Random forest top 30 variable importance utilizing all input variables, including the MODIS bands, spectral indices, and land surface temperature. by Varun Tiwari (9258130)

Indices calculated using MODIS bands (B1-B7) which were used to estimate <i>boro</i> rice yields, where MODIS bands relate to indices as follows: B1 = RED, B2 = NIR1, B3 = BLUE, B4... by Varun Tiwari (9258130)

A proposed model on the impact of DHA and ARA supplementation on <i>L. vannamei</i> health. by Virak Visudtiphole (11574169)

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)

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. …”