Showing 701 - 720 results of 8,689 for search 'significant ((((((teer decrease) OR (we decrease))) OR (nn decrease))) OR (mean decrease))', query time: 0.59s Refine Results
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    Multi-organ differential gene expression changes statistically significant at hypertension onset. by Eden Hornung (20148295)

    Published 2024
    “…<i>Tgfb1</i> is significantly decreased in male SHR kidney compared to female at 16 weeks of age (p = 0.004). …”
  14. 714

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

    Published 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.. …”
  15. 715

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

    Published 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. 716

    Fitting formula parameter table. by Hongqi Wang (2208238)

    Published 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. 717

    Test plan. by Hongqi Wang (2208238)

    Published 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. 718

    Fitting surface parameters. by Hongqi Wang (2208238)

    Published 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. 719

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

    Published 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. 720

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

    Published 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. …”