Showing 1 - 20 results of 88,140 for search '(( significant ((gap decrease) OR (_ decrease)) ) OR ( significant temporal scales ))', query time: 0.77s Refine Results
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    Temporal scale or landmark dependent significant correlations. by Bastien Perroy (14134841)

    Published 2025
    “…<p>Temporal scale or landmark dependent significant correlations.…”
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    Regenerating axons are increased by ATRA and decreased by clodronate. by Valeria De La Rosa-Reyes (6658613)

    Published 2021
    “…Compared to PBS-treated, the number of regenerating axons is doubled by ATRA treatment, decreased by 30% by clodronate, and, in the case of ATRA combined with clodronate, is not significantly different.…”
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    Overexpression of Gαq decreases cell number and cell size. by Dharsan K. Soundarrajan (11632145)

    Published 2021
    “…Student t-test was used for statistical significance testing.</p> <p>(TIF)</p>…”
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    Syndecan ectodomain fragments decrease endothelial resistance via rho kinase. by Melanie Jannaway (6703577)

    Published 2019
    “…(B) rhS3ED fragments induced a highly significant drop in TER (n = 6, p<0.0001). (C) rhS4ED fragments significantly decreased TER (n = 8, p = 0.0002). …”
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    Post hoc t-test comparisons of FuzzyEn across temporal scales in groups with significant ANCOVA results. by Ayumu Ueno (20899814)

    Published 2025
    “…<p>As the post hoc <i>t</i>-test, The average of FuzzyEn dependency on the temporal scale in the groups (TD, ADHD, and drug-naïve ADHD) were compared in the groups that were significant in the ANCOVA. …”
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    Multi-scale temporal attention component. by Kang Xu (708915)

    Published 2025
    “…<div><p>Accurate traffic flow prediction is vital for intelligent transportation systems but presents significant challenges. Existing methods, however, have the following limitations: (1) insufficient exploration of interactions across different temporal scales, which restricts effective future flow prediction; (2) reliance on predefined graph structures in graph neural networks, making it challenging to accurately model the spatial relationships in complex road networks; and (3) end-to-end training, which often results in unclear optimization directions for model parameters, thereby limiting improvements in predictive performance. …”
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