S2 Table - by Yong-Rae Kim (19797278)

“…In addition, the vigor score was significantly increased while total mood disturbance was significantly decreased when viewing the wood-attached ECD. …”

Participant characteristics (<i>n</i> = 34). by Yong-Rae Kim (19797278)

“…In addition, the vigor score was significantly increased while total mood disturbance was significantly decreased when viewing the wood-attached ECD. …”

S4 Table - by Yong-Rae Kim (19797278)

“…In addition, the vigor score was significantly increased while total mood disturbance was significantly decreased when viewing the wood-attached ECD. …”

S6 Table - by Yong-Rae Kim (19797278)

“…In addition, the vigor score was significantly increased while total mood disturbance was significantly decreased when viewing the wood-attached ECD. …”

Forest plot of the effect of high-dose TXA on transfusion rate. by Xin Liu (43569)

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Forest plot of the effect of high-dose TXA on intraoperative blood loss. by Xin Liu (43569)

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Forest plot of the effect of high-dose TXA on operation time. by Xin Liu (43569)

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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. …”

The technical route of the study. by Hongliang Zou (20707270)

“…The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. …”

Point cloud fusion instance effect. by Hongliang Zou (20707270)

“…The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. …”

Experimental results in the SubT-MRS dataset. by Hongliang Zou (20707270)

“…The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. …”

3D reconstruction of a control PFC PV interneuron using Neurolucida showing the soma in light blue, the dendrites in green, and the axon in light blue. by Sara Hijazi (21656615)

The percentage of received patients who migrated for primary knee arthroplasty in different provinces. by Fanqiang Meng (4786071)

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Patient migration rates for primary knee arthroplasty from 2013 to 2018. by Fanqiang Meng (4786071)

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Comparison of postoperative outcomes and financial burden between migrated and non-migrated primary knee arthroplasty patients. by Fanqiang Meng (4786071)

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Sankey diagram of patient migration for primary knee arthroplasty in China. by Fanqiang Meng (4786071)

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