[Purpose/Significance] The library is currently in a critical period of development for the "15th Five-Year Plan", and the intelligent strategy is one of the key areas of the library's "15th Five-Year Plan". The large-scale modeling technologies represented by DeepSeek, ZhipuAI, ChatGPT, etc. are reshaping the boundaries and forms of knowledge services through the deep integration of new-generation artificial intelligence technologies and knowledge service systems, providing important theoretical and technical support for the development of library intelligence strategies during the 15th Five-Year Plan period. Therefore, exploring how DeepSeek enhances library knowledge services has become one of the most cutting-edge issues worth paying attention to in the library and information science (LIS) field. [Method/Process] On the basis of a brief review of the current state of research on the integration of DeepSeek and library knowledge service theory, this article designs and proposes a theoretical model for DeepSeek to enhance library knowledge services. It explores the innovative model of DeepSeek that empowers library knowledge services from five aspects: knowledge discovery, knowledge acquisition, knowledge analysis, knowledge recombination, and knowledge utilization and thoroughly analyzes the four core dimensions of technology empowerment, business empowerment, user empowerment, and ecological empowerment. It also elaborates on the security issues of large models caused by the open source strategy, the intellectual property risks caused by technological innovation, the knowledge illusion problems caused by data traps and defects, and the information cocoon problems caused by technological applications. This study aims to provide some reference and inspiration for the research of related issues. [Results/Conclusions] The library is currently in a critical period of development for the "15th Five-Year Plan". DeepSeek's inherent technological advantages such as low cost, high performance, and open source ecosystem not only enable the library knowledge service system in multiple dimensions, reshape the boundaries and forms of knowledge services, comprehensively enhance users' knowledge service experience, but also provide stronger impetus for library construction, management, and service in the "15th Five-Year Plan" period. The theoretical model of DeepSeek empowerment of library knowledge services mainly includes four core dimensions: technology empowerment, business empowerment, user empowerment, and ecological empowerment. It has an impact on library service innovation in five aspects: knowledge discovery, knowledge acquisition, knowledge analysis, knowledge recombination, and knowledge utilization. At the same time, it can bring many problems, such as model security, intellectual property risks, knowledge illusions, and information cocoons. From the existing public information, DeepSeek can provide important technical support and core driving force for library knowledge service innovation in the era of artificial intelligence from four aspects: technical algorithms, training cost, open source ecology, and local lightweight deployment. Since the gradual formation of the DeepSeek open source ecosystem, more and more enterprises, communities, research institutions, teams, and developers have actively participated in and built the industry ecosystem, showing a strong magnetic field effect. Libraries should adhere to the principle of "join if you can't win", actively integrate into the DeepSeek open source ecosystem, and build an ecosystem of knowledge service ecosystems with library industry characteristics and disciplinary features.
[Purpose/Significance] With the rapid development of generative artificial intelligence (AI) and large language models (LLMs), the role of "prompt librarians" has emerged. This study constructs a theoretical framework for prompt librarians and explores the rationality, feasibility, and significance of the transition of librarians to this role from the perspective of new quality productive forces. Driven by the new quality productive forces represented by AI, transforming librarians into prompt librarians can not only optimize application scenarios and user experience, but also improve work efficiency and effectively promote the intelligent transformation of libraries. There is currently no research on this topic in the existing literature. This study, for the first time, proposes a theoretical framework for prompt librarians and the implementation path for the transition of librarians, filling the research gap in this area. [Method/Process] Through a review of relevant national and international literature, this study examines the impact of AI on the role and positioning of librarians within the library industry. Taking the new quality productive forces as the theoretical foundation and driving factor, the study explains the necessity of the transition of public librarians to prompt librarians, and analyzes the rationality, feasibility, and significance of this transition. Furthermore, a theoretical framework for prompt librarians is constructed, encompassing concepts, scope of functions, work processes, and core competencies. Additionally, through the method of literature review and online surveys, the study examines the current status of information and knowledge services in public libraries, focusing on the top thirty libraries ranked by online influence in China. It identifies the major challenges faced by librarians in the transition. Based on the theoretical framework of prompt librarians and real-world challenges, the study explores the implementation path for the transition of librarians to prompt librarians, ensuring the scientific, logical, and innovative nature of the research. [Results/Conclusions] As an emerging role that combines the library industry with AI technology, prompt librarians, driven by user needs, explore the unique resources of their collections in depth, revitalizing literature, diverse information resources, and other materials through AI pathways. They act as guides and translators between knowledge and AI, effectively driving the intelligent transformation of libraries. However, the transition of librarians faces many challenges. To facilitate a smooth transition, this study proposes implementation pathways, such as the establishment of dedicated prompt librarian positions, a "three-step leap" training model for librarians, robust top-level planning, the construction of multi-modal resource service platforms, AI ethics considerations, and interdisciplinary collaboration. Through these explorations, the study aims to provide innovative ideas and practical guidance for the transition of librarians in the AI era, enrich research on the application scenarios of new quality productive forces, and enhance the service quality and competitiveness of libraries.
[Purpose/Significance] Satisfaction is the patient's evaluation and emotional feedback on the entire mobile healthcare experience. Not only does it directly affect the patient's experience, but it also significantly influences user adoption and retention. Therefore, this study aims to explore the influencing factors, hierarchical relationships, and associated pathways of user satisfaction with mobile health applications, and provide scientific evidence and practical recommendations for the healthy development of mobile health applications, thereby promoting the construction of a healthy China and intelligent healthcare. By clarifying the key drivers of user satisfaction and their interactions, the study provides theoretical support for enhancing user experience, optimizing service quality, and increasing user retention. [Method/Process] This study first crawled, cleaned, and filtered negative user review data from mobile health applications, resulting in 539 valid data points after processing. Using the grounded theory, the study extracted factors influencing user satisfaction with mobile health applications by coding the review data. Subsequently, based on the interpretive structural model (ISM), the internal logic and associated pathways between these influencing factors were explored. Finally, the cross-impact matrix multiplication (MICMAC) method was used to examine the dependencies and driving forces among the influencing factors, and to identify the key factors affecting user satisfaction with mobile health applications. [Results/Conclusions] The study found that user satisfaction with mobile health applications is influenced by 23 factors across eight dimensions, including physician service quality, management service quality, system quality, information quality, transaction quality, perceived value, perceived risk, and perceived cost. Perceived cost and perceived risk are key drivers that directly affect user satisfaction. The middle-level factors transmit the effects of the bottom-level factors to the top level, acting as "mediators," and consist of factors from the dimensions of system quality, information quality, perceived value, transaction quality, and perceived risk. The bottom-level factors are the primary driving forces, including the quality of medical service, management service quality, system quality, and information quality. Based on the analysis results, this study proposes the following practical recommendations: strictly review the qualifications of doctors and establish a service quality evaluation mechanism; provide communication training for doctors and simplify medical terminology; add artificial intelligence and human services, and regularly train management service staff; design a simple interface and offer personalized customization; ensure information security and privacy, follow the principle of minimal data collection, and allow users to view and delete their personal information. Subsequent research, based on the expansion of the types of mobile health applications, will use a combination of qualitative and quantitative research methods to explore more deeply the relationships among the various factors that influence user satisfaction.
[Purpose/Significance] With the rapid development of artificial intelligence generated content (AIGC) technology and the deepening of social impact, it is an important responsibility and historical mission of university libraries to cultivate and enhance students' critical information literacy (IL) in the application of AIGC. The research aims to explore the content and pedagogical strategies of critical IL education in university libraries for AIGC applications, promote the ability of college students to critically recognize and apply AIGC in the AI era, and also provide reference for the development of critical IL education in university libraries. [Method/Process] By reviewing the relevant literature at home and abroad, this paper summarizes the research status of critical IL education for AIGC applications. Based on the requirements of the "Higher Education Information Literacy Framework" for the cultivation of critical thinking ability, the current situation of critical IL education in university libraries, and the relevant policies and guidelines for the development of AI literacy education at home and abroad are reviewed. The content of critical IL education in university libraries for AIGC applications can be categorized into three aspects: AIGC application knowledge, AIGC application skills, and AIGC application ethics. At the same time, based on the requirements of IFLA library's Strategic Response to Artificial Intelligence' and the lack of IL education system in university libraries, it is proposed that the critical IL education of university libraries from the perspective of AIGC application should be ensured and implemented from the aspects of educational content integration, educational team building, educational mode development and educational system optimization. [Results/Conclusions] The research on critical IL education for AIGC application has a critical role in promoting the cultivation and improvement of students' critical thinking ability for AIGC. University libraries should be aware of their responsibilities, actively respond to the new requirements of critical IL education for AIGC applications, innovate and expand the content and form of IL education, and help students acquire the new IL skills needed for AIGC applications. At the same time, university libraries should also continuously update the content of critical IL education from the perspective of AIGC application, and have the courage to explore new teaching methods and strategies, so as to better cultivate and improve students' IL of AIGC application, help students use AIGC scientifically, correctly and normatively, and realize lifelong learning.
[Purpose/Significance] Public access policy plays a crucial role in raising the awareness of openness, promoting scientific progress and innovation development. Studying the current situation of scientific data sharing in international countries can provide a reference for the practice and development of scientific data sharing activities in China. [Method/Process] Over the past 15 years, an increasing number of funding agencies in the United States have responded to national policy calls to require funded projects to share the research results in order to improve the effectiveness of the grant implementation and to promote scientific development. To this end, many academic institutions have established and provided a variety of data support facilities and services, but these facilities and services are often scattered across different administrative departments. Data management and sharing activities under this model suffer from organizational deficiencies, fragmented activities, overlapping services, inaccessibility, and others that reduce the efficiency of public access to scientific data. In order to understand the reality of scientific data sharing, ARL conducted a fact-based study named the RADS initiative on the scientific data sharing model, with survey respondents coming from six research-intensive universities in the United States, who are involved in scientific data management and sharing, resulting in a relatively comprehensive survey. The article adopts the network research method and literature analysis method, through the interpretation of the first phase of ARL's RADS Initiative series of reports and materials, to comprehensively understand the composition of the entire life cycle of scientific data management and sharing activities, service content and implementation costs of the U.S. academic institutions under the public access policy. We also analyze the behavioral characteristics of the two main actors of the U.S. colleges and universities involved in the practice of scientific data sharing, the characteristics of the activities and support services, and summarize the real problems of scientific data management. The practical problems of scientific data sharing include inter-departmental coordination and linkage, gaps in supply and demand between disciplines, boundaries between disciplines, inadequate cost-benefit evaluation, and the availability of shared data to the public. [Results/Conclusions] On the basis of summarizing the successful experiences and shortcomings of the RADS Initiative, and taking into account the current situation of scientific data sharing in China, this paper puts forward the construction ideas and quality enhancement suggestions to promote the implementation of scientific data sharing activities in China at each level with an emphasis on public participation, We propose to integrate the coordinated development and optimize cost-effectiveness, foster the data literacy and emphasize user feedback, focus on the public access, and construct the core clusters.
[Purpose/Significance] Information literacy (IL) training for farmers has become one of the main contents for farmers in the new era. However, the current implementation of rural revitalization still does not pay enough attention to farmers. At the same time, farmers' IL ability is an important embodiment of farmers' integration into the digital countryside, which can give a strong boost to the modernization of agriculture and rural areas. Therefore, it is of great practical significance for the rural revitalization strategy in the new era to make full use of multiple social subjects and improve farmers' IL. [Method/Process] This paper reviews the concept and definition of IL, and analyzes the research on farmers' IL in recent years. The results show that most of the current research on the cultivation of farmers' IL focuses on a specific topic and lacks holistic research. Therefore, it is necessary to systematically understand the cultivation process of farmers' IL, and guide the cultivation behavior of IL by the all-round cultivation concept. [Results/Conclusions] At present, although the local governments have initially built an IL training model of the new era, with schools and social organizations as participants in the model, farmers still lack information knowledge, information awareness, and IL skills. Several proposals are put forward here to address the above issue. First of all,it is necessary to strengthen the construction of IL education system and improve farmers' information knowledge. The government should give full play to the local government agencies in resource integration, schools and scientific research institutions in professional advantages, and social organizations in providing information services, so as to provide farmers with more systematic IL training. Second, efforts should be made to jointly build IL education space to raise farmers' information awareness: the government should build farmers' IL training base, the schools should promote the transformation of the education model, and social organizations continue to carry out IL training project. The three parties join hands to build a three-dimensional integrated IL education space of "material space, spiritual space and social space", and a new way of the cultivating farmers' information awareness. Finally, IL teachers should be trained to improve farmers' information literacy. The government will attract and retain information talent in rural areas through positive talent polices. Schools will play an educational role in developing farmers' information literacy skills.
[Purpose/Significance] Public emergencies frequently trigger online public opinion, exacerbating public panic and threatening social stability. The intrinsic linkage between public emergencies and online discourse amplifies the dissemination of public emotions, attitudes, and perspectives across online platforms, creating a feedback loop that influences event dynamics. Investigating the generation mechanism of public opinion on hot topics in such contexts provides critical theoretical foundations for mitigating cyber discourse risks, while enhancing the accuracy and efficiency of governmental mangement over online public opinion. [Method/Process] From an information ecology perspective, this study employs fuzzy-set qualitative comparative analysis to examine the online public opinion heat of 50 public emergencies between 2020 and 2022. We analyze eight conditional variables across four dimensions - information, information person, information technology, and information environment - including peak propagation speed, peak event popularity, netizen attention, opinion leaders' communication power, important media participation, central media coverage, the proportion of the overall public opinion field, and event duration. Single-factor necessity detection and configuration analysis were performed, and robustness was tested by adjusting calibration points and consistency thresholds. Finally, based on empirical findings, we interpreted case studies and proposed a mechanism for the generation of online public opinion heat in public emergencies. [Results/Conclusions] The results reveal that information and information people are the primary drivers and key causes of hot public opinion. Although information environment and information technology are not necessary conditions, they still contribute to the process. In public emergencies, multiple factors jointly influence online public opinion, and no single factor alone determines its intensity. Rather, the complementarity of multiple factors can, to some extent, substitute for seemingly necessary conditions. The key findings reveal that the event's peak plays a dominant role in driving high online public opinion intensity, and directly triggers its rapid outbreak, while the absence of major media participation and short event duration - core conditions for non-hot events - significantly reduce public engagement due to limited coverage and transient attention. Additionally, opinion leaders' communication power exhibits a strong positive correlation with public opinion on hot topics, as their amplified expressions attract more attention from netizens and further amplify the momentum of the discourse. These findings will provide valuable insights for effectively managing and controlling online public opinion during emergencies. Future research should examine the impact of emotional shifts, such as positive, negative, and neutral emotions, on the virality of online public opinion during emergencies, while also exploring the underlying mechanisms of such emotional shifts. Additionally, future studies should differentiate between policy stages in emergency development and examine how policy interventions shape the dynamics of public opinion. Finally, network analysis techniques (e.g., forwarding relationship networks, key evolutionary network structures) should be employed to uncover the mechanisms that drive public opinion heat in emergency-related discourse.
[Purpose/Significance] Scientific literature contains rich domain knowledge and scientific data, which can provide high-quality data support for AI-driven scientific research (AI4S). This paper systematically reviews the methods, tools, and applications of arge language models (LLMs) in scientific literature data mining, and discusses their research directions and development trends. It addresses critical shortcomings in interdisciplinary knowledge extraction and provides practical insights to enhance AI4S workflows, thereby aligning AI capabilities with domain-specific scientific needs. [Method/Process] This study employs a systematic literature review and case analysis to formulate a tripartite framework: 1) Methodological dimension: Textual knowledge mining uses dynamic prompts, few-shot learning, and domain-adaptive pre-training (such as MagBERT and MatSciBERT) to improve entity recognition. Scientific data extraction uses chain-of-thought prompting and knowledge graphs (such as ChatExtract and SynAsk) to parse experimental datasets. Chart decoding uses neural networks to extract numerical values and semantic patterns from visual elements. 2) Tool dimension: This explores the core functionalities of notable AI tools, including data mining platforms (such as LitU, SciAIEngine) and knowledge generation systems (such as Agent Laboratory, VirSci), with a focus on multimodal processing and automation. 3) Application dimension: LLMs produce high-quality datasets to tackle the issue of data scarcity. They facilitate tasks such as predicting material properties and diagnosing medical conditions. The scientific credibility of these datasets is ensured through a process of "LLMs + expert validation". [Results/Conclusions] The findings indicate that LLMs significantly improve the automation of scientific literature mining. Methodologically, this research introduces dynamic prompt learning frameworks and domain adaptation fine-tuning technologies to address the shortcomings of traditional rule-driven approaches. In terms of tools, cross-modal parsing tools and interactive analysis platforms have been developed to facilitate end-to-end data mining and knowledge generation. In terms of applications, the study has accelerated the transition of scientific literature from single-modal to multimodal formats, thereby supporting the creation of high-quality scientific datasets, vertical domain-specific models, and knowledge service platforms. However, significant challenges remain, including insufficient depth of domain knowledge embedding, the low efficiency of multimodal data collaboration, and a lack of model interpretability. Future research should focus on developing interpretable LLMs with knowledge graph integration, improving cross-modal alignment techniques, and integrating "human-in-the-loop" systems to enhance reliability. It is also imperative to establish standardized data governance and intellectual property frameworks to promote the ethical utilization of scientific literature data. Such advances will facilitate a shift from efficiency optimization to knowledge generation in AI4S.
[Purpose/Significance] Digital literacy education has become the new educational mission of university libraries. Clarifying the user's perception and utilization mechanism of digital literacy knowledge and optimizing the representation of digital literacy knowledge can promote university libraries to achieve satisfying results in digital literacy education. Based on the frontier of representation theory, this study innovatively puts forward the concept of "sensory digital literacy education", constructs a three-dimensional knowledge perception model including action, image and symbolic representation, and reveals the mechanism of digital literacy knowledge representation and user perception behavior through empirical research. It provides a theoretical anchor for the paradigm shift in library education from tool skills training to cognitive skills training. The "cognition-practice-innovation" teaching system and the "three-in-one" resource construction framework proposed in the study effectively connect the knowledge representation theory with the educational practice scene, and provide a viable way for the three-dimensional implementation of digital literacy education in colleges and universities. [Method/Process] Based on the theories of SOR, TAM and self-efficacy, the theoretical hypothesis model of users' perception and utilization of digital literacy knowledge from the perspective of representation was constructed, and was empirically verified by questionnaire and empirical study. [Results/Conclusions] Action representation, reflexive representation and symbolic representation of digital literacy knowledge all positively affect users' perceived ease of use and perceived usefulness of digital literacy knowledge; perceived ease of use has a positive impact on perceived usefulness; self-efficacy plays a positive moderating role between perceived ease of use, perceived usefulness, and user intention and behavior. Due to the limitations of space and personal energy, the shortcomings of this paper are as follows. First, the methodological level is mainly based on quantitative analysis, and the mining of qualitative dimensions such as details of teacher-student interaction and informal learning scenarios in digital literacy education is insufficient. Secondly, the research object focuses on the groups of teachers and students in colleges and universities, and the issues such as the intergenerational differences of the public's digital literacy and the professional digital literacy needs of professionals have not been covered, and the comparative study of multiple subjects can be expanded in the future. In the future, more research can be done on research methods and research objects. Through the deep coupling of representation theory and educational practice, it is expected to provide a new theoretical mirror for the cultivation of cognitive ability in the digital age, and help to build a three-dimensional educational ecology of "technology empowerment-cognitive development-literacy transfer".
[Purpose/Significance] The rapid advancement of artificial intelligence (AI) technology is transforming various sectors, particularly in higher education. The LLaMA (Large Language Model Meta AI) represents a significant innovation in this arena, making its application within university future learning centers increasingly important. As institutions of higher education strive to create environments conducive to learning and growth, understanding the construction requirements of future learning centers becomes paramount. This study delves into the integration of LLaMA core technologies in these learning spaces and emphasizes the importance of evolving libraries into intelligent learning support systems. [Method/Process] The methodology employed in this research combines technical deconstruction and scenes for validation, allowing for a comprehensive analysis of the legal risks associated with embedding advanced technologies in educational frameworks. By systematically examining these potential risks, the study aims to establish a well-rounded perspective on the implications of AI deployment in educational settings. [Results/Conclusions] The study identifies three principal challenges encountered in the application of the LLaMA within university learning centers. The first challenge arises from reliability risks linked to content generated by the AI, which may be affected by biases present in the training data. Such biases can lead to the dissemination of inaccurate or misleading information, undermining the trustworthiness of educational resources. Secondly, there are privacy leakage risks, particularly associated with the retention of user behavioral data. As AI systems analyze user interactions, there is a potential for sensitive information to be exposed or misused, raising concerns about student privacy and data security. The third challenge involves ownership determination dilemmas regarding the content generated through AI-driven creative processes. These dilemmas are intricately tied to existing copyright law frameworks, which may not adequately address the complexities introduced by human-machine collaboration in content creation. In response to these challenges, the study proposes several pathways for governance aimed at effectively navigating the landscape of AI in education. It suggests the implementation of dynamic data cleansing mechanisms to address reliability risks and inaccuracies. Additionally, establishing tiered privacy protection systems can help safeguard against user data breaches. Legal frameworks also need refinement to ensure clear ownership distribution for outputs of human-machine collaboration. Ultimately, optimizing the application of the LLaMA model in university future learning centers necessitates a careful balance between technological innovation and legal regulation. By focusing on technical refinement, risk control, and relevant regulatory measures, the development and application of AI can be advanced, facilitating a more integrated evolution of artificial intelligence and educational practices.
[Purpose/Significance] Under the background of digital government construction, as a new type of service subject of human-machine collaborative governance, the influence mechanism of the social role positioning of government digital humans on public adoption behavior urgently needs theoretical exploration. Most existing studies have focused on the technical level. This study, based on the perspective of social role theory, explores the influencing mechanism of different role positioning of government digital humans in government service scenarios on public adoption behavior, which is of great significance for optimizing government services and improving the intelligent level of government services. [Method/Process] An experimental research method was adopted to construct a two-factor inter-group experimental design of "social role-business type", and a simulation experiment of government service scenarios was carried out through random grouping. Based on previous studies, we defined the role positioning of "advisors" and "decision-makers" for government digital humans, and constructed experimental scenarios by combining two service scenarios of consultation and approval. The subjects were randomly grouped to complete the role cognition test and human-computer interaction tasks. Data were collected by using the research path combining situation simulation and questionnaire survey. The psychological mechanism and decision-making logic of the public's adoption behavior were analyzed through the data analysis results. [Results/Conclusions] The research findings are as follows: 1) There is a significant interaction effect between the social roles and business types of government digital humans. In approval service scenarios, the decision-maker role is more capable of promoting public adoption behavior than the advisor role; 2) Human-computer trust perception plays a crucial mediating role in the influence path of social roles on the public's adoption behavior, revealing the core value of the trust mechanism in human-computer interaction; 3) The synergy effect between role authority and task fit constitutes an important mechanism influencing public cognition. This study expands the explanatory boundary of the social role theory in the field of intelligent government services and provides theoretical support for the construction of smart government services. However, there are still certain limitations. The service scenario simulation in the experimental design is difficult to fully restore the complexity of real government services. Future research can extend the multi-dimensional role classification system and deepen the mechanism exploration by combining the mixed research method. We have verified the applicability of the theoretical model in real government service scenarios and expand the existing conclusions. In addition, exploration on the dynamic impact of long-term interaction between government digital humans and the public on behavioral evolution is also a potential research direction.
[Purpose/Significance] The ongoing digital transformation has led to significant changes in public cultural services, particularly in content generation, communication channels, and modes of public participation. "Accessibility," a key indicator of the extent to which citizens' cultural rights are realized, is typically assessed along four dimensions: availability, acceptability, accessibility, and adaptability. Previous research has focused primarily on the supply side of accessibility, examining how factors such as the distribution of cultural resources, infrastructure development, and policy support affect user engagement. However, with the widespread adoption of digital technologies, individuals' ability and willingness to access information, utilize services, and provide feedback - collectively referred to as "digital literacy" - has become an increasingly important variable influencing cultural participation. Consequently, this study seeks to explore the relationship between users' digital literacy and the accessibility of public cultural services from a demand-side perspective. It aims to provide a more systematic theoretical framework and practical approach to optimizing the effectiveness of public cultural services. [Methods/Process] This study assesses users' digital literacy by examining their level of digital access, Internet usage, and service availability based on data collected from the Beijing-Tianjin-Hebei region. A structured questionnaire yielded 892 valid responses. To analyze the relationship between users' digital literacy and the accessibility of public cultural services, the study applies a generalized ordered logit model. A generalized ordered logit model is employed to analyze the substitution and overlap effects between users' digital literacy and the various dimensions of service accessibility. [Results/Conclusions] There is currently a digital divide exists between different demographic groups. A significant substitution effect is observed between traditional public cultural accessibility and users' digital literacy, with limited overlap between the two. Digitization has driven the modernization of public cultural resources and services, particularly in terms of technology and service delivery. However, there remains a time lag between the users' digital literacy of users and the digital transformation of the public cultural supply side. This lag suggests that the digital needs of users and the availability of digital cultural services are not fully aligned, which negatively impacts the effectiveness of public cultural services. Therefore, enhancing users' digital literacy, especially improving their ability to adapt to digital cultural resources, is a crucial factor in transitioning public cultural services from "accessibility" to "enjoyment". In promoting the digital upgrading of public cultural services, greater emphasis should be placed on developing users' capabilities and anticipating their needs.
[Purpose/Significance] With the globalization of knowledge sharing and the vigorous development of preprint at home and abroad, the role of preprint platform in academic exchange has been recognized and appreciated by academic community. This paper introduces the evolutionary simulation method for the first time from the previous research on government and enterprises to the research on preprint platform, and takes the three main stakeholders in the construction of preprint platform, that is government, researcher and the public as the main players of the game. Different from the existing research, this paper uses system dynamics theory and software to fill the gap in quantitative analysis of SD model, and combines qualitative and quantitative research to further enrich the research content of the preprint platform through game model construction and simulation analysis. This research aims to guide stakeholders to actively participate in the construction of preprint platform, improve the utilization rate of domestic preprint platform by users, and promote the construction of preprint platform in China. [Method/Process] This study established a tripartite evolutionary game model of "government, researcher, and the public" to analyze the strategic stability of the three stakeholders. Vensim PLE software was used to simulate and analyze the the SD model, focusing on the influence of mixed strategies and external sensitivity variables on stakeholders' decision behavior. [Results/Conclusions] In the construction of preprint platform, the willingness of government supervision is mainly influenced by the supervision cost and credibility Within a reasonable range, the higher the scientific research funding for researchers or the more severe the penalty for their passive participation, the greater the willingness of researchers to participate actively. The public's willingness to cooperate is influenced by the costs of participation and the social dividends. In the future, the construction of the preprint platform can be continuously promoted from three perspectives: formulating the framework of the underlying reward and penalty mechanism of the preprint platform, establishing the reputation evaluation mechanism of researchers, and accelerating the construction of the government's open scientific innovation service. However, due to the limitation of the author's professional ability, the cognition of the preprint platform and the consideration of the relevant policy establishment process are relatively limited. There are many stakeholders involved in the construction of preprint platform, and there are also many factors that can affect the decision-making behavior in the external environment and system. In this study, three stakeholders from three main aspects are selected to model and study the external influence. In the future, we can select stakeholders from different angles and increase the influencing factors to expand the research on preprint platform.
[Purpose/Significance] Promoting the digital transformation of agricultural product circulation through e-commerce has become a crucial way for rural revitalization in China. For three consecutive years, China's No. 1 Central Document has listed the high-quality development of agricultural e-commerce as a priority for upgrading the level of rural industrial development. However, persistent disparities in information literacy and imbalance in risk-benefit perceptions among farmer groups constrain the effective popularization of e-commerce platforms for agricultural products. To address this issue, this study integrates the Theory of Planned Behavior (TPB) and the perceived benefit-risk theory to construct a conceptual framework. It explores the relationship pathways among information literacy, perceived risks, perceived benefits, government support, and farmers' willingness to participate in e-commerce, aiming to provide theoretical insights for governments and enterprises to deepen the high-quality development of agricultural e-commerce business and rural revitalization. [Method/Process] Based on the above background, this paper integrates and proposes a conceptual model that includes the relationship of five potential variables: information literacy, perceived benefits, perceived risks, government support and engagement intention, based on the theory of planned behaviour and the theory of perceived benefits-perceived risks. In order to ensure the appropriateness of the sample distribution as well as the convenience, authenticity and reliability of the data collection, this study used a combination of online (WeChat group of village committees) and offline (recruiting home-based university students for field survey) to collect questionnaires from farmers across the country, and a total of 730 valid farmers' sample data were collected. Finally, based on the above data, the direct paths of perceived benefits, perceived risks and farmers' information literacy on farmers' willingness to participate in agricultural e-commerce were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). We focus our analysis on verifying the mediating roles of perceived benefits and perceived risks, as well as the moderating role of government support, in enhancing farmers' willingness to participate in agricultural e-commerce. [Results/Conclusions] The findings reveal that increased information literacy strengthens farmers' willingness to engage in agricultural e-commerce. Most farmers prefer participation scenarios with high perceived benefits and low perceived risks, where government support plays a key role in endorsing and leading trust. In this regard, local governments should establish tiered training systems and risk-hedging mechanisms (e.g., agricultural insurance, logistics subsidies) to address age-specific demand for information literacy improvement in rural areas and mitigate operational risks. We suggest actively publicizing national high-quality rural e-commerce demonstration cases and improving the perception of benefits to motivate farmers to participate, so as to achieve the high-quality development of agricultural e-commerce in a multi-initiative way. In addition, future research should pay more attention to the breadth of the sample coverage and the depth of the sample research process, and consider using all offline field research to further examine the impact of regional differences and the differences in the digital characteristics of the new farmers (Generation Z) on their willingness to participate in e-commerce. This will provide empirical evidence and guidance for rural revitalization and high-quality development of agricultural e-commerce.
[Purpose/Significance] In recent years, large language models (LLMs) have achieved revolutionary breakthroughs in semantic understanding and generation capabilities through massive text pre-training. This has injected brand-new impetus into the field of knowledge engineering. As a structured knowledge carrier, the knowledge graph has unique advantages in integrating heterogeneous data from multiple sources and constructing an industrial knowledge system. In the context of a paradigm shift in the field of knowledge engineering driven by the emergence of open-source LLMs such as DeepSeek, this study proposes a cost-effective method for constructing domain knowledge graphs based on DeepSeek. We aim to address the limitations of traditional domain knowledge graphs, such as high dependence on expert rules, the high cost of manual annotation, and inefficient processing of multi-source data. [Method/Process] We proposed the semantic understanding-enhanced, cue-engineered domain knowledge extraction technology system, constructed on the methodological framework of manually constructing ontology modelling. In order to process the acquired data, the ETL\MinerU and other tools were used, and the DeepSeek-R1application programming interface was then invoked for intelligent extraction. The ontology model was designed based on domain cognitive features and the multi-source heterogeneous data fusion method was used to achieve the unified characterization of the data structure. Furthermore, the DeepSeek and knowledge extraction were combined. Our system provides a cost-effective reusable technical paradigm for constructing domain knowledge graphs, as well as efficient knowledge extraction, leveraging the advanced powerful textual reasoning ability of the DeepSeek model. [Results/Conclusions] In this study, we take the construction of a domain knowledge map of the entire pig industrial chain as an empirical object. We define the structure of the industrial chain, identify 21 types of core entities and describe their attribute relationships. We achieve the knowledge modelling of the pig industry with a focus on smart farming. The methodology developed in this research was also employed to process and extract knowledge from online and offline resource data. Preliminary experiments demonstrate that DeepSeek-R1 exhibits an F1 value of 0.92 when recognizing the attributes of 161 diseases and 11 types of entities in pig disease control scenarios under zero-sample learning conditions. These experiments also ascertain the reusability of the methodology for other links in the chain. Concurrently, the constructed knowledge map of the entire industrial chain of pigs will be utilized for the design and validation of intelligent application scenarios, with the objective of promoting the intelligent information processing in the pig industry. This study proposes a synergistic paradigm for constructing domain knowledge graphs using DeepSeek, a method that combines deep learning with manual calibration for efficient knowledge extraction and ensure accuracy. This approach ensures the efficiency of knowledge extraction and verifies the knowledge extraction potential of LLMs in vertical domains. The study's findings contribute to the extant literature and offer a practical reference for the promotion of DeepSeek-enabled cost-effective construction of knowledge graphs.
[Purpose/Significance] Red cultural relics are a testimony to the arduous and glorious struggle of the Communist Party of China and its most precious spiritual wealth. In recent years, with the development of digital technology, the digital construction of red relics has made remarkable progress. However, the digital construction of red cultural resources is a complex and multi-dimensional process that still faces numerous challenges. With the comprehensive promotion of the Development Action Plan for the Trusted Data Space (2024-2028), the circulation of data elements, the co-creation of value, and security governance have become key issues in digital construction, which also brings new opportunities for the digital construction of red cultural resources. [Method/Process] Through literature review and online survey, we summarized the achievements made in the theoretical research and practical exploration of the digital construction of red cultural resources, and analyzed the challenges faced in terms of data circulation, technical application, security protection, governance mechanisms, talent and financial support. From the strategic, resource, technical, and social levels, we expounded on the value and significance of the construction of trusted data space in facilitating the digital construction of red cultural resources, and conducted a preliminary exploration of the construction approaches for the trusted data space. [Results/Conclusions] In terms of the key construction points, an operational framework for the trusted data space of red cultural relics will be established around three dimensions: construction of the data space supply system, construction of the core competence system, and cultivation of various types of data spaces. In terms of the implementation path, measures for the construction of the trusted data space of red cultural resources are proposed in four aspects: policy and system, technological empowerment, talent strategy, and social co-governance. Specifically, we provide institutional guidance in four aspects: improving the standardized management system and supervision mechanism, formulating technical specification standards, establishing a capital investment support mechanism and distribution system, and improving a dynamically optimized evaluation and feedback mechanism. We are providing technical empowerment in four aspects: conducting core technology research and development, strengthening the supply of basic capabilities, focusing on the development and application of artificial intelligence, and attaching importance to information security protection. We adopt the talent strategy of "attract, cultivate, utilize and retain" to build a high-level talent team for the trusted data space. We conduct social governance in three aspects: strengthening the overall planning and coordination of the government, promoting exchanges and cooperation among enterprises, and encouraging the public to jointly participate in building a new publicity and promotion matrix for the trusted data space of red cultural resources. The aim is to provide new perspectives for the quality development of the digital construction of red cultural resources.
[Purpose/Significance] High-quality development of new quality productive forces cannot be achieved without the support of intellectual property rights. Intellectual property (IP) has emerged as a new type of production factor with catalytic and leveraging effects, presenting both opportunities and challenges for the intelligent transformation and development of IP information literacy education in university libraries. This study aims to explore the current status and innovative paths of IP information literacy education in university libraries against the backdrop of new quality productive forces, providing theoretical references and practical foundations for the transformation and development of libraries. [Method/Process] Through a review of relevant domestic and international literature, it was found that existing research has primarily focused on investigating and analyzing the status of intellectual property information literacy education in universities, without incorporating an analysis of the demand for new quality intellectual property talent in the context of new quality productive forces. The article summarizes existing research and conducts an online survey of IP education practices in 30 national intellectual property demonstration universities, examining dimensions such as platform setup, participating entities, educational content, educational formats, branding, and special topic settings. From the perspective of participating entities and training objectives, educational formats and talent application, educational content and practical needs, as well as promotion goals and methods, the article discusses the issues currently existing in IP information literacy education in universities against the backdrop of new quality productivity, and proposes corresponding strategies. [Results/Conclusions] New quality productivity is driving the integrated upward development of various industries. As the main institution for IP information literacy education, libraries should seize the development opportunities, cultivate forward-looking new quality intellectual property talented people, continuously strengthen their IP information literacy teaching ability by analyzing their own weaknesses, They should grasp the current wave of emerging technologies, enhance human resources development, constantly innovate educational concepts, innovate service models, attract multiple entities to participate in building an industry-university-research integration community, and thereby promote the high-quality transformation and development of libraries. A limitation of this article is that it only conducts a survey of educational institutions in universities without involving a survey of educational object needs. In subsequent research, a method based primarily on field research will be adopted to expand the scope of the survey.
[Purpose/Significance] The evolution of smart libraries has ushered in a new era, marked by the integration of multimodal learning technologies that combine information from various modalities such as speech, images, and video. This cutting-edge technology is revolutionizing traditional information service systems by providing a more interactive, efficient, and personalized user experience. Unlike traditional studies that focus on single-mode interactions, this research examines the role of multimodal technologies in transforming library services and increasing user engagement. The study highlights its unique contributions to the field of library science, particularly in improving knowledge dissemination, enhancing user-centered services, and addressing emerging challenges in digital information management. These findings not only enrich the theoretical framework of smart libraries, but also provide practical insights into the design and deployment of advanced information services. [Method/Process] This study takes a multidisciplinary approach, drawing from library science, information technology, and human-computer interaction theories. It systematically reviews the historical development and theoretical foundations of multimodal learning technologies while emphasizing their relevance to intelligent library ecosystems. The analysis is organized around key application areas, including intelligent navigation, intelligent question and answer systems, user education with intelligent support, and immersive reading experiences. These areas were explored through a combination of case studies, and a detailed analysis of current library practices. To evaluate the practical impact of these technologies, the study employed qualitative methods, analyzing user feedback and system performance metrics. This comprehensive research also identifies current barriers to adoption, such as data privacy concerns, technology costs, and disparities in user acceptance across different demographics. [Results/Conclusions] The results show that multimodal learning technologies significantly enhance the functionality and user experience of smart libraries. They improve the accuracy of information retrieval, enable more interactive and immersive learning environments, and enable personalized services tailored to individual needs. Despite these advantages, challenges remain, particularly in areas such as securing user data, reducing deployment costs, and increasing accessibility for underprivileged users. The study proposes actionable strategies to address these issues, including enhancing system interoperability, refining ethical frameworks, and fostering human-computer collaboration to reduce barriers to technology adoption. It also identifies gaps in current research, such as the need for more empirical studies of long-term user interaction patterns and the scalability of multimodal systems in large library networks. Future studies could also explore the integration of emerging technologies such as augmented reality (AR) and artificial intelligence (AI) into multimodal library services to further improve their efficiency and reach. By providing a robust framework and practical strategies, this study contributes to the ongoing discourse on smart library innovation, and paves the way for more sustainable and inclusive information service models. It underscores the transformative potential of multimodal technologies to redefine library science and advance the global digital information landscape.
[Purpose/Significance] As artificial intelligence (AI) systems are being widely deployed across diverse domains such as education, healthcare, and public governance, the absence of standardized metadata specifications has led to fragmented descriptions, inconsistent documentation, and difficulties in model evaluation and reuse. This study aims to address the pressing issues of opacity, lack of interpretability, and poor traceability in current AI models, which have increasingly become obstacles to the development of transparent and responsible AI. To overcome these challenges, this study proposes the establishment of a unified metadata specification for AI models to enhance their discoverability, transparency, interoperability, and reusability, thereby advancing the development of trustworthy AI and facilitating effective model governance. [Method/Process] Grounded in metadata quality assessment theory and lifecycle theory, the study adopted a combination of research methods, including literature review, comparative analysis of existing specifications, and questionnaire surveys.We first conducted a systematic examination of domestic and international practices related to AI model metadata specifications to identify representative standards, frameworks, and implementation approaches. Through comparative analysis, the study investigated the structure, element organization, and semantic relationships of different specifications, highlighting their similarities, differences, and areas for improvement. Meanwhile, a targeted questionnaire survey was administered to researchers, developers, and practitioners to explore user awareness, perceptions, practical experiences, and specific needs regarding metadata specification and interoperability. Based on these findings, the study ultimately proposed a lifecycle-oriented framework for metadata specification construction, ensuring that it aligns with the key stages of AI model development, deployment, evaluation, and governance. [Results/Conclusions] The findings reveal that, although users generally recognize the importance of metadata specifications for AI models, they are unaware of of the existing specifications. The current AI model metadata specifications have significant shortcomings in terms of element naming, structural organization, and descriptive granularity. These shortcomings hinder the effective sharing and reuse of model information. In response, the study proposed a comprehensive metadata framework encompassing key entities such as models, datasets, algorithms, technical features, performance evaluations, risks and ethics, legal information, and related resources, as well as the semantic relationships among these entities. The research concluded that establishing a unified metadata specification for AI models not only contributes to effective information management and cross-platform interoperability, but also serves as a critical infrastructure that links technology, ethics, and governance. As the metadata specification system matures and gains wider industry adoption, AI models will become increasingly controllable and trustworthy. This will promote a more regulated, collaborative, sustainable and integrated AI ecosystem.
[Purpose/Significance] Against the backdrop where artificial intelligence generated content (AIGC) is reshaping the paradigm of knowledge production, exploring how to integrate the context-aware capabilities of large models into the knowledge service framework, and on this basis, providing a new interpretation and service expansion of the Five Laws of Library Science, is of great significance for the construction of the AI + knowledge service system. [Method/Process] Starting from the perspective of contextual integration, and with Wilson's Information Behavior Theory and the SECI Model as the theoretical foundation, this study constructs a three-dimensional integrated coupling framework of "demand-knowledge-context". By aligning knowledge context with user context, it dynamically perceives users' knowledge needs and provides proactive responsive services. Based on this framework, it further conducts a contextualized interpretation of the Five Laws of Library Science. [Results/Conclusions] With the integration of all contextual elements as the link, the "demand-knowledge-context" three-dimensional framework forms an effective mechanism for matching user needs with knowledge resources. It also achieves continuous reinforcement learning through information feedback to acquire the ability of self-evolution, aiming to continuously adapt to and better meet users' knowledge needs in complex and changing contexts. Based on this framework, the study conducts contextualized interpretation and theoretical expansion of the Five Laws, endowing library services with new connotations of vitality and intelligence. Furthermore, it proposes a context-driven ecological evolution path for knowledge services: the internal context focuses on the recombination of organizational genes, while the external context emphasizes the dissolution of ecological boundaries, exploring how classic library theories can achieve innovative development with the support of new technologies. As AIGC technology continues to develop, further in-depth research into contextual elements should be conducted, particularly into implicit contextual elements such as users' emotions and psychology. Efforts should be devoted to strengthening interdisciplinary collaboration, incorporate theories and methods from disciplines such as psychology and sociology into library science research, and continuously optimize the knowledge service system of smart libraries. This will make the system more adaptable to complex and changing contexts, provide users with higher-quality, more efficient, and personalized knowledge services, and help library science achieve significant development in the AIGC era. This paper proposes a three-dimensional "demand-knowledge-context" framework that aims to accurately match user needs with knowledge resources by integrating knowledge and user context in depth. Based on this, the "context-driven ecological evolution approach for knowledge services" is put forward as an exploratory implementation approach. However, subsequent research is required to implement, verify and conduct an in-depth empirical analysis of this approach.
[Purpose/Signficance] In the context of the increasingly widespread adoption of digital communication, agriculture-related emergencies often trigger complex and ever-changing public opinion online due to their high level of specialization and the significant cognitive barriers they pose to the general public. Emotional factors play a pivotal role in the evolution and governance of online public opinion. However, current research into how public opinion is guided in relation to agricultural emergencies still fails to systematically address emotional factors. [Method/Process] Therefore, the study constructed an analytical framework for emotional guidance in agricultural-related public opinion, integrating information subjects, information content, and the information environment. The framework was based on three complementary theories: information ecology theory, social amplification of risk theory, and negativity bias theory. It explored the correlations and combined effects of emotional factors with individual audiences, media, and the information environment. A total of 31 online public opinion cases involving agriculture, rural areas, and farmers were selected from the "Public Opinion Daily Reports" published by the People's Daily Online Public Opinion Data Center, covering the period from January 2021 to June 2025. The Weibo platform was chosen for this study, and data were collected by searching for case names and related topics on Weibo to capture raw data for conditional and outcome variables. Sentiment analysis was introduced to identify and quantify emotional characteristics in public opinion, and fuzzy-set qualitative comparative analysis (fsQCA) was employed to investigate how various factors collectively influence the guidance of online public opinion in public emergencies. The aim is to reveal the emotional guidance mechanisms and the logic behind effect formation in online public opinion regarding agricultural emergencies. [Results/Conclusions] The study found that public opinion in agriculture exhibits typical characteristics of equifinal multiple causation, whereby various combinations of factors can produce similar guiding effects. In contexts of high emotional polarisation, the pathways may rely on traffic restriction and emotional substitution regulation. In contexts of low emotional polarization, they may rely on the construction of emotional framing by authoritative media and opinion leaders. In different contexts, information clarity and netizens' emotional involvement can form a substitution relationship with the degree to which the platform intervenes in emotional regulation. This necessitates dynamic adjustments to guidance strategies based on specific situations. Based on this, the governance of agriculture-related public opinion online should shift towards a systematic emotional governance framework that leverages affective computing to expand the range of channels and strengthen the basis of public opinion. Efforts should also be devoted to strengthening dynamic response mechanisms based on real-time emotional monitoring. The aim should be to construct a sentiment guidance system for public opinion featuring dynamic allocation, multi-party collaboration, and precise reach.
[Purpose/Significance] In recent years, the rapid rise of large language model technology has shown significant advantages in understanding semantic context and capturing multidimensional sentiment tendencies. This study explores an aspect-level sentiment analysis method for science and technology policy comments based on large language models, aiming to uncover latent knowledge within these texts and provide data support for evaluating the effectiveness and subsequent optimization of policies. [Method/Process] Taking the electric vehicle industry as an example, a burgeoning sector vital to achieving the "dual carbon" goals and promoting green low-carbon development, this study proposed a policy satisfaction evaluation model. The model uses large language models for fine-grained aspect-level sentiment analysis of policy comment texts. The process includes the following steps: 1) Data collection and preprocessing: Comments related to electric vehicle policies were collected from the "Interactive Topics" section of the "Autohome" website using Python. Deep learning techniques were applied to set rules for the comment texts and automatically add punctuation marks to Chinese texts for data pre-processing. 2) Aspect word extraction: The steps include text tokenization, determining a candidate aspect word set, expanding the aspect word set, and clustering aspect words. A total of 3 405 aspect words were extracted from 35 000 comments, forming six clusters: infrastructure construction, vehicle performance configuration, national policies, technological development, automotive safety, and automotive sales market. Aspect-level sentences were extracted using aspect words and punctuation information, with a subset of sentences manually labeled to build training and validation corpora, resulting in 14 911 aspect-level sentences. 3) Sentiment tendency recognition model training: A prompt template for aspect-level sentiment classification tasks was designed, and the LoRA method was used to fine-tune the large language model with the manually labeled training set. The model's performance was evaluated using a validation set, resulting in the classification of comments on electric vehicle policies into positive, neutral, and negative sentiments. 4) Comparative experiment: The fine-tuned large model was compared with the mainstream sentiment classification model, BERT, to assess the performance of different models in aspect-level sentiment classification tasks. [Results/Conclusions] The results show that compared to the BERT model, the proposed method outperformed other methods in multiple metrics, including accuracy, recall, and F1 score, with improvements of 11.49%, 12.43% and 11.43%, respectively. Overall, public attention is higher towards vehicle performance configuration and automotive sales market, while infrastructure construction receives the lowest attention. The overall public satisfaction with electric vehicles is relatively low, with negative comments outweighing positive comments across all aspects, consistent with the "negative bias" theory in social psychology. Satisfaction issues are particularly prominent in the areas of automotive safety and infrastructure construction. Finally, policy recommendations have been proposed to optimize electric vehicle subsidy policies, strengthen policy promotion, improve infrastructure construction, and enhance after-sales service support systems.
[Purpose/Significance] The rapid advancement of digital infrastructure has precipitated a fundamental transformation in scholarly communication, characterized by an increasing reliance on online platforms. Preprint exchange, as a cornerstone of open science, offers researchers opportunities for immediate dissemination and collaborative engagement. However, the absence of rigorous peer review raises persistent concerns regarding research ethics, data integrity, and the reliability of scholarly outputs, which can undermine public confidence in preprint platforms. Addressing these challenges is essential not only for maintaining the integrity of academic discourse but also for fostering a transparent and trustworthy open science ecosystem. This research contributes to the existing scholarship by systematically examining the trust framework of preprint platforms, positioning itself at the intersection of library and information science and scholarly communication studies. In contrast to previous investigations that have focused predominantly on dissemination efficiency or platform functionality, this study emphasizes the structural dimensions of trustworthiness. It presents an innovative analytical framework that strengthens the theoretical foundations of academic information trust and provides practical strategies for enhancing the governance and legitimacy of preprint platforms. [Method/Process] To ensure both theoretical rigor and empirical depth, first, a comprehensive literature review was conducted to identify potential trust-related vulnerabilities in preprint platforms and to systematically delineate their credibility challenges. This review identified five critical factors influencing the credibility of preprint platforms: academic conflicts of interest, platform reliability, heterogeneous manuscript quality, information overload, and insufficient academic recognition. Drawing upon the DeLone & McLean (D&M) Information Systems Success Model and aligning with the ISO 16363 standard for trustworthy digital repositories, the study analyzed the structural components of trustworthiness through the dimensions of system quality, information quality, and service quality. Subsequently, in-depth case studies of prominent platforms, including arXiv and ChinaXiv, were undertaken to examine their governance architectures, operational methodologies, and practical implementations. This process culminated in evidence-based recommendations for enhancing platform trustworthiness. This integrated methodological framework not only synthesizes theoretical insights with empirical evidence but also ensures the scientific rigor, reliability, and practical applicability of the proposed trust model. [Results/Conclusions] Based on these findings, a three-dimensional trust framework was developed, encompassing system trustworthiness, information trustworthiness, and service trustworthiness. This framework transcends traditional quality control paradigms and offers novel perspectives for the standardized development of preprint platforms. The research further articulates pathways for establishing trustworthiness across three levels: 1) system trustworthiness, adhering to FAIR principles and implementing long-term preservation strategies to provide a stable institutional foundation; 2) information trustworthiness, establishing a comprehensive quality governance continuum that incorporates "pre-screening, dynamic identification, and post-peer review" mechanisms; and 3) service trustworthiness, delivering professional preprint services through collaborative governance models and journal coordination frameworks.While this framework provides a comprehensive analytical perspective, certain limitations should be acknowledged. This study's primary reliance on qualitative methods necessitates broader empirical validation. Furthermore, its focus was on platform functionalities rather than user perceptions. Consequently, future research can adopt a mixed-methods approach, incorporate user perception theories, and establish quantitative metrics for evaluating platform trustworthiness.
[Purpose/Significance] Science and technology have emerged as pivotal domains of competition between China and the United States. This article provides a quantitative analysis of US technology think tanks reports on the electronic information research and industry, with a focus on the evolution of themes and topics over the past decade. This analysis not only reflects their technological priorities but also maps their analytical focus on China, providing decision-making support for China's think tanks development and strategic response. [Method/Process] Based on the "2020 Global Go to Think Tank Index Report" released by the Think Tanks and Civil Societies Program (TTCSP) at the University of Pennsylvania, considering factors such as think tank authority, research topic relevance, and research continuity, we collected a total of 1 360 reports on the electronic information research and industry published between 2015 and 2024 by 8 leading US technology think tanks. Topic analysis was conducted with BERTopic, a topic modeling tool based on Transformer embeddings. The methodology involved several key steps. First, text cleaning was performed using NLTK tools; then, the all-MiniLM-L6-v2 model was employed to generate high-dimensional document embedding vectors. Subsequently, dimensionality reduction was achieved through the UMAP algorithm, followed by density clustering using the HDBSCAN algorithm. Finally, topic words were extracted based on the c-TF-IDF algorithm. [Results/Conclusions] The research identified 31 distinct research themes, of which 6 were directly related to China, specifically: global semiconductor industry competition, Sino-US digital policies and cloud computing competition, 5G network and technology competition, Chinese AI investment, Sino-US science and innovation policies, and Sino-US military technology competition. These 31 research themes were hierarchically clustered using HDBSCAN and could be categorized into 11 major research directions. The US technology think tanks persistently focused on 11 major research directions, which were largely concentrated on key areas of electronic information research and industry, such as semiconductors and microelectronics, artificial intelligence, wireless communication, quantum information technology, network security, and big data. The evolutionary trends across these research directions were generally consistent, with military technology and network security receiving the highest level of attention. The attention attached to China has undergone a significant strategic shift over the years, with drastic increase in semiconductor export control, AI technology and Sino-US digital competition. Based on the identified key themes and topic words, it is highly recommended to establish an evolutionary mapping of China-related topics and to develop a dynamic monitoring and early warning mechanism for technology issues concerning China. Future research could incorporate larger-scale corpus resources and more advanced large language models to continuously optimize topic modeling effectiveness.
[Purpose/Significance] Artificial intelligence is reshaping the landscape of higher education, driving the transformation of university library resource development from a "resource supply-oriented" model to an "efficiency-driven" one. Traditional evaluation systems, constrained by single-dimensional indicators and manual data collection, fail to meet the demands of intelligent transformation. This study takes the Library of Beijing Institute of Technology (BIT), a research university library, as a case study to construct a three-dimensional efficiency evaluation system for resource development. Unlike previous studies that focus on theoretical model construction, this research integrates the practical experience of the BIT Library since 2021, combining team building, technological empowerment and institutional design to form an implementable evaluation system, which provides replicable references for the intelligent development of resource construction in domestic university libraries. [Method/Process] This study adopts a mixed research method combining case study and quantitative-qualitative analysis, with theoretical foundations in library science theories such as resource life cycle management and interdisciplinary theories including artificial intelligence application. The empirical data are derived from the operational data of the BIT Library from 2021 to 2025 (such as procurement records and user behavior data). The construction of the evaluation system consists of five links: 1) We establish a hierarchical training system covering all librarians, offering expert lectures and implementing the pairing model of "data analyst + subject librarian"; 2) We use the analytic hierarchy process (AHP) to determine indicator weights, adding the "interdisciplinary adaptability" indicator for emerging fields, and building a full-process evaluation model; 3) We construct an AI-empowered platform integrating multi-source data, which significantly shortens the duration of manual data processing; 4) We carry out in-depth research in collaboration with colleges and research teams, and conduct benchmarking analysis with top university libraries; 5) We establish a scientific decision-making mechanism linking evaluation data with the University Library Committee and various colleges of the university. [Results/Conclusions] The application of this system in the BIT Library has achieved remarkable results: the accuracy of library resource guarantee has been significantly improved, the efficiency of resource utilization has risen substantially, the capacity for scientific research support has been notably enhanced, and the service satisfaction has undergone a leapfrog improvement. However, the evaluation system still has limitations such as data privacy risks, insufficient AI literacy of some librarians, and lack of inter-library collaboration. Suggestions for targeted action include adopting federated learning technology to protect data privacy, carrying out hierarchical AI training, and establishing a regional evaluation alliance. Future research will explore the specific application of generative artificial intelligence in the evaluation system and establish a dynamic adjustment mechanism for indicators adapted to technological and disciplinary development.
In the digital economy era, the efficient, secure, and compliant circulation of cross-border data flow has become a key issue for the coordination of global industrial chains and the deepening of regional cooperation. It is a driving force for the high-quality development of the global digital economy. Currently, cross-border data flow is confronted with multiple challenges, including the interweaving of driving forces and contradictions, inadequate adaptation between mechanisms and technologies, and poor connection between compliance requirements and practical implementation. There is an urgent need to formulate systematic solutions from both theoretical and practical perspectives. To this end, this journal has invited five experts from universities and enterprises to organize a roundtable discussion on the complete logical chain of "the underlying logic, mechanism construction, trend prediction, compliance governance, and scenario-based implementation of cross-border data flow". The key viewpoints are as follows: 1) Dynamic Mechanism and Governance Logic of Cross-border Data Flow: Cross-border data flow is jointly driven by three major forces: economic interests, technological innovation, and international cooperation. Meanwhile, it faces core contradictions including the trade-off between sovereign security and flow efficiency, fragmentation of rules and institutional coordination, and technological balance and the digital divide. It is necessary to establish a governance philosophy of "dynamic balance" and build a multilateral co-governance system through three types of tools-algorithm-based supervision, technology empowerment, and institutional experimentation-to promote the shift from "fragmented rule-based games" to "systematic coordination". 2) Construction of a Collaborative Mechanism for Cross-border Data Flow: The mechanism for cross-border data flow needs to break through the limitations of a single dimension and form a multi-dimensional collaborative system integrating "policy, technology, and industry". At the policy level, regulatory sandbox pilots, standard mutual recognition, and compliance infrastructure sharing are adopted to address regulatory barriers. At the technical level, scenario-specific needs are met based on a maturity gradient, and the integrated innovation of "technology + management" is promoted. At the industry level, the self-regulatory role of professional fields such as library and information science (LIS) is leveraged to compensate for the rigidity of policies and build a closed-loop governance structure. 3) Trend Evolution and Risk Resilience of Cross-border Data Flow: In the next 3 to 5 years, cross-border data flow will exhibit characteristics of structural growth and domain differentiation. Smart manufacturing and digital trade will drive growth on a large scale, while smart healthcare and modern agriculture will emerge as core sectors. It is imperative to address bottlenecks in infrastructure upgrading and the impact of "black swan" events, establish a risk resilience system from technical, governance and strategic dimensions, and promote service model innovation in LIS as well as advance layout in the agricultural sector. 4) Compliance Governance and China's Path for Cross-border Data Flow: China has established a hierarchical and classified governance framework centered on three fundamental laws, and explored practical paths through institutional innovations such as the negative list system in free trade pilot zones. To tackle challenges including discrepancies in legal compliance requirements, technical barriers, and the complexity of regulatory coordination, it is necessary to strengthen legal synergy and rule mutual recognition, advance infrastructure construction and technological innovation, and improve the compliance service support system, thereby forming a China-specific path that balances security and controllability with high efficiency and convenience. 5) Practice of Cross-border Data Circulation and Credit Product Mutual Recognition: Cross-border data circulation lays a core foundation for the cross-border mutual recognition of credit products, which holds significant strategic value for promoting the facilitation of international trade and supporting the international development of enterprises. Currently, it faces challenges such as data security compliance, standard discrepancies, and high technical costs. To advance the implementation of cross-border mutual recognition of credit products, efforts should be made to improve the legal and regulatory framework and standard system, strengthen the construction of technical infrastructure, deepen international cooperation and mutual recognition mechanisms, and cultivate international credit service institutions.
[Purpose/Significance] From the perspective of value chain collaboration, a trusted data space system adapted to the characteristics of smart library scenarios is constructed, aiming to solve the systematic problems such as fragmented cross-domain integration, a lack of trusted guarantee, and inefficient value transformation in current library data governance. The study will contribute to improving the theoretical framework for governing library data. It also provides practical guidance on balancing the contradiction between data circulation and security, as well as on releasing the operational value of data elements. This helps smart libraries to strengthen their core functions in terms of public cultural service provision and knowledge empowerment. [Method/Process] Adopting a public value approach, we analyzed the coupling logic and value dimension of technical collaboration, rights and responsibilities, and scenario adaptation in the value chain links, as well as the hierarchical improvement laws of the data, knowledge, service and ecosystem layers. This was based on clarifying the four core elements of the trusted data space of smart libraries: data, subject, technology and system. We also examined the characteristics of trusted collaboration and value progression. The collaborative optimization process was examined in conjunctionwas with the links between the various stages of the data lifecycle. The path of expansion for the cross-chain ecosystem was constructed through collaboration between libraries, industry links, and social empowerment. We ensure a high degree of compatibility with the scene requirements of smart libraries. [Results/Conclusions] The trusted data space system of smart libraries consolidates the foundation of data trustworthiness through technological integration, activates the efficiency of the value network through the collaboration of subjects, consolidates the basis of operation guarantee through institutional norms, and extends the coverage boundary of services through value transformation, thus forming a governance pattern of four-dimensional interaction among technology, subjects, systems, and values. Based on this, four collaborative strategies, namely ecological niche reconstruction, capability leap, dynamic risk governance and value closed loop, are proposed. These strategies ultimately facilitate a systematic transition from the aggregation of data resources to the co-creation of ecological value. In the future, the element configuration and collaborative mechanism of the trusted data space can be optimized in combination with the service positioning of different libraries. The goal can be achieved through pilot construction, which will allow us to collect practical data, verify the system's feasibility and effectiveness, and explore the integrated application path of AI large models and trusted data spaces.
[Purpose/Significance] The digital characteristic collections of libraries are facing significant challenges in terms of data circulation and value, which greatly limits their potential utility. To address these issues, this study proposes to establish a trusted data space specifically designed for the digital special collections of libraries. The main objective is to reduce the costs related to trust and promote the full utilization of its multi-dimensional value in areas such as cultural heritage protection, academic research, industrial innovation, and social education. By creating a secure and interoperable environment for data sharing, the plan aims to transform the way digital special collections are managed, accessed and utilized, thereby enhancing their contribution to broader social goals. [Method/Process] This study centers on the trusted data space to explore the cross-domain circulation and value release mechanism of digital specials. It aims to build a dedicated and trusted data space for libraries, break down data barriers, and activate multi-dimensional value. The investigation follows a structured approach centered on requirements analysis, framework construction and strategy formulation. This research is based on the concept and technical foundation of the trusted data space, taking into account the unique attributes and sharing requirements of digital special collections. A comprehensive theoretical framework has been developed and centers around three core capability streams: resource interaction, trusted governance, and value co-creation. These flows are supported by a five-layer architecture model: infrastructure, data interaction, data elementization, intelligent services, and value realization. To illustrate the practical application of this framework, typical usage scenarios were analyzed to demonstrate how special collected data can be transformed from raw resources into valuable assets, and the characteristics and key tasks of specific stages were examined in detail. In addition, a multi-faceted implementation strategy has been proposed to address real-world challenges, including stakeholder reluctance, technological heterogeneity, and conflicts in rights management. These strategies emphasize the development of intelligent resources, the integration of multi-modal and heterogeneous technologies, policy incentive mechanisms, and the establishment of a sound data element market. [Results/Conclusions] The trusted data space proposed in this paper provides a systematic and effective solution for the trusted circulation and efficient utilization of cultural data. It transforms digital characteristic collections into open and reusable assets, thereby significantly enhancing the quality and scope of public cultural services. This development is in line with and supports the national strategic goals of building a "cultural power" and a "Digital China". Looking ahead, future research should prioritize the shift from theoretical conceptualization to practical implementation. This includes integrating technical solutions with actual service workflows and clarifying the unique role of libraries in the broader data ecosystem. To ensure long-term success and influence, key challenges such as sustainable business models and scientific and reasonable evaluation mechanisms must be addressed.
[Purpose/Significance] Rural cultural memory is an important component of social memory. It represents a collection of cultural memories related to villages, village histories, and village customs within specific rural spatial-temporal contexts. In the context of digital-intelligence development, the digital-intelligent transmission of rural cultural memory can promote the protection, revitalization, and utilization of rural cultural resources. This study focuses on how intelligent data can empower the digital-intelligent inheritance of rural cultural memory. It reviews construction projects in the fields of rural memory initiatives and cultural heritage, and proposes paths for leveraging intelligent data to facilitate the digital-intelligent inheritance of rural cultural memory from the perspectives of resource, technology, and service. [Method/Process] The research classifies rural memory and rural digital memory, summarizes the smart data studies in the field of culture heritage, investigates and analyzes the current status of representative rural cultural projects and cultural heritage construction projects from the perspectives of resources, technologies and services. At the resource level, multimodal and high-value rural cultural resources and their associated data are aggregated, with wide-ranging sources and diverse data formats. At the technology level, technical support is provided to achieve the integration and correlation of multimodal data. At the service level, the intelligent platform offers multi-scenario services, such as data acquisition, data correlation analysis, and data crowdsourcing. The practical experience of intelligent cultural heritage projects, along with the concept of intelligent cultural heritage data, provides methodological insights and reference paths for the resource construction, technology application, and service implementation in the digital-intelligent inheritance of rural cultural memory. [Results/Conclusions] Smart data provide new concepts of resource integration, new technology application and intelligent service for the inheritance of rural cultural memory. Existing cultural heritage intelligent projects provide approaches for the digital-intelligent inheritance of rural cultural memory. Finally, this study proposes paths for smart data empowering digital-intelligent inheritance of cultural memory from the perspectives of data resource construction, technological innovation, and service philosophy. At the resource level, multiple stakeholders are coordinated to integrate high-quality data resources. At the technology level, efforts should focus on phased objectives and technology aggregation to unlock the value of rural cultural memory. At the service level, the construction of an intelligent service space for rural cultural memory is recommended to address diverse needs. In the future, the digital-intelligent inheritance of rural cultural memory should align with the characteristics of rural cultural resources to construct interoperable smart data models. This will enable the high-level integration and interconnection of digital rural cultural resources. It will foster a model in which digital intelligent technologies and the utilization of rural cultural resources integrate and reinforce each other mutually.
[Purpose/Significance] As an emerging technology, the use of artificial intelligence-generated content (AIGC) tools is comprehensively influenced by factors such as individuals, tasks, and tools themselves. From an educational perspective, one effective way to influence user behavior is to improve the outcomes of graduate students' use of AIGC tools. This study aims to reveal the key dimensions and influencing factors of AIGC use by analyzing graduate students' spontaneous behaviors when using AIGC tools. It further seeks to improve the application efficiency of AIGC in graduate students' learning and scientific research, and promote deeper integration between tools and academic activities. [Method/Process] The research follows the logic of "from the spontaneous behavior of users to the active guidance of educators", mainly adopting the semi-structured interview method to collect data, and the thematic analysis method to analyze data. Semi-structured interviews were conducted with 25 graduate students from Chinese universities or scientific research institutions. The interviewees included 14 master's students and 11 doctoral students, covering three disciplinary categories: natural sciences (11 students), social sciences (10 students), and humanities (4 students). According to thematic analysis, the interview data were coded, and theoretical saturation was tested. On this basis, a theoretical model of the outcome and its influencing factors of graduate students' use of AIGC tools was constructed, and targeted suggestions were put forward from the perspective of information literacy education. [Results/Conclusions] The use outcome of graduate students' AIGC tool use includes three dimensions: task completion, subjective satisfaction, and process harvest. Its influencing factors involve four aspects: task & situation, personal characteristics, behavioral process, and tool characteristics. 1) task & situation: The use outcome is affected by the matching degree between task demands and application scenarios; 2) personal characteristics: The use outcome is influenced by graduate students' own basic abilities, subjective attitudes, and tool operation skills; 3) behavioral process: The use outcome is significantly impacted by the input of instructions to tools and the provided content; 4) tool characteristics: The use outcome is notably affected by tools' technical functions and operational limitations. Regarding AIGC tool-related education, it is suggested that information literacy educators emphasize the application scenarios of tools, improve the comprehensive ability of graduate students, carry out diversified teaching and training, and pay attention to the dynamics of tool and technology. This study still has some limitations. For instance, it has only identified the dimensions and influencing factors of graduate students' AIGC tool use outcome. Future research will further explore the causal pathways involved in the model through empirical studies.
[Purpose/Significance] The formulation of evidence-based science and technology policy critically relies on the timely and accurate provision of relevant, high-quality evidence. However, current evidence recommendation practices often suffer from significant limitations in both accuracy and efficiency, hindering the scientific rigor and intelligent application of evidence within the policy-making process. These shortcomings hinder policymakers' ability to leverage the most pertinent research and data, potentially leading to suboptimal decisions. Addressing this critical gap, this research proposes a novel knowledge graph-based evidence recommendation method. The primary objective is to substantially enhance the scientific foundation and intelligent capabilities of evidence utilization during policy formulation. This method aims to empower policymakers by providing more reliable, contextually relevant, and efficiently retrieved data support. Ultimately this will foster more robust, transparent, and demonstrably effective science and technology policies grounded in comprehensive research insights. [Method/Process] To achieve these objectives, this study systematically constructs a domain-specific knowledge graph meticulously centered on the intricate citation relationships between policy documents and academic research papers. This graph serves as the foundational semantic network representing entities (policies, articles, topics, authors, institute etc.) and their multifaceted interconnections. Most importantly, we introduce and adapt the Knowledge Graph Attention Network (KGAT) algorithm n an innovative way. Leveraging KGAT's sophisticated graph attention mechanisms, our model effectively captures and learns complex, high-order semantic relationships between policy requirements (represented as queries or specific nodes) and potential evidence sources (research paper nodes). This deep relational understanding enables nuanced evidence relevance scoring and personalized recommendation. To rigorously validate the proposed method's practical efficacy and performance, we conducted an extensive empirical study within the specific domain of agricultural science and technology policy. Furthermore, to demonstrate real-world applicability and provide a tangible tool for policymakers, we designed and implemented a fully functional Evidence Intelligent Recommendation System (EIRS). This system seamlessly integrates the core KG-based recommendation engine and incorporates advanced intelligent analysis capabilities. Significantly, EIRS supports an end-to-end workflow initiated by natural language policy questions posed by users, enabling intuitive interaction and precise, demand-driven evidence retrieval and recommendation. [Results/Conclusions] Experimental results, conducted on real-world datasets within the agricultural science and technology policy domain, demonstrate the superior performance of the proposed KGAT-based recommendation method. It consistently outperforms several state-of-the-art baseline algorithms across multiple key evaluation metrics, including precision, recall, normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR). This quantitatively confirms its significantly stronger recommendation capability. In addition to quantitative metrics, the model inherently offers enhanced explainability due to the transparent nature of the knowledge graph structure and the attention weights learned by KGAT, allowing for insights into why specific evidence is recommended, based on its semantic connections to the policy query. Concurrently, the implemented EIRS has proven to be highly effective in practice. It efficiently identifies and recommends evidence resources exhibiting a strong match with complex policy requirements expressed in natural language. The system's successful deployment underscores its potential to tangibly augment the scientific underpinning of science and technology policy development. By effectively bridging the gap between vast research knowledge and specific policy needs through intelligent, accurate, and explainable recommendations, this research provides a novel, practical pathway towards realizing truly intelligent and rigorously evidence-based policy formulation processes. The methodology and system prototype offer a valuable and adaptable framework for various policy domains beyond the presented case study.
[Purpose/Significance] In order to promote the healthy and orderly development of open science, governments, academia and industry in many countries have agreed that policy should play a role in guiding, supporting and regulating open science. A systematic study on the issue of why open science can be entered the policy field, is of a great significance for revealing the evolutionary logic of open science policy formulation, distinguishing the values pursued by open science policies, and providing theoretical support for the promotion of special open science policies and the sustainable development of open science. [Method/Process] Considering the multiple streams theory and combination with the specific problem thresholds of open science in China,this paper proposes an analytical framework for setting the policy agenda for open science,namely that: 1) the actual requirements for the development of science, technology, and the economy, adverse feedback on the current policies and public health emergencies constitute the problem stream. 2) existing policies and recommendations of experts and scholars constitute the policy stream. 3) the political ideas held by the leadership group and the national emotions represented by the researchers and ordinary people constitute the political stream. On this basis, by analyzing the formulation of relevant policies, the practical progress and research status of open science in China, this article clarifies the reasons why "open science" has become the content regulated by the Science and Technology Progress Law of the People's Republic of China. [Results/Conclusions] This paper believes that: 1) In terms of the problem stream, the achievements of China's national science and technology innovation system construction have provided a large amount of human resources, knowledge resources and infrastructure resources for promoting open science, laying a solid foundation for the development of open science. However, China's requirements for openness and sharing in science and technology are scattered in different policy documents, making it difficult to form a policy synergy to jointly promote the vigorous development of open science. Given the impact of the COVID-19 pandemic on academic communication and research paradigms, it is necessary to shift the focus of open science policy from a social problem to a policy issue. 2) In terms of the policy stream, many policies formulated and implemented by the Chinese government, research funding agencies and scientific research and education institutions involve the relevant content of open science, which provide important support for the entry of open science into law. 3) In the terms of the political stream, the Party and government in China have paid close attention to the issue of open science, treating it as an important topic for national scientific and technological innovation. This has been intertwined with the call from the general public for open science, which has made it a leading force in bringing open science to the attention of policymakers. 4) The convergence of these three streams, with the construction of Digital China rising to the national strategic level and the launch of UNESCO Recommendation on Open Science has pushed open science onto the policy agenda. Finally, this paper suggests that Chinese government departments and relevant institutions take systematic measures to ensure the effective implementation of open science policies.
[Purpose/Significance] Short video platforms have become the core arena for the digital presentation and dissemination of intangible cultural heritage (ICH). However, the "Matthew Effect" in the digital attention economy often causes high-quality ICH content to be submerged. Existing research predominantly suffers from "modal segmentation," focusing on single modalities such as text and visuals in isolation, which fails to explain how these elements synergistically drive user engagement. To address this gap, this study constructs a communication effect evaluation model based on multimodal machine learning. The innovation of this research lies in integrating computational communication methods with traditional persuasion theories, moving beyond simple content analysis to a quantifiable predictive framework. By identifying key influencing factors through data fusion, this study provides a scientific basis for optimizing the digital production strategies of the ICH content, offering significant value for enhancing the visibility of traditional culture and overcoming the barriers of digital dissemination. [Method/Process] This study integrates the elaboration likelihood model (ELM) and media ritual theory to establish a "cognitive-behavioral-cultural" dual-path analytical framework. Theoretically, the study maps content quality (video/audio/text) to the "Central Route" and source credibility (author attributes) to the "Peripheral Route." Empirically, focusing on ICH videos on Douyin as the subject, the study collected data from May 2024 to May 2025. After rigorous cleaning, a dataset of 2,869 valid samples was established. The study employs a multimodal feature engineering approach: visual and textual features are extracted to represent content quality; audio features (including FBank and MFCC) are processed using the OpenSMILE toolkit to capture prosodic and spectral characteristics; and author data are collected to quantify social influence. The Random Forest algorithm is utilized to fuse these heterogeneous data sources, analyze feature importance, and predict communication effectiveness. [Results/Conclusions] The empirical results demonstrate that the multimodal fusion model significantly outperforms single-modality approaches in predicting communication effects, confirming that ICH dissemination is a result of complex symbol interaction. Feature importance analysis reveals a distinct hierarchy: Author attributes make the highest contribution, indicating that the "Peripheral Route" - driven by the creator's social capital - is the decisive factor in determining communication heat. Its persuasive power far surpasses that of the content itself. Regarding content modalities, text and video follow in importance, serving as critical tools for user retention, while the audio modality holds supplementary semantic value by setting the emotional atmosphere. The study does not account for dynamic temporal changes or external trending events. Effective ICH dissemination requires a synergistic strategy: prioritizing the accumulation of the author's social influence as the core driver, while simultaneously optimizing visual and textual quality. Future research should incorporate time-series analysis to capture dynamic communication trends.
[Purpose/Significance] This paper examines the ongoing transformation of library information systems, shifting from platform-oriented architectures to agent-based ones, in the context of generative artificial intelligence. It argues that, although Integrated Library Systems (ILS) and Library Services Platforms (LSP) have improved workflow automation and resource management, they remain constrained by poor semantic understanding, restricted cross-system orchestration, and insufficient support for proactive, personalized services. Building on these observations, the paper proposes a transformation path in which existing ILS/LSP infrastructures are not discarded, but rather re-positioned as providers of capabilities within a broader ecosystem of generative intelligent agents. This provides libraries facing both legacy constraints and pressures for service innovation with a feasible evolution strategy. [Method/Process] The study first reviews service-level limitations of ILS and LSP through the lenses of interaction patterns, data openness, and intelligent service support, and distills typical pain points encountered in cataloging, circulation, reference services, and subject liaison work. On this basis, it constructs a graded capability model for generative intelligent agents that encompasses semantic perception, context modeling, goal-driven behavior, preference adaptation, and reflective evolution. It also discusses how different types of agents can be aligned with specific library roles and task granularities. The study then proposes a three-layer architecture consisting of a basic service layer, an agent coordination layer, and a semantic interaction layer. The bottom layer exposes atomic capabilities such as search, metadata editing, authentication, and logging; the middle layer orchestrates multiple agents via lightweight protocols and shared task states; and the top layer supports natural-language-driven interaction while maintaining semantic consistency and traceable reasoning paths. Finally, leveraging a "Library Assistant" prototype that integrates these components, the study designs and conducts experimental evaluations in bibliographic follow-up and recommendation scenarios, combining task-based user tests with qualitative feedback from librarians and domain experts. [Results/Conclusions] Experimental results indicate that the proposed architecture outperforms traditional models in terms of answer relevance, interaction fluency, and perceived service intelligence, particularly in multi-step information-seeking and follow-up recommendation tasks. At the same time, the study found that the mechanisms for long-term memory, cross-session user modeling, and explicit feedback loops were underdeveloped. This can lead to inconsistencies in sustained interactions and complex task chains. The paper concludes with a discussion of the design implications for the evolution of library systems, suggesting that future work should focus on trustworthy memory management, transparent agent coordination, and robust evaluation metrics. It also recommends the development of governance frameworks that jointly consider system performance, user experience, professional ethics, and institutional policy requirements together. In this way, the study provides both a conceptual blueprint and empirical evidence to guide the transition from platform-oriented systems to agent-based, generative AI-enabled library architectures.
[Purpose/Significance] This study explores the characteristics of organized research collaboration among humanities and social sciences (HSS) researchers and their impact on paper output, aiming to optimize such collaboration and provide theoretical and practical support for enhancing the effectiveness of organized research in HSS. [Method/Process] We selected 163 high-impact scholars serving as chief investigators of Major Project supported by the National Social Science Fundation of China from 2015 to 2021 as the research subjects, and used their papers funded by these projects and published in the Chinese key journals as the data source. Nine explanatory variables (e.g., co-authorship degree, co-authorship rate) and five explained variables (e.g., first-author publication volume, relative publication volume) were designed. Factor analysis was employed to reduce dimensionality and extract three common factors of organized research collaboration: "collaboration stability and intensity," "collaboration breadth and depth," and "collaboration diversity." Combined with control variables such as gender, educational background, and administrative positions, Spearman correlation analysis and multiple linear regression models were used to empirically test the impact of organized research collaboration characteristics on academic paper output. [Results/Conclusions] Under the organized research paradigm: 1) Organized research collaboration characteristics' common factors exhibit a significant inhibiting effect on the quantity of academic paper output; 2) Organized research collaboration characteristics demonstrate a significant enhancing effect on the proportion of high-impact papers; 3) Individual characteristics show no significant effect on academic paper output. Corresponding implications are drawn from the perspective of promoting high-quality development of organized research collaboration in HSS. We put forward some suggestions. Research management institutions should promote interdisciplinary and cross-domain collaborative innovation, optimize research evaluation to guide the quality of cooperation, and strengthen regional collaboration and international cooperation networks. Research institutions should enhance the development and management of research teams, intensify academic exchanges and capacity training, and optimize the allocation of research resources. Researchers should dynamically adjust their cooperation strategies, make good use of digital tools to streamline cooperation processes, balance administrative duties with academic outputs, and attach importance to the training of young scholars.
[Purpose/Significance] With the rapid development of technologies such as artificial intelligence, big data, and cloud computing, digital-intelligent technologies are profoundly revolutionizing the service models and management frameworks of public libraries. This study is based on the development background of the digital-intelligent era under the 15th Five-Year Plan. It investigates the smart library services of theNational Library, the Hong Kong Central Library, the Macao Central Library, libraries in theTaiwan region, and 31 provincial-level public libraries across China. The analysis focuses on the current research progress in smart library services provided by public libraries, examining both service content and methods. [Method/Process] This research employed a comparative analysis method, comparing the smart library services of 31 provincial-level public libraries in China with those in Hong Kong, Macao, and the Taiwan region to identify regional differences and development gaps. The investigation reveals that the development of smart library services in public libraries in China exhibits significant regional imbalances. Public libraries in economically developed regions demonstrate a significantly higher level of smart library services compared to those in less developed areas. / [ResultsConclusions] Based on the findings, this study proposes development strategies for smart library services in public libraries within the digital-intelligent environment. These strategies include building an intelligent technology management system, establishing tiered smart service standards, cultivating a multidisciplinary team of smart librarians, creating an inclusive smart service system, developing an integrated smart resource platform, designing blended physical-virtual smart service spaces, and fostering collaborative innovation in smart service alliances. The challenges faced and the experiences gained by public libraries during the "14th Five-Year Plan" period provide critical insights for the formulation of the "15th Five-Year Plan," while also representing core issues that must be acknowledged and addressed in the journey of the "15th Five-Year Plan." This necessitates the development of scientific and effective strategies by public libraries, which is also a key task of the "15th Five-Year Plan." As a pivotal phase for the innovative development of public libraries, the "15th Five-Year Plan" period should actively implement national policies, with each library formulating development strategies and specific measures for smart library services based on the needs of public cultural development and their own practical circumstances. Grounded in the context of the "15th Five-Year Plan" and building upon the current state of smart library services in provincial-level public libraries during the "14th Five-Year Plan" period, this paper proposes strategies for smart library services in public libraries during the "15th Five-Year Plan" period in the digital-intelligent era, with the aim of contributing to the promotion and development of smart library services in public libraries nationwide.
[Purpose/Significance] Although traditional Chinese Medicine (TCM) classics contain valuable knowledge they remain difficult to process automatically due to their complex page layouts, coexistence of traditional and simplified variant characters, alias-rich terminology, and strong cross-paragraph semantic dependencies. Existing pipelines often split the processes of optical character recognition (OCR), normalization, entity recognition, relation extraction, and entity alignment. This leads to error propagation. Additionally, many studies also focus on modern clinical texts rather than historical sources. This paper addresses these gaps by presenting an end-to-end pipeline that transforms ancient page images to a structured knowledge graph. The central contribution is the CoTCMKE, which is a chain-of-thought (CoT) and ontology-constrained joint model that performs named entity recognition (NER), relation extraction (RE), and entity alignment (EA) simultaneously. By making intermediate reasoning explicit and binding predictions to a TCM ontology, the framework improves batch digitization efficiency, extraction accuracy, and interpretability for digital humanities and library & information science (LIS) applications. [Method/Process] We built a unified pipeline with three steps. 1) Text recognition: a multimodal large language model (MLLM) recognizes text directly from complex pages with mixed vertical/horizontal layouts and performs context-aware traditional-to-simplified conversion. 2) Ontology construction: following semantic completeness, multimodal friendliness, evolvability, and interoperability, experts curate an ontology of core TCM concepts (e.g., diseases, symptoms, formulae, herbs) with aliases and constraints to guide decoding and ensure consistency. 3) Knowledge extraction: CoTCMKE integrates CoT with ontology constraints for multi-task extraction, which is entity localization and normalization, ontology-consistent relation generation, and cross-passage/cross-volume entity alignment. Constraint-aware decoding uses immediate checks and backtracking when a generated entity or relation violates ontology rules or alias mappings. For data, we used Shang Han Lun. Qwen2.5-VL-32B assists OCR, conversion, and initial auto-labeling; two TCM-trained annotators independently review and reconcile results. The final sets contain 2 340 NER items, 1 880 RE items, and 450 EA pairs, evaluated with 10-fold cross-validation. The multimodal large language model (MLLM) was adapted via LoRA with early stopping. The comparisons include traditional deep models, a unified IE framework, prompt-only inference, and a LoRA-SFT baseline. [Results/Conclusions] On Shang Han Lun, CoTCMKE outperformed LoRA-SFT by +3.1 F1 for NER, +1.6 for RE, and +1.3 for EA. In cross-book transfer to Jin Kui Yao Lue, the model maintained stable performance without retraining, indicating robustness and scalability. Ablation results showed that CoT reduced boundary and ambiguity errors, while ontology constraints curbed illegal triples and alias fragmentation. Combining both yielded the best overall results. The analysis yielded the following observations. 1) explicit medical relation templates act as semantic guardrails; 2) proactive alias consolidation before decoding reduces entity scattering and improves alignment; 3) explicit type-path guidance helps disambiguate fine-grained categories (e.g., pulse findings vs. general symptoms). The framework supports the automatic construction of "formula-symptom-herb" triples, as well as alias and variant normalization. It also supports evidence-linked semantic searches and navigation, which benefit LIS workflows, education, and research. Current limitations include the scope of the curated ontology and its focus on two classics. Future work will extend to additional TCM classics and broader historical corpora, support continual incremental learning, and deliver knowledge services based on the constructed graphs.
[Purpose/Significance] In 2025, significant reforms were made to the South African intelligence system. Currently, the academic community in China lacks substantial research on the South African intelligence system and its reform. Providing an explanation and evaluation of the main motivations and basic contents of this reform would give all sectors a comprehensive and systematic understanding of the South African intelligence system. It would also provide a reference point for improving relevant systems in China. [Method/Process] We used a literature analysis method to study the main reasons for South Africa's 2025 intelligence reform, and adopted a normative analysis method to examine the fundamental aspects of this reform. [Results/Conclusions] The 2025 intelligence reform in South Africa is a response to the new era of national security threats, democratic governance of intelligence, and constitutional court rulings. This reform has reorganized the civilian intelligence structure, authorized the bulk interception of communications by intelligence agencies, and increased supervision of these agencies. This reform marks a return to the "intelligence philosophy" of 1994 and could enhance South Africa's capabilities in terms of national security governance. However, it does not clarify the National Security Councils position or its relationship with the intelligence community. Furthermore, intelligence agencies' control of bulk interception procedures is not yet strict. There are also doubts about the rationality of the establishment of the National Intelligence Academy. Although there are differences between China and South Africa in terms of politics, economy, culture, etc., South Africa's intelligence reform in 2025 still has important reference value for China. One is to scientifically allocate national intelligence power, which should be clarified by amending the National Intelligence Law to define the purpose and specific reasons for intelligence reconnaissance measures, and to clarify the applicant, conditions for approval, the subject of approval, the content of reviews, and execution procedures. The second is to strengthen the supervision of intelligence power by improving the mechanism for the full process of the prior authorization, in-process and post-supervision of intelligence reconnaissance measures. Thirdly, we should establish a specialized national intelligence academy to strengthen training management in terms of training philosophy, target selection, training content, and career development, and promote systematic education and training for intelligence personnel. In short, we need to strengthen research not only on the intelligence systems of developed countries, but also on those of countries in the Global South. We should adopt an open attitude, and absorb excellent experiences from around the world, providing intellectual support to help China modernize its national security system and capabilities.
