A bibliometric analysis of acupuncture treatment and cognitive impairment

Cognitive impairment, a prevalent neurological disorder characterized by multisystem dysregulation within the nervous system, has prompted substantial scientific inquiry into complementary therapies. This scientometric investigation systematically examines the evolving bilingual (Chinese-English) research paradigm of acupuncture interventions for cognitive impairment through comparative analysis of 510 publications from the China National Knowledge Infrastructure (CNKI) and 633 articles from Web of Science Core Collection, processed via CiteSpace 6.4.R2. Our multidimensional analysis reveals three principal dimensions: (1) Spatiotemporal evolution demonstrating that scholarly contributions in this domain are predominantly clustered within China. Longitudinal bibliometric analysis demonstrates sustained scholarly productivity in this domain, with annual bilingual (Chinese-English) publication outputs consistently exceeding 40 peer-reviewed articles per annum throughout the 2000–2025 observation window, establishing a robust baseline for continuous knowledge advancement; (2) Network analysis atlas of the research institutions and authors reveals that both research output density and institutional affiliations concentrated in Chinese academic hubs and most authors come from China; (3) Divergent thematic trajectories between linguistic cohorts - Chinese studies emphasize vascular mechanisms, oxidative stress modulation, and pharmacological synergies, whereas English literature prioritizes gut-brain axis interactions, postoperative cognitive recovery, and neuroinflammatory pathways. These findings provide evidence-based insights into acupuncture’s therapeutic mechanisms in cognitive impairment while establishing a conceptual framework to guide future translational studies and clinical protocol optimization in integrative neurology

Construcción y aplicación de un modelo inteligente impulsado por datos de múltiples fuentes para el análisis de la demanda industria-investigación en educación inteligente

In the context of the deepening development of smart education, resolving the structural misalignment between talent cultivation and industry demands has emerged as one of the core challenges in higher education reform. This study proposes a three-phase progressive framework, namely “Data Acquisition–Demand Modeling–Decision Output” to construct the multi-source data-driven intelligent analysis model for industry–research demand, integrating tripartite data from industrial, academic, and policy domains to drive the paradigm shift in educational decision-making from experience-based to data-driven approaches: 1) extracting industry demand profiles are extracted through topic modeling of unstructured recruitment texts to reveal composite competency frameworks; 2) identifying and tracking academic research hotspots and trends through bibliometric and keyword co-occurrence analysis; 3) dynamically calibrating model weights via policy document analysis based on strategic orientations. Applied to the artificial intelligence discipline as an empirical field, the model reveals three domain-specific characteristics: 1) industry demands demonstrate a trinity integration of technical proficiency, industrial applicability, and ethical awareness; 2) academic research undergoes an evolution from technological breakthroughs to scenario-based closed-loop construction, and further to socio-ethical value reconstruction; 3) policy priorities emphasize technological sovereignty and vertical scenario development. Then the model generates hierarchical competency matrices and dynamic-priority knowledge inventories to inform curriculum optimization, accompanied by four evidence-based talent cultivation strategies: 1) establishing a tripartite-integrated educational ecosystem; 2) strengthening industry–academia–research collaborative mechanisms; 3) creating adaptive knowledge renewal and ethical governance frameworks; 4) enhancing interdisciplinary scenario-based innovation capabilities. This study further expands the model’s application scenarios, demonstrating its substantial potential for empowering smart education ecosystems, and outlines future research directions.En el contexto del creciente desarrollo de la educación inteligente, resolver el desajuste estructural entre la formación de talento y las demandas de la industria se ha convertido en uno de los principales desafíos de la reforma de la educación superior. Este estudio propone un marco progresivo de tres fases, denominado "Adquisición de Datos-Modelado de Demanda-Resultados de Decisiones", para construir el modelo de análisis inteligente basado en datos de múltiples fuentes para la demanda de la industria y la investigación. Este modelo integra datos tripartitos de los ámbitos industrial, académico y de políticas para impulsar el cambio de paradigma en la toma de decisiones educativas, pasando de enfoques basados en la experiencia a enfoques basados en datos: 1) la extracción de perfiles de demanda de la industria mediante el modelado temático de textos de reclutamiento no estructurados para revelar marcos de competencias compuestos; 2) la identificación y el seguimiento de los focos y tendencias de la investigación académica mediante análisis bibliométricos y de coocurrencia de palabras clave; 3) la calibración dinámica de las ponderaciones del modelo mediante el análisis de documentos de políticas con base en orientaciones estratégicas. Aplicado a la disciplina de la inteligencia artificial como campo empírico, el modelo revela tres características específicas del dominio: 1) las demandas de la industria demuestran una integración tripartita de competencia técnica, aplicabilidad industrial y conciencia ética; 2) la investigación académica evoluciona desde los avances tecnológicos hasta la construcción de ciclos cerrados basada en escenarios, y posteriormente a la reconstrucción de valores socio éticos; 3) las prioridades políticas enfatizan la soberanía tecnológica y el desarrollo de escenarios verticales. Posteriormente, el modelo genera matrices de competencias jerárquicas e inventarios de conocimiento con prioridad dinámica para fundamentar la optimización curricular, acompañados de cuatro estrategias de desarrollo de talento basadas en la evidencia: 1) establecer un ecosistema educativo tripartito e integrado; 2) fortalecer los mecanismos de colaboración entre la industria, la academia y la investigación; 3) crear marcos adaptativos de renovación del conocimiento y gobernanza ética; 4) mejorar las capacidades de innovación interdisciplinarias basadas en escenarios. Este estudio amplía aún más los escenarios de aplicación del modelo, demostrando su considerable potencial para potenciar los ecosistemas educativos inteligentes y describe futuras líneas de investigación

A visualization analysis of research on arterial compression hemostatic devices using VOSviewer and CiteSpace

BackgroundCardiovascular and cerebrovascular diseases pose a significant health challenge in modern society, with the advancement of interventional therapy and vascular intervention technology playing crucial roles. In the context of post-interventional procedures, the application of suitable pressure at the puncture site is of utmost importance for achieving hemostasis. A variety of arterial compression devices are utilized in clinical settings to facilitate this critical step. A bibliometric analysis is used to assess the impact of research in a particular field. This study seeks to explore the research trends, key themes, and future directions of arterial compression hemostatic devices in international scholarly literature to inform future research endeavors.MethodsEnglish-language literature on arterial compression hemostatic devices was systematically retrieved from the Web of Science (WOS) and Scopus databases until December 31, 2024. In this study, we employed VOSviewer 1.6.18 and CiteSpace 6.2.r4 to systematically analyze a comprehensive set of parameters, which included authorship and institutional affiliations, geographical distribution by country, and thematic categorization through keywords.ResultsIn total, 4,358 relevant publications were retrieved. This study’s results section highlights a growing body of research on arterial compression hemostasis devices, with a significant increase in publications post-2000, reaching 107 in 2022. Department of Cardiology leads in institutional contributions, while ‘Bernat, lvo’ is the most prolific authors. Keyword analysis identifies “human,” “article,” “hemostasis,” “female,” and “male” as key terms, with 7 thematic clusters revealed by hierarchical clustering.ConclusionThe results provide an overview of research on arterial compression hemostatic devices, which may help researchers better understand classical research, historical developments, and new discoveries, as well as providing ideas for future research

A large-scale review of wave and tidal energy research over the last 20 years

Over the last two decades, a large body of academic scholarship has been generated on wave and tidal energy related topics. It is therefore important to assess and analyse the research direction and development through horizon scanning processes. To synthesise such large-scale literature, this review adopts a bibliometric method and scrutinises over 8000 wave/tidal energy related documents published during 2003–2021. Overall, 98 countries contributed to the literature, with the top ten mainly developed countries plus China produced nearly two-thirds of the research. A thorough analysis on documents marked the emergence of four broad research themes (dominated by wave energy subjects): (A) resource assessment, site selection, and environmental impacts/benefits; (B) wave energy converters, hybrid systems, and hydrodynamic performance; (C) vibration energy harvesting and piezoelectric nanogenerators; and (D) flow dynamics, tidal turbines, and turbine design. Further, nineteen research sub-clusters, corresponding to broader themes, were identified, highlighting the trending research topics. An interesting observation was a recent shift in research focus from solely evaluating energy resources and ideal sites to integrating wave/tidal energy schemes into wider coastal/estuarine management plans by developing multicriteria decision-making frameworks and promoting novel designs and cost-sharing practices. The method and results presented may provide insights into the evolution of wave/tidal energy science and its multiple research topics, thus helping to inform future management decisions

Structural analysis and evolutionary exploration based on the research topic network of a field: a case in high-frequency trading

This study aims to systematically analyze the distribution dynamics of research topics and uncover the development state of the research in the specific field, which will provide a practical reference for developing professional subject knowledge services in the era of big data. The research topic network is constructed and analyzed using methods and tools of scientometrics. Basic statistics on network characteristics are performed to reveal the research status. Community detection, node ordering, and other steps are conducted to generate the evolutionary alluvial diagram. Then, relevant results are analyzed to explore the knowledge structure of the specific field and evolutionary context of research topics. Visualization analysis on the network structure of the latest period is executed to distinguish related concepts and predict the research trends. Taking high-frequency trading (HFT) as a case, this study achieves diversified scientometrics analysis of the research topic network and multi-dimensional evolution exploration of the relevant research topics in the specific field, which obtaining some knowledge insights. (1) Six major topics in HFT: liquidity & market microstructure, market efficiency, financial market, incomplete market, cointegration & price discovery, and event study. (2) The research focus about markets gradually transferred from international to emerging, meanwhile continuous attention to volatility/risk related issues. (3) The emphasis will change from theory to practice, technologies (big data, etc.) and theories (behavioral finance, etc.) will have more interaction with HFT. An effective research idea is proposed to reveal the knowledge structure of field and analyze the evolutionary context of research topics, which demonstrating the knowledge insights

a ​bibliometric study

Lopes, N. M., Aparicio, M., & Neves, F. T. (2025). Challenges and prospects of artificial intelligence in aviation: a ​bibliometric study. Data Science and Management, 8(2), 207-223. https://doi.org/10.1016/j.dsm.2024.11.001 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project-UIDB/04152/2020-Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020).The primary motivation for this study is the recent growth and increased interest in artificial intelligence (AI). Despite the widespread recognition of its critical importance, a discernible scientific gap persists within the extant scholarly discourse, particularly concerning exhaustive systematic reviews of AI in the aviation industry. This gap spurred a meticulous analysis of 1,213 articles from the Web of Science (WoS) core database for bibliometric knowledge mapping. This analysis highlights China as the primary contributor to publications, with the Nanjing University of Finance and Economics as the leading institution in paper contributions. Lecture Notes in Artificial Intelligence and the IEEE AIAA Digital Avionics System Conference are the leading journals within this domain. This bibliometric research underscores the key focus on air traffic management, human factors, environmental initiatives, training, logistics, flight operations, and safety through co-occurrence and co-citation analyses. A chronological examination of keywords reveals a central research trajectory centered on machine learning, models, deep learning, and the impact of automation on human performance in aviation. Burst keyword analysis identifies the leading-edge research on AI within predictive models, unmanned aerial vehicles, object detection, and convolutional neural networks. The primary objective is to bridge this knowledge gap and gain comprehensive insights into AI in the aviation sector. This study delineates the scholarly terrain of AI in aviation using a bibliometric methodology to facilitate this exploration. The results illuminate the current state of research, thereby enhancing academic understanding of developments within this critical domain. Finally, a new conceptual framework was constructed based on the primary elements identified in the literature. This framework can assist emerging researchers in identifying the fundamental dimensions of AI in the aviation industry.publishersversionpublishe

Carbon Capture and Storage

Climate change is one of the main threats to modern society. This phenomenon is associated with an increase in greenhouse gas (GHGs, mainly carbon dioxide—CO2) emissions due to anthropogenic activities. The main causes are the burning of fossil fuels and land use change (deforestation). Climate change impacts are associated with risks to basic needs (health, food security, and clean water), as well as risks to development (jobs, economic growth, and the cost of living). The processes involving CO2 capture and storage are gaining attention in the scientific community as an alternative for decreasing CO2 emissions, reducing its concentration in ambient air. The carbon capture and storage (CCS) methodologies comprise three steps: CO2 capture, CO2 transportation, and CO2 storage. Despite the high research activity within this topic, several technological, economic, and environmental issues as well as safety problems remain to be solved, such as the following needs: increase of CO2 capture efficiency, reduction of process costs, and verification of the environmental sustainability of CO2 storage

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data