Showing 181 - 200 results of 18,171 for search 'significantly ((((((we decrease) OR (a decrease))) OR (teer decrease))) OR (linear decrease))', query time: 0.49s Refine Results
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    Data for the G7 countries in 2022. by Luis Alberiko Gil-Alana (14659817)

    Published 2024
    “…The results indicate that only France displays a significant negative trend and thus a continuous decrease in the level of alcohol consumption. …”
  8. 188

    Time series plots: Alcohol consumption. by Luis Alberiko Gil-Alana (14659817)

    Published 2024
    “…The results indicate that only France displays a significant negative trend and thus a continuous decrease in the level of alcohol consumption. …”
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    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. …”
  11. 191

    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. …”
  12. 192

    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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    Detailed information of the observation datasets. by Weidong Ji (129916)

    Published 2025
    “…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”
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    General technical specification for GW154/6700. by Weidong Ji (129916)

    Published 2025
    “…On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”