很多科学研究可以从仪器、材料和发现3个方面去理解。仪器是研究工具；材料是研究对象；发现是研究目标。这里的“仪器”可以是实验工具、理论工具，也可以是实验方法或理论方法。“材料”可以是生命科学中的生命体，也可以是物质科学中的非生命体。多数科学研究要寄托在这些材料基础之上。“发现”主要指科学发现，可以是理论研究的发现，也可以是实验研究的发现。

纤维材料是指具有足够的细度（直径＜100μm）和长径比（长度/直径＞1000），具有定向导向性、可编程性、可柔性加工的物质。自古以来，纤维就与人类生活密切相关。从棉麻丝毛等天然纤维发展到高性能和功能性合成纤维，纤维与人类社会的联系越来越紧密。由于纤维材料的柔性和多样化的可加工特性，其应用已经超越了传统织物和纺织品，在战略新兴产业如人工智能、电子信息、航空航天、新能源、生物医药等领域具有更广泛的应用。纤维新材料的发展具有高科技、高效能、高质量特征，亟需与物理、化学、生物、医学和信息技术等融合，开发具有高性能、多功能、更智能和可持续的纤维材料与器件，实现多功能耦合与杂化，满足未来产业的应用需求。

科技创新是发展新质生产力的核心要素，数字经济是发展新质生产力的重要领域。在以习近平同志为核心的党中央坚强领导下，我国高度重视发展数字经济，规模体量保持稳健增长，数字基础设施扩容提速，关键核心技术不断突破，数据要素市场日趋活跃，数实融合成效更加显著，构筑国家竞争新优势。随着新一轮科技革命和产业变革的深入发展，数据成为新的生产要素、算力成为新的基础设施、人工智能成为新的通用技术，共同构成发展新质生产力的重要引擎。在这个由大数据、大算力、大模型共同塑造的智能时代，通信科技、数字科技、数据科技、运营科技深度融合，为数字经济发展带来深层次、结构性变革。

As a high-tech strategic emerging industry that is a key focus of national cultivation, China's nuclear power industry has long adhered to safe and innovative development, and has initially formed new productive forces in practice; In the face of a new stage of development, the nuclear power industry needs to strengthen the drive of new quality productivity, actively build a modern industrial system, and promote high-quality development of nuclear power. This article is based on the analysis of the development trend of the nuclear power industry at home and abroad, and introduces the characteristics and foundation of the development of new productive forces that China's nuclear power industry has initially possessed. Based on the research results of the academic community on the theory of new quality productivity, it is proposed to actively leverage the doubling effect of new technologies and big data on productivity, and cultivate and develop new quality productivity in nuclear power. At the same time, we will deeply explore the essence of "innovation" in all elements of nuclear power, propose to promote the high-quality development of nuclear power through three practical paths: propose more original innovation, integrated innovation, and systematic innovation, promote the digital transformation of nuclear power and "giving birth to new industries, new models, and new driving forces".

Through more than half a century of development, three generations of polyacrylonitrile (PAN)-based carbon fibers have emerged successively. Since 2010, the third-generation carbon fibers have been successfully developed and achieved an effective combination of high strength and high stiffness characteristics, which are expected to become a priority for the future high-performance carbon fibers. In this article, the development stages and main manufacturers of PAN-based carbon fibers were introduced first. The research background of the third-generation carbon fiber, which combines high strength and high modulus, was elaborated subsequently. Then, the development process of the third-generation carbon fiber at home and abroad was reviewed in detail. Finally, the future development direction of carbon fiber was discussed. With the successful development of Toray M46X carbon fiber in early 2024, the product series of the third-generation carbon fiber is expected to further expand. In addition, for cutting-edge fields such as cable materials for space elevators, foreign countries are also developing carbon fibers of higher strength.

With the rapid development of artificial intelligence (AI) technology, general large models (GLMs) have become a significant research focus in the AI field. GLMs typically possess an extensive number of parameters, are trained on massive datasets and exhibit robust learning and reasoning capabilities. These models demonstrate outstanding performance in various tasks, including natural language processing, image recognition, and code generation. This paper reviews the evolution of GLMs and the key technology nodes, from the early rule-based systems and traditional machine learning models to the rise of deep learning, the introduction of the Transformer architecture, and the advancements in the GPT series and other GLMS over the world. Despite the significant progress, GLMs face numerous challenges, such as high computational resource demands, data bias, ethical issues, and model interpretability and transparency. This paper analyzes these challenges and explores five key future development directions for GLMs: model optimization, multimodal learning, emotionally intelligent models, data and knowledge dual-driven models, and ethical and societal impacts. By adopting these strategies, GLMs are expected to achieve broader and deeper applications, driving continuous progress in AI technology.

Advances in multi-omics technologies, cohort study design, data science, and machine learning are transforming evidence-based medicine, offering a promising outlook for the future of next-generation "deep" medicine. We hereby summarized the development trends in multi-omics experimental techniques, including genomics and epigenomics sequencing, transcriptomics and single-cell transcriptomics, proteomics, metabolomics, microbiomics, imaging, and biosensors. Furthermore, we introduced progress in big data analysis methods such as genome-wide association studies, interpretation of genome-wide association signals, polygenic risk scoring, Mendelian randomization, and artificial intelligence algorithms. Additionally, we discussed the clinical applications of these technologies in disease subtyping, diagnosis and prediction, drug development, and clinical trial design. Finally, we discussed the challenges faced and explored future directions in cohort study design, data management and sharing, and the enhancement of international collaboration.

Dynamic disease risk prediction models are essential for precision prevention strategies. Over the last twenty years, there has been a surge in research focused on these models for precision prevention. However, widely used models（static models） often overlook the impact of changes in predictors over time on disease risk, leading to inevitable calibration drift. This paper reviewed modeling methods for dynamic risk prediction models and provided reference for their development. The conclusions are as follows: As healthcare big data becomes more interconnected and shared, and new methods of statistics and artificial intelligence emerge, the challenge lies in discovering richer predictors, in identifying more accurate modes of action, and in creating interpretable disease risk prediction models which align with biomedical contexts and practical scenarios, to enhance common prevention of common diseases and co-prevention of heterogeneous diseases and to achieve precision and personalized prevention across a spectrum of diseases. This will be a crucial focus for future research on predictive modeling methodologies.

In recent years, the demand for deep-sea mineral resources has gradually increased. In order to solve the shortcomings of traditional pipe lifting mining system, intelligent robots are proposed to realize efficient and accurate mineral collection and health monitoring of the structure. However, due to the complexity of the deep-sea environment, compared with robots on land, the design of underwater robots needs to take into account the effects of resistance, noise, and other aspects brought by the ocean. This paper introduces the development and technical difficulties of mining robots in pipe-lifting deep-sea mining systems from three aspects: hydrodynamics, underwater localization, and underwater vision. Meanwhile, it provides an overview of robotic pipeline inspection technology. On this basis, this paper discusses the technological development direction of underwater robots for deep-sea resource mining.

With the advancement of radio technology, there is a growing demand for high-precision electromagnetic wavefront manipulation. Vortex electromagnetic wave technology and its applications have emerged as an international academic frontier and a research hotspot. With its unique ability to manipulate the electromagnetic waves and characteristics of orbital angular momentum (OAM), vortex wave metasurface provides a new degree of freedom for precison modulation and application of electromagnetic waves. This paper first reviewed the fundamental theory of the vortex wave, including its generating principles and the characteristics of orbital angular momentum which it has carried. Then it introduced the two primary categories of vortex wave metasurfaces: reflective and transmissive metasurfaces. Subsequently, it summarized the measurement technology for vortex waves, providing a scientific and effective approach to evaluate the performance of vortex wave metasurfaces. The paper also discussed the applications of vortex wave metasurfaces in the fields, including to improve the cyberspace security and the data transmission capacity for the wireless communication systems, the target detection capabilities of radar systems, and the imaging quality of holographic imaging technology. Finally, it outlines future development directions for vortex wave metasurfaces, including research of high-order multi-mode OAM waves, solution of the beam divergence in long-distance transmission, design of reconfigurable vortex wave metasurfaces, development of OAM covert signal transmission and multi-channel multiplexing technology, to strengthen the security of cyber space and provide more efficient and flexible technical solutions in the fields of wireless communications, radar detection, optical imaging, and etc.

Coalbed methane is a kind of associated and co-produced gas during the coal formation process. With methane as its main component, coalbed methane is also known as coal mine gas. With green and clean combustion characteristics and superior transportation advantages, methanol is an important basic chemical raw material and energy material. Converting coal mine gas into methanol, a liquid industrial raw material that is easy to store and transport, is a development direction of its comprehensive utilization, which is not only attractive to industrial demand, but also of great significance to alleviate energy crisis, reduce environmental pollution, and help to achieve carbon peaking and carbon neutrality goals. With methane as a model gas, various technical approaches of direct catalytic oxidation of methane to methanol and their recent research progress for are summarized in this paper. Moreover, it also gives a brief outlook on the research direction and development prospects in this field. We hope that this study will provide reference and guidance for the industrial production of methanol from coalbed methane by direct catalytic oxidation in the future.

National energy security is an important guarantee to the sustainable development of national economy. In order to achieve China’s carbon peaking and carbon neutrality goals, this article explores hotspot issues in the development of unconventional energy with a new development perspective of porous media science. In the paper, two aspects of porous media science are highlighted: (1) Multi-scale issues in unconventional petrochemical energy; (2) Multi field coupling theory and application issues related to the development of unconventional energy in porous media. The article reviews the development of the theory and application of multi-field coupling in porous media, as well as the current research hotspots and study trends; It also explores the current hotspots, difficulties, characteristics, transition methods, and development trends of multi-scale problems in porous media science which requires further in-depth research.

Unmanned autonomous systems (UASs) are intelligent systems endowed with autonomous cognition, motion planning, autonomous decision making and reasoning capabilities. Their goals are designed to perform and complete common tasks in complex, open and dynamic scenarios with limited or even no human participation. In terms of the challenges UASs faced in cross-domain collaborative tasks, such as low efficiency of collaborative perception, poor reliability of Ad Hoc network communication, slow resource scheduling, and conflict-prone task allocation, this paper explored how to combine large models and generative artificial intelligence (GAI) technology to construct the“compute-control-test”embodied cognitive intelligence framework of UASs integrating“large model + autonomous unmanned systems + artificial intelligence generated content(AIGC)”. It will provide valuable reference for advancing the technological implementation and practical deployment of UASs with embodied cognitive intelligence.

The existing multi-agent collaborative control methods based on temporal logic tasks usually adopt formal language to describe the temporal task, convert it into an automaton, multiplicate it with the environment model, and finally complete task planning by performing graph-search in the product automata. The advantages and disadvantages of existing methods are compared and summarized in this paper; Starting from the commonly used control methods which have combined temporal logic languages, the development of three key technologies, namely man-machine fusion heterogeneous team control method, system's robust control of task violation degree and coupling task assignment between human-machine collaboration tasks, are emphatically sorted out. Meanwhile, the good performance of emerging temporal languages such as TSTL in human-machine fusion architecture is also reviewed. Finally, an in-depth analysis was conducted on the scientific and technological bottlenecks in current research on collaborative control, including difficulty in task description, decoupling allocation, and large online computing load. These bottleneck issues which urgently need to be solved in the future development, have become the main study trend of this field in the future.

With the development and utilization of renewable energy, lithium-ion (Li-ion) batteries has been regarded as one of the most important energy storage technologies by virtue of their high energy density, long cycle life and low self-discharge. However, the frequent occurrence of fire or explosion caused by thermal runaway of lithium-ion batteries makes it urgent to improve their safety performance. Because of the battery external abuse, Li-ion batteries thermal runaway occurs, resulting in the growth of lithium dendrites inside the battery which will cause short circuits, electrode decomposition and gas precipitation, flammable electrolyte decomposition, leading to combustion and explosion. With the internal components of Li-ion batteries as a starting point, based on the study of the thermal runaway mechanism of Li-ion batteries, this paper made a detailed analysis on the thermal runaway triggers in terms of the positive and negative electrodes and Li-ion battery electrolyte; It also elaborated the reaction processes, within the batteries during the thermal runaway in a comprehensive way; For the thermal runway of Li-ion battery, the author proposed internal improvement strategies such as inhibiting the growth of lithium dendrites, designing electrolyte, reducing the release of positive oxygen and optimizing the diaphragm. Integrated with the external thermal management of the batteries, it will realize dual protection both inside and outside of the Li-ion batteries.

This article systematically reviews the relevant literature in recent years to analyze the development and research trends of the global seed industry and argues that the theoretical innovation research and practical innovation exploration of China's seed industry are not in-depth enough nor forward-looking enough. In view of the basic practical challenges faced by China's seed industry, the paper points out the specific measures of systematically deepening theoretical innovation and practical exploration in six aspects, including "quasi public goods publicity scheme, horizontal and vertical acquisition boundaries of seed companies, collaborative innovation mechanism cluster of seed industry, institution of social capital and insurance participation in innovation chain, transgenic series of social surveys, and introduction and exchange strategy of germplasm resource".

With the advent of ChatGPT, the research of generative artificial intelligence (GAI) has made a breakthrough in the field of multimodal information processing, such as text, image, and video, and has attracted broad attention. This paper aims to systematically review the research progress of GAI and to discuss its future development trend. Being divided into three parts, the paper first reviewed the development history and research status of GAI in terms of natural language models, image and multimodal models; secondly, it discussed the application prospects of GAI in different fields, mainly focusing on content communication, assisted design, content creation, personalized customization, and etc. In the third part, with an in-depth analysis of the main challenges facing GAI, the author summarized the development trends of GAI in the future.