Search Results (2,481)

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. Full article

This paper studies a backscatter-assisted pairwise cooperation scheme in a multi-user wireless powered communication network (WPCN), where pairs of wireless devices (WDs) first harvest wireless energy from an energy node (EN) and then transmit their information to an access point (AP). Under the proposed scheme, the two WDs in each pair first exchange their local messages and then cooperatively transmit to the AP in the uplink. To reduce the time and energy consumption of local information exchange, we exploit the short distance between paired users and realize message exchange through energy-conserving backscatter communication. Meanwhile, the proposed design effectively reuses the wireless power transfer (WPT) signal to enable simultaneous information exchange during the energy harvesting phase, thereby leaving more time and harvested energy for the subsequent cooperative uplink transmission. Based on this transmission protocol, we jointly optimize the time allocation, the user transmit power allocation, and the energy beamforming matrix at the EN to maximize the weighted sum rate. To tackle the resulting non-convex problem, we decompose it into two coupled subproblems and develop an alternating optimization algorithm to update the corresponding variables iteratively. Numerical results show that the proposed scheme achieves significant weighted sum rate improvement over representative benchmark methods. Full article

In this paper, we investigate secrecy energy efficiency (SEE) maximization in a rate-splitting multiple access (RSMA)-enabled UAV communication system, which consists of a communication UAV serving legitimate ground users (GUs) and a cooperative jamming UAV transmitting jamming signals to degrade the channel of the eavesdropper (Eve). Taking into account the propulsion energy consumption of fixed-wing UAVs, we formulate a non-convex SEE maximization problem by jointly optimizing communication scheduling, CUAV transmit power, and the trajectories of both UAVs. To tackle the non-convex problem, an iterative optimization algorithm combined with the Dinkelbach method and successive convex approximation (SCA) is developed to obtain a suboptimal solution. Simulation results demonstrate the convergence of the proposed algorithm and show the proposed joint optimization scheme significantly improves SEE compared with benchmark schemes. Full article

The Gulf Cooperation Council (GCC) countries consistently rank among the highest per capita CO₂ emitters globally, yet rigorous empirical analysis of the structural drivers of these emissions in the post-Paris Agreement era remains scarce. This study investigates the determinants of CO₂ emissions per capita across six GCC economies—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—over the period 2015–2022, using pooled ordinary least squares (OLSs) and country fixed effects (FEs) panel regression models with country-clustered standard errors. The focal explanatory variable is energy use per capita, complemented by GDP per capita, trade openness, urbanization, foreign direct investment (FDI), and industry value added as controls. A quadratic income term explicitly tests the environmental Kuznets curve (EKC) hypothesis. Results consistently show that energy use is the dominant driver of emissions. The EKC hypothesis is supported in the FE framework. The implied turning point of approximately USD 85,500 per capita (constant 2015 USD) is already exceeded by Qatar (panel mean: USD 114,835) and approached by the UAE (USD 71,434), while Bahrain (USD 55,681), Kuwait (USD 51,531), Saudi Arabia (USD 61,232), and Oman (USD 38,591) remain on the EKC’s rising slope, consistent with their continued emissions’ growth trajectories. Urbanization exerts a significant positive within-country effect on emissions. Trade openness reduces emissions in cross-sectional specifications, while FDI is systematically insignificant. These findings support energy efficiency reforms, renewable energy expansion, and low-carbon urban planning as the most effective policy levers for GCC decarbonization. Full article

China is the world’s largest producer of hydrogen, and it has the potential to export renewable hydrogen and its derivatives. Japan has set ambitious targets for developing a hydrogen-based society but is facing cost challenges. There is strong potential for China and Japan to cooperate regarding renewable hydrogen across the value chain. This study evaluates the cooperation opportunities from the 3E perspective (energy security, economics, and the environment). It estimates the renewable hydrogen production potential in both countries, as well as the economics and greenhouse gas (GHG) emissions associated with the production and export of renewable hydrogen from China to Japan using proton exchange membrane (PEM) technology. The renewable hydrogen production potential in China is estimated to be 12.00 Mt/year by 2035 in the base case of this study, providing a strong foundation for exports to Japan. The levelized cost of hydrogen (LCOH) using PEM technology and onshore wind is estimated at 4.27 USD/kg H₂ in China and 11.01 USD/kg H₂ in Japan for projects built in 2025. Even after accounting for liquefaction costs in China, transport costs from China to Japan (Chifeng—Dalian—Kobe) and regasification costs in Japan, renewable hydrogen produced in China remains more cost-effective than that produced in Japan. In terms of GHG emissions, when renewable hydrogen is produced using wind power, and wind power is also used for liquefaction and other electricity-consuming processes, the total emissions within the case study boundary amount to 2.24 kg CO₂-eq/kg H₂, below Japan’s low-carbon hydrogen threshold of 3.4 CO₂-eq/kg H₂. This study also discusses the challenges which are critical to facilitating cooperation, particularly in regards to coordinating standards and certification systems between the two countries. Full article

The “Green ICT @ FMD” competence center brings together the expertise in resource-efficient information and communications technology from 11 Fraunhofer and two Leibniz institutes, which have joined forces to form the Research Fab Microelectronics Germany (FMD). The competence center offers industry a broad portfolio of services focused on the future development of ICT applications, infrastructures, and microelectronic components with a view on resource-efficient production, energy efficiency, and the reduction in greenhouse gas emissions. Various cooperation opportunities have been initiated to support a wide range of companies in responding to customer needs and regulatory requirements through innovative and resource-efficient ideas and developments. We now present the initial results from the success models of the “Green ICT Space” startup and SME program, as well as selected “Validation Projects” with companies that all pursue the common goal of more resource-efficient production and use of ICT. Full article

To address the output volatility of distributed photovoltaics, the low utilization efficiency of energy storage resources, and the challenge of optimal revenue for PV-storage virtual power plants (VPPs) in multi-market environments, this paper proposes a three-stage stochastic optimal operation strategy for PV-storage VPPs under multi-market synergy and develops a benefit allocation model based on the Nash–Harsanyi bargaining game. A Monte Carlo simulation was adopted to capture the uncertainties of market electricity prices and PV power output, and the stochastic dual-dynamic-programming (SDDP) algorithm was employed to solve the three-stage optimization framework consisting of day-ahead bidding, real-time optimization, and real-time frequency regulation. Bargaining power was quantified from four dimensions—the marginal contribution rate, PV prediction accuracy, energy storage capacity, and utilization rate—to establish a fair and reasonable internal benefit allocation mechanism. Case studies verified that the proposed method improved the single-day market revenue by up to 20.79% compared with traditional operation modes, achieved a near-zero curtailment rate for distributed PV, and maintained frequency regulation performance scores above 0.4 at all times. The benefits of all investment entities in the alliance increased by 3.36–99.43%, significantly enhancing the multi-market profitability of PV-storage VPPs and the stability of alliance cooperation. Full article

Rapid digital transformation is reshaping global trade and raising important questions about its environmental impact, particularly in energy-intensive GCC economies. Despite growing interest, existing evidence remains inconclusive and often overlooks potential nonlinear effects. This study explores how digital trade influences environmental sustainability in Gulf Cooperation Council (GCC) countries over the period 2010–2024. Using a balanced panel dataset for the six economies, the analysis applies a fixed-effects approach with Driscoll–Kraay standard errors to account for cross-sectional dependence and other econometric concerns. To better capture the complexity of the relationship, the study also adopts an asymmetric framework that distinguishes between positive and negative changes in digital trade. The findings show that digital trade does not have a significant effect in the linear model. However, once asymmetry is considered, a clearer pattern emerges. Increases in digital trade are associated with lower CO₂ emissions, while decreases tend to raise emissions. Energy consumption remains the primary driver of emissions, while technological readiness helps reduce environmental pressure. Urbanization and political stability, on the other hand, are linked to higher emissions, reflecting ongoing structural challenges in the region. Overall, the results highlight the importance of sustaining digital trade growth and strengthening technological capabilities to support environmental sustainability in GCC economies. Full article

A microscopic model has been elaborated for the description of charge transfer-induced spin transitions in crystals containing tetranuclear Fe₂Co₂ clusters as structural units. The model takes into account the energy spectrum of each Fe₂Co₂ cluster, formed by the states arising from its initial configuration, two low-spin Fe^II and two low-spin Co^III, final configuration two low-spin Fe^III, and two high-spin Co^II, as well as the states that originate from four intermediate configurations of the type of low-spin Fe^II, low-spin Co^III, low-spin Fe^III, and high-spin Co^II. Two different types of cooperative interactions are accounted for in the model, namely, the electron–deformational coupling arising as a result of the observed elongation of the cobalt-nitrogen bonds under the low-spin Co^III$\to$ high-spin Co^II transition and the interaction via the field of phonons that originates from the coupling of the Co-ions with the full symmetric displacements of the nearest ligand surrounding, which are modulated by crystalline vibrations. The role of cooperative interactions is discussed in detail. Different types of spin transitions are predicted, including the gradual and abrupt ones as well as those manifesting hysteretic behavior. Within the framework of the developed approach, a qualitative and quantitative explanation of the experimental data on the {[(Tp*)Fe(CN)₃]₂[Co(bpy^Me)₂]₂}(OTf)₂·2DMF·H₂O compound recently reported oniere is given. Full article

The climate emergency in Latin America and the Caribbean (LAC) has transformed sustainability from an aspirational goal into a strategic imperative, particularly in the context of decoupling economic growth from natural capital depletion. This research analyzes eco-innovation within the frameworks of the National Innovation System (NIS), open innovation, and absorptive capacity, with the objective of examining the moderating role of collaboration in overcoming financial, knowledge, and market-related obstacles to innovative behavior. Employing a quantitative methodology using firm-level microdata from the Latin American Harmonized Innovation Surveys (LAIS) between 2007 and 2017, this study focuses on eco-innovative outcomes specifically linked to reductions in energy and material consumption. By estimating models that assess the role of technical cooperation and public policy support, this study seeks to determine whether collaborative strategies operate as an effective buffer against uncertainty and the limitations of local innovation systems. Expanding the scope of previous analyses centered on a single country, this work provides a regional perspective that underscores institutional and sectoral disparities in emerging contexts. Ultimately, this research examines how integrating an environmental purpose into corporate strategy and strengthening absorptive capacity enable LAC firms to transform ecological pressures into sustainable competitive advantages, mitigating the barriers that traditionally hinder technological progress in the region. Full article

Distributed Electric Propulsion aircraft have gained significant attention for advancing green aviation. However, their application is constrained by the limited energy density of batteries, resulting in weight compensation and flight range limitation. Current research on DEP energy management predominantly focuses on thrust allocation during the cruise phase while largely neglecting the energy regeneration potential during the descent phase. Conventional all-motors active energy recovery strategies force the multi-motor array to operate within a low-efficiency region, since the required drag torque is small under low aerodynamic drag conditions. To solve this issue, this paper proposes an energy recovery strategy that dynamically adjusts the number of activated motors during the descent phase of aircraft. The proposed N-Active strategy can adaptively regulate the number of operating motors, shifting motor operating points from the low-efficiency region to the high-efficiency region, which effectively decouples energy regulation within the longitudinal symmetry plane and maximizes energy recovery benefits. In this study, a high-fidelity simulation platform is established, including nonlinear aerodynamic characteristics and propeller windmilling motor efficiency models. Moreover, the optimal performance of the N-Active multi-motor cooperative energy recovery optimization strategy is verified based on the constructed platform. Simulation results demonstrate that compared with the traditional all motors active strategy, the proposed method improves battery state of charge by 11.96% and reduces virtual weight of battery. This method can effectively alleviate the weight compensation effect of distributed electric propulsion aircraft without additional physical weight increment, thereby enhancing the loading capacity of aircraft. Full article

Rapid localisation of trapped victims after urban disasters is essential but challenging because Bluetooth Low Energy (BLE) beacons are intermittent, radio propagation is obstructed by rubble, UAVs are energy-constrained, and real-world multi-UAV training is impractical in high-risk search-and-rescue (SAR) environments. This study formulates post-disaster victim localisation as a cooperative Dec-POMDP and adapts a model-aided federated multi-agent reinforcement learning framework based on FedQMIX. The proposed pipeline combines a lightweight LoS/NLoS surrogate channel model, PSO-based victim-position estimation, return-to-base and map-feasibility safety checks, an SAR-aligned shaped reward, and a leakage-free centralised training state based on estimated rather than ground-truth victim locations. Each UAV trains locally inside a learned digital-twin simulator and periodically shares only QMIX network parameters, avoiding the exchange of raw trajectories or RSSI logs. The framework is evaluated on two synthetic post-earthquake urban maps representing a compact return-to-base scenario and a larger reach-to-destination scenario. Across five independent seeds per method and map, Model-Aided FedQMIX achieves the highest and most stable victim-localisation performance, with the clearest advantage observed in the larger long-horizon scenario. Additional diagnostic tests examine reward-weight sensitivity, RF channel-shift robustness, BLE/smartphone hardware heterogeneity, non-IID client-data variation, and partial-client FedAvg under missing client updates. The results indicate that combining model-aided localisation cues, decentralised value factorisation, SAR-aligned objective design, and federated parameter sharing can improve the robustness of UAV-based victim-localisation policies. The framework also clarifies deployment considerations for federated SAR coordination, including communication payload, privacy boundaries, heterogeneous client experience, device variability, and intermittent connectivity. This study remains simulation-based, and future validation with real UAVs, BLE devices, and rubble-inspired testbeds is required before operational deployment. Full article

With the ongoing low-carbon transition of energy systems and the increasing penetration of distributed energy resources, the coordinated operation of heterogeneous prosumers has become essential for improving the economic and environmental performance of integrated energy systems. However, existing studies have not sufficiently addressed the joint coordination of electricity sharing, carbon emission trading, green certificate trading, and demand-side flexibility. To address this gap, this paper proposes a hybrid game-based optimal operation model for a multi-energy prosumer alliance coordinated by an Electricity Balance Service Provider (EBSP). The model is developed under coupled carbon emission trading (CET) and green certificate trading (GCT) markets. A piecewise linear dynamic pricing mechanism and a mutual recognition rule are introduced to describe the interaction between CET and GCT. Meanwhile, a price-based demand response model considering reducible and shiftable loads is incorporated to exploit load-side flexibility. On this basis, a Stackelberg-cooperative hybrid game is formulated to coordinate electricity pricing, integrated dispatch, electricity sharing, and benefit allocation between the EBSP and the prosumer alliance. The proposed model is solved using particle swarm optimization and the alternating direction method of multipliers. Case studies show that, compared with the corresponding benchmark scenarios, the proposed method reduces the alliance operating cost by 7.19%, the carbon trading cost by 41.35%, and total carbon emissions by 3.66%. It also decreases the peak-to-valley load difference ratio by 3.78 percentage points. These results demonstrate the effectiveness of the proposed method in improving economic performance, promoting low-carbon operation, and enhancing the peak-shaving and valley-filling capability of the prosumer alliance. Full article

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article

In Latin America and the Caribbean, the circular economy approach is embedded in productive structures characterized by a dependence on natural resources and the persistence of informal economies. The general objective of this article is to analyze the circular economy as an approach to production and consumption in Latin America and the Caribbean through a bibliometric and qualitative analysis of scientific literature. This study adopted a mixed, descriptive, and analytical research design. International and regional databases (Scopus, Web of Science, SciELO, and Redalyc) were used to identify articles published between 2015 and 2025. The selection process followed the PRISMA protocol, resulting in a final qualitative analysis of 47 articles. The results reveal an accelerated and sustained growth in scientific production in the region, with a maximum increase of 250% in 2017, indicating a progressive consolidation of the field. The documentary corpus consists mainly of original articles (65%), with a clear preeminence of environmental sciences, engineering, and energy. Qualitatively, the literature shows a conceptual heterogeneity that adapts the circular economy to sustainable development and industrial ecology, uniquely incorporating grassroots recyclers and cooperatives into a “just transition.” However, there is evidence of an implementation gap: while large industries are making progress in eco-design and remanufacturing, adoption in SMEs and responsible consumption—especially in repair and reuse—remains at incipient levels due to structural and cultural limitations. Ultimately, the results suggest a growing concentration of circular economy research within selected Latin American institutions, indicating the emergence of regionally grounded research agendas that may differ in emphasis from dominant Global North framings. Full article