Search Results (620)

To address the challenges of low-carbon economic optimization and collaborative management for multiple Virtual Power Plants (VPPs), this paper proposes a low-carbon economic optimization and collaborative management method based on a Stackelberg game framework. Firstly, a Stackelberg game model is constructed with the Distribution System Operator (DSO) as the leader and multiple VPPs as followers. The leader (DSO) guides the followers’ behavior through dynamic pricing strategies to maximize its own utility. Meanwhile, the followers (VPPs) develop energy management strategies to minimize their individual costs, taking into account factors such as energy transaction costs, fuel costs, carbon trading costs, operation and maintenance (O&M) costs, compensation costs, and renewable energy generation revenues. Furthermore, the strategy spaces of all participants are defined, and an optimization model is established subjected to constraints including energy balance, energy storage operation, power conversion, and flexible load response. The CPLEX solver and Nonlinear-based Chaotic Harris Hawks Optimization (NCHHO) algorithm are employed to solve the proposed game model. Simulation results demonstrate that the proposed method effectively facilitates collaboration between the DSO and multiple VPPs. While ensuring the safe operation of the system, it balances the profit between the DSO and VPPs, and incentivizes renewable energy consumption and indirect carbon reduction, thereby validating the effectiveness and superiority of the method and providing reliable technical support for the low-carbon collaborative operation of multiple VPPs. Full article

The rapid growth of renewable energy and the inherent volatility of wind power grid integration have imposed stringent requirements on power system security and economic operation. To address this challenge, energy storage systems (ESSs) are widely adopted as flexible regulation tools; however, their high capital costs make the shared energy storage model a more efficient and viable solution. This paper proposes an optimal configuration model for wind farms participating in shared energy storage (SES) based on cooperative game theory. First, integrating wind power output forecasting data and market electricity price information, a wind-storage combined optimization model accounting for wind power uncertainty is first established. Subsequently, a core pricing strategy integrating the core allocation rule with the Vickrey–Clarke–Groves (VCG) auction mechanism is proposed to realize the fair allocation of energy storage resources and effective revenue incentives. Finally, comparative experiments between the proposed core pricing mechanism and the fixed pricing mechanism verify its superiority in terms of social welfare, budget balance, and allocation fairness. The results demonstrate that the proposed mechanism not only enhances the overall social benefits of the wind-storage system but also effectively ensures the incentive compatibility of all participants and the stability of the alliance, providing feasible theoretical and methodological support for the economic dispatch of wind-farm-shared energy storage. Full article

Online ride-hailing platforms increasingly rely on differentiated incentive mechanisms to regulate driver participation and balance supply and demand. However, drivers’ adaptive responses to such incentives introduce dynamic feedback and uncertainty that static equilibrium models fail to capture. This study develops a dual-layer Stackelberg–evolutionary game framework in which the platform acts as a strategic leader setting the order allocation rates and prices, while heterogeneous drivers adapt their working-hour strategies through evolutionary dynamics. Using operational data from Ningbo, China, we calibrated the demand elasticity and driver cost parameters and identified endogenous fatigue-cost thresholds that govern regime shifts in strategy dominance. Simulation results show that uniform incentives tend to drive the system toward single-strategy lock-in, whereas differentiated order allocation and pricing effectively sustain multi-strategy coexistence and mitigate extreme supply polarization. The findings reveal how platform-led differentiation reshapes the evolutionary fitness landscape of drivers, providing actionable guidance for incentive design aimed at stabilizing supply structures, improving platform revenue, and protecting driver welfare. Full article

Most of the existing evaluation systems for hydrocarbon-bearing play are using various evaluation indicators and fixed weights, which are not sensitive to the subjective/objective cognition or the exploration stages. We construct a multi-level and multi-type play evaluation criteria system with unified standards, the subjective uncertainty of which is formulated by the fuzziness of the indicators. Then, a full-stage dynamic fuzzy multi-criteria decision-making (MCDM) method is presented for play evaluation, in which a dynamic fuzzy-game model is built to combine the objective Criteria Importance Through Intercriteria Correlation (CRITIC) weights improved by the Theil index and the subjective Analytic Hierarchy Process (AHP) weights. This approach can simulate hesitation and strategic trade-offs in the human mind to balance the subjective and objective information. Thereafter, a stage-aware model is developed for play assessment by using dynamic fuzzy comprehensive evaluation, covering the regional exploration, pre-exploration, and evaluation stages. Using the data from plays at different exploration stages in the Tarim Basin, empirical application shows that the evaluation results are consistent with actual exploration judgment. Sensitivity analysis and comparative experiments verify the rationality of parameter setting and the effectiveness and reliability of the presented method. This study offers a practical MCDM for optimizing plays and guiding exploration decisions, which overcomes the limitations of traditional methods, including the lack of a unified evaluation framework, insufficient utilization and integration of multi-source information, inadequate characterization of phased priorities, and limited representation of fuzziness in evaluation indicators. Full article

To address the challenges posed by the strong uncertainty of high-proportion renewable energy sources (RES) to the secure and stable operation of distributed real-time power dispatch (D-RTPD) in new-type power systems, this paper proposes an integrated solution combining a neighborhood feature aggregation-based graph attention network (NFA-GAT) and multi-agent deep deterministic policy gradient (MADDPG). First, the D-RTPD problem is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), which effectively captures the stochastic game characteristics of multi-regional agents and the partial observability of grid states. Second, the NFA-GAT is designed to enhance agents’ perception of grid operating states: by introducing a spatial discount factor, it realizes rational aggregation of multi-order neighborhood information while modeling the attenuation of electrical quantity influence with topological distance. Third, a prior-guided mechanism is integrated into the MADDPG framework to eliminate constraint-violating actions by setting their actor logits to negative infinity, improving training efficiency and strategy reliability. Simulation validations on the IEEE 118-bus test system (75.2% RES installed capacity ratio) show that the proposed method achieves efficient training convergence. Compared with the multi-layer perceptron (MLP) structure, it attains higher cumulative reward values and scenario win rates. When compared with traditional model-driven (ADMM) and data-driven (Q-MIX) methods, the proposed method balances solution efficiency, operational safety (98.7% maximum line load rate, zero power flow violation rate), and economic performance ($12,845 daily dispatch cost), providing a reliable technical support for D-RTPD under high-proportion RES integration. Full article

The rapid rise of live-streaming e-commerce has fostered a new “content clipping” model, in which secondary creators edit and republish anchors’ live-streaming content to promote product sales. While this model can expand market reach and enhance revenue, it also introduces copyright disputes, regulatory challenges, and profit-sharing conflicts among platforms, anchors, and secondary creators. This study develops a three-party evolutionary game model to examine strategic choices regarding platform regulation, anchor authorization, and secondary content creation. Results reveal that excessive regulation may undermine equilibrium and profitability, while appropriate authorization can balance risk and reward. Secondary creators’ participation is sensitive to commission rates and cost–benefit trade-offs. This research contributes to the literature by integrating copyright governance into live-streaming e-commerce game theory and offers actionable insights for designing regulatory mechanisms, optimizing authorization policies, and fostering sustainable multi-party collaboration. Full article

Background: Ataxic symptoms are characterized by causing motor, balance and coordination disorders. Virtual reality-based interventions (VRBIs) including video games and exergames can improve ataxic symptoms. The aim of this systematic review with meta-analysis was to assess the effectiveness of VRBI on severity of ataxia, postural balance, mobility and manual dexterity in patients with ataxia. Methods: According to the PRISMA guidelines, we searched PubMed Medline, SCOPUS, WOS, CINAHL, PEDro and other sources for randomized controlled trials (RCTs) that assessed the effectiveness of VRBI, compared to others, on the severity of ataxia, balance, mobility and manual dexterity in patients with ataxia. The pooled effect was calculated using Cohen’s standardized mean difference (SMD) and a 95% confidence interval (95% CI). Results: With data from seven RCTs, providing data from 171 patients with ataxia, our meta-analysis elucidated that VRBI is effective in reducing the severity of ataxia (SMD = −0.43; 95% CI −0.84 to −0.03; p = 0.04) and increasing functional balance (SMD = 0.97; 95% CI 0.16 to 1.78; p = 0.02) and manual dexterity (SMD = −0.63; 95% CI −1.16 to −0.11; p = 0.018). Conclusions: Our findings suggest that VRBI could be a promising and effective therapeutic approach in reducing ataxia disability and increasing balance and manual dexterity in ataxic patients. Full article

This study examines retailers’ information sharing aimed at enhancing product greenness within green supply chains, with consumer participation as a pivotal factor and the overarching goal of advancing the sustainable development of the whole supply chain ecosystem. Each supply chain comprises a green product supplier and a retailer with uncertain demand information. A tripartite evolutionary game model involving manufacturers, retailers, and consumers is constructed to analyze the factors influencing information sharing behavior, which serves as a critical pathway to achieve environmental and economic sustainability in green supply chain operations. The findings highlight two key insights: First, strong consumer willingness to purchase green products may inhibit retailers’ inclination towards information sharing, a counterintuitive outcome that needs to be addressed to align individual stakeholder behaviors with long-term sustainable development goals. Second, lower information sharing costs can motivate retailers to share information with manufacturers; otherwise, manufacturers must adopt technological measures to assist retailers in reducing information sharing-related costs, thereby achieving win–win outcomes across the supply chain and fostering a sustainable and collaborative green supply chain system that balances ecological benefits, economic gains, and social value co-creation. Full article

Enhancement of postural stability in standing is essential for fall prevention in the context of demographic aging. Against such a background, this study proposes a personalized training system based on individual limits of stability (LOS) for a human standing state. The system evaluates LOS in eight directions using center-of-mass (COM) and center-of-pressure (COP) measurement devices and provides game-based feedback, then promotes balance within the relevant LOS parameters. Loading is individualized by applying greater force to virtual objects as the COP approaches the LOS determined for each subject. Experiments with 32 younger and 19 mature subjects produced evaluations for postural stability index (IPS), LOS area, and COP sway. The results revealed two distinct response patterns: LOS expansion and sway reduction, both observed across younger and mature cohorts. These findings suggest that individualized LOS-based training can be applied to improve standing stability with two distinct strategies. These preliminary findings suggest that individualized LOS-based training is associated with changes in standing stability through two distinct response patterns. Full article

With the continuous increase in the penetration rate of renewable energy sources, the uncertainty of new energy output has brought significant risks and challenges to the planning strategy of integrated energy systems. Meanwhile, power grid operators and heat network operators, belonging to different stakeholder entities, exhibit complex cooperative-competitive game relationships, making it difficult to balance the interests of all parties. To address this issue, this paper proposes a game theory and optimal planning strategy for electricity-heat multiple heterogeneous energy systems based on a deep temporal clustering method from the perspective of different stakeholders. Firstly, typical scenarios of renewable energy output are generated through the deep temporal clustering method. Simultaneously, the charging and discharging behaviors of energy storage devices are utilized to assist the distribution system in new energy consumption. This paper incorporates battery life degradation costs into the objective function on the power grid side to achieve accurate accounting of energy storage device dispatch expenses. Additionally, an optimal dispatch model is established on the heat network side, upon which a game framework for multiple heterogeneous energy systems is constructed. The construction capacity and installation location of each flexible device can be determined through planning decisions in typical multi-scenario situations. Considering the non-convex and nonlinear characteristics of the model, this paper employs an improved firefly algorithm to achieve optimal solution search and rapid convergence. Finally, the effectiveness and feasibility of the proposed method are demonstrated through a case study of an electricity-heat energy system. Full article

Ridesplitting is a promising strategy to enhance vehicle efficiency in urban mobility services during mega-events. However, designing dispatching algorithms that effectively balance high service rates with acceptable passenger delays under high-demand, reservation-based scenarios remains a significant challenge. To address this issue, this study proposes a novel two-stage dispatch framework: Offline Grouping and Online Matching (OGOM). In the offline stage, the request grouping problem is formulated as a weighted hypergraph maximum matching (WHMM) problem. A sequence inference (SI) method is introduced to accelerate the construction of candidate ridesplitting trips, and the WHMM problem is solved optimally using the Gurobi solver. In the online stage, the dispatch process is completed within an event-based simulation environment built with MATSim. The framework is validated through a comprehensive case study of the Hangzhou Asian Games. The results demonstrate that the proposed OGOM framework achieves a mean service rate of 92.12%, representing an 8.74% improvement over a rolling horizon batching benchmark. Concurrently, the average passenger delay is maintained between 2 and 4 min across all simulation runs. Furthermore, the framework reduces the average request completion distance by over 30% compared to a non-ridesplitting baseline. The proposed SI method also shows a 49.35% reduction in computation time for hypergraph construction compared to conventional methods. These findings confirm that the OGOM framework provides an effective and scalable operational strategy for mega-event ridesplitting services, simultaneously improving service quality through optimized supply–demand matching and controlled passenger delays. Full article

Esports are a rapidly expanding form of competitive activity that demand high levels of cognitive alertness, motor precision, stress management, and resilience to mental and physical fatigue. At the same time, the sedentary lifestyle, extended screen exposure, and psychological pressures associated with competitive gaming raise concerns for both performance and long-term health. Growing evidence highlights the importance of nutrition and lifestyle behaviors in supporting cognitive performance and overall competitive demands. While balanced dietary patterns and adequate hydration are essential, dietary supplements may provide additional benefits when used appropriately and under professional guidance. However, the current research is limited by a predominance of cross-sectional and self-reported studies, short-term or acute interventions, small sample sizes, and insufficient emphasis on esports-specific and personalized strategies. This review examines existing evidence on individualized nutrition, supplementation, and lifestyle strategies in esports, identifies key methodological limitations, and outlines future directions to inform evidence-based practice for athletes, practitioners, and organizations seeking to optimize cognitive performance, well-being, and long-term sustainability in this emerging field. Full article

This work presents a method for implementing dynamic difficulty adjustment in the arcade game of Air Hockey using reinforcement learning. The resulting AI-controlled opponent is capable of adapting its skill level to the player’s performance to maintain engagement and provide a balanced gameplay experience. The approach relies on generating several AI agents through progressively longer training durations, resulting in distinct and smoothly transitioning difficulty levels that can be switched dynamically. We discuss how this scheme can be extended with manually selected parameters that influence physical aspects of the agent’s behavior—such as movement speed, reaction latency, and control precision—to complement the variations in decision-making quality. The proposed method is applicable to a wide range of video games, and experimental results demonstrate its effectiveness in producing adaptive and varied opponent behavior. Full article

Coupling interference in densely integrated high-frequency communication architectures leads to significant degradation in transmission efficiency, particularly in modern 5G and GHz-range platforms. From a mathematical perspective, mitigating such interference can be formulated as a multi-criteria optimization problem involving competing design objectives and interacting control mechanisms. In this paper, we develop an equilibrium-based optimization framework by modeling coupling suppression as a finite non-cooperative game. Isolation mechanisms are represented as strategic players whose actions are defined over constrained design spaces, while utility functions incorporate coupling minimization, insertion-loss penalties, and fabrication complexity. Under this formulation, stable mitigation strategies are characterized through Nash equilibrium conditions. To address the inherent trade-offs among performance metrics, the equilibrium computation is integrated with a Pareto multi-objective optimization scheme, yielding Nash–Pareto optimal configurations that balance electromagnetic isolation performance with implementation feasibility. Numerical full-wave simulations in the 2–12 GHz frequency band demonstrate that the proposed equilibrium solutions achieve substantial interference suppression, with reductions exceeding 30 dB compared with conventional baseline designs. The proposed framework provides a mathematically structured approach for interference mitigation and offers a generalizable methodology for multi-objective optimization in high-frequency communication systems. Full article

With the rapid development of integrated energy systems (IES) towards integration and marketization, the collaborative planning of multi-energy entities has become a research hotspot. However, in real-world market environments, various energy entities often face information asymmetry and competitive interests, posing significant challenges to the optimal scheduling of the system. To address the incomplete information and competitive constraints among multiple energy hubs (EH) within IES, this paper constructs a multi-entity game planning model that accounts for environmental costs and carbon trading mechanisms. The model employs Bayesian game methods to handle the incomplete information among EH and analyzes the dynamic interactive behaviors of market entities under different strategies through multilateral incomplete information evolutionary game theory. Meanwhile, this paper incorporates carbon trading mechanisms along with the coupling technologies of power-to-gas (P2G) and carbon capture systems (CCS) to balance the economic efficiency and environmental protection. Additionally, in response to investment uncertainty, the real options theory is utilized for evaluation, and then a multi-entity incomplete information planning model is constructed, which is solved by using a nested algorithm proposed in this paper. This approach balances the interests of various entities and enhances the comprehensive long-term investment returns considering options. Simulation results demonstrate that the model effectively reflects the game behaviors among multi-energy entities under incomplete information, yielding optimized scheduling solutions that closely align with real-world scenarios. It improves economic benefits while reducing environmental pollution, providing theoretical foundations and methodological support for the planning of integrated energy systems involving multiple entities in electricity market environments. Full article