diff --git a/UseCases/Anomaly_Detection/Anomaly_Detection_PY_SQL.ipynb b/UseCases/Anomaly_Detection/Anomaly_Detection_PY_SQL.ipynb
index 38837a4c..8f3f3996 100644
--- a/UseCases/Anomaly_Detection/Anomaly_Detection_PY_SQL.ipynb
+++ b/UseCases/Anomaly_Detection/Anomaly_Detection_PY_SQL.ipynb
@@ -68,8 +68,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%capture\n",
-    "# # '%%capture' suppresses the display of installation steps of the following packages\n",
+    "# %%capture suppresses the display of installation steps of the following packages\n",
     "# !pip install tdsense\n",
     "# !pip install imblearn\n",
     "# !pip install xgboost==1.7.3\n",
@@ -175,7 +174,7 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "execute_sql('''SET query_band='DEMO=AnomalyDetection.ipynb;' UPDATE FOR SESSION; ''')"
+    "execute_sql('''SET query_band='DEMO=AnomalyDetection_PY_SQL.ipynb;' UPDATE FOR SESSION; ''')"
    ]
   },
   {
@@ -457,6 +456,17 @@
     "<p style = 'font-size:16px;font-family:Arial'>Next we calculate the derivative by using the lead function and taking the difference of the lead value and the mean value of the resistance. Applying a window function to smooth the resistance curve helps to eliminate noise and makes it easier to see the overall trend. The derivative of the resistance gives an indication of how quickly the resistance is changing, which can be a useful measure for detecting anomalies and predicting potential issues.</p>\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e520bd85-78ba-4356-b7e2-45a431458bd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "copy_to_sql(welding_dataset_smooth,table_name='welding_dataset_smooth', if_exists='replace')\n",
+    "welding_dataset_smooth = DataFrame('welding_dataset_smooth')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1934,7 +1944,7 @@
     "<div style=\"float:left;margin-top:14px\">ClearScape Analytics™</div>\n",
     "<div style=\"float:right;\">\n",
     "<div style=\"float:left; margin-top:14px\">\n",
-    "            Copyright © Teradata Corporation - 2023, 2024. All Rights Reserved\n",
+    "            Copyright © Teradata Corporation - 2023, 2024, 2025. All Rights Reserved\n",
     "</div>\n",
     "</div>\n",
     "</footer>"
diff --git a/UseCases/Anomaly_Detection/Anomaly_Detection_Python_TrustedAI.ipynb b/UseCases/Anomaly_Detection/Anomaly_Detection_Python_TrustedAI.ipynb
index 8bb25caf..715f84cb 100644
--- a/UseCases/Anomaly_Detection/Anomaly_Detection_Python_TrustedAI.ipynb
+++ b/UseCases/Anomaly_Detection/Anomaly_Detection_Python_TrustedAI.ipynb
@@ -482,6 +482,17 @@
     "<p style = 'font-size:16px;font-family:Arial'>Next we calculate the derivative by using the lead function and taking the difference of the lead value and the mean value of the resistance. Applying a window function to smooth the resistance curve helps to eliminate noise and makes it easier to see the overall trend. The derivative of the resistance gives an indication of how quickly the resistance is changing, which can be a useful measure for detecting anomalies and predicting potential issues.</p>\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c494a795-c9bf-4d3b-8ecf-549ba4ad7b79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "copy_to_sql(welding_dataset_smooth,table_name='welding_dataset_smooth', if_exists='replace')\n",
+    "welding_dataset_smooth = DataFrame('welding_dataset_smooth')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,