Relative EEG spectral power during the rest period, in ADHD vs control participants. by Bridget Scalia (21526534)

ASVs abundances across microbiota samples. by Melany Cervantes-Echeverría (22794116)

“…Despite persistent dietary stress, FVT significantly improved glucose tolerance. Longitudinal profiling by virome shotgun metagenomics and bacterial 16S rRNA sequencing revealed marked, durable shifts in both viral and bacterial community composition. …”

S2 File - by Jinfeng Zhao (142391)

“…Exposure to aerobic exercise for 12 weeks significantly improved renal fibrosis and alleviated the intrarenal genetic alterations induced by aging. …”

Ablation Experiment GradCAM Heatmap. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Space-to-depth convolution. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Data augmentation. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Side angle tea picking. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Comparison results of ablation experiments. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Table of dataset division. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Striking image. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Precision, recall, F1-Score curve. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Model comparison experimental results. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Slicing aided hyper inference algorithm. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Loss function variation curve. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Different model detection results comparison. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Inner-IoU. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Comparison of the effectiveness of Models 1, 2a and 2b in estimating fetal fraction (FF) based on telomere content. by Zuzana Holesova (15253816)

“…The RMSE decreased to 0.0216 for the <i>seqFF</i> model and 0.0190 for the <i>comboFF</i> model, indicating a strong predictive improvement. …”

Overview of BDHS data. by Keith Beam (20700854)

“…Our findings demonstrate the long-term success of the SUZY project and provide insights into best practices for impactful zinc scale-up programs including significant pre-launch implementation research addressing key knowledge gaps and partnering with research organizations. …”

Framework synthesis of zinc scale-up programs. by Keith Beam (20700854)

“…Our findings demonstrate the long-term success of the SUZY project and provide insights into best practices for impactful zinc scale-up programs including significant pre-launch implementation research addressing key knowledge gaps and partnering with research organizations. …”

Evaluation of disparities in zinc coverage. by Keith Beam (20700854)

“…Our findings demonstrate the long-term success of the SUZY project and provide insights into best practices for impactful zinc scale-up programs including significant pre-launch implementation research addressing key knowledge gaps and partnering with research organizations. …”