Showing 161 - 180 results of 7,226 for search '(( significant factor decrease ) OR ( significantly ((greater decrease) OR (linear decrease)) ))', query time: 0.79s Refine Results
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    Model A: Logistic structural model. by Abigail A. Lee (19935335)

    Published 2024
    “…Greater distance from care predicts greater HPV vaccine hesitancy and decreased intent to vaccinate against HPV. …”
  6. 166

    Split structural models for belief. by Abigail A. Lee (19935335)

    Published 2024
    “…<p>Trust in government and positive general vaccine attitudes predicted greater intent to vaccinate. No other latent variables or covariates significantly affected intent to vaccinate. …”
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    Test soil parameters. by Yonggang Huang (223155)

    Published 2025
    “…When the length of UHMWPE is greater than 9 mm, there is no significant decrease in the free swelling ratio of the reinforced soil (<i>p</i> = 0.165). …”
  9. 169

    Influence of UHMWPE length on swelling pressure. by Yonggang Huang (223155)

    Published 2025
    “…When the length of UHMWPE is greater than 9 mm, there is no significant decrease in the free swelling ratio of the reinforced soil (<i>p</i> = 0.165). …”
  10. 170

    UHMWPF parameters. by Yonggang Huang (223155)

    Published 2025
    “…When the length of UHMWPE is greater than 9 mm, there is no significant decrease in the free swelling ratio of the reinforced soil (<i>p</i> = 0.165). …”
  11. 171

    Influence of UHMWPE content on swelling pressure. by Yonggang Huang (223155)

    Published 2025
    “…When the length of UHMWPE is greater than 9 mm, there is no significant decrease in the free swelling ratio of the reinforced soil (<i>p</i> = 0.165). …”
  12. 172

    Soil partice-size distribution. by Yonggang Huang (223155)

    Published 2025
    “…When the length of UHMWPE is greater than 9 mm, there is no significant decrease in the free swelling ratio of the reinforced soil (<i>p</i> = 0.165). …”
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    Results of RF algorithm screening factors. 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. …”
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