Extended reality (XR) can give students experiences they might otherwise miss, but its success depends on thoughtful design, accessibility, and support. 

A Harvard study shows that commercially available sensors worn on a runner's body can provide useful data on what researchers call ground-reaction forces. These insights could open avenues to devices and products that deliver this data in real time to help runners avoid injury and improve their form.

JMIR Publications today announced the publication of a timely recap and interview with the co-chair of the Division of Clinical Informatics (DCI) Network’s conference on ethical, effective implementation of artificial intelligence (A) in health care.

From decoding genomes to predicting protein structures, artificial intelligence is rapidly becoming a cornerstone of biology. To help harness its potential for discovery through data analysis, the Stowers Institute for Medical Research has appointed Sumner Magruder, Ph.D., as its first AI Fellow. The new role within the Stowers Fellows Program is designed to accelerate the integration of advanced computational methods into biological research.

Scientists from China have developed a highly scalable on-chip photonic neural network that solves key bottlenecks long limiting the progress of optical computing. The team's new architecture, called a partially coherent deep optical neural network (PDONN), achieves unprecedented network depth by using a cascadable nonlinear activation function with positive net gain. This, combined with the innovative use of more accessible, partially coherent light sources (like LEDs) instead of narrow-linewidth lasers , enable s a chip with the largest input size and deepest structure of its kind to date. The chip successfully performed image classification tasks with high accuracy, marking a critical step toward energy-efficient, scalable, and widely accessible optical computing.

This paper proposes an intermittent measurement-based attitude tracking control strategy for spacecraft operating in the presence ofsensor-actuator faults. A sampled-data (self-)learning observer is developed to estimate both the spacecraft’s states and lumped disturbances, effectively mitigating the impact of faults. This observer acts as a virtual predictor, reconstructing states and actuator fault deviations using only intermittent measurement data, addressing the limitations imposed by sensor failures. The control scheme incorporates compensation based on the predictor’s estimates, ensuring robust attitude tracking despite the presence of faults. We provide the first proof of bounded stability for this learning observer utilizing intermittent information, expanding its applicability. Numerical simulations demonstrate the effectiveness of this innovative strategy,  highlighting its potential for enhancing spacecraft autonomy and reliability in challenging operational scenarios.