Fig 19 - by Zhezhe Zhang (19704587)

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Fig 8 - by Zhezhe Zhang (19704587)

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Mean values and 95% confidence intervals for recovery time, Average NN Interval (AVNN); Standard Deviation of NN Intervals (SDNN), Low Frequency/ High Frequency Ratio (LF/HF); and... by Robert Trybulski (10998702)

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Mean values and 95% confidence intervals for resting flow, arterial occlusion pressure (AOP), biological zero, reactive hyperemia (RHmax), and time to peak across the three conditi... by Robert Trybulski (10998702)

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

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

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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. …”

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)

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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)

Time course of IRF1 and IFITM3 RNA and protein expression across conditions. by Tyron Chang (9922533)

Vaccinia virus infection (MOI = 3) of A549 cells in media supplemented with glucose/galactose. by Tyron Chang (9922533)

Glucose/galactose regulation of IFN-γ induced IRF1 protein is conserved in mammals. by Tyron Chang (9922533)

Characterization of the impact of glucose/galactose on infection outcomes. by Tyron Chang (9922533)