Summary statistics for analysis of single family clades. by Katrine Carlsen (5477300)

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Mode separation results of different methods in Qingdao shallow sea waveguide conditions. by Du Siqi (20721422)

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Results of different Warping transform corrections for negative sound velocity gradient profiles. by Du Siqi (20721422)

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Mode separation results of different methods in Qingdao shallow sea waveguide conditions. by Du Siqi (20721422)

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Pekeris waveguide schematic of the simulated marine environment. by Du Siqi (20721422)

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S3 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. …”

S5 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. …”

S1 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. …”

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. …”

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. …”

<i>C</i>. <i>auris</i> induces IL-1Ra in macrophages to decrease the neutrophil function in the skin. by Abishek Balakumar (20189865)

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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