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

“…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%. …”

GSConv module structure diagram. by Yaojun Zhang (389482)

“…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%. …”

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

“…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%. …”

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

“…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%. …”

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

“…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%. …”

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

“…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%. …”

YOLOv8n structure diagram. by Yaojun Zhang (389482)

“…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%. …”

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

“…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%. …”

Enhanced dataset sample images. by Yaojun Zhang (389482)

“…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%. …”

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

“…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). …”

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

“…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). …”

Effect of the replacement parameter . by Fynn Bachmann (21409831)

Fig S1. by Leonardo Santos (477304)

“…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. …”

Classification of Anti-HlyE IgG Responses. by Jo Walker (22404069)

Profiling overall protein <i>O</i>-glycosylation pathway genes in <i>F. graminearum.</i> by Heeji Moon (17474719)

Profiling overall protein <i>N</i>-glycosylation pathway genes in <i>F. graminearum.</i> by Heeji Moon (17474719)

The head-withdrawal threshold of naïve rats receiving CGRP. by Hiroharu Maegawa (8087543)

“…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. …”

PDS and CEP inhibited DDX60-mediated promotion of CCHFV replication. by Yutong Sui (11151669)

Inverse Simpson index (A) and richness (B) shown for the post-operative and perioperative groups. by Allison J. Collier (13977858)

“…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.…”

The expression of PAX7 and MYOD1 correlate with differential expression of diverse chemokines in HuMuSC. by Katharine Striedinger (15503387)

“…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. …”