Researchers at Shanghai University have developed a physics-constrained, data-efficient artificial intelligence framework that enables accurate thermal field inversion in chiplet-based packaging systems using only limited temperature measurements. The approach addresses a critical challenge in advanced heterogeneous integration, where increasing power density and material heterogeneity complicate thermal monitoring and reliability assessment.

Chiplet-based packaging integrates multiple heterogeneous chiplets into a single system, offering improved flexibility, yield, and cost efficiency compared to traditional system-on-chip designs. However, the resulting increase in power density and deterioration of heat dissipation conditions can lead to localized overheating, threatening system stability and long-term reliability. Accurately reconstructing the temperature field of such systems from sparse sensor data is therefore essential, yet remains a difficult inverse problem in practical engineering applications.

A research team led by Prof. QIAN Peiyuan, Chair Professor of the Department of Ocean Science at The Hong Kong University of Science and Technology (HKUST), in collaboration with the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), and the Yellow Sea Fisheries Research Institute of the Chinese Academy of Fishery Sciences (CAFS), has achieved a significant breakthrough in understanding the adaptive strategies of the deep-sea black coral Bathypathes pseudoalternata (B. pseudoalternata) and its symbiotic microbiome. The study has been published in the top international journal Cell Host & Microbe.

One of the research lines of the group of the EHU-University of the Basque Country is seeking solutions to generate districts or neighbourhoods whose annual net energy consumption is close to zero. Using machine learning techniques, Dr Milagros Álvarez has achieved greater accuracy in energy demand estimates and in the assessment of retrofitting strategies to reduce thermal consumption on the neighbourhood level.

Tokyo, Japan – Researchers from Tokyo Metropolitan University have studied the relationship between numerical information in our vision, and how it affects our perception of space. Volunteers were asked to identify the center of lines and squares filled with numbers; how far they were from the true center revealed unexpected biases. Crucially, their work with squares showed how our perception of space is a complex interplay between “object-based” processing and our processing of numerical information.

A joint research team from NIMS, Tokyo University of Science, and Kobe University has developed a new artificial intelligence (AI) device that exploits ion behavior to perform information processing. The team succeeded in reducing the computational load to about 1/100 of that required for conventional deep learning. The technology is expected to contribute to enhancing the information processing performance of "edge AI" operating directly on terminal equipment (an edge device). This research was published in ACS Nano on October 14, 2025.