Showing 941 - 960 results of 8,902 for search 'significantly ((((((we decrease) OR (largest decrease))) OR (nn decrease))) OR (mean decrease))', query time: 0.57s Refine Results
  1. 941

    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. …”
  2. 942

    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. …”
  3. 943

    Fitting curve 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. …”
  4. 944

    Test instrument. 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. …”
  5. 945

    Empirical model establishment process. 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. …”
  6. 946

    Model prediction error trend chart. 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. …”
  7. 947

    Basic physical parameters of red clay. 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. …”
  8. 948

    BP neural network structure diagram. 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. …”
  9. 949

    Structure diagram of GBDT 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. …”
  10. 950

    Model prediction error analysis index. 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. …”
  11. 951

    Fitting curve 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. …”
  12. 952

    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. …”
  13. 953
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  15. 955

    <i>SLC2A2</i> is essential for liver differentiation in developing vertebrates by Yejin Kim (740789)

    Published 2025
    “…Data are represented as mean ± standard error of the mean (SEM). (G) Quantitative RT-PCR analysis was performed to evaluate the expression levels of igf1r. …”
  16. 956

    Datasets used in the study. by Rajon Banik (12066099)

    Published 2025
    “…</p><p>Conclusion</p><p>The findings indicate a significant increase in the availability of health facilities offering modern family planning services in Bangladesh; however, a slight decline has been observed in their overall mean readiness score. …”
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