Search Results (389)

This paper uses panel data from 30 Chinese provinces (municipalities and autonomous regions) for the period 2007–2024. It examines the impact of the agglomeration of high-end talent on regional innovation efficiency and its underlying mechanisms, based on the theory of external economies of talent agglomeration. Additionally, it analyzes regional heterogeneity and heterogeneity across innovation actors. The study finds that the agglomeration of high-end talent exerts a significant positive effect on regional innovation efficiency, exhibiting a nonlinear inverted U-shaped relationship. These findings hold even after addressing endogeneity issues and conducting various robustness tests. Heterogeneity analysis indicates that the agglomeration of high-end talent has a more pronounced positive effect on regional innovation efficiency, particularly in the eastern and western regions and in universities and research institutions; however, the optimal agglomeration level in the western region is lower than that in the eastern region. The agglomeration of high-end talent in central regions and in enterprises above a certain scale fails to significantly enhance regional innovation efficiency. The agglomeration of high-end talent positively affects regional innovation efficiency in highly marketized regions, but such an effect is not observed in regions with low marketization. The results of mediation analysis suggest that high-end talent agglomeration fosters regional innovation efficiency by facilitating knowledge spillovers and collaborative industry–university–research activities. The above research not only confirms the positive impact of the agglomeration of high-end talent on regional innovation efficiency but also provides policy recommendations for local governments regarding talent development and regional mobility. It holds practical significance for achieving major strategic goals such as building a science and technology powerhouse, a talent powerhouse, and sustainable development. Full article

Drawing on panel data from firms listed on the SME Board and Growth Enterprise Market (GEM) between 2010 and 2024, this study examines how patient capital influences SME innovation. It considers both the quantity and quality of innovation and investigates the underlying mechanisms. Using the China Industrial Enterprises Database (2000–2014), it further explores the innovation effects of patient capital on unlisted SMEs. The empirical findings are as follows. First, patient capital, measured by the proportion of relationship-based debt and stable equity, exhibits a significant and robust positive association with the output and quality of SME innovation, and this association gradually strengthens over time. Second, heterogeneity analyses show that relationship-based debt is more strongly associated with innovation in state-owned enterprises and national-level “Little Giant” firms (specialized, refined, distinctive, innovative SMEs), whereas stable equity is significantly associated with innovation only in private and ordinary enterprises. The association between stable equity and innovation is more pronounced in non-regulated industries, while the association for relationship-based debt remains consistent across industries. Third, mechanism tests reveal that patient capital is linked to SME innovation through four channels: alleviating financing constraints, fostering university–industry–research collaboration, improving knowledge conversion efficiency, and strengthening market power. Fourth, an extended analysis confirms that patient capital is also significantly associated with innovation among unlisted SMEs, indicating strong external validity of the study’s conclusions. Based on these findings, this paper advocates for establishing a long-term financing mechanism oriented toward patient capital, with differentiated allocation and optimization of institutional environments across industries. Such an approach should facilitate three transmission channels—university–industry–research collaboration, knowledge transfer, and market power—while extending policy coverage to unlisted SMEs, thereby nurturing a virtuous cycle ecosystem of “long-term capital → sustained R&D → high-quality innovation.” Full article

Recent statistics reported by the World Health Organization and the International Agency for Research on Cancer indicate that the global incidence of bladder cancer has continued to increase in recent years, particularly in industrialized countries. Therefore, the timely diagnosis of early-stage bladder cancer is of great clinical importance for improving patient prognosis and treatment outcomes. In this context, computational optical sensing frameworks that integrate Spectrum-Aided Visual Enhancer (SAVE) technology with cystoscopy have attracted significant attention to overcome the limitations of conventional visual data interpretation. In this study, an AI-driven optical biosensing framework was evaluated using 1372 white-light cystoscopy (WLC) images of bladder cancer (RGB-WLC) collected in collaboration with Chung Shan Medical University Hospital. Hyperspectral conversion technology was applied to extract precise spectral information from the white-light images. Subsequently, dimensionality reduction was performed based on the characteristic wavelengths of narrow-band imaging cystoscopy at 415 nm and 540 nm to generate hyperspectral reconstructed narrow-band images. The images were categorized into Ta stage (Ta), above T1 stage (Above T1), and four additional classes. The dataset was divided into training and testing sets to establish both a standard white-light cystoscopy model (RGB-WLC) and an advanced hyperspectral biosensing model utilizing the YOLOv8 architecture for enhanced pattern recognition. Model performance was evaluated using sensitivity, F1-score, and overall accuracy. The standard RGB-WLC model achieved an accuracy of 0.852, whereas the SAVE-based biosensing model achieved an accuracy of 0.948, representing an improvement of approximately 11.27%. The results demonstrate that combining algorithmic hyperspectral reconstruction with deep learning architectures effectively addresses the challenges of clinical data interpretation and significantly enhances the detection and staging performance of bladder cancer imaging. Full article

Industry–University–Research (IUR) networks are vital for knowledge sharing and collaborative innovation, yet existing network research largely ignores the maintenance cost of inter-organizational ties, which creates persistent theoretical tension between social capital theory (advocating dense connections) and structural hole theory (advocating sparse non-redundant ties). This study constructs a simulation model integrating barter knowledge exchange and multi-dimensional relational maintenance cost loss and systematically simulates the evolution of average knowledge stock (AKS) under regular, small-world and random network structure. The simulation results show that there exists a stable optimal node degree range of 20–40 for IUR actors, which is robust against changes in network scale, initial knowledge endowment and relational cost coefficients. Under moderate technological complexity, small-world networks realize the highest efficiency of knowledge accumulation; when technological complexity rises to a high level, regular networks with local agglomeration advantages become more efficient. This study supplements a cost-based analytical perspective to reconcile the contradiction between two core network theories and provides preliminary simulation evidence for the contingent design of IUR collaborative networks. From a practical perspective, the findings offer reference for adaptive governance of IUR alliances to balance relational costs and knowledge gains and further respond to the United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Limitations of this simulation-based analysis are clearly acknowledged in the discussion section. Full article

To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge. Full article

AI is currently shaping how people are being entrepreneurial by allowing the establishment of AI-native ventures. The creation of these new types of businesses builds off an infrastructure of data, computing power, and research that typically accompany advanced economies. In contrast, most developing economies are still experiencing institutional and structural barriers that inhibit the formation of new ventures and their subsequent growth. Despite the existence of research that explores some of the ways in which successful ecosystems from developed economies could be applied to developing ecosystems, there is little guidance on how to systematically adapt these successful practices in resource-constrained environments. This research uses a comparative, document-based study design to assess how to benchmark and configure AI-native entrepreneurship ecosystems across heterogeneous institutional environments. Using Switzerland and Jordan as two contrasting analytical cases, we define twelve dimensions of an ecosystem and then create comparative ecosystem profiles using a standardized coding and scoring framework. We combine dimension-level data on talent development, applied research, infrastructure, financing, governance and market access with baseline socio-economic indicators. Our results demonstrate a high level of structural asymmetry between the two ecosystems. Switzerland has a balanced and highly coordinated configuration, whereas there is a strong university anchor and demand for talent in Jordan, but there are also significant weaknesses in terms of infrastructure, financing, and industry linkages. Building from these results, we present the parameter re-weighting and the context-sensitive design model to encourage the emergence of AI-native entrepreneurship in Jordan through coordinated architecture, collaborative experimentation resources and internationalization at an early stage. This article advances both the fields of entrepreneurial ecosystems and digital entrepreneurship by framing AI-native entrepreneurship as a new form of knowledge-intensive venture creation and providing a context-sensitive approach for adapting entrepreneurial ecosystems. The results provide a document-informed basis for policymakers, academic institutions and other ecosystem actors seeking to develop AI-based innovation in resource-constrained economies. Full article

In the context of an increasingly tense relationship between environmental and economic goals, green innovation is of crucial importance for the global green transformation. However, its dual external effects often render a single policy tool ineffective, which makes the implementation of a policy mix necessary. In this study, according to a three-dimensional policy mix framework, four pilot policies in the fields of environment, innovation, and finance were selected and combined in pairs. Panel data from 276 Chinese cities (from 2006 to 2021) were used to compare the differences in the effects of different types of policy mixes on green innovation using the difference-in-differences method and to verify the mediating effects of green finance and university–industry collaboration. The synergy test results show that the cross-domain mix with the differentiation of tool types and the compatibility of mechanisms is the best. The policy effects were more evident in cities outside the Yangtze River Economic Belt and in non-central cities. These findings provide theoretical and practical insights for designing an effective policy mix to promote green transformation. Full article

The rapid development of artificial intelligence has intensified the need for higher education institutions to cultivate students’ capacity to engage with complex and changing innovation problems. Existing research has largely examined the net effects of individual educational resources, leaving less understood how heterogeneous institutional and student resources combine across universities. Drawing on Resource Orchestration Theory, this study examines configurations associated with students’ perceived and self-reported innovation capacity. Survey responses from 14,034 students were aggregated to 126 Chinese universities after tests of aggregation reliability, and the university-level data were analyzed using fuzzy-set qualitative comparative analysis (fsQCA). No single antecedent met the conventional necessity threshold. The analysis identified five configurations associated with high self-reported innovation capacity and three configurations associated with low self-reported innovation capacity, which were summarized into three overarching high-outcome patterns. Additional checks varying consistency, frequency, and calibration choices indicated substantial, although not complete, configurational stability. The study extends resource-orchestration reasoning to higher education while emphasizing that the findings represent cross-sectional set-theoretic associations rather than longitudinal or experimental causal effects. Full article

Structural health monitoring and regular damage inspection are critical to ensure the operational safety of civil infrastructure and reduce life-cycle maintenance costs, while traditional manual inspection suffers from low efficiency, high subjectivity, and occupational safety risks for inspectors in hard-to-reach areas. Although existing reviews have explored image-based damage detection, most focus on single damage types or individual infrastructure categories, with few providing quantitative bibliometric mapping of the whole field. This study combines bibliometric analysis and systematic review to trace the development trajectory, identify unresolved technical bottlenecks and industry–academia gaps, and provide a structured reference for researchers and engineering practitioners. Following PRISMA guidelines, 171 peer-reviewed publications from the Web of Science Core Collection (2009–2025) were included after two rounds of screening (initial retrieval: 892 records). CiteSpace and VOSviewer were jointly used to analyze publication trends, institutional cooperation networks, and emerging research hotspots, followed by a systematic review of technical evolution and engineering applications. Results show that annual publications have maintained a growth rate of over 40% since 2019, with China (54.4%) and the United States (22.2%) as the core global contributors; 89.5% of research outputs come from universities and research institutes, while enterprise participation accounts for only 8.3%, indicating a clear technology translation gap. Technically, the field has evolved from traditional digital image processing to deep learning paradigms (CNN, YOLO, U-Net, GAN, Transformer), integrated with UAV platforms and 3D reconstruction to achieve both intelligent damage identification and 3D quantitative assessment. Key bottlenecks include scarcity of high-quality multi-class annotated datasets, poor model robustness in complex field environments, insufficient pixel-to-engineering scale conversion accuracy, and low model interpretability. Future directions include multimodal sensor fusion, unsupervised domain adaptation for real-world generalization, lightweight edge-deployable detection models, strengthened industry–academia collaboration, and explainable artificial intelligence to accelerate technology deployment in engineering practice. Full article

Industrial doctorates are increasingly seen as a means of linking doctoral education with industrial needs while promoting knowledge exchange between universities and companies. In Spain, this role is formalized through the “Mención Industrial” (industrial doctorate distinction), which provides a framework for collaboration between both sectors. Despite growing interest, its implementation remains limited compared with more established European programmes. This paper presents a comparative conceptual analysis of the Spanish industrial doctorate model, using selected European systems as reference cases to identify opportunities for strengthening university–industry collaboration. The analysis is developed from three complementary perspectives. First, it explores the characteristics that distinguish industrial doctorates from traditional academic PhDs, including supervision, training environment, and expected outcomes. Second, it compares the Spanish model with programmes in France, Denmark, and the United Kingdom. Third, it examines the funding structures that support these initiatives and their influence on university–industry collaboration. The comparison indicates that the effectiveness of industrial doctorate programmes depends not only on institutional design but also on how resources and responsibilities are distributed. In Spain, public support is directed primarily towards the industrial partner, while the university often plays a secondary funding role despite its central contribution to doctoral training. In contrast, other European models provide more balanced funding arrangements. Based on this comparison, a dual-funding approach is proposed in this paper in which both the company and the university receive explicit support. This adjustment aims to improve the alignment between responsibilities and resources, strengthening industrial doctorate programmes. Full article

Artificial intelligence (AI) is reshaping the architecture, engineering and construction (AEC) sector. However, AEC education remains rooted within traditional disciplinary boundaries and a technology-centric training model, creating a widening mismatch between graduates’ capabilities and the cognitive, collaborative, and interdisciplinary demands of AI-enabled practice. Situated within the Chinese higher education context, this study bridges this gap through a multi-stakeholder survey (n = 352) noting perspectives from academia, industry and research. One-way ANOVA and Tukey’s HSD test were used to examine differences across stakeholder groups and disciplines, while a Bayesian Network was developed to model competency pathways, simulate intervention scenarios and identify key leverage points for curriculum reform. The analysis reveals that meaningful AI integration requires a reconstruction of competency, rather than the mere addition of standalone technical or software courses; it calls for fundamental changes in professional formation, curricula and pedagogy. Four core competencies emerged from the data: professional expertise, systems thinking, interdisciplinary collaboration and AI-enabled problem-solving. Bayesian Network simulations further indicated that curriculum expansion alone improved AI knowledge acquisition by 39.6%, but yielded only modest gains in practical skills (13.9%) and application competencies (4.5%). By contrast, integrated interventions that combined teacher development, university–industry collaboration and project-based practice produced substantial improvements in AI application competencies (37.9%), employment adaptability (29.3%) and industry satisfaction (14.1%). These divergent findings highlight the necessity of coordinated educational interventions to reconcile stakeholder expectations and foster AI-oriented competency development. Based on this evidence, the study proposes a competency-oriented framework and a phased curriculum transformation pathway, providing an empirical foundation for AI-driven curriculum reform and competency reconstruction in AEC education. Full article

Trusted Data Spaces (TDSs) have emerged as the core infrastructure for secure, privacy-preserving data circulation across industries and jurisdictions. However, state-of-the-art TDS implementations suffer from centralized platform monopoly, rigid cross-border governance failure, unfair value distribution, and poor scalability for global-scale collaboration. This paper proposes DAO-TDS, a novel decentralized autonomous trusted data space paradigm that enables centerless, cryptography-governed, and value-closed-loop data circulation. We make three core contributions: (1) We formalize the first anti-monopoly, incentive-compatible game-theoretic model for distributed TDS governance, with rigorous provable security guarantees; (2) we design an original Proof of Data Contribution (PoDC) consensus mechanism and a post-quantum secure Crypto-DAO governance protocol, with formal security proofs under the Universal Composability (UC) framework; (3) we implement a full prototype of DAO-TDS and conduct comprehensive, reproducible evaluations, showing that it supports 10,000+ distributed nodes with >12,000 TPS and <2 s 99th-percentile confirmation latency, while delivering >80% of generated value to data contributors (vs. <50% in centralized platforms). While the proposed paradigm demonstrates strong performance and security guarantees, it still faces challenges in adaptive cross-jurisdictional compliance and lightweight edge node deployment, which require further investigation. Full article

This study investigates how community engagement and outreach programs can serve as a vehicle for knowledge co-production between higher educational institutions (HEIs) and industry in developing economies. Despite growing calls for collaboration between HEIs and industry, the existing literature remains policy-driven and largely instrumental, offering a limited understanding of how engagement practices are socially constructed, negotiated, and experienced by diverse stakeholders. There is limited qualitative research on how community voices are integrated into co-creation processes and how these interactions shape sustainable development in emerging economies. The research methodology for the study was developed using the research onion, and an interpretivist research philosophy was adopted. The study employed an inductive research approach to explore actors’ lived experiences and the meanings attached to engagement practices and collected data through in-depth interviews. Participants in the study included representatives from HEIs, industry, and community stakeholders across selected sectors of the economy, and the data were analysed thematically to capture patterns of interactions, power relations, and knowledge and exchange processes underpinning co-production. The study revealed that effective community engagement goes beyond formal partnerships to encompass building lasting relationships which are characterised by trust, mutual learning, and context adaptation. Further, the study also showed that power asymmetries, institutional pressures, and resource constraints have restricted real co-production, resulting in symbolic rather than real engagement. Nevertheless, the use of inclusive and reflective approaches has been shown to foster locally acceptable and essential innovative interventions that can improve sustainable development. The study therefore recommends embedding participatory frameworks within HEIs and industry collaborations to strengthen institutional support for inclusive engagement and to reorient policies towards community-centred knowledge systems. This study, therefore, contributes to the body of knowledge by enhancing theoretical and practical understanding of sustainable higher education–industry collaboration in developing economies by promoting an understanding of engagement as a co-production process. Full article

Nowadays, modern manufacturing industries still rely on the expertise and manual dexterity of highly skilled operators. In this context, Mixed Reality (MR) technologies are emerging as promising solutions for contextualized task guidance to support operators during complex activities. However, their effective adoption in production environments is still limited by the lack of ergonomic evidence regarding their impact on operators’ well-being, usability, and interaction sustainability. This study investigates whether different interaction modalities with holographic instructional content influence the ergonomic suitability of a MR-based task guidance system for wire harnessing, developed in collaboration with Leonardo S.p.A. A between-subjects experimental design was adopted, involving 16 industrial workers randomly assigned to two groups: gaze and gesture interaction (Group A, n = 8), and gaze and voice interaction (Group B, n = 8). Ergonomic evaluation included both physical and cognitive aspects, assessed respectively through the Simulator Sickness Questionnaire (SSQ) and a composite usability index (UI) based on ISO 9241-11, integrating efficiency, effectiveness, and satisfaction. Results suggest comparable usability levels between the two interaction modalities (UI_A = 0.683; UI_B = 0.667). Gesture interaction was perceived as slightly more supportive of operational efficiency, whereas voice interaction was associated with lower cybersickness severity (mean TS_A = 331.5; TS_B = 196.1). Overall, the findings suggest that no single interaction modality universally outperforms the other, but rather that ergonomic suitability depends on the balance between physical workload, cognitive demands, and task characteristics. These results highlight the importance of human-centered ergonomic evaluations in the design of sustainable MR assistance systems for industrial environments. Full article

Inverse kinematics (IK) is fundamental to robot motion planning. Classical analytical solvers require complete Denavit–Hartenberg (DH) parameters that are often proprietary or degraded by mechanical wear, and numerical solvers based on damped least squares (DLS) are sensitive to initialization, particularly near singularities. We propose XGNN, an explainable graph neural network positioned as a model-free, interpretable warm-start initializer for downstream numerical IK refinement rather than as a standalone replacement for analytical solvers. Each IK query is encoded as a 12-node graph in which six pose nodes and six joint nodes are connected through bipartite pose-to-joint attention edges and chain edges along the kinematic structure. GATv2 message passing aggregates information at each joint node; two ablation-validated design contributions (a learnable node-type embedding and an angle-aware composite loss) enable training to convergence. Evaluated on 300,000 trajectory-style samples generated from the ABB IRB 2400 kinematic model, XGNN achieves ${3.66}^{\circ }$ joint mean absolute error (MAE), comparable to a multilayer perceptron baseline (${3.09}^{\circ }$) and a bidirectional LSTM (${3.14}^{\circ }$) under identical training. The standalone joint accuracy of all learned models is too coarse for direct industrial use, but XGNN provides the strongest warm start for DLS refinement: the convergence rate improves from $98.4\%$ to $100\%$, mean iterations drop from 14.6 to 3.2, and wall-clock time per pose drops $5.0\times$ on the IRB 2400. The benefit transfers cross-platform to the Universal Robots UR5 collaborative manipulator (convergence rate $82.2\%$ to $100\%$, $10.0\times$ speedup) and survives DH parameter perturbation of up to $\pm 10\%$, simulating calibration drift or mechanical wear. The GATv2 attention coefficients additionally provide an interpretability signal at zero inference cost. XGNN therefore complements analytical and numerical IK methods as an interpretable, calibration-robust warm start when DH parameters are unavailable, proprietary, or degraded. Full article