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Showing 1 - 20 results of 42 for search '(( significant decrease decrease ) OR ( significantly ((linear increased) OR (slight decrease)) ))~', query time: 0.51s Refine Results
  1. 1
    Significant condition.

    Significant condition. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  2. 2
    Results of significance for regression factors.

    Results of significance for regression factors. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  3. 3
    DDC for four anticoagulants from 2019 to 2023.

    DDC for four anticoagulants from 2019 to 2023. by Wei Luo (80175)

    Published 2025
    “…The trends in the utilization and expenditure of anticoagulants were examined using linear regression analysis.</p><p>Results</p><p>From 2019 to 2023, the DDDs of rivaroxaban demonstrated a significant annual increase in most hospitals (<i>p</i> < 0.05). …”
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  4. 4
    S1 Dataset -

    S1 Dataset - by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  5. 5
    Mineral content of raw materials.

    Mineral content of raw materials. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  6. 6
    Technical parameters of ultrafine cement.

    Technical parameters of ultrafine cement. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  7. 7
    Technical parameters of PVA fiber.

    Technical parameters of PVA fiber. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  8. 8
    Sample preparation and testing flow chart.

    Sample preparation and testing flow chart. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  9. 9
    XRD patterns of MUCG at different curing age.

    XRD patterns of MUCG at different curing age. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  10. 10
    FTIR spectra of MUCG at different curing age.

    FTIR spectra of MUCG at different curing age. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  11. 11
    Description of fitting models.

    Description of fitting models. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  12. 12
    Mineral composition of UFC, B, and SF.

    Mineral composition of UFC, B, and SF. by Junxia Zhou (14381598)

    Published 2024
    “…From Day 7 to Day 28, there was a slight increase in strength, by an amount of 10.66%. …”
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  13. 13
    Model-averaged coefficients, significance, and relative importance (RI) of the predictor variables for the GLMMs to evaluate effects on: (A) overall pollinator abundance; (B) fruit production per female tree, (C) seed production per female tree; and (D) seed weight. Farming system refers to ecological vs. conventional practices; CCA1 refers to the first axis of the CCA and depicts the relationship between the % of natural habitats in the landscape and the composition of the pollinator community (increase abundance of wild bees with the increase in natural habitats); Male-to-female ratio and Male-to-female ratio<sup>2</sup> indicate linear and quadratic terms, respectively. Significant <i>p</i>-values are marked in bold. RI was estimated by summing the Akaike weights over all the models where each explanatory variable appeared, and highlights the importance of these variables in the models [75].

    Model-averaged coefficients, significance, and relative importance (RI) of the predictor variables for the GLMMs to evaluate effects on: (A) overall pollinator abundance; (B) fruit... by Carmelo Gómez-Martínez (9384385)

    Published 2025
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  14. 14
    Data.

    Data. by Aroon La-up (14095691)

    Published 2025
    “…Osteoporosis prevalence remained stable in both males and females. The Linear Mixed-Effects Model analysis revealed significant associations between BMD and several factors: increasing age, female sex, diabetes status and BMI. …”
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  15. 15
    The Date.

    The Date. by Chengyuan Hao (21615653)

    Published 2025
    “…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”
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  16. 16
    Mann-Kendall test for the mean temperature index.

    Mann-Kendall test for the mean temperature index. by Chengyuan Hao (21615653)

    Published 2025
    “…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”
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  17. 17
    Variation curve of the extreme temperature index.

    Variation curve of the extreme temperature index. by Chengyuan Hao (21615653)

    Published 2025
    “…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”
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  18. 18
    Fluctuation trend of the mean temperature index.

    Fluctuation trend of the mean temperature index. by Chengyuan Hao (21615653)

    Published 2025
    “…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”
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  19. 19
    Variation curve of the mean temperature index.

    Variation curve of the mean temperature index. by Chengyuan Hao (21615653)

    Published 2025
    “…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”
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  20. 20
    Data Sheet 1_Unveiling spatiotemporal evolution and driving factors of ecosystem service value: interpretable HGB-SHAP machine learning model.docx

    Data Sheet 1_Unveiling spatiotemporal evolution and driving factors of ecosystem service value: interpretable HGB-SHAP machine learning model.docx by Xiangming Xu (420946)

    Published 2025
    “…The ESV exhibited a slight increase in two counties, while it demonstrated a decrease in the remaining 16 counties at the county scale. …”
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