[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] 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] 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] 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] 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] 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] 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 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] The digital scholarship service ability of librarians is an essential guarantee for providing high-quality digital scholarship services. However, the current domestic research on improving librarians' digital scholarship service capability is mainly concentrated at the theoretical level, and there is a lack of librarians' skill training projects at the practical level. An in-depth analysis of practical projects implemented by foreign university libraries to develop librarians' digital scholarship service skills can provide some references and insights for domestic university libraries to re-skill their librarians at the operational level. [Method/Process] The Developing Librarian Pilot Training Project (DLPTP), implemented by the Digital Humanities Library Group at the University of Florida, has effectively increased librarians' skills and confidence in providing digital scholarship services with limited staff, funding, and space, and has stimulated the willingness and interest of more librarians to participate in digital scholarship services. Taking this project as the research object, this paper introduces its implementation experience from three aspects: implementation background, implementation characteristics and effectiveness evaluation based on the methods of literature research and network survey. This paper summarizes its main implementation characteristics from four aspects: 1) Conduct the top-level design of the training project in advance based on the feedback from team members. 2) Organize librarians to collaboratively develop the project charter, which includes detailed descriptions of the project scope, deliverables, outcomes, target audience, team member roles and responsibilities, timelines and constraints, communication methods, and deadlines. 3) Apply for special funds to cover the training costs of external experts. 4) Establish an independent space for teaching activities and project collaboration to encourage creativity and a playful atmosphere. [Results/Conclusions] Finally, it is suggested that domestic university libraries should focus on improving librarians' digital scholarship service ability from four aspects: 1) The library leadership should recognize the importance of digital scholarship services and incorporate them into the library's long-term development strategy, and advocate the concept of digital scholarship services from top to bottom. 2) The librarian competency training program should be designed from surface to depth. 3) The evaluation of librarian's training projects should be carried out from the surface to the essence, putting more emphasis on their learning process rather than the result.4) The library can first establish a digital scholarship interest group within the library, and then actively seek communication opportunities with external stakeholders such as campus departments, publishers, database vendors, and off-campus research institutions, thus building a digital scholarship practice community from the inside out. Due to the limited conditions, information about DLPTP can only be collected from literature and the Internet, which has certain limitations and needs to be improved in the future.

[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] 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] 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] 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] Virtual communities of interest have rapidly become key sources of information that significantly influence users' decision making. Characterized by resource aggregation, active exchanges, and high interactivity, these communities foster a unique environment that encourages strong user engagement. Understanding the factors that influence information adoption in these settings is essential to meeting user needs and enhancing community management and services. Unlike traditional information contexts, virtual communities emphasize user trust, emotional support, and community identity, which are critical in shaping how users perceive and adopt information. This study aims to deepen the theoretical understanding of information adoption in virtual communities of interest by incorporating information ecology theory and applying both structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). This dual-method approach enables in-depth analysis of individual factors and reveals complex configurations that influence adoption behaviors, providing insights that go beyond what SEM alone can provide. [Method/Process] The research model is based on information ecology theory, which provides a holistic framework that captures the dynamic interplay between factors such as information quality, user support systems, community structures, and platform features. This theory is particularly suited to the study of virtual communities, where multiple interdependent factors create a unique decision-making environment. SEM is used to assess linear relationships between variables, evaluating the influence of information quality, emotional support, community identity, opinion leader participation, content interaction, source credibility, and platform usability on users' information adoption intentions. As a complement to SEM, fsQCA is used to explore configurations of multiple factors, identify pathways through which these factors collectively shape adoption intentions, and capture complex causal relationships that SEM does not address. [Results/Conclusions] The SEM analysis shows that information quality, emotional support, community identity, active participation of opinion leaders, and content interaction significantly increase users' adoption intentions, while information source credibility and platform usability do not. These findings suggest that community-driven aspects may be more important to users in this context than traditional credibility indicators. The fsQCA results further identify two primary modes that drive adoption intentions: a trust-driven mode, where adoption is supported by trust-related factors, and an experience-promoting mode, which focuses on user engagement within the community. Together, these modes comprise six distinct configurations, suggesting that users' adoption intentions are influenced by combinations of factors rather than isolated variables. This study thus highlights the unique value of fsQCA in uncovering the complex interplay of factors in virtual communities and providing detailed insights into user behavior. Future research could explore cultural differences in adoption behaviors and additional factors influencing user engagement in different types of virtual communities.

[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 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] This study aims to analyze the mechanism of collaborative knowledge management capability on the innovation of new think tank smart services, clarify the internal driving force and main methods of providing innovative new think tank smart services, and uncover the internal role "black box" of innovation in new think tank smart services from the perspective of collaborative knowledge management, in order to enhance the innovative new think tank smart services. The contribution of the article lies in the construction of a model for understanding the role of collaborative knowledge management capability in providing the innovative new think tank smart services. The impact of collaborative knowledge management capability on the innovation of new think tank smart services is a complex dynamic process that requires continuous investment of multiple forces and energy. Through system dynamics simulation, it is possible to observe which key factors should be emphasized in long-term development, providing reference basis for organizational development. [Method/Process] This article takes knowledge ecology as the theoretical support, analyzes the composition of collaborative knowledge management capability and its mechanism of action on innovative smart services in new think tanks, and uses system dynamics methods to construct a model of the effect of collaborative knowledge management capability on innovative smart services in new think tanks. It simulates and analyzes the dynamic changes in the impact of each capability on innovative smart services, providing reference for the development of innovative smart services in new think tanks, simultaneously expanding the research perspective of new think tank smart services. [Results/Conclusions] The results show that collaborative knowledge ability, collaborative supporting ability and collaborative management ability have different effects on the smart service innovation of new think tanks, and collaborative knowledge ability is the key factor affecting the smart service innovation level of new think tanks. This article cannot cover all collaborative knowledge management capabilities in the classification of collaborative knowledge management capabilities. In the future research, it is necessary to explore their impact on the innovation of new think tank smart services from a more comprehensive and representative perspective.

[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.

[Purpose/Significance] The rapid development of generative artificial intelligence (GenAI) has led to a growing demand for AI literacy in various fields. However, current AI literacy courses often fail to adequately address the diverse needs of students with different academic backgrounds, expertise, and learning levels. This research aims to design an AI literacy curriculum that balances knowledge dissemination with skill development, ensuring that students can not only understand basic concepts but also apply them in practice. [Method/Process] This study is based on the design of Nanjing University's "Exploration of Frontier Applications of Generative Artificial Intelligence" course, which adopts the "knowledge-skills" navigation framework. The course is divided into four progressively advanced levels: foundational cognition, core understanding, tool application, and innovative development. The foundational cognition level systematically organizes the four key knowledge modules involved in generative artificial intelligence: Machine Learning, Neural Networks, Deep Learning, and Natural Language Processing, helping students to build an initial cognitive framework for GenAI. The core understanding level explores advanced topics in GenAI, covering four main modules: basic model pre-training, downstream task adaptation, human-AI value alignment, and AI agents. This aims to enhance students' comprehensive understanding of the technical principles, application methods, and ethical considerations, providing the necessary technical support and conceptual tools for real-world applications. The tool application level consists of three modules: classification of intelligent tools, tool acquisition and use, and derived applications. It gradually guides students from analyzing the characteristics of tools and their use to exploring state-of-the-art applications in multi-modal, multi-scenario, and integrated contexts. Finally, the innovative development level is the final practical stage of the GenAI learning system and includes environment configuration, basic processes, and frontier development. This includes configuration of hardware and software environments, basic steps for development tasks, and advanced practices for complex functions, forming a complete chain from basic support to high-end applications. Following the "knowledge-skills" navigation, the course will also include teaching designs such as concept cognition modules, multi-modal generation and application skill modules, advanced generative AI knowledge and skill modules, and generative AI governance modules, along with the development of corresponding online open courses, open educational resources, and experimental equipment resources. [Results/Conclusions] The "knowledge-skills" navigation framework effectively enhances students' AI literacy by successfully bridging the gap between theoretical knowledge and practical application. The modular structure of the course, combined with multi-modal learning and hands-on practice, effectively meets the diverse learning needs of students. The course allows students to gradually build a knowledge system from basic concepts to advanced skills, fostering a comprehensive understanding of AI technologies.

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

[Purpose/Significance] 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] Improving artificial intelligence (AI) literacy has emerged as a critical focus in global education, reflecting the growing significance of AI in today's society. This study aims to explore and interpret the core elements and key competencies articulated in the UNESCO AI Competency Framework for teachers and students, and to provide practical guidance for educators and policymakers, offering insights that can facilitate the systematic integration of AI literacy education. A comprehensive approach to AI education is needed to equip both students and teachers with the skills and knowledge necessary to navigate and thrive in an increasingly intelligent era. The results of this study are intended to support the formulation of effective pedagogical strategies, thereby contributing to the enhancement of AI literacy among educational stakeholders. [Method/Process] The study analyzes the preliminary policy foundations and background that led to the creation of the UNESCO AI Competency Framework. It analyzes the content of both the AI CFS and the AI CFT, focusing on key principles and framework structure to systematically interpret the framework's content. In particular, this study explores the policy context in which these frameworks were developed and examines how global educational goals and technological advances have influenced the articulation of AI competencies. By understanding the development and rationale behind the UNESCO AI Competency Framework, this study aims to provide a comprehensive overview that can support the development of effective AI literacy initiatives. It also highlights the connections between the intentions of the frameworks and the practical competencies required of educators and students, thereby contributing to a deeper understanding of how AI literacy can be meaningfully integrated into educational practice. [ [Results/Conclusions] Based on the experience provided by the competency framework and considering the current state of AI literacy education, this study offers insights and recommendations for developing AI literacy education in China from four perspectives: core values, policy refinement, practical application, and future implementation. Specifically, this study emphasizes that all educational stakeholders should work together to improve AI educational content and methods, and move toward a teacher-student-AI interaction model that empowers teachers, fosters student creativity, and integrates AI as a facilitator of personalized, flexible, and multidirectional learning. In terms of policy refinement, this study advocates for the creation of a supportive policy environment that addresses the unique challenges faced by educators and learners in the Chinese context. For practical application, the study provides actionable recommendations for integrating AI literacy into curricula, emphasizing project-based learning, hands-on experiences, and interdisciplinary approaches that foster a comprehensive understanding of AI concepts. Finally, in terms of future implementation, this study highlights the need for ongoing professional development for educators, such as the establishment of assessment mechanisms to monitor and evaluate the effectiveness of AI literacy programs over time.

[Purpose/Significance] In the context of the digital era and the construction of digital villages, digital literacy is crucial for farmers to contribute to rural revitalization and promote the structural transformation of the rural workforce. The defination of the concept and framework construction of farmers' digital literacy is an important basis for the assessment of farmers' digital literacy level, which is of great significance for the education and training of farmers' digital literacy, the promotion of farmers' quality development, and the revitalization of rural talented people. [Method/Process] This study uses the content analysis method to define the concept of farmers' digital literacy based on the perspective of "ability + quality". Farmers' digital literacy refers to digital rural construction, for instance, the ability to use intelligent equipment and digital technology to obtain, produce, use, evaluate, interact, share and innovate digital information, with security guarantee, ethics, and apply to rural life and production practice, so as to improve the quality and ability of digital income increase. Through comparative analysis, this paper reviews the typical and representative digital literacy frameworks at home and abroad, and uses Bloom's cognitive hierarchy theory to construct the farmers' digital literacy framework from the dimensional-level perspective, innovatively introducing farmers' professional skills as the first-level dimension, which is a new skill required to train new professional farmers and high-quality farmers. There is a need to introduce skills related to agricultural occupations. [Results/Conclusions] The framework includes 8 core dimensions of farmers' digital awareness, digital operation, digital information, digital social interaction, digital innovation, digital security, digital application and professional skills, as well as 21 secondary dimensions and several specific elements, including three levels of digital literacy: basic, intermediate and advanced. The basic level consists mainly of "awareness and understanding" and some simple operations with digital technology, tools and equipment. The intermediate level is mainly to be "able to use", which may vary according to individual goals and needs, and professional characteristics. The advanced level mainly refers to "active participation in practice", which can be innovative and practical in a specific area. In the continuum of basic, intermediate and advanced levels, farmers can self-assess against the framework level, providing a pathway for continuous learning and updating of literacy skills. The construction of farmers' digital literacy framework under the background of digital village construction can provide guidance for the formulation of relevant policies and evaluation scales, the updating of the cultivation system of high-quality farmers, and the improvement of farmers' digital literacy. Indeed, as a scientific exploration, the issue of farmers' digital literacy framework still has a huge space for exploration, and it needs to be evaluated by experts, improved from the perspective of farmers' subjects and pre-tested to verify its scientificity and applicability.

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

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