Abstract:DA (data assimilation) is a crucial technical method for improving the accuracy of atmospheric chemical forecasts by integrating the results of atmospheric chemistry models with multi-source observational data, reducing uncertainties in model input data. Centering on DA techniques for atmospheric chemistry models, the transformation process of initial field assimilation for pollutant gases and aerosols from single state variables to multi-state variables was systematically reviewed. Meanwhile, the important progress of pollutant emission source assimilation inversion using ensemble methods and four-dimensional variational methods was focus on the improvement of emission source accuracy, optimization of spatiotemporal resolution, and enhancement of pollutant concentration prediction performance. With the explosive growth of observational data, a core challenge in the current field lied in fully leveraging high-resolution geospatial and remote sensing data for atmospheric chemical DA. The deep integration of DA with artificial intelligence algorithms represented a key research direction to break through this bottleneck and significantly enhanced the accuracy of atmospheric composition analysis and forecasting.

Abstract:The aviation and aerospace transition zones (AATZ), spanning altitudes between 50–250 km, constitutes a strategic arena for hypersonic weapon penetration and electronic warfare operations, serving as a critical battlefield that significantly impacts operational effectiveness. Artificial intelligence (AI) is profoundly empowering the region's information warfare systems, driving their evolution toward dynamic and intelligent capabilities. Key AI technologies and applications across the entire “perception-fusion-prediction-countermeasure” chain are systematically reviewed: relying on deep learning for efficient inversion of environmental parameters; utilizing intelligent fusion to construct digital twins of battlefield environments; enhancing forecast accuracy through physical information; and developing autonomous learning and game-theoretic decision-making capabilities to support precise cognition and counter-interference. AI-enabled environmental information deception and counter-deception confronts four intertwined bottlenecks: inherent uncertainty in multi-source perception, feeble interpretability of deep predictive models, poor cross-domain transferability under heterogeneous conditions, and scarcity of realistic training data. The core challenges facing AI-enabled information warfare include environmental perception uncertainty, weak model interpretability, difficulties in cross-domain transfer, and restricted data acquisition. Finally, the outlook for future development is presented, emphasizing that AI is evolving from a technical tool into a core driving force.

Abstract:Recent advances in information technology and new energy systems have introduced increasingly stringent requirements in regulating energy transport within materials. Conventional material-design paradigms are limited by inherent trade-offs among optical, thermal, and electrical transport properties, creating an urgent need for a new paradigm to fundamentally decouple and reconstruct material functionalities Recent progress on nanoporous aerogels as an enabling platform is systematically summarized, emphasizing how hierarchical structural design and cross-scale assembly of building units allow precise control of diverse energy-carrier transport. Based on this theoretical framework, advanced applications in photo–thermal–electrical energy conversion are highlighted, with particular emphasis on research progress and performance optimization pathways of aerogels for photothermal, photoelectric, thermoelectric, and integrated photo–thermal–electric systems. Finally, future research directions including AI-driven inverse design and synergistic regulation of multiple energy carriers are outlooked, providing new perspectives for the on-demand development of next-generation high-performance photo–thermal–electrical conversion materials.