Producción Científica

 

 

This study fills the void because no specific research on fiscal stimulus using bibliometric analysis in the last decade has been conducted. This study aims to identify trends in fiscal stimulus that can be useful as a decision-making support tool in setting future research priorities (Mejia et al., 2021). This research method adopts Garza and Reyes’s (2015) five-step bibliometric analysis phase: determining search keywords, initializing search results, refining search results, compiling initial data statistics, and analyzing data. Based on the research results, at the initial results stage, 779 datasets were obtained from the Scopus database, reduced to produce 578 data and visualized using VOSviewer. This study lists research trends, active journal publishers, prolific writers, the most active nations and institutions and the most important scientific fields. Based on the distinctive relationships of groups of keywords within clusters, cluster analysis identifies the primary study subjects in fiscal stimulus. The authors conclude that the research areas based on keyword analysis that rarely occur as future study topics related to fiscal stimulus are climate change, multiplier, lockdown and H30 (fiscal policy and behavior of general economic actors), and the liquidity trap.

 

 

Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive, and their use would release pollutants. Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy. Hybrid renewable energy systems (HRES) are viable for remote areas operating in standalone mode. This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades. More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace to present the results with figures and tables. Productive countries and highly cited authors are identified, and hot topics with hotspot articles are shown in landscape and timeline views. Emerging trends and new developments related to techno-economic analysis and microgrids, as well as the application of HOMER software, are predicted based on the analysis of citation bursts. Furthermore, the opportunities of hybrid energy systems for sustainable development are discussed, and challenges and possible solutions are proposed. The study of this paper provides researchers with a comprehensive understanding and intuitive representation of standalone solar-wind hybrid energy systems.

 

 

Este artículo releva el estado actual de la producción científica de la IA en México con técnicas bibliométricas. Considera la especialización de la IA en seis subcampos. Como metodología, emplea los metadatos de 13 265 publicaciones, recolectados del catálogo bibliográfico OpenAlex y realiza un análisis cuantitativo de productividad con base en métricas de publicaciones, autores, citas y colaboraciones internacionales, e identifica sus principales temas de investigación y su desarrollo. Los resultados muestran una estructura científica local extensa con importantes colaboraciones internacionales. Se identifican tanto subcampos maduros, desarrollados desde hace tres décadas, que abarcan la robótica y las redes neuronales; como subcampos emergentes, desarrollados en los últimos cinco años, que comprenden el aprendizaje automático, el procesamiento del lenguaje natural y la visión por computadora. El artículo distingue aplicaciones recientes en las áreas de salud, medio ambiente, finanzas, procesamiento del lenguaje natural y acústica.

 

 

Desde 1990, las investigaciones referentes al capital social (CS) y Desarrollo Sostenible (DS) han aumentado considerablemente debido a que el CS mejora las dimensiones del DS. El objetivo de este estudio es brindar un panorama bibliométrico de la literatura existente en los campos de CS y DS, a través de una visión cuantitativa. Los datos recopilados comprendieron el periodo de 1994-2022, con los conjuntos de datos de Scopus. Las unidades de análisis fueron revistas, países, instituciones, autores y publicaciones. Los métodos bibliométricos utilizados fueron el análisis de desempeño y el mapeo científico. El análisis de desempeño utilizó indicadores como el índice h, la productividad, el umbral de citas y las dimensiones de temporalidad. Para el mapeo científico se manejaron las técnicas de citas conjuntas y la co-ocurrencia de palabras clave; se crearon mapas de redes con el apoyo del software VOSviewer. Los resultados mostraron que Reino Unido es el país más productivo e influyente en publicaciones referentes a CS y DS. Los autores Ann Dale y Lenore Newman, fueron los más representativos. La sustentabilidad, el capital humano y los tipos de capital social: unión, puente y vertical, son los principales temas emergentes relacionados con el CS y DS.

 

 

Background: Natural language processing, such as ChatGPT, demonstrates growing potential across numerous research scenarios, also raising interest in its applications in public health and epidemiology. Here, we applied a bibliometric analysis for a systematic assessment of the current literature related to the applications of ChatGPT in epidemiology and public health. Methods: A bibliometric analysis was conducted on the Biblioshiny web-app, by collecting original articles indexed in the Scopus database between 2010 and 2023. Results: On a total of 3431 original medical articles, “Article” and “Conference paper”, mostly constituting the total of retrieved documents, highlighting that the term “ChatGPT” becomes an interesting topic from 2023. The annual publications escalated from 39 in 2010 to 719 in 2023, with an average annual growth rate of 25.1%. In terms of country production over time, the USA led with the highest overall production from 2010 to 2023. Concerning citations, the most frequently cited countries were the USA, UK, and China. Interestingly, Harvard Medical School emerges as the leading contributor, accounting for 18% of all articles among the top ten affiliations. Conclusions: Our study provides an overall examination of the existing research interest in ChatGPT’s applications for public health by outlining pivotal themes and uncovering emerging trends.

 

 

The present bibliometric analysis used traditional measures and network science techniques to examine how the COVID-19 pandemic influenced research in Clinical Psychology. Publication records from the Web of Science (WoS) were obtained for journal articles published prior to (2015 and 2018), during (2020), and at the end of the pandemic (2022) for the search terms “men and mental health” and “women and mental health”. Network analyses of author-provided keywords showed that COVID-19 co-occurred with fear, anxiety, depression, and stress for both men and women in 2020. In 2022, COVID-19 co-occurred with topics related to world-wide lockdowns (e.g., alcohol use, substance use, intimate partner violence, loneliness, physical activity), and to more fundamental topics in Clinical Psychology (e.g., eating disorders and post-traumatic stress disorder). Although the COVID pandemic was associated with several changes in the research topics that were examined in Clinical Psychology, pre-existing disparities in the amount of mental health research on men compared to women did not appear to increase (in contrast to increases associated with COVID in pre-existing gender disparities observed in other areas of society).

 

 

Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

 

 

Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were “drug assessment”, “cross-species prediction”, “drug–drug interactions”, and “pediatrics and pregnancy drug development”, in which “drug assessment” accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical–biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure–activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.

 

 

This paper presents an author co-citation analysis of the research on L2 vocabulary acquisition that was published in the 2020 calendar year. The most significant influence at this time is Paul Nation—cited in 85% of the publication set—but a number of other important influences can also be identified, notably, Laufer, Hulstijn, Schmitt and Webb. This paper draws some comparisons with data from 1990, and speculates on how “research fronts” might be identified in an author co-citation data set.

 

 

Disinformation is a phenomenon of concern to all political systems, as it poses a threat to freedom and democracy through the manipulation of public opinion aimed at eroding institutions. This paper presents a bibliometric and systematized study which allows the establishment of a comprehensive view of the research and current state of academic investigations on disinformation. To this end, a content analysis of the scientific articles indexed in Scopus up to 31 December 2023 has been carried out based on three categories of analysis: journals, authors and investigations. Similarly, a systematic study of the 50 most cited articles in this sample was performed in order to gain a deeper understanding of the nature, motivations and methodological approaches of these investigations. The results indicate that disinformation is a research topic which has gained great interest in the academic community since 2018, with special mention to the impact of COVID-19 and the vaccines against this disease. Thus, it can be concluded that disinformation is an object of study which attracts significant attention and which must be approached from transdisciplinarity to respond to a phenomenon of great complexity.