Showing 201 - 220 results of 6,121 for search '(( i ((values decrease) OR (largest decrease)) ) OR ( a ((teer decrease) OR (linear decrease)) ))', query time: 0.70s Refine Results
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    Threading Behavior and Dynamics of Ring-Linear Polymer Blends under Poiseuille Flow by Deyin Wang (6028850)

    Published 2024
    “…When the flow field strength exceeds this critical value, ring-linear polymer blends will aggregate into a cluster due to the combination of entanglement between polymers and the large differences in the velocities of the polymers. …”
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    Data Sheet 1_Spatial and temporal variability in blue carbon accumulation in the largest salt marsh in British Columbia, Canada.docx by Karen E. Kohfeld (11936879)

    Published 2025
    “…We combined C measurements with <sup>210</sup>Pb chronologies, in addition to existing data from western Boundary Bay (BBW), to estimate C stocks (g C m<sup>-2</sup>) and accumulation rates (g C m<sup>-2</sup> yr<sup>-1</sup>) for the entire marsh. Total C stocks averaged 71 ± 37 Mg C ha<sup>-1</sup> for high marsh and 41 ± 36 Mg C ha<sup>-1</sup> for low marsh, with higher values in western Boundary Bay (BBW, BBM) compared to the east (BBE, MB). …”
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    Study 1_CFA1. by Chen-Yueh Chen (9740047)

    Published 2025
    “…Findings from Analysis of Covariance (ANCOVA) in Study II indicate that in Experiment I, positive electronic word of mouth does not help improve value co-creation among spectators while negative electronic word of mouth does decrease value co-creation among spectators. …”
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    Maximum and mean SL values of the call types. by Anna N. Osiecka (11739336)

    Published 2024
    “…Those modelled calls were then used in a permuted discriminant function analysis, support vector machine models, and linear models of Beecher’s information statistic, to investigate whether transmission loss will affect the retention of individual information of the signal. …”
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    A Locally Linear Dynamic Strategy for Manifold Learning. by Weifan Wang (4669081)

    Published 2025
    “…For 10-30% noise, where the Hebbian network employs a local linear transform, learning selectively increases signal direction alignment (blue) while simultaneously decreasing noise direction alignment (orange). …”