يعرض 5,841 - 5,860 نتائج من 18,221 نتيجة بحث عن 'significantly ((((((we decrease) OR (a decrease))) OR (linear decrease))) OR (greatest decrease))', وقت الاستعلام: 0.71s تنقيح النتائج
  1. 5841
  2. 5842

    Fig 5 - حسب Varun Tiwari (9258130)

    منشور في 2024
  3. 5843

    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. …"
  4. 5844
  5. 5845
  6. 5846
  7. 5847
  8. 5848
  9. 5849
  10. 5850
  11. 5851
  12. 5852

    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. …"
  13. 5853

    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. …"
  14. 5854

    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. …"
  15. 5855

    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. …"
  16. 5856

    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. …"
  17. 5857

    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. …"
  18. 5858

    Related to Fig 3. حسب Mohammad Nafees Ansari (22232505)

    منشور في 2025
    "…(<b>O</b>, <b>P</b>) Cell proliferation assay confirms an increase in cell proliferation of BT-474_ZFX<sup>OE</sup> cells (mean ± SEM, <i>n</i> = 5) (O), whereas BT-474_ZFX<sup>SL</sup> cells (mean ± SEM, <i>n</i> = 3) show decreased cell proliferation (P). (<b>Q</b>) Tumor growth kinetics recorded a significantly higher growth of BT-474_ZFX<sup>OE</sup> (mean ± SEM, <i>n</i> = 5) than BT-474_VECT<sup>OE</sup> tumors. …"
  19. 5859
  20. 5860

    RICTOR regulates UGCG expression via transcription factor Zinc Finger X-linked (ZFX). حسب Mohammad Nafees Ansari (22232505)

    منشور في 2025
    "…<b>(T)</b> Tumor growth kinetics recorded a significantly higher growth of MCF-7_ZFX<sup>OE</sup> (mean ± SEM, <i>n</i> = 4-6) than MCF-7_VECT<sup>OE</sup> tumors. …"