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significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
significantly point » significantly onto (Expand Search), significantly poorer (Expand Search), significantly lower (Expand Search)
point decrease » point increase (Expand Search)
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1001
YOLOv8n-BWG detection results diagram.
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%. …”
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1002
GSConv module structure diagram.
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%. …”
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1003
Performance comparison of three loss functions.
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%. …”
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1004
mAP0.5 Curves of various models.
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%. …”
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1005
Network loss function change diagram.
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%. …”
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1006
Comparative diagrams of different indicators.
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%. …”
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1007
YOLOv8n structure diagram.
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%. …”
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1008
Geometric model of the binocular system.
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%. …”
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1009
Enhanced dataset sample images.
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%. …”
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1010
Baseline measures of participants.
Published 2025“…Post hoc Wilcoxon-signed rank tests with Bonferroni correction for multiple comparisons revealed significant differences between K-MPAI scores at time points Post 3 and Pre 3 (z = 3.00, adj. p = .040), Post 3 and Pre 2 (z = 3.27, adj. p = .016) and Post 3 and Pre 1 (z = 2.95, adj. p = .048). …”
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1011
Demographic characteristics of participants.
Published 2025“…Post hoc Wilcoxon-signed rank tests with Bonferroni correction for multiple comparisons revealed significant differences between K-MPAI scores at time points Post 3 and Pre 3 (z = 3.00, adj. p = .040), Post 3 and Pre 2 (z = 3.27, adj. p = .016) and Post 3 and Pre 1 (z = 2.95, adj. p = .048). …”
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1012
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1013
Fig S1.
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. …”
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1014
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1015
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1016
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1017
The head-withdrawal threshold of naïve rats receiving CGRP.
Published 2025“…In naïve rats receiving CGRP, a significant decrease was found at 1 day after the CGRP administration as compared to that before administration. * <i>p</i> < 0.05. …”
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1018
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1019
Inverse Simpson index (A) and richness (B) shown for the post-operative and perioperative groups.
Published 2025“…Significant differences were noted in richness (B) overall between time points (p < 0.05) in both the perioperative and post-operative group, and specifically between Baseline and Recheck 1 (p = 0.02 in perioperative and p = 0.01 in the post-operative group), but not between the other time points.…”
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1020
The expression of PAX7 and MYOD1 correlate with differential expression of diverse chemokines in HuMuSC.
Published 2025“…Using linear regression PAX7 slope varies significantly p = 0.0001 and MYOD1 slope also varies significantly p = 0.05 n = 3 <b>(b)</b> Heat map displaying the expression levels of all cytokine protein detected in HuMuSC from collection as they activate progressively at day 0, day 3 and day 6 <i>in vitro</i>, using the high throughput proteomic Multiplex assay. …”