Producción Científica

 

 

The purpose of this study is to analyze the development of research publications on leadership implementation in MSMEs over the last five years. This research used a bibliometric analysis approach with VOSviewer software to develop and visualize the bibliometric network. This research conducted screening based on leadership keywords in MSMEs in the SCOPUS database in the vulnerable years 2018-2023. Publications according to keywords initially amounted to 90 articles, after which 33 articles were filtered. The development of leadership publications in MSMEs in the last 5 years has experienced ups and downs in the number of publications per year, with an increasing trend occurring in 2023, when there were many publications in the field of business, management and accounting, with 33 documents (47.8%). The results of the analysis with VOSviewer and visualization of co-occurrence networks based on leadership keywords in MSMEs revealed 174 items with a division of 12 clusters marked with different colors. The interrelated factors include entrepreneurial leadership, entrepreneurship, entrepreneurial motivation, quality management, digital transformation, competition, and others. The results of the analysis using the VOSviewer application show that there is a relationship between leadership and MSMEs with other networks; thus, additional research could be interesting.

 

 

This research aims to improve problem-solving abilities regarding the greenhouse effect using project-based learning methods supported by Science, Technology, Engineering, and Mathematics (STEM)-Education for Sustainable Development (ESD)-based teaching materials. The study was conducted in multiple phases: (i) utilizing a pretest to gauge students’ prior knowledge, (ii) employing the project-based learning approach to teach the greenhouse effect, and (iii) assessing students’ final knowledge (with a posttest). The research was completed with the observation of temperature variations in a greenhouse prototype. Students monitored and recorded temperature changes over time. Students’ problem-solving abilities increased significantly after being treated using project learning assisted by STEM-ESD teaching materials, imparting more information through the media by stimulating students’ curiosity and interest in science subjects.

 

 

Introduction: Women are underrepresented in the leadership of and participation in randomized controlled trials (RCTs). We conducted a bibliometric review of nephrology RCTs to examine trial leadership by women and participation of women in nephrology RCTs. Methods: A bibliometric review of RCTs published in top medical, surgical, or nephrology journals was conducted using MEDLINE and EMBASE from January 2011 to December 2021. Leadership by women as corresponding authors, women trial participation, and trial characteristics were examined with duplicate independent data extraction. Logistic regression was used to examine associations between trial characteristics and women leadership and trial participation. Results: A total of 1770 studies were screened and 395 RCTs met eligibility criteria. The number (%) of women in corresponding, first, and last authorship positions were as follows: 89 (22%), 109 (28%), and 74 (19%), respectively, without change over time (P = 0.94). The median percentage (interquartile range [IQR]) of women trial participants was 39.0% (13.5%) with no difference between women or men lead authors (P = 0.15). Men lead authors were statistically less likely to enroll women in RCTs. Women lead authors were less likely to be funded by industry (odds ratio [OR]: 0.30; 95% confidence interval [CI]: 0.14–0.63; P = 0.002) or lead international trials (OR: 0.11; 95% CI: 0.01–0.83; P = 0.03). Trials with sex-specific eligibility criteria were more likely to have women leaders (OR: 2.56; 95% CI: 1.19–5.49; P = 0.02) than those without. Discussion: Gender inequalities in RCT leadership and RCT participation exist in nephrology and did not improve over time. Strategies to improve inequalities need to be implemented and evaluated. © 2024 International Society of Nephrology

 

 

The language employed by researchers to define and discuss diseases can itself be a determinant of health. Despite this, the framing of diseases in medical research literature is largely unexplored. This scoping review examines a prevalent medical issue with social determinants influenced by the framing of its pathogenesis: obesity. Specifically, we compare the currently dominant framing of obesity as an addiction to food with the emerging frame of obesity developing from neuroinflammation. We triangulate both corpus linguistic and bibliometric analysis of the top 200 most engaging neuroscience journal articles discussing obesity that were published open access in the past 10 years. The constructed Neurobesity Corpus is available for public use. The scoping review analysis confirmed that neuroinflammation is an emerging way for obesity to be framed in medical research. Importantly, the articles analysed that discussed neuroinflammation were less likely to use crisis terminology, such as referring to an obesity “epidemic”. We highlight a potential relationship between the adoption of addiction frames and the use of stigmatising language in medical research.

 

 

Data-driven expert team formation is a complicated and multifaceted process that requires access to accurate information to identify researchers’ areas and level of expertise and their collaborative prospects. In this respect, bibliometric data represents a valuable and reliable source of information that can be effectively employed in revealing key insights regarding candidates. Due to its complex and complete structure of publication metadata records, IEEE Xplore database may offer the possibility to compute an extensive set of indicators about researchers’ publication production and how they have interacted during time. Considering the case of Politehnica University of Timisoara scholars for the interval 2010–2022, current dataset encapsulates relevant and rich information for assembling multidisciplinary research teams, being also a testing ground for experimenting and calibrating the expert team formation methods and mechanisms.

 

 

Three-dimensional food printing (3DFP) can produce foods with tailored nutritional content, complex shapes and textures. This technology requires food formulations (food inks) with specific rheological properties. Pickering emulsions (PE) have gained attention due to their long-term stability and desirable printable properties, making them an excellent candidate for 3DFP. The purpose of this study is to use bibliometric analysis to identify the most important scientific research on PE used in 3DFP. This includes identifying key authors, countries and universities or institutions where the research was conducted, as well as the primary journals that provide information on this topic. Our study provides insight into the relevance of food ink properties for next-generation 3DFP and the main raw materials used for the development of PE. Out of the 28 original research articles analysed, only 10 countries have studied the application of PE for 3DFP. China and the United Kingdom have been the primary leaders in researching this topic. The Food Hydrocolloids Journal has been the main source of scientific information. The studies cited Pickering stabiliser particles, including soy protein isolate, microcrystalline cellulose and acetylated microcrystalline cellulose, as well as oil phases based on sunflower, canola, olive and soybean oils. High internal phase Pickering emulsions (HIPPEs) have shown great thermal and conductive stability, making them a promising choice for 3DFP post-processing. Further studies should assess the bioavailability and bioaccessibility of the bioactive compounds that are encapsulated. It is also important to explore their potential use in real food systems and to integrate innovative packaging solutions.

 

 

A corncob-derived magnetic solid acid catalyst was synthesized through the sulfonation method and an impregnation process, respectively. In the sulfonation process, the concentrated H2SO4 was utilized as an activation agent to obtain acidic properties. The solution of ferric sulphate-ferrous sulphate was utilized for impregnation to generate the magnetic behaviour of the material. The prepared magnetic acid solid catalyst had a high saturation magnetisation value of 16.48 emu/g and a total acidity of 1.43 mmol/g. The performance of the catalyst was evaluated in the esterification reaction of waste cooking oil. The best result presented 86.12% FFA conversion under reaction conditions of 5% catalyst loading and a 1:15 oil-to-methanol molar ratio at 60oC for 4 h. The catalyst was separated magnetically from the reaction solution and exhibited a good reusability with 61% remaining active after 5 consecutive cycles of reaction. This study resulted in a promising method to obtain magnetic-sulfonated carbon-based catalyst from corncob residue, and it is economical potentially and environmentally friendly for the esterification of low-quality feedstock for biodiesel production.

 

 

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

 

 

The use of chatbots for customer service has gained momentum in recent years. Increasing evidence has shown that chatbots can transform the customer service landscape. Nevertheless, this topic currently lacks adequate bibliometric and visualization research. In order to review and summarise the research on chatbots, the study employs a bibliometric analysis approach to gain a comprehensive understanding of chatbots. The study uses bibliometric analysis of 798 documents sourced from the Scopus database from 2001 to 2022. The combination of biblioshiny (web interface application of Bibliometrix) and VOS viewer software was used to visualize the analysis. The study’s findings reveal three prominent areas in the current research: antecedents of the adoption of chatbots, application of chatbots and behavioural & relational outcomes of the application of chatbots. The future directions and implications have been discussed in the study’s conclusion. © 2024 Ram Arti Publishers. All rights reserved.

 

 

The rapid decline in global biodiversity underscores the critical need for comprehensive monitoring of wildlife distribution and abundance. This study explores the trends in applied hierarchical modeling, which is an important tool in addressing these conservation challenges. By analyzing a dataset of 697 peer-reviewed articles published between 2002 and 2022, we examine the taxonomic focus, detection procedures, study designs, and modeling choices within the field of population ecology. Our findings revealed that most studies concentrated on single taxonomic groups, particularly mammals and birds. Data collection methods included visual surveys, acoustic surveys, camera traps, and traps, with some studies combining multiple techniques. Notably, the United States dominated the geographical focus, accounting for 46% of published papers. In terms of modeling approaches, single-season occupancy was the most prevalent, followed by various other models, including multi-species occupancy and N-mixture models. While hierarchical modeling has gained popularity, citations for these articles remained relatively modest, with only a few achieving over 100 citations. Authorship analysis revealed a highly collaborative network of researchers, with key authors contributing significantly to the field’s development and dissemination. Co-authorship and co-citation networks highlighted the importance of authors who can bridge differing scientific groups and those that have made substantial contributions to hierarchical modeling methods. Despite its growth, the field faces challenges related to standardization in modeling and reporting practices. While efforts to address these issues are currently underway, a cohesive framework for occupancy modeling in ecology is still in an emerging stage.