Serves as the data source for Fig 4. by Shuai Huang (60512)

“…Additionally, 16S rRNA species profiling revealed that the composting process significantly altered the microbial community structure, with an increased abundance of Firmicutes and a decreased abundance of Bacteroidetes in composted pig manure. …”

Serves as the data source for Fig 5. by Shuai Huang (60512)

“…Additionally, 16S rRNA species profiling revealed that the composting process significantly altered the microbial community structure, with an increased abundance of Firmicutes and a decreased abundance of Bacteroidetes in composted pig manure. …”

Serves as the data source for Fig 6. by Shuai Huang (60512)

“…Additionally, 16S rRNA species profiling revealed that the composting process significantly altered the microbial community structure, with an increased abundance of Firmicutes and a decreased abundance of Bacteroidetes in composted pig manure. …”

Serves as the data source for Fig 7. by Shuai Huang (60512)

“…Additionally, 16S rRNA species profiling revealed that the composting process significantly altered the microbial community structure, with an increased abundance of Firmicutes and a decreased abundance of Bacteroidetes in composted pig manure. …”

S4 Data - by Zhen Wang (72451)

“…<div><p>The formation and distribution of residual stress during the micro-milling process significantly affect the crack resistance and service life of alumina bioceramics. …”

S3 Data - by Zhen Wang (72451)

“…<div><p>The formation and distribution of residual stress during the micro-milling process significantly affect the crack resistance and service life of alumina bioceramics. …”

S2 Data - by Zhen Wang (72451)

“…<div><p>The formation and distribution of residual stress during the micro-milling process significantly affect the crack resistance and service life of alumina bioceramics. …”

Thermal properties of alumina bioceramics. by Zhen Wang (72451)

“…<div><p>The formation and distribution of residual stress during the micro-milling process significantly affect the crack resistance and service life of alumina bioceramics. …”

S5 Data - by Zhen Wang (72451)

“…<div><p>The formation and distribution of residual stress during the micro-milling process significantly affect the crack resistance and service life of alumina bioceramics. …”

S6 Data - by Zhen Wang (72451)

“…<div><p>The formation and distribution of residual stress during the micro-milling process significantly affect the crack resistance and service life of alumina bioceramics. …”

S1 Data - by Zhen Wang (72451)

“…<div><p>The formation and distribution of residual stress during the micro-milling process significantly affect the crack resistance and service life of alumina bioceramics. …”

Factors influencing FFM at Q1. by Xiang Pan (5774024)

Factors influencing nurses’ turnover intention. by Şehrinaz Polat (20978625)

Row data. by Xiangyu Wang (341093)

“…<div><p>Objective</p><p>Body image perception significantly impacts university students’ well-being and potentially their creativity. …”

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

“…<div><p>Objective</p><p>Body image perception significantly impacts university students’ well-being and potentially their creativity. …”

Experimental setup. by Shinnosuke Hase (20917896)

“…Conversely, the fast-rhythm auditory guide significantly increased step rate and decreased step length (p <  0.05). …”

Data Sheet 1_Effect of surface roughness on the microbiologically influenced corrosion (MIC) of copper 101.docx by Amit Acharjee (20418776)

“…However, a statistically significant reduction in the corrosion rate was recorded when the surface roughness was decreased from ∼2.71 μm to ∼0.006 μm.…”

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. …”