Search Results (145)

Engineering computer graphics education frequently exhibits a gap between procedural CAD software (e.g. CATIA 2022) training and the strategic engineering reasoning required by industrial practice. This paper documents a holistic redesign of two advanced CAD courses. The study is framed within the Scholarship of Teaching and Learning (SoTL) tradition as a practitioner-led reflective case study. The redesign integrates four pedagogical mechanisms within an enterprise-CAD context: authentic problem-based learning, dual-layered asynchronous video scaffolding, software-agnostic heuristics (including pre-modelling cognitive mapping), and cognitive apprenticeship. The analysis triangulates three institutional data sources: quantitative Course Experience Survey indicators, qualitative student response themes, and twelve consecutive years of cohort-level academic performance records (2013–2024). The 2022 intervention iteration coincided with a marked elevation in academic performance. Grades reached approximately two standard deviations above the historical baseline. Concurrently, qualitative themes highlighted perceived industrial relevance and platform-portable confidence. However, performance in the post-intervention iterations (2023 and 2024) partially regressed. While scores remained above the historical mean, they did not sustain the 2022 peak. This pattern indicates partial sustainment, rather than evidence of a stable or definitive sustained pedagogical effect. This case is reported as descriptive rather than inferential. While the observed patterns align strongly with theoretical predictions, they do not establish definitive causal effects. Ultimately, the primary contribution of this study lies in documenting the integrated operationalization of these four mechanisms. Furthermore, it highlights longitudinal pedagogical sustainability as a critical, under-examined dimension that single-iteration evidence systematically obscures. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

The “Two Joint Operations” policy aims to promote coordinated development between thermal power and renewable energy through institutional incentives and constraints. Under this framework, government participation significantly affects cooperative relationships and benefit realization among energy enterprises. To analyze this coordination mechanism, this study constructs a tripartite cooperative game model involving the government, thermal power enterprises, and renewable energy enterprises. Social Network Analysis (SNA) is introduced to characterize inter-agent cooperative structures, while network centrality indicators are incorporated into key parameters such as coordination efficiency, renewable energy integration capability, and institutional execution efficiency. Furthermore, the Shapley value method is structurally weighted based on network relationships to reflect differences in participants’ actual roles within the coordination system. The results indicate that embedding social network analysis (SNA) structures can effectively release the synergistic effects of institutional optimization and policy coordination, enhance system coordination efficiency, and promote a steady increase in total coalition payoff with a stable positive response pattern. Both thermal power and renewable energy enterprises benefit from the coordination improvement, while renewable energy entities exhibit higher sensitivity, indicating significant heterogeneity in agent responses. The government’s payoff remains generally stable across scenarios. Parameter analysis further verifies that the model demonstrates good structural stability and mechanism robustness. By introducing a network-structure-based analytical perspective, this study provides a potential reference for related research. Full article

Capital structure theory remains a central concern within corporate finance, despite more than six decades of sustained scholarly inquiry. The seminal contributions of Modigliani and Miller established the analytical foundations from which subsequent frameworks emerged, notably the static trade-off theory and its later evolution into dynamic adjustment models. Although competing theoretical perspectives have advanced the debate, their respective limitations have increasingly encouraged a more integrative understanding of firms’ financing behaviour. This study critically examines the limitations of the dynamic trade-off model in explaining the financing decisions of Portuguese small and medium-sized enterprises (SMEs) during the period 2015–2024. The article contributes to the literature by proposing an original comparative methodological framework and introducing an empirical indicator designed to assess the divergence between the model’s theoretical assumptions and observed financing practices. Using dynamic panel estimations based on the Generalized Method of Moments (GMM), the findings reveal that, although SMEs exhibit partial adjustment behaviour towards target leverage rations, several core determinants predicted by the dynamic trade-off framework lose explanatory power when confronted with observed data. In particular, profitability displays patterns more consistent with pecking order behaviour, while variables traditionally associated with debt optimization and collateral effects become statistically weak or inconsistent. These results suggest that the financing behaviour of Portuguese SMEs cannot be fully explained by a single theoretical framework and is strongly shaped by institutional constraints, internal financing preferences, and contextual factors. The study therefore highlights both the continuing relevance and the empirical limitations of the dynamic trade-off model, while reinforcing the need for more pluralistic approaches to capital structure analysis. From a practical perspective, the findings indicate that SME financing decisions should not be interpreted solely through leverage optimization logic, carrying implications for managers, financial institutions, and policymakers involved in SME financing and fiscal policy design. Full article

As a key driver of sustainable urban development, the digital economy transforms urban spatial structures through novel organizational forms such as digital enterprises. Understanding the spatiotemporal distribution of these enterprises is crucial for fostering equitable and efficient urban growth. Focusing on Hangzhou, a leading digital city in China, this study applies kernel density estimation, the standard deviational ellipse, and the nearest neighbor index to analyze the evolution patterns of newly established digital enterprises (NDEs) from 2010 to 2020. It further integrates geodetector and multiscale geographically weighted regression (MGWR) to uncover the drivers behind their spatial differentiation. The results indicate that: (1) The spatial pattern of NDEs evolved from “single-core diffusion” to a “dual-core with multi-center and axial contiguous” structure, yet the density gap between cores and peripheral counties persisted. (2) NDEs exhibited increasing spatial agglomeration over time. (3) Global drivers: the nighttime light index exerts the strongest positive effect, while land costs and population density show negative effects, reflecting cost-squeeze and decentralized locational preferences. (4) Locally, bus accessibility, innovation level and science-education-culture level, display strong spatial heterogeneity; innovation level has very high positive coefficients in innovation poles but negative effects in ecologically sensitive or deindustrialized areas, revealing an “innovation multiplier effect” alongside resource misallocation risks. These findings provide empirical evidence of how digital economy actors spatially manifest, offering insights for urban planners and policymakers to leverage digital growth for guiding sustainable spatial restructuring, enhancing resource allocation efficiency, and promoting balanced regional development. Full article

China’s pollutant discharge permit system mandates total-quantity emission control for industrial volatile organic compounds (VOCs), yet the actual utilization of permitted capacity remains poorly studied. This study developed an “emission idle rate” (IR = 1 − actual/permitted emissions) framework and applied it to 130 chemical enterprises across three cities in Jiangsu Province using 2020–2024 panel data. The mean idle rate reached 78.1%, with no significant inter-city differences (H = 0.96, p = 0.619), attributable to both production underutilization and systematic over-estimation of emission ceilings inherent in the design-capacity-based permit methodology. Ward hierarchical clustering revealed three emission behavioral patterns, Persistent Surplus (n = 74, IR = 0.95), Declining Surplus (n = 32, IR = 0.69), and Growing Surplus (n = 19, IR = 0.59), exhibiting distinct idle rate levels and temporal trajectories. Cluster differentiation was significantly associated only with production-side emission characteristics, while enterprise economic variables showed no significant effects. The estimated tradeable emission surplus reached 668.3 t/a, though its realization faces transaction cost barriers including the lack of standardized transfer mechanisms and formal VOC trading infrastructure. A quadrant-based strategy matrix integrating idle rate levels with temporal trends is proposed for differentiated permit management. Full article

Identifying insider threats in modern enterprise environments presents a unique cybersecurity challenge. Although malicious activity may often appear to be legitimate user activity, it is difficult to recognize the distinction. This study presents an innovative approach to insider threat detection by analyzing enterprise activity logs for session-level behavioural risk monitoring with behavioural biometrics. Behavioural patterns are modelled as temporal sequences across consecutive monitoring windows to capture both short-term behavioural intensity and long-term behavioural drift. The proposed system utilizes a hybrid deep learning architecture that includes a Long Short-Term Memory (LSTM) network and an autoencoder model to model temporal dependence of a user’s behaviour and to identify anomalies through reconstruction error analysis. The LSTM network captures user’s sequential activity and autoencoder determines variance from the user’s typical behavioural profile. The outputs of both models are aggregated using a unified behavioural risk scoring mechanism for session-level risk monitoring and ongoing insider threat assessment. The experimental results from Insider Threat Dataset for Corporate Environments demonstrate that proposed approach is effective in classifying normal versus malicious behaviours of users. The proposed framework achieves an accuracy of 97.65%, a precision of 96.35%, a recall of 99.05%, an F1-score of 97.68%, and a ROC-AUC of 99.20% on a near-balanced benchmark split. Under realistic class imbalance conditions, the framework achieves a PR-AUC of 0.842 and MCC of 0.781, representing the more operationally conservative performance estimate. These findings confirm that the proposed framework constitutes a viable solution for integrating behavioural modelling and anomaly detection within continuous enterprise authentication systems. Full article

Architectural, urban design, and planning practices are increasingly expected to demonstrate measurable impact, accountability, and responsiveness to complex environmental and social challenges. Evidence-based design (EBD) and research-informed design (RID), which ground design decisions in systematically gathered and critically evaluated knowledge, offer a structured pathway to bridge research and practice. Despite growing recognition, however, EBD and RID remain unevenly integrated across professional practice, particularly within small and medium-sized enterprises (SMEs), which constitute the majority of firms in Europe. This paper explores how SMEs understand, adopt, and operationalize research within architectural, urban design, and planning processes, while identifying the factors that enable or constrain the integration of research into practice. Drawing on a qualitative multiple-case study of four European firms located in Cyprus, Portugal, Italy, and Croatia the study uses semi-structured interviews and thematic analysis supported by AI-assisted coding to identify patterns in how systematic research is understood, enacted and positioned in everyday SME practices. The findings show that research integration depends less on firm size than on the interplay between client expectations, organizational culture, and professional ideology. Practices span a spectrum ranging from ad hoc, compliance-oriented, and project-specific inquiry to strategically embedded and, in one case, activist research-led modes. While research engagement can enhance credibility, efficiency, and innovation, persistent barriers—including limited resources, client resistance, deficient knowledge-management routines, and the absence of shared evaluative frameworks—continue to hinder systematic adoption. Building on the cross-case analysis, the paper proposes a conceptual framework of different modes of research integration in SMEs, offering a heuristic lens for understanding how organizational and contextual factors shape the uptake of research in design practice. The findings contribute to ongoing discussions on practice-based research and highlight the need for more context-sensitive approaches to research integration in small and medium-sized design firms. Full article

Under the dual-carbon goals and energy transition backdrop, the photovoltaic cell has become a crucial pillar for optimizing China’s energy structure and promoting green development. From the perspective of patent intelligence, this study systematically investigates the spatiotemporal evolution paths, coupling characteristics, and driving mechanisms of China’s photovoltaic cell industry and innovation chains, using nationwide photovoltaic cell enterprise and patent data from 2005 to 2024 and integrating spatial gravity center modeling, location quotient analysis, and spatial Durbin models. The findings reveal the following: (1) the spatiotemporal evolution of the dual chains exhibits distinct phases, with a notable developmental leap after 2015. The industry chain shows a pattern of “westward shift and eastern optimization,” while the innovation chain evolves from eastern dominance toward a nationally coordinated, multipolar network. (2) At the macro level, the dual chains demonstrate a coupling trend characterized by “coordinated gravity center migration and spatial distance convergence,” yet significant spatial heterogeneity and mismatch persist at the city scale. (3) Industrial agglomeration has an inverted U-shaped effect on innovation, with regional heterogeneity in its impact, driven synergistically by multidimensional factors such as economic foundation, the innovation environment, and openness. Based on these insights, this study proposes recommendations for optimizing the spatial layout of these dual chains, strengthening multifactor synergy, and implementing regionally differentiated policies, aiming to provide decision-making references for achieving sustainable and high-quality development in the photovoltaic cell. Full article

Comprehensive utilization of crop straw (CUCS) is a critical pathway toward sustainable agricultural development, synergizing food security and carbon neutrality goals. However, there remains a lack of systematic empirical evidence regarding its macro-level productivity associations and the conditions under which they materialize. Based on China’s provincial panel data from 2011 to 2023, this paper takes the CUCS pilot policy launched in 2016 as a quasi-natural experiment and employs the difference-in-differences (DID) model to examine the association between CUCS and grain yield, along with its moderating factors and environmental co-benefits. This study yields four main findings. First, CUCS is associated with higher grain yield in pilot regions, and this finding remains robust after a series of endogeneity and robustness checks. Second, the positive association between CUCS and grain output appears to be moderated by fiscal support and innovation–entrepreneurship. The relationship is more pronounced in regions with higher fiscal expenditures on agriculture and environmental protection, as well as more agricultural patents and agricultural enterprises. Third, heterogeneity analysis suggests that the CUCS–grain output association tends to be stronger in regions with richer groundwater resources and more agricultural meteorological observation stations. Fourth, extended analysis indicates that CUCS is also associated with lower particulate matter and agricultural carbon emissions, a pattern consistent with synergistic environmental benefits. By integrating economic and environmental dimensions into a unified analytical framework, this study provides empirical evidence on the contribution of comprehensive straw utilization to grain output and highlights the enabling role of fiscal and innovation environments. These findings offer integrated evidence from China for the policy evaluation of climate-smart agriculture and contribute to the broader sustainable development agenda. Full article

This study investigates the relationship between employees’ task-oriented and social-oriented use of enterprise social media (ESM) and their innovation performance through the accumulation of employees’ social, human, and psychological capital. Integrating Self-Determination Theory and Social Learning Theory, we propose a multiple-mediation framework in which ESM serves as a resource-building infrastructure that supports innovation indirectly by strengthening employee capital. We test the model using survey data from 613 employees in Chinese knowledge-intensive enterprises. Results show that both ESM use orientations are positively associated with all three forms of capital; however, neither orientation has a significant direct effect on innovative performance once the capitals are included. Instead, the ESM–innovation link is transmitted through these capitals, indicating an indirect-only mediation pattern. We further find that empowering leadership amplifies the extent to which human and psychological capital translate into innovative performance, whereas its moderation on the social capital–innovation relationship is comparatively weak. Overall, the findings position ESM as a digital infrastructure that enables a multi-capital pathway to employee innovation in contemporary work settings. Full article

Digital transformation (DT) is becoming of strategic importance for Small, Medium and Micro Enterprises (SMMEs), especially in the retail sector, where a significant portion of customer engagement, operational efficiency, and market competitiveness is shaped by digital technologies. Even though there is a growing availability of smartphones, mobile payment systems, and social media platforms, many South African retail SMMEs struggle to achieve a sustained and meaningful DT. Existing studies offer limited insights into the dynamic interactions between technological, organisational, and human agency factors that enable digital uptake over time. This study investigates the sociotechnical dynamics of DT among retail SMMEs in the Eastern and Western Cape provinces of South Africa. The research integrates Adaptive Structuration Theory (AST) with the Limits to Success Archetype (LSA) to conceptualise DT as an evolving process shaped by the interplay of technology, organisational structures (formal arrangement of roles, responsibilities, authority, and communication patterns within an organisation), and human agency. Using an exploratory qualitative research design, purposively sampled semi-structured interviews were conducted with 23 retail owners, directors and managers. The interviews were transcribed, and the data were analysed thematically using the Braun and Clarke six-step thematic analysis framework on Atlas.ti 25. Findings indicate that DT in retail SMMEs is enabled by pragmatic, tool-level digital adoption, training, education, ongoing skill development, alignment with business capacity, regulatory clarity, operational realities, addressing scams, fraud, data security, a user-friendly interface, and the availability of native language digital tools, structural interventions that reduce inequality, and DT ecosystem support. The study contributes to DT scholarship by integrating sociotechnical and systems-thinking perspectives to explain the trajectories of DT in retail SMMEs. It also provides practical insights for policymakers, support institutions, and digital ecosystem actors seeking to democratise DT in emerging-market retail contexts. Full article

Wireless sensor network (WSN) coverage optimization is a challenging high-dimensional and nonlinear problem that directly affects network performance, including sensing quality, energy efficiency, and system reliability. Although metaheuristic algorithms have been widely applied to this problem, many existing methods still suffer from premature convergence, insufficient population diversity, and an imbalance between exploration and exploitation. To address these issues, this paper proposes a multi-strategy enhanced enterprise development optimization algorithm (MEEDOA) inspired by business management mechanisms. The proposed method integrates a hybrid population initialization strategy, an adaptive activity switching mechanism based on performance feedback, a multi-elite collaborative learning strategy, and a Lévy flight-based stagnation escape mechanism. These strategies are tightly coupled within a unified adaptive framework to improve global search capability, convergence speed, and robustness. Furthermore, a mathematical model for WSN deployment is constructed based on a binary sensing model and discrete coverage evaluation. From the perspective of symmetry, the sensing regions of sensor nodes exhibit significant geometric symmetry in both two-dimensional and three-dimensional deployment spaces. In the two-dimensional case, the sensing and communication regions are modeled as concentric circular structures, while in the three-dimensional case, the sensing regions are represented by isotropic spheres with symmetric spatial distributions. Such symmetry properties provide an effective basis for describing coverage behavior, reducing redundant overlap, and improving the uniformity of node deployment. Meanwhile, the proposed MEEDOA preserves population diversity and enhances search balance, enabling the algorithm to better capture symmetric coverage patterns and more effectively explore complex spatial deployment configurations. Extensive experiments on CEC2014, CEC2017, CEC2020, and CEC2022 benchmark functions demonstrate that MEEDOA achieves superior convergence accuracy, faster convergence speed, and stronger robustness compared with several state-of-the-art algorithms. Additional simulation results in WSN deployment applications verify its effectiveness in improving coverage performance under both symmetric and irregular spatial deployment scenarios. The results indicate that the proposed MEEDOA provides a reliable and efficient solution for complex global optimization problems and practical engineering applications. Full article

Nowadays, Global Value Chains (GVCs) play a vital role in job creation, income generation, knowledge diffusion, and productivity growth. However, significant disparities exist across countries in terms of their integration into GVCs, and Tunisia is no exception to this pattern. In this regard, the question about factors that influence GVCs’ participation is yet to be discussed, to formulate and implement appropriate strategies and reforms. Thus, using firm-level data from the 2025 World Bank Enterprise Survey, this paper examines the role of female leadership and innovation in determining Tunisian firms’ participation in GVCs. Participation in GVCs is captured by a dummy variable indicating the firm’s export and import status. Estimation results from the logit model show that female representation in decision-making positions significantly increases the likelihood of firms’ participation in GVCs. The results also highlight the importance of process innovation in GVC participation, while product innovation appears to have no significant effect. Notably, when firms combine both types of innovation, their likelihood of joining GVCs increases further. Regarding control variables, firm size appears to be an important determinant, as larger firms display a greater tendency to participate in GVCs. The findings further indicate that firm certification and foreign equity participation significantly promote integration into GVCs, while corruption constitutes a major constraint on the integration of Tunisian firms. From a policy perspective, these findings highlight the need to rethink industrial policies, with a stronger focus on process innovation as a key lever of productive sector modernization. Achieving this transformation also requires the development of an inclusive policy ecosystem that supports meaningful and sustainable progress in female’s leadership representation. Full article

Urban innovation spaces are crucial to stimulate innovative thinking and facilitate the integration of science, technology, and humanities. On the one hand, existing research on urban innovation spaces focuses on spatial patterns, associated networks, and spillover effects. They are limited to the macro scale and lack of innovation subject perspective. On the other hand, few studies have explored factors influencing the distribution by examining the needs of innovative talent. This study aimed to identify the evolution mechanism of urban innovation spaces. In total, 36,519 high-tech enterprises in Guangzhou from 2008 to 2023 were selected to represent urban innovation spaces. Spatial analysis methods and statistical methods were employed to investigate the spatiotemporal dynamic characteristics. Furthermore, employing multiscale geographically weighted regression, the study identifies multiple factors influencing the development of innovation spaces from the dual perspectives of the innovation environment and services. The results indicated that characterized by a southeast-northwest orientation, the urban innovation spaces of Guangzhou have displayed an apparent point–axis–face structural evolution, expanding from the central district into sparsely distributed in the suburbs. The factors influencing the distribution of urban innovation spaces, ranked by their degree of impact, were as follows: vehicle carrying, research institutions, public park, living convenience, university resources, business hotel, industrial structure height, and metro station. These findings facilitated the understanding of urban innovation space development and grasped the influencing factors and their functioning mechanisms. They provided references for innovation space planning amidst urban stock development. Full article