يعرض 4,921 - 4,940 نتائج من 21,342 نتيجة بحث عن '(( significantly ((mean decrease) OR (greater decrease)) ) OR ( significant decrease decrease ))', وقت الاستعلام: 0.32s تنقيح النتائج
  1. 4921
  2. 4922
  3. 4923
  4. 4924

    List of triathlon events analyzed. حسب Junhui Zhao (626062)

    منشور في 2024
    الموضوعات:
  5. 4925
  6. 4926
  7. 4927
  8. 4928
  9. 4929

    Flow diagram of study participants. حسب Yushan Shi (16440272)

    منشور في 2024
    الموضوعات:
  10. 4930
  11. 4931

    Setting up for VFSS image. حسب Rie Asayama (693361)

    منشور في 2025
    الموضوعات:
  12. 4932
  13. 4933

    Demographics of the study population. حسب Sohyun Park (78358)

    منشور في 2024
    "…</p><p>Results</p><p>Based on the final clinical diagnosis, 79 patients with iPD and 16 disease controls were included. The mean OBH was significantly smaller in iPD than in disease controls (<i>p</i> < 0.0001). …"
  14. 4934

    Table 3 - حسب Sohyun Park (78358)

    منشور في 2024
    "…</p><p>Results</p><p>Based on the final clinical diagnosis, 79 patients with iPD and 16 disease controls were included. The mean OBH was significantly smaller in iPD than in disease controls (<i>p</i> < 0.0001). …"
  15. 4935

    Mean GHQ scores (2019- September 2021). حسب Mhairi Webster (20454888)

    منشور في 2024
    "…Loneliness accounted for a share of the mental health gender gap, and a more decrease in mental health was recorded for young women experiencing loneliness, compared to older age groups. …"
  16. 4936

    Top 10 significant functional annotations of up-regulated DEGs. حسب Meitner Cadena (22216261)

    منشور في 2025
    "…Functional annotations are ordered by decreasing significance, with color indicating significance according to the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…"
  17. 4937

    Top 10 significant functional annotations of down-regulated DEGs. حسب Meitner Cadena (22216261)

    منشور في 2025
    "…Functional annotations are ordered by decreasing significance, with color indicating significance level based on the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…"
  18. 4938

    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. …"
  19. 4939

    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. …"
  20. 4940

    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. …"