Search Results (834)

Methicillin-resistant Staphylococcus aureus (MRSA) remains a significant multidrug-resistant pathogen, frequently associated with persistent infections and biofilm formation, underscoring the urgent need for alternative antimicrobial strategies. Bioactive compounds derived from fungi have attracted considerable attention due to their structural diversity and demonstrated antibacterial activity against MRSA. This study employed a scientometric approach to assess global research trends, thematic evolution, and collaborative networks concerning fungi-derived anti-MRSA compounds. Bibliographic data were collected from the Scopus database, and a total of 1666 English-language articles and reviews published up to 2025 were analyzed using Bibliometrix/Biblioshiny and VOSviewer. The findings indicate a marked increase in research output after 2010, reflecting heightened scientific interest in fungal natural products for MRSA management. China and the United States emerged as leading contributors in terms of publication volume and international collaboration. Thematic analysis revealed a shift from broad antimicrobial screening to more specialized investigations, including antibiofilm activity, secondary metabolites, endophytic fungi, molecular docking, and antimicrobial resistance. Nonetheless, several challenges persist, such as insufficient mechanistic validation, limited toxicity and pharmacokinetic assessments, and a lack of clinically relevant in vivo studies. Overall, the field is increasingly multidisciplinary, integrating microbiology, natural product chemistry, and computational methodologies to advance the discovery of anti-MRSA agents. Full article

Background: The Vehicle Routing Problem (VRP) is foundational to logistics optimization, yet its alignment with the Triple Bottom Line (TBL) and UN Sustainable Development Goals (SDGs) remains fragmented. This study conducts a strategic bibliometric audit of 301 peer–reviewed publications (1992–2025) to quantify the evolutionary progression and thematic maturity of sustainable routing research. Methods: A four–stage scientometric framework was employed, utilizing Scopus–based data retrieval, longitudinal mapping, and Python 3.14–driven text mining to visualize keyword co–occurrence networks, author collaborations, and regional research clusters. Results: Findings reveal a pronounced “Sustainability Asymmetry,” where 51.5% of studies prioritize economic efficiency, while only 2.6% address the social pillar. Additionally, social sustainability remains an “isolated island” with minimal cross–citation to the research core. Geographic analysis identifies a heavy concentration in China, the USA, and Western Europe, uncovering a critical North–South—collaboration gap. Conclusions: The study proves that while environmental themes reached maturity between 2018 and 2022, social indicators exhibit a significant maturity lag. This quantified social deficit, centered on the neglect of SDG 3 and SDG 10, mandates a fundamental paradigm shift toward a geographically inclusive and socially conscious research agenda to ensure global logistical equity. Full article

This study examines the positioning of biblical narratives within international relations scholarship, with particular emphasis on their function as cultural resources shaping identity, geopolitical discourse, and soft power dynamics. Although religion has gained increasing recognition within international relations, the extent to which scriptural narratives are systematically integrated into analytical frameworks remains insufficiently defined. To address this issue, the study employs a mixed-methods research design that combines a systematic literature review with bibliometric analysis. Bibliographic data were retrieved from the Scopus and Web of Science databases through a structured query linking biblical terminology to diplomacy, geopolitics, and religion–politics interactions, and were analyzed using the Bibliometrix package in R. The analysis draws on two datasets comprising 135 publications from Scopus and 88 from Web of Science, spanning 1989 to 2026. The findings indicate that scholarship examining biblical narratives in international relations is moderately developed and interdisciplinary, yet remains fragmented, with geopolitical themes predominating. Biblical narratives are consistently present but are primarily embedded within broader analytical categories such as identity, discourse, and legitimacy, rather than being treated as central variables. The results further suggest that religious content is often incorporated in indirect or implicit forms, reflecting a broader tendency to approach religion as a contextual rather than a constitutive element. Overall, the findings indicate that biblical narratives function primarily as interpretive and symbolic frameworks in international relations, while their analytical potential remains only partially developed, underscoring the need for more systematic integration of cultural and religious analysis in the study of global politics. Full article

Human activity recognition—HAR—plays a crucial role in the lives of patients battling neurodegenerative diseases. These debilitating conditions, such as Alzheimer’s or Parkinson’s, affect individuals’ ability to perform daily tasks autonomously and safely. HAR technology offers an invaluable solution by enabling real-time monitoring and assistance, helping to maintain independence and quality of life for patients. Additionally, this technology provides a valuable data source for doctors and caregivers, allowing for more precise and personalized care, which can make a difference in managing and treating these neurodegenerative diseases. The objective of this review is to identify the contribution of Transfer Learning and Reinforcement Learning in supporting the processes of daily activity recognition, thus enhancing the quality of life for patients. As this is a trending topic, the literature surrounding it is quite dispersed, which is why this review aims to present the current line of research in this field. To carry out this analysis, the science tree paradigm was used, which establishes two fundamental stages of analysis. The first is delimited by scientometrics, where the leading countries in the application of such technologies can be identified. This review highlights the evolution in the use of transfer learning and reinforcement learning in HAR in the healthcare field, where these techniques have significantly improved the accuracy and adaptability of real-time monitoring systems. The studies reviewed indicate that transfer learning has allowed models to adapt to data variations without requiring large volumes of manual labeling, which is essential in clinical and patient monitoring contexts. Additionally, reinforcement learning has optimized decision-making in complex scenarios, enabling activity recognition systems to dynamically adjust monitoring parameters, enhancing detection and response to critical or unusual activities in multi-user environments. These advances demonstrate that, by integrating these approaches, greater personalization and robustness can be achieved in human activity recognition, thereby improving the quality of life for patients in clinical settings. Full article

Advancement toward the Essential Science Indicators (ESI) Top 1‰ is an important indicator of disciplinary competitiveness, but progress near this boundary is often nonlinear because the global threshold continues to move upward. This study develops a two-layer framework combining an Exponential Decay Model (EDM) for modeling rank trajectories and a Bivariate Logarithmic Difference Decomposition Model (BLDDM) for separating institutional citation growth from external threshold pressure. Using 13 bimonthly ESI update waves from March 2024 to March 2026, we analyze Chemistry, Engineering, and Materials Science at Wuhan Institute of Technology. The results show that the EDM outperforms a linear benchmark in all three disciplines, indicating an asymptotic pattern of advancement near the Top 1‰ boundary. The BLDDM further reveals substantial disciplinary heterogeneity: Engineering faces the strongest threshold pressure, whereas Chemistry is the most favorable near-term candidate for breakthrough. These findings suggest that ESI advancement should be understood as a moving-threshold process rather than a simple accumulation of citations. Full article

Bridge maintenance research has increasingly expanded toward Digital Twin (DT), Structural Health Monitoring (SHM), Artificial Intelligence (AI), sensing technologies, and object-based information management. As maintenance paradigms shift from reactive to preventive and prescriptive approaches, digital twins have gained attention as a means of integrating fragmented technological components. However, the growing emphasis on AI- and DT-based analytics raises questions about how object-based information structures, sensing systems, SHM, AI-based analytics, and interoperability mechanisms are thematically connected and structurally associated. This study conducted a scientometric analysis of publications retrieved from the Web of Science (WoS) database without year restrictions. To avoid predetermining the importance of any single information-modeling technology, the main search query excluded BIM-related terms and combined the bridge domain, DT-related technology layer, and maintenance domain. After applying document type, language, and research-area filters, 406 records were screened by title and abstract. Six records that were not directly related to bridge DT maintenance research were excluded, resulting in a final analytical corpus of 400 records. Among these, 77 records were identified as the BIM-related subset for sensitivity analysis. Using VOSviewer-based bibliographic coupling as the core method, supported by keyword co-occurrence, density and overlay visualization, and CiteSpace analysis, this study examined contemporary research structures and historical intellectual bases. The results show that bridge DT development is not detached from existing technological foundations but reflects the cumulative convergence of object-based information modeling, sensing, SHM, AI-based analytics, and interoperability mechanisms within integrated DT architectures. Full article

Light Detection and Ranging (LiDAR) technology has become a crucial tool in the construction industry, offering precise 3D data capture that addresses inaccuracies and provides accurate building information throughout various construction stages. Despite numerous studies on LiDAR applications, most reviews are isolated and lack a systematic, comprehensive analysis. This study fills that gap by combining scientometric and qualitative analysis to explore LiDAR research trends in construction. The process begins by collecting 671 relevant papers published over the past 15 years. The data is then quantitatively analysed to reveal key research trends. Lastly, the study qualitatively examines the implementation of LiDAR, focusing on three main areas: (1) the significant applications and benefits of LiDAR throughout the entire project life cycle, (2) the challenges and future directions of LiDAR technology organised by workflow stages, and (3) key future research directions aimed at enhancing accuracy, automation, and integrating technologies. This work provides insights for both researchers and practitioners, aiding understandings of the benefits and limitations of LiDAR technology, and offering direction for future research. Ultimately, the study contributes to the long-term management and practical application of LiDAR in the construction life cycle, promoting more intelligent, efficient, and sustainable infrastructure development. Full article

Occupational safety and health (OSH) is increasingly relying on wearable technologies, yet research on such bodycentric antenna systems remains fragmented across diverse disciplines. This review introduces bodycentric antenna systems for occupational safety and health (BASOSH) as a novel unified framework that explicitly links electromagnetic performance, human–body interaction, and OSH objectives within a single conceptual model. Based on a preliminary scientometric analysis, the existing literature is categorized into four application pillars: (i) monitoring workers’ health and safety, (ii) supporting occupational activity, (iii) preventing accidents and mitigating risks, and (iv) rehabilitation and prosthetics. The analysis highlights a lack of integrated design approaches, as most studies address only a subset of the BASOSH framework. To overcome this fragmentation, a parametric design function is proposed, jointly weighting wireless performance, human–body effects, and OSH outcomes, to enable the unified design and comparison of BASOSH. By systematizing a previously scattered research area and establishing a common design language, this work defines BASOSH as a distinct research domain and provides a foundation for the development of personalized and context-aware OSH solutions. Full article

As bridge infrastructure globally transitions from theoretical monitoring toward intelligent digital management, Machine Learning (ML) has emerged as a transformative tool for data-driven lifecycle decision-making. This study presents a systematic and critical review of ML applications across the entire bridge lifecycle, integrating a PRISMA-based scientometric analysis (2020–2025) with a rigorous technical synthesis of 3 major domains. The research reveals a clear hierarchy in deployment readiness; while Design & Optimization and Seismic Fragility Assessment have achieved “High” readiness by leveraging deep learning surrogates to achieve up to a 50-fold computational speedup over traditional simulations, Vibration-Based Damage Identification remains at a “Low–Medium” level due to environmental noise sensitivity and low Signal-to-Noise Ratios (SNR). Technical findings indicate that vision-based models (e.g., ViT, YOLOv8) show strong and promising performance for surface defect detection in controlled or semi-controlled settings, though broader field deployment remains constrained by lighting variability, dataset diversity, and validation at scale. In deterioration modeling and Remaining Useful Life (RUL) prediction, temporal architectures (e.g., LSTM) effectively capture non-linear trends, though operational risks such as “model drift” and “domain shift” in simulation-dependent models necessitate periodic retraining. This review identifies critical bottlenecks, including the “small data” paradox and the “black-box” dilemma. The work concludes by outlining a strategic roadmap centered on Physics-Informed Neural Networks (PINNs), Federated Learning for cross-agency collaboration, and Explainable AI (XAI) to foster professional trust in safety-critical infrastructure management. Full article

This study presents a bibliometric and systematic review of 480 articles meeting the following inclusion criteria: English-language articles, reviews, or proceeding papers focusing on building topics with full text available, retrieved from the Web of Science Core Collection on 9 Jannary 2026 to map the intellectual landscape of smart-sustainable building (SSB) research. Employing the PRISMA framework combined with scientometric mapping (VOSviewer), thematic classification, and qualitative synthesis (no risk of bias assessment was performed as this was a bibliometric review), the analysis reveals exponential publication growth since 2022, identifying three dominant thematic clusters: digital enabling technologies (41.0%), energy systems (30.8%), and advanced building envelopes and materials (28.3%). Keyword analysis identifies “smart buildings,” “green buildings,” and “energy efficiency” as central conceptual anchors, while temporal trends indicate increasing attention to artificial intelligence, digital twins, and blockchain. Notably, 51.4% of articles address two or more themes simultaneously, confirming the field’s interdisciplinary character. Critical analysis reveals persistent fragmentation: sustainable building rating tools (e.g., BREEAM, LEED) and smart building evaluation methods (e.g., Smart Readiness Indicator). Seven challenges, including assessment fragmentation, high costs, and cybersecurity vulnerabilities, are identified as barriers to SSB adoption. Limitations include reliance on a single database (Web of Science) and subjective thematic classification. This review provides a roadmap for future research emphasizing integrated assessment frameworks and interdisciplinary collaboration. Registration: Not pre-registered. Funding: National Key R&D Program of China (2025YFF0521003). Full article

Literature reviews are essential for synthesizing existing knowledge, mapping research domains, identifying intellectual structures, and highlighting research gaps within a field. However, many literature reviews are incomplete because database search strategies are not adequately specified or validated. Search strategies are frequently underreported and undermotivated across the systematic review literature and bibliometrics, while query formulation remains time-consuming, error-prone, and particularly difficult in interdisciplinary or rapidly evolving topics. This article fills that void by developing a guideline for designing a professional topic query in existing academic databases and emphasizing search design as the front-end validity problem in bibliometric research. The article uses the Artificial Intelligence Think Tank framework as a methodological engine and applies it to bibliometric retrieval engineering via structured interaction with generative AI systems and human experts. The paper assists scholars performing bibliometric studies, scientometric analyses, systematic literature reviews, scoping reviews, and hybrid evidence-synthesis projects. Full article

Aligning mental health research with territorial health needs remains a critical goal, yet the global distribution, coherence, and impact of scientific output across income groups remain poorly understood. We conducted a meta-research study combining scientometric analyses with longitudinal data on 60 health and development indicators. Over 386,000 peer-reviewed publications were retrieved from five major databases. Linear regressions, meta-analyses, and meta-regressions were performed, stratified by World Bank income classification. We find that high-income countries (HICs) accounted for 67% of publications, exhibiting the highest research density but the lowest potential marginal health returns. In contrast, low-income countries (LICs) showed the strongest associations between research volume and improvements in life expectancy (β = 0.13; p < 0.01) and child mortality (β = −1.38; p < 0.01). Structural moderators such as governance quality, health expenditure, and education explained up to 48% of between-group variance. In conclusion, the global landscape of mental health research remains unequal. While scientific production is concentrated in HICs, its population-level association is greatest in LICs. These findings underscore the need to redirect investments and enhance research coherence with health needs, particularly through governance safeguards and capacity building in underrepresented regions. Full article

Chromatography represents a cornerstone of modern analytical chemistry, enabling the separation, identification, and quantification of constituents of complex chemical systems. Its development in the Republic of North Macedonia is closely linked to the evolution of national academic institutions and industrial analytical capacity. This study provides a combined historical overview and Scopus-based scientometric analysis of chromatography research in North Macedonia, focusing on institutional development, research groups, methodological evolution, and industrial contribution. Data were obtained from Scopus-indexed publications, institutional records, and key publications. Ss. Cyril and Methodius University in Skopje (UKIM) emerges as the dominant research hub, particularly through the Institute of Chemistry, Faculty of Natural Sciences and Mathematics, and the Faculty of Pharmacy, Faculty of Agriculture, supported by the Macedonian Academy of Sciences and Arts (MANU), Goce Delčev University in Štip, and several specialized applied research institutions. The pharmaceutical industry, particularly Alkaloid AD Skopje and Replek Farm, plays a significant role in chromatographic method application and validation. High-performance liquid chromatography (HPLC), gas chromatography (GC), and liquid chromatography–mass spectrometry (LC–MS) dominate research applications, primarily in phytochemistry, pharmaceutical analysis, food chemistry, and environmental science, while MALDI-TOF mass spectrometry has been integrated into the proteomics activity at MANU. The results demonstrate a highly centralized but scientifically productive research system with increasing methodological sophistication and international cooperation. Full article

The dramatic increase in the volume of postconsumer textile waste poses not only a major environmental problem but also an untapped opportunity for the development of sustainable infrastructure through the use of synthetic and composite textile waste-derived materials (SCTWDMs) in the field of asphalt pavement engineering, contributing to the achievement of the United Nations Sustainable Development Goals (SDGs 9, 11, 12, and 13). This systematic review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A systematic search of the literature in the field of SCTWDMs in asphalt pavement engineering was performed between 2010 and 2025 using the Web of Science and Scopus databases. A total of 65 studies were identified and analysed according to the inclusion and exclusion criteria of the current review. The quality of the studies and the risk of bias were assessed according to the transparency of the methods and the reporting of the results. The triangulated methodological framework consisted of bibliometric analysis, systematic review, and SWOT analysis. The bibliometric analysis was carried out via VOSviewer software version 1.6.20. The results of this study indicate an increase in the number of publications in SCTWDMs; however, there is fragmentation in the field. This denotes poor interrelationships among themes, insufficient collaboration across research streams, and scattered networks of keyword associations, suggesting a lack of a coherent research framework for SCTWDM research. The results of this study indicate that SCTWDMs generally improve the rheological properties, cracking resistance, and mechanical characteristics of asphalt mixtures. However, variability in fibre properties, optimisation of dosage, and limited field validation remain major challenges in SCTWDMs. The SWOT analysis also highlights important technical, institutional, and standardisation barriers, as well as opportunities for further development in sustainable pavement technologies. Despite this, the body of evidence is limited by heterogeneity in study design and a lack of long-term results. The review is not preregistered, but all the methodological procedures are transparently described. In conclusion, this body of evidence offers a strategic direction for further research, policy development, and industry practice, highlighting the importance of linking laboratory results to applications to position SCTWDMs as a viable option within the global sustainability agenda. Full article

Researcher evaluation underpins critical academic decisions, yet traditional bibliometric indicators lack predictive capability and cross-domain generalizability, while most predictive approaches offer limited interpretability and narrow domain validation. This study proposes a SHAP interpretable, multi-domain supervised learning framework for predicting academic award recognition using thirty two publication count-based bibliometric indices. A balanced dataset was constructed across four disciplines, namely Computer Science, Neuroscience, Mathematics, and Civil Engineering, comprising verified awardees from recognized professional societies and matched non-awardee researchers. Eight classifiers were evaluated under stratified five fold cross validation, assessed via accuracy, precision, recall, F1-score, and ROC AUC. The framework achieved domain-specific F1-scores of 0.70 in Computer Science, 0.73 in Neuroscience, 0.72 in Civil Engineering, and 0.78 in Mathematics, with SVM and XGBoost demonstrating the strongest cross-domain robustness across disciplines. SHAP analysis consistently identified normalized h index, h2 family, q2 index, and g index as dominant cross-domain predictors, while domain-specific indicators, including Rm and w indices in Neuroscience and P index in Civil Engineering, reflected disciplinary recognition patterns. By unifying publication-based feature engineering, multi-domain classification, and SHAP explainability within a single reproducible pipeline, this framework offers a scalable, transparent, and evidence-based tool for institutional researcher evaluation. Full article