[Purpose/Significance] Unclassified sensitive information, a special type of information, that falls between state secrets and public information, may threaten national scientific and technological security if it is not managed properly. This study aims to clarify the conceptual connotation and management value of unclassified sensitive information. Additionally, it seeks to improve China's information security protection system by promoting the country's "national unclassified sensitive information management system". [Method/Process] First, based on literature research and content analysis, the characteristics and attributes of unclassified sensitive information and the necessity of its management are systematically explained. Second, the management experience of controlled unclassified information (CUI) in the United States is analyzed by means of case studies. These studies include the creation of special laws and regulations to unify the CUI management, the formation of specialized management institutions to create a complete organizational structure, the development of a CUI management system, and the establishment of CUI classification and labeling. Finally, it proposes strategies to promote the development of China's sensitive information management system, given the current state of management situation in China, with the aim of promoting the safe and efficient management of public information resources. [Results/Conclusions] China needs to strengthen the top-level design of its management system for unclassified sensitive information. First, China should establish a policy and standard system for managing unclassified sensitive information, and clarify the rules for defining and controlling it. Second, the organizational and institutional structure should be improved for the management of unclassified sensitive information, and strengthening the review of information disclosure. Third, efforts should be made to develop a management system for registering directories and labels of sensitive information, and to realize standardized and dynamic management of directories of sensitive information. Fourth, the dissemination, use, training and management check should be strengthened, and thus long-term and healthy management of unclassified sensitive information can be promoted. Fifth, a registration directory and labeling management system for sensitive information should be developed so as to achieve standardized and dynamic management of the sensitive information directory. Finally, efforts should be made to strengthen the dissemination and use of sensitive information, as well as training and management inspections, so as to promote the long-term and healthy management of unclassified sensitive information.

[Purpose/Significance] Generative artificial intelligence (GAI) is currently advancing at an astonishing pace. GAI has unleashed remarkable potential in various fields and is significantly fueling social and economic development. However, this rapid progress has also given rise to a plethora of complex issues, including but not limited to data security breaches, privacy violations, the spread of false information, and intellectual property infringements. Existing research primarily focuses on the governance of AI in general, leaving a gap in in-depth exploration of GAI. This study aims to fill this void by meticulously comparing the governance approaches of Europe and the United States in the realm of GAI. Through this comparison, the study aims to provide valuable insights for China to refine its own governance system. This is not only crucial for China's domestic technological development and social stability but also plays a pivotal role in promoting the harmonization of the global governance framework for GAI. [Method/Process] This research adopts a multi-faceted approach. It commences with a comprehensive review of relevant literature, gathering insights from a wide range of academic sources to understand the current state-of-the-art in GAI governance in Europe and the United States. Additionally, it deploys the case-study method, examining real-world examples such as the development of OpenAI's GPT series in the US and the implementation of the EU's AI Act. By analyzing these cases, it can vividly illustrate the practical implications and impacts of different governance strategies, thus enabling a more in-depth and accurate comparison. [Results/Conclusions] We found that the European Union adopts a regulatory path centered on data protection and ensures the fairness and sustainability of technological development through a strict legal framework. However, this strong regulatory model may stifle innovation vitality to some extent. The United States adopts a governance model oriented towards market accountability, emphasizing technological innovation leadership and free development. It stimulates market vitality through industry self-discipline and flexible regulation, but there is a hidden danger of insufficient ethical risk control. Based on these findings, this paper recommends that China adopt a balanced approach. China should integrate elements of both the U.S. and E.U. models to foster innovation while ensuring ethical and legal compliance. Future research could explore ways to adapt these governance models to emerging trends such as integrating GAI with other emerging technologies and addressing the unique governance challenges posed by cross-border data flows.

[Purpose/Significance] Against the backdrop of an accelerating population aging trend, the integration of big data and intelligent services in multimodal healthcare and eldercare has become pivotal for enhancing the quality of medical and eldercare services. However, existing knowledge service systems for big data in healthcare and eldercare face challenges such as difficulty of integrating multi-source heterogeneous data, the absence of cross-organizational sharing mechanisms, and passive service models. [Method/Process] First, a cross-domain aggregation method is proposed for multi-source heterogeneous medicare big data, including: 1) A method for constructing a clinical, key-feature-based medical case knowledge database. It extracts and categorizes critical features from electronic medical records using natural language processing (NLP). 2) A natural language processing-based cross-domain disease risk factor mining framework. It identifies risk factors from social media via topic-enhanced word embeddings and clustering techniques. 3) An adaptive pointer-constrained generation method for medical text-to-table tasks. It leverages the BART architecture to transform unstructured medical text into structured tables. Next, a knowledge discovery method based on multimodal medicare big data is developed, including: 1) A medical decision support approach integrating case-based reasoning (CBR) and explainable machine learning. It aims to enhance diagnostic interpretability through ensemble learning and case similarity analysis. 2) A large-scale medical model-driven knowledge system. It utilizes multimodal data pretraining and domain adaptation to support the entire diagnosis-treatment process. 3) A personalized recommendation method based on temporal warning signals, generating precise intervention plans via collaborative filtering and dynamic updates. Finally, a smart service model for full-cycle evolving needs is constructed, including: 1) A health information supply-demand consistency matching framework combining deep learning and clustering techniques; 2) A multi-level, cross-scenario health demand and behavior dynamic modeling approach. [Results/Conclusions] The proposed methodological framework significantly improves the efficiency with which medicare big data is integrated and the capabilities of its knowledge services. Key outcomes include: 1) Enabling disease risk prediction and personalized interventions through deep integration of cross-organizational, cross-scenario medicare data via multimodal aggregation and semantic alignment. 2) The CBR-ECC model and WiNGPT large medical models enhance the interpretability and full-process coverage of medical decision-making. These models improve the accuracy of diagnoses made by primary care physicians by over 30%. 3) The temporal warning-based recommendation method increases the dynamic update efficiency of health interventions by 40% and user satisfaction by 25%; 4) Dynamic health demand modeling reveals core pain points for chronic disease patients, providing a basis for precision service strategies. This research provides the theoretical and technical support for developing a proactive health service system that is both data-driven and human-machine collaborative. This system will, advance the implementation of the Healthy China strategy and innovation in aging population governance.

[Purpose/Significance] The advent of the digital era has presented China with significant opportunities to modernize rural governance. Digital literacy is crucial for enabling farmers to participate in rural governance and promote the development of digital villages. Rural residents are direct participants in digital village development, and their digital competence fundamentally determines the modernization level of rural governance. Their proficiency in digital skills affects not only the effectiveness of intelligent rural management, but also serves as a key indicator for measuring the progress of digital rural development. [Method/Process] Based on a literature review and sociopolitical reports, this study combines research objectives and thematic focus to create a questionnaire that investigates farmers' digital competence and rural governance performance. A stratified sampling strategy was implemented to select 306 rural households from diversified villages across township and sub-district jurisdictions, in Xia County, Shanxi Province, for the case study. The dataset encompasses respondents' demographic attributes, familial characteristics, digital proficiency metrics, and multidimensional indicators of rural governance efficiency and capacity building. Through integrated application of exploratory factor analysis (EFA) and multivariate linear regression modeling, this investigation systematically examines the determinants through which digital literacy influences governance outcomes. Together, these approaches establish a theoretical framework and evidence-based pathways for enhancing rural digital transformation initiatives. [Results/Conclusions] The empirical analysis results indicate that farmers' digital literacy significantly impacts rural governance efficacy. Specifically, improvements in the four dimensions of digital literacy - digital awareness, digital skills, digital application, and digital security - positively influence the efficacy of rural governance.In other words, the higher the digital literacy level of farmers, the greater the enhancement in rural governance efficacy. Among these, digital security literacy has the most significant effect on improving governance efficacy. Next is digital application literacy, followed by digital awareness literacy. Digital skill literacy exhibits a relatively weaker impact. Given the significant positive influence of digital literacy on rural governance efficacy, this paper proposes recommendations from three perspectives: strengthening farmers' proactive awareness of digital literacy, enhancing their knowledge in digital literacy, and improving the digital infrastructure construction in rural areas. These suggestions provide practical references for the digital development of rural governance in Xia County, Shanxi Province. Due to various constraints, however, the study only examined Xia County in Shanxi Province as the research area, resulting in notable geographical limitations in the sample. This is because rural regions in different areas exhibit significant disparities in economic development levels, cultural traditions, and policy support, all of which may affect farmers' digital literacy and the efficacy of rural governance. Consequently, the conclusions of this study may not accurately reflect the actual conditions in rural areas across diverse regions. To improve the generalizability of the findings, future research should expand the sample selection to include more representative areas.

[Purpose/Significance] With the rapid advancement of the information society and the ongoing construction of smart cities, public libraries are facing increasing pressure to transition into a smart service model. Smart services leverage cutting-edge technologies to enhance user experiences and improve the efficiency of library services. Public libraries in China, however, are encountering challenges such as mismatched service offerings, unsatisfactory user experiences, and inadequate technological implementation as they move toward a smart service model. It is crucial to identify how to optimize this transition in a manner that prioritizes user needs, ensuring that smart library services meet the demands of a diverse user base. This research aims to explore the dynamic relationships among users, technology, content, and the service environment in public library smart services, thereby promoting innovation and addressing diverse user requirements. [Method/Process] The study develops a model to analyze the influence of various factors on the user experience of smart services in public libraries. Adopting Actor-Network Theory (ANT) constructs an integrated framework for understanding the interactions between various actors in the smart service ecosystem. By combining both online and offline surveys, the research captures library users' perceptions of their smart service experiences and identifies the critical factors that influence user experience and provides valuable data support and strategic recommendations for optimizing smart library services.Principal component analysis is used to identify the key factors affecting user experiences. [Results/Conclusions] The findings show that the core factors influencing user experience in smart services include: "advanced technology support," "network compatibility and flexibility," and "convenient communication channels" within the "library technology actors" dimension; "usability, operability, clarity, and comfort of portal browsing" within the "interaction between human-technology actors" dimension; "effort expectancy, information literacy, and time-energy consumption" within the "user (human actor)" dimension; "professionalism and competence of library staff" within the "librarian (human actor)" dimension; and "social influence and facilitating conditions" within the "interaction between human actors" dimension. These factors have a positive impact on the user experience, with particular attention required for the factors related to the "technology actors" dimension. Libraries need to focus on improving three factors in this area while maintaining and further optimizing the other factors. "Human-technology interaction" activities are crucial in improving the usability and user-friendliness of smart services, especially in more complex technological settings. Social influence and enabling conditions play an important role in enhancing user trust and their overall experience. Based on the empirical findings, the study proposes optimization strategies for public library smart services from three dimensions: the technical actors of the smart service system, the "human-technology actors" interactions, and the "translation" activities among human actors. These strategies include enhancing multi-dimensional collaboration among technical actors in the smart service system, improving the sensory experience of users with the smart service terminals in libraries by increasing their ease of use, empowering digital literacy, and optimizing innovation spaces to drive bidirectional reader participation. The aim is to provide a specific guide for the design and optimization of smart library services.

[Purpose/Significance] Integrating culture and tourism is key to promoting rural revitalization. Constructing a scientific evaluation system and exploring differentiated development paths are core issues for achieving rural cultural and tourism development. [Method/Process] Taking 26 mountainous counties in Zhejiang Province as the research object, a research framework incorporating multi-dimensional analysis methods was constructed based on user-generated content data from tourism platforms. First, travel journal texts were collected to build a cultural and tourism integration database. Second, the BERTopic model was used to identify the potential thematic elements in tourists' narratives. Third, sentiment analysis was applied to quantify the emotional value of different themes in each county. Finally, fuzzy-set qualitative comparative analysis (fsQCA) was employed to reveal the complex development paths of cultural and tourism integration. [Results/Conclusions] 1) Through global topic modeling, eight themes of cultural and tourism integration were identified in 26 mountainous counties in Zhejiang Province, including ecological landscapes, traditional settlements, food culture, transportation, cultural activities, cultural heritage and arts, accommodation facilities, and leisure industries. These eight themes are summarized into four conceptual categories of cultural and tourism integration: natural experience dimension, cultural experience dimension, service support dimension, and leisure consumption dimension. 2) Tourists generally hold a positive attitude towards cultural and tourism experiences in different counties. The natural and cultural experience dimensions are highly regarded, but the service support dimension shows uneven levels, and the leisure consumption dimension displays significant differences, hindering the improvement of the quality of cultural and tourism integration in mountainous counties. Ecological landscapes and traditional settlements constitute the core layer of tourism experiences, while accommodation and leisure business formats, as potential directions, still require further development and activation. The comprehensive performance of cultural and tourism integration in various counties presents a notable spatial pattern of "two poles in the north and south, a developing middle region, and relatively weak coastal areas". 3) Through configurational path analysis, the development paths of cultural and tourism integration in the 26 mountainous counties can be summarized into six configuration paths, including the "ecological landscape + traditional settlements + food culture + transportation" model, the "cultural heritage and arts + leisure industries + food culture" model, the "traditional settlements + food culture" model, the "ecological landscape + cultural activities + transportation" model, the "ecological landscape + cultural heritage and arts + leisure industries + food culture" model, and the "cultural heritage and arts + leisure industries + food culture" model. These paths differ significantly in their combinations of core elements, reflecting the differentiated development strategies of different regions in terms of resource endowment, cultural characteristics, and market positioning. Five of the paths highlight food culture as a core condition, reflecting its foundational role in cultural and tourism integration. Cultural heritage and arts and leisure industries jointly form the core conditions in three successful paths, highlighting the importance of combining in-depth cultural experiences with leisure activities. Accommodation facilities often appear as missing or marginal conditions, indicating that short-term stays are the main form of rural cultural and tourism.

[Purpose/Significance] With the growing demand for intelligent cultural services, libraries are seeking innovative approaches to enhance access to and engagement with historical literature. Generative AI presents promising opportunities for transforming digital reading services, particularly in processing, interpreting, and promoting complex historical documents. This study investigates the integration of generative AI into library-based historical literature promotion, aiming to address persistent access limitations, foster more interactive user experiences, and optimize the depth and breadth of reading engagement. [Method/Process] This research adopts a multi-method approach combining literature review, comparative platform observation, and empirical implementation practice. The study focuses primarily on Shanghai Library's historical digital collections and AI-enabled services. It develops a structured three-layered implementation framework encompassing the data layer, application layer, and service layer-each mapped to corresponding technical and operational phases of digital reading promotion. Within this architecture, a six-step service pathway is articulated: demand analysis, activity planning, content mining, multimodal interaction, content review, and intelligent recommendation. Extensive practical experimentation is conducted across these stages. Key innovations include the application of Retrieval-Augmented Generation (RAG) to support complex historical document Q&A; the use of multimodal creative tools (e.g., Midjourney) to generate engaging visual materials; implementation of voice-based AI interactions to improve accessibility for diverse user groups; and the deployment of dynamic content management modules for librarians to curate and monitor AI-generated materials. Additionally, backend tools such as user profiling dashboards, personalized push notification systems, and topic-based knowledge repositories are developed and tested to enhance librarians' ability to deliver targeted and data-driven reading promotions. [Results/Conclusions] The findings demonstrate that generative AI significantly enhances the efficiency, precision, and user engagement levels of historical literature services. AI-driven methods substantially improve OCR accuracy, streamline metadata generation, facilitate both visual and semantic content creation, and enable real-time interactive services via natural language interfaces. These advancements contribute to a more immersive and responsive digital reading experience. However, several challenges persist, including limited availability of domain-specific training data, the ongoing risk of AI-generated content inaccuracies (hallucinations), and unresolved intellectual property considerations. The study emphasizes the importance of developing domain-specific large language models, establishing expert-assisted validation mechanisms, and formulating clear legal and ethical guidelines for AI-generated content in the library context. While the prototype platform developed in this research exhibits notable gains in user engagement and librarian workflow support, its long-term sustainability hinges on fostering cross-institutional resource collaboration, advancing supportive policy frameworks, and embedding robust ethical safeguards. Future research directions include the exploration of adaptive AI training systems incorporating user feedback loops, integration of cross-library data resources, and the enhancement of multilingual AI capabilities to better serve diverse and global user communities.

[Purpose/Significance] The confluence of digital transformation and the Fourth Industrial Revolution has driven the emergence of data science as an interdisciplinary field. Data science leverages structured and unstructured data to discover knowledge and support decision-making, thereby reshaping research paradigms in information science, computer science, and the social sciences. This study focuses on the development of data science degree programs within member institutions of the iSchools Consortium, a global alliance of information schools. Through systematic empirical investigation, the study aims to unveil innovative features in the program's disciplinary positioning, curriculum architecture, and talent cultivation models. This research aims to inform global information science education institutions on how to optimize their disciplinary strategies and curricular designs for data science. Ultimately, this will address the challenges of knowledge system reconstruction and talent development iteration within the traditional library and information science (LIS) discipline amid its digital transformation. [Method/Process] This study employed a web-based survey and content analysis methodology to create a multi-dimensional analytical framework based on the 2023 iSchools Consortium membership directory. Using a stratified sampling approach that integrated disciplinary influence, as measured by the QS World University Rankings, and program maturity indicators, including curriculum comprehensiveness and industry partnership networks, eight representative U.S. higher education institutions were selected as core samples. A systematic empirical investigation was conducted to thoroughly analyze the current landscape of data science degree programs. The study focused on four critical dimensions: 1) degree-awarding structures such as degree types, concentration specializations, and accreditation standards; 2) credentialing ecosystems such as micro-credentials, stackable certificates, and non-degree pathways; 3) curricular architectures such as core course clusters, elective modules, and interdisciplinary integration mechanisms; and 4) career trajectory outcomes, such as sectoral distribution, occupational roles, and industry-specific skill premiums. [Results/Conclusions] The study summarizes the current state of data science discipline education in international iSchools from several perspectives, including the characteristics of degree program offerings, the reconstruction of disciplinary positioning, pathways for curriculum integration, and insights into employment trends. Based on this, it makes recommendations for developing China's domestic data science discipline. These recommendations include optimizing the disciplinary layout, innovating the curriculum system, and deepening industry-education integration. However, it should be noted that this research is constrained by its small sample size of eight institutions and its geographical scope, which is limited to the United States. In the future, the study could expand to encompass members of the European iSchools consortium, such as the iSchool at University College London and the iSchool at Humboldt University in Berlin, as well as emerging data science programs in the Asia-Pacific region. Through cross-national comparative analysis, it aims to reveal how culture, policies, and industrial ecosystems impact disciplinary development differently. Furthermore, the study could incorporate Learning Analytics technology to model learner behavior in data science courses offered on MOOC platforms, such as Coursera and edX. This would facilitate the refinement of course module granularity and adaptability to better meet learners' needs.