Search Results (777)

Over the past decade, Building Information Modelling (BIM) has become increasingly adopted across the Architecture, Engineering, Construction, and Operation (AECO) industry. As its use in practice has expanded, BIM has also received growing scholarly attention. Existing research has largely concentrated on specific applications of BIM, such as construction management, sustainable building design, infrastructure development, and facility management. However, comparatively limited attention has been given to examining BIM implementation from a global perspective. This study addresses this gap by applying a scientometric approach to analyse global BIM implementation research published between 2004 and 2023. The analysis is conducted using co-authorship, co-word, and co-citation analysis to map the structure and development of the research field. A total of 1349 published articles were obtained from the Scopus database for the analysis. The study identifies the most productive and influential contributors to BIM implementation research, including leading researchers, research institutions, countries, subject areas, and academic journals. In addition, the analysis highlights several key thematic domains within global BIM research. These include topics related to Industry Foundation Classes (IFC), Internet of Things (IoT), Geographic Information Systems (GIS), Historic Building Information Modelling (HBIM), and Digital Twin technologies, which appear as prominent keywords within the BIM implementation literature. Beyond mapping these trends, this paper integrates dispersed scientometric evidence into a coherent global perspective, revealing how BIM implementation research has evolved, matured, and diversified across regions and disciplines. It also establishes a structured knowledge base that can serve as a benchmark for future comparative studies, performance assessments, and policy development initiatives in the digital construction domain. These findings provide valuable insights for researchers, practitioners, and policymakers by illustrating landscape of BIM-related research and highlighting potential directions for future investigation. Full article

(1) Background: Robotics and advanced control architectures are increasingly central to the development of precision agriculture (PA), supporting automated, efficient, and data-driven farm management. This review offers a comprehensive analysis of scientific literature on robotic control systems applied to PA, focusing on technological progress, methodological approaches, and emerging research trends. (2) Methods: A systematic review was conducted according to PRISMA guidelines, combined with a bibliometric analysis using VOSviewer to examine term co-occurrences, thematic clusters, and topic evolution over time. Publications indexed in Scopus between 1976 and 2025 were analyzed. (3) Results: Results reveal a sharp growth in publications after 2010, with a strong acceleration from 2015 onward, reflecting advances in autonomous systems and the integration of artificial intelligence, sensor technologies, and distributed software frameworks. Three principal clusters emerged: algorithmic and control methods (e.g., neural networks, path tracking, simulation); sensing and infrastructure technologies (e.g., LiDAR, SLAM, IMU, ROS, deep learning-based perception); and agronomic applications, including crop monitoring, irrigation, yield estimation, and farm management. Citation trends indicate a shift from foundational control theory to AI-driven solutions. (4) Conclusions: Overall, control architectures are evolving toward modular, scalable, and interoperable systems enabling autonomous decision-making in complex agricultural environments. Full article

Artificial intelligence (AI) is increasingly embedded in banking risk management, yet academic research on this topic remains conceptually fragmented and dispersed across multiple disciplines. This study examines global publication trends and thematic structures related to AI applications in banking risk management through a bibliometric analysis of 83 peer-reviewed articles indexed in the Web of Science Core Collection for the period 2020–2024. The analysis was conducted using Bibliometrix (R-package, version 4.1), its web interface Biblioshiny (2024 release), to evaluate publication dynamics, citation performance, authorship patterns, and thematic clusters. Results show a substantial rise in scientific interest, with annual publication growth of 41.4% and international co-authorship reaching 30%. Five major thematic clusters were identified, including AI-enabled credit risk assessment, fraud detection, operational and cyber-risk mitigation, FinTech adoption, and regulatory compliance. Approximately 30% of the articles appeared in the top ten journals publishing on the topic, and the dataset recorded more than 3800 cited references. The findings indicate that AI contributes to enhanced predictive accuracy, real-time anomaly detection, and supervisory efficiency in banking risk management, while persistent challenges relate to model transparency, data quality, and regulatory adaptation. This study offers a systematic, data-driven understanding of the intellectual landscape and research evolution of AI-driven banking risk management from 2020 to 2024. Full article

Powder coatings, as a widely used green surface treatment material, face significant combustion and explosion risks due to the simultaneous presence of high-concentration combustible dust clouds and electrostatic ignition sources in their application environments. With the advancement of new materials and emerging industrial sectors, research on powder coating explosions has become increasingly interdisciplinary, resulting in a somewhat fragmented knowledge base. To systematically reveal the knowledge structure, research hotspots, and development trends in this field, this study employs bibliometric methods based on 857 relevant publications retrieved from the Web of Science (WOS) Core Collection database between 2015 and September 2025. Using VOSviewer (Version 1.6.20) and CiteSpace (Version 6.4), the analysis examines institutional collaboration, journal distribution, author collaboration patterns, regional differences, co-citation relationships, knowledge foundations, and research frontiers. The results indicate that powder coating explosion research has gradually developed an integrated knowledge system centered on materials science, chemical engineering, and combustion science. Institutions from China, Russia, and India represent some of the most productive contributors in this field. Current research hotspots focus on the explosion mechanisms of powder coatings, explosion-proof materials, risk assessment, numerical simulation, and protective measures for emerging industrial applications. Future trends are expected to focus increasingly on intelligent explosion suppression systems, multi-scale coupling mechanisms, and international collaborative governance. This study provides a comprehensive knowledge map to support scientific planning and safety strategy development in powder coating explosion research. Full article

The increasing global demand for sustainable water purification technologies has accelerated research on catalytic degradation and advanced oxidation processes for the removal of refractory pollutants. This study provides a comprehensive bibliometric analysis of global research trends in catalytic water and wastewater treatment from 2010 to 2025, combining quantitative mapping with a qualitative synthesis of emerging technological directions. Bibliographic data were retrieved from the Scopus database and screened using the PRISMA framework, followed by analysis using VOSviewer (v1.6.20) and OriginPro (version 2023, OriginLab Corporation, Northampton, MA, USA) to examine publication growth, citation patterns, international collaboration networks, and thematic evolution. A total of 1550 publications, including 1265 research articles and 285 review papers, were analyzed. The results show a significant increase in research output after 2015, reflecting growing global attention to water sustainability and environmental remediation. China, the United States, and India were identified as the leading contributors, with strong international collaboration networks. Keyword co-occurrence analysis revealed three dominant research themes: photocatalytic degradation and semiconductor engineering, Fenton and Fenton-like advanced oxidation processes, and emerging hybrid catalytic systems involving carbon-based materials and metal–organic frameworks. The analysis also indicates a recent shift toward multifunctional hybrid catalysts designed to improve efficiency, stability, and performance in complex wastewater systems. These findings highlight key scientific developments and suggest future research priorities, including green catalyst synthesis, reactor and process scale-up, AI-assisted catalyst design, and life-cycle sustainability assessment to support the transition from laboratory research to practical water treatment applications. Full article

The accelerated expansion of advanced technologies—particularly artificial intelligence, intelligent systems, and interactive digital environments—is influencing contemporary media ecosystems and contributing to changes in educational practices. This study provides a systematic and descriptive bibliometric mapping of recent scientific production on artificial intelligence in education, algorithmic ethics, and digital inequality. A total of 229 Scopus-indexed documents published between 2021 and 2026 were analyzed using Biblioshiny and VOSviewer to examine publication patterns, influential authors and sources, and the conceptual structure of the field. Results indicate a marked increase in research output since 2024, with an annual growth rate of 47.58%, an average of 8.68 citations per document, and an international co-authorship rate of 24.45%. These indicators reflect an expanding and increasingly collaborative research landscape, accompanied by a diversification of thematic priorities within the field. The analysis identifies five thematic clusters: (1) the technical foundations of AI and digital transformation; (2) intelligent and immersive learning environments; (3) personalized and adaptive learning systems; (4) AI literacy and pedagogical integration; and (5) ethical considerations, including algorithmic bias and educational robotics. The findings highlight the need for explicit justification of database selection, strengthened critical AI literacy, and context-sensitive strategies that address disparities in access, skills, and institutional capacity. Overall, this study offers a coherent overview of a research area that is currently expanding and undergoing conceptual reorganization, providing evidence-informed insights for future research, policy development, and the design of equitable AI-driven educational technologies. Full article

The safe integration of Large Language Models in clinical environments requires strict adherence to verified medical evidence. As part of the PARROT AI project, this study provides a systematic evaluation of how prompting strategies affect the reliability of Retrieval-Augmented Generation (RAG) pipelines using the MedQA USMLE benchmark ($N=500$). Four prompting strategies were examined: Baseline (zero-shot), Neutral, Expert Chain-of-Thought (Expert-CoT) with structured clinical reasoning, and StrictCitations with mandatory evidence grounding. The experiments covered six modern model architectures: Command R (35B), Gemma 2 (9B and 27B), Llama 3.1 (8B), Mistral Nemo (12B), and Qwen 2.5 (14B). Evaluation was conducted using the Deterministic RAG Evaluator, providing an objective assessment of grounding through the Unsupported Sentence Ratio (USR) based on TF-IDF and cosine similarity. The results indicate that structured reasoning in the Expert-CoT strategy significantly increases USR values (reaching 95–100%), as models prioritize internal diagnostic logic over verbatim context. In contrast, the StrictCitations strategy, while maintaining high USR due to the conservative evaluation threshold, achieves the highest level of verifiable grounding and source adherence. The analysis identifies a statistically significant Verbosity Signal ($r=0.81,p<0.001$), where increased response length serves as a proxy for model uncertainty and parametric leakage, a pattern particularly prominent in Llama 3.1 and Gemma 2. Overall, the findings demonstrate that prompting strategy selection is as critical for clinical reliability as model architecture. This work delivers a reproducible framework for the development of trustworthy medical AI assistants and highlights citation-enforced prompting as a vital mechanism for improving clinical safety. Full article

Additive Manufacturing (AM) and Wearable Technologies (WT) have rapidly evolved over the past decade. AM offers highly customisable fabrication, while WT enables minimally invasive health monitoring. The intersection of these fields presents emerging opportunities in biomedical and engineering domains. This study aims to map the scientific landscape of AM–WT research between 2015 and 2025 through a comprehensive bibliometric analysis. A total of 718 peer-reviewed publications were extracted from Web of Science (WoS), Scopus, and PubMed, following PRISMA-ScR guidelines. Using RStudio and the Bibliometrix package, analyses included co-authorship, citation trends, keyword co-occurrence, and thematic mapping. Custom author disambiguation scripts enhanced data quality and reliability. An annual publication growth of 24.89% was observed, with notable increases after 2020. Core themes included 3D printing, biosensors, microfluidics, and organ-on-a-chip devices. A shift from manufacturing-oriented research to biomedical integration is evident. Research output is dominated by the US, China, and South Korea, with moderate but not yet highly internationalised collaboration. The field of AM–WT research is undergoing a decisive transition from fabrication-focused studies to interdisciplinary, application-driven innovations. This shift is marked by increasing integration in healthcare and bioelectronics, yet hindered by regional imbalances and thematic gaps. Addressing these will be critical to advancing global impact. This study offers a cross-database bibliometric overview of AM–WT research. By combining three major data sources, it provides enhanced coverage and introduces novel analytical dimensions to guide future interdisciplinary efforts in personalised healthcare and wearable device innovation. Full article

Background: Diabetic foot ulcers (DFUs) represent a major chronic complication of diabetes mellitus, often leading to severe infection, amputation, and reduced quality of life. Among various factors affecting DFUs, plantar pressure plays a pivotal role in ulcer formation and recurrence. Despite growing interest in this domain, few studies have comprehensively evaluated the research landscape concerning plantar pressure in the context of DFUs from a bibliometric perspective. Aim: To conduct a comprehensive bibliometric analysis and visualization of global research trends, hotspots, and collaborative networks in the field of plantar pressure-related diabetic foot studies from 2000 to 2024. Methods: A systematic search was conducted in the Web of Science Core Collection (WoSCC) on 16 February 2025, for articles published between 2000 and 2024 using terms related to “diabetic foot” and “plantar pressure”. A total of 2518 records were retrieved, from which 2110 English-language articles and reviews were included. Bibliometric and visual analyses were performed using Microsoft Excel 2021, VOSviewer (v1.6.20), CiteSpace (v6.4.R1), Charticulator, and Scimago Graphica. Analyses included publication trends, country/institution/author collaborations, journal distributions, keyword co-occurrence and clustering, citation bursts, and reference co-citation networks. Results: A total of 2110 publications were identified, showing an overall increase in annual publication output from 2000 to 2024, with some year-to-year fluctuations. The United States led in publication volume (678 articles), citation frequency, and H-index, followed by the United Kingdom and China. Armstrong, David was the most prolific and also had the highest H-index among the listed authors, while the University of Amsterdam was the leading institution. “Journal of Wound Care” had the highest publication count, whereas “Diabetes Care” ranked first in citation frequency. Keyword analysis revealed major research clusters including “diabetic foot”, “plantar pressure”, “wound healing”, “offloading”, and “negative pressure wound therapy”. Recent trends show an increased focus on microcirculation, regenerative medicine, customized footwear, and wound care technologies. Conclusions: The bibliometric analysis reveals research trends and current hotspots in plantar pressure management for diabetic foot ulcers, with a particular focus on managing plantar pressure through personalized offloading strategies and custom footwear. These findings highlight the practical value of tailoring interventions to individual patient needs, emphasizing the importance of biomechanical factors in ulcer prevention and healing. Full article

Geometric morphometrics is an important component of quantitative research on insect morphology, widely applied in taxonomy, intraspecific variation, and phylogenetic studies. However, systematic research in this field remains limited, with few comprehensive summaries of research trends, hotspots, and core theories. This study, based on scientometric methods, analyzed 1321 publications indexed in the Web of Science database up to 31 December 2025, and presents a meta-scientific review from a macro perspective, revealing the research trends, hotspots, and future directions in the field. The results show that: (1) annual publications exhibit overall growth, while research methods evolved from single landmark analysis to multimodal and interdisciplinary approaches; (2) scientists from Brazil, the USA, and France are major contributors, with studies spanning morphology, taxonomy, and ecology; (3) taxonomic studies centered on wing shape analysis constitutes a major research hotspot, closely related to phylogeny, allometry, and sexual dimorphism; (4) highly co-cited studies provide the main theoretical and methodological foundations for the field. Future research, building on existing hotspots, will further integrate geometric morphometrics with genomics, ecological functional data, three-dimensional geometric morphometrics, and artificial intelligence-assisted approaches to advance integrative taxonomy within interdisciplinary and data-driven frameworks. Full article

Vaccines worldwide reduce severe coronavirus disease 2019 (COVID-19) consequences; however, viral evolution escapes immunity, raising global concerns about vaccine protection and requiring monitoring. Bioinformatics is crucial for studying vaccine escape, speeding up variant detection, mapping antibody evasion epitopes and ensuring updated vaccines and public health responses. This study combines bibliometric analysis of the Scopus literature (n = 416) on SARS-CoV-2 immune evasion using bioinformatics tools with descriptive analysis of the top ten most highly cited original articles. Our results showed the United States (USA) as the dominant contributor, leading in publication output, citation impact and collaboration networks. The key themes identified were immune evasion, spike protein mutations, and viral evolution, highlighting the structural, functional and immune evasion mechanisms of spike mutations. Leading authors and journals reveal a globally connected research community that is making advances in our understanding of SARS-CoV-2 vaccine evasion, and supporting the development of future treatments and vaccines. The top ten articles showed molecular docking, dynamics simulations, and protein modeling as crucial to studying vaccine escape. In conclusion, global research led mainly by the USA and supported by active contributions has used bioinformatics to elucidate SARS-CoV-2 immune evasion, guiding variant future vaccine and treatment development, variant monitoring, and preparedness for emerging variants. Full article

Sweet potato (Ipomoea batatas L.) has become a relevant crop in global research due to its remarkable resilience to abiotic stress, richness in bioactive compounds, nutritional relevance, and growing importance within sustainability and circular economy frameworks. This study conducted a comprehensive bibliometric analysis of scientific production indexed in Web of Science, Scopus, and PubMed, mapping how research links the crop’s biochemical properties with sustainability-oriented innovation. Literature on bioactive compounds, food waste management, circular economy strategies, and by-product valorization was examined through keyword co-occurrence, authorship networks, citation patterns, and thematic clustering. Results reveal a rapidly expanding research landscape over the past decade, with strong connections between phytochemical composition, health benefits, sustainable cultivation, and industrial applications. Biology, Chemistry, and Food Science emerged as the most interconnected areas. Collaboration networks remain fragmented, and high-income countries achieve disproportionate citation impact, underscoring structural inequalities. Theoretically, this study contributes to understanding how sweet potato research consolidates as a multidisciplinary field aligned with global sustainability goals. Practically, it highlights opportunities to strengthen equitable international collaboration, advance circular economy approaches, and integrate biotechnology with environmental sustainability to support more resilient food systems. Full article

Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial information. Recent advances in artificial intelligence (AI), particularly large language models and automated information-extraction systems, create new opportunities to accelerate financial analysis; however, their outputs remain probabilistic, context-dependent, and potentially error-prone, making governance and verification essential. This article proposes an AI-assisted value investing framework that integrates automated extraction, valuation modeling, explainability, and human-in-the-loop (HITL) supervision into a unified decision-support architecture. The framework is organized into three layers: (i) a data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling layer for automated key performance indicator (KPI) computation, forecasting support, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer for traceability, verification, model-risk control, and analyst oversight. A central contribution of the paper is the operational characterization of prompt literacy as a determinant of analytical reliability, showing that structured, context-aware prompts materially affect extraction correctness, usability, and verification effort. The framework is evaluated through a case study using Rivanna AI on three large U.S. beverage firms—namely, The Coca-Cola Company, PepsiCo, and Keurig Dr Pepper—selected as a controlled, information-rich setting for comparative analysis. The results indicate that the proposed workflow can reduce end-to-end analysis time from approximately 25–40 h in a traditional manual process to approximately 8–12 h in an AI-assisted setting, including citation/source verification, unit and period reconciliation, and review of key valuation assumptions. Rather than eliminating analyst effort, AI shifts it from manual information processing toward verification, adjudication, and interpretation. Overall, the findings position AI not as an autonomous decision-maker, but as a governed reasoning accelerator whose effectiveness depends on structured human guidance, traceability, and disciplined validation. In value investing, a discipline traditionally grounded in labor-intensive fundamental analysis and disciplined intrinsic value estimation, AI introduces the potential to scale analytical coverage and accelerate evidence synthesis. However, AI systems in financial contexts are probabilistic, context-sensitive, and inherently dependent on human interaction, raising critical questions about reliability, governance, and operational integration. This article proposes a structured framework for AI-driven value investing that preserves the foundational principles of intrinsic value, margin of safety, and economic reasoning, while redesigning the analytical workflow through automation, explainability, and human-in-the-loop (HITL) supervision. The proposed architecture integrates three layers: (i) an AI-enabled data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling and valuation layer combining automated KPI computation, machine learning forecasting, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer ensuring traceability, verification, and model risk control. A central contribution of this work is the operational characterization of prompt literacy, namely the ability to formulate structured, context-aware requests to AI systems, as a critical determinant of system reliability and analytical correctness. Through a focused case study using an AI-assisted analysis platform (Rivanna AI) on three U.S. beverage firms, we provide evidence that structured prompt formulation can improve extraction consistency, reduce verification overhead, and increase workflow efficiency in a human-supervised setting. In this setting, analysis time decreased from a manual range of approximately 25–40 h to 8–12 h with AI assistance and HITL validation, while preserving traceability and decision accountability. The reported hour savings should be interpreted as conservative estimates from the initial deployment phase; additional efficiency gains are expected as operational maturity increases, driven by learning-economy effects. The findings position AI not as an autonomous decision-maker but as a probabilistic reasoning accelerator whose effectiveness depends on structured human guidance, verification discipline, and prompt-driven interaction. These results redefine the role of the financial analyst from manual data processor to reasoning architect, responsible for designing, guiding, and validating AI-assisted analytical workflows. Full article

The tourism sector is widely recognized as a pivotal catalyst for global development and economic growth. However, it faces significant challenges, which have intensified the search for alternative and more sustainable tourism models. Digital technologies have become essential tools for transformation, with the metaverse emerging as a disruptive and promising innovation strategy for the tourism industry. Thus, this study aims to provide a comprehensive review of all previous scientific literature related to the adoption of the metaverse in the context of sustainable tourism, developing a bibliometric analysis (through techniques such as co-citation analysis of references and author keyword co-occurrence) of all articles indexed in Web of Science database from January 2021 to September 2025. Specifically, the references co-citation analysis has concluded three main thematic clusters related to conceptual foundations, technological advances, and metaverse adoption possibilities, respectively. The results obtained indicate that research on the metaverse in sustainable tourism is still at an early stage of development and is primarily conceptual in nature. This study contributes to the emerging research field of metaverse and sustainable tourism by offering a comprehensive review to establish its theoretical foundations and identify opportunities for future research. Full article

Over the past four decades, tinnitus research has grown into a highly interdisciplinary field spanning auditory science, neuroscience, psychology, and clinical medicine. Yet how knowledge across subfields has been inherited, diversified, and integrated over time still lacks traceable structural evidence. To address this gap and move beyond frequency-oriented reviews and bibliometric studies that mainly report “hot topics” and prolific contributors, the present study reconstructs the intellectual evolution of tinnitus research (1984–2025) using citation-network-based main path analysis (MPA). From the Web of Science Core Collection, 6584 records were initially retrieved, of which 6354 formed a mutually linked core citation network (96.5%), indicating high coverage and analyzability. SPLC (Search Path Link Count)–weighted MPA was applied to extract global and key-route main paths capturing dominant knowledge trajectories and major branches. Cluster and co-word analyses were then integrated to delineate seven evolutionary stages and five major thematic clusters. This framework identifies bridging works and turning points and reveals how emerging lines—neuromodulation, implant-related treatments, and digital/telehealth CBT—branch from and later converge with established neurobiological and psychological pathways rather than appearing in isolation. Overall, the field has progressed from early psychoacoustics and spontaneous otoacoustic emissions through cochlear-injury plasticity, central gain, and limbic–auditory network models, and most recently toward mechanism-oriented diagnostics, individualized assessment, and targeted interventions. Full article