Showing 67,541 - 67,560 results of 125,008 for search '(( 5 ((nn decrease) OR (a decrease)) ) OR ( a ((mean decrease) OR (point decrease)) ))', query time: 1.37s Refine Results
  1. 67541

    Table1_Case Report: Immune Microenvironment and Mutation Features in a Patient With Epstein–Barr Virus Positive Large B-Cell Lymphoma Secondary to Angioimmunoblastic T-Cell Lymphom... by Fen Zhang (608307)

    Published 2022
    “…Analysis of 22 kinds of immune cells showed that the numbers of activated NK cells and activated memory T cells increased, while the T-follicular helper population decreased in the transformed sample. In addition, compared with the primary sample, RHOA (G17V) mutation was not detected, while JAK2 and TRIP12 gene mutations were detected in the transformed sample. …”
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    Table 3_Association between platelet-to-red cell distribution width ratio and all-cause mortality in critically ill patients with non-traumatic cerebral hemorrhage: a retrospective... by Rongrong Lu (322302)

    Published 2024
    “…As PRR increased, restrictive cubic splines showed a progressive decrease in the probability of all-cause mortality. …”
  7. 67547

    Table 1_Association between platelet-to-red cell distribution width ratio and all-cause mortality in critically ill patients with non-traumatic cerebral hemorrhage: a retrospective... by Rongrong Lu (322302)

    Published 2024
    “…As PRR increased, restrictive cubic splines showed a progressive decrease in the probability of all-cause mortality. …”
  8. 67548

    Table 2_Association between platelet-to-red cell distribution width ratio and all-cause mortality in critically ill patients with non-traumatic cerebral hemorrhage: a retrospective... by Rongrong Lu (322302)

    Published 2024
    “…As PRR increased, restrictive cubic splines showed a progressive decrease in the probability of all-cause mortality. …”
  9. 67549

    Data_Sheet_1_A pooled analysis of temporal trends in the prevalence of anxiety-induced sleep loss among adolescents aged 12–15 years across 29 countries.docx by Guodong Xu (479781)

    Published 2023
    “…</p>Conclusion<p>Trends in the prevalence of anxiety-induced sleep loss in adolescents varied significantly across different countries. Generally, a stable trend was observed in 21 of the 29 countries surveyed. …”
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  18. 67558

    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. …”
  19. 67559

    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. …”
  20. 67560

    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. …”