Showing 401 - 420 results of 9,590 for search '(( significantly ((we decrease) OR (linear decrease)) ) OR ( significantly impact decrease ))', query time: 0.37s Refine Results
  1. 401

    Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

    Published 2024
    “…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”
  2. 402

    Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

    Published 2024
    “…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”
  3. 403

    Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

    Published 2024
    “…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”
  4. 404

    Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

    Published 2024
    “…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”
  5. 405

    Effect of the Surface Peak–Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature by Yunlong Jiao (6672764)

    Published 2024
    “…When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (<i>Sa</i>), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (<i>Ssk</i> < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (<i>We</i>) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the <i>We</i> increases. …”
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  11. 411

    Geometric manifold comparison visualization by Eloy Geenjaar (21533195)

    Published 2025
    “…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
  12. 412

    Hyperparameter ranges by Eloy Geenjaar (21533195)

    Published 2025
    “…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
  13. 413

    Convolutional vs RNN context encoder by Eloy Geenjaar (21533195)

    Published 2025
    “…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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  19. 419

    Targeted removal robustness analysis. by Fei Wang (19534)

    Published 2025
    “…Distance increased the relative abundance of Actinobacteria, Acidobacteria, and Chloroflexi, but significantly decreased the relative abundance of Proteobacteria and Firmicutes. …”
  20. 420

    The network parameter at different distances. by Fei Wang (19534)

    Published 2025
    “…Distance increased the relative abundance of Actinobacteria, Acidobacteria, and Chloroflexi, but significantly decreased the relative abundance of Proteobacteria and Firmicutes. …”