Transition metal dichalcogenides (TMDs) have garnered considerable attention as advanced electromagnetic wave absorption (EMA) materials due to their unique layered structures, tunable electronic properties, and intrinsic defect-induced polarization mechanisms. This comprehensive review systematically summarizes recent advances in TMDs-based absorbers, with particular focus on two fundamental development strategies. The first involves multiscale structural design of pure phase TMDs spanning from atomic to submillimeter dimensions, achieved through precise defect regulation, phase engineering, and sophisticated morphological manipulation to optimize electromagnetic parameters and attenuation capabilities. The second strategy focuses on constructing multicomponent composite systems, incorporating dielectric matrices, magnetic elements, and multicomponent hybrids to achieve synergistic enhancement through interfacial polarization, conductive loss, and magnetic dissipation mechanisms. The review critically analyzes pioneering research achievements across various subfields while identifying specific challenges and opportunities within each domain. Future perspectives highlight emerging frontiers including atomic level interface engineering, inverse design of multicomponent and multiscale architectures, sustainable large-scale synthesis techniques, and development of multifunctional smart-response systems. This work aims to establish fundamental principles and provide forward-looking guidance for designing next-generation high-performance TMDs-based EMA materials with tailored functionalities.

Future electromagnetic systems require compact devices that can control waves in both frequency and propagation direction. Towards this goal, scientists developed a fully passive sliding metasurface that sharply filters unwanted frequencies while steering beams over a wide angular range. This technique opens a new route to coordinated spectral–spatial wave control, enabling potential applications in interference suppression, multiplexing, sensing, and next-generation wireless communication.

Researchers from Hefei University of Technology and the University of Science and Technology of China have proposed a neural architecture search framework that allows an algorithm to automatically look for suitable network structures for EEG-based seizure prediction.

Researchers from Shenzhen Polytechnic University, the University of California, Riverside, and Tsinghua University evaluated real-world emissions from China V and China VI heavy-duty diesel trucks using portable emissions measurement systems (PEMS). The study found that compliant China VI trucks can substantially reduce NO_x and particle number (PN) emissions, but some vehicles still showed severe emission exceedances under real driving conditions, highlighting the importance of in-use monitoring and after-treatment system performance.

Researchers from McGill University, The Hong Kong University of Science and Technology (Guangzhou), and the University of Luxembourg have developed MILD, an AI-powered mediator system that helps drivers and automated vehicles better understand each other. By combining driver-state monitoring, traffic-scene understanding, and explainable decision-making, the system aims to improve trust, safety, and collaboration in partially automated driving.

To address the dual challenges of reducing fuel consumption and ensuring maritime safety under extreme weather, researchers from Dalian University of Technology, Nanyang Technological University, and Chongqing Jiaotong University have developed a novel ship weather routing framework based on Dual-Mode Deep Reinforcement Learning. Utilizing the D3QN algorithm, this framework effectively navigates both normal sea states and severe typhoon conditions, providing a robust route toward maritime safety security and decarbonization.