Search Results (433)

Pragmatic language is a core component of school-based social participation, yet children with Autism Spectrum Disorder (ASD) and Social Communication Disorder (SCD) frequently experience persistent difficulties in using language appropriately across everyday learning contexts. This study investigated the effectiveness of a culturally adapted, school-based immersive Virtual Reality (VR) learning program designed to enhance pragmatic language and social communication skills among Thai primary school children. Eleven participants aged 7–12 years completed a three-week, ten-session VR program that simulated authentic classroom, playground, and canteen interactions aligned with Thai sociocultural norms. Outcomes were measured using the Social Communication Questionnaire (SCQ) and the Pragmatic Behavior Observation Checklist (PBOC). While SCQ scores showed a small, non-significant reduction (p = 0.092), PBOC results demonstrated significant improvements in three foundational pragmatic domains: Initiation and Responsiveness (p = 0.032), Turn-Taking and Conversational Flow (p = 0.037), and Politeness and Register (p = 0.010). Other domains showed no significant changes. These findings suggest that immersive, culturally relevant VR environments can support early gains in core pragmatic language behaviors within educational settings, although broader social communication outcomes may require longer or more intensive learning experiences. Full article

The Wua-Lai silvercraft community in Chiang Mai is experiencing a widening disconnect with younger visitors, raising concerns about the erosion of intangible cultural heritage. This study evaluates “Silver Craft Journey,” a location-based augmented reality (LBAR) system designed to revitalize cultural engagement and enhance cultural-heritage experience through context-aware, gamified exploration. A quasi-experimental field study with 254 participants across three age groups examined the system’s impact on cultural-heritage experience, knowledge acquisition, and real-world engagement. Results demonstrate substantial knowledge gains, with a mean increase of 7.74 points (SD = 4.37) and a large effect size (Cohen’s d = 1.77), supporting the effectiveness of LBAR in supporting tangible and intangible heritage understanding. Behavioral log data reveal clear age-related engagement patterns: older participants (41–51) showed declining mission completion rates and reduced interaction times at later points of interest, which may reflect increased cognitive and physical demands during extended AR navigation under real-world conditions. These findings underscore the potential of location-based AR to enhance cultural-heritage experience in real-world settings while highlighting the importance of age-adaptive interaction and route-design strategies. The study contributes a replicable model for integrating digital tourism, embodied AR experience, and community-based heritage preservation. Full article

Vision-language models (VLMs) integrate visual and textual information and are increasingly being used as innovative tools in educational applications. However, there is a lack of evidence regarding current practices for integrating VLMs into teaching and learning. To address this research gap and identify the opportunities and challenges associated with the integration of VLMs in education, this paper presents a systematic review of VLM use in formal educational contexts. Peer-reviewed articles published between 2020 and 2025 were retrieved from five major databases: ACM Digital Library, Scopus, Web of Science, Engineering Village, and IEEE Xplore. Following the PRISMA-guided framework, 42 articles were selected for inclusion. Data were extracted and analyzed against six research questions: (1) where VLMs are applied across academic disciplines and educational levels; (2) what types of VLM solutions are deployed and which image–text modalities they infer and generate; (3) the pedagogical roles of VLMs within teaching workflows; (4) reported outcomes and benefits for learners and instructors; (5) challenges and risks identified in practice, together with corresponding mitigation strategies; and (6) reported evaluation methods. The included studies span K-12 through higher education and cover diverse disciplines, with deployments dominated by pre-trained models and a smaller number of domain-adapted approaches. VLM-supported pedagogical functions cluster into five roles: analyst, assessor, content curator, simulator, and tutor. This review concludes by discussing implications for VLM adoption in educational settings and offering recommendations for future research. Full article

This study curates and structures knowledge concerning Buddhist learning resources for tourism, presenting it through a virtual tour of Wat Phra Pathom Chedi Ratchaworamahawihan in Nakhon Pathom Province. Employing a mixed-methods approach that integrates both qualitative and quantitative methodologies, the research first establishes a structured knowledge base. This involves developing a comprehensive metadata schema for cataloging the temple’s diverse resources, including both sacred sites and artifacts, to enhance their searchability and accessibility. Subsequently, this knowledge is rendered into a virtual tour, which serves as an exemplary model of a Buddhist digital learning resource for tourism. The findings reveal the extensive diversity of resources within the temple. The developed virtual tour platform allows users an immersive exploration of the site via 360-degree panoramic views. This research presents significant implications for relevant agencies, offering a scalable model for the digital dissemination of cultural heritage. It is anticipated that this initiative will expand global access to and appreciation of the temple’s cultural value, thereby fostering international interest in visitation. Such engagement is poised to stimulate the local economy and bolster Thailand’s image as a premier cultural tourism destination. Full article

Type 2 diabetes is increasingly prevalent among children and adolescents with overweight or obesity, and although lifestyle interventions remain first-line preventive strategies, long-term adherence and effectiveness are often limited. Metformin has demonstrated efficacy in delaying type 2 diabetes onset in adults at high risk, but its preventive role in pediatric populations remains unclear. This systematic review and meta-analysis aims to evaluate the effectiveness of metformin, alone or in combination with lifestyle interventions, in preventing or delaying type 2 diabetes among children and adolescents with overweight or obesity. The protocol is registered in PROSPERO (CRD42024615622), MEDLINE (PubMed), Embase, Cochrane Library, Scopus, and Web of Science and will be searched from inception to June 2025. Eligible studies include randomized controlled trials, quasi-experimental studies, and prospective cohort studies involving individuals under 18 years of age. The primary outcome is incidence of type 2 diabetes, with secondary outcomes including fasting plasma glucose, HbA1c, insulin resistance, BMI z-score, adherence, and adverse events. Where appropriate, random-effects meta-analyses will be conducted. This review will synthesize current evidence on metformin for pediatric type 2 diabetes prevention and inform future preventive strategies and clinical decision-making. Full article

Background/Objectives: Sexual dysfunction (SD) and broader sexual health problems are common after neurological disorders, yet interventional evidence is fragmented across conditions and outcomes. This umbrella review mapped and appraised systematic review-level evidence on interventions targeting SD and sexual health in neurological populations and qualified conclusions using certainty of evidence. Methods: PubMed, Web of Science, Cochrane Library, Embase, PsycINFO, EBSCOhost, and Scopus were searched from inception to 27 November 2025. Two reviewers screened records, extracted data, assessed review quality with AMSTAR 2, and rated certainty across intervention–outcome pairings using a GRADE-informed approach that integrated review confidence and primary-study risk-of-bias as reported by the source reviews. Results: Twenty-six systematic reviews were included. Overall confidence was frequently limited (17/26 critically low and 6/26 low), with only a small subset rated moderate or higher. Evidence was most coherent for phosphodiesterase type 5 (PDE5) inhibitors improving erectile function in men with spinal cord injury, whereas most other interventions and outcomes were supported by low or very low certainty. Women were represented in 16/26 reviews, yet validated female sexual function outcomes were synthesized in 6/26 reviews and relationship/couple outcomes in 3/26; furthermore, 10/26 reviews restricted inclusion to men, and no review synthesized pediatric intervention trials. Conclusions: Evidence supports PDE5 inhibitors for improving erectile function in men with spinal cord injury, while evidence for other interventions and sexual health domains remains limited. Methodological limitations highlight the need for more inclusive trials, broader standardized outcomes, and longer follow-up within neurorehabilitation pathways. Full article

Background/Objectives: Lung cancer is the leading cause of cancer-related mortality worldwide, and early detection using low-dose computed tomography (LDCT) substantially improves survival outcomes. However, variations in CT acquisition and reconstruction parameters including Hounsfield Unit (HU) calibration, reconstruction kernels, slice thickness, radiation dose, and scanner vendor introduce significant intensity and contrast variability that undermine the robustness and generalizability of deep-learning (DL) systems. Methods: This systematic review followed PRISMA 2020 guidelines and searched PubMed, Scopus, IEEE Xplore, Web of Science, ACM Digital Library, and Google Scholar for studies published between 2020 and 2025. A total of 100 eligible studies were included. The review evaluated preprocessing and harmonization strategies aimed at mitigating CT intensity variability, including perceptual contrast enhancement, HU-preserving normalization, physics-informed harmonization, and DL-based reconstruction. Results: Perceptual methods such as contrast-limited adaptive histogram equalization (CLAHE) enhanced nodule conspicuity and reported sensitivity improvements ranging from 10 to 15% but frequently distorted HU values and reduced radiomic reproducibility. HU-preserving approaches including HU clipping, ComBat harmonization, kernel matching, and physics-informed denoising were the most effective, reducing cross-scanner performance degradation, specifically in terms of AUC or Dice score loss, to below 8% in several studies while maintaining quantitative integrity. Transformer and hybrid CNN–Transformer architectures demonstrated superior robustness to acquisition variability, with observed AUC values ranging from $0.90$ to $0.92$ compared with $0.85$–$0.88$ for conventional CNN models. Conclusions: The evidence indicates that standardized HU-faithful preprocessing pipelines, harmonization-aware modeling, and multi-center external validation are essential for developing clinically reliable and vendor-agnostic AI systems for lung-cancer screening. However, the synthesis of results is constrained by the heterogeneous reporting of acquisition parameters across primary studies. Full article

Background: Climate change is increasingly altering the distribution and burden of infectious diseases in Sub-Saharan Africa, where ecological diversity, fragile health systems, and widespread poverty heighten vulnerability. The One Health approach, which integrates human, animal, and environmental health, provides a useful framework for addressing these climate-sensitive health challenges; its application in the region remains limited. Methods: This review was conducted in accordance with PRISMA-ScR guidelines and synthesized evidence from 30 peer-reviewed studies published between 2019 and 2025, identified through PubMed, Scopus, Web of Science, and the Cochrane Library. Results: Studies consistently showed that rising temperatures, altered rainfall patterns, and extreme weather events shifted malaria transmission into highland zones, modified schistosomiasis risk through changes in snail habitats, and drove diarrheal outbreaks following flooding. While One Health initiatives such as Ghana’s Climate-Smart One Health framework and university-led programmes in East Africa demonstrated promise, their impact remained constrained by donor dependence, institutional silos, and limited policy integration. Conclusions: To enhance climate resilience, national strategies need to integrate climate-informed surveillance, predictive modelling, and One Health governance. Future research should extend beyond malaria and schistosomiasis, incorporate longitudinal data, and establish standardized metrics for assessing One Health interventions. Full article

Industrial Control Systems (ICS) and Cyber–Physical Systems (CPS) are critical infrastructures supporting national sectors, where cyberattacks can directly cause physical process disruptions and safety incidents. Following PRISMA 2020 guidelines, we systematically searched Web of Science, Scopus, IEEE Xplore, and the ACM Digital Library for studies published between 1 January 2021 and 31 October 2025, and finally included 89 primary studies. The literature is categorized into five data modalities—network traffic, operational data, simulation data, hybrid data, and other auxiliary data—and compared in terms of detection objectives, learning paradigms, model families, attack types, and datasets. The analysis shows that network data are effective for detecting cyber-layer attacks such as reconnaissance, DoS, and MITM, while operational data are suited for physical-layer anomalies including process disturbances, FDI, and stealth deviations. Simulation and hybrid data further support rare-scenario generation and cyber–physical consistency checking. However, limitations remain, including reliance on few benchmarks, lack of realistic multi-domain datasets, label sparsity, concept drift, and insufficient consideration of real-time and resource-constrained OT environments. Based on these findings, this review highlights future directions such as multi-domain dataset development, physics- and control-informed model design, hybrid-data-driven integrated detection, and lightweight edge deployment. Full article

This study introduces a Generative AI-Enhanced Robotic Data Automation (AI-ERDA) framework designed to improve accuracy, efficiency, and adaptability in healthcare data workflows. Conducted over a two-month, real-world experiment across three government health platforms—one web-based (NHSO) and two PC-based systems (CHi and TRT)—the study compared the performance of AI-ERDA against a conventional RDA system in terms of usability, automation accuracy, and resilience to user interface (UI) changes. Results demonstrated notable improvements in both usability and reliability. The AI-ERDA achieved a mean System Usability Scale (SUS) score of 80, compared with 68 for the traditional RDA, while Field Exact Match Accuracy increased by 1.8 percent in the web system and by 0.2 to 0.3 percent in the PC systems. During actual UI modifications, the AI-ERDA maintained near-perfect accuracy, with rapid self-correction within one day, whereas the baseline RDA required several days of manual reconfiguration and assistance from the development team to resolve issues. These findings indicate that generative and adaptive automation can effectively reduce manual workload, minimize downtime, and maintain high data integrity across heterogeneous systems. By integrating adaptive learning, semantic validation, and human-in-the-loop oversight, the AI-ERDA framework advances sustainable digital transformation and reinforces transparency, trust, and accountability in healthcare data management. Full article

Manual data entry in dialysis centers is time-consuming, error-prone, and increases the administrative burden on healthcare professionals. Traditional optical character recognition (OCR) systems partially automate this process but lack the ability to handle complex data anomalies and ensure reliable clinical documentation. This study presents the design and evaluation of an AI-enhanced OCR system that integrates advanced image processing, rule-based validation, and large language model-driven anomaly detection to improve data accuracy, workflow efficiency, and user experience. A total of 65 laboratory reports, each containing approximately 35 fields, were processed and compared under two configurations: a basic OCR system and the AI-enhanced OCR system. System performance was evaluated using three key metrics: error detection accuracy across three error categories (Missing Values, Out-of-Range, and Typo/Free-text), workflow efficiency measured by average processing time per record and total completion time, and user acceptance measured using the System Usability Scale (SUS). The AI-enhanced OCR system outperformed the basic OCR system in all metrics, particularly in detecting and correcting Out-of-Range errors, such as decimal placement issues, achieving near-perfect precision and recall. It reduced the average processing time per record by almost 50% (85.2 to 42.1 s) and improved usability, scoring 81.0 (Excellent) compared to 75.0 (Good). These results demonstrate the potential of AI-driven OCR to reduce clerical workload, improve healthcare data quality, and streamline clinical workflows, while maintaining a human-in-the-loop verification process to ensure patient safety and data integrity. Full article

Agriculture has been widely practiced for food production, yet food insecurity remains a critical issue, especially in Africa. Due to the significant role played by small-scale farmers, urban agriculture has been acknowledged as a viable strategy for reducing food insecurity in urban areas of Sub-Saharan Africa. This review analyzes urban household food insecurity factors through a systematic literature approach, retrieving data from various online databases. These databases include ScienceDirect, Wiley Online Library, Web of Science, UNIZULU online library, and PubAg. The search process involved the use of keywords to obtain relevant information along with the application of filters such as geographic location, publication period, language, article type, and accessibility. A total of 37 articles was included in this review after the application of the review eligibility criteria. This was achieved following PRISMA guidelines. Findings reveal a growing trend in the publication of articles on urban farming and an increasing acknowledgment of its importance by high-impact journals. It also shows various factors that determine household food insecurity, categorized as socioeconomic (11), institutional (5), and environmental factors (2). This led to the recommendation that urban government structures including policy makers and stakeholders should support food production and ensure an efficient urban food supply system. Full article

Accurate medical diagnosis is essential for informed decision making and the delivery of effective treatment. Traditionally, this process relies on clinical judgment, integrating data and medical expertise to inform decision making. In recent years, artificial neural networks (ANNs) have proven to be valuable tools for diagnostic support. Attention mechanisms have enhanced ANNs performance, while fuzzy logic has contributed to managing uncertainty inherent in clinical data. This systematic review analyzes how the integration of these three approaches enhances computational models for medical diagnostic support. Following PRISMA 2020 guidelines, a comprehensive search was conducted across five scientific databases (IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and ACM Digital Library) for studies published between 2020 and 2025 that implemented the combined use of ANNs, attention mechanisms, and fuzzy logic for medical diagnostic support. Inclusion and exclusion criteria were applied, along with a quality assessment. Data extraction and synthesis were conducted independently by two reviewers and verified by a third. Out of 269 initially identified articles, 32 met the inclusion criteria. The findings consistently indicate that the integration of ANNs, attention mechanisms, and fuzzy logic significantly improves the performance of diagnostic models. ANNs effectively capture complex data patterns, attention mechanisms prioritize the most relevant features, and fuzzy logic provides robust handling of ambiguity and imprecise information through continuous degrees of membership. This integration leads to more accurate and interpretable diagnostic models. Future research should focus on leveraging multimodal data, enhancing model generalization, reducing computational complexity, and exploring novel fuzzy logic techniques and training paradigms to improve adaptability in real-world clinical settings. Full article

Objectives: Patients with severe to profound sudden sensorineural hearing loss (SSNHL) generally experience poorer hearing recovery; however, the associated risk factors have not been identified. This study synthesizes current evidence to explore prognostic risk factors in this patient group. Methods: Databases were systematically searched through PubMed, Embase, Web of Science, and the Cochrane Library, from their inception to 18 October 2025. Three researchers independently extracted and recorded patient information and relevant data from all selected studies. Any inconsistencies were clarified through discussion or by consulting a fourth researcher. Results: The study included 2632 patients from 15 articles published between 2002 and 2025 and evaluated 8 prognostic risk factors. The results showed that profound hearing loss (OR = 4.68; 95% CI: 3.57–6.13; p < 0.001) and vertigo (OR = 1.95; 95% CI: 1.28–2.98; p = 0.002) were correlated with poorer hearing recovery. Subgroup analyses based on different prognostic criteria confirmed the consistent impact of hearing loss severity on poor outcomes. The remaining 6 risk factors did not show statistically meaningful associations. Conclusions: Profound hearing loss and vertigo are significantly associated with poorer prognosis in patients with severe to profound SSNHL. These findings may help identify high-risk patients early and inform the design of personalized therapeutic approaches in clinical settings. Full article

(1) Background: Climate change has intensified extreme heat and localized rainfall, exposing South Korea’s protected trees to new risks. Despite their ecological and cultural value, prior research has been largely local or qualitative, leaving little basis for nationwide prioritization. (2) Methods: We developed a composite risk index that integrates heat and rainfall exposure with species sensitivities, covering nearly the entire national inventory (≈10,000 individuals). Risks were calculated at the tree level, aggregated to district, provincial, and national scales, and tested for robustness across weighting and normalization choices. Spatial clustering was assessed with Moran’s I and LISA. (3) Results: High-risk clusters were consistently identified in southern and southwestern regions. Mean and tail indicators showed that average-based approaches obscure extreme vulnerabilities, while LISA confirmed significant High–High clusters. Rankings proved robust across scenarios, indicating that results reflect structural signals rather than parameter settings. Priority areas defined by the presence of extreme-risk individuals emerged as stable candidates for intervention. (4) Conclusions: The study establishes a transparent, operational rule for prioritization and offers tailored strategies—such as drainage infrastructure, shading, and root-zone management—while informing medium-term planning. It provides the first nationwide, empirically grounded framework for conserving protected trees under climate transition. Full article