Interaction panel of Treat × Period effect (T × P) for eating time (min/8-h) and sorting index (%) on sieve 2.36 mm and bottom pan as influenced by feeding pasteurized waste milk (... by Shahryar Kargar (18880600)

Accuracy of the random forest regression model with various input variables (all inputs vs. top 30 inputs). by Varun Tiwari (9258130)

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)

Changes in (A) Deaths and (B) DALYs of pancreatitis due to alcohol use according to aging, population growth and epidemiological change from 1990 to 2021 at global level by SDI qui... by Letai Li (19229848)

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

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. …”

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)

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. …”

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

Nitrate anomaly series. by Antonio Bode (1473376)