Search Results (348)

This study, based on the Decomposed Theory of Planned Behavior (DTPB), constructs and validates a systematic model to explore the key drivers influencing consumers’ continuous usage intention of sustainable Ready-to-Drink (RTD) beverage packaging. The model includes perceived value and social normative paths, introducing perceived sustainability (PS) and perceived value fit (PVF) as moderators to reveal the role of contextual perceptions in green consumption decision-making. Empirical results show that all conventional path hypotheses (H1–H6) are supported. Among the moderating effect hypotheses, H7b, H9a, and H10c are not supported, while the rest hold. Theoretical implications include the following: Perceived Usefulness (PU) and Perceived Enjoyment (PE) significantly and positively influence consumer satisfaction (SA), consistent with the Expectation Confirmation Model (ECM). Both external influence (EI) and interpersonal influence (II) significantly enhance subjective norms (SN), aligning with TPB theory. Self-efficacy (SE) and facilitating conditions (FA) positively affect perceived behavioral control (PBC), consistent with the DTPB model. Satisfaction, subjective norms, and perceived behavioral control significantly predict continuous usage intention (UI), with satisfaction showing the strongest effect, highlighting the importance of positive initial experiences for green behavior continuation. Full article

The rapid expansion of online learning platforms has necessitated advanced systems to address scalability, personalization, and assessment challenges. This paper presents a comprehensive review of artificial intelligence (AI)-based decision support systems (DSSs) designed for online learning and assessment, synthesizing advancements from 2020 to 2025. By integrating machine learning, natural language processing, knowledge-based systems, and deep learning, AI-DSSs enhance educational outcomes through predictive analytics, automated grading, and personalized learning paths. This study examines system architecture, data requirements, model selection, and user-centric design, emphasizing their roles in achieving scalability and inclusivity. Through case studies of a MOOC platform using NLP and an adaptive learning system employing reinforcement learning, this paper highlights significant improvements in grading efficiency (up to 70%) and student performance (12–20% grade increases). Performance metrics, including accuracy, response time, and user satisfaction, are analyzed alongside evaluation frameworks combining quantitative and qualitative approaches. Technical challenges, such as model interpretability and bias, ethical concerns like data privacy, and implementation barriers, including cost and adoption resistance, are critically assessed, with proposed mitigation strategies. Future directions explore generative AI, multimodal integration, and cross-cultural studies to enhance global accessibility. This review offers a robust framework for researchers and practitioners, providing actionable insights for designing equitable, efficient, and scalable AI-DSSs to transform online education. Full article

Community-based ecotourism in community forests, such as Suan Pa Ket Nom Klao, Thailand, offers a promising avenue for promoting sustainable development through meaningful tourist experiences. This study develops and validates the Community Forest Intention Model (CFIM) to examine the relationships among Tourist Experience (TE), Tourist Satisfaction (SAT), and Sustainable Intention (SI) using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 128 participants who engaged in six structured activities at Suan Pa Ket Nom Klao, with responses measured via a 5-point Likert scale questionnaire. The results indicate that TE, comprising Service Quality, Accessibility, and Learning Engagement, significantly influences SAT (R² = 0.562), with Learning Engagement exerting the strongest effect (β = 0.413; p < 0.001). SAT, in turn, positively predicts SI (β = 0.502; p < 0.001; R² = 0.252). All hypothesized paths were statistically significant, confirming the model’s validity. These findings highlight the critical role of educational and service-related experiences in fostering tourist satisfaction and sustainable behaviors. This study provides actionable insights for enhancing ecotourism programs to support conservation and community engagement. Full article

STEM education is constantly seeking innovative methods to enhance student learning. Virtual Reality technology can represent a critical tool for effectively teaching complex engineering subjects. This study evaluates an original Virtual Reality software application, entitled Submarine Simulator, which is developed specifically to support competencies in hydrodynamics within an Underwater Engineering course at MINES Paris—PSL. Our application uniquely integrates a customized physics engine explicitly designed for realistic underwater simulation, significantly improving user comprehension through accurate real-time representation of hydrodynamic forces. The study involved a homogeneous group of 26 fourth-year engineering students, all specializing in engineering and sharing similar academic backgrounds in robotics, electronics, programming, and computer vision. This uniform cohort, primarily aged 22–28, enrolled in the same 3-month course, was intentionally chosen to minimize variations in skills, prior knowledge, and learning pace. Through a combination of quantitative assessments and Confirmatory Factor Analysis, we find that Virtual Reality affordances significantly predict user flow state (path coefficient: 0.811) which then predicts user engagement and satisfaction (path coefficient: 0.765). These findings show the substantial educational potential of tailored Virtual Reality experiences in STEM, particularly in engineering, and highlight directions for further methodological refinement. Full article

Background/Objectives: Chronic medical conditions are comorbid with psychological disorders, often attributed to the weight of managing persistent demands associated with debilitating illness. Lifestyle adjustments, physical pain, and costs of health care can impose impairment of functioning, exacerbated by the onset of a chronic disease. While cause-and-effect directionality is difficult to ascertain, it is widely assumed that psychological stress can exacerbate the ability of patients to manage chronic medical conditions. Methods: The current study examined a novel model comprising five psychological factors which might explain variations in patients’ level of adherence, satisfaction, and quality of life. The sample consisted primarily of 124 low-income, female Hispanic patients, who were patients diagnosed with rheumatic and endocrine medical diagnoses. Results: Psychological distress and the lingering psychological effects of the COVID-19 pandemic were negatively associated with patient adherence, satisfaction, and quality of life, and that patients’ reports of the working alliances with their doctors moderated (i.e., significantly lessened) the negative association between the lingering impact of the COVID-19 pandemic and their satisfaction with care. Patients’ self-efficacy, resilience, and working alliance were all positively and significantly associated with adherence, satisfaction, and QOL. The association between working alliance and satisfaction represents a very large effect (r = 0.77, p < 0.001). Path analysis found a direct effect between psychological distress (stand. est. = 0.28, p = 0.05) and treatment adherence and a direct effect between COVID-19 impact and adherence (stand. est. = −0.19, p = 0.05). Conclusions: This study provides evidence of the role that both psychological stress and psychological strengths play in the experience of receiving medical care for low-income patients with rheumatic and endocrine conditions. Psychological stress inhibits adherence, and the physician–patient working alliance moderates the negative correlation between COVID impact and treatment satisfaction. Full article

As large language model (LLM) tutors evolve from scripted helpers into adaptive educational partners, their capacity for self-regulation, ethical decision-making, and internal monitoring will become increasingly critical. This paper introduces the Needs-Driven Consciousness Framework (NDCF) as a novel, integrative architecture that combines Dennett’s multiple drafts model, Damasio’s somatic marker hypothesis, and Tulving’s tripartite memory system into a unified motivational design for synthetic consciousness. The NDCF defines three core regulators, specifically Survive (system stability and safety), Thrive (autonomy, competence, relatedness), and Excel (creativity, ethical reasoning, long-term purpose). In addition, there is a proposed supervisory Protect layer that detects value drift and overrides unsafe behaviours. The core regulators compute internal need satisfaction states and urgency gradients, feeding into a softmax-based control system for context-sensitive action selection. The framework proposes measurable internal signals (e.g., utility gradients, conflict intensity Ω), behavioural signatures (e.g., metacognitive prompts, pedagogical shifts), and three falsifiable predictions for educational AI testbeds. By embedding these layered needs directly into AI governance, the NDCF offers (i) a psychologically and biologically grounded model of emergent machine consciousness, (ii) a practical approach to building empathetic, self-regulating AI tutors, and (iii) a testable platform for comparing competing consciousness theories through implementation. Ultimately, the NDCF provides a path toward the development of AI tutors that are capable of transparent reasoning, dynamic adaptation, and meaningful human-like relationships, while maintaining safety, ethical coherence, and long-term alignment with human well-being. Full article

This paper presents a reliable emergency facility location optimization model that considers complex polygon barriers and the risk of facility disruption. From a sustainable perspective, with capacity, cost, and demand constraints, the model maximizes time satisfaction and minimizes cost as the objective function to determine the optimal facility location and allocation. The paper proposes the barrier path optimization algorithm and the Kepler optimization algorithm (KOA) to solve the model and validates the model and algorithm through simulation experiments of various scales. Finally, the paper conducts a sensitivity analysis of the disruption probability using the control variable method to explore the impact of parameter changes on the decision results and compare the advantages of considering a sustainable perspective versus not considering it. The results show that the model and algorithm designed in this paper can effectively optimize the barrier path and obtain the optimal location-allocation scheme. The research findings will provide mathematical models and methodological strategies for emergency facility location decision-making. Full article

Previous landscape design mostly relies on general standards, failing to fully consider gender differences in landscape visual perception, with relevant research still needing further exploration. This study takes Beijing’s Beihai Park as the research object, using five types of on-site-collected photos (water landscape, plant landscape, architectural landscape, path landscape, and square landscape) as stimuli. Twenty males and twenty females participated in an eye-tracking experiment and a questionnaire survey to analyze gender differences in the visual perception of these five landscapes. The results show the following: (1) females show a “core–radiation” pattern, focusing on mid-short vision and environmental details; males focus on distant views and functional areas. (2) Females have slightly higher APD and fixation counts, with stronger cognitive/emotional fluctuations; males have longer total fixation time and more sustained attention. (3) Males prefer architectural/square landscapes, emphasizing functionality; females favor water/plant landscapes, prioritizing emotional connection with nature. (4) The total fixation time significantly impacts subjective evaluations; the average fixation duration is gender-neutral but uniquely affects evaluations of certain landscape types. This study has guiding significance for enhancing park landscapes’ inclusiveness and attractiveness, promoting different genders’ participation and satisfaction, and boosting space vitality and utilization efficiency. Full article

University students face increased stress and potential school burnout amid rapid digital transformation and competitive academic environments, yet little is known about how socioemotional processes explain the link between burnout and life satisfaction. This study examined how school burnout affects life satisfaction, mediated by loneliness and fear of alienation. A cross-sectional survey of 1783 students was conducted to measure school burnout, loneliness, fear of alienation, and life satisfaction. Structural equation modeling showed that school burnout had a significant negative direct effect on life satisfaction, mediated by loneliness. Higher burnout predicted greater loneliness, which in turn lowered life satisfaction. Although school burnout positively predicted fear of alienation, fear of alienation showed a complex association, with a positive direct path to life satisfaction. However, when loneliness was considered in the full mediation model, the overall indirect effect remained significantly negative. The sequential mediation pathway (school burnout → loneliness → fear of alienation → life satisfaction) highlighted how students’ social disconnection can intensify concerns about exclusion, ultimately affecting their well-being. These findings extend the literature by clarifying the socioemotional mechanisms linking school burnout and life satisfaction. Interventions should address academic demands and bolster emotional support, including resilience training, social skills development, and community-building programs, to mitigate loneliness and manage alienation concerns, thereby promoting students’ life satisfaction and psychological wellness. Full article

The growing adoption of unmanned aerial vehicles (UAVs) for urban parcel delivery highlights the need for efficient distribution center placement. Particularly in dense urban environments, obstacle avoidance usually increases real path distances beyond straight-line measurements. Considering these distance differences, this study first employs the Informed-RRT* algorithm with spatial indexing to calculate real UAV flight paths between locations. We use the real path distances instead of straight-line distances to determine service coverage areas in the distribution center layout model. In this model, we aim to minimize the total economic cost and maximize customer satisfaction, considering the service range, the balance between delivery capability and demand, and the constraints of the distribution mode. An immune-algorithm-enhanced genetic algorithm, the immune genetic algorithm, is used to solve the model, acquiring the distribution center layout. We conduct a simulation experiment in Shenzhen’s low-altitude airspace and compare the layout results between the real path distance criterion and the straight-line path distance criterion. The results show that using the real path distances instead of the straight-line distances leads to changes in distribution center layout, demonstrating the necessity of this framework. Full article

Background/Objectives: Despite the health benefit of regular physical activity, many adults often discontinue it within 3–6 months due to various obstacles. The purpose of this study was to investigate factors affecting physical activity adherence of male office workers based on self-determination theory by constructing a structural equation model. Methods: In total, 257 full-time male office workers who engage in regular physical activity participated. The participants from 15 companies completed a survey asking about perceived physical activity barriers, autonomy support, psychological need satisfaction in physical activity, autonomous motivation, and physical activity adherence. Data analysis was performed using the SPSS 28.0 and the AMOS 26.0 programs to verify the fit of the hypothetical model and identify the direct and indirect effects of variables on physical activity adherence for male office workers. Results: As a result, the path significance test results for the hypothetical model showed that five of the nine paths were significant. The results show that psychological need satisfaction in physical activity and autonomous motivation were significant variables that had a direct effect on physical activity adherence, while autonomy support from significant others and perceived physical activity barriers had a significant indirect effect through psychological need satisfaction and autonomous motivation, explaining 62.0%. Conclusions: Based on these findings, it is recommended to implement customized workplace-specific physical activity interventions to enhance autonomous motivation and the autonomy, competence, and relatedness aspects of psychological need satisfaction in physical activity. Full article

Mental health difficulties in university students are an increasing concern, especially after the COVID-19 global crisis. This study used a cross-sectional design to analyze the effect of psychological factors on students’ psychological well-being. Participants were 190 university students enrolled in undergraduate or graduate programs at a public university. Based on previous research and grounded theoretical models, a conceptual model was proposed to analyze the influence of affect states/experiences (emotion regulation difficulties, anxiety and depression, perceived stress, self-compassion, gratitude, and satisfaction with life) on psychological well-being, including the indirect effect of emotions (negative emotions, positive activation emotions, self-efficacy emotions, prosocial emotions, and serenity emotions), using a path analysis. Multigroup analyses were also performed to test the moderating effect of gender and education level. Findings indicated that self-efficacy emotions had an indirect effect on the relationship between anxiety and depression, self-compassion, and psychological well-being. Both prosocial and self-efficacy emotions indirectly impacted the relationship between gratitude, satisfaction with life, and psychological well-being. Being a female and a bachelor student played a moderating role in the final model. The findings suggest that psychological interventions focused on self-efficacy and prosocial emotions are needed to increase psychological well-being in university students. Full article

This study investigates the creators, effects, and inhibitors of technostress among STEM teachers, addressing a critical yet underexplored issue in the digitalization of education. Grounded in the technostress model and the job demands–resources (JD-R) model, the study examines the relationships among technostress creators, burnout, organizational effects (job satisfaction, organizational commitment, and work performance), and technostress inhibitors. A cross-sectional survey was conducted with 378 STEM teachers from Zhejiang Province, China. Structural equation modeling (SEM) was employed to test the hypothesized paths. The results revealed that technostress creators significantly increased teacher burnout and negatively affected organizational commitment and work performance. Burnout mediated the impact of technostress creators on job satisfaction and organizational commitment. Technostress inhibitors were found to alleviate burnout, mitigate technostress creators, and enhance STEM teachers’ commitment. These findings validate the applicability of the technostress model in the context of K–12 STEM education in China and highlight the importance of organizational mechanisms for supporting teacher well-being and performance. The study contributes to both theory and practice by proposing an integrative model of technostress and offering actionable recommendations for school leadership to effectively manage technostress. Full article

With the increase in the global aging population, the demand for elderly-friendly game products is growing rapidly. To address existing limitations, particularly in user demand extraction and design parameter setting, this study proposed a design framework integrating the BTM–AHP–AD–TOPSIS methods. The goal was to accurately identify the core needs of elderly users and translate them into effective design solutions. User reviews of elderly-friendly game products were collected from e-commerce platforms using Python 3.8-based web scraping. The Biterm Topic Model (BTM) was employed to extract user needs from review texts. These needs were prioritized using the Analytic Hierarchy Process (AHP) and translated into specific design parameters through Axiomatic Design (AD). Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to comprehensively evaluate multiple design schemes and select the optimal solution. The results demonstrate that the proposed design path offers a holistic method for progressing from need extraction to design evaluation. It effectively overcomes previous limitations, including inefficient need extraction, limited scope, unclear need weighting, and unreasonable design parameters. This method enhances user acceptance and satisfaction while establishing rigorous design processes and scientific evaluation standards, making it well suited for developing elderly-friendly products. Full article

StageMix is a video compilation of multiple stage performances of the same song, edited seamlessly together using appropriate editing points. However, generating a StageMix requires specialized editing techniques and is a considerable time-consuming process. To address this challenge, we introduce AutoStageMix, an automated StageMix generation system designed to perform all processes automatically. The system is structured into five principal stages: preprocessing, feature extraction, identifying a transition point, editing path determination, and StageMix generation. The initial stage of the process involves audio analysis to synchronize the sequences across all input videos, followed by frame extraction. After that, the facial features are extracted from each video frame. Next, transition points are identified, which form the basis for face-based transitions, inter-stage cuts, and intra-stage cuts. Subsequently, a cost function is defined to facilitate the creation of cross-edited sequences. The optimal editing path is computed using Dijkstra’s algorithm to minimize the total cost of editing. Finally, the StageMix is generated by applying appropriate editing effects tailored to each transition type, aiming to maximize visual appeal. Experimental results suggest that our method generally achieves lower NME scores than existing StageMix generation approaches across multiple test songs. In a user study with 21 participants, AutoStageMix achieved viewer satisfaction comparable to that of professionally edited StageMixes, with no statistically significant difference between the two. AutoStageMix enables users to produce StageMixes effortlessly and efficiently by eliminating the need for manual editing. Full article