The structure of attention gate block [31]. by Yingying Liu (360782)

“…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …”

DSC block and its application network structure. by Yingying Liu (360782)

“…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …”

The structure of multi-scale residual block [30]. by Yingying Liu (360782)

“…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …”

The structure of IRAU and Res2Net+ block [22]. by Yingying Liu (360782)

“…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …”

Effects of Ni²⁺ on the mitochondrial and glycolytic energy metabolism of BMDM. by Jesse Corbin (21736578)

“…A double dagger (‡) indicates a significant decrease of OCR (slope = −0.14 ± 0.04, <i>R</i>² = 0.40, <i>F</i>(1, 22)=15.0, <i>p </i>< 0.001) from 0 to 72 ppm Ni²⁺. …”

Cortical and subcortical regions feature distinct early and late responses during audition. by Sarah H. McGill (22505225)

“…The mean no-stimulus trial spectrograms were subtracted from the mean maximum-intensity trial spectrograms, and a cluster-based permutation testing was employed to identify significant differences between the conditions (p < 0.05, 5000 iterations). …”

Passive listening and go/No-go tasks feature comparable electrophysiological responses. by Sarah H. McGill (22505225)

“…The mean no-stimulus trial spectrograms were subtracted from the mean maximum-intensity trial spectrograms, and a cluster-based permutation testing was employed to identify significant differences between the conditions (p < 0.05, 5000 iterations). …”

Characterization of Developmentally Dynamic Promoters (DDPs). by Jiang Tan (260415)

The effect of CEP and PDS treatment on recombinant CCHFV. by Yutong Sui (11151669)

Chemogenetic inhibition of Calcrl⁺ neurons attenuates chronic itch in multiple chronic itch models. by Huifeng Jiao (11537806)

“…<p><b>(A-C)</b> Allergic Contact Dermatitis (ACD) Model (n = 7 mice for each group): Experimental timeline showing intraspinal viral injection, ACD model induction, and behavioral testing phases <b>(A)</b>; Quantification of mechanical itch responses revealed significant CNO-mediated suppression in hM4Di mice, with no effect in vehicle-treated controls <b>(B)</b>; Spontaneous scratching frequency decreased following CNO administration <b>(C)</b>. …”

Dataset visualization 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%. …”

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%. …”

Performance comparison of different 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%. …”

C2f and BC2f module structure diagrams. 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 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%. …”

YOLOv8n-BWG model 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%. …”

BiFormer 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%. …”

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%. …”