Search Results (3,196)

The rapid development of telematics technology has greatly supported high-computing applications like autonomous driving and real-time road condition prediction. However, the limited computational resources and dynamic topology of in-vehicle terminals pose challenges such as delay, load imbalance, and bandwidth consumption. To address these, a three-layer vehicular network architecture based on cloud–edge–end collaboration was proposed, with V2X technology used for multi-hop transmission. Models for delay, energy consumption, and edge caching were designed to meet the requirements for low delay, energy efficiency, and effective caching. Additionally, a dynamic pricing model for edge resources, based on load-awareness, was proposed to balance service quality and cost-effectiveness. The enhanced NSGA-III algorithm (ADP-NSGA-III) was applied to optimize system delay, energy consumption, and system resource pricing. The experimental results (mean of 30 independent runs) indicate that, compared with the NSGA-II, NSGA-III, MOEA-D, and SPEA2 optimization schemes, the proposed scheme reduced system delay by 21.63%, 5.96%, 17.84%, and 8.30%, respectively, in a system with 55 tasks. The energy consumption was reduced by 11.87%, 7.58%, 15.59%, and 9.94%, respectively. Full article

This study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors. Full article

This paper presents a computational framework for resolving a nonlinear extension of the Black–Scholes partial differential equation that accounts for transaction costs through a volatility function dependent on the Gamma of the option price. A meshfree radial basis function-generated finite difference procedure is developed using a modified multiquadric kernel. Analytical weight formulas for first- and second-order differentiations are discussed on 3-node stencils for both uniform and non-uniform point distributions. The proposed method offers an efficient scheme suitable for accurately pricing European scenarios when nonlinear transaction cost effects. Full article

Typically, dimension stones, commonly called stone blocks, are cut into multiple small cuboid stones so that multiple sculptures can be produced. To use the stone block as efficiently as possible, it is essential to pack these small cuboids in each stone block as efficiently as possible while satisfying the limitations of the machining. This paper describes methods for packing and cutting stone blocks using nonlinear programming that generate sets of trees, which are also called forests, that decide the packing layout of the small cuboids inside the block. The containers and elements have their own prices and values, respectively. The elements can be translated to the corners of the containers or to the corners of the elements that are already in the containers, if the elements are not outside the containers after the translation. Then, the problem can be interpreted as finding the best forest that packs the elements as efficiently as possible at the lowest total price of containers, which is a subset of all containers. The formula for the score that defines the compactness of the packing is in this paper. The user can define the number of forests so that parallel computing methods can be applied. Each forest is generated randomly. Two different packing methods are introduced: simple packing and slab packing. Simple packing is based on a non-guillotine cutting method and slab packing is a guillotine cutting method for realistic scenarios, such as scenarios with machining limitations. By using this method, it is possible to plan the cutting in a digital environment, which is not possible when using the traditional method with physical templates. Furthermore, by restricting the rotation of the elements, it is possible to make the elements follow the horizontal vein direction of the stone blocks, which is a common vein direction in travertine. Full article

Economic dispatch using wind power plants plays a role in reducing the price of electricity production by dispatching power among different generating units for thermal and wind power plants, and supplying load demand while meeting the power system equality and inequality constraints. Adding wind power plants to the economic dispatch model can significantly reduce electricity production costs and reduce carbon dioxide emissions. In this paper, fuel cost and emission minimization are considered as the objective function of the economic dispatch problem, taking into account transmission loss using the B matrix. The quadratic model of the fuel cost and emission criterion functions is modeled without considering a valve-point loading effect. The real power generation limits for both wind and conventional generating units are considered. In addition, a closed-form expression based on the incomplete gamma function is provided to define the impact of wind power, which includes the cost of wind energy, including overestimation and underestimation of available wind power using a Weibull-based probability density function. In this research work, Lagrange’s algorithm is proposed to solve the Wind–Thermal Economic Emission Dispatch (WTEED) problem. The developed Lagrange classical optimization algorithm for the WTEED problem is validated using the IEEE test systems with 6-, 10-, and 40-generation unit systems. The proposed Lagrange optimization method for WTEED problem solutions demonstrates a notable improvement in both economic and environmental performance compared to other heuristic optimization methods reported in the literature. Specifically, the fuel cost was reduced by an average of 4.27% in the IEEE 6-unit system, indicating more economical power dispatch. Additionally, the emission cost was lowered by an average 22% in the IEEE 40-unit system, reflecting better environmental compliance and sustainability. These results highlight the effectiveness of the proposed approach in achieving a balanced trade-off between cost minimization and emission reduction, outperforming several existing heuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) under similar test conditions. The research findings report that the proposed Lagrange classical method is efficient and accurate for the convex wind–thermal economic emission dispatch problem. Full article

The strategic goal of the transition to a low-carbon economy in Russia requires the active integration of forest-climatic projects into regional sustainable development strategies, especially for areas with high agricultural pressure such as the central forest-steppe of the European part of the Russian Federation. The region contains over 18 million hectares of forest land, which is approximately 2.1% of the area of Russian forests, and intensive agricultural development increases the need for innovative approaches to restoring forest ecosystems. The work uses indicators of the state forest register, data on 18 reforestation projects and 22 afforestation projects, and the results of forecasting the dynamics of greenhouse gas absorption until 2030. It is estimated that by 2030, the sequestration potential of the forests of the central forest-steppe can be increased by 28–30%, which will neutralize up to 12% of emissions from industrial enterprises in the region. In the paper, to unify the assessment, it is proposed to use the carbon intensity factor of investment costs, which, in a number of implemented projects, ranged from 1.2 to 2.7 RUB/1 kg CO₂ eq., reflecting the cost of achieving one ton of absorbed CO₂ equivalent. At ratios above 1, the economic value of the carbon units created exceeds investment costs by at least 20%. Environmental–economic modeling showed that with an increase in the forest cover of the region by 1% (180 thousand hectares), the annual absorption of CO₂ increases by approximately 0.9–1.1 million tons, and the increase in potential income from the sale of carbon units could amount to 1.6–2.2 billion RUB per year at the current price of 1.8–2 RUB/kg CO₂-eq. The use of an integral criterion of environmental and economic efficiency helps increase the transparency and investment-attractiveness of forest-climatic projects, as well as the effective integration of natural and climatic solutions into long-term strategies for the sustainable development of the Central Forest-Steppe of Russia. Full article

Sea–rail intermodal transport offers high efficiency and environmental benefits, yet its development in China remains limited. Existing studies have mainly assessed the macro-level benefits of sea–rail intermodal transport policies, but rigorous evidence on whether incentive policies work and how their effects differ across policy types remains scarce, which limits evidence-based policy design and efficient allocation between subsidies and capacity expansion. To address this gap, a dual-policy identification framework was established that combines a multi-period difference-in-differences model with event study analysis and used station–month data from China to assess the independent effects, underlying mechanisms, and spatiotemporal heterogeneity of railway freight price subsidies and freight train expansion on container throughput. The results indicate that both policies significantly increased container throughput. Railway freight price subsidies exhibited stronger and more persistent effects with a certain lag, whereas freight train expansion produced rapid but short-lived responses. The impacts of both policies were more pronounced in short-distance transport, but weakened or even turned negative over longer distances. Moreover, the number of participating entities served as a key mediating pathway, while information sharing positively moderates policy impacts. This study makes theoretical contributions to the identification of heterogeneity, mechanism analysis, and spatiotemporal characterization of SRIT incentive policy effects, while offering refined and actionable guidance for SRIT policy optimization. Full article

In this paper, we develop a dynamic pricing mechanism for a meal delivery platform that offers multiple transportation modes for order deliveries. We consider orders from heterogeneous customers who select their preferred delivery mode based on individual generalized cost (GC) functions, where GC captures the trade-off between price and delivery latency for each transportation option. Given the logistics of the underlying transportation network, the platform can utilize a pricing mechanism to guide customer choices toward delivery modes that optimize resource allocation across available transportation modalities. By accounting for variability in the latency and cost of modalities, such pricing aligns customer preferences with the platform’s operational objectives and enhances overall satisfaction. Due to the computational complexity of finding the optimal policy, we adopt a deep reinforcement learning (DRL) approach to design the pricing mechanism. Our numerical results demonstrate up to $143\%$ higher profits compared to heuristic pricing strategies, highlighting the potential of DRL-based dynamic pricing to improve profitability, resource efficiency, and service quality in on-demand delivery services. Full article

In the domain of Shape Encoding, the approximation of the Medial Axis of a solid region in ${\mathbb{R}}^3$ with Boundary Representation M, is relevant because the Medial Axis is an efficient encoding for M in Design, Manufacturing, and Shape Learning. Existing Medial Axis approximations include (a) full Voronoi and (b) and partial Shape Diameter Function (SDF)-based ones. Methods (a) produce large high-frequency data, which must then be pruned. Methods (b) reduce computing expenses at the price of not handling some shapes (e.g., prismatic), and currently, they only synthesize 1D Medial Axes. To partially overcome these limitations, this investigation performs a direct synthesis of a 1D and 2D simplex-based Medial Axis approximation by a combination of stochastic geometric reasoning and graph operations on the SDF-originated point cloud. Our method covers one- and two-dimensional Simplicial Complex Medial Axes, thus improving on 1D Medial Axes approximation methods. Our approach avoids the expensive full computing plus pruning of Medial Axis based on Voronoi methods. Future work is needed in the synthesis of Medial Axis approximation for high-frequency neighborhoods of mesh M. Full article

In the context of the coordinated operation of microgrids and community energy storage systems, achieving optimal resource allocation under complex and uncertain conditions has emerged as a prominent research focus. This study proposes a robust collaborative optimization model for microgrids and community energy storage systems under a game-theoretic environment where potential fraudulent behavior is considered. A multi-energy collaborative system model is first constructed, integrating multiple uncertainties in source-load pricing, and a max-min robust optimization strategy is employed to improve scheduling resilience. Secondly, a game-theoretic model is introduced to identify and suppress manipulative behaviors by dishonest microgrids in energy transactions, based on a Nash bargaining mechanism. Finally, a distributed collaborative solution framework is developed using the Alternating Direction Method of Multipliers and Column-and-Constraint Generation to enable efficient parallel computation. Simulation results indicate that the framework reduces the alliance’s total cost from CNY 66,319.37 to CNY 57,924.89, saving CNY 8394.48. Specifically, the operational costs of MG1, MG2, and MG3 were reduced by CNY 742.60, CNY 1069.92, and CNY 1451.40, respectively, while CES achieved an additional revenue of CNY 5130.56 through peak shaving and valley filling operations. Furthermore, this distributed algorithm converges within 6–15 iterations and demonstrates high computational efficiency and robustness across various uncertain scenarios. Full article

This paper addresses the lack of hedging effectiveness that yellow corn 1-month futures of the Chicago Mercantile Exchange (CME) offer for cross-hedging the price of Mexican white corn. For this purpose, the authors tested 1013 combinations (portfolios) of the ten most traded futures on the CME and the New York Mercantile Exchange (NYMEX). The results suggest that using a 51.6741% corn and a 48.3259% wheat portfolio mimics the white corn price with a hedging effectiveness of 0.6180. To test the practical use of such a portfolio, the authors backtested its use from 1 January 2000 to 9 February 2025 as a balancing short position for sale of white corn at t + 1. By using the corn–wheat portfolio, the simulated seller (farmer or intermediary) would have earned MXN 5.7664 per kilo traded. The results in this paper provide the first solution to the Mexican white corn cross-hedging problem with a futures portfolio. This hedge can be used as the balancing (short) position for the strike or minimum buy price that the Mexican Government or a financial institution could offer to farmers and intermediaries to enhance food security. Full article

To analyze the effects of remanufacturing subsidies and emission reduction investments on pricing and quality decisions under cap-and-trade regulation, four profit-maximization Stackelberg game models for a remanufacturing supply chain (RSC), i.e., without remanufacturing subsidies and emission reduction investments, with remanufacturing subsidies only, with emission reduction investments only, and with both remanufacturing subsidies and emission reduction investments, are constructed, derived, compared, and analyzed. Results show that government subsidies and emission reduction investments can improve profits for the RSC members, while possibly leading to more total carbon emissions. Furthermore, it is worth noting that profit growth and emission reduction can be achieved even though reducing remanufacturing subsidies in some scenarios. Moreover, increasing emission reduction targets will reduce profits of the RSC members but does not necessarily contribute to emission reduction. Therefore, to help the RSC improve profits and reduce emission, the policymaker should formulate differentiated policies based on the types of manufacturers. For the non-abating manufacturer, the government should set higher emission reduction targets and cut down subsidies; for the low-efficiency abating manufacturer, higher emission reduction targets and subsidies are more suitable. However, for the high-efficiency abating manufacturer, lower emission reduction targets and subsidies are more effective. Full article

The sharp rise in electricity prices in South Africa has raised a growing concern over household electricity use, affordability, and the need for sustainable consumption patterns. This increasing cost of electricity has added financial pressure on South Africans already burdened by rising prices of water, food, and fuel. This study aims to determine the relationship between residential electricity consumption and electricity prices in South Africa, using annual time series secondary data spanning from 1975 to 2024. To determine the long-run relationship the study employed econometric techniques such as Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS), then, for robustness, the Vector Error Correction Model (VECM) and diagnostics checks. The findings of the study revealed a negative relationship between electricity prices and residential electricity consumption. While disposable income showed a positive relationship with residential electricity consumption, the population growth revealed a negative relationship with residential electricity consumption. Based on the empirical findings of the study, South African policymakers should ensure the affordability of electricity and user-efficiency so that population growth does not worsen energy inequality. Hence, policymakers should ensure basic access for all households by supporting low-income groups and applying higher tariffs for higher consumption. These measures promote fairness, meet essential electricity needs, and encourage responsible use. Full article

This systematic review synthesizes evidence on economic instruments that mobilize renewable-energy investment in emerging economies, analyzing 50 peer-reviewed studies published between 2015 and 2025 under PRISMA 2020. We advance an Institutional Capacity Integration Framework that ties instrument efficacy to regulatory, market, and coordination capabilities. Green bonds have mobilized roughly USD 500 billion yet work only where robust oversight and liquid markets exist, offering limited gains for decentralized access. Direct subsidies cut renewable electricity costs by 30–50% and connect 45 million people across varied contexts, but pose fiscal–sustainability risks. Carbon pricing schemes remain rare given their administrative complexity, while multilateral climate funds show moderate effectiveness (coefficients 0.3–0.8) dependent on national coordination strength. Bibliometric mapping with Bibliometrix reveals three fragmented paradigms—market efficiency, state intervention, and international cooperation—and highlights geographic gaps: sub-Saharan Africa represents just 16% of studies despite acute financing barriers. Sixty-eight percent of articles employ descriptive designs, constraining causal inference and reflecting tensions between SDG 7 (affordable energy) and SDG 13 (climate action). Our framework rejects one-size-fits-all prescriptions, recommending phased, context-aligned pathways that progressively build capacity. Policymakers should tailor instrument mixes to institutional realities, and researchers must prioritize causal methods and underrepresented regions through focused initiatives for equitable global progress. Full article

With rising consumer concerns regarding food safety, cold chain management—which preserves product freshness through low-temperature distribution—has emerged as a critical competitive factor for retailers. This study examines how retail firms can manage quality deterioration over time to maximize profits, with a focus on pricing strategies and discard rates. Through game-theoretic modeling and empirical data analysis of milk products, we find that while individual items exhibit no consistent pattern, bundled fresh food items demonstrate an inverted U-shaped relationship between discount rates and profits, indicating an optimal discount level. Furthermore, we identify a U-shaped relationship between order quantity and disposal rate, highlighting the importance of determining optimal inventory levels to minimize waste and maximize efficiency for a sustainable competitiveness. Full article