Search Results (572)

Background and Objectives: Despite substantial advances in treatment strategies for patients with type 2 diabetes mellitus (T2DM), its complication, particularly sarcopenia, has emerged as a global healthcare challenge. Pay-for-performance (P4P), an incentive-based payment scheme, has long been used to improve the quality of care; however, few studies have explored its effect on sarcopenia prevention. Therefore, we conducted a nested case–control study to investigate the association between P4P participation and the risk of sarcopenia among patients with T2DM. Materials and Methods: Using a large claims dataset, we identified individuals aged 20–70 years with newly diagnosed T2DM between 2001 and 2010 in Taiwan. All enrollees were followed up until 2013 to determine the occurrence of sarcopenia. For each case, we randomly matched two controls without sarcopenia. The risk of sarcopenia in relation to P4P participation was estimated by fitting conditional logistic regression to yield the adjusted odds ratio (aOR) and corresponding 95% confidence interval (CI). Results: A total of 3475 individuals with sarcopenia and 6948 matched controls were included. Patients enrolled in the P4P program had a lower risk of sarcopenia than their matched counterparts (aOR = 0.66; 95% CI: 0.61–0.74). Earlier P4P enrollment (within 1 year of T2DM diagnosis) and high-intensity P4P participation were associated with additional reductions in sarcopenia risk. Conclusions: Integrating P4P into routine T2DM care may help prevent sarcopenia, highlighting the importance of interdisciplinary collaboration and timely program implementation. Full article

Hybrid Split Federated Learning (HSFL for short) in emerging 6G-enabled UAV networks faces persistent challenges in data protection, device trust management, and long-term participation incentives. To address these issues, this study introduces S-HSFL, a security-enhanced framework that embeds verifiable federated learning mechanisms into HSFL and incorporates digital-signature-based authentication throughout the device selection process. This design effectively prevents model tampering and forgery attacks, achieving a defense success rate above 99%. To further strengthen collaborative training, we develop a MAB-GT device selection strategy that integrates multi-armed bandit exploration with multi-stage game-theoretic decision models, spanning non-cooperative, coalition, and repeated games, to encourage high-quality UAV nodes to provide reliable data and sustained computation. Experiments on the Modified National Institute of Standards and Technology (MNIST) dataset under both Independent and Identically Distributed (IID) and non-IID conditions demonstrate that S-HSFL maintains approximately 97% accuracy even in the presence of 30% adversarial UAVs. The MAB-GT strategy significantly improves convergence behavior and final model performance, while incurring only a 10–30% increase in communication overhead. The proposed S-HSFL framework establishes a secure, trustworthy, and efficient foundation for distributed intelligence in next-generation 6G UAV networks. Full article

This study systematically examines the policy and technological pathways for the sustainable development of China’s 3D printing industry under the “Dual Carbon” goals. A three-dimensional sustainability framework is developed, integrating resource efficiency, environmental performance, and socio-economic value. Based on this framework, the study conducts a full-process analysis covering design, material preparation, manufacturing, post-processing, use, and recycling stages. The analysis identifies key carbon-reduction mechanisms of 3D printing, including material savings, reduced energy consumption, lightweight-enabled emission reduction, and distributed manufacturing. A comparative analysis of China, the European Union, and the United States reveals major constraints in China’s 3D printing sector, particularly in top-level policy design, standardization systems, legal frameworks, industrial coordination, and low-carbon core technologies. Based on these findings, the study proposes a dual-driven development pathway integrating policy optimization and technological innovation. From an institutional perspective, this pathway emphasizes green policy incentives, including strategic planning, standard setting, green finance, and collaborative governance. From a technological perspective, it highlights the importance of low-carbon material development, refined energy-efficiency management, life-cycle carbon accounting platforms, and value creation across the product life cycle. Overall, the study demonstrates that effective policy–technology synergy is essential for transforming theoretical carbon-reduction potential into scalable and practical outcomes, providing a systematic analytical framework for academic research and actionable guidance for policymakers and industry stakeholders. Full article

As global demand for critical minerals intensifies with the expansion of energy transition technologies and advanced manufacturing, developing sustainable and efficient exploration strategies has become a national priority. In this shift, AI-driven exploration technologies are emerging as a transformative force, reshaping how mineral resources are discovered, assessed, and managed. This study analyzes the global research landscape in critical mineral exploration by examining patent and scientific publication data to evaluate both research efficiency and knowledge spillover capacity. Using data envelopment analysis and super-efficiency modeling, we compare national R&D performance, identify leading countries, and quantify diffusion dynamics. The results reveal significant disparities: countries such as the United States, South Korea, and Canada demonstrate high research efficiency and strong spillover effects, supported by active innovation ecosystems and technological adoption. In contrast, resource-rich nations including China, Australia, and Russia show limited diffusion due to weaker AI-based innovation incentives and insufficient industry–academia collaboration. Italy stands out as an effective model of policy-driven R&D utilization and technological diffusion. These findings highlight the strategic importance of combining AI-enabled exploration, qualitative research impact, and international cooperation. The study offers policy implications for countries seeking to strengthen resource security and enhance competitiveness through sustainable and innovation-driven mineral exploration. Full article

The sustainable future of dairy farming will depend on how trade-offs between environmental impact, economic viability, and animal welfare are managed. Dairy production contributes significantly not only to human nutrition but also to greenhouse gas (GHG) emissions, ammonia release, and water pollution. Comprehensive sustainability assessments are essential for addressing these impacts, also in light of evolving regulations like the EU Corporate Sustainability Reporting Directive. However, existing research on sustainable dairy farming and intensification often overlooks trade-offs with other ecological aspects like biodiversity, economic viability, or animal welfare. This study evaluated the sustainability performance of Austrian dairy farms using a tool called NEU.rind, which combines life cycle assessment (LCA) with other indicators. Applied to 170 dairy farms, the tool identified four sustainability clusters across the dimensions of environmental conditions, efficiency, animal health, and sustainability: (1) Alpine farms (high cow longevity, medium-to-high emissions per kg milk), (2) efficient low-input farms (low emissions, high cow longevity), (3) high-output lowland farms (high productivity, lower animal welfare), and (4) input-intensive lowland farms (high emissions, especially per hectare; inefficient use of resources). The analysis revealed fundamental trade-offs between production intensity, environmental impact, and animal welfare, particularly when comparing product-based (per kg milk) versus hectare-based indicators. Key improvement strategies include increasing the use of regional feed and pasture as well as adapting manure management. For policymakers, these findings underline the importance of site-specific sustainability assessments and the need for region-specific incentive schemes that reward both environmental efficiency and animal health performance. In this context, NEU.rind provides farm-specific recommendations with minimal data input, making sustainability assessments practical and feasible. Full article

The substantial domain discrepancy inherent in multi-source and multi-view imagery presents formidable challenges to achieving precise drone-based multi-view geo-localization. Existing methodologies primarily focus on designing sophisticated backbone architectures to extract view-invariant representations within abstract feature spaces, yet they often overlook the rich and discriminative frequency-domain cues embedded in multi-view data. Inspired by the principles of $\pi$-Noise theory, this paper proposes a frequency-domain Positive-Incentive Information Screening (PIIS) mechanism that adaptively identifies and preserves task-relevant frequency components based on entropy-guided information metrics. This principled approach selectively enhances discriminative spectral signatures while suppressing redundant or noisy components, thereby improving multi-view feature alignment under substantial appearance and geometric variations. The proposed PIIS strategy demonstrates strong architectural generality, as it can be seamlessly integrated into various backbone networks including convolutional-based and Transformer-based architectures while maintaining consistent performance improvements across different models. Extensive evaluations on the University-1652 and SUES-200 datasets have validated the great potential of the proposed method. Specifically, the PIIS-N model achieves a Recall@1 of 94.56% and a mean Average Precision (mAP) of 95.44% on the University-1652 dataset, exhibiting competitive accuracy among contemporary approaches. These findings underscore the considerable promise of frequency-domain analysis in advancing multi-view geo-localization. Full article

High penetration of renewable energy sources (RES) in power systems introduces substantial source-load uncertainty and flexibility challenges, leading to misalignments between economic optimization and environmental sustainability. An edge-side electricity-carbon coordinated hybrid trading mechanism was proposed to enhance flexibility in microgrid clusters. A three-layer time-varying carbon emission factor (CEF) model is developed to quantify negative emissions as tradable Chinese Certified Emission Reductions (CCERs). An endogenous economic equilibrium point enables dynamic switching between Incentive-Based Demand Response during high-carbon periods and Price-Based Demand Response during low-carbon periods, based on marginal profit comparisons. A Wasserstein distance-based distributionally robust CVaR (WDR-CVaR) strategy constructs a data-driven ambiguity set to optimize decisions under worst-case distributional shifts in edge-side data. Simulations on a modified IEEE 33-bus system show that the mechanism increases the Multi-Energy Aggregator’s (MEA) expected profit by 12.3%, reduces carbon emissions by 17.6%, with WDR-CVaR demonstrating superior out-of-sample performance compared to sample average approximation methods. The approach internalizes environmental values through carbon-electricity coupling and edge intelligence, providing a resilient framework for low-carbon distribution network operations. Full article

The growing emphasis on sustainable manufacturing has motivated the integration of environmental and social factors into traditional assembly line balancing problems (ALBPs). This study introduces a Sustainable Mixed-Model Assembly Line Balancing Problem (S-MMALBP) that jointly considers task precedence, machine selection, worker allocation, carbon-emission control, and green-rating incentives. An exact optimization model is formulated to minimize total operating cost while satisfying sustainability and capacity constraints. To address the problem’s combinatorial complexity, an Adaptive Large Neighborhood Search (ALNS) metaheuristic is developed, incorporating customized destroy and repair operators, adaptive penalty updating, and a simulated-annealing-based acceptance criterion. An analytical lower bound is derived to evaluate the algorithm’s performance, and an enhanced constructive method, Precedence-Driven Task Grouping (PDTG), is proposed to generate high-quality initial solutions. Computational experiments on benchmark instances confirm that the ALNS achieves near-optimal solutions with deviations below 5% from the lower bound, while solving large instances within seconds. A real-world case study on aircraft assembly involving 166 tasks further validates the model’s applicability, achieving a cost deviation below 4% from the theoretical bound under realistic sustainability constraints. The results demonstrate that the proposed model provides an effective and scalable decision-support tool for designing environmentally and socially responsible production systems. The study is the first to incorporate sustainability and worker–machine decisions into a mixed-model ALB framework solved by a tailored ALNS and lower bound. Full article

Amid intensifying challenges of global climate change, China—as the world’s largest carbon emitter and a major manufacturing hub—occupies a pivotal position in the global industrial green transformation. Drawing on environmental federalism theory and China’s decentralized governance model, this study develops a framework of “green finance–local government competition–industrial green transformation.” Using panel data from 283 cities in China, we employ spatial econometrics and mediation effect models to test the dual mechanisms by which green finance promotes industrial green transformation. The findings indicate that (1) green finance promotes industrial green transformation; (2) green finance advances industrial green transformation by dismantling China’s traditional local government competition–based development model and removing the institutional suppression arising from “race-to-the-bottom competition”; (3) the effect of green finance exhibits long-run characteristics and a “benchmark–imitation” pattern; (4) baseline environmental conditions strengthen the influence of green finance on industrial green transformation; (5) incorporating ecological civilization development into officials’ performance evaluations can effectively reshape policy incentives and amplify the positive role of green finance. Thus, we propose differentiated green finance policies, the construction of a governance mechanism that integrates fiscal–financial–ecological compensation, and the optimization of ecological civilization assessment indicators to curb campaign-style governance. Full article

This study investigated the statistical association between public incentives and industrial innovation as reflected in firms’ financial performances. In particular, the analysis was carried out considering a Regional Operational Program, namely, the 2007–2013 ERDF Regional Program in Lombardy, and investigating a dataset of Lombardy-based companies that received support through the mentioned initiative. For each of them, balance sheet variables before and after the acquisition of the incentive and the development of the related innovation project were detected and analyzed by means of both standard and normalized linear regression. Notably, normalized regressions showed that higher subsidy intensity was positively associated with subsequent changes in revenues and intangible assets, especially among manufacturing firms, thereby supporting policies that target sectors with a high innovation capacity. Furthermore, this research underscores the importance of tailoring policy instruments to local and sectoral contexts, recognizing the limitations of one-size-fits-all approaches. In keeping with this exploratory stance, this study does not build a counterfactual control group and makes no causal claims; it simply documents balance sheet associations that may inform future, impact-oriented research. Given the absence of a control group, the design is observational; all findings describe associations and do not allow causal inference. Full article

This study investigates the factors that influence the acceptance of 3D concrete printing technology in South Africa. The purpose is to provide evidence-based insights to guide regulators in developing clear standards and certification pathways for 3DCP in South Africa. In a mixed-method research design, the study gathered data from professionals including architects, civil engineers, quantity surveyors, project managers, contractors, regulators, and local municipalities using a modified Unified Theory of Acceptance and Use of Technology framework, adapted to the institutional and infrastructure contextual nuances of South Africa. The findings indicate significant variability in awareness, exposure, and openness to 3DCP across professions and regions. Regulatory actors express caution due to the absence of national standards but also recognize the potential alignment with sustainable construction goals. Major enablers of acceptance include access to demonstrable case studies, technical training, and policy incentives. Barriers include a lack of local performance benchmarks, cost perceptions, and uncertainty regarding compliance pathways. By incorporating institutional variables such as regulatory clarity and policy maturity, the study advances a theoretical understanding of construction technology diffusion in the global south. The study offers a robust, context-specific model that can be adapted in similar economies seeking to balance innovation with regulatory oversight. Full article

Urban inefficient land redevelopment (UILR) is crucial for sustainable urban development, yet its progress is driven by the interplay of multiple factors. To systematically uncover the driving mechanisms and dynamic patterns of these factors, an integrated analytical approach combining Structural Equation Modeling (SEM) and Fuzzy Cognitive Map (FCM) is developed in this study. Based on 222 valid survey responses from professionals across eight cities in China’s Yangtze River Delta region, five key factors are identified within the “drivers–pressure–enablers” conceptual framework: economic incentives, environmental objectives, social needs, policy guidance, and implementation conditions. SEM is first employed to examine static causal relationships, and the quantified pathway effects are subsequently incorporated into an FCM model to simulate the long-term evolution. The results reveal the following: (i) All five factors exert significant direct effects, with economic incentives, environmental objectives, and policy guidance also demonstrating notable indirect effects. (ii) The factors exhibit distinct temporal characteristics: policy guidance acts as a “fast variable” enabling short-term breakthroughs; economic incentives serve as a “strong variable” driving medium-term progress; and social needs function as a “slow variable” with long-term benefits. (iii) Policy guidance is essential, as its absence leads to persistently low effectiveness, while its synergy with implementation conditions can achieve satisfactory performance even without economic incentives. The combined SEM–FCM approach validates static hypotheses and simulates dynamic scenarios, offering a new perspective for analyzing complex driving mechanisms of UILR and providing practical insights for targeted redevelopment strategy design. Full article

Local United Arab Emirates (UAE) inhabitants have shown heightened awareness and interest in renewable energy (RE), resulting in a rise in the installation of solar photovoltaic (PV) systems in their residences; however, electric utility earnings have decreased due to this tendency. Energy decision-makers are concerned about discriminatory resident access to incentives and publicly funded solar PV frameworks. To reduce solar PV installations, utilities and energy players have adjusted RE initiatives. Utility companies provide solar PV-assisted installations. Nonetheless, adopting such frameworks requires a comprehensive feasibility study of all elements to achieve a win–win condition for all stakeholders, namely energy consumers, grid operators, solar PV company owners, regulators, and financiers. This article predicts the success of numerous local UAE solar PV models using agent-based modeling (ABM) to assess stakeholders’ measurements and objectives. Agents represent prosumers who choose solar PV. The effects of their installation choices on stakeholder performance measures are studied over time. ABM results show that suitable solar community pricing policies can benefit all stakeholders. Therefore, enhanced RE implementation rates can grow equitably. Also, electric utility companies can recoup profit losses from solar PV installations, and solar PV firms can thrive. The proposed modeling technique provides a viable policy design that supports all parties, preventing injustice to any stakeholder. Full article

Understanding how drivers respond to feedback and incentives is crucial for designing data-driven interventions that enhance road safety. This study investigates driver profiling and behavioral change using high-resolution telematics data collected through the OSeven DrivingStar smartphone application within the O7Insurance project. The naturalistic driving experiment was divided into two main phases: a baseline period with personalized feedback (Phase A) and an incentive-based phase (Phase B) comprising two gamified driving challenges with distinct reward criteria. Using data from 86 active participants, K-means clustering identified three driver profiles—Low-Exposure Cautious, Balanced/Average, and High-Risk Drivers—based on exposure, harsh events, speeding, and mobile phone use. The Balanced/Average group exhibited statistically significant improvements during both challenges, reducing speeding frequency and intensity (e.g., from 4.8% to 3.7%, p < 0.01), while High-Risk Drivers achieved moderate reductions in speeding intensity (from 6.4 to 5.3 km/h, p < 0.05). Low-Exposure Cautious Drivers maintained stable, low-risk performance throughout. These findings demonstrate that incentive-based telematics schemes can effectively influence driving behavior, particularly among drivers with moderate risk levels. The study contributes to the growing body of research on gamified driver feedback by linking behavioral clustering with responsiveness to incentives, providing a foundation for adaptive and personalized road safety interventions. Full article

The rapid global shift toward transportation electrification has positioned e-mobility as a key part of low-carbon transition strategies. Saudi Arabia, as a major energy producer undergoing economic diversification under Vision 2030, has recently increased its policy efforts for electric mobility. This study performs a qualitative document analysis of 52 national policies, strategies, and institutional publications issued between 2010 and 2025, creating a longitudinal dataset of 1240 coded references. Using a PRISMA-aligned screening process and NVivo-based thematic coding, the analysis highlights main policy trends, institutional priorities, and implementation challenges influencing the Kingdom’s e-mobility transition. Results show a clear shift from early technology-neutral sustainability rhetoric to a more explicit policy framework focusing on industrial localization, charging infrastructure growth, renewable energy integration, and regulatory development after 2020. Despite these advances, gaps remain in governance coordination, market readiness, charging accessibility, and user adoption incentives. The paper provides a systematically mapped view of Saudi Arabia’s e-mobility policy landscape and places it within global transition trends. The findings offer practical insights for policymakers aiming to strengthen implementation, accelerate adoption, and align transport electrification with national decarbonization goals. Full article