Abstract: [Purpose/Significance] From multidimensional proximity perspective, investigating the mechanisms through which geographical, technological, institutional, cultural, organizational, and network proximity influence the output of organized cross-institutional research collaborations in the humanities and social sciences holds significant importance for optimizing models of organized research collaboration and enhancing the overall effectiveness and impact of such cross-institutional initiatives. [Method/Process] Using 1 443 journal papers involving cross-institutional cooperation from the Major Projects of Key Research Bases of Humanities and Social Sciences of the Ministry of Education between 2002 and 2023 as sample data, Spearman rank correlation analysis and OLS regression analysis were conducted successively, and binary logistics regression was carried out for the robustness test. [Result/Conclusion] ①Geographical, institutional, and network proximities all have significant positive effects on the quantity of cross-institutional research collaboration output. Among these, the interaction between geographical and cultural proximity and the interaction between network and cultural proximity both significantly positively affect the quantity of cross-institutional collaboration output. In contrast, the interactions between technological and network proximity, organizational and network proximity, and organizational and cultural proximity all significantly negatively affect the quantity of cross-institutional collaboration output. ②Geographical and institutional proximities both significantly negatively affect the quality of cross-institutional research collaboration output. The interactions between geographical and technological proximity and between institutional and network proximity both significantly negatively affect the quality of collaboration output. ③Geographical proximity significantly negatively affects the influence of cross-institutional collaboration output, while network proximity significantly positively affects it. None of the interaction terms show significant effects on the influence of collaboration output. Based on the above research results, several suggestions are put forward in order to provide references for the development of relevant fields.

Abstract: [Purpose/Significance] Based on the combination of traditional topic models and large language models, as well as RAG-based large language model, this study designs the topic modeling method, analyzes the diffusion of emerging topics and non-emerging topics, so as to provide a reference for the subsequent identification and diffusion of emerging topics. [Methods/Process] First of all, the topic mining of patents was carried out through the combination of traditional topic models and large language models, and the emerging topics were identified based on novelty, continuity, growth and influence. Secondly, the best topic modeling method was determined by using the large language model score fine-tuned by RAG. Finally, a citation network was constructed to calculate the diffusion speed, breadth, intensity, depth, and region, and analyze the temporal changes of the diffusion indicators of emerging topics and non-emerging topics. [Result/Conclusion] Taking nuclear fusion as the empirical field, the research finds that the topic modeling method based on BERTopic+Doubao has the best effect. At the same time, the diffusion of emerging themes mostly shows a fluctuating upward trend, while non-emerging themes show a steady upward trend first and then a slight downward trend. The method of combining the traditional topic model and the large language model proposed in this paper can effectively identify emerging topics. The RAG fine-tuning large language model optimizes the evaluation effect of the results, and enriches the perspective of topic diffusion measurement.

Abstract: [Purpose/Significance] Against the background of the increasingly prominent reproducibility issues in digital humanities research, this paper systematically analyzes the challenges posed by data complexity, methodological heterogeneity, and the multimodal nature of research outputs to research verifiability, and explores the critical role of academic journals in institutional regulation and technical governance. [Method/Process] From the three dimensions of data, methods, and research outputs, the paper examined the dilemmas of reproducibility and, on this basis, introduced an analytical framework of “boundary-keeping, boundary-breaking, and their dynamic balance”. By integrating data publishing, platform-based collaboration, intelligent empowerment, and human-machine collaboration mechanisms, it constructed practical pathways through which journals can enhance reproducibility. [Result/Conclusion] It is argued that journals should achieve transparent and traceable management of the entire research process through standardized data collection and metadata specifications, centralized platform management, intelligent quality control, and human–machine collaborative peer review. The study concludes that academic journals need to maintain a dynamic balance between safeguarding academic norms and promoting publishing innovation, gradually forming an integrated “data–methods–publishing” ecosystem, so as to enhance the credibility, reproducibility, and academic impact of digital humanities research, and to provide institutional support for building an open, transparent, and sustainable scholarly publishing system.

Abstract: [Purpose/Significance] Scholar profiles are widely applied in various precision services within the academic field, such as paper review, project evaluation, targeted recommendations, and talent recruitment. Topic tags play a critical role in identifying scholars in the same specialized subfield. This paper optimizes the method for calculating scholar topic tags to more accurately identify scholars within specific subdisciplines. [Method/Process] This paper proposed a theoretical framework: scholar profiles should comprehensively incorporate evaluation information such as scholars' research interests and academic capabilities, while also reflecting their research fields and academic standing within those fields. This paper integrated topic tags with evaluation data, using the latter as a factor for weighting topic tags. An algorithm was proposed for calculating high-scoring topic tags for scholars, incorporating three dimensions: the quantity of published papers, the author's position in the author list, and the citations of the papers. [Result/Conclusion] From the perspective of individual scholar profiling, high-score topic tags can delineate the main specialized research areas of a scholar. For the need to identify experts within a discipline, high-score topic tags can help pinpoint the most outstanding and core scholars in that field.

Abstract: [Purpose/Significance] As AI technologies become deeply embedded in social life, AI literacy has emerged as a fundamental competency for the public in the intelligent era. This study aims to systematically review the progress of empirical research on AI literacy assessment worldwide, providing reference and insights for future studies. [Method/Process] Following the principles of systematic review, this study selected 69 empirical studies on AI literacy assessment as the analytical sample. Through a comprehensive examination of the included literature, descriptive characteristics were summarized, and key elements of AI literacy assessment—including assessment targets, assessment dimensions, development and validation of assessment tools, influencing factors, and research limitations—were systematically analyzed. [Result/Conclusion] Existing research shows that AI literacy assessments primarily target the general public, students, and educators. Core assessment dimensions commonly include cognition, application, evaluation, and ethics. The assessment process generally involves sample selection and the development and validation of assessment tools. Empirical findings indicate that AI literacy levels are influenced by variables such as gender, age, nationality, and educational background. Current studies still exhibit limitations regarding assessment tools, sample characteristics, and assessment methods, suggesting the need for further refinement in future research.

Abstract: [Purpose/Significance] Large-scale research infrastructures represent a symbol of national scientific competitiveness, and the scientific data they generate play a crucial role in scientific research, technological advancement, and economic development. However, traditional data management methods face challenges such as data redundancy, difficulties in sharing, and repetitive experiments, urgently requiring empowerment through next-generation artificial intelligence technologies. This paper aims to study a method for constructing an AI-enabled scientific data management and recommendation framework for large-scale research infrastructures, focusing on exploring a personalized recommendation system architecture that integrates knowledge graphs and large language models to enhance the integration and intelligent recommendation capabilities of scientific data. [Method/Process] This paper proposed an artificial intelligence-based framework for scientific data management and recommendation for large-scale research infrastructures. It introduced technologies such as knowledge graphs, link prediction, and large language models (LLMs), proposing a personalized data recommendation system architecture oriented towards the entire research process. [Result/Conclusion] The system architecture includes modules for data collection, knowledge extraction, semantic reasoning, and personalized recommendation. The data layer is responsible for the organization and integration of multi-source heterogeneous data, the analysis layer performs in-depth analysis using graph neural networks and large language models, and the application layer provides accurate recommendation services to researchers. The framework proposed in this paper contributes to improving data utilization, promoting scientific and technological innovation capabilities, and enhancing the integration and intelligent recommendation of multi-source heterogeneous data.

Abstract: [Purpose/Significance] In response to the practical demand for high-quality data support and trustworthy operation of domain large models in specialized and high-risk application scenarios, this study systematically examines the basic connotation, key characteristics, and systematic construction path of the domain large model data foundation. It aims to provide a theoretical framework and methodological support for the practical deployment of trustworthy artificial intelligence, and to offer systematic references for the standardized construction and governance practice of the domain large model data foundation. [Method/Process] Based on a systematic review of the relevant literature, this study clarified the conceptual connotation, major characteristics, and constituent elements of the domain large model data foundation. It introduced the theory of layered modular architecture to construct a layered framework for the domain large model data foundation, mainly covering the data resource layer, knowledge organization layer, knowledge storage and semantic indexing layer, model layer, algorithm layer, application layer, and governance and operational support layer, so as to characterize the overall mechanism of the coordinated operation of data, knowledge, and models. Finally, a desk research method based on public evidence and a cross-case comparison approach were employed to demonstrate the contextual applicability of the framework. [Result/Conclusion] Cross-domain comparisons show that the proposed framework can explain the common mechanisms underlying external knowledge memory access, evidence-constrained generation, and compliance-audit output chains in reference systems across different domains. On this basis, the study further distills reusable methodological elements such as data contracts, index configuration, and evaluation baselines, thereby validating the applicability and transferability of the framework across domains.

Abstract: [Purpose/Significance] People can quickly obtain the necessary knowledge from a vast amount of information, relying on knowledge to represent complex structured and semantic modeling of information. This article describes knowledge from multiple perspectives, constructs a multi-perspective knowledge representation framework model, and explores the innovation of knowledge organization theories and methods, with the aim of further promoting the strategic development of artificial intelligence. [Method/Process] Knowledge representation perspectives were classified into perspectives such as knowledge object, knowledge subject, and knowledge situation based on different perspectives. The theoretical basis of the methods under these knowledge representation perspectives was described. The framework and model of multi-perspective knowledge representation were constructed, the formal representation of knowledge order was realized, and a brand-new operating mechanism of multi-perspective knowledge representation was proposed. [Result/Conclusion] To maximize the effectiveness of knowledge representation in a digital and intelligent environment, multi-perspective knowledge representation can be adjusted in aspects such as establishing a flexible and multi-dimensional holographic knowledge order, integrating multi-modal and contextualized expressions, and dynamically evolving for user problems, so as to better serve specific practical fields.

Abstract: [Purpose/Significance] Under the background of AI, it is of great significance to study the digital transformation of digital governance, the ideas for solving the problems of digital governance, and the cultivation of intelligent talents from a knowledge-based perspective. [Method/Process] Based on the ideas of governance and complex systems, this paper analyzed the process of knowledge-based and knowledge deepening in AI, digital transformation, and digital governance; revealed the dual subject dual intelligence of humans and AI, the spillover symbiotic characteristics of AI's digital intelligence, as well as the elements, characteristics, advantages, and challenges of digital governance. In response to the problem of human cultivation ability, which is attributed to the attitude of "upright, serious, and pragmatic", the cultivation of "humanistic literacy, learning experience, insight, and cognitive ability of thinking logic", this paper analyzed the process of knowledge-based sublimation of knowledge, intelligence, spirituality, cognition, and wisdom, and proposed the idea of smart education to solve governance problems and cultivate intelligent talents. [Result/Conclusion] Digital transformation and governance require intelligent talents with a "correct, serious, and pragmatic" attitude and a profound humanistic background. Through universities, enterprises, urban governance, and personal cultivation, to cultivate the intelligent talents urgently needed for digital transformation and digital governance under AI, and make due contributions to supporting the implementation, expansion, optimization, and strengthening of the digital economy.

Abstract: [Purpose/Significance] This study investigates the underlying mechanism through which library smart knowledge services empower the development of AI literacy among faculty in borderland universities. It aims to provide a regional perspective and practical insights for addressing challenges in teachers' digital-intelligent transformation and for expanding the functional role of academic libraries. [Method/Process] Utilizing a grounded theory approach, in-depth interviews were conducted with 30 faculty members from universities in the Guangxi border region. Data analysis involved a three-level coding process to construct a "Symbiotic Mechanism Model of Library Smart Knowledge Services". [Result/Conclusion] The research reveals an empowerment model characterized by "Preconditions — Multi-dimensional Empowerment — Collaborative Evolution". Faculty's AI literacy challenges and mismatches in library services constitute the preconditions. Libraries then engage in multi-dimensional intervention through four interconnected empowerment pathways: skills, cognition & ethics, culture & localization, and outcomes & governance. This ultimately drives the librarian-faculty relationship towards collaborative evolution, externally manifested in the behavioral progression of "anxiety → trust → integration → co-creation", and internally representing a fundamental shift from one-way support to two-way collaboration. This study enriches the regional theoretical perspective on smart libraries and faculty development, offering concrete implications for the localized and collaborative practices of academic libraries.

Abstract: [Purpose/Significance] Against the backdrop of the widespread application of Generative Artificial Intelligence (AIGC) and the interdisciplinary convergence of new medical science, this study analyzed medical students' actual needs for library information resource services and put forward targeted suggestions for the innovation of library information resource services. It aimed to provide countermeasures and suggestions for medical university libraries to carry out demand-oriented, AIGC-based optimization and innovation of interdisciplinary medical information resource services. [Method/Process] By designing a questionnaire, this study surveyed and analyzed the demands of 813 medical students for medical information resource services provided by medical university libraries, as well as their practices and competencies in accessing and utilizing medical information resources. [Result/Conclusion] Analysis of the survey results found that in the context of AIGC and the interdisciplinary convergence of modern medical science, medical students across different academic years have increasingly diversified demands for medical information resource services, with an urgent need for integrated interdisciplinary resources and intelligent services. However, existing interdisciplinary information resources in medical university libraries are scattered, and intelligent services are insufficient. Simultaneously, medical students' abilities in accessing and utilizing information resources—including retrieving, reading, and managing them—are relatively weak. Furthermore, while their demand for the application of emerging technologies like AIGC is high, their understanding of these technologies is inadequate. Based on the above needs and problems, this study proposes utilizing AIGC to optimize and innovate interdisciplinary medical information resource services in medical university libraries, and conducting multi-tiered information literacy training and education based on AIGC to enhance the abilities and efficiency of medical students in accessing and utilizing interdisciplinary information resources.

Abstract: [Purpose/Significance] This study employs the BERTopic model to analyze the core themes, evolutionary pathways, and frontier dynamics of data element valorization in China, providing a macro perspective and objective foundation for advancing theoretical exploration and guiding practical applications. [Method/Process] Having used 1735 articles published in CNKI from 2019 to the present as the dataset, this research had applied the BERTopic topic modeling approach. First, a semantic embedding space had been constructed using a pre-trained language model, and UMAP had been applied for dimensionality reduction, combined with HDBSCAN for density-based clustering, to have initially generated the incorporation of a temporal analysis method to trace the dynamic evolution of research topics. [Result/Conclusion] The findings reveal that: ① Current research themes focus on five key areas: "Enterprise Innovation and Data Element Market Cultivation", "Data Infrastructure Systems and Industrial Digital Transformation", "Element Marketization Allocation and Institutional Reform", "Data Transaction Systems and Market Competition Mechanisms", and "Data Empowerment for Productivity Improvement and Regulatory Balance". ② Data element valorization exhibits multidimensional collaborative characteristics and forms a dynamic evolutionary mechanism.

Abstract: [Purpose/Significance] The paper aims to reveal richer social relationships beyond formal collaboration networks from the acknowledgment text of the doctoral dissertation, deepen our perception of academic social networks, and provide new evidence for understanding academic social capital and the cultivation of young researchers. [Method/Process] This paper had used the LAC model to extract and identify the people being acknowledged and their social relationships mentioned in the acknowledgment sentences of the acknowledgment text of doctoral dissertations, and had then analyzed the correlation between the social relations between the persons acknowledged and the authors of doctoral dissertations (hereinafter referred to as "dissertation authors") and their cooperation relations in academic publications. [Result/Conclusion] Most of the social relationships mentioned in the acknowledgments did not translate into formal research collaborations. This indicates that acknowledgments can effectively capture implicit social relationships that are often overlooked in traditional collaboration datasets, such as mentorship, peer review, peer networks, academic friendships, and family support. The study shows that nearly half of research collaborations can be traced back to the doctoral stage, demonstrating that social relationships highlighted in acknowledgments have a substantial impact on research output and publication. Among these, mentorship and peer relationships represent the closest types of collaboration, indirectly revealing the patterns of research collaboration among early-career researchers. Moreover, a significant correlation was observed between the order of mention in acknowledgments, the type of relationship, and the intensity of research collaboration, indicating that social networks play a critical and tangible role in the academic growth of early-career researchers.

Abstract: [Purpose/Significance] Addressing the issue of "big data price discrimination" in the digital economy, which severely undermines consumer rights and market fairness, this study focuses on two key governance strategies: information transparency and consumer education. It explores their synergistic efficacy in countering this phenomenon to foster a fair digital market with multi-stakeholder participation. [Method/Process] Based on information asymmetry theory, equity theory, and information processing theory, the research had employed two 2×2 scenario-based experiments (food package ordering and hotel booking) to examine the interactive effect of information transparency and consumer education on perceived price discrimination, and had analyzed the mediating effect of perceived price fairness. [Result/Conclusion] The results indicate that the interaction between information transparency and consumer education significantly enhances consumers' perception of price discrimination. While enhancing either strategy individually improves recognition ability, their combined effect yields more pronounced results. Perceived price fairness partially mediates this interactive influence: improvements in information transparency and consumer education enhance consumers' understanding of pricing procedures, thereby increasing their perception of fairness and indirectly mitigating their ultimately negative judgments.

Abstract: [Purpose/Significance] Exploring young people's attitudes towards the digital modeling of traditional culture plays a significant role in the communication of cultural heritage. [Method/Process] Based on CAPS theory, this study had employed methods such as word frequency analysis, topic identification, and sentiment analysis to investigate the young people's attitudes towards the digital modeling of traditional culture, having used the danmaku and comments from videos showcasing traditional culture on Bilibili as the research subjects. The study had collected and cleaned a total of 22,961 danmaku and comment texts from high-play-volume videos related to the construction of traditional Chinese architecture using the sandbox game Minecraft on Bilibili. [Result/Conclusion] Results show that the younger generation maintains a certain level of interest and awareness in traditional architecture and culture, especially regarding the use of new forms like Minecraft to inherit traditional architectural culture; the younger generation appreciates and recognizes the aesthetic and cultural value of traditional architecture and shows a certain interest in the design and creative skills of traditional construction; nearly 60% of the younger generation hold a positive attitude towards using Minecraft to inherit traditional architectural culture, demonstrating a fondness for historical civilization and pride in traditional culture. It is evident that inheriting traditional architectural culture through game formats is feasible, capable of attracting the attention and participation of the younger generation, and providing new ideas for the protection, inheritance, and promotion of traditional architecture.

Abstract: [Purpose/Significance] In response to the persistent ambiguity and conflation of terminology surrounding citation breaches in academic discourse, this study aims to propose a conceptual analytical framework for the classification and definition of citation breach terms, in order to promote research integrity in China and strengthen academic norms. [Method/Process] By having systematically reviewed academic literature and policy documents from both domestic and international sources, the article had extracted definitional features and usage contexts of citation breach terms. It had constructed a dual-dimensional classification framework based on "research misconduct/questionable research practices" and "increase citation count/decrease citation count/no impact on citation count", alongside a three-element conceptual framework comprising "motivation – process – utility". [Result/Conclusion] Grounded in this framework, the article integrates and reconstructs the terminology system for citation breaches, establishing 11 primary core terms—honorary citation, excessive citation, citation contamination, referencing error, unintentional citation omission, citation bias, citation copying, citation misrepresentation, citation manipulation, concealed citation, fabricated citation—and clarifies the characteristics of each term across the dimensions of motivation, process, and utility. This provides a more operational tool for future institutional governance.

Abstract: [Purpose/Significance] This study aims to explore the motivation of organized scientific research cooperation among philosophy and social science researchers to effectively promote such cooperation. [Method/Process] Based on the theory of value co-creation, the value co-creation behaviors in organized scientific research cooperation were divided into four stages: value consensus, value sharing, value symbiosis, and value win-win. A consensus-based qualitative research method was adopted, and in-depth interviews were conducted with 20 researchers in the field of philosophy and social sciences to identify the motivation at each stage. [Result/Conclusion] The research finds that in the value consensus stage, the motivation of researchers mainly includes academic drive, policy drive, social value drive, and organizational management drive; in the value sharing stage, the motivation mainly includes reciprocal drive, reputation drive, communication and feedback mechanism drive, and external pressure drive; in the value symbiosis stage, the motivation focuses on collaborative innovation drive, role identity drive, personal development drive, and national demand drive; in the value win-win stage, the motivation emphasizes outcome distribution drive, collaborative win-win drive, and spillover effect drive. Meanwhile, there are significant cross-stage common motivations in each stage. Based on these findings, corresponding suggestions are proposed to promote the active participation of philosophy and social science researchers in organized scientific research cooperation.

Abstract: [Purpose/Significance] Given that the evolution of digital technology has triggered a cognitive phase transition of knowledge forms from solid to liquid, this study addresses the gap in existing research, where there is a lack of analytical frameworks for systematically uncovering the mechanisms through which cross-modal reasoning technologies such as DeepSeek reconstruct the paradigm of knowledge production. This study proposes a theoretical framework of knowledge liquidization and reveals the reconstruction mechanism of cross-modal reasoning technologies on the knowledge production paradigm. [Method/Process] An analytical framework of knowledge liquidization encompassing three dimensions, “modality-subject-process”, was constructed. By integrating case analysis and functional analysis methods, this study systematically examined the core drivers through which DeepSeek’s dynamic sparse attention mechanism, mixture of experts (MoE) model, and cross-modal alignment technology reconstructed the knowledge production paradigm. [Result/Conclusion] The findings indicate that DeepSeek is not merely a general influencing factor. Instead, through its technical architecture (such as the MoE model) and open-source model, it fundamentally triggers a threefold paradigmatic reconstruction of knowledge production: in the dimension of modality, transforming from discrete forms to unified forms; in the dimension of subject, shifting from centralized dominance to human-machine distributed collaboration; in the dimension of process, evolving from linear progression to dynamic interaction. While this reconstruction process significantly improves the efficiency of knowledge production, it also gives rise to new governance challenges, such as the dissipation of cognitive authority and ambiguity in responsibility attribution.

Abstract: [Purpose/Significance] This paper aims to comprehensively grasp the theoretical and practical frontiers of data governance in the field of library and information science, construct a systematic cognitive framework, and provide a strong reference for relevant policy formulation, institutional design, and strategic planning implementation. [Method/Process] With the help of CiteSpace and VOSviewer visualization tools, the data source of this paper was CNKI. Through the analysis of the topic information, this paper built the research framework of the field, extracted the frontier hotspots of the field, and explored the development and evolution of the field. [Result/Conclusion] The research literature on data governance in the field of Library and Information Science in China shows an exponential growth trend and can be divided into two stages (2015-2019 and 2020 to now). Through topic clustering, five topics are summarized, including governance theory system, governance technology system, governance business model, governance practice plan, governance policies and laws, and regulations. Further prospects for the research on data governance in China are put forward, which provide theoretical and practical reference for better playing the role of "data element ×" and promoting the modernization of the national governance system.

Abstract: [Purpose/Significance] Focusing on sudden incidents at tourist attractions within the short-video public opinion environment, this study aims to explore the most effective recovery strategies (apology, rectification, compensation) to restore public trust and enhance post-recovery satisfaction. [Method/Process] Using an online experiment, a 4 (recovery strategy: apology, rectification, compensation, no remedy) × 2 (involvement: high, low) between-subjects design was implemented. Short videos of service failures at tourist attractions served as stimulus materials to examine the effects of different strategies on public negative emotions and satisfaction under varying levels of involvement. [Result/Conclusion] The results indicate that among the recovery strategies, compensation is the most effective, followed by rectification, and then apology. Effective recovery enhances satisfaction by mitigating the public’s negative emotions. Public involvement plays a key moderating role: under high involvement, compensation is the optimal strategy for emotional remediation; under low involvement, rectification and compensation are equally effective in alleviating negative emotions.

Abstract: [Purpose/Significance] By constructing a model of online application behavior among fine arts undergraduates, this study aims to provide a basis for art universities to understand and grasp students' online behaviors, thereby monitoring their digital activities and exploring innovative online ideological and political education models in higher education. [Method/Process] Based on grounded theory and using the qualitative data analysis tool NVivo 12, interview texts were subjected to open coding, axial coding, selective coding, and theoretical saturation testing to develop a model of online application behavior for fine arts undergraduates. [Result/Conclusion] The model reveals that under different online and real-world contexts, influenced by personal traits and realistic constraints, fine arts undergraduates' online behaviors exhibit similar group characteristics while differing in behavioral purposes and patterns. The model summarizes six core categories (representation layer, environment layer, goal layer, behavior layer, extension layer, and constraint layer), as well as 18 main categories. Based on these core categories, the study further proposes that art universities should enhance the effectiveness of online ideological and political education by focusing on three aspects: environmental governance, goal guidance, and behavior correction, thereby providing a foundation for properly guiding students' online behaviors.

Abstract: [Purpose/Significance] Addressing issues such as single point of failure, privacy leakage, and insufficient ability of traditional digital resource preservation methods, an integrated model based on blockchain and distributed storage is proposed to solve the core challenges such as easy data tampering, inefficient permission management and a lack of trust in cross-institutional collaboration. [Method/Process] This study designed a hierarchical architecture model that integrates blockchain depositary metadata and IPFS chunked storage files. And smart contracts were utilized to automate processes including resource upload, permission control and data recovery. Data privacy was safeguarded through AES encryption. The model's reliability was rigorously verified based on a hash collision probability calculation (4.26×10^-27) and a quantified disaster tolerance capacity quantification (annual file recoverability rate of 99.99%). [Result/Conclusion] The model significantly outperforms other methods in data immutability, privacy protection (it takes 3.68×10⁵¹ years to crack AES-256) and disaster tolerance capacity. And it is suitable for high-security scenarios such as digital archives and medical data management. The study also proposes strategies such as hierarchical storage, data deduplication and compression to optimize system performance and reduce cost. A modular design reduces development complexity, while the integration of managed services enhances deployment efficiency. This research shows that the collaborative mechanism of blockchain and IPFS provides a reliable path for digital resources preservation. In the future, it is necessary to further balance performance and cost to support large-scale applications.

Abstract: [Purpose/Significance] This study aims to clarify the optimization path of library reader service empowered by AI from the perspective of data elements, and to provide suggestions for libraries to make more effective use of AI for digital intelligent transformation and promote the release of library value. [Method/Process] Guided by the data value chain theory, information ecology theory and perceived value theory, relevant influencing factors were extracted through grounded interviews from the perspective of data elements, and Fuzzy-ISM was used to divide the three-level influencing paths according to the extracted 15 categories. The characteristics of independent group and dependent group were identified by MICMAC analysis. [Result/Conclusion] The influence path of 15 factors in three dimensions of data subject, data environment and data ontology was determined, and the underlying independent group composed of data storage and technical facilities was established, which was the root driving force of service optimization. By influencing other factors, users' perceived value was affected, so as to achieve a closed loop of value. It provides ideas for the transformation of "data-value" in library.

Abstract: [Purpose/Significance] Technology forecasting is a field of research related to future technology development trends and technological innovation. This paper aims to identify the current research hotspots and frontiers in the field of technology forecasting, to provide guidance for the technological innovation of enterprises and organizations, helping them seize opportunities and avoid risks. [Method/Process] Technology forecasting literature from WoS and CNKI (2001—2024) was analyzed using VOSviewer and CiteSpace to identify literature characteristics, hotspots, and frontiers. [Result/Conclusion] The result shows that: ①The volume of articles published in the field of technology forecasting has grown steadily at domestic and international, and the core journals are distributed in the fields of information science, management and other disciplines. ②A stable core authors group has formed in the field of technology forecasting at home and abroad. In China, collaboration remains predominantly intra-institutional, and there are cross-institutional, with cross-institutional ties often stemming from mentor-apprentice relationships. Internationally, cross-institutional collaboration is more prevalent, frequently facilitated by jointly-trained scholars and visiting academics who serve as bridges for research partnerships. Notably, Chinese scholars have emerged as key contributors to international technology forecasting research. ③Regarding research hotspots, domestic research focuses on the research of emerging and disruptive technologies, technology opportunities, and technology forecasting methods. Foreign countries focus on technology development trend research, emerging technology assessment and forecasting research, technology convergence and technology intelligence research, technology opportunity discovery and analysis research. ④In terms of research frontiers, identification and prediction of disruptive and emerging technologies, research on technology convergence and technological innovation, and optimization of technology opportunity identification and discovery methods are the research frontiers in the field of domestic technology forecasting. The research frontiers in the field of foreign technology forecasting mainly focus on the improvement of technology forecasting methods and the discovery and analysis of technology opportunities.

Abstract: [Purpose/Significance] In order to promote the standardization and digital transformation of Hainan sea fishing industry, this paper integrates knowledge management theory and Internet of Things technology to build intelligent management solutions and provide scientific and technological support for the upgrading of recreational fisheries. [Method/Process] Based on the real-time data acquisition and big data analysis technology of HiTo smart bracelet, the "Deshun Haitu" integrated management information service platform was designed and developed. The platform incorporated functional modules such as safety early-warning, resource scheduling, and user behavior analysis. Its efficacy was validated and refined through multiple iterations of case studies, expert evaluations, and simulation tests. [Result/Conclusion] "Desheng Haitu" integrated management information service platform based on HiTo smart bracelet can significantly reduce the rate of sea fishing accidents (down 35%), improve the efficiency of fishery resource allocation (28%), and enhance user activity (up 40%). Through the real-time perception and dynamic analysis of intelligent devices, accurate risk early warning and intelligent resource allocation can be realized, and a replicable technical path and management paradigm can be provided for the digital transformation of recreational fisheries.

Abstract: [Purpose/Significance] Evaluating service efficiency from a user perspective is crucial for grassroots library to enhance their performance and achieve high-quality development. [Method/Process] Based on the theory of uses and satisfaction, this study first constructed an "Awareness-Utilization-Satisfaction" evaluation framework for the service efficiency of grassroots library from the perspective of users. Then, a concise evaluation indicator system was designed with reference to the existing results. The indicator system was further optimized by collecting users' opinions through questionnaires and interviews. Finally, A pilot survey was carried out to collect data for calculating the indicator values. [Result/Conclusion] The constructed evaluation framework and indicator system highlight the user perspective of service efficiency evaluation, aligning with the current trend of international public library evaluation. They could reflect the true level, advantages and disadvantages of grassroots library, thereby serving as a valuable supplement to China's national Evaluation and Grading Standards for Public Library. If it is implemented by the district/county library or the third party organization every year, it would be conducive to improving the awareness rate, utilization rate and satisfaction of the grassroots library, ultimately improving the overall service efficiency of China's public library, and better meeting the public's expectation for the library circle.

Abstract: [Purpose/Significance] Focusing on the public opinion dynamics of generative artificial intelligence, especially ChatGPT, we aim to explore the background and significance of this technology and its social impact, analyze the public acceptance and public opinion evolution process, and provide theoretical support for the application of related technologies and social governance. [Method/Process] 221 564 microblog data related to generative AI between January 2023 and September 2024 were analyzed. By using the BERTopic model to extract the core themes and combining with keyword co-occurrence network analysis, we tracked the discussion hotspots and the evolution of public opinion in different time periods. [Result/Conclusion] This study identified eight major themes, including technology industry, education and robotics, and health care, and revealed the evolutionary path of public discussion: the initial phase focused on technological innovation and potential, the mid-term explored scenario applications such as education, health care, and finance, and the later stage turned to data privacy, security, and social ethics. Keyword analysis shows that terms such as "artificial intelligence" and "technology" run through the entire cycle, while high-frequency words such as "privacy" and "security" in the later period reflect the public's concern about the potential risks of technology. This study provides an in-depth understanding of the social effects of generative artificial intelligence technology and theoretical support for technology optimization and policy governance.