[Purpose/Significance] Generative artificial intelligence (AI) technology has been widely used in many fields, and the application of this technology has become popular among researchers. However, there are few studies on the willingness of researchers willingness to accept generative AI. This leads to an insufficient understanding of the psychological mechanism, cognitive process and behavioral pattern of users' acceptance of generative AI, which limits the ability of theoretical innovation and practical exploration in user information behavior. This study focuses on researchers acceptance of generative AI. By studying the evaluation process of ChatGPT by college students, it explores the acceptance behavior of generative AI. At the same time, it verifies the applicability of the AIDUA model in the new context, and introduces the new variable of school identity, which further extends the AIDUA model. [Method/Process] Based on the cognitive assessment theory and the AI acceptance framework (AIDUA), this paper constructs a theoretical model of the intention to use generative artificial intelligence, and develops and empirically tests the theoretical model of the intention to use generative AI. Taking college students as the main research object, based on the maturity scale in authoritative literature at home and abroad, 8 variables and 29 observation variables such as social influence, hedonic motivation and anthropomorphism were designed. College students with experience in using generative AI were invited to participate in the questionnaire survey. SPSS26.0 was used to analyze the data from 294 valid questionnaires collected. SmartPLS 3.2.9 was used to construct a structural equation model to test the hypothesis, and the JN method was used to detect the regulatory effect. [Results/Conclusions] The study found that users went through three stages of decision making before using generative AI. The PLS-SEM results show that: 1) Social influence, hedonic motivation and anthropomorphism significantly affect performance expectancy and effort expectancy, and anthropomorphism is the strongest variable affecting performance expectancy and effort expectancy. 2) Performance expectancy and effort expectancy are significantly negatively correlated with negative emotions, while hedonic motivation has no significant effect on negative emotions. 3) Negative emotions are significantly negatively correlated with users' intension to use. 4) School identity moderates the relationship between effort expectancy and negative emotions. This study combines anthropomorphic research on college students' acceptance of generative AI, and provides a framework for the acceptance of generative AI. Researchers can use this framework to better study the acceptance of AI. This study has some limitations. In the future, we will focus on the following three aspects: 1) to evaluate the users' acceptance of generative AI in different usage scenarios. 2) to use samples of other groups to test the applicability of the model, such as civil servants, librarians, researchers and other groups. 3) to incorporate variables from other technology acceptance models into the model, such as ease of use and practicality.

[Purpose/Significance] In recent years, the rapid development of artificial intelligence (AI) technology has become an international research hotspot. Embedding ChatGPT-like AI system in libraries will lead to a new direction for the development of their intelligent services, which is a necessary choice to improve the library service level, optimize service efficiency and promote service model innovation. It is also an important measure to adapt to the development trend of the information age, which brings new opportunities and challenges to the information and digital construction of libraries. This article aims to explore the realization path of ChatGPT to help libraries provide intelligent services, provide theoretical reference and practical basis for the intelligent service by libraries, and promote the construction of library intelligent service system. [Method/Process] By reviewing the relevant literature at home and abroad, on the basis of summarizing the core technology of ChatGPT, and studying the practical application cases of some domestic libraries, this article analyzes the application scenarios and directions in which ChatGPT can support library services, explains the current limitations of this technology and the threats and challenges it poses to libraries and librarians, and thus proposes corresponding strategies. [Results/Conclusions] ChatGPT technology will further optimize the knowledge service system of libraries and improve the intelligent service capability of libraries. It brings vitality to the libraries, but also brings threats and challenges. Librarians should grasp the integration capabilities of humans and AI in time, and make full use of their unique advantages. Libraries should strengthen the capacity building of their smart services oriented to ChatGPT, innovate the mode and mechanism of their smart services, and rely on ChatGPT to build a smart service platform, provide smart service resources, build a sound service system, build a service team, and improve the level of their smart services. Continuous efforts should be made to promote the in-depth development of smart library services, so as to meet the diversified and personalized information needs of library readers. To this end, this paper makes a preliminary exploration on the opportunities, challenges and development strategies of enabling smart services in libraries through ChatGPT. Due to the limited conditions, only some practices of domestic libraries are explored, and no cases of international libraries are introduced, which has certain limitations and needs to be improved in future studies.

[Purpose/Significance] Research on the governance system and policy of data elements circulation is an important issue to be solved in the field of data governance in China at present, and research on the policy formulation and governance system of its circulation plays an important role in grasping the security of data circulation in China and promoting the market-oriented allocation of data elements. [Method/Process] First, this study is based on the reality of China's data factor market security and trustworthy, autonomous and controllable requirements. Based on the analysis of the security risk of data circulation, we put forward the data factor market risk governance countermeasures of the "security-fairness-efficiency" triangular structure. Then, based on the three-level system and five-dimensional standards of data factor market governance, we put forward the method of docking the security governance with the trusted ecosystem and the international data governance rule system for cross-border data flow, and constructed a governance system with Chinese characteristics for the national unified data factor market. [Results/Conclusions] Facing the security risks in data sovereignty, data market and data circulation, we should identify and monitor data sovereignty disputes and the operation situation of the circulation market, and establish a multi-party cooperative and joint governance model led by the government, operated by the platform owner, the main body of the enterprise and the participation of users. When assessing the market for data elements, a mixed assessment approach should be adopted, combining qualitative and quantitative aspects, combining expert opinion with objective data, and comparing objectives with results. For different types of data, the control boundaries and scope of use should be clarified in a hierarchical manner, and data ownership, use and income should be clarified; at the same time, a confirmation platform of data rights should be established to audit and register and certify the data service subject, data circulation process, and data circulation rules so as to ensure that the normative nature of data circulation is maintained.

[Purpose/Significance] The advent and emergence of social robots represent a closer development trend in human-computer interaction. However, the study of the information behavior of social robots faces many challenges that arise from the need to simulate human social behavior. This challenge includes technical hurdles such as a multi-level understanding of human emotions, extraction of multi-modal information features, situational awareness, as well as the establishment of long-term user profiling, data privacy, and ethical considerations in personalized interaction. However, existing research tends to focus narrowly on specific applications and lacks a holistic review. This paper attempts to provide a thorough review of both domestic and international studies of social robots in the area of information behavior. It aims to elucidate the theoretical evolution and technological foundations of social robots, thereby enriching our understanding of their role in the landscape of information behavior research. [Method/Process] Using a rigorous literature review methodology, we meticulously analyze the current state and prospective trajectory of research on the information behavior of social robots. First, we extract and scrutinize the theoretical foundations and salient research topics within the field. We then delineate the core tasks of social robots, which include data acquisition, language processing, emotion analysis, information retrieval, and intelligent communication. Furthermore, we synthesize research on the information behavior of social robots in various application domains such as education, healthcare, and service sectors. We delve into the intricacies of human-computer interaction in these contexts and provide comprehensive insights. Finally, we explore future directions in the field. [Results/Conclusions] Our examination of the information behavior of social robots reveals both promising potential and notable challenges. This paper provides a fundamental elucidation of the social robot concept, identifies current research foci, and addresses prevailing challenges. Regarding the construction of data resource and related technologies, we systematically delineate the task architecture of social robots, and highlight their wide-ranging applications in various domains. Furthermore, we provide an in-depth examination of human-computer interaction scenarios in critical domains such as education, healthcare, and service delivery, offering prescient guidance for future research efforts in social robotics. Nonetheless, our findings underscore the nascent stage of development of social robotics, which requires a concerted focus on advancing interaction quality assessment, enhancing social cognitive capabilities, managing user information disclosure, and refining emotional intelligence. By prioritizing these avenues, we aim to improve the quality of human-robot interaction and provide users with enriched and personalized service experiences, thereby catalyzing the continued evolution and broader integration of social robotics technology.

[Purpose/Significance] Interdisciplinary research has emerged as a key driver of knowledge innovation, its essence being integration of knowledge from different disciplines. A deep and nuanced understanding of the research content of interdisciplinary knowledge integration is essential to foster innovation beyond traditional academic boundaries. Most of the research reviews on interdisciplinary research summarize theories, concepts and indicators from the macro level, while the content of interdisciplinary knowledge integration at the micro level is scattered, and lacks systematicity and logic. Therefore, this study attempts to classify and summarize the research methods, research content and research ideas involved in order to provide reference for scholars and researchers engaged in interdisciplinary knowledge integration. [Method/Process] Based on Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) database as the primary data sources, we reviewed the relevant literature on interdisciplinary knowledge integration. By clarifying the intrinsic meaning of interdisciplinary knowledge integration, we have systematically reviewed and summarized the existing research findings from three aspects. First, research on the degree of knowledge integration mainly involves measurement methods. Second, research on the content of knowledge integration is based on citations and keywords. Third, research on the process of knowledge integration involves its integration path and process, and the characteristics of the integration stage. On this basis, this paper also summarizes the limitations and challenges of existing research, and provides research perspectives for subsequent research on interdisciplinary knowledge integration. [Results/Conclusions] The existing results have extensively explored the definition, measurement method, content, and process of interdisciplinary knowledge integration, but there are problems such as limited applicability of knowledge integration measurement methods, insufficient semantic disclosure, and the lack of systematic research on the degree of knowledge integration. Future research in this area should pay attention to the following three aspects. First, the applicability of methods for measuring interdisciplinary knowledge integration should be improved in order to better adapt to the development of interdisciplinary knowledge integration. Second, there is also a need to strengthen the study of knowledge integration from the perspective of knowledge increment, examining how new knowledge is created and how it contributes to the advancement of the area. In addition, efforts should be made to explore new theories and methods, grasp the development law of interdisciplinary knowledge integration, and expand the application scope and application value of interdisciplinary knowledge integration.

[Purpose/Significance] As the cultural treasure of the Chinese nation, cultural heritage plays an important role in highlighting China's cultural background and strengthening national identity. In the process of globalization and digitization, the protection and inheritance of cultural heritage is facing new opportunities and challenges. How to effectively promote the protection and inheritance of cultural heritage and enhance the international influence of Chinese culture is an issue to be considered in the process of building a cultural power. [Method/Process] This paper takes the Chinese tea culture videos on YouTube platform as the research object, constructs the theoretical framework of "Cognitive-Affective-Conative", analyzes users' cognition of Chinese tea culture through LDA topic clustering of video comments, and further explores users' emotional responses. Then, based on the "5W" model, an international communication power measurement model is constructed from three aspects: communication subject, communication audience, and communication content to verify the interaction mechanism among conation, affect and conation. The communication subject factors include influence and credibility. The communication audience factors combine user cognitive themes and user emotions to form user cognitive agreement. The communication content factors are analyzed in terms of video theme, duration, subtitles, and background music. [Results/Conclusions] The video theme of tea sets, the video duration of 4-20 minutes, and the credibility and influence of the communication subject significantly enhance the international communication effect of tea culture videos. While background music and users' cognitive agreement of the tea ceremony negatively affected the communication effect. From the current practice, it can be seen that the application of AI technology in the dissemination of tea culture cannot greatly improve the dissemination effect of videos, which may have much to do with the political nature of video accounts. Based on the above conclusions, this paper further puts forward relevant suggestions for digital intelligence technology to strengthen the protection and inheritance of cultural heritage. The empowerment of digital information technology in the process of cultural heritage protection and inheritance mainly plays a role in the digital storage, data management, digital presentation, and popular dissemination of cultural heritage resources. Looking to the future, with advances in science and technology, new technologies will not only help protect and inherit cultural heritage, but also promote the creative transformation and innovative development of cultural heritage, and maintain its vitality and value.

[Purpose/Significance] New quality productive forces is the latest pattern of productivity development under the background of data-driven intelligence. To explore the mechanism and practical way of bidirectional empowerment of knowledge service and new quality productive forces has important theoretical and practical significance for giving full play to the effectiveness of new quality of data elements, new power of digital intelligence technology and new application of intelligent service, so as to promote the cultivation of new quality productive forces and improve knowledge service. [Method/Process] Based on the summary of the development process of knowledge service and the new interpretation from the perspective of new quality productive forces, this study analyzes the internal relationship and external manifestations of the bidirectional empowerment of knowledge services and new quality productive forces, constructs the bidirectional empowerment mechanism of knowledge services and new quality productive forces, and proposes the bidirectional empowerment path. [Results/Conclusions] "Factor reorganization, scientific and technological innovation, and quality service" has become the internal relationship between knowledge service and new quality productive forces. Data factor as the core production factor, digital intelligence technology as the key production technology, and intelligence-driven new business forms and new models as the common carrier, which is an important basis for intelligent knowledge service and new quality productive forces bidirectional empowerment mechanism. Intelligent knowledge service cultivates the new quality productive forces through three important ways: accelerating scientific and technological innovation, opening up new ways and creating new business forms. New quality productive forces drives the development of intelligent knowledge service and improves the service by solving the resource dilemma, technology dilemma and talent dilemma, and creating new competitive advantages. This is an important manifestation of the bidirectional empowerment mechanism of new quality productive forces and intelligent knowledge service. After clarifying the empowerment basis and the performance of the bidirectional empowerment mechanism, this study proposes that the two kinds of empowerment should be realized from the following four aspects: a) Taking the market demand as the guide by mining new value and responding to new demand, and grasping the new quality growth engine; b) Taking data elements as the center, the compacting intelligent driving base is realized by building the data element empowerment system and improving the standardization of element utilization; c) Taking technological innovation as the key way by releasing the potential of key elements and creating fusion application scenarios to consolidate the foundation of fusion application; d) Starting from the application scenario as the starting point, the bidirectional empowerment mechanism is put into practice in various fields.

[Purpose/Significance] Identifying key core technologies helps to clarify the direction of future efforts, optimize the allocation of innovation resources, accelerate the breakthrough of key core technologies, and promote the development of new quality productive forces by cultivating new types of workers, updating new types of means of labor, and expanding new types of subjects of labor. [Method/Process] Given the importance of key core technology identification in empowering the development of new quality productive forces, the research clarifies the connotation and extension of the key core technologies, and investigates the qualitative and quantitative identification methods of key core technologies at home and abroad. Quantitative methods are divided into three categories: indicator evaluation, social network analysis, and text mining. A comparative analysis of the advantages and disadvantages of each method are reviewed, and the overall shortcomings and problems of existing identification methods are summarized. Based on the analysis of the practical requirements and significance of breakthroughs in key core technologies, this study delves into the inherent logic of identifying key core technologies, which enables the development of new quality productive forces. The method of identifying key core technologies can cultivate new types of workers by improving talent management mechanisms and strengthening skills training for workers, New types of means of labor are updated through promoting technological integration and innovation and engaging in industry-academia-research cooperation and expanding new types of objects of labor by developing new production fields and analyzing market demand. In view of the challenges faced in the process of empowering new quality productive forces through the identification of key core technologies, this study proposes a practical way for empowering the development of new quality productive forces through the identification of key core technologies. [Results/Conclusions] Current research on identifying key core technologies is in its early stages and still faces several challenges, including outdated identification methods, inconsistent standards, the complexity of integrating technologies across disciplines, the need for improving accuracy of identification results, the lack of mining and using of multi-source data, and uncertainties in forecasting market demand. These obstacles hinder the process of using key core technology identification to cultivate new types of workers, update new types of means of labor, and expand new types of objects of labor, thus hindering the accelerated development of new quaity productive forces. Therefore, we need to focus on removing the obstacles that hinder the acceleration of new quality productive forces through the identification of key core technologies. A dynamic identification indicator system can be established to support the training of new types of workers, industry-academia-research cooperation can be strengthened to facilitate the updating of new production materials, and industrial development funds can be established to support the expansion of new types of objects of labor.

[Purpose/Significance] With the development of artificial intelligence (AI) technology, the teaching innovation in higher education driven by digital intelligence technology has gradually gained attention. ChatGPT has brought new opportunities and challenges to the development of higher education and the reconstruction of educational ecology. The purpose of this study is to apply ChatGPT tools to the process of information literacy (IL) education in colleges, so as to create a human-machine collaborative teaching paradigm of IL education in colleges based on ChatGPT applications, promote the innovative reform and improve the teaching efficiency of IL education in colleges and provide reference for the application research of AI tools in the field of higher education in the era of digital intelligence and the development of high-quality IL ducation in colleges. [Method/Process] By combing the relevant literature, on the basis of summarizing the global educational application situation of ChatGPT and revealing the educational theory of ChatGPT, combined with the current teaching problems of IL education in colleges, this paper comprehensively analyzes the application scenarios of ChatGPT in the process of IL education in colleges from the perspective of teachers' teaching and students' learning. At the same time, the study carries out practical teaching comparison, and examines the satisfaction of teachers and students by means of "interview + questionnaire", so as to test the application effect of ChatGPT, summarize the problems in teaching and put forward improvement strategies for teaching. [Results/Conclusions] ChatGPT can effectively promote the innovation of IL education in colleges and improve the teaching efficiency of courses. Teachers and students also give good feedback on the educational application of ChatGPT. The application of ChatGPT can effectively improve teaching efficiency, innovate teaching content, optimize teaching process, improve teaching management, and provide students with more learning options, enhance learning initiative, promote higher-order thinking and scientific research innovation. At the same time, the educational use of ChatGPT will also lead to many problems, such as tool abuse, knowledge misleading, information security, and intellectual property rights. Therefore, we should look at the teaching use of ChatGPT dialectically. On the one hand, we should actively promote and apply ChatGPT; on the other hand, we should also pay attention to the different risks that ChatGPT may cause, and formulate forward-looking instructional management policies and guidelines to standardize the use of ChatGPT, so as to give full play to the educational function of ChatGPT and further promote the reform of IL education and teaching in colleges.

[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] Talent data have become the most important production factor and strategic resource. Building a scientific and technological (S&T) talent database has become an inevitable way to narrow the digital divide and accelerate the digital and intelligent transformation of talent work. Therefore, this study builds an S&T talent database to promote scientific decision-making for talent development, precision in attracting new quality technical talent, reform in evaluating S&T talents, and building talent system for new quality productive forces. [Method/Process] By analyzing the practical requirements and significance of building an S&T talent database, this study first explores and analyzes the intrinsic logic of promoting the development of new quality productive forces through an S&T talent database. It then summarizes the challenges facing the current construction of a S&T talent database, including the scattering and concealment of S&T talent data, the lack of policies and standardized systems for S&T talent data, the inadequate exploration of value-added S&T talent data, the need to expand the application of digital technology in talent work, and the security risks of S&T talent data. In response to these challenges, this paper finally proposes the idea of building an S&T talents database, and introduces the research exploration and application practice on it, including the construction of big data database for S&T talent aimed at the development of new quality productive forces, the development of AI-powered talent data computing engine, research into the system for profiling new quality technical talent, the analysis of talent growth paths for the training of new quality technical talent, the identification method of new quality talented professionals based on big data, the development of an efficient digital platform for talent management, and the development of a strategic analysis platform for technical talent. [Results/Conclusions] The construction of S&T talent database is an objective requirement for the development of the digital era and an inevitable requirement for the formation of new quality productive forces. Building big data for S&T talent, empowering talent workflow with big data and artificial intelligence technology can help empower talent workflow, release the enormous energy contained in digitalization, effectively activate the internal momentum of talented professionals, institutions, society, and government, and then continuously improve the efficiency of talent resource allocation, the operational efficiency of talent work, the overall effectiveness of talent development governance, and promote the development of new quality productive forces.

[Purpose/Significance] The advent of the Internet and the subsequent evolution of new technologies and applications, such as big data and artificial intelligence, have spurred the exponential growth of a new generation of technologies. China has entered the era of data-driven digital intelligence, with social network data privacy protection playing a crucial role in the country's comprehensive security strategy. By studying the evolution of this field, we gain insights into how to protect personal information, foster the healthy development of social platforms, and facilitate the responsible circulation of data elements. Compared to previous studies, this paper offers a broader range of perspectives, exploring the main directions and roles of each element of privacy protection with the information ecology theory, and more comprehensive research content, and combining causes, technologies, management and metrics.We propose an ecological framework for social network data privacy protection, based on a thorough overview of the subject matter. This framework serves as a reference point for the evolving landscape of privacy protection practices. [Method/Process] This study uses two different approaches: literature review and content analysis. The former is utilized to identify and categorize literature related to CNKI and WOS, while the latter is employed to visually analyze and interpret this corpus using Citespace. This study provides a systematic review of data privacy protection in social networks in four dimensions: privacy protection triggers, privacy leakage, privacy protection technology, and privacy protection management. The review is based on the visualization and analysis of both Chinese and international literature. Furthermore, we present our perspective on information ecology, integrating analyses of research themes and limitations for each information topic. We also explore strategies to promote the sustainable and healthy development of behaviors related to data privacy protection in social networks. [Results/Conclusions] Interdisciplinary exchange on data privacy protection in social networks is essential to advance the digital intelligence era. Relying solely on isolated methods of privacy protection is insufficient in the face of an increasingly complex application environment and the trends towards digitalization and intelligence. It is therefore necessary to establish a comprehensive privacy protection system that integrates technical optimization, legal improvements, platform regulations, and user engagement. Specifically, from a technical perspective, it is essential to leverage a variety of technologies to develop solutions for privacy protection. From a legal perspective, there is a need to refine the content, standardize the adjudication process, and strengthen supervision and sanctions. At the platform level, optimizing the content of privacy policies and ensuring their effectiveness are crucial. Finally, at the user level, it is essential to raise awareness of privacy protection and to enhance privacy protection capabilities. Moreover, it is crutial to examine the inherent relationship between each entity and the methods of protection. A key limitation of this paper is the lack of an in-depth analysis of the interaction mechanisms between stakeholders within the ecological framework of social network data privacy protection. Future iterations will address this by incorporating a background on the complex information environment and conducting a more in-depth analysis of the interaction mechanisms.

[Purpose/Significance] The new quality productivity advancing AI technology, especially exemplified by large language models (LLMs), is rapidly updating and attracting wide attention. In order to accelerate the implementation of AI technologies, it is urgent for advanced AI technologies to acquire support from knowledge resources in scientific and technological (S & T) information and libraries. Meanwhile, S & T information provides significant potential service scenarios for the application of AI technologies such as LLMs. This study aims to explore and design the method and path for constructing AI-ready data resources in the field of S & T information, and proposes a comprehensive and operable construction model that adapts to the new technical environment of AI, thereby facilitating comprehensive readiness in the field of intelligence. [Method/Process] This study first focuses on the concept and development status of AI-ready construction, and examines the development of AI-ready construction at home and abroad from three aspects: governments, enterprises and research institutions. The survey shows that the application of artificial intelligence has been highly valued by various fields of scientific research and production. However, the groundwork and preparation for AI applications are still relatively lagging behind, and AI tools cannot be fully implemented in key application scenarios due to the lack of high-quality and refined data resources. Based on the research results, the study made a preliminary definition of AI-ready construction, that is, we defined AI-ready construction as: the various development and improvement actions to adapt the object to the AI technical environment and promote the long-term benefits. The research then focuses on the field of S & T information, and systematically discusses and designs the AI-ready construction mode in the field of S & T information from six aspects: connotation category, construction angle, construction object, construction principle, control dimension and types of construction mode. [Results/Conclusions] The construction of AI-ready S & T information resources is a comprehensive and multi-angle transformation and upgrading process, which is located between the knowledge resource end and the intelligence application end. It is carried out in four aspects, including standards, methods, tools and platforms. The main content of the construction includes channels of AI technology, data transformation, data resources, and data management. At the same time, the construction is comprehensively controlled by six principles and four control dimensions. Besides, this study proposes the way of the practical construction of AI-ready S & T data resources, including the construction of intelligent data systems, and the construction of integrated platforms for the whole life cycle of S&T information data. The path reflects the process of the variation of knowledge resources from diversification to organization and then to integration, which not only serves the scientific information field itself, but also provides more intelligent, convenient, rich and powerful S&T information support for various fields. In the future, it is hoped that further research can delve into more micro and practical aspects, review the specific characteristics of different AI technologies, and provide more detailed suggestions for specific application scenarios at the operational level, providing a solid guarantee for scientific research institutions to achieve the leading strategic position in research and development.

[Purpose/Significance] In order to better realize the goal of digital transformation of education and digital transformation of university libraries, university libraries should strengthen the team building of high-level and high-quality librarians, so as to improve librarians' digital literacy levels and serve the independent training mode of high-level talented professionals in universities. Most of the existing digital literacy research studies farmers, college students, and teachers, whereas few studies focus on the digital literacy of librarians in teaching assistant positions. With the gradual deepening of the digital transformation of libraries and the application of educational technology, the improvement of librarians' digital literacy is particularly important. Therefore, this study aims to establish the correlation between educational technology and librarians, and explore the ways to improve librarians' digital literacy from the perspective of educational technology application, in order to fill in the gaps of current research. [Method/Process] Based on the literature analysis and research background at home and abroad, this paper comprehensively explored the connotation of digital literacy, and the connotation and characteristics of educational technology; through the combination of "online + field", this paper reviewed the practical application of educational technology at home and abroad in university libraries from two aspects of similarity and difference, and discussed the necessity and correlation of librarians to improve their digital literacy. From the three aspects of media literacy, data literacy and ethical literacy, the pathway for librarians to improve their digital literacy was constructed. [Results/Conclusions] This paper extracts three important qualities needed in the application of educational technology, and constructs the way to improve the quality of librarians, so as to improve the effective utilization rate of educational technology and find out the position of educational technology in the library service. Due to the realistic factors such as time and research object, this study still remains in the theoretical aspect, and we need further verify our method's feasibility and practicality. In the future, we will further study the influencing factors of how to improve the digital literacy of librarians, and then improve our method of improving the digital literacy of librarians from the aspects of digital literacy education object, education content, education method, education mode and education evaluation, so as to realize the feasibility of our method.

[Purpose/Significance] This study aims to unravel the complex relationship between new quality productivity (NQP) and rural revitalization, and to deepen understanding of the role of media in rural development. NQP, characterized by advanced technologies, innovative practice, and enhanced efficiency, has the potential to transform rural economies and communities. This research provides a multi-dimensional theoretical perspective and scientific foundation to advance comprehensive rural revitalization in China. By offering novel insights compared to the existing literature, this study establishes its significance in the field of scientific knowledge and demonstrates its potential to address significant real-world challenges. Understanding the synergies between NQP and rural revitalization can help policy makers, researchers, and practitioners to develop effective strategies to promote sustainable rural development and address socio-economic disparities. [Method/Process] The study employed a rigorous methodological approach that involved the training of a naive Bayes classifier to categorize the texts of government work reports. This machine learning technique enabled the extraction and analysis of relevant information from a large corpus of textual data, providing a robust basis for further empirical investigation. The study utilized provincial-level data from 2012 to 2022 to construct comprehensive evaluation frameworks for the NQP and rural revitalization. These frameworks included a wide range of indicators reflecting economic, social, and technological dimensions.Statistical analyses included bi-directional fixed effect models and spatial Durbin models, which allowed for a comprehensive exploration of the impact mechanisms and spatio-temporal dynamics of NQP on rural revitalization. The bi-directional fixed effect model helped to control for unobserved heterogeneity and to capture the dynamic interplay between NQP and rural revitalization over time. The spatial Durbin model was particularly useful in identifying spatial spillovers and understanding regional interdependencies. The theoretical underpinnings of this research were grounded in established frameworks in economic development and media studies. Media practice, including information dissemination, community building, and consumption upgrading, were hypothesized to play a crucial role in facilitating the impact of the NQP on rural revitalization. The empirical foundations were derived from robust provincial-level datasets, ensuring the reliability and validity of the findings. These methodological choices ensured a rigorous examination of the complex dynamics between NQP and rural revitalization in different regions over time. [Results/Conclusions] 1) Media factors demonstrated significant complementary relationships with various drivers of rural revitalization. 2) Media practice, centered on information support, community building, and consumption upgrading, has profoundly altered rural production relations and economic bases. 3) Temporally, both NQP levels and rural revitalization showed rapid growth, with annual growth rates increasing. Spatially, the central and western regions showed a stronger impact of NQP on rural revitalization compared to the eastern regions.4) NQP had significant spatial spillover effects on rural revitalization, not only influencing local revitalization efforts but also fostering stronger indirect effects on surrounding areas, thereby promoting regional interlinked development.

[Purpose/Significance] In the context of the rapid development of the artificial intelligence generated content (AIGC), it is crucial to understand the driving factors of users' psychological resilience and the characteristics of AIGC users' dropout behavior. This research focuses on this area to address the lack of in-depth studies in the existing literature. It aims to contribute to the knowledge system by providing a more comprehensive understanding of user behavior in the context of the AIGC. This is significant for promoting the transformation of the AIGC industry, as it helps to reduce the negative impacts of user loss and transfer, and promotes the sustainable use of the AIGC. It also has practical value in addressing the challenges facing the industry. [Method/Process] This study is based on resilience theory and S-O-R theory, which provide a solid theoretical foundation for the research. A questionnaire survey method is used, which is an appropriate approach for collecting data directly from users. A total of 328 questionnaires were collected from a wide range of AIGC users, ensuring the representativeness and reliability of the data. The empirical analysis and testing of the constructed model helps to validate the research hypotheses and draw meaningful conclusions. [Results/Conclusions] The research shows that psychological resilience is indeed a key factor in reducing dropout among AIGC users. Technological resilience and information quality play an important role in enhancing the psychological resilience of users. Based on these results, specific strategies and suggestions are proposed, such as improving the technological stability and performance of the AIGC, enhancing the quality of the information provided, and providing personalized support and training for users. However, there are some limitations to this study. For example, the sample size may not be large enough to cover all types of AIGC users. Future research could increase the sample size and explore other potential factors that may influence user behavior. In addition, longitudinal studies could be conducted to better understand the dynamic changes in user behavior over time. In conclusion, this study provides valuable insights into the factors influencing AIGC user dropout behavior and offers practical suggestions for promoting user retention and sustainable use. It paves the way for further research in this field and contributes to the development of the AIGC industry.

[Purpose/Significance] The purpose of this study is to provide an in-depth analysis of the widespread application of artificial intelligence (AI) technology in the field of government data governance and its far-reaching implications, with a particular focus on the core issue of algorithmic discrimination. With the rapid development of AI technology, it has demonstrated great potential in government decision support, public service optimization, and policy impact prediction, but it has also sparked extensive debate on issues such as algorithmic bias, privacy invasion, and fairness. Through systematic analysis, this study aims to reveal the potential risks of AI algorithms in government data governance, especially the causes and manifestations of algorithmic discrimination, and then it proposes effective solutions to protect citizens' legitimate rights and interests from being violated, and to maintain government credibility and social justice. [Method/Process] This study adopts the literature induction method to extensively collect domestic and international related data on the application of AI in government data governance, including academic papers, policy documents, and case studies. Through systematic review and in-depth analysis, we clarified the specific application scenarios of AI algorithms in government data governance and their role mechanisms. On this basis, this study further identified the key factors that led to algorithmic discrimination, including but not limited to the one-sidedness of data collection and processing, the subjective bias of the algorithm designers, and the influence of inherent social biases on the algorithms. It then explored the potential risks of algorithmic discrimination, including exacerbating social inequality, restricting civil rights, and undermining government credibility, and provided an in-depth analysis through a combination of theoretical modeling and case studies. [Results/Conclusions] The results of the study show that while the embedding of AI technology in government data governance has significantly improved the efficiency and accuracy of governance, it comes with a risk of algorithmic discrimination that cannot be ignored. To address this issue, this study proposed a series of targeted prevention and control measures, including clarifying the principle of algorithmic fairness, formulating industry norms and standards, improving the accountability mechanism and regulatory system, and optimizing the data collection and processing environment, so as to effectively curb the phenomenon of algorithmic discrimination while making full use of the advantages of AI technology, so that AI technology in government data governance can truly benefit the people, and promote social fairness and justice.

[Purpose/Significance] It is of great significance to analyze the current situation of elderly people's online health information seeking behavior, grasp its hot topics and development trend, to meet the health information needs and improve the health literacy level of the elderly people, and to promote the high-quality development of health services for the elderly people. [Method/Process] In this study, the DTM model was used to perform dynamic topic mining and analysis of Sina Weibo post content from 2016 to 2023, and the topic evolution, topic semantic evolution and topic information entropy trend were each investigated. In this study, data information related to online health searches of the elderly was obtained from the Sina Weibo platform, and the text content and time in the data information were taken as corpus data. After cleaning the data, different time windows are divided in time order, a DTM model is constructed to identify research topics, and "subject-word matrix" and "document-topic matrix" files are obtained. The topic intensity calculation was carried out successively, and the hot topic identification and analysis of online health searches for the elderly was carried out. The evolutionary trend of topic intensity was visualized and the evolutionary path of topic keywords was analyzed at a fine-grained level, so as to explore the focus and changing trend of online health information searches for the elderly people. [Results/Conclusions] The topics of "senile diseases", "old-age care by science and technology", "diet and health care", "mental health" and "social care" have evolved significantly, and the elderly people pay much attention to health information types such as common old age diseases, physical medical maintenance, social assistance and care for the elderly, and clothing, food, housing and transportation, in order to meet their information needs. The topics of "senile diseases", "old-age care by science and technology", "diet and health care", "mental health" and "social care" have evolved significantly, and the elderly pay much attention to health information types such as common old age diseases, physical medical maintenance, social assistance and care for the elderly, and clothing, food, housing and transportation, in order to meet their information needs. The research popularity of "economic trap", "epidemic control", "medical fraud", "virus transmission", "epidemic travel" and "medical health" as a whole showed a trend of first increasing and then decreasing, and the elderly continued to pay gradual attention to health emergencies and economic property security issues that might arise. The research popularity of "sports health care", "high risk" and "cultural and sports tourism" remain moderately stable from 2016 to 2023 and has not changed significantly. Topics such as "senile disease", "sports health", "high risk" and "medical fraud" are semantically stable. The information entropy of "sports health care", "daily life safety" and "virus transmission" is relatively stable, the information entropy of "medical literacy", "epidemic control", "cultural and sports tourism" and "balanced diet" shows a diffusion trend, and the information entropy of "high risk", "diet and health care", "economic trap" and "medical fraud" shows a convergence trend.

[Purpose/Significance] The development of artificial intelligence (AI) technology has brought huge changes and opportunities to library knowledge services. With the improvement of the multimodal capabilities of large language models, the ability of generative artificial intelligence to generate cultural images, cultural audio, and cultural video will also improve, which will effectively promote the intelligent transformation of library knowledge services and support the innovation of library knowledge services. General AI has stronger generalization ability, which can achieve functions such as complex task understanding, multimodal data learning, zero-sample reasoning, and continuous dialogue, completing the transformation from data-driven AI to natural mechanism-driven AI. By studying the theoretical foundation, system framework and application perspective of knowledge services based on artificial general intelligence, we can get new research ideas and practical references for knowledge services. [Method/Process] Based on the classification of the development history of AI, this article examines the state of research in AI for library knowledge services from the dynamic perspective of the evolution from generative AI to artificial general intelligence. This article analyzes the system framework of library knowledge services constructed with generative artificial intelligence and artificial general intelligence as technical bases, and explores the future development trend of library knowledge services based on artificial general intelligence from the aspects of space, content, mechanism and talent. [Results/Conclusions] With the continuous development and optimization of large language model technologies, the gradual implementation and widespread application of artificial general intelligence technology is an inevitable trend in the future. With the promotion of artificial general intelligence, library knowledge services will also undergo significant changes, and the traditional knowledge service model based on existing resources will gradually transform into a digital intelligence service model. Although artificial general intelligence has shown great potential in knowledge understanding and content generation, artificial general intelligence is still at an early stage of development, and its prospects for application in library knowledge services are not yet clear. In order to explore the impact of artificial general intelligence on the library knowledge service model, this article summarizes the different stages of AI technology integration in knowledge services, namely the evolution from automation to cognition and then to autonomy, and proposes an AI agent-driven library knowledge service framework. It is believed that artificial general intelligence technology will open up new development space for library knowledge services, promote academic research and practical applications in knowledge services and enter a new era of rapid development.

[Purpose/Significance] Document classification is one of the fundamental tasks of information service institutions such as libraries. The limited human resources make it challenging to categorize the vast number of documents, and the current automatic indexing technologies are not yet fully integrated into the entire indexing process. Large language models (LLMs), with their excellent capabilities in natural language understanding and processing capabilities, have been utilized for various natural language processing tasks such as text generation, automatic summarization, and text classification, which can be integrated into the entire classification process. [Method/Process] Combining the long-term practical experience of the National Newspaper Index, the research on how to introduce LLMs into the classification and indexing process is conducted from three aspects: reducing the reading pressure on indexers, directly using LLMs for classification, and combining them with automatic indexing models. A prompt-assisted topic classification model is designed to leverage the LLM for intelligent analysis and extraction of document content, guiding the model to output concise information summaries. This allows indexers to quickly understand the basic situation of the research, grasp the essence of key concepts and their interrelationships, and thus quickly and accurately determine how to classify the collections. [Results/Conclusions] When the LLM cannot be directly used for text classification tasks based on the "Chinese Library Classification" (CLC), it is combined with existing automatic models to generate the ACBKSY model. The overall classification accuracy of the model has improved by 2.16%, and the non-rejection accuracy has increased by 3.77%. On this basis, the actual indexing workflow is optimized to increase the systematicity and coherence of the indexing work, ensuring that every step from document input to final classification is more efficient and accurate. This optimized workflow has been put into use in the R and F categories of the collection, and it can improve the efficiency of indexing by 1.1 to 1.4 times. However, there are still some shortcomings in this paper, such as not providing the LLM with sufficient learning to fully understand the category settings of the CLC and some simple rule divisions; the classification based on the CLC is essentially a hierarchical classification, and how to guide the LLM to gradually output classification results in the form of multiple rounds of dialogue needs further study.

[Purpose/Significance] In the digital age, government digital avatars represent a significant innovative application of the integration of generative artificial intelligence and digital government. These digital avatars aim to enhance the efficiency, accessibility, and responsiveness of public services. This study aims to explore the factors and pathways that influence public acceptance of government digital avatars, providing a theoretical basis and practical insights for improving these services and enhancing the user experience. Unlike previous studies that have focused primarily on technological and functional aspects, this research emphasizes users' perceptions and expectations, filling the gap in existing research on user experience. This innovation of this paper lies in the integration of the Expectation Confirmation Theory (ECT) and the Technology Acceptance Model (TAM) and the extension of other user perception factors to systematically analyze how these factors together influence public acceptance. [Method/Process] The study adopts a comprehensive approach, integrating the Expectation Confirmation Theory (ECT) and the Technology Acceptance Model (TAM), and extends the framework to include additional user perception factors such as perceived information quality, perceived intelligence, perceived convenience, perceived attractiveness, perceived usefulness, and AI trust. Structured questionnaires were used to collect data from a diverse sample, measuring constructs such as expectation confirmation, satisfaction, and various user perception factors. The data were analyzed using structural equation modeling (SEM), which provides robust statistical insights into the relationships between these constructs. In addition, mediation effect models were used to examine indirect effects, providing a comprehensive understanding of how these factors influence public acceptance. Data were collected from a diverse group of respondents to ensure the findings are broadly applicable and representative. [Results/Conclusions] The results suggest that expectation confirmation significantly increases public satisfaction with government digital avatars, which in turn positively affects their acceptance. Perceived information quality, perceived intelligence, perceived convenience, perceived attractiveness, perceived usefulness, and AI trust serve as critical mediators in this relationship. In particular, high levels of perceived quality and intelligence significantly increase satisfaction and acceptance, while convenience and attractiveness also play an important role. AI trust emerges as a critical factor, mediating the impact of user perceptions on acceptance. However, the study does have some limitations. First, the lack of understanding of the professional backgrounds of the research population may lead to differences in acceptance between different professional groups. Future research should look more closely at different occupational groups to gain a fuller understanding. Second, the sample consisted mainly of respondents from younger demographic groups, which may affect the generalizability of the conclusions. Future research should broaden the geographical and demographic coverage of the sample to increase diversity and representativeness. In addition, the lack of qualitative research limits the depth of understanding of users' deep-seated views and needs about government digital avatars. Future research should include qualitative components, such as in-depth interviews and focus group discussions, to explore the actual experiences and specific needs of users and to complement the quantitative findings. This study provides practical recommendations for improving user satisfaction and acceptance, and for supporting the development of effective digital governance solutions. By specifically optimizing government digital avatar services, public satisfaction and trust in digital government services can be increased, further promoting the application and development of digital avatar technology in digital government.

[Purpose/Significance] Aiming at the semantic missing and incomplete problems in the process of image organization and retrieval, a framework for semantic description of images in social media is proposed to enrich the existing theoretical system of image description, improve the efficiency and utilization of image retrieval, and provide a reference for the realization of the automatic semantic annotation of images. [Method/Process] First, we conducted a survey and analysis of research progress related to image description both domestically and internationally, summarizing the existing theories of image description and annotation, metadata specifications, and related technical methods. Second, based on the image metadata standards and the theory of hierarchical and categorical description of image features, we constructed a semantic description framework for social media images, focusing on seven layers: external feature layer, content layer, object layer, relationship layer, scene layer, event layer, and emotional layer. We also elaborated in detail the various semantic layers and their interrelationships. Finally, we verified the feasibility of the image semantic description framework by describing the examples of character images and landscape images. [Results/Conclusions] The results of the descriptive examples of character images and landscape images indicate that the image semantic description framework can eliminate the "semantic gap" in image description through semantic associations between different layers, and achieve a multi-faceted, multi-dimensional, and multi-level structured and semantic description of the external and content features of images. It has strong portability and flexibility. However, there are also certain limitations and areas for improvement in this paper: 1) Based on the image semantic description framework proposed in this paper, a prototype system based on image annotation needs to be developed; 2) The images posted by users on social media are closely related to the situation, and they are more likely to express emotions. In the future, more research on the semantic layer of images can be conducted based on the text information posted by users; 3) Future research can further explore the application of deep learning in image and text fusion to achieve more accurate event and emotion recognition. By constructing a more complex neural network structure, the event and emotion information in the image can be deeply mined and fused; 4) When describing images, the study should pay attention not only to static visual features, but also to consider the dynamic course of events. Future frameworks could attempt to combine static and dynamic information to provide richer, more vivid descriptions of images.

[Purpose/Significance] With the popularization of artificial intelligence technology, the cost of information fog has decreased, and its negative impact on national security is becoming increasingly apparent. Information fog not only creates cognitive barriers for users, but also poses serious challenges to various fields such as politics, economy, and society. This article explores the prevention and control of information fog from the perspective of the overall national security concept, with the aim of addressing the risks and challenges posed by information fog. There is a lack of research in the literature on the prevention and control of information fog from the perspective of overall national security. To fill the gap, this article not only provides a new perspective and strategy for the prevention and control of information fog, enriching the connotation of national security research, but also promotes the cross-integration of information security and national security disciplines, providing new theoretical support for research in related fields. It provides reference and guidance to relevant entities such as the government and online platforms in preventing and controlling information fog. [Method/Process] Based on the concept of overall national security, this article summarizes the academic achievements on information fog at home and abroad, including research on stages, scenario applications, governance strategies, and practical case analysis. We summarize the characteristics of information fog and analyze the methods and strategies for prevention and control. [Results/Conclusions] Information fog has the characteristics of wide dissemination, realistic experience, and difficulty in identification. Based on this feature, the article puts forward the following suggestions to strengthen the improvement of legal policies and clarify the division of responsibilities: 1) to strengthen the evaluation and risk warning of online accounts and utilize technology to update anti-counterfeiting tools and improve information authentication capabilities. Governments should intervene in a timely manner to prevent the information fog from escalating. 2) to improve public awareness of discrimination and the level of prevention. In addition, the article also has some shortcomings. First, it does not present other forms of information fog in the security domain. Second, it does not analyze information fog from an algorithmic perspective. Therefore, in future research, we will closely follow the development of society to analyze the characteristics and presentation methods of information fog in various security fields. At the same time, scholars in the fields of computer science, intelligence science, and national security are also invited to conduct in-depth analysis of information fog from the perspective of computer algorithms, in order to propose practical countermeasures and suggestions for preventing and managing information fog from a technological perspective.

[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.

[Purpose/Significance] AI technology has brought unprecedented challenges and opportunities to the knowledge service industry, requiring innovation and reform of knowledge services in various dimensions, including technology, organizational mechanisms, and service models, to adapt to the development of emerging knowledge productivity. AI technology has not only changed the way knowledge is produc ed and disseminated, it has also significantly influenced the processes by which users acquire knowledge and the systems through which they produce knowledge. Simply promoting the empowerment of knowledge services through AI from a technical point of view is not enough to achieve the transformation and upgrading of knowledge service institutions. [Method/Process] This article begins with the multi-level transformative impact of AI technology on emerging knowledge productivity, proposing that generative AI has rapidly become a powerful new force in knowledge production, and that AI agents are gradually becoming revolutionary tools for the flexible design and innovation of complex processes. We argue that the rapid development of AI has deepened the connotations and forms of AI empowerment. The article further explores the barriers in production relations in the development of new quality productive knowledge services and examines the challenges of aligning traditional knowledge services with user knowledge processes and user production systems in the AI environment. We propose to promote the development of the knowledge service industry through multi-level AI empowerment and innovation of the traditional organizational mechanisms of knowledge services. The article emphasizes placing the construction of new production relations at the key point of AI empowerment, developing new user-oriented, user-process-driven knowledge service organizational models, and developing new docking logic and service embedding architectures between knowledge services and user production systems, as well as building user-oriented, service-driven internal organizational models. Specifically, we present possible new directions for knowledge service production relations, such as the Library-Inside model, the Inside-Out+Outside-In model, new docking architectures between knowledge services and user production systems, and reforms in internal organizational models of institutions. [Results/Conclusions] By exploring the multi-level transformative effects of AI technology and analyzing the barriers in the production relations of new quality productive knowledge services, this article proposes to reform and innovate the production relations of knowledge services. In order to promote the development of new quality productive knowledge services, we summarize the construction ideas of new-type knowledge service production relations, aiming to sustainably promote the development of new quality producitve knowledge services in the process of improving users' knowledge productivity and promoting the high-quality development of users' production systems.

[Purpose/Significance] The new round of technological revolution and industrial transformation is accelerating, and its impact on national development and security is becoming deeper and wider. The complexity and uncertainty of the technological innovation system are highlighted, and the technology policy agenda is also undergoing a transformation in order to cope with the increasingly fierce international technological competition. In order to identify the trend of technological development, countries generally engage in data-driven strategic intelligence practice. [Method/Process] Through research on the standard Innovation Management - Tools and Methods for Strategic Intelligence Management - Guide published by the International Organization for Standardization, the Science, Technology, and Innovation Policy Agenda published by the Economic Development Cooperation Organization, Safeguarding the Future of the United States: Framework for Key Technology Assessment issued after the recent national key technology assessment in the United States, The 2023 EU Industrial R&D Investment Scoreboard by the EU, the Japanese R&D Overlook Report, and the Scientific Structure Atlas of the Chinese Academy of Sciences, this study is focused on how to develop and utilize scientific and technological strategic intelligence to support the "evidence-based decision-making" agenda in the report development process. [Results/Conclusions] The essence of technological strategic intelligence is to provide data, knowledge, and evidence for decision making. The operational cycle model of strategic intelligence is DIKI, which is a strategic intelligence data infrastructure and analysis model including indicators, and tools for technology policy issues. There is a need to establish a dedicated strategic intelligence unit within the organization to understand and utilize technology strategic intelligence data, and to consciously incorporate it into the "evidence-based decision-making" agenda. Combining different types of strategic intelligence has become a necessary skill for technology policy makers. Technology innovation policy makers should take responsibility for the generation, maintenance, integrity, and accessibility of a large amount of administrative data related to the monitoring of technology innovation systems and policies.

[Purpose/Significance] Accurately measuring the influence of technical topics is crucial for decision-makers to understand the developmental trends in the technology sector. It is also an important link in identifying emerging, cutting-edge, and disruptive technical topics. Traditional methods of measuring technical topic influence are significantly affected by the latency of patent data approval and citations, lack a forward-looking perspective on the potential influence of technical topics, and suffer from insufficient semantic richness in the extraction of technical topics. This paper presents a method for measuring technical topic influence based on PhraseLDA-SNA and machine learning. It aims to mitigate the impact of delays in patent data approval and citation, while improving the interpretability and accuracy of the results in assessing technical topic influence. [Method/Process] In this study the explicit and implicit determinants of technical topic influence were first analyzed, based on which an index system for measuring technical topic influence was constructed. Then, the PhraseLDA model was used to extract semantically rich technical topics from a large corpus of pre-processed patent texts and to compute the topic-patent association probabilities. PhraseLDA-SNA enhances the semantic richness of technical topic extraction and deepens the analysis of topic content. Machine learning methods leverage their robust data processing and analysis capabilities to predict the high citation potential of patents related to the topics. This research integrates PhraseLDA-SNA and machine learning methods to accurately measure the significance and advanced nature of technical topics in promoting field development, thereby achieving an accurate measurement of the influence of technical topics. Finally, an empirical study was conducted in the field of cellulose biodegradation to compare the high-impact technical topics identified by the proposed method with those identified by the traditional method. Several experts with high academic influence and extensive experience in cellulose biodegradation research were invited to evaluate the high-impact technical topics identified in this study, thus validating the effectiveness of the proposed method. [Results/Conclusions] Compared with the traditional method, the technical topic influence measurement approach based on PhraseLDA-SNA and machine learning reveals more in-depth content. Moreover, this method also analyzes the importance and leading nature of technical topics, which shows superiority in quantitative analysis. Comparing the distribution of high-impact technical topic-related patents identified by the two methods across different years, the topics identified by the proposed method had a higher association ratio in the most recent data, indicating a significant reduction in the impact of patent data approval and citation delays.

[Purpose/Significance] Breeding 4.0, characterized by "biotechnology + artificial intelligence + big data information technology," has brought new requirements for the digital management and intelligent utilization of germplasm resources. In order to meet the diverse support needs for knowledge service forms under an intelligent background, this article aims to propose an effective method for knowledge organization and deep semantic association. This is essential to address the inconveniences that discrete germplasm resource data bring to researchers when collaborating across regions and institutions. Therefore, the article presents a method that integrates fragmented domain data into a systematic knowledge system, which is particularly important. [Method/Process] By analyzing the domain data descriptions and the current organizational status, the ontology construction was performed using the seven-step method developed by Stanford University Hospital. First, existing ontologies such as the Crop Ontology, Gene Ontology, and Darwin Core were referenced and reused, and then integrated with the knowledge framework from the "Technical Specifications for Crop Germplasm Resources" series and example datasets. Consequently, an ontology model was successfully constructed, which covers five major categories of crops: cereals, cash crops, vegetables, fruit trees, and forage and green manure crops. This model defines 11 core classes including phenotypes and genotypes, as well as identification methods and evaluation standards, along with 10 object properties and 56 data properties. [Results/Conclusions] Based on the ontology model, the article proposes a methodology for constructing a knowledge graph of crop germplasm resources. Using rice as an example, a domain-specific fine-grained knowledge graph is developed to facilitate semantic association and querying across multiple knowledge dimensions. The article also outlines prospective designs for new intelligent knowledge service scenarios driven by the knowledge graph, such as intelligent question and answer and knowledge computation, aiming to meet the knowledge service needs of researchers, breeding companies, and the general public. This is intended to provide more accurate and efficient support for computational breeding efforts. Currently, the research focuses only on rice as an example of a cereal crop, with economic crops, vegetables, and other types of crop germplasm resources not yet included in the study. Future work will expand the scope of the study and add new classes and properties specific to different germplasm resources to better address the diverse and personalized knowledge needs of users in the eraa of big data. This approach aims to promote the contextualization, ubiquity, and intelligence of knowledge services, and to further integrate them into different academic disciplines related to the development of new quality digital productivity.

[Purpose/Significance] This study systematically analyzes the basic models and development trends of international open peer review platforms, with the aim of exploring the insights these platforms provide for academic communication and research governance in China. The goal is to accelerate the establishment and development of an open peer review system in China, fostering a more open, collaborative, and efficient academic exchange environment that promotes the free flow of knowledge and the widespread dissemination of scientific ideas. [Method/Process] The article first reviews and analyzes several international open peer review platforms and communities that are led by the scientific community and operate independently of scientific journal publishing, such as Peer Community In (PCI), Sciety, PREreview, and Review Commons. On this basis, it outlines the basic operational models of international open peer review platforms and identifies a number of common features among the three basic models. Then, through an in-depth analysis of the development dynamics of these platforms and communities, the study summarizes their development trends from different perspectives, including research institutions, scientists, scientific community, and international academic communication models. Based on this analysis, and taking into account international experience and the specific characteristics of China's research environment, the article proposes recommendations for building an open peer review system in China. [Results/Conclusions] The study identifies three basic operational models of international open peer review platforms: platforms established for the purpose of open publishing, platforms developed primarily as preprint servers, and platforms built independently for open peer review. It also summarizes the key trends in the development of these platforms: strong support from major research institutions, active participation of leading scientists, the formation and impact of large-scale platforms, recognition of open peer-reviewed research by the scientific community, alignment with the international open access (OA) movement, and the reshaping of international academic communication models.International open peer review platforms and communities are emerging as important forces in driving research innovation and enhancing research quality. In light of China's current situation, the article offers six recommendations to accelerate the development of open peer review platforms and communities: (1) fully recognizing the significance of open peer review platforms for Chinese science; (2) updating scientific publishing concepts to create a supportive environment for the development of open peer review platforms; (3) implementing policies to support the construction of open peer review platforms and communities in China; (4) promoting exemplary cases and innovative practices in the development of open peer review platforms and communities; (5) building robust national open peer review infrastructure to support the development of open peer review communities; and (6) engaging in brand building to create internationalized open peer review platforms and communities. These efforts aim to secure a new position for China in global scholarly communication.

[Purpose/Significance] In the era of digital intelligence, robots technology is playing an increasingly important role in the field of education. The applying of AI chatbots in library scenarios is an important lever for the future construction of learning ecosystems in universities. This study aims to explain the influencing factors of users' willingness to continue using library AI chatbots, and provide a new perspective beyond the IT perspective to understand the impact of the basic characteristics of AI chatbots on human behavioral intentions, in order to better understand the sustainability thinking of interpreters and provide some inspiration for the further development of library AI chatbots in the future learning ecosystem. [Method/Process] Based on U&G theory and the SOR framework, we developed a conceptual model of library AI chatbot users's willingness to continue using the chatbot. Data were collected using a questionnaire survey method, with teachers and students as the main respondents. The variables of the AI chatbot user's continuous usage intention model were set to 8, each consisting of 3-6 options, and then measured using a 7-point Likert scale. Finally, the variables and hypotheses in the model were validated using a mixed research method of PLS-SEM and fsQCA. [Results/ [Conclusions] The research results indicate that three types of satisfaction, hedonic (entertainment and avoidance), social (social presence), and utilitarian (convenience and information consultation), have a significant positive impact on emotional experience (awe experience and emotional participation), with avoidance having the greatest impact on awe experience and social presence having the greatest impact on emotional participation. Emotional experience has a significant positive impact on the intention to continue using, with awe experience having the greatest impact on the intention to continue using. Emotional experience, as a mechanism of action, affects user satisfaction and willingness to use. Based on the data analysis, four suggestions are proposed from the perspective of future learning ecology design and user psychology. When designing library AI chatbots, the usage scenarios should be enriched, and the healing function, immersive experiences and immersive experiences should be emphasized. The limitation of this study is that the use of first-hand cross-sectional data cannot prove whether the influencing mechanism changes over time. In the future, a combination of first-hand and second-hand data can be used to improve the explanatory power. In addition, although this study ensures the validity and reliability of cross-sectional data, there may be geographic and cultural differences in users' behavioral intentions. In the future, a comparative study of the intention to continue using library AI chatbots in different regions and levels can be considered.

[Purpose/Significance] In the context of the rapid development of artificial intelligence and content generation (AIGC) technology, it is particularly urgent and important to explore in depth its impact and evolutionary path on the information culture of university libraries. As a central platform for knowledge dissemination and information exchange, university libraries not only hold a wealth of literature resources, but also serve as important places for teachers and students to obtain information and improve their information literacy (IL). Therefore, the study of the internal logic and development ideas of information culture in university libraries under the influence of the AIGC aims to reveal how new technologies can reshape the information service model of libraries, and how to promote the deep integration of knowledge innovation and educational informatization by optimizing organizational structure and improving service efficiency. This study will provide scientific basis for university libraries to formulate strategic plans that adapt to future development trends, help them to better serve teaching and research in the context of the new era, and cultivate high-quality talented people with high IL skills. [Method/Process] This study focused on both theory and practice of library information culture construction. First, the theoretical foundations of library information culture are considered, and the concept, characteristics, and manifestations in university libraries are systematically reviewed. Second, an in-depth analysis of the impact mechanism of the AIGC technology on information culture was conducted, including changes in information acquisition methods, improvements in information processing capabilities, and innovations in information exchange models. Finally, the integration path of information culture and the AIGC technology was explored, and a framework for cultivating information culture in libraries and at the university level was proposed. [Results/Conclusions] Under the promotion of the AIGC technology, the cultivation of information culture in university libraries has a new trend and characteristics. In order to better achieve the goal of educational informatization, university libraries should actively embrace and respond to change by building information system standards, improving information management systems, enriching information resources, and enhancing information service capabilities. At the same time, they should also promote the cultivation of information culture by integrating information resources for university education, enriching IL systems, cooperating in the development of the information technology, and participating in the construction of information governance systems. By integrating educational resources, cooperating in information technology research and development, and jointly building an open, shared, and mutually beneficial information ecosystem, we can effectively promote the prosperity and development of information culture in university libraries.

[Purpose/Significance] In the context of the digital age, knowledge collaboration platforms such as online Q&A communities, academic forums, and various professional networking platforms have become important venues for knowledge sharing and collective wisdom. These platforms bring together users from different fields, with diverse professional backgrounds and levels of expertise. They actively engage in problem solving, exchange views, and form complex and dynamic social networks. Online knowledge collaboration platforms not only enhance the accessibility of knowledge but also serve as incubators for interdisciplinary communication, problem solving, and innovative thinking by harnessing the collective wisdom and expertise of individuals. This article explores how to optimize the network structure of online knowledge collaboration platforms and balance the internal knowledge and expertise within teams. The goal is to promote cross-domain information flow, prevent the formation of information silos, and promote the creation, dissemination, and application of knowledge through collective knowledge collaboration. [Methods/Process] Due to the diversity of participants' backgrounds, experiences, and viewpoints, effectively managing and coordinating this heterogeneity becomes a critical issue. Additionally, the quality and efficiency of knowledge collaboration is also influenced by the characteristics of the network structure, such as the flow of information paths, the role of key nodes, and the interaction patterns of small groups. This study is based on actual data from Stack Overflow, the world's largest programming Q&A website. It focuses specifically on the following aspects of influence: clustering coefficient, node centrality, edge span, user knowledge heterogeneity, and user experience heterogeneity. By constructing a negative binomial regression model, the study investigates how network structure characteristics and team user heterogeneity affect the quality and efficiency of knowledge collaboration. [Results/Conclusions] The results show that, with respect to network structural characteristics, node centrality significantly improves the quality and efficiency of collaboration, and higher aggregation coefficients and larger span of connecting edges restrict information flow and are detrimental to the efficiency of knowledge collaboration. In terms of user heterogeneity, high heterogeneity in knowledge background and registration duration usually hinders collaboration, heterogeneity in experience heterogeneity in registration duration negatively affects collaboration effectiveness in both cases, heterogeneity in response acceptance rate only negatively affects collaboration quality, while heterogeneity in activity intensity positively affects it. In addition, this study still has shortcomings that deserve further exploration. First, future research could consider expanding the sample to include more questions on different topics and domains to increase the reliability and generalizability of the findings. Second, future research could focus on the dynamic changes of network structure and heterogeneity in order to better understand the impact of network structure on knowledge collaboration and to improve the prediction ability of collaboration effects; it could explore more deeply how different types of heterogeneity affect collaboration dynamics over time.

[Purpose/Significance] This study aims to critically analyze the social philosophical roots of the digital intelligence divide from the perspective of machine functionalism. By uncovering the theoretical origins and generation pathways of the digital intelligence divide, countermeasures can be proposed. The research contributes to understanding the divide's impact on society and provides insights for promoting inclusive development of artificial intelligence (AI) technology. The study fills a gap in the literature by linking machine functionalism to the digital intelligence divide and offers a novel perspective on addressing the unequal use of AI technology. The findings have significant implications for policymakers, technology developers, and researchers in the fields of AI ethics, digital inequality, and social philosophy. [Method/Process] Using the theoretical lens of machine functionalism, this study examines the evolutionary pathways, generation mechanisms, and multiple risks of the digital intelligence divide. It draws on relevant theories, such as the extended mind thesis and the theory of technological determinism, to analyze how machine functionalism influences the design and application of AI technology. The study also draws on empirical evidence from case studies and surveys to illustrate the manifestation of the digital intelligence divide in different contexts. By synthesizing theoretical and empirical insights, the research proposes interventions that address the divide at different levels, from the philosophical underpinnings to the practical implementation of AI technology. [Results/Conclusions] The study shows that machine functionalism, which applies Turing machine principles to explain the mind and views the mind as a physically realized Turing machine. It has become the social philosophical foundation of AI technology. While breaking with the traditional biological essentialist view of the mind, machine functionalism inadvertently creates inequitable uses of AI through three main pathways: the mechanization of the mind, designer bias and algorithmic preference, and technological specialization and barriers to entry. This creates the digital intelligence divide and risks such as the evolution of information access inequality into social inequality and the weakening of information cocoons and public dialogue. The study argues that interventions are needed to mitigate these risks and promote a more equitable distribution of the benefits of AI technology. To bridge the digital intelligence divide, the study suggests a multi-pronged approach. First, future efforts should focus on promoting positive interaction between machines and humans through value-sensitive design, which incorporates ethical considerations into the development and deployment of AI systems. Second, developing ethical algorithms that eliminate designer bias and algorithmic preference is critical to ensuring fair and unbiased AI decision-making. Third, improving the digital intelligence skills of individuals and communities can help break down barriers to entry caused by technological specialization and enable more people to benefit from AI technology. Together, these policies can help break down the barriers of unequal technology use under machine functionalism. The study concludes by emphasizing the importance of a collaborative effort among policymakers, technology developers, researchers, and the public in addressing the digital intelligence divide. It calls for further research on the social implications of machine functionalism and the development of inclusive AI governance frameworks. The findings of this study serve as a foundation for future work to mitigate the risks of the digital intelligence divide and promote the responsible and equitable development of AI technology.

[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] 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] Health is the foundation of survival, and the health of all people is the foundation of a country. However, the aging situation in China's rural areas is serious, and the health information literacy (HIL) of the middle-aged and elderly population is low. Improving the HIL of middle-aged and elderly people in rural areas is of great significance to personal health and the "Healthy China" strategy. In terms of research perspective and content, previous studies either used quantitative methods to measure the health level of rural middle-aged and elderly people, or used qualitative methods to analyze a certain aspect of HIL, but the reasons for the low level of HIL among rural elderly people are not explored from a qualitative perspective. Based on this, the author analyzes the reasons for the low level of HIL of the rural middle-aged and elderly from the perspective of qualitative research and proposes corresponding countermeasures. [Method/Process] Semi-structured interview method and ground theory were used to explore the reasons for low HIL among rural middle-aged and elderly people from five aspects: health information awareness, health information access, health information evaluation, health information utilization and health information service. A theoretical model of the causes of low HIL among rural middle-aged and elderly people was constructed. [Results/Conclusions] It is found that the reasons for the low HIL of the rural middle-aged and elderly people include personal cognitive limitations, objective condition limitations, and service delivery problems. Specifically, the reasons are rejection of digitalization, cognitive misunderstanding, physical condition limitation, digital equipment limitation, low quality of related services, and the lack of related services. Accordingly, from the level of personal cognition, it is proposed that multiple measures should be taken to promote health-related publicity and education to improve the awareness of HIL. For example, offline and online HIL education should be improved and the characteristics of rural social acquaintance should be used to improve publicity and HIL education. From the perspective of objective conditions, it is suggested that the material basis of life should be improved to raise the level of HIL. From the perspective of health information services, health care institutions, village committees, libraries and family members should be involved in the health information service system for the rural middle-aged and elderly people, and the quality of health information services should be improved. The interaction between the various causes can be further explored in the future.

[Purpose/Significance] This paper aims to explore the development and evolution of the library statistical evaluation index system, highlighting its characteristics and changes at different stages of document management, information management, and data management. The research is conducted around three key stages: document level, information level, and data level, analyzing the main content and significance of the library statistical evaluation index system at different development stages. The innovation of this paper lies in the systematic analysis of these transitions, providing a comprehensive perspective that integrates theoretical and methodological advances with practical indicators. [Method/Process] The research methodology includes a systematic analysis of statistical evaluation indicators of libraries in different stages of development. The study uses historical review and theoretical analysis methods, analyzing the development of document organization, information digitization, and data management in libraries. By examining the development of classification, cataloging, and evaluation metrics, the research combines historical documentation with contemporary practices to provide a solid theoretical foundation. The study also draws on existing literature and integrates data from library management systems and user feedback to assess service quality and operational efficiency. This mixed-methods approach ensures a comprehensive understanding of the applicability and effectiveness of the evaluation indicators. [Results/Conclusions] The study shows that the library's statistical evaluation index system has evolved significantly, reflecting the library's adaptation to changing resource types and management needs. The main conclusions can be summarized as follows. The document level in the first stage, focusing on book circulation, including indicators such as book use efficiency, collection development quality, and reader engagement. Key metrics such as cumulative borrowing and utilization rates provide basic service performance data, but lack deep information insights. With the development of information technology, library statistical evaluation indicators have expanded to include service frequency, response time, user satisfaction, and growth rates, enabling libraries to evaluate and improve service strategies based on user feedback and service performance. Currently, the library's statistical evaluation system focuses on research data management and data value assessment. Indicators now include not only resource- and service-related metrics but also operational efficiency, budget utilization, technological updates, scholarly contributions, and social impact. These indicators provide a comprehensive view of the library's performance in resource management, service quality, and social contribution, helping to optimize resource allocation, enhance service quality, and increase impact. The study also acknowledges certain limitations, such as the evolving nature of technology and user needs, which may require continuous updates to the evaluation system. Future research should explore the integration of advanced data analytics and artificial intelligence to further refine evaluation metrics. In addition, ongoing studies are needed to adapt to emerging trends in data management and user behavior to ensure that libraries remain at the forefront of information services in the digital age.

[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] The vigorous development of information literacy (IL) education has led to the increasingly prominent role of academic librarians as teachers in universities. However, most of the librarians who assume the teaching function lack the special training of pedagogy and teaching competence, so they have negative attitudes such as resistance and refusal to accept the role of teachers. These problems will inevitably affect the favorable and sustainable development of library IL education. Therefore, building the teacher role of librarians is of great significance to the development of IL education. [Method/Process] A content analysis method of the research literatures was used to review the historical development and recent debates about the teacher role of librarians were retrospected. We found that librarians currently play multiple teacher roles, including curriculum designers, teaching partners, teachers, academic research participants, and advocates for IL education. However, librarians face many realistic dilemmas in the process of teacher role development. Based on the in-depth analysis of the realistic dilemmas faced by librarians, this paper puts forward the ways of development for the teacher role of librarians from various aspects and perspectives. [Results/Conclusions] The research suggests that the development of the teacher role of librarians faces many realistic difficulties, such as the influence of professional image, the disconnect between library science education and practice, and the lack of teaching competence of librarians themselves. The ways of development: 1) Librarians: As the implementers of the educational mission, librarians in the new era should establish professional self-confidence and play a good role as a member of the educational and academic circles. In addition, they should actively examine their own lack of professional knowledge and pedagogical knowledge, and actively make up for their lack of knowledge through the training of teaching knowledge for librarians, the MOOCs of universities, and the further study and upgrading of academic degrees. It is also necessary to improve teaching skills through course observation, lecture practice, peer discussion, teaching reflection and other forms. In short, promoting librarians' teaching self-confidence and teaching competence is the key to developing the role of teachers; 2) Library science education: Attention should be paid to the establishment of courses related to IL teaching, such as adapting traditional library science courses to meet the teaching responsibility requirements of university librarians in China, and gradually adding courses related to teaching design, method, theory, assessment, introduction to teaching problems, presentation skills, cooperation and communication skills to improve the teaching ability of pre-service librarians. Embedding more demonstration/simulation teaching in LIS/MLIS teaching courses to help pre-service librarians gradually acquire teaching-related skills and experience, so as to prepare librarians for teaching roles; 3) Library departments: Efforts should be made to establish teacher-librarian positions, reallocate the responsibilities of librarians, and provide policy facilities to enable librarians to grow into library education experts; 4) Library industry associations: The dual professional qualification system of "teachers" and "librarians" should be implemented by library industry associations.

[Purpose/Significance] Universities are important bases for cultivating national innovative talent. In the context of China's implementation of the strategy of strengthening the country through talent and innovation, intellectual property literacy (IPL) has become an essential quality for college students to adapt to the society. In order to explore the ways and methods of IPL education in the training of innovative talent in university libraries, this paper aims to discuss the practical experience of IPL education based on innovative talent cultivation among college students. [Method/Process] Taking Yangzhou University Library as an example, we are undertaking the work of IPL education for undergraduates, graduates, and innovation and entrepreneurship students in non-law majors at universities. The IPL education in the cultivation of innovative talent of college students are divided into different stages, including innovation and entrepreneurship cognition stage to cultivate the intellectual property innovation awareness for innovative talent, comprehensive literacy education stage to inspire the intellectual property innovative ways of thinking for innovative talents, innovation ability training stage to improve the intellectual property practical skills for innovative talent, and professional knowledge development stage to improve the quality of intellectual property creation for innovative talented people. Under the guidance of the characteristics of demand for IPL in different educational stages, the content and the objectives of IPL education are formulated, the purpose is to comprehensively enhance students' awareness and ability of intellectual property innovation, which will lay a solid foundation for future technological innovation activities and intellectual property protection. [Results/Conclusions] The IPL of innovative talent plays a crucial role in the country's innovation-driven development. IPL education in the training of innovative talent in universities is a systematic project that requires constant practice and reform in university libraries, only then can we build an IPL education system that is compatible with the concepts of cultivating innovative talent and innovation and entrepreneurship education. We also made some suggestions on IPL education in the cultivation of innovative talent,including strengthening the construction of talent team in IPL education, emphasizing the demand of personalized talent on IPL education, focusing on the practical education of IPL for innovative talents and strengthening the cooperation between university libraries and innovation and entrepreneurship departments. In addition, we should inject more effective vitality into the training of innovative talent in colleges and universities.