Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

Objective: To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.

Methods: Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report.

Restoration of Strength in Polyamide Woven Glass Fiber Organosheets by Hot Pressing: Case Study of Impact and Compression after Impact.

Thermoplastic composite organosheets (OSs) are increasingly recognized as a viable solution for automotive and aerospace structures, offering a range of benefits including cost-effectiveness through high-rate production, lightweight design, impact resistance, formability, and recyclability. This study examines the impact response, post-impact strength evaluation, and hot-pressing repair effectiveness of woven glass fiber nylon composite OSs across varying impact energy levels. Experimental investigations involved subjecting composite specimens to impact at varying energy levels using a drop-tower test rig, followed by compression-after-impact (CAI) tests.

Effect of Post-Cured through Thickness Reinforcement on Disbonding Behavior in Skin-Stringer Configuration.

An experimental investigation of interlaminar toughness for post-cured through-thickness reinforcement (PTTR) skin-stringer sub-element is presented. The improvement in the crack resistance capability of skin-stringer samples was shown through experimental testing and finite element analysis (FEA) modeling. The performance of PTTR was evaluated on a pristine and initial-disbond of the skin-stringer specimen.