In recent years, activation has emerged as a novel concept and pathway for memory institutions such as libraries, archives, and museums to unlock the value of their collections. However, while the practice of activating documentary heritage continues to advance, challenges such as homogenization, lack of motivation, and insufficient effectiveness have become increasingly evident, underscoring the need for systematic theoretical interpretation and futureoriented reflection. Based on a comprehensive review and analysis of existing literature, this paper traces the conceptual evolution of documentary heritage activation and provides a thorough elucidation of its core connotations, operational objectives, and implementation modalities. It proposes that future endeavors should prioritize three key breakthroughs: constructing a more theoretically robust activation framework with enhanced explanatory power, achieving modernized reinterpretation of cultural symbols within documentary heritage, and establishing profound connections between collections and public engagement

This paper probes into the inevitability of the transformation of university librarians into learning curators and the corresponding capacity construction. Based on the new constructivist theory, this transformation points to a more active role for librarians in the future learning ecology, emphasizing their importance in the design of learning experience and the facilitation of learning processes. This paper analyzes the multidimensional responsibilities and essential competencies that librarians must develop during this transformation, while discussing the challenges and opportunities they are likely to encounter. Through systematic literature analysis and field research, it constructs the role framework of learning curators and puts forward strategic suggestions for transformation. This transformation not only redefines librarians professional identity but also serves as an important pathway to improve the quality of library services and foster an environment that supports active learning and knowledge construction. 

Artificial intelligence, represented by Large Language Models (LLMs), has been deeply integrated into all stages of scientific research, driving a paradigm shift toward AI for Science (AI4S). This transformation compels libraries, whose knowledge services have traditionally focused on resource organization and information access, to undergo a fundamental paradigm transition. Based on an analysis of the impacts of AI4S on knowledge production and service mechanisms, this study proposes a new paradigm of library research knowledge service covering the entire lifecycle of scientific research. A hierarchical model, comprising the data foundation layer, knowledge computing layer, intelligent agent layer, and collaborative ecosystem layer, is constructed to elaborate its operational logic, followed by the design of a systematic evolutionary path for libraries toward intelligent transformation. The AI4Soriented knowledge service paradigm can optimize the processes of scientific knowledge organization and enhance knowledge utilization efficiency. It further enables libraries to be deeply embedded in the research innovation process, reshaping their functional boundaries and value positioning within the academic ecosystem, and providing theoretical reference and practical insights for service transformation in the era of artificial intelligence. 

To comply with the objective requirements of national cultural development, and to enable university libraries to overcome development bottlenecks and achieve their own sustainable development, this paper examines the “Star Sea Plan” jointly implemented by Hubei University of Science and Technology and local community as a case of universitycommunity collaboration. It mainly analyzes the bidirectional development model in which universities empower society while society in turn nurtures education, focusing on three aspects: implementation pathways, innovative measures, and longterm development mechanisms. This model not only demonstrates the proactive role of university libraries in promoting the prosperity and development of the library profession, but also meets the public expectations for broader access to the achievements of higher education reform. It also represents an important avenue through which university libraries can participate in higher education reform. The model possesses important academic value and practical reference, offers  a replicable and scalable framework for advancing national reading initiatives. 

Lu Xuns works are an important part of the intellectual wealth of mankind, and the study of Lu Xuns works has dual significance for literary research and historical research. While digital technology continues to facilitate research in the humanities, existing digital initiatives on Lu Xuns works have largely remained at the stage of digital preservation, with limited application of computational methods for indepth analysis. Therefore, this paper takes the sequences of Lu Xuns symbolically significant passages as the content carrier, and employs a knowledge graph as the representational framework to reconstruct literary images across works of different genres, and to map the relationships among their constituent elements, thus proposing a new approach to digital literary studies. In this paper, the SCN2M model is constructed to integrate digital technology and humanities research, and the overall research method is divided into three phases: image element identification based on seedcorpus preparation, semantic matching of image meanings, and reconstruction and analysis of literary images. Particular attention is given to the compatibility between the digital technology method and the corpus text in each phase. The reconstructed Lu Xuns literary images present a passive, heuristic form of analytical support in comparison with what traditional literary research means present, and the research methodology demonstrates both transferability and objectivity.

This study conducts intelligent semantic knowledge organization and questionanswering application research on the special literature Produce in Local Chronicles based on Large Language Models (LLMs) and Graph RetrievalAugmented Generation (GraphRAG) technology. By constructing a knowledge graph for produce in local chronicles and implementing an intelligent questionanswering system, the accessibility and practical value of ancient Chinese texts are effectively enhanced, providing a new paradigm for inheriting and promoting local culture. Taking data from the Yunnan Volume as an empirical object, the research designed structured prompts using the CRISPE framework and evaluated ten mainstream large models such as GPT4o and DeepSeekV3 through multidimensional indicators. Experimental results show that the QwenLong model demonstrates the optimal performance in entityrelation extraction. The knowledge graph constructed by QwenLong successfully integrated 24,215 triples, realizing semantic correlation of previously scattered produce knowledge. Building on this graph with GraphRAG and LangChain framework, an intelligent questionanswering system was built. The knowledge graphenhanced DeepSeekV3 model achieve the best performance across six categories of representative question with an accuracy rate of 86.33%, capable of providing accurate and coherent interactive knowledge services. This research validates the feasibility of LLMbased technology to knowledge organization of ancient Chinese texts and offers methodological references for deep integration of digital humanities. 

With the growing prevalence of internetbased medical services, the value mining of online healthcare and medical derivative data has become increasingly significant. Developing a scientifically grounded evaluation index system facilitates the accurate assessment and effective utilization of data value. We adopted grounded theory to construct the evaluation framework. Through a systematic review of academic literature, three levels of coding were conducted to construct an initial indicator framework. Based on this framework, expert interviews were used to revise and refine the applicability of the indicators. The final evaluation system comprises three primary dimensions—intrinsic value, extrinsic value, and cost—along with 8 secondary indicators and 37 tertiary indicators. The Delphi method was employed to gather input from 12 experts, and the Analytic Hierarchy Process(AHP) was applied to determine the initial indicator weights. Subsequently, a Backpropagation(BP) neural network model was developed. Using 300 user samples, the model was trained via error backpropagation, resulting in a datadriven optimization of the indicator weights. The findings indicate that intrinsic value constitutes the core of healthcarederived data assessment, with data authenticity, completeness, and timeliness being particularly critical. The analysis of extrinsic value highlighted the broad impact of data in realworld applications. In the cost dimension, the significance of privacy protection and risk response increased notably after optimization. The proposed evaluation system offers a practical tool for assessing the value of online healthcare and medical derivative data and provides actionable insights for platform operators seeking to enhance their data management strategies.

Large Language Models(LLMs) encounter accuracy issues such as semantic distortion and hallucination, as well as comprehensiveness issues including information redundancy and the lack of crossdocument semantic associations in multidocument summarization tasks. Existing solutions mitigate these limitations by designing specialized workflows. However, it remains uncertain whether enhancing LLMs capabilities results in diminishing marginal returns and whether the introduction of workflows can counteract these diminishing effects. This study develops a workflow incorporating automatic document classification and hierarchical summarization to investigate the marginal effects of LLMs on both accuracy and comprehensiveness, and to determine whether workflows can overcome these marginal constraints. Experimental results reveal that, in the absence of workflows, text accuracy(measured by ROUGE score) stabilizes at 11.6, while comprehensiveness approaches 0.025. With the proposed workflow, the improvement in accuracy diminishes from 45% to less than 5%, indicating a pronounced effect of diminishing marginal returns. In contrast, the enhancement in comprehensiveness remains substantial, with the improvement margin decreasing from 50 times to approximately 25 times, yet continuing to grow as model capabilities advance. This suggests that workflows can effectively bolster comprehensiveness and overcome marginal effects. The study confirms the presence of marginal effects in both comprehensiveness and accuracy within multidocument summarization using LLMs and underscores the pivotal role of workflows in overcoming these limitations.

This study delves into the factors influencing librarians adoption of Artificial Intelligence Generated Content(AIGC) technologies. Based on the SOR theory, MOA model and TAM model, 422 valid samples were collected through questionnaire surveys, and a structural equation model(SEM) was used for data analysis.The results show that librarians are open to AIGC technology as a way to improve productivity and service quality, but share concerns about information security, technical complexity, and potential impact on professional development. Perceived ease of use and perceived usefulness are identified as the primary determinants of technology acceptance. Technical support, technical competence, and work experience significantly enhance librarians perceptions of the ease of use of AIGC technology, and indirectly influence their perception of the usefulness of AIGC technology. Information security has a significant positive impact on librarians acceptance, while perceived risk has no significant impact on it. In addition, librarians attitude toward AIGC does not significantly affect their usage intention, suggesting that other factors, such as organizational support and personal motivation, may play a more important role. Based on these findings, this paper puts forward corresponding countermeasures and suggestions on how to promote the development and acceptance of AIGC technologies among Chinese libraries and librarians.

This article explores how to optimize the multiobjective collaborative paths in the preservation of library collections, focusing on the instrumental value and application strategies of format conversion technologies. In the context of the intertwining of technological iteration and cultural demands, document preservation faces complex challenges and requires diversified solutions. This research systematically analyzes the characteristics of three types of reproduction formats: preservation facsimile, microfilm, and digital files, as well as the advantages, disadvantages and costeffectiveness of the associated conversion technologies. This research points out that when formulating document protection strategies, the preservation costs, risk factors, and cultural benefits of both originals and reproductions need to be comprehensively assessed, followed by selecting format conversion technologies based on specific needs while allocating resources judiciously to ensure secure storage and effective utilization of information. Additionally, it emphasizes the importance of avoiding overreliance on digitization and recognizing the riskbuffering role of microfilm technology to maintain a balanced technological ecosystem. Through a graded protection framework diagram and data analysis in nine tables, this article provides decisionmaking tools for the graded protection strategy of documents, emphasizing the achievement of an optimal balance between cultural heritage preservation and information dissemination through technological collaboration, aiming to simultaneously meet the needs of multiple objectives.

Currently, the conceptual scope of ancient texts has been significantly expanded, which has extended from traditional paperbased documents to encompass diverse carriers such as printing blocks, bamboo slips, silk manuscripts, stone rubbings, oracle bones, and ancient texts in ethnic minority languages, demonstrating certain overlaps with movable cultural relics and archives. Notably, these ancient texts are mainly preserved in three major systems: libraries, museums and archives, each system has developed distinct management practices， which has led to challenges in the applicability of ancient texts preservation, and there is an urgent need to establish a systematic solution. This study proposes a tripartite improvement strategy, through the coordinated promotion of institutional innovation and technological empowerment can a transformative leap in ancient text preservation be achieved?偆h, thereby ensuring the sustainable safeguarding of cultural heritage.

The pressuresensitive adhesives in pressuresensitive tapes or selfadhesive labels attached to paper poses a serious threat to r the longterm preservation of the substrate. This paper systematically examines the removal methods of pressuresensitive tapes from modern and contemporary paper documents. This study innovatively conducts a comparative analysis of the principles, operation steps, application scopes, advantages and disadvantages of multiple physical, chemical, and comprehensive methods, thereby providing an important reference basis for the protection of paper documents. Through detailed experimental and case studies, the effects of different methods in removing tapes and their residues are clarified, and the importance of selecting appropriate methods according to the specific conditions of the documents is emphasized, which provides significant guidance for practical restoration work.