From f3b3560d268fe8c87475ff1f55480eec3fb80a76 Mon Sep 17 00:00:00 2001 From: liaozhaorun <1300336796@qq.com> Date: Wed, 11 Mar 2026 00:12:05 +0800 Subject: [PATCH] =?UTF-8?q?fix(factors/engine):=20=E4=BF=AE=E5=A4=8D?= =?UTF-8?q?=E5=88=97=E9=80=89=E6=8B=A9=E6=97=B6=E5=9F=BA=E7=A1=80=E5=88=97?= =?UTF-8?q?=E9=87=8D=E5=A4=8D=E7=9A=84=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/experiment/learn_to_rank.ipynb | 2025 +++++++++++++++++++++++----- src/factors/engine/data_router.py | 17 +- 2 files changed, 1692 insertions(+), 350 deletions(-) diff --git a/src/experiment/learn_to_rank.ipynb b/src/experiment/learn_to_rank.ipynb index 907b2cb..6daa45c 100644 --- a/src/experiment/learn_to_rank.ipynb +++ b/src/experiment/learn_to_rank.ipynb @@ -26,11 +26,9 @@ { "cell_type": "code", "metadata": { - "jupyter": { - "is_executing": true - }, "ExecuteTime": { - "start_time": "2026-03-09T16:47:47.027059Z" + "end_time": "2026-03-10T14:55:40.429719Z", + "start_time": "2026-03-10T14:55:39.008639Z" } }, "source": [ @@ -58,7 +56,7 @@ "from src.training.config import TrainingConfig" ], "outputs": [], - "execution_count": null + "execution_count": 1 }, { "cell_type": "markdown", @@ -71,8 +69,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:20.005621Z", - "start_time": "2026-03-09T16:28:19.995941Z" + "end_time": "2026-03-10T14:55:40.441603Z", + "start_time": "2026-03-10T14:55:40.434663Z" } }, "source": [ @@ -257,104 +255,7 @@ " results[f\"ndcg@{k}\"] = np.mean(ndcg_scores) if ndcg_scores else 0.0\n", " \n", " return results\n", - "\n", - "\n", - "def analyze_top_k_strategy(\n", - " df: pl.DataFrame,\n", - " prediction_col: str = \"prediction\",\n", - " return_col: str = \"future_return_5\",\n", - " date_col: str = \"trade_date\",\n", - " k_list: List[int] = [5, 10, 20],\n", - ") -> dict:\n", - " \"\"\"分析 Top-k 选股策略表现\n", - " \n", - " Args:\n", - " df: 包含预测分数和真实收益的 DataFrame\n", - " prediction_col: 预测分数列名\n", - " return_col: 真实收益列名\n", - " date_col: 日期列名\n", - " k_list: 要分析的 k 值列表\n", - " \n", - " Returns:\n", - " 策略分析结果字典\n", - " \"\"\"\n", - " results = {}\n", - " \n", - " for k in k_list:\n", - " # 每日选择 Top-k 股票\n", - " daily_top_k = (\n", - " df.sort([date_col, prediction_col], descending=[False, True])\n", - " .group_by(date_col)\n", - " .head(k)\n", - " )\n", - " \n", - " # 计算每日平均收益\n", - " daily_returns = daily_top_k.group_by(date_col).agg(\n", - " pl.col(return_col).mean().alias(f\"top{k}_return\")\n", - " ).sort(date_col)\n", - " \n", - " # 计算累计收益\n", - " returns_array = daily_returns[f\"top{k}_return\"].to_numpy()\n", - " cumulative_returns = np.cumprod(1 + returns_array) - 1\n", - " \n", - " # 统计指标\n", - " results[f\"top{k}\"] = {\n", - " \"mean_daily_return\": np.mean(returns_array),\n", - " \"std_daily_return\": np.std(returns_array),\n", - " \"sharpe_ratio\": np.mean(returns_array) / (np.std(returns_array) + 1e-8) * np.sqrt(252),\n", - " \"total_return\": cumulative_returns[-1] if len(cumulative_returns) > 0 else 0,\n", - " \"cumulative_returns\": cumulative_returns,\n", - " \"dates\": daily_returns[date_col].to_list(),\n", - " }\n", - " \n", - " return results\n", - "\n", - "\n", - "def plot_strategy_performance(strategy_results: dict, save_path: Optional[str] = None):\n", - " \"\"\"绘制策略表现图\"\"\"\n", - " fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", - " \n", - " # 累计收益曲线\n", - " ax = axes[0, 0]\n", - " for name, result in strategy_results.items():\n", - " ax.plot(result[\"dates\"], result[\"cumulative_returns\"], label=name, linewidth=2)\n", - " ax.set_xlabel(\"Date\")\n", - " ax.set_ylabel(\"Cumulative Return\")\n", - " ax.set_title(\"Top-k Strategy Cumulative Returns\")\n", - " ax.legend()\n", - " ax.grid(True, alpha=0.3)\n", - " \n", - " # 日均收益对比\n", - " ax = axes[0, 1]\n", - " names = list(strategy_results.keys())\n", - " mean_returns = [strategy_results[n][\"mean_daily_return\"] for n in names]\n", - " ax.bar(names, mean_returns)\n", - " ax.set_ylabel(\"Mean Daily Return\")\n", - " ax.set_title(\"Mean Daily Return Comparison\")\n", - " ax.grid(True, alpha=0.3, axis=\"y\")\n", - " \n", - " # 夏普比率对比\n", - " ax = axes[1, 0]\n", - " sharpe_ratios = [strategy_results[n][\"sharpe_ratio\"] for n in names]\n", - " ax.bar(names, sharpe_ratios)\n", - " ax.set_ylabel(\"Sharpe Ratio\")\n", - " ax.set_title(\"Sharpe Ratio Comparison\")\n", - " ax.grid(True, alpha=0.3, axis=\"y\")\n", - " \n", - " # 总收益对比\n", - " ax = axes[1, 1]\n", - " total_returns = [strategy_results[n][\"total_return\"] for n in names]\n", - " ax.bar(names, total_returns)\n", - " ax.set_ylabel(\"Total Return\")\n", - " ax.set_title(\"Total Return Comparison\")\n", - " ax.grid(True, alpha=0.3, axis=\"y\")\n", - " \n", - " plt.tight_layout()\n", - " \n", - " if save_path:\n", - " plt.savefig(save_path, dpi=300, bbox_inches=\"tight\")\n", - " \n", - " plt.show()" + "\n" ], "outputs": [], "execution_count": 2 @@ -372,8 +273,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:20.017203Z", - "start_time": "2026-03-09T16:28:20.009432Z" + "end_time": "2026-03-10T14:55:40.451021Z", + "start_time": "2026-03-10T14:55:40.444975Z" } }, "source": [ @@ -462,8 +363,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:20.025233Z", - "start_time": "2026-03-09T16:28:20.020941Z" + "end_time": "2026-03-10T14:55:40.459051Z", + "start_time": "2026-03-10T14:55:40.454871Z" } }, "source": [ @@ -473,7 +374,7 @@ "VAL_START = \"20240101\"\n", "VAL_END = \"20241231\"\n", "TEST_START = \"20250101\"\n", - "TEST_END = \"20261231\"\n", + "TEST_END = \"20251231\"\n", "\n", "# LambdaRank 模型参数配置\n", "MODEL_PARAMS = {\n", @@ -518,8 +419,8 @@ " 4. 选取当日市值最小的500只股票\n", " \"\"\"\n", " code_filter = (\n", - " ~df[\"ts_code\"].str.starts_with(\"300\") &\n", - " ~df[\"ts_code\"].str.starts_with(\"688\") &\n", + " ~df[\"ts_code\"].str.starts_with(\"30\") &\n", + " ~df[\"ts_code\"].str.starts_with(\"68\") &\n", " ~df[\"ts_code\"].str.starts_with(\"8\") &\n", " ~df[\"ts_code\"].str.starts_with(\"9\") &\n", " ~df[\"ts_code\"].str.starts_with(\"4\")\n", @@ -555,8 +456,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:43.241056Z", - "start_time": "2026-03-09T16:28:20.030078Z" + "end_time": "2026-03-10T14:56:04.604292Z", + "start_time": "2026-03-10T14:55:40.466222Z" } }, "source": [ @@ -715,7 +616,7 @@ "准备数据\n", "================================================================================\n", "\n", - "计算因子: 20200101 - 20261231\n", + "计算因子: 20200101 - 20251231\n", "[FinancialLoader] 加载 financial_fina_indicator 失败: Binder Error: Referenced column \"f_ann_date\" not found in FROM clause!\n", "Candidate bindings: \"ann_date\", \"end_date\", \"ocf_to_debt\", \"arturn_days\", \"nca_to_assets\"\n" ] @@ -732,24 +633,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "数据形状: (7255513, 70)\n", - "数据列: ['ts_code', 'trade_date', 'low', 'open', 'high', 'turnover_rate', 'vol', 'close', 'amount', 'total_assets', 'total_mv', 'f_ann_date', 'total_liab', 'total_cur_liab', 'total_hldr_eqy_exc_min_int', 'total_cur_assets', 'n_income', 'revenue', 'n_cashflow_act', 'ebit', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'high_low_ratio', 'bbi_ratio', 'return_5', 'return_20', 'kaufman_ER_20', 'mom_acceleration_10_20', 'drawdown_from_high_60', 'up_days_ratio_20', 'volatility_5', 'volatility_20', 'volatility_ratio', 'std_return_20', 'sharpe_ratio_20', 'min_ret_20', 'volatility_squeeze_5_60', 'overnight_intraday_diff', 'upper_shadow_ratio', 'capital_retention_20', 'max_ret_20', 'volume_ratio_5_20', 'turnover_rate_mean_5', 'turnover_deviation', 'amihud_illiq_20', 'turnover_cv_20', 'pv_corr_20', 'close_vwap_deviation', 'roe', 'roa', 'profit_margin', 'debt_to_equity', 'current_ratio', 'net_profit_yoy', 'revenue_yoy', 'healthy_expansion_velocity', 'EP', 'BP', 'CP', 'market_cap_rank', 'turnover_rank', 'return_5_rank', 'EP_rank', 'pe_expansion_trend', 'value_price_divergence', 'active_market_cap', 'ebit_rank', 'future_return_5_rank']\n", + "数据形状: (7044952, 70)\n", + "数据列: ['ts_code', 'trade_date', 'amount', 'vol', 'turnover_rate', 'close', 'low', 'high', 'open', 'total_assets', 'total_mv', 'f_ann_date', 'n_income', 'revenue', 'total_liab', 'total_cur_assets', 'total_hldr_eqy_exc_min_int', 'total_cur_liab', 'n_cashflow_act', 'ebit', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'high_low_ratio', 'bbi_ratio', 'return_5', 'return_20', 'kaufman_ER_20', 'mom_acceleration_10_20', 'drawdown_from_high_60', 'up_days_ratio_20', 'volatility_5', 'volatility_20', 'volatility_ratio', 'std_return_20', 'sharpe_ratio_20', 'min_ret_20', 'volatility_squeeze_5_60', 'overnight_intraday_diff', 'upper_shadow_ratio', 'capital_retention_20', 'max_ret_20', 'volume_ratio_5_20', 'turnover_rate_mean_5', 'turnover_deviation', 'amihud_illiq_20', 'turnover_cv_20', 'pv_corr_20', 'close_vwap_deviation', 'roe', 'roa', 'profit_margin', 'debt_to_equity', 'current_ratio', 'net_profit_yoy', 'revenue_yoy', 'healthy_expansion_velocity', 'EP', 'BP', 'CP', 'market_cap_rank', 'turnover_rank', 'return_5_rank', 'EP_rank', 'pe_expansion_trend', 'value_price_divergence', 'active_market_cap', 'ebit_rank', 'future_return_5_rank']\n", "\n", "前5行预览:\n", "shape: (5, 70)\n", - "┌───────────┬────────────┬─────────┬─────────┬───┬────────────┬────────────┬───────────┬───────────┐\n", - "│ ts_code ┆ trade_date ┆ low ┆ open ┆ … ┆ value_pric ┆ active_mar ┆ ebit_rank ┆ future_re │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ e_divergen ┆ ket_cap ┆ --- ┆ turn_5_ra │\n", - "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ ce ┆ --- ┆ f64 ┆ nk │\n", - "│ ┆ ┆ ┆ ┆ ┆ --- ┆ f64 ┆ ┆ --- │\n", - "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ f64 │\n", - "╞═══════════╪════════════╪═════════╪═════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n", - "│ 000001.SZ ┆ 20200102 ┆ 1806.75 ┆ 1817.67 ┆ … ┆ null ┆ null ┆ null ┆ -0.008857 │\n", - "│ 000001.SZ ┆ 20200103 ┆ 1847.15 ┆ 1849.33 ┆ … ┆ null ┆ null ┆ null ┆ -0.01881 │\n", - "│ 000001.SZ ┆ 20200106 ┆ 1846.05 ┆ 1856.97 ┆ … ┆ null ┆ null ┆ null ┆ -0.008171 │\n", - "│ 000001.SZ ┆ 20200107 ┆ 1850.42 ┆ 1870.07 ┆ … ┆ null ┆ null ┆ null ┆ -0.014117 │\n", - "│ 000001.SZ ┆ 20200108 ┆ 1815.49 ┆ 1855.88 ┆ … ┆ null ┆ null ┆ null ┆ -0.017252 │\n", - "└───────────┴────────────┴─────────┴─────────┴───┴────────────┴────────────┴───────────┴───────────┘\n", + "┌───────────┬───────────┬──────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", + "│ ts_code ┆ trade_dat ┆ amount ┆ vol ┆ … ┆ value_pri ┆ active_ma ┆ ebit_rank ┆ future_re │\n", + "│ --- ┆ e ┆ --- ┆ --- ┆ ┆ ce_diverg ┆ rket_cap ┆ --- ┆ turn_5_ra │\n", + "│ str ┆ --- ┆ f64 ┆ f64 ┆ ┆ ence ┆ --- ┆ f64 ┆ nk │\n", + "│ ┆ str ┆ ┆ ┆ ┆ --- ┆ f64 ┆ ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ f64 │\n", + "╞═══════════╪═══════════╪══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 000001.SZ ┆ 20200102 ┆ 2.5712e6 ┆ 1.5302e6 ┆ … ┆ null ┆ null ┆ null ┆ -0.008857 │\n", + "│ 000001.SZ ┆ 20200103 ┆ 1.9145e6 ┆ 1.1162e6 ┆ … ┆ null ┆ null ┆ null ┆ -0.01881 │\n", + "│ 000001.SZ ┆ 20200106 ┆ 1.4779e6 ┆ 862083.5 ┆ … ┆ null ┆ null ┆ null ┆ -0.008171 │\n", + "│ 000001.SZ ┆ 20200107 ┆ 1.2470e6 ┆ 728607.56 ┆ … ┆ null ┆ null ┆ null ┆ -0.014117 │\n", + "│ 000001.SZ ┆ 20200108 ┆ 1.4236e6 ┆ 847824.12 ┆ … ┆ null ┆ null ┆ null ┆ -0.017252 │\n", + "└───────────┴───────────┴──────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘\n", "\n", "[4] 转换为排序学习格式\n", "\n", @@ -759,39 +660,39 @@ "\n", "原始 future_return_5_rank 统计:\n", "shape: (9, 2)\n", - "┌────────────┬────────────┐\n", - "│ statistic ┆ value │\n", - "│ --- ┆ --- │\n", - "│ str ┆ f64 │\n", - "╞════════════╪════════════╡\n", - "│ count ┆ 7.227054e6 │\n", - "│ null_count ┆ 28459.0 │\n", - "│ mean ┆ 0.003978 │\n", - "│ std ┆ 0.073204 │\n", - "│ min ┆ -0.969459 │\n", - "│ 25% ┆ -0.032998 │\n", - "│ 50% ┆ -0.001278 │\n", - "│ 75% ┆ 0.032666 │\n", - "│ max ┆ 10.361925 │\n", - "└────────────┴────────────┘\n", + "┌────────────┬───────────┐\n", + "│ statistic ┆ value │\n", + "│ --- ┆ --- │\n", + "│ str ┆ f64 │\n", + "╞════════════╪═══════════╡\n", + "│ count ┆ 7.01659e6 │\n", + "│ null_count ┆ 28362.0 │\n", + "│ mean ┆ 0.003779 │\n", + "│ std ┆ 0.073221 │\n", + "│ min ┆ -0.969459 │\n", + "│ 25% ┆ -0.033163 │\n", + "│ 50% ┆ -0.001483 │\n", + "│ 75% ┆ 0.032547 │\n", + "│ max ┆ 10.361925 │\n", + "└────────────┴───────────┘\n", "\n", "转换后 future_return_5_rank_rank 统计:\n", "shape: (9, 2)\n", - "┌────────────┬────────────┐\n", - "│ statistic ┆ value │\n", - "│ --- ┆ --- │\n", - "│ str ┆ f64 │\n", - "╞════════════╪════════════╡\n", - "│ count ┆ 7.227054e6 │\n", - "│ null_count ┆ 28459.0 │\n", - "│ mean ┆ 9.493551 │\n", - "│ std ┆ 5.765628 │\n", - "│ min ┆ 0.0 │\n", - "│ 25% ┆ 4.0 │\n", - "│ 50% ┆ 9.0 │\n", - "│ 75% ┆ 14.0 │\n", - "│ max ┆ 19.0 │\n", - "└────────────┴────────────┘\n", + "┌────────────┬───────────┐\n", + "│ statistic ┆ value │\n", + "│ --- ┆ --- │\n", + "│ str ┆ f64 │\n", + "╞════════════╪═══════════╡\n", + "│ count ┆ 7.01659e6 │\n", + "│ null_count ┆ 28362.0 │\n", + "│ mean ┆ 9.495412 │\n", + "│ std ┆ 5.765668 │\n", + "│ min ┆ 0.0 │\n", + "│ 25% ┆ 4.0 │\n", + "│ 50% ┆ 9.0 │\n", + "│ 75% ┆ 14.0 │\n", + "│ max ┆ 19.0 │\n", + "└────────────┴───────────┘\n", "\n", "每日样本数统计:\n", "shape: (9, 2)\n", @@ -800,15 +701,15 @@ "│ --- ┆ --- │\n", "│ str ┆ f64 │\n", "╞════════════╪═════════════╡\n", - "│ count ┆ 1494.0 │\n", + "│ count ┆ 1455.0 │\n", "│ null_count ┆ 0.0 │\n", - "│ mean ┆ 4856.434404 │\n", - "│ std ┆ 564.521537 │\n", - "│ min ┆ 2885.0 │\n", - "│ 25% ┆ 4382.0 │\n", - "│ 50% ┆ 5069.0 │\n", - "│ 75% ┆ 5347.0 │\n", - "│ max ┆ 5476.0 │\n", + "│ mean ┆ 4841.891409 │\n", + "│ std ┆ 560.948186 │\n", + "│ min ┆ 3740.0 │\n", + "│ 25% ┆ 4369.0 │\n", + "│ 50% ┆ 5060.0 │\n", + "│ 75% ┆ 5344.0 │\n", + "│ max ┆ 5458.0 │\n", "└────────────┴─────────────┘\n", "\n", "分位数标签分布:\n", @@ -818,22 +719,22 @@ "│ --- ┆ --- │\n", "│ i64 ┆ u32 │\n", "╞═══════════════════════════╪════════╡\n", - "│ null ┆ 28459 │\n", - "│ 0 ┆ 362270 │\n", - "│ 1 ┆ 361546 │\n", - "│ 2 ┆ 361599 │\n", - "│ 3 ┆ 361755 │\n", + "│ null ┆ 28362 │\n", + "│ 0 ┆ 351599 │\n", + "│ 1 ┆ 350894 │\n", + "│ 2 ┆ 350944 │\n", + "│ 3 ┆ 351077 │\n", "│ … ┆ … │\n", - "│ 15 ┆ 361289 │\n", - "│ 16 ┆ 361218 │\n", - "│ 17 ┆ 361227 │\n", - "│ 18 ┆ 361252 │\n", - "│ 19 ┆ 359483 │\n", + "│ 15 ┆ 350910 │\n", + "│ 16 ┆ 350835 │\n", + "│ 17 ┆ 350848 │\n", + "│ 18 ┆ 350871 │\n", + "│ 19 ┆ 349137 │\n", "└───────────────────────────┴────────┘\n", "\n", "[配置] 训练期: 20200101 - 20231231\n", "[配置] 验证期: 20240101 - 20241231\n", - "[配置] 测试期: 20250101 - 20261231\n", + "[配置] 测试期: 20250101 - 20251231\n", "[配置] 特征数: 49\n", "[配置] 目标变量: future_return_5_rank_rank（20分位数）\n" ] @@ -842,7 +743,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_8380\\3149203115.py:108: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_7864\\551043002.py:108: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n", "(Deprecated in version 0.20.5)\n", " daily_counts = df_ranked.group_by(date_col).agg(pl.count().alias(\"count\"))\n" ] @@ -861,8 +762,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:46.756857Z", - "start_time": "2026-03-09T16:28:43.247911Z" + "end_time": "2026-03-10T14:56:29.070549Z", + "start_time": "2026-03-10T14:56:04.619443Z" } }, "source": [ @@ -870,6 +771,12 @@ "print(\"股票池筛选\")\n", "print(\"=\" * 80)\n", "\n", + "# 先执行 ST 过滤（在股票池筛选之前，与 Trainer.train() 保持一致）\n", + "if st_filter:\n", + " print(\"\\n[过滤] 应用 ST 过滤器...\")\n", + " data = st_filter.filter(data)\n", + " print(f\" ST 过滤后数据规模: {data.shape}\")\n", + "\n", "if pool_manager:\n", " print(\"\\n执行每日独立筛选股票池...\")\n", " filtered_data = pool_manager.filter_and_select_daily(data)\n", @@ -892,6 +799,1450 @@ "股票池筛选\n", "================================================================================\n", "\n", + "[过滤] 应用 ST 过滤器...\n", + " [20200103] 过滤 129 只 ST 股票\n", + " [20200106] 过滤 129 只 ST 股票\n", + " [20200107] 过滤 128 只 ST 股票\n", + " [20200108] 过滤 128 只 ST 股票\n", + " [20200109] 过滤 128 只 ST 股票\n", + " [20200110] 过滤 128 只 ST 股票\n", + " [20200113] 过滤 129 只 ST 股票\n", + " [20200114] 过滤 129 只 ST 股票\n", + " [20200115] 过滤 130 只 ST 股票\n", + " [20200116] 过滤 130 只 ST 股票\n", + " [20200117] 过滤 129 只 ST 股票\n", + " [20200120] 过滤 129 只 ST 股票\n", + " [20200121] 过滤 131 只 ST 股票\n", + " [20200122] 过滤 132 只 ST 股票\n", + " [20200123] 过滤 132 只 ST 股票\n", + " [20200203] 过滤 132 只 ST 股票\n", + " [20200204] 过滤 132 只 ST 股票\n", + " [20200205] 过滤 132 只 ST 股票\n", + " [20200206] 过滤 132 只 ST 股票\n", + " [20200207] 过滤 132 只 ST 股票\n", + " [20200210] 过滤 132 只 ST 股票\n", + " [20200211] 过滤 132 只 ST 股票\n", + " [20200212] 过滤 132 只 ST 股票\n", + " [20200213] 过滤 132 只 ST 股票\n", + " [20200214] 过滤 132 只 ST 股票\n", + " [20200217] 过滤 132 只 ST 股票\n", + " [20200218] 过滤 132 只 ST 股票\n", + " [20200219] 过滤 132 只 ST 股票\n", + " [20200221] 过滤 132 只 ST 股票\n", + " [20200224] 过滤 132 只 ST 股票\n", + " [20200226] 过滤 132 只 ST 股票\n", + " [20200227] 过滤 131 只 ST 股票\n", + " [20200228] 过滤 132 只 ST 股票\n", + " [20200302] 过滤 132 只 ST 股票\n", + " [20200303] 过滤 131 只 ST 股票\n", + " [20200304] 过滤 130 只 ST 股票\n", + " [20200305] 过滤 129 只 ST 股票\n", + " [20200306] 过滤 129 只 ST 股票\n", + " [20200309] 过滤 129 只 ST 股票\n", + " [20200310] 过滤 129 只 ST 股票\n", + " [20200311] 过滤 129 只 ST 股票\n", + " [20200312] 过滤 129 只 ST 股票\n", + " [20200313] 过滤 129 只 ST 股票\n", + " [20200317] 过滤 129 只 ST 股票\n", + " [20200318] 过滤 129 只 ST 股票\n", + " [20200319] 过滤 130 只 ST 股票\n", + " [20200320] 过滤 129 只 ST 股票\n", + " [20200323] 过滤 130 只 ST 股票\n", + " [20200324] 过滤 130 只 ST 股票\n", + " [20200325] 过滤 130 只 ST 股票\n", + " [20200326] 过滤 130 只 ST 股票\n", + " [20200327] 过滤 130 只 ST 股票\n", + " [20200330] 过滤 130 只 ST 股票\n", + " [20200331] 过滤 131 只 ST 股票\n", + " [20200401] 过滤 133 只 ST 股票\n", + " [20200402] 过滤 133 只 ST 股票\n", + " [20200403] 过滤 133 只 ST 股票\n", + " [20200407] 过滤 133 只 ST 股票\n", + " [20200408] 过滤 131 只 ST 股票\n", + " [20200409] 过滤 132 只 ST 股票\n", + " [20200410] 过滤 131 只 ST 股票\n", + " [20200413] 过滤 130 只 ST 股票\n", + " [20200414] 过滤 129 只 ST 股票\n", + " [20200415] 过滤 130 只 ST 股票\n", + " [20200416] 过滤 131 只 ST 股票\n", + " [20200417] 过滤 131 只 ST 股票\n", + " [20200420] 过滤 127 只 ST 股票\n", + " [20200421] 过滤 128 只 ST 股票\n", + " [20200422] 过滤 131 只 ST 股票\n", + " [20200424] 过滤 132 只 ST 股票\n", + " [20200427] 过滤 135 只 ST 股票\n", + " [20200428] 过滤 135 只 ST 股票\n", + " [20200429] 过滤 139 只 ST 股票\n", + " [20200430] 过滤 151 只 ST 股票\n", + " [20200506] 过滤 183 只 ST 股票\n", + " [20200507] 过滤 184 只 ST 股票\n", + " [20200508] 过滤 182 只 ST 股票\n", + " [20200511] 过滤 184 只 ST 股票\n", + " [20200512] 过滤 182 只 ST 股票\n", + " [20200513] 过滤 184 只 ST 股票\n", + " [20200514] 过滤 184 只 ST 股票\n", + " [20200515] 过滤 184 只 ST 股票\n", + " [20200518] 过滤 184 只 ST 股票\n", + " [20200519] 过滤 184 只 ST 股票\n", + " [20200520] 过滤 183 只 ST 股票\n", + " [20200521] 过滤 184 只 ST 股票\n", + " [20200522] 过滤 184 只 ST 股票\n", + " [20200525] 过滤 183 只 ST 股票\n", + " [20200526] 过滤 183 只 ST 股票\n", + " [20200527] 过滤 183 只 ST 股票\n", + " [20200528] 过滤 181 只 ST 股票\n", + " [20200529] 过滤 181 只 ST 股票\n", + " [20200601] 过滤 182 只 ST 股票\n", + " [20200602] 过滤 184 只 ST 股票\n", + " [20200603] 过滤 182 只 ST 股票\n", + " [20200604] 过滤 181 只 ST 股票\n", + " [20200605] 过滤 180 只 ST 股票\n", + " [20200608] 过滤 176 只 ST 股票\n", + " [20200609] 过滤 180 只 ST 股票\n", + " [20200610] 过滤 179 只 ST 股票\n", + " [20200611] 过滤 178 只 ST 股票\n", + " [20200612] 过滤 180 只 ST 股票\n", + " [20200615] 过滤 182 只 ST 股票\n", + " [20200616] 过滤 183 只 ST 股票\n", + " [20200617] 过滤 182 只 ST 股票\n", + " [20200619] 过滤 183 只 ST 股票\n", + " [20200622] 过滤 183 只 ST 股票\n", + " [20200623] 过滤 183 只 ST 股票\n", + " [20200624] 过滤 186 只 ST 股票\n", + " [20200629] 过滤 190 只 ST 股票\n", + " [20200630] 过滤 188 只 ST 股票\n", + " [20200701] 过滤 196 只 ST 股票\n", + " [20200702] 过滤 197 只 ST 股票\n", + " [20200703] 过滤 197 只 ST 股票\n", + " [20200706] 过滤 197 只 ST 股票\n", + " [20200707] 过滤 196 只 ST 股票\n", + " [20200709] 过滤 197 只 ST 股票\n", + " [20200710] 过滤 196 只 ST 股票\n", + " [20200713] 过滤 197 只 ST 股票\n", + " [20200714] 过滤 197 只 ST 股票\n", + " [20200715] 过滤 197 只 ST 股票\n", + " [20200716] 过滤 195 只 ST 股票\n", + " [20200717] 过滤 196 只 ST 股票\n", + " [20200721] 过滤 195 只 ST 股票\n", + " [20200722] 过滤 194 只 ST 股票\n", + " [20200723] 过滤 193 只 ST 股票\n", + " [20200724] 过滤 194 只 ST 股票\n", + " [20200727] 过滤 195 只 ST 股票\n", + " [20200728] 过滤 195 只 ST 股票\n", + " [20200729] 过滤 195 只 ST 股票\n", + " [20200730] 过滤 196 只 ST 股票\n", + " [20200731] 过滤 196 只 ST 股票\n", + " [20200805] 过滤 194 只 ST 股票\n", + " [20200806] 过滤 195 只 ST 股票\n", + " [20200807] 过滤 195 只 ST 股票\n", + " [20200810] 过滤 194 只 ST 股票\n", + " [20200811] 过滤 194 只 ST 股票\n", + " [20200812] 过滤 195 只 ST 股票\n", + " [20200813] 过滤 194 只 ST 股票\n", + " [20200814] 过滤 194 只 ST 股票\n", + " [20200817] 过滤 195 只 ST 股票\n", + " [20200818] 过滤 195 只 ST 股票\n", + " [20200819] 过滤 195 只 ST 股票\n", + " [20200820] 过滤 196 只 ST 股票\n", + " [20200821] 过滤 196 只 ST 股票\n", + " [20200825] 过滤 198 只 ST 股票\n", + " [20200826] 过滤 198 只 ST 股票\n", + " [20200827] 过滤 198 只 ST 股票\n", + " [20200828] 过滤 198 只 ST 股票\n", + " [20200831] 过滤 198 只 ST 股票\n", + " [20200901] 过滤 199 只 ST 股票\n", + " [20200902] 过滤 199 只 ST 股票\n", + " [20200903] 过滤 199 只 ST 股票\n", + " [20200904] 过滤 198 只 ST 股票\n", + " [20200907] 过滤 197 只 ST 股票\n", + " [20200908] 过滤 197 只 ST 股票\n", + " [20200909] 过滤 197 只 ST 股票\n", + " [20200910] 过滤 197 只 ST 股票\n", + " [20200911] 过滤 197 只 ST 股票\n", + " [20200914] 过滤 197 只 ST 股票\n", + " [20200915] 过滤 200 只 ST 股票\n", + " [20200916] 过滤 200 只 ST 股票\n", + " [20200917] 过滤 199 只 ST 股票\n", + " [20200918] 过滤 200 只 ST 股票\n", + " [20200921] 过滤 200 只 ST 股票\n", + " [20200922] 过滤 201 只 ST 股票\n", + " [20200923] 过滤 201 只 ST 股票\n", + " [20200924] 过滤 200 只 ST 股票\n", + " [20200925] 过滤 200 只 ST 股票\n", + " [20200928] 过滤 202 只 ST 股票\n", + " [20200929] 过滤 203 只 ST 股票\n", + " [20200930] 过滤 204 只 ST 股票\n", + " [20201009] 过滤 204 只 ST 股票\n", + " [20201012] 过滤 204 只 ST 股票\n", + " [20201013] 过滤 204 只 ST 股票\n", + " [20201014] 过滤 203 只 ST 股票\n", + " [20201015] 过滤 203 只 ST 股票\n", + " [20201016] 过滤 203 只 ST 股票\n", + " [20201019] 过滤 203 只 ST 股票\n", + " [20201020] 过滤 203 只 ST 股票\n", + " [20201021] 过滤 203 只 ST 股票\n", + " [20201022] 过滤 203 只 ST 股票\n", + " [20201023] 过滤 203 只 ST 股票\n", + " [20201026] 过滤 203 只 ST 股票\n", + " [20201027] 过滤 203 只 ST 股票\n", + " [20201028] 过滤 203 只 ST 股票\n", + " [20201029] 过滤 203 只 ST 股票\n", + " [20201030] 过滤 201 只 ST 股票\n", + " [20201102] 过滤 201 只 ST 股票\n", + " [20201103] 过滤 203 只 ST 股票\n", + " [20201104] 过滤 204 只 ST 股票\n", + " [20201105] 过滤 204 只 ST 股票\n", + " [20201106] 过滤 204 只 ST 股票\n", + " [20201109] 过滤 203 只 ST 股票\n", + " [20201110] 过滤 203 只 ST 股票\n", + " [20201111] 过滤 203 只 ST 股票\n", + " [20201112] 过滤 203 只 ST 股票\n", + " [20201113] 过滤 203 只 ST 股票\n", + " [20201116] 过滤 204 只 ST 股票\n", + " [20201117] 过滤 204 只 ST 股票\n", + " [20201118] 过滤 204 只 ST 股票\n", + " [20201119] 过滤 204 只 ST 股票\n", + " [20201123] 过滤 203 只 ST 股票\n", + " [20201124] 过滤 203 只 ST 股票\n", + " [20201125] 过滤 201 只 ST 股票\n", + " [20201126] 过滤 202 只 ST 股票\n", + " [20201127] 过滤 202 只 ST 股票\n", + " [20201130] 过滤 202 只 ST 股票\n", + " [20201201] 过滤 202 只 ST 股票\n", + " [20201202] 过滤 202 只 ST 股票\n", + " [20201203] 过滤 202 只 ST 股票\n", + " [20201204] 过滤 201 只 ST 股票\n", + " [20201207] 过滤 201 只 ST 股票\n", + " [20201208] 过滤 201 只 ST 股票\n", + " [20201209] 过滤 201 只 ST 股票\n", + " [20201210] 过滤 201 只 ST 股票\n", + " [20201211] 过滤 201 只 ST 股票\n", + " [20201214] 过滤 202 只 ST 股票\n", + " [20201215] 过滤 203 只 ST 股票\n", + " [20201216] 过滤 203 只 ST 股票\n", + " [20201217] 过滤 203 只 ST 股票\n", + " [20201218] 过滤 203 只 ST 股票\n", + " [20201221] 过滤 203 只 ST 股票\n", + " [20201222] 过滤 201 只 ST 股票\n", + " [20201223] 过滤 201 只 ST 股票\n", + " [20201224] 过滤 200 只 ST 股票\n", + " [20201225] 过滤 200 只 ST 股票\n", + " [20201228] 过滤 200 只 ST 股票\n", + " [20201229] 过滤 201 只 ST 股票\n", + " [20201230] 过滤 201 只 ST 股票\n", + " [20201231] 过滤 200 只 ST 股票\n", + " [20210104] 过滤 199 只 ST 股票\n", + " [20210105] 过滤 199 只 ST 股票\n", + " [20210106] 过滤 199 只 ST 股票\n", + " [20210107] 过滤 199 只 ST 股票\n", + " [20210108] 过滤 199 只 ST 股票\n", + " [20210111] 过滤 199 只 ST 股票\n", + " [20210112] 过滤 199 只 ST 股票\n", + " [20210113] 过滤 199 只 ST 股票\n", + " [20210114] 过滤 198 只 ST 股票\n", + " [20210115] 过滤 198 只 ST 股票\n", + " [20210118] 过滤 198 只 ST 股票\n", + " [20210119] 过滤 199 只 ST 股票\n", + " [20210120] 过滤 199 只 ST 股票\n", + " [20210121] 过滤 198 只 ST 股票\n", + " [20210122] 过滤 198 只 ST 股票\n", + " [20210125] 过滤 198 只 ST 股票\n", + " [20210126] 过滤 199 只 ST 股票\n", + " [20210127] 过滤 199 只 ST 股票\n", + " [20210128] 过滤 199 只 ST 股票\n", + " [20210201] 过滤 197 只 ST 股票\n", + " [20210202] 过滤 199 只 ST 股票\n", + " [20210203] 过滤 199 只 ST 股票\n", + " [20210204] 过滤 200 只 ST 股票\n", + " [20210205] 过滤 200 只 ST 股票\n", + " [20210208] 过滤 199 只 ST 股票\n", + " [20210209] 过滤 200 只 ST 股票\n", + " [20210210] 过滤 200 只 ST 股票\n", + " [20210218] 过滤 200 只 ST 股票\n", + " [20210219] 过滤 203 只 ST 股票\n", + " [20210222] 过滤 203 只 ST 股票\n", + " [20210223] 过滤 202 只 ST 股票\n", + " [20210224] 过滤 202 只 ST 股票\n", + " [20210225] 过滤 201 只 ST 股票\n", + " [20210226] 过滤 200 只 ST 股票\n", + " [20210301] 过滤 200 只 ST 股票\n", + " [20210302] 过滤 200 只 ST 股票\n", + " [20210303] 过滤 200 只 ST 股票\n", + " [20210304] 过滤 201 只 ST 股票\n", + " [20210305] 过滤 200 只 ST 股票\n", + " [20210308] 过滤 201 只 ST 股票\n", + " [20210309] 过滤 201 只 ST 股票\n", + " [20210310] 过滤 201 只 ST 股票\n", + " [20210311] 过滤 200 只 ST 股票\n", + " [20210312] 过滤 199 只 ST 股票\n", + " [20210315] 过滤 198 只 ST 股票\n", + " [20210317] 过滤 198 只 ST 股票\n", + " [20210318] 过滤 198 只 ST 股票\n", + " [20210319] 过滤 195 只 ST 股票\n", + " [20210322] 过滤 194 只 ST 股票\n", + " [20210323] 过滤 194 只 ST 股票\n", + " [20210324] 过滤 194 只 ST 股票\n", + " [20210325] 过滤 194 只 ST 股票\n", + " [20210326] 过滤 194 只 ST 股票\n", + " [20210329] 过滤 194 只 ST 股票\n", + " [20210330] 过滤 194 只 ST 股票\n", + " [20210331] 过滤 192 只 ST 股票\n", + " [20210401] 过滤 191 只 ST 股票\n", + " [20210402] 过滤 189 只 ST 股票\n", + " [20210406] 过滤 190 只 ST 股票\n", + " [20210407] 过滤 191 只 ST 股票\n", + " [20210408] 过滤 191 只 ST 股票\n", + " [20210409] 过滤 190 只 ST 股票\n", + " [20210412] 过滤 188 只 ST 股票\n", + " [20210413] 过滤 187 只 ST 股票\n", + " [20210414] 过滤 186 只 ST 股票\n", + " [20210415] 过滤 182 只 ST 股票\n", + " [20210416] 过滤 180 只 ST 股票\n", + " [20210419] 过滤 179 只 ST 股票\n", + " [20210420] 过滤 179 只 ST 股票\n", + " [20210421] 过滤 180 只 ST 股票\n", + " [20210422] 过滤 180 只 ST 股票\n", + " [20210423] 过滤 178 只 ST 股票\n", + " [20210426] 过滤 183 只 ST 股票\n", + " [20210427] 过滤 180 只 ST 股票\n", + " [20210428] 过滤 190 只 ST 股票\n", + " [20210429] 过滤 185 只 ST 股票\n", + " [20210430] 过滤 200 只 ST 股票\n", + " [20210506] 过滤 225 只 ST 股票\n", + " [20210507] 过滤 222 只 ST 股票\n", + " [20210510] 过滤 218 只 ST 股票\n", + " [20210511] 过滤 215 只 ST 股票\n", + " [20210512] 过滤 214 只 ST 股票\n", + " [20210513] 过滤 212 只 ST 股票\n", + " [20210514] 过滤 210 只 ST 股票\n", + " [20210517] 过滤 208 只 ST 股票\n", + " [20210518] 过滤 202 只 ST 股票\n", + " [20210519] 过滤 196 只 ST 股票\n", + " [20210520] 过滤 202 只 ST 股票\n", + " [20210521] 过滤 201 只 ST 股票\n", + " [20210524] 过滤 199 只 ST 股票\n", + " [20210525] 过滤 198 只 ST 股票\n", + " [20210526] 过滤 197 只 ST 股票\n", + " [20210527] 过滤 197 只 ST 股票\n", + " [20210528] 过滤 196 只 ST 股票\n", + " [20210531] 过滤 196 只 ST 股票\n", + " [20210601] 过滤 196 只 ST 股票\n", + " [20210602] 过滤 195 只 ST 股票\n", + " [20210603] 过滤 193 只 ST 股票\n", + " [20210604] 过滤 193 只 ST 股票\n", + " [20210607] 过滤 190 只 ST 股票\n", + " [20210608] 过滤 191 只 ST 股票\n", + " [20210609] 过滤 190 只 ST 股票\n", + " [20210610] 过滤 189 只 ST 股票\n", + " [20210611] 过滤 190 只 ST 股票\n", + " [20210615] 过滤 187 只 ST 股票\n", + " [20210616] 过滤 187 只 ST 股票\n", + " [20210617] 过滤 186 只 ST 股票\n", + " [20210618] 过滤 185 只 ST 股票\n", + " [20210621] 过滤 183 只 ST 股票\n", + " [20210622] 过滤 183 只 ST 股票\n", + " [20210623] 过滤 181 只 ST 股票\n", + " [20210624] 过滤 183 只 ST 股票\n", + " [20210625] 过滤 181 只 ST 股票\n", + " [20210628] 过滤 181 只 ST 股票\n", + " [20210629] 过滤 181 只 ST 股票\n", + " [20210630] 过滤 180 只 ST 股票\n", + " [20210701] 过滤 180 只 ST 股票\n", + " [20210702] 过滤 182 只 ST 股票\n", + " [20210705] 过滤 182 只 ST 股票\n", + " [20210706] 过滤 182 只 ST 股票\n", + " [20210707] 过滤 184 只 ST 股票\n", + " [20210708] 过滤 184 只 ST 股票\n", + " [20210709] 过滤 182 只 ST 股票\n", + " [20210712] 过滤 182 只 ST 股票\n", + " [20210713] 过滤 183 只 ST 股票\n", + " [20210714] 过滤 182 只 ST 股票\n", + " [20210715] 过滤 181 只 ST 股票\n", + " [20210716] 过滤 181 只 ST 股票\n", + " [20210719] 过滤 180 只 ST 股票\n", + " [20210720] 过滤 179 只 ST 股票\n", + " [20210721] 过滤 178 只 ST 股票\n", + " [20210722] 过滤 180 只 ST 股票\n", + " [20210723] 过滤 181 只 ST 股票\n", + " [20210726] 过滤 182 只 ST 股票\n", + " [20210727] 过滤 181 只 ST 股票\n", + " [20210728] 过滤 181 只 ST 股票\n", + " [20210729] 过滤 180 只 ST 股票\n", + " [20210730] 过滤 180 只 ST 股票\n", + " [20210802] 过滤 180 只 ST 股票\n", + " [20210803] 过滤 180 只 ST 股票\n", + " [20210804] 过滤 180 只 ST 股票\n", + " [20210805] 过滤 181 只 ST 股票\n", + " [20210806] 过滤 181 只 ST 股票\n", + " [20210809] 过滤 179 只 ST 股票\n", + " [20210810] 过滤 179 只 ST 股票\n", + " [20210811] 过滤 179 只 ST 股票\n", + " [20210812] 过滤 179 只 ST 股票\n", + " [20210813] 过滤 179 只 ST 股票\n", + " [20210816] 过滤 179 只 ST 股票\n", + " [20210817] 过滤 180 只 ST 股票\n", + " [20210818] 过滤 180 只 ST 股票\n", + " [20210819] 过滤 180 只 ST 股票\n", + " [20210820] 过滤 180 只 ST 股票\n", + " [20210823] 过滤 179 只 ST 股票\n", + " [20210824] 过滤 181 只 ST 股票\n", + " [20210825] 过滤 181 只 ST 股票\n", + " [20210826] 过滤 180 只 ST 股票\n", + " [20210827] 过滤 181 只 ST 股票\n", + " [20210830] 过滤 180 只 ST 股票\n", + " [20210831] 过滤 180 只 ST 股票\n", + " [20210901] 过滤 180 只 ST 股票\n", + " [20210902] 过滤 180 只 ST 股票\n", + " [20210903] 过滤 180 只 ST 股票\n", + " [20210906] 过滤 180 只 ST 股票\n", + " [20210907] 过滤 181 只 ST 股票\n", + " [20210908] 过滤 181 只 ST 股票\n", + " [20210909] 过滤 181 只 ST 股票\n", + " [20210910] 过滤 181 只 ST 股票\n", + " [20210913] 过滤 181 只 ST 股票\n", + " [20210914] 过滤 181 只 ST 股票\n", + " [20210915] 过滤 182 只 ST 股票\n", + " [20210916] 过滤 182 只 ST 股票\n", + " [20210917] 过滤 182 只 ST 股票\n", + " [20210922] 过滤 180 只 ST 股票\n", + " [20210923] 过滤 179 只 ST 股票\n", + " [20210924] 过滤 180 只 ST 股票\n", + " [20210927] 过滤 178 只 ST 股票\n", + " [20210928] 过滤 178 只 ST 股票\n", + " [20210929] 过滤 180 只 ST 股票\n", + " [20210930] 过滤 180 只 ST 股票\n", + " [20211008] 过滤 181 只 ST 股票\n", + " [20211011] 过滤 181 只 ST 股票\n", + " [20211012] 过滤 182 只 ST 股票\n", + " [20211013] 过滤 182 只 ST 股票\n", + " [20211014] 过滤 182 只 ST 股票\n", + " [20211015] 过滤 182 只 ST 股票\n", + " [20211018] 过滤 182 只 ST 股票\n", + " [20211019] 过滤 182 只 ST 股票\n", + " [20211020] 过滤 182 只 ST 股票\n", + " [20211021] 过滤 182 只 ST 股票\n", + " [20211022] 过滤 182 只 ST 股票\n", + " [20211025] 过滤 182 只 ST 股票\n", + " [20211026] 过滤 182 只 ST 股票\n", + " [20211027] 过滤 182 只 ST 股票\n", + " [20211028] 过滤 182 只 ST 股票\n", + " [20211029] 过滤 182 只 ST 股票\n", + " [20211101] 过滤 182 只 ST 股票\n", + " [20211102] 过滤 182 只 ST 股票\n", + " [20211103] 过滤 182 只 ST 股票\n", + " [20211104] 过滤 182 只 ST 股票\n", + " [20211105] 过滤 182 只 ST 股票\n", + " [20211108] 过滤 182 只 ST 股票\n", + " [20211109] 过滤 180 只 ST 股票\n", + " [20211110] 过滤 181 只 ST 股票\n", + " [20211111] 过滤 181 只 ST 股票\n", + " [20211112] 过滤 182 只 ST 股票\n", + " [20211115] 过滤 182 只 ST 股票\n", + " [20211116] 过滤 182 只 ST 股票\n", + " [20211117] 过滤 180 只 ST 股票\n", + " [20211118] 过滤 182 只 ST 股票\n", + " [20211119] 过滤 182 只 ST 股票\n", + " [20211122] 过滤 182 只 ST 股票\n", + " [20211123] 过滤 182 只 ST 股票\n", + " [20211124] 过滤 182 只 ST 股票\n", + " [20211125] 过滤 182 只 ST 股票\n", + " [20211126] 过滤 182 只 ST 股票\n", + " [20211129] 过滤 182 只 ST 股票\n", + " [20211130] 过滤 182 只 ST 股票\n", + " [20211201] 过滤 181 只 ST 股票\n", + " [20211202] 过滤 180 只 ST 股票\n", + " [20211203] 过滤 179 只 ST 股票\n", + " [20211206] 过滤 180 只 ST 股票\n", + " [20211207] 过滤 180 只 ST 股票\n", + " [20211208] 过滤 180 只 ST 股票\n", + " [20211209] 过滤 181 只 ST 股票\n", + " [20211210] 过滤 181 只 ST 股票\n", + " [20211213] 过滤 181 只 ST 股票\n", + " [20211214] 过滤 180 只 ST 股票\n", + " [20211215] 过滤 181 只 ST 股票\n", + " [20211216] 过滤 180 只 ST 股票\n", + " [20211217] 过滤 178 只 ST 股票\n", + " [20211220] 过滤 178 只 ST 股票\n", + " [20211221] 过滤 177 只 ST 股票\n", + " [20211222] 过滤 178 只 ST 股票\n", + " [20211223] 过滤 178 只 ST 股票\n", + " [20211224] 过滤 177 只 ST 股票\n", + " [20211227] 过滤 177 只 ST 股票\n", + " [20211228] 过滤 177 只 ST 股票\n", + " [20211229] 过滤 177 只 ST 股票\n", + " [20211230] 过滤 175 只 ST 股票\n", + " [20211231] 过滤 174 只 ST 股票\n", + " [20220104] 过滤 177 只 ST 股票\n", + " [20220105] 过滤 177 只 ST 股票\n", + " [20220106] 过滤 178 只 ST 股票\n", + " [20220107] 过滤 178 只 ST 股票\n", + " [20220110] 过滤 178 只 ST 股票\n", + " [20220111] 过滤 178 只 ST 股票\n", + " [20220112] 过滤 178 只 ST 股票\n", + " [20220113] 过滤 177 只 ST 股票\n", + " [20220114] 过滤 178 只 ST 股票\n", + " [20220117] 过滤 179 只 ST 股票\n", + " [20220118] 过滤 179 只 ST 股票\n", + " [20220119] 过滤 179 只 ST 股票\n", + " [20220120] 过滤 179 只 ST 股票\n", + " [20220121] 过滤 179 只 ST 股票\n", + " [20220124] 过滤 179 只 ST 股票\n", + " [20220125] 过滤 179 只 ST 股票\n", + " [20220126] 过滤 179 只 ST 股票\n", + " [20220127] 过滤 179 只 ST 股票\n", + " [20220128] 过滤 179 只 ST 股票\n", + " [20220207] 过滤 178 只 ST 股票\n", + " [20220208] 过滤 178 只 ST 股票\n", + " [20220209] 过滤 179 只 ST 股票\n", + " [20220210] 过滤 179 只 ST 股票\n", + " [20220211] 过滤 179 只 ST 股票\n", + " [20220214] 过滤 178 只 ST 股票\n", + " [20220215] 过滤 179 只 ST 股票\n", + " [20220216] 过滤 179 只 ST 股票\n", + " [20220217] 过滤 179 只 ST 股票\n", + " [20220218] 过滤 179 只 ST 股票\n", + " [20220221] 过滤 179 只 ST 股票\n", + " [20220222] 过滤 179 只 ST 股票\n", + " [20220223] 过滤 179 只 ST 股票\n", + " [20220224] 过滤 179 只 ST 股票\n", + " [20220225] 过滤 179 只 ST 股票\n", + " [20220228] 过滤 179 只 ST 股票\n", + " [20220301] 过滤 179 只 ST 股票\n", + " [20220302] 过滤 179 只 ST 股票\n", + " [20220303] 过滤 178 只 ST 股票\n", + " [20220304] 过滤 179 只 ST 股票\n", + " [20220307] 过滤 178 只 ST 股票\n", + " [20220308] 过滤 177 只 ST 股票\n", + " [20220309] 过滤 177 只 ST 股票\n", + " [20220310] 过滤 178 只 ST 股票\n", + " [20220311] 过滤 178 只 ST 股票\n", + " [20220314] 过滤 178 只 ST 股票\n", + " [20220315] 过滤 179 只 ST 股票\n", + " [20220316] 过滤 179 只 ST 股票\n", + " [20220317] 过滤 179 只 ST 股票\n", + " [20220318] 过滤 178 只 ST 股票\n", + " [20220321] 过滤 178 只 ST 股票\n", + " [20220322] 过滤 178 只 ST 股票\n", + " [20220323] 过滤 178 只 ST 股票\n", + " [20220324] 过滤 177 只 ST 股票\n", + " [20220325] 过滤 176 只 ST 股票\n", + " [20220328] 过滤 176 只 ST 股票\n", + " [20220329] 过滤 176 只 ST 股票\n", + " [20220330] 过滤 176 只 ST 股票\n", + " [20220331] 过滤 173 只 ST 股票\n", + " [20220401] 过滤 171 只 ST 股票\n", + " [20220406] 过滤 171 只 ST 股票\n", + " [20220407] 过滤 170 只 ST 股票\n", + " [20220408] 过滤 170 只 ST 股票\n", + " [20220411] 过滤 168 只 ST 股票\n", + " [20220412] 过滤 167 只 ST 股票\n", + " [20220413] 过滤 167 只 ST 股票\n", + " [20220414] 过滤 167 只 ST 股票\n", + " [20220415] 过滤 165 只 ST 股票\n", + " [20220418] 过滤 163 只 ST 股票\n", + " [20220419] 过滤 165 只 ST 股票\n", + " [20220420] 过滤 165 只 ST 股票\n", + " [20220421] 过滤 167 只 ST 股票\n", + " [20220422] 过滤 165 只 ST 股票\n", + " [20220425] 过滤 167 只 ST 股票\n", + " [20220426] 过滤 166 只 ST 股票\n", + " [20220427] 过滤 164 只 ST 股票\n", + " [20220428] 过滤 162 只 ST 股票\n", + " [20220429] 过滤 162 只 ST 股票\n", + " [20220505] 过滤 135 只 ST 股票\n", + " [20220506] 过滤 168 只 ST 股票\n", + " [20220509] 过滤 166 只 ST 股票\n", + " [20220510] 过滤 164 只 ST 股票\n", + " [20220511] 过滤 163 只 ST 股票\n", + " [20220512] 过滤 161 只 ST 股票\n", + " [20220513] 过滤 162 只 ST 股票\n", + " [20220516] 过滤 162 只 ST 股票\n", + " [20220517] 过滤 161 只 ST 股票\n", + " [20220518] 过滤 159 只 ST 股票\n", + " [20220519] 过滤 155 只 ST 股票\n", + " [20220520] 过滤 155 只 ST 股票\n", + " [20220523] 过滤 155 只 ST 股票\n", + " [20220524] 过滤 154 只 ST 股票\n", + " [20220525] 过滤 151 只 ST 股票\n", + " [20220526] 过滤 149 只 ST 股票\n", + " [20220527] 过滤 149 只 ST 股票\n", + " [20220530] 过滤 148 只 ST 股票\n", + " [20220531] 过滤 149 只 ST 股票\n", + " [20220601] 过滤 149 只 ST 股票\n", + " [20220602] 过滤 148 只 ST 股票\n", + " [20220606] 过滤 149 只 ST 股票\n", + " [20220607] 过滤 149 只 ST 股票\n", + " [20220608] 过滤 149 只 ST 股票\n", + " [20220609] 过滤 148 只 ST 股票\n", + " [20220610] 过滤 147 只 ST 股票\n", + " [20220613] 过滤 148 只 ST 股票\n", + " [20220614] 过滤 146 只 ST 股票\n", + " [20220615] 过滤 147 只 ST 股票\n", + " [20220616] 过滤 147 只 ST 股票\n", + " [20220617] 过滤 147 只 ST 股票\n", + " [20220620] 过滤 147 只 ST 股票\n", + " [20220621] 过滤 147 只 ST 股票\n", + " [20220622] 过滤 144 只 ST 股票\n", + " [20220623] 过滤 145 只 ST 股票\n", + " [20220624] 过滤 143 只 ST 股票\n", + " [20220627] 过滤 144 只 ST 股票\n", + " [20220628] 过滤 144 只 ST 股票\n", + " [20220629] 过滤 144 只 ST 股票\n", + " [20220630] 过滤 142 只 ST 股票\n", + " [20220701] 过滤 144 只 ST 股票\n", + " [20220704] 过滤 144 只 ST 股票\n", + " [20220705] 过滤 144 只 ST 股票\n", + " [20220706] 过滤 144 只 ST 股票\n", + " [20220707] 过滤 144 只 ST 股票\n", + " [20220708] 过滤 144 只 ST 股票\n", + " [20220711] 过滤 143 只 ST 股票\n", + " [20220712] 过滤 143 只 ST 股票\n", + " [20220713] 过滤 144 只 ST 股票\n", + " [20220714] 过滤 145 只 ST 股票\n", + " [20220715] 过滤 145 只 ST 股票\n", + " [20220718] 过滤 145 只 ST 股票\n", + " [20220719] 过滤 145 只 ST 股票\n", + " [20220720] 过滤 145 只 ST 股票\n", + " [20220721] 过滤 145 只 ST 股票\n", + " [20220722] 过滤 145 只 ST 股票\n", + " [20220725] 过滤 143 只 ST 股票\n", + " [20220726] 过滤 144 只 ST 股票\n", + " [20220727] 过滤 144 只 ST 股票\n", + " [20220728] 过滤 144 只 ST 股票\n", + " [20220729] 过滤 144 只 ST 股票\n", + " [20220801] 过滤 145 只 ST 股票\n", + " [20220802] 过滤 145 只 ST 股票\n", + " [20220803] 过滤 145 只 ST 股票\n", + " [20220804] 过滤 145 只 ST 股票\n", + " [20220805] 过滤 145 只 ST 股票\n", + " [20220808] 过滤 146 只 ST 股票\n", + " [20220809] 过滤 146 只 ST 股票\n", + " [20220810] 过滤 145 只 ST 股票\n", + " [20220811] 过滤 144 只 ST 股票\n", + " [20220812] 过滤 144 只 ST 股票\n", + " [20220815] 过滤 141 只 ST 股票\n", + " [20220816] 过滤 143 只 ST 股票\n", + " [20220817] 过滤 142 只 ST 股票\n", + " [20220818] 过滤 142 只 ST 股票\n", + " [20220819] 过滤 142 只 ST 股票\n", + " [20220822] 过滤 142 只 ST 股票\n", + " [20220823] 过滤 142 只 ST 股票\n", + " [20220824] 过滤 142 只 ST 股票\n", + " [20220825] 过滤 142 只 ST 股票\n", + " [20220826] 过滤 142 只 ST 股票\n", + " [20220829] 过滤 142 只 ST 股票\n", + " [20220830] 过滤 141 只 ST 股票\n", + " [20220831] 过滤 142 只 ST 股票\n", + " [20220901] 过滤 141 只 ST 股票\n", + " [20220902] 过滤 141 只 ST 股票\n", + " [20220905] 过滤 141 只 ST 股票\n", + " [20220906] 过滤 141 只 ST 股票\n", + " [20220907] 过滤 141 只 ST 股票\n", + " [20220908] 过滤 141 只 ST 股票\n", + " [20220909] 过滤 141 只 ST 股票\n", + " [20220913] 过滤 140 只 ST 股票\n", + " [20220914] 过滤 140 只 ST 股票\n", + " [20220915] 过滤 139 只 ST 股票\n", + " [20220916] 过滤 139 只 ST 股票\n", + " [20220919] 过滤 139 只 ST 股票\n", + " [20220920] 过滤 139 只 ST 股票\n", + " [20220921] 过滤 140 只 ST 股票\n", + " [20220922] 过滤 138 只 ST 股票\n", + " [20220923] 过滤 138 只 ST 股票\n", + " [20220926] 过滤 138 只 ST 股票\n", + " [20220927] 过滤 139 只 ST 股票\n", + " [20220928] 过滤 140 只 ST 股票\n", + " [20220929] 过滤 138 只 ST 股票\n", + " [20220930] 过滤 137 只 ST 股票\n", + " [20221010] 过滤 137 只 ST 股票\n", + " [20221011] 过滤 137 只 ST 股票\n", + " [20221012] 过滤 137 只 ST 股票\n", + " [20221013] 过滤 137 只 ST 股票\n", + " [20221014] 过滤 137 只 ST 股票\n", + " [20221017] 过滤 137 只 ST 股票\n", + " [20221018] 过滤 137 只 ST 股票\n", + " [20221019] 过滤 137 只 ST 股票\n", + " [20221020] 过滤 137 只 ST 股票\n", + " [20221021] 过滤 137 只 ST 股票\n", + " [20221024] 过滤 137 只 ST 股票\n", + " [20221025] 过滤 137 只 ST 股票\n", + " [20221026] 过滤 137 只 ST 股票\n", + " [20221027] 过滤 137 只 ST 股票\n", + " [20221028] 过滤 137 只 ST 股票\n", + " [20221031] 过滤 137 只 ST 股票\n", + " [20221101] 过滤 136 只 ST 股票\n", + " [20221102] 过滤 137 只 ST 股票\n", + " [20221103] 过滤 137 只 ST 股票\n", + " [20221104] 过滤 137 只 ST 股票\n", + " [20221107] 过滤 137 只 ST 股票\n", + " [20221108] 过滤 136 只 ST 股票\n", + " [20221109] 过滤 136 只 ST 股票\n", + " [20221110] 过滤 136 只 ST 股票\n", + " [20221111] 过滤 136 只 ST 股票\n", + " [20221114] 过滤 136 只 ST 股票\n", + " [20221115] 过滤 136 只 ST 股票\n", + " [20221116] 过滤 135 只 ST 股票\n", + " [20221117] 过滤 134 只 ST 股票\n", + " [20221118] 过滤 133 只 ST 股票\n", + " [20221121] 过滤 133 只 ST 股票\n", + " [20221122] 过滤 135 只 ST 股票\n", + " [20221123] 过滤 135 只 ST 股票\n", + " [20221124] 过滤 135 只 ST 股票\n", + " [20221125] 过滤 135 只 ST 股票\n", + " [20221128] 过滤 134 只 ST 股票\n", + " [20221129] 过滤 135 只 ST 股票\n", + " [20221130] 过滤 135 只 ST 股票\n", + " [20221201] 过滤 134 只 ST 股票\n", + " [20221202] 过滤 134 只 ST 股票\n", + " [20221205] 过滤 134 只 ST 股票\n", + " [20221206] 过滤 134 只 ST 股票\n", + " [20221207] 过滤 134 只 ST 股票\n", + " [20221208] 过滤 135 只 ST 股票\n", + " [20221209] 过滤 133 只 ST 股票\n", + " [20221212] 过滤 134 只 ST 股票\n", + " [20221213] 过滤 134 只 ST 股票\n", + " [20221214] 过滤 134 只 ST 股票\n", + " [20221215] 过滤 134 只 ST 股票\n", + " [20221216] 过滤 134 只 ST 股票\n", + " [20221219] 过滤 133 只 ST 股票\n", + " [20221220] 过滤 133 只 ST 股票\n", + " [20221221] 过滤 132 只 ST 股票\n", + " [20221222] 过滤 132 只 ST 股票\n", + " [20221223] 过滤 134 只 ST 股票\n", + " [20221226] 过滤 134 只 ST 股票\n", + " [20221227] 过滤 134 只 ST 股票\n", + " [20221228] 过滤 134 只 ST 股票\n", + " [20221229] 过滤 134 只 ST 股票\n", + " [20221230] 过滤 133 只 ST 股票\n", + " [20230103] 过滤 133 只 ST 股票\n", + " [20230104] 过滤 133 只 ST 股票\n", + " [20230105] 过滤 133 只 ST 股票\n", + " [20230106] 过滤 133 只 ST 股票\n", + " [20230109] 过滤 132 只 ST 股票\n", + " [20230110] 过滤 133 只 ST 股票\n", + " [20230111] 过滤 133 只 ST 股票\n", + " [20230112] 过滤 133 只 ST 股票\n", + " [20230113] 过滤 133 只 ST 股票\n", + " [20230116] 过滤 133 只 ST 股票\n", + " [20230117] 过滤 132 只 ST 股票\n", + " [20230118] 过滤 133 只 ST 股票\n", + " [20230119] 过滤 133 只 ST 股票\n", + " [20230120] 过滤 133 只 ST 股票\n", + " [20230130] 过滤 133 只 ST 股票\n", + " [20230131] 过滤 133 只 ST 股票\n", + " [20230201] 过滤 134 只 ST 股票\n", + " [20230202] 过滤 134 只 ST 股票\n", + " [20230203] 过滤 131 只 ST 股票\n", + " [20230206] 过滤 131 只 ST 股票\n", + " [20230207] 过滤 131 只 ST 股票\n", + " [20230208] 过滤 131 只 ST 股票\n", + " [20230209] 过滤 131 只 ST 股票\n", + " [20230210] 过滤 131 只 ST 股票\n", + " [20230213] 过滤 131 只 ST 股票\n", + " [20230214] 过滤 131 只 ST 股票\n", + " [20230215] 过滤 131 只 ST 股票\n", + " [20230216] 过滤 131 只 ST 股票\n", + " [20230217] 过滤 130 只 ST 股票\n", + " [20230220] 过滤 131 只 ST 股票\n", + " [20230221] 过滤 131 只 ST 股票\n", + " [20230222] 过滤 131 只 ST 股票\n", + " [20230223] 过滤 131 只 ST 股票\n", + " [20230224] 过滤 131 只 ST 股票\n", + " [20230227] 过滤 130 只 ST 股票\n", + " [20230228] 过滤 129 只 ST 股票\n", + " [20230301] 过滤 129 只 ST 股票\n", + " [20230302] 过滤 129 只 ST 股票\n", + " [20230303] 过滤 128 只 ST 股票\n", + " [20230306] 过滤 128 只 ST 股票\n", + " [20230307] 过滤 128 只 ST 股票\n", + " [20230308] 过滤 127 只 ST 股票\n", + " [20230309] 过滤 126 只 ST 股票\n", + " [20230310] 过滤 126 只 ST 股票\n", + " [20230313] 过滤 127 只 ST 股票\n", + " [20230314] 过滤 128 只 ST 股票\n", + " [20230315] 过滤 128 只 ST 股票\n", + " [20230316] 过滤 128 只 ST 股票\n", + " [20230317] 过滤 128 只 ST 股票\n", + " [20230320] 过滤 127 只 ST 股票\n", + " [20230321] 过滤 127 只 ST 股票\n", + " [20230322] 过滤 127 只 ST 股票\n", + " [20230323] 过滤 128 只 ST 股票\n", + " [20230324] 过滤 128 只 ST 股票\n", + " [20230327] 过滤 128 只 ST 股票\n", + " [20230328] 过滤 128 只 ST 股票\n", + " [20230329] 过滤 127 只 ST 股票\n", + " [20230330] 过滤 126 只 ST 股票\n", + " [20230331] 过滤 125 只 ST 股票\n", + " [20230403] 过滤 124 只 ST 股票\n", + " [20230404] 过滤 124 只 ST 股票\n", + " [20230406] 过滤 124 只 ST 股票\n", + " [20230407] 过滤 124 只 ST 股票\n", + " [20230410] 过滤 125 只 ST 股票\n", + " [20230411] 过滤 125 只 ST 股票\n", + " [20230412] 过滤 124 只 ST 股票\n", + " [20230413] 过滤 124 只 ST 股票\n", + " [20230414] 过滤 122 只 ST 股票\n", + " [20230417] 过滤 122 只 ST 股票\n", + " [20230418] 过滤 122 只 ST 股票\n", + " [20230419] 过滤 120 只 ST 股票\n", + " [20230420] 过滤 121 只 ST 股票\n", + " [20230421] 过滤 121 只 ST 股票\n", + " [20230424] 过滤 120 只 ST 股票\n", + " [20230425] 过滤 120 只 ST 股票\n", + " [20230426] 过滤 118 只 ST 股票\n", + " [20230427] 过滤 122 只 ST 股票\n", + " [20230428] 过滤 121 只 ST 股票\n", + " [20230504] 过滤 116 只 ST 股票\n", + " [20230505] 过滤 153 只 ST 股票\n", + " [20230508] 过滤 153 只 ST 股票\n", + " [20230509] 过滤 150 只 ST 股票\n", + " [20230510] 过滤 148 只 ST 股票\n", + " [20230511] 过滤 148 只 ST 股票\n", + " [20230512] 过滤 148 只 ST 股票\n", + " [20230515] 过滤 148 只 ST 股票\n", + " [20230516] 过滤 148 只 ST 股票\n", + " [20230517] 过滤 148 只 ST 股票\n", + " [20230518] 过滤 147 只 ST 股票\n", + " [20230519] 过滤 146 只 ST 股票\n", + " [20230522] 过滤 145 只 ST 股票\n", + " [20230523] 过滤 144 只 ST 股票\n", + " [20230524] 过滤 144 只 ST 股票\n", + " [20230525] 过滤 143 只 ST 股票\n", + " [20230526] 过滤 142 只 ST 股票\n", + " [20230529] 过滤 140 只 ST 股票\n", + " [20230530] 过滤 139 只 ST 股票\n", + " [20230531] 过滤 138 只 ST 股票\n", + " [20230601] 过滤 140 只 ST 股票\n", + " [20230602] 过滤 135 只 ST 股票\n", + " [20230605] 过滤 134 只 ST 股票\n", + " [20230606] 过滤 133 只 ST 股票\n", + " [20230607] 过滤 133 只 ST 股票\n", + " [20230608] 过滤 132 只 ST 股票\n", + " [20230609] 过滤 132 只 ST 股票\n", + " [20230612] 过滤 130 只 ST 股票\n", + " [20230613] 过滤 127 只 ST 股票\n", + " [20230614] 过滤 123 只 ST 股票\n", + " [20230615] 过滤 124 只 ST 股票\n", + " [20230616] 过滤 122 只 ST 股票\n", + " [20230619] 过滤 122 只 ST 股票\n", + " [20230620] 过滤 121 只 ST 股票\n", + " [20230621] 过滤 121 只 ST 股票\n", + " [20230626] 过滤 121 只 ST 股票\n", + " [20230627] 过滤 119 只 ST 股票\n", + " [20230628] 过滤 119 只 ST 股票\n", + " [20230629] 过滤 119 只 ST 股票\n", + " [20230630] 过滤 121 只 ST 股票\n", + " [20230703] 过滤 122 只 ST 股票\n", + " [20230704] 过滤 122 只 ST 股票\n", + " [20230705] 过滤 123 只 ST 股票\n", + " [20230706] 过滤 123 只 ST 股票\n", + " [20230707] 过滤 123 只 ST 股票\n", + " [20230710] 过滤 123 只 ST 股票\n", + " [20230711] 过滤 123 只 ST 股票\n", + " [20230712] 过滤 123 只 ST 股票\n", + " [20230713] 过滤 123 只 ST 股票\n", + " [20230714] 过滤 122 只 ST 股票\n", + " [20230717] 过滤 122 只 ST 股票\n", + " [20230718] 过滤 122 只 ST 股票\n", + " [20230719] 过滤 123 只 ST 股票\n", + " [20230720] 过滤 123 只 ST 股票\n", + " [20230721] 过滤 122 只 ST 股票\n", + " [20230724] 过滤 122 只 ST 股票\n", + " [20230725] 过滤 122 只 ST 股票\n", + " [20230726] 过滤 122 只 ST 股票\n", + " [20230727] 过滤 121 只 ST 股票\n", + " [20230728] 过滤 121 只 ST 股票\n", + " [20230731] 过滤 121 只 ST 股票\n", + " [20230801] 过滤 121 只 ST 股票\n", + " [20230802] 过滤 121 只 ST 股票\n", + " [20230803] 过滤 121 只 ST 股票\n", + " [20230804] 过滤 121 只 ST 股票\n", + " [20230807] 过滤 121 只 ST 股票\n", + " [20230808] 过滤 121 只 ST 股票\n", + " [20230809] 过滤 121 只 ST 股票\n", + " [20230810] 过滤 121 只 ST 股票\n", + " [20230811] 过滤 121 只 ST 股票\n", + " [20230814] 过滤 121 只 ST 股票\n", + " [20230815] 过滤 121 只 ST 股票\n", + " [20230816] 过滤 121 只 ST 股票\n", + " [20230817] 过滤 120 只 ST 股票\n", + " [20230818] 过滤 120 只 ST 股票\n", + " [20230821] 过滤 120 只 ST 股票\n", + " [20230822] 过滤 120 只 ST 股票\n", + " [20230823] 过滤 120 只 ST 股票\n", + " [20230824] 过滤 120 只 ST 股票\n", + " [20230825] 过滤 120 只 ST 股票\n", + " [20230828] 过滤 120 只 ST 股票\n", + " [20230829] 过滤 120 只 ST 股票\n", + " [20230830] 过滤 120 只 ST 股票\n", + " [20230831] 过滤 120 只 ST 股票\n", + " [20230901] 过滤 120 只 ST 股票\n", + " [20230904] 过滤 118 只 ST 股票\n", + " [20230905] 过滤 118 只 ST 股票\n", + " [20230906] 过滤 118 只 ST 股票\n", + " [20230907] 过滤 118 只 ST 股票\n", + " [20230908] 过滤 118 只 ST 股票\n", + " [20230911] 过滤 118 只 ST 股票\n", + " [20230912] 过滤 118 只 ST 股票\n", + " [20230913] 过滤 118 只 ST 股票\n", + " [20230914] 过滤 118 只 ST 股票\n", + " [20230915] 过滤 118 只 ST 股票\n", + " [20230918] 过滤 118 只 ST 股票\n", + " [20230919] 过滤 118 只 ST 股票\n", + " [20230920] 过滤 118 只 ST 股票\n", + " [20230921] 过滤 117 只 ST 股票\n", + " [20230922] 过滤 118 只 ST 股票\n", + " [20230925] 过滤 117 只 ST 股票\n", + " [20230926] 过滤 117 只 ST 股票\n", + " [20230927] 过滤 118 只 ST 股票\n", + " [20230928] 过滤 116 只 ST 股票\n", + " [20231009] 过滤 117 只 ST 股票\n", + " [20231010] 过滤 118 只 ST 股票\n", + " [20231011] 过滤 118 只 ST 股票\n", + " [20231012] 过滤 119 只 ST 股票\n", + " [20231013] 过滤 119 只 ST 股票\n", + " [20231016] 过滤 119 只 ST 股票\n", + " [20231017] 过滤 119 只 ST 股票\n", + " [20231018] 过滤 118 只 ST 股票\n", + " [20231019] 过滤 118 只 ST 股票\n", + " [20231020] 过滤 118 只 ST 股票\n", + " [20231023] 过滤 118 只 ST 股票\n", + " [20231024] 过滤 117 只 ST 股票\n", + " [20231025] 过滤 118 只 ST 股票\n", + " [20231026] 过滤 118 只 ST 股票\n", + " [20231027] 过滤 118 只 ST 股票\n", + " [20231030] 过滤 118 只 ST 股票\n", + " [20231031] 过滤 119 只 ST 股票\n", + " [20231101] 过滤 119 只 ST 股票\n", + " [20231102] 过滤 118 只 ST 股票\n", + " [20231103] 过滤 118 只 ST 股票\n", + " [20231106] 过滤 116 只 ST 股票\n", + " [20231107] 过滤 116 只 ST 股票\n", + " [20231108] 过滤 116 只 ST 股票\n", + " [20231109] 过滤 116 只 ST 股票\n", + " [20231110] 过滤 116 只 ST 股票\n", + " [20231113] 过滤 116 只 ST 股票\n", + " [20231114] 过滤 116 只 ST 股票\n", + " [20231115] 过滤 116 只 ST 股票\n", + " [20231116] 过滤 115 只 ST 股票\n", + " [20231117] 过滤 115 只 ST 股票\n", + " [20231120] 过滤 115 只 ST 股票\n", + " [20231121] 过滤 116 只 ST 股票\n", + " [20231122] 过滤 115 只 ST 股票\n", + " [20231123] 过滤 115 只 ST 股票\n", + " [20231124] 过滤 114 只 ST 股票\n", + " [20231127] 过滤 116 只 ST 股票\n", + " [20231128] 过滤 116 只 ST 股票\n", + " [20231129] 过滤 116 只 ST 股票\n", + " [20231130] 过滤 116 只 ST 股票\n", + " [20231201] 过滤 116 只 ST 股票\n", + " [20231204] 过滤 116 只 ST 股票\n", + " [20231205] 过滤 116 只 ST 股票\n", + " [20231206] 过滤 116 只 ST 股票\n", + " [20231207] 过滤 114 只 ST 股票\n", + " [20231208] 过滤 115 只 ST 股票\n", + " [20231211] 过滤 115 只 ST 股票\n", + " [20231212] 过滤 115 只 ST 股票\n", + " [20231213] 过滤 115 只 ST 股票\n", + " [20231214] 过滤 116 只 ST 股票\n", + " [20231215] 过滤 115 只 ST 股票\n", + " [20231218] 过滤 116 只 ST 股票\n", + " [20231219] 过滤 114 只 ST 股票\n", + " [20231220] 过滤 116 只 ST 股票\n", + " [20231221] 过滤 116 只 ST 股票\n", + " [20231222] 过滤 115 只 ST 股票\n", + " [20231225] 过滤 113 只 ST 股票\n", + " [20231226] 过滤 114 只 ST 股票\n", + " [20231227] 过滤 114 只 ST 股票\n", + " [20231228] 过滤 110 只 ST 股票\n", + " [20231229] 过滤 113 只 ST 股票\n", + " [20240102] 过滤 114 只 ST 股票\n", + " [20240103] 过滤 114 只 ST 股票\n", + " [20240104] 过滤 114 只 ST 股票\n", + " [20240105] 过滤 114 只 ST 股票\n", + " [20240108] 过滤 114 只 ST 股票\n", + " [20240109] 过滤 113 只 ST 股票\n", + " [20240110] 过滤 113 只 ST 股票\n", + " [20240111] 过滤 113 只 ST 股票\n", + " [20240112] 过滤 111 只 ST 股票\n", + " [20240115] 过滤 112 只 ST 股票\n", + " [20240116] 过滤 112 只 ST 股票\n", + " [20240117] 过滤 112 只 ST 股票\n", + " [20240118] 过滤 112 只 ST 股票\n", + " [20240119] 过滤 111 只 ST 股票\n", + " [20240122] 过滤 111 只 ST 股票\n", + " [20240123] 过滤 111 只 ST 股票\n", + " [20240124] 过滤 110 只 ST 股票\n", + " [20240125] 过滤 110 只 ST 股票\n", + " [20240126] 过滤 111 只 ST 股票\n", + " [20240129] 过滤 112 只 ST 股票\n", + " [20240130] 过滤 112 只 ST 股票\n", + " [20240131] 过滤 113 只 ST 股票\n", + " [20240201] 过滤 113 只 ST 股票\n", + " [20240202] 过滤 113 只 ST 股票\n", + " [20240205] 过滤 112 只 ST 股票\n", + " [20240206] 过滤 111 只 ST 股票\n", + " [20240207] 过滤 111 只 ST 股票\n", + " [20240208] 过滤 111 只 ST 股票\n", + " [20240219] 过滤 111 只 ST 股票\n", + " [20240220] 过滤 111 只 ST 股票\n", + " [20240221] 过滤 111 只 ST 股票\n", + " [20240222] 过滤 111 只 ST 股票\n", + " [20240223] 过滤 111 只 ST 股票\n", + " [20240226] 过滤 111 只 ST 股票\n", + " [20240227] 过滤 112 只 ST 股票\n", + " [20240228] 过滤 112 只 ST 股票\n", + " [20240229] 过滤 112 只 ST 股票\n", + " [20240301] 过滤 112 只 ST 股票\n", + " [20240304] 过滤 112 只 ST 股票\n", + " [20240305] 过滤 112 只 ST 股票\n", + " [20240306] 过滤 111 只 ST 股票\n", + " [20240307] 过滤 111 只 ST 股票\n", + " [20240308] 过滤 110 只 ST 股票\n", + " [20240311] 过滤 110 只 ST 股票\n", + " [20240312] 过滤 110 只 ST 股票\n", + " [20240313] 过滤 110 只 ST 股票\n", + " [20240314] 过滤 110 只 ST 股票\n", + " [20240315] 过滤 110 只 ST 股票\n", + " [20240318] 过滤 110 只 ST 股票\n", + " [20240319] 过滤 110 只 ST 股票\n", + " [20240320] 过滤 110 只 ST 股票\n", + " [20240321] 过滤 110 只 ST 股票\n", + " [20240322] 过滤 110 只 ST 股票\n", + " [20240325] 过滤 110 只 ST 股票\n", + " [20240326] 过滤 110 只 ST 股票\n", + " [20240327] 过滤 110 只 ST 股票\n", + " [20240328] 过滤 109 只 ST 股票\n", + " [20240329] 过滤 110 只 ST 股票\n", + " [20240401] 过滤 112 只 ST 股票\n", + " [20240402] 过滤 112 只 ST 股票\n", + " [20240403] 过滤 112 只 ST 股票\n", + " [20240408] 过滤 113 只 ST 股票\n", + " [20240409] 过滤 113 只 ST 股票\n", + " [20240410] 过滤 113 只 ST 股票\n", + " [20240411] 过滤 113 只 ST 股票\n", + " [20240412] 过滤 113 只 ST 股票\n", + " [20240415] 过滤 113 只 ST 股票\n", + " [20240416] 过滤 113 只 ST 股票\n", + " [20240417] 过滤 113 只 ST 股票\n", + " [20240418] 过滤 113 只 ST 股票\n", + " [20240419] 过滤 113 只 ST 股票\n", + " [20240422] 过滤 112 只 ST 股票\n", + " [20240423] 过滤 114 只 ST 股票\n", + " [20240424] 过滤 115 只 ST 股票\n", + " [20240425] 过滤 114 只 ST 股票\n", + " [20240426] 过滤 115 只 ST 股票\n", + " [20240429] 过滤 117 只 ST 股票\n", + " [20240430] 过滤 118 只 ST 股票\n", + " [20240506] 过滤 163 只 ST 股票\n", + " [20240507] 过滤 163 只 ST 股票\n", + " [20240508] 过滤 161 只 ST 股票\n", + " [20240509] 过滤 161 只 ST 股票\n", + " [20240510] 过滤 161 只 ST 股票\n", + " [20240513] 过滤 161 只 ST 股票\n", + " [20240514] 过滤 163 只 ST 股票\n", + " [20240515] 过滤 163 只 ST 股票\n", + " [20240516] 过滤 162 只 ST 股票\n", + " [20240517] 过滤 162 只 ST 股票\n", + " [20240520] 过滤 161 只 ST 股票\n", + " [20240521] 过滤 163 只 ST 股票\n", + " [20240522] 过滤 163 只 ST 股票\n", + " [20240523] 过滤 163 只 ST 股票\n", + " [20240524] 过滤 163 只 ST 股票\n", + " [20240527] 过滤 162 只 ST 股票\n", + " [20240528] 过滤 162 只 ST 股票\n", + " [20240529] 过滤 161 只 ST 股票\n", + " [20240530] 过滤 160 只 ST 股票\n", + " [20240531] 过滤 159 只 ST 股票\n", + " [20240603] 过滤 159 只 ST 股票\n", + " [20240604] 过滤 158 只 ST 股票\n", + " [20240605] 过滤 157 只 ST 股票\n", + " [20240606] 过滤 157 只 ST 股票\n", + " [20240607] 过滤 153 只 ST 股票\n", + " [20240611] 过滤 148 只 ST 股票\n", + " [20240612] 过滤 147 只 ST 股票\n", + " [20240613] 过滤 145 只 ST 股票\n", + " [20240614] 过滤 144 只 ST 股票\n", + " [20240617] 过滤 143 只 ST 股票\n", + " [20240618] 过滤 140 只 ST 股票\n", + " [20240619] 过滤 139 只 ST 股票\n", + " [20240620] 过滤 138 只 ST 股票\n", + " [20240621] 过滤 137 只 ST 股票\n", + " [20240624] 过滤 135 只 ST 股票\n", + " [20240625] 过滤 130 只 ST 股票\n", + " [20240626] 过滤 131 只 ST 股票\n", + " [20240627] 过滤 128 只 ST 股票\n", + " [20240628] 过滤 128 只 ST 股票\n", + " [20240701] 过滤 126 只 ST 股票\n", + " [20240702] 过滤 125 只 ST 股票\n", + " [20240703] 过滤 123 只 ST 股票\n", + " [20240704] 过滤 123 只 ST 股票\n", + " [20240705] 过滤 122 只 ST 股票\n", + " [20240708] 过滤 122 只 ST 股票\n", + " [20240709] 过滤 128 只 ST 股票\n", + " [20240710] 过滤 128 只 ST 股票\n", + " [20240711] 过滤 128 只 ST 股票\n", + " [20240712] 过滤 128 只 ST 股票\n", + " [20240715] 过滤 128 只 ST 股票\n", + " [20240716] 过滤 128 只 ST 股票\n", + " [20240717] 过滤 126 只 ST 股票\n", + " [20240718] 过滤 125 只 ST 股票\n", + " [20240719] 过滤 126 只 ST 股票\n", + " [20240722] 过滤 125 只 ST 股票\n", + " [20240723] 过滤 125 只 ST 股票\n", + " [20240724] 过滤 125 只 ST 股票\n", + " [20240725] 过滤 124 只 ST 股票\n", + " [20240726] 过滤 123 只 ST 股票\n", + " [20240729] 过滤 122 只 ST 股票\n", + " [20240730] 过滤 122 只 ST 股票\n", + " [20240731] 过滤 122 只 ST 股票\n", + " [20240801] 过滤 122 只 ST 股票\n", + " [20240802] 过滤 123 只 ST 股票\n", + " [20240805] 过滤 121 只 ST 股票\n", + " [20240806] 过滤 122 只 ST 股票\n", + " [20240807] 过滤 123 只 ST 股票\n", + " [20240808] 过滤 123 只 ST 股票\n", + " [20240809] 过滤 123 只 ST 股票\n", + " [20240812] 过滤 124 只 ST 股票\n", + " [20240813] 过滤 124 只 ST 股票\n", + " [20240814] 过滤 124 只 ST 股票\n", + " [20240815] 过滤 123 只 ST 股票\n", + " [20240816] 过滤 122 只 ST 股票\n", + " [20240819] 过滤 121 只 ST 股票\n", + " [20240820] 过滤 121 只 ST 股票\n", + " [20240821] 过滤 121 只 ST 股票\n", + " [20240822] 过滤 121 只 ST 股票\n", + " [20240823] 过滤 119 只 ST 股票\n", + " [20240826] 过滤 120 只 ST 股票\n", + " [20240827] 过滤 120 只 ST 股票\n", + " [20240828] 过滤 120 只 ST 股票\n", + " [20240829] 过滤 119 只 ST 股票\n", + " [20240830] 过滤 119 只 ST 股票\n", + " [20240902] 过滤 119 只 ST 股票\n", + " [20240903] 过滤 120 只 ST 股票\n", + " [20240904] 过滤 119 只 ST 股票\n", + " [20240905] 过滤 119 只 ST 股票\n", + " [20240906] 过滤 119 只 ST 股票\n", + " [20240909] 过滤 119 只 ST 股票\n", + " [20240910] 过滤 118 只 ST 股票\n", + " [20240911] 过滤 119 只 ST 股票\n", + " [20240912] 过滤 119 只 ST 股票\n", + " [20240913] 过滤 119 只 ST 股票\n", + " [20240918] 过滤 118 只 ST 股票\n", + " [20240919] 过滤 121 只 ST 股票\n", + " [20240920] 过滤 121 只 ST 股票\n", + " [20240923] 过滤 121 只 ST 股票\n", + " [20240924] 过滤 121 只 ST 股票\n", + " [20240925] 过滤 121 只 ST 股票\n", + " [20240926] 过滤 121 只 ST 股票\n", + " [20240927] 过滤 121 只 ST 股票\n", + " [20240930] 过滤 121 只 ST 股票\n", + " [20241008] 过滤 119 只 ST 股票\n", + " [20241009] 过滤 119 只 ST 股票\n", + " [20241010] 过滤 119 只 ST 股票\n", + " [20241011] 过滤 119 只 ST 股票\n", + " [20241014] 过滤 119 只 ST 股票\n", + " [20241015] 过滤 119 只 ST 股票\n", + " [20241016] 过滤 120 只 ST 股票\n", + " [20241017] 过滤 120 只 ST 股票\n", + " [20241018] 过滤 120 只 ST 股票\n", + " [20241021] 过滤 120 只 ST 股票\n", + " [20241022] 过滤 120 只 ST 股票\n", + " [20241023] 过滤 120 只 ST 股票\n", + " [20241024] 过滤 120 只 ST 股票\n", + " [20241025] 过滤 120 只 ST 股票\n", + " [20241028] 过滤 119 只 ST 股票\n", + " [20241029] 过滤 120 只 ST 股票\n", + " [20241030] 过滤 120 只 ST 股票\n", + " [20241031] 过滤 120 只 ST 股票\n", + " [20241101] 过滤 122 只 ST 股票\n", + " [20241104] 过滤 123 只 ST 股票\n", + " [20241105] 过滤 123 只 ST 股票\n", + " [20241106] 过滤 123 只 ST 股票\n", + " [20241107] 过滤 123 只 ST 股票\n", + " [20241108] 过滤 124 只 ST 股票\n", + " [20241111] 过滤 120 只 ST 股票\n", + " [20241112] 过滤 121 只 ST 股票\n", + " [20241113] 过滤 122 只 ST 股票\n", + " [20241114] 过滤 122 只 ST 股票\n", + " [20241115] 过滤 121 只 ST 股票\n", + " [20241118] 过滤 120 只 ST 股票\n", + " [20241119] 过滤 120 只 ST 股票\n", + " [20241120] 过滤 121 只 ST 股票\n", + " [20241121] 过滤 122 只 ST 股票\n", + " [20241122] 过滤 122 只 ST 股票\n", + " [20241125] 过滤 122 只 ST 股票\n", + " [20241126] 过滤 122 只 ST 股票\n", + " [20241127] 过滤 122 只 ST 股票\n", + " [20241128] 过滤 122 只 ST 股票\n", + " [20241129] 过滤 122 只 ST 股票\n", + " [20241202] 过滤 122 只 ST 股票\n", + " [20241203] 过滤 122 只 ST 股票\n", + " [20241204] 过滤 122 只 ST 股票\n", + " [20241205] 过滤 122 只 ST 股票\n", + " [20241206] 过滤 122 只 ST 股票\n", + " [20241209] 过滤 122 只 ST 股票\n", + " [20241210] 过滤 123 只 ST 股票\n", + " [20241211] 过滤 123 只 ST 股票\n", + " [20241212] 过滤 124 只 ST 股票\n", + " [20241213] 过滤 123 只 ST 股票\n", + " [20241216] 过滤 124 只 ST 股票\n", + " [20241217] 过滤 123 只 ST 股票\n", + " [20241218] 过滤 124 只 ST 股票\n", + " [20241219] 过滤 123 只 ST 股票\n", + " [20241220] 过滤 125 只 ST 股票\n", + " [20241223] 过滤 125 只 ST 股票\n", + " [20241224] 过滤 125 只 ST 股票\n", + " [20241225] 过滤 122 只 ST 股票\n", + " [20241226] 过滤 124 只 ST 股票\n", + " [20241227] 过滤 123 只 ST 股票\n", + " [20241230] 过滤 123 只 ST 股票\n", + " [20241231] 过滤 124 只 ST 股票\n", + " [20250102] 过滤 124 只 ST 股票\n", + " [20250103] 过滤 125 只 ST 股票\n", + " [20250106] 过滤 124 只 ST 股票\n", + " [20250107] 过滤 125 只 ST 股票\n", + " [20250108] 过滤 125 只 ST 股票\n", + " [20250109] 过滤 126 只 ST 股票\n", + " [20250110] 过滤 126 只 ST 股票\n", + " [20250113] 过滤 124 只 ST 股票\n", + " [20250114] 过滤 127 只 ST 股票\n", + " [20250115] 过滤 127 只 ST 股票\n", + " [20250116] 过滤 127 只 ST 股票\n", + " [20250117] 过滤 126 只 ST 股票\n", + " [20250120] 过滤 126 只 ST 股票\n", + " [20250121] 过滤 126 只 ST 股票\n", + " [20250122] 过滤 126 只 ST 股票\n", + " [20250123] 过滤 126 只 ST 股票\n", + " [20250124] 过滤 126 只 ST 股票\n", + " [20250127] 过滤 126 只 ST 股票\n", + " [20250205] 过滤 126 只 ST 股票\n", + " [20250206] 过滤 126 只 ST 股票\n", + " [20250207] 过滤 126 只 ST 股票\n", + " [20250210] 过滤 125 只 ST 股票\n", + " [20250211] 过滤 125 只 ST 股票\n", + " [20250212] 过滤 125 只 ST 股票\n", + " [20250213] 过滤 125 只 ST 股票\n", + " [20250214] 过滤 125 只 ST 股票\n", + " [20250217] 过滤 125 只 ST 股票\n", + " [20250218] 过滤 124 只 ST 股票\n", + " [20250219] 过滤 124 只 ST 股票\n", + " [20250220] 过滤 124 只 ST 股票\n", + " [20250221] 过滤 126 只 ST 股票\n", + " [20250224] 过滤 126 只 ST 股票\n", + " [20250225] 过滤 126 只 ST 股票\n", + " [20250226] 过滤 126 只 ST 股票\n", + " [20250227] 过滤 126 只 ST 股票\n", + " [20250228] 过滤 126 只 ST 股票\n", + " [20250303] 过滤 126 只 ST 股票\n", + " [20250304] 过滤 126 只 ST 股票\n", + " [20250305] 过滤 126 只 ST 股票\n", + " [20250306] 过滤 126 只 ST 股票\n", + " [20250307] 过滤 126 只 ST 股票\n", + " [20250310] 过滤 127 只 ST 股票\n", + " [20250311] 过滤 127 只 ST 股票\n", + " [20250312] 过滤 126 只 ST 股票\n", + " [20250313] 过滤 126 只 ST 股票\n", + " [20250314] 过滤 127 只 ST 股票\n", + " [20250317] 过滤 127 只 ST 股票\n", + " [20250318] 过滤 131 只 ST 股票\n", + " [20250319] 过滤 131 只 ST 股票\n", + " [20250320] 过滤 132 只 ST 股票\n", + " [20250321] 过滤 131 只 ST 股票\n", + " [20250324] 过滤 130 只 ST 股票\n", + " [20250325] 过滤 133 只 ST 股票\n", + " [20250326] 过滤 134 只 ST 股票\n", + " [20250327] 过滤 134 只 ST 股票\n", + " [20250328] 过滤 134 只 ST 股票\n", + " [20250331] 过滤 134 只 ST 股票\n", + " [20250401] 过滤 133 只 ST 股票\n", + " [20250402] 过滤 133 只 ST 股票\n", + " [20250403] 过滤 133 只 ST 股票\n", + " [20250407] 过滤 133 只 ST 股票\n", + " [20250408] 过滤 133 只 ST 股票\n", + " [20250409] 过滤 133 只 ST 股票\n", + " [20250410] 过滤 133 只 ST 股票\n", + " [20250411] 过滤 132 只 ST 股票\n", + " [20250414] 过滤 131 只 ST 股票\n", + " [20250415] 过滤 130 只 ST 股票\n", + " [20250416] 过滤 132 只 ST 股票\n", + " [20250417] 过滤 131 只 ST 股票\n", + " [20250418] 过滤 130 只 ST 股票\n", + " [20250421] 过滤 130 只 ST 股票\n", + " [20250422] 过滤 133 只 ST 股票\n", + " [20250423] 过滤 138 只 ST 股票\n", + " [20250424] 过滤 137 只 ST 股票\n", + " [20250425] 过滤 139 只 ST 股票\n", + " [20250428] 过滤 142 只 ST 股票\n", + " [20250429] 过滤 148 只 ST 股票\n", + " [20250430] 过滤 169 只 ST 股票\n", + " [20250506] 过滤 190 只 ST 股票\n", + " [20250507] 过滤 190 只 ST 股票\n", + " [20250508] 过滤 190 只 ST 股票\n", + " [20250509] 过滤 190 只 ST 股票\n", + " [20250512] 过滤 189 只 ST 股票\n", + " [20250513] 过滤 189 只 ST 股票\n", + " [20250514] 过滤 189 只 ST 股票\n", + " [20250515] 过滤 188 只 ST 股票\n", + " [20250516] 过滤 188 只 ST 股票\n", + " [20250519] 过滤 182 只 ST 股票\n", + " [20250520] 过滤 183 只 ST 股票\n", + " [20250521] 过滤 181 只 ST 股票\n", + " [20250522] 过滤 179 只 ST 股票\n", + " [20250523] 过滤 178 只 ST 股票\n", + " [20250526] 过滤 178 只 ST 股票\n", + " [20250527] 过滤 179 只 ST 股票\n", + " [20250528] 过滤 177 只 ST 股票\n", + " [20250529] 过滤 178 只 ST 股票\n", + " [20250530] 过滤 175 只 ST 股票\n", + " [20250603] 过滤 172 只 ST 股票\n", + " [20250604] 过滤 172 只 ST 股票\n", + " [20250605] 过滤 172 只 ST 股票\n", + " [20250606] 过滤 172 只 ST 股票\n", + " [20250609] 过滤 171 只 ST 股票\n", + " [20250610] 过滤 171 只 ST 股票\n", + " [20250611] 过滤 172 只 ST 股票\n", + " [20250612] 过滤 171 只 ST 股票\n", + " [20250613] 过滤 169 只 ST 股票\n", + " [20250616] 过滤 168 只 ST 股票\n", + " [20250617] 过滤 170 只 ST 股票\n", + " [20250618] 过滤 170 只 ST 股票\n", + " [20250619] 过滤 169 只 ST 股票\n", + " [20250620] 过滤 168 只 ST 股票\n", + " [20250623] 过滤 167 只 ST 股票\n", + " [20250624] 过滤 168 只 ST 股票\n", + " [20250625] 过滤 168 只 ST 股票\n", + " [20250626] 过滤 169 只 ST 股票\n", + " [20250627] 过滤 166 只 ST 股票\n", + " [20250630] 过滤 166 只 ST 股票\n", + " [20250701] 过滤 166 只 ST 股票\n", + " [20250702] 过滤 166 只 ST 股票\n", + " [20250703] 过滤 166 只 ST 股票\n", + " [20250704] 过滤 168 只 ST 股票\n", + " [20250707] 过滤 169 只 ST 股票\n", + " [20250708] 过滤 170 只 ST 股票\n", + " [20250709] 过滤 171 只 ST 股票\n", + " [20250710] 过滤 170 只 ST 股票\n", + " [20250711] 过滤 172 只 ST 股票\n", + " [20250714] 过滤 171 只 ST 股票\n", + " [20250715] 过滤 170 只 ST 股票\n", + " [20250716] 过滤 169 只 ST 股票\n", + " [20250717] 过滤 169 只 ST 股票\n", + " [20250718] 过滤 169 只 ST 股票\n", + " [20250721] 过滤 169 只 ST 股票\n", + " [20250722] 过滤 170 只 ST 股票\n", + " [20250723] 过滤 170 只 ST 股票\n", + " [20250724] 过滤 170 只 ST 股票\n", + " [20250725] 过滤 170 只 ST 股票\n", + " [20250728] 过滤 170 只 ST 股票\n", + " [20250729] 过滤 170 只 ST 股票\n", + " [20250730] 过滤 170 只 ST 股票\n", + " [20250731] 过滤 170 只 ST 股票\n", + " [20250801] 过滤 171 只 ST 股票\n", + " [20250804] 过滤 171 只 ST 股票\n", + " [20250805] 过滤 170 只 ST 股票\n", + " [20250806] 过滤 170 只 ST 股票\n", + " [20250807] 过滤 168 只 ST 股票\n", + " [20250808] 过滤 168 只 ST 股票\n", + " [20250811] 过滤 169 只 ST 股票\n", + " [20250812] 过滤 170 只 ST 股票\n", + " [20250813] 过滤 167 只 ST 股票\n", + " [20250814] 过滤 166 只 ST 股票\n", + " [20250815] 过滤 166 只 ST 股票\n", + " [20250818] 过滤 165 只 ST 股票\n", + " [20250819] 过滤 167 只 ST 股票\n", + " [20250820] 过滤 166 只 ST 股票\n", + " [20250821] 过滤 169 只 ST 股票\n", + " [20250822] 过滤 169 只 ST 股票\n", + " [20250825] 过滤 169 只 ST 股票\n", + " [20250826] 过滤 169 只 ST 股票\n", + " [20250827] 过滤 169 只 ST 股票\n", + " [20250828] 过滤 169 只 ST 股票\n", + " [20250829] 过滤 169 只 ST 股票\n", + " [20250901] 过滤 169 只 ST 股票\n", + " [20250902] 过滤 169 只 ST 股票\n", + " [20250903] 过滤 169 只 ST 股票\n", + " [20250904] 过滤 169 只 ST 股票\n", + " [20250905] 过滤 168 只 ST 股票\n", + " [20250908] 过滤 169 只 ST 股票\n", + " [20250909] 过滤 168 只 ST 股票\n", + " [20250910] 过滤 168 只 ST 股票\n", + " [20250911] 过滤 168 只 ST 股票\n", + " [20250912] 过滤 169 只 ST 股票\n", + " [20250915] 过滤 169 只 ST 股票\n", + " [20250916] 过滤 169 只 ST 股票\n", + " [20250917] 过滤 169 只 ST 股票\n", + " [20250918] 过滤 168 只 ST 股票\n", + " [20250919] 过滤 169 只 ST 股票\n", + " [20250922] 过滤 169 只 ST 股票\n", + " [20250923] 过滤 173 只 ST 股票\n", + " [20250924] 过滤 172 只 ST 股票\n", + " [20250925] 过滤 173 只 ST 股票\n", + " [20250926] 过滤 173 只 ST 股票\n", + " [20250929] 过滤 172 只 ST 股票\n", + " [20250930] 过滤 173 只 ST 股票\n", + " [20251009] 过滤 174 只 ST 股票\n", + " [20251010] 过滤 175 只 ST 股票\n", + " [20251013] 过滤 174 只 ST 股票\n", + " [20251014] 过滤 174 只 ST 股票\n", + " [20251015] 过滤 174 只 ST 股票\n", + " [20251016] 过滤 175 只 ST 股票\n", + " [20251017] 过滤 175 只 ST 股票\n", + " [20251020] 过滤 175 只 ST 股票\n", + " [20251021] 过滤 175 只 ST 股票\n", + " [20251022] 过滤 174 只 ST 股票\n", + " [20251023] 过滤 175 只 ST 股票\n", + " [20251024] 过滤 175 只 ST 股票\n", + " [20251027] 过滤 176 只 ST 股票\n", + " [20251028] 过滤 175 只 ST 股票\n", + " [20251029] 过滤 174 只 ST 股票\n", + " [20251030] 过滤 174 只 ST 股票\n", + " [20251031] 过滤 176 只 ST 股票\n", + " [20251103] 过滤 176 只 ST 股票\n", + " [20251104] 过滤 176 只 ST 股票\n", + " [20251105] 过滤 177 只 ST 股票\n", + " [20251106] 过滤 177 只 ST 股票\n", + " [20251107] 过滤 176 只 ST 股票\n", + " [20251110] 过滤 176 只 ST 股票\n", + " [20251111] 过滤 174 只 ST 股票\n", + " [20251112] 过滤 175 只 ST 股票\n", + " [20251113] 过滤 174 只 ST 股票\n", + " [20251114] 过滤 173 只 ST 股票\n", + " [20251117] 过滤 173 只 ST 股票\n", + " [20251118] 过滤 174 只 ST 股票\n", + " [20251119] 过滤 173 只 ST 股票\n", + " [20251120] 过滤 174 只 ST 股票\n", + " [20251121] 过滤 174 只 ST 股票\n", + " [20251124] 过滤 174 只 ST 股票\n", + " [20251125] 过滤 174 只 ST 股票\n", + " [20251126] 过滤 172 只 ST 股票\n", + " [20251127] 过滤 171 只 ST 股票\n", + " [20251128] 过滤 172 只 ST 股票\n", + " [20251201] 过滤 170 只 ST 股票\n", + " [20251202] 过滤 172 只 ST 股票\n", + " [20251203] 过滤 173 只 ST 股票\n", + " [20251204] 过滤 173 只 ST 股票\n", + " [20251205] 过滤 172 只 ST 股票\n", + " [20251208] 过滤 173 只 ST 股票\n", + " [20251209] 过滤 172 只 ST 股票\n", + " [20251210] 过滤 171 只 ST 股票\n", + " [20251211] 过滤 173 只 ST 股票\n", + " [20251212] 过滤 174 只 ST 股票\n", + " [20251215] 过滤 174 只 ST 股票\n", + " [20251216] 过滤 175 只 ST 股票\n", + " [20251217] 过滤 175 只 ST 股票\n", + " [20251218] 过滤 175 只 ST 股票\n", + " [20251219] 过滤 174 只 ST 股票\n", + " [20251222] 过滤 174 只 ST 股票\n", + " [20251223] 过滤 175 只 ST 股票\n", + " [20251224] 过滤 175 只 ST 股票\n", + " [20251225] 过滤 176 只 ST 股票\n", + " [20251226] 过滤 173 只 ST 股票\n", + " [20251229] 过滤 174 只 ST 股票\n", + " [20251230] 过滤 176 只 ST 股票\n", + " [20251231] 过滤 175 只 ST 股票\n", + " ST 过滤后数据规模: (6823808, 71)\n", + "\n", "执行每日独立筛选股票池...\n" ] }, @@ -899,7 +2250,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_8380\\653304.py:63: DeprecationWarning: `is_in` with a collection of the same datatype is ambiguous and deprecated.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_7864\\174861970.py:63: DeprecationWarning: `is_in` with a collection of the same datatype is ambiguous and deprecated.\n", "Please use `implode` to return to previous behavior.\n", "\n", "See https://github.com/pola-rs/polars/issues/22149 for more information.\n", @@ -910,11 +2261,11 @@ "name": "stdout", "output_type": "stream", "text": [ - " 筛选前数据规模: (7255513, 71)\n", - " 筛选后数据规模: (1494000, 71)\n", - " 筛选前股票数: 5694\n", - " 筛选后股票数: 2252\n", - " 删除记录数: 5761513\n" + " 筛选前数据规模: (6823808, 71)\n", + " 筛选后数据规模: (1455000, 71)\n", + " 筛选前股票数: 5678\n", + " 筛选后股票数: 1934\n", + " 删除记录数: 5368808\n" ] } ], @@ -931,8 +2282,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:46.849944Z", - "start_time": "2026-03-09T16:28:46.764139Z" + "end_time": "2026-03-10T14:56:29.152001Z", + "start_time": "2026-03-10T14:56:29.081042Z" } }, "source": [ @@ -972,11 +2323,11 @@ "\n", "训练集数据规模: (970000, 71)\n", "验证集数据规模: (242000, 71)\n", - "测试集数据规模: (282000, 71)\n", + "测试集数据规模: (243000, 71)\n", "\n", "训练集 group 数量: 970\n", "验证集 group 数量: 242\n", - "测试集 group 数量: 282\n", + "测试集 group 数量: 243\n", "训练集日均样本数: 1000.0\n", "验证集日均样本数: 1000.0\n", "测试集日均样本数: 1000.0\n" @@ -986,7 +2337,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_8380\\3149203115.py:132: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_7864\\551043002.py:132: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n", "(Deprecated in version 0.20.5)\n", " pl.count().alias(\"count\")\n" ] @@ -1005,8 +2356,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:47.385207Z", - "start_time": "2026-03-09T16:28:46.854611Z" + "end_time": "2026-03-10T14:56:29.729147Z", + "start_time": "2026-03-10T14:56:29.161969Z" } }, "source": [ @@ -1055,7 +2406,7 @@ "\n", "处理后训练集形状: (970000, 71)\n", "处理后验证集形状: (242000, 71)\n", - "处理后测试集形状: (282000, 71)\n" + "处理后测试集形状: (243000, 71)\n" ] } ], @@ -1072,8 +2423,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:49.943341Z", - "start_time": "2026-03-09T16:28:47.393354Z" + "end_time": "2026-03-10T14:56:32.377628Z", + "start_time": "2026-03-10T14:56:29.735814Z" } }, "source": [ @@ -1127,12 +2478,12 @@ "│ --- ┆ --- │\n", "│ str ┆ f64 │\n", "╞════════════╪══════════╡\n", - "│ count ┆ 969536.0 │\n", - "│ null_count ┆ 464.0 │\n", - "│ mean ┆ 9.821 │\n", - "│ std ┆ 5.444634 │\n", + "│ count ┆ 969665.0 │\n", + "│ null_count ┆ 335.0 │\n", + "│ mean ┆ 9.810091 │\n", + "│ std ┆ 5.346526 │\n", "│ min ┆ 0.0 │\n", - "│ 25% ┆ 5.0 │\n", + "│ 25% ┆ 6.0 │\n", "│ 50% ┆ 10.0 │\n", "│ 75% ┆ 14.0 │\n", "│ max ┆ 19.0 │\n", @@ -1141,7 +2492,7 @@ "开始训练...\n", "Training until validation scores don't improve for 50 rounds\n", "Early stopping, best iteration is:\n", - "[48]\ttraining's ndcg@1: 0.516707\ttraining's ndcg@5: 0.393291\ttraining's ndcg@10: 0.328332\ttraining's ndcg@20: 0.274341\tvalid_1's ndcg@1: 0.401918\tvalid_1's ndcg@5: 0.342001\tvalid_1's ndcg@10: 0.310652\tvalid_1's ndcg@20: 0.268765\n", + "[50]\ttraining's ndcg@1: 0.676684\ttraining's ndcg@5: 0.440728\ttraining's ndcg@10: 0.361258\ttraining's ndcg@20: 0.296362\tvalid_1's ndcg@1: 0.272472\tvalid_1's ndcg@5: 0.215751\tvalid_1's ndcg@10: 0.198035\tvalid_1's ndcg@20: 0.191275\n", "训练完成！\n" ] } @@ -1157,7 +2508,12 @@ }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-10T14:56:34.997174Z", + "start_time": "2026-03-10T14:56:32.382086Z" + } + }, "source": [ "print(\"\\n\" + \"=\" * 80)\n", "print(\"训练指标曲线\")\n", @@ -1219,12 +2575,47 @@ " val_ndcg = evals_result[\"val\"][metric][-1]\n", " print(f\" {metric}: 训练集={train_ndcg:.4f}, 验证集={val_ndcg:.4f}\")" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "训练指标曲线\n", + "================================================================================\n", + "\n", + "重新训练模型以收集训练指标...\n", + "Training until validation scores don't improve for 50 rounds\n", + "Early stopping, best iteration is:\n", + "[50]\ttrain's ndcg@1: 0.676684\ttrain's ndcg@5: 0.440728\ttrain's ndcg@10: 0.361258\ttrain's ndcg@20: 0.296362\tval's ndcg@1: 0.272472\tval's ndcg@5: 0.215751\tval's ndcg@10: 0.198035\tval's ndcg@20: 0.191275\n", + "训练完成，指标已收集\n", + "\n", + "评估的 NDCG 指标: ['ndcg@1', 'ndcg@5', 'ndcg@10', 'ndcg@20']\n", + "\n", + "[早停信息]\n", + " 配置的最大轮数: 1000\n", + " 实际训练轮数: 100\n", + " 早停状态: 已触发（连续50轮验证指标未改善）\n", + "\n", + "最终 NDCG 指标:\n", + " ndcg@1: 训练集=0.7563, 验证集=0.2625\n", + " ndcg@5: 训练集=0.5383, 验证集=0.2203\n", + " ndcg@10: 训练集=0.4355, 验证集=0.2053\n", + " ndcg@20: 训练集=0.3464, 验证集=0.1960\n" + ] + } + ], + "execution_count": 10 }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-10T14:56:35.585609Z", + "start_time": "2026-03-10T14:56:35.001847Z" + } + }, "source": [ "# 绘制 NDCG 训练指标曲线\n", "import matplotlib.pyplot as plt\n", @@ -1271,8 +2662,37 @@ " print(f\" {metric}: {best_val:.4f} (迭代 {best_iter + 1})\")\n", "print(f\"\\n[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\")" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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41pmf1nuakhOTQ71LAMLIvn3SnDnSjBnS++9LO3daVauj1K9fhrKMmYUoU7B+4JH3Nwv+Xvmjz+m/z2kDi3n7peXKlXPGl3j99df15ZdfZiY1AAAQqvNLG2v9kUekX36R1q61+Ir3GetvltWIER6CtUFAsPYIFbQsgY3lZbc6JSYmKpRJcjaQlpeVHrDgpdXSsmxZ27cePXoEZeAxy5ZNSUlxRlH2Bmxtf7IGbi0bwTq3tWvX9vvabdu2ZQZss762QoUKTmauadiwoX7//XctWrTIyWB45ZVXnOWLFy/W+PHjnRINAACEs4MHpR9+kL74ws2itUBtSkru9d55J45gbYz3OcOlv2nocxasz2nZTdxBAwAItS1bpLvukp5/3u1P5CVL6AVFiAHGYphluVpg1oKl1qG2UgFWzzYYLABrpRZuv/12/fPPP049r2effTbzeRvwq3v37k55g7/++svJ+v3++++drFnva++44w7ntb/99puee+65zNda1q2tY7VuLXBr27V6Y1ZqwQY6s5MYu+XMsoqzBoItAGyTSUtLc+btfQEACJZdu6Tvv5emT7damFKnTpZ9595uZuUu33kne6DW7pru29ejiRP/1u23F/3FVuBw0Of09TmtBMIvv/yi9PR057jYYLwWrO3QoQNfLgBA0KWlSWPGSI0aSRMn+gK1CQmWRCd17Gj126XbbvNo9OhdGjyY/mYwkFkbw5o1a+bUcD3zzDOdDuMFF1zgTMFig50NGjTICRZb3dkrrrhCE+23w78mT57sDNJgAy9YqQUbeOHtt9/OfK2tb0HdRo0aObVxbZnXyJEjnVq1n376qVOXN2ttXgvyerNOsvIOJGFOPPFE56cNvma1fAEAONyasr/+Kv38s/TTT9Lvv7vLrCNs1wNtSk+X/vrLfe7vvwt2i3r37lLPnlZvXkpM9GjLllQddRRthPBEn9PX57Q7vu666y5ngF0bt8FKIsyZM0dly5YNaRsBAGKrpJYlB3zzjWR5b9ZX9Spd2k0YuP56qXhx3/KMDOtv/qMqVUqHZJ9jTZwnGPe8R5jdu3c7HaZdu3ZlK/RvmZZr1qxxarpa9mZh2GH23ZYWPoM/RIvRo0c7gdW5c+dmLrNs20cffVT33HOPUwOsfPnyWrFihZ5++mknOJ01k7ew7XUk3wUElmU/e8tpZL3lMpKlHExR72m9o65mbTS2VTSLlfayUup2U8kxx0jHHmu3JOe//o4dbg2vFSvcnzb99psbdLUOrf1J8P7cts1dz4KzR6JWLTerwcb+tMnG7czaJMFqq7z6Rwj88Tzcfgb9zcjqc9LfjByx8jcxGtBWkYX2Ck5f97ulKbphYW/t2imV/Wialv+Y7CQLZGV94CuukB54wO50Dm1b7abPSWYtItOSJUucbITGjRs780899ZTuvffebOtYZm3btm2dzrPdYmYnQzbYWN++fXW9XSYCAMQsC66++qrd0uVmtZoGDaQePdys1ZNOcm//+vNP6dNPfdPq1UWzP9bntbukbYzP+vXdn3Y7mt0Z7WesTQBBQp8TABApLBXzxx+ld991M2dt3um72g0ebi6Q9JOkHIFaSwZ44gmpdetQ7DX8oQwCItLWrVuderY2kJhd2Rk8eLBTUiEnG1TMatMCAOBlt3xZ/Vf7mZV1Zh97zJ0qV5ZKlnSDtYdSqpSbSZua6k7eTFq787lxY6l5c+m449ypSRN3uxac9U6WyVC+vFSsGG0EhBv6nACAcLd8uTR1qjutXJn/utbvbHG81K6dO7Vv7/ZRuQE8vBCsRUTq2rWrc3sgEI0S4xPVs0nPzHkAgWEZtMOHW0307MvPOccNslrmrPeWsK1bc78+KUlq29YNvjZt6ptq1szewbWsXdueBWsJwAKRjT4nACAcrV8vTZli4/m4YyP4YwkCTtJAi0T9c0xP5y6u4c8mqixlZ8MeUQAACDMWoL3ihCtCvRtA1LA6sw8/LD35pPTPP77lzZpJY8dKXbr41ps1S5o5U5o92w26WhmC005zJ8s8yDIWZZ4sW7Yg6wEAAAAFZf1Y66e+9JJkpdNzjkBlyQOnnCL16SN17uyW+LKyXm7oj/PLSEKwFgAARKU9e6THH3fLGuze7Vterpw0apQ0ZIib/epVoYLUr587WaDWpqzPAwAAAMEO0M6b5wZpp03L3qf1suQCC9D27i3VqEH7RANOQQAgzNjo0Fv3u/dgVy5ZWXEUEAIKPeqtDb7+f/8nbduWvYyBBWjvukuqVCn/bXjryQIAAADBZOW43n/fHSjso4+k/ftzr2OD0Q4YIPXv787nh/PLyEOwFgDCTGp6qga96w6YN633NCUnJod6l4CIsH279PTTbrkDK2ngZbd/XXGFdPfdcmp1AQAAAOHmp5+kBx5wM2jtDi9/g9r+5z/S5ZdLHTsWPLGA88vIQ7AWAABE/AALY8ZIzz0n7dvnW25J6ZdcIt17r9SwYSj3EAAAAPBv8WLpvvukd97J/VzlytL550vdu7t1aG3QMEQ/grXI5vLLL1e5cuU01kZcCYAePXqoZcuWutfOlAEACIC//5a++kr64gt3+vJL6cCB7Jm0FqS97Tbp2GM55EA4os8JAIhlf/0lffONm2zw4YfZn6tSxc2etQCtDXDrDhKGWEI1tijTrVs3XXfddbmW7969WyVLltT8+fMVyazWytSpU3XWWWepatWqqlKlik455RRNnDhR6enp2dZ98803ddJJJzmf2wLGAIDIYLd9WRD21VelJ56QRoyQrrnGve3ruOPcgcDOPVd68EHp0099gdrkZMn+BK5eLb38MoFaoCjR5/SxpITExEQdddRRmZP1VwEA8PZtFyxwSxz06CHVrOkOBNazZ/ZArS2zvLk1a6SHHpJOOolAbawiszbKDBo0SIMHD9Zjjz2m4sWLZy5//fXXVb16dZ1xxhmKVKmpqfrPf/7j/LztttvUtm1bJxD7888/66mnntIrr7yi999/X6VLl3bWr1Chgm666Sb99ttvmmZFXwAAYdFZzau+lo1u+9JL0lNPSatWFXybdeu6mbQ33uhmIgAoevQ5fX1Oc95552mmDdUNAMC/LKHg9delhx+Wli3L+7DUqSPdfrs0cKCUJYyDGEZmbZS54IILnCv7OTuLL774oq644gqtW7dOXbp0UeXKlVW+fHmde+65+uOPPwq0bVvPRqWfMmWKGjZs6JRLsFvYDmS59/Ttt992nitbtqwTND548GC2bSxevFhnnnmmE0i1fbj++uszn3vrrbeyvdY6vVnLJ9x8881q0KCBZs+erU6dOqlMmTLOZ7Ws2RdeeMFZf9iwYZnrd+7c2Qnu1rTLVgCAkPnlF+mWW6SqVaUSJaR27aQhQ6QXXpB++EFauVK66SapVi034JpfoNZuA2vd2l3vzTelDRvc7APLVCBQCwQPfU5fnxMAgKz27pUef1xq0EAaMCB3oLZMGcny6OxPyYwZ0m+/SVdfTaAWPmTWHqk2baRNm4JzsKtVk777Lt9VihUrpn79+mnSpEnq06ePs2z58uX67rvvnECqBVaHDh3qZNimpaVlZkXMnTu3wLvx4Ycf6vvvv9eePXvUvn17vfrqq07Q9tdff9Ull1ziBF3POeccPf/8805JhjZ2jGQn1BucQO3o0aP1wQcfKCMjwwneGnut7feMGTOcIKsFl6+55prM1/75559asGCBfvzxR+cz2HZt3dq1a6tDhw6qVq2a7rjjDjVv3lzbt29XxYoVj+BAAwCOlA30ZTc1PP+8W9Igq2+/daf8dOpkmWruoAqVKkn2a91+egO+QMwpYJ8zIJ17+pyF7nNaqTGbt6l37966++67lWy1WQAAUWvdOrdPa8kDGzf6flqYY+fO7Ot26CD997/SiSdKjRrlfacZYAjWHinrNNv/yEOIC+L3zQKw1oG0LFrrWFrgtmvXrpkZpnXtflGntl+y7rzzTp144olO4DS+gL8tRowY4dz2ZdPZZ5/tBFwtWGu1uSzj9XwbqlB2ZehqPWHFBv9lZQpat27tBGG9rN6s8b7WtmcsgJx1kLOPP/5YF154oZNJO2HCBK1evdoJ8O7fv9/JFLZOse1/s2bNnLIHBGsRyRLiEtStYbfMeSDUPB7pgw/cDAELlN5zjxs38ueff6RHH3UnK2uQVVKSdPTReWfOWlyjXz/phhvc2rQACtfnDGZ/09DndPuc1g+98sorVaNGDSdJ4rLLLtPevXuz9YMBANFj82Zp5Ehp4kQpx83EuVh4xDJoO3ZUyHB+GXkI1gYi86AAPIHoSBfwvSxg2a5dO02ePFm33367EyQdP36889zWrVt144036vPPP9euXbucZVYD1rJkrfxAwXbDtx+lSpXSzn8vGW3cuFF1rNhKFlkfW3ZsI7uE5Ie91gLLWR1tZ/T/2rx5c+bzlunQq1cvpwyDTTbAhZcFqL3BaCBSFUsopiFth4R6NwAnSGuDHowaJS1a5Dsg778vXXqpO8CX91e1rWtlCawzunZt9oN37LF2EU667DI3Q9aCuN9/794sYpkHFoPq2lW68kr3eQCH1w8MSH+zgO9l6HO6fc5j7Zfcv4477jg9+OCDTvkxgrUAEF327JEee8xNSrC7yPJiJc179ZJuvTU8Brzl/DLyEKw9UocoS5DJ43Hqt1pmqOLigpLp8H//939Oh9GyZr3ZrsOHD3eyUZcsWeLUjF26dKlOOOEEeews+whZNsFXX32VbdnatWudzF1v4Pajjz7K87XffPNNrtdamQVj+2plFEyLFi2cUgtWciElJcWpYWuf78knn3Syh7MGkwEAhWd/EuzX9V13VdDixf7vunj1Vas1bvXEpXPOke68U1q4MHtt2f793du9rEZt1j99VqfrtNPcCUAA+5xB7m8a+py5FfRuNQBAZNixQ3rtNem++6QtW3zLS5Vyx2GwO8LsRuYaNdyf1tcN0p9hRCl6ElHK6tVu2rTJGZSrf//+Ti1bs3v3bpUsWdLJSLU6WyMtdz9AbDCvefPmadasWc6JwsSJE51SBV6XXnqpFi1a5JQxsGxeCxpbhq/3tVbqwIK59lor3ZD1tVYiwQK06enpzklB/fr11bhxY/Xs2dOpg2sDqtnntWxiL1vXgrlWb8yC0TZv7wuEO/u+7krZ5UyBuJCC2JOe7g7qZde4MjIK/hoLtg4dKtWvbwHYeC1enJT5fPPmbnD2ySd92a/2K/X//s8NumYN1FqW7I8/SpMmSXbNjc4qEL3oc9rgMDOcfrVZuXKlU9PWyncBACKTnYJZX/qRR9x+rg1ia2OjewO1dk302mul1avddWwQsc6d7Y4TyW5YDre+L+eXkYfM2ihl9WQtAGoDdVlw08uCswMGDFD58uVVq1YtZ7AxC3QGggVPp0yZohtuuEHbtm1z6nd5a9Aaez8L5t5yyy1OeYakpCT17dvXqVtrr7VA65AhQ5zX2r5bELZ48eLOay04a1m2VmPXMoafe+45Z/LKzCLJwvZl4MCBmY9LlCjhZPf+8ccfAfm8QFFJTU/VZTMuc+an9Z6m5EQGKEHBzZ/vdh5XrPDVibWKNHa3br16UvnyNhilb7JfnVZD1v4UZM0U8Dr2WI/uvTfOuZXLmyxmdWWtBIKVY0xL8617zDHSmDGSVacJt04qgKJBn9MGU5zmjLfwzz//qGrVqk4A28Z4AACEv7177UJb9snKf61Z43/93r2lBx5wBwmLFJxfRp44D2lbuVj2qdVvtZquZSx//V+WmblmzRrVq1ev0KO72mH2BhTjOIMtEAvgWkfXMnKNdYAtk9Yyg4cNG+aUb7ASDzb67j333OOUQTjppJMK1S6H215H8l1AYNl3YMuWLapSpUrU3HaYcjBFvaf1jrpgbTS2VTj56y/pllvcW7SOlAVwTz/do969d+mKK8ooMdF/e1kn1uIRVnfW6s1ed50bHEZ0/t/Kq3+EwB/Pw+1n0N+MrD4n/c3IQR8mctBWkSVU7WXjJdhNuTZZBu2hWELCuee6Yza0bq2YPL8MZlvtps9JZi3Cx3vvvafTTz/dybh9+umn9ddff2XLzLXMWCuxYBm41nG20XYtkGoZt6NHjw5YoBYAIomNQPvMM1Zf1h24y6tNG6l6dcluJrCgqmUN5KdECbd8gWXQnnee3cLl0ZYtKYqPzzsoZ5m6U6YE8MMAQBDQ5wSA2Owzz54tvfCC/R1wS4Dlxe4+O/VUt09sQdpIyqJFdKAMAsLGnDlznBINVmPWMhzeffddVcwxLHhCQoIzuq5NABDpUlLcQKuNJtu9uzs4waFuvrCg65Il7lhD334rffmlDcjoe75CBemhhyT7Nem96G11t2xgBAvc2usPHPBNVsbABkewDqn99CporVsAiDT0OQEg+lkw9uef3b7yF1+4pcLsTrSc2rZ1++BNmtidFu5k4zdwtxhCiWAtwoZl09oEALHAxle00gHesRTvvtvtHF50kVsLq0ULadcu6fvvfZMFae1WrbzGnbMS5TbgV6VK2ZdbANiufeW4/gUAMYk+JwBEJ0tKsNIG77wjff21tGeP//Vq1JAuv9xNbmjQINh7CRwawVoAAILIShXcfrubUZuTDWhgAxbYZBmylg17KFbOskMH6f77JarBAAAAINbY3WOW9/X8826yQ159Ziv5ZckSVm0xx/jkQFjh63kYGJMNfAcA+GMlBZYtc0sUrFsnlS/vZrNapqv93LBBuvFGaf1632ss0GqZtJYB8NlnvqxZf4Fa61Q2b+7ermWT1aU99li3rhaA6EN/I7bR/gCQ3+9IaeFC6YknpBkzcpfwsrEbTj7ZTWawny1bUtoAkYNgbSEU+/dseP/+/c5gV4hd9h3I+p0AAikhLkGd6nXKnEfRsP/GNrjAvHluZ+6//3VvicpvUAKre7Vzp1tj1jvZ7VZWmsACtD/84AZsC8Lqwz74oHTttVaPW7r5Zndk2unTpWnT3KBvw4bSCSdIrVq5Py0wW7x4wA4BgDBlNfpNWloafc4YZu2f9fsAAJB+/1165RXp5Zel1auzHxHrJ196qXTDDW5JsUONBRErOL+MPBERrB03bpweeeQRbdq0Sccff7yeeuoptWvXzu+6p59+uj799NNcy7t166ZZs2Yd0X5YR6lcuXLasmWL87hkyZKKK+D/frsyfvDgQSUmJhb4NQidvNrLllug1r4D9l2g84yiUCyhmG468SYObhGw896PPpJef93NZLVgq9fo0dJll0m33CI1a+ZbbgFY6wy+9pobTA2Es86Snn1Wqls3+/Jq1aRrrnEnALHL+h/Wz9y6datzYTjeO1rgIdDfjCz5tVdGRobT/vY9sOcBIJYzaC0o+8kn0pQp7rgPOXn70JZ8UaVKKPYyvHF+GXnC/i//1KlTNXToUE2YMEHt27fX2LFj1bVrV61cuVJV/PwvnD59euZVaLN9+3YnwNvb7jENgGr2W0DKDNgWpjNmnS7rbBOsDX+Hai8L1Hq/CwDCm/1J+Phj6e23pZkz864De+CA9OKL7nTeeW55gqlTpR9/LPh72QBhVprApkaN3Pq027e707ZtbnDYamX95z9c6QeQN+t7VK9eXWvWrNGff/5Z4ENFfzOyHKq9bPnRRx/NuQOAmGKhFgvMLl7sm/zVobVfm2ee6Q4UZuEe7j5DNAn7YO2YMWM0ePBgDRw40HlsQVvLkJ00aZJutxFacqhgI7Jk8cYbbzhXpAMVrPV2ni1QfMDO7AvIOmIWOK5YsWKBsyMQOvm1l2W4kFGLoj55S01PdeaLJxTnJO0w/POPNGeOG6C1Ugf+OnhWT/bCC6Vevdwr9OPH+9Z7/313ysqqnnTrJjVp4pYwyDrVquWWKihT5nD2FgByS0pKUqNGjbIlIRwK/c3Icqj2su8A5w0AYsXmzdJDD7mD8Kak5L1e06bSgAFuuQPrg+PQOL+MPGEdrLXO6eLFizV8+PDMZdZh6dy5s7766qsCbeOFF17QxRdfrFJ2Nh1AFqwrTMDOOmMW5EtOTqbTFQFoL4SSBWp7T3MvME3rPU3Jick0SAFuj1q1SvrwQ2n2bGnBAjdgm5P9KbjgAqlvXzfDNSnJXX7OOZL9qZk4UXr88ewDgJ14otS/v5sNa4OEAUCwWL/X+o4FRf8lstBeAGB3oMVpzJg4jRvnjimRk40tYXettW4tnXuu+5PKkoXD+WXkCetg7bZt25Senq6qVatmW26PV6xYccjXL1q0SD///LMTsM1PamqqM3nttvtW/+1A2RQIth3vrU4If7RX5IjGtvJ+Ju98tHy2omgrK1Hw/PNxTpD299/91wMvU8aj88+3DFqPE6DNOj5k1l2xQO5NN7kDflm5BLu6b7VljznG//rRLhr/b0WrYLUV3wUAABAoNkjv6NFxeuKJytq3z9ePt2uUV10ldeniBmYtWAvEmrAO1h4pC9I2b948z8HIvEaPHq2RI0fmWm5F/VPyy78v5AnOrl27nJMpbmcKf7RX5IjGtko5mKK01LTM+tjRklkbqLayeNT8+cX17LMltXBhcb/rVKuWrjPPTFW3binq2DEts4bVnj3udCinneabL2SJ8qgRjf+3olWw2mpPQf7zRMmAtS+99FJmCS6v4sWLZ+sX2vG+5557NHHiRO3cuVMnn3yynnnmGad0AQAAyJuVKbMEiXXrLEjrBmqtv24DhFm1SwK0iHVhHaytVKmSU2pgs6U3ZWGPDzW40759+5x6taNGjTrk+1iZBRvELGtmbe3atVW5cmWVCVABQjuRsnq3tk1OesMf7RU5orGtLFibVNy9P9/qY0dDsNZiPNOne/TFF2Xl8ZRQSkqcU6bAJivHaL/SjznGo4YN3YG5bCpd2n3eBuXyTgsXSk8+GaeVK7Nn0SYmetSxo3T22W72bPPmcYqLs+MW+ccuVKLx/1a0ClZbFeZ2/EgfsNZYH9Ce98o5ANTDDz+sJ598UpMnT1a9evV09913O9tcvnx5RB8rAACKyoYN0g032HmBb1mxYh5deaV0xx1x1KAFIiFYa0X1W7durXnz5qlHjx6ZJyT2+Lrrrsv3tdOmTXNKG1x22WWHfB/LlLApJzvhCeRJj3XyA71NFB3aK3JEW1tlHRU6VJ/LAqiJifb+h7+N9HR3JNfJk90OmVuDKr/64f7LGOTHgrs33mg1ZeP+Hdyr8NtA7PzfimbBaKtI/h4UdsBa7zHNKznAsmot4HvXXXepe/fuzrKXX37ZKdU1c+ZMZ7wEAADgOy+YMMEdIyLrjTpdung0atQ2tWtnAy3SjwciIlhrLAtiwIABatOmjXOrmnWMLWvW29nu37+/atas6ZQyyFkCwQK8NroqACD/K9xLl0o//OD+tMkG6ypXTjr5ZDkZqzZZYX8/17V04ID011/udmyywbl+/12aMSP7QF2BZGUK7IYIG2SgEGM9AohBhztg7d69e1WnTh0nUaBVq1Z68MEHdeyxxzrPrVmzximnYNvwKlu2rJO1a9v0F6xljATkRG3wyEFbRQ7aKrzYecIbb0iPPBKnZct8wdjKlT0aM8ajPn0ytG3bQeriR8CYKMH8v5XBmBnhH6zt06ePUzt2xIgRTqe4ZcuWmj17duagY2vXrs2V6WG3rC1cuFAfffRRiPYaAMLfpk3SoEHSBx/4f/7vv6X333cnY4Fay2S18RgtS9ZbxqAgpb0t8Nunj0edOv2tRo3KqVSpeGegr5Il3QzeP/+UfvvNN1mw2LZtg35lnSpVsu1IrVoF9lgAiF6HM2Bt48aNnazbFi1aOPWAH330UZ100klatmyZatWq5fRJvdvIuU3vczkxRgJyojZ45KCtIgdtFR6sH//GGyU1fnwprV+fPbPikkv266679qh8eY+2bmWMhEgZEyWY/7f2RPg4CTERrDVW8iCvsgcLFizw28H2XjUAgEgTHxevk2ufnDlfFObMsTsT/A+eZaUWmzWzi2EW5PAttyDtsmUFfw8Lwp5zjjRggHTeeW49qi1b0mTlIXP+fW/e3J0AIBx06NDBmbwsUNu0aVM9++yzuu+++w5rm4yRgJyoDR45aKvIQVspJOXT7LzB7qyz6bff4vTaa3aekb2swckne3T//R6deqpvXAnaK0htlJ6mMxqe4cxXq1pNSQnu+CiFEcy2Sqb2f2QEawEgltgfz9s7+q+hGIjO1F132a1IvmVWktECty1bupMN7mWBVrvmZWPr2KBen3/u/ty4UZkZsd6fNtmIrTVryhkUwPvzuOPcTFgv7mYBEGkD1noVK1ZMJ5xwglZZ2r/ze7Na5jaqZxmy2h7bXWD+MEYC/KE2eOSgrSIHbRUc06ZJ99zjni/k18+35A2rRHTKKRa8zV2XlvYqesnxyRp+iq8c1OEKVlvFR/A4CYFCsBYAYoRd6bYyit9+61vWrZv00ktWNyr3+jbGWZMm7mQjtAJAJDqSAWu9rIzCTz/9pG72S1NSvXr1nICtbcMbnN29e7e++eYbDRkypAg/DQAAobV3rzvA76RJea9jsbbevSUbwzOPa5gA8kGwFgCi2K5d0rvvSm++6ZY+sCL/plgx6aGH3I4WFy4BRLvCDlg7atQonXjiiWrYsKF27typRx55RH/++aeu/PfKlWWW3HTTTbr//vvVqFEjJ3h79913q0aNGpkBYQAAos2SJVLfvtKvv/qWWSkzS+6oX9+dGjSQbDzOAt68AsAPgrUAEIYF4HtP6+3MT+s9rdAF4K2g/4wZboD2ww/d0gdZ2SBhNipr69aB3GsAiJ4Ba//++28NHjzYWbd8+fJOZu6XX36pZlbQ+1/Dhg1zAr5XXXWVE9Dt2LGjs03qrAEAoo2VOXj8cbecgTf5wwb/ffppd3wKuyMP0Xt+ieAjWAsAUeKXX6Rnn5UmT5Z27sz9fI0a0qWXSnffLZUuHYo9BIDIGLD28ccfd6b8WHatZeDaBABAtJQ4eOcdd1DhDRuyT3v2+NZr00bOIGI21gWAwCNYCwARLCXFzaKdMEH67LPcz9vtR1Yv6j//sdHMKXkAAAAAwMcGFbbBhF980R00bN++/I/OsGHSffdZTXiOIlBUCNYCQJhZv17avEkqliRt2iTVqZn91qK1a6UPPnCnefOk/fuzvz452W75lawUY8eOUkJC0D8CAAAAgDBmdWenTnUHG7aBiPNSsqRUs6Zbi/aWW6ROnYK5l0BsIlgLAGEwCNinn0pz50offyytWCXJLSmkejdJpYr7CvavWuXeluSPFfa/+mobKEcqXz6oHwEAAABAmFu+XHrrLXf66afcz5cpI118sdS9u1SnjhukLVuWmrRAsBGsBYAQsYG/rrpKeuUVKT0979/MdiuSdab8dahsbJxu3aTLL5dOOYWOFAAAAIDsZQ7GjXOnFStyHxm7g69zZ/d8okcPN5MWQGgRrAWAELHbiGwwsKysZEHbE6WtDd0AbqMu0h+rpD/+cEdetc5Uu3bSuee6QdoTTqAOLQAAAIDcdu92g7A2xkVOJ54oXXihO7bF0Udz9IBwQrAWAELg9delp55y5604v2XYdukinX66lFwqXqM/b+M8N3xUvJIS3MCt1bItXVqqUIEmAwAAAJA3y6Lt2TN7Nq2NZ3HRRVKvXlLt2hy9WBEfF6821dtkziP8EawFgCD7+Wfpyit9jy1oa8FanyTdc/o9uTJurW4UAAAAAOTHMmltHIu9e93H5cq5pdfs7jzEnqSE3OeXCG+E1AEgyLci2e1G+/e7j+22pMGDaQIAAAAAR2bDBmnYMDdz1huobd5c+vZbArVAJCGzFgCCWNx/4EDp11/dxy1bSuPHMygYAAAAgMKzUmkWiJ01y52+/z778337ShMnSqVKcXSBSEKwFgCC5LHHpOnTfbcivf22VKJE7vVSDqbosumXOfOv9HpFyYnJtBEAAAAApaVJ8+e75xLvvCNt3Zr7oFgJtUcflW68kcQQcH4ZiQjWAkARW7ZMevZZN4vWa8oUqX79vF+Tmp5KuwAAAABQSoo0d64vQLtzp/+D0rq1dN55Up8+UtOmHDhwfhmpCNYCQBF1qKwzNWGCtHBh9ufuusvtRAEAAACAP7t2SR98IM2c6f701qDNqmRJqWtXtx5tt25S9eocSyAaEKwFgADasUMaO9bNot2+PftzVvLgmmuke+/lkAMAAADI7p9/pFdfdZM+5s2TDhzIfYRKl5YuuMAdtNgCtRawBRBdCNYCQABs2yaNGSM99VTuq97NmklXXy316+fWqgUAAAAAr9RU6fnnpQcekP76K/dxqVBBOv986aKLpC5dpOLFOXZANCNYCwBHYPNmd+Awy6Tdt8+3vFgxqXdvacgQ6eSTKewPAAAAIDvLnJ08WbrvPmnt2uzP1a4t9egh9ewpnXKKlEj0BogZ/HcHgMPwyy/S449LL7/sXgn3SkqSBg2SbrtNqlOHQwsAAADA5+BBackSt8zBCy9Iq1dnPzoWnLVziXbtSPgAYhXBWgAoII9Hmj/fzaT98MPszyUnS4MHS8OGSbVqHdkhjY+L13GVj8ucBwAAABC5/vhDevddN0D76afu4GE52QBho0ZJrVuHYg8RzTi/jDwEawGgAL74Qrr2WumHH7IvL1PGDdL+73+BG301KSFJozuPpl0AAACACPfSS9JVV/kfLMyceaZ0//1Shw7B3jPECs4vIw/BWgDIR0aG9NBD0t13S+npvuVW4uDGG92SBxawBQAAAICsd+WNGOEGYrOqVEk64wypUyc3UNuoEccMQHYEawEgn8HD+vWT5s71LWvVyq0h1asXRf4BAAAA5JaSIl1xhfT6675ldjee3anXvLkUT6UzAPkgWAsAflg9qcsukzZtch/HxbnZtTYV9UisKQdTNOjdQc78Cxe8oOTEZNoIAAAAiADbtrmDhC1c6DuPGDtWuuGGUO8ZYhXnl5GHYC2AmPXrr9KTT7o/rYaUd0pLc2vT2q1Lplo16dVX3duUgmV36u7gvRkAAACAIy6f9vHHbvbsqlXuspIl3ezaCy7g4CK0OL+MLARrAcSc5culBx6Q3njD7VTl56yzpClTpCpVgrV3AAAAACLF9u3uIGLPPCOtXu1bbgkf778vtW4dyr0DEIkI1gKIGT/+6Bb4f+stX9asP8WKSRUrSkOHSv/7HzWlAAAAAPhs3CgtXiy9/babAJKamv3oHH+89O670tFHc9QAFB7BWgBRa8cOacECaf58d/rll9wjsVpAdtAgqXRpN0ibkODWlQIAAAAAS/L49FN3sgDtd99Jf/3l/7h07iwNGeKWPSjqcS4ARC9+fQCIKlbWYNw46cUXpaVL/WfQVq0q3XqrdPXVUqlSodhLAAAAAOHMziNmz5buuUf69tu81ytXTho40D23OOaYYO4hgGhFsBZAVN2O1L+/NG9e7ucsY7ZNG+mSS6TBg6USJUKxhwAAAADCPUj70UdukPabb/wHZ60OrU1t20rdurkDiQFAoBCsBRAVrHi/lTPYts23rGVL6cwz3emUU6QyZRQR4uPi1ahCo8x5AAAAAEXv66/dMSu+/DJ3DVpbftJJUv36lE1DZOH8MvIQrAUQMeUNrKyB1ZOtUkWqXFlKSpJSUqS77iqtF17wBTVr1JBeeUU64wxFpKSEJI3pOibUuwEAAADEhN27pTvukMaPz15G7bjjpJEjpR49GHQYkYvzy8hDsBZA2Fu/XurZ0y3mn1XZsla4P07bt/sKz1ox/xdecAcPAwAAAID8vPuudM010oYNvmXNmkn33itdeCFBWgDBR7AWQFizW5B69ZI2b8793K5d9m+cM1+8uEdjxsQ5o69a9i0AAAAA+HPwoPTbb9Ldd0tvv+1bbrVn77tPuuEGSwrh2AEIDX79AAhbkybJCb6mpbmP69WTzjpL2rLFN23d6lHTpgf0zDOJOv746IjSph5M1TWzrnHmx587XsUTi4d6lwAAAICIlJoqvfOO9P330ooV0sqV0qpV0oED2dfr2lV65hn3nAOIJpxfRh6CtQDCjnWcrID/U0/5ltkgYW++KVWsmH3djAyPtmzZoSpWyDZKeOTRlv1bMucBAAAAFL4O7XPPSWPGSH/9lfd6Vj7tiSekvn25Qw/RifPLyEOwFkDYsDpRM2ZIL70kLV7sW3799dJjj0nFioVy7wAAAACEu61b4/Xkk3FOluzOnbmfL15catRIatxYatnSvZMvZ0IIAIQSwVoAIfXHH26A9q233Pq0WVlw1kZkvfLKUO0dAAAAgEhgJQ4efzxOL79cWSkpvvJoNp6FDVY8cKA7cFidOlJCQkh3FQDyRbAWQNB4PG6NqM8/900WrPXHOlJ229LJJ9NAAAAAAPyfX3zyiVvqYNYsWxKXLfGjXz/p1lulJk04egAiB8FaAIctJcXNii1dWmrRQqpdO3edp99/lz7+WJo3T1qwwB0ULC/HHitddJE72XzObQEAAADArl3unXk2xsUPP2Q/HqVKZeiqq+I0dGicatXiWAGIPARrARwWq/903nnSF1/4lpUr5wZtbbJArgVp88qcNcnJUvv2Upcu0oUXcsUbAAAAgH///ONmz77+uvszNTX780cfbWNdZOiCC7aqYcPKio8n8wNAZCJYC6DQNm+WunbNfRXbAriffeZO/pQtK3XsKJ1yiju1bu0W+Ed2cYpT7TK1M+cBAACAWLFvn7RuXfbJSqm9/760Z0/u9du1k/73P6lXLyk+3u7k84Rit4Gwxfll5CFYC6BQ/vxT6txZWrXKfVy5sjRggLRsmfTjj9KGDb51k5Lc4GynTu5rWrWSEvmtc0jFE4tr/Lnj+WYCQACNGzdOjzzyiDZt2qTjjz9eTz31lNrZGf4hvPHGG+rbt6+6d++umTNnZi6//PLLNXny5Gzrdu3aVbNnz6bdAKCQJQ0+/dQtm2Z35i1ffujXVK0q9ekjXXqp1Latr3xaRgaHHsiJ88vIQ9gEQIH98otbssAbkLUatdahOuYY3zrbt0s//eTO2zlwyZIcYABAaE2dOlVDhw7VhAkT1L59e40dO9YJrK5cuVJVqlTJ83V//PGHbrnlFp1it4P4cfbZZ+vFF1/MfFyc20UA4JAOHJC+/lqaO9c9l1i0SEpPP/TrypRxS6ddcol0+ukkgQCIXgRrARTI4sVu6QMLxprGjd0OlgVss6pY0e08AQAQLsaMGaPBgwdr4MCBzmML2s6aNUuTJk3S7bff7vc16enpuvTSSzVy5Eh9/vnn2mm1fnKw4Gy1atWKfP8BINJZGYM5c9zzBxt0eO9e/+tZGQMrldaokXueYXVo7adNTZtSQg1AbCBYC+CQrFNlNaC8nSorZ2B3eVoJBARe6sFU3TznZmf+8a6PO7etAAAOT1pamhYvXqzhw4dnLouPj1fnzp311Vdf5fm6UaNGOVm3gwYNcoK1/ixYsMBZp3z58jrzzDN1//33q6JdtQQAyONxzyMefNAtc5AXC8JayTQrnXbaae6gxQACh/PLyEOwFkC+XnlFskSkgwfdx6eeKr37rjtYGIqGRx6t270ucx4AcPi2bdvmZMlWtQKHWdjjFStW+H3NwoUL9cILL2jp0qV5btdKIPTq1Uv16tXT6tWrdccdd+icc85xAsAJCQm51k9NTXUmr927dzs/MzIynCkQbDsejydg20PRor0iB21V2OMlzZghPfRQnBYvzj1YbtWqnn/HtPA4QdqaNXO/nraKDfzfCo70jHSt3bU2c/5w+gnBbKsM+jEEawHkfSX80UelYcN8y3r0kF57TSpRgqMGAIhOe/bsUb9+/TRx4kRVqlQpz/UuvvjizPnmzZurRYsWatCggZNt28miEDmMHj3aKamQ09atW5WSkhKwk5tdu3Y5J1OWPYzwRntFDtqqYPbvj9OMGcmaMKGUVq3KnhfWoMFBXXLJfp1+epqaNj2YOSCY2bKFtopV/N8KjpSDKUpLTXPmt2zZouTE5LBuqz179ijWJUbj6L1WU+zOO+/U9OnTtWPHDtWpU8cZSKJbt25B3W8gUtmFrKFDpSee8C0bMkR66inJT7IQAABhywKulum6efPmbMvtsb96s5YlawOLnX/++bkyPBITE51BySwom1P9+vWd91q1apXfYK2VYbBBzrJm1tauXVuVK1dWGRs1JwBsP+Pi4pxtEqwNf7RX5KCt8vfzz9Jzz8VpyhT73ZY9k7ZVK49uu82jnj3jlZBwlIoabRVZaK/gBWuTiic581a+6XCDtcHqYyQnF37/ok1itI3ea3XJunTp4jz31ltvqWbNmvrzzz9VjsI3QL5ZtHZF2+4Gtem996RZs3zP33+/dMcdynYFHACASJCUlKTWrVtr3rx56mG3iPx7wmGPr7vuulzrN2nSRD/99FO2ZXfddZeT5fHEE084AVZ/1q9fr+3bt6t69ep+n7fByGzKyU54AnnSYydSgd4mig7tFTloq+zS0qS33pKeecZKx+Q+XlY6zc4fzjorzjl2wURbRRbaq+hZn8D7//BI+gjBaqt4+jDhH6wt7Oi9ttyyab/88ksVK1bMWVa3bt2g7zcQCWyQMCv4b+ekfga5drJon3tOuuKKUOwdAACBYRf+BwwYoDZt2jh3Z9nF/3379mX2L/v37+9c4LdSBZbNcdxxx2V7vfeiv3f53r17nZIGF154oZOda9m4w4YNU8OGDZ2kAgCIVlu3uucH48ZJf/2V/Tkrlda3r3T11VLbtqHaQwCIfInRNnrvu+++qw4dOujaa6/VO++846RoX3LJJbrtttv8DvZgGPABsVbo3D7WfffFadSovK9ylynj0SuveHTuuUdW5L+oRWNbeT+Tdz5aPls0tlU0o70iR7DaKpL/7/bp08epDTtixAinrFbLli01e/bszEHH1q5dW6gsDutT/vjjj5o8ebJTfqtGjRo666yzdN999/nNngWAaCh1YCXSbPDhnGW2mzZ1A7T9+9vFrVDtIQBEj8RoG733999/1/z583XppZfqgw8+cOqGXXPNNTpw4IDuuecev69hwAfEUqHznTvjdN11ZTVvnq8OTI0a6WrU6KAaNvRO6WrR4oATsA1kwf+iEI1tlZqeqjLxbv1CCy4UT4iOE/9obKtoRntFjmC1VaQP9mAlD/yVPTA2KFh+XnrppWyPS5QooTlz5gR0/wAgHFgg9scf3TvvLEBrk83nKPvtlEezyjI33CCddhrl0oBwFqc4VSlZJXMe4S+sg7WHe8Ji9Wqfe+45J+vBapRt2LDBGaAsr2AtAz4gVgbosI7XhRfG6fffvfVqPHrwQY9uucVqSVnZELd0SCSJ1rZ65T+vKNpEa1tFK9orcgSrrRjsAQCi0+7d0gcfSG+/7f7cvz/vdW08xCuvtAtgUr16wdxLAIereGJxvdD9BQ5gBEmMptF7jQ3qYLVqs5Y8aNq0qXPLm5VVsEEmcmLAB0R7ofMdO6RXX5Vuu0365x93WcWKNoBfnDp1ivwra9HUVtGOtoostFfkCEZb8TsWAKJrgLDXX5emTZPmznUf56VSJal5c6lnT+nyy6XSpYO5pwAQexKjafRec/LJJ+u1115z1vOeVPz6669OENdfoBaIVtu3SzNnuh2wefOkgwd9z7Vu7V45r1MnlHsIAAAAIJhsWAQ7Rxg2TFq1KvfzltBhY1a0amWDKrpTlSqUOQCAYArrYG1hR+81Q4YM0dNPP60bb7xR119/vX777Tc9+OCDusGK6QAxYMMG+3/g3sKUnp77+SuucEdvTfaVrEWYSUtP0+0f3+7M/1/n/1NSAheaAAAAcGS++87Or6XPP8++vGZNN2v2wguljh2lxLCPEgAoDM4vI09itI3eW7t2bWfAh5tvvlktWrRwArkWuL3N7v8GotzWrVKnTtLKldmXWwbtRRfZ/yepbdtQ7R0KKsOTod92/JY5DwAAAByu9ettnBbplRxDIpx+unTffdJJJ1mpG44vEK04v4w8YR+sPZzRezt06KCvv/46CHsGFE2B/8mT3duNunUreE2oXbuks8/2BWrteka/flLv3m6A1kZsBQAAABA7JQ8mTpRuuUXas8e3vFEj6ZFHpAsu4BwBAMJRRARrgVjx7bfSxRdLv//uPrZSBeec4wZczzsv78CtDRpmna0lS9zHNWpIX3wh1a0bvH0HAAAAEB7WrJGuvFKaP9+3rEIF6Z57pKuvtvFhQrl3AID8cLMDECR790o7d/p/LiNDevRR9xYkb6DWpKRIM2ZIl1ziZtpaLakpU6S///atc+CAG8z97DPfoAA2oiuBWgAAACC22HnFU0+5A4NlDdTauBU2oJgN5UKgFgDCG5m1QBBYR8kK9lupAitJYCOs2nTCCdK2bdKAAdLs2b71TzzRfe7tt6UtW3yBWxu51SYr+n/GGVKvXtKnn0qzZrnrHHWU9OGHUrNmNCsAAAAQK/btc88dbCDhRYt8y48+2i2FcNZZodw7AEBhEKwFipgFUi1Qm5rqPrbOk012C1L16lJ6ui8ga3VlbSy8UaOkYsXcq+I2Wuu0aW7na/Nmd72DB93sWZu8iheX3nuPAcQAAACAWKlJa+cVkyZJr7+evS6tGTJE+r//k8qUCdUeAgAOB8FaoAhZkNVKGFhw1VgpA29g1vz1l2/eBgSzEgdduviWJSS4o7Ta9OST0pdfumURpk+X/vwz+3r2XrYeokOZ4vSqAQAAkJslgdh5w9ix0rJluZ+3u+yeftq9Ew8AOL+MPARrgSLy8svSwIFu3SjTp4/bqbIA7QcfuBm38+a5g4N17SpNnuwGbPNiAdlTTnGnxx6Tli51A7fffy/997/uAGSIDsmJyXq116uh3g0AAACEkd27peeek8aMyZ704S2HZucbVpu2Qwf3jj0AMJxfRh6CtUARmDDBve3Iy4K2VivKAq5WN8pGYLXJArUbNkgNGhSuQ2XrWk1bmwAAAABELztfGD/enXIOWGwDFF95pTvgsAVsAQCRj2AtEED790sjRriZr17XXSc98YQUH597/RIlpIYNaQIAAAAAPjZWxVtvSVOnSgsXuvVpsyZu9OjhjnXRvj1HDQCiDcFaIEA+/tgtR/D7775lw4a5Rf25DQmFkZaepns+uceZH3nGSCUlJHEAAQAAopyVT3vlFbc82oIFvnJqXjYAcb9+0q23Sk2ahGovAUQazi8jD8FaoIC8V7NzBl537JBuuUV68UXfsuLFpQcflG6+mUAtCi/Dk6Gft/6cOQ8AAIDotmePNGCAOyZFTo0bu/VoBw+WatUKxd4BiGScX0YegrVAPsHZ335zM2bnzpU++cQtc1CpklS5sm+y5Vu2+F536qlu4X/rVAEAAABAflatkrp3l5Yv9y2zMS0sQGtT8+YkgABALCFYC+SwZIkNEBan2bMrad263IVmN21yp5zKlJEeecQt8O+vPi0AAAAAZDV7ttS3r2/gsLJlpZdfls4/nwAtAMQqgrXAv376SbrnHu+tR3G5/ntYRq3ddrRtm7R1q5Sa6nvOroSPGyfVrMnhBAAAAJD/HXy7d0vPPCPdcYev3FrTptI770iNGnH0ACCWEaxFzFu5Urr3Xnek1ayjrCYleXTKKVKXLnE66yzp+ON9GbO23t69btDW6tMSpAUAAACQ099/S6+9Jn3xhbRxoztt2OCWV8uqRw83o7Z0aY4hAMQ6grWIWf/84w4ANnFi9pFWq1e3K9wZ6tZti+rWraL4+Bwjiv07yJh1pOhMAQAAAMjKEjs+/VR6/nnprbey35Hnz8iR0l13UUoNAOAiWIuYtH691LOn9N13vmU2WNjw4dLVV7vZslkHDQOCrXhCcQ46AABAhN2xN326NGmSO2iYPzbORY0a7mQl1vr3lzp1CvaeAog1nF9GFoK1iDlffin16iVt3uw+LlXKvZJ93XXSUUe5y7Jm2gLBlpyYrLf+8xYHHgAAIIylp0tff+3WmbXp119zr1OhghuQ7ddPOuYY3/kGAAQL55eRh2AtYopd5R4yREpLcx/Xq+d2rJo3D/WeAQAAAIgEO3ZIjz3mllOzMSz86dxZuvJKtxat3bUHAEBBEaxFTDh4UPrf/6Qnn/QtO+MMado0qWLFUO4ZAAAAgEgZLOyJJ6SxY6U9e7I/ZwMRd+wode/ulluzpBAAAA4HwVpEPcuiveQS6e23fcus5MGYMVKxYqHcM8C/tPQ0jf58tDM//JThSkpI4lABAACEyLZtlklbShMnxmnXLt9yO5c47zw3QHvuuVKlSjQRgPDD+WXkIViLqJaSIvXuLb3/vq9DNX68e0sSEK4yPBn67q/vMucBAAAQhD5YhrRkibR0qbRsmW/auDFeUunM9RITpSuukO64Q6pTh5YBEN44v4w8BGsRtfbvd29B+ugj93Fysluf9qyzQr1nAAAAAMKBx+MGaN94Q5o6VVq3Lu91ExI8uvzyOGdw4rp1g7mXAIBYQrAWUWnvXun886UFC9zHpUq52bWnnx7qPQMAAAAQKjt3Sn/+6U6LFrkB2lWr8l6/QgXp2GM9atp0v265pYQaNYoL5u4CAGIQwVpEHRuR1UZd/fJL93Hp0tKHH0onnxzqPQMAAABQFGzAL+vzz50rp67sgQPZp+3b3QBt1pqzOVl5A7sLz6bjjrMgrVS1qmXferRlyx5VqVKCxgMAFDmCtYho1vGymlJ2Vfybb9zp1199z5crJ82ZI7VrF8q9BAAAAHA4vv1Wuu8+acsWN4DavLk7tWhhZQmk995zBxK2Pn9qauG3HxcnnXGGdPHFUq9eUsWK/kslAAAQLARrEbGsw9a+vfTHH/6ft46WXVk/4YRg7xkAAACAI/HPP9I990iPPeYO/GUsMSNnoLWggVQbaLh2bbfWrA0KZlO9elKXLlL16rQVACB8EKxFxHroodyB2qQkqWVL6cQTpRtvlOrXD9XeAQAAADgcn30mDRqUfy1ZkzVQawFXG1zYpmOOcYOzNllpA/tZooQUH097AADCH8FaRGxW7TPPuPPJydLDD7tZtscfLxUvHuq9A45McmKy3uv7HocRAADElN27peHDpfHjsydjWIbtNddIK1dKP/3km2ywMCthYOULOnQgGAsA/nB+GXkI1iIiPfqoe2uU+e9/peuvD/UeAQAAADgcVmt2wgTp/vulbdt8yy0A+8ILUtOm7mNLzrAJAIBoxo0giDhbt0rjxrnzlkU7bFio9wgAAABAYVkt2ldekZo0kW66yReoLVlSGjtW+vxzX6AWAIBYQWYtIo4NMrB/vzt/1VVSjRqh3iMgsNLS0zTmqzHO/NAOQ5WUkMQhBgAAUcHqzFot2q++ksaMkX74Ifvzl1wiPfCAOxAYAODIcX4ZeQjWIqLY1fann/bVryKrFtEow5OhL9Z94czfdOJNod4dAACAw2alyz791A3OfvONtGiR9Pffudc76yzp//5POuEEDjYABBLnl5GHYC0iyuOPS/v2ufNXXinVqhXqPQIAAABiT0qKtG6dVKGCVL589sG91q6VZs1yp/nzfWNN+NO6tfTQQ1KnTkHZbQAAwh41axExduyQnnrKnS9WTLr99lDvEQAAiBTjxo1T3bp1lZycrPbt22uRpfcVwBtvvKG4uDj16NEj23KPx6MRI0aoevXqKlGihDp37qzffvutiPYeCB/p6W6fvGpV6ZhjpEqV3L55lSpSs2Zu/dk6daRrrnGDtTkDtZUrS+edJ40a5QZy7b8igVoAAHzIrEXEsEEG9uxx56+4QqpdO9R7BAAAIsHUqVM1dOhQTZgwwQnUjh07Vl27dtXKlStVxSJMefjjjz90yy236JRTTsn13MMPP6wnn3xSkydPVr169XT33Xc721y+fLkTEAai0XffSVdfLS1enHugMBsE2KacqleXunWTOneWTjzRDeTGxQVtlwEAiDhk1iIibN8uPfGEO29X7ocPD/UeAQCASDFmzBgNHjxYAwcOVLNmzZygbcmSJTVp0qQ8X5Oenq5LL71UI0eOVP369XNl1VrA96677lL37t3VokULvfzyy9q4caNmzpwZhE8EBNeuXdL110vt2mUP1FoQ9uyzpTZt3AHBjjrKLYfQvr2bOWvrbtggPf+8dPHF7joEagEAyB+ZtQjbEWK//todhMAmGyX2wAH3+csvd6/IAwAAHEpaWpoWL16s4Vmu9MbHxztlC76yEY/yMGrUKCfrdtCgQfr888+zPbdmzRpt2rTJ2YZX2bJlnaxd2+bFFpUCoqBPbiUKpk+XJk+WNm/2Pde8ufTMM9LJJ+d+nWXZZq1fCwAACodgLcKuU3jhhdKMGf6fT0oiqxYAABTctm3bnCzZqlZgMwt7vGLFCr+vWbhwoV544QUtXbrU7/MWqPVuI+c2vc/llJqa6kxeu3fvdn5mZGQ4UyDYdizrN1DbQ9EKp/ayOrQ2iK9Nv/5qffE4pz++fn32egUlS3p0770e3XCDe7dbXrseBh8patsK+aOtIgvtFdzj7J0/nN9lwWyrDH7XEqxFeJkzx3+gtmlT93aqAQOkevVCsWdA8BRPKK5pvadlzgMAgmfPnj3q16+fJk6cqEo2clKAjB492impkNPWrVuVkpISsJObXbt2OSdTlj2M8BbK9lq3LkG33VZG339fTPv3xyktLf8isgkJHp19dqruvXe3atXK0N9/K6bwfyty0FaRhfYKDvs78/SpTzvzu7bv0u4494JxuLbVHu9gRTGMzFqEDbvQY7WtvKwu1gUXSG3b2q2FodwzILhs1PHkRAanAYBAsIBrQkKCNme9h1t2S/dmVatWLdf6q1evdgYWO//883NleCQmJjqDknlfZ9uobqMnZdlmy5Yt/e6HlWGwQc6yZtbWrl1blStXVpkyZQLwSd39tL8htk2CteEvVO21fLnUs2ecNmzIP0BbrJjHGRSsVy+P0yevVCnJ/kcpFvF/K3LQVpGF9oocwWyrZAZqJViL8DF/vuQtHdesmTR2LPWuAADAkUlKSlLr1q01b9489ejRI/OEwx5fd911udZv0qSJfvrpp2zLbCAxy/J44oknnABrsWLFnICtbcMbnLXg6zfffKMhQ4b43Y/ixYs7U052whPIkx47kQr0NlF0gt1eVoP2nHOkHTvcxxUrSrVqSaVKWYkD92f58nKCtOedF/dvwkT+Qd1Ywf+tyEFbRRbaK3IEq63i6cMQrEX4yJpVe/fdBGoRuw6kH9C4b8c589e2vVbFEoqFepcAIKJZRuuAAQPUpk0btWvXTmPHjtW+ffs0cOBA5/n+/furZs2aTqkCy+Y47rjjsr2+XLlyzs+sy2+66Sbdf//9atSokerVq6e7775bNWrUyAwIA+Hm448l+3paXVrTurX04YdS5cqh3jMAQFHi/DLyUAYBYeHTT6XPPnPnGzeWevcO9R4BoZPuSde8NfOc+avbXK1iIlgLAEeiT58+Tm3YESNGOAOAWTbs7NmzMwcIW7t2baGzOIYNG+YEfK+66irt3LlTHTt2dLbJrXsIdVkxC8YmJLgD89pP8/bb0iWXSGlp7uMzzpBmzpQCVIEDABDGOL+MPARrERbuu883f+edvo4lAABAIFjJA39lD8yCBQvyfe1LL73k91bAUaNGORMQart3S5MnS+PGSStX+pbbNQgL2mYdw657d+mNN6wmYEh2FQAAHALBWoTcl19K89wkQjVoIPXtG+o9AgAAAMLfb79JTz8tvfiijZ6d+3kbGy9roPbyy6WJE22wvKDuJgAAKAT+TCPssmrpPAIAAAB5++sv6b//ld57L/dzbdtKRx3lljzwTnFxbhmE//2PcSEAAAh3BGsRcHb1futWd2RZ6xgeakTa2bPd+bp1pcsuo0EAAACAvKxeLXXpIq1Z41tWooTUr5+V+5CaN+fYAQAQyQjWIqC2bJFOPdWtlVWnjnTeedL550unny4VL5593YMHpaxl3oYPl4oxjhIAAADg148/Sl27Sps2uY+rV5duvlkaNEiqUIGDBgBANCBYiwLZsEGaOtUNvh5zTN4ZtT16+AY1+PNPd5ADm+xWrE6d3IHD1q93J+tkWh0tU7u2NGAAjQEAAAD488UXbl985073cbNm0kcfSTVrcrwAAIgm8YoA48aNU926dZWcnKz27dtrkd07n89ovTY6b9bJXocjC9S2a+fWuGrVSvrgg9zreDzuFf2vvnIflymTPUt2717pnXek6dPd0gcbN/oCteb223Nn3gKxqnhCcb3S8xVnsnkAABDbPvzQLX3gDdS2by999hmBWgDAoXF+GXnCPrN26tSpGjp0qCZMmOAEaseOHauuXbtq5cqVqlKlit/XlClTxnneywK2ODz790vdu7vBVbNvn3TBBdKECdKVV/rWe+AB6bXX3PmSJaUFC6QGDdyr/TbwwaxZ0vbt3vaQqlZ1a9paJsCJJ0pXX00LAVl/Z5VNLssBAQAgBv3zj/Trr9KKFe4da7/8Ir31lltCzHTuLM2Y4d65BgDAoXB+GXnCPlg7ZswYDR48WAMHDnQeW9B21qxZmjRpkm63dMw8vojVqlUL8p5GH8t8tdIEixe7j5OS3NFk09OlwYOldeuke++Vpk2T7r7bF4h99VXphBPcxxdd5E72Gutslirl1taybQEAAACQVq1y+9Q2LV3q3rXmj/WrX3mFO9IAAIhmYR2sTUtL0+LFizXcRp76V3x8vDp37qyvvPfb+7F3717VqVNHGRkZatWqlR588EEde+yxQdrr6GGBWLuKb0qXlhYutDIT0uOPu8tscDAb5GD2bN9rRo9269bmZLVqra4WgEM7kH5Azy953pm/stWVKpbAyHsAAEQiC7q+/LJbDswyYb13ltnPihWtH11KH34YpyVL8t+OlRe77jrpkUfcfjUAAAXF+WXkCetg7bZt25Senq6qds98FvZ4hd0X5Efjxo2drNsWLVpo165devTRR3XSSSdp2bJlqmW9Ij9SU1OdyWv37t3OTwv22hQIth2PxxOw7RU1K2lw331uSeP4eI9ee82j446THn3U7VzeckucPJ44zZzpe82AAR7dcot9RkW8SGuvWBaNbWV/TGf9NsuZH3D8ACXERcdZWTS2VTSjvSJHsNqK/7tA4dhdaFY2zMqC+Wd97dK5lrZsKbVoITVp4k6NG7vlxRjfAQBwONI96fpglTv40MATBqqYSAYKd2EdrD0cHTp0cCYvC9Q2bdpUzz77rO677z6/rxk9erRGjhyZa/nWrVuVkpISsBMcCx7byZRlB4ezJUuK6corK2Q+HjFij9q02a8tW9zHl1ximbbFdf315ZSa6tYDbt8+TaNG7dDWrYoKkdResS4a2yrlYIrSUtOc+S1btig5MToGSYzGtopmtFfkCFZb7dmzp8i2DURbNu2LL0o332xJIAV7TZs20n/+45Y5qFevqPcQAACEs7AO1laqVEkJCQnavHlztuX2uKA1aYsVK6YTTjhBq6wQVB6szIINYpY1s7Z27dqqXLmyM1hZoE6krJaubTOcgxQWkB00KC4zCDtokEd33XWU4uKyj2AwaJB0zDEe2WGrXNlu70pUpUr+B3yLRJHSXojOtrJgbVJxt7CzDaQYTcHaaGuraEZ7RY5gtVVycnT8LgKK0oYN7tgOH37oW1ajhjs4b8OG0vr1vmnDBo8qV96rAQNKqWFD/i4CAIAICNYmJSWpdevWmjdvnnr8WwjVTkjs8XVWtKkArIzCTz/9pG7duuW5TvHixZ0pJzvhCeRJj51IBXqbgc4CuOYaadMm9/Fpp0njx8cpIcEN3OZkz3sHH5P8rxPJwr29EL1tZZ/DPpN3Plo+VzS2VbSjvSJHMNqK/7dA/ubMkfr2lf7+27fs8svd8R7KlXMfN23qey4jw6MtW/apSpVSHFoAAJAp6GfLBw8e1Nq1awu8vmW8Tpw4UZMnT9Yvv/yiIUOGaN++fRo4cKDzfP/+/bMNQDZq1Ch99NFH+v3337VkyRJddtll+vPPP3WlFYxCvl5/XZoxw52vVEl6800LmHPQAABAeDhw4ECodwHwm/BgA39Zbog3UFu9uvTee245BG+gFgAAICyDtTbQV71CFGLq06ePM0jYiBEj1LJlSy1dulSzZ8/OHHTMAr9//fVX5vp///23Bg8e7NSptWxaK2nw5ZdfqlmzZkXyeaLFxo3uCLNedqtWleipagAAACLIm2++qbQ0t3a3efrpp1WnTh2nFIOVybKL80A4+Ocf6bLLpGHDLFPWXXbBBdLPP0vnnRfqvQMAAJEorMsgeFnJg7zKHixYsCDb48cff9yZULhsgP/+15cJcPHF0oUXcgQBAEBo9O3b17kYb3W7X3zxRd16660aNmyY2rdvr++//94ZHLZGjRrcOYWQWrdO6tkza1kwG5hXuuceKxsSyj0DAACRLODB2latWuX7/D92+Rlh5eWXpfffd+ctYfnpp0O9R0BsK55QXC9c8ELmPADEGo9dSf7XhAkTnExaC9gau3OqQoUKGj9+PMFaBMXnn0tTpkh79kgpKe5kpzQ//STt2OGuU6qU26fu1YtGAQCEF84vI0/Ag7XLly/XxRdfnGepA8uS+PXXXwP9tjhMNhLtjTf6Hj/3nFSxIocTCPVAQVVKUYcEQGzzDrRo4xCcddZZ2Z6zx7fddluI9gyx5K23rCybr8SBP3ba8847UvPmwdwzAAAKhvPLyBPwYO1xxx3n3KJmA4H5YzVnbcAwhJ4lrdi4a7t2uY/793drbAEAAISajVFQtmxZp07t/v37sz2XkpKSGcwFisqsWVaSI+9ArZU66NGDZAcAABDmwdqTTz5ZK1euzPP50qVL69RTTw302+IwPPOMNGeOO1+jhjR2LIcRCAcHMw7q5R9edub7H99fifERUV4cAAJqwIABmfPz589Xhw4dMh9//fXXatCgAUccRWb+fHcMh4MH3ccDB0p33y0lJ0slSrg/k5KoTQsACH+cX0aegEcAnnjiiXyft471J598Eui3RSEtWiTdfLPv8fPPS+XLcxiBcPljOmPFDGf+kuaXEKwFEHMy8rvn3KmxX9UZZAwoCl9+6d5tlprqPrYyCHZjYEICxxsAEHk4v4w8pGvFoK1bpYsuktLS3Mc33SSdc06o9woAAKBgzjvvPA4VisSSJTaInbRvn/v4/PPdwcUI1AIAgKgI1q5Zs0Zz587V33//rYYNG+qCCy5QsWLFivItcQjp6dIll0jr1rmPO3aUHn6YwwYAAMIT/UkEwz//SC+8IN1zj288h86dpTfflDh9AQAAwRRfFBv1eDy64YYb1LZtWy1cuFA7duzQhAkT1KJFC23cuLEo3hIFNGKE9PHH7nzVqnRAAQBAeKI/iWCwDNrHHpPq15euv17ascNdfvLJ0syZbm1aAACAiM+sHThwoLZs2aJff/1VFSpUyFz+yCOP6Nprr9WMGTO0ZMkStWrVqijeHnl4913pwQfdebuVyzIFqlfncAEAgPBDfxJFaf9+6ckn3UDttm3Zn+vVS5o0SSpVijYAAABREKydM2eOFixYoOXLl+udd97RgQMHMp8rXbq0PvzwQ6WmpmrQoEEaMWKEevbsGehdgB+rVkn9+vkeW+mDU0/lUAEAgPBDfxJFPX6DjdeweHH25RdeKN11l9SyJccfAABEUbD2+eefd0oglCxZUrNnz9Zrr72mevXq6aijjtIPP/ygs88+W/v379ddd92lhx9+mGBtkDqkNqLt7t3uYxtc7Oabg/HOAAAAhUd/EkXlzz+ls86Sfv3VfRwfL/XpI915p3TssRx3AAAQhTVrrbzBKaec4sxbwPb22293yiHY8unTp2vnzp0qX768E7T97rvvtM871CqKxPbt7uAIv/ziPm7SxL2tKy6OAw6Eq+IJxTWu2zhnsnkAiDX0J1EUli1za9F6A7U1a9p3TXrtNQK1AIDoxfll5Al4sHb37t1K/rcS/8yZM9W7d+/M5y644AJ9++232rx5s0qVKqWEhAQneIuiYYfWMgd+/NHXIZ01y8pRcMSBcBYXF6ejyx7tTDYPALGG/iQC7euvJcsn2bDBfXzMMdIXX0jHH8+xBgBEN84vI0/Ag7U1a9bUmjVrnPlatWo5AVuv9957T8WKFVPFihW1bds2paenq1KlSoHeBTgnOdLZZ7vZAqZaNWn+fHekWwAAgHBGfxKBNHu21KmT9Pff7uPWraWFC6U6dTjOAAAgBoK1Xbp00dSpU535xx9/XE8++aSaNGmi1q1bq2/fvho3bpwSExOdIO6JJ56o4sW5xTfQ9u6Vzj1X+uYb93HlytK8eW4GAYDwdzDjoF776TVnsnkAiDX0JxGou8z++193MLH9+91lZ57pJjBY/xgAgFjA+WXkCfgAY9dff72aN2/ulDvo2LGjVq9erS+//FJpaWlq27atatSooR07dmjUqFEaP358oN8+5h08KHXv7mYLmAoVpI8/lpo1i/lDA0TUH9PXf37dme/VtJcS4wP+qxoAwhr9SRwJj0d6803pxhulzZt9y3v1kl59Vfq3YhsAADGB88vIE/DM2qOPPlpPP/20unfvrrlz56ps2bI655xznMcWqF21apU6deqk8847z5kQWFZ1wrIFTLly0ty5UosWHGUAABA56E/icGRkSKtWuXeYXXyxL1B71FHS2LFuAJdALQAACHdFkq7Vr18/lStXToMHD1aVKlV00kknqUSJEvrxxx+1aNEiDRs2TLfeemtRvHXMsyxar0mTpFatYv6QAACACER/EvmZNk16+mlpyxZpzx53vAYrBWZZtVldcIG7Xu3aHE8AABAZiuze2vPPP19nn322Pv30U/300086ePCgrrzySr3xxhsqXbp0Ub1tzPvkE/cQFCsmnXVWzB8OAAAQwehPIicLxt57rzRqVP7HpmZN6amnpJ49OYYAACCyFGkhxGLFiqlz587OhKK3caP066/ufLt2UqlSHHUAABDZ6E/CKzVVuuIK6bXXfMfEShyULStZLkiZMu7PDh0ku4nPHgMAACjWg7UZGRlatmyZM8iYmTBhgjO4mFdCQoKGDBmi+PiAl8uNed6sWnPGGTF/OAAAQISiP4mctm1zs2S9g+jGxUljxriDiNk8AABAtAh4sNbKHFiA9rPPPnMeW21aq1+bmOi+1bZt25ScnKxBgwYF+q1jXtZg7emnx/zhAAAAEYr+ZGyaN0968kkpJcUGmXPrzNrPChWk//3PHTzMlCzpZtd27x7qPQYAAIiAYO2LL76oa6+9Ntsyq1tbv359Z94Cua+88grB2iIM1iYlSSedVBTvACAYkhKSNOasMZnzABBr6E/Glg0bpKFDpTffPPS61apJ770ntWkTjD0DACDycX4ZeQJei2DFihVqk0/v6bTTTtMPP/wQ6LeNeWvXSr//7h6GE0+USpSI+UMCRKz4uHg1qtjImWweAGIN/cnYcOCA9OijUuPGBQvUHnec9M03BGoBACgMzi8jT8CjAFu3bs32+Pfff1fdunWzDRKxb9++QL9tzKNeLQAAiBZF0Z8cN26csw0rx9W+fXstWrQoz3WnT5/uJB9YKa9SpUqpZcuWmjJlSrZ1Lr/8csXFxWWbzj777ELtUyz7+mupZUt3IDBvU1aqJL3wgrRli7R4sTRzplsW4ZZbpBEjpC++cMsiAAAARLOAl0GoWrWqVq5cqQYNGjiPK1eunO35X375RdXs/iUEFMFaIHoczDiod1e+68xf0PgCJcYH/Fc1AIS1QPcnp06dqqFDhzrluCxQO3bsWHXt2tV5jypVquRav0KFCrrzzjvVpEkTJSUl6f3339fAgQOdde11XhactZINXsWLFz/MTxxb5s6VLrjArU1rbICw//5XeuABtz6tsSZv1SqkuwkAQFTg/DLyBDyztlOnTnrAelp+eDwejR492lkHgePx+IK1ycluGQQAkf3H9MWlLzqTzQNArAl0f3LMmDEaPHiwE3Bt1qyZE7QtWbKkJk2a5Hf9008/XT179lTTpk2dgPGNN96oFi1aaOHChdnWs+CsBY29U/ny5Qv5SWPPRx9lD9S2bStZkvMzz/gCtQAAIHA4v4w8AU/XsiyEVq1aOVkLt9xyi4455hhnuWUuPProo87Pl19+OdBvG9PWrJFTs9bYwGIkdQAAgEgWyP5kWlqaFi9erOHDh2cui4+PV+fOnfXVV18d8vUWHJ4/f77zng899FC25xYsWOBk21qQ9swzz9T999+vihUr+t1OamqqM3nt3r3b+ZmRkeFMgWDbsf0N1PYCbc4cqWfPOKWmxjmPe/Tw6I03PCpWzPZdMSfc2ws+tFXkoK0iC+0V3OPsnT+cvzvBbKsM/i4GPlhr2Qdz58516nj16dPHqd9lrFHtVrKPPvpIDRs2DPTbxjRKIAAAgGgSyP7ktm3blJ6e7pRWyMoe20Bmedm1a5dq1qzpBFgTEhI0fvx4denSJVsJhF69eqlevXpavXq17rjjDp1zzjlOANjWz8mygUeOHOm3Pm+KN800ACc3tt92nCwgHU7mz0/SFVeUzwzUduuWoief3Km//1bMCuf2Qna0VeSgrSIL7RUcKQdTlJaa5sxv2bJFyYnJYd1We/bsUawrkkKI7dq10/Lly7V06VL9+uuvzrJGjRrphBNOKIq3i3kEawEAQLQJdX+ydOnSznvv3btX8+bNc2re1q9f3ymRYC6++OLMdZs3b+6USbAgs2Xb+ivRYJm9to2smbW1a9d26vGWKVMmIPtsJ1IW2LZthlPwb/Zs6YorfBm1vXp59NprSSpWLHe94FgSru2F3GiryEFbRRbaK3jB2qTiSc683RF0uMHaYP3NSrb6njGuSIK11vk86qijnJFzbcrauNbhDVSHFNnr1ZYs6db9AgAAiHSB6k9WqlTJyXTdvHlztuX2OL9ByuxExJu9a+9vg5pZdqw3WJuTBXLtvVatWuU3WGv1bf0NQGbvE8iTHjuRCvQ2j8QPP1hw1spAuI8vukh67bU4FSvmBm5jXbi1F/JGW0UO2iqy0F5Fz/7GeO9SOpK/OcFqq3j+JgZ+gLEZM2aoTZs2fm/n+ueff9S2bVu99957gX7bmPXbb9LGje58x45SknuxBAAAIGIFsj+ZlJSk1q1bO9mxWQO+9rhDhw4F3id7TdaaszmtX79e27dvV/Xq1Qu8zVhIKrj2Wl+gtndvC9TKqVELAACAIAVrn3nmGQ0bNswZYTenUqVK6bbbbtPTTz8d6LeNWZRAAAAA0SbQ/UkrPzBx4kRNnjzZyZAdMmSI9u3bp4EDBzrP9+/fP9sAZJZBazVzf//9d2f9xx57TFOmTNFll13mPG+Zvbfeequ+/vpr/fHHH07gt3v37k4mbteuXQNyDKLBG29IX3zhzjdqJE2ZQqAWAAAg6GUQfv75Z2cAhryceuqpuuuuuwL9tjFrwQLf/BlnhHJPAARKUkKSHjzzwcx5AIg1ge5P2iBlNpDXiBEjtGnTJqeswezZszMHHVu7dm22W+4skHvNNdc42bIlSpRwBjV75ZVXnO0YK6vw448/OsHfnTt3qkaNGjrrrLN03333+S11EIv27pVuvdX3+IknrBREKPcIAIDYxPll5Al4sPbvv//WwYMH83z+wIEDzjoIbL3a0qWl1q05qkA0iI+LV/OqzUO9GwAQMkXRn7zuuuucyR8bFCyr+++/35nyYgHcOXPmFOr9Y83o0dKGDe78eedJ55wT6j0CACA2cX4ZeQJeBqFu3br67rvv8nzenqtTp06g3zYmrVhhg2O486ecIiUWyXBxAAAAwUV/MrKtXi09+qg7b+MpPP54qPcIAAAgcgQ8WNurVy/deeeduUbcNXbbmd2yduGFFwb6bWMS9WqB6HQw46Bm/TrLmWweAGIN/cnINnSolJbmm2/YMNR7BABA7OL8MvIEPBfz9ttv1zvvvKNGjRo5gzA0btzYWb5ixQq9+uqrql27trMOjtynn/rmTz+dIwpE0x/TCYsnOPOd6ndSYjxp8wBiC/3JyDV7tvTuu+58jRrSnXeGeo8AAIhtnF9GnoBHAEqXLq0vvvjCGVF36tSpmfXEypUr5wRvH3jgAWcdHLlvv3V/2kDJLVtyRAEAQHSgPxmZLJv2ppt8jx96SDrqqFDuEQAAQOQpknStsmXLOiP4jhs3Ttu2bZPH41HlypUVFxdXFG8Xk7Zvl9ascedPOIF6tQAAILrQn4w848ZJK1e68x06SJdeGuo9AgAAiDxFem/t9u3b9eeffzpB2oSEBFWsWLEo3y6mLFnim2/dOpR7AgAAUHToT0ZOVq13UDHLz3jqKfcnAAAAQjzAmFm2bJlOPfVUVa1aVe3bt1e7du1UpUoVnXnmmVrpvdyOI/Ldd775Nm04mAAAILrQn4wsb70lbdzoznfvTjIBAABA2GTWbtq0SaeddppT9mDMmDFq0qSJUwZh+fLlmjhxok455RT9/PPPTvAWh2/xYt88mbUAACCa0J+MPE884ZvPWrcWAAAAIQ7WPv7446pTp44zyFhycnLm8rPPPltDhgxRx44dnXVGjx4d6LeOyczaUqWkxo1DvTcAAACBQ38ysnz9tbRokTt//PHSqaeGeo8AAAAiV8DLIMydO1e33XZbtkCtV4kSJXTrrbdqzpw5gX7bmLJtm/Tnn77BxRISQr1HAAKpWHwxjTh1hDPZPADEGvqTkWXsWN/8jTdSqxYAgHDC+WXkCXhm7e+//65WrVrl+XybNm2cdRCYEgjUqwWiT0J8gtrWbBvq3QCAkKE/GTnWr3fr1ZrKlaW+fUO9RwAAICvOLyNPwDNr9+zZozJlyuT5fOnSpbV3795Av21MoV4tAACIZvQnI8f48VJ6ujt/9dWSn5vrAAAAEMrMWm8H218ZBLN7925nwDEceb1aQ2YtEH0OZhzUp3986syfVvc0JcYXya9qAAhr9CfD3/790nPPufPFiklDhoR6jwAAQE6cX0aegEcALBB7zDHH5Pt8XFxcoN82JjNrjzpKyudQA4jgP6Zjv3ELAJ589MkEawHEHPqTkeHVV6Xt2935//xHql491HsEAABy4vwy8gQ8WPvJJ58EepMaN26cHnnkEW3atEnHH3+8nnrqKbVr1+6Qr3vjjTfUt29fde/eXTNnzlQ02LpVWrvWN7hYfMALWQAAAIRWUfQnEVh2o9wTT/ge33QTRxgAACAsg7WnnXZaQLc3depUDR06VBMmTFD79u01duxYde3aVStXrlSVKlXyfN0ff/yhW265RaeccoqiCYOLAQCAaBfo/iQCb/58adkyd/6kkyjNBQAAECgBz8uMj49XQkJCvlNiYsFjxGPGjNHgwYM1cOBANWvWzAnalixZUpMmTcrzNenp6br00ks1cuRI1a9fX9GEwcUAAEC0C3R/EoGXNav2xhs5wgAAAIES8F7ujBkz8nzuq6++0pNPPqmMjIwCbSstLU2LFy/W8OHDs3XeO3fu7GwrL6NGjXKybgcNGqTPP/9c0YTBxQAAQLQLZH8SgffXX9L777vztWtLvXpxlAEAAMI2WGv1YXOykgW333673nvvPSfj1YKpBbFt2zYnS7Zq1arZltvjFStW+H3NwoUL9cILL2jp0qUF3ufU1FRn8tq9e7fz004CAnUiYNuxwTKOdHuLF9vgbHEqXdqjBg1sewHZPRRRe6HoRWNbeT+Tdz5aPls0tlU0o70iR7DaKpj/dwPZn0TgffCBW7PW9O8vkeQMAAAQOEV6/9jGjRt1zz33aPLkyU6dWQugHnfccUX2fnv27FG/fv00ceJEVapUqcCvGz16tFMyIaetW7cqJSUlYCc4u3btck6mLDv4cGzbFq9169w6vccdd0Dbtu0IyL6haNoLwRGNbZVyMEVpqWnO/JYtW5ScmKxoEI1tFc1or8gRrLayflYoBLs/iUPzZtWa88/niAEAAIR9sNZOGB588EE99dRTatmypebNm3dYA31ZwNVqkm3evDnbcntcrVq1XOuvXr3aGVjs/Cy9Rm8WiNU1s4yMBg0a5HqdlVmwQcyyZtbWrl1blStXVpkyZRQIth9xcXHONg/3RCprvdoTTyyW7wBrCH17ITiisa3SM9J11xl3OfM1q9VUQnyCokE0tlU0o70iR7DaKjk5uBeOAtWfRGDZzWhz57rzlStLbdtyhAEACGfF4ovptpNvy5xHDAZrH374YT300ENOMPX111/3extbQSUlJal169ZO57xHjx6ZJyT2+Lrrrsu1fpMmTfTTTz9lW3bXXXc5mSBPPPGEE4D1p3jx4s6Uk53wBPKkx06kjmSbS5b45tu2tW1ZSQQUlSNtLwRPtLWVfY5T656qaBRtbRXtaK/IEYy2Cub/20D2JxFYn34q7dvnznfrZt8LjjAAAOHMkn86Ht0x1LuBUAZrrZZYiRIl1LBhQ+d2NZv8mT59eoG2ZxmvAwYMUJs2bdSuXTuNHTtW+/bt08CBA53n+/fvr5o1azqlDCzjI+dtceXKlXN+RsPtclkza1u3DuWeAAAAFJ1A9ydRNCUQzjuPIwsAABD2wVoLnlp2R6D06dPHqR07YsQIbdq0ybkNbvbs2ZmDjq1duzZmMrS++879aZUZGjYM9d4AKMoyCF+t/8qZ71CrQ9SUQQCAUPUnERg2qJg3WGuDip11FkcWAIBwx/ll5Al4sPall14K9Cadkgf+yh6YBQsWBH1/QmHTJmnDBne+VStuOQOi2YGMA3roi4ec+Wm9pxGsBRBzoqX/Fm1WrJDWrHHnTz3VTSAAAADhjfPLyBMbKalRIGsJhDZtQrknAAAAiEWUQAAAACh6BGsjBPVqAQAAEEqzZvnmqVcLAABQNAjWRli9WkNmLQAAAILp77+lhQvd+UaN3AkAAACBR7A2wjJry5aVGjQI9d4AAAAglsyZI6Wnu/Nk1QIAABQdgrURYMsWaeNG3+BiDI4MAACAYKIEAgAAQHAQrI0Av/zimz/uuFDuCQAAAGKNZdR+8IE7X7q01LFjqPcIAAAgeiWGegdwaCtW+OabNuWIAdEuMT5RN7W/KXMeAIBQ+vpraccOd75rVykpifYAACBScH4ZeYgCRFhmbZMmodwTAMH6Y9qpficONgAgLFACAQCAyMX5ZeShDEKEBWvJrAUAAEAwvf+++9PGTTjnHI49AABAUSKzNoKCteXKSVWrhnpvABS19Ix0LflriTPfqnorJcQncNABACGxdq3000/ufLt2UpUqNAQAAJGE88vIQ7A2zO3dK61b58uqtYwGANHtQMYBjfpslDM/rfc0grUAgJCZM8c3f955NAQAAJGG88vIQxmEMLdypW+eEggAAAAIpuXLffMdO3LsAQAAihrB2jDH4GIAAAAIlVWrfPONGtEOAAAARY1gbZhjcDEAAACEOlhbooRUvTrtAAAAUNQI1oY5grUAAABHbty4capbt66Sk5PVvn17LVq0KM91p0+frjZt2qhcuXIqVaqUWrZsqSlTpmRbx+PxaMSIEapevbpKlCihzp0767fffouqpkpPl37/3Z1v0ECK58wBAACgyNHlipBgbfHiUt26od4bAACAyDN16lQNHTpU99xzj5YsWaLjjz9eXbt21ZYtW/yuX6FCBd1555366quv9OOPP2rgwIHONCfLaFsPP/ywnnzySU2YMEHffPONE9S1baakpCharF8vpaW58w0bhnpvAAAAYgPB2jB24IDv1rPGjaWEhFDvEQAAQOQZM2aMBg8e7ARcmzVr5gRYS5YsqUmTJvld//TTT1fPnj3VtGlTNWjQQDfeeKNatGihhQsXZmbVjh07VnfddZe6d+/uPPfyyy9r48aNmjlzpqKxXi3BWgAAgOBIDNL74DCsXi0dPOjON2nCIQRiRWJ8oq5ufXXmPADg8KWlpWnx4sUaPnx45rL4+HinbIFlzh6KBWbnz5+vlStX6qGHHnKWrVmzRps2bXK24VW2bFmnvIJt8+KLL861ndTUVGfy2r17t/MzIyPDmQLBtmP7G6jt/fqrL7ejfn3bz4BsFkXUXig6tFXkoK0iC+0VHPGK11WtrsqcP5y/O8Fsqwz+LhKsDWfUqwVikwVozz3m3FDvBgBEhW3btik9PV1Vq1bNttwer1ixIs/X7dq1SzVr1nQCrAkJCRo/fry6dOniPGeBWu82cm7T+1xOo0eP1siRI3Mt37p1a8BKJ9jJje23nUxZQPpI/fRTaUmlnPlKlXZqy5Z/ayIgLNsLRYe2ihy0VWShvYKnbdm2zs8d23aEfVvt2bNHsY6UrTBGsBYAACA0SpcuraVLl2rv3r2aN2+eU/O2fv36TomEw2GZvbaNrJm1tWvXVuXKlVWmTJmA7LOdSMXFxTnbDMSJ1MaNcZnzbduWU5UqR7xJFGF7oejQVpGDtoostFfkCGZbJScnK9YRrA1jBGuB2JThydCyLcuc+WOrHKv4OE7gAOBwVapUycmM3bx5c7bl9rhatWp5vs5ORBr+W6i1ZcuW+uWXX5zsWAvWel9n26hevXq2bdq6/hQvXtyZ/L1PIE967EQqUNu0klzGdvvoo22bR75/KLr2QtGirSIHbRVZaK/IOb8MVlvF8zeRAcbCmffOvLg4qVGjUO8NgGBJS0/THfPvcCabBwAcvqSkJLVu3drJjs2aHWKPO3ToUODt2Gu8NWfr1avnBGyzbtMyZb/55ptCbTOcWbk4b7C2fn07cQr1HgEAgMPB+WXkIbM2THk8vmBtvXpSiRKh3iMAAIDIZOUHBgwYoDZt2qhdu3YaO3as9u3bp4EDBzrP9+/f36lPa5mzxn7aug0aNHACtB988IGmTJmiZ555JjOz5KabbtL999+vRo0aOcHbu+++WzVq1FCPHj0UDf76S/rnH3f+3wRjAAAABAHB2jC1fr20d68737RpqPcGAAAgcvXp08cZyGvEiBHOAGBWqmD27NmZA4StXbs22y13Fsi95pprtH79epUoUUJNmjTRK6+84mzHa9iwYc56V111lXbu3KmOHTs624yWOmurVvnmCdYCAAAED8HaMEW9WgAAgMC57rrrnMmfBQsWZHtsGbM25ceya0eNGuVM0YhgLQAAQGhQfSpMEawFAABAqBCsBQAACA2CtWHKW6/WNGkSyj0BAABArCFYCwAAEBoEa8MUmbUAAAAIdbA2MVE6+mjaAQAAIFioWRvmwVob96J8+VDvDYBgSoxP1MCWAzPnAQAIJo/HF6ytV88N2AIAgMjE+WXkoesVhnbskLZsceebNg313gAIxR/TXk17ceABACFh/dC9e935hg1pBAAAIhnnl5GHMghhiBIIAAAACBXq1QIAAIQOmbVhiMHFgNiW4cnQ6h2rnfkGFRooPo7ragCA4PntN988mbUAAEQ2zi8jD8HaMERmLRDb0tLTNPSjoc78tN7TlJyYHOpdAgDEEDJrAQCIHpxfRh7StcIQwVoAAACECsFaAACA0CFYG8bB2tKlpZo1Q703AAAAiMVgbXy8VLduqPcGAAAgthCsDTP//CP98Yc736SJFBcX6j0CAABArPB4fMHaOnWkpKRQ7xEAAEBsIVgbZn791e0ke4O1AAAAQLBs3y7t2uXOM7gYAABA8BGsDTPUqwUAAECoUK8WAAAgtAjWhpl163zzDRqEck8AAAAQawjWAgAAhFZiiN8fOezc6ZuvUIHDA8SixPhE9T2ub+Y8AADBQrAWAIDowvll5CEKEGb+/ts3X65cKPcEQCj/mF7S/BIaAAAQdARrAQCILpxfRh7KIIRxsLZ8+VDuCQAAAGI1WBsXJ9WvH+q9AQAAiD1k1oZxGQSCtUBs8ng8WrfbLWBdu0xtxdkZMwAAQQzW1qolJSdzyAEAiHScX0YegrVhnFlbtmwo9wRAqKSmp+raD6515qf1nqbkRM6WAQDB6Ydu3+7ON2zIEQcAIBpwfhl5KIMQppm1ZcpICQmh3hsAAADEitWrffMEawEAAEKDYG2YZtYyuBgAAACCicHFAAAAQo9gbRjxeHyZtdSrBQAAQDARrAUAAAg9grVh5J9/pLQ0d57MWgAAAAQTwVoAAIDQI1gbRrxZtYbMWgAAAATTb7/55hs04NgDAACEAsHaMKxXa8isBQAAQLCkp0s//ujO164tlSrFsQcAAAiFiAjWjhs3TnXr1lVycrLat2+vRYsW5bnu9OnT1aZNG5UrV06lSpVSy5YtNWXKFEUCMmsBmMT4RPVs0tOZbB4AgKK2cqW0d68737YtxxsAgGjB+WXkCfsowNSpUzV06FBNmDDBCdSOHTtWXbt21cqVK1WlSpVc61eoUEF33nmnmjRpoqSkJL3//vsaOHCgs669LpyRWQvA+8f0ihOu4GAAAILm22998wRrAQCIHpxfRp6wz6wdM2aMBg8e7ARcmzVr5gRtS5YsqUmTJvld//TTT1fPnj3VtGlTNWjQQDfeeKNatGihhQsXKtxlDdZSsxYAAADBQrAWAAAgPIR1Zm1aWpoWL16s4cOHZy6Lj49X586d9dVXXx3y9R6PR/Pnz3eycB966KE810tNTXUmr927dzs/MzIynCkQbDu2P/ltzw3WuvHzsmXtvQPy1iii9kJ4iMa2ss+zdf9WZ75yycqKi4tTNIjGtopmtFfkCFZb8X83un33nW++detQ7gkAAAikaD2/jGZhHazdtm2b0tPTVbVq1WzL7fGKFSvyfN2uXbtUs2ZNJwCbkJCg8ePHq0uXLnmuP3r0aI0cOTLX8q1btyolJUWBOsGx/bL/JBZw9mf9ehvJobQzHxe3S1u2+ALICK6CtBfCQzS2VcrBFF019ypn/rkuzyk5MVnRIBrbKprRXpEjWG21Z8+eIts2QistTVq61J1v1IiBbgEAiCap6aka9O4gZ35a72lRc34ZzcI6WHu4SpcuraVLl2rv3r2aN2+eU/O2fv36TokEfyxz19bJmllbu3ZtVa5cWWXKlAnYiZRdvbBt5nUideCA7+pG3bpl5ackL4KkIO2F8BCNbWXB2qTiSc681duOlj+m0dhW0Yz2ihzBaisb6BXR6eef7U4zd556tQAAAKEV1sHaSpUqOZmxmzdvzrbcHlerVi3P19mJSsOGDZ35li1b6pdffnGyZ/MK1hYvXtyZ/G0nkCc9diKV3zZ37vTNV6hg6wXsrVEE7YXwEW1tZZ/De2tKNH2uaGyraEd7RY5gtBX/b6MX9WoBAADCR1ifLSclJal169ZOdmzW7BF73KFDhwJvx16TtSZtuMoarGWAMQAAAAQDwVoAAIDwEdaZtcbKEwwYMEBt2rRRu3btNHbsWO3bt08DBw50nu/fv79Tn9YyZ439tHUbNGjgBGg/+OADTZkyRc8884zCnTvAmKtcuVDuCQAAAGJtcDFLzD7hhFDvDQAAQGwL+2Btnz59nIG+RowYoU2bNjllDWbPnp056NjatWuz3ZZngdxrrrlG69evV4kSJdSkSRO98sorznYiJbM2KUkqUSLUewMAAIBot3+/W7PWHHusVLJkqPcIAAAgtoV9sNZcd911zuTPggULsj2+//77nSkSeTNrLav233KVAAAAQJFZulRKT3fnGVwMAAAg9CIiWBsrvJm11KsFYltCXIK6NeyWOQ8AQFGhXi0AANGN88vIQ7A2TFhGw+7d7jz1aoHYViyhmIa0HRLq3QAAxACCtQAARDfOLyOPr9grwiKr1pBZCyDY6tatq8aNGzt1wZs2bapLLrnEqQF+uF566SWtWLGiwOu///77To3xRo0aqVevXtrtvXqVw5NPPqnjjjtOzZs3V4sWLZya5F4PPPCAs//eqUyZMs4glWb+/PnOIJXNmjXTscceq2HDhikjI8N57o8//lBCQkK2165evfqwPzsAROLgYjZmQvPmod4bAAAAEKwNEwRrAXht2rNJL37/op765im98fMb2rx3c1AOztSpU7V06VItW7ZMu3btcgKuwQjW7t27V4MGDdLMmTP122+/qUaNGrrvvvv8rmuB1i+++EI//fSTZs2apZtuuikzsHrnnXc6+2/TN998o2LFiunSSy91nitfvrzeeOMNLV++XIsXL9aXX36pl19+OXO7pUuXznytTQ0aNDjszw4gPI0bN865MJWcnKz27dtr0aJFea47ceJEnXLKKc7vDps6d+6ca/3LL79ccXFx2aazzz5bkWTXLmnlSnf++OPdgC0AAIguHo9Hu1J2OZPNI/wRrA2zwcUMZRCA2PTT5p/U962+qvV4LV3x7hW6YfYN6vu2+9iW2/PBkJaWpv379zsBCq9HH33UyUxt1aqVE4z4888/neXvvfeek+Fq2aiW8frOO+/o+eef13fffaebb77ZWf7BBx/k+34ffvihTjjhBCez1lxzzTV6/fXX/a7bqVMnlS1b1pmvXbu2qlWrpnXr1uVazwK/9nzr1q2dx7b9+vXrO/MWqLH9soxaALHBLkZZpv0999yjJUuW6Pjjj1fXrl21ZcuWPAew7du3rz755BN99dVXzu+Ts846Sxs2bMi2nv0+/OuvvzKnvH53havFi33zDC4GAEB0Sk1P1WUzLnMmm0f4I1gbJsisBWLbnFVz1O75dnpr+VtK9/w7LPe/DmYc1Fu/vOU8b+sVlT59+jhBTAuAxsfH6z//+Y+z/LXXXtPKlSudgIUFOSxb1QKq5q677tKzzz7rZKP++OOPOu2003TllVeqTZs2evzxx53l3bp108aNG51Arz9r165VnTp1Mh9b5psFPQ4ePJjv/n788cf6+++/1dZPhOGFF15wsnX92bRpk9566y2dd955mcus5INtx/Zx1KhRSvcOjQ4gKowZM0aDBw/WwIEDnXIoEyZMUMmSJTVp0iS/67/66qvO7zn7nWgXkuwilJVOmTdvXrb1ihcv7vzO9E5ZL3JFAurVAgAAhB8GGAsTZNYCscsyZntM7aHUg6nyyP9tKRawTc9Id9ZbdOUiJSUkaWfKTrWv1T6gmWcWmLAg6X//+1/ddttteuyxx5ws1W+//TYzSzVrINMyXW+88UZddNFFTtaZvd4fK21ggd68stgKy8ogWNDF9rlUqVLZnrOs34ULFzplD3KyWrjnn3++U7PWAsqmevXqTrZclSpVtGPHDidobZ/b1gEQ+exuASt/Mnz48MxldkHKShvYRaiCsLsNDhw4oAoVKuTKwLXfHRakPfPMM3X//ferYsWKfreRmprqTF7e2twWBPbW0D5Sth27vbGg21u0KE6STVLr1rYfAdkNFFF7IXRoq8hBW0UW2iu4x9k7fzh/d4LZVhn8XSRYGy7IrAVi14OfP6iD6QfzDNR62fMH0g+o34x+ql22ttrXbB/QYK1XYmKiLrzwQt16661O0NL+KFuQ46qrrvKbrWY1bu1W4QEDBjhZt4UNch599NGaO3du5mMrT2ABVNsPf6zurGXFWkZcx44dcz3/4osvqnv37rmCKnv27HFuWbbnvAOPeTPjLNhi7DVXXHGFk01MsBaIDtu2bXMuMlWtWjXbcntc0NradvHKLjpZgNfLfp/YgIj16tVzamffcccdOuecc5wAsA1amNPo0aM1cuTIXMu3bt2qlJQUBerkxmqO2+9tC0gfyqJFlSUlqGTJDFWosEUBup6GImovhA5tFTloq8hCewVHysEUpaWmOfOWPJOcmBzWbbVnzx7FOjJrwwSZtUBsssHDrMTBQU/+t/x7WYmEHzf/qEubX6qTap+kT9Z8on8O/qN/Dvzj/BHOb97qEw1uNdh53aHMnz9fjRs3duZ79OjhBG0te9aCmZZd9vPPPzt1YC3QYYN+2WTB1Y8++sh5TZkyZZw/5gVhAY9rr73W2Zbdbjx+/HhdfPHFftf95ZdfnLIKzz33nLp06eK3E2HBWiuDkHMQM3sfm6x0Q1bWYbGsOBuQzLLepk+f7nw2ADD/93//52TqWxat1bz2yvp7qnnz5k79bhuc0Nazuw5ysoteWS8UWWat1cKtXLmy8zszEOx3oA10Zts81InU1q3S+vXuOq1bx6l6dfeiFYKnMO2F0KKtIgdtFVlor+Cw88Gk4u4oopakcrjB2mD9zUrO0t+KVQRrwwSZtUBsWvDHAqfEQWFYhu0by97QZ2s/k1JSlLBvv0qUq6Lk0uVUIrGEM9kf4BLFSqhSyUqZ87a8YYWGeW7Xbv8vUaKEUwbBashaTUdj2bLbt2/XGWec4Ty25y371AKalklm9WyTkpKc+o/PPPOMs45l4f7vf/9z6tY++OCDTnkEC7LOnj071/uWLl3aqQdpQWHbtg1UNnny5MznvYOUWVbbDTfc4ASBLcvNJvPQQw85AwV569ha5yFnoOSJJ55wRnK32rQWjDW9e/fWnXfe6ZRMGDFihJMJZ+9vtzLbcgDRoVKlSs7/782bN2dbbo+tzmx+bHBFC9ba7xYLxubHBjG091q1apXfYK1l8duUk/3OCuRJj51IFWSb2QcXs9e45RAQXAVtL4QebRU5aKvIQnsVPfsbY8fZO3+4f3OC1Vbx/E0kWBsuyKwFYtOetMO7xaN+XAU9ODdBdV/9RIlp6W7FwYYNpZNPljq2kqw8gGXG/vtH+VCs9EB+LEhqU07ewGdOVqYg6wBeJr+atRdccIEz+WODlHllLZfgj9XNXbNmTa7lFnzNKwBrtzHbBCA62cUkq7ltg4PZRSHjHSzsuuuuy/N1Dz/8sB544AHNmTMns8Z1ftavX+9c2LIyLpGAwcUAAADCE5dwwwSZtVHkp5/svnFp/Hjp3yLeQF5KJ5U+rIPz11cf64697ygj/d9ArVm1SrKM1MGDpaZNLVXWRgPj4AOIeVZ+YOLEiU7WvpVTGTJkiJNpbwMVmv79+2cbgMwy9u+++26nNnbdunW1adMmZ7KSKsZ+Wl3vr7/+2rnYZYFfq4fdsGHDzEz/cEewFgCA2JAQl6BO9To5k80j/FEGIUyQWRsl/vlH6tlTWr1aeucdad066cEHC5zdGHNsQJXnnnMzQCPk5Paw/fijdMcdkt1ye/fdUp06zuLT656uxPjEQpVCSEyX3nhLSkuUipet4H7nbJAcO/NOcwvHO6ZNk049VconcwwAYoGVebGBvKzkiQVdrbyKlWXxDjq2du3abLfcWUmXtLQ0p1Z3Vvfcc4/uvfdep6zCjz/+6AR/d+7c6ZRpscz+++67z2+pg3Bj15K/+86dt7EY69cP9R4BAICiUiyhmG468SYOcAQhWBuGmbVly4ZyT3BE/u//3EBt1scHDkiPPELA1t+Z4mWXSW+/7T6eOVPq3j06v4D2Gfv3l/bvdx+/+qp06602vLiqHlVVF9Xqqrf+mKWD8QUL1PZeJtUqf7RkA9UMGiQddZQv+G1FCD/+WLr3XneZ3fp/4YVShNyWCyDLVdwNG6SaNaVy5fgbEgBW8iCvsgc2KFhhSsNYfW8rjxCp7KvlLeFrFR64pgwAABA+CNaGWWatBWoTyEqPTL/+6gZnjTWi9/bzxx5zA7Zjx3I2lNW4cb5ArbFgpqX5NGqkqJGR4QZN77sv+3ILqtqySZOkBx7QHc+v1MwzJPvGePIJ2MZ57Jd2nIZ3e0B64xapWLHsK9iomVaz1qa1a93t797tBnVff71oPiOAwNm+XZoxw82KnzfP93ekVCnp6KPdyYK3OTI34zwelbE7O268UTrhBFoEh2R/Irysag4AAIheHo9HqempznzxhOKZg40hfBGsDbNgrSXPIEKzRK+5xncL+i23uIM9XXWV+9yTT7oB26eftqENFbUsc/TNN6WXXnKDDFa3t3nz3OtZUPZ//8u+zIKKlgH61VduYCLS2efp1096913fMsskrlLF/T4cPOimNl1+uewIzdws9bgkTgfj4nXQk7vOrJVKsGlmn5lq3rAAJSMeesgtxWHBnzfekKwu41lnBfhDAkFkFzksK90Gqbv2WqlMmeg4/BZktYspU6dmD9BmtW+f9Msv7uSHdbdL2vUhG6SPYC0KYMcO37yVQQAAANHLArW9p/V25qf1nqbkxORQ7xIOIYqjRpHDYnneMgjlywfxjf/8073tfOTI2BgIy06Ii+pzWjDMTrKN1SK1mqRXXim9+KIvm/aZZ9yBn/4dnCSqLF/uZnRZxpcFBT/9VFq4UDrpJGnWrOzr2pf9P//xBbavvlpq0sQ3OJs3wB3Jli2TOnTwBWotQG8Z1i+/7P78+Wfp3HOzvaTrltJadO5M9T72P7mKvluQtnez3lp05SJ1LUig1lSq5Jbf8LLglgW7QmnMGJWzsg2WhQ4UVGqq+/vTsu7t96rVfj7xROm33yI/WmYZ9vY3w/5ffPRR9kCtLb/0UqlLF7eud4kSodxbRPFYCQRrAQAAwguZtWGSjGhJl0HPrLVb9i2YZNOZZ0qnnKKoNXu2ZIOEWCaWnRxffnng6k1Y8NFuM/eyrElvZuiAAVJionuLv90Sb7elW23WG25wB32qWFFhyb6QlpHpL+Btg6bZ/ZMW7LefNnDW11/7344Fpi3TywKUFsw1FmxZs8adb99eeuIJt85vu3bu+q+95gY6w21QLPucVmfWMmP79HGzVJOSsq9jx8Gb0eoNONt/asuYy5rVaoGX9993v5c2+vj69dLkyWrevptea3+B/q/z/6nn1J7OoGO3nnSrzmpwlqqUqlL4fbbvn33nLHC+apX7f95byzbYvvxS8bfeKruG67F9WbIkOjKoUbS/hyxL//77s9+zbSzD1H5n5Py/FQnsd+iYMdLEiW7GbFYWoO3d272glbOQqP1Osd/LGzfmyr7NyMjQjh07VKF16yB9CERTZm1QEwUAAABwSARrw2xwsaB2mO12c69XXjm8YK2dTNvJpAUkw9WuXW62p50U22TBQquXakHCQASo77pL2rTJnbdMZQtOZmWZUVZb1H7are92hmQBM8t6tCxSC/TWqqWwypI99VT/wdpDsZqpF18sXXGF9NRTbt1FC1LffLMbXLEMWm+dWm8Q0wKeVjDPspAtSGFs/Vat3KCt3fJsmag2WSadZdlZoMKmgwcVl56uksccI40YUXQlJrZulbp1c8sWeP+/WCqSlW3o29cNYluQ9rPPsr/OPpddDLGSGP6cfbY7WRAmS1DGArM1Stdw5i9qdtHh36Zix8MyEu22aPvujR4tXXKJZMcr2Owixr/iLLP2ppvcYFU0sOCZ1RAN14svkVhCxIK0jz9uozxlf+78892LO/Z7yv54nnOO+7vUfmeEc+0ty2r/4AO3jIP9TrD/j1n/n1pw1i5o2QWsvD6HLbeMeZtyysjQQftdSS0lFBCZtQAAAOErjCNssdlhDtp5lgWXLPjlZUE1C67lzBTMj2URdu3qZhp+/rlUrZrCkmUueoOpXt9/7wYk7QT54YfdbKbDYbVXrS6rKVkyW0AqG3uf445zMxstc9QCjRY4tmCE1bG1wJWVowj1ba4WNLz++sIHai0oaeUMrEar94qDDXJlwVnv4FrPPZf9NRaMyXrcLfPZ6thaFq4FMiw4akHubdvyfWsLa1jlSo/9R7LjH+iAjbWVBWS9gVovC7pbsNFfwLFGDTd4NGRIwbJHizLIZN87O64WTLbSE1Zbee7c4Aa2LHP4rbeyL3v+eff3h7V7JLPBoOz/t12omD7dvWUdh8eCsPZ3yLLB9+zJ/pz9PrDfkZZtmrUetF0Msu/3Dz+43yULWNoQ9/bTLrJYFruVACnqQLr9zvLeImPs/5f9PrWLohagtYtUduEwK/vO2IUt2//69Yt2/4AcyKwFAAAIX9SsjdXM2qVLs99GaYGuDz8s+OvtBNkCUXbSbLc033OPwpKdKE+Y4M5b0MwGcWnZ0ve8DYZlAUULuBT2xNzq1Nrt8N7b3S1b1kbqzkuzZm7NUgtI2C3+dqJu7ATfMsOOP94NegfC4dZ8tfqy8+e781WrSj16ZJ8s89UygceOdQNTFqy2oIjVaLXSDlm/wJYtNmqUm4Wa8yKABTItCzknC2afdpo7b4GNQwRqs4qzoLe9PtCsXb31iO2YWLtbdqq/IKwFhl54Qfr9d3eQuXC5zd9qKHsD4/ZZLMs761WiombZvf/+vjlg33Mvq+Fst4TnZLe8W4DNvms5bxMPJ5YhbKUm7PeBlfCwrE8rb3GkLFBpWZaWER2I7YU7C7Ta7xerSWt3PGQN1FpA336P2+8mC9QaK2djv7PtrgYv+91qdzXYnRN33uluxy6M2d+mevXc/8f29yrQVqxw682WLu1esPNOduHNfnbq5AafswZq7QKn7buVkrG7PAjUIgTIrAUAAAhjHuSya9cui3Q5PwMlPT3d89dffzk/c3r3XYusudOoUUFqkCee8L2pd7roooK//uWXs782IcHjWbHCE1bS0jye5s19+zhmjLv84EGP57nnPJ7KlX3PJSV5PJ98cuj22r/f4xk3zuOpXz/75z/uOPf9CmPzZo9n+HD3vbNu69prPZ7duwu3rR07PJ733/d4br/d4+nY0eMpXtzjqVrV4xk40ON5++2Cbc/2v3Fj335Mm+YJmC++8B3vDh08ntTUvNfdtMm3H9Wrezxdung8/8/efYBHUXVhAP5SCKH3LlIEpXfBjgqCigVBBESa/GDBRhFFBcSGFRGkKAoiWBBBEFBQqqII0lSKiAgC0kF6CSTzP98dZ7ObbEISNruzyfc+z8Bmd3Z2du5mc+fMuef26mVZ779vWT/+aFm//WZZ69db1saNlvXnn1b8qFG+x++99wK33zym3p/xxYsTHzt2zLI+/dSy2rSxrDvusKwvvuAHJyAve/LMSeuWj28xC28HxMyZvsfpggssa/58K9Pxd6ZIEfOaCdHR1p41a6wEHjNnP665xv6dJP4/bJhl5cmT+Pjttyc+7ibHj9u/90m/R6OjLeuzzzK+3e++s6wKFRK3lzt3yL5bU/u7FTBz5lhWrly+xzA21rK6d7d/18+Fx5rHKGk7+FsKF7asV16xf3fP19KlltWypWVFRKTttfPmtaxOnSxr7lzLOnPGCsu2yqT+UXYW7P6mt5tvTvx47t0bsJcXl/7uyvlTW4UPtVV4UXsFRyDOL4PZVofV57QUrA3SByO1D7Z33HPECCs4Onb0DS7wfwb4Dh1KW/ClbNnkJ6OtW1uu8vLLiftWr17yE2S+1/btE9fJn9+yVq/23158zy++6BvgdZYGDezAYUYx8HjZZb7bvPBCO4qfkJDy806etKw33/QNSKe0MCDcrJlljR5tWadO+d/e228nrn/llam/dkYwoDx7duqBWgeDc2n8/WMbHXnmmcR9j4y0rBkzzn9///rLsgoWTNzua69ZwXL67GlryPdDzMLbATN5su974tKnT8qfiUBgkP2/10po397+vdq/3/6MO/vwwguW9csvltWwof/P72OPWa7C3w0G3pz9q1rVstq29f0M8os9PdgGjz/uP/hXv37afm8CLNM7ZPw85siR+D7LlLGsl16yrH370rcdBrOffda+2vnOO/aFE14g4vf5ffcl/o1zlnz5LKtcOcu66CLLuvhiy6pWzf4bwW3wuz418+bZFxiStlGBApZ17bWWdd119v/OwgsTfJ8M7mciBWvDUyiDtbxu6nx8M+H6gaSRghThQ20VPtRW4UXtFT7nlwrWBpeCtS7oPHsnuU6caAVHlSr2C/JEuUePxB0YN+7cz2VmkrN+kyaWVbJk4s8//WS5wubNidlaDJysWJFyNmmLFon7z/eyebNvezH7sFKl5CfnDH7ysUAENRmcZOA1aYZZrVqWNWmSb9YubzMgwcBGSsFZZuZ5Zyd6LwwgJP1sM3BdtKj72jENTFvt3GklMKDnnZn3/fdp2wCzjv/80850ZgCc7cn/69ZN3B4zZwMdvA6Vbdss6/rrfT8T1atb1r33WlaXLpbVubN9Meeee+ws1/P5HuQxq13b8zrxS5cm/l4xg5S/m07WctKAGoNc3vcF7UpWGjAz39kv/p7xggt/h3kMnfsZdOXv6bkwSsIszaRZusyQ9850f+IJK6gSEqz4X3+1DkydasUzw/zzz+0/UHzvvBDGbPe777aspk3tC0b8zrn0Uvv7lMeBWf78TuN78/e7w+14B6Zbtcq8iwb8/eZnOi1ZsPyuZ0A2qU2bLOu225KvX7q0fSEnxJmmCtaGp1AGa52vF167kNBRkCJ8qK3Ch9oqvKi9woeCtcGlYK0LOs+DByee9/GcONPxfTknrcwK5cm0d/A1NcyIYwaREwTlMFVmazrPb9w47UEtBsmYzcmMVQYHA5W9xddv3jxxnx59NPX1mfHkndlaqZIVv2uXtXvdOiuBwSvvE3O+53btLGvVKivTggrMzEoaEGAWGKP6zNZjNpi/zDu+TwZUdu2yt8WA49df22UVvLMYuTCLjMFJR79+iY/x/YURz+8Wg14dOiS+D2aQnmsYNYP4zKj2PjYMEHoHuhm8SUvGeTjh79obbyQvweFv4e87y3WwPEV6LVqUuJ1GjZJ/Dw4cmPz1eCHJCbSzpIX37x5LOYTaypX2KARnvz75JPExvi/+vnm/n+LFLeuKK+xMXGZ+fvCB/Z3HrH5ejPHeFhe2yauv2sFfvpaTecrv7AULgvMet2+3L+qc67OR1oVlY556yrLWrk0+6oFLt27BSe1bt87+fuPIEF6Y4wWqQoXsaFXSQC7bixm+/DvFQHnS3xV+Tpk1nplZ6emgYG14CmWwll9NTvdCQkdBivChtgofaqvwovYKHwrWBpeCtS7oPHsnBC5ZYmU+1mZ1XvCBB+zgphMA5AnrP/+k/FzvnWX2lJPpWbly4v1ffZX66zs1Y50zBWdhiQFm9bEG4fkEx7zrSjD7NC31WhmE5lDm/56XUK2adfa/Ops+pQF4sp/Z+BlhndmUhoR7L8z04vDxc2EbM3Dm/Z7YZlu3WtaWLYmBCAaO+HMY8fnd4lBx70B9aoFWfi78ZUx7L8x0TsvxDVe//uqT+Zrqws8Gh5QzwM1h594LPzP+LtIwW9J5/kcfJf8eZICOv1d8nEFJBm+TBr8YKHa2wSA6A5ihwlIe3vVkGZhNisehb9+MBTXZFmwTbwzcen+fHTiQsX1nGzHTl9+xzHplmQ9/WEKAtV3Ts9/8/uBz0pK56n38uLD0gxuy1vnd7nwWnYXfl94jR5w62hMmBO7iYoAoWBueQhWs5a+cM3ChTp2AvbRkgIIU4UNtFT7UVuFF7RU+FKwNLgVrXdB55rmzcx4YjFigz8k/M4OSZrgx4y6l0gJOlheDWDt2JD7GAKvzfGaLpXSSwOGlfPxcJ/Q8i2CWLksuMHiRlpN5BlIYTPIOGDDwkJ7h4Zx0Kem+MPNyzJjgn5w7AVbvWUCchUOPM1KqgMO1vd8jgz/cVqiGWmfG79bRo3bmsPOeGDD09/nxrjnKoC2Hb7MWJcsf8GcGs9Pz+QmgTJlgLCU8Nqy5zKxHfj4YfP3jD8tavtyeoM67pmhqC8/4WZPYOdYMDjplDhjgOn3a//cga4R+/LE9zNwfrutdD5bb4lWtYP8+cv84zN/ZD15MSSmrksdg7Fi7lAH3N6VjxvIPzNDk5I7MnPe3Pb5P77IVXDct34fMrGftZgaUvS+mOQvbhnXGWduV2+MIg/vv91knoWxZ62jPnlb8oEH23w2WomC2MycfZBkLfm54McTZH16IY8Y+M9r5Xc/gMCcIdD4HSZchQyxX4bHmPjujR5IGpHnhIL2TPwaJgrXhKVTBWv6ZdD7a/HqR0FGQInyorcKH2iq8qL2CQxOMhR8Fa13QeeZk506needOK/PddVfiCzpZXDzpdu5jsMof74DJ00/7PsaTdZZU8Fd8lyfz06db1q23Jj8BZrCAgVDOqp1SjVUuDDBydvBPP7WDqklfm6+XdPKvjAznZ7Scw2KdYAVrlaaWaRwsbCcOFeaH5XyHQv/9t28tTGfhkOAwHO7v93eLWYNe7ZjsAgTrADuPcQg0y0+4SFCDtefCizLMFOVxSkvQltmJixfbGZPOfRz+fz6dMQYeWUog6XcCM/2dwC2/B1gCZO5cy3r9dTsYz5qqDAiyHAi/XDOSwekEXr2/n5hFyqz0tGJkhBna06bZdVxZOoG/02kdQs828P48jxyZcrCav8MsM1CiRNraywk8c5KtJN/NnAguIBOMsYTG8OGJ5WYYpOb3vlvxc+T9945/B3ix0sUUrA1PoQrWshvg3Q2T0FGQInyorcKH2iq8qL2CQ8Ha8BPBfyA+jhw5ggIFCuDw4cPInz9/QI5OQkIC9u7di+LFiyMyMtLnscaNge++s2+fOAHkypXJDVKxIrBlC5A7N3D4MBAdbd9/6aXAihX27fXrgapVE5/z889Aw4b27WLFgD//BJIemwULgCZN7NvlygH33APMnw8sX84D4Ltu/frA0KHANdck3nf6tH0gZs8GvvoK2LQp5fdQtixwxRXAZZcBX34JLFyY+FjevMDzzwMPPZT43tJjwwZY776LQ/Xro8Dddydrryxh3z7gppuAlSsT7xs5EnjwQYSbFH+3Zs0Cbr3Vvh0VBSxaBFx1FfDXX0CdOsDRo/ZjkyYBHTrATU6dPYU2U9qY21PaTEFsdGyodwk4dAh4/33gt9+SP/brr8Dq1b738ZjHxwMxMcC2bUCJEql+D57T/v3A1VcDv/+e/LFSpYCzZ+3PdWqKFwdq1wYuuggoX95e+F3F//lY0n3ia3bvDkyfnnhfpUrAp5/a32HBNHUqcOedvu+Zn+/bbweuv95un7feAkaN4h8x3+fye5Dfl82a2X9wFi8G3n4b2L07+evw7wK3060bEiwr4+2Vkh077P8vuACut2qV/Zng94XLndfvVoj7R9lZsPubjl9+SfxY8yvu3XcD8tLi4t9dOX9qq/Chtgovaq/wOb8MZlsdUZ+T19Ql1JkOTlUAloTMdJwwxTsDzhszvpJmzrKW64ABvsNCOSlYSpo1Sz2DizNnp7XeH4cdMxvrxhuTT8KT0sIUEU6Oc56yxRU+DuflhHLOBGWsPRyGUm0r73qnHI7ODEXvWsDMvnQhV2XWpjX7lHWWveo+exbWeQnU79WxY5Y1frxdGiSt5RnSunCYOyfCYvkVTlTHzOCk9Ur/9z87SzZUevTwv+/M+k36HclyMCyZ8OWX/ofuM6uX38UsX+E8h7c3bMhe34NZhDJrw1OoMms5QMf5tef8ohI6+p4NH2qr8KG2Ci9qr6ydWVuuXDnr4osvtmrXrm1VqVLFat++vXWM53QZ7CONHz/e2uB1vnIuM2fOtC655BKrUqVK1h133JFin+utt96yqlevbtWoUcOqWbOmNdFrpPgnn3xi9p+Pc3mdozi9vPfee2b7FStWtP73v/9Zcf/FVX788UfzPC7VqlWzevToYZ1Kx+TEuoTrAv/+a/9fsGAQXsw7k5KZtN7atUvMLGO2Yb9+dtYZs1SZgUvVqgE9eqS8/ZdfBiIifO/jcx55BJgxw87I7dQpeQabP8xge/hh4OuvgYMH7f+fecbOIsuTx3ddZsYxI/fzz8MjY8sN8uUDvvnGzppm1mmOHMhynnsOuO46+/auXUCtWnamt/P5YnahnD/+zrdqZWfdTpgAVKiQmF3bq1fgjjB/77t0sX/X9+61X+uWW4DYWDsztmlToHdvYPx4+7uOGb8TJwJ9+9qPFS2a8rbj4uysa2adfvQR8NpriZmnRYoAX3wBjB1rZ+6HyujR9ntmRi3fs+P4cXtkAvH3+H//szOQp0yx1+XvelI5c9rfxcwe/f574OOPgZ9+AqpUCd77EZGQ9jupcGE1goiISFa359ge7Dy6E9sOb8OU9VPMz8EyefJkrFmzBuvWrTOjiT744IMMb+uDDz7A7/5GWvpx7NgxdOvWDdOnT8emTZtQunRpPM/Ylh/Vq1fHDz/8gN9++w2zZ8/GY489hs2bN5vHypYtizlz5mDt2rVmndGjR2MR4yfggPUtGDBgAL7//nv8+eef2LNnD979b8hS7dq18fPPP5v3zu0yK3kUR0GmUQbGiEugcfQqFSoUhGPLwJyjQQPfx0qWtAMaDOD9/bcdrPAeRtu5sx24TS2oV7euHdRgSQRun2URSpc+//3m0Nwbb7QX4pBnBmKWLrWDJ23a2OtI+jBonvRzkJXwc/vJJ/bnksFaBv2d+xmc8hfEkoxjcJYBQF74mTkTKFPGLjuQGXh1i6/FhQliSS8SOWrWtEuyENdjqYStW+2F33PO7e3b7cX5QnY0b24Hf1l2wA2/r857ZoB23jz7IhiD13xv/I5+7DH7uKcVjxvLg4hItuH8KQxa31NERERC4rc9v+Gl718yAdp4K97c1+mLToiOjMadVe/EU1c/hZolagZlX+Li4nDixAkU8up8vP766/jss89w9uxZU17hnXfeQbly5fA1E/XA05SrTPmFF198Efv27cOKFSvQq1cvPPvss3jppZdw8803p/h63EbdunVR5b9klAcffBDNmjXDa95xrv80ccp5/hecLVmyJLZv346LLroIV155pecxlq/i9rby/BHMFfwct912m1mf7r//frNfPXv2RG6v+BTf+8mTJxGR0jmrHwrWhhhjjk7pzKBk1jo1af1l1hJrdzJY6519xYJmjz8OXHhh2l6DNRS5ZCYG2+rVsxeR1JQowct5doYta6jSSy/5//xLYLBObevWwTuaaf2jx/WYgcvFqcGd1LFjiYFbfinzYoYb6wgyyzgY37UikuUos1ZERCTrm/vnXLSc3BJn4896ArWOswln8fmGzzF943RMbzsdzSs1z7T9aNu2LXLlymUCnPXr18ddd91l7v/444+xceNGLF26FFFRUZg4caIJqDKz9YUXXjDrLFmyBHnz5jU1bAsWLIhJkyaZrNeWLVuax3fu3GkCtsxeTWrbtm0m8OsoX748du3aZQLD0anMbTRv3jz8+++/uNRPvGD9+vVmf8eMGZPia/A+B9/z7bffbrJ0W7RoYd5fWrnwDDR7caoLBD2ztkABexh4UsxQ5aRdfJxDhzkR2YgRaQ/UirgRJ6biFyozaTt2BPr0gZtFRkSiQakGZuFtCSJm6nNyRU7GxYCuGwO1IiLnQZm1IiIiWT+jloHa02dP46x11u86DNjyca7H9Z37LI7Yy4QyCPv37zfBzCeeeMLcz/IEDIwygFunTh28+uqrnkBnY06KDGDYsGH49ddfTaDWH5Y28BeozSiWK+jatavZ5zxJSm/u2LHDBF4ZqL0gjaU3+X5/+eUX7N69G6dPn8a0adPSvC86C3VRdkOmZ9bu3GkvxJnM/QUhcuUCfvzR3jGmh7th6K9IILCO55EjwIcfuj4AFxMVg0HXDjILb4uIiASKMmtFRESyNpY+YEathdQDr3ycAdrnv3se41aPQ5spbbBmd+CCn96Yzdq6dWtT/9W8tmWhf//+Jtjq1HXlYvb/pZfM/ywl0LlzZxPITa8LL7wQf7PsnVeWa6lSpVLMqmXW7C233IJx48aZ8gvemMHbtGlTPPPMM2jDBMdUXoP3JcXs4Hbt2uEjzo2SRu6OWGQD3uURMz2z9lwlELyHCqejloaIiIiIhAdl1oqIiGRdnDyMJQ5SyqhNypREWP85pm2YhrbV25oatgykMuv28KnD2HV0F7b8uwUbtq/Cxg9fwQ9bv8P8v+Zj1h+zMGXdFMz7a16a923BggW45JJLzG2WMmCW6sH/OiZnzpzB6tWrze0//vjD/N+jRw888MAD+IkTIQPInz+/maQsLW688UasWrXKMyEZJ/diwNSfDRs2mHIKnBzshhtu8HmMpRNY05YZwQwce2Pw+csvvzSZszxmfD/Oa3DCMb4np2btF198gVqc8DyNVLM2O2XWpja5mIiIiIhkecqsFZEsLyEh1HsgEjKLti4yAdj0YIbtsbhj+PrPr/HF71/g5JmTiVm5cWeArVvsEplxZ2BFLkZEmTLIEZkDsdGxqFCwAppWbHrOmrWsFcv6rk691w4dOuDAgQO4jnPLmPmczuLee+81k4INHjzY3McM13z58mH06NGe4G2fPn3w5ptvmuxblk9IqWYtn/fee++ZoDC3XaNGDUyYMMHzOJ/71VdfmVIKjzzyiAkCMyDrlGl45ZVX0Lx5cwwcONCUZ3jrrbfMQo8++qgpl1CxYkWzr84kZNdeey3uu+8+T2B6+PDhph4vX58B3wEDBqS5TSKsQBekyAJYvJizvLGxGLkPBM5gt3fvXjPDXaTXEOwpU4D/6iubqgMsE5tpbroJ+C/l3Mx+7lUIWdLWXuI+WbGtTp09hXum3WNuT2o1yfwRzAqyYltlZWqv8BGstsqM/lF2Fsz+pjdWwlq1CoiKYhaLBlOFkr5nw0dYtZVzep9NR0paDLS8/jqjPIjo3x9o3jzbHotw4JrfLf5B3LUL+Ocfe+GVzcKF7cmqnYXzn5zrs3T6NDBxIjB/PnD33cCttyLY3lv1HrrP7J7u5zWNqYqOte5BvgqXIDZHbuQ6dAy5PpuGXJ/PQOzRk8h1Bsh1FshZrRZy/bQC0VE5kFmOqM+pzFo3ZTdkahkE/tF2MmuLFdOEYSIudzr+dKh3QUREsnDfk/1OxS9EspiFC4GHH7Z/0Xv3BjjzOOckyS4WL0aEU9vyu+/spW5dgEHbVq3sq1SBzuA9ftwO4mWm7duBcePszMYKFYDKlROXTB+em0VxLpOpU4FPPuGsUsCePYkXOlISGwvUq8ex7/bnqXz5xMeOHgXeeQcYOtQO+tKnn9q/g2+8YT83SPLFZOzz+O/WDfhmxdN4bG0eNCheF1i2zA5i/8eKjMSp229HzgEDEJmJgVqxqQxCViyDwC8aXtHxLmzMTNoDBxJLIKh3LiIiIpJta9Zm+lwJIhLcwBMzSv8bXmxwyOabbwKDBgFdu3J2n8AHELnts2eByy8HOAy4evXAB0TTivvxyCPJ72cNTA5lvfhiO4DNepIFCvjfRnw88O23dtC7dGn7fdWpA8R4TfgbF8fxzQBndZ8xA9i3D7j3Xjsgl9J2M4KBQ77OyJH266RU2qF4cRbnBDp2BDicPFTHPxww8Dh3LjBpkn1MT51K3/O5Pidj59Knjz1UhYHbEyeAt9/2nZDIMWoUsGSJHbitWjVtr7N8OTB7tv2HumZNe2E7p8WxY7h29b+ITgDOpiNROToeGDcdWFEGiDx6HPhjSeKDDDTfey+sXr1wOG9ekwUtmU/B2qw2wdiiRcAtt9hX+Pil/fTTZghImicXExEREZEsiXEIZ14Oju4UkSyAwafu3e3gaVIczt2jh11vb+BAoGRJYP9+e2EiDzOHLroI4OzmfCytvvgC6NYtMfOIw76JJV0uuwy4+mrgzjuBKlUQNMxq/PVXc/NMrVqIevppRDLLduVK+3FOWHT//cBjjwF33AFwoqCmTe3g5ubNwPjxwAcf2MfMGwNVDMoxcLtzJzBrlh0c9/b++3a5wXffBW6+OePv4dgxYO1aYOlSe1v/TYyUqr17gQ8/tJcyZVgI1A7cXnABp7cH1q1L/J/txcmdatRIDAKWLRv4RC4Gzim9Fwi4fwyW81jyvTM46r1wu5Uq2dnSXJjlygsEOXOee9usP9mzpx1cT4rBR5aI5PHjceP//CPJq5tMhOPC48wEOH5WHPxsOZ8vB48lP1+MubDuKvebn0smzI0YYV848Xe8GYxngJYlPJgR7m8f2V58v7zw4CxsP15A+PprOyA8cyZKnDyJO1sDn1cDzqYhdh8dEY02pa9FrceboxYD0Tt/ZAagnU3IY8aLIHx97iOPgwSFataGuIYY/17w7wrx95zfNxnGLzT+EUl6RYd/LPk++MtPX34Zktop4cQ1tXMk29asbTOljbk9pc0U1ayVkMiKv1tZlWrWhqdQ1KxlbKZoUfs2r+nz3E5CR9+z4cOVbcWAIQOPDDI68uQBXn7ZPv/jRDYzZ6ZtW3xPTZrYgT4GmlL6Tjp50s5O9c7gTQ2DnPfcY2ezpicYnF4MPjNw9V/w+MCsWSh0002IZFCMwb8hQ+ykpqSYPcuyAj/8kLHXZYkJHjsmSjkYBGZGMzOxuF98XWbqLl5sB/8Y9PJeGAzesMEeiu8dCPRWqhTASYtatAB27AA2bbIXBqCZOZw0eJweLOHA41CkiB2gdP5nUJDtxs9USvh5+OYbOw7x11+Jy7ZtdhCcSWMMiHNhcNU765eBvz17kLB5M47PnIm8S5Yg4qef0j85XI4cdkYxM1h54cEfTgrF3xVvfJ98f/x8NmqU9oA13yvLJ3DhsffeDwbJH3888SIFA+98DQbKHTwmPLYMDrOMAv9n+zMzm/+nFz8/PK7en0EAvxUHGt4XgdNR9uRhKYlABHJG58Ty/y1HzRI1E7O6GZTlZ9grqzyY34NHVLNWwdpgfTBS+mDzd3fyZPs2v9f4tyKFnbKviPGKjL8rVLxCxKuY3Ajx6hJLIfjDGiqZ+ccyC3Blh0yyTVspWCtukBV/t7IqBWvDUyiCtX/+aZc4JM578tFHAXlZySB9z4ZxW/E8a+xYOxjKZJlOnYCKFYO3Q0yRb9bMHi7tYLCV++R9QsksuaeesgOF6Qn+8GpO7dp20IkLA6EMJLZv7xt44hDwRx+1R3Ey4Mll9+6Ug8EMTjKg6r3wPh6/Ll0SryalFwOZzERlnKlTJ+x55ZXk34PMjGLm7McfJ9aDSYpBLwZE+QXJq1vMcOUxdM6xiaUOmPjEmqWcvIwBWWY2M2jp4Lk239d/mb4Zds01wEMPAS1b2sFAf5i5yWxfZjd/9VViVmsgMFjHY8t9YLapg58FBuxZRzelY+lvW/xdYVyDAWdmMHvVQ/WLgUJ+Hhnb4P8M5CbNfHbkzWsHbBl8dQKvDDpypDGD9Q62L98TP+MpHdO04ueCiXA85m3b2lmuSbFEQq9ens9nmjD7mcFl7h+D+M7iLys4KQahmSnfrh3mljqOllNa42z8WZy1kn8uoiOjzTK97XQ0r9T8nJtWsDa4lFkb4s4zv9+d73V+z/kthcBffqa88yoO/2jyi5GBWe8v6Ouvt/+YUK1a9tU7fmnzi8l7+AS/ZPnlKBlqL3GfrNhWCtaKG2TF362sSsHa8BSKYC3jOkwgIo5sZIk9CR19z4ZhWxUsiMgJE4AXXkh+TsXAGgOOHP6fkQmnGFhisO255+wgzfDh/oddMlDLk0hO/kP8/mBWHssS+MsO5HadoeXMBGUwh0FR/s8AFx9jAJOTV6WGgUzWUiFuh/uX9DX5WtwOA1isC5p0iPi5AnMM/jKQxmOZ1kxHvgaHnPO18+VDwu+/Y29kZMr9Fw4Z54hTBm6d4CaDY6w7y6Cxv6QmDoNnQDp3brs2r3cNW+d9M8OZWcdOrRl/x48BXAZ3/QUpuW2WJ+C5PBdmi/Ln9GAwj5lgrKfL12AGp/fCYe0bN9oZnwz+8X8mhPF5nCArJUwWYzCSAU5e5eNnKSX8PPLCBQPxf/+dvv3nhYGbbrJLSTAD1d+kXBxFvGYNsGqVndnKuId3AJeBdgZtmRHMzxIDyg5mm7M0QSjm72EZBl7YcCYf84cZ8czMZUDZ32eXn0O2HzOqvRcGwJm9zIspjAt5BaF/2/MbhiwZgs/WfYZ4K94nUNumWhv0v6p/YkbtOShYG1wK1oa488wOMzvO/L7g3wm/58P8EvL+Q82VefWOgVh+4fILyUnP5RAG/uFmrRX7hYHp04GXXrK/0Fi3h8XmJUPtJe6TFdsqLj4OgxYOMrcHXzcYMVFJOoRhKiu2VVam9gofCtaGp1AEa1nakufazjkrY0ISOvqeDR8JcXE4OmoU8r/1FiJYtzI1DLoxYFKtWmJmKpfUJijh8GcG+ryDYAy48HyPWXnO7zODMgzUcrg4MejKYFV6g3pJMdjIbTIQx/NKBhRTwuShTz5J22RJTBriNhm4TXrcGNDmd5+/TElm8jITl6NKufBY+htdynNdBvWcpKXXX0dCr15p728ye5bBVWYjByKAx/fCACGDwdwez+EZdOXCQBzfM481X9Opg8ogKd8vA5yh7B8zuMvsMS4MKDJTl22XWvYrg9YM4jK4yBIEfA/8nPO9830y83TePPuCACdLc2ocs8wC4xVly8IqUwZHKlZEvjZtEJmR7HTW+WVNVe9yICwvwGPqZMVxf3hxgRnCocRjwsA4g9j8feD/TkDbKceQSXYc2YGHvnoIcWfjcE/te9C0YlMUz5O+icIUrA0uBWtD3HnmdwjLzXA0hb/JAw1eGWL6Q1LFitlXHVkvhXj1iMWoUyp8y+E6aSm+Leo8hxGd6IQPtVV4UXuFDwVrw1MogrWMr/AaPw0daseAJHT0PRskzEQbNsyeHIoBRo5YZHCTAdSUzo2YQcoh7Dy3+v57WN9/j4ikE+twUmf+Ev38s52lea7JoEqUsGu4MvjITFD+z9dnpt/IkSkPX7/hBoDZvDzX49UWJzDJzFgGavl+Aon7wRNUHje+Jy68zcAm69lyf/1lPJ4rSMUMQH4vMZjHhCMn+Mr7Wb6BwTa+hj98vTp17OxQBvSchVm1Dz5or8P2/OUXJERHhzY5gO+Vx4/n6gGZQTyEGLRlLGL0aN+2YTCUk+8wG5nvMy34O8XfQQZqvergBux7kJNrcZ+SZjbzogcvFtx1V8a3LYFtqzQ4opq1SOf0fBJoToA21e9x58opPfCAfZWLV5B4VcYJ1PKXhV9Qqc1QpkCtiIiISLblJDURz5dFzgsnF2IyCANv5xvc2r7dDo7+8otdXoDZkszYS2k2eZ4HcRInDuFmeTgGYpJm5TFzjenjDHQ6kxZxeL73sHQ+h5mwDOjwtbhwfzhE3GvSJp+cS9aK5XadLDhm0fbrZw+X5GvxnMz7l83BTEoOu+fi4Ot6Zy6y5uUrr9jDvDkikpiVyIDshRcmTmjEQC0zFQMdqCUeAwa105I5m1bMbGSpAX+YvfTaa3ZpCQ7f5+zbDJKzHbzL/vGc2Pu8OClmTjLTM70TVAUa3yvfU1bAic2efx7o39/OsmVg/dpr7YsG3pOFpQXX91fTNVA4GRC/C5ih6kwYxzIfHGXMLG2RMKNgbQjx74/zdzzVPo5z9ZTBVl4VZpFsXsVl3RMH7+fVXRERERERP7zngQn3hC8Jsc8/t4d7M4uNySQMXqbnQ8UPI+fhYG0OBmmTDjHkYwMH2kEi1hF1aoQyu+/114ERIxJnP58xw16PmaoM2jJ4yjqVzNZMbQg3M/2YAZkGVv78OH355Yjp3x+RjRv7D9AxeMuFxaAZfHayUp3MVAaik07G5Owfa8A++aRdro7BY9aeZFCY753ZiHzfTmYjr7TMn2/XNc1KeK7L982FgXKW8GOdWGbP8n/OkJgSZvwyA1kyBz+TLMPodsz4XbTI/n7gZ4YXUVi2QyQMKVgbQpwY0BntkmLfhleNnT9MHDbDjgonCfvsM7um0Xvv2VeK2EkSkSwzwVi3L7uZ2+/f9j5io9M51ExERMQPZdbKeWOAlLOU8xzEweAks0lZX5XDolMbHsusWWbEMnvSCbamhPUuGSDisHsGXXhexASVlCZCYnCGS1LMiuGkPZwhnZmBzuRKXPgaDJjypMyZPMspWcAao1yuuQZW9eo4dOCAGf57Tnz/5crZC+vLemfqMNvX2U+WT+DPfA1mlSbNOuQ5HgPZ//ufnR3oHajN6gEoloZhBicXBy8MbN5st5n3wnqCmi1RvDPDVeMnGZ1fhp+wCNaOHDkSr732Gnbv3o3atWtjxIgRaNiwod91x44diw8//BBr+UfYxDfr46WXXkpxfbd0mFMM1noP9bj8ct/HOPzAmSVCRLKUI6cTh96JiIgEgjJrJVWc6IYZqTyPYjCQmYqsreqUIuAQfGY9MkvUwccY6OSEVAysMgjLrDbWF2WJBA5f58JAK+tespxb0mxXJqLw9bgwW5SBurfeSpwciAFeTiDkjQksPXrYmacsB8BJsZwSAQ4OgWZguU+fxGGMlSvbkyH5w2AqA7Z8P8zy9J5wKhBD67k9TmTFhYHjtGC5A5YG+PhjYMkS+zgEsjxBOGFQliX/Uiv7JyIp0vlleHF9sHby5Mno3bs3xowZg0aNGmHYsGFo3rw5Nm7c6PfK5qJFi9C+fXtcccUViI2NxSuvvIJmzZph3bp1KMOOgEuDtSmWQXBKIPgL1oqIiIiIZKDvqZq1YjBwOnOmXTKAJQmcOqEMEA4aZAfIWFaAAUZmL8bF2Y9zgiAGZZs2tYfuc9QfMWM0LecsnDCqWzegd+/ktWbp5pvtGrAvvuhbZ5Y1Xvm8p55KzERlQPmJJ+ySBiwTx2QXTkTFbad18iMnmOrUrXUT7leHDvYiIiLZgsv+EiU3dOhQdO/eHV27djU/M2g7e/ZsjBs3Dk+yrk8SH7HwtZf33nsPU6dOxfz589GJV15dxLs0U4qZtQrWioiIiEgAKLNWTDB22zY7EPrjj8Ann9gTX6WEQ8+/+ML3PmY28nnOJErMauUs7Mz6/G90Y4oY/O3ZE3j0UeBcJQU4MpL1aFnrlYFi1s3k8GbWpfSHWbMM4oqIiIQ5Vwdr4+LisHLlSvRnwfj/REZGomnTpljqHcRMxYkTJ3DmzBkUdmH6wDkzazkEhx0p4uyfpUsHbd9EREREJGtx+p6cy4iJjSHFoN68ecAVV9iZkd5DziWwGJxlQgszTpctSzk4yxqrrI/KSYs5qdO339pt5ExsRSwp8NJLiRN+Oa67zi5DwInBmJXLsgH8kDkLywrUrWvXtGWZg/RgeQRm/oqIiGQTrg7W7t+/H/Hx8SjBAu9e+PPvnFUzDZ544gmULl3aBHhTcvr0abM4jnD2SVOaKMEsgcDtWJblsz07u8EuwF+wIF8ryZN++QWRnIWMJZQaNYIVoH2RjLWXuFNWbCvnPTm3s8p7y4ptlZWpvcJHsNoq3H93Az0HAo/5oEGDzLqHDh3ClVdeidGjR6Mys/tcnlmb4oiuYGD90uefB155JXFCpwsusGdz58LJltw2DD1cMelj6FDg8899J8/yxmN9++12vVnWqHUmB2PNWZYb4O89g7A//GBPdnzllSm/Hrf10EP2IiIiIhmWpXtCL7/8Mj799FNTx5b1a1MyZMgQDOYso0ns27cPp9ihDNAJzuHDh03HntnBtH17bk51aW5HRBzB3r2+r5Xr229R4L/bR2vUwIm9ewOyL5Kx9hJ3yoptxdk6407bNeH27t2L2OhQpz8FRlZsq6xM7RU+gtVWR1OagT0MZMYcCK+++iqGDx+OCRMmoEKFChgwYIDZ5vr161Ptd7ohszZkwVoOu2cAMGnSBSeQYv1TLpxQ6a677KHyrDsqvphgsm6dHUBleQAGYTn6znth7VYGaRlgTYrD+XjRoVEj+3/Wl+UxTwm/Uxik5SIiIiJB4epgbdGiRREVFYU9SYbq8OeSJUum+tzXX3/dBGvnzZuHWpxVNBUss8AOvHdmbdmyZVGsWDHkT+8wnVROpCIiIsw2nRMp7wvc5crlR/Hivq8V4VXzKe8NNyDvueo6ScD4ay9xp6zYVnHxcaheyj5BLVmiJGKikgw1DFNZsa2yMrVX+AhWW7k1ABmKORAYGGfA95lnnsHtzEoETCYuR39Nnz4d7dq1g9sw/+DkSft20KuDHTtm1xJl3VFnAitOFMU6p5s328PtOdEVccj96NH2wqH1Dz8M3Hpr9sy2ZUk0ng+wfAGzZFetsgO1vD89OMkWg9/8XDLzW3+DRUSylciISFQuXNlzW9zP1b2emJgYM+yMHeOWLVt6Tkj480OpDK9hpsOLL76IuXPnokGDBud8nZw5c5olKZ7wBPKkhydS3tv0nmCscGHen+QJrCll7yAieTVbHaugStpe4l5Zra1iI2Px5o1vIivKam2V1am9wkcw2ipcf28zYw6ELVu2mHIK3mW2ChQoYLJ2uU03Bmu950oIamYtR4Zx6Pyffybex4zO998HatRInMTqq6/siaxmz+YBt+9fuNBeOHcDJ6R67LGs3x9mxuxnn9mTDP/8c+KxyIhq1QAmpHTo4IIixSIiEipM/hnafKgaIIy4OlhLzHjt3LmzCbqyThizGI4fP+7JjGB2A4ejsZQBcZjawIED8fHHH6N8+fKmI0158+Y1i5t4B2uTdZr37Uvs1DJQm7SIv4iIiIiEZA4Ep3/pb5vOY26ZI8Gxfz//tQOdhQpxnf8yXDNZxKBBiPivT2vlygWL9WofeQSIirLroVK+fEDbtvbCwO2HHyJi5EhEcDi/M0FWnz5IYIbuM88gq0jWXp98gohOnRCRwufB4jGrWtXUk7U4WRfryubODezcCezahQj+z8WyYPFYNmuWOHFbmNecDjXVcQ8faqvwovYKH8FsqwT9zXJ/sLZt27amdiwDsOz81qlTB3PmzPF0jrdt2+aT6cGJHZhBceedd/pshxNAPPvss3BrhgPLR/ngcCcHa0mJiIiIiGvnQDiXUM2R4PjrrxwA7NqkOXOewN69mV+DOGrbNhR97z173/LkwYFvvkF8xYp2qYPUMNDYpg1iFi9G7vHjkXPePESwfMKrr2Jfq1awihZFVuDdXrmnT0eBhx/2CdTGX3AB4urXx5l69eyFNXxz5Uq+ofLl/b8Akz8k4G0VrqMMsgu1VXhRe4WPYLbV0TCeJyHbBGuJJQ9SKnvAjrO3rVu3IlykmlnrPSxPwVqRbOX02dN4cPaD5vaoFqOQMzp5mRYREQndHAjO87iNUqVK+WyTiQVumiPB4ZSKpQsuyIXixf0E/QIs4oknEPFffdWI3r1R5LLL0rcBJ9uW9VbHjEHk8eMo/v77sN54A1mB017Fv/kGkV6BWut//4P17LOIKFUK7AGoFxB6quMePtRW4UXtFbzzy55f9zS3R940MkPnl8Fsq1iV7gmPYG1W5WTW8nOY7LOoYK1ItmXBwt4Tez23RUTEXXMgVKhQwQRsuQ0nOMvg67Jly/DAAw+4ao4EB6sLpDpXQqBt2ABMmmTfLlQIEX36ICKjLzpwIDBhgpkhLWL0aBP4RdmyCBt//23XouXkXixj4HUcck2ZgsjHHrMzh+mBBxDx9tsZP1aSaVTHPXyorcKL2isIxzgyAvtO7PPczmi/I1htFam/gQrWhlL79gATDLwzHQxmIHDGV+KECqVLh2L3RERERLKEQM+BwJOVxx57DC+88AIqV65sgrcDBgwwdW2dgLDbHDyYePu/edIyFwOszpD+J57gDGwZ3xazl1nn9pVXWPwXYDmJ/8or+GBN2xEj7FIBd98NFC+OkImPtydNGzMG+PrrxA4/D/5VVwFXX23uK8DsY+cxZhBz/506syIiIpItKbM2hLwmJfb122+JM7+qBIKIiIiI6+ZA6Nevnwn49ujRA4cOHcJVV11ltunWoXvecyUkK78VaKtWAZ9/bt/mMU4lgznN+vWzA59MER4/Hnj8ceCSSxIf54RtN98M/PBD4voMnHfvDjRpkpjNyiAqs35//tmezJcT+d56K5CDNX0DYPt2e//GjgV27PAfNf/yS7P45CU9/DDw1lsK1IqIiIiCta6kEggiIiIirp4Dgdm1zz33nFnCQVAza595xvd2njznv03uNAO03B4zdpm5O3my/RgDuDfd5NuHPnMGmDLFXipUAJo1A9atswPJTlKEg6PYevSwA7vpHdHGyeG+/x6YOxeYM8d+jaQ4Uo6BfwaHlyzxbQyWPHrkEUQMG6ZArYiIiBjKrHUj745meidiEBEREREJVWYtM1s57N8JUjIAGiiPPgoMHw7s3Qt89pldXqFiRaB588QSYgzqduoEfPyxvR5t2QK8807K2925E2DG9PPP29m4995rlynIly/l7NmZM4FZsxjpN7V0k2Emb4sWwH33ATfeCERF2fcz0Pz77ybAa/38M45WqIC8Tz5pgv8iIiIipGCtG/30k/0/J6GoWzfUeyMiIiIiYS4ombWsvfr004k/Dxpk92cDJW9eO7OW9Wupb1/g6FFgxQr756JFgXnzgNq17fq2DKi++y7wzTeJ2yhfHrj0UnthMJnZuTNm2EFUlkiYOtVeGGzl5HEM2nJhxi2zZ1nCgJOF+cOAa8OGdjmGLl3s7SfF7VarZhare3ec2LsXeRWoFRERES8K1rrNvn32ECliDa2YmFDvkYgEWQQiUDZ/Wc9tERGRQGbWFiyYCcfTqSW7eLH988UX2xmugcZyBW+8Afz9N7BwYeL9xYoBCxYANWrYP7MP3bq1vWzbZmfXMkjK9by1bWtnyrLGLJf/JpMzwVuWTODCWrIpYRCXmb1cmjYFihQJ/HsWERE5Dzq/DD8K1ro1q5Y0uZhItpQzOidGtRgV6t0QEZEsmFlboEDiiPzzdvy4XQrg00/t0genTyc+xlq+0ZlwqsFMXZYs6No18b7ixe1AbfXq/p/DDFd/Wa6OsmXt/R0wwH4/zM5lHdq1a+1s4aQaNLAnJbvtNjuLV5mxIiLiYjq/DD8K1rqNJhcTERERkUzKrA1YvdohQ4AXXkg+WRfdcQfQpg0yTceOwJtvAr/+CpQsaQdqq1Y9/+3myGHvOxfnoP34ox24ZV3bK68EbrkFKFPm/F9LREREJAUK1rqNgrUiIiIiEkBMDnWCtQGpV8varU895XtfiRJ2gLZdO3t0GGuzZhamBjv1Y5nhWqpU5rwOI9ucJIyLiIiISJAoWOsmZ88CP/+cOByLNbBEJNs5ffY0es3tZW6/2fxNM2xFREQkozgHF+fOCkhm7ZkzQC/7b5TRoQPQrRtwzTUBrK+QBsyoZf1aERERSZXOL8OPgrVuwrpYrP1Fqlcrkm1ZsLD9yHbPbRERkUDUqw1IZu2YMcCGDfbtyy4DJk5UzVYREREX0/ll+MnE8UmSbppcTEREREQCzCmBcN6ZtQcOAIMGJf781lsK1IqIiIgEmIK1bq1Xy0wFERERERG3ZNY++2xi5JeTfDVsqLYRERERCTAFa90YrI2JAerWDfXeiIiIiEh2yqzlTGQscXDyZPLH1q0DRo+2b+fODQwZEvgdFREREREFa12Dw8o2bbJv16sH5NSEQiIiIiISxMxaThRWrRpQvjzw5puJQVsGcXv3TpylrH9/oEwZNY2IiIhIJlBmrVuoXq2IiIiIhCqzdvZsYPx4+/bevXZwtlIlYNQo4IsvgG++sR8rVw7o00ftJCIiIpJJojNrw3Ie9Wovv1yHTyQbi0AEiucu7rktIiKSqZm1J04APXsmv3/nzuT3v/oqkCuXGkRERCRM6Pwy/ChY68bMWk0uJpKt5YzOifdvfz/UuyEiItkls/a554C//7ZvN2kCDB0KDBoETJ/uu97VVwNt2mTuzoqIiEhA6fwy/KgMghuw/teyZfZt1v8qWzbUeyQiIiIi2SGzdu1a4I03Eie5ZdmDWrXs0gcrVgA332w/lj8/MHw4EKERHyIiIiKZSZm1brB+PXDsmH1bWbUiIiIiEozM2oQE4P77gbNnEycOu/jixMfr17dr2TLrloHcUqXULiIiIiKZTMFaN1C9WhHxEhcfhyfnPWluv9z0ZcRExej4iIjIeWfWRkcDefN6PTBuHPDDD/btypWBJ+2/PclwUjEREREJSzq/DD8K1rqBgrUi4iXBSsCmg5s8t0VERAKRWcusWk8Vg337gH79ElcaPRqIjdWBFhERyWJ0fhl+VLPWTZOL5cgB1KsX6r0RERERkSyYWetTr7Zv38QobocO9sRiIiIiIhJyCta6off8++/27bp1ldEgIiIiIgFz5gxw9GiSerWffw58+KF9u2DBxAnGRERERCTkFKwNtWXLEm9rcjERERERCaBDhxJvm8zajRuBrl0T73z9daBECR1zEREREZdQsNYtJRDo8stDuSciIiIiksU4lQ6oZN5jQKtWwLFjieUP7r03ZPsmIiIiIslpgjE3TS6mzFoRERERyYR6tYCFB9b0AP5Yb/9YvTrwzjteM46JiIiIiBsoWBtKCQmJZRBKlgTKlQvp7oiIe+TPmT/UuyAiIlkos/ZBjEKDPz6xf8iXD5g6FciTJ6T7JiIiIsGh88vwomBtKG3YABw5klgCQZkNIgIgNjoWH7X6SMdCREQCklnbCD/hTfRKvHP8eOCSS3R0RUREsgGdX4Yf1awNJZVAEBEREZFMdHLbPkxBG8TgjH1Hnz5A69Y65iIiIiIupWBtKGlyMRERERHJLPHxuP69u1EWO8yPB6pfDQwZouMtIiIi4mIK1rohszY6GqhfP6S7IiLuERcfh/7z+puFt0VERDIkMhJrSzdDPCKxGyWw+cXJQI4cOpgiIiLZiM4vw49q1oZKfDxQtapdSKxMGSB37pDtioi4S4KVgLX71npui4iIZEhEBFZe/zg++fNSHDoahaEXl9KBFBERyWZ0fhl+FKwNlago4PPPActKnGRMRERERCSABg/mP9fqmIqIiIiECZVBCLWICKBAgVDvhYiIiIiIiIiIiISYgrUiIiIiIiIiIiIiLqBgrYiIiIiIiIiIiIgLKFgrIiIiIiIiIiIi4gKaYExExIVyRuUM9S6IiIiIiIhIFqDzy/CiYK2IiMvERsfi87s+D/VuiIiIiIiISJjT+WX4URkEERERERERERERERdQsFZERERERERERETEBRSsFRFxmbj4OAxeNNgsvC0iIudv5MiRKF++PGJjY9GoUSMsX748xXXXrVuH1q1bm/UjIiIwbNiwZOs8++yz5jHvpUqVKmoqERERcRWdX4Yf1awVEXGZBCsBK3at8NwWEZHzM3nyZPTu3RtjxowxgVoGX5s3b46NGzeiePHiydY/ceIEKlasiDZt2qBXr14pbrd69eqYN2+e5+foaHWtRURExF10fhl+lFkrIiIiIlna0KFD0b17d3Tt2hXVqlUzQdvcuXNj3Lhxfte/9NJL8dprr6Fdu3bImTNnittlcLZkyZKepWjRopn4LkREREQkO1CwVkRERESyrLi4OKxcuRJNmzb13BcZGWl+Xrp06Xlte9OmTShdurTJwu3QoQO2bdsWgD0WERERkexMY7VEREREJMvav38/4uPjUaJECZ/7+fPvv/+e4e2ynMIHH3yASy65BLt27cLgwYNx9dVXY+3atciXL1+y9U+fPm0Wx5EjR8z/CQkJZgkEbseyrIBtTzKX2it8qK3Ch9oqvKi9gnucndsZ6ScEs60S1I9RsFZEREREJL1uuukmz+1atWqZ4G25cuXw2WefoVu3bsnWHzJkiAnoJrVv3z6cOnUqYCc3hw8fNidTzB4Wd1N7hQ+1VfhQW4UXtVdwnDp7CnGn7Ymr9+7di9joWFe31dGjR5HdKbNWRERERLIs1pGNiorCnj17fO7nz6wzGygFCxbExRdfjD///NPv4/379zeTnHln1pYtWxbFihVD/vz5A7IPPJGKiIgw21Sw1v3UXuFDbRU+1FbhRe0VvGBtTM4Yc5sTq2Y0WBusPkZsbPr3L6tRsNYPJz3cGZ4WCPxg8+oAP3TqPLuf2it8ZMW24h/TMyfOeL6H4qLtq6DhLiu2VVam9gofwWorp1/k9JPCRUxMDOrXr4/58+ejZcuWnmPGnx966KGAvc6xY8ewefNmdOzY0e/jnKjMe7Iy5zjyeYFqN74vbi9Xrlz6ng0Daq/wobYKH2qr8KL2Ct755dmTZ81t9hPORtu33dpWx44dC8s+ZyApWJtKyjWzHUREQqnE/3xrLIqIuKGfVKBAAYQTZrR27twZDRo0QMOGDTFs2DAcP34cXbt2NY936tQJZcqUMaUKnEnJ1q9f77n9zz//YM2aNcibNy8qVapk7u/bty9uvfVWU/pg586dGDRokMngbd++fZr2Sf1NERERCbZwOr88GoZ9zkBRsNYPzuq7fft2MzkE07wDwRnqxu0GaqibZB61V/hQW4UPtVV4UXuFj2C1FbMb2GlmPynctG3b1tSGHThwIHbv3o06depgzpw5nknHtm3b5pMlwuBr3bp1PT+//vrrZmncuDEWLVpk7tuxY4cJzB44cMAMCbzqqqvw008/mdtpof6m6Hs2fKitwofaKryovcJHMNvKCuM+Z6BEWNk5rzjIH2xeEWBBZgVr3U/tFT7UVuFDbRVe1F7hQ20l+iyEJ/3uhg+1VfhQW4UXtVf4UFsFl4oGioiIiIiIiIiIiLiAgrUiIiIiIiIiIiIiLqBgbZBw9l9OPOE9C7C4l9orfKitwofaKryovcKH2kr0WQhP+t0NH2qr8KG2Ci9qr/Chtgou1awVERERERERERERcQFl1oqIiIiIiIiIiIi4gIK1IiIiIiIiIiIiIi6gYK2IiIiIiIiIiIiICyhYKyIiIiIiIiIiIuICCtYGyciRI1G+fHnExsaiUaNGWL58ebBeWlIwZMgQXHrppciXLx+KFy+Oli1bYuPGjT7rnDp1Cj179kSRIkWQN29etG7dGnv27NExDbGXX34ZEREReOyxxzz3qa3c459//sE999xjfm9y5cqFmjVrYsWKFZ7HLcvCwIEDUapUKfN406ZNsWnTppDuc3YVHx+PAQMGoEKFCqYtLrroIjz//POmjRxqr9D47rvvcOutt6J06dLm+2769Ok+j6elXQ4ePIgOHTogf/78KFiwILp164Zjx44F+Z1IMKm/6T7qb4Yv9TfdT33O8KD+prupz+lOCtYGweTJk9G7d28MGjQIq1atQu3atdG8eXPs3bs3GC8vKVi8eLEJxP7000/49ttvcebMGTRr1gzHjx/3rNOrVy/MnDkTU6ZMMevv3LkTrVq10jENoZ9//hnvvPMOatWq5XO/2sod/v33X1x55ZXIkSMHvv76a6xfvx5vvPEGChUq5Fnn1VdfxfDhwzFmzBgsW7YMefLkMd+JDLhLcL3yyisYPXo03n77bWzYsMH8zPYZMWKE2ivE+LeI/QUG3/xJy+8RA7Xr1q0zf+NmzZplOuM9evQI4ruQYFJ/053U3wxP6m+6n/qc4UP9TXdTn9OlLMl0DRs2tHr27On5OT4+3ipdurQ1ZMgQHX0X2bt3L1PJrMWLF5ufDx06ZOXIkcOaMmWKZ50NGzaYdZYuXRrCPc2+jh49alWuXNn69ttvrcaNG1uPPvqouV9t5R5PPPGEddVVV6X4eEJCglWyZEnrtdde89zH9suZM6f1ySefBGkvxdGiRQvr3nvv9TkgrVq1sjp06KD2chH+3fniiy/S9Xu0fv1687yff/7Zs87XX39tRUREWP/880+Q34EEg/qb4UH9TfdTfzM8qM8ZPtTfDB/qc7qHMmszWVxcHFauXGmGJzoiIyPNz0uXLs3sl5d0OHz4sPm/cOHC5n+2G7NtvduuSpUquPDCC9V2IcJM6BYtWvi0Camt3OPLL79EgwYN0KZNG1NepG7duhg7dqzn8S1btmD37t0+bVigQAFTHkbficF3xRVXYP78+fjjjz/Mz7/88guWLFmCm266Se3lYmn5PeL/LH3A30cH12cfhJm4krWovxk+1N90P/U3w4P6nOFD/c3wpT5n6ESH8LWzhf3795saLSVKlPC5nz///vvvIdsv8ZWQkGDqn3L4do0aNcx9PBGOiYkxJ7tJ246PSXB9+umnpowIh6UlpbZyj7/++ssMq2fpl6eeesq01yOPPGJ+lzp37uz53fH3najfq+B78sknceTIEXMhKioqyvy9evHFF83weVJ7uVNa2oX/84KJt+joaHNBUr9rWY/6m+FB/U33U38zfKjPGT7U3wxf6nOGjoK1Iv9dQV+7dq3JKBP32b59Ox599FFTd5GT9Im7T0SZyffSSy+Zn5lZy98t1tVksFbc5bPPPsNHH32Ejz/+GNWrV8eaNWvMhStOaqX2EhEJLPU33U39zfCiPmf4UH9TJP1UBiGTFS1a1GQr7dmzx+d+/lyyZMnMfnlJg4ceeshMvLJw4UJccMEFnvvZPhxWeOjQIZ/11XbBxzIHnJCvXr16JjOMCyfs4OQ6vM1sMrWVO3Bm+mrVqvncV7VqVWzbts3cdr739J3oDo8//rjJdmjXrh1q1qyJjh07msn6OHs5qb3cKS3twv+TTmR69uxZHDx4UP2PLEj9TfdTf9P91N8ML+pzhg/1N8OX+pyho2BtJuPQ3/r165uagN5XAfnz5ZdfntkvL6lg/Wx2nL/44gssWLAAFSpU8Hmc7cYZ7b3bbuPGjSbopLYLriZNmuC3334zWX/OwuxNDtV2bqut3IGlRPh74o31UMuVK2du8/eMf/S9f684DJ81NPV7FXwnTpwwNUy98QIj/06R2sud0tIu/J8XGxl8cPBvHduWtW0la1F/073U3wwf6m+GF/U5w4f6m+FLfc4QCvUMZ9nBp59+amZo/uCDD8zszD169LAKFixo7d69O9S7lq098MADVoECBaxFixZZu3bt8iwnTpzwrHP//fdbF154obVgwQJrxYoV1uWXX24WCb3GjRtbjz76qOdntZU7LF++3IqOjrZefPFFa9OmTdZHH31k5c6d25o0aZJnnZdfftl8B86YMcP69ddfrdtvv92qUKGCdfLkyZDue3bUuXNnq0yZMtasWbOsLVu2WNOmTbOKFi1q9evXz7OO2it0s5GvXr3aLOyuDR061Nz++++/09wuN954o1W3bl1r2bJl1pIlS6zKlStb7du3D9E7ksym/qY7qb8Z3tTfdC/1OcOH+pvupj6nOylYGyQjRowwQb+YmBirYcOG1k8//RSsl5YU8OTX3zJ+/HjPOjzpffDBB61ChQqZgNMdd9xhArrivs6z2so9Zs6cadWoUcNcpKpSpYr17rvv+jyekJBgDRgwwCpRooRZp0mTJtbGjRtDtr/Z2ZEjR8zvEf8+xcbGWhUrVrSefvpp6/Tp05511F6hsXDhQr9/o3jCk9Z2OXDggAnO5s2b18qfP7/VtWtX0yGXrEv9TfdRfzO8qb/pbupzhgf1N91NfU53iuA/oczsFRERERERERERERHVrBURERERERERERFxBU0wJiIiIiIiIiIiIuICCtaKiIiIiIiIiIiIuICCtSIiIiIiIiIiIiIuoGCtiIiIiIiIiIiIiAsoWCsiIiIiIiIiIiLiAgrWioiIiIiIiIiIiLiAgrUiIllA+fLlMWzYsFDvhoiIiIhkUepviogEh4K1IiLp1KVLF7Rs2dLcvvbaa/HYY48F7Rh+8MEHKFiwYLL7f/75Z/To0SNo+yEiIiIimUf9TRGR7Cs61DsgIiJAXFwcYmJiMnwoihUrpsMoIiIiIilSf1NEJDwos1ZE5DwyHhYvXoy33noLERERZtm6dat5bO3atbjpppuQN29elChRAh07dsT+/fs9z2VG7kMPPWSycosWLYrmzZub+4cOHYqaNWsiT548KFu2LB588EEcO3bMPLZo0SJ07doVhw8f9rzes88+63dY2rZt23D77beb18+fPz/uuusu7Nmzx/M4n1enTh1MnDjRPLdAgQJo164djh49qs+DiIiIiEuovykikv0oWCsikkEM0l5++eXo3r07du3aZRYGWA8dOoTrr78edevWxYoVKzBnzhwTKGXA1NuECRNMNu0PP/yAMWPG2F/KkZEYPnw41q1bZx5fsGAB+vXrZx674oorTECWwVfn9fr27ZtsvxISEkyg9uDBgyaY/O233+Kvv/5C27ZtfdbbvHkzpk+fjlmzZpmF67788sv6PIiIiIi4hPqbIiLZj8ogiIhkELNRGWzNnTs3SpYs6bn/7bffNoHal156yXPfuHHjTCD3jz/+wMUXX2zuq1y5Ml599VWfbXrXv2XG6wsvvID7778fo0aNMq/F12RGrffrJTV//nz89ttv2LJli3lN+vDDD1G9enVT2/bSSy/1BHVZAzdfvnzmZ2b/8rkvvviiPhMiIiIiLqD+pohI9qPMWhGRAPvll1+wcOFCU4LAWapUqeLJZnXUr18/2XPnzZuHJk2aoEyZMiaIygDqgQMHcOLEiTS//oYNG0yQ1gnUUrVq1czEZHzMOxjsBGqpVKlS2Lt3b4bes4iIiIgEj/qbIiJZlzJrRUQCjDVmb731VrzyyivJHmNA1MG6tN5Y7/aWW27BAw88YLJbCxcujCVLlqBbt25mQghm8AZSjhw5fH5mxi6zbUVERETE3dTfFBHJuhSsFRE5DyxNEB8f73NfvXr1MHXqVJO5Gh2d9q/ZlStXmmDpG2+8YWrX0meffXbO10uqatWq2L59u1mc7Nr169ebWrrMsBURERGR8KH+pohI9qIyCCIi54EB2WXLlpms2P3795tga8+ePc3kXu3btzc1Yln6YO7cuejatWuqgdZKlSrhzJkzGDFihJkQbOLEiZ6Jx7xfj5kUrC3L1/NXHqFp06aoWbMmOnTogFWrVmH58uXo1KkTGjdujAYNGqi9RURERMKI+psiItmLgrUiIuehb9++iIqKMhmrxYoVw7Zt21C6dGn88MMPJjDbrFkzEzjlxGGsGetkzPpTu3ZtDB061JRPqFGjBj766CMMGTLEZ50rrrjCTDjWtm1b83pJJyhzyhnMmDEDhQoVwjXXXGOCtxUrVsTkyZPV1iIiIiJhRv1NEZHsJcKyLCvUOyEiIiIiIiIiIiKS3SmzVkRERERERERERMQFFKwVERERERERERERcQEFa0VERERERERERERcQMFaERERERERERERERdQsFZERERERERERETEBRSsFREREREREREREXEBBWtFREREREREREREXEDBWhEREREREREREREXULBWRERERERERERExAUUrBURERERERERERFxAQVrRURERERERERERFxAwVoRERERERERERERF1CwVkRERERERERERMQFFKwVERERERERERERcQEFa0VERERERERERERcQMFaERERERERERERERdQsFZERERERERERETEBRSsFQkT1157LSIiIsyydevWDG3jgw8+8Gzj2WefRXazaNEiz/vv0qWL69qT/zv38XE3vB9+TpzX4OdHREREsi71N8+f+pvpp/6miIgvBWtF0ql8+fKe4NW5FnbWJPwcP34cefLk8bTjX3/95Xe9yy+/3LPORx99hHB06NAh00Hm4vZgrPcJJNtn//79Kf5u/v77737vj4yMRM6cOVGiRAk0atQI/fr1O+fFj3nz5qFt27a48MILERsbi+LFi6Nhw4YYPHgwtm3blmz9LVu2oE+fPqhduzYKFCiAXLlyoUKFCmb/hwwZgj///DPF1+K+9O3bF9WqVTPvsWjRoua1Xn31VRw8eNDvc3h///790bhxY+TOnTtNAfyVK1fi9ttvR5EiRcx74utx3+Li4lI9FiIiEhzqb2Z96m+6k/qb/vubq1evxpNPPokrrrgCZcqUQUxMDIoVK4Zbb70V33//vd/nsM/boUMH0+9m//uiiy7CE088gSNHjmRyK4pkAZaIpEu5cuUs/uqkZVm4cGHAju6vv/5qff/992Y5depUhraxZ88ezzb+/vtvK7thezht07lz51TXbdeunWfdIUOGJHt827ZtVkREhHk8V65c1tGjR9O1L40bN/Zsf8uWLeY+tqvTPmzvQL6flPC1nW1wn5Li58TZJ35+Qsn7mHHp379/ir+bGzZs8Hu/vyVHjhzWO++8k+z14uLirI4dO6b63EcffdTnOWPHjrViYmJSfU7r1q39vr+hQ4dasbGxKT6vZMmS1ty5c5M9b/Xq1X7XT+kzwW2ktI/NmjWzzp49m+Y2ERGRzKH+ZvhSfzM59TfDv7953333pficyMhIa+rUqT7rr1mzxipQoIDf9evUqWMdOXIk3b9bItlJdKiDxSLh5vPPP8epU6c8P7dp0wa7d+82t4cPH466det6HqtZs2aqV9LTI6VtpQczArnIubVr1w6ffvqpuf3ZZ5+ZK8nepkyZwotd5naLFi2QN2/e8z6svOJ81VVXuap5mE3KxY1GjhxpMmMLFiyY5ufwd5S/S3///TfGjx+PxYsX48yZM7jvvvtMdsAdd9zhWbdXr16YOHGiuc2M3O7du+OWW24xmai//fZbskzkL774Aj169PB8Lph5zecwO+ro0aNYsWIFJk+e7He/mKXrlCa59NJL0bFjR1StWtVkuq5fvx4ff/yxyWi47bbb8O233+Lqq6/2PJeZDddcc43JdNi7dy/GjRuX4vs/efIkunbt6smgfeaZZ8x31qBBg7B27Vp88803GDNmDHr27JnmYyoiIoGn/mb2oP6mTf1N9/c3qWTJkujWrZs5X/n333/N9jZu3IiEhAT07t0brVq18qzL/ubhw4fNbe4vz5feeOMNfPfdd1izZg2ee+45vPbaawH8bRLJYkIdLRbJSpkP3pm0Sa8gL1682LrsssvMlUwn4+29994zmWxly5a1cufObeXMmdOqVKmS9dBDD1n79u07ZyZm0tdYvny5de2115pMzxIlSlhPP/20FR8f79nG+PHjPesPGjTI77Z/+eUX8/rFihUz+3rjjTdaW7du9dkXbnPw4MFWmTJlzGvxNZnd528fU7Jjxw6ra9euVq1atawiRYpY0dHRVqFChazrrrvO+uKLL1LNUJgzZ47VoEEDc7x47N56661k23f2h++B+/nss89a3377bZozUU+fPm0VLFjQs/6mTZt8HmdbOo85V5ID3Z4ZfT/8rN15553mtXlFm1fyS5UqZbVp08a0r4PPSekKufP6/Jw49/Hz423lypXmdfhZ42vwf17FX7Fihc96ST93EydOtKpXr24yAipXrmxNnjw51bbwd8ychZ/D9GTWev+OJiQkmP13Hitfvrx15swZ8xifzywB57ERI0Yk2x8+//fffze3+Tw+31mfx9r7d8/7OUmzpr/77jvzWmyrWbNm+X3vfN6oUaOsqKgo0/7Hjh3zu97o0aNT/YzzWDuPN2/e3HP/0qVLPffXqFHD77ZFRCR01N9Uf1P9TfU3Q9nf5Ci748ePJ8ue9e6XO6Pwli1b5rmvatWqZru0c+dOz8hEnvcxq1hE/FOwViQInefSpUv7DDdxgigMlqQULOMftpMnT6Y5uMdgHAOnSbfDYTLpCdZWrFgx2TauvPJKn/f8yCOPJFuHf/i9Ow/nCtZ6B4f8LRMmTPAbrOXx9g6iOQsDlw4GVv0Nu2FgOK3BWrr33ns967/44os+pQGcjkb+/Pk97RTI9vQO1qb3/bBsQ0r7wSDy+vXrzztYO2PGDBOg9fdc3s/H/X3u/H2+2J5O0DM13seMwXr+X7hwYU8JivQGa51yFt6fJ3ZE6bnnnvPcx6D3uUoDsAPs/X7++ecfK62uueYa0yn+4YcfzM8LFiywqlWrZuXJk8fq3bu31bNnT8/n5O233za3/Q2jS0uw9uGHH/Yb6Gbn37s9Dx48mOb9FxGRzKf+pvqb6m+qv+mW/qaDwVvvPr3TJ3/jjTc89zE5x1uFChU8jzEZRUT80wRjIkGwc+dOXHDBBZg0aRK++uortGzZ0tzPSYs4ZHn27NlmMjL+36lTJ/PYhg0bMG3atDS/xq5du1CvXj3MmDEDjzzyiOf+d955J137um/fPjMMmvvqDC//4YcfsG7dOnObQ11GjBjhGaozcOBAzJw500yCdK6JmpIOo3n55ZcxdepUM4HTwoULMWHCBDMUnV544QW/z+PwdRay52ty6Ji/9zlgwADPsBsO8Z4+fbrZ59QmdvLHe/ssheA9NNEZesRJmjhMKdDt6S2974dtwce//PJLc1w5jOmVV14xj504cQJvvvmmuf3000+bcg6OOnXqmAkCuDhtnFIZDw6BYvkAeuCBB8zn+sEHHzQ/834+zvWS4mRtfGzWrFlo0qSJuY9Dp9577710HZPHHnvMlBLh5FqjRo1CRpUtW9ZMkuDgsCz65ZdfPPdxeFlUVFSq2/Fe/5JLLkHp0qV9HluyZInPcvr0afMYJyjjcDAOX3PKGPDzzaFoPH5Dhw7F3LlzPdvisea2ebwzwvt3lJM9OKKjo1G4cGG/64mISHhQfzM59TfTTv3N5NTfTL2/yfM4B0smOGXhUupvkndJPk7KKyL+qWatSBAwqMngFIM43po2bYrnn3/eBCvZwXYCOA7WHLr77rvT9BqsW8k/mPyDyDpHDH4xMJfeACXrB7F+JzGoxMAtcTvVq1c3wWAnUMn6nqxVRFdeeaUJerEmZlqwrhI70MOGDTP1mBiMdLZLmzZtMjOF5s+fP9kfeNZhYn1X1lpy6so675OBPx5rB4POnOmeWFv4xRdfTPOxuP76683rMYDGgNsff/yBiy++2Cdw2759+0xpT0dG3s9ll11mAq7vvvsuNm/ebD4HSfeDKleujBw5cnjuL1CgQJpq5rKu6f79+83t+vXre4KlN910E5YtW4aVK1eaxxkkdi5MOGrXru0JzBYtWhTz5883t9P7OS1SpAjuv/9+U/uKy8MPP4yMKlWqFLZv325uO0Fx53/yDrymxHv9pDV0GZzmMfHGzil/B5zZczt37mz+Hz16tAnS8rPNmmI8PgzYen+XMKjO34+M8A6g8zvDm/fP/gLtIiLibupvJqf+Ztqov+mf+psp9zfZt3X63zwvc5JBSP1NkfOnzFqRIGBQLGmglgXgmUk3duxYE7hJGtijQ4cOpfk1qlSp4rlyyc56oUKF0r0Naty4sU8HJem+MDPS0ahRI89tvh73Ia34B71Lly4mWMVtewdqk75m0kAkOwQp7R8Dq8eOHTO3mXnpBDadjNP0YDYlJ5BzMEjLTMjly5d7Xv+GG27IlPZ0ZOT9MIDMQB+D4EkDtRndD28MWvv7DCTdJ+/10vr5So++ffuarGYeIwamM+qff/7xCVh7/08MvJ+L9/o7duxI82s72+bkDsQ2I17EuPnmm00gulKlSj7PYRYsT6oywntiw6SfUWfSsaTriYhIeFB/Mzn1N9NG/c2Uqb+ZHBN6mNTCZAX2Sz/55BOTwOFQf1Pk/ClYKxIESYd/ODN5OkEdBjmZLcrApfdVyfQEZJzgrIN/ODPCezve2/AXTI2IiEBGeQ+z79evn8kg5PuvWbNmqu8/PfsXiP31zpxlsJZlA5zXat26tWcfAt2eaeHv/TCYzPIHxKFIzHplSQYumbUf59qnQLVfUszM/t///mduczZZ72BjWjGw7h2MZdaqkwHs+OmnnxAfH5/qdrzXZ/CX5Tq8M5n5/vx9DyQ9Fs57cIaRUb58+XwyFX788UeUK1cOGcEMI8eePXs8t8+ePYsDBw74XU9ERMKD+pvJqb95/tTfVH8z6Qi75s2bmxGQTKBheTiOtkxLf9MZGeioUKFCAD6hIlmTgrUiQeCvk+OdzdezZ0/cddddZgj6qVOnXN0mF110kef2zz//7Ln977//4vfff0/zdpz3z+xK1lPl1VnWY/U+LhnBsgXO1VwGtlgr1sEh+unFbNkLL7zQk/X41ltv+Q3kZlZ7pvf9eO8HO1KsccpsVicbOSlmYac3iMtSEA4ny9jfz97rZRYG+jl8n++bdZvTgwHUPn36eALFDIAyc5uYUe0cG5Yc8Je5y+exhrNT19b5nPA4cr/SWoKBWKOWKlasaP5nRi07wQy8O3V0Wf+LdZRZYuK2225DRniXuWDQ1/t3mQFbqlGjRrKLPyIi4n7qbyan/mbaqL+ZOvU34UlO4dwKHLnH8xPOz8H5O1Lrby5dutTT1+bvIxNLiH1NltgTEf8UrBUJEe/MOE5KxQmDhg8fnuLEWm7BP8jOyQBr5LJGK/9Qc3KttNar9X7/zObjRGN8/61atTITRp0PBthYs9fRsWNHU2eXGaYcWp5efK98bw6ntinrmF5zzTXJ3k+g2zO978d7PxYsWGCGJTEj+J577vG7fe+gHIPRnLyMQ5ucjpQ/zZo185QwYNboQw89hK+//trUrXLq4bIerVMiIjNxgjBnEre04HtcvHixmcyOQWx2Oh0MkDoZrsyOZqDbwffGCdT4WWdNYh57ZuGyxizxea+//rpnfR5zHicef2Y1839/dWCdzixrERNLg9CHH35oSivw982pgXvdddeZ+sWcSJB1cB3sMDOrgcvq1as99zO717nfyfRlB9upwcvMCEQzXZMAAOcxSURBVE4yx4nvOMGZg7WARUQka1B/U/3NtFB/M3Xqb8KMLmQyA0eB8fxo0KBBJhnE3wS6LIvGJBxiYgPnQ2ECAufucAK37Mt6z50hIklYInJeypUrx784Zlm4cKHn/i1btnjub9y4cbLnHTlyxCpVqpRnHWe58sorPbc7d+7sWZ/bcO7nts/1Gt775Rg/frznvkGDBqW6beI6zv18ruORRx5Jtt/58+f3eU3v7fjz2muvJdtG0aJFrUsuuSTZNnhc/R0Tcu7nazv++OMPsz9Jt1+5cuUUt5OaVatWJdvWo48+GrT2TO/7adGiRar74X2sqH79+snWdz4fKX0Gpk+fbuXIkSPZ87jw/hkzZpzzc3eu35GkvI/Z119/7bl/8+bNVlRUlM8+bNiwwfO49+cypf0dM2ZMsteLi4uzOnbsmOpzk34ORowYYUVHR6f6nIiICGv79u2e51x22WVWzpw5rZUrV5qfX3rpJc/7adSokdWtWzdzOzY21tw+dOiQz2t6H8eUFu+2mzt3rhUTE+N3vWbNmllnz549Z1uIiEhwqb+p/iapv6n+Zqj6mzzXOFd/0/v8b/Xq1VaBAgX8rlenTh3zWRaRlCmzViREWIfy22+/NcP/WZ+yTJkyeO6558zidpydnhNYMUOPEzxdffXVWLhwoU+WZu7cuVPdRq9evUzWKTM+uO61115rMkFZhzQQE2xwf5j5yiu+3OYTTzzhU7csPXhlOOkEcRyOHqz2TO/7mThxIjp37myyW5mVyWzcmTNnprh9Zn3eeOON6Rr6zoxPDmu68847zdA5ZpYWK1bMZEdzeH1Gh+lnBEsH8Ep9WvEqPve1QYMG6N27tyktwSv+/tZjhiszUJlJcMEFF5iSC8wqZnbrgAEDzPO9Mct47dq1JguX2bn8bHM7rCPIzFh+HlhWgdtyMDudWQqcUOy7775D//79TR3dVatWmSyFp556Cr/88ovJCn7vvfd8JjPLCGb8so2YZcs252eKE5y99NJL5nPCifVERCRrUH9T/c20Un8zdepvpg9HoLHMFvvoPFdgH5o1allSgqPcvOdkEJHkIhix9XO/iEiK+LWRtC4ayxmwZieHZDNAyJ+966GKSMoYkB0yZIi5zbIXDIazQ3vmzBlTz5blKRhg5aR1rI8rIiKS1am/KRJY6m+KhI+MTRcvItkaa3OytiyDSgzQsh4mswwZqE06OZOInBuzWpmRzYx11qXlkhTr5jZq1EiHU0REsgX1N0UCS/1NkfChzFoRSTcGlAYPHuz3MQ6nZvafMwGViKQdJ2FgeQuWXtixY4eZaffKK6/EY489ZkqFiIiIZBfqb4pkDvU3RdxPwVoRSTfObs9shzVr1mDfvn2mBhHrXN1xxx2mFi0zBEVEREREMkr9TRERya4UrBURERERERERERFxARWVFBEREREREREREXEBBWtFREREREREREREXEDBWhEREREREREREREXiA71DrhRQkICdu7ciXz58iEiIiLUuyMiIiIScpZl4ejRoyhdujQiI3W9/3ypvykiIiKiPqc/Ctb6wUBt2bJl/R4wERERkexs+/btuOCCC0K9G2FP/U0RERGRlG3Pxn1OBWv9YEat88HInz9/wLIn9u3bh2LFiikbJQyovcKH2ip8qK3S5tTZU+j0RSdz+8M7PkRsdCxCQe0VPoLVVkeOHDEXs51+kpwf9TdF37PhQ20VPtRW4SWU7eWWPne4CGZbHVGfU8Faf5zSBwzUBjJYe+rUKbM9DR10P7VX+FBbhQ+1VdrEnI1Bjtw5zG3+zQhlsFZ/t8JDsNtKJaICexzV38y+9D0bPtRW4UNtFV5C2V5u6XOHi1C0VUQ2LkuqzFoREREXiY6MRtc6XT23RUREREREfW7JPnQWKCIi4iIM0Laq2irUuyEiIiIikmWpzy1upmBtBsXHx+PMmTPpShnn+kwbVxkE9zuf9sqRIweioqIybd9EREQk+/RH4uLi0rW++pvhQ/1NERER8UfB2nSyLAu7d+/GoUOH0v08dsiOHj2aretuhIvzba+CBQuiZMmSamsRSbcEKwGbD242ty8qfBEiI4Jbv0tE3IFB2i1btpj+SFqpvxle1N8UEQkd9bnFzRSsTScnUFu8eHHkzp07zcE4dsbOnj2L6OhoBfDCQEbbi887ceIE9u7da34uVapUJu6liGRFcfFx6P1Nb3N7SpspmuxAJA1GjhyJ1157zfTTateujREjRqBhw4Z+1502bRpeeukl/PnnnyYLtXLlyujTpw86duzoWadLly6YMGGCz/OaN2+OOXPmeH4+ePAgHn74YcycOdOMwmndujXeeust5M2b97zbjP2JXbt2mZE6ZcuWTfMoH/U3w4v6myIioaM+t7iZgrXpLH3gBGqLFCmSrgOtznN4OZ/2ypUrl/mfAVt+VlQSQUREJPNMnjwZvXv3xpgxY9CoUSMMGzbMBFY3btxo/g4nVbhwYTz99NOoUqUKYmJiMGvWLHTt2tWsy+c5brzxRowfP97zc86cOX2206FDBxNQ/fbbb03Ql9vo0aMHPv744/N+T+yD8OJv6dKlTXJAWqm/GV7U3xQRERF/NLYyHZwatenpNEv25HxG0lPXWERERNJv6NCh6N69uwmWVqtWzQRt+Xd43Lhxfte/9tprcccdd6Bq1aq46KKL8Oijj6JWrVpYsmSJz3oMzrKkkbMUKlTI89iGDRtMlu17771nAsRXXXWVyeb99NNPsXPnzoAkCBCDySIpUX9TREQka1KwNgNUc1b0GREREXFHXdeVK1eiadOmnvtYMoA/L126NE2ZjfPnzzdZuNdcc43PY4sWLTLZtpdccgkeeOABHDhwwPMYt8369A0aNPDcx9fkay9btixg7099TtHnQ0REJPtRGQRJ0bZt20yGyj///IMCBQpk+pGaPn06HnvsMWzdulWtIiIiIue0f/9+k4VaokQJn/v58++//57i8w4fPowyZcrg9OnTplzRqFGjcMMNN/iUQGjVqhUqVKiAzZs346mnnsJNN91kgrRcn7Vxk5ZYYOkklljgY/7wtbg4jhw5Yv7nBGJJJxHjzwwkO0t6OOun93mh7nNWr14dO3bsCFqfs1evXmYCt1A7n/ZyPh/+PkMSWM7vpI6z+6mtwkso28t5bee2fr/TdryCcZwS9DdNwdqsxntSi5MnT5oThxw5cpifr776anz99ddp3taFF16IY8eOIZzxRO2NN94wWTP//vuvOXnjydbjjz9u6sA5WHPuvvvuw4oVK8ztVatWoUaNGsk69nweg9f16tUzQx9Z705ERETCS758+bBmzRrTz2EfgTVvK1asaEokULt27Tzr1qxZ05RJYMkEZts2adIkQ685ZMgQDB48ONn9+/btw6lTp3zuYxklnqiwdi2XtOJJlFNCIbOzcr3LQiTtc7IsBCdeSyv2ydhPo/S834xyjlEgX4t9TtZLXrhwoXkvDOYz6M/Plnef86uvvsLrr7+OtWvXmuN15ZVXmr4qJ5JzzJgxA/379zd9zrp165rSHv76nNx/fk6Y9e0ce8kcPM68yMPfsbRO+CehobYKL6Fsr1NnTyHudJxnvpnY6Nigvn64CWZbHT16FNmdMmuzGO/gKk84WrZsabJV/XXumBmSlYfXffjhh+akiJ3kZ5991nSU9+zZg88//9wErjlpiDPkkV827FA/88wzpvZcUhweyYlEOIkJhzlyFunbb78d69atMycnIiIiEnxFixY1/Rn+fffGn1lnNiX8u1+pUiVzu06dOqYGLYOpTrA2KQZy+Vp//vmnCdZy2zyxS9q3OnjwYIqvy+Ab+yTembUM0BUrVgz58+f3WZfBW56osI+RkX5GMAJ33idS1113nekXhUuf05n8NVB9OPY5n3vuOZOty76nd5/z+uuvN/WTnT4n++pPPPEEGjdubI7JQw89hHvuuQc//PCDp8/ZuXNnU//Y6XPeeeedJribdH/5Mz/LnPg4NlZBhswOUrC9+PuqYK27qa3CSyjbi8HamJx2bXheYFOw1j1tFau/aapZm53wF+vtt982GaN58uQxnUVOylG5cmWTYcKMET7uYDkCPufQoUPm5y5dupgJPJhtwvVZw40ZJinhCQ9PTDizMtdnNupvv/3meZxD3Zo1a2ZOUOrXr4/169f7PJ8nMezAlitXzqxz6aWXYvv27Z7ncrii81x2ZMuXL+95Locpvvrqq+b/nj17mqGOfC88geI2OSnI//73P8+JBjNuH3zwQTRs2NDve5k0aZI5EbnlllvMF8eAAQPMSdr333+f4fYQEfEnOjIa7Wu0Nwtvi0jKOAEX+wHMjvU+meDPl19+eZoPHZ/jXaIgKfY7mL1YqlQp8zO3zf4R6+U6FixYYLbj76KvM2EZ+y3eC/GEx9/Cfkt6F/L+P1hL0tfj/o8cOdJkJXPU1/Hjx/Hmm2/i4osvNu+bgXI+7qz/999/m+cwY4c/c7K4Hj16oH379mZ9ZpUuXrw4xddnH42lKnjh3ekbMrjpPM4MVfZHWWKBdYYZnPfeZ/YHH374YdOX5DrsD7LNneeyv+o8l0F9lsdwnvvTTz/htddeM31O9jEvuOAC8174WeE22edk/5n9bq7Pi//sT7JvzP4412GdY2b78vGPPvrIvJ9bb70VuXLlwsCBA02fkxPgpfT+U/oMaQnsMdCxDp/PlNoq9G0QDu0VEx2Du2vebRbeDvVxCIclmG2V3ekIZDMff/wxvvnmGxMIZQeRgVCeXPBnDuvnMH/nyr4/zCy9//77zQlKx44dTQA3NRMnTjRBUw4HYweXHVLH3XffbTqyrO3GjunYsWN9nsttM4OFnV++3rvvvms6rc5zue/MWvjkk0/w/vvv+zyXHdvRo0ebK2TTpk0zAWlmObCDzSAzf+7UqVOKM0Un9euvv5rMG++MFdbz5f0iIoHEAK3TcVSwVuTcmK3KPsSECRNMEI6TgTE4yIAf8e89Lx472Bf49ttv8ddff5n1OQSd/RVmNxKDauwPMQjHC9cM/DJrlAFGBvyoatWqJjDIINzy5ctN34mBOl7Q9h7ynp2pz5m2Picv/PPz5GTNqs8pIhIc6nOLmyll5zxxEuAU5pEI+OHmqLoVK85rE+jXr5/PSUTr1q09t3kVnychzJZl/Sx/br75Zs8QQZ4EMcOUmSYcfuUPT3xq165tbnNIF09siBmy7JxyeFju3LlNxgSDwAywEoOwX3zxhcm0cPaXNbu8n8sgLIO3zNLgc5mhQcyM4WQgLHXA4YjMzGB9MD7/6aef9mTO8Oe01lPjiRtnffbGn1VLRUREJLTatm1r6r7yQi0vAPPiKrMZnUnHOHmVd4YGA7kcTcPMSfYj2AfhCBpuxxkiz4AZg7+8WMx+CDMrn3/+eZMd6+CFZgZoWRaB22efavjw4S7oc55/9159zuD0OVevXm1KdX322Wee+9TnFBEREQVrzxM7zf/8k5Y13VGni5OGeeOJBjNKmDnCoXsnTpwwQ7tS4l2HjZm5xIBlSsHapOs7NXV37txpygl4z6TMTFkHg7Q8IUq6v97PZe04f++LwWPndZmZyzpzTnkDBoydDjE74N6lE1LDIXwcmueNP3MIm4hIILFo//YjdsmXsvnLuqrOo4QGJyretIklfoBffgFYnYhVfLjwz+rRoxE4fLgoPviAJYiyZysxaMrFn6Qlm1544QWzpIRBublz557zNQsXLmyyR93V53TP94X6nKn3OVkajEkQnJSMpb0c6nOKiASH+twpHRfg55+BP/6w+x07dzr/R2DbtmLo2xfwU6JeAkzB2vOUytwVSVjn3ZFO+2ulzDuzhJkmDF4y+4TZshx+xQnJ+KWV2ZilwskzWIPLCdhyf7wDt8xGYOfWe3Zc7+fu37/fE7D1fi5PnpyJRjhkkcMcOUSRWQ3MnCHWmBs1ahRmz56dpv3lLNCcNdp7lmbW2GUtNhGRQDodfxo9v+ppbk9pM0WTHWRDp04By5YBS5bYAdqffuKFyNSewX5FNA4eTAjeTkrQpa0feP79zbS/VurU50y5z8lALScPY0kO1rD1pj6niEhwqM+d3IIFwNNP233P5NiviMLWrZkfLxIFa89bWssSMP7J2XAZEHVLkhSzXBmYZbCUHWoO22I9Ww7hymwMwLLUwpNPPmmGkjHY+s4773ge59BF1ofjUDPW0uXPv/zyi8nScJ7LiSTeeustM4yR9WwdzLrlOqx1ywlAuF3WuOV77datm2kHztjLrGLvQDADwI64uDjzM0s0cDgkyzlwMjYeIw53ZOeagWJnZl8REZHzDc4yAZQLA7SpzHWVTGyshdy5LSQoVovs3ud0Y3+T1OdM7HOuW7fOBGqZ3c2SYmwvb+pziohIsDE4yyAtg7WpKVw4AdHRLupgZGHKrM3GOEEW62ldf/31Zgba2267zSzBwqGDDJ4yWMwaYPfee6/PJGOsFffEE0+YiclYaoGTL0ydOtXzXK7PIC4nbmDH1nso4uDBg03QmTMHs4acd21eBnmdExlvzuRldNlll5n/Ofkaa/lygghm5T766KMmOFyvXj18+eWXybYhIiKSVn//DQwdCnCOzOPHU16vcGHg8suBK64AGjXiCBMOlQZYiYcViaKiLJ+RKiJuoz5nYn/x9ddfNzWWe/XqZRYHR2wxKUF9ThERyQzx8cC+fcD+/b7/z5kDJC2rXr065yhiSSO731mmDBPqEnD4sNPfVMA2s0VYwRjzHmaOHDmCAgUKmJqk+fPn99zPTMstW7aYmq7M3kwPHubETAd9sAONma4MrHJ2ZwezbdkhHjRokJnYrFChQvj999/x9ttvm+C0dyZvoNvrfD4rkj6stewEKbyHXIr7qK3S5tTZU2gzpU3IyyCovTLPb78Br74KfPKJ3XFOiqUtWXuWgzc432flykg1SzJYbZVS/0gCfzwz2o9QfzO8+pzqb4YP/U0MH2qr8BLK9nJLnzuzcI4DZsz++KO9cBTXf1MIpeiii5j4BrRrx2SA0LXVEfU5lVkr4WnVqlWmRAGzD3h7xIgRZjZdb8ysvfTSS03nuX///uZkiJONtW/fHg8//HDI9l1ERLKfkycBxnZYtSdpuXQO7LjrLuD664HGjVm3PVR7KSJJqc8pIiLh0tf87js7U5Z9znXr0v5cZs4OGgR06QLkyJGZeylppTHcEpY4fIz1bDmRGK/sdO/e3ZRUSIqTirE2rYiISLAdPmwHZqdNszvOSUsdsLwBrx0+9BDw33yZIuIy6nOKiIgbcYz8+vV2YJb9zMWL7XkQUgvI1qkDsIpBsWJ235P/lyplJwtoQLC7KFgrYal58+ZmeKCIiIgbsJzBxo3MwktcOOTszJnk67L+V58+AK8xsuasiLiX+pwiIuIWDIFwErD58+3/9+zxvx6rFNSta5fT4pwHXLzmVpcwoGCtiIiIi0RHRuOOKnd4bos7MQjL2l/ffGN3mNesAU6cSHl9Zi9wDs877mDwR0PMRERERELJ7X1uJgJwngOn5uwPPwBbt6a8/gUX2H3MG28EmjQBChUK5t5KoLnvEykiIpKNsbN4b917Q70b4gdnzJ0yxQ7QMpvh6NHUDxMnCmOAtlUrO7PBa0J4EREREQkhN/a5OSnY5MnA1KnA0qWpTwiWN69dvoBzHjBIW61a6hPSSnjRaYOIiIhIKhIS7InBnnzSrkOb0uy59erZC4edcWFNMBERERGR1DJoWXf2gw+A6dOB06f9r8easpddZgdnmTl76aUaqZWVKVgrIiLiIpZlYd+JfeZ2sdzFEKFL5CG1di1w33328LOkZQ2aNbOXG24ASpcO1R6KiIiISDj0uTkp2I4d9sRg69bZCycH27kz+brsWzo1Z/l/7dpATEym76K4hIK1IiIiLnI6/jS6fdnN3J7SZgpio2NDvUvZ0smTwAsvAK++Cpw9m3h/ly7Aww/bs+ly8gYRERERCT/B6nMzc5YZs2+/DaxcmXoZLSYD3H030LmzPUpLORvZl4K14qNLly4oWLAghg0bFpAj07JlS9SpUwfPPvusjrSIiLjW8ePAqlXA8uX28v33wK5diY9Xrgy88w5w3XWh3EuRrEN9ThERycri4oBJk+wL/xs3prwe5zRo0cJOCLj5ZmXPik05IVnMzTffjIceeijZ/UeOHEHu3LmxgDOihPlQhcmTJ6NZs2YoUaIEihcvjquvvhpjx45FPC9Zefnss89wxRVXmPfNgHFSZ86cMceqUKFCKFy4MB5++GGc9U6fEhGRLG3DBmDQIDtzIX9+4JprgL59+fcjMVCbIwcwYADw668K1Ip4U58z0eOPP45LLrkE+fLlQ4UKFTBkyBCfY6U+p4hI9sCQxJ9/Asx943wG3br5BmrLlrUDs48/DowfbycI/PuvnXnbsqUCtZJImbVZTLdu3dC9e3e88cYbyJkzp+f+Tz75BKVKlcJ1YZwSdPr0adx1113m/yeeeAKXXnqpCcSuXbsWI0aMwKRJkzBr1izTUSYGYB977DFs2rQJUzh9dxIvvPAClixZgvUsGAPgpptuwksvvYSBAwcG/b2JiEhwbN5sz7LLhQHYlOTObU/g8Mor9uy6IuJLfc7EPmdsbCymTZuGKlWqmH7njTfeiCJFiqBHjx6p9jkH8EqQiIiEbWCWcxosXWrXnuU8B0wEYCmtpBo3Bvr3t+c6UGkDSQtl1mYxt912G6KjozGdl2a8jB8/Hvfeey+2b9+OG264AcWKFTMZpS1atMDWrVvTtG2ux6LbEydORKVKlUy5BA5hY7aAY+rUqeaxAgUKmKBx0kzVlStX4vrrrzeBVO4Ds1kdn3/+uc9zb7nlFp/yCb169cJFF12EOXPmoEmTJsifP795r8yaff/99836/fr186zftGlTE9wtU6aM3/czbtw4PPPMMyaIzeXpp5822xERkayFGQ4MunLW3EqVgKef9g3UstPMSRu6dwfGjgV++QU4fBiYOVOBWpGUqM+Z2Od8/vnnUb16dURFRZmAbatWrUxw1qE+p4hI1sDwxsKFwIMPAgwzcFTWE08AH35ol9NKGqi97TY7oLtoEdC8uQK1knYK1mYxOXLkQMeOHU2n0MGr+CtWrDCB1YSEBPTu3dsEbf/++2+TmcrAaHp8/fXXWL16tdnu/Pnz8dFHH5n7//jjD9x999148803ceDAAdSvX98EVh3//POPCdTeeeed2Llzp3l9BlOd53K/3377bfPchg0bYu7cuZ7nct1Fixbh1VdfNcHh++67z5RA4GuwlAEzFvr06YPvvvvOPP9c/v33X+zYscOnPAJvb9u2DYd5hi4iImHtjz/sCcL4Nc96s08+CaxY4bvO5ZcDb71lz8q7Zg3w7rvA//4H1Kpl1w8TkZSpz+m/z8mSXeyP1uIXifqcIiJhz7LscgX33w+ULm2PvBo9Gtizx3c9Tjx78cXAHXfYJbR++w2YMcPub4qkl05FzleDBsDu3cE52CVLJj/TTGFYWs2aNU1AtmzZsiZw27x5c0+Gafny5T1DtphNetlll5kgbmQap7VmmQAO++LCYV7MlmUgmLVkmfF66623mvXuv/9+vMWz4P+wTAGDqw/yMtR/WG+WnOdye8QAsvckZ/PmzUPr1q1NJu2YMWOwefNmE+A9ceKEyRRu06aN2f9q1aqZ4WccepaaY8eOmf+ZHexwbh89etRk7YqISPhh0PW554AvvvD/OOvTtmsH8Frhf38ORbJUnzMgnXv1OTPc5+SoLa77wAMPpKnPmSdPnkC0mIiIBBhzuD7+2J5glqOukoqNZVkbO3uWyQFVqtj3iQSCgrXni53mf/4552oRCB52HpmZOmHCBDz55JMmSDpq1Cjz2L59+/Doo4/i+++/92SQsgYsO4ssP5AWJdmB/w87mIcOHTK3mS1brlw5n3W9f2Z2bGWmN/nB5zKw7O3CCy/03N6zZ4/n8V9//dUML2NHlwsnuHAwQO0Eo1OTN29e8z+PQdGiRT23yak/JiISClERUbi50s2e23L+QdpGjYA77wRatQIqVtQRlazb5wxmf5PU5/Ttc7788sv49NNPsXjxYk8QVn1OERF397ktTsh+KAp/7mZcwv5Ty0o2n34KnDjh+5xcuTjBJtCmjf2/QgeSWRSsPV9egcvU8AvgvDvSaXwtJ7uWHcYaNWqYrFkn27V///7mav+qVatMzdg1a9agbt26ZsjW+SpdujSWsrq2F5YVYOauE7j95ptvUnzusmXLkj23Ec+wAbOvLKNAHFbG+rYsuXDq1ClTaoHvb/jw4SZ72DuYnBLW673gggvM+2cdXOJtBoQZtA7E8RARyYgcUTnwwKV2Rpakjh1o1g177z17Fl1vHKbWq5edQet17U8kfKWhfxOQ/mYaX8uhPqeN/W5m4jJQyz5mWvucSed3EBGRzMfT/Xnf5MDHLz2AlSuBnn4mBfPWsCFw3312v/K/vC+RTKWateeLZQlY7O5cy/btOLtli/k/Tev7W9JQAsHRtm1b7N6920zK1alTJ1NXjI4cOWLq1DIjlXW2Bg8ejEBh/VnWsJ09e7bpeI4dO9YMG3N06NABy5cvNx1ZZvMyaMwMX+e5LHXAYC6fy9IN3s9liQQGaOPj481JQcWKFXHJJZfgjjvuMHVwOaEa3y+ziR1cl8Fc1rhl8JW3+bqOrl274sUXXzTP48JZef/HYoUiIuJamzbZdWZZNadwYeCWW3wDtQzSjhgBbN4M9O2rQK1ksz5nIPqb6nOmu8/JORU4im3hwoXJRpmR+pwiIu7BC/1XXWVnxjKDNumkYA5WRmQFx9WrAeaV3XuvArUSPMqszaI4lJ8B0PHjx5uOpoPB2c6dO3uu8nOyMXY6A4Ed2YkTJ+KRRx7B/v37TU0vpwYt8fUYzO3bt68pzxATE4P27duburV8Lju9rO/F53Lf2SHOmTOneS47ysyyZY1dZi68++67ZnEwwMvaYt64L+wcO3LlymU60Fu3bjU/DxgwwASsq1atan6+55578NRTTwXkWIiIZBQvLh05fcTczp8zPyIigj2w2T3OnGHpG3sWXWfZts3/uqVKcfQIa56rXphIMKnPyZnAnzCJEZwzwsH+LSflJfU5RURCn0nLQcCc+GvBAs+9QM4j4DW2yuXyo0zpCHCaH17456gsTiSmsuISKhGWxnsnw+xTDktiDVPviaaYmbllyxZUqFDBTM6VHjzMTkAxO594pwcDuJzMjBm5dPLkSZPVwMzgfv36mfINLPGwYMECDBo0yJRBuOKKKwLy2ufbXufzWZH04Wdg7969KF68eJonyZPQUFulzamzp9BmShtze0qbKYiNjs1W7ZWQYGfKcpZdBmeT1grzxpHGnNjBWbLr122w2iql/pEE/nhmtB+h/mZ49TnV3wwf6sOED7VVeMms9mJ/cv164LvvAA7m5ZK07HuVGqeQp2sblCoJTLkrdH3ucBHM360j6nMqs1bcY+bMmbj22mtNxu3bb7+NXbt2+WTmMjOWJRaYgcuO8/r1600glRm3Q4YMCVigVkREgi8uDvjoI+CVV4CNG/2vkzs3wDLozZvbQ9eqVwd0/VNE0kt9ThGRrOfvv4G5c+1l0SLg4EH/67F8OKtBtrwTaDct2HspkjYqgyCuMXfuXFOigTVmmeHw5ZdfokiRIj7rREVF4d577zWLiIiEv+PHgbFjgTfesEtleitb1q4pxmtxXGrVApJUvBERSTf1OUVEwh+no2H9WVacYYA2pYv9xHIGl18OtGsHdOoEcEqfU5rfUVxMpzziGsym5SIiItkjSMtSB6++Cuzb5/tY48bAk0/aGbTKnBWRQFOfU0QkfPuPc+YA06YBs2ZxuLz/9TgJ7TXXsH64vdSpYwdoRcKFgrUiIiIS1E72mDF2kHbvXt/HbrvNDtIy80FERERE5PBhlq8BPv/czqA9dSr5MYmKsvuPvNDPpV49+z6RcKVgrYiIiGT6DLy//mpnQAwf7hukZeYsh6Q99RRQo4YaQkRERCS7+/dfYMYMO0D7zTfAmTPJ1ylY0L7Qz6VpU6BAgVDsqUjmULA2g7PgiegzIiKSMpY2+PZbOwOCnezdu30fZ5C2bVtgwACgWjUdSRF/LF7pENE5iYhkA/Hxdr+RI7BYh/asn5qyJUoALVsCrVsD116r0gaSdSlYmw4xMTGIjIzEzp07UaxYMfNzRBqL6bGzffbsWURHR6f5ORI6GW0vPi8uLg779u0znxV+RkRE0iMqIgpNKjTx3A43v/0GvPQS8NlnvLiZ/HF+pd51lx2krV49FHso4n45cuQw/Q/2J9jnVH8za1J/U0QE2L8/EuPHA+++C2zdmvyIlCljB2e5XHll4MobhHufW7I2BWvTgcG3ChUqYNeuXSZgm97OGDNyuQ0Fa93vfNsrd+7cuPDCC83zRUTSI0dUDjx22WNhd9B+/hl48UV7yFpSuXMD110HNGsGtGgBXHRRKPZQJHxERUXhggsuwI4dO7DV35lrCtTfDC/qb4pIdrZpE/DssxGYMqUYzpyJSBag5QisO+8EGjViLCbwrx+ufW7JHhSsTSdmSjIIx6zLeObppxEDfwcOHECRIkUUwAsD59NePMFSBrWIZAf//AP8+CPw/vv2sDVvxYoBXboAN95oZ0HkzBmqvRQJT3nz5kXlypVxxl+hvhSovxle1N8Ukexaj/b554G334ZPkJY5Upwc7IEHgJtvBqIVrZJsTB//DGCmJYencUlPZ4zrx8bGKlgbBtReIhLKTKvT8afN7ZxROV01GmP9emDePGDpUjtIu21b8nVKlwb69QO6d7czakUk43gBmEtaqf8SXtReIpKd8NrjO+8wmxY4cCDx/kKFEvC//0Xg/vsjULFi8PbHzX1uEQVrRUREXISdxjZT2pjbU9pMQWx0rCsmC2MA9oMPUl6nfHngySftbFpl0YqIiIgIca7MmTOBJ54Afv898ZjkygX07WuhS5d9KF++GCIjgxssdWOfW8ShYK2IiIj4xQnC3nsP6N8fOHjQ9zFmzbKG2OWX22UObrhBM/KKiIiISGKQlmWyBg605zbw1qEDMGQIa9Na2LvX0iETSULBWhEREUnm119ZyqAwVqxIrNtdoICdPcuJwmrVUi0xEREREUkepF2wwA7SsmyWN17kf/NN+4K/kxggIslpqnoRERHxOHQI6NULaNAgAitWxPhkQHDoGoO19eopUCvuMnLkSJQvX97MDdCoUSMsX748xXWnTZuGBg0aoGDBgsiTJw/q1KmDiRMneh7nhF5PPPEEatasaR4vXbo0OnXqhJ07d/psh6/H+nbey8svv5yp71NERMRtjh+35zMYNQro0QOoWxdo2tQ3UMuL/NOnAz/8kBioFZGUKbNWRERETGbD+PF2yQPWqAXsumEXX2xh1KgINGmigyTuNHnyZPTu3Rtjxowxgdphw4ahefPm2LhxI4oXL55s/cKFC+Ppp59GlSpVEBMTg1mzZqFr165mXT7vxIkTWLVqFQYMGIDatWvj33//xaOPPorbbrsNK1as8NnWc889h+6cTe8/+fLlC8p7FhERCbVp04DBg4HffrOzaf2pWtVep3VrIFKpgiJppmCtiIhINsdsiEceAbzjULlyWXjooWMYPDgPcuXS7LjiXkOHDjUBUwZciUHb2bNnY9y4cXiSqeBJXHvttT4/MxA7YcIELFmyxARrCxQogG+//dZnnbfffhsNGzbEtm3bcOGFF/oEZ0uWLJlp701ERMRt9u8HHnqIF0v9Px4RYWfSPv440K4dEBUV7D0UCX+6tiEiIpINxcfbkz60aQNccYVvoPauu4D16y307n0cOXOGci9FUhcXF4eVK1eiKcdb/icyMtL8vJRXIc7BsizMnz/fZOFec801Ka53+PBhU+aApRO8sexBkSJFULduXbz22ms4e/asmkxERLKsqVOBatV8A7UMzHbrxgubdpmDI0eANWvsEloK1IpkjDJrRUREXCQyIhJXlr3SczvQ/vgD+OAD4MMPgX/+8X2sZk1g+HBmHtplEfbuDfjLiwTU/v37ER8fjxIlSvjcz59/Z5HlVIKvZcqUwenTpxEVFYVRo0bhhhtu8LvuqVOnTA3b9u3bI3/+/J77H3nkEdSrV8+UVfjxxx/Rv39/7Nq1y2T6+sPX4uI4wrNZM7lKglkCgdthADpQ25PMpfYKH2qr8KG2yhx//w088UQEpkxJHG1VuLCF4cMtkz3LbFrfdgiD9rKAKy64wnNbfzvd01YJ6scoWCsiIuImMVExePKq5EO3M+LkSWDdOju7gcuyZb4ZtI5ixYBBg4D77tPEYZI9sHzBmjVrcOzYMZNZy5q3FStWTFYigZON3XXXXebkZPTo0T6P8TmOWrVqmfq39913H4YMGYKcflLSef9gFu5LYt++fSYgHKiTGwaiub/MMBZ3U3uFD7VV+FBbBcb27ZFYujTGLD/+GINt23zz/G688RReeeUIihdP+G+ug/Bsr66V7RJKhw4cCvprh5tgttXRo0eR3SmzVkREJAvhhWjGlMaMATZssMsd+MNhaS1aAF262P/HxAR7T0XOX9GiRU1m7J49e3zu58+p1ZLlSUalSpXM7Tp16mDDhg0mmOodrHUCtX///TcWLFjgk1XrDyc3YxmErVu34pJLLkn2ODNvvQO8zKwtW7YsihUrds5tp+dEiuUauE0Fa91P7RU+1FbhQ22VcewzfvIJ8MorEVi/3v98BYnZtDGIiCiK86X2Ch/BbKvY2FhkdwrWioiIZBGbNtk1w77/PuV1atSwA7T33MOh4sHcO5HAYzZr/fr1TXZsy5YtPScT/Pkhzn6SRnyOd4kCJ1C7adMmLFy40NSlPRdm6vLkpXjx4n4fZ7atv4xbPieQJz08kQr0NiXzqL3Ch9oqfKit0n+h//PPgWeftS/0J8U/XZddBlx/PUdhRaBEicBOPKv2Ch/BaqtI9WHcMcHYyJEjUb58eRM9Z1bC8uXL0/S8Tz/91HxYnM65g2nZAwcORKlSpZArVy4zyQQ72yIiIm536uwp3PrJrWbh7bRmQgwbBtSu7RuorVPHDsy++SawcCFw4ADw229Anz4K1ErWwWzVsWPHYsKECSZD9oEHHsDx48fRtas9tLFTp04mq9XBDNpvv/0Wf/31l1n/jTfewMSJE3EPr2D8F6i98847sWLFCnz00UemJu7u3bvNwgnNiJOXDRs2DL/88ovZDtfr1auX2UahQoVCdCRERETOzbLsScC2bLEnDKtbF2jb1jdQy+Asg7eLFgGHDtn/DxyYtfqPGelzi2SbzNrJkyebTvaYMWNMoJYd3+bNm5tZeVPKTCAOMevbty+uvvrqZI+9+uqrGD58uOm0V6hQAQMGDDDbXL9+vdKpRUQky2BHe/16oG9fe/ZdR8WKwLhxQOPGodw7keBo27atqfvKC/UMqLKswZw5czyTjm3bts0nQ4OB3AcffBA7duwwF/WrVKmCSZMmme3QP//8gy+//NLc5ra8McuWpRKYIcukgWeffdZk5LK/yWCtd5kDERERt/QXWTL96685MSdw8GDKZbKuvBJ4/nnguuuCvZci4qpgLWfM7d69uyf7gUHb2bNnY9y4cXjySf8TrDDDoUOHDmaShu+//x6HeKnHK6uWAd9nnnkGt99+u7nvww8/NB326dOnox2nKhQREQkzf/xhB2A5wT1n5N261c50SOqRR4CXXgLy5AnFXoqEBksepFT2YBHTgby88MILZkkJR3uxP5maevXq4aeffsrg3oqIiATH3LlA9+6cMCz19Ro2tIO0N9zAoe5qHZFsHazlULKVK1f6DE1j5gPLFnB4WUqee+45k3XbrVs3E6z1tmXLFpNVwW04ChQoYLJ2uU1/wVpmRHjXKeOED079Mi6BwO2w4x+o7UnmUnuFD7VV+FBbpe84ObdXrUrAyy9HmFpilpVy7/miiyy8956Fa65xtqP2yi6C9bulPoyIiEh44AV9lr3ihX5HjhxAmTKcJMxeWI69aFHg5puBm25SkFbETUIarN2/f7/JknWGqTn48+9MHfJjyZIleP/9980kDv4wUOtsI+k2nceSYu0yZukmxSF1p06dCtgJzuHDh83JlIolu5/aK3yorcKH2iptWDMr7nQcDh2KQIsWcVg0L3eydaKiLJQuHY+yZeNxwQUJqF79DO655wRy5wb27lV7ZTfB+t06evRopm1bREREzh+v286aBTz4IMv6JN7fpAnw3nscPaKjLBIOQl4GIb0nCR07djSTSBTlJaAAYWavd40xZtaWLVsWxYoVQ/78+QN2IsXJ0LhNBWvdT+0VPtRW4UNt5R8Hc6xebdee3bAhAus2nsLSsjlxmtcKF8V61ite3EKvXpaZAIJZEdHRDMo5gTnOMJ9X7ZVNBet3ixPBioiIiLscOwZ8+y0wcyYwe7bvhft8+YDXX7dLIai8gUj4CGmwlgHXqKgo7Nmzx+d+/lyyZMlk62/evNlMLHbrrbcmG5IXHR1tJiVznsdtlCpVymebSSeJcHCSCC5J8YQnkCc9PJEK9DYl86i9wofaKnyorRJxUvk332RpH+DECa8HoiOANok/lisHPP44cO+9EciVK7hFxNRe4SMYbaX+i4iIiDvs3AlwLswZM4AFC+x+ZVLNmwPvvgtceGEo9lBEwjZYGxMTg/r162P+/Plo2bKlJ/jKn/1NEsHZen/77Tef+ziRGDNu33rrLZMNmyNHDhOw5Tac4CwzZZctW4YHHnggSO9MREQkZQsXAj17MpPWz4NWJHIfboDixYEB70Wi4912jTERERERyb5YKXL6dHtZtsz/OiyJxUnC7r4baNNG2bSpiYyIRINSDTy3Rdwk5GUQWH6gc+fOaNCgARo2bIhhw4bh+PHj6Nq1q3m8U6dOKFOmjKkry+F3NWrU8Hl+wYIFzf/e9z/22GNmlt/KlSujQoUKGDBgAEqXLu0JCIuIiITCrl1A377Axx8n3sdEyHvuAS6/HKhWDahaNQbFig1SA4mIiIhkcywXP2kSMHo0kCRvzeOCCwAOPuZy3XUsWxTsvQxPMVExGHSt+tziTiEP1rZt29ZM5DVw4EAzARizYefMmeOZIGzbtm3pHnbXr18/E/Dt0aMHDh06hKuuuspsU7XWREQkmE6eBFatAn76yV7mzrU73Y6GDe3Od716ahcRERERsa1da/cRP/zQrkmbVK1aAHPRbr8dqFtXGbQiWU3Ig7XEkgf+yh7QokWLUn3uBx984Ldu23PPPWcWERGRYGbO/vADsGQJ8OOPwJo1wJkzydcrVAh45RWgWzc7s1ZEREREsq/du+3SBsuX2+Wyli5Nvs5llwF33WUHaCtWDMVeiki2CtaKiIiEo/h4YPJk4Jtv7ADt5s2pr583L9C+PfDii0CxYv7XOXX2FO6Zdo+5PanVJMRGayybiIiISFby77/AtGnAnDl2kHb7dv/r5cljl8vi9Du1awd7L7M29bnFzRSsFRERyYAdO4COHTkCJOV1WIOWWRDOwp+jos697dPxp9UmIiIiIlkIyxl8+SXw6ad2kNbf6CtH1ap2gLZTJ6BAgWDuZfaiPre4lYK1IiIi6TRjBnDvvcDBg4n35cxp16C96ip74YRhLHcgIiIiItkXM2eHDwe++MKez8DfyKsGDex+ZKNG9v9lyqgOrUh2pmCtiIhIGrGD3bcvMGqU7wy877wDNGliB2xFREREJHs7exaYOhUYNsyeZDap0qU52bq9MFCblpFXIpJ9KFgrIiKShtq08+cDffrYs/M6WrUCxo4FChfWIRQRERHJ7rZuteczGDkyeR3aIkWANm3s+Qs4CkuTzIpIShSsFRERScEffwATJgAffmjXqHXkygW8+SbQo4eGqImIiIhk5wzaH38EZs8GZs0C1q9Pvk6tWsBjj9lB2ljNGysiaaBgrYiIiJdt2+zO9kcf2Z3vpGrWtCeG4GRhIiIiIpL9/PUX8Prrdp/w33+TPx4RAdxyix2kve46XdwXkfRRsFZERLK1hARg+XI7QDtzJvDrr8nXYR2xm28GunQBbr0VyJEj8/YnMiISNYrV8NwWEREREXdYtw54+WXgk0/sMllJA7SXXQa0aAHcdRdQuXKo9lLSQn1ucTMFa0VEJFs6dQoYPRp47TVg1y7/69SoAXTtCnToAJQoEZz9iomKwZCmQ4LzYiIiIiJyTrywP2QIMH267/1589rBWS433ggUK6aDGS7U5xY3U7BWRESyXW2xDz4ABg/2rUPruPRSO3v2ttvsGmPMkhARERGR7Df6irVoWe7gu++STxbGEgc9ewKFCoVqD0Ukq1KwVkREsk2Q9vPPgYEDgU2bfB9jTbGWLe2siJIlQ7WHIiIiIhJqJ08CEycCQ4cCGzf6Pla6NNC3rz3JbJ48odpDEcnqFKwVEZEs2cleswZYvTrx/99+A06f9l2PGbTPPw/Urg3XOHX2FLp92c3cfv+29xEbrWmDRURERDLbvn3AqFHAyJH2bW9VqgC9ewOdOgE5c6otsgL1ucXNFKwVEZEs48gR4K237EyIQ4dSXu/aa4GXXgIuvxyudOT0kVDvgoiIiEi2wOxZ9h0//NCe0yBpn7FPH3ui2UjN+5rlqM8tbqVgrYiIZIkg7YgRwBtvAP/+m/xx1p3ljLx16wL33gvccINq0YqIiIhkV2fOAAsW2Fm0M2f6PhYVBbRpY5c7qF8/VHsoItmZgrUiIhK2Dh4E3nnHnviBt7072XfdBVx1lR2grVnTnq1XRERERLJ3gHbKFGD6dODAAd/H8+UDuncHHnkEKFcuVHspIqJgrYiIhBnLAhYvBsaOBaZO9a1DyyBtx47AM88AF10Uyr0UERERETdYtw4YNgyYNs334r7jgguARx+1A7UFCoRiD0VEfCmzVkREwgInehg/HnjvPWDTJt/HWEPsnnuAAQOASpVCtYciIiIi4hY//2zPUcAs2qRy5wZuucUeiXXbbUCOHKHYQxER/xSsFRERV1u71s6GmDTJN4uWChe2Z+V94AHg4otDtYciIiIi4pYRWD/+mAOjR0dg3jzfx/LkAW69FbjzTuCmm+yArYiIGylYKyIiruxoz50LvPkm8M03yR+//np7qNoddwA5cyJLiYyIROXClT23RURERCT1fuPq1XZ5rKlTI7BxYxGfx0uXticLY99RcxiI+twSDhSsFRERV0z48OuvwE8/AcuWAUuWAFu2+K6TP7/dyWYWbVauRxsTFYOhzYeGejdEREREXG3lSuDjj+1atFu3OvdGeB6vWBF44gmgc+esd3Ffzp/63OJmCtaKiEjIfPUV8PLLdk2xU6f8r8OONid96NrVnqVXRERERLKn+Hhgxgxg6FDghx+SPx4RYaFhwzPo2TMa7dtHIloRDxEJQ/rqEhGRoDt2DOjdGxg71v/jMTHAlVcCDz9sT/oQFRXsPRQRERERtzh6FBg3DnjrreSjrxiQve46oHVr9hstREQcRPHixc0EtCIi4UjBWhERCaoff7SHo/31V+J9FSoAl18ONGpkL3XqZN/haqfPnsaDsx80t0e1GIWc0dn0QIiIiEi2dvgw8PXXdibt7Nl2wNZbtWr26CtOGMZJZykhAdi7NyS7K2FGfW5xMwVrRUQkKOLigCFD8uLttyNMR9qZlZfD2FiLNiKxxFi2ZsHC3hN7PbdFREREsstEYX/+aU8uywDtokX2vAZJNW9uj9C64Qb1H+U8Pm/qc4uLKVgrIiKZ3vGePh0YMCAC69bl9dzPTNqJE7P2ZGEiIiIi4t/Zs8AvvwDff29PLstlzx7/6xYoYGfQPvYYUKOGjqiIZG0K1oqISKYFaTl0bcAAYNUq3mOnzkZHWxg8OAL9+tk1xkREREQk+/QPWRJr0iTgs8+AgwdTXvfCC4Hbb7eXa64BcuQI5p6KiISOTpNFRCTgnfAFC4BnngF++sn3sXr14vDOO9Fo0EA1D0RERESyiz/+sAO0XJJOEObIn9+eYPaqq4CbbwZq11aZAxHJnhSsFRGRgGFw9qmngIULfe+vWxcYPDgBDRocRIkSxXXERURERLK4+Hhg1izgrbeS9w0pVy6gRQvg2mvtAC3LG0RFhWJPRUTcJTLUOyAiIuHv11/tIWqsQ+vdGa9eHZg6FVi50u6MaxIxEckMI0eORPny5REbG4tGjRph+fLlKa47bdo0NGjQAAULFkSePHlQp04dTGQBbS+WZWHgwIEoVaoUcuXKhaZNm2LTpk0+6xw8eBAdOnRA/vz5zba6deuGY8eOqYFFJNs7fBgYNgy4+GKgZUvfvmFkpD0x2IQJdn3aKVOAnj3tLFoFakVEbMqsFRGRDOHsvEuXAu+8A3zyiV3+wFGpEjNpgbZtEzve3o9LyiIQgbL5y3pui0jqJk+ejN69e2PMmDEmUDts2DA0b94cGzduRPHiyTP5CxcujKeffhpVqlRBTEwMZs2aha5du5p1+Tx69dVXMXz4cEyYMAEVKlTAgAEDzGPr1683AWFioHbXrl349ttvcebMGbONHj164OOPP1aTiUi2k5BgTxDGr8CPPgKSXrti3/C++4C77wZKlw7VXookUp9b3CzCYuqA+Dhy5AgKFCiAw4cPm2yJQEhISMDevXvNiUAkLyeKq6m9wofaKrh27ADmzLEnDps3j9+Xvo+XKQMMGgR06ZJ8Egi1VXhRe4WPYLVVZvSPAoEB2ksvvRRvv/2253iULVsWDz/8MJ588sk0baNevXpo0aIFnn/+eZNVW7p0afTp0wd9+/Y1j/M9lyhRAh988AHatWuHDRs2oFq1avj5559Nli7NmTMHN998M3bs2GGefy7qb4q+Z8OH2so/RhJ++cUO0PLCPfuJSTGL9tFHgZtusrNqM5vaKryovcJHMNvqiEv7nMGkqKGIiKTowAG7jMFDD9klDcqWBbp35zBi30Bt0aLA0KHAn3/aj2u2XhEJhri4OKxcudKUKXDwBII/L2Xq/zkwMDt//nyThXsNpxoHJ77Zgt27d/tskycMDAo72+T/LH3gBGqJ6/O1ly1bFuB3KSLiviAta9GydAHnJXjtNd9Abe7cdhbtunXAN9/YpbCUryQiknYqgyAiIj6d759/5rBiYP58O1siJUWKAM2a2ZkSrEeWL58OpIgE1/79+xEfH2+yXr3x599//z3F5zFTo0yZMjh9+jSioqIwatQo3MD0L8AEap1tJN2m8xj/T1piITo62pRYcNZJiq/FxTtrxMlU4RII3A4D0IHanmQutVf4UFslYgC2d+8IzJvnW6opOtoy/cL27S3cdhuQN69z7NRWot+trCCY34MJ6scoWCsiIsDff9v1xT78ENi40f8RYe1ZJpHdeKMdoOVtTQQReKfPnkavub3M7Tebv4mc0Tn1ERUJsHz58mHNmjVmQjBm1rLmbcWKFXEtpyTPJEOGDMFgFvNOYt++fTh16lTATm4YiObJlMpuuZ/aK3yorTjaKgKvv54XH36YGwkJiYHaOnXi0K7dSdxyyykUKWJXWDxxwl7UVuLm363T8acx6MdB5vbgKwYjZ5T63G5pq6NHjyK7U2atiEg2zaBlZgSzZ6dPBxYtSr5ORIQ9tO266+zl6quBbFoyKKgsWNh+ZLvntoikrGjRoiYzdg+nFPfCn0uWLJni83iSUYmz3ZhAQx1Tg5bBVAZrnedxG6VKlfLZJtclrsO6bd7Onj2LgwcPpvi6/fv3N0Fh78xa1tYtVqxYQOdIiIiIMNtUsNb91F7hI7u21f79wMKF7C9GYMoU4NChxCBtuXIWXnnFwp13RiMigsOr3DHEKru2VbgKZXudOnsK+8/sN7f5+rHR9gSiEvq2iv1vMtfsTMFaEZFs4p9/gLlz7QAtlySxDY/GjYGOHYE77uCs6cHeSxGRtIuJiUH9+vVNdmxL1mP572SCPz/EYttpxOc4JQoqVKhgAq7chhOcZWCVtWgfeOAB8/Pll1+OQ4cOmXq5fH1asGCB2Q5r2/qTM2dOsyTFE55AnvTwRCrQ25TMo/YKH9mhrXgxn6W5eSGfE8muWWPf5y1PHuCpp+xSCLGxvqUQ3CI7tFVWEqr24uvxtZ3b+ry4p60i9burYK2ISFbHjjYnfmAd2vh4/+tccokdoO3QAShfPth7KCKSccxW7dy5s5nsq2HDhhg2bBiOHz+Orl27msc7depk6tMyc5b4P9e96KKLTID2q6++wsSJEzF69GjPichjjz2GF154AZUrVzbB2wEDBqB06dKegHDVqlVx4403onv37hgzZgzOnDljgsPt2rUz64mIhJMzZ+wJZTlZLOcu8IeTx95zD/Dii4DXoAMREckEyqwVEcmCmAWxYAHw6qv2LLxJcdIHZtA2aWIvNWvaZQ9ERMJN27ZtTd3XgQMHmsm9mA07Z84czwRh27Zt88nQYCD3wQcfxI4dO5ArVy5UqVIFkyZNMttx9OvXz6zXo0cPk0F71VVXmW16D8v76KOPTIC2SZMmZvutW7fG8OHDg/zuRUQy7tAh4L33AH51bbcrMPlgOSz2E5s2Ba66ys6qFRGRzKdgrYhIFsJa7J99BowaBaxa5ftY0aJAjx7AzTcDDRvaGRIiIlkBg6YplT1YlKQoNzNmuaSG2bXPPfecWVJSuHBhfPzxxxncYxGR4Dt7Fli92r6gz+X774GTJ33XYfWXRx8FWrRgHU+1kohIKChYKyKSBbJolywBxo2zA7VJZ9+tUAHo2xfo0gXInTtUeykiIiIiocDA7FtvAYsXA4cP+1/n1ltZVsYeeaXRViIioaVgrYhIGEpIAFauBGbNApjY9eefydfhnDf9+gGtWgHR+rYPGxGIQPHcxT23RURERDIiLg545hl77gJ/ypQBbr8deOQRe/4CkexEfW5xM52+i4iEiePH7Zl5Z84EZs8Gdu9Ovk6BAsDddwP33msHa5UZEX5yRufE+7e/H+rdEBERkTD2119Au3a+E4axJNb11yculSqpryjZl/rc4mYK1oqIuBgDsgzOzphhB2pPn/a/Hid/YID2jjuAXLmCvZciIiIi4haffALcd589lwFxnoJXXrFr0XrNtygiIi6lYK2IiMvs2gV8+KEdoP3pJ7smbVIMyN5wA3DLLfYEEKVLh2JPRURERMQtOLnsG2/YJbIczJ799FN7xJWIiIQHBWtFRFzi11+BoUPtDvaZM8kfZ0CWkz9w4dA1ZdBmTXHxcXhy3pPm9stNX0ZMVEyod0lERERc6t9/7b7j++8Dq1f7PtaxIzByJJAvX6j2TsS91OcWN1OwVkQkhJg1+803dhbEt98mf7xGDXviBy7MiNDQtawvwUrApoObPLdFREREktq0CXjuOeDzz4FTp3wfy58fGDEC6NRJx01EfW4JRwrWioiEyJYtQPv2wLJlvvcXKgTcfz/QrRtw0UWh2jsRERERcZuTJ4GXX7aXuDjfxxo2BP73P6BtWztgKyIi4UnBWhGREPjqK+Cee+yha46KFYFevYCuXYE8edQsIiIiIpLo66+Bhx4C/vrL9yI/yx3wIn+tWjpaIiJZgYK1IiJBFB8PDB4MPP+878QPnKGXpQ6iotQcIiIiImJLSLDnNWDfcdq0xKMSHQ306QMMGKCL/CIiWY2CtSIiQbJ/P3D33b61ae+4Axg/HihQQM0gIiIikt3xwv6aNcDixfby/fe+I7Ho2mvticOqVQvVXoqISGZSsFZEJJP9+ScweTIwejTwzz/2fcygZa0xZkRERKgJRERERLIr1p6dN8+eLGzGDODgQf/rFS9uT0rboYP6jyIiWZmCtSIimeDvv4HPPrODtCtX+j5WooR9f+PGOvTiX/6cmhVEREQkKzt8GFi0CJg6FfjyS/tnfwoXBq65BrjuOqBTJ6BgwWDvqUjWpT63uJWCtSIiAWJZwPz5wOuvA3Pn+l/nxhuBceOAUqV02MW/2OhYfNTqIx0eERGRLGTfPmDJEuC77+yFpQ5YjzapvHmB5s3tUge8sF+9OhAZGYo9Fsna1OcWN1OwVkTkPJ05A0yZArz2mt3xTqpePaBdO+Cuu4By5XS4RURERLIDBmNnzbL7iAzUpoRzF9x2G9C6NdCsGZArVzD3UkRE3EbBWhGR86gvNmaMXTts2zbfx8qXB7p1A9q2BSpX1iEWERERyU59xI8/toO069cnf5zzFdSqBVx9NXDTTUCTJkDOnKHYUxERcSMFa0VEMlDugJM/PP64PXmYtwYN7PtbtQKi9Q0rGRAXH4dBCweZ24OvG4yYqBgdRxERkTApdfDhh8CwYcCOHb6PVakC3HKLXdrgyiuBQoVCtZciQupzi5u5ovrNyJEjUb58ecTGxqJRo0ZYvnx5iutOmzYNDRo0QMGCBZEnTx7UqVMHEydO9FmnS5cuiIiI8FluZKFIEZHztHo1cP31wB13+AZqW7SwJ4ng1xfLHShQKxmVYCVg7b61ZuFtERERca9Tp+xyWCxjULo00Levb6D2qquAmTOBdevsTFsGbBWoFQk99bnFzUKe9zV58mT07t0bY8aMMYHaYcOGoXnz5ti4cSOKFy+ebP3ChQvj6aefRpUqVRATE4NZs2aha9euZl0+z8Hg7Pjx4z0/59S4EhE5D7t2AU8/DXzwgZ1Z62B2xNChdl1aEREREcn64uOB778HPvmE57PA4cPJ12Hw9okngCuuCMUeiohIOAt5sHbo0KHo3r27CbgSg7azZ8/GuHHj8OSTTyZb/1pOi+nl0UcfxYQJE7BkyRKfYC2DsyVLlgzCOxCRrOzECbsm7SuvAMePJ95/0UV2dkTLlnbdMRERERHJ2pOFcZKwzz+3M2l3706+zgUXAPfcA3TubJc9EBERCbtgbVxcHFauXIn+/ft77ouMjETTpk2xdOnScz7fsiwsWLDAZOG+wkiKl0WLFpls20KFCuH666/HCy+8gCJFivjdzunTp83iOHLkiPk/ISHBLIHA7XB/A7U9yVxqr/CRWW3FzTFb4qmnIrBjR2I0tkABCwMGWOjZE4iJsbNsvTNtJfhtlVWPk3M7VMdL7RU+gtVW+t0VkayMf3rXrgXmzLFrz548aS8sc3DiRASWLSuGnTuTVxHMkwdo3Rro1ImJRUBUVEh2X0REspCQBmv379+P+Ph4lChRwud+/vz777+n+LzDhw+jTJkyJsAaFRWFUaNG4YYbbvApgdCqVStUqFABmzdvxlNPPYWbbrrJBIC5flJDhgzB4MGDk92/b98+nOJf5wCd4HC/eTLFgLS4m9or+7QVO+b//huBffuisG9fpGeZPj0Wq1cnTuwUFWWhU6cT6NPnGIoUsXDoUIDfSDag36u0OXX2FOJOx5nbe/fuRWx0LEJB7RU+gtVWR48ezbRti4iEAq9xcb6BadOAL75IPnFsIl64TzyPZIW9m26y5ylguQMGbEVERLJMGYSMyJcvH9asWYNjx45h/vz5puZtxYoVPSUS2rVr51m3Zs2aqFWrFi666CKTbdukSZNk22NmL7fhnVlbtmxZFCtWDPnz5w/YiRQnOuM2Fax1P7VX1m+r334DJkyIwMcfA3v2pF7H4KabLLz6qoVq1XIB4CLBbKvsGKyNyWlfKOAIkVAGa9Ve4SFYbcWJYEVEwh3LWs2fD8yeDcyaBezcmbbn5chhoVkznmtGmABtgE4TRURE3BWsLVq0qMl03bNnj8/9/Dm1erM8EalUqZK5XadOHWzYsMFkxyatZ+tgIJev9eeff/oN1rK+rb8JyPg6gTzp4YlUoLcpmUftlfXaav9+u7QBJwlbterc261e3Z48rFkzBnNVmDaYbZWd8dg4AdpQHyu1V/gIRlvp91ZEwmXyL8454L0wQMsMWgZoFy5kGbzkz+PXJyeOveMOoFYtIFcuIHdu+/+cORNw5sxelCtXHJGR6hOKZBU5o5LHgUSQ3YO1MTExqF+/vsmObclZev7LDuHPDz30UJq3w+d415xNaseOHThw4ABKlSoVkP0WkfDBMgfffQeMHm0PcTtzxvdx1p29+mp7QghWZOHCa0UXXghcdhkQHZbjDyScMVD7+V2fh3o3REREwsKBA8APPwDff28vK1cCZ8+m7bnM12G2LAO0t97KZKKUyyXs3RvQ3RaREFOfW9ws5GEIlh/o3LkzGjRogIYNG2LYsGE4fvw4unbtah7v1KmTqU/LzFni/1yXZQ0YoP3qq68wceJEjGYkBjClEVh/tnXr1iY7lzVr+/XrZzJxmzdvHtL3KiLBc/gw8OGHwJgxwPr1yR+/9FKgSxcOZQMKF1bLiIiIiLg9Y3brVrtft26d/T8Ds/76eakpUwa45RagRQvg+utVb1ZERNwn5MHatm3bmom8Bg4ciN27d5uyBnPmzPFMOrZt2zafYXcM5D744IMmWzZXrlyoUqUKJk2aZLZDLKvw66+/YsKECTh06BBKly6NZs2a4fnnn/db6kBEsg5O+jV3LjBzpj1JBIe9eWO2ROfOdpC2Ro1Q7aWIiIiIpMXu3cDkycBnnwGrVwMnT577OZdcYo+QYgkDZ2EpA46iuvFGu8RBhCoZiIiIi4U8WEsseZBS2QNOCubthRdeMEtKGMCdy2iNiGSL7Nm//gJmzMiNxYsjzNA3Zl0kxTIH998PtG5tD3cTcbO4+DgM+d4eTdL/6v6IibInGxMREcku/TtedOcksJwIjCUIUhIVBdSrB1x1ld3f4//FigVzb0UkXKnPLW7mimCtiEhq4uKAr7+2Fw5/277dXo4e5aPMvE8+HS9n6L3nHuCBB5RFK+ElwUrAil0rPLdFRESyQ19vzhxg0iTgyy/9TwB20UVAzZr2BLDVqtkLs2iZNSsikl7qc4ubKVgrIq6dGOynn4CJE+3hbwcPnvs57MRzcgjWIWN2BScPExEREZHw7OtVqADcfbe9MDgrIiKSHShYKyKuceSIPZsvq59MnQps3ux/PZYyKFvWWSxUqHAUd92VF1WrRqoGmYiIiIgLnTkDrFkDLFli9/f4/549yddjGQNOR9KhA9CokerLiohI9qNgrYiEdEIw1pllcPa774BVq/zXJePwtjvusDvtl15qTxTmTAyRkGBh794TKF48rwK1IiIiIi7DicGefhpYvDj55K/efb2WLe0SVjfcAOTIEey9FBERcQ8Fa0UkaDi8zQnOssPO7AoOgfOHwdgmTexOe6tWQL58aigRERGRcKpD++KLwEsvAWfPwu/8ApwQjFm0vCivvp6IiIhNwVoRydSyBgzOLlgALFyYenCWatQAGjdOXIoXV+OIiIiIhGM2bZcuwK+/Jt5XujRw7bXAlVfaQVpOFBYVFcq9FBERcScFa0XkvDFbYts24M8/7WXTJmDpUmDFCiA+PuXM2dq17U47A7OcEKxIETWGiIiISLg6dQoYMsQ3mzY6GnjqKbsUgiZ/FREROTcFa0UkQx1xljGYPRuYN88O0HLSiHNhcPa66+wALYOzhQvr4IskFRsdi5ntZ+rAiIiI63HE1Lp1wDff2Av7h+wnOmrVAj74AKhbN5R7KSKSnPrc4maRod4BEQkPBw4AY8fakz9wgq8bbwRGjAA2bEg5UFu1KtCzJzB1KrB/v10G4c03gdtvV6BWREQCZ+TIkShfvjxiY2PRqFEjLF++PMV1x44di6uvvhqFChUyS9OmTZOtHxER4Xd57bXXPOvw9ZI+/vLLL6tZJVvghfoHHwTKlAFq1gT69AHmzk0M1DKbduBA4OefFagVERFJL2XWikiqOGvvsGEAzz+PHvXzJRJtB2UrVwYqVbIX3uZ9JUro4IqISOaaPHkyevfujTFjxphA7bBhw9C8eXNs3LgRxf0UP1+0aBHat2+PK664wgR3X3nlFTRr1gzr1q1DGUaeAOzatcvnOV9//TW6deuG1q1b+9z/3HPPoXv37p6f82mGJMni1q61yxx8+imQkJD88QsuAJo1Ax5+GKhTJxR7KCIiEv4UrBURv1hrdtIk4JlngB07fB/jue9NNwG33ALccANQoIAOokigxMXHYejSoeZ278t7IyYqRgdXJBVDhw41AdOuXbuanxm0nT17NsaNG4cnn3wy2fofffSRz8/vvfcepk6divnz56NTp07mvpIlS/qsM2PGDFx33XWoWLGiz/0MziZdVySrlTk4dMgudfD66/xd8H08d267vBUDtFyqVLHnJRARcTv1ucXNFKwVEYOTQOzbx2wie2gbJ4b45ZfEgxMZCdx7L8AEogYN/t/efYBHUbVtHH9SCAHp0gRpCoIiAtLEhgqCXUQRFRtix/6qiAVsiKAioii+KlYUxF7xQxQriMCLSrWL0kF6SUiy33WfYZJNSCCBTXZ28/95jZnsbnYne0Jy9t5nnmPucwCRlxXKsm/+/sbt33DYDTzFwE6kp6fbzJkzbcCAAWF/rxJda4OpWumyEDZv3mzbtm2zagU0Ul++fLkLf1988cUdrlPbg/vuu8/q169v5513nt14442WrFNO8pGWluY23/r1693HrKwst0WC7icUCkXs/lC8gjZemv+9/rrZ118n2IoV+tn35obbtu2YvlavHrIbbgi5Vgjhb9or3NUWb4I2VigYYxVbojleGZkZ9vWir93+de2vs+QE4rGgjFUWv2sJa4HSXDk7Zowqisz++subjBf0O1EVtGqD0Lx5SR8lAAAFW7VqlWVmZlqtPH139PmCBQsK9dT179/f6tSp4wLe/CikVQVtjx49cl1+3XXX2aGHHupC3m+//dYFxmqfoErf/AwZMsTuueeeHS5fuXKlbQ1fkWkPX9ysW7fOvZhSaI1gC8J4LV6caO+/n2rvvFPOfvihzC5vX7t2pl111SY7//wtVr58yPT+g4LdeBeEsULhMFaxJZrjtTVjq6Wnpbv9FStWuAXHEIyx2pBf/8VShrcOgFJIK/Vef33uytn8tGljprVUjj22pI4MAICSo8rYcePGuT626l+bH7VT6N279w7Xq0+u75BDDrGUlBS74oorXChbtmzZHe5HYW7416iytl69elajRg2rVKlSxF5IaaEz3SeBUvBFc7xUeH7XXQn2+ef59yxITg65tlfaatTwPnbqFLLzz0+wsmUrmJm20oN/W7GDsYot0RwvhbUpZb12Y+pxT1gbnLFKLWBOVpoQ1gKlyJ9/mt1yi9kbb+y4GMQ++3ibWu9pU1CrilqKBwAAQVW9enVLSkpyrQrC6fNd9ZJ9+OGHXVj76aefurA1P1999ZVbqEyLmO2KFjfLyMiwP//805o2bbrD9Qpw8wtx9YInki969EIq0veJ4lPS4/X772Zq5Txhwo7Xae53zjlmWkevYcOEfHrPlu5mtPzbih2MVWyJ1njp8fTY/j5/N4MzVonMYQhrgXhvdTBvntn06WbffqtFVdQzL+f61q3NHnvM7KijonmUAADsHlWztmnTxi0O1r179+zKD31+zTXXFPh1w4YNs8GDB9snn3xibdWIvQDPPfecu/+WLVvu8lhmz57tXlyoOgcIGi0Sdv/9Zo8/rl7POZc3bmx20UVmvXqZNWkSzSMEAAA+KmuBOKIFHWbP9ipnFc7OmGG2ceOOt9PrSC0gdvHFZklJ0ThSAAAiQ60FLrroIhe6tm/f3kaMGGGbNm2yPn36uOsvvPBCq1u3rmtPIEOHDrWBAwfaq6++ag0bNrRly5a5yytUqOC28DYFEyZMsEceeWSHx9TiZd99950de+yxrp+tPtfiYueff75VrVqVoUVgLFpk9swzZk89ZbZ6dc7lam1w771ml16qlgfRPEIAAJAXf5qBOLBwodlrr5mNG+ftF0RnX153ndmdd5pFqD0eAABR1atXL7dIlwJYBa+tWrWyiRMnZi86tmjRolyn0z311FOWnp5uZ511Vq77GTRokN19993Zn6uXrRbROPfcc3d4TLUz0PW6fVpamjVq1MiFteE9aYFonln18cdmo0d7H8MXkFUbQP2Y9u/PXBAAgKAirAVi0JIlZtOmedukSV41bX7q1VMPPbP27b2P6kW2114lfbQAiqJsUlmb0HNC9j6AXVPLg4LaHmjxsHDqKVsYl19+udvyc+ihh9o0/REGAhbSPvGEmYrB//4793U6k+q887xWCPXrR+sIASA4mHMjyAhrgYDbtMls1iyz7783++47L6DVKW35UX909Z9VEdBpp5nVqVPSRwsgEo37WY0WAFAUv/3mtbf6+usd37jXew6XXMK8EACYcyNWENYCAbNhg9fSYOpUL6CdPz/36Wv5adfOC2h79jTbd9+SOlIAAABEe70CtTu4+WazzZtz3rw/+WSzK680O+EE1icAACDWENYCAaEJ9pNPauETs1WrCr6d2hgonO3Y0eyww7z2Btvb8gGIA9syt9mo70e5/X7t+lmZpDLRPiQAQAD9849Z375m//d/OZftt5/ZCy94Z1oBAArGnBtBRlgLRNnWrWb//a+ZFqneviB1Nq3Oe8ghZm3begGtPh58MKv2AvEsM5Rpk/+Y7PavbHullTHCWgBA7mpanYXVr5/Z2rU5l19xhdnDD5tVqMCzBQDMuRHLCGuBErJwobcQ2OrVZv/+m/Pxs8+8ygifTl1TSwOtk9K6tbdqLwAAAKC541VXmb3+es5zoTUKnnvOa3kAAABiH2EtUMwUyt52m9mzz+76tmeeaXbPPWbNmzMsAAAAyKF2B336mC1ZknOZ3uAfNcqsalWeKQAA4kVitA8AiFdaFGzMGLOmTXcd1J5yitmsWWZvvEFQCwAAgJyWB3//bXbttWbduuUEtVWqmI0bZ/bqqwS1AADEGyprgWLw44/eKWrffptzWcWKZjfc4C38sPfeZtWqeR+1OBjVEAAAAKWbgtkVK8x++MFs+nRv+/77Hdc0OP54s+efN6tbN1pHCgAAihNhLRBBmZlm999vdt993r7vnHPMHnnE6ykGAACA0mv9erNFi8z++MNs5szy9s8/CbZggdm8eWZr1hT8dVrH4KGHzK6+2iyR8yMBAIhbhLVAhKjq4bzzzD7/POeyAw7w+oh16cLTDAAAUBrbYqldwfjxZn/+afbXX2br1vnXKnGttNOvV7uDtm3N2rc3u/hisyZNSuKoAQBANBHWAhEwebJZ795my5d7n6va4a67zAYMMCtblqcYQOGVTSprr5zxSvY+ACA2qR3WjTd67QwKY999zQ480Fu/wA9o99+fKloAKA7MuRFkhLXAHlCrg3vv9doeqM+YqNXBa6+ZHX00Ty2AoktISLDKqZV56gAgRql6tn9/r5o2XJkyZvXqmTVoYFa/vvZDVqPGOuvQoZIddFCiW98AAFAymHMjyAhrgSKGs+onpkqJb74x++or75Q2n1bpffllsxo1eFoBAABKk61bzQYP9vrKpqXlXK5KWa1doIXBwnvNZmWFbMWKrVazZiV60AIAgMiEtevWrbNl25cnrV27tlWuTCUQ4sPmzWYLFiS7UPb3381++cXbZs70FoXISxNvLSymKgoWfACwJ7ZlbrNnZz3r9i899FIrk1SGJxRxLW17qlWWvkGIYTNmmF10kfemvq96de/sq0svNUumRAYAAoU5N4Jst6YNzz77rA0fPtwWLlyY6/KmTZvaf/7zH+vbt2+kjg8oNtu2mb34otmXX5qtWpV727BBZQ/Vd3kfel152GHeRPyooxgsAHsuM5RpH/36kdvv07qPlTHCWsSfSZMm2aOPPmpTp0619dvfBa1UqZJ17NjRbrrpJuvCypyIEenp3jxwyBDvDCy/3cH115vdcYe3QBgAIHiYcyOuwtqHHnrI7r77brvuuuusW7duVqtWLXf58uXL7f/+7//s+uuvtzVr1tjNN99cHMcL7DH1ln3nHbPbbjP7+eeifW3t2mZHHGF2+OHe1ro1C4gBAFAUL774ol166aV21llnucA271zypJNOsueee84uuOACnlgE2uzZXjXtjz/mXHbooV4xwMEHR/PIAABAqQprn3jiCXv++eft7LPPznX5gQceaMccc4y1bNnSbrnlFsJaBNK0aWa33GL29dc7XpeQYLb33t4pa9Wrh6x27a128MFlrUmTRGvc2NxWrVo0jhoAgPgxePBgGzFihPXr12+H6y6++GI78sgj7d577yWsReD8/bfZ1Kk5m1of+NW0anNw111mAwZ4lbUAAAAlFtauWLHCWrRoUeD1um6VziMHAlRJ+913ZsOHm02YkPs6tS7QqWta+KFqVbOkpPAFH9ZZzZo16UELAEAELVq0aKdtDjp37uzaagFBsHGjty7BK6+YLV6c/2300kjVtDrjCgAAYE+FrUdaOO3atbMHH3zQMjIydrguMzPThg4d6m4DRNuGDWajR3sT544dcwe1TZt6rRC++MKsUyevmtYPagEAQPFp3ry5a3NQkDFjxthBBx3EECDqPvjAe0N/6ND8g1rNJ++916uwJagFAABRbYOgXrW1a9e2o48+OlefsS+//NJSUlJcvzEgmlW0zz9vNnas2aZNua+vWdPs7ru9VXk5RQ0AgJL3yCOP2CmnnGITJ050Fbbhc8nJkyfb77//bh9++CFDg6hRMKsFwt58M/eisjojSwvLqghAH2mPBQAAAhHWHnLIIfbzzz/bK6+8YtOmTXMTalF4e//999t5553nVvMFSjKg/eEHs3HjvO2vv3a8TYcOZldeaaZWy+XLMzYAAESL1jiYM2eOPfXUU24uuWzZsuy55IknnmhXXnmlNWzYkAFCiVP/2aeeMrv9du8MLd/xx5s9+aS3fgEAAEDgwlqpWLGiXXXVVW4Dojmhfuwxs//+12zhwh2vr1DBrHdvsyuu4NQ0ALGjbFJZe+6057L3gXikMFats4Cg+Okns8su887QCj8j69FHzc4911uIFgAQP5hzI+7C2p3Ztm2bLV261OrXrx/puwayrV9vdt55ZnnPklTfWa1Z0quX2Vln6Y0FnjQAsSUhIcFq7lUz2ocBAKXCli3eYrMPPWQWviSHgtsHH6TVAQDEK+bcKFVh7bx58+zQQw91i40BxeGPP8xOPdVs7lzvc1U6HH202TnnmJ15plmNGjzvAAAE2ZNPPmlvvfWWVatWza644grr3Llz9nWrVq2y9u3bZ7faAorL55+bXX652a+/5lzWrJl31pb60wIAAERDYlQeFdhNX31l1r59TlBbtarZp5+aTZni9aQlqAUQ6zKyMmzM/8a4TftAvBk5cqTdcsst1qxZMytbtqyddNJJNmTIkOzr9Yb/X/k1oAcitNbB11+bnX662XHH5QS1Wnh20CCz2bMJagGgNGDOjbiqrFXV7M5s0blEQDF4/nmv/+y2bd7nTZuavf++WZMmPN0A4mvi+PaCt93+eS3Os+TEiJ8EA0TV008/bc8884xblFa0BkL37t3dHPLee+9ldFAsdNLfe++ZDRtmNm1a7uuOOMKrpj3oIJ58ACgtmHMjyJJ3p83BOeecY40aNcr3evWr/fnnnyNxbIAzZ47ZXXeZvfNOzhPStavZ+PFmVarwJAEAEEv++OMPO/zww7M/1/5nn31mXbp0cWsf3HDDDVE9PsSfcePMBg40++WX3JfXrevNMdWfNpHzDQEAQKyGtQcffLB16NDBVUHkZ/bs2a5aAohEb1qdjvbKK94pa75rrzUbPtwsmWIzAABiTvXq1e3vv/+2hg0b5ppfKrA97rjjbMmSJVE9PsQPzR9VrH333bkvP/hgs1tu8dY7SEmJ1tEBAADkr8jvIR9xxBG2cOHCAq+vWLGiHa3VnoDdtHy52TXXeG0OXn45J6itU8fsxRfV646gFgCAWHXkkUe6xcXyOuigg2zy5Mn28ccfR+W4EF+yssyuuy53UHvssWb68frxR7MLLySoBQAAwVTk2sTHHntsp9fvv//+9rmWVgWKSKHsa695Qe2aNTmXV6tmdttt3uXlyvG0AgAQy2677TabOXNmvtc1b97cVdi++eabJX5ciD1paWZly+54eXq62cUXe/NKn87KuvHGEj08AACA3cKJ5AiEFSu0wIhZeKHNXnt5k+qbbzarXDmaRwcAACLlkEMOcVtB1BJBG1CQDRu8eePYsWaNG5t17mzWpYtXOZuaanbWWWYTJ3q3TUoyGzPGq6QFAACI+7BWC0RMmjTJ1qxZY40bN7bTTjvNypQpE7mjQ6mggPbKK81Wrsy5TD3ERowwq1UrmkcGAACKE3NJFNXcuWZnnmnmd2X79Vdve/pps4QE9UTOmVMquH39dbNTT+V5BgAAsWO31j0NhUJ23XXXWbt27ezrr7+2f//910aPHu2qJFgUAoWlMyAVymrC7U+qNcGeMME7bY2gFkBpVDaprI06aZTbtA/EI+aSyM8335i1b2928slmr75qtnlz7utVSavr/aC2fHmz8DoRtdTy55SVKpl98glBLQAgf8y5EXeVtX369LEVK1bYzz//bNXUUHS7hx56yPr162dvv/22zZo1yw499NBIHiviwPr1XhD73/+azZqV+7ru3c1GjyakBVC6JSQkWP3K9aN9GECxYi6JvL76yuzEE802bfI+/+gjswoVzHr0MDv/fLO33zZ76qmc27dsaabWxrVre1/76afe9sMPZvvua/b++2atWvE8AwDyx5wbcRXWfvLJJzZlyhSbN2+evfvuu7Zt27bs6ypWrOhW8E1LS7O+ffvawIED7Ywzzoj0MSNGaHGHP/80+/13b5sxwzsVzZ+E+6pWNRs50qx3b+/0NQAAEL+YSyK/itqTTtpxjrhxo9lLL3lbuD59zEaNyll49oQTvM0vDNCiY/ktPAYAABCXYe2zzz7rWiCUL1/eJk6caK+++qo1atTIKlSoYD/88IOdcMIJtnnzZrvzzjtt2LBhhLWlzJYtZnfc4VU6/P23dzpaQdq1M7v8cq8VgionAABmGVkZ9vrc191TcXbzsy05kbVAEV+YSyLct996QauCWdH+rbd6bRDUGmvdupzbKoBVSNu3b8HPodofAACwK8y5EVc9a9Xe4KijjnL7Cmxvu+021w5Bl7/11lu2du1aq1q1qgttZ8yYYZvyvkWej1GjRlnDhg0tNTXVOnToYNOnTy/wtnqMtm3bWpUqVWyvvfayVq1a2csvv7xDHzRV9e6zzz5Wrlw569Kli/3yyy9F/VZRRFrcoWNHs0cfNVu0KP+gtmJFb/VetUDQMF96KUEtAOSdOL425zW3aR+IN9GeSz7zzDPu8fUY2jRPzHv7iy++2J0eGb7peMJpzYbevXtbpUqV3LxUZ5Vt9BNHFMq0abmD2q5dvXYHxx6rcTJbtsw7K0sn6nXpYjZ16s6DWgAACos5N+IqrF2/fr2bCMs777xjPXv2zL7utNNOs++//96WL1/ugtSkpCQ34d6Z8ePH20033WSDBg1yk/SWLVtat27dXE/c/KhH7h133GFTp061H3/80fU806ZT6nyq6B05cqRb9Oy7775zx6L73Lp1a1G/XRTSW2+ZtWnj9QnzKx9UOdurl9mAAd6E+7PPzJYuNXvySbPWrXlqAQAojaI9l1Q7r3PPPdc+//xzN5+sV6+ede3a1RYvXpzrdgpnly5dmr29pqb7YRTUzp071yZNmmQffPCBffnll3a5ThlCoXz3nVm3bmYbNnifH3+8fh7Mtv9oONrXj4fmmZMmMX8EAAClQ5HD2rp169off/zh9vfdd183yfa9//77VqZMGdt7771t1apVlpmZadWrV9/p/Q0fPtwuu+wyF7gedNBBLmBVlcWYMWPyvf0xxxzjWisceOCBtv/++9v1119vhxxyiH399dfZVbUjRoxwbRhOP/10d91LL71kS5YsyXWsKBot2KDJ8g03mD3/vNn//meWlmamlsX/+Y/ZmWd6PcKkaVOzmTO9ytlx48weeMCroFWVxF578cwDAFCaRXsuOXbsWLv66qvd2VnNmjVzbRmysrJs8uTJuW5XtmxZq127dvamKlzf/PnzXTswfa0qeY888kh7/PHHbdy4cW7OiZ1ThayqaP25Y+fOXlDr96AFAAAozYrcCO/44493FQyqfHj00Uete/furm+tqh8WLFjgTkNLTk52E+/DDjvMTXQLkp6ebjNnzrQBKr3cLjEx0Z2OpkqHXVEw+9lnn9nChQtt6NCh7jJN/pctW+buw1e5cmU3kdZ9nqMGqSgSrbx7zTVmWVm5L09ONtt7b7Ply3MuUyWtqmjV7gAAACDIc0nRWgtaMFdnb+WtwK1Zs6YLaY877ji7//77XYgsum+1PlBrLp8eU4+ts7ryW2BXC/BqC68wFgXF2iJB96P5caTurziovuLkkxNs40ZvVdljjw3ZO++EXBVtgA+7WMTCeMHDWMUOxiq2RHO8/Mf29/ldXLjnqySepyz+LhY9rL322mutRYsW7hQ1VRH89ttv9u2337rJcrt27axOnTquh9e9995rT+p8953wKyZq1aqV63J9rsl6QdatW+eqMjTh1elxehxN/EVBrX8fee/Tvy4vJs/507+PAQMS7OGHvcl0XhkZOUFtmTIhe+SRkF19tVlCQuxPtvkjHzsYq9jBWBXtefL3ozVZYbxiR0mNVaTuPwhzyXD9+/d3jxn+Rr9aIPTo0cMtoqvju/322+3EE090Ia3mnppTKsgNp4BZgW9B880hQ4bYPffcs8PlK1eujFirLo2R5sn6eVBwHDTfflvGLrigqm3e7M0tjz46zZ59do3rWVsa2/0GfbyQg7GKHYxVbInmeG3N2GrpaeluX62TUpPD+vAgqmO1we+RVIoVOaytX7++PfHEE67FwIsvvuhCUk1efb/++qvrPXbKKae4rThUrFjRZs+e7RZx0Clr6lO23377uRYJu4PJ8470muH66yvbe+/lnI925ZWb7Nhj02zu3GSbO7eMzZuXbL/8kmx162bak0+us0MP3WYrV1pc4I987GCsYgdjFVsTR8YrdpTUWEVq4hyEuaTvwQcfdK0LVEXr99GV8DOxFCyrrZbab+l2nXXO/m5Q9a/mrOGVteqXW6NGDbdIWaR+FrQYmu4zaOGf1i44//wE27LFC2q7dg3ZW2+VsXLlcofepUmQxwu5MVaxg7GKLdEcL825U8qmuH29AUtYG5yxSg1vYF9KFTmslQsuuMCd+qX+YPqhPvzww61cuXJuwS+tpnvrrbfaLbfcssv7UQ8yVSdoEYlw+ly9wQqiH4zGjRu7ffUbU98wBa4Ka/2v033ss88+ue5Tt81PaZ8857V6tTeZ/uYbbzKdmBiykSNDdtVVCm7L2Vln5dw2I8N7YZiYmNPHLR7E0niVdoxV7GCsYmviyHjFjpIaq0hOnKM9l5SHH37YhbWffvqpC2N3RkUBeiwFyQprdd95FzDLyMhwFcEFPa7aOeTX0sGbR0Vu3PSzEOn73FNaHOy007xiADnpJLM330yw1NT8z94qTYI4XsgfYxU7GKvYEq3x0uPpsf19fg8HZ6wS+Zu4e2GtnHrqqe4UsS+++MJ++uknN0G99NJLXXWCKl8LIyUlxdq0aeOqY9WvTPwFHq5Rk9RC0tf4PcB0upomyboPP5xV+Kr+YVdddVW+X1+aJ895LVrkrcb788/e5+XLa5XlBDvllPwn0ylenhCXYmG84GGsYgdjtWupZVLt0W6PZu8nJkTvdxDjFTtKYqwifd/RnEsOGzbMBg8ebJ988kmuvrMF+eeff2z16tXZhQAdO3a0tWvXun65enzROgp6bK2TgBxqHXzqqd7CtKLQ9vXXNf/mWQIARE9KUooN7zo8ex+Ii7BWtFqv+nuF9/gqKlW0XnTRRW6i3L59exsxYoRt2rTJregrF154oetPq8pZ0UfdVqeiKaD96KOP7OWXX7antArW9hcrN9xwg1sEokmTJi68veuuu1wvMn8Sj/z99pvZccd5ga2oMOSDD8y2vwYBAJQAhbNN9m7Cc41SIRpzSS1KO3DgQLeoWcOGDbN7zFaoUMFtarOl3rJnnnmmKwBQz1pV+uqsrm7durnbHnjggS5oVmXw6NGj3QJlCofVPkFzTnj+/ttMa635Qa32x42L7zf7AQCxgTk34iqsVcXA3LlzXf8u0QRVC0L4dCqaKlgLW33Rq1cvt7CCJs2aLKsaduLEidkLRSxatCjXfWnyffXVV7sKB50u16xZM3vllVfc/fg0odbtLr/8clf1oMUrdJ/0vSjY/PlmasG2dKn3eZMm3ilrDRoU9ScEAAAguHNJvcGvxzsrvK+TmQ0aNMjuvvtu9/hqx6B+uppHKnzt2rWr3XfffbnOxBo7dqwLaNUWQfevcHfkyJEM/XabN5upTsLvUHHssTpbSwE9TxEAAMDOJIT8JacLSVUImlR/+eWX7nOdpqaeY1oB11+VVxUNffv2tViltgmVK1d2i3VEsmetepupL1vQTqv/4Qev9YG/OFjz5maffupV1pZWQR4v5MZYxQ7GqnAysjLsvYXvuf3Tmp5myYl7dBLMbmO8YkdJjVWk5kelYS5Z2uebenVx7rleOCuNGpl9/73Z3ntH7ZACKSjjhV1jrGIHYxVbojleQZlzx4qSHKv1xTBHijVFfoaff/5569evX67L1Gvsjz/+cNtDDz3kKl0RG6ZP9yod/KD20EPNpkwp3UEtAESTJo7Pz37ebdoH4g1zyfinjhN+UFuhgtl77xHUAgCChTk34iqsXbBgwU4XYujUqZP9oFJNBN6MGWZqEbdmjfd5x45mkydrZeVoHxkAAIhXzCXjm4LZO+7w9rXI9tixZgcfHO2jAgAAiB1FrvNWT7Bwv//+u+0ddk6TFopQv1gEmxZ6uOACsw0bvM+POcbs/fe96gcAAIDiwlwyfs2da9a7d87n991ndtpp0TwiAACAUlBZq8UaFi5cmP15jRo1cvWrmD9/vls5F8H2wAOqbPH2VSj90UcEtQAAoPgxl4xPW7aY9exptnGj97nW/r399mgfFQAAQCkIa7Xi7eDBg/O9TmuVDRkyxN0GwTVnjtdLTLSWx3PPmZUrF+2jAgAApQFzyfh0660q2vD2W7Y0GzPGa4MAAACAYm6DcMcdd9ihhx5qHTp0sJtvvtkOOOAAd7mqbR9++GH38aWXXirq3aKEZGaaXXqp2bZt3uf9+5sdcghPPwAAKBnMJePPxx+bPfGEt5+aavbaa2bly0f7qAAAAEpJWLv//vvbpEmT7OKLL7ZevXpZwva3zFVV26xZM/u///s/a9y4cXEcKyLgySfNvvvO21fOfuedPK0AAKDkMJeMLytWmPXpk/P5ww+bHXhgNI8IAACglIW10r59e5s3b57Nnj3bfv75Z3dZkyZNrHXr1pE+PkTQokW5e4c984xX/QAACI6UpBR74LgHsveBeMRcMj6EQmaXXWa2fLn3+Yknml19dbSPCgCAXWPOjbgLa9evX28VKlSwVq1auc2XlZVlGzdutEqVKkXyGBGhyfRVV+Us+nDFFWZHH81TCwBBk5iQaC1qtYj2YQDFirlkfNAb/++95+1Xr06fWgBA7GDOjbhaYOztt9+2tm3b2tatW3e4bsuWLdauXTt7//33I3V8iJDx480++sjb32cfs6FDeWoBAEDJYy4ZH3Ry3Y035nyuBcVq147mEQEAAJTSsPapp56yW2+91crns2rAXnvtZf3797cn/BUGEAhbtuSeTKtvbeXK0TwiAEBBMrIy7MOfP3Sb9oF4w1wy9mVlmV1wgdnmzTlnbJ16arSPCgCAwmPOjbgKa+fMmWPHHHNMgdcfffTR9tNPP+3pcSGCnn3WbNkyb/+008y6d+fpBYAgTxxHzxztNsJaxCPmkrHvm2/Mpk/PWbD2kUeifUQAABQNc27EVVi7Zs0ay8gouNJn27Zt7jYIhrS03C0P7r03mkcDAABKO+aSse/ll3P277pLZ9dF82gAAABKeVjbsGFDmzFjRoHX67oGDRrs6XEhQl580WzxYm9fp6e1bMlTCwAAooe5ZGzTshWvv+7tK6Q944xoHxEAAEApD2t79Ohhd9xxhy1fvnyH65YtW2Z33nmnnXnmmZE6PuyBbdvMhgzJ+fzOO3k6AQBAdDGXjG0ffmi2bp2336MHVbUAAACRllzUL7jtttvs3XfftSZNmtj5559vTZs2dZcvWLDAxo4da/Xq1XO3QfS9+qrZn396+127mrVvH+0jAgAApR1zyfhpgaBFxgAAABDlsLZixYr2zTff2IABA2z8+PHZ/WmrVKniwtvBgwe72yC6MjPNHnggdz8xAACAaGMuGbtWrzb76CNvv04ds+OOi/YRAQAAxJ8ih7VSuXJle/LJJ23UqFG2atUqC4VCVqNGDUtISIj8EWK3vPGG2c8/e/udOpkdeSRPJAAACAbmkrFp/HivzZacd55ZUlK0jwgAACD+7FZY61u9erX99ddfLqRNSkqyvffeO3JHht2WlWV2//05n9OrFgBiR5nEMjbw6IHZ+0A8Yy4Zuy0Qzj8/mkcCAMCeYc6NuFpgTObOnWtHH3201apVyzp06GDt27e3mjVr2nHHHWcLFy6M/FGiSN57z2zOHG//sMPMOnfmCQSAWJGUmGTt6rZzm/aBeMRcMvb8+qvZtGnefosWZi1bRvuIAADYfcy5EVeVtcuWLbNOnTq5tgfDhw+3Zs2auTYI8+bNs2eeecaOOuoomzNnjgtvUfJCIbP77stdVUt3CgAAEBTMJWPTK6/k7LOwGAAAQIDC2kcffdQaNGjgFhlLTU3NvvyEE06wq666yo488kh3myFDhkT6WFEIU6aYzZrl7bdubXbSSTxtABBLMrIy7Is/v3D7nRp2suTEPepYBAQOc8nYLAbww1oVAahfLQAAsYw5N+KqDcKkSZOsf//+uYJaX7ly5eyWW26xTz75JFLHhyIaNy5n/5ZbqKoFgFicOI74boTbtA/EG+aSsWfqVLPffvP21V6rbt1oHxEAAHuGOTfiKqz9/fff7dBDDy3w+rZt27rboORlZJi99Za3X7682WmnMQoAACBYmEvG9sJitEAAAAAIWFi7YcMGq1SpUoHXV6xY0TZu3Linx4XdbIGwapW3f/LJZnvtxdMIAACChblkbElLMxs/PqcYoEePaB8RAABAfEve3Ul2fm0QZP369W7BMZS8CRNy9nv2ZAQAAEAwMZeMHRMnmq1Z4+13725WoUK0jwgAACC+FTmsVRB7wAEH7PT6BK08gKi2QGBhMQAAEETMJWPLtGk5+2efHc0jAQAAKB2KHNZ+/vnnxXMk2CO0QAAAALGAuWRsWbgwZ79Fi2geCQAAQOlQ5LC2U6dOxXMk2CO0QAAAALGAuWRs+fln72PZsmYNGkT7aAAAAOJfkcPaxMTEXbY50PUZOi8fJYIWCAAQP8oklrH+R/TP3gfiDXPJ2JGZafbrr95+48ZmSUnRPiIAACKDOTfiKqx9++23C7xu6tSpNnLkSMvKytrT40IR0AIBAOJHUmKSHVn/yGgfBlBsmEvGjr/+MktL8/abNo320QAAEDnMuRFXYe3pp5++w2ULFy602267zd5//33r3bu33XvvvZE6PhQCLRAAAECsYC4Zm/1qCWsBAABKRuKefPGSJUvssssusxYtWri2B7Nnz7YXX3zRGtDQqsTQAgEA4ktmVqZ9vehrt2kfiGfMJYONsBYAEK+YcyPuwtp169ZZ//79rXHjxjZ37lybPHmyq6o9+OCDI3+E2ClaIABAfNmWtc2GfjPUbdoH4hFzydhAWAsAiFfMuRFXbRCGDRtmQ4cOtdq1a9trr72W76lsKDm0QAAAALGEuWTsIKwFAACIgbBWvWnLlSvnqmrV8kBbft56661IHB92ghYIAAAg1jCXjL2wtkYNs6pVo300AAAApUOR2yBceOGFdvbZZ1u1atWscuXKBW4ofrRAAAAAsaY45pKjRo2yhg0bWmpqqnXo0MGmT59e4G2feeYZO+qoo6xq1apu69KlS67bb9u2zbX70poMe+21l9WpU8cds/rrhtPjJSQk5NoefPBBixcbNqinsLfP4mIAAAABrqx94YUXiudIUGThxcs9e/IEAgCA4Iv0XHL8+PF200032ejRo11QO2LECOvWrZstXLjQatasucPtp0yZYueee64dfvjhLtxVe6+uXbu6dRjq1q1rmzdvtlmzZtldd91lLVu2tDVr1tj1119vp512ms2YMSPXfd17771usV1fxYoVLV78/HPO/gEHRPNIAAAASpcih7UIjh9+yNk/4YRoHgkAAEB0DB8+3AWmffr0cZ8rtP3www9tzJgxruVCXmPHjs31+bPPPmtvvvmmWzBXFbSq6p00aVKu2zzxxBPWvn17W7RokdWvXz9XOKt1HOIR/WoBAACig7A2hv3xh/dRrxHiqJADAACgUNLT023mzJk2YMCA7MsSExNda4OpU6cW6j5USavWB2rLUJB169a5NgdVqlTJdbnaHtx3330uwD3vvPPsxhtvtOTk/KfXaWlpbvOtX7/efczKynJbJOh+QqFQRO5vwYIEM9Nm1qSJjjECB4hiGy8UL8YqdjBWsSWa4+U/tr/P7+LCPV8l8Txl8XeRsDZWbdlitnSpt9+wYbSPBgAQKcmJyXZDhxuy9wEUbNWqVZaZmWm1atXKdbk+X7BgQaGeOvWnVV9aBbz52bp1q7uNWidUqlQp+/LrrrvODj30UBfyfvvtty4wXrp0qav0zc+QIUPsnnvu2eHylStXuseI1IsbBct6MaXQek/8+KP6Bpdz+9Wrr7YVKzIjcowonvFC8WKsYgdjFVuiOV4ZWRl20QEXuf1/V/3LvDtAY7VBjfNLOV4Fxqi//srZb9QomkcCAIgkBbSd9+vMkwqUAFXGjhs3zvWxVf/avFRxq8XQ9MLkqaeeynWd+uT6DjnkEEtJSbErrrjChbJly5bd4b4U5oZ/jSpr69WrZzVq1MgVAu/pCylVAOs+9/SF1F9/eVW1SUkha9dub0tJicghopjGC8WLsYodjFVsifZ4nVn7zBJ/zFhVkmOVms+crLQhrI1Rf/6Zs09YCwAASqPq1atbUlKSLV++PNfl+nxXvWQffvhhF9Z++umnLmwtKKj966+/7LPPPttloKrFzTIyMuzPP/+0pk2b7nC9Atz8Qly94Inkix69kNrT+9TZh/4CY/vtl2CpqV5wi8iLxHihZDBWsYOxii2MV+woqbFK5G+iMSuI8X61QhsEAIgfmVmZ9v3i792mfQAFUzVrmzZt3OJg4ZUf+rxjx44Fft2wYcNcr9mJEyda27ZtCwxqf/nlFxfm7r333rschtmzZ7sXFzVr1oz5IVu8WL18vf18cmcAAGIec24EGZW1cRDWUlkLAPFjW9Y2u/fLe93+hJ4TLCkxKdqHBASaWgtcdNFFLnRt3769jRgxwjZt2mR9+vRx11944YVWt25d155Ahg4dagMHDrRXX33VGjZsaMuWLXOXV6hQwW0Kas866yybNWuWffDBB64nrn8b9adVQKzFy7777js79thjrWLFiu5zLS52/vnnW9WqVS3WLVyYs09YCwCIR8y5EWSEtTGKNggAAABmvXr1cot0KYBVqNqqVStXMesvOrZo0aJcp9Op92x6eroLZMMNGjTI7r77blu8eLG999577jLdV7jPP//cjjnmGNfOQL1udfu0tDRr1KiRC2vDe9LGMsJaAACA6CGsjfHKWr32qFcv2kcDAAAQPddcc43b8qPFw8Kpp+zOqNpWC4rtzKGHHmrTpk2zeEVYCwAAED30rI3xsLZuXfVri/bRAAAAIF4Q1gIAAEQPYW0M2rDBbPVqb59+tQAAACiOsLZyZbM4WC8NAAAgphDWxiD61QIAAKA4bNmiPr85i4slJPA8AwAAlCTC2hhugSBU1gIAACBSfvnFzG/Zq7AWAAAAJYsFxmI8rG3YMJpHAgCItOTEZLuyzZXZ+wBQkuhXCwAoDZhzI8h4FRiDaIMAAPE9cTz5gJOjfRgASinCWgBAacCcG0FGG4QYRBsEAAAAFAfCWgAAgOiisjaGw9oyZczq1In20QAAIikrlGVzV8x1+81rNrfEBN5XBVDyYa0WFmvcmGceABCfmHMjyAhrY4wWfPDD2vr1zZKSon1EAIBISs9Mt9s/u93tT+g5wVKTU3mCAZTYPNMPaxs0MCtXjiceABCfmHMjyAJRrjNq1Chr2LChpaamWocOHWz69OkF3vaZZ56xo446yqpWreq2Ll267HD7iy++2BISEnJtJ5xwgsWDNWvMNmzw9hs1ivbRAAAAIF4sX262fr2337RptI8GAACgdIp6WDt+/Hi76aabbNCgQTZr1ixr2bKldevWzVasWJHv7adMmWLnnnuuff755zZ16lSrV6+ede3a1RYvXpzrdgpnly5dmr299tprFg/oVwsAAIDiQL9aAACA6It6WDt8+HC77LLLrE+fPnbQQQfZ6NGjrXz58jZmzJh8bz927Fi7+uqrrVWrVtasWTN79tlnLSsryyZPnpzrdmXLlrXatWtnb6rCjQeEtQAAACgOhLUAAAClPKxNT0+3mTNnulYG2QeUmOg+V9VsYWzevNm2bdtm1apV26ECt2bNmta0aVO76qqrbPXq1RZvYW3DhtE8EgAAAMQTwloAAIBSvsDYqlWrLDMz02rVqpXrcn2+YMGCQt1H//79rU6dOrkCX7VA6NGjhzVq1Mh+++03u/322+3EE090AXBSPitypaWluc23fnuzLlXsaosE3U8oFNrj+/vjjwStz+v2GzTQ8UXk8FBM44Xix1jFDsaqaM+Tvx+t30OMV+woqbHib2L8I6wFAAAo5WHtnnrwwQdt3LhxropWi5P5zjnnnOz9Fi1a2CGHHGL777+/u13nzp13uJ8hQ4bYPffcs8PlK1eutK1bt0bsBc66devciylVD++uhQvVzqGs269YcZWtWEGYWBwiNV4ofoxV7GCsCmdrxlZLT0t3++rfnpqc8/etJDFesaOkxmqDv8Ip4j6sLV/erG7daB8NAABA6RTVsLZ69equ0nW5lp4No8/VZ3ZnHn74YRfWfvrppy6M3Zn99tvPPdavv/6ab1g7YMAAt8hZeGWtFi6rUaOGVapUySL1QiohIcHd5568kFqyxKuqLVcuZAcdVN0SvE8RYZEaLxQ/xip2MFaFk5GVYZe3v9zt16ldx5ITo/OnmvGKHSU1VuFvjCP+6CQzv91W06bGHBMAENc0x+7Tqk/2PhAkUf2JTElJsTZt2rjFwbp37+4u8xcLu+aaawr8umHDhtngwYPtk08+sbZt2+7ycf755x/Xs3afffbJ93otRqYtL73gieSLHr2Q2pP71Fmxf/7p7TdsmGBJSSS1xWlPxwslh7GKHYzVrqUkpthZzc+yIGC8YkdJjBV/D+Pbb7+ZZWZ6+82aRftoAAAoXgpoexzYg6cZgRT1FEoVrc8884y9+OKLNn/+fLcY2KZNm6xPH+8djgsvvNBVvvqGDh1qd911l40ZM8YaNmxoy5Ytc9vGjRvd9fp4yy232LRp0+zPP/90we/pp59ujRs3tm7dulksUwGy35WhUaNoHw0AAADiBf1qAQAAgiHqtd69evVyvWEHDhzoQtdWrVrZxIkTsxcdW7RoUa5KjqeeesrS09PtrLNyVx0NGjTI7r77btdW4ccff3Th79q1a93iY127drX77rsv3+rZWOKfmiaEtQAQn7JCWfbbv7+5/f2r7W+JCVF/XxVAKRC+ti+VtQCAeMecG0EW9bBW1PKgoLYHWhQsnKpld6ZcuXKuPUI8IqwFgPiXnpluN/2f10d9Qs8JUVtgDEDpQmUtAKA0Yc6NIKNcJ4aE59QNG0bzSAAAABCvlbUHHBDNIwEAACjdCGtjCJW1AAAAiDQtYutX1tavb1a+PM8xAABAtBDWxhDCWgAAAETaihVma9d6+/SrBQAAiC7C2hgMaytXNqtaNdpHAwAAgHhAv1oAAIDgIKyNEZmZZosWefv0qwUAAEBx9KulshYAACC6CGtjxOLFZhkZ3n6jRtE+GgAAAMQLKmsBAACCIznaB4DCoV8tAJQOyYnJdu7B52bvA0Bxo7IWAFDaMOdGkPEqMEb8+WfOPpW1ABDfE8fzWpwX7cMAUAoraytUMKtTJ9pHAwBA8WPOjSCjDUIMVtbSsxYAAACRkJaWM89s2tQsIYHnFQAAIJqorI0RtEEAgNIhFArZ3+v/dvv1KtWzBJITAMXo11/NsrJywloAAEoD5twIMsLaGEFlLQCUDmmZadbvo35uf0LPCZaanBrtQwIQx+hXCwAojZhzI8hogxBjPWtr1PD6iQEAAACR6lcrVNYCAABEH2FtDEhPN1u82NunXy0AAAAihcpaAACAYCGsjZFJtN9LrEmTaB8NAAAA4q2yVu2xmWcCAABEH2FtDJg9O2e/VatoHgkAAADiRSiUU1nboIFZuXLRPiIAAAAQ1saAH37I2W/ZMppHAgAAgHixfLnZ+vXePv1qAQAAgoGwNgYQ1gIAABRs1KhR1rBhQ0tNTbUOHTrY9OnTC7ztM888Y0cddZRVrVrVbV26dNnh9qFQyAYOHGj77LOPlStXzt3ml19+yXWbf//913r37m2VKlWyKlWqWN++fW3jxo0xNUz0qwUAAAgewtoYOD3ND2tr1fI2AED8Sk5MtjOaneE27QPYufHjx9tNN91kgwYNslmzZlnLli2tW7dutmLFinxvP2XKFDv33HPt888/t6lTp1q9evWsa9eutthfzdXMhg0bZiNHjrTRo0fbd999Z3vttZe7z61bt2bfRkHt3LlzbdKkSfbBBx/Yl19+aZdffnlM9qsVKmsBAKUJc24EGWFtwC1darZqlbdPCwQAKB0Tx0taX+I2wlpg14YPH26XXXaZ9enTxw466CAXsJYvX97GjBmT7+3Hjh1rV199tbVq1cqaNWtmzz77rGVlZdnkyZOzq2pHjBhhd955p51++ul2yCGH2EsvvWRLliyxd955x91m/vz5NnHiRPe1quQ98sgj7fHHH7dx48a528UKKmsBAKVVtObcOhOoadOmbh5y4IEH2nnnnWebNm3a7ft74YUXbEH4H/Rd0BvMmv80adLEevToYev9fkh5fPjhh9amTRsrW7as3XDDDbmuW758uftazZH0PWje5Bs8eLD73vxNZyDpTXXfc8895x57//33d/O3bdu27db3He8o2YmhxcUIawEAAHKkp6fbzJkzbcCAAdmXJSYmurYFqpotjM2bN7sXCtWqVXOf//HHH7Zs2TJ3H77KlSu7UFb3ec4557iPan3Qtm3b7Nvo9npsVeKeccYZOzxOWlqa23z+iyMFxdoiQfejsLmw97dgQYKZaTNr0kTHEZHDQDGNF6KHsYodjFVsieZ4Ld+43Kb8NcU2pG+wiikV7ZgGx1itCiVzKvNrr73mgkx936eddpo9//zz7o3k3Q1rFYgecMABu7yt2jWpbZPOLlJge+2119q9997rzijKS2Gq3pR+44033NeFj9WNN97oQlpdp6BZ7aU6duxo7dq1c3Myf16mec++++7rzmjS12mOddddd9mMGTOsVq1a1r17d3v66ad3+N6z+LtIWBtL/WpbtYrmkQAASoImQSs3r3T7NcrXsIQEL0gBsKNVq1ZZZmamm/CH0+eFrTLp37+/1alTJzucVVDr30fe+/Sv08eaNWvmuj45OdkFvv5t8hoyZIjdc889O1y+cuXKXO0V9oRe3Kxbt879HlFwvCvz5lV3tRsVKmRZUtIKK6BzBIpJUccL0cNYxQ7GKrZEY7zmr55vj/3vMfvg9w8sM5SZfXlSQpKdst8pdn3r6+3AvQ8stsfXvEV979WuSX//9f3re/fbNz311FP23nvvWUZGhlWvXt0FqWrZ9H//93/24IMPutvquttuu81Wr15t33//vat8Va99haSdO3cu8LHff/99dxaS5it6vLPPPtsFqTfffPMOt9Wb0toUuOqNbd3eHyu1ndLX+cesN6+1JkCDBg1y3ce7777r+v/r+HXbF198MfvNbc1/9Ab4Y489ZmeddVaur9uwYYOVdlTWBhyLiwFA6ZKWmWZ93+vr9if0nGCpyanRPiQgbulFj1oXqI+tFicrTnoBFX4aoCpr9eKlRo0ariImUi969QaP7nNXL3qVD//9t/dmULNmCVarVu7wGcWvKOOF6GKsYgdjFVtKerw++e0T6/F2D8vIysgV1Io+//CPD+2TPz+xt3q9Zd3271Ysx5CUlGT9+vVzC5j++eefrtXApZde6t70ffXVV10PfS18qtu9/PLLrie/Whc88sgjLhBVBaueN80jFKYqgL3uuutclaqoHdMpp5ziAtW8FLaqBYH/hnOFChVcSwOFt3r8/Khvv85A0tf4Y9W+fXvXDurEE090gfFXX33lKnvzvpGtylv18/cvV0itil7/c60zkN8b4KnFPCeLBYS1MRLWli3Lwg8AAADhVHGiFzN6oRFOn9euXXunT9bDDz/swtpPP/3U9Vzz+V+n+1A1SPh96pRF/zZ5FzBTlYtehBT0uOr5pi0vvTiN5AtUvZAqzH3+9pu3kK0f1iYmUsUfDYUdL0QfYxU7GKvYUlLj9dPyn6zH6z1cYUTItv8BzMOFuJbpbjf90um2T8V97JNfP7ETm5xo1cp57ZIitTiq5hSaO1xxxRXuDV2FsaqoVaWs2gn4Vbii50YVs2o/oCpULYzqz0n86/3nT20HZof308zzXPvPt/91eb9+Z1/jf9R6AarGVdCsoPWYY45xlbLh9/HXX3/ZN998475X//KdPX64RP4mssBYkG3ebPbzz95+8+Y6vS7aRwQAABAcKSkp7oWCvziY+IuFqfKkIDql8L777nNVIeF9Z6VRo0YucA2/T1WvqBetf5/6uHbtWtcv1/fZZ5+5x1Zv21iwcGHOftOm0TwSAACK3wNfPWAZmRkFBrU+Xa/bXfHBFW77+NePbcu2LUV6LLUKKAxVs5555pluPuJ/nYJbha3afvrpJ7eJAlL1ttUiqhdddFG+fWZ3pX79+i5E9amyV29MF1RVu7M3y9Ur94cffrBJkya5ALa5QqswOlYt1OqvCVDQ4+sy7Ij4L8DmzNELDm+fxcUAAAB2pNYCetGi0FWn5WlFYi120adPH3f9hRdeaHXr1nU9Y2Xo0KGur5tONdSKzH6PWZ0KqE0vONT77f7773enCiq81WIY6mvrn2KoRTVOOOEEt4rx6NGj3emB11xzjeu9ptvFgvCWvs2aRfNIAAAo/sXE3pj/hmWEMgp1e91u2j9T7dr219lxjY6z75d8b18t+sqFtlsyttjWjK22edtm9zF8379u34r72uMnPV6ox9KbvU23v2uqeYYqbFU9q5BT84s5c+ZY69atXS9+BaLaFK6qh62olZLaGxSG5i5qwaD7UjuCJ5980s1dikqtD/S4ZcqUsf/973/2zjvvuI8+vXmtsPa5557L9XUKpo888ki7++673VoAmkPtzuOXBoS1AUa/WgAAgJ3r1auXO/VOAayCV50WqAoVf4GwRYsW5TqdTgt3pKen77CYhXrC6cWD3HrrrS7wVZ81VdDqhYXuM7yH2tixY11Aq9MSdf96ATJy5MiYGS4qawEAcU9tBL791qZ8ONQyyhUuqPWpNvariU/b75U+sNRa+1r5fepZaspebj2J8mXKu4+VylaymnvVzP7cv65+5fq7nLuoZ63aIGhRLoWW0rt3bxeEHnvsse5zXX/JJZe4sPb222+3hQsXurOKVF2r+YxorvKf//zHHn30UXvggQfcPOikk07KtxVCxYoV7dlnn3WhsO774IMPdot++fS1H330kXvjWWcY6c1wnV2kil/1nx18//12wTnnuJ666pOr0Fj3+frrr+dqHaUWU37rhnD77befW2z1iCOOcJ+rfYLaQGBHCaHC1meXIvphrFy5snt3IpILPqi3mfp5FLb/xjXXmI0a5e1PmWLWqVNEDgXFNF6IDsYqdjBWhaN343tO6Bn1BcYYr9hRUmNVHPOj0iza8021w5sxQ/3jvNZbrOVR8vg9GzsYq9jBWMXoeGmBsb//NvvuO7Pp09Uo3kyLTqkPvN581cfq1c3S0vQHNPemv3cVK3qb/p7qo/rKv/ee2QcfmK1aZc8eanbZaUU/viMWmd3yjdkJv6r3fHkztTrSac/qF6uPBx2knkwWl/Rcz51rptD3f/+z0OzZbrPhwy3xssuK9aHXM+eksjZWKmvD1r0AAAAAdptKNfzK2oYNCWoBxBm9A6WKyngquvn3X7NNm8zq1bPAWrPG7KWXzF54wQtLDz7YrHXrnK1xY7MNG8z++Sd7S/jrL6sybZolKPzIs3BnJFVM272va7jWbExrs6wEszMWbDb7/HNv86nXa5s2ZnfeaXbyyd47oLFo1SovgFIY63+cP1+lvdk3Sdi+hcKDKhQb2iAElHrV+v8G1G+5atVoHxEAoCQkJSTZSY1Pyt4HgEhbutR7vSz0qwUQN/791xIuvdRqvfuuWVKS2b77mjVokLPJ6tXepnBKH8uUUaNQNTg3q1t3z49BgepHH3lhpAJWbXocfVTV5wkneKFe2CnjBdq61asO1Wnqn3ziBdCq5lRP9t69vUrTPbV2rfdc5K1WrVDBC1v322/nobfe/Zs61ezpp81ef907Zt+SJWbb+6o6GhN9D2EU/hXrOWR77eWe82NOPsaS/77BMkK5H39nkhOTbfiIn6zcV9MstexnZusne99TOIWZqgY+9VQznfL/yCN7vuCQwqDffjPTwlx7771n96XxWbTI7PvvvdNp9HO5caP3c+pvK1d6E4NCyKxb1xJVuYxiR1gbUH/+mTOJZnExACg9yiSVsavaXRXtwwAQx/RazdeoUTSPBAAiRKfO9+xpCQqm/MDrjz+8rTBfq8rI4483u/his9NPNytXruhVpeph+NhjXvhZkDffzOlFo4CvWzcvGN22zQv+tGmxqLfeMhs/3gtTw6mi64YbzG65xfv6Sy4x69rVC50LS4/x/vve8U6evPPbli9v1ry5F9zuv78X7il41verTcHFL7/s+HWVK3vfR7g8QW24UNWqlqAWA+3be60G9MdJz6PaIWghUG0KvTUuCr39TcGhAkkFzApQ/I+qcFV4etxx7vQRdbE/641v7I15hVtkTEFtz4N6Ws16zczO03axd4WqfzUG/qafnZ9/9q7Tc6kKYo3Jffd5Yevvv3vXa1MAq/BVx6WerWXL5n5QPY8K5l9+2QtYpUYN713VAw/0Nn2/+pnwn3/tq5Jc96Xnxt8UjKuFgUJahbFFpa/X4+n7UcuH1q0tq0ULW5mR4VotxWj9cEwhrA0oFhcDAABAcQh//czZWwBimoK6J54w+89/vMBTOa2Cv/r13Sn2O4Sd4cIrPRXuqnpVm4JGVa5q4aNd9SNUiPjoo1q9MqfaqjAUomkbOLBwt1eVsPq2qjpS9L0q0NWmUFABsxbOVBCYNwT0KbR79lnvWNUftjAUBPrHuitVqphddJFWvPKCvsWLXa9Tt82a5QWXCh/1vWzfsvbZx1bXrGl7d+hgCRqPYnT7UbfbOwvfscyMTAu55cPyl2AJLqwdcOSAHa9UH12F+tr8nz8F8Lfe6r0xoM+fe84LXBWK6+cqrwce8ALVo47y7kfVv7q9KpTzGzNtX31lxUIN6xV6a7z8Xrza1Is378+RvpdibFWB3AhrA4qwFgBKJ637uT5tvdvXCrMJsdr7CkBMhLXKJAAgKlSlOWeOV3EYvqmaUr+c9G6Sgkh/U9CnsEwLTumjQqabbzabMCH7LkNHHGGrRo606q1aWYJO31elpUJbVSrqc1U2alMLAX29KkP9Pqva939JPvmkt3Xs6IW2Z5/tBWwKZBWY6tR3bR9/7C3E5NNjnHOO1+rAfyz/+FVdqYpWbYXp+6mq1jPP9ALQY47xwuV588yef94L9/Q8iSpddZk2fU+quG3aNKf9gt/6QaFpenrux1C1rCpZ9XV6zv1qVVW1/vSTt2lMClqXXsekr1dA6z9HPj+U1fEUJCvLMhUAlsB8t0WtFvZOr3es+/julpGZkW+FrUJabbqdbr9LOm6F5PoeH3/cq6jVz1ze5zmvLVu8FhHhbSLCf4ZUEaw3EtQ3Vm8I7C793KmK299UpavxVTW3fr6KOSDH7iOsDajw3916YwMAUDqkZabZ+W+f7/Yn9JxgqcnF2skLQCkUXmhGWItABXdauf2bb8zq1PFOh9bCPXl/SNUTUwGfgieFIqec4gVTxUEhnBbamTbNyuuU4549vVPCsWdUFapga9Agr39mpNx8s4Xuv9+yNFY+hY8tWnhbfnS6vY7jrru86kWFtuq9qopSUbWjNrUeUPCoU8vzCy5VhahesmpPoD6v+dHPtDYFegqQ9fM+c6YX+GmhqvBNVY5nnOEFa+FU8fjQQ151pqqAX3nF7MMPc55H/ZsYO3bnz5MeT2Fyv35eC4VdLcSmf5sKiVWNGx6i66OOL4YWcuvWuJtNv3S6Dfl6iL0+93XLDOth67c+UEVtoYLavOOvNw4UrN97rzcmeo4OOCBnUzD+669mkyZ5W97+t2o1oa9XVXd4T2P90V6wwNv0O0nPuzZVMuujKnN1uQJgf9PnWkFUP98UfsQkwtqAh7V6w6Og3/UAAABAUVFZi8BQqKDFmNSbU8GVPg+nkEFBrCrCFJD5K5SH975UQHLssWZXXuktFJWSkvs+FLrpa1RhqCoybQrD9FEhk0INBcDa9PgKvX780QW0fiWioqhKuq977vECFVUQKrgN2gp9/inrCitVOapKVG0KdQoTqOk5VvWnqlD1HKkPqr/pOlVY+qHRwoXeJgqWFLD7mxbz6tLFO7U6b1D09ddmV1/tVWwWRAGgAsL8TiHPj74/haxqBVDYr8lLz0+nTt42YoQXgmrRLP84FZjl11JBYdmll5rdeGPhFg3z6TlSWLq7NCZ6o0KbfnZVofnGG96CZHl7xfp0fAoCr7qqaCGDwkC/MjMOKIh99cxX7cEuD9oZ48+wjKwMu+XwW6zr/l2t5l419+zO9e9Nb0Roy4+qkDUG+vekf0effuqN10knef1h8wtW9fN92GHehlKDsDaA9G/V74OuNwBj6I0qAAAABBxhbSmjU6OHDTOrV8/rj6gQTVV7u1rh3Q8uddq3QkBVaKmSJBJU/TdggLeYjkK5nR2HHw7uzOefe5tOjdcCUerDWJhTuHeHqnq1qdeoXqypV6XClz2tXtMx6nh1irwCR42VwrGdnaas06O//dYLQFWRrL6g6pOZl+5Dz43CZb/KVJuqNFXh+cUX3vbllzmn1heFqgW15aWfGZ0erkBRj6XqVf08+vSc9eplduihXtWhNoWICmv1HOhnTyG7fyq/enfqlHkdo/9R4fDtt0d2tURVjypIVaisqlqFtqq2VUWwnjeFZgrd9FEVk9F+wa6f99NO8zadfq+fBf27UmWn34pBgZ8CeGRTMFunYh23f9ZBZ5Xs2Wz62fcXDQPywb/WANIbuT7NowAAAIDiCGv1+h1xSgHrNdfkhGMKPHXqrfgrkisc8wMwf1MoppAsb4WiAiGdOn3uuV4VWHhvyqKYMsULVBUShlMlqPpzqjpSx6FV1rXplMPtC0e5sEltCLavTu4uf+aZnNXY9XUKpiNFQdxhh1lW+/a28d9/reInn1iCgjCfwtULLvCqMEeP9k473p2AVj1XtfmVqqIwWOPUrZv3fLdt612vQNbftIBTYagSeelSb1OoHQkKKBWsakx0v/lVc6oCaeRIb8tLAa0WulJbgILuX6GptkgGsUUN1A4/3Nu0MJeeR/07CDJVlqvSHEBMI6wNIBYXAwAAQHGhsrYUUJWjFr0paBEjBbKqFCwKnWqtVc+1qcJWLQcUIqp6T5WQ/qZ3APJ7F0CVunfcYfboo7lPr1ZlpTYtqBNe+afejaI2BQo0VR2a3wrl//mPF0AqLH377dyVpQqUFe6qGrJuXS+A1vUK3fyV2hW+5d0UDqpyUt+bZGXZ5hUrrMIdd1iCAlI9B2rdoFYJot6hao8weLAXkOdXDatqYoWtCpb9jwpcf/ml4Odc4/Tqq95WGKrSO+II7/i1QJS/kry2f/7Z9UruGr8jj/TCcAXJCsP9Tc9V/fpeda42Bf3hY6FKTj8QVh9WtbVQtW7eSl/1kFW/VbWtiKXFjfx2EABQAghrA4iwFgAAAMWFsDbAtMq8TrHX6XUK/woKhxSe/f67F4SpR6IqMP3g6513vKBT4aBoxW9Vn6oyU5W16pGocDO/SkiFk6pw9VeEVyjrLyCknph+2Kf2CKom1ZYfnXLvn2p/yCHeffbv7/WO9ak3qPqM7qoaVYGgjn1n1Y8KerWpLYCCU39RKYWWkQ4E1U5Ci01pUyCp/p8KQhVW6rLXXjPr29fr+6ox0qZWDApMd0bfx9FHe71wFRh//LH3nKvKOT+qOFV1qipTFbB27Oj9HOyMjsFvD6FN1db6Gr9Xq37udvdUeQXvjRt721FHec+Ffsb0Pai1g1o16Pt78EGz2rV37zEAoJQgrA1wWKu/1wUtWgkAQCQ1bNjQypYta+XKlbO0tDRr1aqVDVaF0G564YUX7LDDDrNmhVx85YMPPrCbb77ZMjMzrUWLFu7rK+nFdh4jR460//73v5aQkOC2W2+91c4//3x33ffff2/XX3+9zZ4927p27WrvKLAIc//999vz208HPuecc7K/vy1bttgVV1xhs1ThZDqrcz977rnnrIYCECAOxVxYq9POFYYpxIp2b8jiNHGi19vTXzxLgZ3COFV46kWBKjq1Er3CXFVm+q0B/BcOfn9KvyWA6HewqkBVkeqf1q9enAp5tXiWFkxSsKpNgerOKgf1NWphoDBS91nQIkaiUHfyZG/LL3wdMsTs+usjP54KAf2K3JKg8VIAqf67Tz7pXfbdd95WGAqSVQmrBct69Mi9QJUCX42xfv61CJuqpTWe+pnQtjurvOvvmh9slwT9gtG/W20AgEIjrA0YzYH8BSf1pmSkevgDAGLDqk2rrFpqNUvLTLM3571pXfbrYrUq1CqRxx4/frwLabOysuyUU05xn/dXJdRuUNhapUqVQoW1GzdutL59+9oXX3zhbn/NNdfYfffdZw899NAOt23evLl98803VrlyZfv777+tdevW1rFjR9t///1tn332sREjRtj//vc/+1gVSWG+/PJLe+211+zHH3+05ORkO+KII+zwww+3k08+2Z5++mnbvHmz/fTTTy4Avuyyy9xjD4tk30MgQPyMTTmRii4DTZWLWqBIFFLed5/FJX1vCuz8oNZvO6DFo7Ttik5Z1ynz2nxqLaCKWlXG5qXqyaKu7K6v0eJk2hRMqkpXlbKq/AzfVL2p7ye/SlKFjC+/nBMexwO9sThqlNfL99JLc/ed9akFg/q7Nm3qBeb+R12mHqMFUXh+zDHeBiCikhKSrHOjztn7QJAQ1gaMWhZpXiYsLgYApcdPy3+yB756wN6Y/4ZlZHn93cbPHW/Jicl21oFn2e1H3W4tapXM6Rbp6emu2lSBqO/hhx+2119/3TIyMqxmzZou4GzQoIG9//77dscdd1hiYqK7TtWqK1eutBkzZtiNN95od999tz3wwAN2kvoaFkDBqkJXP9i9+uqrXWVsfmFtZy2Is129evWsdu3aLrRVWLvvvvu6bd68eTt8nYLnCy64wPbSaZpmdskll7jwVmGtAlqFtdu2bXPfh8JjVfcC8UrFlKJ/4nu6gH2x0gJUF16Y87lOn1YAqfYA8URVqKrQ9E931+JAqhDV4lo6fT6/0FRhn3qxqier35PU71GqcFaVnuqdWlwDrOpYHbO2gixf7lWhaPVkvcjR79XLLovfvp9qRTB7ttkbb5j9+6/X01VhrNo87O5ibACKTZmkMnbDYTfwDCOQCGsDhn61AFD6fPLrJ9Z9fHfLyMywjFDuhTgU3CrAfWfhO/ZOr3esW+NuxXYcvXr1cm0Q/vzzT2vTpo2ddtpp7vJXX33VFi5caFOnTrWkpCR7+eWXXaD64Ycf2p133umCW1W3qiJ3/fr1rqL2lVdesRtuuMG6awEaM1uyZIkLbNWiIK9Fixa54De8JcPSpUtd+Ksq2IJ8+umntmbNGmtXiOowPcaReiEd9hjjxo1z+2qB8O2337oQWt9fhw4dXHUvEO+VtYFugZCe7lWarlmT+xS0yy/3el/GSzuErVst4bzzvGBa2rTx+ntuf2PJBbDff+/1FlV1poLqJk12HniqyjYIKXytWt6mStzSQq0rtrfmAQBgd8XJLCd+qA+9T2+YAwDiv6JWQW1aRtoOQW14YKvrdTvdvrio+lRh6qpVq1x4qh6vot6vCkYV4KpNgtoDKPz0K13VJ1aXqcWAgtr81KlTJ9+gdnepZUGfPn3cMfvVsrvr//7v/1zQvGzZMhcS63sYOHBgxI4VCBLleDER1t56q1dZKurNqYBSpk41e/ppiwuhkFW+6SZL0PckCmPfey8nqBX1kT3xRLMbb/TCa7UP2FVlahCCWgAIuFAoZFsztrpN+0CQENYGjM6YCZ+bAQDim1ofqKI2ZDufJOp6hba6/YwlM6zfh/3svzP/WyzHpGrWHj162OdaLXz7ZHbAgAEubNWmoFSbDB8+3C3aVb58ebvooot2q89r/fr17S+/qszMVfaq/2xBVbVqc6CeumPGjMlVLVvUx9BlogXLzjjjDEtNTbWUlBTr3bt39vcOxJstW7wCVSngvZXo0+JVjz3m7auf54QJ+oeac/1tt6lcP/KPq8WcFJyqkjUrq+Db/f67t9K9FoPSpj6savWiBaHuuMNszBivfcGuXvzfd5+Ve/ttb1/Ng1VRW6dOZL8nAEC+tD5Ezwk93aZ9IEhogxAw4Wd6Va0azSMBABS35RuXez1qC6iozUthrfrYrt6y2trVaWcnNcnpA6tAVRPNLdu2uAqBLRlbdtjPCmVZp4adLDU5dZePpbBSfWBFrQweeeQRO+uss6xatWqut+ucOXNcn9kFCxa4Rb+0KVxVlapUqlTJ1u1spfAwJ5xwgvXr18/dl/rWPvnkk3bOOefke9v58+e7dgoKWI8//ngrrJ49e7rHuPbaa91xKuhVP13Zb7/93HGfq8VhzFx7h4PjrScmsF34P8uoV9aq1YGqRMMrQbXi/SWX5Hw+YoTXGkD69DF7/nmz9evNrr/eC3Ej8YRMnOhVtH70UU5DX4WwZ5xhduaZZkcf7a3GpoW+hg/X6Qa5w9xly/K/b70hpP6zxx3nBboKcHWGwf/+5z4mbn8DKZSQYAmvvmrWuvWefz8AACDmEdYGuLK2WrVoHgkAoLhN+XNK9mJihaUKWwWwG9I22H1f3OcFsRlbXJuEXVXn7lVmLztg7wOsUdVGO+1Zq16xqjq9b/uq66o0Xb16tR2r0MG1jcxwC3QprL399ttdP1tVpKq69qmnnnK3ufzyy+0///mPPfroo26BMbVPKKhnbcWKFe3ZZ591obDuW0Hpiy++mH29vvajjz5yrRSuu+46FwL379/fbTJ06FDr1q2bOw61ZdBiYVogTYuN6fjUX/eYY45x35+/cJj2VZ0rCm11vH5Aq8BYfXiBeBSIsFYVpwpkX3hB7+x4bQ60CJO2yZO9MFb0ps2VV+Z8nRYdVPWp+rhqESftn3rq7lVHKOh9/XWzL77IKTUOt3Sp2ZNPeptOd1OLgvDFJfz+pHvv7S2kld99qF2MfpeF/T7L9+kYOtQSTj+96N8HAACIS4S1AUNYCwClx4b07St/F1FmVqbtV3U/q5xa2cqVKWflksu5atnwfW3ly5TP3td1iQkFdz9SW4Bw6uG6QiuUb6eQVFteb731Vr73pyDUD0N9O+tZq8XM/AXN8gr/ukmTJhV4H02bNrV//vmnwOvVhza/XrSqFn5DwQ9QCgQirH33XS+oFQWzCkHzBqEHHOC1PgivulUwqkpbfwGnfv28ytUKFbwAODPTC01VratK2HBbt5p98IHZ2LFeBa2qevPSE3LCCV6viE8+MUvbflqswmFtvtq1za69VqsTesekKtvVq70KW7VnmDXL7LPPvIXQ9Lj5qVDBQi1b2vrTTrOKN920O88iAACIU4S1AW2DoDZ9e7heCgCguCkcmDLFq6467LAiL+pSMaXibj3strTN9s0/39jZB51tPZv3tFJBIYyq4P7+2zsteXuLBgAxFtYqCNViWeGtAhRwhlemqn+r3kCpmM/vyPPOM3vpJa0M6P0+UN8wP6gNp9/LCnE1odZH3dav2A2nql69UaTtqKNyFu/asEE9Ubz+uQp3N282a9nSTMFqr15mZcvm3EdiolmNGt6m6v1u3cwGDPDC3u++84LbBQu831utWnntDvbbz50LsWXFCqvIgmAAACAMYW1AK2vVAoF5GwAE2MaNZpdeajZ+vPe5XqCr8rR3b7Ny5Xb+tQoWtmyxY8ofZMkJSZYRyhMy7ERyptnL98+z78+rYRuq/WXW3OLfl196i/moz6Oo/YF6SKp/5VlneUEMgELxW7JGLaxVKwO/kr9zZ5XLe5Wpixd7l6s6vm1br7I2P5ogqzWB2paoajW/9gOi67SFV8SGV8aqR7UqdBWc5jfpVlCsNgzaFNQq5dbXFWWCrkBXv6u05Wdni5gBAIBSi7A2wGEtACCg5s3zqjtVKeX76Sezyy7zgsTLL/eqv9THcOHCnE0L5+gUClV3ZWZaLTM760yzNw4yy8hzxm5BQW3PuWZNl6Zb00cmmY2cYnbhAm9l9MaNd/7FM2d6q6srZFZ1lzZ9jT5q9XEFyAoOVJ2mj1u3WrK+J1WXqe+iFsLRR6U7WixHIYsW4ClOf/xhduutXoVdfgGutmuu8QLbBg286jZ/0ynQBx3krdCu01UARL+yVmHskCHevv5djhzphZ/696oKW22Fod9bL79sNmyY2bZt3n35m+5Ll+l3Xfim4FTVs3pDTb/D8rZJ2BlV+moDAAAoAbx6CRDNK/WaWHRGFwAggF57zQtlN23yPtfiOM2amU2fnvOu24MPelsh3P6V2TvNzDITtSJ4wbdLsARLTkmxAY3ONiv/plfppT8czz3nrY7eo4cXbLZrl/sLdXrx7bd7pw0rkC0kdbetXtCVejxp3tysSxcv+DjkEC9oUVBaFAqx1ddRpwurQs7vOalTltWv0u8ZKToFWauzjxuXE5RrHHa2eI9CXFU89+0bxQadQDDD2ipVSvjB//OfnB6u+nepN1R2l96k0QYAwG7QWg5H1Dsiex8IEsLagJ6WRmUtAASM3k1T6PnEEzmXqfWB+hk2aWL2/fde5ar6qipEzY+C3Zo1vY/aKla0FpUq2TsVUq17xiuWkZVpGaEdT+lNTkx22zu93rEWjbuZ9X/Ee6zHH/eqdFUJq+pTbZ06eaGtPg4f7oXGCnaLw9y53qZjEVWeHXigtymE0WnKCljr1ct96rAWLlPgqsq4GTN2/Th6zgYP9toeqBpOi4SpD6RCY91Pfn0ofaoIVkA0aJC3+ryqnrUgkF+Bq+NSj0qNB1AKRK2y9tNPtSKht1+rlvdvEgCAKElJSrHbjryN5x+BRFgbwBYIQlgLAHt4qu38+V4Il5LibdpX1ebvv5v98kvOplPtVV6mUDF80ymzqvj86ivvdHv1Sw3vL3jRRV7fRP/UWFW0vvKK149RFaFz5niVpk2bepuqbxU65tPvsJuZTV9+vQ35eohNmDfBMrJyAluFtD0P6mkDjhxgLWq18C7UIjb33292yy3eMSgsVcsF+eILb9Pxh1el6nu8+26zU07xvufffsvZFJ76rQO0JSZaKCnJtlStaqlNm1piw4Zehaq+H4WfCl20KaAOf04UCqvdgrZwemxV3mrTY0+cuONiQPnRuKlX7R135A5T9RxqQTdtWhlex6FqvfA2DjrtWWHwxx97X6PPdcq1tvy0aeOFuqrU8xcYCpKVK72fQYXjeuNAz/X2LWHjRquid3z1M6Fen0DQwlq9gaVKWt/QobxBAgAAUICEUKgI50SWEuvXr7fKlSvbunXrrFKEKm2ysrJsxYoVVrNmTUss4BTRadPMOnb09jWf9QuVUPIKM14IBsYqxsZq2TKruX69JaoX6g8/eJvCQlVhqpeg+otWL/Dk+11T6HjnnWbPPlukU/6LRCGoKlq1uFgxrAS5YtMKm/LnFFuftt4qla1kxzQ8xmruVXPnX6SgUsHkww+b/fxz7usUvl59tVfFporSSP7bUkA4ZYr3B0zhuHr5KgwvyqI5WtxHC/ho4Z7wvpPaFDyqn+6e0HHpD6raQGgV+l1RIK2AWOOb30r0xUXtHBTIajEk/6P+bcya5YW0WnRpF7LeftsSu3ePqflRaRat+abeZ9I/B/+fh95HKnaq8tebIaI3Wb75pugtU+IQc5jYwVjFDsYqtjBesaMkx2o9c04qa4OEyloAcWvbNku4/nqr+cILlphfYKbEQO0E9If/iCO84Fan0KuCUGVgOs1dHxUCtm9vdtRRuZstpqd7Aeq99+78lPj8aJEs/QIOr0LNj45Hj3vVVV77g2KigPblH192+xN6TrDU5NRdf1FqqtdHV31Z33vPq+799lsv/FaAq7YExUFjoHAwPCBUcKzAWBWgP/7obQrltdK7b999vUV+LrjA63tbnPS9jx7ttVFQf1/1Fvarb/1N/XH1BoJoEbWbbjK75x6vH68qAhWk+pWs6qer41elsV9xrI+tWhV9ASK9oaCQXe01wp+f3VVc7S4QV0q8slZnOqiqX/QGl1rJENQCAKJsa8ZW6zmhZ9Hm3EAJoQ1CgGiBcB8LjAGIGwqkLrvMEl580fKtQ1Xlp39KvIIztR3QtjN6wa9w7JhjzA44wKvaUksDnyoiFV6WK+cFuQrc9FH3r3BNPWa1aUXxvfbyAjgtdOVX+2pT8KVg+OijvQC5CFWpUaMAxA9PFZoqxC1peky/5cG55+Zcvnq1F4jqej2vJR3WaPzUy7egn1G1jlCw/eGHOYmW3kDIj4Lo/IJr/cxdc03hVrRX9ewVV5i9/faub6uqS1UgH3qo16JDbTD81en32suyUlNt1ebNVr1x413fF0q98DUSij2s1ZsQnTvnrKCrinW1HAEAAECww9pRo0bZQw89ZMuWLbOWLVva448/bu31Qi4fzzzzjL300ks2R70AXYu5NvbAAw/kur06OwwaNMjddu3atXbEEUfYU089ZU30wjzAqKwFEJfUb/TFF91uSD1ITzrJEhS0+r1htfiUFotSRei773qh6a4oXNOp4dryhriqLlXvTi1gU1g65V4VntrOO8/iQjSC2l2FpQrXg0g/Nzo2bWrloPBfFa8K+PO2wJD8qrCVgKmiWV/bo4fZ9debHX54/q0y3n/fC63UtsOnU8P1b0FtQBTGalPVt/6tNGq083A7K8uydF/+8QGFqKzVrz29n7XD71a9kaCqgZ31blYP6Bde8P696E0H/S7PSz+Tqk5XaxRRvwUteAgAAIBgh7Xjx4+3m266yUaPHm0dOnSwESNGWLdu3WzhwoWuF0ZeU6ZMsXPPPdcOP/xwS01NtaFDh1rXrl1t7ty5VrduXXebYcOG2ciRI+3FF1+0Ro0a2V133eXuc968ee5rgoqwFkDc0emuQ4a43VBCgq0dNcoqX3KJJeQNnlS5qk2Lziis1aJQ+qWosi9VFfofVe2qxb5UBanq1/C+tGpRoN6kqkAEdpf6J6vn8aOPeqGVKq+1KdVSFbgfZmmhNW06xXv2bLMJE7xwV1Xi2temheVUva35ib9NnWo2ZkzO4ymcffppL+AFSjCs1a/VHd5L0Jtr+p2tNwvUqkQNbvWGQXgAq5Yzo0blnBKmBRW12IJah/h9nvX7+/jjzRYs8D7fbz9vUUJW0AUAAAh+WDt8+HC77LLLrE+fPu5zhbYffvihjRkzxm677bYdbj927Nhcnz/77LP25ptv2uTJk+3CCy90VbUKfO+88047/fTT3W1UiVurVi1755137BwtYhJQtEEAEHPU31OhqSqmdAp2+Cv/N97Itfp36LHHLO2UU3Z9nwq4tBXED7UUBqhdgk6tV5hw8snFsuAXSimFTvktMKafMb2ZrK1du5zLH3nEC12ffNJs+XLvMr3xsLNKcf17eOYZb3E1oITD2vC2347eeBg2zNvXGxIjRnibWppceKHZr7961bRqsRJOb1DozY3XX/du37Wr2QkneP2qRT2eJ0/23qwAAABAsMPa9PR0mzlzpg0YMCD7Mq0q16VLF5uqypNC2Lx5s23bts2qbX+n/o8//nDtFHQfPq20q6pd3WeQw1oqawEEnnq+zpjhncattgX+i3FRL1i9SFc1laoQzz8/p/JVv+f79ct92vee0u99vSm3/Y05IKrUdmPgQLP+/b3QSlXls2Z5/ZDzqlDBC7UuuYQ3GFCi9Cs5vLI2F73h4PcPD6ff8zffnPsy9VBQ5a1+7yvgVYCr/rQ9e3rV4qtW5fy7UFCrRfgAAAAQ/LB21apVlpmZ6apew+nzBf5pU7vQv39/q1OnTnY4q6DWv4+89+lfl1daWprbfOu3rySepR5wCiYiQPejqt+d3d/q1aoI86rCqlTRY0fkoVFM44VgYKwiSL/71Dt26lRLmD49Z2V5v1pVH+fPt4QCfpe6U8JVJagtTEhnPdx3H2NVxJ9pfz9av4f4t7Wb1OdTIZY2jZ0qFBVi+ZsWu9MbDFqETOMc3soj4GPF38TYp1/rfh6bK6zVG2lq/yFauE5nTKhtgfqNT5uW+40G9ahVT2b1WBa1Srj2WrOPPvI+94NavaGm+9AikAAAAIidNgh74sEHH7Rx48a5PrZ70ot2yJAhdo/6bOWxcuVK25r3VK89eIGzbt0692JK1cP5WblS1cEpbj89fUVEC9AQ+fFCMMT1WKWlWcKGDZa4caP7qC1UsaJlaLHESPTfzsy0lC++sNT/+z8rM2OGJSuILWLYk96qlW3r0MGS5861lOnTLSHPgkxpxx1na7TY18qV8T1WEZSemW4HVjrQ7a9aucpSkry/CyWN8YoQvcmh08C1hYvgH/mSGqsNGzYU232jZPhVtTuEtSNH5rQ3uPxyr62Ntiuv9Fp5aPFHBbVagDFv/wT1o/3gA7O33/ZC3H/+8XqM/9//mR18cMl8YwAAFFFiQqK13adt9j4QJFENa6tXr25JSUm23O/ttp0+r72L/m0PP/ywC2s//fRTO0S9tLbzv073sY9WUQ67z1bhCySEURsGLXIWXllbr149q1GjhlXSZDNCL6QSEhLcfRb0QmrDBq96rWLFkNWtu+Piaig5hRmvUkNVYDNnugWdEtassZD6hbZvb0ERF2OlF8hz5rhTphN02vT//uc+TyjgzaKQFjlST9dDDrGQfv9pFW4tzpVff838LFhgCaqWeuUVS1iypEiHGlJ7gy5dLKRemyefbMn77JP9hyS0ebOFvvrKEiZNMvv6a7ewUpmnn7aaeoEfL2NVQobuMzTah8B4xZCS+rcV5EVaR40aZQ899JA7i6ply5b2+OOPW/sC/lZpUdqBAwe6Vlx//fWXPfroo3bDDTfkuk3Dhg3ddXldffXV7rHkmGOOsS+02GGYK664wq2/EFNhrc6q2P49ucrw//wn9xfp782tt+76TQnND9QKR38D9NzToxYAEGAqiBh0zKBoHwYQvLA2JSXF2rRp4xYH6969e/YLDn1+zTXXFPh1w4YNs8GDB9snn3xibdt674T4GjVq5AJb3Ycfzip8/e677+yqq67K9/7Kli3rtrz0gieSL3r0Qmpn9+n3rK1WTbdjkZxo29V4xTWd/qjTGfUiVKHbpk3ZVyU89JB3Cu9995m1aGFBEDNjpdOdtSCXglktiuVv8+fn39eyAAk6h3XePLcljBvnXagAt0MHs86dve2ww7yegkuXeo/5999q6u1VR4Wf0urTc6fg9/DDva1jR7M6dXKO23/slBT3WPn+hlIoe+KJ3ubfPlbHCg7jFTtKYqyC+u92/Pjx7k13haRao0ALzXbr1s0WLlxoNbUQXD7rHey3337Ws2dPu/HGG/O9z++//9616vLNmTPHjj/+ePc14bRI7r333pv9eXm1EAiwtWvzCWu1MJ5/hXqN560CLwr9HTjjjD07SAAAgFIu6m0QNLm+6KKLXOiqCghNsDdt2mR9+vRx11944YVWt25d16pAhg4d6qohXn31VVf14PehrVChgtv0YkXVEffff781adLEhbd33XWX62vrB8JBpCxkzRpvv2rVaB8NYp5OZf/tN69CM3zTC229aaEKmfwqpFRyo0VE/L51BVHgp8WltGCfWojotPzi/geiRQd1SuVee3nVOnoxqY9hFfSBpRfBWmxIW54zCQqsUNLpp/reVN3vb6qc1e88LfaisFZVzz6FCt9+620K0vUGlALg/BaL8SnMPflkr9+gAt4InUkAACVp+PDhLjT1544KbT/88EMbM2aM3XbbbTvcvl27dm6T/K4XVSmH09lc+++/v3Xq1CnX5Qpnd3U2WKAra3UGx/DhOX97dlVBCwAAgPgPa3v16uV6wyqAVfCqatiJEydmLxC2aNGiXJUcTz31lKWnp9tZZ52V634GDRpkd999t9u/9dZbXeB7+eWX29q1a+3II4909xnk0/dUuOjnLlqPASgy9QrVQh4TJnhhqp/+5zVokNlLL3nB4Qkn5Fz+4YfeoiFaACecqiuPOcbb9BgPPGCmU+cVoL72mrfquSoxGzXyVnvWpn0FuPpaf3Gq3aHH0/0/9pjZjBn53kS/HfZWTzz1RT3ttGCtrK5FVrTi++OPe6eZFhSYNmtmduihZm3aeB/V1mBXLQ30C0MLMSq4VZCt1bbDF2YMWzRxB6qgVaih3oP5VJ0hurZmbLXz3zrf7b/S4xVLTQ7u3y4g2jQnVDsDtbTyad6ohWen6ndjhB7jlVdecQUGKgoIN3bsWHedAttTTz3VFQgEubp2h7D25Ze9NwFFFbH6ewQAQCnAnBtBFvWwVtTyoKC2B1o8LNyff/65y/vTRFqnpIWflhZ04bkalbWlhKot339fP9ReVaMCz6JQWKoqze+/N3vjDS+gDX8Vltfee3uPqUpLVd3qVHX1lxs4UE2gXf/SbAoKFeqq3cH+++cOQC+5RO+aeKHt6tXe/X31lbflpcpQ9a3zN7Um0ZsvquQJ33Qf/qro/qaed3oc/0XkTpRRWwFVzqviSW0atldMRY1aDzzyiHf8Wnrbp1YFXbp4z4NaSCg0VS9AtRYoKvUV1H1o06rzoqBdoa02tTpQFbJWnPc3rdzdvDkLvsSAtMydhO0Asq1atcq1K/Df5Pfp8wXhb2DtgXfeece9+X/xxRfnuvy8886zBg0auLO3fvzxR+vfv79rvfDWW2/lez9paWlu86lNl98CTFsk6H600FxB9+fNN70iiMoVtllo2LDsdjVZt9zinZmDErOr8UJwMFaxg7GKLdEcLz2mAlt/n9/FwRmrLP4uBiOsRU6/WqGyNo6p0lKhqsJVBWp+ObUCUy0QpRdKp57qBZrhVMmq/rHqJfvrrzlbWC/ZXBS2HnecV6WpYLB1a69twNy5Wh0lJ1jVC8q8Lyq7dTP773+9cC8/WmBKC/JddplXNapAUuFkfnS5vl9te0rfg45d35tCSa02vXixhdS3VWGt6DlSKNyrlxcma4XqkqQVsxV8q3JZVcHhwapaDeh0W4XfxUVtIS680NsAABHx3HPP2YknnuhC2XA6g8vXokULt7Bt586d7bfffnMtE/JSS6971DooD51htrWABSV358XNunXr3Iup/HoML1miql+v5U2jWS9bguYSCpKPPNLW6MyYFSsichyIzHghOBir2MFYxZZojpeC2vQ07zXbihUrOJstQGO1YcMGK+0IawOCsDYOqEJGi3Ip/PzsMy+sU8Wkvymw0wJPBfUQ/eYbb1OlpVZiVlWkKsu1/fLLrh9f/UbVBkAtQhS45tf2Q+0CFGiqila9acNflFWp4oWvCvoK00pAoeldd3mbfplq1WxVCWvT96nT81X1u7Nq313RHwGdlnn99WZHHpnvcYUyM23tCy9YlaFDLcF/nsaPN3vzTbOjj/Z6smo74IA9a5Gg/q8aH1VFqZehturVvedZVazDhqn8KtdiXK5vrEJthfAFhd8AgN1WvXp1S0pKsuV5+oHr80j0kv3rr7/s008/LbBaNpwWN5Nff/0137BWrRrUSiG8srZevXquP26lCPUM1wspnWGm+8zvhVRmpv93MGTtJj+RfXmZO+/MdzE2FK9djReCg7GKHYxVbInmeCmsTSnrneGov4G0HgvOWKUGuIVpSSGsDQjaIMQoVY5OnOgFgzptP7yacld0SvqZZ3oLST35pLdglF+dGVatky+dTq++sPpa9YY9/nizrl29cHBXFFhecIFXwauKXvWr06n56gubp2qo0BTcKgjWFk6nLyhAnT7d2/S9KbTWL19/0zGrb6t/bP6mIFSrUjdosMvvJ+3kky10/vmWoIXRVLW0cqUXrio016bwWy+cFdqqQlfPvd8WYFd/CPQ9qBJaofTPP+94vUL1vBXOetGthdwUMsfCAmgAEKNSUlKsTZs2Nnny5OyFZPViQp8X1GKrKJ5//nn3Au5k/f3YhdmzZ7uPqrDNT9myZd2Wl17wRPJFj15IFXSffvv0A22+Vfrtf94nbdpYouYQQer5XorsbLwQLIxV7GCsYku0xkuP5/ei5/dwsMYqkb+JhLVBEajKWh2MTtdXgBa+KWRTMLU7/TXjgSom1f/u669ztt9/z/+2GkT1z1N462+qyFTVil5MKqRVX1X/hZGCvY8/9vqtqvI1nJ53ndqvBb7U11aVtwoadfmeUCXtyJFeSFtcL9D0S1bHq00BcXHS89Gvn/c46her6uHw8VGfXn2/eWlMdHxHHeX1vNVzXKGCN94akzvu0Cvwgh83PKjVC/Qbb/QWaotQlRQAYOdUrXrRRRdZ27ZtrX379jZixAi30GwfLaRoOmHkQqtbt65rQ+AvGDZv+xuk2l+8eLELWitUqGCN9Sbodgp9FdbqvpP9NxW3U6uDV1991U466STbe++9Xc/aG2+80Y4++mg7RP3IA8o/2WV/+y3nQgXRBLUAAACBQWVtaQ9rFTjq1G5VPPqbFo3aVSimMEubKirVa/WJJ+I3xFVop+BPod3ffxd8O1Wl6pR9Ldql0+/zvLDbZajpn66v1gE6jV+9YRXQduxoVpwrS8fbCzSFpKquvftu7+f5gw/MPvzQ+1lXtW1eagWhTX181edW49amjXfdd9/lvq0CXW16M0ObKni1KfhWuwMt9FWY6mYAQMT06tXL9X0dOHCgLVu2zFq1amUTJ07MXnRs0aJFuSo0lixZYq11lsV2Dz/8sNs6deqUa2FbtT/Q116ihTXzqejV9X4wrHYGZ555pt15552BHlmtMyr1bVHOhbs6gwUAAAAlirC2NLdBuPdes0GDiv51WhRLB+wftKpTVFGYz6IZgfe//3m9VlVNmV+vNvVdvfZaL+jLS6GcKl7VS1W9YrUfiXJ9Vdxqw56H0M2aeZv686qcSIGseuouWuQF7/qoz7WAm0+Bbt6QVgu1KcjlNFGUgMSERDu4xsHZ+wB2TS0PCmp7EB7ASsOGDd3iGLvStWvXAm+ncPaLvGfCxAC/sjZXWEtPdQBAKcScG0FGWFtaK2sVPuYXrqo6VKeEa1V5LYSl4MrfdCr/5s1mGzd6C0rpoxb00AsZBVk6tT/Ap/5l0/FOnmw2eLC3eJcf7KmCVaGrttq1reKdd1rCCy/kXhDsuOO8xbsU0Kr6kirK2FG5stkpp+R/ncLaL7/0WlBomz/fu/zAA83uu8+rlo63CmQEVkpSig3p4p2uDQDFEdY2TFikNcY8hLUAgFKIOTeCjLC2NIa1Wl1CfT21cJLccIP3+QEHeK0NikKLLt1/vxfm6jTBadPyP/1fVYz//a8LQV0FqkLdkg46FdK+/74X0mqxq7zXffutt912myWkpNhe4YuFqYfd44+bnXBCyR4zSobepDjnHG8TtUVQOxD9m9BibgAAxFFY2yhpkZnfGUiLbQIAACAwCGtLYxuE667zTv0W9d98+OHdD6TUm+2tt7xWCDNnegs79e+f+zY//eRVoy5dmnOZ+tu2auUFty1aeG0UFORqU4+5SPa/1QJQr73mLS6lYwmnME4BrCpt587Nvjhhe1AbKlfOEtSrVqfRU0VbeqglRn5tMQAAiIOwtp7fBqFGDa9HPgAAAAKDsDZglbUqSi1qcWuRTJhg9uKL3r4WB3vppT2rHFSAOWaM1/NVlbrqgdu9u9dKQaZONTvppJwVLXwKQ1XdmrfC1Ve9ulnz5l4FbsuW3kd9XpSFtn7+2eypp8yefz7n1YlP93f77WZnnZXz/f/2m1d5+957FvruO0s77jhLGTnSEho1KtJTAgB7YmvGVuv7Xl+3/9xpz1lqcipPKIA9ppOIdHJVsm2zmhnbe7XTAgEAUEox50aQEdYGrLJWLRCKrTXm4sVmV1yR8/moUVplY8/vt0MHsxtv9Kpq09LM+vb1+n9OmuT1+lSfW9GiWWq38P33Xki7cGHB97lqVU7/UJ8W71Il8PXXe31l8wuZt271AtdnnvEeP6/DDvNCWvUuzftE77+/1xLihhsslJVla1essJpUVwKIgvVp63neAUSUTjRSG/76tsSSbHsrLMJaAEApxpwbQUVYG7DK2mJrgaCq14svzkmFe/Y0O//8yN3/vfeavfuu2a+/mn3zjdl553ntEbZt867v3Nns7be9al6fqm1nzDD74w+zZctyb7osvG2C/z34Aa5C5muv9YLhSpW8Cl5VCY8fv2MVr6p/zz3XrF8/s7ZtI/c9AwAAxAj/JKP6fgsE90n9qB0PAAAA8kdYGwBam0unpUVkcTEFmlrwS9WreWfoX32Vs5jS6NGRLeFVe4JnnzU75hjvc4WmvjPPNBs7dseer1WqmHXpsvPqWvWY/fFHsx9+8I5fYbCo5+5//mM2cKDXW1Thbl5qX3DVVd7CZ3vvHZFvEwAAIBb572UT1gIAAAQbYW0AhBeC7nFlrfrHqm/szqhn7R6nwvno1MkLR9Un1nfppV4wvDt9cdW39thjvc0PotXaYMQIs4kTc87pCw9qFRorHFa7heOO27N+vAAAAHGCyloAAIDYkBjtA0BOCwTZowxVLQceeKDg61VJqyB3Z9Wse2roUG8hMD3WgAFm//1v5AJT9azt1s3s44/N5s/3gmGFs3ostVlQCL18udcO4fjjCWoBAAC2I6wFAACIDVTWxlNYq1YDfpWpwspXX819fWqqWYUKVqzUk3bmTK9cuFat4nucZs3MnnzS7NFHvZC6uL8vAACAGEZYCwAAEBsIawPAX/Nrj9ogqPHt4ME5n999t9dGIBrUm7Y4g9q8j5W3Fy4AxLDEhERrUq1J9j4AFEtYm5Li9f0HAKAUYs6NICOsjZfK2nHjchbfUkuAww+PyLEBAEpWSlKKDe82nKcdQPGGtfXqeS2mAAAohZhzI8iYocVDWJuZaXb//TmfDxwYkeMCAABA/IS1lWydVbb13gX160f7kAAAAJAPwtp4aIMwYYLZwoXefqdOZkcfHbFjAwAAQHyEtdlVtUJYCwAAEEiEtbFeWZuVZXbffTmf33VXxI4LAFDy0jLSrO+7fd2mfQCIBK39miusbdCAJxYAUGox50aQ0bM2YJW1LqwNhcxWrvSqZcO3jRvNTjzR7PzzzfbZx/uCt94ymzfP21ef2uOOi843AQCIiJCFbMXmFdn7ABAJVNYCAMCcG7GBsDZglbV7b11sdmDnnLYGeX3+udmAAWYnnGB28cW5q2rVqzYhofgPGAAAADEX1h5OGwQAAIDAI6wNWFhb9bUnCw5qwxcU+/BDb/O1b2/WtWvxHSQAAABiFpW1AAAAsYGwNkBtECpUMEv68D3vE1XIXn21WbNmZk2beltamtlLL5m9+KLZ33/nvhOqagEAAFDYsLZePZ4rAACAACKsDVBl7SGV/jSbM8f7pEMHsyee2PHGantwzz1eO4Tnnzf77DOzk082O+mkkj1oAAAAxGZYW726Wfny0T4kAAAA5IOwNsq0lpgf1p6W8H7OFaedVvAXJSaade7sbQAAAMAu5pub1mVYXVvsXVC/Ps8XAABAQBHWRtnmzWbbtnn7nTdvb4Egp54atWMCAERPgiVYvUr1svcBYE9t3GhWK7TUki3Tu4CwFgBQyjHnRpAR1kaZX1VbydZZy7VfeJ80bGjWvHlUjwsAEB1lk8vakyc/ydMPIGLWrs3Tr5awFgBQyjHnRpAlRvsASjs/rO1mn1iZ0LacFghaYAwAAACI9OJihLUAAACBRVgbZWvWeB9PtbB+tbRAAAAAQIQQ1gIAAMQOwtoAVNYmWYadbB96F1SqZHb00dE+LABAlKRlpNnVH17tNu0DwJ4irAUAgDk3Ygc9awNQWXu4fWvVbHuJ7QknmKWkRPuwAABRErKQ/b3+7+x9ANhThLUAADDnRuygsjYAlbW0QAAAAEBJhLWZSWXMatXiyQYAAAgowtoAhLWn2XtuPysxyeykk6J9SAAAAIjTsHZL9XpmibwEAAAACCpmalGW8sdCa2o/u/3NrY4wq1Yt2ocEAACAOLJ1xXqramvd/rba9aN9OAAAANgJwtooazz//ez9bSecGtVjAQAAQPxJXOz1wZbMfQlrAQAAgoywNsoOWZQT1pY587SoHgsAAADiT9nlXgsESWhAWAsAABBkydE+gFJt9WprvvYbt7vQDrADWh8Q7SMCAERZgiVYzfI1s/cBYE+VX5UT1qbsT1gLAABzbgQZYW00ffyxJVum251c7lRrymtyACj1yiaXtedOf67UPw8AIqfS2pywNvUAwloAAJhzI8hogxBN7+e0QPi2Oi0QAAAAEHlVN+aEtWWorAUAAAg0wtpoSU+30Mcfu91/rar9WefwqB0KAAAA4leNLTlhrdWrF81DAQAAwC4Q1kZLYqKtf+4Ne9yusWftUqu8Nx0pAABm6ZnpdtMnN7lN+wB2bdSoUdawYUNLTU21Dh062PTp0wu87dy5c+3MM890t09ISLARI0bscJu7777bXRe+NWvWLNdttm7dav369bO9997bKlSo4O5z+fLlgRyufdK9sHZNYjWzChWifTgAAEQdc24EGWFttCQn24pWXe06e9z62zCrVi1qRwIACJCsUJb98u8vbtM+gJ0bP3683XTTTTZo0CCbNWuWtWzZ0rp162YrVqzI9/abN2+2/fbbzx588EGrXbt2gffbvHlzW7p0afb29ddf57r+xhtvtPfff98mTJhgX3zxhS1ZssR69OgRuOHK2pZpdUL/uP0VqfSrBQDA/X1kzo0AI6yNon//zdmvWjWaRwIAABCbhg8fbpdddpn16dPHDjroIBs9erSVL1/exowZk+/t27VrZw899JCdc845VrZs2QLvNzk52YW5/la9evXs69atW2fPPfece+zjjjvO2rRpY88//7x9++23Nm3aNAuSjb8uszKW4fZXlyesBQAACDrC2oCEtVTWAgAAFE16errNnDnTunTpkn1ZYmKi+3zq1Kl79HT+8ssvVqdOHVeF27t3b1u0KKfvqx5z27ZtuR5XbRLq16+/x48baZsX5Bz32sqEtQAAAEFHo9QoWrMmZ5+wFgAAoGhWrVplmZmZVqtWrVyX6/MFCxbs9tOpvrcvvPCCNW3a1LVAuOeee+yoo46yOXPmWMWKFW3ZsmWWkpJiVapU2eFxdV1+0tLS3OZbv369+5iVleW2SND9hEKhXPeX9suf2fsbq9WL2GOheMYLwcRYxQ7GKrZEc7z8x/b3+V1cuOerJJ6nLP4uEtZGE20QAAAAgufEE0/M3j/kkENceNugQQN7/fXXrW/fvrt1n0OGDHGhb14rV650i5VF6sWNWjToxZQqjGXjvIXZ16+rVKvAXr4oefmNF4KJsYodjFVsieZ4bc3Yaulp3mK++tuYmpxaoo8fa0pyrDZs2GClHZW1UUQbBAAAgN2nPrJJSUm2fPnyXJfr850tHlZUqqA94IAD7Ndff3Wf677VgmHt2rW5qmt39rgDBgxwC6GFV9bWq1fPatSoYZUqVYrYC6mEhAR3n/4LqS2rVmVfn9iwsdWsWTMij4XiGS8EE2MVOxir2BLN8VJYm1I2xe3rbyNhbXDGKjWV4JywNiBtEFhgDADgq1Q2MsENEO/UikCLe02ePNm6d++e/WJCn19zzTURe5yNGzfab7/9ZhdccIH7XI9ZpkwZ9zhnnnmmu2zhwoWur23Hjh3zvQ8tZpbfgmZ6wRPJFz16IRV+n2uq7Gf/2JFW3xZZqF5DQsGAyTteCC7GKnYwVrElWuOlx6ucWjl7n9/DwRmrRP4mEtZGE5W1AIC89K7+2B5jeWKAQlK16kUXXWRt27a19u3b24gRI2zTpk3Wp08fd/2FF15odevWdW0IRBWx8+bNy95fvHixzZ492ypUqGCNGzd2l99888126qmnutYHS5YssUGDBrkK3nPPPdddX7lyZdcOQY9drVo1Vxl77bXXuqD2sMMOC9TYfXfETXb1WK+i9/kG0T4aAACCgTk3gozK2ihigTEAAIA906tXL9f3deDAgW5xr1atWtnEiROzFx1TtWt4hYbC19atW2d//vDDD7utU6dONmXKFHfZP//844LZ1atXu9P9jjzySJs2bZrb9z366KPuflVZq4XDunXrZk8++WTghnPt2pz9yl4BEQAAAAKMsDaKWGA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+ }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[指标分析]\n", + " 各NDCG指标在验证集上的最佳值:\n", + " ndcg@1: 0.2725 (迭代 50)\n", + " ndcg@5: 0.2203 (迭代 97)\n", + " ndcg@10: 0.2108 (迭代 74)\n", + " ndcg@20: 0.1970 (迭代 86)\n", + "\n", + "[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\n" + ] + } + ], + "execution_count": 11 }, { "cell_type": "markdown", @@ -1285,8 +2705,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:50.036021Z", - "start_time": "2026-03-09T16:28:49.947604Z" + "end_time": "2026-03-10T14:56:35.982083Z", + "start_time": "2026-03-10T14:56:35.604003Z" } }, "source": [ @@ -1344,143 +2764,44 @@ "\n", "NDCG 评估结果:\n", "----------------------------------------\n", - " ndcg@1: 0.0000\n", - " ndcg@5: 0.0000\n", - " ndcg@10: 0.0000\n", - " ndcg@20: 0.0000\n", + " ndcg@1: 0.3919\n", + " ndcg@5: 0.3768\n", + " ndcg@10: 0.3831\n", + " ndcg@20: 0.3935\n", "\n", "特征重要性（Top 20）:\n", "----------------------------------------\n", - " 1. max_ret_20 3634.79\n", - " 2. turnover_rank 2905.90\n", - " 3. drawdown_from_high_60 1816.20\n", - " 4. overnight_intraday_diff 1484.73\n", - " 5. std_return_20 791.58\n", - " 6. volume_ratio_5_20 757.43\n", - " 7. current_ratio 694.13\n", - " 8. revenue_yoy 605.02\n", - " 9. kaufman_ER_20 507.48\n", - " 10. close_vwap_deviation 372.14\n", - " 11. roa 281.62\n", - " 12. active_market_cap 280.69\n", - " 13. sharpe_ratio_20 278.22\n", - " 14. turnover_rate_mean_5 274.94\n", - " 15. healthy_expansion_velocity 266.26\n", - " 16. net_profit_yoy 263.69\n", - " 17. high_low_ratio 252.26\n", - " 18. volatility_squeeze_5_60 245.18\n", - " 19. return_5_rank 243.28\n", - " 20. volatility_20 228.92\n" + " 1. max_ret_20 4720.20\n", + " 2. turnover_rank 4345.39\n", + " 3. overnight_intraday_diff 3623.13\n", + " 4. volatility_5 2632.23\n", + " 5. turnover_rate_mean_5 2518.52\n", + " 6. std_return_20 1819.15\n", + " 7. close_vwap_deviation 1525.61\n", + " 8. return_20 1014.14\n", + " 9. return_5_rank 694.17\n", + " 10. EP 412.96\n", + " 11. ma_5 403.47\n", + " 12. BP 369.00\n", + " 13. ma_20 345.33\n", + " 14. upper_shadow_ratio 342.03\n", + " 15. bbi_ratio 339.14\n", + " 16. roa 289.77\n", + " 17. roe 269.37\n", + " 18. drawdown_from_high_60 248.74\n", + " 19. min_ret_20 247.30\n", + " 20. high_low_ratio 215.36\n" ] } ], - "execution_count": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.7 Top-k 选股策略分析" - ] + "execution_count": 12 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T16:28:50.725016Z", - "start_time": "2026-03-09T16:28:50.043768Z" - } - }, - "source": [ - "print(\"\\n\" + \"=\" * 80)\n", - "print(\"Top-k 选股策略分析\")\n", - "print(\"=\" * 80)\n", - "\n", - "# 分析策略表现\n", - "strategy_results = analyze_top_k_strategy(\n", - " df=test_data,\n", - " prediction_col=\"prediction\",\n", - " return_col=LABEL_NAME,\n", - " k_list=[5, 10, 20],\n", - ")\n", - "\n", - "# 打印结果\n", - "print(\"\\n策略表现统计:\")\n", - "print(\"=\" * 60)\n", - "for name, result in strategy_results.items():\n", - " print(f\"\\n{name}:\")\n", - " print(f\" 日均收益: {result['mean_daily_return']:.4f}\")\n", - " print(f\" 日收益标准差: {result['std_daily_return']:.4f}\")\n", - " print(f\" 年化夏普比率: {result['sharpe_ratio']:.4f}\")\n", - " print(f\" 累计收益: {result['total_return']:.4f}\")\n", - "\n", - "# 绘制图表\n", - "print(\"\\n生成策略表现图...\")\n", - "plot_strategy_performance(strategy_results)" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================\n", - "Top-k 选股策略分析\n", - "================================================================================\n", - "\n", - "策略表现统计:\n", - "============================================================\n", - "\n", - "top5:\n", - " 日均收益: nan\n", - " 日收益标准差: nan\n", - " 年化夏普比率: nan\n", - " 累计收益: nan\n", - "\n", - "top10:\n", - " 日均收益: nan\n", - " 日收益标准差: nan\n", - " 年化夏普比率: nan\n", - " 累计收益: nan\n", - "\n", - "top20:\n", - " 日均收益: nan\n", - " 日收益标准差: nan\n", - " 年化夏普比率: nan\n", - " 累计收益: nan\n", - "\n", - "生成策略表现图...\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.7 保存结果" - ] - }, - { - "cell_type": "code", - "metadata": { - "ExecuteTime": { - "end_time": "2026-03-09T16:28:50.888298Z", - "start_time": "2026-03-09T16:28:50.733200Z" + "end_time": "2026-03-10T14:56:36.204207Z", + "start_time": "2026-03-10T14:56:35.991175Z" } }, "source": [ @@ -1534,15 +2855,35 @@ "output_type": "stream", "text": [ "\n", - "预测结果已保存: output\\learn_to_rank_predictions.csv\n", - "特征重要性已保存: output\\feature_importance.csv\n", - "策略统计已保存: output\\strategy_statistics.csv\n", + "[1/1] 保存每日 Top 5 股票...\n", + " 保存路径: output\\rank_output.csv\n", + " 保存行数: 1215（243个交易日 x 每日top5）\n", + "\n", + " 预览（前15行）:\n", + "shape: (15, 3)\n", + "┌────────────┬──────────┬───────────┐\n", + "│ trade_date ┆ score ┆ ts_code │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ f64 ┆ str │\n", + "╞════════════╪══════════╪═══════════╡\n", + "│ 2025-01-02 ┆ 0.603437 ┆ 600530.SH │\n", + "│ 2025-01-02 ┆ 0.580176 ┆ 600322.SH │\n", + "│ 2025-01-02 ┆ 0.538287 ┆ 605080.SH │\n", + "│ 2025-01-02 ┆ 0.488088 ┆ 000679.SZ │\n", + "│ 2025-01-02 ┆ 0.480476 ┆ 603958.SH │\n", + "│ … ┆ … ┆ … │\n", + "│ 2025-01-06 ┆ 0.963227 ┆ 600807.SH │\n", + "│ 2025-01-06 ┆ 0.737975 ┆ 002816.SZ │\n", + "│ 2025-01-06 ┆ 0.644058 ┆ 603617.SH │\n", + "│ 2025-01-06 ┆ 0.637653 ┆ 000573.SZ │\n", + "│ 2025-01-06 ┆ 0.521599 ┆ 605580.SH │\n", + "└────────────┴──────────┴───────────┘\n", "\n", "训练流程完成！\n" ] } ], - "execution_count": 12 + "execution_count": 13 }, { "cell_type": "markdown", diff --git a/src/factors/engine/data_router.py b/src/factors/engine/data_router.py index 65f64fc..8ed7b6d 100644 --- a/src/factors/engine/data_router.py +++ b/src/factors/engine/data_router.py @@ -262,10 +262,11 @@ class DataRouter: if stock_codes is not None: df = df.filter(pl.col("ts_code").is_in(stock_codes)) - # 选择需要的列 - select_cols = ["ts_code", "trade_date"] + [ - c for c in columns if c in df.columns - ] + # 选择需要的列（避免重复） + base_cols = ["ts_code", "trade_date"] + extra_cols = [c for c in columns if c in df.columns and c not in base_cols] + select_cols = base_cols + extra_cols + return df.select(select_cols) def _load_from_database( @@ -314,10 +315,10 @@ class DataRouter: if col not in df.columns and col not in ["ts_code", "trade_date"]: raise ValueError(f"表 {table_name} 缺少字段: {col}") - # 选择需要的列 - select_cols = ["ts_code", "trade_date"] + [ - c for c in columns if c in df.columns - ] + # 选择需要的列（避免重复） + base_cols = ["ts_code", "trade_date"] + extra_cols = [c for c in columns if c in df.columns and c not in base_cols] + select_cols = base_cols + extra_cols return df.select(select_cols)