Mount Sinai-led research can transform how hospitals triage, risk-stratify, and counsel patients to save lives

The FAU Center for Connected Autonomy and Artificial Intelligence (CA-AI) has received a $799,759 grant from the U.S. Department of Defense to develop a state-of-the-art platform for testing and evaluating connected AI autonomous systems. The funding positions FAU as a national leader in this field and one of the first institutions to host a high-end NVIDIA GPU infrastructure for AI-driven autonomy research.

A research article published by the Beijing Institute of Technology presented a piezoelectric energy harvester (PEH) weighing only 46 milligrams. By matching the thoracic vibration frequency and optimizing the center of gravity distribution of bees, the device achieved high energy output (5.66 V and 1.27 mW/cm³), with experimental verification showing minimal interference with normal flight behaviors.

Researchers from the University of Shanghai for Science and Technology and the University of Illinois Urbana-Champaign have developed and validated a coupled xenon-transport and reactor dynamics model for molten salt reactors. This model enables simulations of dynamic and frequency response of liquid-fueled MSRs, which is validated against a broad spectrum of experiment data from the MSRE. The new simulation framework reveals the unique power-to-flowrate and power-to-void frequency responses of the MSRE. In addition, plant responses during the unique initiating events of off-gas system blockage and loss of circulating voids are simulated.

Plant-based seafood alternatives should have similar flavors, textures and nutritional content to the foods they mimic. And recreating the properties of fried calamari rings, which have a neutral flavor and a firm, chewy texture after being cooked, has been a challenge. Building off previous research, a team publishing in ACS Food Science & Technology describes successfully using plant-based ingredients to mimic calamari that matches the real seafood’s characteristic softness and elasticity.

Scientists developed a machine-learning tool that can teach itself, with minimal external guidance, to differentiate between aerial images of flowering and nonflowering grasses — an advance that will greatly increase the pace of agricultural field research, they say. The work was conducted using images of thousands of varieties of Miscanthus grasses, each of which has its own flowering traits and timing.