Search Results (124)

This paper presents a qualitative case study on the design, implementation, and validation of the Art Nouveau Path, a mobile augmented reality game developed to foster sustainability competences through engagement with Aveiro’s Art Nouveau built heritage. Grounded in the GreenComp framework and developed through a Design-Based Research approach, the game integrates location-based interaction, narrative storytelling, and multimodal augmented reality and multimedia content to activate key competences such as systems thinking, futures literacy, and sustainability-oriented action. The game was validated with 33 in-service schoolteachers, both through a simulation-based training workshop and a curricular review of the game. A mixed-methods strategy was used, combining structured questionnaires, open-ended reflections, and curricular review. The findings revealed strong emotional and motivational engagement, interdisciplinary relevance, and alignment with formal education goals. Teachers emphasized the game’s capacity to connect local identity with global sustainability challenges through immersive and reflective experiences. Limitations pointed to the need for enhanced pedagogical scaffolding, clearer integration into STEAM subjects, and broader accessibility across technological contexts. This study demonstrates that these games, when grounded in competence-based frameworks and inclusive design, can meaningfully support multimodal, situated learning for sustainability and offer valuable contributions to pedagogical innovation in Education for Sustainable Development. Full article

The growing integration of renewable energy sources (RESs) into modern power systems calls for enhanced flexibility and control mechanisms. Conventional demand response (DR) strategies, such as price-based and incentive-driven methods, often encounter challenges that limit their effectiveness. This paper proposes a novel DR approach grounded in Customer Directrix Load (CDL) and formulated through Stackelberg game theory. A bilevel optimization framework is established, with air conditioning (AC) systems and electric vehicles (EVs) serving as the main DR participants. The problem is addressed using a genetic algorithm. Simulation studies on a modified IEEE 33-bus distribution system reveal that the proposed strategy significantly improves RES accommodation, reduces power curtailment, and yields mutual benefits for both system operators and end users. The findings highlight the potential of the CDL-based DR mechanism in enhancing operational efficiency and encouraging proactive consumer involvement. Full article

To address the challenges of real-time decision-making and resource optimization in multi-agent cooperative interception tasks within dynamic environments, this paper proposes a hierarchical framework for reinforcement learning-based interception algorithm (HFRL-IA). By constructing a hierarchical Markov decision process (MDP) model based on dynamic game equilibrium theory, the complex interception task is decomposed into two hierarchically optimized stages: dynamic task allocation and distributed path planning. At the high level, a sequence-to-sequence reinforcement learning approach is employed to achieve dynamic bipartite graph matching, leveraging a graph neural network encoder–decoder architecture to handle dynamically expanding threat targets. At the low level, an improved prioritized experience replay multi-agent deep deterministic policy gradient algorithm (PER-MADDPG) is designed, integrating curriculum learning and prioritized experience replay mechanisms to effectively enhance the interception success rate against complex maneuvering targets. Extensive simulations in diverse scenarios and comparisons with conventional task assignment strategies demonstrate the superiority of the proposed algorithm. Taking a typical scenario of 10 agents intercepting as an example, the HFRL-IA algorithm achieves a 22.51% increase in training rewards compared to the traditional end-to-end MADDPG algorithm, and the interception success rate is improved by 26.37%. This study provides a new methodological framework for distributed cooperative decision-making in dynamic adversarial environments, with significant application potential in areas such as maritime multi-agent security defense and marine environment monitoring. Full article

Developing prefabricated buildings (PBs) and optimizing the construction supply chain represent effective strategies for reducing carbon emissions in the construction industry. Prefabricated building supply chain (PBSC) carbon reduction suffers from synergistic difficulties, limited rationality, and environmental complexity. Therefore, investigating carbon emission reduction in PBSC is essential. In this study, PBSC participants are divided into four categories according to the operation process. Gaussian white noise is introduced to simulate the random perturbation factors, and a four-way stochastic evolutionary game model is constructed and numerically simulated. The study found the following: Stochastic perturbation factors play a prominent role in the evolution speed of the agent; the emission reduction benefit and cost of the participant significantly affect the strategy selection; the operation status of the PBSC is the key to strategy selection, and it is important to pay attention to the synergy of the participants at the first and the last end of the PBSC; the influence of the external environment on strategies is mainly manifested in the loss caused and the assistance provided; and the information on emission reduction is an important factor influencing strategies. Finally, we provide suggestions for promoting carbon emission reduction by participants in the PBSC from the perspective of resisting stochastic perturbation, enhancing participants’ ability, and strengthening PBSC management; external punishment and establishing a cross-industry information sharing platform is more important than the reward. Full article

Fog computing introduces a new dimension to the network edge by pooling diverse resources (e.g., processing power, memory, and bandwidth). However, allocating resources from heterogeneous fog nodes often faces limited capacity. To overcome these limitations, integrating fog nodes with cloud resources is crucial, ensuring that Service Providers (SPs) have adequate resources to deliver their services efficiently. In this paper, we propose a game-theoretic model to describe the competition among non-cooperative SPs as they bid for resources from both fog and cloud environments, managed by an Infrastructure Provider (InP), to offer paid services to their end-users. In our game model, each SP bids for the resources it requires, determining its willingness to pay based on its specific service demands and quality requirements. Resource allocation prioritizes the fog environment, which offers local access with lower latency but limited capacity. When fog resources are insufficient, the remaining demand is fulfilled by cloud resources, which provide virtually unlimited capacity. However, this approach has a weakness in that some SPs may struggle to fully utilize the resources allocated in the Nash equilibrium-balanced cloud solution. Specifically, under a nondiscriminatory pricing scheme, the Nash equilibrium may enable certain SPs to acquire more resources, granting them a significant advantage in utilizing fog resources. This leads to unfairness among SPs competing for fog resources. To address this issue, we propose a price differentiation mechanism among SPs to ensure a fair allocation of resources at the Nash equilibrium in the fog environment. We establish the existence and uniqueness of the Nash equilibrium and analyze its key properties. The effectiveness of the proposed model is validated through simulations using Amazon EC2 instances, where we investigate the impact of various parameters on market equilibrium. The results show that SPs may experience profit reductions as they invest to attract end-users and enhance their quality of service QoS. Furthermore, unequal access to resources can lead to an imbalance in competition, negatively affecting the fairness of resource distribution. The results demonstrate that the proposed model is coherent and that it offers valuable information on the allocation of resources, pricing strategies, and QoS management in cloud- and fog-based environments. Full article

The variable valve mechanism, as a critical component for the efficient and low-carbon development of internal combustion engines, faces increasingly stringent requirements regarding its driving efficiency, output force, precision, and energy consumption. To address the limitations of existing technologies, a new composite electromagnetic valve train is proposed, characterized by a high force-to-power ratio, fast response, and high precision, along with a unique single/double drive mode, which offers greater flexibility in controlling valve timing parameters; however, it also introduces complex coupling relationships and increases the difficulty of optimization design. To this end, this paper establishes a thermodynamic model of the engine based on the composite electromagnetic valve mechanism. First, it analyzes the effects of different valve timing parameters and drive modes on engine performance; second, a multi-objective game theory optimization algorithm is employed to optimize the valve timing parameters and obtain the optimal solution set; finally, taking into account the energy consumption of the valve mechanism, engine emissions, and performance, a control strategy for valve timing parameters is developed based on an entropy-weighted method combined with a superiority and inferiority solution distance analysis. The results indicated that, under all the operating conditions of the engine, the average torque increased by 2.56%, the effective fuel consumption rate decreased by 6.23%, and nitrogen oxide emissions reduced by 9.86%. Meanwhile, an efficient and economical operational mode for the variable valve mechanism was obtained, providing new insights for the development of variable valve timing technology. Full article

Integrating Artificial Intelligence (AI) and Extended Reality (XR) technologies into agriculture presents a transformative opportunity to modernize education and sustainable food production. Traditional agriculture training remains resource-intensive, time-consuming, and geographically restrictive, limiting scalability. This study explores an AI-driven Digital Twin (DT) system embedded within a gamified XR environment designed to enhance decision-making, resource management, and practical training in viticulture as well as woody crop management. A survey among stakeholders in the viticultural sector revealed that participants are increasingly open to adopting Virtual Reality (VR) combined with AI-enhanced technologies, signaling a readiness for digital learning transformation in the field. The survey revealed a 4.48/7 willingness to adopt XR-based training, a 4.85/7 interest in digital solutions for precision agriculture, and a moderate climate change concern of 4.16/7, indicating a strong readiness for digital learning transformation. Our findings confirm that combining AI-powered virtual educators with DT simulations provides interactive, real-time feedback, allowing users to experiment with vineyard management strategies in a risk-free setting. Unlike previous studies focusing on crop monitoring or AI-based decision support, this study examines the potential of combining Digital Twins (DTs) with AI-driven personal assistants to improve decision-making, resource management, and overall productivity in agriculture. Proof-of-concept implementations in Unity and Oculus Quest 3 demonstrate how AI-driven NPC educators can personalize training, simulate climate adaptation strategies, and enhance stakeholder engagement. The research employs a design-oriented approach, integrating feedback from industry experts and end-users to refine the educational and practical applications of DTs in agriculture. Furthermore, this study highlights proof-of-concept implementations using the Unity cross game engine platform, showcasing virtual environments where students can interact with AI-powered educators in simulated vineyard settings. Digital innovations support students and farmers in enhancing crop yields and play an important role in educating the next generation of digital farmers. Full article

Environmental degradation and climate change are of paramount concern and require urgent action. Physical education holds an important role in addressing environmental issues and contributing to environmental sustainability. The present study assesses the effectiveness of an intervention program to cultivate environmental awareness in fifth-grade children through physical activities and games in physical education. The participants were 74 children (39 girls and 35 boys) divided into two groups, the experimental and control group. Participants completed a scale assessing their knowledge of environmental issues, which had been shown by previous research to be valid and reliable for this population. The experimental procedure of the study included three stages: completing the questionnaire before the implementation of the intervention and at the end, as well as a retention measurement after four months. The results indicated that the students of the experimental group maintained a significantly higher level of knowledge of fundamental environmental concepts at the end of the program compared to their counterparts in the control group (p < 0.05). In addition, children of the experimental group seemed to retain their knowledge as a result of the intervention program (p > 0.05). The findings suggest that interventions using physical education can serve as a meaningful strategy for promoting environmental care and sustainability and turn young tourists into tomorrow’s responsible consumers and travelers who respect and protect nature. Finally, such a process could potentially aim to increase environmental awareness as a learning experience through the provision of physical activity and recreation. Full article

Background: Basketball matches involve numerous conditioning factors which, when put together, make for a complex prediction event. However, unraveling all these elements in different studies allows the control of certain conditioning factors of the game, giving rise to more stable and controlled games. Objectives: Due to the absence of studies that analyze professional matches in women’s basketball, the aim of the present research is to analyze the influence of the result, partial result and specific positions on the load in official competition between two women’s teams that play a match simultaneously. Methods: Using an ex post facto design, 19 professional players were measured in an official match of the Spanish second division of women’s basketball, monitoring both opponents simultaneously, obtaining at all times the contrast of loads between both teams. Inertial devices were used to measure the external and internal load of each player. Parametric and non-parametric statistical difference analyses were performed, as well as two linear mixed models. Results: The results reveal significant differences in external loading when loads are analyzed as a function of match outcome. Differences between external and internal load are found when taking into account specific positions, and when including several comparisons in the analysis. The team that obtained the highest kinematic and neuromuscular load demands was the one that won the match. The specific position of guard obtained a greater physical load at the end of the match than the centers. Conclusions: Coaches should prioritize high-intensity training that replicates match demands, considering positional differences in workload to optimize player conditioning and recovery strategies for sustained performance. Full article

This study provides the game-theoretical framework to investigate the relationship between the blockchain service and mass customization in the environment of information sharing and contract coordination. Specifically, we construct the game-theoretical models of the manufacturer and the retailer to discuss the optimal strategy of information sharing by the retailer in the case of mass customization. The result explores the conditions of information sharing for the retailer because she understands the end market information of nearby consumers. This discussion helps us to understand that the motivation of the manufacturer pays for the retailer’s construction of a blockchain system in the case of two types of products, such as a standard product and a customization product. Finally, we use the method of contract coordination to obtain the optimal strategy. Results reveal that information costs significantly impact sharing decisions, and cost-sharing contracts can incentivize retailers to share market data. This study has two main contributions. On the one hand, this study adopts the blockchain service for mass customization by supporting contract coordination, showing the technical value of avoiding false information and tampering-proof. On the other hand, although big data has the same information sharing function, this technology can’t play the role of secure data transmission. In order to increase the accuracy of information sharing, we analyze the fusion results of two technologies in the aspect of increasing the accuracy of data sharing, which better reveals the technical value. Full article

Steganography is a technique for hiding secret information in imperceptible carriers and transmitting it. Unlike traditional embedding-based steganography, generative steganography can generate stego-carriers directly from secret messages, thus avoiding modifications to natural carriers that steganalysis can detect. As a branch of generative steganography, game-behavior-based steganography transmits secret information by encoding game behavior. It can naturally integrate with real interaction scenarios, exhibiting strong concealment and undetectability. To this end, this paper proposes a generative steganography based on Chinese Chess record construction. Firstly, an AlphaZero model was trained to achieve a high level in Chinese Chess, then transmit secret information by encoding chess behavior. Specifically, in each chess step, the model generates all the current feasible moves and encodes the moves that meet the threshold strategy according to probability. Then, the appropriate move will be selected according to the secret information. To ensure the reasonableness of the generated chess records, this paper controlled the game process and designed a database of fixed opening chess records. The proposed method can hide an average of 413 bits of information for each carrier and effectively resist common image attacks. Regarding anti-steganalysis, the proposed method achieved accuracy rates of 0.498 and 0.497 on XuNet and YeNet, respectively, outperforming other behavior-based steganography techniques. Full article

The Machine-Managed Bidding (MMB) system is an innovative bidding mode implemented by the Chinese government to mitigate collusive bidding behavior. Prior studies have focused minimally on the bidding mechanism and the possible collusive bidding behavior under this mode. The objectives of this study are to analyze the bidding mechanism and the dynamic evolution of collusive bidding behavior under the MMB system and provide targeted regulation countermeasures. To this end, this study develops an evolutionary game model among collusion initiators, free bidders, and regulators, explores possible scenarios for evolutionarily stable strategies, and performs sensitivity analysis of critical parameters utilizing MATLAB software (Version R2024a) based on empirical data. Results indicate that: (1) The MMB model significantly mitigates vertical collusive bidding behavior but lacks measures for governing transverse collusive bidding; (2) The game model has five evolutionarily stable strategies, with the one where the collusion initiator adopting the “non-collude” strategy, the free bidder adopting the “bid” strategy, and the regulator adopting the “negative regulate” strategy being the optimal evolutionary stable strategy; (3) Decreasing the costs associated with preparing bid documents, enhancing supervision costs, increasing the technical complexity of collusive bidding, and expanding the total number of construction enterprises with high-credit and low-credit ratings can expedite the evolution of the three participants toward the optimal evolutionarily stable strategy. This study supplements current knowledge on the regulation of collusive bidding behavior and enriches the knowledge framework of the MMB model. This study also provides insights for policymakers to guarantee the smooth implementation of the MMB. Full article

Effective energy management techniques are essential for the full utilization of energy in the field of extended-range electric vehicle research, with the goals of lowering energy consumption and exhaust emissions, enhancing driving comfort, and extending battery life. To achieve optimal comprehensive usage costs, this article uses bargaining game theory to design an adaptive energy management strategy (EMS_ad-bg) that focuses on battery life. In the study, a power system model was first built based on AVL/Cruise software and MATLAB/Simulink software. The impact of discount factors on strategy results was analyzed through simulation experiments. The results showed that the discount factor for auxiliary power unit (APU) focused more on energy optimization, while the discount factor for battery focused more on optimizing the degradation of battery life. When the initial state of charge (SoC) is high, the specific value of the discount factor also has a significant impact on the battery SoC value at the end of the trip. To improve the strategy’s adaptability to various initial SoC values, a fuzzy controller was created that can adaptively modify the discount factor based on the battery SoC. The results of the simulation experiment demonstrate that the bargaining game strategy taking SoC into account has more pronounced advantages in terms of overall usage cost when compared to the strategy of the fixed discount factor. The creation of an EMS_ad-bg that takes battery life into account based on a bargaining game can serve as a helpful model for the creation of a clever EMS that lowers the total cost of operating a vehicle. Full article

To enhance the interests of all stakeholders in the multi-microgrid integrated energy system and to promote photovoltaic consumption, this paper proposes a master–slave game operation optimization strategy for a multi-microgrid system considering photovoltaic consumption and integrated demand response. Initially, an energy interaction model was established to delineate the relationships between each microgrid and the distribution network, as well as the interactions among the microgrids. Additionally, an integrated demand response model for end-users was developed. This framework leads to the formulation of a one-leader multi-follower interaction equilibrium model, wherein the multi-microgrid system acts as the leader and the users of the multi-microgrid serve as followers. It is proven that a unique equilibrium solution for the Stackelberg game exists. The upper level iteratively optimizes variables such as energy-selling prices, equipment output, and energy interactions among microgrids, subsequently announcing the energy-selling prices to the lower level. The lower level is responsible for optimizing energy load and returning the actual load demand to the upper level. Finally, the rationality and effectiveness of the proposed strategy are demonstrated through the case analysis. Thus, the profitability of the multi-microgrid system is enhanced, along with the overall benefits for each microgrid user, and the amount of photovoltaic curtailment is significantly reduced. Full article

Disney animation studios created and subsequently shaped the animation genre for the last two-thirds of the 20th century, but the appearance of Pixar in the industry and their unstoppable success changed the rules of the game. (1) Apart from a new and revolutionary technology, Pixar introduced a new type of storytelling in animation based on characters and stories that we believe ended up tremendously influencing Disney’s storytelling starting in 2006, when the big animation studio purchased its most threatening competitor. Our study also tries to shed some light on whether the changes happened only at the level of storytelling or also crystallized into better box office and rating data. (2) We aim to clarify this belief and turn it into a reality through the content analysis of Disney animation features before and after the purchase of Pixar. (3) Our results show that Pixar’s influence on Disney is remarkable, both in the movies’ narrative and in their reception by the audience and the critics. (4) This confirms not only the change in the story-telling strategies of the company, enhancing psychological construction of the protagonists of Disney animation features, but also the subsequent impact on its audiences. Full article