A future where all homes and vehicles in the U.S. are fully electrified could overwhelm power supply and risk outages unless key upgrades are made, says a new study conducted by Purdue University engineers. But a few strategies could cut two-thirds of the potential costs of reinforcing the nation’s distribution grid to handle this demand.

As modern communication and sensing systems demand ever higher speeds and efficiencies, integrated photonics has emerged as a critical enabling technology.

To solve a problem, we have to see it clearly. Whether it’s an infection by a novel virus or memory-stealing plaques forming in the brains of Alzheimer’s patients, visualizing disease processes in the body is the first step toward alleviating human suffering. It’s also often the most difficult and costly. But an artificial intelligence (AI) breakthrough by Virginia Tech computer scientists published Sept. 16 in Cell Systems — a high-impact journal dedicated to biological research — is bringing those fog-bound processes into focus.

Muography can provide a noninvasive approach to examining subterranean infrastructure, and in the Journal of Applied Physics, researchers apply this technique to the Shanghai Outer Ring Tunnel, which runs under the Huangpu River. Using a combination of a spatial scan over the length of the tunnel and a simulation of muons passing through a simplified tunnel model, the researchers mapped the thickness of the sediment. They took 10 minutes of data per location at 50-meter intervals as proof of their technique.

In Chaos, researchers in Istanbul develop a more efficient and flexible algorithm to model traffic. The model, which they call the data-driven macroscopic mobility model, relies on simple observations that city planners routinely collect, like how packed the streets are. The researchers tested their model on both synthetic benchmarks and real-world traffic data, and the faster simulation speeds and easier data requirements mean city planners may have the tools to design better, smarter cities.

SAN ANTONIO — September 16, 2025 — Southwest Research Institute (SwRI) and The University of Texas at San Antonio (UT San Antonio) are collaborating to create a detection system to identify pre-ignition in hydrogen internal combustion engines (H₂-ICE). Researchers will combine machine learning (ML) algorithms and artificial intelligence with onboard sensors to help detect pre-ignitions based on their tell-tale signs.