Ultimate Lateral resistance of Tetrapod Pile Foundation in Rate-dependent and Strain-softening Clay by Zi-hao Zhao (21085578)

“…The results show that the ultimate lateral resistance gradually decreases with <b>a reduction in</b> strain-dependent parameters. …”

Fungal relative abundance links to FAV-eQTLs and variation in tissue-specific relative gene expression. by Emily P. Van Syoc (22169869)

“…<p><b>(A–F)</b> Individual genotypes for each FAV-eQTL associate with increased relative gene expression, decreased relative gene expression, or are heterozygous and possess one increased and decreased expression allele. …”

Supplementary file 1_Differential effects of electroacupuncture and manual acupuncture on spontaneously hypertensive rats: insights from intestinal microbiota and metabolomics.docx by Ji-Peng Liu (15918956)

“…Background<p>Hypertension is a significant risk factor for cardiovascular disease, and acupuncture has demonstrated therapeutic effects in managing hypertension. …”

Supplementary file 1_Mechanical properties of 3D voxel-printed materials for cardiovascular tissue imitation.docx by Joël Illi (21449474)

“…In the second step, varying the orientation of the structures had a significant influence on the elastic modulus, which was measured. …”

Table 1_Immunomodulatory peptide–drug conjugate MEL-dKLA suppresses progression of prostate cancer by eliminating M2-like tumor-associated macrophages.docx by Ik-Hwan Han (8605281)

“…In vivo, MEL-dKLA treatment significantly reduced tumor growth, decreased the number of CD163⁺ M2 macrophages, and increased CD8⁺ T cell infiltration in tumor tissues. …”

Table 2_Immunomodulatory peptide–drug conjugate MEL-dKLA suppresses progression of prostate cancer by eliminating M2-like tumor-associated macrophages.docx by Ik-Hwan Han (8605281)

“…In vivo, MEL-dKLA treatment significantly reduced tumor growth, decreased the number of CD163⁺ M2 macrophages, and increased CD8⁺ T cell infiltration in tumor tissues. …”

PPFIA4 Knockdown Suppresses Expression, Proliferation, and Migration in Colorectal Cancer Cells. by Wenliang Yuan (7842908)

“…<p>(A) qPCR analysis showing a significant reduction in PPFIA4 mRNA expression in the si-PPFIA4 group.…”

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. …”