Search Results (644)

A growing international consensus holds that science education must advance beyond content coverage to cultivate robust understanding of the Nature of Science (NoS)—how scientific knowledge is generated, justified, revised, and socially negotiated. Yet naïve conceptions persist among students and teachers, and effective, scalable classroom strategies remain contested. This narrative review synthesizes research and practice on games and playful activities that make epistemic features of science visible and discussable. We organize the repertoire into six families—(i) observation–inference and discrepant-event tasks; (ii) pattern discovery and rule-finding puzzles; (iii) black-box and model-based inquiry; (iv) activities that dramatize tentativeness and anomaly management; (v) deliberately underdetermined mysteries that cultivate warrant-based explanations; and (vi) moderately contextualized games. Across these designs, we analyze how specific mechanics afford core NoS dimensions (e.g., observation vs. inference, creativity, plurality of methods, theory-ladenness and subjectivity, tentativeness) and what scaffolds transform playful engagement into explicit, reflective learning. We conclude with pragmatic guidance for teacher education and curriculum design, highlighting the importance of language supports, structured debriefs, and calibrated contextualization, and outline priorities for future research on equity, assessment, and digital extensions. Full article

The crisis of research integrity triggered by academic misconduct, such as scientific fraud and paper retractions, has emerged as a critical issue demanding urgent resolution within the academic community. Blockchain (BC), with its core features of distributed ledger, peer-to-peer transmission, consensus mechanisms, timestamps, and smart contracts, offers novel technical solutions for research institutions seeking efficient models of research credit supervision. By incorporating the psychological factors of risk perception among decision-makers and the dynamic evolution of behavioral decision-making, and drawing on prospect theory, this study has constructed an evolutionary game model involving researchers, scientific research institutions, and governmental entities to examine BC-enabled research credit supervision. This model analyzes the key determinants influencing scientific research institutions’ adoption of blockchain regulation (BC regulation), elucidates the behavioral characteristics and boundary conditions of research integrity among researchers under this new regulatory paradigm, and reveals the dynamic evolutionary trajectory of collaborative supervision between governments and scientific research institutions. The findings indicate the following: (1) Compared to traditional regulation, the BC regulation demonstrates superior regulatory effectiveness at equivalent levels of researcher integrity and misconduct costs, as well as under identical settings for reputational loss and penalties. (2) In addition to cost considerations and government subsidies, factors such as loss aversion coefficient, risk preference coefficient, and privacy breach losses are critical in influencing research institutions’ decisions to implement BC regulation. (3) The evolution of blockchain-empowered regulatory models encompasses three distinct evolutionary patterns. This study provides a theoretical foundation and a simulation case to optimize regulatory strategy formulation and resource allocation, thereby enhancing the effectiveness of research credit supervision. Full article

With origins in video gaming, 3D virtual worlds (VWs) are digital environments where people engage and interact synchronously using digital characters called avatars. VWs may have future potential for delivering youth mental health (YMH) services. Despite progress in developing VW-based YMH interventions, limited consultation with young people may be contributing to mixed uptake and engagement. This study aimed to understand how young people with experiences accessing YMH services view the potential (i.e., hypothetical) use of VWs for YMH service delivery to understand qualitative factors influencing uptake. Eleven 18–25-year-old consumers (M = 22.91 years; five women, five men, and one non-binary person) took part in one-on-one, semi-structured interviews via videoconferencing. Interviews explored anticipated ease of use, helpfulness, and perceived intention to use VW-based YMH interventions if they were made available. Interviews were analysed using reflexive thematic analysis. Four themes were produced: (1) VWs as unique therapeutic spaces; (2) creative engagement for therapy; (3) VW communication promoting both connection and distance; (4) flexible access. All participants expressed a level of openness towards the potential use of VWs for YMH interventions. Features such as creative world-building and avatar customisation, increased anonymity, and remote accessibility were seen as ways to improve access to convenient, personalised, and engaging mental healthcare. Concerns included technology misuse, privacy risks, and reduced physical and emotional presence. Future research and service development should test real-world outcomes to ensure clinical benefit and employ codesign approaches that leverage servicer-users’ expectations to ensure accessible and acceptable delivery. Full article

The unequal status between manufacturers and live-streamers often undermines supply chain profitability and social welfare. However, the “volume guarantee” commission mode, designed to mitigate this issue, has proven ineffective in practice. This paper adopts a Nash bargaining fairness framework to analyze this paradox, incorporating two defining features of live-streaming commerce: the social network effect and the streamer’s cost of purchasing public domain traffic. We develop a dynamic game model involving the platform, manufacturer, streamer, and consumers to examine commission mode selection and supply chain decision-making. Our analysis yields four key findings: (1) Under Nash bargaining fairness, the “volume guarantee” mode is invariably redundant, regardless of who sets the sales threshold. Bargaining power only influences profit distribution via commission rates without distorting optimal product pricing or traffic acquisition decisions. (2) The social network effect boosts product prices, traffic purchases, total profit, and social welfare, with its impact amplified by the streamer’s fanbase size. Thus, collaborating with top-streamers is advantageous for manufacturers. (3) While higher platform traffic costs do not affect the optimal product price, they reduce traffic purchase volume, thereby decreasing supply chain profits and social welfare. (4) To enhance social welfare, platforms can implement differentiated traffic pricing, offering discounts to top-streamers. This study provides critical managerial insights for designing fair contracts and fostering equitable cooperation in live-streaming ecosystems. Full article

This study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions. Full article

Public understanding of complex sustainability concepts like life cycle assessment (LCA) is crucial for promoting environmentally responsible consumption yet remains a significant educational challenge. This study introduces and evaluates Paper Goes Green, a competitive board game designed to make abstract LCA principles tangible and personally relevant. The game simulates the paper production chain, compelling players to make strategic decisions about resource allocation, production pathways (conventional vs. green), and waste management to fulfill paper orders. Through a single-group pre-test/post-test design with 85 participants (25 environmental educators and 60 public members), the game’s efficacy was assessed. Paired-sample t-tests revealed significant improvements in participants’ perceived knowledge of green chemistry/LCA (pre-game mean 2.05, post-game 3.24 on a 5-point scale, p < 0.001), pro-environmental attitudes (3.38 to 4.22, p < 0.001), and behavioral intentions toward green consumption (3.97 to 4.44, p < 0.001). These gains correspond to medium-to-large effect sizes (Cohen’s d = 0.94 for knowledge, 0.70 for attitude, 0.71 for behavior), indicating substantial practical impact. Qualitative feedback further highlighted the game’s engaging and thought-provoking nature. Notably, specific design features—such as immediate feedback, player control, and interactivity—were identified as key contributors to learning, fostering systems thinking in players. These findings suggest that Paper Goes Green is a promising educational tool for translating complex environmental science into an engaging, impactful learning experience. The game effectively bridges the gap between abstract concepts and real-world consumer choices, fostering life cycle thinking and empowering players to make greener choices in their daily lives. Full article

This study evaluates the feasibility of using a lightweight, off-the-shelf sensing system for short-term stress detection during exergaming. Most existing studies in stress detection compare rest and task conditions, providing limited insight into continuous stress dynamics, and there is no agreement on optimal sensor configurations. To address these limitations, we investigated dynamic stress responses induced by a cognitive–motor task designed to simulate rehabilitation-like scenarios. Twenty-three participants completed the experiment, providing electrodermal activity (EDA), blood volume pulse (BVP), self-report, and in-game data. Features extracted from physiological signals were analyzed statistically, and shallow machine learning classifiers were applied to discriminate among stress levels. EDA-based features reliably differentiated stress conditions, while BVP features showed less consistent behavior. The classification achieved an overall accuracy of 0.70 across four stress levels, with most errors between adjacent levels. Correlations between EDA dynamics and perceived stress scores suggested individual variability possibly linked to chronic stress. These results demonstrate the feasibility of low-cost, unobtrusive stress monitoring in interactive environments, supporting future applications of dynamic stress detection in rehabilitation and personalized health technologies. Full article

Against the backdrop of inclusive development and modernization of employment governance, the limitations of traditional approaches to promoting employment for persons with disabilities—such as information asymmetries and inefficient resource allocation—have become increasingly salient. Building a systematic promotion framework for disability employment has therefore emerged as a critical agenda for advancing modern social governance. Drawing on bounded rationality and information asymmetry theories, this study develops a tripartite evolutionary game model encompassing government, employers, and persons with disabilities. By incorporating key elements such as initial intentions, skill matching, and policy signal transmission, the model analyzes the strategic choices and dynamic interactions among stakeholders. We conduct numerical simulations using delay differential equations (DDEs), perform stability and sensitivity analyses in MATLAB R2024b, and triangulate findings with a practice-based case from Shanghai. The results indicate that persons with disabilities exhibit the highest policy responsiveness within the employment ecosystem and act as the core driver of convergence toward desirable equilibria through four mechanisms: skill-matching effects, policy signal diffusion, perceived institutional fairness, and system-level synergy gains. Although employer subsidies and penalties directly target firms, they exert the strongest psychological incentive effects on persons with disabilities, revealing a “misaligned incentives” feature in policy signaling. Systemic synergy gains activate market network effects, facilitating a pivotal shift from “policy transfusion” to “market self-sustenance.” Based on these findings, we propose a diversified policy toolkit, enhanced policy signaling mechanisms, and innovations in concentrated employment models to support the modernization of disability employment governance. Full article

As global copper demand continues to grow, China, being the largest copper consumer, faces increasingly complex challenges in ensuring the security of its supply chain. However, a substantive gap remains: prevailing assessments rely on static index systems and discrete scenario analyses that seldom model uncertainty-driven, continuous-time strategic interactions, leaving the conditions for self-enforcing cooperation and the attendant policy trade-offs insufficiently identified. This study models the interaction between Chinese copper importers and foreign suppliers as a continuous-time stochastic differential game, with feedback Nash equilibria derived from a Hamilton–Jacobi–Bellman system. The supply security utility is specified as a diffusion process perturbed by Brownian shocks, while regulatory intensity and profit-sharing are treated as structural parameters shaping its drift and volatility—thereby delineating the parameter region for self-enforcing cooperation and clarifying how sudden disturbances reconfigure equilibrium security. The research findings reveal the following: (i) the mean and variance of supply security utility progressively strengthen over time under the influence of both parties’ maintenance efforts, while stochastic disturbances causing actual fluctuations remain controllable within the contract period; (ii) spontaneous cooperation can be achieved under scenarios featuring strong regulation of domestic importers, weak regulation of foreign suppliers, and a profit distribution ratio slightly favoring foreign suppliers, thereby reducing regulatory costs; this asymmetry is beneficial because stricter oversight of domestic importers curbs the primary deviation risk, lighter oversight of foreign suppliers avoids cross-border enforcement frictions, and a modest supplier-favored profit-sharing ratio sustains participation—together expanding the self-enforcing cooperation set; (iii) sudden events exert only short-term impacts on supply security with controllable long-term effects; however, an excessively stringent regulatory environment can paradoxically reduce long-term supply security. Security effort levels demonstrate positive correlation with supply security, while regulatory intensity must be maintained within a moderate range to balance incentives and constraints. Full article

This paper addresses the cold-start problem and rare event prediction challenges in Olympic medal forecasting by proposing a predictive framework that integrates multi-granularity transfer learning with extreme value theory. The framework comprises two main components, a Multi-Granularity Transfer Learning Core (MG-TLC) and a Rare Event Analysis Module (RE-AM), which address multi-level prediction for data-scarce countries and first medal prediction tasks. The MG-TLC incorporates two key components: Dynamic Feature Space Reconstruction (DFSR) and the Hierarchical Adaptive Transfer Strategy (HATS). The RE-AM combines a Bayesian hierarchical extreme value model (BHEV) with piecewise survival analysis (PSA). Experiments based on comprehensive, licensed Olympic data from 1896–2024, where the framework was trained on data up to 2016, validated on the 2020 Games, and tested by forecasting the 2024 Games, demonstrate that the proposed framework significantly outperforms existing methods, reducing MAE by 25.7% for data-scarce countries and achieving an AUC of 0.833 for first medal prediction, 14.3% higher than baseline methods. This research establishes a foundation for predicting the 2028 Los Angeles Olympics and provides new approaches for cold-start and rare event prediction, with potential applicability to similar challenges in other data-scarce domains such as economics or public health. From a symmetry viewpoint, our framework is designed to preserve task-relevant invariances—permutation invariance in set-based country aggregation and scale robustness to macro-covariate units—via distributional alignment between data-rich and data-scarce domains and Olympic-cycle indexing. We treat departures from these symmetries (e.g., host advantage or event-program changes) as structured asymmetries and capture them with a rare event module that combines extreme value and survival modeling. Full article

The global creator economy has generated standardised monetisation strategies, yet their effectiveness varies significantly across regional contexts. This study examines how South African student influencers adapt global monetisation approaches to local market conditions on TikTok and Instagram. Using a mixed-methods approach, we collected data from 20 student influencers (aged 18–28, 1000–50,000 followers) through structured surveys and thematic coding of social media content across diverse categories including beauty, lifestyle, and gaming. Our findings reveal three key adaptation patterns: (1) Strategic localisation—influencers modify brand partnership approaches to align with local business practices and payment capabilities; (2) Platform arbitrage—creators leverage platform-specific features differently than global best practices due to regional access limitations, particularly TikTok’s creator fund restrictions; and (3) Resource-constrained innovation—student influencers develop alternative monetisation methods, including direct product sales and educational content, to overcome socio-economic barriers. Beauty influencers demonstrate the highest adaptation success with brand sponsorships (35% of participants), whilst micro-influencers pivot towards affiliate marketing and entrepreneurial ventures. The study contributes to platform economy literature by demonstrating that successful monetisation requires strategic adaptation rather than direct replication of global models. These findings offer practical insights for creators in emerging markets and platform developers seeking to support regional creator economies. The research highlights the need for context-sensitive approaches to digital entrepreneurship in the Global South. Full article

Serious games in nursing education provide students with unique opportunities to increase knowledge and enhance decision-making and problem-solving skills. Hence, serious games from simple quizzes that test students’ knowledge to Virtual Reality simulations that gauge students’ ability skills have been developed. This evidence-based review examines the latest initiatives in serious games for nursing curriculum focusing on the design and their technological features to highlight the need of pre-selecting the appropriate elements when conceptualizing a nursing serious game. Using search algorithms in Scopus and PubMed, 1969 articles published between 2019 and 2023 were screened, resulting in 81 studies and 69 unique nursing serious games involving over 7000 nursing students. Geographical distribution of serious games, the games’ type, teaching subject, nursing courses incorporating the games, technologies embarked, and different gaming platforms/engines utilized for their development are reported. Furthermore, common gamification elements (e.g., score, avatars, and quests) and key-design features (e.g., player mode, player–game interaction, feedback provision, and failure option) are described. By reporting on the latest technological advancements, a useful guide is formed, enabling both programmers and educators to easily grasp the newest trends on serious game design and use the produced knowledge to further enhance the nursing curriculum. Full article

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments. Full article

Lower limb functional degeneration has become prevalent, notably reducing the core strength that drives motor control. Squats are frequently used in lower limb training, improving overall muscle strength. However, performing continuously with improper techniques can lead to dynamic knee instability. It worsens with little to no motivation to perform these power training motions. Hence, it is crucial to have a gaming-based exercise tracking system to adaptively enhance the user experience without causing injury or falls. In this work, 28 healthy subjects performed exergame-based squat training, and dynamic kinematic features were recorded. The five features acquired from a depth camera-based inertial measurement unit (IMU) (1—Knee shakiness, 2—Knee distance, and 3—Squat depth) and an Anima forceplate sensor (4—Sway velocity and 5—Sway area) were assessed using a Spearman correlation coefficient-based feature selection method. An input vector that defines knee instability is used to train and test the Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models for binary classification. The results showed that knee instability events can be successfully classified and achieved a high accuracy of 96% in both models with sets 1, 2, 3, 4, and 5 and 1, 2, and 3. The feature selection results indicate that the LSTM network with the proposed combination of input features from multimodal sensors can successfully perform real-time tracking of knee instability. Furthermore, the findings demonstrate that this multimodal approach yields improved classifier performance with enhanced accuracy compared to using features from a single modality during lower limb therapy. Full article

A significant problem in cybersecurity is to accurately detect malicious network activities in real-time by analyzing patterns in socket-level packet transmissions. This challenge involves distinguishing between legitimate and adversarial behaviors while optimizing detection strategies to minimize false alarms and resource costs under intelligent, adaptive attacks. This paper presents a comprehensive framework for network security by modeling socket-level packet transmissions and extracting key features for temporal analysis. A long short-term memory (LSTM)-based anomaly detection system predicts normal traffic behavior and identifies significant deviations as potential cyber threats. Integrating this with a zero trust signaling game, the model updates beliefs about agent legitimacy based on observed signals and anomaly scores. The interaction between defender and attacker is formulated as a Stackelberg game, where the defender optimizes detection strategies anticipating attacker responses. This unified approach combines machine learning and game theory to enable robust, adaptive cybersecurity policies that effectively balance detection performance and resource costs in adversarial environments. Two baselines are considered for comparison. The static baseline applies fixed transmission and defense policies, ignoring anomalies and environmental feedback, and thus serves as a control case of non-reactive behavior. In contrast, the adaptive non-strategic baseline introduces simple threshold-based heuristics that adjust to anomaly scores, allowing limited adaptability without strategic reasoning. The proposed fully adaptive Stackelberg strategy outperforms both partial and discrete adaptive baselines, achieving higher robustness across trust thresholds, superior attacker–defender utility trade-offs, and more effective anomaly mitigation under varying strategic conditions. Full article