Energy absorbing efficiency is a key determinant of a structure's ability to provide mechanical protection and is defined by the amount of energy that can be absorbed prior to stresses increasing to a level that damages the system to be protected. Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system.
View Article and Find Full Text PDFBackground: Evaluation of the residual risk in patient with chronic coronary syndrome is challenging in daily practice. Several types of events (myocardial infarction, ischemic stroke, bleeding, and heart failure [HF]) may occur, and their impact on subsequent mortality is unclear in the era of modern evidence-based pharmacotherapy.
Methods: CORONOR (Suivi d'une cohorte de patients Coronariens stables en région Nord-pas-de-Calais) is a prospective multicenter cohort that enrolled 4184 consecutive unselected outpatients with chronic coronary syndrome.