يعرض 6,801 - 6,820 نتائج من 18,438 نتيجة بحث عن 'significantly ((largest decrease) OR (((((we decrease) OR (a decrease))) OR (mean decrease))))', وقت الاستعلام: 0.64s تنقيح النتائج
  1. 6801

    Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s... حسب Mijung Song (13134633)

    منشور في 2025
    "…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …"
  2. 6802

    Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s... حسب Mijung Song (13134633)

    منشور في 2025
    "…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …"
  3. 6803

    Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s... حسب Mijung Song (13134633)

    منشور في 2025
    "…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …"
  4. 6804

    Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s... حسب Mijung Song (13134633)

    منشور في 2025
    "…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …"
  5. 6805

    Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s... حسب Mijung Song (13134633)

    منشور في 2025
    "…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …"
  6. 6806

    Direct Observation of Liquid–Liquid Phase Separation and Core–Shell Morphology of PM<sub>2.5</sub> Collected from Three Northeast Asian Cities and Implications for N<sub>2</sub>O<s... حسب Mijung Song (13134633)

    منشور في 2025
    "…As the shell becomes more viscous, the diffusivity of N<sub>2</sub>O<sub>5</sub> decreases, thereby lowering the N<sub>2</sub>O<sub>5</sub> uptake coefficient by 1–3 orders of magnitude and significantly restricting N<sub>2</sub>O<sub>5</sub> uptake. …"
  7. 6807
  8. 6808

    Prediction of transition readiness. حسب Sharon Barak (4803966)

    منشور في 2025
    "…In most transition domains, help needed did not decrease with age and was not affected by function. …"
  9. 6809

    Dataset visualization diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  10. 6810

    Dataset sample images. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  11. 6811

    Performance comparison of different models. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  12. 6812

    C2f and BC2f module structure diagrams. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  13. 6813

    YOLOv8n detection results diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  14. 6814

    YOLOv8n-BWG model structure diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  15. 6815

    BiFormer structure diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  16. 6816

    YOLOv8n-BWG detection results diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  17. 6817

    GSConv module structure diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  18. 6818

    Performance comparison of three loss functions. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  19. 6819

    mAP0.5 Curves of various models. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  20. 6820

    Network loss function change diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"