Showing 1,121 - 1,140 results of 18,438 for search 'significantly ((largest decrease) OR (((we decrease) OR (((a decrease) OR (mean decrease))))))', query time: 0.68s Refine Results
  1. 1121

    Presentation 1_Prehospital tranexamic acid decreases early mortality in trauma patients: a systematic review and meta-analysis.pdf by Yi Li (1144)

    Published 2025
    “…</p>Conclusion<p>Prehospital TXA decreases early (24-h) mortality in trauma patients without a significant increase in the risk of VTE and other complications, and further studies are still needed to improve and optimize its management strategy.…”
  2. 1122
  3. 1123
  4. 1124
  5. 1125

    Raw data. by Jia Zhu (135506)

    Published 2025
    “…The remaining working conditions did not exhibit a significant difference. However, the observed decreasing trend was consistent with previously documented research. …”
  6. 1126
  7. 1127

    Fig 4 - by Hyuk Sung Yoon (20208672)

    Published 2024
    Subjects:
  8. 1128

    Top view of the experimental setup. by Muhammad Hammad Bucha (21736111)

    Published 2025
    “…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”
  9. 1129

    Parameters of energy harvesting. by Muhammad Hammad Bucha (21736111)

    Published 2025
    “…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”
  10. 1130

    Graph for Max Amplitude/Length at G<sub>y</sub> = 0. by Muhammad Hammad Bucha (21736111)

    Published 2025
    “…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”
  11. 1131

    Graph for maximum Frequency at G<sub>y</sub> = 0. by Muhammad Hammad Bucha (21736111)

    Published 2025
    “…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”
  12. 1132

    Graph for maximum Power at G<sub>y</sub> = 0. by Muhammad Hammad Bucha (21736111)

    Published 2025
    “…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”
  13. 1133

    Summary of experimentation results. by Muhammad Hammad Bucha (21736111)

    Published 2025
    “…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”
  14. 1134

    Piezoelectric eel. by Muhammad Hammad Bucha (21736111)

    Published 2025
    “…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”
  15. 1135
  16. 1136

    Geometric manifold comparison visualization by Eloy Geenjaar (21533195)

    Published 2025
    “…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. 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. …”
  17. 1137

    Hyperparameter ranges by Eloy Geenjaar (21533195)

    Published 2025
    “…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. 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. …”
  18. 1138

    Convolutional vs RNN context encoder by Eloy Geenjaar (21533195)

    Published 2025
    “…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. 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. …”
  19. 1139
  20. 1140