Conventional photovoltaic-thermal (PV-T) collectors have coupled electrical and thermal outputs, limiting the temperature of the delivered thermal energy typically to < 60 °C. Concentrating PV-T (CPV-T) collector designs can reach higher temperatures, but cell overheating from similar coupling limitations reduces their electrical efficiency. Spectral splitting—dividing the solar spectrum so that only useful wavelengths reach the PV cells—promises to break this compromise, yet to go beyond previous studies and propose advanced spectral-splitting CPV-T designs capable of breakthrough performance, it is necessary to develop fully coupled optical, electrical and thermal-fluid models validated at the collector scale.

With the development of the Internet and intelligent education systems, the significance of cognitive diagnosis has become increasingly acknowledged. Cognitive diagnosis models (CDMs) aim to characterize learners’ cognitive states based on their responses to a series of exercises. However, conventional CDMs often struggle with less frequently observed learners and items, primarily due to limited prior knowledge. Recent advancements in large language models (LLMs) offer a promising avenue for infusing rich domain information into CDMs. However, integrating LLMs directly into CDMs poses significant challenges. While LLMs excel in semantic comprehension, they are less adept at capturing the fine-grained and interactive behaviours central to cognitive diagnosis. Moreover, the inherent difference between LLMs’ semantic representations and CDMs’ behavioural feature spaces hinders their seamless integration. To address these issues, this research proposes a model-agnostic framework to enhance the knowledge of CDMs through LLMs extensive knowledge. It enhances various CDM architectures by leveraging LLM-derived domain knowledge and the structure of observed learning outcomes taxonomy. It operates in two stages: first, LLM diagnosis, which simultaneously assesses learners via educational techniques to establish a richer and a more comprehensive knowledge representation; second, cognitive level alignment, which reconciles the LLM’s semantic space with the CDM’s behavioural domain through contrastive learning and mask-reconstruction learning. Empirical evaluations on multiple real-world datasets demonstrate that the proposed framework significantly improves diagnostic accuracy and underscoring the value of integrating LLM-driven semantic knowledge into traditional cognitive diagnosis paradigms. 

In a paper published in SCIENCE CHINA Earth Sciences, a team of researchers conducted a comprehensive review of direct measurement techniques for optical and chemical properties of atmospheric aerosols, as well as the impacts of aerosols on climate and environment, and health risks associated with exposure to high concentrations of ultrafine particles. It serves as a valuable reference for advancing future research and instrumentation development in the field of aerosol science.

A new paper published in SCIENCE CHINA Earth Sciences has identified the topographic heating of the Tianshan Mountains as a key driver of a distinctive dipole precipitation pattern in Arid central Asia. The research, focusing on the mid-Holocene warm period, indicated that the dipole pattern is closely linked to high-resolution model's effective representation of the Tianshan thermal effects. It provides crucial insights for refining climate models to more accurately project future hydrological changes in this vulnerable region.