يعرض 5,921 - 5,940 نتائج من 18,262 نتيجة بحث عن 'significantly ((((((we decrease) OR (linear decrease))) OR (a decrease))) OR (larger decrease))', وقت الاستعلام: 0.47s تنقيح النتائج
  1. 5921

    Fig 7 - حسب Varun Tiwari (9258130)

    منشور في 2024
  2. 5922
  3. 5923
  4. 5924
  5. 5925

    Fig 5 - حسب Varun Tiwari (9258130)

    منشور في 2024
  6. 5926

    RICTOR silencing inhibits cell proliferation via UGCG regulation. حسب Mohammad Nafees Ansari (22232505)

    منشور في 2025
    "…(<b>C</b>) Cell proliferation studies show a decrease in the proliferation of MCF-7_RICTOR<sup>SH</sup> cells (mean ± SEM, <i>n</i> = 4) compared to MCF-7_SCRAM<sup>SH</sup> cells. …"
  7. 5927
  8. 5928
  9. 5929
  10. 5930
  11. 5931
  12. 5932
  13. 5933
  14. 5934
  15. 5935

    Trends in spatial beta diversity over time. حسب Zoë J. Kitchel (21688386)

    منشور في 2025
    "…A lack of significant trend is shown in blue. The average linear trend across surveys (black line with 95% confidence interval in gray) is also plotted from a linear mixed effect model with a random slope and intercept for survey. …"
  16. 5936

    Major hyperparameters of RF-SVR. حسب Jintao Li (448681)

    منشور في 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. …"
  17. 5937

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

    منشور في 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. …"
  18. 5938

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

    منشور في 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. …"
  19. 5939

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

    منشور في 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. …"
  20. 5940

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

    منشور في 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. …"