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. …”

Effects of warming and nitrogen deposition on plant species diversity. Different lowercase letters indicated that there were significant differences in different nitrogen depositio... by Xuemei Xiang (20756894)

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Baseline characteristics of study subjects. by Md. Akeruzzaman Shaon (21511929)

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Demographic characteristics of the breast cancer patients enrolled in the study. by Md. Akeruzzaman Shaon (21511929)

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Box plots to show various parameters of the rectal waves generated in AB larvae at 7 dpf that were fed a standard diet (SD). by Jessica C. M. Hui (20370522)

The pooled effect of all-cause mortality rate in non-health care workers and health care workers. by Zhuoyang Xia (21354186)

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The pooled effect of COVID-19 asymptomatic infection rate in non-health care workers and health care workers. by Zhuoyang Xia (21354186)

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Agarose gel electrophoresis validation of <i>Kif15</i>^<i>-/-</i>zebrafish complement; M, 2000 bp marker. by Xiaomei He (795333)

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Association between urinary BPA exposure levels in childhood and serum thyroid hormone concentrations in adolescence. by Jung Eun Choi (563339)

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Linear regression analysis results for the effect of log-transformed urinary BPA concentrations at two time points on serum thyroid concentrations at ages 10–12. by Jung Eun Choi (563339)

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Vasopressor titration regimen in the balanced multimodal group. by Žiga Kalamar (22145810)

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The pooled effect of COVID-19 mild or moderate infection rate in non-health care workers and health care workers. by Zhuoyang Xia (21354186)

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The distribution of PSD95 and KIF15 in the cortex and hippocampus of 3-week, 6-week, and 11-week <i>Kif15</i>^<i>+/+</i> and <i>Kif15</i>^<i>-/-</i> mice was ex... by Xiaomei He (795333)

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The schedule of enrollment, interventions and assessments. by Žiga Kalamar (22145810)

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The pooled effect of COVID-19 infection rate in non-health care workers and health care workers. by Zhuoyang Xia (21354186)

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The pooled effect of COVID-19 severe infection rate in non-health care workers and health care workers. by Zhuoyang Xia (21354186)

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