Acid attack test of HPC at 28, 56, and 90 days. by Fadi Althoey (13040091)

“…Activated WSA-RA concrete showed significantly improved early strength. The mixture with 30% RA, 40% WSA, WTSFs, and activator exhibited the highest strength at 90 days. …”

XRD spectra of HPC at 28, 56, and 90 days. by Fadi Althoey (13040091)

“…Activated WSA-RA concrete showed significantly improved early strength. The mixture with 30% RA, 40% WSA, WTSFs, and activator exhibited the highest strength at 90 days. …”

Mix details of samples (kg/m³). by Fadi Althoey (13040091)

“…Activated WSA-RA concrete showed significantly improved early strength. The mixture with 30% RA, 40% WSA, WTSFs, and activator exhibited the highest strength at 90 days. …”

Systematic All-Hydrocarbon Stapling Analysis for Cecropin A Generates a Potent and Stable Antimicrobial Peptide by Yejiao Shi (6391688)

“…Compared to cecropin A, its increased helicity and hydrophobicity as well as the decreased net charge also enabled its improved stability and biocompatibility, facilitating its enhanced antibacterial and anti-inflammatory efficacy for the effective treatment of mice with peritonitis sepsis. …”

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. …”