Knowing the correct phase connectivity information plays a significant role in maintaining high-quality power and reliable electricity supply to end-consumers. However, managing the consumer-phase connectivity of a low-voltage distribution network is often costly, prone to human errors, and time-intensive, as it involves either installing expensive high-precision devices or employing field-based methods. Besides, the ever-increasing electricity demand and the proliferation of behind-the-meter resources have also increased the complexity of leveraging the phase connectivity problem. To overcome the above challenges, this paper develops a data-driven model to identify the phase connectivity of end-consumers using advanced metering infrastructure voltage and current measurements. Initially, a preprocessing method that employs linear interpolation and singular value decomposition is adopted to improve the quality of the smart meter data. Then, using Kirchoff’s current law and correlation analysis, a discrete convolution optimization model is built to uniquely identify the phase to which each end-consumer is connected. The data sets utilized are obtained by performing power flow simulations on a modified IEEE-906 test system using OpenDSS software. The robustness of the model is tested against data set size, missing smart meter data, measurement errors, and the influence of prosumers. The results show that the method proposed correctly identifies the phase connections of end-consumers with an accuracy of about 98%.

This paper presents the synthesis of Co-doped nano-dendritic Cu₂O/Cu heterojunctions via a facile, green chemical replacement method. Atomic Co enhances nitrate adsorption, while metallic Co boosts active hydrogen (*H) generation. By tuning the Co doping concentration and form, *H levels on the catalyst surface are precisely controlled, optimizing ammonia selectivity in nitrate reduction by balancing *H availability and suppressing competing hydrogen evolution. This strategy effectively improves electrocatalytic ammonia synthesis performance.

Professor Jiawen Chen and Associate Researcher Yan Wang from South China Normal University, in collaboration with Professor Ben L. Feringa's team at the University of Groningen, Netherlands, designed a novel molecular machine with both rotational and shuttle motion modes. This molecular machine combines a sterically hindered olefin molecular motor, an H-type benzimidazole, and a crown ether system, achieving for the first time the control of rotaxane shuttle motion through the rotational motion of the molecular motor. The motion mechanism of this molecule was elucidated in detail using methods including two-dimensional proton NMR spectroscopy and theoretical calculations. This work demonstrates the tuning effect of two different motion modes within a single molecular machine, providing a solid experimental foundation for the future design of multifunctional molecular machines with complex mechanical functions. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.

Researchers at the Nara Institute of Science and Technology and Osaka University have developed a computational model of how human emotions are formed. This system integrates body signals, sensory input, and language, forming emotional concepts that match the self-reported human emotional judgment with 75% accuracy. The findings highlight new ways of building emotionally aware artificial intelligence, with potential applications in mental health care, interactive robots, and assistive technologies.

Researchers created an AI-based learning tool to help neurotypical people learn how to better communicate with autistic people.