh3Iv/xHmXihhRUTKgh6RarULotjJyzBReencmsdgdO/x93R4vLpcbt8eLxWSkvrGZNIt5m+zkQCCIWCwa0lLFtvZOItEI9dVrcFmbOfnMS1Bq0xhZWZ7Yprx8BIIA78x4kaqNK3HN/4xNm2u48083DFmTGIfTRVe3FY1aTVtHFxKxGP3IoRVmQ6EQIpFoyBoAJYvH6+WyK68jQ+3A6Y2itIzk5j9eAcQFcrvdCcTLiWVSKUUFeTsUaCORCPX1dTS32Tl1RDlplr3LXqLZm4EkvIS//vUv3HjDLej1Gn5ZuRajQYvT2oTSUEhZceF2fwNhS/a6Xqfl+GOmc+WdL7Fw8ank5f4/e/cdHkXdtXH8O1vSew8hJITee1NQUUAFRRG72Av2rs9rfWyPvXfsvWDHiqAUURAUlN6kl3TSy7Z5/1iIhISQuiHh/lyXV8jszOzZZBM39545vza0S25T8bPqdDpZvW4Dbpe7Yl5UWFhIrV/w7gnRTI+b2d+9yZbCaB4+YWwjfRWan91up11yG846bXxFV2lUVASBgQHExEQd8CqFPZ0NVquVpOQ0ktqmMG/eLxXB7M70DPJyM0hJa7xLwPd0UR9/3BjWrornyw+fZ/rMOQzs1xvDYhAVGVHxB0NxUSFlDvd+f+/7imGx0SG1HXN+n0F+QWG963E4nDV2tbrd7v3+jgu1FzDmxPM0iqae6vo6RmqvTbsOsO43Vq0OpffAkYTW4eqoPdq178SvK8vxDwzijBOG8cOnz7Bp80SOG3MMOaXe7nCXs4wff5pNnwGHMWjYkZSWFNN5V2mtzm8PjCA+MIvvf/gBl9vkz+x4ni8AAQAASURBVN++obxgB0OGj+KYow7H4/GwdME3fDXzby694iZSO3Rj0JDD6/w49hYc2Zag/MVM//xlFq8t4JGHHqCsrJyS4jy2rf2Nw0afy/FjjqmyQrhIa+arqwPS2idXhFFt2ySwKK0H8+b/SV7eCWzfvIKl6zJ55PH7CAz0p22bhEarKygosMnmhUdEhHHpFTdz2ln5BAcHYbfZ2LpxKT9+8RI/Wy2Uuzycc+xIUpK9YV5CfOzuWbpbCQjwdvHvKyw0lMLQYkL26sAESE1J5ubbnyA0NJgundLI3ZVPWFgIoSHBFVfBlpSWUlxcst969zRTWCyWiiaNxvrb02azkZKcRLnDUeuxOdFRkdxz/0N89M5LrFo6j14DjuDUU08hqZqvi68ZhkFaajKe3Qu0gzfD2TOSISw0hIAA/wM2B7Rt155xp13DqtWrmPX1qzz/6E2UO1zqmN2HgllpNnvebamLPf+D2nPJR128+dKjbNm6gwceeZrhw/ozdOgmPv7gNTp1eACP20NhYTEulwubzUbbNglM+24m3Tp3oE2bBKKjIrHZrGTvWIs1KK7O7ziGhYbQs1tnLBYLeXkFbNqyjaQ28bU+T3hYKN27dMRT0pviVz9kzboNdO3cAbfbjWFUfgfU6XSyfUc6paVluD0mRcXFlJc5vGMFdtu6fSclJaU4nE4iwsOIj41plMtNnU4nfy1fRVJCLB+/8xzrdjgYMOhwLFYLFsOCiUlifCw70jNJiIvljEmXgmny3SfP8dE3X3LEEYdzzBGHVQQzhUXFBAcF1vh18ng8uN3uKi9eHA4HhYXFFBWVkJ2dw/qNW+jUIQV//9r9j7I28gsKsdlszRbQLJw/h5TIQkaPv5zYCDtffv4uLz5+C4Z/NEGBfgw5/FisVguRUfF06tiewqIi/P33H6BkZuXwz7qV5BU5GTKwX4u7BPm5x+5l/bq1fPXZm3zy5j34+1lxOhzYrIZ3gb7jTq5153Bqj2OYNHYeX379HeeefXrFCr2GYbB563YyMzL49bcFBASFMOaYo3ZfbvTvH7Iej4f8/EJCQ4OrvPGRnpFFTm4euZmb+fqnv7n6xntqXPSlJdq3i3jPm2sul6tOnZ2xMdF07TWUhe++R35BIeFhoWxYMZc5f2zl0TMb9/J9m81Gm4Q4/OwD+Xl6EvN+/ID0bWuJTUhl8MABtEmIw2q1sis3i3Knd+Xa5mQYBt2HTMTy3lS++uYHTp1wQp27t91uN+s3bKJNYjzhYaEV35uysnIsFgt+fnaysnNJz8wmIjyU1Hb/XnbvcDjIytrFsDFdWsyClAeb1vZzfzDpO/R4nnrte9569UE6d0qr19faYrHw5huvUVRUQkREGH/9MY8fvnqblQs/ZVcxjDvlUiylm9mWA5ffdDFWqwWLxagIIA4kMq49S1ZlYAsP5dJLbuWzqe+y+Z8l/DJ/Ie1TXqKgMJ9PP5vK8KPHc8r4Y7FZrZSVl9f5cewttdNA7nt6KheOziTQ4WbV6vWY7hK+mfoc3/66k9fO7kNSYu1fn4pI7e3bIXjk6In88MP3PP3gVbjcHuKSutK9a0egccPixporWx2bzUbi7svyu3fpSLnDQVBoNIWlHs656AYG9O1J756VR/BZLBbS2rfb7+tBf3+/audgh4eFcvjQARiGQUCAP0l7BXt73vQPCgxs1k7MPXOFa8swDGKiIjn9nMm89d5UTjh+NJ07ph408/n3baaw2+1VxpEd6HW93W73dhPbbXTq+Ch/zP4A56IFBCmYrUTBrDQbX7/o27P6913/dwMAZaVD6LJ8NU8+/j+GHXE8Hdq3I6mNd7TC2sXTWPTTm/zwhZuUbodzy/VXUVqUy87sItok1m8+z57HGxERRs+Qzjiczjodb7fb6dihE907JTN73gIS4mPZtGU7VquF+NgYAgL8yS8oJD+/kHnzF1KU8TfPvjOXk45MYcaCTfTqPYj3X38at9tNTs4uADKzc8jNzSMnZxdJbeKJiqx5rEJNnE4nvy9cyIev3EtiQix/rcvlhhtuIjwslLCwkEqrnO75d1l5Obm5eYydcAGYbl594z06paUSEx1JRHgYGzZtITAggM4d21d7ny6Xi23b0ykoKqJTWmqld94uuOw6CovL6NcxlPysDZSWu3E4nZx8whgS4mIbFDo6nU6KS0pZ8PtiikrLGD5sIG33swJzU/G4HeRsWUi5fwf69h8IwFU39yEzYwd//fkbv8+fzZwfPyIoMIDiol3sKAjllFNO44arL97vOdes+pvZP3xAaFQSoaFV5yEd7IICA7H7B3Pq2Vezbv16Nm3dSZuEWNqnpHBSYRH9+/ao9bmS26US334QWfM/4/9u+5Nrrr2B1HbJ+PnZmT1jGvN+/ozQ0HB2ZGSza1ce4084jmGD/w0K167fSFlZOWGhIbRPTcYwjIo3ftIzMikqKmH9mr/wD4llUP/erfJd4+pCwvp0548YeRxfffI6P86cy7gxR/D6G6/QZ8goBg3oc+CD6yg2JpqY6CjOu+RWvvrqU7ZuWMmqP3/k3Q+juffO/9C5U3t27NiKzV7zG0a+0i65Db0GHMncmV/Qr29venTtVOvf4W63m1m/LODbH37m/LMnEhQUSLu2bXC53Wzesh2X20WAvz+z5i4gMMCPwMCAimDWNE2WzP+ObVmljDl6RFM+RJF6OWzIEF589nHiYmMIbsAf6clJbbDbbLjcLsz+h9MurSfFJaWsWzaH5b99zNb0PI4edyGdOqQAYLNZMajdm0+HDTuMrXkBPHPbRQzo14sunbtQlJ/J/ff+H9u2b2P9inlsznRx9X9OrOhgbWiYf8LxR/PNJ2/wyIN3kRJRwKdvPYDhKWVrUTzPP/M4Qwb19dnoK5FDXce09tx+33OsXbsBm83G4IF9qp1HerCLj4shJjoSq9WKn5+dtp0OY8k/X/NU/15EhIdV+3qpvuOXAgMDmmwWcHNJTIgjMiKcC849na6dOjRpkN5cwkJD6NWjC5lZOZQOOZmHX5lJSHDLe643JQWzcsjw8/MjqU08pWXe2YCFhcWMHHcJi+Z9y5qFn5Kx3M3qbeWktO9GWoKNTQVxjOiwi1mL5jJrbn/6pdn47pd1XHD+8AbXYrPZ6hVQBIVE0q9XB+bN/5OxY0ZiejyUu1zs2Jnh3cHjZNf2P5n33RuUOO2cdFgEmzZv5eGbT+b6h6Yx97dF+NkN3n7teaJCTNJ3bmFrrj//+b+7KHc62ZmRRVJiPIGBAXg8Zp0uZcvKzmXJ/G/BL5qA6O6MPn44E048ttpLXPf8zzg5KZGkxHh25eUzfNTJrP/nMd55/yNOnXAS2dk5fPzp1xw5Yuh+g9mt23byx5Kl7MrLx89up2vnDgCYpps+idmEx6Tyz84yTpp0O2v//IJVq9ZyxGGDycsvwG6zERwcRHRUBJu3bKdjWmqNYe2OnRk4HE5SU9qycfM2Sgp3Me29e/l9vcHbr72Mx+MhLCSE8PBQn8x6NN3lrNucQ2Lb9qQkJ+Ef4MfmLduJi2/DmLGnMmbsqf/WvmMrO9fO5e3P3qsSzJaWlpGRmU1RUSEfvP4YzuCu3H/flU1ef1Pw87PTvWsntu9Ip1PHjnTq2LHitqCgwIrZg7Vhs9k4acKZLF29lQFRm7nv/vu5/f/+D6srm9kzP2PoqAsZcdhgFvzyA0sWzea+h9fx6XsvEBwURHm5g+XLVzLr5x9I69yHUyeMIzkpkVVr/8HlKOPR+6/H7XQQEWIlMXkIiQnNf7nSwSw2JpbBgwfxypvv0jbWxtYMB7fdd26T/ZwZhkGPHl0IDDoXgKK8dL778i0++OQrrpl8Pls2b8Lmd/AEF8efcDpPP3gNjz/1EnfffiMx0ZEEBgRQUlLK1h07SUttV/G73OPxVFzut2tXLn/8/DZbli/nyWe2c+SRRzOgb0+cjnK+/vozCsvsHDmsH4tmvERJuUluoYsundLo06s77vJ8Fv7+K2EJvdX1KQclf38/BvTt1eDfr9FREYB3tEpoSAhr/9lIZEQ4bducwVFjTqewqJiOaSl1Wkh2Dz8/O99//hb+/n7Ex8V4F58MDiQq1M5TD91MQYnJ8adcSPtGXCDOYrEQGRHGHXf9j/c+/pKkiHCiI0M5LimJ7l06KZQV8SGLxUJsdBTWrlYsFoPIiKZdcLYp7f2m8ITxY+nVozsd0lKa5LVaS1mYuC4CAvzp1rlji7tasS6sViuJCXHEx8WwbNGsVv1Y60PBrBwyQoKDCAkOquhai4wIp6S0lLS0DixftoQFi/7m4rED+fuP2WRsW8HRR5zA4QM60qHLXGZ88QJ/hloJjUhj0hknN9tjsPiF0KdbO179dBrvvfY4q1cuIa/YpNeAkbRrE8P2tXP4au5mBg89mpsvO5flK5bRtbCM9t07ccJhf7B8+VKsRavZvGULqaNOplPfsSz/7UPee/lOtuTaufHG/2C32XA4nZges+KXZ238/N27fPX9XCaccSmHDx1EQIA/YaE1z3Q1DAOr1bvIRY/ewzjmhIv44cvXeCEzm9QYF/mbVvDYL18QGvICh+2z6rjbUciTj93D2jUriA61svjvlbz89AMA5GTv5K91Bdx8+nkct7s7d+s/KZSsnsEjD66lW5/DGTywP3Gx0WzZtoP8/ELaJSft938Q6RlZbN+RwY6dGZimydzZMynJ/ItFa0vp2S6AyVdexd133U1SYjwd0lIa/d1up9NJRlYO0ZER5O7KJ6lNPKbbyYYt6QwfdRSRkd4Xct26dKS0tMy7UJ7DURFAt01OIdhvBHwys8o8ya3bdpKZncOW9Uv4Z2cZjz85mbZJ8fsr5aBmGAZ+fnbapyZTWFRMWVlZxbyrcoezzm+GxMfFcPllF+MqL2ZLxt18/vb9bM1y07HnkYw4fAhBgQGMOPpEEqP9ePTFz1i7biPR0ZHMnfkFP347FawBTF/1J0FBQfTp1Y2f5/yGvyeH/BILN9z8EPkFRRw+dGCrfIHZ2I466hhe/egeZs74joCQmCYPs8NCQ2iblEBxSal3wcr2Hdn083Qe/O9CgvwNrH4Hz0zVHt27csFlN/Phe6/w3/se5PprriI8PJQtW7axfNU6CgYUkdquLW3bJJCekUVmVg5hoSHM+f4tpv6wjLPPOgtX3hpmfPkCG1f0wubJ4+/VO0mM9ue7qT/xx9pS3nrzbaa+cR8zfp5H757d2Lr5H2bMW87EMy5p7ocvsl/7Xm5ZH3uHDQEB/nTv0hG320O5w0F6RhY2u61B9xMfF1OxMOmeS3dPu+hOigq9gW/71ORGX7i0c8f2uN1u4uNjK66gSm6b2CoXSBU52LVr26biqruD4UqcxtI2KVGvb+voUAkqLRZLs6/TcDBSMCuHnD3hjMVioUP7FFat/YeevfrRs1c/ADp17sGsub9x/LFHY7NaSEyMY85yBx169+CSIw5v1u4gwzBok5SKn5nL/AULmHjuDeDYxdI/5/LL2j/AHsG1197EgH69sFgMRgwfTlFRCYkJsSQlxrH0l/dZtN7FrTffSq8eXQDo0qUTLkcR7718D2++dC/5ZluOGtyJIlcQp04YR1RkOKbpvex1f5db5+fn8dOM7xg34UJOm3ACDqeTkKCgOv0PJjoqkkFDhlNUVMSmFT+zduVO0vqfSnTsn/z+x5KKYDYrOweb1YafO4O8nJ0ktEll/Ki+3Pb4V8ycdRwp7dqy4u/5lJRDj26dKCt3kBgfR8e064mM70hexlr+/uNb1v7xGQ7Tn1A/B/NXlTDuxFN57IHbqtRVXFLCwoW/M+OLZ/l12S5OHjeGNct/JSC6N0ePmcAp449h5rQ3+ei1+yn3a88F55/L2DFH1f2bW4PMrFzS0zPJys7F6XQSHBxIoKWcjVvSuWqfuU2BgQEV36c9ixU5nU5yIwPwtxsUFRcT5RdRsf81112DnyeH5Fg79uB4EuJjW/Q79nuEhgRXCsjrM6vLYrGQ3CYRt8fN9f95mLy8QoqKi0lLbUfnju3x9/ejtLQMizGKrklf8Mwzj3PGmeeyYslcotOO5vxzTmPNok9586MpLF8QTVlJEaa7nKCozsQnxBEbF0vU7m4sqVmHbsM4Z1xv/vj7Dw4/4qx6dafVVUx0FDG7F1cPDLyIjdkB9O7ali2bN9O958Amv//a8vf3o3OPQQwbtpblC6dx+y2X0abDYAJKl1PqtPLqq69w/733EHzEML74/GN+mPkL/QcMYfPyOVw2+WqOPeYI8gsK6dh7JbNmz8Hil8oFl51L+5RkXC4Xl5gmkRHhdOzcky9mzuDY0Ucyf+Y01m/OZPy40c398EX2qylCAe8VT96fu8b4PVRdGHrk4YPZum0nAQH+TbIIp2EY2Gw24mO9oXCAvz92u00hikgzaU2B7B66mkZq0lwLZx/MDIejvHUN6agDl8vFiHEX8Mu3bzXqqvTSspimSXFJKbm78rDbbBQWFVNSUkrXzh0wDIPSsjI2bd4GeBewSW3ES8rqoyBnM3fedB6/rnLx+quvVrtPcttEIiPC8Xg8lJaVExoSzK+/zefxZ1/j+qsvI6VdEv5+fgQHB1FYWExWdg4FhUVYnVm8/cojlJR7CA2PYvmGEu6//wEiwsOw2qx06ZiGn58dp9Pp3d9ipbiklO+nvcv3M+by3/serdOMw325XC7Wb9jM5k0b+ejTz7n+6muY8dXzrM0M5arJ5+Pv5w3CDMMgd9sfXPmf53nrjSmEB9v45O3HyLd04vDBfVjw07uU2Nvz/FOPVjr/0hWr8bg9OJ0unA4H69Yu44uPX2XIwD78+Gc+s779oEpNf87/gRefe4LYdv3pEOfgx9kL6TzwZE47eRwxMVG43W5yc/NYvnQJO9fPYdpvOcyb8Xm9Hv++du3KJ2fXLt597XG+/nEBSTF2Nma4ufrq6zh6aBqTLrqOuXN+qdWLH5ejiAknHcvzr0ytWJjENE2uu/g4wtuNYNiww+nWtSPtU5IbpfbWJid3F5lZOTicTjp3aF/lTYqZP8/l95lvsmTZGnbmWbjnvofp26sbOTk5/LnwV77++guOGH06XTqlkdY+uWJWp/4Qrr2f58zjzPOvZtnCmbXu5G9MK1evw+MxcblcTdIZ31Bbt+3gkadepkObAP5ZOpOFawp58tFH+eidZ/hzVQbHHz+OlX9M5/DhI1jw21w251h484136NQhlcKiYnJydpGXXwB4551ZLAbFxaVERHgXblu5bBGvPHULbVO6Me/3ZXQdcDwP339HMz9qkdbH4/EArTOsERERkaqURsohzzCMijEHAIlAUXFJRdjl7+9HbEw0drutSToX6io0qi25hW4cnkA6dUzF4zHJydlFucNBaEgw4eGhFTPCLBYLoSHeH/OBAwbw39vDvZflJidVBEIhwUEkJsSyIz2TvLwIBo+5guS2ScRFh/DpO4/y/lsvkBgXTnqewRVXXE63zh3ZviOdwqJili5fSVRUFDs3raBjj2F07dShQe+A2Ww22qckExMdSdvkFLp37ciSBSnMnPM5n38WTEgA/LlgJrklfvTtEExap8707tmVwIAAElN6suO3b/hw5Uz8Qttx512Tq5y/e5eOOJ0uMrNzKC93EBI2jF59B5O17kc+nvEjpaVlFUPld+XlY7Va+fi91yCyH2dNugCb1Upw25EMHtCbqKgIggIDCQjwJy4mGpvdRlJ8KD/OfZhNW7YRHxtD7q582iTG1Tl8Ky0tw8/Pzm+/L2bj+mWsXP43100+i/c//pyLTunHcy9OwZE3iqiY2Fq/I22x+hMWHEBhYXHFtuLCHDbsKODWS44lNjqyWcKuliI6KrLSAnb76tWzOzExt9Bt6Fb8/e2MOupwDMM7L8yw2OjRZwhxsdGU7+7gViBbd8OHDWHO9E+Jid7/96EpdemUhtVqxeFwYrcffC+fktu24ZbrJ5NfUEhodAo9Rph07dGHK258hNULp/Lt99PJccZy1Njz6Tn4RKIiI+jUIRX4t8O8pLQUq8VasfBE7F6/Err2GMDKrAR2emycfcltHDViaDM8SpHWT4GsiIjIoeXg+8tC5CCw76JXSW0OnpmbhmGlXe+TOP3ifhUBbG06t/YEshERYZVCoT1/ALRtk0DbNgnEREfidLmwWiwcNuYCvvpiKgVlftjLV/PAQ0/xfzddzTefvcLs+csY2NHKn2tLiY+0ccKZ4xtlNo6/vx/+/lEEBQViGAZDj5rI0jU7Kc7dyObcfHoMGkegpYTVG7Zz1/9dQdDu1ZYnnHEpq7aUccEpJxIUHEhaatXOzz2Lru3dMZqfX0iAuyc28xMWL11BWkoyFouFtes24CgvZO2mnVx98w306NoJgAB/f9qnJlf6wykwMICe3TpT1r4dnT94gdff+oBxx41m+vTvOeKIkRw2dECtV9jMyytg05ZtlJbkM/X1/2IPCKPPYRM47OhT6DtsLIHksXHzQ2xa+TPJqYNq/XU1LDZCgv3JLyio2JaxaQmb0t2MGKY5pw0VHxdDaGgwYaEhhIQEV3w9bTYbnTqksisvv8ZgVw7Mz89OWmq7Zrv8ac/9HswzwPb8bmufkkx5eTkx0VEEBwUREXEZW4oSObtnd3p170JO7q5qx3sE1bB6vcVi4Y0pT5Ods4vE+NiKRZFERERERKT+NMpAowykBcrOySUoKLDGP6Lry+PxVISORcUlrP9nEwCFuZv58qMpFJc52LijlGNHHcWvi1Yw6YzxTPv+Jx64/+EmC7CXr1yLy+UCvCGox+OhvNxBl05plS4pX/fPJoKDAgkLC60SrtckK2M7N151BjtzXBx93GnM++lTIoItuD2QXmBjxvQZ+PnVLlj93//uZ82S6VgsEBtuY8GmCJ565D4G9u9d43GmabJ1+07effNFfv99IeEBpazYXMYbr79NXGw0bRLjKSsrJyDAn5WrVvPQIw9z+eXXcPjQAbV+nDddcTJdh5zOqSeNw88Ov//0Llfd9yWr/pxZ63OISMu0bUc60ZER+50VXhtOpxOLxaLZYCIiIiIijUTBrIJZaYH2XErriy7HwqJiLBYD04Sly1by86w59OzVi+5dO2EYBna7DafTVXFJbFNwuVw4nE4cDifhYaGYponT6arShWqaZr2/Jm9/8Dnb1/7C5nV/EuxvsMs+kMOH9KZP334MOkCoure8/ALmzf+Dbdt3EmBzsnbJ9/ywYCczvv2K6P1cgu1yudi2ZQPPPvM4m/5ZzqjjziBnVz69+g3hmKOGExIcVOlxORxOtmzbTlpquzpd8jj50nPJytjO8ePPpltKKJ9++ytL1mQy5/uPan0OEREREREREWkcCmYVzIrUWklpKVu37SQhPpbwsFDKyx34+/tV6rJtqXbtyie/oJBXpzzLvCWbeO+1Z7BZrYSGhtSp+3bPfNqS0jLCQkPYsGE9rzx5M/4Jw3nqkXsoL3dQWloGBoSFhrArL58nH72bjM1/sSM/iDHjJnLahJNwOp3ExUXvtyu6PiF0ekYGzz79KCv++g23CSXWVO649RqOOfKwOp1HRERERERERBpOwayCWRHZS0lpKQ6Hk4jwsEY5n2mavP3Kw3w7eyX/u/8+/lz8J1u3bsPjgT49O7Ji8SwyNv3Juu0OzrrkdsaMHEFkZHij3Pe+XC4X23dmkJmZQ2lZGTHRkXRon1Lr+bciIiIiIiIi0niURoqI7CUosHFn9xqGQYfOvQmY8S2P3TOZvMJS4uLbYHic/PXrJ+S5YokLcNFz2ClMHH9ck75JtGfhszYJcTiczorF40RERERERETE9xTMiog0sf5DRpI8dyVdO3egc8c0YqMjMTHJytlFfEw0TpeLDu3b+axz3263V7siu4iIiIiIiIj4joJZEZEmFhwUxB23XIXFsBAQ4F8xG7ZjWmrzFiYiIiIiIiIizUbBrIiID2hsgIiIiIiIiIjsrWUvoy4iIiIiIiIiIiLSAimYFREREREREREREfExBbMiIiIiIiIiIiIiPqZgVkRERERERERERMTHFMyKiIiIiIiIiIiI+JiCWREREREREREREREfUzArIiIiIiIiIiIi4mMKZkVERERERERERER8TMGsiIiIiIiIiIiIiI8pmBURERERERERERHxMVtzF9CcTNMEwOVyNXMlIiIiIiIiIiIi0tpZrVYMwwAO8WDW7XYDMPKkS5q5EhEREREREREREWntfvn2LWw2byRrOBzlZjPX02w8Hg8Oh6NSUt2STLr8Nt57+aHmLkMOQnpuSHX0vJDq6Hkh1dHzQqqj54VUR88LqY6eF1IdPS9kfw6154Y6ZnezWCwEBAQ0dxn1ZhhGRcIusjc9N6Q6el5IdfS8kOroeSHV0fNCqqPnhVRHzwupjp4Xsj+H8nNDi3+JiIiIiIiIiIiI+JiC2RZs4omjm7sEOUjpuSHV0fNCqqPnhVRHzwupjp4XUh09L6Q6el5IdfS8kP05lJ8bh/SMWREREREREREREZHmoI5ZERERERERERERER9TMCsiIiIiIiIiIiLiYwpmRURERERERERERHxMwayIiIiIiIiIiIiIjymYFREREREREREREfExBbMiIiIiIiIiIiIiPqZgVkRERERERERERMTHFMyKiIiIiIiIiIiI+JiCWREREREREREREREfUzArIiIiIiIiIiIi4mMKZkVERERERERERER8TMGsiIiIiIiIiIiIiI8pmJWDzoTzruejz39oceeWg8e3P85l9CmXNfg8TqeLUy+4kaUr1jb4XFfe8gBPvfRug8/TEKZp8vDTrzNm4mSGHTuJtf9sbtZ6WqJ9v4+XXPdfZv2ysBkrEhERERERkZbK1twFHAzuf3wK3834pcr2qW88TnJSQjNU1HDf/jiXp19+jxmfv1Ljfm63h/c//YZvf/yF9Mxs/P38SE5K4KTjj2L88SMBbxDRKS2FG6441xelN6vX3v2M19/7osr2dm0T+fj1x5qhoqb12rufMfe3P3nnpQd9er9ffTeLT6fNYPvODKxWK4kJsRxzxBDOP3O8T+s4kC++/Yk2CXH07tG5wed66K7rsdmsjVBV/S34YynfzpjLC4/eQVJiHOHhoc1aT0NMOO96zjj5OM485bgGn2vx3yu56tYH+fGzKYSGBNfp2AvOOplnprzHkYcPxGLRe50iIiIiIiJSewpmdxs6sDd33lS5wy4iPKxe53I6XdjtLeNL+/p7n/Pldz9z01Xn061ze4qLS1m1biOFhcXNXVq9ud0eDIN6hyRpKW159uH/q7TNat1/oFbd97u+NTS09pbg6+lzePrl97jhynPp16srTqeL9Ru3sGHTtuYurRLTNPl02gwuPW9ig86z5/kRHhbSSJXV37YdGcRERTQoaDZNE7fHg62Gn4lDybBBfXjo6deYv+hvDh/Sr7nLERERERERkRakZaSHPuBntxMdFVHtbYuXruL5Vz9k/cYthIUGM3bUCC674LSKYOLKWx4gLSUZq9XC9J9/pUNqMi88dgf/bNrK869+yN/L1xAQ4M+QAb24bvIkInZ3qXk8Hj749Du+/O5nMrNziYoI5+SxR3PB2ScB8MJrHzHntz/IzM4lOjKcMUcfxsXnTMBm837b1v2zmadffo/V6zaCAcltEvjPdRdRWlrGA094O2WHHTsJgIsnTeCSc6sGTPMWLGbiiaM45oghFds6dUip+Pf9j09hydLVLFm6mqlfTgfg87efIi42moefeZ0//1pJzq48EuKiOeWEUZwx4bhKxxYVldC7R2c+/Pw7nE43o44ayg2XT6p4DLl5+Tz45GssWrKc6MgIJl9wapUaP/zsO775cS47dmYRFhrM8KH9uOqSswgKDAD+7Q6++5bJvPjGx2zdls4nbz6Bf4DfAc9dHavVst/nAng79U489ki2bs9g7vw/OOrwQfTv3a3aGoKDg3jqpXf59ffFOJwu+vXqyo1XnlfRib2/2hMTYqvcb22ehx3bt8PPz86072djt9uYMO7oar/vtbV+41aefuldlq1aR4C/PyOHD+LayedUfO1dbjfPTnmf72fOw2KxMP64o8jZlUdxcSmP3HNDteecN38xxxwxmPHHHVWxLS21bZX9vp4+hw8//Y5tOzMICw3mqMMHc/PV5wMHfk5UZ+5vf/L6+5+zafMOYqIjGDt6BOefddJ+A8bV6zayfWcGhw3uW2n7gX4u93Qhnzp+NG99OI30zGx+++HdKp3nE867nvHHHcWWbenM+XUR4WEh3HjlefTs1omHnnqNP/5aQZvEOO648VK6dU4DIL+gkCdeeJsly9ZQWFRMUmIc5585njEjD9vv495j7ysDhh07iYT4GL5452kcDifPv/YhM2cvoLiklK6d23Pd5HPo3qUD8G836ZMP3MKUtz7hn01beebB/9C3V1fenfoNX303i5xdebRLSuTCc07m6BGDK+5zw6ZtvPD6R/y1fDWY0CmtHXfePJm2beJZueYfXn7zE9b+swmXy02nDilcP/kcunRqD3gD4Nff+5xvps8lNy+f8NAQRo4YzI1XnseVtzxAekY2z0x5j2emvAfA/Onv1fj4d2Zk88QLb7N0xRqcTjeJ8TFcfelZtG+XxFW3ejvGx0ycDMDY0SO46+bJlJaV8eizbzHn10UEBQVw9sRxVc5rtVoYNqgPM+csUDArIiIiIiIidaJg9gAys3O56c7HGTtmBHffcjmbt+3g4adfx8/PXinw+n7mL0w44RimPHk3AIVFxVxz60OceNyRXDd5EuUOBy++/hF3/u85nn/0dgBeemMqX30/i+suP4c+PbqQk5vH5q07Ks4ZFBTAnTddRmx0JOs3beXhp18nODCQSaefAMA9j7xE544p3HLNhVitFtb+sxmb1Uqv7p25/vJJvPrOZxWX3wfuJ7CKigznj79WcsoJo4iMqNohfMMV57JlWzodUttWdA5GhIdhmh7iYqL4353XEB4WwrKV63j46TeIjopg1JFDK47/8++VREdF8Pwjd7BtRwZ3Pfg8ndNSOGmsd0zCA4+/QnbOLl549A5sNitPvvgOu/IKKtVgGBZuvOI82iTEsj09k8eee4sXXvuQW665sGKfsvJy3p36DbddfwnhYSFERoRx+wPPHvDc9fXBp99x0TkTuHjSBAD+Xr6m2hrufugFtu5I59F7biQ4KJAXXv+IG+98jA9ffaQiyKvuuH3V9nn43YxfOGvi8bz+7L0sW7mOB554hd7dOzN4QK86P8bSsjJuuP0RenbrxBvP3ceuvAIeeuo1nnjhbe662RtgvffxN0z/+TfuuOlSUpOTmPrldOb+9icD+nTf73mjIsNZsmw1OzOySYyPqXafz7+eyTOvvM+VF53BsEF9KCouYemKdRW31+Y5sbe/lq3mvsde5oYrz6Vvz65s35HBw8+8AcDFk06p/pjla0hOSiQ4KLDS9gP9XIK3M3XWvEU8dPd1WGvofv7o8x+4/MLTuOick/no8++599GX6dW9EycceyRXX3oWL7z+Efc99jIfvPIIhmFQ7nDSpWN7Jp1+IsFBgfy28C/ue/RlkhLj6dG1w37vB7w/y0mJcXz13SzeeO6+iq7sF17/kFnzFnHXLZNJiIvhvU++4frbH+WTN5+o1OX74usfc82lZ9EmMY7QkGDe+ehrfvj5V2699kKSkxL4a9lq7n3kJSLCQ+nfuxuZ2blccfMD9O/djecfuZ3goECWrliL2+0GoKS0jLGjh3PjlecBJh989h033vU4U994nOCgQGbNW8RHn//A/bdfTfuUJHJy81m/YQvgHQtx3hW3c9LYkZy0e+TKgTz+/Fu4XC5efPwuAgP82bh5O4GBAcTFRvPQXddx2/3P8PHrjxEcFIi/vx8Az7/6IX8tW8Uj99xAZEQ4L785lTXrN9EpLaXSubt36cC7U7+uVR0iIiIiIiIieyiY3e3X35dw9EkXV3w+dFAfHrzzWj7/eiZxsVHcfNX5GIZBars2ZOfs4sXXP+aicyZUhBttk+K5+pKzKo5/84Mv6dwxhSsuOqNi2x03XspJk65jy7adREdFMPXL6dx01XmMG32E9xxt4unTs0vF/heefXLFvxMTYtkycScz5yyoCIDSs7I5+7SxpLZrA1BpHm5IcBCGYdTY+Qlw3eRJ3P7AM5xw1lW0T2lLr26dOOKwAQwb1KfiPHa7FX9/v33OZal0iXebhDiWrVzPz3N/rxTMhoYGc9NV52O1Wkht14bDBvfhj79WcNLYkWzZtpP5i/7m9WfvrejOu/2GSznr0lsr1bj3DMnEhFgmX3Aajz77RqUQzuVyc8vVF1R0+9b23NX5Z9PWSs8FgGOPPpz/XHdRxecD+nbn7FPHVnz+9/I1VWrYuj2dXxYsZsqTd1dcOn7v/13JSZOuY85vf1Z0Ke97XHVq+zzs2D65ImhMTkrg02kz+OOvFfUKZn/8eT7lDid33zqZwABvsH/TVedzy3+f4KqLzyQqMpxPpv3IeWecyFGHD6q4ff6iv2o878WTJnDb/c9wynnX065tAj27dWLYoD4cPWJwxeN488OvOHvi2Eod2Hu+j1C758TeXn/vC84948SKn7WkxDguO38iL7z20X6D2fSMbGKiI6psP9DPJYDT5eLuWy6vNmTf22GD+zBh3DEAXHTOBD7/5ie6dU6reG6ce/qJXHr9PeTuyic6KoK4mCjOOe3frs3TThrDgj+W8tPc3w8YzIYEBxEUGIhlr47w0rIyPv/mJ+68aXLFz/xt11/MwsXL+Xr6bCad9u9juvS8iRXPI4fDydsfTePZh/+PXt07Ad6v6d8r1vDltz/Tv3c3Pps2g5DgQO6//aqKNyHatU2sON/Avj0q1fd/113M6ImXsWTpaoYP7UdGZjbRUeEM6tcDm81GQlxMxWMMDwvBYrUQFBh4wN9xe2Rk5TBy+CA6tk+uqHePsFDvXNnIiLCKGbMlpWV8PX0O/731Cgb16wnAXTdP5qRJ11Y5d2x0BJlZOXg8nlY9hkREREREREQal4LZ3fr36c4t11xQ8XlggD8Am7buoFe3jhiGUXFb7+6dKSktIzM7l4Q4b8df147tK51v3YYt/Pn3yioBH8D2nZkUFpXgcDoZ2K9Hldv3mDl7AVO/ms72nZmUlpbhdnsqde+ddcrxPPTU6/ww81cG9e/B0SOG0LZNfJ0ed/uUJN6f8jCr121k6Yp1/LV8Nbfc/QRjx4zg9hsurfHYT6fN4Jvpc8jIyqG83IHT5arSSZaWkoTV+m9QERMdwT8bvbNEN23ZgdVqpWunf792qe3aEBoSVOkcCxcv552Pp7F5605KSkpxud04HE7KysoJ2P19stttdExrV3FMbc9dnXZtE3n0nhsrbQsOrtw12a1TWpXjqtawHavVSo+uHSu2hYeF0q5tIpu27NjvcdWp7fOwwz7niYmKILeeXcKbtm6nU1q7ilAWoHePzng8Jpu37cTPz07urny67xUIWq0WunRqj+kx93vemOhIXn36Hv7ZtJW/lq1m2cp13P/4FL7+YTZP/e9W8goKyc7ZVSW421ttnhN7W79xC0tXruXtD7+q2Ob2eGo8ptzhwN/PXmX7gX4uARLiYg4YygJ02B0SgreTuOo27zl25RUQHRWB2+3h7Y++4qe5v5OVvQuXy4XD6SJgd4dnXW3fkYnL5a40c9Zms9G9S1ql5yhA187//ixt25FBWXk51932cKV9nC4XnTukAt7fgX16dqkIZfeVuyufKW99wuKlq9iVV4DH46Gs3EFGVjYAR48YwsdfTGfi+TcydGBvhg3uw/Ch/es92/b0k8bw6HNv8fufyxjUr6c3pK3h5277jgycTlelwDs8LKRSuLyHv58fHo/ZoO+FiIiIiIiIHHoUzO4WGOBfqeO0rvYNdkpLyxg+pD9XXnxGlX1joiPYvjOrxvMtW7mOex55kUvOnciQgb0ICQ5ixuz5fPjZ9xX7XHLuRMaMPIxfF/7FgkV/89q7n3PfbVdVdC/WlsVioXuXDnTv0oEzTzmOH36ax72PvswFZ51Em4S4ao+ZMXs+z736AddedjY9u3UiKDCA9z/9lpWr/6m0n82671PMwGN6al3bzvQsbrn7CSaccAyXX3A6YaHB/L1iLQ8++SpOl4sAvF93fz+/SqFlQ9httgM+F6oL8upbQ2PWvm9oZRgGprn/kLQ5dUhNpkNqMhNPHM2EcWu4/Kb7WbJ0daUAsDq1fU7sraS0jEvPnciRhw+scptfNeErQERYaMWbCHvU5ucS/n1j50D2/vnY8xyw2f79Hhp4t3l2fw/f//Qbpn45nesvn0SH1GQCAvx5+uX3cLnctbq/htj7MZWWlQHw+P03ExsdWWk/P7v36+nvV3NAed9jL1NQWMQNV5xLQlwMdruNy264F6fT+1ji46L56PXHWLRkOYsWL+fx59/i/U++46XH79hv2FuT8cePZMiA3vy68C8WLl7GOx9P49rLzuG0k8bU+Vz7KigsJjDAX6GsiIiIiIiI1ImuuTyA1OQ2LFu1vlK4tXTlWoKCAoiLidrvcV06prJh8zYSE2JJTkqo9F9gQADJSfH4+/vxx5IV1R6/bOU6EuJjuODsk+jWOY3kpATSM7Or7NeubSJnnXI8zzz0fxx1+EC+/XEu4O1683hqH4DurX27JABKy8oBb1C577mWrlhLr+6dmHjiaLp0TCU5KYHtOzLrdD8pyYm43W7v4mW7bd66g8KikorPV6/biMf07A6AO9KubSLZObsa5dxNLbVdEm63mxWr11dsyy8oZMu2nbRPSarbuer5PGyI1OQk1m3YUhHCgff7brEYpLRNJCQ4iKjIcFat2VBxu9vtYc36TXW/r72ec8FBgSTGx/LHX9X/bNTnOdGlYyqbt+2s8rOYnJSw30vPO3dMZfPWHZW+5rX9uWwqS1esY8SwARx3zHA6dUghKTGOrdvT632+pDZx2O02lq5YW7HN5XKxau2Git8D1Ultl4Sf3U5GZk6Vr2d8XDQAHdOSd4/4cFV7jmUr13HaScdy2OC+pKW2xc9uJy+/sNI+Af5+jBjanxuvPI8XHr2D5avW8c/GrUD1v5cOJD4umlNOOIaH776esyaO5avvZwFUBL17ny+pTTw2m5UVe73ZVFBYzNZtVb/e/2zeSucaRpGIiIiIiIiIVEcdswdwyomj+PiL6TzxwjucOn40W7bt5LV3P+esU46vcZbgxPGjmfb9bO5+6AUmnTaOsNAQtu3IYObs+dx2w6X4+/lx7ukn8PxrH2Gz2ejdozN5+YVs2LyN8ccdRXJSPOmZOcyYPZ9undP4beFfzPn1z4rzl5U7eP7VDzl6xCASE+LIzM5l5ZoNjBzuXRE9MT6GktIyFi1ZTqe0FAL8/art8rz9/mfo1aMzvbt3Iioygp3pmbz05lTatU0gJbnN7nPFsmL1P+xMzyIwMICw0GCSkxL4fuY8FvyxlDYJsXz/0zxWrd1Am4TYWn9tU5LbMHRgbx559g1uveZCrFYrT7/0bsXCO+Cdu+tyufnkqx8ZPrQ/S1es5Ytvf2qUc++P2+0hJzev0jbDMCouNa+t5KQEjhg2gIeffp3/XHcRQYEBvPjGx8RGR3LEsP51Old9n4e1Ue5wsPafzZW2BQUGcOzRh/Hau59x/2NTuPjcU8jLK+TJF9/huGOGV3wtThs/hnc+nkbbNvGkJCfyyVczKCwshhoagB999k1ioiMY0LcHcTFR5OTm8eYHXxIZHkav7t6xDxefO4FHn32TyIgwhg3qQ0lJKUtXruO0k8bU6zlx0TkTuPnuJ0iIi2bk8MFYLAbrNmxhw6ZtTL7gtGqPGdCnG6VlZWzYvI0Oqd7xAgf6uWxqyUnxzPplEUtXrCUsNJgPP/ue3F35NYaoNQkMCGDCuGN4/rUPCQsNrlj8q6zMwYnHHbXf44KDAjn71LE8M+U9PKaHPj26UFxcwtKV6wgKCmDc6CM4dfxoPvnqR+568AXOO/NEQoKDWL5qPd27pJGS3Ia2SfH88NM8unVuT3FJKc+/+mGln89vf5yL2+OhR9cOBPj788PPv+Lv70fC7gXjEuNj+GvZakYdNRQ/u52I8NAaH+tTL73LsEF9aNc2gcLCYhb/vZLU3b/jEuJjMAyDeb8v4bBBffH39yMoMIATjz2K51/7sGJRvilvfoLFUvXJ/ffyNfWa4ywiIiIiIiKHNgWzBxAXE8UTD9zM869+yHlXziIsNJgTjz2SC/ZaAKg6sdGRTHnqbl54/SOuv/0RHE4XCXExDB3Yu+IP+wvPPhmrxcqr735Gds4uYqIiOHn3QkAjhg3gzFOO44kX3sbpdHHY4L5cePbJvP7e5wBYLRYKCgu577Ep5OblEx4WylGHD+SS87wLGfXu0ZkJ447hrgefJ7+giIsnTeCScydWqXPIgN7MmD2fdz/6mqKSEqIjwxnQpzsXnzux4rL4s08dy/2PT+Gsy/5DebmDz99+ipPHHs3a9Zu468HnMQwYfdQwTjlxFAsW/V2nr++dN13Gg0+9xpU3/4+oyDAuO/80Mt75tOL2Th1SuHbyObw39RteenMqfXt24YoLz+C+x15u8Ln3Z8PmbZxw1tWVtvnZ7cz55s06PTaAO266jKdeepeb734Cp8tF355dePKBW+p8KXZ9n4e1sWVbOudfeUelbQP79uC5R27jqQf/w9MvvctF19xNgL8/I4cP4trJ51TsN+mME8jZlcd9j72MxWLh5LEjGTKgV6W5wvsa1K8H3/w4hy+++Yn8wiLCw0Lp1a0jzz5yG+Fh3nBt3OgjcDicfPT5Dzz36gdEhIUycoT3TYf6PCeGDuzN4/fdxBvvf8G7U7/BZrWSktyG8TWEj+FhoRx52ECm//wbV+5exO9AP5dN7YKzTmbHzixuuONR/P39OGnsSI44bADFxaX1PueVF5+BaZrc99jLlJSU0bVze55+8NaKBbH257LzTyUiPJR3PvqaHemvExocTOeOKZx/1kmA9+v3/CO38/xrH3Llzf/DYjXonJZSMc/29hsu5ZFnXuf8q+4kPjaayy88nede/aDi/CEhQbz78dc8O+V9PB4PHdon89i9N1Y8Ry4971QeeeYNTrvgJhxOJ/Onv1djvR6Ph8eff4us7F0EBwUyZGBvrr/c+1yOi4niknNP4aU3PuZ/T7zK8aOGc9fNk7n60rMoLSvjlrufJCgogLMmHk9RSeWu+8zsXO+Ii1uvqNsXXkRERERERA55hsNRfnAOoBSRFsfj8XDWpbdy9BFDmHx+9Z2oLcn6DVu49raH+fStJwkKDDjwAXLIeeG1jygsKub/rq+60KOIiIiIiIhITdQxKyL1tjMjm4V/LqNf7644nC4+nfYjO9KzOHbkYc1dWqPomNaOqy4+kx3pWXRsn9zc5chBKDIijLMmHt/cZYiIiIiIiEgLpI5ZEam3jMwc7nroeTZs2oaJSVpKMldefAb9enVt7tIOOemZ2Zx96X/2e/sHrz5CQlyMDyvyrRvueJS/l6+p9rbzzhzPBbtHLIiIiIiIiIgcLBTMioi0Ai63m53pWfu9PTEhtmJudGuUmZ1Lebmj2tvCQkMIDwvxcUUiIiIiIiIiNVMwKyIiIiIiIiIiIuJj+186XURERERERERERESahIJZERERERERERERER87pINZ0zRxuVyYpqY5iIiIiIiIiIiIiO/YfHEnW7enc99jU8gvKCQkOJA7b5pMWmrbKvtN+2E27378NaZpMqBPd2655gJsNhvLVq7jsefeBMDlctO7Z2duvOI8/PzsNR53IG63mxHjLuCXb9+q1f4iIiIiIiIiIiIijcEnHbOPPPMGJ48dydQ3HmfS6SfywBNTquyzIz2TV9/+lJefuItP3nyC3Lx8vvxuFgCd0trxxnP38c5LD/LelIfYlVfAZ9/MPOBxIiIiIiIiIiIiIgejJg9mc/PyWbVuA8ceczgAI4cPIiMrl63b0yvt9/MvCxk+tD/RUREYhsGEcccwY/Z8AAIC/Cs6Wp0uF+XlDgyMAx4nIiIiIiIiIiIicjBq8mA2MyuXmKgIbFYrAIZhEB8bTUZWTqX9MjJzSIiPqfg8MT6WjMx/99mZnsW5l9/O8addQUhwEBNPHFWr40REREREREREREQONi1msGpiQizvvvwgJaVl3PvIS8z+dRGjjxpW5/N8Om0Gn309A0CLfomIiIiIiIiIiEizaPJgNi42iuzcPFxuNzarFdM0ycjKIT42utJ+8XHRbN+RWfH5zows4uOi9z0dQYEBjDpqKNN//o3RRw2r9XF7nDp+NKeOHw2Ay+VixLgLGvgIRUREREREREREROqmyUcZREWE06VjKtN/+hWAWfMWERcTRXJSQqX9Rg4fzLwFi8nJzcM0Tb749idGHTkUgK3b03G5XAA4nS7m/PoHHdsnH/A4ERERERERERERkYORT0YZ/Ofai3jgiVd4+6NpBAcFcsdNlwHw4FOvMmJof0YMG0BSYhyXnDuRyTfeB0C/3t2YMO5oAP78eyWffPkjFosFt9vNwH49uPCckwFqPE5ERERERERERETkYGQ4HOWH7KDVPaMMfvn2LWy2FjNuV0RERERERERERFq4Jh9lICIiIiIiIiIiIiKVKZgVERERERERERER8TEFsyIiIiIiIiIiIiI+pmBWRERERA5O7jKMLR81dxUiIiIiIk1CwayIiIiIHJSM7V9h+/18MA/ZtWpFREREpBVTMCsiIiIiBydroPejq6h56xARERERaQIKZkVERETk4GT1934sz2jeOkREREREmoCCWRERERE5OLnLATDKMpu5EBERERGRxqdgVkREREQOTu4y70cFsyIiIiLSCimYFREREZGDk8cbzBrlCmZFREREpPVRMCsiIiIiBw/TrPin4S71/kMdsyIiIiLSCimYFREREZGDgrH9K2xfREF5lnfDnlEGWvxLRERERFohBbMiIiIiclCw7PgWw12CddFk74bdi39Z0n8EZ2EzViYiIiIi0vgUzIqIiIjIwcFdgrvz9Vh2fguOXeAuxZN6PmZwKta/bmzu6kREREREGpWCWRERERE5OLhLITgFMyARy8oHMMrSMW1BuAe/gbHpXSjZ1twVioiIiIg0GltzFyAiIiIiAoCrBNMaBH4RWNc97900/AsITAJrELg0zkBEREREWg8FsyIiIiJycHCXegNYwwqA8+RMsId7b7MGVMycFRERERFpDTTKQERERESaX/5yLDnzwRaIJ+1SPJED/g1lAawBGJ6y5qtPRERERKSRKZgVERERkWZnXXHf7n8E4ul4Oe5Rv1XeweIPbgWzIiIiItJ6KJgVERERkebncXk/WoOqv90aoGBWRERERFoVBbMiIiIi0vxM9+5/GNXfbA1UMCsiIiIirYqCWRERERFpVkbmLIysubs/2c/LU2sAaMasiIiIiLQituYuQEREREQObda/b8PT4y48CcdBWLfqd9KMWRERERFpZRTMioiIiEjzcu7CjBoI4d33v481AMNdhum7qkREREREmpRGGYiIiIhI83IWYdpCa95HowxEREREpJVRMCsiIiIizctVCLaQmvexBGiUgYiIiIi0KgpmRURERKT5eBwYnnKwH6hj1h88Dt/UJCIiIiLiAwpmRURERKT5OAu9Hw8wysC02BXMioiIiEiromBWRERERJqPqxATC1iDat7P4qdgVkRERERaFQWzIiIiItJ8XCVgCwbDqHk/BbMiIiIi0soomBURERGR5uNxeEPXA1EwKyIiIiKtjIJZEREREWk2hukEi/3AO1r8MDzOpi9IRERERMRHFMyKiIiISPPxOMGoXTCrjlkRERERaU0UzIqIiIhI8/E4wWI78H4Wu4JZEREREWlVFMyKiIiISPOpwygDBbMiIiIi0poomBURERGR5lPLUQamglkRERERaWUUzIqIiIhI8/GoY1ZEREREDk0KZkVERESk+ZguzFoHs86mr0dERERExEdqsdJCw23dns59j00hv6CQkOBA7rxpMmmpbavsN+2H2bz78deYpsmAPt255ZoLsNls/PHXCl58/WNKy8owMDhsSF+uvOgMLBYLO9OzOPXCG+mQmlxxngfvuo62beJ98dBEREREpCFqOcpAHbMiIiIi0tr4JJh95Jk3OHnsSMaNOYKff1nIA09M4Y3n7q+0z470TF59+1PeeuEBoiLDufWeJ/nyu1mcOn40oSHB3H/71SQlxlHucHDt/z3M9zPnMW7MEQAEBQbyzksP+uKhiIiIiEhj8jhqOcrAjqFgVkRERERakSYfZZCbl8+qdRs49pjDARg5fBAZWbls3Z5eab+ff1nI8KH9iY6KwDAMJow7hhmz5wPQpWMqSYlxAPj7+dEpLYWdGVlNXbqIiIiINDWPEyy16BVQx6yIiIiItDJNHsxmZuUSExWBzWoFwDAM4mOjycjKqbRfRmYOCfExFZ8nxseSkVl5H4Cc3DxmzVvI4UP6VWwrLSvnomvu4vyr7uD1977A7fY00aMRERERkcZkmBplICIiIiKHJp+MMmgsxcUl3PLfJ5h02ji6dU4DIDoqgmkfPEtURDj5BUXc9eDzfPjZd0w6/YRqz/HptBl89vUMAEzT9FntIiIiIlINj7OWowy0+JeIiIiItC5NHszGxUaRnZuHy+3GZrVimiYZWTnEx0ZX2i8+LprtOzIrPt+ZkUV83L/7FJeUcv0djzFi2ADOmji2Yrufn50ov3AAwsNCOOHYI/hx1vz9BrOnjh/NqeNHA+ByuRgx7oLGeqgiIiIiUld1CmbVMSsiIiIirUeTjzKIiginS8dUpv/0KwCz5i0iLiaK5KSESvuNHD6YeQsWk5Obh2mafPHtT4w6cigAJaVl3HDHowwd2JsLzz650nG5efm4XC4AHA4ns3/9g84dUpr6YYmIiIhIYzBdtRplYCqYFREREZFWxiejDP5z7UU88MQrvP3RNIKDArnjpssAePCpVxkxtD8jhg0gKTGOS86dyOQb7wOgX+9uTBh3NABTv5jOyjUbKCsrZ86viwA4esQQLjj7JJYuX8ur73yGxWLB7XYzoG93LjjrJF88LBERERFpKHXMioiIiMghynA4yg/ZQat7Rhn88u1b2GwtatyuiIiISKtgWX4vRtlO3ANfrnnHovXYpvfDNbHQN4WJiIiIiDSxJh9lICIiIiKyX6YT06jFG+QWPwyPA7R4q4iIiIi0EgpmRURERKT51GWUAXhn0oqIiIiItAIKZkVERESk+Xic/4auNdmzj+bMioiIiEgroWBWRERERJqPx6FgVkREREQOSQpmRURERKTZGOqYFREREZFDlIJZEREREWk+HkftZswa9n/3FxERERFpBRTMioiIiEjzMWu5+JdhYBp2BbMiIiIi0moomBURERGR5lPbGbPg3U/BrIiIiIi0EgpmRURERKT51DmYdTZtPSIiIiIiPqJgVkRERESaj8eBWYdg1lDHrIiIiIi0EgpmRURERKT5eFz/Lux1IBY/8JQ3bT0iIiIiIj6iYFZEREREmk+dRhlo8S8RERERaT0UzIqIiIhI86lLMGv1B7c6ZkVERESkdVAwKyIiIiLNxvA4vJ2wtWHx1ygDEREREWk1FMyKiIiISPPxOGsdzJoWf40yEBEREZFWQ8GsiIiIiDQfjTIQERERkUOUglkRERERaT4epxb/EhEREZFDkoJZEREREWk+demYtfhjaMasiIiIiLQSCmZFREREpPl4HJh1CGa1+JeIiIiItBYKZkVERESk+Zi1X/zLO2NWowxEREREpHVQMCsiIiIizcfjqH0wq45ZEREREWlFFMyKiIiISPMwTYw6zJg1LXYFsyIiIiLSaiiYFREREZHmYbq8H+s0Y1ajDERERESkdVAwKyIiIiLNw+P0fqxtMGv1B7c6ZkVERESkdVAwKyIiIiLNY0/3q1Hbxb+CMNylTVePiIiIiIgP7TeY9Xg8bNi0DZfb7ct6RERERORQsSeYrW3HrC0EXIVNV4+IiIiIiA/tN5i1WCxcfO1/sVrUVCsiIiIiTaAimK1dx6xpDwNnQRMWJCIiIiLiOzWmrh3Tktm2I8NXtYiIiIjIocR0Yho2MGrZCGALBVdR09YkIiIiIuIjtppuHDGsP7f890kmnjiKuNgoLIax120Dmrw4EREREWnFPI7ajzEAsIdgqGNWRERERFqJGoPZL7+dBcCHn31fabthKJgVERERkQbyOGs9xgAAW5g6ZkVERESk1agxmP38nad8VYeIiIiIHGrq2DFr2kPBqcW/RERERKR1qDGYBfB4PKxcs4HM7FziY6Po1jkNixYEExEREZEGMjzOuo0ysIWCqwBM03sJl4iIiIhIC1ZjMLsjPZNb7n6SjKwcYqIjyc7ZRVxMFI/ddxNJiXG+qlFEREREWiOPo26jDOyhGKYbPGVgDWy6ukREREREfKDGYPbx599myMDeXHHh6djtNpxOF1Pe/oTHn3+Lp/53q69qFBEREZHWyOMAoy4zZkO9H50FCmZFREREpMWrcSbByjX/VISyAHa7jcvOO5WVazb4pDgRERERacXcdex8tfhjGnZwac6siIiIiLR8NQazIcFBbNuRUWnbjvRMQkOCmrQoERERETkEuEsx6xLMGgbYQ8FZ1HQ1iYiIiIj4SI2jDE45YRTX3/4Ip508hsT4WHZmZPHZtBmcOn6Mr+oTERERkdbKXQq2Oo4ksIViuAowm6YiERERERGfqTGYPfvUsUSEhzL951/JzM4lLiaKS887lbGjR/iqPhERERFppQx3Sd1nxdpDwalRBiIiIiLS8u03mHW53dz90Av899bLGxzEbt2ezn2PTSG/oJCQ4EDuvGkyaaltq+w37YfZvPvx15imyYA+3bnlmguw2Wz88dcKXnz9Y0rLyjAwOGxIX6686AwsFu8khnkLlvDcqx/g8Xjo0D6Zu266jOBgjVsQEREROai5S+sczJq2UM2YFREREZFWYb8zZm1WK0tXrMFqtTb4Th555g1OHjuSqW88zqTTT+SBJ6ZU2WdHeiavvv0pLz9xF5+8+QS5efl8+d0sAEJDgrn/9qv58NVHefOF+1m2ch3fz5wHQElpGQ8+9SqP/PcGPnnzCWKiInnjgy8bXLOIiIiINDFXKVjr+Ga6PRRDHbMiIiIi0grUuPjXyWOP5r2p3zToDnLz8lm1bgPHHnM4ACOHDyIjK5et29Mr7ffzLwsZPrQ/0VERGIbBhHHHMGP2fAC6dEwlKTEOAH8/PzqlpbAzIwuA+Yv+pnOHFFLbtQFg4omjKo4TERERkYOYuxTTGlC3Y9QxKyIiIiKtRI0zZufOX8yGTdv4+IsfiI2OxLAYFbe9/cL/anUHmVm5xERFYNvdeWsYBvGx0WRk5ZCclFCxX0ZmDgnxMRWfJ8bHkpGZU+V8Obl5zJq3kMfvu2k/x8WQk5uHy+2uuE8REREROQi5S+rRMRumGbMiIiIi0irUGMyeMeFYX9VRK8XFJdzy3yeYdNo4unVOq9c5Pp02g8++ngGAaWo9XxEREZFm4y4Fv+g6HWLaQtQxKyIiIiKtQo2Lf33+9U+8+Pgd+Pv51fsO4mKjyN6rg9U0TTKycoiPrfwiPD4umu07Mis+35mRRXzcv/sUl5Ry/R2PMWLYAM6aOLbScQsXL9vruGyi9+rQ3dep40dz6vjR3sfocjFi3AX1fmwiIiIiUn+GuxTTVseOWVsoRumOpilIRERERMSHalz8K3dXXoPvICoinC4dU5n+068AzJq3iLiYqEpjDABGDh/MvAWLycnNwzRNvvj2J0YdORTwLvB1wx2PMnRgby48++RKxw0d2Ju16zezaYv3BfpnX89k1JHDGly3iIiIiDQxdynUdcasXTNmRURERKR1qHGUwblnjOfZKe9z5cVnEhwUWO87+c+1F/HAE6/w9kfTCA4K5I6bLgPgwadeZcTQ/owYNoCkxDguOXcik2+8D4B+vbsxYdzRAEz9Yjor12ygrKycOb8uAuDoEUO44OyTCA4K5LYbLuY/9z6F2+2mQ2oyd90yud61ioiIiIiPuOoxY9YWDK6ipqlHRERERMSHDIejfL+DVsdMvIySkjJMTIICAzGMfxf/+vGzKT4psCntGWXwy7dvYbPVmFGLiIiISCOzzj4WT+okzNRza32MseldLJvexX3Uj01XmIiIiIiID9SYRj783xt8VYeIiIiIHGrc9eiYtQZ6RyCIiIiIiLRwNQaz/Xt381UdIiIiInKIMeozY9Ya4D1ORERERKSFq3bxr9vue6bS52+8/0Wlzy+57r9NV5GIiIiIHBrcJWBTx6yIiIiIHJqqDWYXLllW6fOPPv+h0ucbN29vuopERERE5NDgLvMGrXVhDfQeJyIiIiLSwlUbzFa1z/pgRvV7iYiIiIjUmrsUs44zZk11zIqIiIhIK1HLYFZJrIiIiIg0MldJPWbMKpgVERERkdah2sW/TI/J+o1bwfR2yno8nkqfmx6zusNERERERGrH9GB4yqCOHbMVwaxpgqHmARERERFpuaoNZsvKHZx/5e17clgAzrvi9op/6zWwiIiIiDTInjmx9Zgxa2CCxwFW/8avS0RERETER6oNZn/74V1f1yEiIiIih5I94whsdeyYtQV7P7qKFMyKiIiISItWyxmzIiIiIiKNyF3i/Wip44xZWzCmLRTK0hu/JhERERERH1IwKyIiIiK+5y7FtAbWb0ZWYBuM0u2NX5OIiIiIiA8pmBURERER33OX1n2+7G5mYBso3dHIBYmIiIiI+JaCWRERERHxOaMBwSz+sRjl2Y1bkIiIiIiIjymYFRERERHfc5XUv2PWHg7O/EYuSERERETEt2z7bnjgiVdqdeCdN13W6MWIiIiIyCGiIR2zfhEKZkVERESkxavSMRsSHFTxn8ViMGPWfHJ35eNnt7MrL58Zs+djtajRVkREREQawF2KaQ2q37H2cAwFsyIiIiLSwlXpmL3+8kkV/779/mf4353XMnxov4ptv/6+hG+mz/VNdSIiIiLSOrlLwRpQv2M1ykBEREREWoEaW19/X7yMwwb3qbRt6MA+LFyyrEmLEhEREZFWzuMEi71eh5r2cIySrWCajVyUiIiIiIjv1BjMJsbH8s30OZW2fTtjLglxMU1alIiIiIi0bobpqn8wG3sEFKyGwtWNXJWIiIiIiO9UGWWwt5uuOp//3PMkH38xnYT4aNIzcsjKyeWRe270VX0iIiIi0hqZLjBqfCm6f4GJEJiEUZ6DemZFREREpKWq8dVwv15d+eztp/j19yVk5+QRc1QEhw3uR1hosK/qExEREZHWyOOsfzAL4BcBzl2NVo6IiIiIiK/V+Gr4nY+ncd4Z4znumOGVtr839RsmnX5CkxYmIiIiIq1YA0YZAJj2CHBoATARERERablqnDH79kfTqt3+7tRvmqQYERERETlEeNwN7pg11DErIiIiIi1Yta+G12/YAoDpMVm/sfKKt9t2ZuDvX//uBhERERERTCdYGhDMqmNWRERERFq4al8Nn3flHRjG7n9fcXvFdsOAqMgILjv/VJ8UJyIiIiKtlKcBi38BpmbMioiIiEgLV+2r4d9+eBeAi6/9L68/e69PCxIRERGRQ4DpxGxgx6xRtKHx6hERERER8bEaZ8zuG8oWFBY3aTEiIiIicohoYMcs6pgVERERkRau2mD2uxm/MH/R3xWfr1m/iZPOuZbjT7+cMy+5la3b031WoIiIiIi0QqYLLPVft8DUjFkRERERaeGqDWY/+vx7IiPCKj5//Pm36Ne7K++89CB9e3XhpTc+9lmBIiIiItIKeZwN7pg11DErIiIiIi1YtcFsemYOndJSAMjdlc+qNRu45tKz6ZCazFUXn8nSlWt9WqSIiIiItDJmA0cZqGNWRERERFq4GmfMAqxYvZ7EhFiioyIACAkOorSsvKnrEhEREZFWzDDdDRtl4BepGbMiIiIi0qJVG8x27pDCN9PnADD9598Y3L9nxW3pmTmEhQT7pjoRERERaZ0aOsrAHo7hKvKeR0RERESkBao2mL3iotN54fWPOPKEC1m8dBVnnzqu4raf5iygV/dOPitQRERERFoh0wWWhsyYjfR+dGqcgYiIiIi0TNW+Gu7RtSNfvvcMW7btJDkpgeCgwIrbRgzrz7HHHO6zAkVERESkFfI0cMasNRDT4g+OXeAf03h1iYiIiIj4yH5fDQcFBtC1U/sq21OS2zRpQSIiIiJyCDCdDeuYBbBHYDjzMRunIhERERERnzrg4l8iIiIiIo2uoR2zAP5RUJ7dOPWIiIiIiPiYglkRERER8T3ThWmxN+wUgW2gdEcjFSQiIiIi4lsKZkVERETE9zzOhnfMBrbFKN3WOPWIiIiIiPhYrV4NZ2bnkpmVS89uHet1J1u3p3PfY1PILygkJDiQO2+aTFpq2yr7TfthNu9+/DWmaTKgT3duueYCbDYbO9OzuP+JKaxdv5k2CbG889KDFccs/nslN9z5GCltEyu2vfL0PQT4+9WrVhERERHxAXcJWAMPvF8NzMAkjJLtjVSQiIiIiIhv1RjMZufs4q6HXmDZirX4+dn5+avXmTlnAYsWL+e2Gy6p9Z088swbnDx2JOPGHMHPvyzkgSem8MZz91faZ0d6Jq++/SlvvfAAUZHh3HrPk3z53SxOHT+aoKBAJp9/GkXFJUx565Mq509pm1gprBURERGRg5tRlgEB8Q07SUAc5P3dOAWJiIiIiPhYjaMMHnvuLXp07cBPX72GzWYFYFC/Hvzx14pa30FuXj6r1m3g2GMOB2Dk8EFkZOWydXt6pf1+/mUhw4f2JzoqAsMwmDDuGGbMng9AeFgIfXp2ITDAv04PTkREREQOQqYJZRmYDQxmTf8YcOQ0UlEiIiIiIr5VYzC7dOVaLr/wdPz9/AADgPCwUPILimp9B5lZucRERWCzeoNdwzCIj40mI6vyi+iMzBwS4mMqPk+MjyUjs3YvtLfvzOT8q+7gomvu4rOvZ9S6NhERERFpBq4iDHcJBCQ07Dx+0Rjl2Y1Tk4iIiIiIj9U4yiA4KJCCwiKiIsIrtmVm5RAVGV7DUb7VpWMqX73/LCHBQWRm5XDjXY8THhbKqCOHVrv/p9NmVIS3pmn6slQRERERAXDkej/aIxp0GtM/BsrVMSsiIiIiLVONweyYkYdxz8Mvce3kcwCTLdt28syU9xk7ekSt7yAuNors3Dxcbjc2qxXTNMnIyiE+NrrSfvFx0WzfkVnx+c6MLOLjovc9XRXBwUF73Vc0o48axt/L1+w3mD11/GhOHT8aAJfLxYhxF9T6sYiIiIhII/A4MC3+YBgNO09gG3DmeYNev6hGKU1ERERExFdqHGVw0aQJdO6YwuQb7qWouJQLr76L1HZtmHT6CbW+g6iIcLp0TGX6T78CMGveIuJiokhOqnzp2sjhg5m3YDE5uXmYpskX3/6033B1b9k5u/B4PAAUl5Ty6+9L6Nwxtdb1iYiIiIiPeRxgaYS1A/xjILwnRsbPDT+XiIiIiIiPGQ5Hea2u58/LLyQ8LASjHp0Nm7fu4IEnXiG/oIjgoEDuuOkyOrZP5sGnXmXE0P6MGDYAgK++m8W7U78GoF/vbvzn2gux2WyUlZVz+sU343S6KCouITIijOOOGc6VF53BJ1/9yBff/ITVasXtdnP0EYO5eNIptapzT8fsL9++hc1WY/OwiIiIiDSWXYux/TIe1/htDT6VZeltGOU5uAe90giFiYiIiIj4Tq2D2dZIwayIiIiI7xk5C7DOPxvXCRsafq5tn2NZ/TjuUb81QmUiIiIiIr5TJY0cM/Ey4MDdpj9+NqUp6hERERGR1s5dDha/xjlXYFuMkoZ33oqIiIiI+FqVYPbh/97QHHWIiIiIyKHCU944M2YBMygJozxj99zaRgp7RURERER8oEow2793t+aoQ0REREQOFZ5G7JgNSMA0rFC6HYLbN845RURERER84ICDVXdmZLNs5VryC4owzX/H0Z5+8rFNWpiIiIiItFIeJ6a1cTpmMawQ0AajZBumglkRERERaUFqDGZ//mUh9z76EinJbdi4eRvtU9qyYdM2+vTorGBWREREROrH3XijDADMoLbejlkRERERkRakxmD29Xc/566bJzPqyKGMmXgZ77z4P76ePodNW/TCV0RERETqqTFHGQAEJnk7ZhvvjCIiIiIiTc5S043pWdkcc8SQStuOHzWcH376tUmLEhEREZHWy2jkhbq8HbPbGu18IiIiIiK+UGMwGxYaQkFhEQCx0VGs/WczOTl5OBxOnxQnIiIiIq2Qpxwaa8YsQFBbjBIFsyIiIiLSstQ4ymDUEUP5/c9ljBl5GCccdyRX3fI/rFYLo44c6qv6RERERKS1aewZs4FtsRSsBI8LLAdc21ZERERE5KBQ4yvXqy45s+LfZ51yPN27dKCkpJShA3s3eWEiIiIi0kq5y8Aa0GinM+OPgT+vxMhZgBk7vNHOKyIiIiLSlGoMZh0OJxaLgc3m3a1Pj864XC6cThd+fnafFCgiIiIirYyrCNMW0njns4dhRvSGonWgYFZEREREWogaZ8xef8cjrFyzodK2FWs2cOOdjzVpUSIiIiLSirmKoDGDWcAM6YBR9E+jnlNEREREpCnVGMyu37CVnt06VtrWs2tH1v6zuUmLEhEREZHWy3AVN3owiz0cnIWNe04RERERkSZUYzDr72entLSs0rbSsjLsdi2qICIiIiL15CoCW3DjntOwgelq3HOKiIiIiDShGoPZgf168PSU93E6vS9ynU4Xz77yPgP6dPNJcSIiIiLSCjX2jFkAw4phehr3nCIiIiIiTajG1tdrLj2bm+9+nONOv5zY6CiycnJJaZvIY/fe5Kv6RERERKS1aYpRBuqYFREREZEWpsZgNioynNefvY+Va/4hPTOHhLgYundJwzAMX9UnIiIiTcE0wVMG1sDmrkQOQYYzvwlGGVjBdDfuOUVEREREmlCNowwADMOgR9eOHHPEEJKTEhTKioiItHRlGVhnHY3t207gKsKy9DaMHd80d1VyqChYBcWbMCMHNO55LTYFsyIiIiLSolQbzH434xfmL/q74vM16zdx8qTrOP70yznzklvZuj3dZwWKiIhI4zIy52A488AahP2LaCzrp2BZ90JzlyWHCOvaZzHbnQEBcY17YsMCHo0yEJEGKFqP9bczm7sKERE5hFQbzH70+fdERoRVfP7Yc2/St1cX3nnpQfr26sJLb3zsswJFRESkcRnOPMygdng6X4MZ2gX34VMx8pc3d1lyKDA9GFun4ulwWeOf21DHrIg0jGX711i2f+Gdgy0iIuID1Qaz6Zk5dEpLASB3Vz6r127kmkvPpkNqMlddfCZLV671aZHSiPKWYWx4rbmrEBGR5uTMB78IPJ2uwXXcUsywHhjlmeAub+7KpLUr3QGuEszwXo1/bsOqxb9EpGE8DgCss0eB6WneWkRE5JBQ4+JfACtWrycxIZboqAgAQoKDKC3TH24tUnk2tplDMEw3LosfZup5zV2RiIg0B0cepj3i388D4jENO5Rug5AOzVaWtH5G0ToISgarfxOcXB2zItIwRvFGzPBeWHYtxpP5M2b8qOYuSUREWrlqO2Y7d0jhm+lzAJj+828M7t+z4rb0zBzCQhp5FV3xCevv52O2OQF3t//DuvzeineERUTkEOPcBX4R/35uWCCwDUbJ1mYrSQ4Nxo5vMGOPaKKTq2NWRBqoeCPuTlfj7nIzlg1vNnc1IiJyCKg2mL3iotN54fWPOPKEC1m8dBVnnzqu4raf5iygV/dOPitQGo9RsBpP5+vw9LgbrIEYW6Y2d0kiItIMDEc+2MMqbTODkqFkWzNVJIcKo2gDZtSAJjm3aVHHrIg0jFG0EYLb42l/AcaOaVCe3dwliYhIK1ftKIMeXTvy5XvPsGXbTpKTEggOCqy4bcSw/hx7zOE+K1AaiWlCeSamfxwYVjydr8O69mlcKeeAYTR3dSIi4kuuQrCFVt4WlIxRug2zeSqSQ4WnHNMa0DTnNqzgUTArIvXkcULpNszgVAhOwYweimXz+3g6X9fclYmISCtWbccsQFBgAF07ta8IZf9e4V3wKyW5DbHRkb6pThqPqwDD44CAeAA8qZOgdCdG5s/NXJiIiPicqwTTVnkskRnUFjTKQJqauxwsTRXMqmNWRBqgZCtgQFBbADyp52Fs+ah5axIRkVZvv8Hsvm6889GmrEOaWlkmpsX/3w4payCeDpdg2fB689YlIiK+5y4GW0jlbYFtNWNWmp67DJqyY1bBrIjUk1G8EYJTvL9LADPmMIz85VqXQ0REmlStg1lp4fwicfd/ptLYAjOsO5TubMaiRESkORiuYrAGVdpmBrXF0IxZaWKGpwys/k10cpsW/xKRejPyV2CGdv53Q3B7DI8D6+/nN19Rjc3jwLLuBYzs35q7EhER2a32wayGzrVs/jGY7S+svM0ejuHMa5ZyRESkGblKYN9RBiEdoOgfKFjdTEXJIcFd1oSjDNQxKyL1Z+QswIwettcG75/Klm2fQ3lOM1XVeCyrH8P+WSjWv27EuuiS5i5HRER2q3Uw+9OXrzVlHdIc/CLAmd/cVYiIiK+5iqrMmCWsG57O12KbOxaKNzVLWXII8DiasGPWgqGOWRGpJyPnd8zoIZW2eRKO3X3b/OYoqVFZNn8IgBnWDUp3gEe/L0VEDga2mm5csqz6rhk/u4342GhitAhYi2baw8GR19xliIiIr7mrjjIA8PS8D5wFWOefg3vUr81QmLR6Tdkxa9HiXyItlXXmMDydrsFMOdv3d+4qwdjyATjzMKMGVrrJPWIa/HE5RvZ8zDYn+L62xmKaULwB14hpmLFHYPsqCQrXQHiP5q5MROSQV2Mw+597nqSktByPx4Ofnx2Hw4nFYsHfz05ZeTm9u3fmnv9cSXxctK/qlcZkj8BwF4PHCRZ7c1cjIiK+4HFheBxVF/8CMAw8vR7ANq0NFG2AkDTf1yetm7sMsykX/1IHmEiLYuQuwvrzSAzTCeuex90Mwaztp8PAWYB72AfV/r/RE3MYlg1v+ryuRuUuxnCXYkYNBmsgZkQfjF1LMBXMiog0uxpHGUy+4DTGjh7Odx+/yOxpb/DtRy9wwpgjuOriM5n2/nNER0Xw+Atv+ahUaXR+kZgYUJbe3JWIiIiPGLmLMO0R4L+fN1XtoRDUDqN4g0/rkkNEky/+pY5ZkZbE8s+r3lAWMApWg7vUtwW4SzEKVuEaNR9z99iCfZnRQzF2/dGyf7+UZWIaNrBHAGBG9sPYtbh5axIREeAAwezbH03j5qsuICI8FIDIiDCuv+Jc3vroK6KjIrj12otYtnKdTwqVJmAN8P5POWtec1ciIi2YseE1rL+c2LL/YDmEWDa+iSflLLD47XcfMygZSrb6sCo5JJimt1u7SRf/UsesSEtgZM3DyJyDsfVTXMf8hvOUfAiIx/55BOQt810hJdswLX7gH7v/fYKSvb+7WvACYEZ5JvjHgWEAu4PZnPm+D8JFRKSKGoNZt9tDZnZupW3ZObtwubx/fAcFBeB2e5quOmlyZmQ/jIKVzV2GiLRQlr//D+uSm7Ck/wglW5q7HKkFI2sOZpvxNe5jhqRhXX4v1j8u91FVckjwlHs/NmnHrF6XihzsjIyfsM0+BtucMWALwYwa4G0YCe/uvd2HixMbpdsgMAmMGv4stgZiWoOhPNtndTUq08Sy5mnM2BH/boofheEuw/ZDb72xLiLSzGoMZscfdxTX3/4IH372HT/N/Z0PP/uO629/hJOOHwnAb7//RWq7Nj4pVJpIcHsMrb4tIvVk2fwB7iO+xrSFgEtdFwc9VxFG8SbM8J417ubp/TDuPo9i2fim/mCTxuMu8X6sZuG5RmFY9XwVOZh5HFgXXoxt7lhcA17wbrOHVtzs7v2Q9x++/Dku2e69SuRAAmIxyrOavp4mYGT+jJH1C+5+T/y7MTAR1+iFULodSrY1X3EiIlLz4l+XnX8qiQmxzJg1n+zcXcRERXLemeM58dgjATh8SD8OG9LXF3VKEzGDUzG2T2vuMkSkJfI4MMozMEM6gjXIu6hEc9ckNTIKVmH6x0JAXM072sMwE8Z4/+0qqfSHs0i9uYq8s+2tgU1zfotNowxEDmKWDa9hbJ+Gu++TmGmXwJ9XVX6jJrQzZlh37yxqH/F2zLY94H6mfyw4WuYoA6NoA2bUwKrjGix2CErBKPoHMn7ydi03w+JrIiKHuhqDWcMwGH/cUYw/7qhqb7daa2y4lZYgsA2GFv8Skfoo3e5dSCIg3hu07OmGk4NX/grMsFquwGwL9n50FyuYlcbhKvaGMDVdMtwApjpmRQ5qxs4f8PS4E0+nqwBwjfgaMySt0j6mNQDcvgtmKdmKGXTgYJbg9hgFa1rmG9Cuon//n74PMyQN29zjMW2hmMmn4lYwKyLiczUGswDLVq7juxm/kJmdS1xMFGNHj6BX9051upOt29O577Ep5BcUEhIcyJ03TSYtter/AKf9MJt3P/4a0zQZ0Kc7t1xzATabjZ3pWdz/xBTWrt9Mm4RY3nnpwVodJwdm+sdBWTqYZsUweBGRGrnLwZGLkTELwrp7Lx+2BSmYbQGMgpWY4d1qt7PFjmnYvWGaSCMwXMVgC2nCO7B5fz/pNY3IwcddhpE1F3efRyo2VVyZsTdLgPfn2EeMkm2Y4b0OuJ8ZPRQj/QcfVNQEavrda/HO/DZchZiOXT4sSkRE9qixZWHG7Plcf/sjmJj07dkFwzC44Y5H+XHWb3W6k0eeeYOTx45k6huPM+n0E3ngiSlV9tmRnsmrb3/Ky0/cxSdvPkFuXj5ffjcLgKCgQCaffxr3/t+VdTpOaiEg3rvKqA+H7ItIy2ZZ+T9s36Rh+/MK3J2uBsC0BmnGbAtg5K+A2nbMgjdwdylwl0ZSQ9dWowjpCLYgjIwZTXcfIlIvRvY88IuB0K4172j1B7fvXk8Ypdtq1THriTkMI+f3lrnAoKsIcz+/e939nsAMTsOM6AflOVjWPed9c0tERHymxmD2rQ++4vH7b+b/rruYc884kVuvvZDH77uJtz74qtZ3kJuXz6p1Gzj2mMMBGDl8EBlZuWzdXvny+Z9/Wcjwof2JjorAMAwmjDuGGbPnAxAeFkKfnl0IDKi6im9Nx0kt2EIxLQFQntHclYhIC2HZ/iWevo/jGvIuZso53o1Wdcw2uUb4Q8koWI0ZVsuOWQBrMIa+r9JYmjqYtQVhxh6BsWtJ092HiNSLkbMIM3b4gbvZrYEYPpwxS8m22i3+Fd4LPC4oWAnFG7GsfgxKdzR9fQ1Vsg3r2qcqOmOrCG6Pa+wq3N1vx5L9C9a/boayFvC4RERakRqD2aycXHp371xpW6/uncnOrf1lDplZucRERWCzWgHv3Nr42GgysioPT8/IzCEhPqbi88T4WDIyDzxgvb7HyW6G4e2aLVMwKyL7YZoYOQu8/3aVQOE6PG0nYLY73bvYDoAt0KcdLocSyz+vQOlObJ+HY2z5uP4n8ji8c4FD2tf+GFugRhlI43GVYDblKAPADO+Bkb+8Se9DROqhPAszIOHA+/lwxqyx4xvvXOrgtAPvbLFhRg/Ckv0blq2fYV12J7bvu2Nsr33DUnMwClZ5Px7o6khrwL/HFG1oypJERGQfNQ5ibZ/Sli+/+4mJJ46u2PbV97NIbZfU5IU1lU+nzeCzr72XuJm6TAMAMyAeyjKbuwwROVjl/Y3t5yNxDXoVS/Z8CEmDgMTK+1gDMdwlLXNRjIOZaWJdfA3WxdcAYP1jMq6YYRDUru7nKtnqncG57/euJtZg7+JfIo3BVeQdj9GEzPAeWLZMbdL7EJG6Mxw5tZrl6rMZs6U7sC66DPeA52u9wKUZfRhGzgLMwCTcaZdCYCKWzR/iTjqpiYutPyP3D+8/HLk17meG7TViomgDxI5owqpERGRvNQaz118+iRvvfIzPps0kMSGGnenZ7Mov4MkHbqn1HcTFRpGdm4fL7cZmtWKaJhlZOcTHRlfaLz4umu07/g0Hd2ZkER8Xve/pqqjrcaeOH82p471Bs8vlYsS4C2r9WFqtgDiMsgwFKiJSLUvWXABsiy7Fk3wGrqHvVr0U0apZpE3C4wDA3f0OPKnnYv+uK5b1U/D0/l+dT2WU7oDANmDUeLFMJaYtRB2z0miM8mzvjMkmZIb1gMI1UJaJZeObeDpcCn5RTXqfPmG6MbZ/hRk9DALr8OaKyMGiPBuzNj+L1gDwwSgDy8a3MKMHY7Y7s9bHmDHDsCz+EDNhNPhFYEYPwbLpvSassuEsG14FwAxsU/OOQe1wnlaOddFkjOKN+rtQRMSHavzrrFvnND558wnOP+sk+vXqxvlnncTUNx6nW+daXO6xW1REOF06pjL9p18BmDVvEXExUSQnVb6UZeTwwcxbsJic3DxM0+SLb39i1JFDD3j++h4ne/GPA40yEJH9ceTiSRiDa+gHuIe+A5H9q+xi2kPBWdAMxbVyu8dDeDpfD8Htcfd+CKO4npcYukvq3q0YmIhRsq1+9yeyr5JNmMH16Paui+BUsPhj2fIBluX/xTZ9QMsfs1Keg23GEGzzz8KyY1pzVyNSL0Z5DvgfuOnG9NXiX+VZmAdaiGwfZvQQKN6MUbAG7BGYftHgKmyiAhtBWQaU7sA5djWePo/U6hAzJE2jDEREfKzGjlmAkOAgjj36sIrPnU4XE8+/gc/efqrWd/Kfay/igSde4e2PphEcFMgdN10GwINPvcqIof0ZMWwASYlxXHLuRCbfeB8A/Xp3Y8K4owEoKyvn9Itvxul0UVRcwvhzruG4Y4Zz5UVn1Hic1I4ZkIBRtrO5yxCRg5UzHzOsB2byxP3vYw+HA80vk7rb88epNRAAM6wblo1vAWBs+wIzpANGwSrMNicceFEld5n3EtE6MIPbQ/HGulYtUi2jeAtmRN8mvhMLZnh3jPSZmFGDMRw5GDunY7Y9+d99TE+dOsebk2X9i1iX3IAZ3gtP0kngPIhDIJGaOHLAvxYd86GdMbZ92eTlGI5dmGHd63aQPRzCe2DJmoOr3em75+EevG/8GHl/QUgHCK79bHkzJA1jx9dNV5SIiFRxwGB2X6ZpsjMju07HpCS34dWn76my/fYbLq30+UljR3LS2JFV9gsI8Gfa+8/t9/z7O05qKSAO8v5q7ipE5CBlOPMxQzvXvJM9HKNkq28KOpS4SzENK1jsAJiRA6BwLTjysC67E6NoPQCuwW9gppxzgHOVY+4OeGstuB3GjmX1qVykCqNkC576zEeuI0/7C7H9eSWe+GPwRPXHyJrjDWaL1mNd9RjG1qm4jv0LglOavJYGKVqP5e//A8CTOsn7s38wd+eJ1KQ829thegCehGOx/XWrd86s1b/p6nHkgl9knQ/zRA/Fmr/MG9JaA71jnEyz6oing4Cx6y/MyH51OyhYHbMiIr5Wr3aBg/D/O9IA3sW/MsDjau5SRORg5MgDe1jN+9jD6tYxW7INY+sn3sWAZP/cZRXdsoD3jbSgdhiZszCK1uPueAUARsHqA5/LU1bnP3JNe7g69KRxmCaUbMH0QRhqpp4PgFGyDTN6KEbunwBYNr6Nkf6D92coe16T19FQlu1fYcaPwnlauXeciS1MI2OkZXKVYLhLatcxG5wGthAoWFF5u2libPu8YvZ6gzl21W7m7T7MqIHej2HdvQuf4gHT2Tg1NTIj7686X6VghqRhOHJ0FZSIiA+1jOu4pGn5x2EUrsH+WTDG5g+buxoROdg4CzDtETXu4g3wah8YWNY8iW3BJCzL7say5imMnAUNLLKV8uwTzAJm9GAs61/EDErB0+9p3P2f+3fV5RoY7jLvZZd1YQvGcGvxL2kEjmwMdyn4oGMWy+4LwsqzMcO6Y+Qv94Y6xZvxdLzSG9YWrm/6OhqqeEvlqxXsYRh6o0RaIkcOJkbtOlQNAzOyL8auxf9uK0vH+tvp2OafhfX3C7HOPxsjd1GDSjLq2TFrxo/yhp27g1ng4BxnULAKy7bPMSP71u04v0hMeySoa1ZExGeqHWUw9cvp+z3A7XY3WTHSPMyAeIzdXWtGzgLMlLOauSIROZgYzjzvJXs1sYdhlGfV+nI+o3AtnqhBWLZ8hOHIwdPuTNzRWrhxX4a7tGowGzUI69+34kk+AwBP1EBsy+70XvVgqWFCkbu0zjNmsQarq1kahVG8xXsZ84FmITcS0x6BGdEHQjuDPRTL8ru9HbuJx0N5ZpXntZH7B2Z4r5q7ysuzMHZ8hxk1AMJ7NvEj8I5+MBNG/7vBHgoudcxKC1SeA35RYFhrtbsZ2Q9j12LM3Z9b/7gSy85vva8btn2KGd4b659X4Rq9sP411bNjlqC2uEb/vruwvYLZvV8nFa711h/ZD8u6F/H0uu/Ar6MamWXHN5gWf8yYEXU+ds8CYHUegyAiIvVS7V9wc36tufOmb8+6rWApB7mA+Ip/GnlLm7EQETkoOfMP+AeFGTUEHHkYm9/DTD33gKc0CtfiHjQFM24klvUvY+z8vrGqbV3cpVW6XM3oYd6Pe7pgIvp6L6fM/gUzroZ56+7yenTMhnjn54k0VMkW33TL7uY6YaM3BLL64zryB2wzDwfTiRnWDaNwDUZZxl47F2OdPRr34Z9gxo/a7zmtiy71Brhh3XEf9WPTPgDTjZG7CE+XG//dZAvD0CgDaYEMRzb4H3i+7B5mZH8sa5789/j85biO/BEj+1fIXYS7/zNYZ4/ePe6njv9fA+8CgI5d4BdR92P3ZrF758Dv7pg1Nr2HGdEL28JLoDwToyx9zx3i6f9sw+6rjozcRXh6/rd+c3pD0jCKN1YE4yIi0rSqDWZfeOwOX9chzckWWvFPI39pi1qtWER8wJmPeaA/XvyjcQ94Aesfk3GlTKq5a7Y8C6Nkc8XcMzMwEWv6D/DbGVi2f4nzpJ3ezhrZHcxW7Zh1934YT7szvRsMC56k8Rjbvqg5mPXU/Q9Y0xaijllpFEbJZsxg3wWz2IL+/XdYN9z9nsK6/B6I6AUZM8H17ygDY+d33vmXzgIoWI111SOYkX3wdLoOy5JrMQrWeBfDcWTj7vsE1t0LcjUlI2chmB7MmMP+3WgP08xnaZnKczD9ajFfdjczsp93BInHAYYdynZiBrbB2P3/MDN6KAQkYGTNxUwYU/d6nAXe2bCN8VrDGugNiF3FWBddChYbhseB86SdWOdNBHsIlo1v4+lwOYR3b/j91ZKR+weeTlfX61gzOA2j6J9GrkhERPZH6ZtUBCieuKO9HVXFG5u5IBE5aJgeb1hRi0vwzLiR3pltzl017mfk/oEZ0qlitpsZ2R8zrAdGurcDzShc1/C6WwtXCaY1qPI2w8DT5QYITKzYZCadjGX7NO/3a3/cZZh17pgNAlexd0SFSEOUZ2P6xzXb3Zvtz8c1bo23i9YeWingtGz9xPvxn1exrH8Ry5YPsP79H4ytn2D95xXMxGPBkQ0Wf8y4IzFcBWA27WgvI2MGZvwxlS/9tod671ukhTHK69YxS3AHb+CZv8I7n9rjgMAkPO0vxN3nETAs3tcc9Z1PX56BiaVSc0p9Ga4ijOz5WJbdiYEH14mbcZ6wAfyicB89C/eIrzETj8WS/kOD76vWSrdD6U7MyP71OtwMSYNizZgVEfEVBbMCgGvENNzDPoTw7hhZc5u7HBE5WLgKMTBrNxvNHupdMKJka427GbmLMKMH/7shKBnXsYtxD3zJ+3nx5gYU3LoYZTsrBbD7Y8YeAeVZULLP185Z4L0c1F2Odc0TGHX92tpCvF1FnrK6HSeyL4+jfpfUNiaLHwCmLfTfTnDTU/GmkCXzZywb38Z15I94Ivtj+/1czJBOeLrchGvsalwnboKABO9xjrwmLdVI/xHP3vNlAWz16Jh1l3mP8Rycq8bXSt4yLP+80txVSEM4csC/9h2zGAZmhHfOrJH1K2ZQiveNQr9IPJ2vB8AMbodRuqNe5VgXXuz9f1stZuLXhu3PK7Bkz8eTeoG3CzcwqdLtpn+sdyyUjxg5i7zdubaQ+p0guD1GkRp1RER8RcGsAGAmHAt+EZiRA7D9cTlGxs/NXZKIHAwcebu7Smr54j4oCaM2wWzUwCrbzXZn4kk+HWPfcPFQVrIdc58/8KplsXtnwhWurbTZ2PYZ1qW3YV18rffzA3xvqrCFer//xZvqdpzIvtzlYGnmYHYPW0jFoqeUZ2O4S/+9zNo/BjN2BO6jf8F5aimuMbvXXQhI8HbwWYMxDdsBrwxokLy/MfJXYCaOq7TZtId6r2CoA8uKe7F/GYP9sxCsv53p/T60FI48rL+Mxz5jINbF1zR3NdIQjl2Y9og6HWJG9sH255VYfz8fT9ebqt4emAQl2+pXjz0CT+r59Tt2H64jf8AMSsHIW4In+dT93l9Tv5mzN8vWqXjqM+JhNzMkzfsmu8fRiFWJiMj+KJiVSty9H8L0i8LI+Km5SxGRg8Gehb9qOXfatIV4O7T2w0ifgSX9R8yoQdUf7x8LZZn1KrU1Mkq3V+m82R8zpEOVDhdj12I8kQO8l0XbQnAd/kndCrD640m7EOuyO+t2nMg+DI/j4AlmA5Og6B/vvOuCVZi2sIqbXMO/8P6+s9i8H/cd/2EY4BeJ4Wi67jfr2mfxpE6qeum3PQzDXVynMQpG3jI88aNxHTUTI2c+RvYvjVxt4zJyF2FZcgNG5myMbZ9jlG7HNexDTMNe86gWObh5HBUd67VlJhyHGdIR1zG/4OkwueoOQW0x6jt+zVWIp4aF/urCjBuJu9f93n8Htql+J78IDF91zJbuxNjxNZ60S+p/jsAk7xu+elNWRMQnFMxKZX4RuId9gGX9Cy37sjcRaRSGM9+74ExtWfz232FhmlhWPoAZ0gkzsmrHLAD+sRiOnLoX2lqVbscMalu7ffe+PHs3ozwbM/k0XGPXeFepD25f5xI8Pe/FSJ8JhWvqfKxIBU95848y2M2MGoAZ1g3LigewzRnjndu6e1V1Inof+AT2iKbrmC1YhbH1UzydqukQ3RMg12KcgbF9GvZP/DGy5uLu/SBm7AjMmOFYl9558L75VbgG66yjsez4FtucY7H9eQWeDpdjJh6PYTqxLLm+uSuUejI8Lm/QVwdm/NG4jl+x359JM2qQ9w2Wsoy611OWAQGxdT5uf8ykk/C0ORFC0qrfwR4GzrxGu7+aWH89BTP6MAjpUP+TGBbM8J4YuYsarzAREdkvBbNShRl7pHexiYLVzV2KiDQ3Zx74RdR+f4t9/8Fs4RosOQtw93tq/3Pd/GO8s1IFAKOk9h2zWIP+DZf2KM/2diFb7HUL2PfmHwv+0RiOJrx0W1o/d3mdg5mmZCadhCVjBu4uN+M8cSuenv/FXcsOM9MvsmkuS3bkYp/eF0/HyyG0c9XbbcGYGFCLBcAsG9/EDGiDa8yiimDL0/kaKFiF/etkrAvObezqG8woWANh3XCNW4tr2Me4hrzj7fqzBgJg/WdKM1co9Wa6wLA17jn9oiCiL0bmrDrW4oaydMw986IbgzUA9+GfVjxXq9ylPQIjf2XdZ0TXlenGsmsx7r6PNfxUSSdj2fROIxQlIiIHomBWqjIsmBF9MfKWNHclItLcHPl1mwtn8fNeslwNI/cPPFFDMPdd0GYvpn8MRpmCWWD3H487ajdjFjBtgeAuqbTNKMus24Ir+2MNqBr6itSF5yCaMQt4ut6M6/iVeHr/DwLi8HS+Ds+AF2p3sF9Ek7xRYeT+iekfi6f3w/vZYfcq8rUJd8rSvW+ChXap2GRGD8V1ciamLQTL1qmNVHXjMUq2YAYlA2C2PRmz3RkVb+I5T87GtPjBPnO0pYXwOJvkjRlPwmgs6TPqdlDeMm9IXN2bH03EjD8Go2Qz9i9jIH95nWdF11rJVkzDCmHdGnwqT4dLMXIXY2TNa4TCRESkJgpmpVpmZF+MXQpmRQ51jTbKwFmAdeX9mO1Oq/FwM7I/FK7FyJxdt0Jbo7IM70zFwMTa7W8NAlflYJbyTMyAuIbXYg1UMCsNczDNmG0oe2TjX5Zsmlj+meJd8KumleLtYRi1CHWMsqzqL9W2+uMaNd/beVuPS8CbVMlWzKB21d9mD8WMG4llxze+rUkah+n0zm1u7NMmjMFIn1Gn+cOW7F8wYw7zXh3oK3u9QWqbeyL2L2OhcF2j342Rv8I7sqgxQnC/SDwdL8ey/sWGn0tERGqkYFaqZUb0xdj1V3OXISLNzZnvnadYW0Y1owxME+viazGD0/B0vKrm44NT8PT+H9Y/rqhzqa2NUbodAuJrv2CKNQhj7/C0LBPDkQshnRpci2kNrHFRN5ED8pRjHiQzZhvK9AuHxuyYLd2J7YdeGHlLcfd+sOZ97aEHHGVgWfMERulW7xiT6gS1w8DE9s1+5mE2E6NkC+zumK2OmXgcRsZMH1YkjcbTBKMM8HaB4y6pUzOJkfULZuwRjV7LgThHzcc56neMsh0AWFc9gpHxc6Peh5Exo9EWNQMwowdjNFaXurscY9uX3rE2IiJSiYJZqZYZ2Q8j7+86rfwrIq2QY5c3hKgti1+VhQONzFkYO7/DPfh176W4B+Bpdwb/z959h0dRbg8c/74zu+m9kULvvRcBkaKAggXFclX02nsv12v3qj977713xYKCBUUEBQTpvfeWkJAA6Tvz/v6YJBBI2U3bhJzP8/hIdmdnziabzeyZ856jsjfU3lK/hiJnu9dtDAA4rJWB2rccHdLCSeRUl1TMiuqyjq6K2ZpsZaB2/4o6sNbpCxkYW+G22hVRcSsDKx9zyV3Ov8tLzJpBeIb95lQMal3FqGtBRRWzgB07CJU+16fqSFFP6NppZYDhRjc7C2Pti17GoVFpf6Hjj635WCoT3Ruie2I3PQMAte1rjE3v1+gh1N7F6Nj+NbY/HdICsjeXfG2sf927iyNaO8n4YtmbcH8dgWv2OdK3VgghyiCJWVG28I5OUrYWltkIIRoOlbsDgpO9f0BZrQxyd6Cjeni/H3e0k3zI3uT9cY9CKmeL94O/4IhWBiprOTqyc80EIz1mRXXVsx6z1RIYU6MVs8rKwY4fik45rfKN3eEVXrRSWz5BR3TCM2QSBESXu52O6oGy8+tsUrw3VM5WqCAxS2hLlGc/eA7UXVCiZtgetKqd4X9Wx9tRWz73rvdyfhqqYI9zTuIn1sBPKTwzD6vPS5CzteZ2bBegspahI7vW3D5DW6A8+0ranpgLbsA1Y2y5mxsrH8P1Y1dcP7TENSnFaWM1bSjmghtKtlFbPqu5+IQQ4ighiVlRNsOFjuqGylzk70gqJtPbhahdudvRwU293lwbR7YyUAUZFSYIjqAUhDZH5WyufNujmNq3Eh3RofINi2gzpHTydN8KdESXmgnGDC7dJkF4RaXNrH99PP1E2fm1UzHnBzogDgoyam6H+XsqTkgeyh3hJErKYaROx252DjpxdCX7CUe7wiB3hw+B1iIrD5W3q2T4V5ncEU5v3HqUTBZeqqUes4DTU9UVAjlbKt+2MBNtBDmrQPxJKaelSPamGqtaV1u/gqAEiOxWI/sDwB2JHd0Hc86FkJdaeQzpf2MnjnRWSLkjMdY8j5E+B2PXzxSOWkjhqdtQGfNg36qai1EIIY4CkpgV5Qtrhzqw0d9RVMg9qSlq+3f+DkOIo5bK2Vr9itmCvRBQ8fLcw2l3tLQy2LcSHeFDxaurdGJWZa1AR9ZQYtYIkh6zPjIW34Fr+gkYq5/1dyj1g10AR0mPWQJjUflpGKufQe2cUv395aWiDxkOVBEd0gy1b3W59zsXdLycyB6c7KyKqA9yt6GNAKevdnmU4QyjLMiqu7hEzbA9Tg/62lCc5PTiYq4qyISAqNqJw0c6qodzQWLPn9XfWcFezGX3Y7e7zquWUb6wjvkQAGPlowdvLO/8zJONju6DbnICdquLSv7+WR1ug8jOEBiPThkn7QyEEOIwkpgV5dLBiZC3099hVOqIXkc521B7ZvsnGCGOJjnbIG8n2pfqizITs+loXypmAdxhKG+WJR7FVN5OH1sZBKOKe8xqjdq3wrfEbiX7llYGvjHXPAeAKpQkEuAMfDlqWhnEQd5uzCV3Yi66vdq7U/l7IKicfrCH0cmnorZ/W/6Fkpxt6NAW3u0rKLla53lqz1811tLBuQiYUnlSyR2FkorZhkd7aq9iFtDh7TC2fl35YKnCTN8GmtYmdzg6fggqY261d2VsfAdyd2K3vKAGAjtMWBusXk9jrnvl4G0HNpS9recAuMIAsJuegbKysVNOx+7+fyWb6NgBqNqsmLVynSFj9al/thBCVEISs6J8QUmo3HqcmC0a/mDs/Knkyq3598W4J7fB9fswZ6mMEKLKVNYSCG/vW3VJGYlZVbAXAmJ8O7grXPoI5md4XUUHlO4xa2U7CcFQL5dHV0JLj1nf5KcDYPV8WpZsFivY6/sFmnpKB8ajCpyfsTqwDmP5w9XbYf4edHmDug4/dtxgCIxDbfum7A2sPKcntDeCKznP0xbG6qePHLZ1YD3uLwNx/T4Cc9Y5Tm9PrWHfSu+Oexi1/TvMP8agvfk7YRdgbHxfJrs3NHZh7VXMAlb3R1FZSzF/H1HxcLiCTHQ9qZgF0BGdUVlV+70pJX8PdpvLSpKiNS6yK3bLCwHQkd1QB9aVuZnyHABXqPNFREd0ZFf0YechOigR8ovaIuxfXXpIWA1QqdNxzT4H8+8LZIi1EKLBkMSsKJcOToK8Xf4Oo3y2c1Kucrbg/jYec9ow1NbPKRy7HqvNFRgrHvWqH5IQohyeHLQr3LfHGAEou7D0bQUZ3n3gPoR2h4OnEVfM2gVOH0lfvm+HVrUWLzN0RdRMPFIx6xN1YB06sAl2ymlONVRj/1vkyXZez0FJ/o6kZoS2xDN8Gp6BzhAbc8VD1dqdyk91qnC92lhht74MY8ObZd9ve5+Y1cFJkLu9/A1ytmAuueuIPrRq3woAPMf/hSrMwpx7Meb8a3D/3BOyVhwSi6fURPfyGKufQ2E7F+Qqk5+KsflDXFPaQ30uHjiaFWbh+r6ld8O2iii7FnvMAoS1wTNiBip3G2rjO+XHUbi3/lTMgjOgc9+KyjeshMrP8P0CuI+sfm9SeJKzEkcdWF/2Rp7sUslhq+czJQndEkEJqLzdUJiF+6fuGCuKqmkL95caYFpluTuxo/uiMuZjrH+j+vsTQog6IIlZUb76XjFblCSwer+EZ8gkjPTZEN4RQppid7jVOXH8fbifgxSiAbNynCpMX5TZyqAKHxhcFU8eP+oVDxYK9L43r3aFOD8zgMJ9zjCwmvogbAajpMes9/avQYe3h5Bm6Jj+GNvLqW5sLPJ2Ov1DazlxUJd03GB0/HEHb6jOMLD8PRCY4PXmdssLUBnzSydBAWwPSlvet4wITq74PK+owvHwPrQqdyd24mh0TF88fV+FvN1ow412R2Gk/VGynbHi/3BPaV95HMp0/u+uvNrPM3YthWdkgXKXW7UnapHWmDNORuXtdC4OeFvtqD2gajExC2AGYre+HCNtZvnbHNjgdauPuqAjOqP2rTzyvMlXBel18/4a1gYd1qaCxOwBZ6hgEZ0wFCK7ltpEBzUpKqpx3vOK319ck9tiTj++2iGqvN0Q0QGr17MYS+7CWHiTrMASQtR7kpgV5dLBSajcrU6fyfqoKElgt7oYnTiawpNW4Bn6k3NfaEusQZ85J+2ebD8GKUTDpTw54PJxcrHhLqOVQQb4uoTZFY7yHEBl/NM4l4LnpzvVykaA948xg0uqTZRnP7gjay4eqZj1idq/FsLbAaCbnelMy27EVO5Op1pWKX+HUrMCYyk8bbeTXEmdUbV9aLuolYEPbUsCYtDxQzA2f1y612zRSiKvK2Yr6zFbvAz4sKpaY+HNB9+bontjHT8Tu/cL6MTRpRIgKnNhpTGotJlOFSN4dyEwOMV5focNOxR1Q235BLVvBVaHWzDWvYZrSkfwJkFuFzrnB7UtMK78i7pWrtP7O6Jj7cfhrfAO6IhOuCeGYyy5s+r7KdiL9nHIalXpsFYYm96HnK2H3aGLesyGVryDsHZYHW7FbjEBq/M94HF+XqowE2PvguoHmLcTHZSITjoRz8i5qL2LcX8Ti9ryBWQtq/7+hRCiFkhiVpSvaMmhe3IbjPWv+zmYMli5aOU6WBEW1gaCDqk4CYhDuyJqt8G8EEczKwfMSk6wD+cKcz4Uafvgh7WCvWgfKj8Bp69t7k7MORc4HwAaGVWV6hczBGXnOd/7wixw+9iGosJ9S49ZX6gDa9HhbQFnAIqRNgNVURXX0S5vl9NX8GgUEIWdMAyVOq1qjy/IcJbxe9ljtoRSmKufwvVTd9SeWc5txYlZrytmk46ohi2lKDGrcrYccptGaU/pauHiu9xhpZe35++pNARz7qWo4mSJL+8xZnD5A9BE7bA9mEvuxur7Onb3R/GMWYmO7oGx6aPKH6s9dZKY1e6IclssqMylANhJJ9V6HF4zXFjHfoMOa4e5+pmK++OW9fCl92HOPtdpmVNDPeUroxNHAaDS5zj/T/sTlT4H84/RKO2p/EK8UtjdH8Hq/zY6OKXGV0epvF1Q/PcmvB3Wcd9jxw3BnH8t7l/61N+CIyG8ZeWjtn1dM60/RL0hiVlRPlcIOrQ1VpsrUDum+DuaI1n5FVeFKIVOORVz+gmoje/XeHN5IY56Vq5TleQDHd4WtXcBats3uH/s4pywF2Z53z+xiB0/BGP3VFT2Bp/62B018tN9q6CDgz8rKxcK96Nrqr8sSMWsj1RxKwOA4CTslHGNutedyt0BwUdJf9ky6Jh+qMwlVXtwfprTdqSyKrPD2K0vw255IXarizHnXeZUqxUnKr1MzOrgZGeWQHkDcopvPzSRYRetVmpx3pHbu8JKV8wWJ2Yrmo5esBerw63Ov33oxawlMVvnVMZc0B5009OLbjCL+h2/U/mQJbsOWhkAuMOdftZlyduJjuoJoa1qPw5fBDXBM3oBGqPins9lMLZ9DTnb0AnD0bEDaynAwwQlYjf/V8l7nmv68bimDUVlLcUzeKJvK6TKSKSb/1wF+9dUPb7cXU7/7GKuMKzhv+I5PQ0dmOAkboVowIz1r+KafS6uHzt5dQFUNAySmBUV8oxZiW4ysuKKCj9RVq6TLKiA1etZ7JYXYK58FHP2v+ooMiGOEp4c58OvD3REZ1RBOua8ywHnhN3qePvB6gVvRXbHThiODu/oLMtvbKpYMQuAlYNKnw2BNddvTpIg3lHbJ2HO+hcqaxn6kL56dsppGFu/QG371n/B+dPRXDELEN6uyv1OVf4e36tlAZ1yGla/N7E73Ay5uyBrCdj5aGV631s6KMnpSZufVs5BiitmD1myXDJYsIyKfFeYM5W9WPEHxvIu6mgL5dmH3fYa5zi+JEykir/u7V+Ljuh8sCcwoJuMdJaiV5ZI03XUysAVUW4FpsrdWTphV58YARDWCpW13PvHZG+C7I1Yx36LNWQSqLr7WG8njnYuNmrtJJQBz/F/opNP9m1H7nBUzhaMBTc6+005DWPjuxhbPvetktYucP7+zrkAI+Pv8s85A+Mhv5EP4xQNnspYgNX1QXRYO9yTUlAb3nLayNXn2UCiUpKYFZXSIU2PmMhbL9h5YFTSR80dgd37Baw+L6P2r66buIQ4WlRl+FdQEwpHLXSWj3W6E89xU7C7VmFiuVJYQ3/Cbnt1oxwCpvLTfa4yLq6Sc09qirH5U6xuj9RcQFIxW7n9azFnnYOx/RsnkX1oVVbRh0Rjw1t+Cs6/qvR6bkB0eHsnCVmVD0X5aegqJGZLmEHoxFEY2793Lp5Udl5U6rGBTl/K8uIuWlatdv8G+9c6t3n2O4PczDKqcg+tmLXyDl5UK6eCUW390vlHQDSefm9iDXjXh9iDnQv0onblbsecNtQZLJezBUIOWy5vuNBRvZx+8BWxPU77sVqm3RGo7I0YK/7vyArs7E1OlXg9peOHonZP9Xp7Y9dUdPyxPg0JrSm66emowkyn4j6sjVMpG9bG9x25wpxBctsnAWB1uQ+r4x2YKx7G/W087i8DMda+VOlujHWv4Jp1Fir1dye+kKZlxx0UD3nlXIgSoqHI2YwObYnV50XspuMxF92OOfdS3D+0PHIoqGgwJDErKhfayqneOrzJu79ZeV4PuEC5pJWBED5SVWhlAEBkZzwnzMLufDe6yfHVGvhTUb+4o1b2Zowtn6N9XW5Z9H32HPMRnpOWQ1S3movJDJIkSCXMZfdht76UwrPy8YwrvbRMh7Z0/lGYWedx1Qt2oW+D7BoadyQ6pi9q968Yyx/2aTmyyk+rdtLaTj4ZY8cPTsVMWQnTigQnocqLV1todyR260twzTrHSboW7neqEstySGJW7f4NHdLC+XdRb88jNv/7384/zBB0ywvRcYO9j1sqZuuEylyMkT4HY8MbTouW4veyQ+iYvqiMeeXvZN9K8Oz3/py9OooucqgdUzAX3nywjYbWGDt+QDc5ofZjqCK7+b8wNn/i/es6byeEtKzVmMplBqODU1BZSyBvd8nvuq90aCt0UBKe0fOxOt4BYW3QMX0BsFOclhnmolsx511Z4SBYtfs3rM734hm7lsJRC8pvVxGY4LznCtGAqeyii2QRnbAGfoLd5R6MrV8AYK5+ys/RiaqSxKyoXEA0hLbAPbktrh/aYiz+b8X9wuqKlev9SZ7hqrz/lRCiNE+Ob9VXh1JmzSxbrKhf3NHILsQ1bSh23EDszr5PaC48Kx/d7Cynb1tNkorZiuVsRe34AbvTf5yvD19KHtaawpOWozIXO/3RGxtd6P3y+gZKx/TD2PQB5oqHnJ6b3qpiK4NSx046CTIXo7I3+fyerYOTUeVWzFpOH9Fu/4d2hzuTzfP3gDus7M1d4SWJWWPH99gpp6GjemJseLviIKpy8c4MPjjsTNSe/AwAjCV3Y2z9Ajt57BGbOInZ+WU/3i7ANW0YdrvrIbxjbUbqCGlK4fj9WAPeQe34HrXnL+f2rCWQuwOdeGLtx1BFOv44p73Ils8q3nDvAlxfR2Gu+D90DbYs8pXd+nLMmac552jBVWxVE5yM55RNEBCD3e1BJ+GbOLqof/aFAHgGfQl5u3D/3ANj6T1l7kZlLnEKAcxgiOxS7uG0tDIQDZ3WzkqboIPnDXb7m/Ec9yOFI/9Bbf0K5cs5iKg3JDErvOIZ+ClWjyfRIU0x1zx78ETHn6x87/tfKpczEVYI4b266glXEVcEFGb5N4a6lLMV8tOwez1fvyoMzWAoPFBqsI84SGUtc5ZxHr7M91ChbcAdicpcWHeB1Rd1NfjHj3RkN4y0GWgzFGPNC94n4PNSS33AqpLAWHR0b4wdk8H08X0jKNmpvCuD0kU/N8OFNew3tBmMueg2cEeWvS9XqNNjVluoHT+gU07F6vUsKm1mmdPm7bjBWG2v9i3eIloqZuuEytuF3exsPCP/xjPoC4judcQ2OqaPUzlZ1mu+cD+qMBO7893VWj3jEyMAwjugm55R0hrA2DoRnTy2aquA6opSWB1uxlx6X/krhQr34f51IDrCST4qHwbm1TS7853ouEHOFwE12E7BDMTq9yY6aQyFp6ejU04tuVBtrnryyM+gdqFTtRtawd/fYkEJKGllIBoyzwGULiz9O6cMdJMRENUNa+CnuOZfjbHE9+IO4V+SmBXeie6N3f4GrOG/Ybe8ELVzsr8j8q1iVloZCOE72+P3KjcdlOD0iasPVfp1QGVvdJJ79ay6UAcloPJ345rU1FkuLUrzZriVUuj4oU7SrrHRHv9f5KllOuU0tCsMzwl/geH2OgHvDP+qfv9d3eR4jE3vO1WzvjwuOKnSilnASc4O+hK73XVYfV4te3t3uJNQ2r8GPNno2IHomH5OZWvm4iM2V54DTpVbVUgVf93I24kOagLh7dEpp5W9TWgbMENRWWW0rLBynP/XRRuDw9hJY0p6l6r9q9Exfeo8Bl/pFhOc/slpf5R5v9rxA1q5sQZ+jGfAB1id767jCA/jCi0KrJZSCi6nOl/H9APATj4Vc+ZpmHMuPHihOD8NhYbAhEp3JxWzosEryHAG7pVzgVQnj8VudTHm6mecixaiwZDErPCNMrETR2Hs8r45fa2xve8xqw2pmBXCZ9oDys/JlOCmKCun0fTmVFnL0eHt/B3GkcLa4BnyAwQmYKx60t/R1DsqdxcEVT7t2+r5JGrH93BgQx1EVY/YhUd9xSyBsXhOT4eITujksRgbvBxklb+nesO/iuhEp3em1f0x3x4X2grKG456aGIW0IkjsdtcXn6CyxUGnmwnORzawrnAZLjRCcMwNn2ISpsBBZkHty/IBHeUT/GWkMRsKWrL55CfBtkba3a/mcvQEZ0r2Ug5VbNlDQCzctFGYO0l7iqgk06CA+ud13dBBjqgAQwgVAq72XjMJXeWeUHaSJ2O3f5GCG2Jbn4OhLX2Q5AH6ejedXMgZTotKgZ/iU4cjbH1c9Se2c59ebudIYbeXPwLjJces6JhK8hw2kxW8J5q9XkFHZSMSp1ed3GJapPErPCZThgBWcurNn24Bikr1/teatLKQAjfaY9zUcOf3OFoVwRkb/ZvHHVB26jtk9BJ9bMHnk4cidX3FYz1b5S5LLneyU93qr6zltd+xXXudnRw5YlZgpPRkd0rHpRzNNIe9FFeMXsou82VqO3feFWtovJTq91jFkDHHoOO7Ird+hLfHhd3jJNQKyvWwxKzle7LFQae/aicLehD2nrYrS7CXPcyrukjcU1Kxv1lIOYfJ6JyNqOrkZhVVl7VHnu0KcjA9feFuCa3xz2lI+bcS2tmv1qjMheho3tWvmlMv3ISs3lOEt0f3OHoJidgbPsOlZ8OfuzH6gu7y/2Ql4pKnXbEfSp1OjphWN0HVQ67870Unrqjbg5W1N7JGvAeOqoHquiCksreCMHJ3u0jKMHp6y1EA6UK0iGgkvcyZWA3PR1j29d1E5SoEZKYFb4r6mWmdv/m3zisfB9aGZhSzi+Er+pJX0idcgqu6cdjTht+VPebNRbfjrFnJnbyyf4OpVw6/jgo2AsH1vk7lAqp9Lm4JyXj+iYW9y+9MTa8WbvHO7AWHdbWu42jupe95PdoVk/eS+qKju7tJA73/Fn5xjUw/AsAIwDPqPnl938tT2hrlJ1fdrJCW75VOrrCnBUOOVshOOXgbore06x21+EZuxYd3hGVswU7aQxEdPIt3mJGoJP0E6i9C9AhLYqqpBIxNn+EseQu5+JUdeRsdpaLR5Q/TKmYju6Fylx0yGO3QP4ep4jC9F9fV7vp6ZjL7kXtW16zfVBrkxmE3ekOzMV3lB5cnL3RGWBW3Ne1PjBcEFjH31fDjZ04CpW13Ply54/YXrZE0YHxjao9ls8OrENVNqxR+Fd+BtqL3zndbDzGxncwVjxaxp3y+q+PJDErqkQnjsRc/iBkLfNfEFau91fhpZWBEL7TVr1Iplj93sYzfBqqMAtz9vmoTR81jIpNX9gFGJs/wer+SMUDpPzNCICQ5j73sawTdgFkLcNY+TjG8gewm52DNfQnrE53oXb/WnvHtfJQWSshooNXm+vQlqicbbUXT32kG1diFmWgk8agdlTSj1/bRa0M/LjE2nA7la5ltYvxsWK2uB+kyt7s9Ac/ROG4NOzuj0NwCp4TF+M5aQXWsd9UvZ+2tDIoofYuRMf0Qbc4F88pmyk8YTbm6qcxlj9Qzf0ugojOYAZWvnFQ4sHkfs423JPb4fpjtG/zIGqBTj4FHdnd+bevFy38yG57LRTsRR3SOk6lTkfH9j/Y17UR0xFdYN+KokGDU9DJp3j3wMAEZ3DSUXyRvzrMhTfjmn8NxuL/onb9XPrCgKgXVEFG5RWzgI4d6GxffL6utfNfXiqub+MwVj9Ti1GKqpDErKgS3eQEVM5mXDO8/ENYG6w8tDcniyDDv4SoCruwfgyhUgqiemD1ehZj91Rc8y7FWHSLv6OqUWr79+COxG5/s79DqZQObYmqb60ltMb852rcv/TB2PguaI3V5W503EB0bH9U6h+YM8bU/OCyrBW4fhuCDm2BjvZusIwOTobc7TUbR33XCIZ/Hc5OHoOxY3LFlSkFGSjsmqmYrQ53tPNh73BVTMySvenI6kR3RI3+PdGSmC2h9i4o/f4T3Rur+6POYLnq7DdzETq6l1fb6oBop/eh1hibP0KHtYP9a5z2Bv5qZQAQEIVn1Dw8w3+HcO8untULZiA6pi/G8v85FeiAkfoHOn6onwOrH3RkF1TWipI+szpuoHcPdIWhjSAZAFaW/WtRmcuwOt6Oyt2Oa+apqI3v+TsqcTgvE7MoA6vzvYBzDmL+cxWub+Mx1r6I8hzAWP4g7F1Qu7EKn0hiVlSJjh3g/MMdfsiNdumhDrXN9qFvlXI5H36Otio7IWpTfRj+dQidMJTC8dl4hv2GsXXiUXWxRaVNx045zS8DUnylQ1s6iZd6xFj1OGr3b3j6v4dn+DSsoT+WfAjXcYOxW1+KSp8LWStq9ri7fkRlLcE67gfvq8KCk53EdmP6e9QYhn8dRicc7yTgs9eXv5HnAFqZ/q+AK06qHc7XxKzhRhuBqOxNtV8FbAZLK4MiKmP+EQlUHZToLNmuzn73LkRH9fBu44AYpyWGlYtKn4vd5nLslhdirHjYSaL7mY4b5FzkbUjcURh7F2Bs/gRzzgUYWz5Fxx/r76jqh/D24DmA2voFOmGo9+9TSkF4e8yFN2Gsfbl2Y2xIMpfg+qk7mIHYHW/HOuZDrA63YZTVN1r4V0E62pvELEBA1MG/7dkbwBWGueoJrG4PY7e+FHPtS7UWpvBd/f8EKOonw43nuB/Bznc+XO5fjeunrrh+6Vt3Mfgy/Ku4SkOWZAjhPe2pHxWzhzJc6NhjwBXiTLc/Sigrz6koawhCW9avVgb5aRjLH8Zz7LfoFuceOQTEHYHd/RGnB2LWkpo9duE+rNaX+tTXU0f3AWVgLLq1ZmOpx1R9fC+pba4QCGmKyt5S/jZ2fslAG3/SATFO7+gj7vAxMQtOn9n81NrvOykVs478dGeI2uGVrUEJqLzd1dq1ylzs1eAvAIqHuBVkQH4qOrgpdvubUFYuqqZXKjQSVo/HsFtMQKX/jbH1C8AZsiYAMwgd3RNz/evoyG4+PdTqco8zbHXzR7UUXMNjbJuITjkVz5hVJeczOm6Q086gaMhao+bJxljzfL3II6gDG71eZVPqb7uVh9XzaTzDp2O3uwG71SUYmz/G/WUgKs2Lfvii1tXJWfLW7bt48MnXydq3n7DQYO659Upat2x6xHaTfprOh59/j9aaPj06c/v1F+FyuSq8b8HiFdx8z5O0aHpwGvIbzz1AUKD/T3SPdjq8LWRvwfVtPLgi0KEtUOl/O4naOqj6Ula+91fhiyt1tAeoPxWAxYyVT4ArBLvddf4ORYgSyvb4/qG8LhgudFQvZ/K3v2OpKVaeM8ymAdChLTGX3o299Qx003F+f40Yq55yPqxWlkAITEDlp9Xsa6Yg82BCwlsBUXiG/oTrpx7Yba6AwARAQ3GFoSfbabkQEF2TkfpXI6yYBdCBCRUvmbUL60ViluAkVM7WI383qpCYVQXOwCkdM6BmYiuPGeRc0GrkVOZCdGirI5a26rA2zvCuvN0Q1MT3HeftgrxdJf1ZK2W40AExqPTZRT0QoyG8nXNfWdXYonIB0djNz8Gcewk6ohNWu+sazgXcOmD3eg41Ywx20kk+PU6nnIYV1hbXb0Pq7DNrvbZ/LebKx/AcV7onuk46CZ04GtcvffGMnFv1QY1HAbX9O8zF/4HCLOwu9/kvjvQ5qNTpWL2f8+4BAdElf5OVJxtcIQfbfkR2cVYgbnwH1/TjsZufhzXg3doJXHilTt6JHn/+HcaNGc4X7zzFhLNP4eGnXz9imx27Unnz/a947el7+fLdp8nIzOLbKb9Xeh9Ai6ZJfPDqIyX/SVK2jgQ3dT5QGIHYzc/C6v2i0y6grhqqW7neDSSAgx8I62rpc/4enwajqbTpGEvvLekjJUS9UJ8H9gRE1W3rlNpm5Xn/fuZnOm4QdovzMRfejLHkLoxFt6I2feCfSoK9CzHXPIfd+c5KN9UBUVBQs3+fVGGW74lZgNCW6IShmGuexzV1AOai20ruMtY8jznv8poLsj5ohD1mgaKqxYoSswX1IjFb3K/xyDssdFX/BgREVSumSplBUjFLUbuB6N5H3hHaCh3VA7V7WhX3uwjC2pZuWVYJu9nZGLt/KzU1vHDMKjzHflulGAToxFF4Tt2GZ/QidOvL/B1OvaJjB+A5Pb3yi7JlCW8HaFTajJoOq2HJ24X7p64A6IThpe9TBlbfVyG8I+rAOj8EV3+o9L+xE090Cqn8OCPAWPkYdvsbvR4SrIOaHGxpY2Uf0TZJxx+L1f8dPH3fwNjyScU98UWtq/XEbEZmFivXbmD08YMBGH5sP3anZbB1+65S202bOZdjj+lNbEwUSilOH3s8U6fPrvQ+4UfKwO75BJ6Rf2P3eAIiu6KVG4quzNQ6y4ces8ahFbO1JHcnautXqN3TcM0Yi2vOBV4/VGVvgZCmuH7qhrHu1aOqd6ZowOrL8K8yaHdk2VPEGyo73/vWLP4WnIzV/x3sDjdhrnkOI/UPzCX3oLZ+WeehGKlOb16dOKryjd1RUFjGcu3qKMyqegVTUBOMje+iI9qj0uccvD13O2r3r07Sad9KjOUPYf45zhly0lBPmu16fJGnFunA+AorZpVdUC8q5XVEJ9S+shOz/q6IL5f0mAWK+8uWkZgFiOiCylqCseQu1I4ffNtv9kZ0ccWrtyI7O0mLwsyDFf+hrSDKt6XmQtQ6IwDd7Excf4yG/Wv8HY3fmItuw252NoVn7Cv3vV4HJVS7X3VDp3K2ohNHoaO6Yf59kf8CydvtfXsZcGYaFKSDlQ+eHDDL7mevm50JgLHsXmfVlvCLWk/MpqZlEBcThct0ftmVUjSJj2V3Wunk3e7UdBKbHBwUkNQknt2p6ZXeB7B9Zyr/vvZuLrn+XiZ+P7U2n444jN3mSghp5nyhlNP8P7+OlixZuWhvh62oWkjMagtz2lCM5Q9Bzjanx+6c83HNOAmtDNi30rvlW9qCnK14Bn2BdcxHGGtfwvVLH9SGdxr9H0LhZ/Vs+FcpAdFOteLRwspHN5CK2WJ2uxvwHD8Lz/DfsFuch5H6R90Hkb3BWbLrjYBoVE1XWRdkOJW4VaCLJthbvV8oVYGh8vegrFzM6aNw/dLXWRq8ZxbmjLG4fuzUMIeGNcYeswBBTVB5aeXfb+XXi0piHdnFSU4c3gu0HidmdU30mM3e1HAvdhRRexeUm5i14wZirn4Gc/XTGGte8G3Hnmwoeo/ylg5sgtq70PkioJZ7DAtRTVbfN7Cje6Ma6WR6tWcWxtYvsdvfVPGKraI2UI2ZytmKDmmG3f0xpx+rXeifODwHfHtfDox3Bozm7XSGjZY3aNQVimfYVIydP2MsuatmghU+a/BnyR3atuS7j18gLDSE1LR0brn3KSIjwjlh6DFlbv/VpKklyVvdwE/G6qWgJs4v/+G0xljyXzBDsLveX/q+3B2YS+9xBvrsX4NOGI5OHlv5sXypMKvpVgb5e1Dbv8NIn4POS8Vc8TA6pAWevq+hcrZit70W849RGFu/cnoIViRrhfOBNawdOqITniYjMRfcgLnyMVh6D1a3B9GtLm1402RFw6et+ptMcUc6rQy0RqXPOdgzqaGy8xpOxWwxw4WO6QOAjuqGWv9mnYegDqxHp5zu1bY6IApV1oCjqsre5CRFYss+36iM3e1hp6+4K9SpnCxe1p6/BwAjYy6e4yajm5zgDPhJm4E59xKMxf/B7vlUzT2PumDX44s8tSkwHir60F9PWhkQ0sKp3N2/FiK7HLy9HidmCU52KjML9vrUj1llzMNY/xYqczEqcyFW53uxu9xTe3HWpvIGfxXRrS6isNnZkL0J12+DUel/o2O97P3rOXLZa6WCmqDy07C6/s/3xwpR15RyhoJmLkE3/5e/o6lbBXsx51yAHTOg0gpMHZyIse2boq8Udqc7aj28+kLtnobKXOzkJyK7QmhLCE7BWPsidodb6j6gQh8Ts8qEiM4Yq59BWTkVvi/r+OOwOtyEseHtGghUVEWtf+JOiI9hT0YmHsvCZZpordmdlk6T+NJXUpskxLJ9x8HqwJ2702iSEFvpfaGhIYccK5aRwwayeNnqchOzZ546kjNPHQmAx+NhyNiLauR5CoeO6IjatxKdclqp29XGdzDWvoTSHnTyySUf5gFU2kyMzR+jd/+OytuB3vYNHm8Ss1au960Mij9Y1ETF7N4FuH47FqUttBmKZ/RC3F9HooMS0c3OKhmeoZPGYi64HjtlHAQllLs7I32WM7imOAFmBmL1c/owq80f45p7CZ7wDuj4IdWPXQhf1OPlxzo4BSNrGSr1N1wzxuIZPr1BJ2eVld9gesyWKbw9av/aOj+sOrAB29uK2bC2qKylNZZsUtmbISjJOVGvClcohLUBzwHna082BASg8lOxer+EDkpwkrIAgbHopqdj5+5A7ZhU7djrnC5E19eLPLVIV1YxW09aGaAMCG+PsXMKdg0kZqvcl9YXgXEQ1hqVNgOdfIrXA3zMORPQCSOwmwyH5mdjLrkTY8ckrB5PohOG1m7MNTxoSGUtQYe0qDgx7QqByM7Yne/GnHupM8THFVL+9sWsHLTpxXaH0DH98Az6Ap18qk+PE8JvIrv73ObjaKBSp4MrBGvE9Erfk+z2N6IyFqC2T0JlLcVud7137yENndaY865ABydid/8/CGsNgNX/bcy/xmO3vMC5+FqXPAfQLu/7fgN4Bn+J65d+zheVXTALa+esesjfc3Agragztd7KICYqkg5tW/Lzb38B8Puf80iIi6FZSmKp7YYf258/5ywgPSMTrTXfTP6tJLla0X170vdi286yvuycXP76eyHt27as7aclyqGje2Euux+17WunzL9oWZyx8yfsTv8FQG35pNRj1P7V2C0uwDNiOp4BHzo9ajOXVHos5cuwHKWcUv4aSMyqvQsgrA128sl4TtvhDKAACIgstZ2dcgoA5l9nVDjUS+2ZhY4bVOZ9usX56KBEXNNPqHbcQvisHg//0kknoUOaY/5V1Bdp/Rt+jqia7Lz6kaCpIh3eHlWwx7fp2wV7nav4mz6q2tBIuxBytqCLTpYrjTHuWNAWas8s349VlvxUZ7BCdRUnPzwHnB5gB9ZjJ514xAVOKKpM3r+++sesa424Ylbl7XYm3Je19NEuQNeHillAh7fFXHoP5O445EbL50Ri4ci5eEYvrOHoyma3ugTXrLNxTelYUmle8QMKUdmbsLrci939UewOt2C1vdpZufXn6ajUP5z3IttTvRYHdsERK7SM+dfg+rXsc72qUhnznSoub0LqcDMohdr1k3c7r0rFrOFy3rdkhZdoIHRUd+eCbSOjMuZixw/x7v09KBFr2M9Yw39H2QWYcy+p/QDrAbX1S9CFWMN+c1Y3FdEJQ9HxQzBWPV23AWntnCf6+r4c2gq7w81odxS4K15doqN7o2MHYKz4v6rHKaqs1hOzAHfccAnfTpnG2Zfcxoeff8/dtzpLux959k1mzp4PQEpSApddMJ4rb3mQsy6+lajICE4fO6LS+37/cx4TrryTC666i8tvfID+vbty8qjj6uJpiTLY7W/CM/BTzLmX4J4YhntiOK6femDsmISO7omn/zsYG95x+noBWHkYa1/BjhsIoS3Qzc/GbnkB5prnKj+YL8O/wEkw1UArA5W5FDvpJKzBE0uSslbvl7B6PVt6w4hOFJ6RibILcU9u63wwO4Sx5C7M309AbfsGu4KluNbAT9GoBt8HTTRA2lN/q9wMN9bQn7A734PV9UHUrp+cvswNlZV/8CJPQ+SOdPoL+jBEQ+36BXPJnZjLH8RYep/vx8zZAhgH+5xXxnBhtzgfY8mdNTLNXeXtrnA1hPc7MpzKNE82KnMRBMRBcNMyN9URnSF3m28J8PqgkfaY1ZHdwHMA9/ctMJbdf+QG9aWVAWD1fxcd1aP0ILqqVMxG9YDw9jUbXDnsDrdSOHY9Kmcz5oIbK39AcfL2kEogu+ezeE7dhtXzCVx/jML1XTKuKR1Q27+rclyuXwdiznRWfqndv2EsewBzw9uozIWQX0MDcrWNseZ57OSTvdtemejQVihvEthQ6bJXIY4GOrIbKm+X9zM9tOX83/Y4iTs/DiBUaTNQu36BA+udC38+fE5U6fPQMf19O6AZiNXreVTWMh8jbZiMrV86CdkyisDs1pdiVONvRJVYuShscPtWMQtgd7oLz2k7K79oZriwm53t07m8qDl1cpbcolkybz73wBG333Xz5aW+Pm3McE4bM7zMfZR331mnjeKs07yYxizqhjLQTc/AsvIxVz+F3ewcdHhbXLPPRYe2gYhO6N3TMBfejDX4a9SOyeCOQLc6ePXNbncDrl/6QNcHISjRuZpX1hU9K8+3RIZy1UzFbNYy7FYXlbrNbnN52RubwXiOm4J7UrIzxdEMxm5+LrrZWZirn0YHxGIN/73CibU6sgsKDZ59Tl9NIeqKXVhvK2YB5wSi420ATvXsqifxtG6gV/Lt/AZdMQtFk90zF3vdc1XtX43d8kLs+KEYmz7w+XjqwAYIbeFT4sju+j/MP07EWHY/do8nfD5mybFTp2Osew07sWbOP5SVgzn/Wow9f6KDm5V/8hwYB+HtcE0fjdXraXR8A7kQXd/fS2pLQBSeUfNQWctw/TkOY/OneE7ecPDnaxeAWT8SsxhudFg71IENJS2ZVH3uMQvO9zGkKYWjF+H6dRBqy2cV94rM34N2R5ZOhhf9LHTry/BEdsecdY5TWZvxN7rpuKqFlbUMBVj712DOGItucR6evm9gbP8Gc95lWIO+qNbQN7XpA4wN76DyU9EtL/D+gQHRTm92b3iyD1bzC3G0coejQ5qj9q9GH3ah1VjxKBhu5zxTa0Dj/ioEHd4RHRiHsedPPP3fRbc4r87DNlY+jrms9AVtq9fz2G2vqvzBtge1dz66t48DAQG7xbkYi25xVoJ6e1G8gVIZ87A73FTmfTp+CCp7Q90u+ffsc/7v41BGoOjvnJcrGVwhNVK84CuV/jfGxvew+r5a58euL+qkYlY0PrrFuXhGzcfu9B900zMoPHUbRHYGpbB6PIHaMxu1eyrmkjuxO95e+kNoeDt00omYcy/GNbkd5tyLD14FzN9zcMmrnedM5fWW4UYVZDiPz9lStaWzWqOyljpVMN4KjMUz6AtU1jJ0RGfMv/8NOZsA8Jy0olS/3TK5ItCuMGcKpBB1qSFVuYW39W4p66EKMupP5aGVh27IFbOATj4Jte1bLzfWqN3T0FE9IDDW6yquQ6kD69He9pctZgZhd74bY+P7uL5vheubOGcavY+MDe+gYwdgd/2fz48td597/kQbAdid/lPhdp5BX6JDmqF2TKmxY9ca24M59zLnwkNjrb4LaYZOPBGr9eWovB2QufDgOY2dX28qZgF0WBtU6nTMeVc4ibn6npgtFtEJa9BnmP9cA/tWlruZyk+r8EO0ju2PZ+xqrK4PYK5+psJ9ecP9Uzd07ACs/u+gW/0bq/97qOxNGGt9T4gcDFJjLr0XHdMXzzEf+ZTg1e4oZ2CaNzzZ5U/wFuJoEhB98Pcie5NTMb/xXczlD2AuvRvzt+Nw/ToA1zfOfBu7xbno5DFYba/FXPGw83ly38qi85pfMWef5/0FkCow/xqPuew+rN4voAPjKRy7FqvtNZgLb3QGSlf2+PnXOBXxER18P7g7Ep0wAmPje74/tiEp3IfK24kO71j2/e5IdFASxrav6ywkY+tX6MjutX/OYAajPDm1e4xi2ZsxFlwPODOHKKihFSUNVAP5xC0avEObYwfGYjc/B9fMU7Bj+mG3vvSIza1ez2KsfQm7+bmYKx/FnH4CdutLMHb9AsqF1f9t52qODxVmqjAT1+8jSr7WYW3xnLTct+eRu9Xp7xJRzht1OXTyqXiSTwWlMLZ/i/nP1c4d3lTAKoXd/THMOROwBrxXZt9BIWpFPR7+dTgdGAcFe7wermIsfxBj5eMQnOIkB12hWO1vgkqm09YaX1cA1EN20/G4ltwN+WmVDkQw1jyLytmC1WIC6sBa35PqANkbvO4veygdfxyqMBMKM7GbnIC56kmsfm/6thMrBx07vEpLyspjxx+HNWxq5RtGdEQ3OR5z0S3ONPmqVE/UleyNqK2f4xk1H4KT/R2N/yiF3ecllOcArjkXgOcAdttrwR1WvxKzTY7HXPU4WrkxIjo4sTWExCygE0ejmwzH2PUzdkSnsjfK3YEOSqp4R0YAutXF2Lt+wdj0IXb3R3wLpKhllqf/e+joXqWHAwZEYbe5HGPhrdjJp0J4O9/2DbB/NRTsxe72sO9/MwKinRYs3pBWBqKR0K4IKNwPOHNEVPYmzMXOXBTPwM9wzXaq8D1DfkCHNDv4GVDbqJmrcX+bULKtSpuB2vkTrmnHYfV5BR1/bA0GqlF7ZmLs+AFPvzfRLS/EbnOlc1+gE4NKn42O7FzBPmyMTe9jtb22yu/tdsdbMWeOc4ZfVXX4aX2mNcbqZ9FByRAYW+5mdrPxGCsewW51cbVWQFTKysNY/j+Mda9iDfys9o5TzAxx3v/rgLHlU8z1b6CbnYPa/h266Rl1ctz6SipmhV/olhOc/zc5vuwkSnAKdvdH0a0vwXPCHHTKqZhL7sbY8hlq20Qo2Iuycn3qMesZ+DmeQV8evCFnG2r3NJ/iVvtWQmhr30+GlSqpCrb6v4uR+vvB271gt7kcq98bTjWIJ9u3Y9dXfuzLJLxUj4d/HSEw3ll2W7C34u0KMjAW3Yax7nWsoT+hQ1ugQ5ujA2NxTR+J2vwJauN7sLduhtcAzrJZK7vhtyoJaYaO6Yex/GHn67xdZf6eq9TpGMsfxhr0JQREoQNiUfm7MacN9anKxGll4HtiFjMQO3EUOqQFVreHUZs+rPx1czgrB12DU4k9x36LNfBTr7cvbqGg0mbUWAy1QeXtdBKykV38HUq9YKeMQx1Yh8rbhbH6aWfYW31KzMYfR+EJs7H6v42x7H+ojAUNZ9UEoMM7ojKXlNtrUWVvAG+q7JXCThyFyvjH9yCKPtDq5DFOAuew80U7ZRw66URcUwfg+qk7as9fPu3e2DkZHTuwahfyAqK8fq9TuTuhJoYbClHfucNRhc4ycZW9CbvJSDxjVqLNEHT8EKz2N2N1+i86cWTpwhxlYA3+CgAdEIO58BaMXT9j9XoGu9lZmH+Og70Lqh2eseopjCV3Yiy4HvOvM7FaX4pueWGpbex212HHDMA1/xqMhTeXv7O8XWgUdvdHqxyPjh8GAdG4ZlXQNqYBM9a9jLHpAzxDKu4ha3d/HIISnGFoxb2Ha4HaMRljy5d4RvyBTjqx1o5Twgyus8/oqmjmkGv68aj9aw9eaGikJDEr/EJH9y36fyXL+MGpsG1/I9YxHzlLCkKa4ZpStPzCh6v5uuk4dMqp2NG9sXo+g93xdox1L/sWeF4quppVPzq2P54hk7B97A2om57pVDvs+qVax/c3teN7yN2B++vIouE9ol6yC5zexg3lQ7kZijaCnGrN8hxYj+uH1hhpM/CMmO6ccA+bit3zaeyeT2P1eAxz1ZO4/rkSc/61dRd78RK6gIqnpTYEdpd7nSVudgHu71vg/joS89dBGKueLNnGWPcadvsbD7ZxCUoEQBVkOh9kcnc6y/EOG5hYSuF+VPrf6KpUnAHWsZPwnLgEontBRCdnGrsvPLk12n9RJ53kW5+y8HbYLc6vWuKoLuVsRRf9fAXolFPwDJtK4Zk54I7E2PplvUrMohRE90Y3PweddCLGlk98a93kZ7rJcIzNH+P+Kghz9rml7lMZ8zG2f48Oa+vdvmL6ofbO9/0Dd/HF8/LeH4KTsY79Buu471H7V/t8TmdseAe7ir3UdWAC5Hsx5EjbkLvNqQ4U4mjnjgCPUzHLgfXo6J4QEIPnjL0QGIfd47Hy2xaZwRSesQ/PyRtQeTucFkvRfbC73Ivd+W7cvw7E/OPEqrXPK2KseR6V+gfGpg+xBk/E7vNKGc8hHGvEH3j6vIzau6jcfansjRCcUuZAK68p5axcLf6e1WNq11Rnpau3crdjLL0Xq+eTENW94m0NF54hP6BS/0AVF1zVApWfio7q6gzWrAPaVXcVsyp9Lp5BX+Dp8yqe4dOc/raNmCRmhX8oReFZ+eiUU71+iI4/Fs+Ji7F6Po0O74in72tVarhtnTAbu921zhKEHT84PU3KCnHrRMjZVvq2/D0VLmvwlk4c7d2S1VIHV9hNz6jTfjY1xVj5GMbSezAW34HrrzNLWjn4nAwRdUNrzAU3Or2MAhtIxYxSEJRQYa9SY8f36PgheEbOLXMJqW59KZ7RC7HaXu2cuNaVgr1oI9CnFQD1lU4YhrLzMGce0nLFFY7aPqloA41Km+EkIkvuD6HwrHw8J/wFQU1w/9AS9y+9cH/fAsqZ/qsyFzm7q+rwLaVKKs7sJiMwVj+D+8tAryczKyvH7z8vHd2n3iRm1fZJmHMudOIp3IfaPc25beEtENzU3+HVH8p0BrYpEzvlNFTmInRA9c8paoPV9UE8w37D7nyXv0Pxmm5yAoWjFuIZ+Clq+3dOghHgwHrM34dhJwzFbnOFdzuL6OQ8ft9q34KwctDKXenSVh03GLvlRb6vSslPR5fXqqEywcmo3B2Vb5e3G2UXyO+uaBS0K7wkcVq13vXO+VvhCXOcr4uqau32NzlzVbJWYCy5u2rFKFY+Kj8Va/BXeE5PR8cPKX9bpSC8A0b6LKdVV1nnT/tWVqkF1OF0cDLk7ih3dUK9YOXhmnmycx7iJWPdqxAQjU4a490DghLQSSeidvuQmM3dgdozGzwHUJs+xFhwY8XbF2RAXZ4nmMHOap7alrsdtW85OqYvuvUlziyiRk4Ss6LB0YmjsI6fgW51cfV2FNEJu+VFmAtvchKExSfwALYH15zzcP3YBQ6sO3h7/h6nl6Wf6MSR5SaS6yNj2f2Yf1+Muex+1N6FqOzNWB3vQGU5vX2NDW/7OUJRFrVnJmrb13iO/abhVMxS1Ge2vIpZbaO2fIFOrHwZkI4/zjnhrCOqIBMCYurseLVKmXhG/IFKnY52hTlLAHs9jZExF2PF/2EsuA5VkI4uqxLBFYY18DOsHk/gOW4KdtyQ8gdMFGQ4yfMa6H9pd7oLHdPbCX+fl33HrRy/X9nXMX1RGfP9/8FIa8z512Fs/Rzzj9G4v43HNeMkzGX3oaN7OpU14kjBRZXEIfU0+RXRsWb7I9aVyM7oxNGlWtuorGUQ3hG751POcn5vGC50dG9Uxlzfju/xvjerdoX61p5KW84KiyqurvA2maKylqJDWzX4vudCeMUdgbHlC1xTj0Glz61aiySA6F4Ujs8+eFFGKez2N+I57nuMPbNw/dSzpJett9T2b51/BDXx6nxchzR3HrfjB1y/DnaKjA6sdy6a2gWY615HJ5/iUwxlCk5xLlAXXSSvFtvjnMvkbHW+1lbV5g4cRm39wvl/2kzn57pvVSVxFGJs+war+yM+vffpyK6o/V5cwMvdjjnvSmew+R8nYqx5EXPe5ZjrXwMrv/zHFWSg6/IzghnizPHxHDhYVOGL/HSvWua4fyj6PavLQph6ruF84haiFljtrsFc+Tjmn6ejrGzslhdi9XgctX8t2ghA2Xm4f+xC4RmZzpTC/D3VbmVQHTq6F+Tthtzt9f+NLD8Nc+Vj2K0uwer031LLgOwu90HeDlxTOmLOuwId1Q277XVe99wVtctY97rTzL6+JgzKExiHytlKWR85VdofqH0rsZqdVeludFg754P8gQ1QA5UFlSrI8D5Z0ADo2GPQKadhbP/GWXIb5lQnm8sfPLhReSe9RR9kAKygJrimDUXHHYtuOq70dgV70TX1PQuMxe71HOrABlTWCnTC8MofU8OtDKpCR3ZzXjt5O/z690BlzAU7n8Lx+50WE6m/o5PH+r2iuL7TRasRZLl4LXCFos0QZ9l+YCzmghvKvhhUCR3TD5UxD93y316fnzjV9F6+N7hCvev5answFt0C7ginxVBVP6QHp4Bd4LSJCS5/CJpKnY6OHVC1YwjR0IQ0R+VsxkocBa0vRccNrPq+ykqeRvXAM3oBrp97onb9jI7t77SwqazNz96FuP6+0KnA9/YidGgLPMf/iY7ui9r8Ea455wGgQ1pgNxsPyqiZPp7uCKyu/8M1bRh2h1uwu95f5V2pnZOd1jNaY3e8FZUxD5W9Bc+YlQc3sguc5KW3A1cL92EuvhNP3zcwdk7GnOasVLGO/eaIlVYq9XfUvlUlczV00/E+xa/D2mAs+S9qxw/o5JOP3CBvN+asf6HydqKyN+Lp+zqqcB/m4tvRRoDz3PLTyv3MpfIz0BEdfIqpWlwhKGyMlY9jrnqCwrHrndjsQoyN72Inn1z+MFfbg2vqAFTuVuyEEdhd7kPHHlPm308d2tK7lpaNiFTMisYtqgfWwE/QzYrehLOW45rUFPOfq9HJJzvLiwG1+RMoyEDt+N75g+ovrjCI7Ozd8lVPDsbKJ1Bpf2Kse83nq7TVpXb8gDaDsfq+emRvJsMFIc2xBn2BDmmKueg23/v9itJyd/jWR6kCas+f6JTTKt+wnlE5WzEX347a9MGRd2Zvdj5oBiVUvqOobuhmZ2KueqLmgyyDseVzdIwf31dqgd32KucfgfFO65rT0yk8Mw8dEIv2dnlsZFes/u84gxUOe/9SBRk1XmWsk8ZgbHzXu15wVraT+PEnVwiEt3OGHfmR2voFOmWc80EzMBbd7ExJynqjqOJaR3b1cyBHqcB4VF6qsxQ4bxd2W9/7huuYfhgZ83D92OngUMNyqLQ/IWu509PQ22p6VxiquGK2gipWtfcfjC2fY2x8r6jtTRXfe1yhEN7B6Z1bnoJMjPVvYre+vGrHEKKBsZuMcP7f7UHsNpfXWt9vO+U0jI3v4Z7cDtfkdqj0v2HfyrI31rpkCb414H2fjqNj+oFS6JYX4Bk8Ec/xf6FyNmOufgbPgPeq11/2EHan/2IN/w1j5aPefcbM34PaM6v0ClVA7VuNbjoea/AXmKuedL7/2ZvALix6QhpzzgW4v43z+rOssfpZdGRndMsLsQZ9gWf8Aay+r2LOmYDa+D7G/GswFt2K2vY15sxTMVY+jrHycawej1XahuZwOqo7Slu4/hqP2nzIEFetMda9huuHVhAQhd38X3iGT0O3ugi7xXnYLSZg9X7RaWdU1mo/bTlxbvkEHVa1eQpV4gpHm8EYq58FcF6n+1fj+qkbxqLbMJeVn4RX274COx/P8Gmo/D2YM8bg+rET7F97xLY6IA7bi2KZxkQSs0IAVu+XKBy1AOuEWVj93irqedIPz7jdePq+hmv+Nbi+b4GO7u3VUuja5FRwVJyYNaePxPV9M8xl9+KafjzGysdxTekIRVNHSxRkVrx8ogqMda9A9iZc/1x18I9qOXTyydhd7sNOOQ1z0a3Vao7fKFl5uH7sjGtiOO4fWmEs/k/192kXQt7uBlnFZfV5Gavr/zAX3oyx5gXIXIL55xkYq5/F2POXT/3yrI63ORdkDuszXeMyl6K2fY3V+e7aPU4d0/FD8Qz77eCSW1cYKIXnxMV4Rv7t/X5SToPA2COXExdkomt4WJrd6iJnaNm3CaitX1W8sScHXP5PPuqoHv5NzO5dgLn2JexmZ/ovhgbK6TH6b6eXqahxOqwVxppnMZbegzaCqtSPWsf2R2UuRmVvxFjxsHPOVJbMJbimH4/7l96YKx7yrWLWcwBytuD+Kgi1+9cyN1O7p6GbHI/V/52iHsVVX12ko7qjslaUfeeBdZizzkJH92qYbSyEqIrw9hSemVvrLaXslNMwdk/Fjh+Gbv4vzOkjcf/c8+AS/pINPahdP6IOOMksXdSztip08snomL4Hb6jhvzc6pq/TuzpzYfkbWbmYf5yI6/vmmH+Mxpx1ltNz98B6OLABY82z6PD26MST8PR9A2vQV857XO522L8aY/mDqD1/oQObYGx4E7SNsewB1O7fQNu4fu6F+fsJGKufxvVDG4xl92Ps+AG77dUH3ysNF7rlhdid78L1zxWown0YaX/hmn0udrvrUHk7wcqv2tyCkGZYXR7A6v0i5j9XYqx4FABz/jUYy+7H6vc21qDPsbs+gI4b7DwmMA6r/9voVhc5FxHLSMwaK5/A3PA2dpMTnFVIdcUMwnPyBqwhk7Cbjnf+/u36BcxgPCP/Rm3+2FnpofXBPsZ2IWrje5jzr8Xq+SQ6bjCeUfPwnLYDHTsQc/XTRxzGWV0i7XIOJa0MhADn6mFkFwB0i/Ow9i7Abn4umMHo5ufBP1ehk8Y6Uxr9vNxeR/dBbZsIhVmo3dMgIAadMLTkfpX2J0baDKw2V6L2LsTImOssoSnulxvWDnP+NejAOIz1b0JkZzwn/F0zz+vAOicptvo5NAaeMd4NzbAGfQG/n4Cx/g3sjrdXPw5wrkIHNQF3tLNMJWMedrtrnQTRUULtnY86sB6rx5PosDaYs85G7V+H3fxsCGnhvC58vfKftxNQlS+xqod03OCS/8y5l2IuLnot7ZyMNkOwO/3X+51FdELHH4ex7Svs9jdVcFBdrd8dc+k9ToVGaIsq76NeUqrsD/aB8T7vSscNdk7Kmxx/cPe525weiDXJDMIzejHGulcw1jyP1XR82T9bbaHsPDC96yNZm3RUd9TeCj4Q1TJz+UNOHN60fxClBTXB6veGv6M4aumYfpirnkRHbHH+9qsq1KIccoFSNzke93dN8Ayb6iRHD2EuewAd0hy7wy3O6qq9C7yL0RWG8hzA2PwZ4AzR01G9nAtaxfFauRhbv8Rqd70zY6GqAw+LjxnSHHI2O194SvfKNpc/DK5wrF7PVusYQjQ4VXl/8FVULzyDvnTOZVyh0O9NXFM6ofavOVgMkZ+Oe5KzTNzq/gh2h1trP65q0vHHoXb9gg5KwjXnfKzuj5Y+X9v2DSpvF56x66EwE2Pju6j9q3H/2BntjsRudpaTRDVc6Fb/dh4U2RVz3hWozCXo8PZYx3wEyoU5YyzGmudRebvQa15AWc6KA7vDbZhL7sKOGYCx9iWU5wC66HP9oez2Nzmf74OaQM42jC2fYbe7HowAZ+BXFecW2J3vdL4X0b0wfz8etX81avt3WEN/dJbyV/T9C22B2reqdFLY9mCsehLP8N/RcYOqFFO1BMSgm4xAp05H7ZiMsnKwm53tJPaDm2KsfgYK9mJueBM7pj9GUfGEZ+gvpXISmMHYba/EnHkqdHuo9GcAX9r+NBKSmBWiDHbPpw5+YQYWNXOvH78uOrIrxor/w5x5Kio/HfJ2Yre6BJ1wHObyh1CZi7F6v+Qke7KWY3uynQmToS1Q2Zth7yKMrV9gtbsBu9uDGMsfxpx1Jnazc9DNz65WbMaOydixA9Epp6DdUT71KLU73oo57wpnYrI7slpxALjmTIDcXRAY5zSRt7LRgfHO5MeaYuU7PYm8HPRR4w5swI4bgt3+BgA8Jy3D2PoV5orHivr7DHeWpER0dJaWByVVfPJpezA2vAuhzevN670qdPwQPKPmF53spkBgglMJ7ONz0k1GYGz8AB0Qg25xwcEknZWHsfJRjLUvQ2AcnhOX+rz0CYD9a1Cp02U4UiV03CBngEXJDRq1dwF2yriaP5hS2Cmn4l50C3rDG2X3Yiuu7K8HfYF1VHen/YK/2IVYvV9s0O8X4uikE4bBqifxDPkWigbiVIXn+L+gMBMdOwgW3Yo5/1rnb6tyYfd8GvatRO3+Dc/YNc6HfThiqW65XOEYqdPQexdidbwDc9XjmOtfd3rz930VAHPBjWhXOLrFeVV+DqWENsdY+Rnml85y5sIz9jnFCftWoVJ/xzrm46PvQqEQ9YFS6JRTS92kw9s6c02KEpnm0nvQQUlg52O3vqLGDu05fpbTz7QW2K0udma1ZG9GZS7G/HMcOqY/1rHfOH2x9692kpPBSRCchN3jCbA9MGMsuulp2G2vOTLeY7/D2PA2dvsb0EljS86/PSP+wP2r0//aM24XxsrHISgRu83l6JRT0cHJqP1rnGX4oW3KDrj4fTqkKXbH25zncHjLvSrSMf2w+r2Jyt6EHX8cOqbyXt06cTRqxyQo+ixH7g7cPziFB/7u9W23vRozewM6d0dJizK76RkYG952BvliYLe9Bl14rvN3sYyKbB0zAN3keMzZE7CG/nTws5Qn1+9DdOsbOZMWwhv16EOnDop3llzk7cJzyhbI34O57D6MRZNAufAc+y066SRn48guBwchhbbE2PK5k7jt9ZxzdRLACERlzMP19wV4rFx0ywuqduV4/1qM1c9hdfsfuuWFvj+vxBPREZ0w51+PdcwHkJeKsfopZwhVeUtvcraisjcChtOs/9Arnbm7sNvfgNq/BqvbgxgrHkEdWF/mYKiqck3pgA5vhzVsag3u1XvqwHoIO+TEI7QVdsfbsdvfiNq70FkW6QrH2PgO5qonsNpchd37+bJ3pjXmX2egDmzAGlBGj9aGxh2OjjmkqXwVemrZzc7EXHInrnmXY+Vsxe58t/N9WnA9KnMpVp+XMBfcgPnXGditL/O+L29hFhhBzoCxiE7OxQNRLjthKK5FtzlTYs1gjGX3Oq0MipeE1bSQZlid78ZY9RTsX4eO6YdudtbBk8nCLLQy60fFbHQfyN2B2vkTOqnu2+yonC3Y1Uh6CVFbdMJwPAM+hODqteU5dBmw1esZjDXPQ+52jM2flLSV0s3PPvhhH7w+hyr+0G31eQnd7ExnuaqdjznjZOjzklN9u/kjPCetqLEln3bcsaiY31HbnTY9KnORs9pp3uUQ3r70300hRK3SUT0OVtgXZqE2vofnpKUQ1rZmj1OLv9c6YSh2j8cx1r2C1fsF7GZnY848xemJHdEJc+VjWJ3vKf0gw4U17OfydxqchN3lniNvj+55yD4CsLvcezCOovdTHdIM65CK3bqmm5/j02dNO/lkXIvvwJw2HKvvK7j+vgjAWblQxQreGhOc5FQrH8Lu8Rh2j8ecVhN2AYS2qvj5KoXV9zVcP3bFWHY/OulEpwrYyvX/rIZ6RhUU5NdknqJB8Xg8DBl7ETMnv4fLVX8Sb0JUqCAT93dN0K4IPKeX0Sy8HMaqpzCX3o3V+R7sTneUXuJuF+Ke6Czx1xFdsNtchjaD0S0v8nqZtmvqMdjxx2L3qEa7hwPrcP3cx6maNQMxNr4Phfuwhv/qNLM/jPnnOIydPwJg9Xj84JJz24NrYhiesWtLliIaq5/G2PQRdpvLsZNPKbVEsUrsAtwTnemgnmO/QSeeVOdtLsw5F6Ijuzg/z0qo9DmYM0/DM2532Rtkb8L1Y2c8p+2skYrlo0ZhFmRvwT21L1aPx9FBSZgLb8JzwmwIbQlZyzFXPgr5e5wrwcWyVmCufgY76URnGFIxK98Z+pDv/BzsFhOkYrYyWuOa2he71SXYzc/GPakphaMXQzX6rlXKykNt/gRj968Y2yZixw7E6vOy0/Jm7yJcM8bgOW1H7R3fB8aKR1Hpc7CGfOf7gz3ZqKyllS61K1P2Jlw/dcMzdl3ppJQQjUHBXowtn6H2zMLq+kDpi6Q+UDt+QCeOPrjqwi7E9XUk1uCvMLZ/DwfW1crFX/PPMzB2TkZjoLCxU07HGvRZjR9HCFE+tftXzD/HY7e9ErXX6WftGbvG32FVm7H4P5hrnkcHJaKDm2L1eQmie9XMzvevAW0dXf3ZM5diLrgeI302dvxxWMdNrrUhdP6iNn+Kuew+5/PSgPdxzTqLwpNWVPlv59FIspFCNDTFSTMfe6Xaba5AR3Y5WE17KMNN4Zl5oAtxTwzHXHgz2gxF75iCNejzyqs/9q2ErGXYhy5RqIqwtlgDP8ZY9SRG+hysHo+jUqej0ueWmZglf48z1bH99RjLHsRueqbTPqFgDwrtLGEvfv7NzoG8NNTWibhWP4udNAadfDKYgaj0udit/l1h/0u18yfUnlnYXe7DnHcpKm0mWrnRLS/AnPNvrAHvOvurSwfWH7Esqjw6tCWqMNO5unnIH3u17Vuw8zB2TQVXuCRlD+eOhKhu2C3Ox1hyD0oXYvV8xknKAkR2wW57Debsg8tM1e5fMf8600kobv6Qwph+zlLavF2o3B3OxNLj/4R9K50P5KJiSmF1uBVz5ePomL7owCa1m5QFMIPQrS/Ban0JVv4ezHmXY2z+CLv7o87vUT36PbFbno97+QPo9W86LWx8YKx+FnPFQ3hGzCi9ZK5wP9j5EBiH64e2EBiHDk50ljkXtW4xdvyAjh8iSVnROAVEOyuPilcfVdER5w2GGwJicP15OnbcsU6rkFpgDfoce9cvEBCFDk6RXn9C+IFOOB6rz0sYOyaDO/zo6TseEAuAZ8j3ENW9Zvcd3r5m91cfRHXD6v08xtT+TgutoywpC6BbnIun+b8wFt6I+U/R3035u1OKJGaFaGiKEp86JMW3x7kjyk7KHrpfFYDV5QF0bD90dB9cv/RB7ZyMTj7F6QdUTksHY9MHzlK+Gui5qJNPxg6IRf0xCjv5ZIz9ayCvjCpPKxe1dxGeExdDWBvUrqmo3b+iW12EsX0SOqRF6eXrIU2dpRfachrHZ/yDMfdSVP5utCsClfFPudUiavOnmPMuQ2kPavevqMJ9WP3fdabCR3VHxfTDnPUvdOwx2F3ucXrb1QGVvQEd1tq7jYsT+Z4DpSbPmnMmoHQh2h1d0j9IHMnq/w70fR3QR5ww6chuzkCDVU9hd7gVY+tX2G2udJZ2LbgB11/jsdpdj+sfp1+YHTfYudBQ1sUGUSbd9HRYeJOzNC6shod+VSYwzumPtehWJ7FemFW/hgiGNMdz3I+Ys85GB0Q5bRe8kbcbY9tX6KBkzOmj0FHdsYZMgrzduH7uCe4odPwQVO5WrHbXYi75L3b63+gmIwDnAoRuMrLWnpYQjZVn9GJwh9fuh3PDXbeTvoUQR1IK3fICrJYX+DuSGqVj+jq9cms6KXs0i+pxsOf30Uop7N4voBNH4vrrTDCD/R1RvSKJWSEaIM+QH9CRnWtl38WTJaGoofuCG2H5w05P21HzS/XDVKnTi4aKbUHX1BIVQMcNxDN+v/NFUCIqZ3vRHdpJBgQnOYPPAuMh1ElM6uieGDu+R+fvwVjzAlbPJ8reuTKdyckAhftwTR2A3eJ81K6fyt48fS7m/Guwjv3aWYY+6yw8x3xcauqkbn0JnoShzjT3pfdgHf9njXwfSmLY9i3Gls/QYW1QeTuxuj0EZgiqIKPk+VfKDEGjnEq4QxKzuCOdVgyx/Ws05qNSeQO+3OFYx3yIueBmjHWvoHK34zluCgB2z6dRs87C9c8VWG2vhdCW6KPxan9tM4Oxm/8Lc/3rWD2erPPD2wnDUHFD0CHNUWkzsKs5Gb2m6SYjsLvej2vOBKy0Gdjtb8Jcdj8qbaYzhbjDzc6GnmzUvhXoqF6YcyagI7s5Fx2sXMy5l+L6oTVYec4gjeg+GDunOC0kOtyMylpalIwdAXYBKnUGVteH/PvEhTgaBcb6OwIhhKgy3eR4PKds8ncYDc/RnJQ9hE4c7Zw/uiP8HUq9IolZIRognVg3VUp2x9vRwUnOAKktn2LOvw5r4KcYq57A2PgeKntDybYeL5fU+0oHp2Cu+D84sBZcYag9s53l+NqDbnb2wQrihBHOECYrH7vzXc59lXFH4BmzEpX6O8bmj8vcxFh4I3anO0uWnBeeshWCEo7cMKwNdpsrcK1/E+zC8pN4vrI9TmuJ0OaY279xepyisNtejQ6I9b5KWRnOEmRP9sHbrFxUwZ66r0A8CunkU/DED8NY8TCENC2ZsIvhxhr8NXbq787Fi0OT4sInuuW/Yf3r2K0vqfuDR3bFGv5r3R/XB3bCCEzA2PQx5npnOaTV4wnMxf+BA+uxuz/iTE7OmAsBcai8HRSOXeu8VxlurEFfQNYyCIiG4BSnhUSriw7uP/lUzGX3Y3f7P4xFtwIaIrv65bkKIYQQQogGyAjA7vQff0dR70hiVghRPjMI3foyAKwmI3D9Ohhz9rmonT+im5+Dyt6A1ftFzAXXo8Pb1UoIusV5eCI6w4F1qH2rsLo9jMpPQ+2Zjd3+hoPbJZ2IZ/BEdNJYn/vc6oA4KEgv4w4blbUCq/+7B28rKylbLKyds/Rw30pn+Y5dCMpVtb67WmPOvRi1byW4I7CGT8PeMRkd3g73zz1h3yp0ZBff9ukKR3n2H5yemZ/mVNFKsrBmuMOxezx+5O3KOJioFVWmY/pQeGaO/6fU1leRXZxe4UphTh+NSvsDu90N6MAEjLUvYs4YgzqwHs+p2zHnXoIOHO30Pi6mFER1K3f3OnYA7F+N+dd41J6/nGFodTzwUAghhBBCiKONJGaFEN4JjMcz5DvMRbdh93wSu/XlWL2eAVcYdquLaq8XmhmMjhsIcQNLEooajkx0KbPqw7cCY1GFWZjTR2EN+Q5z4S2QtRy1fw3KzgNv+7gqAx3ZFde8K7DaXYdr3qVY3R/B7nCr97EUZELeLkBjbPkUT9/Xnd7AyiwZ9GV1uA1z9VNY7a7z7Xm6Qp0es8Xy052krCS6REMhr9WKFSVKrcFfAMrpX9fiXKzYfrh/7IKdfLJzoWfwV77vOzgJ3exMdFgbrN7PQ0izmo1dCCGEEEKIRkgSs0II70V0wjpu8sGviwfgNPTpkYEJ2E3PxNj2FcbXUQDosDbopNHYcYN9en5Wn5cx5ztJWQBj3evouCFe93A1/7kSY/u3TgxmCPqQpcTF7G4POz03fV3S7QpF7V1YktRW+WmlegYLIY4S7sjSX4e1pXD0IghKqtZurWM+qtbjhRBCCCGEEKVJYlYIIQwX1sCPsez3cU8MBcAa8D46pp/v+4rsgjXidywATw7Giocx/xjtNME/PFlSTGtn+M7MUzH2zKRwzCpU9mZ0ef0blcLuco/Podkppzs9UHN3oJNOwljzHFqq3oRoHCI6+TsCIYQQQgghxGEMfwcghBD1huHCM2ImnsFfVy0pezhXiFPd6grFWHpf+YddcC3ub6JRudvxDPkBQluhE4bVeDWr3flOrGM+xNjxA+bsc9Hh7bH6v12jxxBCCCGEEEIIIYR3pGJWCCEO4W3LAa8pA7vz3ahdPxcdQEN+Ghgup7/r3kWYG5zkqNXzSXTiyJo9/mF08il4kk9x4pDBPUIIIYQQQgghhN9IYlYIIWqZDmmOufNH9KJbMTZ9hCrMBMCOHYTKXOT8O2YAOmls3QUlSVkhhBBCCCGEEMKvJDErhBC1TEd2RSsTtIXV6zl08slQuBdj60TsHo+hMuZiNx0vyVIhhBBCCCGEEKIRkcSsEELUttAWeM7MKX2bOxy7w80A6NgBfghKCCGEEEIIIYQQ/iTDv4QQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pjL3wH4k9YaAI/H4+dIhBBCCCGEEEIIIYQQRzvTNFFKAY08MWtZFgDDT7vMz5EIIYQQQgghhBBCCCGOdjMnv4fL5aRkVUFBvvZzPH5j2zYFBQWlMtUNyYSr7uSj1x71dxiiHpLXhiiLvC5EWeR1IcoirwtRFnldiLLI60KURV4XoizyuhDlaWyvDamYLWIYBkFBQf4Oo8qUUiUZdiEOJa8NURZ5XYiyyOtClEVeF6Is8roQZZHXhSiLvC5EWeR1IcrTmF8bMvxLCCGEEEIIIYQQQggh6pgkZhuw8aeM9HcIop6S14Yoi7wuRFnkdSHKIq8LURZ5XYiyyOtClEVeF6Is8roQ5WnMr41G3WNWCCGEEEIIIYQQQggh/EEqZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZoUQQgghhBBCCCGEEKKOSWJWCCGEEEIIIYQQQggh6pgkZsVR66GnXueOB571dxg1bvIvMxh5xhXV3k9hoYczL7qFJcvX1EBU1ffWhxO58Oq7KtzGm5/pgsUrGDh6AvsPZNdkeMLPdu5KY+DoCaxZvxmAjZu3c+r515Obl+fnyIQQQgghhBBCiKqRxCxOsmfg6AlH/Ld1+y5/h1Zl3ibvykuGHZ4E8adrbn+YZ1/90OfH3Xz1Bdxzm28JzIGjJ/DHrH98Ptbhrrn94ZLX0dCTL+bsS27j/c8mobX2aT+nX3gTn339U6nbjh96DJ+/82S1Y/xm8m8kJybQvUv7UrfPX7SCW+55ktFnXsWwUy/h3Mv/wwuvf0zqngyg8sTnvY+8xE13PV7qttnzFjNw9ATe+nBiqdvf+nAi4ybcCMB5Z47lxcfv9Ok5VPW1UZ68/AKGn3ppub/7Zb1PXHnLgzV2fH/w9ntY09/r6mrVIoWuHdvy6cQf/R2KEEIIIYQQQghRJS5/B1BfHNO3O/fcWjqJFxUZUaV9FRZ6cLvlW1td1f0+hoWG1GA0vjvtpOFcfuF4Cgo9zF+0nMeef4fw0BDOOOWEau03KDCAoMCAau1Da81Xk6Zy+YXjS93+zeTfeOql9zjphCE8eu8NJDWJZ1daOj/+OpNPJ07hxisnVLrv3j068dKbn+KxLFymCcCCxStpEh/LgiUrS207f/FKevfoBEBIcBAEB1XreVXXvAVLSWwSS7OUxHK3uefWKzimb/eSr12uqr9GPR5PtR7f2I0ddRyPPfc2F/7r1JLXmhBCCCGEEEII0VBIRqBIgNtNbExUmfctWLKSl978lHUbtxARHsqYE4ZwxUVnlSQCrrn9YVq3aIZpGvw87S/atGzGy0/ezfpNW3npzU9ZvGw1QUGBDOjTjRuvnEBUZDgAtm3zyVdT+HbKNFL3ZBATFcm4MSO46LzTAHj5rc/4Y9Y/pO7JIDY6klEjBnHp+aeXJHLWrt/Mc699xKq1G0FBs+RE7rjxEnJz83j46TcAp8IP4NIJp3PZBeMPf2pem/zLDJ577SMeuus6nnvtI1LT0unepT333HoFcbHRJdt9//MffPrVFLbt3E1EeCjDBvfntuv+DcD+A9m8+OYnzJy9gILCQjq1a82NV55PuzYtAKd6csas+Zx56kje+3QSu1L3cNIJx7JwySoWLlnFF9/+DMDX7z9LQnwsjz3/NvMXrSB9byaJCbGccfIJnHP6iSWxPPTU6xw4kMPjD9xc8nNq26o5AQFuJv04HbfbxeljR5R8X06/8CYA/vu/5wBIbBLHK0/czfiLbuHtF/5Hp/atS/b92dc/8fk3PzLx/WcxjLILzwMDA0peUyePHspXk6Yyd+GyksTsth27eeH1j1m2ah15efm0bJ7MVRefQ//eXUvi3bV7D8+//hHPv/4RALN//qjkZzH16zdKjvX197/yycQp7E5LJzkxnovOHcdJJxxb7s9z1dqNbN+5m0H9e5bclpqWzrOvfshZp43mpqsOJmCTEuPp1a2j160B+vToTE5uHqvWbKRrp7aA8zt0wTkn88Ibn5BfUEBgQAD5BQWsWLWek0cdBxz8+X/w6iMAWJbNS299wg8/z8AwDE45cWipiuOHnnq9zNfGoc/xlbc/Z+OW7bRr3YJ7br2cFs2SK4x9xuwFDDmmd4XbhIWFlPleYds2737yHd/9OI3MrP20bJbM1Zecw8B+PQCnCv2Mf9/MQ3dex8QffmXFqvX854aLGTvqOCb9+DufTPyRnbvSSGwSx9njRjH+lJEl+05NS+eltz5lzj9LKSz00LJ5Mrdd92+6dGxb6esIYOL3U/ns659ITcsgNDSYnl078Mi9N5b7PUxKjK/we1CWxctW8+o7n7Ny7UaiIsIZOrgvV19yNsFBQbz6zuf8s2gFb7/wv1KPueCquxh2bD8unXA6QKXfh8P1792NffuzWbhkJf16dS13OyGEEEIIIYQQoj6SxGwlUvdkcOs9TzFm1BDuu/0qNm/bwWPPvU1AgLtUovPHX2dy+snH8/oz9wFOEvL6/zzKKScO5cYrJ5BfUMArb3/GPf/3Ii894bQOePWdL/jux9+58arz6dGlA+kZmWzeuqNknyEhQdxz6xXEx0azbtNWHnvubUKDg5lw9skAPPD4q7Rv24Lbr78Y0zRYs34zLtOkW+f23HTVBN78YCKfv+0seQ+ugUrEvPx8PvlqCvf/5yoMpXjgiVd58c1P+d9/rwGc5ODzb3zMNUXJqAPZOSxZvrbk8Xc//CKBgW6eefh2wkJD+GbyNK7/76N8/vZTREaEAU6y8vc/5/HofTdiGgaJTeLYsm0XbVo2LanujIqMQGubhLgY/u+e64mMCGPpirU89tw7xMZEccLQY8p9DlOmzuTc8Sfx9gv/Y+mKtTz89Bt079ye/n268c4LDzLmnGtKKiINwyA6KoJ+vbrwwy8zSiVmJ/8ygzEjjys3KXsorTWLl61m89adpSoxc3PzGNi/B1defBYBbjdTfp3J7fc/zedvP0liQhyP3nsTF159F6eNGc5pJw0vd//T/5rHs699yE1XTaBfr6789fdC/u/pN0iIi6FPz85lPmbRstU0S0kiNCS45LbfZs6lsNDDhLPGlvmY8LDQSp8rQPOmScTFRjN/8Qq6dmpLdk4uq9dt4qkHb+XL76aybMU6+vTszNIVaykoLKR3j7Jj/HTiFCb/MpO7b7mcls2T+WTiFP6Y9Q99e3QBnFYVZb02du5OA+D1977k+ivOIzoygideeIf/e+ZN3nj2/nLjtm2bv/5eyOP33+zV8zzc59/+zKcTp3DHjZfQvk0Lfvh5Bv954Bk+eePxUj/3V975nBuuOI/2bVsQ4Hbz87S/ePODidx67b9p37YFa9Zt5tHn3iYoKJCxI48jJzePa27/P+Jjo3nyf7cQEx3J6nWbsG0nSV3Z62jlmg08+8qH3Pefq+jWuT379h9g8bLVFX4PfbVtx25uvvsJrvj3Wdx96xXszdzH06+8z9Mvvc89t13J6BGD+eDz79m2YzdNk5sAsGHTNtZt3MIj994AUOn3oSxut4t2bZqzeNlqScwKIYQQQgghhGhwJDFb5K+/FzLitEtLvj6mXw8euecGvv7+VxLiY7jt2n+jlKJl82T2pO/llbc/55LzTy9JzDVNacJ1l51b8vh3P/mW9m1bcPUl55Tcdvctl3PahBvZsm0nsTFRfPHtz9x67YUlSYemyU3o0bVDyfYXnzeu5N9JifFsGb+TX/+YU5KY3ZW2h/POGkPL5k4V4KHJn7DQEJRS5VYBV4XHY/GfGy4uSayceepI3vn424PP+dPvOG/8mFJVq507tAGcaroVq9cz5fNXCAhwA3DDFecxY9Y//P7nXMaNGQFAocfDfbdfRXTUweSQ222Wqj51GKWW4ScnJrB0xTqmzfi7wsRs21bNuHTCGYDz/fpq0lT+WbSc/n26lRzz8IrIU04cxhMvvMuNV5xPQICb1Ws3sn7TVp54oOIE3tc//Mr3P02n0OPB47EICHBz1mmjSu5v16ZFSbUwwJX/PosZf81n5uwFnHXaKCIjwjBMg5Dg4Ap/jp98NYWxI48rqSxs3jSJZSvX8cnEyeUmZnft3kNcbOl9btu+i9CQ4FIV0FXVp0cnFi5Zyb//dSqLl62meUoi0VER9OzWgQVLVtKnZ2cWLF5JcmI8SU3iytzH59/8xIX/OoVhx/YD4D83XMLf/ywtuT8sNKSc14bjyovOond3p03CBeecwq33PlVSrVuWZSvXAdClY5sKn9t9j76MeUhC/v47rmbooL588tUUJpx9MiOHDQTg2sv+xfzFK/jsm5+4/bqLSrY/5/TRJc8J4M0PJnL9FeeV3JacmMDGLdv5dvLvjB15HL/8Pou9Wft4+4UHSy5gHPq7XtnraHdqOkFBgQwe0IvQkGCSmsTRoW1Lr76H3vrg80mMGjGIf51xYkl8t1x9Idfc/jC333AxrVs2pV3r5vzy+ywuOd+pjv152l906dim5LlU9n0oT1xsNLt276ly7EIIIYQQQgghhL9IYrZI7x6duf36i0q+Dg4KBGDT1h1069QWpVTJfd07tycnN4/UPRkkJjhJpY5tW5Xa39oNW5i/eEWpZG+x7TtT2X8gh4LCQvr26lJuTL9On8MX3/3M9p2p5ObmYVl2qQrHc884iUeffZuffv2Lfr27MGLIgJKkaW0ICgwstf/YmCj2Zu4DICMziz3pe+nbs+zns3bDFnLz8jjxrKtK3Z5fUMD2HaklXycmxJVKylbkq0lT+eHnP9idlk5+fgGFHg/tWreo8DFtWjcv9XVcTBQZRc+hPEMH9eXpl9/nj1n/MHLYQCZPnUnvHp0qXe49evgg/n3uaew/kM1bH35Nt87tSg3aysnN460Pv2bW3EWkZ2RiWRb5BQXsTkuv5JmXtmnLDk4bU7qitnuX9iVL08viJCjdpW7TmlKv8+ro3b0Tz732ER6PhwVLVtKrqI9sr26d+HbKNAAWLllZbrXsgewc9mRk0qVj25LbXKZJx/atwMv5aW1bHfxZFycd92buK/mdPdzM2QsYPKBXpVXQN145gX6H/N7GxUaRnZ3DnvS9RwxS696lPes2bCl1W8dDKq9z8/LYvjOVR559i8eee7vkdsuyCQ11ftfXrt9M+zYtS5Kyh6vsddSvd1cSm8Rx5kW3cEzf7hzTtztDB/UlqOg9rias27CFdRu38su0WSW3aQ22rdm5K42WzVMYNWIQP/w8g0vOPx2tNVOnz+Hc8Sd5/X0oT2BAAHn5BTX2XIQQQgghhBBCiLoiidkiwUGBFQ78qczhSY7c3DyOHdCbay4954ht42Kj2L4zrcL9LV2xlgcef4XLLhjPgL7dCAsNYer02aUmkF92wXhGDR/EX3MXMWfeYt768GsevPNahg3uV8GeSwsNCeZAdu4Rt+/PzgEg7JCkiMtVeriOUqqk52d5VYjFcnPziI2J4uUn7j7ivkOXyAd7mSyaOn02L775CTdccR5dO7UjJDiIj7+azIpV6yt83OEDgg59DuVxu12cdPyxTP5lBsMG9+OX32dx89UXVBpjaGhIyWvq4buv56yLb6VLx7YlvT9ffOMT5i1cxnWXn0vT5EQCA9zc/fALFBZ6Kt13dUVFhLN+47ZStzVrmugkRNP3VrtqtnePzuTm5bNi9QYWLF7BeWc67RF6de/II8+8Sda+AyxfvZ5xY0dU6zgVOfT1Wpxw1nb5P+uZcxZwzSVH/r4eLjYm8oj3iuyi3xdvHPoaz83NB+DOmy4tqS4vZppOgriy363KXkehIcG89/LDLFi8krkLlvLmBxN568OveefFB71uT1GZnNx8xo0ZUaoivFhxInzUsIG88vbnrF67kbyCQlLT0jl+6ADAu+9DefbtP0BKUu1dkBJCCCGEEEIIIWpL5Q0yG7mWzZJZunJdqeTdkhVrCAkJIiEuptzHdWjbkg2bt5GUGE+zlMRS/wUHBdEspQmBgQH8s3B5mY9fumItiU3iuOi80+jUvjXNUhLZlXrkct3mTZM494yTeP7R/zJscF8m/zIDcCbF27Zd6fNr3jSJ1D0ZZOzNKnX76nWbCAhw0yS+7OrCwzlLpOP5Z1HZz6dD25ZkZGThMs0jvh/Fw9DK4y7juSxZvoZundsx/pSRdGjbkmYpiaUqb6vK5TKxrSO/b6eeNIx5C5cx8ftfsSyboT4kvwFCgoM4e9xoXnrzk5LX0pIVaxgzcgjDBvejbatmxMZEsfOwJdllPffDtWyezNJDevmC8/1p2Tyl3Me0b9uSzVt3lHpdjzi2P263i4++nFzmY7wd/gVOW44m8bH8OWcBa9ZvKWkpkBAXQ3xcNJ9OnEJhoafcitmw0BDiYqJYvmpdyW0ey2L12k2ltvPm++ONrdt3sSt1T6mBWb4IDQ0hLjaaJcvXlLq9sp9DTHQkcbHRbN+ZesTvRXJiAgBtWjdj7frNZO07UOY+vHkduUyT/r27ct1l5/Lha4+wc/ce5i9aAdTM97BD25Zs3Lz9iOfQLCURt9u5/pcQH0uvbh35edosfpn2F/16dyUmKtLr70N5NmzaRvu2FVfKCyGEEEIIIYQQ9ZEkZitxxiknkJqWwdMvf8CmLTuYMWs+b334NeeecVKFS57HnzqS/fuzue/Rl1mxej3bduxmzj9LePip17Esm8CAAC44+2ReeuszpkydybYdu1m2ch2TfpoOQLOUJuxKTWfq9Nls27GbL779mT/+ml+y/7z8Ap566X0WLF7Bzt17WLx8DStWb6BlMycJlNQkjpzcPOYtXEZm1n7y8vLLjHNA3+60aJrEfY++zJLla9i+M5VpM+fyxntfcs640ZVWqx3q0gtO55OJU/ji25/Zun0Xq9du5MvvfgGc5dRdO7Xljv89y9/zl7JzVxpLlq/htXe/YOWaDRXuN6lJPMtXrWfnrjQys/Zj2zbNUhJZtWYjc/5ZwpZtO3n9/S8r3Y83ipPL6RmZ7Nt/MBHZsnkKXTq25ZV3PmPksIEEBVZcxViWcWNGsGX7Ln7/cx7g9OGc/tc/rFm/mbXrN3P/Yy9j69IJsqQmcSxauorUPRlkZu0vc7/nnzWWyVNn8PX3v7J1+y4+nTiFP/76h/PPHFNuLH16dCI3L48Nmw9WzTZJiOXGK8/ni29/5v+eeZMFS1aWvLYee/5t3j2kn7A3evfoxMQffqVpchNioiNLbu/ZrSNfTvqF5k0Tia+gMvfscaP58PMf+GPWP2zasoOnXnyP/dmlk8NlvTaqYsas+fTr1aVay/vPP3MsH33xA79On8PmrTt45e3PWLthM+ecPrrCx11+wRl88Pn3fPHtz2zZtpN1G7fyw89/8OnEKQCMGjaI2JhI/vu/Z1lc9Dv6+8y5LF3hJOMrex39OWchX3z7M2vWb2bn7j38OPVPtLZp3jQJ8O17mJm1nzXrN5f6L2NvFhecfTJLV67lqZfeZ836zWzdvosZs+bz1Evvl3r86BGDmPrHHKbNnMvoEYN8+j6UZeeuNNLS98rgLyGEEEIIIYQQDZK0MqhEQlwMTz98Gy+9+SkXXvM7EeGhnDJ6KBcdMpirLPGx0bz+7H28/PZn3HTX4xQUekhMiOOYvt0xDGdZ9cXnjcM0TN78cKKzfDwminFjjwdgyMA+/OuME3n65fcpLPQwqH9PLj5vHG9/9DUApmGwb/9+HnzydTIys4iMCGfY4L5cdqEz2Kp7l/acPvZ47n3kJbL2HeDSCadz2QXjj4jTZZo89+gdvPbuF9z/2MvszdpPcpN4zh43uqT/o7fGjjyOgoJCPvv6J1588xOiIsIZPqQ/4Cwlf/rh23n9vS95+Ok3yMzaR2x0FD27dSipmivPeWeO4aGnXufcK+4gP7+Ar99/lnFjRrBm3SbufeQllIKRwwZyxiknMGfeYp9iPtz1V5zHC69/zHc/Tic+LppvPniu5L5TThzG0hVrOXl0+YOIKhIZEcZJxx/L2x9+zbDBfbnxivP5v2fe5Iqb/0dURDgTzj6Z7JzSbSUuv/BMHn/+Hc666FYKCguZ/fNHR+x36KC+3HzVBXwycQrPvvYhyYnx3H3rFeVWozqxhDN0UF9+njar1PL98aeMpFlKEp98NZn//u858gsKSGoSz+ABPTn3DN9eD316dObHX/+k9/BOpW7v1b0jk3+ZQe9h5ccHcO6ZY9iTkclDT76OYShOHjWUoYP6kn1I642yXhtVMXP2fMaMHFKlxxY7e9wosnNyeOHNj9mbuY9WzVN44oFbKm2RcupJwwkMDOTjrybz0lufEhQYSJtWzUoSum63i+ce+S8vvvExt97zJJZl06pFMrdeexFApa+j8LAQpv81j7c+/JqCwkKaJSfyv/9eS+uWTYGyv4fl9U/+5fdZ/PL7rFK3XfHvM7n4vHG88uTdvP7el1x960NorUlJalLSqqDY8CH9efrlDzAMg+MG9fXp+1BmPNNn079313IHyAkhhBBCCCGEEPWZKijI93KUjhCN2zsff8O0mXP56LVH/R1KjVi3YQs33PkYX733DCHBQf4Ox28ys/Zz8rnXMenjF0pV9or6rbDQw9mX3MoD/72WHocNXRNCCCGEEEIIIRoCaWUgRCVycvNYv2krX02aylmnHjncqKFq27o51176L3bsqngQ3dFu3/4D3Hjl+ZKUbWB2pe7hwn+dKklZIYQQQgghhBANllTMClGJh556nanTZ3PcwD7877/X+tR3VwghhBBCCCGEEEKIskhiVgghhBBCCCGEEEIIIeqYlP4JIYQQQgghhBBCCCFEHZPErBBCCCGEEEIIIYQQQtQxScwKIYQQQgghhBBCCCFEHWvUiVmtNR6PB62lza4QQgghhBBCCCGEEKLuNOrErGVZDBl7EZZl+TsUIYQQQgghhBBCCCFEI9KoE7NCCCGEEEIIIYQQQgjhD5KYFUIIIYQQQgghhBBCiDomiVkhhBBCCCGEEEIIIYSoY5KYFUIIIYQQQgghhBBCiDomiVkhhBBCCCGEEEIIIYSoY5KYFUIIIYQQQgghhBBCiDomiVkhhBBCCCGEEEIIIYSoY5KYFUIIIYQQQgghhBBCiDomiVkhhBBCCCGEEEIIIYSoY5KYFUIIIYQQQgghhBBClOv0C29izNnX4PF4Sm6bv2gFA0dP4NlXP/R5f19+9wsPPfU6ADNnz+e51z6qsVjLsnzVOi646i7OvuQ2rvvPI6TuyShzuxWr13P5Tf9j2KmXcMcDz5a6Lzcvj4efep3zr/wv51x6O6+8/Rla62rFJYlZIYQQQgghhBBCCCFEhZokxDJz9oKSr7//eTqd2req9n6HDOzDTVdNqPZ+ymPbNvc/9go3XTWBL955ioH9epSbCI6LieKmqyZw45XnH3Hf+59OwrI1H732KB+//ihrN2xh2sy51YrNVa1HC9GAzHv5Hzqf3ZnQ+BB/hyKEEEIIIYQQQgjRoIwddRw//PIHw4f050B2DstWrmPk8IHk5OSVbPPxl5P5bcbfWLZFdGQEd9x4KUlN4sjOyeXRZ99i7YbNREVG0KpFSsljJv8ygxmz5vP4AzeTnpHJfY++THZOLgUFhfTu0ZlbrrkAwzCY/MsMfvrtL6Kjwlm/aRsBbjcP3309KUkJFca9au1GTNOkT8/OAIwbO4I33v+K/IICAgMCSm2bEB9LQnwsm7ZsP2I/azdsYdTwQSilcLlc9O/dlZ9++5PjjxtQ5e+pVMyKRmPqzVP5aMRH1S4zF0IIIYQQQgghhGhsundpz85de0hL38svv89mxHEDMI2DqcWfp81iy7advPncA7z/8v8xesRgnnrpXQDe+fgb3G43n731JE8/dBuLlq4u8xhhYSE8+eCtvPfyw3z42qPs3J3GbzP+Lrl/5ZoNXHXx2Xz8+mP069WFD7/4vuS+C6++i7T0vUfsc3dqOklN4kq+Dg0JJjQkmD3pmT49/47tWjFt5t8UFBSSk5vHjFnz2bl7j0/7OJxUzIpGJX11On/cP4NhDw71dyhCCCGEEEIIIYQQXlGffY7x+ecAWB+8j3HvvajNW9BdumBfczXmtdcBYF96CRR6MD74wNn2zTcwnnoKtXoNum0b7DvvxLz0Mmfb889HnznepzhOPGFwSYXr//57DT9P+6vkvhmz/mHlmo1cfN09zrFtu+S+fxYt58YrJ6CUIiw0hFHDB7J9Z+oR+9e25uW3P2PJsjVoNHsz99GmZVNGDhsIQNdObUlOTCj6dzu+/O6Xksd+8OojPj0XX11wzim8+s5nXHbj/YSGhtCpQxvmL15erX1KYlY0GkHRQZz44mh+uHwyHU5rT1KfJH+HJIQQQgghhBBCCFEp/a9zsP51TsnX9nPPlbrf+ubr0l+fPu7gto89VuG2vjjphCFcdO09NE9JpFlKYukYgQv/dQrjxoyodD9KqTJv//TrH9mbuY+3XniAwIAAnn/9I/ILCkvuP7T1gGEYWLZV6bGaJMSWqmzNzsnlQE4OcbFRlT72UEGBAdx89YUlX3/w+SRat2jq0z4OJ60MROOhIa5jHH2u6s2Uq3/0dzRCCCGEEEIIIYQQDUp8bDRXX3w211z6ryPuGzqoD99M/o2sfQcA8Hg8rF63CYB+vboy+ZcZaK3Jzs5h6u+zy9z//v3ZxEZHEhgQQHpGJtNmVG+4FjgtCCzLYv6iFQB8O3kaxw7odUR/2cpkZ+eQl5cPwI5dqXzzw2+cO35MtWKTilnRaGhbg4LBdw1m3kv/sHf9XqLbRPs7LCGEEEIIIYQQQogG4+TRZbeHHD1iMFn7DnDdf5yWApZlcfLooXRo25KLzxvHo8++xb8uu52oyAi6d21PYaHniH2cffpo7nroBc67/A7iYqPp16uL13FdePVdPP3w7cTHls71GIbB/f+5msdfeIeCgkLiYqO4//arS+6/5Z4nufzC8XRq35rNW3dw/X8fJS+vgPyCAk49/3r+/a9TGX/KSLbvSuOe/3sR0zQwTZMbr5xA+zYtvI6vLKqgIN+vk5C2bt/Fg0++Tta+/YSFBnPPrVfSumXpMuB/Fi3nlbc/JzcvD4Vi0ICeXHPJORiGwY5dqdz10AvYto1l2bRonsx/b7yUiPDQSo/t8XgYMvYiZk5+D5dLctRHu6din+bCPy4koWs8n53yOe4QNye+OJrQhMpfK0IIIYQQQgghhBBC1CS/tzJ4/Pl3GDdmOF+88xQTzj6Fh59+/YhtwsNCeeiu6/j0zSd49+WHWLpiLT/++icAcTHRvPbMfXzw6iN8/MZjxMdG8fZHE+v6aYiGQIMynB4mJ74wmvx9+fwoLQ2EEEIIIYQQQgghhB/4NTGbkZnFyrUbGH38YACGH9uP3WkZbN2+q9R2Hdq2JCXJmbgWGBBAu9Yt2Lk7DYCAADdBgU5PCMuyyc3LB8puICwaN21rintLR7WK4sQXRrP+5w3kZeb5NzAhhBBCCCGEEEII0ej4NTGbmpZBXEwULtMEnIlsTeJj2Z2WXu5j0jMy+f3PuQwe0KvktsJCDxdefRcnnX0VW7fv5vILx9d67KLh0VrDIVP/YtrFENc5jjWT1vgxKiGEEEIIIYQQQgjRGDWoxqrZ2Tncfv/TTDhrLJ3aty653e128cGrj1BY6OHpV97n28nTmHD2yWXu46tJU5n4/VSgKFEnGg8N6rBLEZ3P6sSKL1fS/cLu/olJCCGEEEIIIYQQQjRKfq2YTYiPYU9GJh7LApxE6e60dJrExx6xbXZOLjfd/SRDBvbh3PFjytyf2+3i5FFD+fG3P8s95pmnjuTTN5/g0zef4KPXHq2ZJyIaBKeVQek2F53O7MSmaZvYtXBXOY8SQgghhBBCCCGEEKLm+TUxGxMVSYe2Lfn5t78A+P3PeSTExdAsJbHUdjm5edx89xMc07c7F583rtR9O3fvIS8vHwDbtpk242/atmpeJ/GLhsVpZVD6tqhWUQz931A+O+VzslOz/ROYEEIIIYQQQgghhGh0/N7K4I4bLuHhp9/g/c8mERoSzN23XgHAI8++yZBjejNkYB+++OZnVqzeQF5ePn/8NQ+AEUMGcNF5p7Fu4xZef+9LwKmIbN+2Jbdcc4Hfno+oxzRHVMwCDLztGDb8soFlnyxnwE39/RCYEEIIIYQQQgghhGhsVEFBfqNttOrxeBgy9iJmTn4Pl8vvOWpRyx4NfoyrV1xFVKuoI+5b8sES5r30D5fOvaTuAxNCCCGEEEIIIYQQjY5fWxkIUZe0fWQrg2IdxnUgfVU6acvT6jYoIYQQQgghhBBCCNEoSWJWNB7ltDIACIwIpN0p7VjwxoI6DkoIIYQQQgghhBBCNEaSmBWNgtZOxw5llFMyCwy+czDLP1vBP6/8U1dhCSGEEEIIIYQQQohGShKzonEo7qRcfl6WhK7xnPbBqcx8+E9sj10nYQkhhBBCCCGEEEKIxkkmXolGoaRitpxWBsVandAKwzTYPGML+7buo+XwFkQ2j6yLEIUQQgghhBBCCCFEIyIVs6JxKKqYraiVAYBhGnQ8owPzXpjLlKum8Nejs+ogOCGEEEIIIYQQQgjR2EhiVjQK2i7OzFa+bcfxnVg3ZT3xneNZ9skyDuw8wB/3/8H+7ftrN0ghhBBCCCGEEEII0WhIYlY0CsWtDKiklQFAs8FNSeyVyIkvjSaxVyIvtHiRWY/PZuFbC7EKLNLXpNdytEIIIYQQQgghhBDiaCc9ZkXj4H1eFsM0uHjORSilOP6JESx6ezEtjmvOjIdmEp4Szo/X/MSt6bcQGB5YuzELIYQQQgghhBBCiKOWVMw2EntWpfN2v3cOVo42MiXDvyrpMVuseEhYSv8Uxr4+ho5ndiQnLYdlnywHYPG7i2snUCGEEEIIIYQQQgjRKEhitpGIbh3F3vV7SV2c6u9Q/MKXHrNlcQW6OP6J48lOzabNia2Z9+I/ZG3OqrkAhRBCCCGEEEIIIUSjIonZRsIMMGk6uClbZ2/zdyj+UdLKoIqZWaDXpT25atmVnPX1WbQ5sTXvDnqP/H35NRSgEEIIIYQQQgghhGhMJDHbiCT1TmTLjC3+DsMvfG1lUBHDZXDiiycS0TSClRNXVXt/QgghhBBCCCGEEKLxkcRsI9Ljoh6s/2k9W2c1vqrZ6rYyKEu3C7ux5IMlNbdDIYQQQgghhBBCCNFoSGK2EYlqFcWgOwYx/Z7p/g6l7tVAK4PDdTmnMzvn7SRjXUaN7VMIIYQQQgghhBBCNA6SmG1kOp7RgR3zdmAVWP4OpU7VZCuDYiFxIbQ7pR2/3f4bnnxPje1XCCGEEEIIIYQQQhz9JDHbyMS0jcEd7CZ1Waq/Q6lTtdHKAODEl0ZzYHc2v90xrWZ3LIQQQgghhBBCCCGOapKYbWSUoUjqm8jOf3b6O5S6VQutDABCYkMY+fQJLP1gKemr02t030IIIYQQQgghhBDi6CWJ2UYoqW8yO+Y1rsRsUSeDGq+YBUg5JoWuE7ry0w0/l7RMEEIIIYQQQgghhBCiIpKYbYSS+yWzY94Of4dRt2qhx2wxpRTDHhpK5sZMfpbkrBBCCCGEEEIIIYTwgiRmG6HkvknsWbmH/P35/g6lzhT3mK3pVgbFgiKDmPDr+az6ZjUbftlQK8cQQgghhBBCCCGEEEcPScw2QmFJYUS3jmLei//4O5S6U4utDIpFNo+kx4XdWfLB0to7iBBCCCGEEEIIIYQ4KkhitpE649MzmPfCXOa+OM/fodQJXYutDA7V7YJurP1+LXmZebV6HCGEEEIIIYQQQgjRsElitpFq0rMJZ3wxnhkPzGDn/J28P+T9o7o3am23MigW1ymOhG4WFpfDAABziUlEQVTxrPhiRa0eRwghhBBCCCGEEEI0bJKYbcRaHNecwIhApt72K9v/3sGeFXv8HVLtqYNWBsUG3DyA3+6YxqqvV9X+wYQQQgghhBBCCCFEgySJ2UYuZUAK2/7ahjvUzcZpm/wdTq0paWVQyxWzAJ3O7MTJb53M5CunkL+v8QxYE0IIIYQQQgghhBDek8RsI9d0YApmgMnA2weyauLRW+GpbV0n1bLFOo3vSEzbaJZ/Li0NhBBCCCGEEEIIIcSRJDHbyHU8oyMnPH0Cfa7uzfY529m/fb+/Q6odum6qZQ/V89KeLHp7UZ0eUwghhBBCCCGEEEI0DJKYbeQimkXQ58reBEcHE9c5js9P+4K1P6z1d1g1Tuu6rZgF6Hx2Z9JXp7PgzYV1e2AhhBBCCCGEEEIIUe9JYlaUGPvGGOI6xbH042X+DqXmaVBG3WZmAyMCOfvbs/jj3ums+mZ1nR5bCCGEEEIIIYQQQtRvkpgVJZL7JtPrsp5sm72tZFjW0ULbus5bGQC0GNqC/jcNYPG7i+r82EIIIYQQQgghhBCi/nL5O4Ct23fx4JOvk7VvP2Ghwdxz65W0btm01Db/LFrOK29/Tm5eHgrFoAE9ueaSczAMg3Ubt/L0S++RkbkPl2nSqUNrbrvuIoICA/z0jBq2pL5JZO/OZt+WfUS2iPR3ODXGH60MinU7vyszH5rJgZ0HCEsK808QQgghhBBCCCGEEKJe8XvF7OPPv8O4McP54p2nmHD2KTz89OtHbBMeFspDd13Hp28+wbsvP8TSFWv58dc/AQgMcHPrtf/m87ef5INXHyEvL5+Pvvi+rp/GUSMgNIDEnk3YNmubv0OpWX5oZVAsolkELYY2Z9G7i/1yfCGEEEIIIYQQQghR//g1MZuRmcXKtRsYffxgAIYf24/daRls3b6r1HYd2rYkJSkBgMCAANq1bsHO3WkANEtJpG3r5gCYpkGn9q3ZuXtPHT6Lo0/TQU3ZOvvoSsz6q5VBsYH/GcTsJ2az5IMlfotBCCGEEEIIIYQQQtQffk3MpqZlEBcThcs0AVBK0SQ+lt1p6eU+Jj0jk9//nMvgAb2OuC83L49JP01nyMDetRZzY5ByTFNWf7uaz075nMLcQn+HUyO0xm+tDABaDmvB+C/O4MdrfyIvM89/gQghhBBCCCGEEEKI/2/vrgOrrvc/jj/POeuNdW+sx+ju7hAECQMTE/Xa3d5rX9triygoBkpIitKS0l0bNVasO0/8/tiVKz9AiW1ng9fjLznn8/1+3x/5eITXPuf9qRfs3mP2XJSUlPLo829x/ZXDadYk5qT3qqrMPPPyB3Tp0Iq+PTqd8R4z5i5m5rzFABfdAVc1Jap/JJG9Izi89AhpG9OJ7B1h75IunM1mt1YGf4gZHENAiwD2ztxHu1vb2rUWERERERERERGxL7sGs4EBvmTn5mO2WHAwmbDZbBzPyiEowO+UsSWlZTzw9Bv06taB8WMvO+k9s9nMM6+8j7+vNw/edcNfPnPcyEGMGznoxHW9hk+osflcLNz83Lhi2hX8dMMcDv96qF4Hsxlbq9teBLcL/stx9m5l8IdW17dk1ze7FMyKiIiIiIiIiFzi7NrKwNfbi4S4KH5ZugaA5as3EujvS+Owk0O20rJyHnz6dbp2bM3N115x0ntmi4VnX/kQz0YePPHArfUifLtYtLi6Obu+343NWn93Fn/R5Uu+6PLl39do51YGf2h+dQsytmRwaPEhNn20iYrCCnuXJCIiIiIiIiIidmD3VgaP33cLL731GVO/n4u7mytPP3wHAK+8M4leXdvTq1sHfpj9C3v2H6K8vIKVazYC0L9XFyZcO4qlK9ezYs1G4qIjuOnupwFo1aIJj94zwV5TumjEDImhqriS1N9TCe8Wbu9yTsvB1QFzmZmUdSk07tH4jONstvqxY9Y9wI2h7w/h++HTASjJLKXPP3vbuSoREREREREREalrhsrKivq7HbKW/dHKYNWCKTg42D2jrpfmTpiLg6sjl308zN6lnNYb3m8S1DYIz/BGjJw6EqPp9JvAj287zrdDv+XBjAfruMLTWzBxIdu/3I5PnA8TVt+Eq6+rvUsSEREREREREZE6ZNdWBlL/9Xq2F3um7+HQr4fsXcppWc1Wej7dg4xtx/nhih+xVFpOO85ms0E92DH7hyH/Gcwtv99MQPMAvu43DXOF2d4liYiIiIiIiIhIHVIwK3/JJ9aHvi/1YcmjS6vDzXrGarHiG+fLzWsnkL03m8NLDp92XHUwW8fF/QUHZweC2wUzZvpobDYbe6bvsXdJIiIiIiIiIiJShxTMyt9qc3Mb8g7mkZeUZ+9STmKz2bBZbBgdjDh7OpNwRQL75xw4w2AwGOtRMvtfRpORzvd3Zv3bv2O1WO1djoiIiIiIiIiI1BEFs/K3HF0dCe0cSvLqY/Yu5SQ2S/UOXqND9TJOGNWExPmJpw04bdb6cfjX6bS6riU2q43PWn1G0s9J9i5HRERERERERETqgIJZOSuNezYm+bdke5dxkj8CWIOpOnAN7x6OwWRg6+fbThlb31oZ/JmDiwO3briFVje0Yuljy+plywgREREREREREalZCmblrET2juBYPdsxazVXB7N/7Jg1moyM/nY0y55YRsq6lJMH19NWBn9wcHGg8wOdKc4oJvX3VHuXIyIiIiIiIiIitUzBrJyVsK5hlGSWcHjZEXuXcsIfrQz+2DELENGzMa1vbMWub3edPLYetzL4g6OrIy2uac6OKTvsXYqIiIiIiIiIiNQyBbNyVpw8nBj4xgDm3zqf8oJye5cDnLpj9g9NRjbhwLxEbNb/tQSw2ai3rQz+rM2ENuz5YS9Ze7LtXYqIiIiIiIiIiNQiBbNy1trd3g4XHxcO/XrY3qUAZw5mI3pHUFVSRfrm9P+9aKv/O2YBgtsH0+m+TnzV5ysKkgvsXY6IiIiIiIiIiNQSBbNy1gwGA1H9IkleVT8OAfujlYHRdPIyNjmaiLssjgNzDvxvbD3vMfsHg8FAn3/2JrJ3BNu+2H7GcVazld3T9+igMBERERERERGRBkrBrJyTiF4RJK9MJudADgfmJdq1lj92zP65x+wfmo1rxtYvtpGbmAtU95htCK0M/tD2trZs/XwrJZklp33/8NLDzLlhDkseWYLVYq3j6kRERERERERE5EIpmJVz0rhXY7L3ZfNV369ZcMcCu4aCVrMVg8lw2hYF8SPiaH1ja6aP+gFLlaXBtDL4Q+zQWCL7RPL98OkUHD21pcH0y38A4OCiQ6x7fV1dlyciIiIiIiIiIhdIwaycEzc/N6IHRNHpHx3BZiNtY5rdarFarKe0MfiDwWCg38t9sZqtHPz54H9bGdRxgRfAYDAw8svLCW4XxI9jZ5zUsuCPncIjv7ycAf/uz5bPtp54TUREREREREREGoYGFFVJfTF+4Xh6Pt2TmCGxHPjpwN9fUEusZtspB3/9mdFkpNmYpuyfc+C/rQwazo5ZAJOTicHvDqbwWCFHVxw98XrJ8RIwQLOrmhE7LBaDyUDifPu2lRARERERERERkXOjYFbOW5ub27B96g6qyqrs8nybxXra/rJ/1uSKJiQtSMRaZWlILWZPcHRzpNUNrfh2yHcsf2o5NquNguQCPII9MDmaMJqMtLutHb+/t8Fuvw8iIiIiIiIiInLuFMzKeYvsE4FvvC/fXfY9pTmldf58q9n6lztmAcI6h2F0MnFw0UEwNsRoFno80Z2eT/dg9w972D51B8dWHyO0Y8iJ99vd3hZrpYVJbT8nc1eWHSsVEREREREREZGzpWBWzpvBYODaReOxma3smLqzzp9vtfx9MGswGujzz95s+nAzuQdy66iymuXm70bv53vT86kebPpgE4eXHiFqYPT/3vdz46ZVNxHVL5LVL6+2Y6UiIiIiIiIiInK2FMzKBXF0c6TZuGYcXXGkzp9ts9j+tpUBQNtb2uId4137BdWyFte0oDClkCPLjxA9IOqk9wxGA90e60bivEQKUwrtU6CIiIiIiIiIiJw1BbNywaL6RZG86hiWSkudPvdsWhn8YdTUkbS8tkUtV1S7HN0caXtzGzwbe+Ib73vK+z4xPsQNj2PagG9Y/9Z6O1QoIiIiIiIiIiJny8HeBUjDF9AyAEc3R9I2ptG4R+M6e67VYsNoOrtgNqxLGGFdwmq5otrX/YnuNB3bFIPh9DuFR301kqSFB1lw+wJCO4cS0SuijisUEREREREREZGzoR2zcsEMRgORfSM5suwIWz7bQnF6cZ08t3rHbMM80Ot8uXi7ENop9IzvOzg70HR0Ah3v6cjaf6+tw8pERERERERERORcKJiVGhE9IIoDcxNZdM8vLHl0SZ0802a2YjjLHbOXmg53tif19zTm3DiHvEN59i5HRERERERERET+H6VaUiOaX9Ucc4UZgOx9OXXyTKvFetatDC41HsEe3Lb5VoozSph363x7lyMiIiIiIiIiIv+PUi2pEU4eToz9YSytb2pN1q4sSnNKa/2Z53L416XIK8KLsT+OIWtXFsmrksnem01xRt20mRARERERERERkb+mVEtqjH9TP0ZMGo5/M3/2z95f68+zmm0YTJdWj9lz5eLlQteHujBj3Ew+azOJT1t+hs1ms3dZIiIiIiIiIiKXPAWzUuP6vtSHJY8uJX1Leq0+x2bRjtmz0f2J7oz+9gqaXB6PzWbjp+vnUFFYYe+yREREREREREQuaUq1pMbFD4+nzc1t2Pzxllp9jloZnB2DwUD0gGjGzRzHnbsnUphcwNp/r7N3WSIiIiIiIiIilzSlWlIr4kfEc3jJ4Vr92rzNolYG58oj2INBbw9i00ebKMkssXc5IiIiIiIiIiKXLAWzUisadw+nLLeM7L05tfYM7Zg9P6GdQono1ZgN7220dykiIiIiIiIiIpcspVpSKxxcHIjoHcHhxYdq7RlW9Zg9bz2f6cnmjzez+pU19i5FREREREREROSS5GDvAo6lZvDCG59SUFiEh7srzzw8kZio8JPGbNq2m48mT6esvBwDBrp3acvdt1yN0WiktKycp158j32Jh7FYrCye9ZmdZiL/X8zAaA4tOUzn+zvXyv2tFhtGtTI4L2Gdw7hx5Q1M6TGVJiObENgywN4liYiIiIiIiIhcUuy+3fDf733BFZf144cv3uT6qy7npbc+PWVMIw93XnzqHr6b9DpffvgiO/ck8vOS1QA4mExcf9UI/vPak3VduvyN2KGxJP+WTPrm9Fq5v1oZXJjAVoG0ntCaNdo1KyIiIiIiIiJS5+yaauXmF7A38RBDBvQAoF/PThzPyuVYasZJ4xLioggLCQTA2cmJ+JhI0o9nAeDk5EjHti1o5O5Wt8XL3/JL8KP3872ZceVMzOXmGr+/zWLFYFQweyG6P9qNxPmJJK9KtncpIiIiIiIiIiKXFLumWplZufj7euNgMgFgMBgICvDjeNaZD4zKyc1n+eoN9OjSrq7KlAvQ5cHqNgbH1hyr8Xtrx+yF82zsyYA3BvD9iOnsn3PA3uWIiIiIiIiIiFwy7N5j9lyUlJTy6PNvcf2Vw2nWJOa87jFj7mJmzlsMgM1mq8ny5DQMBkN1r9nFh4keEF2j97aabQpma0CHie0xGGDZk8uIHhiFk7uTvUsSEREREREREbno2TXVCgzwJTs3H7PFAlQHpcezcggK8DtlbElpGQ88/Qa9unVg/NjLzvuZ40YO4rtJr/PdpNeZ9smr530fOXvRg2I4vPhwjd/XZrFi0OFfNaLtrW1x8XJmy6db7V2KiIiIiIiIiMglwa7BrK+3FwlxUfyytPrwoeWrNxLo70vjsOCTxpWWlfPg06/TtWNrbr72CjtUKhciqn8kmTsz2fCfDTV6X7UyqDlGk5Hez/dmzStrSJyfaO9yREREREREREQuenZvZfD4fbfw0lufMfX7ubi7ufL0w3cA8Mo7k+jVtT29unXgh9m/sGf/IcrLK1i5ZiMA/Xt1YcK1owC4/s4nyS8opKS0jJHX3UuHNs15/rG77DYnOZmbnxvjZoxl3i3zcfNzo+V1LWvkvlazFaN2zNaY2KGxDP1gCHNumsuwD4fS4poW9i5JREREREREROSiZaisrLhkG62azWZ6DZ/AqgVTcHCwe0Z90ds3ax+/PrSYew7+A6Ppwne6rvznb5RkFHPZJ+ff2kJOteubXax4fiV37p6Ig7P+uxARERERERERqQ36HrjUmSajmmAuM5O+Kb1G7mczWzGolUGNa35Nc5zcnZh/63wytmbYuxwRERERERERkYuSUi2pM0aTkYQrEtg+dUeN3M9qsamVQS0wmoyM/WEMTp7OfNXna/IP59u7JBERERERERGRi46CWalTra5vyYG5B7CarRd8L6tFh3/VFr8EPy77aBjNxjVl1Uur7V2OiIiIiIiIiMhFR6mW1KmwrmF4BHnw3WXfU1lSeUH3spqtGGqgV62cWa9ne7F/9n6WP7Ucm/WSbUctIiIiIiIiIlLjlGpJnTI5mrhx5Q0UpRWROC/xgu5ls1hr5BAxOTPvaG8mrLmJXd/vZs8Pe+xdjoiIiIiIiIjIRUOpltQ5Jw8nmo5pSuKCpAu6j9VsUyuDOuDfzJ+eT/Vg9StrtGtWRERERERERKSGKNUSu2hyeTwHFx3EUmU573vYLFYMOvyrTrS6oRVVJVVs+ngz5nKzvcsREREREREREWnwFMyKXYR0CMHRzZEN727AZju/XZhWsw7/qisOzg4MfGMA695Yx7fDvjvv3zMREREREREREammVEvswmA0MPq70Wz6aDO/PvDred1DwWzdajqmKXfumkj2nmyOLD9q73JERERERERERBo0pVpiN427h3P1vKvZPmUHVaVV53y91WLDqFYGdcrJw4lO93Rk1b9+w2q22rscEREREREREZEGS8Gs2FVAC38ahXpweOmRc75WO2bto/MDnaksrmJq768oSi2ydzkiIiIiIiIiIg2SUi2xK4PBQMtrW7LujXXnfK3VrMO/7MG5kTMT1tyEd7Q3s6//CUuVhazdWaRtTLN3aSIiIiIiIiIiDYaCWbG7Tvd2In1TOjkHcs7pOpvFph2zduLg4sCgtweRfyifBbcvYM6Nc5nSYyqVJZX2Lk1EREREREREpEFQqiV25+LtQvuJ7fhxzAzKcsvO+jqrRa0M7MkjyJ0bll9P9r4cMndmAjC542TSt6TbuTIRERERERERkfpPqZbUCwPfHIh7oDvbJm8762tsFisGk5awPfnE+HDTqhtx9Xfltk23knBFUxbcvlAHg4mIiIiIiIiI/A2lWlIvGE1GWt3Qiv1zDpz1NVazWhnUByZHEw+mPUBg60B6/7MX5rIqtn6+1d5liYiIiIiIiIjUa0q1pN6IHxFP+uZ0ilKLzmq81WzFaNThX/WJg3N179llTy5n00ebsFlt9i5JRERERERERKReUjAr9YZ7gBuNezRm/9wD2Gx/H+ipx2z9FDs0litnjWP9W7/z9YBpmCvM9i5JRERERERERKTeUaol9UrCFU1Y/tRyXm/0BkdWHP3LsTazFYOC2Xopql8UV3wzipQ1KSx5ZAlVpVX2LklEREREREREpF5RqiX1Sttb2jJ2+hiaXdmMfTP3/uVYq8WG0aRWBvVVeNdw7thxB+mbM5h3y3x7lyMiIiIiIiIiUq8omJV6xdHNkZjBMbS8tgWJ85P+sqWB1axWBvWdf1M/rpw1jkO/HuLY2hR7lyMiIiIiIiIiUm8o1ZJ6KaJ3BBWFFaRvTj/jGKvZikE7Zus9j2APuj/ejR9H/8ia19ZitVjtXZKIiIiIiIiIiN1dcDCbfjybr6bP5c0PpvLV9LmkZ2TVRF1yiXNwdqDtrW2Ze+NcitKKTjvGZrFpx2wD0e2xboz+bjRbP9/Kjq922rscOUe7vt3FzKtnYamy2LsUERERERERkYvGBaVaG7fu4trbH2fL9r1YrVa27tjHdROfYMOWXTVVn1zCBrzWH/9m/qx9fd1p31crg4bDYDAQ3T+K/q/257d//abDwBqY3dP3kDg/kV8fXKwdzyIiIiIiIiI15IJSrY8mT+e5Ryfy7iuP89h9N/POy4/x3KN38uHk72qqPrmEGYwG2t3RjsR5iaftNWuzWjGYFMw2JM3GNaVRaCOWPLYUc4XZ3uXIWbBUWkj+LZmx08dwcNFBPoz7iJ1fn3nXc86BHPbO+OuD+0RERERERETkAoPZ1PTj9OnR8aTXenXrQFq62hlIzYjsG0l5fjnHtx0/5T2rWa0MGhqDwcDlk0eQuj6VD2I//MsewmJ/qb+nMqnd5zi4OBA3PI679t7JwDcGsOjeX0jfcvrfu21fbGf2tT+x/u3fqSrTzmgRERERERGRM7mgVCskOIDV67ee9NrajdsICQ64oKJE/uDg7EDskFgOzD1wynvVrQx0+FdD49/Mn1vW30zbW9oy79b5amtQj214byO5ibn4N/PHYDBgcjTRbFwzej7dg2n9v2HVi6tOucZgMODX1I/d3+/m8/afU5ZbZofKRUREREREROq/Cwpm777lGp577UMefvYN3vhgCg8/+wbPvfIhd99ydU3VJ0KTy+PZN2s/VvPJvS2tFitGtTJokIwORno92xM3fzc+7ziZ/MP59i5JTsNmtdHzmZ5cOWvcSa93e7Qb1y+9js2fbGH71B0nvVeeX07CFQnc8vvN+Df1Z94t88hNyiXvYF5dli4iIiIiIiJS711QqtWlQyu+/vgVWjVvAjYbrZo34etPXqFrx9Y1VZ8I8ZfHYzAaWHjnwpN6zerwr4bN5Gji2l/GE9oxhBXPrbR3OXIaVrMVV18XXLxdTnkvpEMIo6aO5NcHfmXbl9uxVFqwWqyU55Xj6uOCwWBg+KThWKqsfNL8Uz5u9gkrnl1JblKuHWYiIiIiIiIiUv84XOgNGocFM2H8qJqoReS0nDycGL/wGj5u9gmd7+9MYKtAAGxmGwajWhk0ZEaTkb4v9WVS28/Z8tkWfOJ8ie4fZe+y5L/+7ocf0QOjGTFpOMufWcHCiQsxOhqxVlmJGRIDgJu/G+MXXENlcSXHVh9j2xfbmTbgG27ZcAseQe51NQ0RERERERGReumcg9lZ85cyZsQAAH746ZczjrvqiiHnX5XI/+MR4kHMkBj2/7T/RDBrtWjH7MXAK8KL/q/1Y/lTK7BUWrh10y34NfGzd1kC2KxWDH/TLqTZuGaEdAxhUtvPGfTOIBZOXIh3lNdJY5w8nIgdGkvMkBjm3DCH2dfOptM9nYgfHofJyVSbUxARERERERGpt845mF29fsuJYHblmk2nHWMwGM46mD2WmsELb3xKQWERHu6uPPPwRGKiwk8as2nbbj6aPJ2y8nIMGOjepS1333I1RqPxvzVt5f1J32K1WomNbsyzD9+Bu7vbuU5N6rmEUU1Y//bvdLirAzabWhlcTNrf0Z52t7dj0b2/sPzpFYz7cay9SxLO/r8x7yhvHs1/BIA2E1pjMJx+J7vBYGDYx8NYcMdCFj+8mMNLDjPsw6E1WrOIiIiIiIhIQ2GorKyw/f2w2nPPY68wbGBPhg/uzbJVG5j2wzy+eP/Fk8bsTzqCh7sbYSGBVFRWct8TrzFyaF+GD+5NaVk54yY8xEdvPENURChvfjAVZ2dH7r392r99ttlsptfwCaxaMAUHhwvu6iC1rDy/nLcD3wEDxA6J4ciyo9y+7TZ843ztXZrUkOLjJXzS7BP6vtyXDhPbYzAaKDhaQObOTOJHxNu7vEvON4O/pdX1LWl9Y833DS9ILuCLLl/S48nudLy7o37IIiIiIiIiIpecC/qb8HUTnzjt6zfe9dRZXZ+bX8DexEMMGdADgH49O3E8K5djqRknjUuIiyIspPrr685OTsTHRJJ+PAuAdRu30yQ2kqiIUADGXj6QxSvWndd8pH5z8XZh/KLxjJg0nMNLj2CptCjMuch4BLkz9scxrH9rPYvuq26Vsvnjzfw4Zgb5h/PtW9wlyGqxYvybVgbnyyvCi9HfjWbj+5uY2msqVWVVtfIcERERERERkfrqgv7GnXE8+/SvZ+ac1fWZWbn4+3rjYKruMWgwGAgK8ON41pmvz8nNZ/nqDfTo0g6A45k5BAf5n3g/JMifnNx8zBbL2U5DGpDo/lG0vrE1QW2CAGotNBL7iR4QzQ3LrmfH1B2seGYFlqrq/5Yntf+cFc+uxGaz6yb/S4rNbMVQiz/8iOobyZ27J2J0NDHn+jlYzdZae5aIiIiIiIhIfXNe399/79NpAJjNlhP//Ie09KwTu1trWklJKY8+/xbXXzmcZk1izuseM+YuZua8xQAKeBqwZmObkr4pXTtmL1JeEV5MWHUTM6+ZRf6hfHo+05O4y2KZPvIHQjuF0GRkE6D66/DpmzNoOjrBzhVfnOqij7PJycRVP13JN4O/5eNmnzDi8+FE9oms1WeKiIiIiIiI1Afn9TfuoqISiopKsNqsJ/65qKiE4pIyoiJCeenpe8/qPoEBvmT/aXerzWbjeFYOQQGnnsheUlrGA0+/Qa9uHRg/9rITrwcF+p20czf9eDZ+f9qF+/+NGzmI7ya9zneTXmfaJ6+ey7SlHmk6tikGkwGTi050v1gFtQ1i4o47cPF2IbBlAKEdQ+nzrz4sfmQJ2fuqd9VveG8Ds66excFFB+1c7cXJarZhNJ3+IK+a5Orryi3rb6bLg52ZMXYmu6fvoaqsivL8ckA/RBMREREREZGL03ntmH3mkYkAJMRHc+Wowef9cF9vLxLiovhl6RqGD+7N8tUbCfT3pXFY8EnjSsvKefDp1+nasTU3X3vFSe917diatz6YypHkNKIiQpk5bwkD+3Q775qkYfCO8uYfB+7G1cfV3qVILTI5mXgo88ETv257Sxty9mXzRZcvuHHFDRxZdpTGvRqz+OElHN9+HO9ob5pf1dyOFV9crJba3zH7B6ODkY53d8TB1ZEVz65gzg1zABj11Ujm3DiXG5ZfT+MejeukFhEREREREZG6YKisrKiRrUglpWXwp11N7u5uZ3Xd0WNpvPTWZxQUFuPu5srTD99BXHRjXnlnEr26tqdXtw5M+XYOn0+bRUxk2Inr+vfqwoRrRwGwat1mPvj8eywWC7FRjXn20Yl4nMXzzWYzvYZPYNWCKTg4nFdGLSJ2sOKZFeyZsZf8w/ncf+w+Vjy7kpwDOaSsSeH2bbcT0NyfyuJKHFwd1If4AnzWdhIDXutP7NDYOn2u1WJly2db2TtjLylrUzA5mnB0c6DrI93o8mBn/Z6KiIiIiIjIReGCgtmMzGxefedztu8+QFXVySdqr/n56wsurrYpmBVpmCqLK3nT9y0Anqp88sTry55Yxr7Z+xn6wRBWPv8bRWlF9HupL62ub2WvUhu0T1p+yuB3BhEz6Px6etcEc7kZo6ORpIUHWfLoElpc3Zw+/+pjt3pEREREREREasoFpZFvvD+FRh7ufPbO89z9yEt8/OYzfD5tFj06t62h8kRETuXk4cS1v4ynqvTkHwj1fbkvnhGezBg3E5vFRp9/9eaX+3/FPcjdruFiQ2Wz2ux+wJ6DS/X/pppcHo9PjDdTe32Fq78bCSObkL03+6TdvJYqCwajQTtqRUREREREpEG4oGB2194kZn/9Lm6uLhgMBuJjI3nqwdu48+EXGTmsX03VKCJyiqh+Uae8ZjRV9ynNO5jHxg820fGejhidTCy862f6vdwP3zgfQjqE1H2xDZTVXHc9Zs9GQIsAxkwfzZJHl7Lk4SUAhHUJpenYZnS+vxPTR/5Azr4cxv88Hv+mpx4iKSIiIiIiIlKfXFAwazIacXKsvoW7m2t1n1h3NzKzcmukOBGR89HnhT5E9IrAwdmBjnd3ADhxmNT9qffjHnBqD+qi1CIahTWq0zrrO6vZiqGe7T6NGRTDrRsi+W7499UtKmw2Vr20mvTN6RxZeoT4EfFM7jSZXs/2ovtjOghSRERERERE6q8LCmbjYyPYtG0PXTu2pk3LBF5993NcnJ2Iigj7+4tFRGqJk7sTCVckANW7aDvf2wm/eF/W/HstX/WeyuVfXE54t/AT4ysKK/gg7kNGfnk5za9uTvKqY7j5uRLQIsBeU6gXbBYbRgeDvcs4hcnJxPWLrzvx6/Du4ax6cTWtbmjF5ZNHcHzbcb7q9zVBrQPr/OAyERERERERkbN1QcHsUw/ejtVWfXbYg3fdwMdfTKe0rJz7Jl73N1eKiNSt2KGxRA+KZu1ra5lz41zu2H47jm6OAJTnlWOz2Pj5H4vI3JHJhv9sxGAy0PrGVgx8cyAmJxMGQ/0LKGtbfWtlcCZ+Tfy44utRJ34d1DaIYR8O5ccxMwjvHs7YH8bg6utqxwpFRERERERETnVBf+N2cXEiKMAXAG+vRtxx0zi8vRpx/5Ov1UhxIiI1yWgy0uPJHrgHurHuzfUnXq8oqMDF24Xhnw0n6eeDtBjfgok77uDA3EReb/QG/4l8n6SFSXas3D6sZmuDPUir5bUtuS/5Xly8nPlxzAzK88s5uvKovcsSEREREREROeG8/sa9L/Ewo2+4n8uuvpsR4+9h++4DzJq3hKtueYSMzGw++PdTNV2niEiNMBgNDH53MOvfWs+yJ5djqbJQXliBk6cTzcY25fattzFi0nC8Ir0Y9NZAALo+1JWFd/1MRWGFnauvWzaLrd71mD0Xbv5ujJo2CpvVxn8i3+ebQd+y5bMt2P77TQ8RERERERERezqvVgbvf/YtA/t047JBvZi7aDnPvPQfAvx9+OjNZ0iIi6rhEkVEalZop1BuXHEDs6/7Cfcgd3zjfXD2dD5lXLNxzWgyqglGByOJ8xOZd8t8IvtF0vGuDhiMF0drg+1TdxDSPpjAVoGnvNdQWhn8FUdXR25Ydj2HFh+iJLOUJY8sYfuUHYz7cawOexMRERERERG7Oq+/cR88coyJN19JdGQYEydcRW5+Aa//8yGFsiLSYAS3C2bYR8P47Z+/sXfGPpy9Tg1mAUyO1f1lh304FKODkXWvrzupDUJDZi43s+D2BXzeYTIL71yI1WI96f3qYLbhB9BGByNxw+Joc1Nr7j18D4GtApnU7nMOLjqo3bMiIiIiIiJiN+cVzFaZzTiYTAC4ODvh7uaGv59PjRYmIlLbovpGMvDNASSvSsYjyOMvx/ol+DHm+9Fc9dOVrH55NUkLk6gsqayjSmvGgjsW8PM9iyjNKQWgLKcMgIk77+DIiqO83ugNjm8/TsHRAqxma4PuMXsmTh5ODP/0Mvq+3Jc5N83lx9EzsFRZ7F2WiIiIiIiIXILOq5WBucrCDz/9cuLXVVXmk34NcNUVQy6sMhGROtDutna0vrH1WYdzwe2CGfjmQObdOp+ynDJih8Uy+O1B+MTW/x9O7fhqJzarjcR5idyy/mZKs0px8XHBL8GPq+dcxfzb5jO50xcAtBjfApvV1uBbGZxJ+9vb0fyqZnzdbxq/3PcrQz8YctGF0CIiIiIiIlK/GSorK875e5x3P/oSBs789VaDwcAHr9f/A8DMZjO9hk9g1YIpODicV0YtIpcoc7mZpIVJ7J2xj8QFiVz367WEdQmzd1l/6fMOk+nzr97snbGXorRiuj7UhcUPL+HO3RNPjMnem83Kf/7G8e3HyT+Uz/2p9+Me4GbHqmtX4bFCpg38BqvFyrgfxxLcLtjeJYmIiIiIiMgl4ryC2YuFglkRqQnr31rP1snbuG3zrTi6Otq7nDP6pOWnDH5nEGFdw5jSYyo5+3MI7x7OjctvOGWszWoj/3B+g9gJfKGsZitrXlvLtsnbuHntBDxC/rqthYiIiIiIiEhNUBopInKBOj/Qmb0z9jJ3wjyC2wUT1T+SsM51s3s2a082Ac39z2qstcqKycmEcyNn7th2Ozn7czA6nv7r+waj4ZIIZaH6cLCeT/cg/1AeX3T9kk73diKkQzBR/aLsXZqIiIiIiIhcxNRQT0TkAhlNRsZMH4ODs4k90/fw03VzyNmfc+L9/MP5vOL0Ktu+3I7VbK2x52btzmJS20ls/mTzWY03V5gxOVcf3GgwGvBv5o9vnG+N1dOQGQwGhk8azrCPhrL8qeV8O+Q7XnP/Nyv/+Rs22yX7xRIRERERERGpRdoxKyJSA7wivBj11ShsVhuLH1nC5E5fMPyzy2hxTQtyDlSHtGtfW8vxbRkMee/0hyOay804uJz9x3LB0QIAlj25nNBOoYR0CPnL8ZZKCw7O+tg/E6PJSPzweO7aeydVZWYqiyqYedUsqkqr6DCx/SWzg1hERERERETqhnbMiojUIIPRwOC3BzFuxlgWTFzI0seXkrElg8h+kYxfeA07vtrJ4WVHTrnuyPIjvB34DokLEs/6WYUphcQMiaHXs734ut80Fj+8+C93d1oqLBgdTeczrUuKT6wPgS0DCO8WzviF15C1K5PPO0wmaWGSvUsTERERERGRi4iCWRGRWhAzOIYrZ40jLymPlc//RnF6MT6xPvR/tR8/XfcTmz/dcmJsUWoRs66eTcLoBObfOv+kNgh/pfBYEZ7hnnR9qAu3bb6VfbP2s+2L7WccX71jVsHsuQhsFcj4heMZMXkEs6/7ia2Tt1FVWmXvskREREREROQioO+0iojUkugB0UQPiGbzJ5tx9XMDoP3E9jQK82T+bfPxCHIn4YoEtk/ZTuMe4YyaOpLlT6/g846T6fZIV3o/3/sv71+YUohvXPXX633jfRn+2WXMGDeTnP059HupLyan/4WwNpsNS4XlpNfk7DUb2xSb1cbql1ezbfI2xi+8BhdvF3uXJSIiIiIiIg2YdsyKiNSyDnd2oPmVzYDqQ6aaXB7P4HcGsejeX8g/kk/O/hxCOoUC0O/lvty8dgLr3/qdyZ2+4MiKo2e8b2FKIY3CPU/8OmZQDDcsv57Diw+z/JkVJ43949Axk3bMnrfmVzbjlt9vxs3flekjf+Dob8n2LklEREREREQaMAWzIiJ20GJ8C6IHRvNRwsfs/n7PiZ2vUP31+cs+HkbB0QJmjpvJ1snbTnuPopRCPMMbnfRaSPsQxkwfzbbJ29j44SYqiyuB6v6ygHbMXiAHZwdGfzeasK5hzBgzg00fb7Z3SSIiIiIiItJAqZWBiIgdGAwGRn55OX3+2Zvf39tA7LDYk95veV1LWl7XksNLDzNj3EwCWwYQ1iXsxPs2m43ClOoes/+fXxM/rpg2iuVPr+C3f/7GTatuwi3AFdCO2Zrg5O7EwNcH0GxsU7677HuKjhXiGeFJu9vaYXTQzztFRERERETk7OhvkCIiduQV6cXgtwfh3Mj5tO9HD4imzwt9mDbgGxbeuRBLlYW8Q3kUHC3AUmHBs/GpwSxA3LA4bt9yG21vbcucG+eQvScb0I7ZmhTWJYxrFlzN8Z2Z/HLfryy69xdKc0rP6142mw2bzVbDFYqIiIiIiEh9ZqisrLhk/yZoNpvpNXwCqxZMwcFBm4dFpP7KTcxl9rU/UVVaRW5iLs6ezliqLDxW8OhfXmeptDD/tvns/n4PAE9WPIHBYKiLki8pBckF/DhmBpk7Mmkyqgl9X+yLf1O/s75+xTMr2PX9bjrf24lO93bCYNTvkYiIiIiIyMVOO2ZFRBoA33hfblp9I53u7ci1v4zHPdiduGFxf3udycnEqK9GcX/KfVz+xQiFsrXEK8KL2zbdyn1H76WyuJLJnSbzzZBvqSiqOKvrC5ILCG4TxOZPNrPgjgWU5ZUBUJ5fzrdDv+PAvMTaLF9ERERERETsQDtmtWNWRBogq8WKwWhQ0FpPVRRWMOuaWVgqrfT5V2/CuoT9Zf/Z6aN+IH54HPHD45l9/U+krE0huG0whSmFWCotWKusNLuyGb2e7YlXhFcdzkRERERERERqi3bMiog0QEaTUaFsPebs6czYH8fiEezOjCtnMvfmedisZ/45aEV+Oc5eLjQKa8SNy2/g6jlXEdg6kJhB0dx/7D5uXHkD5rIqvhn0LQXJBXU4ExEREREREakt2jGrHbMiIlKLio+X8FWfr3B0c6TDne1pd3u7U0L1z9pOYsBr/YkdGnvG+9isNhbd+wvbvtiGb5wvV84ah2+87ylj1J9WRERERESkYdCOWRERkVrkEeTOzesm0OWBzvz2r1W8E/wue2fuw1JpOTGmoqACZ2/nv7yPwWhg6AdDuHP3ROIui2XawG/YN3v/iftYKi28G/4eP1zxIynrUlj5/Eq+HfodiQvUn1ZERERERKQ+0jZRERGRWubq40rrG1sT3i2cNa+tZdmTy9jy2RaGvDcEJ3dHSrNLcfVx/dv7GAwGfGJ86P9afxqFNmLp40vZ/MlmBr89iNKcMgwGA/7N/Pl++HScvZxpe2tbfrpuDgPeGEBIh2B+vnsRra5vSezQWHxifdQOQ0RERERExI7UykCtDEREpI5VFlfyVZ+vydyZeeK1J8oex2g6ty+yVBZXMu/W+RxcdBBzmZlmVzZj9DdXUJZbRmVRJV6RXiT9nMSyJ5aTvTcbgJjB0Rz69TChnUJod0d7mo1tipOHU43OT0RERERERP6e3YPZY6kZvPDGpxQUFuHh7sozD08kJir8pDHpGVm8+NanHEg6SmhwAF99/MqJ96xWKx98/j2/b9qB2WKhdYsmPHbvzTg6/n3QqmBWRETspbKkEqvZSsraFFJ/T6PPP3uf970slRb2zdpHSIeQU/rOAthsNja8t5HAlgFED4ymOKOYPdP3sOOrnVgqLYz9cSz+Tf0uZDoiIiIiIiJyjuwezN7z2CsMG9iT4YN7s2zVBqb9MI8v3n/xpDEFhcUcSU6luKSUT6f8eFIwO2fhchavWMc7Lz+Gg4OJ196dTOPwYK6/csTfPlvBrIiIXMqsFiu/PvAr26fsoNnYplz26WU4OJ/6/0NzhZn9Px2g2dimGB3Unl5ERERERKQm2PVvV7n5BexNPMSQAT0A6NezE8ezcjmWmnHSOC9PD9q0TMDV5dSDURIPJdOxXQscHR0wGAx069SGRUvX1En9IiIiDZnRZGTo+0P5R+Ld5OzP4dsh35G0MOmUcQcXHWLODXP4tNWnbHhvAzbrJdsFSUREREREpMbYNZjNzMrF39cbB5MJqD7UJCjAj+NZOWd9j6bxUaxev4WSklLMZjNLf/ud9ONZtVWyiIjIRccj2IPrFl9HRO8I5t02n5XPr8Rm+1/4mvzbUdpMaE3fl/qx8YNNTOk5lcPLjiigFZFTHN9+nN3f7yZlfQornlvJujfWkfRz0kmfKSIiIiJSrcF/f3/44N5kZGZz16Mv4+zkRKd2LTBtNp1x/Iy5i5k5bzGA/oAoIiLyX04eTvR9oQ8tr23Jt0O+JWVdKiXHSyhILsDkaGLYR0NpNrYp8cPjWPXiaubcMIeofpFc/uXlmBzP/P9dEbl0HFtzjK/7TcMzwpOq4krKcsuJHxHP+rd/J7RTCHGXxREzKAafWJ+TrrPZbCTOT+Lw0sN0ub8z3tHef/kcm9XG4SWHieofpfYqIiIi0qDZNZgNDPAlOzcfs8WCg8mEzWbjeFYOQQFnfwCJwWDgthvGctsNYwFYvGIdMZFhZxw/buQgxo0cBPyvx6yIiIhU82/qx81rJ7Dvp/0cWXKY5lc1A4OBmCExADi4ONDv5b50vr8T3132PdMGfEPTMU1pfVMrXH1cT9wn72Aezl7OuPm72WsqIlLHUn9PI6p/FON/voaq0ipKjpfgE+NDaU4pG97byIF5iSx7Yjltb2uLm58reYfycfJw5PiOTLJ2ZRHZN5JJ7T4nbngcra5rSdxlcWd4Tirfj5iOe5A7Uf0i6fdKPzzDPet4tiIiIiIXzq7BrK+3FwlxUfyydA3DB/dm+eqNBPr70jgs+KzvUVFZSUVFFZ6N3MkvKOLr6fO4/aZxtVi1iIjIxa1RWCM6/aMjnf7R8Yxj3APduX7JdWz/cjv7Z+9jxbMr8Aj2oMnIeAb8ewBzbpxDYWoR/V/pR+yw2JNCWxG5OGXvzSasaxgGgwEndyecYpwAcPNzo+8LfQBIWZ/C9i+2s2vaTqL6R+Pi7Yx3pBe9nu1FVN9I0rekk7QgibkT5hHZL5KmY5qecvBg9t5swnuE0++lvmybvI3P2kwisncE0YOiaX9He3KT8nAPdMPF2wWD0WCXfxciIiIiZ8NQWVlh1+/zHz2WxktvfUZBYTHubq48/fAdxEU35pV3JtGra3t6detAeXkFV936CFVVZopLSvHx9mTogJ7cfcvV5OYVcPejL2M0GLDabFx1xRDGjBhwVs/+Y8fsqgVTcHBo8F0dRERE7MJms5G1M4vijGKWPLoURzcHMrYdp8cT3TkwN5G8Q3lc8fUo4kfE27tUEalF00dOJ35EPO3vaH/B9ypOL+b3d3/nwNwDGB1NtLi6OT5xvkT2jmD1q2swORoZ9Fb1t+DSNqWRsjaFrZ9vw1JpIf9QPgDOXs6EdwsjolcE4d3DcQ90xyvKS+1XREREpN6wezBrTwpmRUREalZ5fjk7p+2kUWgjmo5pCsDemfuYd8s8/Jv6E9IxhG6PdP3bHpIi0vB80eVLejzVg4RRTWrsnuZyMzun7eToymSOrT5GUWoRGODqOVcROzT25LEVZg79coiKwgrCu4VTmlPKgTmJ5OzPIXlVMuV55Th5OBF/eTwD/t0fj2CPGqtTRERE5HwomFUwKyIiUuvKcstIWphE0s8H2TdzHzFDYhj11UhcvFywWW36urHIReA/Ue8z5vvRhHcNr7VnlBeUU3CkgMDWgRgMZ/+5UVVWhbncTOHRQta9uY6khQcJ6RhClwc6n7GXrYiIiEhtUzCrYFZERKROlWSWMP+2+WRsPU7zq5qx8f1N9Hy6B82vboF/07M/AFRE6g+b1ca/PV5n4q478InxsXc5f+v49uMcWX6UNa+uwSvCC584Hzrf3+mkUNlms51T+CsiIiJyrpRGioiISJ1yD3TnqjlXseeHvax8fiUA6Vsy+P2dDYyYPIJmY5ue1X2KUotI35JBk8vVu1bE3spyy7CarbgHudu7lLMS1CaIoDZBtLyuJUeXH+H49ky+HfIdrr6uRPSOwNXXhe1f7iB6YDTe0V5E9I4kfkTc3wa1+2btwzfOF//m/icdWCYiIiJyOtoxqx2zIiIidmOz2bBZbBgdjCQtTGL2dT8RNyyOTvd1/NuvQ88YN5MDcw/Q9eGu9HquJ46ujnVUtYj8f1m7s5ja6yseyX3Y3qWct/L8cjJ3ZrLru91k78mmy4NdyNiaQXleGXt+3Etgy0Dih8fR9ta2OHk4nXK9ucLM643eAMDB1YE2N7eh9/O9cPVxPWVsblIu69/6Hb8mvgS2CSKyTwRGk4JcERGRS42CWQWzIiIi9Ubmzkx2f7+HTR9uotUNLfFt4kebCa1xbuR8YkxuUi5fdptCVWkV1/5yLYvuWUTBkQIGvN6/Rk6DF5Fzd3jZERbds4i79txp71JqRVluGdu/3M7+OQcoTCmk3W3tCGodSOyw2BOBasHRAj5K+JjHih4la1cWy59ZQfLKZEK7hNLrmZ5E9Ys6cb/Nn27hl3t/ocmoJqSsTcHkbCJ6QDSxQ2Np3LMx7oFuaqMgIiJyCVAwq2BWRESk3klelcyBuQdI/T2N4vRiPCM8KckoITcpF994X3IP5DJq6khajG+BzWbj6IqjzLpmNs2vakbCFQlE9o3U7jORC1RVWkXK+lSc3B0J6Rjyl/9N7f5uN5s/3cKNK26owwrrns1mY/e3u0ladJCUdSmYHI0ENA8gsl8kQW2D+en6n7jvyL0nxuYdzGPfrP2se2MdbgFuxA6JocdTPdn4n42U5ZYx7MOhWKosHPr1EIeXHGb3d7spyy3H1c+VNhPa0GRkPEFtg3B0dSRpYRK+8b74xvva+d+CiIiI1BQFswpmRURE6i2bzcbmjzdTml2GpdyMwWjAxdeV8G5hhHc7udVB1p5s1v57LUeWHSG8WzjDP7sMF28XO1Uu0vDNGDeTY6uTAfCO9qbVja3Z++NeIvtG0vzKZvg19Tuxq3PDexs4tiaFsT+MsWfJdcpSaSFxfiKFxwrZMmkrBUcKCG4XxE2rbjplbEVhBcfWHGPrpG0cXnoYc5mZXs/1otczPU87NmNrBps+2syxNccA6HhXB9b+ex02m42wrmE0G9eMpmOakrE5nch+kTg46+8yIiIiDZGCWQWzIiIiF5XSnFJmX/sTx1Yfo+uDXejzYh99JVjkHFUUVfB24DvctfdOPII92Pj+RpJXJVNRUIGjhxPHVh0jvHs4Q/4zGN94X5Y/tYLKkkqG/meIvUu3C0uVhZx9Obj6utIorNFfjs3em83R35KJ7h/1t7tfbTYbB+Ymsvu73bgFuNLlgS4cXnaEjf/ZSM7+HFz9XXHxcqbtLW2JGRxDUJugmpxWjchNzKU8v5zQTqH2LkVERKTeUTCrYFZEROSilLkzkx/HzMA7xhuTo5Hj2zO5as6VBLcLVlAr8jeK0op4P+oDnix/AoPx1P9eygvKWf7UCrZ+vhVXX1ecPJxofVPr0+4AlZpntVgpySjBPdidnV/vJOnngxxcdJCEKxKIHRxDYOtAAlsF2rtMABZMXMj2L7cT0iGYqP7RdHmoM25+bvYuS0REpF5QMKtgVkRE5KJVklXK9i+3s+XTLZjLzVSVVhHcLpjR316BR7DHed3z8LIjJK88SnFGCc6eTrS4pgXB7RX2ysWl4GgBHzb5iKcqnvzLcZXFlaRtSmflcyvp9mg3mlweX0cVyv+XfySfDe9t4OjKZLJ2ZeGX4Ef0oGi6P9oNc4WZ7VN24B7kjkewB9l7s6ksqiRueBwRPRufdJ+i1CKOLDuCwWggelA07oHuF1TXD1f8QEjHUHxjfdj57S5S16XSuGdjWl7XkmZjm542+BcREblUKJhVMCsiInLJqCypZN7N89n/037Cu4cz4PX+hHUOO6d7zLhyJsXpxRSlFeHgbKIks5SQDiFc/sUIPMM9a6lykbqVdzCPT1t/xhMlj9u7FDkP5QXlJK9MZuc3uzj0yyGcGjnh1MgJvya+5OzPwT3IA/+mfuyduQ/feF8iejWmyagEAlsGsOaVNax/+3eC2weTtSuL6IFRNBvbjPiR8bh4nXvf7i+6fkmPJ7qTcEUCAGmb0ji6IplNH20CG4R1CaPn0z0IbBVISWYJlcWVeEV66QBHERG5JCiYHT6Bta6lOBjA8tVUjM8+i+FoMrYWLbDefRemf9wDgPXWW6DKjPGrrwCwTPoM45tvYth/AFtcLNYnn8R0623VY6+7DtxcMU76vHrs+//BOOlzDDt2YGscjvW11zBdd3312HHjICQY4/sfVI998w2M03/AsHEjBAZi+ehDTOOurB478nJo0gTjm29Vj335ZYw/L8Sweg14emKZOgXTlVeB2Yxt8CBsnTphfPmV6rHPPYtx7VoMS5aCszOW77/DdMONUFyMrXcvrAMHYXruuernPPYo7N6NccHC6mtnz8J02+2Qk4Ota1esY0Zjeqz6D+nWB+6H5GMYZ82qHvvdtxgfeQRDahq2tm2x3jwB0/0PVI+9cyIUFGL87rvqsVO+xPjCCxgOHcbWtCnWB+7HdOdd1WMnVB+aYJwytXrsJx9jfPc9DPv2YYuJxvrcc5gm3Fw9dvx48PLE+Mmn1WPfexfjl1MwbNuGLSwU65tvYhp/bfXYMWMgojHGd9+rHvv6vzHOmo1h/Xrw88Py+SRMo6sPrbAOvwxatMD4+hvVY194AeOSxRh+WwUeHli+/grTNeOhogLbwAFYu3fH9MKL1dc+/RSGjRsx/LoYHByw/PgDppsmQGEhtp49sA67DNPTT1ePfeRhOHAA49x51c+Z8SOmu/8BmZnYOnXCevVVmB55tHrsvfdAegbGGTOqx34zDeMTT2A4loKtdWust9+G6d77qsfefhuUlmH85pvqsZM/x/jqqxiSDmJLaIL1kUcw3X5H9dgbbwRHB4yTv6ge++EHGD/6GMPu3dgiI7C++CKmG6t/T6xXXw1+vhg/+rh67DtvY5z2DYbNm7GFBGN97z1MV11dPfaKURATg/Htd6rHvvoKxnnzMaxdCz4+WL6YjGnsOLBasQ0biq1tW4yvvlY99vnnMf62EsPyFeDqiuXbbzBdex2UlWHr1xdr7z6Y/vWv6uc8+QSGbdsw/LwIjEYsM2dguuVWyMvD1r071stHYHryqeqxDz0Ihw5h/GlO9XN+mI7x/vsxpGdg69AB6/XXYXrwoeqxd98FObkYp0+vHqvPiOrn6DOieqw+IxrsZ4R5xo9Yr7mR4j0p7DjUCMPYUXQ+OgMXbxdsjzz0t58RaZ2vxDvaG/eXHoOcXGzTviV7TzZTUwczOnITMU2MlIfFYnz4Plwff0ifEfqMaJCfEfnDrmbFQwu5YlDRJfcZcbH9OSK/0hXHTz/GPcAN27tvnfQZUfLqW1gGj6Isr5yt2Y3JKPOiB2sIbhuM25T/UDFtJuXzlpJ73MyMqlFc7z4LrzB33G4YjaFr57/8jDBefwOZaw+xJzOAhE/vJXTGhyd9RhjmLaAsr5zfOj9M2IfP4OFYyZHyYPbQnCGmxfhEe+P0rydwL8/GOGu2PiPq2WfEpfzniIvtM0J/jtBnhD4j6vYzwtarF3+mYFY7ZkVERC5Zx7cdZ9PHm9n17S6MJiPRA6Lo9Wwvgtqe+QCdD+M/YsTkEUT2jjjp9aLUIhbds4iU9amU55fjHeVF35f64eThSGTfM5+aXpJZQlluOf5N/QCwWav/aKav94o9Ze7K4uu+X/Nw9kP2LkXqiNVspaKoguL0Evyb+Z3SniVjawZHVyaz8f2NFKcXE9AygO6Pd8fJw5HyvHI8I7w4vPgQzl4uxI+IIy8pj1nXzKb9ne3p9UxPnDyczvjs4vRi8g7nU3A4n9hhseTsz2HlP38jeWUyfgl+dHu0K/Ej4nHxPvcduyIiIvWZglkFsyIiIpc8m9VG1u4sdn2zi82fbqHLA51pcU0L/BL8Th5ns/G65xvctvlW/Jr4nXIfq8XKrm92EdU/it3f72Hn1zspyy3DwcUBg9FA1IAoBr4xACf3/wUUU3pMIW1jOgEtAogfEc+BuQcoLyhn3IyxhHbUKeZiHxlbM/jusu95MP0Be5ci9YzVYqUwuZCDvx5izStrwAA2iw2D0YCl0oKzpzOFKYVYq6z4xvlw5547z/tZVaVV7Pp2Fxvf30TO/hwi+0XSZkIbovpH4R6gA8RERKThUzCrYFZERET+JHlVMhve3cDhpUcY9uFQWl7X8sR7ZXllvBP0Lg/nPIRzI+ezup+l0sLBXw5hNBlY/coajm87TvTAaPq+2IeAlgG8HfAOV0wbRUFyAdu/3E6La1pgtdhY9cIqYofG0vOZngS2DKit6dpVVWkV6VsyaNw9XDuE65m0TWn8OHoG9x+7z96lSD1ms9nOePBhUWoRVaVV+Mb71sizilKL2D51B/tm7SNrdxYJVyQQ1iWMJiPj8YnxqZFniIiI1DUFswpmRURE5DQOLz3MrKtn4xPng7OXM5d/PoKKokqm9JjCo3mPnNc9bVYbOQdy2fzJZrZ/sZ3YYbHsn72fh7Mfwtnz5KA3c2cm26fuYPuX2xn4xgASRifg6uN64n2rxcqhXw8RMzim3h+Sk7kzk4OLDtIotBFRA6LwCPagvKCcH0b9SMraFMK7h9P+9nbEjYg7r8OFpOalrEth9rU/ce/he+xdisgp8o/ks/WzrWTtyeLw0iPEj4gnvFsYrr6uBLUJwr+5f73/XBQREQEFswpmRURE5IwKUwpJWpjE/jkHSF2fiqO7I5ZyCw9lPnjB987Zn8OOr3ZSlFbEyC8vP+O4/XMO8Ns/f6PwWCE9nupB6xtb4ebvxu7vdjPnprlE9Y9i0NuDTtsTsj7Y/f1u5tw4l6C2QVQVV1KcUULUgCgOzDlAWNcwrpw9jk0fbCJxfhKl2aUMfHMgsUNjTmr3cLYslRZMTqZamMWlJ3lVMvNunc8/Dtxt71JE/lLOgRz2/LCXvT/uJf9wPgaTAa8IL2IGR2O12AjrEoajqwN+CX6ntKcRERGxNwWzCmZFRETkb9hsNjK2ZjD/tgVYq6xM3HlHndewf84B1r2xjvRN6YR1DSN9Uzr9Xu7L8e3H2TtjH47ujnR7tBttbm590s5ae/tmyLccXX6U2zbfSmCrQNK3pJO08CA5+7Lp8VRPApr7A9X/jrdP2cHql1ZTnlfO0A+G0PLaln9z9//JTcrlk+af4hvvS+sbWxEzOIaAlgGYHBXUno8jy4/w8z8WcdcF9AcVqUs2mw1s1YeYbZ+ynex9OVQVV7Jv1n4ahTUi/3A+zt7ONAptRMygGCL7RhDSMUS79EVExK4UzCqYFRERkXNgqbLYNewrySzh93c2kLYpjfELr8HkaMJSaeHoyqMsf2oFmbsyCekQQttb2xI/It7uB+TMumY2YV3D6PJA57Mab7PZSFqYxPxb5+OX4EfC6KZ0vLvD3+6EPbz0MDOvmsXlk0ew7Mll5B3Mx9XXhSH/GULzq5rXxFQuKYd+PcTiR5YwcUfd/xBCpDaU5pSSvTeHwqMFJC5MIm1DGqVZpbS5pQ0xg6KJ6heFg4v+TigiInVLwayCWREREbmI5B3MY9sX2zm89DBZu7Joc0sbXLxc8I7xptX1Les8VP5x9I9ED46h410dzum6kswSDsxLZPPHmyk8VkhIh2CC24VgrjDj4GSix9M9MJeZcfV1xWA0sPv73Wz+eDM3rrwRqO7nu2/2fhb942dc/dwI6xJKr2d74R3tXQuzPH+bP91CxpYM4kfEE9k34qwPlattSQuTWP7MCm7fcpu9SxGpNemb09n04SaOrUnBarbS6vqWuHi70Pqm1rj61p9vHoiIyMVLwayCWREREblIpW9JZ9sX2ynPLefw0sO4B7rTdGxTuj/eDUdXxzqp4bvLvqPZlc1pe3Ob87rearaSsS2DjC0ZpG/OwOhgJGtPFjn7cyjLLiN6YBQD3xjInh/3krUzk3Ezx510fVluGWkb0tg3ez87p+3EK9ILz3BPej3Xi7DOoXbtSVuWW8Y7we8S0jGE8vxyKosqGfbhUOJHxGMw2rdf8P45B1jzyhpu+f1mu9YhUhdsVht7pu/h0OJDFBwrJGNzBiEdQ/Bv7k+HOzvgl+BbL3t4i4hIw6c0UkREROQiFdI+hJD2IQBUlVZxYF4imz7YyLYvthHdP4r4EfE0HdO0VkNAc4UFhwsIP40ORkI7hhLaMfTEa1aLlYM/H8S3iS+rX1rN5x0mY7PaSBidcMr1rr6uxA6NJXZoLL2e6cn+uQc4vPgQM6+cibOnM8M/uwyPEA9S1qXScnyLOg1qN3+yhci+kVz367XYbDZ2fbOruo+xxUrXh7vS7dGudmubYTVbMTooiJJLg8FooMX4FrQY3wKo3kmbtTuLI8uPMqndJBycHWg2rinNr2pOYJsgPILc7VyxiIhcLLRjVjtmRURE5BJiqbJwcNEh0jens2PqDrwivWh1fUta39S6VkLAKT2m0OWhrjQb27TG7/0Hm81G2oY0nDycCGgRcHbXWG38/u4GVr+8msqiSpwaOeHg6kB4t3AGvz0Iz8aetVYvgLnCzPvRHzBqykhiBseceN1SZSFlXSq/3v8rZbllxA2Po++LfXDzq9tewf+/NYTIpcpcbiZ7bzYbP9hE6u+p5B3Mw7+pPwEt/En6+SB+CX5E9I6g5fgWBLYO1M5aERE5JwpmFcyKiIjIJaq8oJytn21lx9c7Kc8vp8cT3Wk2rhnugTW3G+zzjpPp88/exI+Ir7F71iSrxUrJ8RLc/N1I25TO9i+3c2DOAZpf05z2d7QnsOXZBb3nKjcpl8/aTOLx4sdOG+RYKi0cW3OMje9vIm1jGk1GNmHf7H2EdQ4ldlgcbSa0xsH55D+/bvtyOwEt/AnpEILRZLyg+nZO28n2KTu4fsl1F3QfkYtN3qE80jakkZuYS86BXGKHxHDo10Psm70fB2cHwnuE0/neToR0CsHFy8Xe5YqISD2nNFJERETkEuXi5UK3R7vR9eGu7PlhD5s/2cLSx5aRMDqBTvd2JKxz2AU/w1xurtcnnRtNRhqFNgKgcfdwGncP5/D4FuydsZepPaeScEUTwrqF0/bmNjXa5qCyqBLnRk5n3F1ncjIR1S+KyL6RJM5P4vDSw/R/tT9FKYVs/Wwrmz7YRMd7OpIwsgkeIR5YqiwsnLgQAFdfF9rf2YFW17fEN873vOqrbmVwYeGuyMXIJ8YHnxifk15rdX0rhhVXkncwjz3T9zD/jgWUZZfRfmI74kfE4x7ojn8zfztVLCIi9Vn9/VOyiIiIiNQJg9FAi2ta0OKaFuQm5rLp4818N/R7vGO8aXtrWxJGNTkRXgIUHy9hw7sbaNwjHBcfF/ya+p3xq/aWSgsmZ/sdsHU+ovtHEd0/ina3tSVxfhJbPtnCtsnb6PFkDxr3CMfV1/WCQ8vK4kqcGjn/7TiDwUCTy+Npcvn/dhx3f7w7Wz7bwt4f97LimRX0eq7XidD48eLHSFmXwuqX17D+zfXEj4hn0FsDaRTW6EyPOC2r2YrRUcGsyNly8nAiqE0QQW2C6PdKPzK2ZrD23+uYf9sCitKKCOkQQljXMNpMaENAc4W0IiJSTa0M1MpARERE5BSlOaVs/2I7iQuTSF2fil8TP9wC3YjqG0nqhjQO/nwQZ09njA4GjE4mhn0wlJghMad8vf69iP9w5cxxhHYKPcOT6j9zhZl1r69j9/Q95B7IxehoxC/Bj453d6DJyCY4ezqf867gpIVJLH96Bbdvve2Cakv6OYk1r64lY2sGTu6OPJjx4In3io+XsPjBxeyduZfQjiEMeX/IicPgzOVmEucnEj0oml3f7sZSYaHNhNa4eFd/9XrzJ5s5uOgQV/105QXVJyLVn6cH5hzg2Opj7PlxL77xvoR1CaP9He0IahukvrQiIpcwBbMKZkVERET+UtaebLL3ZlNZXMmG9zZQllPG7dtuOxHibf9yOyueW4mlwsJln1x20kFfbwe9w3W/XktQmyB7lV9jbDYbJRklVJVWkbIuhTWvrSX3QC7uQe70f7UfkX0iaRTe6KxClt3T97Dpw03c9FvNHK5ls9mwmq2nPcCtNKeUzR9vYd0b6whsFUjLa1uQtiGNfbP2Y6m04N/MH0c3BzJ3ZRHQ3J+uj3SjKK2I5JXJjJsxtkbqE5FqJVmlpKw5xqElh9n51U5cfF1IGNWEZuOaEdAiAFdfV3uXKCIidUjBrIJZERERkbNmrjBTUVBxygFhNpuN/bP3s2DiQvyb+hHWNQzvaG+WPraM2zbfil+Cn50qrj02qw1zuZkD8xJZ9sQyitKKiBsWR4+nuuPq64qDswOejT1Pe+3WydvYN2sf4xdcU2f1Fh4r5PCyI2z+eDMVhRVcv/g6SrNLCWwVCAbI2p1NyroUVr2wisriSuKGxTH62yvqrD6RS01VWRWpv6exY8p2jq5MpjyvnHa3tyV2SCwhHUNO/PBLREQuXgpmFcyKiIiI1JiitCKOLDvC1snbyNyRiUeQOzetvumS2AVWmlPK0keXkrggifK8cgD8Evxod3tbOv6jIwaj4cRu2t/f3UDKuhTGTh9jl1ptNtsZd/ZWFleS+nsqnuGeF2WgLlJfpW9J5/d3NpC2MY3itGKiB0YT2jmUFtc0p7KwkoriSsK7han1gYjIRcTuweyx1AxeeONTCgqL8HB35ZmHJxITFX7SmPSMLF5861MOJB0lNDiArz5+5cR7VquV9yd9y/pNOzCZTHg18uCJB26lcVjw3z5bwayIiIhI7bFZbRiMl2aAUJ5fjrnCQsqaYyx/ejkVhZVYKix0e6wboZ1C+On6ObS/ox29n+9t71JFpB7K2pPNwUUHSf7tKAcXHcLB1QGTowmPEA+C2wcT3j2chCsScPN3VVArItKA2T2YveexVxg2sCfDB/dm2aoNTPthHl+8/+JJYwoKizmSnEpxSSmfTvnxpGB25dpNfPX9PD59+1kcHBz48tufSDqUzMvP3Pe3z1YwKyIiIiK1rbKkkrQNadgsNpY+voz8I/m0vK4lg94ciMnp1J6wIiJ/Vp5fjqO7I5ZKCwd/Pkj23mwOzE3k+Pbj+DX1I6J3BMHtgmk6OuGS+HaCiMjFxK5pZG5+AXsTD/Huq48D0K9nJ976cCrHUjNO2vHq5elBm5YJbNm+55R7GDBQVVVFRWUVJpOJktIyAgN862wOIiIiIiJ/xcndiah+UQDctvlW+xYjIg3OH71mTY4mmo1rBkCvZ3tRUVjBvtn7ydiawY6pO1jyyBLiLosjrGsYbW9ug5OHkz3LFhGRs2DXYDYzKxd/X28cTNU7BQwGA0EBfhzPyjmrVgQAPbu2Y/P2PYy45h7c3FwI8PPhozefqc2yRUREREREROzK2dOZNje1ps1NrQFI25TGkaVH2P3dbla/uBqPEA9CO4fiEeROSMcQYofG4uCib4qKiNQnDf5Tee+Bwxw6ksLcb/+Du5srH30xndf/8wX/fPzu046fMXcxM+ctBqoPPRARERERERFp6EI7hhLaMZRuj3UjfVM6xRklHPzlIIUpheybtY9f7v+VqH6RxI+IJ2F0AkaT0d4li4hc8uwazAYG+JKdm4/ZYsHBZMJms3E8K4eggLM//fXnJavp0LY5jTzcAbhsYC/uf+rfZxw/buQgxo0cBPyvx6yIiIiIiIjIxcBgMBDaKRSAJpfHA9WHMR5ecpiUdSksfWIZSx9fRkTvCFpc05yYwTE6QExExE7sGsz6enuREBfFL0vXMHxwb5av3kigv+9ZtzEACAsJYO2G7Vw3bjiOjg6s+X0rsVHhtVi1iIiIiIiISMNhMBqIGRxDzOAYejzVg6PLj3J0ZTLzbplPVWkVvvG+RPRqTElmKfEj4mk2tilGB+2oFRGpbYbKygq7fp//6LE0XnrrMwoKi3F3c+Xph+8gLroxr7wziV5d29OrWwfKyyu46tZHqKoyU1xSio+3J0MH9OTuW66msrKKtz6cyvbdB3BwMOHn48Vj991CWEjg3z77jx2zqxZMwcGhwXd1EBERERERETlrlkoLuYm5ZO7K4ujyI7gFuLN7+m7KsssI6RhCwugEovtH4dvEl5KMEtI3pxM9MFq9akVEaojdg1l7UjArIiIiIiIi8j9Wi5XsPdkcWX6EpIUHObbmGAaDAXO5mUZhjagqrSKiZ2MMDkbcA90IbBVIQMtACpML8EvwI6htkFojiIicJaWRIiIiIiIiIgKA0WQksFUgga0C6XxfZ6rKqsjanYWzpzO+8b6krk8ldX0qFYUVVJWZ2fHVTtI2pBHQIoDCY4V4R3vj4uuCi7cLMYNjCO0YQlCbIHtPS0SkXtKOWe2YFRERERERETkvNpuNopQiPBt7UlFYQeL8RKxmK/mH80n5b4gb3D4Y31gfXHxdaTa2KUFtg6gqrcLFy8Xe5YuI2JXSSBERERERERE5LwaDAc/GngA4ezrT8tqWJ71fmlPK7u/3UJZdSv6RAr4b9j0VhRUAuHi74BXlhaXCgn8zf1pe15LoAVE4ujnW+TxEROxBwayIiIiIiIiI1Ao3Pzc6/aPjiV/brDYyd2RiMFX3rT2y7Aj+zfzJ2JrB4ocWU5hSSFDrIJqNa0pgq0BCOobg5u9mxxmIiNQetTJQKwMRERERERERu7PZbBQmF5K8Kpm9M/aSm5RHwZECgtsFEdIxhBbjW5C0IAm/BD+iB8VgNBlw9XW1d9kiIudNwayCWREREREREZF6Ke9gHmkb0khadJC9P+4lemAU+UcKyNmXA0B4j3ASRjYhoncE/s381QZBRBoUBbMKZkVERERERETqPUuVBZOjCZvNRlVJFVVlZnZ/t4vklckcXnaEqpIqGoU3ot2tbWkysgl+Tf0wOZpOe6/CY4Vk7sokqHUQHqEeQHW/XBGRuqRgVsGsiIiIiIiISINmtVgpyykjfVM6mz7aRMraVJw8nWg6OgH3QHcslRb8mvgR2CYI/2Z+zBgzg8QFSRiMBhxcHTA5mgjtHEJYlzDCuoQR0ikEVx+1SRCR2qVgVsGsiIiIiIiIyEXFZrWROD+Ro78lU5ZdSkVhJSXHi8nclYWrrys2m40bl9+Aq58rmbuyMDoYSN+cQdrvqaT+nkreoXz8m/kT2TeSyD6RNLk8HqOD0d7TEpGLjIJZBbMiIiIiIiIil4SKwgoO/nKImMHRuHi5nHFcSVYpR5cfIXVDGvt/2o+53IxfEz8iejUmbngcfgl+f3m9iMjZUDCrYFZEREREREREzsBqsZKyLpX8Q3kcmHOAlN9TqSyqJGFUEzxCPAjrEoZnY09cvF3wifNRr1oROWsKZhXMioiIiIiIiMg5yNyVxZ7peyjLKyNlTQql2aWU55Xjl+BH7NBYGvcMJ2ZwDEaT2h+IyJkpjRQREREREREROQeBLQMIbNnnpNeqSqvY8+NeUtel8PPdi7BUWfCO9MLVz5WgtsEEtwtWr1oROYl2zGrHrIiIiIiIiIjUIEuVhbSN6RSlFlFwJJ+8Q/kcXnKYqrIqPMM88Qhxp8moBMK6hBHQ3N/e5YqInSiNFBERERERERGpQSZHE427h5/0mtViJWVtCgVHCig4VsjOaTv59YFfaRTqgbOXC1H9oojsG4G53EJJZgkewR6Edg7FI8jdTrMQkdqmHbPaMSsiIiIiIiIidlCeX07ahjTK88s5MC+RjK0ZmJxMmBxNmCvM5CXlETskhpCOITQZ2YSAFgH2LllEapDSSBERERERERERO3DxdiFmcAwAza9qfsr7uUm57J+9n/TNGax5dS3uwe4EtwumxdXN8W8egGfjRji5O9V12SJSQxTMioiIiIiIiIjUQ75xvnR7tBsAZXllHN92nKMrk/ntX6vIP5KPucyMq68Llior0QOjiewbSfSAKPya+Nm5chE5G2ploFYGIiIiIiIiItLA2Gw2ynLKKDxWSHlBBcm/JZO2IZWjK5PxjffFI8QDc7kZrwgvfON9aRTqQfrmdBJGJRDZNxKjg9HeUxC55CmNFBERERERERFpYAwGA27+brj5uwEQ1TcSgJLMElLWpVKcUYzNYqU0u4yMLRkcmFNISMcQ5t48j4qCCkI6htBifAvCOoUS0DJAQa2IHWjHrHbMioiIiIiIiMglwmqxkpuYx5Flh9k7Yx+ZOzJxauREwugEwruGETssFudGzvYuU+SSoGBWwayIiIiIiIiIXKKsFisHFx3k0OLDJK9MJjcpF/cgd1y8XGg6tikBzf2J6h+Fs6fCWpGapmBWwayIiIiIiIiICAA5B3LIO5hHUVoxR5cfIXNnFnkH83ALdMNgMODX1I/wrmE07tmYxj0bY3I02btkkQZLaaSIiIiIiIiIiADg18QPvyZ+ALS7tS0AeQfzKEwtoiK/nMLUIlLXpbBl0lbKcsoIaB5AeI9w/Jv5U3SsEO8Yb/yb++PcyBmfWB8cXBQ9iZyJ/usQEREREREREZEz8on1wSfW58SvO97VAZvVRt6hPDJ3ZHJk+RF2fbOLRmGNSN2QRur6VMzlZgACWgYQPyIe7ygv4i6LwyPYw17TEKl3FMyKiIiIiIiIiMg5MRgN+Mb54hvnS9MxTU96z2at7ppZklnCoV8PcWz1MZJXJbPonl9wD3Insm8k/s388YnxJrhd8Emhr8ilRD1m1WNWRERERERERKTWFacXk5uUy/6f9lOUWkzB0Xwyd2bh4OqApcJCaOdQwjqHEntZHI17hGMwGOxdskitUhopIiIiIiIiIiK1ziPEA48QDyJ6RZx4raKogrykPPKPFFCSWULGlgxmjJ1BZVElPrE+NB2TgEdoI/KScgnrEoZfU3/8Enx16JhcFLRjVjtmRURERERERETqDXO5maLUItK3ZJA4P5GitCIcXR0ozighNzEXg9FASIcQwjqHEjMkhsDWgbh4udi7bJFzpjRSRERERERERETqDQcXhxMHjjW/stlJ79msNo5vP07G1gyOrkxm9nU/UZJRgn8zf0zOJvyb+RM9IIqgNkEEtAzAaDLaaRYif0/BrIiIiIiIiIiINAgGo4HgdsEEtwum7S1tgeretcmrkjE5O5C+MY1tX24nc0cmzp7ORPaNJLBVIAlXNME7yhuDUX1rpf6weyuDY6kZvPDGpxQUFuHh7sozD08kJir8pDHpGVm8+NanHEg6SmhwAF99/MqJ9+b/spIffvrlxK8zs3Np26oprz33wN8+W60MREREREREREQuPlazlUOLD5Gx9TjJvyVzdOVRTI4mQruEEjskFpOTkdDOoQS0CMC5kbO9y5VLlN2D2Xsee4VhA3syfHBvlq3awLQf5vHF+y+eNKagsJgjyakUl5Ty6ZQfTwpm/7/r7niC224YQ79enf/22QpmRUREREREREQufpZKC3mH8jn060FS1qZSUVRB+sZ0yvPL8YzwpOnoBAJaBhLeNQy/BD97lyuXCLumkbn5BexNPMS7rz4OQL+enXjrw6kcS82gcVjwiXFenh60aZnAlu17/vJ+u/clkZdfSK9u7Wu1bhERERERERERaThMTib8m/rh39SPzvdVv2az2SjPKydlbQpJiw6y46sdLLpnER7BHgS3Dya4bRA+cb6EdwvDM9zTvhOQi5Jdg9nMrFz8fb1xMJkAMBgMBAX4cTwr56Rg9mzNW7SSoQN6aPeriIiIiIiIiIj8JYPBgKuvK/Ej4okfEQ9AeUE5GVsySFmXyvEdmRyYe4C52zPxjvLCt4kvUf2jCGgZiHeUF56NPTE5muw8C2nILpoEs6y8nMUr1/H5u//6y3Ez5i5m5rzFQPVPRkRERERERERERABcvFyI6hdFVL+oE6+VZJZwfPtxsvdkc3jJYTZ/vJmCo4XYbDY8wz0J7x5ORO8IwrqEEdDCH4NBB4zJ2bFrMBsY4Et2bj5miwUHkwmbzcbxrByCAs69l8ey3zYQExlOdGTYX44bN3IQ40YOAv7XY1ZEREREREREROR03APdiRkUQ8ygGDrfX32mkdVipSiliLzD+RxZcpg9P+xhycNLcPFxIahNIPEj4onqF4VXlBdGk9HOM5D6yq7BrK+3FwlxUfyydA3DB/dm+eqNBPr7nmcbgxVcPqRPLVQpIiIiIiIiIiLyP0aTEa9IL7wivYjqGwlAVVkVaRvSyNh6nO1TdvDL/b9iNBkJbhdEULtgwjqF4hXtzZpX15B/KB+vKC8a92hMQHN/vCK9CGobdGK3rdVixWgykjg/kdykPCwVZhr3iiCghT8uXi72nPoFK0guwNHNEVc/10t+d7GhsrLCrt/nP3osjZfe+oyCwmLc3Vx5+uE7iItuzCvvTKJX1/b06taB8vIKrrr1EaqqzBSXlOLj7cnQAT25+5arT9zjlnufY+637+Pu5nrWz/5jx+yqBVPUl1ZERERERERERGqMpcpC3sF80jakkrkzkyPLj1KaVYqTuyMD3xpIQXIhhxcfpjClkNzEXBzdHPGO9sbV14VDiw/jHe1N7oFcovpH4eBiIm1jOqVZpQS3DyZ2SAxBbYKIGRKDk7vTaZ+/54c95B3Kx7+ZP5F9InDxPn2gW1VWxeQOkzGYjLgHu+MR5I5HaCOC2wQR0CIAv6Z+OLhceG6246sdbPpwM8d3HMdmseHi7UJkv0jGTh9zwfduqOwezNqTglkREREREREREbE3c4WZ9M0ZFCYXkLYpncjeEdhskLUrkx5P9Tixs7Q0u5QDcw9wbE0KaRvSyDuUh3uQO7FDYgntFEJRahH5RwrwivRi9Uur8Y72xsHFgbyDeQS0CCCwVQBNxzQluH0wHsEeAGTuymJqr6mMnT6G4oxiSo6XUJhSSMbW6r66VWVVBLYKJKBFAMHtgwntGEJgq0Ac3RzPaY5f9f0ajxAPLvtkGAajgbykPCpLqojo2bjG/302FApmFcyKiIiIiIiIiEgDY7PZyD+cT8HRAvbN2kduUh5OHk54RXhSllNG/tECrpl/NU7uTuQdzOP4jkxS1qWQOO8AeQfz8Y3zoXGvCHKTcik8Vsg/Dtx9xmdkbMng+PZMMramk7H1OJVFlcQMisa/RQARvRoT0TsCB+e/ztY+bfUZg94eSMygmNr6V9LgKI0UERERERERERFpYAwGAz4xPvjE+BDVL+ovx/rE+uAT60PT0QkMfH0AFYUVHF1xlLSN6RQcLaDVdS3/9hnNxjUDqsPa7L05HPw5iZz9Ocy/fQElGSX4JfjRuFdjQjuGENIxlIAW/if1kC3NKsHN363G5n8xUDArIiIiIiIiIiJyCXH2dKbJyCY0GdnknK81GAwENPcnoLk/UB3UFqUUkbYpndT1Kez4eie/PriYqpIq3ALdaDIintDOoZTlleMWoGD2zxTMioiIiIiIiIiIyHkxGAx4NvbEs7EnTUcnANU9c4vTiyk4WkDi/ER2f7+HoNZBuAe627na+kXBrIiIiIiIiIiIiNQYB2cHvKO88Y7yJrJPpL3LqbeM9i5ARERERERERERE5FKjYFZERERERERERESkjimYFREREREREREREaljCmZFRERERERERERE6piCWREREREREREREZE6pmBWREREREREREREpI4pmBURERERERERERGpYwpmRUREREREREREROqYglkRERERERERERGROqZgVkRERERERERERKSOOdi7AHuy2WwAmM1mO1ciIiIiIiIiIiIiFzuTyYTBYAAu8WDWYrEA0G/UbXauRERERERERERERC52qxZMwcGhOpI1VFZW2Oxcj91YrVYqKytPSqobkuvvfJJpn7xq7zKkHtLakNPRupDT0bqQ09G6kNPRupDT0bqQ09G6kNPRupAzudTWhnbM/pfRaMTFxcXeZZw3g8FwImEX+TOtDTkdrQs5Ha0LOR2tCzkdrQs5Ha0LOR2tCzkdrQs5k0t5bejwLxEREREREREREZE6pmC2ARt7+SB7lyD1lNaGnI7WhZyO1oWcjtaFnI7WhZyO1oWcjtaFnI7WhZzJpbw2LukesyIiIiIiIiIiIiL2oB2zIiIiIiIiIiIiInVMwayIiIiIiIiIiIhIHbs0jzyrhyoqK3nulQ85nJyKs5MTPt6ePHrvBBqHBZObX8ALr39CanomTo6OPHLvBNq1agrAlO/m8POSVRxLPc6rz91Pn+4dT9zTZrMxedosfl2+DkdHB7w9G/HhG0/ba4pyHmpjXdx63/NUVVUBYLFYOXQ0ha8/foW4mAi7zFHOXW2si937DvLOx19RWWWmsrKKEYN7c/1VI+w1RTkPtbEu9uw/yLufTKOsrAKDAe6beB0d27aw1xTlPJzvunjpzU/ZsScRZycnXF2deeDO62meEAtAeXkFr7wziT37D2E0Grnz5qvo36uzPacp56E21sa8X1by/ayfOZqcxj23X8s1Y4bac4pyHmpjXXz8xXRWrNmEk6MjDg4mJk64kq4dW9tzmnKOamNdfPLlD6xavwWTsXqv2A1XX86gvt3sNkc5d7WxLv5wJDmVCfc8y6hh/XjwrhvsMT05T7WxLl5881M2bNmFj1cjADq1b8m9t19rtznWNPWYrScqKivZvG0P3Tq1wWAw8OOcX1m+egMfvfEML731GcGBftx2w1j27D/IEy+8y6yp7+Dg4MDufQfx9mrEy29/xtWjh570F+rpsxexdec+XnzyHhwdHcjJzcfP19t+k5RzVhvr4s+WrdrA5Gmz+ObT1+p4ZnIhamNd3HjXU9x+41h6detAQWEx4297jA/feJroyDA7zlTORU2vC5vNxqjr7+OZhyfSuX1LklPSue+J1/h+8hu4ODvZebZyts53Xaxat5lundviYDKxev1W3vpoKrO/eheAydNmk5aRybOPTCQtI5Pb7vsn333+b7w8G9l3snJOamNtJB48iqOjA1O/n0dCXJSC2QaoNtbFuo3bade6GS7OTiQePMpdj7zEvO/ex9XFxb6TlbNWG+uiqLiERh7uAGRm5zL+9seYOeUdvL30/5KGojbWBYDZbOaex14hKNAfb69GCmYbmNpYFy+++SnxMZEX7Z8r1MqgnnB2cqJ757YYDAYAWjaLI/14NgDLfvud0cMHANA8IRZ/Xx+27NgHQIumsYSFBJ72nt/MWMDdt1yNo2P1xmiFsg1PbayLP5u3aAWXD+lbO8VLramVdWEwUFRcClTvhnNwNOHZyL2WZyI1qabXRUFhMfkFRXRu3xKAiPAQPDzcWL9xe11MR2rI+a6LXt064GAynbgmKzsPs8UCwNKV609cFxocSLvWTVm5ZlOdzksuXG2sjfjYSKIiwjAaDXU9HakhtbEuunVqc+IHerHRjbFhIz+/qE7nJRemNtbFH6EsQFlZOdjAarPW2ZzkwtXGugCY/M1s+vfuQuOwoLqcjtSQ2loXFzO1MqinfvjpF3p3a09BYRFmi+WkUDUkyJ/jWTl/eX1JSSm5eYX8tm4zy1dtBGD8mGEM7Nu1NsuWWnah6+LPjmfmsHXnPp5/7K5aqFTqUk2si2cevoPH//k2n02dQV5BIY/fd4t+mNPAXei68PZqhJ+vN0tWrmdgn67s2X+Q5JR00o9n1XLlUpvOZ11M/2kR3Tu1OfGH5eNZOQQH+v3vuuAAMjLP/v8/Uj/VxNqQi09Nr4v5v/5GWHAgwUH+tVm21LKaWhc//PQLM+ctJjMrjycfvBVfb6+6KF9qSU2si937kti1J4n/vPYEk6fNqqvSpRbV5OfF/F9XEhTgx8QJV9IkNrIuyq8T2jFbD035bg4pace56+arz/seZosVi8VCRUUVk//zL1566h7e+3QaiQeP1mClUpdqYl382YLFv9Gjc1t9XaiBq6l18fX0edx589X8NO09vv3s33w65UcOH02toSqlrtXUunj9+QeZ/8tKbrz7aX746Rdat2iCSQFMg3U+62LR0tUs++13nnjg1lqsTOxNa0NOp6bXxcatu/hi2mxefOqeEzuppOGpyXVx1RVDmD75TT575zmmfj+XgkLtpG6oamJdlJdX8Mb7U3jywVv1GXGRqKnPi4kTrmTGlLeY9smrXD60Dw898zqlZeW1UbJdKJitZ775cQEr12zi7ZcexcXFGS/PRpiMJnJy80+MST+eTVCA35lvAnh5euDm6sLQAT2A6t0srVo0Ye+BQ7VZvtSSmloXf7DZbCz49TcuH9q3dgqWOlFT6yK/oIiVazcxpH93AMJCAmnRLI4duw/UZvlSS2ry8yI+NpJ3X3mcrz56mX8+fjfZOfnqO9xAnc+6WLJiPZOnzea9V5/A1+d/u5iCAvxO2iGbnpF10g5aaVhqcm3IxaOm18WWHXt5+a1JvPGvh4hsHFpX05AaVlufF/GxkQT4+bBl+97anoLUgppaFynpmRzPyuEfj73C6BsfYPrs6h2SL7zxSV1PSWpATX5eBPr7YvzvQYF9e3TC3c2V5JT0OptLbVMwW498N3Mhi1es471Xnzip507/3p2ZvWApUH1CdlZOHu1bN/3b+w3q2431m6p7ARYUFrN3/0FioyNqp3ipNTW9LgA2bduNxWI90TtSGp6aXBeNPNxxcXZm07bdQHVQu2ffQWKiwmtvAlIravrzIjsn78Q/z1m4HFcXZzq2bVHzhUutOp91sWTlej6d+iP/ee1JggNP/srxn69Ly8hk64599D7DIZNSv9X02pCLQ02vi6079/HC65/w738+SPxF9NXTS01Nr4s/fzMrJe04Bw4e1Q9/G6CaXBdx0Y35+YePmf3Vu8z+6l2uHj2EEYP78Nyjd9btpOSC1fTnReaf2h3s2ptEQWEx4aEXTw9iQ2Vlhc3eRUj1Qht1/f2EhQTi5lp9QqmjoyOT//MvcvMK+NfrH5OWkYWjgwMP/+MmOrRtDsCX3/7E7AVLyS8ows3VBScnR6Z++DI+3p4UFBbx0lufkZZe3Q9wzOUDGHv5ILvNUc5dbawLgOde/ZDGYcHcfuNYu81Nzl9trIsNW3bx0eTvsVismC1mRg7ty/ixl9lzmnKOamNdTJ42i1+WrcVmsxEVEcoj/5hAkHZGNijnuy56XnYTfj5eeHl6nLjX+/9+Ei/PRpSVl/PyW5PYl3gYo9HIHTeNY2Af9bBvaGpjbSz49Tc+nfojRUWlODiYcHV15o1/PUxCXJQ9pijnoTbWxZU3P0xJaRn+f+or+NxjdxEX3bhO5ybnrzbWxcPPvkl6RhYODiZMJhPXXzWCAb272GV+cn5qY1382edfz6SouJQH77qh7iYlF6w21sW9j79Kbn4BJqMRZ2cn7pxw1YnrLgYKZkVERERERERERETqmFoZiIiIiIiIiIiIiNQxBbMiIiIiIiIiIiIidUzBrIiIiIiIiIiIiEgdUzArIiIiIiIiIiIiUscUzIqIiIiIiIiIiIjUMQWzIiIiIiIiIiIiInVMwayIiIiIiIiIiIhIHVMwKyIiIiIiIiIiIlLHHOxdgIiIiIiIvdz96Evs2puEo4MDBoOBoAA/unRoxQ1XX46Pt+ffXv/51zNJPJjMv//5YB1UKyIiIiIXE+2YFREREZFL2t23XMPSnz5n8azPeOnpe8nKyePme54lN6/A3qWJiIiIyEVMO2ZFRERERACDwUB0ZBjPP34XN931NN/OXMgt143mn699xK69SVRWVREfE8FDd99IfGwkK9duYur3c7FZbfQfdSsAy+ZMxmaz8eOcX5k5bwm5eQXEx0bw2L03ExURZucZioiIiEh9oh2zIiIiIiJ/4mAy0bt7B7bu2IfNamVwv+7M/OptFkz/kCZxkTzzyvvYbDb6dO/ITdeMpEeXdiybM5llcyYDMGv+EuYtWsmbLzzMzz9+TN8enXjkubeoqjLbeWYiIiIiUp8omBURERER+X8C/HwoLCrG3d2NgX274urigrOTE7fdMJbklAyycvLOeO3MuUu4/caxNA4LxsFk4qorhlBRWcXufUl1OAMRERERqe/UykBERERE5P/JysnDs5EH5RWVvP/ZN6zduJ3ComKMhup9DQUFRQT6+5722vTj2fzr9Y8xGv+3B6LKbCYzO7dOahcRERGRhkHBrIiIiIjIn5gtFn5bt5nundry3cyF7Es8wqdvPUtggB9FxSUMHjsR23/HGgynfgEtMMCXB+68nm6d2tRt4SIiIiLSoKiVgYiIiIjIfx1JTuPFNz6lpKSM8WOHUVJahpOTI40auVNaVs4nX/5w0nhfH0/SM7MxWywnXhs3chCTvprJ0WNpAJSUlPLb2s2UlJbV6VxEREREpH7TjlkRERERuaR99MX3TPpqBgaDgQB/H7p1bMMXH7yAr7cX48cM4/nXPmL41f/Ay6sRd9w4jlnzl564tn+vLvy6fB2XXXUXNhssnvUZ40YOwmg08OQL73E8Owc3V1fatGhCh7bN7ThLEREREalvDJWVFba/HyYiIiIiIiIiIiIiNUWtDERERERERERERETqmIJZERERERERERERkTqmYFZERERERERERESkjimYFREREREREREREaljCmZFRERERERERERE6piCWREREREREREREZE6pmBWREREREREREREpI79H0izrd/uFUGCAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1400x1000 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
"\n",
"ax = axes[0]\n",
"ax.plot(\n",
" arima_df.index,\n",
" arima_df[\"arima_forecast\"],\n",
" linewidth=0.8,\n",
" label=f\"ARIMA({p},{d},{q}) forecast (median)\",\n",
")\n",
"ax.plot(arima_df.index, arima_df[\"actual\"], linewidth=0.5, alpha=0.5, label=\"Actual GK RV\")\n",
"ax.fill_between(\n",
" arima_df.index,\n",
" arima_df[\"arima_forecast\"] - 1.96 * arima_df[\"arima_forecast_std\"],\n",
" arima_df[\"arima_forecast\"] + 1.96 * arima_df[\"arima_forecast_std\"],\n",
" alpha=0.2,\n",
" label=\"95% PI\",\n",
")\n",
"ax.set_title(f\"ARIMA({p},{d},{q}) Forecast on Garman-Klass RV (Log-Normal Back-Transform)\")\n",
"ax.set_ylabel(\"Annualized Volatility\")\n",
"ax.legend()\n",
"\n",
"ax = axes[1]\n",
"ax.plot(arima_df.index, arima_df[\"arima_forecast_std\"], linewidth=0.8, color=\"orange\")\n",
"ax.set_title(\"Forecast Standard Error on Log Scale (arima_forecast_std)\")\n",
"ax.set_ylabel(\"Log-Scale Std Error\")\n",
"\n",
"ax = axes[2]\n",
"ax.plot(arima_df.index, arima_df[\"forecast_uncertainty_ratio\"], linewidth=0.8, color=\"purple\")\n",
"ax.axhline(\n",
" arima_df[\"forecast_uncertainty_ratio\"].median(),\n",
" color=\"red\",\n",
" linestyle=\"--\",\n",
" linewidth=0.5,\n",
" label=f\"Median: {arima_df['forecast_uncertainty_ratio'].median():.2f}\",\n",
")\n",
"ax.set_title(\"Forecast Uncertainty Ratio (CI Width / Forecast Level)\")\n",
"ax.set_ylabel(\"Ratio\")\n",
"ax.set_xlabel(\"Date\")\n",
"ax.legend()\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f8018afa",
"metadata": {
"papermill": {
"duration": 0.003465,
"end_time": "2026-06-13T03:39:18.158611+00:00",
"exception": false,
"start_time": "2026-06-13T03:39:18.155146+00:00",
"status": "completed"
}
},
"source": [
"**Interpretation**: The forecast standard error spikes during volatile periods\n",
"(COVID, 2022 rate hikes) — precisely when the model is least confident.\n",
"The log-normal back-transform produces asymmetric prediction intervals on the\n",
"original scale: wider on the upside, reflecting that volatility spikes are\n",
"unbounded while the floor is zero. The order was selected by AutoARIMA on\n",
"the first window; periodic reselection would guard against structural change."
]
},
{
"cell_type": "markdown",
"id": "f1fe062a",
"metadata": {
"papermill": {
"duration": 0.003319,
"end_time": "2026-06-13T03:39:18.165356+00:00",
"exception": false,
"start_time": "2026-06-13T03:39:18.162037+00:00",
"status": "completed"
}
},
"source": [
"## Feature Summary"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "69b4be83",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:39:18.172905Z",
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{
"name": "stdout",
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"text": [
"SV Uncertainty Features (Walk-Forward):\n"
]
},
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>vol_posterior_mean</th>\n",
" <th>vol_posterior_std</th>\n",
" <th>vol_ci_width</th>\n",
" <th>vol_of_vol</th>\n",
" <th>vol_persistence</th>\n",
" </tr>\n",
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" <th>count</th>\n",
" <td>252.0000</td>\n",
" <td>252.0000</td>\n",
" <td>252.0000</td>\n",
" <td>252.0000</td>\n",
" <td>252.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.6925</td>\n",
" <td>0.1331</td>\n",
" <td>0.5342</td>\n",
" <td>0.2531</td>\n",
" <td>0.5391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0308</td>\n",
" <td>0.0340</td>\n",
" <td>0.1274</td>\n",
" <td>0.0521</td>\n",
" <td>0.1539</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.6506</td>\n",
" <td>0.0911</td>\n",
" <td>0.3791</td>\n",
" <td>0.1975</td>\n",
" <td>0.3632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.6782</td>\n",
" <td>0.1089</td>\n",
" <td>0.4423</td>\n",
" <td>0.2098</td>\n",
" <td>0.4139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.6912</td>\n",
" <td>0.1296</td>\n",
" <td>0.5197</td>\n",
" <td>0.2427</td>\n",
" <td>0.5184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.7055</td>\n",
" <td>0.1538</td>\n",
" <td>0.6115</td>\n",
" <td>0.2860</td>\n",
" <td>0.6436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.7371</td>\n",
" <td>0.1818</td>\n",
" <td>0.7183</td>\n",
" <td>0.3296</td>\n",
" <td>0.7564</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" vol_posterior_mean vol_posterior_std vol_ci_width vol_of_vol \\\n",
"count 252.0000 252.0000 252.0000 252.0000 \n",
"mean 0.6925 0.1331 0.5342 0.2531 \n",
"std 0.0308 0.0340 0.1274 0.0521 \n",
"min 0.6506 0.0911 0.3791 0.1975 \n",
"25% 0.6782 0.1089 0.4423 0.2098 \n",
"50% 0.6912 0.1296 0.5197 0.2427 \n",
"75% 0.7055 0.1538 0.6115 0.2860 \n",
"max 0.7371 0.1818 0.7183 0.3296 \n",
"\n",
" vol_persistence \n",
"count 252.0000 \n",
"mean 0.5391 \n",
"std 0.1539 \n",
"min 0.3632 \n",
"25% 0.4139 \n",
"50% 0.5184 \n",
"75% 0.6436 \n",
"max 0.7564 "
]
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"text": [
"ARIMA Uncertainty Features (AutoARIMA on Log-Vol):\n"
]
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>arima_forecast_std</th>\n",
" <th>arima_ci_width</th>\n",
" <th>forecast_uncertainty_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2243.0000</td>\n",
" <td>2243.0000</td>\n",
" <td>2243.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.0057</td>\n",
" <td>0.0223</td>\n",
" <td>0.1884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0033</td>\n",
" <td>0.0131</td>\n",
" <td>0.0137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.0019</td>\n",
" <td>0.0073</td>\n",
" <td>0.1679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.0035</td>\n",
" <td>0.0138</td>\n",
" <td>0.1802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.0046</td>\n",
" <td>0.0181</td>\n",
" <td>0.1858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.0070</td>\n",
" <td>0.0274</td>\n",
" <td>0.1950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.0291</td>\n",
" <td>0.1139</td>\n",
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" arima_forecast_std arima_ci_width forecast_uncertainty_ratio\n",
"count 2243.0000 2243.0000 2243.0000\n",
"mean 0.0057 0.0223 0.1884\n",
"std 0.0033 0.0131 0.0137\n",
"min 0.0019 0.0073 0.1679\n",
"25% 0.0035 0.0138 0.1802\n",
"50% 0.0046 0.0181 0.1858\n",
"75% 0.0070 0.0274 0.1950\n",
"max 0.0291 0.1139 0.2323"
]
},
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"source": [
"print(\"SV Uncertainty Features (Walk-Forward):\")\n",
"display(sv_features_df.describe().round(4))\n",
"\n",
"print(\"ARIMA Uncertainty Features (AutoARIMA on Log-Vol):\")\n",
"display(\n",
" arima_df[[\"arima_forecast_std\", \"arima_ci_width\", \"forecast_uncertainty_ratio\"]]\n",
" .describe()\n",
" .round(4)\n",
")"
]
},
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"source": [
"### Production Notes\n",
"\n",
"The walk-forward protocol implemented above is the correct production pattern:\n",
"SV features are re-estimated at periodic boundaries and carried forward between\n",
"refits. ARIMA features update daily with negligible cost (refit on a rolling\n",
"window and call `get_forecast()`).\n",
"\n",
"For production hardening beyond this notebook:\n",
"- **Particle filtering** provides online posterior updates without full MCMC\n",
" re-estimation — useful for daily SV updates between quarterly refits\n",
"- AutoARIMA reselects the order at each window automatically; for even\n",
" faster rolling forecasts, fix the order after an initial selection pass"
]
},
{
"cell_type": "markdown",
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},
"source": [
"## Save Uncertainty Features"
]
},
{
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"outputs": [
{
"name": "stdout",
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"text": [
"Saved ARIMA uncertainty features to case_studies/etfs/models/time_series/arima_uncertainty.parquet\n",
" Shape: (2243, 5)\n",
"Saved SV uncertainty features to case_studies/etfs/models/time_series/sv_uncertainty.parquet\n",
" Shape: (252, 6)\n"
]
}
],
"source": [
"MODEL_DIR = get_case_study_dir(\"etfs\") / \"models\" / \"time_series\"\n",
"MODEL_DIR.mkdir(parents=True, exist_ok=True)\n",
"\n",
"arima_save_cols = [\n",
" \"arima_forecast\",\n",
" \"arima_forecast_std\",\n",
" \"arima_ci_width\",\n",
" \"forecast_uncertainty_ratio\",\n",
"]\n",
"arima_output = pl.from_pandas(arima_df[arima_save_cols].reset_index())\n",
"arima_path = MODEL_DIR / \"arima_uncertainty.parquet\"\n",
"arima_output.write_parquet(arima_path)\n",
"print(f\"Saved ARIMA uncertainty features to {arima_path}\")\n",
"print(f\" Shape: {arima_output.shape}\")\n",
"\n",
"sv_output = pl.from_pandas(sv_features_df.reset_index())\n",
"sv_path = MODEL_DIR / \"sv_uncertainty.parquet\"\n",
"sv_output.write_parquet(sv_path)\n",
"print(f\"Saved SV uncertainty features to {sv_path}\")\n",
"print(f\" Shape: {sv_output.shape}\")"
]
},
{
"cell_type": "markdown",
"id": "02f56496",
"metadata": {
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},
"source": [
"## Key Takeaways\n",
"\n",
"1. **Walk-forward SV preserves point-in-time integrity** — the filtered final\n",
" state from each training window produces features conditioned only on past\n",
" data, unlike full-sample smoothed posteriors\n",
"2. **Student-t observation model handles fat tails** — the estimated $\\nu$\n",
" posterior confirms whether Gaussian is adequate; values near 46 show\n",
" Student-t materially improves the fit\n",
"3. **Vol persistence ($\\phi$) and vol-of-vol ($\\sigma_\\eta$)** are complementary:\n",
" $\\phi$ near 1 means shocks decay slowly; high $\\sigma_\\eta$ means the\n",
" volatility process itself is unstable\n",
"4. **Log-vol ARIMA ensures positive forecasts** — the log-normal back-transform\n",
" produces exact, asymmetric prediction intervals on the original scale\n",
"5. **MCMC diagnostics go beyond divergences** — $\\hat{R}$, ESS, and the $\\nu$\n",
" posterior all provide information about posterior reliability\n",
"6. **Computational cost scales with universe** — full MCMC is practical for\n",
" tens of assets; large panels require variational inference or sector aggregates\n",
"\n",
"**Previous**: `09_har_rough_volatility` for multi-horizon volatility decomposition.\n",
"**Next**: `11_hmm_regimes` for regime detection via Hidden Markov Models."
]
}
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