In 2025, nuclear physics research has reached a critical juncture characterized by cross−scale, interdisciplinary integration. From exploring nucleon binding mechanisms, the nature of nuclear forces, and shell evolution, to simulating quark–gluon plasma under extreme temperatures and densities; from tracing the origins of heavy elements and the evolution of dense astrophysical matter, to testing weak interactions, fundamental symmetries, and quantum information applications—these fields highlight the key challenges in contemporary nuclear physics. These topics are not only widely discussed at the international forefront in nuclear physics but have also become important areas where Chinese scholars actively participate and contribute. This article provides an accessible review of representative achievements in 2025 across areas such as nuclear structure, heavy−ion collisions, nuclear astrophysics, and symmetry measurements, with a focus on high−level research involving Chinese teams. It also offers an outlook on the development trends in nuclear physics over the next decade.

Against the backdrop of the global transition toward green and low−carbon development, electrocatalytic synthesis technology utilizes renewable electricity to drive chemical reactions, offering a highly promising pathway for the direct synthesis of chemicals under mild conditions. By precisely regulating electrode potential to achieve high−selectivity synthesis, this approach combines the advantages of atom economy and low−carbon efficiency, positioning itself as a critical link between renewable energy and future intelligent manufacturing. In the context of the "dual carbon" goals, this review systematically summarizes key advances in the field of electrocatalytic synthesis over the past year. In terms of inorganic molecular conversion, it focuses on the interfacial microenvironment engineering and electrolyzer design for CO₂ reduction reaction, the exploration of novel catalysts and mechanisms for nitrogen reduction reaction, and the development of highly efficient and stable catalysts for water electrolysis toward hydrogen production. In the area of organic electrosynthesis, it covers mechanism−driven innovations and process intensification, including potential−mediated precise synthesis of aryl halides, green electrochemical synthesis of amino acids, and the upcycling of plastic waste and biomass−derived molecules. The coordinated development of electrocatalytic synthesis technology provides robust support for achieving the "dual carbon" goals and offers valuable references for future research directions in this field.

Triboelectric nanogenerator (TENG) is an emerging platform technology for achieving electro−mechanical energy conversion, with great potential for applications in various fields such as artificial intelligence, the Internet of things, and high entropy energy. This article provides a brief overview of the latest strategies and methods to improve the output performance of TENG since 2025, including composite triboelectric dielectric materials, unlocking accumulated charges at interfaces, and constructing bipolar symmetric step-down converters. Additionally, it reviews the latest progress of TENG in the fields of micro/nano energy, self−powered sensors, blue energy, wearable electronics, contact−electro−catalysis, and engineering applications, so that more scientific and technological workers can understand the latest development trends of TENG and promote faster development in related fields.

This review synthesizes the progress and trends in global environmental science for the year 2025, based on research published in leading journals such as Nature, Science, and National Science Review. Current international environmental research is characterized by multi−scale interdisciplinary integration and technology−policy synergy. Key frontiers are identified, including the accounting of global biogeochemical cycles, attribution of extreme climate events, mechanisms of ecosystem functional responses, health effects of atmospheric pollution, and the design of carbon neutrality pathways. Significant breakthroughs have been reported in understanding carbon sink dynamics, data−driven prediction, and carbon emission reduction technologies. China has made prominent contributions in addressing complex local environmental issues and promoting green technology applications. Particularly in the fields of greenhouse gas accounting, PM_2.5 toxicity control, and sectoral decarbonization pathways, practical and distinctive "Chinese solutions" have been developed. However, gaps remain in leading global fundamental scientific inquiries and constructing major original theoretical systems. To advance China's environmental science research to the world forefront, future efforts should focus on deepening global collaborative observation, strengthening interdisciplinary integration, accelerating technology industrialization, and enhancing discourse power in environmental governance.

Hydrogeological intelligent computing represents an emerging scientific paradigm that integrates physical principles with artificial intelligence. An analysis of key 2025 research trends reveals that in core applied fields such as groundwater resource assessment, mine water hazard prevention, and contaminant transport remediation, hydrogeology is transitioning from traditional data−driven approaches toward physics−informed fusion. This shift moves beyond isolated technological breakthroughs toward constructing a comprehensive technical system encompassing "data sensing, knowledge extraction, and simulation−driven decision−making". Although challenges remain in mechanism modeling, data quality, and standardization, intelligent computing has significantly enhanced prediction accuracy and decision reliability in complex scenarios such as groundwater flow simulation and surface–subsurface water coupling. Looking ahead to 2026, deeper integration of artificial intelligence and large−scale models into mechanistic research is expected to enable more accurate, interpretable, and trustworthy intelligent simulation systems and early−warning decision−support frameworks.

Emotional Intelligence (EI) refers to an individual's ability to recognize, comprehend, regulate, and apply emotional information. In recent years, with the rapid advancement of affective computing technologies, emotion−enabled health monitoring and intervention have evolved into one of the core issues in the field of public health. This article systematically reviews research progress in key areas—including multimodal emotion recognition, psychological frameworks based on large models, digital emotion regulation interventions, and AI virtual agents—along with their specialized applications in mental health. Furthermore, it discusses current challenges. Specific challenges include: group differences and recognition accuracy issues, ethical and efficacy concerns in AI−based psychological interventions, and emotional data privacy and governance challenges. Future directions are proposed, such as advancing multimodal emotional reasoning and planning, promoting standardized diagnosis and treatment alongside personalized in−home support, and establishing an ethical framework centered on data governance and regulation.

In 2025, unmanned aerial vehicle technology will develop in directions such as intelligence, autonomy, systemaltization, and low−cost becoming the core driving force for the large−scale application of low−altitude economy and the construction of a global intelligent airspace system. This paper systematically elaborates on the development trends of unmanned aerial vehicle technology in 2025 from multiple dimensions including unmanned aerial vehicle technology innovation, key unmanned aerial vehicle technologies, unmanned aerial vehicle application verification, anti−unmanned aerial vehicle tactics, and unmanned aerial vehicle management policies. At the critical stage of the global implementation of low−altitude economy on a large scale, the optimization of communication networking efficiency, the intelligent collaboration of heterogeneous platforms, and the construction of a secure and trustworthy airspace system have become the forefront of global technological competition and jointly promote the formation of a new ecosystem of the unmanned aerial vehicle industry where humans, machines, and objects are integrated. In the future, unmanned aerial vehicles will be driven by distributed collaboration and based on intelligent safe airspace, continuously injecting strong new technological impetus for the high−quality development and digital transformation of the low−altitude economy.

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.

China's pharmaceutical industry has made remarkable progress in development, yet it also faces significant challenges in technological innovation and industrial upgrading. This paper provides a systematic overview of the technological architecture underlying synthetic biomanufacturing, highlighting its core advantages grounded in the use of renewable feedstocks, environmentally benign processes, and superior atom economy. On this basis, the paper offers an in−depth discussion of the innovative applications and recent advancements of synthetic biomanufacturing in the synthesis of chemical active pharmaceutical ingredients, bioactive constituents of modernized traditional Chinese medicine, and macromolecular therapeutics including proteins and antibodies. Despite its promising outlook, the field still faces key constraints such as suboptimal technology translation efficiency, barriers to interdisciplinary integration, and limited end−to−end process consolidation across the value chain. To overcome these limitations, it is imperative to strengthen AI−enabled enzyme engineering and metabolic pathway optimization, while promoting deeper convergence with materials science, chemical engineering, and related disciplines to establish next−generation biomanufacturing platforms. In conclusion, synthetic biomanufacturing represents both a strategic driver and an indispensable pathway for advancing China's pharmaceutical industry toward greater precision, efficiency, and intelligence, thereby reinforcing its global competitiveness.

The biopharmaceuticals have diverse types, complex sources, low contents, and variable structures. Separation and purification are the core of biopharmaceutical manufacturing. This article first elaborates on the main methods in the purification of biopharmaceuticals and the challenges currently faced. Secondly, it analyzes the limited types of marketed chromatography media and equipment, as well as the low domestication level, which makes it difficult to meet the requirements for efficient biopharmaceuticals manufacturing. It then introduces the key progress made in the development of new−generation chromatography media and efficient separation equipment, including media with uniform−sized media, superporous media, high−capacity media, media with controllable surface properties, mixed−mode media, affinity media, and porous membrane media, as well as continuous flow chromatography, anti−pollution membrane components, reaction and separation coupled systems, and separation and detection coupled systems. It also covers the establishment of relevant quality standards. Finally, it proposes suggestions for the future development of advanced chromatography media and integrated equipment, namely expanding the separation mechanisms, forming an innovative material and equipment cluster, strengthening the multiple synergies between efficient chromatography media and equipment and major and frontier biopharmaceuticals, overcoming core technical difficulties and obstacles in the industrial chain development, highlighting the key points of domestication, greenness, and intelligence in industrialization development.

Amidst the deep integration of technological revolution and industrial transformation, food biomanufacturing, powered by synthetic biology, is reconstructing the production paradigms of the food industry through cutting−edge approaches such as precision gene editing, AI−assisted enzyme engineering, and intelligent fermentation. This review systematically summarizes recent advances in the bio−manufacturing of fundamental ingredients such as proteins, carbohydrates, and lipids, as well as food additives including colorants, sweeteners, and acidulants. It highlights the potential of microbial proteins, artificial starch, and functional lipids in enhancing production efficiency and reducing environmental impacts. However, several technical bottlenecks remain, including challenges in texture and flavor modulation of proteins, high costs in scale−up production, and the lack of comprehensive safety evaluation systems. To address these challenges, we propose strengthening AI−driven strain design, developing efficient carbon−fixing chassis cells, and establishing standardized nutrition and safety assessment frameworks to accelerate industrial translation. In conclusion, food bio−manufacturing holds promise for restructuring the global food supply chain and paves the way toward a green, efficient, and sustainable food system.

Renewable energy−driven biological conversion of carbon dioxide (CO₂) represents an emerging carbon−neutral technology integrating clean energy with biotechnology. By harnessing energy from renewable sources such as solar power and green electricity, this approach drives microbial or enzyme−catalyzed systems to convert CO₂ into high−value chemicals, fuels, materials etc., which demonstrates significant potential. This paper reviews CO₂ bioconversion pathways driven by clean energy sources including solar energy, green electricity (photovoltaic, wind, etc.), and geothermal/biomass energy. It summarizes progress and key case studies on achieving CO₂ bioconversion through various critical technological approaches: nature−artificial hybrid systems, photoelectrochemical microbial coupling, microbial electrochemistry, and enzyme−electrocatalysis. Research indicates that despite continuous breakthroughs in enhancing carbon fixation efficiency and expanding product diversity, core challenges persist, including low energy transfer efficiency, limitations of natural carbon fixation pathways, complex metabolic network regulation, and low product yields. Consequently, this paper recommends that future research focus on designing efficient bio−abiotic interfaces, developing dynamic metabolic regulation strategies, and innovating low−energy, high−economic−value integrated technological processes. The review demonstrates that optimizing carbon fixation pathways and carbon flow direction to establish a sustainable "renewable energy−carbon conversion−high−value products" industrial chain enables the transformation of CO₂ into high−value chemicals. This approach synergistically advances carbon reduction, pollution mitigation, green growth, and carbon neutrality development.

To achieve the "Dual Carbon" goals, industrial biomanufacturing must transits toward green sustainability. Bottlenecks such as high−water consumption, sterilization energy demands, and discontinuous processes have driven the development of next generation biomanufacturing centered on extremophiles (e.g., Halomonas spp.). Their non−sterile open fermentation significantly reduces energy consumption and operational costs. This review highlights Halomonas bluephagenesis as a chassis strain: Through synthetic biology approaches—including the development of specific genetic regulatory tools, optimization of gene editing, accelerated evolution methods, metabolic pathway and cell morphology engineering, Halomonas bluephagenesis has been successfully constructed into an efficient platform. It can utilize diverse and low−cost waste carbon sources (e.g., starch, cellulose, acetate, food wastes) to synthesize biodegradable bioplastics (PHA), high−value small molecules, amino acids, and proteins. Future efforts should focus on developing more versatile synthetic biology toolkits, enhancing the robustness of large−scale fermentation processes, and improving the integration between carbon source pretreatment and process engineering. In conclusion, next generation Halomonas−based biomanufacturing, leveraging the combined advantages of extreme contamination−resistant chassis+synthetic biology tools+process simplification, effectively overcomes inherent limitations of traditional methods. Its significant economic efficiency and environmental compatibility provide crucial support for building a green, sustainable biomanufacturing system and realizing the dual carbon goals.

With China's "Dual Carbon" goals (carbon peaking by 2030 and carbon neutrality by 2060) entering a critical implementation window, the next five years represent a decisive phase for determining the success of this transition. As one of the core supports of clean energy systems, electrochemical energy technology is witnessing unprecedented development opportunities. Based on the latest policy orientations and technological trends, this study analyzes the current status, target pathways, and strategic actions for electrochemical energy storage and conversion against the "countdown" backdrop of the Dual Carbon initiative. Against the escalating global climate crisis and growing energy security concerns, clean energy has emerged as a central direction for the worldwide energy transition. The development of clean energy not only helps reduce dependence on fossil fuels and cut greenhouse gas emissions but also promotes the diversification of energy mix and enhances energy security. Consequently, the clean energy sector is facing new development opportunities and challenges. This study aims to provide a systematic exploration of the development status, technological innovations, market trends, and application prospects across five key areas: electrocatalysis, solar cells, fuel cells, lithium batteries, and bioenergy, thereby offering insights to support the further deployment and sustainable development of clean energy.

The dual pressures of urbanization and climate change are intensifying the urban heat island effect, carbon emissions, and air pollution, posing significant challenges to environmental sustainability and urban livability. As the demand for multiobjective coordinated management of urban ecological environments continues to increase, integrating heat, carbon, and pollution into a unified framework for comprehensive assessment has become a key direction for future urban planning and policy−making. This article systematically compares and analyzes the consistency between global development agendas and the goals of reducing urban heat, carbon, and pollution, highlighting the significant potential and advantages of new−generation information technologies in intelligent optimization and coordinated scheduling, data fusion and analysis, real−time monitoring and feedback, and decision support and simulation. From the new perspective of urban spatial form, it comprehensively reviews the specific content, challenges, and future issues in conducting multi−scale, multi−dimensional "heat−carbon−pollution" multi−objective coordinated reduction planning. It provides innovative solutions for multi−objective coordinated management and sustainable development of "heat−carbon−pollution" in Chinese cities.

Hydrogen energy, recognized globally as a clean energy source, demonstrates significant potential in supporting climate commitments and energy transition. Driven by the "dual carbon" target, China's hydrogen energy industry has entered a rapid development phase. This paper focuses on emerging hydrogen energy application sectors, such as transportation, electricity, and construction. Relevant policies and cases from several developed countries in recent years are summarized. Current status and achievements of domestic hydrogen energy applications in the three sectors are reviewed and also some key issues. Finally, a development pathway for hydrogen energy which conforms to China's national conditions and some forward−looking recommendations are proposed. The purpose of this paper is to provide support for the steady development of hydrogen energy in China.

This paper systematically reviews the concept of green methanol fuel, and conducts a comparative analysis of the main manufacturing technology paths, emission reduction potential, as well as the advantages and disadvantages for the major routes, including the biomass path, the electricity−based path and the electricity−biomass coupling path, etc. We summarize the relevant policies on methanol fuel for the water transportation industry at the national and industry levels, clearly stating to promote the pilot application of methanol fuel in coastal and inland river vessels, and to accelerate the construction of methanol refueling stations, storage facilities and other supporting facilities. A review has been conducted of the current green methanol−related standards in China, including guidelines for methanol fuel for ships and group standards focusing on the evaluation of green methanol and the carbon footprint assessment of green methanol products. Related policies and standards are still in the initial stage. At the same time, based on the low−emission analysis platform LEAP, a comprehensive assessment model for carbon emissions prediction of China's water transportation industry is constructed to quantitatively calculate the total energy consumption and carbon emissions of China's water transportation industry in the medium and long term. It is estimated that the total energy consumption will be about 58~85 million tce, and the total emissions will decrease to 30~120 million tons CO₂. Based on the above analysis, suggestions for the future development of marine green methanol fuel industry in China are proposed, including: continuously tracking and deeply participating in the IMO’s negotiations on the net−zero framework and related rules construction; combining the internal and external water industry advantages to conduct in−depth quantitative research on the dual−carbon transformation path of the water industry; and taking the lead through trials and experiments, step by step to systematically promote the development of green methanol in China.

Methanol as fuel, particularly for the green methanol by its clean burning characteristics and easy availability with liquified energy as well as abundant application scenarios is assist it transform from shipping, transportation and industry and so on to the global energy transition as well as realization for the goal of "Double Carbon". According to the transformation of methanol from chemical to fuel, the review reports that various forms of methanol as fuel are used in industry and power units respectively, particularly introducing the status of usages in internal combustion engines and their technical characteristics in applications for reference in futural development. Taking the advantage of the huge productive capability of our country, and promoting the green methanol application as well as its development, many proposals are provided based on the following issues such as the policy support and technical improvements and so on.

Biological carbon fixation is crucial to the Earth's carbon cycle and is one of the effective ways to transform CO₂ and manage carbon emissions. Chemoautotrophs, with their unique metabolic strategies and environmental adaptability, play an important role in this process. They are able to convert CO₂ into valuable organic products, solving the problem of limited CO₂ utilization. However, the carbon fixation potential of chemoautotrophs in controlled systems has not been fully explored. This review illustrates the possible challenges of stable culture of chemoautotrophic bacteria in bioreactor. Based on this, a series of physical, chemical and biological methods are proposed to regulate the carbon metabolism of chemoautotrophic bacteria and improve their carbon fixation efficiency. Further, the application prospects of chemoautotrophic carbon fixation in controlled systems are expected, including improving the primary productivity of natural ecosystems, reducing carbon emissions in specific sites, and producing high−value microbial by−products. This review highlights the advantages and challenges of these applications, providing important insights into carbon capture, fixation and conversion by chemoautotrophs.

Oxygen blast furnace (OBF) has many advantages such as a high coal injection rate, reduced coke ratio, lower CO₂ emissions, and improved production efficiency, and is considered one of the most promising low−carbon ironmaking processes for large−scale application. Therefore, it has received extensive research attention. This article analyzes the current state and development trends of OBF research from various aspects, including its development history, industrial experiments, physical models, and mathematical models. Carbon reduction potential of the OBF is analyzed from the perspectives of internal production state production indexes, material flow, and energy flow. It is found that OBF has significant carbon reduction advantages compared to traditional blast furnaces (TBF), and its carbon reduction potential can be further enhanced with CO₂ capture and storage technologies. Then, the progress made by China Baowu Hydrogen−enriched Carbonic oxide Recycling Oxygenate Furnace (HyCROF) was elaborated in more detail. This process achieved a utilization coefficient of 5.0 t/(m³·d), reducing the cost per ton of iron by approximately 150 yuan compared to previous stages. Finally, it looks ahead to accelerating the integration of the "blast furnace−converter" long process by replacing carbon with hydrogen, combining carbon capture and storage (CCS) technology and pure hydrogen reduction technology, so as to build an intelligent, efficient and high−yield development direction for the steel industry. Finally, the direction of low−carbon iron making in China is prospected.

This paper reviews low−carbon metallurgical technologies in China, Japan, and Europe, analyzing the EU's ULCOS project, Japan's COURSE50 project, and various low−carbon technological advancements in China. Countries are actively addressing the carbon reduction challenges in the iron and steel industry by developing technologies such as hydrogen direct reduction, molten reduction, and electrolytic steelmaking. For instance, the ULCOS project aims to reduce CO₂ emissions per ton of steel through top gas recycling and novel direct reduction processes. The COURSE50 project integrates hydrogen injection with CO₂ capture technologies to achieve its reduction targets. Under the "dual carbon" strategy, China has rapidly developed technologies such as hydrogen−rich carbon−circulating blast furnaces and gas−based direct reduction processes, demonstrating significant carbon reduction potential.

In order to achieve green, low carbon and high−quality development of the iron and steel industry, the development and application of low carbon emissions steelmaking technology should be noted. The study systematically analyzes the structural transformation of the current steelmaking process, with a focus on achieving low−carbon operation in the converter process through high scrap steel ratio smelting and the use of clean raw materials such as direct reduced iron (DRI). At the same time, an in−depth analysis was conducted on how electric arc furnace steelmaking technology can move towards the forefront of low−carbon and even "near zero carbon" smelting through technological solutions such as green electricity, intelligent power supply, biomass carbon sources, and reducing auxiliary material consumption. Finally, a systematic low−carbon emission steelmaking development path was proposed from the dimensions of technology integration, policy guidance, and energy structure transformation, providing theoretical support and practical reference for the high−quality and sustainable development of the steel industry.

The production of ferroalloys necessitates the consumption of substantial quantities of alloy materials. Achieving the national "dual carbon" strategic goals and reducing energy consumption in the steel industry necessitates the implementation of scientific and practical methods and approaches for ferroalloy charging. The objective of alloy reduction technology in the steelmaking alloying process is twofold: first, to minimize the use of alloying elements, and second, to reduce production costs, while ensuring that the final steel retains the required properties and characteristics. The present paper introduces the physicochemical properties of ferroalloys and employs drum tests to quantitatively evaluate their pulverization performance. During handling, alloys should be stored in tiered arrangements based on particle size and density to ensure absorption rates. It is imperative to mitigate the occurrence of collisions during storage, transportation, and utilization to avert pulverization losses prior to furnace entry. An intelligent control system for alloy reduction in steelmaking, developed using neural networks and big data models, has been successfully implemented in over ten domestic steel enterprises. The substitution of customized alloy recycling plans, derived from field operation data and process analysis, has been demonstrated to reduce ferroalloy usage costs for steel producers. In the process of smelting particular steel grades, it is imperative to exercise caution with regard to the presence of deleterious elements within the alloy. Concurrently, precise selection should be made based on changes in the main alloy components to reduce cost increases caused by fluctuations in alloy composition. By analyzing current alloy reduction technologies in steelmaking, this study proposes future improvement directions and trends for ferroalloy reduction methods. Initial efforts must concentrate on the enhancement of ferroalloy quality, with the objective of reducing the usage of superfluous alloy elements and averting the squandering of resources. Secondly, the advancement of digitalization and automation technologies has the potential to enhance the stability and controllability of steelmaking operations by enabling the monitoring and control of the alloying process.

As the world's largest steel producer, China's steel industry generates over 1.4 trillion cubic meters of by−product gas annually, which contains energy equivalent to 266 million tons of standard coal. However, currently, the by−product gas from China's steel industry is mainly used as fuel for combustion, remaining at a stage with high carbon emissions and low comprehensive utilization rate. In view of this, to promote the improvement of the utilization efficiency of by−product gas in China's steel industry, this paper elaborates on the composition, calorific value and other characteristics and availability of three core by−product gases: blast furnace gas, coke oven gas and converter gas. It also analyzes the current utilization status and limitations mainly based on fuel combustion, and explores the high−value utilization pathways under the steel−chemical integration and hydrogen metallurgy approaches, including pressure swing adsorption (PSA) and chemical absorption for purifying CO/CO₂, as well as biological fermentation of converter gas and reforming of coke oven gas to produce methanol/ethanol and hydrogen energy. It focuses on the application advantages and practices of coke oven gas in Midrex and Energiron−ZR direct reduction ironmaking. Research shows that by−product gas can be transformed from a single fuel to a chemical raw material through technological upgrades, such as reducing emissions by 10%~20% through high−pressure injection of coke oven gas into blast furnaces, and the feasibility of Baowu's HyCROFTM hydrogen−rich carbon cycle blast furnace technology has been verified. Based on this, it is concluded that the core directions for efficient and low−carbon utilization are steel−chemical integration and synergy, hydrogen metallurgy coupling, and the integration of CCUS technology, which can drive the industry to shift from "carbon metallurgy" to "hydrogen metallurgy" and provide technical support for the green and low−carbon transformation of the steel industry.

Autonomous intelligent unmanned systems operating in real−world open environments—characterized by dynamic complexity, multi−agent coupling, incomplete information, and strong social constraints—face critical challenges such as insufficient compliance modeling, limited social risk perception, complex collaborative conflicts, and delayed abnormal response. To address these issues, this paper proposes an Embodied Social Perception Intelligence Framework, which integrates embodied perception (including proprioceptive, internal, exteroceptive, interactive, and intention perception) with social radar, and introduces Agentic AI as a top−level decision−making and control mechanism to achieve multi−level and autonomous cognitive decision−making. The framework adopts a five−layer architecture—perception, reasoning, execution, feedback, and meta−control—establishing a dynamic closed loop from multimodal perception to compliant behavior generation. By fusing physical and social environmental information, the proposed framework significantly enhances the task adaptability, collective coordination efficiency, and compliance reliability of autonomous intelligent unmanned systems in complex and uncertain scenarios such as urban governance, emergency rescue, and social security. This work provides a new technical pathway toward trustworthy, explainable, and sustainable autonomous intelligent systems.

Embodied intelligence represents a new stage in the evolution of artificial intelligence, marking a transition from "perception−cognition" to an integrated paradigm of "perception−cognition−action." The Vision−Language−Action (VLA) model provides a critical technological pathway for enabling autonomous agent operation in the real world by unifying visual perception, language understanding, and action generation. This paper systematically reviews the development trajectory and representative achievements of VLA technologies, and summarizes their architectural paradigm, which includes multi−modal perception, semantic fusion mechanisms, reinforcement and imitation learning, world models, and hierarchical action output. By considering application scenarios such as autonomous driving, human–computer interaction, and industrial equipment, we further analyze the core challenges faced by VLA development, including the scarcity of data resources, limited generalization and transferability, insufficient interpretability, and increasing computational demands, and we outline the future development trends.

With the convergence and innovation of emerging information technologies, Digital Twin (DT) technology has become a key enabler for digital transformation and the evolution of intelligent systems. It has been widely applied and received significant attention in fields such as industrial manufacturing, smart cities, and intelligent transportation. However, existing research on traditional DT technologies has predominantly focused on the modeling and analysis of physical entities ("objects"), with limited systematic integration of "human" and "environmental" factors. The lack of exploration into human−environment interactions makes it difficult for current digital twin frameworks to meet the advanced demands of complex intelligent systems for multi−level, comprehensive interaction capabilities. In view of this, this paper innovatively introduces the "object−human−environment" interactive vision and comprehensively and systematically analyzes the research frontiers and progress of digital twin technology from the three core dimensions of intelligent physical entity (object), intelligent individual (human), and virtual−real fusion environment (environment). Firstly, the paper analyzes the traditional digital twin technology system with "object" as the core and focuses on its theoretical origin, framework, and application. Secondly, it discusses the definition, development context, national policies, and core technologies of digital people driven by AI. Finally, expand the vision to the dimension of "environment" and explore the application practice of "environment" in multiple scenes of the meta−universe, deeply discuss the deep integration and interaction mechanism of the three elements of "object", "human" and "environment", reveal how the three interact and promote each other, and provide support for the construction of the meta−universe. Furthermore, this study discusses the current research challenges and future development trends of digital twin technology from the perspective of "Object−Human−Environment" interaction and proposes three key research directions: (1) developing an intelligent, multi−layered data fusion framework; (2) exploring AIGC−enabled intelligent virtual−real mapping and native virtual evolution; and (3) constructing novel virtual economy architectures and intelligent governance systems. The research outcomes provide both theoretical foundations and practical insights for building next−generation digital twin systems characterized by multi−agent collaborative perception, multimodal intelligent interaction, and closed−loop integration of virtual and real environments.

The intelligence of unmanned vessel systems is undergoing a profound transformation from remote control to embodied autonomous forms, with the core being the realization of advanced intelligent behavior through multimodal perception, environmental interaction, and closed−loop learning. This paper systematically reviews the key advances of embodied intelligence in unmanned vessels, highlighting that semantic control loops, digital twin validation, and evaluation systems are moving from methodological exploration to engineering integration, and have already begun to provide preliminary application support in port and inland waterway scenarios. However, current technologies still face bottlenecks in perception stability, rule interpretability, and deployment resources. Therefore, this paper recommends focusing on breakthroughs in strengthening autonomous closed−loop intelligence systems, establishing standard and trustworthy validation environments, and promoting lightweight and collaborative deployment to enhance system reliability, compliance, and scalability, providing support for the development of intelligent ship technology and the implementation of our marine strategy.

Humanoid robots, benefiting from their human−like morphology and locomotion capability, are regarded as promising platforms for future service, rescue, and industrial applications; however, achieving stable and reliable walking in unstructured environments remains highly challenging. This paper provides a comprehensive review of recent advances in humanoid locomotion planning and control, with a focus on gait planning, trajectory generation, whole−body control, and learning−driven approaches. We summarize the core concepts and implementation frameworks of representative methods, compare their applicable scenarios, strengths, and limitations, and present a hierarchical categorization of existing research. Moreover, this work discusses key technical bottlenecks that hinder environmental adaptability and dynamic stability. Finally, we outline future research directions, including multimodal perception integration, co−optimization of learning and control, whole−body motion skill learning, and safety assurance, and offer suggestions toward standardization and large−scale deployment.

The Chinese Hα Solar Explorer (CHASE), dubbed "Xihe" – Goddess of the Sun, is China's first solar space mission for both scientific and technological experiments. Since its launch on October 14, 2021, it has been operating well in orbit with excellent scientific data quality. Based on a new type of satellite platform with ultra-high pointing accuracy and ultra-high stability, the Hα Imaging Spectrograph (HIS) onboard the CHASE mission has, for the first time in the world, achieved space-based spectroscopic observations in the solar Hα waveband. The pixel spectral resolution reaches~0.024 Å, the full-disk scanning time is~46 seconds, and the spatial resolution is~1.2 arcseconds. Using the high-quality data from CHASE, researchers have achieved a series of original scientific results in the dynamic processes of solar activities in the lower atmosphere, the formation, evolution, and eruption of solar filaments, as well as comparative studies of solar and stellar eruptions.

Kuafu-1, also known as the Advanced Space-based Solar Observatory (ASO-S), is China's first comprehensive solar exploration satellite. It was successfully launched on October 9, 2022, from the Jiuquan Satellite Launch Center. After nearly one year of in-orbit testing, the satellite was delivered to the Purple Mountain Observatory of the Chinese Academy of Sciences at the end of September 2023 and officially entered the scientific operation phase. This article summarizes in brief the preliminary observational research results achieved in less than one and a half years, up to February 2025, following the mission's delivery. Some findings are highlighted, including the pivotal role of magnetic cancellation in the dissipation of β-type sunspots, an exhaustive diagnostic on quasi-periodic pulsations (QPP) in flaring hard X-ray emissions, a joint analysis of dual-angle observed hard X-ray imagery, the features of white-light flares at 360 nm wavelength, Carriton maps in the Lyman-alpha (Lyα) band, and so on. In the future, by utilizing observation data from ASO-S and integrating it with data from other advanced solar observation satellites both domestically and internationally, joint research on multi-wavelength and even stereoscopic observations can be carried out. This is expected to yield significant progress in understanding the nature of and correlations between the "one magnetic field and two storms" (i.e., the solar magnetic field, solar flares, and coronal mass ejections).

A brief introduction is provided to the observational system of the 1-meter New Vacuum Solar Telescope (NVST) at Fuxian Lake, Chengjiang, operated by Yunnan Observatories, Chinese Academy of Sciences. Over the past decade, both domestic and international researchers have used NVST observational data to conduct outstanding scientific studies in several areas, including the observational characteristics and fine physical processes of magnetic reconnection, the structure, formation, and evolution of solar filaments, the fine structure and dynamic evolution of prominences, small-scale solar activities, fine physical processes of photospheric activities, as well as image processing and feature recognition methods. Prospects are also presented for the construction of large-aperture ground-based solar telescopes and the scientific issues they aim to address.

The paper introduces the growing trend of world solar radio researches and points out the importance and uniqueness of solar radio observations. The Mingantu Spectral Radioheliograph (MUSER) in China has been developed and constructed to image the solar atmosphere over continuous wide band radio spectrum in 3-D from the lower atmosphere up into the mid-corona to monitor solar activities. The results of solar radio bursts and multi-frequency (or 3-D) radio images are demonstrated, exhibiting the significant role of MUSER in solar physics and space weather studies.

During May 10-11, 2024, Solar Active Region (AR) 13664 experienced one of the most intense solar storm events since the Carrington Event of 1859, triggering a G5-level geomagnetic storm (Dst index reaching -412 nT) and global auroral displays. AR 13664 exhibited a dense and complex magnetic field distribution, accompanied by rapid magnetic field evolution and high activity such as abundant magnetic emergence, topological restructuring, and the generation of multiple flares and coronal mass ejections (CMEs). AR 13664 may represent a typical process of energy accumulation and release in intense solar eruptions, making it an ideal subject for studying magnetic complexity, energy storage and release mechanisms, and the causes of strong solar eruptions. This paper reviews current relevant research findings, focusing on multi-band observations, magnetohydrodynamic modeling, and nonlinear force-free field extrapolations, to reveal the full chain physical processes from magnetic flux emergence to near-Earth space responses of AR 13664. The research results around AR 13664 indicate: (1) the region exhibited an extremely high rate of magnetic flux emergence, peaking at 2.2×10²² Mx/day, rapidly forming a complex βγδ-type magnetic structure, with a total unsigned magnetic flux of up to 1.35×10²³ Mx, laying the magnetic topological foundation for efficient energy storage; (2) magnetic topology analysis indicates that the energy release process is closely related to the evolution of quasi-separatrix layers (QSLs) and the development of multiple current sheets, revealing the energy release mechanism in local non-potential energy regions; (3) multi-stage magnetic shearing processes were clearly observed, showing the gradual formation of magnetic rope structures and enhanced instability, closely associated with subsequent eruptions of 12 X-class flares and multiple halo CMEs; (4) the associated CMEs exhibit large-scale trans-equatorial source structures and propagate swiftly through solar-terrestrial space. Some of these CMEs reach projected speeds surpassing 2000 km/s and showcase pronounced southward magnetic fields (with the North-south magnetic field Bz dropping to a minimum of -50 nT) at 1 AU. These characteristics lead to significant impacts on Earth's magnetosphere, instigating intense magnetic storms and disturbances in the ionosphere. These studies systematically depict the full chain evolution process of extreme space weather events from the solar source to near-Earth space, providing innovative insights into the triggering, energy accumulation, release, and propagation mechanisms of solar eruptions, and offering an important research foundation for establishing more accurate and predictable space weather models.

This article focuses on the triggering process of an X1.0 class flare that occurred on May 8, which is the first event in a series of solar eruptions related to the massive geomagnetic storm event on May 10-11, 2024. Multi-wavelength observations show that the solar source region of this event is composed of two closely-related active regions, AR 13668 and AR 13664. There are four filaments in the core area of the activity, and the corresponding high-temperature observations exhibit four sets of bright corona loops, corresponding to one twisted magnetic flux rope and three weakly twisted sheared magnetic arcades in the nonlinear force-free field model. The complex triggering process of X1.0 class flares is analyzed, and it is found that they are associated with the eruption of two M-class flares and two hot channels. The eruption of the hot channel HC1 during the first M-class flare provides favorable conditions for the fast rise of the hot channel HC2 after its formation, which in turn propels the fast rise of HC1. Eventually, the two hot channels merge and successfully erupt, forming a halo coronal mass ejections. The joint analysis of multi-wavelength observations and nonlinear force-free field shows that the formation of the hot channel HC2 is caused by the tether-cutting magnetic reconnection of two sheared magnetic arcades triggered by the magnetic flux cancellation. This process occurs during the second M-class flare, and the fast rise of the hot channel HC2 subsequently triggers the onset of the X1.0 class flare. The study reveals the complex triggering process of the first X-class solar flare related to the massive geomagnetic storm in 2024 May, which involves coupling between multiple flares and hot channel eruptions, deepening our understanding of the triggering process of extreme space weather events in the source region.

Organizational resilience is fundamental for an organization to maintain its survival and achieve sustainable development when encountering severe risks. The study of organizational resilience in disaster scenarios aims to establish a dialogue channel between theoretical research on resilience and emergency management practice, and to guide the research and practice of disaster emergency management with the rich achievements and management inspirations of organizational resilience research. Therefore, based on raising the research issues of organizational resilience in disaster scenarios, this paper uses Citespace software to sort out the core literature in the field of disaster resilience, sort out the research topics of concern, and form an organizational resilience research and analysis framework based on the entire process of disaster emergency management, and then proposes future research prospects.

Post−disaster restoration and reconstruction after major disasters is an open and complex mega−system project. This paper attempts to interpret the historical development of post−disaster restoration and reconstruction in China from the system engineering perspective. First, we define major disaster events and provide classification and outline significant catastrophic events since 1949. Secondly, we use Hall's three−dimensional morphology and Wuli−Shili−Renli system approach to construct the system structure of post−disaster restoration and reconstruction from three dimensions: development stages, critical activities, and science and technology. Then, by examining the specific measures of post−disaster restoration and reconstruction in typical catastrophic events and their relationship with the evolution of human−environment interactions, the traditional, sustainable and intelligent patterns of post−disaster restoration and reconstruction systems are revealed. Finally, the historical experiences of China's post−disaster restoration and reconstruction are summarized in five aspects: institutional advantages and legal guarantees, cross−departmental coordination and information sharing, intelligent monitoring and precise needs assessment, infrastructure and cultural construction, and catastrophe relief and insurance.