Questionnaire description. by Cristobal Padilla-Fortunatti (21376807)

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Decreasing oxygen saturation with corresponding levels of NLR and D-dimer for understanding the interplay of inflammation. by Narendran Gopalan (110148)

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. …”

Screening for gHgLgO interaction partners reveals decreased binding of the gO249 mutant to cell surfaces. by Lena Thiessen (17605194)

Characteristics of census tracts that are projected to have high (groups: 250-399 and 400+) and low (groups: 50-99 and 100-249) MHC utilization for COVID-19 vaccination before Marc... by Fatih Gezer (21485468)

The variation of creep strain of rock samples with different dip angles of double fissures with stress level. by Dengke Yang (842532)

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Clusters of creep curves under different freeze-thaw cycles. by Dengke Yang (842532)

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Creep failure mode and sketch map of slow-step fractured rock. by Dengke Yang (842532)

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Creep failure mode and sketch map of slow-slow fractured rock. by Dengke Yang (842532)

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Creep curve clusters of rock samples with different fracture dip angles. by Dengke Yang (842532)

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Creep long-term strength of intact rock samples under different freeze-thaw cycles. by Dengke Yang (842532)

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Raw creep-test data for double-fractured rock samples under freeze-thaw cycles. by Dengke Yang (842532)

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The variation of creep strain with the number of freeze-thaw cycles. by Dengke Yang (842532)

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The variation of instantaneous strain of rock samples with different dip angles of double fissures with stress level. by Dengke Yang (842532)

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Mean values of physical parameters of rock samples. by Dengke Yang (842532)

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Creep failure mode and sketch map of step-step fractured rock. by Dengke Yang (842532)

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