Table 15_Genome-wide association study of seedling–plant resistance to stripe rust in bread wheat (Triticum aestivum L.) genotypes.xlsx by Genet Atsbeha (17595417)

“…<p>Fungal diseases, such as stripe rust, are major bottlenecks in Ethiopian wheat production. They can significantly reduce yields and impact regional food security. …”

Table 11_Genome-wide association study of seedling–plant resistance to stripe rust in bread wheat (Triticum aestivum L.) genotypes.xlsx by Genet Atsbeha (17595417)

“…<p>Fungal diseases, such as stripe rust, are major bottlenecks in Ethiopian wheat production. They can significantly reduce yields and impact regional food security. …”

Table 3_Genome-wide association study of seedling–plant resistance to stripe rust in bread wheat (Triticum aestivum L.) genotypes.xls by Genet Atsbeha (17595417)

“…<p>Fungal diseases, such as stripe rust, are major bottlenecks in Ethiopian wheat production. They can significantly reduce yields and impact regional food security. …”

Table 8_Genome-wide association study of seedling–plant resistance to stripe rust in bread wheat (Triticum aestivum L.) genotypes.xlsx by Genet Atsbeha (17595417)

“…<p>Fungal diseases, such as stripe rust, are major bottlenecks in Ethiopian wheat production. They can significantly reduce yields and impact regional food security. …”

Table 14_Genome-wide association study of seedling–plant resistance to stripe rust in bread wheat (Triticum aestivum L.) genotypes.xlsx by Genet Atsbeha (17595417)

“…<p>Fungal diseases, such as stripe rust, are major bottlenecks in Ethiopian wheat production. They can significantly reduce yields and impact regional food security. …”

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%. …”

data.zip by Zhizhang Dong (21158474)

“…Compared with the control group, the thickness of retina in the experimental group was significantly reduced (t=5, P=0.0075), the number of retinal pigment epithelium was statistically decreased (t=4.243, P=0.0132), and the pyrolytic related proteins NLRP3, Caspase-1, GSDMD, IL-1β and IL-18 were strongly red positive in retinal pigment epithelium (P<0.05). …”

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%. …”

DC parameters. by Kai Wen (261662)

“…The results show that the system can effectively recover waste heat from the DC, significantly reducing cooling electricity consumption during the heating season and decreasing original heating steam consumption by about 25%. …”

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%. …”

Table 1_Study on the effect of different types of sugar on proliferation and inflammatory in goose fatty liver.docx by Shuang Yi (8698824)

“…Glucose combined with si-CPT1A treatment decreased CyclinD3 while increasing p21 expression. …”

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%. …”