Search Results (3,553)

Digitalization and greening are two fundamental forces shaping the current technological revolution and industrial transformation, serving as key pathways for nations to achieve sustainable development goals. Drawing on panel data from 30 Chinese provinces from 2012 to 2022, this study constructs indicators of digitalization and greening from the perspectives of data empowerment and technological efficiency improvement and examines how their synergistic development influences industrial structure optimization. The findings reveal the following: (1) although the overall synergy between digitalization and greening has steadily increased, regional disparities persist, displaying an “East strong–West weak” pattern, with inter-regional differences being the primary source of overall imbalance; (2) through the mediating role of environmental regulation, the coordinated advancement of digitalization and greening exerts a significant positive effect on industrial structure optimization; (3) heterogeneity analysis indicates a gradient empowerment effect, showing that the impact of digitalization–greening synergy on industrial structure optimization follows a “West > Central > East” pattern. These results provide both theoretical and empirical evidence for understanding how digitalization and greening jointly drive sustainable development. The study offers practical insights for guiding traditional industries to integrate into circular economy systems through “digitalization + greening” transformation and recommends that governments adopt differentiated strategies tailored to local conditions, enhance digital infrastructure, promote green initiatives, deepen reforms, and innovate regulatory frameworks to foster the synergistic advancement of digitalization and greening. Full article

Background: Poor nutrition drives chronic disease, health disparities, and rising health care costs in the United States. Medically tailored meals (MTMs), designed by registered dietitians, are a Food-as-Medicine intervention with potential to improve outcomes and reduce costs. This review synthesizes evidence on the clinical, economic, and policy implications of MTMs. Methods: We conducted a narrative review of peer-reviewed studies, real-world program evaluations, and policy analyses. Sources included PubMed, Google Scholar, and grey literature from government, nonprofit, and industry organizations. Articles and reports were included if they examined MTMs in Medicare, Medicaid, or other high-risk populations. Results: Evidence demonstrates that MTMs improve health outcomes, reduce hospitalizations, and lower total cost of care. Case studies from Medicaid and Medicare Advantage plans, including those administered by Mom’s Meals^®, report reductions in emergency department visits, hospital readmissions, and total cost of care, alongside sustained high member satisfaction. Despite these findings, gaps in coverage and limited stakeholder awareness hinder broader access and adoption. Conclusions: Federal policy action can expand MTM availability and maximize utilization of existing benefits. Opportunities include establishing a Medicare Fee-for-Service demonstration, expanding and encouraging use in Medicare Advantage, and leveraging MTMs within Center for Medicare and Medicaid Innovation models. Broader implementation and utilization could reduce the nation’s chronic disease burden, advance health equity, and promote value-based care. Full article

This review examines recent advances in Gallium Nitride (GaN) power semiconductor devices and their growing impact on the development of high-efficiency power conversion systems. It explores innovations in device design, packaging methods, and gate-driving strategies that have improved both performance and reliability. Key metrics such as switching speed, conduction losses, thermal management, and device robustness are analyzed, supported by reliability assessment techniques including Double-Pulse Testing (DPT). The discussion extends to current market dynamics and strategic industry initiatives that have catalyzed widespread GaN adoption. These combined insights highlight GaN’s role as a transformative material offering compact, efficient, and durable power solutions while identifying challenges that remain for broader implementation across diverse industries. Full article

Against the backdrop of frequent internal and external shocks, as a core driver of the consumption segment in the digital economy, the impact mechanism and actual effectiveness of information consumption on urban economic resilience urgently require systematic exploration. Based on panel data of 280 prefecture-level cities in China from 2010 to 2022, this study treats the information consumption pilot policy as a quasi-natural experiment and employs a multi-period Difference-in-Differences (DID) method to empirically examine the policy’s impact on urban economic resilience and its internal mechanisms. The results show that the information consumption pilot policy significantly enhances urban economic resilience, with a policy effect coefficient of 0.084, and this conclusion remains robust after multiple robustness tests. Mechanistic analysis indicates that the policy indirectly strengthens urban economic resilience by promoting consumption growth, stimulating technological innovation, and improving human capital. Meanwhile, the level of digital infrastructure plays a positive moderating role in the policy effect. Heterogeneity analysis finds that the policy has a more pronounced effect of enhancing economic resilience on cities with larger population sizes, higher economic density, and non-resource-dependent characteristics. Further extended research confirms that the information consumption pilot policy exhibits a significant spatial spillover effect on urban economic resilience, and this spillover effect presents a phased characteristic of “resource homogeneous competition → positive synergistic driving → cross-regional resource siphoning → spatial attenuation of the effect” with changes in geographical distance. Full article

This study examines the role of the supply chain finance (SCF) ecosystem as an innovative financial framework in driving the high-quality development of rural industries. Using panel data from 31 Chinese provinces (2004–2022), we employ a fixed-effects model to analyze this relationship, confirming that the SCF ecosystem has a significant promoting effect. Mechanism analysis reveals that this positive effect operates primarily through two channels: enhancing rural industrial integration and stimulating technological innovation. Furthermore, we identify significant regional heterogeneity, with the most substantial positive spillover effects observed in the Southwest and South China. These results underscore the critical importance of the SCF ecosystem in rural revitalization and provide a basis for formulating regionally tailored financial policies. Full article

Digital video surveillance systems are now common in the security infrastructure of modern times, but proprietary file systems provided by large manufacturers are a major challenge to the work of the forensic investigator. This paper proposes a forensic recovery methodology of Hikvision and Dahua surveillance systems by utilizing three major innovations: (1) adaptive temporal sequencing, which dynamically changes gap detection thresholds; (2) dual-signature validation with header–footer matching of DHFS frames; and (3) automatic manufacturer identification. The strategy puts into practice direct binary analysis of proprietary file systems, frame-based parsing and automatic video reconstruction. Testing on 27 surveillance hard drives showed a recovery rate of 91.8, a temporal accuracy of 96.7% and a false positive rate of 2.4%—the lowest of the tools tested with statistically significant improvements over commercial tools (p < 0.01). Better results with fragmented streams (87.2 vs. 82.4% with commercial tools) meet key forensic needs of determining valid evidence chronology. The open methodology offers the necessary algorithmic transparency to be court-admissible, and the automated MP4 conversion with metadata left intact makes the integration of forensic workflow possible. The study provides a scientifically validated approach to proprietary surveillance formats, which evidences technical innovativeness and practical usefulness to digital forensics investigations. Full article

Take-over time is a critical factor affecting safety. Accurately predicting the take-over time provides a more reliable basis on issuing take-over requests, assessment of take-over risks, and optimization of human–machine interaction modes. Although there has been substantial research on predicting take-over time, there are still shortcomings in personalized prediction (particularly in accounting for individual differences in driving experience, cognitive abilities, and physiological responses). To gain a comprehensive understanding of the characteristics and applicability of take-over time prediction methods, this review covers four aspects: literature search information, factors influencing take-over time, data acquisition and processing methods, and take-over time prediction methods. Through literature search, research hotspots in recent years have been summarized, revealing the main research directions and trends. Key factors influencing take-over time, including driver factors, autonomous driving systems, and driving environments, are discussed. Data preprocessing stages, including data acquisition and processing, are systematically analyzed. The advantages and disadvantages of classical statistical, machine learning, and cognitive architecture models are summarized, and the shortcomings in current research are highlighted (for instance, the limited generalizability of models trained predominantly on simulator data to real-world driving scenarios). By thoroughly summarizing the strengths and weaknesses of existing research, this review explores under-researched areas and future trends, aiming to provide a solid theoretical foundation and innovative research perspectives for optimizing take-over time prediction, thereby promoting the widespread application and efficient development of autonomous driving technology. Full article

Understanding and accurately estimating cognitive workload is crucial for the development of adaptive, user-centered interactive systems across a variety of domains including augmented reality, automotive driving assistance, and intelligent tutoring systems. Cognitive workload assessment enables dynamic system adaptation to improve user experience and safety. In this work, we introduce a novel framework that leverages smooth pursuit eye movements as a non-invasive and temporally precise indicator of mental effort. A key innovation of our approach is the development of trajectory-independent algorithms that address a significant limitation of existing methods, which generally rely on a predefined or known stimulus trajectory. Our framework leverages two solutions to provide accurate cognitive load estimation, without requiring knowledge of the exact target path, based on Kalman filter and B-spline heuristic classifiers. This enables the application of our methods in more naturalistic and unconstrained environments where stimulus trajectories may be unknown. We evaluated these algorithms against classical supervised machine learning models on a publicly available benchmark dataset featuring diverse pursuit trajectories and varying cognitive workload conditions. The results demonstrate competitive performance along with robustness across different task complexities and trajectory types. Moreover, our framework supports real-time inference, making it viable for continuous cognitive workload monitoring. To further enhance deployment feasibility, we propose a federated learning architecture, allowing privacy-preserving adaptation of models across heterogeneous devices without the need to share raw gaze data. This scalable approach mitigates privacy concerns and facilitates collaborative model improvement in distributed real-world scenarios. Experimental findings confirm that metrics derived from smooth pursuit eye movements reliably reflect fluctuations in cognitive states induced by working memory load tasks, substantiating their use for real-time, continuous workload estimation. By integrating trajectory independence, robust classification techniques, and federated privacy-aware learning, our work advances the state of the art in adaptive human–computer interaction. This framework offers a scientifically grounded, privacy-conscious, and practically deployable solution for cognitive workload estimation that can be adapted to diverse application contexts. Full article

This article integrates diverse strands of theory and empirical research to combine views on economic complexity and entrepreneurial ecosystems in the context of mountain regions, focusing on the role of orchestrator platforms in shaping innovation and growth. Mountains are often marginal, dispersed, and loosely integrated areas that suffer from scarce opportunities for resource combination and interaction at the basis of the innovative process. The research relies on the case study of the European Alps, combining multiple sources of data—surveys, interviews, and ethnographic studies—to highlight the strengths and weaknesses of the mountain ecosystems. Orchestrator platforms emerged as the central actors in the innovative network that overcomes the difficulties and constraints of the mountains. The article provides a comprehensive perspective on how economic complexity can drive development in mountain regions, offering both theoretical and practical contributions to the broader discourse on entrepreneurship and regional growth. Full article

This study proposes a novel framework for analyzing Vehicle-Integrated Photovoltaic (VIPV) systems, integrating optical, thermal, and electrical models. The model modifies existing fixed PV methodologies for VIPV applications to assess received irradiance, PV module temperature, and energy production, and is available as an open-source MATLAB tool (VIPVLIB) enabling simulations via a smartphone. A key innovation is the integration of meteorological data and real-time driving, dynamically updating vehicle position and orientation every second. Different time resolutions were explored to balance accuracy and computational efficiency for optical model, while the thermal model, enhanced by vehicle speed, wind effects, and thermal inertia, improved temperature and power predictions. Validation on a minibus operating within the University of Palermo campus confirmed the applicability of the proposed framework. The roof received 45–47% of total annual irradiation, and the total yearly energy yield reached about 4.3 MWh/Year for crystalline-silicon, 3.7 MWh/Year for CdTe, and 3.1 MWh/Year for CIGS, with the roof alone producing up to 2.1 MWh/Year (c-Si). Under hourly operation, the generated solar energy was sufficient to fully meet daily demand from April to August, while during continuous operation it supplied up to 60% of total consumption. The corresponding CO₂-emission reduction ranged from about 3.5 ton/Year for internal-combustion vehicles to around 2 ton/Year for electric ones. The framework provides a structured, data-driven approach for VIPV analysis, capable of simulating dynamic optical, thermal, and electrical behaviors under actual driving conditions. Its modular architecture ensures both immediate applicability and long-term adaptability, serving as a solid foundation for advanced VIPV design, fleet-scale optimization, and sustainability-oriented policy assessment. Full article

Against the backdrop of China’s “carbon peaking” and “carbon neutrality” goals, as well as the advancement of new industrialization, the oil and gas industry is undergoing a critical transformation from resource-dependent growth toward innovation-driven, low-carbon, and high-quality development. The integrated advancement of high-end, intelligent, and green transformation—collectively referred to as the “Three Modernizations”—has become a vital pathway for promoting industrial upgrading and sustainable growth. Based on panel data from 30 Chinese provinces from 2009 to 2023, this study constructs a comprehensive evaluation index system covering 19 secondary indicators across three dimensions: high-end, intelligent, and green development. Using the entropy-weighted TOPSIS method, kernel density estimation, Dagum Gini coefficient decomposition, and σ–β convergence models, the study examines the spatiotemporal evolution, regional disparities, and convergence characteristics of HIG integration, and further explores its driving mechanisms through a two-way fixed effects model and mediation effect analysis. The results show that (1) the overall HIG integration index rose from 0.34 in 2009 to 0.46 in 2023, forming a spatial pattern of “high in the east, low in the west, stable in the center, and fluctuating in the northeast”; (2) regional disparities narrowed significantly, with the Gini coefficient declining from 0.093 to 0.058 and σ decreasing from 7.114 to 6.350; and (3) oil and gas resource endowment, policy support, technological innovation, and carbon emission constraints all positively promote integration, with regression coefficients of 0.152, 0.349, 0.263, and 0.118, respectively. Heterogeneity analysis reveals an increasing integration level from upstream to downstream, with eastern regions leading in innovation-driven development. Based on these findings, the study recommends strengthening policy and institutional support, accelerating technological innovation, improving intelligent infrastructure, deepening green and low-carbon transformation, promoting regional coordination, and establishing a long-term monitoring mechanism to advance the integrated high-quality development of China’s oil and gas industry. Overall, this study deepens the understanding of the internal logic and spatial dynamics of the “Three Modernizations” integration in China’s oil and gas industry, providing empirical evidence and policy insights for accelerating the construction of a low-carbon, secure, and efficient modern energy system. Full article

Digital tools and platforms offer significant potential to address critical gaps in market access, credit availability, and agricultural knowledge, particularly in urban and peri-urban areas. This is especially relevant in regions like Sudan, where these opportunities remain largely underexplored. By providing real-time market information, facilitating financial access, and offering essential agricultural training, these tools can help bridge traditional barriers, improve decision-making capabilities, and contribute to sustainable agriculture. Such advancements strengthen economic resilience and promote equity in agriculture, enabling these farmers to drive innovation and sustainability in the industry. Our study was conducted in Omdurman’s Algamwai area during 2022 and 2023, and involved interviews with 100 female farmers. It explored the intersection of gender, technology, and socioeconomic equity. It highlighted how technological advancements can enhance agricultural productivity and market access while addressing challenges such as limited digital literacy and socioeconomic constraints. Despite structural inequalities—including restricted land ownership (45%), limited credit access (5%), and inadequate extension services—female farmers are driving innovation and sustainability by adopting sustainable practices, enhancing food security, and building community resilience. Digital urban agriculture provides income opportunities (76% rely on it) and serves as a platform for equitable participation. From a social science perspective, this research underscores the need to address systemic disparities to unlock the full potential of farmers. Policies ensuring equitable access to resources, credit, and technology are essential for fostering participation and maximizing the socio-economic benefits of digital agriculture in Sudan and similar contexts. Full article

New quality agricultural productivity is essential for advancing agricultural modernization, consolidating and expanding achievements in poverty alleviation, and driving rural revitalization. However, leveraging this productivity to facilitate industrial upgrading and support the transition of smallholder farmers remains challenging. This paper constructs a tripartite evolutionary game model involving the government, agricultural enterprises, and farmers within the policy framework of new quality agricultural productivity. By applying evolutionary game theory, we analyze the strategic interactions among policy implementation, farmer welfare, and the development of new quality agricultural productivity. Equilibrium analysis reveals that the government, as a regulatory actor, should provide appropriate subsidies to agricultural enterprises and farmers, undertake initial infrastructure improvements, diversify subsidy instruments, establish special incentives for agricultural technology innovation, and increase investment in cultivating new agricultural talent. Agricultural enterprises, as dynamic agents, should adopt proactive and systematic transformation strategies. Furthermore, they need to strengthen benefit-linked mechanisms with farmers to ensure sustained collaboration. Full article

Since increasing attention has been given to predictive maintenance (PdM) of industrial equipment, in order to enhance operational efficiency, improve reliability, and reduce downtime, this powerful strategy offers significant benefits, holds clearly great promises, and is now regarded as a key for future perspective in Industry 4.0. There are various approaches to PdM, each offering its own set of advantages and disadvantages which are single and hybrid approaches to carrying out diagnostics and prognostics in PdM. In this paper we will compare these approaches according to different aspects such as complexity of data and interpretability of results. Moreover, we also discuss the barriers to successful adoption, such as data quality, system complexity, and the need for workforce training. Finally, this paper concludes by identifying future research directions in response to scientific problems, which will drive the next wave of innovation in predictive maintenance solutions. Full article

Based on the strategy tripod framework, this study identifies 27 feature variables that influence the persistence of enterprise green innovation. In addition, utilizing data from Chinese listed enterprises between 2012 and 2022, this study employs machine learning models and the SHAP method to analyze the driving factors and their underlying mechanisms. The findings indicate that the persistence of enterprise green innovation results from multiple factors, among which enterprise size, R&D investment, and technological utilization capability rank as the top three most important determinants. Enterprise size has a positive linear effect on the persistence of green innovation, while market competition has a negative linear effect. R&D investment, technological utilization capability, enterprise green culture, financing capacity, and integration capability all show non-linearly positive effects. The conclusions provide theoretical guidance and micro-level evidence for promoting high-quality enterprise green development in enterprises and supporting governmental policy formulation. Full article