Predictors in ordinal regression model for GDS. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

Classification of hand grip strength. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

Rating scale for functional severity [28]. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

Regression model coefficients. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

ICOPE screening positive participant’s responses. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

WHO BMI classification for adults. حسب Shane Naidoo (20148021)

"…Conversely in a linear regression model, depression (<i>B</i> = -2.01, <i>p</i> = .004) and physical activity (<i>B</i> = -.001, <i>p</i> = .008) were predictors for decreases in BMI.…"

Gating strategy of experiment using 30 min EdU pulses experiments in mOr2-Piwi1 juvenile polyps. حسب Eudald Pascual-Carreras (12115380)

Flowchart of participants in this study. حسب Yubo Teng (21246745)

"…Participants were categorized into quartiles based on UACR levels. Multivariate linear regression models were used to evaluate the association between UACR and cognitive scores. …"

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 (... حسب Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for ether extract intake (g/d) as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein cal... حسب Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for starter intake (g/d), body weight (kg), average daily gain (g/d), and feed efficiency (g/g) as influenced by feeding pasteuri... حسب Shahryar Kargar (18880600)

Interaction panel of Treat × Period effect (T × P) for body measurements as influenced by feeding pasteurized waste milk (WM) or milk replacer (MR) to newborn Holstein calves (n =... حسب Shahryar Kargar (18880600)

Major hyperparameters of RF-SVR. حسب 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. حسب 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. حسب 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. حسب 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. حسب 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. …"

Attitude towards NTDs in the study Area. حسب Uchechukwu M. Chukwuocha (6685790)

"…<div><p>Background</p><p>Neglected Tropical Diseases (NTDs) continue to significantly impact marginalized communities, contributing to high morbidity, stigma, and social exclusion. …"

Dataset of results. حسب Uchechukwu M. Chukwuocha (6685790)

"…<div><p>Background</p><p>Neglected Tropical Diseases (NTDs) continue to significantly impact marginalized communities, contributing to high morbidity, stigma, and social exclusion. …"

Respondents’ perception about the public artwork. حسب Uchechukwu M. Chukwuocha (6685790)

"…<div><p>Background</p><p>Neglected Tropical Diseases (NTDs) continue to significantly impact marginalized communities, contributing to high morbidity, stigma, and social exclusion. …"