Search Results (36,991)

Background: Chronic Obstructive Pulmonary Disease (COPD) poses a substantial public health challenge in China owing to its increasing prevalence and substantial economic burden. In response, the diagnosis–intervention packet (DIP) payment reform was implemented to control healthcare costs and enhance service efficiency. Methods: To evaluate the effect of the DIP reform on medical costs, hospitalization days, and individual out-of-pocket payments for COPD inpatients in M City, a pilot city in central China, we conducted an interrupted time series (ITS) analysis using monthly reimbursement records from January 2020 to December 2023. The study included 84,410 hospitalized patients from a city-wide database of 3,241,233 inpatient records with COPD who met the inclusion criteria. The analysis focused on the total healthcare costs, length of stay, and individual out-of-pocket costs. Results: The DIP reform resulted in a 3.7% reduction (95% CI: 0.9% to 6.5%) in the total hospitalization costs in the first month post-reform, with a sustained monthly decline of 0.8% (95% CI: 0.5% to 1.1%). The length of stay decreased from 9.53 (95% CI: 9.31 to 9.75) to 8.74 days (95% CI: 8.62 to 8.86). Conversely, the proportion of out-of-pocket payments relative to total costs increased. Conclusions: While the DIP reform effectively reduced hospitalization costs and days, it led to an increase in individual out-of-pocket payments. Future research should focus on optimizing payment rules, enhancing the supervision of medical services, and refining health insurance policies to achieve the reform’s objectives better and alleviate the financial burden on patients. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

Despite increasing emphasis on circular economic practices, the construction sector remains slow to adopt circular approaches, largely due to limited stakeholder engagement. In this context, understanding how value is perceived by stakeholders is critical for motivating their participation in circular economy practices. This study presents a systematic literature review conducted in accordance with PRISMA guidelines to examine value creation models and frameworks across multiple disciplines. A total of 49 studies were identified and analysed through a structured screening and qualitative content analysis process. The review clarifies the conceptual underpinnings of “value” in a circular system, examines how value can be created and delivered, and identifies the essential elements required for a value creation framework in construction to motivate stakeholders toward circular practices. The findings highlight that a circular value creation framework needs to collectively generate, deliver, and capture economic, environmental, and social value for multiple stakeholders. Based on these insights, the study develops a stakeholder-centred conceptual framework for value creation in construction waste management. The originality of the framework lies in its integration of stakeholder value perceptions with circular economic implementation, explicitly addressing the challenges of the complex and project-based construction environment. Full article

In the digital era, online attention has become a key indicator of tourism competitiveness and destination visibility. This study proposes a two-dimensional framework to evaluate the competitive state of online attention by combining its current magnitude and growth dynamics. Using Baidu Index data, the study applies the Boston Consulting Group (BCG) matrix and the coefficient of variation to analyze online attention patterns of Beijing’s 5A–level tourist attractions from 2011 to 2025. The results show clear polarization in online attention. A small number of iconic attractions consistently dominate digital visibility, while many other sites exhibit unstable and uneven attention trajectories. These patterns reflect the cumulative effects of consumer behavior, information-seeking preferences, and algorithmically mediated content environments, which reinforce attention concentration and competitive inequality over time. External shocks, particularly the COVID–19 pandemic, caused sharp declines in online attention in 2020, followed by an uneven recovery in subsequent years, highlighting the volatility of digital attention systems. The study also demonstrates the managerial value of the proposed framework. By classifying attractions according to attention levels and growth potential, the framework supports differentiated marketing and demand–redistribution strategies. For instance, increasing the visibility of high-potential but under-visited attractions can help redirect visitors away from overcrowded “Star/GC” sites and encourage more balanced spatial and temporal visitation. Overall, this study proposes a quantitative and replicable framework that integrates digital attention dynamics, algorithmic filtering, and consumer behavior into destination competitiveness analysis. The framework supports evidence-based and sustainability-oriented destination management by informing adaptive marketing and demand management strategies that can help alleviate overtourism and balance visitor flows. However, the study relies on a single digital platform and lacks direct sustainability indicators. Future research should integrate multi-platform data and link online attention metrics to measurable environmental, social, and economic sustainability outcomes. Full article

The Variable Cycle Engine (VCE) is a key enabling technology for addressing the economic and environmental challenges of next-generation supersonic civil aircraft. This paper presents a multidisciplinary design analysis and optimization (MDAO) approach to quantitatively assess the potential benefits of Variable Cycle Engines (VCE) in the conceptual design of supersonic civil aircraft. In this approach, component-level models of a conventional Mixed-Flow Turbofan (MFTF) and a double-bypass VCE with a Core Driven Fan Stage (CDFS) are integrated into the MDAO process. Employing a multi-point optimization strategy, the engine design parameters and off-design control schedules are first determined. Subsequently, for each given engine design (MFTF and CDFS VCE), the airframe geometry parameters are optimized to minimize the aircraft Maximum Take-off Weight (MTOW). The application of this approach is illustrated through a case study of a medium-sized supersonic civil transport. The results indicate that, under the assumption of identical weights for the VCE and the MFTF, the design with the VCE reduces the MTOW by 2.8%, block fuel consumption by 5.7%, and total mission Nitrogen Oxides (NOx) emissions by 24.2% compared to the design with the MFTF. Additionally, lateral noise and flyover noise during the take-off phase are decreased by 2.2 EPNdB and 1.9 EPNdB, respectively. To account for the potential weight increase caused by the structural complexity of the VCE, a parametric weight sensitivity analysis is conducted. Results show that the VCE retains its advantages in MTOW, fuel efficiency, noise, and emissions for weight penalty factors up to 1.15. Full article

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

This study investigates the relationship between banking sector stability and economic growth in Ethiopia, employing a dynamic panel data approach with the Two-Step System Generalized Method of Moments (GMM). The analysis uses a balanced dataset from 13 Ethiopian commercial banks covering 2014 to 2023, gathered from the World Bank database, the National Bank of Ethiopia, and audited financial statements. Banking sector stability is assessed using indicators such as Z-score, non-performing loan (NPL) ratio, capital adequacy ratio (CAR), liquidity ratio (LR), return on assets (ROA), and loan-to-deposit ratio (LDR), along with key macroeconomic and institutional factors. The results show that banking stability, as indicated by Z-score, liquidity ratios, and profitability, has a positive and significant effect on economic growth, confirming the sector’s role in promoting development. Surprisingly, a positive correlation between NPLs and economic growth suggests unique structural features in the Ethiopian banking system that warrant further investigation. Other variables, such as inflation rates, government expenditure, and gross domestic savings, positively influence economic growth, whereas foreign direct investment is negatively associated with it. The study highlights the importance of enhancing the stability of the banking sector by implementing robust regulatory frameworks, prudent risk management practices, and improved profitability to support sustainable economic development in Ethiopia, while calling for additional research into the unexpected effects of NPLs and FDI amid ongoing financial reforms. Full article

The rapid accumulation of genome-scale data has transformed plant biology from descriptive genetics to predictive and increasingly mechanistic genomics. Longan (Dimocarpus longan Lour.) is an economically important subtropical fruit tree in China and Southeast Asia, but compared with model plants and major temperate fruit crops, its genomic resources and functional studies have developed relatively late. Here, we review recent progress in longan genomics with emphasis on three interrelated areas: genome assembly and annotation, transcriptomic resources, and representative gene family studies associated with flowering, somatic embryogenesis, and transporter-mediated stress tolerance. The progression from the first draft genome of ‘Honghezi’ to the chromosome-scale assemblies of ‘Jidanben’ and ‘Shixia’ has substantially improved contiguity and gene annotation, thereby enabling population-genomic analysis, genome-wide gene family identification, and candidate-gene discovery. Available transcriptomic datasets further support studies of reproductive development, stress responses, and embryogenic competence, although cross-study integration remains limited. We also summarize how gene family analyses have advanced the current understanding of floral induction, continuous flowering, somatic embryogenesis, mineral transport, and sugar transport in longan. Importantly, the field is still dominated by cataloguing and expression-based inference, whereas causal validation, pan-genomic analysis, and multi-omics integration remain insufficient. We therefore argue that future progress in longan molecular breeding will depend on integrating high-quality genomic resources with functional validation, standardized comparative annotation, and improved transformation or regeneration systems. Full article

Socio-environmental conflicts in mining regions are often examined through political, economic, or social lenses, while the role of land-system transformation remains less integrated into quantitative analysis. This study examines the co-evolution of socio-environmental conflict and territorial change in Las Bambas (Apurímac, Peru) as a socio-territorial process. Annual conflict records from the Peruvian Ombudsman’s Office (2007–2024) were combined with annual land-cover data from MapBiomas. Yearly conflict influence zones were reconstructed from reported affected communities and geographic features using buffered spatial entities and concave hull polygons. Clustering methods (K-medoids, DBSCAN, and agglomerative hierarchical clustering) and FP-Growth association rule mining were applied to 23 unique conflicts consolidated from the original records and encoded with 10 root causes. The most intense conflict phases were accompanied by measurable landscape transformations, including the emergence of mining-related land cover from 2012 onward, sustained loss of high-Andean natural vegetation, expansion of agricultural mosaics, urban growth along the Apurímac–Cusco corridor, and hydrological alterations in wetlands and headwaters. Three conflict typologies were identified, with unfulfilled company commitments emerging as the most recurrent co-occurring grievance. The dynamic polygon approach offers a replicable framework for linking conflict records with land-system change in extractive regions. Full article

Poultry litter represents a promising feedstock for biogas production through anaerobic digestion (AD), offering potential benefits for both on-farm energy supply and organic waste management. This opportunity is particularly relevant in resource-constrained countries, where limited access to reliable energy and inadequate waste management remain critical challenges. This study investigates the integration of poultry litter-based biogas production into a decentralized energy system supplying a poultry farm and a nearby household in Yaoundé, Cameroon. A techno-economic optimization framework based on mixed-integer linear programming is used to determine the cost-optimal configuration of the energy system. The results show that anaerobic digesters are only selected when constraints on poultry litter disposal are introduced. Total annual system costs increase from approximately 2680 EUR·y⁻¹ in the unconstrained scenario to 3720 EUR·y⁻¹ when up to 50% of the poultry litter is valorized locally through AD. Increasing biogas production primarily substitutes liquefied petroleum gas (LPG) used for heating and progressively reduces electricity purchases from the grid. Overall, the analysis indicates that anaerobic digestion is currently not economically competitive when evaluated solely on energy supply benefits, mainly due to the high capital cost of digesters. However, when waste management objectives or external investment support are considered, poultry litter-based biogas systems can contribute to integrated energy–waste management strategies and support circular resource use in small-scale agricultural systems. Full article

Metallurgical-grade silicon (MG-Si) is an important source material for many industrial applications, including the manufacture of alloys, solar photovoltaics, and electronics. The process to refine raw materials into MG-Si is energy-intensive, with the predominant method of submerged-arc furnaces requiring energy consumption of approximately 11–13 kWh/kg Si. Recent research has discussed promising methods for reducing the energy required for the silicon production process, including the use of molten-salt electrolysis (MSE), a technique that offers potential savings in energy consumption without requiring carbon inputs for the process. This paper presents a techno-economic study of a potential industrial-scale MSE plant for MG-Si production to evaluate the trade-offs between capital and operating costs of the system. Capital costs are sourced from recent MG-Si plants and an existing cost model developed for MSE processes that includes the size of the plant and the operating temperature among its inputs. The results show that MSE technology has the potential to be an economically cost-competitive option for MG-Si production if the technology successfully scales to industrial production and matures enough to allow for financing costs similar to that of a comparably sized submerged-arc furnace plant. Full article

Agrivoltaics (AV) has emerged as an integrated land-use innovation capable of simultaneously addressing food, energy, and water challenges, yet its systemic implications for farming system sustainability remain insufficiently synthesized. This review adopts a farming system dynamics perspective to examine how AV systems reorganize biophysical, ecological, and socio-economic interactions across agroecosystems. Drawing upon agroecological principles, pathways of sustainable intensification and ecological intensification, and resource-loop strategies in circular economy, we identify the key elements and cause-and-effect relationships that shape AV system performance. Evidence indicates that the co-location of photovoltaics (PV) structures and crop cultivation generates new system properties, altered light distribution, moderated microclimates, redistributed soil moisture, and diversified production functions that influence productivity, resource-use efficiency, ecological services, and farm resilience. Using causal loop analysis, we conceptualize four central feedback dynamics: (i) PV–crop trade-offs and spatial-sharing relationships; (ii) microclimate modifications and crop physiological responses; (iii) ecological performance and landscape-level interactions; and (iv) circularity loops connecting resource conservation, renewable-energy substitution, soil processes, and material flows. This feedback collectively determines eco-efficiency outcomes, including enhanced land-equivalent productivity, improved water-use efficiency, strengthened regulating services, and reductions in external energy dependence. At the farming-system scale, AV diversifies income streams and stabilizes yields under climatic variability, whereas at the landscape scale, it fosters multifunctionality by supporting regenerative resource flows and ecological resilience. Building on these insights, we propose an integrated framework that links agroecological elements with dynamic feedback structures to guide context-specific AV design, management, and governance. This system-oriented synthesis provides a foundation for future research and policy efforts aimed at optimizing AV as a circular, resilient, and sustainable farming system innovation. Full article

This study reconceptualises marginality in China’s inter-provincial border regions as a dynamic, scale-sensitive spatial relationship rather than a static condition of underdevelopment. Using the Hubei–Henan–Anhui border area as a case study, we quantitatively assess marginality across three dimensions—production, livelihood, and ecology—based on panel data from 61 counties for 2000, 2010, and 2021. The entropy-weighted TOPSIS method is used to calculate comprehensive development indices, and geographic detector models identify key driving factors. The results show that production marginality is persistently shaped by economic level and industrial structure. Livelihood marginality exhibits a clear temporal shift: dominant drivers move from healthcare security to cultural amenities and finally to transport accessibility. Ecological marginality remains primarily determined by natural endowments such as habitat quality and ecosystem services. Theoretically, the study advances marginality analysis by integrating spatial, temporal and dimensional perspectives. Practically, it offers a diagnostic framework to support differentiated, cross-administrative governance strategies that can transform peripheral border regions into cooperative hubs. Full article

The development of high-viscosity oil fields requires technologies that provide not only the thermal mobilization of oil, but also an economically justified level of production with a high energy intensity of the process. One of the most effective technologies of this type is steam-assisted gravity oil drainage (SAGD), but its practical effectiveness is determined by the combined influence of reservoir geology, heat-transfer parameters, and market conditions. The paper proposes a reduced physics-guided model for the rapid technical and economic screening of SAGD in high-viscosity oil fields. The methodological contribution lies in linking geological screening, steam energy input, useful heat delivered to the reservoir, production response, and operating profit within one interpretable analytical chain suitable for pre-feasibility assessment. The study is based on an extended-scenario thermoeconomic analysis of representative heavy-oil development conditions. It is shown that, in a favorable mode, at a depth of about 400 m, oil viscosity of 15,000 cP, steam consumption of 500 t/day and heat-transfer coefficient of 0.7, the estimated production reaches 513–520 t/day, and the net profit is 20,000–22,000 USD/day. In an unfavorable mode, with a depth of about 1000 m, a viscosity of 20,000 cP, a heat-transfer coefficient of 0.4, and a high steam cost, production decreases to 210–230 t/day, and the economic result becomes negative. It has been established that the cost of steam, heat transfer, and the price of oil have a decisive impact on profitability. Full article

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article