Showing 1 - 20 results of 4,101 for search '(( making ((a decrease) OR (mean decrease)) ) OR ( a ((larger decrease) OR (marked decrease)) ))', query time: 0.68s Refine Results
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    Modeling the Shape and Stability of Co Nanoparticles as a Function of Size and Support Interactions through DFT Calculations and Monte Carlo Simulations by Enrico Sireci (12127349)

    Published 2025
    “…Increasing MSI led to a flattening of the NPs on the support as well as to decreasing Co dispersion but hardly affected the site distribution, suggesting that they do not alter the NPs intrinsic activity. …”
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    Scheme of g-λ model with larger values λ. by Zhanfeng Fan (20390992)

    Published 2024
    “…<div><p>This paper theoretically explores the propagation attenuation of normally incident P-waves on a single uncoupled joint exhibiting nonlinear deformation behavior. …”
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    Biases in larger populations. by Sander W. Keemink (21253563)

    Published 2025
    “…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …”
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    Force analysis of a single rib. by Jianbo Jia (717814)

    Published 2024
    “…Finally, by making corresponding test blocks, it was found that the peak value of forward resistance when the 10.5°test block crossed the steps of 1mm, 2mm and 3mm height decreased by 19.8%, 25.0% and 13.9% respectively, and the mean value decreased by 30.8%, 27.2% and 24.1% respectively, which was close to the simulation results, and verified the accuracy of the finite element analysis results.…”
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    ROC analysis to mark selectivity results in mostly mixed-selective units. by Thomas S. Wierda (22404198)

    Published 2025
    “…The large number of mixed selective units also results in a significant decrease in accuracy when these neurons are targeted as compared to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013559#pcbi.1013559.g006" target="_blank">Fig 6c</a> where there was no significant effect visible after targeting mixed selective units, likely because there were less mixed selective units present. …”
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    Marginal means – Pooled across scenarios. by Mehdi Mourali (10170245)

    Published 2025
    “…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”