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%. …”

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%. …”

DtpT transports structurally diverse di-/tripeptides. by Imran Khan (109715)

VB2 cell fate analysis of <i>hlh-17</i>, <i>hlh-31</i>, and <i>hlh-32</i> mutant animals. by G. Robert Aguilar (13889674)

<i>Rab23</i> deletion perturbs ciliation in a context-dependent manner. by Wan Ying Leong (22075947)

Effects of Nash equilibrium attack and defense behaviors with the sensory–motor algorithm on encounters, agent numbers, and payoffs. by Hiroyuki Ichijo (13891153)

DataSheet1_The development of patient-specific 3D anatomical models in minimally invasive parathyroidectomy.docx by Zahra J. Haq (20411501)

“…Mental demand showed the greatest decrease in mean workload out of all the 6 subscales tested for in the NASA TLX (210.3 vs 136.7) points. …”

Data Sheet 1_The impact of a home visiting program on the care environment of Brazilian adolescent mothers - an descriptive exploratory study.pdf by Letícia Aparecida da Silva (21178136)

“…No significant differences were found between the groups in the overall IT-HOME scores, but it was found that the relationship between maternal schooling and the score on the subscales emotional and verbal responsibility of the caregiver was greater in the control group (4. 5 points more) in mothers with less schooling (primary school) than in mothers with the same schooling in the control group (p = 0.02), this satisfactory result was obtained in the 6 and 24 month measurements, in the latter the intervention group scored 3 points higher than the control group (p = 0.05).…”

DataSheet1_Bacillus amyloliquefaciens TL promotes gut health of broilers by the contribution of bacterial extracellular polysaccharides through its anti-inflammatory potential.docx by Shijie Li (759509)

“…-TL markedly enhanced chicken growth performance, concomitant with a significant decrease in the expression of genes encoding inflammatory cytokines (e.g., CCL4, CCR5, XCL1, IL-1β, IL-6, IL-8, LITAF, and LYZ) in jejunum and ileum tissues. …”

DataSheet1_A lightweight MHDI-DETR model for detecting grape leaf diseases.pdf by Zilong Fu (20392494)

“…The findings from the experiments suggest that The MHDI-DETR model results in a 56% decrease in parameters and a 49% reduction in floating-point operations, respectively, compared with the RT-DETR model, in terms of accuracy, the model achieved precision rates of 96.9%, 92.6%, and 72.5% for accuracy, mAP50, and mAP50:95, respectively. …”

AgTL2 embryos have altered lipids, metabolites, and transcriptional profiles during development. by Maurice A. Itoe (9930475)

“…(C) Heatmap of metabolites analyzed by LC-MS showing levels of the 60 most highly dysregulated metabolites in AgTL2 embryos compared to controls throughout development (values of <i>t</i> test statistic, range: blue to red = significant decrease to increase) (3 biological replicates represented by triplicate columns at each time point). …”