Search Results (8,139)

The rapid integration of artificial intelligence (AI) accelerates the need for continuous skill acquisition. Consequently, this increases the importance of lifelong learning while raising fundamental questions about pedagogical interaction and human social values. To remain competitive, individuals must constantly acquire new skills and enhance existing ones. The aim of the article is to evaluate the stability of individual social value systems and the role of pedagogical interaction in lifelong learning during AI integration. The study uses a quantitative survey (N = 160) with a retrospective self-assessment model based on Schwartz’s Theory of Basic Human Values. The study processed data in IBM SPSS using non-parametric tests (Wilcoxon signed-rank, Kruskal–Wallis, Kendall’s rank correlation) to analyze how digital skills and sociodemographics influence technology perception. Findings reveal core value systems remain statistically stable; AI integration causes no internal value conflict. Digital skill level, rather than age, is the most significant factor in AI perception. While participants highly rate AI’s potential to customize learning, they express concerns regarding technological dependence. In the lifelong learning ecosystem, AI is viewed as a didactic tool rather than an educator replacement, as technology cannot provide essential social interaction and emotional support. Finally, higher education fosters a critical attitude toward AI’s ethical risks. Full article

Background: Artificial Intelligence (AI) and Machine Learning (ML) technologies, such as conversational agents, are becoming increasingly essential tools across multiple industries, particularly in healthcare. This paper presents a scoping review (PRISMA-ScR) of conversational agents (CAs) in mobile health coaching systems (MHCS). It examines existing applications of MHCS, focusing on development strategies, usage contexts, impacts on users, benefits, and research gaps, emphasizing the ability of explainable artificial intelligence (XAI) in making health guidance and decision-support recommendations transparent, trustworthy, and interpretable, if properly integrated. This scoping review identifies opportunities to maximize the use of conversational agents, explainable AI, and mobile technologies to make mobile health coaching systems more accessible and trustworthy, as well as further research gaps worth exploring. Objective: This scoping review maps the evidence on CAs and XAI-enabled technologies in MHCS, identifies trust-related design criteria, categorizes reported outcomes, and highlights opportunities for explainable conversational agents (XCA) in a mobile health context, especially in tackling general medical conditions pertinent in underserved settings. Eligibility criteria: Reported eligible resources evaluated, designed, or conceptually analyzed existing CAs, XAI techniques, and MHCS, AI-supported medical dialogue systems, e-coaching systems, and mobile health applications. We considered sources only relevant to healthcare, health coaching, trust, explainability, or patient engagement that were published between 2006 and 2025. Sources of Evidence: Searches were conducted in IEEE Xplore, Google Scholar, Springer, ScienceDirect/Elsevier, ProQuest, and ACM Digital Library, supplemented by targeted web searches and backward citation checks. Charting methods: Data were charted by system type, communication mode, health context, operational mode, technology used, XAI/trust features, degree of automation, study designs and outcome classification. We applied a revised outcome classification: generated desired outcome (GDO) and partially generated desired outcome (P-GDO), and did not generate desired outcome (DN-GDO). Results: A total of 201 resources were collected. Charted studies clustered around CAs in health, MHCS for chronic diseases and stress management, XAI methods such as LIME, SHAP, Prospector, and counterfactual explanations, and trust-related elements such as voice quality, communication style, appearance, social intelligence, privacy, and performance quality. Most health CAs and MHCS addressed chronic diseases, mental health, or behavior change; fewer addressed general medical diagnosis or autonomous mobile-based primary care support. Conclusions: Existing evidence suggests that CAs and MHCSs can support engagement, coaching, education, and selected decision-support tasks, but evidence for safe, autonomous, explainable general practice functionality remains limited. Future work should prioritize clinically supervised XCA designs, core safety assessment, interfaces with transparent explanation, data protection, culturally and linguistically responsive implementation, and future-oriented review in underserved mobile health settings. Full article

Cone beam computed tomography (CBCT)-guided bronchoscopy has emerged as a powerful tool in the diagnosis of peripheral lung lesions, offering real-time, image-confirmed biopsy capabilities that enhance precision and diagnostic yield. However, implementation of a CBCT program presents significant logistical and technical challenges. This article presents an experience-based implementation tutorial outlining a stepwise approach to establishing a CBCT-guided bronchoscopy program. The framework is derived from iterative workflow development across more than 300 procedures at our institution, St. Elisabethen Hospital in Frankfurt, Germany, as well as our implementation efforts at Avenue Hospital Parklands in Nairobi, Kenya. Key domains addressed include infrastructure assessment, access strategy, training, procedural logistics, ventilation protocols, case selection, workflow optimization, safety considerations, and business case development. This roadmap aims to support interventional pulmonologists in integrating CBCT into clinical practice, while emphasising the need for local adaptation based on institutional resources and multidisciplinary collaboration. Full article

Asthma is the most prevalent chronic respiratory disease in childhood, and the objective assessment of airway inflammation remains a major challenge, particularly in younger children in whom conventional lung function testing is often not feasible. The aim of this narrative review is to evaluate the clinical role of fractional exhaled nitric oxide (FeNO) in pediatric asthma, focusing on its diagnostic utility, role in treatment guidance, and value in disease monitoring. A structured literature search was conducted in PubMed for studies published between January 2015 and October 2025, using predefined keywords related to FeNO, asthma, and pediatric populations. After applying the eligibility criteria, 47 studies were included in the final synthesis. Evidence from systematic reviews and clinical studies indicates that FeNO has moderate-to-good diagnostic accuracy for childhood asthma, with a pooled sensitivity of 0.79 and specificity of 0.81, and is most useful as an adjunct to clinical assessment and lung function testing. FeNO-guided therapy may reduce exacerbation rates in selected pediatric populations, although its effects on symptom control and corticosteroid use remain inconsistent. In the monitoring setting, serial FeNO measurements may provide additional information on inflammatory control, treatment adherence, and risk of future exacerbations. However, interpretation is influenced by multiple confounding factors, including atopy, allergic rhinitis, corticosteroid therapy, and asthma phenotype. In conclusion, FeNO is a valuable complementary biomarker in pediatric asthma, with particular utility in improving diagnostic and therapeutic precision. Its optimal use requires careful integration within a multimodal clinical framework rather than reliance as a standalone tool. Full article

Urban expressway projects are critical components of modern transportation infrastructure, yet their construction quality is often threatened by multi-source, latent, and dynamic risks. Traditional expert-driven risk identification methods frequently suffer from subjective bias and low efficiency, failing to meet the rigorous management requirements of complex engineering environments. To address these challenges, this study proposes a robust risk assessment framework integrating Large Language Models (LLMs) and the Delphi method within a Bayesian Network (BN) structure. First, LLM technology is leveraged to perform semantic mining on extensive engineering texts, including construction specifications and project reports, to pre-identify potential risk factors. Second, the Delphi method is applied through multiple rounds of expert consultation to refine a comprehensive inventory comprising 32 risk factors across five dimensions: personnel, machinery, materials, methods, and environment. Finally, a BN-based evaluation model is developed, utilizing forward inference, backward diagnosis, and sensitivity analysis to quantify risk levels and pinpoint critical risk drivers. The framework was empirically validated using the T Expressway Project in Hangzhou as a case study. Results demonstrate that the model effectively transforms empirical management into precise, data-driven diagnosis, providing project managers with a quantitative tool for optimizing construction quality control and decision making in complex urban bridge projects. Full article

Arthritis (RA) is characterized by immune-mediated chronic inflammation and endothelial dysfunction, ultimately resulting in clinically overt cardiovascular complications. As a prototypical disease of microvascular dysfunction, RA represents an ideal model to study microvascular alterations. The dermal capillary network offers an easily accessible window to the peripheral microcirculation, whose function can be easily assessed using Nailfold videocapillaroscopy (NVC) or laser techniques. Whereas the clinical significance of structural alterations is not always clear, functional abnormalities may provide more direct insight into the dynamic status of the microvasculature and endothelial integrity. The present narrative review aims to provide an integrative overview of available studies evaluating functional abnormalities of the dermal microcirculation in RA, with particular emphasis on the emerging role of NVC as a dynamic vascular assessment tool. Several studies in RA have assessed the structure and morphology of the peripheral microvasculature using NVC, but far fewer data exist on functional alterations assessed with this method. The study of functional alterations of the dermal microvascular network in RA has largely been based on laser techniques, which consistently point towards altered microvascular reactivity. By contrast, functional NVC-related approaches remain limited, despite their potential ability to simultaneously assess structural and dynamic capillary abnormalities in vivo. Available evidence supports that NVC may be reframed as a promising functional vascular biomarker in RA. However, the available literature is characterized by small sample sizes, predominantly cross-sectional designs, and methodological heterogeneity, highlighting the need for standardized prospective studies. Full article

Accurate three-dimensional documentation has become an essential tool for the analysis, interpretation, and preservation of archaeological heritage, particularly in the case of large and complex architectural remains. This paper presents a high-resolution three-dimensional documentation and quantitative study of the Great Cistern of Sierra Aznar, a major Roman water-storage structure located in Arcos de la Frontera (Cádiz, southern Spain). A geometrically reliable three-dimensional model was generated through the integration of image-based photogrammetry and terrestrial laser scanning, ensuring high spatial resolution and complete geometric coverage. The resulting model provided the basis for detailed metric analyses, including planimetric documentation, estimation of maximum storage capacity, and assessment of sediment accumulation within the structure. The results indicate that the cistern had an estimated storage capacity of approximately 2180 m³, while sediment deposits currently occupy nearly 37.5% of its original volume, offering valuable evidence for the long-term evolution and post-depositional history of the monument. In addition, the spatial and altimetric relationships between the cistern, nearby sedimentation basins (piscinae limariae), and an associated fountain are consistent with a coordinated water-management landscape, although direct hydraulic connections are not preserved. The Great Cistern of Sierra Aznar is thus presented as a significant archaeological case study illustrating how rigorous three-dimensional documentation can support quantitative analysis, contextual interpretation, and the long-term preservation of complex hydraulic heritage. Full article

Under the combined impacts of climate change and urbanization, flood disasters have exhibited increasing non-stationarity, low-frequency but high-impact characteristics, and enhanced spatial dependence. Traditional indemnity-based flood insurance has certain limitations in claim efficiency and loss assessment. In contrast, index-based flood insurance, characterized by objective triggering mechanisms, rapid claim settlement, and low operational costs, has gradually become an important tool for flood catastrophe risk management. Based on a literature review approach, this study systematically reviews the index system, pricing mechanisms, and basis risk of index-based flood insurance, and provides a comprehensive analysis from the perspectives of index construction, threshold determination, and payout design. The results indicate that index systems have evolved from single hazard indicators to coupled indices integrating hazard characteristics and loss information, and multiple pricing approaches have been developed, including fixed, linear, piecewise payout, and probabilistic payout schemes (payouts determined by loss probabilities rather than fixed thresholds). Among the reviewed approaches, inundation-area-based indices generally show stronger consistency with actual losses at urban scales, whereas precipitation-based indices are more suitable for large-scale regional applications due to their rapid triggering capability. However, basis risk remains a critical issue, mainly arising from index errors, spatial scale mismatches, and inappropriate threshold settings. Therefore, to address the identified limitations of basis risk, threshold uncertainty, and spatial mismatches, future research should focus on multi-dimensional risk indices, dynamic threshold setting, and optimized spatial risk zoning, as well as the integration of remote sensing and machine learning methods to improve the consistency between indices and actual losses. The findings provide practical guidance for insurers in product design, for policymakers in regional flood risk financing, and for disaster managers in improving climate adaptation strategies. Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, with growing applications in pediatric cardiology. AI techniques, particularly machine learning and deep learning, enable the analysis of complex and heterogeneous data, supporting diagnosis, risk stratification, and clinical decision-making. This paper provides an overview of current AI applications in this field, discusses existing challenges, and explores future perspectives. In pediatric cardiology, AI has shown promising results across multiple domains. In electrocardiography, AI algorithms improve diagnostic accuracy and enable early detection of cardiac conditions, even in asymptomatic patients, while facilitating telecardiology-based care pathways. In cardiac auscultation, AI-assisted digital stethoscopes enhance the distinction between innocent and pathological murmurs, supporting primary care physicians and optimizing referral to pediatric cardiologic centers. Multimodality imaging represents one of the most advanced areas of AI applications. In echocardiography, magnetic resonance and computed tomography, AI improves image acquisition, view classification, and automated quantification, contributing to more standardized and reproducible assessments. Additionally, emerging technologies such as virtual reality, integrated with AI, offer innovative tools for education, surgical planning, and patient-specific modelling. Despite these advances, several limitations remain, including limited availability of large pediatric datasets, challenges in model generalizability and issues related to interpretability and integration into clinical workflows. In conclusion, AI represents a powerful complementary tool in pediatric cardiology, with the potential to improve diagnostic accuracy, optimize healthcare resources and support the transition toward precision medicine. Full article

The increasing energy demand and environmental impact of transportation systems have intensified the need for more efficient railway energy management strategies. Although electric railway systems provide a sustainable alternative, the dynamic nature of traction power systems and the inadequate use of regenerative braking energy still result in significant energy losses. In order to improve energy efficiency and state-of-charge (SOC) stability, this study proposes an optimized fractional-order proportional-integral (FOPI) controller for the control of a wayside energy storage system (ESS) in a DC railway network. The parameters of the FOPI controller are tuned via recent metaheuristic tool of barrel theory-based optimizer (BTO) such that the error between the desired and actual charging/discharging voltages of the ESS is minimized under nonlinear and time-varying operating conditions. The BTO is characterized by strong exploration/exploitation balance that prevents the approach from falling in local optima. Also, the approach has low sensitivity to user-defined parameters. The proposed approach was evaluated using a MATLAB/Simulink (version 2021b) model of a double-track DC railway system incorporating realistic train operations and three distinct traffic scenarios including ideal, perturbed, and stochastic conditions. The BTO was compared to other approaches of particle swarm optimization (PSO) and gray wolf optimizer (GWO). Also, statistical tests using the Friedman, Kruskal–Wallis, ANOVA, and Wilcoxon rank tests were conducted to assess the suggested approach. The obtained results confirm the robustness and competence of the proposed controller compared to either the conventional static control approach or optimized controller via the comparable approaches. As a result, the suggested controller achieved higher total energy savings, improved utilization of regenerative braking energy, and enhanced power demand distribution across substations. While minor increases in SOC deviation were observed in certain scenarios, the overall system performance showed improved robustness and adaptability. These findings highlight the effectiveness of integrating fractional-order PI control designed via the suggested BTO for advanced energy management in railway applications. Full article

The rapid integration of large language models (LLMs) into education has shifted research focus from questions of capability, such as what LLMs can do and how accurately—to questions of deployability, including how they can be operated effectively for many learners at once. In school environments, system reliability, scalability, and real-time responsiveness are critical, as delays or interruptions can directly reduce learner engagement, particularly during synchronous activities. This study evaluates the performance of open-source LLaMA models ranging from 1 billion to 70 billion parameters across one-, dual-, triple-, and quad-GPU configurations suitable for educational settings. Performance is assessed using four key indicators: success rate (percentage of completed requests), generation speed (tokens per second), throughput (completed responses per second), and latency (time until full response generation). These metrics were measured under progressively increasing numbers of simultaneous users to identify system capacity limits and trade-offs between model size, responsiveness, and scalability. The results indicate that smaller models (1B–3B) deliver faster, more stable performance under concurrent use, while larger models (8B–70B) experience substantial slowdowns and reduced reliability, even on high-end GPU systems. These findings suggest that effective educational deployment should prioritize empirical performance and infrastructure compatibility over model size alone. The paper concludes by proposing a practical framework to guide educators, administrators, and developers in selecting and configuring locally hosted GPU systems that balance model capability, response speed, and resource efficiency for real-time applications such as AI tutors, classroom chatbots, and automated feedback tools. Full article

Advanced imaging is the cornerstone of modern radiation oncology, contributing to each phase of patient care, from diagnosis and treatment planning to delivery and follow-up. It has evolved from providing purely geometric guidance to enabling biological and dynamic precision, capturing detailed spatial and functional information about tumors and surrounding tissues. This progress has also generated vast amounts of complex data that remain largely underexplored. AI-based methods have shown promises to unlock the potential of these data, ensuring quality and standardization while extracting previously inaccessible insights. AI-driven tools can enhance accuracy, efficiency, and personalization of radiation oncology through precision diagnosis, automated segmentation, adaptive treatment planning, real-time image guidance, and predictive response assessment. In this review, we conducted a systematic bibliometric analysis of relevant literature published in the last decade and explored current advancements in AI and radiomics applications across radiation oncology. We also addressed ongoing challenges, such as data heterogeneity, model interpretability, and clinical implementation, and discussed future directions for integrating AI-powered imaging solutions into routine practice to advance precision cancer care. Full article

Fatigue remains a persistent contributor to safety incidents in construction; however, limited research has examined how industry professionals perceive and prioritize fatigue monitoring approaches in real-world settings, particularly within the context of increasing digitization and data-driven safety management. To address this gap, this study conducted an exploratory survey of 103 construction professionals, including workers, supervisors, safety personnel, and project managers, to assess their familiarity with subjective, objective, and hybrid fatigue monitoring methods, along with their implementation preferences and perceived challenges. Descriptive statistical analysis and qualitative interpretation were used to evaluate familiarity levels and method preferences. The results indicate that subjective approaches, such as self-assessments and rating-based check-ins, are more widely recognized (mean ≈ 2.1/5), while awareness of objective, sensor-based systems remains lower (≈1.5/5). Despite this disparity, approximately 38% of respondents preferred hybrid approaches that integrate subjective inputs with wearable or physiological data, and a similar proportion perceived these approaches as the most reliable for operational decision-making. Additionally, more than 85% of participants indicated that fatigue monitoring could moderately to significantly improve job-site safety. These findings suggest that successful adoption depends on usability, user acceptance, and the effective integration of digital monitoring tools into construction workflows. Full article

The integration of sustainability into higher education architectural curricula and student Diploma Projects (DPs) remains limited, necessitating further investigation to improve overall outcomes. This study aims to identify, characterise, and compare existing keyword sources to determine their efficacy in detecting sustainability-related solutions within DPs and to define the characteristics of the most suitable datasets for this purpose. A total of 132 academic, professional, and policy-related Keyword Databases (KDs) were identified and analysed through a multi-stage process. Nine of the best-performing KDs were selected for further development into Keyword Search Lists (KSLs), and their effectiveness in identifying sustainability-related solutions in DPs’ descriptions was tested, confirming the correlation of the results with expert assessments. As a result, a method for identifying, developing, and analysing KSLs was developed, titled Mapping the Linguistic Landscape of Architectural Sustainability (MLLAS). This framework provides a practical tool for the large-scale analysis of how sustainable development is linguistically represented within architectural theses, as well as a theoretical basis for understanding the level of sustainability’s incorporation in architectural education. The results indicate that keyword search constitutes an effective identification method within DPs, regardless of KSL size. The future implementation of the MLLAS framework has been proposed. Full article

Recent research highlights the importance of effective teaching methodologies to enhance scientific learning from the earliest years of schooling. The present study investigates the effects of the Spaced Learning (SL) methodology in science education in Italian primary schools, with particular attention to scientific knowledge and students’ scientific reasoning skills. The study involved 401 third- and fourth-grade pupils (aged 8–11) from three primary schools in Palermo, Italy, during the 2024/2025 school year. A quasi-experimental design was adopted, with classes assigned to an experimental group that adopted SL or to a control group that followed traditional teaching. The intervention lasted seven months and was supported by continuous teacher training and collaboration with university researchers. The data were collected through a pre-test/post-test questionnaire developed and validated by experts in physics education. The tool assessed the students’ general scientific reasoning skills through multiple-choice items inserted in everyday life contexts. Descriptive statistics were calculated and between-group comparisons were made by Student’s t-test or Welch’s t-test when the assumption of homogeneity of variances was not met. The results indicate that students exposed to the SL methodology achieved higher post-test scores than those who received traditional education, suggesting a positive effect of time-distributed, movement-integrated learning on science learning outcomes. Such results support the effectiveness of SL as a promising teaching approach to promote meaningful and lasting scientific learning in primary school. Full article