S2 File - by John N. Staffurth (20636565)

“…Increases in quality of life, sexual activity and/or sexual function, and decrease in specific symptoms (e.g. bowel-related or urinary) between 6 and 12 months were observed. …”

S3 File - by John N. Staffurth (20636565)

“…Increases in quality of life, sexual activity and/or sexual function, and decrease in specific symptoms (e.g. bowel-related or urinary) between 6 and 12 months were observed. …”

S7 File - by John N. Staffurth (20636565)

“…Increases in quality of life, sexual activity and/or sexual function, and decrease in specific symptoms (e.g. bowel-related or urinary) between 6 and 12 months were observed. …”

S1 Data - by John N. Staffurth (20636565)

“…Increases in quality of life, sexual activity and/or sexual function, and decrease in specific symptoms (e.g. bowel-related or urinary) between 6 and 12 months were observed. …”

S5 File - by John N. Staffurth (20636565)

“…Increases in quality of life, sexual activity and/or sexual function, and decrease in specific symptoms (e.g. bowel-related or urinary) between 6 and 12 months were observed. …”

S1 File - by John N. Staffurth (20636565)

“…Increases in quality of life, sexual activity and/or sexual function, and decrease in specific symptoms (e.g. bowel-related or urinary) between 6 and 12 months were observed. …”

S2 Data - by John N. Staffurth (20636565)

“…Increases in quality of life, sexual activity and/or sexual function, and decrease in specific symptoms (e.g. bowel-related or urinary) between 6 and 12 months were observed. …”

S6 File - by John N. Staffurth (20636565)

“…Increases in quality of life, sexual activity and/or sexual function, and decrease in specific symptoms (e.g. bowel-related or urinary) between 6 and 12 months were observed. …”

Right Ankle fNIRS MI Dataset by Hammad Gilani (8060012)

“…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”

Comparison to state of the art. by Nicholas A. Rondoni (21573261)

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Introduction<p>Internalizing disorders (depression, anxiety, somatic symptom disorder) are among the most common mental health conditions that can substantially reduce daily life function. Early adolescence is an important developmental stage for the increase in prevalence of internalizing disorders and understanding specific factors that predict their onset may be germane to intervention and prevention strategies.…”

Frequency response analysis of the commonly used indicator GCaMP6s, <i>k</i>_<i>f</i> = 0.0514, <i>k</i>_<i>r</i> = 7.6, <i>L</i> = 30. by Nicholas A. Rondoni (21573261)

Data Sheet 1_Antibodies and cryptographic hash functions: quantifying the specificity paradox.pdf by Robert J. Petrella (5146220)

“…The results illustrate and quantify how the humoral immune system achieves both high specificity and high degeneracy simultaneously by effectively decoupling the two properties, similarly to programs in cryptography called secure hash algorithms (SHAs), which display the same paradoxical features. …”

Data Sheet 2_Antibodies and cryptographic hash functions: quantifying the specificity paradox.pdf by Robert J. Petrella (5146220)

“…The results illustrate and quantify how the humoral immune system achieves both high specificity and high degeneracy simultaneously by effectively decoupling the two properties, similarly to programs in cryptography called secure hash algorithms (SHAs), which display the same paradoxical features. …”

Structure diagram of DCNv3. by Jiexiang Yang (19980594)

“…<div><p>Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model’s loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. …”

Ablation experiment. by Jiexiang Yang (19980594)

“…<div><p>Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model’s loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. …”

PR comparison. by Jiexiang Yang (19980594)

“…<div><p>Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model’s loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. …”

Flexi-YOLO structure. by Jiexiang Yang (19980594)

“…<div><p>Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model’s loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. …”

Experimental environment. by Jiexiang Yang (19980594)

“…<div><p>Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model’s loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. …”

Loss curve. by Jiexiang Yang (19980594)

“…<div><p>Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model’s loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. …”