ABSTRACT. High-Tech Industrial Development Zone (HTZ) plays an important role in China's regional innovation system, yet the determinants shaping the spatial distribution of top-tier HTZs remain insufficiently understood. This study examines how economic strength, innovation inputs, innovation outputs, higher-education resources, and industrial innovation capacity jointly influence provincial-level differences in HTZ development. Using cross-provincial data for 2024, we construct a set of theoretically grounded indicators—including GDP per capita, R&D intensity, patents per 10,000 people, the number of 985/211 universities, and the number of high-tech enterprises—to evaluate their relationships with HTZ presence and scale. Three models are implemented: logistic regression for the presence of top HTZs, Poisson regression for HTZ counts, and a negative binomial model as a robustness check. This research highlights R&D intensity has the strongest relationship to both the presence and scale of top HTZs, followed by GDP per capita. The study contributes to understanding regional drivers of high-tech development in China and provides a data-driven basis for evaluating innovation investment, optimizing HTZ planning, and informing future research on spatial innovation dynamics.

ABSTRACT. Electric vehicles have become a cornerstone of the global energy transition. This paper examines the ripple effects of the European Union’s tariffs on Chinese electric vehicles, focusing on their transmission through critical metal supply chains and their implications for global welfare. We develop a multi-country, multi-sector quantitative trade model, calibrated with global input–output data, and analyze three key metals—copper, nickel, and aluminum. Our results show that tariffs reshape not only EU–China trade in electric vehicles but also generate heterogeneous shocks across upstream and midstream metal sectors via price and demand channels. The copper supply chain is the most affected, experiencing sharp fluctuations in exports and prices; nickel shows high sensitivity in the mining stage; while aluminum demonstrates relative resilience due to its diversified downstream demand. Welfare effects are highly uneven: China, as the major producer and consumer, suffers substantial losses; EU manufacturing powers benefit from domestic substitution; and resource exporters such as Russia and Australia gain from their endowment advantages. Overall, we highlights how trade barriers in the electric vehicle sector reverberate through critical metal supply chains, underscoring the importance of supply-chain heterogeneity in shaping policy outcomes and offering new empirical evidence on the broader consequences of trade protection in the green transition.

ABSTRACT. How do innovation ecosystems evolve in response to global technological shifts and institutional adaptation? How does the high-tech sector in small emerging economies engage with the global innovation ecosystem? This study investigates the transformation of the artificial intelligence (AI) innovation ecosystem in Taiwan, focusing on how firms engage in knowledge exchange, follow international regulations, and adapt to industrial transformation. Adopting a multi-level perspective (MLP) as the framework, we analyze the interactions between technological niches, regimes, and landscapes. Combining bibliometrics, scientometric mapping, social network analysis (SNA), and system dynamics analysis, we trace knowledge flows, collaboration patterns, and policy feedback loops in Taiwan’s AI sector from 2000 to 2023. Our findings show that Taiwan’s AI innovation ecosystem is closely connected to global knowledge networks, particularly in semiconductor and biomedical applications. Taiwanese firms rely on their hardware strengths but seek strategic partnerships with global technology leaders to address weaknesses in software and algorithms. However, despite strong global connections, Taiwan’s AI sector faces challenges in establishing a distinctive niche beyond its traditional hardware dominance. This study contributes to the literature on innovation systems by providing empirical evidence of industrial transformation. The findings offer insights into how firms in small emerging economies adapt to global AI regulations and participate in knowledge markets. Our mixed-methods approach offers a new way to analyze the co-evolution of technology, institutions, and firm strategies. This study extends the MLP framework by integrating qualitative system dynamics to map policy-innovation feedback loops in small open economies. The findings provide suggestions for policymakers to enhance innovation capabilities and for firms to develop competitive AI business models.

ABSTRACT. How do technology sectors in emerging economies transition to green innovation? How do institutional factors stimulate the transformation of green technologies? This study adopts an integrated Sectoral/Technological Innovation System (STIS) framework, combining Multi-Criteria Decision-Making (MCDM) with qualitative insights from elite interviews. The analysis focuses on the Taiwanese green technology sector, drawing data from a panel of 15 experts and 12 high-level stakeholders from industry, government, and academia, each with over a decade of strategic experience. Our analysis identifies a specific 'Policy-Driven STIS Model,' characterized by the dominance of Cross-Sector Collaboration and Government Industrial Policy over purely market-based drivers. Notably, a divergence emerged between qualitative and quantitative data: while experts qualitatively emphasized the pandemic's impact, they prioritized structural factors in quantitative rankings. This implies that the pandemic served as a context-dependent accelerator for digitalization rather than a fundamental driver of structural change. These findings offer a replicable framework for policymakers in emerging economies, highlighting the need for hybrid innovation policies that align institutional support with technological capabilities.

ABSTRACT. To address three bottlenecks in technology opportunity identification—namely, the coarse granularity of analysis, limited capability for large-scale data processing, and the continued reliance on experts’ subjective judgments in the evaluation process—this study presents a fine-grained identification and evaluation framework built upon a Knowledge graph. Using patent documents as the data source, the framework first constructs a domain-specific Knowledge graph by extracting technology elements and their relationships. Building on this graph, a Relation-aware link prediction algorithm is employed to automatically identify potential combinations of technology elements and generate a corresponding set of candidate technology opportunities. The framework further integrates topological features of the Knowledge graph with technological semantic information to formulate an objective set of evaluation indices spanning the dimensions of novelty, feasibility, and value. The entropy weight method and the TOPSIS method are then used to support data-driven screening of technology opportunities. An empirical study in the quantum information domain demonstrates that the framework can identify and prioritize high-potential technology opportunities while revealing specific implementation paths, thereby improving interpretability and practical operability. Overall, the identification and evaluation framework offers methodological support and valuable references for innovation decision-making in frontier technological domains.

ABSTRACT. Understanding the evolutionary trajectories of emerging technologies is critical for strategic decision-making amid intensifying global scientific competition. Current methods, predominantly reliant on single structural features, exhibit analytical limitations that compromise prediction accuracy. To address this gap, we propose a novel multi-feature fusion approach integrating three patent-derived dimensions: citation network structures, classification co-occurrence networks, and semantic text content. We construct multidimensional feature vectors, apply temporal clustering to delineate technology clusters within sequential intervals, and compute inter-cluster similarities to trace evolutionary pathways. The method’s efficacy is validated using a dataset of 61,683 Chinese new energy vehicle patents. We identify 17 distinct technology themes for 2022, visualize their evolutionary relationships via similarity-based linkages across time windows, and detect thematic convergence/differentiation patterns. By synthesizing empirical evolution paths, thematic prominence trends, policy analysis, and expert validation, we make predictions about the key future trends in China's new energy vehicle: (1) battery performance breakthroughs, (2) waste battery recycling. Our findings demonstrate that fusing multidimensional patent features with time-series cluster analysis significantly enhances the detection of emerging technology evolution paths, offering a robust framework for innovation tracking and strategic planning.

ABSTRACT. Key generic technologies (KGTs) are crucial for industrial upgrading and competitiveness. Existing studies are mostly ex-post and single-source. This paper proposes a multi-source, multi-indicator framework for prospective KGT identification. BERTopic extracts technology topics, a multi-dimensional indicator system supports evaluation, and XGBoost explores evolution from “emerging” to “key generic.” Using the smart-grid domain, the study identifies multiple ETs and KGTs, three evolutionary paths, and five transition patterns, priority cultivation, reserve enhancement, maturity upgrading, baseline maintenance, and wait-and-see, providing actionable insights for technology foresight, policy, and industrial planning.

ABSTRACT. With the rapid development of artificial intelligence (AI) technologies, identifying potential application scenarios has become a critical task for bridging technological supply and industrial demand. However, traditional approaches to application scenario identification largely rely on shallow semantic similarity matching, which makes it difficult to uncover deep, cross-domain, and latent associations between technologies and application scenarios. To address this limitation, this study proposes an integrated methodological framework that combines large language models (LLMs) with heterogeneous graph neural networks (HGNNs) to enhance both the breadth and quality of application scenario identification in the AI domain. First, large language models are employed to achieve deep semantic understanding of patent and academic publication texts and to automatically extract interpretable technology themes and application scenario themes. Based on these results, a heterogeneous graph comprising multiple types of nodes and relationships is constructed. A heterogeneous graph transformer (HGT) is then applied to jointly aggregate structural and semantic information, enabling the learning of rich representations for technology and application scenario nodes. Finally, a link prediction approach is used to identify novel associations between technology themes and application scenario themes that have not been explicitly documented. Empirical results demonstrate that the proposed framework can comprehensively and effectively identify multiple categories of potential application scenarios in the AI field. By integrating deep semantic understanding with complex relationship modeling, this study provides a novel methodological framework for application scenario identification and offers valuable insights for strategic R&D planning and the practical deployment of artificial intelligence technologies.

ABSTRACT. With the development and progress of the times, disruptive technology research and development have become crucial starting point for technological innovation and are among the country's major strategic tasks. Accurately identifying and measuring potential disruptive technologies is of great significance for the country, enterprises and universities to seize the technological development opportunities. To achieve this, we start by identifying a list of potentially disruptive technologies through expert analysis, utilizing improved Support Vector Machine(SVM)algorithms and an Latent Dirichlet Allocation(LDA) model. Furthermore, we construct an indicator system for measuring disruptive technology across four dimensions. In the end, we validate the effectiveness and applicability of our model in the field of artificial intelligence. In this article, the systematic fusion model of potential disruptive technology measurement we proposed offers distinct advantages in identifying these technologies, and the research idea from disruptive technology "identification" to "measurement" enables a more precise discovery of disruptive technologies within the subject area.

ABSTRACT. This article discusses the approach of employing explainable artificial intelligence (XAI) framework to dissect opportunities and risks for technological innovation, that is, patentability of technological topics. As samples for analysis, 10579 patents related to lithium-ion battery research applied from 2021 to 2023 are collected. The Derwent titles of patents are processed by biterm topic model (BTM) to accurately extract technology topics from short texts and to reduce dimensions of classification models’ inputs. Ten distinct types of machine learning and deep learning algorithms have been deployed to categorize patent documents. The main driving features, that is, technology topics crucial for authorization and rejection of patent applications, are derived from the model with optimal classification outcomes using Shapley additive explanations (SHAP). Opportunities for technological innovation are defined as these main driving features for authorization, while risks for technological innovation are defined as these main driving features for rejection. Several opportunities and risks for technological innovation in field of lithium-ion battery are discovered by the proposed approach.

ABSTRACT. Emerging topics detection plays a significant role in various research fields in the era of rapidly evolving innovation. However, traditional methods like co-word and co-citation analysis often lack content-level granularity and interpretability. To overcome these limitations, this study extracts Subject-Action-Object (SAO) triples from article abstracts and constructs a multilayer semantic network incorporating an Issues-Solutions-Effects framework. This study extracts multidimensional features and measurement indicators of multilayer semantic networks from macro, meso, and micro perspectives, and employs the Analytic Hierarchy Process (AHP) to integrate multiple indicators, thereby enabling the identification of emerging themes. The findings indicate that, empirical validation on temperature and tactile-receptor research shows the index effectively traces topical evolution: from 1995–2003, the focus was on clinical aspects of cardiovascular and neurological disorders; during 1995–2009, attention shifted to neuroanatomical fundamentals; after 2015, studies on cold hypersensitivity gained significant attention, correlating with a Nobel Prize in 2021. In conclusion, the emerging topic identification method based on multilayer semantic networks proposed in this study can promptly detect emerging topics and their semantic relationships, thereby deepening our understanding of approaches for identifying emerging themes.

ABSTRACT. Existing Technology Opportunity Analysis (TOA) methods often rely solely on retrospective patent data, which lack foresight, while similarity-based and machine-learning-based approaches tend to overlook global structural information. To address these limitations, this study proposes an innovative TOA framework that integrates patent and literature data with advanced graph-based link prediction. We combine Generative Topographic Mapping (GTM) for technology opportunities identification by using both patent and literature data with Variational Graph Auto-Encoder (GVAE) for link prediction to evaluate technology opportunities. The framework was empirically tested using China’s digital publishing dataset (2005–2022).The dataset included 1,942 literature records and 4,708 patents. The GVAE outperformed 14 baselines, achieving 0.868 precision, 0.539 recall, 0.665 F1-score, and 0.806 AUC. It further evaluated 63 technological opportunities identified by GTM and predicted their practical development probability, which ranged from 0.892 to 0.666. Integrating literature with patents and applying GVAE-based link prediction enhances foresight and prediction accuracy, offering robust guidance for technology management.

ABSTRACT. Identifying technology opportunities that can effectively support idea generation remains a central challenge in technology opportunity analysis (TOA). Existing patent mapping–based approaches are effective in revealing patent vacuums, understood as data-level vacant regions within patent landscapes, but often lack interpretative mechanisms to translate such model-level outputs into actionable innovation insights. Addressing this limitation, this study proposes an idea-support-oriented technology opportunity identification framework that starts from technology gap information mining and progressively transforms patent vacuums identified by Generative Topographic Mapping (GTM) into technology gap points, defined as analytically derived and semantically interpretable units for opportunity interpretation. The proposed framework integrates GTM-based patent vacuum identification, deep semantic analysis, and hierarchical topic modeling, enabling a stepwise analytical process from vacuum detection to semantic enrichment and opportunity interpretation. Using scientific publications and patents in the field of 3D bioprinting as an empirical case, the study enhances SAOX semantic structures by incorporating a domain-specific technical term dictionary and constructs hierarchical scientific themes to provide contextual support for interpreting technology gap points. By structurally linking technology gap points to scientific themes and semantic features, the framework extracts explicit technical problems and potential improvement directions embedded in gap information, thereby forming interpretable technology opportunity units that can support subsequent idea generation. The results demonstrate that the proposed approach effectively transforms dispersed and implicit patent vacuum information into structured technical descriptions, improving the explanatory power of patent vacuum analysis without prescribing specific solutions. Overall, the study confirms the feasibility of a progressive analytical pathway from patent vacuum detection to idea-supporting technology opportunity units through semantic enrichment and hierarchical interpretation.

ABSTRACT. Discovering breakthrough technology opportunities (BTOs) is essential for guiding innovation and advancing scientific and technological progress. Existing studies characterize science-technology (S-T) interactions as nonlinear and bidirectional processes, and found that S-T knowledge interactions can foster the generation of technology opportunities. In this study, we present a novel framework for discovering BTOs that starts with the technology problem-driven innovation. First, a Subject-Action-Object (SAO) semantic bi-layer network is constructed to mine technological solutions and problems contained in papers and patents, respectively. The “S” in papers and “AO” in patents are treated as nodes, and the inter-layer edges mapping from patent-to-paper citations explicitly express S-T interactions. Second, weak-signal “AO” nodes are identified to represent breakthrough technology signals through a two-stage process including weak-signal indicator screening and novelty evaluation. Further, weak-signals “AO” nodes and the original “S” nodes are used to construct a weak-signal SAO semantic bi-layer network. Third, a similarity-based link-prediction method is used to obtain new cross-layer links in the weak-signal SAO semantic bi-layer network, which represent technology problem-driven solutions, and these links are treated as candidate BTOs. Finally, Louvain algorithm is applied to the original SAO semantic bi-layer network to detect knowledge subfields, and the association similarities between subfields across the two layers are calculated to select weak-association knowledge subfields. Only the predicted links within the weak-association knowledge subfields are retained as the final BTOs. Empirical studies of two biomedical datasets validate the research framework. The insights from technology problem-driven innovation can reveal how problem demands from technology knowledge integrate scientific solution knowledge, and are expected to provide practical guidance for discovering BTOs.

ABSTRACT. Research topic selection is a pivotal decision in a scholar’s academic trajectory, yet identifying viable topics remains challenging due to information overload in academic literature. To address this problem, we propose a GNN–LLM synergy framework that integrates structural connectivity learning with semantic expert reasoning for personalized scholar-topic recommendation. Unlike approaches relying solely on graph topology or semantic generation, our framework decomposes the recommendation process into two stages: (1) constructing a scholar–paper–topic heterogeneous bibliometric network and learning embeddings via HetGNNs to capture long-term research trajectories; and (2) empowering large language models (LLMs) as domain experts through Chain-of-Thought (CoT) prompting for reasoning-based filtering and re-ranking. By constraining LLMs to reason over graph-derived candidate topics, the framework effectively suppresses hallucinations while alleviating sparsity in graph-based methods. Experiments on Scopus data from Renmin University of China, with cross-institutional validation on Peking University and Tsinghua University, demonstrate superior performance, achieving an F1-score of up to 85.91% and bridging structural topology with deep semantic insight in scientometric recommendation.

ABSTRACT. In knowledge-intensive industries, the forward-looking patent layout in key or core technology fields has become an important indicator for measuring an enterprise's technological strength and innovation capabilities. However, existing external patent recommendation and matching methods are difficult to fully reveal the strategic needs of enterprises in different technology fields and the potential relationships between technology fields. Considering the outstanding ability of graph neural networks and attention mechanisms in depicting the intrinsic relationships within different technological fields in recent years, this paper proposes a patent recommendation method based on a relational graph attention network. Firstly, a time-integrated enterprise technology portrait model is constructed using patent IPC numbers. On this basis, a heterogeneous information graph containing three types of nodes: enterprises, patents, and IPCs is built. The relational graph attention network (RGAT) is introduced to learn the semantic and structural information between heterogeneous nodes, thereby achieving high-precision patent recommendations oriented to the technological needs of enterprises. In the empirical analysis section, the heavy gas turbine field is selected. The experimental results show that this method significantly outperforms traditional baseline models in key indicators such as Precision, Recall, and F1 score, and the ablation experiments further demonstrate that the dynamic technology portrait mechanism of long-term and short-term data fusion has a significant synergetic effect. The results of this research provide a new path reference for the R&D decision-making and technology management of enterprises in the heavy gas turbine field and also offer certain theoretical and methodological references for the intelligence of patent recommendation systems and the assessment of enterprise innovation capabilities.

ABSTRACT. Local Citation Recommendation aims to retrieve appropriate references from the candidate pool based on the context of specific citation position. Existing methods primarily rely on the semantic representation of citation contexts for candidate matching. However, they fail to effectively distinguish differences in citation intent across various writing scenarios. Consequently, the recommendation results often struggle to precisely align with users' actual needs. Meanwhile, existing methods mainly employ end-to-end architectures and lack the ability to explain recommendation results, which limits user trust and adoption rate. To address these issues, we propose Citation Intent-Aware Explainable Local Citation Recommendation method (IAE-LCR). The IAE-LCR identifies citation intent and embeds it throughout the entire recommendation procedure, achieving to align accurate recommendations and explainable outputs. Specifically, we first design a citation intent recognition module based on Pre-trained Language Models to extract citation intent features from the context. Second, we construct an intent-enhanced recall and re-ranking mechanism, where the intent information is integrated into both stages to achieve complementary optimization. Finally, we propose a templated explanation generation strategy based on intent labels and keyword evidence. This approach ensures standardization while effectively avoiding the hallucination risks associated with free-form generation. Experimental results on SciCite, ACL-200, and RefSeer benchmark datasets demonstrate the effectiveness of our proposed method. It achieves Macro-F1 of 86.95% on the intent recognition task. For the citation recommendation task on ACL-200, our performance significantly outperforms all baseline models. Furthermore, human evaluation indicates that the generated explanations receive high ratings in terms of relevance, faithfulness, and usefulness.

ABSTRACT. Industry demand-oriented technology transfer is crucial for accelerating the transformation of university patents and enhancing innovation efficiency. Accurately matching enterprise technological demands with transferable university patents remains challenging due to semantic heterogeneity, structural complexity, and limited observable transfer records. This paper proposes a heterogeneous graph neural network–based solution for industry demand-driven university patent recommendation. First, large language models are employed to extract and represent enterprise technological demands and university patent knowledge from patent texts, capturing fine-grained semantic information. Then, a heterogeneous network is constructed to jointly model enterprises, technological demands, university patents, and technology fields, incorporating semantic, structural, and classification-based relationships. Based on this network, a heterogeneous graph neural network is applied to learn demand–patent representations through relation-aware neighbourhood aggregation, enabling effective identification of patents with high potential for technology transfer. Finally, a demand-oriented patent recommendation framework is developed to rank university patents for enterprise-specific technological needs. An empirical case study in the artificial intelligence domain demonstrates that the proposed approach outperforms multiple baseline methods, validating its effectiveness and robustness in supporting university–industry technology transfer decision-making.

ABSTRACT. To scientifically and effectively evaluate the transfer and transformation potential of patents, this study integrates relevant patent evaluation guidelines, suggestions from field experts, and data availability to enrich the existing patent transformation evaluation system. It innovatively incorporates semantic-dimensional indicators into the evaluation of patent technical value, designs 20 evaluation indicators from four dimensions (technical value, legal value, economic value, and applicant entity), and constructs a comprehensive evaluation system. Aiming at the imbalance of patent transformation samples, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted to balance the categories. Two gradient boosting decision tree models, XGBoost and CatBoost, are used to perform classification tasks, and the SHAP method is applied to explain the importance of classification features. The empirical data consist of 139,273 authorized invention patents of Chinese institutions in Sichuan Province, retrieved from the Incopat Global Patent Search Database. The results show that transformed patents have slightly higher technical novelty and lower technical dependence; their average examination duration and average patent life are both longer than those of non-transformed patents; they also have a greater number of simple patent families, extended patent families, and more countries covered by the patent family, while having fewer patents under the same IPC main classification code. After oversampling, the CatBoost model demonstrates better performance, with an F1-score of 92.0%.

ABSTRACT. Against the backdrop of cross disciplinary technology integration becoming the core driving force of innovation, university patents are an important carrier reflecting the trend of technology integration. The existing patent research in universities has limitations, such as a single research perspective and insufficient exploration of inter domain correlations. To address these gaps, this study uses patents from Wuhan University of Technology from 2007 to 2025 as research samples, covering areas such as ships, materials, and others. It constructs a multidimensional intelligent analysis framework by integrating text mining, topic clustering, and social network analysis methods. The results indicate that patents exhibit overall cross disciplinary but locally clustered characteristics, with materials being the main core area; The turning point in the evolution of patent themes occurred in 2018, with research focus shifting towards green energy and data-driven approaches; Strategic emerging industry patents have high innovation potential. This study constructs a multi method integrated patent analysis framework, providing data support and decision-making references for universities to optimize research layout and promote technology transfer.

ABSTRACT. Against the backdrop of intensifying global technological competition and the deepening of the digital economy, early-stage tech startups serve as crucial engines of innovation. However, existing innovation potential evaluation systems face two core limitations: reliance on long-term operational data that startups lack and overgeneralized industry-agnostic indicators that fail to capture sector-specific innovation logic. To address these critical gaps, this study constructs a four-dimensional evaluation framework—integrating innovation accumulation, innovation team, innovation capital, and innovation ecology—grounded in the and innovation system theory. Tailored indicators are developed for four strategic emerging industries (semiconductors and integrated circuits, artificial intelligence, biopharmaceuticals, high-end medical devices) to enhance industry relevance. Using 20 AI startups as empirical samples, multi-source validation and case studies confirm the framework’s validity and strong discriminatory power. A key methodological contribution lies in prioritizing accessible "soft indicators" (e.g., core team background, patent quality, policy alignment) over traditional financial metrics, enabling effective capture of startups’ implicit technical value and team potential. This framework provides actionable tools for investors, policymakers, and enterprises to optimize innovation resource allocation, offering a targeted solution to the long-standing challenge of evaluating early-stage tech startups with limited data and heterogeneous industry characteristics.

ABSTRACT. This study reviews the current state of governance of health disinformation generated by Artificial Intelligence Generated Content (AIGC) on social media. It highlights the new challenges posed by AIGC and explores future governance pathways, aiming to provide a comprehensive analytical framework and strategic guidance for effective governance in this context. The study begins by summarizing past research on governing traditional health disinformation, examining governance models, correction mechanisms, evaluation systems, and key factors. It identifies the core focus and limitations of traditional research. With the rapid growth of AI-generated content (AIGC) in health information dissemination, the study examines the challenges posed by its swift production, broad reach, high fidelity, and significant impact on existing governance systems. To address these challenges, the study proposes future governance pathways in three dimensions: collaborative governance, technological innovation, and legal regulation. Collaborative governance involves a multi-stakeholder system with governments, academia, industries, media, and users. Technological innovation emphasizes cross-platform technology sharing, data supervision, standardized data collection, and using AI to filter misleading information. Legal regulation focuses on clarifying responsibility for AIGC content, balancing privacy protection and freedom of speech, and providing a solid legal foundation for governance actions.

ABSTRACT. Intellectual property securitization (IPS) has emerged as a novel financial instrument for mobilizing intangible assets and supporting innovation driven growth. Despite growing policy attention, systematic firm level evidence on the innovation incentives of IPS remains limited. This study exploits the introduction of local intellectual property backed securities as a quasi-natural experiment to examine the impact of IPS on firm innovation, using panel data on SMEs listed on China’s A share markets from 2015 to 2023. A difference in differences approach is employed to identify the causal effect of IPS on innovation outcomes. The results indicate that IPS significantly enhances firms’ innovation activity. Mechanism analyses further show that internal pressure weakens the innovation enhancing effect of IPS, whereas stronger external supervision substantially amplifies it. These findings suggest that although IPS can alleviate financing constraints by transforming future intellectual property income into tradable securities, its effectiveness depends critically on firms’ internal governance conditions and the strength of external monitoring. Additional heterogeneity analyses reveal that the innovation promoting effect of IPS is more pronounced for firms with fewer collateralizable tangible assets and stronger securitization capacity. Overall, this study provides new evidence on how intellectual property based financial instruments influence firm innovation behavior. It contributes to the literature on technology finance and innovation policy and offers policy relevant implications for improving intellectual property centered financial support systems.

ABSTRACT. Today, rapid technological change and the shortening of technology life cycles are strengthening the demand for systems that enable firms and nations to assess technology maturity in a timely and consistent manner. Technology Readiness Level (TRL) is used as an indicator to evaluate technology maturity in a staged manner; however, it relies heavily on expert judgment, which limits its reproducibility and scalability. In addition, patent-based indicators and bibliometric indicators also have difficulty adequately explaining the stage-to-stage transition processes of technology maturity and their interpretation. Therefore, to address these limitations, this study proposes a Retrieval-Augmented Generation (RAG)-based multi-agent framework that emulates expert panel–based TRL evaluation structures. The proposed framework consists of three stages. (1) Diverse technology-related documents, including academic papers, patents, corporate reports, and industry news, are integrated to construct role-specific RAG-based knowledge databases that reflect the entire technology life cycle. (2) Multiple expert agents representing the perspectives of basic research, technology development, system implementation, and the external environment independently evaluate TRL using role-specialized RAG structures based on the same technology information. Their evaluation results are then integrated through a consensus-based procedure to converge on a final TRL stage. (3) Based on the derived TRL results, the development period required for transitions between specific TRL stages is estimated and quantified as time delays in inter-firm technology gaps with respect to a common target TRL. By integrating automated TRL assessment with time-based technology gap analysis into a unified framework, this study presents a data-driven decision-support tool for technology strategy formulation, R&D investment prioritization, and inter-firm technology competitiveness comparison.

ABSTRACT. Recommending scientific collaborators with high academic potential, strong collaborative potential, and targeted relevance can facilitate knowledge sharing, integrate innovative resources, and enhance research quality and efficiency. Existing studies often neglect scholars' academic potential, which limits the full realization of researchers' capabilities during collaboration and impedes the development of high-quality scientific cooperation. To address these limitations, this study proposes the hybrid scientific collaborator recommendation method (HSCR) that integrating multiple dimensions—academic potential, collaborative potential, and research interests—enabling the simultaneous measurement of recommended collaborators' academic potential and the prediction of collaborative potential while ensuring targeted recommendations. An empirical study was conducted in the hydrogen fuel cell domain to validate the proposed hybrid scientific collaborator recommendation method. The results demonstrate that the HSCR method effectively identifies potential collaborators characterized by high academic potential, strong collaborative compatibility, and targeted relevance, thereby better accommodating personalized needs in scientific collaborator recommendation.

ABSTRACT. Large language models (LLMs) are increasingly used as reviewer assistants, yet their reliability in real peer-review settings remains insufficiently characterized. Using publicly available OpenReview records from ICLR 2017–2019, we construct a stratified benchmark (N=280 submissions) of real submissions and review artifacts for evaluating LLM-assisted reviewing. We define a protocolized task with fixed inputs (Title/Abstract/Keywords) to reflect a triage setting and standardized outputs (recommendation, overall score, self-reported confidence, and rationale), enabling controlled cross-model comparisons. We introduce a three-dimensional evaluation that separates validity, reliability, and robustness, measuring alignment with human outcomes and ranking signals, stability across repeated runs, and drift under information-preserving perturbations. LLMs provide informative decision and ranking signals, but stability is uneven: instability concentrates near the decision boundary, with elevated flip risk, and confidence is only partially predictive of unstable cases. Variability arises not only from run-to-run stochasticity but also from subtle, information-preserving changes in input presentation and instruction framing. We therefore position LLM outputs as calibratable process signals for triage and targeted human review rather than substitutes for final acceptance decisions and discuss implications for human–AI reviewing workflows that balance efficiency, quality, and accountability.

ABSTRACT. Generative artificial intelligence (GenAI) has been increasingly penetrating the scientific community, emerging as a valuable asset for a researcher’s individual human capital. From early pioneers to late adopters, researchers adopted it at different stages of scientific research. Drawing on the Theory of Scientific and Technical Human Capital (STHC), we argue that a researcher’s social capital influences the adoption of GenAI, which in turn reshapes the researcher’s subsequent collaboration. To test this relationship, we propose an LLM-assisted approach to identify a cohort of domain scientists who first adopted GenAI in their research between 2021 and 2023, and we then track their research collaborations in the one-year periods before and after adoption. We measure generative AI skills by keyword adoption and intensity, while social capital is measured by multi-dimensional collaboration metrics of collaborative network centralities, average citation per collaboration and institutional diversity. Then we employ a Cox proportional hazards model to test the effect of social capital on the speed of AI adoption, and a two-way fixed effects model to test the effect of AI adoption on researchers’ social capital development. Data on researchers and their publications are drawn from OpenAlex, and we focus on the fields of molecular biology and mechanical engineering to capture domains with differing levels of modularity. The results of this empirical study are expected to first prove the interplay between researchers’ human capital and social capital in the context of GenAI adoption. Thereby offering actionable insights for researchers’ career strategies and institutional policies aimed at fostering AI4Science.

ABSTRACT. Effective scientific collaborator recommendation is crucial for fostering academic partnerships, accelerating knowledge dissemination, and promoting interdisciplinary research.This study tackles the core challenge of capturing dynamic evolution patterns in scholarly collaboration networks. Scholarly collaboration networks are inherently heterogeneous, comprising multiple types of nodes and edges, while their topological structures evolve dynamically over time due to shifting research interests, emerging trends, and evolving collaboration patterns. Existing methods primarily focus on static homogeneous networks, which struggle to address the temporal evolution and multi-source heterogeneity inherent in dynamic heterogeneous networks, particularly failing to adapt to the dynamic nature of academic ecosystems where researchers' expertise evolves, new collaborations form, and existing partnerships strengthen or dissolve over time. To resolve this issue, we propose DynHGN, a novel dynamic heterogeneous network embedding model. DynHGN integrates hierarchical attention mechanisms to learn heterogeneous information and combines recurrent neural networks (RNNs) with temporal attention mechanisms to capture dynamic evolution patterns in collaboration networks. This dynamic modeling capability addresses the critical need for time-sensitive recommendations that reflect researchers' evolving expertise and current collaboration trends, thereby significantly improving recommendation precision. Experimental results demonstrate that DynHGN achieves improvements of 2.17%, 2.99%, and 3.2% in F1, MRR, and nDCG metrics, respectively, compared to the best baseline methods. Our model provides a novel framework for dynamic academic network analysis and can be extended to applications such as social networks and knowledge graphs.

ABSTRACT. [Purpose/Significance] Original theories originating from non-English-speaking countries have long been insufficiently valued in the academic community, which has restricted their development. This study takes such theories as the research object, aiming to reveal the characteristics of their communication and diffusion. [Methods/Approach] Focusing on the spillover effects of theories, this study carries out research from the perspectives of theoretical citation and the diffusion process of related topics. Quantitative measurements of the theoretical diffusion process are conducted from three dimensions, namely temporal dimension, spatial dimension and structural dimension, so as to clarify the diffusion trajectories and paths of such theories and explore their essential attributes and characteristics. [Results/Conclusions] The analysis in this study reveals that the overall diffusion of original theories from non-English-speaking countries follows the S-curve diffusion law. Meanwhile, they also exhibit diffusion characteristics similar to the sleeping beauty phenomenon during the diffusion process.

ABSTRACT. To address the issue of unidimensional and incomplete technological main paths due to time lags in patent citations, this study proposes an integrated framework for extending main paths for technological development and identifies multi-category technology innovation opportunities. The multi-dimensional technological main paths are initially extracted combining the community detection and SPC algorithms. Then the technical-efficacy matrix based on SAO semantic analysis is mapped to acquire hot technical efficacies of each main path. Finally, we apply FOS to retrieve the latest scientific papers from similar technical domains with the hot efficacies, and then use technological novelty indicators and similarity indicators to screen and identify the technology innovation categories, thereby extending the original main paths. In summary, five main paths and three types of innovation opportunities in the field of ASSLIBs are identified.

ABSTRACT. Against the backdrop of a new round of scientific and technological revolution and industrial transformation, we examine in this study from a demand-side perspective how industrial innovation performance shapes the structure of technology convergence. Using four-digit CPC co-occurrence in biopharmaceutical patents, we construct a technology convergence network, incorporate network endogenous structure, structural positions, industrial innovation performance, and factors related to knowledge proximity, resource dependence, and path dependence, and apply an exponential random graph model (ERGM) to identify multiple mechanisms from the perspective of micro-level tie formation. The results show that the technology convergence network is globally sparse but locally clustered, and nodes with high centrality act as hubs in network expansion. After controlling for structural factors, industrial innovation performance, collaboration intensity, and policy support significantly promote the formation of technological ties, whereas certain knowledge diversity indicators and excessively strong market demand inhibit the creation of new cross-domain ties; disruptive technologies participate more frequently in cross-domain connections. This study provides empirical evidence for understanding the evolution of technology convergence structures under demand-side drivers and for optimizing industrial innovation policies.

ABSTRACT. To address the limitations of existing quantitative methods that rely on single indicators, such as traditional co-classification and co-citation, which struggle to identify synergistic relationships between technologies at the semantic level, this study constructs a systematic conceptual model of patent complementarity and proposes an intelligent identification method integrating Large Language Models. The research aims to achieve precise and efficient semantic identification of patent complementarity, providing a reference for technology layout and strategic decision-making. First, through literature review and conceptual analysis, a conceptual system for patent complementarity is established across dimensions including connotation, extension, core features, constituent elements, and multi-dimensional classifications (technical, legal, market, temporal, and contextual). This clarifies the essential characteristics of value dependence and synergistic enhancement and categorizes nine types of complementarity across the problem, solution, and effect layers within the technical dimension. Second, a method for "Problem-Solution-Effect" (P-S-E) extraction and complementarity identification is designed. This conceptual model is utilized to guide the design of LLM prompts, enhancing semantic understanding and logical reasoning capabilities. Finally, using 7,536 patents in the field of humanoid robotics as experimental samples, 23 categories of technical problems, 27 categories of technical solutions, and 40 categories of technical effect themes were extracted. A total of 5,919 (78.54%) complementary patents were identified, and high-value complementary patents were recommended by integrating complementarity value and endogenous value. Through expert evaluation and multi-model cross-validation, the method demonstrated stable performance in tasks involving P-S-E extraction (P1), complementarity identification (P2), complementarity type identification (P3), and complementarity rationale generation (P4). The overall accuracy reached 90% or higher, with the DeepSeek model performing best in hallucination control. The results indicate that the conceptual model and associated methods effectively break through the limitations of traditional quantitative research on patent complementarity, significantly enhancing the capability to identify complex technical complementarity relationships. This study still has limitations, such as coarse granularity in technical element representation, insufficient adaptability to corporate strategies, and a single data modality. Future research will further develop a hierarchical technical element system and integrate corporate strategic contexts and multi-modal data to improve the interpretability of identification results and decision-support capabilities.

ABSTRACT. This paper proposes a multi-dimensional graph convolutional patent classification model based on cross-modal fusion and technological evolution. It aims to improve classification efficiency while reducing manual effort in intellectual property management. Method: In this paper, graphs are constructed from both spatial and temporal dimensions, and two Graph Convolutional Networks (GCNs) are employed to extract hierarchical structural features and dynamic historical technological features. The structured features and the unstructured representations of patent texts are treated as heterogeneous bimodal data. To bridge these modalities, a novel cross-modal attention mechanism is introduced to uncover latent correlations in the semantic space, thereby achieving semantic alignment and effective fusion between hierarchical structural features and textual patent features. Results: Experiments on Chinese and English patent datasets demonstrate that the proposed model outperforms all baseline models across every evaluation metric.

ABSTRACT. Against the backdrop of globalization and technological transformation, technological convergence continues to drive innovation and profoundly reshape the global industrial landscape. However, current research on technological convergence has primarily focused on binary convergence, with insufficient exploration of multi-technology convergence. Moreover, existing studies on predicting technological convergence generally rely on existing network edges and overlook the informational gains from introducing new edges. Therefore, this study uses pharmaceutical patent data as a case study to construct a higher-order network and focuses on ternary relationships—a typical pattern of multi-technology interaction—to predict potential ternary technology convergence trends from the perspective of network edge dynamics. Specifically, we first construct a multimodal heterogeneous network based on patent information, linking "inventors–CPC–technical terms." Using a meta-path approach, we uncover latent connections between edges within the CPC co-occurrence network layer. Based on this, we form an expanded CPC co-occurrence network that includes all existing edges as well as newly identified links. Additionally, we develop a multidimensional technical similarity metric based on CPC classification codes, inventor information, and technical terms. Finally, we design three comparative experiments to systematically analyze, through machine learning models, the impact of network edge dynamics (i.e., the introduction of new edges) and multidimensional technical similarity metrics on technology convergence outcomes. Experiment 1 serves as the baseline control, predicting potential future ternary technology convergence using only the initial network state and the CPC-based technical similarity metric. Experiment 2 recalculates the CPC-based technical similarity metric within the expanded network and predicts ternary technology convergence, aiming to validate the improvement in prediction accuracy brought by the introduction of new edges. Experiment 3 builds upon Experiment 2 by further incorporating inventor similarity and technical term similarity, exploring the optimization of prediction performance through a multidimensional similarity metric system. The results indicate that introducing new edges into the CPC co-occurrence network used to characterize technology convergence significantly enhances the model's predictive performance and generalization capability. Furthermore, incorporating inventor similarity and terminology similarity metrics further optimizes the prediction outcomes. This study not only provides a novel methodological framework for predicting technology convergence but also offers important scientific evidence for deepening the understanding of the technology convergence process and optimizing technology convergence management practices.

ABSTRACT. Technology convergence is a critical driver of innovation in complex ecosystems like Digital Health. However, existing measurement approaches often rely on macro-level indicators (e.g., citations, IPC codes), failing to reveal the granular micro-mechanisms of how technologies combine at structural versus functional levels. To address this gap, this study proposes a novel analytical framework utilizing Large Language Models (LLMs) and Subject-Action-Object (SAO) semantic analysis. By employing the DeepSeek V3 model for precise knowledge extraction and Bertopic for topic modeling, we construct a “Dual-Perspective Network”—distinguishing between Structural Composition and Functional Interaction networks based on semantic action categorization. Applying this framework to 26,727 digital health patent families (2011-2024), we introduce a “Normalized Coreness Shift Index” to quantify evolutionary dynamics. Results reveal a striking divergence: the functional network evolves towards high complexity with “Super Topics,” while the structural network maintains a stable core. Specifically, “Sensor Module Design” exhibits a continuous core-evolution trend, whereas “Telemedicine Services” shows an infrastructure-sinking effect. These findings provide a high-resolution view of convergence, offering strategic insights for R&D decision-making.

ABSTRACT. At the macro level, the tension between the demands of public governance in post-industrial societies and the logic of technological rationality has become increasingly pronounced. This tension manifests in three structural contradictions: the mismatch between escalating societal complexity and the lagging linear decision-making paradigm; the supply–demand conflict arising from the expanding scale of public services and the growing scarcity of governance-related technical resources; and the compatibility dilemma between heterogeneous discretionary technologies and the homogeneity inherent in traditional bureaucratic systems. Collectively, these dynamics signal that modern governance is approaching a critical threshold of “decision-making crisis.” To address this crisis, algorithmic decision-making (ADM)systems—organized through a closed-loop coupling of data, models, and computational power—have emerged. At the micro level, the deluge of big data serves as the foundational fuel of algorithmic systems and releases administrative value; large-scale models function as the central arena for distributed inference, contextualized discretion, and the enactment of technological rationality; and ultra-computing capacity operates as the driving engine that powers big-data enablement and large-model operations, thereby breaking through performance bottlenecks. From a meso-level perspective, Algorithmic decision-making systems constitute the pivotal technological support underpinning the transformation and upgrading of public governance in the digital society. Technological rationality provides the basis for governance alignment; technological embedding enables the empowerment of public governance in the digital era; and technological leaps drive a qualitative shift toward data-intensive discretion in public administration.

ABSTRACT. To address the issue of unidimensional and incomplete technological main paths due to time lags in patent citations, this study proposes an integrated framework for extending main paths for technological development and identifies multi-category technology innovation opportunities. The multi-dimensional technological main paths are initially extracted combining the community detection and SPC algorithms. Then the technical-efficacy matrix based on SAO semantic analysis is mapped to acquire hot technical efficacies of each main path. Finally, we apply FOS to retrieve the latest scientific papers from similar technical domains with the hot efficacies, and then use technological novelty indicators and similarity indicators to screen and identify the technology innovation categories, thereby extending the original main paths. In summary, five main paths and three types of innovation opportunities in the field of ASSLIBs are identified.

ABSTRACT. Against the backdrop of a global sustainability transition, synergies and trade-offs among the Sustainable Development Goals (SDGs) have become a central scientific problem for policy integration and systemic governance. Focusing on the construction of an SDG–SDG interlinkage network, this study integrates bibliometric analysis and rule-based information extraction. Specifically, based on the abstracts of 3,475 publications retrieved from the Web of Science (WoS) Core Collection and the full texts of 195 sampled papers, we (i) use tools such as VOSviewer to analyze countries, journals, and keyword co-occurrence patterns to depict the knowledge structure and the evolution of research hotspots; and (ii) extract sentence-level “SDG–relation–SDG” statements to construct a directed, weighted SDG–SDG network, quantify network density, reciprocity and centrality, and conduct community detection. The results show that synergy accounts for 56.1% of extracted relations, whereas trade-offs account for 43.9%. The network exhibits a hub-dominated structure led by a small set of goals, and four cross-module coupled communities can be identified. This study provides structural evidence for identifying synergy corridors and trade-off hotspots, and for optimizing multi-objective policy portfolios and risk governance.

ABSTRACT. Interdisciplinary research often faces challenges due to fragmented theoretical systems, hindering systematic knowledge integration and development. Using Information Resources Management as a case study, this research proposes an AI assisted framework to identify, classify, and visually represent core theories. The framework employs a five stage hybrid paradigm that combines theoretical foundation building, automated literature processing, multidimensional classification, expert validation, and knowledge graph construction. By integrating natural language processing, large language models, and human expert input, the study establishes a scalable theoretical maturity assessment system and uncovers relational structures among theories. This approach improves the objectivity, efficiency, and systematization of theory extraction in interdisciplinary fields. The framework is designed to be transferable and offers a methodological foundation for theory driven knowledge discovery. Future work will focus on refining the classification system through expert feedback and extending the methodology to domains such as innovation management.

ABSTRACT. AI systems are increasingly deployed in high-risk decision-making contexts, where the credibility of AI-assisted judgments depends not only on outcome correctness but on whether reasoning processes can be examined, audited, and attributed, revealing a structural mismatch between knowledge-oriented organizational frameworks and procedural, temporally unfolding reasoning. Accordingly, this paper introduces Reasoning Organization (RO) as an organizational and governance-level interface that treats reasoning processes as explicit objects of organization, specifying how AI-assisted reasoning should be structured, constrained, recorded, rather than describing internal model computations or algorithmic implementations. The RO framework operates through two cross-cutting dimensions—Semantic Alignment, which ensures consistent interpretation of concepts, evidence, and states throughout reasoning, and Human–AI Collaboration, which allocates responsibilities across the reasoning workflow. At its core lies the Reasoning Object Stream O(t), which represents the judgment carrier that is progressively reconstructed over the course of reasoning. Reasoning Organization is realized through a layered architecture in which the Rule Layer defines admissible state transitions through explicit rule objects that constrain the Execution Layer, the Execution Layer reconstructs O(t) and produces execution traces, and the Accountability Layer audits O(t) and its traces by reorganizing execution records and feeding governance back to the Rule Layer through rule revision. From this layered architecture, three structural properties emerge as necessary consequences: inspectability, whereby rules, states, and transitions are explicitly identifiable and examinable; verifiability, whereby execution traces enable reasoning paths to be replayed and checked for consistency; and governability, whereby reasoning outcomes can be audited and corrected through rule revision and responsibility reallocation. These properties form a logical dependency chain from structured to executable and ultimately accountable reasoning. As a Research-in-Progress study, this paper contributes a conceptual and organizational framework for reasoning governance, while domain-specific instantiation and system-level implementation are reserved for future research.

ABSTRACT. This study conceptualizes brand communities as information systems composed of heterogeneous user interactions. Using large-scale Reddit data and a theory-driven LLM framework, community discourse is structured into brand competitiveness assessment and brand value co-creation processes. The results reveal distinct emotional dynamics across discourse types, highlighting how information structures shape collective sentiment in brand communities.

ABSTRACT. Purpose: This study aims to systematically examine the role of technological innovation in advancing the United Nations Sustainable Development Goals (SDGs), identifying both general-purpose and goal-specific technologies that drive progress. Design/methodology/approach: Drawing on patent data from the PatentSight database (2015–2024), we construct an SDG–technology mapping matrix covering 100 technology categories across 13 SDGs. We develop and apply the Technology Contribution Index (TCI), which integrates coverage, patent volume, diversity, and contribution intensity. Additionally, cosine similarity is applied to capture interlinkages among SDGs. Findings: The results reveal marked heterogeneity in technological engagement across the SDGs. SDG 09 (Industry, Innovation and Infrastructure), SDG 07 (Affordable and Clean Energy), and SDG 13 (Climate Action) dominate in portfolio size and form a tightly interconnected cluster, whereas socially oriented goals such as SDG 05 (Gender Equality) and SDG 01 (No Poverty) remain weakly supported by patents. Large-scale, general-purpose technologies (e.g., Advanced Manufacturing, Blockchain, Internet of Things) provide broad systemic support across multiple SDGs, while specialized technologies (e.g., Clean Cooking, Sex-Disaggregated Data Management) play indispensable roles in individual goals. These findings highlight the coexistence of systemic platforms and niche drivers in the global innovation ecosystem. Research limitations:Patent data may underrepresent non-technological contributions to socially oriented SDGs (e.g., equity, education, institutional reform), which rely more heavily on policy, cultural, or behavioral interventions. Practical implications: The TCI framework offers a tool for policymakers to prioritize investment in enabling technologies with broad spillover effects, while also promoting targeted support for niche innovations to address technology gaps in under-supported SDGs. Originality/value: By integrating patent analytics with the SDGs framework, this study introduces a novel quantitative approach—the TCI—for systematically identifying key enabling and goal-specific technologies. The findings enrich the literature on sustainable development and innovation policy, and provide actionable insights for aligning science, technology, and innovation (STI) strategies with the 2030 Agenda.

ABSTRACT. The “AI for Science” (AI4Science) revolution driven by AI-empowered scientific research is transforming modes of knowledge production and innovation. This paper empirically examines how the knowledge composition of scientists influences the formation and evolution of collaboration networks in the context of AI-empowered scientific research. We first proposed LLM-based approach to identify AI4Science publications. Using AI4Science publications in the field of materials chemistry, we construct collaboration networks with researchers as nodes and co-authorship ties as edges. The Stochastic Actor-Oriented Models (SAOMs) are applied to model the network dynamics across different periods, enabling a longitudinal analysis of collaboration patterns. The findings indicate that domain experience, AI experience, and AI4Science experience play distinct roles, where scientists with more AI or AI4Science experience are more inclined to establish AI4Science partnerships, while those with more domain experience are the opposite.

ABSTRACT. In the context of de-globalization, emerging economies face the dual challenge of sustaining leadership in advantaged industries while enabling breakthroughs in disadvantaged ones. Drawing on innovation ecosystem theory, this paper proposes a "dual transition" framework to explain divergent industrial pathways under changing global conditions. Using multi-source data and comparative case analysis of the new energy vehicle and biopharmaceutical industries, the study identifies two distinct innovation ecosystem trajectories. Advantaged industries undergo an "outward transition" driven by technological upgrading and international expansion, whereas disadvantaged industries follow an "inward transition" centered on import substitution and indigenous innovation. Based on these findings, the paper develops an "outward expansion–inward breakthrough" framework, highlighting the need to balance global competitiveness with indigenous innovation to support industrial upgrading in emerging economies.

ABSTRACT. Under conditions of deglobalization and techno-nationalism, critical technologies are increasingly governed through security-oriented policy interventions rather than efficiency-driven market coordination. Focusing on the global semiconductor value chain, this study examines how such shifts translate into concrete governance outcomes. The paper develops an analytical framework linking structural drivers, policy instruments, operative mechanisms, and governance outcomes, and applies comparative policy analysis and process tracing to three cases: overseas fabrication investment, export controls on advanced manufacturing equipment, and competition in advanced chips. The analysis suggests that security-oriented instruments—such as export controls, industrial subsidies, investment screening, and alliance-based coordination—reconfigure value chain dynamics by reshaping technological diffusion, production location, and rule alignment. Rather than producing uniform decoupling, these mechanisms generate differentiated and fragmented forms of technology governance, increasing regulatory complexity for multinational firms. The study contributes to debates on global technology governance under geopolitical uncertainty by unpacking the mechanisms through which techno-nationalist policies reshape value chain governance.

ABSTRACT. Existing topic modeling approaches face two principal limitations: clustering-based methods (e.g., BERTopic) rely heavily on dimensionality reduction, resulting in inevitable information loss, while methods based on large language models (LLMs) (e.g., TopicGPT) often fail to capture the intrinsic structure of document collections. To address these issues, this paper introduces a novel hybrid topic modeling framework that creatively integrates graph-based clustering and LLMs, aiming to ensure both the robustness of topic structure discovery and the integrity of topic semantics. Its core mechanism lies in abandoning the traditional dimensionality reduction steps and directly conducting computations in the original high-dimensional semantic space. Specifically, the proposed framework first employs an embedding model to obtain document vectors and constructs a K-nearest neighbor graph (K-NNG). The Leiden algorithm is then applied for community detection, forming initial document clusters. To enhance cluster purity, a semantic similarity-based document filtering mechanism is introduced. Finally, LLMs are utilized to automatically transform semantically coherent document clusters into interpretable topic labels and detailed descriptions. Experiments conducted on the Bills and Wiki datasets demonstrate that the proposed framework outperforms mainstream baseline methods such as BERTopic in key evaluation metrics including topic coherence and alignment. Human evaluations further confirm its superior interpretability. Notably, our analysis reveals that the choice of embedding model has limited impact on final topic quality, offering practical guidance for model selection in resource-constrained scenarios. Overall, this research contributes an innovative hybrid framework that effectively combines graph-based clustering and LLMs for topic modeling, experimentally validates its superiority, and provides a novel and practical solution to the field.

ABSTRACT. Understanding how artificial intelligence (AI) enables carbon neutrality (CN) technologies is critical for advancing low-carbon innovation. Existing studies on technology convergence largely rely on citation-based or co-occurrence-based relationships, and often treat convergence as a symmetric process, limiting their ability to capture early-stage and enabling interactions. This study proposes a semantic-based framework to examine AI–carbon neutrality convergence using patent data. An AI-agent approach is employed to match patents with relevant technology keywords. A heterogeneous patent-keyword graph is constructed, and a graph convolutional network (GCN) is used to jointly learn semantic representations of patents and technology keywords. Based on the learned vectors, we develop multidimensional convergence indicators capturing directionality, integration depth, temporal dynamics, and quality. The proposed framework provides a flexible tool for identifying AI-enabled carbon-neutral innovations and contributes to technology convergence research and climate-related policy analysis.

ABSTRACT. Accurate matching between enterprise technology demands and academic supplies is crucial for technology transfer. Traditional keyword methods struggle with fine-grained semantic relations. This study presents a demand-driven matching framework combining LLM-based semantic structuring and task-adaptive embeddings. Demands are decomposed into structured components, and similarity is computed via lexical and embedding models. Contrastive learning and multi-model ranking enhance retrieval. Experiments in the biopharmaceutical domain show significant gains in Precision, Recall, and F1, confirming the approach’s effectiveness.

ABSTRACT. Big science facilities are core infrastructures for scientific advancement, playing a vital role in discovering novel solutions to achieve Sustainable Development Goals (SDG). We constructed a dedicated dataset to systematically compared SDGs contributions made by the publications supported by big scientific facilities, the publication profiles of different types of facilities in both SDG and non-SDG domains, as well as the distribution and topical landscape of big scientific facilities in SDG-related research, thereby revealing the association between large scientific facilities and SDG research from a macro perspective. Results show a rising focus on big scientific facilities in SDG-related research, which is oriented towards technological applications rather than social systems, and synchrotron light sources (SLSs) stand out as the most frequently discussed facility type. This study will expand the research landscape of big scientific facilities and SDGs, revealing the macro-level distribution status of big scientific facilities in SDG research.

ABSTRACT. This paper summarizes the current achievements of domestic and international research on the characteristics and identification of false health information on social media, and delves into the new changes in the content, user behavior, and transmission patterns of false health information in the era of Artificial Intelligence Generated Content (AIGC). Driven by AIGC technology, the identification techniques and methods for false health information are confronted with new challenges and transformations. While the advanced generation capabilities of AIGC technology enhance the confusion of false information, they also present opportunities for developing more efficient identification tools and optimization strategies. Notably, this paper emphasizes the unique role of digital libraries in addressing the challenges posed by false health information on social media. As crucial institutions for knowledge storage, dissemination, and utilization, digital libraries not only possess abundant digital resources but also boast powerful capabilities in information organization, retrieval, and analysis. Through collaboration with social media platforms, digital libraries can provide users with more comprehensive, accurate, and authoritative health knowledge, jointly establishing a mechanism to verify the authenticity of health information. Furthermore, they can enhance the public's information literacy and discrimination capabilities, assisting users in defending against the intrusion of false health information.

ABSTRACT. In this paper, we propose a cross-modal tech mining framework to integrate patent schematics and open-source code, aiming to uncover innovation patterns missed by text-only analysis. Traditional tech mining relies on patent and literature text, overlooking rich information in patent drawings and in code repositories. By embedding patent figures and code documentation together via vision language models (CLIP), we bridge the “implementation gap” between design (patents) and execution (code) in additive manufacturing. A pilot case using shape-memory-alloy and self-healing-polymer technologies demonstrates hidden links and innovation trajectories that text-based methods cannot reveal.

ABSTRACT. The analysis of the evolution of patent technology is a crucial method for identifying technological trajectories and forecasting frontier directions. However, extant approaches are beset by significant challenges in dynamic environments, particularly with regard to the robustness of their handling of conflicting evidence from multi-source data and their ability to capture the temporal dynamics of relational weights. In order to address these limitations, this study proposes a novel patent technology topic evolution identification model integrating the Vector Space Model (VSM) and Dempster-Shafer (D-S) evidence theory. The model employs VSM for feature quantification and similarity calculation within small-sample time windows, generating confusion matrices to dynamically derive fusion weights for D-S evidence theory. This design enhances robustness in high-conflict scenarios and reveals the differential integration of multivariate relationships—textual co-occurrence (MB), citation coupling (MC), and applicant coupling (MP)—across various technological stages. An empirical study in the graphene sensing technology domain validates the framework. The findings indicate a substantial degree of complementarity among the three heterogeneous networks. Compared to the static entropy weight method, the proposed dynamic weighting strategy effectively overcomes the issue of early-stage data sparsity caused by independent time-window segmentation, particularly by restoring latent cross-temporal MP associations. The constructed temporal topic networks and subsequent CONCOR clustering successfully delineate the field's evolution from fundamental material analysis towards diversified, integrated, and application-oriented research themes.

ABSTRACT. Under the combined pressures of de-globalization, techno-nationalism, and an increasingly volatile external environment, future industries can no longer rely on single organizations to integrate critical innovation resources. Drawing on innovation ecosystem theory, this paper argues that the coordinated development of a "dual-cycle framework" constitutes the core mechanism for reconstructing innovation ecosystems in future industries. Taking quantum technology as a case study, this research employs multi-source data and comparative case analysis to examine how technological barriers imposed by major economies, particularly the United States, have fragmented traditional global value chains. It further investigates China's practices of domestically driven independent innovation under the internal cycle, alongside regionally oriented external-cycle collaboration with the European Union and other partners. The findings indicate that ecosystem adaptability is fundamental to the functioning of the dual-cycle framework, while proactive government intervention in collaborative technological R&D significantly enhances systemic resilience. This study provides theoretical insights and policy-relevant implications for innovation ecosystem reconstruction under de-globalization.

ABSTRACT. This study compares the corporate basic research models of State Grid and Huawei, analysing how distinct ownership structures and industry contexts shape research strategies, governance, and knowledge outcomes. Through a qualitative comparative case study, it identifies two contrasting models. State Grid, a state-owned monopoly in strategic infrastructure, pursues a mission-oriented and system-driven model. Its centrally coordinated research is embedded in national energy strategy, focusing on large-scale engineering challenges like grid stability. It generates system-level foundational knowledge primarily diffused through standards and infrastructure deployment, prioritizing reliability and systemic impact over rapid commercialization. In contrast, Huawei, a private firm in the competitive global ICT sector, follows a capability-oriented and long-term investment model. Driven by technological uncertainty, it invests in high-risk theoretical research through autonomous entities, emphasizing researcher autonomy and tolerating failure. Knowledge is transformed internally into technology platforms and ecosystems to build sustainable innovative capability. The comparison reveals that effective corporate basic research models are highly contingent on ownership and industry characteristics, with no single optimal approach. State-Owned Enterprises (SOEs) in strategic sectors facilitate mission-oriented, system-level research, while private firms contribute through long-term capability building. Consequently, the study argues for differentiated policy and evaluation frameworks that support these diverse organisational models, rather than applying uniform metrics, to foster a more resilient and complementary national innovation system.