Search Results (96)

Precast concrete (PC) component production scheduling is essential to the efficiency and reliability of industrialized construction. Although intelligent algorithms have been widely applied in this field, the relationships among research evolution, collaboration patterns, and industrial applicability remain insufficiently understood. To address this issue, this study presents a bibliometric review of 1272 publications indexed in the Web of Science Core Collection from 1990 to 2025. CiteSpace was employed to analyze publication trends, collaboration networks, co-citation structures, keyword co-occurrence, and burst terms. On this basis, a technology adaptability evaluation framework was developed to assess the alignment between algorithmic advances and industrial implementation in terms of dynamic adaptability, verification completeness, and technological generation gap. The results indicate that the field has evolved through four broad stages, from early static optimization to multi-objective coordination, digital twin-enabled dynamic scheduling, and emerging human-centric intelligent autonomous systems. The analysis also shows an increasing convergence of operations research, computer science, and civil engineering. However, a gap remains between academic output and industrial application. Specifically, 32% of the retrieved studies focused on genetic algorithms, whereas only 6% reported full-process industrial validation. In addition, Gen 4.0-related studies showed a technological generation gap of 82.5%, indicating that many frontier technologies have not yet reached broad industrial implementation. The collaboration network further reveals a “high-output, low-synergy” pattern, in which major publishing countries contribute substantially to the literature but exhibit limited cross-institutional integration. This study provides a structured overview of the development of PC component production scheduling research and highlights future directions for digital twin integration, human–robot collaboration, and cross-sector validation platforms. Full article

This paper presents the development of two adaptive autopilots for the Cessna Citation X business jet aircraft. The two adaptive control strategies, including a dynamic inversion controller and a neural network controller, provide dual adaptation. The control objective consists of tracking the vertical speed, altitude, and heading commands. Dynamic inversion is applied on each output variable, and then the neural network (NN) controller is updated using adaptive law, derived from backpropagation. Dynamic inversion (DI) is achieved locally using a Recursive Least Squares (RLS) algorithm for state estimation. An inner control loop for the pitch, roll and yaw rates is integrated within the autopilots. The longitudinal states were separated from the lateral states in order to differentiate between longitudinal and lateral control. Robustness tests were conducted under turbulence and wind-gust conditions. The autopilot results were compared with flight simulation data from a Cessna Citation X research flight simulator. Results have shown that the autopilots accurately track the vertical speed, altitude and heading reference signals. The flight simulation comparison has shown that the proposed adaptive controllers were better than the one currently on board the Cessna Citation X. 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

This brief proposes a novel Virtual-Geography Hawkes Process (VG-Hawkes) to model citation dynamics considering academic networks. The VG-Hawkes model incorporates academic relationships between authors as virtual distances by extending the conventional temporal Hawkes process, enabling a more detailed and realistic representation of citation behavior. Validation results on real-world datasets show that the VG-Hawkes model consistently achieves higher log-likelihood scores than temporal Hawkes models and effectively captures citation peaks and distributional patterns. While this study focuses on selected datasets and pairwise interactions, the model is general and readily extensible. Future work includes scaling to broader datasets and incorporating more complex author relationships. The VG-Hawkes model provides a novel and flexible framework for academic network analysis and scientific impact prediction. Full article

Background/Objectives: Dental trauma is common in childhood and may negatively affect oral health-related quality of life (OHRQoL). Given the growing volume and diversity of publications on this topic, a bibliometric approach is suitable for mapping scientific production, collaboration patterns, thematic evolution, and citation dynamics. This study aimed to perform a bibliometric analysis of the literature addressing the impact of dental trauma on OHRQoL in a paediatric population up to 14 years of age. Methods: A bibliometric study was conducted using Clarivate’s Web of Science Core Collection (WoS-CC), selected for its standardized citation indexing and suitability for bibliometric analyses. Publications retrieved up to August 2025, without restrictions on language or year, were analyzed using VOSviewer (version 1.6.20) and Biblioshiny (Bibliometrix package). Indicators included scientific output, collaboration networks, keyword co-occurrence, thematic evolution, and citation performance. Spearman’s correlation was used to explore relationships between citation counts, journal impact factor, and year of publication. Results: A total of 107 articles published between 2006 and 2025 were included. Scientific output increased steadily, with publications concentrated in specific countries, notably Brazil and India. The predominant research focus concerned the impact of dental trauma on children’s quality of life. Dental Traumatology was the most productive journal and showed high local citation impact. Citation analysis demonstrated a weak positive correlation between citation counts and journal impact factor (rho = 0.37, p < 0.001) and a strong negative correlation with year of publication (rho = −0.84, p < 0.001). Conclusions: This bibliometric analysis identifies research trends, thematic stability, and collaboration patterns in studies on dental trauma and OHRQoL in children, highlighting regional concentration and limited international collaboration. Full article

Air pollution has become a pressing global challenge that threatens ecological security, public health, and sustainable socioeconomic development, prompting extensive academic and policy attention on air pollution control and environmental governance. To systematically clarify the knowledge structure, evolutionary trends, and interdisciplinary characteristics of this field, this study employs bibliometric methods combined with CiteSpace, VOSviewer, and Tableau tools for in-depth analysis of the global literature published in the last 25 years. Key dimensions including keyword clustering, co-occurrence networks, national cooperation patterns, journal co-citation relationships, and policy evaluation methodology evolution are explored. The results reveal that research output in this field has maintained sustained rapid growth, with distinct interdisciplinary integration across environmental science, economics, energy engineering, and public health. Notably, the evolutionary path of research themes presents a clear transformation: shifting from early emphasis on “emission standards” and “end-of-pipe treatment” to market-oriented policy instruments such as “carbon tax” and “carbon emission trading”, and further expanding toward systematic solutions including “green finance” and “collaborative environmental governance”. In terms of policy evaluation methodologies, there is a developmental trend from single-indicator monitoring to integrated assessment frameworks combining quasi-experimental approaches (e.g., difference-in-differences, regression discontinuity design) and multi-model coupling. Furthermore, national collaboration analysis identifies China as a core hub in the global research network, while European and American countries maintain advantages in research impact. While this observation is based on absolute metrics, a data normalization approach (e.g., by population) reveals more distinct relative differences and a complementary global dynamic: China’s scale-driven output aligns with large-scale, engineering-intensive governance challenges, whereas the markedly higher per capita research impact of Western nations reflects a deeper focus on policy innovation and systemic mechanisms. Burst term detection highlights emerging frontiers such as the “Porter hypothesis”, reflecting growing focus on the synergistic relationship between environmental regulation, green innovation, and economic development. This study also identifies critical research gaps, including insufficient attention on cross-regional pollution transport policy coordination and emergency policy evaluation under extreme weather conditions. The findings provide a comprehensive academic map of global air pollution control and environmental governance research, offering valuable insights for optimizing environmental policy design, promoting interdisciplinary collaboration, and guiding future research directions in this field. Full article

Freshwater fish habitat simulation is a vital technology for assessing the state and dynamics of aquatic ecosystems under changing environments. Based on a comprehensive dataset spanning 1991–2024, this study constructs a domain knowledge map by integrating co-citation analysis, keyword burst detection, and social network metrics. The bibliometric results quantitatively identify leading contributors and trace the field’s exponential growth. Complementing this, a critical technical review reveals a significant paradigm shift in modeling methodologies: moving from traditional univariate suitability curves to advanced multivariate and artificial intelligence (AI)-based frameworks. Despite these advancements, our analysis highlights critical gaps in addressing habitat connectivity and broad environmental stressors. To overcome these limitations, we propose a novel framework that integrates landscape pattern indices with circuit theory to quantify habitat patch arrangement and ecological flows. Furthermore, we advocate for future research to explicitly incorporate climate change scenarios (e.g., thermal regime shifts) and geomorphological processes. This study offers both a macroscopic overview of the discipline’s evolution and a roadmap for developing robust, ecosystem-based management tools. Full article

Programmable mechanical metamaterials enable precise regulation of mechanical responses through geometric design, ushering in transformative paradigms for transformable structures. To systematically map the knowledge landscape and development trends in this field, this study employs knowledge mapping methods to analyze the current research status, core hotspots, and future directions of programmable mechanical metamaterials. During the research process, we expanded keywords using the litsearchr tool to optimize the retrieval strategy. Bibliometric tools, including CiteSpace 6.3.R3 and bibliometrix, were utilized to conduct multidimensional analyses on 2017 original papers related to mechanical metamaterials in transformable architecture from 2015 to 2025. These analyses encompass co-word analysis, co-citation clustering, and structural variation analysis. Key aspects include (1) identifying core journals and their attributes to clarify interdisciplinary dynamics, (2) mapping research themes and evolutionary trends through keyword analysis and clustering, and (3) pinpointing research hotspots and future directions based on citation networks and clustering results. The results reveal significant interdisciplinary characteristics, with core knowledge emerging from the intersection of materials science, mechanics, and civil engineering. Mathematical system theory provides a cross-scale modeling foundation for metamaterial microstructure design. The field is evolving from static structural design toward environment-adaptive intelligent systems. Future efforts should prioritize multi-physics collaborative regulation, engineering integration, and technical chain refinement. These findings offer a theoretical reference for the innovative development of transformable architecture. Full article

The objective of this study was to analyze social perception, trust, and vaccine hesitancy through a combined approach of bibliometric analysis and qualitative synthesis, based on the most cited articles in the recent scientific literature. A systematic search was conducted in indexed databases, identifying patterns of production, collaboration, citation, thematic networks, and conceptual trends associated with the study of public trust in vaccines. The results reveal a marked geographic concentration of scientific production, dominated by the United States and the United Kingdom, as well as a strong articulation of thematic clusters linked to digital disinformation, health communication, risk perception, and psychosocial determinants of vaccine acceptance. The qualitative synthesis of the most influential studies reveals that vaccine hesitancy is a multidimensional phenomenon, determined by sociocultural, cognitive, emotional, and structural factors that interact dynamically according to each context. Disinformation, institutional trust, community narratives, and the credibility of sources emerge as central components in individual decision-making. Together, the integrated results enable a deeper understanding of vaccine hesitancy beyond traditional cognitive models, highlighting the need for contextualized communication strategies, intercultural approaches, and health policies based on trust and social participation. This study provides an integral view of the scientific landscape and establishes priority lines for future research and the design of effective public health interventions. Full article

This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to map citation networks, keyword co-occurrence patterns, and thematic evolution. The results identify nine major clusters spanning machine learning, natural language processing, semantic modeling, expert systems, knowledge-based decision support, and emerging hybrid techniques. Collectively, these findings indicate a field-wide shift from manual codification toward scalable, context-aware, and semantically enriched approaches that better support tacit knowing in organizational practice. Building on these insights, the paper introduces the AI–Tacit Knowledge Co-Evolution Model, which situates AI as an epistemic partner—augmenting human interpretive processes rather than merely codifying experience. The framework integrates Polanyi’s concept of tacit knowing, Nonaka’s SECI model, and sociotechnical learning theories to elucidate how human–AI interaction transforms the dynamics of knowledge creation. The review consolidates fragmented research streams and provides a conceptual foundation for guiding future methodological development in AI-enabled tacit knowledge management. Full article

Generative artificial intelligence (GenAI) is rapidly permeating research practices, yet knowledge about its use and topical profile remains fragmented across tools and disciplines. In this study, we present a cross-disciplinary map of GenAI research based on the Web of Science Core Collection (as of 4 November 2025) for the ten tool lines with the largest number of publications. We employed a transparent query protocol in the Title (TI) and Topic (TS) fields, using Boolean and proximity operators together with brand-specific exclusion lists. Thematic similarity was estimated with the Jaccard index for the Top–50, Top–100, and Top–200 sets. In parallel, we computed volume and citation metrics using Python and reconstructed a country-level co-authorship network. The corpus comprises 14,418 deduplicated publications. A strong concentration is evident around ChatGPT, which accounts for approximately 80.6% of the total. The year 2025 shows a marked increase in output across all lines. The Jaccard matrices reveal two stable clusters: general-purpose tools (ChatGPT, Gemini, Claude, Copilot) and open-source/developer-led lines (LLaMA, Mistral, Qwen, DeepSeek). Perplexity serves as a bridge between the clusters, while Grok remains the most distinct. The co-authorship network exhibits a dual-core structure anchored in the United States and China. The study contributes to bibliometric research on GenAI by presenting a perspective that combines publication dynamics, citation structures, thematic profiles, and similarity matrices based on the Jaccard algorithm for different tool lines. In practice, it proposes a comparative framework that can help researchers and institutions match GenAI tools to disciplinary contexts and develop transparent, repeatable assessments of their use in scientific activities. Full article

Non-suicidal self-injury (NSSI) has become an increasingly salient mental health concern among adolescents, and it commonly co-occurs with depression, anxiety, borderline personality disorder, substance use, and childhood maltreatment, forming a complex psychological risk structure. Despite a growing body of literature, a systematic understanding of the structural links between NSSI and psychiatric comorbidities remains limited. This study uses bibliometric and visualization methods to map the developmental trajectory and knowledge structure of the field and to identify research hotspots and frontiers. Drawing on the Web of Science Core Collection, we screened 1562 papers published between 2005 and 2024 on adolescent NSSI and comorbid psychological problems. Using CiteSpace 6.3.R1, VOSviewer 1.6.20, and R 4.3.3, we constructed knowledge graphs from keyword co-occurrence, clustering, burst-term detection, and co-citation analyses. The results show an explosive growth of research in recent years. Hotspots center on comorbidity mechanisms of mood disorders, the impact of childhood trauma, and advances in dynamic assessment. Research has evolved from describing behavioral features toward integrative mechanisms, with five current emphases: risk factor modeling, diagnostic standard optimization, cultural sensitivity, stratified intervention strategies, and psychological risks in special populations. With big data and AI applications, the field is moving toward dynamic prediction and precision intervention. Future work should strengthen cross-cultural comparisons, refine comorbidity network theory, and develop biomarker-informed differentiated interventions to advance both theory and clinical practice. Full article

The constant evolution of Decentralized Finance (DeFi) calls for the continuous monitoring of its developments and implications through a critical review of the academic literature. While DeFi holds promise for enhancing economic activity by expanding market access for enterprises and promoting financial inclusion, concerns remain that digital assets are primarily used for speculative purposes rather than for financing the real economy. This study employs bibliometric methods to investigate whether and how the current academic literature addresses the potential influence of DeFi on real economic dynamics. Employing bibliometric methods—including co-citation, bibliographic coupling, and keyword co-occurrence analyses—focused on DeFi-related publications in the Economics and Business subject areas within the Scopus database, the study maps the knowledge base, author networks, and thematic trends and their temporal evolution, supporting regulators, researchers, and practitioners. The findings reveal that the integration of DeFi with the real economy has received limited attention in scholarly research. This highlights the need for further investigation into DeFi’s implications for financial stability, productive investment, and long-term economic growth. Full article

This study conducts a comprehensive bibliometric analysis of research applying data envelopment analysis (DEA) to the evaluation of sustainability and performance in complex socioeconomic systems between 2010 and mid-2025. DEA has become an increasingly valuable tool for measuring efficiency, benchmarking practices, and supporting decision-making in contexts where sustainability challenges intersect with economic, environmental, and governance dimensions. To capture global research dynamics, we extracted and merged bibliographic data from Web of Science and Scopus, analyzing publication trends, thematic clusters, co-authorship networks, citation structures, and keyword co-occurrences using bibliometric tools such as VOSviewer and Bibliometrix. Our findings reveal a consistent growth trajectory of the field, with research outputs peaking in 2020 and subsequently diversifying across multiple thematic areas. Conceptual mapping highlights two dominant domains: (i) policy, governance, and planning and (ii) environmental, ecological, and management applications, both linked through the overarching theme of sustainable development. The analysis further underscores the geographic diversity of contributions, the concentration of knowledge in key publication outlets, and the increasing connectivity of international collaboration networks. By identifying thematic gaps and underexplored intersections, this study emphasizes the need for more interdisciplinary approaches that integrate bibliometric insights with practical sustainability outcomes. The results provide a structured overview of the field’s evolution, offering researchers and policymakers a valuable reference point for advancing DEA applications in sustainability research. Full article

Objective: This study investigates how artificial intelligence (AI) research relates to firm value, focusing on dominant thematic trends, theoretical foundations, and global collaboration patterns. Methods: A PRISMA-guided systematic review was conducted on 219 peer-reviewed articles published between 2013 and May 2025 in the Web of Science Social Sciences Citation Index. Bibliometric techniques, including co-word, co-citation, and collaboration network analyses, were performed using the bibliometrix (version 4.2.3) in R (version 4.4.2) package to map key concepts, intellectual structures, and international research partnerships. Results: The literature is primarily grounded in strategic management theories such as the resource-based view (RBV) and dynamic capabilities. Emerging research streams emphasize digital transformation, big data analytics, and decision support systems. Frequently co-occurring terms include “firm performance,” “artificial intelligence,” “dynamic capabilities,” “information technology,” and “decision-making.” Collaboration mapping highlights the United States, United Kingdom, and China as leading hubs, with increasing contributions from emerging economies such as India, Malaysia, and Saudi Arabia. The alignment between co-word and co-citation structures reflects a shift from foundational theory to applied AI capabilities in firm-value creation. Implications: By integrating a systematic review with advanced bibliometric and science-mapping methods, this work establishes a structured foundation for theory development, empirical testing, and policy formulation in AI-driven business landscapes. Full article