Search Results (563)

Urban Digital Twins (UDTs) represent a recent application of Digital Twins (DTs), with the objective of replicating cities and providing a framework for urban planning. The utilization of UDTs provides a structured approach for the modeling and analysis of urban environments, incorporating a range of geospatial data presented in both two-dimensional (2D) and three-dimensional (3D) formats. This article details the process of processing, modeling, and integrating urban geospatial data into a Digital Twin. Two integrations for end-user platforms were demonstrated using a Geographic Information System (GIS) and an Unreal Engine (UE5) game platform. GIS-based dashboard systems provide professionals with the tools necessary to monitor, analyze, and create scenarios, thereby promoting collaboration between authorities and citizens. Game engines have the potential to play a pivotal role in the development of future UDTs by facilitating the creation of immersive experiences that aid users in comprehending their environment and promoting citizen engagement. Full article

The collaboration between economic and ecological departments in land-use planning is crucial for advancing sustainable development. However, existing research has largely focused on macro-level policies and technical instruments, paying insufficient attention to the micro-level logics of behavior and strategic interactions between these two departments. This research employs a rigorous mixed-methods approach to bridge empirical depth with analytical rigor. The qualitative phase, encompassing 41 semi-structured interviews and analysis of 327 internal documents, examines the departments’ real-world motivations, strategic behaviors, and the cost–benefit structures underlying their decision-making. Based on these empirical findings, a tailored evolutionary game theory model is constructed to formally simulate the dynamic pathways and stable equilibria of collaboration between the Economic and Ecological Departments. Our analysis reveals that the evolutionary game system converges toward a dichotomy of stable states: a non-cooperative equilibrium characterized by development-oriented land-use planning with adaptive regulation, and a cooperative equilibrium underpinned by green-coordinated planning supported by stringent regulatory enforcement. A cooperative equilibrium is more readily achieved when both departments demonstrate a willingness to simultaneously increase their cost investment parameters in sustainable land-use planning. Conditions contrary to this mutual commitment lead to a non-cooperative equilibrium. Building on these findings, the study synthesizes this interplay into a novel “Institutional-Situational-Behavioral” (ISB) framework. This framework provides a cohesive theoretical lens for diagnosing and fostering interdepartmental collaboration in sustainable land governance. The research thus offers a theoretical foundation for analyzing the evolutionary dynamics of interdepartmental collaboration and delivers mechanism-informed policy guidance for enhancing sustainable land-use planning. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

The development of large-scale cascade hydropower complexes has improved the navigation conditions of mountainous rivers but creates unique “continuous dam zones,” presenting complex challenges for port site selection due to hydrological variability and geological risks. To address the lack of specialized evaluation tools for this specific context, this paper constructs a comprehensive evaluation indicator system tailored for mountainous reservoir areas. The proposed system explicitly integrates critical engineering and physical constraints—specifically fluctuating backwater zones, geological hazards, and dam-bypass mileage—alongside ecological and social requirements. The Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are integrated using a Game Theory model to determine combined weights, and the Evaluation based on Distance from Average Solution (EDAS) model is applied to rank the alternatives. An empirical analysis of the Xiluodu Reservoir area on the Jinsha River demonstrates that operational efficiency, geological safety, and environmental feasibility constitute the critical decision-making factors. The results indicate that Option C (Majiaheba site) offers the optimal solution (ASi = 0.9695), effectively balancing engineering utility with environmental protection. Sensitivity analysis further validates the consistency and stability of this ranking under different decision-making scenarios. The findings provide quantitative decision support for project implementation and offer a replicable reference for infrastructure planning in similar complex mountainous river basins. Full article

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics. Full article

Amidst the surge of high-density urban development and the growing demand for high-quality spaces, the Usable Area Coefficient (UAC) has emerged as a pivotal metric in the architectural planning. The rational calibration of the UAC for primary school buildings is key to balancing intensive land use, educational demands, and the well-being of children. Taking primary schools in a district of Qingdao as the research subject, this research rationally optimizes the range of UAC by constructing an evolutionary game model, based on quantitatively analyzing the divergent perspectives and requirements of three stakeholders: the government, school administrators, and students. After further identifying the key factors that influence the ultimate decision, the study yields the following insights: (1) The incremental comprehensive benefit emerges as the linchpin influencing the UAC. (2) The government’s risk compensation to schools and the benefit-sharing coefficient between schools and students exert significant impacts on system evolution. (3) Effective control of construction and land costs, coupled with enhanced availability of open activity spaces, paves the way for consensus on low UAC. This research not only furnishes a theoretical framework and practical guidance for harmonizing land use efficiency with educational excellence but also steers the design of salubrious primary school environments and informs pertinent policy-making. 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

Planning serves as a vital tool for achieving orderly land management and utilization. The success of conservation planning hinges on its ability to translate cultural heritage preservation needs into rational allocation and guidance of land resources, ultimately realizing a win–win outcome that fosters cultural continuity, social harmony, and economic development. Historic and cultural towns are highly representative urban and rural historic and cultural heritage sites. However, the participation components in the conservation planning of historic towns are complex, and the misalignment of the functions, rights and responsibilities, and interest demands of the participants often leads to a loss of actual benefits. To help achieve a reasonable transformation of the protection needs of historic towns and guide the cultural inheritance and socially harmonious development of urban and rural construction, based on game theory and the logic of planning rights games, this paper begins with an understanding of the relevant laws and regulations, conducts an empirical analysis of the game processes and situations of conservation planning in two provinces and four towns, and incorporates publicly available data from the internet for argumentation to explore the game states and operation mechanisms of conservation planning in historic and cultural towns. The findings reveal the following regarding historic town conservation planning: (1) it proceeds lawfully and rationally, reflecting collective rationality; (2) it exhibits two equilibrium modes: relatively static and dynamic; (3) game conflicts mainly manifest as multi-planning conflicts and the resulting conflicts among systems and inter-systems. The game dynamics are influenced by the value of the historic town, resource allocation, and the relationship between rights, responsibilities, and interests. To overcome the game dilemma, it is essential to establish effective cooperative mechanisms at the legal and regulatory levels based on the value of the historic town, allocate resources reasonably, and achieve a balance between rights, responsibilities, and interests. Full article

This study explored the influence of game-specific on-court drills on external load in junior and professional male tennis players. Using wearable inertial technology, a total of 345 drills performed during a training microcycle were analyzed. Drills were classified according to the usual tennis game situations (i.e., serve, return, baseline, net play, and all-court), and load was quantified through distance covered, explosive distance, accelerations, decelerations, and Player Load. Significant differences were found in load across playing situations, with baseline and all-court drills producing the highest demands, especially in distance and Player Load. Serve drills consistently showed the lowest external load, while acceleration and deceleration values remained stable. Age group comparisons revealed that juniors covered more distance and experienced higher overall load in return and baseline situations, while professionals showed greater acceleration and deceleration values. These findings highlight the relevance of adapting training load to the specific demands of the game situations, the developmental stage, and the skill level of players. Coaches and sports scientists can use these insights to better plan, monitor, and individualize training programs for injury prevention and performance optimization in high-performance tennis. Full article

Cross-modal and immersive technologies offer new opportunities for experiential learning in early childhood, yet few studies examine integrated systems that combine multimedia, mini-games, 3D exploration, virtual reality (VR), and augmented reality (AR) within a unified environment. This article presents the design and implementation of the Solar System Experience (SSE), a cross-modal extended reality (XR) learning suite developed for preschool education and deployable on low-cost hardware. A dual-perspective evaluation captured both preschool teachers’ adoption intentions and preschool learners’ experiential responses. Fifty-four teachers completed an adapted Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) questionnaire, while seventy-two students participated in structured sessions with all SSE components and responded to a 32-item experiential questionnaire. Results show that teachers held positive perceptions of cross-modal XR learning, with Subjective Norm emerging as the strongest predictor of Behavioral Intention. Students reported uniformly high engagement, with AR and the interactive eBook receiving the highest ratings and VR perceived as highly engaging yet accompanied by usability challenges. The findings demonstrate how cross-modal design can support experiential learning in preschool contexts and highlight technological, organizational, and pedagogical factors influencing educator adoption and children’s in situ experience. Implications for designing accessible XR systems for early childhood and directions for future research are discussed. Full article

The cross-fertilization of video games and tourism has expanded in recent years, with digital narratives increasingly shaping real-world travel behavior, yet the mechanisms linking mythological video games to pre-trip travel intention remain underexplored. Using the Chinese mythological game Black Myth: Wukong as a case, this study examines how digital myth narratives relate to overseas audiences’ perceptions of, and travel intentions towards, Chinese tourist destinations in a cross-cultural context. Based on a large corpus of YouTube comments, we integrate topic modeling, sentiment analysis, and interpretable machine learning to identify semantic cues associated with travel intention. The results indicate that multidimensional perceptions elicited by digital myth narratives are associated with a gradual evolution of destination image from cognitive to affective and then intentional. Cultural symbol perception, cross-cultural understanding, aesthetic appreciation, and emotional resonance show positive relationships with travel intention and appear as important predictors in the model. SHAP analysis further suggests a nonlinear threshold effect, whereby the probability that a comment is classified as expressing travel intention increases when overall perception reaches a relatively high level. Embedding the cognition–emotion–intention path within a digital game context, this study provides empirical evidence on destination image and behavioral intention in digital narrative settings and offers implications for cross-cultural communication and sustainable tourism planning. Full article

The dual imperative of mitigating carbon emissions and maximizing hydrocarbon recovery has amplified global interest in carbon capture, utilization, and storage (CCUS) technologies. These integrated processes hold significant promise for achieving net-zero targets while extending the productive life of mature oil reservoirs. However, their effectiveness hinges on a nuanced understanding of the complex interactions between geological formations, reservoir characteristics, and injection strategies. In this study, a comprehensive machine learning-based framework is presented for estimating CO₂ storage capacity and enhanced oil recovery (EOR) performance simultaneously in subsurface reservoirs. The methodology combines simulation-driven uncertainty quantification with supervised machine learning to develop predictive surrogate models. Simulation results were used to generate a diverse dataset of reservoir and operational parameters, which served as inputs for training and testing three machine learning models: Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN). The models were trained to predict three key performance indicators (KPIs): cumulative oil production (bbl), oil recovery factor (%), and CO₂ sequestration volume (SCF). All three models exhibited exceptional predictive accuracy, achieving coefficients of determination (R²) greater than 0.999 across both training and testing datasets for all KPIs. Specifically, the Random Forest and XGBoost models consistently outperformed the ANN model in terms of generalization, particularly for CO₂ sequestration volume predictions. These results underscore the robustness and reliability of machine learning models for evaluating and forecasting the performance of CO₂-EOR and sequestration strategies. To enhance model interpretability and support decision-making, SHapley Additive exPlanations (SHAP) analysis was applied. SHAP, grounded in cooperative game theory, offers a model-agnostic approach to feature attribution by assigning an importance value to each input parameter for a given prediction. The SHAP results provided transparent and quantifiable insights into how geological and operational features such as porosity, injection rate, water production rate, pressure, etc., affect key output metrics. Overall, this study demonstrates that integrating machine learning with domain-specific simulation data offers a scalable approach for optimizing CCUS operations. The insights derived from the predictive models and SHAP analysis can inform strategic planning, reduce operational uncertainty, and support more sustainable oilfield development practices. Full article

Integrated hydro-wind-solar-storage (HWSS) bases are pivotal for advancing new power systems under the low carbon goals. However, the independent decision-making of diverse generation investors, coupled with limited transmission capacity, often leads to a dilemma in which individually rational decisions lead to collectively suboptimal outcomes, undermining overall benefits. To address this challenge, this study proposes a novel cooperative game-based method that seamlessly integrates grid congestion into capacity allocation and benefit distribution. First, a bi-level optimization model is developed, where a congestion penalty is explicitly embedded into the cooperative game’s characteristic function to quantify the maximum benefits under different coalition structures. Second, an improved Shapley value model is introduced, incorporating a comprehensive correction factor that synthesizes investment risk, congestion mitigation contribution, and capacity scale to overcome the fairness limitations of the classical method. Third, a case study of a high-renewable-energy base in Qinghai is conducted. The results demonstrate that the proposed cooperative model increases total system revenue by 20.1%, while dramatically reducing congestion costs and wind/solar curtailment rates by 86.2% and 79.3%, respectively. Furthermore, the improved Shapley value ensures a fairer distribution, appropriately increasing the profit shares for hydropower (from 28.5% to 32.1%) and energy storage, thereby enhancing coalition stability. This research provides a theoretical foundation and practical decision-making tool for the collaborative planning of HWSS bases with multiple investors. Full article

Background: Rising obesity rates among young adults increase long-term health risks, especially cardiometabolic conditions such as type 2 diabetes mellitus. Digital interventions can offer scalable solutions to promote and support healthy behaviors by integrating personalized diet, physical activity promotion, and behavioral support. Objective: To assess the feasibility, user friendliness, adherence, and satisfaction of the Healthy Lifestyle Recommender System (HLRS). Secondary outcomes will include measures of metabolic health and obesity. Methods: A 3-month, single-arm pilot study conducted across European countries, including Bulgaria, Germany, Italy, Netherlands, Portugal, and Spain, enrolling 351 young adults (18–25 years old, BMI 18.5–29.9 kg/m²). The intervention includes a mobile app for meal planning (Nutrida v.1), gamified physical activity encouragement (GameBus), and real-time monitoring via a wearable smartwatch device. Primary outcomes are adherence and engagement, measured through app usage and participant feedback; secondary outcomes include anthropometry, physical activity, dietary patterns, psychological well-being, and selected biomarkers of metabolic health. Expected Outcomes: Improved engagement is expected to enhance lifestyle behaviors, supporting weight management and overall well-being. Findings will guide future large-scale interventions. Conclusions: This study will contribute to minimizing the impact of obesity in Europe. Full article