[Purpose/Significance] In the era of data elements, the innovation in public data development and utilization has promoted the development of new quality productive forces. Exploring its innovation mechanism is conducive to unlocking the value of public data. [Method/Process] Focusing on the innovation mechanism of public data development and utilization, and based on grounded theory, interviews and data coding analysis were conducted with 18 innovation participants. Consequently, an innovation mechanism model for public data development and utilization was constructed, covering 4 core categories, 12 main categories, 46 basic categories, and 170 initial concepts. [Result/Conclusion] The research finds that the innovation in public data development and utilization is a dynamic process of “trigger-evolution-regulation-guarantee”, which includes four stages: idea generation, refinement and verification, implementation support, and implementation diffusion. Its influencing factors include innovation-triggering factors, innovation-regulating factors, and innovation-guaranteeing factors. The research systematically analyzes the process logic and operational mechanism of each element in the innovation in public data development and utilization, providing a theoretical framework and practical guidance for deepening the development and utilization of public data.

[Purpose/Significance] This study explores the cognitive characteristics of users in AIGC multi-modal information search, aiming to provide references for optimizing AIGC multi-modal search tools and enhancing users’ search capabilities. [Method/Process] Based on the Revised Bloom’s Taxonomy, this study collected data through experimental methods. Empirical analyses were conducted using two-way analysis of variance, one-way analysis of variance, paired-samples t-test, Kruskal-Wallis test, Spearman correlation analysis, and multiple linear regression. Focusing on user cognition in the process of multi-modal information search under the AIGC context, the study proceeded along two lines of inquiry.First, it examined the effects of perceived task difficulty, task type, task context, and familiarity on cognition. Second, it investigated the influence of cognition on multi-modal search behavior and outcome satisfaction. Drawing on cognitive psychology theories, the study further analyzed the underlying generative mechanisms and constructed a cognitive process model linking multimodal information stimuli to outcome satisfaction. [Results/Conclusion] First, the perceived difficulty of image tasks is lower than that of video and music tasks. In image search tasks, the cognitive dimensions of remembering, applying, and analyzing are significantly higher than those in video and music tasks. Task context has no significant effect on cognition, while familiarity with image and music tasks is positively correlated with users’ cognition. Second, applying and evaluating positively affect the number of searches, while creating is more associated with long-term search behavior. In addition, understanding, applying, and analyzing positively affect outcome satisfaction.

[Purpose/Significance] International Artificial Intelligence (AI) alliances serve as crucial platforms for promoting the integration of artificial intelligence resources and fostering collaborative innovation. Analyzing their systemic structures can provide strategic references for the development of similar alliances in China. [Method/Process] Drawing on the triadic structural model of information ecology theory, this paper constructed an analytical framework from three dimensions: ontology, subject, and environment. By integrating thematic analysis with tools such as ArcGIS and Gephi, it systematically examined the core topics, member composition, and operational foundations of international AI alliances. Furthermore, it extracted the synergistic interaction mechanisms among the elements of the information ecology. [Result/Conclusion] International AI alliances, grounded in technological infrastructure and knowledge resource sharing, have formed six major core themes. The alliance members mainly consist of four types of entities, i.e. enterprises, non-profit organizations, research institutions, and government or public agencies. The institutional environment plays a dual regulatory role in alliance operations, while the social environment may subtly influence their value orientation. The evolution of alliance forms is closely tied to generational leaps in technology and exhibits a “center-periphery” spatial distribution pattern. There are three types of interactions among information ecological elements: horizontal, vertical, and cross-dimensional. Based on these findings, the future development of AI alliances in China should focus on introducing diverse members to enhance subject density, providing targeted policy support to optimize the information environment, and advocating for an open-source culture to facilitate information flow. These efforts will help strengthen China’s competitiveness and discourse power in the global AI arena.

[Purpose/Significance] Obliteration by incorporation (OBI) refers to the phenomenon where methods, theories, and other forms of knowledge cease to be explicitly cited after becoming widely accepted as common knowledge. This process results in an underestimation of the academic influence of such knowledge, posing challenges to the accuracy and validity of academic evaluation. Therefore, in-depth research on this phenomenon and its underlying mechanisms is important to enhance academic assessment frameworks. [Method/Process] Utilizing the arXiv repository as the data source, this study measures the extent of OBI in physics by analyzing the trends of explicit citations, implicit citations, and citation rates. It then calculates semantic evolution indicators of knowledge phrases from multiple dimensions. A multiple regression analysis is applied to explore the associations between semantic evolution and OBI. [Result/Conclusion] The findings reveal a temporal delay between the peaks of explicit and implicit citations. The reduction of semantic variation, coupled with the enrichment of application contexts, facilitates the transition of knowledge phrases into common knowledge, leading to an increased degree of OBI. This study explains the mechanism underlying OBI from the perspective of semantic evolution, thereby contributing to a deeper understanding of OBI and offering valuable insights for refining citation-based academic evaluation systems.

[Purpose/Significance] Against the backdrop of intensifying global technological competition, how to effectively identify and predict underdeveloped technical fields and untapped technical opportunities in technological development remains an urgent problem to be solved. [Method/Process] This paper proposed a national science and technology intelligence analysis framework based on technology control policies texts. By integrating prompt engineering for large language models (LLMs) and generative topographic mapping (GTM), it constructed GTM patent maps and multi-dimensional GTM maps to mine underdeveloped technology nodes and technological gaps. Then, it designed a multi-dimensional evaluation index system and conducted a comprehensive assessment to identify the technological opportunity points with CRITIC method. Ultimately, inverse mapping technology was utilized for technological interpretation. [Result/Conclusion] Taking the lithography technology clauses in the Commercial Control List as an empirical case, the framework identifies 6 underdeveloped key technology nodes and 3 potential technological innovation opportunities in China, and verifies its scientificity and effectiveness. This framework is intended to provide scientific basis for the formulation of science and technology policies, and technology strategy layout of relevant departments.

[Purpose/Significance] In the context of the Fifth Research Paradigm, artificial intelligence(AI) technology is deeply integrated with scientific research scenarios. Focusing on the demand for the intelligent transformation of agricultural research services under the new paradigm, and based on the evolutionary trends of research paradigms and the domain uniqueness of agricultural research, this study aims to construct a theoretical framework and practical pathway for the intelligent development of agricultural research. [Method/Process] First, by combining agricultural research practices with a literature review approach, this paper distinguishes the core differences between traditional agricultural research and AI-driven models, and extracts the characteristic dimensions of the new agricultural research paradigm. Subsequently, it systematically deconstructs an AI-enabled agricultural research service mode from five dimensions: service positioning, target users, service content, operational mechanisms, and supporting elements. Finally, drawing on systems theory, an intelligent service platform with a four-layer architecture(data, model, application, interaction) is proposed, with clearly defined functions and technical logic for each layer. [Result/Conclusion] This study proposes an AI-driven agricultural research service framework with the aim of fostering a corresponding service ecosystem. Furthermore, practical strategies for the service framework are elaborated from the dimensions of institutional support, scenario innovation, model transformation, and talent cultivation, to provide, a theoretical basis and practical guidance for the intelligent upgrading of agricultural research.

[Purpose/Significance] With the frequent occurrence of natural disasters, government Weibo accounts have become an essential way for the public to obtain event-related information and to prevent and respond to risks. This study aims to construct an effective information disclosure quality assessment system, which provides theoretical guidance for the government's evaluation of information disclosure quality and its current status. Based on this, some strategies are proposed to provide references for the government to enhance the quality of information disclosure. [Method/Process] Based on the framing theory, the evaluation framework is constructed from five aspects, including construction subject, textual content, post structure, presentation form, and volume control. Indicators are extracted from related literature. Then, the entropy weight method is used to calculate the weight of indicators, and the TOPSIS and RSR methods are used to evaluate and classify the government Weibo information disclosure quality. Finally, based on the characteristics of information disclosure and the identification of benchmark accounts, a model for high-quality information disclosure of government Weibo is constructed to help the government improve the quality of information disclosure. [Result/Conclusion] The results indicate that the level of quality can be classified into four grades: excellent, good, medium and average. The information disclosure quality of D1 is the highest, while B2 is the lowest, indicating that the quality is not proportional to the level of government. Furthermore, the characteristics of high-quality information disclosure include timely and accurate replies, a high follower count, and appropriate post length.

[Purpose/Significance] Researching a digital twin model for museum exhibition services can provide a theoretical framework for building next-generation digital museum applications that reflect the advantages and characteristics of digital twin technology. It can also offer practical guidance for museums to achieve high-quality digital transformation. [Method/Process] First, this paper analyzes the main features of museum exhibit services based on digital twin technology and their distinctions from traditional systems. Second, it proposes a foundational conceptual model for multi-granularity digital twins, summarizing the structure and characteristics of the digital twin environment from a multi-granularity information perspective. Then, it presents a digital twin model for museum exhibit services, with information recommendation services and functions for organizing display content as its core components. Finally, using the example of touring the Palace Museum, it introduces the implementation mechanism of the digital twin process for related cultural relic exhibits. [Result/Conclusion] From the perspective of multi-granularity information modeling, this paper proposes a digital twin model for museum exhibit services and discusses the design mechanisms of its core functions, providing a reference for the construction of digital museums in the new era.

[Purpose/Significance] Characterized by cloud services, intelligent data integration, and microservice mechanisms, next-generation library services platforms offer a critical solution to the challenges faced by regional university library consortia(RULCs), including resource decentralization and diminishing collaborative efficiency. [Method/Process] This study systematically traces the technological evolution of library services platforms and the development trajectory of RULCs, thereby revealing the core bottlenecks in the transformation toward knowledge services within traditional consortium platforms: closed architectures lead to inefficiencies in digital services; heterogeneous systems cause data format conflicts; loosely coupled cooperation models restrict deep business-process synergy; and closed ecosystems result in data silos. [Result/Conclusion] The study demonstrates that next-generation services platforms can significantly enhance the intelligent service capabilities of RULCs through architectural innovation, resource integration, process re-engineering, and consortium ecological collaboration. Based on this, the research proposes a migration stage model for consortium-level regional university libraries toward next-generation services platforms, promoting the leapfrogging of domestic RULCs from “paper-based resource consortia” to “intelligent service symbiosis.”

[Purpose/Significance] A trusted data space is an emerging infrastructure for the circulation and utilization of data. Exploring the construction path of a trusted data space for the value co-creation of open public data is of great significance for promoting the open development and the circulation and utilization of public data resources. [Method/Process] Based on the logic of the trusted data space empowering the co-creation of open public data value, the social network analysis method was used to evaluate the level of value co-creation and point out the challenges of spatial empowerment. Subsequently, the DART model(Dialogue, Access, Risk, Transparency) for value co-creation was introduced to outline the construction pathways of trusted data spaces, proposing multiple spatial development strategies to enhance co-creation effectiveness. [Result/Conclusion] From the dual logical analysis of theory and practice, space construction can facilitate value co-creation. Due to the low degree of subject aggregation, reliance on individual node driving and other factors, the level of open public data value co-creation needs to be improved. The trusted data space can be constructed from the four aspects of dialogue, access, risk, and transparency by providing a multilateral dialogue platform with centralized resources and diversified access channels with linkage of development paths, accurately enabling the development of open public data value co-creation.

[Purpose/Significance] This study aims to reveal the influencing factors and pathways of algorithmic resistance behavior of mobile short-video users, analyze the dynamic evolution mechanism of cognition and emotion, and provide theoretical basis and countermeasures for the construction of a human-machine collaborative algorithmic governance framework. [Method/Process] The article preliminarily synthesized influencing factors using meta-ethnography and employed the triangular fuzzy number DEMATEL to analyze the causal relationships among these factors. Following this, it utilized the DANP method to quantify the weights of the influencing factors to determine their significance. Subsequently, it employed the AISM method to construct a hierarchical adversarial topological network and ultimately integrated the dual system theory to deconstruct the mechanisms of resistance behavior. [Result/Conclusion] The study identifies 22 influencing factors related to information quality. Based on the dual-system theory, it reveals the impulsive path dominated by System I, the reflective path governed by System II, and the dynamic game mechanism between the two. It elucidates the complex interactions among context, cognition, and emotion in the algorithmic resistance behavior of mobile short-video users. Finally, it proposes management strategies for this behavior from three perspectives: regulatory systems, algorithm design, and literacy cultivation.

[Purpose/Significance] This study explores the pathways, challenges, and development strategies of AI digital humans empowering library service innovation, aiming to provide theoretical support and practical guidance for the intelligent upgrading and smart service system construction of libraries, and to promote the innovative development of smart libraries. [Method/Process] Based on a comprehensive literature review, this study first reviewed the definition, characteristics, and technical architecture of AI digital humans, clarifying their multidimensional value in library services. Second, from the perspectives of library service categories and diverse user needs, it identified the logical path through which AI digital humans are embedded in library services. Finally, by analyzing the challenges libraries faced during the pre-introduction and post-introduction of AI digital humans, the study proposed corresponding development strategies for AI-driven library service innovation. [Result/Conclusion] The findings reveal that the logical path of AI digital human integration into library services comprises four progressive stages: classification of AI-enabled library service categories, configuration of intelligent cores, construction of role types, and adaptation and evolution of service scenarios and capabilities, demonstrating a transition from functional integration to contextual fusion. In terms of service scenarios, AI digital human applications can be categorized into six typical contexts: user consultation and reception, navigation and spatial guidance, academic research and knowledge discovery, teaching and tutoring, companionship and psychological interaction, and brand communication and cultural engagement, each with distinctive service emphases. In terms of development strategies, the study proposes three key approaches, promoting modular development, consolidating data foundations, and advancing pilot implementation, to facilitate the deep integration of AI digital humans into library services.

[Purpose/Significance] This study systematically reviews the current development of government service robots, explores multidimensional factors influencing user preference, and provides theoretical and empirical foundations for establishing user-friendly and efficient government service systems. [Method/Process] Based on the behavioral reasoning theory, the mind perception theory, status quo bias theory, and procedural fairness theory, it constructs a multidimensional and higher-order research model of user preference for government service robots. This model elucidates the formation mechanism of user preference through dual pathways of acceptance and rejection toward government service robots. [Result/Conclusion] The results confirm the significant impact of values on reasons and attitudes, as well as the impact of reasons on attitudes and user preference. Functional values and public values are key in shaping values. Competence is an important factor to form the user acceptance reasons. And perceived risk is the core factor to form the user rejection reasons.

[Purpose/Significance] This study aims to mitigate data conflicts caused by time lags, enhance semantic complementarity and accuracy of multi-source data, and improve the synergistic effect of knowledge fusion. [Method/Process] Focusing on topic time lag, this study incorporates the data source as a covariate in the structural topic model (STM). The direction and duration of the lag are measured by analyzing topic content, evolutionary trends, and similarity. By combining time lag and semantic similarity, the knowledge element semantics are separated into three categories: non-lagging semantics, lagging-similar semantics, and lagging-dissimilar semantics. And semantic separation and fusion strategies are proposed. Furthermore, an implementation framework for a multi-source data integration knowledge fusion model is constructed. This framework is applied and validated using artificial intelligence domain data from NSF and WoS. [Result/Conclusion] The proposed approach of local time lag processing mitigates their negative impact on data fusion while maintaining the characteristics of the data source. It is the basis for designing topic time lag measurement and knowledge element similarity fusion strategies. The calculation reveals that topic time lags of NSF and WoS data manifest as both leads and lags, and exhibit different lag directions and durations on different topics. The fusion of knowledge element semantics significantly reduces semantic redundancy and noise, achieves alignment and deduplication of divergent semantics in multi-source data, while preserving the integrity of semantic information. The proposed workflow of “Time Lag Measurement→Semantic Separation→Semantic Fusion” can effectively support the implementation of multi-source data integration knowledge fusion model.

[Purpose/Significance] In light of the weakness in theoretical research and the lack of replicable operational paradigms for data value realization in this field, this study is to uncover the driving forces and evolutionary paths of data value realization, providing theoretical insight for the efficient utilization and precise empowerment of smart elderly care data. [Method/Process] Adopting a multiple case study approach, the research selects Y Community in Wuhou District, Chengdu, and X Home-Based Elderly Care Service Center in Xinwu District, Wuxi, as case sites. Applying grounded theory methodology, the study conducts three levels of coding on the case materials and constructs a mechanism model of data value release in community-home-based smart elderly care. [Results/Conclusion] The findings reveal that the release of data value in community-home-based smart elderly care is primarily driven by three core forces: stakeholder participation, technological embedding, and institutional guidance. It evolves through a four-stage path—value perception, value activation, value realization, and value emergence—forming a mechanism model of data value release structured around the main logic of “core motivation, path evolution, and continuous release.” This model offers theoretical guidance and methodological reference for fully unlocking the value of elderly care data and optimizing community-home-based smart elderly care services.

[Purpose/Significance] In an open access environment, public digital cultural resources of considerable value or effectiveness constitute a potential economic ‘substitution’ with non-public digital cultural resources. Exploring the factors influencing the substitutability of public digital cultural resources provides a reference path for enhancing the attractiveness and irreplaceability of public digital cultural resources. [Method/Process] The original corpus was obtained through the semi-structured interview method, and the interview texts were analyzed using the rooting theory and the push-pull theory to summarize the factors influencing the substitutability of public digital cultural resources and construct the push-pull model. [Result/Conclusion] In the push-pull model: (1) the internal environment shapes the public/non-public digital cultural resource push-pull; (2) the external environment regulates the public/non-public digital cultural resource push-pull; (3) the public digital cultural resource pull and the non-public digital cultural resource push weaken the public digital cultural resource substitutability; (4) the public digital cultural resource push and the non-public digital cultural resource pull reinforce enhance public digital cultural resources substitutability; (5) individual user factors regulate public digital cultural resources substitutability through users’ willingness to use.

[Purpose/Significance] In the era of big data, scientific and technological texts exhibit characteristics such as multi-source, multi-type, and multi-structured. Designing topic detection methods to accurately organize scientific and technological information from massive texts has become a supportive task for formulating scientific and technological development strategies, optimizing resource allocation, and promoting scientific and technological innovation. [Method/Process] This paper proposes a neural network model, NNMMFF, based on multi-view feature fusion. The model utilizes a self-supervised training framework to extract feature vectors of scientific and technological texts from three perspectives: network, statistical, and contextual. It uses an embedded “multi-view feature fusion module” to integrate the three feature vectors and obtain fused text features. Finally, the effectiveness of this approach is validated through the topic detection task. [Result/Conclusion] Experiments are conducted on a Chinese dataset from the “National Security” domain (2015–2023). The results show that in NNMMFF, a deep feature-level fusion algorithm using gated multimodal units can effectively fuse feature vectors from the network, statistical, and contextual perspectives. In contrast, shallow feature-level fusion algorithms, such as feature concatenation and feature weighting, perform relatively poorly. The findings suggest a clear performance hierarchy for topic detection in multi-source scientific and technological texts: three-view fusion outperforms two-view fusion, which in turn outperforms a single view.

[Purpose/Significance] This study aims to enhance knowledge fusion at the user level, enabling a more systematic and feasible approach to embedding user needs into knowledge services. [Method/Process] Based on the thematic, collaborative, and hybrid relationships between author knowledge elements and semantic knowledge elements, this paper designs four recommendation algorithms within a neural network framework. These algorithms are categorized into three types: content-based, collaborative filtering, and hybrid recommendation. The dataset is divided into training set, test set, and calibration set in a 7:2:1 ratio. An implementation framework for a knowledge fusion model to mine user needs is constructed. The artificial intelligence domain data in WoS database is selected for implementation comparison and result validation. [Result/Conclusion] This paper proposes four knowledge recommendation models: content-based(C), collaborative filtering(CF), content-based collaborative filtering(CCF), and collaborative filtering content-based recommendation(CFC). The outputs from these models can serve as an auxiliary representation for user needs, support the implementation of the knowledge fusion model, and validate the feasibility of the “semantic supplementation-semantic recommendation” path. In addition, the CFC recommendation model achieves significantly higher accuracy than other models in both content evaluation and quantitative evaluation.

[Purpose/Significance] Constructing an industrial technology chain risk measurement method and systematically evaluating the risk evolution trajectory over a long period can provide a methodological reference for breaking through the “bottleneck” dilemma of key core technologies. [Method/Process] Starting from the connotation characteristics of the industrial technology chain and technology gap theory, this study constructed a three-dimensional risk framework of “technology level-technology value-technology complexity”(LVC), and proposed a competitiveness index and risk index measurement method integrating Entropy Weight-TOPSIS based on this. It quantitatively identified the advantages and disadvantages of each link of the technology subjects, and revealed the risk differences and time-series evolution trajectories of different subjects from the perspective of the overall technology chain and technology links. [Result/Conclusion] Taking the “New Energy Vehicle” industry as an example, the empirical results show that the measurement method based on the LVC three-dimensional risk framework can significantly improve the dimensional completeness and decision-making support of the industrial technology chain risk assessment. This study expands and deepens the theory and measurement methods of industrial chain risks, and provides scientific support for the optimization of industrial technology policies, forward-looking technology layouts and technology chain security governance.

[Purpose/Significance] This study integrates indicator features reflecting patent value with semantic features derived from patent texts and leverages the Sparrow Search Algorithm(SSA) to address two major challenges in the identification of potential high-value patents: incomplete feature representation and the complexity of parameter optimization. [Method/Process] It proposes a potential high-value patent identification model, named SSA-BERT-CNN-BiLSTM(SSA-BCB), which incorporates both semantic and indicator features. Semantic features are extracted from patent texts using the BERT model, while indicator features are mined from co-occurrence networks. These heterogeneous features are fused via vector concatenation. The CNN-BiLSTM model parameters are subsequently optimized through SSA to enhance classification performance. [Result/Conclusion] Experimental evaluation demonstrates that the proposed SSA-BCB model achieves a 4.9% improvement in the F1-score over benchmark models, with the area under the ROC curve(AUC) reaching 0.927. Furthermore, the integration of an attention mechanism yields an additional average performance gain of 4.2%. These findings confirm that SSA effectively enhances the identification of potential high-value patents, that the complexity of semantic relationships can substantially influence model performance, and that the combination of optimized parameters with attention mechanisms can further improve identification accuracy.

[Purpose/Significance] From the perspective of technology readiness improvement, this study adopts a three-phase evolution model of application-oriented basic research in chemical industry, dividing the paper citation network chronologically into three phases. By examining the structural characteristics and evolutionary patterns of the network from multiple angles, this article reveals the supporting mechanisms of application-oriented basic research for emerging technology directions, aiming to optimize technological innovation management and promote technological progress. [Method/Process] Taking lithium iron phosphate(LFP) battery technology as an example, this article conducted an empirical analysis through dynamic growth cumulative network topology analysis, community clustering of time-sliced subnets, cross-phase bipartite network knowledge flow analysis, and identification of three-phase paper characteristics in the entire network. [Result/Conclusion] The citation network structure of application-oriented basic research papers exhibits self-similarity, but the undirected self-similar structure is constrained by directed connections, which may impede technological innovation. Papers from the “technology integration and systematization phase” act as bridges connecting the “core technical research phase” and the “technological upgrading phase”. “Knowledge backtracking” helps overcome bottlenecks in technological readiness improvement. Research focuses across the three phases exhibit a “concentration-expansion-concentration” trend, with cross-phase citation patterns breaking the preferential attachment(rich-get-richer) mechanism. This study proposes network intervention strategies to foster technological innovation, emphasizing the importance of monitoring emerging core nodes with growing control and influence to preemptively address potential industrial restructuring risks. The findings provide critical decision-making references for optimizing R&D resource allocation and advancing the readiness of emerging technology directions.

[Purpose/Significance] Investigating the impact mechanism of user concerns on information suppression behavior in online health communities aims to provide a theoretical basis for the “mitigating suppression and increasing the flow” transformation, and to promote the dissemination of health information. [Method/Process] Grounded in the cognitive-affective personality system theory, and combined with protective motivation theory, self-monitoring theory, face theory, and regulatory focus theory, this study formulates hypotheses regarding the paths among latent variables, such as privacy concerns, evaluation apprehension, and prevention focus. A model of the impact mechanism of privacy concerns on information suppression behavior in online health communities is constructed and empirically tested. [Result/Conclusion] In online health communities, privacy concerns, evaluation apprehension, and prevention focus all have a significant positive effect on user information suppression behavior. Evaluation apprehension and prevention focus play a sequential mediating role in the relationship between privacy concerns and information suppression behavior. User privacy concerns have no significant effect on prevention focus. Drawing on the research findings, targeted intervention strategies are proposed to regulate information suppression behavior in online health communities, including strengthening privacy protection settings, improving community evaluation mechanisms, and refining the classification of personality traits.

[Purpose/Significance] Research on the concept of information makes significant contributions to the academic discourse system. From the perspective of information theory, the classical definition is that information is something that can be used to remove uncertainty. Research on the origin and evolution of the definition can reveal the innovative contributions of Chinese scholars to the idea of “uncertainty” in the conceptual history of information, which inspires us to value subjectivity and originality in basic research. [Method/Process] The origin of the concept of information was identified through textual research. Initially, the Chinese literature on the origin of this definition was reviewed. Subsequently, further textual research was conducted on the relevant primary sources in other languages. Ultimately, the true origin of the concept was traced to the early research by Chinese scholars in information theory and information science. [Result/Conclusion] This definition is widely believed to have been proposed by Claude Elwood Shannon(1916-2001), the founder of information theory, in his 1948 work, A Mathematical Theory of Communication, but there is no such statement in his original text. This definition is a creative interpretation of Shannon's information theory by Chinese information scientist Zhong Yixin. It emerged during the period when the “San Lun”(i.e., general systems theory, cybernetics, and information theory) was widely studied in China around the 1980s, and was first published in Zhong Yixin's article The Present and Future of Information Science in 1978. It was widely disseminated due to the significant academic impact of Zhong's book XINXI KEXUE YUANLI(i.e., Principles of Information Science) in 1988, but its content has been misinterpreted. This definition reflects the theoretical connotation and local value of the meta-concept of information science, constituting the ideological core of the academic discourse system of information resource management in China.

[Purpose/Significance] This study aims to explore the intrinsic formation mechanism and influencing factors of the information cocoon effect in the process of public information disclosure acquisition during public health emergencies. The findings are expected to provide decision-making references for governments at all levels, facilitate the effective “breaking of the cocoon”, and enhance the efficiency of public information acquisition and utilization. [Method/Process] Based on the S-O-R theory, this study identified the influencing factors of the information cocoon effect from the dimensions of internal and external stimuli and their behavioral responses to construct a theoretical model. A questionnaire was designed, and 341 valid responses were collected. Structural equation modeling and the bootstrap method were employed to analyze the data, revealing the formation mechanism and operational pathways of the information cocoon effect. [Result/Conclusion] In the specific context of public health emergencies, the information cocoon effect in public information disclosure acquisition behavior is a complex system formed by the interaction of internal and external factors. Essentially, it is a biased behavioral outcome driven by the combined stimulation of subjective and objective factors. Furthermore, information literacy plays a moderating role and serves as a key factor in mitigating the information cocoon effect in the process of public information acquisition in public health emergencies.

[Purpose/Significance] The integration of artificial intelligence technology into archival open access review work helps to improve the efficiency of archival open access review and, promote the open utilization of archival resources, which is one of the key priorities of future archival open access review work. [Method/Process] This article combines SOR theory to analyze the elements of the adoption behavior of artificial intelligence assisted archival open access review in comprehensive archives from three dimensions of “stimulus organism response”. Based on the interview texts of 45 comprehensive archives, the internal mechanism of such behavior is explored using phenomenological analysis research methods. [Results/Conclusion] The study finds that the generation mechanism of this behavior is “contextual trigger-cognitive judgment-behavioral feedback”. The contextual trigger mechanism includes multiple stimulus factors such as laws and regulations, data security, personnel security, input cost and output benefit, peer experience, and confidentiality bureau supervision. In the process of active or passive information search and communication in comprehensive archives, their psychological attitudes change positively or negatively, leading to behavioral feedback. By analyzing its mechanism content and functional relationships, a comprehensive archive artificial intelligence assisted behavior generation mechanism model for archival open access review is constructed to promote the intellectualization of archival open access review and improve the quality and efficiency of archival open access review work.

[Purpose/Significance] In the process of using generative artificial intelligence(Generative AI), users often find that the generated content does not meet their expectations and must optimize their interaction with the AI to obtain satisfactory results. However, research on the mechanisms underlying dialogue optimization remains underdeveloped. [Method/Process] This study explored the mechanisms of user dialogue optimization in the context of generative AI. Using grounded theory, the study conducted a coding analysis of 22 interview transcripts and 6 online documents to investigate the influencing factors, strategy content, and behavioral outcomes of user dialogue optimization in the context of generative AI usage. Based on this, a theoretical model of the mechanisms of user dialogue optimization behaviors was proposed. [Result/Conclusion] The findings reveal that task, user, and technology factors influence the content generation, which in turn affects user dialogue optimization. Typical strategies for dialogue optimization include content provision and instruction clarification, output format and structure optimization, feedback mechanisms and iterative optimization, task decomposition and step-by-step execution, the use and optimization of prompt engineering, and the leveraging of external resources. These strategies lead to enhanced user experience, user learning and alignment.

[Purpose/Significance] The data broker system in China is in the initial exploration stage, and a perfect data broker system has not yet been established. As an intermediary between the supply and demand sides of data market, and the promotion of value mining and ecological chain construction, analysis of data brokers' mechanism is helpful to solve the problem of information asymmetry and promote the circulation and transaction of data. [Method/Process] A data elements circulation trading system including data brokers participate was built based on the theory of information ecology. System dynamics method was used to analyze the causal relationship between factors, identify key variables, and carry out system simulation and sensitivity analysis. [Result/Conclusion] Data brokers play a matching role through intermediary services, and their professional ability and services directly affect the efficiency of data circulation transactions. Its mechanism of operation varies at different stages. In the stage of supply and demand matching, the efficiency of supply and demand docking is improved through accurate intermediary matching. In the stage of transaction matching, as an intermediary connector, the circulation channels of data resources is broadened, and the incoming transactions is matched. In the transaction execution stage, relying on third-party services such as compliance review and pricing evaluation, the risk is reduced and the compliance transaction is orderly promoted. In the future, the data broker system should be optimized, and the intermediary service efficiency and transaction conversion rate should be improved, to accelerate the realization of trusted circulation and value transformation of data.

[Purpose/Significance] This study proposes a citation pattern for identifying research contributions in scientific paper and refines its typology to offer an operational and structured classification standard. The aim is to support the automated analysis and evaluation of scientific papers and to enable the semantic-level quantification of research contributions. [Method/Process] The definitions and classification frameworks of research contributions were systematically reviewed to clarify their essential characteristics. Based on a large-scale corpus of English citation sentences, typical organizational structures of contribution expressions were identified, citation patterns were extracted, and the classification dimensions of the core elements within the pattern were further refined. [Result/Conclusion] A citation pattern for research contributions was established, formulated as: research contribution = contribution field + innovative work + influence value. Innovative work was categorized into four types: theoretical, methodological, applied, and cognitive. Influence value was classified along two horizontal dimensions and four vertical levels. The proposed citation pattern and classification framework provide a foundation for the automated identification and quantitative evaluation of research contributions and offer methodological support for advancing academic assessment from traditional bibliometric indicators to semantic-level analysis.

[Purpose/Significance] The trusted data space is an important carrier of the national data element market. A data catalog serves as a fundamental component for the orderly organization, discovery, transaction, and operation and development of data products in the data space. Therefore, building a unified data catalog system to support data operation and governance within trusted data space is of strategic significance. [Result/Conclusion] Based on a systematic review of domestic and international research and practical efforts related to data catalog system in trusted data space, this paper adopts a model-driven approach to construct a system architecture for a unified data catalog system. Inspired by semantic web ontology, it further proposes an ontology model tailored for trusted data spaces and discusses potential application scenarios of catalog ontology. [Results/Conclusion] The proposed unified data catalog system of the trusted data spaces use data products as its core description objects, applies ontology as the description mechanism, and leverages semantic data networks for representation. It supports multi-dimensional semantic browsing and intelligent data discovery, enabling application scenarios such as data discovery and supply, usage control, supply-demand matching, audit and evaluation, and end-to-end traceability and other application scenarios.