What if a prosthetic hand could adapt to the person instead of the other way around? Researchers have developed a personalized AI-powered prosthetic system that combines custom 3D-printed wearable sleeves with soft magnetic sensors to decode each user’s unique muscle movements in real time. Designed for every individual’s anatomy, the technology delivers more natural, reliable control and remained highly accurate during repeated testing, offering a promising step toward prosthetic devices that feel more intuitive, comfortable and responsive.

China's massive reforestation effort has transformed more than 77 million hectares of land, but new research shows that planted forests face different drought risks from natural forests. Using satellite observations from 1990 to 2024 and machine learning analysis, researchers found that planted forests are more vulnerable to rapid-onset flash droughts but less prone to prolonged slow droughts. The differences are linked to forest structure: natural forests have greater species diversity, more complex canopies, and deeper root systems that help buffer sudden water shortages, while planted forests are typically younger and less diverse. The findings highlight a trade-off in drought resilience and suggest that future forest management should prioritize mixed-species, structurally diverse forests to better withstand increasing climate extremes.

As large models advance, there’s growing demand to use knowledge distillation to produce smaller, more portable models (student) that match higher-performing larger models (teacher). However, when a teacher’s capacity far exceeds the student’s, distillation often degrades, which is known as capacity mismatch. This mismatch caps the student model’s performance and has become a bottleneck in large-model distillation. Existing mitigation techniques remain ad hoc, and no study has yet systematically explained its root causes or proposed targeted methods to enable larger teachers to yield better distillation results.

In recent years, domain generalization (DG) has garnered significant attention for its goal of learning models from multiple source domains to mitigate domain shift and enable generalization to unseen target domains. Most existing methods e.g., domain-invariant representation, focus on learning a universal sample-to-label mapping (function) across domains, overlooking semantically intrinsic domain-specific information and failing to maintain invariance across diverse unseen target domains.

Skill-based reinforcement learning has become the mainstream approach to solve sparse-rewards decision making tasks. The skills extracted from the demonstration datasets provide the temporal abstraction. However, in previous skill-based RL methods, the skills are kept fixed during online learning, which brings in sub-optimal asymptotic performance when the dataset contains only sub-optimal behavior modes.

With the growing use of multiple social platforms, aligning user identities across networks, known as Social Network Alignment (SNA), has become crucial for personalized services, fraud detection, and online safety. However, existing SNA methods face significant challenges when dealing with sparse connections, heterogeneous structures, and dynamic changes in real-world social networks.

Researchers led by Worcester Polytechnic Institute (WPI) Assistant Professor Jiawei Yang have designed a modular system that could potentially improve hydrogel implants in the body by customizing the materials for stiffness and functionality.

At left, hydrogels developed by Jiawei Yang and a team of researchers exhibit different colors according to their coatings. At right, a microscopic image of a hydrogel surface. Photos courtesy of Jiawei Yang. The system, described in the peer-reviewed journal Science Advances, uses coatings to treat the surface of hydrogels, which are flexible, water-loaded polymers. 

Artificial intelligence is beginning to transform not only drug discovery but also the design of advanced biomaterials. In a new study, researchers combined machine learning with laboratory validation to develop a data-driven strategy for identifying bioactive nucleoside hydrogels for periodontitis treatment. By integrating predictive modeling with innovative molecular scoring methods, the work aims to streamline biomaterial discovery and accelerate the development of targeted therapies for oral health applications.