A research team from Soochow University has developed a novel artificial intelligence (AI) method to improve emotion cause extraction in conversations, enabling machines to better understand the nuanced triggers behind human emotions. Published in Frontiers of Computer Science, this breakthrough addresses key challenges in identifying fine-grained emotional causes within complex dialogues, offering potential applications in mental health support, customer service chatbots, and human-computer interaction systems.

Researchers have demonstrated, for the first time, that transfer learning can significantly enhance material Z-class identification in muon tomography, even in scenarios with limited or completely unlabeled data. Led by Professor Liangwen Chen, the team developed lightweight neural network models that achieve over 96% overall accuracy—and nearly 99% accuracy for high-Z materials—when detecting shielded objects, paving the way for practical applications of transfer learning in nuclear security and inspection technologies.

Whether a smartphone battery lasts longer or a new drug can be developed to treat incurable diseases depends on how stably the atoms constituting the material are bonded. The core of 'molecular design' lies in finding how to arrange these countless atoms to form the most stable molecule. Until now, this process has been as difficult as finding the lowest valley in a massive mountain range, requiring immense time and costs. Researchers at KAIST have developed a new technology that uses artificial intelligence to solve this process quickly and accurately.

In this work, the authors systematically advance macro-micro integration with feedback (MMIF) as a transformative paradigm for analyzing urban mega-mobility systems, synthesizing the state-of-the-art developments in typical constituent subsystems under this unified perspective. The MMIF paradigm bridges the gap between theoretical abstraction and empirical practice, contributing to scientifically sound urban development by harmonizing emergent patterns with granular behavioral dynamics. Building upon this paradigm, we investigate the key methods and technologies empowered by artificial intelligence (AI) that enable MMIF, and critically analyze the enduring challenges and prospective research directions. As urban mobility systems increasingly serve as testbeds for complexity science, the MMIF paradigm using AI promises to reshape interdisciplinary collaboration, offering a blueprint for building intelligent, adaptive, and human-centric cities.

The research team of Weihong Tan, Xiaohong Fang, and Tao Bing from the Hangzhou Institute of Medical Sciences, Chinese Academy of Sciences, proposed a new method for nucleic acid aptamer sequence analysis based on machine learning. This method can directly parse the secondary structure of nucleic acid aptamers from single-round screening data, thereby obtaining detailed secondary structure information of nucleic acid aptamers without iterative enrichment. This enables rational truncation and optimization of high-affinity nucleic acid aptamers, and even the design of nucleic acid aptamer molecules, significantly accelerating the discovery and optimization process of nucleic acid aptamers. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.

A new study reveals how advanced carbon based nanofluids could significantly improve heat transfer in technologies ranging from microelectronics to renewable energy systems. By combining numerical modeling with artificial intelligence, researchers have developed a powerful method to optimize cooling performance under complex physical conditions.