Search Results (2,002)

The implementation of community power generation technology not only increases the flexibility of electricity use but also improves the power system’s load distribution, increases the overall system efficiency, and optimizes energy allocation. This article first outlines the operational context of the system and analyzes the roles and missions of the various participants. Subsequently, optimization models are developed for microgrid operators, community power storage facility service providers and load aggregators. Next, the paper explores the game relationship between microgrid operators and load aggregators, proposing a model based on the Stackelberg game theory and proving the presence and singularity of the Stackelberg equilibrium solution. Finally, simulations are conducted using Yalmip tools and the CPLEX solution on the MATLAB R2023a software platform. A combination of heuristic algorithms and solver methods is employed to optimize the strategies of microgrid operators and load aggregators. The research findings show that the proposed framework is not only able to achieve an effective balance of interests between microgrid operators and load aggregators but also creates a win–win situation between load aggregators and shared energy storage operators. Additionally, the solution algorithms used ensure the protection of data privacy. Full article

Understanding the impact of creep on the long-term mechanical features of concrete is crucial, and constructing an accurate prediction model is the key to exploring the development of concrete creep under long-term loads. Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. Simultaneously, the contributions of the input features are ranked, and the optimal model’s prediction outcomes are explained through SHapley Additive exPlanations (SHAP). The research results show that the optimized SVM, RF, and XGBoost models increase their accuracies on the test set by 9.927%, 9.58%, and 14.1%, respectively, and the XGBoost has the highest precision in forecasting the concrete creep. The verification results of four scenarios confirm that the optimized model can precisely capture the compliance changes in long-term creep, meeting the requirements for forecasting the nature of concrete creep. Full article

The growing importance of critical infrastructure systems (CIS) makes maintaining their normal operation against deliberate attacks such as terrorism a significant challenge. Combining game theory and complex network theory provides a framework for analyzing CIS robustness in adversarial scenarios. Most existing studies focus on single-layer networks, while CIS are better modeled as multilayer networks. Research on multilayer network games is limited, lacking methods for constructing incomplete information through link hiding and neglecting the impact of cascading failures. We propose a multilayer network Stackelberg game model with incomplete information considering cascading failures (MSGM-IICF). First, we describe the multilayer network model and define the multilayer node-weighted degree. Then, we present link hiding rules and a cascading failure model. Finally, we construct MSGM-IICF, providing methods for calculating payoff functions from the different perspectives of attackers and defenders. Experiments on synthetic and real-world networks demonstrate that link hiding improves network robustness without considering cascading failures. However, when cascading failures are considered, they become the primary factor determining network robustness. Dynamic capacity allocation enhances network robustness, while changes in dynamic costs make the network more vulnerable. The proposed method provides a new way of analyzing the robustness of diverse CIS, supporting resilient CIS design. Full article

The proliferation of community energy storage systems (CESSs) necessitates effective energy management to address financial concerns. This paper presents an efficient energy management scheme for heterogeneous power consumers by analyzing various cost factors relevant to the power system. We propose an authority transaction model based on a multi-leader multi-follower Stackelberg game, demonstrating the existence of a unique Stackelberg equilibrium to determine optimal bidding prices and allocate authority transactions. Our model shows that implementing a CESS can reduce total electricity costs by 16% compared to the conventional case that does not account for authority transactions among CESS users, highlighting its effectiveness in practical power systems. Full article

Studying the resilience of metro stations in mountainous cities to heavy rain and flooding is of significant importance for enhancing the stability and safety of metro station operations. Considering the topographical and climatic characteristics of mountainous urban areas, this study analyzes the mechanisms through which heavy rain and flooding affect metro station resilience. Based on this analysis, 27 factors, influencing metro station resilience, are identified across 4 dimensions: absorptive capacity, resistance capacity, recovery capacity, and adaptive capacity. A water disaster resilience evaluation index system and corresponding rating standards are established for metro stations in mountainous cities. By combining the advantages of objective and subjective weighting, the combination weights of evaluation indicators are calculated using game theory. The extension theory is combined with the cloud model to establish a model for assessing the water disaster resilience of metro stations in mountainous urban areas. The applicability and feasibility of the model are validated through its implementation at Shapingba Station within Chongqing Rail Transit. The evaluation results obtained from the established model indicate a resilience level of IV for Shapingba metro station, reflecting a high level of resilience that aligns with real-world conditions. These findings further validate the proposed evaluation standards and the method for assessing the water disaster resilience of metro stations based on the combination weighting method and extension cloud model. This evaluation method considers the uncertainty in the evaluation process, demonstrating good feasibility and reliability. It offers a new perspective and methodology for assessing and analyzing the resilience of similar metro stations in mountainous cities. Full article

Since Darwin, evolutionary population dynamics has captivated scientists and has applications beyond biology, such as in game theory where economists use it to explore evolution in new ways. This approach has renewed interest in dynamic evolutionary systems. In this paper, we propose an information-theoretic method to estimate trait parameters in a Darwinian model for species with single or multiple traits. Using Fisher information, we assess estimation errors and demonstrate the method through simulations. Full article

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers. Full article

Given integer n and k such that $0<k\le n$ and n piles of stones, two players alternate turns. On each move, a player is allowed to choose any k piles and remove exactly one stone from each. The player who has to move but cannot is the loser in the normal version of the game and (s)he is the winner in the misère version. Cases $k=1$ and $k=n$ are trivial. For $k=2$, the game was solved for $n\le 6$. For $n\le 4$, the Sprague–Grundy function was efficiently computed (for both versions). For $n=5,6$, a polynomial algorithm computing P-positions was obtained for the normal version. Then, for the case $k=n-1$, a very simple explicit rule that determines the Smith remoteness function was found for the normal version of the game: the player who has to move keeps a pile with the minimum even number of stones; if all piles have an odd number of stones, then (s)he keeps a maximum one, while the $n-1$ remaining piles are reduced by one stone each in accordance with the rules of the game. Computations show that the same rule works efficiently for the misère version too. The exceptions are sparse. We list some. Denote a position by $x=(x_1,\dots ,x_n)$. Due to symmetry, we can assume wlog that $x_1\le \dots \le x_n$. Our computations partition all exceptions into the following three families: $x_1$ is even, $x_1=1$, and odd $x_1\ge 3$. In all three cases, we suggest formulas covering all found exceptions, but it is not proven that there are no others. Full article

With the continuous evolution of international trade, the global market has been steadily expanding while also facing increasing challenges, particularly in relation to the introduction of environmental policies such as carbon barriers. Our research explores how China’s power battery manufacturers can adapt their export strategies to the EU’s carbon barrier policies. Additionally, we examine the roles of government regulations, research institutions, and manufacturers in either facilitating or hindering compliance with carbon reduction objectives. Using evolutionary game theory, we construct models involving government entities, manufacturers, and research institutions to systematically analyze market evolution, strategic interactions, and outcomes among these stakeholders. Our analysis focuses on understanding the competitive dynamics faced by exporting countries under stringent environmental policies and provides strategic insights to guide export strategies. Taking the EU’s carbon barrier policy as a case study, we explore Chinese battery manufacturers’ adaptive strategies and decision-making processes as they respond to shifting market demands and regulatory environments. The findings not only offer valuable insights into exporting countries but also provide policymakers with information on international trade and industrial policy design. Furthermore, we validate our model through numerical simulations and conduct sensitivity analyses on key parameters. The results underscore the importance of governmental adoption of punitive and incentive policies, revealing their substantial impact on stakeholder behavior. Additionally, the study highlights how participants’ pre-cooperation losses and post-cooperation gains influence participation rates and the speed at which stakeholder consensus is reached. By offering a novel approach with which to address carbon barrier challenges, this research contributes valuable perspectives on environmental regulations’ strategic and policy implications in global trade. Full article

This paper, based on differential game theory, examines governance models and cooperative strategies for managing cross-border water pollution in regions with uneven economic development. To address cross-regional water pollution, three differential game models are constructed under different scenarios: the Nash noncooperative mechanism, the pollution control cost compensation mechanism, and the collaborative cooperation mechanism. This study analyzes the dynamic changes in pollution emissions, governance investments, and economic returns within each model. The results indicate that the collaborative cooperation mechanism is the most effective, as it significantly reduces pollution emissions, maximizes overall regional benefits, and achieves Pareto optimality. In comparison, the pollution control cost compensation mechanism is suboptimal under certain conditions, while the Nash noncooperative mechanism is the least efficient, resulting in the highest pollution emissions. Furthermore, the research explores the influence of cooperation costs on the selection of governance models. It finds that high cooperation costs reduce local governments’ willingness to engage in collaborative cooperation. However, an appropriate compensation mechanism can effectively encourage less-developed regions to participate. Numerical analysis confirms the dynamic evolution of pollution stocks and economic returns under different models, and provides corresponding policy recommendations. This paper offers theoretical insights and practical guidance for cross-regional water pollution management, highlighting the importance of regional cooperation and cost-sharing in environmental governance. Full article

In this article, we present the mathematical foundations of generative machine intelligence and link them with mean-field-type game theory. The key interaction mechanism is self-attention, which exhibits aggregative properties similar to those found in mean-field-type game theory. It is not necessary to have an infinite number of neural units to handle mean-field-type terms. For instance, the variance reduction in error within generative machine intelligence is a mean-field-type problem and does not involve an infinite number of decision-makers. Based on this insight, we construct mean-field-type transformers that operate on data that are not necessarily identically distributed and evolve over several layers using mean-field-type transition kernels. We demonstrate that the outcomes of these mean-field-type transformers correspond exactly to the mean-field-type equilibria of a hierarchical mean-field-type game. Due to the non-convexity of the operators’ composition, gradient-based methods alone are insufficient. To distinguish a global minimum from other extrema—such as local minima, local maxima, global maxima, and saddle points—alternative methods that exploit hidden convexities of anti-derivatives of activation functions are required. We also discuss the integration of blockchain technologies into machine intelligence, facilitating an incentive design loop for all contributors and enabling blockchain token economics for each system participant. This feature is especially relevant to ensuring the integrity of factual data, legislative information, medical records, and scientifically published references that should remain immutable after the application of generative machine intelligence. Full article

Today, immersive virtual reality (IVR) is increasing in popularity in a broad range of fields, including science, pedagogy, engineering and so forth. Therefore, this study discusses the Unified Theory of Acceptance and Use of Technology (UTAUT), which can be used to examine the factors that influence the adoption of immersive VR in the classroom, particularly in second language (L2) vocabulary acquisition. The sample for this study included 32 intermediate students and their teacher. For the purpose of evaluation, the researcher used partial least squares structural equation modelling (PLS-SEM) techniques to analyze the results. The findings of the students’ survey showed that performance expectancy, effort expectancy and social influence were seen to have had a positive impact on the intention to use immersive VR. Likewise, the findings indicated that facilitating conditions were seen to have had a positive impact on the use behavior of actually using immersive VR, whereas behavioral intentions did not. In addition, the teacher’s survey demonstrated a favorable view regarding the potential of immersive VR technology to support teaching L2 vocabulary acquisition. This particular study encouraged educators and educational technologists to utilize immersive VR games as a teaching–learning tool to reduce the challenge of L2 vocabulary acquisition. Full article

The scarcity of land drives urban village redevelopment projects, which involve interest redistribution among stakeholders with distinct demands. This paper utilizes evolutionary game theory and simulation methods, constructing a tripartite game model under the institutional arrangement of bottom-up with private developer funding. This study identifies the stable strategies and evolutionary trends of the tripartite interactions under four distinct scenarios and validates these strategies through simulations. The redevelopment of XC village validates the assumptions of the model and theoretical analysis, suggesting that when private developers adopt forced demolition strategies, although villagers ultimately choose to sign the contract of property exchange, it can easily lead to social conflicts. These research findings can enlighten the government to form a tripartite alliance to smooth urban village redevelopment. Full article

Cooperative governance of water pollution is an effective initiative to implement the strategy for the protection of the Yangtze River Basin. Based on the stakeholder theory, this paper constructs a tripartite evolutionary game model of water pollution in the Yangtze River Basin from the perspective of “cost–benefit”. This paper analyzes the stability of possible equilibrium points of the evolutionary game system by scenarios and further explores the influence of key factors on the evolution of the cooperative governance system of water pollution in the Yangtze River Basin using numerical simulation. According to the findings, (1) the watershed system comprises three key stakeholders: local governments, enterprises, and the public. Each stakeholder’s behavioral strategy choice is influenced by its unique factors and the behavioral strategy choices of the other two stakeholders. (2) Equilibrium points represent the potential strategic equilibrium presented by each stakeholder. When the net income of a particular behavioral strategy within the set exceeds zero, stakeholders will be more inclined to choose that behavioral strategy. (3) The key influencing factors in the evolutionary game are regulatory costs, reputation loss, material rewards, and violation fines. Therefore, this paper proposes to construct a cooperative governance mechanism for water pollution in the Yangtze River Basin from three aspects: an efficient regulatory mechanism, a dynamic reward and punishment mechanism, and a multi-faceted incentive mechanism, with a view to promoting a higher-quality development of the ecological environment in the Yangtze River Basin. Full article

In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decision-making with a small sample size in actual production processes. The lane-changing decision judgment accuracy rate of the SSA-SVM algorithm model can reach 93.6% compared with the SVM algorithm model without algorithm optimization; the SSA-SVM algorithm model has obvious advantages in decision performance and running speed. Therefore, the proposed new algorithm can effectively solve the problem of the objective consideration of the payoff function in conventional decision game theory. Full article