A recent study reports (Al,Ga, Sc)N thin films with record-high scandium levels, with exciting potential for ultra-low-power memory devices, as reported by researchers from Institute of Science Tokyo (Science Tokyo). Using reactive magnetron sputtering, they fine-tuned the composition of ternary alloys to overcome previous stability limits.
An interdisciplinary research team including mechanical science and engineering professor Mickey Clemon from the Grainger College of Engineering at the University of Illinois Urbana-Champaign is investigating cooling methods for heat sinks by performing experiments onboard a satellite currently orbiting Earth.
“University-sponsored satellites have a very low success rate of making it into space, so we’re very happy that we made it into space and that our system works,” Clemon said.
The team has published the recent findings from their ongoing study, “Investigating the performance of a heat sink for satellite avionics thermal management: From ground-level testing to space-like conditions,” in the International Journal of Heat and Mass Transfer.
The rise of Artificial General Intelligence (AGI) has the potential to change many aspects of human life. One of the most interesting possibilities is that AGI could create a universal language. This change could deeply transform how we think. Unlike today’s AI, which is made for specific tasks, AGI refers to machines that can perform any intellectual task a human can. These machines can learn, reason, and innovate in many different areas.
This ability makes AGI a powerful tool in technology and in changing how we communicate and understand the world. By studying patterns from all human languages, AGI could design a universal language with the potential to connect different cultures and languages. More than just making communication easier, such a language could also improve human thinking. In fact, this could change the way we think, perceive, and interact with our environment.
Despite their astounding success, custom-made base editors and prime editors will need time to broaden their clinical impact.
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The ability to analyze gene expression at the single-cell level—known as single-cell RNA sequencing (scRNA-seq)—has transformed life sciences, driving discoveries across immunology, oncology, and developmental biology. Over 40,000 studies have leveraged this technique to map the complex diversity of cells within tissues and organisms.
Yet beneath this explosive growth lies a persistent problem: clustering instability. When researchers attempt to group cells by expression patterns to identify cell types or disease states, they often face inconsistent results—even when analyzing the same dataset repeatedly.
Inaccurate clustering can lead to misclassifying normal cells as cancerous or missing rare but critical cell types—jeopardizing interpretation and therapeutic decisions. This “reliability crisis” forces scientists to rerun analyses or rely on computationally expensive pipelines to extract trustworthy insights.
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