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%. …”

Evaluation of chronic <i>in vivo</i> efficacy in 21 days rat MCT model in prevention mode. by Tobias G. Schips (21799838)

Depletion of viral UL87 LTF in late infection does not affect viral long promoter transcription. by Qiaolin Hu (8677473)

S5 Fig - The human medial temporal lobe represents memory items in their ordinal position in both declarative and motor memory domains by Ainsley Temudo (21673160)

“…This effect was less pronounced in the PHC, PER and HC, where a decrease in similarity was only observed in lag 5 compared to other lags (lag 5 < 2–4, all <i>p</i>_<i>corr</i> < .05 and lag 5 < 1, in HC only, <i>p</i>_<i>corr</i> < .005). …”

Robustness of the computational results. by Farhad Razi (22076077)

miR-184 does not downregulate target pSJ and TCJ mRNA levels but changes Nrv2 expression. by Sravya Paluri (5475260)

Overexpression of miR-184 disrupts the blood-brain barrier and larval locomotion. by Sravya Paluri (5475260)

Recognition of <i>F. novicida</i> strains by the mitochondria and Golgi complex in THP-1 cells. by Dhandy Koesoemo Wardhana (21956781)

Recognition of <i>F. novicida</i> strains by lysophagosomes and lysosomes in THP-1 cells. by Dhandy Koesoemo Wardhana (21956781)

Characterization of purified esAEVs based on protein content and the localization of Mif on the surface. by Safia A. Essien (20108379)

Recognition of <i>F. novicida</i> strains by lysophagosomes and lysosomes in HeLa–FcγRII cells. by Dhandy Koesoemo Wardhana (21956781)

Recognition of <i>F. novicida</i> strains by the mitochondria and Golgi complex in HeLa–FcγRII cells. by Dhandy Koesoemo Wardhana (21956781)