Showing 3,501 - 3,520 results of 18,479 for search 'significant ((((gap decrease) OR (((a decrease) OR (greater decrease))))) OR (mean decrease))', query time: 0.68s Refine Results
  1. 3501

    Data Sheet 3_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

    Published 2025
    “…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”
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    Impact of Cold Plasma Treatment on the Shelf Life and Metabolite Profiles of Strawberries during Storage by D. Abouelenein (22365684)

    Published 2025
    “…Although ascorbic acid decreased after treatment, greater stability was noted during storage. …”
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    Flow chart for inclusion and exclusion criteria. by Maleda Tefera (9025154)

    Published 2025
    “…Similarly, children who breastfed until the second year of life had a greater chance of underweight and stunting. This study highlights a significant prevalence of undernutrition among children. …”
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    Major hyperparameters of RF-SVR. by Jintao Li (448681)

    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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    Pseudo code for coupling model execution process. by Jintao Li (448681)

    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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    Major hyperparameters of RF-MLPR. by Jintao Li (448681)

    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. …”
  10. 3510

    Results of RF algorithm screening factors. by Jintao Li (448681)

    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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    Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

    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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    <b>Data for s</b><b>easonal variations in coral lipids and their significance for energy maintenance in the </b><b>South China Sea</b> by Hongyan Mo (19721569)

    Published 2024
    “…<p dir="ltr">In recent years, the intensification of global warming and extreme climate have led to an increase in the frequency and severity of coral bleaching. Coral bleaching means a decrease in symbiotic zooxanthellae density (ZD). …”
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    This is the raw data used for this study. by James M. Friedman (22522150)

    Published 2025
    “…Average age was 48. 37% were male. 89% of patients who received a preoperative subcoracoid injection reported a significant decrease in presenting symptoms. 6 months after PM release, median VAS pain scores decreased from 8 to 2. …”
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    Pre-operative versus post-operative symptoms. by James M. Friedman (22522150)

    Published 2025
    “…Average age was 48. 37% were male. 89% of patients who received a preoperative subcoracoid injection reported a significant decrease in presenting symptoms. 6 months after PM release, median VAS pain scores decreased from 8 to 2. …”