News from Science China Press

Researchers developed a walking-support robot that uses large-area tactile contact at the waist and underarms to continuously follow movement intent and provide balance support. In tests with healthy adults, the same touch-based interaction enabled forward, backward and turning assistance, responded to mild balance disturbances, and reduced hamstring activity compared with handle-based support.

The human brain performs perception, memory, learning and decision-making with only about 20 watts of power. A new study in National Science Review proposes a physics-based framework to explain this extraordinary efficiency. By modeling cooperative neuronal activity, the researchers suggest that brain information may be stored and processed through dynamic electrical patterns, offering new clues for brain science and energy-efficient artificial intelligence.

A new study demonstrates that ramped pyrolysis/oxidation (RPO) can directly distinguish the sources of organic carbon transported by rivers after earthquakes and extreme rainstorms. By tracing carbon from vegetation, soils, and sedimentary rocks, the technique reveals how different natural hazards reshape carbon cycling and provides a powerful new tool for investigating climate-carbon feedbacks in rapidly changing mountain landscapes. The study was recently published in Science China Earth Sciences.

How can the long-term restoration effects of China’s Shan-Shui Initiative, recognized by the United Nations as one of the first World Restoration Flagships, be scientifically assessed? A research team from Chang’an University used long-term remote sensing and climate datasets from 2011 to 2024 to conduct a quantitative assessment of 25 early Shan-Shui Initiative across China. By developing an improved Remote Sensing Ecological Index, or kRSEI, the study found that vegetation greening does not always translate into broader improvements in satellite-observed ecological conditions. It also identified region-specific restoration pathways for forests, grasslands and croplands, and highlighted soil moisture as a key factor associated with the long-term stability of restoration outcomes. The findings provide a remote-sensing-based assessment framework that may support the Beautiful China initiative and offer a quantitative reference for evaluating large-scale ecological restoration projects.

Prof. Ben Zhong Tang of The Chinese University of Hong Kong (Shenzhen) and Professor Anjun Qin of South China University of Technology, together with a number of scholars who have long been engaged in research on organic room-temperature phosphorescence, aggregation-induced emission, theoretical calculations, and organic/polymer optoelectronic functional materials, jointly authored a review article entitled "Organic room-temperature phosphorescence materials."

Existing electrochemical biosensing systems generally rely on external computing units for data analysis. The necessity of analog-to-digital conversion and data transfer leads to high complexity, low working efficiency and concern of privacy. Zhao et al. realize the in-sensor computing in electrochemical biosensing for the first time, which uses a single neuromorphic organic photoelectrochemical transistor to simultaneously perform multi-target biosensing and artificial neural network classification. The strategy permits in-situ training using real-time data, helping solving the issue of device-to device variations. The proof-of-concept application is demonstrated by direct classification of microRNA fingerprints in real biological samples, which provides a new paradigm for simpler, faster and more secure AI-driven electrochemical biosensing.

Researchers have developed BattFailScholar, a knowledge-augmented large language model framework that dramatically improves battery failure diagnosis. By equipping AI with a structured "case library" of over 11,000 battery failure examples, the system boosts diagnostic accuracy by 19.7% over standard LLMs, effectively handles rare failure types, and provides reliable risk assessments with confidence scores. Beyond diagnosis, it can infer missing links in failure chains with over 85% accuracy, offering a new tool for safer battery development.