Search Results (2,664)

Single-player role-playing games (RPGs) combine two promises that do not always align: delivering a compelling narrative experience (world, characters, choices, and consequences) while sustaining a demanding ludic trajectory in which players face obstacles, master systems, and progress over time. This Systematic Literature Review (SLR) synthesizes existing evidence on the evolution of narrative and challenge in single-player RPGs from a player-centered perspective, with particular attention paid to immersion, engagement, flow, and perceived agency. A multi-database search strategy was conducted across Google Scholar, Scopus, IEEE Xplore, and the ACM Digital Library using query strings targeting narrative/agency, challenge and dynamic difficulty adjustment (DDA), adaptive difficulty, and the historical evolution of RPG narrative design, following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-reported selection flow and Rayyan-supported screening. From 423 identified records, duplicates and non-eligible records were removed through staged screening, yielding 43 reports sought for retrieval; because six were not accessible in full text at consolidation, the synthesis was conducted on 37 full-text articles. The findings indicate (i) a predominance of work on narrative and agency, where agency is framed as a design effect rather than merely the presence of explicit branching choices; (ii) a recent rise in challenge/adaptation research, frequently tied to flow, fairness, and differentiated player profiles; and (iii) the emergence of artificial intelligence (AI)-driven approaches, including non-player character (NPC) systems, combat AI, reinforcement learning, and large language model (LLM)-based narrative control, which amplify core design trade-offs between narrative coherence and perceived agency. Beyond synthesizing a dispersed body of literature, the review contributes an integrated player-centered analytical framework that brings together narrative, challenge, and player experience, while also highlighting the need for more consistent measurement practices, stronger comparative designs, and longer-term empirical work in single-player RPG research. Full article

This paper presents an adaptive optimal consensus tracking control scheme for canonical nonlinear multi-agent systems (MASs) with unknown dynamics, employing an actor–critic reinforcement learning (RL) framework. The scheme integrates a sliding mode mechanism to suppress tracking errors and ensure consensus tracking between the followers and the leader. Additionally, optimal control is designed to find a Nash equilibrium in a graphical game. To address the intractability of obtaining an analytical solution for the coupled Hamilton–Jacobi–Bellman (HJB) equation, a policy iteration algorithm is utilized. Within this algorithm, a critic neural network (NN) approximates the gradient of the optimal value function, while an actor NN approximates the optimal control policy. Together, these networks form a compact actor–critic (AC) architecture that achieves optimal consensus tracking. Furthermore, the proposed method guarantees the boundedness of all closed-loop signals while ensuring consensus tracking. Finally, two simulations are conducted to verify the effectiveness and advantages of the proposed method. Full article

Challenges remain in developing a comprehensive understanding of spatial cognition, including gender and developmental differences, partly due to limitations of well-established spatial measures. Many traditional tasks face accessibility constraints and are not well suited for use across broad age ranges, populations, or ability levels. The present study introduced two game-based tasks, Q-bitz^® and Spot it!^®, designed to assess mental rotation and object location memory, respectively. We examined whether these game-based measures meaningfully complement established spatial tests, the Mental Rotation Test (MRT) and the Object Location Memory (OLM) task, across a wide age range (7–79 years, N = 114). Results indicated that MRT scores were strongly related to Q-bitz performance, whereas OLM scores were strongly related to Spot it! performance, supporting the convergent validity of the game-based tasks. Notably, gender-specific patterns emerged in the relationships among spatial measures, suggesting differences in spatial function. Age was associated with performance on speeded tasks (Q-bitz and Spot it!) but not with accuracy-based MRT or OLM performance. Together, these findings demonstrate that game-based assessments capture meaningful spatial constructs and reveal gender-specific patterns across the lifespan, providing a practical and ecologically valid approach for advancing research on spatial cognition. Full article

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator. Full article

Digital game-based learning environments (DGBLEs) are increasingly integrated into classrooms as learning tools, yet limited research exists regarding the impact of students’ discrete emotions on digital gameplay performance. This study examined the role of emotions and arousal in predicting performance outcomes during digital gameplay. Thirty-two grade 5 students (M_age = 10.99, 62.5% male) played four digital games (two math; two identically designed non-math). During gameplay, real-time heart rate and affective data were collected and analyzed using an interpretable machine learning approach (XGBoost). Results suggest that students performed better on non-math games, as compared to math games. Real-time anger was associated with lower performance, particularly in games, whereas other emotions and physiological measures were not significant predictors. This pilot investigation suggests that discrete emotions, particularly anger, may play a more important role in performance during math gameplay than in comparable non-math activities. The results highlight the importance of supporting emotional regulation during digital math learning, as unmanaged anger may impact performance. This study contributes to the growing literature on affective dynamics in digital game-based learning. Full article

Against the backdrop of low-carbon transportation and urban logistics transformation, metro-based freight is regarded as an important pathway for emission reduction. This paper constructs a tripartite evolutionary game model involving the government, logistics enterprises, and metro operators, and analyzes multi-agent strategy evolution and the influence of key parameters using replicator dynamics equations and numerical simulation. The results show that well-designed subsidies and penalties can effectively promote a stable state characterized by “active government intervention, active response from logistics enterprises, and low-carbon integrated passenger and freight transportation by metro operators”. Reducing the cost of transformation can improve evolutionary efficiency, while excessively high subsidies may weaken the government’s willingness to intervene. This study provides insights for optimizing low-carbon transportation policies and supporting the development of metro-based freight systems. Full article

Financial literacy is critical for individual well-being and sustainable economic development, yet significant gaps remain among Portuguese young adults. Using a two-phase design, this study combines a diagnostic assessment and multi-criteria decision analysis to identify and prioritise effective financial education strategies. In Phase 1, a diagnostic questionnaire administered to 172 first-year university students revealed pronounced deficiencies in core financial concepts. Only 29.1% correctly answered a question on compound interest, and almost half were unable to understand the concept of inflation. Additionally, 62.8% reported low exposure to financial education during compulsory schooling, and 59.9% strongly agreed that it should be included in the mandatory curriculum, indicating both unmet need and strong receptiveness. Phase 2 employed the Analytic Hierarchy Process (AHP) to evaluate five educational alternatives across four criteria. Engagement and motivation (0.32) and knowledge acquisition (0.31) were prioritised over behavioural impact (0.22) and accessibility (0.15). Based on expert assessments weighted by student preferences, in-person courses emerged as the most effective strategy (0.42), substantially outperforming online courses (0.22), videos and digital content (0.14), books (0.13), and games (0.10). The findings point to the need for policy-driven integration of structured, educator-led financial education within formal curricula, supported by approaches that prioritise active engagement and knowledge acquisition over convenience, with digital tools serving as complements rather than replacements. Full article

To explore the evolutionary dynamics of green product markets under bounded rationality, this study develops a tripartite evolutionary game model involving the government, passenger vehicle enterprises, and consumers, using China’s new energy vehicle (NEV) market as a case study. By integrating system dynamics with real-world data and policies, the paper simulates strategy evolution paths and identifies equilibrium conditions. The results show a unique evolutionarily stable strategy: the government refrains from regulation, enterprises actively produce NEVs, and consumers actively purchase green products. The government’s strategy is primarily influenced by enterprises, while enterprises’ strategy is mainly driven by consumers. Numerical analysis reveals that when the premium payment ratio of green products (price difference relative to conventional vehicles) is controlled between 27.27% and 31.82%, the market evolves most rapidly toward the ideal equilibrium. Furthermore, when the additional positive benefit ratio of green consumption falls below 36.36%, market formation and development are severely hindered; raising this ratio to 40.91% yields significant promotion effects, beyond which marginal benefits diminish. These findings provide quantitative benchmarks for policy design and strategic decision-making to foster self-sustaining green product markets. Full article

Competence-oriented Education for Sustainable Development requires evidence that immersive and gamified learning experiences elicit sustainability-relevant change beyond short pre–post windows. This study examines the Art Nouveau Path, a location-based mobile augmented reality heritage game implemented in Aveiro, Portugal, using a four-wave repeated cross-sectional design with anonymous student samples: baseline (S1-PRE, N = 221), immediate post-activity (S2-POST, N = 439, validated n = 438), follow-up (S3-FU, N = 434), and distant follow-up (S4-DFU, N = 69, validated n = 67). Analyses were anchored in a shared 25-item GreenComp-based questionnaire (GCQuest) block targeting Embodying Sustainability Values (ESVs; scale of 1 to 6) and combined distribution-aware descriptives, nonparametric omnibus, and pairwise tests with Holm correction, and planned robustness checks including equal-n downsampling and alternative scoring. Results displayed a pronounced post-activity peak (S2-POST), partial attenuation at follow-up (S3-FU), and convergence toward baseline at distant follow-up (S4-DFU), accompanied by loss of the high-agreement tail. Item-level contrasts suggested that later-wave declines concentrated in effortful self-regulation and critical appraisal items, whereas value endorsement items were more stable. These findings indicate that field-deployable mobile AR heritage paths may generate strong proximal competence-aligned signals; nevertheless, durable enactment-oriented change is likely to require structured reinforcement and integration into broader curricular sequences. Full article

Promoting the effective use of abandoned farmland has become a key policy priority for strengthening food security in China. However, disentangling the decision-making processes among diverse participating actors is a foundational prerequisite for addressing the governance challenge of abandoned farmland utilization. Building on this, the present study employs a four-actor evolutionary game model and sensitivity analysis of key parameters to systematically examine the interactions among four key actors—local governments, village collectives, family farms, and farmers—and to identify the corresponding evolutionarily stable strategies (ESSs) across different stages of abandoned farmland utilization. The results show that: (1) Multi-actor strategic interactions in abandoned farmland utilization exhibit a multi-stage evolutionary trajectory, in which all actors gradually shift their strategic choices under changing cost–benefit structures, regulatory intensity, and coordination conditions, leading to different evolutionary stable equilibria across governance stages. (2) The configuration in which local governments adopt loose regulation, the village collective plays an active coordinating role, family farms pursue long-term operations, and farmers choose recultivation is a key condition for achieving a Pareto-optimal equilibrium. (3) Although farmers’ production willingness and behavioral choices form the basis for the utilization of abandoned farmland, spontaneous individual action alone is insufficient to address the structural contradictions currently facing abandoned farmland utilization in China. To effectively promote the evolution of abandoned farmland governance toward a stable collaborative equilibrium and ultimately realize sustainable utilization, it is necessary to further optimize governmental administrative control models and incentive mechanisms, strengthen the organizational and coordinating functions of village collectives, and improve long-term operational support systems for family farms. This study systematically elucidates the underlying logic of China’s abandoned farmland utilization from the perspective of multi-actor behavioral decision-making, providing policy-referential insights for optimizing policy design, reducing coordination costs, and improving the efficiency of abandoned farmland utilization. Full article

As major sources of pollution and carbon emissions, energy enterprises have long faced challenges in their technological transformation due to the industry’s characteristics of high investment costs and strong lock-in effects. While formal mechanisms such as government subsidies can impose short-term constraints, they fail to stimulate the sector’s intrinsic motivation. Can the reputation incentive mechanism be the key to breaking the deadlock? This paper constructs a three-party evolutionary game model involving energy enterprises, the public, and the government from the perspective of informal institutions. For the first time, it incorporates the dual effects of reputation gains and losses into a unified framework. The results show that reputation incentives are not merely a “cherry on top,” but rather independently drive transformation by moderating enterprises’ cost–benefit structures. The evolution of the three-party strategies exhibits dynamic synergy, and the system equilibrium depends on the threshold matching of key parameters. Subsidy policies are effective in the short term, but may crowd out the role of reputation in the long term. This paper reveals the underlying logic by which the integration of informal institutions and formal regulations drives profound transformation, offering new theoretical perspectives and practical guidance for designing incentive-compatible multi-stakeholder governance frameworks. Full article

Aging is a progressive and heterogeneous biological process influenced by multiple factors that may compromise physical and cognitive capacities and increase the risk of frailty, functional decline, and falls in older adults. Falls represent a major public health concern due to their impact on independence and long-term care demand. Immersive virtual reality (IVR) delivered through active video games (exergames) has emerged as a preventive strategy that integrates sensory, motor, and cognitive stimulation within controlled and engaging environments, particularly where traditional programs face challenges related to adherence and individual adaptation. This study aims to determine the feasibility and implementation of an IVR-based program for falls prevention in older adults at risk of frailty in primary health care (PHC). A quasi-experimental pre–post design will be conducted with an intervention group (IVR/exergames) and a conventional control group, including a total sample of 40 participants (20 per group). The protocol comprises three phases: baseline assessment and IVR familiarization; a 12-week intervention delivered twice weekly; and post-intervention assessment. The primary outcome will be fall risk assessed using the Timed Up and Go (TUG) test. Secondary outcomes include physical performance (Short Physical Performance Battery, SPPB, and handgrip dynamometry) and psychological aspects related to falls (Falls Efficacy Scale International, FES-I, and Activities-specific Balance Confidence Scale, ABC). Feasibility indicators will include recruitment, adherence, retention, and cybersickness. A reduction in TUG time is expected, providing preliminary evidence on the feasibility of integrating IVR-based programs for falls prevention within PHC systems. Full article

Multi-source environmental data are increasingly used for measurement, reporting and verification, and for coordinating low-carbon innovation across interorganizational networks. However, voluntary data sharing remains limited because participants face asymmetric costs, leakage and compliance risks, and uncertainty in value capture. This study develops a network evolutionary game model to examine how cooperative data sharing emerges and stabilizes in green innovation networks. We specify a two-strategy game in which heterogeneous agents choose between sharing and withholding. The payoff structure integrates private innovation gains from their own data, cross-partner synergy, external incentives, fixed governance costs, and stock-scaled sharing and risk burdens. Agents interact on a Barabási–Albert scale-free network and update strategies via local imitation under a Fermi rule. Simulations show that cooperation can diffuse from low initial participation and converge to a high-sharing regime when benefit allocation and incentive intensity jointly offset cost and risk frictions. Several governance levers exhibit threshold-type effects, including the allocation share, the opportunity loss of non-sharing, and the marginal cost–risk burden. Multi-source synergy and subsidies further raise the attainable cooperation level, but with diminishing marginal returns. Degree heterogeneity accelerates diffusion once hub organizations adopt sharing, while also raising fairness concerns when benefits concentrate on central nodes. Overall, the findings provide green-innovation-specific governance conditions that translate threshold regions into implementable design targets for sustainable environmental data sharing. Full article

The study explores the relatively underexamined role of artificial intelligence policies in sustainable agricultural development by investigating how governments, enterprises, and farmers interact under different policy incentives. A combination of tripartite evolutionary and Stackelberg game models is employed to examine how artificial intelligence can support more effective policy design, improve the speed of response, and foster greater collaboration among stakeholders. The analysis primarily draws on simulated data, reflecting the impact of policy incentives across various contexts. Findings suggest that artificial intelligence policies can meaningfully enhance cooperation, thereby promoting sustainable agricultural development. Higher levels of government incentives appear to encourage participation from both enterprises and farmers, while artificial intelligence contributes to faster and more precise policy adjustments. Theoretically, the study offers a framework for understanding artificial intelligence policy in agriculture and elucidates the mechanisms governing stakeholder interactions. From a practical perspective, the results provide cautious guidance for the design of artificial intelligence policies aimed at fostering sustainability. Full article

Background: Technology such as telerehabilitation, virtual reality, robotics, and wearable systems are reshaping pediatric physiotherapy. While evidence remains fragmented, there is little guidance on how these approaches can be integrated into coherent, family-centered care pathways. Objective: To develop the Digital Pediatric Physiotherapy Framework (DPPF) based on a systematic review of randomized evidence on digital interventions in pediatric physiotherapy. Methods: Several databases were searched for randomized trials published after 1 January 2020, including PubMed, Web of Science Core Collection, and Google Scholar. The included studies assessed the results of physiotherapist-delivered or physiotherapist-supervised digital interventions in children and adolescents aged 18 and younger. Population, intervention, outcome, implementation, and safety data were extracted. Considering the substantial heterogeneity of the findings, they were synthesized narratively. Cochrane RoB 2 was used to assess risk of bias, and GRADE was used to evaluate certainty of evidence. Results: Twenty-nine trials involving 1196 participants were included. Most studies examined virtual reality and gaming-based interventions, with fewer evaluating telerehabilitation/tele-exercise and robotic or wearable technologies. Digital interventions were most often directed at body-function and activity-level outcomes, while participation outcomes were less frequently studied. The strongest evidence supported short-term benefits in balance, gross motor function, upper-limb activity, pain, and selected fitness outcomes, particularly in children with cerebral palsy. Evidence for telerehabilitation and robotic or wearable approaches was more limited but generally promising. Implementation, equity, cost, and long-term outcomes were rarely reported. No eligible trial directly evaluated electronic patient-reported outcome measures, digital triage, or clinical decision support as stand-alone interventions. Conclusions: Digital interventions have the potential to strengthen pediatric physiotherapy, particularly for short-term motor and functional outcomes. The proposed DPPF provides an implementation-informed structure to guide future research, pathway design, and more purposeful integration of digital health into pediatric rehabilitation practice. Full article