With the confirmation of data elements as the latest production factor, accelerating data sharing and value mining on the basis of ensuring data security has become an industry consensus. Privacy computing is the core technology for achieving "data available but invisible". Privacy computing platforms have flourished and gradually forming a phenomenon of "platform islands" with privacy computing platforms as the core. How to solve platform silos and further promote the integration and value of data elements has become an important research direction. This paper summarizes the typical functions of privacy computing platforms at home and abroad, the current status of the interconnection ecology of privacy computing platforms in China, and industry practice cases. The research scope of the paper is clarified as node interconnection and algorithm interconnection. It analyzes the different modes of node interconnection as well as algorithm, and finally summarizes the algorithm, data, security, and performance difficulties of privacy computing cross platform interconnection, and elaborats on the trend and prospects. Although privacy computing platforms face various challenges in interconnection, cross−platform interconnection remains a significant trend for future development. In terms of standards and regulations, both the financial industry and the telecommunications industry are exploring their own internal standards for interconnecting privacy computing platforms. From a technical perspective, the dual adaptation functions under the two−way adaptation model will gradually be replaced by "adapter"−style conversion products. Meanwhile, large−scale, open protocol−based platform interconnection is increasingly becoming the preferred choice for more technical providers.

The definition of spatio−temporal Intelligence (STI) is presented, and its characteristics of digitalization, informatization, networking, intelligence, and real−time performance in geomatics, remote sensing, and geographic information science are revealed, as well as its significant position in the field of artificial intelligence. The innovative evolution of STI from positioning, navigation, and timing (PNT) services to positioning, navigation, timing, remote sensing, and communication (PNTRC) services is reviewed and discussed. Furthermore, the application cases of STI in various fields, including the low−altitude economy, public safety and health, autonomous driving and robotics, smart grids and smart factories, forest and grassland monitoring, intelligent management of national parks, and emergency management, are explored. The important role of STI in promoting the development of new quality productive forces is demonstrated.

Artificial intelligence (AI) is profoundly transforming the paradigms and methodologies of advanced materials research and development. This review systematically examines cutting−edge advances in AI applications across materials composition/structure design, property prediction, synthesis optimization, and industrial implementation. By integrating data−driven approaches, physics−informed modeling, and autonomous experimental systems, AI has enabled high−accuracy cross−scale performance prediction, inverse design of materials with extreme properties, intelligent optimization of synthesis processes, and non−destructive defect detection, significantly accelerating development cycles while overcoming performance bottlenecks. The work highlights breakthroughs in representative case studies including high−throughput screening of stable crystals, targeted development of radiative cooling materials, and optimization of electrolytes for high−voltage batteries, while elucidating how techniques such as few−shot learning, transfer learning, and physics−constrained algorithms address challenges in data scarcity and multiscale modeling. Looking forward, the synergistic convergence of AI with quantum computing and generative design will propel materials innovation toward an accelerated transition to advanced paradigms characterized by data−driven workflows, autonomous decision−making, and intelligent iteration.

Artificial intelligence (AI) technology is profoundly transforming the research paradigms in the field of materials science, driving the analysis methods for material microstructures to shift from traditional human−experience−dominated approaches to data−driven intelligent recognition. AI−based microstructure recognition and quantification, characterized by high accuracy and efficiency, have significantly advanced the development of high−throughput microstructure analysis techniques. This review focuses on the emerging field of AI−assisted microstructure analysis of metallic materials. Following the development from qualitative analysis toward refined quantitative analysis of microstructures, it systematically summarizes the research progress in traditional machine learning algorithms, deep learning−based classification, object detection, and semantic segmentation algorithms for the classification, recognition, and quantification of metallic material microstructures. Particular emphasis is placed on the current state of widely adopted semantic segmentation algorithms. Meanwhile, addressing the challenges faced by semantic segmentation in this domain, such as high microstructural complexity and limited annotated samples, the innovative strategies proposed by researchers in data augmentation and model architecture improvements, along with their enhanced performance, are discussed. Finally, the existing limitations and future directions of AI−based microstructure analysis methods are summarized and outlooked.

Dual−phase steel with improved formability (DH steel) is developed as an evolution of conventional dual−phase steel (DP steel) to meet the increased ductility requirements associated with the fabrication of complex−shaped automotive components. Currently, DH steel with a tensile strength of 980 MPa has reached mass production, while the development of DH steel with a tensile strength of 1180 MPa has attracted significant research interest. In this study, a performance−driven machine learning methodology was employed to design the chemical composition and processing parameters of 1180 MPa−grade DH steel. Additionally, interpretable machine learning techniques were used to elucidate the fundamental relationships between the microstructural characteristics and mechanical properties. Initially, leveraging data extracted from the literature, a composition and process−performance predictive model was developed using a neural network algorithm. Subsequently, a multi−objective genetic algorithm was implemented to efficiently design the chemical composition of the novel DH steel. Following this, based on orthogonal experimental data concerning the processing parameters of the newly designed DH steel, a random forest algorithm was applied to construct predictive models for tensile strength and fracture elongation, with processing parameters serving as input variables. An optimized set of preparation process parameters was determined using a multi−objective genetic optimization algorithm. The resulting parameters are as follows: a coiling temperature of 510°C, an annealing temperature of 860°C, an annealing duration of 160 s, a slow cooling temperature of 715°C, an over−aging temperature of 340°C, and an over−aging duration of 110 s. The resulting DH steel demonstrated an exceptional balance between strength and ductility, achieving a tensile strength of 1214 MPa and an elongation after fracture (A₈₀) of 15.5%. Finally, SHAP analysis was conducted to reveal the influence patterns of microstructural features on mechanical performance, thereby providing theoretical insights to guide the design and microstructure−performance optimization of advanced high−strength steels.

With the continuous advancement of technologies in data acquisition, deep learning, and model generation, data−driven methods have provided a powerful tool for predicting the properties of fiber−reinforced composites, leveraging their unique advantages in uncovering high−dimensional nonlinear relationships, constructing surrogate models, and processing multimodal data. This review systematically reviews recent progress in this field, categorizing digital characterization methods into four types: collection of intrinsic material parameters, image−driven feature extraction, physics−informed feature engineering, and cross−scale data−driven techniques. It summarizes the modeling strategies and prediction accuracy of data−driven models in predicting the mechanical, thermal, acoustic, and electrical properties of composites. The engineering significance of interpretability analysis and uncertainty quantification techniques is elaborated, highlighting their roles in enhancing model transparency and quantifying prediction risks. This review aims to provide a comprehensive perspective—from theoretical foundations to engineering applications—for the deeper application of data−driven methods in predicting the properties of composites.

Cross−seasonal heat storage technology can effectively coordinate the mismatch between energy supply and demand in time and space, and use solar energy, air energy and other energy clean energy or industrial waste heat and building air conditioning waste heat as heat sources to realize the summer storage and winter use of energy, and provide a new technical route for building winter heating, coal to clean energy, and regional energy supply. This paper summarizes the classification and system working principle of cross−seasonal heat storage technology, compares the main technologies, lists relevant policies at the national and local levels, reviews the research status of cross−seasonal heat storage technology at home and abroad, and the patent development of this technology, and looks forward to the future development of cross−seasonal heat storage technology.

Ergothioneine (EGT) is a potent natural sulfur−containing antioxidant with broad application potential in the pharmaceutical, cosmetic, and nutraceutical industries. However, its traditional production methods are inefficient and fail to meet market demand. Recent advancements in synthetic biology offer promising avenues for the efficient bio−manufacturing of EGT. This review systematically summarizes the research progress on using Schizosaccharomyces pombe as a promising chassis organism for EGT production. It focuses on its endogenous biosynthetic capabilities and metabolic engineering strategies, such as promoter engineering, nutrient stress regulation, and mutagenesis screening. We also discuss the major challenges hindering the industrial application of S. pombe, including gaps in fundamental knowledge, unclear physiological functions of EGT, and a lack of standardized analytical methods. Finally, future research directions are proposed, including elucidating the metabolic regulatory network, integrating green production processes, and establishing standardized evaluation systems. This review aims to provide a theoretical foundation for the further development and optimization of S. pombe as a robust platform for EGT synthesis.

Ecological stoichiometry characteristics of carbon (C), nitrogen (N), and phosphorus (P) in plant organs and sediments of the exotic mangrove Sonneratia apetala and the native Kandelia obovata were compared, and the relationship between the ecological stoichiometry parameters of mangrove plant organs and key physico−chemical properties of sediments was examined. The C, N, and P contents in leaves, branches, roots, and the 0~1 m sediment layer were analyzed for an unvegetated mudflat, 12−year−old (SA12) and 18−year−old (SA18) S. apetala plantations, and a 40−year−old native K. obovata forest in Qi'ao Island, Zhuhai. It was shown that no significant difference in C and N contents of the same plant organ existed between the different−aged S. apetala forests. Higher P content and a lower C∶P ratio were observed in the leaves of the 12−year−old S. apetala trees compared to those of the 18−year−old trees, indicating more rapid growth in the younger stand. The SOC and N contents in the 0~20 cm sediment layer of the S. apetala forests were found to be significantly higher than those in the unvegetated mudflat. The SOC content, N content, and C∶N ratio in the 0~20 cm sediment layer of the 12−year−old S. apetala forest were found to be significantly lower than those of the 18−year−old forest. In the 0~60 cm sediment layer, the SOC and N contents, as well as the C∶N ratio, were found to be significantly lower in the S. apetala forests than in the K. obovata forest. It was indicated by correlation analysis (RDA) that sediment bulk density was the major factor affecting the ecological stoichiometry of mangrove plant organs. These findings suggest that for long−term carbon sequestration in mangrove restoration, native species like K. obovata should be prioritized; management of S. apetala plantations should be age−specific (e.g., addressing N limitation in young stands and P limitation in older stands); and the improvement of sediment physical structure (e.g., bulk density) can enhance the overall ecosystem functionality.

In view of the deficiencies in the spatial layout control, management policies, and regulation of sea use of far offshore wind power in China currently, this paper analyses the current situation of sea management for far offshore wind power in the domestic and overseas, through drawing on the management experience of far offshore wind power from abroad and the management practice of offshore wind power in China's near−sea, the following countermeasures and suggestions are proposed: First, on the basis of clarifying the resource and ecological background of deep sea, formulate a spatial plan for far offshore wind power, and promote the implementation of far offshore wind power in a coordinated manner. Second, study on the management policies of far offshore wind power, such as approval for sea use, development model, and integrated development with other industries, then clarify specific implementation paths. Third, innovate regulatory models such as improving regulation systems, enhancing monitoring capabilities, and strengthening post−assessment to ensure regulation of far offshore wind power timely and effectively.

Using the two documents related to his application for membership in the Communist Party of China (CPC) of Hou Yunde, academician of the Chinese Academy of Engineering, as the primary historical sources, supplemented by research articles, award documentation, and interview accounts, this study employs close textual reading in combination with a sociology−of−science approach to examine how his party convictions informed key choices in his scientific career. The article shows that Hou closely tied his scholarly life to national needs, oriented his work toward "what the country requires and the people expect" and consistently advanced a "science for the people" agenda through industrial translation.