Showing 5,041 - 5,060 results of 17,947 for search 'significantly ((((larger decrease) OR (teer decrease))) OR (((nn decrease) OR (a decrease))))', query time: 0.66s Refine Results
  1. 5041

    Scheme of the SiTFarm tool–farm to sector level. by Jure Brečko (20314959)

    Published 2024
    “…However, the variability is significant, with a coefficient of variation 0.74. Only 25% of farms exceeded 17.15 €/h, while 25% did not surpass 4.46 €/h. …”
  2. 5042

    S1 File - by Jure Brečko (20314959)

    Published 2024
    “…However, the variability is significant, with a coefficient of variation 0.74. Only 25% of farms exceeded 17.15 €/h, while 25% did not surpass 4.46 €/h. …”
  3. 5043

    GHG emissions in TAHs. by Jure Brečko (20314959)

    Published 2024
    “…However, the variability is significant, with a coefficient of variation 0.74. Only 25% of farms exceeded 17.15 €/h, while 25% did not surpass 4.46 €/h. …”
  4. 5044

    Variation law of UCS. by Wenyu Lv (20139458)

    Published 2025
    “…Notably, the attenuation constant λ follows a monotonically decreasing pattern with increasing loading rate. …”
  5. 5045

    Detail of the personalized-enhanced GCN. by Yuanming Ding (12842858)

    Published 2025
    “…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
  6. 5046

    Enhanced multi-component module. by Yuanming Ding (12842858)

    Published 2025
    “…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
  7. 5047

    The architecture of the TCBiL. by Yuanming Ding (12842858)

    Published 2025
    “…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
  8. 5048

    Detail of the encoder. by Yuanming Ding (12842858)

    Published 2025
    “…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
  9. 5049

    Detail of the Fourier transform. by Yuanming Ding (12842858)

    Published 2025
    “…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
  10. 5050

    Detail of the decoder. by Yuanming Ding (12842858)

    Published 2025
    “…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
  11. 5051

    Encoder-decoder architecture. by Yuanming Ding (12842858)

    Published 2025
    “…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
  12. 5052

    Dataset description. by Yuanming Ding (12842858)

    Published 2025
    “…The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. …”
  13. 5053
  14. 5054

    Study design. by Moazzam Tanveer (20775814)

    Published 2025
    “…<div><p>Background</p><p>Childhood obesity poses a significant public health challenge, yet effective school-based physical activity (PA) interventions remain scarce, especially in Pakistan. …”
  15. 5055
  16. 5056

    Prevaelnce of different pathogens by location. by Amete Mihret Teshale (12072758)

    Published 2025
    “…<div><p>Diarrheal illness remains a major global health challenge, causing millions of deaths annually. …”
  17. 5057

    Frontier Analysis Based on ASDR and SDI. by Guanghui Yu (423945)

    Published 2025
    “…<div><p>Background and Objectives</p><p>Hypertension is a major risk factor for aortic aneurysm (AA), but the global, regional, and national patterns of its related disease burden are not well studied. …”
  18. 5058
  19. 5059

    Model vs. WKY group KEGG pathways (Top 15). by Xiao Zhang (152326)

    Published 2025
    “…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”
  20. 5060

    Behavioral data. by Xiao Zhang (152326)

    Published 2025
    “…</p><p>Results</p><p>From Day 1–5, compared with the WKY group, both the Model and SHR groups exhibited shortened incubation periods and slower average swimming speeds (<i><i>P</i></i> < 0.01); On day 6, compared with the WKY group, the Model group showed a significant decrease in the number of platform crossings (<i><i>P</i></i> < 0.05), time spent in the target quadrant (<i><i>P</i></i> < 0.05), and total distance traveled in the target quadrant (<i><i>P</i></i> < 0.05). …”