Search Results (2,556)

This article presents a systematic bibliometric analysis on academic research into Artificial Intelligence (AI) applications in Architectural Conceptual Design (ACD). Based on a curated selection of publications indexed in the Web of Science (WoS) and Scopus databases between 2010 and 2025, this article shows a study that maps the intellectual evolution, thematic composition, and methodological trends of the field. By using the software tool VOSviewer, this study generates a series of knowledge graphs, including Keyword Co-Occurrence and International Collaboration Networks. The findings from this study reveal a rapid acceleration in AI-related research focused on the conceptual design stage, highlighting its transformative potential for architectural practice. Through a critical analysis of bibliometric results, this study identifies dominant research emphases, emerging directions, and persistent frictions between academic approaches and industry adoption. This review article contributes to the theoretical consolidation of AI applications in ACD and provides a structured foundation for future ACD-related research and practice. Full article

Background: Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, and mitochondrial dysfunction. Emerging evidence indicates that the gut microbiota plays a critical role in modulating neuroinflammatory, and metabolic pathways involved in AD pathogenesis through the microbiota-gut-brain axis. Objective: This systematic review aims to comprehensively evaluate the role of the microbiota-gut-brain axis in Alzheimer’s disease, with a particular focus on its mechanistic links to oxidative stress, mitochondrial dysfunction, and amyloid pathology, as well as its therapeutic potential. Methodology: A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science databases, focusing on studies evaluating gut microbiota composition, metabolomic changes, oxidative stress markers, mitochondrial activity, and therapeutic interventions in AD models and patients. Results: Altered gut microbial composition in AD is associated with increased pro-inflammatory taxa (Escherichia-Shigella, Bacteroides) and depletion of short-chain fatty acid (SCFA) producing bacteria (Faecalibacterium, Roseburia). Dysbiosis contributes to systemic inflammation, disrupted intestinal permeability, and microglial activation, leading to oxidative damage and mitochondrial impairment in neurons. Preclinical and clinical studies indicate that probiotics, prebiotics, and fecal microbiota transplantation can restore redox balance, reduce neuroinflammation, and improve cognitive outcomes. Multi-omics and AI-based models are emerging as tools for identifying microbiome-derived biomarkers for early AD detection. Conclusion: The gut microbiota-mitochondria-oxidative stress axis represents a promising therapeutic target in Alzheimer’s disease. Future research should focus on longitudinal human studies, standardized microbial profiling, and personalized microbiome-based interventions to translate these mechanistic insights into clinical benefit. Full article

Background: Shared decision-making (SDM) is particularly relevant in rheumatoid arthritis (RA), where multiple treatment options with distinct benefit–risk profiles require alignment with patient values and preferences. This study describes the development of a web-based PtDA to support treatment decision-making in RA and represents the first structured, standards-aligned PtDA in the Greek healthcare context. Methods: Guided by the Ottawa Decision Support Framework and the International Patient Decision Aid Standards, a multistage, user-centered methodology was applied, including evidence synthesis, iterative prototyping, and alpha and beta testing. Qualitative methods, including focus group discussions, semi-structured interviews, and think-aloud protocols, were used, while usability was assessed with the System Usability Scale (SUS). Methodological quality was evaluated using IPDASi v3 and UCD-11 criteria. Results: The final PtDA provides a three-step pathway supporting values clarification, comparison of medication options, and reflection on decisional confidence. It was developed as a publicly accessible, web-based tool compatible with multiple devices, with core elements also available in printable format. The tool showed good usability (mean SUS: 75.93) and strong alignment with IPDASi (83.3/100), and user-centered design criteria (11/11). Conclusions: Developing digital PtDAs is inherently complex, underscoring the importance of established methodological frameworks. The findings demonstrate acceptable usability and alignment with established standards within this early-stage development study. Further research is required to examine the tool’s impact on decision-making processes, value–choice concordance, and longer-term clinical outcomes. Full article

This systematic review analyzes how the introduction and progressive deployment of 5G networks have influenced the evolution of telemedicine between 2014 and 2024, focusing on their impact on performance, accessibility, and the feasibility of advanced clinical applications across the pre-COVID-19, COVID-19, and post-COVID-19 periods. The review was conducted in accordance with PRISMA guidelines and included publications retrieved from SCOPUS, PubMed, and Web of Science using a PICO-based search strategy. Studies were selected based on predefined inclusion and exclusion criteria, and extracted data included clinical parameters, network characteristics such as bandwidth and latency, geographic setting, and type of telemedicine service. A total of 45 studies met the inclusion criteria, with most published between 2020 and 2024. The most frequently reported applications were telediagnosis, particularly robotic ultrasound, followed by telesurgery and teleconsultation. The low latency enabled by 5G networks supported complex telesurgical procedures over distances exceeding 5000 km, while in ultra-remote areas, hybrid solutions combining 5G and fiber-optic networks were often adopted to ensure stable connections. The integration of robotic platforms and AI-based tools further enhanced the precision and reliability of remote procedures. Overall, 5G technology has significantly advanced telemedicine by enabling real-time, high-quality care over long distances, improving access to specialist services and supporting more equitable and efficient digital healthcare delivery, particularly in underserved regions. Full article

This study advances the conceptual and practical scope of social education by integrating Security and Safety Education (SSE) categories into its theoretical foundation. We demonstrate that SSE encompasses multidimensional areas highly relevant to social education and offer a structured competence model to guide curriculum design. Using a mixed-methods approach, 2926 Web of Science publications were analysed through an NVivo Word Frequency Query to identify key domains associated with security and safety. The temporal scope of the corpus (2019–2021) provides a coherent analytical baseline, capturing intensified security and health-related discourse during the COVID-19 period while preceding geopolitical disruptions that could otherwise distort thematic patterns. The results show that security is associated with broad social and geopolitical issues, including food, political, economic, public, national, and international affairs, as well as health and information. In contrast, safety is mainly linked to transport-related concerns, although both domains converge in areas such as health, social, public, national, and information matters. These findings indicate that SSE encompasses multidimensional areas relevant to social education. To support curricular integration, we propose an eMEDIATOR-derived competence model that structures SSE content into measurable, outcomes-based components. Ultimately, this research provides actionable tools to elevate social education and promote active, informed citizenship in times of global uncertainty. Full article

Background/Objectives: Joubert syndrome (JS) is a rare neurodevelopmental disorder characterized by cerebellar and brainstem malformations, resulting in a complex and heterogeneous motor phenotype. Despite increasing clinical recognition, functional assessment and physiotherapy strategies in this population remain insufficiently characterized. This study aimed to synthesize current rehabilitation evidence and to propose a conceptual framework for functional motor assessment in children with JS. Methods: A structured narrative review was conducted across PubMed, Scopus, Web of Science, EBSCOhost, the Cochrane Library, and PEDro databases, including studies published between 2000 and 2026. Eligible studies involved pediatric patients (0–18 years) with JS and reported physiotherapy or motor-related outcomes. Data were synthesized descriptively, and recurring functional domains were identified to inform the development of a conceptual framework. Results: Ten studies (eight case reports and two case series) were included. Rehabilitation approaches were heterogeneous and predominantly multidisciplinary, focusing on postural control, trunk stability, and motor milestone acquisition. Functional improvements were reported across studies; however, outcome measures were primarily based on generic pediatric tools such as GMFM-88 and WeeFIM. These tools did not fully capture the multidimensional nature of motor impairment, particularly in relation to regulatory and sensorimotor domains. Evidence also suggested that postural control and gross motor performance may not fully correspond, highlighting additional functional components such as axial control and thoracoabdominal organization. Given the absence of formal risk-of-bias assessment and the low methodological quality of included studies, all findings should be interpreted as exploratory. Conclusions: Current functional assessment in JS may not adequately reflect the interaction between regulatory processes, sensorimotor integration, and motor control. The proposed conceptual framework provides a multidimensional, hypothesis-generating perspective that may support clinical reasoning and physiotherapy planning. Further research is required to validate this framework and to develop more sensitive, syndrome-specific assessment tools. Full article

Digital transformation in the interior design industry has opened new opportunities for innovation; however, many cost-conscious homeowners still face difficulties in selecting and customizing design packages that achieve a balance between overall cost and sustainable quality. Existing interior design platforms lack seamless support and often require homeowners to invest considerable time and effort to tailor services to their needs while staying within budget. To address these challenges, this paper explores the use of machine learning to build a predictive modelling framework that supports personalized and value-driven interior design recommendations. The proposed approach uses a hybrid recommendation system that combines content-based and collaborative filtering. It also incorporates lightweight techniques such as TF–IDF (Term Frequency–Inverse Document Frequency) and logistic regression to more effectively capture user preferences, budget limits, and several interior-design service categories. Primary data was collected from small to medium-sized interior design companies. To demonstrate the proposed approach, a user-friendly web application tool is developed to integrate machine learning-enabled recommendation services. The resulting solution provides access to professional interior design services, enhancing customization and customer satisfaction while reducing the time and effort required from homeowners. To validate and compare the performance of the proposed approach, several machine learning models including Random Forest, XGBoost and KNN (K-Nearest Neighbors) were tested using standard metrics such as accuracy, precision, recall, and ROC-AUC (Receiver Operating Characteristic-Area Under the Curve). The proposed logistic regression hybrid model achieved the strongest overall results, with an accuracy of 83.62%. These findings demonstrate the significant contribution of this work to enhancing personalization and accessibility in the interior design sector via machine learning-enabled recommendation systems. The proposed approach bridges the gap between expert-level services and financial limits, making it a practical choice for cost-conscious homeowners. Full article

This study presents a scalable decision support system (DSS) framework designed to meet the growing demands of instant data-driven decision-making environments. The architecture integrates key technologies, including Apache Kafka for parallel data streaming, a Python-based data analytics module for distributed processing, JWT-based secure user authentication, and WebSocket communication for instantaneous prediction delivery. The system performs mitochondrial localization in electron microscopy (EM) images using multiple versions of the YOLO (You Only Look Once) object detection model. The publicly available CA1 Hippocampus dataset was used for detection evaluation. Among the evaluated models, YOLOv10x achieved the highest detection performance, yielding a mean average precision (mAP) score of 95.2%. Experimental evaluations of the DSS were conducted under simulated load conditions using the Artillery tool to assess the system’s scalability and responsiveness. Empirical results indicate consistent low-latency performance across varying consumer group sizes, confirming the architecture’s ability to scale the analytics module horizontally without compromising responsiveness. These findings validate the system’s suitability for just-in-time decision support applications. In particular, the system may support clinicians in the task of mitochondrial analysis, where structural abnormalities can be indicative of pathological conditions, including cancer. By enabling early detection of such abnormalities, the proposed framework has the potential to contribute to the timely diagnosis of diseases such as cancer. The proposed study differs from existing studies by combining deep learning with real-time scalable data processing technologies, such as Kafka and WebSocket, in a web-based DSS application for mitochondria detection. Full article

Background: Radical hysterectomy with pelvic lymphadenectomy is a standard surgical procedure for early-stage cervical cancer. However, even with optimal treatment, some patients still experience disease recurrence. We aimed to develop and validate a prediction model to classify patients according to the risk of recurrence which can better assist clinicians to tailor the postoperative treatment and improve patient outcomes. Methods: Data of women diagnosed with early-stage cervical cancer who underwent radical hysterectomy were collected and analyzed. The primary outcome was recurrence-free survival (RFS). A prediction model based on Cox proportional hazard regression was developed by using the backward elimination procedure. Internal validation was performed by bootstrapping. The model’s discriminative ability was demonstrated by the concordance index (C-index). The model’s calibration was examined through a calibration plot. The final prognostic model was presented as a nomogram and a web-based calculator, which were further used to categorize patients into low, moderate, and high-risk groups for clinical application. Results: Among the 1309 patients, 115 (8.8%) experienced a recurrence. The median follow-up was 72.2 months. The 3-year and 5-year RFS rates were 93.0% (95% CI, 91.5–94.6%) and 90.7% (95% CI, 88.9–92.5%), respectively. Tumor size, histological subtype, number of positive lymph nodes, lymphovascular space invasion, and platelet-to-lymphocyte ratio were significantly associated with RFS. These factors were employed to construct a prediction model. The model exhibited a good overall fit with minimal overfitting and good calibration. The model’s discriminative performance, as measured by the C-index, was 0.73. Conclusions: Our proposed survival model offers a potentially valuable tool for therapeutic decision-making in patients with early-stage cervical cancer. This model demonstrates robust discriminative performance and predictive calibration. Nevertheless, external validation across diverse datasets should be conducted to assess the reproducibility and applicability of this predictive model across a broader spectrum of patients. Full article

The digital and green transitions in the AEC sector require rapid, data-driven workflows to redefine sustainability through real-time performance evaluation. However, the high computational cost of traditional energy simulations often lacks evidence-based feedback during early-stage design. This study introduces a surrogate machine learning framework (S-TCML) designed to bypass traditional energy simulation by providing an instantaneous assessment of thermal comfort. Using a parametric Grasshopper–Honeybee environment, a dataset of 3072 configurations was generated for an office room in Cairo, Egypt. Six machine learning algorithms were benchmarked, with Gradient Boosting and Random Forest demonstrating superior performance in capturing non-linear thermal physics. Validation against the EnergyPlus engine confirmed that S-TCML models deliver predictions in milliseconds—a 99.9% reduction in computational time. The Gradient Boosting model achieved exceptional accuracy with an R² of 0.999 and RMSE of 0.013 for PMV and an R² of 0.995 and RMSE of 0.46% for PPD prediction. Feature importance analysis proved that a tree-based ML model can capture the underlying physical relationship between variables. To bridge the feedback gap, a web-based graphical user interface (GUI) was developed to facilitate proactive design exploration. This framework supports sustainable decision-making and design efficiency, offering scalable, user-friendly tools that protect occupant health and ensure thermal resilience in hot–arid environments. Full article

Against the backdrop of the global pursuit of carbon neutrality, research on coal pore structure has shifted from a single focus on coal mine safety to a dual orientation of hazard prevention and carbon sequestration, forming two distinct research directions worldwide. To clarify the evolutionary trajectory, research heterogeneity and integration paths of this field, this study systematically analyzes 722 core publications on coal pore structure from the CNKI and Web of Science core databases during 2015–2025, combining knowledge visualization analysis and systematic literature sorting (using CiteSpace as an auxiliary analysis tool). The results show that global research on coal pore structure has experienced three developmental stages (embryonic, developmental, and explosive growth) and entered an exponential growth phase after 2020, driven by the dual carbon goals. A clear research divergence has formed between regional engineering practices and international theoretical models: Chinese research is highly oriented to on-site coal mine engineering needs, focusing on the characterization of coal pore structure and its engineering application in gas extraction and outburst prevention of structural coal; international research prioritizes the theoretical exploration of carbon sequestration and CO₂-ECBM, with core research on gas adsorption kinetics, multiphysics coupling mechanisms of coal pore structure, and numerical simulation of reservoir modification. This research disconnect between engineering practice and theoretical modeling has become a key bottleneck restricting the safe application of coal pore structure theory in carbon capture, utilization, and storage (CCUS) projects. To address this issue, a Safety–Sustainability Nexus framework is proposed, which integrates field-based mine safety protocols with theoretical carbon storage models, and realizes cross-scale validation from micro-scale pore characterization to field-scale engineering application. Further, this study points out that the cross-scale data fusion of artificial intelligence and machine learning is the core direction to bridge the gap between engineering practice and theoretical models. In future CO₂-ECBM pilot projects, traditional gas outburst prevention indicators must be taken as mandatory safety thresholds to realize the dynamic matching of carbon injection parameters and coal reservoir stress sensitivity. This study sorts out the global research context and hotspots of coal pore structure, and provides a theoretical and practical reference for the synergy and integration of coal mine gas control engineering and carbon sequestration theoretical research under the dual carbon goals. CBM, coalbed methane; CNKI, China National Knowledge Infrastructure; WOS, Web of Science; CCUS, carbon capture, utilization, and storage; ECBM, Enhanced Coalbed Methane; CO₂-ECBM, CO₂-Enhanced Coalbed Methane. Full article

Global Navigation Satellite Systems (GNSS) offer precise movement analyses based on distance and speed in open-water sports. Despite the influence of swimming in triathlon, its performance analysis remains underdeveloped due to methodological limitations in capturing continuous data in aquatic environments. This review aimed to: (1) systematically analyse and compare the sensor-based technologies applied to open-water movement analysis, and (2) propose a framework for continuous GNSS-based assessment of triathlon swim performance. A systematic search was conducted prior to the 14 August 2025 across four databases (Web of Science, SPORTDiscus, PubMed, and SPONET). Studies were eligible if they analysed open-water sports using GNSS-based technologies for continuous movement or performance analysis. Studies limited to indoor swimming, inertial sensors, or non-sporting applications were excluded. Methodological quality and potential sources of bias were evaluated using a custom scheme based on GNSS reporting guidelines, as methodological heterogeneity precluded the application of standardised tools. Following screening and eligibility assessment, articles were analysed qualitatively. In total, 20 articles were included and focused on surfing, sailing, water skiing, windsurfing, kitesurfing, stand-up paddling (SUP), and swimming. Most studies focused on board- and sail-based sports, employed sampling frequencies between 1 and 15 Hz, and demonstrated substantial variability in device specifications and reporting quality. Different sensors and GNSS-derived variables were central to discipline-specific performance analysis. The strength of evidence is limited by the heterogeneous methodologies, and variable reporting quality. The proposed framework provides methodological guidance for implementing high-resolution GNSS-based monitoring in triathlon swimming to improve pacing analysis and race strategy development. Full article

Building Information Modelling (BIM) offers a pivotal opportunity to automate Life Cycle Assessment (LCA) within the Architecture, Engineering, and Construction (AEC) industry. However, seamless integration is persistently hindered by a semantic gap, a critical misalignment between the object-oriented, geometric definitions of BIM and the process-based material data required by Life Cycle Inventory (LCI) databases. This paper presents a comprehensive review of data interoperability challenges and evaluates advanced methodologies designed to bridge this divide, moving beyond simple tool comparison to analyse structural integration barriers. Through a systematic review of 124 primary studies published between 2010 and 2025, this research inductively derives the BIM-LCA Interoperability Triad. This framework analyses causal dependencies across three dimensions, including Semantic and Ontological Structures, Workflow and Temporal Integration, and System Architecture and Interoperability. Furthermore, by establishing a comparative challenge–solution matrix, the analysis reveals a maturity paradox in current methodologies. While semi-automated commercial plugins dominate practice due to accessibility, they frequently function as opaque black boxes with limited transparency. Conversely, advanced approaches utilising Semantic Web technologies and Machine Learning demonstrate superior capability in resolving terminological mismatches but currently face significant barriers regarding infrastructure and expertise. This study contributes a novel theoretical model for understanding integration failures. It concludes that future research must pivot from static schema mapping towards AI-driven semantic healing, dynamic Digital Twins, and explicit system boundary harmonisation to achieve truly automated, context-aware environmental assessments and support whole-life circularity. Full article

As high-dimensional behavioral datasets grow, interactive 3D visualization is increasingly limited by rendering and annotation bottlenecks rather than data availability. Existing web-based tools often degrade sharply under large node counts, making real-time exploration impractical. We propose a web-based 3D point-cloud visualization system optimized for rendering performance under condition-locked experimental settings (load × guidance). To enable reproducible evaluation without proprietary data, the system uses a synthetic surrogate dataset with clustered structure and three node types (user/attribute/action) and provides guided/free exploration workflows with interaction logging. We report a technical benchmark across two load scales (N = 500 vs. N = 5000) and two modes (guided vs. free). Under the high-load setting (N = 5000), the system maintains real-time rendering performance while supporting interactive selection (point/cluster), tooltips/inspector, and session logging. We discuss practical strategies for controlling on-screen annotations under overload conditions and outline limitations and future work for validating the approach on real-world embeddings. Full article

Recent advances in artificial intelligence have expanded the use of large language models (LLMs) beyond speech-based applications and increased interest in their potential roles in dental education. However, evidence regarding LLM performance in postgraduate dental education, particularly in prosthodontics, remains limited. Therefore, this study aimed to compare the accuracy of responses from prosthodontic residents and LLMs to standardized multiple-choice questions in prosthodontics and to explore the potential role of artificial intelligence in prosthodontic education. Thirty-two prosthodontic residents participated in this cross-sectional study. Participants completed a standardized 30-item multiple-choice test comprising four demographic items and 26 questions assessing basic knowledge, general dentistry, and advanced prosthodontic specialty questions. The same questions were administered to seven large language models (LLMs): ChatGPT-4o, ChatGPT-o1, ChatGPT-o3-mini, Claude Sonnet 3.7, Gemini 2.5 Pro, Microsoft Copilot (web interface, accessed in August 2025), and DeepSeek V3. Response accuracy and consistency were evaluated. Statistical analyses were performed using IBM SPSS Statistics (version 27.0), with statistical significance set at p < 0.05. A statistically significant difference was observed between prosthodontic residents and LLMs in responses to advanced-level prosthodontic specialty questions (p < 0.05), with higher correct response rates recorded for LLMs. No statistically significant differences were identified between the two groups for basic knowledge and general dentistry questions (p > 0.05). In addition, no significant association was found between the duration of prosthodontic residency training and residents’ response accuracy (p > 0.05). LLMs achieved high scores on this structured MCQ-based assessment, particularly in advanced theoretical prosthodontic items. However, these findings should be interpreted with caution within the limits of a written examination format and do not represent overall clinical competence or real-world patient care performance. Accordingly, artificial intelligence may be considered a supportive educational tool in postgraduate prosthodontic education rather than a replacement for clinical training. Full article