diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 41c5307f..f56e9f0c 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,13 @@
 Changes - from version >= 1.x
 =============================
 
+2026-02-21
+----------
+
+**version 1.2.2**
+
+* [experimental] LSA-method Ordinary Kriging tests and experiments
+
 2025-12-26
 ----------
 
diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/compare-lsa.ipynb b/analyses/differences-between-non-lsa-and-lsa-solving/compare-lsa.ipynb
new file mode 100644
index 00000000..9a41e9b9
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/compare-lsa.ipynb
@@ -0,0 +1,544 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:22:39.831488Z",
+     "start_time": "2026-01-18T14:22:38.956271Z"
+    }
+   },
+   "source": "import pandas as pd",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:23:25.297300Z",
+     "start_time": "2026-01-18T14:23:25.294450Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "f1 = 'lsa-comp-dem2180.csv'\n",
+    "f2 = 'lsa-comp-meuse.csv'"
+   ],
+   "id": "d69139aa1e1fcf7d",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:38:00.273630Z",
+     "start_time": "2026-01-18T14:38:00.235619Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df1 = pd.read_csv(f1, index_col=False)\n",
+    "df1.drop(columns=['Unnamed: 0'], inplace=True)\n",
+    "df1.head()"
+   ],
+   "id": "f14b77f8eb959389",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       rmse  time-linalgsolve  rmse_lsa  time-lsa  nn  dataset\n",
+       "0  4.542369          0.024086  4.542369  0.030443   4  dem2180\n",
+       "1  4.518797          0.023378  4.518797  0.031923   8  dem2180\n",
+       "2  5.163855          0.024277  5.163855  0.033809  16  dem2180\n",
+       "3  5.259920          0.028030  5.259920  0.043930  32  dem2180\n",
+       "4  4.847665          0.032389  4.847665  0.086493  64  dem2180"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>time-linalgsolve</th>\n",
+       "      <th>rmse_lsa</th>\n",
+       "      <th>time-lsa</th>\n",
+       "      <th>nn</th>\n",
+       "      <th>dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.542369</td>\n",
+       "      <td>0.024086</td>\n",
+       "      <td>4.542369</td>\n",
+       "      <td>0.030443</td>\n",
+       "      <td>4</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.518797</td>\n",
+       "      <td>0.023378</td>\n",
+       "      <td>4.518797</td>\n",
+       "      <td>0.031923</td>\n",
+       "      <td>8</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.163855</td>\n",
+       "      <td>0.024277</td>\n",
+       "      <td>5.163855</td>\n",
+       "      <td>0.033809</td>\n",
+       "      <td>16</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.259920</td>\n",
+       "      <td>0.028030</td>\n",
+       "      <td>5.259920</td>\n",
+       "      <td>0.043930</td>\n",
+       "      <td>32</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.847665</td>\n",
+       "      <td>0.032389</td>\n",
+       "      <td>4.847665</td>\n",
+       "      <td>0.086493</td>\n",
+       "      <td>64</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:38:09.918132Z",
+     "start_time": "2026-01-18T14:38:09.904922Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df2 = pd.read_csv(f2, index_col=False)\n",
+    "df2.drop(columns=['Unnamed: 0'], inplace=True)\n",
+    "df2.head()"
+   ],
+   "id": "f42a3cee932272b1",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       rmse  time-linalgsolve  rmse_lsa  time-lsa  nn dataset\n",
+       "0  0.747242          0.018661  0.747242  0.022680   4   meuse\n",
+       "1  0.585143          0.019339  0.585143  0.029146   8   meuse\n",
+       "2  0.655050          0.016812  0.655050  0.024158  16   meuse\n",
+       "3  0.687484          0.018080  0.687484  0.025354  32   meuse\n",
+       "4  0.630097          0.016673  0.630097  0.027855  64   meuse"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>time-linalgsolve</th>\n",
+       "      <th>rmse_lsa</th>\n",
+       "      <th>time-lsa</th>\n",
+       "      <th>nn</th>\n",
+       "      <th>dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.747242</td>\n",
+       "      <td>0.018661</td>\n",
+       "      <td>0.747242</td>\n",
+       "      <td>0.022680</td>\n",
+       "      <td>4</td>\n",
+       "      <td>meuse</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.585143</td>\n",
+       "      <td>0.019339</td>\n",
+       "      <td>0.585143</td>\n",
+       "      <td>0.029146</td>\n",
+       "      <td>8</td>\n",
+       "      <td>meuse</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.655050</td>\n",
+       "      <td>0.016812</td>\n",
+       "      <td>0.655050</td>\n",
+       "      <td>0.024158</td>\n",
+       "      <td>16</td>\n",
+       "      <td>meuse</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.687484</td>\n",
+       "      <td>0.018080</td>\n",
+       "      <td>0.687484</td>\n",
+       "      <td>0.025354</td>\n",
+       "      <td>32</td>\n",
+       "      <td>meuse</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.630097</td>\n",
+       "      <td>0.016673</td>\n",
+       "      <td>0.630097</td>\n",
+       "      <td>0.027855</td>\n",
+       "      <td>64</td>\n",
+       "      <td>meuse</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:38:49.813912Z",
+     "start_time": "2026-01-18T14:38:49.808550Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df = pd.concat([df1, df2], axis=0)",
+   "id": "aab8f8917aea1e1",
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:38:50.642895Z",
+     "start_time": "2026-01-18T14:38:50.637869Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "len(df) == (len(df1) + len(df2))",
+   "id": "c07b1cdd30b1c946",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:38:51.711205Z",
+     "start_time": "2026-01-18T14:38:51.700466Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df.head()",
+   "id": "b780630b9579ce87",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       rmse  time-linalgsolve  rmse_lsa  time-lsa  nn  dataset\n",
+       "0  4.542369          0.024086  4.542369  0.030443   4  dem2180\n",
+       "1  4.518797          0.023378  4.518797  0.031923   8  dem2180\n",
+       "2  5.163855          0.024277  5.163855  0.033809  16  dem2180\n",
+       "3  5.259920          0.028030  5.259920  0.043930  32  dem2180\n",
+       "4  4.847665          0.032389  4.847665  0.086493  64  dem2180"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>time-linalgsolve</th>\n",
+       "      <th>rmse_lsa</th>\n",
+       "      <th>time-lsa</th>\n",
+       "      <th>nn</th>\n",
+       "      <th>dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.542369</td>\n",
+       "      <td>0.024086</td>\n",
+       "      <td>4.542369</td>\n",
+       "      <td>0.030443</td>\n",
+       "      <td>4</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.518797</td>\n",
+       "      <td>0.023378</td>\n",
+       "      <td>4.518797</td>\n",
+       "      <td>0.031923</td>\n",
+       "      <td>8</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.163855</td>\n",
+       "      <td>0.024277</td>\n",
+       "      <td>5.163855</td>\n",
+       "      <td>0.033809</td>\n",
+       "      <td>16</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.259920</td>\n",
+       "      <td>0.028030</td>\n",
+       "      <td>5.259920</td>\n",
+       "      <td>0.043930</td>\n",
+       "      <td>32</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.847665</td>\n",
+       "      <td>0.032389</td>\n",
+       "      <td>4.847665</td>\n",
+       "      <td>0.086493</td>\n",
+       "      <td>64</td>\n",
+       "      <td>dem2180</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:39:35.683605Z",
+     "start_time": "2026-01-18T14:39:35.680883Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "# average RMSE per nn",
+   "id": "f0b2831ae02ca8bf",
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:40:12.525831Z",
+     "start_time": "2026-01-18T14:40:12.519541Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "avg_rmse_nn = df.groupby('nn')[['rmse', 'rmse_lsa']].mean()",
+   "id": "74406bce9fa50365",
+   "outputs": [],
+   "execution_count": 15
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:40:44.350344Z",
+     "start_time": "2026-01-18T14:40:44.344587Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "avg_rmse_nn['rmse'] - avg_rmse_nn['rmse_lsa']",
+   "id": "5aed1357d8b6cbbe",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nn\n",
+       "4     -4.440892e-16\n",
+       "8      0.000000e+00\n",
+       "16     0.000000e+00\n",
+       "32    -8.881784e-16\n",
+       "64    -3.552714e-15\n",
+       "128    2.341096e-10\n",
+       "256   -7.472416e-06\n",
+       "512    5.260083e-07\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:41:08.524344Z",
+     "start_time": "2026-01-18T14:41:08.521564Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "# average time per nn",
+   "id": "2c38c4ea5190299c",
+   "outputs": [],
+   "execution_count": 18
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:41:41.057661Z",
+     "start_time": "2026-01-18T14:41:41.054036Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "avg_time_nn = df.groupby('nn')[['time-linalgsolve', 'time-lsa']].mean()",
+   "id": "b982b044aaf4873c",
+   "outputs": [],
+   "execution_count": 19
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:42:10.117937Z",
+     "start_time": "2026-01-18T14:42:10.111960Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "avg_time_nn['time-linalgsolve'] - avg_time_nn['time-lsa']",
+   "id": "776ecebc294348a9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nn\n",
+       "4     -0.006057\n",
+       "8     -0.006906\n",
+       "16    -0.009496\n",
+       "32    -0.013793\n",
+       "64    -0.034989\n",
+       "128   -0.214857\n",
+       "256   -0.763580\n",
+       "512   -3.212537\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "# the only difference seems to be in computation times",
+   "id": "3012f233d288d988"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/dem2180.csv b/analyses/differences-between-non-lsa-and-lsa-solving/dem2180.csv
new file mode 100644
index 00000000..2b8a6aaa
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/dem2180.csv
@@ -0,0 +1,6896 @@
+longitude,latitude,dem
+238012.30175002094,551466.8052219786,91.27559661865233
+237878.00129414318,548982.3511107564,96.5482940673828
+237685.3253934355,545416.7083154013,51.25455093383789
+237674.14030058548,545209.6713154893,48.958282470703125
+237449.25487021718,541045.9347496564,16.81786346435547
+237320.08999164688,538653.5180322705,16.29390525817871
+237187.2381191512,536192.0942486105,16.54066276550293
+237056.90784774156,533776.6832719631,19.450933456420895
+237050.70259316498,533661.6638223836,19.24267578125
+236890.6368568528,530694.1658183495,45.66201019287109
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diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-dem2180-duplicated-coords.csv b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-dem2180-duplicated-coords.csv
new file mode 100644
index 00000000..924705cc
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-dem2180-duplicated-coords.csv
@@ -0,0 +1,801 @@
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diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-dem2180.csv b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-dem2180.csv
new file mode 100644
index 00000000..38e6f3f0
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-dem2180.csv
@@ -0,0 +1,801 @@
+,rmse,time-linalgsolve,rmse_lsa,time-lsa,nn,dataset
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diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-meuse.csv b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-meuse.csv
new file mode 100644
index 00000000..41e35706
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-comp-meuse.csv
@@ -0,0 +1,501 @@
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diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-dem2180-duplicated-coords.ipynb b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-dem2180-duplicated-coords.ipynb
new file mode 100644
index 00000000..815ad95c
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-dem2180-duplicated-coords.ipynb
@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:57:44.942541Z",
+     "start_time": "2026-01-04T13:57:44.938565Z"
+    }
+   },
+   "source": [
+    "import geopandas as gpd\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from pyinterpolate import (\n",
+    "    build_experimental_variogram,\n",
+    "    build_theoretical_variogram,\n",
+    "    ordinary_kriging\n",
+    ")\n",
+    "from pyinterpolate.kriging.point.ordinary import experimental_ordinary_kriging_lsa"
+   ],
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:57:46.168243Z",
+     "start_time": "2026-01-04T13:57:45.498193Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Load DEM data\n",
+    "\n",
+    "df = pd.read_csv(\n",
+    "    'dem2180.csv'\n",
+    ")\n",
+    "# Populate geometry column and set CRS\n",
+    "\n",
+    "dem_geometry = gpd.points_from_xy(x=df['longitude'], y=df['latitude'], crs='epsg:2180')\n",
+    "dem = gpd.GeoDataFrame(df, geometry=dem_geometry)\n",
+    "\n",
+    "# Transform crs to metric values\n",
+    "dem.plot(column='dem', cmap='gist_earth', alpha=0.6, vmin=0, legend=True);"
+   ],
+   "id": "5ef555bb83f52fb",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:57:46.391247Z",
+     "start_time": "2026-01-04T13:57:46.388231Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "step_size = 500  # meters\n",
+    "max_range = 10000  # meters"
+   ],
+   "id": "8e50ef8493c57fbb",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T17:27:25.019256Z",
+     "start_time": "2026-01-04T15:39:03.411692Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from tqdm import tqdm\n",
+    "from time import perf_counter\n",
+    "\n",
+    "\n",
+    "neighbors_number = [4, 8, 16, 32, 64, 128, 256, 512]\n",
+    "number_of_trials = 100\n",
+    "\n",
+    "outputs = []\n",
+    "\n",
+    "for _ in range(number_of_trials):\n",
+    "    for nn in tqdm(neighbors_number):\n",
+    "        # Divide dataset into train and test set\n",
+    "        train = dem.sample(n=int(0.1*len(dem)),\n",
+    "                           replace=True)\n",
+    "\n",
+    "        unique_indexes = pd.unique(train.index)\n",
+    "        train_linalg = dem.loc[dem.index.isin(unique_indexes)]\n",
+    "        test = dem.loc[~dem.index.isin(unique_indexes)]\n",
+    "\n",
+    "        # Take sample from test, we don't need a whole dataset\n",
+    "        test_sample = test.sample(n=500)\n",
+    "\n",
+    "        # Fit semivariogram model\n",
+    "        exp_var = build_experimental_variogram(\n",
+    "            values=train['dem'],\n",
+    "            geometries=train['geometry'],\n",
+    "            step_size=step_size,\n",
+    "            max_range=max_range\n",
+    "        )\n",
+    "        theo_var = build_theoretical_variogram(\n",
+    "            experimental_variogram=exp_var,\n",
+    "            models_group='linear'\n",
+    "        )\n",
+    "\n",
+    "        # Predict new values\n",
+    "        ty0 = perf_counter()\n",
+    "        yhats = ordinary_kriging(\n",
+    "            theoretical_model=theo_var,\n",
+    "            known_values=train_linalg['dem'],\n",
+    "            known_geometries=train_linalg['geometry'],\n",
+    "            unknown_locations=test_sample['geometry'],\n",
+    "            no_neighbors=nn,\n",
+    "            use_all_neighbors_in_range=False,\n",
+    "            progress_bar=False\n",
+    "        )\n",
+    "        ty = perf_counter() - ty0\n",
+    "\n",
+    "        yhats = yhats[:, 0]\n",
+    "        avg_rmse = np.sqrt(np.mean((test_sample['dem'] - yhats)**2))\n",
+    "\n",
+    "        # Predict using lsa\n",
+    "        ty_lsa0 = perf_counter()\n",
+    "        yhats_lsa = experimental_ordinary_kriging_lsa(\n",
+    "            theoretical_model=theo_var,\n",
+    "            known_values=train['dem'],\n",
+    "            known_geometries=train['geometry'],\n",
+    "            unknown_locations=test_sample['geometry'],\n",
+    "            no_neighbors=nn,\n",
+    "            use_all_neighbors_in_range=False,\n",
+    "            progress_bar=False\n",
+    "        )\n",
+    "        ty_lsa = perf_counter() - ty_lsa0\n",
+    "\n",
+    "        yhats_lsa = yhats_lsa[:, 0]\n",
+    "\n",
+    "        avg_rmse_lsa = np.sqrt(np.mean((test_sample['dem'] - yhats_lsa)**2))\n",
+    "\n",
+    "        outputs.append([avg_rmse, ty, avg_rmse_lsa, ty_lsa, nn, len(train_linalg), len(train)])"
+   ],
+   "id": "f9fd0c82b22907f9",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 8/8 [00:24<00:00,  3.07s/it]\n",
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+     ]
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T17:27:25.339260Z",
+     "start_time": "2026-01-04T17:27:25.331779Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf = pd.DataFrame(data=outputs, columns=['rmse', 'time-linalgsolve', 'rmse_lsa', 'time-lsa', 'nn', 'len-train', 'len-train-lsa'])",
+   "id": "69cd879e82df6044",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T17:27:25.696629Z",
+     "start_time": "2026-01-04T17:27:25.677254Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf.groupby(['nn']).mean()",
+   "id": "888b0744308b170d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "            rmse  time-linalgsolve     rmse_lsa   time-lsa  len-train  \\\n",
+       "nn                                                                      \n",
+       "4       4.843679          0.115438     4.861627   0.149970     656.02   \n",
+       "8       4.621145          0.112475     4.631273   0.150855     656.08   \n",
+       "16      4.554350          0.116192     4.555354   0.165388     655.86   \n",
+       "32      4.676973          0.127551     4.677548   0.215316     655.47   \n",
+       "64      4.594973          0.157098     4.594912   0.421879     655.21   \n",
+       "128    39.239077          0.235130    51.784165   1.174458     656.53   \n",
+       "256  2773.190427          0.548827   890.985764   4.016728     655.88   \n",
+       "512  4879.208899          2.173357  2387.786438  17.057617     655.34   \n",
+       "\n",
+       "     len-train-lsa  \n",
+       "nn                  \n",
+       "4            689.0  \n",
+       "8            689.0  \n",
+       "16           689.0  \n",
+       "32           689.0  \n",
+       "64           689.0  \n",
+       "128          689.0  \n",
+       "256          689.0  \n",
+       "512          689.0  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>time-linalgsolve</th>\n",
+       "      <th>rmse_lsa</th>\n",
+       "      <th>time-lsa</th>\n",
+       "      <th>len-train</th>\n",
+       "      <th>len-train-lsa</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nn</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.843679</td>\n",
+       "      <td>0.115438</td>\n",
+       "      <td>4.861627</td>\n",
+       "      <td>0.149970</td>\n",
+       "      <td>656.02</td>\n",
+       "      <td>689.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.621145</td>\n",
+       "      <td>0.112475</td>\n",
+       "      <td>4.631273</td>\n",
+       "      <td>0.150855</td>\n",
+       "      <td>656.08</td>\n",
+       "      <td>689.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>4.554350</td>\n",
+       "      <td>0.116192</td>\n",
+       "      <td>4.555354</td>\n",
+       "      <td>0.165388</td>\n",
+       "      <td>655.86</td>\n",
+       "      <td>689.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>4.676973</td>\n",
+       "      <td>0.127551</td>\n",
+       "      <td>4.677548</td>\n",
+       "      <td>0.215316</td>\n",
+       "      <td>655.47</td>\n",
+       "      <td>689.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>4.594973</td>\n",
+       "      <td>0.157098</td>\n",
+       "      <td>4.594912</td>\n",
+       "      <td>0.421879</td>\n",
+       "      <td>655.21</td>\n",
+       "      <td>689.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>39.239077</td>\n",
+       "      <td>0.235130</td>\n",
+       "      <td>51.784165</td>\n",
+       "      <td>1.174458</td>\n",
+       "      <td>656.53</td>\n",
+       "      <td>689.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>2773.190427</td>\n",
+       "      <td>0.548827</td>\n",
+       "      <td>890.985764</td>\n",
+       "      <td>4.016728</td>\n",
+       "      <td>655.88</td>\n",
+       "      <td>689.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>4879.208899</td>\n",
+       "      <td>2.173357</td>\n",
+       "      <td>2387.786438</td>\n",
+       "      <td>17.057617</td>\n",
+       "      <td>655.34</td>\n",
+       "      <td>689.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T17:27:26.212397Z",
+     "start_time": "2026-01-04T17:27:26.208978Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf['dataset'] = 'dem2180-duplicated-coords'",
+   "id": "dbbfd687e2957e80",
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T17:27:26.696025Z",
+     "start_time": "2026-01-04T17:27:26.676154Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf.to_csv('lsa-comp-dem2180-duplicated-coords.csv')",
+   "id": "536b2c35872e245a",
+   "outputs": [],
+   "execution_count": 14
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "ed48a99ac74bce18"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-dem2180.ipynb b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-dem2180.ipynb
new file mode 100644
index 00000000..f26ed8fd
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-dem2180.ipynb
@@ -0,0 +1,462 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:38:35.608731Z",
+     "start_time": "2026-01-04T13:38:33.221129Z"
+    }
+   },
+   "source": [
+    "import geopandas as gpd\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pyinterpolate import (\n",
+    "    build_experimental_variogram,\n",
+    "    build_theoretical_variogram,\n",
+    "    ordinary_kriging\n",
+    ")\n",
+    "from pyinterpolate.kriging.point.ordinary import experimental_ordinary_kriging_lsa"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:38:36.831141Z",
+     "start_time": "2026-01-04T13:38:36.176322Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Load DEM data\n",
+    "\n",
+    "df = pd.read_csv(\n",
+    "    'dem2180.csv'\n",
+    ")\n",
+    "# Populate geometry column and set CRS\n",
+    "\n",
+    "dem_geometry = gpd.points_from_xy(x=df['longitude'], y=df['latitude'], crs='epsg:2180')\n",
+    "dem = gpd.GeoDataFrame(df, geometry=dem_geometry)\n",
+    "\n",
+    "# Transform crs to metric values\n",
+    "dem.plot(column='dem', cmap='gist_earth', alpha=0.6, vmin=0, legend=True);"
+   ],
+   "id": "5ef555bb83f52fb",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:38:37.427359Z",
+     "start_time": "2026-01-04T13:38:37.423965Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "step_size = 500  # meters\n",
+    "max_range = 10000  # meters"
+   ],
+   "id": "8e50ef8493c57fbb",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:50:45.318685Z",
+     "start_time": "2026-01-04T13:38:37.948735Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from tqdm import tqdm\n",
+    "from time import perf_counter\n",
+    "\n",
+    "\n",
+    "neighbors_number = [4, 8, 16, 32, 64, 128, 256, 512]\n",
+    "number_of_trials = 100\n",
+    "\n",
+    "outputs = []\n",
+    "\n",
+    "for _ in range(number_of_trials):\n",
+    "    for nn in tqdm(neighbors_number):\n",
+    "        # Divide dataset into train and test set\n",
+    "        train = dem.sample(n=int(0.1*len(dem)))\n",
+    "        test = dem.loc[~dem.index.isin(train.index)]\n",
+    "\n",
+    "        # Take sample from test, we don't need a whole dataset\n",
+    "        test_sample = test.sample(n=100)\n",
+    "\n",
+    "        # Fit semivariogram model\n",
+    "        exp_var = build_experimental_variogram(\n",
+    "            values=train['dem'],\n",
+    "            geometries=train['geometry'],\n",
+    "            step_size=step_size,\n",
+    "            max_range=max_range\n",
+    "        )\n",
+    "        theo_var = build_theoretical_variogram(\n",
+    "            experimental_variogram=exp_var,\n",
+    "            models_group='linear'\n",
+    "        )\n",
+    "\n",
+    "        # Predict new values\n",
+    "        ty0 = perf_counter()\n",
+    "        yhats = ordinary_kriging(\n",
+    "            theoretical_model=theo_var,\n",
+    "            known_values=train['dem'],\n",
+    "            known_geometries=train['geometry'],\n",
+    "            unknown_locations=test_sample['geometry'],\n",
+    "            no_neighbors=nn,\n",
+    "            use_all_neighbors_in_range=False,\n",
+    "            progress_bar=False\n",
+    "        )\n",
+    "        ty = perf_counter() - ty0\n",
+    "\n",
+    "        yhats = yhats[:, 0]\n",
+    "        avg_rmse = np.sqrt(np.mean((test_sample['dem'] - yhats)**2))\n",
+    "\n",
+    "        # Predict using lsa\n",
+    "        ty_lsa0 = perf_counter()\n",
+    "        yhats_lsa = experimental_ordinary_kriging_lsa(\n",
+    "            theoretical_model=theo_var,\n",
+    "            known_values=train['dem'],\n",
+    "            known_geometries=train['geometry'],\n",
+    "            unknown_locations=test_sample['geometry'],\n",
+    "            no_neighbors=nn,\n",
+    "            use_all_neighbors_in_range=False,\n",
+    "            progress_bar=False\n",
+    "        )\n",
+    "        ty_lsa = perf_counter() - ty_lsa0\n",
+    "\n",
+    "        yhats_lsa = yhats_lsa[:, 0]\n",
+    "\n",
+    "        avg_rmse_lsa = np.sqrt(np.mean((test_sample['dem'] - yhats_lsa)**2))\n",
+    "\n",
+    "        outputs.append([avg_rmse, ty, avg_rmse_lsa, ty_lsa, nn])"
+   ],
+   "id": "f9fd0c82b22907f9",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:53:43.755268Z",
+     "start_time": "2026-01-04T13:53:43.750484Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf = pd.DataFrame(data=outputs, columns=['rmse', 'time-linalgsolve', 'rmse_lsa', 'time-lsa', 'nn'])",
+   "id": "69cd879e82df6044",
+   "outputs": [],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:53:45.552519Z",
+     "start_time": "2026-01-04T13:53:45.537931Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf.groupby(['nn']).mean()",
+   "id": "888b0744308b170d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "            rmse  time-linalgsolve     rmse_lsa  time-lsa\n",
+       "nn                                                       \n",
+       "4       4.577432          0.029551     4.577432  0.036269\n",
+       "8       4.461550          0.029602     4.461550  0.037161\n",
+       "16      4.476758          0.030471     4.476758  0.041436\n",
+       "32      4.521744          0.034222     4.521744  0.053655\n",
+       "64      4.541094          0.042573     4.541094  0.104571\n",
+       "128    18.985349          0.063804    18.985349  0.278661\n",
+       "256   662.150692          0.125173   662.150700  0.888754\n",
+       "512  1090.390462          0.439284  1090.390461  3.651821"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>time-linalgsolve</th>\n",
+       "      <th>rmse_lsa</th>\n",
+       "      <th>time-lsa</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nn</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.577432</td>\n",
+       "      <td>0.029551</td>\n",
+       "      <td>4.577432</td>\n",
+       "      <td>0.036269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.461550</td>\n",
+       "      <td>0.029602</td>\n",
+       "      <td>4.461550</td>\n",
+       "      <td>0.037161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>4.476758</td>\n",
+       "      <td>0.030471</td>\n",
+       "      <td>4.476758</td>\n",
+       "      <td>0.041436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>4.521744</td>\n",
+       "      <td>0.034222</td>\n",
+       "      <td>4.521744</td>\n",
+       "      <td>0.053655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>4.541094</td>\n",
+       "      <td>0.042573</td>\n",
+       "      <td>4.541094</td>\n",
+       "      <td>0.104571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>18.985349</td>\n",
+       "      <td>0.063804</td>\n",
+       "      <td>18.985349</td>\n",
+       "      <td>0.278661</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>662.150692</td>\n",
+       "      <td>0.125173</td>\n",
+       "      <td>662.150700</td>\n",
+       "      <td>0.888754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>512</th>\n",
+       "      <td>1090.390462</td>\n",
+       "      <td>0.439284</td>\n",
+       "      <td>1090.390461</td>\n",
+       "      <td>3.651821</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:56:05.141255Z",
+     "start_time": "2026-01-04T13:56:05.137513Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf['dataset'] = 'dem2180'",
+   "id": "dbbfd687e2957e80",
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-04T13:56:08.410274Z",
+     "start_time": "2026-01-04T13:56:08.393967Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf.to_csv('lsa-comp-dem2180.csv')",
+   "id": "536b2c35872e245a",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "ed48a99ac74bce18"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-meuse.ipynb b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-meuse.ipynb
new file mode 100644
index 00000000..4f35ba90
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/lsa-vs-non-lsa-meuse.ipynb
@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:08:36.059180Z",
+     "start_time": "2026-01-18T14:08:31.195940Z"
+    }
+   },
+   "source": [
+    "import geopandas as gpd\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pyinterpolate import (\n",
+    "    build_experimental_variogram,\n",
+    "    build_theoretical_variogram,\n",
+    "    ordinary_kriging\n",
+    ")\n",
+    "from pyinterpolate.kriging.point.ordinary import experimental_ordinary_kriging_lsa"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:10:43.853932Z",
+     "start_time": "2026-01-18T14:10:43.644743Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Load elements data\n",
+    "\n",
+    "# Variogram parameters\n",
+    "STEP_SIZE = 100\n",
+    "MAX_RANGE = 1600\n",
+    "ALLOWED_MODELS = 'safe'\n",
+    "\n",
+    "df = pd.read_csv(\n",
+    "    'meuse.csv', usecols=['x', 'y', 'zinc']\n",
+    ")\n",
+    "# Populate geometry column and set CRS\n",
+    "\n",
+    "zinc_geometry = gpd.points_from_xy(x=df['x'], y=df['y'], crs='epsg:2180')\n",
+    "gdf = gpd.GeoDataFrame(df, geometry=zinc_geometry)\n",
+    "gdf['z'] = np.log(gdf['zinc'] + 1)\n",
+    "\n",
+    "# Transform crs to metric values\n",
+    "gdf.plot(column='z', cmap='gist_earth', alpha=0.6, vmin=0, legend=True);"
+   ],
+   "id": "5ef555bb83f52fb",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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ZNeQKLTKpGDIpKkhYueRXqYERORiBQLAgyLV6pKOR8bCWpKouoUKrm5Qzadx8BQYPPY2ofwh5ns0yXkmmNaF+wyVcHVaOrXEnlBoTvEMHEA0MsgHRW5pha9jK3kt534yEDQu9d66qJAypLReLA+hnMnJUkSYqzBYG8VsUCAQLAlWIpYI+LlOmkuTyJH82GYdMoSoNEMukEkiE3Lzwk7FoOeu9iPoGEAsMs8CiSmuGsWYdFKrJ3et0X6N9HQy2Ns65cFWZ/NT7lWMwN0Oh1HNSX6U2VRyj50onQzBZ2+EefBN+9xHWKaPBZNaazbDX74RSZVhBjZa5ed9jqREGRiAQzAtK3qfCAaQiIWSzGeTSqcL4Y5l8fNHPs3HROuvZW/D07kNw+DgyySgv8nzMUgdH+9mcb5kpdG9qzJwOMir2uu0Y6StMsqUqMrqOihFoIBnNiolHRhH0dHG4jIxLJhXHcM8LXABAFWYrofs/l88hN88QF91jqREGRiAQzBlK2keHB5BJxtl4SFi3hb8uQyqTQa7TQ6HR8Vhk6osZ63oDvsGDbFRUeitXh2XTCYTdPUgnomjc9i4+FvEPIBYe5bQNSb8YrM1TljKfjrqWi/g+npG3EQsPcwiOjIxKY4VUIkMs7ILOUDB+BPXI5HImRINDGOl7Ca0bZz8HZcHJU+XcPA2EMDACgWC1QCGXqGuQQ2JytbYUoiIzkE2nkM+mOWxWbK5MRnwIuk5w2Euu0pbuI1dqOO9BITNv/36Eg328uFNOhCAjoDXXoXHjVZCPeyxUajxTmX3KpzSsuxT2uh1cVUZhMPJkaDux/6GC8OW4cTl1jQxKtYk9GxLJXAlezGpEGBiBQDAn0tEQlyaX51uKyBRK9m5SQX/JwER9Q8imk1BoJuc1yDshQ+A68RIyshS0JAUjLzRkZtJJ+N3H4PUchUpXMAYanYNDX5QrmamhmSh2GfSe5HHJNCemGhQyS0THkEoEl93A5JdpHsx8EQZGIBDMCfJcqEly4rf/IpSDIT2yYlUZGRfeP0UnPo02JmkYrbMBMnmhrJiuTST9SGbDyKVSbMzI44gEBxENDSER9aK+7ZI5Se+TQSPjRAu3RDb5M+TLKsyWm/wChMjmHWKbA6IPRiAQzJ1ZLOwUCiOm6sKnfhZIJZDKTn3vzWRiiMfdbAxI4iWfSXGORGuohUyhhXvoTUSCA3N6dK2xHmqNlSVkqkH71Vo7d/wL5oYwMAKBYE7I1JRHkUwZuqFKLU7ujxshnb2JjQypJFc7lyYuSpWKCm8kkQhwLoamUFIBQXmpLZUQUz7FP3a08l65DBIJP5JJarKcWoaGmi8djbs5dJSM+0vf8LnJM+aFRJJHTePZK6InJj+uRTbfbalZ/t+cQCBYlSi0epbiz8QikKk0pVAZi1umk4VEuclSOl+pMcLStBWenj2c8Jer9RyiSiejSEQ9yMvzSGXCUKZiLOHCYbVsoiQ/wx35E0QppTIV4lEP/0xyL273XoyN7UcyGWTvSq+vh7NmFyyWzqphNEr8U3Ole/ANxMIjBY8sXzBezpZLWHF5JZBfpSEyYWAEAsGcIIOiq2tCdLi/TGOMFugcJHIFtI46NkLlWJu3Qq5UIzB0lJWU06kIYjEX8tI852zSyTACo0d5BLLO1ACJRFYwWKSiLJVBpZnQLJnLcJMleS3dPY/B4znI3o5CoeMFNRjoRjg8gObmy1Hr3D35M0ikqG0+D1bnZoS83chk4pzcN1mpybPy2QWzRxgYgUAwZ0h639C0jpssyZOhEJaUEvFTDBgjL8JU1wmjsx3BsZPoP/I4S/tTrwvlXmJKLaLBYR4cRkUB5BnlsilI8pQzqYNceaqzn4wKhbfMtg54vUfg9RyGWm2BXH7Ky1Eq9YjHvRgcfAFmUzvUU1SMkcdir9+BlUoeOd7me4+lRhgYgUAwL6iBUmWy8Dbja6RSrgIjoUmtqb4UviKpfQqPxUm8MhWBQm2EVleDbD7DY5CLcpk0CTMZ80BvaoLZ3okTXb/jY+XGpQgZnUhkBH7/cdTVnYvVSJ5DZPMtUxYGRiAQrGKo1Dg81stKyITGWAODvWVSqS8tdjTauNAwWZ4bkfDMFgpPxYLDqG07H9b6bRjoeoqbJJMJP59D9zPa2tHccRVS6TD8geMcRkskfFAqjRWJ+WKfDOdlBEuKMDACgWBGsDhkLIS434Vshubaq6Gx1kGhLoStYgEXho48i0TEV5IlocVdY3KiYfPlUOttp+7FVU2ZKeVfCkPGyDDkOe/SvvVGRMPDLOtC+zR6J7T6GgwMPMd5l0TczVVoqVSQy5l1ujqo1JUeFe1freRFkl8gEKxVyCD4eg8iMtrLVVcsN5YH5KqjMDVugMbqZMn9ZMwPtcHOFWTF8uNYYISPte26viROSYPBuGEyNAwljFXfjxwb5XgHPXk5emMDb0X6+p6Gy/UGeyw6XT0i0RE2ItlcCpHIAL+HUmlAOh2DTKaEydSK1Uoun10AsUtRpiwQCFYggYFjCA2dgEypgUqvHy8bziOTiMLfewBhby+SUR80ppoK6RZK3KuNDsRDboTGemCp38T76XoKfUUCg5xPKWqMlbr3Y17WAjPa26s+D/W5eDwHoFAY2IjI5SokkwGkM3HIpCrkcknEYx6+VyoVhsO+FQZDI1Yr+VXqwYhGS4FAMC3ZVALh0R5I5UrIqd9lPGdCfyo0evZk/MNH2WOopgtW8GYkiPgGK/ZbnBthqd2MVNyPeGQM6VSUJ1DGQsMso1/f+U4olKdEMcsJh/uRTke5SqzwHkoYja1QKgzI5dM8UyaR8CCbTcLpPAttbe+esWaZYOEQHoxAIJiWRJiGiMWh1E8hCqnWIutPQCabXMFVhD2eCZ3klKhv3nQ1dOYG+IYOsDQLGQFr3VbYG3ZAb2ma8n48iVIiqTAaVEFmsXSw4aGEfi6Xxob1H4DZPPMZMyuV/AKMPBZilwKBYOXBhqFYIDwZWuRpcafczFTjkikBrzHYJ11LRsbReBb3oGTSNFNGVhEumwqVyszvyxMoZcryp4FCoUcmk4BW64DRuHrDYuWIEJlAIFiTyDUGSOUK5DKpqsdpYBgJQtKMl3Q8NMm4JKN+KNV6GJ0d0/bFUMlyKhlE0HeS1ZLJKE2F0dgCrdbJYbCJCycZHZKYsdu3c1e/YPkQHoxAIJgWpc4EtcmBqGeIPY5yeX6qEqMcjblpI/KyHNxdr3G5Molaks+TTcXZcNR2XgSVduqZKtHwCIZ6X0AkNMid+5S30ehqUNt0Piz2DZPOpz6XlpYr0dX1e0Sjw5DLdWxMSH2ZQmMWywbU1OzCWiEvQmQCgWAtwhVfbdt5LHIy7GdvhqrDcpk097JorXUwN27gIgDqdQmMHOPhYlRnbHJ2wlK3EVpz7ZT3j0VGcfLIb1nRmEqX5RoLN03GIi70Hvsj525osNhEDIYmbNjwAbjdb8PvP4Z8PgONxg6HYztvq7nvZSJCKkYgEKxZaAqlc/OFiLj7eaNwmVJnhL6mBbqaZh55TOitTbwVZfJnMgjMNfgaknEfdIaGU2OX5Spo9bWIRdwY6X8ZZltn1cFfWm0NWluvRHPzOznxT0ZFVIutHGb1X+K+++7D9u3bYTQaebvgggvw+OOPl47feeedaG9vh0ajgcPhwPXXX4+jR0/NavB6vXj3u9+N+vp6qFQqNDU14VOf+hRCocq47XPPPYddu3bxOR0dHXjggQcmPcuPfvQjtLa2Qq1W47zzzsPrr79ecTyRSOCTn/wkbDYb9Ho9brzxRoyOjs7m4woEgjIox0KhsIZdV6LxnGtQt+NyGOs7SsalnEKF1+mNSyoZQsjfA6XKVPV8tcaCRNyLcLB/2vtQeIwKDdaqccnn86VE/9y3qWfjLBaz+q/R2NiIb3/723jrrbfw5ptv4vLLL2cjcujQIT6+e/du3H///Thy5AiefPJJ/kBXXXUVstlC7E8qlfL5v//973H8+HE2HE8//TQ+/vGPl96jp6cH1113HS677DLs27cPn/nMZ/DRj36U71fkoYcewuc+9zl8/etfx549e7Bjxw5cffXVcLvdpXM++9nP4tFHH8XDDz+M559/HsPDw7jhhhsW4ncmEJwZsjDxKIfETknxFyBDQDmSuYwpnghVjlE4bKpwFnkttDjSeWcy+fEczHy3pUaSn6dZs1qtuOeee/CRj3xk0rH9+/fz4t/V1cWeTTV+8IMf8PUDA4Wxp1/84hfxxz/+EQcPHiyd88EPfhCBQABPPPEEvyaP5ZxzzsEPf/hDfk1NVeQNffrTn8aXvvQlBINB9qAefPBB3HTTTXwOeVKbNm3CK6+8gvPPP39Gn408K5PJxPcjj00gOBMgoxIaOolk2Mf5DzImKpMdxoZ2TvgvJOTBHN5zP899Icn8idAQMfJgOrfcBJO1+hqyXCzF+hAaf49n/+PvodfOL6cUiSVx+Yf/dUnXszn7k+SV/OIXv0A0GuVQ2URoP3kzbW1tvPhXg7yK3/zmN7j00ktL+8gAXHHFFRXnkXdC+4lUKsUeVPk55BnR6+I5dDydTlecs3HjRjQ3N5fOqUYymeT/oOWbQHAmkQz54D2+B3GfixP5cpWOe1NinmF4ju9BKrKwisRKlZENB5UnT/yuyyXOcR80WjsMpuYFfd9T75Hj3M1KJ0/POc9tVfTBHDhwgHMalB+h0NYjjzyCzZtPVXjce++9fJw2ys889dRTUCorY7S33nortFotGhoa2JL+/Oc/Lx1zuVxwOp0V59NrWuzj8Tg8HpJ/yFY9h64t3oPe02w2T3lONe6++27+tlDcpjKMAsFahBb04OBxZFIJ7tqXKVSQymSQKdX8OhOPIjTUteCxfGfjuVBrbKyWTHIx1P9CjZJUXUYhsvqWd1RN8M+HaHQUvb1PYe+++7B33704cvQX8HgOTdt7s5zkV2mIbNYGZsOGDZwbee211/CJT3wCd9xxBw4fPlw6ftttt2Hv3r2c91i/fj1uvvlmTriX893vfpdzJ7/73e9w8uRJzqesBL785S+z+1jcimE7geBMIB0NIhUJsPz+xPwKvSZJmETIywKXCwkNFGvf/Few2Deydlg85ubRyaSc3LbhvVX7YMgQkJglbZTDmQ2BQDeOHnsIwyOvckNmcbRy18nfs0LzcnzTPx3zT/DP3oMZGhrCX//1X3OhFBVubdu2jXPvi1qmTJ4BVXYVk/pvvPEGvv/97+MnP/kJ7yt+++/s7ORch8ViYS+HvJYitbW1vFHYinI4F198Mb72ta+hrq6O90+s9qLX5OnQh5TRNyqZrOo5dG3x/hRKo7xNuRdTfk41yCujTSA4E6ERxblsFnJ19WWBvAhqqsymkixyuZBo9U42MqSiTJMsC2XKzklVYbRIjrn3Y2x0LxJxmjsDqNQm2Gt2oKb2rIpBY9Ugz6i3709IpaLQ60+VRatUJtYwG3XvYdVlu30LzmT8fj8uuugiLraiSBTltE+cOMHr+WyYd00fJdgpdzF1aV1+yuPF64niOZTPeeaZZyrOoTBbMc9DBo4MW/k5dA96XTyHjisUiopzjh07hv7+/qr5IoFAgFKX/kRRygqBSamMGy0XA1rsNTo7jJZW6Ax1VYxLHoP9z6Pv5BOIR8egUGihUOp4ymV/71Po7zm99+H3n0A87oVWa5/kpSkUOn6PsbH9y1LSOx15ZBdkmyn/8i//wikCyqOfe+65nEuniuCpirUWxIOhENI111zDyfJwOMxVWtSzQiXE3d3dXD5MD0HWbnBwkEuayeu49tpr+frHHnuMvQiqAKMcDZU3/8M//ANbSuppISivQ9VhX/jCF/DhD38Yzz77LH75y19yZVkRCqlRaO7ss8/mD/+9732Piwo+9KEP8XHyoKiqjc4jD4m8H6owI+My0woygeBMg/IsCq0B6WhoUrVYcfYLScbQOctBJDwE98geNio0A6aIXK7moWIe99swWzthtky9CNIcGZovMJWnQ0YmFh/jfIVEIl+TYpehCcVL1SI31EpCxVUf+MAHON1B+fL//b//N/7X//pfs3rPWf0Gqc/k9ttvx8jICC/i1HRJxuXKK6/kirAXXniBF3tyryihfskll+Dll19GTU0NX0/G5mc/+xn3qJDHQhaSelOotLgIWUoyJnQOhd6o94aKAOjDFrnlllswNjaGu+66i5P2O3fu5BLm8sQ/5XmouowaLOm96HoqQBAIBNUh78XY0AHfyf1IRgKAVFLIUeRykOSom98IY337gvS/zAW/7zgy2QTUGuukY+TNJBMB+DxHpjUw5Tpq1SDDIpUo12zDJjGxeIn6Cb/xjW9U7COHgRrr6Uv6V77yFU6F/O3f/i1HkOjL/ZL1waxlRB+M4EwkNHISw4eeYxVkDpdJCtIthpo2NOy4AgqVblb3y2Ti8PtOIOjvQS6XYnkXq30jtLrKStDTceLorxH0d015HSX8NToHNm+begEMhfpx5Oj/b3wKZuX8GloKI5Eh1NWeh7a2q1ZUH8wTP/0YdNrJigmzIRpL4d0f+ykXL5U/bzUPhgwJRYjIQShCBoYMzXStHhNZOT6gQCBYdmimi3/4CHJIQ2NyQCKTc94ljzxiIRdGDj+Pxm1XzjgPQ/mO7hOPcglyQT5Gxp6Ie3QPGpouRk3t7hl7RGQQpisjphwRqSpPByXwTaZ18PmOsjAmhdcK12YRj3ugUhrhqNmOtSx2aRyX+poOKrgqbz8hqFH917/+9azeUxgYgeAMhuT2k0EvGxaZXIFEzI9YYARqvZ0bLcuRyeSskhzx9sPoPH2ylxb83pOPce5Ep3OW8h6Fwp8ABvue53CXybxuRs9K+RXP2H5kM0n2qCrfK80LqNU2uaS5HAp9ta8r5ISDwW42gGzg8nnuxWltuRJ63dSVpmcKF110ERdGlUPyXi0tLbO6jzAwAsEZStQzjPBQF2uNkYfC+0LDPI+FPJeJFJodJQh7ZmZgQsE+RMLDPFmyPKlOC7pabeFjHveBGRsYk7kNZnMHfL5jUKmMPLmSyKRjSCQDMJlbYbauP+19KJG/Yf2NCIcHEAoNsMy/Sm2BxbweCsXUY5+XkzwWYB7MLKrIKAd+4YUX4v/+3//LvYwkJvzTn/6Ut9kgDIxAcAZCUjCB3kPI5/KQa/SFMFguh6yvB5lUHNlEjBsrqyXJc5mp2w7KiUZGeF5M5UjjU1A1WDjUz57O6fpXCDqnteNaSHtV7MlEoy5IQBpmRtjsW9Dc9i7IJ3g203kyNBWTttVAnkJkVGkxz3vMFKr0pf5Fqhz+p3/6Jy6+ogIuaqSfDcLACARnGGRIwiM93FRZXo5MxkOpMXEXfToWhkytpfz+qevyee6ap3NmzgzyK7OoM6IkfJzKiMnjosiWJA+5Ug2LbUNF6bJg/rznPe/hbT4IAyMQnGGQ8UjFwjzfZSJqnQ3x8CgyqRhy6STrkRWhffSaqslmAlV7kdEiRWRZFS2xdCrGMjAz1RkjiZfuk39ANp3gSjTyjLLZFBIJH3p7Huf3slqnz8GsWiQzs9WnvccSs3aLvQUCwZSJfe5tkcomHVOojdAYa9koUJkyCV9SyCwR9rJMjKVxCzQm54xzJjpdHWuLlVd/cZI/EeAigplWbFGT4MjQyzwXRqurLYXd6E+djp43hZGhV1asWOV8IU9tIbalRngwAsEZBqkj0+LOg76klTkLSsBrDfWQQgqJSoZMMs7ffDWmGpjrN8BY2zHjsmLOmbRfw2XKMcqXSGQ8WyaTTXHJcX3jO2Ayt/NzUH9KJhPj/QZj86S8TSzm5pwLFQcU3590xdLpCBsfui8dj0ZHuBRZsDIQBkYgOMOg5L3aZEfMOwypXFlhMDjPkorBWNsJW8dODosR1Fx5ui74qZSSN2z+IPy+Ywj4T3KFmkbnhM2+CTp9PQL+4xgceIF7UKipk5LvVC5c33AhJ+6Lz5bNJMaLAZRcTRWJDCOR9E+Y5ZJHKNgrDMwKQhgYgeAMgxZtkoRJx0JIRwKQljyaDHKpBBRaPUyNnTwLRqmZf+JcrtBwWIvKimk0Msnwk3cT8Heh++Rj3NdCRoXyNNTPQnphvd1PsLGx2QvNfgolXatgVYBk0o9Y3M3GRi7X8uchw5XOxDAy8iosls5ZqwSseCSrMwcjDIxAcAZCRsS2fhcirj7E/aNcekw5GX1tM/TOVj6+EERCgxjs+wt38nN/jVTOEyqdDefC7d7LBoPyNEVPRSpVcAI/Fh3lnIrFup6NkVptg8HQgjHPAaRTITYupTBaPo9sLg2VyoJ0Og63ex9a205pF64F8guQQxE5GIFAsGTQTBdL2xYYGzuRy6Q4XCZTzE/vqpxoeAQnj/4OyWSIcycyjZo9lHjMi55jv0cmn4LWQDNfJn+1psZHKkcmFQCjqYXPaWh8B3z+o4hlYlDI9YUpjdS7k0sWPCNDPedz/P7jaGq+bMr+G8HSIarIBIIzHDIqZGwmGhdKnsfDHkQDI0jFw7O+r2voda4W0+nrOExGRoIWfcrL5PJZJGN+bpSsBnkyVBFG1WFFdPpaOJ0060lHMr18jLrTyXiZTG28nzykXJ6q5LJrM0Qmmee2xAgPRiA4A6Bv+pl4FPlslmVg5BptKWlPiX0alUw5GUKpM7Mysad/HxKhMU6kk/aX0dEGR9vZUGlPTYmdimQiiFCgB0q1uaqHQjkXCoMl4n7oDJPzJTQmgIwRdfuXQxVmGo2Nw2GU1KewHlfCjb8FVaKRiOVErbLVTp6bSud/j6VGGBiBYI2TCgcR94xyHwt3zZMnoVRD46iFVKGAr/sAEsEx5Gm2fV6CVCqISNQFmVIJpc4ChVTOzY2+wYOIh8bQsvPa03bzUwkxGSYleRtVIG9DJlMjlQxCq3dUzF8hg0fjkIt9NOWYze1Qq61IJoPQaGsqjFc2SyOf03A4tq/peS6rCWFgBII1blyiIwPswciUqtJI5GwqjshQLxJRD1KxEBRqHaRqPRsFEpOk8mS5Qs2eAC3iUpkecqUG8dAovP0HULfhHdO+Lw0Ak1KnfabgiUx6rmSIvY+cJAev9yi0GjtrilGOhoyOSmVGY9Olk7wfktdvar4cPT2PIRodhlJh4LBYJh3lkBl18pOBWXNI6IvBPJP0IskvEAgWCvYEvG42KOXClRxWUmoQ840gHnBDY60piU2mEkHkskko1QZkkwlkkyR6qStdR/IywdHjqFl3ToWMzESUKgPMlg6MufZyifEpjyKPcHgY0cgwJVo4hEbNksFIP5RJPXSaGtgd21Bbdx7nXKpBRkQh12DUvRehYA97LmSQHDU7UFOzkxP+a428CJEJBIKVBCkik5GoZgjIM+AmSprTXvbFlkqJC8elSOeiiAQHIUtoIJMr2RhI5Sr2fkiyZToDQ9Q2notIaIBLlFVqM2RyNaLRUUTCg+zdGKytUGlMJekYCs3ZnDvR2nL5aT8b5WJoS6ejXAzAHtMMFJkFS4v4LyIQrFFILZkqwchTqEah0qowbKsIhZvomljCzQlzOizNKFm9WBZxQamyFEqOZ5BEp36X9k1/hZGBVzjhTwKb8dgYh9r05hb2kkrzYTQWlpP3+4+iseHC0qTJ08EVZYIVizAwAsEaRSqXl3Iu1QaIkZFI5UMVBkilsSCTjyOdiUAqUUBGeRiFir0MquyKhYdhtDazkZgJVJLcvvF6rhYLBLrQ0/041LqaqnNbVCoTS8ZEoiMwm2am2HzGIBGd/AKBYAUhU2kg1+iQjoQhUcsmaY7JlFrIFIpCk+W4waB8BnXHSfJS9lrYQNH/yBPK5VlUMptP8vUzFb0kyEPRpGogkSv4HtWgsBy9z5rrYVkARCe/QCBYUZAB0NidyCaTPBaZGikLkyuzyKZTUBkskOs0iI4NIpdOcelyIjbGxkOuJKmYXEFMMptmJWSF2gCFxsA9LjSUTKk2zup5NBorFHIt503IW5kIJfvpOPWxCNYGwsAIBGsY8mD0DS2I+9zIxCLIp7OAVAKV0QK1zQGZSg21yYbwaB8y8Qj3kZDwpc7cCLlaw7NgqMSZ9lFYLJNOIJsu7JstNHHSat2EEddrLFJZPoSMypOpt8VZswtq9ekbOc84JCJEJhAIViDUta+vb2EvpdDJT2XKp3IgemcLdI4mNibR4BASh4KQUP6GvBZVpehlOhmBWmuFQj03MczGxosQT3gQDHZDIpFzSXEul2RPyWRsQ1PTpfP+vGuRvChTFggEyw0ZkUwyweGxghxMId/BOmBlRmUilGuhZkuTqgO6wQaEfb2QGeoqZsBQaXI+l4GtYducS4Kp6mt95w3weg9jzHMQqVQIGrUVdvsW2GxboFDMrHhAsDoQBkYgWAPkMmlERweRCHiQz2QKcjAqNbS2Wqht1RWLp0q0N264An0HH0UsPAKpTMXhMZKKoRCLtW4rbPU75vWsVILsdO7ibTbFAnQuKSzTUDEa6axSGWE2d7CQ5ppHIjr5BQLBMkCDwkJ9x5EMBVhbjKrHuKw4mUBoqBvZTBr62qYZ30+jt2PdWTfB7zqCwOhRNi46Uz2stZthqulc0IbGmRoXkoEZ6HsWXs8hniFTEILPs6w/SfPThMy1TF6EyAQCwXKQ9HuQDAcqQ2K0BMu0yKaSiHtGoDbbKuRiTgdJvThbzuVtJTDY/zxGXW+xXplObS1MscxlkYh70dvzOIfWjKbW5X5MwQSE5KhAsMpJ+Mc4tFU0LkWKPSWpSBC+Y28j1H8CqXCAvZvVBI1Q9noOchUabaemX8qg0To4NzTq2rPqPteZMA9GGBiBYBXDobB0kivDKvaTYQn5kY4Ekc+kkUlEER0bhr/rICLDPatqMQ6H+pFOx6BQFqRlyiFjQ15N4Zwo1i75BdqWFmFgBIJVDEvpy5WT+lJSkRAbHpDhYfVkNZQ6AyRyWaEYwDeK1QI3e0IyZb5GKpGNe2t0nmAlIQyMQLDKUVvs7LEUjQxVlOXSSa7+4uIjqRTS8XHIRQXkmGdk1XgxlMinEGD5+ORyCt6Nfk0LX+YlC7OtaANz3333Yfv27TAajbxdcMEFePzxx0vH77zzTrS3t0Oj0cDhcOD666/H0aNHS8fffvtt3HrrrWhqauJzNm3ahO9///sV7/Hcc8/xN5WJm8vlqjjvRz/6EVpbW6FWq3Heeefh9ddfrzieSCTwyU9+EjabDXq9HjfeeCNGR1fPtzaBYKaoLQ4odEYeiUwSMLQVDA5piGULg8bKQmgkGZOJxwoTLhcBMlyxqBsjw69iaOAFjLnfnlf4ymhs5tkwNMaZNdHKIO00EuGkIWPSMmWANYdkdeZgZlVF1tjYiG9/+9vo7Ozkv0T/+Z//yUZk79692LJlC3bv3o3bbrsNzc3N8Pl8+MY3voGrrroKPT09kMlkeOutt1BTU4P//u//ZiPz8ssv42Mf+xgf+9SnPlXxXseOHWMjVoSuK/LQQw/hc5/7HH784x+zcfne976Hq6++mq8pnvfZz34Wf/zjH/Hwww/DZDLx/W+44Qa89NJL8/+tCQTLAP2by6XT7KlwaEyhKHgncgVMLesRGenjJH4ulSxI8EupuVLL1WOTwksSDjot+DNWlBNnTxmw4oRKu2PrrO9JZdHNrVeg+8TveVCZXKHjfZTcJ4kZC02xdJ6FtUx+lYpdSvLz9JOtVivuuecefOQjH5l0bP/+/dixYwe6urrYs6kGeRlHjhzBs88+W/JgLrvsMvj9fpjN1TWJyKicc845+OEPf8ivc7kcG6xPf/rT+NKXvoRgMMge1IMPPoibbrqJzyFPijymV155Beeff/6MPlsoFGLjRPcrN3YCwVKTTaWQicVY6qWUrJVKuZlSrtGUDEgmEUcy6EVooAsylarqULB0LMIej3X9jlkpIs+Evt6n4Bp+HUqViUNWdH/youJxH1d9dXS+Hybzujndm2bJjLn3w+c9ynkZKqUmz8Ves21ZplguxfoQGn+PX/3qg9BpJ4+eng3RWAo33fSLJV3P5twHk81m2TuIRqMcKpsI7b///vvR1kb6QlM3edGHJSM1kZ07dyKZTGLr1q3sCV100UW8P5VKsSf05S9/uXSuVCrFFVdcwcaDoOPpdJr3Fdm4cSN7VtMZGHo/2sr/4woEyw15LelohOXyKdRVWLTz7Mlk49R0CCi0hR4XEqiUqRq4NJnKlzkPM16+XPCACnkMrb12QY0LLfhBfzfcI2+xkKWS1ZgLkKYZKSRHoyPcy2I0tc3pvakkubn1XWhsvpTfTyZTlo1iPgOQYNUxawNz4MABNiiU46DcxiOPPILNmzeXjt977734whe+wAZmw4YNeOqpp6BUVre8FCKjcBeFsorU1dVx6Ovss8/mxf7nP/853vnOd+K1117Drl274PF42Lg5nc6Ke9HrYr6H8jX0nhM9IDpnYi6nnLvvvhvf/OY3Z/srEQgWlUwiQSs4Dw0rLsycm5TJeGoldezL1eqSbhgdMzZ1IJ/NcMisEH8vDB5jpWRnE9TWwr8fFrj0DCIeGOXjKoMNensTlLrJcvrVoJyIx/U23MN7uVSYJ1YqdNzgqTU4KrTQlEojwpFBpFNhLi2eKxQeO9PGI+fJiM8zwrUcJR2z/q9ERmPfvn3sefzqV7/CHXfcgeeff75kZCgHc+WVV2JkZAT/+q//iptvvpnzHpSML+fgwYOcv/n617/OeZry+9NW5MILL8TJkyfx3e9+F//1X/+FxYS8IsrtlHsw03lfAsFiQ14KVYXRQl3tWz8PBMtm2MuhkFgRSuyb27cgGfRxyCyXybB3UywIoHslwl64j72OVDQwbpwkiHqHERrugm3dThic03fGk0c03Pcij0SmUmHSGGODQiGxkAvZTAIGa3PJy6AQGVW35fJioNiZwqwNDHkGHR0d/DMl9d944w2uBPvJT37C+yheSBsVAlAoymKxsJdD1WNFDh8+jHe9612c4P/qV7962vc899xz8eKLL/LPdrudiwImVoTR69raWv6Z/qRQWiAQqPBiys+phkql4k0gWClwipS2KUJKHC4rnDjpGHkrGmsNbxPJZlIYO/4GGxeV3lryfuj90vEQPN17ebiY2mib8tniUTfcw29BodCyR5JKhdmISGQKyKRypOJB3lRaC59PlWTkxSgVc5P6P5PJj/9vvvdYauYdwKQEe3neohyOE+fzFccPHTrESXzyfL71rW/N6D3IY6LQWdHAkWF75plnKp6BXhdzQXRcoVBUnEMVZv39/VXzRQLBSqVQpk+jhKsvDvmi8SmT1Z8JMe8wkmxcLBWS/PR+Co2RS5gj7r5p7xHwdiFT1mGvUOg5/0KeC3tclNeM+U+VE2cSXEW2psuJF4ncAm0r2oOhENI111zDyfJwOMxVWlT19eSTT6K7u5vzKRTuogquwcFBLmmmfpdrr722FBa7/PLLuaSYQlHFfAh5JHQNQSXHVBhAZc+U56EcDFWY/elPfyo9B11LBoryNOTd0DWU8/nQhz7Ex8mDoqo2Oo8KCKhigirMyLjMtIJMIFgJcBmyUsnVYfm8tCJMxl/gaICYXMYly7MhEfax1zNRv4zfk0ugVYgFps5XEqlkiBP45XkhvaEBoWDfeN9LHulUFLGYm5PyVE6sNzRhoP/PCEeGIZVIuaKMplyS9L5g7TErA+N2u3H77bdzfoUWcWq6JONCOZfh4WG88MILvNhTiTEl1C+55BJO5Bd7UyhnMzY2xn0wtBVpaWlBb28v/0yhrc9//vMYGhqCVqvl93j66afZ6ylyyy238H3uuusuNlJUcfbEE09UJP4pZ0PVZdRgSR4UGTUqQBAIVhtUhkwzXkh2n0Utxxd0SsqT5yLXVulzWYrnUtBYgMrvxVSaTEYjkfAV5slIZdBqa2B3bEMeOZw4/iuk0xGeM4N8DoFAF9yje7Cu/b0wGBqX/DOsFnL5+Sf553v9svTBrGVEH4xgJSX7M/E4cqlUYVEfb7SUq9Sz9l6I8GgvRo+9CpWuMkTG70Vh7bAXpob1cHTsnvoewX6cOPBLyJU6zsOUQx5LLDKKpvYr4Gw4B5HIEI4d+yU/u3pcbr/wXjlEoy4uY96y+fZVNTxsKftg/t/DN0M7zz6YWCyF2z7wyyVdz86gInKBYPXCI411OihNJihNZqhMZij1hjkZF0JrredS5GTUX+GFFJL8YW7QNNS0THsPvbERFscmJON+JBPBguAkXZ+KIBZxwWBqht25jY0Jd/anYxXGhT+XRAqt1ol43AN/4PicPotgcaD+w4mSXdRPOBvOrGJygWAFTaGk8mFSPaZchUKrh8pkZ52w0xmahQiG0fvUdJ4D97HXkAz7SmXQ9FykvGxr2wm10T79s0ikaO64CnKFFj73QUQjlLPJQy7XwFazDY3r3lnySEKhXt5fLZRHYTTKB9H1Dsf8xjGvVXLLFCKjXDilKIrI5bMzGcLACARLDEm1BHuPsqdQXl4sU2thatkAlaFQ1rvYkAGp334ZN1rG/C6eEEn79I4mqHTVZZomIpMp0LTuMjgbzkY0TArNOWh0dmi0E40TlVOfZoVbhjzSaiG3AFVgc7meDMp0rR2nvX7OVwoEgllDTZPB3iNIx8KQawxl/SeUYykYHuv6nZCr5paLSCXC8LuPIOjpQi6bgkbvhMW5CQZLS1XvQa7Scq6FtnIo1BUNDMI/egTR0Ah7OCZ7ByzOjVBpJhsf0gWjbSpIHsY18irfd+JzUL6GK9B0hVYEwWSK7VDzoXj9RAms6fr/Tpw4gfr6em6UpypcUjuhKuKZIgyMQLCEJAIe9mDKjUsx3ET70tEQEj439HXT5z+qEQu70Hv4j4hH3AWdLqmM95HBqWk6G3VtF8+o2oyMgLv/dbh6XkE2k4RMriok4/2D8A3vR/Pma6E3z67iy27fCq/3IGKREU79ZlJh9mcohEYGhvI5ZkvnrD+zYPZMVCchNRXKt1QTFX7ggQdYWYUqh0lG6+KLL+Z2E4Nh6i8T5QgDIxAsIOVFmdUW86I2WLXKrUJfihTJkLfCwNAxGnlMlWRcNSafnKfJZdPoP/okEtEx6Ix1FSKQ5NWQwWBvpuaUDNNUhP19cPW8DIlUDp2pvuI54mEXBo48ic6zb4NcUSn/NB16fR3stm3o6XoUmUyCpWWIRMzLFWjW5o0sNSOYLkSWX5AQ2cDAQEUV2VTeC/U8FqF2ETI41FLyy1/+sqp6fjWEgREIFgBWNk6lWBOsKO1CFV4ypXKCMaFFoqxZMptFJhljwUq6jjwFmtNCQ8NozkvC70ZktI9DavQeNJlSY62Foa61Qoo/5OtFPDIKjc4xSWFYqTYgGorAO3IAZsf603oxvpFDyGVS0Joq8yh0nVpfg0R4FCFPF6x1M5/tQk2ZAe9xqNX2wiyXTJzvJyfZmHwWY663YLVtgFY3WdZGgAUNkRUHRs4Wkt1av349j1+ZKaJMWSBYiB6VGBmJJKseM2RwksnCDJfiPpbVN3CDIUvnUyVZ2I9MLFoqFS4YnDgCJw8hMtILX/cBPoeMjUyl4ePh4W74uvazEShCngsboClkWGikMHkf5OlM+1nGcy80qKwaVPFFyfp4xDOr35Hfd4ybLw2GBvZmzOZ1MJnaoNM6uEw5lY5wKbNg5RKJRFh4uCjbNROEgREIFkBOn9SKuYSY5rWU/Uly+iy3P47K7OAyYAp5paNh5Kk7Xy5nYUpauKVyeaE/JexHoK8wfoJeU1iMzqHplEqdEYmgF9GxoVMPcdrcSqE5c0Yd/3TOab4uz1Y5IBImaRgqhZ685LAnI1Ox3L9g5WiR/f3f/z0r5ZPKCimy/NVf/RXLepULF58OYWAEgmkoCraSd1D8ueI47S8alwmLbrE5jY4XJlFS1ZYaxqbOQhgsESuEPrLZwiCwPHk4Rpbd556UVIpl9ydCyXsyRDHPcMnz0RnqeF4MJeWrQZpgBnPzaYUmeZaMrY2bIquJfJAHRPpjWuPsKr5mYpAWY4TzWiHPQjvz22arpkx6kmRMKMlPY1dsNhteffXVkm7kTBA5GIGgCkWjMv7i1IFx5eJTemAFwzMxaV9+Pp/DA8MKiW2ayWJIJTkMRv/o+ZYKDZcmU46F71t8X3qEyXqUbCgoT1MQu5RCb26G3tSEkK8bWn1NyZCw7EvMB5lcDVv99hl9dsqtBEaPIhH1QK2zlz4rhfSoQk1nbmQjNBv0hkZ43Ad4fDIZqHK4iCGb5FJmwcrhF7/4xbzvIQyMQFAFNi4TDct4ppWPlRmZ033v5uMTvsHLtTrI1BoeAjaVojE/xxTfOvPUOyJTnJoYKZWieePV6DvyGCKBQf7OSmrF1DypUOlRv+4S7oWZCVSF1rjhCgwd/zNiwWF+Dy4+kEigMzWgZdM1s5bcJyVl1/AbiEZHodPRuOZT/T+ktqxSmWB1nJqMK1i8JP9SIgyM4IyCwz5kICh8NYXXUREaKjcME4wM51nIK6H70D3HPZQKisZowjEKhZHHQoUAck31hHphJHJmknwMLcrkvRhrmio+AzVAtu/4AELek1xqTOEs8kCockyttWI2UHMmhcEC7uPstUilCugtTTDZ27kvZrYolDq0dVyHnq4/IhoZGTcwEvZolCoTWtddDY1m6uFmZzq5Zerkny/CwAjOnEqvZILzGsVK4UJllorzGTP+uliWAC92pdM9uFqMci3F64r5l3weivGcSjlSmQxaRz1Cgye5RFmqVJXOp2Ff5DXQ8UTIg0wiBpmKekQkyGfT/Jqq0XSOxqrSLZaajbzNFzJYzpZzsVAYjI3YtPWv4fcdRSg4wEsezY+x2DayByNYewgDIzgjjEs6GilUetFCP77YZ1NJlm4hlWIyNpOYLjFd3lBJ3sl4Mr/iFDIyCgVXiVVD46hnDyU2NoQM9bmMp7kpD6Ora2YDE3H1Iuoe5IozsoyUyCdRTHPzBq4om9XvgXIdmUJFGzU1LscMGfJkamp38yaYOTkqa59njGu+188FYWAEZ04ZMYW0JoS88tnCnBWFXj55wS2OI65GaZ5JHtnxMmQ2JuPXFf8pSyhERhVkVcJxrL9V1wK1tQapkJ+NHRk6lcnKpcyEsaEdupompCL+8U5+LRQ646yMAz2j13cMLvc+RKMk1QLodHWordkJm3XDshgaweygv0/zNQ/LMfhLGBjBmoYWZSoBnqqMGDIpV2JxqXFxtkrRyymGysqvmyAFQ4arWply8Sfqg6EOf/JiplrIKRcjd0wtbkk5GI3l1LTWidB44kCgG+lMDAq5BmZzO0+WLDxuHgNDL2Fg6MVCvkNR0JAKBE8iGOpFU8M70NRwkTAyK5zcKp1oKQyMYNXC3kMyPm5AZFyZNbGRr6DxRXGn6gsoG4nykuSi4RnPh4zfZOJFp1SQyTjx7ae+f7EHZjafq/QcpznP7d6LwaGXkEwFS/tVSiPq6y9ArfNshCNDGBp+FTKZCmrVKRVkpdKARDLAx8ymVhjFuGLBIiAMjGBVkolHER0d5IFdNJueDAuV/VLegkJMFSXEkqnDXZyo53MmeDdkQMZ7WEoGpnjPKt7QfKHnIN2xmHcYqWiI34O8Fq29HgqNvuo1Hs9B9PY9RQ8Enba2IOOSzyKRCKCv7xnIpEpEYm6kM3EY9A2TrlcpTWyAPN6jwsCscHLjzZLzvcdSIwyMYNVBVVTB/hPIJuJcXSWRqQtik4kYwoPdrPVFzYyMVMoSK1kSoZQUqr5IpiUR8iKbTnKviNJghcI4uYqJjciE3MmkMBvldcoqyiZC+ydVqVU5J9h/DFF3//j5SuSzGYSGuhHzjsDSthVqU2UJL0ncj7he5/N1ZcO9qImRyn2pt4SOS2SagnR/lWfjCjiZks8VrGzyIgcjECwNcY+LjYtcqz/lqchkkGp0bHyi7iEojVYuBS6WEVO1FuVDYt4hxDwDBWkWIg9IfSNIxQKwtG1hY1TO6TyVYi8M3buiw78ov085FFJUnuY+cd8oGxepQlWhkCxT5VlFOdB3BDWbz6uodItGXYjFx6BSV588qVKZec69WuPkpsypoGNz6WsRCGaC0CITrCqo0ioZ8nMpb7VFm6qvqHkxHTmVkyDZfMrPpEIeLvuljjO52sDNjgqDGVKVGlH3ACsXp6LB0yoOTxJq1GjYmHFBAeVkaBvP6UipB+Y0HkxBUyxfYVyK96bwGHtcgbHK30OOem6oW7/6vUkSn8JlBkN9QceqipHheyAPq7ljxp9XsLxJ/tw8t6VGeDCCVQVXbfFclOpSJYXke0EKvxypXIZEaAxSmbSQ0yDbRAUBEgq5RZGOh+Hr2Y/A8DHurDc422Bu2ABZleFek95TRgUGWn42auZMpSLsCam0Fg6PTee9cI9OLFR1iFjp8+TzSCeiFftVKiMrEGcyMU7YTySdjkEmU8Nh34JI1IVQqB8ajaM01Iv6YeLxMRiNzbBaK8clC1YeOZGDEQgWH16wpYXSYkwIZxEFz0EyKdRFRQHpeISbE8tlW5LRIBJhD9UTc/6C8zWpBHw9+5AM+1C76cIZ6W6Rt+Bxvw2v6wBSyWAh8W6og71uJ8y2zqmNTLEkuqyKrfpplcEGtdrK5cjusbchl2s5wV/+LMlkADbbZhj0jVjf8T6c7H4cwfAA4onCHBeSfrGY29G+7houbRYIFgNhYASrCm5ENFoQ945WDZOR7ArlXBR6U5XS38ppkhQKS0Z8BWdGoWajRQaGCgcoFBf1DCA82gtT/fSz4inU1Hf8CXjdByGVqrhbncJXIV8PIsEBNK67DI76XVWvpefXmGsQHu2DLK+Z9HnoOagEW2WwTLq2seESxOIeRCJDUCr0XIqczaZ4eJdeV4umpksL91dbsWXTrQiG+jl3Q5DgpMnYXHU+i2DlkRdilwLB0qCx13IpbyYW4ZwL5TioVJmkX2jB1NU0cE6kHPJcZAp1oXJsPFlOYSdOcpNxIfFK6m8Z93zoHPKUQqM9pzUw/rGj8I0dhkptq5hTT4YmEfNhuO8lGC1tUGkmGwmCSpFjPhcy8TDkGn1p0acwXzoRYQOkNExO5ms0VmxYfxNGR/fA4z2MbDYJqUyJRsdFcDp3Qa0+9X50T+p3oU2w+sgvgFilqCITCCZAiyxJuXAISyqDTKNlY2Fq7kR0dAjpaBDZeJKNAe2nPhi1ebIqL3kmWkcDlwMXJVmK1VUFheVsQXCyzDBJ5SpkEpGC8Zlq3gvNsHcf5j/LjUsRMiqx8DAC3i44G8+per1Sb+IKNppgmY6GSguBlHphzA4+NpWnQc2TLc2Xo7HhHZxXoRwLlR4L1hY5oUUmECwctOinwyEkg/6CSvH4xEPyVlRmMxR6I4wtneOd/BRGklbt5C/HUNfGCfW418V5D0rKU2kN3b9YaVYeomLvRqWdVvSSGyRjXsimyGMUVAGAVDI07efVWGrY0FCzJZVaU8kzhcVUBuu0xq0IGRVhWAQrDWFgBCuSdCSMhM/Di3u5lD15Hwmflw2JQm8oKApPdhxKUOgs6h/mUBN5MYbG9dwhH/MMIRH2I52MQqpSsvx9+UJenLlibtw8bRUY99nI1Uin4lM/A5Ugz2DxpzJlEracCfR8mVSMf5YrKw2jYO2RF42WAsHCQCGpVIj6WCQVw7Z4MVcoOdeSDAUg151qtKwGGZbRE68gHhobn11Ps1n0sDRugWPD2Vzp5T7+OoIjxyFJxVmpmMhlUkjFQlDpLTDWrjvt81odmzDY/WzVUBrNtqdGRoNlYXIf9DkoJOcZ2Y94bIwLFDQ6J+x1O2BxbBSGZo2SE2KXAsHCwHNa0qnqM1rGE/C5VLowDVJd3X0hozJ48Gn2XJQ6M/ezcM9JIgx39xtsbGrWnQNHx24ufQ6P9iAR9hYqymRy6Kz1sLfvhkJdUCWeDmvNZvjchxCLjECttbFHw7NXUhEOjVmdm6E3TtYCm5hrinoHCiXTlLvR26C3NVX8Duiew70vYHTgNf42quD+lzzCwX5EQgNIxH2oa75QGBnBikEYGMHKg79qnW4WC9VtTl1X4xs8hFQ8BI3JeUpORiqFUmti78Q3cBCW+k1QqPVwtO+GuWEjEkHydLJQaIxQG+0zXqiVaiPaNr0P/V1PIRoaRi7u45pQuVILR/1ONKy7bNrcEJVKjxx9oeRpSTjhJIHaYEPtxouhMRZ01ajk2T30JmQKHZSqU82VCqUeqUQQo4Ovc7Wa3lg/o+cWrB5yq3Rk8qyK4O+77z5s374dRqORtwsuuACPP/546fidd96J9vZ2aDQaOBwOXH/99Th69Gjp+Ntvv41bb70VTU1NfM6mTZvw/e9/f9L7PPfcc9i1axdUKhU6OjrwwAMPTDrnRz/6EVpbW6FWq3Heeefh9ddfrzieSCTwyU9+EjabDXq9HjfeeCNGR0dn83EFywRLq5AycK66zD31q5CxmMrDyaQTCHt62fuoZiRYfiUVQ8Q3eGqfWgeDsxXG2nZoTI5ZewEanQPrt38QndtuQUvn1WjdeB027vxrtKy/ptQ9X41sOoHhw88jFhiFSmuG1lgDjakGKp2FvZmRw88jk4yVyqGpz6XcuJSeX2VENh1HwHNsVs8tWB3kF+h/K9rANDY24tvf/jbeeustvPnmm7j88svZiBw6dIiP7969G/fffz+OHDmCJ598kl36q666CtnxeRh0XU1NDf77v/+br/nHf/xHfPnLX8YPf/jD0nv09PTguuuuw2WXXYZ9+/bhM5/5DD760Y/y/Yo89NBD+NznPoevf/3r2LNnD3bs2IGrr74abvcpVdjPfvazePTRR/Hwww/j+eefx/DwMG644YaF+J0JFhmu6FJrCrIwE0orOdGfzbCcy1RyMVQIwBL+0uoOesGboCqyccHLBYLuazA3wVF/Fuy12zlcdjrCY31sSDQGW4X6AP2sNtiRiPgQcvfwvnjMA6l0CkkZKoaQKpGIFkJsAsFKQJKf+C94llitVtxzzz34yEc+MunY/v37efHv6upiz6Ya5GWQQXr22Wf59Re/+EX88Y9/xMGDB0vnfPCDH0QgEMATTzzBr8ljOeecc0qGKZfLsVf06U9/Gl/60pcQDAbZg3rwwQdx00038TnkSZHH9Morr+D888+f0WcLhUIwmUx8P/LYBEsHTYGMj7n4T5ZBoeQ5iUnmspApVdDWOLmTvxrUod/1ykPsyah0kxsUyUBRWKp5x7thrDl9En8xGTr0LEKj3dAYTknul0N5Ia21Hs07r8HJQ7/hfhqtvrbqudRvY63ZgrZN713kpxYs1foQGn+Prz/wfqi1p5csmo5ELI1v/s1vl3Q9m7NOBHklv/jFLxCNRjlUNhHaT95MW1sbL/5TQR+WjFQRMgBXXHFFxTnkndB+IpVKsSdUfg41pNHr4jl0PJ1OV5yzceNGNDc3l86pRjKZ5P+g5ZtgeSCJe21NHVQmc8G45HPIIYt43odQuh9j7v2IRdyTPByCtMNMdRs4/DRR9JLOJ2l+CkHprDMrCV5MONxXJl8zEdZdGw8VmmwdnKOpqow8rgBtslX/IidYG2XK+XluKz7Jf+DAATYolOOg3MYjjzyCzZs3l47fe++9+MIXvsAGZsOGDXjqqaegVFb/pvnyyy9zuIs8liIulwtOZ+X8cXpNi308Hoff72fjVu2cYr6H7kHvaTabJ51Dx6bi7rvvxje/+c1Z/kYEiwWFwNRWO1RmK4K+bgz2/pmbGou6YpTbMNs3orn9XZNmmtiatiDmH0TENwS5UjMuB5PlKjKZQgNnx3mQTZHDWUpUBhtC7u6qA8uKfT8UKiNINJOq0SjZr9LYWTmAG1LTMQQiA8grFOgafgka/2E4bZths66HbIowoUCwIj0YMhqUG3nttdfwiU98AnfccQcOHy5IZRC33XYb9u7dy3mP9evX4+abb2ZjNBEKgVH+hvIolKdZCVA+iDyq4jYwMLDcj7TmoQWS5rsE+47De3QvfMf38yhkKlUuQv0efV1PIBH3QqN1cJWUzlDPRsXj2ov+k89M8mSogqtp+9VwtO1mj4YS5eTNUEisefvVyx4aK2KsaYNCpUMqFpz0GdgYKtUwOgvPKldo0LbxPezJZNJhRMPDiIQHMBo+gXA+ioxMgljSB7f3CA4e/w2OnPgdMtlTv0fB6peKyc1zW2pm/fWGPAOq7Com9d944w2uBPvJT37C+yheSFtnZyfnOiwWC3s5VD1WhAzSu971LnzsYx/DV7/61Yr719bWTqr2otcUM6TKM5lMxlu1c+ja4j0olEZ5m3IvpvycalDVGm2CpYEW1MhIL0+o5BkvMhqSlUc6GkbCPwZjcycUWj3GRvZxPwkZlVLJMQ3jUtKsegkCnqOIN+yGVu+cZGRqOy+AvrYNPn83l/6aTE3QGpaujJdVlYN98HuPIZkMQqnUw2JdD6O5jYeCUajO0X4O3F2vcZmyXEkVZxIO71GVnGPd2dAYayq0zTq23oRIaAjxyCj6R/cgH47DZmyEvEwtgHTJXJ6DLIjZ3nzZkn1eweKQW6VlyvP2nynBTrmLatBiwd9Qy45T9RhVn5Hn861vfWvSNRR+e+yxxyr2UZitmOchA0eG7ZlnnsH73//+0jPQ60996lP8mo4rFAreR+XJxLFjx9Df3181XyRYHpIBDyfyJXKqGju1OHKTYjyC8OBJmNo2IODrYmXiaqXDcoUWyUQA4UDfJANDi2xX35/h9hxCKjMuqyJVwmxsQee6q6DTnL7Kaz5QXqSv50/wjh3kvAnNYKE/PWMHYLFuQGv7tZDLVTDXb+D+HFIUiPqG+Fq9vROm2k5oLfXVq9VMTVCojYgP/wVara3CuPDnlKuhVGjhGjuA5rrzoFAUVAoEq5P8mSDXTyGka665hpPl4XCYq7SoZ4VKiLu7uzmfQuEuquAaHBzkkmbyOq699tpSWIyMCyXtqcy4mA8hj4SuIT7+8Y9zdRjlcT784Q9zddkvf/nLijwNXUsG6uyzz8a5556L733ve5zz+dCHPsTHyYOiqjY6jwoIyPuhCjMyLjOtIBMsLmRE4j431+bLJ1SD8RhitRbpeAyJkK9QciyplN8vP5f+l82lJ3kOR08+DtfYfqiURhh19SWj4/EfR+p4FDs23QJVlWmQC4Vr5A2MufdBpbJULPD0DF7PIZ5E2dxWKETRmmt5K0jaTB4wVo1IbIxnvxh0dVWP0/3jcS+icQ/MiuYF+1wCwaIYGOozuf322zEyMsKLODVdknG58soruc/khRde4MWeEvGUUL/kkks4kU+9L8SvfvUrjI2NcR8MbUVaWlrQ29vLP1PVGRkT6mOh0Bv13vz85z9no1Tklltu4fvcddddbKR27tzJJczlif/vfve7XF1GHgx5UHQ9FSAIVgZkNDLx2DSjgmVcOZZPZ6BSmxGLjFZtMOSKKgn4nHICoQGM+Y5Cq7ZWLO4KhQYGWR1C4SGMeg6huX5xvnBkMkmecEljiyd6D+xdKA3weg6jtuG8ipHHsxkANh4sHC8QmOorK4vfzOOTCFYC+QUYmbwcjZbz7oNZy4g+mMUjl83Ce3QP105SX0s10rEw9PWtiGbc6O96AiqNtaIrnr2gqBtKtRmbz7qjopLsZN+f0T3wF5gNjVXvHYm5odc5cc72Dy/CpwMi4SEcPfjfUKktVZWUyVOh6ZLrN34AZuv0A82mIpWK4vX9P2MjqykbLlYkFvdCqdDhnB0fhVwmcouruQ/mC//xPqjm2QeTjKXxnQ//fnX0wQgE84EmTtL8k6m66amLn7wYktG3O7fC6tiMZNyHWNSNdCqCZCLIVVRUWdW07vJJZcrpbHLa/hKZVIF0emqJ/fkys+9t+YrzY6FR+IYOwjd8CImI97RXK5U61Dq2I52OTvosZHwoFFdfc5YwLoJlQxTJC5YNjdWJVMjPA7ZkqlPz6Kn5kAaJqUxWKHQG3t+y/lroTU3wjh7gpD7lZBx1O+Go3QldFXFHtZK+oRWKTKoVB1AIy2hoWLzPprWz2jEZQ5nmVCNxkVQqDIVCz1L7JMo5dOzPiHj7kM0UCmLIcBprOlC//lLu45mK1sZ38L0o3BdP+Ngo86A0mQqNtWejqf7cRfuMgqUjfyYk+QWChURpMEHf0IrISD9XjRVyBWQQpFAazTA0risZB5lMgZr6XWxUspkEG5iJXks5Dut69A+9jEQyMCl8lM4kOB5d69i2aJ+NQnn2mu0Y6n8O8Vi+IM5JHplCz9VlqWQYtfXnQiZVouft3yLi64dKa4FSWzBGmVQU3sH9LGDZsuN9Bbmcau8jU2Jjx3v5s4z5jnPSn4oaHLYNMBuaZpXTEaxccguQg5nv9XNBGBjBsnsxSp0JyaAPmWS8IKmvN0FpMFcdFUwLJpUmnw6d1o6Wxotwsv/PCEVd7NHQtalUBJlcErX2rXDYNmIx0RvqONcSDJwsyPBLpCzAqVIZYa/ZgYbmixF0HUfUPwCNwVkhdkmD0ahBNDTWg7C3FybH1BIwUokUVvM63uh9UulY4b2EcREsM8LACJYdmUoNbc3CNz9ShRiVIQ+53uKkPi2+apUJdc4daKzdvagyKpTk7+l6jKaXcVMlTbaknAiFr6jB0mzpgFyuQdDdxd5YuXEpQh4azachIzOdgSEo0T/ofhuDrn2IUqhMIoXN1IKm2t2wm9sW7XMKloacmGgpEKwsKLxW69gKp30z4gk/52PIwFC4bTGh9xkZfhWpZBB6fcOkHBAVKoyOvAGbYwvPpaEJmlN/BhmHyU5nXA52PYbB0X3s9akUejamw2OH4PGfxOb2d6OhZseCfT7B0pNbpZ38wocWrHno27xWY+Ow2WIbFyKZDCAc7INKZa5aYKDWWFhXLRzqh0pnRW48sV9VCSOX4S7/6XB5jmDI/TY0ajOMulqolHqoVUaY9PXI5nM41vssEkmhDC6YO9Q0T3+XaT7XbBAGRiBYYKgSjKVhqvS/ECQZQx4GnWeu3cCVX+TJTCSdLIhdmmvWT/t+Q+79XLRA0jDl0IKg19gQTwYx6hWTLlczuWUUuyS9SdKapMb62SIMjGBZIHHLRNCL0NBJhIa6EPeNcvPlWoA696k6jKrdqkFjj7miTKmD0dYGa/02pOJBJCIeHpKWScURD7s5NGZv3gWNsVJjbaKXE46NQSGvXvhQTPTHkoEF+nSC5QyR5ea5zZZIJMIK+T/72c9YuHi2iByMYMkh6Xx/z2Gkwv7SMC3SOqGmSkvrFq4iW80oKTxl7cCYax8rPpdXc5FBSMQ90OrroDc0cs6kYdPlUBts3GRJw9CoWltrdMLWuA2W+i1Vw2wTS5Wp9Hpq8txYKli9ZPN53uZ7j9lCE4dphD0Nb/znf/7nWV8vDIxgSSEvxXfyIFJhH+QaKsUt/BXkYWDREHwn98O+8WzIVVM3F64G6urPRzQ8hEhkiHMxcpmaBTmTCT8UCh0ami7hajKCvBlH8y7YGrazJ0PGVqUxFfTYZlLIYN+M431/LpVCl5NKxyGTqmAzty7aZxWsLkITJvVONaaEJhbv2bOHQ2RzRYTIBEtKMuhBKuKHXGOoKM1lWRidiRsu496pp45SbiMR9yHBVWHLURcz807+jg03wFGzo/DMCR8ymThMlg60b3g/zJbJZcf0+1DrbVDrrDMyLkUaarbDoHUgGBlBJluQ3uExGakoYgkvaqydsBqFmvJaELvMzWMril3SCPvi3C7aaJLvRGjY4t/93d/h//2//we1+pT+32wRHoxgSUmECiOPSYusqvS+TI54wA1DfWXvBi3SY+63MTb6NpJJP3f903TLGudZsNlPH0ZaSCiHEouOFvpqNDYeIlYNer51ne9jaRvSCyP5Fjp/oZ9Vp7Fix4a/wqGTjyEUcSHHhjcPhVzNxmfzuneLpstVTm4By5TJeJSLXVbzXt566y1Wz9+1a1dpH42q/8tf/sLjVEihnsasnA5hYARLntyfboGlnARpkVVck8+hv/dpuEf3cCe8UmHgb2PUzBiNDPECXt940aIbmVwuC/foW/wcJLZJz0AJfat1E+obL+TQVzVolMDEcQKT7s3yMRS6oPCYedYGwWxowAXbPwRPoBuRuBdSiQwWYxOXLS+l8RWsfIxG42nVlGni8IEDByr20bytjRs34otf/OKMjAshDIxgSVFodLRSTylCmcukobZUVk0F/CfZe1GqTBWLOC3uNIbYNfI6d8br9FOPw54v9LxDA89jZPg1lnAhGX4yAuSZjIy8injCi47Ov+IJlbOBJ1wO74VnZD9SiUJsXKN3wF63E1bnFtYW8/q7kE7H2COxmtuhnsJYUVMmhcKsptZJEy4Fq5schbnyS6dFZjAYsHXr1op9Op0ONptt0v7pEAZGsKRoLE5EXL3IJKKQqyvHIGdTCc49aG2VExp93iPsPVTzEJRKI3syPt+xRTUwsdgo3KN7ubSY3rOISmUqSL4Euvk5a5w7Z3xP+kz9x5+E13UAUqmSK85oGYgGRxANDmNkbB9CSQ+SqfD4FdTrokdD3bloabiw5OWQh+fyHMaQey8iUTe5gbAaW9BA4UMhE7MmyK3STn5hYARLCo1CNjZtQLDvCNLRIKQK+sYv4W52WjD1da1QGSvl7amsdyrlZDJQVI1F1VmLCXlRlKTXqSdL79NAMQpJ0Rjk2RiYoLcLvtFD7A2RPH8REvP0hfsxMvQyjyIw6Ov4d0OGhDryewee57LjpvrzeN/x3mfQP/I6p3LJAJEu+8jYQXgCXdjQehUaZvFMAsFUPPfcc5gtwsAIlhydvR5ypRpRzzCSQRqslYfG7IDW3gC1pWZS6Ewm1yKXc095PxKELJ90uRik0xEuH54qn0Fd+6Q9Nhv8o4fZQJQbF4L2xXMR9nBkOKWKTH+SHEwsnsHw6Juoc+6EP9iPAdcbPLmShD2LkGR/LO7Bif5nYTG1QFtl4qVg9ZBdpj6Y+SIMjGBZIC+FNkr682RHlpevvnhbbRtY8p7lVyYoIJPcCi28JvP0asPzhfI9rA02Ve4om4JCa5/VPRMxH2SyyYYxlY3zJpcquGJtImqVGdHYGELhQYyMHUA2l4G+zLgQ9IykvxaMDMHtPYrWhgtm9WyClUVuHlIv5fdYakTtomBZoaoxyrtMV+lktW7krvdYxMWy98WFnqZFxmJjMJs7YFrkXAO9B3lJ7MlMIJdLI5fPwG7fMqt70qRKMpoTKfT35CFBnkNv1ZL5dA5dG46OQiGr3pRanAkTjZ9+/LJAsBgID0aw5HATYNCDmHeEZWMoD0OJfQqTVWswpBBSR+f16O35EysQU5Ml75dr4KjZjubWd03ybBYara4WDscOuFxvcEkxycFwFVkqgmQqxAbOats0q3taHBsR9vdN8szkMhUkeQknZel9JpLOxNjYqdUWzv+QcZsKMkRLoSAtWFxyIskvEJwekoTx9xxExD2AfDZTmCGfzyE6NgCNtRa2jp2QySeX2FIfyfqNH0A0MoJYzM0ej05fD43GviR9HvQeTS2XcSXb2Ng+xONjbCgpdOZ07mLpFzJ4s8Hs2Aiv6yAiwUGotVbIxq/PpZNQQo6kDJAqKkNouXyWZ9vUWDdBp3HAaduIQHigqkxMJptkD8hmEpVkq53cKg2RCQMjWFLCIz0Iu3ohV2kh0xor+l/inmEEFWpY26vLgrP8vKGet+WAvAxqqKypPQvRiIurtjQaG2uNzQWFUou2ze/FQNcziAT6kYyPe2YKLZob34FA2oNQZBgKhYZzNZSPob4bqipra76sNFCNZsHQeXptDXs0RDoTRzTugcPSwaOUBaubHOUbF+AeS40wMIIlI5fNIDLazwu1jMuTTyGVKyBTahDzDsPY2MEGaKVCnspccz7ZTArpRLigSKAxQqWxoH3rjYiFXUhEPVypRqXJ5NGkUhGMuN/G6Nh+NhgKuQaNtWejrmZnqdlSozJhW+f7cbj7j4hER9nDoZoJMjQ11g3Y3P6eRR0NLRBMh/ibJ1gy0rEw51ymUkqm4VokhJmKBFe0gZkLNOfF078X/uHDyKSihZJjUy3szWfB5GiHzljHWzmkcdbSeBGa6s9n74VyKdVyTSZDPc7d+jfwBrsRiY3xvc2GRt6EBtnaICdCZALBTDldzmTp/yEsJuS19B94HMGxE+z9yJU67t0Je3sRCwyjYeNlsDZsm/J6kvOXSqfP75DxIY+FNsHaIyeS/ALB9Cg0eg6DkSSMVFNQIKaQTjThQiQ+jFQ6DKlEAWXEAYXRzAn0tYB/+BBCY11Q6xwVBQxkaBIRL0a6XobBvg4KVXWxTIFgtSIMjGDJoDyLvqYJgf4jyKZTkMikcAf3I5p0sbwJ5w4UWQz0P4dA8CTWrb8eKvXqm26ZySQQ8J1AwN/FjaDh0R5IIGGRzImodBbEQy6Exk7C1jj7meeCM4OcCJEJBKfHUN+OdCKK2Ngg/LEehFMDUEi1kErkkKk1UJvsXJ0VDvZjsPfPWLfh+lUlN089Ot3HH0UkPMhGhRQKovFhbo5EQga9uq7i8xRzJOnk5AZOgaBILj9/A0H3WGqEgREsKTRozNa+A2qLHaMHDnBJLqkTkwimTKUtDB2DlAUgSaE4HhuDVleD1QD1ovSdfALh0AB0Oicn5OnfdDLkQzaXRDQ5wqOTNcpTgpnUS0OuWzXvRiBY7YgSE8HyyMOoFZCqFNDbmqA2OyZJ95PhocqrxCqSOQmHBnmj3phitRd9IrXOwp35ZEziKc+4USlAFWUyhQYGW+syPrlgpZNdoG2pER6MYFng0JCEFt2paltIVPJUCGk1EIu6WJdsorKzSmtBKhbk4WEpRLiCjL7bUT8MGRh78y6o9bMTyhScWeRWaQ5mVv9677vvPmzfvr00cvOCCy7A448/Xjp+5513or29HRqNBg6HA9dffz2OHj1acY+//du/xe7du3kO9M6dk+dU9Pb2FsIkE7ZXX3214ryHH36Yx3eq1Wps27YNjz32WMVx+pZ41113oa6ujp/niiuuwIkTJ2bzcQXzJJNKIDTcDfeR1zB66GUE+o9yLwyh1lj5m35hTPBkUskwD+DS6St7Q1YjNMtGb2uGXKlFPpdBLOTixD65NzVt56F+/SWrKs8kECyKgWlsbMS3v/1tvPXWW3jzzTdx+eWXsxE5dOgQHyfDcf/99+PIkSN48skneZG/6qqrkJ0wY/3DH/4wbrnllmnf6+mnn8bIyEhpo3sXefnll3HrrbfiIx/5CPbu3Yv3v//9vB08eLB0zne+8x384Ac/wI9//GO89tprPO7z6quvRiKRmM1HFswRyjuMHngRvu63Efe5kAh6EOg7DNfBF1mHjEJIjtqzSJysMN++7NsVjQcmEUmrY2tVsceVipbzLgquIqsm2KnSmWGr24aWrdegZft1WH/+X7NxWYj8C/3+SDSz/PcoWHt9MLl5bkuNJD/Pv5FWqxX33HMPL/YT2b9/P3bs2IGuri72bMr5xje+gd/+9rfYt2/fJA+mra2NDUc1D4cg4xSNRvGHP/yhtO/888/n88mg0Eeqr6/H5z//efz93/89Hw8Gg3A6nXjggQfwwQ9+cEafLRQKwWQy8bXksQlmBpUgjx54AalYCEq9uWy0bx7peJgbB2u2XAClzoThgZfhHn6DjQp9i6dzSObEat+M5varVpUSMIX7jh1+CEF/dynJX9ifRyoVRjabwLrO98Jqn53q8nSk0jEMufdj0P02UqkolEodGhzb0FCzHSoewSxYLJZifQiNv8eVP7gcCs38MhrpeAZP/e2zS7qezTnATV7JL37xC17oKVQ2EdpP3gwZi6amplnf/33vex9qamrwjne8A7///e8rjr3yyisc8iqHvBPaT/T09MDlclWcQ/+RzjvvvNI51Ugmk/wftHwTzJ64b6RgXHSnjAtBBkShMXCjZdQ9yMfqmy7Chm23obH1nXDU7kJ90zuwfssH0dp5zaoyLgR9ntZ174bB2MiKz9Goi+fVRCLDnJupa7wQFtvGBXs/Gp/81pFf4lD3E4jGvMgjz7NfDnf/iffHZzlhU7ByyeUlyM5zo3ssNbM2iQcOHGCDQqEmvV6PRx55BJs3by4dv/fee/GFL3yBDcyGDRvw1FNPQamcLL8+FXTPf/u3f8NFF10EqVSKX//61xz+Im+HjA5BxoO8kXLoNe0vHi/um+qcatx999345je/OeNnFVQnGQ6UqsUmQkZGqlAiERwrvaYy5PmWIpP3QIs5zUYhdWMaIbwcqDUWrN98M/zeEwj6u1gyX6NxwGqnoWkNC5prOTHwF3gDPTDqayGTnjLGNOHSF+zD8b7nsGP99Qv2fgLBohsYMhoU1iI361e/+hXuuOMOPP/88yUjc9ttt+HKK6/kvMm//uu/4uabb8ZLL73EyfiZYLfb8bnPfa70+pxzzsHw8DCH4YoGZrH48pe/XPHe5MHMxfsSLF0egMJPHu9hDLveRDQ2yoaGVIftts1orL+ABSOXGh6E5tzO22JB3onLexRqlbHCuBCknkwqy27fccTifmg1lkV7DsHSkAMNoJPO+x4r3sCQN9LR0cE/U+L9jTfewPe//3385Cc/KYWiaOvs7OS8iMViYS+HkvJzhUJb5AkVqa2txejoaMU59Jr2F48X91EVWfk5U+V1CKpso00wP1QGC8KuHuRzuZIXQwnoWHoMyUwY2UQcVuPmqkOy4hEPQp4upBIRyBUqGGxt0JnqpyxXdo3uQU/f08jlc1CrTNwxTxMfB4dfQSTqwqb1N64ZTbNyonEf560M2kovvQgZ1lCENN68wsCsBfKSwjbfeywx824yyOVynLuoRnF2+lTHZwp5TOWGgkJ0zzzzTMU5ZICKuSDK+5CRKT+HvBGqJquWLxIsLBprHRRaI1LRQKG5MO3DYPBluEJ74I0cQSDbh+HAazh+7GFOfhN0nqvnVZx460EMnXgOY4NvYaTnRZzc+0v0H3mCFYknkkyFMTD0IhsVva6WPQcqEFCrzPw6EOrlWSprEZpUSZ+b579MMTmUw5FVRlALBCvSg6EQ0jXXXIPm5maEw2E8+OCDeO6557gkubu7Gw899BCXJVMPzODgIJc0Uw/KtddeW7oHVZRFIhHOhcTj8VIVGYXYyDv6z//8T/7zrLPO4v2/+c1v8B//8R/4+c9/XrrH3/3d3+HSSy/lXM11113HxQZUNv3Tn/6Uj9M/rM985jP453/+Z/akyOB87Wtf48oyyucIFheZQsmjj70n9iIaHII7dQiZPI0B1kAq17OHA4Ucfv9x5LvzWL/+JvhHj8LV8yJkcjW07LEUvm1lUjF4h/fz/sb1l1e8j9/fhWQqBL1u8oRLKheWS1Vwj+1HQ925q6phcyZQ3kWrtnCozKB1TDpO++m4Wb880z8FC0seEt7me48VbWDcbjduv/12zq9QGIyaLsm4UM6F8iQvvPACvve978Hv93NC/ZJLLuGeFaoGK/LRj36UczZFioaEKr9aWwtyGf/n//wf9PX1QS6XczMlGa6bbrqpdM2FF17Ixu2rX/0qvvKVr7ARoSKArVu3ls4pFhp87GMfQyAQ4Gq0J554Ysa5IMH8UBttcG69CD3H/4icOwON3Aa5UgO5RseqyoRG60Ao2INAoBueIfqiIYFSU6meTM2JVIHldx1GTfPZUKpPlVdSZzxdM5XxoI56CiPRsK6J3fVLRTIVgcd/gr0t0iGzWdqh09jmfV+5TInW+nNw6OQTiCUCnHMplnlTdVkml0Jn3dmQy0XId02QX50hsnn3waxlRB/M/Nn/9k+RTAWh0VSXQomEh+Cw70Bk6CQbE2pInAjlaqjzvXXb9bA4T5X5Do+8gZM9T0Cvr16dFY97Of+ya8edSx4qon9WQ6N70T3wF/ayyBDS56DqtgbnWWhvfue8n4nu1zXwInqHX0MyHeXvp/SPWanQoqXuHHQ2XwrpGvPcztQ+mHd+92rINfMr28/E03jus08u6XomtMgE8yKXzSARGEMy6OO4v0Krh9pSw8PF+HguXZCqnxIJn0PzYKYOYxWMx0TdMou5HQqljhdwSvCXQ3pflOyvrz1nWfIQbu9RHO/5E0v2G3Uk0S8t5CNTIfQOvcy5onVNF8/rPeienc2XoM6xBW7fCaTSUSjlGtRYO6GvEjYTrF7yZ0KITCAoJ5OIItBzGKlIkG0ALXgJ/yg3URrq10FX0widrg4+3xEyB5OuLxiMPPSGeqRVHqSTUdbtmkg2Hef9al1laEmjsaKuZhcGhl5CPFfof6FnyGTiiCe8nOh31uzAUkOfa2Dkde7JIeNShLwsMoSUmB9y7UFj7a4F6dfRa2zQN8w/7CZYweSlhW2+91hihP8smBNUghzoPYpUJMBeC8m+KLQGKHQm1hcLD3WxHpndvpU9mFSqcqAWV5fFPVCpTLDZt8BatxXZTJwnQJaTy2WRjPmhtzZDo5/cjNnUeDFami/jHEs05kIkOoxMJgabdQM2rr8BarUZSw1104ejo1Arq0/jpP2JVAiB0MCSP5tAsJQID0YwJ5JhP9LRIOQaPSRlISj6lk6zXciriXmGYW7bDKfzbIyOvol0KgyFQscTK9OpKP/c3HIFlEoDHE27EQu7ERw7ziExmUKNXDaNXCYFnbkBDZ2XV82zUPirqeFC1NachXBkkPtt1GoLdFrnrLvm2eglfEhnEpzH0Kjn1j9SEJ3MlbTIJlIMl9F5AsFMyPM8oXmGyFaDVIxAQKSjIfZipLLqf4VIDiZFkjF5oLnlcg6DjY3tRyw6yuORa2rOgqNmBwyGRj6fQmCtW65DwN0Bn+sIknE/VBozLLWbObFPuZbpUCg0sFo65/x5guEh9A29DH+wF9lcGjKpEjbzOrQ0XAiDvtC4O1OooovUBCgnQtVeEyG1Zdo/VwMmOBORlHKR87vH0iIMjGCOTF98yPPox8+jEJnNthlW6yZO6GcySUTDw0jFAwjn8tAbqQpMyrL1FCqjbSmhUNXBY79BPEnlvmaoFAbWEBsZO4BQZBhbN9wI4yzm0lDlWq1jK7oHX2BF43IpF/Jsogkv7JZOGEWPimCGCA9GcEZBVWLcd5HNQiKbXKVFuRS1uaYifEaL6+jwWxgb2VMaNEYNkTpDHRrbLmMvZ6mhUFV3//NIJIMwlZU7k5IzhcnIwPQOvIBtGz8wq5BbS8P5fK030A25TAWFXI1sNo1UJgqDrhbrW68QQ8YEax6R5BfMCZXJBrnWgHQ8Mql8OJOMs2HR2isNxsjAyxjqe54bH2k4l85QD4XKgHCwH93Hfo94zLPEnwIIR0cQCg9Cq7FOWvDJq6Iwlj/Ui1jcO6v7UnXY9g03Yn3bVXwP0kqjHp91TZdix6ZboJ+nerTgTFyqpQuwLS3CgxHMmGJPLo+xlspgbt2EQM8hpKPhgqglJa+zGc7L6Ota2QgVoamVbtde1gtTlVV2UfWXTF+HaHgI7pF9aGmvnPOz2FB1G3W9a2XVO96p+556V0g1QIfqzaLThcpaGy5AS/15yGRTHCoT2mCCuSBCZII1Cw0Ii3tdiHldyGXSkKvU0NjqoLHVwtp5FhJ+N49E5kZLjQEam5PLlcs9glCgh8cg66rkHXgQmVKPgPcYGlsurtoLs1iwQKZUwYl9CmVNJJtLFXTNZHOXmiFPiEJkAsGZhjAwgtM2U/q7DiAVDUEil7PnQj9ThVgi4IFl3RbonE28TQflZCjxP1XegRL8uWyqUMGFuRkY6pkJRoaRzsShUuhZEHKiOgAl76k8mAwGeROUaNdraxCKjExK5JPHFkv4YTW1ipCWYJmRiCoywdqCFthg/3Eef6zQGSsmVBYkYtyIjBpgbGg/7b3IQ+HrctmqYaJsJsF9MXMVpXR7j6F78EVucCQDQlIsJkMDOpouhcXUjHBkBCOje+D1n2AZGZXSCGfNdtTW7ERL40U40vUowlEXtGorX0shrVjCy9IrLY0Xrjk1ZsEqI786xS6FgRFMSToWRirsh1ylnTT+mPIsUrkScc8I9M4WSOXT/1UyWdZBpbEgGfdBo6vUySKDkEnH4Ww4b8rmxOkY9R7FwROPIpNNQKe2cQUYlUJTBRd11bfVn4fhkdeQTIagVOk55BVL+HCy50kEgn3Y2Hk9NrW/Bz2DLyBKY5fZCMq52ouS8jbz6Q2oQCCYjDAwginJJGLIZbOQqRVTNlPmkglkkzFI5dOrs1IFVUPzxeg7+SdEIyNQqkw8NItkXair32huQU3t1NNGp4KMU/fAC8hmk+OikpJS46VJ3oBAqB9Hu34PjVIPg+FUGTJNfKRwmdd3DK7RfWisPw92ayf3xFCIjUqUTYYmkZQXnLFJ/vvuu4+33t5efr1lyxbcddddPBNspggDI5iS0/ZpUFUZnTPBu5kKW80WHhzmHn4TkfAwMvksJ9nrmi5EbcM5p+3Wr0YgPIhwzA2txlalzFgChUyJaMQNk/6U8SlCSX2pTAnX2Nuor93NXovV3DbrZxAIFh/pApQZz+76xsZGHhpJ87YoXE7DIK+//nrs3buXjc1MEAZGMCVKvQkyhYqryOSqyXNassk4FHoT5OqZz7w3W9s5XJZM+Nn7IB2yajNgZko6HR9P2k+WZCF4RsqEPp2JpcQ0tpkLA5SGOT0DDROjkBx5PXQ/gWAt8N73vrfi9be+9S32aF599VVhYATzg8UYsxlO7se8IwUBSqWqNDWRjAt5L7qaplknwFm2XmNdkOekBZ3KjGmBrza9kSTzuW9nipk0ZJxYpmYOuR/q1B8YeQM+fxe/j0ymQo1tM5rqzhE6Y4KFnwcz3xDZeBUZDTErR6VS8TYd2WwWDz/8ME8JvuCCC2b8nqI0RjAJKkP2d70Nz+E3kAyMcfNkKuRDMuBhef50JMiLtrGxAxqrc1mf1WxohEHn5GT+xOGs9DqdTbMeGA81mwAdJ+/FSoPL5LPzoii3c+Dowxhx72MDpVTQe2TRP/wKDhz7FeIJ/5TX0nm0CQRzK1We61agqamJp2QWt7vvvnvKdzxw4AD0ej0boI9//ON45JFHsHnz5hk/sfBgBJMqx/xd+znBL1NrIVNrIFNpkY6HaWWE2mTnDn212TGr0NhCQiEv0g4jqNy4vflSHDz+W+6B0ZXKjBOIxX3Q65yw6Gvh8x1lj4sMARlH8lxiMQ+Hxeqcu2b1/iT7crL/z0ikgjCW6ZdRiTXdLxQeQv/QK9jQfm3ZM+fh8XdhyL0f/lA/Pwv11zQ6d8Jmap30HqHoKIbHDsEfGmQD5rCsQ519C7Tq6jNmBGub/AIm+QcGBipGJk/nvWzYsAH79u3jMcu/+tWvcMcdd+D555+fsZERBkZQQWSkl40L5VaKC6dEIYVKYWVJmGw6xZMqy0UslwpapEc9B3kaZCTm5n0atRX1zp3Y2nk9eodeRTAyhFySwlUK1Ng2or3pEui1dnT3PYsx70FEIkMFx10CaDV2tLdcAeP4yICZEgwPcHhMq55cWEBVZyqVEW7fUbQ2XcwGh567Z+hldPX/hRtJqQmUhrINu9+G23cMG1uvRFPtKSM3OPo2Dvc8hUQqUsgt5XMY859A38hb2LH+fbCZWhboNyo4EzEajRUGZjqUSiU6Ojr45927d+ONN97A97//ffzkJz+Z0fXCwAgqRCqTQS9kKnXVCjLyaDLxCJLhANRlOmPz1TWb6fk9gy+ysjFFkwsJeQmiUTeOnXwMDbVnY/eWWxGJecYT9nroNPbS/TvXvZvPIeFKGmRGo4stlvaq8jCnI5kM8z2magqlcFsiGWCdM3pOqnQ7Ofgie1aGMkUAjdrMob3jfc/CYmyCXutAIDyMQ91/4pyOpaysmry2QGQE+088igu3/w1/PsGZhHTJq8iqkcvlkExWTp2dDmFgBCVIZ4z+AskU1SuypDIZMpT8z6TmdH8yEplYBKmgH+lEjKVj5Fo9VCYz5JrpS5Spy57CTqTpRcahCFVu0WCvYfde7mOxWwrftqqh1dp5my/kVVDYisJs1YoDyEuhogIyKITLcwjpdAJmQ8PkZ1Jb2QC5PIfR0XwphsYOIpmOVhgXgt7PpKtFMOKCy3sMLXW75/05BKuH/DL0wXz5y1/mnpfm5maEw2E8+OCDeO655/Dkk0/O+B4iyS8oIZWT2i8pIldPQPPsF4mEz5uLcUn4xhAZ7udxytRDw9+Ggj5EhvqQDAWmvX7Me3TcMzFWlcanirdRz2EsBWZjM1eJVUvk8+dMBrhJk8J3RDAyAoW8UIE3EdpHVXAkcUN4Az1Qyqt7kNz0KaHen+FF+VwCQTlutxu3334752He9a53cXiMjMuVV16JmSI8GEEJ6nVRmmyIe0e4S3/iIpdJxiBX66AyzL4ENxOPIenzsOSMVHEqtJTPK5BLJREfc0FOBQXK6iErStjLpPIpQ2pUohyLjWEpoNBYc/35ON7zJKJxDzQqCy/+NFAsHif9Mi0fLw0vkyqRy09dNUbHpNKi1yiZflYo97aKQWVnHPml1yL793//9/m9n/BgBBMx1Law9lg6GkQunSr1w6RJTVkihaFh3ZwS/KS+zBpfE8Jv7BEpVRx2S0cq6/PLUSjU0y/S2QyrAiwV9c5d6Gy9kvMtkdgoV47FEh4OwW3seF+FIkCNtZPLpKs9fzaX4T/tlnX8J1WLpTOJSSXXRLG0mcJngjOwDwbz35Ya4cEIKqDGSkvHNoQHu7nnJU8NlVIZ79fXtkJjnZtsfTYRrzpameBv5BIpssnElNfbzB0YGt3L+mETE/OUC8khy1VjiwV5J4lUwchqVFRhJ0Vj3Tlw2rfAH+zl5yJ9M4uxlSvYynHaN2HAtQehiAsGbU3pOCk2U2jMbGxCjXU972uo2YaB0X0sf0PnFr2VwigCFxcJOG0bFu1zCgQLiTAwgkkodSZY1+/kirFsKslKyQptpVz/rKFrq3wrLyJhXbOp72+1tLOq8Zj3GCfGi5IsmUwc0YQPFmPzohgYGu/c73oLQ6P7eLIlhbBobkxT7W44bZv4OWrs0/cEqJUGbF//fhzsehShqIv7aCT8cWXcC7Ot872lgWRGnRNb26/Foe4n4A8PcViQKsjoGqPWgR2d7+XCBsEZRl7I9QvWECwUqTXwthAodQbEY1EO/UzMIeRzOc47KLRTV5LRQru54304LnuSGxbjyQAnvOVSJRzW9diw7t0LrgNGXsvBE3/AiOcAe01UGkyLvS/Yi0BoEMnWCFrqz53RvWj42Xnb/gZjga7xhL4EJn097Oa2SZVo9Y7NMOmdGPEc4UZLOk69L7X2TVCL8uQzkjykvM33HkuNMDCCJUFpMCEZ9COXiENKfTbj3hAZFwqNyTVaKPTTGzOlUoct6/+KmyzDkWE2SjSNkqZSLkbie9R7hEuMqZ+mfOQxVa1R/wqNCXBYO6Gdoe4YhcZqbZt4Ox06jQ0dTe+Y1/MLBMuNMDCCJYGS+7raRsRGh9ig5Ms9JY0O2tqGGRUP0PmkPUbbYjM8doC9pHLjUoSMSjA8xJM0WxvOX/RnEZzh5EWITCCYFvJSDM3rkI5GSgl9Kk2W60gfbGUVNFIoLxrz8MjkavDzSiRIJKeufBMIFoqFqAITVWSCRYNCUZlYlJP2BMnB0II/r8T9HCAvhcJloG0Fw56VQoNYPF71OJcR5/OQy6urHggEC8oq9WBmtbrQsJnt27eXxNJoLsDjjz9eOn7nnXeivb0dGo0GDoeDp58dPXq04h5/+7d/y6JppOC5c2f1Ebn79+/HxRdfDLVazdLS3/nOdyadQ7MJNm7cyOds27YNjz322KQFgMZ71tXV8fNcccUVOHHiBM5EyFuIDA8g5h5Bwu9F0u9FbHQY0ZHBksERTIbUi6knpVr/CqkK0PwXm7nQvyIQCOZpYIojNN966y28+eabuPzyy9mIHDp0iI+T4bj//vtx5MgRlhSgRf6qq67iYTXlfPjDH8Ytt9xS9T1oGA5d09LSwu9zzz334Bvf+AZ++tOfls55+eWXceutt+IjH/kIj+98//vfz9vBgwdL55BR+sEPfoAf//jHeO2116DT6XD11VcjkZi612ItkstkEHO7CuXGSmWhW16t4ZwIGZ64e3RKaZgznTrHNh61HIqMsEGhv8+FUQEhxOJeOO0beR6NQLDY5PPSBdmWGkm+WsvwLLBarWwEaLGv5ons2LEDXV1d7NmUQ0bjt7/9Lc8amOgl/eM//iNcLhdLRRNf+tKX+NyiN0TGiSar/eEPfyhdd/7557NHRAaFPlJ9fT0+//nP4+///u/5OM0zcDqdeOCBB/DBD35wRp+NjB0N5KFrZypvvdKgyq24Zwwy1WQtLO7STyWhcTgLYSvBJCKxMRylnpTQILK5VKmKrNa+BetbLq86RXOukPGizv6CJM7KykkJlmd9CI2/x9n/9L8gV88vHJtJpPDmXT9b0vVszjmY043QpP3kzbS1tXGYa6a88soruOSSS0rGhSDP41/+5V/g9/thsVj4nM997nMV19E5ZISInp4eNlAUFitC/5HOO+88vnamBmYtQHkXiVQypdAikQj4kM0k2auhJkuhdXUKktDfveU2BMJDrDsmlcjYa9Eu0MhnIp4MYnh0H0Y8B1kRgObF1NdsR51ju2iqFKxqZm1gaIQmGRQKNdEozYkjNO+991584QtfYANDKpxPPfVUhbE4HWQYyCiVQ55H8RgZGPqzuK/8HNpfPK/8umrnVIPmHJTPOpg4u3o1UnBQqxuMbCqOVCxc+HMwxHL8ZGCMjZ08uVJQgLwJmtdC20JDlWpvH/81h+FI14yEManH5mj3k3D7TmD7+hugUk4/ykBwBpA/A5L85SM0Ka/xiU98gkdoHj58Sib9tttu47wIjdVcv349br755lWT96DZ1OWzqmfjea1UqFqMRh1PjIRmUwkkgl7kMxmuJCPDIlOokQj54O3ax8dWA7QYD7kPYMj9NsvizzPiu6TQsx7ve5qNi1FXzw2dapWRm0dp1LM3cBK9wy8v92MKVgD5M0Xs8nQjNIuLc2dnJ+dFyOMgL4eS8jOhtrYWo6OF2RhFiq/p2HTnlB8v7qMqsvJzpqpcKw7YKQ+9kQezWowMJeqzySQn9SkkRuEuSuordHqkw6GCIVEURRjzSEVpJosEErkMeeT4r55ErmBDQ8dCQ11QGa0rNlyWTsdxtPcZuLxHeOAYQQ2RdvM6bGq7Ghq1CalUGJHwMPL5LDQaOzRax4r6PKTC7Av2sbYaz3opg2bEUKjMNXYQbQ0Xct5HIFhtyBdzhGah6iY/qxGbFH6jJH86nYZifEGkMBt5TmSsiuc888wz+MxnPlO6js4p5oIoxEZGhs4pGhQyFkWvayqodJq21UYmkUA6HEY+W5B+Z2iQlVIFhcEAldmKZMDLVWPUh5LNpJDP5ljdOA+65tS3flqASa6flJTTsTCUupVX3EDKwge6/oDhsYOsbGzSF+TrU5kYhscOIZGMoM7QgIDvONLpMH88mVwFo6kVTa2XQz0u7UJ/N2PRUYT8PSxqSWrIZlsnlKql+czRuA+ZTAJadfXx02RU4kk/4omAMDBnOvkFCHHlV7iBmW6EZnd3Nx566CEuMaYemMHBQS5pph6Ua6+9tnQPqiiLRCKcC4nH46UqMsrjkHf0P/7H/8A3v/lNrkr74he/yKXH5CF997vfLd3j7/7u73DppZfi3/7t33DdddfhF7/4BZdNF0uZaZEk4/PP//zP7EmRwfna177GlWVUzryWyKZSSIdCyJMDrFCcmuE+rvHFUidGE1eRpcKhgmw+GaV0HFKloqrTLJXJOS8z19HIiw1NfRz1HeMEfLmMi0qh4xxGcOwgsoEuDjXpdOTNSpDJxODzHkE6FUHnxpsglSkx0PMsfGOHeJGXjP/7Uw68jPrmd8BRu3PRvZ3iADWqHqMRyxOh/hsJpFXHMgvOMPILUGa8DGXK8rmM0BwZGeEwGDVdFkdoDg8P44UXXsD3vvc9rvaihDpVg1HPSk3NqRkiH/3oRzk/U+Sss84qVX61trbyff/0pz/hk5/8JIfg7HY7N0x+7GMfK11z4YUXsnH76le/iq985StsRKiCbOvWraVzioUGdF0gEMA73vEOPPHEE9yYuZbIxuNsTCSKymmP3KEvlyOXSnF4jLr2aWOPMhJAxNcHSbYQFqs2vIs8nYnDwVYKbn8XezHVNMKkuTQk2QTSUjlUanNpv4KMj0yNcHgQXs8hpBIRjI28BaXKBJXaWlrokwk/BnqeYWVmi31x566YDI1Qq0w8YlmrmezFJJJBHg1AuRmBYDUy7z6YtcxK74Ph+e+ewpjgicO86FgqFkQulYDaUgON1VFxbOzwq5zQn1iWzNdFAtBYamDfeM6KylkU2XfsNxwKoybIiaSjo0iFBiFTGlDnmDynhcYqq1QmLnKgv/kq9eT+n2jEBaOpGeu33rLo/SikyNzV/2coFXqolMaSoYsl/MjnM9jS8T7UOU59cRKcmX0wu7/+vyFTzy98n00k8dY3710dfTCCFUCV7wZkIOIBF0LubqTiQU7+UyWZxlILg6UBMpUWCq0e+vp2JEJeRD2DkEjlkMoVkClodHEaCrWWS5VXonEpKhmTB1N9tkwWNF1GraguUknJ80Tci1wmD71hsoEiKAdDRiaZCEC9gP0u1WhtuJAnWw6O0sTLofGS8jxUKgPWNbyTGzoFgnyevnjMU+xSqCkLZgXNs6d8STpV8mCivkH4Bg8in8tArtBBIs8jnYrC33sQ4aET0JsbuRw5l03xdflsmr/Nk7GisJrKaIO1YwdUhpnNOFkOnLaN6B1+nUcYU5K/HJ4WCUCjOhUeKyeTSUIu1yGdjUzZH0ReC3kRtC02VD22vvVdaKjZAU/gJEvSUPUYzZmh8JlAUIDrPDE/hIERzAL69i7TaAqGIpfj2fRB13E2FkqNmftfChVjWShUOh4vnM6SMQHSsRCkChVUjkY+nstm2ShRvobKlFdyo6VRV4vW+nNxcuBFXpDVHFqSIpkKI5vPQqs2o9pkmWw2ySXZ9pqtcA/vQTaTgLyKp5NJR6FQ6qFULsw0z5mg09p5EwjWEsLArHJkajXk6TQy8RiHxjLJKBQqw3hzZY6rwaSyQnWZLKdAKuKDRG2CRCan5hnk02nIlGrQS4JKkyOuPuidLewdrUTos3Q2XwqN2oyBkT2IxMc4XEZeS1PLZVBkMxgZegmxqJvDXWR8qHqMDKzFugF1jRciFnEj6O+GTl5XkWehcmWqsHM2nMelzQLBSiC/AFVkyyF2uTJXEMHs5pYYDNxUGQ0OsXfChkEmRTYeYSND+ll8LvXAUGiM8iwqLXKZLFeZkYEpIlNpkEnG2NCs5DAZGYUm51locGwvJMSRh1Zl5rHEZGxUaiPGXHsQj3n4mEKhR03d2aitP48FKpvaLmejEw0PQ0YSLTIFlyuTF0fVY7X1Zy/3RxQIyhAhMsEywc2RajVUJiukCgUbG8qnpGOUjyivECv0VRS+sZMAJtmj/FRdXVgNUA5DPyG0RL8PR8122OxbOKFPRpZKluVlZc3U1d++/v3weg7D7z3KOSmdvha2mq2wOTbP2XspNBb72VhRaTRVrAkEZyrCwKwhtJY6yNVapBMRKLXGQgEAEgVTQdL82QzUejukeRl3/dNiOFGihBL+MoUGco1+yvehUt9A4DgSCT/3lphMbTAaWyfda7mh59HqTvVgEWRs4l4XYp5hZOJRyCUSNJjOh8Zex3mn+VTOUY/NyMirCIX6kculuenTZF6H+rrzodVVCq8KBLNBVJEJlgxO6JO2mKTQKFlcFOVKNSxNm+E5uQfJaKDQKBmXIJdOcgEAVY9pTU7O11CSn0JmVJpchEqUqSjAUL8OsiqjgMkguVyvY3j4ZaTTkfFqqzxGR9+E2dyBdeuug3yKGfYrATIuof4TiLoH+DUVOZDhjftGkQz5YGreAK29euny6QiH+tHV9VskkyH2Wsh7oaICz9h+RCND6Oi8YVxVQCA4c9SUhYFZRVCPRzIYQDocZAPDemMqCo2ZIdfq2dCY6jew7Ehg6ChSMZKQyXEpslyphc7SyMfIs6G+F+olSSUjkGUUyGXTHDbTORphrK8cDlfE5zuKgYHnIJUqoNM1lAwbhYN8viM8Qnjduvec1gsgY0fvLZMpl7TXJuH3IOoehFSpqjCs9DqTiCI0eILzTqxAXVbWHPCfQDzu4XCjTl8Pk7mtQr6FDNfg4F+4io2OFz8TfT4yNNHIMIaHXkZH51+t2N4igWAxEAZmFXktNPo4HSl4HoW+lzwPFCN9MbW9BiqjedzIdMLgbEMiNIZsJoNsIoZ0JIxsIsqhMaXBDF1tE9LJGJIhL2X/odY4oLU3QGt18v0nvX8+D7d7Ly+mRbHIIpTbUCrN8PuP80Ks1Z5SDSiHmhfHRt9GkKRe8lm+j71mG2yOrUuitxX3jtAvssK4nMph6ZCOBBEPuKF3NvP+cGgAvd1PcEiwmJMir01vaMS6jveUfg/RqAuRyDC/nmhA6HyVyoxgqAeJhA+aKpIwAsHpWAi5/VUh1y9YHsiwkJGQcQJfNkGYMoWEdwwKrY49k+J+yslMHI+cScURHOmCf/Aoh87IC5KrNJBbnSwPU824EKlUCLGYa8reEP6mHg0iEhmqamCCgR70nPgDh5DoXMqPUAVXODSIULAfbe3XcDn1YpKOR6pqrxFsGCQSZBJxfk35pe6uP3A3P+VxigaQqvBCwT50dz2KDZs+yF4KhQtpv1pWXedOJlfze6fTUWFgBHODSozXutilYPlIhYOFnEuZAaB+jVhgBNHACLLpJPzuI7A0b4ahpnVSDwsvoDIpfL1vI+YfZUl+JZUh5/NcluzvO4RMKgF7x66qYZzi6IWpu9+LY5knd7/TOOb+nqdZUUBfFkIiY0XhNe/YIRiMzaipnXpWz0JAv7t8frrREVT0UPhHSM8Uj3uhN9DznvqHSQZFp3MiEh5CMHASVtsmLnQgA8SJfdnk3BXtp+NymeirEZxZLL1JE8wa9j7S6QrjQpVinr49CLiOI5uOc+iKQmKuIy9iYM/j8Hbvh7/nEKJjg5y8J+jnuN/NApfktbBRkEqh0Oi5+izq7kMyXH2SJc1KoVAPfVuvBjUxUm6GBntNJBjo5n4UjXZylRaF16hPx+N+e9GlWUj0k5QKqum7soK0RAqlsRD2CgS6uF+mmtgleVpcMBDs59dkNKnsmUJgVcuWE37o9A3QaCsr2gSCtT7RUhiYVQIZguLCSH/6R44iFQ9DqTFCrtJBrlBDodZzmXFw+Di8J/ciNHIS3hN7MXroZSTDfkQ9/VS7W7VDnyqqqAEz4ulHNDqKWMzNyfjScakcNTU7WOE3nY5VXEsJ+0TCC72+kbeJULipeI9qyBVaDkVR1dViorXVQq7RIRMNsTxOyXhn0sjEIqzDpjIUxC3ps1eb0VJCIin9fuhz1ddfyEKaNMAsywUThXAavabPV19/gUjwC+ZfRTbfbYkRIbJVACeh9UYkfQVJlFQ8gFTcz/piRWFGWvCo+54mVUrlSmRzSfZUiHQ0BF/X25wDmEr+harNItkReAdPQuqhPIUEarUVNc6z4KjZwe/jcOxk4+Px7EcyGWDvo1ARluaqstbWq6t/4yfDMh5iqxp+y2XZgEoki/vXkbw0y7qtCPYdQTpGKgeFhlJ6Po3VCVPLxsIcHdIG09WycagG/77zOWh1p3JNNtsmvtfw0Etc6EBGl+5L4bTGxku4V0ggmCsiyS9YVFQGIzKRELLJJNLxcKHMl3MKOa4wo787NIFSplByhRaVHdM3afZs9Cae8QKppBQuK4fu4QkfRijVB6XMDLXCxh3+tFD29jyBZDKIxqZLecFsbX03zOZ2eDyH2Guh/IPNthFWK00krd6cSfkV6ozPpGNQKCtH/7LBTEfgrNvNci2LjVJvgn3TOUgGvUjHo2zwlHoz/47KjR9VttEETPrs5d34PA4h7uFBZaRrVo7NthkWcyfC4QEOGZKatcHQtOIaUAWCpUIYmFUCNU1qnfWIe9zIR/JsVMhboW/cNA45nYiOyxWRpRkvqR3/xkJeBZc2y2Wcr6F8Q7knE0uNIZwYgkKmhc5QW5JJIQ8llQzB7XoTFut6zjXQYmm1kkHZOONnpy52un7MvZ+fUS7X8mJORpIXa6UB9prtWCrod0H5mAnV1hUYjS2oa7iQPZJIeJgnXJLRZSOp0KG55YqqMjCUn6HufYFgQeEQ13yryIQHc8ZD3kkiXEgWq/RmKLWnFjFqANTVN0GmVSMS6GdjQsPDaMGkMthihVdhaJieQ2VF+Ju62sAJ/ah3iHtBZErqus8jGOljg0U5iIk9IgqlgTvR/b7jbGDmAr1387or+We6TyIRKHkLNNCrufUKrtZaSdDz1TdcyCEuz9hBloGRSiQcCrM7tq+45xWsbfJ5isrOTx9wOWYXCwOzQqAyY2/P24h6BpCh/pQ8eLHXWethW7eTq76KCx/1q5ga1sM/cJilXUgChjwc8mLyNDxMImHNseIizonsbIYNiKFuHRSDRxF19yMTD7NRykkyUGnNUOspwT2xUZAqzeQcKpoP5A21db4HzqiLq69ItZgEKE2W9goRypUEfXazpYO3QoVbsRRbIBDMBGFgVgAUKnIfew0RzwD3p6h4oSejk0BotBuZdAJ1m99RaqIk7G27WC4mPNbLXg+PCuZ+Cxm0lkYoNCakkxHucckk45ybITFH+tPWth3mhvV8HRmjxEAS4XDBI6oG3XshNMZocdbp63hbbVQrXhAIloz8Agicz+L6u+++G7/5zW9w9OhRaDQaXHjhhfiXf/kXbNhQmXc8HcLArABivhFEfcMcDis3InKlhmP6cb8LUd8QN1AWofOcGy9kWZiYf6QgVJmMIxkKcII/NHoS2TRJw2RZCoYqqFxHX4aprpN7akh7izwkqp6yWjcgFOoZN1CKKv0tMlgsHUv6OxEIBKcYL1ycF7O5/vnnn8cnP/lJnHPOOchkMvjKV76Cq666CocPH4ZOV1moMx3CwKwAKCfCg8GqyJgUk/GRscEKA1MKl5lqeCuSCHkx9PYzyKSinINR6XSss0UTLBMBD8YCr0GpM7NxIc8k5h2GwmiGQd/I1U/UTEnVT/R1J5WKIJ0K8VwVg7FlCX4TAoFgJfDEE09UvH7ggQdQU1ODt956C5dccsmM7yMMzAqAQmHThWB4EmU6MaN7kfdCg8X0NVQafCrJTxL90rwUuXya0ywKbUFTjHIzqYAPVvt6KO0mBIPdSEYL+RaqlnLWnYPGpneKUluBYLmT/Pn534MIhUIV+1UqFW/TEQwW1gSrtRC+nynCwKwAqOKLvImpICOg0FQXmZxIPDjK55cbF0ryU/iMcizkyZDMjNpg49fkIVH4LBMKoXnLu5BpvAhxUg+WSKDX1XEiXiAQLDO5qjJ/s78HgKampordX//61/GNb3xj6styOXzmM5/BRRddhK1bt87qLYWBWQHo7U0IuU6y0jHlXSZWl/FYYEflX4qpoHLjiZVOlIcp9sxwj8yEr0IkE0ONmDSETEeS/VPI7QsEgtXPwMAAjEZj6fXpvBfKxRw8eBAvvvjirN9LGJgVgNrkgLG2A8GhY8ilU5CRECWFtVKFpkhTXQe0lplNQyx4OoVGzKLsSTmsAKDSIp72I5IYQSobggQyKHM6mDNbFuHTCQSClRQiI+NSbmCm41Of+hT+8Ic/4C9/+QsaGyfrDJ4OYWCWEJa8Z90tWYWXQT/b1+2EUqNH0HVyvGmyEDoz1q7jSrGZlsnqbI2Qqw1IxQJcqkyd51zCnM8AhaGViEt8CAeGkM2lCjLz2QwiyCE3LEGn8QZWPZ4vJIhJEzB9vmNIZ+LQamwspUIyM6LkVyCYHdRkOe9Gy1lcT2vVpz/9aTzyyCN47rnn0NY2Ny09YWCWACohDo/2IuTu4VwI5UH0jmYYnW3cWU+Qt0HNk4a69kJ/Sj7P3shU4pRTIVeqYW/fhcH9TyPsG+CEP4s65vOQShSQ67UIpUe4eVKlsEBCTZi5FGQaHWJxN3q6H8fGzf9jXkl9kq0/0fU7nplC70NGLBodgcd7BDU1O9HacqUoGhAIlikHMxMoLPbggw/id7/7HQwGA1wuF+83mUzcFzNThIFZZLLpFNzHXuE+F1psKfmeTSXh7zuAqGcQzo0XsCRMEVp4Vbr5JdapwZKMBnX3sxDmeCUaiWHGomPIKpNQqdTIZ1MsT0QhM7XByl4OycKEQ31z1tMiY9bT+yeWViFF4nKJflJzHh3dA522Bk7nLiw2JPYZCPYgFvcWDLihGXpdrejGFwhOw3333cd/vvOd76zYf//99+Nv/uZvMFOEgVlkaDZL1DvMvSfl3kg+r0My7IOnew/qt122YIseS870v819Llqzs2y4VmF+iXd4P1TQQ6ZU86JLygGkSSaRUiZGxjkamjE/VwNDI5PDoX4eDTxx/guVPZORcbvfhsOxY1G9mGB4AF09T/J4geIgM5lMBZulAx1t72bxSoHgTMzBzIRqQ/nmgjAwSxAaoyqtySOMaZKkEcmQl6dIqo32aXtbIr5BLi8mD0hnbeRZMNWgjn8KsRV0xcZHJZfeUwKJXA5kAbXRBolsqgV+7saOBpVNN59eodBzCC2VCkO9SCXQ5LEcPfE7JJJ+aDU1PAaA/sGkMzGMjh1ELp/D5vU3iFyQYNWQX+JO/oVCGJhFhHTAqEGyoFo8GdIFSyfCBYMwhYEJjfVgtOtVJCNelosnFCo9bE3bYW89a9Iimc0kC4O9xr2DXDaLdDzEhQPkwVDIjCRhSMdMNsHA0LWFIVkzq1irCg1Am/aEfKEfZxHDVKNjBxCPe2EwNJR+P6wmrSgMaPMFutjDMQt1AoFgURFf4RYTntJIvSfVvzoUku+FRbkaUf8Qhg4/g2TUxwKYWpMTGqODDcXoyVfh6ds36RpSVqb3pMowGoEc8w0jHvKw6jIfz0m5qozk/umcInTPWGwMekMjjKa5L7zUnEnqyJlM5VjlIuS5aLU1PANmMaDfKRUT0Jjiah6KQq7hXFQg2Lco7y8QLKrYZX6e20o2MJT42b59e6mO+oILLsDjjz9eOn7nnXeivb2dqwwcDgeuv/56VuMsp7+/H9dddx20Wi1r2/zDP/wDi6kVoZI4DuVM2IpVDEV+9KMfobW1FWq1Gueddx5ef/31iuOJRIIrIWw2G/R6PW688UaMjlYfgbtYUJmxSm9BOhmd0sORq9TQmBxVF0rKpZASstpgL4XYaNFUkSimTAHvwH7ulSlHZ21g0cxkLIhEyMNKzHKa/aJQslcjlSih0lp5f9B3kscCRyLD/KfR2IS2ddfMK3RExoOEMRMJP7LZZMXnoTHLNATNWTPZ81o48uPl11Pnd/I87GzyZE+BYGW3OOTnty3DQJhZ/SunRptvf/vbLHj25ptv4vLLL2cjcujQIT6+e/durjI4cuQInnzySf5ApMCZJUVfrurJsnFJpVJ4+eWX8Z//+Z8sonbXXXdNeq9jx45hZGSktJExKvLQQw/hc5/7HEsc7NmzBzt27MDVV18Nt9tdOuezn/0sHn30UTz88MOsDDo8PIwbbrgBSwkZRuphIeOQioVKyWb6vZDhyKYSMDjbONE+EQqdRfxDXMZcLZyk1Bj5HPJyyqEcjWPdbs7oJaP+8ZAVzYNJI5Mig6aB1bYBDt1W6CQ1PNKX+lPaO9+H9Ztu4QFg8/3MVIZstaxnI0OlylQ0QMl/Cs81NF7M77dYkOGiKrVMutLwFqH/BjTdXK0SEjgCwWIjyc/TrJH42T333IOPfOQjk47t37+fF/+uri72bMjbec973sOLvdPp5HN+/OMf44tf/CLGxsagVCrZg7nsssvg9/thNldfBMhjIRnpH/7whyWtHNLXocagL33pSyzMRh4U1XHfdNNNfA55Ups2bcIrr7yC888/f0afjUThqO6b7jfTzteJ0K837OqGr+8geyycP8+T3L4SBmcrbG07qva6JCJedL/+K9YJK44wnkgsOIrGLVfAUr9p0rHR469i5MgLyEsK5SfkvSjVRmiNTq4wI4NDA8oad17FJcoLDYXcgsEe+AMnObejVltgta6HVuucdf6F7hUK9iKVCvE4ARpnrFRN/d/D7TnISX4aaVxeLUb/LWLxMSjkWpy17UNQLVKYTnBmsBDrw0zfo+N/fQEy5fSSLqeD2iO6fvadRX3eBUvykzdC3kE0GuVQ2URoP3kz1AFaFFejxX3btm0l40KQ5/GJT3yCvaCzzjqrtH/nzp1IJpMsrkZCbCS0RpD3Qx7Ul7/85dK5UqkUV1xxBd+foOPpdJr3Fdm4cSOam5unNTD0frQVmag6OhdoMTXWtUNrreNeGGq0JFl+jaWWQ1lTLbZUJUbVZ+TlVDMwlFORSGSc8K+GzlIPrbGWDRRBIbUKAUySklnEQVpULGCxdPI2H4LBXgz0PcvVaUUPkMqda5w7Ud9w0aRSaMJu3QSnoxejY/uQTIWgVOi54TSZikCp0GJdy7uEcRGsLvJLO3BsoZj16nLgwAHOaZBA2sc//nGWEti8+VTI49577+XjtJHH8tRTT7FnQlAepdy4EMXXxRxLXV0dezW//vWveSPjRM0+FAojPB4PG7dq9yneg/6k95zoAZWfM9UUN/q2UNwmqo7OBwqDkaaYtXUbzI0buZlyum/ylKw3OTuQTsa4N6UczmdEA9AY7WxIqqE2OngODIlbkrEqGheScEkkvIhFRiHXGKDULs03mblAuaHurkcRi41CrbZCr68vVLjl8xgafAnDQy9VvY7yL53r3o3OddfBoG9gD4hwOrZh8/qbUGMXmmsCwYr0YGhk5r59+9jN+tWvfoU77riDcxxFI3Pbbbfhyiuv5LzJv/7rv+Lmm2/GSy+9xMn4md6/fCwnjeo8efIkvvvd7+K//uu/sJiQV0S5nXIPZiGNzGyxN+9ELOBCNDAEuVJbGBJG81sSEc7BODsuLJUjT4RyLaRjRooB1O+Sl+QQCveOJ98T/G0mLY9B52qArW7Hiuxud7v2IJUMQqdvKD0fFzlQ/0wScI/ug6NmJ4fCJkKeTZ3zLNTW7GAtNKlExtVtAsFqJH+m9MGQZ9DR0VFK6r/xxhv4/ve/j5/85Ce8r/jtv7Ozk0NRFouFvZxbb70VtbW1k6q9ipVddGwqzj333JJUtN1u5/6NiRVh9Lp4D/qTQmmBQKDCiyk/pxozGbwzHYWy4xwn1hdiwaYqtOYd13C1WNB1nMNlZFCsDZthbdzGZcvTYW3eyurMgeGj8IWOI5ONQiZVQS7TQakzIocs+o8/zeE2Z/O5WElQkj4YOAmF0li9yEFpZH0zys04anZMeR8ySNT/IhCsZvJL3Mm/YhotKcFenreYVFrH5amF45Sr+da3vsXVXsWqMAqhUcKpPMw2EfKYKHRWNHBk2J555hm8//3vLz0DvSZpaYKOKxQK3kflycWqNCqRrpYvmi/UxBga7UFkrK8wHEyt4+owQ00L5z7mA4W3ajsugKN1N1eBUaiLvJmZQMUD9o6zEUt7gNhx9gToeej5qMiASMR8GB14AxbnJihVp/IS1I1P1V+Uu6DwVDUvYTGhUuNcPgvZFIoAZDio5JmeUyBY8+SWVuxyWQwMhZCuueYaTpaHw2Gu0qKqLypJ7u7u5vJhKkumCq7BwUEuaaaemGuvvZavp2NkSP7n//yf+M53vsP5kK9+9avcr1L0HL73ve9xYcCWLVu4l+XnP/85nn32WfzpT38qPQeFsSg0d/bZZ7N3Q9dQUcGHPvQhPk4eFFW10XlU5UYGjCrMyLjMtIJspiQjfowefRnJSIAXbYr/J0JeJIJjiPlG4NxwPif15wsZlvIk/XSQUY/4BuAfPsJlzIHgCUgVClZnnjjQTKUxIxZyIeTrgb1uOyfSR0ff4i2Z9PO95HINV4A1NFwKpbJ6UcFCQ5Ve1CyZTkWr6oZxH4tEAtU01WQCgWB5mZWBIc/j9ttv5/wKLeLUdEnGhXIuVHr8wgsv8GJPJcaUUL/kkku436XorVBoi4bXUNUYLfY6nY4NxT/90z+V3oNCW5///OcxNDTEzZj0Hk8//TSXLhe55ZZbuKyZ+mfISFHF2RNPPFGR+KecDVWXkQdDHhRVq1EBwoJ3jZ/cwzPsVQZrqSJLDh3rkEXG+nk0saV58fo+qj3TWO9bGOt+g0uRaTQAeQPZeBLhdB905jquXivCzyyRlPpGBgf/guHhl7kcWKWy8fFMJgqX6y3E4z50dt44Z6FIKlbwB0/C6z2KeMLP1WB26wbYrBsgk1UaT/K07PatGOh/jo0JPU/5Z4zHPdBo7DCa5janQiBYVYxHg+Z7j1XXB3Mm17nHg24M73+OJ1BW8y6ouZKS7U273j3ruS5zhUQx+/b9odD3ojGyR+L1HkQ2mwG1xNBzGB3rSl4VHScPpmXTtdCa6nD48H8VEukTQmK0yFM1V2vru1Fbe86sn4squbp6Hod77ACHvuQyJbLkheQBi7kd6zveNylXQhVvXSceQTDQM+7RaPg+1A9Dopnr2q+D2VLIB641KOyaDHpZ1of+fqlN9gXxhAWrsw+m7a//AdJ59sHkUkn0/Pc9q6MPRgCkY2Fe8JRThK5IEp/6XqjBcqblwDTDJJdJQiZXz8koBagYIJ2E1lwoZigYCxui0SHISIcrnUQqHmLPikjGA1CqDTBZ12HMe4C9FZ2uYdJ9yYOQSpUYGzswJwMz7HoDLvc+aFSWCg+Icihe/3H09j3DRqYcOq+j8/0Yde2B13OQp3OS4bTZt8Dp3A2Dcfkq/GYCf3fL5UrfPKma73TFH+TlBfuPI+YZYs007syVUMGHDqbmDdBYpi/sEAhWEsLAzAOap8KN+dQpX23hyBUryk7fbpSI+uAdeht+91EuFJAr1LDUboa9YceUzZTViAddXM5cjlbrQCoVRDodYTeZigWyGT1SiSD3XtW3XczvkclQmGxqpWOap0IGiOVWZtGgSUaEjItcppoUXqPQGMm2eP0nWGafRiuXQ2G0xqaLUVd/Hs+SIUO3VHmg+ZDPZpFNpSpCG/z3RS6HVC6f8nccGjiBiKuHFbgV471S1BRLoxr83QchXa/gcKzgDCO3OpP8Qk15HqhNDm6IzJIETBXIc1EZrYWGx2mIh93ofvs3GO19lY0L5R+owXKk6wV07/8tUonwjJ+JvuEXO97LF3GzmURI7axLRgs1GRe11oaW9VfD0VCYLknJfIpZTRU1JfFKubwgeT8b4gkfksnQlArK1GlPs1qisanFSNkQqS2ry7jwFwwJpOS5SKUFY5MujEqoRiYRQ9QzBKlCXRgIV+z9kdLsIAN7ppHRgSX+NIKV1AeTn+e21AgPZp59KqQn5h84wtL8Bal8CS/wFEOnBLuprnPasAgt5kNdzyMR9UBrqi8t3gqljsNvEf8ARvteQ9OGU7I302GwtyEWGJnkVZH3YdA3Q5pTwtlxDoyOdmgNzgqpFbO5A8PDr3COo1oOhsYwOxzb5vCbGp9hNmW679TUzbUAGxAyLjyuodggSqGugjeSz2SQr+LFUPUh9S0pdJNLwulc8mpoQB2N4SZ1bIFgpSMMzDyxtm5nryPi7kMyQbL8hUWDVYtbt0Fnm5zPKCcWpk79QZbQn+gZ0OKvUOrhGdjL314zqSj3wJjs7TA6OnhS40TMdesRGD6CRNgNtd5eNngsjUTYA6O1Cc7W86sWJVAozencxVVkZFDIyNAzkcdDHojJ1AqbbfYyKxq1jcNgiUSgajd9ijXCdDDoCr1Oqxk2IKQeXmZcKigamWy2MF20nPG5QVN+ISFl7GyaE/+CM4v8mdLJL6iEEvGOznNYZyzmd/FCTrpjZFiqyfBPhBLupDas0lbmHgjyQlLJEBIRN7KZOORKPfK5DPyuwzDY2tCy+dpJo5NVWjMat16BocPPIh4eK3kNZGh0lgbUb7iUQ3eUQJardJMWs8bGSzjvQX0wpFlW7IOprd2FhoZL5lSiTIawtmYXunufLBiTsjBXJpNAMhVEXe05izZCeSXBHu4Ux6hajAwQTSGlsNpECv/N1JAK7+XMI08FI6uvTFkYmAVaNCjxOpfkKxko8hIKifPKRSUZ83LoDBIZtIZayBSFJkmSdgmNdWHoxJ/RsuW6SUaCDMm6cz+A8FgPy/7zuGCdFZlYFCOH/8JjnOnbsMZUA0vjJh5SduqzSLlKzOHYPqGTf36Lf51zFxIJHyf7E8kAJ+tz+Qy/n922Ga3Np/qcVjXjoTD+x1zFEymFLqscU5tsUGoNXN6umKC0TV4y6dDp7A3TDlMTCFYSwsAsMzpTA5QaE1LxINS6UwaKDE4iWuiDUGksnN8pQuEtpcaCkOckGyCNfvJETK5C+/+3d+bRUZbXH7/JTGbfJ5M9IYGEBBDCJtvPWqUIUmyt0iO1HEupdWlpz2npQaX1SOs/ehC3o7h0UewfrYo/6SJKfxakgEVZlMVIwpaQkH2ZfSYzk5n3d+4NM84kk2QmySQzyf2c8x4y7/vMuzwkz/e9z12ea+vE4PRaU9VBcHZdpfPgmzItm9xRD25rK2SXLwNNVmTCIvpscN2V0QIHxanFt0CmsQI6Oquh22MlS8mIJf11U6OW3U9FaAVWkQgCPszxifSD9daq6xUecvr3/W66CLRTKqDr0lnwYWUIiZT2oeWCU2oyfRYos5I7NJtJDMJkrUXGjAwUAlPBfGi8ePBaTgoWd0wnK8PncdDAK1dl9nOAoy/GZbOC294aVWDCsTafJ3GRqgwRuTUoNl6nBToun6Sy/33Dm0cbfC6tZgptExkMQ0ZBQF9LyKJBglFlEsmAfhaZxgiZ0+eDs60B3OY2NF0oB0aRmQ9KUz4nW05SBPbBMMPFVLSAIsbaG05SVj2OPVTmJb13agwtnOGCg5y15VJvnbQ+iZs0dabQUD01FCBN9jQYbzBnBsvrY85MqpbXR+tEJJWSFUMiE/SDBXNgovhXwsFK15KSWZRYid+nadQoFg8ziQikZh4MC0wSQH6PkqVgyJ0Fts5acvrj4NpWfxw8bmvU8F1MlkTrR64ePLMbz+X3uge0TnrzZnpzdsYTnDJrajkJbZ1V0NPjIR9NJhbYzFkISsXgFloyi0xoWox2DpzEGg16IWB3C5PCsMAkETg9hpn74X6Yhur/A5/HGREthtaNt9tCgiRT4vTZwGDSJomIP3pyX29SpTDiZQVGKi5VNe+A1X4VJBIliStaMlebj4HZWgszy+4AtSo1Q5gHcugzzGSYImO7O4kx5M0GU+ECqsHltDaB29EOLlszOfY1xqmQX3bzkG/E+BasNk2BHq87aoZ+jwdza2SgMERfenksqG/8mMRFrcoDucxAodAYsqxR54PL3QGX6w+MvJIsw6QwAmfyM6MNRl7lT78ZtKZSsLTWgMdlptwabWYpaDKnxbw+jDavHBydDdBt6wCJUkvfQ+uop9tJEWaGotlUeXk88Hjt0N5ZQ+u69A2/xalDudxI4mN3NJHgMAyTOrDAJDk4yKoNU2gbLlKlDnJn3Ajtl45TNr8XM83TMBJNCcbiSjBOGXjJ4USDFQL8/m7KtYmGWCQDt78Lur020AALzGhDa+vYWsHbbaNpUqUuP+YXF2YMCYxCouVIvz8MWGAmCZhUWThvNbgtreDrtkNauphK+odXG6BS8bYrYLXV0bIBOE1l1JdHzbDHyst2awMVwMQilipN4bASAMlHlCaiKLpopW8w0RNFVhS24BgzOrhsrdB84TA4Lb1LA2A/SxQ6ME1ZAMaCyrgCEpjEIqSoD4YFZhKBA4hCj87y/g5zr88JFy/tBbPlEi0ERnXRBAGuNh6FKUU3QU7W3NAbb1vLSWhpOg6ebktvgABWGlDmQEHxTaDRxmdpKeWZoFLlgNXWABnXKhWEg/XL5DIdaJN87ZdUo9vRAVfOvAceZxdIFHoQKYyUfIsJv43VB+hnFBqGGQns5GdINC7X/gs6uqqpJIxGXQhqVT6oVHkkNrV1H4LZcpnatjWfhPra/RR4oFBkURupTAcORxNcPv8PcNgb47o2Cllh7hIqx+90tZElg+Cqly53F5WTKchdTHkxzOjR0XCKREauyaGVQtFawYAQmcpIIeLtdScoepFJDoQUdfKzwDDgcDaT5YJVj8OTG8nikWdCj98DLW2fg8/rgpamY73VBeTG0JQYlpVRKnPIn9LadCLu62O+y/SSW0GaoQansw1s9kZwOFpoWmxq0XLIy+5dr4YZHXp83WBtvQgZUnXUtX0kch14PXawd14Zl/tjBkm0HOk2xvAUGQN2eyNVNZbJ+ld0RtDHYrM1gNl8ATweK1kuUQt+SjVgtdRSBWiJNL6otGzTbKpL1mW5TNN1YrEUDLppVMafGV1wSW7yeQ1QKaF3cTRM0u0e83tjJhYsMAw50gfLMk+DdAiA79qAIwzozMepFUyQxG04oPWUlTlzWN9lYgercmOkGP5/iiX9/V4oPvirgIveMcmBIIyCk38cUsl4iowBucLUW2BzAGHw+hw0JSaTG6+180VthwMW+lKwSjKTvKC46HIraAG7aIuXeV1mqtatziwel/tj+iMEhFHZxhoWGAZ0mimgVuaBy91GCZjh4GqW+OqTbaoEra4Y5Iqs0EJk4WCIMwqR3jiDnMZMcpNZOJeW6MbiqpgDg+WHenxucGOx1fR0yCldCiIxB1YkDYHU9MGwwExgUAQwXwX9K4OVWkGn/bSSVaCQmygazOVqh+5uM2XPe7w2yMmeB1mZs6ldwZSv0wqXTmcz+LxOyoNBvwx+VqnzISf/+jF9Rmb4de+KK79NQoPTnmi1+L0uUOoLoOi6b4I+l6cqmZHDPpgJCIqJues8dLSdBqejmfYpVblgyqoEnWF6VF+LSpULs2bcDe0dVdDReY6EQ6spAlPmdWDQl4X8Ljr9NCgtv4PyYOy2evD1uMhZnJO3GHLyFoFUOvjSAj6fm0SLItFkBk7mG0ekCi0UzloJ2d1LKfkWk14xTDlaZBkz+RItDx06BE899RScPHkSmpubYc+ePfCd73wnrnOwwExAcWm++l9oavyYnLUYAYZYzBfBZq2DvIIbIDd/adSBXSbVQmH+MtoGQ60tosx9j8dCUyvocxnKIez1OqCh+VNo66ii9V7S09JBo8qH/NxFYNSXjvCpmZEgkalpY5IXYRwExul0QmVlJfzoRz+CO++8c1jXZIGZYDgdTdDc9AnlpoSHE6PQYOZ9c+NR0GiLQaWOr3pyaC35a+DPMpk+pu9i2HHV+Xep9L5EoqJwZhQ/DEm2ORqhbOpqyM68Lq77YRgmsaxevZq2kcACM8Ho6jhHfheVqn9hSIlUCw5HI3R1notJYNDh39VVAx0dZ8HlaqMIMYOhAjIzrxuwOGU0mlo/B4u1lkrChGqKiaSU44LZ+7X1BynnJUPMwQEMk+gVLW02W8RuqVRKWyLgydYJBjroUQiiTYHhPlG6BNyu9iHPg1FhdXX/B5cu/R0slgsQCPjImX/16n+guvpNsNuvxnQ/aKm0tp+hyLJoBSuxegDWG+uyXIrxCRlm8iGMYqmYwsJC0Gq1oe2JJ55I2H2zBTPBQHGJltsQRBCwavHQ5dg7O7+EtrbPaWotPK8FrRqXqwXq6vbBrFkbKLlyMNDfgptYFN06weABAaOYvPYh74lhmJHT0NAAGs1XlTYSZb0gLDATDJ2+FMyd1WQ5YKRWOLgPfSnaIZzq2Kaj43RvNnefpEmMMJLJTDRlZrFcBoOhfNBzYZFKtFywaGb0a/W+VqHPiGGY6IxGomTw+ygu4QKTSHiKbIKhN06nfBSnswX8PZ7QfvzZ6WihY3pD2aDnwAXA3O4OEIujR4bhui0oQt3dHUPeD1pLmYZyslD6JnEi3Vi3LENJPhiGYSZxouXLL78Mc+bMCSng0qVL4YMPPggdf+CBB2DatGkgl8vBZDLB7bffDtXV1RHnqK+vhzVr1oBCoYCsrCzYsmUL9PT0lmgPcvDgQZg/fz6ZbqWlpbBr165+97Jz504oLi4GmUwGixcvhmPHjkUc7+7uhk2bNoHRaASVSgVr166F1tZWmOhgEuTUsm+DVldCYcRYPh83/FmrL6Fj2GYwcAEwrEAWTRCQ3qRNjCqLbYExrIaMVZkxcROTPoM+Hre7iwIS8nIWUIg0wzDJg8PhgFOnTtGG1NbW0s84hidEYAoKCuDJJ5+kxJsTJ07A8uXLSUSqqqro+IIFC+D111+Hc+fOwb/+9S8aiFauXAl+XKKX3oz9JC5erxf++9//whtvvEHi8dhjj4WugQ+BbW6++WZ6mF/84hfw4x//mM4X5K233oLNmzfDtm3b4LPPPqNY7VWrVkFbW1uozS9/+Uv45z//Cbt374b//Oc/0NTUNOxY7lRDJjdA+cy7oWzGXVBY/A3aps9YR/vwWCxWh1ZbAj6fI2oFAL/fTW1UqoKY7kepMMHM6XeAXltCSZZYjh8tLDxHSeFNMCX/hmE9J8NMFgR00vtHuMVpweAYP2/ePNoQHHPx5/DxeijShMFqiMSAwWCgbM97772337EzZ87Q4H/x4kWybNDaue2222iwz87OpjavvPIKPPzww9De3g4SiYR+3rt3L3zxxReh83zve98Di8UC+/bto89osVx//fXw4osv0udAIECRET//+c/hkUceAavVShbUX/7yF/jud79LbdCSmjFjBhw9ehSWLFkS07NhOB9GWeD5xmrOMlmw2erhwoV3wO/voUKXwexutEBwasxgmAFlZWvjysRHiwjFxe2xkF8GV6nkcvxMqjIW44Pt2jX0lb+E9BH6KQN+D5hPPzum49mwfTBojbz55puU7YlTZX3B/WjNlJSU0OCP4OA+e/bskLggaHlgJwatIGyzYsWKiHNhG9yPoPWDFlR4m/T0dPocbIPHfT5fRJuKigooKioKtYmGx+OhewnfJisaTREUF6+iZYyxzhjmzzgcV8HrtYJeXw4lJavjLvOCIoWikmOaDSZjBYsLw0xwH0zcUWRnz54lQUEfB/o2sD7NzJlfFcZ76aWX4KGHHiKBKS8vhw8//JAsE6SlpSVCXJDgZzw2WBsc7N1uN5jNZhK3aG2C/h48B15Tp9P1axO8TjQwHvx3v/tdvF0yYTEaZ4FaXQRmcw3lqmBUmkYzhbZE1KtyOJqho+sc2B0tdC10/BtZiBgmZYlbYFA00DeCZtY777wDGzZsIB9HUGTWr18Pt9xyCxVH27FjB9x1113w8ccfkzM+2dm6dSvNMwZBUQtaX5MVzIPJzl6Y8Os0t5yEKw0Hwetz9ebyXKsi0Nx6EqaXfhtUypyE3wPDJC0BAYS0Ea7nMg7rwcQtMGgZYGRX0Kl//PhxeP755+HVV1+lfcHs0LKyMvJ16PV6snLuvvtuyMnJ6RftFYzswmPBf/tGe+FnnDPE6DSRSERbtDbh58CpNPTbhFsx4W2ikciSCczAWKx1UFf/Ea2qqVblh6becKVNh7MZLlx6D+bM+kFMCaIMMxERcHprhIXHR1oscziMeJ4DHezou4gGxg/gFjyOU2s4xRYe7YVTaCgeQQsI2+zfvz/iPNgm6OdBgUNhC2+D94Cfg23weEZGRkSbmpoaCq+L5i9ixpe29rPQ43f3K9+PYdBKRTY4Xa1g5lIyDJNyiOOdQsLqmugst9vtFKWFOSsYQnz58mUKH8awZIzgunr1KoU0o9XxzW9+k76Px1BI7rnnHti+fTv5Qx599FHKVwlaDg8++CBFh6EfB8tEHzhwAN5++22KLAuC01g4Nbdw4UJYtGgRPPfcc+Tz2bhxIx1HCwqj2rAdRrmhgGGEGYpLrBFkkwEUf5ejBVz2ZlqvW64ygUqDFkT6mN6DxVYHGQMkdWIpGiEQAIezBTKNM8bsvhgmqfAnyTkSKTBoefzgBz8g/woO4ph0ieKCPhcMPT58+DAN9uiIR4f6jTfeSPkumFCJ4NTWe++9Bz/5yU9osFcqlSQUjz/+eOgaGHWGYoJ5LDj1hrk3f/zjHymSLMi6desorBnjsVGk5s6dSyHM4Y7/Z599lqLLMMESLSj8PgYgML14PXZouPgh2Mx14Pd7KXESB3OVthCKSm8BmSL2askjhZccY5iJOUU24jyYicxEzYPBRcIuVv0vWLsuU+KlSCynqSnKcXF1gEpbAGXXfRfEGYohz4WFNZ1dTWDvqIMejwsyZCpQm4pBocultd1j4fylf0Jr22nyv/QFa5hh9eeK6XeyBcNM2jwYbekvIG2EeTCC3wPWi8+N6XjGxS4nISgsdvMVkCuzIhznYrEMFKpccFobwdx+Hkx5uF77wPh7vNBSfQTs7bU0jZWGlZEDfrA01YA2pxSypy+DdNHQv2LZpjnQ2VUDbncnrTMT9MNgORmXsxXUqjzQc60yZhIjpKgFwwIzSQUGw4CjRWVh+fy0dDFYOocWmM66z8HacpHWdhdlfPV21ePtBktTNWTI1ZBZ3FtmYjC0milQUvQNqGv4iBI6cW14FCwBAhSeXDbtNo4gYyY3gSQ5R5ywwExCsFoyishAoMiEV2KOBk6HobiIJfIIcUHEEhkEejxgbToP+oJZIBIPLQ452fPIUunoPAd2rFOWLiarxWiY3m/JAIZhUgMWmCQg4O8Bj6UDfC475YJkKDQg0xlpyikRoN9FCPgogitauRe/3wMyhXHQc3TbO0lkZKro7cRSJXjdNvA4ukChiy1JUqnMpo1hmGhruYzOejBjCQvMOON1WMFS9yX0uBy0siOCg36GUgu64pmQoVCN+jX1meXQ3vQ5eD1WkMoiy+n4vA6KJjOYYnSoDzov/NUvNOVDObvA67JCmkgMCm02iMSc1MowY+U/YR/MJMPv7QZzbRX0uJ2QoVCHoq4Evx+8DgtYaqvAWD4f0sWDL0scL3JVNmQXLobmK0fA5WiGDImalALFBSdqs/IXglo/ZdBzSFUGmh4LRo71BfeLJUqQKvXgcVmg9cJRsHfVg9/XTXk2ErkWDIWzIbOwMuZoM4aZtASS5BxxwgIzjri7WqHH7SBrJSKDXSSiaTKv0wbd5nZQmPJG9bp4rZzCJSCV66Gj+TQlWyJKTS5k5lSCMXvWkMmWGTIlqLOnQlf9F5AulkT4Wfw+D/T4usFUOJNybOpPfwBuWyuJCm5YAsbntkPL+SMQ6PFC9rTFo/p8DMMkByww4wj6XdDPEs0PEnyr99i7Rl1g6PxpaWAwVdB0Wa/lApAhUcaVxW8qWQA93Q5wdNSDVxAoJBn9SVSWP7cUjFMqob3uMxIXuSYr5FNKSxOTZYM+ms6G06DLLQepInKqjmGYr8AFw0boguEpsskGLUk8yJoqOFDjdFkiQaGRSHGKLH4weixv1nJwdNaDva030VIsU4ImqwSUxkJIgzSwtpwHUYYsasBChkwNbmsrODqugLSIBYZhBoJ9MEzc4DSYx2aOGs1FhUIDfhArhjf4jxVotWiyptLWFwx1xg3zWqJBz5wGNJ3GMMzEg72r44jcmA3pGRng97j7iYu/2wUiiRQUhpGF7Y5nJSAUFlGGnDL+B7TgBMybGbokDcNMagLC6GxjDFsw4wg699V5U8HeeInCldMzJDjqkuMbHeeagjIQy+NPMkQnu6OtHhxtVyhSTSSVgyprCqhMRSDCa4wROMWnz6uA5pojEAj00CqV4aAPhoIFTMVjdk8Mk4oIgVHwwYzDuyYLzDiCU0TK7CLIkKvA1dlCDn30W8gNWaAw5oFEHb9fosfjhrbqo9Btbad8E5zC8jot0HGxA1wdVyGrYgmIJLK4rSCvw0zChcInVetjDgbQ584AW3stOLquQoZURaHNwSgyxDR1EUhkyT0NyDDM8GCBSQKRkWqNtAWns6JFlcWK+coX4La2g1Sli3CsY3SXy9wC5vovIbN0fsznc1vawFJ/jjLyMeAAzylR6UBXWAEKQ+6Q3xdLFVA0+1Zoqz0BtraL4HGZSZywAoCxcA7o8rhCMsOMSbFLtmAmNyMRFsTndoCzs4kG9b5RW2jJ4H5nx1XQFc4AsVQ+5PnQCmqvOUbFK9HKoiKUWNbG3gnt54+Dafr1MYkMJmLmz7gJskoW0rQYhmDL1CaqecYwTAyMgsCMdIptOLCTfwKB005YZBKDA6KB+9HhHpyeGnKlyYZq6PG6QarSUyIlCiBWFZAo9eQnwuPkqI8RFBqlPg8U2hwWF4aZBLAFM4HoTc5M67WFo7ztUDj0Nef7UPhcNrJUUBT6WlZUK02mpnI2HrsZZJrBC2MyDDMKiZY8RcaMJ1gfDKOyerpdVNusL7RfriYfylCgpRPw+0Esjf4rggEEmKcT8A1e1p9hmMnrg+EpsgkETl+pc6aSQx+z6oNBA/gvigsKgjp3WkyrTGKWfrD0SzQEv49qpqX3WQuGYZgEEOA8GCYJ0OZPp+x5e3MteO1dvX49odf/gs59TW5sSw+jpSPVZIKrs4lCk8OnyVCwMKBAps0EqdqQuIdhGCalYYGZgH4YQ/EcUGcVg6urmXJXqCKAPhckSm3s50lLo1Bkr9NK+TnBKLKA3we+bieIrwnWSCPfGIaZuFNkLDATECpgqdTGJSjRQOd9VsVisDScA4+tdwXLdJEI5Pos0BVUgFyXNWr3zDDMwLCTn5mQoMhkz/wfiioLZvKjcLHlwjDMULDAMDFbRAzDjBOCF0AY6Utd9KKziYQFhmEYJskRACuuj2xtKGEcBIbDlBmGYZiEwBYMwzBMkiOAC+trjPAcI/v+cGCBYRiGSXqcmJ02wnOMvcDwFBnDMAyTENiCYRiGSYkpMvEIzxG97FMiYQuGYRgmBQRGGIUtXnbu3AnFxcUgk8lg8eLFcOzYsbi+zwLDMAyT9NgBwDbCbeh1oMJ56623YPPmzbBt2zb47LPPoLKyElatWgVtbW0xn4MFhmEYhunHM888A/fddx9s3LgRZs6cCa+88gooFAp47bXXIFbYBzMIwXL3NhuqP8MwzFcEx4XgOJFYqkdtxeO+45lUKqUtHK/XCydPnoStW7eG9qWnp8OKFSvg6NGjMV+LBWYQ7PZek7KwsHC8b4VhmCQeJ7TaxJRSkkgkkJOTAy0tLaNyPpVK1W88wymw3/72txH7Ojo6wO/3Q3Z2dsR+/FxdXR3z9VhgBiEvLw8aGhpArVanVHFHfEPBXyK8d41GM963M6Hgvk0MqdivaLmguOA4kShkMhnU1taSRTEa0LLpfcayvtbLaMICMwhoEhYUFECqgn+oqfLHmmpw3yaGVOvXRFkufUUGt7EkMzMTRCIRtLa2RuzHz2hRxQo7+RmGYZh+U3MLFiyA/fv3h/YFAgH6vHTpUogVtmAYhmGYfmCI8oYNG2DhwoWwaNEieO6558DpdFJUWaywwExAcE4VHXeJnFudrHDfJgbu1+Rj3bp10N7eDo899hgFGcydOxf27dvXz/E/GGnC2MTYMQzDMJMM9sEwDMMwCYEFhmEYhkkILDAMwzBMQmCBYRiGYRICC8w4cejQIfjWt75FWcCYWfu3v/0t4jjui7Y99dRToTZY4fSWW24BnU4HRqMR7r//fnA4HBHnqa+vhzVr1lCRuqysLNiyZQv09ESuC3Hw4EGYP38+RfCUlpbCrl27Rr1sd7L0K/bPz372M0qglcvloSJ+4XR3d8OmTZuoT7G0xtq1a/slnHG/xt+vv//97+Gmm26iREo8h8Vi6Xedrq4uWL9+PbXB3+t777233+/0mTNn4Gtf+xr1GWb/b9++vd95du/eDRUVFdRm9uzZ8P77749aXzBxgFFkzNjz/vvvC7/5zW+Ed999F6P4hD179kQcb25ujthee+01IS0tTbh06RIdb2xsFPR6vfDggw8K1dXVwrFjx4Rly5YJa9euDZ2jp6dHuO6664QVK1YIn3/+OV0zMzNT2Lp1a6jN5cuXBYVCIWzevFn48ssvhRdeeEEQiUTCvn37Qm3efPNNQSKR0D1UVVUJ9913n6DT6YTW1lYh1foV733atGnCRx99JNTW1gqvvvoqPe/f//73UBvs08LCQmH//v3CiRMnhCVLllDfBuF+HV6/Pvvss8ITTzxBG57DbDb3u86tt94qVFZWCp988olw+PBhobS0VLj77rtDx61Wq5CdnS2sX79e+OKLL4S//vWvglwup+sF+fjjj+na27dvp75/9NFHhYyMDOHs2bMJ6x8mOiwwSUC0P9i+3H777cLy5ctDn/EPKisrS/D7/aF9Z86coXNduHAhNCikp6cLLS0toTYvv/yyoNFoBI/HQ58feughYdasWRHXWrdunbBq1arQ50WLFgmbNm0KfcZr5uXl0UCRav2Kz/r4449H7Js/fz4NnojFYqHBaPfu3aHj586do3MdPXqUPnO/xt+v4aAIRRMYFAPcf/z48dC+Dz74gF6s8IUKeemll+jFKtjPyMMPPyyUl5eHPt91113CmjVrIs69ePFi4YEHHhj2MzPDg6fIUgCcntm7dy9NFwTxeDxUzgHrpQXBqQnkyJEj9C+W1cbpgfDEKFwwCAsLVlVVhdpgCe5wsE2wJHewbHd4m+GU7U4Wli1bBv/4xz+gsbGRCv999NFHcP78eVi5ciUdx2f1+XwRz4tTLUVFRaHn5X6Nv19jAZ8bp8UwczwI9gf2y6effhpqc+ONN9Lvfni/1tTUgNlsjqnvmbGDBSYFeOONN6ii85133hnat3z5csquRZ8MDlb4x/XII4/QsebmZvoXj0crtx08NlgbHCzdbvegZbtHq4T4WPLCCy+QfwB9BThI3XrrreQHwUELwWfC/TjQDfS83K/x92ss4HOjPyscsVgMBoNhVPo+Ffs11WGBSQFwBTl0fIZXVJ01axYJz9NPP02OZqxwWlJSQn9I4VYN038g/OSTT+htGy0I7D906P/73/8e71tLabhfmWhwLbIk5/Dhw2T+4/rYffn+979PG06hKZVKiszBZU6nTp1Kx1F0+kYlBaOhgiW38d9oJbkxigen3LBk92iU7U4G0HL49a9/DXv27KEIMGTOnDlw6tQp2LFjB02r4DOhRYgRTuFWTPjzcr/G36+xgM/dd713jMzDyLKh+jV4bLA2qdavEwF+1U1y/vSnP1HZ7MrKygHboNWC4bQoQmjlYOgygmW1z549G/FH++GHH9Igh9MZwTbhJbmDbYIluUerbHcygL4V3PpaeDjQ4zMh+KwZGRkRz4sCj2HJweflfo2/X2MBnxuFHS2gIAcOHKBzYAh3sA2GTOP1wvu1vLwc9Hp9TH3PjCHDDA5gRojdbqcQV9zwv+GZZ56hn69cuRIRkomhrhihFA0MfT158qRQU1MjvPjiixSu+fzzz/cLp125cqVw6tQpCpE1mUxRw2m3bNlC0VI7d+6MGk4rlUqFXbt2UaTP/fffT+G04VFUqdKvX//61yniCSOZ8Nlff/11QSaTUXRSeJhyUVGRcODAAQpTXrp0KW1BuF+H168Ybo/f+cMf/kDnOHToEH3u7OyMCFOeN2+e8OmnnwpHjhwRysrKIsKUMcoPw5TvueceClPGPsR+7humLBaLhR07dlDfb9u2jcOUxwkWmHEiGKrZd9uwYUOoDf7RoGjgH1U08I/MYDBQLsWcOXOEP//5z/3a1NXVCatXr6bzYK7Gr371K8Hn8/W7l7lz59J5pk6dSoNDNDHDQRfbYHgt5imkYr/iIPfDH/6QwoFxAMTw1qeffloIBAKhc7jdbuGnP/0phcPi4HXHHXfQ98Lhfo2/X3Ggj3aO8H5BsUFBUalUFPa9ceNGErdwTp8+Ldxwww0kzvn5+cKTTz7Z737ffvttYfr06dSvKHx79+5NaP8w0eFy/QzDMExCYB8MwzAMkxBYYBiGYZiEwALDMAzDJAQWGIZhGCYhsMAwDMMwCYEFhmEYhkkILDAMwzBMQmCBYRiGYRICCwzDMAyTEFhgGIZhmITAAsMwDMMkBBYYhmEYBhLB/wP0Fkp55AFjfQAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:12:00.210422Z",
+     "start_time": "2026-01-18T14:12:00.207881Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "step_size = 100  # meters\n",
+    "max_range = 2000  # meters"
+   ],
+   "id": "8e50ef8493c57fbb",
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:15:44.564436Z",
+     "start_time": "2026-01-18T14:14:20.680530Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from tqdm import tqdm\n",
+    "from time import perf_counter\n",
+    "\n",
+    "\n",
+    "neighbors_number = [4, 8, 16, 32, 64]\n",
+    "number_of_trials = 100\n",
+    "\n",
+    "outputs = []\n",
+    "\n",
+    "for _ in range(number_of_trials):\n",
+    "    for nn in tqdm(neighbors_number):\n",
+    "        # Divide dataset into train and test set\n",
+    "        train = gdf.sample(n=int(0.1*len(gdf)))\n",
+    "        test = gdf.loc[~gdf.index.isin(train.index)]\n",
+    "\n",
+    "        # Take sample from test, we don't need a whole dataset\n",
+    "        test_sample = test.sample(n=100)\n",
+    "\n",
+    "        # Fit semivariogram model\n",
+    "        exp_var = build_experimental_variogram(\n",
+    "            values=train['z'],\n",
+    "            geometries=train['geometry'],\n",
+    "            step_size=step_size,\n",
+    "            max_range=max_range\n",
+    "        )\n",
+    "        theo_var = build_theoretical_variogram(\n",
+    "            experimental_variogram=exp_var,\n",
+    "            models_group='linear'\n",
+    "        )\n",
+    "\n",
+    "        # Predict new values\n",
+    "        ty0 = perf_counter()\n",
+    "        yhats = ordinary_kriging(\n",
+    "            theoretical_model=theo_var,\n",
+    "            known_values=train['z'],\n",
+    "            known_geometries=train['geometry'],\n",
+    "            unknown_locations=test_sample['geometry'],\n",
+    "            no_neighbors=nn,\n",
+    "            use_all_neighbors_in_range=False,\n",
+    "            progress_bar=False\n",
+    "        )\n",
+    "        ty = perf_counter() - ty0\n",
+    "\n",
+    "        yhats = yhats[:, 0]\n",
+    "        avg_rmse = np.sqrt(np.mean((test_sample['z'] - yhats)**2))\n",
+    "\n",
+    "        # Predict using lsa\n",
+    "        ty_lsa0 = perf_counter()\n",
+    "        yhats_lsa = experimental_ordinary_kriging_lsa(\n",
+    "            theoretical_model=theo_var,\n",
+    "            known_values=train['z'],\n",
+    "            known_geometries=train['geometry'],\n",
+    "            unknown_locations=test_sample['geometry'],\n",
+    "            no_neighbors=nn,\n",
+    "            use_all_neighbors_in_range=False,\n",
+    "            progress_bar=False\n",
+    "        )\n",
+    "        ty_lsa = perf_counter() - ty_lsa0\n",
+    "\n",
+    "        yhats_lsa = yhats_lsa[:, 0]\n",
+    "\n",
+    "        avg_rmse_lsa = np.sqrt(np.mean((test_sample['z'] - yhats_lsa)**2))\n",
+    "\n",
+    "        outputs.append([avg_rmse, ty, avg_rmse_lsa, ty_lsa, nn])"
+   ],
+   "id": "f9fd0c82b22907f9",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 5/5 [00:00<00:00,  5.53it/s]\n",
+      "100%|██████████| 5/5 [00:00<00:00,  6.01it/s]\n",
+      "100%|██████████| 5/5 [00:00<00:00,  6.12it/s]\n",
+      "100%|██████████| 5/5 [00:00<00:00,  6.05it/s]\n",
+      "100%|██████████| 5/5 [00:00<00:00,  5.68it/s]\n",
+      "100%|██████████| 5/5 [00:00<00:00,  5.86it/s]\n",
+      "100%|██████████| 5/5 [00:00<00:00,  6.04it/s]\n",
+      "100%|██████████| 5/5 [00:00<00:00,  6.00it/s]\n",
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+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:16:08.699201Z",
+     "start_time": "2026-01-18T14:16:08.694971Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf = pd.DataFrame(data=outputs, columns=['rmse', 'time-linalgsolve', 'rmse_lsa', 'time-lsa', 'nn'])",
+   "id": "69cd879e82df6044",
+   "outputs": [],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:16:12.656128Z",
+     "start_time": "2026-01-18T14:16:12.636933Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf.groupby(['nn']).mean()",
+   "id": "888b0744308b170d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "        rmse  time-linalgsolve  rmse_lsa  time-lsa\n",
+       "nn                                                \n",
+       "4   0.686030          0.016851  0.686030  0.022247\n",
+       "8   0.670949          0.017058  0.670949  0.023311\n",
+       "16  0.658241          0.017494  0.658241  0.025521\n",
+       "32  0.667449          0.017480  0.667449  0.025632\n",
+       "64  0.656718          0.017562  0.656718  0.025543"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rmse</th>\n",
+       "      <th>time-linalgsolve</th>\n",
+       "      <th>rmse_lsa</th>\n",
+       "      <th>time-lsa</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nn</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.686030</td>\n",
+       "      <td>0.016851</td>\n",
+       "      <td>0.686030</td>\n",
+       "      <td>0.022247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.670949</td>\n",
+       "      <td>0.017058</td>\n",
+       "      <td>0.670949</td>\n",
+       "      <td>0.023311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.658241</td>\n",
+       "      <td>0.017494</td>\n",
+       "      <td>0.658241</td>\n",
+       "      <td>0.025521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>0.667449</td>\n",
+       "      <td>0.017480</td>\n",
+       "      <td>0.667449</td>\n",
+       "      <td>0.025632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0.656718</td>\n",
+       "      <td>0.017562</td>\n",
+       "      <td>0.656718</td>\n",
+       "      <td>0.025543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:16:23.711503Z",
+     "start_time": "2026-01-18T14:16:23.707417Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf['dataset'] = 'meuse'",
+   "id": "dbbfd687e2957e80",
+   "outputs": [],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-18T14:16:26.884575Z",
+     "start_time": "2026-01-18T14:16:26.873417Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "diffdf.to_csv('lsa-comp-meuse.csv')",
+   "id": "536b2c35872e245a",
+   "outputs": [],
+   "execution_count": 15
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "ed48a99ac74bce18"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/analyses/differences-between-non-lsa-and-lsa-solving/meuse.csv b/analyses/differences-between-non-lsa-and-lsa-solving/meuse.csv
new file mode 100644
index 00000000..931ed6c0
--- /dev/null
+++ b/analyses/differences-between-non-lsa-and-lsa-solving/meuse.csv
@@ -0,0 +1,156 @@
+"","x","y","cadmium","copper","lead","zinc","elev","dist","om","ffreq","soil","lime","landuse","dist.m"
+"1",181072,333611,11.7,85,299,1022,7.909,0.00135803,13.6,"1","1","1","Ah",50
+"2",181025,333558,8.6,81,277,1141,6.983,0.0122243,14,"1","1","1","Ah",30
+"3",181165,333537,6.5,68,199,640,7.8,0.103029,13,"1","1","1","Ah",150
+"4",181298,333484,2.6,81,116,257,7.655,0.190094,8,"1","2","0","Ga",270
+"5",181307,333330,2.8,48,117,269,7.48,0.27709,8.7,"1","2","0","Ah",380
+"6",181390,333260,3,61,137,281,7.791,0.364067,7.8,"1","2","0","Ga",470
+"7",181165,333370,3.2,31,132,346,8.217,0.190094,9.2,"1","2","0","Ah",240
+"8",181027,333363,2.8,29,150,406,8.49,0.0921516,9.5,"1","1","0","Ab",120
+"9",181060,333231,2.4,37,133,347,8.668,0.184614,10.6,"1","1","0","Ab",240
+"10",181232,333168,1.6,24,80,183,9.049,0.309702,6.3,"1","2","0","W",420
+"11",181191,333115,1.4,25,86,189,9.015,0.315116,6.4,"1","2","0","Fh",400
+"12",181032,333031,1.8,25,97,251,9.073,0.228123,9,"1","1","0","Ag",300
+"13",180874,333339,11.2,93,285,1096,7.32,0,15.4,"1","1","1","W",20
+"14",180969,333252,2.5,31,183,504,8.815,0.113932,8.4,"1","1","0","Ah",130
+"15",181011,333161,2,27,130,326,8.937,0.168336,9.1,"1","1","0","Ah",220
+"16",180830,333246,9.5,86,240,1032,7.702,0,16.2,"1","1","1","W",10
+"17",180763,333104,7,74,133,606,7.16,0.0122243,16,"1","1","1","W",10
+"18",180694,332972,7.1,69,148,711,7.1,0.0122243,16,"1","1","1","W",10
+"19",180625,332847,8.7,69,207,735,7.02,0,13.7,"1","1","1","W",10
+"20",180555,332707,12.9,95,284,1052,6.86,0,14.8,"1","1","1",NA,10
+"21",180642,332708,5.5,53,194,673,8.908,0.0703468,10.2,"1","1","1","Am",80
+"22",180704,332717,2.8,35,123,402,8.99,0.0975136,7.2,"1","1","1","Am",140
+"23",180704,332664,2.9,35,110,343,8.83,0.113932,7.2,"1","1","1","Ag",160
+"24",181153,332925,1.7,24,85,218,9.02,0.342321,7,"1","2","0","Ah",440
+"25",181147,332823,1.4,26,75,200,8.976,0.385804,6.9,"1","2","0","W",490
+"26",181167,332778,1.5,22,76,194,8.973,0.429289,6.3,"1","2","0","W",530
+"27",181008,332777,1.3,27,73,207,8.507,0.315116,5.6,"1","2","0","Ab",400
+"28",180973,332687,1.3,24,67,180,8.743,0.320574,4.4,"1","2","0","Ag",400
+"29",180916,332753,1.8,22,87,240,8.973,0.249863,5.3,"1","2","0","Ah",330
+"30",181352,332946,1.5,21,65,180,9.043,0.489064,4.8,"1","2","0","Ag",630
+"31",181133,332570,1.3,29,78,208,8.688,0.472778,2.6,"1","2","0","B",570
+"32",180878,332489,1.3,21,64,198,8.727,0.287957,1,"1","2","0","Ag",390
+"33",180829,332450,2.1,27,77,250,8.328,0.271622,2.4,"1","2","0","Ah",360
+"34",180954,332399,1.2,26,80,192,7.971,0.385807,1.9,"1","2","0","B",500
+"35",180956,332318,1.6,27,82,213,7.809,0.418417,3.1,"1","2","0","B",550
+"37",180710,332330,3,32,97,321,6.986,0.244474,1.6,"1","2","0","Ab",340
+"38",180632,332445,5.8,50,166,569,7.756,0.135709,3.5,"1","2","0","Ab",210
+"39",180530,332538,7.9,67,217,833,7.784,0.0484965,8.1,"1","1","1","Am",60
+"40",180478,332578,8.1,77,219,906,7,0,7.9,"1","1","1","W",10
+"41",180383,332476,14.1,108,405,1454,6.92,0.00135803,9.5,"1","1","1","W",20
+"42",180494,332330,2.4,32,102,298,7.516,0.135709,1.4,"1","2","0","Am",170
+"43",180561,332193,1.2,21,48,167,8.18,0.26622,NA,"1","2","0","Ga",320
+"44",180451,332175,1.7,22,65,176,8.694,0.211843,NA,"1","2","0","W",260
+"45",180410,332031,1.3,21,62,258,9.28,0.320572,2,"1","2","0","Ah",360
+"46",180355,332299,4.2,51,281,746,7.94,0.081222,5.1,"1","2","0","Ah",100
+"47",180292,332157,4.3,50,294,746,6.36,0.190086,5.3,"1","2","0","Am",200
+"48",180283,332014,3.1,38,211,464,7.78,0.287941,4.5,"1","2","0","Ah",320
+"49",180282,331861,1.7,26,135,365,8.18,0.423826,4.9,"1","2","0","Ah",480
+"50",180270,331707,1.7,24,112,282,9.42,0.554289,4.5,"1","2","0","Bw",660
+"51",180199,331591,2.1,32,162,375,8.867,0.603225,5.5,"1","2","0","Bw",690
+"52",180135,331552,1.7,24,94,222,8.292,0.614071,3.4,"1","2","0","Ab",710
+"53",180237,332351,8.2,47,191,812,8.06,0.00135803,11.1,"1","1","1","Ah",10
+"54",180103,332297,17,128,405,1548,7.98,0,12.3,"1","1","1","W",10
+"55",179973,332255,12,117,654,1839,7.9,0.0054321,16.5,"1","1","1","W",10
+"56",179826,332217,9.4,104,482,1528,7.74,0.0054321,13.9,"1","1","1","W",10
+"57",179687,332161,8.2,76,276,933,7.552,0.0054321,8.1,"1","1","1","W",20
+"58",179792,332035,2.6,36,180,432,7.76,0.146578,3.1,"1","1","0","Fw",200
+"59",179902,332113,3.5,34,207,550,6.74,0.135684,5.8,"1","1","0","Fw",140
+"60",180100,332213,10.9,90,541,1571,6.68,0.0703333,10.2,"1","1","1","Fw",70
+"61",179604,332059,7.3,80,310,1190,7.4,0.0484831,12,"1","1","1","W",20
+"62",179526,331936,9.4,78,210,907,7.44,0.0054321,14.1,"1","1","1","W",10
+"63",179495,331770,8.3,77,158,761,7.36,0.0054321,14.5,"1","1","1","Fw",10
+"64",179489,331633,7,65,141,659,7.2,0.0316663,14.8,"1","1","1","W",20
+"65",179414,331494,6.8,66,144,643,7.22,0.0122243,13.3,"1","1","1","Ah",10
+"66",179334,331366,7.4,72,181,801,7.36,0.0122243,15.2,"1","1","1","W",20
+"67",179255,331264,6.6,75,173,784,5.18,0.0373395,11.4,"1","1","1","W",20
+"69",179470,331125,7.8,75,399,1060,5.8,0.211846,9,"1","1","0","Ah",270
+"75",179692,330933,0.7,22,45,119,7.64,0.451037,3.6,"1","1","1","Fw",560
+"76",179852,330801,3.4,55,325,778,6.32,0.575877,6.9,"1","1","0","Bw",750
+"79",179140,330955,3.9,47,268,703,5.76,0.0756869,7,"1","1","1","Ab",80
+"80",179128,330867,3.5,46,252,676,6.48,0.12481,6.2,"1","1","1","Ab",130
+"81",179065,330864,4.7,55,315,793,6.48,0.103024,6.5,"1","1","0","W",110
+"82",179007,330727,3.9,49,260,685,6.32,0.157469,5.7,"1","1","0","W",200
+"83",179110,330758,3.1,39,237,593,6.32,0.200976,7,"1","1","1","Ah",260
+"84",179032,330645,2.9,45,228,549,6.16,0.200976,7.3,"1","1","0","W",270
+"85",179095,330636,3.9,48,241,680,6.56,0.26622,8.2,"1","1","0","W",320
+"86",179058,330510,2.7,36,201,539,6.9,0.298835,4.3,"1","1","0","Ah",360
+"87",178810,330666,2.5,36,204,560,7.54,0.0812247,4.4,"1","1","1","Am",80
+"88",178912,330779,5.6,68,429,1136,6.42,0.070355,8.2,"1","1","1","W",100
+"89",178981,330924,9.4,88,462,1383,6.28,0.0122243,8.5,"1","1","1","W",70
+"90",179076,331005,10.8,85,333,1161,6.34,0,9.6,"1","1","1","W",20
+"123",180151,330353,18.1,76,464,1672,7.307,0.0537723,17,"1","1","1","W",50
+"160",179211,331175,6.3,63,159,765,5.7,0.0593662,12.8,"1","1","1","W",80
+"163",181118,333214,2.1,32,116,279,7.72,0.211843,5.9,"1","2","0","W",290
+"70",179474,331304,1.8,25,81,241,7.932,0.12481,2.9,"2","2","1","Ah",160
+"71",179559,331423,2.2,27,131,317,7.82,0.12481,4.5,"2","1","0","W",160
+"91",179022,330873,2.8,36,216,545,8.575,0.0921516,10.7,"2","1","0","W",140
+"92",178953,330742,2.4,41,145,505,8.536,0.113941,9.4,"2","1","0","W",150
+"93",178875,330516,2.6,33,163,420,8.504,0.179216,9,"2","1","0","Ah",220
+"94",178803,330349,1.8,27,129,332,8.659,0.233596,7,"2","1","0","Am",280
+"95",179029,330394,2,38,148,400,7.633,0.336861,6.5,"2","1","1","Am",450
+"96",178605,330406,2.7,37,214,553,8.538,0.070355,9.4,"2","1","1","Am",70
+"97",178701,330557,2.7,34,226,577,7.68,0.0593662,10.2,"2","1","0","Am",70
+"98",179547,330245,0.9,19,54,155,7.564,0.255341,6.4,"2","1","0","W",340
+"99",179301,330179,0.9,22,70,224,7.76,0.364067,7.6,"2","1","0","W",470
+"100",179405,330567,0.4,26,73,180,7.653,0.429295,7,"2","1","0","Am",630
+"101",179462,330766,0.8,25,87,226,7.951,0.380328,5.6,"2","1","0","Am",460
+"102",179293,330797,0.4,22,76,186,8.176,0.249874,6.5,"2","1","0","Am",320
+"103",179180,330710,0.4,24,81,198,8.468,0.266212,6.6,"2","1","0","Ah",320
+"104",179206,330398,0.4,18,68,187,8.41,0.451037,5.9,"2","1","0","W",540
+"105",179618,330458,0.8,23,66,199,7.61,0.30971,6.5,"2","1","0","W",420
+"106",179782,330540,0.4,22,49,157,7.792,0.293359,6.4,"2","1","0","SPO",380
+"108",179980,330773,0.4,23,63,203,8.76,0.532351,7.2,"2","2","0","W",500
+"109",180067,331185,0.4,23,48,143,9.879,0.619513,6.6,"2","3","0","Am",760
+"110",180162,331387,0.2,23,51,136,9.097,0.684725,4.3,"2","2","0","Ah",750
+"111",180451,331473,0.2,18,50,117,9.095,0.809742,5.3,"2","3","0","Fw",1000
+"112",180328,331158,0.4,20,39,113,9.717,0.880389,4.1,"2","3","0","Ah",860
+"113",180276,330963,0.2,22,48,130,9.924,0.749591,6.1,"2","3","0","Ah",680
+"114",180114,330803,0.2,27,64,192,9.404,0.575752,7.5,"2","3","0","Fw",500
+"115",179881,330912,0.4,25,84,240,10.52,0.581484,8.8,"2","3","0","STA",650
+"116",179774,330921,0.2,30,67,221,8.84,0.49452,5.7,"2","3","0","DEN",630
+"117",179657,331150,0.2,23,49,140,8.472,0.32058,6.1,"2","3","0","Fw",410
+"118",179731,331245,0.2,24,48,128,9.634,0.336851,7.1,"2","3","0","Ah",390
+"119",179717,331441,0.2,21,56,166,9.206,0.249852,4.1,"2","2","0","Ah",310
+"120",179446,331422,0.2,24,65,191,8.47,0.0756869,6,"2","1","0","Ah",70
+"121",179524,331565,0.2,21,84,232,8.463,0.0756869,6.6,"2","1","0","W",70
+"122",179644,331730,0.2,23,75,203,9.691,0.162853,6.8,"2","1","1","STA",150
+"124",180321,330366,3.7,53,250,722,8.704,0.0974916,9.1,"2","2","0","Bw",80
+"125",180162,331837,0.2,33,81,210,9.42,0.440142,5.9,"2","2","0","Ah",450
+"126",180029,331720,0.2,22,72,198,9.573,0.4619,4.9,"2","2","0","Aa",530
+"127",179797,331919,0.2,23,86,139,9.555,0.222701,7.1,"2","1","0","W",240
+"128",179642,331955,0.2,25,94,253,8.779,0.103024,8.1,"2","1","1","Tv",70
+"129",179849,332142,1.2,30,244,703,8.54,0.0921353,8.3,"2","1","0","Fw",70
+"130",180265,332297,2.4,47,297,832,8.809,0.0484884,10,"2","1","0","Ah",60
+"131",180107,332101,0.2,31,96,262,9.523,0.168331,5.9,"2","1","0","Ah",190
+"132",180462,331947,0.2,20,56,142,9.811,0.38581,5,"2","2","0","Ah",450
+"133",180478,331822,0.2,16,49,119,9.604,0.489064,4.5,"2","2","0","Am",550
+"134",180347,331700,0.2,17,50,152,9.732,0.57602,5.4,"2","2","0","Am",650
+"135",180862,333116,0.4,26,148,415,9.518,0.0812194,2.3,"2","1","0","Am",100
+"136",180700,332882,1.6,34,162,474,9.72,0.0373369,7.5,"2","1","0","W",170
+"161",180201,331160,0.8,18,37,126,9.036,0.771698,4.6,"2","3","1","Ah",860
+"162",180173,331923,1.2,23,80,210,9.528,0.336829,5.8,"2","2","0","W",410
+"137",180923,332874,0.2,20,80,220,9.155,0.228123,4.4,"3","1","0","Aa",290
+"138",180467,331694,0.2,14,49,133,10.08,0.597761,4.4,"3","2","0","Am",680
+"140",179917,331325,0.8,46,42,141,9.97,0.44558,4.5,"3","2","0","Am",540
+"141",179822,331242,1,29,48,158,10.136,0.396675,5.2,"3","2","0","Am",480
+"142",179991,331069,0.8,19,41,129,10.32,0.581478,4.6,"3","3","0","W",720
+"143",179120,330578,1.2,31,73,206,9.041,0.287966,6.9,"3","1","0","W",380
+"144",179034,330561,2,27,146,451,7.86,0.233596,7,"3","1","0","W",310
+"145",179085,330433,1.5,29,95,296,8.741,0.364067,5.4,"3","1","0","Ah",430
+"146",179236,330046,1.1,22,72,189,7.822,0.331454,6.2,"3","1","0","Ah",370
+"147",179456,330072,0.8,20,51,154,7.78,0.211846,5,"3","1","0","Fw",290
+"148",179550,329940,0.8,20,54,169,8.121,0.103029,5.1,"3","1","0","W",150
+"149",179445,329807,2.1,29,136,403,8.231,0.070355,8.1,"3","1","0","Bw",70
+"150",179337,329870,2.5,38,170,471,8.351,0.146576,8,"3","1","0","Bw",220
+"151",179245,329714,3.8,39,179,612,7.3,0.0537723,8.8,"3","1","0","W",80
+"152",179024,329733,3.2,35,200,601,7.536,0.119286,9.3,"3","1","0","W",120
+"153",178786,329822,3.1,42,258,783,7.706,0.0921435,8.4,"3","1","0","Ah",120
+"154",179135,329890,1.5,24,93,258,8.07,0.249863,7.7,"3","1","0","Am",260
+"155",179030,330082,1.2,20,68,214,8.226,0.37494,5.7,"3","1","0","Ah",440
+"156",179184,330182,0.8,20,49,166,8.128,0.423837,4.7,"3","1","0","Am",540
+"157",179085,330292,3.1,39,173,496,8.577,0.423837,9.1,"3","1","0","Ah",520
+"158",178875,330311,2.1,31,119,342,8.429,0.27709,6.5,"3","1","0","Ah",350
+"159",179466,330381,0.8,21,51,162,9.406,0.358606,5.7,"3","1","0","W",460
+"164",180627,330190,2.7,27,124,375,8.261,0.0122243,5.5,"3","3","0","W",40
diff --git a/src/pyinterpolate/kriging/point/ordinary.py b/src/pyinterpolate/kriging/point/ordinary.py
index c506d814..2f9247e7 100644
--- a/src/pyinterpolate/kriging/point/ordinary.py
+++ b/src/pyinterpolate/kriging/point/ordinary.py
@@ -22,7 +22,8 @@
 from pyinterpolate.kriging.utils.errors import singular_matrix_error
 # Pyinterpolate
 from pyinterpolate.kriging.utils.point_kriging_solve import (get_predictions,
-                                                             solve_weights)
+                                                             solve_weights,
+                                                             __experimental_solve_weights_lsa_only)
 from pyinterpolate.transform.statistical import sem_to_cov
 from pyinterpolate.semivariogram.theoretical.theoretical import TheoreticalVariogram
 
@@ -401,3 +402,236 @@ def ordinary_kriging_from_cov(
         return [zhat, sigma, unknown_location[0], unknown_location[1]]
     except np.linalg.LinAlgError as _:
         singular_matrix_error()
+
+
+def experimental_ordinary_kriging_lsa(
+        theoretical_model: TheoreticalVariogram,
+        unknown_locations: Union[
+            np.ndarray, Point, List, Tuple, GeoSeries, GeometryArray, ArrayLike],
+        known_locations: ArrayLike = None,
+        known_values: ArrayLike = None,
+        known_geometries: ArrayLike = None,
+        neighbors_range=None,
+        no_neighbors=4,
+        max_tick=5.,
+        use_all_neighbors_in_range=False,
+        progress_bar: bool = True
+) -> np.ndarray:
+    """
+    Function predicts value at unknown location using Least Squares
+    Approximation.
+
+    Parameters
+    ----------
+    theoretical_model : TheoreticalVariogram
+        Fitted theoretical variogram model.
+
+    unknown_locations : Union[ArrayLike, Point]
+        Points where you want to estimate value ``(x, y) <-> (lon, lat)``.
+
+    known_locations : numpy array
+        Known locations: ``[x, y, value]``.
+
+    known_values : ArrayLike, optional
+        Observation in the i-th geometry (from ``known_geometries``). Optional
+        parameter, if not given then ``known_locations`` must be provided.
+
+    known_geometries : ArrayLike, optional
+        Array or similar structure with geometries. It must have the same
+        length as ``known_values``. Optional parameter, if not given then
+        ``known_locations`` must be provided. Point type geometry.
+
+    neighbors_range : float, default=None
+        The maximum distance where we search for neighbors. If ``None`` is
+        given then the range is selected from the Theoretical
+        Model's ``rang`` attribute.
+
+    no_neighbors : int, default = 4
+        The number of **n-closest neighbors** used for interpolation.
+
+    max_tick : float, default=5.
+        If searching for neighbors in a specific direction how big should be
+        a tolerance for increasing the search angle (how many degrees more).
+
+    use_all_neighbors_in_range : bool, default = False
+        ``True``: if the real number of neighbors within
+        the ``neighbors_range`` is greater than the ``number_of_neighbors``
+        then take all of them anyway.
+
+    progress_bar : bool, default=True
+        Show a progress bar during the interpolation process.
+
+    Returns
+    -------
+    : numpy array
+        ``[predicted value, variance error, longitude (x), latitude (y)]``
+
+    Raises
+    ------
+    RunetimeError
+        Singular Matrix in the Kriging system.
+
+    Examples
+    --------
+    >>> import geopandas as gpd
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>>
+    >>> from pyinterpolate import (build_experimental_variogram,
+    ...     build_theoretical_variogram, ordinary_kriging)
+    >>>
+    >>> dem = gpd.read_file('dem.gpkg')
+    >>> unknown_locations = gpd.read_file('unknown_locations.gpkg')
+    >>> step_size = 500
+    >>> max_range = 10000
+    >>> exp_variogram = build_experimental_variogram(
+    ...     values=dem['dem'],
+    ...     geometries=dem['geometry'],
+    ...     step_size=step_size,
+    ...     max_range=max_range
+    ... )
+    >>> theo_variogram = build_theoretical_variogram(exp_variogram)
+    >>> interp = ordinary_kriging(
+    ...     theoretical_model=theo_variogram,
+    ...     unknown_locations=unknown_locations['geometry'],
+    ...     known_values=dem['dem'],
+    ...     known_geometries=dem['geometry']
+    ... )
+    >>> print(interp[0])
+    [7.91222896e+01 9.72740449e+01 2.38012302e+05 5.51466805e+05]
+    """
+    # Check if known locations are in the right format
+    if not isinstance(known_locations, VariogramPoints):
+        known_locations = VariogramPoints(points=known_locations,
+                                          geometries=known_geometries,
+                                          values=known_values)
+        known_locations = known_locations.points
+
+    unknown_locations = InterpolationPoints(unknown_locations).points
+
+    interpolated_results = []
+
+    _disable_progress_bar = not progress_bar
+
+    for upoints in tqdm(unknown_locations, disable=_disable_progress_bar):
+        res = __experimental_ok_calc_lsa(
+            theoretical_model=theoretical_model,
+            known_locations=known_locations,
+            unknown_location=upoints,
+            neighbors_range=neighbors_range,
+            no_neighbors=no_neighbors,
+            max_tick=max_tick,
+            use_all_neighbors_in_range=use_all_neighbors_in_range
+        )
+
+        interpolated_results.append(
+            res
+        )
+
+    return np.array(interpolated_results)
+
+
+def __experimental_ok_calc_lsa(
+        theoretical_model: TheoreticalVariogram,
+        unknown_location: ArrayLike,
+        known_locations: ArrayLike = None,
+        known_values: ArrayLike = None,
+        known_geometries: ArrayLike = None,
+        neighbors_range=None,
+        no_neighbors=4,
+        max_tick=5.,
+        use_all_neighbors_in_range=False
+):
+    """
+    Function predicts value at unknown location with Ordinary Kriging
+    technique.
+
+    Parameters
+    ----------
+    theoretical_model : TheoreticalVariogram
+        Fitted theoretical variogram model.
+
+    unknown_location : Union[ArrayLike, Point]
+        Points where you want to estimate value ``(x, y) <-> (lon, lat)``.
+
+    known_locations : numpy array
+        Known locations: ``[x, y, value]``.
+
+    known_values : ArrayLike, optional
+        Observation in the i-th geometry (from ``known_geometries``). Optional
+        parameter, if not given then ``known_locations`` must be provided.
+
+    known_geometries : ArrayLike, optional
+        Array or similar structure with geometries. It must have the same
+        length as ``known_values``. Optional parameter, if not given then
+        ``known_locations`` must be provided. Point type geometry.
+
+    neighbors_range : float, default=None
+        The maximum distance where we search for neighbors. If ``None`` is
+        given then the range is selected from the Theoretical
+        Model's ``rang`` attribute.
+
+    no_neighbors : int, default = 4
+        The number of **n-closest neighbors** used for interpolation.
+
+    max_tick : float, default=5.
+        If searching for neighbors in a specific direction how big should be
+        a tolerance for increasing the search angle (how many degrees more).
+
+    use_all_neighbors_in_range : bool, default = False
+        ``True``: if the real number of neighbors within
+        the ``neighbors_range`` is greater than the ``number_of_neighbors``
+        then take all of them anyway.
+
+    Returns
+    -------
+    : numpy array
+        ``[predicted value, variance error, longitude (x), latitude (y)]``
+
+    Raises
+    ------
+    RunetimeError
+        Singular Matrix in the Kriging system.
+    """
+    # Check if known locations are in the right format
+    # Validate points
+    if not isinstance(known_locations, VariogramPoints):
+        known_locations = VariogramPoints(points=known_locations,
+                                          geometries=known_geometries,
+                                          values=known_values)
+        known_locations = known_locations.points
+
+    # Check if unknown location is Point
+    if isinstance(unknown_location, Point):
+        unknown_location = (
+            unknown_location.x,
+            unknown_location.y
+        )
+        unknown_location = np.array(unknown_location)
+
+    k, predicted, dataset = get_predictions(theoretical_model,
+                                            known_locations,
+                                            unknown_location,
+                                            neighbors_range,
+                                            no_neighbors,
+                                            use_all_neighbors_in_range,
+                                            max_tick)
+
+    k_ones = np.ones(1)[0]
+    k = np.r_[k, k_ones]
+
+    p_ones = np.ones((predicted.shape[0], 1))
+    predicted_with_ones_col = np.c_[predicted, p_ones]
+    p_ones_row = np.ones((1, predicted_with_ones_col.shape[1]))
+    p_ones_row[0][-1] = 0.
+    weights = np.r_[predicted_with_ones_col, p_ones_row]
+
+    output_weights = __experimental_solve_weights_lsa_only(weights, k)
+    zhat = dataset[:, -2].dot(output_weights[:-1])
+
+    sigma = np.matmul(output_weights.T, k)
+
+    if sigma < 0:
+        return [zhat, np.nan, unknown_location[0], unknown_location[1]]
+
+    return [zhat, sigma, unknown_location[0], unknown_location[1]]
diff --git a/src/pyinterpolate/kriging/utils/point_kriging_solve.py b/src/pyinterpolate/kriging/utils/point_kriging_solve.py
index 87fc579a..dbfb833e 100644
--- a/src/pyinterpolate/kriging/utils/point_kriging_solve.py
+++ b/src/pyinterpolate/kriging/utils/point_kriging_solve.py
@@ -147,3 +147,37 @@ def solve_weights(weights: np.ndarray,
                 raise linalgerr
 
     return solved
+
+
+def __experimental_solve_weights_lsa_only(weights: np.ndarray,
+                                          k: np.ndarray) -> np.ndarray:
+    """
+    Solves Kriging System.
+
+    Parameters
+    ----------
+    weights : numpy array
+        Array of weights of size m:m.
+
+    k : numpy array
+        Array of semivariances of size m:n.
+
+    Returns
+    -------
+    solved : numpy array
+        Final weights to estimate predicted value.
+
+    Warns
+    -----
+    ZerosMatrixWarning :
+        Weights or k matrices are zero-matrices.
+    """
+
+    if np.mean(weights) == 0 or np.mean(k) == 0:
+        warnings.warn(ZerosMatrixWarning().__str__())
+        solved = np.zeros(len(k))
+    else:
+        solved = np.linalg.lstsq(weights, k, rcond=None)
+        solved = solved[0]
+    return solved
+