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Showing 1 - 20 results of 3,255 for search '(( significantly vary decrease ) OR ( significantly ((linear decrease) OR (larger decrease)) ))', query time: 0.54s Refine Results
  1. 1
    Larger example of stimuli with cue relevant for Task 3.

    Larger example of stimuli with cue relevant for Task 3. by Marcel F. Hinss (14675457)

    Published 2024
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  2. 2
    Cohesion characteristics and statistical significance analysis of different ingredients across IDDSI levels<sup>b'*'</sup>.

    Cohesion characteristics and statistical significance analysis of different ingredients across IDDSI levels<sup>b'*'</sup>. by Muxi Chen (9294270)

    Published 2025
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  3. 3
    Adhesion characteristics and statistical significance analysis of different ingredients across IDDSI levels<sup>b'*'</sup>.

    Adhesion characteristics and statistical significance analysis of different ingredients across IDDSI levels<sup>b'*'</sup>. by Muxi Chen (9294270)

    Published 2025
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  4. 4
    Hardness characteristics and statistical significance analysis of different ingredients across IDDSI levels<sup>b'*'</sup>.

    Hardness characteristics and statistical significance analysis of different ingredients across IDDSI levels<sup>b'*'</sup>. by Muxi Chen (9294270)

    Published 2025
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  5. 5
    Linear covariate analysis of prognostically significant metabolites. Presenting the effect sizes of metabolites that showed significant differences among prognostic groups in ICU-treated COVID-19 patients. Metabolites were included based on an F-value > 2.5 and a p-value < 0.05. Metabolites marked with a single asterisk (*) were significant in both two-way ANOVA and ASCA, whereas those marked with double asterisks (**) were significant only in two-way ANOVA. Positive effect sizes indicate an increase in metabolite concentration between the compared groups, while negative values indicate a decrease.

    Linear covariate analysis of prognostically significant metabolites.... by Emir Matpan (21655811)

    Published 2025
    “…<p>Linear covariate analysis of prognostically significant metabolites. …”
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  6. 6
    Distribution of the same ingredient across different IDDSI levels (Percentages and frequencies) and statistical significance analysis (Chi-square analysis).

    Distribution of the same ingredient across different IDDSI levels (Percentages and frequencies) and statistical significance analysis (Chi-square analysis). by Muxi Chen (9294270)

    Published 2025
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  7. 7
    Distribution of different ingredients at the same IDDSI level (Percentage and frequency) and statistical significance analysis (Chi-square test)<sup>b'*'</sup>.

    Distribution of different ingredients at the same IDDSI level (Percentage and frequency) and statistical significance analysis (Chi-square test)<sup>b'*'</sup>. by Muxi Chen (9294270)

    Published 2025
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  8. 8
    <i>CTSZ</i> SNPs significantly associated with TB severity in Ugandan household contact study cohorts, sorted by ascending <i>p</i>-value.

    <i>CTSZ</i> SNPs significantly associated with TB severity in Ugandan household contact study cohorts, sorted by ascending <i>p</i>-value. by Rachel K. Meade (22216529)

    Published 2025
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  9. 9
    Multiple linear regression analysis results.

    Multiple linear regression analysis results. by Muxi Chen (9294270)

    Published 2025
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  10. 10
    The linearly decreasing convergence factor a.

    The linearly decreasing convergence factor a. by Junhao Wei (6816803)

    Published 2025
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  11. 11
    Brain regions with significantly decreased activations for the Raven task are shown in blue.

    Brain regions with significantly decreased activations for the Raven task are shown in blue. by Morteza Fattahi (15892028)

    Published 2025
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  12. 12
    Introgression decreases allelic shuffling.

    Introgression decreases allelic shuffling. by Enrique J. Schwarzkopf (6579275)

    Published 2025
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  13. 13
    Statistically significant linear increase in use of antidepressants during the observed time period.

    Statistically significant linear increase in use of antidepressants during the observed time period. by Jelena Filimonovic (22305307)

    Published 2025
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  14. 14
    Parameters used in the simulations.

    Parameters used in the simulations. by Yongfeng Zhu (7361045)

    Published 2025
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  15. 15
    The CDF of normal and tangential contact forces in numerical specimens after compression considering different particle morphologies: (a) normal contact force; (b) tangential contact force; and different particle sizes: (c) normal contact force; (d) tangential contact force.

    The CDF of normal and tangential contact forces in numerical specimens after compression considering different particle morphologies: (a) normal contact force; (b) tangential conta... by Yongfeng Zhu (7361045)

    Published 2025
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  16. 16
    Two ellipsoidal particles in contact.

    Two ellipsoidal particles in contact. by Yongfeng Zhu (7361045)

    Published 2025
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  17. 17
    The normal force between two particles with different equivalent diameters for P3: (a) hertznormal contact force; (b) normal viscous contact force.

    The normal force between two particles with different equivalent diameters for P3: (a) hertznormal contact force; (b) normal viscous contact force. by Yongfeng Zhu (7361045)

    Published 2025
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  18. 18
    Different ellipsoidal particles with De = 6.30 mm used in the discrete element simulations.

    Different ellipsoidal particles with De = 6.30 mm used in the discrete element simulations. by Yongfeng Zhu (7361045)

    Published 2025
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  19. 19
    Average contact force and percentage of strong contact force under different conditions: (a) different particle shapes, (b) different particle sizes.

    Average contact force and percentage of strong contact force under different conditions: (a) different particle shapes, (b) different particle sizes. by Yongfeng Zhu (7361045)

    Published 2025
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  20. 20
    Stress concentration factor value in the numerical samples with different shapes and sizes.

    Stress concentration factor value in the numerical samples with different shapes and sizes. by Yongfeng Zhu (7361045)

    Published 2025
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