Search Results (1,739)

Electric vehicles (EVs) are pivotal for enhancing the flexibility of power systems, with vehicle-grid integration (VGI) constituting the fundamental mechanism for their participation in grid regulation. VGI relies on multi-source information from EVs, charging infrastructure, traffic network, power grid, and meteorology. However, ineffective data integration mechanisms have resulted in data silos, which impede the realization of synergistic value from multi-source data fusion. To address these issues, this paper develops a quadripartite evolutionary game model that incorporates data providers, data users, government, and data service platforms, overcoming the limitation of traditional tripartite models in fully capturing the complete data value chain. The model systematically examines the cost–benefit dynamics and strategy evolution among stakeholders throughout the data-sharing process. Leveraging evolutionary game theory and Lyapunov stability criteria, sensitivity analyses were conducted on key parameters, including data costs and government subsidies, on the MATLAB platform. Results indicate that multi-source data integration accelerates system convergence and facilitates a multi-party equilibrium. Government subsidies as well as reward and punishment mechanisms emerge as critical drivers of sharing, with an identified subsidy threshold of ε_S = 0.02 for triggering multi-source integration. These key factors can also accelerate system convergence by up to 79% through enhanced subsidies (e.g., reducing stabilization time from 0.29 to 0.06). Importantly, VGI data sharing represents a non-zero-sum game. Well-designed institutional frameworks can achieve mutually beneficial outcomes for all parties, providing quantitatively supported strategies for constructing incentive-compatible mechanisms. Full article

This article critically examines the growing interest in what most contemporary scholars consider still a new and underdeveloped mode of environmental storytelling in video games. Different models of games that provide strong narrative techniques within highly detailed, environmentally sophisticated land/soundscapes have been released over the last decade by well-known studios like Fullbright Productions, Giant Sparrow and Campo Santo. This new perspective will draw several critical questions formed from prior research in several foundational articles, the area of game studies and several journals directed at the question of how game spaces function as narrative devices. For example, an early 2016 article by John Barber for the Cogent Arts and Humanities, “Digital storytelling: New opportunities for humanities scholarship and pedagogy” was one of the first essays to explore how Fullbright’s well-known game Gone Home utilizes spatial design, object placement, and ambient details to convey stories without explicit narration. Gone Home, according to Barber and many others, continues to emphasize environmental storytelling as a form of semiotic communication—one where the “text” is the game world itself, inviting players to read and interpret more complex layers of literary meaning. Contemporary scholars have built on these more foundational studies to consider how AI and procedural generation further complicate narrative agency and structure in digital spaces, enabling the current study to consider what could be considered a distinctly post-AI theoretical perspective based upon these primary determinants: (a) how game environments may dynamically adapt narratives in response to player interaction and algorithmic input, and (b) the evolving notion of narrative agency in digital spaces where human and machine contributions intertwine in AI systems. The two chief aims of this proposal are thus to reconsider traditional environmental storytelling within new innovative, post-GenAI narrative frameworks and, looking at contemporary insights from leading examples in the field, deepen current academic understandings of narrative spaces in games from new narratological perspectives. Studies in this area seem uniquely valuable, given the rapid development of GenAI tools in creative content production and what appears to be a new epoch in narrative engagement in all interactive media. Full article

Despite growing research on sustainable aviation, multi-airport systems, and environmentally constrained capacity allocation, critical gaps persist. Existing studies often treat passenger choice, airline competition, and airport regulation in isolation, or evaluate environmental policies such as carbon taxation only as macro-level constraints. Consequently, the endogenous feedback among pricing, capacity reallocation, and regulatory intervention in shaping equilibrium outcomes within multi-airport systems remains underexplored, particularly within a unified dynamic framework that links low-carbon policies to operational decision-making. This study develops such a dynamic framework to support the sustainable transition of carbon-constrained multi-airport regions. Focusing on the Beijing–Tianjin multi-airport system and China’s “Dual Carbon” goals, we construct a three-layer iterative equilibrium game integrating passenger airport choice (modeled using a multinomial logit specification), airline capacity reallocation (formulated as an evolutionary game internalizing carbon taxes), and airport slot regulation (implemented through a multi-objective mechanism balancing economic revenue, hub connectivity, and environmental performance). An agent-based simulation of the Beijing/Tianjin–Nanchang route demonstrates robust convergence to a stable systemic equilibrium. Intensified competition reduces fares and improves accessibility, while capacity shifts from higher-cost Beijing airports to Tianjin Binhai Airport, whose market share rises from 10.6% to 34.0%. Airport utilization becomes more balanced, total airline profits increase slightly, and both total and per-passenger CO₂ emissions decline, indicating improved carbon efficiency despite demand growth. The results further identify a range of carbon-tax levels that jointly promote emission reduction and traffic rebalancing with limited profit loss. Full article

With the advancement of digital technology, serious games have become an essential tool for disseminating and educating individuals about cultural heritage. However, systematic empirical research remains limited with respect to how visual elements influence users’ cognitive and emotional engagement through interactive behaviors. Using the “Macau Historic Centre Science Popularization System” as a case study, this mixed-methods study investigates the mechanisms by which visual elements affect user experience and learning outcomes in digital interactive environments. Eye-tracking data, behavioral logs, questionnaires, and semi-structured interviews from 30 participants were collected to examine the impact of visual elements on cognitive resource allocation and emotional engagement. The results indicate that the game intervention significantly enhanced participants’ retention and comprehension of cultural knowledge. Eye-tracking data showed that props, text boxes, historic buildings, and the architectural light and shadow shows (as incentive feedback elements) had the highest total fixation duration (TFD) and fixation count (FC). Active-interaction visual elements showed a stronger association with emotional arousal and were more likely to elicit high-arousal experiences than passive-interaction elements. The FC of architectural light and shadow shows a positive correlation with positive emotions, immersion, and a sense of accomplishment. Interview findings revealed users’ subjective experiences regarding visual design and narrative immersion. This study proposes an integrated analytical framework linking “visual elements–interaction behaviors–cognition–emotion.” By combining eye-tracking and information dynamics analysis, it enables multidimensional measurement of users’ cognitive processes and emotional responses, providing empirical evidence to inform visual design, interaction mechanisms, and incentive strategies in serious games for cultural heritage. Full article

The fusion of Artificial Intelligence and the Internet of Things (AI-IoT, also widely referred to as AIoT) offers transformative potential for smart cities, yet presents a critical challenge: how to process heterogeneous data streams from intelligent sensing—particularly crowd sensing data derived from citizen interactions like text, voice, and system logs—into reliable intelligence for sustainable urban governance. To address this challenge, we introduce the Intelligent Multimodal Ticket Processing System (IMTPS), a novel AI-IoT smart system. Unlike ad hoc solutions, the novelty of IMTPS resides in its theoretically grounded architecture, which orchestrates Information Theory and Game Theory for efficient, verifiable extraction, and employs Causal Inference and Meta-Learning for robust reasoning, thereby synergistically converting noisy, heterogeneous data streams into reliable governance intelligence. This principled design endows IMTPS with four foundational capabilities essential for modern smart city applications: Sustainable and Efficient AI-IoT Operations: Guided by Information Theory, the IMTPS compression module achieves provably efficient semantic-preserving compression, drastically reducing data storage and energy costs. Trustworthy Data Extraction: A Game Theory-based adversarial verification network ensures high reliability in extracting critical information, mitigating the risk of model hallucination in high-stakes citizen services. Robust Multimodal Fusion: The fusion engine leverages Causal Inference to distinguish true causality from spurious correlations, enabling trustworthy integration of complex, multi-source urban data. Adaptive Intelligent System: A Meta-Learning-based retrieval mechanism allows the system to rapidly adapt to new and evolving query patterns, ensuring long-term effectiveness in dynamic urban environments. We validate IMTPS on a large-scale, publicly released benchmark dataset of 14,230 multimodal records. IMTPS demonstrates state-of-the-art performance, achieving a 96.9% reduction in storage footprint and a 47% decrease in critical data extraction errors. By open-sourcing our implementation, we aim to provide a replicable blueprint for building the next generation of trustworthy and sustainable AI-IoT systems for citizen-centric smart cities. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

Gamification is widely used to enhance student motivation, yet many educational design proposals remain conceptual and provide limited operational guidance for digital learning environments. This paper introduces APAR (Activities, Points, Achievements and Rewards), a content-independent structural framework for designing and implementing educational gamification in learning platforms. Grounded in motivation theories (including Self-Determination Theory and Relatedness–Autonomy–Mastery–Purpose) and reward taxonomies (Status, Access, Power and Stuff), APAR distinguishes high-level design constructs from concrete game elements (e.g., points, badges and leaderboards) and provides a systematic design loop linking learning activities, feedback, intermediate goals and reinforcement. The contribution includes (i) a mapping table relating each APAR construct to motivation models, supported dynamics and typical learning-platform implementations; (ii) an actionable design guide; and (iii) an empirical illustration implemented in Moodle in a higher-education Computer Networks course. In this setting, the proportion of enrolled students taking the final exam increased from 58% to 72% in the first year, and the proportion of enrolled students passing increased from 17% to 38%; in 2022–2023 these values were 70% and 39%, respectively (56% of exam takers passed). While the use case relies on quantitative course-level indicators and is observational, the findings support the potential of structural gamification as an integrated methodological tool and motivate further mixed-method validations. Full article

Motivated by the inefficiencies where multi-agent systems fail to reconcile individual agent self-interest with global optimality and accommodate dynamic tasks as its population increases, this paper investigates a clustering allocation problem for large-scale multi-agent systems. A novel coalitional game clustering allocation scheme that can simultaneously reconcile individual agent self-interest and adapt to dynamic tasks is proposed. In this scheme, a coalition switching strategy is newly constructed and incorporated to select optimal switching operation and obtain stable coalition partition. Simulation and comparative results are provided to verify the effectiveness of the developed allocation scheme. It is shown both theoretically and simulation experimentally that in the case of large-scale multi-agent systems, the generated clustering allocation strategy is Nash stable using the proposed scheme. Full article

In this paper we investigate the chaos of a generalized perturbed Lotka–Volterra model based on considerations by other studies used in the literature. The model, containing N number of free parameters, could be of interest to specialists working in the fields of biological applications, chemistry, reaction kinetics, biostatistics, games theory, etc. With a specially developed software product, we generate the Melnikov equation $M(t)=0$ and examine all its zeros. This opens up an opportunity for the researcher to correctly understand and formulate the classical Melnikov criterion for the possible occurrence of chaos in the dynamical system. Several simulations are composed. We also demonstrate some specialized modules for investigating the dynamics of the proposed model. We further develop our model using the exponential form of the sine function. Thus, the perturbation can be interpreted as a term dependent on the characteristic function of a probability distribution. Although the original formulation leads to a distribution stated on a discrete domain, we can easily generalize the results for arbitrary distributions. Some particular examples are provided. Full article

Standard epidemiological models often treat human behavior as static, failing to capture the dynamic feedback loops that shape epidemic waves. To address this, we developed a compartmental model of COVID-19 that couples the disease dynamics with two co-evolving behavioral games governed by imitation dynamics: an altruistic self-isolation game for infected individuals and a self-interested vaccination game for susceptible individuals. Our simulations reveal a fundamental behavioral paradox: strong adherence to self-isolation, while effective at reducing peak infections, diminishes the perceived risk of disease, thereby undermining the incentive to vaccinate. This dynamic highlights a critical trade-off between managing acute crises through non-pharmaceutical interventions and achieving long-term population immunity. We conclude that vaccination has a powerful stabilizing effect that can prevent the recurrent waves often driven by behavioral responses to non-pharmaceutical interventions. Public health policy must therefore navigate the tension between encouraging short-term mitigation behaviors and communicating the long-term benefits of vaccination to ensure lasting population resilience. Full article

The rapid growth of China’s prepared food industry has created new opportunities for industrial upgrading, but it has also intensified concerns regarding product quality, supervision gaps, and the long-term sustainability of governance structures. In response to these challenges, this study develops a tripartite evolutionary game model involving local governments, enterprises, and consumers, and further integrates a Markov chain framework to capture stochastic disturbances and long-run state transitions. This dynamic–stochastic modeling approach enables an examination of how collaborative supervision evolves under varying regulatory intensities, compliance costs, and consumer reporting costs. The results show that (1) multi-actor collaborative supervision substantially increases firms’ incentives to operate honestly and reinforces positive feedback loops between regulators and consumers; (2) excessive regulatory, compliance, or reporting costs weaken system stability and reduce policy effectiveness; (3) aligning regulatory intensity with penalty mechanisms accelerates the system’s convergence toward a stable equilibrium, balancing industrial development with food safety objectives; and (4) Markov chain simulations confirm the robustness and long-term stationarity of the governance system. Overall, this study provides a dynamic and evidence-based framework for designing sustainable and resilient regulatory mechanisms in the prepared food industry. The findings offer practical guidance for advancing Sustainable Development Goals (SDGs) 3, 12, and 16 through improved food safety, responsible production, and stronger institutional governance. Full article

Modernizing public security risk governance demands a paradigm shift from reactive response to proactive, systems-oriented prevention. Prevailing governance models, with their focus on institutions and technology, often neglect the micro-foundational mechanisms of risk generation: the internal psychological processes of individuals. To address this gap, this study develops a novel theoretical model—the F(T, P, C, R) framework—which integrates self-organization theory with a psychological gaming perspective. We conceptualize an individual’s behavioral choice (F_behavior) as an emergent outcome of the dynamic interplay among four constitutive factors: the situational context of Time (T) and Place (P), and the cognitive assessments of perceived Risk Control power (C) and perceived Risk Destructive power (R). Employing automotive driving behavior—specifically decisions regarding safe distance maintenance and the adoption of autonomous driving technologies—as our primary analytical scenario, we derive a dynamic risk-decision matrix. This matrix categorizes behavioral outcomes into four distinct quadrants (Confirm, Tend-to-Confirm, Tend-to-Deny, Deny) based on the subjective calculus between C and R, thereby elucidating the internal logic of risk-related choices. The study’s main contribution is constituted by this novel micro-behavioral analytical framework that integrates cognitive science with systems-based governance principles. It offers theoretical insights for behavioral public policy and provides a structured toolkit for diagnosing and designing targeted interventions, ultimately aiming to enhance proactive risk management and systemic resilience. Full article

Emergency supplies allocation is a critical task in post-disaster response, as ineffective or delayed decisions can directly lead to increased human suffering and loss of life. In practice, emergency managers must make rapid allocation decisions over multiple periods under incomplete information and highly unpredictable demand, making robust and adaptive decision support essential. However, existing allocation approaches face several challenges: (1) Those traditional approaches rely heavily on predefined uncertainty sets or probabilistic models, and are inherently static, making them unsuitable for multi-period, dynamically allocation problems; and (2) while reinforcement learning (RL) technique is inherently suitable for dynamic decision-making, most existing RL-base approaches assume fixed demand, making them unable to cope with the non-stationary demand patterns seen in real disasters. To address these challenges, we first establish a multi-period and multi-objective emergency supplies allocation problem with demand uncertainty and then formulate it as a two-player zero-sum Markov game (TZMG). Demand uncertainty is modeled through an adversary rather than predefined uncertainty sets. We then propose RESA, a novel RL framework that uses adversarial training to learn robust allocation policies. In addition, RESA introduces a combinatorial action representation and reward clipping methods to handle high-dimensional allocations and nonlinear objectives. Building on RESA, we develop RESA_PPO by employing proximal policy optimization as its policy optimizer. Experiment results with realistic post-disaster data show that RESA_PPO achieves near-optimal performance, with an average gap of only 3.7% in terms of the objective value of the formulated problem, from the theoretical optimum derived by exact solvers. Moreover, RESA_PPO outperforms all baseline methods, including heuristic and standard RL methods, by at least 5.25% on average. Full article

Background/Objectives: Vitamin D plays an important role in muscle metabolism and recovery, yet its kinetics during and after football-specific physical activity remain poorly understood. Therefore, this study aimed to determine whether physical effort during a football match influences the concentration of vitamin D metabolites and to explore the effect of a single high-dose cholecalciferol supplementation combined with physical exercise on the levels of vitamin D metabolites in professional football players. Methods: Twenty professional football players participated in a three-phase, randomized placebo-controlled pilot study. Baseline fitness and blood samples were collected, followed by pre- and post-match measurements during two games. In the final phase, half of the players received a single 500,000 IU dose of vitamin D₃ before a simulated match. Blood samples were collected before and after each session to analyze vitamin D metabolites using the isotope-dilution liquid chromatography–tandem mass spectrometry (ID-LC-MS/MS) method. Results: Physical exercise during the football match significantly increased serum concentrations of 25-(OH)D₃, 24,25-(OH)₂D₃, and 3-epi-25-(OH)D₃ (by up to 25%, p < 0.001). Following supplementation, these effects were further amplified, with 25-(OH)D₃ rising by 98% and 3-epi-25-(OH)D₃ by 424% (p < 0.001). Significant alterations in vitamin D metabolite ratios after exercise and supplementation suggest enhanced metabolic turnover and dynamic regulation of vitamin D pathways in response to physical effort. Conclusions: Football-specific physical activity appears to stimulate the release of vitamin D metabolites. High-dose cholecalciferol supplementation was well tolerated and may rapidly increase vitamin D status in professional athletes. These findings may have implications for optimizing recovery and performance, though larger trials are needed. Full article

Background/Objectives: This cross-sectional study examined how training age, chronological age, and biological maturity influence motor and perceptual–cognitive performance in youth soccer players, with relevance for health and well-being through sport participation. Methods: Forty-one male athletes (age = 14.86 ± 0.81 years) completed a two-day field-based assessment following a holistic framework integrating motor (sprinting, jumping, and agility) and perceptual–cognitive components (psychomotor speed, visuospatial working memory, and spatial visualization). Biological maturity was estimated using the maturity offset method. Results: Regression analyses showed that biological maturity and training age significantly predicted motor performance, particularly sprinting, jumping, and pre-planned agility, whereas chronological age was not a predictor. In contrast, neither maturity nor training experience influenced perceptual–cognitive skills. Among cognitive measures, only psychomotor speed significantly predicted reactive agility, emphasizing the role of rapid information processing in dynamic, game-specific contexts. Conclusions: Youth soccer training should address both physical and cognitive development through complementary strategies. Physical preparation should be tailored to maturity status to ensure safe and progressive loading, while systematic training of psychomotor speed and decision-making should enhance reactive agility and game intelligence. Integrating maturity and perceptual–cognitive assessments may support individualized development, improved performance, and long-term well-being. Full article