Showing 781 - 800 results of 1,471 for search '(( significant decrease decrease ) OR ( significantly ((a decrease) OR (point decrease)) ))~', query time: 0.49s Refine Results
  1. 781

    Demographic characteristics of participants. by Maryam Rafiee (10368504)

    Published 2025
    “…Furthermore, an increase of one point in work ability score leads to a decrease in WMSDs in the neck, wrists/hands, low back and hips/thighs regions by 13.5%, 8%, 11.5%, and 9%, respectively.…”
  2. 782

    Dataset visualization diagram. by Yaojun Zhang (389482)

    Published 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%. …”
  3. 783

    Dataset sample images. by Yaojun Zhang (389482)

    Published 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%. …”
  4. 784

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

    Published 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%. …”
  5. 785

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

    Published 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%. …”
  6. 786

    YOLOv8n detection results diagram. by Yaojun Zhang (389482)

    Published 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%. …”
  7. 787

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

    Published 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%. …”
  8. 788

    BiFormer structure diagram. by Yaojun Zhang (389482)

    Published 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%. …”
  9. 789

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

    Published 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. 790

    GSConv module structure diagram. by Yaojun Zhang (389482)

    Published 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. 791

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

    Published 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. 792

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

    Published 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. 793

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

    Published 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. 794

    Comparative diagrams of different indicators. by Yaojun Zhang (389482)

    Published 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. 795

    YOLOv8n structure diagram. by Yaojun Zhang (389482)

    Published 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. 796

    Geometric model of the binocular system. by Yaojun Zhang (389482)

    Published 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. 797

    Enhanced dataset sample images. by Yaojun Zhang (389482)

    Published 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. 798

    Baseline measures of participants. by Philippa Charlotte Rose Wheble (21578605)

    Published 2025
    “…<div><p>Music Performance Anxiety, a subset of social anxiety disorder (SAD), can significantly impede the lives of professional voice users (PVUs). …”
  19. 799

    Demographic characteristics of participants. by Philippa Charlotte Rose Wheble (21578605)

    Published 2025
    “…<div><p>Music Performance Anxiety, a subset of social anxiety disorder (SAD), can significantly impede the lives of professional voice users (PVUs). …”
  20. 800

    Fig S1. by Leonardo Santos (477304)

    Published 2025
    “…Black arrows indicate the formation of heteroduplexes, which are absent in the controls of uninduced and untransfected cells, as well as in induced but untransfected cells (Ctrl and Ctrl2, respectively). A noticeable decrease in amplicon yield suggests significant modifications following Cas9 cleavage and DNA repair processes. …”