Fig 9 - by Zhezhe Zhang (19704587)

Subjects:

Failure modes of joint surfaces under different τ₁of group C specimens under the UNLCSL path. by Zhezhe Zhang (19704587)

Subjects:

Specimen packaging process. by Zhezhe Zhang (19704587)

Subjects:

Fig 5 - by Zhezhe Zhang (19704587)

Subjects:

Stress path diagram of Ⅱ, Ⅲ and Ⅳ groups of specimens. by Zhezhe Zhang (19704587)

Subjects:

Basic mechanical parameters of concrete specimens. by Zhezhe Zhang (19704587)

Subjects:

Fig 19 - by Zhezhe Zhang (19704587)

Subjects:

Fig 8 - by Zhezhe Zhang (19704587)

Subjects:

Percentage reduction of shear strength parameters. by Zhezhe Zhang (19704587)

Subjects:

Relationship between τ_<i>i</i> and σ_<i>i</i>. by Zhezhe Zhang (19704587)

Subjects:

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. …”

Analysis of host and interferon-induced responses in glucose/galactose infected with vaccinia virus. by Tyron Chang (9922533)

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)

Subjects:

Brain regions with significant metabolic inhibition (p < 0.001, K = 50) (A) and activation (B) in rats following cocaine self-administration compared to baseline. Cluster location... by Christopher Rowan (21499924)

qPCR of vaccinia virus RNAs <i>IRF1</i> KO cells: glucose with and without IFN-γ priming. by Tyron Chang (9922533)

Glucose/galactose with extended interferon-priming does not impact levels of ISG RNAs or non-ISG proteins levels. by Tyron Chang (9922533)