Search Results (859)

The concept of the 15 minutes city is increasingly present in the structure of spatial planning for large urban centers, with the main goal of improving quality of life by facilitating access to basic necessities for the population. This study aims to provide an integrated assessment of spatial accessibility for two urban centers that differ in structure and organization, with the main goal of identifying best practices that can be borrowed from one urban center to another in order to streamline sustainable spatial planning based on the strategic concept of the 15 minutes city. The entire research process is based on the development of a completely new and innovative GIS spatial analysis model that will add value to the specialized literature both through the geoinformational approach to the analysis, integration and through the exclusive use the freely available GIS databases (using the OpenStreetMap database), functionally integrated through network analysis and equations weighing the importance of accessibility needs for the population. For the analysis of pedestrian accessibility, in minutes, a total of 4826 locations were used for Cluj–Napoca and 5050 for Pecs, which were structured into 12 subclasses and five main classes (Recreational and Cultural, Public Services and Safety, Education and Health, Commercial, and Public Transport) established in accordance with the main requirements of the 15 minutes city development methodology. The integration of subclasses and accessibility classes was achieved by weighting their importance according to the responses obtained after the implementation of questionnaires to identify the working population’s perception of accessibility in their daily routine. The comparative analysis of the intermediate and final results of the proposed model leads to the establishment of directions and decision-making in the territorial planning process through the transfer of knowledge, solutions, and techniques between the two urban centers to eliminate or reduce negative hotspots and develop a more sustainable urban center in terms of accessibility and as close as possible to a 15 minutes city. Full article

Gaze estimation, a critical facet of understanding user intent and enhancing human–computer interaction, has seen substantial advancements with the integration of deep learning technologies. Despite the progress, the application of deep learning in gaze estimation presents unique challenges, notably in the adaptation and optimization of these models for precise gaze tracking. This paper conducts a thorough review of recent developments in deep learning-based gaze estimation, with a particular focus on the evolution from traditional methods to sophisticated appearance-based techniques. We examine the key components of successful gaze estimation systems, including input feature processing, neural network architectures, and the importance of data preprocessing in achieving high accuracy. Our analysis extends to a comprehensive comparison of existing methods, shedding light on their effectiveness and limitations within various implementation contexts. Through this systematic review, we aim to consolidate existing knowledge in the field, identify gaps in current research, and suggest directions for future investigation. By providing a clear overview of the state-of-the-art in gaze estimation and discussing ongoing challenges and potential solutions, our work seeks to inspire further innovation and progress in developing more accurate and efficient gaze estimation systems. Full article

Using a panel of Chinese A-share listed companies from 2007 to 2022, this study examines how firms’ structural positions in patent citation networks affect innovation efficiency. We construct a firm-level patent citation network and use betweenness centrality to capture firms’ brokerage-oriented positions in knowledge flows. Based on firm- and year-fixed-effects models, instrumental-variable estimation, and robustness checks, we find that stronger brokerage positions significantly improve innovation efficiency. Mechanism analyses show that this effect operates through two channels: cross-domain knowledge recombination and organizational boundary spanning. Firms in stronger brokerage positions are more likely to access technologically heterogeneous external knowledge and interact with a wider range of external knowledge-bearing entities, thereby improving the efficiency with which innovation inputs are transformed into patent-based outputs. We further find that digital transformation negatively moderates the relationship between brokerage centrality and innovation efficiency. This suggests that digital transformation reduces firms’ marginal dependence on external brokerage positions by strengthening internal data-processing, coordination, and knowledge-integration capabilities. Additional analyses show that the positive effect of brokerage centrality is broadly shared across ownership groups. Regional heterogeneity is more evident in the stronger brokerage premium observed in the western region than in the eastern region. Full article

This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that the cooperative innovation network exhibits pronounced small-world characteristics. In terms of spatio-temporal evolution, China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern. Moreover, the advancement of digital transformation positively contributes to both the quality and quantity of cross-regional cooperative innovation. By enhancing the relational embeddedness among cities, digital transformation facilitates improved outcomes in collaborative innovation. Furthermore, when the volume of digital patent applications surpasses a certain threshold, its positive effect on the quality of cross-regional collaborative innovation accelerates. These results provide empirical evidence from a major emerging economy, offering insights that can inform policies and strategies in other regions undergoing digital transition. The mechanisms identified, such as network structure evolution and relational embeddedness, contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy. Full article

As a sustainability-oriented mode of education, cross-border digital education has distinct advantages, including a low carbon footprint associated with decreased student and staff commute times and expanded accessibility for disadvantaged learners. However, the intrinsic mechanisms by which globally mobile talent, including international students and transnational professionals, utilize their global skills and networks to create sustainable EdTech entrepreneurial initiatives need further investigation. Based on dynamic capability theory and resource orchestration logic, this study examines how human and social capital shape entrepreneurial engagement through resource integration capability (RIC) via PLS-SEM analysis of data collected from 318 transnationally mobile actors. The study finds that neither form of capital has a direct association on entrepreneurial entry; instead, both are associated with entrepreneurial entry indirectly through RIC, allowing mobile talent to combine and allocate knowledge, networks, and digital technologies across institutional and cultural boundaries. The study examines how cross-border EdTech entrepreneurship works towards creating inclusive and equitable quality education, as well as global partnerships, through scalable, adaptable, and low-carbon educational services, while meeting objectives 4 and 17 of the UN Sustainable Development Goals. This study reveals the transformation process centered around RIC, highlighting the need to create innovative ecosystems that transition from talent attraction to talent empowerment. The findings underline the importance of RIC in translating global mobility into sustainable digital education solutions. Full article

This study delves into how the literature conceptualizes banking efficiency as a capability shaping sustainability-oriented pathways under conditions of systemic uncertainty, including recurrent economic–financial disruptions and geopolitical shocks. Using records indexed in the Web of Science Core Collection, the study combines bibliometric mapping with conceptual structuring to examine publication dynamics, collaboration networks, and the thematic evolution of research linking bank efficiency, green finance intermediation, sustainable digital innovation, and risk governance. The study reveals a multidimensional knowledge base organized around two converging streams: (i) research on efficiency, stability, and crisis transmission emphasizing intermediation quality, performance under stress, and prudential responses; and (ii) sustainability and innovation scholarship focusing on how financial systems enable eco-innovation diffusion and low-carbon transition through capital allocation, governance mechanisms, and digitally enabled transformation. Across these streams, banking efficiency is increasingly discussed not merely as a performance ratio, but as a strategic capability that becomes particularly salient in crisis environments: it can reduce intermediation frictions when funding conditions tighten, strengthen screening and monitoring of green projects amid elevated uncertainty, and support the continuity and scaling of eco-innovations by improving decision speed and resource allocation through digital tools. Collaboration patterns indicate growing interdisciplinary engagement—especially among European and Asian institutions—where crisis, sustainability, and innovation perspectives are integrated into systems-based approaches to green finance. Building on these insights, the article outlines a research agenda oriented toward innovation outcomes in turbulent contexts, emphasizing (a) measurement strategies that connect efficiency to eco-innovation diffusion and adoption rates during stress periods; (b) comparative analyses of how policy incentives and green market signals interact with bank efficiency across crisis episodes; and (c) hybrid methodological designs combining econometric identification, network analytics, scenario-based stress framing, and AI-enabled analytical tools to capture nonlinear dynamics in efficiency–innovation linkages. Overall, the study clarifies how banking efficiency may condition the capacity of financial institutions to sustain green investment intermediation and advance eco-innovation pathways when uncertainty is systemic rather than episodic. Full article

This study analyzes 1441 industry–university–research (I-U-R) collaborative invention patents (2004–2023) in China’s new energy vehicle (NEV) industry using social network analysis. We propose the “Proximity–Industry Life Cycle” Fit Theory to systematically investigate the influence mechanisms of industrial proximity, geographical proximity, and technological proximity on the evolution of the industry–university–research collaborative innovation network of the new energy vehicle industry across three industry life cycle stages. Key findings include: (1) the network scale expanded significantly while density declined; (2) State Grid Corporation emerged as the core node after 2010; (3) all three proximity dimensions positively influence network evolution, with varying effects across stages—industrial proximity dominates in the emergent stage, while technological proximity becomes the primary driver in later stages. Policy implications: Governments should formulate stage-differentiated policies—encouraging industrial chain collaboration in early stages while promoting technology alliances in mature stages. Core enterprises should be supported to strengthen I-U-R collaboration, and cross-regional innovation platforms should be established to optimize proximity-driven knowledge transfer. Full article

The importance of financial literacy for professional athletes is undeniable. This study aimed to build on previous research by identifying the financial literacy content areas that require the highest level of competence for professional athletes competing in team sports. To address this, 12 structured one-on-one interviews were conducted with a purposively selected sample of participants drawn from a network of potential actors capable of influencing the financial decisions of professional athletes, as informed by Actor-Network Theory. The research findings show that skills to avoid unethical behaviour, the acumen to navigate the transition to a post-sports career, savings and financial control are the content areas that require a higher level of competence. This study innovatively visualizes research findings through a heatmap to identify and prioritize focus areas. This study offers insights that may assist professional athletes to reduce their exposure to financial risks. The study may also engage sport’s governing bodies, professional clubs, players’ associations, researchers, and financial advisors aiming to deepen their knowledge of the financial literacy competencies required by professional athletes. Full article

Knowledge graph completion via link prediction is critical for intelligent equipment maintenance systems to support accurate fault diagnosis and maintenance decision making. However, existing approaches struggle to simultaneously capture local structural dependencies and perform effective multi-hop reasoning due to limited receptive fields or inefficient path exploration mechanisms. Traditional path-based methods implicitly assume path symmetry, treating all reasoning chains equally without considering their task-specific relevance. To address this issue, we propose a Graph Attention Network (GAT)-guided semantic path reasoning framework that breaks this symmetry through attention-driven asymmetric weighting, integrating local structural encoding with global multi-hop inference. The key innovation lies in a target-guided biased path sampling strategy, which transforms GAT attention weights into probabilistic transition biases, enabling adaptive exploration of high-quality semantic paths relevant to specific prediction targets. GATs learn importance-aware local representations, which guide biased random walks to efficiently sample task-relevant reasoning paths. The sampled paths are encoded and aggregated to form global semantic context representations, which are then fused with local embeddings through a gating mechanism for final link prediction. Experimental evaluations on FB15k-237, WN18RR, and a real-world equipment maintenance knowledge graph demonstrate that the proposed method consistently outperforms state-of-the-art baselines, achieving an MRR of 0.614 on the maintenance dataset and 0.485 on WN18RR. Further analysis shows that the learned path attention weights provide interpretable asymmetric reasoning evidence, enhancing transparency for safety-critical maintenance applications. Full article

With the growing integration of AR/VR/Metaverse technologies across luxury brands, advertising has shifted to providing consumers with a personalised experience in which they can engage with brands via digital avatars. Given the considerable success of Metaverse advertising, it is apparent that organisations need to reinvent their advertising strategies to enhance consumer experience and brand engagement over digital platforms. However, this reinvention would require organisations to develop an advertising strategy that creates a coherent brand experience for consumers and provides them with an immersive brand experience on Metaverse platforms. As a result, this study undertakes a bibliometric approach to provide a comprehensive understanding of how integrating Metaverse platforms with advertising strategies can enhance brand engagement among consumers. More precisely, a keyword search strategy is formulated, and a multi-database search is performed across key databases, including Scopus, EBSCOhost, and ProQuest. In doing so, results from Scopus databases are visualised through network and overlay visualisation maps to understand how key themes/knowledge structures are associated with Metaverse advertising and brand engagement. Besides this, the study also showcases the key theoretical perspectives (i.e., psychological perspectives, value-based perspectives, technology/innovation perspectives, and social interaction perspectives) across these studies to understand how brands have well-infused Metaverse advertising to enhance brand engagement among consumers. Lastly, the study also provides a deeper understanding of the key challenges that are associated with the widespread implementation of Metaverse advertising. Full article

Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a “MasterChain” virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevner’s design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash Algorithm 3 (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit Rivest–Shamir–Adleman (RSA)’s 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility. Full article

The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, an ultra-lightweight neural architecture for resource-constrained CAN intrusion detection. The framework integrates three innovations: (1) Separable Multiscale Convolution Lite (SMC-Lite) blocks capturing multitemporal attack patterns with minimal parameters; (2) Lightweight Channel-Temporal Attention (LCTA) achieving linear $\mathcal{O}\left(N\right)$ complexity through adaptive pruning; and (3) 6-dimensional CAN-optimized features exploiting protocol-specific characteristics for aggressive compression. The framework employs Bayesian hyperparameter optimization and knowledge distillation for systematic model compression. Extensive experiments on CAN and CAN-FD datasets demonstrate that LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs, outperforming existing solutions while meeting real-time constraints of automotive embedded systems, providing a viable edge AI deployment solution. Full article

Multi-sensor fusion provides richer information for bearing fault diagnosis. However, under variable working conditions, the coupling relationships among signals from different sensors exhibit significant non-stationarity and directionality, posing challenges for modeling and practical deployment. Existing methods often rely on fixed or symmetric graph structures or construct correlation relationships entirely based on data-driven approaches; this makes balancing physical consistency, robustness, and computational efficiency difficult. To address these issues, we propose a Physics-guided Adaptive Graph Transformer Network (AGTN) for multi-modal bearing fault diagnosis under variable working conditions. More specifically, we offer innovative improvements across three aspects. Firstly, we introduce domain knowledge priors into the graph structure learning process to adaptively construct sparse and asymmetric dynamic graph structures that capture physically meaningful directional dependencies among different sensor signals. Secondly, we combine a graph-aware transformer to jointly model the temporal features and structural correlations of multi-source signals. Finally, we further introduce a hierarchical subgraph training strategy that significantly reduces memory usage and training time while ensuring diagnostic performance. Experimental results on a self-built multi-condition bearing dataset show that AGTN achieves an average diagnostic accuracy of 99.42% under the same distribution conditions and demonstrates good generalization and robustness, e.g., variable speed and load and sensor failure. In particular, when using only 25% of the nodes for training, the model can still maintain a diagnostic accuracy of 97.9%, while reducing the peak memory usage to about 19% of that of full-graph training. The above results validate the effectiveness of the proposed method under complex industrial conditions, as well as its practical application potential in resource-constrained scenarios. Full article

This paper presents new techniques for electric machine diagnostics that combine advanced signal processing and artificial intelligence (AI)-based techniques using magnetic flux measurements acquired under various operating conditions. Developing an effective electric machine diagnostics tool is paramount for increased industrial productivity and extending the service life of the machine. The existing diagnostic tools face issues, including false indication of faults using classical methods, and the proposed data-driven methods based on machine learning lack transferability of model knowledge on an unseen dataset from different motor types or power ratings due to structural differences. To overcome these diagnostic drawbacks of statistical ML classifiers and classical approaches, innovative feature selection methods were employed in this work to preprocess the measured magnetic flux into a spectrogram image, and the transfer learning (TL) technique was applied to fine-tune convolution neural networks (CNNs) ImageNet pretrained models. The experimental results show the trained statistical ML classifiers and traditional CNN performance on unseen BU data and on the external data, and the performance demonstrated a lack of generalization on external datasets of different power ratings or structures. Models with such drawbacks cannot be used for developing effective diagnostic systems. The TL technique was employed on different deep CNN ImageNet pretrained models with spectrogram images as inputs to the deep CN network. This approach demonstrated an advanced and improved electric machine diagnostic system that addresses the drawbacks of the current ML-based diagnostic systems. The generalized model developed using CNN ResNet50 outperformed other deep CNN ImageNet models in correctly diagnosing faults on both the dataset generated from the authors’ lab and on an external dataset of a different machine from another research lab. Full article

University–employer alliances have expanded as a strategy to foster innovation, employability, and knowledge transfer; however, their growth often results in instrumental arrangements oriented toward short-term metrics (agreements, hours, deliverables) that weaken curricular transformation and Social Responsibility. This article proposes a governance architecture to design and audit sustainable Collaborative Education, understood as a technologically mediated multi-actor network organized by a shared principle of Social Responsibility. The method operates in two moves: (i) a conceptual ordering that uses the substance–accidents distinction and a formative telos to subordinate organizational and technological means to the educational purpose; and (ii) the translation of concepts into decision domains (who decides, with what evidence, under what risks, and with what safeguards), positioning Technological Mediation as governance infrastructure rather than a neutral support. The proposal delivers three managerial outputs: (a) a hierarchy of seven support entities (metaphysical question, Social Responsibility, projects and strategies, institutional management, institutional development, stakeholders, and benefits); (b) governance principles (primacy of purpose, multi-actor accountability, justifiable distribution of benefits and risks, and deliberative traceability); and (c) a compact matrix and checklist applicable through document auditing and platform design review, without requiring field data collection. Taken together, the framework shows how employer-side corporate governance can align incentives, rules of evidence, and data use to enable co-responsibility and avoid capture, strengthening the sustainability of collaboration over time across organizational contexts. Full article