Reliable defect detection is critical for the safe operation of heavy-haul railways. However, ultrasonic inspection signals are often distorted by complex non-Gaussian noise in small-radius curve sections. A research team developed a physics-informed denoising framework that integrates multi-feature fusion within an ideal binary mask paradigm. The proposed method improves defect echo extraction accuracy and reduces the risk of missed detections.

In a new comprehensive review, researchers from Tsinghua University, Xiaomi EV, Beijing Institute of Technology, and University of Electronic Science and Technology of China examine how these two AI paradigms are being applied across the development, validation, and operation of autonomous driving systems, discuss their strengths and limitations, and further review their emerging synergy for more robust, interpretable, and trustworthy autonomous driving systems. 

Using demographic profiles from surveys in Sydney, Hong Kong, and Nanjing, the authors construct role-playing prompts to test three LLMs on lottery-choice tasks. Three findings emerge. First, GPT models are more risk-averse than human benchmarks, whereas Gemini is more risk-seeking. Second, prompt language affects simulated risk attitudes, with non-English prompts often inducing more conservative choices. Third, LLMs fail to reproduce empirical heterogeneity, producing distributions that are either overly concentrated or unrealistically dispersed.

Revolver Therapeutics has won the health category of the Royal Society of Chemistry's Emerging Technologies Competition 2026.

Researchers at the University of Georgia conducted the first empirical study investigating how "one-pedal driving" (OPD) influences electric vehicle car-following behavior. The study reveals a striking contrast: while manual one-pedal driving increases speed oscillations and driver variability, integrating OPD with Adaptive Cruise Control (ACC) significantly enhances traffic stability and produces smoother, more conservative driving patterns compared to traditional two-pedal systems.