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

[Purpose/Significance] In 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] 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] 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] 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] 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 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] 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] 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] 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] In the rapidly evolving digital landscape, generative artificial intelligence (GenAI) has emerged as a transformative force in information literacy (IL) education, presenting unprecedented opportunities and challenges for library-based learning environments. This scoping review comprehensively examines the integration of GenAI within IL education, moving beyond theoretical frameworks to provide a nuanced analysis of practical applications and strategic implementations. In contrast to existing research that primarily emphasizes technological capabilities, this study explores the profound implications of GenAI on educational paradigms and provides critical insights into the systematic transformation of library IL services in the AI era. [Methods/Process] Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, 51 key literature sources selected from the SSCI, A\&HCI, and CSSCI databases were systematically analyzed. The comprehensive analytical framework encompassed four key dimensions: technology acceptance, educational framework construction, AI literacy cultivation, and the integration of artificial intelligence with IL education. This methodological approach enabled a thorough exploration of current practices while identifying critical gaps in existing research. [Results/Conclusions] The results show that GenAI significantly enhances IL education through personalized learning experiences and improved digital teaching effectiveness. Tools such as ChatGPT have significant potential to promote adaptive learning environments and improve student engagement. The research identifies four primary areas of impact: 1) creating dynamically adaptive learning environments tailored to individual needs, 2) enhancing critical thinking through interactive scenarios, 3) facilitating cross-disciplinary knowledge integration, and 4) generating innovative educational content and resources. However, the study also identifies several critical challenges, including concerns about data accuracy concerns, inherent algorithmic biases, risks to academic integrity, and the potential weakening of independent thinking skills due to over-reliance on AI systems. To address these challenges, the research proposes a comprehensive framework that includes: 1) robust ethical guidelines for the implementation of GenAI, 2) systematic assessment mechanisms to monitor learning outcomes, 3) critical thinking training programs, and 4) strategies to maintain academic integrity and intellectual autonomy. The study emphasizes that the integration of GenAI is more than a technological change - it represents a fundamental shift towards AI literacy education. This evolution will require learners to develop skills beyond traditional IL skills, including understanding AI ethics, legal frameworks, and using AI technologies to solve problems. Future research directions should focus on conducting empirical studies in different educational contexts, developing adaptive teaching frameworks that balance technological innovation with traditional educational values, and investigating the long-term impact of GenAI integration on learning outcomes. By systematically examining the opportunities, challenges, and development trajectories of generative AI, this study provides valuable insights for libraries and educational institutions seeking to optimize their IL programs in the AI era. The findings not only contribute to the theoretical understanding of the role of GenAI in education, but also provdie practical guidance for integrating advanced technologies into traditional educational frameworks, ultimately fostering a more adaptive, intelligent, and personalized learning ecosystem.

[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] 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] 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 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] To explore in depth the acceptance and usage habits of AIGC tools by graduate students in the process of academic research, and to promote the positive attention and application of emerging technologies by graduate students is one of the goals of library knowledge service and information literacy education. This paper aims to reveal the influence mechanism of internal and external factors on the use of AIGC tools by graduate students at the user level, clarify the behavioral motivation of graduate students to use AIGC tools to support learning and research, help libraries to design and promote AIGC services according to the actual situation, and promote the implementation of AIGC technology in knowledge services. [Method/Process] Based on the UTAUT2 model, considering related theories such as perceived value and the characteristics of AIGC tool and graduate student group, this study constructed the influencing factor model of graduate students' AIGC tool use behavior, and provided empirical evidence through questionnaire survey and structural equation model analysis. The survey respondents are graduate students in universities or research institutes. In this study, questionnaires were distributed to graduate students through social media platforms, enterprise Wechat contacts, email, etc., and the survey period was from July to August 2024. After the data collection, statistical software such as SPSS and SmartPLS was used to analyze all the valid data obtained, including descriptive statistics, reliability and validity test and structural equation model analysis. [Results/ [Conclusions] Functional value, use value and emotional value in the tool aspect, individual innovation in individual aspect and social influence in environmental aspect have significant positive effects on graduate students' willingness to use AIGC tools, and indirectly affect their use behavior. Facilitating conditions, such as network equipment, as supporting factors, also have a significant positive impact on graduate students' usage. It is suggested that AIGC tool developers and library service designers consider the functional advantages and convenience. On the one hand, it is suggested that they pay attention to the functional value of the tool, that is, the auxiliary role to the graduate study and scientific research; on the other hand, they consider whether the tool is design-friendly, easy to operate, with low technical threshold and easy to use on an ongoing basis. From a graduate education perspective, it is important to promote the deep integration of the tool use with one's own professional learning and research in order to realize the improvement of other qualities through information literacy. Meanwhile, strengthening students' innovative thinking and comprehensive ability training, and guiding AIGC tool application ability and scientific research thinking to promote each other are conducive to new technologies to truly support learning and scientific research, and ultimately achieve the goal of developing high-level innovative talents.

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

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

[Purpose/Significance] 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] 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] The generative natural language processing model represented by ChatGPT is beginning to show great application potential in libraries, and its technical advantages coincide with the development needs of knowledge services, greatly improving the quality and efficiency of user services. [Method/Process] Starting with the introduction of ChatGPT's development history, technical advantages and theoretical and practical research achievements of Chinese and foreign academic circles, this paper explains its technical advantages in cross-modal information organization, text generation, and in-depth mining of user behavior. The most direct use of ChatGPT for a library is to connect the library's collection resources to ChatGPTAPI. Using machine transformer, human feedback reinforcement learning and other technologies to create its own open source chat machine model with intelligent interactive question and answer, text and image multi-mode generation, semantic search and discrimination and other functions, the application scenario covers a range of areas from basic library information services to intelligent knowledge services. During the consultation, users can use natural language to communicate directly with the model, and ChatGPT uses semantic analysis and pre-training models to fine-tune the language environment to provide a more accurate question and answer service in different contexts. During a search, the multi-modal technology of ChatGPT can fully realize the multi-source heterogeneous data input of information resources inside and outside the library, so as to effectively solve the problem of multi-dimensional, multi-level and multi-source cross-mode "heterogeneous aggregation" in information retrieval, and help search engines find more comprehensive search results. In addition, ChatGPT automatically generates subject resources such as abstracts or reviews that are highly relevant to the knowledge content of the user's ongoing conversation. By accurately capturing and analyzing the profile characteristics of users' interests and hobbies, ChatGPT can recommend personalized subject guidance services to them based on knowledge graphs, and quickly realize effective collection, refinement and analysis of knowledge related to required subject areas. At the same time, the application focuses on the risks and challenges posed by technical limitations, intellectual property rights, user privacy, harmful information, data sources, academic integrity and other aspects. [Results/Conclusions] In the future, ChatGPT will be embedded in knowledge services with a new quality of productivity, but it is necessary to recognize the limitations and security risks of this technology. At this stage, libraries should take a series of measures in advance to integrate business platforms and resources, strengthen internal system security prevention, improve the risk supervision mechanism and enhance the professional quality of librarians to fully cope with the crisis. It also provides a new research focus for libraries to actively build their own language interaction model in the future. Given the limited practical experience of ChatGPT in the library knowledge service, this paper only provides a risk analysis and prediction in the application, and the specific implementation path and rules in the future need further study.

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

[Purpose/Significance] At present, we are in a strategic development period of integrated development of digital rural areas and data elements. The government has introduced a series of policies to promote the deep integration and development of digital technology and rural areas, and promote the release of data value. The continuous improvement of rural digital capabilities and data resource systems, as well as the continuous improvement of data element policy system and the value transformation paths, provide unlimited opportunities for unlocking the value of agricultural data. The purpose of this study is to further study and explore the model of enabling "data elements X" in modern agricultural innovation, summarize and extract the model of empowering high-quality development of the agricultural industry with data elements, and explore the vertical application of data elements in the agricultural field, which is of great significance for breaking through the bottleneck of agricultural industry development with the help of data elements. [Method/Process] The research method of this article is based on the review of data elements, digital rural areas, agricultural data resource policy system, and integrated development trends released by the country in recent years. It has used policy analysis, comparative analysis, model analysis, case study and other research methods. The theoretical basis mainly comes from government official policy documents, and the comparative analysis and model analysis mainly rely on industry experience in practical work. The case study mainly takes the Funan Digital Rural Project and Guangxi Pig Data Authorization Operation Project as examples to deeply analyze the practical application of the DOD mode in the special debt of digital rural data assets and agricultural industry data authorization operation, and conducts in-depth analysis and discussion based on the current situation of the industry. This study combines theory and practice. [Results/Conclusions] First, an innovative digital rural construction and operation integration model (DOD) guided by value-added elements is proposed. The connotation and significance of this model are analyzed, and a model for the operation and benefits of this model is further proposed. Two representative cases are used to deeply analyze the practical application of the DOD model. Finally, based on the case practice and the problems faced, targeted work suggestions are proposed to provide reference for local governments to flexibly use the DOD model, explore the use of value-added data elements to support digital rural construction, and empower agricultural industry development.

[Purpose/Significance] Against the backdrop of the increasing popularity of artificial intelligence technology, particularly large language models, this paper aims to explore their applications in the field of digital humanities, with a particular focus on the retrieval of ancient documents. Through the practice and exploration of the ancient document retrieval platform at Peking University Library, this study not only introduces new perspectives and methods to the field of digital humanities, but also promotes academic research and cultural heritage. It also provides practical references for other university libraries, which is an important guide. [Method/Process] The article begins with an overview of the origins and development of the digital humanities, emphasizing its central role in humanities research. The paper then examines the current state of the arts in large language models and analyzes their potential and advantages for identifying and classifying ancient documents, semantic understanding and parsing, and information extraction and association. Through the analysis of practical case studies, this paper constructs a fundamental semantic retrieval model, the core architecture of which consists of two critical components. First, the construction phase of the retrieval engine involves meticulous pre-processing of the ancient document information to generate basic metadata. Using large-scale models, these metadata are subjected to in-depth processing and enhancement to create auxiliary search fields and enriched text. In addition, the text processed by the model and the original text are transformed into semantic vectors, which are then stored in an efficient vector engine for rapid retrieval. Second, the search and sort component is another core part of the model. This part accurately processes the user's search terms through large models to generate extended content and, in conjunction with the search terms, creates accurate semantic vectors. Utilizing the previously constructed vector engine, the model can efficiently retrieve relevant documents and intelligently sort the search results based on specific algorithms, ensuring that users can quickly obtain the most relevant and valuable information. Taking the ancient document system collection data of Peking University Library as the research object, the paper processes over 250,000 records, primarily consisting of ancient books and rubbings, as well as over 10 million metadata items. Using the gradio framework on a server equipped with two NVIDIA RTX 4090 24G graphics cards, a semantic retrieval platform was created to effectively manage and retrieve these vast amounts of data. [Results/Conclusions] The main strengths and contributions of the study lie in the standardized metadata organization, the metadata extension supported by large models, the support for natural language search terms, the fault-tolerant search mechanisms, and the efficient retrieval capabilities of the vector engine. However, there are shortcomings, such as the accuracy of results generated by large models and insufficient comprehensive analysis of user search data. Future efforts will be devoted to improving these issues to increase the effectiveness of the research.

[Purpose/Significance] Artificial intelligence is empowering scientific research and has become a major driver of scientific discovery. High-quality data resources for thematic scenarios are the key to training high-performance AI models. Given the complexity of scientific and technological (S&T) literature data and the limitations of its direct use for large-scale model training, there is a urgent need to build a systematic data construction technology framework to process, refine and curate S&T literature resources, and ultimately build a high-quality training corpus for AI applications. Some experts have conducted a number of studies, but there is still a lack of research on S&T literature AI data system for thematic scenarios. [Method/Process] This article proposes a "3+5 technical framework" plan for the construction of an AI data system for themed scenarios. Focusing on the whole process of AI data system construction, it refined and designed three levels of data content and five stages of data governance. The three-level data structure inclueds the multi-type basic database, the multi-model deconstruction database and fine-grained semantic mining knowledge base. The five-level construction stages are multi-channel data source scanning, multi-type basic data construction, multi-modal deconstruction data construction, fine-grained semantic mining knowledge construction and multi-scenario data application. Taking big data technology and intelligent mining technology as the key elements of data governance, the system architecture and functions of the data governance tool chain are described in detail. The core components of the tool chain are multi-source data aggregation tool, multi-format data parsing tool, data cleaning tool, associated file identification and acquisition tool, data fusion tool, multi-modal deconstruction and reorganization tool, and fine-grained knowledge identification tool. Working together, these tools ensure the efficiency and integrity of the design process from raw data to the AI data system. [Results/Conclusions] To verify the effectiveness of the proposed technical framework, this study has built a knowledge base in the field of rice breeding. The AI data system for thematic scenario of rice intelligent breeding includes a multi-type basic knowledge layer, a multi-modal deconstruction and recombination knowledge layer and a fine-grained semantic mining knowledge layer. The basic knowledge layer includes general scientific papers and patent data; the multi-modal knowledge layer includes the multi-modal data deconstruction of the paper content; the domain semantic mining knowledge layer focuses on the professional knowledge in rice intelligent breeding, such as rice variety validation data, phenotypic characteristics data, and rice lineage network. The results showed that the framework can effectively process S&T literature data and build a high-quality domain knowledge base, providing data support for the application of AI models in rice breeding research, verifying the effectiveness and practicality of the framework.

[Purpose/Significance] Rural public digital cultural service is a new module of public cultural service, and the current relevant research mainly starts from two independent perspectives, namely external institutional structure and internal subjective action, and constructs the research paradigm of "system-behavior". However, this framework still has room for improvement in order to reveal the phenomenon of "technical alienation" in practice. "Technological alienation" is essentially a deviation from the value level, and the original framework is difficult to effectively reveal the influence of value on the impact of digital technology on rural public cultural services, so this study introduces the value factor to provide new theoretical perspectives and analytical frameworks for the existing research, in order to comprehensively reveal the underlying logic of "technological alienation" and to find the path of digital technology-enabled rural public cultural services. [Method/Process] Starting from the current situation of rural public digital cultural services, this study constructs a three-dimensional analysis framework of "value-institution-behavior" and applies the extended case study method to the case of Y Township in Zhejiang Province to explore the phenomenon of "technological alienation" of rural public digital cultural services, such as the de-domainization of public cultural service content, information risk aggregation, and "digital authoritarian" production, which is caused by the fragmented value, institutional imbalance, and unruly action. This "technological alienation" has seriously affected the effectiveness of public digital cultural services. [Results/Conclusions] According to the specific elements of the three-dimensional analysis framework of "values-institutions-behavior" alignment, combined with the extended case study method focusing on the study of macro-micro interactions, it is proposed that the people-centered value concept guides the goal of building rural public digital cultural services, a more complete institutional system protects the process of building rural public digital cultural services, and a more standardized subjective behavior shares the results of building rural public digital cultural services. Based on the above paths, the "technical alienation" of rural public digital cultural services will be abandoned. This study still has shortcomings in the use of research methods in this field, while the framework of "value-institution-behavior" has some explanatory power, but still differs from the complex reality of the society due to the lack of empirical material. In the future, we plan to increase the empirical research on rural public digital cultural services to verify the conclusions of this study and to further refine the measurement of the level of rural public digital cultural services.

[Purpose/Significance] In the context of digital rural development, farmers' digital literacy is greatly influenced by both intrinsic motivations and external influences. Cultivating digital literacy is of great significance in accelerating farmers' integration into the digital process, promoting the construction of digital villages, and facilitating the revitalization of rural talent. As an innovative mechanism for farmer education, the synergistic cultivation of farmers' digital literacy is poised to emerge as a critical research focus in the revitalization of rural talent. [Method/Process] Drawing from activity theory, the static components of this synergistic cultivation - consisting of the interrelated elements of goals, core aspects, and mediating factors - offer a nuanced perspective on the intricacies involved in enhancing farmers' digital literacy. The framework includes production, communication, sharing, and collaboration subsystems that together form a dynamic structure for cultivating farmers' digital literacy. By examining both static activity elements and dynamic operational systems, we conducted an in-depth analysis of the challenges encountered within each subsystem during collaborative cultivation efforts. [Results/Conclusions] We recommend the optimization strategies such as refining the use of tools, improving the regulatory framework, maximizing the potential for collaboration, and establishing sustainable mechanisms. The farmers' digital literacy collaborative cultivation activity model constructed in this paper is based on the background of digital rural construction and high-quality farmer cultivation. With the agricultural broadcasting school (center) as the core subject, it interacts and cooperates with other communities, and through the online digital platform and offline regular practice tools, it realizes the embedding of tool use, the improvement of the rule system, the long-term cooperation of activities, and the close participation and co-construction through the interaction and coordination of the four subsystems of production, sharing, communication and cooperation. It is closer to the digital scene requirements of farmers' cultivation. Moreover, few studies have applied activity theory to the cultivation activities of farmers' digital literacy, and analyzed the interaction and coordination process among elements from a dynamic and holistic perspective. The proposed framework for synergistically cultivating farmers' digital literacy not only provides detailed insights into educational practices, but also offers theoretical foundations for improving practical pathways in this area. The limitation of this article lies in the fact that the collaborative mechanisms and existing problems among the subsystems in the collaborative cultivation model of farmers' digital literacy have not been systematically sorted out. In response to the above limitations, continuous research based on the integration of different perspectives and methods will be conducted on the hot topic of cultivating farmers' digital literacy, which is "always in progress".

[Purpose/Significance] A thorough understanding of algorithm aversion among social media users, encompassing its manifestations and underlying causes, is crucial in the algorithmic era. This understanding serves as the cornerstone for accurately capturing users' information needs and preferences, which are constantly evolving due to technological advances and changes in societal behaviors. By studying how users perceive, interact with, and respond to algorithmic recommendations and personalizations, researchers can gain insight into the effectiveness and limitations of current algorithmic technologies. These insights are invaluable for improving and optimizing algorithms to ensure that they not only meet user expectations, but also enhance their overall experience and satisfaction. Moreover, understanding algorithm aversion can help design more ethical and transparent algorithms, foster trust between users and technology, and ultimately promote the sustainable development of the digital economy. In addition, this research has broader implications for the fields of human-computer interaction, artificial intelligence, and social media studies. By exploring the psychological, social, and cultural factors that influence users' attitudes and behaviors towards algorithms, researchers can contribute to the development of more user-centered and socially responsible technologies. This, in turn, can lead to more inclusive and equitable digital environments, where everyone can benefit from the advances of technology. [Method/Process] This study employed a qualitative research approach, which is well suited for exploring complex and nuanced phenomena such as algorithm aversion among social media users. Qualitative research allows for the collection of rich, detailed, and contextually embedded data, enabling a deeper understanding of the subject matter. To accomplish this, the study included in-depth interviews with 26 respondents, who were selected for their active use of social media and their diverse experiences and perspectives on algorithmic recommendations and personalizations. The interviews were conducted using a semi-structured format that allowed for flexibility in the conversation while still addressing key research questions and themes. This approach allowed the researchers to gain detailed insights into the participants' attitudes, beliefs, and experiences with algorithms, as well as their perceptions of the consequences of algorithm aversion. A rigorous coding process was used to analyze the collected data. This involved breaking down the textual data into smaller, manageable units, or codes, which were then categorized and grouped based on common themes and patterns. The coding analysis focused on three main areas: the expression of algorithm aversion, the formation mechanisms of algorithm aversion, and the consequences of algorithm aversion for social media users. Drawing on qualitative research paradigms, the analysis resulted in the construction of a theoretical model analysis framework specifically tailored to algorithm aversion among social media users. This framework provides a structured way to understand the complex interplay between users' attitudes, beliefs, and behaviors towards algorithms, and the factors that influence these attitudes and behaviors. The framework also highlights key consequences of algorithm aversion, such as reduced trust in social media platforms, decreased engagement with algorithmic recommendations, and potential negative impacts on user experience and satisfaction. [Results/Conclusions] The results reveal three distinct forms of algorithm aversion among social media users: algorithmic interruption, algorithmic complaint, and algorithmic evasion. These forms have significant implications for individuals, organizations, and society. Additionally, the study identifies personal factors, algorithmic technology factors, and social environment factors as key drivers of algorithm aversion. A comprehensive framework for analyzing the formation mechanism of algorithm aversion, based on the concept of "individual-algorithm-social environment," is extracted. Based on this framework, the study proposes research paths and coping strategies from three perspectives: theoretical research, technical research, and humanistic research. These recommendations aim to effectively address and mitigate algorithm aversion among social media users.

[Purpose/Significance] With the advancement of open science, the signing of transformative open access agreements will have a profound and long-term impact on the acquisition of library information resources. How libraries respond to this trend, how to evaluate open access conversion agreements, whether to sign reasonable open access conversion agreements, and actively adopt them suitable for China's domestic conditions, and scientific and effective collection acquisition and transformation strategies in an open publishing environment, how to ensure reasonable investment of institutional publishing funds, and how to play a role in promoting positive academic exchanges in an open scientific environment are all important issues that deserve industry attention and exploration. [Method/Process] We first reviewed the research progress on open access transformation at home and abroad. Second, based on the data statistics and analysis of DOAJ and related research reports and institutions, the relatiave concepts were elaborated, the impact on stakeholders was examined, and the trend of practical development was understood. Through the analysis and induction of practical cases in domestic and foreign university libraries, especially through the case analysis of the library of Shanghai Jiao Tong University, we elaborated on the issues that Chinese university libraries need to pay more attention to in the process of open access transformation, and proposes related strategies. [Results/Conclusions] To cope with the open access movement, on the one hand, we need to be vigilant against the emergence of new knowledge "barriers" and "paywalls" due to profit-driven, high APC, and transformative costs, as well as the lack of regulatory norms for author payments and the existence of financial risks and loopholes such as taxation. On the other hand, all parties adhere to the original intention of promoting the dissemination of academic knowledge, forming a transparent and reasonable APC price ecological market, establishing a hierarchical, reliable, and sustainable open publishing funding support, transformation, and regulation mechanism, and creating an economic and healthy academic information exchange environment. In this process, it is necessary for university libraries to comprehensively coordinate subscription fees and publishing fees, and fully guarantee and restructure knowledge exchange and information dissemination. This paper summarizes the three issues that need to be considered in the process of open access transformation, including the impact of open access on stakeholders, the phenomena that need to be monitored, and the key to ensuring sustainability. We proposed implementation strategies based on practical cases, including research and data preparation, analysis and evaluation, focusing on specific implementation points such as controlling elements of contract terms. The OA transformation is still in the transition period, and in order to avoid the loss of funds due to double payments, some overall management and guidance systems are needed during this period. For some publishers that prioritize commercial profit, it is necessary to send strong signals and strengthen supervision of APC pricing rationality through alliances and other levels. In addtion, we must strengthen China's leadership and discourse power in open science, and carry out the construction of related supporting systems.

[Purpose/Significance] American university libraries have accumulated rich experience in strategic planning with their advanced concepts and practices. This article analyzes the content and development trends of strategic planning in American university libraries from two dimensions, and provides an in-depth understanding of the key elements and future directions of strategic planning in university libraries. In the relevant knowledge system, it provides a new reference perspective for Chinese university libraries. Compared with previous studies, this study provides a more systematic analysis of the content and development trends of strategic planning in American university libraries. It helps to promote in-depth research on strategic planning in the field of library science. By addressing important major practical issues, specific ideas and methods can be provided for the strategic planning of university libraries in China. [Method/Process] This article uses online surveys and content analysis methods. The online survey method can widely collect information related to the strategic planning of American university libraries, which is efficient and convenient; the content analysis method can deeply analyze the main content and development trends, which is relatively systematic and objective. This article is based on the relevant theories of library strategic planning and academic research achievements in libraries. Through online surveys, it obtains strategic planning texts and other materials published by major libraries, and analyzes the actual strategic plans of prominent university libraries in the United States as an basis for research and analysis. [Results/Conclusions] The research suggests that the key elements of strategic planning for prominent university libraries in the United States can be summarized as strengthening collaboration and sharing, promoting world-class collections and services, understanding internal and external environmental factors, improving management levels, and enhancing technical service support. The main trends of its strategic planning development are the trends of service development, collection development, organizational development, and cooperative development. Finally, considerations for the strategic planning of university libraries in China are proposed. Suggestions are put forward for Chinese university libraries to strengthen cooperation, optimize collections, and improve service levels. The limitation of this study lies in its limited scope and lack of in-depth case studies. In the future, the scope of the research can be extended to more countries, in-depth case studies can be conducted, dynamic changes in the implementation of strategic planning can be tracked, and the future direction of university libraries in combination with the development of new technologies can be explored.

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