Search Results (1,516)

With the advancement of digital technology, serious games have become an essential tool for disseminating and educating individuals about cultural heritage. However, systematic empirical research remains limited with respect to how visual elements influence users’ cognitive and emotional engagement through interactive behaviors. Using the “Macau Historic Centre Science Popularization System” as a case study, this mixed-methods study investigates the mechanisms by which visual elements affect user experience and learning outcomes in digital interactive environments. Eye-tracking data, behavioral logs, questionnaires, and semi-structured interviews from 30 participants were collected to examine the impact of visual elements on cognitive resource allocation and emotional engagement. The results indicate that the game intervention significantly enhanced participants’ retention and comprehension of cultural knowledge. Eye-tracking data showed that props, text boxes, historic buildings, and the architectural light and shadow shows (as incentive feedback elements) had the highest total fixation duration (TFD) and fixation count (FC). Active-interaction visual elements showed a stronger association with emotional arousal and were more likely to elicit high-arousal experiences than passive-interaction elements. The FC of architectural light and shadow shows a positive correlation with positive emotions, immersion, and a sense of accomplishment. Interview findings revealed users’ subjective experiences regarding visual design and narrative immersion. This study proposes an integrated analytical framework linking “visual elements–interaction behaviors–cognition–emotion.” By combining eye-tracking and information dynamics analysis, it enables multidimensional measurement of users’ cognitive processes and emotional responses, providing empirical evidence to inform visual design, interaction mechanisms, and incentive strategies in serious games for cultural heritage. Full article

An investigation on a discrete-time infectious disease model that incorporating vertical transmission is presented in this paper. Departing from prior research centered on continuous-time frameworks, our study adopts a discrete-time formulation to better capture the complex epidemiological dynamics. We establish a model and conduct a bifurcation analysis of its equilibrium points. In particular, sufficient conditions for the local stability and the emergence of Neimark–Sacker and flip bifurcations are rigorously derived and analytically verified. As anticipated, variations in the bifurcation parameter give rise to distinct periodic regimes in the system response. To mitigate the instabilities and chaotic behaviors resulting from these bifurcations, we propose and validate two control strategies, which are Hybrid Control Method and State Feedback Control. Numerical simulations futher substantiated the analytical results, demonstrating that appropriate parameter adjustments can shift the system behavior from chaotic attractors and limit cycles toward stable equilibria. Our results show that by dynamically adjusting the intensity of prevention and control measures to mitigate unstable factors such as vertical transmission and high infection rates, or reducing the frequency of system updates to slow down the growth of infections, the epidemic can be transitioned from repeated outbreaks to a stable and manageable state. Full article

Background/Objectives: Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and early dementia, affecting up to 20% of older adults. Sleep disturbances, particularly insomnia, affect around 60% of individuals with MCI, contributing to declines in cognitive and physical function. Although Cognitive Behavioral Therapy for Insomnia (CBT-I) is an evidence-based non-pharmacological treatment, few studies have adapted it for individuals with MCI. This pilot study developed and evaluated Slumber, a clinician-supported mobile CBT-I app tailored for older adults with MCI and insomnia. Methods: The study had three aims: (1) to develop the app for delivering CBT-I to individuals with MCI; (2) to evaluate its usability and refine smart messaging prompts; and (3) to assess the feasibility of outcome measurement while detecting exploratory signals of change through a 6-week pilot trial. N = 19 participants completed the trial. Results: A significant reduction in insomnia severity was observed (mean difference = −2.06; p = 0.0131), while changes in cognitive and physical functioning were not statistically significant. Participants reported high satisfaction with the app’s tracking features and motivational reminders, though some noted technical challenges with presenting and interpreting sleep analysis charts. Conclusions: Findings support the usability of the Slumber app and the feasibility of outcome measurement in this population. The observed improvement in sleep quality provides an initial signal of promise. Future studies should address user feedback, enhance technical features, and evaluate clinical effectiveness in a larger randomized trial. Full article

While there is extensive literature on the strengths of different supervisory models, we have limited understanding of how the relational capacity of front-line staff may impact how they receive and seek feedback from their supervisor. This study examines how mental health providers’ and front-line staff’s own rejection sensitivity may be associated with the supervisory relationship and the ways in which job feedback is sought and received in community-based mental health settings. Cross-sectional survey data were collected from 156 front-line staff of three mental health agencies. Staff were administered an original survey using validated measures related to supervision, feedback, and relational capacities. We found staff with a higher rejection sensitivity (RS) were less likely to actively seek feedback about their performance; and, when feedback was received, were more likely to rate its quality as poor. Staff with a higher RS were more likely to perceive their supervisor and their relationship negatively. This is the first study to examine whether workers’ relational capacities, as expressed through a higher RS, influence their perceptions of supervision and quality of feedback and their feedback-seeking behaviors. These findings build theory related to the important role that staff relational capacities play in influencing organizational dynamics and support. Full article

To mitigate rotor vibrations in magnetic bearing systems arising from mass imbalance, this study proposes a novel suppression strategy that integrates the crested porcupine optimizer (CPO) with an enhanced linear active disturbance rejection control (ELADRC) framework. The approach introduces a disturbance estimation and compensation scheme based on a linear extended state observer (LESO), wherein both the LESO bandwidth ω₀ and the LADRC controller parameter ω_c are adaptively tuned using the CPO algorithm to enable decoupled control and real-time disturbance rejection in complex multi-degree-of-freedom (DOF) systems. Drawing inspiration from the crested porcupine’s layered defensive behavior, the CPO algorithm constructs a state-space model incorporating rotor displacement, rotational speed, and control current, while leveraging a reward function that balances vibration suppression performance against control energy consumption. The optimized parameters guide a real-time LESO-based compensation model, achieving accurate disturbance cancelation via amplitude-phase coordination between the generated electromagnetic force and the total disturbance. Concurrently, the LADRC feedback structure adjusts the system’s stiffness and damping matrices to improve closed-loop robustness under time-varying operating conditions. Simulation studies over a wide speed range (0~45,000 rpm) reveal that the proposed CPO-ELADRC scheme significantly outperforms conventional control methods: it shortens regulation time by 66.7% and reduces peak displacement by 86.8% under step disturbances, while achieving a 79.8% improvement in adjustment speed and an 86.4% reduction in peak control current under sinusoidal excitation. Overall, the strategy offers enhanced vibration attenuation, prevents current saturation, and improves dynamic stability across diverse operating scenarios. Full article

Aging involves progressive changes in sensory perception, appetite regulation, and metabolic flexibility, which together affect dietary intake, nutrient adequacy, and health-related outcomes. Meanwhile, current wearable technologies allow continuous, minimally invasive monitoring of physiological and behavioral markers relevant to metabolic health, such as physical activity, sleep, heart rate variability, glycemic patterns, and so forth. However, digital nutrition approaches have largely focused on physiological signals while underutilizing the sensory dimensions of eating—taste, smell, texture, and hedonic response—that strongly drive dietary intake and adherence. This narrative review synthesizes evidence on the following: (1) age-related sensory changes and their nutritional consequences, (2) metabolic adaptation and markers of resilience in older adults, and (3) current and emerging wearable technologies applicable to nutritional personalization. Following this, we propose an integrative framework linking subjective (implicit) sensory perception and objective (explicit) wearable-derived physiological responses into adaptive feedback loops to support personalized dietary strategies for healthy aging. In this light, we discuss practical applications, technological and methodological challenges, ethical considerations, and research priorities to validate and implement sensory–physiological integrated models. Merging together sensory science and wearable monitoring has the potential to enhance adherence, preserve nutritional status, and bolster metabolic resilience in aging populations, moving nutrition from one-size-fits-all prescriptions toward dynamic, person-centered, sensory-aware interventions. Full article

Background/Objectives: Alcohol-based handrub (ABHR) consumption is commonly used as an indirect proxy for hand hygiene practices. Hand hygiene compliance increased significantly during COVID-19, but sustainability remains uncertain. This study assessed ABHR consumption trends from 2022 to 2024 and compared them with pre-pandemic and pandemic-era rates. Methods: We conducted a follow-up observational study tracking quarterly ABHR consumption in a surgical department and hospital-wide (2022–2024). Consumption was normalized as mL per patient-day and compared with 2019–2020 data. Time-series regression with Newey–West standard errors assessed temporal trends. Results: Surgical department consumption declined 27.5% (55.9 to 40.5 mL/patient-day), returning to 2019 pre-pandemic levels. Hospital-wide consumption increased 36% (36.4 to 49.6 mL/patient-day). Neither trend reached statistical significance (p > 0.05). The 2024 surgical rate remained substantially below the 2020 pandemic peak (320 mL/patient-day). Conclusions: Pandemic-era ABHR consumption gains were not sustained in the surgical department despite maintained educational infrastructure, accessible dispensers, and consistent staffing. The critical missing element was systematic monitoring and feedback. Institutions relying solely on passive education may experience erosion of hand hygiene compliance post-crisis, highlighting the need for active surveillance programs to maintain behavioral gains. Full article

Deep reinforcement learning (DRL) and large foundation models (FMs) have reshaped modern artificial intelligence (AI) by enabling systems that learn from interaction while leveraging broad generalization and multimodal reasoning capabilities. This survey examines the growing convergence of these paradigms and reviews how reinforcement learning from human feedback (RLHF), reinforcement learning from AI feedback (RLAIF), world-model pretraining, and preference-based optimization refine foundation model capabilities. We organize existing work into a taxonomy of model-centric, RL-centric, and hybrid DRL–FM integration pathways, and synthesize applications across language and multimodal agents, autonomous control, scientific discovery, and societal and ethical alignment. We also identify technical, behavioral, and governance challenges that hinder scalable and reliable DRL–FM integration, and outline emerging research directions that suggest how reinforcement-driven adaptation may shape the next generation of intelligent systems. This review provides researchers and practitioners with a structured overview of the current state and future trajectory of DRL in the era of foundation models. Full article

Standard epidemiological models often treat human behavior as static, failing to capture the dynamic feedback loops that shape epidemic waves. To address this, we developed a compartmental model of COVID-19 that couples the disease dynamics with two co-evolving behavioral games governed by imitation dynamics: an altruistic self-isolation game for infected individuals and a self-interested vaccination game for susceptible individuals. Our simulations reveal a fundamental behavioral paradox: strong adherence to self-isolation, while effective at reducing peak infections, diminishes the perceived risk of disease, thereby undermining the incentive to vaccinate. This dynamic highlights a critical trade-off between managing acute crises through non-pharmaceutical interventions and achieving long-term population immunity. We conclude that vaccination has a powerful stabilizing effect that can prevent the recurrent waves often driven by behavioral responses to non-pharmaceutical interventions. Public health policy must therefore navigate the tension between encouraging short-term mitigation behaviors and communicating the long-term benefits of vaccination to ensure lasting population resilience. Full article

Concrete structures suffering from Mg²⁺ environments may suffer severe damage, which mainly has something to do with the coupled effect among Cl⁻, SO₄²⁻, and Mg²⁺. Based on a systematic review of Web of Science and Scopus database (2000–2025), we first summarized the migration behavior, reaction paths, and interaction mechanism of Cl⁻, SO₄²⁻, and Mg²⁺ in cementitious matrices. Secondly, from the perspective of Cl⁻ cyclic adsorption–desorption breaking the passivation film of steel bars, SO₄²⁻ generating expansion products leads to crack expansion, then Mg²⁺ decalcifies C-S-H and transforms into M-S-H; we analyzed the main damage mechanisms, respectively. In addition, under the coexistence conditions of three kinds of ions, the “fixation–substitution–redissolution” process and “crack–transport” coupling positive feedback mechanism further increase the development rate of damage. Then, some anti-corrosion measures, such as mineral admixtures, functional chemical admixtures, fiber reinforcements, surface coatings, and new binder systems, are summarized, and the pros and cons of different anti-corrosion technologies are compared and evaluated. Lastly, from two aspects of simulation prediction for the coupled corrosion damage mechanism and service life prediction, respectively, we have critically evaluated the advances and problems existing in the current research on the aspects of ion migration-reaction coupled models, multi-physics coupled frameworks, phase-field methods, etc. We found that there is still much work to be conducted in three respects: deepening mechanism understanding, improving prediction precision, and strengthening the connection between laboratory test results and actual projects, so as to provide theoretical basis and technical support for the durability design and anti-corrosion strategies of concrete in complex Mg²⁺ environments. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article

With the advancement of the “dual carbon” goals, power transmission and transformation projects face complex challenges arising from the construction of new power systems. Traditional cost management models struggle to meet dynamic management demands, necessitating the establishment of analytical methods that systematically reflect the relationship between cost management levels and cost dynamics. This paper introduces system dynamics theory and methodology to construct a cost management model applicable to all phases of transmission and transformation projects. It aims to deeply analyze the relationship between project cost levels and expenses from the perspectives of system structure, feedback mechanisms, and dynamic behavior. Research indicates that pathways such as controlling cost deviations and optimizing resource allocation significantly impact total project costs. Specifically, enhancing design accuracy can effectively mitigate cost shocks caused by carbon price fluctuations, while timely implementation of cost control measures can significantly improve cost management levels. The system dynamics approach effectively reveals the dynamic interaction mechanism between cost management levels and costs in power transmission and transformation projects, providing theoretical foundations and methodological support for enhancing project cost control efficiency. Full article

Fuzzy Cognitive Maps (FCMs) are a type of recurrent neural network with built-in meaning in their architecture, originally devoted to modeling and scenario simulation tasks. These knowledge-based neural systems support feedback loops that handle static and temporal data. Over the last decade, there has been a noticeable increase in the number of contributions dedicated to developing FCM-based models and algorithms for structured pattern classification and time series forecasting. These models are attractive since they have proven competitive compared to black boxes while providing highly desirable interpretability features. Equally important are the theoretical studies that have significantly advanced our understanding of the convergence behavior and approximation capabilities of FCM-based models. These studies can challenge individuals who are not experts in Mathematics or Computer Science. As a result, we can occasionally find flawed FCM studies that fail to benefit from the theoretical progress experienced by the field. To address all these challenges, this survey paper aims to cover relevant theoretical and algorithmic advances in the field, while providing clear interpretations and practical pointers for both practitioners and researchers. Additionally, we will survey existing tools and software implementations, highlighting their strengths and limitations towards developing FCM-based solutions. Full article

This study proposes a structured multicriteria approach to assist professionals in the selection of appropriate computing platforms for children diagnosed with Autism Spectrum Disorder, particularly those between 4 and 6 years of age. Recognizing the learning limitations and reduced attention span typical of this group, the study addresses a gap in the current selection process, which is often based on professional experience rather than objective and measurable criteria. A Systematic Literature Review (SLR), protocol analysis, and problem-structuring methods identified essential evaluation criteria that incorporated key dimensions of development and behavior. These include personalization and adaptation, interactivity and engagement, monitoring and feedback, communication and language, cognitive and social development, usability and accessibility, and security and privacy. Based on these dimensions, a multicriteria method was applied to rank the alternatives represented by the technologies in question. The proposed framework enables a rigorous and axiomatic comparison of platforms based on structured criteria aligned with established intervention protocols, such as ABA, DIR/Floortime, JASPER, and SCERTS. The results validate the model’s effectiveness in highlighting the most appropriate technological tools for this audience. Although the scope is limited to children aged 4 to 6 years, the proposed methodology can be adapted for use with broader age groups. This work contributes to inclusive education by providing a replicable, justifiable framework for selecting digital learning tools that may influence clinical recommendations and family engagement. Full article

Behavioral and educational researchers increasingly rely on rich datasets that capture how students respond to technology-enhanced instruction, yet few open resources document the full pipeline from experimental design to data curation in authentic classroom settings. This data descriptor presents a clustered quasi-experimental dataset on the impact of an instructional architecture that combines virtual reality (VR) simulations with artificial intelligence (AI)-driven formative feedback to enhance undergraduate students’ communication and problem-solving performance. The study was conducted at a large private university in Mexico during the 2024–2025 academic year and involved six intact classes (three intervention, three comparison; n = 180). Exposure to AI and VR was operationalized as a session-level “dose” (minutes of use, number of feedback events, number of scenarios, perceived presence), while performance was assessed with analytic rubrics (six criteria for communication and seven for problem solving) scored independently by two raters, with interrater reliability estimated via ICC (2, k). Additional Likert-type scales measured presence, perceived usefulness of feedback and self-efficacy. The curated dataset includes raw and cleaned tabular files, a detailed codebook, scoring guides and replication scripts for multilevel models and ancillary analyses. By releasing this dataset, we seek to enable reanalysis, methodological replication and cross-study comparisons in technology-enhanced education, and to provide an authentic resource for teaching statistics, econometrics and research methods in the behavioral sciences. Full article