An automatic diagnosis system based on wearable augmented reality (AR) glasses and an artificial intelligence (AI)model was developed to assess leafminer damage levels, and it achieved 92.38% accuracy. The DeepLab-Leafminer model incorporated an edge-aware module and the Canny loss function into the DeepLabv3+ model, which enhanced its ability to segment the leafminer damaged area in leaves.A mobile application and a web platform were developed to display the diagnostic results of leafminer damage levels for surveyors to guide their scientific decisions for leafminer prevention and control.

Summary: “Mind reading” evokes images of futuristic scanners, but a study published in Nature Neuroscience demonstrates that a simple video may be enough. Using machine learning techniques, a team at the Champalimaud Foundation in Portugal, showed that mice’s facial movements reflect their hidden thoughts. According to the authors, this discovery could offer unprecedented insight into brain function, but also signals a need to consider safeguards for mental privacy. ***SUMMARY YOUTUBE VIDEO LINK***: https://youtu.be/eNjkmeJ-lvE

Liberate AI, an interdisciplinary project uniting researchers from the medical domain, computer science, and trustworthy artificial intelligence (AI), aims to develop an AI model capable of supporting doctors in the treatment of ischemic stroke. Serving as a digital assistance system, the AI model is intended to predict the long-term outcome of patients after mechanical thrombectomy, a minimally invasive treatment, as well as potential complications. Using a technology called Swarm Learning, the AI model will be trained in a privacy-preserving fashion on medical data residing at different sites across Germany. Further challenges addressed in the project include the AI’s explainability as well as its ability to make differentiated predictions for patient subgroups. Funded by the Helmholtz Association with 250,000 euros, Liberate AI is a joint project between Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), the Department of Vascular Neurology at the University Hospital Bonn (UKB), and CISPA Helmholtz Center for Information Security.

Shanghai, September 2025 — Motion sickness is a major barrier to passenger comfort, particularly during nighttime journeys when visual cues are limited. A new study published in Artificial Intelligence and Autonomous Systems (AIAS) provides experimental evidence that warm red in-vehicle lighting can significantly reduce motion sickness, compared with cool blue light or driving in darkness. The article, “Passenger motion sickness binary classification model and analysis of vehicle lighting intervention effect,” authored by Bin Ren*, Xiaoyuchen Wang, Pengyu Ren and Menghan Wu from Shanghai University, investigates how different ambient lighting conditions affect passenger comfort during real-road nighttime driving.

The human brain does more than simply regulate synapses that exchange signals; individual neurons also process information through “intrinsic plasticity,” the adaptive ability to become more sensitive or less sensitive depending on context. Existing artificial intelligence semiconductors, however, have struggled to mimic this flexibility of the brain. A KAIST research team has now developed next-generation, ultra-low-power semiconductor technology that implements this ability as well, drawing significant attention.

KAIST (President Kwang Hyung Lee) announced on September 28 that a research team led by Professor Kyung Min Kim of the Department of Materials Science and Engineering developed a “Frequency Switching Neuristor” that mimics “intrinsic plasticity,” a property that allows neurons to remember past activity and autonomously adjust their response characteristics.