Effects of the observed significant angular changes at individual joints on the leading toe-obstacle clearance in healthy older adults during dual-task obstacle crossing (black sti... by Shiuan-Huei Lu (22430628)

Characteristics of water footprint by sector at the regional level in China. by Jianqin Ma (20328414)

Effects of the observed significant angular changes at individual joints on the trailing toe-obstacle clearance in healthy older adults during dual-task obstacle crossing (black st... by Shiuan-Huei Lu (22430628)

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Related to Fig 3. by Mohammad Nafees Ansari (22232505)

“…(<b>O</b>, <b>P</b>) Cell proliferation assay confirms an increase in cell proliferation of BT-474_ZFX^OE cells (mean ± SEM, <i>n</i> = 5) (O), whereas BT-474_ZFX^SL cells (mean ± SEM, <i>n</i> = 3) show decreased cell proliferation (P). (<b>Q</b>) Tumor growth kinetics recorded a significantly higher growth of BT-474_ZFX^OE (mean ± SEM, <i>n</i> = 5) than BT-474_VECT^OE tumors. …”

Meta-regression of macrolide resistance Mycobacterium isolates over the years; The results of the meta-regression analysis are presented in a scatter plot that shows the trend in t... by Saeid Sadeghi Ghazi Chaki (22582028)

RICTOR regulates UGCG expression via transcription factor Zinc Finger X-linked (ZFX). by Mohammad Nafees Ansari (22232505)

“…<b>(T)</b> Tumor growth kinetics recorded a significantly higher growth of MCF-7_ZFX^OE (mean ± SEM, <i>n</i> = 4-6) than MCF-7_VECT^OE tumors. …”

The mean unfed body weight of uninfected <i>Rhodnius prolixus</i> at different developmental stages and ambient temperatures. by Henri Loshouarn (17896676)

Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…In the normal tissue of L2-IL1B mice, we demonstrate a statistically significant increase (<i>p</i> < 0.001) in Raman band intensities associated with free amino acids and a decrease in bands associated with lipids (<i>p</i> < 0.005) and carotenoids (<i>p</i> < 0.001) compared to healthy controls. …”

Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…In the normal tissue of L2-IL1B mice, we demonstrate a statistically significant increase (<i>p</i> < 0.001) in Raman band intensities associated with free amino acids and a decrease in bands associated with lipids (<i>p</i> < 0.005) and carotenoids (<i>p</i> < 0.001) compared to healthy controls. …”

Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…In the normal tissue of L2-IL1B mice, we demonstrate a statistically significant increase (<i>p</i> < 0.001) in Raman band intensities associated with free amino acids and a decrease in bands associated with lipids (<i>p</i> < 0.005) and carotenoids (<i>p</i> < 0.001) compared to healthy controls. …”

Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…In the normal tissue of L2-IL1B mice, we demonstrate a statistically significant increase (<i>p</i> < 0.001) in Raman band intensities associated with free amino acids and a decrease in bands associated with lipids (<i>p</i> < 0.005) and carotenoids (<i>p</i> < 0.001) compared to healthy controls. …”

Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…In the normal tissue of L2-IL1B mice, we demonstrate a statistically significant increase (<i>p</i> < 0.001) in Raman band intensities associated with free amino acids and a decrease in bands associated with lipids (<i>p</i> < 0.005) and carotenoids (<i>p</i> < 0.001) compared to healthy controls. …”

Probing Field Cancerization in the Gastrointestinal Tract Using a Hybrid Raman and Partial Wave Spectroscopy Microscope by Elena Kriukova (21524544)

“…In the normal tissue of L2-IL1B mice, we demonstrate a statistically significant increase (<i>p</i> < 0.001) in Raman band intensities associated with free amino acids and a decrease in bands associated with lipids (<i>p</i> < 0.005) and carotenoids (<i>p</i> < 0.001) compared to healthy controls. …”

Effects of cell culture patterns on injury and inflammation induced by the S1 protein. by Yanru Liu (1662463)

Importance of the predictor variables for estimating AGB using a random forest model. by Kasip Tiwari (22232186)