Researchers at Lehigh University are developing a faster, more accurate way to predict how metals solidify during 3D printing and other additive manufacturing processes. Supported by a three-year, $350,000 grant from the National Science Foundation, assistant professor Parisa Khodabakhshi is creating a physics-based, data-driven model that connects manufacturing process parameters with the resulting material microstructure. The approach aims to replace costly trial-and-error methods with efficient simulation tools that can guide the design of high-performance metal components. The project’s outcomes could accelerate innovation across industries that rely on advanced manufacturing—such as aerospace, automotive, and healthcare—while helping train the next generation of engineers and scientists.

Accurate land cover maps are fundamental to urban planning, environmental monitoring, and sustainable development. 

In JASA Express Letters, researchers evaluate the relationships between sound quality, speech recognition, and quality-of-life outcomes for cochlear implant (CI) users. They found CI sound quality leads to a 32% variance in users’ quality of life — in contrast, speech recognition has virtually no predictive power over quality of life. In their study, speech recognition only correlated with sound quality under noisy conditions, suggesting it is particularly relevant in situations with background noise and different sound sources — in other words, the real world.

MIT engineers designed a control mechanism, known as DIAL, that allows them to establish a desired protein level, or set point, for any synthetic gene circuit.

Land cover is constantly changing due to climate pressures and human activities, yet traditional satellite monitoring often misses rapid events.