Search Results (3,231)

The rapid influx of generative artificial intelligence (GenAI) into higher education has resulted in a large number of research studies over the past three years. Much of the research to date has focused on the researcher/educator perspective of the optimal usage, benefits and challenges in the implementation of GenAI, often in a very general sense. The importance of the student voice and discipline-specific results must not be lost in this debate. This study investigates students’ decisions to use (or not use) GenAI for mathematics purposes and the reasons they give to support this choice. This was done by surveying almost 700 students in an Irish university, using an online, anonymous survey in March 2025. The students in the sample were all studying at least one mathematics module at the time. In addition, students were asked what they perceive the benefits and challenges to be of GenAI for mathematics in higher education. 91% of respondents were using GenAI for mathematics purposes. Both their reasons for using GenAI and the benefits they perceived for it fell into four main themes: Self-Improvement, Tools, Access, and Support. Their reasons for not using GenAI, and the challenges they perceived, were categorised under Unnecessary/Unknown, Inaccurate, Skill Loss, and Ethical Concerns. Students mentioned using GenAI for self-improvement reasons, such as bettering their understanding of mathematics, and found the lack of interaction with a person to be a benefit rather than a challenge. This study was conducted at a unique moment in time when the usage of GenAI was moving from sporadic to widespread and reflects students’ self-reported reasons for this. Full article

Background: Family-based interventions are effective in mitigating the risk for relapse of schizophrenia. However, the accessibility of these interventions is scarce in many clinical settings. A group-based brief cognitive behavioural intervention facilitated by a therapist using videoconferencing may help address this practice gap and improve the high treatment disengagement that occurs in interventions delivered in self-paced web-based forums or mobile applications. Objective: To examine the feasibility, acceptability, and safety of an online group-based cognitive behavioural family intervention for dyads of individuals with schizophrenia-spectrum disorders and caregivers. Methods: This feasibility study adopted a parallel-group, assessor-blind randomised controlled trial with a twelve-week post-intervention follow-up as well as individual semi-structured interviews. Participants were randomly assigned to the intervention group [i.e., tele-group cognitive behavioural family intervention (tgCBFI) group] and the treatment-as-usual group. Both groups also received biweekly brief telephone support. The feasibility and acceptability were assessed by the recruitment rate, intervention completion rate, retention rate and participants’ service satisfaction. Safety was measured by the number of adverse events. Results: Most intervention group participants (85.7%) attended all six online group sessions (six service user-caregiver dyads, corresponding to 12 participants), while 100% of participants attended the per-protocol number of sessions (≥four sessions). The study recruitment rate was 16.4%, while the study retention rate for follow-up assessments was 95.8%. No adverse events were reported throughout the study. Five themes were generated to illustrate the benefits of and recommendations for the tgCBFI programme to complement the quantitative findings. Conclusions: The preliminary results suggested that the use of videoconferencing to deliver group-based cognitive behavioural intervention was partially feasible and provided exploratory estimates suggesting possible improvement in psychiatric symptoms for individuals with schizophrenia-spectrum disorders, warranting a fully powered trial. Trial Registration: prospectively registered at ClinicalTrials.gov NCT05808244. Full article

Dynamic uplink power allocation is a critical challenge in cell-free massive MIMO (CF-mMIMO) networks, where distributed access points (APs) jointly serve multiple user equipment (UEs) under mobility, time-varying propagation conditions, and strong inter-user interference. Conventional optimization-based methods can improve fairness or spectral efficiency, but they often require repeated numerical solving and are usually designed for a specific objective. Learning-based approaches can reduce online decision time after training; however, their effectiveness depends strongly on the reward design and the selected operating objective. In response to these challenges, we propose a Deep Hybrid Intelligent (DHI) architecture designed to evaluate dynamic uplink power management within cell-free massive MIMO environments. The framework uses Soft Actor-Critic (SAC) learning to generate continuous uplink transmit-power decisions and evaluates objective-specific configurations for fairness, signal-to-interference-plus-noise ratio (SINR) improvement, and spectral-efficiency enhancement. In addition, three optimization-based strategies, namely max-min fairness, max-product SINR optimization, and max-sum-rate maximization, are incorporated to analyze the trade-off among fairness, signal quality, throughput, and computational cost. Limited-memory Broyden-Fletcher-Goldfarb-Shanno with bound constraints (L-BFGS-B) optimization is employed for the max-product and max-sum-rate objectives, while the max-min strategy is evaluated through a fairness-oriented feasibility procedure. Simulation results show that the fairness-oriented configuration achieves the highest Jain’s fairness index, reaching 0.989 at 120 access points, whereas the sum-rate-oriented configuration provides stronger SINR and user-rate performance. The results also indicate execution-time reductions of 51.6%, 83.7%, and 85.0% for the evaluated max-min, max-product, and max-sum-rate strategies, respectively, compared with conventional optimization-based implementations. These execution-time gains are accompanied by a clear performance trade-off: the max-min strategy provides the strongest fairness behavior, the max-sum-rate strategy improves total spectral efficiency and user-rate performance, and the max-product strategy offers a balanced operating point between collective SINR improvement and user-service balance. Therefore, the proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions. These results indicate that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks. Full article

Background/Objectives: Hidradenitis suppurativa (HS), a chronic, inflammatory skin condition, severely affects quality of life. Despite advances in understanding its pathophysiology, major gaps persist in diagnosis and management. This study examined the journeys and unmet needs of Italian participants via an online survey, focusing on diagnostic delays, treatments, and quality of life for HS. Methods: Data were collected through a survey shared by the HS patient association in a dedicated Facebook group, using 45 closed-ended questions through Computer-Assisted Web Interviews. A total of 320 participants completed the survey in January 2023. Results: Participants reported consultations with approximately five clinicians before receiving a formal diagnosis, with a 10-year delay. Dissatisfaction with primary care was high (73%). Dermatologists played a central role in disease management. Only 24% of participants were on biologic therapy, despite the higher satisfaction levels compared to other treatments. HS was associated with quality-of-life impairments, particularly in psychological wellbeing (58%), daily functioning, and economic productivity. Unmet needs included better psychological support, improved access to biologics, nutritional guidance, and increased awareness of the disease among healthcare professionals. Conclusions: HS imposes considerable physical, psychological, and economic burdens. Earlier diagnosis, improved treatment strategies, and enhanced patient-centered care are essential to reducing this burden. Full article

Background: Post-COVID-19 Condition (PCC) causes persistent multisystem symptoms, reduced quality of life, and high healthcare needs. Data on patient experiences in Switzerland are scarce. Objective: To assess healthcare access, treatment pathways, waiting times, and insurance-related burden among individuals with PCC in Switzerland. Methods: We conducted a nationwide anonymous online survey between July and October 2025. Adults living in Switzerland with symptoms persisting ≥12 weeks after SARS-CoV-2 infection were eligible. Recruitment was conducted through support groups, social media, and specialized PCC consultations at several Swiss hospitals. The multilingual questionnaire covered demographics, symptoms, healthcare utilization, treatment experiences, and insurance issues. Results: A total of 333 individuals participated (81.7% women; mean age 45.8 years). The most frequent symptoms were fatigue (98.5%), brain fog (92.5%), post-exertional malaise (91.0%), sleep disturbances (82.3%), and musculoskeletal pain (80.8%). Only 12.6% reported early and adequate medical support, whereas 45.0% had to actively seek care themselves and 24.6% had received no support. General practitioners were the most frequently consulted providers (77.5%), followed by specialized PCC clinics (39.0%). Waiting times were prolonged: 32.1% waited 4–6 months, 32.4% >6 months and 10.8% reported no available consultation service. Satisfaction with care was modest (3.02/5). Difficulties with social insurance were reported by 42.6%, including missing benefits, lack of recognition, and prolonged procedures. Conclusions: Individuals with PCC in Switzerland face major barriers to timely and coordinated care. Strengthening primary care competencies, expanding multidisciplinary services, and implementing structured patient-centered pathways are urgently needed to improve outcomes and reduce long-term disability. Full article

Earthquake surface ruptures provide critical geological evidence for identifying seismogenic structures, reconstructing rupture processes of strong earthquakes, and assessing seismic hazards associated with active faults. The Mainland China Surface Rupture Database (MCSRD) has compiled surface rupture records from 632 data sources and 72 earthquake events, providing an important data foundation for investigating strong-earthquake surface ruptures in mainland China. However, offline data products are limited in supporting online visualization, interactive querying, and cross-platform sharing, which restricts the broader reuse of surface rupture data. In this study, we developed a WebGIS-based data-sharing system for earthquake surface ruptures in mainland China using MCSRD as the core data source and an open-source geospatial technology stack comprising PostgreSQL/PostGIS, GeoServer, Django, and Leaflet. The system enables the unified organization, online publication, interactive querying, result export, and Open Geospatial Consortium (OGC)-compliant service access of multi-source thematic data. Unlike platforms primarily designed for the management of basic active-fault information, the proposed system focuses on event-based surface rupture data from documented strong earthquakes, with emphasis on rupture geometry, coseismic displacement, reliability assessment, and source traceability. Application and performance testing results show that the system can transform offline MCSRD thematic data into standardized spatial information services, enhancing the online accessibility, interoperability, and reusability of earthquake surface rupture data. This study provides a practical implementation framework for the Web-based publication and open sharing of earthquake geology thematic data. Full article

The increasing prevalence of anxiety, depression, and stress has intensified the demand for scalable, evidence-based psychological interventions that can be delivered across traditional and digital modalities. University students represent a high-risk group due to academic demands and developmental transitions, making them a relevant population for evaluating flexible, technology-mediated interventions. Cognitive-behavioral therapy (CBT)-based psychoeducation offers a structured approach that may accommodate variability in accessibility, preferences, and delivery contexts. This quasi-experimental pre–post study evaluated a CBT-based psychoeducation protocol delivered in group settings in both online and in-person formats. A total of 104 undergraduate and graduate students participated and completed the intervention. Symptom severity was assessed using the Depression, Anxiety and Stress Scale (DASS-21), the Hamilton Anxiety Rating Scale (HAM-A), and the Beck Depression Inventory–II (BDI-II). Significant reductions in anxiety, depression, and stress symptoms were observed over the intervention period, with moderate to large effect sizes. DASS-21 scores decreased from moderate to mild severity levels, and reductions were also observed in BDI-II and HAM-A scores. No significant differences were found between delivery modalities. These findings support the potential of CBT-based group psychoeducation as a scalable intervention. Similar patterns of symptom change were observed across delivery formats, contributing to the expansion of accessible and digitally informed mental health care. Full article

The increasing penetration of renewable energy sources and microgrids (MGs) provides additional flexibility for active distribution networks (ADNs), but it also introduces two coupled operational challenges: renewable uncertainty management and Model-Confidentiality-Preserving coordination between the ADN and MGs. This paper aims to improve the ADN-side cost effectiveness and reserve adequacy of ADN–MG systems by enabling MGs to actively support renewable uncertainty balancing while preserving the confidentiality of their internal operation models. To this end, a Model-Confidentiality-Preserving distributionally robust chance-constrained scheduling method is proposed for ADN–MG systems. In the proposed framework, affine recourse strategies are used to coordinate the uncertainty responses of ADN-side dispatchable generators and MGs, while renewable forecast errors are handled through a tractable Wasserstein-based distributionally robust chance constraint (DRCC) reformulation. Meanwhile, each MG provides an aggregated flexibility box to the ADN operator, so that coordinated scheduling can be performed without accessing detailed MG resource parameters and constraints. Case studies on modified IEEE test systems show that the proposed method can maintain out-of-sample reserve adequacy, reduce online computational time, and achieve near-centralized ADN-side operating-cost performance. Full article

Although digital transformation has expanded classroom technology access, the integration of ethical development within digital learning environments remains limited. This study investigates the relationship between executive functions and ethical digital citizenship among primary school students aged 6–12 years. Key contributing factors examined include smart education, digital literacy, ethical competency, teacher agency, institutional support, and executive function. Empirical data were collected from 387 primary school teachers and administrators within the Office of the Basic Education Commission network via online and printed questionnaires, capturing educators’ professional observations and proxy-reports of their students’ behavior. Using partial least squares structural equation modeling (PLS-SEM), the study tested seven hypotheses. Results indicate that smart education directly influences digital literacy, a relationship significantly moderated by teacher agency. Furthermore, digital literacy directly impacts ethical competency, with institutional support serving as a critical moderator. Finally, executive functions demonstrated a significant, direct impact on students’ ethical competency. By highlighting the interconnected roles of these six factors, this study provides a novel, evidence-based framework that policymakers can utilize to effectively foster ethical digital citizenship among young Thai learners. Full article

Background/Objective: Social media platforms have become increasingly integrated into nursing students’ academic and daily lives, influencing how health-related information is accessed, evaluated, and shared. At the same time, eHealth literacy has emerged as an important competency for nursing students within contemporary digital healthcare environments. This study aimed to examine the relationship between multidimensional social networking engagement and perceived eHealth literacy among undergraduate nursing students. Methods: A cross-sectional descriptive correlational design was used among undergraduate nursing students (N = 146). Data were collected using an electronic survey that included demographic characteristics, the Social Networking Usage Questionnaire, and the Revised eHealth Literacy Scale. Descriptive statistics, Pearson’s correlation, and linear regression analyses were performed using SPSS version 26. Results: Participants reported moderate-to-high social networking engagement and generally favorable perceived eHealth literacy. A strong positive correlation was identified between SNUQ and eHEALS-R scores (r = 0.75, p < 0.001), with academic use showing the strongest dimension-level association with perceived eHealth literacy. In the adjusted regression model, social networking engagement remained independently associated with perceived eHealth literacy, whereas demographic variables and daily social media use showed no significant associations. Conclusions: The findings suggest that how nursing students engage with digital platforms is associated with their confidence in accessing, evaluating, and applying online health information. Strengthening purposeful and critical digital engagement within nursing education may help prepare students for contemporary digital healthcare environments. Full article

Agricultural E-commerce alters farmers’ production and marketing decisions, thereby shaping land-use intensity and livelihood stability. In the context of rapid digital transformation, enhancing farmers’ livelihood resilience is critical to sustainable rural development and resilience to socio-economic and environmental shocks. Using survey data from 542 rural communities in China, this study examines the relationship between agricultural E-commerce participation and farmers’ livelihood resilience across its multidimensional components. The empirical results show that participation in agricultural E-commerce is positively associated with farmers’ overall livelihood resilience, with notable positive associations with buffering capacity, learning capacity, and self-organization capacity. Among these, learning capacity exhibits the strongest association, followed by buffering capacity and self-organization capacity. Farmers’ digital capability positively moderates the relationship between E-commerce participation and livelihood resilience by facilitating access to and effective use of digital information, thereby lowering information costs and contributing to returns from online market participation. Heterogeneity analysis further indicates that the positive association between agricultural E-commerce participation and livelihood resilience is stronger among cooperative members, households with adequate production capital, non-agricultural households, and families with sufficient labor resources. By identifying the role of agricultural E-commerce in building livelihood resilience, this study contributes to the empirical understanding of how digital engagement may support inclusive and sustainable rural development under similar institutional conditions. Full article

Background/Objectives: The digital transition in school settings is reshaping children’s learning, writing practices, and mental health trajectories. This narrative review examines whether handwriting still matters in contemporary hybrid educational environments from a multidisciplinary perspective. Methods: Evidence from neuroscience, developmental psychology, educational sciences, pediatrics, and child psychiatry was narratively synthesized, with attention to handwriting, digital exposure, learning, emotional regulation, and vulnerable populations. Results: Handwriting uniquely integrates fine motor control, visuomotor coordination, orthographic processing, attention, and embodied cognition, supporting early literacy, memory consolidation, conceptual learning, and reflective writing. Conversely, excessive or poorly mediated digital exposure may interact with attentional fragmentation, sleep disruption, online stressors, problematic use, and internalizing symptoms, particularly in vulnerable children and adolescents. Digital tools remain essential for personalization, accessibility, and compensatory support, especially for students with neurodevelopmental or learning difficulties. Conclusions: Handwriting and digital technologies should not be framed as competing educational paradigms. A developmentally sensitive hybrid model is needed, preserving handwriting during key stages of literacy and self-regulation while integrating digital tools as purposeful, individualized resources for learning, inclusion, and school mental health promotion. Full article

Background: Public acceptance of vaccines during emerging infectious disease outbreaks is influenced by perceptions of safety, accessibility, and cost. Understanding these determinants during the early phase of a pandemic is important for informing vaccination policy and communication strategies. This study examined the role of vaccine cost and safety perceptions in shaping COVID-19 vaccination intention among adults in Saudi Arabia prior to national vaccine rollout. Methods: A cross-sectional electronic survey was conducted among adults residing in Saudi Arabia between August and September 2020. Participants were recruited through online distribution using social media and electronic communication channels. The questionnaire assessed socio-demographic characteristics, knowledge and perceptions regarding COVID-19, and intention to receive COVID-19 vaccination under five hypothetical scenarios reflecting vaccine cost and the availability of scientific safety and efficacy evidence. Multivariable logistic regression analyses were performed to identify factors associated with vaccination intention. Results: A total of 1293 respondents completed the survey. When vaccination was offered free of charge, 57.5% of participants reported that they would likely receive the vaccine. Vaccination intention declined as the hypothetical vaccine price increased, reaching 38.7% when the cost exceeded 100 SAR. Intention was lowest when vaccination was presented under conditions in which safety and efficacy evidence had not yet been confirmed. Age, gender, educational attainment, and prior seasonal influenza vaccination were associated with COVID-19 vaccination intention across several scenarios. Conclusions: Perceived vaccine affordability and confidence in scientific safety and efficacy evidence were important factors associated with COVID-19 vaccination intention during the early phase of the pandemic in Saudi Arabia. Providing vaccination without direct cost to recipients and communicating safety and efficacy evidence transparently may support public confidence and vaccine acceptance during future large-scale immunization campaigns. These findings should be interpreted as an early-pandemic baseline and should not be extrapolated to current post-rollout attitudes. Full article

Interest in electric vehicles (EVs) is rising as the world shifts toward sustainable transportation, yet consumer adoption remains highly uneven, particularly in developing countries. This study examines how five dimensions of consumption value—functional, social, emotional, novelty, and conditional—influence consumers’ purchase intention toward EVs in Indonesia, while also testing the moderating role of infrastructure readiness. Using a quantitative approach, data were collected through an online survey with purposive sampling, yielding 455 valid responses. Partial least squares structural equation modeling (PLS-SEM) was applied to assess the measurement and structural models. The results reveal that functional, social, emotional, and conditional values significantly influence consumers’ purchase intention toward EVs, whereas novelty value has no significant effect. Infrastructure readiness also significantly moderates most consumption values, with negative coefficients indicating that limited charging access and inadequate maintenance support weaken the positive impact of consumer values on EV adoption. The findings show that although consumers value performance, social image, emotional appeal, and situational factors, poor charging infrastructure hinders purchase intention toward EVs. This study contributes to EV adoption literature by integrating consumption value theory with infrastructure readiness as a moderator. The results emphasize that developing charging infrastructure, expanding service availability, and maintaining supportive government policies are critical steps for accelerating EV adoption in emerging markets. Full article

This study develops a computational framework for quantifying how technological disruptions are associated with shifts in media–public discourse alignment. Using the public release of ChatGPT (30 November 2022) as a temporal breakpoint, we examine whether the broad public availability of generative AI was associated with structural changes in the relationship between media agendas and online public discourse in South Korea. The framework combines PPMI-weighted semantic network construction with QAP correlation, MRQAP regression and Cohen’s q effect-size analysis, applied to 181,081 Korean-language texts encompassing media agendas (national newspapers, economic dailies, regional newspapers, and broadcast news) and public agendas (online communities) over six years (2019–2025). Results reveal that national newspapers lost their dominant agenda-setting position, with public alignment declining sharply (r: 0.678 → 0.430, Cohen’s q = 0.369, large effect), while economic papers rose from lowest to second-highest alignment (r: 0.352 → 0.567) by addressing market and industry dimensions that direct AI experience could not supply. Broadcasting emerged as the dominant structural anchor of public discourse (unique coefficient β: 0.263 → 0.569; its removal alone lowers model fit from R² = 0.517 to 0.347), while national newspapers’ unique contribution reversed in sign. Collective media explanatory power itself remained essentially stable (R² = 0.537 → 0.517), indicating a structural reconfiguration of which media align with public discourse rather than a wholesale weakening of media–public alignment—consistent with agenda-melding, in which publics integrate media coverage with firsthand technological experience. A residual analysis further shows that the variance media agendas leave unexplained is not noise but a structured, public-specific layer of discourse organized around the hands-on use of generative-AI tools (e.g., ChatGPT, image generation)—direct evidence of a bounded public autonomy in which public discourse is distinct from, and not reducible to, media agendas. These findings demonstrate the framework’s utility for detecting how publicly accessible AI adoption—exemplified by ChatGPT—is associated with shifts in media–public structural dynamics within discourse ecosystems and carry implications for computational social science, technology communication, and applied network analysis. Full article