This research develops a physics-based method for measuring lung elasticity from medical imaging to predict which emphysema patients will benefit from lung valve treatment. By creating detailed elasticity maps, the work aims to improve treatment selection, enhance patient outcomes, and provide new quantitative tools for assessing lung health.

Lead contamination in drinking water threatens millions. This research combines physics-based pipe models with machine learning to identify lead pipes using vibration data. Generating thousands of simulated signals enabled a classifier with 99% accuracy, offering a noninvasive, cost-effective method to locate hidden lead pipes and support safer water infrastructure worldwide.