Prof. Shiyuan Liu’s team from Huazhong University of Science and Technology has reported a full-chip EUV curvilinear mask optimization framework that integrates deep learning with gradient-based inverse optimization. This framework achieves an estimated speedup of four orders of magnitude over conventional FDTD methods while preserving accuracy, thereby enabling full-chip EUV mask optimization within a practical runtime. Doctoral student Pinxuan He is the first author of this paper, and Prof. Shiyuan Liu and Dr. Jiamin Liu are the co-corresponding authors. Prof. Honggang Gu, Dr. Song Zhang, Prof. Qi Xia, and Prof. Hao Jiang also contributed to this research.

A case report in Chinese Neurosurgical Journal describes a novel salvage surgical approach for refractory multiple sclerosis-related trigeminal neuralgia. In a single patient who had failed medical, percutaneous, and radiosurgical treatments, the procedure combined internal neurolysis (nerve combing) with targeted glycerol delivery to the proximal trigeminal root. The patient experienced immediate pain relief and remained recurrence-free on the treated side for 43 months, with preserved sensation and reduced medication use.

Researchers proposed an image-aided terminal guidance attitude control method for Explosively Formed Penetrator (EFP)-equipped multirotors to achieve long-range precision strikes, solving disturbance, EFP dispersion and visual-attitude coupling problems. Published in Defence Technology, the work built a multi-source EFP dispersion model, designed an NTSM-STO composite controller, and validated its fast response, strong robustness and high hit rate via Monte Carlo simulations and real flight tests.

An international research collaboration, from Sun Yat-sen University, South China University of Technology, University of Liverpool and JiangSu CheeShine Performance Materials Co., has developed a mechanically adaptive artificial interphase for ultrafast zinc metal batteries. By constructing a spider-web-like nitrile butadiene rubber (NBR) nanofiber interphase, the researchers achieved synergistic regulation of homogeneous Zn²⁺ transport, free-water blocking, and interfacial deformation buffering. This strategy transforms unstable zinc growth into a uniform and confined surface deposition process. Based on this interphase, Zn//Zn symmetric cells can operate stably for more than 6,000 hours, while Zn//V₂O₅ full cells deliver stable cycling for 20,000 cycles at an ultrahigh rate of 50 A g^-1, providing a new interfacial engineering strategy for safe and high-power aqueous zinc batteries.

A team of researchers from the University of Washington, Janelia Research Campus, and EMBL Heidelberg developed a new class of fluorescent imaging tags called NovoTags. This new tagging system is based on an AI model developed by Nobel Laureate David Baker, which allowed the scientists to create proteins from scratch that bind to exceptionally bright fluorescent dyes developed by Luke Lavis at the Janelia Research Campus. NovoTags will allow researchers to identify specific proteins inside cells and observe many at once, accelerating the study of protein-protein interactions. Future developments in this technology could also help researchers combine fluorescence microscopy with in situ cryo-electron tomography, providing deeper insights into structure-function relationships inside cells.

Polymer-based flexible wireless sensors have demonstrated significant potential in health monitoring. However, existing studies have predominantly focused on material design, energy harvesting, and wireless communication, while research addressing signal attenuation, noise accumulation, and data distortion within wireless links remains relatively limited. Physiological signals are highly susceptible to multi-source interference during acquisition and transmission; without effective data processing and intelligent analysis strategies, monitoring outcomes may fail to meet the accuracy and stability requirements of clinical applications. To address this research gap, this review constructs a comprehensive framework encompassing signal sensing, link transmission, and intelligent processing, building on advances in flexible materials and wireless energy supply modes. Special attention is given to the critical roles of channel compensation, noise suppression, feature extraction, lightweight artificial intelligence models, and edge computing in enhancing data quality and reducing energy consumption. Furthermore, the review systematically analyzes the impact of wireless link stability and energy management on monitoring performance. By reinforcing the synergistic mechanisms between data processing and communication, this work aims to provide design principles and development directions for the next generation of high-reliability, low-power, and intelligent wireless flexible health monitoring systems.