Search alternatives:
significant temporal » significant compared (Expand Search)
temporal scales » temporal changes (Expand Search)
gap decrease » a decrease (Expand Search), gain decreased (Expand Search), mean decrease (Expand Search)
_ decrease » _ decreased (Expand Search)
significant temporal » significant compared (Expand Search)
temporal scales » temporal changes (Expand Search)
gap decrease » a decrease (Expand Search), gain decreased (Expand Search), mean decrease (Expand Search)
_ decrease » _ decreased (Expand Search)
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Temporal scale or landmark dependent significant correlations.
Published 2025“…<p>Temporal scale or landmark dependent significant correlations.…”
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Words for describing activities that decrease and/or increase Subjective Well-Being.
Published 2022Subjects: -
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Regenerating axons are increased by ATRA and decreased by clodronate.
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.
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.
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.
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.
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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