Search Results (1,013)

Digital finance has emerged as a rapidly evolving field shaped by advances in financial technology (FinTech), blockchain, artificial intelligence, digital banking, and payment innovations. Our analysis applied bibliometric techniques to examine the evolution, trends, intellectual structure, and future directions of research in the field. Data were collected from the Web of Science Core Collection using a search strategy designed to capture publications that explicitly address research trends, evolution, and future directions in digital finance. After applying predefined screening criteria, a final dataset of 334 publications published between 2016 and 6 March 2026, was analyzed using Bibliometrix, Biblioshiny, and VOSviewer. The analysis included performance indicators, science mapping, co-authorship networks, co-citation analysis, keyword co-occurrence analysis, thematic mapping, thematic evolution, author productivity analysis using Lotka’s Law as a descriptive reference, and Bradford’s Law. The findings indicate a substantial increase in publications over the study period, with an annual growth rate of 33.35%. Research output is highly concentrated in China and India, which together account for approximately 70.06% of the publications. The intellectual structure of the literature is primarily organized around themes related to FinTech, blockchain, cryptocurrency, innovation, and financial inclusion. At the same time, emerging areas include artificial intelligence, sustainability, ESG, green finance, and decentralized finance. The results also reveal fragmented collaboration networks and significant geographical concentration in research production. This study contributes to the literature by providing an overview of the evolution and thematic development of trend-focused digital finance research and identifies promising directions for future investigation. The findings should be interpreted within the scope of the selected trend-oriented literature and not as a representation of the digital finance literature landscape. Full article

Subsurface thermo-hydro-mechanical (THM) coupled processes are fundamental to geomechanics, yet conventional mesh-based methods face high computational costs and limited efficiency in strongly nonlinear simulations and inverse problems. This review examines two representative physics-informed machine learning paradigms for THM modeling: physics-informed neural networks (PINNs) and neural operators (NOs). Relevant studies were identified through iterative keyword-based searches and citation tracking and were comparatively analyzed in terms of physical embedding, data dependence, computational efficiency, inverse capability, generalization, and engineering applications. The analysis shows that PINNs are well suited to physics-constrained simulation and parameter inversion from sparse data but are limited by training instability and loss imbalance. NOs enable rapid repeated forward predictions but depend strongly on representative training data and may perform poorly under out-of-distribution conditions. This review clarifies the complementary roles, trade-offs, and application boundaries of PINNs and NOs and highlights their hybrid integration as a promising route toward efficient and physically consistent subsurface THM simulation. Full article

The multifunctionality of Moringa oleifera is widely recognized; however, its application in small ruminant production remains limited. This study aimed to analyze research trends, scientific structures, and knowledge gaps related to the use of Moringa oleifera in sheep and goat production through a bibliometric approach. A total of 203 articles published between 2002 and 2025 were retrieved from the Scopus and Web of Science databases and analyzed using VOSviewer (version 1.6.20). Data mining identified contributions from 41 countries, 804 authors, 241 institutional/organization affiliations and 109 journals. Publication output increased markedly after 2020, with India, Egypt, and Mexico leading scientific production. Keyword co-occurrence analysis revealed animal nutrition and productive performance as the main research themes, with recent expansion toward reproductive biology and enteric methane mitigation. A. E. Kholif and G. A. Gouda were the most cited authors. Bibliographic coupling network analysis of journals identified four thematic clusters, indicating disciplinary expansion beyond traditional animal science. Important gaps remain regarding the economic impacts of Moringa oleifera use and the standardization of preparation methods, including plant parts, extraction procedures, solvents, and dosages. Full article

Background/Objectives: Nursing research on children with disabilities spans diverse disability groups, care settings, and areas of practice; however, its development and structure have not been comprehensively examined. This study aimed to examine publication trends, major research themes, temporal development, and collaboration patterns. Methods: The Web of Science Core Collection was searched on 29 June 2026 without publication-year restrictions. English-language articles and reviews indexed in Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) were included, yielding 1915 publications from 1977 to 2026. VOSviewer and Biblioshiny were used to analyze publication trends, keyword co-occurrence, thematic structure and evolution, trend topics, and country and institutional collaboration. Results: Research output increased substantially, particularly during the last decade. Across the study period, the United States had the highest publication output and co-authorship connectivity, followed by England, Canada, and Australia. Nursing, children, autism spectrum disorder, intellectual disability, and cerebral palsy were among the largest nodes in the keyword network. The thematic map positioned the autism–pediatrics–developmental disability cluster slightly within the motor themes quadrant, while the disability–children with disabilities–qualitative research, adolescents–communication–transition, and children–nursing–intellectual disability clusters were positioned among the basic themes. Thematic evolution showed both continuity and diversification, with autism, nursing, children, and intellectual disability represented across multiple periods, while quality of life, education, and mental health were represented in the most recent period. Trend topic analysis further showed that well-being, implementation, pediatric nursing, anxiety, and mental health were among the topics with more recent median publication years. Conclusions: Nursing research on children with disabilities has expanded and diversified, with recurring disability-specific topics alongside more recent topics related to psychosocial issues, pediatric nursing, and implementation. Future research could build on the thematic patterns identified in this study through systematic reviews and primary nursing research, while bibliometric studies incorporating additional databases could provide a more comprehensive view of the field. Full article

The rapid global expansion of solar photovoltaic (PV) capacity has increased the operational need for accurate generation forecasting to support grid balancing, dispatch, and market participation. Artificial intelligence (AI) and machine learning (ML) methods now dominate this research area, but the resulting literature is large and methodologically fragmented, making it difficult to establish which methods are used, what data they require, and where the principal gaps lie. This paper combines a bibliometric analysis of 3111 records retrieved from the Web of Science Core Collection (2010–2026) with a technical synthesis of 27 highly cited studies published from 2022 onward, combining the most highly cited works with targeted additions from 2024–2025 covering specific methodological gaps. The bibliometric analysis shows exponential growth in annual output, from three publications in 2010 to 609 in 2025, with keyword evolution tracing a clear methodological trajectory from classical and fuzzy-logic approaches, through shallow and deep neural networks, to transformer- and attention-based architectures since 2023. The technical synthesis finds that classical machine learning remains competitive for day-ahead forecasting with well-structured numerical weather prediction inputs, that convolutional neural network–long short-term memory (CNN-LSTM) hybrids dominate the deep-learning literature, and that graph-based and transformer architectures address multi-site and multi-horizon forecasting, respectively. A comparison of reported results shows that absolute error metrics are not directly comparable across studies due to heterogeneous datasets, metrics, temporal resolutions, and climates, although relative improvements within controlled comparisons are directionally consistent. Seven research gaps are identified, including the absence of standardized benchmarks, limited public dataset availability, weak cross-region generalization, and underdeveloped uncertainty quantification. Full article

Background/Objectives: Population aging has emerged as a major global challenge, particularly in Asian countries experiencing rapid demographic transitions. Among them, South Korea represents one of the fastest aging societies in the world, having rapidly transitioned into a super-aged society. As aging populations expand worldwide, increasing attention has been directed toward well-dying as an important component of quality of life, end-of-life care, and social well-being in later life. This study aims to identify the major research trends and knowledge structures of well-dying studies by applying text net-work analysis and LDA-based topic modeling. Methods: A total of 91 Korean academic studies related to well-dying published between 2016 and March 2026 were collected from publicly accessible scholarly databases and analyzed via keyword frequency, degree centrality, and community detection analyses, as well as LDA-based topic modeling. Results: The results showed that keywords such as “death,” “awareness,” “education,” “older adults,” “medical care,” and “life-sustaining treatment” played central roles in the knowledge network. Community analysis revealed that well-dying research has evolved into interconnected domains involving psychological preparations for death, hospice and palliative care, legal and ethical decision-making, and community-based aging policies. Topic modeling further identified four major themes: (1) psychological well-being and death preparation in later life, (2) social and policy approaches to well-dying, (3) well-dying education and healthcare perceptions, and (4) legal and ethical issues surrounding life-sustaining treatment decisions. Conclusions: The findings suggest that well-dying research is expanding from individual psychological adaptation to broader social, medical, legal, and policy dimensions. As one of the world’s fastest-aging societies, the Korean case provides meaningful implications for other countries facing accelerated population aging and highlights the importance of integrated well-dying policies and community-based support systems in super-aged societies. Full article

Lean Construction (LC) has evolved from an emerging adaptation of production principles into a mature academic domain, yet a comprehensive assessment of its global structure and development remains needed. This study presents a rigorous bibliometric and scoping review of LC research using a sample of 484 highly refined, peer-reviewed documents retrieved from the Scopus database up to 2025. Following the PRISMA-ScR guidelines and utilizing VOSviewer, the co-occurrence networks of keywords, countries, and co-authorships were systematically analyzed. The results reveal a structurally centralized research landscape historically led by the United States and the United Kingdom, though accompanied by an increasing globalization driven by emerging clusters in Asia and South America. Keyword analysis indicates growing research attention to Building Information Modeling (BIM), information management, agile approaches, and environmental sustainability within the analyzed Lean Construction literature alongside traditional operational and planning concerns. However, the literature exhibits persistent fragmentation, with over 70% of identified journals publishing two or fewer articles on the subject within specialized thematic niches. This study concludes that opportunities exist to further explore the relationship between Lean Construction and emerging digital technologies while strengthening cross-regional and interdisciplinary collaborations. These efforts may contribute to a more cohesive and globally representative understanding of the discipline and its role in improving efficiency and sustainability within the built environment. Full article

Sparse count data, such as patent document–keyword matrices, often contain excessive zeros and overdispersion, making conventional distance-based clustering methods less suitable. This study proposes a zero-inflated likelihood-based graph neural clustering method with a zero-inflated negative binomial likelihood, denoted as ZIL-GNC-ZINB. The proposed method combines graph-smoothed node representations with cluster-specific zero-inflation probabilities and count-intensity parameters. By incorporating graph-neighborhood information into the cluster membership update, ZIL-GNC-ZINB jointly accounts for structural zeros, overdispersion, and local graph relationships among observations. The proposed method was applied to a quantum-computing patent document–term matrix consisting of 9416 patent documents and 82 reduced keywords. Compared with K-means, K-means clustering based on principal component analysis (PCA+K-means), and K-means clustering based on graph convolutional networks (GCN+K-means), ZIL-GNC-ZINB achieved the best performance in terms of negative log-likelihood (NLL), zero area underneath the receiver operating characteristic (ROC) curve (AUC), and zero Brier score. The resulting clusters revealed interpretable quantum-computing sub-technologies, including photonic qubit control, hybrid quantum–classical computing, superconducting qubit hardware, and quantum security networks. Simulation experiments under zero proportions of 0.5, 0.7, and 0.9 further showed that the proposed method becomes increasingly effective as zero inflation becomes more severe. In the extreme zero-inflation setting, ZIL-GNC-ZINB achieved the best performance in NLL, Zero AUC, adjusted rand index (ARI), normalized mutual information (NMI), and clustering accuracy (ACC). These results demonstrate that zero-inflated likelihood modeling combined with graph-based clustering provides an effective and interpretable framework for sparse high-dimensional count data. Full article

Plastic pollution continues to threaten marine ecosystems and undermine efforts toward a circular economy. This study systematically maps global research on the three core waste management strategies, i.e., reduce, recycle, and remove (3R’s), to understand their intellectual structure, evolution, and emerging directions. Using both Scopus and the Web of Science, we apply bibliometric performance analysis and keyword co-occurrence network mapping to examine trends across plastic bans and regulation, recycling technologies, and clean-up initiatives. Research on reduction has increasingly incorporated themes related to multi-level governance and circular economy frameworks, based on observed changes in keyword patterns and thematic evolution. Recycling studies show an increasing presence of themes related to advanced chemical, biological, and AI-supported systems alongside established mechanical recycling approaches. Clean-up research increasingly includes themes related to technology-driven solutions, including improved detection and remediation approaches. Despite this progress, key challenges remain such as fragmented policy coordination, technological and economic limits to scaling advanced recycling, and the high cost and complexity of large-scale clean-up. Life-cycle trade-offs and persistent microplastics further constrain impact. Overall, future research must connect prevention, material recovery, and environmental restoration within coherent governance and technological systems to reduce plastic leakage and support long-term marine sustainability. Full article

Mycoplasma pneumoniae (MP) is a major etiological agent of respiratory tract infections. This study sought to systematically map the current landscape, thematic progression, collaborative networks, and emerging priorities in diagnostic research on MP infections. Data were extracted from the Web of Science Core Collection from 1 January 1980, to 12 May 2025. Bibliometric and visualization analyses across countries/regions, institutions, authors, co-cited references, keywords, and disease-related terms were conducted using CiteSpace, VOSviewer, Pajek, and SCImago Graphica. Analysis of 2093 articles revealed consistent growth in research output related to MP diagnosis. Most publications originated in China (n = 623). The United States Centers for Disease Control and Prevention demonstrated the greatest total link strength in institutional collaboration networks. Key high-frequency keywords reflect the ongoing transition from traditional pathogen confirmation to integrated diagnostic approaches incorporating molecular testing, macrolide-resistance detection, co-infection evaluation, and risk stratification. Current research has increasingly targeted diagnostic optimization and the early identification of refractory or severe Mycoplasma pneumoniae pneumonia. This study provides a structured overview of the knowledge structure, thematic evolution, and technological frontiers in MP diagnostic research. The findings highlight key challenges, thereby offering guidance for interdisciplinary innovation and informing future diagnostic research directions. Full article

This study provides a comprehensive bibliometric analysis of photobiomodulation (PBM) research in peripheral nervous system (PNS) disorders, aiming to characterize its structural features and developmental trends. Using a bibliometric workflow, a final dataset of 191 unique publications across 24 standardized PNS disease entities was identified through MeSH- and ICD-11-based classification from 14,670 records retrieved from the Web of Science Core Collection. The results show that PBM research has grown steadily since 2010 but remains concentrated in specific clinical conditions, particularly carpal tunnel syndrome (CTS). Keyword co-occurrence network analysis and functional classification identified three primary domains of PBM research: (1) pain modulation, (2) nerve regeneration and structural repair, and (3) biological/mechanistic regulation. The relative emphasis of these domains differed across disease groups, with compression and entrapment neuropathies emphasizing intervention parameters and assessment, nerve injury/regeneration research showing greater representation of structural repair and biological regulation, and neuropathic pain/neuropathy research showing a prominent pain-related component. Temporal analysis indicated diversification toward a broader range of neuropathic conditions and increasing representation of pain-related and biological themes. In conclusion, PBM research exhibits distinct functional patterns across PNS disease contexts. The function-oriented classification framework provides a structured basis for identifying research gaps and disease-specific research priorities. Full article

The proliferation of artificial objects in Earth’s orbit has transformed space debris into one of the most critical challenges to space sustainability. Radar technologies play a pivotal role in the detection, tracking, and characterization of these non-functional objects, providing the backbone for global Space Situational Awareness (SSA) and collision avoidance systems. This study presents a comprehensive bibliometric analysis of radar-based space debris detection research published between 2005 and 2025. Using data retrieved from the Scopus and Web of Science databases, a curated dataset of 557 peer-reviewed journal articles was examined. The analysis explores publication trends, institutions, countries, and journals, as well as co-citation and keyword co-occurrence networks to map the intellectual and thematic structure of the field. Results reveal a sustained annual growth rate of 3.76%, with China and the United States leading global research output. Core motor themes—such as space-based radar, synthetic aperture radar, and remote sensing—define the technological foundation of the domain, while emerging clusters reflect growing interest in artificial intelligence and active debris removal. The study highlights a high degree of collaboration among researchers but significant geographic concentration of capabilities. By unveiling the evolution, collaboration patterns, and conceptual frontiers of radar-based space debris research, this work provides a quantitative foundation to guide future investigations and policy strategies aimed at ensuring long-term orbital sustainability. Full article

Against the backdrop of rapid development in marine engineering, the construction industry faces practical challenges, such as water scarcity, limited availability of natural river sand, and high raw material transportation costs. This has led to an increasing demand for resource-efficient concrete production technologies and improved construction economic efficiency. Seawater–sea-sand concrete (SWSSC) offers a locally sourced solution that effectively reduces the construction sector’s overreliance on freshwater and river sand, lowers material transportation costs for coastal infrastructure projects, and supports marine engineering and infrastructure development along the Belt and Road Initiative. However, existing research lacks systematic organization and visualized quantitative analysis. Utilizing the CiteSpace 7.0.R0 knowledge graph analysis software, this study selects 982 relevant papers published in the Web of Science (WOS) Core Collection between 2016 and 2025 as the sample. By employing analytical methods—including annual publication volume statistics, collaboration networks among researchers, keyword co-occurrence patterns, and temporal evolution charts—we systematically delineate the overall research landscape, distribution of key research institutions, trends in research hotspots, and future frontier directions in this field. The analysis results indicate that: (1) The total number of publications in the global seawater–sand concrete field has been increasing year by year. From 2016 to 2018, it was the basic exploration period, with an average annual publication volume of less than 10. From 2019 to 2021, it was the deepening and expansion period, with research expanding from the performance of a single material to material modification and structural application. From 2022 to 2025, it was the rapid prosperity period, with the publication volume reaching its peak in 2024–2025 (208 articles and 203 articles), and the publication volume continued to rise. (2) China ranks first globally with 798 publications, but its centrality in international cooperation networks is only 0.24, reflecting low overall collaboration density and loose partnerships between institutions and authors, without the formation of cross-institutional core research teams with global leadership. (3) Research hotspots in this field primarily focus on material properties, durability characteristics, and mechanical strength, among which FRP reinforcement systems serve as a bridge for interdisciplinary research bridging material fundamentals and engineering applications, representing a key research branch. (4) From the perspective of evolutionary trends, the field exhibits three major developmental shifts from macroscopic mechanical performance characterization to in-depth investigation of microscopic damage mechanisms, from single-material studies to composite structural systems, and from short-term laboratory accelerated testing to full life-cycle performance evaluation, with the low-carbon potential of seawater–sand concrete increasingly becoming a prominent research focus. Therefore, this paper advocates strengthening international and inter-institutional academic collaboration, fostering multidisciplinary innovation, and prioritizing breakthroughs in key areas, such as large-scale intelligent performance prediction, long-term performance database development, and digital-twin-based operation and maintenance management, to facilitate the transition of seawater–sand concrete technology toward efficient, low-carbon, safe, and intelligent engineering applications. Full article

Construction equipment monitoring has shifted from manual logbooks and early telematics toward intelligent digital systems, driven by the need to reduce delays in obtaining equipment data and improve decision-making on construction sites. This study examines research on construction equipment monitoring and tracking published between 2000 and 2026 to identify the evolution, major themes, and emerging directions in the field. A scientometric and bibliometric analysis was conducted on 1093 bibliographic records retrieved from Scopus and Web of Science on 20 May 2026. The annual publication trend was evaluated through 2025, the last complete publication year, while records indexed in 2026 were retained for the remaining corpus-level analyses. The datasets were preprocessed and analyzed using Bibliometrix, VOSviewer, and CiteSpace to examine publication trends, keyword networks, collaboration patterns, citation structures, and research clusters. The keyword network was interpreted through six major thematic clusters, which were synthesized into four broader knowledge streams covering operations and sensing, AI-based perception, safety monitoring, and BIM/digital-twin integration. The results show a substantial increase in the representation of AI- and perception-related research across the later publication periods, reflecting a transition from basic sensor-based approaches toward more intelligent and connected site systems. The study identifies leading contributors and comparatively underdeveloped research priorities, particularly real-time idle-state detection, multimodal data fusion, multi-site validation, and the integration of monitoring outputs with BIM and digital-twin decision-support environments. Full article

The extraction of spatiotemporal features from video sequences allows for the recognition of actions and the analysis of behaviors in video, making it a key challenge in automated video processing. The literature shows widespread use of deep learning approaches, specifically convolutional neural networks (CNNs). In this context, researchers face the challenge of identifying the advantages, disadvantages, and emerging trends across different architectures, evaluation metrics, and even dataset selection. The objective of this study is to identify the most common CNN architectures, evaluation metrics, datasets, and trends in spatiotemporal feature extraction for video analysis. The selection of articles used the PRISMA methodology and the Joanna Briggs Institute (JBI) methodological framework. From the databases Scopus, Web of Science and the MDPI platform, and based on the inclusion/exclusion criteria, 31 articles that met the criteria were analyzed and synthesized. The search was conducted primarily using the keywords “Convolutional Neural Network,” “video processing,” and “feature extraction,” limiting the selected works to those published between 2020 and the end of 2025. The results mainly show the use of four neural network architectures: 2D CNNs, 3D CNNs, hybrid models (e.g., CNN–RNN, CNN–Transformer, and multi-stream models), and, to a lesser extent, lightweight architectures. Commonly used datasets were identified (e.g., UCF101 and HMDB51). Additionally, standardized evaluation metrics were identified, ranging from accuracy and F1-score to performance measures specific to each case study. The challenges identified center on the heterogeneity of the study datasets, the lack of standardized evaluation metrics, and maintaining a balance between accuracy and computational resource consumption. On the other hand, strong emerging trends toward the use of hybrid models and those integrating transformers have been identified. This systematic review emphasizes the need for clear and robust guidelines that allow for the appropriate selection of CNN architecture, test datasets, and evaluation metrics in applications for extracting spatiotemporal features from video sequences, as well as identifying trends and future lines of research. Full article