Search Results (3,389)

Rapid progress in lithium-ion batteries and AI-powered autonomous driving is poised to propel electric vehicles to a 50% share of the global automotive market by the year 2035. Today, there is a major focus on recycling electric vehicle motors, particularly on extracting rare earth elements (REEs) from NdFeB permanent magnets (PMs). This research is based on a single-furnace process concept designed to separate metal components within PM motors by exploiting the varying melting points of the constituent materials, simultaneously extracting REEs present within the PMs and transferring them into the slag phase. Thermodynamic modeling, via Factsage Equilib stream calculations, optimized the experimental process. Simulated materials substituted the PM motor, which optimized modeling-directed melting within an induction furnace. The 2FeO·SiO₂ fayalite flux can oxidize rare earth elements, resulting in slag. The neodymium oxidation reaction by fayalite exhibits a ΔG° of −427 kJ when subjected to an oxygen partial pressure ($P_{O_2}$) of 1.8 × 10⁻⁹, which is lower than that required for FeO decomposition. Concerning the FeO–SiO₂ system, neodymium, in Nd³⁺, exhibits a strong bonding with the ${\mathrm{S}\mathrm{i}\mathrm{O}}_4^{4^{-}}$ matrix, leading to its incorporation within the slag as the silicate compound, Nd₂Si₂O₇. When 30 wt.% fayalite flux was added, the resulting experiment yielded a neodymium extraction degree of 91%, showcasing the effectiveness of this fluxing agent in the extraction process. Full article

The gearbox, as an important transmission component in wind turbines, connects the blades to the generator and is responsible for converting wind energy into mechanical energy and transmitting it to the generator. Its ability to reduce vibrations directly affects the operational lifespan of the wind turbine. When designing the gearbox’s elastic support structure, it is essential to evaluate how the design parameters influence various performance metrics. Neural networks offer a powerful means of capturing and interpreting the intricate associations linking structural parameters with performance metrics. However, conventional neural networks are usually optimized for a single task, failing to fully account for task differences and shared information. This can lead to task conflicts or insufficient feature modeling, which in turn affects the learning efficiency of inter-task correlations. Furthermore, physical experiments are costly and provide limited training, making it difficult to meet the large-scale dataset requirements for neural network training. To address the high cost and limited scalability of traditional physical testing for gearbox rubber damping structures, in this study, we propose a low-cost performance prediction method that replaces expensive experiments with simulation-driven dataset generation. An optimal Latin hypercube sampling technique is employed to generate high-quality data at minimal cost. On this basis, a multi-task prediction model called multi-gate mixture-of-experts with LSTM (PLE-LSTM) is constructed. The adaptive gating mechanism, hierarchical nonlinear transformation, and effective capture of temporal dynamics in the LSTM significantly enhance the model’s ability to model complex nonlinear patterns. During training, a dynamic weighting strategy named GradNorm is utilized to counteract issues like the early stabilization in multi-task loss convergence and the uneven minimization of loss values. Finally, ablation experiments conducted on different datasets validate the effectiveness of this approach, with experimental results demonstrating its success. Full article

Anaerobic digestion (AD) offers a sustainable solution by treating biodegradable waste while recovering bioenergy, enhancing the share of renewable energy. Thus, this study aims to investigate the AD for managing and valorizing residues from the potato chip industry: potato peel (PP), potato offcuts (OC), waste cooking oil (WCO), wastewater (WW), and sewage sludge (SS). In particular, the biochemical methane potential (BMP) of each residue, anaerobic co-digestion (AcoD), and greenhouse gas (GHG) emissions of an AD plant are assessed. WW, OC, and SS present a BMP of around 232–280 NmL_CH4/kg of volatile solids (VS). PP and WCO reach a BMP slightly lower than the former substrates (174–202 NmL_CH4/g_VS). AcoD results in methane yields between 150 and 250 NmL_CH4/g_VS. An up-scaled anaerobic digester is designed to manage 1.60 Mg/d of PP. A residence time of 12 days and a digester with 165 m³ is estimated, yielding 14 Nm³_CH4/Mg_VS/d. A simulated AD plant integrated with a combined heat and power unit results in a carbon footprint of 542 kg of CO₂-eq/Mg_db PP, primarily from biogenic GHG emissions. These findings highlight the potential of AD to generate renewable energy from potato industry residues while reducing fossil fuel-related GHG emissions and promoting resource circularity. Full article

In the context of carbon trading policy, carbon emissions in the supply chain of new energy vehicles have received much attention in academic research and practice. Consumer preference for environmental friendliness is also growing in new energy vehicle supply chain operations, which has prompted new energy vehicle manufacturers to invest in carbon abatement technologies to improve the environmental friendliness of new energy vehicles. At the same time, the increased demand for new energy vehicles will also increase the green promotion of third-party power battery recycling companies to facilitate the recycling of power batteries. Considering these special features in the new energy vehicle supply chain, we applied a differential game model to examine the carbon emission reduction behaviors and green promotion technologies of the new energy vehicle supply chain members from a long-term and dynamic perspective. Supply chain equilibrium strategies under four different scenarios were analyzed and compared, numerical experiments were conducted to validate the theoretical results, and sensitivity analyses were performed to identify further insights. The results of the study show that a unit carbon trading price reaching a critical threshold is a prerequisite for technical cooperation between the new energy vehicle manufacturer and the third-party power battery recycling company. It provides a theoretical basis for the government to set the carbon price, and it effectively stimulates the cooperation and emission reduction drive of new energy vehicle companies. The study breaks through the traditional cost–benefit framework, internalizes the carbon price as a supply chain cooperation drive, and opens up a new paradigm for new energy vehicle industry research. Full article

Electricity markets with a high share of variable renewable energy require significant balancing reserves to ensure stability by preserving the balance of supply and demand. However, they were originally conceived for dispatchable technologies, which operate with predictable and controllable generation. As a result, adapting market mechanisms to accommodate the characteristics of variable renewables is essential for enhancing grid reliability and efficiency. This work studies the strategic behavior of a wind power producer (WPP) in the Iberian electricity market (MIBEL) and the Portuguese balancing markets (BMs), where wind farms are economically responsible for deviations and do not have support schemes. In addition to exploring current market dynamics, the study proposes new market designs for the balancing markets, with separate procurement of upward and downward secondary balancing capacity, aligning with European Electricity Regulation guidelines. The difference between market designs considers that the wind farm can hourly bid in both (New 1) or only one (New 2) balancing direction. The study considers seven strategies (S1–S7) for the participation of a wind farm in the past (S1), actual (S2 and S3), New 1 (S4) and New 2 (S5–S7) market designs. The results demonstrate that new market designs can increase the wind market value by 2% compared to the optimal scenario and by 31% compared to the operational scenario. Among the tested approaches, New 2 delivers the best operational and economic outcomes. In S7, the wind farm achieves the lowest imbalance and curtailment while maintaining the same remuneration of S4. Additionally, the difference between the optimal and operational remuneration of the WPP under the New 2 design is only 22%, indicating that this design enables the WPP to achieve remuneration levels close to the optimal case. Full article

The safety of power transmission lines is crucial to public well-being, with insulators being prone to failures such as self-detonation. However, images captured by unmanned aerial vehicles (UAVs) carrying optical sensors often face challenges, including uneven object scales, complex backgrounds, and difficulties in feature extraction due to distance, angles, and terrain. Additionally, conventional models are too large for UAV deployment. To address these issues, this paper proposes RML-YOLO, an improved insulator defect detection method based on YOLOv8. The approach introduces a tiered scale fusion feature (TSFF) module to enhance multi-scale detection accuracy by fusing features across network layers. Additionally, the multi-scale feature extraction network (MSFENet) is designed to prioritize large-scale features while adding an extra detection layer for small objects, improving multi-scale object detection precision. A lightweight multi-scale shared detection head (LMSHead) reduces model size and parameters by sharing features across layers, addressing scale distribution imbalances. Lastly, the receptive field attention channel attention convolution (RFCAConv) module aggregates features from various receptive fields to overcome the limitations of standard convolution. Experiments on the UID, SFID, and VISDrone 2019 datasets show that RML-YOLO outperforms YOLOv8n, reducing model size by 0.8 MB and parameters by 500,000, while improving AP by 7.8%, 2.74%, and 3.9%, respectively. These results demonstrate the method’s lightweight design, high detection performance, and strong generalization capability, making it suitable for deployment on UAVs with limited resources. Full article

The Renewable Energy Community (REC) has emerged in Europe, encouraging the use of renewable energy sources (RESs) within localities, bringing social, economic, and environmental benefits. RESs are characterized by various loads, including household consumption, storage systems, and the increasing integration of electric vehicles (EVs). EVs offer opportunities for distributed RESs, such as photovoltaic (PV) systems, which can be economically advantageous for RECs whose members own EVs and charge them within the community. This article focuses on the integration of PV systems and the management of energy loads for different participants—consumers and prosumers—along with a small EV charging setup in the REC. A REC consisting of a multi-unit building is examined through a mathematical and numerical model. In the model, hourly PV generation data are obtained from the PVGIS tool, while residential load data are modeled by converting monthly electricity bills, including peak and off-peak details, into hourly profiles. Finally, EV hourly load data are obtained after converting the data of voltage and current data from the charging monitoring portal into power profiles. These data are then used in our mathematical model to evaluate energy fluxes and to calculate self-consumed, exported, and shared energy within the REC based on energy balance criteria. In the model, an energy management system (EMS) is included within the REC to analyze EV charging behavior and optimize it in order to increase self-consumption and shared energy. Following the EMS, it is also suggested that the number of EVs to be charged should be evaluated in light of energy-sharing incentives. Numerical results have been reported for different seasons, showing the possibility for the owners of EVs to charge their vehicles within the community to optimize self-consumption and shared energy. Full article

Background: This study investigates the electrophysiological (EEG) correlates underlying negotiation dynamics in dyads engaged in a shared decision-making process. Methods: Using EEG hyperscanning, we examined single-brain and inter-brain neural activity in 26 participants (13 dyads) during a structured negotiation task. The participants, selected for their group-oriented decision-making preference, discussed a realistic group decisional scenario while their EEG activity was recorded. EEG frequency bands (delta, theta, alpha, beta, and gamma) were analyzed and Euclidean Distances were computed for measuring dissimilarity at the inter-brain neural level. Results: At the single-brain level, the results show increased delta and theta power in frontal regions, reflecting emotional engagement and goal-directed control, alongside heightened beta and gamma activity in parieto-occipital areas, linked to cognitive integration and decision-monitoring during the negotiation process. At the inter-brain neural level, we observed significant dissimilarity in frontal delta activity compared to temporo-central and parieto-occipital one, suggesting that negotiation involves independent cognitive regulation within the members of the dyads rather than complete neural synchrony. Conclusions: These findings highlight the dual role of negotiation as both a cooperative and cognitively demanding process, requiring emotional alignment and strategic adaptation. This study advances our understanding of the neurophysiological bases of negotiation and provides insights into how inter-brain dynamics shape collaborative decision-making. Full article

In a multi-beam communication scenario where Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communications coexist, the limited spectrum of resources force V2V users to reuse the orthogonal frequency bands allocated to I2V, inevitably introducing cross-layer interference between I2V and V2V. Furthermore, the adoption of a multi-beam communication architecture exacerbates beam interference, significantly degrading the overall network’s communication and sensing performance. To address these challenges, this paper proposes an integrated sensing and communication (ISAC) beam allocation algorithm, termed IMSBA, which jointly optimizes beam direction, transmission power, and spectrum resource allocation to effectively mitigate the interference between I2V and V2V while maximizing the overall network performance. Specifically, IMSBA employs a joint optimization framework combining Multi-Agent Proximal Policy Optimization (MAPPO) with a Stackelberg game. Within this framework, MAPPO leverages vehicle perception data to dynamically optimize V2V beam steering and frequency selection, while the Stackelberg game reduces computational complexity through hierarchical decision-making and optimizes the joint power allocation among V2V users. Additionally, the proposed scheme incorporates a V2V cooperative sensing domain-sharing mechanism to enhance system robustness under adverse conditions. The experimental results demonstrated that, compared with existing baseline schemes, IMSBA achieved a 92.5% improvement in V2V energy efficiency while significantly enhancing both communication and sensing performance. This study provides an efficient and practical solution for spectrum-constrained scenarios in millimeter-wave Internet-of-Things (IoT), offering substantial theoretical insights and practical value for the efficient operation of intelligent transportation system (ITSs). Full article

This paper presents an adaptive wireless droop control scheme that uses an adaptive virtual resistance to regulate the DC voltage and control the active power. The proposed methodology is implemented to address the power mismatch problem in a fixed-droop control for multi-terminal HVDC (MT-HVDC or MTDC) systems. Each inverter calculates available power and adjusts its output power accordingly while adapting the virtual resistance to mimic the behavior of a mesh system that is based on loading effects. The main objective of this methodology is to increase the reliability of the MTDC system by eliminating the need for fast communication links and ensuring proper power sharing between inverters. Additionally, this communication-free scheme includes a power management algorithm that controls power sharing during peak hours of the inverters among the rectifiers as per mutual agreements between the operators to mitigate the risk of a system overload and optimize the power sharing. A simulation of a five-terminal mesh MTDC system has been verified by using PSCAD/EMTDC to validate the performance and effectiveness of the proposed method. The results show the flexibility and feasibility of the proposed control method in three different modes. Full article

Tennessee’s ecological diversity, spanning forests, farmland, and urban areas, provides an ideal foundation for applying the One Health approach, which integrates human, animal, and environmental health. This review examines Tennessee’s current One Health landscape, highlighting active initiatives, ongoing challenges, and future directions. Key efforts involve workforce development, disease surveillance, outbreak response, environmental conservation, and public education, led by a coalition of state agencies, universities, and the Tennessee One Health Committee. These programs promote cross-sector collaboration to address issues such as zoonotic diseases, climate change, land use shifts, and environmental contaminants. Notably, climate-driven changes, including rising temperatures and altered species distributions, pose increasing threats to health and ecological stability. Tennessee has responded with targeted monitoring programs and climate partnerships. Education is also a priority, with the growing integration of One Health into K–12 and higher education to build a transdisciplinary workforce. However, the state faces barriers, including limited funding for the One Health workforce, undefined workforce roles, and informal inter-agency data sharing. Despite these obstacles, Tennessee’s successful responses to outbreaks like avian influenza and rabies demonstrate the power of coordinated action. To strengthen its One Health strategy, the state must expand funding, formalize roles, improve data systems, and enhance biodiversity and climate resilience efforts positioning itself as a national leader in interdisciplinary collaborative solutions. Full article

Distributed power trading is becoming the future development trend of electric energy trading, and virtual power plant (VPP), as a kind of aggregated optimization scheme to enhance energy utilization efficiency, has received more and more attention for studying distributed trading among multiple VPPs. However, how to guarantee the economy, credibility, security, and efficiency of distributed transactions is still a key issue to be overcome. To this end, a multi-VPP power sharing trusted transaction strategy based on dual-chain blockchain is proposed. First, a dual-chain blockchain electric energy transaction architecture is proposed. Then, the VPP-independent operation cost model is constructed, based on which, the decision model of multi-VPP electric energy sharing transaction based on Nash negotiation theory is constructed. Again, an improved-Practical Byzantine Fault Tolerant (I-PBFT) consensus algorithm combining the schnorr protocol with the Diffie–Hellman key exchange algorithm and a smart contract for multi-VPP electricity trading are designed to realize trusted, secure, and efficient distributed transactions. Finally, the example results verify the effectiveness of the strategy proposed in this paper. Full article

Extracting causal relations from natural language texts is crucial for uncovering causality, and most existing causal relation extraction models are single-task learning-based models, which can not comprehensively address attributes such as part-of-speech tagging and chunk analysis. However, the characteristics of words with multi-domains are more relevant for causal relation extraction, due to words such as adjectives, linking verbs, etc., bringing more noise data limiting the effectiveness of the single-task-based learning methods. Furthermore, causalities from diverse domains also raise a challenge, as existing models tend to falter in multiple domains compared to a single one. In light of this, we propose a multi-task sequence tagging model, MPC−CE, which utilizes more information about causality and relevant tasks to improve causal relation extraction in noised data. By modeling auxiliary tasks, MPC−CE promotes a hierarchical understanding of linguistic structure and semantic roles, filtering noise and isolating salient entities. Furthermore, the sparse sharing paradigm extracts only the most broadly beneficial parameters by pruning redundant ones during training, enhancing model generalization. The empirical results on two datasets show 2.19% and 3.12% F1 improvement, respectively, compared to baselines, demonstrating that our proposed model can effectively enhance causal relation extraction with semantic features across multiple syntactic tasks, offering the representational power to overcome pervasive noise and cross-domain issues. Full article

Diversified application scenarios and business models are effective ways to improve the utilization and economic benefits of energy storage systems. In response to the current problems of single application scenarios, high idle rates, and imperfect price formation mechanisms faced by energy storage on the power generation side, a robust two-stage optimization operation strategy for shared energy storage is proposed, taking into account leasing demand and multiple uncertainties, from the perspective of the sharing concept. A multi-scenario application framework for shared energy storage is established to provide leasing services for wind farm clusters, as well as auxiliary services for participating in the electric energy markets and frequency regulation markets, and the participation sequence is streamlined. Based on the operating and opportunity costs of shared energy storage, a pricing mechanism for leasing services is designed to explore the driving forces of wind farm clusters participating in leasing services from the perspective of cost assessment. Considering the uncertainty of wind power output and market electric prices, as well as the market operational characteristics, an optimized operation model for shared energy storage in the day-ahead and real-time stages is constructed. In the day-ahead stage, a Stackelberg game model is introduced to depict the energy sharing between wind farm clusters and shared energy storage, forming leasing prices, leasing capacities, and energy storage pre-scheduling plans at different time periods. In the real-time stage, the real-time prediction results of wind power output and electric prices are integrated with scheduling decisions, and an improved robust optimization model is used to dynamically regulate the pre-scheduling plan for leasing capacity and shared energy storage. Based on actual data from the electricity market in Guangdong Province, effectiveness verification is conducted, and the results showed that diversified application scenarios improve the utilization rate of shared energy storage in the power generation side by 52.87%, increasing economic benefits by CNY 188,700. The proposed optimized operation strategy has high engineering application value. Full article

With the increasing integration of renewable energy sources, distributed shared energy storage (DSES) systems play a critical role in enhancing power system flexibility, operational resilience, and energy sustainability. However, conventional scheduling methods often suffer from excessive communication burdens, limited scalability, and poor real-time responsiveness, especially when handling fast-changing frequency regulation signals and fluctuating renewable energy outputs. To address these challenges, this paper proposes a consensus-driven distributed online convex optimization method that enables a decentralized scheduling of energy storage units by leveraging the consensus algorithm for local decision-making while maintaining global consistency. Additionally, an adaptive event-triggered mechanism is designed to dynamically adjust the communication frequency based on system state variations, reducing redundant information exchange and ensuring convergence and stability in a fully distributed environment. Simulation results on the IEEE 14-bus test system show that the strategy reduces the communication load by 33–60% and improves the convergence speed by over 40% compared to baseline methods. It also demonstrates a strong adaptability to storage unit disconnection and reconnection. By enabling a fast and efficient response to grid services such as frequency regulation and renewable energy balancing, the proposed approach contributes to the development of intelligent and sustainable power systems. Full article