A generative AI-driven system enables non-experts to design and build common objects like furniture simply by describing the objects with words.

The team created a new tool that reduces bias and improves cancer diagnosis across populations

Weed control is essential in apple orchards because weeds compete with trees for nutrients, water and sunlight, which can reduce fruit yields. However, physically removing weeds is not only labor-intensive, but it also can damage soil structure and tree roots. Using chemical sprays to kill weeds can lead to other problems, such as pollution, herbicide resistance and excess chemical residues on apples. Another option called precision weed management — detecting and measuring weeds with high accuracy then applying small amounts of herbicide to control them efficiently — can help farmers avoid wasting chemicals or causing injury to crops or the environment, according to a team of researchers at Penn State. To help growers achieve such precise management, the researchers are developing an automated, robotic weed-management system.

Kharon and Digital Science have entered into a strategic partnership, bringing together Kharon’s risk intelligence and Digital Science’s world-leading Dimensions Research Security platform.

A research paper by scientists at University of Rome Tor Vergata introduced a novel deep learning framework based on a graph deviation network (GDN) to analyze spiking activity from human forebrain organoids (hFOs) and predict network-level alterations associated with autism spectrum disorder (ASD) risk..

The research paper, published on Nov. 6, 2025 in the journal Cyborg and Bionic Systems.