Search Results (620)

Digital technologies such as the Internet, social media, and online games have become integral to an adult’s everyday life, yet their implications for health-related behaviors remain the subject of ongoing debate. While existing research has extensively examined risks and benefits of digital media use, evidence focused specifically on adult populations and on the psychological processes supporting health-oriented engagement remains fragmented. This systematic review with narrative synthesis, conducted in accordance with PRISMA 2020 guidelines, examined peer-reviewed studies published between 2015 and 2025 involving adults (≥18 years). Searches across PubMed, Scopus, Web of Science, and PsycINFO identified 27 eligible studies addressing spontaneous use of the Internet, social media, or online games in relation to actual health behaviors. Across studies, a consistent pattern emerged in which self-efficacy, health literacy, motivation, risk perception, and perceived social support were associated with the adoption of health-related behaviors, particularly physical activity, preventive practices, healthy eating, and health information seeking. However, the literature was characterized by a predominance of cross-sectional designs, a strong geographical concentration in East Asian contexts, and a marked imbalance across digital environments, with social media and informational Internet use being far more frequently studied than online games. Overall, the findings suggest that digital technologies are neither inherently beneficial nor harmful for adult health; rather, their effects depend on users’ psychological competencies and modes of engagement. By synthesizing evidence across digital contexts, this review proposes a competence-oriented framework that helps explain how everyday digital media use may translate into health-promoting behaviors in adulthood, while also highlighting critical gaps that future longitudinal, cross-cultural, and gaming-focused research should address. Full article

With the development of the digital intelligence era, generative AI is being widely used in scientific research, and its impact on graduate students’ research competence has attracted much attention from the academic community. Based on cognitive distribution theory and self-efficacy theory, this study classifies AI applications into three levels from basic to advanced—technical support AI use, text development AI use, and transformation AI use—explores their effects on graduate students’ research competence, and examines the mediating effect of critical thinking and the moderating effect of research self-efficacy. The results of the empirical analysis show that all three types of AI use behaviors are significantly correlated with research competence, with the strongest correlation for text development type and the weakest for technical support type. In the relationship between the three types of AI use behaviors and research competence, critical thinking plays a significant positive mediating role, and research self-efficacy plays a significant moderating role. Universities and tutors should guide students to focus on higher-order AI use behaviors in the text development and transformation categories, promoting the use of critical thinking to avoid technology misuse and improving research self-efficacy to help students accumulate confidence and support their research. Full article

Triboelectric nanogenerators (TENGs) have been gaining significant attention owing to their excellent energy conversion efficiency and their integration towards a large number of practical applications in energy harvesting, wearables, and self-powered sensing. In recent advancements, the utilization of flexible triboelectric composite films can help to enhance the TENG’s electrical output performance, as they possess excellent mechanical and dielectric properties and tunable surface characteristics. Moreover, by combining flexible active layers with triboelectric nanogenerators, the advantages of each component result in sensor devices which offer superior characteristics, including high sensitivity, biocompatibility, less weight, and mechanical flexibility. This review mainly focuses on the applications of TENGs in mechanical energy harvesting, self-powered wearable sensor systems, as well as the latest research progress in the TENG field. The working principles of TENG will be first explained in detail, including four basic operational modes of TENG, simulation results, and the working mechanism of the contact–separation mode TENGs. The fabrication techniques of triboelectric flexible films, along with TENG construction, will then be introduced. Common applications of TENGs are based on mechanical energy harvesting and powering portable electronic devices, which will subsequently be classified and summarized. Additionally, the applications of various wearable and self-powered sensor applications are elucidated. Finally, the current limitations and future directions of the TENG will be explained in detail and proposed. By exploring these innovations, the review underscores the importance of triboelectric flexible film-based TENGs in driving the future of energy harvesting and sensor technologies. Full article

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening. Full article

Education 4.0 and digital learning have led to a technology-driven transformation in educational methodologies and the roles of teachers, primarily at Higher Education Institutions (HEIs). From an educational standpoint, the extant literature on Education 4.0 highlights its technological features and benefits; however, there is a lack of studies that assess its impact on students’ learning outcomes. Seemingly, Education 4.0 features are taken for granted, as if the technology in itself were enough to guarantee students’ learning, self-efficacy, and engagement. Seeking to address this lack, this study describes the implications of tailoring Education 4.0 tenets and digital learning in an engineering curriculum. Four case studies conducted in the last four years with 119 students are presented, in which technologies such as digital twins, a Modular Production System (MPS), low-cost robotics, 3D printing, generative AI, machine learning, and mobile learning were integrated. With these case studies, an educational methodology with active learning, hands-on activities, and continuous teacher support was designed and deployed to foster cognitive and affective learning outcomes. A mixed-methods study was conducted, utilizing students’ grades, surveys, and semi-structured interviews to assess the approach’s impact. The outcomes suggest that including Education 4.0 tenets and digital learning can enhance discipline-based skills, creativity, self-efficacy, collaboration, and self-directed learning. These results were obtained not only via the technological features but also through the incorporation of reflective teaching that provided several educational resources and oriented the methodology for students’ learning and engagement. The results of this study can help complement the concept of Education 4.0, helping to find a student-centered approach and conceiving a balance between technology, teaching practices, and cognitive and affective learning outcomes. Full article

The rapid adoption of generative artificial intelligence (AI) in higher education has intensified a pedagogical dilemma: while AI tools can increase immediate classroom engagement, they do not necessarily foster the self-regulated learning (SRL) capacities required for ethical and reflective professional practice, particularly in human-service fields. In this two-time-point, pre-post cohort-level (repeated cross-sectional) evaluation, we examined a six-week AI-integrated curriculum incorporating explicit SRL scaffolding among social work undergraduates at a Taiwanese university (pre-test N = 37; post-test N = 35). Because the surveys were administered anonymously and individual responses could not be linked across time, pre-post comparisons were conducted at the cohort level using independent samples. The participating students completed the AI-Enhanced Learning Attitude Scale (AILAS); this is a 30-item instrument grounded in the Technology Acceptance Model, Attitude Theory and SRL frameworks, assessing six dimensions of AI-related learning attitudes. Prior pilot evidence suggested an engagement regulation gap, characterized by relatively strong learning process engagement but weaker learning planning and learning habits. Accordingly, the curriculum incorporated weekly goal-setting activities, structured reflection tasks, peer accountability mechanisms, explicit instructor modeling of SRL strategies and simple progress tracking tools. The conducted psychometric analyses demonstrated excellent internal consistency for the total scale at the post-test stage (Cronbach’s α = 0.95). The independent-samples t-tests indicated that, at the post-test stage, the cohorts reported higher mean scores across most dimensions, with the largest cohort-level differences in Learning Habits (Cohen’s d = 0.75, p = 0.003) and Learning Process (Cohen’s d = 0.79, p = 0.002). After Bonferroni adjustment, improvements in the Learning Desire, Learning Habits and Learning Process dimensions and the Overall Attitude scores remained statistically robust. In contrast, the Learning Planning dimension demonstrated only marginal improvement (d = 0.46, p = 0.064), suggesting that higher-order planning skills may require longer or more sustained instructional support. No statistically significant gender differences were identified at the post-test stage. Taken together, the findings presented in this study offer preliminary, design-consistent evidence that SRL-oriented pedagogical scaffolding, rather than AI technology itself, may help narrow the engagement regulation gap, while the consolidation of autonomous planning capacities remains an ongoing instructional challenge. Full article

CO₂ pipeline transportation is a core link in the CCUS (Carbon Capture, Utilization, and Storage Technology) industry. Ensuring the flow safety of CO₂ pipelines under transient conditions is currently a key and challenging issue in industry research. This paper focuses on the phase migration and safety control during the shutdown process of supercritical carbon dioxide pipelines. Taking a supercritical carbon dioxide transportation pipeline in Xinjiang Oilfield, China, as the research object, a hydro-thermal coupling model of the pipeline is established to simulate the pipeline and elucidate the coordinated variation patterns of temperature, pressure, density, and phase state. It was found that there were significant differences in the migration paths of the CO₂ phase at different positions. The accuracy of the simulation results was verified through the self-built high-pressure visual reactor experimental system, and the influences of the initial temperature, initial pressure, and ambient temperature before pipeline shutdown on the slope of the phase migration path were explored. The phase migration line slope prediction model was established by using the least squares method and ridge regression method, the process boundary ranges and allowable shutdown time ranges for pipeline safety shutdowns in both summer and winter were further established. The research results show that when the pipeline operates under the low-pressure and high-temperature boundary, the CO₂ in the pipeline vaporizes earlier from the starting point after the pipeline is shut down, and the safe shutdown time of the pipeline is shorter. There is a clear safety operation window in summer, while vaporization risks are widespread in winter. The phase migration path prediction formula and the safety zone division method proposed in this paper provide a theoretical basis and engineering guidance for the safe shutdown control of supercritical carbon dioxide pipelines, which can help reduce operational risks and lower maintenance costs. Full article

Artificial intelligence (AI) is transforming countless aspects of society, including possibly even who we are as persons. AI technologies may affect our flourishing for good or for ill. In this paper, we put forward principled considerations concerning flourishing and AI that are oriented towards ensuring AI technologies are conducive to human flourishing, rather than impeding it. The considerations are intended to help guide discussions around the development of, and engagement with, AI technologies so as to orient them towards the promotion of individual and societal flourishing. Five sets of considerations around flourishing and AI are discussed concerning: (i) the output provided by large language models; (ii) the specific AI product design; (iii) our engagement with those products; (iv) the effects this is having on human knowledge; and (v) the effects this is having on the self-realization of the human person. While not exhaustive, it is argued that each of these sets of considerations must be taken seriously if these technologies are to help promote, rather than impede, flourishing. We suggest that we should ultimately frame all of our thinking on AI technologies around flourishing. Full article

As municipalities increasingly adopt artificial intelligence (AI) and generative AI (GenAI) to automate self-service technologies (SSTs), concerns related to fairness, transparency, accountability, and citizen trust have become central to sustainable public-sector governance. While existing studies emphasize either AI adoption or high-level ethical principles, limited empirical research explains how governance mechanisms translate into user-level outcomes in municipal services, particularly in the context of emerging GenAI capabilities. This study addresses this gap by examining how governance antecedents and system design attributes shape user satisfaction, trust, and perceived fairness in AI-enabled municipal SSTs in the United Arab Emirates (UAE). A mixed-methods research design was employed, combining a comparative analysis of international and UAE AI governance frameworks with semi-structured interviews (n = 16) and a survey of municipal employees and service users (n = 272). Qualitative findings reveal persistent concerns regarding data privacy, fairness, explainability, and the absence of standardized municipal-level accountability instruments. Quantitative analysis shows that perceived helpfulness significantly increases user satisfaction, while perceived fairness strongly predicts continued usage intentions. In contrast, system responsiveness exhibits a negative association with satisfaction, highlighting an expectation–performance gap in automated service delivery. Based on these findings, the study proposes a governance–implementation–outcomes model that operationalizes ethical AI principles into measurable governance and service-design mechanisms. Unlike prior adoption-focused or purely normative frameworks, this model empirically links governance instrumentation to citizen-centered outcomes, offering practical guidance for inclusive and sustainable AI and GenAI deployment in municipal self-service systems. The findings contribute to debates on sustainable digital governance by demonstrating how ethically governed AI systems can reinforce public trust, service equity, and long-term institutional resilience. Full article

Wearable technologies, such as smartwatches, are increasingly used by people with Chronic Obstructive Pulmonary Disease (COPD) for health monitoring and self-management. However, there is limited evidence-informed guidance available to help patients and healthcare practitioners use these tools effectively in everyday life. Objectives: This study aimed to co-create a digital resource for people with COPD and healthcare practitioners to support the use of smartwatches for self-management. Methods: A participatory co-creation methodology was used, based on the Three Co’s Framework (co-define, co-design, co-refine). Participants included people with COPD, carers, family, or friends of people with COPD; healthcare practitioners; and researchers who attended workshops and individual think-aloud interviews to develop a website and video resource. The resource was refined based on real-time feedback. Data were analysed using rapid qualitative analysis. Results: Twenty-one participants engaged and identified key informational needs, including understanding smartwatch features, interpreting health data, and setting personalised goals. The co-created website and video resource were positively received. Participants valued the inclusion of real-life experiences and practical guidance tailored to both patients and healthcare practitioners. Conclusions: This study presents the first co-created resource for COPD and healthcare practitioners on using smartwatches. The co-creation process was successfully delivered online and face-to-face, demonstrating a robust, inclusive approach to managing multiple stakeholders. The resource offers practical value for patients and practitioners and contributes to the growing field of remote interventions for chronic respiratory conditions. Future research is needed to evaluate its effectiveness. Full article

As emotion becomes increasingly digitized, there is a growing risk that computational systems may overreach, shaping or managing affect in ways that undermine human autonomy. This study builds a cross-cultural dialog between Daoist affective philosophy and Western theories of emotion to address this problem. By comparing their assumptions about emotional life—what emotions are, how they should be guided, and what counts as appropriate intervention—the paper develops a set of ethical principles for the design of affective technologies. Through textual analysis and a historical–conceptual review, the study identifies three safeguards drawn from Daoist thought—minimality, autonomy, and reversibility—and translates them into practical guidance for data use, system behavior, and user interaction. A brief case from Finland’s well-being initiatives illustrates how these principles can redirect technological design toward supporting inner balance and self-directed regulation rather than external control. The paper’s contribution lies in offering a clear boundary ethics for affective computing, showing how Daoist ideas of moderation and self-cultivation can help prevent emotional alienation while still allowing technological systems to enhance human well-being. Full article

In the financial sector, machine learning has become essential for credit risk assessment, often outperforming traditional statistical approaches, such as linear regression, discriminant analysis, or model-based expert judgment. Although machine learning technologies are increasingly being used, further research is needed to understand how they can be effectively combined and how different models interact during credit evaluation. This study proposes a technique that integrates hierarchical clustering, namely Agglomerative clustering and Balanced Iterative Reducing and Clustering using Hierarchies, along with individual supervised models and a self organizing map-based consensus model. This approach helps to better understand how different clustering algorithms influence model performance. To support this approach, we performed a detailed unsupervised component analysis using metrics such as the silhouette score and Adjusted Rand Index to assess cluster quality and its relationship with the classification results. The study was applied to multiple datasets, including a Taiwanese credit dataset. It was also extended to a multiclass classification scenario to evaluate its generalization ability. The results show that the quality metrics of the cluster correlate with the performance, highlighting the importance of combining unsupervised clustering and self organizing map consensus methods for improving credit evaluation. Full article

Background/Objectives: In today’s reality, the mobile phone accompanies people in almost every area of life. Technological progress offers a range of conveniences, facilities, and opportunities. At the same time, researchers observe new phenomena such as phubbing, which is defined as ignoring others in favor of one’s smartphone and is increasingly being perceived as a normative behavior. Methods: The study was conducted using an online survey. A total of 201 adults aged between 18 and 75 participated. The research employed a proprietary questionnaire designed to collect data on phone and social media use, as well as the Revised UCLA Loneliness Scale (R-UCLA), the Generic Scale of Phubbing (GSP), and the Self-Esteem Scale (SES). Results: The results showed a positive relationship between loneliness and phubbing, and a negative relationship between loneliness and self-esteem. Additionally, a negative relationship was found between self-esteem and phubbing behavior. In line with the main objective of the study, it was demonstrated that self-esteem acts as a mediating factor in the relationship between loneliness and phubbing behavior. Conclusions: Individuals experiencing loneliness may have lower self-esteem, which in turn may lead them to engage in phubbing behavior more frequently. Identifying factors related to phubbing behavior helps expand knowledge about this new, yet increasingly common phenomenon, which carries psychosocial consequences. At the same time, the topic highlights the need for further research to deepen our understanding of the phenomena of loneliness and phubbing. Full article

Background: Cardiac implantable electronic devices (CIEDs) have become indispensable tools in the management of bradyarrhythmia and heart failure, prompting a surge in research activity. To characterize the evolving research landscape, we conducted a bibliometric analysis focused on institutional contributions, author networks, journal trends, funding patterns, and emerging thematic hotspots in the field of cardiac devices to highlight keywords and identify knowledge development timelines and emerging trends, providing a comprehensive overview of the current state of research in this area. Methods: We conducted a bibliometric analysis of cardiac devices using the Web of Science Core Collection (WOSCC) on 27 November 2024, with search terms “ST (cardiac defibrillator) OR (pacemaker)”. Data from 1 January 2019 to 1 January 2024 resulted in 3753 articles, refined to 1000 after excluding non-English and methodologically inappropriate papers. VosViewer, Excel, and Drawio facilitated data visualization, creating networks where node size indicates frequency, line thickness shows association strength, and colors denote clusters. This approach helped identify key research trends and collaborations in the field. Results: The United States led in publication volume (362 papers) and citations (7198), with Emory University emerging as the most prolific institution. Heart Rhythm was the most productive journal, while Europace was the most co-cited. Kurt Stromberg was the leading author by publications and citations. Funding was predominantly from U.S. agencies, with the NIH and HHS each supporting 127 studies. Co-citation and keyword analyses revealed three dominant research clusters: (1) leadless pacemakers; (2) permanent pacemaker implantation following transcatheter aortic valve replacement (TAVR); and (3) development of self-powered pacing technologies, including piezoelectric and bioresorbable systems. Conclusions: This study offers a comprehensive overview of recent trends and intellectual structures in cardiac device research. By identifying key contributors, collaborative networks, and thematic evolutions, it provides a valuable reference for researchers, clinicians, and innovators seeking to navigate or shape the rapidly advancing field of cardiac electrophysiology and device therapy. Full article

Newly brewed Baijiu often contains harmful substances such as mercaptan and methanol, which are spicy and harmful to health. At present, this is mainly solved by long-term cellaring, but this is faced with some problems such as a long cycle, high cost, high fire hazards and so on. Therefore, based on the principle of liquid jet cavitation explosion catalyzing the heterogeneous association of Baijiu molecules, this paper first developed the physical aging process and equipment without radiation and additives. Then, based on the traditional computational fluid dynamics (CFD) model of high-speed jet simulation, an N-CFD model which can accurately simulate the cavitation explosion catalytic process of high-speed jet of Baijiu was established by optimizing the three sub models of conservation, turbulence and VOF. Finally, the N-CFD model was used to optimize the distance between the nozzle and the reaction chamber wall of the new aging equipment. Through the 15 min aging experiment on 100 L Baijiu, the methanol concentration of Baijiu decreased by 68.14 ± 2.25%, and the concentration of ethyl acetate, ethyl lactate and ethyl palmitate increased from 6.105 ± 0.014, 3.498 ± 0.015 mg/L and 0.621 ± 0.010 mg/L to 6.332 ± 0.016, 4.868 ± 0.012 mg/L and 0.681 ± 0.008 mg/L. The results show that the aging technology equipment can adjust the self-coupling characteristics and dynamic characteristics of various molecules in Baijiu through high-speed jet, and catalyze the alternating phase transition and association of various molecules. Finally, the goal of high-efficiency and healthy aging Baijiu without additives was achieved, which helps the rapid and healthy development of the Baijiu brewing industry. Full article