[Purpose/significance] To address the limitations of existing disruptive technology identification methods,which primarily rely on static analysis and struggle to continuously characterize dynamic evolution or accurately quantify the direction of trajectory shifts and breakout rates, this study constructs a disruptive singularity identification computational model integrating Natural Language Processing (NLP) and geometric dynamics, aiming to provide quantitative intelligence support for technological foresight and strategic decision-making. [Method/process] Utilizing the pretrained SciBERT model and sliding window techniques, discrete patent texts are mapped into a continuous, dynamic semantic vector space to extract the technological centroid. Drawing upon physical kinematics theory, the tangential acceleration and normal evolutionary curvature of the centroid's trajectory are calculated to represent the short-term concentration of R&D resources and technological route mutations, respectively. Furthermore, by combining Kernel Density Estimation (KDE) with the long-tail characteristics of the data, an identification mechanism for disruptive singularities is established using the superposition of dual statistical extremes (Top10%) as the strict criterion. [Result/conclusion] Empirical research in the gene-editing domain demonstrates that this model successfully captured the disuptive singularities associated with three transformative paradigm shifts. Compared to traditional patent volume mutation models, it can identify key signals 2-3 years in advance; compared to static semantic novelty models, it effectively filters out the pseudo-mutation noise of conventional incremental innovations. This study achieves a methodological expansion in technological evolution analysis from static scientometrics to dynamic kinematic characterization, providing robust intelligence support for technological foresight and strategic decision-making.

[Purpose/significance] Emerging research topics are important carriers for the incubation of scientific and technological frontiers and the diffusion of knowledge. Their early identification provides support for scientific and technological strategic planning and industrial decision-making. This paper integrates multi-source heterogeneous data to construct an integrated framework for early identification and uncertainty measurement, aiming to improve the accuracy and stability of identification, and to provide a quantitative basis for frontier monitoring and strategic assessment through uncertainty analysis. [Method/process] Within the framework of "information perception—cognitive processing—evidence fusion", this paper proposes a multi-source data fusion method based on neural networks and DempsterShafer (DS) evidence theory. First, topics are extracted from multi-source data, a comprehensive emerging-degree indicator is constructed, and features are input into a BP neural network to obtain posterior probabilities. These probabilities are then transformed into basic probability assignments, and multi-source decision-level fusion is achieved through DS evidence theory, thereby identifying emerging research topics. Second, belief intervals are constructed using belief functions and plausibility functions to analyze the evolutionary trends of topic uncertainty. Finally, an empirical study is conducted in the field of stem cells, and development strategies are proposed according to the uncertainty trends of emerging research topics. [Result/conclusion] Compared with single data sources, the multi-source data fusion method can effectively improve the accuracy of identifying emerging research topics and clearly characterize the changing trends of uncertainty in their early stages.

[Purpose/significance] To achieve the quantitative measurement and early-stage prediction of the original innovation impact of individual scientific papers, this study proposes a method for measuring and predicting original innovation impact. [Method/process] Centered on three core characteristics—original pioneering, original breakthrough, and original leadership—this study constructs a measurement framework for original innovation impact from the perspective of knowledge diffusion, incorporating both short-term and long-term temporal dimensions. On this basis, a prediction method is further developed by integrating semantic information from domain-specific knowledge and
journal-level characteristics. An empirical analysis is conducted using publications in the field of attosecond science
as the research sample. [Result/conclusion] The proposed measurement framework can distinguish papers with high
original innovation impact from highly cited papers, avoiding the simple conflation of impact with citation accumulation. The proposed prediction method can be used for the relative assessment of the potential original innovation impact of newly published papers, providing a quantitative basis for the early identification of research outputs with high original innovation impact.

[Purpose/significance] Centering on the enhancement of data governance capabilities, systematically investigate the intrinsic mechanisms and implementation pathways through which trusted data spaces enable value co-creation between government and enterprise data elements. [Method/process] The article elucidates the logical progression of this spatial empowerment: an evolutionary shift from static exchange to dynamic value addition, data silos to data interconnection, value sharing to value co-creation, and ad hoc cooperation to routine collaboration. It then details
the enabling mechanisms: trust-building for government-enterprise data exchange through mutual trust and interconnection, allocation efficiency of data elements via integration and optimization, and the multiplier effect of data value through sharing and coordination. Regarding implementation pathways, drawing on the TIME framework, it proposes a four-dimensional strategy: advancing data element iteration at the technological level, fostering a multi-stakeholder and trusted data circulation ecosystem at the institutional level, optimizing data circulation types at the model level,and establishing an agile data governance system at the regulatory level. [Result/conclusion] The practical recommendations offered in this paper provide a valuable reference for facilitating government-enterprise data cooperation and offer actionable insights for accelerating the high-quality development of data governance in China.

[Purpose/significance] In the current era where algorithmic recommendations are deeply embedded in social operations, traditional metaphors represented by the "information cocoon" are insufficient to fully explain the complex information ecology characterized by collaboration, dynamism, and constructiveness, due to their static, critical,and binary perspectives. This study introduces the new metaphor of the "information hive" to overcome the limitations of existing theoretical paradigms. [Method/process] The study first deconstructs the elements of the information hive,constructing a three-dimensional theoretical framework encompassing the boundary environment layer, the core mechanism layer, and the value precipitation layer. Subsequently, by comparing the differences between information cocoon and information hive in terms of spatial form, operating mechanism, subject relationship, and governance orientation,the study points out that information hive achieves a theoretical shift from a closed, static, and one-way warning perspective to an open, dynamic, and mutually constructive perspective. Furthermore, the study elaborates on the theoretical innovation and practical guidance value of the information hive from three aspects: theoretical expansion, governance paths, and practical mechanisms. [Result/conclusion] The study shows that the information hive, as a more inclusive metaphor, provides a new analytical path for understanding and shaping an open, healthy, and sustainable information cology in the algorithmic age.

[Purpose/significance] To explore the influencing factors of the users′ transfer behavior between humanhuman surrogate search behavior and human-machine surrogate search behavior, providing theoretical foundations
and practical strategies for optimizing generative artificial intelligence. [Method/process] First, semi-structured interviews were conducted with 30 respondents focusing on critical incidents of users′ surrogate information seeking behaviors. Second, based on the critical incident technique and the COM-B model, the influencing factors of transfer behavior in surrogate information seeking were identified and the influence factor model was constructed. Finally, the combined weights of the entropy weight method and the coefficient of variation method were calculated using the geometric mean, in order to compare the differences in the influence of each factor before and after the transfer behavior. [Result/conclusion] The influencing factors of both types of transfer behavior include capability factors (physical capability and psychological capability), opportunity factors (physical opportunity and social opportunity), and motivation factors (automatic motivation and reflective motivation), and these factors differ before and after the transfer.

[Purpose/significance] Integrating information field theory from the perspective of user information behavior, it is to analyze the altmetrics data generation mechanism, aiming to clarify the core logic of data generation and promote it as a reliable bridge connecting science and society. [Method/process] Facing the scientific information communication process based on the network platform, the information field theory is introduced, and the stakeholder needs involved in the communication process are taken as the basis to construct a "Context-Cognition-Behavior-Data" (CCBD) analysis framework, which take the platform as the information field and interpret the generation mechanism of altmetrics data. [Result/conclusion] The network information platform is a virtual information field for scientific information communication. The altmetrics data generation process from the perspective of user information behavior includes five stages: triggering, acquisition, judgment, utilization, and dissemination. Four types of generation mechanisms of altmetrics data under the CCBD framework are revealed: stimulus-disturbance, competition-selection, overlap-self-organi-zation, and consensus-collaboration. The four types of mechanisms do not exist independently, and present a hierarchical progressive relationship, which are "micro triggering-meso selecting-macro aggregation-multiparty collaboration".

Abstract：[Purpose/significance] To solve the current dilemma of emphasizing form over knowledge in immersive
reading experience scenarios of public libraries, return to the core of knowledge transmission and cultural inheritance,and provide theoretical and practical guidance for the high-quality construction of immersive reading experience scenarios in libraries in the intelligent era. [Method/process] This paper analyzes the roots of the formalization dilemma of scenarios, defines the connotation and motivation of knowledge reconstruction, constructs a three-dimensional and two-step knowledge reconstruction model based on four core theories, and conducts practical verification with relevant excerpts from Tien-kung Kai-wu as a case. [Result/conclusion] Knowledge as the core is the core orientation to break the dilemma, and the constructed model can achieve accurate adaptation of knowledge to readers, technologies and scenarios; case verification shows that the model is operable, and knowledge reconstruction is the key path to promote the transformation of scenarios from sensory experience to cognitive improvement and highlight the core functions of libraries.

[Purpose/significance] Against the backdrop of intensifying strategic rivalry among major powers over critical technologies and resources, this study investigates strategic layout organization to explore human-AI collaborative
models, so as to help our country acquire superiority in cognitive confrontation. [Method/process] This paper analyses the requirements and characteristics of intelligence cognition processes, categorizing them into three stages: resource perception, knowledge representation, and fact integration. Based on the specific implementation methods of manual approaches and intelligent assistance technologies within each stage, a human-AI collaborative model integrating the strengths of both has been established. Furthermore, this paper evaluates the effectiveness of the constructed human-AI collaborative model by examining the strategic layout of the US project as a case study, comparing the advantages and disadvantages across each intelligence cognition stage. [Result/conclusion] The human-AI collaborative model developed in this paper employs intelligent assistive technologies as the primary approach in resource perception and knowledge representation, supplemented appropriately by manual methods. In the fact compilation stage, manual methods take precedence with intelligent assistive technologies serving as reference. Empirical findings indicate this operational model proves more effective, facilitating the precise and comprehensive mapping of intelligence landscapes for organizing strategic layout.

[Purpose/significance] Cultivating new quality productive forces presents new demands for accurately assessing the development trends of sci-tech innovation. It is an important tool for sci-tech development planning to pool
massive sci-tech information resources, deeply track the weak phenomena and interactions characterized in the early
stage of sci-tech innovation, and foresee the trends of emerging technologies evolving to frontier or core technologies.[Method/process] This study conducted a literature review on technology foresight analysis, explored key challenges in emerging technology foresight from the perspectives of evolutionary mechanisms and analytical methodologies, and constructed a graph learning-based emerging technology foresight framework from the weak signal perspective by integrating rules for judging the evolutionary stages of emerging technologies with representation learning from dynamic multiplex knowledge networks. [Result/conclusion] This framework employs a full-cycle analysis and revelation of evolutionary features and representational factors to deeply dissect the evolution mechanisms of emerging technologies.It proposes detailed steps for tracking and modeling the diverse evolutionary features of emerging technologies using dynamic multiplex knowledge networks, holding significant theoretical and practical implications for the field of sci-tech policy and management.

[Purpose/significance] This study aims to identify the influencing factors of enterprise application innovation behavior in Open Government Data (OGD) and to uncover the underlying driving mechanisms, thereby providing a theoretical foundation and practical insights for stimulating enterprise application innovation and advancing China's digital economy. [Method/process] Based on the TOE framework, this study preliminarily identifies the influencing factors of enterprise application innovation behavior in OGD. Integrating the "sheep-grass ecosystem" multi-agent model, a multi-agent model of enterprise application innovation behavior in OGD is constructed on the NetLogo platform. Simulation experiments are conducted sequentially to assess the driving effects and efficacy of these factors. [Result/conclusion] Enterprise application innovation behavior is driven by the level of government open data resource development, enterprise application innovation readiness, and market demand. These correspond to the data element empowerment mechanism, innovation readiness promotion mechanism, and market demand catalysis mechanism, respectively. Moreover, throughout the development of OGD, the efficacy of each influencing factor evolves dynamically.