Search Results (5,664)

The operational reliability of Insulated Gate Bipolar Transistor (IGBT) modules in demanding transportation applications, such as traction systems, is critically challenged by solder layer and bond wire failures under cyclic thermal stress. To address this, this paper proposes a novel health monitoring framework that innovatively synergizes micro-scale spatial thermal analysis with microsecond electrical dynamics inversion. The method requires only non-invasive temperature measurements on the module baseplate and utilizes standard electrical signals (load current, duty cycle, switching frequency, DC-link voltage) readily available from the converter’s controller, enabling simultaneous diagnosis without dedicated voltage or high-bandwidth current sensors. First, a non-invasive assessment of solder layer fatigue is achieved by correlating the normalized thermal gradient ($\nabla T_{\mathrm{P}}$) on the baseplate with the underlying thermal impedance ($Z_{\mathrm{JC}}$). Second, for bond wire aging, a cost-effective inversion algorithm estimates the on-state voltage ($V_{\mathrm{ce},\mathrm{on}}$) by calculating the total power loss from temperature, isolating the conduction loss ($P_{\mathrm{cond}}$) with the aid of a Foster-model-based junction temperature ($T_{\mathrm{J}}$) estimate, and finally computing $V_{\mathrm{ce},\mathrm{on}}$ at a unique current inflection point ($I_{\mathrm{C},\inf }$) to nullify $T_{\mathrm{J}}$ dependency. Third, the health states from both failure modes are fused for comprehensive condition evaluation. Experimental validation confirms the method’s accuracy in tracking both degradation modes. This work provides a practical and economical solution for online IGBT condition monitoring, enhancing the predictive maintenance and operational safety of transportation electrification systems. Full article

Nail clipping can cause fear, anxiety, and stress (FAS) in dogs and cats and possibly long-term aversion to veterinary care. We conducted an anonymous, online, mixed-methods survey to characterise the knowledge, attitudes, and practices of Australian veterinary nurses, technicians, and other veterinary staff regarding nail clipping in dogs and cats. Among 242 valid responses, 71.9% (n = 174/242) performed nail clipping multiple times per week. Dogs (72.4%, n = 165/228) and cats (59%, n = 134/227) frequently displayed FAS (score > 2/5) during nail clipping. Most respondents (79.8%, n = 193/242) reported being injured while performing nail clipping. Respondents reporting a negative attitude towards nail clipping were 5.5 times (95% CI = 1.7–17.8) more likely to report being injured during a nail clip compared to those with a positive attitude. Respondents reported feeling pressure to persevere with nail clipping when it was unnecessary or when animals exhibited severe FAS. Where used, pre-visit pharmaceuticals and sedation were reported to effectively reduce FAS (dog: p = 0.015; cat: p = 0.152), along with non-pharmaceutical interventions such as gentle handling and counter-conditioning. Respondents perceived nail-clipping to be a difficult, undervalued task. Increased training of veterinary team members regarding nail clipping could improve animal welfare and the safety of veterinary team members. Full article

Green production technology, serving as a core approach to coordinate agricultural production and ecological conservation, acts not only as a technological engine driving the transition towards sustainable agricultural models but also as a critical pathway to achieve environmental sustainability. To promote the adoption and diffusion of green production technologies, this study utilizes survey data from 2566 farm households in Jiangxi Province and employs an Ordered Probit model to thoroughly investigate how the application of digital technology enhances farmers’ adoption of green production practices. The findings indicate that the application of digital technology—measured through digital information acquisition, digital technology use, and digital online sales—significantly promotes the intensity of farmers’ adoption of green production technologies. Furthermore, the positive effect of digital technology application on the adoption of green production technologies strengthens with increased government intervention. The influence also varies significantly across households with different farm sizes and education levels, showing a more pronounced effect on medium- and large-scale farmers as well as those with higher education levels. This study provides new evidence on the mechanism through which digital technology application affects farmers’ adoption of green production technologies, and offers insights conducive to leveraging digital technologies in promoting sustainable environmental development. Full article

High-precision non-contact online voltage monitoring has attracted considerable attention due to its improved safety. Based upon existing research works and validation of non-contact voltage measurement techniques, an enhanced approach for online voltage monitoring is proposed in this paper. By analyzing the influence of the relationship between coupling capacitance and input capacitance on monitoring results, an RC-type signal input circuit with enhanced adaptability has been designed for practical engineering scenarios that may involve large input capacitance. Furthermore, a mixed-signal measurement method based on phase dithering is proposed to eliminate detection errors caused by relative phase drift during synchronous sampling in existing signal injection approaches. This improvement enhances measurement accuracy and offers a more robust theoretical basis for selecting injection signal frequencies. The hardware circuit architecture and data processing scheme presented in this work are straightforward and have been validated using an experimental prototype tested at 50 Hz/500 V and 2000 Hz/300 V. Long-term energized testing demonstrates that the system operates stably at room temperature with a relative measurement error below 0.5%. This study provides a high-precision, easily implementable non-contact measurement solution for online monitoring of low-frequency, low-voltage signals in complex electromagnetic environments such as industrial control signals, low-voltage power signals, and rail transit signals. Full article

(1) Background: Drug–drug interactions (DDIs) are a frequent cause of medication-related harm, particularly in ambulatory care. Community pharmacists are uniquely positioned to identify and manage these risks. This study assessed DDI knowledge, practices, and barriers among community pharmacists in the Jazan Region, Saudi Arabia. (2) Methods: A structured, self-administered questionnaire was distributed to community pharmacists. The survey assessed DDI knowledge using 26 clinically relevant drug pairings and included questions on professional behavior, training exposure, software use, and educational needs. Descriptive and inferential statistics were applied to identify associations between knowledge scores and demographic or practice-related variables. (3) Results: A total of 219 pharmacists participated in the study. The mean knowledge score was (9.63 ± 4.58) out of 26, reflecting suboptimal to moderate awareness. Female pharmacists demonstrated significantly higher DDI knowledge scores than males (10.74 ± 5.4 vs. 9.08 ± 4.2; p = 0.016). Knowledge scores also differed significantly by academic qualification (p < 0.001), with PharmD holders scoring higher than B. Pharm and postgraduate degree holders. Pharmacists with less than 10 years of experience had significantly higher scores compared with those with longer practice duration (p = 0.002). Additionally, pharmacists who graduated from Saudi institutions scored higher than those trained outside Saudi Arabia (10.22 ± 4.7 vs. 8.44 ± 4.2; p = 0.005). Pharmacists who had received professional development training and those who attended workshops regularly also scored significantly higher. Familiarity with guidelines showed a positive trend. Reported barriers to effective DDI counseling included time constraints, limited patient understanding, and poor collaboration with prescribers. Self-rated awareness of DDIs was positively associated with actual knowledge scores. Pharmacists expressed strong preferences for workshops, online courses, and webinars as future training formats. (4) Conclusions: Pharmacists in the Jazan Region demonstrate moderate awareness of DDIs, with variation influenced by training, experience, and qualifications. Enhancing access to structured professional development and integrating clinical decision support tools could strengthen pharmacists’ role in preventing DDIs in community practice. Full article

Background: Brain–Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) and Extended Reality (XR) offer promising solutions for highly wearable applications, but their classification performance can be affected by systematic effects in stimulus presentation. Novelty: This study introduces a novel online compensation method to compensate for systematic effects in the Refresh Rate (RR) of XR displays, enhancing SSVEP classification without requiring additional training or invasive measurements. Methods: A non-invasive monitoring module was incorporated into the developed BCI pipeline to measure frame rate variations in the XR display, allowing deviations between nominal RR and measured values to be automatically detected and compensated for. Classification performance was evaluated using Filter Bank Canonical Correlation Analysis (FBCCA). Statistical significance was assessed using Student’s t-test. Materials: Two datasets were used: a dataset based on Moverio BT-350, including 9 subjects, and a dataset based on HoloLens 2, including 30 subjects, all collected by the authors. Results: The proposed compensation method led to significant improvements in SSVEP classification accuracy, proportional to the magnitude of fps deviations. In some cases, classification accuracy increased by up to 300% relative to its original value. Statistical analyses confirmed the reliability of the results across subjects and datasets. Conclusions: These findings show that the proposed method effectively enhances SSVEP-based BCIs in XR environments and provides a robust foundation for practical applications requiring high reliability. Full article

The rapid emergence of generative artificial intelligence (GAI) is reshaping academic work in higher education. While classical technology acceptance models primarily emphasize cognitive and instrumental determinants, the adoption of GAI also raises ethical concerns related to trust in AI systems and the protection of personal and institutional data. To address this gap, this study examines the determinants of GAI acceptance and use among academic staff in Slovenian higher education institutions by applying a UTAUT-based model that integrates trust and privacy. In this study, GAI is conceptualized as a class of text-based generative AI tools commonly used in academic practice, including applications such as ChatGPT, Copilot, Scholar AI, Gemini, Consensus, and similar systems. A quantitative research design was employed, based on a structured online survey administered to academic staff across 20 higher education institutions in Slovenia (n = 201). Data were analyzed using multilevel confirmatory factor analysis and generalized estimating equations. The results indicate that performance expectancy and attitude toward using significantly predict behavioral intention to use GAI (B = 0.49, p < 0.001 for both), while behavioral intention is the primary predictor of actual use behavior (B = 0.93, p < 0.001). Effort expectancy is positively associated with use behavior independent of behavioral intention (B = 0.23, p = 0.012), whereas trust does not show a statistically significant association with use behavior (B = 0.05, p = 0.458) or behavioral intention (B = −0.01, p = 0.840). Privacy exhibits a positive, but non-statistically significant, association with use behavior (B = 0.12, p = 0.058). The findings highlight the relevance of considering both cognitive and ethical factors when examining generative AI adoption in academic contexts and provide initial empirical insights for refining UTAUT-based frameworks in the context of emerging AI technologies. Full article

This report describes the development and deployment of the Public Health Quality Tool (PHQTool), an online resource designed to help mobile health clinics (MHCs) assess and improve the quality of their public health services. MHCs provide essential clinical and public health services to underserved populations but have historically lacked tools to assess and improve the quality of their work. To address this gap, the PHQTool was developed as an online, evidence-based, self-assessment resource for MHCs, hosted on the Mobile Health Map (MHMap) platform. This report documents the collaborative development process of the PHQTool and presents preliminary evaluation findings related to usability and relevance among mobile health clinics. Drawing from national public health frameworks and Honore et al.’s established public health quality aims, the PHQTool focuses on six aims most relevant to mobile care: Equitable, Health Promoting, Proactive, Transparent, Effective, and Efficient. Selection of the six quality aims was guided by explicit criteria developed through pilot testing and stakeholder feedback. The six aims were those that could be directly implemented through mobile clinic practices and were feasible to assess within diverse mobile clinic contexts. The remaining three aims (“population-centered,” “risk-reducing,” and “vigilant”) were determined to be less directly actionable at the program level or required system-wide or data infrastructure beyond the scope of individual mobile clinics. Development included expert consultation, pilot testing, and iterative refinement informed by user feedback. The tool allows clinics to evaluate practices, identify improvement goals, and track progress over time. Since implementation, 82 MHCs representing diverse organizational types have used the PHQTool, reporting high usability and identifying common improvement areas such as outreach, efficiency, and equity-driven service delivery. Across pilot and post-pilot implementation phases, a majority of respondents agreed or strongly agreed that the tool was user-friendly, relevant to their work, and appropriately scoped for mobile clinic practice. Usability and acceptance were assessed using descriptive statistics, including percentage agreement across Likert-scale items as well as qualitative feedback collected during structured debriefs. Reported findings reflect self-reported perceptions of feasibility, clarity, and relevance rather than inferential statistical comparisons. The PHQTool facilitates systematic quality assessment within the mobile clinic sector and supports consistent documentation of public health efforts. By providing a standardized, accessible framework for evaluation, it contributes to broader efforts to strengthen evidence-based quality improvement and promote accountability in MHCs. Full article

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care. Full article

Effective signaling in instructional materials—through cues such as highlights, arrows, and annotations—can guide learner attention, reduce cognitive load, and enhance comprehension in multimedia-rich online courses. While the benefits of signaling are well documented, little is known about how combinations of signaling strategies influence both the average performance and the consistency of student outcomes. In this study, we propose a data-driven approach to evaluate and optimize signaling strategies in online teaching. Using lecture materials from three semesters of introductory and intermediate statistics courses, we extracted multiple features of textual and visual signaling, including highlighted words, annotated formulas, arrows, and notes. Principal Component Analysis identified four distinct signaling strategies employed by the instructor. We then applied a heteroscedastic beta regression model to link these strategies to topic-level exam performance, allowing simultaneous assessment of mean learning outcomes and their variability. Results show that strategies combining formula highlighting with arrows and detailed notes improve both the average proportion of successful learners and the stability of outcomes, while relying solely on formula highlighting increases variability. Our findings provide actionable guidance for instructors to design effective signaling strategies, and demonstrate a flexible framework for data-driven evaluation of teaching practices in online learning environments. Full article

Endometriosis affects one in seven women in Australia and is a significant public health concern. Access to appropriate health information is essential for informed decision-making and quality of life, especially for culturally and linguistically diverse (CALD) women who may face additional communication and health literacy barriers. This study explored the information-seeking behaviours and experiences of CALD women living with endometriosis using semi-structured interviews. Through convenience and snowball sampling via social media, eleven women were recruited. Data were analysed using thematic analysis. The results showed that although women often did not view their cultural background as influential, taboos and stigma can shape information-seeking behaviours. Women primarily relied on healthcare professionals, online resources, and other women with endometriosis as information resources. Healthcare professionals were appreciated for providing tailored information, but some were perceived to have limited knowledge of endometriosis, reducing their usefulness. Online information was abundant and easily accessible but often overwhelming and difficult to navigate. Information from other women with lived experience provided both practical insights and validation, though participants recognised its limited transferability to their own circumstances. These findings highlight the need for information pathways, including better patient education through healthcare providers, as well as accessible and evidence-based online resources. Full article

Prosocial behavior is an important manifestation of socialization in young children. As the primary setting for socialization of young children, the family bears the significant responsibility of fostering prosocial behavior in young children. Drawing on family systems theory and Goleman’s emotional intelligence theory, the purpose of this study was to investigate the relationship between parenting styles and children’s prosocial behavior and the mediating role of children’s emotional intelligence in it. In this study, an online questionnaire was distributed to 869 young children’s parents using the Parenting Style Questionnaire, Children’s Prosocial Behavior Questionnaire, and Children’s Emotional Intelligence Questionnaire. The results indicated that democratic parenting style positively influenced children’s prosocial behavior, while indulgent parenting style, permissive parenting style and inconsistent parenting style negatively impacted it. Authoritarian parenting style had no significant effect on children’s prosocial behavior. Children’s emotional intelligence mediated the relationship between parenting styles and prosocial behavior. This study explored factors influencing children’s prosocial behavior from both external family systems and internal individual perspectives and revealed their underlying mechanisms, providing theoretical support for research and educational practice on children’s prosocial behavior. Full article

Background/Objectives: Children in paediatric palliative care often face school attendance barriers due to complex health needs. This study describes post-training perceptions of a training program by a pediatric hospice team to prepare school care assistants to safely include children with complex conditions, focusing on procedural skills, knowledge of the child, and family partnership. Methods: Care assistants who completed a structured course at the Paediatric Palliative Care Centre, University Hospital of Padua (2023–2024), were surveyed immediately after training. The program combined classroom instruction with hands-on simulation using high-fidelity mannequins and standard devices, including suction, pulse oximetry, ventilation, enteral feeding, and tracheostomy care. It also covered modules on urgent and emergency management, as well as family communication. An anonymous online questionnaire gathered socio-demographic data, prior training, clinical tasks performed, self-efficacy levels, and open-ended feedback. Quantitative results were analyzed descriptively, while qualitative comments were subjected to thematic analysis. Results: Of 130 invited assistants, 105 participated (81%). Participants reported strong perceived confidence: 85% selected the upper end of the 5-point scale (“very” or “extremely”) for routine-management ability, and 60% selected these same response options for emergency-management ability. In the most severe events recalled, 60.5% of incidents were resolved autonomously, 7.6% involved contacting emergency services, and 3.8% involved community or hospice nurses. Seventy-five percent judged the course comprehensive; thematic analysis of 102 comments identified satisfaction, requests for regular refreshers, stronger practical components, and requests for targeted topics. Conclusions: Immediately after the session, participants tended to select the upper end of the self-assurance item for both routine and emergency tasks. Combining core emergency procedures with personalized, child-specific modules and family-partnership training may support safety, trust, and inclusion. Regular refreshers and skills checks are advised. Full article

This study examines how Generation Z’s digital practices on TikTok and Instagram shape their music festival experiences, focusing on event perception, engagement, and the development of collective identity. The aim is to identify key factors connecting online and offline aspects of festival participation. The research adopts a quantitative approach based on an online survey of 248 respondents born between 1995 and 2010 from various regions of Serbia. Data were analyzed in SPSS 26.0 using Spearman correlation, quantile regression, and the Mann–Whitney test. Given the exploratory nature of the study, the findings should be interpreted accordingly. Findings show that frequent social media use has a positive but limited effect on how important these platforms are perceived for the festival experience. However, user-generated content created by attendees plays a more significant role in shaping engagement and attitudes than influencer content. Influencer credibility also influences how festivals are interpreted digitally. The interplay between online interaction and offline participation motivates content sharing and reinforces a sense of community. Overall, the study concludes that social media and digital narratives are central to Generation Z’s festival experience. Authentic, attendee-created content strongly contributes to collective identity, helping bridge digital and physical dimensions—insights valuable for festival organizers, influencers, and cultural tourism. Full article

In the digital tourism marketplace, online reviews have become a decisive source of information for travelers who cannot directly assess the quality of hospitality services before purchase. However, it remains unclear how service-related and sustainability-related cues interact to shape guest perceptions in sustainable hotel contexts. This study investigates how intrinsic and extrinsic cues are reflected in online user reviews of sustainable hotels. Drawing on Cue Utilization Theory, a longitudinal content analysis was conducted on a worldwide online travel platform’s (OTP) reviews of hotels certified for their sustainability practices (2004–2024) by user selection. A coding framework was developed deductively based on prior studies on hotel service quality and sustainability practices, and data were analyzed using MAXQDA, Leximancer, and VADER sentiment analysis. Findings indicated that reviews primarily emphasize service quality attributes—particularly staff efficiency, food quality, and accommodation facilities—while sustainability themes appear less frequently, often in nature-related contexts. Sentiment analysis revealed a predominantly positive emotional tone shaped by service quality experiences. Overall, intrinsic cues play a central role in forming guest perceptions, highlighting the need for emotionally engaging sustainability communication strategies. Full article