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significantly increased » significant increase (Expand Search)
increased decrease » increased release (Expand Search), increased crash (Expand Search)
greater decrease » greatest decrease (Expand Search), greater increase (Expand Search), greater disease (Expand Search)
degs decrease » mean decrease (Expand Search), teer decrease (Expand Search), we decrease (Expand Search)
significantly increased » significant increase (Expand Search)
increased decrease » increased release (Expand Search), increased crash (Expand Search)
greater decrease » greatest decrease (Expand Search), greater increase (Expand Search), greater disease (Expand Search)
degs decrease » mean decrease (Expand Search), teer decrease (Expand Search), we decrease (Expand Search)
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2321
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2322
Major hyperparameters of RF-SVR.
Published 2024“…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 R<sup>2</sup> 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. …”
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2323
Pseudo code for coupling model execution process.
Published 2024“…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 R<sup>2</sup> 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. …”
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2324
Major hyperparameters of RF-MLPR.
Published 2024“…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 R<sup>2</sup> 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. …”
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2325
Results of RF algorithm screening factors.
Published 2024“…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 R<sup>2</sup> 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. …”
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2326
Schematic diagram of the basic principles of SVR.
Published 2024“…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 R<sup>2</sup> 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. …”
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2327
Yearly crude rates for alcohol-induced deaths among females by state in 2019, 2021, and 2024.
Published 2025Subjects: -
2328
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2329
Alcohol-induced fatalities and crude rates in the United States at the county level.
Published 2025Subjects: -
2330
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2331
Yearly crude rates stratified by age from 1999 to 2024 and by race from 1999 to 2020.
Published 2025Subjects: -
2332
Maps of alcohol-induced crude rates in the United States at the county level for 1999, 2021, 2024.
Published 2025Subjects: -
2333
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2334
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2335
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2336
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2337
Yearly crude rates for alcohol-induced deaths among males by state in 2019, 2021, and 2024.
Published 2025Subjects: -
2338
PGAM5 knockdown prevented rTsSPc from disrupting Caco-2 monolayer integrity.
Published 2025Subjects: -
2339
Inhibition of PGAM5 and apoptosis alleviated the rTsSPc-damaged Caco-2 monolayer barrier integrity.
Published 2025Subjects: -
2340