Soil contamination remains a global challenge, threatening ecosystems, agriculture, and human health. Conventional remediation strategies, while effective, are often energy-intensive and carbon-heavy, limiting their long-term sustainability.

Artificial intelligence (AI) data centers use huge amounts of electricity and face future risks from quantum computers that could break today’s encryption. To tackle this, researchers developed a new system that combines ultrafast data transmission with quantum key distribution for security. In tests, the system delivered data at terabit-per-second capacity with low power consumption, while generating secure encryption keys in real time, pointing to a promising path for the development of more efficient and secure data transmission systems to handle the surging demands of AI applications such as autonomous vehicles and large language models.

Doctors and researchers at the University of Pittsburgh and UPMC have developed a new treatment for epilepsy patients who don’t respond to medication and aren’t candidates for surgery. Their approach, published today in Nature Communications, uses deep brain stimulation (DBS) that is tailored to each patient’s unique brain wiring.

Perovskite materials offer exceptional optoelectronic properties for photodetectors, but precise patterning is critical to optimize their performance. This review surveys five key patterning strategies—template-guided growth, inkjet printing, vapor deposition, seed-induced growth, and photolithography—highlighting their roles in controlling perovskite microstructures. The resulting patterned films enable high-sensitivity photodetectors across zero- to three-dimensional architectures, facilitating breakthroughs in flexible wearables and biomimetic vision systems. These advances pave the way for next-generation imaging, health monitoring, and human-machine interfaces.

- Development of an AI method that enables efficient large-scale model training while protecting personal information with only a single model transmission

- Overcomes previous limitations of computational cost and overfitting… expected to be applied in medical image analysis

- Research results published in Medical Image Analysis, October 2025, a top-tier journal in the field of medical image analysis

A research team led by Associate Professor Tomoya Kataoka at Ehime University, in collaboration with Yachiyo Engineering Co., Ltd., Nippon Koei Co., Ltd., and Wageningen University’s Associate Professor Tim van Emmerik, has developed AI software using deep learning to detect, classify, and track plastics flowing in rivers. The system automatically estimates transport volumes from video data, supporting continuous monitoring and advancing understanding of land-to-sea plastic transport. The findings appear in Water Research.