Chinese researchers have used CRISPR gene-editing technology to improve the production efficiency and nutritional value of the fungus Fusarium venenatum.
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Faraday effects emerging from the optical magnetic field
Scientific Reports volume 15, Article number: 39,566 (2025) Cite this article.
Over a decade in the making: Lanthanide nanocrystals illuminate new possibilities
In a discovery shaped by more than a decade of steady, incremental effort rather than a dramatic breakthrough, scientists from the National University of Singapore (NUS) and their collaborators demonstrated that great ideas flourish when paired with patience.
Flashback to 2011: a small group of young researchers gathered around an aging optical bench at the NUS Department of Chemistry, watching a faint, flickering glow on a screen. Their goal seemed deceptively simple: make an insulating crystal emit light when electricity flowed through it. The challenge, however, was nearly impossible.
Lanthanide nanocrystals, known for their chemical stability and pinpoint color purity, were insulators, notoriously resistant to electrical excitation.
New solar-powered Nissan EV can drive 3,000 km a year without ever plugging in
Nissan just announced a solar-powered EV based on the Nissan Sakura for this year’s Japan Mobility Show.
Built using the super popular kei car as a platform, the solar-powered Sakura promises ‘free’ motoring thanks to its solar panels.
In theory, you can drive it for a year without ever plugging it in.
The cost of thinking: Reasoning models share aspects of information processing with human brains
Large language models (LLMs) like ChatGPT can write an essay or plan a menu almost instantly. But until recently, it was also easy to stump them. The models, which rely on language patterns to respond to users’ queries, often failed at math problems and were not good at complex reasoning. Suddenly, however, they’ve gotten a lot better at these things.
A new generation of LLMs known as reasoning models are being trained to solve complex problems. Like humans, they need some time to think through problems like these—and remarkably, scientists at MIT’s McGovern Institute for Brain Research have found that the kinds of problems that require the most processing from reasoning models are the very same problems that people need to take their time with.
In other words, they report in the journal PNAS, the “cost of thinking” for a reasoning model is similar to the cost of thinking for a human.