Search Results (8,389)

The flexible job shop scheduling problem (FJSP), which simultaneously involves machine assignment and operation sequencing under multiple constraints, is a typical NP-hard combinatorial optimization problem, and efficient scheduling is of great importance for improving production efficiency and manufacturing flexibility. To address this problem, the success-history beaver behavior optimizer (SHBBO) is introduced to solve FJSP with the objective of minimizing the makespan. First, considering the discrete characteristics of FJSP, an effective encoding and decoding scheme is designed to represent operation sequences and machine assignments. Then, the adaptive success-history mechanism of SHBBO is employed to dynamically adjust the search parameters during the optimization process, enabling a better balance between global exploration and local exploitation. Meanwhile, the behavioral update strategy of SHBBO is adapted to the scheduling environment so that candidate solutions can be effectively evolved in the discrete solution space. In addition, a population updating strategy and elite-guided search mechanism are incorporated to enhance solution quality and convergence performance. Finally, extensive experiments are conducted on benchmark FJSP instances to verify the effectiveness of the proposed method. Experimental results show that SHBBO achieves the best average results on 11 out of 12 CEC2022 benchmark functions, with particularly notable improvements over the original beaver behavior optimizer (BBO) on functions such as F6 (56.69%), F5 (12.20%), and F10 (9.18%). On the BRdata benchmark instances, SHBBO obtains the best or tied-best makespan on all 10 instances, with an average percentage relative deviation (PRD) of 0, and reduces the makespan by 7.69% on MK10 and 6.25% on MK06 compared with BBO. Full article

This study addresses the growing necessity to understand how artificial intelligence (AI) competency and soft skills jointly influence organizational innovation and performance in the era of digital transformation. Despite the rapid adoption of AI technologies across industries, organizations continue to face significant challenges in effectively integrating technical AI capabilities with essential human-centric soft skills such as communication, adaptability, and leadership. This gap often limits the realization of AI-driven value and sustainable competitive advantage. The primary challenge in this research area is the lack of comprehensive models that simultaneously examine AI competency and soft skills within a unified framework, particularly in emerging economies where digital maturity varies widely. Existing studies tend to focus either on technical competencies or behavioral factors in isolation, leading to fragmented insights. To address these challenges, this study proposes a novel integrated research model that examines the combined effects of AI competency and soft skills on innovation outcomes and organizational performance. The model is empirically validated using structural equation modeling (SEM), providing robust evidence of the interrelationships among key constructs. The findings reveal that both AI competency and soft skills significantly contribute to innovation capability, which in turn enhances organizational performance. The study offers important theoretical and practical implications by bridging the gap between technical and human dimensions of AI adoption, thereby providing a more holistic understanding of digital transformation success. Full article

Despite the rapid penetration of digital technologies into the workplace, many enterprises are undergoing digital transformation, making safeguarding employees’ occupational health increasingly important. Drawing on social cognition and the conservation of resources theories, this study developed a moderated mediation model to explore the relationship between digital leadership and employee resilience, as well as the mediating roles of work gamification and workplace mindfulness and the moderating role of AI anxiety. Survey data from 293 employees revealed that digital leadership significantly and positively predicted employee resilience. Both work gamification and workplace mindfulness mediate the relationship between digital leadership and employee resilience. AI anxiety positively regulated the positive relationship between workplace mindfulness and employee resilience but did not significantly moderate the indirect pathway through work gamification. In addition, AI anxiety negatively regulates the direct positive effect of digital leadership on employee resilience. These findings clarify the mechanisms and boundary conditions through which digital leadership promotes employee resilience in digitally transformed workplaces. This study also offers practical implications for organizations seeking to protect employee well-being and reduce burnout during digital transformation. Full article

The increasing integration of Virtual Reality (VR) technologies in cultural and historical contexts has significantly transformed the way heritage and legacy are preserved, studied, and experienced. This study provides a bibliometric analysis of the current research landscape surrounding the use of VR in heritage and legacy research. The results obtained highlight a research environment dominated by European institutions—primarily Italian and Spanish—complemented by Asian and French contributions that demonstrate a trend toward progressive internationalization. This field of research combines immersive technologies, photogrammetry for 3D digitization and user-centered designs, moving from conservationist approaches to holistic approaches that prioritize accessibility, educational dissemination and tourism. The results reveal a duality between digital documentation and immersive experience, while, among the countries with the most World Heritage sites, Italy leads in terms of quantity and average citations, China in terms of total volume, and Spain shows underutilized bibliometric potential despite its rich historical heritage. This analysis aims to trace the evolution of this field of research, uncover gaps, and suggest directions for future work that leverages virtual reality to safeguard and disseminate cultural heritage in an immersive and impactful way. Full article

The digital transformation of healthcare requires stronger digital competencies among pharmacists, yet evidence on the effectiveness of structured training remains scarce. This study examines the impact of a blended digital health training programme delivered to practicing pharmacists in Bihor County, Romania, as part of the Romanian pilot of the EU-funded European Health Professionals’ and the DigitAl team SkillS (H-PASS) project. A single-group pre–post educational design was applied to pharmacists from Bihor County, Romania, participating in a modular digital health training programme delivered between May and July 2025. A total of 84 pharmacists completed both pre-training and post-training self-reported competency assessments comprising 18 items across three modules: digital innovation and change management, communication and collaboration, and data management and digital literacy. Paired samples t-tests, Cohen’s d effect sizes, Cronbach’s alpha, moderator analyses, and ceiling effect analyses were conducted using Python-based statistical workflows. Statistically significant improvements were observed across all three modules (all p < 0.0001), with large effect sizes (d = 1.04–1.30). Post-training internal consistency increased substantially, with overall Cronbach’s alpha reaching 0.74. The greatest item-level gains were recorded in adaptive communication, cultural adaptation of care, and data protection ethics. No significant moderation effects were found for age, gender, or years of experience. Course satisfaction showed a moderate positive correlation with competency gains (r = 0.528), while perceived improvement was not significantly associated with observed score change. A ceiling effect indicated greater gains among participants with lower baseline competencies. The Romanian implementation of the H-PASS training programme was associated with improved self-reported digital health competencies among practicing pharmacists, high-lighting its potential as a scalable model for digital upskilling in healthcare. Full article

Digital management of modern railway bridges, a substantial part of high-speed railway networks, is often hindered by manual interpretation of construction drawings for Building Information Modeling (BIM). While individual technologies like optical character recognition (OCR) and neural networks are well-established, their generic application often fails on complex engineering documents. To address this, a domain-adaptive automatic recognition and semantic interpretation framework is proposed for railway bridge construction drawings. The novelty of this work lies in a specialized hybrid data fusion strategy that intelligently merges vector CAD file parsing with morphology-denoised OCR, resolving spatial and semantic conflicts. Furthermore, a back-propagation (BP) neural network is explicitly adapted to classify the extracted text into specific engineering categories, overcoming the challenges of dense layouts and overlapping symbols. Finally, the framework achieves end-to-end integration by transforming these semantic entities directly into structured, IFC-compatible BIM parameters. Evaluated on 250 real-world drawings, the framework achieved an average F1-score of 91.0% in semantic classification and improved processing efficiency by 6.5 times compared to manual methods. Moreover, 93.8% of the extracted entities achieved strict BIM parameter correctness, defined as seamless mapping to Revit IFC attributes without manual intervention. Full article

Despite increasing investments in digital transformation and sustainability initiatives, many organizations struggle to translate these efforts into measurable sustainable organizational performance, particularly in emerging economies, where resource constraints and institutional pressures persist. This study examines how digital capability and environmental strategy orientation contribute to sustainable organizational performance through the sequential mediating roles of environmental management accounting (EMA) integration and managerial decision quality. Drawing on dynamic capability theory and the natural resource-based view, this study proposes a moderated mediation model incorporating technology readiness and environmental regulatory pressure. Data were collected from 479 middle- and senior-level managers of ISO 14001-certified manufacturing firms in Pakistan and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that both digital capability and environmental strategy orientation significantly enhance EMA integration, which in turn improves managerial decision quality and ultimately drives sustainable organizational performance. The findings confirm the presence of sequential mediation through EMA integration and decision quality. Furthermore, technology readiness strengthens the relationship between digital capability and EMA integration, whereas environmental regulatory pressure does not significantly moderate the relationship between environmental strategy orientation and EMA integration. This study contributes to the sustainability literature by introducing a novel sequential mediation mechanism linking digital and strategic capabilities to sustainability outcomes through accounting-based processes. It also provides empirical evidence offering practical insights for managers and policymakers aiming to enhance sustainability performance. The findings provide context-specific insights from an emerging economy and contribute to advancing organizational sustainability in line with SDGs 8, 12, and 13. Full article

Political polarization and policy uncertainty have become increasingly consequential for regional economic adjustment, yet their joint role in shaping socioeconomic resilience remains underdeveloped in the literature. This study advances the debate by conceptualizing regional resilience as the outcome of a multi-layer socioeconomic system in which external policy disturbances, institutional fragmentation, and structural adaptive capacity interact over time. Using balanced panel data for 16 Korean regions from 2004 to 2023, the analysis develops an integrated empirical framework that combines panel local projections, threshold estimation, structural moderation tests, dynamic robustness checks, and forward-looking machine-learning prediction. The results show that policy uncertainty is associated with lower regional socioeconomic resilience and that this effect persists over time. More importantly, political polarization does not simply accompany weaker resilience; it amplifies the transmission of uncertainty shocks, especially once institutional fragmentation crosses a critical threshold. Structural conditions further shape this process. Digital transformation, industrial diversification, and financial depth reduce vulnerability, whereas trade exposure intensifies it. These findings indicate that resilience is not determined by economic structure alone, nor by institutional instability in isolation. It emerges from the interaction between disturbance, amplification, and adaptive capacity within a regional system. The predictive analysis reinforces this interpretation. Variables identified as central in the econometric models also carry forward-looking information about future vulnerability states. This study therefore contributes not only by combining methods, but by linking explanation and prediction within a single systems-oriented account of regional resilience. The Korean case shows how institutional coherence and structural adaptability jointly condition resilience under uncertainty. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Deciphering the dynamic fracture evolution of rock masses, particularly the interaction between dynamic stress waves and localised weak interlayers, is essential for optimising dynamic rock excavation in mining engineering. To systematically explore how these structural planes halt propagating cracks and generate a dynamic shielding effect, this study integrated Split Hopkinson Pressure Bar experiments, Digital Image Correlation techniques, and computational modeling. The findings demonstrate that altering the geometric orientation of the soft layer dictates the ultimate failure pattern. While an orthogonal interface (i.e., an interface with 0° inclination perpendicular to the loading direction) allows a tension-driven crack to cleave directly through the entire composite specimen, introducing an inclined obliquity of 15° forces the advancing fracture to deviate and permanently halt inside the soft stratum. Macroscopically, this barrier capability is validated by a rapid decrease in fracture speed, which drops abruptly by 75.5% upon encountering the inclined zone. Microscopic numerical evaluations confirm that this fracture arrest originates from wave mode conversion at the misaligned boundary. The angled interface forces incoming compressional pulses to transform into intense shear stresses, promoting extensive fracture. Substantial energy dissipation within the interlayer fully deprives the primary crack of the tensile stress required for propagation, effectively confining the stress-propagated hard rock within an energy shadow zone and suppressing further fragmentation. Full article

Under the coordinated implementation of the “dual carbon” goals and digital rural development strategy, digital technology has become a critical support for solving key problems in agricultural carbon reduction and advancing the green and low-carbon transformation of agriculture. Based on panel data from 31 provincial-level regions in China from 2010 to 2023, this study uses the fixed-effect model, mediating the effect model and threshold effect model to systematically examine the impact and transmission mechanism of digital technology on agricultural carbon emission intensity. The results show that: (1) Digital technology markedly lowers agricultural carbon emission intensity, and this conclusion remains steady after endogeneity correction and robustness checks. (2) Digital technology reduces emissions through two core channels: enhancing environmental regulation to constrain high-carbon behaviors via precise monitoring, and improving agricultural socialized services to promote intensive production and lower the adoption threshold of low-carbon technologies. (3) The emission reduction effect of digital technology exhibits a threshold characteristic related to agricultural industrial agglomeration, with the marginal effect of emission reduction showing an increasing trend as the agglomeration level rises. (4) The carbon reduction effect of digital technology shows obvious heterogeneity across grain production functional zones. The inhibitory effect is significant in major grain-producing areas and grain production–consumption balance areas, but not significant in major grain-consuming areas. (5) The carbon reduction effect also presents heterogeneity under different topographic relief conditions. The effect is significant in low-relief areas but not significant in high-relief areas, because complex terrain restricts the construction of digital infrastructure and large-scale application of digital technologies, which further reflects the regulatory role of natural geographical conditions. Accordingly, this paper proposes to strengthen the empowering role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively improve the capacity of agricultural carbon emission reduction and sequestration. Therefore, it is imperative to strengthen the enabling role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively enhance the capacity of agriculture for carbon emission reduction, sequestration and sustainable development. Full article

Understanding the impact of rural digitalization on the resilience of the swine industry is crucial to promoting its transformation toward efficient and low-carbon production. However, existing research has not yet clarified how rural digitalization influences the resilience of the swine industry, and there is a particular lack of discussion regarding potential nonlinear relationships. Based on panel data from 30 Chinese provinces for the period 2011–2023, we employed the entropy method to measure the level of rural digitalization and the resilience of the swine industry. Two-way fixed-effects, mediation, and threshold models were adopted to empirically examine the relationship and underlying mechanisms. The findings indicated that rural digitalization significantly enhances the resilience of the swine industry, and this finding remained robust after multiple robustness checks and endogeneity treatments. This effect is primarily mediated by two pathways: industrial-scale expansion and industrial agglomeration. Additionally, well-designed environmental policies and higher rural household incomes can strengthen the beneficial effect of rural digitalization on industrial resilience. Heterogeneity analysis further reveals that the positive influence is stronger in regions with poor transportation infrastructure and in central and western China, where digitalization effectively strengthens the industry’s shock resistance and adaptive capacity. This study offers meaningful implications for policymakers seeking to accelerate rural digitalization and promote high-quality development of the swine industry in the digital age. Full article

Frequency response analysis (FRA) is recognized as an effective method in power transformer winding fault diagnosis. However, the traditional numerical index methods focus on the overall features of FRA curves, making it difficult to capture subtle deformations in transformer windings. Similarly, existing digital image processing methods rely on a single feature or a simple feature combination without adaptive fusion. These methods ignore differences in the data distributions of features, leading to feature mismatch, the loss of sensitive fault information, and lower diagnostic accuracy. To solve this problem, a novel adaptive multiple-image-feature fusion method for transformer winding fault diagnosis is proposed. First, a multi-dimensional feature space combining image pixel matrix similarity, morphological features, and image texture features is built to decode the difference in fault of FRA images. Second, the multiple kernel learning (MKL) framework is used to dynamically adjust the fusion weights of different kernels to make features compatible and remove redundant information. Finally, comparative and ablation experiments show that the proposed method outperforms the traditional methods in identifying different types and levels of faults. The method achieves over 99% accuracy in fault type identification across SVM, KNN, and RF classifiers. For radial deformation (RD) severity prediction, the accuracy of the proposed model is 93.37% with SVM and 94.85% with KNN, outperforming the full-feature concatenation method. These results confirm the method’s robustness and diagnostic precision. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Electricity infrastructure is widely regarded as a fundamental prerequisite for supporting sustainable industrial development and driving corporate digital transformation under energy constraints. Taking the quasi-natural experiment of changes in electricity supply resulting from the cross-regional power transmission of the Three Gorges Project, and using data from China’s A-share listed manufacturing companies over the period 2000 to 2023, this paper constructs a multi-period difference-in-differences model to investigate whether improvements in electricity infrastructure promote corporate digital transformation and to examine their potential role in supporting sustainable economic development. The empirical results indicate that improvements in electricity infrastructure significantly enhance the level of corporate digital transformation. In the mechanism analysis, the alleviation of financing constraints and the increase in R&D investment play important mediating roles in the process through which electricity infrastructure affects corporate digital transformation. Further heterogeneity analysis reveals that the above effects are more pronounced in non-STAR Market enterprises, labor-intensive enterprises, asset-intensive enterprises, state-owned enterprises, and regions characterized by relatively lower levels of marketization. This study reveals the intrinsic relationship between electricity infrastructure and corporate digital transformation at the micro level, provides empirical evidence for understanding how energy infrastructure supports sustainable digital transformation and enhances long-term economic resilience, and offers policy implications for promoting the coordinated development of energy security and the digital economy. Full article