Search Results (15,186)

Gestational Diabetes Mellitus (GDM) represents a significant public health concern due to its association with adverse maternal and neonatal outcomes, as well as elevated long-term metabolic risks. Its prevalence varies substantially depending on the diagnostic criteria used and the population studied. Women with GDM frequently experience heightened stress, anxiety, and uncertainty, underscoring the need for accessible information, counseling, and ongoing support to navigate glucose monitoring, dietary adjustments, and treatment regimens. Although clinical management has been extensively studied, research has largely focused on metabolic monitoring and therapeutic interventions, often underemphasizing prevention strategies, women’s informational needs, and maternal psychological well-being. Emerging evidence and international guidelines increasingly advocate for integrating these components into structured, woman-centered GDM care plans that actively involve families. Such approaches empower women to engage in self-management, enhance health literacy, support adherence to lifestyle and pharmacological interventions, and promote sustainable behavioral changes. This narrative review presents a comprehensive, holistic model of care across the perinatal continuum, emphasizing early risk identification, preventive strategies, and multidisciplinary coordination. Core elements include individualized antenatal education, empathetic communication, and family engagement, fostering self-efficacy, continuity of care, and integration of medical, educational, and psychosocial interventions. Equipping healthcare professionals with the competencies to deliver this holistic, woman-centered framework is essential to optimize maternal and neonatal outcomes and mitigate the long-term health consequences of GDM. Full article

Background/Objectives: Higher education requires students to communicate in complex academic and social contexts, including oral presentations, group work, help-seeking, assessment, and peer interaction. For students with developmental communication disorders, and communication-related developmental profiles, these demands may create hidden participation vulnerabilities that affect mental health, academic engagement, and belonging. This scoping review mapped empirical evidence among tertiary students, focusing on mental health, academic participation, social belonging, institutional support, and contextual influences. Methods: A scoping review was conducted in accordance with PRISMA-ScR guidance. Five databases, PubMed, PsycINFO, CINAHL, Scopus, and Web of Science, were searched for English-language, peer-reviewed empirical studies published from 2000 onwards. Eligible studies involved university, college, or tertiary students with developmental speech, language, fluency, pragmatic communication, or communication-related developmental profiles, who reported at least one mental health, academic, or social participation outcome. Data were charted and synthesised thematically, with methodological quality appraised using CASP-informed criteria. Results: Twenty-one studies were included. Evidence was strongest for stuttering and fluency-related participation, while research on developmental language disorder, speech sound disorder, pragmatic language impairment, cluttering, and mixed communication profiles was limited. Across studies, communication needs intersected with anxiety, depression, stress, self-efficacy, oral assessment, help-seeking, disclosure, stigma, accommodation access, and belonging. Key limitations included reliance on self-report, cross-sectional or retrospective designs, inconsistent diagnostic confirmation, and limited evidence for intervention. Conclusions: The available evidence suggests that developmental communication disorders and communication-related developmental profiles can function as hidden participation vulnerabilities in higher education. These vulnerabilities are shaped by students’ communication profiles and by communication-intensive university environments. Universities may therefore need communication-accessible teaching, flexible assessment, visible support pathways, and coordinated support across disability services, counselling, academic support, and speech–language pathology. Full article

Reducing carbon emissions from daily travel has become an important goal of 15-minute living-circle planning, yet it remains unclear which facility configurations are most supportive of green travel. Using 634 living circles and 20 million mobile-phone travel records and point-of-interest (POI) data, this study examines how facility layout within a 15-minute cycling circle influences residents’ walking and cycling travel behavior. Extreme Gradient Boosting (XGBoost) models and Shapley Additive Explanations (SHAP) suggest that low accessibility is generally associated with lower green travel shares, while moderate facility density promotes green travel, yet for some facility types, high density may show diminishing marginal benefits. Vegetable markets and primary schools emerge as key facilities, with education facilities driven mainly by accessibility, entertainment facilities by density, and commercial and healthcare facilities by both. K-means clustering identifies three types of low-green-travel-performing living circles—characterized by low density and poor accessibility—concentrated in peripheral and newly developed areas. The methodology is transferable, and the derived numerical ranges and living-circle typologies offer context-specific implications for Tangshan, and identified differences in facility importance and diminishing marginal benefits enrich 15-minute city theory. Full article

Higher education increasingly addresses topics that are complex, interdisciplinary, and context-dependent, creating challenges for traditional lecture-based instruction. This study explores the potential of AI-assisted interactive storytelling as a pedagogical approach for such learning contexts, using healthy buildings as an instructional case relevant to architecture, engineering, and construction (AEC) education. Grounded in constructivist learning theory, a set of interactive stories was developed using generative AI and implemented in Twine to create a decision-based learning experience. The intervention was tested in a class using a pretest–posttest design along with a student perception survey. The results showed a significant improvement in knowledge following the intervention. Student feedback was also positive across all measured dimensions, including perceived learning, cognitive engagement, emotional engagement, motivation to learn, and comparison with traditional lectures. These findings suggest that interactive storytelling can support both learning and engagement when teaching complex, multidimensional topics. This study further indicates that generative AI can serve as a practical development partner by reducing the time and technical effort required to create interactive educational materials. Overall, this paper contributes to higher education research by positioning and demonstrating AI-assisted interactive storytelling as a promising instructional approach for complex learning areas. Full article

The paper investigates tourists’ willingness to pay (WTP) an increased tourist tax to support the development of accessible tourism. To achieve the research objective, a structured survey was conducted among 452 tourists who spent their holidays in the Ukrainian Carpathians. It has been confirmed that the WTP a higher tourist tax that varies significantly depending on the possible direction of its use. It has been established that in the context of a humanitarian crisis, social inclusion is a more powerful factor of tax loyalty than a traditional environmental programme. A statistically significant relationship was found between WTP an increased tourist tax for the development of accessible tourism and several factors, including respondents’ level of education, income level, frequency of tourist trips over the past five years, current trip expenditure, and perceived accessibility of infrastructure. Consumers’ willingness to voluntarily pay a tourist tax for a specific purpose may suggest that higher tax rates would be publicly acceptable, potentially generating financial resources to support the development of accessible tourism. Full article

The rapid emergence of generative AI tools is transforming software development. Consequently, software engineering education must adapt to ensure that students not only learn traditional development methods but also understand how to use these new technologies effectively and responsibly. In particular, project-based courses provide an effective setting in which to explore and evaluate the integration of AI assistance into real-world development practices. This paper presents our approach and a user study conducted in the context of a university programming project in which students collaboratively developed computer games. The study investigates how participants used generative AI tools across different phases of the software development process, identifies the tasks for which these tools were perceived as most useful, and analyzes the challenges students encountered. Building on these insights, we further examine a repository-aware, locally deployed large language model (LLM) assistant designed to provide project-contextualized support. The system employs retrieval-augmented generation (RAG) to ground its responses in relevant documentation and source code, thereby enabling a qualitative analysis of model behavior, parameter sensitivity, and common failure modes. These findings deepen our understanding of context-aware AI support in educational software projects and inform the future integration of AI-based assistance into software engineering curricula. Full article

Introduction: Obstructed HemiVagina and Ipsilateral Renal Anomaly (OHVIRA) syndrome, also known as Herlyn–Werner–Wunderlich syndrome, is a rare congenital Müllerian duct anomaly, characterized by uterus didelphys, obstructed hemivagina, and ipsilateral renal agenesis. Symptoms typically appear shortly after menarche and include dysmenorrhea and pelvic pain. The psychological burden associated with fertility and reproductive outcomes in women with OHVIRA syndrome remains poorly investigated. Materials and methods: A 30-year-old primigravida with left renal agenesis and a history of vaginal abscess, dysmenorrhea, and chronic pelvic pain received a delayed OHVIRA syndrome diagnosis. The patient had previously been informed that spontaneous conception and an uncomplicated pregnancy were highly unlikely because of her congenital gynecological condition, resulting in significant fertility-related anxiety and psychological distress. Under careful supervision and counseling, she conceived successfully, and the pregnancy progressed without complications; an elective cesarean section was performed at term. A literature search using the PubMed and Embase databases was conducted between November 2025 to April 2026 to identify studies reporting reproductive outcomes and psychological aspects in patients diagnosed with OHVIRA syndrome and other Müllerian anomalies. Results: Evidence-based counseling contributed to improvement of quality of life and reduction of pregnancy-related anxiety of the reported patient with OHVIRA syndrome. A limited number of studies discuss the mental burden and fertility-related anxiety of patients with OHVIRA syndrome and other Müllerian anomalies. Conclusions: Spontaneous conception and uncomplicated pregnancy are possible for women with OHVIRA syndrome. The psychological burden associated with congenital gynecological conditions remains under-recognized and requires further investigation. Comprehensive counseling and interdisciplinary care are essential to improve reproductive education, mental health support, and pregnancy outcomes in patients with congenital gynecological anomalies. Full article

Background: Cardiac point-of-care ultrasound (POCUS) is widely used in emergency and acute care settings. Still, broader use remains limited by operator dependence and variability in image acquisition and interpretation. Artificial intelligence (AI), including machine learning and deep learning methods, has been applied to cardiac POCUS to support image acquisition, automate quantitative measurements, and assist interpretation. Methods: We performed a narrative review of current applications of AI-assisted cardiac POCUS. A targeted literature search of PubMed and Google Scholar from 2018 to 2026 was conducted using terms related to AI, machine learning, deep learning, and cardiac ultrasound. Studies evaluating AI-assisted cardiac ultrasound in clinical, educational, or image-acquisition settings were included, with emphasis on recent, clinically relevant applications. Results: The most developed application of AI-assisted cardiac POCUS is an automated assessment of left ventricular systolic function, particularly the left ventricular ejection fraction (LVEF), where multiple studies report agreement with expert interpretation or formal echocardiography and improved performance among novice users. AI-assisted tools have also been evaluated for pericardial effusion detection, guidance for image acquisition, and education. More complex applications, including diastolic function assessment and hemodynamic measurements such as LVOT-VTI, remain less well validated and more dependent on image quality. Across studies, performance is closely linked to image acquisition quality and has often been evaluated under controlled rather than real-world conditions. Conclusions: Current evidence supports AI-assisted cardiac POCUS primarily as a decision-support tool, with the strongest data for automated assessment of LVEF. Other applications remain investigational. Full article

Fostering the coupled and coordinated progress of agricultural pollution control and carbon emission reduction (AGR) is integral to agricultural modernization and high-quality agricultural development. As China’s green finance policies have been steadily refined, green finance has emerged as a pivotal driver of AGR enhancement. To examine the influence of green finance on AGR, this study analyzes their nexus over the 2008–2023 period. The results demonstrate that green finance markedly improves AGR, and this effect exhibits heterogeneity. In terms of mechanism, green finance strengthens AGR by bolstering agricultural technological innovation (INN) and raising farmers’ income (INC). Agricultural fiscal support (FIN) and the state of rural education (EDU) serve as important external conditions for green finance to effectively promote AGR. The findings further suggest that green finance is progressively evolving into a major force for advancing AGR. Full article

This paper describes design, implementation and initial evaluation of Digital Twin Metaverse Classroom for higher education. Digital Twin Metaverse Classroom refers to highly realistic digital replicas or virtual replicas or prototypes of university classrooms or learning spaces. This paper focuses on creating high-fidelity digital replica of typical university lecture room. The main purpose of the Digital Twin Metaverse Classroom is to support teaching and learning in addition to traditional videoconferencing. The pilot involved thirty-two undergraduate students. A single-group pre-test/post-test quiz measured short-term learning, while the Technology Acceptance Model (TAM) measured acceptance through perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention. A single session raised the mean quiz score from 6.41 to 9.19, a within-session gain that reached statistical significance, while all four TAM constructs scored highly. Because the sample was small and confined to one institution, with neither a control group nor a follow-up, these findings are best read as early evidence of feasibility, short-term improvement, and favorable acceptance rather than as proof of comparative effectiveness. Full article

Background: Antimicrobial resistance (AMR) poses an escalating threat to global health, agriculture, and the environment, demanding urgent multisectoral action under the One Health framework. Despite global awareness efforts, understanding of AMR among the general population remains insufficient, particularly in low- and middle-income countries such as Brazil. This study aimed to evaluate the knowledge, attitudes, and perceptions (KAP) of the Brazilian population regarding AMR. Methods: An online questionnaire was distributed through social media platforms between April and August 2025, resulting in 945 valid responses after data cleaning. Quasi-Poisson models were applied to identify demographic predictors of KAP scores while logistic regression models were used to assess the association between KAP scores and antibiotic use-related practices. Results: Education level was the strongest predictor of higher KAP scores, whereas age and gender showed inconsistent influence. Only 40.3% of respondents correctly identified antibiotics among commonly used medicines, and 25.9% reported proper disposal of antibiotic packaging. More than half (54.2%) were willing to pay more for antibiotic-free products, although only 26.7% had ever noticed such labeling. Network analysis of open-ended responses indicated that concerns about potential health risks and AMR awareness were the primary motivators for purchasing antibiotic-free products. Conclusions: These findings reveal significant gaps in public understanding of antibiotic use and resistance in Brazil, emphasizing the urgent need for targeted educational initiatives, improved public communication, and behavioral interventions to support antimicrobial stewardship and sustainable antibiotic use. Full article

Background/Objectives: Discharging patients after hospitalization for an acute exacerbation of chronic obstructive pulmonary disease (COPD) is a critical transition in care associated with a high risk of early readmission. This survey aimed to describe physician-reported discharge practices following COPD exacerbations, identify perceived gaps and organizational barriers, explore attitudes toward structured COPD discharge summaries, and use a SWOT analysis as an interpretative framework. Methods: In this cross-sectional observational survey, 100 physicians involved in COPD care were recruited from the official mailing list of the Respiratory Society of Serbia, which represents approximately 71% of the Society’s members. The survey assessed discharge procedures, multidisciplinary practices, patient education, comorbidity management, perceived causes of readmission, and barriers to structured discharge summaries. Data were analyzed descriptively and complemented with a structured SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Results: Most respondents worked in tertiary care settings and were involved in managing patients hospitalized for COPD exacerbations. Although 24% of physicians routinely used structured discharge summaries, 45% reported never using them. The most frequently perceived contributors to 30-day readmissions were active smoking (90%), poor treatment adherence (81%), comorbidities (77%), and incorrect inhaler technique (72%). Major barriers to implementing structured discharge summaries included the lack of standardized templates, time constraints, poor coordination across healthcare levels, and technical limitations. Willingness to implement structured discharge tools was high (mean score 8.86/10). SWOT analysis identified strong professional support for discharge standardization alongside organizational and system-level barriers to implementation. Conclusions: This exploratory survey identified important gaps between recommended and routine COPD discharge practices and highlighted organizational barriers to implementation. The findings may inform future evaluation and development of structured discharge tools in this healthcare setting. Full article

Artificial intelligence (AI) is transforming higher education, although most research addresses its integration in terms of frequency of use or technological acceptance, without examining how it translates into specific curricular and instructional decisions. That is why this study has a dual aim: to develop and validate the AI Instructional Design Questionnaire for Critical Thinking (AID-CTQ) and to analyze how university faculty integrate AI into instructional design practices in higher education. The sample included 144 faculty members from a university in Madrid, selected by convenience. Exploratory and confirmatory factor analyses of the questionnaire supported a three-factor structure: Activity Design (F1), Critical Thinking Assessment (F2), and Self-Regulation and Reflection (F3). The final 12-item model shows good model fit (CFI = 0.98, TLI = 0.98, RMSEA = 0.05, SRMR = 0.05) and adequate overall reliability (α = 0.86). At the item level, responses related to assessment and reflective practices showed consistently high agreement, whereas items linked to activity design displayed greater variability. Faculty members with more than 10 years of experience obtained significantly higher scores, indicating that the educational value of AI depends less on the tools used and more on the quality of instructional decisions. Reported use of AI was high, with ChatGPT and Copilot being the most frequently used tools. Overall, the findings indicate that the integration of AI in higher education is evolving from predominantly instrumental uses toward more pedagogical and curriculum-oriented forms of implementation. Accordingly, the educational value of AI lies less in the tool itself than in the quality of the instructional decisions through which it is meaningfully embedded in the curriculum. Full article

Over the past decade, developments in artificial intelligence (AI) have sparked a new wave of debate and research across nearly all areas of life, including education. In English as a Foreign Language (EFL) education, AI-based technologies are also widely adopted to support learners and instructors. This trend has led to numerous studies focused on understanding AI’s role in identifying potential opportunities and challenges. This study offers a systematic review of relevant research, highlighting the benefits and obstacles of AI use in the Saudi EFL context. About 60 peer-reviewed articles were selected following PRISMA guidelines. The findings reveal multiple opportunities for AI integration in Saudi Arabia, such as improved language skills, personalized learning experiences, increased self-regulated learning, boosted motivation and confidence among learners, expanded learning opportunities, and support for pedagogy and institutional performance. Major challenges include biased and inaccurate data, students’ overdependence on technology, ethical concerns, and a lack of technological skills among users. The study also suggests future directions, including localizing AI tools, conducting long-term impact studies, providing faculty and student training, and establishing ethical guidelines within institutions. Full article

Digital transformation has become an essential component of sustainable rural development, yet substantial inequalities persist in how communities access, adopt, and benefit from digital technologies. Understanding these disparities requires not only information about technological resources but also knowledge of the relational structures through which information, support, and opportunities circulate. This article presents a reproducible synthetic socio-digital network dataset designed to support the analysis of digital gaps in community-based tourism (CBT) environments. Rather than containing original respondent-level observations, the repository was computationally reconstructed from aggregate statistics derived from field studies conducted in three rural communities in the province of Guayas, Ecuador: Bucay (5 de Septiembre), Manglares Churute, and Ruta de los Chirijos. All node-level records, survey variables, and support relationships included in the repository were synthetically generated to preserve aggregate community characteristics while protecting participant confidentiality and preventing individual re-identification. The repository contains synthetic actor metadata, reconstructed socio-digital variables, directed support networks, graph representations in interoperable formats, and precomputed Social Network Analysis (SNA) indicators. The dataset includes 90 synthetic actors, more than one thousand generated support interactions distributed across multiple socio-digital dimensions, machine-readable metadata, and reusable scripts for preprocessing, validation, graph construction, and metric computation. The represented dimensions include financial assistance, training support, information exchange, technological support, social media promotion, institutional collaboration, trust, and emotional closeness. To facilitate reuse, all resources are distributed in standardized formats compatible with NetworkX, Gephi, Neo4j, and graph-learning frameworks. The repository follows FAIR principles and includes documentation intended to support transparency, reproducibility, and methodological benchmarking. Potential applications include social network analysis, graph mining, graph neural networks, digital inequality research, computational social science, community resilience studies, and educational activities. By providing an openly documented synthetic dataset and reproducible computational workflow, the repository contributes to the study of socio-digital systems, privacy-preserving data sharing, and community-level digital transformation processes. Full article