[Purpose/Significance] With the advancement of communication technology and the popularization of smart mobile devices, social media platforms have developed rapidly. The increased exchange of information via social media platforms has increased the complexity and the spread of public opinion. Understanding the topological structure of information dissemination networks across platforms and the mechanisms of public opinion dissemination on them is of great significant guiding importance for predicting trends in online public opinion and formulating guiding strategies. [Methods/Processes] Based on the SEIR model of infectious diseases, a model of the spread of public opinion on cross-social platform coupled networks was constructed. Using the Monte Carlo simulation method, the experiment explored the effect of the degree of coupling between users and the type of coupling between platforms on the speed and extent of the spread of public opinion, revealing the laws of the spread of public opinion in cross-social platform coupled networks. [Results/Conclusions] The simulation results indicate that, compared to single-layer networks, the inter-layer edges formed by users across platforms significantly promote the spread of public opinion. As the degree of inter-layer coupling in the network increases, the speed of public opinion diffusion accelerates and the extent of diffusion increases. For inter-layer coupling modes, homophily links are more conducive to the spread of public opinion information than heterophily links. The results of this study provide valuable guidance for public opinion management. In a networked environment, government departments should disseminate accurate and authoritative information promptly through a variety of channels to gain the upper hand in public opinion. Given the positive correlation between inter-layer coupling and the speed at which public opinion spreads, it is crucial to closely monitor other platforms with a high degree of user overlap with the current one, and to accurately understand key data such as user overlap, activity levels, and differentiation between platforms. This will help to identify key users with high influence and activity, and enable more targeted, personalized guidance strategies. For platforms with a high degree of user assortative connectivity, the emergency response to public opinion should be enhanced. Conversely, platforms with a high degree of user disassortative connectivity should implement more stringent information filtering and response measures. The model constructed in this paper can simulate the basic structure of real coupled social networks and the process of spreading public opinion to some extent, but there are still shortcomings. Future research can introduce more factors, such as user behavior characteristics and content attributes, to construct a more refined public opinion diffusion model, thereby enhancing the model's capability to describe the real-world mechanisms of public opinion information diffusion.

[Purpose/Significance] With the proliferation of artificial intelligence (AI) technology, knowledge workers' basic understanding of AI and their ability to evaluate its application, known as AI literacy, is particularly important. This study addresses the current state of the AI literacy gap among knowledge workers and explores how AI technology itself can be used to bridge this gap, with the goal of shortening the AI literacy gap. The article analyzes the reasons for the existence of the AI gap in multiple dimensions and the need to bridge the AI gap. In today's era of proliferating AI tools, mastering the use of AI tools and improving AI literacy is the best assistant for knowledge workers. Learning to use AI tools can significantly improve the efficiency of information collection, processing, and storage, and greatly facilitate the building of information resources. [Method/Process] From the dimensions of cognition, practice, and assessment, the study explores how AI technology can assist in enhancing AI literacy. At the cognitive level, the research examines how AI agents can provide personalized knowledge services and help users build a systematic body of knowledge in a particular domain; at the practical level, it analyzes how AI tools can simplify professional tasks such as data analysis, and suggests how to improve knowledge workers' own ways and means of using AI tools through human-computer interaction and mastering effective AI problem-solving aids; at the assessment level, the application of AI tools in verifying the authenticity of information and the importance of the "human-in-the-loop" model in AI applications are discussed, emphasizing the need for human oversight. The article comprehensively draws on the excellent literature on AI, library intelligence, and learning literacy at home and abroad, and combines its own practical situation of using AI tools to achieve AI literacy, which helps knowledge workers establish basic cognition, practical operation ability, and evaluation of answer results. [Results/Conclusions] AI technology has significant advantages in closing the AI literacy gap. AI agents can provide customized knowledge explanation based on user needs, and AI tools can automate the execution of professional tasks and provide factual evidence. These applications not only compensate for the shortcomings of traditional educational methods but also offer new directions for innovation in the field of education. In presenting the arguments, the number of AI tools chosen is small, but fundamentally representative of the problem. Subsequent research will focus on improving AI literacy from other perspectives, and on ways and means to make knowledge workers more comfortable with AI tools. With the continuous development of AI technology, its role in enhancing AI literacy will become more pronounced, contributing to the construction of a more intelligent and efficient information environment.

[Purpose/Significance] With the rapid advancement of information technology, library services are undergoing transformative changes. The emergence of generative artificial intelligence (Generative AI) presents unprecedented opportunities and challenges for innovation in smart library services. By enhancing service efficiency and user experience, generative AI supports core library functions, such as personalized recommendations, intelligent question answering, and automatic summarization. This research explores the implications of applying generative AI technology to library services, with the goal of understanding its transformative impact on the field and addressing its potential risks. Unlike traditional studies that focus primarily on functionality, this study emphasizes the ethical, technical, and management risks associated with the use of generative AI in libraries. The study occupies an important place in the advancement of knowledge in this area and contributes to the development of sustainable, user-centered library services capable of addressing significant contemporary challenges related to information accessibility and data security. [Method/Process] This study uses a systematic literature review and case analysis to examine the current state of generative AI applications in smart libraries. A comprehensive approach is taken to understand how generative AI can enhance library services in areas such as personalized recommendation systems, intelligent Q&A, and automated summarization. The study draws on both theoretical and empirical sources, utilizing qualitative analysis to examine trends in the use of generative AI in different types of library services. This review also includes a thorough examination of the potential risks associated with implementing these technologies. Technical risks include data security vulnerabilities and model bias, while ethical risks focus on the issues surrounding user privacy, misinformation, and intellectual property rights. Management risks are also discussed, including the challenges of maintaining system stability and ensuring regulatory compliance. The multi-dimensional risk framework developed in this study provides a robust structure for analyzing these complex challenges and serves as a foundation for future empirical research in smart library applications. [Results/Conclusions] The research reveals that while generative AI can significantly improve the quality of library services and user satisfaction, it also poses significant risks. These include challenges related to data security, model bias, ethical standards, and management complexity. To address these, the study proposes a number of risk mitigation strategies. Key recommendations include strengthening data security through advanced encryption and access controls, increasing model transparency to build user confidence, and ensuring system stability through rigorous testing and monitoring. In addition, the study advocates for the establishment of ethical guidelines that prioritize user privacy, transparency, and content accuracy. It also underscores the need for ongoing regulatory adjustments to keep pace with technological advances. The study concludes by identifying limitations, such as the lack of quantitative data and real-time experiments, and suggests areas for future research. Future studies should focus on empirically validating the proposed framework, exploring the long-term impact of generative AI on library services, and developing best practices for balancing innovation with ethical responsibility. The continued evolution of generative AI is likely to deepen its integration with smart libraries, enabling innovative service models that meet the diverse and dynamic needs of users while safeguarding against potential risks. This research provides a foundational reference for library managers and policymakers seeking to implement generative AI responsibly and sustainably, and to promote the progressive transformation of library services in the information age.

[Purpose/Significance] As China enters the threshold of an aging society, the health problem of the elderly group has become the focus of attention from all walks of life. The demand for health information among this group has increased dramatically, not only because of the physiological changes associated with aging, but also because they have high expectations of how health information can improve their quality of life. As a unique branch in the field of information behavior, the research on the health information behavior of the elderly presents specific complexity in its object, phenomenon and mechanism. The acquisition, processing and application of health information has a profound impact on the health behavior of older people, particularly the potential for inappropriate drug use and overuse of drugs, which is often closely linked to their health information behavior. Therefore, in-depth research in this area is urgent and important. [[Methods/Process] Based on the topic differentiation perspective, this paper conducted a comprehensive and in-depth literature review on the occurrence mechanism, internal influencing factors (such as cognition, emotion, social support, etc.) and external environment (such as family, community, and medical system) of health information behavior of older people. This review aims to identify the status quo, hot spots and shortcomings of current research, and to provide a valuable reference frame of reference and inspirational direction for future research. [[Results/Conclusions] The study found that there were significant differences between the concerns of the elderly and the young and middle-aged in health information behavior, mainly reflected in the preferences for types of health information and factors influencing health information behavior. There are also large differences between urban and rural areas, social backgrounds at different stages of development, different social characteristics (such as educational background, economic status), individual characteristics (such as age, gender, health status) and health information behaviors in different scenarios. In terms of study objects, future studies can design experiments with control groups to explore more precisely the specific differences in health information behaviors of older people at different stages of development and with different characteristics. In terms of research content, we should broaden our horizons and introduce a variety of theoretical models in combination with the trend of social development, so that the research field not only covers a wider area, but also improves the depth of research. In terms of research methods, future research should actively explore new perspectives, adopt innovative research methods, such as the survey experiment method, and try to combine modern technical means, such as big data analysis and artificial intelligence to obtain more comprehensive and accurate data resources, while exploring and developing new research methods and analysis tools to promote the continuous in-depth development of research in this field.

[Purpose/Significance] Improving the policy and governance systems to promote the development of strategic industries such as artificial intelligence was explicitly proposed in the resolution of the Third Plenary Session of the 20th Central Committee of the Communist Party of China. In recent years, the conflict between AI companies' desire for copyrighted data and the copyright holders' protection of copyrighted data has become increasingly apparent. There have been a number of lawsuits and disputes around the world regarding copyright infringement caused by artificial intelligence. The dilemma of copyright protection of AI training data has become a difficulty and bottleneck that urgently needs to be resolved in the development of high-quality data system for AI. [Method/Process] Based on the academic research and industrial practice on the copyright protection of AI data, this study systematically summarizes six representative approaches to address the copyright dilemma of AI training data, and provides a comparative analysis of the advantages, disadvantages, and applicability of these approaches. The six representative approaches are: signing a license agreement by both parties, initiating special plans or forming alliances, introducing a copyright notice mechanism, introducing a copyright risk guarantee mechanism, replacing with synthetic data, and applying copyright detection tools to large language models. For the copyright dilemma of AI training data, there is no optimal solution that can both encourage the supply of AI copyright training data and protect the copyright of data. [Results/Conclusions] In order to provide helpful references for increasing the supply of AI copyright data, formulating relevant policies, and promoting related work, this study has proposed a concept of general implementation path to build a high-quality data system for AI to solve the copyright dilemma of AI training data, based on the comparative analysis of the above six representative approaches and combined with China's four unique advantages. These include: 1) Integrating existing platforms to build a national-level integrated service platform for copyright data for AI, with state-owned enterprises (SOEs) under the direct administration of the central government taking the lead in establishing a national copyright data alliance and connecting copyright data to the platform. 2) To collaborate with local pilots of data intellectual property rights, explore and promote comprehensive reform pilot programs of copyright data adapted to the development of AI, and continuously strengthen the cooperation efforts and willingness between AI enterprises and copyright holders. 3) The focus should be on principled or critical issues, establishing and improving legislation related to copyright data for AI and promoting industry self-regulation.

[Purpose/Significance] AI literacy is becoming increasingly important, not only to adapt to the future development of higher education and the needs of future society, but also to cultivate innovative thinking and problem-solving skills, to enhance decision-making abilities and, most importantly, to emphasize ethical education to avoid the abuse and misuse of AI technology. Existing research emphasizes the importance of AI literacy, with a focus on discussing AI literacy frameworks and pathways. Although some scholars have classified and discussed the AI literacy for teachers and students, there has not been a comprehensive analysis of the skill requirements for different roles in the context of "AI + higher education". [Method/Process] AI literacy education is a multidimensional and multi-level systematic problem. Based on 18 application cases, this study analyzes the specific application scenarios of AI in the educational process, summarizes the development characteristics of "AI+higher education", and analyzes its AI literacy requirements for university teachers, students, managers, and teaching assistants. Therefore, four-role framework for AI literacy is constructed to provide a theoretical reference for future AI literacy education in higher education. [Results/Conclusions] In the context of "AI + Higher Education," future higher education will continue to develop towards ubiquitous teaching, personalized learning, diversified evaluation, and scientific management, ultimately achieving the complete intellectualization of higher education. For teachers, the skills required are innovative teaching and technological integration; for students, active learning and diversified skills; for administrators, forward-thinking leadership and data-driven decision-making; and for educational support staff, intelligent integration of services and resources. The core elements of AI literacy can be summarized as four key components: thinking, knowledge, skills, and attitudes. In specific educational scenarios, the AI skills of teachers, students, administrators, and educational support staff have similarities but also exhibit differences. Due to space limitations, this study did not construct an AI literacy education pathway. In future research, we will continue to deepen the connotation of AI literacy and propose targeted AI literacy education pathways based on the skill requirements of different roles.

[Purpose/Significance] In recent years, the development momentum of the artificial intelligence (AI) technology industry has been strong. From the perspective of policy instruments, in-depth discussion on the high-quality development path of China's AI technology industry is of great significance to the selection of high-quality development path of China's provincial AI technology industry and the formulation and optimization of government policies in the AI industry. [Method/Process] The mechanism of the high-quality development path of the AI technology industry is relatively complex. The article is based on the theory of policy instruments, selecting supply-oriented, environment-oriented, and demand-oriented policy instruments as the analytical framework. The platforms such as Peking University Treasure, Law Star, and various provincial administrative region government portals were used to review policy texts. A total of 42 policy texts were selected as the objects of analysis, and NVivo software was used to encode the text content and assign conditional variables. The evaluation index of regional competitiveness of AI technology industry in 31 provincial administrative regions was selected as the result variable of QCA analysis, and the fuzzy set qualitative comparative analysis method was used to explore the diversified combination driving path of policy instrument elements. [Results/Conclusions] Research has shown that the high-quality development of the AI technology industry is influenced by multiple policy instruments, including demonstration and promotion, infrastructure, technical support, cooperation and exchange, and target planning. There are three combined paths, namely the supply-oriented path, the supply-demand synergy path, and the supply-demand-environment synergy path. The government should promote the high-quality and sustainable development of China's AI technology industry by improving the basic support system, continuously promoting infrastructure construction, improving the environmental impact mechanism, creating a sound and favorable policy environment, optimizing the structure of policy instruments, and strengthening demand-oriented public services. This study has several limitations. On the one hand, the selection of conditional variables needs to be further optimized; on the other hand, the article has not further verified the key combination path that affects the high-quality development of China's AI technology industry. In subsequent research, we will continue to improve the variable selection of policy instrument elements, draw on and explore more scientific variable assignment standards and methods, and conduct in-depth analysis of the specific combination path obtained in the article to verify the feasibility and scientificity of the key combination path for the high-quality development of the AI technology industry. This will further enrich the theoretical achievements of AI policy research and provide strong theoretical support for the formulation of AI technology industry policies.

[Purpose/Significance] Farmers' digital literacy is the prerequisite and foundation for the construction and development of digital rural areas. How to quantitatively assess farmers' digital literacy, accurately identify the influencing factors, and then purposefully improve farmers' digital literacy level, and stimulate farmers' vitality in digital village construction has become an urgent task for the current construction of digital villages in China. [Method/Process] Based on the definition of the relevant concepts of farmers' digital literacy, this article constructs an evaluation index system for farmers' digital literacy in Beijing based on theoretical and practical bases, and combines 236 pre-survey data to comprehensively use exploratory factor analysis and confirmatory factor analysis to test the rationality of the evaluation index system. Based on 1500 questionnaire survey data from 13 agricultural areas in Beijing, the current level of digital literacy among farmers in Beijing was evaluated and analyzed. A regression model was constructed to analyze and explore the influencing factors of digital literacy among farmers in Beijing from three dimensions: personal factors, resource supply factors, and environmental factors. [Results/Conclusions] Based on the KAP theoretical model and the practical development needs of Beijing's digital countryside and smart city construction, the article constructs an evaluation system for Beijing farmers' digital literacy indicators, which includes six primary indicators: digital concept awareness, digital general literacy, digital technology literacy, digital innovation literacy, digital social literacy, and digital security awareness, as well as 26 secondary indicators. According to the calculation, the overall digital literacy level of Beijing farmers is 68.15, and there are significant differences between different regions, dimensions, and groups. From a regional perspective, the digital literacy level of farmers in Fengtai District is relatively high, while that in Daxing District is relatively low. In terms of indicators, farmers have the highest level of digital security literacy, while their levels of digital innovation literacy and digital technology literacy are relatively low. In terms of group characteristics, small farmers and older people aged 60 and over have the lowest levels of digital literacy. Farmers' digital literacy is negatively influenced by age, but positively influenced by education level, income level, digital training, digital services, popularization of digital science, digital living atmosphere, and digital network. It is suggested to effectively enhance farmers' digital literacy and promote their participation in digital rural areas by strengthening top-level design, creating a good atmosphere, building a lifelong learning system, and establishing a guarantee mechanism.

[Purpose/Significance] Information literacy (IL) training for farmers has become one of the main contents for farmers in the new era. However, the current implementation of rural revitalization still does not pay enough attention to farmers. At the same time, farmers' IL ability is an important embodiment of farmers' integration into the digital countryside, which can give a strong boost to the modernization of agriculture and rural areas. Therefore, it is of great practical significance for the rural revitalization strategy in the new era to make full use of multiple social subjects and improve farmers' IL. [Method/Process] This paper reviews the concept and definition of IL, and analyzes the research on farmers' IL in recent years. The results show that most of the current research on the cultivation of farmers' IL focuses on a specific topic and lacks holistic research. Therefore, it is necessary to systematically understand the cultivation process of farmers' IL, and guide the cultivation behavior of IL by the all-round cultivation concept. [Results/Conclusions] At present, although the local governments have initially built an IL training model of the new era, with schools and social organizations as participants in the model, farmers still lack information knowledge, information awareness, and IL skills. Several proposals are put forward here to address the above issue. First of all,it is necessary to strengthen the construction of IL education system and improve farmers' information knowledge. The government should give full play to the local government agencies in resource integration, schools and scientific research institutions in professional advantages, and social organizations in providing information services, so as to provide farmers with more systematic IL training. Second, efforts should be made to jointly build IL education space to raise farmers' information awareness: the government should build farmers' IL training base, the schools should promote the transformation of the education model, and social organizations continue to carry out IL training project. The three parties join hands to build a three-dimensional integrated IL education space of "material space, spiritual space and social space", and a new way of the cultivating farmers' information awareness. Finally, IL teachers should be trained to improve farmers' information literacy. The government will attract and retain information talent in rural areas through positive talent polices. Schools will play an educational role in developing farmers' information literacy skills.

[Purpose/Significance] The research paradigm is gradually shifting towards a data-intensive model, where research data has become the cornerstone in the realm of academic endeavors. Effective research data management can enhance the research efficiency of scientific researchers, reduce redundant data collection, and reduce costs. As a central repository for the storage of scholarly research outputs, it is essential that university institutional repositories fulfill their role in research data management. [Method/Process] To gain a full understanding of the evolving landscape, we embarked on a meticulous network-based research investigation. We specifically selected the institutional repositories of 24 prestigious American universities as our research subjects, with the aim of exploring the diverse range of services they provide at different stages of the research lifecycle. Our research was firmly grounded in the data lifecycle framework, which enabled us to systematically examine a wide range of research data management (RDM) services. This included critical aspects such as developing comprehensive research data management plans, establishing robust data organization services and standardized protocols, providing reliable long-term data storage solutions to ensure continued accessibility, enhancing data sharing policies to foster collaboration, strengthening research data quality control measures to maintain integrity, and developing comprehensive research data management training programs to empower researchers. Furthermore, we conducted an in-depth analysis to summarize the characteristics and valuable experiences of American universities in building and maintaining the basic infrastructure of their institutional repositories. [Results/Conclusions] Given the unique circumstances of China's modernization process, this paper distills effective insights and strategies from the institutional repositories of domestic university libraries in the field of research data management services. Our findings highlight the importance of building a localized research data management platform tailored to the specific needs and contexts of Chinese academia. Enhancing the quality of research data management is critical to building a trusted institutional knowledge base and fostering an environment of credibility and reliability. By applying the FAIR (Findable, Accessible, Interoperable, Reusable) and TRUST (Transparent, Responsible, Usable, Sustainable, and Trustworthy) principles, we can facilitate the open and seamless sharing of research data, breaking down barriers to collaboration and innovation. Finally, building a professional scientific research data management team is essential to provide the human capital necessary to navigate the complexities of data management and to promote the development and adoption of best practices in scientific research data sharing. Taken together, these findings help to improve the abiity of the scientific community to harness the full potential of research data to drive the creation and dissemination of knowledge.

[Purpose/Significance] Previous studies or reviews of digital twins have focused either on conceptual analysis and theoretical models, or on the current state of the art and implementation, with only a few studies analyzing human-digital twin interaction and collaboration. This paper explores the collaboration between human and digital twin systems, and offers recommendations on how digital twins can catalyze the progress of societal digitization. It envisions a future where the interaction and collaboration between humans and digital twins is not only deepened but also transcended, moving closer to the harmonious integration of man and machine. The proposed strategies aim to unlock the full potential of digital twins in promoting a more connected and intelligent world. [Method/Process] This paper is a systematic literature review focusing on the partnership between human and digital twin systems, emphasizing the role of artificial intelligence. We analyze the value positioning, key technologies and practical applications through 45 papers from home and abroad. Then, we explore the construction path of digital twin technology-enabled information resource management. [Results/ [Conclusions] The study shows that human and digital twin systems have different unique values in the whole ecology, the irreplaceable wisdom of the human brain is reflected in innovation and decision-making, and the core function of the digital twin system is to support and enhance the communication between man and machine. AI technology plays the role of the pedestal in the interaction. There are common enabling technologies for human intelligence collaboration in the digital twin ecosystem, and the types of key technologies supporting human intelligence collaboration in different twin modules are not identical but synergistic with each other. The supporting technologies in digital twin environment mainly involve data acquisition and data transmission, model twin mainly lies in data fusion and management, image recognition and processing, and process twin mainly involves human-computer interfaces, immersive perception and other key technologies. In terms of application areas, human-intelligent information system cooperation at home and abroad has rich applications in industry, healthcare, smart cities and public cultural services, especially in public cultural services, where cooperation has accelerated the intelligentization process of public cultural service institutions. Finally, the study categorizes the human-intelligent information system collaboration methods in the digital twin ecology into three types: pre-determined, collaborative, and autonomous, proposes a holistic framework for the twin co-intelligence system of intelligent connections, and summarizes the current obstacles and development strategies from a realistic perspective. There are some limitations due to the limited samples, which can be increased in the future to deepen the mining and analysis to optimize the form of human-intelligent information system collaboration in the digital twin ecosystem.

[Purpose/Significance] In the open science environment, the mode of collaborative knowledge production is widely used, and the types of scientific outputs are becoming increasingly diverse. Therefore, effective identification and recognition of various contribution contents made by different contributors in open research is crucial to improve transparent disclosure and effective use of research output's contribution data. [Method/Process] Based on the systematic study of domestic and foreign scholarly contribution theories and practices, this study proposes a scholarly contribution analysis system for open science. From the perspective of the creation process of scientific research outputs, research on the attribution of scientific contributions is carried out, focusing on three aspects: the identification of contributors and contribution elements, the calculation of the degree of contribution, and the representation of contribution data. In addition, a paper from the open access platform PLoS One is selected as an empirical case to demonstrate the validity of the process and method of attributing scholarly contributions. It was found that the contribution score is inversely proportional to the number of contributors, i.e., the more the number of contributors, the less likely it is to have a high contribution score. The more contribution elements contributors participate in, the higher the contribution score they receive. If contributors participate in the same contribution elements, and their contribution scores may also be the same. For the same contribution factor, the higher the number of contributors, the lower the contribution degree of each contributor. This paper summarizes the application scenarios of academic contributions from three aspects: clearly distinguishing author and non-author contributors, establishing correlation between contribution data and other LOD ontologies, establishing attribution and responsibility mechanism for academic contributions, and improving the index system of scientific research evaluation. [Results/Conclusions] Academic contribution is an important part of the open science system. It is an inevitable trend to improve the openness and transparency of academic contributions to various scientific research results. However, existing methods for attributing contributions are scattered, lack systematicity, and mainly apply to a certain type of achievement, which has certain limitations in terms of disciplines and achievements. In the future, the applicability of the contribution attribution method can be improved by expanding the application objects of contributions to include research papers, scientific data, scientific software, preprints, scientific notes, and more, establishing a common contribution element system for all types of research outputs, and optimizing the method of calculating the contribution rate. Finally, it is necessary to continuously enrich and expand the application scope and application scenarios of scientific research output's contribution data.

" AI for Science " (AI4S) is a new scientific research paradigm that deeply integrates AI technology with scientific research to promote the discovery of new knowledge and the solution of scientific problems. As the application of AI4S in the natural sciences and humanities and social sciences advances, its development line, opportunities and challenges, needs and tasks, and ways of realization deserve further discussion. In order to advance AI4S research, promote scientific and technological (S&T) innovation and progress, and facilitate the effective strengthening of the discipline of information resources management, our journal has invited seven experts to organize this academic conversation on AI4S. 1) Supporting knowledge services for AI4S: In the current landscape of intelligent knowledge services, the requirements for supporting AI4S have increased, including the need for multi-level knowledge discovery and acquisition, cross-disciplinary research and innovation, and user-friendly participatory services. In addition, knowledge service scenarios are moving towards diversification, complexity, depth, specialization, and personalization in ubiquitous knowledge discovery, generative content services, and multi-round interactive service exploration. In response, professional science and technology information organizations need to reassess the role of knowledge services in the AI4Science environment and their significance in comprehensively supporting the S&T innovation process. This involves establishing a broad literature perspective, deepening full-text knowledge elements, balancing universal and specialized depth, autonomously developing core products, and deeply engaging with professional fields to support interdisciplinary innovation. 2) As a knowledge base for AI4S: In the development of AI4S, S&T literature serves as a high-quality corpus of great importance and utility. The Documentation and Intelligence Center of the Chinese Academy of Sciences has developed the concept and general framework for an AI4S knowledge base utilizing S&T literature. It is dedicated to building four types of knowledge bases to support intelligent services such as evidence-based retrieval, situational awareness, inference prediction, and insight generation required for AI4S applications. In addition, to advance the AI for Science knowledge base, it is essential to actively promote the construction of an intelligent data system, develop an AI engine for technical literature knowledge, conduct key technology research on in-depth mining and intelligent analysis of S&T literature, and promote collaboration with scientific research units across various fields, leading AI companies, and teams of field scientists. This approach aims to fully exploit the innovative and developmental value of the discipline of information resource management. 3) Powering AI4S with scientific data: Effective aggregation of scientific data is the foundation for unleashing the powerful capabilities of AI4S. This is essential for libraries to adapt their roles and functions in the AI era and is a crucial prerequisite for catalyzing the transformation of scientific research services, deepening scientific research support, and accelerating S&T innovation. Currently, libraries face various macro and meso challenges in effectively aggregating valuable scientific data to provide support for AI4S. To address these challenges, the following ways can be pursued: defining the roles and functions of libraries in scientific data management; promoting a conducive environment for scientific data management; establishing a collaborative network for scientific data management; and enhancing the service capacity of scientific data management. 4) AI4S and intelligent language modeling for classical literature: AI4S technology can be used to analyze documents and texts, enabling a faster and more comprehensive understanding of a vast amount of historical documents and cultural materials. The development of intelligent language modeling for classical literature represents a significant breakthrough in the field of ancient literature research, bringing new opportunities and challenges. With the increasing popularity of multimodal and generative GPT models in the context of AI4S, the intelligent language modeling of classical literature will focus on integrating diverse information, enhancing adaptability, improving knowledge representation, and addressing a wider range of application scenarios. 5) Library Digital Scholarly Services for AI4S: The concept of using LLM-based AI4S and AIGC to drive the development of smart libraries is consistent with the vision for digital scholarly services in libraries, and presents both opportunities and challenges. Given the trends towards AI4S platformization and the characteristics of "middle-end" digital scholarly service, as well as the longstanding tradition of libraries in serving scholarly research, the reengineering path for the library's digital scholarly services platform includes three approaches: building an AI4S service platform independently, purchasing and utilizing third-party AI4S platforms, and promoting embedded knowledge services as a component of scientific intelligence. This innovative approach addresses the dilemmas of financial resources, human resources, cognitive and practical gaps, and emphasizes the importance of user needs in the AI4S environment. It also focuses on knowledge organization and service delivery to meet user needs in the AI4S landscape. 6) Historical evolution and logical structure of the scientific intelligence paradigm (AI4S): AI4S is a scientific paradigm change dominated by the full application of AI technology to various disciplines, and its logical structure includes "data+model"-driven, knowledge ecology created by machine conjecture, and application scenarios expanded by algorithmic thinking. In the era of digital civilization, AI4S-driven scientific progress and social development must carry forward the value of science and technology for the good, effectively select the theoretical arguments and proposals for extending AI4S to the field of social sciences and humanities, and improve the series of mechanisms for integrating human decision-making and machine intelligence. 7) Development opportunities and prospects of AI4S in the era of generative AI: With the advances in generative AI, pre-training algorithms and large-scale pre-trained models have provided significant opportunities for AI4S in various disciplinary domains. These technologies have shown immense potential and value for applications in diverse fields such as industrial inspection, robotics, and medicine. Additionally, it is crucial to emphasize the importance of key factors such as the constraints of technical implementation conditions for large pre-trained models, the sustainability of data/computing resources, and the transparency, fairness, and accessibility of the technology.

[Purpose/Significance] The continuous development of metaverse technology marks the transition of mankinds from information society to information civilization. How to understand the relationship between human beings, physical space and information space in the future society has become a key problem of the era. The mixed reality (MR) Technology is a new intelligent technology that integrates the advantages of augmented reality (AR) and virtual reality (VR), makes virtual objects coexist in the physical world, and integrates the functions of human perception, computer processing and environmental input. The new generation of MR technology has improved the traditional global understanding of digital reality interaction, and also has been bringing technological innovation opportunities for the development of smart libraries. Exploring the new application scenarios of MR technology is helpful in expanding the depth and breadth of the research on smart libraries. [Method/Process] By using the methods of literature review, content analysis and website analysis, this paper reviews the current research status of MR Technology in the field of library science at home and abroad. In addition, through practical cases, this paper summarizes the relevant experience and existing gaps in the application of MR Technology in domestic and foreign libraries. Therefore, it is clear that the research of this paper aims to further stimulate and release libraries' demand and potential for MR Technology. Specifically speaking, by examining the characteristics of high realism, more intelligent and omni-directional MR technology, this paper further explores the ability of smart libraryies in four dimensions of service, knowledge, experience and collaboration, which will contribute to building a new application scenario of smart libraries with the vision of MR technology. It is hoped that this paper can promote the formation of a new type of smart libraries that combines dynamic and static, actively data, blending virtual-real and multi-dimensional expansion. [Results/Conclusions] In the wave of rapid innovation of VR, the construction of smart libraries should be considered in four dimensions: problem orientation, theoretical supports, talent management and subject co-creation. It can provide a better understanding of the future smart libraries with the possible risks, urgent internal and external needs. It is expected to build a future ecological picture of the integration of smart libraries and MR technology. However, due to the limitation of the author's knowledge level and the lack of practical ability, this paper provides a relatively macro guidance. Libraries vary in their application of MR technology. On specific issues, we need specific analysis and different solutions. Therefore, in the future research, we will continuously improve and refine the research in this aspect, and provide reference basis and application value for the effective practice of MR technology in the smart libraries.

[Purpose/Significance] Investigating the influencing factors in the general requirements of public digital cultural service in Fujian Province can provide reference for its sustainable development in this field. Referring to the previous research, this paper took the lead in using the binary logistic regression model to study public demand for digital cultural services in Fujian Province, and selecting universities in Fujian Province as the research object. It provides a significant practical reference basis for the construction of public digital cultural service in Fujian Province, and also has a certain theoretical reference value for research related to public digital cultural service. [Method/Process] Culture is related to people's well-being and the all-round development of people. In order to meet the people's growing need for a better life and protect the people's basic cultural rights and interests, there are more urgent requirements. Public digital cultural service, in the form of digitalization, breaks the restrictions of time and space, and serves as an important magic weapon to get through the "last mile" of public cultural service and improve the accessibility of the service. Digital construction helps to enhance the accessibility of public cultural service, improve the coverage and dissemination efficiency of the service, and promote public cultural participation. On the basis of the relevant literature, the article intends to discuss the impact and mechanism of public digital cultural service, that is, which factors have an impact on public cultural needs? How do these factors affect the public digital cultural service? What is the impact? Under what conditions is the impact more significant? This study was carried out by combining the methods of literature survey, quantitative and qualitative analysis, and obtaining sample data of users through the questionnaire to analyze the statistics with the SPSS 21 software and verify relevant research hypotheses and regression models. [Results/Conclusions] It is found that resource, subject, platform, infrastructure, and service efficiency can effectively strengthen the requirements for the public digital cultural service in Fujian Province. In view of this, we suggest establishing diversified platforms for digital service, integrating cultural resources, enhancing the establishment of service personnel, strengthening promotion and popularization, optimizing the facility and equipment of public culture, and improving the environment of the places. In the analysis of the influencing factors of public digital cultural service, this study is not comprehensive enough. In the future, it is possible to sort out the factors affecting public digital cultural service more comprehensively, and a more comprehensive sample can be used in our study.