Search Results (444)

Background: Problematic Internet gaming (PIG), considered an early stage of Internet gaming addiction (IGA), has become increasingly prevalent among adolescents. This study focused on deviant peer affiliation (DPA) and hedonic gaming experience (HGE) as key mediators and examined four psychosocial risk factors closely related to them: interpersonal incompetence (II), perceived stress (PS), frustration (FR), and emotional loneliness (EL). Specifically, the study investigated how these four psychosocial risk factors influence adolescents’ DPA, HGE, and PIG, and whether DPA and HGE mediate these relationships. Methods: Based on existing validated scales, we developed a questionnaire to measure these seven constructs (II, PS, FR, IC, DPA, HGE, and PIG), proposed 14 hypotheses, and collected 214 valid responses from adolescents. Structural equation modeling (SEM) was used to test the hypothesized model. Findings: The results showed that all 14 hypotheses were supported. Specifically, interpersonal incompetence significantly predicted perceived stress; stress led to frustration; and frustration, in turn, contributed to emotional loneliness. Furthermore, all four psychosocial risk factors significantly predicted deviant peer affiliation, hedonic gaming experience, and ultimately, problematic Internet gaming among adolescents. Both DPA and HGE mediated the effects of psychosocial risk factors on adolescent problematic Internet gaming (PIG), with the model explaining moderate-to-high variance. This study highlights the importance of segmenting adolescents into more specific subgroups based on the distinct developmental pathways leading to PIG. Implications: Understanding the step-by-step mechanisms and psychological drivers of different adolescent subtypes can provide a more solid foundation for early identification and targeted intervention efforts. Full article

This study investigates collaborative disaster response strategies involving the government and social organizations from a dynamic perspective, incorporating stochastic disturbances that influence emergency resource supply. To examine the strategic interactions among the participants, three stochastic differential game models are formulated under distinct scenarios: centralized decision making for collusive emergency response, decentralized emergency response without a cost-sharing contract, and decentralized emergency response with a cost-sharing contract. Under an infinite-horizon planning framework, the closed-form solutions for the optimal response efforts and the corresponding value functions are derived for all three scenarios and comparatively analyzed. The results indicate that compared with the purely decentralized scenario, introducing a cost-sharing mechanism achieves a Pareto improvement by optimizing both overall system efficiency and emergency supply availability. Although the centralized collusive model results in the highest expected level of emergency resource supply, it is also associated with the greatest uncertainty. Furthermore, a numerical simulation based on emergency resource allocation during the Wenchuan earthquake is conducted. The results show significant differences in resource availability and response performance under different response mechanisms. Centralized collaboration, together with a well-designed cost-sharing mechanism, can significantly enhance the robustness and efficiency of the overall system, offering important insights for optimizing real-world disaster response strategies. Full article

Game commentary enhances viewer immersion and understanding, particularly in football video games, where dynamic gameplay offers ideal conditions for automated commentary. The existing methods often rely on predefined templates and game state inputs combined with an LLM, such as GPT-3.5. However, they frequently suffer from repetitive phrasing and delayed responses. Recent studies have attempted to mitigate the response delays by employing traditional machine learning models, such as SVM and ANN, for event prediction. Nonetheless, these models fail to capture the temporal dependencies in gameplay sequences, thereby limiting their predictive performance. To address these limitations, an integrated framework is proposed, combining a lightweight convolutional model with multi-scale temporal filters (OS-CNN) for real-time event prediction and an open-source LLM (LLaMA 3.3) for dynamic commentary generation. Our method incorporates prompt engineering techniques by embedding predicted events into contextualized instruction templates, which enables the LLM to produce fluent and diverse commentary tailored to ongoing gameplay. Evaluated in the Google Research Football environment, the proposed method achieved an F1-score of 0.7470 in the balanced setting, closely matching the best-performing GRU model (0.7547) while outperforming SVM (0.5271) and Transformer (0.7344). In the more realistic Balanced–Imbalanced setting, it attained the highest F1-score of 0.8503, substantially exceeding SVM (0.4708), GRU (0.7376), and Transformer (0.5085). Additionally, it enhances the lexical diversity (Distinct-2: +32.1%) and reduces the phrase repetition by 42.3% (Self-BLEU), compared with template-based generation. These results demonstrate the effectiveness of our approach in generating context-aware, low-latency, and natural commentary suitable for real-time deployment in football video games. Full article

(1) Background: Integrating assessment with training helps to enhance precision prosthetic rehabilitation of trans-radial amputees. This study aimed to validate a self-developed closed-loop rehabilitation platform combining accurate measurement in comprehensive assessment and immediate interaction in virtual reality (VR) training in refining patient-centered myoelectric prosthesis rehabilitation. (2) Methods: The platform consisted of two modules, a multimodal assessment module and an sEMG-driven VR game training module. The former included clinical scales (OPUS, DASH), task performance metrics (modified Box and Block Test), kinematics analysis (inertial sensors), and surface electromyography (sEMG) recording, verified on six trans-radial amputees and four healthy subjects. The latter aimed for muscle coordination training driven by four-channel sEMG, tested on three amputees. Post 1-week training, task performance and sEMG metrics (wrist flexion/extension activation) were re-evaluated. (3) Results: The sEMG in the residual limb of the amputees upgraded by 4.8%, either the subjects’ number of gold coins or game scores after 1-week training. Subjects uniformly agreed or strongly agreed with all the items on the user questionnaire. In reassessment after training, the average completion time (CT) of all three amputees in both tasks decreased. CTs of the A1 and A3 in the placing tasks were reduced by 49.52% and 50.61%, respectively, and the CTs for the submitting task were reduced by 19.67% and 55.44%, respectively. Average CT of all three amputees in the ADL task after training was 9.97 s, significantly lower than the pre-training time of 15.17 s. (4) Conclusions: The closed-loop platform promotes patients’ prosthesis motor-control tasks through accurate measurement and immediate interaction according to the sensorimotor recalibration principle, demonstrating a potential tool for precision rehabilitation. Full article

Traditional culture plays a vital role in shaping national identity and emotional belonging, making it imperative to explore innovative strategies for its digital preservation and engagement. This study investigates how interaction design in cultural digital games influences users’ emotional experiences and cultural understanding. Centering on the Chinese intangible cultural heritage puppet manipulation, we developed an interactive cultural game with three modes: gesture-based interaction via Leap Motion, keyboard control, and passive video viewing. A multimodal evaluation framework was employed, integrating subjective questionnaires with physiological indicators, including Functional Near-Infrared Spectroscopy (fNIRS), infrared thermography (IRT), and electrodermal activity (EDA), to assess users’ emotional responses, immersion, and perception of cultural content. Results demonstrated that gesture-based interaction, which aligns closely with the embodied cultural behavior of puppet manipulation, significantly enhanced users’ emotional engagement and cultural comprehension compared to the other two modes. Moreover, fNIRS data revealed broader activation in brain regions associated with emotion regulation and cognitive control during gesture interaction. These findings underscore the importance of culturally congruent interaction design in enhancing user experience and emotional resonance in digital cultural applications. This study provides empirical evidence supporting the integration of cultural context into interaction strategies, offering valuable insights for the development of emotionally immersive systems for intangible cultural heritage preservation. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational safety. This study focuses on the roof water inrush hazard in coal seams of the Banji coal mine, China. The conventional water-conducting fracture zone height estimation formula was calibrated through comparative analysis of empirical models and analogous field measurements. Eight principal controlling factors were systematically selected, with subjective and objective weights assigned using AHP and EWM, respectively. Game theory was subsequently implemented to compute optimal combined weights. Based on this, the vulnerability index model and fuzzy comprehensive evaluation model were constructed to assess the roof water inrush risk in the coal seams. The risk in the study area was classified into five levels: safe zone, relatively safe zone, transition zone, relatively hazardous zone, and hazardous zone. A zoning map of water inrush risk was generated using Geographic Information System (GIS) technology. The results show that the safe zone is located in the western part of the study area, while the hazardous and relatively hazardous zones are situated in the eastern part. Among the two models, the fuzzy comprehensive evaluation model aligns more closely with actual engineering practices and demonstrates better predictive performance. It provides a reliable evaluation and prediction model for addressing roof water hazards in the Banji coal seam. Full article

Mastering client-side Web programming is essential for the development of responsive and interactive Web applications. To support novice students’ self-study, in this paper, we propose a novel exercise format called the phrase fill-in-blank problem (PFP) in the Web Programming Learning Assistant System (WPLAS). A PFP instance presents a source code with blanked phrases (a set of elements) and corresponding Web page screenshots. Then, it requests the user to fill in the blanks, and the answers are automatically evaluated through string matching with predefined correct answers. By increasing blanks, PFP can come close to writing a code from scratch. To facilitate scalable and context-aware question creation, we implemented the PFP instance generation algorithm in Python using regular expressions. This approach targets meaningful code segments in HTML, CSS, and JavaScript that reflect the interactive behavior of front-end development. For evaluations, we generated 10 PFP instances for basic Web programming topics and 5 instances for video games and assigned them to students at Okayama University, Japan, and the State Polytechnic of Malang, Indonesia. Their solution results show that most students could solve them correctly, indicating the effectiveness and accessibility of the generated instances. In addition, we investigated the ability of generative AI, specifically ChatGPT, to solve the PFP instances. The results show 86.7% accuracy for basic-topic PFP instances. Although it still cannot fully find answers, we must monitor progress carefully. In future work, we will enhance PFP in WPLAS to handle non-unique answers by improving answer validation for flexible recognition of equivalent responses. Full article

Serious games are an important tool for education and training that offers interactive and powerful experience. However, a significant challenge lays with adapting a game to meet the specific needs of each player in real-time. The present paper introduces a framework to guide the development of serious games using a phased approach. The framework introduces a level of standardization for the game elements, scenarios and data descriptions, mainly to support portability, interpretability and comprehension. This standardization is achieved through semantic annotation and it is utilized by digital twins to support self-adaptation. The proposed approach describes the game environment using ontologies and specific semantic structures, while it collects and semantically tags data during players’ interactions, including performance metrics, decision-making patterns and levels of engagement. This information is then used by a digital twin for automatically adjusting the game experience using a set of rules defined by a group of domain experts. The framework thus follows a hybrid approach, combing expert knowledge with automated adaptation actions being performed to ensure meaningful educational content delivery and flexible, real-time personalization. Real-time adaptation includes modifying the game’s level of difficulty, controlling the learning ability support and maintaining a suitable level of challenge for each player based on progress. The framework is demonstrated and evaluated using two real-word examples, the first targeting at supporting the education of children with syndromes that affect their learning abilities in close collaboration with speech therapists and the second being involved with training engineers in a poultry meat factory. Preliminary, small-scale experimentation indicated that this framework promotes personalized and dynamic user experience, with improved engagement through the adjustment of gaming elements in real-time to match each player’s unique profile, actions and achievements. Using a specially prepared questionnaire the framework was evaluated by domain experts that suggested high levels of usability and game adaptation. Comparison with similar approaches via a set of properties and features indicated the superiority of the proposed framework. Full article

This study compares the Stackelberg game model and the entropy weight method for allocating intercity water rights in Heilongjiang Province (2014–2021). The entropy method objectively determines indicator weights, while the Stackelberg framework simulates leader–follower interactions between the water authority and users to balance efficiency and satisfaction. Under the same total water rights cap, the Stackelberg scheme achieves a comprehensive benefit of CNY 14,966 billion, 4% higher than the entropy method (CNY 14,436 billion). The results and comprehensive benefits of the two schemes are close to each other in the cities of Qiqihaer, Daqing, Hegang, etc., but the allocation method of the game theory is more in line with the practical needs and can meet the water demand of each region, and the entropy right method is more useful for the cities of Jiamusi, Jixi, and Heihe, while for other cities the water rights allocation appeared to be unreasonable. While the entropy approach is transparent and data-driven, it lacks dynamic feedback and may under- or over-allocate in rapidly changing contexts. The Stackelberg model adapts to varying demands, better aligning allocations with actual needs. We discuss parameter justification, sensitivity, governance assumptions, and potential extensions, including hybrid modeling, climate change integration, stakeholder participation, and real-time monitoring. The findings provide methodological insights for adaptive and equitable water allocation in regions with strong regulatory capacity. Full article

The progression of content during the training and development of young athletes is essential, while considering the developmental stages of the students/athletes. Therefore, it is crucial to monitor training sessions to ensure that content progression is followed and to assess how it is implemented. The aim of this study was to analyze the associations between different male categories of sports development in basketball through pedagogical variables and external loads planned by the coaches. The sample consisted of 148 sessions and 896 tasks, and the SIATE tool was used to observe both the pedagogical variables and the primary external load variables. Significant differences were observed primarily in the U16 category compared to the U12 and U14 categories. In examining the pedagogical variables, three key aspects were highlighted: content type, training methods, and level of opposition. The external load variables were aligned with the pedagogical variables, suggesting a progression of content. This indicates that instruction should follow an order, in which tactical load evolves from the simplest to the most complex, in accordance with the development and training stage of the students/athletes. The analyzed male basketball team demonstrated a content progression focused on the comprehensive development of the student/athlete, encouraging decision-making, and creating a complex, unpredictable, and random environment that closely resembles the dynamics of the real game. Full article

This literature review provides an overview of the food safety and toxicological characteristics of various heavy metals and metalloids and the public health significance of their occurrence in food. Metals also occur as natural components of the environment, but they can enter food of animal origin and the human body primarily due to anthropogenic (industrial, agricultural, transport-related) activities. The persistent heavy metals (e.g., Hg, Pb, Cd) found in the environment are not biodegradable, can accumulate, and can enter the bodies of higher animals and subsequently, humans, where they are metabolized into various compounds with differing toxicity. Thus, due to their environmental contamination, they can accumulate in living organisms and their presence in the food chain is of great concern for human health. Regulations of the European Community in force lay down maximum levels for a limited number of metals, and the types of regulated foodstuffs of animal origin are also narrower than in the past, e.g., wild game animals and eggs are not included. The regulation of game meat (including offal) deserves consideration, given that it is in close interaction with the environmental condition of a given area and serves as indicator of it. Full article

The concept of Nash equilibria in pure strategies for quantum extensions of the general form of the Prisoner’s Dilemma game is investigated. The process of quantization involves incorporating two additional unitary strategies, which effectively expand the classical game. We consider five classes of such quantum games, which remain invariant under isomorphic transformations of the classical game. The resulting Nash equilibria are found to be more closely aligned with Pareto-optimal solutions than those of the conventional Nash equilibrium outcome of the classical game. Our results demonstrate the complexity and diversity of strategic behavior in the quantum setting, providing new insights into the dynamics of classical decision-making dilemmas. In particular, we provide a detailed characterization of strategy profiles and their corresponding Nash equilibria, thereby extending the understanding of quantum strategies’ impact on traditional game-theoretical problems. Full article

This paper investigates the robust tracking control of unmanned aerial vehicles (UAVs) against external time-varying disturbances. First, by introducing a virtual position controller, we innovatively decouple the UAV dynamics into independent position and attitude error subsystems, transforming the robust tracking problem into two zero-sum differential games. This approach contrasts with conventional methods by treating disturbances as strategic “players”, enabling a systematic framework to address both external disturbances and model uncertainties. Second, we develop an integral reinforcement learning (IRL) framework that approximates the optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equations without relying on precise system models. This model-free strategy overcomes the limitation of traditional robust control methods that require known disturbance bounds or accurate dynamics, offering superior adaptability to complex environments. Third, the proposed recursive Ridge regression with a forgetting factor (${\mathrm{R}}^3{\mathrm{F}}^2$ ) algorithm updates actor-critic-disturbance neural network (NN) weights in real time, ensuring both computational efficiency and convergence stability. Theoretical analyses rigorously prove the closed-loop system stability and algorithm convergence, which fills a gap in existing data-driven control studies lacking rigorous stability guarantees. Finally, numerical results validate that the method outperforms state-of-the-art model-based and model-free approaches in tracking accuracy and disturbance rejection, demonstrating its practical utility for engineering applications. Full article

Trade-in programs have become a vital mechanism for promoting sustainable consumption and reducing negative impacts on the environment, gaining substantial support from branders, e-platforms, and consumers in recent years. Concurrently, the emergence of professional recyclers has provided firms with viable alternatives for the outsourcing of recycling processes. To investigate the optimal leadership and recycling model with respect to trade-in operations, this study examines the strategy selection in a platform-based supply chain under a resale model. A two-period game-theoretic framework is developed, encompassing four models: self-recycling and outsourcing models under the leadership of the brander or platform. The main findings are as follows: (1) In markets characterized by a low consumer price sensitivity, both branders and platforms tend to choose the self-recycling model to capture the closed-loop value. In contrast, in highly price-sensitive markets, both parties exhibit a preference for “free-riding” strategies. (2) Once the recycling leader is determined, adopting a self-recycling model can lead to a relative win–win outcome in high price sensitivity contexts. (3) With a short product iteration cycle, both the brander and platform should strategically lower their prices in the first period, sacrificing short-term profits to enhance trade-in incentives and maximize long-term gains. (4) When the brander leads the recycling process, they should consider reusing the resources derived from old products; however, in platform-led models, the brander can only consider reusing the recycled resources in a low price sensitivity market. This study provides strategic insights for the sustainable development of the supply chain through the analysis of a game between a brander and an e-commerce platform, enriching the literature on CLSCs through integrating trade-in leadership selection and the choice to outsource, offering theoretical support for dynamic pricing strategies over multi-period product lifecycles. Full article