يعرض 801 - 820 نتائج من 8,891 نتيجة بحث عن 'significant ((((step decrease) OR (((teer decrease) OR (we decrease))))) OR (mean decrease))', وقت الاستعلام: 0.57s تنقيح النتائج
  1. 801

    Maternal stroke volume. حسب Pedro Henrique Salles Brito (10441343)

    منشور في 2024
    الموضوعات:
  2. 802

    Maternal heart rate. حسب Pedro Henrique Salles Brito (10441343)

    منشور في 2024
    الموضوعات:
  3. 803

    Fetal heart rate. حسب Pedro Henrique Salles Brito (10441343)

    منشور في 2024
    الموضوعات:
  4. 804
  5. 805
  6. 806

    Maternal cardiac output. حسب Pedro Henrique Salles Brito (10441343)

    منشور في 2024
    الموضوعات:
  7. 807

    Maternal blood pH. حسب Pedro Henrique Salles Brito (10441343)

    منشور في 2024
    الموضوعات:
  8. 808

    Maternal blood pO2. حسب Pedro Henrique Salles Brito (10441343)

    منشور في 2024
    الموضوعات:
  9. 809
  10. 810

    Multi-organ differential gene expression changes statistically significant at hypertension onset. حسب Eden Hornung (20148295)

    منشور في 2024
    "…<i>Tgfb1</i> is significantly decreased in male SHR kidney compared to female at 16 weeks of age (p = 0.004). …"
  11. 811

    Key safety measures including adverse events. حسب Anuja Dokras (8679261)

    منشور في 2025
    "…Waist circumference (mean change (MC) −2.23 cm; 95% CI [−3.98, −0.49]; <i>p</i> = 0.01), BMI (MC −0.49 kg/m<sup>2</sup>; 95% CI [−0.88, −0.10[; <i>p</i> = 0.01), and android fat mass measured by DXA (MC −167 g; 95% CI [−264, −71[; <i>p</i> < 0.001) decreased in the COCP group over the study period whilst there was no statistically significant changes in these parameters in the metformin only group when compared to baseline.. …"
  12. 812

    Structure diagram of ensemble model. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"
  13. 813

    Fitting formula parameter table. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"
  14. 814

    Test plan. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"
  15. 815

    Fitting surface parameters. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"
  16. 816

    Model generalisation validation error analysis. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"
  17. 817

    Empirical model prediction error analysis. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"
  18. 818

    Fitting curve parameters. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"
  19. 819

    Test instrument. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"
  20. 820

    Empirical model establishment process. حسب Hongqi Wang (2208238)

    منشور في 2024
    "…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …"