Droplet-based microfluidics can be used to model clustered bacterial growth. by Justin Greene (306625)

Litter clump weight across experimental groups and days in the study. by Allison Andrukonis (11846404)

Algorithm training accuracy experiments. by Yingying Liu (360782)

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

Repeat the detection experiment. by Yingying Liu (360782)

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

Detection network structure with IRAU [34]. by Yingying Liu (360782)

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

Ablation experiments of various block. by Yingying Liu (360782)

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

Kappa coefficients for different algorithms. by Yingying Liu (360782)

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

The structure of ASPP+ block. by Yingying Liu (360782)

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

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. …”

Loss of C1-FDX results in a reduction of the CI and CV activity. by Baoyin Chen (2635399)

SEM images of cobalt-based catalysts supported on Al₂O₃ at different magnifications. by Ning Zuo (17295415)

Study on the expression of mRNA related to biofilm formation of <i>C. albicans</i> by CSR extract (100 μg/mL). by Xuxi Cheng (22453447)

Prediction of transition readiness. by Sharon Barak (4803966)

“…In most transition domains, help needed did not decrease with age and was not affected by function. …”

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%. …”