Dataset used in the manuscript. by Saeun Park (20410160)

“…Caregivers had more severe symptoms of depression and/or anxiety if they experienced greater changes in living situations or decreases in physical activity (CESD-20: β = 3.35, 95% CI [1.00, 5.70], p = .01), food insecurity (HSCL-25: β = 3.25, 95% CI [0.41, 6.10], p = .03, CESD-25: β = 3.09, 95% CI [0.79, 5.39], p = .01), and domestic violence (HSCL-25: β = 3.82, 95% CI [0.94, 6.70], p = .01) during COVID-19. …”

S1 Graphical abstract - by Hai Nguyen Ngoc Dang (19200560)

“…A longer diabetes duration was linked to a greater ASI (r = 0.43, p < 0.05), while the AS decreased (r = -0.37, p < 0.05).…”

Parameters of screws. by Pilan Jaipanya (12861176)

“…For these reasons, surgeons may avoid CPS insertion despite its benefit of greater biomechanical strength. Therefore, an improvement in the CPS design is needed to avoid this catastrophic complication.…”

Restricted cubic spline plots showing the association between changes in PAL and risk of CVD. by Haiwei Li (282229)

Innovation and night light in large and small cities. by Saul Estrin (8629173)

Spectral-domain optical coherence tomography (SD-OCT) images taken from a 72-year-old female patient with neovascular age-related macular degeneration (nAMD) who received ranibizum... by Zheyao Gu (20910794)

Multilayered Fabrication Containing Wind Turbine Blade Solid Wastes for High-Performance Composite Fibers by Varunkumar Thippanna (17960141)

“…The individual layer thickness in the multilayered PAN-GF fibers decreased progressively with an increasing number of layers, with 32-layered fibers exhibiting comparatively thicker layers, while 256-layered fibers demonstrated significantly thinner layers. …”

PCA projection of variables related to bromatological composition, polyphenol content, methylxanthines and antioxidant activity in cocoa beans fermented in different cocoa bean pro... by Andrés Felipe Ramírez González (19797070)

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 =... by 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... by 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... by Shahryar Kargar (18880600)

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)

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. …”

Variability of gait features during adaptation. by Samantha Jeffcoat (22783930)

Step length asymmetry and step width during split-belt adaptation. by Samantha Jeffcoat (22783930)

Changes in rumen fluid metabolites. by Shiqi Zhang (709298)