A research team led by UCLA and the University of Rochester has demonstrated a promising evolution of an imaging system designed to capture details within “complex media,” which scatter light, from depicting structures inside body tissue to seeing obstacles through heavy fog. The system uses physics-based machine learning to improve upon an existing imaging technique. 

In tests with standard calibration images obscured by complex media, the new system more than doubled the signal-to-noise ratio compared to a previous generation of the technology. The system also created images in close to real time — thousandths of a second.

New research found that the century-old yardstick used across many industries to estimate air-conditioning and refrigeration energy demand — called cooling degree days — is missing something fundamental. The study reports a new, physics-based version of the metric — effective cooling degree days — that captures how temperature and humidity together impact the actual work a cooling system is required to do. Applying it across North America, they found that cooling efficiency has been quietly declining by 2–4% per decade since 1971, and that the old temperature-only metric misjudges how much cooling is required in different regions, overstating demand in some places and understating it in others.

Computationally designed tellurium catalysts achieve enantioselective chalcogen-bonding catalysis using neutral, non-covalent interactions.