ASV species taxonomic information sheet. by Xiaona Lyu (20871872)

“…Microbial communities under I2 (PCo1 = 11.13%) showed greater homogeneity while preserving treatment-specific patterns, suggesting slight deficit irrigation balances stability and functional differentiation. …”

Effective sequence statistics table. by Xiaona Lyu (20871872)

“…Microbial communities under I2 (PCo1 = 11.13%) showed greater homogeneity while preserving treatment-specific patterns, suggesting slight deficit irrigation balances stability and functional differentiation. …”

Row data. by Xiangyu Wang (341093)

“…Moreover, Life Sciences & Medicine students demonstrated a greater tendency toward negative self-perception, low psychological well-being level, and decreased creative self-efficacy, compared to peers in other disciplines.…”

Univariate linear regression analysis of scales. by Xiangyu Wang (341093)

“…Moreover, Life Sciences & Medicine students demonstrated a greater tendency toward negative self-perception, low psychological well-being level, and decreased creative self-efficacy, compared to peers in other disciplines.…”

Effects of increasing amounts of gravel on escape latency and aversiveness of gravel. by Ella R. Dockendorf (21533334)

“…<p>(A) Escape latency during trial 1 was higher when greater amounts of gravel were in the connecting chamber. …”

Metformin lotion inhibits scar tissue formation in rat skin after 10 days of wound healing. by Jianying Zhang (256508)

Model selection based on best fit. by Angelina Mageni Lutambi (22097223)

“…<div><p>Malaria remains a significant public health challenge, particularly among vulnerable populations in high-burden countries like Tanzania. …”

Group results for Experiment 1 and 2. by Adam M. Roth (19845864)

S1 Data - by Taher Mohammadizad (19786989)

“…The study involved 672 male broiler chickens in 7 treatment groups, including a thermoneutral control (TNC) group and six HS treatments. …”

Raw data. by Changzhi Liu (518454)

“…The incidence of fractures was significantly greater among participants with low RC levels than among those with high RC levels (6.4% vs. 3.1%, P < 0.01). …”

Baseline characteristics of the subjects. by Changzhi Liu (518454)

“…The incidence of fractures was significantly greater among participants with low RC levels than among those with high RC levels (6.4% vs. 3.1%, P < 0.01). …”

GO and KEGG analysis of DEPs. by Huiyang Jia (14444661)

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 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 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 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. …”