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. …”

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)

Significant Electrode of Supercapattery Devices: the Induced Charge Storage Capability of MoSe₂/rGO Nanosheets Bedecked by MnO₂ Nanorod Composites by Kumar R (3991646)

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

The pooled effect of all-cause mortality rate in non-health care workers and health care workers. by Zhuoyang Xia (21354186)

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(A) Rapid light curves, (B) maximum electron transport rates (ETR_max) and (C) minimum saturating irradiances (E_k) derived from the model fitted according to P... by Malte Ostendarp (13217973)

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Dynamic Tracking of <i>In Vivo</i> Receptor Availability in Tumor Using Paired-Agent Imaging by Yichen Feng (450964)

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(A) Rapid light curves, (B) maximum electron transport rates (ETR_max) and (C) minimum saturating irradiances (E_k) derived from the model fitted according to P... by Malte Ostendarp (13217973)

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The depressive behavior in <i>Kif15</i>^<i>-/-</i> zebrafish was rescued following <i>Kif15</i> supplementation. by Xiaomei He (795333)

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Emotional changes in <i>Kif15</i>^<i>-/-</i> mice from adolescence to adulthood. by Xiaomei He (795333)

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The pooled effect of COVID-19 asymptomatic infection rate in non-health care workers and health care workers. by Zhuoyang Xia (21354186)

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Symbiodiniaceae cell density of (A) <i>Acropora</i> sp. and (B) <i>P. verrucosa</i>, areal chlorophyll content of (C) <i>Acropora</i> sp. and (D) <i>P. verrucosa</i>, and chlorophy... by Malte Ostendarp (13217973)

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Supplementation of five Lactobacillus strains affected the colonization of <i>M</i>. <i>pneumoniae</i> and lung injuries. by Huanbing Long (19949784)

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Manhattan plot for genome-wide association study (GWAS) in a European population with depression. by Xiaomei He (795333)

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