Food intake in groups A, B and C. by Sohail Afzal (10850709)

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General characteristics of relative handgrip strength at baseline. by Yeo Ju Sohn (18536198)

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This dataset includes detailed information for 22,016 individuals. by Yeo Ju Sohn (18536198)

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Flow diagram of participants meeting the inclusion and exclusion criteria. by Yeo Ju Sohn (18536198)

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Initial participants by wave*. by Yeo Ju Sohn (18536198)

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These files contain the STATA program codes used in the study. by Yeo Ju Sohn (18536198)

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Generalized estimating equation model covariance structure analysis. by Yeo Ju Sohn (18536198)

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Raw dataset collected from the KLoSA from 2006 to 2020. by Yeo Ju Sohn (18536198)

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General characteristics of research participants. by Yeo Ju Sohn (18536198)

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Generalized estimation equation model analysis of diabetes mellitus incidence according to relative handgrip strength. by Yeo Ju Sohn (18536198)

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Trends analysis in diabetes mellitus diagnosis according to relative handgrip strength. by Yeo Ju Sohn (18536198)

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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. …”

Busulfan treatment induces orchitis. by Lingjun Zhao (21763122)

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Effects of busulfan treatment (6 mg/kg) on the body and testes weights of mice over three weeks. by Lingjun Zhao (21763122)

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Effect of busulfan treatment on androgens and androgen receptors. by Lingjun Zhao (21763122)

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Effects of busulfan treatment (6 mg/kg) on the epididymis and sperm of mice over a three-week period. by Lingjun Zhao (21763122)

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